Fall 2022 - STAT 261 D100

Laboratory for Introductory R for Data Science (1)

Class Number: 4700

Delivery Method: In Person


  • Course Times + Location:

    Tu 4:30 PM – 5:20 PM
    AQ 3148.2, Burnaby

  • Prerequisites:

    One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 260.



A hands-on application of the R programming language for data science. Using the R concepts covered in STAT 260, students will explore (visualize, transform, and summarize) and wrangle (read and tidy) data. No prior computer programming experience required. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.


STAT 261 Labs will start on Tuesday Sept 13 and will run until Thursday Dec 1. There will not be any Labs on Wednesday Sept 7, Thursday Sept 8, nor Tuesday Dec 6.

Week Number STAT 260 STAT 261
1 Getting started: installing R, RStudio, the tidyverse and other packages, RStudio projects, RMarkdown files and basics In-class exercises for setting up R 
2 and 3 Exploring Data: visualisation Using ggplot to create plots of different types of data 
4 Exploring Data: transformation and summary statistics Plotting transformed data (e.g., bar plots, smoothed functions)
5 Data Wrangling: data frames and tibbles, importing data Reading in data in different formats
6 Data Wrangling: tidy data Cleaning a messy data set
7 Data Wrangling: relational data Linking multiple data sets
8 Data Wrangling: strings Handling text data
9 Data Wrangling: factors, dates and times Handling factors and date and time data
10 Programming: functions Writing functions to minimize code length, prevent bugs, and improve readability
11 Programming: vectors Types of vectors and incorporating vectors in functions
12 Programming: vectors For loops
13 Summary Summary


  • Lab Quizzes 100%


Above grading is subject to change.



R for Data Science, by Garrett Grolemund and Hadley Wickham, available online for free at https://r4ds.had.co.nz/


Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

Students with Disabilities:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or caladmin@sfu.ca.  

Tutor Requests:
Students looking for a tutor should visit https://www.sfu.ca/stat-actsci/all-students/other-resources/tutoring.html. We accept no responsibility for the consequences of any actions taken related to tutors.

Registrar Notes:


SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html