create interval categories from a column including both numbers and characters in R-CodePudding

Assuming I have data below, I want to add column c such that I have categories <0, 0, 0-3, >3 if column b contains only positive or negative values otherwise category in column c would be whatever column b contains.

df <- data.frame(a= 1:14,
                 b= c(-1,-10,-2,0,0,2,1,4,10,12,6, "apple", "apple", "Orange"))
df 
    a      b
1   1     -1
2   2    -10
3   3     -2
4   4      0
5   5      0
6   6      2
7   7      1
8   8      4
9   9     10
10 10     12
11 11      6
12 12  apple
13 13  apple
14 14 Orange


df2
    a      b      c
1   1     -1     <0
2   2    -10     <0
3   3     -2     <0
4   4      0      0
5   5      0      0
6   6      2    0-3
7   7      1    0-3
8   8      4     >3
9   9     10     >3
10 10     12     >3
11 11      6     >3
12 12  apple  apple
13 13  apple  apple
14 14 Orange Orange

I am trying to apply case_when and cut. I am getting the result I need. I would appreciate any help and hint with it.

df %>%
mutate(c = case_when( b %in% grepl("apple|orange", b) ~ b),
                      TRUE ~ cut(as.numeric(b),
                                                       breaks = c(-999, 0, 1, 4, 999),
                                                       labels = c("<0", "0", "0-3", ">3"),
                                                       right = F))

CodePudding user response：

It may be better to subset the numeric from the non-numeric and do it separately. In base R, we can do the assignment twice on each of the subset

i1 <- grepl("^-?[0-9] $", df$b)
df$c[i1] <- as.character(cut(as.numeric(df$b[i1]), 
   breaks = c(-999, 0, 1, 4, 999), labels = c("<0", "0", "0-3", ">3"), right = FALSE))
df$c[!i1] <- df$b[!i1]

-output

> df
    a      b      c
1   1     -1     <0
2   2    -10     <0
3   3     -2     <0
4   4      0      0
5   5      0      0
6   6      2    0-3
7   7      1    0-3
8   8      4     >3
9   9     10     >3
10 10     12     >3
11 11      6     >3
12 12  apple  apple
13 13  apple  apple
14 14 Orange Orange

If we want to use dplyr

library(dplyr)
df %>%
mutate(c = coalesce(case_when( !grepl("apple|orange", b)  ~ as.character(cut(as.numeric(b),
                                                       breaks = c(-999, 0, 1, 4, 999),
                                                       labels = c("<0", "0", "0-3", ">3"),
                                                       right = FALSE))), b))

-output

  a      b      c
1   1     -1     <0
2   2    -10     <0
3   3     -2     <0
4   4      0      0
5   5      0      0
6   6      2    0-3
7   7      1    0-3
8   8      4     >3
9   9     10     >3
10 10     12     >3
11 11      6     >3
12 12  apple  apple
13 13  apple  apple
14 14 Orange Orange

NOTE: case_when or ifelse apply the function on the whole data, so when we do as.numeric, the non-numeric elements are coerced to NA, and thus the first option got overrided. Instead, either use replace or coalesce with b column after case_when