The document details a series of steps in R programming to manage football data, starting from creating directories and downloading files to data analysis and visualization. It includes actions such as unarchiving files, renaming, copying, and reading CSV data, as well as performing a logistic regression to predict passing probabilities based on game scenarios. The process culminates in saving results, committing to a repository, and sending an email with the findings.

1. R for Everything
Jared P. Lander

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5. Giants
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6. Compressed Data Online
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8. Create Directory
# See if directory exists
dir.exists('FootballTemp')
[1] FALSE
# create it
dir.create('FootballTemp')
# check again
dir.exists('FootballTemp')
[1] TRUE
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10. Download Files
download.file('http://www.jaredlander.com/data/Football1415.tar.gz',
destfile='FootballTemp/football.tar.gz',
method='curl')
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11. Untar
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12. getXKCD('1168')
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13. Untar the File
untar('FootballTemp/football.tar.gz', exdir='FootballFiles')
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14. Did They Extract?
dir('FootballFiles')
[1] "pbp‐2014.csv" "pbp‐2015.csv"
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15. Delete Tar
unlink('FootballTemp/football.tar.gz')
dir('FootballTemp')
character(0)
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16. Inspect One File
file.info('FootballFiles/pbp‐2014.csv')
size isdir mode mtime
FootballFiles/pbp‐2014.csv 10280324 FALSE 666 2016‐03‐25 00:14:23
ctime atime exe
FootballFiles/pbp‐2014.csv 2016‐04‐04 22:48:47 2016‐04‐04 22:48:47 no
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17. Inspect All Files
dir('FootballFiles') %>% file.info
size isdir mode mtime ctime atime exe
pbp‐2014.csv NA NA <NA> <NA> <NA> <NA> <NA>
pbp‐2015.csv NA NA <NA> <NA> <NA> <NA> <NA>
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18. Inspect All Files
dir('FootballFiles', full.names=TRUE) %>% file.info
size isdir mode mtime
FootballFiles/pbp‐2014.csv 10280324 FALSE 666 2016‐03‐25 00:14:23
FootballFiles/pbp‐2015.csv 10671016 FALSE 666 2016‐03‐25 00:14:23
ctime atime exe
FootballFiles/pbp‐2014.csv 2016‐04‐04 22:48:47 2016‐04‐04 22:48:47 no
FootballFiles/pbp‐2015.csv 2016‐04‐04 22:48:47 2016‐04‐04 22:48:47 no
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19. Better Names
file.rename(from=dir('FootballFiles', full.names=TRUE),
to=sprintf('FootballFiles/Football%s.csv', 14:15))
[1] TRUE TRUE
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20. Better Names
dir('FootballFiles')
[1] "Football14.csv" "Football15.csv"
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21. Make Copies
dir.create('FootballFiles/Backup')
file.copy(dir('FootballFiles', full.names=TRUE, pattern='.csv'),
sprintf('FootballFiles/Backup/Footballl%s.csv', 14:15))
[1] TRUE TRUE
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22. Make Copies
dir('FootballFiles', recursive=TRUE)
[1] "Backup/Footballl14.csv" "Backup/Footballl15.csv" "Football14.csv"
[4] "Football15.csv"
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23. Count Columns
count.fields('FootballFiles/Football14.csv', sep=',') %>% head(15)
[1] 45 45 45 45 15 45 45 45 45 45 45 45 45 45 45
count.fields('FootballFiles/Football15.csv', sep=',') %>% head(15)
[1] 45 45 45 45 45 45 45 45 45 NA 15 45 45 45 45
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24. Line Count
system('wc ‐l FootballFiles/Football14.csv')
45696 FootballFiles/Football14.csv
system('wc ‐l FootballFiles/Football15.csv')
46278 FootballFiles/Football15.csv
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25. Reference Files
dataPath <‐ 'FootballFiles'
file.path(dataPath, 'Football14.csv')
[1] "FootballFiles/Football14.csv"
file.path(dataPath, 'Football15.csv')
[1] "FootballFiles/Football15.csv"
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26. Read Data
theFiles <‐ dir(dataPath, pattern='.csv', full.names=TRUE)
games <‐ theFiles %>% map_df(read.csv2, sep=',', header=TRUE, stringsAsFactors=FALSE)
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27. See the Data
DT::datatable(data=games%>% slice(sample(nrow(games), size=500, replace=FALSE)),
rownames=FALSE,
options = list(
dom = "rtiS",
