In NJLyon-Projects/helpR: Support Functions for Wrangling and Visualization
#| include: false
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
#| echo: false
#| message: false
# devtools::install_github("njlyon0/supportR", force = TRUE)
Overview
The supportR package is an amalgam of distinct functions I've written to accomplish small data wrangling, quality control, or visualization tasks. These functions tend to be short and narrowly-defined. An additional consequence of the motivation for creating them is that they tend to not be inter-related or united by a common theme. To help account for that, I've divided this vignette into groups of functions that do have similar purposes.
If any of these functions don't work for you, please open an issue so that I can repair the issue as soon as possible.
# install.packages("supportR")
library(supportR)
Example Data
In order to demonstrate some of the data wrangling functions of supportR, we'll use some some example data from Dr. Allison Horst's palmerpenguins R package.
#| include: false
penguins <- structure(list(species = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), levels = c("Adelie", "Chinstrap", "Gentoo"), class = "factor"),
island = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), levels = c("Biscoe", "Dream", "Torgersen"
), class = "factor"), bill_length_mm = c(39.1, 39.5, 40.3,
NA, 36.7, 39.3, 38.9, 39.2, 34.1, 42, 37.8, 37.8, 41.1, 38.6,
34.6, 36.6, 38.7, 42.5, 34.4, 46, 37.8, 37.7, 35.9, 38.2,
38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9,
36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36, 44.1,
37, 39.6, 41.1, 37.5, 36, 42.3, 39.6, 40.1, 35, 42, 34.5,
41.4, 39, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4,
41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5,
42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3,
41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34, 39.6, 36.2,
40.8, 38.1, 40.3, 33.1, 43.2, 35, 41, 37.7, 37.8, 37.9, 39.7,
38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7,
38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6,
38.8, 41.5, 39, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1,
35.6, 40.2, 37, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39, 39.2,
36.6, 36, 37.8, 36, 41.5, 46.1, 50, 48.7, 50, 47.6, 46.5,
45.4, 46.7, 43.3, 46.8, 40.9, 49, 45.5, 48.4, 45.8, 49.3,
42, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1,
44.5, 47.8, 48.2, 50, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4,
42.6, 44.4, 44, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6,
45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45,
43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8,
46.2, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6,
47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50, 44.9, 50.8,
43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8,
49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 47.3, 46.8,
41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1,
44.5, 48.8, 47.2, NA, 46.8, 50.4, 45.2, 49.9, 46.5, 50, 51.3,
45.4, 52.7, 45.2, 46.1, 51.3, 46, 51.3, 46.6, 51.7, 47, 52,
45.9, 50.5, 50.3, 58, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6,
46.7, 52, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51, 49.7,
47.5, 47.6, 52, 46.9, 53.5, 49, 46.2, 50.9, 45.5, 50.9, 50.8,
50.1, 49, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2,
45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6,
50.8, 50.2), bill_depth_mm = c(18.7, 17.4, 18, NA, 19.3,
20.6, 17.8, 19.6, 18.1, 20.2, 17.1, 17.3, 17.6, 21.2, 21.1,
17.8, 19, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2,
18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17,
21.1, 20, 18.5, 19.3, 19.1, 18, 18.4, 18.5, 19.7, 16.9, 18.8,
19, 18.9, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6,
17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17, 18.2, 17.1, 18, 16.2,
19.1, 16.6, 19.4, 19, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8,
19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5,
18.6, 19.2, 18.8, 18, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6,
