Data and information on the web is growing exponentially. All of us today use Google as our first source of knowledge – be it about finding reviews about a place to understanding a new term. All this information is available on the web already.
With the amount of data available over the web, it opens new horizons of possibility for a Data Scientist. I strongly believe web scraping is a must have skill for any data scientist. In today’s world, all the data that you need is already available on the internet – the only thing limiting you from using it is the ability to access it. With the help of this article, you will be able to overcome that barrier as well.
Most of the data available over the web is not readily available. It is present in an unstructured format (HTML format) and is not downloadable. Therefore, it requires knowledge & expertise to use this data to eventually build a useful model.
In this article, I am going to take you through the process of web scraping in R. With this article, you will gain expertise to use any type of data available over the internet.
Web scraping is a technique for converting the data present in unstructured format (HTML tags) over the web to the structured format which can easily be accessed and used.
Almost all the main languages provide ways for performing web scraping. In this article, we’ll use R for scraping the data for the most popular feature films of 2016 from the IMDb website.
We’ll get a number of features for each of the 100 popular feature films released in 2016. Also, we’ll look at the most common problems that one might face while scraping data from the internet because of the lack of consistency in the website code and look at how to solve these problems.
If you are more comfortable using Python, I’ll recommend you to go through this guide for getting started with web scraping using Python.
I am sure the first questions that must have popped in your head till now is “Why do we need web scraping”? As I stated before, the possibilities with web scraping are immense.
To provide you with hands-on knowledge, we are going to scrape data from IMDB. Some other possible applications that you can use web scraping for are:
There are several ways of scraping data from the web. Some of the popular ways are:
We’ll use the DOM parsing approach during the course of this article. And rely on the CSS selectors of the webpage for finding the relevant fields which contain the desired information. But before we begin there are a few prerequisites that one need in order to proficiently scrape data from any website.
The prerequisites for performing web scraping in R are divided into two buckets:
install.packages('rvest')
Using this you can select the parts of any website and get the relevant tags to get access to that part by simply clicking on that part of the website. Note that, this is a way around to actually learning HTML & CSS and doing it manually. But to master the art of Web scraping, I’ll highly recommend you to learn HTML & CSS in order to better understand and appreciate what’s happening under the hood.
Now, let’s get started with scraping the IMDb website for the 100 most popular feature films released in 2016. You can access them here.
#Loading the rvest package library('rvest') #Specifying the url for desired website to be scraped url <- 'http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature' #Reading the HTML code from the website webpage <- read_html(url)
Now, we’ll be scraping the following data from this website.
Here’s a screenshot that contains how all these fields are arranged.
Step 1: Now, we will start by scraping the Rank field. For that, we’ll use the selector gadget to get the specific CSS selectors that encloses the rankings. You can click on the extension in your browser and select the rankings field with the cursor.
Make sure that all the rankings are selected. You can select some more ranking sections in case you are not able to get all of them and you can also de-select them by clicking on the selected section to make sure that you only have those sections highlighted that you want to scrape for that go.
Step 2: Once you are sure that you have made the right selections, you need to copy the corresponding CSS selector that you can view in the bottom center.
Step 3: Once you know the CSS selector that contains the rankings, you can use this simple R code to get all the rankings:
#Using CSS selectors to scrape the rankings section rank_data_html <- html_nodes(webpage,'.text-primary') #Converting the ranking data to text rank_data <- html_text(rank_data_html) #Let's have a look at the rankings head(rank_data) [1] "1." "2." "3." "4." "5." "6."
Step 4: Once you have the data, make sure that it looks in the desired format. I am preprocessing my data to convert it to numerical format.
#Data-Preprocessing: Converting rankings to numerical rank_data<-as.numeric(rank_data) #Let's have another look at the rankings head(rank_data) [1] 1 2 3 4 5 6
Step 5: Now you can clear the selector section and select all the titles. You can visually inspect that all the titles are selected. Make any required additions and deletions with the help of your curser. I have done the same here.
Step 6: Again, I have the corresponding CSS selector for the titles – .lister-item-header a. I will use this selector to scrape all the titles using the following code.
