在R中对dplyr函数进行重采样和循环 [英] Resample and looping over dplyr functions in R

问题描述

我具有以下数据集(日期)，其中包含8个独特的治疗组.我想从每个唯一组中抽取3个点，并存储它们的均值和方差.我想使用循环将所有值存储在输出中进行1000次(带替换的样本).我尝试执行此循环，并在以下情况中遇到意外的'=':"output [i]<-summarise(group_by(new_df [i]，肥料，作物，水平)，平均值[i] ="

I have the following data-set (dat) with 8 unique treatment groups. I want to sample 3 points from each unique group and store their mean and variance. I want to do this 1000 times over (sample with replacement) using a loop to store all the values in output. I tried to do this loop and I keep running into unexpected '=' in:"output[i] <- summarise(group_by(new_df[i], fertilizer,crop, level),mean[i]="

有关如何修复或进一步完善的任何建议

Any suggestions on how to fix it, or make it more

fertilizer <- c("N","N","N","N","N","N","N","N","N","N","N","N","P","P","P","P","P","P","P","P","P","P","P","P","N","N","N","N","N","N","N","N","N","N","N","N","P","P","P","P","P","P","P","P","P","P","P","P")

crop <- c("alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group","alone","group")

level <- c("low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","high","low","low","high","low")

growth <- c(0,0,1,2,90,5,2,5,8,55,1,90,2,4,66,80,1,90,2,33,56,70,99,100,66,80,1,90,2,33,0,0,1,2,90,5,2,2,5,8,55,1,90,2,4,66,0,0)

dat <- data.frame(fertilizer, crop, level, growth)

library(dplyr)

for(i in 1:1000){
  new_df[i] <- dat %>% 
                  group_by(fertilizer, crop, level) %>% 
                  sample_n(3)
  output[i] <- summarise(
                  group_by(new_df[i], fertilizer, crop, level),
                  mean[i] = mean(growth), 
                  var[i] = sd(growth) * sd(growth))
}

推荐答案

我认为您不需要循环.您可以通过以下方式更快地执行此操作:一次对每个组采样 3 * 1000 个值，分配 sample_id 并将其添加到分组变量中，最后对 summaryize 进行分组获得所需的值.这样，您只调用一次所有函数.-

I don't think you need a loop. You can do this faster by sampling 3*1000 values per group at once, assign sample_id and add it to grouping variables, and finaly summarize to get desired values. This way you are calling all functions only once. -

dat %>% 
  group_by(fertilizer, crop, level) %>% 
  sample_n(3*1000, replace = T) %>% 
  mutate(sample_id = rep(1:1000, each = 3)) %>% 
  group_by(sample_id, add = TRUE) %>% 
  summarise(
    mean = mean(growth, na.rm = T),
    var = sd(growth)^2
  ) %>% 
  ungroup()

# A tibble: 8,000 x 6
   fertilizer crop  level sample_id  mean      var
   <chr>      <chr> <chr>     <int> <dbl>    <dbl>
 1 N          alone high          1 30.7  2640.   
 2 N          alone high          2  1       0    
 3 N          alone high          3 60.3  2640.   
 4 N          alone high          4  1.33    0.333
 5 N          alone high          5  1.33    0.333
 6 N          alone high          6 60.3  2640.   
 7 N          alone high          7  1.33    0.333
 8 N          alone high          8 30.3  2670.   
 9 N          alone high          9  1.33    0.333
10 N          alone high         10 60.7  2581.   
# ... with 7,990 more rows

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