This repository will house all the materials for the first course in the data science specialization using R, developed for the UO COE.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Term: Fall 2018
- Time: MW, 10-11:20
- Classroom: 115 ED
- Instructor: Daniel Anderson, PhD
- email: (preferred contact method) [email protected]
- phone: 541-346-3317
- office: 175 Lokey
- office hours: Wednesday, 10-12:00 (shared with multiple regression)
This is the first course in a sequence of courses that will eventually lead to a data science in educational research specialization. The first three courses will be offered this fall/winter/spring. All courses will be taught through R, a free and open-source statistical computing environment. This course, in particular, will introduce students to R and RStudio, version control with git and GitHub, dynamic and reproducible workflows with R Markdown, and basic data wrangling and visualization with the tidyverse suite of packages.
Please install R from CRAN, as well as the latest version of RStudio for your machine.
Register for an account on GitHub.
By the end of this course, students should be able to:
- Understand the R package ecosystem (how to find, install, load, and learn about package functionality)
- Read "flat" (i.e., rectangular) datasets into R
- Perform basic data wrangling and transformations in R, using the tidyverse
- Leverage appropriate functions for introductory data science tasks (pipeline)
- "clean up" the dataset using scripts and reproducible workflows
- Use version control with R via GitHub
- Use R Markdown to create reproducible, dynamic reports, including APA manuscripts
- Understand and create different types of data visualizations
All course readings are freely available online or will be provided by the instructor.
- Bryan, J. (2018). Happy git and GitHub for the useR.
- Ismay, C. and Kim, A. Y. (2018). ModernDive: An introduction to statistical and data sciences via R.
- Wickham, H. and Grolemund, G. (2017). R for data science. Sebastopol, CA: O'Reilly.
- Freely available online in the link above. A full-color paper copy is also available from Amazon for (currently) $18.17.
- Aust, F. and Barth, M. (2018). papaja: Reproducible APA manuscripts with R
- Healy, K. (2018) Data visualization: A practical introduction. New Jersey: Princeton University Press.
- Wilke, C. O. (2018). Fundamentals of data visualization.
We have a class subscription to DataCamp. This means all the courses they offer (which is a lot) are available to you. I encourage you to explore the website and play around. There are far more topics covered by DataCamp than we will cover in this course or this five-course specialization.
In addition to having DataCamp as a resource for your own exploration of topics not covered by this class, we will use DataCamp content to supplement lectures, and as a place for you to practice the skills you learn in class.
Our DataCamp website is located here. I suggest bookmarking the page as we will use it throughout the term.
Regardless of how you identify, but particularly if you identify as a woman or gender non-binary, I highly recommend connecting with the R-Ladies group, which is now a global organization. They are an amazing organization of incredibly supportive individuals who routinely share great information. I cannot speak highly enough of them.
Twitter can be a great place to make connections and learn about new things. If you're not on twitter I would recommend creating an account just so you can follow people. Below is a small list of people I would recommend following to help you learn R and data science.
- Mara Averick
- R4DS online learning community
- Hadley Wickham
- Jenny Bryan
- Jesse Maegan
- Claus Wilke
- Maëlle Salmon
- Colin Fay
- David Robinson
- Julia Silge
- Alison Hill
- Chester Ismay
The last two on this list helped design the content for this course and the specialization as a whole, along with [Andrew Bray] (https://andrewpbray.github.io) (and are wonderful, amazing people that happen to also be good twitter follows). This list is not intended to be comprehensive. There are plenty of other great follows out there that I left off. I tried to focus on people who are active and share lots of good resources. Many (most) of these individuals also maintain blogs, which are worth visiting as well (and they will often tweet out links when they put out new blog posts).
The RStudio community is similar to stackoverflow but is generally more friendly, willing to engage in more philosophical and deeper discussions than "how do you do X", and more opinionated about workflows/software design. This last part is important because these discussions will generally be biased toward the RStudio philosophy, and so it's important that you understand that going in. However, this course is also biased toward the RStudio philosophy, as I think it's a good one. The RStudio community is also much less likely to yell at you than users in stackoverflow. That's not to say stackoverflow doesn't have a place---I think it does---but rather that the RStudio community is a better place for beginners (and there's some serious need for some cultural shifts in the stackoverflow community, which I think is changing, but slowly).
