Adding error bars to ggplot2 in R plots is easiest if you include the width of the error bar as a variable in your plot data. Read my blog post to see an example.
“AI on the edge” was a new term for me that I learned from Marc Staimer, founder of Dragon Slayer Consulting, who was interviewed in a podcast. Marc explained how AI on the edge poses a data storage problem, and my blog post proposes a solution!
Time series plots in R are totally customizable using the ggplot2 package, and can come out with a look that is clean and sharp. However, you usually end up fighting with formatting the x-axis and other options, and I explain in my blog post.
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.
Making upset plots with R package UpSetR is an easy way to visualize patterns of attributes in your data. My blog post demonstrates making patterns of co-morbidities in health survey respondents from the BRFSS, and walks you through setting text and color options in the code.
Making box plots in R affords you many different approaches and features. My blog post will show you easy ways to use both base R and ggplot2 to make box plots as you are proceeding with your data science projects.
Convert CSV to RDS is what you want to do if you are working with big data files in R GUI and want to improve efficiency. Read my blog post for an explanation and video demonstrations of this process!
GPower case example shows a use-case where we needed to select an outcome measure for our study, then do a power calculation for sample size required under different outcome effect size scenarios. My blog post shows what I did, and how I documented/curated the results.
Querying the GHDx database is challenging because of its difficult user interface, but mastering it will allow you to access country-level health data for comparisons! See my demonstration!