Arbitrary sample selection based on groups

Question

I have a df created by almost 50,000 lines spread across 15 different identifiers (each identifier has thousands of cases). df looks like this:

ID Year Temp ph 1 P1 1996 11.3 6.80 2 P1 1996 9.7 6.90 3 P1 1997 9.8 7.10 ... 2000 P2 1997 10.5 6.90 2001 P2 1997 9.9 7.00 2002 P2 1997 10.0 6.93 

I want to take 500 random lines for each ID (so 500 for P1, 500 for P2, ....) and create a new df. I'm trying to:

 new_df<-df[df$ID %in% sample(unique(dfID),500),] 

But it takes one identifier randomly, while I need 500 random strings for each ID.

Answer 1

Try the following:

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

Answer 2

This is available as the sample_n function in dplyr :

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

Answer 3

Here is one approach in the R base.

Firstly, preliminary sample data for working 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 

Secondly, the sample:

 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 

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There is also a strata from the sampling package, which is convenient if you want to choose 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 

Answer 4

The approach if there is <500 by identifiers. Here I used the mtcars set:

 n <- 8 df <- mtcars df$ID <- df$cyl FUN <- function(x, n) { if (length(x) <= n) return(x) x[x %in% sample(x, n)] } df[unlist(lapply(split(1:nrow(df), df$ID), FUN, n = 8)), ] 

Answer 5

 mydata1 is your original data(not tested) mydata2<- split(mydata1,mydata1$ID) names(mydata2)<-paste0("mydata2",1:length(levels(ID))) mysample<-Map(function(x) x[sample((1:nrow(x)),size=500,replace=FALSE),], mydata2) library(plyr)# for rbinding the mysample ldply(mysample) 

Answer 6

Although this is not a very elegant solution, but it may work.

 library(data.table) df <- data.table(df) f <- list() for(i in unique(df1$ID)){ f[[i]] <- df1[id == i][sample(.N,(500))] } dfnew <- rbindlist(f) 

Answer 7

 library(data.table) #1 df <- data.table(df) #2 df[,group_num := sample(2,.N,replace = TRUE,prob = c(500,.N-500)/.N),by = "ID"] #3 df_sample = df[group_num == 1,] #4 

or you can change lines No. 3 and No. 4 to:

 df[,random_num := sample(.N,.N),by="ID"] df_sample = df[random_num <=500,]