Let us start with loading tidyverse.
library(tidyverse)
The cartoon below illustrates the gist of the problem, where the starting point is a dataframe with multiple numerical columns and the output is another dataframe, but the numerical columns are categorized based on some conditions.
Let us created a simple dataframe with three numerical columns.
set.seed(2021)
df <- tibble(id=paste0(rep(letters[1:5],5)),
x1 = rnorm(25,mean=20,sd=10),
x2 = rnorm(25,mean=15,sd=5),
x3 = rnorm(25,mean=12,sd=6))
df
In this simple illustration we also have a character variable with unique values.
df ## # A tibble: 25 x 4 ## id x1 x2 x3 ## <chr> <dbl> <dbl> <dbl> ## 1 a 18.8 15.5 9.00 ## 2 b 25.5 7.72 -1.54 ## 3 c 23.5 13.2 12.3 ## 4 d 23.6 14.5 9.79 ## 5 e 29.0 20.5 6.24 ## 6 a 0.774 5.18 12.6 ## 7 b 22.6 7.76 14.6 ## 8 c 29.2 20.1 11.0 ## 9 d 20.1 7.89 2.71 ## 10 e 37.3 12.0 2.97 ## # … with 15 more rows
Categorizing a numerical vector with cut()
We can categorize a single vector using cut() function. The versatile cut() function takes in a vector, and a specification on how to categorise and optional labels to name the category levels. In this example, we categorise a single numerical vector into three categories low, middle, and high. We specify the intervals for low, middle and high using breaks().
df %>%
pull(x1) %>%
cut(breaks=c(-Inf,10,20,Inf),
labels=c("low", "middle", "high"))
## [1] middle high high high high low high high high high
## [11] low middle high high high low high high high high
## [21] middle middle low high low
## Levels: low middle high
Categorizing Multiple numerical columns with pivot_longer, cut and pivot_wider()
To convert multiple numerical columns with base R, we can use apply() function on columns and apply the cut function to categorize each column. However, a disadvantage is that the input data has to be a matrix.
With tidyverse, we can categorise multiple numerical columns in a dataframe containing other type of variables.
One of the ways to do is to reshape the dataframe into tidy form with pivot_longer() first and then categorize the numerical variables using cut() function and then reshaping into the original wide form.
Here is the first step converting the wide dataframe into loing form using pivot_longer() from tidyr 1.0.0.
df %>%
pivot_longer(-id,
names_to = "vars", values_to="groups")
## # A tibble: 78 x 3 ## id vars groups ## <chr> <chr> <dbl> ## 1 a x1 18.8 ## 2 a x2 7.72 ## 3 a x3 12.3 ## 4 b x1 25.5 ## 5 b x2 13.2 ## 6 b x3 9.79 ## 7 c x1 23.5 ## 8 c x2 14.5 ## 9 c x3 6.24 ## 10 d x1 23.6 ## # … with 68 more rows
Next we use mutate() to categorize the numerical variables into categorical variables using cut() function at once.
df %>%
pivot_longer(-id,
names_to = "vars", values_to="groups") %>%
mutate(groups=cut(groups,breaks=c(-Inf,10,20,Inf),
labels=c("low", "middle", "high")))
## # A tibble: 78 x 3 ## id vars groups ## <chr> <chr> <fct> ## 1 a x1 middle ## 2 a x2 low ## 3 a x3 middle ## 4 b x1 high ## 5 b x2 middle ## 6 b x3 low ## 7 c x1 high ## 8 c x2 middle ## 9 c x3 low ## 10 d x1 high ## # … with 68 more rows
Finally, use pivot_wider() to reshape the tidy data into original dataframe.
df %>%
pivot_longer(-id,
names_to = "vars", values_to="groups") %>%
mutate(groups=cut(groups,breaks=c(-Inf,10,20,Inf),
labels=c("low", "middle", "high"))) %>%
pivot_wider(names_from = vars, values_from = groups)
## # A tibble: 26 x 4 ## id x1 x2 x3 ## <chr> <fct> <fct> <fct> ## 1 a middle low middle ## 2 b high middle low ## 3 c high middle low ## 4 d high high middle ## 5 e high low middle ## 6 f low low middle ## 7 g high high low ## 8 h high low low ## 9 i high middle middle ## 10 j high low middle ## # … with 16 more rows
How to Categorize Multiple numerical columns using column-wise function across()?
Starting from dplyr 1.0.0, we can easily perform colum-wise operations using across() function.
Here we first across() and provide numerical columns using where(is.numeric) function and then use cut() function to categorize each column as before.
df %>%
mutate(across(where(is.numeric),
~ cut(.x, breaks=c(-Inf,10,20,Inf),
labels=c("low", "middle", "high"))))
With a fewer line of code we have categorized multiple numerical columns at once using across().
## # A tibble: 26 x 4 ## id x1 x2 x3 ## <chr> <fct> <fct> <fct> ## 1 a middle low middle ## 2 b high middle low ## 3 c high middle low ## 4 d high high middle ## 5 e high low middle ## 6 f low low middle ## 7 g high high low ## 8 h high low low ## 9 i high middle middle ## 10 j high low middle ## # … with 16 more rows
As we can guess, the approach to use across() to categorize multiple columns seems to be faster as well. A quick runtime estimate from 500 reps using Sys.time() show that using across() function is faster for the small dataframe example.
Pseudo-code to compute the runtime by both the methods.
for (i in 1:500){
start_time <- Sys.time()
# code to categorize by method A
end_time <- Sys.time()
run_time[i] <- end_time - start_time
}
