web-scraping/stack_scrap_function.R at master · luizfcp/web-scraping · GitHub

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# Navegando como em webbrowser - StackOverflow

library(tidyverse)
library(rvest)

sof_scraper <- function(n, search){

  results <- c()

  # Conexão e preenchimento de formulário

  url <- 'https://stackoverflow.com'

  session <- html_session(url)

  form <- html_form(session)

  filled_form <- form

  filled_form[[1]] <- set_values(filled_form[[1]], q = search)

  session <-  submit_form(session = session, form = filled_form[[1]])

  for (i in 1:n) {

    next_page_link <- paste0('/questions/tagged/r?page=',i,'&sort=newest&pagesize=15')

    session <- session %>% jump_to(next_page_link)

    # Perguntas da pagina

    question_id <- session %>%
      html_nodes("div.question-summary") %>%
      html_attr('id')

    for (j in 1:length(question_id)) {

      xpath_id <- paste0('//*[@id="', question_id[[j]], '"]')

      # Título da pergunta

      question <- session %>%
        html_nodes(xpath = xpath_id) %>%
        html_nodes('h3') %>%
        html_text()

      # Link da pergunta

      link <- session %>%
        html_nodes(xpath = xpath_id) %>%
        html_nodes('h3') %>%
        html_nodes('a') %>%
        html_attr('href')

      # Votos

      votes <- session %>%
        html_nodes(xpath = xpath_id) %>%
        html_nodes('div.votes') %>%
        html_nodes('span.vote-count-post') %>%
        html_text() %>%
        as.numeric()

      # Tags

      tags <- session %>%
        html_nodes(xpath = paste0(xpath_id,'/div[2]/div[2]')) %>%
        html_nodes("a") %>%
        html_text()

      # Usuário

      user <- session %>%
        html_nodes(xpath = xpath_id) %>%
        html_nodes('div.user-details') %>%
        html_node("a") %>%
        html_text()

      # Acessar texto da pergunta

      session <- session %>%
        follow_link(question)

      text <- session %>%
        html_node("div.post-text") %>%
        html_nodes('p') %>%
        html_text() %>%
        paste(collapse = '')

      session <- session %>% back()

      # Consolidação dos dados raspados

      results_j <- data.frame(pergunta = question,
                              usuario = user,
                              votos = votes,
                              texto = text,
                              Tag1 = NA,
                              Tag2 = NA,
                              Tag3 = NA)


      results_j[1, c("Tag1", "Tag2", "Tag3")] <- tags[1:3]

      results <- rbind(results, results_j)

      Sys.sleep(3)

    }


  }

  return(results)
}

dados <- sof_scraper(n = 1, search = 'r')

# Text Mining

library(wordcloud)
library(tm)

docs <- Corpus(VectorSource(paste(dados$texto, collapse = ''))) %>%
  tm_map(content_transformer(tolower)) %>%
  tm_map(removeNumbers) %>%
  tm_map(removeWords, stopwords("english")) %>%
  tm_map(removePunctuation) %>%
  tm_map(removeWords, c("want")) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)

dtm <- TermDocumentMatrix(docs)

m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)

set.seed(1234)
wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35,
          colors=brewer.pal(8, "Dark2"))

# Frequência de temas

tags_all <- data.frame(tags = c(dados$Tag1, dados$Tag2, dados$Tag3))

tags_all %>%
  group_by(tags) %>%
  count() %>%
  filter(tags != 'NA', tags != 'r') %>%
  ggplot(aes(x = tags, y = n)) +
  geom_bar(stat = 'identity') +
  coord_flip()