Over the past few days I’ve written a few blog posts about a Wimbledon data set I’ve been building and after running the scripts a few times I noticed that it was taking much longer to run that I expected.
To recap, I started out with the following function which takes in a URI and returns a data frame containing a row for each match:
library(rvest)
library(dplyr)
scrape_matches1 = function(uri) {
matches = data.frame()
s = html(uri)
rows = s %>% html_nodes("div#scoresResultsContent tr")
i = 0
for(row in rows) {
players = row %>% html_nodes("td.day-table-name a")
seedings = row %>% html_nodes("td.day-table-seed")
score = row %>% html_node("td.day-table-score a")
flags = row %>% html_nodes("td.day-table-flag img")
if(!is.null(score)) {
player1 = players[1] %>% html_text() %>% str_trim()
seeding1 = ifelse(!is.na(seedings[1]), seedings[1] %>% html_node("span") %>% html_text() %>% str_trim(), NA)
flag1 = flags[1] %>% html_attr("alt")
player2 = players[2] %>% html_text() %>% str_trim()
seeding2 = ifelse(!is.na(seedings[2]), seedings[2] %>% html_node("span") %>% html_text() %>% str_trim(), NA)
flag2 = flags[2] %>% html_attr("alt")
matches = rbind(data.frame(winner = player1,
winner_seeding = seeding1,
winner_flag = flag1,
loser = player2,
loser_seeding = seeding2,
loser_flag = flag2,
score = score %>% html_text() %>% str_trim(),
round = round), matches)
} else {
round = row %>% html_node("th") %>% html_text()
}
}
return(matches)
}Let’s run it to get an idea of the data that it returns:
matches1 = scrape_matches1("http://www.atpworldtour.com/en/scores/archive/wimbledon/540/2014/results")
> matches1 %>% filter(round %in% c("Finals", "Semi-Finals", "Quarter-Finals"))
winner winner_seeding winner_flag loser loser_seeding loser_flag score round
1 Milos Raonic (8) CAN Nick Kyrgios (WC) AUS 674 62 64 764 Quarter-Finals
2 Roger Federer (4) SUI Stan Wawrinka (5) SUI 36 765 64 64 Quarter-Finals
3 Grigor Dimitrov (11) BUL Andy Murray (3) GBR 61 764 62 Quarter-Finals
4 Novak Djokovic (1) SRB Marin Cilic (26) CRO 61 36 674 62 62 Quarter-Finals
5 Roger Federer (4) SUI Milos Raonic (8) CAN 64 64 64 Semi-Finals
6 Novak Djokovic (1) SRB Grigor Dimitrov (11) BUL 64 36 762 767 Semi-Finals
7 Novak Djokovic (1) SRB Roger Federer (4) SUI 677 64 764 57 64 FinalsAs I mentioned, it’s quite slow but I thought I’d wrap it in system.time so I could see exactly how long it was taking:
> system.time(scrape_matches1("http://www.atpworldtour.com/en/scores/archive/wimbledon/540/2014/results"))
user system elapsed
25.570 0.111 31.416About 30 seconds! The first thing I tried was downloading the file separately and running the function against the local file:
> system.time(scrape_matches1("data/raw/2014.html"))
user system elapsed
25.662 0.123 25.863Hmmm, that’s only saved us 5 seconds so the bottleneck must be somewhere else. Still there’s no point making a HTTP request every time we run the script so we’ll stick with the local file version.
While browsing rvest’s vignette I noticed a function called html_table which I was curious about. I decided to try and replace some of my code with a call to that:
matches2= html("data/raw/2014.html") %>%
html_node("div#scoresResultsContent table.day-table") %>% html_table(header = FALSE) %>%
mutate(X1 = ifelse(X1 == "", NA, X1)) %>%
mutate(round = ifelse(grepl("\\([0-9]\\)|\\(", X1), NA, X1)) %>%
mutate(round = na.locf(round)) %>%
filter(!is.na(X8)) %>%
select(winner = X3, winner_seeding = X1, loser = X7, loser_seeding = X5, score = X8, round)
> matches2 %>% filter(round %in% c("Finals", "Semi-Finals", "Quarter-Finals"))
winner winner_seeding loser loser_seeding score round
1 Novak Djokovic (1) Roger Federer (4) 677 64 764 57 64 Finals
2 Novak Djokovic (1) Grigor Dimitrov (11) 64 36 762 767 Semi-Finals
3 Roger Federer (4) Milos Raonic (8) 64 64 64 Semi-Finals
4 Novak Djokovic (1) Marin Cilic (26) 61 36 674 62 62 Quarter-Finals
5 Grigor Dimitrov (11) Andy Murray (3) 61 764 62 Quarter-Finals
6 Roger Federer (4) Stan Wawrinka (5) 36 765 64 64 Quarter-Finals
7 Milos Raonic (8) Nick Kyrgios (WC) 674 62 64 764 Quarter-FinalsI had to do some slightly clever stuff to get the ’round’ column into shape using zoo’s na.locf function which I wrote about previously.
