Muhammad Imdad Ullah

Student and Instructor of Statistics and business mathematics. Completed my Ph.D. in Statistics from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. l like Applied Statistics, Mathematics and Statistical Computing. Statistical and Mathematical software used is SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis, etc.

Missing Values In R

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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.

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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R Package Questions and Answers

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The post is about some important R package questions and answers. The R Package Questions and Answers are about how to load, install, and remove an R package.

R Package Questions and Answers

Question: What is an R Package?
Answer: The r package is a collection of objects that the R Language can use. A package contains functions, data sets, and documentation (which helps how to use the package) or other objects such as dynamically loaded libraries of already compiled code.

Question: How do I see which packages I have available?
Answer: To see which packages you have to use the command at the R prompt

library()

Question: Which packages do I already have?
Answer: To see what packages are installed one can use the installed.packages() command an R prompt. The output will show the packages installed.

installed.packages()
installed.packages()[1:5,]

Loading R Packages

Question: How one can load a Package in R language?
Answer: Basic packages are already loaded. If you want to load a downloaded version of packages use the command

library("package name")
library("car")

where the package name is the name of the package you want to load. Here in the example, we used the “car”, which means the “car” package will be loaded.

Getting Help in R Language

Question: How one can see the documentation of a particular package?
Answer: To see the documentation of a particular package use the command

library(help="package name")
help(package="package name")
help(package="car")
library(help="car")

for more information about getting help follow the link: Getting Help in R Language

Question: How do I see the help for a specific function?
Answer: To get help with a function in R use the command

help("function name")
? function name
?Manova
help("Manova")

Question: What functions and datasets are available in a package?
Answer: To check what functions and datasets are in a package using the help command at the R prompt. This will provide package information giving a list of functions and datasets.

help(package = "MASS")

Note that once a package is loaded, the help command can also be used with all available functions and datasets.

Installing and Removing R Packages

Question: How can one add or delete a package?
Answer: A package can be installed using the command

install.packages("package name")

and a package can be removed or deleted using the command

remove.packages("package name")
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Data Frame in R Language

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Please load the required data set before running the commands given below in R FAQs related to the data frame. As an example for R FAQs about data frame in R, we are assuming the iris data set is available already in R. At R prompt write data(iris).

Naming/ Renaming Columns in a Data Frame

Question: How do you name or rename a column in a data frame?
Answer: Suppose you want to change/ rename the 3rd column of the data frame, then on R prompt write

names (iris)[,3] <- "new_name"

Suppose you want to change the second and third columns of the data frame

names(irisi)[c(2,4)] <- c("A", "D")

Note that names(iris) command can be used to find the names of each column in a data frame.

Question: How you can determine the column information of a data frame such as the “names, type, missing values” etc.?
Answer: There are two built-in functions in R to find the information about columns of a data frame.

str(iris)
summary(iris)
Data Frame in R Language

Exporting a Data Frame in R

Question: How a data frame can be exported in R so that it can be used in other statistical software?
Answer: Use the write.csv command to export the data in comma-separated format (CSV).

write.csv(iris, "iris.csv", row.names = FALSE)

Question: How one can select a particular row or column of a data frame?
Answer: The easiest way is to use the indexing notation []

Suppose you want to select the first column only, then at the R prompt, write

iris[,1]

Suppose we want to select the first column and also want to put the content in a new vector, then

new <- iris[,1]

Suppose you want to select different columns, for example, columns 1, 3, and 5, then

newdata <- iris[, c(1, 3, 5)]

Suppose you want to select a first and third row, then

iris[c(1,2), ]

Dealing with Missing Values in a Data Frame

Question: How do you deal with missing values in a data frame?
Answer: In R language it is easy to deal with missing values. Suppose you want to import a file named “file.csv” that contains missing values represented by a “.” (period), then on the R prompt write

data <- read.csv("file.csv", na.string = ".")

If missing values are represented as “NA” values then write

dataset <- read.csv("file.csv", na.string = "NA")

For the case of built-in data such (here iris), use

data <- na.omit(iris)

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