Binning by value is the only original binning method implemented in this package. It is inspired by the case in marketing when accounts need to be binned by their sales. For example, creating 10 bins, where each bin represent 10% of all market sales. The first bin contains the highest sales accounts, thus has the small total number of accounts, whereas the last bin contains the smallest sales accounts, thus requiring the most number of accounts per bin to reach 10% of the market sales.
tibble::tibble(SALES = as.integer(rnorm(1000L, mean = 10000L, sd = 3000))) -> sales_data
sales_data %>%
bin_cols(SALES, bin_type = "value") -> sales_data1
#> Warning: SALES contains negative values. Negative values are treated as 0.
sales_data1
#> # A tibble: 1,000 × 2
#> SALES SALES_va10
#> <int> <int>
#> 1 8125 2
#> 2 7412 2
#> 3 10957 6
#> 4 7264 2
#> 5 9146 3
#> 6 12182 7
#> 7 9108 3
#> 8 7237 2
#> 9 12102 7
#> 10 15464 10
#> # ℹ 990 more rowsNotice that the sum is equal across bins.
sales_data1 %>%
bin_summary() %>%
print(width = Inf)
#> # A tibble: 11 × 14
#> column method n_bins .rank .min .mean .max .count .uniques
#> <chr> <chr> <int> <int> <int> <dbl> <int> <int> <int>
#> 1 SALES equal value 10 10 14457 15668. 19157 64 64
#> 2 SALES equal value 10 9 13198 13757. 14451 72 67
#> 3 SALES equal value 10 8 12397 12764. 13119 78 75
#> 4 SALES equal value 10 7 11692 12061. 12395 82 78
#> 5 SALES equal value 10 6 10949 11315. 11684 88 82
#> 6 SALES equal value 10 5 10188 10582. 10939 94 89
#> 7 SALES equal value 10 4 9351 9761. 10187 102 98
#> 8 SALES equal value 10 3 8480 8934. 9348 111 108
#> 9 SALES equal value 10 2 7088 7786. 8479 128 120
#> 10 SALES equal value 10 1 896 5512. 7079 180 174
#> 11 SALES equal value 10 0 -1935 -1935 -1935 1 1
#> relative_value .sum .med .sd width
#> <dbl> <int> <dbl> <dbl> <int>
#> 1 100 1002721 15539 1006. 4700
#> 2 87.8 990522 13664. 386. 1253
#> 3 81.5 995591 12754. 218. 722
#> 4 77.0 989040 12074 188. 703
#> 5 72.2 995699 11284 233. 735
#> 6 67.5 994665 10583 219. 751
#> 7 62.3 995573 9784. 254. 836
#> 8 57.0 991721 8955 251. 868
#> 9 49.7 996641 7726 389. 1391
#> 10 35.2 992103 5790. 1195. 6183
#> 11 -12.4 -1935 -1935 NA 0