mutate: Create, modify, and delete columns
In javifar/TIDYVERSE-DPLYR: A Grammar of Data Manipulation

Description Usage Arguments Value Useful mutate functions Grouped tibbles Methods See Also Examples

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

mutate(.data, ...)

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

transmute(.data, ...)

`.data`	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
`...`	<`data-masking`> Name-value pairs. The name gives the name of the column in the output. The value can be: A vector of length 1, which will be recycled to the correct length. A vector the same length as the current group (or the whole data frame if ungrouped). `NULL`, to remove the column. A data frame or tibble, to create multiple columns in the output.
`.keep`	\Sexpr[results=rd]{lifecycle::badge("experimental")} This is an experimental argument that 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`	\Sexpr[results=rd]{lifecycle::badge("experimental")} <`tidy-select`> Optionally, control where new columns should appear (the default is to add to the right hand side). See `relocate()` for more details.

An object of the same type as .data. The output has the following properties:

Rows are not affected.
Existing columns will be preserved according to the .keep argument. New columns will be placed according to the .before and .after arguments. If .keep = "none" (as in transmute()), the output order is determined only by ..., not the order of existing columns.
Columns given value NULL will be removed
Groups will be recomputed if a grouping variable is mutated.
Data frame attributes are preserved.

+, -, log(), etc., for their usual mathematical meanings
lead(), lag()
dense_rank(), min_rank(), percent_rank(), row_number(), cume_dist(), ntile()
cumsum(), cummean(), cummin(), cummax(), cumany(), cumall()
na_if(), coalesce()
if_else(), recode(), case_when()

Because mutating expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped mutate:

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starwars %>%
  select(name, mass, species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

With the grouped equivalent:

starwars %>%
  select(name, mass, species) %>%
  group_by(species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

The former normalises mass by the global average whereas the latter normalises by the averages within species levels.

These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

mutate(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("mutate")}.
transmute(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("transmute")}.

Other single table verbs: arrange(), filter(), rename(), select(), slice(), summarise()

# Newly created variables are available immediately
starwars %>%
 select(name, mass) %>%
 mutate(
  mass2 = mass * 2,
  mass2_squared = mass2 * mass2
)

# As well as adding new variables, you can use mutate() to
# remove variables and modify existing variables.
starwars %>%
 select(name, height, mass, homeworld) %>%
 mutate(
  mass = NULL,
  height = height * 0.0328084 # convert to feet
)

# Use across() with mutate() to apply a transformation
# to multiple columns in a tibble.
starwars %>%
 select(name, homeworld, species) %>%
 mutate(across(!name, as.factor))
# see more in ?across

# Window functions are useful for grouped mutates:
starwars %>%
 select(name, mass, homeworld) %>%
 group_by(homeworld) %>%
 mutate(rank = min_rank(desc(mass)))
# see `vignette("window-functions")` for more details

# By default, new columns are placed on the far right.
# Experimental: you can override with `.before` or `.after`
df <- tibble(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.
# Experimental: You can override with `.keep`
df <- tibble(x = 1, y = 2, a = "a", b = "b")
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()

# Grouping ----------------------------------------
# The mutate operation may yield different results on grouped
# tibbles because the expressions are computed within groups.
# The following normalises `mass` by the global average:
starwars %>%
  select(name, mass, species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

# Whereas this normalises `mass` by the averages within species
# levels:
starwars %>%
  select(name, mass, species) %>%
  group_by(species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))

# Indirection ----------------------------------------
# Refer to column names stored as strings with the `.data` pronoun:
vars <- c("mass", "height")
mutate(starwars, prod = .data[[vars[[1]]]] * .data[[vars[[2]]]])
# Learn more in ?dplyr_data_masking