# brain of mat kelcey

## moving average of a time series in R

June 15, 2010 at 04:15 PM | categories: simple stuff i keep forgetting, r | View Comments

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...

## e11.3 at what time does the world tweet?

October 28, 2009 at 09:22 PM | categories: e11, twitter, r | View Comments

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 vsĀ 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...

## simple statistics with R

October 03, 2009 at 03:43 PM | categories: statistics, r, language | View Comments

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!...

old projects...

- latent semantic analysis via the singular value decomposition (for dummies)
- semi supervised naive bayes
- statistical synonyms
- round the world tweets
- decomposing social graphs on twitter
- do it yourself statistically improbable phrases
- should i burn it?
- the median of a trillion numbers
- deduping with resemblance metrics
- simple supervised learning / should i read it?
- audioscrobbler experiments
- chaoscope experiment