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Data Visualization

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Data Visualizations

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.

Why Visualization?

We use visualizations both to discover new information and to share it with others. Data visualizations can:

  • Reveal patterns that are obscured when looking at the dataset alone. Often datasets can look similar before they are visualized, with the nuanced differences emerging in graphical representation.
  • Compare datasets at a glance. When working with a large corpora, a visualization can demonstrate the similarities and differences in data sets in a way an observer can easily understand.
  • Understand massive amounts of data, helping us to comprehend large scale projects and phenomenon by placing them into a structure that can be parsed.

Types of Data Visualization

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:

Data visualization catalog  

Types of visualization and when to use them

Articles on Data Visualization

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 RMetagenomic 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

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Acknowledgement

The guide was updated by William Borkgren, Scholars Lab Graduate Assistant in Fall 2024.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 Generic License.