How to randomize (or permute) a dataframe rowwise and columnwise?

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I have a dataframe (df1) like this.

     f1   f2   f3   f4   f5
d1   1    0    1    1    1  
d2   1    0    0    1    0
d3   0    0    0    1    1
d4   0    1    0    0    1

The d1...d4 column is the rowname, the f1...f5 row is the columnname.

To do sample(df1), I get a new dataframe with count of 1 same as df1. So, the count of 1 is conserved for the whole dataframe but not for each row or each column.

Is it possible to do the randomization row-wise or column-wise?

I want to randomize the df1 column-wise for each column, i.e. the number of 1 in each column remains the same. and each column need to be changed by at least once. For example, I may have a randomized df2 like this: (Noted that the count of 1 in each column remains the same but the count of 1 in each row is different.

     f1   f2   f3   f4   f5
d1   1    0    0    0    1  
d2   0    1    0    1    1
d3   1    0    0    1    1
d4   0    0    1    1    0

Likewise, I also want to randomize the df1 row-wise for each row, i.e. the no. of 1 in each row remains the same, and each row need to be changed (but the no of changed entries could be different). For example, a randomized df3 could be something like this:

     f1   f2   f3   f4   f5
d1   0    1    1    1    1  <- two entries are different
d2   0    0    1    0    1  <- four entries are different
d3   1    0    0    0    1  <- two entries are different
d4   0    0    1    0    1  <- two entries are different

PS. Many thanks for the help from Gavin Simpson, Joris Meys and Chase for the previous answers to my previous question on randomizing two columns.

permutation r random
8 Answers

If the goal is to randomly shuffle each column, some of the above answers don't work since the columns are shuffled jointly (this preserves inter-column correlations). Others require installing a package. Yet a one-liner exist:

df2 = lapply(df1, function(x) { sample(x) })
user_29260720

You can also "sample" the same number of items in your data frame with something like this:

nr<-dim(M)[1]
random_M = M[sample.int(nr),]
user_90762670
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