问题

                                              然而，在                                               I           I           Using     I                    However,       Data                                                                           df

-输出 specific list elements
d-color:#272822;">我有一些看起来像这样的数据： price unleveragedData <dbl> <list> 1 450000 <list [5]> 2 400000 <list [5]> 3 400000 <list [5]> 4 397000 <list [5]> 5 750000 <list [5]> 6 550000 <list [5]> 我试图将列表的第 `5` 个元素放入新列中，即： price unleveragedData element5 <dbl> <list> 1 450000 <list [5]> ----- 2 400000 <list [5]> ----- 3 400000 <list [5]> 4 397000 <list [5]> 5 750000 <list [5]> 6 550000 <list [5]> ----- 使用以下代码： df$unleveragedData %>% map(., ~ pluck(., c(5))) 我可以得到我想要的输出： [[1]] [1] 260551.4 [[2]] [1] 330786.9 [[3]] [1] 330786.9 [[4]] [1] 287739.3 [[5]] [1] 566416 [[6]] [1] 271879.7 `mutate` 函数内部，我无法让它正常工作。 df %>% mutate( element5 = map(unleveragedData, ~ map_dbl(., pluck(., c(5)))) ) 数据： df = structure(list(price = c(450000, 400000, 400000, 397000, 750000, 550000), unleveragedData = list(list(-0.0547083151944441, c(-450000, 15533.28, 16475.2128, 17473.760928, 18532.32229728, 280205.849576444 ), "450000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income"), Year1 = c(16560, 828, 15732, 199, 15533), Year2 = c(17554, 878, 16676, 201, 16475), Year3 = c(18607, 930, 17676, 203, 17474 ), Year4 = c(19723, 986, 18737, 205, 18532), Year5 = c(20907, 1045, 19861, 207, 19654), Year6 = c(22161, 1108, 21053, 209, 20844), purchasePriceCapRate = c("450000-0.08", "450000-0.08", "450000-0.08", "450000-0.08", "450000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 260551.350870592), list(0.0224165566243759, c(-400000, 19720.512, 20916.35712, 22184.0790912, 23527.991786112, 355739.600331834), "400000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(21024, 1051, 19973, 252, 19721), Year2 = c(22285, 1114, 21171, 255, 20916), Year3 = c(23623, 1181, 22441, 257, 22184), Year4 = c(25040, 1252, 23788, 260, 23528), Year5 = c(26542, 1327, 25215, 263, 24953), Year6 = c(28135, 1407, 26728, 265, 26463), purchasePriceCapRate = c("400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 330786.932409621), list(0.0224165566243759, c(-400000, 19720.512, 20916.35712, 22184.0790912, 23527.991786112, 355739.600331834), "400000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(21024, 1051, 19973, 252, 19721), Year2 = c(22285, 1114, 21171, 255, 20916), Year3 = c(23623, 1181, 22441, 257, 22184), Year4 = c(25040, 1252, 23788, 260, 23528), Year5 = c(26542, 1327, 25215, 263, 24953), Year6 = c(28135, 1407, 26728, 265, 26463), purchasePriceCapRate = c("400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 330786.932409621), list(-0.00700507916565851, c(-397000, 17154.144, 18194.36544, 19297.1098944, 20466.129841344, 309444.720836595), "397000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(18288, 914, 17374, 219, 17154), Year2 = c(19385, 969, 18416, 222, 18194), Year3 = c(20548, 1027, 195 style="color:#f92672"><details> style="color:#f92672"><summary>英文:</summary> have some data which looks like: price unleveragedData <dbl> <list> 1 450000 <list [5]> 2 400000 <list [5]> 3 400000 <list [5]> 4 397000 <list [5]> 5 750000 <list [5]> 6 550000 <list [5]> am trying to put into a new column the element `5` of the lists - i.e. price unleveragedData element5 <dbl> <list> 1 450000 <list [5]> ----- 2 400000 <list [5]> ----- 3 400000 <list [5]> 4 397000 <list [5]> 5 750000 <list [5]> 6 550000 <list [5]> ----- the following: df$unleveragedData %>% map(., ~ pluck(., c(5))) can get the output I want: [[1]] [1] 260551.4 [[2]] [1] 330786.9 [[3]] [1] 330786.9 [[4]] [1] 287739.3 [[5]] [1] 566416 [[6]] [1] 271879.7 inside the `mutate` function I can't get it to work. df %>% mutate( element5 = map(unleveragedData, ~ map_dbl(., pluck(., c(5)))) ) style="color:#f92672">: df = structure(list(price = c(450000, 400000, 400000, 397000, 750000, 550000), unleveragedData = list(list(-0.0547083151944441, c(-450000, 