You can use the coalesce() function from the package in R to return the first non-missing value in each position of one or more vectors.

There are two common ways to use this function:

Method 1: Replace Missing Values in Vector

library(dplyr)

#replace missing values with 100
coalesce(x, 100)

Method 2: Return First Non-Missing Value Across Data Frame Columns

library(dplyr)

#return first non-missing value at each position across columns A and B
coalesce(df$A, df$B)

The following examples show how to each method in practice.

Example 1: Use coalesce() to Replace Missing Values in Vector

The following code shows how to use the coalesce() function to replace all missing values in a vector with a value of 100:

library(dplyr)

#create vector of values
x <- c(4, NA, 12, NA, 5, 14, 19)

#replace missing values with 100
coalesce(x, 100)

[1]   4 100  12 100   5  14  19

Notice that each NA value in the original vector has been replaced with a value of 100.

Example 2: Use coalesce() to Return First Non-Missing Value Across Data Frame Columns

Suppose we have the following data frame in R:

#create data frame
df <- data.frame(A=c(10, NA, 5, 6, NA, 7, NA),
                 B=c(14, 9, NA, 3, NA, 10, 4))

#view data frame
df

   A  B
1 10 14
2 NA  9
3  5 NA
4  6  3
5 NA NA
6  7 10
7 NA  4

The following code shows how to use the coalesce() function to return the first non-missing value across columns A and B in the data frame:

library(dplyr)

#create new column that coalesces values from columns A and B
df$C <- coalesce(df$A, df$B)

#view updated data frame
df

   A  B  C
1 10 14 10
2 NA  9  9
3  5 NA  5
4  6  3  6
5 NA NA NA
6  7 10  7
7 NA  4  4

The resulting column C contains the first non-missing value across columns A and B.

We can simply add one more value to the coalesce() function to use as the value if there happen to be NA values in each column:

library(dplyr)

#create new column that coalesces values from columns A and B
df$C <- coalesce(df$A, df$B, 100)

#view updated data frame
df

   A  B   C
1 10 14  10
2 NA  9   9
3  5 NA   5
4  6  3   6
5 NA NA 100
6  7 10   7
7 NA  4   4

Notice that the NA value in row 5 of column C has now been replaced by a value of 100.

The following tutorials explain how to perform other common functions using dplyr: