# How to create a new column for factor variable with changed factor levels by using mutate of dplyr package in R?

We know that a factor variable has many levels but it might be possible that the factor levels we have are not in the form as needed. For example, if we want to have capital letters as a factor level but the original data has small letters of English alphabets. In this situation we can convert those factor levels by using mutate of dplyr package.

## Example

Consider the below data frame −

x <-letters[1:20]
y <-20:1
df <-data.frame(x,y)
str(df)
'data.frame': 20 obs. of 2 variables:
$x: Factor w/ 20 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10 ...$ y: int 20 19 18 17 16 15 14 13 12 11 ...
df

## Output

x y
1 a 20
2 b 19
3 c 18
4 d 17
5 e 16
6 f 15
7 g 14
8 h 13
9 i 12
10 j 11
11 k 10
12 l 9
13 m 8
14 n 7
15 o 6
16 p 5
17 q 4
18 r 3
19 s 2
20 t 1

Creating a new column with changed levels −

## Example

df%>% mutate(x,levels=LETTERS[1:20])

## Output

x y levels
1 a 20 A
2 b 19 B
3 c 18 C
4 d 17 D
5 e 16 E
6 f 15 F
7 g 14 G
8 h 13 H
9 i 12 I
10 j 11 J
11 k 10 K
12 l 9 L
13 m 8 M
14 n 7 N
15 o 6 O
16 p 5 P
17 q 4 Q
18 r 3 R
19 s 2 S
20 t 1 T

Lets’ have a look at another example −

F <-rep(c("Cold","Hot","Sweet","Bitter"),times=5)
Count <-sample(1:50,20)
Taste <-data.frame(F,Count)
Taste

## Output

F Count
1 Cold 49
2 Hot 18
3 Sweet 28
4 Bitter 9
5 Cold 29
6 Hot 13
7 Sweet 39
8 Bitter 30
9 Cold 11
10 Hot 5
11 Sweet 45
12 Bitter 31
13 Cold 36
14 Hot 41
15 Sweet 44
16 Bitter 15
17 Cold 20
18 Hot 48
19 Sweet 8
20 Bitter 43

## Example

Taste%>% mutate(F,levels=rep(c("Very Cold","Very Hot","Very Sweet","Very Bitter"),times=5))

## Output

F Count levels
1 Cold 49 Very Cold
2 Hot 18 Very Hot
3 Sweet 28 Very Sweet
4 Bitter 9 Very Bitter
5 Cold 29 Very Cold
6 Hot 13 Very Hot
7 Sweet 39 Very Sweet
8 Bitter 30 Very Bitter
9 Cold 11 Very Cold
10 Hot 5 Very Hot
11 Sweet 45 Very Sweet
12 Bitter 31 Very Bitter
13 Cold 36 Very Cold
14 Hot 41 Very Hot
15 Sweet 44 Very Sweet
16 Bitter 15 Very Bitter
17 Cold 20 Very Cold
18 Hot 48 Very Hot
19 Sweet 8 Very Sweet
20 Bitter 43 Very Bitter

Updated on: 21-Aug-2020

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