Questions to ask:

Where do people tend to die (urban, rural, mixed)

distance to clinics, pharmacies, hospitals

correlation between deaths and admissions to these distances?

treat$Admissions[is.na(treat$Admissions)] <- 0

gg <- ggplot(treat, aes(y=Admissions, x=FiscalYear))
gg <- gg + geom_bar(stat="identity")
gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
#gg <- gg + facet_wrap(~state, scale="free", ncol=5)
#gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
#        labels=c("2012", "2016"))
gg <- gg + labs(x=NULL, y=NULL, 
                title="Opioid Related Treatment Admissions by Town",
                subtitle="",
                caption="Department of Mental Health and Addiction Services")
gg <- gg + theme_minimal(base_family="Lato Regular")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(plot.title=element_text(family="Lato Black"))
gg

# gg <- ggplot(treat, aes(y=Unduplicated.Clients, x=FiscalYear))
# gg <- gg + geom_bar(stat="identity")
# gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
# #gg <- gg + facet_wrap(~state, scale="free", ncol=5)
# #gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# #        labels=c("2012", "2016"))
# gg <- gg + labs(x=NULL, y=NULL, 
#                 title="Opioid Related Treatment Admissions (unduplicated clients) by Town",
#                 subtitle="",
#                 caption="Department of Mental Health and Addiction Services")
# gg <- gg + theme_minimal(base_family="Lato Regular")
# gg <- gg + theme(panel.grid.major.y=element_blank())
# gg <- gg + theme(panel.grid.minor=element_blank())
# gg <- gg + theme(plot.title=element_text(family="Lato Black"))
# gg
treat$Admissions[is.na(treat$Admissions)] <- 0
treat <- ctpopulator(Town, treat)
## [1] "Checking to see if names match..."
treat$per_capita_a <- round(treat$Admissions/treat$pop2013*1000, 2)

gg <- ggplot(treat, aes(y=per_capita_a, x=FiscalYear))
gg <- gg + geom_bar(stat="identity")
gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
#gg <- gg + facet_wrap(~state, scale="free", ncol=5)
#gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
#        labels=c("2012", "2016"))
gg <- gg + labs(x=NULL, y=NULL, 
                title="Opioid Related Treatment Admissions per 1,000 residents by Town",
                subtitle="",
                caption="Department of Mental Health and Addiction Services")
gg <- gg + theme_minimal(base_family="Lato Regular")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(plot.title=element_text(family="Lato Black"))
gg

Mapped

town_shape <- readOGR(dsn="maps", layer="ctgeo")
## OGR data source with driver: ESRI Shapefile 
## Source: "maps", layer: "ctgeo"
## with 169 features
## It has 6 fields
town_shape_df <- fortify(town_shape, region="NAME10")

names(treat)[names(treat) == 'Town'] <- 'id'
treat$id <- str_to_title(treat$id)
town_map <- left_join(town_shape_df, treat)

gg <- ggplot(town_map, aes(long,lat, group=group, fill=per_capita_a)) 
gg <- gg +  geom_polygon()
gg <- gg +  geom_path(color = "grey73")
gg <- gg +  coord_equal()
gg <- gg + facet_wrap(~FiscalYear, ncol=2)
gg <- gg + scale_fill_gradient(low="grey73", high="blue") 
gg <- gg + labs(x=NULL, y=NULL, title="Opioid Related Treatment Admissions by town",
                subtitle="Per 1,000 residents",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank())
gg <- gg + theme(axis.text.x=element_blank())
gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))

gg

Towns with most check-ins per capita

tab1 <- treat %>%
  select(id, FiscalYear, per_capita_a) %>%
  spread(FiscalYear, per_capita_a) %>%
  arrange(-`2016`)
  kable(head(tab1, 10))
id 2012 2013 2014 2015 2016
Canaan 28.75 24.39 20.91 32.23 31.36
Windham 16.87 20.26 22.36 27.53 25.08
Torrington 10.40 13.37 16.06 19.94 23.74
New London 16.09 17.40 23.27 18.92 20.41
Hartford 18.17 17.59 18.60 20.34 19.12
Sharon 10.47 13.00 14.08 15.53 18.06
Griswold 10.29 12.54 15.30 15.72 16.97
Waterbury 12.08 12.47 12.90 16.16 16.66
Hampton 5.51 14.88 6.06 13.77 16.53
Sprague 11.72 11.72 16.08 15.07 16.08