Unfortunately I couldn’t work out how to extract the flag with this version – that value is hidden in the ‘alt’ tag of an img and presumably html_table is just grabbing the text value of each cell. This version is much quicker though!
system.time(html("data/raw/2014.html") %>%
html_node("div#scoresResultsContent table.day-table") %>% html_table(header = FALSE) %>%
mutate(X1 = ifelse(X1 == "", NA, X1)) %>%
mutate(round = ifelse(grepl("\\([0-9]\\)|\\(", X1), NA, X1)) %>%
mutate(round = na.locf(round)) %>%
filter(!is.na(X8)) %>%
select(winner = X3, winner_seeding = X1, loser = X7, loser_seeding = X5, score = X8, round))
user system elapsed
0.545 0.002 0.548What I realised from writing this version is that I need to match all the columns with one call to html_nodes rather than getting the row and then each column in a loop.
I rewrote the function to do that:
scrape_matches3 = function(uri) {
s = html(uri)
players = s %>% html_nodes("div#scoresResultsContent tr td.day-table-name a")
seedings = s %>% html_nodes("div#scoresResultsContent tr td.day-table-seed")
scores = s %>% html_nodes("div#scoresResultsContent tr td.day-table-score a")
flags = s %>% html_nodes("div#scoresResultsContent tr td.day-table-flag img") %>% html_attr("alt") %>% str_trim()
matches3 = data.frame(
winner = sapply(seq(1,length(players),2), function(idx) players[[idx]] %>% html_text()),
winner_seeding = sapply(seq(1,length(seedings),2), function(idx) seedings[[idx]] %>% html_text() %>% str_trim()),
winner_flag = sapply(seq(1,length(flags),2), function(idx) flags[[idx]]),
loser = sapply(seq(2,length(players),2), function(idx) players[[idx]] %>% html_text()),
loser_seeding = sapply(seq(2,length(seedings),2), function(idx) seedings[[idx]] %>% html_text() %>% str_trim()),
loser_flag = sapply(seq(2,length(flags),2), function(idx) flags[[idx]]),
score = sapply(scores, function(score) score %>% html_text() %>% str_trim())
)
return(matches3)
}Let’s run and time that to check we’re getting back the right results in a timely manner:
> matches3 %>% sample_n(10)
winner winner_seeding winner_flag loser loser_seeding loser_flag score
70 David Ferrer (7) ESP Pablo Carreno Busta ESP 60 673 61 61
128 Alex Kuznetsov (26) USA Tim Smyczek (3) USA 46 63 63 63
220 Rogerio Dutra Silva BRA Kristijan Mesaros CRO 62 63
83 Kevin Anderson (20) RSA Aljaz Bedene (LL) GBR 63 75 62
73 Kei Nishikori (10) JPN Kenny De Schepper FRA 64 765 75
56 Roberto Bautista Agut (27) ESP Jan Hernych (Q) CZE 75 46 62 62
138 Ante Pavic CRO Marc Gicquel (29) FRA 46 63 765 64
174 Tim Puetz GER Ruben Bemelmans BEL 64 62
103 Lleyton Hewitt AUS Michal Przysiezny POL 62 6714 61 64
35 Roger Federer (4) SUI Gilles Muller (Q) LUX 63 75 63
> system.time(scrape_matches3("data/raw/2014.html"))
user system elapsed
0.815 0.006 0.827It’s still quick – a bit slower than html_table but we can deal with that. As you can see, I also had to add some logic to separate the values for the winners and losers – the players, seeds, flags come back as as one big list. The odd rows represent the winner; the even rows the loser.
