Here I will post some tutorial videos on how to use R for data analysis and visualization. I think the best videos would probably take one or two very specific analyses methods or programming techniques and go into detail on those - each video lasting 15-20 minutes. I will add some of those and these will probably be useful to intermediate users. The first videos however are slightly longer ones for beginners.

1. Introduction to Data Analysis and Visualization This is the video that accompanies the lecture I gave in the Sports Management Masters program at Columbia in how to use R to analyze large sports datasets. The example is data from the PGA tour. The aim of this class/video was to show how to take a dataset of players and to perform some exploratory anlayses and visualizations of what may be the most important variables to look at and how to start evaluating individual differences in these variables. The aim of this video is to give some ideas as to how start performing these analyses. Up to around 50 minutes is stuff mainly for beginners and then after that I do a few more in depth concepts.

What's covered?

00.00.00 Getting Data into R

00.09.30 Simple Histograms

00.10.45 Intro to dplyr - install, select & filter

00.17.00 Simple boxplots

00.19.00 dplyr II - getting Summary data - n(), group_by, summarize & summarize_each

00.29.15 Simple scatterplots & ggplot intro

00.44.45 Correlation tests & intro to linear and multiple regression

00.55.45 purrr - map

01.02.00 Factor Analysis

01.20.00 dplyr III - mutate, arrange, joins

01.29.15 Non-metric multidimensional scaling

Caveat: Each of these are covered fairly quickly. I think it's important to know more background about each statistical test and when it's appropriate or not to apply to one's data. I would recommend the following readings to get more information:

Some useful multiple regression stat tutorials are here and here (Note - at around 52mins I say 'multivariate regression' when I meant to say 'multiple regression' ! I corrected it in the posted code - see below).

Factor Analysis vs PCA - SV Budaev, 2010, Using Principal Components and Factor Analysis in Animal Behaviour Research: Caveats and Guidelines, Ethology. 116: 472-480.

This is a nice introduction to Non-metric multidimensional scaling in R

The R code for this is available here. The data are available here or here.

You may also be interested in a shiny application that I made using this dataset where you can play around with some interactive analyses.