I am working on a project that requires me to go through various pages of links, and within these links find the xml file and parse it. I am having trouble extracting the xml file. There are two xml files within each link and I am interested in the one that is bigger. How can I extract the xml file, and find the the one with the max size. I tried using the grep function but its constantly giving me an error.
sotu<-data.frame()
for (i in seq(1,501, 100))
{
securl <- paste0("https://www.sec.gov/cgi-bin/srch-edgar?text=abs-
ee&start=", i, "&count=100&first=2016")
main.page <- read_html(securl)
urls <- main.page %>%
html_nodes("div td:nth-child(2) a")%>%
html_attr("href")
baseurl <- "https://www.sec.gov"
fulllink <-paste(baseurl, urls, sep = "")
names <- main.page %>%
html_nodes ("div td:nth-child(2) a") %>%
html_text()
date <- main.page %>%
html_nodes ("td:nth-child(5)") %>%
html_text()
result <- data.frame(urls=fulllink,companyname=names,FilingDate=date, stringsAsFactors = FALSE)
sotu<- rbind(sotu,result)
}
for (i in seq(nrow(sotu)))
{
getXML <- read_html(sotu$urls[1]) %>%
grep("xml", getXML, ignore.case=FALSE )
}
Everything works except when I try to loop over every link and find the xml file, I keep getting an error. Is this not the right function?
With some help from dplyr we can do:
sotu %>%
rowwise() %>%
do({
read_html(.$urls) %>%
html_table() %>%
as.data.frame() %>%
filter(grepl('.*\\.xml', Document)) %>%
filter(Size == max(Size))
})
or, as the type is always 'EX-102' at least in the example:
sotu %>%
rowwise() %>%
do({
read_html(.$urls) %>%
html_table() %>%
as.data.frame() %>%
filter(Type == 'EX-102')
})
This also get rid of the for loop, which is rarely a good idea in R.
Related
i am having a problem when trying to scrape some data, i have created a function that is properly working, problems occurs when i run this function for many different code.
require ("rvest")
library("dplyr")
getFin = function(ticker)
{
url= paste0("https://it.finance.yahoo.com/quote/",ticker,
"/key-statistics?p=",ticker)
a <- read_html(url)
tbl= a %>% html_nodes("section") %>% html_nodes("div")%>% html_nodes("table")
misureval = tbl %>% .[1] %>% html_table() %>% as.data.frame()
prezzistorici = tbl %>% .[2] %>% html_table() %>% as.data.frame()
titolistat = tbl %>% .[3] %>% html_table() %>% as.data.frame()
dividendi = tbl %>% .[4] %>% html_table() %>% as.data.frame()
annofiscale = tbl %>% .[5] %>% html_table() %>% as.data.frame()
redditivita = tbl %>% .[6] %>% html_table() %>% as.data.frame()
gestione = tbl %>% .[7] %>% html_table() %>% as.data.frame()
contoeco = tbl %>% .[8] %>% html_table() %>% as.data.frame()
bilancio = tbl %>% .[9] %>% html_table() %>% as.data.frame()
flussi = tbl %>% .[10] %>% html_table() %>% as.data.frame()
info1 = rbind(ticker, misureval, prezzistorici, titolistat, dividendi, annofiscale, redditivita, gestione, contoeco, bilancio, flussi)
}
What i am trying to do is to use
finale <- lapply(codici, getFin)
where codici is linked to many different Ticker which will be used in the function to generate one url at time and scrape data.
I have tried with 50 ticker and the function works properly, however when i increase the number i get this error:
Error in xml_nodeset(NextMethod()) : Expecting an external pointer:
[type=NULL].
i don't know if this may be related to the number of request or something other. i have also tested a non existing ticker and the function still works, problems just arises when the number is large.
Solved problem, i just need to add Sys.sleep in order to reduce the frequency of requests.
the best number in this case is 3, so Sys.sleep(3) at the end of the for cycle.
I'm trying to webscrape the government release calendar: https://www.gov.uk/government/statistics and use the rvest follow_link functionality to go to each publication link and scrape text from the next page. I have this working for each single page of results (40 publications are displayed per page), but can't get a loop to work so that I can run the code over all publications listed.
