Matrix in R Language (2015): Key Secrets

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The matrix is an important data type in R language similar to the data frame. It has two dimensions as the arrangement of elements is in rows and columns.

Matrix In R Language

Question: What is a matrix in R Language?
Answer: In R language matrices are two-dimensional arrays of elements all of which are of the same type, for example, numbers, character strings, or logical values.

Matrices may be constructed using the built-in function “matrix”, which reshapes its first argument into a matrix having a specified number of rows as the second argument and a number of columns as the third matrix.

Creating a Matrix in R Language

Question: Give an example of how the matrix is constructed in R language.
Answer: A 3 by 3 matrix (3 rows and 3 columns) matrix may be constructed such as:

matrix(1:9, 3, 3)
matrix(c(1,2,3,4,5,6,7,8,9), 3, 3)
matrix(runif(9), 3,3)

First, two commands construct a matrix of 9 elements having 3 rows and 3 columns consisting of numbers from 1 up to 9. The third command makes a matrix of 3 rows and 3 columns with random numbers from a uniform distribution.

Question: How the matrix elements are filled?
Answer: A matrix is filled by columns, unless the optional argument byrow is set to TRUE as an argument in matrix command, for example

matrix(1:9, 3, 3, byrow = TRUE)

Question: Can the matrix be stored in R?
Answer: Any matrix can be stored in R such as

m <- matrix(1:9, 3, 3)
mymatrix <- matrix( rnorm(16), nrow=4 )
Matrix in R Language

Matrices are stored in “m” and “mymatrix” objects. The second command constructs a matrix having 16 elements with 4 rows from a normal distribution having mean 0 and variance 1.

Attributes of Matrix Object in R

Question: What is the use of the dim command in R?
Answer: The dim (dimension) is an attribute of the matrix in R language, which tells the number of rows and the number of columns of a matrix, for example,

dim(mymatrix)

This will result in output showing 4  4, meaning 4 rows and 4 column matrix.

Question: Can we name rows of a matrix in R Language?
Answer: Yes in R language we can name rows of a matrix according to one’s requirements, such as

rownames(mymatrix) &lt;- c("x1", "x2", "x3", "x4")
mymatrix

Question: Can column names be changed or updated in R?
Answer: The procedure is the same as changing the column name. For this purpose colnames command is used, for example

colnames(mymatrix)&lt;-c("A", "B", "C", "D")
mymatrix

Question: What is the purpose of the attributes command for the matrix in R Language?
Answer: The attributes function can be used to get information about the dimension of the matrix and dimnames (dimension names). For example;

attributes(mymatrix)

In summary, the primary function for creating a matrix in R language is matrix(). It takes a few arguments:

  • data: This is a vector containing the elements for the matrix.
  • nrow: The number of rows in the matrix.
  • ncol: The number of columns in the matrix.

FAQs about Matrices in R

  1. How to create a matrix in R?
  2. How elements are filled in R?
  3. How to convert a data object to a matrix object in R?
  4. How different attributes of a matrix in R can be checked?
  5. How matrices can be stored in a variable?
  6. How one can name the rows and columns of a matrix in R?
  7. What is the difference between dim and dimnames commands?
  8. How one can create a matrix of order 3 by 3 (3 rows and 3 columns) with elements from a probability Distribution.
  9. What is the primary function of matrix() function in R Language.

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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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