library(gapminder) library(tidyverse) #> Loading tidyverse: ggplot2 #> Loading tidyverse: tibble #> Loading tidyverse: tidyr #> Loading tidyverse: readr #> Loading tidyverse: purrr #> Loading tidyverse: dplyr #> Conflicts with tidy packages ---------------------------------------------- #> filter(): dplyr, stats #> lag(): dplyr, stats
I see a fair amount of student code where variables are copied out of a data frame, to exist as stand-alone objects in the workspace.
life_exp <- gapminder$lifeExp year <- gapminder$year
Problem is, ggplot2 has an incredibly strong preference for variables in data frames; it is virtually a requirement for the main data frame underpinning a plot.
ggplot(aes(x = year, y = life_exp)) + geom_jitter() #> Error: ggplot2 doesn't know how to deal with data of class uneval
Just leave the variables in place and pass the associated data frame! This advice applies to base and
lattice graphics as well. It is not specific to ggplot2.
ggplot(data = gapminder, aes(x = year, y = life_exp)) + geom_jitter()
What if we wanted to filter the data by country, continent, or year? This is much easier to do safely if all affected variables live together in a data frame, not as individual objects that can get “out of sync.”
Don’t write-off ggplot2 as a highly opinionated outlier! In fact, keeping data in data frames and computing and visualizing it in situ are widely regarded as best practices. The option to pass a data frame via
data = is a common feature of many high-use R functions, e.g.
t.test(), so make this your default modus operandi.
If your data is already lying around and it’s not in a data frame, ask yourself “why not?”. Did you create those variables? Maybe you should have created them in a data frame in the first place! The
tibble() function is an improved version of the built-in
data.frame(), which makes it possible to define one variable in terms of another and which won’t turn character data into factor. If constructing tiny tibbles “by hand”,
tribble() can be an even handier function, in which your code will be laid out like the table you are creating. These functions should remove the most common excuses for data frame procrastination and avoidance.
my_dat <- tibble(x = 1:5, y = x ^ 2, text = c("alpha", "beta", "gamma", "delta", "epsilon")) ## if you're truly "hand coding", tribble() is an alternative my_dat <- tribble( ~ x, ~ y, ~ text, 1, 1, "alpha", 2, 4, "beta", 3, 9, "gamma", 4, 16, "delta", 5, 25, "epsilon" ) str(my_dat) #> Classes 'tbl_df', 'tbl' and 'data.frame': 5 obs. of 3 variables: #> $ x : num 1 2 3 4 5 #> $ y : num 1 4 9 16 25 #> $ text: chr "alpha" "beta" "gamma" "delta" ... ggplot(my_dat, aes(x, y)) + geom_line() + geom_text(aes(label = text))
dplyr::mutate(), which adds new variables to a data frame, this gives you the tools to work within data frames whenever you’re handling related variables of the same length.
This is an entire topic covered elsewhere:
This is an entire topic covered elsewhere:
Inspired by this question from a student when we first started using ggplot2: How can I focus in on country, Japan for example, and plot all the quantitative variables against year?
Your first instinct might be to filter the Gapminder data for Japan and then loop over the variables, creating separate plots which need to be glued together. And, indeed, this can be done. But in my opinion, the data reshaping route is more “R native” given our current ecosystem, than the loop way.
We filter the Gapminder data and keep only Japan. Then we gather up the variables
gdpPercap into a single
value variable, with a companion variable
japan_dat <- gapminder %>% filter(country == "Japan") japan_tidy <- japan_dat %>% gather(key = var, value = value, pop, lifeExp, gdpPercap) dim(japan_dat) #>  12 6 dim(japan_tidy) #>  36 5
japan_dat has 12 rows. Since we are gathering or stacking three variables in
japan_tidy, it makes sense to see three times as many rows, namely 36 in the reshaped result.
Now that we have the data we need in a tidy data frame, with a proper factor representing the variables we want to “iterate” over, we just have to facet.
p <- ggplot(japan_tidy, aes(x = year, y = value)) + facet_wrap(~ var, scales="free_y") p + geom_point() + geom_line() + scale_x_continuous(breaks = seq(1950, 2011, 15))
Here’s the minimal code to produce our Japan example.
japan_tidy <- gapminder %>% filter(country == "Japan") %>% gather(key = var, value = value, pop, lifeExp, gdpPercap) ggplot(japan_tidy, aes(x = year, y = value)) + facet_wrap(~ var, scales="free_y") + geom_point() + geom_line() + scale_x_continuous(breaks = seq(1950, 2011, 15))
This snippet demonstrates the payoffs from the rules we laid out at the start:
popnaturally belong together in one variable. But gathering them was by far the easiest way to get this plot.