# Analyze incarceration data (This is from the agent log, there is a separate incarceartion log)
rm(list=ls())
# Load R environment ---------
renv::activate()
# Load packages ---------
library(here)
## here() starts at /users/akhann16/code/cadre/data-analysis-plotting/Simulated-Data-Analysis/r
library(data.table)
library(yaml)
library(ggplot2)
# Read RDS file ------------
incarceration_release_log_env <-
readRDS("/users/akhann16/code/cadre/data-analysis-plotting/Simulated-Data-Analysis/r/incarceration-log-analysis/rds-outs/incarceration_log_env.RDS")
names(incarceration_release_log_env)
## [1] "incarceration_release_dt"
# Load data ------------
incarceration_release_dt <- incarceration_release_log_env[["incarceration_release_dt"]]
# Explore data ------------
str(incarceration_release_dt)
## Classes 'data.table' and 'data.frame': 3045 obs. of 8 variables:
## $ tick : int 9 25 29 30 45 62 71 84 87 93 ...
## $ id : int 723 5259 9190 723 9190 3899 4534 3899 723 4534 ...
## $ age : int 44 48 77 44 77 75 78 75 44 78 ...
## $ race : chr "Hispanic" "Black" "White" "Hispanic" ...
## $ female : int 0 0 0 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 1 3 1 1 1 1 0 1 1 0 ...
## $ smoking_status: chr "Current" "Current" "Never" "Current" ...
## $ event_type : chr "Incarceration" "Incarceration" "Incarceration" "Release" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(incarceration_release_dt)
## [1] 3045 8
incarceration_release_dt[,.N]
## [1] 3045
# Filter incarceration and release data ------------
incarceration_dt <- incarceration_release_dt[event_type=="Incarceration",,]
release_dt <- incarceration_release_dt[event_type=="Release"]
incarceration_dt[,.N]
## [1] 1540
str(incarceration_dt)
## Classes 'data.table' and 'data.frame': 1540 obs. of 8 variables:
## $ tick : int 9 25 29 62 71 87 97 102 107 113 ...
## $ id : int 723 5259 9190 3899 4534 723 9177 3418 537 8766 ...
## $ age : int 44 48 77 75 78 44 30 67 61 47 ...
## $ race : chr "Hispanic" "Black" "White" "White" ...
## $ female : int 0 0 0 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 1 3 1 1 0 1 1 0 3 1 ...
## $ smoking_status: chr "Current" "Current" "Never" "Never" ...
## $ event_type : chr "Incarceration" "Incarceration" "Incarceration" "Incarceration" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(incarceration_dt)
## [1] 1540 8
release_dt[,.N]
## [1] 1505
str(release_dt)
