# 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': 2853 obs. of 8 variables:
## $ tick : int 1 2 10 24 38 72 81 89 106 106 ...
## $ id : int 5012 2438 1095 1095 5308 5012 1336 1336 3118 2438 ...
## $ age : int 23 84 22 22 33 23 33 33 77 84 ...
## $ race : chr "Black" "White" "White" "White" ...
## $ female : int 0 0 0 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 1 1 3 3 1 1 0 0 1 1 ...
## $ smoking_status: chr "Current" "Current" "Never" "Never" ...
## $ event_type : chr "Incarceration" "Incarceration" "Incarceration" "Release" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(incarceration_release_dt)
## [1] 2853 8
incarceration_release_dt[,.N]
## [1] 2853
# 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] 1440
str(incarceration_dt)
## Classes 'data.table' and 'data.frame': 1440 obs. of 8 variables:
## $ tick : int 1 2 10 38 81 106 122 125 140 145 ...
## $ id : int 5012 2438 1095 5308 1336 3118 236 6435 4586 9402 ...
## $ age : int 23 84 22 33 33 77 64 52 31 22 ...
## $ race : chr "Black" "White" "White" "Hispanic" ...
## $ female : int 0 0 0 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 1 1 3 1 0 1 0 0 0 3 ...
## $ smoking_status: chr "Current" "Current" "Never" "Current" ...
## $ event_type : chr "Incarceration" "Incarceration" "Incarceration" "Incarceration" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(incarceration_dt)
## [1] 1440 8
release_dt[,.N]
## [1] 1413
str(release_dt)
## Classes 'data.table' and 'data.frame': 1413 obs. of 8 variables:
## $ tick : int 24 72 89 106 123 128 158 167 180 218 ...
## $ id : int 1095 5012 1336 2438 5308 3118 4586 6726 6044 8395 ...
## $ age : int 22 23 33 84 33 77 31 69 34 62 ...
## $ race : chr "White" "Black" "Black" "White" ...
## $ female : int 0 0 0 0 0 0 0 0 0 0 ...
## $ alc_use_status: int 3 1 0 1 1 1 0 1 0 0 ...
## $ smoking_status: chr "Never" "Current" "Current" "Current" ...
## $ event_type : chr "Release" "Release" "Release" "Release" ...
## - attr(*, ".internal.selfref")=<externalptr>
dim(release_dt)
## [1] 1413 8
# Analyze state distributions for all incarceration events ------------
n_inc_events <- length(incarceration_dt$id)
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(race)]
## race Count Proportion
## 1: Black 719 0.499305556
## 2: White 423 0.293750000
## 3: Hispanic 290 0.201388889
## 4: Asian 8 0.005555556
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(female)]
## female Count Proportion
## 1: 0 1389 0.96458333
## 2: 1 51 0.03541667
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(alc_use_status)]
## alc_use_status Count Proportion
## 1: 1 487 0.33819444
## 2: 3 481 0.33402778
## 3: 0 417 0.28958333
## 4: 2 55 0.03819444
incarceration_dt[, .(Count = .N,
Proportion = .N/nrow(incarceration_dt)),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 1125 0.78125000
## 2: Never 206 0.14305556
## 3: Former 109 0.07569444
# Analyze state distributions for first-time incarceration events ------------
first_incarceration_dt <- incarceration_dt[order(id, tick), .SD[1], by = id]
head(first_incarceration_dt)
## id tick age race female alc_use_status smoking_status event_type
## 1: 45 10089 61 Hispanic 0 0 Never Incarceration
## 2: 54 6305 47 White 0 1 Former Incarceration
## 3: 67 5690 56 White 0 0 Current Incarceration
## 4: 98 3943 40 White 0 1 Current Incarceration
## 5: 106 2229 79 White 0 0 Current Incarceration
## 6: 168 1157 50 White 0 0 Former Incarceration
dim(first_incarceration_dt)
## [1] 573 8
n_first_inc_agents <- length(first_incarceration_dt$id)
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(race)]
