Question

I have a dataframe in long form for which I need to aggregate several observations taken on a particular day.

Example data:

long <- structure(list(Day = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), 
Genotype = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), View = structure(c(1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1", 
"2", "3"), class = "factor"), variable = c(1496L, 1704L, 
1738L, 1553L, 1834L, 1421L, 1208L, 1845L, 1325L, 1264L, 1920L, 
1735L)), .Names = c("Day", "Genotype", "View", "variable"), row.names = c(NA, -12L),
class = "data.frame")

> long
   Day Genotype View variable
1    1        A    1     1496
2    1        A    2     1704
3    1        A    3     1738
4    1        B    1     1553
5    1        B    2     1834
6    1        B    3     1421
7    2        A    1     1208
8    2        A    2     1845
9    2        A    3     1325
10   2        B    1     1264
11   2        B    2     1920
12   2        B    3     1735

I need to aggregate each genotype for each day by taking the cube root of the product of each view. So for genotype A on day 1, (1496 * 1704 * 1738)^(1/3). Final dataframe would look like:

  Day Genotype  summary
1   1        A 1642.418
2   1        B 1593.633
3   2        A 1434.695
4   2        B 1614.790

Have been going round and round with reshape2 for the last couple of days, but not getting anywhere. Help appreciated!

Answer 1

I’d probably use plyr and ddply for this task:

library(plyr)

ddply(long, .(Day, Genotype), summarize, 
      summary = prod(variable) ^ (1/3))
#-----
  Day Genotype  summary
1   1        A 1642.418
2   1        B 1593.633
3   2        A 1434.695
4   2        B 1614.790

Or this with dcast:

dcast(data = long, Day + Genotype ~ ., 
      value.var = "variable", function(x) prod(x) ^ (1/3))
#-----
  Day Genotype       NA
1   1        A 1642.418
2   1        B 1593.633
3   2        A 1434.695
4   2        B 1614.790