I am new to R.
I need help assigning web scraping data to "salary". Somehow, my variable "salary" is showing character (empty) in my environment. I have used SelectorGadget to find the html nodes.
Would really appreciate it if someone can explain it to me. Thanks!
library(rvest)
library(tidyverse)
library(magrittr)
nba_player_salaries <- read_html("https://hoopshype.com/salaries/players/2018-2019/")
salary <- nba_player_salaries %>%
html_nodes("tbody .hh-salaries-sorted") %>%
html_text2()
You can directly extract the table from the page :
library(rvest)
library(dplyr)
url <- 'https://hoopshype.com/salaries/players/2018-2019/'
url %>%
read_html() %>%
html_table() %>%
.[[1]] %>%
setNames(.[1, ]) %>% #Since column names are in 1st row
slice(-1) %>% #Remove 1st row
select(-1) #Remove 1st column
# Player `2018/19` `2018/19(*)`
# <chr> <chr> <chr>
# 1 Stephen Curry $37,457,154 $38,320,489
# 2 Russell Westbrook $35,665,000 $36,487,029
# 3 Chris Paul $35,654,150 $36,475,929
# 4 LeBron James $35,654,150 $36,475,929
# 5 Kyle Lowry $32,700,000 $33,453,690
# 6 Blake Griffin $31,873,932 $32,608,582
# 7 Gordon Hayward $31,214,295 $31,933,741
# 8 James Harden $30,570,000 $31,274,596
# 9 Paul George $30,560,700 $31,265,082
#10 Mike Conley $30,521,115 $31,224,584
# … with 566 more rows
Related
I'm trying to scrape all the data from this website. There are icons over some of the competitors names indicating that the person was disqualified for being a 'no-show'.
I would like create a data frame with all the competitors while also specifying who was disqualified, but I'm running into two issues:
(1) trying to add the disclaimer next to the persons name produces the error cannot coerce class ‘"xml_nodeset"’ to a data.frame.
(2) trying to extract the text from just the icon (and not the competitor names) produces a blank data frame.
library(rvest); library(tidyverse)
html = read_html('https://web.archive.org/web/20220913034642/https://www.bjjcompsystem.com/tournaments/1869/categories/2053162')
dq = data.frame(winner = html %>%
html_nodes('.match-card__competitor--red') %>%
html_text(trim = TRUE),
opponent = html %>%
html_nodes('hr+ .match-card__competitor'),
dq = html %>%
html_nodes('.match-card__disqualification') %>%
html_text())
This approach generally works only on tabular data where you can be sure that the number of matches for each of those selectors are constant and order is also fixed. In your example you have:
127 matches for .match-card__competitor--red
127 matches for hr+ .match-card__competitor
14 matches for .match-card__disqualification (you get no results for this because you should use html_attr("title") for title attribute instead of html_text())
Basically you are trying to combine columns of different lengths into the same dataframe. Even if it would work, you'd just add DSQ for 14 first matches.
As you'd probably want to keep information about matched, participants, results and disqualifications instead of just having a list of participants, I'd suggest to work with a list of match cards, i.e. extract all required information from a single card while not breaking relations and then move to the next card.
