4.6 Dealing with Missing Values
A common task in data analysis is dealing with missing values. In R, missing values are often represented by NA
or some other value that represents missing values (i.e. 99
). We can easily work with missing values and in this section you will learn how to:
- Test for missing values
- Recode missing values
- Exclude missing values
4.6.1 Test for missing values
To identify missing values use is.na()
which returns a logical vector with TRUE
in the element locations that contain missing values represented by NA
. is.na()
will work on vectors, lists, matrices, and data frames.
# vector with missing data
x <- c(1:4, NA, 6:7, NA)
x
[1] 1 2 3 4 NA 6 7 NA
is.na(x)
[1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
# data frame with missing data
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)
# identify NAs in full data frame
is.na(df)
col1 | col2 | col3 | col4 |
---|---|---|---|
FALSE | FALSE | FALSE | FALSE |
FALSE | TRUE | FALSE | FALSE |
FALSE | FALSE | FALSE | FALSE |
TRUE | FALSE | FALSE | TRUE |
To identify the location or the number of NAs we can leverage the which()
and sum()
functions:
4.6.2 Recode missing values
To recode missing values; or recode specific indicators that represent missing values, we can use normal subsetting and assignment operations. For example, we can recode missing values in vector x
with the mean values in x
by first subsetting the vector to identify NA
s and then assign these elements a value. Similarly, if missing values are represented by another value (i.e. 99
) we can simply subset the data for the elements that contain that value and then assign a desired value to those elements.
# recode missing values with the mean
# vector with missing data
x <- c(1:4, NA, 6:7, NA)
x
[1] 1 2 3 4 NA 6 7 NA
x[is.na(x)] <- mean(x, na.rm = TRUE)
round(x, 2)
[1] 1.00 2.00 3.00 4.00 3.83 6.00 7.00 3.83
# data frame that codes missing values as 99
df <- data.frame(col1 = c(1:3, 99), col2 = c(2.5, 4.2, 99, 3.2))
# change 99s to NAs
df[df == 99] <- NA
df
col1 | col2 |
---|---|
1 | 2.5 |
2 | 4.2 |
3 | NA |
NA | 3.2 |
4.6.3 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 | this | TRUE | 2.5 |
2 | NA | FALSE | 4.2 |
3 | is | TRUE | 3.2 |
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
## [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 |
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.
col1 | col2 | col3 | col4 | |
---|---|---|---|---|
1 | 1 | this | TRUE | 2.5 |
3 | 3 | is | TRUE | 3.2 |