Topic Schedule

We meet on Wednesdays from 11:00am-12:00pm in the Weigle Information Commons (WIC) Seminar Room, on the first floor of the Van Pelt-Dietrich Library Center.

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.

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

  • … (We will be meeting each week, but no specific topic is planned yet)
  • (Beginning August 29th, 2018, we will meet Wednesdays from 11:00am-12:00pm in the Weigle Information Commons (WIC) Seminar Room, on the first floor of the Van Pelt-Dietrich Library Center)

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).

Selected Past Dates with specific topics

  • June 28th, 2018
    R from Basics to Using Shiny, Continued
  • June 21st, 2018
    R from Basics to Using Shiny, Continued
  • June 14th, 2018
    R from Basics to Using Shiny
  • June 7th, 2018
    R for Data Science: Chapter 19, Functions
  • April 6, 2018
    Using RMarkdown to create screen-reader-ready HTML from a dataset on accessible spaces on campus
    Alice McGrath, Jacob Levernier
    Note one-time different location: The Seminar Room, inside the Weigle Information Commons (WIC) in the Van Pelt Library
  • March 2, 2018
    Beginning of the month: R “fundamentals”
     Papaja for APA-style manuscripts, continued
    Jacob Levernier
  • 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`