Creating Conditional Variables and Renaming Variables

Creating Conditional Variables with `case_when()`

The case_when() function inside mutate() allows you to build new variables based on conditional logic, similar to “if…else” statements but more readable.

For example, we can classify participants into categories of gendered maturity based on their age and gender:

library(dplyr) # load dplyr
bfi <- mutate(bfi,
              gendered_maturity = case_when(
                  age < 18 & gender == "male"   ~ "boy",
                  age < 18 & gender == "female" ~ "girl",
                  age >= 18 & gender == "male"  ~ "man",
                  age >= 18 & gender == "female"~ "woman",
                  TRUE ~ NA_character_),
              gendered_maturity = as.factor(gendered_maturity))
str(bfi)

'data.frame':   2800 obs. of  39 variables:
 $ A1               : int  2 2 5 4 2 6 2 4 4 2 ...
 $ A2               : int  4 4 4 4 3 6 5 3 3 5 ...
 $ A3               : int  3 5 5 6 3 5 5 1 6 6 ...
 $ A4               : int  4 2 4 5 4 6 3 5 3 6 ...
 $ A5               : int  4 5 4 5 5 5 5 1 3 5 ...
 $ C1               : int  2 5 4 4 4 6 5 3 6 6 ...
 $ C2               : int  3 4 5 4 4 6 4 2 6 5 ...
 $ C3               : int  3 4 4 3 5 6 4 4 3 6 ...
 $ C4               : int  4 3 2 5 3 1 2 2 4 2 ...
 $ C5               : int  4 4 5 5 2 3 3 4 5 1 ...
 $ E1               : int  3 1 2 5 2 2 4 3 5 2 ...
 $ E2               : int  3 1 4 3 2 1 3 6 3 2 ...
 $ E3               : int  3 6 4 4 5 6 4 4 NA 4 ...
 $ E4               : int  4 4 4 4 4 5 5 2 4 5 ...
 $ E5               : int  4 3 5 4 5 6 5 1 3 5 ...
 $ N1               : int  3 3 4 2 2 3 1 6 5 5 ...
 $ N2               : int  4 3 5 5 3 5 2 3 5 5 ...
 $ N3               : int  2 3 4 2 4 2 2 2 2 5 ...
 $ N4               : int  2 5 2 4 4 2 1 6 3 2 ...
 $ N5               : int  3 5 3 1 3 3 1 4 3 4 ...
 $ O1               : int  3 4 4 3 3 4 5 3 6 5 ...
 $ O2               : int  6 2 2 3 3 3 2 2 6 1 ...
 $ O3               : int  3 4 5 4 4 5 5 4 6 5 ...
 $ O4               : int  4 3 5 3 3 6 6 5 6 5 ...
 $ O5               : int  3 3 2 5 3 1 1 3 1 2 ...
 $ gender           : Factor w/ 2 levels "male","female": 1 2 2 2 1 2 1 1 1 2 ...
 $ education        : int  NA NA NA NA NA 3 NA 2 1 NA ...
 $ age              : int  16 18 17 17 17 21 18 19 19 17 ...
 $ agree            : num  4 4.2 3.8 4.6 4 4.6 4.6 2.6 3.6 5.4 ...
 $ consc            : num  2.8 4 4 3 4.4 5.6 4.4 3.4 4 5.6 ...
 $ extra            : num  3.8 5 4.2 3.6 4.8 5.6 4.2 2.4 3.25 4.8 ...
 $ neuro            : num  2.8 3.8 3.6 2.8 3.2 3 1.4 4.2 3.6 4.2 ...
 $ open             : num  3 4 4.8 3.2 3.6 5 5.4 4.2 5 5.2 ...
 $ agree_z          : num  -0.726 -0.503 -0.948 -0.058 -0.726 ...
 $ consc_z          : num  -1.541 -0.279 -0.279 -1.33 0.141 ...
 $ extra_z          : num  -0.3253 0.8058 0.0518 -0.5138 0.6173 ...
 $ neuro_z          : num  -0.3028 0.5331 0.3659 -0.3028 0.0315 ...
 $ open_z           : num  -1.963 -0.726 0.264 -1.715 -1.221 ...
 $ gendered_maturity: Factor w/ 4 levels "boy","girl","man",..: 1 4 2 2 1 4 3 3 3 2 ...

levels(bfi$gendered_maturity)

[1] "boy"   "girl"  "man"   "woman"

with(bfi, table(minor = age < 18, gender, gendered_maturity))

, , gendered_maturity = boy

       gender
minor   male female
  FALSE    0      0
  TRUE    84      0

, , gendered_maturity = girl

       gender
minor   male female
  FALSE    0      0
  TRUE     0    164

, , gendered_maturity = man

       gender
minor   male female
  FALSE  835      0
  TRUE     0      0

, , gendered_maturity = woman

       gender
minor   male female
  FALSE    0   1717
  TRUE     0      0

Renaming Variables

Finally, the rename() and rename_with() functions from dplyr provide a clean way to rename variables, individually or in groups.

