dplyr，dunn测试，dim（robj）<-c（dX，dY）中的错误：dims [产品0]与对象的长度不匹配 [英] dplyr, dunn test, Error in dim(robj) <- c(dX, dY) : dims [product 0] do not match the length of object

问题描述

我正试图将通过变量值过滤的数据集从R中的 asbio包传递给pairw.kw函数。

I am trying to pass a dataset filtered by a variable value to the pairw.kw function from the "asbio" package in R.

example.df <- data.frame( 
                 species = sample(c("primate", "non-primate"), 50, replace = TRUE),
                 treated = sample(c("Yes", "No"), 50, replace = TRUE), 
                 gender = sample(c("male", "female"), 50, replace = TRUE), 
                 var1 = rnorm(50, 100, 5)
               )

library(dplyr)
library(asbio)

with(example.df, pairw.kw(var1, species, conf=0.95))

此代码有效。但是，

example.df %>% 
   filter(treated=="No") %>% 
   {pairw.kw("var1", "species",conf = 0.95)}

提供错误消息

dim（robj）中的错误<-c（dX，dY）：
dims [产品0]与对象[1]的长度不匹配。

Error in dim(robj) <- c(dX, dY) : dims [product 0] do not match the length of object [1]

除了假设应用滤波器后，被比较的两个向量的长度变得不同。

I cannot understand what is causing this, other than to assume that the two vectors being compared become different lengths after the filter is applied.

除了将数据显式设置为新数据框并使用它之外，是否有其他方法可以解决此问题？我知道这行得通，但想知道是否存在更优雅的解决方案。

Is there a way to fix this other than subsetting the data explicitly to a new dataframe and using that instead? I know that will work, but wanted to know if a more elegant solution exists.

推荐答案

首先，％>％管道传递数据。框架作为第一个参数到 pairw.kw 函数。其次， pairw.kw 函数需要两个向量作为输入。您可以使用 magrittr 包中的％$％管道实现此目标。它的工作方式类似于具有$code>功能的。

First of all %>% pipe passes a data.frame to the pairw.kw function as a first argument. Secondly, pairw.kw function wants two vectors as an input. You can achive this with %$% pipe from magrittr package. It works similar to with function.

library(magrittr)

example.df %>% 
   filter(treated=="No") %$% 
   pairw.kw(var1, species, conf = 0.95)

要回答的问题：

Answer to question in comment:

library(tidyverse)
library(magrittr)
library(asbio)

example.df %>% 
  group_by(treated) %>%
  nest() %>%
  mutate(
    kw = map(
      data,
      ~ .x %$% pairw.kw(var1, species, conf = 0.95)
    ),
    p_val = map_dbl(kw, ~ .x$summary$`Adj. P-value`)
  )

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