in this a sliding window of 3 elements
> x = c(3,1,4,1,5,9,2,6,5,3,5,8) > ra_x = filter(x, rep(1,3)/3) > ra_x Time 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 lons
some interesting observations with unanswered questions…
speaking of correlation here’s a scatterplot of tweets with lat lons vs without. we can see that time period uncorrelatedness that occurs past 17:00 as a quite obvious cluster.
i’m learning a new statistics language called R and it’s pretty cool.
make a vector …
> 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 8
turn it into a frequency table …
> table(c(3,1,4,1,5,9,2,6,5,3,5,8)) 1 2 3 4 5 6 8 9 2 1 2 1 3 1 1 1
sort by frequency …
> sort(table(c(3,1,4,1,5,9,2,6,5,3,5,8))) 2 4 6 8 9 1 3 5 1 1 1 1 1 2 2 3
and plot!
> barplot(sort(table(c(3,1,4,1,5,9,2,6,5,3,5,8))))so simple!
