Which of the following functions let you display smaller groups, or subsets, of your data?

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.

Data: starwars

To explore the basic data manipulation verbs of dplyr, we’ll use the dataset starwars. This dataset contains 87 characters and comes from the Star Wars API, and is documented in ?starwars

dim(starwars) #> [1] 87 14 starwars #> # A tibble: 87 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 Darth Vader 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

Note that 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 //tibble.tidyverse.org; in particular you can convert data frames to tibbles with as_tibble().

Single table verbs

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:

• Rows:
  • filter() chooses rows based on column values.
  • slice() chooses rows based on location.
  • arrange() changes the order of the rows.
• Columns:
  • 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.

The pipe

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 rows with filter()

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 TRUE.

For example, we can select all character with light skin color and brown eyes with:

starwars %>% filter(skin_color == "light", eye_color == "brown") #> # A tibble: 7 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Leia Organa 150 49 brown light brown 19 fema… femin… Aldera… #> 2 Biggs Darkl… 183 84 black light brown 24 male mascu… Tatooi… #> 3 Cordé 157 NA brown light brown NA fema… femin… Naboo #> 4 Dormé 165 NA brown light brown NA fema… femin… Naboo #> # … with 3 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

This is roughly equivalent to this base R code:

starwars[starwars$skin_color == "light" & starwars$eye_color == "brown", ]

Arrange rows with arrange()

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) #> # A tibble: 87 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Yoda 66 17 white green brown 896 male mascu… <NA> #> 2 Ratts Tyere… 79 15 none grey, … unknown NA male mascu… Aleen … #> 3 Wicket Syst… 88 20 brown brown brown 8 male mascu… Endor #> 4 Dud Bolt 94 45 none blue, … yellow NA male mascu… Vulpter #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

Use desc() to order a column in descending order:

starwars %>% arrange(desc(height)) #> # A tibble: 87 × 14 #> name height mass hair_c…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Yarael Poof 264 NA none white yellow NA male mascu… Quermia #> 2 Tarfful 234 136 brown brown blue NA male mascu… Kashyy… #> 3 Lama Su 229 88 none grey black NA male mascu… Kamino #> 4 Chewbacca 228 112 brown unknown blue 200 male mascu… Kashyy… #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

Choose rows using their position with slice()

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) #> # A tibble: 6 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Leia Organa 150 49 brown light brown 19 fema… femin… Aldera… #> 2 Owen Lars 178 120 brown,… light blue 52 male mascu… Tatooi… #> 3 Beru Whites… 165 75 brown light blue 47 fema… femin… Tatooi… #> 4 R5-D4 97 32 <NA> white,… red NA none mascu… Tatooi… #> # … with 2 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

It is accompanied by a number of helpers for common use cases:

• slice_head() and slice_tail() select the first or last rows.

starwars %>% slice_head(n = 3) #> # A tibble: 3 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> # … with 4 more variables: species <chr>, films <list>, vehicles <list>, #> # starships <list>, and abbreviated variable names ¹​hair_color, ²​skin_color, #> # ³​eye_color, ⁴​birth_year, ⁵​homeworld

• slice_sample() randomly selects rows. Use the option prop to choose a certain proportion of the cases.

starwars %>% slice_sample(n = 5) #> # A tibble: 5 × 14 #> name height mass hair_color skin_c…¹ eye_c…² birth…³ sex gender homew…⁴ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Dud Bolt 94 45 none blue, g… yellow NA male mascu… Vulpter #> 2 Bossk 190 113 none green red 53 male mascu… Trando… #> 3 Shaak Ti 178 57 none red, bl… black NA fema… femin… Shili #> 4 Dormé 165 NA brown light brown NA fema… femin… Naboo #> # … with 1 more row, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​skin_color, ²​eye_color, ³​birth_year, ⁴​homeworld starwars %>% slice_sample(prop = 0.1) #> # A tibble: 8 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Qui-Gon Jinn 193 89 brown fair blue 92 male mascu… <NA> #> 2 Dexter Jett… 198 102 none brown yellow NA male mascu… Ojom #> 3 R4-P17 96 NA none silver… red, b… NA none femin… <NA> #> 4 Lama Su 229 88 none grey black NA male mascu… Kamino #> # … with 4 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

Use replace = TRUE to perform a bootstrap sample. If needed, you can weight the sample with the weight argument.

