按组创建具有相关性和 p 值的数据框?

Create dataframe with correlation and p-value by group?

我正在尝试根据 R 中的特定组 (COUNTY) 关联多个变量。虽然我能够通过这种方法成功地找到每一列的相关性,但我似乎无法找到一种方法来保存每个组的 table 的 p 值。有什么建议吗?

示例数据:

crops <- data.frame(
    COUNTY = sample(37001:37900), 
    CropYield = sample(c(1:100), 10, replace = TRUE), 
    MaxTemp =sample(c(40:80), 10, replace = TRUE),
    precip =sample(c(0:10), 10, replace = TRUE), 
    ColdDays =sample(c(1:73), 10, replace = TRUE))

示例代码:

crops %>% 
     group_by(COUNTY) %>%
     do(data.frame(Cor=t(cor(.[,2:5], .[,2]))))

^这为我提供了每一列的相关性,但我还需要知道每一列的 p 值。理想情况下,最终输出应如下所示。

Desired Output

每个国家只有 1 个观测值,所以它不起作用。我为每个国家设置了更多示例:

set.seed(111)
crops <- data.frame(
    COUNTY = sample(37001:37002,10,replace=TRUE), 
    CropYield = sample(c(1:100), 10, replace = TRUE), 
    MaxTemp =sample(c(40:80), 10, replace = TRUE),
    precip =sample(c(0:10), 10, replace = TRUE), 
    ColdDays =sample(c(1:73), 10, replace = TRUE))

我认为您需要转换为长格式,并为每个国家/地区和变量

执行 cor.test
calcor=function(da){
data.frame(cor.test(da$CropYield,da$value)[c("estimate","p.value")])
}

crops %>% 
pivot_longer(-c(COUNTY,CropYield)) %>% 
group_by(COUNTY,name) %>% do(calcor(.))

# A tibble: 6 x 4
# Groups:   COUNTY, name [6]
  COUNTY name     estimate p.value
   <int> <chr>       <dbl>   <dbl>
1  37001 ColdDays    0.466   0.292
2  37001 MaxTemp    -0.225   0.628
3  37001 precip     -0.356   0.433
4  37002 ColdDays    0.888   0.304
5  37002 MaxTemp     0.941   0.220
6  37002 precip     -0.489   0.674

以上为您提供了每个县的每个变量与作物产量的相关性。现在是将其转换为宽格式的问题:

crops %>% 
pivot_longer(-c(COUNTY,CropYield)) %>% 
group_by(COUNTY,name) %>% do(calcor(.)) %>%
pivot_wider(values_from=c(estimate,p.value),names_from=name)

  COUNTY estimate_ColdDa… estimate_MaxTemp estimate_precip p.value_ColdDays
   <int>            <dbl>            <dbl>           <dbl>            <dbl>
1  37001            0.466           -0.225          -0.356            0.292
2  37002            0.888            0.941          -0.489            0.304
# … with 2 more variables: p.value_MaxTemp <dbl>, p.value_precip <dbl>