knitr::opts_chunk$set(collapse = T, comment = "#>") options(tibble.print_min = 4L, tibble.print_max = 4L) library(dplyr) set.seed(1014)
When working with data you must:
Figure out what you want to do.
Describe those tasks in the form of a computer program.
Execute the program.
The dplyr package makes these steps fast and easy:
By constraining your options, it helps you think about your data manipulation challenges.
It provides simple "verbs", functions that correspond to the most common data manipulation tasks, to help you translate your thoughts into code.
It uses efficient backends, so you spend less time waiting for the computer.
This document introduces you to dplyr's basic set of tools, and shows you how to apply them to data frames. dplyr also supports databases via the dbplyr package, once you've installed, read
vignette("dbplyr") to learn more.
To explore the basic data manipulation verbs of dplyr, we'll use the dataset
starwars. This dataset contains
r nrow(starwars) characters and comes from the Star Wars API, and is documented in
starwars is a tibble, a modern reimagining of the data frame. It's particularly useful for large datasets because it only prints the first few rows. You can learn more about tibbles at https://tibble.tidyverse.org; in particular you can convert data frames to tibbles with
dplyr aims to provide a function for each basic verb of data manipulation. These verbs can be organised into three categories based on the component of the dataset that they work with:
filter()chooses rows based on column values.
slice()chooses rows based on location.
arrange() changes the order of the rows.
select()changes whether or not a column is included.
rename()changes the name of columns.
mutate()changes the values of columns and creates new columns.
relocate() changes the order of the columns.
Groups of rows:
summarise()collapses a group into a single row.
All of the dplyr functions take a data frame (or tibble) as the first argument. Rather than forcing the user to either save intermediate objects or nest functions, dplyr provides the
%>% operator from magrittr.
x %>% f(y) turns into
f(x, y) so the result from one step is then "piped" into the next step. You can use the pipe to rewrite multiple operations that you can read left-to-right, top-to-bottom (reading the pipe operator as "then").
filter() allows you to select a subset of rows in a data frame. Like all single verbs, the first argument is the tibble (or data frame). The second and subsequent arguments refer to variables within that data frame, selecting rows where the expression is
For example, we can select all character with light skin color and brown eyes with:
starwars %>% filter(skin_color == "light", eye_color == "brown")
This is roughly equivalent to this base R code:
starwars[starwars$skin_color == "light" & starwars$eye_color == "brown", ]
arrange() works similarly to
filter() except that instead of filtering or selecting rows, it reorders them. It takes a data frame, and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:
starwars %>% arrange(height, mass)
desc() to order a column in descending order:
starwars %>% arrange(desc(height))
slice() lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows.
We can get characters from row numbers 5 through 10.
starwars %>% slice(5:10)
It is accompanied by a number of helpers for common use cases:
slice_tail()select the first or last rows.
starwars %>% slice_head(n = 3)
slice_sample()randomly selects rows. Use the option prop to choose a certain proportion of the cases.
starwars %>% slice_sample(n = 5) starwars %>% slice_sample(prop = 0.1)
replace = TRUE to perform a bootstrap sample. If needed, you can weight the sample with the
slice_max()select rows with highest or lowest values of a variable. Note that we first must choose only the values which are not NA.
starwars %>% filter(!is.na(height)) %>% slice_max(height, n = 3)
Often you work with large datasets with many columns but only a few are actually of interest to you.
select() allows you to rapidly zoom in on a useful subset using operations that usually only work on numeric variable positions:
# Select columns by name starwars %>% select(hair_color, skin_color, eye_color) # Select all columns between hair_color and eye_color (inclusive) starwars %>% select(hair_color:eye_color) # Select all columns except those from hair_color to eye_color (inclusive) starwars %>% select(!(hair_color:eye_color)) # Select all columns ending with color starwars %>% select(ends_with("color"))
There are a number of helper functions you can use within
contains(). These let you quickly match larger blocks of variables that meet some criterion. See
?select for more details.
You can rename variables with
select() by using named arguments:
starwars %>% select(home_world = homeworld)
select() drops all the variables not explicitly mentioned, it's not that useful. Instead, use
starwars %>% rename(home_world = homeworld)
Besides selecting sets of existing columns, it's often useful to add new columns that are functions of existing columns. This is the job of
starwars %>% mutate(height_m = height / 100)
We can't see the height in meters we just calculated, but we can fix that using a select command.
starwars %>% mutate(height_m = height / 100) %>% select(height_m, height, everything())
dplyr::mutate() is similar to the base
transform(), but allows you to refer to columns that you've just created:
starwars %>% mutate( height_m = height / 100, BMI = mass / (height_m^2) ) %>% select(BMI, everything())
If you only want to keep the new variables, use
.keep = "none":
starwars %>% mutate( height_m = height / 100, BMI = mass / (height_m^2), .keep = "none" )
Use a similar syntax as
select() to move blocks of columns at once
starwars %>% relocate(sex:homeworld, .before = height)
The last verb is
summarise(). It collapses a data frame to a single row.
starwars %>% summarise(height = mean(height, na.rm = TRUE))
It's not that useful until we learn the
group_by() verb below.
