brain of mat kelcey

in this a sliding window of 3 elements123456789> x = c(3,1,4,1,5,9,2,6,5,3,5,8)> ra_x = filter(x, rep(1,3)/3)> ra_xTime Series:Start = 1 End = 12 Frequency = 1 [1] NA 2.666667 2.000000 3.333333 5.000000 5.333333 5.666667 4.333333 [9] 4.666667 4.333333 5.333333 NA...

consider the graph below which shows the proportion of tweets per 10 min slot of the day (GMT0)it compares 4.7e6 tweets with any location vs  320e3 tweets with identifiable lat lonssome interesting observations with unanswered questions...the ebb and flow is not just a result of the time of day for high twitter traffic areas. the reduction between 06:00 and 10:00 comes close to zero. this is false, there is never a worldwide time when internet traffic hits zero. does twitter turn down it's gatdenhose for capacity reasons?the number of tweets with lat lons are correlated to those without EXCEPT past...

i'm learning a new statistics language called R and it's pretty cool.make a vector ...12> c(3,1,4,1,5,9,2,6,5,3,5,8) [1] 3 1 4 1 5 9 2 6 5 3 5 8turn it into a frequency table ...123> table(c(3,1,4,1,5,9,2,6,5,3,5,8))1 2 3 4 5 6 8 92 1 2 1 3 1 1 1sort by frequency ...123> sort(table(c(3,1,4,1,5,9,2,6,5,3,5,8)))2 4 6 8 9 1 3 51 1 1 1 1 2 2 3and plot!1> barplot(sort(table(c(3,1,4,1,5,9,2,6,5,3,5,8))))so simple!...