From Data to Manuscript in R

Winter 2022: CHDV 20550/30550, MACS 30550, MAPS 30550, PSYC 30550

Tuesdays and Thursdays 09.30-10.50 Chicago time

Office hours: Tuesdays 11.00–12.30 in Rosenwald 318A or by appointment

Instructor: Marisa Casillas (mcasillas@uchicago.edu)

This course tackles the basic skills needed to build an integrated research report with the R programming language. We will cover every step from data to manuscript including: Using R’s libraries to clean up and re-format messy datasets, preparing data sets for analysis, running statistical tools, generating clear and attractive figures and tables, and knitting those bits of code together with your manuscript writing. The result will be a reproducible, open-science friendly report that you can easily update after finishing data collection or receiving comments from readers. Never copy-paste your way through a table again! The R universe is large, so this course will focus specifically on: The core R libraries, the tidyverse library, and R Markdown. Students will also learn about the use of GitHub for version control.

Course materials:

Any other course materials will be available online on Canvas and the ChatterLab course webpage.

Grading

Students enrolled in this course will be graded on the following basis:

ComponentUndergraduates
Mini assignments60%
Participation20%
Scientific report20%

Mini assignments

There is a mini assignment for each lecture. Each assignment will involve working with your own data, data provided for that assignment, or both and is designed to give you hands-on practice with using R. Assignments typically begin in class and may be completed after class (each one is highlighted in orange) on a pass/fail basis. Students must push their completed assignment to the appropriate GitHub repository, tagging Dr. Casillas with the assignment number so that she receives a notification.

When are the assignments due? Assignments listed as an ‘in-class activity’ are due before the start of the next class. Assignments listed as ‘pre-class preparation’ are due before that class begins.

Need help? We will do much of this work in class, so ask your questions while we’re together. Each student will be assigned to a support group of other students to help answer questions (in addition to access to help from Dr. Casillas during office hours). Finally, you’re strongly encouraged to start developing your search skills when it comes to debugging your code (look for advice and solutions from others who have faced similar problems on sites like stackoverflow and github)!

Participation

Students are generally expected to come to class prepared to practice using R together. That means: come with your charged laptop, prepared with any data you are using and any installed software that is required (see the ‘preparation’ note for each class). Be prepared to share your screen with your support group and with Dr. Casillas. That said, please stay in touch about your limitations regarding in-person participation; Please do not come to class if you are feeling at all ill. Students earn participation credit through their attendance and participation in their support group and discussion of course content in office hours.

Scientific report

Your final assignment will be a scientific report in R Markdown that: is developed via a GitHub repository; includes data read-in, pre-processing, and analysis; includes at least one figure and one table with captions and in-text references; includes citations; and includes at least 1500 words of the manuscript in at least four subsections (Introductions, Methods, Results, Discussion; unless otherwise agreed with Dr. Casillas). Each student must come to office hours to negotiate their manuscript plan with Dr. Casillas by the end of Week 7. Grading will be contingent on the completion of that agreed-upon plan for each individual student.

Course schedule

Module 1: GitHub basics

Tuesday, 11 January 2022 (1.1): Part 1

Pre-class preparation

None!

In-class activities

  • Presentation: Syllabus and course goals (and non-goals)
  • Presentation: Introduction to Git and GitHub [slides]
  • Laptops out:

Thursday, 13 January 2022 (1.2): Part 2

Pre-class preparation

  • Read the dataset selection guidelines for the class
  • Select a dataset for you to use in the class. Contact Dr. Casillas ASAP if you are unsure which to choose.

