R Packages

The post is about “Packages in R for Data Analysis”. It is the form of Questions and Answers. The Questions and answers about “Packages in R for Data analysis” describe a short description or function of the R Packages.

What are R Packages? (or What are Packages in R?)

Packages in R are the collections of sample data, R functions, and compiled code in a well-defined format and these packages are stored in a directory called ‘library’ in the R environment. One of the strengths of R is the user-written function in R language. By default, R installs a standard set of packages during installation. Other R packages are available for download and installation. Once R packages are installed, they have to be loaded into the session to be used.

What is Procedural Programming in R?

Procedural programming is a programming paradigm, derived from structured programming, based on the concept of the procedure call. Procedures, also known as routines, subroutines, or functions (not to be confused with mathematical functions, but similar to those used in functional programming), simply contain a series of computational steps to be carried out. Any given procedure might be called at any point during a program’s execution, including by other procedures or itself.

What is a Compiler in R?

A compiler is computer software that transforms computer code written in one programming language (the source language) into another computer language (the target language).

Define MATLAB Package

The MATLAB package includes wrapper functions and variables. Also, these functions are used to replicate Matlab function calls.

Differentiate between library() and require() Functions in R Language

The following are the differences between library() and require() functions in R language.

Consider a related R code example for the above differentiation:

## Example for loading single R Package

## library() function
library(mctest, character.only = TRUE)

## require() function
if(!require(mctest, character.only = TRUE, quietly = TRUE)){
  install.packages(package)
}

## OR

if(!require(mctest, character.only = TRUE, quietly = TRUE)){
  install.packages(mctest)
  library(mctest, character.only = TRUE)
}

## Example for loading multiple R Package
for (package in c(", ")){
  if(!require(package, character.only = TRUE, quietly = TRUE)){
    install.packages(package)
    library(package, character.only = TRUE)
    }
}

Which Packages are used for Exporting the Data in R?

There are many ways (packages or methods to export the data into another format like SPSS, SAS, Stata, or Excel Spreadsheet. For Excel use the package xlsReadWrite and for SAD, SPSS, and STATA use foreign the package.

What is the Use of the coin Package in R?

The coin package is used to achieve the re-randomization or permutation-based statistical tests.

Why vcd package is used?

The vcd package provides different methods for visualizing multivariate categorical data.

What is the Use of the lattice Package?

The lattice package is to improve on base R graphics by giving better defaults and it can easily display multivariate relationships.

Why library() function is used?

The library() function is used to show the packages which are installed.

What is the Use of the doBY Package?

The doBy is used to define the desired table using function and model formula.

Define the relaimpo Package.

The relaimpo is used to measure the relative importance of each of the predictors in the model.

Why the Package car is Used?

The car provide a variety of regression including scatter plots, and variable plots and it also enhances diagnostic.

Define the robust Package.

The robust provides a library of robust methods including regression.

In which R package Survival Analysis is Defined?

The survival analysis is defined under the R package named survival.

What is the use of the MASS Package?

The MASS package in R language includes those functions that perform linear and quadratic discriminant function analysis.

What is the use of the forecast Package?

The forecast provides the functions that are used for the automatic selection of ARIMA and exponential models.

What is the Use of the party Package?

The party is used to provide a non-parametric regression for ordinal, nominal, censored, and multivariate responses.

Which Package provides the Bootstrapping?

The boot package is used which provides bootstrapping.

What is the Use of the Matrix Package?

The Matrix package includes those functions that support sparse and dense matrices like Lapack, BLAS, etc.

What is the iPlots?

The iPlots is a package that provides bar plots, and mosaic plots. Also, it provides box plots, parallel plots, scatter plots, and histograms.

Which Package is Used for Power Analysis in R?

The Pwr package is used for power analysis in R.

What is the npmc?

The npmc is a package that gives nonparametric multiple comparisons.

Online MCQs and Quiz website

Packages in R Language store all the functions, datasets, and help files that significantly expand the language’s functionality beyond its core capabilities. When a package is loaded, its contents are available to work with. It makes the packages more efficient (as the full list takes more memory and time to search than a subset). The packages are also protected from name clashes with other codes.

Why Use R Packages?

• Specialized Functionality: R packages offer tailored solutions for various domains, such as,
  • Biostatistics
  • Data mining
  • Machine learning
  • Financial analysis
  • Geospatial analysis
  • Text mining

• Efficient Code and Algorithms: Many packages incorporate highly optimized C or C++ code, boosting performance and enabling complex computations.

• Community-Driven Innovation: The R community actively develops and shares packages, ensuring a constant stream of new tools and techniques.