15533.28, 16475.2128, 17473.760928, 18532.32229728, 280205.849576444 ), "450000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(16560, 828, 15732, 199, 15533), Year2 = c(17554, 878, 16676, 201, 16475), Year3 = c(18607, 930, 17676, 203, 17474 ), Year4 = c(19723, 986, 18737, 205, 18532), Year5 = c(20907, 1045, 19861, 207, 19654), Year6 = c(22161, 1108, 21053, 209, 20844), purchasePriceCapRate = c("450000-0.08", "450000-0.08", "450000-0.08", "450000-0.08", "450000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 260551.350870592), list(0.0224165566243759, c(-400000, 19720.512, 20916.35712, 22184.0790912, 23527.991786112, 355739.600331834), "400000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(21024, 1051, 19973, 252, 19721), Year2 = c(22285, 1114, 21171, 255, 20916), Year3 = c(23623, 1181, 22441, 257, 22184), Year4 = c(25040, 1252, 23788, 260, 23528), Year5 = c(26542, 1327, 25215, 263, 24953), Year6 = c(28135, 1407, 26728, 265, 26463), purchasePriceCapRate = c("400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 330786.932409621), list(0.0224165566243759, c(-400000, 19720.512, 20916.35712, 22184.0790912, 23527.991786112, 355739.600331834), "400000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(21024, 1051, 19973, 252, 19721), Year2 = c(22285, 1114, 21171, 255, 20916), Year3 = c(23623, 1181, 22441, 257, 22184), Year4 = c(25040, 1252, 23788, 260, 23528), Year5 = c(26542, 1327, 25215, 263, 24953), Year6 = c(28135, 1407, 26728, 265, 26463), purchasePriceCapRate = c("400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08", "400000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 330786.932409621), list(-0.00700507916565851, c(-397000, 17154.144, 18194.36544, 19297.1098944, 20466.129841344, 309444.720836595), "397000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(18288, 914, 17374, 219, 17154), Year2 = c(19385, 969, 18416, 222, 18194), Year3 = c(20548, 1027, 19521, 224, 19297), Year4 = c(21781, 1089, 20692, 226, 20466), Year5 = c(23088, 1154, 21934, 228, 21705), Year6 = c(24473, 1224, 23250, 231, 23019), purchasePriceCapRate = c("397000-0.08", "397000-0.08", "397000-0.08", "397000-0.08", "397000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 287739.317917958), list(0.00205549716813258, c(-750000, 33768, 35815.68, 37986.4368, 40287.657168, 609143.15125314 ), "750000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(36000, 1800, 34200, 432, 33768), Year2 = c(38160, 1908, 36252, 436, 35816), Year3 = c(40450, 2022, 38427, 441, 37986), Year4 = c(42877, 2144, 40733, 445, 40288), Year5 = c(45449, 2272, 43177, 450, 42727), Year6 = c(48176, 2409, 45767, 454, 45313), purchasePriceCapRate = c("750000-0.08", "750000-0.08", "750000-0.08", "750000-0.08", "750000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 566415.98015346), list(-0.0866171399087425, c(-550000, 16208.64, 17191.5264, 18233.489664, 19338.07544064, 292388.712601507), "550000-0.08", structure(list(` ` = c("Revenue", "Vacancy", "Gross Revenue", "Operating Expenses", "Net Operating Income" ), Year1 = c(17280, 864, 16416, 207, 16209), Year2 = c(18317, 916, 17401, 209, 17192), Year3 = c(19416, 971, 18445, 212, 18233), Year4 = c(20581, 1029, 19552, 214, 19338), Year5 = c(21816, 1091, 20725, 216, 20509), Year6 = c(23125, 1156, 21968, 218, 21750), purchasePriceCapRate = c("550000-0.08", "550000-0.08", "550000-0.08", "550000-0.08", "550000-0.08")), row.names = c(NA, -5L), class = "data.frame"), 271879.670473661))), class = c("rowwise_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), groups = structure(list( .rows = structure(list(1L, 2L, 3L, 4L, 5L, 6L), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"))) style="color:#f92672"></details> style="color:#75715e"># 答案1 style="color:#f92672">**得分**: 4 ```markdown 存在一个`rowwise`分组，如果要使用`map`，应该将其解除分组(`ungroup`) ```R style="color:#a6e22e">library(dplyr) style="color:#a6e22e">library(purrr) %>% ungroup() %>% mutate( element5 = map_dbl(unleveragedData, ~ nth(.x, 5)) )


# 一个 tibble: 6 × 3
   price unleveragedData element5
   <dbl> <list>             <dbl>
1 450000 <list [5]>       260551.
2 400000 <list [5]>       330787.
3 400000 <list [5]>       330787.
4 397000 <list [5]>       287739.
5 750000 <list [5]>       566416.
6 550000 <list [5]>       271880.


另外，由于是rowwise，我们也可以直接提取
df %>%
   mutate(element5 = nth(unleveragedData, 5)) %>%
   ungroup()

-输出
# 一个 tibble: 6 × 3
   price unleveragedData element5
   <dbl> <list>             <dbl>
1 450000 <list [5]>       260551.
2 400000 <list [5]>       330787.
3 400000 <list [5]>       330787.
4 397000 <list [5]>       287739.
5 750000 <list [5]>       566416.
6 550000 <list [5]>       271880.

或者使用 pluck
df %>% 
   mutate(element5 = pluck(unleveragedData, 5)) %>%
   ungroup()

-输出
# 一个 tibble: 6 × 3
   price unleveragedData element5
   <dbl> <list>             <dbl>
1 450000 <list [5]>       260551.
2 400000 <list [5]>       330787.
3 400000 <list [5]>       330787.
4 397000 <list [5]>       287739.
5 750000 <list [5]>       566416.
6 550000 <list [5]>       271880.


在rowwise上使用map也是可能的，只需要将其包装在pick中
df %>%
   mutate(element5 = map_dbl(pick(unleveragedData), pluck, 5))

-输出
# 一个 tibble: 6 × 3
# Rowwise: 
   price unleveragedData element5
   <dbl> <list>             <dbl>
1 450000 <list [5]>       260551.
2 400000 <list [5]>       330787.
3 400000 <list [5]>       330787.
4 397000 <list [5]>       287739.
5 750000 <list [5]>       566416.
6 550000 <list [5]>       271880.


<details>
<summary>英文:</summary>
There is a `rowwise` grouping, which should be `ungroup`ed if we want to use `map`

library(dplyr)

library(purrr)

df %>%

ungroup %>%

mutate(

element5 = map_dbl(unleveragedData, ~ nth(.x, 5))

)
-output

A tibble: 6 × 3
price unleveragedData element5

<dbl> <list>             <dbl>

1 450000 <list [5]>       260551.

2 400000 <list [5]>       330787.

3 400000 <list [5]>       330787.

4 397000 <list [5]>       287739.

5 750000 <list [5]>       566416.

6 550000 <list [5]>       271880.

---
Also, as it is `rowwise`, we can directly extract as well

df %>%

mutate(element5 = nth(unleveragedData, 5)) %>%

ungroup
-output

A tibble: 6 × 3
price unleveragedData element5

<dbl> <list>             <dbl>

1 450000 <list [5]>       260551.

2 400000 <list [5]>       330787.

3 400000 <list [5]>       330787.

4 397000 <list [5]>       287739.

5 750000 <list [5]>       566416.

6 550000 <list [5]>       271880.

Or with `pluck`

df %>%

mutate(element5 = pluck(unleveragedData, 5)) %>%

ungroup
-output

A tibble: 6 × 3
price unleveragedData element5

<dbl> <list>             <dbl>

1 450000 <list [5]>       260551.

2 400000 <list [5]>       330787.

3 400000 <list [5]>       330787.

4 397000 <list [5]>       287739.

5 750000 <list [5]>       566416.

6 550000 <list [5]>       271880.

---
It is possible to do this in `map` on a `rowwise`, if we wrap it in `pick`

df %>%

mutate(element5 = map_dbl(pick(unleveragedData), pluck, 5))
-output

A tibble: 6 × 3
Rowwise:
price unleveragedData element5

<dbl> <list>             <dbl>

1 450000 <list [5]>       260551.

2 400000 <list [5]>       330787.

3 400000 <list [5]>       330787.

4 397000 <list [5]>       287739.

5 750000 <list [5]>       566416.

6 550000 <list [5]>       271880.

</details>







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