CODEWITHSUNDEEP
rggplot2
ambrish dhaka
ambrish dhaka

Reputation: 749

Positioning of label text in ggplot2 in R

I am making line graphs using geom_smooth and labelling it using geom_dl. The lines also have geom_points as an additional display. The texts are positioned on the points and I want the text to be positioned on the tails of the smooth lines. My code is given as below.

library(ggplot2)
library(directlabels)

ggplot(gedu5, aes(x = Year, y = gedu, colour = Country, group = Country)) + 
  geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE)+
  geom_point(stat = "identity") +
  scale_colour_discrete(guide = 'none') +
  geom_dl(aes(label = Country), method = list(dl.combine("first.points", "last.points"), cex = 1)) +
  labs(title = "Government expenditure on education, total (% of GDP)", cex.main = 2.5) +
  theme(axis.title = element_blank()) +
  facet_wrap(.~Region, 2, scales="free")

The Image it renders is given as below. As you can see in the image image that labels are actually pegged on the points and not on the lines. I want to stick the labels to the lines and not to the points.

The data for the above image is here:

structure(list(Year = c(1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 
2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 
2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 
2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 
2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 
2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 
2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 
2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 
2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 
2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 
2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 
2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 
2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 
2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 
2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 
2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 
2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 1999L, 2000L, 
2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 
2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 1998L, 
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 
2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 
2017L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 
2015L, 2016L, 2017L), Country = c("Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", 
"Afghanistan", "Afghanistan", "Afghanistan", "Bangladesh", "Bangladesh", 
"Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", 
"Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", 
"Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", 
"Bangladesh", "Bangladesh", "Bangladesh", "Bhutan", "Bhutan", 
"Bhutan", "Bhutan", "Bhutan", "Bhutan", "Bhutan", "Bhutan", "Bhutan", 
"Bhutan", "Bhutan", "Bhutan", "Bhutan", "Bhutan", "Bhutan", "Bhutan", 
"Bhutan", "Bhutan", "Bhutan", "Bhutan", "India", "India", "India", 
"India", "India", "India", "India", "India", "India", "India", 
"India", "India", "India", "India", "India", "India", "India", 
"India", "India", "India", "Maldives", "Maldives", "Maldives", 
"Maldives", "Maldives", "Maldives", "Maldives", "Maldives", "Maldives", 
"Maldives", "Maldives", "Maldives", "Maldives", "Maldives", "Maldives", 
"Maldives", "Maldives", "Maldives", "Maldives", "Maldives", "Nepal", 
"Nepal", "Nepal", "Nepal", "Nepal", "Nepal", "Nepal", "Nepal", 
"Nepal", "Nepal", "Nepal", "Nepal", "Nepal", "Nepal", "Nepal", 
"Nepal", "Nepal", "Nepal", "Nepal", "Nepal", "Pakistan", "Pakistan", 
"Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", 
"Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", 
"Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", 
"Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", 
"Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", 
"Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", 
"Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", "Sri_Lanka", 
"World", "World", "World", "World", "World", "World", "World", 
"World", "World", "World", "World", "World", "World", "World", 
"World", "World", "World", "World", "World", "World", "South_Asia", 
"South_Asia", "South_Asia", "South_Asia", "South_Asia", "South_Asia", 
"South_Asia", "South_Asia", "South_Asia", "South_Asia", "South_Asia", 
"South_Asia", "South_Asia", "South_Asia", "South_Asia", "South_Asia", 
"South_Asia", "South_Asia", "South_Asia", "South_Asia", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Sub.Saharan_Africa", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Sub.Saharan_Africa", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Sub.Saharan_Africa", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Sub.Saharan_Africa", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Sub.Saharan_Africa", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Sub.Saharan_Africa", "Sub.Saharan_Africa", 
