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Visualising Twitter User Timeline Activity in R

I’ve largely avoided “time” in R to date, but following a chat with @mhawksey at #dev8d yesterday, I went down a rathole last night exploring a few ways of visualising a Twitter user timeline. As a result, I also had a quick initial play with some time handling features of R, such as timeseries objects, as well as a go at generating daily, weekly and monthly summary counts of data values.

To start, let’s grab a user timeline. As Martin started it (?!), we’ll use his…;-)

#the most tweets we can bring back from a user timeline is the most recent 3600...

#As I've done in previous scripts, pull out the names of folk who have been "old-fashioned RTd"...
trim <- function (x) sub('@','',x)

tw.df$rt=sapply(tw.df$text,function(tweet) trim(str_match(tweet,"^RT (@[[:alnum:]_]*)")[2]))
tw.df$rtt=sapply(tw.df$rt,function(rt) if (is.na(rt)) 'T' else 'RT')

The returned data includes a created attribute (of the form “2012-02-17 11:40:25″) and a replyToSN attribute that includes the username of a user Martin was replying to via a particular tweet.

The simplest way I can think of displaying the data is to just display the screenName atrribute of the sender (which in this case is always mhawskey) against time:


As ever, things are never that simple… some tweets with old dates appear to have crept in somehow… A couple of things I tried realting to time based filtering caused R to have all sorts of malloc errors, so here’s a fudge I found to just display tweets that were created within the last 8,000 hours…


Okay, so not very interesting… It shows that Martin tweets…

Picking up on views of the style doodled in Visualising Activity Around a Twitter Hashtag or Search Term Using R, where we look at when new users appear in a hashtag stream, we can plot when Martin replies to another twitter user, arranging the user names in the order in which they were first publicly replied to:

#Order the replyToSN factor levels in the order in which they were first created
tw.dfx=ddply(tw.dfs, .var = "replyToSN", .fun = function(x) {return(subset(x, created %in% min(created),select=c(replyToSN,created)))})
tw.dfs$replyToSN=factor(tw.dfs$replyToSN, levels = tw.dfxa$replyToSN)

#and plot the result

The line at the top are tweets where the replyToSN value was NA (not available).

We can then go a little further and plot when folk are replied to or retweeted, as well as tweets that are neither a reply nor an old-style retweet:

ggplot()+geom_point(data=subset(tw.dfs,subset=(!is.na(replyToSN))),aes(x=created,y=replyToSN),col='red') + geom_point(data=subset(tw.dfs,subset=(!is.na(rt))),aes(x=created,y=rt),col='blue') + geom_point(data=subset(tw.dfs,subset=(is.na(replyToSN) & is.na(rt))),aes(x=created,y=screenName),col='green')

Here, the blue dots are old-style retweets, the red dots are replies, and the green dots are tweets that are neither replies nor old-style retweets. If a blue dot appears on a row before a red dot, it shows Martin RT’d them before ever replying to them. If blue dots are on a row that contains no red dot, then it shows Martin has RT’d but not replied to that person. A heavily populated row shows Martin has repeated interactions with that user.

We can generate an ordered bar chart showing who is most heavily replied to:

#First we need to count how many replies a user gets...
r_table <- table(tw.dfs$replyToSN)
#..rank them...
r_levels <- names(r_table)[order(-r_table)]
#..and use this ordering to order the factor levels...
tw.dfs$replyToSN <- factor(tw.dfs$replyToSN, levels = r_levels) 

#Then we can plot the chart...
ggplot(subset(tw.dfs,subset=(!is.na(replyToSN))),aes(x=replyToSN)) + geom_bar(aes(y = (..count..)))+opts(axis.text.x=theme_text(angle=-90,size=6))

(Hmmm… how would I filter this to only show folk replied to more than 50 times, for example? [UPDATE: here’s a partial, related recipe: ****BUT IT SEEMS TO BE BROKEN AND I CAN”T SEE HOW TO FIX IT ATM…
#Filter to retain users with Freq above some threshold, then drop spare levels

Note that the order of the factors needs rearranging. Something like this maybe?

  factor(dfc.name, levels = reorder(dfc.name,dfc.val))


