Question: Can missing values be handled on R?
Answer: Yes, in R language one can handle missing values. 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: In R missing values or data appears as NA. Note that NA is not a string nor a numeric value.

Question: Can 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 y vector the fifth value of strong “NA” not a missing value.

Question: How one can check that there is missing value in a vector/ Matrix?
Answer: To check which values in a matrix/vector recognized as missing value 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 a missing value. For example

> is.na(x)    # 5th will appear as TRUE while all other 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: In R language, can missing values be used comparisons?
Answer: No missing values in R 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); x1

Question: Provide an example for introducing NA in the matrix?
Answer: 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)

For further detail how to deal with missing values in R visit: Missing Values in R