twitteR.R

#' ---
#' title: "R-Ladies Melbourne - Twitter Workshop"
#' author: "Anna Quaglieri & Saskia Freytag"
#' date: "15th May 2017"
#' output:
#'   github_document:
#'     toc: yes
#'     toc_depth: 3
#'   html_document:
#'     toc: yes
#'     toc_depth: 3
#' linkcolor: magenta
#' urlcolor: magenta
#' ---
#' 
## ----echo = FALSE, message=FALSE-----------------------------------------
# Chunk options
knitr::opts_chunk$set(fig.width=8, fig.height=6, echo=T, warning=FALSE, message=FALSE,prompt=T,tidy=T,tidy.opts=list(width.cutoff=50),include=TRUE,cache=TRUE)

# To be changed according to where the repository is saved
dir <- "~/Documents/varie/Rladies/twitter_workshop"

library(png)
library(grid)

#' 
#' \clearpage
#' 
#' # Set up a twitter API 
#' 
#' Before you can download data from Twitter, you need to setup your API by becoming a Twitter developer. First, to become a Twitter developer you need to have a Twitter account. Then, you can sign up [here](https://dev.twitter.com/resources/signup) (Figure 1). Once you have signed up click on the *My Apps* buttom on the left panel as in Figure 2 and then click on *Create a new app*. You should provide a brief description about your App and a few other details. For today's workshop we set up the the *Rladies* App shown in Figure 3. By clicking onto your App's name you can now manage and see your settings as well as the codes that you need to get started with R. You can find the keys that you need in the *Keys and Access Tokens* button. Here you can find your four codes (highlighted in Figure 4) to use into R. 
#' 
#' ***
#' 
## ----fig.cap = "Figure 1", echo = FALSE----------------------------------
img <- readPNG(file.path(dir,"Figures/signup1.png"))
grid.raster(img)

#' 
#' **Figure 1**
#' 
#' ***
#' 
## ----fig.cap = "Figure 2", echo = FALSE----------------------------------
img <- readPNG(file.path(dir,"Figures/signup2.png"))
grid.raster(img)

#' 
#' **Figure 2**
#' 
#' ***
#' 
## ----fig.cap = "Figure 3", echo = FALSE----------------------------------
img <- readPNG(file.path(dir,"Figures/twitter_apps.png"))
grid.raster(img)

#' 
#' **Figure 3**
#' 
#' ***
#' 
## ----fig.cap = "Figure 4", echo = FALSE----------------------------------
img <- readPNG(file.path(dir,"Figures/APIs_tokens.png"))
grid.raster(img)

#' 
#' **Figure 4**
#' 
#' ***
#' 
#' \clearpage
#' 
#' # Let's get started with RuPaul's drag race's tweets!
#' 
#' RuPaul's drag race is an American reality competitions showing RuPaul's search for "America's next drag superstar". Just to tease your curiosity, Figure 5 shows you all the Queens which are competing in Season 9!
#' 
#' ***
#' 
## ----fig.cap = "Figure 5", echo = FALSE----------------------------------
img <- readPNG(file.path(dir,"Figures/queens.png"))
grid.raster(img)

