Learn how to analyze your datasets in R! insert link here
The workshop duration is 3hrs per class.
title : R for Data Science
description : The R for Data Science workshop series is a four part course, designed to take novices in the R language for statistical computing and produce programmers who are competent in finding, displaying, analyzing, and publishing data in R.
subtitle : Basics of R
description : Students will understand the motivation behind object orientation, and how that relates to computation. Students will be able to perform basic functions in R necessary to use the software on their computers and conduct basic arithmetic. Students will understand data types and data structures, and why and how they are different from each other.
knowledge requirements : Programming Fun!damentals, or equivalent prior knowledge
tech requirements : Laptop required; please install R version 3.2 or greater in advance (University laptops will need to have R installed by an administrator); the RStudio IDE is recommended but not required
subtitle : Clean and tidy data
description : Students will be introduced to DRY principles and best practices for sanitizing and tidying data. Students will learn what missingness is, and how best to accommodate missing data in their research designs. Students will be able to read in files from disk or a database, clean the data found within them, select specific data from them, and merge them with other datasets.
knowledge requirements : R-for-Data-Science Part 1 or equivalent prior knowledge
tech requirements : Laptop required; please install R version 3.2 or greater in advance (University laptops will need to have R installed by an administrator); the RStudio IDE is recommended but not required
subtitle : Analyzing data
description : Students will be introduced to the principles behind the grammar of graphics and the general linear model. Students will understand the implementation of plotting in R. Students will be able to explore, summarize, and analyze data using R's implementation of exploratory and inferential data analysis.
knowledge requirements : R-for-Data-Science Part 2 or equivalent prior knowledge
tech requirements : Laptop required; please install R version 3.2 or greater in advance (University laptops will need to have R installed by an administrator); the RStudio IDE is recommended but not required
subtitle : Functions and packages
description : Students will be introduced to the principles behind functional programming. Students will learn how to write and import functions, add looped and vectorized computation to their functions, and control the flow of data through a function. Students will understand the basics of name spaces, and how that relates to assigning values within functions. Students will see how to successfully package a function for CRAN.
knowledge requirements : R-for-Data-Science Part 2 or equivalent prior knowledge
tech requirements : Laptop required; please install R version 3.2 or greater in advance (University laptops will need to have R installed by an administrator); the RStudio IDE is recommended but not required