## race Count Proportion
## 1: Hispanic 121 0.2111693
## 2: White 277 0.4834206
## 3: Black 169 0.2949389
## 4: Asian 6 0.0104712
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(female)]
## female Count Proportion
## 1: 0 529 0.92321117
## 2: 1 44 0.07678883
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: 0 196 0.34205934
## 2: 1 254 0.44328098
## 3: 2 18 0.03141361
## 4: 3 105 0.18324607
first_incarceration_dt[, .(Count = .N,
Proportion = .N/n_first_inc_agents),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Never 161 0.2809773
## 2: Former 69 0.1204188
## 3: Current 343 0.5986038
# 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: 67 6100 58 White 0 0 Current Incarceration
## 2: 177 2226 51 Black 0 1 Current Incarceration
## 3: 177 2593 52 Black 0 1 Current Incarceration
## 4: 177 3674 55 Black 0 1 Current Incarceration
## 5: 177 3872 56 Black 0 1 Current Incarceration
## 6: 205 2468 68 Black 0 1 Current Incarceration
## 7: 205 2645 69 Black 0 1 Current Incarceration
## 8: 205 2920 69 Black 0 1 Current Incarceration
## 9: 205 3931 72 Black 0 1 Current Incarceration
## 10: 205 4786 75 Black 0 1 Current Incarceration
## 11: 236 6902 83 Black 0 0 Never Incarceration
## 12: 254 3419 30 Black 0 1 Current Incarceration
## 13: 254 4237 33 Black 0 1 Current Incarceration
## 14: 254 4477 33 Black 0 1 Current Incarceration
## 15: 325 9455 47 Black 0 0 Current Incarceration
## 16: 325 10224 49 Black 0 0 Current Incarceration
## 17: 332 10200 73 Black 0 1 Current Incarceration
## 18: 332 10739 75 Black 0 1 Current Incarceration
## 19: 470 6522 61 Hispanic 0 2 Current Incarceration
## 20: 535 5622 78 Hispanic 0 3 Current Incarceration
## 21: 535 5916 79 Hispanic 0 3 Current Incarceration
## 22: 535 6192 80 Hispanic 0 3 Current Incarceration
## 23: 535 6450 80 Hispanic 0 3 Current Incarceration
## 24: 535 6760 81 Hispanic 0 3 Current Incarceration
## 25: 535 7604 84 Hispanic 0 3 Former Incarceration
## 26: 594 1471 60 Black 0 0 Current Incarceration
## 27: 594 2032 61 Black 0 0 Current Incarceration
## 28: 594 2184 62 Black 0 0 Current Incarceration
## 29: 594 2721 63 Black 0 0 Current Incarceration
## 30: 594 3061 64 Black 0 0 Current Incarceration
## 31: 594 3658 66 Black 0 0 Current Incarceration
## 32: 776 1229 47 Hispanic 0 3 Current Incarceration
## 33: 776 1447 48 Hispanic 0 3 Current Incarceration
## 34: 776 1472 48 Hispanic 0 3 Current Incarceration
## 35: 776 1750 49 Hispanic 0 3 Current Incarceration
## 36: 776 1821 49 Hispanic 0 3 Current Incarceration
## 37: 776 1935 49 Hispanic 0 3 Current Incarceration
## 38: 776 2682 51 Hispanic 0 3 Current Incarceration
## 39: 776 2829 52 Hispanic 0 3 Current Incarceration
## 40: 776 3758 54 Hispanic 0 3 Current Incarceration
## 41: 776 3854 55 Hispanic 0 3 Current Incarceration
## 42: 776 4052 55 Hispanic 0 3 Current Incarceration
## 43: 776 4320 56 Hispanic 0 3 Current Incarceration
## 44: 776 4809 57 Hispanic 0 3 Current Incarceration
## 45: 776 5026 58 Hispanic 0 3 Former Incarceration
## 46: 776 5578 59 Hispanic 0 3 Current Incarceration
## 47: 776 6058 61 Hispanic 0 3 Current Incarceration
## 48: 776 6273 61 Hispanic 0 3 Current Incarceration
## 49: 776 6630 62 Hispanic 0 3 Current Incarceration
## 50: 776 6706 63 Hispanic 0 3 Current Incarceration
## 51: 820 7158 50 White 0 3 Current Incarceration
## 52: 873 5334 67 Hispanic 0 1 Current Incarceration
## 53: 873 5364 67 Hispanic 0 1 Current Incarceration
## 54: 940 10483 72 Black 0 0 Current Incarceration
## 55: 940 10636 72 Black 0 0 Current Incarceration
## 56: 941 2943 79 White 0 0 Current Incarceration
## 57: 975 7475 45 Black 0 3 Never Incarceration
## 58: 975 8060 46 Black 0 3 Never Incarceration
## 59: 975 9127 49 Black 0 3 Never Incarceration
## 60: 