Tools for binning data
rbin is not available on CRAN yet. You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("rsquaredacademy/rbin")rbin includes two addins for manually binning data:
rbinAddin()rbinFactorAddin()
bins <- rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56))
bins
#> Binning Summary
#> ---------------------------
#> Method Manual
#> Response y
#> Predictor age
#> Bins 10
#> Count 4521
#> Goods 517
#> Bads 4004
#> Entropy 0.5
#> Information Value 0.12
#>
#>
#> # A tibble: 10 x 7
#> cut_point bin_count good bad woe iv entropy
#> <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 < 29 410 71 339 -0.484 0.0255 0.665
#> 2 < 31 313 41 272 -0.155 0.00176 0.560
#> 3 < 34 567 55 512 0.184 0.00395 0.459
#> 4 < 36 396 45 351 0.00712 0.00000443 0.511
#> 5 < 39 519 47 472 0.260 0.00701 0.438
#> 6 < 42 431 33 398 0.443 0.0158 0.390
#> 7 < 46 449 47 402 0.0993 0.000942 0.484
#> 8 < 51 521 40 481 0.440 0.0188 0.391
#> 9 < 56 445 49 396 0.0426 0.000176 0.500
#> 10 >= 56 470 89 381 -0.593 0.0456 0.700
# plot
plot(bins)
# combine levels
upper <- c("secondary", "tertiary")
out <- rbin_factor_combine(mbank, education, upper, "upper")
table(out$education)
#>
#> primary unknown upper
#> 691 179 3651
# bins
bins <- rbin_factor(out, y, education)
bins
#> Binning Summary
#> ---------------------------
#> Method Custom
#> Response y
#> Predictor education
#> Levels 3
#> Count 4521
#> Goods 517
#> Bads 4004
#> Entropy 0.51
#> Information Value 0.01
#>
#>
#> # A tibble: 3 x 7
#> level bin_count good bad woe iv entropy
#> <fct> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 primary 691 66 625 0.201 0.00572 0.455
#> 2 unknown 179 25 154 -0.229 0.00227 0.583
#> 3 upper 3651 426 3225 -0.0228 0.000422 0.520
# plot
plot(bins)
bins <- rbin_quantiles(mbank, y, age, 10)
bins
#> Binning Summary
#> -----------------------------
#> Method Quantile
#> Response y
#> Predictor age
#> Bins 10
#> Count 4521
#> Goods 517
#> Bads 4004
#> Entropy 0.5
#> Information Value 0.12
#>
#>
#> # A tibble: 10 x 7
#> cut_point bin_count good bad woe iv entropy
#> <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 < 29 410 71 339 -0.484 0.0255 0.665
#> 2 < 31 313 41 272 -0.155 0.00176 0.560
#> 3 < 34 567 55 512 0.184 0.00395 0.459
#> 4 < 36 396 45 351 0.00712 0.00000443 0.511
#> 5 < 39 519 47 472 0.260 0.00701 0.438
#> 6 < 42 431 33 398 0.443 0.0158 0.390
#> 7 < 46 449 47 402 0.0993 0.000942 0.484
#> 8 < 51 521 40 481 0.440 0.0188 0.391
#> 9 < 56 445 49 396 0.0426 0.000176 0.500
#> 10 >= 56 470 89 381 -0.593 0.0456 0.700
# plot
plot(bins)
bins <- rbin_winsorize(mbank, y, age, 10, winsor_rate = 0.05)
bins
#> Binning Summary
#> ------------------------------
#> Method Winsorize
#> Response y
#> Predictor age
#> Bins 10
#> Count 4521
#> Goods 517
#> Bads 4004
#> Entropy 0.51
#> Information Value 0.1
#>
#>
#> # A tibble: 10 x 7
#> cut_point bin_count good bad woe iv entropy
#> <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 < 30.2 723 112 611 -0.350 0.0224 0.622
#> 2 < 33.4 567 55 512 0.184 0.00395 0.459
#> 3 < 36.6 573 58 515 0.137 0.00225 0.473
#> 4 < 39.8 497 44 453 0.285 0.00798 0.432
#> 5 < 43 396 37 359 0.225 0.00408 0.448
#> 6 < 46.2 461 43 418 0.227 0.00482 0.447
#> 7 < 49.4 281 22 259 0.419 0.00927 0.396
#> 8 < 52.6 309 32 277 0.111 0.000811 0.480
#> 9 < 55.8 244 25 219 0.123 0.000781 0.477
#> 10 >= 55.8 470 89 381 -0.593 0.0456 0.700
# plot
plot(bins)
bins <- rbin_equal_length(mbank, y, age, 10)
bins
#> Binning Summary
#> ---------------------------------
#> Method Equal Length
#> Response y
#> Predictor age
#> Bins 10
#> Count 4521
#> Goods 517
#> Bads 4004
#> Entropy 0.5
#> Information Value 0.17
#>
#>
#> # A tibble: 10 x 7
#> cut_point bin_count good bad woe iv entropy
#> <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 < 24.6 85 24 61 -1.11 0.0347 0.859
#> 2 < 31.2 822 106 716 -0.137 0.00358 0.555
#> 3 < 37.8 1133 115 1018 0.134 0.00425 0.474
#> 4 < 44.4 943 82 861 0.304 0.0172 0.426
#> 5 < 51 623 52 571 0.349 0.0147 0.414
#> 6 < 57.6 612 66 546 0.0660 0.000574 0.493
#> 7 < 64.2 229 43 186 -0.582 0.0214 0.697
#> 8 < 70.8 34 12 22 -1.44 0.0255 0.937
#> 9 < 77.4 25 13 12 -2.13 0.0471 0.999
#> 10 >= 77.4 15 4 11 -1.04 0.00517 0.837
# plot
plot(bins)
Please note that the ‘rbin’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.