r - Aggregate data frame by date and apply different functions to corresponding columns? -


i have following data frame "df" part of larger one:

             x1  x2            x3 x4 x5 4468 2010-03-24   3  1.000000e+00  1  2 7662 2010-03-24   9  3.000000e+00  2  1 1272 2010-03-25   8  2.000000e+00  1  1 1273 2010-03-26   9  0.000000e+00  1  1 1274 2010-03-27   8  0.000000e+00  1  1 4469 2010-03-28   4  0.000000e+00  1  2 7663 2010-03-28   4  3.000000e+00  3  1 8734 2010-03-28   7  4.000000e+00  2  3 1275 2010-03-29   8  0.000000e+00  1  1

as can see first column contains date. want follows: want transform dataframe new 1 "df2" there 1 row per date corresponding column values:

x2, average  x3, sum x4, maximum

of previous values per date. x5 not relevant , can removed. result:

             x1  x2            x3 x4 7662 2010-03-24   6  4.000000e+00  2   1272 2010-03-25   8  2.000000e+00  1   1273 2010-03-26   9  0.000000e+00  1   1274 2010-03-27   8  0.000000e+00  1   8734 2010-03-28   5  7.000000e+00  3   1275 2010-03-29   8  0.000000e+00  1

does know how accomplish this? appreciated!

you can use ddply function plyr package arbitrary aggregations or other transforms grouping variable.

for question code like:

library(plyr) result <- ddply(df, .(x1), function(df) {   with(df, data.frame( x1=mean(x1), x2=sum(x2), x3=max(x3) ) ) } )

if medium-large project may want set progress argument show progress bar. large problem can set use parallel processing.


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