This is the code I run first to get the list of publications (just from the first 10 pages of results):
#Loading the rvest package
library('rvest')
library('dplyr')
library('tm')
#######PUBLISHED RELEASES################
###function to add number after 'page=' in url to loop over all pages of published releases results (only 40 publications per page)
###check the site and see how many pages you want to scrape, to cover months of interest
##titles of publications - creates a list
publishedtitles <- lapply(paste0('https://www.gov.uk/government/statistics?page=', 1:10),
function(url_base){
url_base %>% read_html() %>%
html_nodes('h3 a') %>%
html_text()
})
##Dates of publications
publisheddates <- lapply(paste0('https://www.gov.uk/government/statistics?page=', 1:10),
function(url_base){
url_base %>% read_html() %>%
html_nodes('.public_timestamp') %>%
html_text()
})
##Organisations
publishedorgs <- lapply(paste0('https://www.gov.uk/government/statistics?page=', 1:10),
function(url_base){
url_base %>% read_html() %>%
html_nodes('.organisations') %>%
html_text()
})
##Links to publications
publishedpartial_links <- lapply(paste0('https://www.gov.uk/government/statistics?page=', 1:10),
function(url_base){
url_base %>% read_html() %>%
html_nodes('h3 a') %>%
html_attr('href')
})
#Check all lists are the same length - if not, have to deal with missings before next step
# length(publishedtitles)
# length(publisheddates)
# length(publishedorgs)
# length(publishedpartial_links)
#str(publishedorgs)
#Combining all the lists to form a data frame
published <-data.frame(Title = unlist(publishedtitles), Date = unlist(publisheddates), Organisation = unlist(publishedorgs), PartLinks = unlist(publishedpartial_links))
#adding prefix to partial links, to turn into full URLs
published$Links = paste("https://www.gov.uk", published$PartLinks, sep="")
#Drop partial links column
keeps <- c("Title", "Date", "Organisation", "Links")
published <- published[keeps]
Then I want to run something like the below, but over all pages of results. I've ran this code manually changing the parameters for each page, so know it works.
session1 <- html_session("https://www.gov.uk/government/statistics?page=1")
list1 <- list()
for(i in published$Title[1:40]){
nextpage1 <- session1 %>% follow_link(i) %>% read_html()
list1[[i]]<- nextpage1 %>%
html_nodes(".grid-row") %>% html_text()
df1 <- data.frame(text=list1)
df1 <-as.data.frame(t(df1))
}
So the above would need to change page=1 in the html_session, and also the publication$Title[1:40] - I'm struggling with creating a function or loop that includes both variables.
I think I should be able to do this using lapply:
df <- lapply(paste0('https://www.gov.uk/government/statistics?page=', 1:10),
function(url_base){
for(i in published$Title[1:40]){
nextpage1 <- url_base %>% follow_link(i) %>% read_html()
list1[[i]]<- nextpage1 %>%
html_nodes(".grid-row") %>% html_text()
}
}
)
But I get the error
Error in follow_link(., i) : is.session(x) is not TRUE
I've also tried other methods of looping and turning it into a function but didn't want to make this post too long!
Thanks in advance for any suggestions and guidance :)
It looks like you may have just need to start a session inside the lapply function. In the last chunk of code, url_base is simply a text string that gives the base URL. Would something like this work:
df <- lapply(paste0('https://www.gov.uk/government/statistics?page=', 1:10),
function(url_base){
for(i in published$Title[1:40]){
tmpSession <- html_session(url_base)
nextpage1 <- tmpSession %>% follow_link(i) %>% read_html()
list1[[i]]<- nextpage1 %>%
html_nodes(".grid-row") %>% html_text()
}
}
)
To change the published$Title[1:40] for each iteraction of the lapply function, you could make an object that holds the lower and upper bounds of the indices:
lowers <- cumsum(c(1, rep(40, 9)))
uppers <- cumsum(rep(40, 10))
Then, you could include those in the call to lapply
df <- lapply(1:10, function(j){
url_base <- paste0('https://www.gov.uk/government/statistics?page=', j)
for(i in published$Title[lowers[j]:uppers[j]]){
tmpSession <- html_session(url_base)
nextpage1 <- tmpSession %>% follow_link(i) %>% read_html()
list1[[i]]<- nextpage1 %>%
html_nodes(".grid-row") %>% html_text()
}
}
)
Not sure if this is what you want or not, I might have misunderstood the things that are supposed to be changing.