975 9689 51 Black 0 3 Never Incarceration
## 61: 975 10821 54 Black 0 3 Never Incarceration
## 62: 981 331 40 Black 0 3 Current Incarceration
## 63: 981 378 40 Black 0 3 Current Incarceration
## 64: 981 2035 44 Black 0 3 Current Incarceration
## 65: 981 2089 45 Black 0 3 Current Incarceration
## 66: 981 2230 45 Black 0 3 Current Incarceration
## 67: 981 2330 45 Black 0 3 Current Incarceration
## 68: 981 2401 45 Black 0 3 Current Incarceration
## 69: 981 2514 46 Black 0 3 Current Incarceration
## 70: 981 3592 49 Black 0 3 Former Incarceration
## 71: 981 4019 50 Black 0 3 Current Incarceration
## 72: 981 4080 50 Black 0 3 Current Incarceration
## 73: 981 4403 51 Black 0 3 Current Incarceration
## 74: 981 4471 51 Black 0 3 Current Incarceration
## 75: 981 6917 58 Black 0 3 Current Incarceration
## 76: 981 7122 58 Black 0 3 Current Incarceration
## 77: 981 7155 58 Black 0 3 Current Incarceration
## 78: 981 7354 59 Black 0 3 Current Incarceration
## 79: 981 7573 60 Black 0 3 Current Incarceration
## 80: 981 7588 60 Black 0 3 Current Incarceration
## 81: 981 7688 60 Black 0 3 Current Incarceration
## 82: 981 7722 60 Black 0 3 Current Incarceration
## 83: 981 7879 60 Black 0 3 Current Incarceration
## 84: 981 8046 61 Black 0 3 Current Incarceration
## 85: 981 8194 61 Black 0 3 Current Incarceration
## 86: 981 9134 64 Black 0 3 Former Incarceration
## 87: 1064 5962 73 White 0 1 Former Incarceration
## 88: 1290 6033 67 White 0 3 Current Incarceration
## 89: 1290 6465 68 White 0 3 Current Incarceration
## 90: 1290 6655 68 White 0 3 Current Incarceration
## 91: 1290 6879 69 White 0 3 Current Incarceration
## 92: 1290 7055 69 White 0 3 Current Incarceration
## 93: 1290 7935 72 White 0 3 Current Incarceration
## 94: 1290 8572 73 White 0 3 Current Incarceration
## 95: 1290 8650 74 White 0 3 Current Incarceration
## 96: 1290 8835 74 White 0 3 Current Incarceration
## 97: 1322 5401 58 White 0 3 Current Incarceration
## 98: 1322 5474 58 White 0 3 Current Incarceration
## 99: 1329 3074 39 Black 0 1 Former Incarceration
## 100: 1329 3312 40 Black 0 1 Current Incarceration
## id tick age race female alc_use_status smoking_status event_type
dim(repeat_incarceration_dt)
## [1] 867 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: White 146 0.168396770
## 2: Black 550 0.634371396
## 3: Hispanic 169 0.194925029
## 4: Asian 2 0.002306805
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(female)]
## female Count Proportion
## 1: 0 860 0.991926182
## 2: 1 7 0.008073818
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: 0 221 0.25490196
## 2: 1 233 0.26874279
## 3: 2 37 0.04267589
## 4: 3 376 0.43367935
repeat_incarceration_dt[, .(Count = .N,
Proportion = .N/n_repeat_inc_events),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 782 0.90196078
## 2: Never 45 0.05190311
## 3: Former 40 0.04613610
# Analyze state distributions for all release events ------------
n_rel_events <- length(release_dt$id)
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(race)]
## race Count Proportion
## 1: White 416 0.294409059
## 2: Black 702 0.496815287
## 3: Hispanic 287 0.203113942
## 4: Asian 8 0.005661713
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(female)]
## female Count Proportion
## 1: 0 1363 0.9646143
## 2: 1 50 0.0353857
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(alc_use_status)]
## alc_use_status Count Proportion
## 1: 3 471 0.33333333
## 2: 1 475 0.33616419
## 3: 0 412 0.29157820
## 4: 2 55 0.03892427
release_dt[, .(Count = .N,
Proportion = .N/nrow(release_dt)),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Never 200 0.14154282
