World Bank Education Data

Δεδομένα Εκπαίδευσης στην R

Συνολικές δημόσιες δαπάνες για την εκπαίδευση (% του ΑΕγχΠ)

Code - Time Series Plot - GRC

#Government expenditure on education, total (% of GDP)
dat <- wb_data(
  indicator = "SE.XPD.TOTL.GD.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.TOTL.GD.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Συνολικές δημόσιες δαπάνες για την εκπαίδευση (% του ΑΕγχΠ)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.TOTL.GD.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Government expenditure on education, total (% of GDP)
dat <- wb_data(
  indicator = "SE.XPD.TOTL.GD.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Συνολικές δημόσιες δαπάνες για την εκπαίδευση (% του ΑΕγχΠ)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.TOTL.GD.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Σύνολο δημόσιων δαπανών για την εκπαίδευση (% των δημόσιων δαπανών)

Code - Time Series Plot - GRC

#Government expenditure on education, total (% of government expenditure)
dat <- wb_data(
  indicator = "SE.XPD.TOTL.GB.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.TOTL.GB.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Σύνολο δημόσιων δαπανών για την εκπαίδευση (% των δημόσιων δαπανών)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.TOTL.GB.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 23 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Government expenditure on education, total (% of government expenditure)
dat <- wb_data(
  indicator = "SE.XPD.TOTL.GB.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Σύνολο δημόσιων δαπανών για την εκπαίδευση (% των δημόσιων δαπανών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.TOTL.GB.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δαπάνες για την τριτοβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)

Code - Time Series Plot - GRC

#Expenditure on tertiary education (% of government expenditure on education)
dat <- wb_data(
  indicator = "SE.XPD.TERT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.TERT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δαπάνες για την τριτοβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.TERT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 29 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Expenditure on tertiary education (% of government expenditure on education)
dat <- wb_data(
  indicator = "SE.XPD.TERT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δαπάνες για την τριτοβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.TERT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δημόσιες δαπάνες ανά σπουδαστή, τριτοβάθμια (% του κατά κεφαλήν ΑΕΠ)

Code - Time Series Plot - GRC

#Government expenditure per student, tertiary (% of GDP per capita)
dat <- wb_data(
  indicator = "SE.XPD.TERT.PC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.TERT.PC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δημόσιες δαπάνες ανά σπουδαστή, τριτοβάθμια (% του κατά κεφαλήν ΑΕΠ)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.TERT.PC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 47 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Government expenditure per student, tertiary (% of GDP per capita)
dat <- wb_data(
  indicator = "SE.XPD.TERT.PC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δημόσιες δαπάνες ανά σπουδαστή, τριτοβάθμια (% του κατά κεφαλήν ΑΕΠ)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.TERT.PC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δαπάνες για τη δευτεροβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)

Code - Time Series Plot - GRC

#Expenditure on secondary education (% of government expenditure on education)
dat <- wb_data(
  indicator = "SE.XPD.SECO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.SECO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δαπάνες για τη δευτεροβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.SECO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 29 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Expenditure on secondary education (% of government expenditure on education)
dat <- wb_data(
  indicator = "SE.XPD.SECO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δαπάνες για τη δευτεροβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.SECO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δημόσιες δαπάνες ανά σπουδαστή, δευτεροβάθμια (% του κατά κεφαλήν ΑΕΠ)

Code - Time Series Plot - GRC

#Government expenditure per student, secondary (% of GDP per capita)
dat <- wb_data(
  indicator = "SE.XPD.SECO.PC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.SECO.PC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δημόσιες δαπάνες ανά σπουδαστή, δευτεροβάθμια (% του κατά κεφαλήν ΑΕΠ)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.SECO.PC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 46 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Government expenditure per student, secondary (% of GDP per capita)
dat <- wb_data(
  indicator = "SE.XPD.SECO.PC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δημόσιες δαπάνες ανά σπουδαστή, δευτεροβάθμια (% του κατά κεφαλήν ΑΕΠ)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.SECO.PC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δαπάνες για την πρωτοβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)

Code - Time Series Plot - GRC

#Expenditure on primary education (% of government expenditure on education)
dat <- wb_data(
  indicator = "SE.XPD.PRIM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.PRIM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δαπάνες για την πρωτοβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.PRIM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 43 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Expenditure on primary education (% of government expenditure on education)
dat <- wb_data(
  indicator = "SE.XPD.PRIM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δαπάνες για την πρωτοβάθμια εκπαίδευση (% των δημόσιων δαπανών για την εκπαίδευση)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.PRIM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δημόσιες δαπάνες ανά σπουδαστή, πρωτοβάθμια εκπαίδευση (% του κατά κεφαλήν ΑΕΠ)

Code - Time Series Plot - GRC

#Government expenditure per student, primary (% of GDP per capita)
dat <- wb_data(
  indicator = "SE.XPD.PRIM.PC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.PRIM.PC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δημόσιες δαπάνες ανά σπουδαστή, πρωτοβάθμια εκπαίδευση (% του κατά κεφαλήν ΑΕΠ)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.PRIM.PC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 63 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Government expenditure per student, primary (% of GDP per capita)
dat <- wb_data(
  indicator = "SE.XPD.PRIM.PC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δημόσιες δαπάνες ανά σπουδαστή, πρωτοβάθμια εκπαίδευση (% του κατά κεφαλήν ΑΕΠ)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.PRIM.PC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Τρέχουσες δαπάνες για την εκπαίδευση, σύνολο (% των συνολικών δαπανών στα δημόσια ιδρύματα)

Code - Time Series Plot - GRC

#Current education expenditure, total (% of total expenditure in public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CTOT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.CTOT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Τρέχουσες δαπάνες για την εκπαίδευση, σύνολο (% των συνολικών δαπανών στα δημόσια ιδρύματα)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.CTOT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 40 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Current education expenditure, total (% of total expenditure in public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CTOT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Τρέχουσες δαπάνες για την εκπαίδευση, σύνολο (% των συνολικών δαπανών στα δημόσια ιδρύματα)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.CTOT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Τρέχουσες δαπάνες για την τριτοβάθμια εκπαίδευση (% των συνολικών δαπανών στα τριτοβάθμια δημόσια ιδρύματα)

Code - Time Series Plot - GRC

#Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CTER.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.CTER.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Τρέχουσες δαπάνες για την τριτοβάθμια εκπαίδευση (% των συνολικών δαπανών στα τριτοβάθμια δημόσια ιδρύματα)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.CTER.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 40 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CTER.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Τρέχουσες δαπάνες για την τριτοβάθμια εκπαίδευση (% των συνολικών δαπανών στα τριτοβάθμια δημόσια ιδρύματα)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.CTER.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Τρέχουσες δαπάνες για την εκπαίδευση, δευτεροβάθμια (% των συνολικών δαπανών σε δημόσια ιδρύματα δευτεροβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#Current education expenditure, secondary (% of total expenditure in secondary public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CSEC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.CSEC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Τρέχουσες δαπάνες για την εκπαίδευση, δευτεροβάθμια (% των συνολικών δαπανών σε δημόσια ιδρύματα δευτεροβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.CSEC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 40 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Current education expenditure, secondary (% of total expenditure in secondary public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CSEC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Τρέχουσες δαπάνες για την εκπαίδευση, δευτεροβάθμια (% των συνολικών δαπανών σε δημόσια ιδρύματα δευτεροβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.CSEC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Τρέχουσες δαπάνες για την εκπαίδευση, πρωτοβάθμια εκπαίδευση (% των συνολικών δαπανών σε δημόσια ιδρύματα πρωτοβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#Current education expenditure, primary (% of total expenditure in primary public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CPRM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.XPD.CPRM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Τρέχουσες δαπάνες για την εκπαίδευση, πρωτοβάθμια εκπαίδευση (% των συνολικών δαπανών σε δημόσια ιδρύματα πρωτοβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.XPD.CPRM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 57 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Current education expenditure, primary (% of total expenditure in primary public institutions)
dat <- wb_data(
  indicator = "SE.XPD.CPRM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Τρέχουσες δαπάνες για την εκπαίδευση, πρωτοβάθμια εκπαίδευση (% των συνολικών δαπανών σε δημόσια ιδρύματα πρωτοβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.XPD.CPRM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Τριτοβάθμια εκπαίδευση, ακαδημαϊκό προσωπικό (% γυναικών)