I'm trying to scrape two things. I want to extract the links from each individual school on a page with this code:
scraped_links <- read_html("https://www.scholenopdekaart.nl/middelbare-scholen/zoeken/") %>%
html_nodes("a.school-naam") %>%
html_attr("href") %>%
html_table() %>%
as.data.frame() %>%
as.tbl()
Then I want to scrape the tabels on these pages:
scraped_tables <- read_html("https://www.scholenopdekaart.nl/Middelbare-scholen/146/1086/Almere-College/Slaagpercentage") %>%
html_nodes(xpath = "/html/body/div[3]/div[3]/div[1]/div[3]/div[3]/div[3]") %>%
html_table() %>%
as.data.frame() %>%
as.tbl()
They both return empty data frames. I tried css selectors, multiple xpaths, but I can't get it to work... Hope someone can help me.
I seem to always have a problem scraping reference sites using either Python or R. Whenever I use my normal xpath approach (Python) or Rvest approach in R, the table I want never seems to be picked up by the scraper.
library(rvest)
url = 'https://www.pro-football-reference.com/years/2016/games.htm'
webpage = read_html(url)
table_links = webpage %>% html_node("table") %>% html_nodes("a")
boxscore_links = subset(table_links, table_links %>% html_text() %in% "boxscore")
boxscore_links = as.list(boxscore_links)
for(x in boxscore_links{
keep = substr(x, 10, 36)
url2 = paste('https://www.pro-football-reference.com', keep, sep = "")
webpage2 = read_html(url2)
home_team = webpage2 %>% html_nodes(xpath='//*[#id="all_home_starters"]') %>% html_text()
away_team = webpage2 %>% html_nodes(xpath='//*[#id="all_vis_starters"]') %>% html_text()
home_starters = webpage2 %>% html_nodes(xpath='//*[(#id="div_home_starters")]') %>% html_text()
home_starters2 = webpage2 %>% html_nodes(xpath='//*[(#id="div_home_starters")]') %>% html_table()
#code that will bind lineup tables with some master table -- code to be written later
}
I'm trying to scrape the starting lineup tables. The first bit of code pulls the urls for all boxscores in 2016, and the for loop goes to each boxscore page with the hopes of extracting the tables led by "Insert Team Here" Starters.
Here's one link for example: 'https://www.pro-football-reference.com/boxscores/201609110rav.htm'
When I run the code above, the home_starters and home_starters2 objects contain zero elements (when ideally it should contain the table or elements of the table I'm trying to bring in).
I appreciate the help!
I've spent the last three hours trying to figure this out. This is how it shoudl be done. This is given my example but I'm sure you could apply it to yours.
"https://www.pro-football-reference.com/years/2017/" %>% read_html() %>% html_nodes(xpath = '//comment()') %>% # select comments
html_text() %>% # extract comment text
paste(collapse = '') %>% # collapse to single string
read_html() %>% # reread as HTML
html_node('table#returns') %>% # select desired node
html_table()
Question
I wanted to rvest specific parts of the websites (car sales platform).
The CSS is frankly too confusing for me to figure out what's wrong on my own.
#### scraping the website www.otomoto.pl with used cars #####
baseURL_otomoto = "https://www.otomoto.pl/osobowe/?page="
i <- 1
for ( i in 1:7000 )
{
link = paste0(baseURL_otomoto,i)
out = read_html(link)
print(i)
print(link)
### building year
build_year = html_nodes(out, xpath = '//*[#id="body-container"]/div[2]/div[1]/div/div[6]/div[2]/article[1]/div[2]/div[3]/ul/li[1]') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
mileage = html_nodes(out, xpath = '//*[#id="body-container"]/div[2]/div[1]/div/div[6]/div[2]/article[1]/div[2]/div[3]/ul/li[2]') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
volume = html_nodes(out, xpath = '//*[#id="body-container"]/div[2]/div[1]/div/div[6]/div[2]/article[1]/div[2]/div[3]/ul/li[3]') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
fuel_type = html_nodes(out, xpath = '//*[#id="body-container"]/div[2]/div[1]/div/div[6]/div[2]/article[1]/div[2]/div[3]/ul/li[4]') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
price = html_nodes(out, xpath = '//div[#class="offer-item__price"]') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
link = html_nodes(out, xpath = '//div[#class="offer-item__title"]') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
offer_details = html_nodes(out, xpath = '//*[#id="body-container"]/div[2]/div[1]/div/div[6]/div[2]/article[1]/div[2]/div[3]/ul') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
Any guesses what might be the reason for this behaviour?
