Course Materials

The syllabus for this year will be posted in the summer. Here is a PDF version of 2017 Method Camp's 2017 Syllabus.


Dates of camp: Tuesday September 4th to Friday September 7th

Time structure of camp:

Breakfast: 9:00 AM

Morning session, math: 9:30 - 11:30 AM

Lunch session (qualitative methods): 12:00 - 1:30 PM

Afternoon session, programming : 2:00 - 4:00 PM

Location: 165 Wallace Hall

Office hours: 4:30-6:30 PM every day, 165 Wallace Hall (Note: in the past, students have found it helpful to use office hours as a time to just work on the homework together in one place)

Materials for over the summer and during the camp

This will be updated in the next few weeks, to be finalized in mid-June 2018

Assignments during on-campus portion

Programming is learned best through practice rather than through passive listening, so each day we're going to have a short activity/assignment where you will apply the lessons learned in that morning's programming lecture. The assignment will be due at 9 AM the following day (right before next day's programming lecture) on Blackboard .

For working on the assignment in and out of class, we'll randomly draw pairs at the end of each programming lecture and your pair will turn in one copy as a group. You'll have some time at the end of each programming lecture to work on the activity but may also need to spend some time on it out of class.

Because the focus will be on these worksheets, there are no required DataCamp modules-- instead, we'll list the relevant DataCamp modules as optional references if you want more practice at another time. Likewise, the listed Gill readings can be used as references but aren't required before camp.

We'll be available in office hours and on Piazza to offer help on the assignment.

Schedule for on-campus portion of camp
Day 1

Math: Calculus and its Applications

Lunchtime qualitative speaker: TBA

Computing: Review of Basics

Day 2

Math: Matrix Algebra

Lunchtime qualitative speaker: TBA

Computing: Functions and the Apply family

Day 3

Math: Basic Probability

Lunchtime qualitative speaker: TBA

Computing: Data Cleaning and more Advanced Manipulation

Day 4

Math: Basic Optimization; Open-ended Q & A

Lunchtime qualitative speaker: TBA

Computing: Plotting and Review of Day 1 material; Open-ended Q & A

Summer DataCamp Exercises

Here are DataCamp modules that cover topics in using R that we expect you to be familiar with by the start of camp. Some of you are already very familiar with R, and as a result, we’re not requiring that the modules be completed (in contrast, we are requiring completion of the written assignment). Instead, the modules are there for those new to R to learn the basics of the language so that they have a foundation that we’ll spend a lot of time building upon the week of the camp! R and Rmarkdown each have a learning curve, so as mentioned on the assignment, we encourage you to setup a time to talk on skype or to use Piazza once available to get help. If you’re getting stuck on a module, definitely feel free to just view the solution and take notes to learn from it rather than getting frustrated/giving up.

  1. All modules in Introduction to R: Intro to Basics. Be patient with this one, it starts at a pretty easy pace! It covers Vectors; Matrices; Factors; Data frames; Lists. Expected time: 4 hours 

  2. All modules in Data Visualization with ggplot2 (Part 1). Complete all modules within ggplot2 (1). It covers intro, data, aesthetics, geometrics, and qplot. Expected time: 5 hours. 

    • Note: R has a built-in plot function called plot. But ggplot2 really has much better flexibility and allows you to do much more to visualize data than basic plotting in R. As a result, we’re going to get you started on plotting using the package. We’ll discuss it more in detail on our day focused on plotting/graphics during the methods camp, but the ggplot2 (1) provide a good baseline. The .rmd file we posted for the assignment also has many examples where we plot things for you in ggplot2.

  3. All modules in Reporting with R Markdown. We’re sending you the .rmd we used to create the assignment, so if you’re able to pick up the structure of Rmarkdown well enough from that, there’s no need to complete this module. But if you’re still confused, this module is helpful (section 4 on configuring rmarkdown is less useful). Expected time: 3 hours.

    • Other helpful online resources for Rmarkdown are: Cheatsheet; Markdown Basics.
    • Remember that after you’re done, go to Knit pdf to compile the document for the pdf to turn in. If you get a knitting error, google it! (and then post on piazza if you can’t find anything helpful). Almost everything you will encounter has been encountered by others.
    • If you encounter Errors in LaTeX code: this will prevent you from knitting – you’ll usually have a line number of the issue.
    • If you encounter Errors involving package installation: make sure to call every package you use using the code: library(packagename). If you have code installing the package, you need to remove that before you knit so we recommend using the RStudio console (that box at the bottom) to install any packages using the code: install.packages(“packagename”).

  4. All modules in Data Manipulation in R with dplyr. dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges. We are going all-in on the Tidyverse! Expected time: 3 hours.

  5. Advanced Modules. If the above is review, you can check out: Joining Data with dplyr; ggplot2- Part 2; ggplot2- Part 3.

Reference Materials

Here is Method Camp's 2016 Syllabus.

You can also check out the syllabi for the Princeton Sociology PhD statistics courses, taken by all sociology students in their first year, SOC 500 and SOC 504.