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Working on resolving the Ubuntu/Windows issues with the `palmerpengui…
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…ns` data loaded for the vignette
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@njlyon0
njlyon0 committed Jun 13, 2024
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Expand Up @@ -36,14 +36,265 @@ library(supportR)

In order to demonstrate some of the data wrangling functions of `supportR`, we'll use some some example data from Dr. [Allison Horst](https://allisonhorst.com/allison-horst)'s [`palmerpenguins` R package](https://github.com/allisonhorst/palmerpenguins).

```{r ex-data-pngn}
# Load library
library(palmerpenguins)

# Get the penguins data
penguins <- palmerpenguins::penguins
```{r penguin-data-get}
#| 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")
```

# Glimpse the penguins dataset
```{r penguin-data-str}
# Check the structure of the penguins dataset
str(penguins)
```

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