Annoyingly we’ve now lost the ’round’ column because that appears as a table heading so we can’t extract it the same way. I ended up cheating a bit to get it to work by working out how many matches each round should contain and generated a vector with that number of entries:
raw_rounds = s %>% html_nodes("th") %>% html_text()
> raw_rounds
[1] "Finals" "Semi-Finals" "Quarter-Finals" "Round of 16" "Round of 32"
[6] "Round of 64" "Round of 128" "3rd Round Qualifying" "2nd Round Qualifying" "1st Round Qualifying"
rounds = c( sapply(0:6, function(idx) rep(raw_rounds[[idx + 1]], 2 ** idx)) %>% unlist(),
sapply(7:9, function(idx) rep(raw_rounds[[idx + 1]], 2 ** (idx - 3))) %>% unlist())
> rounds[1:10]
[1] "Finals" "Semi-Finals" "Semi-Finals" "Quarter-Finals" "Quarter-Finals" "Quarter-Finals" "Quarter-Finals"
[8] "Round of 16" "Round of 16" "Round of 16"Let’s put that code into the function and see if we end up with the same resulting data frame:
scrape_matches4 = function(uri) {
s = html(uri)
players = s %>% html_nodes("div#scoresResultsContent tr td.day-table-name a")
seedings = s %>% html_nodes("div#scoresResultsContent tr td.day-table-seed")
scores = s %>% html_nodes("div#scoresResultsContent tr td.day-table-score a")
flags = s %>% html_nodes("div#scoresResultsContent tr td.day-table-flag img") %>% html_attr("alt") %>% str_trim()
raw_rounds = s %>% html_nodes("th") %>% html_text()
rounds = c( sapply(0:6, function(idx) rep(raw_rounds[[idx + 1]], 2 ** idx)) %>% unlist(),
sapply(7:9, function(idx) rep(raw_rounds[[idx + 1]], 2 ** (idx - 3))) %>% unlist())
matches4 = data.frame(
winner = sapply(seq(1,length(players),2), function(idx) players[[idx]] %>% html_text()),
winner_seeding = sapply(seq(1,length(seedings),2), function(idx) seedings[[idx]] %>% html_text() %>% str_trim()),
winner_flag = sapply(seq(1,length(flags),2), function(idx) flags[[idx]]),
loser = sapply(seq(2,length(players),2), function(idx) players[[idx]] %>% html_text()),
loser_seeding = sapply(seq(2,length(seedings),2), function(idx) seedings[[idx]] %>% html_text() %>% str_trim()),
loser_flag = sapply(seq(2,length(flags),2), function(idx) flags[[idx]]),
score = sapply(scores, function(score) score %>% html_text() %>% str_trim()),
round = rounds
)
return(matches4)
}
matches4 = scrape_matches4("data/raw/2014.html")
> matches4 %>% filter(round %in% c("Finals", "Semi-Finals", "Quarter-Finals"))
winner winner_seeding winner_flag loser loser_seeding loser_flag score round
1 Novak Djokovic (1) SRB Roger Federer (4) SUI 677 64 764 57 64 Finals
2 Novak Djokovic (1) SRB Grigor Dimitrov (11) BUL 64 36 762 767 Semi-Finals
3 Roger Federer (4) SUI Milos Raonic (8) CAN 64 64 64 Semi-Finals
4 Novak Djokovic (1) SRB Marin Cilic (26) CRO 61 36 674 62 62 Quarter-Finals
5 Grigor Dimitrov (11) BUL Andy Murray (3) GBR 61 764 62 Quarter-Finals
6 Roger Federer (4) SUI Stan Wawrinka (5) SUI 36 765 64 64 Quarter-Finals
7 Milos Raonic (8) CAN Nick Kyrgios (WC) AUS 674 62 64 764 Quarter-FinalsWe shouldn’t have added much to the time but let’s check:
> system.time(scrape_matches4("data/raw/2014.html"))
user system elapsed
0.816 0.004 0.824Sweet. We’ve saved ourselves 29 seconds per page as long as the number of rounds stayed constant over the years. For the 10 years that I’ve looked at it has but I expect if you go back further the draw sizes will have been different and our script would break.
For now though this will do!
| Reference: | R: Speeding up the Wimbledon scraping job from our JCG partner Mark Needham at the Mark Needham Blog blog. |