#Using CSS selectors to scrape the title section title_data_html <- html_nodes(webpage,'.lister-item-header a') #Converting the title data to text title_data <- html_text(title_data_html) #Let's have a look at the title head(title_data) [1] "Sing" "Moana" "Moonlight" "Hacksaw Ridge" [5] "Passengers" "Trolls"
Step 7: In the following code, I have done the same thing for scraping – Description, Runtime, Genre, Rating, Metascore, Votes, Gross_Earning_in_Mil , Director and Actor data.
#Using CSS selectors to scrape the description section description_data_html <- html_nodes(webpage,'.ratings-bar+ .text-muted') #Converting the description data to text description_data <- html_text(description_data_html) #Let's have a look at the description data head(description_data) [1] "\nIn a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same." [2] "\nIn Ancient Polynesia, when a terrible curse incurred by the Demigod Maui reaches an impetuous Chieftain's daughter's island, she answers the Ocean's call to seek out the Demigod to set things right." [3] "\nA chronicle of the childhood, adolescence and burgeoning adulthood of a young, African-American, gay man growing up in a rough neighborhood of Miami." [4] "\nWWII American Army Medic Desmond T. Doss, who served during the Battle of Okinawa, refuses to kill people, and becomes the first man in American history to receive the Medal of Honor without firing a shot." [5] "\nA spacecraft traveling to a distant colony planet and transporting thousands of people has a malfunction in its sleep chambers. As a result, two passengers are awakened 90 years early." [6] "\nAfter the Bergens invade Troll Village, Poppy, the happiest Troll ever born, and the curmudgeonly Branch set off on a journey to rescue her friends. #Data-Preprocessing: removing '\n' description_data<-gsub("\n","",description_data) #Let's have another look at the description data head(description_data) [1] "In a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same." [2] "In Ancient Polynesia, when a terrible curse incurred by the Demigod Maui reaches an impetuous Chieftain's daughter's island, she answers the Ocean's call to seek out the Demigod to set things right." [3] "A chronicle of the childhood, adolescence and burgeoning adulthood of a young, African-American, gay man growing up in a rough neighborhood of Miami." [4] "WWII American Army Medic Desmond T. Doss, who served during the Battle of Okinawa, refuses to kill people, and becomes the first man in American history to receive the Medal of Honor without firing a shot." [5] "A spacecraft traveling to a distant colony planet and transporting thousands of people has a malfunction in its sleep chambers. As a result, two passengers are awakened 90 years early." [6] "After the Bergens invade Troll Village, Poppy, the happiest Troll ever born, and the curmudgeonly Branch set off on a journey to rescue her friends." #Using CSS selectors to scrape the Movie runtime section runtime_data_html <- html_nodes(webpage,'.text-muted .runtime') #Converting the runtime data to text runtime_data <- html_text(runtime_data_html) #Let's have a look at the runtime head(runtime_data) [1] "108 min" "107 min" "111 min" "139 min" "116 min" "92 min" #Data-Preprocessing: removing mins and converting it to numerical runtime_data<-gsub(" min","",runtime_data) runtime_data<-as.numeric(runtime_data) #Let's have another look at the runtime data head(runtime_data) [1] 1 2 3 4 5 6 #Using CSS selectors to scrape the Movie genre section genre_data_html <- html_nodes(webpage,'.genre') #Converting the genre data to text genre_data <- html_text(genre_data_html) #Let's have a look at the runtime head(genre_data) [1] "\nAnimation, Comedy, Family " [2] "\nAnimation, Adventure, Comedy " [3] "\nDrama " [4] "\nBiography, Drama, History " [5] "\nAdventure, Drama, Romance " [6] "\nAnimation, Adventure, Comedy " #Data-Preprocessing: removing \n genre_data<-gsub("\n","",genre_data) #Data-Preprocessing: removing excess spaces genre_data<-gsub(" ","",genre_data) #taking only the first genre of each movie genre_data<-gsub(",.