External IT Accounts

While most of the work in the class will be done using R using publicly available data sets, you will need to set up accounts with the following services. Some of them might require billing enabled and most of them would require 2FA. You are responsible for monitoring them and ensuring that the charges are within your budget.

• StackOverflow for Teams. This is a private StackOverflow team for the class. You can use this to ask questions and troubleshoot issues with your peers. You should have received an invite to join the team. If you haven’t, please let me know.
• Github Education. Free access to Copilot in Rstudio and StackOverflow.
• RStudio Cloud. Free access to RStudio in the cloud. You can use this to share your code and troubleshoot issues with your peers.
• Claude. Free access to Claude 3.5 for R code generation and troubleshooting.
• US Census API. Free access to US Census data.

Others maybe required for specific portions of the course.

Logistics

• This course relies on learning from one another. Your learning depends on how you help others troubleshoot their code and problems they are having. We will use cooperative learning and over-the-shoulder learning techniques. This means you will have cooperatively work together to learn from one another synchronously and asynchronously.

• StackOverflow will be used for troubleshooting. You should have received an invite to join PLAN 672 group on StackOverflow. You can sign in using your Github login. You can also access this from the Canvas website You are required to ask questions using a Minimum Reproducible Example (MRE). To create a MRE, you can also use R studio Cloud. please create an account there (see below).

• Your health and well-being is of paramount importance. You may also be primary care givers and might have substantial demands on your time. You may not be able meet the requirements of the course, for any number of other reasons. You may have differential aptitude and learning styles. Reach out to me early and often, if you need any help. I will deal with these on ad-hoc basis.

• I don’t need any advance notification for intermittent absences. You should make appropriate judgements based on your health and your peers. However, you are responsible for keeping up with the material. Because the materials are posted on-line and in advance, you should be able to work through the code. If you have issues, please use StackOverflow, Office Hours and other resources available to you.

• Canvas will continue to be used for HW, lab and assignment submissions.

• You should expect that the datasets may not be available because of server outages or missing links. We will cross those bridges when we get to them.

Deadlines & Extension Requests

Completed lab session materials are due by the end of lab (8 PM) in Canvas. You only need to submit one lab work for each topic.

Homework assigned for the week is due on the deadline specified in Canvas

If there is a reason to extend the deadline for the entire class, please discuss with me at least a week ahead and make a cogent case.

All labs and homework needs to be submitted as two files 1) a R markdown file (*.Rmd) and 2) html output (*.html) of the Markdown file.

Readings/Resources

The weekly readings are provided as resources and references. You are not required to read all or any the materials in detail. But the readings are useful to learn the material in depth and troubleshoot some issues. In some cases, the software and techniques in the Resources may be dated. Please use the web to adapt and update them.

Tutorials

Often labs are accompanied by tutorials. The tutorials are usually self-contained and self-explanatory. In R, there are multiple ways to achieve the results, each with their own advantages and disadvantages. The tutorials may include different ways of data munging and analysis to expose you to different techniques. It is not implied that one is better than the others, though we all have our own preferences. If in doubt, rely on benchmarking.

Equipment

We will conduct the class in the New East lab and you are expected to use the computers in the lab. Occasionally we will use other campus resources such as virtuallab and research computing.

Drive space that yours is accessible at \\storage.unc.edu\\cas_a\\City_Course\\PLAN672 .

This space is accessible whether you are on campus or off campus (using VPN).

Each of you have a folder where you can store your work. Please do not store any files locally on the computer.

If you are using your own computer, you should have a computer with at least 16 GB of RAM and a 64-bit operating system. You will also need to install R and RStudio. RStudio is an integrated development environment (IDE) for R that makes it easier to write and run R code.

Accessing campus resources requires a VPN and MFA. You can find instructions on how to install and use VPN here. MFA instructions are here.

Grading

While all assignments are posted on this website, they are to be submitted exclusively on Canvas and on time. Please refrain from emailing your submissions to the instructor.

