A continuation of... Extracting data from an API using R - r

Question

I'm a super new at this and working on R for my thesis. The code in this answer finally worked for me (Extracting data from an API using R), but I can't figure out how to add a loop to it. I keep getting the first page of the API when I need all 3360.
Here's the code:
library(httr)
library(jsonlite)
r1 <- GET("http://data.riksdagen.se/dokumentlista/?
sok=&doktyp=mot&rm=&from=2000-01-01&tom=2017-12- 31&ts=&bet=&tempbet=&nr=&org=&iid=&webbtv=&talare=&exakt=&planering=&sort=rel&sortorder=desc&rapport=&utformat=json&a=s#soktraff")
r2 <- rawToChar(r1$content)
class(r2)
r3 <- fromJSON(r2)
r4 <- r3$dokumentlista$dokument
By the time I reach r4, it's already a data frame.
Please and thank you!
Edit: originally, I couldn't get a url that had the page as info within it. Now I have it (below). I still haven't been able to loop it.
"http://data.riksdagen.se/dokumentlista/?sok=&doktyp=mot&rm=&from=2000-01-01&tom=2017-12-31&ts=&bet=&tempbet=&nr=&org=&iid=&webbtv=&talare=&exakt=&planering=&sort=rel&sortorder=desc&rapport=&utformat=json&a=s&p="

Answer 1

I think you can extract the url of the next page from r3 as follows:
next_url <- r3$dokumentlista$`#nasta_sida`
# you need to re-check this, but sometimes I'm getting white spaces within the url,
# you may not face this problem, but in any case this line of code solved the issue
next_url <- gsub(' ', '', n_url)
GET(next_url)
Update
I tried the url with the page number with 10 pages and it worked
my_dfs <- lapply(1:10, function(i){
my_url <- paste0("http://data.riksdagen.se/dokumentlista/?sok=&doktyp=mot&rm=&from=2000-01-01&tom=2017-12-31&ts=&bet=&tempbet=&nr=&org=&iid=&webbtv=&talare=&exakt=&planering=&sort=rel&sortorder=desc&rapport=&utformat=json&a=s&p=", i)
r1 <- GET(my_url)
r2 <- rawToChar(r1$content)
r3 <- fromJSON(r2)
r4 <- r3$dokumentlista$dokument
return(r4)
})
Update 2:
The extracted data frames are complex (e.g. some columns are lists of data frames) which is why a simple rbind will not work here, you'll have to do some pre-processing before you stack up the data together, something like this would work
my_dfs %>% lapply(function(df_0){
# Do some stuff here with the data, and choose the variables you need
# I chose the first 10 columns to check that I got 200 different observations
df_0[1:10]
}) %>% do.call(rbind, .)