#' 
#' **Figure 5**
#' 
#' ***
#' 
#' \clearpage
#' 
#' ## Research your hashtags
#' 
#' The first thing to do when you want to analyse a topic using Twitter's data is to know what is or are the most useful *hastag/s* that can describe your questions. This means those hastags that are most commonly used when what you are interested in is tweeted. In this example we are interested into the trends of [RuPaul's drag race Season 9](http://logosrupaulsdragrace.wikia.com/wiki/RuPaul%27s_Drag_Race_(Season_9)). A little bit of research through Twitter or Goggle is needed to define a set of good hastags. For example, we found a web page listing the top hashtags for RuPaul's drag race https://top-hashtags.com/hashtag/rupaul/. For simplicity we will limit our search to two hastags and we decided to use one specific for season 9, `#rpdr9`, and a more general one, `#rupaulsdragrace`. 
#' 
#' ## Initialize functions needed for the analyses
#' 
#' First, start by installing and loading all the necessary packages.
#' 
## ----eval = FALSE--------------------------------------------------------
## ## Initialize functions needed for the analyses
## # Packages that you need to setup your API in R and download twitter data
## install.packages("twitteR")
## install.packages("ROAuth")
## install.packages("RCurl")
## install.packages("RJSONIO")
## 
## # Packages for sentiment analyses and wordclouds
## install.packages("RSentiment")
## install.packages("tm")
## install.packages("wordcloud")
## 
## # Genral R packages for plotting and manipulating data
## install.packages("tidyr")
## install.packages("dplyr")
## install.packages("ggplot2")
## install.packages("plotly")
## install.packages("stringr")
## install.packages("knitr")

#' 
## ----message=FALSE-------------------------------------------------------
# Require all packages
require("tm")
require("wordcloud")
require("twitteR")
require("ROAuth")
require("RCurl")
require("RJSONIO")
require("tidyr")
require("dplyr")
require("ggplot2")
require("plotly")
library("stringr")
library("knitr")
source(file.path(dir,"score_sentiment.R"))

#' 
#' Then setup your `access keys` as below. If you don't remember where to find these codes see Figure 1).
#' 
## ----eval = FALSE--------------------------------------------------------
## api_key<-'yourAPIKey'
## api_secret<- "yourAPISecret"
## token <- "yourToken"
## token_secret <- "yourTokenSecret"
## 
## twitteR::setup_twitter_oauth(api_key, api_secret, token, token_secret)

#' 
#' ## Start and plan your search
#' 
#' Twitter only allows you to retrieve tweets up to about two weeks before. Therefore, if you are planning to analyse some particular tweets across few weeks or months you will have to plan the downloading about every two weeks. For example, for this workshop we downloaded data on the 5th of May and on the 14th of May using the code below: 
#' 
#' ### Download the tweets
#' 
## ----eval = FALSE--------------------------------------------------------
## ### Download the tweets
## # Download only English tweets
## # 5th of May
## twittes_rpdr9_1 <- twitteR::searchTwitter("#rpdr9", n = 3000, lang= "en", since= "2017-03-01")
## # 14th May
## twittes_rpdr9_2 <- twitteR::searchTwitter("#rpdr9", n = 3000, lang= "en", since= "2017-03-01")
## twittes_rpdr <- twitteR::searchTwitter("#rupaulsdragrace", n = 3000, lang= "en", since= "2017-03-01")
## 
## # Save output as R objects
## saveRDS(twittes_rpdr9_2, file.path(dir,"Data","twitter_rpdr9.rds")
## saveRDS(twitter_rpdr9, file.path(dir,"Data","twitter_rpdr9_2.rds")
## saveRDS(twittes_rpdr, file.path(dir,"Data","twitter_rupoldragrace.rds")

#' 
#' Now, read into R the two sets of tweets that we previously downloaded. 
#' 
## ------------------------------------------------------------------------
# Load the files got from twitteR::searchTwitter run three times
twitter_rpdr9_1 <- readRDS(file.path(dir,"Data","twitter_rpdr9.rds"))
twitter_rpdr9_2 <- readRDS(file.path(dir,"Data","twitter_rpdr9_2.rds"))
twitter_rpdr <- readRDS(file.path(dir,"Data","twitter_rupoldragrace.rds"))
class(twitter_rpdr9_1)

# Convert the lists to data.frames 
toDF_rupol_1 <- twitteR::twListToDF(twitter_rpdr9_1)
toDF_rupol_2 <- twitteR::twListToDF(twitter_rpdr9_2)
toDF_rupol_3 <- twitteR::twListToDF(twitter_rpdr)

