DS 202 - Working in teams

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# DS 202 - Working in teams
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# Tools for collaborating in teams

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## A test case

The dataset `brfss_iowa.csv` (linked from website) contains 6227 records from the Behavioral Risk Factor Surveillance System (BRFSS) for Iowans.

> The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors.

It is conducted annually by the Center for Disease Control and Prevention (CDC).

Codebook with detailed explanations of variables is [https://www.cdc.gov/brfss/annual_data/2015/pdf/codebook15_llcp.pdf](https://www.cdc.gov/brfss/annual_data/2015/pdf/codebook15_llcp.pdf).

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## Your turn

You are asked to investigate the relationship between height and weight of Iowans based on the [2015 BRFSS records](https://raw.githubusercontent.com/Stat585-at-ISU/materials-2022/main/01_collaborative-environment/data/brfss_iowa.csv).

In the next 20 mins:

0. Introduce yourself to your neighbor(s).

0. Complete the following tasks and **write instructions / documentation** for a collaborator to be able to reproduce your findings.

1. Verify that there are 6227 cases (= number of interviews) in the data.

2. Verify that there are variables `WEIGHT2` and `HEIGHT3` in the data and read the description of the variables in the codebook.

3. How are height and weight related? Find correlations of weight and height by gender (`SEX`). How many values are the correlations based on for each gender?

2. Write a short report of your findings. Address potential problems in the data.

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## Your turn

In the next 10 mins:

1. Introduce yourself to another team.

2. Swap the report and the instructions / documentation with your collaborator - **BUT DO NOT TALK ABOUT THE DATA OR THE ANALYSIS** (yet)

3. Try to reproduce the other team's findings.

3. Only in case things are not working - talk to the other team and walk them through your report.

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## How did that go?

![](images/desperation-0.jpg)

- Where did things get problematic?

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## Reflection

- What tools did you use?

- Were you successful in reproducing each others' work? 
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- What would happen if your collaborator is no longer available to walk you through 
their analysis?

- What made it easy / hard for reproducing your partners' work?

- What would have to happen if 
    - you had to swap out the dataset or extend the analysis further?
    - you caught further data errors and had to re-create the analysis
    with corrections?
    - you had to revert back to the original dataset?

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## Summary

- Everyone struggles with reproducibility and it is a hindrance to moving 
science forward

- Even with a fairly simple analysis challenges were faced in four main areas:
organization, documentation, automation, and dissemination

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# Facets of reproducibility

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## Four facets of reproducibility

1. **Documentation:** difference between binary files (e.g. docx) and text files and
why text files are preferred for documentation
    - *Protip:* Use markdown to document your workflow so that anyone can pick up your
    data and follow what you are doing
    - *Protip:* Use literate programming so that your analysis and your results are
    tightly connected, or better yet, unseparable

2. **Organization:** tools to organize your projects so that you don't have a single 
folder with hundreds of files

3. **Automation:** the power of scripting to create automated data analyses

4. **Dissemination:** publishing is not the end of your analysis, rather it is a way 
station towards your future research and the future research of others