• Standardized Data Formats: R Packages often include standard data formats, making it easier to work with diverse data sources.
• Reproducibility: By using R packages, one can share his/her code and analyses more easily, making them reproducible for others

Seeing the Installed Packages

To see what packages are installed in your computer system, use the following command without arguments.

library()

To load particular packages in R (for example, mctest (https://CRAN.R-project.org/package=mctest) package containing functions to compute the multicollinearity diagnostics), use the command like:

library(mctest)

Installing and Updating Packages in R

One can install an R package if a system is connected to the internet using install.packages(). A package can also be updated by using the update.packages() command. (The installation of a package is also available through the Packages menu in the Windows and OS X GUIs.

# Installing a package
install.packages("mctest")

# Install Multiple Packages in R
install.packages(c("mctest", "lmridge", "liureg"))

# Updating a package
update.packages("mctest")

Currently Loaded Packages

One can see the packages that are currently loaded in the more by using the command

search()

Note that some packages may be loaded but not available on the search list, such packages may be seen by using

loadedNamespaces()

One can see a list of all available help topics in an installed package, by using the command

help.start()

An HTML help system will start. One can easily navigate to the package listing in the reference section.

Standard/ Base Packages in R

The base or standard packages are considered part of the R source code. The base packages contain the basic functions that allow R to work, and the datasets, standard statistical, and graphical functions that are described in this manual. These packages are automatically available in any R installation.

Contributed Packages and CRAN

There are thousands of contributed/ customized/ user-defined packages for R, written by many different authors. Some of these packages implement specialized statistical methods, some give access to data or hardware, and others are designed to complement textbooks. Most of the R packages are available for download from CRAN (https://CRAN.R-project.org/ and its mirrors).

Key R Package Repositories

• CRAN: The primary repository for R packages, offering a vast array of options.
• Bioconductor: Specializes in bioinformatics and computational biology tools.
• GitHub: Hosts user-contributed packages and open-source projects.

Commonly Used Packages

• Data Manipulation:
  • dplyr: For data manipulation and transformation.
  • tidyr: For tidying data.
• Data Visualization:
  • ggplot2: For creating elegant and customizable plots.
  • plotly: For interactive visualizations.
• Statistical Computing:
  • stats: The Base R package for statistical computations.
  • MASS: For more advanced statistical methods.
• Machine Learning:
  • caret: For a unified interface to various machine learning algorithms.
  • randomForest: For random forest models.
  • xgboost: For gradient boosting machines.
• Text Mining:
  • tidytext: For text mining and analysis.
• Web Scraping:
  • rvest: For extracting data from websites.

Data Analysis and Statistics

The post is an introduction tutorial about Packages in R Programming. In R language functions and datasets are all stored in packages. The content of a package is only available when a package is loaded using the library() function.

To see which R packages are installed, write the following command (without argument)

library( )

To load a particular installed package, use the package name as the argument to the library() function, that is,

library(MASS)

Installing and Updating Packages in R Programming

If the computer system is connected to the internet and a required package is not installed on one’s computer, the user can use the install.packages() function to install the required package. To update the already installed package one can use the update.package() function. The search() function can be used to see which packages are loaded into computer memory.

Classification of R Packages

R packages can be classified as standard (base) packages and contributed packages. The standard (or base) packages are considered part of the R source code. The base packages contain the basic functions that allow R to work. The base packages also contain datasets and standard statistical and graphical functions. The standard R functions are automatically available in any R installation, that is, you do not need to install them.

The standard R packages are written by authors. These packages implement some specialized statistical methods, and access to datasets and hardware. The contributed packages are distributed with every binary distribution of R and are available for download from CRAN and other repositories such as Bioconductor.

R Namespace

R packages can have a namespace. Namespaces (i) allow the package writer to hide functions and data that are meant only for internal use, (ii) prevent functions from breaking when a user picks a name that clashes with one in the packages, and (iii) provide a way to refer to an object within a particular package.

For example, in R the t() function is the transpose function. A user can define his own t() function. The namespaces will prevent the user’s definition from taking procedure and breaking every function that tries to transpose the matrix.

Two operators work with namespaces, (i) :: double colon operator and triple colon operator :::. The double colon operator selects definitions from a particular namespace. For example, the t() function is available as the base::t, because it is defined in the base package. The function that is exported from the package can be retrieved with a double colon operator.

The tiple colon operator acts as a double colon operator but it also allows access to hidden objects. The getAnywhere() function can be used to search for multiple packages.

Note: Packages are interdependent, and loading one package may cause other packages to be automatically loaded. The colon operators also cause automatic loading of the associated package. the package is not added to the search list when a package with namespaces is loaded automatically.

FAQs about R Packages

1. What is an R package?
2. How an R package can be loaded in a session?
3. What is the use of getAnywhere() function in R?
4. What is the use of the colon operator for package loading
5. What is namespace in R language?
6. Who writes or develops R packages?

SPSS Data Analysis

MCQs General Knowledge