#' 
#' Now we would like to combine the two sets of tweets and remove overlapping tweets since there is overlap in the days.
#' 
## ------------------------------------------------------------------------
# Time interval of the tweets dates 
# Set 1
min(toDF_rupol_1$created)
max(toDF_rupol_1$created)
# Set 2
min(toDF_rupol_2$created)
max(toDF_rupol_2$created)
# Set 3
min(toDF_rupol_3$created)
max(toDF_rupol_3$created)
# There is a two days overlap

# Combine the data.frame and remove duplicated tweets
combineTweetsDupl <- rbind(toDF_rupol_1,toDF_rupol_2,toDF_rupol_3) 
duplicatedRows <- duplicated(combineTweetsDupl)
sum(duplicatedRows)
combineTweets <- combineTweetsDupl[!duplicatedRows, ]
sum(duplicated(combineTweets))

colnames(combineTweets)

# Save the clean data to file
write.csv(combineTweets,file.path(dir,"Data","tweets_hastags_combined.csv"),row.names = FALSE)

#' 
#' With the help of [tidyr](https://cran.r-project.org/web/packages/tidyr/index.html) we can reorganise and manipulate the dataset. For example, we can plot the number of tweets per day.
#' 
## ----fig.cap="Total number of tweets per day"----------------------------
toDF_rupol_daily <- combineTweets %>% tidyr::separate(created, into = c("Day","Time"), sep = " ") %>%
			dplyr::group_by(Day) %>% 
			dplyr::summarise(tweetsPerDay = length(text)) %>%
			ggplot(aes(x = Day, y = tweetsPerDay)) + 
      geom_bar(stat = "identity", fill = "#88398A") + theme_bw() +
			theme(axis.text.x = element_text(angle = 45, hjust = 1)) + coord_flip()
toDF_rupol_daily

#' 
#' April 29th is the first day of our sets of tweets with a higher frequency of tweets and indeed it corresponds to RuPaul's Drag Race Episode 6 which in America was the night of April 28th (for the list of all the episodes see [RuPaul's Wikia](http://logosrupaulsdragrace.wikia.com/wiki/RuPaul%27s_Drag_Race_(Season_9))). The same thing apply for May 6th. You could also stratify every day by time and check at which time the highest number tweets was written. This is not going to be addressed here but for examples on how to deal with dates and times in R you can look at this tutorial from Berkeley https://www.stat.berkeley.edu/~s133/dates.html. 
#' 
#' ### User's information
#' 
#' Another functionality of *twitteR* is to download the information about the users that wrote the the tweets. This can be done with the functions *getUser()* and for multiple lookups with *lookupUsers()* and . Of course, you are able to access user's information according to the user's permission. Therefore, you normally expect a lot of missing or fake data! The search can be done by providing on of *screenNames* or *userIDs*.
#' 
## ------------------------------------------------------------------------
head(combineTweets[,c("id","screenName")])
# Create list of screenNames to look up for
screen_names <- unique(as.character(combineTweets$screenName))

#' 
#' There is a cap in the number of users that can be looked up in one go. The maximum is capped at 100 as stated on the [Twitter Developer Documentation](https://dev.twitter.com/rest/reference/get/users/lookup). Therefore, we need to look up *screen_names* in blocks and then combine the results.
#' 
## ----eval = FALSE--------------------------------------------------------
## block_size <- round(seq(1,length(screen_names), length.out = 50))
## # Size of the blocks
## diff(block_size)
## 
## # Create list of screen names
## screen_names_blocks <- list()
## for(i in 1:(length(block_size)-1)){
##   start <- ifelse(block_size[i] == 1, 1, block_size[i] + 1)
##   end <-block_size[i+1]
##   screen_names_blocks[[i]] <- screen_names[start:end]
## }
## 
## # Wrapper for lookupUsers
## getUser_wrapper <- function(name_block){
##   user_infos <- twitteR::lookupUsers(name_block, includeNA = FALSE)
##   user_infosToDF <- twitteR::twListToDF(user_infos)
##   return(user_infosToDF)
## }
## 
## user_infos_list <- lapply(screen_names_blocks, function(x) getUser_wrapper(x))
## user_infosDF <- do.call(rbind, user_infos_list)
## 
## saveRDS(user_infosDF, file.path(dir,"Data","Users_infos.rds"))