"Sub.Saharan_Africa", "Middle_income", "Middle_income", "Middle_income", 
"Middle_income", "Middle_income", "Middle_income", "Middle_income", 
"Middle_income", "Middle_income", "Middle_income", "Middle_income", 
"Middle_income", "Middle_income", "Middle_income", "Middle_income", 
"Middle_income", "Middle_income", "Middle_income", "Middle_income", 
"Middle_income", "Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Central_Europe_and_the_Baltics", "Central_Europe_and_the_Baltics", 
"Euro_area", "Euro_area", "Euro_area", "Euro_area", "Euro_area", 
"Euro_area", "Euro_area", "Euro_area", "Euro_area", "Euro_area", 
"Euro_area", "Euro_area", "Euro_area", "Euro_area", "Euro_area", 
"Euro_area", "Euro_area", "Euro_area", "Euro_area", "Euro_area", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Middle_East_North_Africa", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Middle_East_North_Africa", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Middle_East_North_Africa", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Middle_East_North_Africa", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Middle_East_North_Africa", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Middle_East_North_Africa", 
"Middle_East_North_Africa", "Middle_East_North_Africa", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "Latin_America_Caribbean", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "Latin_America_Caribbean", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "Latin_America_Caribbean", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "Latin_America_Caribbean", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "Latin_America_Caribbean", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "Latin_America_Caribbean", "Latin_America_Caribbean", 
"Latin_America_Caribbean", "North_America", "North_America", 
"North_America", "North_America", "North_America", "North_America", 
"North_America", "North_America", "North_America", "North_America", 
"North_America", "North_America", "North_America", "North_America", 
"North_America", "North_America", "North_America", "North_America", 
"North_America", "North_America", "Low_income", "Low_income", 
"Low_income", "Low_income", "Low_income", "Low_income", "Low_income", 
"Low_income", "Low_income", "Low_income", "Low_income", "Low_income", 
"Low_income", "Low_income", "Low_income", "Low_income", "Low_income", 
"Low_income", "Low_income", "Low_income"), gedu = c(3.5, 3.8, 
3.7, 3.3, 2.9, 2.6, 2.7, 3, 3.4, 3.7, 3.8, 3.7, 3.5, 3.5, 2.6, 
3.5, 3.7, 3.3, 4.2, 4.1, 2.1, 2.1, 2.1, 2.2, 2, 2.1, 1.9, 2.1, 
2.1, 2.2, 2, 1.9, 2, 2.1, 2.2, 2, 1.5, 1.3, 1.5, 2.5, 5.5, 5.7, 
5.5, 5.7, 6.2, 6.8, 6.4, 7.1, 5.4, 4.8, 4.8, 4.6, 4, 4.7, 5.5, 
5.6, 5.9, 7.3, 6.8, 7, 3.6, 4.4, 4.3, 4.2, 4, 3.6, 3.4, 3.2, 
3.1, 3.2, 3.1, 3.3, 3.4, 3.8, 3.9, 3.8, 3.7, 3.9, 3.9, 3.8, 5.8, 
5.5, 4.9, 4.7, 5.8, 5.3, 4.7, 5, 4.7, 4.4, 4.5, 5.8, 4.1, 3.5, 
3.8, 3.8, 3.5, 4.4, 4.1, 4.1, 2.9, 2.9, 3, 3.7, 3.1, 3.1, 3.2, 
3.4, 3.6, 3.5, 3.8, 4.7, 3.6, 3.8, 3.8, 3.5, 4, 3.7, 4.4, 5.5, 
2.6, 2.6, 1.8, 1.5, 1.6, 1.9, 2, 2.2, 2.6, 2.6, 2.8, 2.6, 2.3, 
2.2, 2.1, 2.5, 2.5, 2.6, 3, 2.9, 3, 5.2, 6.6, 7.3, 7.5, 7.2, 
6.6, 5.7, 4.7, 3.7, 2.8, 2.1, 1.7, 1.8, 1.5, 1.6, 1.9, 2.2, 3.5, 
2.8, 4.2, 4.2, 3.9, 4, 4, 4.1, 4, 4.1, 4.2, 4.2, 4.3, 4.7, 4.5, 
4.3, 4.3, 4.6, 4.6, 4.7, 4.5, 4.5, 3, 2.8, 2.5, 2.8, 3.2, 3.1, 
2.6, 3.2, 2.9, 2.6, 2.8, 2.6, 3.4, 2.8, 2.4, 3, 3.1, 3, 3.5, 
2.9, 3.3, 3.3, 3.1, 3.4, 3.1, 3.2, 3.3, 3.6, 3.6, 3.5, 3.6, 3.9, 
3.8, 3.9, 4.1, 4, 4.3, 4.5, 3.9, 4.5, 4.1, 4.1, 3.6, 3.7, 3.4, 
3.6, 3.5, 3.9, 3.9, 3.6, 4, 4.5, 4.5, 4, 4.2, 4.3, 4.4, 4.3, 
4.2, 4.4, 5, 4.6, 4.9, 5.1, 5.2, 5.1, 4.9, 4.8, 4.7, 4.6, 4.8, 
5, 4.8, 4.6, 4.5, 4.6, 4.6, 4.8, 4.7, 4.7, 4.5, 4.8, 4.6, 5.2, 
5.3, 5.1, 5, 5, 4.8, 4.7, 5.1, 5.6, 5.5, 5.1, 5, 5.3, 5.3, 4.9, 
4.8, 4.8, 6.2, 6.3, 6.3, 6.2, 6.1, 6, 5.4, 5.6, 4.3, 5.2, 4.5, 
4.7, 4.5, 4.4, 4.2, 4.2, 4.1, 4.2, 4.3, 4.5, 3.3, 3.8, 3.8, 3.8, 
3.9, 3.5, 3.8, 3.9, 3.8, 3.9, 4.6, 5.3, 4.7, 4.4, 4.4, 5, 5.3, 
4.9, 4.5, 4.5, 5.5, 5.5, 5.4, 5, 5, 5, 5, 4.8, 4.8, 4.8, 4.6, 
4.8, 5.4, 5.3, 5.3, 4.9, 5, 5, 4.9, 5, 3.2, 3.2, 2.9, 3.3, 3.4, 
3.2, 3.1, 3.1, 3.3, 3.4, 3.4, 3.5, 3.5, 3.8, 3.8, 3.1, 3.3, 3.8, 
3.1, 3.5), Region = c("South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "South Asia", "South Asia", "South Asia", 
"South Asia", "South Asia", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region", 
"Region", "Region", "Region", "Region", "Region", "Region", "Region"
)), row.names = c(NA, -360L), class = "data.frame")

