Advanced Data Manipulation with R

Data analysis involves a large amount of janitor work - munging and cleaning data to facilitate downstream data analysis. This workshop is designed for those with a basic familiarity with R who want to learn tools and techniques for advanced data manipulation. It will cover data cleaning and "tidy data," and will introduce participants to R packages that enable data manipulation, analysis, and visualization using split-apply-combine strategies. Upon completing this lesson, participants will be able to use the dplyr package in R to effectively manipulate and conditionally compute summary statistics over subsets of a "big" dataset containing many observations.

Date: Feb 26, 2015
Time: 9:00am - 11:00am
Location: Health Sciences Library, Carter Classroom

Pre-requisites:

This is not a beginner course. This workshop requires basic familiarity with R: data types including vectors and data frames, importing/exporting data, and plotting. You can refresh your R knowledge with DataCamp's Intro to R or TryR from CodeSchool.
Bring a laptop to the course with the software installed as detailed below.
Print and bring this R Cheat Sheet as a refresher from the intro R course.
Print and bring the Data Wrangling Cheat Sheet from this link.

Registration: Click here.

Course material:

Click here

Before coming

You must bring a laptop with the necessary software installed to the course. Please install the software below prior to the course - we will not have time during the workshop to troubleshoot installation issues. Please email me (sd...@virginia.edu) if you have any trouble.

Software setup

Note: R and RStudio are separate downloads and installations. R is the underlying statistical computing environment, but using R alone is no fun. RStudio is a graphical integrated development environment that makes using R much easier. You need R installed before you install RStudio.

Download data. Download the gapminder.csv and malebmi.csv files from bioconnector.org/data. Save them somewhere easy to find. Optionally, open them up in Excel and look around.
Install R. You'll need R version 3.1.2 or higher. Download and install R for Windows or Mac OS X (download the latest R-3.x.x.pkg file for your appropriate version of OS X).
Install RStudio. Download and install the latest stable version of RStudio Desktop. Alternatively, download the RStudio Desktop v0.99 preview release (the 0.99 preview version has many nice new features that are especially useful for this particular workshop).
Install R packages. Launch RStudio (RStudio, not R itself). Ensure that you have internet access, then enter the following commands into the Console panel (usually the lower-left panel, by default). Note that these commands are case-sensitive. At any point (especially if you've used R/Bioconductor in the past), R may ask you if you want to update any old packages by asking Update all/some/none? [a/s/n]:. If you see this, type a at the propt and hit Enter to update any old packages. If you're using a Windows machine you might get some errors about not having permission to modify the existing libraries -- don't worry about this message. You can avoid this error altogether by running RStudio as an administrator.

# Install packages from CRAN
install.packages("dplyr")
install.packages("ggplot2")
install.packages("tidyr")
install.packages("knitr")
install.packages("rmarkdown")

You can check that you've installed everything correctly by closing and reopening RStudio and entering the following commands at the console window:

library(dplyr)
library(ggplot2)
library(tidyr)
library(knitr)
library(rmarkdown)

These commands may produce some notes or other output, but as long as they work without an error message, you're good to go. If you get a message that says something like: Error in library(packageName) : there is no package called 'packageName', then the required packages did not install correctly. Please do not hesitate to email me prior to the course if you are still having difficulty.