Nothing
inst/doc/supportR.R
In supportR: Support Functions for Wrangling and Visualization
## ----knitr-mechanics----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
## ----pre-setup----------------------------------------------------------------
# devtools::install_github("njlyon0/supportR", force = TRUE)
## ----setup--------------------------------------------------------------------
#install.packages("supportR")
library(supportR)
## ----penguin-data-get---------------------------------------------------------
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")
## ----penguin-data-str---------------------------------------------------------
# Check the structure of the penguins dataset
str(penguins)
## ----summary_table------------------------------------------------------------
# Summarize the data
supportR::summary_table(data = penguins, groups = c("species", "island"),
response = "bill_length_mm", drop_na = TRUE)
## ----safe-rename--------------------------------------------------------------
# Make a dataframe to demonstrate
df <- data.frame("first" = 1:3, "middle" = 4:6, "second" = 7:9)
# Invoke the function
safe_rename(data = df, bad_names = c("second", "middle"),
good_names = c("third", "second"))
## ----crop_tri-----------------------------------------------------------------
# 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)
## ----array_melt---------------------------------------------------------------
# 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)
## ----diff_check---------------------------------------------------------------
# Make two vectors
vec1 <- c("x", "a", "b")
vec2 <- c("y", "z", "a")
# Compare them!
supportR::diff_check(old = vec1, new = vec2, sort = TRUE, return = TRUE)
## ----num_check----------------------------------------------------------------
# Make a dataframe with non-numbers in a number column
fish <- data.frame("species" = c("salmon", "bass", "halibut", "eel"),
"count" = c(1, "14x", "_23", 12))
# Use `num_check` to identify non-numbers
num_check(data = fish, col = "count")
## ----date_check---------------------------------------------------------------
# Make a dataframe including malformed dates
sites <- data.frame("site" = c("LTR", "GIL", "PYN", "RIN"),
"visit" = c("2021-01-01", "2021-01-0w", "1990", "2020-10-xx"))
# Now we can use our function to identify bad dates
supportR::date_check(data = sites, col = "visit")
## ----date_format_guess--------------------------------------------------------
# Make a dataframe with dates in various formats and a grouping column
my_df <- data.frame("data_enterer" = c("person A", "person B",
"person B", "person B",
"person C", "person D",
"person E", "person F",
"person G"),
"bad_dates" = c("2022.13.08", "2021/2/02",
"2021/2/03", "2021/2/04",
"1899/1/15", "10-31-1901",
"26/11/1901", "08.11.2004",
"6/10/02"))
# Now we can invoke the function!
supportR::date_format_guess(data = my_df, date_col = "bad_dates",
group_col = "data_enterer", return = "dataframe")
# If preferred, do it without groups and return a vector
supportR::date_format_guess(data = my_df, date_col = "bad_dates",
groups = FALSE, return = "vector")
## ----theme_lyon---------------------------------------------------------------
# Load ggplot2
library(ggplot2)
# Create a plot and allow default ggplot themeing to be added
ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
geom_boxplot(outlier.shape = 24)
# Compare with the same plot with my theme
ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
geom_boxplot(outlier.shape = 24) +
supportR::theme_lyon()
## ----ordination---------------------------------------------------------------
# Load data from the `vegan` package
utils::data("varespec", package = "vegan")
# Make a columns to split the data into 4 groups
treatment <- c(rep.int("Trt_1", (nrow(varespec)/4)),
rep.int("Trt_2", (nrow(varespec)/4)),
rep.int("Trt_3", (nrow(varespec)/4)),
rep.int("Trt_4", (nrow(varespec)/4)))
# And combine them into a single data object
data <- cbind(treatment, varespec)
# Actually perform multidimensional scaling
mds <- vegan::metaMDS(data[-1], autotransform = FALSE,
expand = FALSE, k = 2, try = 10)
# With the scaled object and original dataframe we can use this function
ordination(mod = mds, grps = data$treatment, x = "bottomright", legend = paste0("Treat-", 1:4))
## ----github_ls----------------------------------------------------------------
# # List all files in a GitHub repository
# supportR::github_ls(repo = "https://github.com/njlyon0/supportR",
# recursive = TRUE, quiet = FALSE)
#
# # Or list files in only a particular folder
# supportR::github_ls(repo = "https://github.com/njlyon0/supportR", folder = "R",
# recursive = FALSE, quiet = TRUE)
## ----github_tree--------------------------------------------------------------
# # Create a file tree diagram of a GitHub repository
# supportR::github_tree(repo = repo = "https://github.com/njlyon0/supportR",
# exclude = c("docs", "man", ".github"), quiet = FALSE)
## ----tabularize_md------------------------------------------------------------
# # Identify URL to the NEWS.md file in `supportR` GitHub repo
# md_cxn <- url("https://raw.githubusercontent.com/njlyon0/supportR/main/NEWS.md")
#
# # Transform it into a table
# md_df <- tabularize_md(file = md_cxn)
#
# # Close connection (just good housekeeping to do so)
# close(md_cxn)
#
# # Check out the table format
# str(md_df)
## ----rmd_export---------------------------------------------------------------
# # Authorize R to interact with GoogleDrive
# googledrive::drive_auth()
#
# # Use `rmd_export()` to knit and export an .Rmd file
# supportR::rmd_export(rmd = "my_markdown.Rmd",
# in_path = file.path("Folder in my WD with the .Rmd named in `rmd`"),
# out_path = file.path("Folder in my WD to save the knit file to"),
# out_name = "desired name for output",
# out_type = "html",
# drive_link = "<Full Google Drive link>")
Try the supportR package in your browser
Any scripts or data that you put into this service are public.
supportR documentation built on June 22, 2024, 10:17 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.
