Homework

This section provides optional exercises for those who want to practise and solidify the concepts introduced in the practicals. The exercises focus on subsetting data frames using the penguins dataset and more advanced vector subsetting with conditionals.

This is entirely optional

Some of the later exercises are quite advanced, so you may choose to skip them for now and revisit them later, after the course, to practise what you’ve learnt.

Subsetting data frames

  1. Load the palmerpenguins library and then load the penguins data frame by typing data(penguins).
library(tidyverse)
library(palmerpenguins)
data(penguins)
glimpse(penguins)
Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
  1. Select the third column (bill_length_mm) by name:
  1. Using the $ syntax;
  2. Using single square brackets (penguins[...]);
  3. Using double square brackets (penguins[[...]])
penguins$bill_length_mm
  [1] 39.1 39.5 40.3   NA 36.7 39.3 38.9 39.2 34.1 42.0 37.8 37.8 41.1 38.6 34.6
 [16] 36.6 38.7 42.5 34.4 46.0 37.8 37.7 35.9 38.2 38.8 35.3 40.6 40.5 37.9 40.5
 [31] 39.5 37.2 39.5 40.9 36.4 39.2 38.8 42.2 37.6 39.8 36.5 40.8 36.0 44.1 37.0
 [46] 39.6 41.1 37.5 36.0 42.3 39.6 40.1 35.0 42.0 34.5 41.4 39.0 40.6 36.5 37.6
 [61] 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
 [76] 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
 [91] 35.7 41.1 34.0 39.6 36.2 40.8 38.1 40.3 33.1 43.2 35.0 41.0 37.7 37.8 37.9
[106] 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
[121] 36.2 37.7 40.2 41.4 35.2 40.6 38.8 41.5 39.0 44.1 38.5 43.1 36.8 37.5 38.1
[136] 41.1 35.6 40.2 37.0 39.7 40.2 40.6 32.1 40.7 37.3 39.0 39.2 36.6 36.0 37.8
[151] 36.0 41.5 46.1 50.0 48.7 50.0 47.6 46.5 45.4 46.7 43.3 46.8 40.9 49.0 45.5
[166] 48.4 45.8 49.3 42.0 49.2 46.2 48.7 50.2 45.1 46.5 46.3 42.9 46.1 44.5 47.8
[181] 48.2 50.0 47.3 42.8 45.1 59.6 49.1 48.4 42.6 44.4 44.0 48.7 42.7 49.6 45.3
[196] 49.6 50.5 43.6 45.5 50.5 44.9 45.2 46.6 48.5 45.1 50.1 46.5 45.0 43.8 45.5
[211] 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
[226] 46.5 46.4 48.6 47.5 51.1 45.2 45.2 49.1 52.5 47.4 50.0 44.9 50.8 43.4 51.3
[241] 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
[256] 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
[271] 47.2   NA 46.8 50.4 45.2 49.9 46.5 50.0 51.3 45.4 52.7 45.2 46.1 51.3 46.0
[286] 51.3 46.6 51.7 47.0 52.0 45.9 50.5 50.3 58.0 46.4 49.2 42.4 48.5 43.2 50.6
[301] 46.7 52.0 50.5 49.5 46.4 52.8 40.9 54.2 42.5 51.0 49.7 47.5 47.6 52.0 46.9
[316] 53.5 49.0 46.2 50.9 45.5 50.9 50.8 50.1 49.0 51.5 49.8 48.1 51.4 45.7 50.7
[331] 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
penguins[, "bill_length_mm"]
# A tibble: 344 × 1
   bill_length_mm
            <dbl>
 1           39.1
 2           39.5
 3           40.3
 4           NA  
 5           36.7
 6           39.3
 7           38.9
 8           39.2
 9           34.1
10           42  
# ℹ 334 more rows
penguins[["bill_length_mm"]]
  [1] 39.1 39.5 40.3   NA 36.7 39.3 38.9 39.2 34.1 42.0 37.8 37.8 41.1 38.6 34.6
 [16] 36.6 38.7 42.5 34.4 46.0 37.8 37.7 35.9 38.2 38.8 35.3 40.6 40.5 37.9 40.5
 [31] 39.5 37.2 39.5 40.9 36.4 39.2 38.8 42.2 37.6 39.8 36.5 40.8 36.0 44.1 37.0
 [46] 39.6 41.1 37.5 36.0 42.3 39.6 40.1 35.0 42.0 34.5 41.4 39.0 40.6 36.5 37.6
 [61] 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
