library(dplyr)
library(readxl)
library(stringr)
library(tidyr)
library(data.table)
library(knitr)

stops <- read_excel("ignore/RP-2014-2015-Data-3-9-16.xlsx", sheet=1)
names(stops)[names(stops) == 'Department Name'] <- 'DepartmentName'


stops$ethnicity <- ifelse(((stops$SubjectRaceCode ==  "W") & (stops$SubjectEthnicityCode =="N")), "White", "Minority")
stops$RE <- paste0(stops$SubjectRaceCode, stops$SubjectEthnicityCode)
stops$RE <- gsub("AH", "Hispanic", stops$RE)
stops$RE <- gsub("AM", "Middle-eastern", stops$RE)
stops$RE <- gsub("AN", "Asian", stops$RE)
stops$RE <- gsub("BH", "Black", stops$RE)
stops$RE <- gsub("BM", "Black", stops$RE)
stops$RE <- gsub("BN", "Black", stops$RE)
stops$RE <- gsub("IH", "Indian", stops$RE)
stops$RE <- gsub("IM", "Middle-eastern", stops$RE)
stops$RE <- gsub("IN", "Indian", stops$RE)
stops$RE <- gsub("WH", "Hispanic", stops$RE)
stops$RE <- gsub("WM", "Middle-eastern", stops$RE)
stops$RE <- gsub("WN", "White", stops$RE)

# Adjusting for state police troops

stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0000", "State Police: Headquarters", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0023", "State Police: Headquarters", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0029", "State Police: Headquarters", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP1900", "State Police: Headquarters", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP2900", "State Police: Headquarters", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP3800", "State Police: Headquarters", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0200", "State Police: Troop A", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0300", "State Police: Troop B", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0400", "State Police: Troop C", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0500", "State Police: Troop D", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0600", "State Police: Troop E", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0700", "State Police: Troop F", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0800", "State Police: Troop G", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP0900", "State Police: Troop H", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP1000", "State Police: Troop I", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP1100", "State Police: Troop J", stops$DepartmentName)
stops$DepartmentName <- ifelse(stops$OrganizationIdentificationID=="CTCSP1200", "State Police: Troop K", stops$DepartmentName)

officer_departments <- stops %>%
  dplyr::select(DepartmentName, ReportingOfficerIdentificationID) %>%
  unique()

officer_departments$ReportingOfficerIdentificationID <- paste0(officer_departments$DepartmentName, "--", officer_departments$ReportingOfficerIdentificationID)
stops$ReportingOfficerIdentificationID <- paste0(stops$DepartmentName, "--", stops$ReportingOfficerIdentificationID)

stops$VehicleSearchedIndicator <- gsub("0", "No", as.character(stops$VehicleSearchedIndicator))
stops$VehicleSearchedIndicator <- gsub("1", "Yes", as.character(stops$VehicleSearchedIndicator))

stops$ContrabandIndicator <- gsub("0", "No", as.character(stops$ContrabandIndicator))
stops$ContrabandIndicator <- gsub("1", "Yes", as.character(stops$ContrabandIndicator))

stops$CustodialArrestIndicator <- gsub("0", "No", as.character(stops$CustodialArrestIndicator))
stops$CustodialArrestIndicator <- gsub("1", "Yes", as.character(stops$CustodialArrestIndicator))

What’s the overall rate for searches from traffic stops?

overall
by race

What’s the overall contraband discovery rate from those searches?

overall
by race

state_search <- stops %>%
  group_by(VehicleSearchedIndicator) %>%
  summarise(total=n()) %>%
  spread(VehicleSearchedIndicator, total) %>%
  mutate(not_searched_p = round(No/(No+Yes)*100,2), searched_p = round(Yes/(No+Yes)*100,2) )

names(state_search)[names(state_search) == 'No'] <- 'not_searched'
names(state_search)[names(state_search) == 'Yes'] <- 'searched'
state_search$RE <- "State average"
state_search <- state_search[c("RE", "searched", "not_searched", "searched_p", "not_searched_p")]  
  
state_search_race <- stops %>%
  group_by(RE, VehicleSearchedIndicator) %>%
  summarise(total=n()) %>%
  spread(VehicleSearchedIndicator, total) %>%
  mutate(not_searched_p = round(No/(No+Yes)*100,2), searched_p = round(Yes/(No+Yes)*100,2) )


names(state_search_race)[names(state_search_race) == 'No'] <- 'not_searched'
names(state_search_race)[names(state_search_race) == 'Yes'] <- 'searched'

searches_all <- rbind(state_search, state_search_race)

names(searches_all)[names(searches_all) == 'RE'] <- 'Category'
kable(searches_all)

Category	searched	not_searched	searched_p	not_searched_p
State average	17079	569770	2.91	97.09
Asian	111	10862	1.01	98.99
Black	4373	80972	5.12	94.88
Hispanic	3465	66675	4.94	95.06
Indian	42	4154	1.00	99.00
Middle-eastern	230	11250	2.00	98.00
White	8858	395857	2.19	97.81

Contraband found out of all stops

state_contra <- stops %>%
  group_by(ContrabandIndicator) %>%
  summarise(total=n()) %>%
  spread(ContrabandIndicator, total) %>%
  mutate(no_contra_p = round(No/(No+Yes)*100,2), contra_p = round(Yes/(No+Yes)*100,2) )

names(state_contra)[names(state_contra) == 'No'] <- 'no_contra'
names(state_contra)[names(state_contra) == 'Yes'] <- 'contra'
state_contra$RE <- "State average"
state_contra <- state_contra[c("RE", "contra", "no_contra", "contra_p", "no_contra_p")]  
  
state_contra_race <- stops %>%
  group_by(RE, ContrabandIndicator) %>%
  summarise(total=n()) %>%
  spread(ContrabandIndicator, total) %>%
  mutate(no_contra_p = round(No/(No+Yes)*100,2), contra_p = round(Yes/(No+Yes)*100,2) )


names(state_contra_race)[names(state_contra_race) == 'No'] <- 'no_contra'
names(state_contra_race)[names(state_contra_race) == 'Yes'] <- 'contra'

contra_all <- rbind(state_contra, state_contra_race)

names(contra_all)[names(contra_all) == 'RE'] <- 'Category'
kable(contra_all)