| Week | Theme | Topics | Lab | Assignment Assigned | Assignment Due | Reading |
|---|---|---|---|---|---|---|
| 1a (Sep 24) |
Introduction to the course and getting started | Oh the places you'll go! An overview of the course and (some of) the R ecosystem | DC: Orientation DC: Data wrangling Final Project |
MD 2 R4DS 4 |
||
| 1b (Sep 26) |
The Data Science pipeline and getting your computer ready | Getting ready, Intro to R and R Studio | Lab 1 DC: Projects DC: Data visualization |
DC: Orientation DC: Data wrangling Final Project |
MD 1.1 R4DS 6 |
|
| 2a (Oct 1) |
Introduction to data visualization | Data visualization with ggplot2; Grammar of Graphics | Scatterplots with smooths, line plots | Lab 2 DC: Types of visualizations |
Lab 1 DC: Projects DC: Data visualization Final Groups Finalized |
MD 3.1-3.4 R4DS 3.1-3.6 |
| 2b (Oct 3) |
More data vis basics with ggplot2 | histograms, boxplots, barplots | Lab 3 DC: Grouping & summarizing |
Lab 2 DC: Types of visualizations |
MD 3.5-3.9 R4DS 3.7-3.10 Healy 3 |
|
| 3a (Oct 8) |
Basic data wrangling | Data wrangling with dplyr (skimr, ggplot2 with summarized data) R Sudio Projects Importing flat files with rio |
import different data types skimr group_by %>% summarize |
Lab 4 |
Lab 3 DC: Grouping & summarizing |
MD 5.1-5.7 R4DS 8 rio vignette (skim) |
| 3b (Oct 10) |
More data wrangling with dplyr | The lesser known stars of dplyr | Lab 5 DC: R Markdown Ch 1-3 |
Lab 4 | R4DS 5 MD 5.9 |
|
| 4a (Oct 15) |
Dynamic and reproducible documents | Intro to R Markdown Workflow advice (here::here) |
Put your scripts into an Rmd (one complete knitted file) |
Lab 6 | Lab 5 DC: R Markdown Ch 1-3 |
R4DS 27 Bryan 4-8 (9 optional), 13 https://github.com/jennybc/here_here |
| 4b (Oct 17) |
GitHub | Push Rmd + project to GitHub Case study with dplyr & ggplot2 in Rmd/github_document on GitHub |
Lab 7 | Lab 6 | Bryan 19, 23-28 | |
| 5a (Oct 22) |
Dynamic and reproducible APA manuscripts | Intro to the papaja package | Transform your prior Rmd to an APA Doc | Lab 8 | Lab 7 | Aust & Barth: 2 |
| 5b (Oct 24) |
Citations, equations, and formatting concerns | Prettifying your APA doc | Lab 9 DC: Data in the Tidyverse Ch 1-2 |
Lab 8 | Aust & Barth: 3-4 | |
| 6a (Oct 29) |
Tidy data 1 | Taming and tidying data | DC: Data in the Tidyverse Ch 3 | Lab 10 (DC: Data in the Tidyverse Ch 3) | Lab 9 DC: Data in the Tidyverse Ch 1-2 |
R4DS12 Wickham (2014): http://www.jstatsoft.org/v59/i10/paper MD 4.1 - 4.2; 4.4 |
| 6b (Oct 31) |
Taming variables | DC: Data in the Tidyverse Ch 4 | Lab 11 (DC: Data in the Tidyverse Ch 4) DC: Categorical Data Ch 1-3 |
Lab 10 (DC: Data in the Tidyverse Ch 3) | R4DS 14, 16 | |
| 7a (Nov 5) |
Taming unwieldy variables | Taming variables (Part 2) | DC: Categorical Data case study | Lab 12 (DC: Categorical Data Ch 4) | Lab 11 (DC: Data in the Tidyverse Ch 4) Categorical Data DC 1-3 |
R4DS 15 |
| 7b (Nov 7) |
Work day: (import + tame + tidy) | Lab 12 (DC: Categorical Data Ch 4) | ||||
| 8a (Nov 12) |
Exploratory data analysis | Exploratory data analysis / Review dplyr & ggplot2 | EDA case study | Lab 13 | R4DS 7 | |
| 8b (Nov 14) |
Work day | Lab 13 Final Project Data Script Draft |
||||
| 9a (Nov 19) |
Review and work week | RMD slides, review, and more GitHub | Publish slides in GH | Lab 14 | ||
| 9b (Nov 21) |
Looking forward to Course 2 & Work Day | Lab 14 Peer Review Final Project Data Script |
||||
| 10a (Nov 26) |
Final Presentations | Student Presentations 1 | Final project slides (published) | |||
| 10b (Nov 28) |
Student Presentations 2 | |||||
| 11 | No class: Final papers due | Final Project | ||||
As outlined in the table above, most class meetings will include a lab. All labs will also count toward homework assignments, with roughly half the assignment completed during lab, and the other half completed out of class. The DataCamp platform will also be used for additional homework assignments, and the course will conclude with a final project. More detail about each is provided below.
