使用扫帚(增强)和dplyr与黄土适合时出错

Question

我试图在黄土适合使用扩充,但我收到以下错误:

Error in data.frame(..., check.names = FALSE) : 
  arguments imply differing number of rows: 32, 11

在错误消息中,11恰好等于一个段中的观察数,32是观察总数.代码如下.

require(broom)
require(dplyr)

# This example uses the lm method and it works
regressions <- mtcars %>% group_by(cyl) %>%  do(fit = lm(wt ~ mpg, .))
regressions %>% augment(fit)

# This example uses the loess method and it generates the error
regressions2 <- mtcars %>% group_by(cyl) %>%  do(fit = loess(wt ~ mpg, .))
regressions2 %>% augment(fit)

# The below code appropriately plots the loess fit using geom_smooth. 
# My current # workaround is to do a global definition as an aes object in geom_smooth`
cylc = unique(mtcars$cyl) %>% sort()
for (i in 1:length(cyl)){
  print(i)
  print(cyl[i])
  p<- ggplot(data=filter(mtcars,cyl==cylc[i]),aes(x=mpg,y=wt)) + geom_point() + geom_smooth(method="loess") + ggtitle(str_c("cyl = ",cyl[i]))
  print(p)
}

Answer 1

这似乎是与do()运算符相关的问题:当我们检查model.frame()其中一个LOESS模型对象时,我们返回所有32行而不是与该模型对应的子集.

解决方法是保留数据而不仅仅是模型,并将其作为第二个参数传递给augment():

regressions2 <- mtcars %>%
  group_by(cyl) %>%
  do(fit = loess(wt ~ mpg, .),
     data = (.)) %>%
   augment(fit, data)

augment()无论如何,这通常是推荐的,因为model.frame()没有获得所有原始列.

顺便说一下,我是扫帚的维护者,我一般不再推荐这种do()方法(因为dplyr大部分都在远离它).

相反,我建议使用tidyr nest()和purrr map(),如R4DS的本章所述.这使得保持数据和合并更容易一些augment().

library(tidyr)
library(purrr)

mtcars %>%
  nest(-cyl) %>%
  mutate(fit = map(data, ~ loess(wt ~ mpg, .))) %>%
  unnest(map2(fit, data, augment))