*","",genre_data) #Convering each genre from text to factor genre_data<-as.factor(genre_data) #Let's have another look at the genre data head(genre_data) [1] Animation Animation Drama Biography Adventure Animation 10 Levels: Action Adventure Animation Biography Comedy Crime Drama ... Thriller #Using CSS selectors to scrape the IMDB rating section rating_data_html <- html_nodes(webpage,'.ratings-imdb-rating strong') #Converting the ratings data to text rating_data <- html_text(rating_data_html) #Let's have a look at the ratings head(rating_data) [1] "7.2" "7.7" "7.6" "8.2" "7.0" "6.5" #Data-Preprocessing: converting ratings to numerical rating_data<-as.numeric(rating_data) #Let's have another look at the ratings data head(rating_data) [1] 7.2 7.7 7.6 8.2 7.0 6.5 #Using CSS selectors to scrape the votes section votes_data_html <- html_nodes(webpage,'.sort-num_votes-visible span:nth-child(2)') #Converting the votes data to text votes_data <- html_text(votes_data_html) #Let's have a look at the votes data head(votes_data) [1] "40,603" "91,333" "112,609" "177,229" "148,467" "32,497" #Data-Preprocessing: removing commas votes_data<-gsub(",","",votes_data) #Data-Preprocessing: converting votes to numerical votes_data<-as.numeric(votes_data) #Let's have another look at the votes data head(votes_data) [1] 40603 91333 112609 177229 148467 32497 #Using CSS selectors to scrape the directors section directors_data_html <- html_nodes(webpage,'.text-muted+ p a:nth-child(1)') #Converting the directors data to text directors_data <- html_text(directors_data_html) #Let's have a look at the directors data head(directors_data) [1] "Christophe Lourdelet" "Ron Clements" "Barry Jenkins" [4] "Mel Gibson" "Morten Tyldum" "Walt Dohrn" #Data-Preprocessing: converting directors data into factors directors_data<-as.factor(directors_data) #Using CSS selectors to scrape the actors section actors_data_html <- html_nodes(webpage,'.lister-item-content .ghost+ a') #Converting the gross actors data to text actors_data <- html_text(actors_data_html) #Let's have a look at the actors data head(actors_data) [1] "Matthew McConaughey" "Auli'i Cravalho" "Mahershala Ali" [4] "Andrew Garfield" "Jennifer Lawrence" "Anna Kendrick" #Data-Preprocessing: converting actors data into factors actors_data<-as.factor(actors_data)
But, I want you to closely follow what happens when I do the same thing for Metascore data.
#Using CSS selectors to scrape the metascore section metascore_data_html <- html_nodes(webpage,'.metascore') #Converting the runtime data to text metascore_data <- html_text(metascore_data_html) #Let's have a look at the metascore data head(metascore_data) [1] "59 " "81 " "99 " "71 " "41 " [6] "56 " #Data-Preprocessing: removing extra space in metascore metascore_data<-gsub(" ","",metascore_data) #Lets check the length of metascore data length(metascore_data) [1] 96
Step 8: The length of the metascore data is 96 while we are scraping the data for 100 movies. The reason this happened is that there are 4 movies that don’t have the corresponding Metascore fields.
Step 9: It is a practical situation which can arise while scraping any website. Unfortunately, if we simply add NA’s to last 4 entries, it will map NA as Metascore for movies 96 to 100 while in reality, the data is missing for some other movies. After a visual inspection, I found that the Metascore is missing for movies 39, 73, 80 and 89. I have written the following function to get around this problem.
for (i in c(39,73,80,89)){ a<-metascore_data[1:(i-1)] b<-metascore_data[i:length(metascore_data)] metascore_data<-append(a,list("NA")) metascore_data<-append(metascore_data,b) } #Data-Preprocessing: converting metascore to numerical metascore_data<-as.numeric(metascore_data) #Let's have another look at length of the metascore data length(metascore_data) [1] 100 #Let's look at summary statistics summary(metascore_data) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 23.00 47.00 60.00 60.22 74.00 99.00 4
Step 10: The same thing happens with the Gross variable which represents gross earnings of that movie in millions. I have use the same solution to work my way around:
#Using CSS selectors to scrape the gross revenue section gross_data_html <- html_nodes(webpage,'.ghost~ .text-muted+ span') #Converting the gross revenue data to text gross_data <- html_text(gross_data_html) #Let's have a look at the votes data head(gross_data) [1] "$269.36M" "$248.04M" "$27.50M" "$67.12M" "$99.47M" "$153.67M" #Data-Preprocessing: removing '$' and 'M' signs gross_data<-gsub("M","",gross_data) gross_data<-substring(gross_data,2,6) #Let's check the length of gross data length(gross_data) [1] 86 #Filling missing entries with NA for (i in c(17,39,49,52,57,64,66,73,76,77,80,87,88,89)){ a<-gross_data[1:(i-1)] b<-gross_data[i:length(gross_data)] gross_data<-append(a,list("NA")) gross_data<-append(gross_data,b) } #Data-Preprocessing: converting gross to numerical gross_data<-as.numeric(gross_data) #Let's have another look at the length of gross data length(gross_data) [1] 100 summary(gross_data) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 0.08 15.52 54.69 96.91 119.50 530.70 14
Step 11: Now we have successfully scraped all the 11 features for the 100 most popular feature films released in 2016. Let’s combine them to create a dataframe and inspect its structure.
