结合两个财务数据集，具有随时间变化的交互式账户余额

Question

我有一个与金融交易数据集相关的问题。我有两个数据集：

第一个包含带有时间戳的金融交易。

   Account_from  Account_to  Value  Timestamp  
1        1            2        25       1
2        1            3        25       1
3        2            1        50       2
4        2            3        20       2
5        2            4        25       2
6        1            2        40       3
7        3            1        20       3
8        2            4        25       3

另一个数据集包含帐户信息：

   Account_id  initial deposit
1       1            200
2       2            100
3       3            150
4       4            200

现在我想创建一个包含金融交易和原始账户余额的数据集。此外，我希望账户余额随着每笔交易的时间而变化，例如：

   Account_from  Account_to  Value  Timestamp  Initial_deposit  Old_bal_org  New_bal_org  Old_bal_des  New_bal_des
1        1            2        25       1            200            200          175         100          125
2        1            3        25       1            200            175          150         150          175
3        2            1        50       2            100            125          75          150          200
4        2            3        20       2            100            75           55          175          195
5        2            4        25       2            100            55           30          200          225
6        1            2        40       3            200            200          160         30           70
7        3            1        20       3            150            195          175         160          180
8        2            4        25       3            100            70           45          225          250

这怎么可能？

重现数据：

dftrans <- structure(list(Account_from = c(1L, 1L, 2L, 2L, 2L, 1L, 3L, 2L
), Account_to = c(2L, 3L, 1L, 3L, 4L, 2L, 1L, 4L), Value = c(25, 
                                                             25, 50, 20, 25, 40, 20, 25), Timestamp = c(1L, 1L, 2L, 2L, 2L, 
                                                                                                        3L, 3L, 3L)), class = "data.frame", row.names = c(NA, -8L))

dfacc <- structure(list(Account_id = c(1L, 2L, 3L, 4L), Initial__deposit = c(200, 100, 150, 200)), class = "data.frame", row.names = c(NA, -4L))

提前致谢

Answer 1

一种可能的方法：

dftransFinal <- dftrans %>% 
  # create a record id to keep track of each transaction
  rowid_to_column(var = 'recordID') %>% 
  pivot_longer(cols = c(Account_to, Account_from), names_to = 'accountType',
               values_to = 'Account_id') %>% 
  left_join(dfacc, by = 'Account_id') %>% 
  # If the record is a 'from' set value to negative so that it is subtracted from balance
  mutate(Value = if_else(accountType == 'Account_from', -Value, Value)) %>% 
  group_by(Account_id) %>% 
  mutate(sum_changes = cumsum(Value),
         # calculate the cumulative sum with a lag for old_bal
         sum_changes_lag = lag(cumsum(Value), k = 1, default = 0),
         Old_bal_org = Initial__deposit + sum_changes_lag,
         New_bal_org = Initial__deposit + sum_changes) %>% 
  pivot_wider(names_from = 'accountType', values_from = c('Account_id', 'Old_bal_org',
                                                      'Initial__deposit',
                                                      'New_bal_org', 'Value'),
              id_cols = c('recordID', 'Timestamp')) %>% 
  # select, rename, and order columns
  select('Account_from' = 'Account_id_Account_from', 'Account_to' = 'Account_id_Account_to',
         'Value' = 'Value_Account_to', Timestamp, 'Initial__deposit' = 'Initial__deposit_Account_from',
     'Old_bal_org' = 'Old_bal_org_Account_from', 'New_bal_org' = 'New_bal_org_Account_from',
     'Old_bal_des' = 'Old_bal_org_Account_to', 'New_bal_des' = 'New_bal_org_Account_to')