Topic Schedule

We meet on Fridays from 01:00-02:30pm in the Goldstein Electronic Classroom, at the entrance to the Weigle Information Commons (WIC) in the Van Pelt Library.

We welcome individuals of all skill levels. Whether you regularly use R or are brand new to the language and writing code, please join us during any week! If you are a new use-R, we will help you to get set up and learning, regardless of our topic for the week.

With that in mind (i.e., that everyone is welcome every week!), the first week of each month will be a “fundamentals” / “basics” day to talk about R from scratch.

In general, our meetings comprise three types of activity:

  • General consulting, and learning through collaboratively working to answer questions from group members
  • Performing “deep-dives” into a topic, especially through presentations by group member. Presentations are meant to be low-stakes: they can take 10 minutes and include mostly questions, or might take 90 minutes and be full of code to review and share. They can be on topics a group member is already expert in, or a useful motivator for prompting learning more in order to present on it.
  • Writing code to understand current best practices in R development: currently, by working through Garrett Grolemund and Hadley Wickham’s R for Data Science.

Upcoming Dates

  • March 2, 2018
    Beginning of the month: R “fundamentals”
    Papaja for APA-style manuscripts, continued
    Jacob Levernier
  • April 6, 2018
    Note one-time different location: The Seminar Room, inside the Weigle Information Commons (WIC) in the Van Pelt Library

Other dates’ topics coming soon. If a topic is not listed for a given week, we default to talking through small-scale training-project questions.
If you would like to suggest a topic, please find us on our Slack channel (you’ll need to create an account with your U. Penn email address).

Past Dates with specific topics

  • February 23, 2018
    Papaja for APA-style manuscripts, continued (building an example manuscript)
    Jacob Levernier
  • February 16, 2018
    Geospatial mapping
    Ivonne Soto
    Papaja for APA-style manuscripts, continued
    Jacob Levernier
  • February 9, 2018
    Papaja for APA-style manuscripts
    Preparing figures for publication
    Briana Last
    Jacob Levernier
  • January 26, 2018
    Data Wrangling in the Tidyverse
    Pre-reading (if you’re able): R for Data Science, Ch. 5, Data Transformation
    Jacob Levernier
  • January 19, 2018
    An introduction to the Tidyverse
    Jacob Levernier
  • January 5, 2018
    Note location: Goldstein Electronic Classroom, at the entrance to the Weigle Information Commons (WIC) in the Van Pelt Library
    Planning the term; and
    Beginning of the month: R “fundamentals” / “basics”
    Jacob Levernier
  • December 15, 2017
    Part 1: Version control with Git in RStudio
    Part 2: Code hygiene — code style, handling multiple files for a project
    Jacob Levernier
  • December 8, 2017
    More on ggplot: creating violin plots from count data, making boxplots, and more
    Kevin Ahmaad Jenkins
  • December 1, 2017
    Beginning of the month: R “fundamentals” / “basics”
    Jacob Levernier
  • November 24, 2017
    (As this was the day after Thanksgiving, we did not have a meeting.)
  • November 17, 2017
    Plotting with ggplot
    Jacob Levernier
  • November 10, 2017
    Consultations, and Using papaja to render APA-style manuscripts
    Jacob Levernier
  • November 3, 2017
    Basics / R from scratch
    Jacob Levernier
  • October 13, 2017
    Introduction to R, finding duplicates in a dataset
    Jacob Levernier
  • September 29, 2017
    Regression diagnostics (checking model assumptions)
    Steve Brooks
  • September 22, 2017, 10:00-11:30am
    Using Plotly
    Liis Hantsoo
    Output: Blog post
  • September 8, 2017, 10:00-11:30am
    Creating presentations with Beamer through RStudio
    Jacob Levernier
  • Friday, September 1, 2017, 10:00-11:30am (Note new time)
    Introduction to R, Planning the academic term

    Jacob Levernier
  • August 17, 2017
    Consult: Data wrangling
  • August 10, 2017
    Self-led instruction day
  • August 3, 2017
    Consult: Data wrangling
    Jacob Levernier
  • July 27, 2017
    Self-led instruction day
    (Many of us were at the Causal Inference and Big Data Summer Institute!)
  • July 20, 2017
    Consult, Demo: Introductory syntax and RMarkdown
    Steve Brooks, Jacob Levernier
  • July 13, 2017
    Demo: k-Means Clustering
    Kat Placek
  • July 6
    Demo: Principle Components Analyses in R
    Kat Placek
    Output: Blog post
  • June 29th
    Demo: Mixed-effects models
    Steve Brooks
    Output: Blog post
  • June 22, 2017
    Consult: What to know about plots in R before starting plotting (saving, display devices, resolution, file formats)
    Naomi Nevler
  • June 15, 2017
    Demo: Propensity score models
    Patricia Posey
  • June 8, 2017
    Demo: R Syntax: An introduction to if/else statements loops, and apply statements
    Jacob Levernier
    Output: (None)
  • June 2, 2017
    Demo, Tutorial: Regular expressions in R
    Jacob Levernier
    Output: (None)
  • May 25, 2017
    (Note date and time change)
    Demo: Further introduction to R; Resources; What is scripting?; RMarkdown revisited
    Jacob Levernier
    Output: (None)
  • May 17, 2017
    Demo: Introduction to R, Reading regression output
    Jacob Levernier
    Output: (None)
  • May 10, 2017
    Demo, Hackathon: An introduction to Markdown and RMarkdown; Writing APA-style manuscripts using the papaja package
    Jacob Levernier
    Output: (None)
  • May 3, 2017
    Show-and-Tell: Using R in Political Science research
    Patricia Posey
    Description: Patricia’s dissertation examines how financial services such as pawnshops, check-cashings, auto title loans, and payday loans (collectively known as the “fringe economy”) influence the political engagement and political attitudes of racial and ethnic minorities.
    Output: (None)
  • April 26, 2017
    Hackathon: Reshaping data
    Output: Blog post
  • April 19, 2017
    Demo: Descriptive statistics in R
    Steve Brooks
    Output: Blog post
  • April 12, 2017
    Demo: An introduction to ggplot2
    Jacob Levernier
    Resources: Midwest dataset from ggplot2
    Output: Blog post

Not Yet Scheduled

  • (Working through Hadley Wickham’s R for Data Science book)
  • Using Shiny
  • More with papaja (publishing workflow)
  • Alternatives to papaja?
  • Using tableone
  • Updating base R and transferring packages alongside it
  • Geo-mapping with leaflet.js
  • Git
  • Getting started with R — what is this thing?
  • Network mapping with visnetwork
  • Linear mixed effects models
  • Nested models
  • Basic stats analyses:
    • t-test
    • Mann-Whitney U
    • Chi-square
    • AN(C)OVA
  • Spatial Econometrics (analyzing geocoded data) using R
  • Using Tesla (UPenn’s computing cluster)
  • Re-visiting RMarkdown (with the papaja package)
  • Re-visiting ggplot2
  • Using the likert package
  • Simple linear regression (with categorical IVs), and with time variable, and interaction
    • Manually dummy/effects coding categorical variables
  • Merging datasets (on one or multiple variables)
  • Clustering, finding repeatedly mutated genes (with heatmap)
  • Bayesian inferential analyses using `brms` and `stan`