There are three advantages that the if_else() function in has over the ifelse() function in base R:

1. The if_else() function verifies that both alternatives in the if else statement have the same data type.

2. The if_else() function does not convert Date objects to numeric.

3. The if_else() function offers a ‘missing’ argument to specify how to handle NA values.

The following examples illustrate these differences in practice.

Example 1: if_else() Verifies that Both Alternatives Have the Same Type

Suppose we have the following data frame in R that contains information about various basketball players:

#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
                 points=c(22, 20, 28, 14, 13, 18, 27, 33))

#view data frame
df

  team points
1    A     22
2    A     20
3    A     28
4    A     14
5    B     13
6    B     18
7    B     27
8    B     33

If we use the ifelse() function from base R to create a new column that assigns a value of ‘Atlanta’ to rows with a team value of ‘A’ and 0 to rows with a different value, we won’t receive any error even though ‘Atlanta’ is a character and 0 is a number:

#create new column based on values in team column
df$city <- ifelse(df$team == 'A', 'Atlanta', 0)

#view updated data frame
df

  team points    city
1    A     22 Atlanta
2    A     20 Atlanta
3    A     28 Atlanta
4    A     14 Atlanta
5    B     13       0
6    B     18       0
7    B     27       0
8    B     33       0

However, if we use the if_else() function from dplyr to perform this same task, we’ll receive an error that lets us know we used two different data types in the if else statement:

library(dplyr)

#attempt to create new column based on values in team column
df$city <- if_else(df$team == 'A', 'Atlanta', 0)

Error: `false` must be a character vector, not a double vector.

Example 2: if_else() Does Not Convert Date Objects to Numeric

Suppose we have the following data frame in R that shows the sales made on various dates at some store:

#create data frame
df <- data.frame(date=as.Date(c('2022-01-05', '2022-01-17', '2022-01-22',
                        '2022-01-23', '2022-01-29', '2022-02-13')),
                 sales=c(22, 35, 24, 20, 16, 19))

#view data frame
df

        date sales
1 2022-01-05    22
2 2022-01-17    35
3 2022-01-22    24
4 2022-01-23    20
5 2022-01-29    16
6 2022-02-13    19

If we use the ifelse() function from base R to modify the values in the date column, the values will automatically get converted to numeric:

#if date is before 2022-01-20 then add 5 days
df$date <- ifelse(df$date < '2022-01-20', df$date+5, df$date)

   date sales
1 19002    22
2 19014    35
3 19014    24
4 19015    20
5 19021    16
6 19036    19

library(dplyr)

#if date is before 2022-01-20 then add 5 days
df$date <- ifelse(df$date < '2022-01-20', df$date+5, df$date)

#view updated data frame
df

        date sales
1 2022-01-10    22
2 2022-01-22    35
3 2022-01-22    24
4 2022-01-23    20
5 2022-01-29    16
6 2022-02-13    19

Example 3: if_else() Offers a ‘missing’ Argument to Specify How to Handle NA Values

Suppose we have the following data frame in R:

#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', NA, 'B'),
                 points=c(22, 20, 28, 14, 13, 18, 27, 33))

#view data frame
df

  team points
1    A     22
2    A     20
3    A     28
4    A     14
5    B     13
6    B     18
7   <NA>   27
8    B     33

If we use the ifelse() function from base R to create a new column, there is no default option to specify how to handle NA values:

#create new column based on values in team column
df$city <- ifelse(df$team == 'A', 'Atlanta', 'Boston')

#view updated data frame
df

  team points    city
1    A     22 Atlanta
2    A     20 Atlanta
3    A     28 Atlanta
4    A     14 Atlanta
5    B     13  Boston
6    B     18  Boston
7   <NA>   27    <NA>
8    B     33  Boston

However, if we use the if_else() function from dplyr then we can use the missing argument to specify how to handle NA values:

library(dplyr)

#create new column based on values in team column
df$city <- ifelse(df$team == 'A', 'Atlanta', 'Boston', missing='other')

#view updated data frame
df

  team points    city
1    A     22 Atlanta
2    A     20 Atlanta
3    A     28 Atlanta
4    A     14 Atlanta
5    B     13  Boston
6    B     18  Boston
7   <NA>   27   other
8    B     33  Boston

Notice that the row with an NA value in the team column receives a value of ‘other’ in the new city column.