Scraping data from APIs
Pablo Barbera
August 1, 2018
Scraping web data from APIs
To understand how APIs work, we’ll take the New York Times API as an example. This API allows users to search articles by string and dates, and returns counts of articles and a short description of each article (but not the full text). You can find the documentation here. Get a new API token and paste it here:
apikey <- '05e7c6c6f4896fba6c08531808067326:10:67731177'The fist step is to identify the base url and the parameters that we can use to query the API. Now we can do a first API call using the httr package. (You can use my API key for now, let’s hope we don’t hit the rate limit!)
base_url <- "http://api.nytimes.com/svc/search/v2/articlesearch.json"
library(httr)
r <- GET(base_url, query=list(q="inequality","api-key"=apikey))
r## Response [http://api.nytimes.com/svc/search/v2/articlesearch.json?q=inequality&api-key=05e7c6c6f4896fba6c08531808067326%3A10%3A67731177]
## Date: 2018-07-30 14:33
## Status: 200
## Content-Type: application/json;charset=UTF-8
## Size: 160 kBFrom the output of r, we can see that the query was successful (Status: 200), the content is in json format, and its size is 17.3kB.
To extract the text returned by this API call, you can use content. You can write it to a file to take a look at it.
content(r, 'text')writeLines(content(r, 'text'), con=file("nyt.json"))We can save the output into an object in R to learn more about its structure.
json <- content(r, 'parsed')
class(json); names(json) # list with 3 elements## [1] "list"## [1] "status" "copyright" "response"json$status # this should be "OK"## [1] "OK"names(json$response) # the actual data## [1] "docs" "meta"json$response$meta # metadata## $hits
## [1] 33906
##
## $offset
## [1] 0
##
## $time
## [1] 18If we check the documentation, we find that we can subset by date with the begin_date and end_date parameters. Let’s see how this works…
r <- GET(base_url, query=list(q="inequality",
"api-key"=apikey,
"begin_date"=20160101,
"end_date"=20161231))
json <- content(r, 'parsed')
json$response$meta## $hits
## [1] 1497
##
## $offset
## [1] 0
##
## $time
## [1] 49Between these two dates, there were 1840 articles in the NYT mentioning “inequality”.
Now imagine we want to look at the evolution of mentions of this word over time. Following the best coding practices we introduced earlier, we want to write a function that will take a word and a set of dates as arguments and return the counts of articles.
This would be a first draft of that function:
nyt_count <- function(q, date1, date2){
r <- GET(base_url, query=list(q=q,
"api-key"=apikey,
"begin_date"=date1,
"end_date"=date2))
json <- content(r, "parsed")
return(json$response$meta$hits)
}
nyt_count(q="inequality", date1=20160101, date2=20160131)## [1] 143Ok, so this seems to work. But we want to run this function multiple times, so let’s write another function that helps us do that.
nyt_years_count <- function(q, yearinit, yearend){
# sequence of years to loop over
years <- seq(yearinit, yearend)
counts <- rep(NA, length(years))
# loop over periods
for (i in 1:length(years)){
# information message to track progress
message(years[i])
# retrieve count
counts[i] <- nyt_count(q=q, date1=paste0(years[i], "0101"),
date2=paste0(years[i], "1231"))
}
return(counts)
}# and let's see what happens...
nyt_years_count(q="inequality", yearinit=1980, yearend=2017)Oops! What happened? Why the error? We’re querying the API too fast. Let’s modify the function to add a while loop that will wait a couple of seconds in case there’s an error:
nyt_count <- function(q, date1, date2){
r <- GET(base_url, query=list(q=q,
"api-key"=apikey,
"begin_date"=date1,
"end_date"=date2))
json <- content(r, "parsed")
## if there is no response
while (r$status_code!=200){
Sys.sleep(2) # wait a couple of seconds
# try again:
r <- GET(base_url, query=list(q=q,
"api-key"=apikey,
"begin_date"=date1,
"end_date"=date2))
json <- content(r, "parsed")
}
return(json$response$meta$hits)
}And let’s see if this does the trick…
counts <- nyt_years_count(q="inequality", yearinit=1980, yearend=2017)## 1980## 1981## 1982## 1983## 1984## 1985## 1986## 1987## 1988## 1989## 1990## 1991## 1992## 1993## 1994## 1995## 1996## 1997## 1998## 1999## 2000## 2001## 2002## 2003## 2004## 2005## 2006## 2007## 2008## 2009## 2010## 2011## 2012## 2013## 2014## 2015## 2016## 2017plot(1980:2017, counts, type="l", main="Mentions of inequality on the NYT, by year",
xlab="Year", ylab="Article count")
Let’s try to generalize the function even more so that it works with any date interval, not just years:
nyt_dates_count <- function(q, init, end, by){
# sequence of dates to loop over
dates <- seq(from=init, to=end, by=by)
dates <- format(dates, "%Y%m%d") # changing format to match NYT API format
counts <- rep(NA, length(dates)-1)
# loop over periods
for (i in 1:(length(dates)-1)){ ## note the -1 here
# information message to track progress
message(dates[i])
# retrieve count
counts[i] <- nyt_count(q=q, date1=dates[i],
date2=dates[i+1])
}
# improving this as well so that it returns a data frame
df <- data.frame(date = as.Date(dates[-length(dates)], format="%Y%m%d"), count = counts)
return(df)
}And now we can count articles at the month level…
counts <- nyt_dates_count(q="trump", init = as.Date("2015/01/01"), end = as.Date("2018/07/31"), by="month")## 20150101## 20150201## 20150301## 20150401## 20150501## 20150601## 20150701## 20150801## 20150901## 20151001## 20151101## 20151201## 20160101## 20160201## 20160301## 20160401## 20160501## 20160601## 20160701## 20160801## 20160901## 20161001## 20161101## 20161201## 20170101## 20170201## 20170301## 20170401## 20170501## 20170601## 20170701## 20170801## 20170901## 20171001## 20171101## 20171201## 20180101## 20180201## 20180301## 20180401## 20180501## 20180601plot(counts$date, counts$count, type="l", main="Mentions of 'Trump' in the NYT, by month",
xlab="Month", ylab="Article count")