18.5, 16.1, 18.5, 17.9, 20, 16, 20, 18.6, 18.9, 17.2, 20,
17, 19, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17, 20.5, 17,
18.6, 17.2, 19.8, 17, 18.5, 15.9, 19, 17.6, 18.3, 17.1, 18,
17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9,
17.1, 17.2, 15.5, 17, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1,
17.1, 18.5, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3,
13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2,
14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 14.3, 15,
14.3, 15.3, 15.3, 14.2, 14.5, 17, 14.8, 16.3, 13.7, 17.3,
13.6, 15.7, 13.7, 16, 13.7, 15, 15.9, 13.9, 13.9, 15.9, 13.3,
15.8, 14.2, 14.1, 14.4, 15, 14.4, 15.4, 13.9, 15, 14.5, 15.3,
13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 14.4, 16.2, 14.2, 15,
15, 15.6, 15.6, 14.8, 15, 16, 14.2, 16.3, 13.8, 16.4, 14.5,
15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14, 17, 15, 17.1,
14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15, 17, 15.5,
15, 13.8, 16.1, 14.7, 15.8, 14, 15.1, 15.2, 15.9, 15.2, 16.3,
14.1, 16, 15.7, 16.2, 13.7, NA, 14.3, 15.7, 14.8, 16.1, 17.9,
19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8,
20.3, 17.3, 18.1, 17.1, 19.6, 20, 17.8, 18.6, 18.2, 17.3,
17.5, 16.6, 19.4, 17.9, 19, 18.4, 19, 17.8, 20, 16.6, 20.8,
16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5,
19.1, 17, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19, 17.3,
19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17,
19.8, 18.1, 18.2, 19, 18.7), flipper_length_mm = c(181L,
186L, 195L, NA, 193L, 190L, 181L, 195L, 193L, 190L, 186L,
180L, 182L, 191L, 198L, 185L, 195L, 197L, 184L, 194L, 174L,
180L, 189L, 185L, 180L, 187L, 183L, 187L, 172L, 180L, 178L,
178L, 188L, 184L, 195L, 196L, 190L, 180L, 181L, 184L, 182L,
195L, 186L, 196L, 185L, 190L, 182L, 179L, 190L, 191L, 186L,
188L, 190L, 200L, 187L, 191L, 186L, 193L, 181L, 194L, 185L,
195L, 185L, 192L, 184L, 192L, 195L, 188L, 190L, 198L, 190L,
190L, 196L, 197L, 190L, 195L, 191L, 184L, 187L, 195L, 189L,
196L, 187L, 193L, 191L, 194L, 190L, 189L, 189L, 190L, 202L,
205L, 185L, 186L, 187L, 208L, 190L, 196L, 178L, 192L, 192L,
203L, 183L, 190L, 193L, 184L, 199L, 190L, 181L, 197L, 198L,
191L, 193L, 197L, 191L, 196L, 188L, 199L, 189L, 189L, 187L,
198L, 176L, 202L, 186L, 199L, 191L, 195L, 191L, 210L, 190L,
197L, 193L, 199L, 187L, 190L, 191L, 200L, 185L, 193L, 193L,
187L, 188L, 190L, 192L, 185L, 190L, 184L, 195L, 193L, 187L,
201L, 211L, 230L, 210L, 218L, 215L, 210L, 211L, 219L, 209L,
215L, 214L, 216L, 214L, 213L, 210L, 217L, 210L, 221L, 209L,
222L, 218L, 215L, 213L, 215L, 215L, 215L, 216L, 215L, 210L,
220L, 222L, 209L, 207L, 230L, 220L, 220L, 213L, 219L, 208L,
208L, 208L, 225L, 210L, 216L, 222L, 217L, 210L, 225L, 213L,
215L, 210L, 220L, 210L, 225L, 217L, 220L, 208L, 220L, 208L,
224L, 208L, 221L, 214L, 231L, 219L, 230L, 214L, 229L, 220L,
223L, 216L, 221L, 221L, 217L, 216L, 230L, 209L, 220L, 215L,
223L, 212L, 221L, 212L, 224L, 212L, 228L, 218L, 218L, 212L,
230L, 218L, 228L, 212L, 224L, 214L, 226L, 216L, 222L, 203L,
225L, 219L, 228L, 215L, 228L, 216L, 215L, 210L, 219L, 208L,
209L, 216L, 229L, 213L, 230L, 217L, 230L, 217L, 222L, 214L,
NA, 215L, 222L, 212L, 213L, 192L, 196L, 193L, 188L, 197L,
198L, 178L, 197L, 195L, 198L, 193L, 194L, 185L, 201L, 190L,
201L, 197L, 181L, 190L, 195L, 181L, 191L, 187L, 193L, 195L,
197L, 200L, 200L, 191L, 205L, 187L, 201L, 187L, 203L, 195L,
199L, 195L, 210L, 192L, 205L, 210L, 187L, 196L, 196L, 196L,
201L, 190L, 212L, 187L, 198L, 199L, 201L, 193L, 203L, 187L,
197L, 191L, 203L, 202L, 194L, 206L, 189L, 195L, 207L, 202L,
193L, 210L, 198L), body_mass_g = c(3750L, 3800L, 3250L, NA,
3450L, 3650L, 3625L, 4675L, 3475L, 4250L, 3300L, 3700L, 3200L,
3800L, 4400L, 3700L, 3450L, 4500L, 3325L, 4200L, 3400L, 3600L,
3800L, 3950L, 3800L, 3800L, 3550L, 3200L, 3150L, 3950L, 3250L,
3900L, 3300L, 3900L, 3325L, 4150L, 3950L, 3550L, 3300L, 4650L,
3150L, 3900L, 3100L, 4400L, 3000L, 4600L, 3425L, 2975L, 3450L,
4150L, 3500L, 4300L, 3450L, 4050L, 2900L, 3700L, 3550L, 3800L,
2850L, 3750L, 3150L, 4400L, 3600L, 4050L, 2850L, 3950L, 3350L,
