Coding Lab: Level 1

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.

Before Day 1:

• 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.

Materials

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

Class	Videos	Slides & Code	Problem sets	Additional Resources
Pre-work: Why R?	- video part 1: why R? (~ 10 min) - video part 2: a quick introduction to R, Rstudio and tidyverse (~ 14 min)	- slides - slide code	- lab 0 - lab code! - solved lab	Note: Make sure to download the lab code!
Class 1: Reading data files and manipulating data with dplyr	- video 1: reading data (~ 10 min) - video 2: manipulating data with dplyr (~ 18 min)	- slides: reading data - slides: manipulating data with dplyr - slide code: reading data - slide code: manipulating data with dplyr	- fall lab 1 - solutions - basics review	- push lab 1 - solution push lab - FED data from slides (you can download to follow along) - texas data from slides - drug cartel data from slide (you’ll need to download the dta from Dataverse)
Class 2: and Data Types	- video (~ 22 min)	- slides - slide code	- basics review - fall lab 2 - solution	- push lab 2 - solved push lab - push lab Rmd template
Class 3a: If Statements	- video (~ 20 min)	- slides - slide code	- basic review - fall lab 3 updated 10/14 (zip file) - fall lab 3 solutions	- push lab 3 - push lab 3a Rmd template - solved push lab
Class 3b: Grouped analysis with dplyr	- video (~ 13 min)	- slides - slide code	- basic review	- lab 4 - push lab code - push lab 4 Rmd template - solved push lab
Class 4: Iteration	- video (~ 22 min)	- slides - slide code	- basics review - fall lab 4 - fall lab 4 zip - solved lab	- fall lab 4 Rmd template
Class 5: Functions	- video (~ 16 min)	- slides - slide code	- basics review - fall lab 5	- push lab Rmd template - push lab - solved push lab
Bonus Class: Visualizing data with ggplot	- video (~ 18 min)	- slides - slide code	- basic review

Final Project

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.

Questions

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.

Additional Resources

• tidyverse cheetsheets start with dplyr and ggplot
• 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
    • introduction to programming in r
    • working with vectors
    • vectorized functions
  • Data frames
    • working with data frames
    • dplyr to investigate frequency distributions of data
  • Control flow and if statements
    • control flow
  • 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.
  • They’re always adding content, so we probably missed some good ones.