## Classes 'data.table' and 'data.frame': 1505 obs. of 8 variables:
## $ tick : int 30 45 84 93 102 109 158 158 218 225 ...
## $ id : int 723 9190 3899 4534 723 9177 5259 1469 5259 3453 ...
## $ age : int 44 77 75 78 44 30 48 54 48 71 ...
## $ race : chr "Hispanic" "White" "White" "Black" ...
## $ female : int 0 0 0 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 1 1 1 0 1 1 3 0 3 1 ...
## $ smoking_status: chr "Current" "Never" "Never" "Current" ...
## $ event_type : chr "Release" "Release" "Release" "Release" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(release_dt)
## [1] 1505 8
# Analyze state distributions for all incarceration events ------------
n_inc_events <- length(incarceration_dt$id)
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(race)]
## race Count Proportion
## 1: Hispanic 220 0.142857143
## 2: Black 976 0.633766234
## 3: White 342 0.222077922
## 4: Asian 2 0.001298701
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(female)]
## female Count Proportion
## 1: 0 1497 0.97207792
## 2: 1 43 0.02792208
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(alc_use_status)]
## alc_use_status Count Proportion
## 1: 1 632 0.41038961
## 2: 3 452 0.29350649
## 3: 0 397 0.25779221
## 4: 2 59 0.03831169
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 1263 0.82012987
## 2: Never 175 0.11363636
## 3: Former 102 0.06623377
# Analyze state distributions for first-time incarceration events ------------
first_incarceration_dt <- incarceration_dt[order(id, tick), .SD[1], by = id]
head(first_incarceration_dt)
## id tick age race female alc_use_status smoking_status event_type
## 1: 29 586 41 White 0 1 Current Incarceration
## 2: 30 6858 83 White 0 0 Current Incarceration
## 3: 55 388 42 Black 0 0 Current Incarceration
## 4: 74 1069 78 Black 0 3 Former Incarceration
## 5: 81 5656 57 Hispanic 0 1 Current Incarceration
## 6: 84 10111 56 Hispanic 0 0 Current Incarceration
dim(first_incarceration_dt)
## [1] 559 8
n_first_inc_agents <- length(first_incarceration_dt$id)
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(race)]
## race Count Proportion
## 1: White 252 0.450805009
## 2: Black 183 0.327370304
## 3: Hispanic 123 0.220035778
## 4: Asian 1 0.001788909
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(female)]
## female Count Proportion
## 1: 0 518 0.92665474
## 2: 1 41 0.07334526
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(alc_use_status)][
order(alc_use_status)
]
## alc_use_status Count Proportion
## 1: 0 184 0.32915921
## 2: 1 273 0.48837209
## 3: 2 19 0.03398927
## 4: 3 83 0.14847943
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 349 0.6243292
## 2: Former 58 0.1037567
## 3: Never 152 0.2719141
# Analyze state distributions of repeat incarceration events ------------
repeat_incarceration_dt <- incarceration_dt[order(id, tick), .SD[-1], by = id]
head(repeat_incarceration_dt, 100)
## id tick age race female alc_use_status smoking_status event_type
## 1: 55 759 43 Black 0 0 Current Incarceration
## 2: 55 1139 44 Black 0 0 Current Incarceration
## 3: 55 1239 45 Black 0 0 Current Incarceration
## 4: 55 1634 46 Black 0 0 Current Incarceration
## 5: 55 2346 48 Black 0 0 Current Incarceration
## 6: 55 2701 49 Black 0 0 Current Incarceration
## 7: 55 2837 49 Black 0 0 Current Incarceration
## 8: 55 5399 56 Black 0 0 Current Incarceration
## 9: 55 6409 59 Black 0 0 Current Incarceration
## 10: 55 8913 66 Black 0 0 Current Incarceration
## 11: 55 9078 66 Black 0 0 Current Incarceration
## 12: 55 9406 67 Black 0 0 Current Incarceration
## 13: 55 9539 67 Black 0 0 Current Incarceration
## 14: 55 9676 68 Black 0 0 Current Incarceration
## 15: 55 10075 69 Black 0 0 Current Incarceration
## 16: 55 10172 69 Black 0 0 Current Incarceration
## 17: 55 10328 70 Black 0 0 Current Incarceration
## 18: 55 10491 70 Black 0 0 Current Incarceration
## 19: 55 10879 71 Black 0 0 Current Incarceration
## 20: 216 2588 47 Black 0 1 Current Incarceration
## 21: 216 2790 48 Black 0 1 Current Incarceration
## 22: 216 3798 51 Black 0 1 Former Incarceration
## 23: 359 1955 77 Black 0 0 Current Incarceration
## 24: 