Delivered by Jared Lander (Author, R for Everyone) at the 2016 New York R Conference on April 8th and 9th at Work-Bench.

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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