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.

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.

Upcoming Dates

  • November 17, 2017
    Plotting with ggplot
  • November 24, 2017
    (As this is the day after Thanksgiving, we will not have a meeting.)
  • December 1, 2017
    More on ggplot: creating violin plots from count data
    Kevin Ahmaad Jenkins
  • December 8, 2017
    Version control with Git in RStudio
    Jacob Levernier
  • December 15, 2017
    Code hygiene — code style, handling multiple files for a project
    Jacob Levernier

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

  • 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)

 

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