 [76] 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
 [91] 35.7 41.1 34.0 39.6 36.2 40.8 38.1 40.3 33.1 43.2 35.0 41.0 37.7 37.8 37.9
[106] 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
[121] 36.2 37.7 40.2 41.4 35.2 40.6 38.8 41.5 39.0 44.1 38.5 43.1 36.8 37.5 38.1
[136] 41.1 35.6 40.2 37.0 39.7 40.2 40.6 32.1 40.7 37.3 39.0 39.2 36.6 36.0 37.8
[151] 36.0 41.5 46.1 50.0 48.7 50.0 47.6 46.5 45.4 46.7 43.3 46.8 40.9 49.0 45.5
[166] 48.4 45.8 49.3 42.0 49.2 46.2 48.7 50.2 45.1 46.5 46.3 42.9 46.1 44.5 47.8
[181] 48.2 50.0 47.3 42.8 45.1 59.6 49.1 48.4 42.6 44.4 44.0 48.7 42.7 49.6 45.3
[196] 49.6 50.5 43.6 45.5 50.5 44.9 45.2 46.6 48.5 45.1 50.1 46.5 45.0 43.8 45.5
[211] 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
[226] 46.5 46.4 48.6 47.5 51.1 45.2 45.2 49.1 52.5 47.4 50.0 44.9 50.8 43.4 51.3
[241] 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
[256] 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
[271] 47.2   NA 46.8 50.4 45.2 49.9 46.5 50.0 51.3 45.4 52.7 45.2 46.1 51.3 46.0
[286] 51.3 46.6 51.7 47.0 52.0 45.9 50.5 50.3 58.0 46.4 49.2 42.4 48.5 43.2 50.6
[301] 46.7 52.0 50.5 49.5 46.4 52.8 40.9 54.2 42.5 51.0 49.7 47.5 47.6 52.0 46.9
[316] 53.5 49.0 46.2 50.9 45.5 50.9 50.8 50.1 49.0 51.5 49.8 48.1 51.4 45.7 50.7
[331] 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
  1. Select the fifth column (flipper_length_mm) by position:
  1. Using single square brackets (penguins[...]);
  2. Using double square brackets (penguins[[...]])
penguins[[5]]
  [1] 181 186 195  NA 193 190 181 195 193 190 186 180 182 191 198 185 195 197
 [19] 184 194 174 180 189 185 180 187 183 187 172 180 178 178 188 184 195 196
 [37] 190 180 181 184 182 195 186 196 185 190 182 179 190 191 186 188 190 200
 [55] 187 191 186 193 181 194 185 195 185 192 184 192 195 188 190 198 190 190
 [73] 196 197 190 195 191 184 187 195 189 196 187 193 191 194 190 189 189 190
 [91] 202 205 185 186 187 208 190 196 178 192 192 203 183 190 193 184 199 190
[109] 181 197 198 191 193 197 191 196 188 199 189 189 187 198 176 202 186 199
[127] 191 195 191 210 190 197 193 199 187 190 191 200 185 193 193 187 188 190
[145] 192 185 190 184 195 193 187 201 211 230 210 218 215 210 211 219 209 215
[163] 214 216 214 213 210 217 210 221 209 222 218 215 213 215 215 215 216 215
[181] 210 220 222 209 207 230 220 220 213 219 208 208 208 225 210 216 222 217
[199] 210 225 213 215 210 220 210 225 217 220 208 220 208 224 208 221 214 231
[217] 219 230 214 229 220 223 216 221 221 217 216 230 209 220 215 223 212 221
[235] 212 224 212 228 218 218 212 230 218 228 212 224 214 226 216 222 203 225
[253] 219 228 215 228 216 215 210 219 208 209 216 229 213 230 217 230 217 222
[271] 214  NA 215 222 212 213 192 196 193 188 197 198 178 197 195 198 193 194
[289] 185 201 190 201 197 181 190 195 181 191 187 193 195 197 200 200 191 205
[307] 187 201 187 203 195 199 195 210 192 205 210 187 196 196 196 201 190 212
[325] 187 198 199 201 193 203 187 197 191 203 202 194 206 189 195 207 202 193
[343] 210 198
penguins[5]
# A tibble: 344 × 1
   flipper_length_mm
               <int>
 1               181
 2               186
 3               195
 4                NA
 5               193
 6               190
 7               181
 8               195
 9               193
10               190
# ℹ 334 more rows