359 2199 78 Black 0 0 Current Incarceration
## 25: 359 2769 79 Black 0 0 Current Incarceration
## 26: 359 2910 80 Black 0 0 Current Incarceration
## 27: 359 3119 80 Black 0 0 Current Incarceration
## 28: 359 3535 82 Black 0 0 Current Incarceration
## 29: 359 3933 83 Black 0 0 Current Incarceration
## 30: 359 4035 83 Black 0 0 Current Incarceration
## 31: 359 4363 84 Black 0 0 Current Incarceration
## 32: 408 3704 80 Asian 0 1 Current Incarceration
## 33: 419 9327 82 White 0 3 Current Incarceration
## 34: 432 961 81 Black 0 3 Current Incarceration
## 35: 432 1205 82 Black 0 3 Current Incarceration
## 36: 432 1225 82 Black 0 3 Current Incarceration
## 37: 432 1382 82 Black 0 3 Current Incarceration
## 38: 432 1486 83 Black 0 3 Current Incarceration
## 39: 432 1675 83 Black 0 3 Current Incarceration
## 40: 432 1835 84 Black 0 3 Current Incarceration
## 41: 432 1922 84 Black 0 3 Current Incarceration
## 42: 432 1950 84 Black 0 3 Current Incarceration
## 43: 446 7575 64 Hispanic 0 0 Current Incarceration
## 44: 510 2012 33 Black 0 2 Current Incarceration
## 45: 510 2388 34 Black 0 2 Current Incarceration
## 46: 510 2542 35 Black 0 2 Current Incarceration
## 47: 510 2574 35 Black 0 2 Current Incarceration
## 48: 510 3379 37 Black 0 2 Current Incarceration
## 49: 510 3593 38 Black 0 2 Current Incarceration
## 50: 510 3830 38 Black 0 2 Current Incarceration
## 51: 510 4618 40 Black 0 2 Current Incarceration
## 52: 510 5565 43 Black 0 2 Current Incarceration
## 53: 510 5813 44 Black 0 2 Current Incarceration
## 54: 533 3802 59 White 0 1 Current Incarceration
## 55: 534 10075 47 Black 0 0 Current Incarceration
## 56: 534 10456 48 Black 0 0 Current Incarceration
## 57: 534 10651 48 Black 0 0 Current Incarceration
## 58: 537 4377 72 White 0 3 Never Incarceration
## 59: 537 5133 74 White 0 3 Never Incarceration
## 60: 603 10142 55 White 0 3 Current Incarceration
## 61: 604 959 22 Black 0 0 Current Incarceration
## 62: 604 2454 26 Black 0 0 Current Incarceration
## 63: 604 2559 27 Black 0 0 Current Incarceration
## 64: 604 4443 32 Black 0 0 Current Incarceration
## 65: 604 4583 32 Black 0 0 Current Incarceration
## 66: 604 4723 33 Black 0 0 Current Incarceration
## 67: 604 5030 33 Black 0 0 Current Incarceration
## 68: 604 7047 39 Black 0 0 Current Incarceration
## 69: 604 7760 41 Black 0 0 Current Incarceration
## 70: 604 8428 43 Black 0 0 Current Incarceration
## 71: 604 9532 46 Black 0 0 Current Incarceration
## 72: 604 9756 46 Black 0 0 Current Incarceration
## 73: 604 10377 48 Black 0 0 Current Incarceration
## 74: 604 10601 49 Black 0 0 Current Incarceration
## 75: 604 10932 50 Black 0 0 Current Incarceration
## 76: 614 1657 40 Black 0 1 Current Incarceration
## 77: 614 1863 41 Black 0 1 Current Incarceration
## 78: 723 87 44 Hispanic 0 1 Current Incarceration
## 79: 942 6364 82 White 0 1 Current Incarceration
## 80: 947 3018 75 White 0 1 Current Incarceration
## 81: 953 353 64 Black 0 0 Current Incarceration
## 82: 953 1306 66 Black 0 0 Current Incarceration
## 83: 953 3035 71 Black 0 0 Current Incarceration
## 84: 953 3498 72 Black 0 0 Current Incarceration
## 85: 953 4025 74 Black 0 0 Current Incarceration
## 86: 953 4569 75 Black 0 0 Former Incarceration
## 87: 953 5540 78 Black 0 0 Current Incarceration
## 88: 953 6011 79 Black 0 0 Current Incarceration
## 89: 953 6308 80 Black 0 0 Current Incarceration
## 90: 953 6808 82 Black 0 0 Current Incarceration
## 91: 953 7075 82 Black 0 0 Current Incarceration
## 92: 953 7269 83 Black 0 0 Current Incarceration
## 93: 953 7624 84 Black 0 0 Current Incarceration
## 94: 959 1192 49 White 0 0 Current Incarceration
## 95: 1010 4686 78 Hispanic 1 0 Current Incarceration
## 96: 1034 10820 69 White 0 1 Current Incarceration
## 97: 1078 4893 40 Black 0 1 Current Incarceration
## 98: 1078 5409 42 Black 0 1 Current Incarceration
## 99: 1078 5588 42 Black 0 1 Current Incarceration
## 100: 1078 5651 42 Black 0 1 Current Incarceration
## id tick age race female alc_use_status smoking_status event_type
dim(repeat_incarceration_dt)
## [1] 981 8
n_repeat_inc_events <- nrow(repeat_incarceration_dt)