4100L, 3050L, 4450L, 3600L, 3900L, 3550L, 4150L, 3700L, 4250L,
3700L, 3900L, 3550L, 4000L, 3200L, 4700L, 3800L, 4200L, 3350L,
3550L, 3800L, 3500L, 3950L, 3600L, 3550L, 4300L, 3400L, 4450L,
3300L, 4300L, 3700L, 4350L, 2900L, 4100L, 3725L, 4725L, 3075L,
4250L, 2925L, 3550L, 3750L, 3900L, 3175L, 4775L, 3825L, 4600L,
3200L, 4275L, 3900L, 4075L, 2900L, 3775L, 3350L, 3325L, 3150L,
3500L, 3450L, 3875L, 3050L, 4000L, 3275L, 4300L, 3050L, 4000L,
3325L, 3500L, 3500L, 4475L, 3425L, 3900L, 3175L, 3975L, 3400L,
4250L, 3400L, 3475L, 3050L, 3725L, 3000L, 3650L, 4250L, 3475L,
3450L, 3750L, 3700L, 4000L, 4500L, 5700L, 4450L, 5700L, 5400L,
4550L, 4800L, 5200L, 4400L, 5150L, 4650L, 5550L, 4650L, 5850L,
4200L, 5850L, 4150L, 6300L, 4800L, 5350L, 5700L, 5000L, 4400L,
5050L, 5000L, 5100L, 4100L, 5650L, 4600L, 5550L, 5250L, 4700L,
5050L, 6050L, 5150L, 5400L, 4950L, 5250L, 4350L, 5350L, 3950L,
5700L, 4300L, 4750L, 5550L, 4900L, 4200L, 5400L, 5100L, 5300L,
4850L, 5300L, 4400L, 5000L, 4900L, 5050L, 4300L, 5000L, 4450L,
5550L, 4200L, 5300L, 4400L, 5650L, 4700L, 5700L, 4650L, 5800L,
4700L, 5550L, 4750L, 5000L, 5100L, 5200L, 4700L, 5800L, 4600L,
6000L, 4750L, 5950L, 4625L, 5450L, 4725L, 5350L, 4750L, 5600L,
4600L, 5300L, 4875L, 5550L, 4950L, 5400L, 4750L, 5650L, 4850L,
5200L, 4925L, 4875L, 4625L, 5250L, 4850L, 5600L, 4975L, 5500L,
4725L, 5500L, 4700L, 5500L, 4575L, 5500L, 5000L, 5950L, 4650L,
5500L, 4375L, 5850L, 4875L, 6000L, 4925L, NA, 4850L, 5750L,
5200L, 5400L, 3500L, 3900L, 3650L, 3525L, 3725L, 3950L, 3250L,
3750L, 4150L, 3700L, 3800L, 3775L, 3700L, 4050L, 3575L, 4050L,
3300L, 3700L, 3450L, 4400L, 3600L, 3400L, 2900L, 3800L, 3300L,
4150L, 3400L, 3800L, 3700L, 4550L, 3200L, 4300L, 3350L, 4100L,
3600L, 3900L, 3850L, 4800L, 2700L, 4500L, 3950L, 3650L, 3550L,
3500L, 3675L, 4450L, 3400L, 4300L, 3250L, 3675L, 3325L, 3950L,
3600L, 4050L, 3350L, 3450L, 3250L, 4050L, 3800L, 3525L, 3950L,
3650L, 3650L, 4000L, 3400L, 3775L, 4100L, 3775L), sex = structure(c(2L,
1L, 1L, NA, 1L, 2L, 1L, 2L, NA, NA, NA, NA, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, NA, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, NA, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
NA, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L,
NA, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L), levels = c("female",
"male"), class = "factor"), year = c(2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
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2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L,
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2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L,
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L,
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L)), row.names = c(NA,
-344L), class = "data.frame")
# Check the structure of the penguins dataset
str(penguins)
Summarize Within Groups
With that data loaded, we can use the summary_table function to quickly get group-wise summaries and retrieve generally useful summary statistics.
The groups argument supports a vector of all of the column names to group by while response must be a single numeric column. The drop_na argument allows group combinations that result in an NA to be automatically dropped (i.e., if a penguin didn't have an island listed that would be dropped). The mean, standard deviation (SD), sample size, and standard error (SE) are all returned to facilitate easy figure creation. There is also a round_digits argument that lets you specify how many digits you'd like to retain for the mean, SD, and SE.
# Summarize the data
supportR::summary_table(data = penguins, groups = c("species", "island"),
response = "bill_length_mm", drop_na = TRUE)
Safely Rename Columns
The safe_rename function allows--perhaps predictably--"safe" renaming of column names in a given data object. The 'bad' column names and corresponding 'good' names must be specified. The order of entries in the two vectors must match (i.e., the first bad name will be replaced with the first good name), but that order need not match the order in which they occur in the data!
