我从 booking.com 的页面上抓取数据并创建数据框，我注意到并非所有酒店都有评级。
我尝试过这个例如：
# Got the elements from Inspect code of the page
titles_page <- page %>% html_elements("div[data-testid='title'][class='fcab3ed991 a23c043802']") %>% html_text()
prices_page <- page %>% html_elements("span[data-testid='price-and-discounted-price']") %>% html_text()
ratings_page <- page %>% html_elements("div[aria-label^='Punteggio di']") %>% html_text()
# The variable ratings
tryCatch(expr ={
ratings_page <- remDr$findElements(using = "xpath", value = "div[aria-label^='Punteggio di']")$getElementAttribute('value')
},
#If the information does not exist in this way it writes NA to the ratings element
error = function(e){
ratings_page <-NA
})
它不会改变任何东西。
当对象没有价值时如何报告 NA？
这是基于此链接中的策略的解决方案：如何将项目整合在一起以免丢失索引？ https://stackoverflow.com/questions/56673908/how-do-you-scrape-items-together-so-you-dont-lose-the-index/56675147#56675147.
这里的关键是使用html_element()（没有 s）。html_element()总是会返回一个答案，即使它是 NA。这样，如果父节点中缺少该元素，NA 将填补空白。
library(rvest)
library(dplyr)
#read the page
url <-"https://www.booking.com/searchresults.it.html?ss=Firenze%2C+Toscana%2C+Italia&efdco=1&label=booking-name-L*Xf2U1sq4*GEkIwcLOALQS267777916051%3Apl%3Ata%3Ap1%3Ap22%2C563%2C000%3Aac%3Aap%3Aneg%3Afi%3Atikwd-65526620%3Alp9069992%3Ali%3Adec%3Adm%3Appccp&aid=376363&lang=it&sb=1&src_elem=sb&src=index&dest_id=-117543&dest_type=city&ac_position=0&ac_click_type=b&ac_langcode=it&ac_suggestion_list_length=5&search_selected=true&search_pageview_id=2e375b14ad810329&ac_meta=GhAyZTM3NWIxNGFkODEwMzI5IAAoATICaXQ6BGZpcmVAAEoAUAA%3D&checkin=2023-06-11&checkout=2023-06-18&group_adults=2&no_rooms=1&group_children=0&sb_travel_purpose=leisure&fbclid=IwAR1BGskP8uicO9nlm5aW7U1A9eABbSwhMNNeQ0gQ-PNoRkHP859L7u0fIsE"
page <- read_html(url)
#parse out the parent node for each parent
properties <- html_elements(page, xpath=".//div[@data-testid='property-card']")
#now find the information from each parent
#notice html_element - no "s"
title <- properties %>% html_element("div[data-testid='title']") %>% html_text()
prices <- properties %>% html_element("span[data-testid='price-and-discounted-price']") %>% html_text()
ratings <- properties %>% html_element(xpath=".//div[@aria-label]") %>% html_text()
data.frame(title, prices, ratings)
title prices ratings
1 Sweetly home in Florence US$1.918 <NA>
2 Pepi Red US$3.062
3 hu Firenze Camping in Town US$902 8,4
4 Plus Florence US$1.754 7,9
5 Artemente Florence B&B US$4.276
6 Villa Aruch US$1.658
7 Hotel Berna US$2.184
8 Hotel Gioia US$2.437
9 Hotel Magenta US$3.250
10 Villa Neroli US$3.242
11 Residenza Florentia US$2.792 8,0
12 Ridolfi Sei Suite Florence US$1.243 <NA>
...
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