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Reduce portfolio by merging redundant date ranges

Source: R/time_reduce.R
merge_date_ranges.Rd

Transform all the date ranges together as a set to produce a new set of date ranges. Ranges separated by a gap of at least min.gapwidth days are not merged.

Usage

merge_date_ranges(
  df,
  begin,
  end,
  ...,
  agg_cols = NULL,
  agg = "sum",
  min.gapwidth = 5
)

reduce(df, begin, end, ..., agg_cols = NULL, agg = "sum", min.gapwidth = 5)

Arguments

df

A data.frame or data.table.

begin

Name of column in df with begin dates.

end

Name of column in df with end dates.

...

Names of columns in df used to group date ranges by.

agg_cols

List of columns in df to aggregate (default = NULL).

agg

Aggregation function as character string (default = "sum").

min.gapwidth

Ranges separated by at least min.gapwidth days are not merged (default = 5).

Value

A data.table of class "reduce", with attributes:

  • begin — name of the begin-date column

  • end — name of the end-date column

  • cols — grouping columns

Details

This function is inspired by IRanges::reduce(), adapted for insurance portfolios.

Author

Martin Haringa

Examples

portfolio <- data.frame(
  policy_nr   = rep("12345", 11),
  productgroup= rep("fire", 11),
  product     = rep("contents", 11),
  begin_dat   = as.Date(c(16709,16740,16801,17410,17440,17805,17897,
                          17956,17987,18017,18262), origin="1970-01-01"),
  end_dat     = as.Date(c(16739,16800,16831,17439,17531,17896,17955,
                          17986,18016,18261,18292), origin="1970-01-01"),
  premium     = c(89,58,83,73,69,94,91,97,57,65,55)
)

# Merge periods
pt1 <- merge_date_ranges(portfolio, begin = begin_dat, end = end_dat,
    policy_nr, productgroup, product, min.gapwidth = 5)

# Aggregate per period
summary(pt1, period = "days", policy_nr, productgroup, product)
#>         date type count policy_nr productgroup  product
#> 1 2020-02-01  out     1     12345         fire contents
#> 2 2018-10-01   in     1     12345         fire contents
#> 3 2018-01-01  out     1     12345         fire contents
#> 4 2017-09-01   in     1     12345         fire contents
#> 5 2016-02-01  out     1     12345         fire contents
#> 6 2015-10-01   in     1     12345         fire contents

# Merge periods and sum premium per period
pt2 <- merge_date_ranges(portfolio, begin = begin_dat, end = end_dat,
    policy_nr, productgroup, product, agg_cols = list(premium),
    min.gapwidth = 5)

# Create summary with aggregation per week
summary(pt2, period = "weeks", policy_nr, productgroup, product)
#> Warning: The default behavior of week() is changing. Previously ('legacy' mode), week numbers advanced every 7th day of the year. The new 'sequential' mode ensures the first week always has 7 days. For example, as.IDate('2023-01-07') returns week 2 in legacy mode but week 1 in sequential mode (week 2 starts on '2023-01-08'). To adopt the new behavior now, set options(datatable.week = 'sequential'). To keep the old results and silence this warning, set options(datatable.week = 'legacy'). See https://github.com/Rdatatable/data.table/issues/2611
#> Warning: The default behavior of week() is changing. Previously ('legacy' mode), week numbers advanced every 7th day of the year. The new 'sequential' mode ensures the first week always has 7 days. For example, as.IDate('2023-01-07') returns week 2 in legacy mode but week 1 in sequential mode (week 2 starts on '2023-01-08'). To adopt the new behavior now, set options(datatable.week = 'sequential'). To keep the old results and silence this warning, set options(datatable.week = 'legacy'). See https://github.com/Rdatatable/data.table/issues/2611
#>      week type count policy_nr productgroup  product
#> 1 2020W06  out     1     12345         fire contents
#> 2 2018W40   in     1     12345         fire contents
#> 3 2018W02  out     1     12345         fire contents
#> 4 2017W35   in     1     12345         fire contents
#> 5 2016W06  out     1     12345         fire contents
#> 6 2015W40   in     1     12345         fire contents