Add new auto R web scraping example for Feb 2019 Meetup

502c4393 · Rebecca Merrett · 97577125 · 502c4393
Commit 502c4393 authored Feb 06, 2019 by Rebecca Merrett
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r_web_scraping_meetup_share.r web_scraping_R-master/r_web_scraping_meetup_share.r +120 -0

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--- a/web_scraping_R-master/r_web_scraping_meetup_share.r
+++ b/web_scraping_R-master/r_web_scraping_meetup_share.r
+# Data Science Dojo Meetup: Automated Web Scraping in R
+# Let's start scraping the main head and paragraph text/comments of a single Reddit page
+#install.packages("rvest")
+library(rvest)
+reddit_wbpg <- read_html("https://www.reddit.com/r/politics/comments/a1j9xs/partisan_election_officials_are_inherently_unfair/")
+reddit_wbpg %>%
+  html_node("title") %>%
+  html_text()  
+reddit_wbpg %>%
+  html_nodes("p.s90z9tc-10") %>%
+  html_text()
+# Let's scrape the time and URL of all latest pages published on Reddit's r/politics
+reddit_political_news <- read_html("https://www.reddit.com/r/politics/new/")
+time <- reddit_political_news %>%
+  html_nodes("a._3jOxDPIQ0KaOWpzvSQo-1s") %>% 
+  html_text()
+time
+urls <- reddit_political_news %>%
+  html_nodes("a._3jOxDPIQ0KaOWpzvSQo-1s") %>%
+  html_attr("href")
+urls
+# Create a dataframe containing the URLs of the Reddit news pages and their published times
+reddit_newspgs_times <- data.frame(NewsPage=urls, PublishedTime=time)
+#Check the dimensions 
+dim(reddit_newspgs_times)
+# Filter dataframe by rows that contain a time published in minutes (i.e. within the hour)
+reddit_recent_data <- reddit_newspgs_times[grep("minute|now", reddit_newspgs_times$PublishedTime),]
+#Check the dimensions (# items will be less if not all pages were published within mins)
+dim(reddit_recent_data)
+# Loop through urls, grab the main head and paragraph text of comments, 
+# store in their own vectors, and create a dataframe to get it ready for analysis/modeling
+titles <- c()
+comments <- c()
+for(i in reddit_recent_data$NewsPage){ 
+  reddit_recent_data <- read_html(i)
+  body <- reddit_recent_data %>%
+    html_nodes("p.s90z9tc-10") %>%
+    html_text()
+  comments = append(comments, body)
+  reddit_recent_data <- read_html(i)
+  title <- reddit_recent_data %>%
+    html_node("title") %>%
+    html_text()
+  titles = append(titles, rep(title,each=length(body)))
+}
+reddit_hourly_data <- data.frame(Headline=titles, Comments=comments)
+dim(reddit_hourly_data)
+head(reddit_hourly_data$Comments)
+# Remove disclaimer comments included in all pages so this doesn't flood the comments and skew results
+disclaimers <- c(
+  "As a reminder, this subreddit is for civil discussion.",
+  "In general, be courteous to others. Attack ideas, not users. Personal insults, shill or troll accusations, hate speech, any advocating or wishing death/physical harm, and other rule violations can result in a permanent ban.",
+  "If you see comments in violation of our rules, please report them.",
+  "I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns."
+)
+reddit_hourly_data_no_disclaimers <- subset(
+  reddit_hourly_data, !(Comments %in% c(disclaimers))
+)
+dim(reddit_hourly_data_no_disclaimers)
+head(reddit_hourly_data_no_disclaimers$Comments)
+# Score the overall sentiment of each comment
+# This library scores sentiment by taking into account the whole sentence
+# It takes into account surrounding words of a target word such as 'not happy'
+# which cancels out positive sentiment
+# A negative value means sentiment is more negative than positive
+# A positive values means the sentiment is more positive than negative
+#install.packages('sentimentr')
+library(sentimentr)
+# Comment out this line so it does not cause errors when scheduling to run the script
+#sentiment(reddit_hourly_data_no_disclaimers$Comments)
+# Treat comments as characters, not factors
+# Convert to a format sentiment() function accepts
+reddit_hourly_data_no_disclaimers$Comments <- as.character(reddit_hourly_data_no_disclaimers$Comments)
+sentiment_scores <- sentiment(reddit_hourly_data_no_disclaimers$Comments)
+head(sentiment_scores)
+# Average the scores across all comments
+average_sentiment_score <- sum(sentiment_scores$sentiment)/length(sentiment_scores$sentiment)
+average_sentiment_score
+# Email the results of the analysis
+#install.packages("sendmailR")
+library(sendmailR)
+from <- "<[email protected]>"
+to <- "<[email protected]>"
+subject <- "Hourly Sentiment Score on Current US Political Situation"
+body <- c("On a scale of 1 to -1 people feel: ", average_sentiment_score)            
+mailControl <- list(smtpServer="ASPMX.L.GOOGLE.COM") #Use Google for Gmail accounts
+sendmail(from=from,to=to,subject=subject,msg=body,control=mailControl)
+# Schedule this script to run every hour to keep track of the overall sentiment 
+# Idea to take this further: Instead of emailing the hourly results, 
+# store the average sentiment score in a table every hour to plot it 
+# over time or see how changes over time
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