## 2: Current 1107 0.78343949
## 3: Former 106 0.07501769
# Analyze state distributions for first-time release events ------------
first_release_dt <- release_dt[order(id, tick), .SD[1], by = id]
head(first_release_dt)
## id tick age race female alc_use_status smoking_status event_type
## 1: 45 10110 61 Hispanic 0 0 Never Release
## 2: 54 6328 48 White 0 1 Former Release
## 3: 67 5826 57 White 0 0 Current Release
## 4: 98 3953 40 White 0 1 Current Release
## 5: 106 2256 79 White 0 0 Current Release
## 6: 168 1170 50 White 0 0 Former Release
dim(first_release_dt)
## [1] 567 8
n_first_rel_agents <- length(first_release_dt$id)
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(race)]
## race Count Proportion
## 1: Hispanic 121 0.21340388
## 2: White 274 0.48324515
## 3: Black 166 0.29276896
## 4: Asian 6 0.01058201
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(female)]
## female Count Proportion
## 1: 0 524 0.92416226
## 2: 1 43 0.07583774
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: 0 195 0.34391534
## 2: 1 250 0.44091711
## 3: 2 18 0.03174603
## 4: 3 104 0.18342152
first_release_dt[, .(Count = .N,
Proportion = .N/n_first_rel_agents),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Never 156 0.2751323
## 2: Former 68 0.1199295
## 3: Current 343 0.6049383
# 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: 67 6214 58 White 0 0 Current Release
## 2: 177 2360 51 Black 0 1 Current Release
## 3: 177 2666 52 Black 0 1 Current Release
## 4: 177 3697 55 Black 0 1 Current Release
## 5: 177 3930 56 Black 0 1 Current Release
## 6: 205 2482 68 Black 0 1 Current Release
## 7: 205 2668 69 Black 0 1 Current Release
## 8: 205 3129 70 Black 0 1 Current Release
## 9: 205 4075 73 Black 0 1 Current Release
## 10: 205 4805 75 Black 0 1 Current Release
## 11: 236 7056 83 Black 0 0 Never Release
## 12: 254 3431 30 Black 0 1 Current Release
## 13: 254 4297 33 Black 0 1 Current Release
## 14: 254 4489 33 Black 0 1 Current Release
## 15: 325 9476 47 Black 0 0 Current Release
## 16: 325 10252 49 Black 0 0 Current Release
## 17: 332 10322 74 Black 0 1 Current Release
## 18: 332 10844 75 Black 0 1 Current Release
## 19: 470 6664 61 Hispanic 0 2 Current Release
## 20: 535 5774 79 Hispanic 0 3 Current Release
## 21: 535 6054 79 Hispanic 0 3 Current Release
## 22: 535 6353 80 Hispanic 0 3 Current Release
## 23: 535 6558 81 Hispanic 0 3 Current Release
## 24: 535 6775 81 Hispanic 0 3 Current Release
## 25: 535 7614 84 Hispanic 0 3 Former Release
## 26: 594 1576 60 Black 0 0 Current Release
## 27: 594 2041 61 Black 0 0 Current Release
## 28: 594 2283 62 Black 0 0 Current Release
## 29: 594 2915 64 Black 0 0 Current Release
## 30: 594 3075 64 Black 0 0 Current Release
## 31: 594 3790 66 Black 0 0 Current Release
## 32: 776 1394 48 Hispanic 0 3 Current Release
## 33: 776 1454 48 Hispanic 0 3 Current Release
## 34: 776 1552 48 Hispanic 0 3 Current Release
## 35: 776 1784 49 Hispanic 0 3 Current Release
## 36: 776 1843 49 Hispanic 0 3 Current Release
## 37: 776 1972 50 Hispanic 0 3 Current Release
## 38: 776 2691 52 Hispanic 0 3 Current Release
## 39: 776 2954 52 Hispanic 0 3 Current Release
## 40: 776 3838 55 Hispanic 0 3 Current Release
## 41: 776 4011 55 Hispanic 0 3 Current Release
## 42: 776 4188 56 Hispanic 0 3 Current Release
## 43: 776 4451 56 Hispanic 0 3 Current Release
## 44: 776 4832 57 Hispanic 0 3 Current Release
## 45: 776 5107 58 Hispanic 0 3 Former Release
## 46: 776 5603 59 Hispanic 0 3 Current Release
## 47: 776 6076 61 Hispanic 0 3 Current Release
## 48: 776 6411 62 Hispanic 0 3 Current Release
## 49: 776 6651 62 Hispanic 0 3 Current Release
## 50: 776 6725 63 Hispanic 0 3 Current Release
## 51: 820 7257 50 White 0 3 Current Release
## 52: 873 5359 67 Hispanic 