r - Aggregate data frame to coarser spatial resolution -


i have dataset 0.25 * 0.25 degree grid resolution. i've 1 * 1 degree resolution , want make them comparable (both changed 1 * 1 resolution). area 28.5 36.5 longitude , -4.5 4.5 latitude monthly data 2003 2012.i've attached first few lines e.g. i've attached excel file of full data if below not help! https://onedrive.live.com/redir?resid=9e74848e574367c6!2625&authkey=!aocpfttyhvqu8ca&ithint=file%2ccsv i've been looking 'sp' (aggregate) , 'raster' packages cannot figure out. appreciated always!

> head(cur.data, 40)     longitude latitude year month decimdate  rainfall  rainanom 1     28.625   -4.375 2012    12  2012.917 173.69343  73.74917 2     28.875   -4.375 2012    12  2012.917 186.91148  86.96723 3     29.125   -4.375 2012    12  2012.917 158.17921  58.23495 4     29.375   -4.375 2012    12  2012.917 194.71006  94.76581 5     29.625   -4.375 2012    12  2012.917 160.84774  60.90349 6     29.875   -4.375 2012    12  2012.917 192.01070  92.06645 7     30.125   -4.375 2012    12  2012.917 225.45180 125.50755 8     30.375   -4.375 2012    12  2012.917 234.98836 135.04410 9     30.625   -4.375 2012    12  2012.917 182.77813  82.83388 10    30.875   -4.375 2012    12  2012.917 226.08477 126.14052 11    31.125   -4.375 2012    12  2012.917 271.90741 171.96316 12    31.375   -4.375 2012    12  2012.917 272.48264 172.53839 13    31.625   -4.375 2012    12  2012.917 265.44591 165.50166 14    31.875   -4.375 2012    12  2012.917 266.44458 166.50033 15    32.125   -4.375 2012    12  2012.917 270.94755 171.00329 16    32.375   -4.375 2012    12  2012.917 257.85288 157.90863 17    32.625   -4.375 2012    12  2012.917 265.42979 165.48554 18    32.875   -4.375 2012    12  2012.917 255.23151 155.28726 19    33.125   -4.375 2012    12  2012.917 249.32812 149.38387 20    33.375   -4.375 2012    12  2012.917 165.93709  65.99284 21    33.625   -4.375 2012    12  2012.917 165.71675  65.77250 22    33.875   -4.375 2012    12  2012.917 201.09573 101.15148 23    34.125   -4.375 2012    12  2012.917 216.02753 116.08328 24    34.375   -4.375 2012    12  2012.917 255.53255 155.58830 25    34.625   -4.375 2012    12  2012.917 202.72544 102.78119 26    34.875   -4.375 2012    12  2012.917  71.00399 -28.94026 27    35.125   -4.375 2012    12  2012.917  69.55960 -30.38465 28    35.375   -4.375 2012    12  2012.917  84.89567 -15.04858 29    35.625   -4.375 2012    12  2012.917 144.54023  44.59598 30    35.875   -4.375 2012    12  2012.917 227.81630 127.87205 31    36.125   -4.375 2012    12  2012.917 193.73762  93.79337 32    36.375   -4.375 2012    12  2012.917 156.49207  56.54782 33    28.625   -4.125 2012    12  2012.917 182.18007  82.23582 34    28.875   -4.125 2012    12  2012.917 208.52020 108.57595 35    29.125   -4.125 2012    12  2012.917 175.20243  75.25817 36    29.375   -4.125 2012    12  2012.917 166.91644  66.97218 37    29.625   -4.125 2012    12  2012.917 156.38420  56.43994 38    29.875   -4.125 2012    12  2012.917 167.72313  67.77888 39    30.125   -4.125 2012    12  2012.917 175.48325  75.53899 40    30.375   -4.125 2012    12  2012.917 210.74390 110.79965

i think raster package way go:

require(raster)  cur.data <- read.csv("lvb_trmm_monthly_rainfall.csv")  r <- cur.data coordinates(r) <- ~ longitude + latitude gridded(r) <- true  # stack each variable band in raster: tmp <- raster(r, layer = 1) (i in 2:5) {   tmp <- stack(tmp, raster(r, layer = i)) } r <- tmp  # aggregate 1 degree resolution (4 times coarse): r <- aggregate(r, fact = 4, fun = mean)  # dataframe coords <- as.matrix(coordinates(r)) df <- data.frame(longitude = coords[, 1], latitude = coords[, 2],                  as.data.frame(r))

this gives:

  longitude latitude year month decimdate rainfall  rainanom 1        29        1 2003     1      2003 60.69725 -39.24700 2        30        1 2003     1      2003 39.60284 -60.34141 3        31        1 2003     1      2003 34.12878 -65.81548 4        32        1 2003     1      2003 46.32009 -53.62416 5        33        1 2003     1      2003 71.27627 -28.66799 6        34        1 2003     1      2003 82.92516 -17.01909

however, see have data different time periods. perhaps want repeat above steps each time period.


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