#Combining all the lists to form a data frame movies_df<-data.frame(Rank = rank_data, Title = title_data, Description = description_data, Runtime = runtime_data, Genre = genre_data, Rating = rating_data, Metascore = metascore_data, Votes = votes_data, Gross_Earning_in_Mil = gross_data, Director = directors_data, Actor = actors_data) #Structure of the data frame str(movies_df) 'data.frame': 100 obs. of 11 variables: $ Rank : num 1 2 3 4 5 6 7 8 9 10 ... $ Title : Factor w/ 99 levels "10 Cloverfield Lane",..: 66 53 54 32 58 93 8 43 97 7 ... $ Description : Factor w/ 100 levels "19-year-old Billy Lynn is brought home for a victory tour after a harrowing Iraq battle. Through flashbacks the film shows what"| __truncated__,..: 57 59 3 100 21 33 90 14 13 97 ... $ Runtime : num 108 107 111 139 116 92 115 128 111 116 ... $ Genre : Factor w/ 10 levels "Action","Adventure",..: 3 3 7 4 2 3 1 5 5 7 ... $ Rating : num 7.2 7.7 7.6 8.2 7 6.5 6.1 8.4 6.3 8 ... $ Metascore : num 59 81 99 71 41 56 36 93 39 81 ... $ Votes : num 40603 91333 112609 177229 148467 ... $ Gross_Earning_in_Mil: num 269.3 248 27.5 67.1 99.5 ... $ Director : Factor w/ 98 levels "Andrew Stanton",..: 17 80 9 64 67 95 56 19 49 28 ... $ Actor : Factor w/ 86 levels "Aaron Eckhart",..: 59 7 56 5 42 6 64 71 86 3 ...
You have now successfully scraped the IMDb website for the 100 most popular feature films released in 2016.
Once you have the data, you can perform several tasks like analyzing the data, drawing inferences from it, training machine learning models over this data, etc. I have gone on to create some interesting visualization out of the data we have just scraped. Follow the visualizations and answer the questions given below. Post your answers in the comment section below.
library('ggplot2') qplot(data = movies_df,Runtime,fill = Genre,bins = 30)
Question 1: Based on the above data, which movie from which Genre had the longest runtime?
ggplot(movies_df,aes(x=Runtime,y=Rating))+ geom_point(aes(size=Votes,col=Genre))
Question 2: Based on the above data, in the Runtime of 130-160 mins, which genre has the highest votes?
ggplot(movies_df,aes(x=Runtime,y=Gross_Earning_in_Mil))+ geom_point(aes(size=Rating,col=Genre))
Question 3: Based on the above data, across all genres which genre has the highest average gross earnings in runtime 100 to 120.
I believe this article would have given you a complete understanding of the web scraping in R. Now, you also have a fair idea of the problems which you might come across and how you can make your way around them. As most of the data on the web is present in an unstructured format, web scraping is a really handy skill for any data scientist.
Also, you can post the answers to the above three questions in the comment section below. Did you enjoy reading this article? Do share your views with me. If you have any doubts/questionsns feel free to drop them below.
