This project accompanies the course
"R2 - Data projects with R and Github"
at the Dr. Eberle Centre for Digital Competencies at the University of Tübingen.
All tutorials are summarized within the
During the course, you have to formulate a data wrangling project. That is, you should name or provide a data set, say how the data should be (re)structured and set some visualization goals. You can use a data set you are working on (note, you might have to make it anonymous to share it) or a data set freely available online, from a publication, whatever. I strongly suggest "dirty" data that has to be cleaned up and reformatted! Data cleanup, transformation and extension should be one (big) part of your project!
The formulated projects are the set of exercises you and your fellow students will pick from during the rest of the course, which is discussed below. But first, some details concerning project definition.
Before you can define a project you need some data! In order to select something, you might want to "reach high"! That is, think about something you would like to know or see and what data might be needed for that. Don't think in terms of "I know how to do" but more "I would like to see" (like "A BOSS")! Given an idea, start looking for data sets that might help you to provide the information for your idea. Either you find something useful, or you might change your idea while looking for and investigating available data.
Best are data set you are working on anyway or that are connected to your field of interest, such data makes most sense to you and you are most creative about possible analyses. Or something you have discussed in some other course or project. It would be best, if the data is already in some table form but not tidy, i.e. there is still need for some (extensive) data cleaning, formatting, ...
You might not have a data set at hand, so check out open data repositories, websites, etc. Some open data repositories or search engines are listed at
- https://libguides.graduateinstitute.ch
- https://handsondataviz.org
- https://github.com/awesomedata
Note down where you got your data from, since you will later have to provide some details about your data!
Eventually: the more ugly the data the better! 😜 Don't try to be nice but provide what you have. Reality is neither nice nor without errors, bugs and misformatted data... Let's get used to it!
online vs. local
If the data set is online and available for download without registration or user accounts, you can directly link it. This is often the case for data from databases or supplements from articles.
If user credentials are needed to access the data, please
- ensure you are allowed to share it! (licence? this is a public github repository!)
- download the data to your project folder
- link the local file in your data description (to make people aware it is accessible)
- add a link where you got the data from
If the data is large (>50MB per file),
- you cannot upload it to github (file size limits)
- you should shrink it
Next, formulate some rough idea what you would like to see. If you want to (re)produce a plot you have seen, store the image. Or just draw a sketch by hand of how it should look like and make a photo. Anything to transport your idea is fine.
Try to think of something "non-standard"...
Double check that you think the data set you picked provides (somehow) all information needed to draw your plot of interest.
Write an R Markdown file project-description.Rmd to
- introduce shortly the general topic of your task (to put it in context)
- introduce the data set(s), it's origin and important information of what-is-what, etc.
- e.g. show the first line of the data set/file and, if needed, explain the column names (or tell where to find such information)
- define data manipulation goals if needed (also fine if this is the main focus of the project!)
- define the visualization goal(s) and possible intermediates
It is fine to be vague at some points but you should formulate a clear goal and roadmap.
The output format should be "normal" Markdown! To this end you have to
- use
output: md_documentin the Rmd header!
In order to submit your project proposal, you have to upload it to GitHub as part of this project! To this end:
- (if not done already) create a subfolder with your user account name in the
Projectfolder - copy the following files to the folder:
- your
project-description.Rmdfile from above - all files linked to, loaded or referenced within the project description (data sets, images, ...)
- your
- knit the file to Markdown (
.md) output (you have to use in the headeroutput: md_document) Pullthe recent project version from the GitHub repositoryCommitall new files to git versioning- R-Markdown, images,
.mdoutput file, ...
