mutate {poorman}

Create or transform variables

Description

mutate() adds new variables and preserves existing ones; transmute() adds new variables and drops existing ones. Both functions preserve the number of rows of the input. New variables overwrite existing variables of the same name. Variables can be removed by setting their value to NULL.

Usage

mutate(.data, ...)

## S3 method for class 'data.frame'
mutate(
  .data,
  ...,
  .keep = c("all", "used", "unused", "none"),
  .before = NULL,
  .after = NULL
)

transmute(.data, ...)

Arguments

.data

A data.frame.

...

Name-value pairs of expressions, each with length 1L. The name of each argument will be the name of a new column and the value will be its corresponding value. Use a NULL value in mutate to drop a variable. New variables overwrite existing variables of the same name.

.keep

This argument allows you to control which columns from .data are retained in the output:

• "all", the default, retains all variables.

• "used" keeps any variables used to make new variables; it's useful for checking your work as it displays inputs and outputs side-by-side.

• "unused" keeps only existing variables not used to make new variables.

• "none", only keeps grouping keys (like transmute()).

Grouping variables are always kept, unconditional to .keep.

.before, .after

<poor-select> Optionally, control where new columns should appear (the default is to add to the right hand side). See relocate() for more details.

Useful mutate functions

• +, -, log(), etc., for their usual mathematical meanings

• lead(), lag()

• dense_rank(), min_rank(), percent_rank(), row_number(), cume_dist(), ntile()

• cumsum(), cummin(), cummax()

• na_if(), coalesce()

• if_else(), recode(), case_when()

Examples

mutate(mtcars, mpg2 = mpg * 2)
mtcars %>% mutate(mpg2 = mpg * 2)
mtcars %>% mutate(mpg2 = mpg * 2, cyl2 = cyl * 2)

# Newly created variables are available immediately
mtcars %>% mutate(mpg2 = mpg * 2, mpg4 = mpg2 * 2)

# You can also use mutate() to remove variables and modify existing variables
mtcars %>% mutate(
  mpg = NULL,
  disp = disp * 0.0163871 # convert to litres
)

# By default, new columns are placed on the far right.
# You can override this with `.before` or `.after`.
df <- data.frame(x = 1, y = 2)
df %>% mutate(z = x + y)
df %>% mutate(z = x + y, .before = 1)
df %>% mutate(z = x + y, .after = x)

# By default, mutate() keeps all columns from the input data.
# You can override with `.keep`
df <- data.frame(
  x = 1, y = 2, a = "a", b = "b",
  stringsAsFactors = FALSE
)
df %>% mutate(z = x + y, .keep = "all") # the default
df %>% mutate(z = x + y, .keep = "used")
df %>% mutate(z = x + y, .keep = "unused")
df %>% mutate(z = x + y, .keep = "none") # same as transmute()

# mutate() vs transmute --------------------------
# mutate() keeps all existing variables
mtcars %>%
  mutate(displ_l = disp / 61.0237)

# transmute keeps only the variables you create
mtcars %>%
  transmute(displ_l = disp / 61.0237)