Category	contra	no_contra	contra_p	no_contra_p
State average	6408	580441	1.09	98.91
Asian	34	10939	0.31	99.69
Black	1480	83865	1.73	98.27
Hispanic	1133	69007	1.62	98.38
Indian	14	4182	0.33	99.67
Middle-eastern	63	11417	0.55	99.45
White	3684	401031	0.91	99.09

Contraband found out out of those searched

state_contra2 <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(ContrabandIndicator) %>%
  summarise(total=n()) %>%
  spread(ContrabandIndicator, total) %>%
  mutate(no_contra_p = round(No/(No+Yes)*100,2), contra_p = round(Yes/(No+Yes)*100,2) )

names(state_contra2)[names(state_contra2) == 'No'] <- 'no_contra'
names(state_contra2)[names(state_contra2) == 'Yes'] <- 'contra'
state_contra2$RE <- "State average"
state_contra2 <- state_contra2[c("RE", "contra", "no_contra", "contra_p", "no_contra_p")]  
  
state_contra_race2 <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(RE, ContrabandIndicator) %>%
  summarise(total=n()) %>%
  spread(ContrabandIndicator, total) %>%
  mutate(no_contra_p = round(No/(No+Yes)*100,2), contra_p = round(Yes/(No+Yes)*100,2) )


names(state_contra_race2)[names(state_contra_race2) == 'No'] <- 'no_contra'
names(state_contra_race2)[names(state_contra_race2) == 'Yes'] <- 'contra'

contra_all2 <- rbind(state_contra2, state_contra_race2)

names(contra_all2)[names(contra_all2) == 'RE'] <- 'Category'

colnames(contra_all2) <- c("Category", "contra_only", "no_contra_only", "contra_p_only", "no_contra_p_only")

mega_contra <- left_join(contra_all, contra_all2)

## Joining by: "Category"

kable(mega_contra)

Category	contra	no_contra	contra_p	no_contra_p	contra_only	no_contra_only	contra_p_only	no_contra_p_only
State average	6408	580441	1.09	98.91	5768	11311	33.77	66.23
Asian	34	10939	0.31	99.69	27	84	24.32	75.68
Black	1480	83865	1.73	98.27	1294	3079	29.59	70.41
Hispanic	1133	69007	1.62	98.38	967	2498	27.91	72.09
Indian	14	4182	0.33	99.67	14	28	33.33	66.67
Middle-eastern	63	11417	0.55	99.45	55	175	23.91	76.09
White	3684	401031	0.91	99.09	3411	5447	38.51	61.49

# Arrests out of all stops

state_arrests <- stops %>%
  group_by(CustodialArrestIndicator) %>%
  summarise(total=n()) %>%
  spread(CustodialArrestIndicator, total) %>%
  mutate(no_arrests_p = round(No/(No+Yes)*100,2), arrests_p = round(Yes/(No+Yes)*100,2) )

names(state_arrests)[names(state_arrests) == 'No'] <- 'no_arrests'
names(state_arrests)[names(state_arrests) == 'Yes'] <- 'arrests'
state_arrests$RE <- "State average"
state_arrests <- state_arrests[c("RE", "arrests", "no_arrests", "arrests_p", "no_arrests_p")]  
  
state_arrests_race <- stops %>%
  group_by(RE, CustodialArrestIndicator) %>%
  summarise(total=n()) %>%
  spread(CustodialArrestIndicator, total) %>%
  mutate(no_arrests_p = round(No/(No+Yes)*100,2), arrests_p = round(Yes/(No+Yes)*100,2) )


names(state_arrests_race)[names(state_arrests_race) == 'No'] <- 'no_arrests'
names(state_arrests_race)[names(state_arrests_race) == 'Yes'] <- 'arrests'

arrests_all <- rbind(state_arrests, state_arrests_race)

names(arrests_all)[names(arrests_all) == 'RE'] <- 'Category'
kable(arrests_all)

Category	arrests	no_arrests	arrests_p	no_arrests_p
State average	10764	576085	1.83	98.17
Asian	86	10887	0.78	99.22
Black	1939	83406	2.27	97.73
Hispanic	2075	68065	2.96	97.04
Indian	47	4149	1.12	98.88
Middle-eastern	162	11318	1.41	98.59
White	6455	398260	1.59	98.41

Arrests out of those searched

state_arrests2 <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(CustodialArrestIndicator) %>%
  summarise(total=n()) %>%
  spread(CustodialArrestIndicator, total) %>%
  mutate(no_arrests_p = round(No/(No+Yes)*100,2), arrests_p = round(Yes/(No+Yes)*100,2) )

names(state_arrests2)[names(state_arrests2) == 'No'] <- 'no_arrests'
names(state_arrests2)[names(state_arrests2) == 'Yes'] <- 'arrests'
state_arrests2$RE <- "State average"
state_arrests2 <- state_arrests2[c("RE", "arrests", "no_arrests", "arrests_p", "no_arrests_p")]  
  
state_arrests_race2 <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(RE, CustodialArrestIndicator) %>%
  summarise(total=n()) %>%
  spread(CustodialArrestIndicator, total) %>%
  mutate(no_arrests_p = round(No/(No+Yes)*100,2), arrests_p = round(Yes/(No+Yes)*100,2) )


names(state_arrests_race2)[names(state_arrests_race2) == 'No'] <- 'no_arrests'
names(state_arrests_race2)[names(state_arrests_race2) == 'Yes'] <- 'arrests'

arrests_all2 <- rbind(state_arrests2, state_arrests_race2)

names(arrests_all2)[names(contra_all2) == 'RE'] <- 'Category'

colnames(arrests_all2) <- c("Category", "arrests_only", "no_arrests_only", "arrests_p_only", "no_arrests_p_only")

mega_arrests <- left_join(arrests_all, arrests_all2)

## Joining by: "Category"

sca_state <- left_join(searches_all, mega_contra)

## Joining by: "Category"

sca_state <- left_join(sca_state , mega_arrests)

