Housekeeping

Announcements

  • Office hours:
    • Dr. Çetinkaya-Rundel:
      • Mondays 3:30 - 5:30pm
      • by appointment
    • Nayib: Tuesdays 5-6pm - Wilson 3rd Floor common room
    • Gloria: Wednesdays 5-6pm - Wilson 3rd Floor common room

Today’s agenda

  • Introduction to tools for reproducible data analysis

Reproducibility: who cares?

Science retracts gay marriage paper without agreement of lead author LaCour

  • In May 2015 Science retracted a study of how canvassers can sway people’s opinions about gay marriage published just 5 months ago.

  • Science Editor-in-Chief Marcia McNutt: Original survey data not made available for independent reproduction of results. + Survey incentives misrepresented. + Sponsorship statement false.

  • Two Berkeley grad students who attempted to replicate the study quickly discovered that the data must have been faked.

  • Methods we’ll discuss today can’t prevent this, but they can make it easier to discover issues.

Source: http://news.sciencemag.org/policy/2015/05/science-retracts-gay-marriage-paper-without-lead-author-s-consent

Seizure study retracted after authors realize data got “terribly mixed”

From the authors of Low Dose Lidocaine for Refractory Seizures in Preterm Neonates:

“The article has been retracted at the request of the authors. After carefully re-examining the data presented in the article, they identified that data of two different hospitals got terribly mixed. The published results cannot be reproduced in accordance with scientific and clinical correctness.”

Source: http://retractionwatch.com/2013/02/01/seizure-study-retracted-after-authors-realize-data-got-terribly-mixed/

Bad spreadsheet merge kills depression paper, quick fix resurrects it

  • The authors informed the journal that the merge of lab results and other survey data used in the paper resulted in an error regarding the identification codes. Results of the analyses were based on the data set in which this error occurred. Further analyses established the results reported in this manuscript and interpretation of the data are not correct.

  • Original conclusion: Lower levels of CSF IL-6 were associated with current depression and with future depression […].

  • Revised conclusion: Higher levels of CSF IL-6 and IL-8 were associated with current depression […].

Source: http://retractionwatch.com/2014/07/01/bad-spreadsheet-merge-kills-depression-paper-quick-fix-resurrects-it/

Reproducibility: why should we care?

Two-pronged approach

#1 Convince researchers to adopt a reproducible research workflow



#2 Train new researchers who don’t have any other workflow

two prongs

Reproducible data analysis

  • Scriptability \(\rightarrow\) R

  • Literate programming \(\rightarrow\) R Markdown

  • Version control \(\rightarrow\) Git / GitHub

Scripting and literate programming

Donald Knuth “Literate Programming (1983)”

“Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer- what to do, let us concentrate rather on explaining to human beings- what we want a computer to do.”

  • These ideas have been around for years!
  • and tools for putting them to practice have also been around
  • but they have never been as accessible as the current tools

Reproducibility checklist

  • Are the tables and figures reproducible from the code and data?
  • Does the code actually do what you think it does?
  • In addition to what was done, is it clear why it was done? (e.g., how were parameter settings chosen?)
  • Can the code be used for other data?
  • Can you extend the code to do other things?

Toolkit

Toolkit

toolkit

Demo

Logging on to RStudio

  • Go to gort.stat.duke.edu:8787

  • Log on with your Net ID and password

Live R/RStudio demo

  • R as a calculator
2 + 2
## [1] 4
factorial(20)
## [1] 2.432902e+18
  • Working with variables
x <- 2
x * 3
## [1] 6

Working with GitHub

  • Create a GitHub account at https://github.com/
    • This will be a public account associated with your name
    • Choose a username wisely for future use
    • Don’t worry about details, you can fill them in later
  • Create a repository called intro_demo
    • Give a brief and informative description
    • Choose “Public”
    • Check the box for “Initialize this repository with a README”
    • Click “Create Repository”

Cloning the repository

  • Go to RStudio

  • File -> New Project
    • Version Control: Checkout a project from a version control repository
    • Git: Clone a project from a repository
    • Fill in the info:
      • URL: use HTTPS address
      • Create as a subdirectory of: Browse and create a new folder call sta112

  • Note for the future: Each course component you work on (an application exercise, a homework assignment, project, exam, etc.) should be its own repository, and should be fully contained in a folder inside the folder sta112.

Merge conflicts

  • On GitHub (on the web) edit the README document and Commit it with a message describing what you did.

  • Then, in RStudio also edit the README document with a different change.
    • Commit your changes
    • Try to push – you’ll get an error!
    • Try pulling
    • Resolve the merge conflict and then commit and push

  • As you work in teams you will run into merge conflicts, learning how to resolve them properly will be very important.

Documenting and reporting

R Markdown

  • Fully reproducible reports

  • Simple markdown syntax for text

  • Code goes in chunks

Tip: Keep the Markdown cheat sheet handy, we’ll refer to it often as the course progresses.

[Live demo – follow along]

On to data analysis

Task

Visualize relationship between life expectancy and GDP per capita in 2007 in countries. Also make a plot

Step 0: Load necessary packages

Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data.

  • In the following exercises we’ll use dplyr (for data wrangling) and ggplot2 (for visualization) packages.

  • Load these packages in your markdown file

library(dplyr)
library(ggplot2)

Step 1: Load data

gapminder <- read.csv("https://stat.duke.edu/~mc301/data/gapminder.csv")

Step 2: Subset data

  • Start with the gapminder dataset

  • Filter for cases (rows) where year is equal to 2007

  • Save this new subsetted dataset as gap07

gap07 <- gapminder %>%
  filter(year == 2007)

Step 2: Explore and visualize

Task: Visualize the relationship between gdpPercap and lifeExp.

qplot(x = gdpPercap, y = lifeExp, data = gap07)

Step 3: Dig deeper

Task: Color the points by continent.

qplot(x = gdpPercap, y = lifeExp, color = continent, data = gap07)

Step 3: Commit and push all changes

  • Stage

  • Commit (with a message)

  • Push

What’s next?

Update your analysis

What if you wanted to now change your analysis

  • to subset for 1952

  • plot life expectancy (lifeExp) vs. population (pop)

  • and size the points by GPD (gpdPercap)
    • hint: add argument size = gpdPercap to your plotting code

Once you’re done, commit and push all your changes with a meaningful message.

Homework