Content
The tutorial will guide from beginner level (level 1) to the Pro level in barplot and boxplot. Some of the functions used in this tutorial are introduced in the scatter plot tutorial, Below is the list of topics that are covered in this page.
- Simple barplot using ggplot
- Edit color and format of barplot
- Barplot by group
- Order variables in barplot
- Barplot in multiple panels
- Export ggplot image
- Simple boxplot using ggplot
- Edit format of boxplot
- Boxplot by group
- log10 scale
# We will use "iris" dataset that comes in R by default. Check the data
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
# Lets say we want to create a barplot of the mean Petal Length under each Species
# Calculating mean for each Species
df = aggregate(iris[,1:4], by = list(iris$Species), FUN = mean)
df
## Group.1 Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026
# Typical barplot
barplot(Petal.Length~Group.1, data = df,xlab = c('Species'), ylab = c('Petal Length'))
Bar plot using ggplot
Level 1: Simple ggplot
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.3
ggplot(df, aes(x = Group.1, y = Petal.Length)) +
geom_bar(stat="identity") +
xlab('Species') + ylab('Petal Length')
Level 3: Barplot by group
- To plot multiple variables in the same barplot, we need to arrange the data in a suitable format.
- Lets first see how the iris data looks like after rearranging. It would be easier to explain from that.
- We will use a function called melt under the library “reshape2”.
library(reshape2) # if not installed, then install using install.packages('reshape2')
md.df = melt(df, id.vars = c('Group.1'))
# md.df has three columns where we brought all variables in same column and all Species info in the same column.
head(md.df)
## Group.1 variable value
## 1 setosa Sepal.Length 5.006
## 2 versicolor Sepal.Length 5.936
## 3 virginica Sepal.Length 6.588
## 4 setosa Sepal.Width 3.428
## 5 versicolor Sepal.Width 2.770
## 6 virginica Sepal.Width 2.974
p = ggplot(md.df, aes(x = Group.1, y = value, group = variable, fill = variable)) +
geom_bar(stat="identity",color='black', position = "dodge") +
xlab('Species') + ylab('Values') + theme_bw()+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
ggtitle("Barplot by group")
p1 = p + scale_fill_discrete(name = "Characteristics", labels = c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"))
grid.arrange(p, p1, ncol=2)
Level 4: Order variables in barplot
- There are multiple ways to order barplot by variables. I found the following method the easiest.
# use the following three lines of code to order the plot. Here, tmp stores the desired order
tmp = c("virginica","setosa","versicolor")
md.df2 = md.df[order(match(md.df$Group.1, tmp)),]
md.df2$Group.1 = factor(as.character(md.df2$Group.1), levels = unique(md.df2$Group.1))
ggplot(md.df2, aes(x = Group.1, y = value, group = variable, fill = variable)) +
geom_bar(stat="identity",color='black', position = "dodge") +
xlab('Species') + ylab('Values') + theme_bw()+
ylim(0,8)+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
ggtitle("Order variables in barplot")+
geom_text(aes(label=value), vjust=-0.3, size=4, # adding values
position = position_dodge(0.9))
Level 5: Barplot in multiple panels
- Use facet_wrap() function to plot in multiple panels.
p = ggplot(md.df, aes(x = Group.1, y = value, fill = variable)) +
geom_bar(stat="identity",color='black', position = "dodge") +
xlab('Species') + ylab('Values') + theme_bw()+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = 'none')+ #removing legend
ggtitle("Barplot in multiple panels")+
facet_wrap(~ variable, ncol = 2)
p
Export ggplot image
#setting working directory to export image.
setwd('C:/sarfaraz/Project_R_tutorials/R-tutorial/R_beginner_part3_files/')
ggsave(p,filename="barplot.png",
width = 20, height = 15, units = "cm")
Boxplot using ggplot
# Typical boxplot
boxplot(Petal.Length~Species, data = iris, xlab = c('Species'), ylab = c('Petal Length'))
Level 1: Simple ggplot
ggplot(iris, aes(x = Species, y = Petal.Length)) +
geom_boxplot() +
xlab('Species') + ylab('Petal Length')
Level 2: Edit format, axis, title
Stepwise tutorial of editing each item is shown in an earlier tutorial on scatter plot Here, the functions are directly applied.
p = ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
geom_boxplot() +
xlab('Species') + ylab('Petal Length') + theme_bw()+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=90, hjust=.5, vjust = 0.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
scale_y_continuous(breaks = seq(0, 8, by=1), limits=c(0,8))+
ggtitle("Boxplot using ggplot")
# Draw a horizontal line using geom_hline(). Vertical line can be drawn using geom_vline(). The legend can be removed using theme(legend.position = "none").
p1 = p + geom_hline(yintercept=3.5, linetype="dashed",
color = "red", size=1)+
theme(legend.position = "none") +
labs(title = "Horizontal line + no legend",
subtitle = "subtitle here",
caption = 'caption here')+
theme(
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(color = "red", size = 11, face = "bold"),
plot.subtitle = element_text(color = "blue", size = 9),
plot.caption = element_text(color = "black", face = "italic")
)
# Manual change in axis values in the following way. Here, 2, 4 and 7 are replaced with a, b and c. User can input other names and break intervals
p2 = ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
geom_boxplot() +
xlab('Species') + ylab('Petal Length') + theme_bw()+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
theme(legend.position = "none") +
scale_y_continuous(breaks=c(2,4,7),
labels=c("a","b","c"))+
scale_x_discrete(breaks=c('setosa','versicolor','virginica'),
labels=c("type1","type2","type3"))+
ggtitle("Manually change axis labels")+
theme(plot.title = element_text(color = "black", size = 11, face = "bold"))
library(gridExtra)
grid.arrange(p, p1,p2, ncol=3)
Level 3: Boxplot by group
- To plot multiple variables in the same boxplot, we need to arrange the data in a suitable format.
- Lets first see how the iris data looks like after rearranging. It would be easier to explain from that.
- We will use a function called melt under the library “reshape2”.
library(reshape2) # if not installed, then install using install.packages('reshape2')
md.df = melt(iris, id.vars = c('Species'))
# md.df has three columns where we brought all variables in same column and all Species info in the same column.
head(md.df)
## Species variable value
## 1 setosa Sepal.Length 5.1
## 2 setosa Sepal.Length 4.9
## 3 setosa Sepal.Length 4.7
## 4 setosa Sepal.Length 4.6
## 5 setosa Sepal.Length 5.0
## 6 setosa Sepal.Length 5.4
p = ggplot(md.df, aes(x = Species, y = value, fill = variable)) +
geom_boxplot() +
xlab('Species') + ylab('Values') + theme_bw()+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
ggtitle("Boxplot by group")+
scale_fill_discrete(name = "Characteristics", labels = c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"))
p1 = p +
geom_jitter(position=position_dodge(0.8))+
ggtitle("Boxplot with point jitters")
grid.arrange(p, p1, ncol=2)
Level 4: Log scale
Here, a boxplot is presented that has y-axis in log scale. To do so, I have first created a dataset that has high order values and then created the boxplot.
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
df = data.frame(Species = iris[,c('Species')], s.length = iris[,c('Sepal.Length')])
# Converting the data to higher order for plotting convenience.
df$s.length = 10^(df$s.length)
p = ggplot(df, aes(x = Species, y = s.length)) +
geom_boxplot( fill="#56B4E9") +
xlab('Species') + ylab('Values') + theme_bw()+
scale_y_log10(limits=c(1000,80000000), )+
theme(text = element_text(size=16),
axis.text.x = element_text(angle=0, hjust=.5),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
ggtitle("Y-axis in log scale")
p
