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r - R中的分类“网格”

问题描述

我正在使用 R 编程语言。假设我有以下数据：

library("dplyr")

df <- data.frame(b = rnorm(100,5,5), d = rnorm(100,2,2),
                 c = rnorm(100,10,10))

a <- c("a", "b", "c", "d", "e")
a <- sample(a, 100, replace=TRUE, prob=c(0.3, 0.2, 0.3, 0.1, 0.1))

a<- as.factor(a)
df$a = a

> head(df)
           b          d          c a
1  3.1316480  0.5032860  4.7362991 a
2  4.3111450 -0.1142736 -0.5841322 c
3  2.8291346  3.6107839 16.0684492 a
4 14.2142245  4.9893987 -1.8145138 a
5 -6.7381302  0.0416782 -7.7675387 c
6  0.4481874  0.3370716 17.4260801 a

我还有以下函数（“my_subset_mean”），它在给定特定输入选择的情况下评估“列 c”的平均值：

my_subset_mean <- function(r1, r2, r3){  
  subset <- df %>% filter(a %in% r1, b > r2, d < r3)
  return(mean(subset$c))
}

my_subset_mean(r1 = c("a", "b"), r2 = 5, r3 = 1 ) 
[1] 5.682513

我的问题：我正在尝试以“r1”、“r2”和“r3”的随机组合来评估函数“my_subset_mean”。例如：

 my_subset_mean(r1 = c("a", "b"), r2 = 5, r3 = 1 ) 
[1] 11.46365

 my_subset_mean(r1 = c("a", "b"), r2 = 5, r3 = 1 ) 
[1] 11.46365

 my_subset_mean(r1 = c("a"), r2 = 2, r3 = 0 ) 
[1] 14.59809

my_subset_mean(r1 = c("a", "b", "c"), r2 = 3.1, r3 = 0 ) 
[1] 11.26508

 #I am not sure how to get this one to work (i.e. ignore "r1" all together and only calculate the mean using r2 and r3)

 my_subset_mean(r1 = "NA", r2 = 3.1, r3 = 0 ) 
[1] NaN

etc.

是否可以制作一个“网格”，其中包含“r2”和“r3”的随机值（例如，“r2”和“r3”的随机值介于 0 和 5 之间）以及“r1”的随机子集（例如“a ", "c, d", "b, a, e", "d"):

> head(my_grid)
           r2          r3   r1
1  3.1316480  0.5032860     a, b
2  4.3111450 -0.1142736     c, d, e
3  2.8291346  3.6107839     a
4 14.2142245  4.9893987     b, e
5 -6.7381302  0.0416782     NA
6  0.4481874  0.3370716     e

然后在“my_grid”的每一行评估“my_subset_mean”？例如

#desired result

 > head(final_answer)
               r2          r3   r1         my_subset_mean
    1  3.1316480  0.5032860     a, b         0.3
    2  4.3111450 -0.1142736     c, d, e      0.1
    3  2.8291346  3.6107839     a            0.55
    4 14.2142245  4.9893987     b, e         0.6
    5 -6.7381302  0.0416782     NA           0.51
    6  0.4481874  0.3370716     e            0.16

如果不涉及“因子变量”，我想我可以用一个迭代的“for循环”来做到这一点。但我不确定如何使用“my_grid”“喂养”函数（“my_subset_mean”）。有人可以告诉我如何做到这一点吗？

谢谢！

标签： rloopsdplyriterationdata-manipulation

解决方案

我认为这段代码可能会对您有所帮助

library(tidyverse)

r1_sim <- c("a", "b", "c", "d", "e")
r2_sim <- seq(0,1,.2)
r3_sim <- seq(0,1,.2)

expand_grid(r1 = r1_sim,r2 = r2_sim, r3 = r3_sim) %>% 
  rowwise() %>% 
  mutate(my_subset_mean(r1,r2,r3))

# A tibble: 180 x 4
# Rowwise: 
   r1       r2    r3 `my_subset_mean(r1, r2, r3)`
   <chr> <dbl> <dbl>                        <dbl>
 1 a       0     0                           16.5
 2 a       0     0.2                         12.9
 3 a       0     0.4                         12.9
 4 a       0     0.6                         12.9
 5 a       0     0.8                         12.9
 6 a       0     1                           13.4
 7 a       0.2   0                           16.5
 8 a       0.2   0.2                         12.9
 9 a       0.2   0.4                         12.9
10 a       0.2   0.6                         12.9
# ... with 170 more rows

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