Random samples from each data.frame column
I want to draw a random sample from each row data.frame
independently of the other rows. Here is the MWE. This code selects the same column for each row, but I need independent column selection for each row. Any help would be much appreciated. Thanks to
library(plyr)
set.seed(12345)
df1 <- mdply(data.frame(mean=c(10, 15)), rnorm, n = 5, sd = 1)
df1
mean V1 V2 V3 V4 V5
1 10 10.58553 10.70947 9.890697 9.546503 10.60589
2 15 13.18204 15.63010 14.723816 14.715840 14.08068
> df1[ , -1]
V1 V2 V3 V4 V5
1 10.58553 10.70947 9.890697 9.546503 10.60589
2 13.18204 15.63010 14.723816 14.715840 14.08068
> sample(df1[, -1], replace = TRUE)
V3 V2 V5 V4 V4.1
1 9.890697 10.70947 10.60589 9.546503 9.546503
2 14.723816 15.63010 14.08068 14.715840 14.715840
> t(apply(df1[, -1], 1, sample))
[,1] [,2] [,3] [,4] [,5]
[1,] 10.70947 9.890697 10.60589 10.58553 9.546503
[2,] 14.71584 13.182044 14.08068 15.63010 14.723816
Edited
df1[ , -1]
V1 V2 V3 V4 V5
1 10.58553 10.70947 9.890697 9.546503 10.60589
2 13.18204 15.63010 14.723816 14.715840 14.08068
sample(df1[, -1], replace = TRUE)
V3 V2 V5 V4 V4.1
1 9.890697 10.70947 10.60589 9.546503 9.546503
2 14.723816 15.63010 14.08068 14.715840 14.715840
sample(df1[, -1], replace = TRUE)
selects columns V3
, V2
, V5
, V4
and V4
for both strings. But I insist that he could choose the columns V3
, V2
, V5
, V4
and V4
for first row
, and / or any combination of the five columns for second row
.
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You can look at the column indices right away and then use a subset of the matrices to avoid using apply
:
## Determine how many indices are required (nrow x (ncol - 1))
nsamp <- prod(dim(df1[, -1]))
## Sample from the number of desired columns, here 5 = ncol(df1[, -1])
mySamp <- sample.int(5, nsamp, replace = TRUE)
## Create a matrix of row and column indices
## Have to add 1 to mySamp to ignore first column of df1
myIdx <- cbind(rep(seq_len(nrow(df1)), ncol(df1) - 1), mySamp + 1)
## Return the corresponding values
matrix(df1[myIdx], nrow = nrow(df1))
# [,1] [,2] [,3] [,4] [,5]
# [1,] 9.890697 10.60589 9.546503 9.546503 10.70947
# [2,] 15.630099 14.71584 15.630099 14.723816 14.72382
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