# 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': 2427 obs. of 8 variables:
## $ tick : int 60 76 80 106 114 114 128 129 132 144 ...
## $ id : int 4866 5738 4866 4226 6245 7796 7796 8538 4226 7785 ...
## $ age : int 20 69 20 33 72 83 83 47 33 53 ...
## $ race : chr "White" "Black" "White" "Hispanic" ...
## $ female : int 0 0 0 1 0 0 0 0 1 0 ...
## $ alc_use_status: int 3 3 3 3 1 1 1 1 3 3 ...
## $ smoking_status: chr "Current" "Current" "Current" "Never" ...
## $ event_type : chr "Incarceration" "Incarceration" "Release" "Incarceration" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(incarceration_release_dt)
## [1] 2427 8
incarceration_release_dt[,.N]
## [1] 2427
# 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] 1227
str(incarceration_dt)
## Classes 'data.table' and 'data.frame': 1227 obs. of 8 variables:
## $ tick : int 60 76 106 114 114 129 144 146 171 174 ...
## $ id : int 4866 5738 4226 6245 7796 8538 7785 4861 8538 8638 ...
## $ age : int 20 69 33 72 83 47 53 82 47 66 ...
## $ race : chr "White" "Black" "Hispanic" "Hispanic" ...
## $ female : int 0 0 1 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 3 3 3 1 1 1 3 1 1 3 ...
## $ smoking_status: chr "Current" "Current" "Never" "Current" ...
## $ event_type : chr "Incarceration" "Incarceration" "Incarceration" "Incarceration" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(incarceration_dt)
## [1] 1227 8
release_dt[,.N]
## [1] 1200
str(release_dt)
## Classes 'data.table' and 'data.frame': 1200 obs. of 8 variables:
## $ tick : int 80 128 132 151 167 206 214 221 225 244 ...
## $ id : int 4866 7796 4226 8538 7785 7785 6245 8091 4861 2906 ...
## $ age : int 20 83 33 47 53 53 72 29 83 39 ...
## $ race : chr "White" "Black" "Hispanic" "Black" ...
## $ female : int 0 0 1 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 3 1 3 1 3 3 1 1 1 3 ...
## $ smoking_status: chr "Current" "Current" "Never" "Current" ...
## $ event_type : chr "Release" "Release" "Release" "Release" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(release_dt)
## [1] 1200 8
# Analyze state distributions for all incarceration events ------------
n_inc_events <- length(incarceration_dt$id)
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(race)]
## race Count Proportion
## 1: White 311 0.253463733
## 2: Black 744 0.606356968
## 3: Hispanic 164 0.133659332
## 4: Asian 8 0.006519967
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(female)]
## female Count Proportion
## 1: 0 1184 0.96495518
## 2: 1 43 0.03504482
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(alc_use_status)]
## alc_use_status Count Proportion
## 1: 3 447 0.36430318
## 2: 1 580 0.47269764
## 3: 2 108 0.08801956
## 4: 0 92 0.07497963
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 991 0.80766096
## 2: Never 167 0.13610432
## 3: Former 69 0.05623472
# Analyze state distributions for first-time incarceration events ------------
first_incarceration_dt <- incarceration_dt[order(id, tick), .SD[1], by = id]
head(first_incarceration_dt)
## id tick age race female alc_use_status smoking_status event_type
## 1: 51 620 42 Black 0 1 Current Incarceration
## 2: 56 1601 64 Black 0 3 Never Incarceration
## 3: 58 2782 65 Black 0 1 Current Incarceration
## 4: 71 5702 57 White 0 3 Current Incarceration
## 5: 115 8132 65 White 0 3 Current Incarceration
## 6: 140 7020 63 Black 0 3 Current Incarceration
dim(first_incarceration_dt)
## [1] 538 8
n_first_inc_agents <- length(first_incarceration_dt$id)
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(race)]
