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grouped-df.r
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grouped-df.r
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utils::globalVariables(c("old_rows", ".rows", "new_indices", "new_rows"))
make_grouped_df_groups_attribute <- function(data, vars, drop = FALSE) {
data <- as_tibble(data)
assert_that(
(is.list(vars) && all(sapply(vars, is.name))) || is.character(vars)
)
if (is.list(vars)) {
vars <- deparse_names(vars)
}
unknown <- setdiff(vars, tbl_vars(data))
if (n_unknown <- length(unknown)) {
if(n_unknown == 1) {
abort(glue("Column `{unknown}` is unknown"))
} else {
abort(glue("Column `{unknown}` are unknown", unknown = glue_collapse(unknown, sep = ", ")))
}
}
# Only train the dictionary based on selected columns
grouping_variables <- select(ungroup(data), one_of(vars))
c(old_keys, old_rows) %<-% vec_split_id(grouping_variables)
# Keys and associated rows, in order
orders <- vec_order(old_keys)
old_keys <- vec_slice(old_keys, orders)
old_rows <- old_rows[orders]
map2(old_keys, names(old_keys), function(x, n) {
if (is.factor(x) && anyNA(x)) {
warn(glue("Factor `{n}` contains implicit NA, consider using `forcats::fct_explicit_na`"))
}
})
groups <- tibble(!!!old_keys, .rows := old_rows)
if (!isTRUE(drop) && any(map_lgl(old_keys, is.factor))) {
# Extra work is needed to auto expand empty groups
uniques <- map(old_keys, function(.) {
if (is.factor(.)) . else vec_unique(.)
})
# Internally we only work with integers
#
# so for any grouping column that is not a factor
# we need to match the values to the unique values
positions <- map2(old_keys, uniques, function(.x, .y) {
if (is.factor(.x)) .x else vec_match(.x, .y)
})
# Expand groups internally adds empty groups recursively
# we get back:
# - indices: a list of how to vec_slice the current keys
# to get the new keys
#
# - rows: the new list of rows (i.e. the same as old rows,
# but with some extra empty integer(0) added for empty groups)
c(new_indices, new_rows) %<-% expand_groups(groups, positions, vec_size(old_keys))
# Make the new keys from the old keys and the new_indices
new_keys <- pmap(list(old_keys, new_indices, uniques), function(key, index, unique) {
if(is.factor(key)) {
new_factor(index, levels = levels(key))
} else {
vec_slice(unique, index)
}
})
names(new_keys) <- names(grouping_variables)
groups <- tibble(!!!new_keys, .rows := new_rows)
}
structure(groups, .drop = drop)
}
#' A grouped data frame.
#'
#' The easiest way to create a grouped data frame is to call the `group_by()`
#' method on a data frame or tbl: this will take care of capturing
#' the unevaluated expressions for you.
#'
#' @keywords internal
#' @param data a tbl or data frame.
#' @param vars a character vector or a list of [name()]
#' @param drop When `.drop = TRUE`, empty groups are dropped.
#'
#' @import vctrs
#' @importFrom zeallot %<-%
#'
#' @export
grouped_df <- function(data, vars, drop = FALSE) {
if (!length(vars)) {
return(as_tibble(data))
}
# structure the grouped data
new_grouped_df(
data,
groups = make_grouped_df_groups_attribute(data, vars, drop = drop)
)
}
#' Low-level construction and validation for the grouped_df class
#'
#' `new_grouped_df()` is a constructor designed to be high-performance so only
#' check types, not values. This means it is the caller's responsibility
#' to create valid values, and hence this is for expert use only.
#'
#' @param x A data frame
#' @param groups The grouped structure, `groups` should be a data frame.
#' Its last column should be called `.rows` and be
#' a list of 1 based integer vectors that all are between 1 and the number of rows of `.data`.
#' @param class additional class, will be prepended to canonical classes of a grouped data frame.
#' @param ... additional attributes
#'
#' @examples
#' # 5 bootstrap samples
#' tbl <- new_grouped_df(
#' tibble(x = rnorm(10)),
#' groups = tibble(".rows" := replicate(5, sample(1:10, replace = TRUE), simplify = FALSE))
#' )
#' # mean of each bootstrap sample
#' summarise(tbl, x = mean(x))
#'
#' @importFrom tibble new_tibble
#' @keywords internal
#' @export
new_grouped_df <- function(x, groups, ..., class = character()) {
stopifnot(
is.data.frame(x),
is.data.frame(groups),
tail(names(groups), 1L) == ".rows"
)
new_tibble(
x,
groups = groups,
...,
nrow = NROW(x),
class = c(class, "grouped_df")
)
}
#' @description
#' `validate_grouped_df()` validates the attributes of a `grouped_df`.
