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mutate.R
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mutate.R
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#' Create, modify, and delete columns
#'
#' `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`.
#'
#' @section Useful mutate functions:
#'
#' * [`+`], [`-`], [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()]
#'
#' @section Grouped tibbles:
#'
#' 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:
#'
#' ```
#' 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.
#'
#' @export
#' @inheritParams arrange
#' @param ... <[`data-masking`][dplyr_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.
#' @family single table verbs
#' @return
#' 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.
#' @section Methods:
#' These function are **generic**s, 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")}.
#' @examples
#' # 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
mutate <- function(.data, ...) {
UseMethod("mutate")
}
#' @rdname mutate
#' @param .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`.
#' @param .before,.after \Sexpr[results=rd]{lifecycle::badge("experimental")}
#' <[`tidy-select`][dplyr_tidy_select]> Optionally, control where new columns
#' should appear (the default is to add to the right hand side). See
#' [relocate()] for more details.
#' @export
mutate.data.frame <- function(.data, ...,
.keep = c("all", "used", "unused", "none"),
.before = NULL, .after = NULL) {
keep <- arg_match(.keep)
cols <- mutate_cols(.data, ..., caller_env = caller_env())
out <- dplyr_col_modify(.data, cols)
.before <- enquo(.before)
.after <- enquo(.after)
if (!quo_is_null(.before) || !quo_is_null(.after)) {
# Only change the order of new columns
new <- setdiff(names(cols), names(.data))
out <- relocate(out, !!new, .before = !!.before, .after = !!.after)
}
if (keep == "all") {
out
} else if (keep == "unused") {
used <- attr(cols, "used")
unused <- names(used)[!used]
keep <- intersect(names(out), c(group_vars(.data), unused, names(cols)))
dplyr_col_select(out, keep)
} else if (keep == "used") {
used <- attr(cols, "used")
used <- names(used)[used]
keep <- intersect(names(out), c(group_vars(.data), used, names(cols)))
dplyr_col_select(out, keep)
} else if (keep == "none") {
keep <- c(
# ensure group vars present
setdiff(group_vars(.data), names(cols)),
# cols might contain NULLs
intersect(names(cols), names(out))
)
dplyr_col_select(out, keep)
}
}
#' @rdname mutate
#' @export
transmute <- function(.data, ...) {
UseMethod("transmute")
}
#' @export
transmute.data.frame <- function(.data, ...) {
dots <- check_transmute_args(...)
mutate(.data, !!!dots, .keep = "none")
}
# Helpers -----------------------------------------------------------------
check_transmute_args <- function(..., .keep, .before, .after) {
if (!missing(.keep)) {
abort("`transmute()` does not support the `.keep` argument")
}
if (!missing(.before)) {
abort("`transmute()` does not support the `.before` argument")
}
if (!missing(.after)) {
abort("`transmute()` does not support the `.after` argument")
}
enquos(...)
}
mutate_cols <- function(.data, ..., caller_env) {
mask <- DataMask$new(.data, caller_env)
old_current_column <- context_peek_bare("column")
on.exit(context_poke("column", old_current_column), add = TRUE)
on.exit(mask$forget("mutate"), add = TRUE)
rows <- mask$get_rows()
dots <- dplyr_quosures(...)
if (length(dots) == 0L) {
return(NULL)
}
new_columns <- set_names(list(), character())
withCallingHandlers({
for (i in seq_along(dots)) {
mask$across_cache_reset()
context_poke("column", old_current_column)
# get results from all the quosures that are expanded from ..i
# then ingest them after
quosures <- expand_across(dots[[i]])
quosures_results <- vector(mode = "list", length = length(quosures))
for (k in seq_along(quosures)) {
quo <- quosures[[k]]
quo_data <- attr(quo, "dplyr:::data")
if (!is.null(quo_data$column)) {
context_poke("column", quo_data$column)
}
# a list in which each element is the result of
# evaluating the quosure in the "sliced data mask"
# recycling it appropriately to match the group size
#
# TODO: reinject hybrid evaluation at the R level
chunks <- NULL
# result after unchopping the chunks
result <- NULL
if (quo_is_symbol(quo)){
name <- as_string(quo_get_expr(quo))
if (name %in% names(new_columns)) {
# already have result and chunks
result <- new_columns[[name]]
chunks <- mask$resolve(name)
} else if (name %in% names(.data)) {
# column from the original data
result <- .data[[name]]
chunks <- mask$resolve(name)