PS#1.
How to rvest all build_type, mileage and fuel_type data from offers available on the analysed website at once as a data.frame? using classes (xpath = '//div[#class=...) didn't work in my case
PS#2.
I wanted to rvest details of the actual offers using f.i.
gear_type = html_nodes(out, xpath = '//*[#id="parameters"]/ul[1]/li[10]/div') %>%
html_text() %>%
str_replace_all("\n","") %>%
str_replace_all("\r","") %>%
str_trim()
the arguments
in ul[a] are for a in (1:2) &
in li[b] are for b in (1:12)
Unfortunately though this concept fails as the resulting data frame is empty. Any guesses why?
First and foremost, learn about CSS selectors and XPath. Your selectors are very long and extremely fragile (some of them did not work for me at all, mere two weeks later). For example, instead of:
html_nodes(out, xpath = '//*[#id="body-container"]/div[2]/div[1]/div/div[6]/div[2]/article[1]/div[2]/div[3]/ul/li[1]') %>%
html_text()
you can write:
html_nodes(out, css="[data-code=year]") %>% html_text()
Second, read documentation of libraries that you use. str_replace_all pattern may be regular expression, which saves you one call (use str_replace_all("[\n\r]", "") instead of str_replace_all("\n","") %>% str_replace_all("\r","")). html_text can do text trimming for you, which means that str_trim() is not needed at all.
Third, if you copy-paste some code, step back and think if function wouldn't be better solution; usually it would. In your case, personally, I would probably skip str_replace_all calls until data cleaning step, when I would call them on data.frame holding entire scrapped data.
To create data.frame from your data, call data.frame() function with column names and content, like that:
data.frame(build_year = build_year,
mileage = mileage,
volume = volume,
fuel_type = fuel_type,
price = price,
link = link,
offer_details = offer_details)
Or you could initialize data.frame with one column only and then add further vectors as columns:
output_df <- data.frame(build_year = html_nodes(out, css="[data-code=year]") %>% html_text(TRUE))
output_df$volume <- html_nodes(out, css="[data-code=engine_capacity]") %>%
html_text(TRUE)
Finally, you should note that data.frame columns must all be the same length, while some of data that you scrap is optional. At the moment of writing this answer I had few offers without engine capacity and without offer description. You have to use two html_nodes calls in succession (as single CSS selector will not match what doesn't exist). But even then, html_nodes will silently drop missing data. This can be worked around by piping html_nodes output to html_node call:
current_df$volume = out %>% html_nodes("ul.offer-item__params") %>%
html_node("[data-code=engine_capacity]") %>%
html_text(TRUE)
The final version of my approach to loop internals is below. Just make sure that you initialize empty data.frame before calling it and that you merge output of current iteration with final data frame (using for example rbind), or each iteration will overwrite results of previous one. Or you could use do.call(rbind, lapply()), which is idiomatic R for such task.
As a side note, when scraping large amount of quickly changing data, consider decoupling data downloading and data processing steps. Imagine that there is some corner case that you haven't accounted for which will cause R to terminate. How will you proceed if such condition appear in the middle of your iterations? The longer you stay on one page, the more duplicates you introduce (as more offers appear and existing ones are pushed down on further pages), and more offers you miss (as sale is concluded and offers disappear forever).
current_df <- data.frame(build_year = html_nodes(out, css="[data-code=year]") %>% html_text(TRUE))
current_df$mileage = html_nodes(out, css="[data-code=mileage]") %>%
html_text(TRUE)
current_df$volume = out %>% html_nodes("ul.offer-item__params") %>%
html_node("[data-code=engine_capacity]") %>%
html_text(TRUE)
current_df$fuel_type = html_nodes(out, css="[data-code=fuel_type]") %>%
html_text(TRUE)
current_df$price = out %>% html_nodes(xpath="//div[#class='offer-price']//span[contains(#class, 'number')]") %>%
html_text(TRUE)
current_df$link = out %>% html_nodes(css = "div.offer-item__title h2 > a") %>%
html_text(TRUE) %>%
str_replace_all("[\n\r]", "")
current_df$offer_details = out %>% html_nodes("div.offer-item__title") %>%
html_node("h3") %>%
html_text(TRUE)