1 Purpose

To extract and visualise tweets and re-tweets of #dockercon for 17 - 21 April, 2017 (DockerCon17).

Borrowing extensively from http://thinktostart.com/twitter-authentification-with-r/

We used the Twitter search API to extract ‘all’ tweets with the #dockercon hashtag. As the Twitter search API documentation (sort of) makes clear this may not be all such tweets but merely the most relevant (whatever that means) from within a sample (whatever that means).

“It allows queries against the indices of recent or popular Tweets and behaves similarly to, but not exactly like the Search feature available in Twitter mobile or web clients, such as Twitter.com search. The Twitter Search API searches against a sampling of recent Tweets published in the past 7 days.” https://dev.twitter.com/rest/public/search, Accessed 12/5/2017

It is therefore possible that not quite all tweets have been extracted although it seems likely that we have captured most human tweeting which was our main intention. Future work should instead use the Twitter streaming API.

2 Load Data

Data should have been already downloaded using collectData.R. This produces a data table with the following variables (after some processing):

##  [1] "text"             "favorited"        "favoriteCount"   
##  [4] "replyToSN"        "created"          "truncated"       
##  [7] "replyToSID"       "id"               "replyToUID"      
## [10] "statusSource"     "screenName"       "retweetCount"    
## [13] "isRetweet"        "retweeted"        "longitude"       
## [16] "latitude"         "location"         "language"        
## [19] "profileImageURL"  "createdLocal"     "obsDateTimeMins" 
## [22] "obsDateTimeHours" "obsDateTime5m"    "obsDateTime10m"  
## [25] "obsDateTime15m"   "obsDate"          "isRetweetLab"

The table has 7,975 tweets (and 11,285 re-tweets) from 6,429 tweeters between 2017-04-16 19:01:03 and 2017-04-21 09:54:11 (Central District Time).

3 Analysis

3.1 Tweets and Tweeters over time

All (re)tweets containing #dockercon 2017-04-17 to 2017-04-21

3.1.1 Day 1 - Monday (Workshops)

This plot is zoomable - try it!

All (re)tweets containing #dockercon Monday 17th April 2017

3.1.2 Day 2 - Tuesday (Main Day 1)

This plot is zoomable - try it!

All (re)tweets containing #dockercon Tuesday 18th April 2017

3.1.3 Day 3 - Wednesday (Main Day 2)

This plot is zoomable - try it!

All (re)tweets containing #dockercon Wednesday 19th April 2017

3.1.4 Day 4 - Thursday (Main Day 3)

All (re)tweets containing #dockercon Thursday 20th April 2017

3.4 Screen name

Next we’ll try by screen name.

Top tweeters:

Top 15 tweeters

screenName	nTweets
DockerCon	335
theCUBE	187
BettyJunod	148
jpetazzo	129
climbingkujira	127
solomonstre	126
jeanepaul	104
ManoMarks	99
kaslinfields	94
OpenShiftNinja	89
vmblog	86
sitspak	85
SFoskett	82
jameskobielus	77
stefscherer	75

And here’s a really bad visualisation of all of them tweeting over time! Each row of pixels is a tweeter (the names are illegible) and a green dot indicates a few tweets in the 5 minute period while a red dot indicates a lot of tweets.

N tweets per 5 minutes by screen name

So let’s re-do that for the top 50 tweeters so we can see their tweetStreaks!

N tweets per 5 minutes by screen name (top 50, most prolific tweeters at bottom)

4 About

Analysis completed in: 51.2 seconds using knitr in RStudio with R version 3.4.0 (2017-04-21) running on x86_64-apple-darwin15.6.0.

A special mention must go to twitteR (Gentry, n.d.) for the twitter API interaction functions and lubridate (Grolemund and Wickham 2011) which allows timezone manipulation without too many tears.

Other R packages used:

• base R - for the basics (R Core Team 2016)
• data.table - for fast (big) data handling (Dowle et al. 2015)
• readr - for nice data loading (Wickham, Hester, and Francois 2016)
• ggplot2 - for slick graphs (Wickham 2009)
• plotly - fancy, zoomable slick graphs (Sievert et al. 2016)
• knitr - to create this document (Xie 2016)