Make license history table

Source: R/license-history.R

The license history table accounts for multi-year/lifetime licenses by including 1 row for every year a license is held. The input data frame should only have 1 record per customer-year (ensured by running rank_sale beforehand).

make_history(sale_ranked, yrs, carry_vars = NULL, yrs_lapse = yrs,
  show_diagnostics = FALSE)

Arguments

sale_ranked

data frame: Sales table from which license history will be made; must include at least 3 variables (cust_id, year, duration)
yrs

numeric: Years in sales data (column 'year') from which to create license history
carry_vars

character: additional variables to carry over from previous year (for multi-year and lifetime licenses).
yrs_lapse

numeric: years to include in lapse calculation (defaults to yrs). If NULL, lapse will not be calculated (useful for mid-year results)
show_diagnostics

logical: If TRUE, will include intermediate variables in the output dataset, necessary for running checks: history_check.

Details

The output data frame includes the following variables:

  • cust_id: Customer ID

  • year: License Year

  • carry_vars: One or more optional variables to include; ensures multi-year/lifetime records are complete in future years.

  • duration_run: A duration variable that accounts for multi-year/lifetimes.

  • lapse: Lapse next year? (1=Yes, 0=No). Only included if yrs_lapse != NULL

  • R3: R3 group this year (1=carried, 2=renewed, 3=reactivated, 4=recruited). Only included if more than 5 years are present.

See also

Salic Function Reference: salic

Other license history functions: history_check, rank_sale

Examples

library(dplyr)
data(sale, lic)
sale_ranked <- inner_join(lic, sale) %>% rank_sale()
#> Joining, by = "lic_id"
history <- make_history(sale_ranked, 2008:2018, "res")

# history includes more rows than sale_ranked if multi-year/lifetimes are present
left_join(count(history, year), count(sale_ranked, year), by = "year")
#> # A tibble: 11 x 3
#>     year   n.x   n.y
#>    <int> <int> <int>
#>  1  2008  6393  6393
#>  2  2009  7591  7428
#>  3  2010  7775  7475
#>  4  2011  7819  7306
#>  5  2012  8243  7447
#>  6  2013  8273  7245
#>  7  2014  8970  7757
#>  8  2015  9086  7660
#>  9  2016  9161  7568
#> 10  2017  9389  7673
#> 11  2018  9206  7281