Code - Time Series Plot - GRC

#Tertiary education, academic staff (% female)
dat <- wb_data(
  indicator = "SE.TER.TCHR.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.TCHR.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Τριτοβάθμια εκπαίδευση, ακαδημαϊκό προσωπικό (% γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.TER.TCHR.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 17 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Tertiary education, academic staff (% female)
dat <- wb_data(
  indicator = "SE.TER.TCHR.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Τριτοβάθμια εκπαίδευση, ακαδημαϊκό προσωπικό (% γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.TCHR.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, τριτοβάθμια, άνδρες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, tertiary, male (% gross)
dat <- wb_data(
  indicator = "SE.TER.ENRR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.ENRR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, τριτοβάθμια, άνδρες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.TER.ENRR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, tertiary, male (% gross)
dat <- wb_data(
  indicator = "SE.TER.ENRR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, τριτοβάθμια, άνδρες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.ENRR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, τριτοβάθμια, γυναίκες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, tertiary, female (% gross)
dat <- wb_data(
  indicator = "SE.TER.ENRR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.ENRR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, τριτοβάθμια, γυναίκες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.TER.ENRR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, tertiary, female (% gross)
dat <- wb_data(
  indicator = "SE.TER.ENRR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, τριτοβάθμια, γυναίκες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.ENRR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, τριτοβάθμια (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, tertiary (% gross)
dat <- wb_data(
  indicator = "SE.TER.ENRR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.ENRR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, τριτοβάθμια (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.TER.ENRR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, tertiary (% gross)
dat <- wb_data(
  indicator = "SE.TER.ENRR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, τριτοβάθμια (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.ENRR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Αναλογία μαθητών-διδασκόντων, τριτοβάθμια

Code - Time Series Plot - GRC

#Pupil-teacher ratio, tertiary
dat <- wb_data(
  indicator = "SE.TER.ENRL.TC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.ENRL.TC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Αναλογία μαθητών-διδασκόντων, τριτοβάθμια",
    caption = "https://data.worldbank.org/indicator/SE.TER.ENRL.TC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Pupil-teacher ratio, tertiary
dat <- wb_data(
  indicator = "SE.TER.ENRL.TC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Αναλογία μαθητών-διδασκόντων, τριτοβάθμια",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.ENRL.TC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο πρόγραμμα σύντομου κύκλου, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.ST.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.ST.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο πρόγραμμα σύντομου κύκλου, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.ST.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.ST.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο πρόγραμμα σύντομου κύκλου, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.ST.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο κύκλο βραχείας διάρκειας, πληθυσμός 25+, άνδρες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.ST.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.ST.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο κύκλο βραχείας διάρκειας, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.ST.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.ST.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο κύκλο βραχείας διάρκειας, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.ST.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο πρόγραμμα σύντομου κύκλου, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)

Code - Time Series Plot - GRC

#Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.ST.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.ST.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο πρόγραμμα σύντομου κύκλου, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.ST.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.ST.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο τριτοβάθμιο πρόγραμμα σύντομου κύκλου, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.ST.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.MS.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.MS.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.MS.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.MS.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.MS.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.MS.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.MS.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.MS.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.MS.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.MS.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.MS.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.MS.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.MS.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.MS.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον μεταπτυχιακό ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.MS.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.DO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.DO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.DO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.DO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.DO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.DO.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.DO.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.DO.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.DO.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.DO.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.DO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.DO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.DO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.DO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, Διδακτορικό ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.DO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.BA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.BA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.BA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.BA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.BA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.BA.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.BA.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.BA.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.BA.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.BA.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.BA.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.TER.CUAT.BA.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.TER.CUAT.BA.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.TER.CUAT.BA.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον πτυχίο Bachelor ή ισοδύναμο, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.TER.CUAT.BA.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Έφηβοι εκτός σχολείου (% της ηλικίας της κατώτερης δευτεροβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#Adolescents out of school (% of lower secondary school age)
dat <- wb_data(
  indicator = "SE.SEC.UNER.LO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.UNER.LO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Έφηβοι εκτός σχολείου (% της ηλικίας της κατώτερης δευτεροβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.UNER.LO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Adolescents out of school (% of lower secondary school age)
dat <- wb_data(
  indicator = "SE.SEC.UNER.LO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Έφηβοι εκτός σχολείου (% της ηλικίας της κατώτερης δευτεροβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.UNER.LO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Έφηβοι εκτός σχολείου, άνδρες (% της ηλικίας των ανδρών της κατώτερης δευτεροβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#Adolescents out of school, male (% of male lower secondary school age)
dat <- wb_data(
  indicator = "SE.SEC.UNER.LO.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.UNER.LO.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Έφηβοι εκτός σχολείου, άνδρες (% της ηλικίας των ανδρών της κατώτερης δευτεροβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.UNER.LO.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 43 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Adolescents out of school, male (% of male lower secondary school age)
dat <- wb_data(
  indicator = "SE.SEC.UNER.LO.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Έφηβοι εκτός σχολείου, άνδρες (% της ηλικίας των ανδρών της κατώτερης δευτεροβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.UNER.LO.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Έφηβοι εκτός σχολείου, γυναίκες (% της ηλικίας των γυναικών στην κατώτερη δευτεροβάθμια εκπαίδευση)

Code - Time Series Plot - GRC

#Adolescents out of school, female (% of female lower secondary school age)
dat <- wb_data(
  indicator = "SE.SEC.UNER.LO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.UNER.LO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Έφηβοι εκτός σχολείου, γυναίκες (% της ηλικίας των γυναικών στην κατώτερη δευτεροβάθμια εκπαίδευση)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.UNER.LO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 43 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Adolescents out of school, female (% of female lower secondary school age)
dat <- wb_data(
  indicator = "SE.SEC.UNER.LO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Έφηβοι εκτός σχολείου, γυναίκες (% της ηλικίας των γυναικών στην κατώτερη δευτεροβάθμια εκπαίδευση)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.UNER.LO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί (% γυναίκες)

Code - Time Series Plot - GRC

#Secondary education, teachers (% female)
dat <- wb_data(
  indicator = "SE.SEC.TCHR.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCHR.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί (% γυναίκες)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCHR.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, teachers (% female)
dat <- wb_data(
  indicator = "SE.SEC.TCHR.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί (% γυναίκες)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCHR.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί, γυναίκες

Code - Time Series Plot - GRC

#Secondary education, teachers, female
dat <- wb_data(
  indicator = "SE.SEC.TCHR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCHR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί, γυναίκες",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCHR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, teachers, female
dat <- wb_data(
  indicator = "SE.SEC.TCHR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί, γυναίκες",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCHR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί

Code - Time Series Plot - GRC

#Secondary education, teachers
dat <- wb_data(
  indicator = "SE.SEC.TCHR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCHR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCHR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, teachers
dat <- wb_data(
  indicator = "SE.SEC.TCHR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, εκπαιδευτικοί",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCHR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in secondary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in secondary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in upper secondary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.UP.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.UP.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in upper secondary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.UP.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in upper secondary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.UP.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.UP.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in upper secondary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.UP.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in upper secondary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.UP.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.UP.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in upper secondary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.UP.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην ανώτερη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in secondary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in secondary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in lower secondary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.LO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.LO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in lower secondary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.LO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση, άνδρες (% ανδρών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in lower secondary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.LO.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.LO.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση, άνδρες (% ανδρών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in lower secondary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.LO.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση, άνδρες (% ανδρών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in lower secondary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.LO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.LO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in lower secondary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.LO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην κατώτερη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in secondary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.TCAQ.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.TCAQ.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in secondary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.SEC.TCAQ.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στη δευτεροβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.TCAQ.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προαγωγή στη δευτεροβάθμια εκπαίδευση (%)

Code - Time Series Plot - GRC

#Progression to secondary school (%)
dat <- wb_data(
  indicator = "SE.SEC.PROG.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.PROG.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προαγωγή στη δευτεροβάθμια εκπαίδευση (%)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.PROG.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 19 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Progression to secondary school (%)
dat <- wb_data(
  indicator = "SE.SEC.PROG.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προαγωγή στη δευτεροβάθμια εκπαίδευση (%)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.PROG.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προαγωγή στη δευτεροβάθμια εκπαίδευση, άνδρες (%)

