ds-freq-numeric.R

ds_freq_numeric <- function(data, variable, bins = 5) {

  check_df(data)
  var_name <- deparse(substitute(variable))
  varyable <- rlang::enquo(variable)
  check_numeric(data, !! varyable, var_name)

  fdata <-
    data %>%
    dplyr::pull(!! varyable) %>%
    stats::na.omit()

  if (!is.numeric(bins)) {
    stop("bins must be integer value")
  }

  if (is.numeric(bins)) {
    bins <- as.integer(bins)
  }

  var_name <-
    data %>%
    dplyr::select(!! varyable) %>%
    names()

  n_bins   <- bins
  inta     <- intervals(fdata, bins)
  result   <- freq(fdata, bins, inta)
  data_len <- length(fdata)
  cum      <- cumsum(result)
  per      <- percent(result, data_len)
  cum_per  <- percent(cum, data_len)

  na_count <-
    data %>%
    dplyr::pull(!! varyable) %>%
    is.na() %>%
    sum()

  if (na_count > 0) {
    na_freq <- na_count
  } else {
    na_freq <- 0
  }

  n_obs <-
    data %>%
    dplyr::pull(!! varyable) %>%
    length()

  lower_n <- n_bins + 1

  freq_data <-
    tibble::tibble(lower        = inta[-lower_n],
           upper        = inta[-1],
           frequency    = result,
           cumulative   = cum,
           freq_percent = per,
           cum_percent  = cum_per)

  out <- list(freq_data   = freq_data,
              breaks      = inta,
              frequency   = result,
              cumulative  = cum,
              percent     = per,
              cum_percent = cum_per,
              bins        = n_bins,
              data        = fdata,
              na_count    = na_freq,
              n           = n_obs,
              varname     = var_name
  )

  return(out)
}

plot_ds_freq_numeric <- function(x, ...) {

  x_lab <- magrittr::use_series(x, varname)

  k <-
    x %>%
    magrittr::use_series(varname) %>%
    magrittr::extract(1) %>%
    rlang::sym()

  bins <-
    x %>%
    magrittr::use_series(frequency) %>%
    length()

  p <-
    x %>%
    magrittr::use_series(frequency) %>%
    tibble::enframe(name = NULL) %>%
    tibble::add_column(x = seq_len(bins), .before = 1) %>%
    ggplot2::ggplot() +
    ggplot2::geom_col(
      ggplot2::aes(x = x, y = value), width = 0.999,
      fill = "blue", color = "black"
    ) +
    ggplot2::xlab(x_lab) + ggplot2::ylab("Count") +
    ggplot2::ggtitle(paste("Histogram of", x_lab))

  return(p)

}