I am going to use a ‘Specification Grading’ in this course. The deliverables are as follows:

Ongoing assignments

• Lab reports to be submitted at the end of the class day for the Topic (due 8 PM on class days). (Individual/Collaborative)

• (Mostly) Bi monthly homework (HW) programming assignments (due 5 pm on specific due dates) (Individual/Collaborative)

Ad-hoc major assignments

• Weekly data visualisation critique (Individual)

• Data collection and story telling assignment (Group)

• Final term project (Individual)

The assignments will be graded on an Satisfactory/Unsatisfactory scale. Satisfactory grade is equivalent to a B+ letter grade. The focus of these assignments is on learning outcomes such as mastery of the material, making innovative connections in the material and on-time submission.

Group assignments will get a single grade for the group.

You will need to achieve Satisfactory grades on at least 70% of the on-going assignments and 2 of the 3 major assignments to achieve a low passing grade (L/C).Fewer than 50 % Satisfactory grades in the ongoing assignments will automatically result in a failing grade regardless of performance in other assignments. In addition to 80% of the on-going assignments, Satisfactory grades should be achieved in all three major assignments for a P/B+. Exceptional performance in the final term project, in addition to Satisfactory grade in 90% of other requirements, will result in H/A grade.

In addition, discretionary points will be awarded for enhancing collective learning. These include, but are not limited to:

• StackOverflow questions and answers
• Group participation and management
• Over the shoulder learning in class

This grading scale will be adjusted if the deliverables change depending course progress. Equivalent grades for undergraduates are assigned accordingly.

Weekly data visualisation critique.

Every week, one of you will lead a discussion about the critique of data visualisation that is found in the wild. Signup sheet is here.

The purpose is to learn from others’ successes and failures in data visualisation. You will need to find an example of data visualisation in the wild (e.g. newspaper, magazine, website, social media). We will spare 20 minutes of in-class time for this. There is no need to submit a written critique, but the deliverable is leading an effective discussion in class. You will be graded on your ability to lead the discussion through pointed questions and your ability to engage the class about the data visualisation and drawing out lessons.

Consent to Share

I am reserving the right to post your ungraded submissions (including HW) in Canvas (restricted to the class) for current and future students, as examples, without comment or recommendation. If you wish to decline to consent, please send me a note. No explanation is necessary and opting out does not affect your grades.

Attendance and Participation

If you don’t attend classes, but submit the requirements on time, there is no penalty. Continuous absences that affect the progress in the course should be discussed with the instructor to figure out remedial action.

E-mail

Canvas messaging system should be the preferred way to communicate with the instructor. Before you email either of us about homework or lab sessions, you should use resources on the web and on Canvas. Google, StackOverflow are your friends.

Asynchronous Communication & Troubleshooting

We will use StackOverflow for asynchronous communication and troubleshooting. We can follow guidelines like these that allow you to get to answers quickly:

• https://stackoverflow.com/help/how-to-ask

• https://codereview.stackexchange.com/help/how-to-ask

We could also use RStudio Cloud for troubleshooting in this course. Think of RStudio Cloud as an instance of RStudio in the cloud where you can share not only your script but also the whole environment. This increases the likelihood that others can replicate your results or troubles. Instructions are located here.

Academic Conduct

I firmly believe in learning from your peers and from others. All homework and lab submissions could benefit from collaborations, however, the submissions are individual. This means that interpreting the data and the results, producing the visualisations, drawing appropriate conclusions from the data, is necessarily individual even when the strategies can be discussed and developed with others. All help including fragments of borrowed or AI generated code, however, should be explicitly acknowledged. Penalties are imposed for non-attribution. In particular, please pay attention to the copyright restrictions and attribution requirements associated with the R-code that you might find elsewhere.

Additional Help

Please set up a time on my calendar to discuss any additional help you may require.

Odum Institute has walk-in consultations and some of them have expertise in R.

Phil McDaniel and Amanda Henley are excellent resources for tracking down geospatial datasets and troubleshooting issues with them.

There are organisations that are devoted to ensuring diversity in the R community. See for example, R-ladies meetup groups and Slack channels. Local groups may or may not be active.

Introductory materials