My purrr is far from perfect, but perhaps something like this:
library(rvest)
library(magrittr)
library(purrr)
library(dplyr)
library(tibble)
library(tidyr)
# helpers -----------------------------------------------------------------
# to keep matches with details (when/where) in header
is_valid_match <- function(element){
return(length(html_elements(element, ".bracket-match-header")) > 0)
}
# detect winner
is_winner <- function(element){
return(length(html_elements(element, ".match-competitor--loser")) < 1 )
}
# extract data from competitor sections
comp_details <- function(comp_card, prefix="_"){
l = lst()
l[paste(prefix, "n", sep = "")] <- comp_card %>% html_element(".match-card__competitor-n") %>% html_text()
l[paste(prefix, "name", sep = "")] <- comp_card %>% html_element(".match-card__competitor-name") %>% html_text()
l[paste(prefix, "club", sep = "")] <- comp_card %>% html_element(".match-card__club-name") %>% html_text()
l[paste(prefix, "dq", sep = "")] <- comp_card %>% html_element(".match-card__disqualification") %>% html_attr("title")
l[paste(prefix, "won", sep = "")] <- comp_card %>% html_element(".match-competitor--loser") %>% length() == 0
return(l)
}
# scrape & process --------------------------------------------------------
html <- read_html('https://web.archive.org/web/20220913034642/https://www.bjjcompsystem.com/tournaments/1869/categories/2053162')
html %>%
# collect all match cards
html_elements("div.tournament-category__match") %>%
keep(is_valid_match) %>%
# apply anonymous function to every item in the list of match cards
map(function(match_card){
match_id <- match_card %>% html_element(".tournament-category__match-card") %>% html_attr("id")
where <- match_card %>% html_element(".bracket-match-header__where") %>% html_text()
when <- match_card %>% html_element(".bracket-match-header__when") %>% html_text()
competitors <- html_nodes(match_card, ".match-card__competitor")
# extract competitior data
comp01 <- competitors[[1]] %>% comp_details(prefix = "comp01_")
comp02 <- competitors[[2]] %>% comp_details(prefix = "comp02_")
winner_idx <- competitors %>% detect_index(is_winner)
# lst for creating a named list
l <- lst(match_id, where, when, winner_idx)
# combine all items and comp lists into single list
l <- c(l,comp01, comp02)
return(l)
}) %>%
# each resulting list item into single-row tibble
map(as_tibble) %>%
# reduce list of tibbles into single tibble
reduce(bind_rows)
Result:
#> # A tibble: 65 × 14
#> match_id where when winne…¹ comp0…² comp0…³ comp0…⁴ comp0…⁵ comp0…⁶ comp0…⁷
#> <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 match-1-1 FIGH… Sat … 2 58 Christ… Rodrig… <NA> FALSE 66
#> 2 match-1-9 FIGH… Sat … 2 6 Melvin… GF Team Disqua… FALSE 66
#> 3 match-1-… FIGH… Sat … 2 47 Eric R… Atos J… <NA> FALSE 66
#> 4 match-1-… FIGH… Sat … 1 47 Eric R… Atos J… <NA> TRUE 10
#> 5 match-1-… FIGH… Sat … 2 42 Ivan M… CheckM… <NA> FALSE 66
#> 6 match-1-… FIGH… Sat … 2 18 Joel S… Gracie… <NA> FALSE 47
#> 7 match-1-… FIGH… Sat … 1 42 Ivan M… CheckM… <NA> TRUE 26
#> 8 match-1-… FIGH… Sat … 2 34 Matthe… Super … <NA> FALSE 18
#> 9 match-2-9 FIGH… Sat … 1 62 Bryan … Team J… <NA> TRUE 4
#> 10 match-2-… FIGH… Sat … 2 22 Steffe… Six Bl… <NA> FALSE 30
#> # … with 55 more rows, 4 more variables: comp02_name <chr>, comp02_club <chr>,
#> # comp02_dq <chr>, comp02_won <lgl>, and abbreviated variable names
#> # ¹winner_idx, ²comp01_n, ³comp01_name, ⁴comp01_club, ⁵comp01_dq,
#> # ⁶comp01_won, ⁷comp02_n
Created on 2022-09-19 with reprex v2.0.2
Also note that not all matches have a winner and both participants can be disqualified (screenshot), so splitting them to winners & opponents might not be optimal.
I'm trying to substitute the years 2000-2020 into the url by using vectors. The error I get is Can't combine 1$Rnk and 4$Rnk . How can I fix this?