Check-ins by town type

source("urban_rural_mixed.R")

town_count <- town_count[c("NAME10", "Type", "perc_urban")]
colnames(town_count) <- c("id", "town_type", "perc_urban")

tab2 <- treat %>%
  select(id, FiscalYear, Admissions, pop2013, per_capita_a)

tab2_joined <- left_join(tab2, town_count)

gg <- ggplot(tab2_joined, aes(x=town_type, y=Admissions,fill=town_type))
gg <- gg +  geom_boxplot()
gg <- gg +  facet_wrap(~FiscalYear, ncol=5)
gg <- gg + labs(x=NULL, y="Admissions", title="Overall opioid-related checkins in Connecticut",
                subtitle="",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg +  theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))

gg

gg <- ggplot(tab2_joined, aes(x=town_type, y=per_capita_a,fill=town_type))
gg <- gg +  geom_boxplot()
gg <- gg +  facet_grid(.~FiscalYear)
gg <- gg + labs(x=NULL, y="Admissions per capita", title="Adjusted opioid-related checkins in Connecticut",
                subtitle="Per 1,000 residents",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))

gg

Looking at deaths

Prepping the data

drug_deaths <- read.csv("https://data.ct.gov/api/views/rybz-nyjw/rows.csv?accessType=DOWNLOAD", stringsAsFactors = F)

# Cleaning up race
drug_deaths$R <- "Unknown"
drug_deaths$R <- ifelse(drug_deaths$Race=="Asian Indian", "Asian", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Asian, Other", "Asian", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Chinese", "Asian", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Hispanic, Black", "Hispanic", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Hispanic, White", "Hispanic", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Hispanic, Black", "Hispanic", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Native American, Other", "Other", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="", "Unknown", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Asian", "Asian", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Black", "Black", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="Hispanic", "Hispanic", drug_deaths$R)
drug_deaths$R <- ifelse(drug_deaths$Race=="White", "White", drug_deaths$R)

# Cleaning up death and residence city locations

drug_deaths <- ctnamecleaner(Death.City, drug_deaths)
## Your file with fixed town names has been exported. 
## Unfortunately, no matches were found for  4  They can be found in your folder. The file is called no_matches.csv
names(drug_deaths)[names(drug_deaths) == 'real.town.name'] <- 'town.of.death'
drug_deaths$Residence.City <- gsub("  ", " ", drug_deaths$Residence.City)
drug_deaths <- ctnamecleaner(Residence.City, drug_deaths)
## Your file with fixed town names has been exported. 
## Unfortunately, no matches were found for  150  They can be found in your folder. The file is called no_matches.csv
names(drug_deaths)[names(drug_deaths) == 'real.town.name'] <- 'town.of.residence'
drug_deaths$residence.and.death <- ""
drug_deaths$residence.and.death <- ifelse(drug_deaths$town.of.death == drug_deaths$town.of.residence, "Same town of death and residence", "Different town of death than residence")
drug_deaths$residence.and.death <- ifelse(is.na(drug_deaths$town.of.death), "Town of death unknown", drug_deaths$residence.and.death)
drug_deaths$residence.and.death <- ifelse(is.na(drug_deaths$town.of.residence), "Residence unknown", drug_deaths$residence.and.death)
drug_deaths$residence.and.death <- ifelse((is.na(drug_deaths$town.of.residence) & (drug_deaths$Residence.State!="CT")), "Out of town resident, local death", drug_deaths$residence.and.death)
drug_deaths$residence.and.death <- ifelse((is.na(drug_deaths$town.of.residence) & (drug_deaths$Residence.City=="")), "Unknown residence", drug_deaths$residence.and.death)

drug_deaths$date.fixed <- mdy(drug_deaths$Date)
drug_deaths$Year <- year(drug_deaths$date.fixed)
drug_deaths$row <- row.names(drug_deaths)

# fentanyl only
new_fentanyl <- drug_deaths %>%
  mutate(cause=str_to_lower(drug_deaths$ImmediateCauseA)) %>%
  filter(grepl("fent", cause) | Fentanyl=="Y" )

new_fent <-  select(new_fentanyl, row)
new_fent$update_fent <- "Yes"
new_fent <- as.data.frame(new_fent)
drug_deaths <- left_join(drug_deaths, new_fent)
  
# heroin only

new_heroin <- drug_deaths %>%
  mutate(cause=str_to_lower(drug_deaths$ImmediateCauseA)) %>%
  filter(grepl("heroin", cause) | Heroin=="Y" )

new_her <- select(new_heroin, row)
new_her$update_heroin <- "Yes"
new_her <- as.data.frame(new_her)
drug_deaths <- left_join(drug_deaths, new_her)

