R/create_tweet_features.R
In haukelicht/politicaltweets: Classify political tweets
Defines functions
compute_tweet_features
Documented in
compute_tweet_features
#' Compute tweet features
#'
#' Internal helper to \code{\link{create_tweet_features}}
#'
#' @param x a \code{data.frame} \code{\link[data.table]{data.table}}, or \code{\link[tibble]{tibble}}
#' recording tweets.
#' For required column (naming and typing conventions) refer to \code{?\link{required.tweets.df.cols}}.
#' For an example see \code{?\link{tweets.df.prototype}}.
#' @param .as.data.table logical. Whether or not to return a \code{\link[data.table]{data.table}}.
#' Defaults to \code{TRUE}.
#' If \code{FALSE}, the returned object is a \code{\link[tibble]{tibble}}.
#' @param .mentions.regexp unit-length character vector,
#' specifying the regular expression pattern used to match, count, and remove
#' mentions in the tweet text (applied to column \code{text}).
#' @param .hashtags.regexp unit-length character vector,
#' specifying the regular expression pattern used to match, count, and remove
#' hashtags in the tweet text (applied to column \code{text}).
#' @param .url.regexp unit-length character vector,
#' specifying the regular expression pattern used to match, count, and remove
#' URLs in the tweet text (applied to column \code{text}).
#'
#' @return A \code{\link[data.table]{data.table}} if \code{.as.data.table = TRUE} (default),
#' otherwise a \code{\link[tibble]{tibble}}.
#' The return object contains all columns contained in \code{x} \emph{plus}
#' the created tweet features.
#'
#' @import dplyr
#' @import dtplyr
#' @importFrom data.table as.data.table
#' @importFrom lubridate as_date ymd
#' @importFrom stringr str_count str_replace_all
#' @importFrom purrr map map_int map2_int map_lgl discard
#' @importFrom tidyr replace_na
compute_tweet_features <- function(
x
, .as.data.table = TRUE
, .mentions.regexp = "(?<=^|\\W)(@\\w{1,15})(?=\\s|$|\\W)"
, .hashtags.regexp = "(?<=\\.|^|\\s)(#\\w{1,139})(?=\\s|$|\\W)"
, .url.regexp = "\\b(([a-z][\\w-]+:(/{1,3}|[a-z0-9%])|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)([^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:\\'\\\".,<>?«»“”‘’]))"
) {
has_n_judgments <- "n_judgments" %in% names(x)
has_judgment_entropy <- "judgment_entropy" %in% names(x)
if (.as.data.table)
require(dplyr)
preprep <- ifelse(.as.data.table, lazy_dt, dplyr::as_tibble)
postprep <- ifelse(.as.data.table, as.data.table, identity)
x %>%
# declare lazy data.table
preprep() %>%
# filter(tweet_exists) %>%
mutate(
# tweet date
date = as_date(created_at)
# text w/o URLs
, text_no_urls = str_replace_all(text, c("\\bhttps?://\\S+" = "", "\\bwww\\.\\S+" = ""))
, nchar_no_urls = nchar(text_no_urls, type = "chars")
# is reply to other tweet?
, is_reply = !is.na(reply_to_status_id)
# is indirect, i.e., retweet, quote or reply
, is_indirect = as.logical(is_retweet + is_quote + is_reply)
# No. mentions
, n_mentions = str_count(text, .mentions.regexp)
# No. hashtags
, n_hashtags = lengths(map(hashtags, discard, is.na))
# No. symbols
, n_symbols = lengths(map(symbols, discard, is.na))
# No. URLs
, n_urls1 = lengths(map(urls_expanded_url, discard, is.na))
, n_urls2 = str_count(text, .url.regexp)
# No. media
, temp1 = lengths(map(media_expanded_url, discard, is.na))
, temp2 = lengths(map(ext_media_expanded_url, discard, is.na))
, n_media = temp1 + temp2
# has media?
, has_media = n_media > 0
# has photo?
, has_photo = !is.na(map_int(media_type, match, x = "photo"))
