R 基于事务数据集中现有列的新列变量“count”

我有一个包含三列的事务数据集。每行代表一个事务

  Account_from  Account_to  Value 
1       1           2        25.0
2       1           3        30.0
3       2           1        28.0
4       2           3        10.0
5       2           4        12.0
6       3           1        40.0

我想创建一个新的列变量，其中包含关于每个帐户进行和收到两列交易的数量的信息。它看起来如下所示：

  Account_from  Account_to  Value  Count_out  Count_in 
1       1           2        25.0      2          2
2       1           3        30.0      2          2
3       2           1        28.0      3          1
4       2           3        10.0      3          1
5       2           4        12.0      3          1
6       3           1        40.0      1          2

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如何一次对整个数据集执行此操作？

tidyverse提供了有用的功能-假设数据存储在数据框df中：

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我们可以使用dplyr通过一些连接操作来实现这一点

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这是一个在R基中的解决方案，其中使用了ave

或使用以下代码：

df <- cbind(df, with(df, list(Count_out = ave(1:nrow(df),Account_from,FUN = length), 
                              Count_in = ave(1:nrow(df),Account_to,FUN = length)[match(Account_from,Account_to,)])))

资料

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你能展示失败的地方吗？@Florisvis这是一个带有base R的解决方案，因此代码看起来可能比那些带有包dplyr或tidyverse的长

library(dplyr)

inner_join(df %>% count(Account_from, name = 'Count_out'), 
           df %>% count(Account_to, name = 'Count_in'), 
           by = c('Account_from' = 'Account_to')) %>%
right_join(df) %>%
select(names(df), Count_out, Count_in)

#  Account_from Account_to Value Count_out Count_in
#         <int>      <int> <dbl>     <int>    <int>
#1            1          2    25         2        2
#2            1          3    30         2        2
#3            2          1    28         3        1
#4            2          3    10         3        1
#5            2          4    12         3        1
#6            3          1    40         1        2

df <- within(df, 
             list(Count_out <- ave(1:nrow(df),Account_from,FUN = length),
                  Count_in <- ave(1:nrow(df),Account_to,FUN = length)[match(Account_from,Account_to,)]))

> df
  Account_from Account_to Value Count_in Count_out
1            1          2    25        2         2
2            1          3    30        2         2
3            2          1    28        1         3
4            2          3    10        1         3
5            2          4    12        1         3
6            3          1    40        2         1

df <- cbind(df, with(df, list(Count_out = ave(1:nrow(df),Account_from,FUN = length), 
                              Count_in = ave(1:nrow(df),Account_to,FUN = length)[match(Account_from,Account_to,)])))

> df
  Account_from Account_to Value Count_out Count_in
1            1          2    25         2        2
2            1          3    30         2        2
3            2          1    28         3        1
4            2          3    10         3        1
5            2          4    12         3        1
6            3          1    40         1        2

df <- structure(list(Account_from = c(1L, 1L, 2L, 2L, 2L, 3L), Account_to = c(2L, 
3L, 1L, 3L, 4L, 1L), Value = c(25, 30, 28, 10, 12, 40), Count_out = c(2L, 
2L, 3L, 3L, 3L, 1L), Count_in = c(2L, 2L, 1L, 1L, 1L, 2L)), class = "data.frame", row.names = c(NA, 
-6L))