#' import_events_df
#'
#' Import events from a data frame.
#'
#' @return A boolean (`TRUE` if no errors)
#'
#' @param input A data frame.
#' @param stat_unit A string containing the stat_unit label.
#' @param date A string containing the date label.
#' @param tag A string containing the tag label.
#' @param optional_data A vector containing label to import in descriptions
#' table.
#' @param date_format_func A function to format date with (not required).
#' Default: `lubridate::parse_date_time(x, date_format_reg)`
#' If you want to use milliseconds [look at this](https://bit.ly/33JGr6s).
#' @param date_format_reg A expression to format date with (not required).
#' Default: `"ymd-HMS"`
#' For more details see [this documentation](https://bit.ly/3bp3FD0).
#' @param force_date_format Boolean to force date format func (not required).
#' Default: `FALSE`
#' @param model An AnalysR env.
#' Default: `analysr_env`
#'
#' @export
import_events_df <-
function(input,
stat_unit = "stat_unit",
date = "date",
tag = "tag",
optional_data,
date_format_func =
(function(x) lubridate::parse_date_time(x, date_format_reg)),
date_format_reg = "ymd-HMS",
force_date_format = FALSE,
model = analysr_env) {
result <- tibble::tibble(input)
n <- nrow(result) # get row number only one time
hash <- get_hash(n)
# using missing https://bit.ly/2QJJyb6
if (!missing(optional_data)) {
fill_descriptions(hash, optional_data, result, n, model)
}
result <- result[c(stat_unit, date, tag)]
# we could use dplyr to extract colums https://bit.ly/32lGkNR
colnames(result) <- c("stat_unit", "date", "tag")
if (!isDate(result$date) || force_date_format) {
result$date <- date_format_func(result$date)
}
add_stat_units(result$stat_unit, model)
result <- dplyr::bind_cols(
hash = hash,
result
)
model$events <- rbind(model$events, result)
TRUE
}
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