I created a tutorial on building an online book using the R bookdown package. The tutorial is available on my RPubs page, which is located here.
I plan on creating more tutorials with the bookdown package, so stay tuned.
Building an HTML Presentation using R Markdown - Part 2
This is the second part of a series on how to create HTML presentations using R Markdown.
I posted it on my Rpubs site and my GitHub site.
I plan on writing more tutorials on this subject, so stay tuned.
Tweedie GLM model in R for Cost Data
I wrote a tutorial on using a Tweedie distribution for a GLM gamma model for cost data in R. Unlike Stata, R is very particular with zeroes when constructing GLM models. Hence, I opted to use the Tweedie distribution to mix and match the link function with the Gamma distribution. I settled on the identity link because it doesn’t involve retransformation and is each to interpret.
My tutorial is available on my RPubs site and GitHub site.
Two-part models in R - Application with cost data
I created a tutorial on how to use two-part models in R for cost data. I used the healthcare expenditures from the Medical Expenditure Panel Survey in 2017 as a motivating example. Normally, I use Stata when I construct two-part models. But I wanted to learn how I could do this in R. Fortunately, R has a package called twopartm that was developed by Duan and colleagues. You can find their document for the twopartm package here.
The tutorial I created is located on my GitHub page and RPubs page.
Building an HTML Presentation Using R Markdown
I learned how to construct an HTML presentation using R Markdown. In order to help myself remember how to do this, I began writing a tutorial. I posted the first part of the tutorial on my RPubs page.
I plan to write more tutorials on this subject, so stay tuned.
Interpreting regression models
I wrote a short explanation on how to interpret regression models.
I have posted this on RPubs, and the code is saved on my GitHub site.
MEPS Tutorial - Part 3: Applying survey weights using R
In this tutorial, we will review how survey weights from the Medical Expenditure Panel Survey (MEPS) are applied using R.
The tutorial is available on my GitHub site and RPubs.
The R Markdown code I used to generate this tutorial is available on my GitHub site.
MEPS Tutorial - Part 2: Merging Data with R
MEPS Tutorial - Part 1: Loading Data into R
For the last couple of years, I have used Stata whenever I worked with MEPS data. Stata is a great statistical program that allows me to script and analyze data from complex survey designs similar to MEPS. However, R is another powerful statistical program that researchers have been using to evaluate and analyze MEPS data. R is free/open source and has a large community that constantly builds packages to improve its utility. Because of its advantages, I wanted to start writing tutorials on how to use R to analyze MEPS data.
This first tutorial provides instructions on how to load MEPS data into R, which is a critical step for data analysis.
You can find the tutorial on my RPubs page (link); I also posted this on my GitHub page (link).
For those of you who are interested in how I developed this tutorial, the R Markdown code is located on my GitHub page (link).
In the coming months, I’ll continue to write more tutorials using R with MEPS data, so stay tuned.
R Markdown: Adding icons using the "fontawesome" package -- a short tutorial
I discovered an interesting package that allowed me to insert icons into my R Markdown documents. I learned how to use some of the basic commands and wrote a short tutorial on how to do this. I posted the tutorial on my GitHub page. I also posted the R Markdown code on my GitHub site.
I also encourage you to check out the Font Awesome GitHub page to learn more about the different icons that are available.