In-class activities

  • Presentation: READMEs and dataset selection [slides]
  • Share: What dataset will you be working with?
  • Laptops out:
    • Push a README briefly describing the study/dataset you will be working with. Imagine this from the perpsective of someone visiting your repository who hopes to review your code and work with your data. NOTE: If you intend to make this repository public, never push any private data. It will be stored in your history even if you delete it! Make at least three commits during this process.
    • Manage repository settings

Module 2: R and tidyverse basics

Tuesday, 18 January 2022 (2.1): Part 1

Pre-class preparation

  • All students: Confirm that your dataset is okay for the course with Dr. Casillas (note: this should be done via email, not GitHub)
  • Install R and RStudio (if you’re unsure how to do this, check these Appendices A and B from Garrett Grolemund’s Hands-On Programming with R)
  • Read about R basics (Ch 2), data types (Ch 3), and programming basics (especially 7.3, 7.4, and 7.5) in Douglas et al.’s An Introduction to R; if you are brand new to R, try to follow along with the example code in your RStudio console

In-class activities

  • Presentation: R, RStudio, packages, and a first glimpse at tidyverse [slides]
  • Laptops out:
    • Confirm/troubleshoot installations
    • Hello world demo and code commenting

Thursday, 20 January 2022 (2.2): Part 2

Pre-class preparation

  • Install the tidyverse package in RStudio (overview here)
  • Prepare and push an anonymous version of your dataset for use in class
  • Read about tibbles, data import, and pipes in Hadley Wickham’s R for Data Science

In-class activities

  • Presentation: Tibble and readr basics [slides]
  • Laptops out:
    • Read in and write out demo
    • Read in and write out your own dataset; troubleshoot in support groups

Tuesday, 25 January 2022 (3.1): Part 3

Pre-class preparation

  • Read about dplyr and tidyr in Hadley Wickham’s R for Data Science

In-class activities

[recording]

  • Presentation: Basic data manipulation and preparation functions [slides]
  • Laptops out:
    • dplyr and tidyr demo
    • Create a mockup of your desired data format (push as an image, sample spreadsheet file, and README description)

Thursday, 27 January 2022 (3.2): Part 4

Pre-class preparation

  • Review the cheatsheets for tibble, readr, dplyr, and tidyr
  • Prepare any supplementary data you need for your desired format

In-class activities

[recording]

  • Laptops out:
    • Data preparation demo [slides]
    • Plan how you will prep your own dataset and begin prepping

Tuesday, 01 February 2022 (4.1): Part 5

Pre-class preparation

  • Continue working on your data prep (we will continue in class; finishing this task is today’s assignment)

In-class activities

[no recording today—in class work session]

  • Laptops out:
    • Finish your data preparation; troubleshoot in support groups [slides]

Module 3: Plotting your data

Thursday, 03 February 2022 (4.2): Part 1

Pre-class preparation

  • Read about ggplot in Hadley Wickham’s R for Data Science
  • Finish implementing your data preparation if you didn’t manage to complete that in class

In-class activities

[recording]

  • Presentation: Introduction to ggplot [slides]
  • Laptops out:
    • ggplot one-variable demo
    • exercise: recreate this plot

Tuesday, 08 February 2022 (5.1): Part 2

Pre-class preparation

  • Imagine two plots you would like to see of your data

In-class activities

[recording]

  • Presentation: Plotting multiple variables at once [slides]
  • Laptops out:
    • ggplot two- and three-variable demo
    • mini data cleaning/reformatting demo
    • exercise: recreate this plot

Thursday, 10 February 2022 (5.2): Part 3

Pre-class preparation

  • Create initial drafts of your planned plots

In-class activities

[recording]

  • Presentation: Customizing: Axes, labels, facets, colors, shapes, and themes [slides]
  • Laptops out:
    • ggplot customization demo
    • exercise: recreate this plot

Tuesday, 15 February 2022 (6.1): Part 4

Pre-class preparation

  • Review/adjust the initial drafts of your planned plots
  • Review the cheatsheet for ggplot

In-class activities

[no recording today—in class work session]

  • Presentation: Share planned plots with each other [slides]
  • Laptops out:
    • create your plot!