You may have noticed that the syntax and function of all these verbs are very similar:
The first argument is a data frame.
The subsequent arguments describe what to do with the data frame. You can
refer to columns in the data frame directly without using
The result is a new data frame
Together these properties make it easy to chain together multiple simple steps to achieve a complex result.
These five functions provide the basis of a language of data manipulation. At the most basic level, you can only alter a tidy data frame in five useful ways: you can reorder the rows (
arrange()), pick observations and variables of interest (
select()), add new variables that are functions of existing variables (
mutate()), or collapse many values to a summary (
The dplyr API is functional in the sense that function calls don't have side-effects. You must always save their results. This doesn't lead to particularly elegant code, especially if you want to do many operations at once. You either have to do it step-by-step:
a1 <- group_by(starwars, species, sex) a2 <- select(a1, height, mass) a3 <- summarise(a2, height = mean(height, na.rm = TRUE), mass = mean(mass, na.rm = TRUE) )
Or if you don't want to name the intermediate results, you need to wrap the function calls inside each other:
summarise( select( group_by(starwars, species, sex), height, mass ), height = mean(height, na.rm = TRUE), mass = mean(mass, na.rm = TRUE) )
This is difficult to read because the order of the operations is from inside to out. Thus, the arguments are a long way away from the function. To get around this problem, dplyr provides the
%>% operator from magrittr.
x %>% f(y) turns into
f(x, y) so you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom (reading the pipe operator as "then"):
starwars %>% group_by(species, sex) %>% select(height, mass) %>% summarise( height = mean(height, na.rm = TRUE), mass = mean(mass, na.rm = TRUE) )
The dplyr verbs can be classified by the type of operations they accomplish (we sometimes speak of their semantics, i.e., their meaning). It's helpful to have a good grasp of the difference between select and mutate operations.
One of the appealing features of dplyr is that you can refer to
columns from the tibble as if they were regular variables. However,
the syntactic uniformity of referring to bare column names hides
semantical differences across the verbs. A column symbol supplied to
select() does not have the same meaning as the same symbol supplied
Selecting operations expect column names and positions. Hence, when
select() with bare variable names, they actually represent
their own positions in the tibble. The following calls are completely
equivalent from dplyr's point of view:
# `name` represents the integer 1 select(starwars, name) select(starwars, 1)
By the same token, this means that you cannot refer to variables from
the surrounding context if they have the same name as one of the
columns. In the following example,
height still represents 2, not 5:
height <- 5 select(starwars, height)
One useful subtlety is that this only applies to bare names and to
selecting calls like
c(height, mass) or
height:mass. In all other
cases, the columns of the data frame are not put in scope. This allows
you to refer to contextual variables in selection helpers:
name <- "color" select(starwars, ends_with(name))
These semantics are usually intuitive. But note the subtle difference:
name <- 5 select(starwars, name, identity(name))
In the first argument,
name represents its own position
1. In the
name is evaluated in the surrounding context and
represents the fifth column.
For a long time,
select() used to only understand column positions.
Counting from dplyr 0.6, it now understands column names as well. This
makes it a bit easier to program with
vars <- c("name", "height") select(starwars, all_of(vars), "mass")
Mutate semantics are quite different from selection semantics. Whereas
select() expects column names or positions,
We will set up a smaller tibble to use for our examples.
df <- starwars %>% select(name, height, mass)
When we use
select(), the bare column names stand for their own
positions in the tibble. For
mutate() on the other hand, column
symbols represent the actual column vectors stored in the tibble.
Consider what happens if we give a string or a number to
mutate(df, "height", 2)
mutate() gets length-1 vectors that it interprets as new columns in
the data frame. These vectors are recycled so they match the number of
rows. That's why it doesn't make sense to supply expressions like
"height" + 10 to
mutate(). This amounts to adding 10 to a string!
The correct expression is:
mutate(df, height + 10)
In the same way, you can unquote values from the context if these values represent a valid column. They must be either length 1 (they then get recycled) or have the same length as the number of rows. In the following example we create a new vector that we add to the data frame:
var <- seq(1, nrow(df)) mutate(df, new = var)
A case in point is
group_by(). While you might think it has select
semantics, it actually has mutate semantics. This is quite handy as it
allows to group by a modified column:
group_by(starwars, sex) group_by(starwars, sex = as.factor(sex)) group_by(starwars, height_binned = cut(height, 3))
This is why you can't supply a column name to
amounts to creating a new column containing the string recycled to the
number of rows:
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