Sort data by factor and output into a matrix (or df) R -

i have looked through other posts , think have idea of do, want clear!

i have large data frame contains 4 variables , number of rows.

  chain resid resnum    energy 1     c   o17    500 -37.03670 2       arg      8  -0.84560 3       leu     24  -0.56739 4       asp     25  -0.98583 5     b   arg      8  -0.64880 6     b   leu     24  -0.58380 7     b   asp     25  -0.85930 

each row contains chain (a, b, or c), resid, resnum, , energy. sort data of energy values belonging specific resid , num in each chain clustered together. cluster mean of values "arg 8" grouped or of rows containing "arg 8" grouped. don't know more efficient. ideally, output residues

arg 8 0.000 0.000 0.000 

where "0.000" energy values arg 8 or o17 , on.

sorry header breaks, wanted data clean, can't insert images.

data

structure(list(chain = structure(c(3l, 1l, 1l, 1l, 2l, 2l, 2l ), .label = c("a", "b", "c"), class = "factor"), resid = structure(c(4l,  1l, 3l, 2l, 1l, 3l, 2l), .label = c("arg", "asp", "leu", "o17" ), class = "factor"), resnum = c(500l, 8l, 24l, 25l, 8l, 24l,  25l), energy = c(-37.0367, -0.8456, -0.56739, -0.98583, -0.6488,  -0.5838, -0.8593)), .names = c("chain", "resid", "resnum", "energy" ), class = "data.frame", row.names = c(na, -7l)) 

if want convert wide format

library(reshape2) dcast(df, resid+resnum~paste0('energy.',chain), value.var='energy') #   resid resnum energy.a energy.b energy.c #1   arg      8 -0.84560  -0.6488       na #2   asp     25 -0.98583  -0.8593       na #3   leu     24 -0.56739  -0.5838       na #4   o17    500       na       na -37.0367