我有一个数据框，其中包含两个物种A和B以及某些变量ab，这些变量与总共100行相关。
我想创建一个采样器，这样在一个set中，它从df数据集中随机选择6行reps。然而，A的样本必须只来自与sp相关的行，A来自df,B也是如此。我想对A和B这两个物种各做500次。
我尝试了一个for循环，当我运行sampling时，它显示了一行6列。如有任何指导，我将不胜感激。
a <- rnorm(100, 2,1)
b <- rnorm(100, 2,1)
sp <- rep(c("A","B"), each = 50)
df <- data.frame(a,b,sp)
df.sample <- for(i in 1:1000){
sampling <- sample(df[i,],6,replace = TRUE)
}
#Output in a single row
a a.1 sp b sp.1 a.2
1000 1.68951 1.68951 B 1.395995 B 1.68951
#Expected dataframe
df.sample
set rep a b sp
1 1 1 9 A
1 2 3 2 A
1 3 0 2 A
1 4 1 2 A
1 5 1 6 A
1 6 4 2 A
2 1 1 2 B
2 2 5 2 B
2 3 1 2 B
2 4 1 6 B
2 5 1 8 B
2 6 9 2 B
....
我将这样做(使用tidyverse):
数据:
a <- rnorm(100, 2,1)
b <- rnorm(100, 2,1)
sp <- rep(c("A","B"), each = 50)
df <- data.frame(a,b,sp)
# create an empty table with desired columns
library(tidyverse)
output <- tibble(a = numeric(),
b = numeric(),
sp = character(),
set = numeric())
# sampling in a loop
set.seed(42)
for(i in 1:500){
samp1 <- df %>% filter(sp == 'A') %>% sample_n(6, replace = TRUE) %>% mutate(set = i)
samp2 <- df %>% filter(sp == 'B') %>% sample_n(6, replace = TRUE) %>% mutate(set = i)
output %>% add_row(bind_rows(samp1, samp2)) -> output
}
> head(output, 20)
# A tibble: 20 × 4
a b sp set
<dbl> <dbl> <chr> <dbl>
1 2.59 3.31 A 1
2 1.84 1.66 A 1
3 2.35 1.17 A 1
4 2.33 1.95 A 1
5 0.418 1.11 A 1
6 1.19 2.54 A 1
7 2.35 0.899 B 1
8 1.19 1.63 B 1
9 0.901 0.986 B 1
10 3.12 1.75 B 1
11 2.28 2.61 B 1
12 1.37 3.47 B 1
13 2.33 1.95 A 2
14 1.84 1.66 A 2
15 3.76 1.26 A 2
16 2.96 3.10 A 2
17 1.03 1.81 A 2
18 1.42 2.00 A 2
19 0.901 0.986 B 2
20 2.37 1.39 B 2
可以先按物种划分df。x[sample(nrow(x), 6), ]可以绘制每个物种的随机行。将其传入replicate()，可以进行多次采样。这里dplyr::bind_rows()用于组合样本，并添加一个新的列set表示采样索引。
lapply(split(df, df$sp), function(x) {
dplyr::bind_rows(
replicate(3, x[sample(nrow(x), 6), ], FALSE),
.id = "set"
)
})
$A
set a b sp
1 1 1.52480034 3.41257975 A
2 1 1.82542370 2.08511584 A
3 1 1.80019901 1.39279162 A
4 1 2.20765154 2.11879412 A
5 1 1.61295185 2.04035172 A
6 1 1.92936567 2.90362816 A
7 2 0.88903679 2.46948106 A
8 2 3.19223788 2.81329767 A
9 2 1.28629416 2.69275525 A
10 2 2.61044815 0.82495427 A
11 2 2.30928735 1.67421328 A
12 2 -0.09789704 2.62434719 A
13 3 2.10386603 1.78157862 A
14 3 2.17542841 0.84016203 A
15 3 3.22202227 3.49863423 A
16 3 1.07929909 -0.02032945 A
17 3 2.95271838 2.34460193 A
18 3 1.90414536 1.54089645 A
$B
set a b sp
1 1 3.5130317 -0.4704879 B
2 1 3.0053072 1.6021795 B
3 1 4.1167657 1.1123342 B
4 1 1.5460589 3.2915979 B
5 1 0.8742753 0.9132530 B
6 1 2.0882660 1.5588471 B
7 2 1.2444645 1.8199525 B
8 2 2.7960117 2.6657735 B
9 2 2.5970774 0.9984187 B
10 2 1.1977317 3.7360884 B
11 2 2.2830643 1.0452440 B
12 2 3.1047150 1.5609482 B
13 3 2.9309124 1.5679255 B
14 3 0.8631965 1.3501631 B
15 3 1.5460589 3.2915979 B
16 3 2.7960117 2.6657735 B
17 3 3.1047150 1.5609482 B
18 3 2.8735390 0.6329279 B
如果我很好地理解了您想要的内容，则可以按照以下代码完成
# Create the initial data frame
a <- rnorm(100, 2,1)
b <- rnorm(100, 2,1)
sp <- rep(c("A","B"), each = 50)
df <- data.frame(a,b,sp)
# Rows with sp=A
row.A <- which(df$sp=="A")
row.B <- which(df$sp=="B")
# Sampling data.frame
sampling <- data.frame(matrix(ncol = 5, nrow = 0))
# "rep" column for each iteration
rep1 <- rep(1:6,2)
# Build the dara.frame
for(i in 1:500){
# Sampling row.A
s.A <- sample(row.A,6,replace = T)
# Sampling row.B
s.B <- sample(row.B,6,replace = T)
# Data frame with the subset of df and "set" and "rep" values
sampling <- rbind(sampling, set=cbind(rep(i,12),rep=rep1,df[c(s.A,s.B),]))
}
# Delete row.names of sampling and redefine sampling's column names
row.names(sampling) <- NULL
colnames(sampling) <- c("set", "rep", "a", "b", "sp")
输出如下所示:
set rep a b sp
1 1 3.713663 2.717456 A
1 2 2.456070 2.803443 A
1 3 2.166655 1.395556 A
1 4 1.453738 5.662969 A
1 5 2.692518 2.971156 A
1 6 2.699634 3.016791 A