## race Count Proportion
## 1: Black 172 0.31970260
## 2: White 252 0.46840149
## 3: Hispanic 107 0.19888476
## 4: Asian 7 0.01301115
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(female)]
## female Count Proportion
## 1: 0 498 0.92565056
## 2: 1 40 0.07434944
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 49 0.09107807
## 2: 1 282 0.52416357
## 3: 2 51 0.09479554
## 4: 3 156 0.28996283
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 341 0.63382900
## 2: Never 151 0.28066914
## 3: Former 46 0.08550186
# 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: 51 900 43 Black 0 1 Current Incarceration
## 2: 51 919 43 Black 0 1 Current Incarceration
## 3: 51 1717 45 Black 0 1 Current Incarceration
## 4: 51 2744 48 Black 0 1 Current Incarceration
## 5: 51 5148 54 Black 0 1 Current Incarceration
## 6: 51 5442 55 Black 0 1 Current Incarceration
## 7: 51 5819 56 Black 0 1 Current Incarceration
## 8: 58 3885 68 Black 0 1 Current Incarceration
## 9: 58 4267 69 Black 0 0 Current Incarceration
## 10: 58 4738 70 Black 0 0 Current Incarceration
## 11: 140 7524 65 Black 0 3 Current Incarceration
## 12: 140 8045 66 Black 0 2 Current Incarceration
## 13: 191 8977 82 Hispanic 0 1 Current Incarceration
## 14: 195 5514 66 White 0 3 Current Incarceration
## 15: 195 5732 67 White 0 3 Current Incarceration
## 16: 271 7793 47 Black 0 3 Never Incarceration
## 17: 271 8147 48 Black 0 3 Never Incarceration
## 18: 284 6253 57 Hispanic 0 1 Current Incarceration
## 19: 325 3850 73 Black 0 3 Current Incarceration
## 20: 325 4422 74 Black 0 1 Current Incarceration
## 21: 325 4485 75 Black 0 1 Current Incarceration
## 22: 325 4540 75 Black 0 1 Current Incarceration
## 23: 325 4783 75 Black 0 1 Current Incarceration
## 24: 325 4922 76 Black 0 1 Current Incarceration
## 25: 325 5245 77 Black 0 1 Current Incarceration
## 26: 325 5650 78 Black 0 1 Current Incarceration
## 27: 325 6217 79 Black 0 1 Current Incarceration
## 28: 325 7387 82 Black 0 1 Current Incarceration
## 29: 325 7502 83 Black 0 1 Current Incarceration
## 30: 325 7925 84 Black 0 1 Current Incarceration
## 31: 458 1950 31 Hispanic 0 3 Current Incarceration
## 32: 1138 2653 83 Black 0 1 Current Incarceration
## 33: 1165 941 33 Black 0 3 Current Incarceration
## 34: 1165 1012 33 Black 0 3 Current Incarceration
## 35: 1165 1249 34 Black 0 3 Current Incarceration
## 36: 1165 1522 34 Black 0 3 Current Incarceration
## 37: 1165 1836 35 Black 0 3 Current Incarceration
## 38: 1165 2414 37 Black 0 3 Current Incarceration
## 39: 1165 2663 38 Black 0 3 Current Incarceration
## 40: 1165 2770 38 Black 0 3 Current Incarceration
## 41: 1165 2922 38 Black 0 3 Current Incarceration
## 42: 1165 2946 38 Black 0 3 Current Incarceration
## 43: 1165 3287 39 Black 0 3 Current Incarceration
## 44: 1165 3367 40 Black 0 3 Current Incarceration
## 45: 1165 3436 40 Black 0 3 Current Incarceration
## 46: 1165 3867 41 Black 0 3 Current Incarceration
## 47: 1165 4571 43 Black 0 3 Current Incarceration
## 48: 1165 4745 43 Black 0 3 Current Incarceration
## 49: 1165 5706 46 Black 0 1 Current Incarceration
## 50: 1165 6452 48 Black 0 1 Current Incarceration
## 51: 1165 7592 51 Black 0 1 Current Incarceration
## 52: 1165 7722 51 Black 0 1 Current Incarceration
## 53: 1177 5966 81 White 0 3 Current Incarceration
## 54: 1179 9464 58 Hispanic 0 1 Current Incarceration
## 55: 1256 7363 54 Hispanic 0 0 Current Incarceration
## 56: 1343 6827 73 Black 0 0 Current Incarceration
## 57: 1386 1813 40 Black 0 3 Current Incarceration
## 58: 1386 1965 40 Black 0 3 Current Incarceration
## 59: 1386 2083 41 Black 0 3 Current