Lorem ipsum dolor sit amet, consectetur adipiscing elit,
Good One
Hi Sajid, I'm glad you found it useful!
for ratings we may also use: ".ratings-imdb-rating strong" for gross earnings I used: ## 11.Gross ------------------- gross_data_html <- html_nodes(webpage, ".sort-num_votes-visible span:nth-child(5)") gross_data <- html_text(gross_data_html) gross_data <- gsub("M","",gross_data) gross_data <- gsub("\\$","",gross_data) gross_data <- as.numeric(gross_data) for (i in c(28,34,35,46,55,60,67,69,73,75,77,83,84,92,99)){ a <- gross_data[1:(i-1)] b <- gross_data[i:length(gross_data)] gross_data <- append(a, -1) # used -1 in place of NA's gross_data <- append(gross_data, b) } gross_data <- na.exclude(gross_data)
Hi Sharadananda, You can do that as well. Best, Saurav.
Never knew R was so Powerful!!
Hi Karthik, Yes, R is really powerful with several functionalities. Moreover, You can always add new functionalities in form of a package if you feel that something is missing. Best, Saurav.
Hi Saurav, It's really interesting. I just wanted to know whether this is applicable on a single webpage or for the entire website.
Hi Surya, You can scrap the entire website this way but you have to go through one webpage at a time. Best, Saurav.
Hi, For q1 , its the movie "Silence" , genre is "adventure" For q2, the genre is "Action" For q3, the genres are "Action,Animation & biography"
Hi Priyadharshini Ajay, Yes, similarly there are multiple directors and actors too. You can take the second genre, director and actor if they are present otherwise fill the value with NA using the same concepts demonstrated in the article. Best, Saurav.
Excellent article !!! its so easy in R !!
Hi Priyadharshini Ajay, I'm glad you liked it. Yes, its really easy in R! Best, Saurav.
The article was wonderful and very powerful.I guess we can read data from any website in this way. Finding NA in any of the field is a tuff task.. You have doe them manually. here data set is only 100 so we could do this but when the daa set would be large it will be difficult to manage. If there could be any easy way or any code to find out the missing values then it would have been great. Please let me know if u have any work around on this.
Hi Jayanti, Firstly, I'm happy that you found this article helpful. Secondly, You are absolutely spot on and what you mentioned can be a problem in scaling up. Unfortunately, It's a really practical problem and if the the web developer who created the website didn't put in the CSS selector and left it empty for the missing fields, there isn't much you can do other than doing what I did. Best, Saurav.
Jayanti, there is a workaround. Since each lister-item has the same structure, we can analyze each individually, then use R to iterate over each item extracting the data. Then, it is easy to add an NA for any node that has length zero. There are different ways to approach it, but I did it by making a custom wrapper for the html_text function like this: html_text2 <- function(checknode) { if (length(checknode)==0) return(NA) return(html_text(checknode)) } For full explanation see my April 2018 comment below.
How to scrap the writers name also, since when you click on the movie title the listing gets open and you are able to find the additional info for eg name of the writer also for the movie. This url as mentioned in post "http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature" provides the excerpt part of the listings. But how we can dig into each single listing and also get the writers info from it.
Hi Sandeep, Yes, you can scrap individual description page of each movie as well by following the same steps as I took. A really helpful thing would be to look for same patterns in the HTML and CSS codes on different description pages to save yourself extra effort. Best, Saurav.
It's really a nice write up
This is so powerful
Quite useful and easy and to follow!
Thanks a lot Saurav! I have always wanted to learn web scrapping and this has been spot on. Used it for my first project on a different website, Big up!
Nice article. Can you help me how can I scrap through infinite scroll paging and navigating pages through page numbers. I have done it using data miner but want to try using R. If possible share source code on my mail.Thanks in advance. :)
so F-ing cool. Thank you sir.
Hi Saurav, This is an excellently written article !! I am working on a project where I have to extract data which runs into multiple pages. Wanted to check with you how do we deal with pagination. Your response will be greatly appreciated ! Cheers, Venkatesh
Hi Saurav, I have sent you an email regarding web scraping assignment. Can you please check and revert?
I have worked for Amazon for data scraping and found yours useful. We used a different method though. I have faced the N/A issue as well. Do you think there is anything you can do about it? Lets brainstorm some ideas.
This is very helpful, Saurav! Thank you very much.
