Visualize your data with ggplot

Author

Lara Leal

Published

October 10, 2025

After you have finished your downstream analyses, you will want to visualize your results.

R has a powefull package, called ggplot that is very versatile. We work with it a lot, so we will present it to you today.

In order to understand better the possibilities of ggplot, we are going to try them out in this tutorial from Roy Francis at NBIS.

# -----------------------------
# Load packages safely
# -----------------------------

# List all packages you need
packages <- c(
  "dplyr", "tidyr", "stringr",
  "ggplot2", "ggrepel", "patchwork", "extrafont"
)

# Function to check if package is installed, load if yes
safe_load <- function(pkg) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    message(sprintf("Package '%s' is not installed. Skipping.", pkg))
  } else {
    library(pkg, character.only = TRUE)
  }
}

# Load all packages
invisible(lapply(packages, safe_load))

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
Registering fonts with R
# -----------------------------
# Load fonts (optional)
# -----------------------------
if ("extrafont" %in% installed.packages()[, "Package"]) {
  # Only try to load fonts if extrafont is installed
  suppressMessages(loadfonts())  # suppress messages during Quarto render
}

# -----------------------------
# Data loading and plots 
# -----------------------------

data("iris")
head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
ggplot(data=iris)+
  geom_point(mapping=aes(x=Petal.Length,y=Petal.Width)) #basic scatter plot

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point()+
  geom_smooth(method="lm") #add a regression line
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width,color=Species))+
  geom_point()+
  geom_smooth(method="lm") #add colours
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species))+
  geom_smooth(method="lm") #add colours but keep only one regression line
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species),size=3)+
  geom_smooth(method="lm") #change the size of the points
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width))+
  geom_smooth(method="lm") #add another subgoup, in this case the sepal.width
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_manual(values=c("red","blue","green")) #specify colours
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm") #map the colours to a continous variable
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_continuous(name="New Legend Title")+
  labs(title="This Is A Title",subtitle="This is a subtitle",x=" Petal Length", 
       y="Petal Width", caption="This is a little caption.") #add titles
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_continuous(name="New Legend Title")+
  scale_x_continuous(breaks=1:8)+
  labs(title="This Is A Title",subtitle="This is a subtitle",x=" Petal Length", 
       y="Petal Width", caption="This is a little caption.") #modify the axis, start with 1,2,3
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_continuous(name="New Legend Title")+
  scale_x_continuous(breaks=1:8)+
  labs(title="This Is A Title",subtitle="This is a subtitle",x=" Petal Length", 
       y="Petal Width", caption="This is a little caption.")+
  facet_wrap(~Species) #create subplots using faceting function
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_continuous(name="New Legend Title")+
  scale_x_continuous(breaks=1:8)+
  labs(title="This Is A Title",subtitle="This is a subtitle",x=" Petal Length", 
       y="Petal Width", caption="This is a little caption.")+
  facet_wrap(~Species)+
  theme_bw() #change the look of the plot by using the default theme_bw option
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_continuous(name="New Legend Title")+
  scale_x_continuous(breaks=1:8)+
  labs(title="This Is A Title",subtitle="This is a subtitle",x=" Petal Length", 
       y="Petal Width", caption="This is a little caption.")+
  facet_wrap(~Species)+
  theme_bw()+
  theme(
    axis.title=element_text(color="Blue",face="bold"),
    plot.title=element_text(color="Green",face="bold"),
    plot.subtitle=element_text(color="Pink"),
    panel.grid=element_blank()
  ) #use the theme to chnage the colours of the axis and title/subtitle
`geom_smooth()` using formula = 'y ~ x'

newtheme <- theme(
  axis.title=element_text(color="lightblue1",face="bold"),
  plot.title=element_text(color="khaki4",face="bold"),
  plot.subtitle=element_text(color="lavender"),
  panel.grid=element_blank()) #this will save the theme and afterwards this same theme can be reused

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Sepal.Width))+
  geom_smooth(method="lm")+
  scale_color_continuous(name="New Legend Title")+
  scale_x_continuous(breaks=1:8)+
  labs(title="This Is A Title",subtitle="This is a subtitle",x=" Petal Length", 
       y="Petal Width", caption="This is a little caption.")+
  facet_wrap(~Species)+
  theme_bw()+
  newtheme
`geom_smooth()` using formula = 'y ~ x'

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width)) #it shows 2 legends

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width))+
  guides(size="none") #we removed one legend

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width),show.legend=FALSE) #turning off both legends using geom

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width))+
  theme(legend.position="top",
        legend.justification="right") #use theme to move the position of the legends

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species,size=Sepal.Width))+
  guides(size=guide_legend(nrow=2,byrow=TRUE),
         color=guide_legend(nrow=3,byrow=T))+
  theme(legend.position="top",
        legend.justification="right") #control of the legends rows

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species))+
  geom_text(aes(label=Species,hjust=0),nudge_x=0.5,size=3) # add labels to the data/points

