Working with These Notes

  1. This document is broken down into multiple chapters. Use the table of contents on the left side of the screen to navigate, and use the hamburger icon (horizontal bars) at the top of the document to open or close the table of contents.
  2. At the top of the document, you’ll see additional icons which you can click to
    • search the document,
    • change the size, font or color scheme of the page, and
    • download a PDF or EPUB (Kindle-readable) version of the entire document.
  3. The document will be updated (unpredictably) throughout the semester.

The 431 Course online

The main web page for the 431 course in Fall 2020 is https://thomaselove.github.io/431/. Go there for all information related to the course.

What You’ll Find Here

These Notes provide a series of examples using R to work through issues that are likely to come up in PQHS/CRSP/MPHP 431. What you will mostly find are brief explanations of a key idea or summary, accompanied (most of the time) by R code and a demonstration of the results of applying that code.

While these Notes share some of the features of a textbook, they are neither comprehensive nor completely original. The main purpose is to give 431 students a set of common materials on which to draw during the course. In class, we will sometimes:

  • reiterate points made in this document,
  • amplify what is here,
  • simplify the presentation of things done here,
  • use new examples to show some of the same techniques,
  • refer to issues not mentioned in this document,

but what we don’t do is follow these notes very precisely. We assume instead that you will read the materials and try to learn from them, just as you will attend classes and try to learn from them. We welcome feedback of all kinds on this document or anything else.

Everything you see here is available to you as HTML or PDF. You will also have access to the R Markdown files, which contain the code which generates everything in the document, including all of the R results. We will demonstrate the use of R Markdown (this document is generated with the additional help of an R package called bookdown) and RStudio (the “program” we use to interface with the R language) in class.

All data and R code related to these notes are also available to you.

Setting Up R

These Notes make extensive use of

  • the statistical software language R, and
  • the development environment R Studio,

both of which are free, and you’ll need to install them on your machine. Instructions for doing so are in found in the course syllabus.

If you need an even gentler introduction, or if you’re just new to R and RStudio and need to learn about them, we encourage you to take a look at http://moderndive.com/, which provides an introduction to statistical and data sciences via R at Ismay and Kim (2019).

These notes were written using R Markdown. R Markdown, like R and R Studio, is free and open source.

R Markdown is described as an authoring framework for data science, which lets you

  • save and execute R code
  • generate high-quality reports that can be shared with an audience

This description comes from http://rmarkdown.rstudio.com/lesson-1.html which you can visit to get an overview and quick tour of what’s possible with R Markdown.

Another excellent resource to learn more about R Markdown tools is the Communicate section (especially the R Markdown chapter) of Grolemund and Wickham (2019).

Initial Setup of R Packages

To start, I’ll present a series of commands I run at the beginning of these Notes. These particular commands set up the output so it will look nice as either an HTML or PDF file, and also set up R to use several packages (libraries) of functions that expand its capabilities. A chunk of code like this will occur near the top of any R Markdown work.

knitr::opts_chunk$set(comment = NA)

library(knitr)
library(magrittr)
library(janitor)
library(NHANES)
library(palmerpenguins)
library(patchwork)
library(rms)
library(mosaic)
library(Epi)
library(broom) # note: tidymodels includes the broom package
library(tidyverse) # note: tidyverse includes the dplyr and ggplot2 packages

theme_set(theme_bw())

I have deliberately set up this list of loaded packages to be relatively small, and will add some others later in these Notes. You only need to install a package once, but you need to reload it every time you start a new session.

The Love-boost.R script

Starting in October, we’ll make use of a few scripts I’ve gathered for you.

source("data/Love-boost.R")

Additional R Packages installed for this book

Some packages need to be installed on the user’s system, but do not need to be loaded by R in order to run the code presented in this set of notes until later. These additional packages include the following.

boot
car
GGally
gt
psych
modelsummary
naniar
visdat

References

Grolemund, Garrett, and Hadley Wickham. 2019. R for Data Science. O’Reilly. http://r4ds.had.co.nz/.

Ismay, Chester, and Albert Y. Kim. 2019. ModernDive: Statistical Inference via Data Science. http://moderndive.com/.