Thanks Saurav for a nicely written article. Here is another way to extract ratings and scores with all 100 documents properly filled (including NA) - library('magrittr') library(data.table) # Function to parse a particular 'div' and extract ratings and (potentially) metascore parse_node <- function(node) { rating % html_node('.ratings-imdb-rating strong') %>% html_text metascore % html_nodes('.metascore') %>% html_text list(rating=rating[1],metascore=metascore[1]) } # extract nodes, parse and merge webpage %>% html_nodes('.ratings-bar') %>% lapply(parse_node) %>% rbindlist
Hi Saurav, how did you find the css selectors, like for example in votes how did you manage to find out that it is " .sort-num_votes-visible span:nth-child(2)". I found it really difficult to find some of the css selectors. Can you please elaborate on the tricks used to find the selectors. Thanks!
Hi Saurav, Can we find specific text / keyword 'facebook' or 'linkedin' from different websites? I have list of 10k websites, I want to search Whether specific word is present in it or not?
This is quite useful. I can already see that the first two steps of html_nodes and html_text can be pushed though a single custom function and then use the custom function through lapply/sapply to extract everything in one step. The cleaning of data will then remain. However, the manual pushing of NA's is something which is a road block to automating this. At best one can ignore those vectors which have a different length to make this automated. Can anything else be done?
i ma not able to scrapping the data properly when selecting number of movie in ascending order , i am getting only one value. for eg- 1. movie name. 2. movie name 3.so on. so while selecting the numbers 1,2,3, getting on;ly one number in the text format.
How would you make this into a loop? Like if there were multiple pages.
saurav very well done. Keep up the work and you will do wonders.
It is always amazing to find something so clear. I can understand and follow even with very limited knowledge of R and programming in general. Fantastic work!
Thank you very much! It is super handy and well explained!
hi i have an excel file with 100 urls in it. How do i extract major elements from them and analyze them using rvest. How can i select which element is more profitable among them
Very good article. I stumbled across this as I am trying to find out whether you can use R to perform searches within a website? For instance, depending what I have in an excel file, can it search for the name of a file within a website and carry on this procedure until i finish my list?
Minor typos on your page: I think "Table of Content" should be "Table of Contents" and "Ways to scrap data" should be "Ways to scrape data." Thanks for the great overview of scraping in R!
Very nice write up giving a clear understanding of Web Scrapping!!! Thank You!!
Great resource!
Hey.. This content is super awesome and very helpful. But I am only able to download few reviews from a web page........ how can i download all reviews from a webpage....... Thankyou so much :):):)
Hi Saurav, great article! I'm learning a lot! I got stuck in a minor detail. I'm using the css selector from Firefox, and I've noticed that the directors and actors are in the same selector ('.lister-item-content p a'), giving me a vector of 517 characters. I saw that you were able to get only the first director with the expression "'.text-muted+ p a:nth-child(1)'" but I didn't understand. Could you explain it to me?
Thank you! It is very helpful!
Great work. This article is really helpful in implementing web scraper in R and easy to understand.
How to go about web scraping when you have a dynamic website where you can select values from a series of dropdowns and generate a content. For example I want to get the Air Quality Index data, the website has dropdowns for State, City, Region, Pollutant. Finally a value for that combination of inputs is displayed on the screen along with a graph.
Hey Saurav, Thanks for sharing such a super useful knowledge! My question is about, how to make "R" click on next to run over the entire list? Is that possible? Thanks in advance!
Hi Why do you only limit only to the first available name in director and actor variables per title and why do you convert them in factors?
Why did you only take the first genre per movie? A movie is a combination of one or more genre, is it right to take only the first value and drop rest of the values?
Thanks for the info and the post. Never knew web scraping could be that easy! Can't wait to put it to use. Great job.
Hi there, I am doing the same web scraping analysis for a project and got a problem in RStudio while calling the function webpage <- read_html(url) RStudio says such a function couldn't be found. Would you plz give me a way out? or some other functions? I've tried it with webpage <- readline as well but no success. Thanks a lot in beforehand.
Good Article !!! Helped me learn how to scrape. I am trying to scrape zomato reviews but only able to capture the popular reviews. I would like to scrape all reviews for a particular restaurant but since clicking on it doesn't change main page just run some java script i guess I am not able to do so. Are there ways where i can try to capture all review link ->> https://www.zomato.com/bangalore/double-decker-1-brigade-road/reviews
Hi, zomato.com doesn't allow its website to be scraped.