Taking the simplification (?!) further:

  tmp2$Var1=factor(tmp2$Var1, levels = reorder(tmp2$Var1,tmp2$Freq))

..or even further…



Sometimes, a text view is easier…

#eg returns:
#psychemedia        wilm     ambrouk    sheilmcn  dajbelshaw  manmalik 
        394          66          59          53          48        43     
#Hmm..can we generalise this?
  r_table <- table(dfc)
  r_levels <- names(r_table)[order(-r_table)]
  head(table(factor(dfc, levels = r_levels)),num)
#so now, for example, I should be able to display the most old-style retweeted folk?
#or the 10 most replied to...

Let’s try some time stuff now… From the R Cookbook, I find I can do this:

#label a tweet with the month number
tw.dfs$month=sapply(tw.dfs$created, function(x) {p=as.POSIXlt(x);p$mon})
#label a tweet with the hour
tw.dfs$hour=sapply(tw.dfs$created, function(x) {p=as.POSIXlt(x);p$hour})
#label a tweet with a number corresponding to the day of the week
tw.dfs$wday=sapply(tw.dfs$created, function(x) {p=as.POSIXlt(x);p$wday})

What this means is we can now chart a count of the number of tweets by day, week, or hour… For example, here’s hour vs. day of the week:


Note that this jittered scattergraph, where each dot is a tweet, only approximates the time each tweet occurred – the jitter applied is a random quantity designed to separate out tweets posted within the same hour-and-day-of-the-week bin.

What about Martin’s tweeting behaviour over time?

#We can also generate barplots showing the distribution of tweet count over time:
ggplot(tw.dfs,aes(x=created))+geom_bar(aes(y = (..count..)))
#Hmm... I'm not sure how to manually set binwidth= sensibly, though?!

Here’s a plot of the number of counts per… I’m not sure: the bin width was calculated automatically…

How about using the number of tweets in a particular day or hour bin to see what times of day or days of week Martin is tweeting?

#We can also plot the number of tweets within particular hour or time bins...
ggplot(tw.dfs,aes(x=wday))+geom_bar(aes(y = (..count..)),binwidth=1)
ggplot(tw.dfs,aes(x=hour))+geom_bar(aes(y = (..count..)),binwidth=1)

This chart shows activity (in terms of count…) per hour of day.

As well as doing the count of tweets per hour, for example, via a ggplot statistical graphical function, we can also get day, week, month, quarter and year counts from a set of functions associated with a particular sort of timeseries object…

Each element in a time series typically has two elements – a timestamp, and a numeric value. We can generate a time series of a sort around a twitter usertimeline by creating a dummy quantity – such as the unit value, 1 – and associate it with each timestamp:

#The xts function creates a timeline from a vector of values and a vector of timestamps.
#If we know how many tweets we have, we can just create a simple list or vector containing that number of 1s

#We can now do some handy number crunching on the timeseries, such as applying a formula to values contained with day, week, month, quarter or year time bins.
#So for example, if we sum the unit values in daily bin, we can get a count of the number of tweets per day
#also apply. weekly, monthly, quarterly, yearly

#If for any resason we need to turn the timeseries into a dataframe, we can:
ts.sum.df=data.frame(date=index(ts.sum), coredata(ts.sum))


#We can then use ggplot to plot the timeseries...

#Having got the data in a timeseries form, we can do timeseries based things to it... such as checking the autocorrelation:

Hmmm.. so, one day is much the same as another, but there also appears to be a weekly (7 day periodicity) pattern…

Finally, here’s a handy script I found on the Revolution Analytics site for Charting time series as calendar heat maps in R:

 #                        Calendar Heatmap                                    #
 #                                by                                          #
 #                         Paul Bleicher                                      #
 # an R version of a graphic from:                                            #
 # http://stat-computing.org/dataexpo/2009/posters/wicklin-allison.pdf        #
 #  requires lattice, chron, grid packages                                    #

## calendarHeat: An R function to display time-series data as a calendar heatmap 
## Copyright 2009 Humedica. All rights reserved.

## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.

## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## GNU General Public License for more details.

