• Office hours:
  • Dr. Cetinkaya-Rundel: Mondays 1 - 3pm + Wednesdays by appt (213 Old Chem)
  • Kyle: Wed 9 - 10am + Fri 10 - 11am at Old Chem 211A
• Course website:
  • http://www2.stat.duke.edu/courses/Fall17/sta112.01/
  • Review the syllabus page for exam and HW due dates
  • Mini HWs will be added as we go

• What is reproducible data analysis, and why do we care?
• What is version control, and why do we care?
• What is R vs RStudio?
• What is git vs GitHub (and do I need to care)?

• Scriptability \(\rightarrow\) R

• Literate programming \(\rightarrow\) R Markdown

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

"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

Near-term goals:

• 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?)

Long-term goals:

• Can the code be used for other data?
• Can you extend the code to do other things?

• Go to https://vm-manage.oit.duke.edu/containers/rstudio

• Log on with your Net ID and password

• R as a calculator

2 + 2

## [1] 4

factorial(20)

## [1] 2.432902e+18

• Working with variables

x = 2
x * 3

## [1] 6

• The statistical programming language we'll use is R

• The software we use to interface with R is RStudio

• But how do I get you the course materials that you can build on for your assignments?
  • Hint: I'm not going to email you documents, that would be a mess!

• Follow the link to create a repository on GitHub

• Connect an R project to Github repository

• Working with a local and remote repository

• Staging, Committing, Pushing and Pulling

(There is just a bit more of GitHub that we'll use in this class, but for today this is enough.)

• Fully reproducible reports

• Simple markdown syntax for text

• Code goes in chunks

Tip: Keep the R Markdown cheat sheet and Markdown Quick Reference (Help -> Markdown Quick Reference) handy, we'll refer to it often as the course progresses.

• Exercise 0
  • Load any necessary packages and data
• Exercise 1
  • Visualize the relationship between life expectancy and GDP per capita in 2007 using a scatter plot.
• Exercise 2
  • Repeat the visualization from Exercise 1, but now color the points by continent.

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 the readr (for loading data), dplyr (for data wrangling), and ggplot2 (for visualization) packages.

• To use these packages, we must first load in our markdown file

library(dplyr)
library(ggplot2)
library(readr)

gapminder = read_csv("https://stat.duke.edu/~mc301/data/gapminder.csv")

## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_double(),
##   gdpPercap = col_double()
## )

• 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)

Task: Visualize the relationship between gdpPercap and lifeExp.

ggplot(data = gap07, aes(x = gdpPercap, y = lifeExp)) + 
  geom_point()

Task: Color the points by continent.

ggplot(data = gap07, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point()

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

• We introduced GitHub as a platform for collaboration

• But it's much more than that…

• It's actually desiged for version control

• Simple formal system for tracking all changes to a project

• Time machine for your projects
  • Remove the fear of breaking things

• Learning curve is a bit steep, but when you need it you REALLY need it

• What is reproducible data analysis, and why do we care?
• What is version control, and why do we care?
• What is R vs RStudio?
• What is git vs GitHub (and do I need to care)?

• Readings for next Tuesday posted

• A brief mini homework will be posted tonight, you'll receive a link to create your repo (just like we did today) and complete a short data visualization task using an R Markdown file I provide
  • Having difficulty? Ask on Slack or come to office hours!