Hello, thanks for sharing it! Can you tell us how to do it for the whole ranking (all pages) and also, getting info from each publication?
Hi Pica, To scrape the same content from the 2nd page, just update the url with this link 'https://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature&page=2&ref_=adv_nxt'. For the 3rd page, replace 'page=2' with 'page=3' in the url. The same procedure can be followed for the rest of the pages. You can use a for loop to do this task. Hope this helps!
Hi Saurav, thank you so much, this information is very useful. I'm trying to do the same with this webpage, but I can't get the expected results, http://www.arrendamientosnutibara.com.co/ I want to get the information of "Tipo", "Municipio", "Sector", etc. of each house. I think the problem is that the webpage is in Angular, but I'm not sure. If you can help me with this I would be very grateful.
Now IMDB have removed the page numbers for review page instead they made load more button to load reviews in a single page (same URL), Can anyone tell me how to extract reviews in this case as the reviews will not load until the button is pressed. https://www.imdb.com/title/tt1431045/reviews?ref_=tt_ql_3
Hi Vijay, rvest has a drawback here, it lacks the functionality to scrape dynamic content.
Saurav - I learned a lot from this tutorial. I noticed that you can improve it using the function power of R, iterating through each of the 100 items, and adding an NA on the spot if any of your nodes is empty. #Specifying the url for desired website to be scrapped url <- 'http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature' #Reading the HTML code from the website webpage <- read_html(url) item_content <- html_nodes(webpage, '.lister-item-content') get_nth_film <- function(item) { description_data_html <- html_nodes(item, '.ratings-bar+ .text-muted') description_data % gsub("\n","",.) runtime_data_html <- html_nodes(item, '.text-muted .runtime') runtime_data % gsub("min","",.) %>% as.numeric() genre_data_html <- html_nodes(item, '.text-muted .genre') genre_data % gsub("\n|,.*", "", .) %>% as.factor() ratings_data_html <- html_nodes(item, '.ratings-imdb-rating strong') ratings_data % as.numeric() metascore_data_html <- html_nodes(item, '.metascore') metascore_data % as.numeric() votes_data_html <- html_nodes(item, '.sort-num_votes-visible span:nth-child(2)') votes_data % gsub(",","",.) %>% as.numeric() director_data_html <- html_nodes(item, '.text-muted+ p a:nth-child(1)') director_data % as.factor() star_data_html <- html_nodes(item, '.ghost+ a') star_data % as.factor() df <- data.frame(description_data, runtime_data, genre_data, ratings_data, metascore_data, votes_data, director_data, star_data) return(df) } html_text2 <- function(checknode) { if (length(checknode)==0) return(NA) return(html_text(checknode)) } movies_df <- do.call(rbind, lapply(item_content, get_nth_film))
This is great to scrape the first 100 movies, but there are 11,027 titles so there are 111 pages to scrape, How would you loop through them?
Could anyone tell, how can we write some code to find missing value in R programming? Ex, for (i in c(17,39,49,52,57,64,66,73,76,77,80,87,88,89))
Just enters numbers of movies that haven't missing values.
Hey..great information! Thanks for sharing :)
Hi, Thanks for this wonderful tutorial. I tried to implement this technique on another page that ranks top 100 books. It was easier to scrap the title and the author. However, when it came to scraping the ratings section. I found it hard. How? The rating includes "average rating as well as total number of ratings" in the text. Example: "4.26 — 3,586,915". The left part indicates average rating, while the other - total number of ratings. I only want the average ratings. How do i do remove "total number of ratings" from the text?
Hi, thanks for this. In Step 6, you are required to click 'Alphabetical' as well as the Movie titles to get the correct CSS Selector. This doesn't seem intuitive, and was only obvious because you included a screenshot. How would we know to select this if you hadn't provided the screenshot?
Good Day!!! Thanks for sharing, very helpful.
It is so amazing the way you did never thought r can do such statistical process. if you have more such examples it would help me also. As I have just started with r. Thank you once again for your efforts in making it simple,