根据群体随机抽取样本

Question

我有一个由15个不同ID分散的近50,000行的df(每个ID有数千个观察值).df看起来像:

        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

我想为每个ID获取500个随机行(对于P1为500,对于P2为500,....)并创建一个新的df.我尝试:

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

但它随机需要一个ID,而每个ID需要500个随机行.

Answer 1

这可用作以下sample_n功能dplyr:

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

Answer 2

试试这个:

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

Answer 3

这是基地R的一种方法.

首先,要使用的先决条件样本数据:

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

二,抽样:

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

也有strata来自sampling包,当你想品尝来自各组不同尺寸是方便:

# 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

如果您有大数据集，data.table解决方案可能是这样的：

library(data.table)

# Generate 26 mil rows random data
set.seed(2019)
dt <- data.table(c1 = sample(length(LETTERS)*10^6), 
                 c2 = sample(LETTERS, replace = TRUE))

# For each letter, sample 500 rows
dt_sample <- dt[, .SD[sample(x = .N, size = 500)], by = c2]

# We indeed sampled 500 rows for each letter
dt_sample[, .N, by = c2][order(c2)]
#>     c2   N
#>  1:  A 500
#>  2:  D 500
#>  3:  G 500
#>  4:  I 500
#>  5:  M 500
#>  6:  N 500
#>  7:  O 500
#>  8:  P 500
#>  9:  Q 500
#> 10:  R 500
#> 11:  S 500
#> 12:  T 500
#> 13:  U 500
#> 14:  V 500
#> 15:  W 500
#> 16:  Y 500
#> 17:  Z 500

^{由reprex 包(v0.2.1)于 2019 年 4 月 23 日创建}

如果您的数据不平衡，因为某些组恰好小于您想要的样本大小（作为行数），那么您需要设置一个防御性技巧，例如样本大小应该是min(500, .N)- 请参阅每个组中的样本随机行一个数据表。所以像：

dt[, .SD[sample(x = .N, size = min(500, .N))], by = c2]