Good enough practices in scientific computing. Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, et al. (2017) Good enough practices in scientific computing. PLOS Computational Biology 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510

Ten Simple Rules for Reproducible Computational Research. Sandve GK, Nekrutenko A, Taylor J, Hovig E (2013) Ten Simple Rules for Reproducible Computational Research. PLOS Computational Biology 9(10): e1003285. https://doi.org/10.1371/journal.pcbi.1003285

The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. Kitzes, J., Turek, D., & Deniz, F. (Eds.). (2018). Oakland, CA: University of California Press. https://www.gitbook.com/book/bids/the-practice-of-reproducible-research/details

• Candy trade
  • Review your selection of candy. Rate how happy you are with your selection on a scale from 1 (unhappy) to 10 (very happy). Record it on your green sticky note and label it as "pre-trade".
  • Find one trading partner. Trade the candy you don't like for candy you do like with that partner only. Rate how happy you are with your selection on a scale from 1 (unhappy) to 10 (very happy). Record it on your green sticky note and label it as "trade 1".
  • Now trade with everyone in the room. Trade candy you don't like for candy you do like. Rate how happy you are with your selection on a scale from 1 (unhappy) to 10 (very happy). Record it on your green sticky note and label it as "trade 2".

• Create one repository or directory that holds all related research files.
• Organize your research to separate data, code, and results.
  • Import your data via the Github link: https://github.com/aprilcs/candy_trade
• Resource on reproducible organization:
  • Karl Broman: http://kbroman.org/steps2rr/pages/organize.html

• Use literate programming to document your analyses.
  • Configure the environment for Jupyter Notebook:
    • Base Environment: Python 3 with Anaconda
    • conda installer: jupyter, numpy, pandas
  • Run -> Launch interactive Jupyter Session.
  • Analyze your data!
    • Load your libraries.
    • Load your data.
    • Create a line graph showing each individual's change in happiness over the trades.
    • Create a box plot comparing the distribution of happiness over trades.
    • Download your notebook "as Notebook".

• Use nbconvert to render your notebook.
  • Upload your notebook into the code subdirectory.
  • In your run.sh script, use nbconvert to render your notebook.
  • Check the rendering by hitting "Run".

• Share your code.
  • Create two python code files: one for each figure you created (line graph & box plot).
  • Configure your run.sh file to run them in order.
  • Save the figures in the results subdirectory.
  • Check the configuration by hitting "Run"

• Make a codebook or data dictionary.
• Resources on making a great codebook or data dictionary:
  • DataONE: https://www.dataone.org/best-practices/create-data-dictionary
  • McGill Codebook Cookbook: http://www.medicine.mcgill.ca/epidemiology/joseph/pbelisle/CodebookCookbook.html
  • UPenn: https://guides.library.upenn.edu/datamgmt/documentation
  • Karl Broman: http://kbroman.org/dataorg/pages/dictionary.html
• Example codebook:
  • AJPS Replication Package: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EZSJ1S

• Create a README.txt or README.md.
• Resource on making a great README file:
  • Cornell (includes a template): https://data.research.cornell.edu/content/readme
• Resource on using markdown:
  • GitHub: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet

• Choose a license for your data and your code.
• Resource on choosing a data licence:
  • Digital Curation Center: Digital Curation Centre
• Resource on choosing a code licence:
  • Karl Broman: http://kbroman.org/steps2rr/pages/licenses.html
  • Open Source Initiative: https://opensource.org/licenses

• Specify your environment and package versions.
• Example of documenting packages in your README:
  • AJPS Replication Package: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EZSJ1S
• Example of documenting dependencies:
  • Binder: http://mybinder.readthedocs.io/en/latest/using.html#preparing-a-repository-for-binder

• Change absolute paths to relative paths.
• Resource explaining paths:
  • Karl Broman: http://kbroman.org/steps2rr/pages/organize.html

• Make a master script (aka driver or controller script) and set it as main.
  • Resource on automation using a driver script: https://www.practicereproducibleresearch.org/core-chapters/3-basic.html

• Share your code and data!