creating a loop for "load" and "save" processes - r

Question

I have a data.frame (dim: 100 x 1) containing a list of url links, each url looks something like this: https:blah-blah-blah.com/item/123/index.do .
The list (the list is a data.frame called my_list with 100 rows and a single column named col and is in character format $ col: chr) together looks like this :
1 "https:blah-blah-blah.com/item/123/index.do"
2" https:blah-blah-blah.com/item/124/index.do"
3 "https:blah-blah-blah.com/item/125/index.do"
etc.
I am trying to import each of these url's into R and collectively save the object as an object that is compatible for text mining procedures.
I know how to successfully convert each of these url's (that are on the list) manually:
library(pdftools)
library(tidytext)
library(textrank)
library(dplyr)
library(tm)
#1st document
url <- "https:blah-blah-blah.com/item/123/index.do"
article <- pdf_text(url)
Once this "article" file has been successfully created, I can inspect it:
str(article)
chr [1:13]
It looks like this:
[1] "abc ....."
[2] "def ..."
etc etc
[15] "ghi ...:
From here, I can successfully save this as an RDS file:
saveRDS(article, file = "article_1.rds")
Is there a way to do this for all 100 articles at the same time? Maybe with a loop?
Something like :
for (i in 1:100) {
url_i <- my_list[i,1]
article_i <- pdf_text(url_i)
saveRDS(article_i, file = "article_i.rds")
}
If this was written correctly, it would save each article as an RDS file (e.g. article_1.rds, article_2.rds, ... article_100.rds).
Would it then be possible to save all these articles into a single rds file?

Answer 1

Please note that list is not a good name for an object, as this will
temporarily overwrite the list() function. I think it is usually good
to name your variables according to their content. Maybe url_df would be
a good name.
library(pdftools)
#> Using poppler version 20.09.0
library(tidyverse)
url_df <-
data.frame(
url = c(
"https://www.nimh.nih.gov/health/publications/autism-spectrum-disorder/19-mh-8084-autismspecdisordr_152236.pdf",
"https://www.nimh.nih.gov/health/publications/my-mental-health-do-i-need-help/20-mh-8134-mymentalhealth-508_161032.pdf"
)
)
Since the urls are already in a data.frame we could store the text data in
an aditional column. That way the data will be easily available for later
steps.
text_df <-
url_df %>%
mutate(text = map(url, pdf_text))
Instead of saving each text in a separate file we can now store all of the data
in a single file:
saveRDS(text_df, "text_df.rds")
For historical reasons for loops are not very popular in the R community.
base R has the *apply() function family that provides a functional
approach to iteration. The tidyverse has the purrr package and the map*()
functions that improve upon the *apply() functions.
I recommend taking a look at
https://purrr.tidyverse.org/ to learn more.

Answer 2

It seems that there are certain url's in your data which are not valid pdf files. You can wrap it in tryCatch to handle the errors. If your dataframe is called df with url column in it, you can do :
library(pdftools)
lapply(seq_along(df$url), function(x) {
tryCatch({
saveRDS(pdf_text(df$url[x]), file = sprintf('article_%d.rds', x)),
},error = function(e) {})
})

Answer 3

So say you have a data.frame called my_df with a column that contains your URLs of pdf locations. As by your comments, it seems that some URLs lead to broken PDFs. You can use tryCatch in these cases to report back which links were broken and check manually what's wrong with these links.
You can do this in a for loop like this:
my_df <- data.frame(url = c(
"https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf", # working pdf
"https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pfd" # broken pdf
))
# make some useful new columns
my_df$id <- seq_along(my_df$url)
my_df$status <- NA
for (i in my_df$id) {
my_df$status[i] <- tryCatch({
message("downloading ", i) # put a status message on screen
article_i <- suppressMessages(pdftools::pdf_text(my_df$url[i]))
saveRDS(article_i, file = paste0("article_", i, ".rds"))
"OK"
}, error = function(e) {return("FAILED")}) # return the string FAILED if something goes wrong
}
my_df$status
#> [1] "OK" "FAILED"
I included a broken link in the example data on purpose to showcase how this would look.
Alternatively, you can use a loop from the apply family. The difference is that instead of iterating through a vector and applying the same code until the end of the vector, *apply takes a function, applies it to each element of a list (or objects which can be transformed to lists) and returns the results from each iteration in one go. Many people find *apply functions confusing at first because usually people define and apply functions in one line. Let's make the function more explicit:
s_download_pdf <- function(link, id) {
tryCatch({
message("downloading ", id) # put a status message on screen
article_i <- suppressMessages(pdftools::pdf_text(link))
saveRDS(article_i, file = paste0("article_", id, ".rds"))
"OK"
}, error = function(e) {return("FAILED")})
}
Now that we have this function, let's use it to download all files. I'm using mapply which iterates through two vectors at once, in this case the id and url columns:
my_df$status <- mapply(s_download_pdf, link = my_df$url, id = my_df$id)
my_df$status
#> [1] "OK" "FAILED"
I don't think it makes much of a difference which approach you choose as the speed will be bottlenecked by your internet connection instead of R. Just thought you might appreciate the comparison.