TDF_wtables <- function(url){
url %>%
read_html() %>%
# extract the table part of the html code
html_node("table") %>%
# create R dataset from webpage contents
html_table() %>%
# only Year and Gross are of interest in our analysis
select(c("Rnk", "Rider", "Team", "Time")) %>%
as_tibble()
}
Year <- 2000:2020
TDFurls <- str_c("https://www.procyclingstats.com/race/tour-de-france/",Year,"/gc")
Maps <- map_dfr(TDFurls, TDF_wtables, .id = "Year")
Maps
You get that error because sometimes there are rows like this
For any table that contains rows like the one shown above, the program cannot naturally tell if the Rnk column should be of a character type or an integer type. The simplest solution to this would be just converting all everything into character
TDF_wtables <- function(url){
url %>%
read_html() %>%
# extract the table part of the html code
html_node("table") %>%
# create R dataset from webpage contents
html_table() %>%
# only Year and Gross are of interest in our analysis
select(c("Rnk", "Rider", "Team", "Time")) %>%
as_tibble() %>%
# all columns as character
mutate(across(.fns = as.character))
}
Output
> Maps <- map_dfr(TDFurls, TDF_wtables)
> Maps
# A tibble: 3,225 x 4
Rnk Rider Team Time
<chr> <chr> <chr> <chr>
1 1 Zanini StefanoMapei - Quickstep Mapei - Quickstep 3:12:363:12:36
2 2 Zabel ErikTeam Telekom Team Telekom ,,0:00
3 3 Vainšteins RomānsVini Caldirola - Sidermec Vini Caldirola - Sidermec ,,0:00
4 4 Rodriguez FredMapei - Quickstep Mapei - Quickstep ,,0:00
5 5 van Heeswijk MaxMapei - Quickstep Mapei - Quickstep ,,0:00
6 6 Magnien EmmanuelLa Française des Jeux La Française des Jeux ,,0:00
7 7 Simon FrançoisBonjour - Toupargel Bonjour - Toupargel ,,0:00
8 8 McEwen RobbieFarm Frites Farm Frites ,,0:00
9 9 Commesso SalvatoreSaeco Saeco ,,0:00
10 10 Piziks ArvisMemoryCard - Jack & Jones MemoryCard - Jack & Jones ,,0:00
# ... with 3,215 more rows
Update
That Time column has two spans nested in each row. They have the same text, so you have to trim off one of them to avoid repetition. Also, I just realised that your code does not get you the table you want. You only want the table on the "GC" tab, right? Consider the following function:
TDF_wtables <- function(url){
gc_table <-
url %>%
read_html() %>%
html_node("div[class$='resultCont '] > table")
timeff <-
gc_table %>%
html_nodes("tbody > tr > td > span.timeff") %>%
html_text()
gc_table %>%
html_table() %>%
select(c("Rnk", "Rider", "Team", "Time")) %>%
as_tibble() %>%
mutate(Time = timeff, across(.fns = as.character))
}
Output
# A tibble: 3,222 x 4
Rnk Rider Team Time
<chr> <chr> <chr> <chr>
1 NA Armstrong LanceUS Postal Service US Postal Service " 92:33:08"
2 2 Ullrich JanTeam Telekom Team Telekom "6:02"
3 3 Beloki JosebaFestina - Lotus Festina - Lotus "10:04"
4 4 Moreau ChristopheFestina - Lotus Festina - Lotus "10:34"
5 5 Heras RobertoKelme - Costa Blanca Kelme - Costa Blanca "11:50"
6 6 Virenque RichardPolti Polti "13:26"
7 7 Botero SantiagoKelme - Costa Blanca Kelme - Costa Blanca "14:18"
8 8 Escartín FernandoKelme - Costa Blanca Kelme - Costa Blanca "17:21"
9 9 Mancebo FranciscoBanesto Banesto "18:09"
10 10 Nardello DanieleMapei - Quickstep Mapei - Quickstep "18:25"
# ... with 3,212 more rows
I have used rvest package to extract a list of companies and the a.href elements in each company, which I need to proceed with the data collection process. This is the link of the website: http://www.bursamalaysia.com/market/listed-companies/list-of-companies/main-market.
I have used the following code to extract the table but nothing comes out. I used other approaches as those posted in "Scraping table of NBA stats with rvest" and similar links, but I cannot obtain what I want. Any help would be greatly appreciated.
my code:
link.main <-
"http://www.bursamalaysia.com/market/listed-companies/list-of-companies/main-market/"
web <- read_html(link.main) %>%
html_nodes("table#bm_equities_prices_table")
# it does not work even when I write html_nodes("table")
or ".table" or #bm_equities_prices_table
web <- read_html(link.main)
%>% html_nodes(".bm_center.bm_dataTable")
# no working
web <- link.main %>% read_html() %>% html_table()
# to inspect the position of table in this website
The page generates the table using JavaScript, so you either need to use RSelenium or Python's Beautiful Soup to simulate the browser session and allow javascript to run.
