This website (harris-coding-lab.github.io) contains the content for the Harris School's Accelerated Coding Lab. The workshops aim to introduce R programming concepts with a focus on preparing and analyzing data, and conducting statistical simulations. We will cover how to read data into R, manipulate data with a suite of tools from the tidyverse
package dplyr
. We will also discuss some basic programming concepts including data types, operators, control flow with if statements and for loops, as well as how to write your own functions.
-
We ask that Coding Lab attendees have the latest stable versions of - R and RStudio pre-installed on their local machine. We have instructions for Mac and Windows users in this google doc. We can help you if your stuck, and you can email the amazing staff of Harris IT at hsit-servicedesk@uchicago.edu as well.
-
Complete the "Pre-work". Watch the intro videos and complete the lab 0. (see below for links.)
-
Watch videos for Class 1.
Note: the lab material is subject to change.
Links to materials for each week's workshop will be posted here as provided. For each class, watch the video. Then, go to lab and attempt the lab
Your final project is quite simple. You will pick a data set that speaks to you and try to uncover something interesting which you will visualize in a plot. You will also compute some summary statistics that you will show in a summary table. We'll provide feedback on your submission. Click on the link for details.
Please send questions via the Q and A google form at least 1 hour prior to Q and A sessions. We will address your questions either in lectures with Ari, in TA sessions or in written form.
Link to QA Slides Rmd Note: these are not polished and may contain typos or other ambiguities.
- tidyverse cheetsheets start with
dplyr
andggplot
- R for Data Science: free online book with clear explanations of many
tidyverse
functions, the book to read on data analysis with R - DataQuest.io: online modules about specific programming concepts, access provided by Harris. For students who would like additional guided practice we recommend:
- Vectors
- Data frames
- Control flow and if statements
- Functions
- writing custom functions
- working with functionals (includes discussion of map)
- Extensions
- random sampling with
sample()
- basic string manipulation
gather()
and correlations- "step 2" is all about
ggplot
and potentially useful.
- "step 2" is all about
- random sampling with
- They're always adding content, so we probably missed some good ones.