There are 14 labs during the course, which must be submitted to the instructor prior to the start of the following class. These labs will be scored on a "best honest effort" basis, which generally implies zero or full credit (i.e., the assignment was or was not fully completed). However, many of the labs require students complete specific portions before moving on to the next sections. If you find yourself stuck and unable to proceed, please contact the instructor for help rather than submitting incomplete work. Contacting the instructor is part of the "best honest effort", and can result in full credit for an assignment even if the the work is not fully complete. If the assignment is not complete, and the student has not contacted the instructor for help or visited office hours, it is likely to result is a score of zero.
DataCamp is an excellent resource to get additional instruction and/or go more in-depth on any of the topics covered in this class. There are also some topics that we will not have the time to cover in much detail, and DataCamp can be helpful for these topics as well. However, in addition to providing supplemental support, select chapters will be assigned and scored as part of homework, at five points per chapter. A total of 17 chapters will be assigned (note that many of these chapters are very brief).
The final project in this class is a group project, requiring students use a “real world” dataset to write, essentially, a miniature manuscript, including an introduction (paragraph or two), methods, results, and discussion (again, maybe 2-3 paragraphs). Ideally, students would work with a dataset that includes variables they are interested in using beyond just this class; however, if students do not have access to a dataset, the instructor will provide one. Students who do not have access to data should plan to meet with the instructor as soon as possible so a dataset can be provided. Additionally, the dataset must be able to be shared publicly, as the full project will be required to be housed on GitHub and be fully reproducible. If making your data publicly available is a problem for you, please contact the instructor as soon as possible. We can work together to either find a dataset that will work for you, or simulate a dataset that is similar to the data you'd like to work with in reality (and then all your code should work for the real dataset, but you can share the simulated data with your classmates). Students are required to work in groups of 2-4 people. The final assignment is assigned during the first class, and groups must be finalized by the end of Week 2 (at which point students who have not self-selected into groups will be randomly assigned).
A basic outline of the final project is due at the end of Week 5. The outline should include a description of the data to be used, a discussion of what preparatory work will need to be done and how the requirements of the final project will be met. The outline is intended to be low-stakes and is primarily designed to be a means for you to obtain feedback on the feasibility of the project and areas to consider.
At the end of Week 8, you must have a draft of the data preparation complete, including moving the data from its raw to tidy form and a variety of exploratory data visualizations.
Following the submission of the data preparation scripts, you will be assigned to review your peers code. The purpose of this exercise is to learn from each other. Programming is an immensely open-ended enterprise and there are lots of winding paths that all ultimately end up at the same destination. During your peer review, you must note (a) at least three areas of strength, (b) at least one thing you learned from reviewing their script, and (c) at least one and no more than three areas for improvement.
Making your code publicly available can feel daunting. The purpose of this portion of the final project is to help us all learn from each other, not to denigrate. Under no circumstances will negative comments be tolerated. Any comments that could be perceived as negative, and outside the scope of the code, will result in an immediate score of zero. Be constructive in your feedback. Be kind. We are all learning.
Each group will present on their final project during Week 10, which is expected to still be in progress. These presentations are expected to be informal, and emphasize what learning occurred during the project. Specifically, the presentations are to commiserate with each other about the failures and challenges experienced along the way, while also celebrating the successes. Learning R is a difficult task, and we should all take solace knowing that others are struggling along with us. The final presentation should be equal parts “journey” and substantive findings/conclusions/results. Students are expected to present for approximately 10 minutes each (20-40 minutes per group), although the time may change depending on the enrollment of the class.
The purpose of the final project is to allow students an opportunity to demonstrate all the skills they have learned throughout the course. The final project must (a) be a reproducible and dynamic APA manuscript produced with R Markdown, via the {papaja} package and include references to the extant literature; (b) be housed on GitHub, with contributions from all authors obvious; (b) demonstrate moving data from its raw “messy” format to a tidy data format through the R Markdown file, but not in the final document; (c) include at least two exploratory data visualizations, and (d) include at least summary statistics of the data in tables, although fitted models of any sort are an added bonus (not literally, there are not extra points for fitting a model). The points for the final project are broken down as follows:
- Writing (abstract, intro, methods, results, discussion, references): 30 points (25%)
- Document is fully reproducible and housed on GitHub: 25 points (21%)
- Demonstrate use of inline code: 5 points (4%)
- Demonstrate tidying messy data: 30 points (25%)
- Two data visualizations: 10 points each, 20 points total (17%)
- Production of at least one table (of summary statistics or model results): 10 points (8%)
I will investigate the commits made by different authors when evaluating the final project. If it is obvious that one person did not utilize GitHub, and instead added all of their contributions through a single or only a few commits, I will dock points from that individual. There should be numerous commits by each author, and they should be roughly even in terms of contribution activity (which GitHub has metrics to track, both in terms of the number of commits as well as the number of lines modified).
