R Glassdoor Web Scraping

I have been tasked to collect Glassdoor Reviews for different hospitals and I am having difficulties extracting the Pros, Cons, Advice to management, Recommend, CEO Approval, Business Outlook, and the small rating drop down. I have been able to extract the rest from the code below. Any help would be greatly appreciated.

library(rvest)
library(tidyverse)
library(tidyverse)
library(stringr)

   url <- "https://www.glassdoor.com/Reviews/Montefiore-Nyack-Hospital-Reviews-E2312619.htm?            sort.sortType=RD&sort.ascending=false&filter.iso3Language=eng"
   page <- read_html(url)

# Extract review titles

review_titles <- page %>%
html_nodes(".reviewLink") %>%
html_text()

# Extract review dates

review_dates <- page %>%
html_nodes(".middle.common__EiReviewDetailsStyle__newGrey") %>%
html_text()

#Extract Pros
review_pros <- page %>%
html_nodes("v2__EIReviewDetailsV2__fullWidth ") %>%
html_text()
print(review_pros)

# Extract review ratings

review_ratings <- page %>%
html_nodes(".ratingNumber.mr-xsm") %>%
html_text() %>%
str_extract("\d+") %>%
as.integer()

# Extract review recommendations

recommendations <- page %>%
html_nodes("html body.main.loggedIn.lang-en.en-US.gdGrid._initOk div#Container div.container-max-width.mx-auto.px-0.px-lg-lg.py-lg-xxl div.d-flex.row.css-zwxlu7.e1af7d9i0 main.col-12.mb-lg-0.mb-md.css-yaeagj.ej1dgw00 div#ReviewsRef div#ReviewsFeed ol.empReviews.emp-reviews-feed.pl-0 li#empReview_76309432.noBorder.empReview.cf.pb-0.mb-0 div.p-0.mb-0.mb-md-std.css-w5wad1.gd-ui-module.css-rntt2a.ec4dwm00 div.gdReview div.mt-xxsm div.mx-0 div.px-std div div.d-flex.my-std.reviewBodyCell.recommends.css-1y3jl3a.e1868oi10") %>%
html_text()

# Convert recommendations to numeric values

recommendations_numeric <- ifelse(grepl("css-hcqxoa-svg", recommendations), 1,
ifelse(grepl("css-1y3jl3a-svg", recommendations), -1, 0))

# Create data frame

reviews <- data.frame(Title = review_titles, Rating = review_ratings, Date = review_dates)

# View data frame

reviews

I was able to pull pros and cons as such:

library(tidyverse)
library(rvest)

data <- "https://www.glassdoor.com/Reviews/Montefiore-Nyack-Hospital-Reviews-E2312619.htm?%20%20%20%20%20%20%20%20%20%20%20%20sort.sortType=RD&sort.ascending=false&filter.iso3Language=eng" %>% 
  read_html() %>% 
  html_elements(".empReview")

tibble(
  title = data %>% 
    html_element(".reviewLink") %>% 
    html_text2(), 
  date = data %>%  
    html_element(".middle.common__EiReviewDetailsStyle__newGrey") %>% 
    html_text2(), 
  pros = data %>% 
    html_element(".v2__EIReviewDetailsV2__fullWidth:nth-child(1) span") %>% 
    html_text2(), 
  cons = data %>%  
    html_element(".v2__EIReviewDetailsV2__fullWidth+ .v2__EIReviewDetailsV2__fullWidth span") %>% 
    html_text2() %>% 
    str_trim()
) %>% 
  separate(col = date, into = c("date", "position"), sep = " - ")

# A tibble: 10 × 5
   title                             date         position                         pros       cons 
   <chr>                             <chr>        <chr>                            <chr>      <chr>
 1 Great place to work               Mar 3, 2023  Manager                          Excellent… "Do …
 2 Don't bother                      May 10, 2023 Practice Manager                 Being loc… "Hor…
 3 Skeleton staffing                 Nov 16, 2022 Registered Nurse, Emergency Room Co-worker… "No …
 4 Nyack hospital                    Mar 7, 2023  Patient Care Associate (PCA)     The food … "No …
 5 Its ok                            Mar 22, 2023 Registered Nurse, BSN            one weeke… "sho…
 6 pca                               Jan 18, 2023 Patient Care Assistant (PCA)     good pay … "non…
 7 Just for starters                 Feb 3, 2023  Registered Nurse, Critical Care  Coworkers… "No …
 8 PCA                               Oct 22, 2022 Emergency Care Assistant         there sta… "the…
 9 Great way to support the Hospital Sep 16, 2022 Donor Relations Manager          Most ever… "Lon…
10 great place to work               Sep 5, 2022  Registered Nurse                 lots of o… "lim…

The data you are looking for is stored in a script. This answer is based on a similar question. https://stackoverflow.com/questions/72835014/web-scraping-data-that-is-not-displayed-on-a-webpage-using-rvest

It took a while searching and trial and error to get it correct. In the script there is a section that starts with "reviews": and ends with }]}. In this case it was after the second occurrence of reviews. It is a matter of extracting out this part and converting from JSON.

library(stringr) 
library(xml2)
library(rvest) 
library(dplyr)

url <- "https://www.glassdoor.com/Reviews/Montefiore-Nyack-Hospital-Reviews-E2312619.htm?sort.sortType=RD&sort.ascending=false&filter.iso3Language=eng"
page <- read_html(url)


#the ratings are stored in a data structure in a script
#find all the scripts and then search
scripts<-page %>% html_elements(xpath='//script')

#search the scripts for the ratings
ratingsScript <- which(grepl("ratingCareerOpportunities", scripts))

#Extract text for the reviews from the script.  this is the second reviews section This is almost valid JSON format
reviews <-scripts[ratingsScript] %>% html_text2() %>% 
   str_extract("\"reviews\":.+?\\}\\]\\}") %>% substring(10) %>% str_extract("\"reviews\":.+?\\}\\]\\}") 
nchar(reviews)  #debugging status

#add a leading { to make valid JSON and convert
answer <-jsonlite::fromJSON(paste("{", reviews))
answer[ , c(ratingRecommendToFriend, ratingCeo, ratingBusinessOutlook)]

There is a lot of potentially useful information in the answer data frame. Job status, comments, reviewers id, star reviews, etc.