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Add transactions to an experience study

Source: R/transactions.R
add_transactions.Rd

Attach summarized transactions to a data frame with exposure-level records.

Usage

add_transactions(
  .data,
  trx_data,
  col_pol_num = "pol_num",
  col_trx_date = "trx_date",
  col_trx_type = "trx_type",
  col_trx_amt = "trx_amt"
)

Arguments

.data

A data frame with exposure-level records with the class exposed_df. Use as_exposed_df() to convert a data frame to an exposed_df object if necessary.

trx_data

A data frame containing transactions details. This data frame must have columns for policy numbers, transaction dates, transaction types, and transaction amounts.

col_pol_num

Name of the column in trx_data containing the policy number

col_trx_date

Name of the column in trx_data containing the transaction date

col_trx_type

Name of the column in trx_data containing the transaction type

col_trx_amt

Name of the column in trx_data containing the transaction amount

Value

An exposed_df object with two new columns containing transaction counts and amounts for each transaction type found in trx_data. The exposed_df's trx_types attributes will be updated to include the new transaction types found in trx_data.

Details

This function attaches transactions to an exposed_df object. Transactions are grouped and summarized such that the number of rows in the exposed_df object does not change. Two columns are added to the output for each transaction type. These columns have names of the pattern trx_n_{*} (transaction counts) and trx_amt_{*} (transaction_amounts).

Transactions are associated with the exposed_df object by matching transactions dates with exposure dates ranges found in exposed_df.

All columns containing dates must be in YYYY-MM-DD format.

See also

expose(), as_exposed_df()

Examples

expo <- expose_py(census_dat, "2019-12-31", target_status = "Surrender")
add_transactions(expo, withdrawals)
#> 
#> ── Exposure data ──
#> 
#> • Exposure type: policy_year
#> • Target status: Surrender
#> • Study range: 1900-01-01 to 2019-12-31
#> • Transaction types: Base and Rider
#> 
#> # A tibble: 141,252 × 19
#>    pol_num status issue_date inc_guar qual    age product gender wd_age premium
#>      <int> <fct>  <date>     <lgl>    <lgl> <int> <fct>   <fct>   <int>   <dbl>
#>  1       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  2       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  3       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  4       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  5       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  6       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  7       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#>  8       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#>  9       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#> 10       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#> # ℹ 141,242 more rows
#> # ℹ 9 more variables: term_date <date>, pol_yr <int>, pol_date_yr <date>,
#> #   pol_date_yr_end <date>, exposure <dbl>, trx_amt_Base <dbl>,
#> #   trx_amt_Rider <dbl>, trx_n_Base <dbl>, trx_n_Rider <dbl>