multiply <- function(x, y) {
x * y
}
multiply(36, 52)[1] 187211 Introduction to R
11.1 Objects
11.1.1 Exercise 1
In R it is possible to use = and <- to assign values to objects, but <- is the preferred way. = is used to assign function parameters a value, which you will encounter later.
11.2 Functions
11.2.1 Exercise 1
11.2.2 Exercise 2
Make sure you know which variable appears on what sign of the minus sign when using the function.
subtraction <- function(x, y) {
x - y
}
subtraction(36, 52)[1] -1611.2.3 Exercise 3
Here we see the usage of = in assigning function parameters values.
subtraction(y = 52, x = 36)[1] -1611.2.4 Exercise 4
subtraction(y <- 52, x <- 36)[1] 16Instead of passing x and y as parameters to the function, <- sets the values of the objects x and y to 36 and 52 respectively, which we can see by printing the variables:
x[1] 36y[1] 52The output is the same as the following code
x <- 36
y <- 52
subtraction(y, x)[1] 16Never use <- inside a function call.
11.3 Vectors
11.3.1 Exercise 1
The operations, \(+, -, \cdot, /\), are performed \(elementwise\), that is the first element of bot vectors gets added/subtracted/multiplied/divided and so on.
vec1 <- c(1, 2)
vec2 <- c(3, 4)
vec1 + vec2[1] 4 6vec1 - vec2[1] -2 -2vec1 * vec2[1] 3 8vec1 / vec2[1] 0.3333333 0.500000011.3.2 Exercise 2
We try running the code:
vec1 <- c(1, 2)
vec2 <- c(3, 4)
vec3 <- c(3, "four")
vec1 + vec2[1] 4 6vec2 + vec3Error in vec2 + vec3: non-numeric argument to binary operatorWe get an error for vec2 + vec3. Vectors must always contain the same data type, so in this case vec3 becomes a vector of strings:
vec3[1] "3" "four"We can see that 3 is now also a string by the quotes, "3".
11.4 Data frames
11.4.1 Exercise 1
We run the code and see that the output is similar to the head function.
# We comment the installation to not run it twice on our machine
# install.packages("palmerpenguins")
library(palmerpenguins)
penguins# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>11.4.2 Exercise 2
We type the dollar operator straight after the tibble and then the column name to print the values in it.
penguins$flipper_length_mm [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 19811.4.3 Exercise 3
When we press tab we see all the columns in the tibble as suggestions. In general you can use tab to autocomplete in R.
11.4.4 Exercise 4
We see some output when loading tidyverse which you can ignore for now. If you have questions about the functions, run ?<function>, e.g. ?glimpse in the console.
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsglimpse(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…summary(penguins) species island bill_length_mm bill_depth_mm
Adelie :152 Biscoe :168 Min. :32.10 Min. :13.10
Chinstrap: 68 Dream :124 1st Qu.:39.23 1st Qu.:15.60
Gentoo :124 Torgersen: 52 Median :44.45 Median :17.30
Mean :43.92 Mean :17.15
3rd Qu.:48.50 3rd Qu.:18.70
Max. :59.60 Max. :21.50
NA's :2 NA's :2
flipper_length_mm body_mass_g sex year
Min. :172.0 Min. :2700 female:165 Min. :2007
1st Qu.:190.0 1st Qu.:3550 male :168 1st Qu.:2007
Median :197.0 Median :4050 NA's : 11 Median :2008
Mean :200.9 Mean :4202 Mean :2008
3rd Qu.:213.0 3rd Qu.:4750 3rd Qu.:2009
Max. :231.0 Max. :6300 Max. :2009
NA's :2 NA's :2 head(penguins)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
# ℹ 2 more variables: sex <fct>, year <int>tail(penguins)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Chinstrap Dream 45.7 17 195 3650
2 Chinstrap Dream 55.8 19.8 207 4000
3 Chinstrap Dream 43.5 18.1 202 3400
4 Chinstrap Dream 49.6 18.2 193 3775
5 Chinstrap Dream 50.8 19 210 4100
6 Chinstrap Dream 50.2 18.7 198 3775
# ℹ 2 more variables: sex <fct>, year <int>nrow(penguins)[1] 344ncol(penguins)[1] 8names(penguins)[1] "species" "island" "bill_length_mm"
[4] "bill_depth_mm" "flipper_length_mm" "body_mass_g"
[7] "sex" "year"