# has video?
, has_video = !is.na(map_int(media_type, match, x = "video"))
# tweet text character-based statistics:
# No. characters
, nchar = nchar(text, type = "chars")
# No. alnum characters
, nchar_alnum = str_pattern_width(text, "[[:alnum:]]")
, nchar_alnum_no_urls = str_pattern_width(text_no_urls, "[[:alnum:]]")
# No. punctuation characters
, nchar_punct = str_pattern_width(text, "[[:punct:]]")
, nchar_punct_no_urls = str_pattern_width(text_no_urls, "[[:punct:]]")
# No. spacec characters
, nchar_space = str_pattern_width(text, "[[:space:]]")
# combined No. character length of all mentions
, nchar_mentions = str_pattern_width(text, .mentions.regexp)
# combined No. character length of all hashtags
, nchar_hashtags = str_pattern_width(text, .hashtags.regexp)
# compute emoji stats (list-column, unpacked below)
, emoji_stats = str_emoji_stats(text)
# No. characters
, nchar_unicode = str_unicode_count(text)
, char_ratio_unicode = str_unicode_char_ratio(text)
# quotes statistics
, quote_stats = str_detect_quote(text)
) %>%
mutate_at(vars(ends_with("_count")), replace_na, 0L) %>%
# we need to separate this block of computing on created columns to make the query data.table-comfortable
mutate(
# No. emojis
n_emojis = map_int(emoji_stats, "count")
, nchar_emojis = n_emojis
# in-text quotes
, has_quotes = map_lgl(quote_stats, "quotes")
, n_quotes = map_int(quote_stats, "n_quotes")
, nchar_quotes = map_int(quote_stats, "n_chars")
, n_urls = map2_int(n_urls1, n_urls2, max)
# has URL(s) ?
, has_urls = n_urls > 0
, has_mentions = n_mentions > 0
, has_hashtags = n_hashtags > 0
, has_symbols = n_symbols > 0
, has_emojis = n_emojis > 0
) %>%
# compute width ration (width/nchar of text occupied by character type vs total text width/nchar)
mutate(
# alpha-numeric character width/total text width
char_ratio_alnum = nchar_alnum/nchar
, char_ratio_alnum_no_urls = nchar_alnum_no_urls/nchar_no_urls
, char_ratio_alnum_dwidth = nchar_alnum_no_urls/display_text_width
# punctuation character width/total text width
, char_ratio_punct = nchar_punct/nchar
, char_ratio_punct_no_urls = nchar_punct_no_urls/nchar_no_urls
, char_ratio_punct_dwidth = nchar_punct_no_urls/display_text_width
# space character width/total width
, char_ratio_space = nchar_space/nchar
, char_ratio_space_dwidth = nchar_space/display_text_width
# total width of mentions/total text width
, char_ratio_mentions = nchar_mentions/nchar
, char_ratio_mentions_no_urls = nchar_mentions/nchar_no_urls
# total width of hasthags/total text width
, char_ratio_hashtags = nchar_hashtags/nchar
, char_ratio_hashtags_no_urls = nchar_hashtags/nchar_no_urls
# total width of emojis/total text width
, char_ratio_emojis = nchar_emojis/nchar
, char_ratio_emojis_no_urls = nchar_emojis/nchar_no_urls
# total width of quotes/total text width
, char_ratio_quotes = nchar_quotes/nchar
, char_ratio_quotes_no_urls = nchar_quotes/nchar_no_urls
, char_ratio_quotes_dwidth = nchar_quotes/display_text_width
) %>%
mutate(
nchar_display = display_text_width
, tweet_type = factor(case_when(is_reply~"reply", is_quote~"quote", TRUE~"tweet"))
, nchar_limit = factor(date > ymd("2017-11-07"), c(F, T), c("140", "280"))
) %>%
# replace non-sensical NAs
mutate_at(vars(ends_with("_count")), replace_na, 0L) %>%
# replace non-sensical NaNs
mutate_at(vars(dplyr::matches("^char_ratio.+_no_urls$")), ~ifelse(is.nan(.), .0, .)) %>%
# keep the following columns
select(
# -- input columns -- #
!!setdiff(colnames(x), c("lang", "is_reply", "is_retweet", "is_quote", "favorite_count", "retweet_count", "quote_count", "reply_count"))
# -- generated columns -- #
# tweet metadata-level
, date
, lang
, tweet_type
, is_reply
, is_retweet
, is_quote
, is_indirect
, favorite_count
, retweet_count
, quote_count
, reply_count
, n_media
, has_media
, has_photo
, has_video
# tweet text-level
, nchar
, nchar_no_urls
, nchar_display
, nchar_limit
, n_mentions
, n_hashtags
, n_emojis
, n_symbols
, n_urls
, n_quotes
, has_mentions
, has_hashtags
, has_emojis
, has_symbols
, has_urls
, has_quotes
## character type counts
, nchar_alnum
, nchar_punct
, nchar_space
, nchar_mentions
, nchar_hashtags
, nchar_emojis
, nchar_quotes
, nchar_unicode
, nchar_alnum_no_urls
, nchar_punct_no_urls
## character type ratios
, char_ratio_unicode
, char_ratio_alnum
, char_ratio_punct
, char_ratio_space
, char_ratio_mentions
, char_ratio_hashtags
, char_ratio_emojis
, char_ratio_quotes
, char_ratio_alnum_no_urls
, char_ratio_punct_no_urls
, char_ratio_mentions_no_urls
, char_ratio_hashtags_no_urls
, char_ratio_emojis_no_urls
, char_ratio_quotes_no_urls
, char_ratio_alnum_dwidth
, char_ratio_punct_dwidth
, char_ratio_space_dwidth
, char_ratio_quotes_dwidth
) %>%
postprep()
}
#' Create tweet features
#'
#' Given a data frame of tweets data, \code{create_tweet_features()} creates tweet features.
