如何将观察结果转化为变量并根据变量条件总结每个值

Question

我有一个包含不同列/观察的数据集

见下面的数据集

merchant	Status	Face value
a	processing	10
b	processing	5
c	success	40
d	Transaction declined	30
e	success	32
f	pending	21
g	Transaction declined	23
h	Success	45
i	Transaction declined	66
j	success	76
k	pending	87
l	processing	89

我想要一种情况，我可以将状态列转换为变量并汇总每个商家的面值

是否可以使用 janitor 包来汇总值而不是计数？

就像在下面的这种情况下，我使用 janitor 包来单独汇总计数

这是我使用管理员包汇总事务计数的代码行

report_21st%>%
  tabyl(Merchant , Status)%>%
  adorn_totals("row")%>%
  adorn_percentages("row")%>%
  adorn_pct_formatting()%>%
  adorn_ns("front")

这是下面代码的结果

merchant	pending	processing	success	Success	Transaction declined
a 0 (0.0%)	1(100.0%)	0 (0.0%)	0 (0.0%)		0 (0.0%)
b 0(0.0%)	1(100.0%)	0 (0.0%)	0 (0.0%)		0 (0.0%)
c 0 (0.0%)	0(0.0%)	1 (100.0%)	0 (0.0%)		0 (0.0%)
d 0 (0.0%)	0(0.0%)	0 (0.0%)	0 (0.0%)		1 (100.0%

所以这次我想为 Value 做同样的事情，不算数 您可以建议我任何可以处理此问题的 r 包，或者我是否也可以使用看门人来处理价值，如果它是 tidyverse 或 dplyr 包，请提供示例代码。 请再提供一行代码或代码示例将不胜感激

再加上我是 r 中的一个大人物

非常感谢

Answer 1

您可以使用pivot_wider 代替tabyl，然后使用janitor 代码。

library(tidyr)
library(janitor)

report_21st %>%
  pivot_wider(names_from = Status, values_from = Face.value, values_fill = 0) %>%
  adorn_totals("row")%>%
  adorn_percentages("row")%>%
  adorn_pct_formatting()%>%
  adorn_ns("front")

# merchant   processing      success Transaction declined      pending     Success
#        a  10 (100.0%)   0   (0.0%)           0   (0.0%)   0   (0.0%)  0   (0.0%)
#        b   5 (100.0%)   0   (0.0%)           0   (0.0%)   0   (0.0%)  0   (0.0%)
#        c   0   (0.0%)  40 (100.0%)           0   (0.0%)   0   (0.0%)  0   (0.0%)
#        d   0   (0.0%)   0   (0.0%)          30 (100.0%)   0   (0.0%)  0   (0.0%)
#        e   0   (0.0%)  32 (100.0%)           0   (0.0%)   0   (0.0%)  0   (0.0%)
#        f   0   (0.0%)   0   (0.0%)           0   (0.0%)  21 (100.0%)  0   (0.0%)
#        g   0   (0.0%)   0   (0.0%)          23 (100.0%)   0   (0.0%)  0   (0.0%)
#        h   0   (0.0%)   0   (0.0%)           0   (0.0%)   0   (0.0%) 45 (100.0%)
#        i   0   (0.0%)   0   (0.0%)          66 (100.0%)   0   (0.0%)  0   (0.0%)
#        j   0   (0.0%)  76 (100.0%)           0   (0.0%)   0   (0.0%)  0   (0.0%)
#        k   0   (0.0%)   0   (0.0%)           0   (0.0%)  87 (100.0%)  0   (0.0%)
#        l  89 (100.0%)   0   (0.0%)           0   (0.0%)   0   (0.0%)  0   (0.0%)
#    Total 104  (19.8%) 148  (28.2%)         119  (22.7%) 108  (20.6%) 45   (8.6%)

数据

report_21st <- structure(list(merchant = c("a", "b", "c", "d", "e", "f", "g", 
"h", "i", "j", "k", "l"), Status = c("processing", "processing", 
"success", "Transaction declined", "success", "pending", "Transaction declined", 
"Success", "Transaction declined", "success", "pending", "processing"
), Face.value = c(10L, 5L, 40L, 30L, 32L, 21L, 23L, 45L, 66L, 
76L, 87L, 89L)), row.names = c(NA, -12L), class = "data.frame")