facet_wrap() is most commonly used to facet by a plot by a single categorical variable.

ggplot(mpg,aes(x=cty))+geom_histogram()+facet_wrap(~drv)#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Andfacet_grid() is commonly used to facet by a plot by two categorical variables.

ggplot(mpg,aes(x=cty))+geom_histogram()+facet_grid(cyl~drv)#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Notice that this results in some empty panes (e.g. 4-wheel drive and 5 cylinders) as there are no cars in thempg dataset that fall into such categories.

You can also usefacet_wrap() with to facet by two categorical variables. This will only create facets for combinations of the levels of variables for which data exists.

ggplot(mpg,aes(x=cty))+geom_histogram()+facet_wrap(cyl~drv)#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Infacet_wrap() you can control the number of rows and/or columns of the resulting plot layout using thenrow andncol arguments, respectively. Infacet_grid() these values are determined by the number of levels of the variables you’re faceting by.

Similarly, you can also usefacet_grid() to facet by a single categorical variable as well. In the formula notation, you use a. to indicate that no faceting should be done along that axis, i.e. cyl ~ . facets across the y-axis (within a column) while. ~ cyl facets across the x-axis (within a row).

ggplot(mpg,aes(x=cty))+geom_histogram()+facet_grid(cyl~.)#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

ggplot(mpg,aes(x=cty))+geom_histogram()+facet_grid(.~cyl)#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Suppose you have the following plot, and you want to add a vertical line at the mean value ofhwy (highway mileage) for each pane.

ggplot(mpg,aes(x=hwy))+geom_histogram(binwidth=5)+facet_wrap(~drv)

First, calculate these means and save them in a new data frame.

library(dplyr)#>#> Attaching package: 'dplyr'#> The following objects are masked from 'package:stats':#>#>     filter, lag#> The following objects are masked from 'package:base':#>#>     intersect, setdiff, setequal, unionmpg_summary<-mpg|>group_by(drv)|>summarise(hwy_mean=mean(hwy))mpg_summary#> # A tibble: 3 × 2#>   drv   hwy_mean#>   <chr>    <dbl>#> 1 4         19.2#> 2 f         28.2#> 3 r         21

Then, add ageom_vline() layer to your plot that uses the summary data.

ggplot(mpg,aes(x=hwy))+geom_histogram(binwidth=5)+facet_wrap(~drv)+geom_vline(data=mpg_summary,aes(xintercept=hwy_mean))

Suppose you have the following faceted plot. By default, both x and y scales are shared across the facets.

ggplot(mpg,aes(x=cty, y=hwy))+geom_point()+facet_grid(cyl~drv)

You can control this behaviour with thescales argument of faceting functions: varying scales across rows ("free_x"), columns ("free_y"), or both rows and columns ("free"), e.g.

ggplot(mpg,aes(x=cty, y=hwy))+geom_point()+facet_grid(cyl~drv, scales="free")

If you also want to make sure that a particular value or range is included in each of the facets, you can set this withexpand_limits(), e.g. ensure that 10 is included in the x-axis and values between 20 to 25 are included in the y-axis:

ggplot(mpg,aes(x=cty, y=hwy))+geom_point()+facet_grid(cyl~drv, scales="free")+expand_limits(x=10, y=c(20,25))

Settingstrip.text toelement_blank() will remove all facet labels.

ggplot(mpg,aes(x=cty, y=hwy))+geom_point()+facet_grid(cyl~drv)+theme(strip.text=element_blank())

You can also remove the labels across rows only withstrip.x.text or across columns only withstrip.y.text.

ggplot(mpg,aes(x=cty, y=hwy))+geom_point()+facet_grid(cyl~drv)+theme(strip.text.x=element_blank())

In the data frame below we have 100 observations, 50 of them come from one group and 50 from another. These groups have very long names, and so when you facet the ploy by group, the facet labels (strips) get cut off.

df<-data.frame(  x=rnorm(100),  group=c(rep("A long group name for the first group",50),rep("A muuuuuuuuuuuuuch longer group name for the second group",50)))ggplot(df,aes(x=x))+geom_histogram(binwidth=0.5)+facet_wrap(~group)

You can control the maximum width of the facet label by setting thewidth in thelabel_wrap_gen() function, which is then passed to thelabeller argument of your faceting function.

ggplot(df,aes(x=x))+geom_histogram(binwidth=0.5)+facet_wrap(~group, labeller=labeller(group=label_wrap_gen(width=25)))

Suppose you have data price data on a given item over a few years from two countries with very different currency scales.

df<-data.frame(  year=rep(2016:2021,2),  price=c(10,10,13,12,14,15,1000,1010,1200,1050,1105,1300),  country=c(rep("US",6),rep("Japan",6)))df#>    year price country#> 1  2016    10      US#> 2  2017    10      US#> 3  2018    13      US#> 4  2019    12      US#> 5  2020    14      US#> 6  2021    15      US#> 7  2016  1000   Japan#> 8  2017  1010   Japan#> 9  2018  1200   Japan#> 10 2019  1050   Japan#> 11 2020  1105   Japan#> 12 2021  1300   Japan

You can plotprice versustime and facet bycountry, but the resulting plot can be a bit difficult to read due to the shared y-axis label.

ggplot(df,aes(x=year, y=price))+geom_smooth()+facet_wrap(~country, ncol=1, scales="free_y")+scale_x_continuous(breaks=2011:2020)

With the following you can customize the facet labels first withas_labeller(), turn off the default y-axis label, and then place the facet labels where the y-axis label goes ("outside" and on the"left").

ggplot(df,aes(x=year, y=price))+geom_smooth()+facet_wrap(~country, ncol=1, scales="free_y",             labeller=as_labeller(c(US="US Dollars (USD)", Japan="Japanese Yens (JPY)")),             strip.position="left")+scale_x_continuous(breaks=2011:2020)+labs(y=NULL)+theme(strip.background=element_blank(), strip.placement="outside")#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'