Another alternative is to use awesome package by #hrbrmstr called decapitated, which basically runs headless Chrome browser session in the background.
#devtools::install_github("hrbrmstr/decapitated")
library(decapitated)
library(rvest)
res <- chrome_read_html(link.main)
main_df <- res %>%
rvest::html_table() %>%
.[[1]] %>%
as_tibble()
This outputs the content of the table alright. If you want to get to the elements underlying the table (href attributes behind the table text), you will need to do a bit more of list gymnastics. Some of the elements in the table are actually missing links, extracting by css proved to be difficult.
library(dplyr)
library(purrr)
href_lst <- res %>%
html_nodes("table td") %>%
as_list() %>%
map("a") %>%
map(~attr(.x, "href"))
# we need every third element starting from second element
idx <- seq.int(from=2, by=3, length.out = nrow(main_df))
href_df <- tibble(
market_href=as.character(href_lst[idx]),
company_href=as.character(href_lst[idx+1])
)
bind_cols(main_df, href_df)
#> # A tibble: 800 x 5
#> No `Company Name` `Company Website` market_href company_href
#> <int> <chr> <chr> <chr> <chr>
#> 1 1 7-ELEVEN MALAYS~ http://www.7elev~ /market/list~ http://www.~
#> 2 2 A-RANK BERHAD [~ http://www.arank~ /market/list~ http://www.~
#> 3 3 ABLEGROUP BERHA~ http://www.gefun~ /market/list~ http://www.~
#> 4 4 ABM FUJIYA BERH~ http://www.abmfu~ /market/list~ http://www.~
#> 5 5 ACME HOLDINGS B~ http://www.suppo~ /market/list~ http://www.~
#> 6 6 ACOUSTECH BERHA~ http://www.acous~ /market/list~ http://www.~
#> 7 7 ADVANCE SYNERGY~ http://www.asb.c~ /market/list~ http://www.~
#> 8 8 ADVANCECON HOLD~ http://www.advan~ /market/list~ http://www.~
#> 9 9 ADVANCED PACKAG~ http://www.advan~ /market/list~ http://www.~
#> 10 10 ADVENTA BERHAD ~ http://www.adven~ /market/list~ http://www.~
#> # ... with 790 more rows
Another option without using browser:
library(httr)
library(jsonlite)
library(XML)
r <- httr::GET(paste0(
"http://ws.bursamalaysia.com/market/listed-companies/list-of-companies/list_of_companies_f.html",
"?_=1532479072277",
"&callback=jQuery16206432131784246533_1532479071878",
"&alphabet=",
"&market=main_market",
"&_=1532479072277"))
l <- rawToChar(r$content)
m <- gsub("jQuery16206432131784246533_1532479071878(", "", substring(l, 1, nchar(l)-1), fixed=TRUE)
tbl <- XML::readHTMLTable(jsonlite::fromJSON(m)$html)$bm_equities_prices_table
output:
> head(tbl)
# No Company Name Company Website
#1 1 7-ELEVEN MALAYSIA HOLDINGS BERHAD http://www.7eleven.com.my
#2 2 A-RANK BERHAD [S] http://www.arank.com.my
#3 3 ABLEGROUP BERHAD [S] http://www.gefung.com.my
#4 4 ABM FUJIYA BERHAD [S] http://www.abmfujiya.com.my
#5 5 ACME HOLDINGS BERHAD [S] http://www.supportivetech.com/
#6 6 ACOUSTECH BERHAD [S] http://www.acoustech.com.my/
I'm having difficulties writing a scrape function with the purrr package (first time). I want to scrape multiple pages by changing two characters of the designated url. The following code works for only one season of football players data.
page_func <- function(page) {
cat(".")