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(race)]
## race Count Proportion
## 1: Black 793 0.808358818
## 2: Asian 1 0.001019368
## 3: White 90 0.091743119
## 4: Hispanic 97 0.098878695
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(female)]
## female Count Proportion
## 1: 0 979 0.997961264
## 2: 1 2 0.002038736
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(alc_use_status)][
order(alc_use_status)
]
## alc_use_status Count Proportion
## 1: 0 213 0.21712538
## 2: 1 359 0.36595311
## 3: 2 40 0.04077472
## 4: 3 369 0.37614679
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 914 0.93170234
## 2: Former 44 0.04485219
## 3: Never 23 0.02344546
# Analyze state distributions for all release events ------------
n_rel_events <- length(release_dt$id)
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(race)]
## race Count Proportion
## 1: Hispanic 219 0.145514950
## 2: White 328 0.217940199
## 3: Black 956 0.635215947
## 4: Asian 2 0.001328904
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(female)]
## female Count Proportion
## 1: 0 1462 0.97142857
## 2: 1 43 0.02857143
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(alc_use_status)]
## alc_use_status Count Proportion
## 1: 1 616 0.40930233
## 2: 0 392 0.26046512
## 3: 3 439 0.29169435
## 4: 2 58 0.03853821
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 1235 0.82059801
## 2: Never 172 0.11428571
## 3: Former 98 0.06511628
# Analyze state distributions for first-time release events ------------
first_release_dt <- release_dt[order(id, tick), .SD[1], by = id]
head(first_release_dt)
## id tick age race female alc_use_status smoking_status event_type
## 1: 29 611 41 White 0 1 Current Release
## 2: 30 6867 84 White 0 0 Current Release
## 3: 55 415 42 Black 0 0 Current Release
## 4: 74 2578 83 Black 0 3 Former Release
## 5: 81 5760 57 Hispanic 0 1 Current Release
## 6: 84 10251 57 Hispanic 0 0 Current Release
dim(first_release_dt)
## [1] 545 8
n_first_rel_agents <- length(first_release_dt$id)
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(race)]
## race Count Proportion
## 1: White 241 0.442201835
## 2: Black 181 0.332110092
## 3: Hispanic 122 0.223853211
## 4: Asian 1 0.001834862
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(female)]
## female Count Proportion
## 1: 0 504 0.92477064
## 2: 1 41 0.07522936
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(alc_use_status)][
order(alc_use_status)
]
## alc_use_status Count Proportion
## 1: 0 182 0.33394495
## 2: 1 265 0.48623853
## 3: 2 19 0.03486239
## 4: 3 79 0.14495413
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 342 0.62752294
## 2: Former 54 0.09908257
## 3: Never 149 0.27339450
# Analyze state distributions of repeat release events ------------
repeat_release_dt <- release_dt[order(id, tick), .SD[-1], by = id]
head(repeat_release_dt, 100)
## id tick age race female alc_use_status smoking_status event_type
## 1: 55 800 44 Black 0 0 Current Release
## 2: 55 1179 45 Black 0 0 Current Release
## 3: 55 1304 45 Black 0 0 Current Release
## 4: 55 1650 46 Black 0 0 Current Release
## 5: 55 2504 48 Black 0 0 Current Release
## 6: 55 2713 49 Black 0 0 Current Release
## 7: 55 5003 55 Black 0 0 Current Release
## 8: 55 5467 56 Black 0 0 Current Release
## 9: 55 8540 65 Black 0 0 Current Release
## 10: 55 8929 66 Black 0 0 Current Release
## 11: 55 9102 66 Black 0 0 Current Release
## 12: 55 9463 67 Black 0 0 Current Release
## 13: 55 9664 68 Black 0 0 Current Release
## 14: 55 9733 68 Black 0 0 Current Release
## 15: 55 10113 69 Black 0 0 Current Release
## 16: 55 10196 69 Black 0 0 Current Release
## 17: 55 10373 70 Black 0 0 Current Release
## 18: 55 10603 70 Black 0 0 Current Release
## 19: 55 10903 71 Black 0 0 Current Release
## 20: 216 2617 47 Black 0 1 Current Release
## 21: 216 2836 48 Black 0 1 Current Release
## 22: 216 3815 51 Black 0 1 Former Release
## 23: 359 1979 77 Black 0 0 Current Release
## 24: 359 2225 78 Black 0 0 Current Release
## 25: 359 2845 80 Black 0 0 Current Release
## 26: 359 3092 80 Black 0 0 Current Release
## 27: 359 3137 80 Black 0 0 Current Release
## 28: 359 