R/RcppExports.R

In YTLogos/dplyr: A Grammar of Data Manipulation

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

loc <- function(data) {
    .Call('dplyr_loc', PACKAGE = 'dplyr', data)
}

dfloc <- function(df) {
    .Call('dplyr_dfloc', PACKAGE = 'dplyr', df)
}

plfloc <- function(data) {
    .Call('dplyr_plfloc', PACKAGE = 'dplyr', data)
}

strings_addresses <- function(s) {
    .Call('dplyr_strings_addresses', PACKAGE = 'dplyr', s)
}

gp <- function(x) {
    .Call('dplyr_gp', PACKAGE = 'dplyr', x)
}

#' Enable internal logging
#'
#' Log entries, depending on the log level, will be printed to the standard
#' error stream.
#'
#' @param log_level A character value, one of "WARN", "INFO", "DEBUG", "VERB",
#'   or "NONE".
#'
#' @keywords internal
init_logging <- function(log_level) {
    invisible(.Call('dplyr_init_logging', PACKAGE = 'dplyr', log_level))
}

arrange_impl <- function(data, quosures) {
    .Call('dplyr_arrange_impl', PACKAGE = 'dplyr', data, quosures)
}

#' Do values in a numeric vector fall in specified range?
#'
#' This is a shortcut for `x >= left & x <= right`, implemented
#' efficiently in C++ for local values, and translated to the
#' appropriate SQL for remote tables.
#'
#' @param x A numeric vector of values
#' @param left,right Boundary values
#' @export
#' @examples
#' x <- rnorm(1e2)
#' x[between(x, -1, 1)]
between <- function(x, left, right) {
    .Call('dplyr_between', PACKAGE = 'dplyr', x, left, right)
}

flatten_bindable <- function(x) {
    .Call('dplyr_flatten_bindable', PACKAGE = 'dplyr', x)
}

bind_rows_ <- function(dots, id = NULL) {
    .Call('dplyr_bind_rows_', PACKAGE = 'dplyr', dots, id)
}

cbind_all <- function(dots) {
    .Call('dplyr_cbind_all', PACKAGE = 'dplyr', dots)
}

combine_all <- function(data) {
    .Call('dplyr_combine_all', PACKAGE = 'dplyr', data)
}

combine_vars <- function(vars, xs) {
    .Call('dplyr_combine_vars', PACKAGE = 'dplyr', vars, xs)
}

distinct_impl <- function(df, vars, keep) {
    .Call('dplyr_distinct_impl', PACKAGE = 'dplyr', df, vars, keep)
}

n_distinct_multi <- function(variables, na_rm = FALSE) {
    .Call('dplyr_n_distinct_multi', PACKAGE = 'dplyr', variables, na_rm)
}

filter_impl <- function(df, quo) {
    .Call('dplyr_filter_impl', PACKAGE = 'dplyr', df, quo)
}

grouped_df_impl <- function(data, symbols, drop) {
    .Call('dplyr_grouped_df_impl', PACKAGE = 'dplyr', data, symbols, drop)
}

as_regular_df <- function(df) {
    .Call('dplyr_as_regular_df', PACKAGE = 'dplyr', df)
}

ungroup_grouped_df <- function(df) {
    .Call('dplyr_ungroup_grouped_df', PACKAGE = 'dplyr', df)
}

grouped_indices_grouped_df_impl <- function(gdf) {
    .Call('dplyr_grouped_indices_grouped_df_impl', PACKAGE = 'dplyr', gdf)
}

group_size_grouped_cpp <- function(gdf) {
    .Call('dplyr_group_size_grouped_cpp', PACKAGE = 'dplyr', gdf)
}

semi_join_impl <- function(x, y, by_x, by_y, na_match) {
    .Call('dplyr_semi_join_impl', PACKAGE = 'dplyr', x, y, by_x, by_y, na_match)
}

anti_join_impl <- function(x, y, by_x, by_y, na_match) {
    .Call('dplyr_anti_join_impl', PACKAGE = 'dplyr', x, y, by_x, by_y, na_match)
}