What’s the difference between the single ([]) and double brackets ([[]])?

Creating new columns

With the penguins data frame:

  1. Tabulate the number of missing values (NA) in the sex column.
Hint

You’ll need to use the is.na and table functions and table.

table(is.na(penguins[["sex"]]))

FALSE  TRUE 
  333    11 
# Or, equivalently:
table(is.na(penguins$sex))

FALSE  TRUE 
  333    11 
# This would also work, but is fairly verbose:
sum(is.na(penguins$sex))
[1] 11
sum(!is.na(penguins$sex))
[1] 333
  1. Using assignment, create a new column, containing bill_length_mm stored as an integer.
penguins$bill_int <-  as.integer(penguins$bill_length_mm)
  1. Create a new column containing bill_depth_mm formatted to two decimal places.
Hint

You’ll need to use sprintf for this, setting the format argument to %.2f. See the help file for details.

penguins$bd_2dp <- sprintf("%.2f", penguins$bill_depth_mm)
  1. Use paste to concatenate the species and island columns. Store the result as a new column.
penguins$si <-  paste(penguins$species, penguins$island)

Months of the year

  1. Define a character vector containing the months of the year (i.e., January, February, …, December).
moy <- c(
  "January",
  "February",
  "March",
  "April",
  "May",
  "June",
  "July",
  "August",
  "September",
  "October",
  "November",
  "December"
)

# Or use the built-in constant 'months.name'
moy <- month.name

# NOTE: I'm using 'moy' as short for 'Months of the year', but
# you can use any label you like.
  1. Select the third element of this vector.
moy[3]
[1] "March"
  1. Select the 6th, 7th, and 8th elements of this vector.
moy[c(6, 7, 8)]
[1] "June"   "July"   "August"
# Or, even shorter:
moy[6:8]
[1] "June"   "July"   "August"
  1. Select the last five elements of this vector.
moy[8:12]
[1] "August"    "September" "October"   "November"  "December" 
# The above answer will work, but it assumes that
# there are always 12 elements in the vector. This
# is true for this example, but it's good practice
# write code that can handle various input lengths.

tail(moy, 5)
[1] "August"    "September" "October"   "November"  "December"
  1. (Harder) Select all months ending in “er” (Hint: str_ends).
moy[str_ends(moy, "er")]
[1] "September" "October"   "November"  "December"
  1. (Harder) Select all months starting with “A” (Hint: str_starts).
moy[str_starts(moy, "A")]
[1] "April"  "August"
# As always, there are multiple ways of achieving this.
# The above method is quite efficient, but you could
# also try:

# Using regular expressions:
moy[str_detect(moy, "^A")]
[1] "April"  "August"
# Using purrr
keep(moy, \(x) str_detect(x, "^A"))
[1] "April"  "August"
# Or, if you really want to over-complicate it:
a_months <- c()
for (m in moy) {
  if (substring(m, 1, 1) == "A") {
    a_months <- c(a_months, m)
  }
}  # You shouldn't do this! But try to see what's happening here.