Upvotes: 1

Views: 374

Answers (2)

Paul
Paul

Reputation: 9087

This is a way to do it by creating a custom method for directlabels.

Custom methods are functions with parameters d and ... which return a data frame. d is a data frame with x, y, groups and a few other columns.

Create a custom method that creates the lm for each group. Keep the first and last values and set the hjust appropriately.

library(tidyverse)
library(directlabels)

first_last_poly3 <- function(d, ...) {
  d %>%
    group_by(groups) %>%
    group_modify(function(g, ...) {
      mutate(g, y = predict(lm(y ~ poly(x, 3), data = g)))
    }) %>%
    filter(row_number() %in% c(1, n())) %>%
    mutate(hjust = ifelse(row_number() == 1, 1, 0))
}

Create the plot and use first_last_poly3 as the geom_dl method.

ggplot(gedu5, aes(x = Year, y = gedu, colour = Country, group = Country)) + 
  geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE)+
  geom_point(stat = "identity") +
  scale_colour_discrete(guide = 'none') +
  geom_dl(aes(label = Country), method = first_last_poly3) +
  labs(title = "Government expenditure on education, total (% of GDP)", cex.main = 2.5) +
  theme(axis.title = element_blank()) +
  facet_wrap(.~Region, 2, scales="free")

plot

Upvotes: 1

stefan
stefan

Reputation: 123783

The issue is that geom_dl uses the value of gedu in your dataset and not the fitted values computed by geom_smooth.

I tried

  1. passing stat="smooth" to geom_dl which resulted in no labels appearing on the plot

  2. adding labels at the endpoints manually using stat_smooth with geom="text" which however did not work

Therefore the only solution I could figure out is to compute the fitted values manually which can then be mapped on y in geom_dl. For computing the fittes values I

  1. tidyr::nest the df by Country and Region
  2. use purrr::map and broom::augment to do the estimation and to get the fitted values
  3. tidyr::unnest and keep only the desired columns
library(ggplot2)
library(directlabels)

library(purrr)
library(broom)
library(tidyr)
library(dplyr)

gedu5 %>% 
  # Manually compute the fitted values
  nest(data = -c(Region, Country)) %>% 
  mutate(mod = map(data, ~ lm(gedu ~ poly(Year, 3), data = .x)),
         mod = map2(mod, data, augment)) %>% 
  unnest(mod) %>% 
  select(Country, Region, Year, gedu, .fitted) %>% 
  ggplot(aes(x = Year, y = gedu, colour = Country, group = Country)) + 
  geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE) +
  geom_dl(aes(y = .fitted, label = Country), method = list(dl.combine("first.points", "last.points"), cex = 1)) +
  geom_point(stat = "identity") +
  scale_colour_discrete(guide = "none") +
  labs(title = "Government expenditure on education, total (% of GDP)", cex.main = 2.5) +
  theme(axis.title = element_blank()) +
  facet_wrap(.~Region, 2, scales="free")

Upvotes: 1

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