# Make a dataframe to demonstrate
df <- data.frame("first" = 1:3, "middle" = 4:6, "second" = 7:9)
# Invoke the function
supportR::safe_rename(data = df, bad_names = c("second", "middle"),
good_names = c("third", "second"))
Handle Special Characters
When loading text data into R you may have encountered pernicious 'special' characters that can affect the meaning of character data. These characters are often non-ASCII (see here for more context on "ASCII" characters) and supportR offers the replace_non_ascii function to find and replace these characters with visually similar equivalents in a given vector.
Note that letters with accents above or below them (e.g., umlaut, accent circumflex, etc.) are technically non-ASCII characters. Because they are often a part of proper nouns rather than being categorically incorrect I've split off whether they should be replaced into an include_letters argument that defaults to FALSE.
Also, non-ASCII characters trigger errors in the R package submission process so the example below uses "u-escape" codes for ASCII characters to still demonstrate the function without causing errors when I submit version updates of supportR to CRAN.
# Make a vector of non-ASCII characters
(non_ascii <- c("\u201C", "\u00AC", "\u00D7"))
# Replace them with ASCII equivalents
supportR::replace_non_ascii(x = non_ascii, include_letters = F)
Crop Triangles
crop_tri allows dropping one "triangle" of a symmetric dataframe / matrix. It also includes a drop_diag argument that accepts a logical for whether to drop the diagonal of the data object. This is primarily useful (I find) in allowing piping through this function as opposed to using the base R notation for removing a triangle of a symmetric data object.
# Define a simple matrix wtih symmetric dimensions
mat <- matrix(data = c(1:2, 2:1), nrow = 2, ncol = 2)
# Crop off it's lower triangle
supportR::crop_tri(data = mat, drop_tri = "lower", drop_diag = FALSE)
# Drop the diagonal as well
supportR::crop_tri(data = mat, drop_tri = "lower", drop_diag = TRUE)
"Melt" Arrays into Dataframes
array_melt allows users to 'melt' an array of dimensions X, Y, and Z into a dataframe containing columns "x", "y", "z", and "value" where "value" is whatever was stored at those coordinates in the array.
# Make data to fill the array
vec1 <- c(5, 9, 3)
vec2 <- c(10:15)
# Create dimension names (x = col, y = row, z = which matrix)
x_vals <- c("Col_1","Col_2","Col_3")
y_vals <- c("Row_1","Row_2","Row_3")
z_vals <- c("Mat_1","Mat_2")
# Make an array from these components
g <- array(data = c(vec1, vec2), dim = c(3, 3, 2),
dimnames = list(x_vals, y_vals, z_vals))
# "Melt" the array into a dataframe
melted <- supportR::array_melt(array = g)
# Look at that top of that
head(melted)
NJLyon-Projects/helpR documentation built on April 13, 2025, 6:11 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#| include: false knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
#| echo: false #| message: false # devtools::install_github("njlyon0/supportR", force = TRUE)
Overview
The supportR package is an amalgam of distinct functions I've written to accomplish small data wrangling, quality control, or visualization tasks. These functions tend to be short and narrowly-defined. An additional consequence of the motivation for creating them is that they tend to not be inter-related or united by a common theme. To help account for that, I've divided this vignette into groups of functions that do have similar purposes.
If any of these functions don't work for you, please open an issue so that I can repair the issue as soon as possible.
# install.packages("supportR") library(supportR)
Example Data
In order to demonstrate some of the data wrangling functions of supportR, we'll use some some example data from Dr. Allison Horst's palmerpenguins R package.