## Joining by: "Category"

sca_table <- sca_state[c("Category", "searched_p", "contra_p_only", "arrests_p_only")]
colnames(sca_table) <- c("Category", "searched", "contrabound found", "arrested")
kable(sca_table)

Category	searched	contrabound found	arrested
State average	2.91	33.77	26.19
Asian	1.01	24.32	27.03
Black	5.12	29.59	21.68
Hispanic	4.94	27.91	24.76
Indian	1.00	33.33	28.57
Middle-eastern	2.00	23.91	20.43
White	2.19	38.51	29.10

What’s the overall rate for searches from traffic stops by town?

overall
by race

What’s the overall contraband discovery rate from those searches by town?

overall
by race

town_search <- stops %>%
  group_by(DepartmentName, VehicleSearchedIndicator) %>%
  summarise(total=n()) %>%
  spread(VehicleSearchedIndicator, total) %>%
  mutate(not_searched_p = round(No/(No+Yes)*100,2), searched_p = round(Yes/(No+Yes)*100,2) )


names(town_search)[names(town_search) == 'No'] <- 'not_searched'
names(town_search)[names(town_search) == 'Yes'] <- 'searched'
town_search$RE <- "State average"
town_search <- town_search[c("RE", "searched", "not_searched", "searched_p", "not_searched_p")]  
  
town_search_race <- stops %>%
  group_by(DepartmentName, RE, VehicleSearchedIndicator) %>%
  summarise(total=n()) %>%
  spread(VehicleSearchedIndicator, total) %>%
  mutate(not_searched_p = round(No/(No+Yes)*100,2), searched_p = round(Yes/(No+Yes)*100,2) ) %>%
  dplyr::select(DepartmentName, RE, searched_p) %>%
  spread(RE, searched_p)

colnames(town_search_race) <- c("DepartmentName", "asian_s", "black_s", "hispanic_s", "indian_s", "middle_eastern_s", "white_s")

Contraband found?

town_contra <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(DepartmentName) %>%
  summarise(searches=n())

town_contra_race2 <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(DepartmentName, RE, ContrabandIndicator) %>%
  summarise(total=n()) %>%
  spread(ContrabandIndicator, total) %>%
  mutate(no_contra_p = round(No/(No+Yes)*100,2), contra_p = round(Yes/(No+Yes)*100,2) ) %>%
  dplyr::select(DepartmentName, RE, contra_p) %>%
  spread(RE, contra_p)

colnames(town_contra_race2) <- c("DepartmentName", "asian_c", "black_c", "hispanic_c", "indian_c", "middle_eastern_c", "white_c")

town_all <- left_join(town_contra, town_search_race)

## Joining by: "DepartmentName"

town_all <- left_join(town_all, town_contra_race2)

## Joining by: "DepartmentName"

kable(town_all)

