subsetting in r based on a vector of conditions - r

Question

This is a restatement of my poorly worded previous question. (To those who replied to it, I appreciate your efforts, and I apologize for not being as clear with my question as I should have been.) I have a large dataset, a subset of which might look like this:
a<-c(1,2,3,4,5,1)
b<-c("a","b","a","b","c","a")
c<-c("m","f","f","m","m","f")
d<-1:6
e<-data.frame(a,b,c,d)
If I want the sum of the entries in the fourth column based on a specific condition, I could do something like this:
attach(e)
total<-sum(e[which(a==3 & b=="a"),4])
detach(e)
However, I have a "vector" of conditions (call it condition_vector), the first four elements of which look more like this:
a==3 & b == "a"
a==2
a==1 & b=="a" & c=="m"
c=="f"
I'd like to create a "generalized" version of the "total" formula above that produces a results_vector of totals by reading in the condition_vector of conditions. In this example, the first four entries in the results_vector would be calculated conceptually as follows:
results_vector[1]<-sum(e[which(a==3 & b=="a"),4])
results_vector[2]<-sum(e[which(a==2),4])
results_vector[3]<-sum(e[which(a==1 & b=="a" & c=="m"),4])
results_vector[4]<-sum(e[which(c=="f"),4])
My actual data set has more than 20 variables. So each record in the condition_vector can contain anywhere from 1 to more than 20 conditions (as opposed to between 1 and 3 conditions, used in this example).
Is there a way to accomplish this other than using a parse(eval(text= ... approach (which takes a long time to run on a relatively small dataset)?
Thanks in advance for any help you can provide (and again, I apologize that I wasn't as clear as I should have been last time around).
Spark

Answer 1

Here using a solution using eval(parse(text=..) here, even if obviously you find it slow:
cond <- c('a==3 & b == "a"','a==2','a==1 & b=="a" & c=="x"','c=="f"')
names(cond) <- cond
results_vector <- lapply(cond,function(x)
sum(dat[eval(parse(text=x)),"d"]))
$`a==3 & b == "a"`
[1] 3
$`a==2`
[1] 2
$`a==1 & b=="a" & c=="m"`
[1] 1
$`c=="f"`
[1] 11
The advantage of naming your conditions vector is to access to your results by condition.
results_vector[cond[2]]
$`a==2`
[1] 2

Answer 2

Here is a function that takes as arguments the condition in each column (if no condition in a column, then NA as argument) and sums in a selected column of a selected data.frame:
conds.by.col <- function(..., sumcol, DF) #NA if not condition in a column
{
conds.ls <- list(...)
res.ls <- vector("list", length(conds.ls))
for(i in 1: length(conds.ls))
{
res.ls[[i]] <- which(DF[,i] == conds.ls[[i]])
}
res.ls <- res.ls[which(lapply(res.ls, length) != 0)]
which_rows <- Reduce(intersect, res.ls)
return(sum(DF[which_rows , sumcol]))
}
Test:
a <- c(1,2,3,4,5,1)
b <- c("a", "b", "a", "b", "c", "a")
c <- c("m", "f", "f", "m", "m", "f")
d <- 1:6
e <- data.frame(a, b, c, d)
conds.by.col(3, "a", "f", sumcol = 4, DF = e)
#[1] 3
For multiple conditions, mapply:
#all conditions in a data.frame:
myconds <- data.frame(con1 = c(3, "a", "f"),
con2 = c(NA, "a", NA),
con3 = c(1, NA, "f"),
stringsAsFactors = F)
mapply(conds.by.col, myconds[1,], myconds[2,], myconds[3,], MoreArgs = list(sumcol = 4, DF = e))
#con1 con2 con3
# 3 10 6
I guess "efficiency" isn't the first you say watching this, though...