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species))+
  geom_label(aes(label=Species,hjust=0),nudge_x=0.5,size=3) #edit the labels theme

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species))+
  geom_text_repel(aes(label=Species),size=3) #use ggrepel to avoid having the labels overlapping
Warning: ggrepel: 130 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

ggplot(data=iris,mapping=aes(x=Petal.Length,y=Petal.Width))+
  geom_point(aes(color=Species))+
  annotate("text",x=2.5,y=2.1,label="There is a random line here")+
  annotate("segment",x=2,xend=4,y=1.5,yend=2) #add customn annotations

ggplot(data=iris,mapping=aes(x=Species,y=Petal.Width))+
  geom_bar(stat="identity") #BARPLOT

ggplot(data=iris,mapping=aes(x=Species,y=Petal.Width))+
  geom_bar(stat="identity")+
  coord_flip() #flip the axes

dfr <- iris %>% group_by(Species) %>% 
  summarise(mean=mean(Sepal.Length),sd=sd(Sepal.Length)) %>%
  mutate(high=mean+sd,low=mean-sd) #mean and standard deviation 

ggplot(data=dfr,mapping=aes(x=Species,y=mean,color=Species))+
  geom_point(size=4)+
  geom_errorbar(aes(ymax=high,ymin=low),width=0.2) #plotting the error bars

###### ECONOMIST SCATTERPLOT #######

ec <- read.csv("/Users/lara/Downloads/data_economist.csv",header=T) #import the data from the .csv file
head(ec) #read the data
  X     Country HDI.Rank   HDI CPI            Region
1 1 Afghanistan      172 0.398 1.5      Asia Pacific
2 2     Albania       70 0.739 3.1 East EU Cemt Asia
3 3     Algeria       96 0.698 2.9              MENA
4 4      Angola      148 0.486 2.0               SSA
5 5   Argentina       45 0.797 3.0          Americas
6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
str(ec) #check that HDI and CPI are numerical variables and Region is a categorical one/factor
'data.frame':   173 obs. of  6 variables:
 $ X       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Country : chr  "Afghanistan" "Albania" "Algeria" "Angola" ...
 $ HDI.Rank: int  172 70 96 148 45 86 2 19 91 53 ...
 $ HDI     : num  0.398 0.739 0.698 0.486 0.797 0.716 0.929 0.885 0.7 0.771 ...
 $ CPI     : num  1.5 3.1 2.9 2 3 2.6 8.8 7.8 2.4 7.3 ...
 $ Region  : chr  "Asia Pacific" "East EU Cemt Asia" "MENA" "SSA" ...
levels(ec$Region) # we can't see the levels, therefore we need to modify the Regions variable
NULL
ec$Region <- factor(ec$Region,levels = c("EU W. Europe",
                                         "Americas",
                                         "Asia Pacific",
                                         "East EU Cemt Asia",
                                         "MENA",
                                         "SSA"),
                    labels = c("OECD",
                               "Americas",
                               "Asia &\nOceania",
                               "Central &\nEastern Europe",
                               "Middle East &\nNorth Africa",
                               "Sub-Saharan\nAfrica")) #use factor levels and labels to rename factors

levels(ec$Region) #check that we have made the change
[1] "OECD"                        "Americas"                   
[3] "Asia &\nOceania"             "Central &\nEastern Europe"  
[5] "Middle East &\nNorth Africa" "Sub-Saharan\nAfrica"        
ggplot(ec,aes(x=CPI,y=HDI,color=Region))+
  geom_point(shape=21,size=3,stroke=0.8,fill="white") #generate a plot with points without fill. 

ggplot(ec,aes(x=CPI,y=HDI,color=Region))+
  geom_point(shape=21,size=3,stroke=0.8,fill="white")+
  geom_smooth(method="lm",formula=y~poly(x,2),se=F,size=0.6,color="red") #add a trendline in red
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

p <- ggplot(ec,aes(x=CPI,y=HDI,color=Region))+
  geom_smooth(aes(fill="red"),method="lm",formula=y~poly(x,2),se=F,color="red",size=0.6)+
  geom_point(shape=21,size=3,stroke=0.8,fill="white") #we want the points on top of the line so we assign the plot to a variable and alter the order of geom_smooth and geom_point
p #to print/see the plot

labels <- c("Congo","Afghanistan","Sudan","Myanmar","Iraq","Venezuela","Russia","Argentina","Brazil","Italy","South Africa","Cape Verde","Bhutan","Botswana","Britian","New Zealand","Greece","China","India","Rwanda","Spain","France","United States","Japan","Norway","Singapore","Barbados","Germany")

p+geom_text(data=subset(ec,Country %in% labels),aes(label=Country),color="black") #add the labels to the new coutries on the plot

