migrate() summarizes the transition amount (or percentage) of a numeric
variable from each beginning credit risk state category to each ending credit
risk state, given a data frame input.
Usage
migrate(
data,
id,
time,
state,
metric = NULL,
percent = TRUE,
fill_state = NULL,
verbose = TRUE
)Arguments
- data
A data frame or data frame extension (e.g., a tibble or data.table) containing a minimum of three (3) column variables representing a time, a credit risk state, and an ID identifying the credit facility (we would expect to see most unique values in this column variable appear twice in the dataset; once at the first unique
timevalue and again at the second uniquetimevalue, unless the ID only existed at one of those two times).- id
The column variable of the
datadata frame argument that contains the unique identifier to track where a particular credit facility migrated to/from. If left null,migrate()will attempt to use the first column variable from the data frame provided in thedataargument.- time
The column variable of in the
datadata frame representing the timepoint (e.g., a Date) of each observation; this column should contain two unique values (migration from Time A to Time B)- state
The column variable of the
datadata frame argument that contains the credit risk state values.- metric
(Optional) The column variable of type "numeric" in the
datadata frame argument that contains the continuous metric values to weight the state migration by- percent
If
FALSE, will calculate the migration on an absolute basis (rather than a percentage basis, which is the default)- fill_state
(Optional) A value (e.g., a character string such as "No Rating" or "NR") to be used as the filler
statefor anyidvalues that only exist at one timepoint in thedata.- verbose
If
TRUE, the function returns an informational message about the transition period
Value
A data frame containing three (3) column variables representing the unique combinations of starting & ending credit risk states and the calculated migration observed during the period.
Examples
# Return the percent migration of the number of credit facilities
migrate(
data = mock_credit,
id = customer_id,
time = date,
state = risk_rating
)
#> ℹ Migrating from 2020-06-30 to 2020-09-30
#> # A tibble: 49 × 3
#> risk_rating_start risk_rating_end prop
#> <ord> <ord> <dbl>
#> 1 AAA AAA 0.774
#> 2 AAA AA 0.194
#> 3 AAA A 0.0323
#> 4 AAA BBB 0
#> 5 AAA BB 0
#> 6 AAA B 0
#> 7 AAA CCC 0
#> 8 AA AAA 0.101
#> 9 AA AA 0.663
#> 10 AA A 0.157
#> # ℹ 39 more rows
# Return the absolute migration in `principal_balance`
migrate(
data = mock_credit,
id = customer_id,
time = date,
state = risk_rating,
metric = principal_balance,
percent = FALSE
)
#> ℹ Migrating from 2020-06-30 to 2020-09-30
#> # A tibble: 49 × 3
#> risk_rating_start risk_rating_end principal_balance
#> <ord> <ord> <dbl>
#> 1 AAA AAA 29042000
#> 2 AAA AA 6575000
#> 3 AAA A 20000
#> 4 AAA BBB 0
#> 5 AAA BB 0
#> 6 AAA B 0
#> 7 AAA CCC 0
#> 8 AA AAA 6445000
#> 9 AA AA 58095000
#> 10 AA A 13045000
#> # ℹ 39 more rows
# Provide a filler `state` value when a unique `id` is missing a timepoint
migrate(
data = head(mock_credit, n = 995), # drop the last 5 observations
id = customer_id,
time = date,
state = risk_rating,
fill_state = "NR",
percent = FALSE
)
#> ℹ Migrating from 2020-06-30 to 2020-09-30
#> ℹ 5 IDs have a missing timepoint:
#> • Migrating 5 IDs with missing end timepoint to new class 'NR'
#> • Migrating 0 IDs with missing start timepoint from new class 'NR'
#> # A tibble: 64 × 3
#> risk_rating_start risk_rating_end count
#> <ord> <ord> <int>
#> 1 AAA AAA 24
#> 2 AAA AA 5
#> 3 AAA A 1
#> 4 AAA BBB 0
#> 5 AAA BB 0
#> 6 AAA B 0
#> 7 AAA CCC 0
#> 8 AAA NR 1
#> 9 AA AAA 9
#> 10 AA AA 59
#> # ℹ 54 more rows