Incarceration
## 60: 1386 2146 41 Black 0 3 Current Incarceration
## 61: 1386 3031 43 Black 0 1 Current Incarceration
## 62: 1399 8392 56 White 0 3 Current Incarceration
## 63: 1399 8470 56 White 0 3 Current Incarceration
## 64: 1399 8577 57 White 0 3 Current Incarceration
## 65: 1399 9365 59 White 0 3 Current Incarceration
## 66: 1413 6581 63 Black 0 1 Current Incarceration
## 67: 1413 7357 65 Black 0 1 Former Incarceration
## 68: 1449 4390 38 Black 1 1 Current Incarceration
## 69: 1479 2730 38 White 0 1 Current Incarceration
## 70: 1637 6808 40 Black 0 3 Current Incarceration
## 71: 1637 7830 43 Black 0 1 Current Incarceration
## 72: 1637 7900 43 Black 0 1 Current Incarceration
## 73: 1637 8028 43 Black 0 1 Current Incarceration
## 74: 1637 10709 51 Black 0 1 Current Incarceration
## 75: 1637 10752 51 Black 0 1 Current Incarceration
## 76: 1744 5822 60 White 0 0 Current Incarceration
## 77: 1773 3054 65 Hispanic 0 1 Current Incarceration
## 78: 1802 293 34 White 0 3 Current Incarceration
## 79: 1802 427 34 White 0 3 Current Incarceration
## 80: 1832 3841 53 Hispanic 0 3 Current Incarceration
## 81: 1832 6857 61 Hispanic 0 1 Former Incarceration
## 82: 2028 4804 56 Black 0 3 Current Incarceration
## 83: 2028 4933 56 Black 0 0 Current Incarceration
## 84: 2074 3673 29 Black 0 1 Former Incarceration
## 85: 2074 4145 30 Black 0 1 Current Incarceration
## 86: 2074 4893 32 Black 0 1 Current Incarceration
## 87: 2074 5181 33 Black 0 1 Current Incarceration
## 88: 2074 7137 38 Black 0 1 Current Incarceration
## 89: 2074 7355 39 Black 0 1 Current Incarceration
## 90: 2074 7648 40 Black 0 1 Former Incarceration
## 91: 2108 10699 75 White 0 1 Current Incarceration
## 92: 2380 4288 41 White 0 0 Current Incarceration
## 93: 2402 9166 81 Black 0 1 Current Incarceration
## 94: 2462 6710 77 Black 0 3 Current Incarceration
## 95: 2462 6784 77 Black 0 3 Current Incarceration
## 96: 2462 7396 78 Black 0 3 Current Incarceration
## 97: 2462 7480 79 Black 0 2 Current Incarceration
## 98: 2462 7611 79 Black 0 2 Current Incarceration
## 99: 2847 1461 74 Black 0 1 Current Incarceration
## 100: 2847 1552 74 Black 0 1 Current Incarceration
## id tick age race female alc_use_status smoking_status event_type
dim(repeat_incarceration_dt)
## [1] 689 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 572 0.830188679
## 2: Hispanic 57 0.082728592
## 3: White 59 0.085631350
## 4: Asian 1 0.001451379
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(female)]
## female Count Proportion
## 1: 0 686 0.995645864
## 2: 1 3 0.004354136
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 43 0.06240929
## 2: 1 298 0.43251089
## 3: 2 57 0.08272859
## 4: 3 291 0.42235123
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 650 0.94339623
## 2: Never 16 0.02322206
## 3: Former 23 0.03338171
# Analyze state distributions for all release events ------------
n_rel_events <- length(release_dt$id)
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(race)]
## race Count Proportion
## 1: White 302 0.251666667
## 2: Black 732 0.610000000
## 3: Hispanic 158 0.131666667
## 4: Asian 8 0.006666667
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(female)]
## female Count Proportion
## 1: 0 1157 0.96416667
## 2: 1 43 0.03583333
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(alc_use_status)]
## alc_use_status Count Proportion
## 1: 3 436 0.36333333
## 2: 1 569 0.47416667
## 3: 2 103 0.08583333
## 4: 0 92 0.07666667
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 969 0.80750000
