Research Guides: Data Analysis and Visualizations: Additional Resources

Data Analysis and Visualization Resources and Tools

The Research Support & Engagement department assists with:

Open-access tools in analyzing and visualizing data.
Identify what types of visualization best fit your needs
Provide tips on creating your visualization
Offer guidance on interpreting data analysis results

What is R? What is RStudio?

R is more of a programming language than just a statistics program. it is “a language for data analysis and graphics.” You can use R to create, import, and scrape data from the web; clean and reshape it; visualize it; run statistical analysis and modeling operations on it; text and data mine it; and much more.

RStudio is a user interface for working with R. It is called an Integrated Development Environment (IDE): a piece of software that provides tools to make programming easier. RStudio acts as a sort of wrapper around the R language.

Install R and RStudio

R and RStudio are two separate pieces of software:

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis
RStudio is an integrated development environment (IDE) that makes using R easier. In this course we use RStudio to interact with R.

Windows

Download R from the CRAN website.
Run the .exe file that was just downloaded
Go to the RStudio download page
Under Installers select RStudio x.yy.zzz - Windows Vista/7/8/10 (where x, y, and z represent version numbers)
Double click the file to install it
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

MacOS

Download R from the CRAN website.
Select the .pkg file for the latest R version
Double click on the downloaded file to install R
It is also a good idea to install XQuartz (needed by some packages)
Go to the RStudio download page
Under Installers select RStudio x.yy.zzz - Mac OS X 10.6+ (64-bit) (where x, y, and z represent version numbers)
Double click the file to install RStudio
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

Open Access e-book: Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. " O'Reilly Media, Inc.".

Python

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.

- from WIKIPedia

Introduction to Python Programming - OpenStax

Tableau

With Tableau, users can upload data from spreadsheets, cloud-based data management software, and online databases and merge them to identify trends, filter databases, and forecast outcomes. Users also can drag and drop information to transform data and instantly create charts and visualizations. Start your free trial of Tableau here.

You can also find more information about Tableau here in this LibGuide: Data Visualization: Getting Started with Tableau

Power BI

Microsoft’s Power BI software provides business intelligence and data analytics tools to clean and transform data, merge data from different sources, and perform grouping, clustering, and forecasting to find patterns in the data. Start Power BI for free here.

Google Charts

Google’s free Charts software creates customizable charts, maps, and diagrams from imported datasets.

Additional Resources

Visualize This by Nathan Yau
ISBN: 9781394214884

Publication Date: 2024-04-17

Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design Details tools that can be used to visualize data graphics for reports, presentations, and stories, for the web or for print, with major updates for the latest R packages, Python libraries, JavaScript libraries, illustration software, and point-and-click applications Contains numerous examples and descriptions of patterns and outliers and explains how to show them Information designers, analysts, journalists, statisticians, data scientists--as well as anyone studying for careers in these fields--will gain a valuable background in the concepts and techniques of data visualization, thanks to this legendary book.
Data visualization by Lauren Magnuson
ISBN: 9781442271128

Publication Date: 2016

Data Visualization: A Guide to Visual Storytelling for Libraries is a practical guide to the skills and tools needed to create beautiful and meaningful visual stories through data visualization. Understanding and communicating trends from your organization's data is essential. Whether you are looking to make more informed decisions by visualizing organizational data, or to tell the story of your library's impact on your community, this book will give you the tools to make it happen.
Storytelling with Data by Cole Nussbaumer Knaflic
ISBN: 9781119002253

Publication Date: 2015-11-02

Featuring refreshed visuals, expanded content, and new insights, this collector's edition builds on the classic's foundational lessons, empowering you to craft persuasive data stories. Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. There is a story in your data--Storytelling with Data will give you the skills and power to tell it.
R Graphics Cookbook by Winston Chang
ISBN: 9781449363116

Publication Date: 2012-12-06

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R's graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.
Python data science handbook: essential tools for working with data by Vanderplas, Jacob T.
ISBN: 9781098121198

Publication Date: 2023

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

O'Reilly
The O’Reilly Learning Platform is a powerful resource for developing skills across a wide range of topics, including data science, programming, business, and more. It offers access to a vast library of books, video tutorials, and interactive learning paths, making it an ideal platform for both beginners and professionals seeking to expand their knowledge.