#'
#' @param x a \code{data.frame} \code{\link[data.table]{data.table}}, or \code{\link[tibble]{tibble}}
#' recording tweets.
#' For required column (naming and typing conventions) refer to \code{?\link{required.tweets.df.cols}}.
#' For an example see \code{?\link{tweets.df.prototype}}.
#'
#' @param .req.columns.mapping a two-column \code{data.frame} mapping column names to
#' (character vectors specifying) expected column classes.
#' The first column must be named \code{colname} and have type character.
#' The second column must be a list-column of character vectors and named \code{accepted_types}.
#' (see \code{?\link{required.tweets.df.cols}}).
#'
#' @param ... Additional arguments passed to \code{\link{compute_tweet_features}}.
#'
#' @return A \code{\link[data.table]{data.table}}.
#' If, in addition, you pass the parameter-argument mapping \code{.as.data.table = FALSE}
#' (e.g. \code{create_tweet_features(df, .as.data.table = FALSE)}),
#' the return object is a \code{\link[tibble]{tibble}} instead.
#'
#' @note Names and types/classes of \code{x} are expected to match the values mapped in
#' \code{\link{required.tweets.df.cols}} (for an example, see \code{\link{tweets.df.prototype}}).
#' An error will be raised if
#' (a) any required column is missing (column name-based check),
#' or (b) any the class of any required column does not match any of the accepted classes.
#'
#' @section Complementary with the \code{rtweet} package:
#' The column naming and typing conventions (see \code{\link{required.tweets.df.cols}})
#' conform with the data returned by \code{rtweet} functions
#' \code{\link[rtweet]{lookup_statuses}} and \code{\link[rtweet]{lookup_tweets}}
#' if called with \code{parse = TRUE} (the default behavior).
#'
#'
#' @export
create_tweet_features <- function(
x
, .req.columns.mapping = required.tweets.df.cols
, .as.data.table = TRUE
) {
stopifnot("`x` needs to be a/inherit from data.frame object." = inherits(x, "data.frame"))
tmp <- validate_column_mapping(.req.columns.mapping)
if (!tmp$result)
stop("`.req.columns.mapping` has not the expected structure/format: ", tmp$message, call. = FALSE)
if (length(idxs_ <- which(!.req.columns.mapping$colname %in% names(x))) > 0)
stop("The following columns are required but missing from `x`: ", col_msg(idxs_, .req.columns.mapping), call. = FALSE)
col_class_check <- tryCatch(check_column_types(x, .req.columns.mapping), error = function(err) err)
if (inherits(col_class_check, "error"))
stop("Error raised when trying to check required columns' classes. Error message reads ", col_class_check$message, call. = FALSE)
if (any(!na.omit(col_class_check))) {
idxs_ <- which(.req.columns.mapping$colname %in% names(col_class_check)[!col_class_check])
stop("The following columns of `x` have wrong classes (correct classes in parantheses): ", col_msg(idxs_, .req.columns.mapping), call. = FALSE)
}
compute_tweet_features(x, .as.data.table = .as.data.table)
}
haukelicht/politicaltweets documentation built on July 3, 2023, 4:11 a.m.