• slice_min() and 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) #> # A tibble: 3 × 14 #> name height mass hair_c…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Yarael Poof 264 NA none white yellow NA male mascu… Quermia #> 2 Tarfful 234 136 brown brown blue NA male mascu… Kashyy… #> 3 Lama Su 229 88 none grey black NA male mascu… Kamino #> # … with 4 more variables: species <chr>, films <list>, vehicles <list>, #> # starships <list>, and abbreviated variable names ¹​hair_color, ²​skin_color, #> # ³​eye_color, ⁴​birth_year, ⁵​homeworld

Select columns with select()

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) #> # A tibble: 87 × 3 #> hair_color skin_color eye_color #> <chr> <chr> <chr> #> 1 blond fair blue #> 2 <NA> gold yellow #> 3 <NA> white, blue red #> 4 none white yellow #> # … with 83 more rows # Select all columns between hair_color and eye_color (inclusive) starwars %>% select(hair_color:eye_color) #> # A tibble: 87 × 3 #> hair_color skin_color eye_color #> <chr> <chr> <chr> #> 1 blond fair blue #> 2 <NA> gold yellow #> 3 <NA> white, blue red #> 4 none white yellow #> # … with 83 more rows # Select all columns except those from hair_color to eye_color (inclusive) starwars %>% select(!(hair_color:eye_color)) #> # A tibble: 87 × 11 #> name height mass birth…¹ sex gender homew…² species films vehic…³ stars…⁴ #> <chr> <int> <dbl> <dbl> <chr> <chr> <chr> <chr> <lis> <list> <list> #> 1 Luke … 172 77 19 male mascu… Tatooi… Human <chr> <chr> <chr> #> 2 C-3PO 167 75 112 none mascu… Tatooi… Droid <chr> <chr> <chr> #> 3 R2-D2 96 32 33 none mascu… Naboo Droid <chr> <chr> <chr> #> 4 Darth… 202 136 41.9 male mascu… Tatooi… Human <chr> <chr> <chr> #> # … with 83 more rows, and abbreviated variable names ¹​birth_year, ²​homeworld, #> # ³​vehicles, ⁴​starships # Select all columns ending with color starwars %>% select(ends_with("color")) #> # A tibble: 87 × 3 #> hair_color skin_color eye_color #> <chr> <chr> <chr> #> 1 blond fair blue #> 2 <NA> gold yellow #> 3 <NA> white, blue red #> 4 none white yellow #> # … with 83 more rows

There are a number of helper functions you can use within select(), like starts_with(), ends_with(), matches() and 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) #> # A tibble: 87 × 1 #> home_world #> <chr> #> 1 Tatooine #> 2 Tatooine #> 3 Naboo #> 4 Tatooine #> # … with 83 more rows

But because select() drops all the variables not explicitly mentioned, it’s not that useful. Instead, use rename():

starwars %>% rename(home_world = homeworld) #> # A tibble: 87 × 14 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender home_…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 Darth Vader 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​home_world

Add new columns with mutate()

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 mutate():

starwars %>% mutate(height_m = height / 100) #> # A tibble: 87 × 15 #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 Darth Vader 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 5 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, height_m <dbl>, and abbreviated variable #> # names ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

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()) #> # A tibble: 87 × 15 #> height_m height name mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender #> <dbl> <int> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> #> 1 1.72 172 Luke Skywa… 77 blond fair blue 19 male mascu… #> 2 1.67 167 C-3PO 75 <NA> gold yellow 112 none mascu… #> 3 0.96 96 R2-D2 32 <NA> white,… red 33 none mascu… #> 4 2.02 202 Darth Vader 136 none white yellow 41.9 male mascu… #> # … with 83 more rows, 5 more variables: homeworld <chr>, species <chr>, #> # films <list>, vehicles <list>, starships <list>, and abbreviated variable #> # names ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year

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()) #> # A tibble: 87 × 16 #> BMI name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <dbl> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 26.0 Luke … 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 26.9 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 34.7 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 33.3 Darth… 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 5 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, height_m <dbl>, and abbreviated variable #> # names ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld

If you only want to keep the new variables, use transmute():

starwars %>% transmute( height_m = height / 100, BMI = mass / (height_m^2) ) #> # A tibble: 87 × 2 #> height_m BMI #> <dbl> <dbl> #> 1 1.72 26.0 #> 2 1.67 26.9 #> 3 0.96 34.7 #> 4 2.02 33.3 #> # … with 83 more rows

Change column order with relocate()

Use a similar syntax as select() to move blocks of columns at once

starwars %>% relocate(sex:homeworld, .before = height) #> # A tibble: 87 × 14 #> name sex gender homew…¹ height mass hair_…² skin_…³ eye_c…⁴ birth…⁵ #> <chr> <chr> <chr> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> #> 1 Luke Skywal… male mascu… Tatooi… 172 77 blond fair blue 19 #> 2 C-3PO none mascu… Tatooi… 167 75 <NA> gold yellow 112 #> 3 R2-D2 none mascu… Naboo 96 32 <NA> white,… red 33 #> 4 Darth Vader male mascu… Tatooi… 202 136 none white yellow 41.9 #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​homeworld, ²​hair_color, ³​skin_color, ⁴​eye_color, ⁵​birth_year

Summarise values with summarise()

The last verb is summarise(). It collapses a data frame to a single row.

starwars %>% summarise(height = mean(height, na.rm = TRUE)) #> # A tibble: 1 × 1 #> height #> <dbl> #> 1 174.

It’s not that useful until we learn the group_by() verb below.

Commonalities

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 (filter() and select()), add new variables that are functions of existing variables (mutate()), or collapse many values to a summary (summarise()).