DepartmentName	searches	asian_s	black_s	hispanic_s	indian_s	middle_eastern_s	white_s	asian_c	black_c	hispanic_c	indian_c	middle_eastern_c	white_c
Ansonia	151	NA	3.40	NA	NA	5.41	2.37	NA	14.81	18.18	NA	23.91	12.50
Avon	10	2.22	1.71	NA	NA	NA	0.60	NA	50.00	NA	NA	NA	NA
Berlin	145	3.90	4.82	5.21	NA	3.88	1.64	NA	10.34	17.95	NA	NA	39.13
Bethel	22	NA	1.03	1.31	NA	NA	0.58	NA	NA	20.00	NA	NA	86.67
Bloomfield	150	NA	4.02	3.68	NA	NA	1.33	NA	41.44	53.85	NA	NA	50.00
Branford	97	NA	4.10	3.78	NA	NA	1.66	NA	18.18	15.38	NA	NA	20.55
Bridgeport	476	NA	12.35	10.03	NA	3.53	3.64	NA	12.35	7.79	NA	NA	14.71
Bristol	117	13.33	3.94	3.29	6.25	NA	1.26	NA	31.82	60.00	NA	NA	54.10
Brookfield	31	NA	7.29	1.32	2.94	NA	1.21	NA	42.86	50.00	NA	NA	80.95
Canton	24	NA	3.92	NA	NA	NA	1.55	NA	NA	NA	NA	NA	45.45
CAPITOL POLICE	3	NA	3.17	2.04	NA	NA	NA	NA	NA	NA	NA	NA	NA
CCSU	8	1.61	NA	1.29	NA	NA	0.11	NA	NA	NA	NA	NA	NA
Cheshire	78	NA	3.60	3.75	NA	NA	1.00	NA	40.00	27.27	NA	NA	55.32
Clinton	134	NA	7.22	4.29	NA	NA	4.67	NA	71.43	22.22	NA	NA	54.24
Coventry	26	NA	5.17	1.64	NA	NA	1.45	NA	33.33	NA	NA	NA	36.36
Cromwell	21	5.26	1.48	NA	NA	3.77	0.93	NA	25.00	NA	NA	NA	57.14
Danbury	390	2.80	7.40	12.02	4.35	12.50	5.62	NA	15.15	3.73	NA	NA	3.16
Darien	62	NA	4.59	3.76	NA	NA	1.85	NA	42.86	33.33	NA	NA	45.45
Derby	261	NA	11.26	15.29	NA	3.70	8.04	NA	2.00	5.77	NA	NA	5.70
DMV	6	NA	0.24	NA	NA	NA	0.31	NA	NA	NA	NA	NA	NA
East Hampton	29	NA	13.64	25.00	NA	NA	5.54	NA	NA	NA	NA	NA	65.22
East Hartford	379	0.89	5.46	4.49	NA	1.16	3.57	NA	50.28	42.86	NA	NA	50.00
East Haven	97	NA	5.06	4.17	NA	3.57	2.68	NA	8.33	15.79	NA	NA	20.00
East Windsor	17	NA	1.97	NA	NA	NA	1.73	NA	33.33	NA	NA	NA	21.43
Easton	4	NA	NA	2.17	NA	NA	0.60	NA	NA	NA	NA	NA	NA
Enfield	160	NA	4.81	4.46	NA	2.74	2.40	NA	37.04	27.78	NA	50.00	27.43
Fairfield	205	2.91	3.92	3.64	NA	1.00	2.13	33.33	51.06	57.14	NA	NA	50.00
Farmington	143	3.16	3.51	4.77	7.41	0.81	2.64	40.00	26.67	36.84	NA	NA	43.43
Glastonbury	331	2.70	8.60	10.25	NA	4.35	7.43	50.00	34.38	24.32	NA	NA	31.37
Granby	10	NA	NA	7.69	NA	NA	0.85	NA	NA	NA	NA	NA	62.50
Greenwich	92	NA	2.66	2.70	2.17	0.52	0.94	NA	26.67	11.43	NA	22.22	15.62
Groton City	33	2.90	2.72	4.33	NA	NA	0.70	NA	66.67	16.67	NA	NA	NA
Groton Town	83	NA	1.94	2.54	NA	NA	1.26	NA	50.00	25.00	NA	NA	54.39
Guilford	24	NA	NA	2.59	NA	NA	0.79	NA	NA	33.33	NA	NA	38.10
Hamden	60	NA	2.23	1.70	NA	0.30	0.74	NA	17.14	NA	NA	50.00	NA
Hartford	61	NA	1.47	1.21	7.14	NA	0.45	NA	23.53	17.65	NA	NA	44.44
Madison	35	NA	NA	1.89	NA	1.24	0.86	NA	NA	NA	NA	12.50	58.33
Manchester	129	NA	4.52	4.19	NA	1.56	1.33	NA	44.64	35.48	NA	NA	46.34
Meriden	174	8.33	9.95	6.59	NA	2.27	5.33	NA	30.23	28.33	NA	NA	28.99
Middlebury	2	NA	NA	9.09	NA	NA	0.65	NA	NA	NA	NA	NA	NA
Middletown	234	3.12	11.01	6.27	NA	NA	6.40	NA	56.94	52.63	NA	NA	50.00
Milford	267	2.08	16.59	12.00	6.67	7.69	6.69	NA	32.35	22.22	NA	NA	41.88
Monroe	62	3.12	1.64	2.38	NA	NA	0.94	NA	20.00	33.33	NA	NA	51.06
Naugatuck	238	NA	6.15	6.13	NA	1.61	4.47	NA	29.03	34.38	NA	NA	36.78
New Britain	320	1.33	5.35	4.41	NA	1.27	2.71	NA	37.65	34.25	NA	50.00	40.70
New Canaan	50	NA	2.11	1.75	NA	1.96	0.77	NA	71.43	55.56	NA	NA	57.58
New Haven	794	1.45	9.02	6.17	1.92	3.03	3.06	NA	9.29	12.34	NA	NA	13.77
New London	116	6.67	11.25	9.84	NA	NA	5.88	NA	20.00	30.00	NA	NA	28.00
New Milford	50	NA	4.97	1.39	NA	2.78	1.07	NA	44.44	NA	NA	NA	45.71
Newington	225	0.83	5.64	6.52	3.45	3.03	3.02	NA	17.02	24.00	NA	33.33	17.35
Newtown	96	0.65	1.62	2.15	NA	1.96	0.83	NA	NA	23.08	NA	NA	35.21
North Branford	19	NA	1.89	20.51	NA	NA	1.12	NA	NA	37.50	NA	NA	90.00
North Haven	96	NA	9.95	12.98	NA	2.04	4.20	NA	13.64	23.53	NA	NA	23.21
Norwalk	243	NA	9.58	6.07	NA	2.78	2.32	NA	27.62	24.24	NA	60.00	25.37
Norwich	361	1.48	8.53	8.65	6.25	NA	5.05	NA	33.00	30.99	NA	NA	34.59
Old Saybrook	119	1.69	0.94	2.11	NA	NA	3.75	NA	NA	25.00	NA	NA	60.18
