R for Data Analytics – Inspecting packages

R for Data Analytics – Inspecting packages



R, a powerful programming language and environment for statistical computing and graphics, has become an indispensable tool for data analysts and statisticians. One of R’s most remarkable strengths is its extensive library of packages, which extend its functionality and enable users to tackle complex analytical tasks with ease. In this article, we will explore how to inspect packages in R for data analytics, including how to search, install, load, and use them effectively.

Discovering Packages

There are thousands of packages available in R, covering a wide range of applications, from data manipulation and visualization to machine learning and text mining. To discover packages that can be useful for your data analytics tasks, you can use the following resources:

CRAN (Comprehensive R Archive Network): The official repository for R packages, containing thousands of packages. Browse through the CRAN task views (https://cran.r-project.org/web/views/) to find packages organized by topic.

RDocumentation (https://www.rdocumentation.org/): A search engine that allows you to search for R packages and functions across multiple repositories, including CRAN, Bioconductor, and GitHub.

GitHub (https://github.com/): Many R package developers host their code on GitHub. Use the search bar to find R packages by entering “language:R” along with your search keywords.

Installing Packages

Once you have identified the packages you need, you can install them directly from R or RStudio using the install.packages() function. For instance, to install the popular “dplyr” package, run:


To install multiple packages at once, pass a vector of package names:

install.packages(c("dplyr", "ggplot2", "tidyverse"))

Loading Packages

After installing a package, you need to load it into your R session using the library() function before using its functions. For example, to load the “dplyr” package, run:


Inspecting Package Contents

To explore the contents of a package, such as the available functions and datasets, you can use the following methods:

help(package = “packageName”): Provides an overview of the package, including a description, author information, and a list of functions and datasets.

ls(“package:packageName”): Returns a list of the functions and datasets in the package.

vignette(package = “packageName”): Displays a list of vignettes (long-form documentation) included in the package.

help.search(“keyword”, package = “packageName”): Searches for help documentation related to the given keyword within the specified package.

Updating Packages

It is essential to keep your packages updated to benefit from the latest features, bug fixes, and performance improvements. To update all your installed packages, run:


To update a specific package, reinstall it using the install.packages() function.


Inspecting packages in R is crucial for streamlining your data analytics workflow and ensuring you have access to the best tools for your tasks. By exploring, installing, loading, and maintaining packages, you can leverage the power of the R ecosystem and enhance your data analytics capabilities.


Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

R for Data Analytics – Inspecting packages

Loader Loading...
EAD Logo Taking too long?

Reload Reload document
| Open Open in new tab

Download PDF [76.66 KB]

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!