Blog

Ramblings of an applied statistician

`dtplyr` and `tidyfast` are teaming up (well, at least in this blog post)

data.table

data wrangling

packages

With the advent of a more cohesive and complete dtplyr, I’ve been wanting to write about how it can be used with tidyfast to use the syntax of the tidyverse while relying on the speed and efficiency of data.table. This workflow is…

Six Things I Learned While Making `tidyfast`

data.table

data wrangling

packages

This post highlights six major themes of what I learned while creating the tidyfast R package. This process taught me about the tidyverse, data.table, R, and data science in general.

Guest Post: Interpreting Interactions in Multilevel Models

regression

This is a guest post by Jeremy Haynes, a doctoral student at Utah State University.

Fast and Readable ‘If Else’ in R

data.table

data wrangling

As I’ve spent time learning about different approaches to working with data, I’ve seen several subtle, but important, differences in how to do things. This very short post is presenting how one can perform vectorized “if else” functions in…

Data Joins: Speed and Efficiency of `dplyr` and `data.table`

data.table

data wrangling

This short post is looking at data joins for both dplyr and data.table. There are a lot of moving parts when assessing these things, so the results here are just for this situation. It may differ in others. However, the results here…

Comparing Efficiency and Speed of `data.table`: Adding variables, filtering rows, and summarizing by group

data.table

packages

As of late, I have used the data.table package to do some of my data wrangling. It has been a fun adventure (the nerd type of fun). This was made more meaningful with the renewed development of the dtplyr package by Hadley Wickham and…

New Preprint: List-columns in data.table: Nesting and unnesting data tables and vectors

data.table

data wrangling

reproducibility

I posted a new preprint named “List-columns in data.table: Nesting and unnesting data tables and vectors” in PsyArXiv. It can be downloaded here. It has been submitted to the R Journal (Oct 2019) so updates to the manuscript may follow…

New Preprint: Six Reasons to Consider Using R in Psychological Research

reproducibility

I posted a new preprint named “Six Reasons to Consider Using R in Psychological Research” in PsyArXiv. It can be downloaded here.

Using `mice` and `furniture` to understand missing data

data.table

furniture

I get a lot of questions about how to investigate missing values in a data set. So I’m putting this short post together to help me better explain how to handle it.

Bayesian Power Analysis with `data.table`, `tidyverse`, and `brms`

data.table

bayesian

visualization

I’ve been studying two main topics in depth over this summer: 1) data.table and 2) Bayesian statistics. I’ve loved learning both and, in this post, I will combine them into a single workflow.

Why I Chose to Learn `data.table` (and such related things)

data.table

packages

The data.table package has been a fun thing to learn about this week.

`data.table` and Text Analysis: Analyzing the Four Gospels

data.table

data wrangling

visualization

I am really enjoying quantitative text analysis as of late, sparked by a project I helped on regarding Autoscore. I’ve also had the desire to use data.table more often in my work. A post showing data.table being used with pipes (which…

`rinstalled` for Keeping Your Packages When Updating R

packages

When we update R to a new version, the packages you downloaded for the previous version won’t get loaded. Some code has been dispersed to take care of this, but there are so notably shortcomings to this.

Using Jamovi: Chi Square

jamovi

regression

packages

This post is part of a series–demonstrating the use of Jamovi–mainly because some of my students asked for it. Notably, this is using version 0.8.6.0. Today’s topic is correlation and linear regression.

Using Jamovi: Logistic Regression

jamovi

regression

packages

This post is part of a series–demonstrating the use of Jamovi–mainly because some of my students asked for it. Notably, this is using version 0.8.6.0. Today’s topic is logistic regression (with a binary outcome).

Using Jamovi: Correlation and Regression

jamovi

regression

packages

This post is part of a series–demonstrating the use of Jamovi–mainly because some of my students asked for it. Notably, this is using version 0.8.6.0. Today’s topic is correlation and linear regression.