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Defines functions compute_tweet_features
Documented in compute_tweet_features
#' Compute tweet features
#'
#' Internal helper to \code{\link{create_tweet_features}}
#'
#' @param x a \code{data.frame} \code{\link[data.table]{data.table}}, or \code{\link[tibble]{tibble}}
#' recording tweets.
#' For required column (naming and typing conventions) refer to \code{?\link{required.tweets.df.cols}}.
#' For an example see \code{?\link{tweets.df.prototype}}.
#' @param .as.data.table logical. Whether or not to return a \code{\link[data.table]{data.table}}.
#' Defaults to \code{TRUE}.
#' If \code{FALSE}, the returned object is a \code{\link[tibble]{tibble}}.
#' @param .mentions.regexp unit-length character vector,
#' specifying the regular expression pattern used to match, count, and remove
#' mentions in the tweet text (applied to column \code{text}).
#' @param .hashtags.regexp unit-length character vector,
#' specifying the regular expression pattern used to match, count, and remove
#' hashtags in the tweet text (applied to column \code{text}).
#' @param .url.regexp unit-length character vector,
#' specifying the regular expression pattern used to match, count, and remove
#' URLs in the tweet text (applied to column \code{text}).
#'
#' @return A \code{\link[data.table]{data.table}} if \code{.as.data.table = TRUE} (default),
#' otherwise a \code{\link[tibble]{tibble}}.
#' The return object contains all columns contained in \code{x} \emph{plus}
#' the created tweet features.
#'
#' @import dplyr
#' @import dtplyr
#' @importFrom data.table as.data.table
#' @importFrom lubridate as_date ymd
#' @importFrom stringr str_count str_replace_all
#' @importFrom purrr map map_int map2_int map_lgl discard
#' @importFrom tidyr replace_na
compute_tweet_features <- function(
x
, .as.data.table = TRUE
, .mentions.regexp = "(?<=^|\\W)(@\\w{1,15})(?=\\s|$|\\W)"
, .hashtags.regexp = "(?<=\\.|^|\\s)(#\\w{1,139})(?=\\s|$|\\W)"
, .url.regexp = "\\b(([a-z][\\w-]+:(/{1,3}|[a-z0-9%])|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)([^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:\\'\\\".,<>?«»“”‘’]))"
) {
has_n_judgments <- "n_judgments" %in% names(x)
has_judgment_entropy <- "judgment_entropy" %in% names(x)
if (.as.data.table)
require(dplyr)
preprep <- ifelse(.as.data.table, lazy_dt, dplyr::as_tibble)
postprep <- ifelse(.as.data.table, as.data.table, identity)
x %>%
# declare lazy data.table
preprep() %>%
# filter(tweet_exists) %>%
mutate(
# tweet date
date = as_date(created_at)
# text w/o URLs
, text_no_urls = str_replace_all(text, c("\\bhttps?://\\S+" = "", "\\bwww\\.\\S+" = ""))
, nchar_no_urls = nchar(text_no_urls, type = "chars")
# is reply to other tweet?
, is_reply = !is.na(reply_to_status_id)
# is indirect, i.e., retweet, quote or reply
, is_indirect = as.logical(is_retweet + is_quote + is_reply)
# No. mentions
, n_mentions = str_count(text, .mentions.regexp)
# No. hashtags
, n_hashtags = lengths(map(hashtags, discard, is.na))
# No. symbols
, n_symbols = lengths(map(symbols, discard, is.na))
# No. URLs
, n_urls1 = lengths(map(urls_expanded_url, discard, is.na))
, n_urls2 = str_count(text, .url.regexp)
# No. media
, temp1 = lengths(map(media_expanded_url, discard, is.na))
, temp2 = lengths(map(ext_media_expanded_url, discard, is.na))
, n_media = temp1 + temp2
# has media?
, has_media = n_media > 0
# has photo?
, has_photo = !is.na(map_int(media_type, match, x = "photo"))
