Data Cleaning

The article is about Handling Missing Values in R Language.

Question: What are the differences between missing values in R and other Statistical Packages?

Answer: Missing values (NA) cannot be used in comparisons, as already discussed in the previous post on missing values in R. In other statistical packages (software) a “missing value” is assigned to some code either very high or very low in magnitude such as 99 or -99 etc. These coded values are considered as missing and can be used to compare to other values and other values can be compared to missing values.

In R language NA values are used for all kinds of missing data, while in other packages, missing strings and missing numbers are represented differently, for example, empty quotations for strings, and periods, large or small numbers. Similarly, non-NA values cannot be interpreted as missing while in other package systems, missing values are designated from other values.

Handling Missing Values in R

Question: What are NA options in R?
Answer: In the previous post on missing values, I introduced is.na() function as a tool for both finding and creating missing values. The is.na() is one of several functions built around NA. Most of the other functions for missing values (NA) are options for na.action(). The possible na.action() settings within R are:

• na.omit() and na.exclude(): These functions return the object with observations removed if they contain any missing (NA) values. The difference between these two functions na.omit() and na.exclude() can be seen in some prediction and residual functions.
• na.pass(): This function returns the object unchanged.
• na.fail(): This function returns the object only if it contains no missing values.

To understand these NA options use the following lines of code.

getOption("na.action")

(m <- as.data.frame(matrix(c(1 : 5, NA), ncol=2)))
na.omit(m)
na.exclude(m)
na.fail(m)
na.pass(m)

Note that it is wise to investigate the missing values in your data set and also make use of the help files for all functions you are willing to use for handling missing values. You should be either aware of and comfortable with the default treatments (handling) of missing values or specifying the treatment of missing values you want for your analysis.

FAQs about Missing Values in R

1. What is meant by a missing value?
2. How one can handle missing values in R?
3. What is NA in R?
4. How one can identify missing values in R?
5. What is is.na() function?
6. What is the use of na.omit() function in R?
7. Why it is importance of investigate missing values before performing any data analysis?

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The article is about the missing Values in R Language. A discussion is about how missing values are introduced in vectors or matrices and how the existence of missing observations can be checked in R Language.

Understanding Missing Values in R Language

Question: Can missing values be handled in R?
Answer: Yes, in R language one can handle missing observations. The way of dealing with missing values is different as compared to other statistical software such as SPSS, SAS, STATA, EVIEWS, etc.

Question: What is the representation of missing values in R Language?
Answer: The missing values or data appear as NA. Note that NA is not a string nor a numeric value.

Question: Can the R user introduce missing value(s) in matrix/ vector?
Answer: Yes user of R can create (introduce) missing values in vector/ Matrix. For example,

x <- c(1,2,3,4,NA,6,7,8,9,10)
y <- c("a", "b", "c", NA, "NA")

Note that on the $y$ vector the fifth value of strong “NA” is not missing.

How to Check Missing Values in a Vector/ Matrix

Question: How one can check that there is a missing value in a vector/ Matrix?
Answer: To check which values in a matrix/vector are recognized as missing values by R language, use the is.na function. This function will return a vector of TRUE or FALSE. TRUE indicates that the value at that index is missing while FALSE indicates that the value is not missing. For example

is.na(x)    # 5th will appear as TRUE while all others will be FALSE
is.na(y)    # 4th will be true while all others as FALSE

Note that “NA” in the second vector is not a missing value, therefore is.na will return FALSE for this value.

Question: Can missing values be used for comparisons?
Answer: No missing values cannot be used in comparisons. NA (missing values) is used for all kinds of missing data. Vector $x$ is numeric and vector $y$ is a character object. So Non-NA values cannot be interpreted as missing values. Write the command, to understand it.

x <- 0
y == NA
is.na(x) <- which(x==7)
x

Question: Provide an example for introducing NA in the matrix.
Answer: The following command will create a matrix with all of the elements as NA.

matrix(NA, nrow = 3, ncol = 3)
matrix(c(NA,1,2,3,4,5,6,NA, NA), nrow = 3, ncol = 3)

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