Module 4: R Markdown and Papaja basics

(“papaja” is a package for “Preparing APA Journal Articles”)

Thursday, 17 February 2022 (6.2): Part 1

Pre-class preparation

  • Go through your existing code and make sure you have informative comments
  • Install the papaja package in Rstudio (follow the instructions here)
  • Read about the basics of RMarkdown in Hadley Wickham’s R for Data Science; for a more detailed introduction, check out chapter 2 in Yihui Xie et al.’s R Markdown: The Definitive Guide

In-class activities

[recording]

  • Presentation: Introduction to RMarkdown, code chunks, formatting, and knitting [slides]
  • Laptops out:
    • create and compare rmd files
    • insert comments into the code chunks and in the text

Tuesday, 22 February 2022 (7.1): Part 2

Pre-class preparation

  • Review markdown syntax for text formatting (see the rightmost column on this cheatsheet)

In-class activities

[recording]

  • Presentation: Introduction to Papaja [slides]
  • Laptops out:
    • Initialize a papaja .rmd file
    • Insert your author information, libraries, and read-in, prep, and plotting code chunks
    • Insert a footnote

Thursday, 24 February 2022 (7.2): Part 3

Pre-class preparation

REMINDER: You must meet with Marisa by the end of this week (i.e., before Friday, February 25th at 17.00 Chicago time) to negotiate your plan for the scientific report assignment, which is due on Tuesday of Finals Week

In-class activities

[recording]

  • Presentation: Tables, figures, images, captions, and chunk references [slides]
  • Laptops out:
    • group demo based on chapters 9, 10, and 12 of Nicholas Tierney’s RMarkdown for Scientists
    • Add one table, one figure, and one image to your rmd, each with captions and a chunk reference in the text.

Tuesday, 01 March 2022 (8.1): Part 4

Pre-class preparation

  • Insert real text/lorem ipsum-style text/outline into your rmd, including references to your code chunks, and see whether it knits to the desired style
  • Brainstorm what else you would like to change about report and how you could implement that change; add as a commented-out ’to-do’ list in your document

In-class activities

[recording]

  • Presentation: Sourcing code and organizing rmds for others’ eyes [slides]
  • Laptops out:
    • Brainstorm with group about how to organize code
    • Come up with a plan for reorganizing your code and adding comments to maximize re-use potential (even if this is just for your future self)

Module 5: Bibtex and in-line references

Thursday, 03 March 2022 (8.2): Part 1

Pre-class preparation

  • Implement your code reorganization from the last class; ensure that it knits

In-class activities

[recording]

  • Presentation: BibTeX part 1 [slides]
  • Laptops out:
    • Create a .bib file with at least 5 of your references

Tuesday, 08 March 2022 (9.1): Part 2

Pre-class preparation

In-class activities

[recording]

  • Presentation: BibTeX part 2 [slides]
  • Laptops out:
    • Citation styles demo
    • Add non-cited references
    • Add at least 10 in-text bibliographic references into your rmd

Thursday, 10 March 2022 (9.2): Part 3

Pre-class preparation

None!

In-class activities

  • Presentation: In-line code [slides]
  • Laptops out:
    • in-line reference addition demo
    • add two in-line numerical references, one figure reference, and one table reference to your rmd. note this means you need to have code chunks that generate numbers, a figure, and a table to refer to!

Final deadline (Tuesday, 15 March 2022)

Scientific report due

Contact

Email: mcasillas@uchicago.edu

Office: Rosenwald 318A

Office hours: Tuesdays 11.00–12.30 in Rosenwald 318A or by appointment

If you require any accommodations for this course, as soon as possible please provide your instructor with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) so that you may discuss with him/her how your accommodations may be implemented in this course. The University of Chicago is committed to ensuring the full participation of all students in its programs. If you have a documented disability (or think you may have a disability) and, as a result, need a reasonable accommodation to participate in class, complete course requirements, or benefit from the University’s programs or services, you are encouraged to contact Student Disability Services as soon as possible. To receive reasonable accommodation, you must be appropriately registered with Student Disability Services. Please contact the office at 773-834-4469/TTY 773-795-1186 or gmoorehead@uchicago.edu, or visit the website at disabilities.uchicago.edu. Student Disability Services is located in Room 233 in the Administration Building located at 5801 S. Ellis Avenue.