Using Jamovi: Introduction

jamovi

data wrangling

packages

Jamovi is a new piece of software that makes running most basic analyses quicker, easier, and better. Shockingly, this is a free program based on the R programming language. It has the goal of “free and open software to bridge the gap…

Using the `afex` R package for ANOVA (factorial and repeated measures)

regression

visualization

packages

We recently switched our graduate statistics courses to R from SPSS (yay!). It has gone fairly well. However, once we get into ANOVA-type methods, particularly the repeated measures flavor of ANOVA, R isn’t as seamless as almost every…

Writing Your Dissertation (or Thesis) in RMarkdown

reproducibility

A few days ago I announced that I had completed the requirements for a PhD in Quantitative Psychology and (maybe more interestingly) that I had written my entire dissertation in Rmarkdown. It was more of a shoutout to @RStudio but the…

Simple Table 1 in R

furniture

data wrangling

packages

This has been updated to work with the most recent version of the furniture package.

Recent Accepted Paper in the R Journal

furniture

packages

Recently, our paper Furniture for Quantitative Scientists was posted in the R Journal. This paper discusses the use of descriptive statistics in exploring data through the furniture package. See the R Journal for the manuscript.

Introducing the Furniture R Package!

furniture

data wrangling

packages

This has been updated to work with the most recent version of the furniture package.

Reshaping Data with Long() and Wide()

furniture

data wrangling

packages

As part of the newest CRAN version of furniture, I included two new functions that allow simple reshaping of data frames. It is simply a wrapper of reshape() but has some added benefits. Check out the cheatsheet below.

Reading In and Joining Together Multiple Files in a Single Step

data wrangling

reproducibility

It is a common problem in the social sciences where we have several disparate data files that all have information we want combined into one beautiful, complete data set. But how to do this?

Reshaping Your Data (Long to Wide, Wide to Long)

data wrangling

I am currently teaching an Introductory R class for Health, Behavioral, Educational, and Social Scientists. As such, I created a PDF handout describing the base R function reshape() to help the students to better comprehend each of the…

Introducing Average Marginal Effects

regression

reproducibility

Average Marginal Effects** (AMEs) provide a powerful framework from which to interpret generalized linear models (GLMs). However, their use is uncommon in most fields (there is some use in econometrics literature).

Reproducibility and Regularized Regression

regression

reproducibility

Below is a Prezi presentation I have prepared for a presentation I am doing on Reproducibility and Regularized Regression. It briefly presents a step by step on how to use regularized regression (specifically elastic net) to explore…

Announcing Furniture Version 1.5.0

furniture

packages

table1()now has the ability to apply any function to numeric variables that the user supplies (all functions that work withtapply()should work withtable1(). Other than that, the changes are mainly internal changes intable1()`…

The Accuracy and Statistical Power of Average Marginal Effects

regression

Below is a Prezi presentation I have prepared for a seminar about the accuracy and statistical power of Average Marginal Effects (AME’s). It presents some simulations that I ran to assess the bias, the statistical power, and the CI…

Leaflet of Violent Crime Rates by State

data wrangling

visualization

packages

The map below is an example of using the leaflet R package to demonstrate statistics about a location (in this example, by state). There are a few great tutorials on leaflet and creating something useful and beautiful is fairly…

Furniture 1.4.1

furniture

packages

The latest stable version of the furniture R package is version 1.4.1. It contains several bug fixes in addition to improvements in table1().

Colors in R

visualization

This is a fantastic guide to the colors available in R. I’m not sure who made it but it appears to be very comprehensive. Just click on R Colors for the PDF.

Furniture Version 1.2.0

furniture

packages

We are excited to announce that we are updating our furniture package from 1.0.1 to 1.2.0. We included a new operator (%xt%) for simple cross tabulations that includes a chi-square test and some data from NHANES 2005-2010. Although…

Wash Your Data

furniture

data wrangling

packages

A bit part of being a data scientist, analyst, or anything else data related, is cleaning up your data. There are several things that have to be done to get the data ready. To help you in this process, the washer() function in the…