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!
Wondering what we mean by the National Institutes of Health (NIH)? In my blog post, I explain the NIH’s role in appropriating funding for research, and demystify its role in implementing federal priorities.
Can you name categories other than “food” and “drugs” that are regulated by the FDA in the US? Read this blog post to learn what they are, and what the FDA does in the US.
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!
Variable names in SAS and R are subject to different “rules and regulations”, and these can be leveraged to your advantage, as I describe in this blog post.
Referring to variables in processing is different conceptually when thinking about SAS compared to R. I explain the differences in my blog post.
Interview preparation for data science jobs can involve taking several simple, actionable steps to make yourself feel confident and ready to answer questions with ease. Read my blog post for my tips and tricks!