#' 
#' Now we can merge tweets with user information.
#' 
## ----eval = FALSE--------------------------------------------------------
## user_infosDF <- readRDS(file.path(dir,"Data","Users_infos.rds"))
## 
## toDF_rupol_time <- combineTweets %>% tidyr::separate(created, into = c("Day","Time"), sep = " ", remove = FALSE)
## combine_data <- merge(toDF_rupol_time[,c("screenName","text","Day","Time","retweetCount","isRetweet","retweeted")],
## 	user_infosToDF[,c("screenName","description","followersCount","friendsCount","location")], all.x = TRUE, all.y = TRUE)
## combine_data <- combine_data[!duplicated(combine_data),]
## 
## write.csv(combine_data, file.path(dir,"Data","Users_infos_and_tweets.csv"), row.names = FALSE)

#' 
#' ## Some analysis
#' 
#' ### How many times is a drag queen mentioned daily?
#' 
#' Now that we have combined the tweets and the user's information we can start with some analyses. For example, how many times was each queen mentioned daily? Of course, each mention could have a positive or negative connotation but nonetheless this would tell us about the overall popularity of the queen.
#' 
#' First, let's load the data where we saved the queen names and their twitter screen name:
#' 
## ------------------------------------------------------------------------
queens <- read.csv(file.path(dir,"Data","queens_name.csv"))
knitr::kable(queens)

#' 
#' We are going to do the search through the *grep* function and for every queen we need a unique vector of names. This is because a queen can be mentioned via her twitter name, her queen name or even her real name. For simplicity and to avoi overlap between queens we will perform the search using their drag queen names and their twitter names. 
#' 
## ------------------------------------------------------------------------
# Read in data.frame containing tweets and User's infos
combine_data <- read.csv(file.path(dir,"Data","Users_infos_and_tweets.csv"), stringsAsFactors = FALSE)

# Separate queen name into their components
queens <- queens %>% 
  tidyr::separate(Queen.Name, into = c("Queen1","Queen2","Queen3"), sep = " ", remove = FALSE)
kable(queens[,c("Queen1","Queen2","Queen3","Twitter.Name")])

# Wrapper function that creates a vector of key names for every queen
queen_vector <- function(x){
	vec <- c(x[c("Queen1","Queen2","Queen3","Twitter.Name")])
	vec <- vec[!is.na(vec)]
}

# List containing the vectors for every queen
queens_vecs <- apply(queens, 1,queen_vector)
queens_grepKey_prepare <- lapply(queens_vecs, function(x) paste0(x, collapse = "|"))

# Set the encoding of the tweets as latin to avoid issues with for example emoji
Encoding(combine_data$text) <- "latin1"
grep_queens <- lapply(queens_grepKey_prepare, function(x) grep(x,combine_data$text))
names(grep_queens) <- queens$Twitter.Name
# Index referring to the raw in combine_data where a queen was mentioned
head(grep_queens[[1]])

# Frequency of tweets per queen
# 1. Exctract rows where a queen was mentioned and extract only columns that we need for this analysis
freq_mention_Day <- lapply(grep_queens, function(x){
	mention_data <- combine_data[x,c("Day","Time","text","location","followersCount","friendsCount","retweetCount", "isRetweet", "retweeted")]
})
# 2. Combine mention for every queen into a data.frame 
freq_mention_DayToDF <- do.call(rbind,freq_mention_Day)
# 3. Creat a column $queen_name which will tell us whose queen the tweet belongs to
number_mention <- sapply(freq_mention_Day,function(x) nrow(x))
freq_mention_DayToDF$queen_name <- rep(names(freq_mention_Day), times = number_mention)

