R ggplot tutorial: scatter plot

Learning objectives

Learn how to create a useful and attractive scatter plot using ggplot.
Create scatter plot where color and size of the points vary with variables and values.
Learn how to modify axis and plot properties.
Export ggplot image in desired resolution/dimension.

Content

The tutorial will guide from beginner level (level 1) to the Pro level in scatter plot. A lot of the functions used in the tutorial will be useful while plotting barplot, boxplot, line plot, etc. Below is the list of topics that are coverd in this page.

Simple ggplot
Axis limit and interval
ggplot themes
Point colors
Color of points by values
Point color/size by values
Draw best fit line
Pro in scatter plot
Export ggplot image

library(ggplot2) # if you haven't installed ggplot2, then enter install.packages('ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.3

# Check default dataset in R
data()

# we will use R default dataset "trees". It contains diameter, 
# height and volume for Black Cherry Trees
head(trees)

##   Girth Height Volume
## 1   8.3     70   10.3
## 2   8.6     65   10.3
## 3   8.8     63   10.2
## 4  10.5     72   16.4
## 5  10.7     81   18.8
## 6  10.8     83   19.7

plot(trees[,'Height'], trees[,'Volume'], xlab = c('Height'), ylab = c('Volume'))

Level 1: Simple ggplot

ggplot(trees, aes(x = Height, y = Volume)) +
  geom_point() +
  xlab('This is height') + ylab('This is volume')

Level 2: Axis limit and interval

use xlim() and ylim() functions to fix the axis limit
use scale_x_continuous() and scale_y_continuous() to add intervals to the axis labels

ggplot(trees, aes(x = Height, y = Volume))+
  geom_point() +
  xlab('This is height')+ ylab('This is volume')+
  xlim(c(30,90)) +
  ylim(c(0,100))

  # scale_y_continuous(breaks = seq(0, 80, by=10), limits=c(0,80))+
  # scale_x_continuous(breaks = seq(0, 100, by=10), limits=c(0,100))

Level 3: ggplot themes

ggplot(trees, aes(x= Height, y = Volume))+
  geom_point()+
  xlab('This is height')+ ylab('This is volume')+
  theme_bw()

  # theme_classic()+
  # theme_minimal()+
  # theme_gray()+
  # theme(axis.text.x = element_text(color = "grey20", size = 10),
  #     # axis.text.x = element_text(color = "grey20", size = 10, angle = 0, hjust = .5, vjust = .5),
  #       axis.title.x = element_text(color = "grey20", size = 12))+
  # theme(text = element_text(size=15))+

Level 4: Point colors

ggplot(trees, aes(x= Height, y = Volume))+
  geom_point(color = 'red',size = 2)+
  # geom_point(color = 'black',size = 2, fill = 'red',pch=21)+
  theme_bw()

  # definition of pch can be found here https://www.datanovia.com/en/blog/ggplot-point-shapes-best-tips/

Level 5: Color of points by values

ggplot(trees, aes(x= Height, y = Volume, fill = Girth))+
  geom_point(color = 'black',size = 3, pch=21)+
  theme_bw()+
  scale_fill_gradientn(colours = terrain.colors(10))

  # scale_fill_gradient(name = 'aa', low = "blue", high = "red")+
  # theme(legend.position = 'bottom',
  #       legend.direction = "horizontal")+
  # theme(legend.position = c(0.1, 0.7),
  #       legend.background = element_rect(linetype='solid', color='black', size =0.5))
  # ggtitle("Plot of height vs volume")

Level 6: Point color/size by values

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(iris, aes(x= Sepal.Length, y = Petal.Length, fill = Species))+
# ggplot(iris, aes(x= Sepal.Length, y = Petal.Length, fill = Species, size = Petal.Length))+
  geom_point(color = 'black', pch=21)+
  # scale_fill_gradientn(colours = terrain.colors(10))+
  # scale_fill_gradient(name = 'aa', low = "blue", high = "red")+
  # theme(legend.position = 'bottom',
  #       legend.direction = "horizontal")+
  # ggtitle("Plot of height vs volume")+
  theme_bw()

Level 7: Draw best fit line

ggplot(trees, aes(x= Height, y = Volume, fill = Girth))+
  geom_point(color = 'black',size = 3, pch=21)+
  geom_smooth(method='lm',se=F,alpha=.5)+
  theme_bw()

## `geom_smooth()` using formula 'y ~ x'

ggplot(iris, aes(x= Sepal.Length, y = Petal.Length, fill = Species))+
  geom_point(color = 'black',size = 3, pch=21)+
  geom_smooth(aes(color = Species),method='lm',se=F,alpha=.5)+
  theme_bw()

## `geom_smooth()` using formula 'y ~ x'

Level 8: Pro in scatter plot

p =ggplot(iris, aes(x= Sepal.Length, y = Petal.Length, fill = Species))+
  geom_point(color = 'black',size = 3, pch=21)+
  scale_y_continuous(breaks = seq(1, 8, by=1), limits=c(1,8))+
  scale_x_continuous(breaks = seq(3, 9, by=1), limits=c(3,9))+
  xlab('Sepal length')+ ylab('Petal length')+
  theme_bw()+
  theme(axis.text.y = element_text(color = "black", size = 12),
        axis.title.y = element_text(color = "black", size = 13),
        axis.text.x = element_text(color = "black", size = 12, angle = 0, hjust = .5, vjust = .5),
        axis.title.x = element_text(color = "black", size = 13))+
  scale_fill_manual(values=c('#999999','#E69F00','#56B4E9'))+
  theme(legend.position = c(0.2, 0.8),
        legend.background = element_rect(linetype='solid', color='black', size =0.5))+
  annotate("text", x = 8, y = 2, label = "Scatter plot", size = 5)

p

Export ggplot image

# export the image in png format 
#setting working directory to export image.
setwd('C:/sarfaraz/Project_R_tutorials/R-tutorial/R_beginner_part3_files/')
ggsave(p,filename="scatterplot.png",
       width = 20, height = 15, units = "cm")