# R

## Counting "Select All That Apply" Questions in Qualtrics

Qualtrics Messy Data My friend Devon Cantwell reached out with an interesting messy data caused by how Qualtrics produces “select all that apply” variables. For example, in her (mock) survey, she asks students to select all the colors that they personally find attractive from a list.

## Developing Race and Gender Estimates for US Law Enforcement Leadership

Introduction Researchers might be interested in developing a descriptive understanding of the gender and race composition of a particular industry, organization, or other institution. Oftentimes this is done with sampling from a population.

## Week 10 Solutions

So you want to be a data scientist This week can feel like a bit of a doozy, as the difficulty really ramps up. Something to remember - most data scientists spend most of their time cleaning, transforming, and tidying their data.

## Fun With the Scholar Package

Playing around with the updated scholar package.

## Website migrated

This is a post to breadcrumb the migration of the previous website. Turns out that updating the hugo package, the blogdown package, the academic theme, and installing the Rstudio preview build all in the same day was a good way to blow up the previous iteration.

## Chapter 3

One of the wonderful features of the R statistical universe is the number of free, high-quality instructional materials available. Throughout the remainder of the course, we will be learning and working from the R for Data Science book (you’ll see this book shorthanded as R4DS in this course and all over the web), by two giants of the R space: Hadley Wickham and Garrett Grolemund .

## The Effect of Body-Worn Camera Activation and Auditing Policies on Perceptions of Monitoring Fairness

Presented at the Political Research Colloquium, University of Utah, Oct. 30, 2020

## Tidying STM with tidytext

Libraries library(tidytext) library(ggthemes) library(tidyverse) library(ggplot2) library(dplyr) library(scales) Load Previous STM Objects I have previously run stm models for topics ranging from 3 to 25. Based on the fit indices, a six-topic model was selected.

## Testing Netlify hosting with R Markdown

This report was generated on 2020-12-30, as a demo of textclean from https://github.com/trinker/textclean#check-text This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

## New Website

I really appreciate Dan Quintana for his walkthrough on getting an Academic themed website up and running. Thank you to Peter Paul Pichler for his script adapated from Lorenzo Busetto’s original script to import publication data from bibtex files (I use Zotero, but other citation software should work fine).