scrollY = 400, scrollX=TRUE,
scrollCollapse = TRUE),
filter=list(position='top')
)
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28. See the Data
Showing 1 to 10 of 500 entries
2016010300 2016-01-03 1 12 31 BUF NYJ
2015120608 2015-12-06 2 15 0 ATL TB
2015122100 2015-12-21 1 15 0 DET NO
2014111610 11/16/2014 1 6 6 DET ARI
2015112904 2015-11-29 2 1 23 IND TB
2015122710 2015-12-27 2 14 7 GB ARI
2014101203 10/12/2014 1 11 20 PIT CLE
2015101102 2015-10-11 2 8 2 CIN SEA
GameId GameDate Quarter Minute Second OffenseTeam DefenseTeam Down ToGo
All All All All All All All All All
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29. Pass vs Rush
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30. Focus on One Team's Offense
oneOff <‐ games %>%
filter(OffenseTeam == 'NYG', PlayType %in% c('PASS', 'RUSH')) %>%
mutate(PlayType=factor(PlayType, levels=c('RUSH', 'PASS')),
Down=factor(Down, levels=c(1, 2, 3, 4)))
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31. Probability of a Pass
passRushMod <‐ glm(PlayType ~ Down + ToGo ‐ 1, data=oneOff, family=binomial)
coefplot(passRushMod, trans=arm::invlogit, title='Probability of Pass')
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32. Scenarios
# make grid of scenarios
scenarios <‐ expand.grid(ToGo=1:15, Down=1:4) %>% as.tbl %>%
mutate(Down=factor(Down, levels=c(1, 2, 3, 4)))
# make prediction based on model
scenarioPredict <‐ predict(passRushMod,
newdata=scenarios, type='response', se.fit=TRUE)
# build confidence intervals
scenarios <‐ scenarios %>% mutate(Prediction=scenarioPredict$fit,
Lower=Prediction ‐ 2*scenarioPredict$se.fit,
Upper=Prediction + 2*scenarioPredict$se.fit)
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33. Scenarios
ToGo Down Prediction Lower Upper
1 1 0.2754536 0.2135514 0.3373558
2 1 0.2959441 0.2371832 0.3547051
3 1 0.3172914 0.2621339 0.3724488
4 1 0.3394361 0.2882498 0.3906223
5 1 0.3623061 0.3153154 0.4092968
6 1 0.3858171 0.3430379 0.4285962
knitr::kable(head(scenarios))
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34. Probability of Pass
ggplot(scenarios, aes(x=ToGo)) + scale_y_continuous(label=scales::percent) +
geom_ribbon(aes(ymin=Lower, ymax=Upper), fill='lightgrey') +
geom_line(aes(y=Prediction)) + facet_wrap(~Down, nrow=2)
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35. Get Eli's Stats
eliPage <‐ read_html('http://www.pro‐football‐reference.com/players/M/MannEl00.htm')
eliStats <‐ eliPage %>% html_nodes("#passing") %>%
html_table(header=TRUE) %>% getElement(1)
useful::topleft(eliStats, c=7, r=8)
Year Age Tm Pos No. G GS
1 2004 23 NYG qb 10 9 7
2 2005 24 NYG QB 10 16 16
3 2006 25 NYG QB 10 16 16
4 2007 26 NYG QB 10 16 16
5 2008* 27 NYG QB 10 16 16
6 2009 28 NYG QB 10 16 16
7 2010 29 NYG QB 10 16 16
8 2011* 30 NYG QB 10 16 16
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37. Save Them
dir.create('results')
ggsave('results/EliPass.png')
write.table(eliStats, file='results/eliStats.csv', sep=',', row.names=FALSE)
[1] TRUE
[1] TRUE
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39. Commit Them
repo <‐ repository(getwd())
add(repo, file.path('results', c('eliPass.png', 'eliStats.csv')))
commit(repo, message='Tracking plot and csv')
push(repo)
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41. Email Them
footballResults <‐ mime(
To = "jared@landeranalytics.com",
From = "jared@jaredlander.com",
Subject = "Eli Results",
body = "See the attached graph and data.") %>%
attach_file('results/EliPass.png') %>%
attach_file('results/eliStats.csv')
send_message(footballResults)
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42. Things We've Done
Create Directories
Query Directories
Untar Files
Read XKCD
Delete Files
Get File Info
Move Files
Copy Files
Count Columns
Run System Commands
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Build File Paths
Read Data
Munge Data
Fit a GLM
Make Predictions
Generate Plots
Save Files
Scrape a Website
Commit and Push to Git
Send an Email
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43. Jared P. Lander
Chief Data Scientist of Lander Analytics
Author of R for Everyone
Adjunct Professor at Columbia University
Organizer of New York Open Statistical Programming (The R) Meetup
Website: http://www.jaredlander.com
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44. Packages
rvest
ggplot2
dplyr
purrr
coefplot
magritrr
useful
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45. The Tools
R
RStudio
knitr
Pandoc
ioslides
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