Code - Time Series Plot - GRC

#Progression to secondary school, male (%)
dat <- wb_data(
  indicator = "SE.SEC.PROG.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.PROG.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προαγωγή στη δευτεροβάθμια εκπαίδευση, άνδρες (%)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.PROG.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 19 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Progression to secondary school, male (%)
dat <- wb_data(
  indicator = "SE.SEC.PROG.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προαγωγή στη δευτεροβάθμια εκπαίδευση, άνδρες (%)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.PROG.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προαγωγή στη δευτεροβάθμια εκπαίδευση, γυναίκες (%)

Code - Time Series Plot - GRC

#Progression to secondary school, female (%)
dat <- wb_data(
  indicator = "SE.SEC.PROG.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.PROG.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προαγωγή στη δευτεροβάθμια εκπαίδευση, γυναίκες (%)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.PROG.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 19 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Progression to secondary school, female (%)
dat <- wb_data(
  indicator = "SE.SEC.PROG.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προαγωγή στη δευτεροβάθμια εκπαίδευση, γυναίκες (%)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.PROG.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές σε σχολεία, δευτεροβάθμια, ιδιωτικά (% του συνόλου της δευτεροβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#School enrollment, secondary, private (% of total secondary)
dat <- wb_data(
  indicator = "SE.SEC.PRIV.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.PRIV.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές σε σχολεία, δευτεροβάθμια, ιδιωτικά (% του συνόλου της δευτεροβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.PRIV.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 39 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary, private (% of total secondary)
dat <- wb_data(
  indicator = "SE.SEC.PRIV.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές σε σχολεία, δευτεροβάθμια, ιδιωτικά (% του συνόλου της δευτεροβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.PRIV.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, δευτεροβάθμια, άνδρες (% καθαρά)

Code - Time Series Plot - GRC

#School enrollment, secondary, male (% net)
dat <- wb_data(
  indicator = "SE.SEC.NENR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.NENR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια, άνδρες (% καθαρά)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.NENR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary, male (% net)
dat <- wb_data(
  indicator = "SE.SEC.NENR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια, άνδρες (% καθαρά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.NENR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, δευτεροβάθμια, γυναίκες (% καθαρά)

Code - Time Series Plot - GRC

#School enrollment, secondary, female (% net)
dat <- wb_data(
  indicator = "SE.SEC.NENR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.NENR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια, γυναίκες (% καθαρά)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.NENR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary, female (% net)
dat <- wb_data(
  indicator = "SE.SEC.NENR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια, γυναίκες (% καθαρά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.NENR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, δευτεροβάθμια εκπαίδευση (% καθαρά)

Code - Time Series Plot - GRC

#School enrollment, secondary (% net)
dat <- wb_data(
  indicator = "SE.SEC.NENR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.NENR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια εκπαίδευση (% καθαρά)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.NENR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary (% net)
dat <- wb_data(
  indicator = "SE.SEC.NENR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια εκπαίδευση (% καθαρά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.NENR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, δευτεροβάθμια, άνδρες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, secondary, male (% gross)
dat <- wb_data(
  indicator = "SE.SEC.ENRR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, δευτεροβάθμια, άνδρες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary, male (% gross)
dat <- wb_data(
  indicator = "SE.SEC.ENRR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, δευτεροβάθμια, άνδρες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, δευτεροβάθμια, γυναίκες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, secondary, female (% gross)
dat <- wb_data(
  indicator = "SE.SEC.ENRR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, δευτεροβάθμια, γυναίκες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary, female (% gross)
dat <- wb_data(
  indicator = "SE.SEC.ENRR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, δευτεροβάθμια, γυναίκες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, δευτεροβάθμια εκπαίδευση (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, secondary (% gross)
dat <- wb_data(
  indicator = "SE.SEC.ENRR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια εκπαίδευση (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary (% gross)
dat <- wb_data(
  indicator = "SE.SEC.ENRR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, δευτεροβάθμια εκπαίδευση (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, μαθητές επαγγελματικής εκπαίδευσης (% γυναικών)

Code - Time Series Plot - GRC

#Secondary education, vocational pupils (% female)
dat <- wb_data(
  indicator = "SE.SEC.ENRL.VO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.VO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές επαγγελματικής εκπαίδευσης (% γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.VO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, vocational pupils (% female)
dat <- wb_data(
  indicator = "SE.SEC.ENRL.VO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές επαγγελματικής εκπαίδευσης (% γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.VO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, μαθητές επαγγελματικής εκπαίδευσης

Code - Time Series Plot - GRC

#Secondary education, vocational pupils
dat <- wb_data(
  indicator = "SE.SEC.ENRL.VO",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.VO)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές επαγγελματικής εκπαίδευσης",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.VO"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, vocational pupils
dat <- wb_data(
  indicator = "SE.SEC.ENRL.VO",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές επαγγελματικής εκπαίδευσης",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.VO"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Αναλογία μαθητών-διδασκόντων, ανώτερη δευτεροβάθμια εκπαίδευση

Code - Time Series Plot - GRC

#Pupil-teacher ratio, upper secondary
dat <- wb_data(
  indicator = "SE.SEC.ENRL.UP.TC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.UP.TC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Αναλογία μαθητών-διδασκόντων, ανώτερη δευτεροβάθμια εκπαίδευση",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.UP.TC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 40 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Pupil-teacher ratio, upper secondary
dat <- wb_data(
  indicator = "SE.SEC.ENRL.UP.TC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Αναλογία μαθητών-διδασκόντων, ανώτερη δευτεροβάθμια εκπαίδευση",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.UP.TC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Αναλογία μαθητών-εκπαιδευτικών, δευτεροβάθμια

Code - Time Series Plot - GRC

#Pupil-teacher ratio, secondary
dat <- wb_data(
  indicator = "SE.SEC.ENRL.TC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.TC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Αναλογία μαθητών-εκπαιδευτικών, δευτεροβάθμια",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.TC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Pupil-teacher ratio, secondary
dat <- wb_data(
  indicator = "SE.SEC.ENRL.TC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Αναλογία μαθητών-εκπαιδευτικών, δευτεροβάθμια",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.TC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Αναλογία μαθητών-διδασκόντων, κατώτερη δευτεροβάθμια εκπαίδευση

Code - Time Series Plot - GRC

#Pupil-teacher ratio, lower secondary
dat <- wb_data(
  indicator = "SE.SEC.ENRL.LO.TC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.LO.TC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Αναλογία μαθητών-διδασκόντων, κατώτερη δευτεροβάθμια εκπαίδευση",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.LO.TC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 40 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Pupil-teacher ratio, lower secondary
dat <- wb_data(
  indicator = "SE.SEC.ENRL.LO.TC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Αναλογία μαθητών-διδασκόντων, κατώτερη δευτεροβάθμια εκπαίδευση",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.LO.TC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, μαθητές γενικής εκπαίδευσης (% γυναικών)

Code - Time Series Plot - GRC

#Secondary education, general pupils (% female)
dat <- wb_data(
  indicator = "SE.SEC.ENRL.GC.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.GC.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές γενικής εκπαίδευσης (% γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.GC.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, general pupils (% female)
dat <- wb_data(
  indicator = "SE.SEC.ENRL.GC.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές γενικής εκπαίδευσης (% γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.GC.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, μαθητές γενικής εκπαίδευσης

Code - Time Series Plot - GRC

#Secondary education, general pupils
dat <- wb_data(
  indicator = "SE.SEC.ENRL.GC",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.GC)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές γενικής εκπαίδευσης",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.GC"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, general pupils
dat <- wb_data(
  indicator = "SE.SEC.ENRL.GC",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές γενικής εκπαίδευσης",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.GC"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, μαθητές (% γυναικών)

Code - Time Series Plot - GRC

#Secondary education, pupils (% female)
dat <- wb_data(
  indicator = "SE.SEC.ENRL.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές (% γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, pupils (% female)
dat <- wb_data(
  indicator = "SE.SEC.ENRL.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές (% γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, μαθητές

Code - Time Series Plot - GRC

#Secondary education, pupils
dat <- wb_data(
  indicator = "SE.SEC.ENRL",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.ENRL)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές",
    caption = "https://data.worldbank.org/indicator/SE.SEC.ENRL"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, pupils
dat <- wb_data(
  indicator = "SE.SEC.ENRL",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, μαθητές",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.ENRL"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Δευτεροβάθμια εκπαίδευση, διάρκεια (έτη)