Orange	72	NA	3.61	1.74	NA	2.48	0.96	NA	35.48	30.00	NA	33.33	25.00
Plainfield	30	NA	3.70	2.86	10.00	NA	1.61	NA	NA	NA	NA	NA	4.00
Plainville	227	NA	10.58	10.89	33.33	5.88	6.09	NA	34.48	48.72	NA	50.00	39.74
Plymouth	57	NA	7.14	3.92	NA	5.00	2.46	NA	14.29	25.00	NA	NA	37.78
Portland	4	NA	NA	NA	NA	NA	2.35	NA	NA	NA	NA	NA	25.00
Putnam	15	NA	NA	8.33	NA	NA	1.41	NA	NA	NA	NA	NA	71.43
Redding	18	NA	1.28	1.26	NA	NA	0.92	NA	NA	NA	NA	NA	6.67
Ridgefield	40	0.72	1.75	0.76	NA	4.55	0.41	NA	16.67	33.33	NA	NA	23.08
Rocky Hill	83	1.35	3.61	3.75	NA	0.77	1.84	NA	40.00	27.27	NA	NA	43.64
SCSU	13	NA	1.19	1.25	NA	NA	1.43	NA	14.29	NA	NA	NA	60.00
Seymour	98	NA	5.37	7.94	NA	NA	2.32	NA	7.69	NA	NA	NA	8.82
Shelton	14	NA	NA	2.17	NA	NA	2.74	NA	NA	NA	NA	NA	15.38
Simsbury	28	NA	2.42	0.90	NA	NA	0.80	NA	25.00	NA	NA	NA	65.22
South Windsor	151	1.00	6.36	5.62	3.70	4.88	2.74	NA	48.84	33.33	NA	NA	48.75
Southington	9	NA	1.12	0.77	NA	3.33	0.13	NA	NA	NA	NA	NA	40.00
Stamford	194	1.09	4.02	5.38	2.99	NA	2.29	NA	13.51	24.62	NA	NA	25.00
State Police: Headquarters	162	0.26	1.69	1.73	1.78	2.47	0.79	NA	30.23	20.00	33.33	25.00	43.21
State Police: Troop A	520	1.05	6.10	4.06	1.97	0.39	1.85	NA	34.25	34.21	75.00	NA	36.51
State Police: Troop B	119	NA	1.68	3.07	NA	1.30	1.37	NA	33.33	53.85	NA	NA	44.44
State Police: Troop C	638	0.78	4.26	4.49	0.76	0.93	2.16	12.50	45.36	25.97	50.00	16.67	51.35
State Police: Troop D	327	0.80	4.30	3.72	1.12	2.51	1.68	33.33	38.89	42.42	NA	20.00	51.64
State Police: Troop E	404	NA	4.08	1.85	0.46	1.12	1.65	NA	33.66	24.14	NA	66.67	35.56
State Police: Troop F	209	0.15	1.54	1.42	0.51	0.51	0.72	NA	37.84	39.29	NA	NA	56.74
State Police: Troop G	386	0.72	2.50	1.63	0.22	1.46	1.13	60.00	25.50	24.32	NA	33.33	28.57
State Police: Troop H	461	0.63	4.17	4.07	0.24	2.33	1.35	66.67	34.22	29.57	NA	20.00	32.00
State Police: Troop I	152	0.35	1.83	2.44	0.63	0.75	0.71	NA	23.91	38.10	NA	NA	27.87
State Police: Troop J	319	0.93	3.75	4.04	NA	0.94	1.27	NA	32.79	36.51	NA	NA	40.00
State Police: Troop K	283	NA	5.12	4.97	NA	4.56	2.05	NA	40.00	52.38	NA	36.36	46.67
Stonington	19	NA	0.91	1.56	NA	NA	0.67	NA	NA	NA	NA	NA	70.59
Stratford	297	8.11	11.80	11.57	NA	6.90	7.10	33.33	23.39	23.44	NA	NA	25.00
Suffield	28	NA	3.45	4.00	NA	4.55	2.03	NA	50.00	50.00	NA	NA	30.43
Thomaston	24	NA	6.25	6.67	NA	50.00	3.09	NA	NA	50.00	NA	NA	30.00
Torrington	78	NA	3.68	3.45	NA	NA	1.17	NA	27.27	7.69	NA	NA	40.74
Trumbull	72	NA	3.23	1.62	NA	4.55	2.52	NA	36.84	42.86	NA	NA	40.91
UCONN	80	2.13	2.40	7.24	NA	2.96	3.10	NA	50.00	45.45	NA	50.00	44.44
Vernon	293	NA	9.54	12.58	NA	6.67	7.42	NA	50.00	51.22	NA	NA	52.02
Wallingford	705	3.82	10.01	13.19	NA	6.77	5.79	40.00	46.07	36.54	NA	33.33	44.39
Waterbury	436	12.50	26.85	22.54	NA	NA	10.19	NA	30.81	16.20	NA	NA	38.89
Waterford	185	5.26	5.01	5.81	NA	NA	3.60	NA	48.28	31.03	NA	NA	45.53
Watertown	24	NA	11.01	5.41	NA	NA	0.75	NA	33.33	25.00	NA	NA	NA
West Hartford	676	1.15	7.15	10.31	1.74	11.90	7.76	33.33	48.94	59.87	66.67	NA	68.36
West Haven	216	NA	4.44	5.12	NA	6.86	2.65	NA	8.96	14.29	NA	NA	20.25
Weston	3	NA	3.12	NA	NA	NA	0.65	NA	NA	NA	NA	NA	50.00
Westport	212	4.40	10.23	6.79	NA	4.08	2.73	25.00	32.26	43.33	NA	NA	48.25
Wethersfield	253	3.12	7.58	5.62	9.09	NA	5.04	NA	35.94	25.00	NA	NA	30.51
Willimantic	124	3.23	9.55	4.73	NA	2.44	2.97	NA	23.81	26.32	NA	50.00	40.00
Wilton	413	3.45	12.83	16.86	1.45	13.64	7.02	33.33	18.87	5.88	NA	NA	6.37
Windsor	85	NA	1.70	2.58	10.00	1.01	1.05	NA	32.56	23.08	NA	NA	26.92
Windsor Locks	92	NA	3.90	5.45	NA	4.29	4.05	NA	7.69	NA	NA	33.33	19.40
Winsted	17	NA	11.54	NA	NA	25.00	2.61	NA	66.67	NA	NA	NA	76.92
Wolcott	24	NA	6.90	11.11	NA	NA	6.15	NA	NA	NA	NA	NA	15.79
Woodbridge	23	NA	4.69	0.74	NA	NA	0.39	NA	22.22	NA	NA	NA	50.00
Yale	97	2.56	12.47	22.45	NA	5.26	2.72	NA	44.90	51.52	NA	NA	53.85