- R-Markdown, images,
Pushyour changes to GitHub- Check online if your Markdown file is listed on GitHub
- Change this
README.mdfile and add your project to the following list ofAvailable projectsbelow- to this end, you have to put a relative link to the
.mdpage, see example link
- to this end, you have to put a relative link to the
CommitandPushyour changes to GitHub- wait a minute (to get things published automatically)
- check the project website if your project draft is listed, linked and correctly rendered
Example:
- Martin (martin-raden) - Example project
Current projects:
- Luis (kickbones1) - Phone Usage Time
- Silin (Selena-qian) - Silin's project
- Charlotte (janaelodie) - Charlottes project
- Gulmira (GulmiraKairr) - Gulmira's project
- Dennis (shad-lad-flad) - Dennis' project
- Yuguang (Yuguang Chen) - Yuguang's project
- Yue (Yue_Z9) - Yue's project
- Jungi (JungiHong) - Volleyball Passing
- Beilei (Beilei-Wang) - Income distributions
- Celine (celineony) - Behavorial data
- Felix (felix-gogollok) Historical data analysis regarding genocides
- Manuel (ManuDoerrich) - Nutrition data
- Nicolas (NicolasDelgadoL) - social media & personality
- Sun (MoonSunKyung) - [Shopping Trends by Age group and Sex](Projects/SunKyoung Moon/project_description.md)
At the end of Phase 1 you will have a better understanding of
- How to write stuff in R Markdown
- How to produce presentable output from it (here "normal" Markdown)
- How to get things into version control in GitHub
- See the integration of "normal" Markdown in GitHub Pages
To ensure the drafted projects are understandable and doable, we will do a peer reviewing. To this end, you will get assigned to two projects to give feedback for them. Review comments should be done via GitHub issues, where you can also discuss you ideas and suggestions with the respective project owner.
For each project draft, we will assign two reviewers at random. The reviewer assignments are as follows:
- YOU will review projects of: the two subsequent students in the list of Available projects
- if you are the last in the list, you will review the first two students, etc.
- use respective GitHub user names for contact
Each reviewer is supposed to
- read the respective project draft on GitHub Pages
- open an Issue on GitHub within the course
- provide constructive feedback w.r.t.
- understandability
- missing information
- helpful data example views, etc.
- possible extensions or other ideas in the scope of the project and data
- "ping" the project owner by referencing his/her user name with an "@", like
Hi @martin-raden, what do you think about my comments!
Each project owner is supposed to
- carefully check the reviews for his/her project
- respond to the reviews (verbosely! not just "Yes, sounds good!")
- you can also argument why you don't want to do this or that etc..
- it is YOUR rebuttal of the review!
DON'T CHANGE THE PROJECT DRAFT SO FAR!!! (Since this will interfere with the second review!)
At the end of Phase 2 you will
- Get used to the Issue-based communication on GitHub
- Practice your "constructive criticism" skills
- Get and discuss feedback on your own project draft
Now it is time to rework your project draft in the light of the received reviews and the project drafts you have reviewed yourself. You might want/need to change a few bits and pieces. In the end, you might do the following:
Pullthe current state of the project (just to be up-to-date)- revise your R Markdown project description
- double check the Issues with the review comments to see if you incorporated everything
- knit your description to
.mdoutput (see Project description guide) Pullagain the current project state from GitHubCommityour changesPushyour committed changes to GitHub- Check your changes on the project website (after a minute or two)
- Close the Issues you received for your project (with a nice comment) :-)
At the end of Phase 3 you will
- have exercised a feedback loop of project work
- be familiar with basic git commands in RStudio
- have experienced the Issue feature of GitHub from both sides (raise and close)
Given a project description, you will try to solve the task. In order to practice real work flow life cycles, you will create your solution first in your own git branch and suggest it via a pull request on GitHub. This provides the project owner the possibility to review your solution and to give you feedback, which you can discuss within the pull request. Once all are happy with the solution it can be merged into the main branch of the course repository and thus be published.
This workflow is described and summarized in
Note: we are still working all on ONE GITHUB REPOSITORY! We do not create a fork, i.e. our own copy of the repository on GitHub, which is also detailed in the linked material. The latter (forking) is needed, if you don't have writing permissions to a repository. But the overall workflow is more or less the same.