#| include: false penguins <- structure(list(species = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("Adelie", "Chinstrap", "Gentoo"), class = "factor"), island = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("Biscoe", "Dream", "Torgersen" ), class = "factor"), bill_length_mm = c(39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, 42, 37.8, 37.8, 41.1, 38.6, 34.6, 36.6, 38.7, 42.5, 34.4, 46, 37.8, 37.7, 35.9, 38.2, 38.8, 35.3, 40.6, 40.5, 37.9, 40.5, 39.5, 37.2, 39.5, 40.9, 36.4, 39.2, 38.8, 42.2, 37.6, 39.8, 36.5, 40.8, 36, 44.1, 37, 39.6, 41.1, 37.5, 36, 42.3, 39.6, 40.1, 35, 42, 34.5, 41.4, 39, 40.6, 36.5, 37.6, 35.7, 41.3, 37.6, 41.1, 36.4, 41.6, 35.5, 41.1, 35.9, 41.8, 33.5, 39.7, 39.6, 45.8, 35.5, 42.8, 40.9, 37.2, 36.2, 42.1, 34.6, 42.9, 36.7, 35.1, 37.3, 41.3, 36.3, 36.9, 38.3, 38.9, 35.7, 41.1, 34, 39.6, 36.2, 40.8, 38.1, 40.3, 33.1, 43.2, 35, 41, 37.7, 37.8, 37.9, 39.7, 38.6, 38.2, 38.1, 43.2, 38.1, 45.6, 39.7, 42.2, 39.6, 42.7, 38.6, 37.3, 35.7, 41.1, 36.2, 37.7, 40.2, 41.4, 35.2, 40.6, 38.8, 41.5, 39, 44.1, 38.5, 43.1, 36.8, 37.5, 38.1, 41.1, 35.6, 40.2, 37, 39.7, 40.2, 40.6, 32.1, 40.7, 37.3, 39, 39.2, 36.6, 36, 37.8, 36, 41.5, 46.1, 50, 48.7, 50, 47.6, 46.5, 45.4, 46.7, 43.3, 46.8, 40.9, 49, 45.5, 48.4, 45.8, 49.3, 42, 49.2, 46.2, 48.7, 50.2, 45.1, 46.5, 46.3, 42.9, 46.1, 44.5, 47.8, 48.2, 50, 47.3, 42.8, 45.1, 59.6, 49.1, 48.4, 42.6, 44.4, 44, 48.7, 42.7, 49.6, 45.3, 49.6, 50.5, 43.6, 45.5, 50.5, 44.9, 45.2, 46.6, 48.5, 45.1, 50.1, 46.5, 45, 43.8, 45.5, 43.2, 50.4, 45.3, 46.2, 45.7, 54.3, 45.8, 49.8, 46.2, 49.5, 43.5, 50.7, 47.7, 46.4, 48.2, 46.5, 46.4, 48.6, 47.5, 51.1, 45.2, 45.2, 49.1, 52.5, 47.4, 50, 44.9, 50.8, 43.4, 51.3, 47.5, 52.1, 47.5, 52.2, 45.5, 49.5, 44.5, 50.8, 49.4, 46.9, 48.4, 51.1, 48.5, 55.9, 47.2, 49.1, 47.3, 46.8, 41.7, 53.4, 43.3, 48.1, 50.5, 49.8, 43.5, 51.5, 46.2, 55.1, 44.5, 48.8, 47.2, NA, 46.8, 50.4, 45.2, 49.9, 46.5, 50, 51.3, 45.4, 52.7, 45.2, 46.1, 51.3, 46, 51.3, 46.6, 51.7, 47, 52, 45.9, 50.5, 50.3, 58, 46.4, 49.2, 42.4, 48.5, 43.2, 50.6, 46.7, 52, 50.5, 49.5, 46.4, 52.8, 40.9, 54.2, 42.5, 51, 49.7, 47.5, 47.6, 52, 46.9, 53.5, 49, 46.2, 50.9, 45.5, 50.9, 50.8, 50.1, 49, 51.5, 49.8, 48.1, 51.4, 45.7, 50.7, 42.5, 52.2, 45.2, 49.3, 50.2, 45.6, 51.9, 46.8, 45.7, 55.8, 43.5, 49.6, 50.8, 50.2), bill_depth_mm = c(18.7, 17.4, 18, NA, 19.3, 20.6, 17.8, 19.6, 18.1, 20.2, 17.1, 17.3, 17.6, 21.2, 21.1, 17.8, 19, 20.7, 18.4, 21.5, 18.3, 18.7, 19.2, 18.1, 17.2, 18.9, 18.6, 17.9, 18.6, 18.9, 16.7, 18.1, 17.8, 18.9, 17, 21.1, 20, 18.5, 19.3, 19.1, 18, 18.4, 18.5, 19.7, 16.9, 18.8, 19, 18.9, 17.9, 21.2, 17.7, 18.9, 17.9, 19.5, 18.1, 18.6, 17.5, 18.8, 16.6, 19.1, 16.9, 21.1, 17, 18.2, 17.1, 18, 16.2, 19.1, 16.6, 19.4, 19, 18.4, 17.2, 18.9, 17.5, 18.5, 16.8, 19.4, 16.1, 19.1, 17.2, 17.6, 18.8, 19.4, 17.8, 20.3, 19.5, 18.6, 19.2, 18.8, 18, 18.1, 17.1, 18.1, 17.3, 18.9, 18.6, 18.5, 16.1, 18.5, 17.9, 20, 16, 20, 18.6, 18.9, 17.2, 20, 17, 19, 16.5, 20.3, 17.7, 19.5, 20.7, 18.3, 17, 20.5, 17, 18.6, 17.2, 19.8, 17, 18.5, 15.9, 19, 17.6, 18.3, 17.1, 18, 17.9, 19.2, 18.5, 18.5, 17.6, 17.5, 17.5, 20.1, 16.5, 17.9, 17.1, 17.2, 15.5, 17, 16.8, 18.7, 