(Disclaimer: I’ve no grudge against python programming language per se. I think its equally great. In the following post, I’m merely recounting my experience.)

It’s been quite a while since I last posted. The reasons are numerous, notable being, unable to decide which programming language to choose for web data scraping. The contenders were data analytic maestro, R and data scraping guru, python. So, I decided to give myself some time to figure out which language will be best for my use case. My use case was, Given some search keywords, scrape twitter for related posts and visualize the result. First, I needed the live data. Again, I was at the cross-roads, “R or Python”. Apparently python has some great packages for twitter data streaming like twython,python-twitter, tweepy. Equivalent R libraries are twitteR,rwteet. I chose the rtweet package for data collection over python for following reasons;

• I do not have to create a credential file (unlike in python) to log in to my twitter account. However, you do need to authenticate the twitter account when using the rtweet package. This authentication is done just once if using the rtweet package. Your twitter credentials will be stored locally.
• Coding and code readability is far more easier as compared to python.
• The rtweet package allows for multiple hash tags to be searched for.
• To localize the data, the package also allows for specifying geographic coordinates.

So, using the following code snippet, I was able to scrape data. The code has following parts;

1. A custom search for tweets function which will accept the search string. If search string is NULL, it will throw a message and stop, else it will search for hash tags specified in search string and return a data frame as output.

   #required libraries

    > library(rtweet)
    > library(tidytext)
    > library(tidyverse)
    > library(stringr)
    > library(stopwords)
    > library(rtweet) # for search_tweets()
    # Create a function that will accept multiple hashtags and will search the twitter api for related tweets
   
    > search_tweets_queries <- function(x, n = 100, ...) {
      ## check inputs
      stopifnot(is.atomic(x), is.numeric(n))
      if (length(x) == 0L) {
    stop("No query found", call. = FALSE)
      }  
      ## search for each string in column of queries
      rt <- lapply(x, search_tweets, n = n, ...)
      ## add query variable to data frames
      rt <- Map(cbind, rt, query = x, stringsAsFactors = FALSE)
      ## merge users data into one data frame
      rt_users <- do.call("rbind", lapply(rt, users_data))
      ## merge tweets data into one data frame
      rt <- do.call("rbind", rt)
      ## set users attribute
      attr(rt, "users") <- rt_users
      ## return tibble (validate = FALSE makes it a bit faster)
      tibble::as_tibble(rt, validate = FALSE)
    }

2. A data frame containing the search terms. Note, here my search hash-tags are KTM, MRT and monorail.

    # create data frame with query column
    > df_query <- data.frame(
      query = c("KTM", "monorail","MRT"),
      n = rnorm(3), # change this number according to the number of searchwords in parameter query. As of now, the parameter got 3 keywords, therefore this nuber is set to 3.
      stringsAsFactors = FALSE
    )

3. Using the search_tweets_queries defined in step 1, to search for tweets. Note, the usage of retryonratelimit=TRUE indicates if search rate limit reached, then the crawler will sleep for a while and start again. Refer to the rtweet documentation for more information.

    # pass query column to the search_tweet_queries function defined above
    > df_collect_tweets <- search_tweets_queries(df_query$query, include_rts = FALSE, retryonratelimit = TRUE, 
    #geocode for Kuala Lumpur
    geocode = "3.14032,101.69466,93.5mi"
    )

4. Once the data is collected, I’ll keep some selected columns only.

    # Select and keep only relevant columns
    > df_select_tweets<- df_collect_tweets %>%
      select(c(user_id,created_at,screen_name, !is.na(hashtags),text,
       source,display_text_width>0,lang,!is.na(place_name),
       !is.na(place_full_name),
       !is.na(geo_coords), !is.na(country), !is.na(location),
       retweet_count,account_created_at, account_lang, query)
     )

5. Text mining: The collected data need to be cleaned. Therefore, I’ve used the basic gsub() function and str_replace_all() from the stringr library.

