Baseball Data Wrangling with Vagrant, R, and Retrosheet

Name: Baseball Data Wrangling with Vagrant, R, and Retrosheet
Rating: 4.6 (173 reviews)

Analytics with the Chadwick tools, dplyr, and ggplot.

Created byCharles Redmond

Last updated 6/2015

English

What you'll learn

install VirtualBox and Vagrant
run a virtual Linux machine
install the Chadwick software tools
extract game and play-by-play baseball data from Retrosheet files
produce graphs with ggplot

Course content

4 sections • 28 lectures • 2h 9m total length

Introduction1:22
This is our course introduction in which I detail the structure of the course.
Installing VirtualBox0:57
After viewing this lecture, you will be able to install VirtualBox on your machine.
Installing Vagrant0:38
After viewing this lecture, you will be able to install Vagrant on your machine.
Creating a Project Folder3:17
After this lecture, you will create a folder for your project and navigate to the folder via the command-line. You will also check to make sure you have Vagrant installed.
Vagrant Up3:11
After viewing this lecture, you will download your Linux box and start it.
Directory Structure4:34
After viewing this lecture, you will be able to ssh into your Linux box and navigate the directory structure.

Downloading the Chadwick Software6:50
After viewing this lecture, you will be able to download the Chadwick software and copy files from one directory to another on your Linux machine.
Installing the Chadwick Software8:46
After viewing this lecture, you will be able to install the Chadwick software to your Linux machine.
The Retrosheet Files3:55
After viewing this lecture, you will be familiar with the contents of the Retrosheet event files.
cwevent and cwgame6:06
After viewing this lecture, you will be able to work with the cwevent and cwgame programs from the Chadwick software.

Data Extraction11:08
After viewing this video, you will be able to extract the information you need for our first project using the Chadwick software. You will also see how to work with wildcards in Linux.
Reading our data into R6:23
After viewing this lecture, you will be able to assign names to data frame columns. We will also review how to read csv files into R.
The Result Column5:07
After viewing this lecture, you will be able to work with some of the logical operators in R. We will also review the mutate verb in dplyr.
The Date Column3:39
After viewing this lecture, you will be able to work with the substr function in R.
The Date Column Part II4:29
After viewing this lecture, you will be able to work with the paste function and the as.Date function in R.
The Player Data Frames6:46
In this video, we extract the information we need for our player data frames from our main bdat data frame.
ggplot Crash Course8:22
After viewing this video, you will understand enough of the essentials of ggplot to create our cumulative home run plots.
Cumulative Home Run Plots2:25
After viewing this video, you will be able to put multiple plots on one graph.
Colors and Legend5:25
After viewing this lecture, you will be able to add axis labels, a title, and a legend to your graph. You will also understand how to use color within the aesthetics.

Project Description3:20
In this video, I give the details of project #2.
Data Extraction3:02
In this video, we return to our Linux machine and extract the data we need for our project.
Reading the data into R3:41
In this video, we read our data into R.
The Date Column3:18
In this video, we generate a column of date objects in the default R date format.
The Result and AB Columns3:15
In this video, we modify the AB column and generate an H (hits) column.
The Player Data Frames9:41
In this video, we generate the player data frames. We accumulate the AB and H columns and then divide them to obtain a batting average column.
The Plots3:54
In this video, we finally generate our plots.
The Four-Hundred Line4:31
In this video we add a horizontal line to our graph to represent the .400 batting average line.
The Marchi/Albert Book and Course Wrap-Up1:30
In this video, I recommend a text for additional ideas and examples.

Requirements

Students will need to have R and RStudio installed on their own computers.

Description

This course is for those interested in doing baseball analytics with the Retrosheet game-by-game and play-by-play data. The main tools for working with such data are in the Chadwick software. We install a virtual Linux machine, on which we will install the Chadwick software. We will then learn how to extract baseball data with the Chadwick software, how to further filter the data with dplyr in R, and how to plot our results with ggplot.

For the first part of the course, in which we install the virtual Linux machine and learn how to work with the Chadwick software, there are no prerequisites. To follow the second part of the course, knowledge of dplyr is necessary. This can be obtained through my course "Baseball Database Queries with SQL and dplyr".

At a relaxed pace, the course should take two to three weeks to complete.

Who this course is for:

This course is for those interested in doing baseball analytics with Retrosheet files.
No background is needed for the first part of the course. A background in the R package dplyr is necessary to follow the second part of the course.

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Course content

Setting up Vagrant6 lectures • 14min

Installing and Working with the Chadwick Software4 lectures • 26min

Project #1: Mike Schmidt and Greg Luzinski9 lectures • 54min

Project #2: Dykstra, Murray, and Brett9 lectures • 36min

Requirements

Description

Who this course is for: