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[英]Create a new column in dplyr by appending values to a list from other columns?
[英]New List Column from other vector columns with dplyr and rowwise
我有下面的tibble,我想創建第4列,它是來自A,B和C的聯合載體。我明白dplyr :: unite()可以做到這一點創建一個新的角色向量,但我正在尋找使用向量創建列表列。
現在rowwise工作,但不保持輸入tibble。 有關將A_Vector列保存到C_Vector的建議嗎?
這是代碼:
library(tidyverse)
My_Data <- tibble(A_Vector = rnorm(10),
B_Vector = rnorm(10),
C_Vector = rnorm(10)) %>%
rowwise() %>%
do(Port_Weights = matrix(c(.$A_Vector,.$B_Vector,.$C_Vector),3,1))
結果如下:
Source: local data frame [10 x 1]
Groups: <by row>
# A tibble: 10 x 1
Port_Weights
* <list>
1 <dbl [3 x 1]>
2 <dbl [3 x 1]>
3 <dbl [3 x 1]>
4 <dbl [3 x 1]>
5 <dbl [3 x 1]>
6 <dbl [3 x 1]>
7 <dbl [3 x 1]>
8 <dbl [3 x 1]>
9 <dbl [3 x 1]>
10 <dbl [3 x 1]>
這不起作用:
My_Data <- tibble(A_Vector = rnorm(10),
B_Vector = rnorm(10),
C_Vector = rnorm(10)) %>%
mutate(Port_Weights = rowwise() %>% do(matrix(c(.$A_Vector,.$B_Vector,.$C_Vector),3,1)))
長版本,顯然沒有意義:
My_Data <- tibble(A_Vector = rnorm(10),
B_Vector = rnorm(10),
C_Vector = rnorm(10))
Data_Unite <- My_Data %>%
rowwise() %>%
do(Port_Weights = matrix(c(.$A_Vector,.$B_Vector,.$C_Vector),3,1))
My_Data <- as.tibble(cbind(My_Data,Data_Unite))
但確實提供了追捧的結果:
# A tibble: 10 x 4
A_Vector B_Vector C_Vector Port_Weights
* <dbl> <dbl> <dbl> <list>
1 -1.23504457 -0.3750408 -0.4214122 <dbl [3 x 1]>
2 -0.90678699 0.5261914 1.1191229 <dbl [3 x 1]>
3 -0.62944085 0.5995529 0.2096462 <dbl [3 x 1]>
4 2.06171633 1.5399094 2.2972950 <dbl [3 x 1]>
5 0.08761555 0.1424207 -1.4758585 <dbl [3 x 1]>
6 -1.07334432 -1.9112787 0.4820864 <dbl [3 x 1]>
7 -0.18655423 -1.3698855 0.6672621 <dbl [3 x 1]>
8 -0.97961789 -0.8194373 -0.4158516 <dbl [3 x 1]>
9 0.68112936 -1.9864507 1.0193449 <dbl [3 x 1]>
10 0.61455438 0.5885380 -1.0925312 <dbl [3 x 1]>
數據 :
library(tidyverse)
my_tibble <- tibble(A_Vector = rnorm(10),
B_Vector = rnorm(10),
C_Vector = rnorm(10))
要向數據框添加列,請使用mutate
而不是do
,並使用Map
並行循環遍歷這三個向量,並構造每行的矩陣:
my_tibble %>%
mutate(Port_Weights = Map(function(...) matrix(c(...), 3, 1), A_Vector, B_Vector, C_Vector))
# A tibble: 10 x 4
# A_Vector B_Vector C_Vector Port_Weights
# <dbl> <dbl> <dbl> <list>
# 1 0.62674726 -0.5432169 -1.66763618 <dbl [3 x 1]>
# 2 -0.47346722 -0.4436020 -1.04892634 <dbl [3 x 1]>
# 3 0.19059238 -1.6733052 2.79275828 <dbl [3 x 1]>
# 4 -0.23501873 -1.1664704 -0.19324676 <dbl [3 x 1]>
# 5 0.66552642 -1.3328070 -1.53575954 <dbl [3 x 1]>
# 6 -0.41251920 -0.2056882 1.66537220 <dbl [3 x 1]>
# 7 0.48396052 0.3968486 0.16110407 <dbl [3 x 1]>
# 8 0.43035213 -0.6433268 1.61640228 <dbl [3 x 1]>
# 9 0.06747126 -1.0146385 -0.47824193 <dbl [3 x 1]>
#10 0.79916411 -1.2349901 -0.05151402 <dbl [3 x 1]>
如果元素不必是矩陣:
my_tibble %>% mutate(Port_Weights = Map(c, A_Vector, B_Vector, C_Vector))
這相當於(使用data.table::transpose
):
my_tibble %>% mutate(Port_Weights = data.table::transpose(as.list(.)))
由於您使用的是tidyverse
,您還可以考慮purrr
包中的pmap
函數,它是tidyverse
一部分。
set.seed(123)
library(tidyverse)
My_Data <- tibble(A_Vector = rnorm(10),
B_Vector = rnorm(10),
C_Vector = rnorm(10))
My_Data2 <- My_Data %>%
mutate(Port_Weights = pmap(.l = list(A_Vector, B_Vector, C_Vector),
.f = function(x, y, z) matrix(c(x, y, z), 3, 1)))
My_Data2
# A tibble: 10 x 4
A_Vector B_Vector C_Vector Port_Weights
<dbl> <dbl> <dbl> <list>
1 -0.56047565 1.2240818 -1.0678237 <dbl [3 x 1]>
2 -0.23017749 0.3598138 -0.2179749 <dbl [3 x 1]>
3 1.55870831 0.4007715 -1.0260044 <dbl [3 x 1]>
4 0.07050839 0.1106827 -0.7288912 <dbl [3 x 1]>
5 0.12928774 -0.5558411 -0.6250393 <dbl [3 x 1]>
6 1.71506499 1.7869131 -1.6866933 <dbl [3 x 1]>
7 0.46091621 0.4978505 0.8377870 <dbl [3 x 1]>
8 -1.26506123 -1.9666172 0.1533731 <dbl [3 x 1]>
9 -0.68685285 0.7013559 -1.1381369 <dbl [3 x 1]>
10 -0.44566197 -0.4727914 1.2538149 <dbl [3 x 1]>
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