mutate: Create or transform variables

In olascodgreat/samife: A Grammar of Data Manipulation

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.

Usage

mutate(.data, ...)

transmute(.data, ...)

Arguments

.data

A tbl. All main verbs are S3 generics and provide methods for tbl_df(), dtplyr::tbl_dt() and dbplyr::tbl_dbi().

...

Name-value pairs of expressions, each with length 1 or the same length as the number of rows in the group (if using group_by()) or in the entire input (if not using groups). The name of each argument will be the name of a new variable, and the value will be its corresponding value. Use a NULL value in mutate to drop a variable. The arguments in ... are automatically quoted and evaluated in the context of the data frame. They support unquoting and splicing. See vignette("programming") for an introduction to these concepts.

Value

An object of the same class as .data.

Useful functions available in calculations of variables

• +, -, 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()

Scoped mutation and transmutation

The three scoped variants of mutate() (mutate_all(), mutate_if() and mutate_at()) and the three variants of transmute() (transmute_all(), transmute_if(), transmute_at()) make it easy to apply a transformation to a selection of variables.

Tidy data

When applied to a data frame, row names are silently dropped. To preserve, convert to an explicit variable with tibble::rownames_to_column().

See Also

Other single table verbs: arrange, filter, select, slice, summarise

Examples

# Newly created variables are available immediately
mtcars %>% as_tibble() %>% mutate(
  cyl2 = cyl * 2,
  cyl4 = cyl2 * 2
)

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


# window functions are useful for grouped mutates
mtcars %>%
 group_by(cyl) %>%
 mutate(rank = min_rank(desc(mpg)))
# see `vignette("window-functions")` for more details

# You can drop variables by setting them to NULL
mtcars %>% mutate(cyl = NULL)

# 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)


# mutate() supports quasiquotation. You can unquote quosures, which
# can refer to both contextual variables and variable names:
var <- 100
as_tibble(mtcars) %>% mutate(cyl = !!quo(cyl * var))

olascodgreat/samife documentation built on May 13, 2019, 6:11 p.m.