In this tutorial, we will see how to make a grid of bar plots using facet_grid(). Such a grid may be useful when your data set contains several categorical predictor variables, and displaying the data in a single graph makes it hardly comprehensible. If you are not so familiar with bar plots or facet_grid(), have a quick look at these two pages:

We will plot the precipitations recorded monthly in 2017, 2018 and 2019 at two Norwegian locations: Lygra and Østerbø. We will thus have three categorical variables: month, year and location, and one response variable precipitations. Here is the code for the dataframe:

# dataframe
df <- data.frame(location, year, month, precipitations)
# structure of the dataframe
## 'data.frame':    72 obs. of  4 variables:
##  $ location      : Factor w/ 2 levels "Lygra","Østerbø": 1 1 1 1 1 1 1 1 1 1 ...
##  $ year          : Factor w/ 3 levels "2017","2018",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ month         : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ precipitations: num  135.8 88.4 91 111.7 31 ...

Our plan is to make a matrix displaying 6 panels, each of which is a bar plot. In these bars plot, the predictor variable month and the response variable precipitations shall be plotted on the X- and Y-axis, respectively. Eventually the matrix shall show location in columns and year in rows. To obtain this matrix, we must:

Here is the code, and the corresponding faceted plot:

ggplot(df, aes(x = month, y = precipitations)) +
  geom_col() + 

If the plan was to set up a matrix with location in rows and year in columns, we should have inverted the variables in facet_grid():

ggplot(df, aes(x = month, y = precipitations)) +
  geom_col() + 

However, in this particular case, the X-axis is overloaded and the labels are unreadable.

Improving the look

You may improve the look of a grid by tuning the labels of the matrix. This is further explained HERE.

Alternative plot

This data set may be alternatively plotted in the form of a grid of clustered/grouped bars. HERE is a tutorial for making such a plot.