df <- read_html(paste0("http://www.voetbal.com/spelerslijst/ned-eredivisie-2017-2018/nach-name/",
page)) %>%
html_nodes("table") %>%
html_table() %>%
as.data.frame() %>%
as.tbl() %>%
select(Speler, Team, Geboren, Lengte, Positie) %>%
add_column(seizoen = "2017-2018")
}
raw_seizoen_17_18 <- map_df(1:11, page_func)
Output:
# A tibble: 541 x 6
Speler Team Geboren Lengte Positie seizoen
<chr> <chr> <chr> <chr> <chr> <chr>
1 Amir Absalem FC Groningen 19.06.1997 ??? VD 2017-2018
2 Asumah Abubakar Willem II 10.05.1997 183 cm AV 2017-2018
3 Ragnar Ache Sparta Rotterdam 28.07.1998 182 cm AV 2017-2018
4 Marouane Afaker SBV Excelsior 09.05.1999 ??? AV 2017-2018
5 Gor Agbaljan Heracles Almelo 25.04.1997 183 cm MV 2017-2018
6 Thomas Agyepong NAC Breda 10.10.1995 168 cm AV 2017-2018
Now I want to scrape all seasons from 1956-1957 untill 2017-2018 in one function, but I can't yet figure out how to manipulate these two variables with purrr.
page_season_func <- function(seizoen, page) {
cat(".")
df <- read_html(paste0("http://www.voetbal.com/spelerslijst/ned-eredivisie-",
seizoen,
"/nach-name/",
page)) %>%
html_nodes("table") %>%
html_table() %>%
as.data.frame() %>%
as.tbl() %>%
select(Speler, Team, Geboren, Lengte, Positie) %>%
add_column(year = seizoen)
}
seasons <-
1956:2017 %>%
paste(., . + 1, sep = "-")
res <-
cross2(seasons, 1:11) %>%
transpose() %>%
pmap_df(page_season_func)
You can use map2_dfr, with the .id tag to specify the year in your output:
page_span <- 1:11
year_span <- 1956:1958
years <- sort(rep(year_span, length(page_span)))
names(years) <- years # need to name years for .id to work
pages <- rep(page_span, length(year_span))
map2_dfr(years, pages, page_season_func, .id="year")
Output:
# A tibble: 6 x 6
year Speler Team Geboren Lengte Positie
<chr> <chr> <chr> <chr> <chr> <chr>
1 1956 Sjeng Adang Roda JC Kerk… 04.07.19… ??? MV
2 1956 Wim Anderiesen jr. AFC Ajax 02.09.19… ??? VD
3 1956 Wim Andriesen AFC Ajax 09.02.19… ??? MV
4 1956 Aad Bak Feyenoord 18.06.19… ??? MV
5 1956 Huub Bisschops Roda JC Kerk… 22.01.19… ??? AV
6 1956 Wim Bleijenberg AFC Ajax 05.11.19… ??? AV
A couple of changes to page_season_func():
seizoen2 is created, which makes the y1-y2 format from y1 as input
no need to add a year column, now that you can use map2_dfr's .id argument
page_season_func <- function(seizoen, page) {
cat(".")
seizoen2 <- paste(seizoen, seizoen+1, sep="-")
df <- read_html(paste0("http://www.voetbal.com/spelerslijst/ned-eredivisie-",
seizoen2,
"/nach-name/",
page)) %>%
html_nodes("table") %>%
html_table(fill=TRUE) %>%
as.data.frame() %>%
as.tbl() %>%
select(Speler, Team, Geboren, Lengte, Positie)
}
I'm trying to write a script that will go through a list of players provided by the website Transfermarkt and gathering some information about them. For that, I've created the script below, but faced a problem with 1 of the 29 players in the list. Due to one page being arranged differently than the others, the code outputs a list of only 28 players since it can't find information on the aforementioned page.
I understand why the code I've written doesn't find any information on the given page and thus giving me a list of 28, but I don't know how to rewrite a code in order to achieve what I want:
for the script to simply replace the entry with a "-" if it does not find anything, in this case a nationality, for the node on a particular page and return a full list with 29 players with all the other info in it.
The player page in question is this and while the other pages has the node used in the code for nationality, here it's ".dataValue span".
I'm still quite new to R and it might be an easy fix, but atm I can't figure it out. Any help or advise is appreciated.