Arrested?

town_arrests_race2 <- stops %>%
  filter(VehicleSearchedIndicator=="Yes") %>%
  group_by(DepartmentName, RE, CustodialArrestIndicator) %>%
  summarise(total=n()) %>%
  spread(CustodialArrestIndicator, total) %>%
  mutate(no_contra_p = round(No/(No+Yes)*100,2), contra_p = round(Yes/(No+Yes)*100,2) ) %>%
  dplyr::select(DepartmentName, RE, contra_p) %>%
  spread(RE, contra_p)

colnames(town_arrests_race2) <- c("DepartmentName", "asian_a", "black_a", "hispanic_a", "indian_a", "middle_eastern_a", "white_a")

town_all <- left_join(town_all, town_arrests_race2)

## Joining by: "DepartmentName"

kable(town_all)

DepartmentName	searches	asian_s	black_s	hispanic_s	indian_s	middle_eastern_s	white_s	asian_c	black_c	hispanic_c	indian_c	middle_eastern_c	white_c	asian_a	black_a	hispanic_a	indian_a	middle_eastern_a	white_a
Ansonia	151	NA	3.40	NA	NA	5.41	2.37	NA	14.81	18.18	NA	23.91	12.50	NA	18.52	18.18	NA	15.22	14.29
Avon	10	2.22	1.71	NA	NA	NA	0.60	NA	50.00	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Berlin	145	3.90	4.82	5.21	NA	3.88	1.64	NA	10.34	17.95	NA	NA	39.13	NA	20.69	10.26	NA	20.00	33.33
Bethel	22	NA	1.03	1.31	NA	NA	0.58	NA	NA	20.00	NA	NA	86.67	NA	50.00	40.00	NA	NA	13.33
Bloomfield	150	NA	4.02	3.68	NA	NA	1.33	NA	41.44	53.85	NA	NA	50.00	NA	20.72	7.69	NA	NA	30.77
Branford	97	NA	4.10	3.78	NA	NA	1.66	NA	18.18	15.38	NA	NA	20.55	NA	9.09	NA	NA	NA	28.77
Bridgeport	476	NA	12.35	10.03	NA	3.53	3.64	NA	12.35	7.79	NA	NA	14.71	NA	14.74	10.39	NA	NA	19.12
Bristol	117	13.33	3.94	3.29	6.25	NA	1.26	NA	31.82	60.00	NA	NA	54.10	12.50	13.64	28.00	NA	NA	22.95
Brookfield	31	NA	7.29	1.32	2.94	NA	1.21	NA	42.86	50.00	NA	NA	80.95	NA	14.29	NA	NA	NA	4.76
Canton	24	NA	3.92	NA	NA	NA	1.55	NA	NA	NA	NA	NA	45.45	NA	NA	NA	NA	NA	50.00
CAPITOL POLICE	3	NA	3.17	2.04	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
CCSU	8	1.61	NA	1.29	NA	NA	0.11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Cheshire	78	NA	3.60	3.75	NA	NA	1.00	NA	40.00	27.27	NA	NA	55.32	NA	5.00	18.18	NA	NA	19.15
Clinton	134	NA	7.22	4.29	NA	NA	4.67	NA	71.43	22.22	NA	NA	54.24	NA	14.29	44.44	NA	NA	52.54
Coventry	26	NA	5.17	1.64	NA	NA	1.45	NA	33.33	NA	NA	NA	36.36	NA	33.33	NA	NA	NA	40.91
Cromwell	21	5.26	1.48	NA	NA	3.77	0.93	NA	25.00	NA	NA	NA	57.14	NA	NA	NA	NA	NA	35.71
Danbury	390	2.80	7.40	12.02	4.35	12.50	5.62	NA	15.15	3.73	NA	NA	3.16	NA	15.15	14.29	NA	NA	11.58
Darien	62	NA	4.59	3.76	NA	NA	1.85	NA	42.86	33.33	NA	NA	45.45	NA	28.57	26.67	NA	NA	30.30
Derby	261	NA	11.26	15.29	NA	3.70	8.04	NA	2.00	5.77	NA	NA	5.70	NA	4.00	11.54	NA	NA	13.92
DMV	6	NA	0.24	NA	NA	NA	0.31	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	20.00
East Hampton	29	NA	13.64	25.00	NA	NA	5.54	NA	NA	NA	NA	NA	65.22	NA	NA	66.67	NA	NA	52.17
East Hartford	379	0.89	5.46	4.49	NA	1.16	3.57	NA	50.28	42.86	NA	NA	50.00	NA	22.03	23.47	NA	NA	27.45
East Haven	97	NA	5.06	4.17	NA	3.57	2.68	NA	8.33	15.79	NA	NA	20.00	NA	25.00	36.84	NA	NA	23.08
East Windsor	17	NA	1.97	NA	NA	NA	1.73	NA	33.33	NA	NA	NA	21.43	NA	33.33	NA	NA	NA	14.29
Easton	4	NA	NA	2.17	NA	NA	0.60	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Enfield	160	NA	4.81	4.46	NA	2.74	2.40	NA	37.04	27.78	NA	50.00	27.43	NA	NA	11.11	NA	NA	15.93
Fairfield	205	2.91	3.92	3.64	NA	1.00	2.13	33.33	51.06	57.14	NA	NA	50.00	66.67	17.02	23.81	NA	NA	33.04
Farmington	143	3.16	3.51	4.77	7.41	0.81	2.64	40.00	26.67	36.84	NA	NA	43.43	80.00	13.33	31.58	75.00	NA	48.48
Glastonbury	331	2.70	8.60	10.25	NA	4.35	7.43	50.00	34.38	24.32	NA	NA	31.37	NA	15.62	21.62	NA	33.33	23.53
Granby	10	NA	NA	7.69	NA	NA	0.85	NA	NA	NA	NA	NA	62.50	NA	NA	NA	NA	NA	37.50
Greenwich	92	NA	2.66	2.70	2.17	0.52	0.94	NA	26.67	11.43	NA	22.22	15.62	NA	33.33	34.29	NA	22.22	18.75
Groton City	33	2.90	2.72	4.33	NA	NA	0.70	NA	66.67	16.67	NA	NA	NA	NA	66.67	16.67	NA	NA	10.00
Groton Town	83	NA	1.94	2.54	NA	NA	1.26	NA	50.00	25.00	NA	NA	54.39	NA	14.29	16.67	NA	NA	15.79