#'
#' @rdname new_grouped_df
#' @export
validate_grouped_df <- function(x) {
assert_that(is_grouped_df(x))
groups <- attr(x, "groups")
assert_that(
is.data.frame(groups),
ncol(groups) > 0,
names(groups)[ncol(groups)] == ".rows",
is.list(groups[[ncol(groups)]]),
msg = "The `groups` attribute is not a data frame with its last column called `.rows`"
)
n <- nrow(x)
rows <- groups[[ncol(groups)]]
for (i in seq_along(rows)) {
indices <- rows[[i]]
assert_that(
is.integer(indices),
msg = "`.rows` column is not a list of one-based integer vectors"
)
assert_that(
all(indices >= 1 & indices <= n),
msg = glue("indices of group {i} are out of bounds")
)
}
x
}
setOldClass(c("grouped_df", "tbl_df", "tbl", "data.frame"))
#' @rdname grouped_df
#' @export
is.grouped_df <- function(x) inherits(x, "grouped_df")
#' @rdname grouped_df
#' @export
is_grouped_df <- is.grouped_df
group_sum <- function(x) {
grps <- n_groups(x)
paste0(commas(group_vars(x)), " [", big_mark(grps), "]")
}
#' @export
tbl_sum.grouped_df <- function(x) {
c(
NextMethod(),
c("Groups" = group_sum(x))
)
}
#' @export
group_size.grouped_df <- function(x) {
group_size_grouped_cpp(x)
}
#' @export
n_groups.grouped_df <- function(x) {
nrow(group_data(x))
}
#' @export
groups.grouped_df <- function(x) {
syms(group_vars(x))
}
#' @export
group_vars.grouped_df <- function(x) {
groups <- group_data(x)
if (is.character(groups)) {
# lazy grouped
groups
} else if (is.data.frame(groups)) {
# resolved, extract from the names of the data frame
head(names(groups), -1L)
} else if (is.list(groups)) {
# Need this for compatibility with existing packages that might
# use the old list of symbols format
map_chr(groups, as_string)
}
}
#' @export
as.data.frame.grouped_df <- function(x, row.names = NULL,
optional = FALSE, ...) {
x <- ungroup(x)
class(x) <- "data.frame"
x
}
#' @export
as_tibble.grouped_df <- function(x, ...) {
x <- ungroup(x)
class(x) <- c("tbl_df", "tbl", "data.frame")
x
}
#' @export
ungroup.grouped_df <- function(x, ...) {
ungroup_grouped_df(x)
}
#' @importFrom tibble is_tibble
#' @export
`[.grouped_df` <- function(x, i, j, drop = FALSE) {
y <- NextMethod()
if (isTRUE(drop) && !is_tibble(y)) {
return(y)
}
group_names <- group_vars(x)
if (!all(group_names %in% names(y))) {
tbl_df(y)
} else {
grouped_df(y, group_names, group_by_drop_default(x))
}
}
#' @method rbind grouped_df
#' @export
rbind.grouped_df <- function(...) {
bind_rows(...)
}
#' @method cbind grouped_df
#' @export
cbind.grouped_df <- function(...) {
bind_cols(...)
}
#' Select grouping variables
#'
#' This selection helpers matches grouping variables. It can be used
#' in [select()] or [vars()][scoped] selections.
#'
#' @inheritParams tidyselect::select_helpers
#' @seealso [groups()] and [group_vars()] for retrieving the grouping
#' variables outside selection contexts.