## 2: Never 163 0.13583333
## 3: Former 68 0.05666667
# Analyze state distributions for first-time release events ------------
first_release_dt <- release_dt[order(id, tick), .SD[1], by = id]
head(first_release_dt)
## id tick age race female alc_use_status smoking_status event_type
## 1: 51 631 42 Black 0 1 Current Release
## 2: 56 2240 65 Black 0 3 Never Release
## 3: 58 2884 65 Black 0 1 Current Release
## 4: 71 5823 57 White 0 3 Current Release
## 5: 115 8152 65 White 0 3 Current Release
## 6: 140 7141 64 Black 0 3 Current Release
dim(first_release_dt)
## [1] 526 8
n_first_rel_agents <- length(first_release_dt$id)
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(race)]
## race Count Proportion
## 1: Black 171 0.32509506
## 2: White 245 0.46577947
## 3: Hispanic 103 0.19581749
## 4: Asian 7 0.01330798
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(female)]
## female Count Proportion
## 1: 0 486 0.92395437
## 2: 1 40 0.07604563
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 49 0.09315589
## 2: 1 277 0.52661597
## 3: 2 48 0.09125475
## 4: 3 152 0.28897338
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 333 0.63307985
## 2: Never 148 0.28136882
## 3: Former 45 0.08555133
# 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: 51 916 43 Black 0 1 Current Release
## 2: 51 1341 44 Black 0 1 Current Release
## 3: 51 2431 47 Black 0 1 Current Release
## 4: 51 2778 48 Black 0 1 Current Release
## 5: 51 5248 55 Black 0 1 Current Release
## 6: 51 5461 55 Black 0 1 Current Release
## 7: 51 5922 57 Black 0 1 Current Release
## 8: 58 3940 68 Black 0 1 Current Release
## 9: 58 4293 69 Black 0 0 Current Release
## 10: 58 4752 70 Black 0 0 Current Release
## 11: 140 7541 65 Black 0 3 Current Release
## 12: 140 8100 66 Black 0 2 Current Release
## 13: 191 9027 82 Hispanic 0 1 Current Release
## 14: 195 5582 66 White 0 3 Current Release
## 15: 195 5891 67 White 0 3 Current Release
## 16: 271 7807 47 Black 0 3 Never Release
## 17: 271 8317 48 Black 0 3 Never Release
## 18: 284 6269 57 Hispanic 0 1 Current Release
## 19: 325 3867 73 Black 0 3 Current Release
## 20: 325 4446 74 Black 0 1 Current Release
## 21: 325 4508 75 Black 0 1 Current Release
## 22: 325 4568 75 Black 0 1 Current Release
## 23: 325 4803 75 Black 0 1 Current Release
## 24: 325 5101 76 Black 0 1 Current Release
## 25: 325 5309 77 Black 0 1 Current Release
## 26: 325 5771 78 Black 0 1 Current Release
## 27: 325 6393 80 Black 0 1 Current Release
## 28: 325 7418 83 Black 0 1 Current Release
## 29: 325 7513 83 Black 0 1 Current Release
## 30: 458 1978 31 Hispanic 0 3 Current Release
## 31: 1138 2734 83 Black 0 1 Current Release
## 32: 1165 961 33 Black 0 3 Current Release
## 33: 1165 1021 33 Black 0 3 Current Release
## 34: 1165 1259 34 Black 0 3 Current Release
## 35: 1165 1616 35 Black 0 3 Current Release
## 36: 1165 1979 36 Black 0 3 Current Release
## 37: 1165 2447 37 Black 0 3 Current Release
## 38: 1165 2724 38 Black 0 3 Current Release
## 39: 1165 2783 38 Black 0 3 Current Release
## 40: 1165 2930 38 Black 0 3 Current Release
## 41: 1165 3277 39 Black 0 3 Current Release
## 42: 1165 3295 39 Black 0 3 Current Release
## 43: 1165 3376 40 Black 0 3 Current Release
## 44: 1165 3617 40 Black 0 3 Current Release
## 45: 1165 4033 41 Black 0 3 Current Release
## 46: 1165 4651 43 Black 0 3 Current Release
## 47: 1165 4832 44 Black 0 3 Current Release
## 48: 1165 6342 48 Black 0 1 Current Release
## 49: 1165 6581 48 Black 0 1 Current Release
## 50: 1165 7602 51 Black 0 1 Current Release
## 51: 1165 7821 52 Black 0 1 Current Release
## 52: 1177 5983 82 White 0 3 Current Release
## 53: 1179 