}
if (inherits(.data, "rowwise_df") && vec_is_list(result)) {
sizes <- list_sizes(result)
wrong <- which(sizes != 1)
if (length(wrong)) {
# same error as would have been generated by mask$eval_all_mutate()
group <- wrong[1L]
mask$set_current_group(group)
abort(x_size = sizes[group], class = "dplyr:::mutate_incompatible_size")
}
}
}
if (is.null(chunks)) {
chunks <- mask$eval_all_mutate(quo)
}
if (is.null(chunks)) {
next
}
# only unchop if needed
if (is.null(result)) {
if (length(rows) == 1) {
result <- chunks[[1]]
} else {
result <- withCallingHandlers(
vec_unchop(chunks <- vec_cast_common(!!!chunks), rows),
vctrs_error_incompatible_type = function(cnd) {
abort(class = "dplyr:::error_mutate_incompatible_combine", parent = cnd)
}
)
}
}
quosures_results[[k]] <- list(result = result, chunks = chunks)
}
for (k in seq_along(quosures)) {
quo <- quosures[[k]]
quo_data <- attr(quo, "dplyr:::data")
quo_result <- quosures_results[[k]]
if (is.null(quo_result)) {
if (quo_data$is_named) {
name <- quo_data$name_given
new_columns[[name]] <- zap()
mask$remove(name)
}
next
}
result <- quo_result$result
chunks <- quo_result$chunks
if (!quo_data$is_named && is.data.frame(result)) {
new_columns[names(result)] <- result
mask$add_many(result, chunks)
} else {
# treat as a single output otherwise
name <- quo_data$name_auto
new_columns[[name]] <- result
mask$add_one(name, chunks)
}
}
}
},
error = function(e) {
local_call_step(dots = dots, .index = i, .fn = "mutate", .dot_data = inherits(e, "rlang_error_data_pronoun_not_found"))
call_step_envir <- peek_call_step()
error_name <- call_step_envir$error_name
error_expression <- call_step_envir$error_expression
show_group_details <- TRUE
if (inherits(e, "dplyr:::mutate_incompatible_size")) {
size <- vec_size(rows[[mask$get_current_group()]])
x_size <- e$x_size
bullets <- c(
i = cnd_bullet_column_info(),
i = glue("`{error_name}` must be size {or_1(size)}, not {x_size}."),
i = cnd_bullet_rowwise_unlist()
)
} else if (inherits(e, "dplyr:::mutate_mixed_null")) {
show_group_details <- FALSE
bullets <- c(
i = cnd_bullet_column_info(),
x = glue("`{error_name}` must return compatible vectors across groups."),
i = "Cannot combine NULL and non NULL results.",
i = cnd_bullet_rowwise_unlist()
)
} else if (inherits(e, "dplyr:::mutate_not_vector")) {
bullets <- c(
i = cnd_bullet_column_info(),
x = glue("`{error_name}` must be a vector, not {friendly_type_of(e$result)}."),
i = cnd_bullet_rowwise_unlist()
)
} else if(inherits(e, "dplyr:::error_mutate_incompatible_combine")) {
show_group_details <- FALSE
bullets <- c(
i = cnd_bullet_column_info(),
x = glue("`{error_name}` must return compatible vectors across groups"),
i = cnd_bullet_combine_details(e$parent$x, e$parent$x_arg),
i = cnd_bullet_combine_details(e$parent$y, e$parent$y_arg)
)
} else {
bullets <- c(
i = cnd_bullet_column_info(),
x = conditionMessage(e)
)
}
bullets <- c(
cnd_bullet_header(),
bullets,
i = if(show_group_details) cnd_bullet_cur_group_label()
)
abort(
bullets,
class = c("dplyr:::mutate_error", "dplyr_error"),
error_name = error_name, error_expression = error_expression,
parent = e,
bullets = bullets
)
},
warning = function(w) {
# Check if there is an upstack calling handler that would muffle
# the warning. This avoids doing the expensive work below for a
# silenced warning (#5675).
if (check_muffled_warning(w)) {
maybe_restart("muffleWarning")
}
local_call_step(dots = dots, .index = i, .fn = "mutate")
warn(c(
cnd_bullet_header(),
i = cnd_bullet_column_info(),
i = conditionMessage(w),
i = cnd_bullet_cur_group_label(what = "warning")
))
# Cancel `w`
maybe_restart("muffleWarning")
})
is_zap <- map_lgl(new_columns, inherits, "rlang_zap")
new_columns[is_zap] <- rep(list(NULL), sum(is_zap))
used <- mask$get_used()
names(used) <- mask$current_vars()
attr(new_columns, "used") <- used
new_columns
}
check_muffled_warning <- function(cnd) {
early_exit <- TRUE
# Cancel early exits, e.g. from an exiting handler. This way we can
# still instrument caught warnings to avoid confusing
# inconsistencies. This doesn't work on versions of R older than
# 3.5.0 because they don't include this change:
# https://github.com/wch/r-source/commit/688eaebf. So with
# `tryCatch(warning = )`, the original warning `cnd` will be caught
# instead of the instrumented warning.
on.exit(
if (can_return_from_exit && early_exit) {
return(FALSE)
}
)
muffled <- withRestarts(
muffleWarning = function(...) TRUE,
{
signalCondition(cnd)
FALSE
}
)
early_exit <- FALSE
muffled
}
on_load(
can_return_from_exit <- getRversion() >= "3.5.0"
)