Update: I’ve included another way of saving a separate plot by group in this article, as pointed out
@monitus. Actually, this is the preferred
dplyr::do() is deprecated, according to Hadley Wickham himself.
I’ll be honest: the title is a bit misleading. I will not use
purrr that much in this blog post.
Actually, I will use one single
purrr function, at the very end. I use
dplyr much more.
However Make ggplot2 purrr sounds better than Make ggplot dplyr or whatever the verb for
dplyr would be.
Also, this blog post was inspired by a stackoverflow question and in particular one of the answers. So I don’t bring anything new to the table, but I found this stackoverflow answer so useful and so underrated (only 16 upvotes as I’m writing this!) that I wanted to write something about it.
Basically the idea of this blog post is to show how to create graphs using
ggplot2, but by
grouping by a factor variable beforehand. To illustrate this idea, let’s use the data from the Penn World
Tables 9.0. The easiest way to get this data is to
install the package called
and then load the data with:
Now, let’s load the needed packages. I am also using
ggthemes which makes themeing your ggplots
very easy. I’ll be making Tufte-style plots.
library(ggplot2) library(ggthemes) library(dplyr) library(tidyr) library(purrr) library(pwt9)
First let’s select a list of countries:
country_list <- c("France", "Germany", "United States of America", "Luxembourg", "Switzerland", "Greece") small_pwt <- pwt9.0 %>% filter(country %in% country_list)
Let’s us also order the countries in the data frame as I have written them in
small_pwt <- small_pwt %>% mutate(country = factor(country, levels = country_list, ordered = TRUE))
You might be wondering why this is important. At the end of the article, we are going to save the
plots to disk. If we do not re-order the countries inside the data frame as in
name of the files will not correspond to the correct plots!
Update: While this can still be interesting to know, especially if you want to order the bars
of a barplot made with
ggplot2, I included a suggestion by
that does not require your data to be ordered!
Now when you want to plot the same variable by countries, say
avh (Average annual hours worked by
persons engaged), the usual way to do this is with one of
ggplot(data = small_pwt) + theme_tufte() + geom_line(aes(y = avh, x = year)) + facet_wrap(~country)
ggplot(data = small_pwt) + theme_tufte() + geom_line(aes(y = avh, x = year)) + facet_grid(country~.)
As you can see, for this particular example,
facet_grid() is not very useful, but do notice its
country~., which is different from
facet_wrap()’s argument. This way, I get the graphs
stacked horizontally. If I had used
facet_grid(~country) the graphs would be side by side and completely
Now, let’s go to the meat of this post: what if you would like to have one single graph for each
country? You’d probably think of using
dplyr::group_by() to form the groups and then the graphs. This
is the way to go, but you also have to use
dplyr::do(). This is because as far as I understand,
ggplot2 is not
dplyr-aware, and using an arbitrary function with groups is only possible with
Update: As explained in the intro above, I also added the solution that uses
# Ancient, deprecated way of doing this plots <- small_pwt %>% group_by(country) %>% do(plot = ggplot(data = .) + theme_tufte() + geom_line(aes(y = avh, x = year)) + ggtitle(unique(.$country)) + ylab("Year") + xlab("Average annual hours worked by persons engaged"))
And this is the approach that uses
# Preferred approach plots <- small_pwt %>% group_by(country) %>% nest() %>% mutate(plot = map2(data, country, ~ggplot(data = .x) + theme_tufte() + geom_line(aes(y = avh, x = year)) + ggtitle(.y) + ylab("Year") + xlab("Average annual hours worked by persons engaged")))
If you know
dplyr at least a little bit, the above lines should be easy for you to understand.
But notice how we get the title of the graphs, with
ggtitle(unique(.$country)), which was actually
the point of the stackoverflow question.
Update: The modern version uses
tidyr::nest(). Its documentation tells us:
There are many possible ways one could choose to nest columns inside a data frame.
nest() creates a list of data frames containing all the nested variables: this seems to be the most useful form in practice.
Let’s take a closer look at what it does exactly:
small_pwt %>% group_by(country) %>% nest() %>% head()
## # A tibble: 6 x 2 ## country data ## <ord> <list> ## 1 Switzerland <tibble [65 × 46]> ## 2 Germany <tibble [65 × 46]> ## 3 France <tibble [65 × 46]> ## 4 Greece <tibble [65 × 46]> ## 5 Luxembourg <tibble [65 × 46]> ## 6 United States of America <tibble [65 × 46]>
This is why I love lists in R; we get a
tibble where each element of the column
data is itself a
We can now apply any function that we know works on lists.
What might be surprising though, is the object that is created by this code. Let’s take a look at
## # A tibble: 6 x 3 ## country data plot ## <ord> <list> <list> ## 1 Switzerland <tibble [65 × 46]> <S3: gg> ## 2 Germany <tibble [65 × 46]> <S3: gg> ## 3 France <tibble [65 × 46]> <S3: gg> ## 4 Greece <tibble [65 × 46]> <S3: gg> ## 5 Luxembourg <tibble [65 × 46]> <S3: gg> ## 6 United States of America <tibble [65 × 46]> <S3: gg>
dplyr::do()’s documentation tells us, the return values get stored inside a list. And this is
exactly what we get back; a list of plots! Lists are a very flexible and useful class, and you cannot
spell list without
purrr (at least not when you’re a ne
Here are the final lines that use
purrr::map2() to save all these plots at once inside your working directory:
Update: I have changed the code below which does not require your data frame to be ordered according
to the variable
# file_names <- paste0(country_list, ".pdf") map2(paste0(plots$country, ".pdf"), plots$plot, ggsave)
As I said before, if you do not re-order the countries inside the data frame, the names of the files and the plots will not match. Try running all the code without re-ordering, you’ll see!
I hope you found this post useful. You can follow me on twitter for blog updates.
Update: Many thanks to the readers of this article and for their useful suggestions. I love the R community; everyday I learn something new and useful!