18.6, 18.4, 17.8, 18.1, 17.1, 18.5, 13.2, 16.3, 14.1, 15.2, 14.5, 13.5, 14.6, 15.3, 13.4, 15.4, 13.7, 16.1, 13.7, 14.6, 14.6, 15.7, 13.5, 15.2, 14.5, 15.1, 14.3, 14.5, 14.5, 15.8, 13.1, 15.1, 14.3, 15, 14.3, 15.3, 15.3, 14.2, 14.5, 17, 14.8, 16.3, 13.7, 17.3, 13.6, 15.7, 13.7, 16, 13.7, 15, 15.9, 13.9, 13.9, 15.9, 13.3, 15.8, 14.2, 14.1, 14.4, 15, 14.4, 15.4, 13.9, 15, 14.5, 15.3, 13.8, 14.9, 13.9, 15.7, 14.2, 16.8, 14.4, 16.2, 14.2, 15, 15, 15.6, 15.6, 14.8, 15, 16, 14.2, 16.3, 13.8, 16.4, 14.5, 15.6, 14.6, 15.9, 13.8, 17.3, 14.4, 14.2, 14, 17, 15, 17.1, 14.5, 16.1, 14.7, 15.7, 15.8, 14.6, 14.4, 16.5, 15, 17, 15.5, 15, 13.8, 16.1, 14.7, 15.8, 14, 15.1, 15.2, 15.9, 15.2, 16.3, 14.1, 16, 15.7, 16.2, 13.7, NA, 14.3, 15.7, 14.8, 16.1, 17.9, 19.5, 19.2, 18.7, 19.8, 17.8, 18.2, 18.2, 18.9, 19.9, 17.8, 20.3, 17.3, 18.1, 17.1, 19.6, 20, 17.8, 18.6, 18.2, 17.3, 17.5, 16.6, 19.4, 17.9, 19, 18.4, 19, 17.8, 20, 16.6, 20.8, 16.7, 18.8, 18.6, 16.8, 18.3, 20.7, 16.6, 19.9, 19.5, 17.5, 19.1, 17, 17.9, 18.5, 17.9, 19.6, 18.7, 17.3, 16.4, 19, 17.3, 19.7, 17.3, 18.8, 16.6, 19.9, 18.8, 19.4, 19.5, 16.5, 17, 19.8, 18.1, 18.2, 19, 18.7), flipper_length_mm = c(181L, 186L, 195L, NA, 193L, 190L, 181L, 195L, 193L, 190L, 186L, 180L, 182L, 191L, 198L, 185L, 195L, 197L, 184L, 194L, 174L, 180L, 189L, 185L, 180L, 187L, 183L, 187L, 172L, 180L, 178L, 178L, 188L, 184L, 195L, 196L, 190L, 180L, 181L, 184L, 182L, 195L, 186L, 196L, 185L, 190L, 182L, 179L, 190L, 191L, 186L, 188L, 190L, 200L, 187L, 191L, 186L, 193L, 181L, 194L, 185L, 195L, 185L, 192L, 184L, 192L, 195L, 188L, 190L, 198L, 190L, 190L, 196L, 197L, 190L, 195L, 191L, 184L, 187L, 195L, 189L, 196L, 187L, 193L, 191L, 194L, 190L, 189L, 189L, 190L, 202L, 205L, 185L, 186L, 187L, 208L, 190L, 196L, 178L, 192L, 192L, 203L, 183L, 190L, 193L, 184L, 199L, 190L, 181L, 197L, 198L, 191L, 193L, 197L, 191L, 196L, 188L, 199L, 189L, 189L, 187L, 198L, 176L, 202L, 186L, 199L, 191L, 195L, 191L, 210L, 190L, 197L, 193L, 199L, 187L, 190L, 191L, 200L, 185L, 193L, 193L, 187L, 188L, 190L, 192L, 185L, 190L, 184L, 195L, 193L, 187L, 201L, 211L, 230L, 210L, 218L, 215L, 210L, 211L, 219L, 209L, 215L, 214L, 216L, 214L, 213L, 210L, 217L, 210L, 221L, 209L, 222L, 218L, 215L, 213L, 215L, 215L, 215L, 216L, 215L, 210L, 220L, 222L, 209L, 207L, 230L, 220L, 220L, 213L, 219L, 208L, 208L, 208L, 225L, 210L, 216L, 222L, 217L, 210L, 225L, 213L, 215L, 210L, 220L, 210L, 225L, 217L, 220L, 208L, 220L, 208L, 224L, 208L, 221L, 214L, 231L, 219L, 230L, 214L, 229L, 220L, 223L, 216L, 221L, 221L, 217L, 216L, 230L, 209L, 220L, 215L, 223L, 212L, 221L, 212L, 224L, 212L, 228L, 218L, 218L, 212L, 230L, 218L, 228L, 212L, 224L, 214L, 226L, 216L, 222L, 203L, 225L, 219L, 228L, 215L, 228L, 216L, 215L, 210L, 219L, 208L, 209L, 216L, 229L, 213L, 230L, 217L, 230L, 217L, 222L, 214L, NA, 215L, 222L, 212L, 213L, 192L, 196L, 193L, 188L, 197L, 198L, 178L, 197L, 195L, 198L, 193L, 194L, 185L, 201L, 190L, 201L, 197L, 181L, 190L, 195L, 181L, 191L, 187L, 193L, 195L, 197L, 200L, 200L, 191L, 205L, 187L, 201L, 187L, 203L, 195L, 199L, 195L, 210L, 192L, 205L, 210L, 187L, 196L, 196L, 196L, 201L, 190L, 212L, 187L, 198L, 199L, 201L, 193L, 203L, 187L, 