inner_join_impl <- function(x, y, by_x, by_y, suffix_x, suffix_y, na_match) {
    .Call('dplyr_inner_join_impl', PACKAGE = 'dplyr', x, y, by_x, by_y, suffix_x, suffix_y, na_match)
}

left_join_impl <- function(x, y, by_x, by_y, suffix_x, suffix_y, na_match) {
    .Call('dplyr_left_join_impl', PACKAGE = 'dplyr', x, y, by_x, by_y, suffix_x, suffix_y, na_match)
}

right_join_impl <- function(x, y, by_x, by_y, suffix_x, suffix_y, na_match) {
    .Call('dplyr_right_join_impl', PACKAGE = 'dplyr', x, y, by_x, by_y, suffix_x, suffix_y, na_match)
}

full_join_impl <- function(x, y, by_x, by_y, suffix_x, suffix_y, na_match) {
    .Call('dplyr_full_join_impl', PACKAGE = 'dplyr', x, y, by_x, by_y, suffix_x, suffix_y, na_match)
}

mutate_impl <- function(df, dots) {
    .Call('dplyr_mutate_impl', PACKAGE = 'dplyr', df, dots)
}

select_impl <- function(df, vars) {
    .Call('dplyr_select_impl', PACKAGE = 'dplyr', df, vars)
}

compatible_data_frame_nonames <- function(x, y, convert) {
    .Call('dplyr_compatible_data_frame_nonames', PACKAGE = 'dplyr', x, y, convert)
}

compatible_data_frame <- function(x, y, ignore_col_order = TRUE, convert = FALSE) {
    .Call('dplyr_compatible_data_frame', PACKAGE = 'dplyr', x, y, ignore_col_order, convert)
}

equal_data_frame <- function(x, y, ignore_col_order = TRUE, ignore_row_order = TRUE, convert = FALSE) {
    .Call('dplyr_equal_data_frame', PACKAGE = 'dplyr', x, y, ignore_col_order, ignore_row_order, convert)
}

union_data_frame <- function(x, y) {
    .Call('dplyr_union_data_frame', PACKAGE = 'dplyr', x, y)
}

intersect_data_frame <- function(x, y) {
    .Call('dplyr_intersect_data_frame', PACKAGE = 'dplyr', x, y)
}

setdiff_data_frame <- function(x, y) {
    .Call('dplyr_setdiff_data_frame', PACKAGE = 'dplyr', x, y)
}

slice_impl <- function(df, dots) {
    .Call('dplyr_slice_impl', PACKAGE = 'dplyr', df, dots)
}

summarise_impl <- function(df, dots) {
    .Call('dplyr_summarise_impl', PACKAGE = 'dplyr', df, dots)
}

test_comparisons <- function() {
    .Call('dplyr_test_comparisons', PACKAGE = 'dplyr')
}

test_matches <- function() {
    .Call('dplyr_test_matches', PACKAGE = 'dplyr')
}

assert_all_white_list <- function(data) {
    invisible(.Call('dplyr_assert_all_white_list', PACKAGE = 'dplyr', data))
}

shallow_copy <- function(data) {
    .Call('dplyr_shallow_copy', PACKAGE = 'dplyr', data)
}

#' Cumulativate versions of any, all, and mean
#'
#' dplyr adds `cumall()`, `cumany()`, and `cummean()` to complete
#' R's set of cumulate functions to match the aggregation functions available
#' in most databases
#'
#' @param x For `cumall()` and `cumany()`, a logical vector; for
#'   `cummean()` an integer or numeric vector
#' @export
cumall <- function(x) {
    .Call('dplyr_cumall', PACKAGE = 'dplyr', x)
}

#' @export
#' @rdname cumall
cumany <- function(x) {
    .Call('dplyr_cumany', PACKAGE = 'dplyr', x)
}

#' @export
#' @rdname cumall
cummean <- function(x) {
    .Call('dplyr_cummean', PACKAGE = 'dplyr', x)
}

# Register entry points for exported C++ functions
methods::setLoadAction(function(ns) {
    .Call('dplyr_RcppExport_registerCCallable', PACKAGE = 'dplyr')
})