1.1 Meeting Time/Place

ASEC 303, MW 1200-1315

1.2 Office Hours

1.3 Philosophy

Although our primary focus is learning how to use R for geospatial analysis, the course also introduces additional skills and concepts related to reproducible research.

1.4 Caveats/things to consider

Here are some other aspects regarding this course that you may wish to consider before committing:

1. Assignments in this class are problem-oriented, not recipe-based:

   I believe that the best way to learn R (and any human or computational language, for that matter) is to figure out how to use it to solve specific problems. There are usually multiple paths that can be taken to solve a problem, particularly in R, which has a huge number of contributors and over 10,000 packages. Coding recipes that spell out precise steps needed to arrive at a solution will not take you as far towards R fluency, and will prevent you from learning the diversity of this language.

   It can be very rewarding to figure out programming problems (I personally prefer programming to writing papers), but it can often be frustrating. Feeling frustrated is quite normal, and please don’t take it as a sign that you are not good at programming, or that it is not for you.

2. The order in which material is introduced will occasionally be non-linear:

   Primarily because we are introducing reproducibility concepts up front in this class, which entails learning about some things that people might ordinarily get around to after they know a bit of R code. However, I believe that this order of things will enable you to learn R (or any other programming language) more rapidly. It might even make it more fun.

3. This is a flipped class:

   Materials and problems are expected to be done before class. This is particularly true this semester, when we are working more than half the time in an online mode. There will be some lecturing on key concepts, but class is intended to function more like a lab (and the lab period is intended for students seeking extra help), in which you work through practical problems, clarify concepts that are unclear, or present your work to your peers.

4. I am not a computer scientist:

   Full disclosure: I am self-taught programmer. That means that the way I teach this course might differ from the way a formally trained computer scientist would teach it. I don’t know if that’s the case, but those of you have taken computer science courses might notice some differences.

1.5 Required Texts, Reading, and Assignments

There is no required textbook for this course. There is a huge amount of well-developed R material that is freely available on the web. We draw on and those resources for this class and integrate them into our own content (citing/linking to show where they come from).

Readings and assignments should be completed before the class they are listed under. This is key to learning the language, as it is difficult to learn programming by just listening to lectures.

Practical assignments. During the first two units, there will be a total of 5 assignments (see links within the individual modules). The sixth assignment is your project overview. The week assignments are due is listed on the syllabus, to be submitted by midnight on the Friday of that week. You will undertake and submit your work through github repositories that exist under the Agricultural Impacts Research Group’s github organization, where there is a team setup for this class. You will need to join GitHub (it’s free!) to be able to submit assignments, as we will need your github name to add you to the team. You will manage your individual assignments under private personal repositories that will be listed under your own individual sub-team.

Projects Each student will be required to undertake a final project. Please see the projects page for more detail.

1.6 Style

There are many ways to write code and get the results you want. However, not all ways of writing code are equal. Some code is messy and hard to read. Other code is organized, clean, and easy to read. The latter is what we are aiming for, as it helps to foster reproducibility. In this class, we will follow Hadley Wickham’s style guide. Please study it.

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1.7 Assessment and engaged time

Your progress in this class will be assessed as follows:

Grading will be based on the rubrics found under the Assessment vignette.

Courses at Clark are worth 4 credit hours, which equates to 180 hours of engaged academic time. The breakdown of that time is estimated to be:

1.8 Expectations

Since class is flipped, this is a time for questions and discussion, between us and you, and often between yourselves. However, please keep any conversations low and necessary to the task at hand if they are one-on-one.

Class attendance is expected. It is the primary time in which to get help on understanding reading materials and assignments (see next section on Communications). Late assignments (including presentations and final report) are not accepted, barring any emergency or reasonable conflicts that prevent on-time submissions.

For assignments in the first two units, students can work together to figure out coding problems and to understand the material, but final assignments should reflect each student’s own work and coding effort (i.e. not copying code from someone else). For final projects, many, if not most, of the projects will be team-based (2-3 per team, depending on the nature of the assignment) on some if not all of the available topics.

We will follow the University’s policies on plagiarism and cheating. Please familiarize yourself with the University’s policy on academic integrity, particularly section I.

1.9 Title IX

Clark University and its faculty are committed to creating a safe and open learning environment for all students. Clark University encourages all members of the community to seek support and report incidents of sexual harassment to the Title IX office (titleix@clarku.edu). If you or someone you know has experienced any sexual harassment, including sexual assault, dating or domestic violence, or stalking, help and support is available.

Please be aware that all Clark University faculty and teaching assistants are considered responsible employees, which means that if you tell me about a situation involving the aforementioned offenses, I must share that information with the Title IX Coordinator, Brittany Rende (titleix@clarku.edu). Although I have to make that notification, you will, for the most part, control how your case will be handled, including whether or not you wish to pursue a formal complaint. Our goal is to make sure you are aware of the range of options available to you and have access to the resources you need.

If you wish to speak to a confidential resource who does not have this reporting responsibility, you can contact Clark’s Center for Counseling and Professional Growth (508-793-7678), Clark’s Health Center (508-793-7467), or confidential resource providers on campus: Prof. Stewart (als.confidential@clarku.edu), Prof. Palm Reed (kpr.confidential@clarku.edu), and Prof. Cordova (jvc.confidential@clarku.edu).

2.1 Unit 1. An introduction to R and related reproducibility skills

In this first part of the course, we will learn the basics of working with R, starting with non-spatial data. We will also learn some additional skills that foster reproducibility, which can be loosely defined as the ability for you and others to easily repeat the steps of your analysis. including the use of git and github, how to create an R package, and the use of Rmarkdown to document and present your analyses.

The detailed readings and assignments for each week and day can be found in the accompanying Unit 1 vignette, as well as the overall learning goals for the unit.

2.2 Unit 2. Handling and analyzing spatial data with R

In this part of the course we will start to learn to use R as a GIS. The detailed syllabus can be found in the Unit 2 vignette.

2.3 Unit 3. Projects

3.1 Books etc

• The R Inferno
• Efficient R Programming
• Swirl Stats and the swirl package

3.2 The intertubes