Code - Time Series Plot - GRC

#Secondary education, duration (years)
dat <- wb_data(
  indicator = "SE.SEC.DURS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.DURS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Δευτεροβάθμια εκπαίδευση, διάρκεια (έτη)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.DURS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Secondary education, duration (years)
dat <- wb_data(
  indicator = "SE.SEC.DURS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Δευτεροβάθμια εκπαίδευση, διάρκεια (έτη)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.DURS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο ανώτερο δευτεροβάθμιο επίπεδο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.UP.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.UP.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο ανώτερο δευτεροβάθμιο επίπεδο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.UP.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο ανώτερο δευτεροβάθμιο επίπεδο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο ανώτερο δευτεροβάθμιο επίπεδο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.UP.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.UP.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο ανώτερο δευτεροβάθμιο επίπεδο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.UP.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο ανώτερο δευτεροβάθμιο επίπεδο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη ανώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.UP.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.UP.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη ανώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.UP.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη ανώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο μεταδευτεροβάθμιο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.PO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.PO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο μεταδευτεροβάθμιο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.PO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο μεταδευτεροβάθμιο, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο μεταδευτεροβάθμιο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.PO.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.PO.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο μεταδευτεροβάθμιο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.PO.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο μεταδευτεροβάθμιο, πληθυσμός 25+, άνδρες (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον μετά τη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)

Code - Time Series Plot - GRC

#Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.PO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.PO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον μετά τη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.PO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον μετά τη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.LO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.LO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.LO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, άνδρες (%) (σωρευτικά)

Code - Time Series Plot - GRC

#Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.LO.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.LO.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, άνδρες (%) (σωρευτικά)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.LO.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, άνδρες (%) (σωρευτικά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)

Code - Time Series Plot - GRC

#Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.LO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CUAT.LO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.SEC.CUAT.LO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένη κατώτερη δευτεροβάθμια εκπαίδευση, πληθυσμός 25+, γυναίκες (%) (σωρευτικά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Lower secondary completion rate, total (% of relevant age group)
dat <- wb_data(
  indicator = "SE.SEC.CMPT.LO.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CMPT.LO.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 46 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Lower secondary completion rate, total (% of relevant age group)
dat <- wb_data(
  indicator = "SE.SEC.CMPT.LO.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Lower secondary completion rate, male (% of relevant age group)
dat <- wb_data(
  indicator = "SE.SEC.CMPT.LO.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CMPT.LO.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 46 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Lower secondary completion rate, male (% of relevant age group)
dat <- wb_data(
  indicator = "SE.SEC.CMPT.LO.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Lower secondary completion rate, female (% of relevant age group)
dat <- wb_data(
  indicator = "SE.SEC.CMPT.LO.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.CMPT.LO.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 46 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Lower secondary completion rate, female (% of relevant age group)
dat <- wb_data(
  indicator = "SE.SEC.CMPT.LO.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ολοκλήρωσης της κατώτερης δευτεροβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ηλικία έναρξης της κατώτερης δευτεροβάθμιας εκπαίδευσης (έτη)

Code - Time Series Plot - GRC

#Lower secondary school starting age (years)
dat <- wb_data(
  indicator = "SE.SEC.AGES",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.SEC.AGES)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ηλικία έναρξης της κατώτερης δευτεροβάθμιας εκπαίδευσης (έτη)",
    caption = "https://data.worldbank.org/indicator/SE.SEC.AGES"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Lower secondary school starting age (years)
dat <- wb_data(
  indicator = "SE.SEC.AGES",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ηλικία έναρξης της κατώτερης δευτεροβάθμιας εκπαίδευσης (έτη)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.SEC.AGES"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Παιδιά εκτός σχολείου (% της ηλικίας του δημοτικού σχολείου)

Code - Time Series Plot - GRC

#Children out of school (% of primary school age)
dat <- wb_data(
  indicator = "SE.PRM.UNER.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.UNER.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Παιδιά εκτός σχολείου (% της ηλικίας του δημοτικού σχολείου)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.UNER.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 42 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Children out of school (% of primary school age)
dat <- wb_data(
  indicator = "SE.PRM.UNER.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Παιδιά εκτός σχολείου (% της ηλικίας του δημοτικού σχολείου)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.UNER.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Παιδιά εκτός σχολείου, αγόρια (% της ηλικίας των αρρένων στο δημοτικό σχολείο)

Code - Time Series Plot - GRC

#Children out of school, male (% of male primary school age)
dat <- wb_data(
  indicator = "SE.PRM.UNER.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.UNER.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Παιδιά εκτός σχολείου, αγόρια (% της ηλικίας των αρρένων στο δημοτικό σχολείο)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.UNER.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Children out of school, male (% of male primary school age)
dat <- wb_data(
  indicator = "SE.PRM.UNER.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Παιδιά εκτός σχολείου, αγόρια (% της ηλικίας των αρρένων στο δημοτικό σχολείο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.UNER.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Παιδιά εκτός σχολείου, δημοτικού, αρσενικού

Code - Time Series Plot - GRC

#Children out of school, primary, male
dat <- wb_data(
  indicator = "SE.PRM.UNER.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.UNER.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Παιδιά εκτός σχολείου, δημοτικού, αρσενικού",
    caption = "https://data.worldbank.org/indicator/SE.PRM.UNER.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Children out of school, primary, male
dat <- wb_data(
  indicator = "SE.PRM.UNER.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Παιδιά εκτός σχολείου, δημοτικού, αρσενικού",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.UNER.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Παιδιά εκτός σχολείου, κορίτσια (% της ηλικίας των γυναικών στην πρωτοβάθμια εκπαίδευση)

Code - Time Series Plot - GRC

#Children out of school, female (% of female primary school age)
dat <- wb_data(
  indicator = "SE.PRM.UNER.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.UNER.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Παιδιά εκτός σχολείου, κορίτσια (% της ηλικίας των γυναικών στην πρωτοβάθμια εκπαίδευση)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.UNER.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Children out of school, female (% of female primary school age)
dat <- wb_data(
  indicator = "SE.PRM.UNER.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Παιδιά εκτός σχολείου, κορίτσια (% της ηλικίας των γυναικών στην πρωτοβάθμια εκπαίδευση)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.UNER.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Παιδιά εκτός σχολείου, δημοτικού, θηλέων

Code - Time Series Plot - GRC

#Children out of school, primary, female
dat <- wb_data(
  indicator = "SE.PRM.UNER.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.UNER.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Παιδιά εκτός σχολείου, δημοτικού, θηλέων",
    caption = "https://data.worldbank.org/indicator/SE.PRM.UNER.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Children out of school, primary, female
dat <- wb_data(
  indicator = "SE.PRM.UNER.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Παιδιά εκτός σχολείου, δημοτικού, θηλέων",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.UNER.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Παιδιά εκτός σχολείου, δημοτικού

Code - Time Series Plot - GRC

#Children out of school, primary
dat <- wb_data(
  indicator = "SE.PRM.UNER",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.UNER)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Παιδιά εκτός σχολείου, δημοτικού",
    caption = "https://data.worldbank.org/indicator/SE.PRM.UNER"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 42 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Children out of school, primary
dat <- wb_data(
  indicator = "SE.PRM.UNER",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Παιδιά εκτός σχολείου, δημοτικού",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.UNER"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια, αρσενικά (% των παιδιών ηλικίας δημοτικού)

Code - Time Series Plot - GRC

#Adjusted net enrollment rate, primary, male (% of primary school age children)
dat <- wb_data(
  indicator = "SE.PRM.TENR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TENR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια, αρσενικά (% των παιδιών ηλικίας δημοτικού)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TENR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Adjusted net enrollment rate, primary, male (% of primary school age children)
dat <- wb_data(
  indicator = "SE.PRM.TENR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια, αρσενικά (% των παιδιών ηλικίας δημοτικού)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TENR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια, γυναίκες (% παιδιών ηλικίας δημοτικού)

Code - Time Series Plot - GRC

#Adjusted net enrollment rate, primary, female (% of primary school age children)
dat <- wb_data(
  indicator = "SE.PRM.TENR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TENR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια, γυναίκες (% παιδιών ηλικίας δημοτικού)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TENR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Adjusted net enrollment rate, primary, female (% of primary school age children)
dat <- wb_data(
  indicator = "SE.PRM.TENR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια, γυναίκες (% παιδιών ηλικίας δημοτικού)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TENR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια εκπαίδευση (% των παιδιών ηλικίας πρωτοβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#Adjusted net enrollment rate, primary (% of primary school age children)
dat <- wb_data(
  indicator = "SE.PRM.TENR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TENR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια εκπαίδευση (% των παιδιών ηλικίας πρωτοβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TENR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Adjusted net enrollment rate, primary (% of primary school age children)
dat <- wb_data(
  indicator = "SE.PRM.TENR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προσαρμοσμένο καθαρό ποσοστό εγγραφής, πρωτοβάθμια εκπαίδευση (% των παιδιών ηλικίας πρωτοβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TENR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Πρωτοβάθμια εκπαίδευση, εκπαιδευτικοί (% γυναικών)