#' 
#' Now we can plot the number of time a queen was mentioned in a tweet every day.
#' 
## ----fig.cap="Total number of times that a queen was mentioned in a tweet."----
dailyMention <- freq_mention_DayToDF %>% dplyr::group_by(Day, queen_name) %>% 
  dplyr::summarise(Nmention = length(text)) %>%
  ggplot(aes(x = Day, y = Nmention, colour = queen_name, group = queen_name)) + 
  geom_line() + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  geom_vline(xintercept= c(4,11,18), linetype = "dotted")
dailyMention
# ggplotly(dailyMention) 

#' 
#' 
## ----fig.cap="Unique tweets per queen per day."--------------------------
# Unique different tweets
dailyMention <- freq_mention_DayToDF %>% dplyr::group_by(Day, queen_name) %>% 
  dplyr::summarise(Nmention = length(unique(text))) %>%
  ggplot(aes(x = Day, y = Nmention, colour = queen_name, group = queen_name)) + 
  geom_line() + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  geom_vline(xintercept= c(4,11,18), linetype = "dotted") + labs(y = "N unique tweets sent")
dailyMention
# ggplotly(dailyMention) 

# Episodes in America
# Airdate: April 28, 2017 (29th in AU)
# Airdate: May 5th 2017 (6th in AU) - ajaqueen and atlsexyslim - bottom two - Aja eliminated
# Airdate: May 12th 2017 (13th in AU)

#' 
#' ### How come @atlsexyxlim has such huge number of tweets?
#' 
#' The answer is in the retweets!
#' 
## ----fig.cap="Number of retweets by number of unique tweets where a queen was mentioned per day."----
retweets <- freq_mention_DayToDF %>% dplyr::group_by(Day, queen_name) %>%
  dplyr::summarise(NUniqueTweet = length(unique(text)), Nretweet = sum(isRetweet)) %>%
  ggplot(aes(x = NUniqueTweet, y = Nretweet, colour = queen_name)) + geom_point() + theme_bw()
retweets
# ggplotly(retweets)

#' 
#' ### What are the most common words used in @atlsexyslim tweets? 
#' 
#' To answer this question we will generate a word cloud using **@atlsexyslim** tweets. At the beginning of this tutorial there is the list functions that are loaded for this step. 
#' 
## ------------------------------------------------------------------------
## Create a world clous from @atlsexyslim tweets
# Extract tweets 
some_txt <- freq_mention_DayToDF$text[freq_mention_DayToDF$queen_name=="@atlsexyslim"]
# Clean text
# remove punctuation
some_txt <- gsub("[[:punct:]]", "", some_txt)
# remove numbers
some_txt <- gsub("[[:digit:]]", "", some_txt)
# remove html links
some_txt <- gsub("http\\w+", "", some_txt)
# remove unnecessary spaces
some_txt <- gsub("[ \t]{2,}", "", some_txt)
some_txt <- gsub("^\\s+|\\s+$", "", some_txt)

# Create objects for the worldcloud
corpus <- tm::Corpus(tm::VectorSource(some_txt))

# Create termdocoumentmatrix
tdm <- tm::TermDocumentMatrix(corpus,
                    control = list(removePunctuation = TRUE,
        stopwords = c("machine", "learning", tm::stopwords("english")),
        removeNumbers = TRUE, tolower = TRUE))   
tdm

# define tdm as matrix
dm <- as.matrix(tdm)
# get word counts in decreasing order
word_freqs <- sort(rowSums(dm), decreasing=TRUE) 
# create a data frame with words and their frequencies
dm <- data.frame(word = names(word_freqs), freq = word_freqs)

#' 
#' Before plotting the wordcloud it is always useful to check and maybe remove the first few words of the list which are probably going to be excpected words, like the hastags that we used for the search. 
#' 
## ----fig.cap="Wordcloud of the top 100 words for the tweets where @atlsexyxlim was mentioned."----
knitr::kable(head(dm))
dm <- dm[-(1:4),]
# Plot the word cloud
wordcloud::wordcloud(dm$word, dm$freq, random.order=FALSE, max.words = 100, colors=brewer.pal(8, "Dark2"))

