One data manipulation task that you need to do in pretty much any data analysis is recode data. It’s almost never the case that the data are set up exactly the way you need them for your analysis.
In R, you can re-code an entire vector or array at once. To illustrate, let’s set up a vector that has missing values.
A <- c(3, 2, NA, 5, 3, 7, NA, NA, 5, 2, 6)
A
[1] 3 2 NA 5 3 7 NA NA 5 2 6
We can re-code all missing values by another number (such as zero) as follows:
A[ is.na(A) ] <- 0
A
[1] 3 2 0 5 3 7 0 0 5 2 6
Let’s re-code all values less than 5 to the value 99.
A[ A < 5 ] <- 99
A
[1] 99 99 99 5 99 7 99 99 5 99 6
However, some re-coding tasks are more complex, particularly when you wish to re-code a categorical variable or factor. In such cases, you might want to re-code an array with character elements to numeric elements.
gender <- c("MALE","FEMALE","FEMALE","UNKNOWN","MALE")
gender
[1] "MALE" "FEMALE" "FEMALE" "UNKNOWN" "MALE"
Let’s re-code males as 1 and females as 2. Very useful is the following re-coding syntax because it works in many practical situations. It involves repeated (nested) use of the ifelse() command.
ifelse(gender == "MALE", 1, ifelse(gender == "FEMALE", 2, 3))
[1] 1 2 2 3 1
If you’ve never seen this type of nested command, it can be a bit confusing. The ifelse command takes some statement that may be true or false. If it is true, we assign the value after the first comma, if it is false, we assign the value after the second comma.
In this case, we first check if gender is male, and if so assign a 1 to the observation. If the gender isn’t male, we go to the second part of the code which checks for being female, assigning a 2 if female and a 3 otherwise. Together this made the element with unknown gender get re-coded as a 3. Make a note of this syntax. It’s great for re-coding within R programs.
Another example, this time using a rectangular array.
A <- data.frame(Gender = c("F", "F", "M", "F", "B", "M", "M"), Height = c(154, 167, 178, 145, 169, 183, 176))
A
Gender Height
1 F 154
2 F 167
3 M 178
4 F 145
5 B 169
6 M 183
7 M 176
We have deliberately introduced an error where gender is misclassified as B. This one gets re-coded to the value 99. Note that the Gender variable is located in the first column, or A[ ,1].
A[,1] <- ifelse(A[,1] == "M", 1, ifelse(A[,1] == "F", 2, 99))
A
Gender Height
1 2 154
2 2 167
3 1 178
4 2 145
5 99 169
6 1 183
7 1 176
You can use the same approach to code as many different levels as you need to. Let’s re-code for four different levels.
My last example is drawn from the films of the Lord of the Rings and the Hobbit.
The sets where Peter Jackson produced these films are just a short walk from where I live, so the example is relevant for me.
S <- data.frame(SPECIES = c("ORC", "HOBBIT", "ELF", "TROLL", "ORC", "ORC", "ELF", "HOBBIT"), HEIGHT
= c(194, 127, 178, 195, 149, 183, 176, 134))
S
SPECIES HEIGHT
1 ORC 194
2 HOBBIT 127
3 ELF 178
4 TROLL 195
5 ORC 149
6 ORC 183
7 ELF 176
8 HOBBIT 134
We now use nested ifelse commands to re-code Orcs as 1, Elves as 2, Hobbits as 3, and Trolls as 4.
S[,1] <- ifelse(S[,1] == "ORC", 1, ifelse(S[,1] == "ELF", 2, ifelse(S[,1] == "HOBBIT", 3, ifelse(S[,1] == "TROLL", 4, 99))))
S
SPECIES HEIGHT
1 1 194
2 3 127
3 2 178
4 4 195
5 1 149
6 1 183
7 2 176
8 3 134
We can recode back to character just as easily.
S[,1] <- ifelse(S[,1] == 1, "ORC", ifelse(S[,1] == 2, "ELF", ifelse(S[,1] == 3, "HOBBIT", ifelse(S[,1] == 4, "TROLL", 99))))
S
SPECIES HEIGHT
1 ORC 194
2 HOBBIT 127
3 ELF 178
4 TROLL 195
5 ORC 149
6 ORC 183
7 ELF 176
8 HOBBIT 134
The general approach is the same as before, but now you have a few additional sets of parentheses.
That wasn’t so hard! In our next blog post we will learn about how to work with multiple plots at once using the par command.
About the Author: David Lillis has taught R to many researchers and statisticians. His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R. David holds a doctorate in applied statistics.
See our full R Tutorial Series and other blog posts regarding R programming.
Great article about r programming tutorial, thanks for sharing I guess you should have a look on http://programmer.science/category/r-programming-tutorial, hope this can help you jessica (sorry not sure of your name) anyway cheers…
FYI, Part 19 of this tutorial is currently 404 / not found.
Good morning
Do you mean I should use 99 instead of NA or what
Er, not to be negative, but this is a pretty sloppy way of recoding. What if I want to change 5 values? Am I meant to nest four ifelse() calls?
To take a page from Hadley Wickham’s advanced-R (adv-r.had.co.nz), it’s much easier to create a named vector.
For your data frame A, you can do
gender_recode <- c('F' = 1, 'M' = 2)
A$Gender <- gender_recode[A$Gender]
Which would then code the 'B' value as missing. Note that you can also go from numeric to string (although you must escape the numeric names with the backtick, `)
Using match() or merge() are alternative solutions (as is the dplyr packages recode() ).
Loved your idea! I had 12 values to recode. Your method is very clean and easy to maintain. Thanks very much!!