Guilford	24	NA	NA	2.59	NA	NA	0.79	NA	NA	33.33	NA	NA	38.10	NA	NA	66.67	NA	NA	61.90
Hamden	60	NA	2.23	1.70	NA	0.30	0.74	NA	17.14	NA	NA	50.00	NA	NA	14.29	NA	NA	NA	18.75
Hartford	61	NA	1.47	1.21	7.14	NA	0.45	NA	23.53	17.65	NA	NA	44.44	NA	17.65	41.18	NA	NA	55.56
Madison	35	NA	NA	1.89	NA	1.24	0.86	NA	NA	NA	NA	12.50	58.33	NA	NA	33.33	NA	37.50	37.50
Manchester	129	NA	4.52	4.19	NA	1.56	1.33	NA	44.64	35.48	NA	NA	46.34	NA	16.07	29.03	NA	NA	26.83
Meriden	174	8.33	9.95	6.59	NA	2.27	5.33	NA	30.23	28.33	NA	NA	28.99	NA	23.26	30.00	NA	NA	31.88
Middlebury	2	NA	NA	9.09	NA	NA	0.65	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Middletown	234	3.12	11.01	6.27	NA	NA	6.40	NA	56.94	52.63	NA	NA	50.00	NA	8.33	10.53	NA	NA	14.08
Milford	267	2.08	16.59	12.00	6.67	7.69	6.69	NA	32.35	22.22	NA	NA	41.88	NA	51.47	55.56	NA	NA	42.50
Monroe	62	3.12	1.64	2.38	NA	NA	0.94	NA	20.00	33.33	NA	NA	51.06	NA	40.00	33.33	NA	NA	14.89
Naugatuck	238	NA	6.15	6.13	NA	1.61	4.47	NA	29.03	34.38	NA	NA	36.78	NA	29.03	21.88	NA	NA	34.48
New Britain	320	1.33	5.35	4.41	NA	1.27	2.71	NA	37.65	34.25	NA	50.00	40.70	NA	28.24	26.03	NA	NA	27.91
New Canaan	50	NA	2.11	1.75	NA	1.96	0.77	NA	71.43	55.56	NA	NA	57.58	NA	NA	44.44	NA	NA	3.03
New Haven	794	1.45	9.02	6.17	1.92	3.03	3.06	NA	9.29	12.34	NA	NA	13.77	NA	10.51	13.64	NA	NA	15.94
New London	116	6.67	11.25	9.84	NA	NA	5.88	NA	20.00	30.00	NA	NA	28.00	NA	48.57	43.33	NA	NA	50.00
New Milford	50	NA	4.97	1.39	NA	2.78	1.07	NA	44.44	NA	NA	NA	45.71	NA	11.11	20.00	NA	NA	11.43
Newington	225	0.83	5.64	6.52	3.45	3.03	3.02	NA	17.02	24.00	NA	33.33	17.35	NA	10.64	9.33	NA	NA	17.35
Newtown	96	0.65	1.62	2.15	NA	1.96	0.83	NA	NA	23.08	NA	NA	35.21	NA	33.33	46.15	NA	NA	38.03
North Branford	19	NA	1.89	20.51	NA	NA	1.12	NA	NA	37.50	NA	NA	90.00	NA	NA	NA	NA	NA	30.00
North Haven	96	NA	9.95	12.98	NA	2.04	4.20	NA	13.64	23.53	NA	NA	23.21	NA	NA	17.65	NA	NA	26.79
Norwalk	243	NA	9.58	6.07	NA	2.78	2.32	NA	27.62	24.24	NA	60.00	25.37	NA	20.00	28.79	NA	20.00	22.39
Norwich	361	1.48	8.53	8.65	6.25	NA	5.05	NA	33.00	30.99	NA	NA	34.59	50.00	24.00	26.76	NA	NA	28.11
Old Saybrook	119	1.69	0.94	2.11	NA	NA	3.75	NA	NA	25.00	NA	NA	60.18	NA	NA	75.00	NA	NA	45.13
Orange	72	NA	3.61	1.74	NA	2.48	0.96	NA	35.48	30.00	NA	33.33	25.00	NA	38.71	30.00	NA	NA	50.00
Plainfield	30	NA	3.70	2.86	10.00	NA	1.61	NA	NA	NA	NA	NA	4.00	NA	50.00	NA	NA	NA	8.00
Plainville	227	NA	10.58	10.89	33.33	5.88	6.09	NA	34.48	48.72	NA	50.00	39.74	NA	6.90	5.13	NA	50.00	5.77
Plymouth	57	NA	7.14	3.92	NA	5.00	2.46	NA	14.29	25.00	NA	NA	37.78	NA	NA	50.00	NA	NA	37.78
Portland	4	NA	NA	NA	NA	NA	2.35	NA	NA	NA	NA	NA	25.00	NA	NA	NA	NA	NA	NA
Putnam	15	NA	NA	8.33	NA	NA	1.41	NA	NA	NA	NA	NA	71.43	NA	NA	NA	NA	NA	57.14
Redding	18	NA	1.28	1.26	NA	NA	0.92	NA	NA	NA	NA	NA	6.67	NA	NA	NA	NA	NA	46.67
Ridgefield	40	0.72	1.75	0.76	NA	4.55	0.41	NA	16.67	33.33	NA	NA	23.08	NA	NA	16.67	NA	NA	19.23
Rocky Hill	83	1.35	3.61	3.75	NA	0.77	1.84	NA	40.00	27.27	NA	NA	43.64	NA	13.33	NA	NA	NA	20.00
SCSU	13	NA	1.19	1.25	NA	NA	1.43	NA	14.29	NA	NA	NA	60.00	NA	28.57	NA	NA	NA	20.00
Seymour	98	NA	5.37	7.94	NA	NA	2.32	NA	7.69	NA	NA	NA	8.82	NA	30.77	NA	NA	NA	25.00
Shelton	14	NA	NA	2.17	NA	NA	2.74	NA	NA	NA	NA	NA	15.38	NA	NA	NA	NA	NA	15.38
Simsbury	28	NA	2.42	0.90	NA	NA	0.80	NA	25.00	NA	NA	NA	65.22	NA	25.00	NA	NA	NA	30.43
South Windsor	151	1.00	6.36	5.62	3.70	4.88	2.74	NA	48.84	33.33	NA	NA	48.75	NA	4.65	16.67	NA	NA	3.75
Southington	9	NA	1.12	0.77	NA	3.33	0.13	NA	NA	NA	NA	NA	40.00	NA	NA	NA	NA	NA	20.00
Stamford	194	1.09	4.02	5.38	2.99	NA	2.29	NA	13.51	24.62	NA	NA	25.00	NA	8.11	9.23	NA	NA	9.09
State Police: Headquarters	162	0.26	1.69	1.73	1.78	2.47	0.79	NA	30.23	20.00	33.33	25.00	43.21	NA	18.60	26.67	NA	50.00	23.46
State Police: Troop A	520	1.05	6.10	4.06	1.97	0.39	1.85	NA	34.25	34.21	75.00	NA	36.51	66.67	28.08	22.81	25.00	NA	32.14
State Police: Troop B	119	NA	1.68	3.07	NA	1.30	1.37	NA	33.33	53.85	NA	NA	44.44	NA	16.67	15.38	NA	NA	18.18
