How to add a column to a dataframe in R?

Question

I'm working with the ToothGrowth data:

library(datasets)
data(ToothGrowth)

Here I have three columns, lenght, supplement and dosis. I want to a add a fourth column with a categorical variable that depends on the dosis amount, like for example if dosis = 0.5, then "D5", if dosis = 1, then "D1", I tried the following:

data(ToothGrowth)
df_TD <- ToothGrowth
dosiscatg <- NULL
for(i in 1:nrow(df_TD)) {
  if df_TD$dose==0.5 {
    dosiscatg <- c(dosiscatg, "D0.5")
  } else if df_TD$dose==1 {
    dosiscatg <- c(dosiscatg, "D1")
  } else if df_TD$dose==2 {
    dosiscatg <- c(dosiscatg, "D2")
  }
}

But I keep getting an error with the brackets "{}", also I don't know if that code is correct.

Answer 1

Using dplyr:

library(dplyr)

df_TD <- df_TD %>%
  mutate(dosiscatg = case_when(
    dose==0.5 ~ 'D0.5',
    dose==1   ~ 'D1',
    dose==2   ~ 'D2',
    TRUE ~ NA_character_
  ))

Answer 2

Try this:

data(ToothGrowth)
df_TD <- ToothGrowth
dosiscatg <- NULL
for(i in 1:nrow(df_TD)) {
  if df_TD$dose[i]==0.5 {
    dosiscatg <- c(dosiscatg, "D0.5")
  } else if df_TD$dose[i]==1 {
    dosiscatg <- c(dosiscatg, "D1")
  } else if df_TD$dose[i]==2 {
    dosiscatg <- c(dosiscatg, "D2")
  }
}

Edit: As people have pointed out this is only correcting the syntax problems of the code but the solution alltogether is not encouraged

Answer 3

Here is another option using mutate and cut :

library(dplyr)    
df_TD %>%  
  dplyr::mutate(dosiscatg = cut(dose, breaks = c(0, 0.5, 1.0,2.0), labels = c("D0.5", "D1", "D2")))

    len supp dose dosiscatg
1   4.2   VC  0.5      D0.5
2  11.5   VC  0.5      D0.5
3   7.3   VC  0.5      D0.5
4   5.8   VC  0.5      D0.5
5   6.4   VC  0.5      D0.5
6  10.0   VC  0.5      D0.5
7  11.2   VC  0.5      D0.5
8  11.2   VC  0.5      D0.5
9   5.2   VC  0.5      D0.5
10  7.0   VC  0.5      D0.5
11 16.5   VC  1.0        D1
12 16.5   VC  1.0        D1
13 15.2   VC  1.0        D1
14 17.3   VC  1.0        D1
15 22.5   VC  1.0        D1
16 17.3   VC  1.0        D1
17 13.6   VC  1.0        D1
18 14.5   VC  1.0        D1
19 18.8   VC  1.0        D1
20 15.5   VC  1.0        D1
21 23.6   VC  2.0        D2
22 18.5   VC  2.0        D2
23 33.9   VC  2.0        D2
24 25.5   VC  2.0        D2
25 26.4   VC  2.0        D2
26 32.5   VC  2.0        D2
27 26.7   VC  2.0        D2
28 21.5   VC  2.0        D2
29 23.3   VC  2.0        D2
30 29.5   VC  2.0        D2
31 15.2   OJ  0.5      D0.5
32 21.5   OJ  0.5      D0.5
33 17.6   OJ  0.5      D0.5
34  9.7   OJ  0.5      D0.5
35 14.5   OJ  0.5      D0.5
36 10.0   OJ  0.5      D0.5
37  8.2   OJ  0.5      D0.5
38  9.4   OJ  0.5      D0.5
39 16.5   OJ  0.5      D0.5
40  9.7   OJ  0.5      D0.5
41 19.7   OJ  1.0        D1
42 23.3   OJ  1.0        D1
43 23.6   OJ  1.0        D1
44 26.4   OJ  1.0        D1
45 20.0   OJ  1.0        D1
46 25.2   OJ  1.0        D1
47 25.8   OJ  1.0        D1
48 21.2   OJ  1.0        D1
49 14.5   OJ  1.0        D1
50 27.3   OJ  1.0        D1
51 25.5   OJ  2.0        D2
52 26.4   OJ  2.0        D2
53 22.4   OJ  2.0        D2
54 24.5   OJ  2.0        D2
55 24.8   OJ  2.0        D2
56 30.9   OJ  2.0        D2
57 26.4   OJ  2.0        D2
58 27.3   OJ  2.0        D2
59 29.4   OJ  2.0        D2
60 23.0   OJ  2.0        D2