将嵌套的小标题的所有列取消嵌套到小标题列表 [英] Unnest all columns of nested tibble to a list of tibbles
问题描述
我正在为数据集中的每个组拟合模型。我通过分组变量嵌套数据，然后使用map将模型拟合到每个组。然后，将整理过的模型信息作为列存储在嵌套的小块中。
I am fitting a model to each group in a dataset. I am nesting the data by the grouping variable and then using map to fit a model to each group. Then I store the tidied model information as columns in a nested tibble.
我想将这些列中的每一个另存为自己的文件，此示例将它们保存为工作表在Excel工作簿中。
I'd like to save each of these columns as its own file, this example saves them as sheets in a excel workbook.
是否有一种方法可以不将每个列都嵌套为新小标题？是否可以立即将所有列都取消嵌套到新的小标题列表中？可以在其他功能（例如编写excel文件）中使用的那个吗？
Is there a way to not to unnest each column individually as a new tibble? Can all columns be unnested at once to a new list of tibbles? One that can be used in other functions (like writing an excel file)?
library(tidyverse)
library(broom)
data(mtcars)
df <- mtcars
nest.df <- df %>% nest(-carb)
results <- nest.df %>%
mutate(fit = map(data, ~ lm(mpg ~ wt, data=.x)),
tidied = map(fit, tidy),
glanced = map(fit, glance),
augmented = map(fit, augment))
glanced.df <- results %>%
unnest(glanced, .drop=T)
tidied.df <- results %>%
unnest(tidied, .drop=T)
augmented.df <- results %>%
unnest(augmented, .drop=T)
myList <- list(glanced.df, tidied.df, augmented.df)
names(myList) <- c("glance", "tidy", "augment")
openxlsx::write.xlsx(myList, file = "myResults.xlsx")
推荐答案
编辑
再次阅读您的问题，我错过了最后一个rt。
Edit
read your question again, I missed the last part. But now it should be pretty much what you are searching for.
像这样吗？
library(tidyverse)
library(broom)
data(mtcars)
df <- mtcars
nest.df <- df %>% nest(-carb)
results <- nest.df %>%
mutate(fit = map(data, ~ lm(mpg ~ wt, data=.x)),
tidied = map(fit, tidy),
glanced = map(fit, glance),
augmented = map(fit, augment))
results %>% select(glanced, tidied, augmented ) %>%
map(~bind_rows(.))
#> $glanced
#> # A tibble: 6 x 11
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 0.696 0.658 2.29 18.3 2.70e-3 2 -21.4 48.7
#> 2 0.654 0.585 3.87 9.44 2.77e-2 2 -18.2 42.4
#> 3 0.802 0.777 2.59 32.3 4.62e-4 2 -22.6 51.1
#> 4 0.00295 -0.994 1.49 0.00296 9.65e-1 2 -3.80 13.6
#> 5 0 0 NaN NA NA 1 Inf -Inf
#> 6 0 0 NaN NA NA 1 Inf -Inf
#> # … with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>
#>
#> $tidied
#> # A tibble: 10 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 27.9 2.91 9.56 0.0000118
#> 2 wt -3.10 0.724 -4.28 0.00270
#> 3 (Intercept) 44.8 6.49 6.90 0.000982
#> 4 wt -7.81 2.54 -3.07 0.0277
#> 5 (Intercept) 39.4 3.09 12.7 0.00000137
#> 6 wt -5.92 1.04 -5.68 0.000462
#> 7 (Intercept) 17.5 22.1 0.791 0.574
#> 8 wt -0.312 5.73 -0.0544 0.965
#> 9 (Intercept) 19.7 NaN NaN NaN
#> 10 (Intercept) 15 NaN NaN NaN
#>
#> $augmented
#> # A tibble: 32 x 9
#> mpg wt .fitted .se.fit .resid .hat .sigma .cooksd .std.resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 2.62 19.7 1.17 1.25 0.263 2.38 0.0730 0.639
#> 2 21 2.88 19.0 1.04 2.04 0.205 2.29 0.129 1.00
#> 3 14.3 3.57 16.8 0.761 -2.50 0.111 2.23 0.0838 -1.16
#> 4 19.2 3.44 17.2 0.796 1.99 0.121 2.31 0.0594 0.929
#> 5 17.8 3.44 17.2 0.796 0.594 0.121 2.43 0.00527 0.277
#> 6 10.4 5.25 11.6 1.22 -1.20 0.283 2.39 0.0760 -0.620
#> 7 10.4 5.42 11.1 1.32 -0.663 0.333 2.43 0.0315 -0.355
#> 8 14.7 5.34 11.3 1.27 3.39 0.310 1.90 0.714 1.78
#> 9 13.3 3.84 16.0 0.725 -2.67 0.100 2.20 0.0842 -1.23
#> 10 15.8 3.17 18.0 0.895 -2.24 0.153 2.27 0.102 -1.06
#> # … with 22 more rows
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