migrateThis package is intended to serve as a set of tools to help convert credit risk data at two time points into traditional credit state migration (aka, “transition”) matrices. At a higher level, migrate is intended to help an analyst understand how risk moved in their credit portfolio over a time interval.
One of the more difficult aspects of making a credit state migration matrix in R (or Python, for that matter) is the fact that the output doesn’t satisfy the structure of a traditional data frame object. Rather, the output needs to be a matrix, which is a data structure that R does support. In the past, there has been difficulty converting a matrix to something more visual-friendly. More recently, however, tools like the kableExtra and gt packages allow us to present visually appealing output that extends the structure of a data frame. Using the matrix-style output of migrate’s functions with a visual formatting package such as the two mentioned above will hopefully help analysts streamline the presentation of their credit portfolio’s state migration matrices to an audience.
If you haven’t done so already, first install migrate with the instructions in the README section.
First, load the package & the mock dataset (as a data frame) using library()
library(migrate)The package has a built-in mock dataset, which can be loaded into the environment like so:
data("mock_credit")
head(mock_credit[order(mock_credit$customer_id), ]) # sort by 'customer_id'| customer_id | date | risk_rating | principal_balance |
|---|---|---|---|
| Customer_1001 | 2020-06-30 | A | 915000 |
| Customer_1001 | 2020-09-30 | A | 1328000 |
| Customer_1002 | 2020-06-30 | AAA | 979000 |
| Customer_1002 | 2020-09-30 | AAA | 354000 |
| Customer_1003 | 2020-06-30 | BBB | 1400000 |
| Customer_1003 | 2020-09-30 | BBB | 356000 |
Note that an important feature of the mock_credit dataset is that there are exactly two (2) unique values in the date column variable; if the time argument passed to migrate has more than two (2) unique values, the function will throw an error.
unique(mock_credit$date)
#> [1] "2020-06-30" "2020-09-30"To summarize the migration within the data, use the migrate() function
migrated_df <- migrate(
data = mock_credit,
id = customer_id,
time = date,
state = risk_rating,
)
#> === Migrating from: `2020-06-30` --> `2020-09-30` ===
head(migrated_df)
#> # A tibble: 6 x 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 0To create the state migration matrix, use the build_matrix() function
build_matrix(migrated_df)
#> Using `risk_rating_start` as the 'state_start' column variable
#> Using `risk_rating_end` as the 'state_end' column variable
#> Using `prop` as the 'metric' column variable
#> AAA AA A BBB BB B CCC
#> AAA 0.774193548 0.19354839 0.03225806 0.00000000 0.00000000 0.00000000 0.00000000
#> AA 0.101123596 0.66292135 0.15730337 0.07865169 0.00000000 0.00000000 0.00000000
#> A 0.008333333 0.06666667 0.72500000 0.16666667 0.03333333 0.00000000 0.00000000
#> BBB 0.000000000 0.00000000 0.11363636 0.68181818 0.14772727 0.05681818 0.00000000
#> BB 0.000000000 0.00000000 0.00000000 0.11392405 0.63291139 0.16455696 0.08860759
#> B 0.000000000 0.00000000 0.00000000 0.01388889 0.09722222 0.62500000 0.26388889
#> CCC 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.14285714 0.85714286Or, to do it all in one shot, use the %>%
mock_credit %>%
migrate(
id = customer_id,
time = date,
state = risk_rating,
metric = principal_balance,
percent = FALSE,
verbose = FALSE
) %>%
build_matrix(
state_start = risk_rating_start,
state_end = risk_rating_end,
metric = principal_balance
)
#> AAA AA A BBB BB B CCC
#> AAA 29042000 6575000 20000 0 0 0 0
#> AA 6445000 58095000 13045000 14467000 0 0 0
#> A 804000 7898000 85330000 21015000 5829000 0 0
#> BBB 0 0 12461000 65315000 13911000 8140000 0
#> BB 0 0 0 11374000 45986000 14057000 5723000
#> B 0 0 0 413000 6700000 47402000 17132000
#> CCC 0 0 0 0 0 2094000 14843000