Code - Time Series Plot - GRC

#Primary education, teachers (% female)
dat <- wb_data(
  indicator = "SE.PRM.TCHR.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TCHR.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Πρωτοβάθμια εκπαίδευση, εκπαιδευτικοί (% γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TCHR.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary education, teachers (% female)
dat <- wb_data(
  indicator = "SE.PRM.TCHR.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Πρωτοβάθμια εκπαίδευση, εκπαιδευτικοί (% γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TCHR.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Πρωτοβάθμια εκπαίδευση, εκπαιδευτικοί

Code - Time Series Plot - GRC

#Primary education, teachers
dat <- wb_data(
  indicator = "SE.PRM.TCHR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TCHR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Πρωτοβάθμια εκπαίδευση, εκπαιδευτικοί",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TCHR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary education, teachers
dat <- wb_data(
  indicator = "SE.PRM.TCHR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Πρωτοβάθμια εκπαίδευση, εκπαιδευτικοί",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TCHR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επιμορφωμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in primary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.PRM.TCAQ.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TCAQ.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επιμορφωμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TCAQ.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in primary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.PRM.TCAQ.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επιμορφωμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TCAQ.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in primary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.PRM.TCAQ.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TCAQ.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TCAQ.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in primary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.PRM.TCAQ.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση, άνδρες (% των ανδρών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TCAQ.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in primary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.PRM.TCAQ.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.TCAQ.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.TCAQ.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in primary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.PRM.TCAQ.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην πρωτοβάθμια εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.TCAQ.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επαναλήπτες, πρωτοβάθμια, σύνολο (% των συνολικών εγγραφών)

Code - Time Series Plot - GRC

#Repeaters, primary, total (% of total enrollment)
dat <- wb_data(
  indicator = "SE.PRM.REPT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.REPT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επαναλήπτες, πρωτοβάθμια, σύνολο (% των συνολικών εγγραφών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.REPT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Repeaters, primary, total (% of total enrollment)
dat <- wb_data(
  indicator = "SE.PRM.REPT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επαναλήπτες, πρωτοβάθμια, σύνολο (% των συνολικών εγγραφών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.REPT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επαναλήπτες, πρωτοβάθμια, άνδρες (% των εγγραφών ανδρών)

Code - Time Series Plot - GRC

#Repeaters, primary, male (% of male enrollment)
dat <- wb_data(
  indicator = "SE.PRM.REPT.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.REPT.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επαναλήπτες, πρωτοβάθμια, άνδρες (% των εγγραφών ανδρών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.REPT.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Repeaters, primary, male (% of male enrollment)
dat <- wb_data(
  indicator = "SE.PRM.REPT.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επαναλήπτες, πρωτοβάθμια, άνδρες (% των εγγραφών ανδρών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.REPT.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επαναλήπτες, πρωτοβάθμια, γυναίκες (% των εγγραφών γυναικών)

Code - Time Series Plot - GRC

#Repeaters, primary, female (% of female enrollment)
dat <- wb_data(
  indicator = "SE.PRM.REPT.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.REPT.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επαναλήπτες, πρωτοβάθμια, γυναίκες (% των εγγραφών γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.REPT.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Repeaters, primary, female (% of female enrollment)
dat <- wb_data(
  indicator = "SE.PRM.REPT.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επαναλήπτες, πρωτοβάθμια, γυναίκες (% των εγγραφών γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.REPT.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εμμονή στον τελευταίο βαθμό της πρωτοβάθμιας, ολική (% της κοόρτης)

Code - Time Series Plot - GRC

#Persistence to last grade of primary, total (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRSL.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRSL.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εμμονή στον τελευταίο βαθμό της πρωτοβάθμιας, ολική (% της κοόρτης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRSL.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Persistence to last grade of primary, total (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRSL.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εμμονή στον τελευταίο βαθμό της πρωτοβάθμιας, ολική (% της κοόρτης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRSL.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επιμονή έως τον τελευταίο βαθμό πρωτοβάθμιας εκπαίδευσης, άνδρες (% κοόρτης)

Code - Time Series Plot - GRC

#Persistence to last grade of primary, male (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRSL.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRSL.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επιμονή έως τον τελευταίο βαθμό πρωτοβάθμιας εκπαίδευσης, άνδρες (% κοόρτης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRSL.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Persistence to last grade of primary, male (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRSL.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επιμονή έως τον τελευταίο βαθμό πρωτοβάθμιας εκπαίδευσης, άνδρες (% κοόρτης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRSL.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επιμονή έως τον τελευταίο βαθμό πρωτοβάθμιας εκπαίδευσης, θήλεις (% κοόρτης)

Code - Time Series Plot - GRC

#Persistence to last grade of primary, female (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRSL.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRSL.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επιμονή έως τον τελευταίο βαθμό πρωτοβάθμιας εκπαίδευσης, θήλεις (% κοόρτης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRSL.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Persistence to last grade of primary, female (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRSL.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επιμονή έως τον τελευταίο βαθμό πρωτοβάθμιας εκπαίδευσης, θήλεις (% κοόρτης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRSL.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εμμονή έως τον βαθμό 5, συνολικά (% της κοόρτης)

Code - Time Series Plot - GRC

#Persistence to grade 5, total (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRS5.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRS5.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εμμονή έως τον βαθμό 5, συνολικά (% της κοόρτης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRS5.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Persistence to grade 5, total (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRS5.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εμμονή έως τον βαθμό 5, συνολικά (% της κοόρτης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRS5.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επιμονή στον βαθμό 5, άνδρες (% της κοόρτης)

Code - Time Series Plot - GRC

#Persistence to grade 5, male (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRS5.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRS5.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επιμονή στον βαθμό 5, άνδρες (% της κοόρτης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRS5.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Persistence to grade 5, male (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRS5.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επιμονή στον βαθμό 5, άνδρες (% της κοόρτης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRS5.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επιμονή έως τον βαθμό 5, γυναίκες (% της κοόρτης)

Code - Time Series Plot - GRC

#Persistence to grade 5, female (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRS5.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRS5.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επιμονή έως τον βαθμό 5, γυναίκες (% της κοόρτης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRS5.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 13 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Persistence to grade 5, female (% of cohort)
dat <- wb_data(
  indicator = "SE.PRM.PRS5.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επιμονή έως τον βαθμό 5, γυναίκες (% της κοόρτης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRS5.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές σε σχολεία, πρωτοβάθμια, ιδιωτικά (% του συνόλου της πρωτοβάθμιας εκπαίδευσης)

Code - Time Series Plot - GRC

#School enrollment, primary, private (% of total primary)
dat <- wb_data(
  indicator = "SE.PRM.PRIV.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.PRIV.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές σε σχολεία, πρωτοβάθμια, ιδιωτικά (% του συνόλου της πρωτοβάθμιας εκπαίδευσης)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.PRIV.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary, private (% of total primary)
dat <- wb_data(
  indicator = "SE.PRM.PRIV.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές σε σχολεία, πρωτοβάθμια, ιδιωτικά (% του συνόλου της πρωτοβάθμιας εκπαίδευσης)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.PRIV.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Υπερήλικες μαθητές, πρωτοβάθμια εκπαίδευση (% των εγγραφών)

Code - Time Series Plot - GRC

#Over-age students, primary (% of enrollment)
dat <- wb_data(
  indicator = "SE.PRM.OENR.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.OENR.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Υπερήλικες μαθητές, πρωτοβάθμια εκπαίδευση (% των εγγραφών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.OENR.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Over-age students, primary (% of enrollment)
dat <- wb_data(
  indicator = "SE.PRM.OENR.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Υπερήλικες μαθητές, πρωτοβάθμια εκπαίδευση (% των εγγραφών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.OENR.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Υπερήλικες μαθητές, πρωτοβάθμια, άνδρες (% των εγγεγραμμένων ανδρών)