#'
#' @examples
#' gdf <- iris %>% group_by(Species)
#'
#' # Select the grouping variables:
#' gdf %>% select(group_cols())
#'
#' # Remove the grouping variables from mutate selections:
#' gdf %>% mutate_at(vars(-group_cols()), `/`, 100)
#' @export
group_cols <- function(vars = peek_vars()) {
if (is_sel_vars(vars)) {
matches <- match(vars %@% groups, vars)
if (anyNA(matches)) {
abort("Can't find the grouping variables")
}
matches
} else {
int()
}
}
# One-table verbs --------------------------------------------------------------
# see arrange.r for arrange.grouped_df
.select_grouped_df <- function(.data, ..., notify = TRUE) {
# Pass via splicing to avoid matching vars_select() arguments
vars <- tidyselect::vars_select(tbl_vars(.data), !!!enquos(...))
vars <- ensure_group_vars(vars, .data, notify = notify)
select_impl(.data, vars)
}
#' @export
select.grouped_df <- function(.data, ...) {
.select_grouped_df(.data, !!!enquos(...), notify = TRUE)
}
#' @export
select_.grouped_df <- function(.data, ..., .dots = list()) {
dots <- compat_lazy_dots(.dots, caller_env(), ...)
select.grouped_df(.data, !!!dots)
}
ensure_group_vars <- function(vars, data, notify = TRUE) {
group_names <- group_vars(data)
missing <- setdiff(group_names, vars)
if (length(missing) > 0) {
if (notify) {
inform(glue(
"Adding missing grouping variables: ",
paste0("`", missing, "`", collapse = ", ")
))
}
vars <- c(set_names(missing, missing), vars)
}
vars
}
#' @export
rename.grouped_df <- function(.data, ...) {
vars <- tidyselect::vars_rename(names(.data), ...)
select_impl(.data, vars)
}
#' @export
rename_.grouped_df <- function(.data, ..., .dots = list()) {
dots <- compat_lazy_dots(.dots, caller_env(), ...)
rename(.data, !!!dots)
}
# Do ---------------------------------------------------------------------------
#' @export
do.grouped_df <- function(.data, ...) {
index <- group_rows(.data)
labels <- select(group_data(.data), -last_col())
attr(labels, ".drop") <- NULL
# Create ungroup version of data frame suitable for subsetting
group_data <- ungroup(.data)
args <- enquos(...)
named <- named_args(args)
mask <- new_data_mask(new_environment())
n <- length(index)
m <- length(args)
# Special case for zero-group/zero-row input
if (n == 0) {
if (named) {
out <- rep_len(list(list()), length(args))
out <- set_names(out, names(args))
out <- label_output_list(labels, out, groups(.data))
} else {
env_bind_do_pronouns(mask, group_data)
out <- eval_tidy(args[[1]], mask)
out <- out[0, , drop = FALSE]
out <- label_output_dataframe(labels, list(list(out)), groups(.data), group_by_drop_default(.data))
}
return(out)
}
# Add pronouns with active bindings that resolve to the current
# subset. `_i` is found in environment of this function because of
# usual scoping rules.
group_slice <- function(value) {
if (missing(value)) {
group_data[index[[`_i`]], , drop = FALSE]
} else {
group_data[index[[`_i`]], ] <<- value
}
}
env_bind_do_pronouns(mask, group_slice)
out <- replicate(m, vector("list", n), simplify = FALSE)
names(out) <- names(args)
p <- progress_estimated(n * m, min_time = 2)
for (`_i` in seq_len(n)) {
for (j in seq_len(m)) {
out[[j]][`_i`] <- list(eval_tidy(args[[j]], mask))
p$tick()$print()
}
}
if (!named) {
label_output_dataframe(labels, out, groups(.data), group_by_drop_default(.data))
} else {
label_output_list(labels, out, groups(.data))
}
}
#' @export
do_.grouped_df <- function(.data, ..., env = caller_env(), .dots = list()) {
dots <- compat_lazy_dots(.dots, env, ...)
do(.data, !!!dots)
}
# Set operations ---------------------------------------------------------------
#' @export
distinct.grouped_df <- function(.data, ..., .keep_all = FALSE) {
dist <- distinct_prepare(
.data,
vars = enquos(...),
group_vars = group_vars(.data),
.keep_all = .keep_all
)
vars <- match_vars(dist$vars, dist$data)
keep <- match_vars(dist$keep, dist$data)
out <- as_tibble(distinct_impl(dist$data, vars, keep, environment()))
grouped_df(out, groups(.data), group_by_drop_default(.data))
}
#' @export
distinct_.grouped_df <- function(.data, ..., .dots = list(), .keep_all = FALSE) {
dots <- compat_lazy_dots(.dots, caller_env(), ...)
distinct(.data, !!!dots, .keep_all = .keep_all)
}