The function in R can be used to replace all occurrences of a certain pattern within a string in R.

To replace multiple patterns at once, you can use a nested gsub() statement:

df$col1 <- gsub('old1', 'new1',
           gsub('old2', 'new2',
           gsub('old3', 'new3', df$col1)))

However, a much faster method is the stri_replace_all_regex() function from the stringi package, which uses the following syntax:

library(stringi)

df$col1 <- stri_replace_all_regex(df$col1,
                                  pattern=c('old1', 'old2', 'old3'),
                                  replacement=c('new1', 'new2', 'new3'),
                                  vectorize=FALSE)

The following examples show how to use each method in practice.

Method 1: Replace Multiple Patterns with Nested gsub()

Suppose we have the following data frame in R:

#create data frame
df <- data.frame(name=c('A', 'B', 'B', 'C', 'D', 'D'),
                 points=c(24, 26, 28, 14, 19, 12))

#view data frame
df

  name points
1    A     24
2    B     26
3    B     28
4    C     14
5    D     19
6    D     12 

We can use a nested gsub() statement to replace multiple patterns in the name column:

#replace multiple patterns in name column
df$name <- gsub('A', 'Andy',
           gsub('B', 'Bob',
           gsub('C', 'Chad', df$name)))

#view updated data frame
df

  name points
1 Andy     24
2  Bob     26
3  Bob     28
4 Chad     14
5    D     19
6    D     12

Notice that A, B, and C in the name column have all been replaced with new values.

Method 2: Replace Multiple Patterns with stringi

A much faster way to replace multiple patterns is by using the stri_replace_all_regex() function from the stringi package.

The following code shows how to use this function:

library(stringi)

#replace multiple patterns in name column
df$name <- stri_replace_all_regex(df$name,
                                  pattern=c('A', 'B', 'C'),
                                  replacement=c('Andy', 'Bob', 'Chad'),
                                  vectorize=FALSE)

#view updated data frame
df

  name points
1 Andy     24
2  Bob     26
3  Bob     28
4 Chad     14
5    D     19
6    D     12

Notice that the resulting data frame matches the one from the previous example.

Additional Resources