Topic Schedule

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.

Upcoming Dates

We will be switching to a new time, Fridays between 10:00 and 11:30am, starting on September 1st, 2017.

  • September 15th, 11:00am-12:00pm (Later time due to room scheduling conflict)
  • September 22nd, 10:00-11:30am
    Using Plotly
    Liis Hantsoo
  • September 29th
    Regression diagnostics (checking model assumptions)
    Steve Brooks

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

  • September 8th, 10:00-11:30am
    Creating presentations with Beamer through RStudio
    Jacob Levernier
  • Friday, September 1st, 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
  • Shiny
  • Using Tesla (UPenn’s computing cluster)
  • Re-visiting RMarkdown (with the papaja package)
  • Re-visiting ggplot2
  • Using the likert package