Data visualization needs tools to turn raw data into meaningful insights, and should be capable of uncovering correlations, patterns, and achieving comparisons of data.

Therefore, we compare several commonly used tools based on the following critiria:

• Interactivity of graphs
• Ease of use (syntax or operations) and flexibility in making figures
• Types of versatile graphs included in the tool

In the following table, we compare the pros and cons of these tools. To summerize, we recommend the use of Plotly for Python users, Plotly or Shiny for R users, ArcGIS for GIS users, and Tableau for non-programmers who also aim to visualize data in projects.

Table 1. Pros and cons of tools for (scientific) data visualization (Last update: Jun 9, 2022)

Additional reading materials about above-mentioned tools:

1. https://towardsdatascience.com/top-6-python-libraries-for-visualization-which-one-to-use-fe43381cd658
2. https://towardsdatascience.com/matplotlib-vs-seaborn-vs-plotly-f2b79f5bddb
3. https://www.shanelynn.ie/plotting-with-python-and-pandas-libraries-for-data-visualisation/
4. https://analyticsindiamag.com/plotly-vs-seaborn-compari/
5. https://datascienceplus.com/how-does-visualization-in-plotly-differ-from-seaborn/
6. https://datapane.com/u/leo/reports/87NNEJ7/python-visualisation-guide-814e8638/
7. https://nl.mathworks.com/products/matlab/plot-gallery.html
8. https://medium.com/analytics-vidhya/microsoft-power-bi-vs-tableau-which-one-is-best-38389b16f22f
9. https://towardsdatascience.com/a-guide-to-data-visualisation-in-r-for-beginners-ef6d41a34174#0689
10. https://www.mit.edu/~amidi/teaching/data-science-tools/tutorial/data-visualization-with-r/
11. http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
12. https://gephi.org/
13. https://cytoscape.org/
14. https://www.trustradius.com/compare-products/arcgis-vs-qgis

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