UC Business Analytics R Programming Guide

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Exclude Missing Values

We can exclude missing values in a couple different ways. First, if we want to exclude missing values from mathematical operations use the na.rm = TRUE argument. If you do not exclude these values most functions will return an NA.

# A vector with missing values
x <- c(1:4, NA, 6:7, NA)

# including NA values will produce an NA output
mean(x)
## [1] NA

# excluding NA values will calculate the mathematical operation for all non-missing values
mean(x, na.rm = TRUE)
## [1] 3.833333

We may also desire to subset our data to obtain complete observations, those observations (rows) in our data that contain no missing data. We can do this a few different ways.

# data frame with missing values
df <- data.frame(col1 = c(1:3, NA),
                 col2 = c("this", NA,"is", "text"), 
                 col3 = c(TRUE, FALSE, TRUE, TRUE), 
                 col4 = c(2.5, 4.2, 3.2, NA),
                 stringsAsFactors = FALSE)

df
##   col1 col2  col3 col4
## 1    1 this  TRUE  2.5
## 2    2 <NA> FALSE  4.2
## 3    3   is  TRUE  3.2
## 4   NA text  TRUE   NA

First, to find complete cases we can leverage the complete.cases() function which returns a logical vector identifying rows which are complete cases. So in the following case rows 1 and 3 are complete cases. We can use this information to subset our data frame which will return the rows which complete.cases() found to be TRUE.

complete.cases(df)
## [1]  TRUE FALSE  TRUE FALSE

# subset with complete.cases to get complete cases
df[complete.cases(df), ]
##   col1 col2 col3 col4
## 1    1 this TRUE  2.5
## 3    3   is TRUE  3.2

# or subset with `!` operator to get incomplete cases
df[!complete.cases(df), ]
##   col1 col2  col3 col4
## 2    2 <NA> FALSE  4.2
## 4   NA text  TRUE   NA

An shorthand alternative is to simply use na.omit() to omit all rows containing missing values.

# or use na.omit() to get same as above
na.omit(df)
##   col1 col2 col3 col4
## 1    1 this TRUE  2.5
## 3    3   is TRUE  3.2