Efficient subsetting in R using 2 dataframes - r

Question

I have a big time series full in one dataframe and a list of timestamps in a different dataframe test. I need to subset full with data points surrounding the timestamps in test. My first instinct (as an R noob) was to write the below, which was wrong
subs <- subset(full,(full$dt>test$dt-i) & (full$dt<test$dt+i))
Looking at the result I realized that R is looping through both the vectors simultaneously giving the wrong result. My option is to write a loop like the below:
subs<-data.frame()
for (j in test$dt)
subs <- rbind(subs,subset(full,full$dt>(j-i) & full$dt<(j+i)))
I feel that there might be a better way to do loops and this article implores us to avoid R loops as much as possible. The other reason is I might be hitting up against performance issues as this would be at the heart of an optimization algorithm. Any suggestions from gurus would be greatly appreciated.
EDIT:
Here is some reproducible code that shows the wrong approach as well as the approach that works but could be better.
#create a times series
full <- data.frame(seq(1:200),rnorm(200,0,1))
colnames(full)<-c("dt","val")
#my smaller array of points of interest
test <- data.frame(seq(5,200,by=23))
colnames(test)<-c("dt")
# my range around the points of interset
i<-3
#the wrong approach
subs <- subset(full,(full$dt>test$dt-i) & (full$dt<test$dt+i))
#this works, but not sure this is the best way to go about it
subs<-data.frame()
for (j in test$dt)
subs <- rbind(subs,subset(full,full$dt>(j-i) & full$dt<(j+i)))
EDIT:
I updated the values to better reflect my usecase, and I see #mrdwab 's solution pulling ahead unexpectedly and by a wide margin.
I am using benchmark code from #mrdwab and the initialization is as follows:
set.seed(1)
full <- data.frame(
dt = 1:15000000,
val = floor(rnorm(15000000,0,1))
)
test <- data.frame(dt = floor(runif(24,1,15000000)))
i <- 500
The benchmarks are:
test replications elapsed relative
2 mrdwab 2 1.31 1.00000
3 spacedman 2 69.06 52.71756
1 andrie 2 93.68 71.51145
4 original 2 114.24 87.20611
Totally unexpected. Mind = blown. Can someone please shed some light in this dark corner and enlighten as to what is happening.
Important: As #mrdwab notes below, his solution works only if the vectors are integers. If not, #spacedman has the right solution

Answer 1

Here's a real R way to do it. Functionally. No loops...
Starting with Andrie's example data.
First, an interval comparison function:
> cf = function(l,u){force(l);force(u);function(x){x>l & x<u}}
An OR composition function:
> OR = function(f1,f2){force(f1);force(f2);function(x){f1(x)|f2(x)}}
Now there's sort of a loop here, to construct a list of those comparison functions:
> funs = mapply(cf,test$dt-i,test$dt+i)
Now combine all those into one function:
> anyF = Reduce(OR,funs)
And now we apply the OR composition to our interval testing functions:
> head(full[anyF(full$dt),])
dt val
3 3 -0.83562861
4 4 1.59528080
5 5 0.32950777
6 6 -0.82046838
7 7 0.48742905
26 26 -0.05612874
What you've got now is a function of a single variable that tests if the value is in the ranges you defined.
> anyF(1:10)
[1] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
I don't know if this is faster, or better, or what. Someone do some benchmarks!

Answer 2

I don't know if it's any more efficient, but I would think you could also do something like this to get what you want:
subs <- apply(test, 1, function(x) c((x-2):(x+2)))
full[which(full$dt %in% subs), ]
I had to adjust your "3" to "2" since x would be included both ways.
Benchmarking (just for fun)
#Spacedman leads the way!
First, the required data and functions.
## Data
set.seed(1)
full <- data.frame(
dt = 1:200,
val = rnorm(200,0,1)
)
test <- data.frame(dt = seq(5,200,by=23))
i <- 3
## Spacedman's functions
cf = function(l,u){force(l);force(u);function(x){x>l & x<u}}
OR = function(f1,f2){force(f1);force(f2);function(x){f1(x)|f2(x)}}
funs = mapply(cf,test$dt-i,test$dt+i)
anyF = Reduce(OR,funs)
Second, the benchmarking.
## Benchmarking
require(rbenchmark)
benchmark(andrie = do.call(rbind,
lapply(test$dt,
function(j) full[full$dt > (j-i) &
full$dt < (j+i), ])),
mrdwab = {subs <- apply(test, 1,
function(x) c((x-(i-1)):(x+(i-1))))
full[which(full$dt %in% subs), ]},
spacedman = full[anyF(full$dt),],
original = {subs <- data.frame()
for (j in test$dt)
subs <- rbind(subs,
subset(full, full$dt > (j-i) &
full$dt < (j+i)))},
columns = c("test", "replications", "elapsed", "relative"),
order = "relative")
# test replications elapsed relative
# 3 spacedman 100 0.064 1.000000
# 2 mrdwab 100 0.105 1.640625
# 1 andrie 100 0.520 8.125000
# 4 original 100 1.080 16.875000

Answer 3

There is nothing inherently wrong with your code. To achieve your aim, you need a loop of some sort around a vectorised subset operation.
But here is more R-ish way to do it, which might well be faster:
do.call(rbind,
lapply(test$dt, function(j)full[full$dt > (j-i) & full$dt < (j+i), ])
)
PS: You can significantly simplify your reproducible example:
set.seed(1)
full <- data.frame(
dt = 1:200,
val = rnorm(200,0,1)
)
test <- data.frame(dt = seq(5,200,by=23))
i <- 3
xx <- do.call(rbind,
lapply(test$dt, function(j)full[full$dt > (j-i) & full$dt < (j+i), ])
)
head(xx)
dt val
3 3 -0.83562861
4 4 1.59528080
5 5 0.32950777
6 6 -0.82046838
7 7 0.48742905
26 26 -0.05612874

Answer 4

one more way using data.tables:
{
temp <- data.table(x=unique(c(full$dt,(test$dt-i),(test$dt+i))),key="x")
temp[,index:=1:nrow(temp)]
startpoints <- temp[J(test$dt-i),index]$index
endpoints <- temp[J(test$dt+i),index]$index
allpoints <- as.vector(mapply(FUN=function(x,y) x:y,x=startpoints,y=endpoints))
setkey(x=temp,index)
ans <- temp[J(allpoints)]$x
}
benchmarks:
number of rows in test:9
number of rows in full:10000
test replications elapsed relative
1 spacedman 100 0.406 1.000
2 new 100 1.179 2.904
number of rows in full:100000
test replications elapsed relative
2 new 100 2.374 1.000
1 spacedman 100 3.753 1.581