## You can find a copy of the GNU General Public License, Version 2 at:
## http://www.gnu.org/licenses/gpl-2.0.html

calendarHeat <- function(dates, 
                         date.form = "%Y-%m-%d", ...) {
if (class(dates) == "character" | class(dates) == "factor" ) {
  dates <- strptime(dates, date.form)
caldat <- data.frame(value = values, dates = dates)
min.date <- as.Date(paste(format(min(dates), "%Y"),
                    "-1-1",sep = ""))
max.date <- as.Date(paste(format(max(dates), "%Y"),
                     "-12-31", sep = ""))
dates.f <- data.frame(date.seq = seq(min.date, max.date, by="days"))

# Merge moves data by one day, avoid
caldat <- data.frame(date.seq = seq(min.date, max.date, by="days"), value = NA)
dates <- as.Date(dates) 
caldat$value[match(dates, caldat$date.seq)] <- values

caldat$dotw <- as.numeric(format(caldat$date.seq, "%w"))
caldat$woty <- as.numeric(format(caldat$date.seq, "%U")) + 1
caldat$yr <- as.factor(format(caldat$date.seq, "%Y"))
caldat$month <- as.numeric(format(caldat$date.seq, "%m"))
yrs <- as.character(unique(caldat$yr))
d.loc <- as.numeric()                        
for (m in min(yrs):max(yrs)) {
  d.subset <- which(caldat$yr == m)  
  sub.seq <- seq(1,length(d.subset))
  d.loc <- c(d.loc, sub.seq)
caldat <- cbind(caldat, seq=d.loc)

#color styles
r2b <- c("#0571B0", "#92C5DE", "#F7F7F7", "#F4A582", "#CA0020") #red to blue                                                                               
r2g <- c("#D61818", "#FFAE63", "#FFFFBD", "#B5E384")   #red to green
w2b <- c("#045A8D", "#2B8CBE", "#74A9CF", "#BDC9E1", "#F1EEF6")   #white to blue
assign("col.sty", get(color))
calendar.pal <- colorRampPalette((col.sty), space = "Lab")
def.theme <- lattice.getOption("default.theme")
cal.theme <-
   function() {  
  theme <-
    strip.background = list(col = "transparent"),
    strip.border = list(col = "transparent"),
    axis.line = list(col="transparent"),
lattice.options(default.theme = cal.theme)
yrs <- (unique(caldat$yr))
nyr <- length(yrs)
print(cal.plot <- levelplot(value~woty*dotw | yr, data=caldat,
 layout = c(1, nyr%%7),
   between = list(x=0, y=c(1,1)),
   main = paste("Calendar Heat Map of ", varname, sep = ""),
   scales = list(
     x = list(
               at= c(seq(2.9, 52, by=4.42)),
               labels = month.abb,
               alternating = c(1, rep(0, (nyr-1))),
               cex = 0.7),
          at = c(0, 1, 2, 3, 4, 5, 6),
          labels = c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday",
                      "Friday", "Saturday"),
          alternating = 1,
          cex = 0.6,
   xlim =c(0.4, 54.6),
   cuts= ncolors - 1,
   col.regions = (calendar.pal(ncolors)),
   xlab="" ,
   colorkey= list(col = calendar.pal(ncolors), width = 0.6, height = 0.5),
    ) )
panel.locs <- trellis.currentLayout()
for (row in 1:nrow(panel.locs)) {
    for (column in 1:ncol(panel.locs))  {
    if (panel.locs[row, column] > 0)
    trellis.focus("panel", row = row, column = column,
                  highlight = FALSE)
xyetc <- trellis.panelArgs()
subs <- caldat[xyetc$subscripts,]
dates.fsubs <- caldat[caldat$yr == unique(subs$yr),]
y.start <- dates.fsubs$dotw[1]
y.end   <- dates.fsubs$dotw[nrow(dates.fsubs)]
dates.len <- nrow(dates.fsubs)
adj.start <- dates.fsubs$woty[1]