# has video?
, has_video = !is.na(map_int(media_type, match, x = "video"))
# tweet text character-based statistics:
# No. characters
, nchar = nchar(text, type = "chars")
# No. alnum characters
, nchar_alnum = str_pattern_width(text, "[[:alnum:]]")
, nchar_alnum_no_urls = str_pattern_width(text_no_urls, "[[:alnum:]]")
# No. punctuation characters
, nchar_punct = str_pattern_width(text, "[[:punct:]]")
, nchar_punct_no_urls = str_pattern_width(text_no_urls, "[[:punct:]]")
# No. spacec characters
, nchar_space = str_pattern_width(text, "[[:space:]]")
# combined No. character length of all mentions
, nchar_mentions = str_pattern_width(text, .mentions.regexp)
# combined No. character length of all hashtags
, nchar_hashtags = str_pattern_width(text, .hashtags.regexp)
# compute emoji stats (list-column, unpacked below)
, emoji_stats = str_emoji_stats(text)
# No. characters
, nchar_unicode = str_unicode_count(text)
, char_ratio_unicode = str_unicode_char_ratio(text)
# quotes statistics
, quote_stats = str_detect_quote(text)
) %>%
mutate_at(vars(ends_with("_count")), replace_na, 0L) %>%
# we need to separate this block of computing on created columns to make the query data.table-comfortable
mutate(
# No. emojis
n_emojis = map_int(emoji_stats, "count")
, nchar_emojis = n_emojis
# in-text quotes
, has_quotes = map_lgl(quote_stats, "quotes")
, n_quotes = map_int(quote_stats, "n_quotes")
, nchar_quotes = map_int(quote_stats, "n_chars")
, n_urls = map2_int(n_urls1, n_urls2, max)
# has URL(s) ?
, has_urls = n_urls > 0
, has_mentions = n_mentions > 0
, has_hashtags = n_hashtags > 0
, has_symbols = n_symbols > 0
, has_emojis = n_emojis > 0
) %>%
# compute width ration (width/nchar of text occupied by character type vs total text width/nchar)
mutate(
# alpha-numeric character width/total text width
char_ratio_alnum = nchar_alnum/nchar
, char_ratio_alnum_no_urls = nchar_alnum_no_urls/nchar_no_urls
, char_ratio_alnum_dwidth = nchar_alnum_no_urls/display_text_width
# punctuation character width/total text width
, char_ratio_punct = nchar_punct/nchar
, char_ratio_punct_no_urls = nchar_punct_no_urls/nchar_no_urls
, char_ratio_punct_dwidth = nchar_punct_no_urls/display_text_width
# space character width/total width
, char_ratio_space = nchar_space/nchar
, char_ratio_space_dwidth = nchar_space/display_text_width
# total width of mentions/total text width
, char_ratio_mentions = nchar_mentions/nchar
, char_ratio_mentions_no_urls = nchar_mentions/nchar_no_urls
# total width of hasthags/total text width
, char_ratio_hashtags = nchar_hashtags/nchar
, char_ratio_hashtags_no_urls = nchar_hashtags/nchar_no_urls
# total width of emojis/total text width
, char_ratio_emojis = nchar_emojis/nchar
, char_ratio_emojis_no_urls = nchar_emojis/nchar_no_urls
# total width of quotes/total text width
, char_ratio_quotes = nchar_quotes/nchar
, char_ratio_quotes_no_urls = nchar_quotes/nchar_no_urls
, char_ratio_quotes_dwidth = nchar_quotes/display_text_width
) %>%
mutate(
nchar_display = display_text_width
, tweet_type = factor(case_when(is_reply~"reply", is_quote~"quote", TRUE~"tweet"))
, nchar_limit = factor(date > ymd("2017-11-07"), c(F, T), c("140", "280"))
) %>%
# replace non-sensical NAs
mutate_at(vars(ends_with("_count")), replace_na, 0L) %>%
# replace non-sensical NaNs
mutate_at(vars(dplyr::matches("^char_ratio.+_no_urls$")), ~ifelse(is.nan(.), .0, .)) %>%
# keep the following columns
select(
# -- input columns -- #
!!setdiff(colnames(x), c("lang", "is_reply", "is_retweet", "is_quote", "favorite_count", "retweet_count", "quote_count", "reply_count"))
# -- generated columns -- #
# tweet metadata-level
, date
, lang
, tweet_type
, is_reply
, is_retweet
, is_quote
, is_indirect
, favorite_count
, retweet_count
, quote_count
, reply_count
, n_media
, has_media
, has_photo
, has_video
# tweet text-level
, nchar
, nchar_no_urls
, nchar_display
, nchar_limit
, n_mentions
, n_hashtags
, n_emojis
, n_symbols
, n_urls
, n_quotes
, has_mentions
, has_hashtags
, has_emojis
, has_symbols
, has_urls
, has_quotes
## character type counts
, nchar_alnum
, nchar_punct
, nchar_space
, nchar_mentions
, nchar_hashtags
, nchar_emojis
, nchar_quotes
, nchar_unicode
, nchar_alnum_no_urls
, nchar_punct_no_urls
## character type ratios
, char_ratio_unicode
, char_ratio_alnum
, char_ratio_punct
, char_ratio_space
, char_ratio_mentions
, char_ratio_hashtags
, char_ratio_emojis
, char_ratio_quotes
, char_ratio_alnum_no_urls
, char_ratio_punct_no_urls
, char_ratio_mentions_no_urls
, char_ratio_hashtags_no_urls
, char_ratio_emojis_no_urls
, char_ratio_quotes_no_urls
, char_ratio_alnum_dwidth
, char_ratio_punct_dwidth
, char_ratio_space_dwidth
, char_ratio_quotes_dwidth
) %>%
postprep()
}
#' Create tweet features
#'
#' Given a data frame of tweets data, \code{create_tweet_features()} creates tweet features.
