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DrSkippy/data.csv

Created January 25, 2012 19:11
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R script for plotting pulse of social activity with pulse function fit
Raw
data.csv
ts1 ngFit ts2 twFit timestamp date twitter newsgator
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1326699609.000000 0.706958 1326699609.000000 457.007119 1326699609 1/16/12 2:40 530 2
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1326699969.000000 0.616653 1326699969.000000 413.732365 1326699969 1/16/12 2:46 376 0
1326700029.000000 0.600790 1326700029.000000 406.924277 1326700029 1/16/12 2:47 279 0
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1326700149.000000 0.569260 1326700149.000000 393.641093 1326700149 1/16/12 2:49 325 0
1326700209.000000 0.553710 1326700209.000000 387.162611 1326700209 1/16/12 2:50 428 1
1326700269.000000 0.538364 1326700269.000000 380.790596 1326700269 1/16/12 2:51 347 1
1326700329.000000 0.523256 1326700329.000000 374.523345 1326700329 1/16/12 2:52 402 0
1326700389.000000 0.508414 1326700389.000000 368.359169 1326700389 1/16/12 2:53 363 1
1326700449.000000 0.493858 1326700449.000000 362.296396 1326700449 1/16/12 2:54 354 2
1326700509.000000 0.479606 1326700509.000000 356.333375 1326700509 1/16/12 2:55 371 1
1326700569.000000 0.465668 1326700569.000000 350.468473 1326700569 1/16/12 2:56 313 0
1326700629.000000 0.452053 1326700629.000000 344.700085 1326700629 1/16/12 2:57 340 0
1326700689.000000 0.438767 1326700689.000000 339.026628 1326700689 1/16/12 2:58 291 1
1326700749.000000 0.425812 1326700749.000000 333.446543 1326700749 1/16/12 2:59 263 0
1326700809.000000 0.413190 1326700809.000000 327.958295 1326700809 1/16/12 3:00 237 0
1326700869.000000 0.400900 1326700869.000000 322.560375 1326700869 1/16/12 3:01 238 0
1326700929.000000 0.388939 1326700929.000000 317.251298 1326700929 1/16/12 3:02 386 0
1326700989.000000 0.377304 1326700989.000000 312.029602 1326700989 1/16/12 3:03 313 0
1326701049.000000 0.365992 1326701049.000000 306.893850 1326701049 1/16/12 3:04 253 1
1326701109.000000 0.354996 1326701109.000000 301.842627 1326701109 1/16/12 3:05 251 1
1326701169.000000 0.344313 1326701169.000000 296.874543 1326701169 1/16/12 3:06 270 0
1326701229.000000 0.333934 1326701229.000000 291.988228 1326701229 1/16/12 3:07 258 0
1326701289.000000 0.323855 1326701289.000000 287.182339 1326701289 1/16/12 3:08 260 0
1326701349.000000 0.314069 1326701349.000000 282.455550 1326701349 1/16/12 3:09 280 0
1326701409.000000 0.304569 1326701409.000000 277.806560 1326701409 1/16/12 3:10 274 0
1326701469.000000 0.295348 1326701469.000000 273.234088 1326701469 1/16/12 3:11 255 1
1326701529.000000 0.286399 1326701529.000000 268.736876 1326701529 1/16/12 3:12 260 0
1326701589.000000 0.277715 1326701589.000000 264.313684 1326701589 1/16/12 3:13 269 0
1326701649.000000 0.269290 1326701649.000000 259.963294 1326701649 1/16/12 3:14 261 0
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1326702009.000000 0.223778 1326702009.000000 235.324060 1326702009 1/16/12 3:20 261 0
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1326702189.000000 0.203969 1326702189.000000 223.894530 1326702189 1/16/12 3:23 226 0
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1326702369.000000 0.185905 1326702369.000000 213.020125 1326702369 1/16/12 3:26 181 1
1326702429.000000 0.180245 1326702429.000000 209.513985 1326702429 1/16/12 3:27 214 0
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1326702849.000000 0.145159 1326702849.000000 186.534730 1326702849 1/16/12 3:34 192 0
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1326702969.000000 0.136450 1326702969.000000 180.444841 1326702969 1/16/12 3:36 154 0
1326703029.000000 0.132294 1326703029.000000 177.474865 1326703029 1/16/12 3:37 140 0
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1326703689.000000 0.094132 1326703689.000000 147.860807 1326703689 1/16/12 3:48 122 2
1326703749.000000 0.091264 1326703749.000000 145.427138 1326703749 1/16/12 3:49 121 0
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1326703989.000000 0.080640 1326703989.000000 136.086483 1326703989 1/16/12 3:53 148 0
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1326704229.000000 0.071252 1326704229.000000 127.345770 1326704229 1/16/12 3:57 132 1
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Raw
plotFit.r
#!/usr/bin/env Rscript
library(ggplot2)
library(gridExtra)
fitData <- read.csv(file="./data.csv", header=TRUE, sep=",")
fitData$dates <- as.POSIXct(fitData$date, format = "%m/%d/%y %H:%M")
summary(fitData)
###
p0 <- qplot(fitData$ts1, fitData$twFit, data=fitData,
, ylab = "timestamp"
, xlab="Number of Tweets/Min"
, geom="line" )
p2 <- qplot(fitData$ts1, fitData$twitter, data=fitData,
, ylab = "timestamp"
, xlab="Number of Tweets/Min" )
p1 <- qplot(fitData$ts1, fitData$ngFit, data=fitData,
, ylab = "timestamp"
, xlab="Number of Articles/Min"
, geom="line")
p3 <- qplot(fitData$ts1, fitData$newsgator, data=fitData,
, ylab = "timestamp"
, xlab="Number of Tweets/Min" )
png(filename = "./fitsraw.png", width = 640, height = 900, units = 'px')
print(
grid.arrange(p0, p1, p2, p3, ncol=1)
)
dev.off()
p5 <- qplot(fitData$dates, fitData$twitter, data=fitData
, xlab = "Jan 16, 2012 (GMT)"
, ylab="Tweets/Min"
, geom="bar"
, fill = "red"
, main = "Twitter"
, stat = "identity") +
scale_x_datetime(format="%H:%M") +
opts(legend.position = "none") +
geom_line(aes(fitData$dates, fitData$twFit), fitData, color="blue")
p7 <- qplot(fitData$dates, fitData$newsgator, data=fitData
, xlab = "Jan 16, 2012 (GMT)"
, ylab="Articles/Min"
, geom="bar"
, fill = "red"
, main = "Newsgator"
, stat = "identity") +
scale_x_datetime(format="%H:%M") +
opts(legend.position = "none") +
geom_line(aes(fitData$dates, fitData$ngFit), fitData, color="blue")
png(filename = "./pubfits.png", width = 534, height = 700, units = 'px')
print(
grid.arrange(p5, p7, ncol=1)
)
dev.off()
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