Initial R code files

R Code Files for Introduction to Data Science

Author: jmstanto

MappingScripts.R

@@ -0,0 +1,57 @@
+# Mapping scripts
+# EnsurePackage(x) - Installs and loads a package if necessary
+EnsurePackage<-function(x)
+{
+  x <- as.character(x)
+  if (!require(x,character.only=TRUE))
+  {
+    install.packages(pkgs=x,repos="http://cran.r-project.org")
+    require(x,character.only=TRUE)
+  }
+}
+
+
+# Format an URL for the Google Geocode API
+MakeGeoURL <- function(address) 
+{
+  
+  root <- "http://maps.google.com/maps/api/geocode/"
+  
+  url <- paste(root, "json?address=", address, "&sensor=false", sep = "")
+  
+  return(URLencode(url))
+}
+
+Addr2latlng <- function(address) 
+{
+  url <- MakeGeoURL(address)
+  
+  apiResult <- getURL(url)
+  
+  geoStruct <- fromJSON(apiResult, simplify = FALSE)
+  
+  lat <- NA
+  lng <- NA
+  
+  
+  try(lat <- geoStruct$results[[1]]$geometry$location$lat, silent=TRUE)
+  try(lng <- geoStruct$results[[1]]$geometry$location$lng, silent=TRUE)
+  
+  return(c(lat, lng))
+}
+
+# Process a whole list of addresses
+ProcessAddrList <- function(addrList)
+{
+  resultDF <- data.frame(atext=character(),X=numeric(),Y=numeric(),EID=numeric())
+  i <- 1
+  
+  for (addr in addrList)
+  {
+    latlng = Addr2latlng(addr)
+    resultDF <- rbind(resultDF, data.frame(atext=addr,X=latlng[[2]],Y=latlng[[1]], EID=i))
+    i <- i + 1
+  }
+  
+  return(resultDF)
+}

MyMode.R

@@ -0,0 +1,7 @@
+MyMode <- function(myVector)
+{
+  uniqueValues <- unique(myVector)
+  uniqueCounts <- tabulate(match(myVector,uniqueValues))
+  
+  return(uniqueValues[which.max(uniqueCounts)])
+}

twitterSupport.R

@@ -0,0 +1,125 @@
+# EnsurePackage(x) - Installs and loads a package if necessary
+EnsurePackage<-function(x)
+{
+  x <- as.character(x)
+  if (!require(x,character.only=TRUE))
+  {
+    install.packages(pkgs=x,repos="http://cran.r-project.org")
+    require(x,character.only=TRUE)
+  }
+}
+
+# PrepareTwitter() - Load packages for working with twitteR
+PrepareTwitter<-function()
+{
+  EnsurePackage("bitops")
+  EnsurePackage("RCurl")
+  EnsurePackage("RJSONIO")
+  EnsurePackage("twitteR")
+}
+
+# TweetFrame() - Return a dataframe based on a search of Twitter
+TweetFrame<-function(searchTerm, maxTweets)
+{
+  tweetList <- searchTwitter(searchTerm, n=maxTweets)
+  
+  # as.data.frame() coerces each list element into a row
+  # lapply() applies this to all of the elements in twtList
+  # rbind() takes all of the rows and puts them together
+  # do.call() gives rbind() all the rows as individual elements
+  tweetDF <- do.call("rbind", lapply(tweetList,as.data.frame))
+  
+  # This last step sorts the tweets in arrival order
+  return(tweetDF[order(as.integer(tweetDF$created)), ])
+}
+
+# CleanTweets() - Takes the junk out of a vector of tweet texts
+CleanTweets<-function(tweets)
+{
+  # Remove redundant spaces
+  tweets <- str_replace_all(tweets,"  "," ")
+  # Get rid of URLs
+  tweets <- str_replace_all(tweets, "http://t.co/[a-z,A-Z,0-9]{8}","")
+  # Take out retweet header, there is only one
+  tweets <- str_replace(tweets,"RT @[a-z,A-Z]*: ","")
+  tweets <- str_replace_all(tweets,"#[a-z,A-Z]*","")
+  tweets <- str_replace_all(tweets,"@[a-z,A-Z]*","")
+  return(tweets)
+}
+
+# ArrivalProbability - Given a list of arrival times
+# calculates the delays between them with lagged differences
+# then computes a list of cumulative probabilties of arrival
+# for a list of time increments
+# times - A sorted, ascending list of arrival times in POSIXct
+# increment - the time increment for each new probability
+# max - the highest time increment
+#
+# Returns - an ordered list of probabilities in a numeric vector
+# suitable for plotting with plot()
+ArrivalProbability<-function(times, increment, max)
+{
+  # Initialize an empty vector
+  plist <- NULL
+  
+  # Probability is defined over the size of this sample
+  # of arrival times
+  timeLen <- length(times)
+  
+  # May not be necessary, but checks for input mistake
+  if (increment>max) {return(NULL)}
+  
+  for (i in seq(increment, max, by=increment))
+  {
+    # diff() requires a sorted list of times
+    # diff() calculates the delays between neighboring times
+    # the logical test <i provides a list of TRUEs and FALSEs
+    # of length = timeLen, then sum() counts the TRUEs
+    plist<-c(plist,(sum(as.integer(diff(times))<i))/timeLen)
+  }
+  return(plist)
+}
+
+# Like ArrivalProbability, but works with an unsorted list
+# of delay times
+DelayProbability<-function(delays, increment, max)
+{
+  # Initialize an empty vector
+  plist <- NULL
+  
+  # Probability is defined over the size of this sample
+  # of arrival times
+  delayLen <- length(delays)
+  
+  # May not be necessary, but checks for input mistake
+  if (increment>max) {return(NULL)}
+  
+  for (i in seq(increment, max, by=increment))
+  {
+    # the logical test <i provides a list of TRUEs and FALSEs
+    # of length = timeLen, then sum() counts the TRUEs
+    plist<-c(plist,(sum(delays<=i)/delayLen))
+  }
+  return(plist)
+}
+
+# Compare tweets - Run poisson.test() on rate ratio for two tweet streams
+# search1 - the first hashtag or search twerm to look for
+# search2 - the second search term or hashtag to look for
+# numEvents - the number of events to sample for each search
+CompareTweets <- function(search1, search2, numEvents)
+{
+  tweetDF <- TweetFrame(search1, numEvents)
+  sortweetDF<-tweetDF[order(as.integer(tweetDF$created)), ] 
+  eventDelays1 <- as.integer(diff(sortweetDF$created))
+  meanDelays1 <- round(mean(eventDelays1))
+  
+  tweetDF <- TweetFrame(search2, numEvents)
+  sortweetDF<-tweetDF[order(as.integer(tweetDF$created)), ] 
+  eventDelays2 <- as.integer(diff(sortweetDF$created))
+  
+  eventCount1 <- sum(eventDelays1<=meanDelays1)
+  eventCount2 <- sum(eventDelays2<=meanDelays1)
+  
+  return(poisson.test(c(eventCount1,eventCount2),c(numEvents,numEvents)))
+}