    # Saving the selected columns data
    > df_select_tweets_1 = data.frame(lapply(df_select_tweets, as.character), stringsAsFactors=FALSE)
    ### Text preprocessing
       
    # 1. Remove URL from text
    # collapse to long format
    > clean_tweet<- df_select_tweets_1
       
    #clean_tweet<- paste(df_select_tweets_1, collapse=" ")
    > clean_tweet$text = gsub("&amp", "", clean_tweet$text)
    > clean_tweet$text = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", clean_tweet$text)
    > clean_tweet$text = gsub("@\\w+", "", clean_tweet$text)
    > clean_tweet$text = gsub("[[:punct:]]", "", clean_tweet$text)
    > clean_tweet$text = gsub("[[:digit:]]", "", clean_tweet$text)
    > clean_tweet$text = gsub("http\\w+", "", clean_tweet$text)
    > clean_tweet$text = gsub("[ \t]{2,}", "", clean_tweet$text)
    > clean_tweet$text = gsub("^\\s+|\\s+$", "", clean_tweet$text)
   
    #get rid of unnecessary spaces
    > clean_tweet$text <- str_replace_all(clean_tweet$text," "," ")
    # Get rid of URLs
    > clean_tweet$text<- str_replace_all(clean_tweet$text, "https://t.co/[a-z,A-Z,0-9]*","")
    > clean_tweet$text<- str_replace_all(clean_tweet$text, "http://t.co/[a-z,A-Z,0-9]*","")
    # Take out retweet header, there is only one
    > clean_tweet$text <- str_replace(clean_tweet$text,"RT @[a-z,A-Z]*: ","")
    # Get rid of hashtags
    > clean_tweet$text <- str_replace_all(clean_tweet$text,"#[a-z,A-Z]*","")
    # Get rid of references to other screennames
    > clean_tweet$text <- str_replace_all(clean_tweet$text,"@[a-z,A-Z]*","")

   a. Next, I’ll use the tidytext library for token extraction

    # Unnest the tokens
    > df.clean<- clean_tweet %>%
      unnest_tokens(word, text)
       
    > clean_tweets<- tibble()
    > clean_tweets<- rbind(clean_tweets, df.clean)
       
    # Basic calculations
    # calculate word frequency
    > word_freq <- clean_tweets %>%
      count(word, sort=TRUE)
    > word_freq 
   
    # A tibble: 5,291 x 2
       wordn
       <chr>   <int>
     1 mrt   596
     2 ktm   582
     3 ke455
     4 kl259
     5 ni251
     6 naik  221
     7 the   214
     8 at208
     9 sentral   195
    10 nak   193
    # ... with 5,281 more rows

   b. It should be noted, the national language of Malaysia is Bahasa Melayu (BM). To remove the stop words in BM, I’ve used the stopwords library. lots of stop words like the, and, to, a etc. Let’s remove the stop words. We can remove the stop words from our tibble with anti_join and the built-in stop_words data set provided by tidytext.

    > clean_tweets %>%
      # remove the stopwords in Bahasa Melayu (BM). Use `ms` for BM. See this reference for other language codes: https://en.wikipedia.org/wiki/ISO_639-1
      anti_join(get_stopwords(language="ms", source="stopwords-iso")) %>%
      # remove the stopwords in english
      anti_join(get_stopwords(language="en", source="stopwords-iso")) %>%
      count(word, sort=TRUE) %>%
      top_n(10) %>%
      ggplot(aes(word,n, fill=word))+
      geom_bar(stat = "identity")+
      xlab(NULL)+
      ylab(paste('Word count'))+
      ggtitle(paste('Most common words in tweets')) +
      theme(legend.position="none") +
      theme_minimal()+
      coord_flip()

6. Finally, I present a basic bar plot to show the trending words.

   
   Barplot: Trending twitter words in kuala lumpur, malaysia

Area’s of further improvement

• How to extract tweets within a given time range?

See the code on my Github account