2018-08-16 07:04:29
Acknowledgments
Agenda
- Welcome and introductions
- Housekeeping
- Schedule
- Aspirations & Philosophy
- The stoRy of R
Welcome and introductions
- Michael Hallquist
- Rick Gilmore
- Nilam Ram
Welcome and introductions
- Dan Albohn
- Kayla Brown
- Ben Johnson
- Alicia Vallorani
Who R You?
Housekeeping
- Bootcamp materials on GitHub: https://github.com/psu-psychology/r-bootcamp-2018
- Bootcamp website: http://psu-psychology.github.io/r-bootcamp-2018
- Survey: Please complete this anonymous (and light-hearted) survey before lunch.
- Sessions are being video recorded. Last year's is on Databrary
Schedule
- Day 1 (Thursday, August 16, 2018): 104 Keller & 104 Rackley
- Day 2 (Friday, August 17, 2018): 104 Keller
Day 1 Schedule
| Plenary session, 104 Keller | |
|---|---|
| 09:00 am • Welcome, introductions, & housekeeping | |
| 09:15 am • Why We R Here | |
| 09:45 am • Break |
Day 1 Schedule
| Slow-R Track, 104 Keller | Fast-R Track, 104 Rackley | |
|---|---|---|
| 10:00 am - 12:00pm • RStudio, R console, object classes & data types (Rick Gilmore) | 10:00 am - 12:00pm • Introduction to exploratory factor analysis (Nilam Ram) |
| Lunch, Moore 1st floor lobby | |
|---|---|
| 12:00 pm • Lunch (thanks to the Child Study Center (CSC)) |
Day 1 Schedule
| Slow-R Track, 104 Keller | Fast-R Track, 104 Rackley | |
|---|---|---|
| 1:00 pm - 1:45 pm • Data indexing and subsetting (Rick Gilmore) | 1:00 pm - 2:00 pm • Introduction to structural equation modeling in lavaan (Michael Hallquist) | |
| 1:45 pm - 3:00 pm • Packages, scripts, & functions (Rick Gilmore) | 02:00 pm - 3:00 pm • Parallel computing, batch processing, and big data in R (Michael Hallquist) | |
| 03:00 - 03:15 pm • Break | 03:00 pm - 03:15pm • Break |
Day 1 Schedule
| Plenary, 104 Keller | |
|---|---|
| 03:15 pm • R-eproducible Science with R (Rick Gilmore) | |
| 04:30 pm • End |
Day 2 Schedule
| Plenary | |
|---|---|
| 09:00 am • Welcome back and Q&A | |
| 09:15 am • Data wrangling & pipelines (primarily tidyverse) | |
| 10:30 am • Break | |
| 10:45 am • Data visualization (primarily ggplot2) | |
| 12:00 pm • Lunch (pizza & drinks thanks to SLEIC) |
Day 2 Schedule
| Plenary | |
|---|---|
| 01:00 pm • Core programming skills: loops, apply functions, writing functions | |
| 02:00 pm • Best practices in R: script management, versioning, modular coding | |
| 02:45 pm • Break | |
| 03:00 pm • Basic data analyses in R: correlation, regression, ANOVA, categorical data | |
| 04:30 pm • End | |
| 04:45 pm • Optional Happy Hour @ Whisker's |
Aspirations & Philosophy
- You can learn to program
- You should learn to program
- R is a good language for learning programming
The storRy of R
The storRy of our R
- What is R?
- A programming language, written by and for statisticians & data scientists
- Why is it called R?
- TLDR: it's the free, open-source version of a commercial program called S developed in the 1970s at Bell Labs
- Bell Labs is also the home of C, and the Unix operating system
The storRy of R
- Features of R
- Written in C, Fortran, and R
- 14th most popular programming language
- Base R (now 3.5.1) + thousands of packages
- Object-oriented
Using R
- On your own computer
- In the cloud from your browser
Some of ouR own eye-candy
Your turn
- Why do you want to learn R?
- If you know some R, what else do you want to learn?
- Complete the survey!
- Testimonials?
Next steps
- Slow R (stay here in 104 Keller)
- Fast R (104 Rackley Moore)
- Lunch (Moore Building Lobby)