Siddharth Chattoraj

I conducted a comparative evaluation of interactive data visualization libraries for beginners to help them choose the best library for their needs. The libraries evaluated were D3.js (JavaScript), Chart.js (JavaScript), Plotly Express (Python), Bokeh (Python), and ggplot2 with the ggiraph extension (R). The files that I created as part of my evaluation and are referred to as "Supplemental Code" in the paper are available in the GitHub repository linked above.

I designed a resource guide in LaTeX for analysts with the San Francisco District Attorney's Office Data, Research, and Analytics team to help them conduct spatial joins and geospatial data analysis, create geospatial maps in Python/R/Datawrapper, use ArcGIS to map locations and create timelapses, and use Microsoft Excel to create 3D Maps.

I taught a workshop on how to use Datawrapper to create interactive charts and maps for the Daily Nexus Data Team. The workshop covered how to create a Datawrapper account, upload data, and create interactive and customized graphs and maps. The workshop primarily focused on charts, tables, heatmaps, symbol maps, and locator maps. The datasets used in the workshop are linked in the speaker notes of the presentation slides.

I designed a resource guide in LaTeX for reporters on the Daily Nexus Data Team to help them with brainstorming ideas, writing an article, interviewing, collecting data, creating charts, web scraping, or finding additional resources.

I taught a workshop on how to visualize data in Python with Matplotlib and Plotly in order to create static and interactive charts. The datasets used are linked in the notebooks within the GitHub repository. The workshop covered how to install Python, Jupyter Notebook, and necessary libraries and primarily focused on creating line, bar, pie, and dropdown charts, and customize the charts with titles, labels, colors, and themes.