2020
The Ups and Downs of Communicating Complex Statistics
In the age of “big data” there is an information overload. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. Accordingly, the effective communication of statistical concepts to diverse audiences is currently an education and public health priority. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content, specifically addressing how to help the general public read past headlines to the actual evidence, or lack there of. We will discuss engaging with the public via organizations such as TED Ed - focusing both best practices and lessons learned.
Causal Inference in R
In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.
Best Practices for Teaching R A Randomized Controlled Trial
We are interested in studying best practices for introducing students in statistics or data science to the programming language R. The “tidyverse” is a suite of R packages created to help with common statistics and data science tasks that follow a consistent philosophy. We have created two sets of online learning modules, one that introduces tidyverse concepts first and then dives into idiosyncrasies of R as a programming language, the second that takes a more traditional approach, first introducing R broadly and then following with an introduction to a particular suite of packages, the tidyverse. We have created a randomized study to examine whether the order certain concepts are introduced impacts whether learning objectives are met and/or how engaged students are with the material. This talk will focus on the mechanics of this study: how it was designed, how we enrolled participants, and how we evaluated outcomes.
Panel: Engaging Students during the COVID-19 Health Crisis
In this session we will discuss three different aspects of engaging students during the COVID-19 health crisis:
How do we engage students with a sensitive topic like COVID-19?
How do we engage students with COVID-19 data?
How do we engage students in a virtual environment?
Tools for Analyzing R code the Tidy Way
With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted. In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data analysis pipeline that contribute to the variability in results, we have created two R packages: matahari and tidycode. These packages build on methods created for natural language processing; rather than allowing for the processing of natural language, we focus on R code as the substrate of interest. The matahari package facilitates the logging of everything that is typed in the R console or in an R script in a tidy data frame. The tidycode package contains tools to allow for analyzing R calls in a tidy manner. We demonstrate the utility of these packages as well as walk through two examples.
Using RStudio Cloud in the Classroom
This workshop covers set up, implementation, and tips and tricks for integrating RStudio Cloud in your classroom. RStudio Cloud is a great way to incorporate R in the classroom without the hassle of installation and complex set up.
2019
Challenges in Augmenting Randomized Trials with Observational Health Records
This talk addresses challenges with making health record data and clinical trial data compatible. The data collected in trials is collected regularly and in an organized way, while data from health records is messier and more haphazard. A clinical trial has a clear start and endpoint, while health record data is collected continuously. Additionally, clinical trial participants may be healthier than patients we see in health records. Covariates are defined in advance for a trial, but must be predicted or imputed from the health record. In this talk I will discuss some of the challenges we have encountered in trying to integrate trial data with observational health records to improve power and design new trials.
There and Back Again, a Data Scientist’s Tale
We are in an exciting new age with access to an overwhelming amount of data and information. This talk will focus on three areas that have become increasingly important as a result. First, we will discuss the importance of reproducibility during this age of information overload. As quantitatively minded people, we are being pushed to innovate and develop best practices for reproducibility. We will talk a bit about tools that make this possible and the next steps in this important area. We will then discuss new opportunities for developing innovative methods, particularly in the observational research space. This portion will include a brief introduction to causal inference for the data scientist. Finally, we will examine the importance of well-developed communication skills for quantitatively savvy people. These aspects will be discussed in the context of my winding path to data science, speckled with some advice and lessons learned.
Data Visualizations with ggplot2
“If you’re navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it’s a relief. It’s like coming across a clearing in the jungle.” – David McCandless.
The ability to create polished, factual, and easily-understood data visualizations is a crucial skill for the modern statistician. Visualizations aid with all steps of the data analysis pipeline, from exploratory data analysis to effectively communicating results to a broad audience. This tutorial will first cover best practices in data visualization. We will then dive into a hands on experience building intuitive and elegant graphics using R with the ggplot2 package, a system for creating visualizations based on The Grammar of Graphics.