r - Aggregate a data.frame by time series and with different functions -


i have lots of measurement values, recorded each minute. of values have mean, min , max values given minute. i'd summarize/aggregate whole data.frame have 1 entry every 30 minutes, 

str(wgdata) 'data.frame':   115200 obs. of  7 variables:  $ timestamp          : posixct, format: "2012-11-24 00:00:00" "2012-11-24 00:01:00" "2012-11-24 00:02:00" 7"2012-11-24 00:03:00" ...  $ record             : int  11683 11684 11685 11686 11687 11688 11689 11690 11691 11692 ...  $ tpanel             : num  -0.075 -0.075 -0.075 -0.095 -0.095 -0.095 -0.095 -0.118 -0.118 -0.118 ...  $ vbattery           : num  13.8 13.8 13.8 13.8 13.8 ...  $ vbatteryheating_avg: num  12.2 12.2 12.2 12.2 12.2 ...  $ vbatteryheating_min: num  12.2 12.2 12.2 12.2 12.2 ...  $ vbatteryheating_max: num  12.2 12.2 12.2 12.2 12.2 ...

so i'd calculate every 30 minutes: timestamp, mean of tpanel (temperatur of panel), mean of vbattery, mean of vbatteryheating_avg, min of vbatteryheating_min, max of vbatteryheating_max

i had success doing

wgdata30min <- aggregate(list(tp = wgdata$tpanel, vb=wgdata$vbatteryheating_avg, vb_min=wgdata$vbatteryheating_min, vb_max=wgdata$vbatteryheating_min),                list(timestamp = cut(wgdata$timestamp, "30 min")),                mean) head(wgdata30min)             timestamp         tp       vb   vb_min   vb_max 1 2012-11-24 00:00:00 -0.1621667 12.15467 12.15333 12.15333 2 2012-11-24 00:30:00 -0.4751667 12.13333 12.13133 12.13133 3 2012-11-24 01:00:00 -0.5647333 12.11167 12.11067 12.11067 4 2012-11-24 01:30:00 -0.4573667 12.09133 12.08967 12.08967 5 2012-11-24 02:00:00 -0.4923667 12.07100 12.07000 12.07000 6 2012-11-24 02:30:00 -0.6469000 12.04933 12.04733 12.04733

... did not manage pass array of functions apply columns. appreciated.

i believe data looks this

seconds <- seq(0,100000, by= 600) dates <- as.posixlt(seconds, origin = "2012-11-24", tz = "utc") tpanel <- rnorm(167) vbatteryheating_avg <- rcauchy(167) vbatteryheating_min <- runif(167) vbatteryheating_max <- rexp(167)  wgdata <- data.frame(timestamp = dates,                       tpanel = tpanel,                       vbatteryheating_avg = vbatteryheating_avg,                       vbatteryheating_min = vbatteryheating_min,                       vbatteryheating_max = vbatteryheating_max)  head(wgdata) ##             timestamp     tpanel vbatteryheating_avg vbatteryheating_min vbatteryheating_max ## 1 2012-11-24 00:00:00  0.4770116          10.2937806          0.80151633           0.8722767 ## 2 2012-11-24 00:10:00  0.0304906         -20.7057773          0.32311092           0.7172383 ## 3 2012-11-24 00:20:00  1.4875903           0.5749393          0.74020471           0.5857239 ## 4 2012-11-24 00:30:00  0.4933884           6.6567398          0.73824231           0.3691020 ## 5 2012-11-24 00:40:00 -0.0369843           3.4332840          0.06552402           0.2455765 ## 6 2012-11-24 00:50:00  0.7339858          -3.3787044          0.06451802           0.5952835

probably best solution use plyr. first, use cut before make indicator 30-minute chunks. use ddply, splitting data frame variable.

wgdata$timestamp30min <- cut(wgdata$timestamp,"30 min")  library(plyr)  out <- ddply(wgdata, .(timestamp30min), summarize,              tp = mean(tpanel),              vb = mean(vbatteryheating_avg),              vb_min = min(vbatteryheating_min),              vb_max = max(vbatteryheating_max))  head(out) ##        timestamp30min         tp          vb     vb_min    vb_max ## 1 2012-11-24 00:00:00  0.6650308 -3.27901911 0.32311092 0.8722767 ## 2 2012-11-24 00:30:00  0.3967966  2.23710649 0.06451802 0.5952835 ## 3 2012-11-24 01:00:00 -0.1326459 -1.20082543 0.50358789 1.0569388 ## 4 2012-11-24 01:30:00  0.7845420 -0.07520645 0.14500901 0.9656004 ## 5 2012-11-24 02:00:00 -0.4523882  0.40472169 0.24997021 1.4056166 ## 6 2012-11-24 02:30:00 -0.2317818  0.61860868 0.64909054 0.2338781

alternatively, use aggregate each function (mean, min, , max) , use merge on results, 2 data frames @ time.


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