# prescription opioids

new_presc <- drug_deaths %>%
  mutate(cause=str_to_lower(drug_deaths$ImmediateCauseA)) %>%
  filter(grepl("oxy", cause) | Oxycodone=="Y" | Oxymorphone=="Y" |
           grepl("hydro", cause) | Hydrocodone=="Y" |
           grepl("tramad", cause) | Tramad=="Y" |
           grepl("morphine", cause) | Morphine..not.heroin.=="Y" )


new_pre <- select(new_presc, row)
new_pre$update_presc <- "Yes"
new_pre <- as.data.frame(new_pre)
drug_deaths <- left_join(drug_deaths, new_pre)

#leftover <- subset(drug_deaths, is.na(update_fent) & is.na(update_heroin) & is.na(update_presc))
#ggplot(leftover, aes(Year)) + geom_bar()

# There were about 461 cases that did not involve overdoses of prescription opioids, heroin, or fentanyl

# OK, checkins and deaths by year and town


# Prepping town and year dataframe

towns_only <- data.frame(town_count[,1])
colnames(towns_only) <- "town.of.residence"
towns_only$Year <- 0

for (i in 2012:2016) {
  towns_only$Year <- i
  if (i == 2012) {
    mega_towns <- towns_only
  } else {
    mega_towns <- rbind(mega_towns, towns_only)
  }
}

# First, deaths by town (just opioids)

opioid_deaths <- drug_deaths %>%
  filter(update_presc=="Yes") %>%
  group_by(town.of.residence, Year) %>%
  summarise(overdose.deaths=n())
# 
checkins <- tab2
colnames(checkins) <- c("town.of.residence", "Year", "admissions", "pop", "per_capita_a")

test <- left_join(mega_towns, checkins)
test <- left_join(test, opioid_deaths)

cor(test$admissions, test$overdose.deaths, use="complete.obs")
## [1] 0.7008847
cor(test$per_capita_a, test$overdose.deaths, use="complete.obs")
## [1] 0.3995875
test2 <- test
test2$per_capita_o <- round(test2$overdose.deaths/test2$pop*10000,2)
test2$overdose.deaths[is.na(test2$overdose.deaths)] <- 0
cor(test2$admissions, test2$overdose.deaths, use="complete.obs")
## [1] 0.7249749
cor(test2$per_capita_a, test2$overdose.deaths, use="complete.obs")
## [1] 0.36486
cor(test2$per_capita_a, test2$per_capita_o, use="complete.obs")
## [1] 0.02540601
test3 <- test2 %>%
  select(town.of.residence, Year, per_capita_a, per_capita_o) %>%
  gather("Type", "per.capita", 3:4)

gg <- ggplot(test3, aes(x=Year, y=per.capita, group=Type, color=Type))
gg <- gg + geom_line()
gg <- gg + facet_wrap(~town.of.residence, scales="free", ncol=5)
gg

test4 <- test2 %>%
  select(town.of.residence, Year, admissions, overdose.deaths) %>%
  gather("Type", "Count", 3:4)

gg <- ggplot(test4, aes(x=Year, y=Count, group=Type, color=Type))
gg <- gg + geom_line()
gg <- gg + facet_wrap(~town.of.residence, scales="free", ncol=5)
gg

opioid_deaths2 <- drug_deaths %>%
  filter(update_presc=="Yes") %>%
  group_by(town.of.residence) %>%
  summarise(overdose.deaths=n())
# 
checkins <- tab2
colnames(checkins) <- c("town.of.residence", "Year", "admissions", "pop", "per_capita_a")

checkins2 <- checkins %>%
  group_by(town.of.residence) %>%
  summarise(admissions=sum(admissions, na.rm=T))

town_totals <- left_join(towns_only, opioid_deaths2)
## Joining, by = "town.of.residence"
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## character vector and factor, coercing into character vector
town_totals <- left_join(town_totals, checkins2)
## Joining, by = "town.of.residence"
cor(town_totals$admissions, town_totals$overdose.deaths, use="complete.obs")
## [1] 0.882802
town_totals <- ctpopulator(town.of.residence, town_totals)
## [1] "Checking to see if names match..."
## Joining, by = "name2"
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
town_totals$per_cap_o <- round(town_totals$overdose.deaths/town_totals$pop2013*1000,2)
town_totals$per_cap_a <- round(town_totals$admissions/town_totals$pop2013*1000,2)
cor(town_totals$per_cap_a, town_totals$per_cap_o, use="complete.obs")
## [1] 0.2476643
gg <- ggplot(town_totals, aes(x=overdose.deaths, y=admissions))
gg <- gg + geom_point()
gg <- gg + labs(x="Overdoses", y="Admissions", 
                title="Overdose deaths compared to clinical admissions by town",
                subtitle="",
                caption="Department of Mental Health and Addiction Services")
gg
## Warning: Removed 45 rows containing missing values (geom_point).

gg <- ggplot(town_totals, aes(x=per_cap_o, y=per_cap_a))
gg <- gg + geom_point()
gg <- gg + labs(x="Overdoses per capita", y="Admissions per capita", 
                title="Overdose deaths compared to clinical admissions by town (per 1,000 residents)",
                subtitle="",
                caption="Department of Mental Health and Addiction Services")
gg
## Warning: Removed 45 rows containing missing values (geom_point).

