missing values

Missing values In R

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: 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 $y$ vector the fifth value of strong “NA” is not missing.

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.

Missing Values in R

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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