r - Combine multiple data frames and calculate average -

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i have 3 data frames below. wish combine them 1 data frame according lon & lat, , average 3 values each 'cell'. have read (calculate average on multiple data frames) , attempted utilise aggregate no avail....any pointers appreciated. 

> head(csr.grace[,c(1:14)],10)     lon  lat   january  february     march     april       may     june        july     august  september   october    november  december 1  28.5 -4.5 17.710425 13.855327 12.385712 13.558101 12.789865 6.913783  1.03770075 -5.3901741 -6.6351015 -7.661375 -3.09337944 6.0659410 2  29.5 -4.5 14.010154 10.257435  9.009641 10.275778  9.598241 5.166972  0.73570247 -4.2733162 -5.0861417 -5.850192 -2.93521806 4.1240150 3  30.5 -4.5 16.288443 10.467614  9.275714 10.904162 10.228808 5.364853  0.50089883 -4.7478741 -5.4320069 -6.316568 -3.80160315 3.8494745 4  31.5 -4.5 18.560677  9.932461  9.239592 11.037748 10.551886 5.281853  0.01181973 -4.9034324 -5.3504391 -6.438050 -4.41695714 3.3432301 5  32.5 -4.5 10.171202  4.476512  4.509140  5.448872  5.338991 2.556262 -0.22646611 -2.3274204 -2.4376636 -3.103697 -2.27586145 1.3641930 6  33.5 -4.5 14.040068  5.349344  5.772618  7.158792  7.121341 3.407587 -0.30616689 -2.6800099 -2.7955420 -3.803622 -2.77898997 1.4021380  > head(gfz.grace[,c(1:14)],10)     lon  lat   january  february     march     april       may      june     july     august september   october   november  december 1  28.5 -4.5 15.642782 15.521720 11.823875 19.825865 17.335761 11.208188 5.080615 -3.0897644 -5.733351 -4.196604 -1.6697661 10.744696 2  29.5 -4.5 12.164074 10.931418  8.622238 15.341911 12.969769  8.521280 4.072790 -2.4301791 -4.551170 -3.055914 -1.2260079  7.592880 3  30.5 -4.5 13.579305 10.267520  8.787406 16.567715 13.745143  9.121496 4.497849 -2.6723491 -5.022949 -3.269881 -1.0691039  7.377143 4  31.5 -4.5 14.501465  8.600480  8.259757 16.981533 14.054429  9.318550 4.582672 -2.7917893 -5.249895 -3.636936 -0.5141342  6.770836 5  32.5 -4.5  7.311216  3.249596  3.513870  8.430777  6.941659  4.572560 2.203461 -1.4106516 -2.661226 -2.113089  0.2459282  3.049897 6  33.5 -4.5  9.121348  3.113245  3.584976 11.040761  8.732950  5.772059 2.811168 -1.8554437 -3.524447 -3.272863  1.2493973  3.750694  > head(jpl.grace[,c(1:14)],10)     lon  lat   january  february     march     april       may     june     july     august  september    october   november   december 1  28.5 -4.5 19.559790 14.544438 12.035112 13.944141 11.931011 7.513007 3.095003 -3.6165702 -6.5945043 -7.2498567 -4.5402436  6.3935236 2  29.5 -4.5 15.740160 11.192191  8.549782 10.783359  9.401173 5.834498 2.267822 -2.6354346 -4.8939197 -5.5912996 -3.7295148  4.1461123 3  30.5 -4.5 18.984714 12.014807  8.510139 11.628697 10.635699 6.448064 2.260429 -2.6979695 -5.2102337 -6.2646164 -4.2713238  3.5089825 4  31.5 -4.5 22.794356 11.993054  8.162500 11.813746 11.747350 6.955983 2.164615 -2.5707902 -5.3448873 -6.7473006 -4.5777496  2.5609555 5  32.5 -4.5 13.233634  5.606305  3.880347  5.753024  6.388978 3.742596 1.096214 -1.1103189 -2.6367831 -3.4102675 -2.2860237  0.7826054 6  33.5 -4.5 19.260989  6.761722  4.978247  7.373498  9.135645 5.421030 1.706414 -1.0796434 -3.3122886 -4.2114588 -2.8110246  0.4825075

you can do:

library(data.table)  rbindlist(list(jpl.grace,gfz.grace,csr.grace))[,lapply(.sd,mean), list(lon, lat)]

explanations:

first, data.frames put list , 'superposed horizontaly' using rbindlist (which returns data.table). alternative approach have been do.call(rbind, list(jpl.grace,gfz.grace,csr.grace)). since data.frame has same structure (same number , name of columns, same type of data).

secondly, loop on each distinct pair of lon, lat. .sd represents data.table associated each pair. can see doing:

dt = rbindlist(list(jpl.grace,gfz.grace,csr.grace)) dt[,print(.sd), list(lon, lat)]

for each of these .sd, loop on columns , compute means.


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