########
#IF YOU WAN TO CHANGE THE FONT 
font_import(pattern="Georgia",prompt=FALSE)
Scanning ttf files in /Library/Fonts/, /System/Library/Fonts, /System/Library/Fonts/Supplemental, ~/Library/Fonts/ ...
Extracting .afm files from .ttf files...
/System/Library/Fonts/SFGeorgian.ttf : -SFGeorgian-Regular already registered in fonts database. Skipping.
/System/Library/Fonts/SFGeorgianRounded.ttf : -SFGeorgianRounded-Regular already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia Bold Italic.ttf : Georgia-BoldItalic already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia Bold Italic.ttf : Georgia-BoldItalic already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia Bold.ttf : Georgia-Bold already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia Bold.ttf : Georgia-Bold already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia Italic.ttf : Georgia-Italic already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia Italic.ttf : Georgia-Italic already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia.ttf : Georgia already registered in fonts database. Skipping.
/System/Library/Fonts/Supplemental/Georgia.ttf : Georgia already registered in fonts database. Skipping.
Found FontName for 0 fonts.
Scanning afm files in /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library/extrafontdb/metrics
# load fonts for pdf
#loadfonts()
# list available fonts in R
#fonts()
########

p+geom_text(data=subset(ec,Country %in% labels),aes(label=Country),
            color="black",family="Georgia") #change the font of the labels

library(ggrepel)
p <- p+geom_text_repel(data=subset(ec,Country %in% labels),aes(label=Country),
                       color="black",box.padding=unit(1,'lines'),segment.size=0.25,
                       size=3,family="Georgia") #solve the overlap of the labels
p

p <- p+scale_x_continuous(name="Corruption Perceptions Index, 2011 (10=least corrupt)",
                          breaks=1:10,limits=c(1,10))+
  scale_y_continuous(name="Human Development Index, 2011 (1=best)",
                     breaks=seq(from=0,to=1,by=0.1),limits=c(0.2,1)) #adjust the axes
p

p <- p+scale_color_manual(values=c("#23576E","#099FDB","#29B00E", "#208F84","#F55840","#924F3E"))+
  scale_fill_manual(name="trend",values="red",labels=expression(paste(R^2,"=52%"))) #to change the colors of the points and the red fill label 
p

#I don't want to change the colors so I'm just going to add the new label
p <- p+scale_fill_manual(name="trend",values="red",labels=expression(paste(R^2,"=52%")))
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
p

p <- p+labs(title="Corruption and human development",
            caption="Sources: Transparency International; UN Human Development Report") #add a title to the plot 
p

p <- p+guides(color=guide_legend(nrow=1))+
  theme_bw(base_family="Georgia")+
  theme(legend.position="top") #move the legend to the top and change the font in all the text
p

### Now we do some careful refining with themes.

#Turn off minor gridlines
#Turn off major gridlines on x-axis
#Remove the gray background
#Remove panel border
#Remove legend titles
#Make axes titles italic
#Turn off y-axis ticks
#Change x-axis ticks to color grey60
#Make plot title bold
#Decrease size of caption to size 8

p+theme(panel.grid.minor=element_blank(),
        panel.grid.major.x=element_blank(),
        panel.background=element_blank(),
        panel.border=element_blank(),
        legend.title=element_blank(),
        axis.title=element_text(face="italic"),
        axis.ticks.y=element_blank(),
        axis.ticks.x=element_line(color="grey60"),
        plot.title=element_text(face="bold"),
        plot.caption=element_text(hjust=0,size=8))

sessionInfo()
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.6.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Stockholm
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] extrafont_0.20  patchwork_1.3.2 ggrepel_0.9.6   ggplot2_4.0.0  
[5] stringr_1.5.2   tidyr_1.3.1     dplyr_1.1.4    

loaded via a namespace (and not attached):
 [1] Matrix_1.7-3       gtable_0.3.6       jsonlite_2.0.0     compiler_4.5.1    
 [5] tidyselect_1.2.1   Rcpp_1.1.0         splines_4.5.1      scales_1.4.0      
 [9] yaml_2.3.10        fastmap_1.2.0      lattice_0.22-7     R6_2.6.1          
[13] labeling_0.4.3     generics_0.1.4     knitr_1.50         tibble_3.3.0      
[17] pillar_1.11.1      RColorBrewer_1.1-3 rlang_1.1.6        Rttf2pt1_1.3.14   
[21] stringi_1.8.7      xfun_0.53          S7_0.2.0           cli_3.6.5         
[25] mgcv_1.9-3         withr_3.0.2        magrittr_2.0.4     digest_0.6.37     
[29] grid_4.5.1         nlme_3.1-168       lifecycle_1.0.4    vctrs_0.6.5       
[33] evaluate_1.0.5     glue_1.8.0         extrafontdb_1.1    farver_2.1.2      
[37] rmarkdown_2.30     purrr_1.1.0        tools_4.5.1        pkgconfig_2.0.3   
[41] htmltools_0.5.8.1