I have climatic data which have been collected during a whole year along an altitude gradient. Shaped like that:
clim <- read.table(text="alti year month week day meanTemp maxTemp minTemp
350 2011 aug. 31 213 10 14 6
350 2011 aug. 31 214 12 18 6
350 2011 aug. 31 215 10 11 9
550 2011 aug. 31 213 8 10 6
550 2011 aug. 31 214 10 12 8
550 2011 aug. 31 215 8 9 7
350 2011 sep. 31 244 9 10 8
350 2011 sep. 31 245 11 12 10
350 2011 sep. 31 246 10 11 9
550 2011 sep. 31 244 7.5 9 6
550 2011 sep. 31 245 8 10 6
550 2011 sep. 31 246 8.5 9 8", header=TRUE)
and I am trying to reshape this data in order to have only one row per altitude and to calculate the mean data for each month and for the whole year. I would be great if it could be shaped like that:
alti mean_year(meanTemp) mean_year(maxTemp) mean_aug.(meanTemp) mean_aug.(maxTemp) mean_sep.(meanTemp) [...]
350 10.333 12.667 10.667 14.3 10 ...
550 8.333 9.833 8.667 10.333 7.766 ...
Any idea to perform this reshaping & calculation?
Here's another variation of data.table solution, but this requires the current devel version, v1.9.5:
require(data.table) # v1.9.5+
setDT(clim)
form = paste("alti", c("year", "month"), sep=" ~ ")
val = c("meanTemp", "maxTemp")
ans = lapply(form, function(x) dcast(clim, x, mean, value.var = val))
Reduce(function(x, y) x[y, on="alti"], ans)
# alti meanTemp_mean_2011 maxTemp_mean_2011 meanTemp_mean_aug. meanTemp_mean_sep. maxTemp_mean_aug. maxTemp_mean_sep.
# 1: 350 10.333333 12.666667 10.666667 10 14.33333 11.000000
# 2: 550 8.333333 9.833333 8.666667 8 10.33333 9.333333