Here are three examples (along with some sample data that I think is representative of what you described).
Here's the sample data:
set.seed(1)
mydf <- data.frame(
company = LETTERS[1:4],
earnings_2012 = runif(4),
earnings_2011 = runif(4),
earnings_2010 = runif(4),
assets_2012 = runif(4),
assets_2011 = runif(4),
assets_2010 = runif(4)
)
mydf
# company earnings_2012 earnings_2011 earnings_2010 assets_2012 assets_2011 assets_2010
# 1 A 0.2655087 0.2016819 0.62911404 0.6870228 0.7176185 0.9347052
# 2 B 0.3721239 0.8983897 0.06178627 0.3841037 0.9919061 0.2121425
# 3 C 0.5728534 0.9446753 0.20597457 0.7698414 0.3800352 0.6516738
# 4 D 0.9082078 0.6607978 0.17655675 0.4976992 0.7774452 0.1255551
Option 1: reshape
One limitation is that it won't handle "unbalanced" datasets (for example, if you didn't have "assets_2010" as part of your data, this wouldn't work).
reshape(mydf, direction = "long", idvar="company",
varying = 2:ncol(mydf), sep = "_")
# company time earnings assets
# A.2012 A 2012 0.26550866 0.6870228
# B.2012 B 2012 0.37212390 0.3841037
# C.2012 C 2012 0.57285336 0.7698414
# D.2012 D 2012 0.90820779 0.4976992
# A.2011 A 2011 0.20168193 0.7176185
# B.2011 B 2011 0.89838968 0.9919061
# C.2011 C 2011 0.94467527 0.3800352
# D.2011 D 2011 0.66079779 0.7774452
# A.2010 A 2010 0.62911404 0.9347052
# B.2010 B 2010 0.06178627 0.2121425
# C.2010 C 2010 0.20597457 0.6516738
# D.2010 D 2010 0.17655675 0.1255551
Option 2: The "reshape2" package
Quite popular for its syntax. Needs a little bit of processing before it can work since the column names need to be split in order for us to get this "double-wide" type of data. Is able to handle unbalanced data, but won't be the best if your varying columns are of different column types (numeric, character, factor).
library(reshape2)
dfL <- melt(mydf, id.vars="company")
dfL <- cbind(dfL, colsplit(dfL$variable, "_", c("var", "year")))
dcast(dfL, company + year ~ var, value.var="value")
# company year assets earnings
# 1 A 2010 0.9347052 0.62911404
# 2 A 2011 0.7176185 0.20168193
# 3 A 2012 0.6870228 0.26550866
# 4 B 2010 0.2121425 0.06178627
# 5 B 2011 0.9919061 0.89838968
# 6 B 2012 0.3841037 0.37212390
# 7 C 2010 0.6516738 0.20597457
# 8 C 2011 0.3800352 0.94467527
# 9 C 2012 0.7698414 0.57285336
# 10 D 2010 0.1255551 0.17655675
# 11 D 2011 0.7774452 0.66079779
# 12 D 2012 0.4976992 0.90820779
Option 3: merged.stack from "splitstackshape"
merged.stack from my "splitstackshape" package has pretty straightforward syntax and should be pretty fast if you need to end up with this "double-wide" type of structure. It was created to be able to handle unbalanced data and since it treats columns separately, won't have problems with converting column types.
library(splitstackshape)
merged.stack(mydf, id.vars="company",
var.stubs=c("earnings", "assets"), sep = "_")
# company .time_1 earnings assets
# 1: A 2010 0.62911404 0.9347052
# 2: A 2011 0.20168193 0.7176185
# 3: A 2012 0.26550866 0.6870228
# 4: B 2010 0.06178627 0.2121425
# 5: B 2011 0.89838968 0.9919061
# 6: B 2012 0.37212390 0.3841037
# 7: C 2010 0.20597457 0.6516738
# 8: C 2011 0.94467527 0.3800352
# 9: C 2012 0.57285336 0.7698414
# 10: D 2010 0.17655675 0.1255551
# 11: D 2011 0.66079779 0.7774452
# 12: D 2012 0.90820779 0.4976992
