Classification crosswalks are easy to make, and can help you reduce cardinality in categorical variables, making for insightful data science portfolio projects with only descriptive statistics. Read my blog post for guidance!
Text and arrows in dataviz, if used wisely, can help your audience understand something very abstract, like a data pipeline. Read my blog post for tips in choosing images for your data visualizations!
Table editing in R is easier than in SAS, because you can refer to columns, rows, and individual cells in the same way you do in MS Excel. Read my blog post for example R table editing code.
Data curation solution that I posted recently with my blog post showing how to do upset plots in R using the UpSetR package was itself kind of a masterpiece. Therefore, I thought I’d dedicate this blog post to explaining how and why I did it.