## ----knitr-mechanics----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
## ----pre-setup----------------------------------------------------------------
# devtools::install_github("njlyon0/supportR", force = TRUE)
## ----setup--------------------------------------------------------------------
#install.packages("supportR")
library(supportR)
## ----penguin-data-get---------------------------------------------------------
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")
## ----penguin-data-str---------------------------------------------------------
# Check the structure of the penguins dataset
str(penguins)
## ----summary_table------------------------------------------------------------
# Summarize the data
supportR::summary_table(data = penguins, groups = c("species", "island"),
response = "bill_length_mm", drop_na = TRUE)
## ----safe-rename--------------------------------------------------------------
# Make a dataframe to demonstrate
df <- data.frame("first" = 1:3, "middle" = 4:6, "second" = 7:9)
# Invoke the function
safe_rename(data = df, bad_names = c("second", "middle"),
good_names = c("third", "second"))
## ----crop_tri-----------------------------------------------------------------
# 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)
## ----array_melt---------------------------------------------------------------
# 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)
## ----diff_check---------------------------------------------------------------
# Make two vectors
vec1 <- c("x", "a", "b")
vec2 <- c("y", "z", "a")
# Compare them!
supportR::diff_check(old = vec1, new = vec2, sort = TRUE, return = TRUE)
## ----num_check----------------------------------------------------------------
# Make a dataframe with non-numbers in a number column
fish <- data.frame("species" = c("salmon", "bass", "halibut", "eel"),
"count" = c(1, "14x", "_23", 12))
# Use `num_check` to identify non-numbers
num_check(data = fish, col = "count")
## ----date_check---------------------------------------------------------------
# Make a dataframe including malformed dates
sites <- data.frame("site" = c("LTR", "GIL", "PYN", "RIN"),
"visit" = c("2021-01-01", "2021-01-0w", "1990", "2020-10-xx"))
# Now we can use our function to identify bad dates
supportR::date_check(data = sites, col = "visit")
## ----date_format_guess--------------------------------------------------------
# Make a dataframe with dates in various formats and a grouping column
my_df <- data.frame("data_enterer" = c("person A", "person B",
"person B", "person B",
"person C", "person D",
"person E", "person F",
"person G"),
"bad_dates" = c("2022.13.08", "2021/2/02",
"2021/2/03", "2021/2/04",
"1899/1/15", "10-31-1901",
"26/11/1901", "08.11.2004",
"6/10/02"))
# Now we can invoke the function!
supportR::date_format_guess(data = my_df, date_col = "bad_dates",
group_col = "data_enterer", return = "dataframe")
# If preferred, do it without groups and return a vector
supportR::date_format_guess(data = my_df, date_col = "bad_dates",
groups = FALSE, return = "vector")
## ----theme_lyon---------------------------------------------------------------
# Load ggplot2
library(ggplot2)
# Create a plot and allow default ggplot themeing to be added
ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
geom_boxplot(outlier.shape = 24)
# Compare with the same plot with my theme
ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
geom_boxplot(outlier.shape = 24) +
supportR::theme_lyon()
## ----ordination---------------------------------------------------------------
# Load data from the `vegan` package
utils::data("varespec", package = "vegan")
# Make a columns to split the data into 4 groups
treatment <- c(rep.int("Trt_1", (nrow(varespec)/4)),
rep.int("Trt_2", (nrow(varespec)/4)),
rep.int("Trt_3", (nrow(varespec)/4)),
rep.int("Trt_4", (nrow(varespec)/4)))
# And combine them into a single data object
data <- cbind(treatment, varespec)
# Actually perform multidimensional scaling
mds <- vegan::metaMDS(data[-1], autotransform = FALSE,
expand = FALSE, k = 2, try = 10)
# With the scaled object and original dataframe we can use this function
ordination(mod = mds, grps = data$treatment, x = "bottomright", legend = paste0("Treat-", 1:4))
## ----github_ls----------------------------------------------------------------
# # List all files in a GitHub repository
# supportR::github_ls(repo = "https://github.com/njlyon0/supportR",
# recursive = TRUE, quiet = FALSE)
#
# # Or list files in only a particular folder
# supportR::github_ls(repo = "https://github.com/njlyon0/supportR", folder = "R",
# recursive = FALSE, quiet = TRUE)
## ----github_tree--------------------------------------------------------------
# # Create a file tree diagram of a GitHub repository
# supportR::github_tree(repo = repo = "https://github.com/njlyon0/supportR",
# exclude = c("docs", "man", ".github"), quiet = FALSE)
## ----tabularize_md------------------------------------------------------------
# # Identify URL to the NEWS.md file in `supportR` GitHub repo
# md_cxn <- url("https://raw.githubusercontent.com/njlyon0/supportR/main/NEWS.md")
#
# # Transform it into a table
# md_df <- tabularize_md(file = md_cxn)
#
# # Close connection (just good housekeeping to do so)
# close(md_cxn)
#
# # Check out the table format
# str(md_df)
## ----rmd_export---------------------------------------------------------------
# # Authorize R to interact with GoogleDrive
# googledrive::drive_auth()
#
# # Use `rmd_export()` to knit and export an .Rmd file
# supportR::rmd_export(rmd = "my_markdown.Rmd",
# in_path = file.path("Folder in my WD with the .Rmd named in `rmd`"),
# out_path = file.path("Folder in my WD to save the knit file to"),
# out_name = "desired name for output",
# out_type = "html",
# drive_link = "<Full Google Drive link>")
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