3635 82 Black 0 0 Current Release
## 29: 359 3964 83 Black 0 0 Current Release
## 30: 359 4174 83 Black 0 0 Current Release
## 31: 359 4371 84 Black 0 0 Current Release
## 32: 408 3912 81 Asian 0 1 Current Release
## 33: 419 9417 83 White 0 3 Current Release
## 34: 432 1138 82 Black 0 3 Current Release
## 35: 432 1221 82 Black 0 3 Current Release
## 36: 432 1365 82 Black 0 3 Current Release
## 37: 432 1464 83 Black 0 3 Current Release
## 38: 432 1666 83 Black 0 3 Current Release
## 39: 432 1725 83 Black 0 3 Current Release
## 40: 432 1901 84 Black 0 3 Current Release
## 41: 432 1945 84 Black 0 3 Current Release
## 42: 446 7600 64 Hispanic 0 0 Current Release
## 43: 510 2117 34 Black 0 2 Current Release
## 44: 510 2536 35 Black 0 2 Current Release
## 45: 510 2570 35 Black 0 2 Current Release
## 46: 510 2863 36 Black 0 2 Current Release
## 47: 510 3510 37 Black 0 2 Current Release
## 48: 510 3697 38 Black 0 2 Current Release
## 49: 510 3858 38 Black 0 2 Current Release
## 50: 510 4690 41 Black 0 2 Current Release
## 51: 510 5602 43 Black 0 2 Current Release
## 52: 510 5821 44 Black 0 2 Current Release
## 53: 533 3890 59 White 0 1 Current Release
## 54: 534 10094 47 Black 0 0 Current Release
## 55: 534 10475 48 Black 0 0 Current Release
## 56: 534 10676 48 Black 0 0 Current Release
## 57: 537 4396 72 White 0 3 Never Release
## 58: 537 5410 75 White 0 3 Never Release
## 59: 603 10217 55 White 0 3 Current Release
## 60: 604 1519 24 Black 0 0 Current Release
## 61: 604 2474 26 Black 0 0 Current Release
## 62: 604 2623 27 Black 0 0 Current Release
## 63: 604 4458 32 Black 0 0 Current Release
## 64: 604 4659 32 Black 0 0 Current Release
## 65: 604 4882 33 Black 0 0 Current Release
## 66: 604 6956 39 Black 0 0 Current Release
## 67: 604 7056 39 Black 0 0 Current Release
## 68: 604 7775 41 Black 0 0 Current Release
## 69: 604 8444 43 Black 0 0 Current Release
## 70: 604 9547 46 Black 0 0 Current Release
## 71: 604 9767 46 Black 0 0 Current Release
## 72: 604 10391 48 Black 0 0 Current Release
## 73: 604 10700 49 Black 0 0 Current Release
## 74: 604 10945 50 Black 0 0 Current Release
## 75: 614 1741 40 Black 0 1 Current Release
## 76: 614 1890 41 Black 0 1 Current Release
## 77: 723 102 44 Hispanic 0 1 Current Release
## 78: 942 6389 82 White 0 1 Current Release
## 79: 947 3100 75 White 0 1 Current Release
## 80: 953 455 64 Black 0 0 Current Release
## 81: 953 2245 69 Black 0 0 Current Release
## 82: 953 3086 71 Black 0 0 Current Release
## 83: 953 3603 73 Black 0 0 Current Release
## 84: 953 4173 74 Black 0 0 Current Release
## 85: 953 4592 75 Black 0 0 Former Release
## 86: 953 5566 78 Black 0 0 Current Release
## 87: 953 6039 79 Black 0 0 Current Release
## 88: 953 6360 80 Black 0 0 Current Release
## 89: 953 6969 82 Black 0 0 Current Release
## 90: 953 7233 83 Black 0 0 Current Release
## 91: 953 7292 83 Black 0 0 Current Release
## 92: 959 1244 49 White 0 0 Current Release
## 93: 1010 4694 78 Hispanic 1 0 Current Release
## 94: 1078 4919 40 Black 0 1 Current Release
## 95: 1078 5430 42 Black 0 1 Current Release
## 96: 1078 5602 42 Black 0 1 Current Release
## 97: 1078 5697 42 Black 0 1 Current Release
## 98: 1078 6487 45 Black 0 1 Current Release
## 99: 1243 6136 74 Black 0 0 Current Release
## 100: 1243 6388 74 Black 0 0 Current Release
## id tick age race female alc_use_status smoking_status event_type
dim(repeat_release_dt)
## [1] 960 8
n_repeat_rel_events <- nrow(repeat_release_dt)
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(race)]
## race Count Proportion
## 1: Black 775 0.807291667
## 2: Asian 1 0.001041667
## 3: White 87 0.090625000
## 4: Hispanic 97 0.101041667
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(female)]
## female Count Proportion
## 1: 0 958 0.997916667
## 2: 1 2 0.002083333
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(alc_use_status)][
order(alc_use_status)
]
## alc_use_status Count Proportion
## 1: 0 210 0.218750
## 2: 1 351 0.365625
## 3: 2 39 0.040625
## 4: 3 360 0.375000
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 893 0.93020833
## 2: Former 44 0.04583333
## 3: Never 23 0.02395833