9527 58 Hispanic 0 1 Current Release
## 54: 1256 7391 54 Hispanic 0 0 Current Release
## 55: 1343 6910 73 Black 0 0 Current Release
## 56: 1386 1960 40 Black 0 3 Current Release
## 57: 1386 2018 40 Black 0 3 Current Release
## 58: 1386 2110 41 Black 0 3 Current Release
## 59: 1386 2254 41 Black 0 3 Current Release
## 60: 1386 3122 43 Black 0 1 Current Release
## 61: 1399 8406 56 White 0 3 Current Release
## 62: 1399 8576 57 White 0 3 Current Release
## 63: 1399 8689 57 White 0 3 Current Release
## 64: 1399 9409 59 White 0 3 Current Release
## 65: 1413 6600 63 Black 0 1 Current Release
## 66: 1413 7366 65 Black 0 1 Former Release
## 67: 1449 4405 39 Black 1 1 Current Release
## 68: 1479 2751 38 White 0 1 Current Release
## 69: 1637 7592 42 Black 0 3 Current Release
## 70: 1637 7851 43 Black 0 1 Current Release
## 71: 1637 7975 43 Black 0 1 Current Release
## 72: 1637 8053 43 Black 0 1 Current Release
## 73: 1637 10720 51 Black 0 1 Current Release
## 74: 1637 10778 51 Black 0 1 Current Release
## 75: 1744 5895 60 White 0 0 Current Release
## 76: 1773 4926 70 Hispanic 0 1 Current Release
## 77: 1802 308 34 White 0 3 Current Release
## 78: 1802 447 35 White 0 3 Current Release
## 79: 1832 3969 53 Hispanic 0 3 Current Release
## 80: 1832 6882 61 Hispanic 0 1 Former Release
## 81: 2028 4825 56 Black 0 3 Current Release
## 82: 2028 5053 56 Black 0 0 Current Release
## 83: 2074 3758 29 Black 0 1 Former Release
## 84: 2074 4285 30 Black 0 1 Current Release
## 85: 2074 4962 32 Black 0 1 Current Release
## 86: 2074 6750 37 Black 0 1 Current Release
## 87: 2074 7241 38 Black 0 1 Current Release
## 88: 2074 7522 39 Black 0 1 Current Release
## 89: 2074 7669 40 Black 0 1 Former Release
## 90: 2108 10862 75 White 0 1 Current Release
## 91: 2380 4421 42 White 0 0 Current Release
## 92: 2402 9180 81 Black 0 1 Current Release
## 93: 2462 6738 77 Black 0 3 Current Release
## 94: 2462 6945 77 Black 0 3 Current Release
## 95: 2462 7417 78 Black 0 3 Current Release
## 96: 2462 7567 79 Black 0 2 Current Release
## 97: 2462 7774 79 Black 0 2 Current Release
## 98: 2847 1474 74 Black 0 1 Current Release
## 99: 2847 1678 74 Black 0 1 Current Release
## 100: 2847 3639 79 Black 0 3 Current Release
## id tick age race female alc_use_status smoking_status event_type
dim(repeat_release_dt)
## [1] 674 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 561 0.83234421
## 2: Hispanic 55 0.08160237
## 3: White 57 0.08456973
## 4: Asian 1 0.00148368
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(female)]
## female Count Proportion
## 1: 0 671 0.995548961
## 2: 1 3 0.004451039
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 43 0.06379822
## 2: 1 292 0.43323442
## 3: 2 55 0.08160237
## 4: 3 284 0.42136499
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 636 0.94362018
## 2: Never 15 0.02225519
## 3: Former 23 0.03412463
# Sort data by id --------
incarceration_release_dt[order(id)]
## tick id age race female alc_use_status smoking_status
## 1: 620 51 42 Black 0 1 Current
## 2: 631 51 42 Black 0 1 Current
## 3: 900 51 43 Black 0 1 Current
## 4: 916 51 43 Black 0 1 Current
## 5: 919 51 43 Black 0 1 Current
## ---
## 2423: 10924 15168 35 Asian 0 1 Current
## 2424: 10272 15313 65 Hispanic 0 3 Never
## 2425: 10309 15313 65 Hispanic 0 3 Never
## 2426: 10539 15467 23 White 0 0 Current
## 2427: 10681 15467 24 White 0 0 Current
## event_type
## 1: Incarceration
## 2: Release
## 3: Incarceration
## 4: Release
## 5: Incarceration
## ---
## 2423: Release
## 2424: Incarceration
## 2425: Release
## 2426: Incarceration
## 2427: Release