#' 
#' \clearpage
#' 
#' ### Sentiment analysis
#' 
#' Sentiment analysis is defined as the process to computationally classify opinions from a text. Texts are sequences of qualitative information that can be seen as unstructured data. Sentiment analysis aims to classify the positivity or negativity of each sentence so that the results could be used quantitatively. To perform the following analysis we will use the package [RSentiment](https://cran.r-project.org/web/packages/RSentiment/vignettes/Introduction.html). 
#' 
## ------------------------------------------------------------------------
## Sentiment analysis
# Extract tweets and store them into a character vector with a unique ID
freq_mention_DayToDF$uniqueID <- rownames(freq_mention_DayToDF)
some_txt <- freq_mention_DayToDF$text
names(some_txt) <- freq_mention_DayToDF$uniqueID

#' 
#' Before we can process the data with `RSentiment` a set of data cleaning are necessary. Below is the list of the most important ones. 
#' 
## ------------------------------------------------------------------------
# Clean the text
# remove retweet entities
some_txt <- gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", some_txt)
# remove at people
some_txt <- gsub("@\\w+", "", some_txt)
# remove punctuation
some_txt <- gsub("[[:punct:]]", "", some_txt)
# remove numbers
some_txt <- gsub("[[:digit:]]", "", some_txt)
# remove html links
some_txt <- gsub("http\\w+", "", some_txt)
# remove unnecessary spaces
#some_txt <- gsub("[ \t]{2,}", "", some_txt)
#some_txt <- gsub("^\\s+|\\s+$", "", some_txt)
#some_txt <- gsub("\n","",some_txt)

# Convert every word to lower case
# define "tolower error handling" function 
# The functin will return the text or NA is the text cannot be converted to lower case
try.error = function(x){
  # create missing value
  y = NA
  # tryCatch error - it will return the text or the error 
  try_error = tryCatch(tolower(x), error = function(e) e)
  # if not an error
  if (!inherits(try_error, what = "error")) y = tolower(x)
  # result
  return(y)
}
# lower case using try.error with sapply 
some_txt = sapply(some_txt, try.error)

# Text and names
key_text <- data.frame(txt = some_txt, uniqueID = names(some_txt))

# remove NAs in some_txt
some_txt = some_txt[!is.na(some_txt)]
# names(some_txt) = NULL
some_txt_unique <- unique(some_txt)

#' 
#' Now that the text is clean we can run the sentiment analysis with the `calculate_sentiment()` function!
#' 
## ------------------------------------------------------------------------
# Import positive and negative English words
pos = readLines(file.path(dir,"Data","positive_words.txt"))
neg = readLines(file.path(dir, "Data","negative_words.txt"))

# Apply function score.sentiment
scores <- lapply(some_txt,function(x) score.sentiment(x, pos, neg))
scoresDF <- do.call(rbind, scores)
scoresDF$uniqueID <- rownames(scoresDF)
sum(scoresDF$uniqueID != freq_mention_DayToDF$uniqueID)
combine_sentiment <- cbind(freq_mention_DayToDF,score = scoresDF[,2])
# Breakdown the score
combine_sentiment$score_breakdown <- cut(combine_sentiment$score, breaks = c(-Inf,-2,-0.5,0.5,2,Inf), 
          labels = c("Very Negative","Negative","Neutral","Positive","Very Positive"),include.lowest = TRUE, right = FALSE)

ggplot(combine_sentiment, aes(x = queen_name, fill = score_breakdown)) + 
geom_bar(stat = "count", position = "fill") + coord_flip()


#' 
#' 
## ----eval = FALSE--------------------------------------------------------
## # extract R code
## knitr::purl(file.path(dir,"twitteR.Rmd"), output = file.path(dir,"twitteR.R"), documentation = 2)

#' 
#' 
#'