In this tutorial, we will see how to make a grid of clustered/ grouped bar plots using facet_wrap(). 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. Compared to a grid of non-grouped bar plots (introduced HERE), it allows for side-by-side comparison of related groups in the form of color-coded clusters.

If you are not so familiar with bar plots, clustered bar plots or facet_wrap(), have a quick look at these 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 grid displaying 2 panels, each of which is a clustered 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. Three color-coded bars shall display the monthly precipitations for the recorded years in clusters defined by the variable year. Finally, the grid shall show the two locations on top of each other (two panels displayed in a single column). To obtain this grid, we must:

Here is the code, and the corresponding faceted plot:

ggplot(df, aes(x = month, y = precipitations)) +
  geom_col(aes(fill = year), position = "dodge") + 
  facet_wrap(~location, ncol=1)

If the plan was to set up a grid with location in a single row instead of a single column, we should have used facet_wrap(~location, nrow=1):

ggplot(df, aes(x = month, y = precipitations)) +
  geom_col(aes(fill = year), position = "dodge") + 
  facet_wrap(~location, nrow=1)

However, in this particular case, the design is not so attractive since the bars become very thin and the labels of the X-axes quite close to each other.

facet_wrap() vs facet_grid()

Here we have made use of facet_wrap(), but we could have written the code with facet_grid() to achieve the same results. facet_wrap() is easier to use when making a grid based on one variable (here location); on the opposite, facet_grid() requires the use of two variables, unless overridden by:

ggplot(df, aes(x = month, y = precipitations)) +          # left plot, levels in rows
  geom_col(aes(fill = year), position = "dodge") + 
  facet_grid(rows = vars(location))

ggplot(df, aes(x = month, y = precipitations)) +          # right plot, levels in columns
  geom_col(aes(fill = year), position = "dodge") + 
  facet_grid(cols = vars(location))

Improving the look

You may improve the look of a grid by tuning the labels of the matrix. This is further explained HERE.
Since colors might be important for the interpretation of the data, have a look at this page which shows how to color frames and/or boxes as a function of a variable, and this page that tells you more about color palettes.

Alternative plot

This data set may be alternatively plotted in the form of a grid of non-grouped bar plots. HERE is a tutorial for making such a plot.