I have a large dataframe and I want to check whether the values a set of (factor) variables uniquely identifies each row of the data or not.
My current strategy is to aggregate by the variables that I think are the index variables
dfAgg = aggregate(dfTemp$var1, by = list(dfTemp$var1, dfTemp$var2, dfTemp$var3), FUN = length)
stopifnot(sum(dfAgg$x > 1) == 0)
But this strategy takes forever. A more efficient method would be appreciated.
Thanks.
The data.table
package provides very fast duplicated
and unique
methods for data.table
s. It also has a by=
argument where you can provide the columns on which the duplicated/unique results should be computed from.
Here's an example of a large data.frame:
require(data.table)
set.seed(45L)
## use setDT(dat) if your data is a data.frame,
## to convert it to a data.table by reference
dat <- data.table(var1=sample(100, 1e7, TRUE),
var2=sample(letters, 1e7, TRUE),
var3=sample(as.numeric(sample(c(-100:100, NA), 1e7,TRUE))))
system.time(any(duplicated(dat)))
# user system elapsed
# 1.632 0.007 1.671
This takes 25 seconds using anyDuplicated.data.frame
.
# if you want to calculate based on just var1 and var2
system.time(any(duplicated(dat, by=c("var1", "var2"))))
# user system elapsed
# 0.492 0.001 0.495
This takes 7.4 seconds using anyDuplicated.data.frame
.
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