## <int> <chr> <dbl> ## 1 1 A 0.7 ## 2 1 B 0.5 ## 3 1 C 0.3 ## 4 1 D 0.4 ## 5 2 A 1 ## 6 2 B 0.7 ## 7 2 C 0.6 ## 8 2 D 0.7 ## 9 3 A 1.5 ## 10 3 B 0.9 ## 11 3 C 1 ## 12 3 D 1.2 ## 13 4 A 1.8 ## 14 4 B 1.3 ## 15 4 C 1.2...
1. Aggregating values: Sometimes, the wide-format data being transformed may contain multiple values within a single cell. In such cases, we can use an aggregator function, such as `tidyr::separate_rows()`, within `pivot_longer` to split the values into separate rows. 2. Handling missing...
Step 1: pivot_longer as usual Don't forget to exclude the new religionClass column from the pivot. relig_income %>% pivot_longer(-c(religion, religionClass), names_to = "income", values_to = "count") ## # A tibble: 180 x 4 ## religion religionClass income count ## <chr> <...