Code - Time Series Plot - GRC

#Over-age students, primary, male (% of male enrollment)
dat <- wb_data(
  indicator = "SE.PRM.OENR.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.OENR.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Υπερήλικες μαθητές, πρωτοβάθμια, άνδρες (% των εγγεγραμμένων ανδρών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.OENR.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Over-age students, primary, male (% of male enrollment)
dat <- wb_data(
  indicator = "SE.PRM.OENR.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Υπερήλικες μαθητές, πρωτοβάθμια, άνδρες (% των εγγεγραμμένων ανδρών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.OENR.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Υπερήλικες μαθητές, πρωτοβάθμια, γυναίκες (% των εγγεγραμμένων γυναικών)

Code - Time Series Plot - GRC

#Over-age students, primary, female (% of female enrollment)
dat <- wb_data(
  indicator = "SE.PRM.OENR.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.OENR.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Υπερήλικες μαθητές, πρωτοβάθμια, γυναίκες (% των εγγεγραμμένων γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.OENR.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Over-age students, primary, female (% of female enrollment)
dat <- wb_data(
  indicator = "SE.PRM.OENR.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Υπερήλικες μαθητές, πρωτοβάθμια, γυναίκες (% των εγγεγραμμένων γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.OENR.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καθαρό ποσοστό εισαγωγής στην 1η τάξη (% του επίσημου πληθυσμού σχολικής ηλικίας)

Code - Time Series Plot - GRC

#Net intake rate in grade 1 (% of official school-age population)
dat <- wb_data(
  indicator = "SE.PRM.NINT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.NINT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καθαρό ποσοστό εισαγωγής στην 1η τάξη (% του επίσημου πληθυσμού σχολικής ηλικίας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.NINT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 48 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Net intake rate in grade 1 (% of official school-age population)
dat <- wb_data(
  indicator = "SE.PRM.NINT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καθαρό ποσοστό εισαγωγής στην 1η τάξη (% του επίσημου πληθυσμού σχολικής ηλικίας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.NINT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καθαρό ποσοστό εισαγωγής στην 1η τάξη, άνδρες (% του επίσημου πληθυσμού σχολικής ηλικίας)

Code - Time Series Plot - GRC

#Net intake rate in grade 1, male (% of official school-age population)
dat <- wb_data(
  indicator = "SE.PRM.NINT.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.NINT.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καθαρό ποσοστό εισαγωγής στην 1η τάξη, άνδρες (% του επίσημου πληθυσμού σχολικής ηλικίας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.NINT.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 48 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Net intake rate in grade 1, male (% of official school-age population)
dat <- wb_data(
  indicator = "SE.PRM.NINT.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καθαρό ποσοστό εισαγωγής στην 1η τάξη, άνδρες (% του επίσημου πληθυσμού σχολικής ηλικίας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.NINT.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καθαρό ποσοστό εισαγωγής στην 1η τάξη, γυναίκες (% του επίσημου πληθυσμού σχολικής ηλικίας)

Code - Time Series Plot - GRC

#Net intake rate in grade 1, female (% of official school-age population)
dat <- wb_data(
  indicator = "SE.PRM.NINT.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.NINT.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καθαρό ποσοστό εισαγωγής στην 1η τάξη, γυναίκες (% του επίσημου πληθυσμού σχολικής ηλικίας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.NINT.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 48 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Net intake rate in grade 1, female (% of official school-age population)
dat <- wb_data(
  indicator = "SE.PRM.NINT.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καθαρό ποσοστό εισαγωγής στην 1η τάξη, γυναίκες (% του επίσημου πληθυσμού σχολικής ηλικίας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.NINT.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, δημοτικό, άνδρες (% καθαρά)

Code - Time Series Plot - GRC

#School enrollment, primary, male (% net)
dat <- wb_data(
  indicator = "SE.PRM.NENR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.NENR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, δημοτικό, άνδρες (% καθαρά)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.NENR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary, male (% net)
dat <- wb_data(
  indicator = "SE.PRM.NENR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, δημοτικό, άνδρες (% καθαρά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.NENR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, δημοτικό, γυναίκες (% καθαρά)

Code - Time Series Plot - GRC

#School enrollment, primary, female (% net)
dat <- wb_data(
  indicator = "SE.PRM.NENR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.NENR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, δημοτικό, γυναίκες (% καθαρά)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.NENR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary, female (% net)
dat <- wb_data(
  indicator = "SE.PRM.NENR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, δημοτικό, γυναίκες (% καθαρά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.NENR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, πρωτοβάθμια (% καθαρά)

Code - Time Series Plot - GRC

#School enrollment, primary (% net)
dat <- wb_data(
  indicator = "SE.PRM.NENR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.NENR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, πρωτοβάθμια (% καθαρά)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.NENR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary (% net)
dat <- wb_data(
  indicator = "SE.PRM.NENR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, πρωτοβάθμια (% καθαρά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.NENR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ακαθάριστης εισαγωγής στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Gross intake ratio in first grade of primary education, total (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.GINT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.GINT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ακαθάριστης εισαγωγής στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.GINT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Gross intake ratio in first grade of primary education, total (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.GINT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ακαθάριστης εισαγωγής στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.GINT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ακαθάριστη αναλογία εισακτέων στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Gross intake ratio in first grade of primary education, male (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.GINT.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.GINT.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ακαθάριστη αναλογία εισακτέων στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.GINT.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Gross intake ratio in first grade of primary education, male (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.GINT.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ακαθάριστη αναλογία εισακτέων στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.GINT.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ακαθάριστη αναλογία εισακτέων στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Gross intake ratio in first grade of primary education, female (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.GINT.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.GINT.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ακαθάριστη αναλογία εισακτέων στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.GINT.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Gross intake ratio in first grade of primary education, female (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.GINT.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ακαθάριστη αναλογία εισακτέων στην πρώτη τάξη της πρωτοβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.GINT.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, δημοτικό, άνδρες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, primary, male (% gross)
dat <- wb_data(
  indicator = "SE.PRM.ENRR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.ENRR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, δημοτικό, άνδρες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.ENRR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary, male (% gross)
dat <- wb_data(
  indicator = "SE.PRM.ENRR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, δημοτικό, άνδρες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.ENRR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, δημοτικό, γυναίκες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, primary, female (% gross)
dat <- wb_data(
  indicator = "SE.PRM.ENRR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.ENRR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, δημοτικό, γυναίκες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.ENRR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary, female (% gross)
dat <- wb_data(
  indicator = "SE.PRM.ENRR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, δημοτικό, γυναίκες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.ENRR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, πρωτοβάθμια (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, primary (% gross)
dat <- wb_data(
  indicator = "SE.PRM.ENRR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.ENRR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, πρωτοβάθμια (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.ENRR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary (% gross)
dat <- wb_data(
  indicator = "SE.PRM.ENRR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, πρωτοβάθμια (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.ENRR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Αναλογία μαθητών-εκπαιδευτικών, πρωτοβάθμια

Code - Time Series Plot - GRC

#Pupil-teacher ratio, primary
dat <- wb_data(
  indicator = "SE.PRM.ENRL.TC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.ENRL.TC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Αναλογία μαθητών-εκπαιδευτικών, πρωτοβάθμια",
    caption = "https://data.worldbank.org/indicator/SE.PRM.ENRL.TC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Pupil-teacher ratio, primary
dat <- wb_data(
  indicator = "SE.PRM.ENRL.TC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Αναλογία μαθητών-εκπαιδευτικών, πρωτοβάθμια",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.ENRL.TC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Πρωτοβάθμια εκπαίδευση, μαθητές (% γυναικών)

Code - Time Series Plot - GRC

#Primary education, pupils (% female)
dat <- wb_data(
  indicator = "SE.PRM.ENRL.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.ENRL.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Πρωτοβάθμια εκπαίδευση, μαθητές (% γυναικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.ENRL.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary education, pupils (% female)
dat <- wb_data(
  indicator = "SE.PRM.ENRL.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Πρωτοβάθμια εκπαίδευση, μαθητές (% γυναικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.ENRL.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Πρωτοβάθμια εκπαίδευση, μαθητές

Code - Time Series Plot - GRC

#Primary education, pupils
dat <- wb_data(
  indicator = "SE.PRM.ENRL",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.ENRL)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Πρωτοβάθμια εκπαίδευση, μαθητές",
    caption = "https://data.worldbank.org/indicator/SE.PRM.ENRL"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary education, pupils
dat <- wb_data(
  indicator = "SE.PRM.ENRL",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Πρωτοβάθμια εκπαίδευση, μαθητές",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.ENRL"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Πρωτοβάθμια εκπαίδευση, διάρκεια (έτη)