for (k in 0:6) {
 if (k < y.start) {
    x.start <- adj.start + 0.5
    } else {
    x.start <- adj.start - 0.5
  if (k > y.end) {
     x.finis <- dates.fsubs$woty[nrow(dates.fsubs)] - 0.5
    } else {
     x.finis <- dates.fsubs$woty[nrow(dates.fsubs)] + 0.5
    grid.lines(x = c(x.start, x.finis), y = c(k -0.5, k - 0.5), 
     default.units = "native", gp=gpar(col = "grey", lwd = 1))
if (adj.start <  2) {
 grid.lines(x = c( 0.5,  0.5), y = c(6.5, y.start-0.5), 
      default.units = "native", gp=gpar(col = "grey", lwd = 1))
 grid.lines(x = c(1.5, 1.5), y = c(6.5, -0.5), default.units = "native",
      gp=gpar(col = "grey", lwd = 1))
 grid.lines(x = c(x.finis, x.finis), 
      y = c(dates.fsubs$dotw[dates.len] -0.5, -0.5), default.units = "native",
      gp=gpar(col = "grey", lwd = 1))
 if (dates.fsubs$dotw[dates.len] != 6) {
 grid.lines(x = c(x.finis + 1, x.finis + 1), 
      y = c(dates.fsubs$dotw[dates.len] -0.5, -0.5), default.units = "native",
      gp=gpar(col = "grey", lwd = 1))
 grid.lines(x = c(x.finis, x.finis), 
      y = c(dates.fsubs$dotw[dates.len] -0.5, -0.5), default.units = "native",
      gp=gpar(col = "grey", lwd = 1))
for (n in 1:51) {
  grid.lines(x = c(n + 1.5, n + 1.5), 
    y = c(-0.5, 6.5), default.units = "native", gp=gpar(col = "grey", lwd = 1))
x.start <- adj.start - 0.5

if (y.start > 0) {
  grid.lines(x = c(x.start, x.start + 1),
    y = c(y.start - 0.5, y.start -  0.5), default.units = "native",
    gp=gpar(col = "black", lwd = 1.75))
  grid.lines(x = c(x.start + 1, x.start + 1),
    y = c(y.start - 0.5 , -0.5), default.units = "native",
    gp=gpar(col = "black", lwd = 1.75))
  grid.lines(x = c(x.start, x.start),
    y = c(y.start - 0.5, 6.5), default.units = "native",
    gp=gpar(col = "black", lwd = 1.75))
 if (y.end < 6  ) {
  grid.lines(x = c(x.start + 1, x.finis + 1),
   y = c(-0.5, -0.5), default.units = "native",
   gp=gpar(col = "black", lwd = 1.75))
  grid.lines(x = c(x.start, x.finis),
   y = c(6.5, 6.5), default.units = "native",
   gp=gpar(col = "black", lwd = 1.75))
   } else {
      grid.lines(x = c(x.start + 1, x.finis),
       y = c(-0.5, -0.5), default.units = "native",
       gp=gpar(col = "black", lwd = 1.75))
      grid.lines(x = c(x.start, x.finis),
       y = c(6.5, 6.5), default.units = "native",
       gp=gpar(col = "black", lwd = 1.75))
       } else {
           grid.lines(x = c(x.start, x.start),
            y = c( - 0.5, 6.5), default.units = "native",
            gp=gpar(col = "black", lwd = 1.75))

