## How to randomly select row from a dataframe for which the row skewness is larger that a given value in R

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I am trying to select random rows from a data frame with 1000 lines (and six columns) where the skewness of the line is larger than a given value (say Sk > 0.3).

I've generated the following data frame

df=data.frame(replicate(6,sample(10:100,1000,rep=TRUE)))

I can get row skewness from the fbasics package:

rowSkewness(df) gives:

[8] -0.2243295435  0.5306809351  0.0707122386  0.0341447417  0.3339384838 -0.3910593364 -0.6443905090
[15]  0.5603809206  0.4406091534 -0.3736108832  0.0397860038  0.9970040772 -0.7702547535  0.2065830354

But now, I need to select say 10 rows of the df which have rowskewness greater than say 0.1... May with

for (a in 1:10) {
sample.data[a,] = sample(x=df[wich(rowSkewness(df[sample(1:nrow(df),1)>0.1),], size = 1, replace = TRUE)
}

or something like this?

Any thoughts on this will be appreciated. thanks in advance.

you can use the sample_n() function or sample_frac() - makes your version a little shorter:

library(tidyr)
library(fBasics)
df=data.frame(replicate(6,sample(10:100,1000,rep=TRUE)))
x=df %>% dplyr::filter(rowSkewness(df)>0.1)  %>% dplyr::sample_n(10)

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Got it:

x=df %>% filter(rowSkewness(df)>0.1)
for (a in 1:samplesize) {
sample.data[a,] = sample(x=x, size = 1, replace = TRUE)
}

Subset Data Frame Rows in R, We will also show you how to remove rows with missing values in a given column . Remove missing values; Select random rows from a data frame; Select top n rows ordered by a Select rows when any of the variables are greater than 2.4:. Select random rows from a data frame. It’s possible to select either n random rows with the function sample_n() or a random fraction of rows with sample_frac(). We first use the function set.seed() to initiate random number generator engine. This important for users to reproduce the analysis.

Just do a subset:

res1 <- DF[fBasics::rowSkewness(DF) > .1, ]

#    X1 X2 X3 X4 X5 X6
# 7  56 28 21 93 74 24
# 8  33 56 23 44 10 12
# 12 29 19 29 38 94 95
# 13 35 51 54 98 66 10
# 14 12 51 24 23 36 68
# 15 50 37 81 22 55 97

Or with e1071::skewness:

res2 <- DF[apply(as.matrix(DF), 1, e1071::skewness) > .1, ]

stopifnot(all.equal(res1, res2))
##### Data
set.seed(42); DF <- data.frame(replicate(6, sample(10:100, 1000, rep=TRUE)))

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