View, summarise and print a triangle — triangle

as.matrix() gives one segment and measure of a triangle as an origin x development matrix: origin labels as row names, ages in months as column names, NA where a cell is not observed. Key arguments choose the segment, one value each (compared as character); keys not named may take any value, but the choice must leave one segment, so a triangle with one segment needs none. column names the measure and may be left out when there is only one. This is Python's Triangle.view(column=None, **keys).

Arguments

x, object: A triangle.
column: Name of the measure, or NULL for the only one.
max_rows, max_cols: Rows and development ages shown before the middle ones are left out; 0 for no limit.
...: For as.matrix(), key conditions as key = value; unused otherwise.

Value

as.matrix(): a numeric matrix. summary(): a data.frame. format(): a character vector of lines. print(): x, invisibly.

Details

summary() gives one row per segment and measure: the key columns, column, n_origins (origins with an observed value), first_origin and last_origin of those, valuation (the last day of the latest valuation with an observed value), latest (the sum over origins of the latest cumulative value, so for an incremental triangle the sum of every increment) and cumulative. Origins and valuation are NA for a segment with no observed value of the measure.

print() and format() show the grid of as.matrix() for a triangle with one segment and one measure, and the summary() table otherwise, with numbers rounded for reading; long tables show their first and last rows around a .... Python prints the same text.

as.matrix() is a method on base R's generic rather than a new view() verb, which would mask tibble::view() once the tidyverse is attached. A key named x or column cannot be chosen this way; use subset() first.

Errors: an unknown key, value or column, a choice that matches several segments, a missing column with several measures, or a key with the name of a summary column.

Examples

long <- data.frame(lob = rep(c("auto", "home"), each = 3), year = c(2020, 2020, 2021),
                   age = c(12, 24, 12), paid = c(100, 150, 110, 40, 60, 45))
tri <- triangle(long, "year", "age", "paid", keys = "lob")
tri
#> Triangle: 2 segments x 1 column, keys lob (cumulative, valuation 2021-12)
#> lob   column  n_origins  first_origin  last_origin  valuation  latest
#> auto  paid            2          2020         2021    2021-12     260
#> home  paid            2          2020         2021    2021-12     105
as.matrix(tri, lob = "auto")
#>       development
#> origin  12  24
#>   2020 100 150
#>   2021 110  NA
summary(tri)
#>    lob column n_origins first_origin last_origin  valuation latest cumulative
#> 1 auto   paid         2         2020        2021 2021-12-31    260       TRUE
#> 2 home   paid         2         2020        2021 2021-12-31    105       TRUE
subset(tri, lob = "home")
#> Triangle: paid, lob=home (cumulative, valuation 2021-12)
#>       12  24
#> 2020  40  60
#> 2021  45