197L, 191L, 203L, 202L, 194L, 206L, 189L, 195L, 207L, 202L, 193L, 210L, 198L), body_mass_g = c(3750L, 3800L, 3250L, NA, 3450L, 3650L, 3625L, 4675L, 3475L, 4250L, 3300L, 3700L, 3200L, 3800L, 4400L, 3700L, 3450L, 4500L, 3325L, 4200L, 3400L, 3600L, 3800L, 3950L, 3800L, 3800L, 3550L, 3200L, 3150L, 3950L, 3250L, 3900L, 3300L, 3900L, 3325L, 4150L, 3950L, 3550L, 3300L, 4650L, 3150L, 3900L, 3100L, 4400L, 3000L, 4600L, 3425L, 2975L, 3450L, 4150L, 3500L, 4300L, 3450L, 4050L, 2900L, 3700L, 3550L, 3800L, 2850L, 3750L, 3150L, 4400L, 3600L, 4050L, 2850L, 3950L, 3350L, 4100L, 3050L, 4450L, 3600L, 3900L, 3550L, 4150L, 3700L, 4250L, 3700L, 3900L, 3550L, 4000L, 3200L, 4700L, 3800L, 4200L, 3350L, 3550L, 3800L, 3500L, 3950L, 3600L, 3550L, 4300L, 3400L, 4450L, 3300L, 4300L, 3700L, 4350L, 2900L, 4100L, 3725L, 4725L, 3075L, 4250L, 2925L, 3550L, 3750L, 3900L, 3175L, 4775L, 3825L, 4600L, 3200L, 4275L, 3900L, 4075L, 2900L, 3775L, 3350L, 3325L, 3150L, 3500L, 3450L, 3875L, 3050L, 4000L, 3275L, 4300L, 3050L, 4000L, 3325L, 3500L, 3500L, 4475L, 3425L, 3900L, 3175L, 3975L, 3400L, 4250L, 3400L, 3475L, 3050L, 3725L, 3000L, 3650L, 4250L, 3475L, 3450L, 3750L, 3700L, 4000L, 4500L, 5700L, 4450L, 5700L, 5400L, 4550L, 4800L, 5200L, 4400L, 5150L, 4650L, 5550L, 4650L, 5850L, 4200L, 5850L, 4150L, 6300L, 4800L, 5350L, 5700L, 5000L, 4400L, 5050L, 5000L, 5100L, 4100L, 5650L, 4600L, 5550L, 5250L, 4700L, 5050L, 6050L, 5150L, 5400L, 4950L, 5250L, 4350L, 5350L, 3950L, 5700L, 4300L, 4750L, 5550L, 4900L, 4200L, 5400L, 5100L, 5300L, 4850L, 5300L, 4400L, 5000L, 4900L, 5050L, 4300L, 5000L, 4450L, 5550L, 4200L, 5300L, 4400L, 5650L, 4700L, 5700L, 4650L, 5800L, 4700L, 5550L, 4750L, 5000L, 5100L, 5200L, 4700L, 5800L, 4600L, 6000L, 4750L, 5950L, 4625L, 5450L, 4725L, 5350L, 4750L, 5600L, 4600L, 5300L, 4875L, 5550L, 4950L, 5400L, 4750L, 5650L, 4850L, 5200L, 4925L, 4875L, 4625L, 5250L, 4850L, 5600L, 4975L, 5500L, 4725L, 5500L, 4700L, 5500L, 4575L, 5500L, 5000L, 5950L, 4650L, 5500L, 4375L, 5850L, 4875L, 6000L, 4925L, NA, 4850L, 5750L, 5200L, 5400L, 3500L, 3900L, 3650L, 3525L, 3725L, 3950L, 3250L, 3750L, 4150L, 3700L, 3800L, 3775L, 3700L, 4050L, 3575L, 4050L, 3300L, 3700L, 3450L, 4400L, 3600L, 3400L, 2900L, 3800L, 3300L, 4150L, 3400L, 3800L, 3700L, 4550L, 3200L, 4300L, 3350L, 4100L, 3600L, 3900L, 3850L, 4800L, 2700L, 4500L, 3950L, 3650L, 3550L, 3500L, 3675L, 4450L, 3400L, 4300L, 3250L, 3675L, 3325L, 3950L, 3600L, 4050L, 3350L, 3450L, 3250L, 4050L, 3800L, 3525L, 3950L, 3650L, 3650L, 4000L, 3400L, 3775L, 4100L, 3775L), sex = structure(c(2L, 1L, 1L, NA, 1L, 2L, 1L, 2L, NA, NA, NA, NA, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, NA, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, NA, 2L, 1L, NA, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L), levels = c("female", "male"), class = "factor"), year = c(2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L)), row.names = c(NA, -344L), class = "data.frame")
# Check the structure of the penguins dataset str(penguins)
Summarize Within Groups
With that data loaded, we can use the summary_table function to quickly get group-wise summaries and retrieve generally useful summary statistics.