| Lower percent | Lower point range | Grade | Upper point range | Upper percent |
|---|---|---|---|---|
| 0.97 | (422 pts) | A+ | ||
| 0.93 | (405 pts) | A | (422 pts) | 0.97 |
| 0.90 | (392 pts) | A- | (405 pts) | 0.93 |
| 0.87 | (378 pts) | B+ | (392 pts) | 0.90 |
| 0.83 | (361 pts) | B | (378 pts) | 0.87 |
| 0.80 | (348 pts) | B- | (361 pts) | 0.83 |
| 0.77 | (335 pts) | C+ | (348 pts) | 0.80 |
| 0.73 | (318 pts) | C | (335 pts) | 0.77 |
| 0.70 | (304 pts) | C- | (318 pts) | 0.73 |
| F | (304 pts) | 0.70 |
Graduate: 1 credit hour = 40 hours of student engagement (3 credit hours = 120 hours of student engagement)
| Educational activity | Hours student engaged | Explanatory comments (if any): |
|---|---|---|
| Course attendance | 26.5 | 20 meetings, at 80 minutes per meeting |
| Assigned readings | 27 | Weekly readings are assigned, and are expected to take roughly as long to complete as the in-seat time per week. |
| Projects | 30 | Final project, as described above |
| Homeworks | 36.5 | 14 Labs, at approximately two hours per lab spent out of class (28 hours), plus 17 DataCamp chapters, at approximately 0.5 hours per chapter |
| Total hours: | 120 |
Attendance at all class and discussion groups is expected and required, as described above.
Students must contact the instructor in case of illness or emergencies that preclude attending class sessions. Messages can be left on the instructor's voice mail or e-mail at any time of the day or night, prior to class. If no prior arrangements have been made before class time, the absence will be unexcused. Excused absences will involve make-up assignments, with make-up assignment procedures to be discussed in class on the first day.
If you are unable to complete an assignment due to a personal and/or family emergency, you should contact your instructor or discussion leader as soon as possible. On a case-by-case basis, the instructor will determine whether the emergency qualifies as an excused absence.
Students are expected to participate in classroom activities. If use of digital devices or engagement in other non-class activities during class for purposes not regarding classroom activities is critically necessary, the student should engage in these activities while on break, or check with the instructor to arrange for a 10-minute break for unusual circumstances, but only if critically necessary.
It is the policy of the University of Oregon to support and value equity and diversity and to provide inclusive learning environments for all students. To do so requires that we:
- respect the dignity and essential worth of all individuals.
- promote a culture of respect throughout the University community.
- respect the privacy, property, and freedom of others.
- reject bigotry, discrimination, violence, or intimidation of any kind.
- practice personal and academic integrity and expect it from others.
- promote the diversity of opinions, ideas and backgrounds which is the lifeblood of the university.
In this course, class discussions, projects/activities and assignments will challenge students to think critically about and be sensitive to the influence, and intersections, of race, ethnicity, nationality, documentation status, language, religion, gender, socioeconomic background, physical and cognitive ability, sexual orientation, and other cultural identities and experiences. Students will be encouraged to develop or expand their respect and understanding of such differences.
Maintaining an inclusive classroom environment where all students feel able to talk about their cultural identities and experiences, ideas, beliefs, and values will not only be my responsibility, but the responsibility of each class member as well. Behavior that disregards or diminishes another student will not be permitted for any reason. This means that no racist, ableist, transphobic, xenophobic, chauvinistic or otherwise derogatory comments will be allowed. It also means that students must pay attention and listen respectfully to each other’s comments.
Appropriate accommodations will be provided for students with documented disabilities. If you have a documented disability and require accommodation, arrange to meet with the course instructor within the first two weeks of the term. The documentation of your disability must come in writing from the Accessible Education Center in the Office of Academic Advising and Student Services. Disabilities may include (but are not limited to) neurological impairment, orthopedic impairment, traumatic brain injury, visual impairment, chronic medical conditions, emotional/psychological disabilities, hearing impairment, and learning disabilities. For more information on Accessible Education Center, please see http://aec.uoregon.edu
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Several options, both informal and formal, are available to resolve conflicts for students who believe they have been subjected to or have witnessed bias, unfairness, or other improper treatment.
It is important to exhaust the administrative remedies available to you including discussing the conflict with the specific individual, contacting the Department Head, or within the College of Education, you can contact the Associate Dean for Academic Affairs and Equity (Krista Chronister, 346-2415, [email protected]). Outside the College, you can contact:
- UO Bias Response Team: 346-3216 http://bias.uoregon.edu/whatbrt.htm
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