Combining functions with %>%

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) ) #> Adding missing grouping variables: `species`, `sex` #> `summarise()` has grouped output by 'species'. You can override using the #> `.groups` argument. #> # A tibble: 41 × 4 #> # Groups: species [38] #> species sex height mass #> <chr> <chr> <dbl> <dbl> #> 1 Aleena male 79 15 #> 2 Besalisk male 198 102 #> 3 Cerean male 198 82 #> 4 Chagrian male 196 NaN #> # … with 37 more rows

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) )

Patterns of operations

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.

Selecting 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 to mutate().

Selecting operations expect column names and positions. Hence, when you call 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) #> # A tibble: 87 × 1 #> name #> <chr> #> 1 Luke Skywalker #> 2 C-3PO #> 3 R2-D2 #> 4 Darth Vader #> # … with 83 more rows select(starwars, 1) #> # A tibble: 87 × 1 #> name #> <chr> #> 1 Luke Skywalker #> 2 C-3PO #> 3 R2-D2 #> 4 Darth Vader #> # … with 83 more rows

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) #> # A tibble: 87 × 1 #> height #> <int> #> 1 172 #> 2 167 #> 3 96 #> 4 202 #> # … with 83 more rows

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)) #> # A tibble: 87 × 3 #> hair_color skin_color eye_color #> <chr> <chr> <chr> #> 1 blond fair blue #> 2 <NA> gold yellow #> 3 <NA> white, blue red #> 4 none white yellow #> # … with 83 more rows

These semantics are usually intuitive. But note the subtle difference:

name <- 5 select(starwars, name, identity(name)) #> # A tibble: 87 × 2 #> name skin_color #> <chr> <chr> #> 1 Luke Skywalker fair #> 2 C-3PO gold #> 3 R2-D2 white, blue #> 4 Darth Vader white #> # … with 83 more rows

In the first argument, name represents its own position 1. In the second argument, 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 select():

vars <- c("name", "height") select(starwars, all_of(vars), "mass") #> # A tibble: 87 × 3 #> name height mass #> <chr> <int> <dbl> #> 1 Luke Skywalker 172 77 #> 2 C-3PO 167 75 #> 3 R2-D2 96 32 #> 4 Darth Vader 202 136 #> # … with 83 more rows

Mutating operations

Mutate semantics are quite different from selection semantics. Whereas select() expects column names or positions, mutate() expects column vectors. 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():

mutate(df, "height", 2) #> # A tibble: 87 × 5 #> name height mass `"height"` `2` #> <chr> <int> <dbl> <chr> <dbl> #> 1 Luke Skywalker 172 77 height 2 #> 2 C-3PO 167 75 height 2 #> 3 R2-D2 96 32 height 2 #> 4 Darth Vader 202 136 height 2 #> # … with 83 more rows

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) #> # A tibble: 87 × 4 #> name height mass `height + 10` #> <chr> <int> <dbl> <dbl> #> 1 Luke Skywalker 172 77 182 #> 2 C-3PO 167 75 177 #> 3 R2-D2 96 32 106 #> 4 Darth Vader 202 136 212 #> # … with 83 more rows

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 tibble: 87 × 4 #> name height mass new #> <chr> <int> <dbl> <int> #> 1 Luke Skywalker 172 77 1 #> 2 C-3PO 167 75 2 #> 3 R2-D2 96 32 3 #> 4 Darth Vader 202 136 4 #> # … with 83 more rows

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) #> # A tibble: 87 × 14 #> # Groups: sex [5] #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 Darth Vader 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld group_by(starwars, sex = as.factor(sex)) #> # A tibble: 87 × 14 #> # Groups: sex [5] #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <fct> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 Darth Vader 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 4 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, and abbreviated variable names #> # ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, ⁵​homeworld group_by(starwars, height_binned = cut(height, 3)) #> # A tibble: 87 × 15 #> # Groups: height_binned [4] #> name height mass hair_…¹ skin_…² eye_c…³ birth…⁴ sex gender homew…⁵ #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> #> 1 Luke Skywal… 172 77 blond fair blue 19 male mascu… Tatooi… #> 2 C-3PO 167 75 <NA> gold yellow 112 none mascu… Tatooi… #> 3 R2-D2 96 32 <NA> white,… red 33 none mascu… Naboo #> 4 Darth Vader 202 136 none white yellow 41.9 male mascu… Tatooi… #> # … with 83 more rows, 5 more variables: species <chr>, films <list>, #> # vehicles <list>, starships <list>, height_binned <fct>, and abbreviated #> # variable names ¹​hair_color, ²​skin_color, ³​eye_color, ⁴​birth_year, #> # ⁵​homeworld

This is why you can’t supply a column name to group_by(). This amounts to creating a new column containing the string recycled to the number of rows:

group_by(df, "month") #> # A tibble: 87 × 4 #> # Groups: "month" [1] #> name height mass `"month"` #> <chr> <int> <dbl> <chr> #> 1 Luke Skywalker 172 77 month #> 2 C-3PO 167 75 month #> 3 R2-D2 96 32 month #> 4 Darth Vader 202 136 month #> # … with 83 more rows

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