(Builds on: Manipulation basics)
(Leads to: Scoped verbs with predicates, Tidy evaluation)
You’ll often want to operate on multiple columns at the same time. Luckily, there are scoped versions of dplyr verbs that allow you to apply that verb to multiple columns at once.
Scoped verbs are powerful. They allow you to quickly carry out complex wrangling that would otherwise be much more difficult.
Each dplyr verb comes in three scoped variants. The name of each variant consists of the dplyr verb plus one of three suffixes: _at, _all, or _if. In this reading, you’ll learn about the _all and _at scoped verbs.
We’ve also created a scoped verbs cheat sheet to help you organize all the information involved in scoped verbs.
x is a simple tibble.
x <-
tibble(
number_1 = c(1, 1, 51),
number_2 = c(3, 42, NA),
letter = c("w", "x", "w")
)
x
## # A tibble: 3 x 3
## number_1 number_2 letter
## <dbl> <dbl> <chr>
## 1 1 3 w
## 2 1 42 x
## 3 51 NA w
We can use summarize() to find the number of distinct values for each variable.
x %>%
summarize(
number_1 = n_distinct(number_1),
number_2 = n_distinct(number_2),
letter = n_distinct(letter)
)
## # A tibble: 1 x 3
## number_1 number_2 letter
## <int> <int> <int>
## 1 2 3 2
There are only three variables in x. If x had more columns, however, writing out each n_distinct() call would be a hassle. Instead, we can use a scoped verb to succinctly summarize all columns at once. This will save time and reduce code duplication.
Each scoped verb has a suffix and a prefix. The prefix specifies the dplyr verb and the suffix specifies the scoped variant. There are two suffixes you’ll learn about in this reading:
_all: applies the dplyr verb to all variables_at: applies the dplyr verb to selected variablesTo summarize all the variables in x as we did above, we’ll use the scoped verb summarize_all().
Each scoped verb takes a tibble and a function as arguments. In this case, the function is n_distinct().
x %>%
summarize_all(n_distinct)
## # A tibble: 1 x 3
## number_1 number_2 letter
## <int> <int> <int>
## 1 2 3 2
Notice that we wrote n_distinct, and not n_distinct(). Recall that n_distinct is the name of the function, while n_distinct() calls the function.
To summarize just variables number_1 and number_2, we’ll use summarize_at(). The _at verbs take an additional argument: a list of columns specified inside the function vars().
x %>%
summarize_at(vars(number_1, number_2), n_distinct)
## # A tibble: 1 x 2
## number_1 number_2
## <int> <int>
## 1 2 3
Inside vars(), you can specify variables using the same syntax as select(). You can give their full names, use contains(), remove some with -, etc.
x %>%
summarize_at(vars(contains("number")), n_distinct)
## # A tibble: 1 x 2
## number_1 number_2
## <int> <int>
## 1 2 3
mutate()If you want to apply mutate() to multiple columns, the same logic applies. mutate_all() will apply the same function to each column, changing all of them in the same way.
x %>%
mutate_all(lag)
## # A tibble: 3 x 3
## number_1 number_2 letter
## <dbl> <dbl> <chr>
## 1 NA NA <NA>
## 2 1 3 w
## 3 1 42 x
And mutate_at() changes just the variables specified inside vars().
x %>%
mutate_at(vars(starts_with("number")), lag)
## # A tibble: 3 x 3
## number_1 number_2 letter
## <dbl> <dbl> <chr>
## 1 NA NA w
## 2 1 3 x
## 3 1 42 w
n_distinct() and lag() are both named functions. However, scoped verbs can also take anonymous functions.
In a scoped verb, you start an anonymous function with ~, and use . to refer to the function’s argument.
For example, the following code tells us which variables have more than two distinct values.
x %>%
summarize_all(~ n_distinct(.) > 2)
## # A tibble: 1 x 3
## number_1 number_2 letter
## <lgl> <lgl> <lgl>
## 1 FALSE TRUE FALSE
The . is a placeholder. It refers to each column specified in the scoped verb in turn. In this case, it refers to number_1, then number_2, then letter.
...The scoped verbs all take ... as a final argument. You can use ... to specify arguments to a named function without having to write an anonymous function.
For example, you might not want to count NAs as distinct values. We could write an anonymous function that doesn’t count NAs.
x %>%
summarize_all(~ n_distinct(., na.rm = TRUE))
## # A tibble: 1 x 3
## number_1 number_2 letter
## <int> <int> <int>
## 1 2 2 2
It’s simpler, however, to just specify the additional argument after the function name.
x %>%
summarize_all(n_distinct, na.rm = TRUE)
## # A tibble: 1 x 3
## number_1 number_2 letter
## <int> <int> <int>
## 1 2 2 2
The ... functionality makes the code easier to read, avoiding the extra syntax involved in anonymous functions. You can use it to add any number of arguments.
Unfortunately, you cannot use ... to supply columns of the original tibble as function arguments. For example, the following code tries to combine number_1 and number_2 with letter to create strings. However, mutate_at() says it can’t find letter.
x %>%
mutate_at(vars(contains("number")), str_c, letter)
## Error in list2(...): object 'letter' not found
You have to use an anonymous function if you want to reference any of the tibble’s columns in the function.
x %>%
mutate_at(vars(contains("number")), ~ str_c(., letter))
## # A tibble: 3 x 3
## number_1 number_2 letter
## <chr> <chr> <chr>
## 1 1w 3w w
## 2 1x 42x x
## 3 51w <NA> w
You can use list() to supply the scoped variants of mutate(), summarize(), and transmute() with multiple functions.
x %>%
summarize_at(
vars(number_1, number_2),
list(median = median, distinct = n_distinct),
na.rm = TRUE
)
## # A tibble: 1 x 4
## number_1_median number_2_median number_1_distinct number_2_distinct
## <dbl> <dbl> <int> <int>
## 1 1 22.5 2 2
Your list of functions must be named for this functionality to work. In this example, we gave median() the name “median” and n_distinct() the name “distinct.” summarize_at() then names the new variables by appending these names (“median” and “distinct”) to the end of the existing variable names.
select() and rename()The scoped variants of select() and rename() work very similarly to those of mutate(), transmute(), and summarize(). However, they apply the specified function(s) to column names, instead of to column values.
The following code changes all column names to lowercase.
capitals <-
tribble(
~Country, ~Capital,
"Namibia", "Windhoek",
"Georgia", "Tbilisi"
)
capitals %>%
rename_all(str_to_lower)
## # A tibble: 2 x 2
## country capital
## <chr> <chr>
## 1 Namibia Windhoek
## 2 Georgia Tbilisi
The scoped variants of rename() will generally be more helpful than those of select().