Code - Time Series Plot - GRC

#Primary education, duration (years)
dat <- wb_data(
  indicator = "SE.PRM.DURS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.DURS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Πρωτοβάθμια εκπαίδευση, διάρκεια (έτη)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.DURS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary education, duration (years)
dat <- wb_data(
  indicator = "SE.PRM.DURS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Πρωτοβάθμια εκπαίδευση, διάρκεια (έτη)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.DURS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, σύνολο (%) (σωρευτικό)

Code - Time Series Plot - GRC

#Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.PRM.CUAT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.CUAT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, σύνολο (%) (σωρευτικό)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.CUAT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)
dat <- wb_data(
  indicator = "SE.PRM.CUAT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, σύνολο (%) (σωρευτικό)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.CUAT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, άνδρες (%) (σωρευτικά)

Code - Time Series Plot - GRC

#Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.PRM.CUAT.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.CUAT.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, άνδρες (%) (σωρευτικά)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.CUAT.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)
dat <- wb_data(
  indicator = "SE.PRM.CUAT.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, άνδρες (%) (σωρευτικά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.CUAT.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, γυναίκες (%) (σωρευτικά)

Code - Time Series Plot - GRC

#Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.PRM.CUAT.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.CUAT.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, γυναίκες (%) (σωρευτικά)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.CUAT.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)
dat <- wb_data(
  indicator = "SE.PRM.CUAT.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μορφωτικό επίπεδο, τουλάχιστον ολοκληρωμένο πρωτοβάθμιο εκπαίδευση, πληθυσμός 25+ ετών, γυναίκες (%) (σωρευτικά)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.CUAT.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Primary completion rate, total (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.CMPT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.CMPT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.CMPT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary completion rate, total (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.CMPT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, σύνολο (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.CMPT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Primary completion rate, male (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.CMPT.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.CMPT.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.CMPT.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary completion rate, male (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.CMPT.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, άνδρες (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.CMPT.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)

Code - Time Series Plot - GRC

#Primary completion rate, female (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.CMPT.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.CMPT.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.CMPT.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 45 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary completion rate, female (% of relevant age group)
dat <- wb_data(
  indicator = "SE.PRM.CMPT.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό ολοκλήρωσης πρωτοβάθμιας εκπαίδευσης, γυναίκες (% της σχετικής ηλικιακής ομάδας)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.CMPT.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ηλικία έναρξης του δημοτικού σχολείου (έτη)

Code - Time Series Plot - GRC

#Primary school starting age (years)
dat <- wb_data(
  indicator = "SE.PRM.AGES",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRM.AGES)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ηλικία έναρξης του δημοτικού σχολείου (έτη)",
    caption = "https://data.worldbank.org/indicator/SE.PRM.AGES"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 11 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Primary school starting age (years)
dat <- wb_data(
  indicator = "SE.PRM.AGES",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ηλικία έναρξης του δημοτικού σχολείου (έτη)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRM.AGES"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Επιμορφωμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση (% του συνόλου των εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in preprimary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.PRE.TCAQ.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.TCAQ.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Επιμορφωμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.TCAQ.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in preprimary education (% of total teachers)
dat <- wb_data(
  indicator = "SE.PRE.TCAQ.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Επιμορφωμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση (% του συνόλου των εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.TCAQ.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση, άνδρες (% ανδρών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in preprimary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.PRE.TCAQ.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.TCAQ.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση, άνδρες (% ανδρών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.TCAQ.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in preprimary education, male (% of male teachers)
dat <- wb_data(
  indicator = "SE.PRE.TCAQ.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση, άνδρες (% ανδρών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.TCAQ.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Καταρτισμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)

Code - Time Series Plot - GRC

#Trained teachers in preprimary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.PRE.TCAQ.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.TCAQ.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Καταρτισμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.TCAQ.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 65 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Trained teachers in preprimary education, female (% of female teachers)
dat <- wb_data(
  indicator = "SE.PRE.TCAQ.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Καταρτισμένοι εκπαιδευτικοί στην προσχολική εκπαίδευση, γυναίκες (% των γυναικών εκπαιδευτικών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.TCAQ.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, προσχολική, άνδρες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, preprimary, male (% gross)
dat <- wb_data(
  indicator = "SE.PRE.ENRR.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.ENRR.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, προσχολική, άνδρες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.ENRR.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, preprimary, male (% gross)
dat <- wb_data(
  indicator = "SE.PRE.ENRR.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, προσχολική, άνδρες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.ENRR.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφές στο σχολείο, προσχολική, γυναίκες (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, preprimary, female (% gross)
dat <- wb_data(
  indicator = "SE.PRE.ENRR.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.ENRR.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφές στο σχολείο, προσχολική, γυναίκες (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.ENRR.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, preprimary, female (% gross)
dat <- wb_data(
  indicator = "SE.PRE.ENRR.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφές στο σχολείο, προσχολική, γυναίκες (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.ENRR.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, προσχολική (% ακαθάριστο)

Code - Time Series Plot - GRC

#School enrollment, preprimary (% gross)
dat <- wb_data(
  indicator = "SE.PRE.ENRR",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.ENRR)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, προσχολική (% ακαθάριστο)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.ENRR"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, preprimary (% gross)
dat <- wb_data(
  indicator = "SE.PRE.ENRR",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, προσχολική (% ακαθάριστο)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.ENRR"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Αναλογία μαθητών-εκπαιδευτικών, προσχολική

Code - Time Series Plot - GRC

#Pupil-teacher ratio, preprimary
dat <- wb_data(
  indicator = "SE.PRE.ENRL.TC.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.ENRL.TC.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Αναλογία μαθητών-εκπαιδευτικών, προσχολική",
    caption = "https://data.worldbank.org/indicator/SE.PRE.ENRL.TC.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 18 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Pupil-teacher ratio, preprimary
dat <- wb_data(
  indicator = "SE.PRE.ENRL.TC.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Αναλογία μαθητών-εκπαιδευτικών, προσχολική",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.ENRL.TC.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Προσχολική εκπαίδευση, διάρκεια (έτη)

Code - Time Series Plot - GRC

#Preprimary education, duration (years)
dat <- wb_data(
  indicator = "SE.PRE.DURS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.PRE.DURS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Προσχολική εκπαίδευση, διάρκεια (έτη)",
    caption = "https://data.worldbank.org/indicator/SE.PRE.DURS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 54 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Preprimary education, duration (years)
dat <- wb_data(
  indicator = "SE.PRE.DURS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Προσχολική εκπαίδευση, διάρκεια (έτη)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.PRE.DURS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μαθησιακή φτώχεια: Ποσοστό αρρένων παιδιών στο τέλος της πρωτοβάθμιας ηλικίας κάτω από την ελάχιστη ικανότητα ανάγνωσης, προσαρμοσμένο από παιδιά εκτός σχολείου (%)

Code - Time Series Plot - GRC

#Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)
dat <- wb_data(
  indicator = "SE.LPV.PRIM.MA",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.LPV.PRIM.MA)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μαθησιακή φτώχεια: Ποσοστό αρρένων παιδιών στο τέλος της πρωτοβάθμιας ηλικίας κάτω από την ελάχιστη ικανότητα ανάγνωσης, προσαρμοσμένο από παιδιά εκτός σχολείου (%)",
    caption = "https://data.worldbank.org/indicator/SE.LPV.PRIM.MA"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 64 rows containing missing values or values outside the scale range
(`geom_line()`).

`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

Code - Map Plot

#Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)
dat <- wb_data(
  indicator = "SE.LPV.PRIM.MA",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μαθησιακή φτώχεια: Ποσοστό αρρένων παιδιών στο τέλος της πρωτοβάθμιας ηλικίας κάτω από την ελάχιστη ικανότητα ανάγνωσης, προσαρμοσμένο από παιδιά εκτός σχολείου (%)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.LPV.PRIM.MA"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Μαθησιακή φτώχεια: Ποσοστό κοριτσιών στο τέλος της πρωτοβάθμιας ηλικίας κάτω από την ελάχιστη ικανότητα ανάγνωσης, προσαρμοσμένο από παιδιά εκτός σχολείου (%)

Code - Time Series Plot - GRC

#Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)
dat <- wb_data(
  indicator = "SE.LPV.PRIM.FE",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.LPV.PRIM.FE)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Μαθησιακή φτώχεια: Ποσοστό κοριτσιών στο τέλος της πρωτοβάθμιας ηλικίας κάτω από την ελάχιστη ικανότητα ανάγνωσης, προσαρμοσμένο από παιδιά εκτός σχολείου (%)",
    caption = "https://data.worldbank.org/indicator/SE.LPV.PRIM.FE"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 64 rows containing missing values or values outside the scale range
(`geom_line()`).

