Take randomly sample based on groups

This is available as the sample_n function in dplyr:

library(dplyr)
new_df <- df %>% group_by(ID) %>% sample_n(500)

Try this:

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

Here is one approach in base R.

First, the prerequisite sample data to work with:

set.seed(1)
mydf <- data.frame(ID = rep(1:3, each = 5), matrix(rnorm(45), ncol = 3))
mydf
#    ID         X1          X2          X3
# 1   1 -0.6264538 -0.04493361  1.35867955
# 2   1  0.1836433 -0.01619026 -0.10278773
# 3   1 -0.8356286  0.94383621  0.38767161
# 4   1  1.5952808  0.82122120 -0.05380504
# 5   1  0.3295078  0.59390132 -1.37705956
# 6   2 -0.8204684  0.91897737 -0.41499456
# 7   2  0.4874291  0.78213630 -0.39428995
# 8   2  0.7383247  0.07456498 -0.05931340
# 9   2  0.5757814 -1.98935170  1.10002537
# 10  2 -0.3053884  0.61982575  0.76317575
# 11  3  1.5117812 -0.05612874 -0.16452360
# 12  3  0.3898432 -0.15579551 -0.25336168
# 13  3 -0.6212406 -1.47075238  0.69696338
# 14  3 -2.2146999 -0.47815006  0.55666320
# 15  3  1.1249309  0.41794156 -0.68875569

Second, the sampling:

do.call(rbind, 
        lapply(split(mydf, mydf$ID), 
               function(x) x[sample(nrow(x), 3), ]))
#      ID         X1          X2         X3
# 1.2   1  0.1836433 -0.01619026 -0.1027877
# 1.1   1 -0.6264538 -0.04493361  1.3586796
# 1.5   1  0.3295078  0.59390132 -1.3770596
# 2.10  2 -0.3053884  0.61982575  0.7631757
# 2.9   2  0.5757814 -1.98935170  1.1000254
# 2.8   2  0.7383247  0.07456498 -0.0593134
# 3.13  3 -0.6212406 -1.47075238  0.6969634
# 3.12  3  0.3898432 -0.15579551 -0.2533617
# 3.15  3  1.1249309  0.41794156 -0.6887557

---

There is also strata from the sampling package, which is convenient when you want to sample different sizes from each group:

# install.packages("sampling")
library(sampling)
set.seed(1)
x <- strata(mydf, "ID", size = c(2, 3, 2), method = "srswor")
getdata(mydf, x)
#            X1          X2         X3 ID ID_unit Prob Stratum
# 2   0.1836433 -0.01619026 -0.1027877  1       2  0.4       1
# 5   0.3295078  0.59390132 -1.3770596  1       5  0.4       1
# 6  -0.8204684  0.91897737 -0.4149946  2       6  0.6       2
# 8   0.7383247  0.07456498 -0.0593134  2       8  0.6       2
# 9   0.5757814 -1.98935170  1.1000254  2       9  0.6       2
# 14 -2.2146999 -0.47815006  0.5566632  3      14  0.4       3
# 15  1.1249309  0.41794156 -0.6887557  3      15  0.4       3