Stat 585 - Working in teams

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.title[
# Stat 585 - Working in teams
]
.author[
### Heike Hofmann
]

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# Tools for collaborating in teams

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## One last thing ...

### Imagine the following situation:

it's your last day before you go on a 4-week vacation after a stressful stretch of work. 
The last week seemed to have been filled with deadline after deadline for all of your projects.

You are planning to leave in 30 minutes.

Your boss storms in ... "I'm glad I still caught you, I need to know, what the average height and weight of Iowans was in 2015, and while you are at it, calculate the correlation as well."

Before you can explain that you won't be here for the next four weeks, she is gone.

Two minutes later you get a text with two links:

"Don't forget to put in a blurb about the source of the data and stuff"

- [2015 BRFSS records](https://raw.githubusercontent.com/Stat585-at-ISU/materials-2023/main/01_collaborative-environment/data/brfss_iowa.csv)
- [Codebook](https://www.cdc.gov/brfss/annual_data/2015/pdf/codebook15_llcp.pdf)

---
<!--
## 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 [available here](https://www.cdc.gov/brfss/annual_data/2015/pdf/codebook15_llcp.pdf).
-->

## ... still imagining

You talk to your colleague ...

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## Yay for colleagues!

Your colleague is willing to help out, but is busy right now finishing up a project of their own.

"Just do as much as you can, I'll pick things up after the weekend."

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## Your turn

Prompt:

> I need to know, what the average height and weight of Iowans was in 2015, and while you are at it, calculate the correlation as well."

> Don't forget to put in a blurb about the source of the data and stuff

Two related links:

In the next 20 mins:

1. Write down the time

2. Write out a plan of work

3. Write down the time

4. Fill in the plan as far as you come

5. When the alarm rings, save your document and submit it to Canvas.

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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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## This is where we are

How confident are you  ...

![Barchart of the number of minutes spent planning.](01_test-case_files/figure-html/unnamed-chunk-3-1.png)

On average, you spent 7.5 minutes to plan the work.

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## ... meanwhile at the office

### Imagine you are the colleague

Again, you don't have time, and you want to get this extra piece of work done as quickly as possible

You are deeply regretting having been suckered in ... but you (as of now, still) like your colleague

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## Your Turn
In the next 10 mins:

1. Go to Canvas, find the assignment for peer-review

2. Download all of the documents

3. Pick up where your colleague left off.

4. When the alarm rings, stop the work and go back to Canvas.

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## How did that go?

Thanks to  Jenny Bryan and Mine Cetinkaya-Rundel for the idea for the original  example.

![](images/meanwhile-at-home.png)

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## Reflection

- Were you successful in reproducing each others' work?

- Where did things get problematic? 
  
  - time is always too short, no matter how much you have
  - time is also our most strictly limited resource for now
  - a good system will help us save time

- What tools did you use?

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## What if?

- 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?
    
    - you had to 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

- [THE checklist](checklist.md), developed by Jenny Bryan and Mine Cetinkaya-Rundel

- Even with a fairly simple analysis we face challenges 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
    - *We will:* Use markdown to document our workflow so that anyone can pick up our
    data and follow what we are doing
    - *We will:* Use literate programming so that our analysis and our results are
    tightly connected, or better yet, inseparable

2. **Organization:** tools to organize our projects so that we 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  future research and the future research of others