This document discusses using R to scrape baseball box score data from the website baseball-reference.com. It describes breaking down the scraping process into steps: iterating through each MLB team from 2005-2010, each date from April to October, and accessing the box score URLs and tables. The document outlines the R code used to systematically scrape over 78,000 URLs and extract the data tables. It concludes that R provides a powerful platform for web scraping and accessing large amounts of publicly available data.

1. Using R for Scraping Data
Ryan Elmore
National Renewable Energy Lab
rtelmore@gmail.com
Twitter: rtelmore
June 13, 2012
useR! 2012

2. A Baseball Challenge
Question: Has the minimum number of pitches
per (full) inning (6 pitches) has ever been
attained?
Answer: I don’t know; scrape the boxscores at
baseball-reference.com.
http://www.baseball-reference.com/boxes/COL/COL201104010.shtml

3. A Baseball Challenge
Question: Has the minimum number of pitches
per (full) inning (6 pitches) has ever been
attained?
Answer: I don’t know; scrape the boxscores at
baseball-reference.com.
http://www.baseball-reference.com/boxes/COL/COL201104010.shtml

4. The Boxscore
This column seems useful!

5. The Boxscore
This column seems useful!

6. Dissecting the URL
http://www.baseball-reference.com/boxes/COL/COL201104010.shtml
Just step through
all of the teams: YearMonthDay Game ID
COL, BOS, etc.

7. How Do We Proceed?
The most systematic way that I could find
was to break it down like this:
• 30 Teams
• 2005 - 2010
• Everyday from Apr 1 through Oct 31
• This is a little more than 78K URLs!
• My program took about 3 hrs 25 min.

8. How Do We Proceed?
The most systematic way that I could find
was to break it down like this:
• 30 Teams
• 2005 - 2010
• Everyday from Apr 1 through Oct 31
• This is a little more than 78K URLs!
• My program took about 3 hrs 25 min.

9. R Code
for (team in teams){
for (year in years){
out.string <- paste(Sys.time(), "--", team, year, sep = " ")
print(out.string)
for (month in months){
for (day in days){
for (i in 0:1){
full.url <- paste(paste(base.url, team, date.url,
sep="/"), i, ".shtml", sep="")
table.stats <- readHTMLTable(full.url)
## Process the list of data.frames returned by
## the call to readHTMLTable
}
}
}
}
}

10. R Code
for (team in teams){
for (year in years){
out.string <- paste(Sys.time(), "--", team, year, sep = " ")
print(out.string)
for (month in months){
for (day in days){
for (i in 0:1){
full.url <- paste(paste(base.url, team, date.url,
sep="/"), i, ".shtml", sep="")
table.stats <- readHTMLTable(full.url)
## Process the list of data.frames returned by
## the call to readHTMLTable
}
}
}
}
}

11. Tools
• base: paste, strsplit, unlist, lapply
• XML: readHTMLTable, htmlTreeParse,
getNodeSet, xmlValue, xmlSApply
• httr, stringr, and other Hadley things
• useful, but not necessary: regex, xpath,
XML, etc.

12. Tools
• base: paste, strsplit, unlist, lapply
• XML: readHTMLTable, htmlTreeParse,
getNodeSet, xmlValue, xmlSApply
• httr, stringr, and other Hadley things
• useful, but not necessary: regex, xpath,
XML, etc.

13. Conclusions/Discussion
• There is a lot of data available on the web!
• You can access this data from a browser;
however, you can access A LOT more data
if you let your computer do the work.
• R and its libraries provide a great platform
for scraping data and data mining.
• Download data and see where you go.

14. Conclusions/Discussion
• There is a lot of data available on the web!
• You can access this data from a browser;
however, you can access A LOT more data
if you let your computer do the work.
• R and its libraries provide a great platform
for scraping data and data mining.
• Download data and see where you go.

15. Was That Minimum Attained?
• NO! Unless there is an error in my code.
• Did we learn something? Of course.
• The skills are transferrable to other
websites with data.