I have a dataframe like this:
experiment iter results
A 1 30.0
A 2 23.0
A 3 33.3
B 1 313.0
B 2 323.0
B 3 350.0
....
Is there a way to tally results by applying a function with conditions. In the above example, that condition is all iterations of a particular experiment.
A sum of results (30 + 23, + 33.3)
B sum of results (313 + 323 + 350)
I am thinking of "apply" function, but can't find a way to get it work.
Answer
There are a lot of alternatives to do this. Note that if you are interested in another function different from sum, then just change the argument FUN=any.function, e.g, if you want mean, var length, etc, then just plug those functions into FUN argument, e.g, FUN=mean, FUN=var and so on. Let's explore some alternatives:
aggregate function in base.
> aggregate(results ~ experiment, FUN=sum, data=DF)
experiment results
1 A 86.3
2 B 986.0
Or maybe tapply ?
> with(DF, tapply(results, experiment, FUN=sum))
A B
86.3 986.0
Also ddply from plyr package
> # library(plyr)
> ddply(DF[, -2], .(experiment), numcolwise(sum))
experiment results
1 A 86.3
2 B 986.0
> ## Alternative syntax
> ddply(DF, .(experiment), summarize, sumResults = sum(results))
experiment sumResults
1 A 86.3
2 B 986.0
Also the dplyr package
> require(dplyr)
> DF %>% group_by(experiment) %>% summarise(sumResults = sum(results))
Source: local data frame [2 x 2]
experiment sumResults
1 A 86.3
2 B 986.0
Using sapply and split, equivalent to tapply.
> with(DF, sapply(split(results, experiment), sum))
A B
86.3 986.0
If you are concern about timing, data.table is your friend:
> # library(data.table)
> DT <- data.table(DF)
> DT[, sum(results), by=experiment]
experiment V1
1: A 86.3
2: B 986.0
Not so popular, but doBy package is nice (equivalent to aggregate, even in syntax!)
> # library(doBy)
> summaryBy(results~experiment, FUN=sum, data=DF)
experiment results.sum
1 A 86.3
2 B 986.0
Also by helps in this situation
> (Aggregate.sums <- with(DF, by(results, experiment, sum)))
experiment: A
[1] 86.3
-------------------------------------------------------------------------
experiment: B
[1] 986
If you want the result to be a matrix then use either cbind or rbind
> cbind(results=Aggregate.sums)
results
A 86.3
B 986.0
sqldf from sqldf package also could be a good option
> library(sqldf)
> sqldf("select experiment, sum(results) `sum.results`
from DF group by experiment")
experiment sum.results
1 A 86.3
2 B 986.0
xtabs also works (only when FUN=sum)
> xtabs(results ~ experiment, data=DF)
experiment
A B
86.3 986.0