R 关于公式的pmap工作

我试图让

purr:：pmap

在公式上工作，但我遗漏了一些明显的东西

library(plyr)
library(tidyverse)
library(broom)

这只是一个容器，我将在其中跟踪单独的数据帧

d1 <- expand.grid(vs = c(0, 1),
                  am = c(0, 1))

d1$mods <- 1:4 %>% 
  map(function(i)

lm(mpg ~ wt,
   data = mtcars %>%
     filter(vs == d1$vs[[i]],
            am == d1$am[[i]]
            )
   ) %>% 
  tidy
)

返回此错误：

 Error in mutate_impl(.data, dots) : Binding not found: ..1.

---

在

pmap

内部使用

plyr:：mutate

：

library(broom)
library(tidyverse)

d1 <- expand.grid(vs = c(0, 1), am = c(0, 1))

d1$mods <- 1:4 %>% 
  map(function(i)

lm(mpg ~ wt,
   data = mtcars %>%
     filter(vs == d1$vs[[i]],
            am == d1$am[[i]]
            )
   ) %>% 
  tidy
)

pmap(
 list(d1$vs, d1$am, d1$mods),
 ~..3 %>% 
 plyr::mutate(vs = ..1, am = ..2)
)

---

如果您的目标是将

lm

摘要放在每个

vs

和

am

组合的旁边，这样可以吗

# Libraries
library(broom)
library(tidyverse)

#Data
d1 <- expand.grid(vs = c(0, 1), am = c(0, 1))


d1 %>%
  mutate(data = pmap(list(vs, am), ~ (lm(mpg ~ wt,
                                         data = (mtcars %>%
                                                  filter(vs %in% .x,
                                                         am %in% .y))) %>% 
                                                  tidy()))) %>% 
  unnest()

#>   vs am        term  estimate std.error statistic      p.value
#> 1  0  0 (Intercept) 25.059424 3.5111642  7.137070 3.152928e-05
#> 2  0  0          wt -2.438894 0.8421098 -2.896171 1.593780e-02
#> 3  1  0 (Intercept) 31.527152 8.9769855  3.511998 1.706422e-02
#> 4  1  0          wt -3.376121 2.7961588 -1.207414 2.812677e-01
#> 5  0  1 (Intercept) 42.363570 3.3009614 12.833707 2.125032e-04
#> 6  0  1          wt -7.913760 1.1414756 -6.932921 2.272629e-03
#> 7  1  1 (Intercept) 44.126436 6.9567852  6.342935 1.437588e-03
#> 8  1  1          wt -7.767647 3.3627090 -2.309937 6.891266e-02
```

#库
图书馆（扫帚）
图书馆（tidyverse）
#资料
d1%
突变（数据=pmap（列表（vs，am），~（lm）（mpg~wt，
数据=（mtcars%>%
过滤器（与%.x中的百分比相比，
am%（单位为%.y））%>%
整洁（））%>%
unnest（）
#>vs am术语估计标准误差统计p值
#>10 0（截距）25.059424 3.5111642 7.137070 3.152928e-05
#>2 0 0 wt-2.438894 0.8421098-2.896171 1.593780e-02
#>3110（截距）31.527152 8.9769855 3.511998 1.706422e-02
#>4 1 0重量-3.376121 2.7961588-1.207414 2.812677e-01
#>5 0 1（截距）42.363570 3.3009614 12.833707 2.125032e-04
#>6.01重量-7.913760 1.1414756-6.932921 2.272629e-03
#>7 1（截距）44.126436 6.9567852 6.342935 1.437588e-03
#>8 1 wt-7.767647 3.3627090-2.309937 6.891266e-02
```

由（v0.2.0）于2018年9月16日创建

[[1]]
         term  estimate std.error statistic      p.value vs am
1 (Intercept) 25.059424 3.5111642  7.137070 3.152928e-05  0  0
2          wt -2.438894 0.8421098 -2.896171 1.593780e-02  0  0

[[2]]
         term  estimate std.error statistic    p.value vs am
1 (Intercept) 31.527152  8.976986  3.511998 0.01706422  1  0
2          wt -3.376121  2.796159 -1.207414 0.28126767  1  0

[[3]]
         term estimate std.error statistic      p.value vs am
1 (Intercept) 42.36357  3.300961 12.833707 0.0002125032  0  1
2          wt -7.91376  1.141476 -6.932921 0.0022726287  0  1

[[4]]
         term  estimate std.error statistic     p.value vs am
1 (Intercept) 44.126436  6.956785  6.342935 0.001437588  1  1
2          wt -7.767647  3.362709 -2.309937 0.068912660  1  1

# Libraries
library(broom)
library(tidyverse)

#Data
d1 <- expand.grid(vs = c(0, 1), am = c(0, 1))


d1 %>%
  mutate(data = pmap(list(vs, am), ~ (lm(mpg ~ wt,
                                         data = (mtcars %>%
                                                  filter(vs %in% .x,
                                                         am %in% .y))) %>% 
                                                  tidy()))) %>% 
  unnest()

#>   vs am        term  estimate std.error statistic      p.value
#> 1  0  0 (Intercept) 25.059424 3.5111642  7.137070 3.152928e-05
#> 2  0  0          wt -2.438894 0.8421098 -2.896171 1.593780e-02
#> 3  1  0 (Intercept) 31.527152 8.9769855  3.511998 1.706422e-02
#> 4  1  0          wt -3.376121 2.7961588 -1.207414 2.812677e-01
#> 5  0  1 (Intercept) 42.363570 3.3009614 12.833707 2.125032e-04
#> 6  0  1          wt -7.913760 1.1414756 -6.932921 2.272629e-03
#> 7  1  1 (Intercept) 44.126436 6.9567852  6.342935 1.437588e-03
#> 8  1  1          wt -7.767647 3.3627090 -2.309937 6.891266e-02
```