- before you start working on your project solution:
- create YOUR branch (remember Step 4: Branch)
- ensure
- you are in
mainbranch BEFORE creating your new branch !!! Otherwise, your new branch will be an offspring of the currently loaded branch (and its changes..)! - AFTER creation: check you have switched to the branch in RStudio (upper right corner in "Git" pane)
- you are in
- now you are ready to create your solution file:
- go into the project folder of the target project you are working on
- create a new R Markdown (File > New File > ...) with name
youGithubName.Rmd- use
output: md_documentin your header to produce a "normal" Markdown file - your will program your solution within this file
- on "Save", RStudio will automatically create an
.mdoutput file for you- you can check it using Ctrl+Shift+K (or Cmd+Shift+K on Mac)
- use
- Commit and Push the following files
- your R Markdown file
yourGithubName.Rmd - your solution output file
yourGithubName.md(knit it, if not existing yet)
- your R Markdown file
When you work on your solution, you should at least once a day
- Commit and Push your changes to GitHub (remember Step 5: Work, commit and push)
This ensures you will not loose your work (backup) and store the stuff where it belong.
Furthermore, it opens up a new way to get help! In case you get stuck somewhere, it is a good idea to
- Commit and Push to GitHub
- Call for help (and tell us the branch, file and problem)
- either by raising an Issue, describing your problem (preferred solution)
- or writing an Email to us
- or rushing our office (hope we are there)
- or ...
At some point you will be satisfied with your project solution and all changes are committed and pushed to GitHub.
Now it is time to open a pull request.
- create a new pull request of your branch (select from dropdown) into main branch
- post a (Markdown) link in the pull request that allows to see your output
.mdpage in the browser:- e.g.
https://github.com/Dr-Eberle-Zentrum/Data-projects-with-R-and-GitHub/blob/main/Projects/martin-raden/project-description.md
- e.g.
- invite the project owner as a reviewer
- suggest him/her as reviewer (upper right in GitHub Pull Request interface)
- send him/her a message via a comment "@USERNAME"
At the end of Phase 1 you will
- know about branches in git and how to use them in RStudio
- have worked on a data wrangling and visualization project (in your own independent git branch)
- have created a pull request on GitHub to incorporate your suggested project solution
Now it is time for the project owner to check your solution and for both of you to discuss possible changes, extensions, ... This should, as before, be done on GitHub, but now directly within the pull request! All comments, answers, changes etc. will be listed there. Even if you are meeting in person, please note down the main points and goals within the pull request (together).
The project owner should
- check the solution using the link provided in the pull request
- compare the solution with your own project description
- provide constructive feedback within the pull request
- positive writing
- suggest changes, extensions, ...
- you can even (in discussion and agreement) change the goal of this solution! Your are not fixed to the initial project plan from this point on!
- provide hints/ideas how to tackle the project (if known to you or needed)
- ...
The solution author should
- be open minded to suggestions and criticism
- at the end formulate a plan of changes, i.e.
- what will you change
- try to implement
- ...
- your can use GitHub Markdown checkboxes to later keep track what you have done and what is still open
- best put your plan in the first post of the pull request by editing it. That way it is found and updated most easily!
You can already work on the changes while you are discussing! Any change you commit to your branch is automatically visible in the pull request (and this HTML visualizing link you provided).
Thus, you can directly discuss if you meet the ideas of the project owner or suggest alternative ideas.
You will get a loooot of GitHub emails this week! 😁
At the end of Phase 2 you will
- have experienced the pull request workflow from both sides
- have integrated an interactive reviewing cycle in your project solution
- be ready for the next project! 😁
Finally, it is not only about content but presentation matters. Thus, you will have to beautify your HTML output. Here some ideas where to start:
- hide
- loading information + warnings
- code chunks
- add
- a narrative why you do/show things
- add document structure using headers etc.
- (shortly) explain and interpret what you show
- provide used formulas etc. to compute statistics/values/...
- ...
If your solutions generates HTML output files, you cannot directly view/render them on GitHub, since the page is made to work on source files not rendered output.
In case your HTML file works without JavaScript (just static text and image output), you can use https://htmlpreview.github.io/
- open the file on github.com in your browser
- click on the
Rawbutton in the upper right corner - copy the URL of the now shown file
- either paste the URL to the linked page from above
- or just prefix it with
http://htmlpreview.github.io/?
- or just prefix it with
Note: htmlpreview is only working for HTML pages without JavaScript content!
In case your HTML file is making use of JavaScript, you can use https://raw.githack.com/
The procedure is the same as above but the final URL is slightly different, see website.