inserting a column of categories based on other columns in R

Question

My dataframe currently looks like this:


Tree Cookie Age 
C1T1   A     10
C1T1   A     20
C1T1   A     30
C1T1   B     15
C1T1   B     20
C1T1   B     25
C1T2   A     12
C1T2   A     20
C1T2   B     5
C1T2   B     13

So for each "Tree" I have several "Cookies", and for each cookie I have different ages (basically representing different parts of the tree's life). I would like to add another column that bins each tree by its max age - the oldest age of the oldest cookie, in this case it would be the last age of cookie A in both trees (so will classify a tree as "young" if the max age is < 40, "mid-age" if max age is > 40 and < 120, and "old" if max age is > 120). Any advice on this is greatly appreciated!

score 1 · Accepted Answer · answered Oct 27 '20 at 16:59

Ok, here it goes: I used the dplyr library to do this, which gives me the %>% operator and the summarise() function. I also named your data frame trees. Then:

library(dplyr)

trees2 <- trees %>%
  group_by(Tree = Tree) %>%
  summarise(Age = max(Age))

trees2$Cat <- ifelse(trees2$Age < 40, "young", ifelse(trees2$Age > 120, "old", "mid-age"))
trees$Category = trees2$Cat[match(trees$Tree, trees2$Tree)]

Before, trees2 would have bee this:

> trees2
# A tibble: 2 x 2
  Tree  Age  
  <chr> <chr>
1 C1T1  30   
2 C1T2  5  
  
> trees2$Cat <- ifelse(trees2$Age < 40, "young", ifelse(trees2$Age > 120, "old", "mid-age"))

> trees2
# A tibble: 2 x 3
  Tree  Age   Cat  
  <chr> <chr> <chr>
1 C1T1  30    young
2 C1T2  5     old

After, using the recommendations in this post by cory, I finished by putting this tibble in the original table with this final line:

trees$Category = trees2$Cat[match(trees$Tree, trees2$Tree)]

And this gave me:

> trees
   Tree Cookie Age Category
1  C1T1      A  10    young
2  C1T1      A  20    young
3  C1T1      A  30    young
4  C1T1      B  15    young
5  C1T1      B  20    young
6  C1T1      B  25    young
7  C1T2      A  12      old
8  C1T2      A  20      old
9  C1T2      B   5      old
10 C1T2      B  13      old

score 1 · Answer 2 · answered Oct 27 '20 at 17:02

An approach using cut:

trees_max <- trees %>%
  group_by(Tree) %>%
  summarise(max_age = max(Age))

breaks <- c(0, 40, 120, Inf)
labels <- c("young", "mid-age", "old")

trees_max$cat <- cut(trees_max$max_age, breaks, labels)

gives you

> trees_max
# A tibble: 4 x 3
  Tree  max_age cat    
  <chr>   <dbl> <fct>  
1 C1T1       30 young  
2 C1T2       20 young  
3 C1T3       35 young  
4 C1T4       77 mid-age

inserting a column of categories based on other columns in R

2 Answers2