The groups argument supports a vector of all of the column names to group by while response must be a single numeric column. The drop_na argument allows group combinations that result in an NA to be automatically dropped (i.e., if a penguin didn't have an island listed that would be dropped). The mean, standard deviation (SD), sample size, and standard error (SE) are all returned to facilitate easy figure creation. There is also a round_digits argument that lets you specify how many digits you'd like to retain for the mean, SD, and SE.
# Summarize the data supportR::summary_table(data = penguins, groups = c("species", "island"), response = "bill_length_mm", drop_na = TRUE)
Safely Rename Columns
The safe_rename function allows--perhaps predictably--"safe" renaming of column names in a given data object. The 'bad' column names and corresponding 'good' names must be specified. The order of entries in the two vectors must match (i.e., the first bad name will be replaced with the first good name), but that order need not match the order in which they occur in the data!
# Make a dataframe to demonstrate df <- data.frame("first" = 1:3, "middle" = 4:6, "second" = 7:9) # Invoke the function supportR::safe_rename(data = df, bad_names = c("second", "middle"), good_names = c("third", "second"))
Handle Special Characters
When loading text data into R you may have encountered pernicious 'special' characters that can affect the meaning of character data. These characters are often non-ASCII (see here for more context on "ASCII" characters) and supportR offers the replace_non_ascii function to find and replace these characters with visually similar equivalents in a given vector.
Note that letters with accents above or below them (e.g., umlaut, accent circumflex, etc.) are technically non-ASCII characters. Because they are often a part of proper nouns rather than being categorically incorrect I've split off whether they should be replaced into an include_letters argument that defaults to FALSE.
Also, non-ASCII characters trigger errors in the R package submission process so the example below uses "u-escape" codes for ASCII characters to still demonstrate the function without causing errors when I submit version updates of supportR to CRAN.
# Make a vector of non-ASCII characters (non_ascii <- c("\u201C", "\u00AC", "\u00D7"))
# Replace them with ASCII equivalents supportR::replace_non_ascii(x = non_ascii, include_letters = F)
Crop Triangles
crop_tri allows dropping one "triangle" of a symmetric dataframe / matrix. It also includes a drop_diag argument that accepts a logical for whether to drop the diagonal of the data object. This is primarily useful (I find) in allowing piping through this function as opposed to using the base R notation for removing a triangle of a symmetric data object.
# Define a simple matrix wtih symmetric dimensions mat <- matrix(data = c(1:2, 2:1), nrow = 2, ncol = 2) # Crop off it's lower triangle supportR::crop_tri(data = mat, drop_tri = "lower", drop_diag = FALSE) # Drop the diagonal as well supportR::crop_tri(data = mat, drop_tri = "lower", drop_diag = TRUE)
"Melt" Arrays into Dataframes
array_melt allows users to 'melt' an array of dimensions X, Y, and Z into a dataframe containing columns "x", "y", "z", and "value" where "value" is whatever was stored at those coordinates in the array.
# Make data to fill the array vec1 <- c(5, 9, 3) vec2 <- c(10:15) # Create dimension names (x = col, y = row, z = which matrix) x_vals <- c("Col_1","Col_2","Col_3") y_vals <- c("Row_1","Row_2","Row_3") z_vals <- c("Mat_1","Mat_2") # Make an array from these components g <- array(data = c(vec1, vec2), dim = c(3, 3, 2), dimnames = list(x_vals, y_vals, z_vals)) # "Melt" the array into a dataframe melted <- supportR::array_melt(array = g) # Look at that top of that head(melted)
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