Data visualization is a method of presenting data in graphical form, to ease and to better understand the data. It is easier to figure out trends and patterns through data visualization. Simple examples would include pie charts, bar charts, line graphs, etc.
The selection of a proper visualization is one of the most important steps in the data visualization process. This selection depends on the data and the expected output. As such, it is necessary to understand the different types of visualizations that exist, to be able to choose the appropriate type. Given below are few resources that you can refer to:
Batt, S., Grealis, T., Harmon, O., & Tomolonis, P. (2020). Learning Tableau: A data visualization tool. The Journal of Economic Education, 51(3–4), 317–328. https://doi.org/10.1080/00220485.2020.1804503
Coleman, A., Bose, A., & Mitra, S. (2023). Metagenomics Data Visualization Using R. Metagenomic Data Analysis, 2649, 359–392. https://doi.org/10.1007/978-1-0716-3072-3_19
Grömping, U. (2022). Pro Data Visualization Using R and JavaScript : Analyze and Visualize Key Data on the Web. Journal of Statistical Software, 102(Book Review 1), 1–4. https://doi.org/10.18637/jss.v102.b01
Janvrin, D. J., Raschke, R. L., & Dilla, W. N. (2014). Making sense of complex data using interactive data visualization. Journal of Accounting Education, 32(4), 31–48. https://doi.org/10.1016/j.jaccedu.2014.09.003
Laurent, A., Lyu, X., Kyveryga, P., Makowski, D., Hofmann, H., & Miguez, F. (2021). Interactive Web‐based Data Visualization and Analysis Tool for Synthetizing on‐farm Research Networks Data. Research Synthesis Methods, 12(1), 62–73. https://doi.org/10.1002/jrsm.1440
Beautiful Evidence by
Science and art have in common intense seeing, the wide-eyed observing that generates empirical information. Beautiful Evidence is about how seeing turns into showing, how empirical observations turn into explanations and evidence presentations. The book identifies excellent and effective methods for presenting information, suggests new designs, and provides tools for assessing the credibility of evidence presentations.Here we will see many close readings of serious evidence presentations-ranging through evolutionary trees and rocket science to economics, art history, and sculpture. Insistent application of the principles of analytical thinking helps both insiders and outsiders assess the credibility of evidence.
Data Storytelling and Visualization with Tableau by
With the tremendous growth and availability of the data, this book covers understanding the data, while telling a story with visualization including basic concepts about the data, the relationship and the visualizations. All the technical details that include installation and building the different visualizations are explained in a clear and systematic way. Various aspects pertaining to storytelling and visualization are explained in the book through Tableau. Features Provides a hands-on approach in Tableau in a simplified manner with steps Discusses the broad background of data and its fundamentals, from the Internet of Everything to analytics Emphasizes the use of context in delivering the stories Presents case studies with the building of a dashboard Presents application areas and case studies with identification of the impactful visualization This book will be helpful for professionals, graduate students and senior undergraduate students in Manufacturing Engineering, Civil and Mechanical Engineering, Data Analytics and Data Visualization.
Data Visualization with Excel Dashboards and Reports by
Large corporations like IBM and Oracle are using Excel dashboards and reports as a Business Intelligence tool, and many other smaller businesses are looking to these tools in order to cut costs for budgetary reasons. An effective analyst not only has to have the technical skills to use Excel in a productive manner but must be able to synthesize data into a story, and then present that story in the most impactful way. Microsoft shows its recognition of this with Excel. In Excel, there is a major focus on business intelligence and visualization. Data Visualization with Excel Dashboards and Reports fills the gap between handling data and synthesizing data into meaningful reports. This title will show readers how to think about their data in ways other than columns and rows. Most Excel books do a nice job discussing the individual functions and tools that can be used to create an "Excel Report". Titles on Excel charts, Excel pivot tables, and other books that focus on "Tips and Tricks" are useful in their own right; however they don't hit the mark for most data analysts. The primary reason these titles miss the mark is they are too focused on the mechanical aspects of building a chart, creating a pivot table, or other functionality. They don't offer these topics in the broader picture by showing how to present and report data in the most effective way. What are the most meaningful ways to show trending? How do you show relationships in data? When is showing variances more valuable than showing actual data values? How do you deal with outliers? How do you bucket data in the most meaningful way? How do you show impossible amounts of data without inundating your audience? In Data Visualization with Excel Reports and Dashboards, readers will get answers to all of these questions. Part technical manual, part analytical guidebook; this title will help Excel users go from reporting data with simple tables full of dull numbers, to creating hi-impact reports and dashboards that will wow management both visually and substantively. This book offers a comprehensive review of a wide array of technical and analytical concepts that will help users create meaningful reports and dashboards. After reading this book, the reader will be able to: Analyze large amounts of data and report their data in a meaningful way Get better visibility into data from different perspectives Quickly slice data into various views on the fly Automate redundant reporting and analyses Create impressive dashboards and What-If analyses Understand the fundamentals of effective visualization Visualize performance comparisons Visualize changes and trends over time
Hands-On Data Visualization by
Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub. Hands-On Data Visualization takes you step-by-step through tutorials, real-world examples, and online resources. This practical guide is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and maps Learn key data visualization concepts to help you choose the right tools Convert and transform tabular and spatial data to tell your data story Edit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others
Interactive Web-Based Data Visualization with R, Plotly, and Shiny by
The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.
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The guide was updated by Anusha Ravi, Scholars Lab Graduate Assistant in Fall 2023.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 Generic License.