 if (y.start == 0 ) {
  if (y.end < 6  ) {
  grid.lines(x = c(x.start, x.finis + 1),
   y = c(-0.5, -0.5), default.units = "native",
   gp=gpar(col = "black", lwd = 1.75))
  grid.lines(x = c(x.start, x.finis),
   y = c(6.5, 6.5), default.units = "native",
   gp=gpar(col = "black", lwd = 1.75))
   } else {
      grid.lines(x = c(x.start + 1, x.finis),
       y = c(-0.5, -0.5), default.units = "native",
       gp=gpar(col = "black", lwd = 1.75))
      grid.lines(x = c(x.start, x.finis),
       y = c(6.5, 6.5), default.units = "native",
       gp=gpar(col = "black", lwd = 1.75))
for (j in 1:12)  {
   last.month <- max(dates.fsubs$seq[dates.fsubs$month == j])
   x.last.m <- dates.fsubs$woty[last.month] + 0.5
   y.last.m <- dates.fsubs$dotw[last.month] + 0.5
   grid.lines(x = c(x.last.m, x.last.m), y = c(-0.5, y.last.m),
     default.units = "native", gp=gpar(col = "black", lwd = 1.75))
   if ((y.last.m) < 6) {
      grid.lines(x = c(x.last.m, x.last.m - 1), y = c(y.last.m, y.last.m),
       default.units = "native", gp=gpar(col = "black", lwd = 1.75))
     grid.lines(x = c(x.last.m - 1, x.last.m - 1), y = c(y.last.m, 6.5),
       default.units = "native", gp=gpar(col = "black", lwd = 1.75))
   } else {
      grid.lines(x = c(x.last.m, x.last.m), y = c(- 0.5, 6.5),
       default.units = "native", gp=gpar(col = "black", lwd = 1.75))
lattice.options(default.theme = def.theme)

If we pass the dataframed time series data counting the sum (count) of tweets per day, we can get a calendar heatmap view of Martin’s twitter activity:

calendarHeat(ts.sum.df$date, ts.sum.df$sum, varname="@mhawksey Twitter activity")

I’m not sure if this is even interesting, let alone useful, but I do think now I’ve found out a little bit about working with time in R, that could be handy…

Still to do: extract hashtags and visualise them; extend the twitteR library so it exposes things like retweet counts. But that’s for another day…

Written by Tony Hirst

February 17, 2012 at 3:26 pm

Posted in Rstats

14 Responses

Subscribe to comments with RSS.

  1. Ignoring the gross invasion of privacy ;) my favourite summary is the calendar heatmap [who’d thought it would take so many lines of R code to produce this #theyMustBeDoingSomethingWrong].

    Immediate questions are what was I saying on Tuesday 13th December and what would it look like if you excluded my more gabberish moments?



    February 17, 2012 at 9:37 pm

  2. Wow!! :) It’d be great to have the same scripts to analize a whole community (Friends timeline)


    February 18, 2012 at 7:20 pm

    • @Victor I think the friends timeline only applies to an authenticated user. At the moment, I am exploring what I can do without any authentication.

      Tony Hirst

      February 19, 2012 at 2:16 pm

  3. Hi This is wonderful, I am trying to replicate this with 5 years of data. only last year is retained with a warning message
    In trellis.panelArgs() :
    Plot spans multiple pages, only last page can be updated
    being a novice could’nt figure it out. Could you help me this. rgds, Krishna


    February 20, 2012 at 9:21 am

    • I’m a complete novice with this too – the calendarheatmap is code i found on the web and I haven’t really looked to see how it works (or doesn’t…). If there’s a problem with it, it may be worth following the link to the original author and asking them…

      Tony Hirst

      February 20, 2012 at 11:40 am

  4. […] Visualising Twitter User Timeline Activity in R […]

  5. I know this is probably a stupid question, but I am very new to R and do not have much experience with programming languages. How do you actually import someone’s twitter into R?


    March 6, 2012 at 1:08 am

    • @jack If you instal the twitteR package from CRAN, all you need to do then is:


      Tony Hirst

      March 6, 2012 at 10:57 am

  6. Hey! Nice graphs!

    Suppose you want R to retrive more than 3600 tweets, in order to analyze a given period of time (a month or more). Do you know how can I achieve this?

    Thank you in advance.


    June 15, 2012 at 9:28 pm

  7. Looks great.


    August 5, 2012 at 10:26 am

  8. […] it might in and of itself provide a post hoc “what happened” timeline. Something like Visualising Twitter User Timeline Activity in R might be helpful in reporting […]

  9. […] or Not?, Small World? A Snapshot of How My Twitter “Friends” Follow Each Other…,  Visualising Twitter User Timeline Activity in R, Blogging Academic Lectures, Twitter Powered Subtitles for Conference Audio/Videos on […]

  10. Really nice. I only have a problem: in The colored plot I can’t see blue dots (I only have green and red, even using your same Twitter timeline). What can I do?

    Stefania Spina (@sspina)

    February 2, 2013 at 7:13 pm

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