Sample Random Rows Within Each Group in a Data.Table

Sample random rows within each group in a data.table

Maybe something like this?

> DT[,.SD[sample(.N, min(3,.N))],by = a]
   a   b
1: 1 744
2: 1 497
3: 1 167
4: 2 888
5: 2 950
6: 2 343

(Thanks to Josh for the correction, below.)

R data.table: Random sample of rows from second table by group

A direct translation of your needs is:

DT2[DT1, on=.(group), allow.cartesian=TRUE, .(var1, obs=obs[sample(.N, 2L)]), by=.EACHI]

This might be faster:

gn <- DT1[, .(nsamp=2*.N), keyby=.(group)]
DT2[gn, on=.(group), .(obs=obs[sample(.N, nsamp, replace=TRUE)]), by=.EACHI][,
    var1 := rep(DT1$var1, each=2L)]

data:

set.seed(0L)
library(data.table)
DT1 <- data.table(var1=101:120, group=c(1,1,1,1,1,2,2,2,2,3,3,3,4,4,4,4,4,4,4,4))
DT2 <- data.table(obs=201:213, group=c(1,1,1,2,2,2,3,3,3,4,4,4,5))

sample output:

    group var1 obs
 1:     1  101 203
 2:     1  101 201
 3:     1  102 202
 4:     1  102 203
 5:     1  103 203
 6:     1  103 201
 7:     1  104 203
 8:     1  104 202
 9:     1  105 202
10:     1  105 203
11:     2  106 204
12:     2  106 206
13:     2  107 204
14:     2  107 205
15:     2  108 205
16:     2  108 206
17:     2  109 205
18:     2  109 206
19:     3  110 209
20:     3  110 207
21:     3  111 209
22:     3  111 208
23:     3  112 207
24:     3  112 208
25:     4  113 210
26:     4  113 212
27:     4  114 211
28:     4  114 210
29:     4  115 211
30:     4  115 212
31:     4  116 211
32:     4  116 210
33:     4  117 211
34:     4  117 210
35:     4  118 210
36:     4  118 211
37:     4  119 212
38:     4  119 211
39:     4  120 210
40:     4  120 211
    group var1 obs

Take random sample by group

Try this:

library(plyr)
ddply(df,.(ID),function(x) x[sample(nrow(x),500),])

random sample of rows with at least x of each group with several conditions

In dplyr you could do this...

library(dplyr)

df2 <- mydf %>% group_by(age, region, gender) %>% sample_n(1) #select one from each group

sample <- mydf %>% sample_n(24 - nrow(df2)) %>%               #select rest randomly
            bind_rows(df2)                                    #add first set back in

Your example data does not cover all the possible groups because of the way you have constructed it (6=2*3, so very cyclic), but this approach should work in a more general case.

Sample n random rows per group in a dataframe with dplyr when some observations have less than n rows

You can sample minimum of number of rows or x for each group :

library(dplyr)

x <- 2
df %>% group_by(samples, groups) %>% sample_n(min(n(), x))

#  samples groups
#  <chr>    <dbl>
#1 A            1
#2 A            1
#3 A            2
#4 B            1
#5 B            1

However, note that sample_n() has been super-seeded in favor of slice_sample but n() doesn't work with slice_sample. There is an open issue here for it.

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However, as @tmfmnk mentioned we don't need to call n() here. Try :

df %>% group_by(samples, groups) %>% slice_sample(n = x)

How to efficiently sample from a datatable by column in R?

You can use sample on .N for each group and select 1 random row.

library(data.table)
set.seed(123)
dt[, .SD[sample(.N, 1)], A]

#   A   B   C
#1: A  31 143
#2: D  16 175
#3: B 100 165
#4: E  27 190
#5: C  90 197

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dplyr has slice_sample (previously sample_n) function for it :

library(dplyr)
dt %>% group_by(A) %>% slice_sample(n = 1)

Flag randomly selected N rows by group in data.table

dt[, C3 := 1:.N %in% sample(.N, min(.N, 2)), by = C1]

Or use head, but I think that should be slower

dt[, C3 := 1:.N %in% head(sample(.N), 2) , by = C1]

If the number of flagged rows is not constant you can do

flagsz <- c(2, 1, 2, 3)
dt[, C3 := 1:.N %in% sample(.N, min(.N, flagsz[.GRP])), by = C1]

from data table, randomly select one row per group

OP provided only a single column in the example. Assuming that there are multiple columns in the original dataset, we group by 'z', sample 1 row from the sequence of rows per group, get the row index (.I), extract the column with the row index ($V1) and use that to subset the rows of 'dt'.

dt[dt[ , .I[sample(.N,1)] , by = z]$V1]

Sample n random rows per group in a dataframe

You can assign a random ID to each element that has a particular factor level using ave. Then you can select all random IDs in a certain range.

rndid <- with(df, ave(X1, color, FUN=function(x) {sample.int(length(x))}))
df[rndid<=3,]

This has the advantage of preserving the original row order and row names if that's something you are interested in. Plus you can re-use the rndid vector to create subset of different lengths fairly easily.

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