0 1 Current Release
## 53: 873 5381 67 Hispanic 0 1 Current Release
## 54: 940 10632 72 Black 0 0 Current Release
## 55: 940 10653 72 Black 0 0 Current Release
## 56: 941 2970 80 White 0 0 Current Release
## 57: 975 7492 45 Black 0 3 Never Release
## 58: 975 8337 47 Black 0 3 Never Release
## 59: 975 9137 49 Black 0 3 Never Release
## 60: 975 9856 51 Black 0 3 Never Release
## 61: 975 10892 54 Black 0 3 Never Release
## 62: 981 351 40 Black 0 3 Current Release
## 63: 981 2006 44 Black 0 3 Current Release
## 64: 981 2084 45 Black 0 3 Current Release
## 65: 981 2221 45 Black 0 3 Current Release
## 66: 981 2327 45 Black 0 3 Current Release
## 67: 981 2340 45 Black 0 3 Current Release
## 68: 981 2409 45 Black 0 3 Current Release
## 69: 981 2674 46 Black 0 3 Current Release
## 70: 981 3608 49 Black 0 3 Former Release
## 71: 981 4031 50 Black 0 3 Current Release
## 72: 981 4201 50 Black 0 3 Current Release
## 73: 981 4422 51 Black 0 3 Current Release
## 74: 981 6548 57 Black 0 3 Current Release
## 75: 981 6954 58 Black 0 3 Current Release
## 76: 981 7140 58 Black 0 3 Current Release
## 77: 981 7229 59 Black 0 3 Current Release
## 78: 981 7544 59 Black 0 3 Current Release
## 79: 981 7585 60 Black 0 3 Current Release
## 80: 981 7672 60 Black 0 3 Current Release
## 81: 981 7707 60 Black 0 3 Current Release
## 82: 981 7810 60 Black 0 3 Current Release
## 83: 981 8044 61 Black 0 3 Current Release
## 84: 981 8090 61 Black 0 3 Current Release
## 85: 981 8205 61 Black 0 3 Current Release
## 86: 1064 5980 73 White 0 1 Former Release
## 87: 1290 6059 67 White 0 3 Current Release
## 88: 1290 6599 68 White 0 3 Current Release
## 89: 1290 6710 68 White 0 3 Current Release
## 90: 1290 6886 69 White 0 3 Current Release
## 91: 1290 7166 70 White 0 3 Current Release
## 92: 1290 7960 72 White 0 3 Current Release
## 93: 1290 8613 74 White 0 3 Current Release
## 94: 1290 8668 74 White 0 3 Current Release
## 95: 1290 9128 75 White 0 3 Current Release
## 96: 1322 5414 58 White 0 3 Current Release
## 97: 1322 5618 59 White 0 3 Current Release
## 98: 1329 3088 39 Black 0 1 Former Release
## 99: 1329 3352 40 Black 0 1 Current Release
## 100: 1329 3428 40 Black 0 1 Current Release
## id tick age race female alc_use_status smoking_status event_type
dim(repeat_release_dt)
## [1] 846 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: White 142 0.167848700
## 2: Black 536 0.633569740
## 3: Hispanic 166 0.196217494
## 4: Asian 2 0.002364066
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(female)]
## female Count Proportion
## 1: 0 839 0.991725768
## 2: 1 7 0.008274232
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: 0 217 0.25650118
## 2: 1 225 0.26595745
## 3: 2 37 0.04373522
## 4: 3 367 0.43380615
repeat_release_dt[, .(Count = .N,
Proportion = .N/n_repeat_rel_events),
by = .(smoking_status)]
## smoking_status Count Proportion
## 1: Current 764 0.90307329
## 2: Never 44 0.05200946
## 3: Former 38 0.04491726
# Sort data by id --------
incarceration_release_dt[order(id)]
## tick id age race female alc_use_status smoking_status
## 1: 10089 45 61 Hispanic 0 0 Never
## 2: 10110 45 61 Hispanic 0 0 Never
## 3: 6305 54 47 White 0 1 Former
## 4: 6328 54 48 White 0 1 Former
## 5: 5690 67 56 White 0 0 Current
## ---
## 2849: 10926 14679 43 Black 0 1 Current
## 2850: 10361 14715 37 Hispanic 1 1 Current
## 2851: 10373 14715 37 Hispanic 1 1 Current
## 2852: 10761 15310 35 White 0 0 Current
## 2853: 10925 15310 36 White 0 0 Current
## event_type
## 1: Incarceration
## 2: Release
## 3: Incarceration
## 4: Release
## 5: Incarceration
## ---
## 2849: Release
## 2850: Incarceration
## 2851: Release
## 2852: Incarceration
## 2853: Release