R Packages

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?

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MCQs General Knowledge

The packages can have namespaces in R Language, and currently, all of the base and recommended packages do except the dataset packages. Understanding the use of namespaces is vital if one plans to submit a package to CRAN because CRAN requires that the package plays nicely with other submitted packages on CRAN.

Namespaces in R Language

Namespaces in R Language are essential tools for organizing code and preventing naming conflicts.
They become especially important when dealing with multiple packages, each potentially containing functions or objects with the same names.

Namespaces in R Language ensure that other packages will not interfere with your code and that the package works regardless of the environment in which it’s run. In R Language, the namespace environment is the internal interface of the package. It includes all objects in the package, both exported and non-exported to ensure that every function can find every other function in the package.

For example, plyr and Hmisc both provide a function namely summarize(). Loading plyr package and then Hmise, the summarize() function will refer to the Hmisc. However, loading the package in the opposite order, the summarize() function will refer to the plyr package version.

To avoid confusion, one can explicitly refer to the specific function, for example,

Hmisc::summarize

and

plyr::summarize

Now, the order in which the packages are loaded would not matter.

The Namespaces in R Language do three things:

• Namespaces allow the package writer to hide functions and data that are meant only for internal use,
• Namespaces prevent functions from breaking when a user (or other package writers) picks a name that clashes with one in the package, and
• Namespaces in R provide a way to refer to an object within a particular package

Namespace Operators

In R language, two operators work with namespaces.

• Doule-Colon Operator
  The double-colon operator:: selects definitions from a particular namespace. The transpose function t() will always be available as the base::t because it is defined in the base package. Only functions exported from the package can be retrieved this way.
• Triple-Colon Operator
  The triple-colon operator ::: acts like the double-colon operator but also allows access to hidden objects. Users are more likely to use the getAnywhere() function, which searches multiple packages.

Packages are often interdependent, and loading one may cause others to be automatically loaded. The colon operators will also cause automatic loading of the associated package. When packages with namespaces are loaded automatically they are not added to the search list.

Benefits of using namespaces:

• Clarity: Namespaces clarify the code by avoiding ambiguity when using common function names across different packages.
• Fewer conflicts: Namespaces prevent errors that might arise if a user accidentally overwrites an object from another package with the same name.
• Modular design: Namespaces promotes a modular approach to code organization, making managing and reusing code across projects easier.

FAQs about Namespaces in R Language

1. What is namespace in R Language?
2. What do namespaces in R language ensure?
3. List and discuss namespace operators.
4. Write a note on the benefits of using namespaces in R Language.
5. What is the purpose of getAnywhere() function in R?
6. Discuss double colon and triple colon operators.

R Language Basics: Frequently Asked Questions

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