To rename a few columns directly:

bfi <- rename(bfi, gen = gender, ed = education, gm = gendered_maturity)
head(bfi)

      A1 A2 A3 A4 A5 C1 C2 C3 C4 C5 E1 E2 E3 E4 E5 N1 N2 N3 N4 N5 O1 O2 O3 O4
61617  2  4  3  4  4  2  3  3  4  4  3  3  3  4  4  3  4  2  2  3  3  6  3  4
61618  2  4  5  2  5  5  4  4  3  4  1  1  6  4  3  3  3  3  5  5  4  2  4  3
61620  5  4  5  4  4  4  5  4  2  5  2  4  4  4  5  4  5  4  2  3  4  2  5  5
61621  4  4  6  5  5  4  4  3  5  5  5  3  4  4  4  2  5  2  4  1  3  3  4  3
61622  2  3  3  4  5  4  4  5  3  2  2  2  5  4  5  2  3  4  4  3  3  3  4  3
61623  6  6  5  6  5  6  6  6  1  3  2  1  6  5  6  3  5  2  2  3  4  3  5  6
      O5    gen ed age agree consc extra neuro open     agree_z    consc_z
61617  3   male NA  16   4.0   2.8   3.8   2.8  3.0 -0.72583913 -1.5406915
61618  3 female NA  18   4.2   4.0   5.0   3.8  4.0 -0.50322161 -0.2793220
61620  2 female NA  17   3.8   4.0   4.2   3.6  4.8 -0.94845665 -0.2793220
61621  5 female NA  17   4.6   3.0   3.6   2.8  3.2 -0.05798656 -1.3304633
61622  3   male NA  17   4.0   4.4   4.8   3.2  3.6 -0.72583913  0.1411345
61623  1 female  3  21   4.6   5.6   5.6   3.0  5.0 -0.05798656  1.4025040
          extra_z     neuro_z     open_z    gm
61617 -0.32527289 -0.30281564 -1.9627661   boy
61618  0.80583785  0.53307316 -0.7257147 woman
61620  0.05176403  0.36589540  0.2639264  girl
61621 -0.51379134 -0.30281564 -1.7153558  girl
61622  0.61731939  0.03153988 -1.2205352   boy
61623  1.37139322 -0.13563788  0.5113367 woman

To rename several variables programmatically, use rename_with(). For instance, we can convert all item names ending in a number to lowercase:

bfi <- rename_with(bfi, .fn = tolower, .cols = matches("\\d$"))
head(bfi)

      a1 a2 a3 a4 a5 c1 c2 c3 c4 c5 e1 e2 e3 e4 e5 n1 n2 n3 n4 n5 o1 o2 o3 o4
61617  2  4  3  4  4  2  3  3  4  4  3  3  3  4  4  3  4  2  2  3  3  6  3  4
61618  2  4  5  2  5  5  4  4  3  4  1  1  6  4  3  3  3  3  5  5  4  2  4  3
61620  5  4  5  4  4  4  5  4  2  5  2  4  4  4  5  4  5  4  2  3  4  2  5  5
61621  4  4  6  5  5  4  4  3  5  5  5  3  4  4  4  2  5  2  4  1  3  3  4  3
61622  2  3  3  4  5  4  4  5  3  2  2  2  5  4  5  2  3  4  4  3  3  3  4  3
61623  6  6  5  6  5  6  6  6  1  3  2  1  6  5  6  3  5  2  2  3  4  3  5  6
      o5    gen ed age agree consc extra neuro open     agree_z    consc_z
61617  3   male NA  16   4.0   2.8   3.8   2.8  3.0 -0.72583913 -1.5406915
61618  3 female NA  18   4.2   4.0   5.0   3.8  4.0 -0.50322161 -0.2793220
61620  2 female NA  17   3.8   4.0   4.2   3.6  4.8 -0.94845665 -0.2793220
61621  5 female NA  17   4.6   3.0   3.6   2.8  3.2 -0.05798656 -1.3304633
61622  3   male NA  17   4.0   4.4   4.8   3.2  3.6 -0.72583913  0.1411345
61623  1 female  3  21   4.6   5.6   5.6   3.0  5.0 -0.05798656  1.4025040
          extra_z     neuro_z     open_z    gm
61617 -0.32527289 -0.30281564 -1.9627661   boy
61618  0.80583785  0.53307316 -0.7257147 woman
61620  0.05176403  0.36589540  0.2639264  girl
61621 -0.51379134 -0.30281564 -1.7153558  girl
61622  0.61731939  0.03153988 -1.2205352   boy
61623  1.37139322 -0.13563788  0.5113367 woman

Practice 1

Notation

The following problem statement uses these abbreviations

O = Openness to Experience (open)
E = Extraversion (extra)

Use dplyr::mutate() and case_when() to create a new factor called type that satisfies the following logic

type = “adventurous” when O is higher than the mean of O and E is higher than the mean of E
type = “inquisitive” when O is higher than the mean of O and E is lower than or equal to the mean of E
type = “quiet” when O is lower than or equal to the mean of O and E is lower than or equal to the mean of E
type = “chatty” when O is lower than or equal to the mean of O and E is higher than the mean of E

Interactive Editor
Solution

bfi <- mutate(bfi,
              type = case_when(
                  open > mean(open, na.rm = TRUE) & extra > mean(extra, na.rm = TRUE) ~ "adventurous",
                  open > mean(open, na.rm = TRUE) & extra <= mean(extra, na.rm = TRUE) ~ "inquisitive",
                  open <= mean(open, na.rm = TRUE) & extra <= mean(extra, na.rm = TRUE) ~ "quiet",
                  open <= mean(open, na.rm = TRUE) & extra > mean(extra, na.rm = TRUE) ~ "chatty",
                  TRUE ~ NA_character_),
              type = as.factor(type))

Practice 2

Exclude the raw scale items from the modified ‘bfi’ data.
Save the dataset from (a) as an RDS file.

Interactive Editor
Solution

# a)
bfi_mod <- select(bfi, -starts_with(c("A", "C", "E", "N", "O"))) 

#b)
saveRDS(bfi_mod, file = "bfi_mod.rds")

Creating Conditional Variables with case_when()

Renaming Variables

Creating Conditional Variables with `case_when()`