This post explains how to build grouped, stacked and percent stacked
barplots with R and
ggplot2. It provides a reproducible example with code for each type. Note
that this
online course has a
dedicated section on barplots using the
`geom_bar()` function.

A grouped barplot display a numeric value for a set of entities
split in groups and subgroups. Before trying to build one, check
how to make a
basic barplot
with `R` and `ggplot2`.

A few explanation about the code below:

`value), and 2 categorical variables for the group (`
`specie) and the subgroup (`
`condition) levels.`
`aes()call, x is the group (`
`specie), and the subgroup (`
`condition) is given to the`
`fillargument.`
`geom_bar()call,`
`position="dodge"must be specified to have the bars one beside each other.`

`# library library(ggplot2) # create a dataset <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) ) specie <- rep(c("normal" , "stress" , "Nitrogen") , 4) condition <- abs(rnorm(12 , 0 , 15)) value <- data.frame(specie,condition,value) data # Grouped ggplot(data, aes(fill=condition, y=value, x=specie)) + geom_bar(position="dodge", stat="identity")`

A stacked barplot is very similar to the grouped barplot above. The subgroups are just displayed on top of each other, not beside.

The only thing to change to get this figure is to switch the
`position` argument to `stack`.

`# library library(ggplot2) # create a dataset <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) ) specie <- rep(c("normal" , "stress" , "Nitrogen") , 4) condition <- abs(rnorm(12 , 0 , 15)) value <- data.frame(specie,condition,value) data # Stacked ggplot(data, aes(fill=condition, y=value, x=specie)) + geom_bar(position="stack", stat="identity")`

Once more, there is not much to do to switch to a percent stacked
barplot. Just switch to `position="fill"`. Now, the
percentage of each subgroup is represented, allowing to study the
evolution of their proportion in the whole.

`# library library(ggplot2) # create a dataset <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) ) specie <- rep(c("normal" , "stress" , "Nitrogen") , 4) condition <- abs(rnorm(12 , 0 , 15)) value <- data.frame(specie,condition,value) data # Stacked + percent ggplot(data, aes(fill=condition, y=value, x=specie)) + geom_bar(position="fill", stat="identity")`

As usual, some customization are often necessary to make the chart look better and personnal. Let’s:

`# library library(ggplot2) library(viridis) library(hrbrthemes) # create a dataset <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) ) specie <- rep(c("normal" , "stress" , "Nitrogen") , 4) condition <- abs(rnorm(12 , 0 , 15)) value <- data.frame(specie,condition,value) data # Small multiple ggplot(data, aes(fill=condition, y=value, x=specie)) + geom_bar(position="stack", stat="identity") + scale_fill_viridis(discrete = T) + ggtitle("Studying 4 species..") + theme_ipsum() + xlab("")`

Small multiple can be used as an alternative of stacking or
grouping. It is straightforward to make thanks to the
`facet_wrap()` function.

`# library library(ggplot2) library(viridis) library(hrbrthemes) # create a dataset <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) ) specie <- rep(c("normal" , "stress" , "Nitrogen") , 4) condition <- abs(rnorm(12 , 0 , 15)) value <- data.frame(specie,condition,value) data # Graph ggplot(data, aes(fill=condition, y=value, x=condition)) + geom_bar(position="dodge", stat="identity") + scale_fill_viridis(discrete = T, option = "E") + ggtitle("Studying 4 species..") + facet_wrap(~specie) + theme_ipsum() + theme(legend.position="none") + xlab("")`

You are watching: Grouped, stacked and percent stacked barplot in ggplot2. Info created by GBee English Center selection and synthesis along with other related topics.