`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

Code - Map Plot

#Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)
dat <- wb_data(
  indicator = "SE.LPV.PRIM.FE",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Μαθησιακή φτώχεια: Ποσοστό κοριτσιών στο τέλος της πρωτοβάθμιας ηλικίας κάτω από την ελάχιστη ικανότητα ανάγνωσης, προσαρμοσμένο από παιδιά εκτός σχολείου (%)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.LPV.PRIM.FE"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, τριτοβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)

Code - Time Series Plot - GRC

#School enrollment, tertiary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.TERT.FM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ENR.TERT.FM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, τριτοβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    caption = "https://data.worldbank.org/indicator/SE.ENR.TERT.FM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, tertiary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.TERT.FM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, τριτοβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ENR.TERT.FM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, δευτεροβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)

Code - Time Series Plot - GRC

#School enrollment, secondary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.SECO.FM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ENR.SECO.FM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, δευτεροβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    caption = "https://data.worldbank.org/indicator/SE.ENR.SECO.FM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, secondary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.SECO.FM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, δευτεροβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ENR.SECO.FM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, πρωτοβάθμια και δευτεροβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)

Code - Time Series Plot - GRC

#School enrollment, primary and secondary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.PRSC.FM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ENR.PRSC.FM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, πρωτοβάθμια και δευτεροβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    caption = "https://data.worldbank.org/indicator/SE.ENR.PRSC.FM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary and secondary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.PRSC.FM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, πρωτοβάθμια και δευτεροβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ENR.PRSC.FM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Εγγραφή στο σχολείο, πρωτοβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)

Code - Time Series Plot - GRC

#School enrollment, primary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.PRIM.FM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ENR.PRIM.FM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Εγγραφή στο σχολείο, πρωτοβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    caption = "https://data.worldbank.org/indicator/SE.ENR.PRIM.FM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 15 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#School enrollment, primary (gross), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ENR.PRIM.FM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Εγγραφή στο σχολείο, πρωτοβάθμια (ακαθάριστη), δείκτης ισότητας των φύλων (GPI)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ENR.PRIM.FM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Υποχρεωτική εκπαίδευση, διάρκεια (έτη)

Code - Time Series Plot - GRC

#Compulsory education, duration (years)
dat <- wb_data(
  indicator = "SE.COM.DURS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.COM.DURS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Υποχρεωτική εκπαίδευση, διάρκεια (έτη)",
    caption = "https://data.worldbank.org/indicator/SE.COM.DURS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 39 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Compulsory education, duration (years)
dat <- wb_data(
  indicator = "SE.COM.DURS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Υποχρεωτική εκπαίδευση, διάρκεια (έτη)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.COM.DURS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, σύνολο ενηλίκων (% ατόμων ηλικίας 15 ετών και άνω)

Code - Time Series Plot - GRC

#Literacy rate, adult total (% of people ages 15 and above)
dat <- wb_data(
  indicator = "SE.ADT.LITR.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.LITR.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, σύνολο ενηλίκων (% ατόμων ηλικίας 15 ετών και άνω)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.LITR.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, adult total (% of people ages 15 and above)
dat <- wb_data(
  indicator = "SE.ADT.LITR.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, σύνολο ενηλίκων (% ατόμων ηλικίας 15 ετών και άνω)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.LITR.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, ενήλικοι άνδρες (% των ανδρών ηλικίας 15 ετών και άνω)

Code - Time Series Plot - GRC

#Literacy rate, adult male (% of males ages 15 and above)
dat <- wb_data(
  indicator = "SE.ADT.LITR.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.LITR.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, ενήλικοι άνδρες (% των ανδρών ηλικίας 15 ετών και άνω)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.LITR.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, adult male (% of males ages 15 and above)
dat <- wb_data(
  indicator = "SE.ADT.LITR.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, ενήλικοι άνδρες (% των ανδρών ηλικίας 15 ετών και άνω)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.LITR.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, ενήλικες γυναίκες (% των γυναικών ηλικίας 15 ετών και άνω)

Code - Time Series Plot - GRC

#Literacy rate, adult female (% of females ages 15 and above)
dat <- wb_data(
  indicator = "SE.ADT.LITR.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.LITR.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, ενήλικες γυναίκες (% των γυναικών ηλικίας 15 ετών και άνω)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.LITR.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, adult female (% of females ages 15 and above)
dat <- wb_data(
  indicator = "SE.ADT.LITR.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, ενήλικες γυναίκες (% των γυναικών ηλικίας 15 ετών και άνω)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.LITR.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, σύνολο νέων (% των ατόμων ηλικίας 15-24 ετών)

Code - Time Series Plot - GRC

#Literacy rate, youth total (% of people ages 15-24)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.1524.LT.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, σύνολο νέων (% των ατόμων ηλικίας 15-24 ετών)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.1524.LT.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, youth total (% of people ages 15-24)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, σύνολο νέων (% των ατόμων ηλικίας 15-24 ετών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.1524.LT.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, νέοι άνδρες (% των ανδρών ηλικίας 15-24 ετών)

Code - Time Series Plot - GRC

#Literacy rate, youth male (% of males ages 15-24)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.MA.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.1524.LT.MA.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, νέοι άνδρες (% των ανδρών ηλικίας 15-24 ετών)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.1524.LT.MA.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, youth male (% of males ages 15-24)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.MA.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, νέοι άνδρες (% των ανδρών ηλικίας 15-24 ετών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.1524.LT.MA.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, νέοι (ηλικίες 15-24), δείκτης ισότητας των φύλων (GPI)

Code - Time Series Plot - GRC

#Literacy rate, youth (ages 15-24), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.FM.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.1524.LT.FM.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, νέοι (ηλικίες 15-24), δείκτης ισότητας των φύλων (GPI)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.1524.LT.FM.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, youth (ages 15-24), gender parity index (GPI)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.FM.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, νέοι (ηλικίες 15-24), δείκτης ισότητας των φύλων (GPI)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.1524.LT.FM.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot

Ποσοστό αλφαβητισμού, νέοι γυναίκες (% των γυναικών ηλικίας 15-24 ετών)

Code - Time Series Plot - GRC

#Literacy rate, youth female (% of females ages 15-24)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.FE.ZS",
  country = c("GRC")
) %>% 
  select(country, date, value = SE.ADT.1524.LT.FE.ZS)

ggplot(dat) +
  aes(x = date, y = value, color = country) +
  geom_line() +
  labs(
    x = "", y = "",
    title = "Ποσοστό αλφαβητισμού, νέοι γυναίκες (% των γυναικών ηλικίας 15-24 ετών)",
    caption = "https://data.worldbank.org/indicator/SE.ADT.1524.LT.FE.ZS"
  ) +
  theme_pander() +
  NULL

Plot - Time Series Plot - GRC

Warning: Removed 36 rows containing missing values or values outside the scale range
(`geom_line()`).

Code - Map Plot

#Literacy rate, youth female (% of females ages 15-24)
dat <- wb_data(
  indicator = "SE.ADT.1524.LT.FE.ZS",
  mrv = 10
) %>% 
  rename(value = 5) %>% 
  filter(is.na(value) == FALSE) %>%
  group_by(iso3c) %>%
  filter(date == max(date)) %>% 
  ungroup()

world <- ne_countries(scale = "medium", returnclass = "sf") %>%
  select(iso3c = iso_a3, geometry) 

DF <- left_join(world, dat, by = "iso3c") %>% 
  st_as_sf()

ggplot(DF) +
  geom_sf(aes(fill = value), linewidth = 0.1, color = "white") +
  coord_sf(crs = "ESRI:54030") +
  scale_fill_viridis_c(
    option = "C",     
    na.value = "grey90",
    name = "",
    labels = label_number(accuracy = 1)
  ) +
  labs(
    title = "Ποσοστό αλφαβητισμού, νέοι γυναίκες (% των γυναικών ηλικίας 15-24 ετών)",
    subtitle = "World Bank (latest year varies by country)",
    caption = "Data: https://data.worldbank.org/indicator/SE.ADT.1524.LT.FE.ZS"
  ) +
  theme_void(base_size = 12) +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold")
  )

Plot - Map Plot