#'
#' @param x a \code{data.frame} \code{\link[data.table]{data.table}}, or \code{\link[tibble]{tibble}}
#' recording tweets.
#' For required column (naming and typing conventions) refer to \code{?\link{required.tweets.df.cols}}.
#' For an example see \code{?\link{tweets.df.prototype}}.
#'
#' @param .req.columns.mapping a two-column \code{data.frame} mapping column names to
#' (character vectors specifying) expected column classes.
#' The first column must be named \code{colname} and have type character.
#' The second column must be a list-column of character vectors and named \code{accepted_types}.
#' (see \code{?\link{required.tweets.df.cols}}).
#'
#' @param ... Additional arguments passed to \code{\link{compute_tweet_features}}.
#'
#' @return A \code{\link[data.table]{data.table}}.
#' If, in addition, you pass the parameter-argument mapping \code{.as.data.table = FALSE}
#' (e.g. \code{create_tweet_features(df, .as.data.table = FALSE)}),
#' the return object is a \code{\link[tibble]{tibble}} instead.
#'
#' @note Names and types/classes of \code{x} are expected to match the values mapped in
#' \code{\link{required.tweets.df.cols}} (for an example, see \code{\link{tweets.df.prototype}}).
#' An error will be raised if
#' (a) any required column is missing (column name-based check),
#' or (b) any the class of any required column does not match any of the accepted classes.
#'
#' @section Complementary with the \code{rtweet} package:
#' The column naming and typing conventions (see \code{\link{required.tweets.df.cols}})
#' conform with the data returned by \code{rtweet} functions
#' \code{\link[rtweet]{lookup_statuses}} and \code{\link[rtweet]{lookup_tweets}}
#' if called with \code{parse = TRUE} (the default behavior).
#'
#'
#' @export
create_tweet_features <- function(
x
, .req.columns.mapping = required.tweets.df.cols
, .as.data.table = TRUE
) {
stopifnot("`x` needs to be a/inherit from data.frame object." = inherits(x, "data.frame"))
tmp <- validate_column_mapping(.req.columns.mapping)
if (!tmp$result)
stop("`.req.columns.mapping` has not the expected structure/format: ", tmp$message, call. = FALSE)
if (length(idxs_ <- which(!.req.columns.mapping$colname %in% names(x))) > 0)
stop("The following columns are required but missing from `x`: ", col_msg(idxs_, .req.columns.mapping), call. = FALSE)
col_class_check <- tryCatch(check_column_types(x, .req.columns.mapping), error = function(err) err)
if (inherits(col_class_check, "error"))
stop("Error raised when trying to check required columns' classes. Error message reads ", col_class_check$message, call. = FALSE)
if (any(!na.omit(col_class_check))) {
idxs_ <- which(.req.columns.mapping$colname %in% names(col_class_check)[!col_class_check])
stop("The following columns of `x` have wrong classes (correct classes in parantheses): ", col_msg(idxs_, .req.columns.mapping), call. = FALSE)
}
compute_tweet_features(x, .as.data.table = .as.data.table)
}
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