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content/docs/modules/week7/slides.Rpres

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@@ -22,7 +22,9 @@ Welcome to the final lecture in dPrep!
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If you haven't done so, please **explore the exam page & example questions** at [https://dprep.hannesdatta.com/docs/exam]().
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<!--
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The __course evaluation__ is live at https://app.evalytics.nl. Please voice your opinions!
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-->
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<!--
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- Team project:
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- delete stuff that isn't needed, roll back when you want
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- Oh no, not authenticated!
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- Oh no! Can't push! (pull first!)
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- GUIs (e.g., in R) are available, too!
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- GUIs (e.g., in R or VS Code) are available, too!
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Lessons learnt #1: Versioning and Project Management with Git(Hub) (II)
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========================================================
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- Ready to use Git/versioning in a business
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- Know what to version, and what not
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- Purpose of `.gitignore`
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- How to work together (issues, feature branches, PRs)
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- How to work together (issues, feature branches, pull requests/PRs)
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- Collaborate on open source projects
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- You know what forks are!
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- You may even actually contributed to public projects!
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- RMarkdown! (mixing code with reporting)
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- ability to quickly produce clean docs to share!
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- uh... but how to run it w/ make?
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- __verify you can render markdown documents on your computer!__
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- __verify you can render markdown documents!__
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- Doing data quality checks?
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- getting back to your data supplier if needed!
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- reporting summary stats
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- Make ITO blocks
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- setup to load libraries, __then__ input, transformation, output!
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- super crucial for `make`, too!
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- Modularize code, iterate, and use functions
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- Modularize code, loop, and use functions
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Lessons learnt #4: Pipeline building and automation
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========================================================
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- Gradually implement across classes or projects
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- e.g., some projects just benefit from better directory structure, while others may need "more"
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- risk of "losing" the skill (!)
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- Realize it takes time to learn
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- it took me years to become proficient
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- Become part of the open science community at Tilburg
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- contribute to [Tilburg Science Hub](https://tilburgsciencehub.com)
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- improve [music-to-scrape](https://music-to-scrape.org)
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- follow us on [LinkedIn](https://www.linkedin.com/company/tilburgsciencehub/) and [Twitter](https://twitter.com/tilburgscience)
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- improve [music-to-scrape.org](https://music-to-scrape.org)
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- follow us on [LinkedIn](https://www.linkedin.com/company/tilburgsciencehub/) and [X](https://twitter.com/tilburgscience)
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- Invest in your research skills (e.g., Docker, coding)
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Looking ahead: After your studies
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Next steps: Submissions and preparing for the exam
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========================================================
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- Deadline for project coming up!
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- Deadline for project coming up next week!
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- remember: __make it work__ on different computers and operating systems
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- __do not use__ absolute paths!
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- verify data can be downloaded smoothly
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- https://dprep.hannesdatta.com/docs/exam/examplequestions/
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Exam overview: Theoretical part
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Exam planning
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=============
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- Organization
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- When: 16 October, 1-2pm (1 hour)
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- How?
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- closed book, no software like R, __just TestVision__
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- 4 April (time tba; 3 hours)
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- On campus, using __TestVision__
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- Software & materials
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- access to R/RStudio, Git, make
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- access to github.com/course-dprep and classroom.github.com; no access to ChatGPT or other AI tools
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- I'm making selecting resources available on the instruction page - [check them out here](https://github.com/hannesdatta/course-dprep/raw/master/content/docs/exam/cheatsheets-exam.zip)
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- What?
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- How to prepare?
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- familiarize yourselves with [how TestVision works with a practice test](https://oefentoetsen.testvision.nl/online/fe/login_ot.htm?campagne=tlb_demo_eng&taal=2)
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- let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/#theoretical-part)
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- let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/)
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Exam overview: Practical part
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=============
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- Organization
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- When: 17 October, 10am - 11.59am + 1 minute, take home
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- Work max. 2 hours on this part
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- How?
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- open book, on your computer
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- ChatGPT and other tools ONLY when explicitly asked for.
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- What?
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- let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/#practical-part)
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- prep well - expect new datasets that are big (too big maybe even) -- aggregation, selection of number of rows, etc.
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Some tips for your exam
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=======
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incremental: true
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- Expect an unexpected data set & data wrangling
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- Know common data operations in `dplyr` & become fast!
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- When handing in documents, check what I require (for `.Rmd`, I sometimes ask for rendered `.pdf` documents - does it work on your computer?)
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- Be prepared to make commits to GitHub repositories -- know how clone, fork, write issues, do PRs, roll back to previous versions, etc.
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- Be prepared to run, correct and develop new `make` workflows
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- Be prepared to work with Git Bash
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- know how to make commits with commit messages, create branches, switch branches, etc.
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- download git repository, unzip, do your commits, zip again and submit as a zipfile
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- Be prepared to work with GitHub
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- know how to clone, fork, write issues, do PRs
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- know how to roll back to previous version
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- Be prepared to use `make`
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- run, correct and develop new `make` workflows
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- Be prepared to download data sets from Testvision (`.Rdata`) - on the Cover page of the exam or in a specific question on the exam.
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Learning goals + distribution of points
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==========
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- 100 points in total, about 25 questions
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- Mix of open and closed questions
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- Learning goals & question weights
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1. Use R to clean and transform data for analysis (e.g., aggregation, merging, de-duplication, reshaping, data conversions, regular expressions) [synthesis; __20% of points__]
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2. Use GitHub for managing empirical research projects (e.g., GitHub Issues and Project Boards) [evaluation; __10% of points__]
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3. Use Git/GitHub for versioning files and collaborating on privately-shared and publicly-available (open science) GitHub repositories [application; __30% of points__]
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4. Use R for generating automatic reports (e.g., to assess data quality, to report research findings in a paper) and deploying research findings in novel ways (e.g., apps) [comprehension; __15% of points__]
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5. Use Workflow Management Tools to create and run portable, automated, and reproducible research pipelines [application; __25% of points__]
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Next steps: Official course evaluation
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========================================================
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- Course evaluation has been immensely important to this course
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- this semester: new data set, coaching sessions with breakout groups on campus and online
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- last semester: developed new on-campus tutorials, released example projects
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- this semester: switched order of sessions, revised GitHub tutorial/onboarding
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- last semester: new data set, coaching sessions with breakout groups on campus and online
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- Course evaluation has been critical to my career
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- Without my past evaluations, I wouldn't be teaching to you today
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- I will look at all comments.
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- __Scores are most important to show importance of this course__
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- You will be invited via [Evalytics](https://app.evalytics.nl/#/login)
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- You will be invited via [Evalytics](https://app.evalytics.nl/#/login) at the end of the week
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Next steps: Self- and peer assessment
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========================================================
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- You will be invited via e-mail
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- To be filled in using Google Forms
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Informal feedback
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incremental: true
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- Coaching sessions?
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- Coaching sessions? (Online, offline)
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- How was it for beginners?
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- What's are three things you'd like me to change?
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Stay in touch!
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