State Police: Troop C	638	0.78	4.26	4.49	0.76	0.93	2.16	12.50	45.36	25.97	50.00	16.67	51.35	NA	10.31	7.79	50.00	NA	14.13
State Police: Troop D	327	0.80	4.30	3.72	1.12	2.51	1.68	33.33	38.89	42.42	NA	20.00	51.64	33.33	16.67	42.42	NA	NA	24.18
State Police: Troop E	404	NA	4.08	1.85	0.46	1.12	1.65	NA	33.66	24.14	NA	66.67	35.56	NA	15.84	20.69	NA	66.67	21.11
State Police: Troop F	209	0.15	1.54	1.42	0.51	0.51	0.72	NA	37.84	39.29	NA	NA	56.74	NA	27.03	32.14	NA	NA	34.04
State Police: Troop G	386	0.72	2.50	1.63	0.22	1.46	1.13	60.00	25.50	24.32	NA	33.33	28.57	40.00	28.19	40.54	NA	NA	42.86
State Police: Troop H	461	0.63	4.17	4.07	0.24	2.33	1.35	66.67	34.22	29.57	NA	20.00	32.00	33.33	32.09	36.52	NA	40.00	48.67
State Police: Troop I	152	0.35	1.83	2.44	0.63	0.75	0.71	NA	23.91	38.10	NA	NA	27.87	NA	41.30	42.86	NA	NA	47.54
State Police: Troop J	319	0.93	3.75	4.04	NA	0.94	1.27	NA	32.79	36.51	NA	NA	40.00	NA	29.51	39.68	NA	NA	38.95
State Police: Troop K	283	NA	5.12	4.97	NA	4.56	2.05	NA	40.00	52.38	NA	36.36	46.67	NA	17.14	14.29	NA	27.27	28.72
Stonington	19	NA	0.91	1.56	NA	NA	0.67	NA	NA	NA	NA	NA	70.59	NA	NA	NA	NA	NA	11.76
Stratford	297	8.11	11.80	11.57	NA	6.90	7.10	33.33	23.39	23.44	NA	NA	25.00	33.33	25.81	26.56	NA	NA	33.65
Suffield	28	NA	3.45	4.00	NA	4.55	2.03	NA	50.00	50.00	NA	NA	30.43	NA	NA	NA	NA	NA	NA
Thomaston	24	NA	6.25	6.67	NA	50.00	3.09	NA	NA	50.00	NA	NA	30.00	NA	NA	NA	NA	NA	75.00
Torrington	78	NA	3.68	3.45	NA	NA	1.17	NA	27.27	7.69	NA	NA	40.74	NA	9.09	38.46	NA	NA	31.48
Trumbull	72	NA	3.23	1.62	NA	4.55	2.52	NA	36.84	42.86	NA	NA	40.91	NA	15.79	14.29	NA	NA	25.00
UCONN	80	2.13	2.40	7.24	NA	2.96	3.10	NA	50.00	45.45	NA	50.00	44.44	33.33	16.67	27.27	NA	16.67	44.44
Vernon	293	NA	9.54	12.58	NA	6.67	7.42	NA	50.00	51.22	NA	NA	52.02	NA	21.15	14.63	NA	NA	28.79
Wallingford	705	3.82	10.01	13.19	NA	6.77	5.79	40.00	46.07	36.54	NA	33.33	44.39	60.00	58.43	64.10	NA	22.22	58.07
Waterbury	436	12.50	26.85	22.54	NA	NA	10.19	NA	30.81	16.20	NA	NA	38.89	NA	32.97	23.94	NA	NA	43.52
Waterford	185	5.26	5.01	5.81	NA	NA	3.60	NA	48.28	31.03	NA	NA	45.53	25.00	34.48	27.59	NA	NA	36.59
Watertown	24	NA	11.01	5.41	NA	NA	0.75	NA	33.33	25.00	NA	NA	NA	NA	25.00	NA	NA	NA	25.00
West Hartford	676	1.15	7.15	10.31	1.74	11.90	7.76	33.33	48.94	59.87	66.67	NA	68.36	66.67	27.66	25.66	33.33	NA	45.65
West Haven	216	NA	4.44	5.12	NA	6.86	2.65	NA	8.96	14.29	NA	NA	20.25	NA	29.85	33.93	NA	42.86	31.65
Weston	3	NA	3.12	NA	NA	NA	0.65	NA	NA	NA	NA	NA	50.00	NA	NA	NA	NA	NA	NA
Westport	212	4.40	10.23	6.79	NA	4.08	2.73	25.00	32.26	43.33	NA	NA	48.25	25.00	24.19	23.33	NA	NA	22.81
Wethersfield	253	3.12	7.58	5.62	9.09	NA	5.04	NA	35.94	25.00	NA	NA	30.51	NA	21.88	33.82	NA	NA	27.97
Willimantic	124	3.23	9.55	4.73	NA	2.44	2.97	NA	23.81	26.32	NA	50.00	40.00	NA	9.52	21.05	NA	25.00	23.33
Wilton	413	3.45	12.83	16.86	1.45	13.64	7.02	33.33	18.87	5.88	NA	NA	6.37	33.33	16.98	9.80	NA	NA	12.35
Windsor	85	NA	1.70	2.58	10.00	1.01	1.05	NA	32.56	23.08	NA	NA	26.92	NA	16.28	30.77	NA	NA	19.23
Windsor Locks	92	NA	3.90	5.45	NA	4.29	4.05	NA	7.69	NA	NA	33.33	19.40	NA	7.69	11.11	NA	33.33	25.37
Winsted	17	NA	11.54	NA	NA	25.00	2.61	NA	66.67	NA	NA	NA	76.92	NA	NA	NA	NA	NA	23.08
Wolcott	24	NA	6.90	11.11	NA	NA	6.15	NA	NA	NA	NA	NA	15.79	NA	NA	NA	NA	NA	21.05
Woodbridge	23	NA	4.69	0.74	NA	NA	0.39	NA	22.22	NA	NA	NA	50.00	NA	5.56	NA	NA	NA	NA
Yale	97	2.56	12.47	22.45	NA	5.26	2.72	NA	44.90	51.52	NA	NA	53.85	NA	40.82	24.24	NA	NA	46.15

search and contraband

Andrew Ba Tran

June 21, 2016

What’s the overall rate for searches from traffic stops?

What’s the overall contraband discovery rate from those searches?

Contraband found out of all stops

Contraband found out out of those searched

Arrests out of those searched

What’s the overall rate for searches from traffic stops by town?

What’s the overall contraband discovery rate from those searches by town?

Contraband found?

Arrested?