I have a large data.frame that was generated by a process outside my control, which may or may not contain variables with zero variance (i.e. all the observations are the same). I would like to build a predictive model based on this data, and obviously these variables are of no use.
Here's the function I'm currently using to remove such variables from the data.frame. It's currently based on apply, and I was wondering if there are any obvious ways to speed this function up, so that it works quickly on very large datasets, with a large number (400 or 500) of variables?
set.seed(1)
dat <- data.frame(
A=factor(rep("X",10),levels=c('X','Y')),
B=round(runif(10)*10),
C=rep(10,10),
D=c(rep(10,9),1),
E=factor(rep("A",10)),
F=factor(rep(c("I","J"),5)),
G=c(rep(10,9),NA)
)
zeroVar <- function(data, useNA = 'ifany') {
out <- apply(data, 2, function(x) {length(table(x, useNA = useNA))})
which(out==1)
}
And here's the result of the process:
> dat
A B C D E F G
1 X 3 10 10 A I 10
2 X 4 10 10 A J 10
3 X 6 10 10 A I 10
4 X 9 10 10 A J 10
5 X 2 10 10 A I 10
6 X 9 10 10 A J 10
7 X 9 10 10 A I 10
8 X 7 10 10 A J 10
9 X 6 10 10 A I 10
10 X 1 10 1 A J NA
> dat[,-zeroVar(dat)]
B D F G
1 3 10 I 10
2 4 10 J 10
3 6 10 I 10
4 9 10 J 10
5 2 10 I 10
6 9 10 J 10
7 9 10 I 10
8 7 10 J 10
9 6 10 I 10
10 1 1 J NA
> dat[,-zeroVar(dat, useNA = 'no')]
B D F
1 3 10 I
2 4 10 J
3 6 10 I
4 9 10 J
5 2 10 I
6 9 10 J
7 9 10 I
8 7 10 J
9 6 10 I
10 1 1 J
Don't use table() - very slow for such things. One option is length(unique(x)):
foo <- function(dat) {
out <- lapply(dat, function(x) length(unique(x)))
want <- which(!out > 1)
unlist(want)
}
system.time(replicate(1000, zeroVar(dat)))
system.time(replicate(1000, foo(dat)))
Which is an order magnitude faster than yours on the example data set whilst giving similar output:
> system.time(replicate(1000, zeroVar(dat)))
user system elapsed
3.334 0.000 3.335
> system.time(replicate(1000, foo(dat)))
user system elapsed
0.324 0.000 0.324
Simon's solution here is similarly quick on this example:
> system.time(replicate(1000, which(!unlist(lapply(dat,
+ function(x) 0 == var(if (is.factor(x)) as.integer(x) else x))))))
user system elapsed
0.392 0.000 0.395
but you'll have to see if they scale similarly to real problem sizes.
You may also want to look into the nearZeroVar() function in the caret package.
If you have one event out of 1000, it might be a good idea to discard these data (but this depends on the model). nearZeroVar() can do that.
Simply don't use table - it's extremely slow on numeric vectors since it converts them to strings. I would probably use something like
var0 <- unlist(lapply(df, function(x) 0 == var(if (is.factor(x)) as.integer(x) else x)))
It will be TRUE for 0-variance, NA for columns with NAs and FALSE for non-zero variance
Use the Caret Package and the function nearZeroVar
require(caret)
NZV<- nearZeroVar(dataset, saveMetrics = TRUE)
NZV[NZV[,"zeroVar"] > 0, ]
NZV[NZV[,"zeroVar"] + NZV[,"nzv"] > 0, ]
Well, save yourself some coding time:
Rgames: foo
[,1] [,2] [,3]
[1,] 1 1e+00 1
[2,] 1 2e+00 1
[3,] 1 3e+00 1
[4,] 1 4e+00 1
[5,] 1 5e+00 1
[6,] 1 6e+00 2
[7,] 1 7e+00 3
[8,] 1 8e+00 1
[9,] 1 9e+00 1
[10,] 1 1e+01 1
Rgames: sd(foo)
[1] 0.000000e+00 3.027650e+00 6.749486e-01
Warning message:
sd(<matrix>) is deprecated.
Use apply(*, 2, sd) instead.
To avoid nasty floating-point roundoffs, take that output vector, which I'll call "bar," and do something like bar[bar< 2*.Machine$double.eps] <- 0 and then finally your data frame dat[,as.logical(bar)] should do the trick.
How about using factor to count the number of unique elements and looping with sapply:
dat[sapply(dat, function(x) length(levels(factor(x)))>1)]
B D F
1 3 10 I
2 4 10 J
3 6 10 I
4 9 10 J
5 2 10 I
6 9 10 J
7 9 10 I
8 7 10 J
9 6 10 I
10 1 1 J
NAs are excluded by default, but this can be changed with the exclude parameter of factor:
dat[sapply(dat, function(x) length(levels(factor(x,exclude=NULL)))>1)]
B D F G
1 3 10 I 10
2 4 10 J 10
3 6 10 I 10
4 9 10 J 10
5 2 10 I 10
6 9 10 J 10
7 9 10 I 10
8 7 10 J 10
9 6 10 I 10
10 1 1 J NA
I think having zero variance is equivalent to being constant and one can get around without doing any arithmetic operations at all. I would expect that range() outperforms var(), but I have not verified this:
removeConstantColumns <- function(a_dataframe, verbose=FALSE) {
notConstant <- function(x) {
if (is.factor(x)) x <- as.integer(x)
return (0 != diff(range(x, na.rm=TRUE)))
}
bkeep <- sapply(a_dataframe, notConstant)
if (verbose) {
cat('removeConstantColumns: '
, ifelse(all(bkeep)
, 'nothing'
, paste(names(a_dataframe)[!bkeep], collapse=',')
, ' removed', '\n')
}
return (a_dataframe[, bkeep])
}
Check this custom function. I did not try it on data frames with 100+ variables.
remove_low_variance_cols <- function(df, threshold = 0) {
n <- Sys.time() #See how long this takes to run
remove_cols <- df %>%
select_if(is.numeric) %>%
map_dfr(var) %>%
gather() %>%
filter(value <= threshold) %>%
spread(key, value) %>%
names()
if(length(remove_cols)) {
print("Removing the following columns: ")
print(remove_cols)
}else {
print("There are no low variance columns with this threshold")
}
#How long did this script take?
print(paste("Time Consumed: ", Sys.time() - n, "Secs."))
return(df[, setdiff(names(df), remove_cols)])
}
来源：https://stackoverflow.com/questions/8805298/quickly-remove-zero-variance-variables-from-a-data-frame