subsetting in data.table

I am trying to subset a data.table ( from the package data.table ) in R (not a data.frame). I have a 4 digit year as a key. I would like to subset by taking a series of years. For example, I want to pull all the records that are from 1999, 2000, 2001.

I have tried passing in my DT[J(year)] binary search s开发者_StackOverflow中文版yntax the following:

1999,2000,2001
c(1999,2000,2001)
1999, 2000, 2001

but none of these seem to work. Anyone know how to do a subset where the years you want to select are not just 1 but multiple years?

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What works for data.frames works for data.tables.

subset(DT, year %in% 1999:2001)

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The question is not clear and does not provide sufficient data to work with BUT it is usefull, so if some one can edit it with the data I provide hereafter, one is welcome. The title of the post could also be completed : Matthew Dowle often answers the subsetting-over-two-vectors question, but less frequently the subsetting-according-a-in-statement-on-one-vector one. I have been looking a while for an answer, untill finding one for character vectors here.

Let's consider this data :

library(data.table)
n <- 100
X <- data.table(a=sample(c(10,20,25,30,40),n,replace=TRUE),b=1:n)

The data.table-style query corresponding to X[X$a %in% c(10,20),] is somehow surprising :

setkey(X,a)
X[.(c(10,20))]
X[.(10,20)] # works for characters but not for integers
            # instead, treats 10 as the filter
            # and 20 as a new variable

# for comparison :
X[X$a %in% c(10,20),]

Now, which is best? If your key is already set, data.table, obviously. Otherwise, it might not, as prove the following time-measurements (on my 1,75 Go RAM computer) :

n <- 1e7
X <- data.table(a=sample(c(10,20,25,30,40),n,replace=TRUE),b=1:n)
system.time(X[X$a %in% c(10,20),])
# utilisateur     système      écoulé (yes, I'm French) 
#        1.92        0.06        1.99
system.time(setkey(X,a))
# utilisateur     système      écoulé 
#       34.91        0.05       35.23 
system.time(X[J(c(10,20))])
# utilisateur     système      écoulé 
#        0.15        0.08        0.23

But maybe Matthew has better solutions...

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[Matthew] You've discovered that sorting type numeric (a.k.a. double) is much slower than integer. For many years we didn't allow double in keys for fear of users falling into this trap and reporting terrible timings like this. We allowed double in keys with some trepidation because fast sorting isn't implemented for double yet. Fast sorting on integer and character is pretty good because those are done using a counting sort. Hopefully we'll get to fast sorting numeric one day! (Now implemented - see below).

Timings on data.table pre-1.9.0

n <- 1e7
X <- data.table(a=sample(c(10,20,25,30,40),n,replace=TRUE),b=1:n)      
system.time(setkey(X,a))
#   user  system elapsed 
# 13.898   0.138  14.216 

X <- data.table(a=sample(as.integer(c(10,20,25,30,40)),n,replace=TRUE),b=1:n)
system.time(setkey(X,a))
#   user  system elapsed 
#  0.381   0.019   0.408 

Rememeber that 2 is type numeric in R by default. 2L is integer. Although data.table accepts numeric it still much prefers integer.

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Fast radix sort for numerics is implemented since v1.9.0.

From v1.9.0 on

n <- 1e7
X <- data.table(a=sample(c(10,20,25,30,40),n,replace=TRUE),b=1:n)      
system.time(setkey(X,a))
#    user  system elapsed 
#   0.832   0.026   0.871 

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Like the above, but more data.table esque:

DT[year %in% c(1999, 2000, 2001)]

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This will work:

sample_DT = data.table(year = rep(1990:2010, length.out = 1000), 
                       random_number = rnorm(1000), key = "year")
year_subset = sample_DT[J(c(1990, 1995, 1997))]

Similarly, you can key an already existing data.table with setkey(existing_DT, year) and then use the J() syntax as shown above.

I think the problem may be that you didn't key the data first.