URL <- "http://www.transfermarkt.de/fc-bayern-munchen/leistungsdaten/verein/27/reldata/%262016/plus/1"
WS <- read_html(URL)
Team <- WS %>% html_nodes(".spielprofil_tooltip") %>% html_attr("href") %>% as.character()
Team <- paste0("http://www.transfermarkt.de",Team)
Catcher <- data.frame(Name=character(),Nat=character(),Vertrag=character())
for (i in Team) {
WS1 <- read_html(i)
Name <- WS1 %>% html_nodes("h1") %>% html_text() %>% as.character()
Nat <- WS1 %>% html_nodes(".hide-for-small+ p .dataValue span") %>% html_text() %>% as.character()
Vertrag <- WS1 %>% html_nodes(".dataValue:nth-child(9)") %>% html_text() %>% as.character()
if (length(Nat) > 0) {
temp <- data.frame(Name,Nat,Vertrag)
Catcher <- rbind(Catcher,temp)
}
else {}
cat("*")
}
num_Rows <- nrow(Catcher)
odd_indexes <- seq(1,num_Rows,2)
Catcher <- data.frame(Catcher[odd_indexes,])
It's honestly easier to scrape the whole table, just in case things move around. I find purrr is a helpful complement for rvest, allowing you to iterate over URLs and node lists and easily coerce results to data.frames:
library(rvest)
library(purrr)
# build dynamically if you like
urls <- c(boateng = 'http://www.transfermarkt.de/jerome-boateng/profil/spieler/26485',
friedl = 'http://www.transfermarkt.de/marco-friedl/profil/spieler/156990')
# scrape once, parse iteratively
html <- urls %>% map(read_html)
df <- html %>%
map(html_nodes, '.dataDaten p') %>%
map_df(map_df,
~list(
variable = .x %>% html_node('.dataItem') %>% html_text(trim = TRUE),
value = .x %>% html_node('.dataValue') %>% html_text(trim = TRUE) %>% gsub('\\s+', ' ', .)
),
.id = 'player')
df
#> # A tibble: 17 × 3
#> player variable value
#> <chr> <chr> <chr>
#> 1 boateng Geb./Alter: 03.09.1988 (28)
#> 2 boateng Geburtsort: Berlin
#> 3 boateng Nationalität: Deutschland
#> 4 boateng Größe: 1,92 m
#> 5 boateng Position: Innenverteidiger
#> 6 boateng Vertrag bis: 30.06.2021
#> 7 boateng Berater: SAM SPORTS
#> 8 boateng Nationalspieler: Deutschland
#> 9 boateng Länderspiele/Tore: 67/1
#> 10 friedl Geb./Alter: 16.03.1998 (19)
#> 11 friedl Nationalität: Österreich
#> 12 friedl Größe: 1,87 m
#> 13 friedl Position: Linker Verteidiger
#> 14 friedl Vertrag bis: 30.06.2021
#> 15 friedl Berater: acta7
#> 16 friedl Akt. Nationalspieler: Österreich U19
#> 17 friedl Länderspiele/Tore: 6/0
Alternately, that particular piece of data is in three places on those pages, so if one is inconsistent there's a chance the others are better. Or grab them from the table with the whole team—countries are not printed, but they're in the title attribute of the flag images, which can be grabbed with html_attr:
html <- read_html('http://www.transfermarkt.de/fc-bayern-munchen/leistungsdaten/verein/27/reldata/%262016/plus/1')
team <- html %>%
html_nodes('tr.odd, tr.even') %>%
map_df(~list(player = .x %>% html_node('a.spielprofil_tooltip') %>% html_text(),
nationality = .x %>% html_nodes('img.flaggenrahmen') %>% html_attr('title') %>% toString()))
team
#> # A tibble: 29 × 2
#> player nationality
#> <chr> <chr>
#> 1 Manuel Neuer Deutschland
#> 2 Sven Ulreich Deutschland
#> 3 Tom Starke Deutschland
#> 4 Jérôme Boateng Deutschland
#> 5 David Alaba Österreich
#> 6 Mats Hummels Deutschland
#> 7 Javi Martínez Spanien
#> 8 Juan Bernat Spanien
#> 9 Philipp Lahm Deutschland
#> 10 Rafinha Brasilien, Deutschland
#> # ... with 19 more rows