add note + examples for downsampling data for plotting - see #32.

R/vis_dat.R

@@ -26,6 +26,12 @@
 #'
 #' @seealso [vis_miss()]
 #'
+#' @note Some datasets might be too large to plot, sometimes creating a blank
+#'   plot - if this happens, I would recommend downsampling the data, either
+#'   looking at the first 1,000 rows or by taking a random sample. This means
+#'   that you won't get the same "look" at the data, but it is better than
+#'   a blank plot! See example code for suggestions on doing this.
+#'
 #' @examples
 #'
 #' vis_dat(airquality)
@@ -34,6 +40,20 @@
 #' vis_dat(airquality, palette = "cb_safe")
 #' vis_dat(airquality, palette = "qual")
 #'
+#' \dontrun{
+#' # if you have a large dataset, you might want to try downsampling:
+#' library(nycflight13)
+#' library(dplyr)
+#' flights %>%
+#'   sample_n(1000) %>%
+#'   vis_dat()
+#'
+#' flights %>%
+#'   slice(1:1000) %>%
+#'   vis_dat()
+#'
+#' }
+#'
 #' @export
 vis_dat <- function(x,
                     sort_type = TRUE,

R/vis_miss.R

@@ -28,6 +28,12 @@
 #'
 #' @seealso [vis_dat()]
 #'
+#' @note Some datasets might be too large to plot, sometimes creating a blank
+#'   plot - if this happens, I would recommend downsampling the data, either
+#'   looking at the first 1,000 rows or by taking a random sample. This means
+#'   that you won't get the same "look" at the data, but it is better than
+#'   a blank plot! See example code for suggestions on doing this.
+#'
 #' @examples
 #'
 #' vis_miss(airquality)
@@ -36,6 +42,20 @@
 #'
 #' vis_miss(airquality, sort_miss = TRUE)
 #'
+#' #' \dontrun{
+#' # if you have a large dataset, you might want to try downsampling:
+#' library(nycflight13)
+#' library(dplyr)
+#' flights %>%
+#'   sample_n(1000) %>%
+#'   vis_miss()
+#'
+#' flights %>%
+#'   slice(1:1000) %>%
+#'   vis_miss()
+#'
+#' }
+#'
 #' @export
 vis_miss <- function(x,
                      cluster = FALSE,