The goal of migrate is to provide credit analysts with an easy set of tools for building state migration matrices (also known as “state transition matrices”).
You can install the released version of migrate from CRAN with:
And the development version from GitHub with:
First, load the package & the mock dataset (as a data frame) using library()
We can get a look at the data using dplyr::glimpse()
library(dplyr)
glimpse(mock_credit)
#> Rows: 900
#> Columns: 4
#> $ customer_id <chr> "Customer_1083", "Customer_1468", "Customer_1430", "Customer_1498", "Customer_1272", "Customer_1058", "Customer_1397", "Customer_1061", "Customer_1199", "Customer_1120", "Customer_1053", "Customer_1106", "Customer_1165", "Customer_1008", "Customer_1196", "Customer_1064", "Customer_1231", "Customer_1315", "Customer_1127", "Customer_1184", "Customer_1361", "Customer_1290", "Customer_1004", "Customer_1096", "Customer_1130", "Customer_1297", "Customer_1401", "Customer_1425", "Customer_1437", "Customer_1005", "Customer_1357", "Customer_1316", "Customer_1208", "Customer_1065", "Customer_1423", "Customer_1087", "Customer_1162", "Customer_1147", "Customer_1251", "Customer_1268", "Customer_1245", "Customer_1206", "Customer_1236", "Customer_1063", "Customer_1246", "Customer_1079", "Customer_1371", "Customer_1091", "Customer_1261", "Customer_1284", "Customer_1157", "Customer_1278", "Customer_1441", "Customer_1037", "Customer_1176", "Customer_1181", "Customer_1171",...
#> $ date <date> 2020-06-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-06-30, 2020-09-30, 2020-09-30, 2020-06-30, 2020-06-30, 2020-06...
#> $ risk_rating <fct> 6, 10, 8, 5, 5, 6, 8, 8, 8, 11, 7, 8, 6, 10, 10, 5, 6, 5, 6, 7, 10, 4, 11, 10, 8, 5, 5, 6, 9, 10, 9, 10, 10, 8, 7, 7, 11, 7, 6, 7, 7, 8, 9, 6, 8, 7, 8, 10, 11, 8, 9, 8, 11, 8, 11, 7, 6, 6, 8, 9, 6, 6, 6, 6, 7, 9, 10, 10, 7, 11, 11, 6, 7, 6, 7, 6, 11, 9, 8, 5, 6, 5, 7, 6, 11, 7, 7, 9, 11, 11, 8, 12, 7, 6, 8, 6, 8, 9, 7, 7, 5, 6, 10, 9, 7, 8, 7, 8, 9, 6, 10, 6, 6, 4, 7, 12, 11, 12, 9, 8, 8, 11, 6, 4, 11, 5, 6, 7, 7, 10, 9, 8, 6, 8, 6, 10, 12, 7, 7, 6, 11, 10, 8, 6, 8, 12, 7, 11, 6, 11, 6, 7, 6, 4, 5, 9, 7, 10, 4, 4, 8, 7, 6, 8, 10, 9, 11, 6, 6, 9, 5, 11, 9, 6, 9, 11, 6, 6, 11, 6, 7, 5, 6, 8, 10, 7, 10, 8, 10, 9, 7, 9, 9, 8, 6, 7, 9, 7, 8, 10, 9, 10, 8, 9, 9, 4, 6, 10, 8, 6, 11, 4, 6, 7, 7, 9, 6, 5, 7, 8, 9, 6, 9, 11, 6, 7, 6, 6, 7, 8, 4, 9, 8, 6, 8, 7, 5, 7, 7, 9, 7, 7, 7, 9, 7, 11, 6, 9, 8, 8, 9, 4, 7, 12, 9, 7, 8, 8, 9, 6, 7, 8, 10, 9, 5, 6, 9, 8, 4, 7, 9, 8, 11, 9, 8, 11, 7, 6, 10, 9, 6, 7, 8, 9, 11, 6, 7, 6, 10, 9, 5, 8, 9, 11, 6, 8, 6, 8, 9, 7, 6, 8, ...
#> $ principal_balance <dbl> 2201000, 1538000, 3447000, 1302000, 2306000, 666000, 938000, 737000, 799000, 1747000, 722000, 1206000, 1040000, 1372000, 510000, 400000, 890000, 1021000, 1849000, 1205000, 503000, 637000, 750000, 673000, 3658000, 932000, 856000, 2881000, 989000, 1177000, 242000, 659000, 375000, 669000, 546000, 976000, 253000, 1232000, 1850000, 743000, 1156000, 787000, 1737000, 2218000, 1284000, 1283000, 1233000, 2013000, 853000, 1600000, 774000, 695000, 2672000, 1659000, 1273000, 1420000, 363000, 224000, 1932000, 2161000, 3197000, 2763000, 916000, 4921000, 253000, 619000, 877000, 618000, 2194000, 464000, 334000, 854000, 1493000, 515000, 4343000, 983000, 943000, 814000, 1259000, 1108000, 833000, 576000, 1223000, 492000, 2963000, 327000, 1261000, 961000, 1374000, 1424000, 376000, 2269000, 299000, 1065000, 1149000, 1964000, 947000, 390000, 1041000, 1270000, 2598000, 933000, 1793000, 551000, 1268000, 837000, 1686000, 967000, 312000, 593000, 484000, 223000, 509000, 1380000, 2...Note that an important feature of the dataset is that there are exactly two (2) unique values in the date column variable
To summarize the migration within the data, use the migrate() function
migrated_df <- migrate(
data = mock_credit,
date = date,
rating = risk_rating,
metric = principal_balance
)
head(migrated_df)
#> # A tibble: 6 x 3
#> risk_rating_start risk_rating_end principal_balance
#> <fct> <fct> <dbl>
#> 1 4 4 -557000
#> 2 4 5 -2732000
#> 3 4 6 -939000
#> 4 4 7 0
#> 5 4 8 0
#> 6 4 9 0To create the state migration matrix, use the build_matrix() function
build_matrix(migrated_df)
#> Using risk_rating_start as the 'rating_start' column variable
#>
#> Using risk_rating_end as the 'rating_end' column variable
#>
#> Using principal_balance as the 'metric' column variable
#>
#> 4 5 6 7 8 9 10 11 12 13
#> 4 -557000 -2732000 -939000 0 0 0 0 0 0 0
#> 5 -4882000 6396000 182000 0 0 0 0 0 0 0
#> 6 0 -3720000 -4721000 637000 0 0 0 0 0 0
#> 7 0 -1447000 3073000 -778000 -12258000 1997000 0 0 0 0
#> 8 0 0 -486000 825000 -3634000 3706000 750000 0 0 0
#> 9 0 0 0 0 0 54000 3288000 1281000 -765000 0
#> 10 0 0 0 0 258000 97000 3711000 187000 1192000 0
#> 11 0 0 0 0 0 0 239000 7516000 2082000 -2280000
#> 12 0 0 0 0 0 0 0 -309000 2175000 2052000
#> 13 0 0 0 0 0 0 0 0 0 0Or, to do it all in one shot, use the %>%