This is a sample collection of non-interactive data visualizations. See the code used to develop these visualizations on GitHub. R Markdown was used to create this gallery; the next iteration will, hopefully, be built using Quarto.

California, Oregon, and Nevada saw the largest decreases in uninsured persons after the Affordable Care Act.

On average, the uninsured rate in the United States decreased six percent from 2010 to 2015. The top 10 states with the largest decreases in uninsured persons had decreases of more than eight percent.


In 2021, the counties of ‘Silicon Slopes’ had some the highest percentages of teleworkers in Utah.

Unsurprisingly, the three counties comprising UT’s technology hub (Salt Lake, Summit, and Utah) were among the top five counties with the highest percentages of teleworkers.

  • The telework data were obtained from the U.S. Census Bureau’s ACCESS BROADBAND Dashboard. This dashboard is a result of work done by the Census Bureau and the National Telecommunications and Information Administration (NTIA) following the passage of the ACCESS BROADBAND Act of 2021. The original source of the teleworker data was the five-year American Community Survey from 2017-2021.

  • In the ACCESS Dashboard data dictionary, the definition for the percentage of teleworkers is as follows: “The percentage of workers ages 16 years and older that reported their residential address as the geographic location at which they carried out their occupational activities.” See the complete data dictionary online or in “/data/file_layout_ACCESS_BROADBAND_Dashboard.xlsx” within the R project on GitHub.

  • Geomcomputation with R was helpful for learning which functions could be used to subset an existing shapefile, and union the resulting shapefiles, to create a new shapefile.


McDonald’s ‘Big Breakfast’ is a high-cholesterol start to the day.

Of the breakfast items listed, the Big Breakfast (with or without hotcakes) has the worst nutritional value.

  • The data visualized in this heatmap are from Kaggle.

  • This Data Science Tutorials’ heatmap tutorial distributed via R-Bloggers was quite helpful for recalling how the data must be structured for a heatmap made with ggplot2::geom_tile().

  • The code for a beautifully formatted horizontal colorbar is courtesy of Cédric Scherer’s workshop materials from Data Visualization Society’s Outlier Conf 2021.

  • Two Stack Overflow forums (1 and 2) were helpful for learning how to configure the height/weight arguments to ensure the tiles were large enough when the heatmap was saved as a PNG.


In 2018, Oklahoma had the highest prevalence of elderly, female Medicare enrollees with 6+ chronic conditions.

The prevalence of six or more chronic conditions among Medicare beneficiaries assigned female at birth aged 65 years or older was 20.5% in OK in 2018.

  • This visualization is a hexbin map of the United States; it features data from the Centers for Medicare and Medicaid Services’ (CMS) Multiple Chronic Conditions (MCC) dataset.

  • The MCC data were retrieved through the CMS API. More information about the dataset can be viewed in the data dictionary and methodology documentation.

  • Yan Holtz’s excellent hexbin map tutorial on The R Graph Gallery provided the data source for the hexagonal shapefile—as well as helpful hints for manipulating the file.

  • The hexagonal shapefile of the United States was obtained from Carto. It can be downloaded in other formats like GeoJSON.

  • Tips and tricks for handling NA values in a continuous choropleth map were learned from R for the Rest of Us’ Mapping with R course taught by Charlie Hadley.


Toyin L. Ola