Income

data2014 <- getCensus(name="acs5", 
                      vintage=2014,
                      key=census_key, 
                      vars=c("NAME", "B19013_001E"), 
                      region="county subdivision:*",  regionin="state:09")
data2014$town.of.residence <- gsub(" town.*", "", data2014$NAME)
data2014 <- subset(data2014, !is.na(B19013_001E))
data2014 <- data2014[,5:6]

colnames(data2014) <- c("median.income", "town.of.residence")

town_totals2 <- town_totals 
town_totals2$town.of.residence <- str_to_title(town_totals2$town.of.residence)
town_totals2 <- left_join(town_totals2, data2014)
## Joining, by = "town.of.residence"
cor(town_totals2$per_cap_a, town_totals2$median.income, use="complete.obs")
## [1] -0.5560087
gg <- ggplot(town_totals2, aes(x=median.income, y=per_cap_a))
gg <- gg + geom_point()
gg <- gg + labs(x="Median income", y="Admissions per capita", 
                title="Clinical admissions by town (per 1,000 residents) compared to median income",
                subtitle="",
                caption="Department of Mental Health and Addiction Services")
gg

cor(town_totals2$per_cap_o, town_totals2$median.income, use="complete.obs")
## [1] -0.2370663
gg <- ggplot(town_totals2, aes(x=median.income, y=per_cap_o))
gg <- gg + geom_point()
gg <- gg + labs(x="Median income", y="Overdoses per capita", 
                title="Opioid overdoses by town (per 1,000 residents) compared to median income",
                subtitle="",
                caption="Department of Mental Health and Addiction Services")
gg
## Warning: Removed 45 rows containing missing values (geom_point).

tab3_joined <- town_count
colnames(tab3_joined) <- c("town.of.residence", "town_type", "perc_urban")
tab3_joined <- left_join(test2, tab3_joined)


gg <- ggplot(tab3_joined, aes(x=town_type, y=per_capita_o, group=town_type, fill=town_type))
gg <- gg +  geom_boxplot()
gg <- gg +  facet_grid(.~Year)
gg <- gg + labs(x=NULL, y="Overdoses per capita", title="Adjusted opioid-related overdoses in Connecticut",
                subtitle="Per 1,000 residents",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg

gg <- ggplot(tab3_joined, aes(x=per_capita_a, y=per_capita_o, group=town_type, color=town_type))
gg <- gg +  geom_point()
gg <- gg +  facet_wrap(~Year, ncol=2)
gg <- gg + labs(x="Admissions per capita", y="Overdoses per capita", title="Opioid-related overdoses compared to treatment admissions",
                subtitle="Per 1,000 residents",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
## Warning: Removed 523 rows containing missing values (geom_point).

combined <- tab3_joined %>%
  group_by(town.of.residence, town_type) %>%
  summarise(admissions=sum(admissions, na.rm=T), pop=mean(pop), overdose.deaths=sum(overdose.deaths, na.rm=T)) %>%
  mutate(per_capita_a=round(admissions/pop*1000,2), per_capita_o=round(overdose.deaths/pop*1000,2))

gg <- ggplot(combined, aes(x=per_capita_a, y=per_capita_o, group=town_type, color=town_type))
gg <- gg +  geom_point()
gg <- gg + labs(x="Admissions per capita", y="Overdoses per capita", title="Opioid-related overdoses compared to treatment admissions",
                subtitle="Per 1,000 residents",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg

Death found

town_count_merge <- town_count
colnames(town_count_merge) <- c("town.of.residence", "town_type", "perc_urban")
drug_deaths_mega <- left_join(drug_deaths, town_count_merge)
## Joining, by = "town.of.residence"
## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector
kable(table(drug_deaths_mega$InjuryPlace, drug_deaths_mega$town_type))
Mixed Rural Urban
36 0 13
Alleyway 0 0 0
Apartment 4 0 1
Apartment House 1 0 0
Automobile 13 0 4
Bar or Night Club 1 0 0
Beach 2 0 0
Boat/Marina 1 0 0
Campgrounds 0 1 0
car in hartford 0 0 1
Casino 1 0 0
Convenience Store 0 0 1
Detoxification Center 0 0 1
Driveway 3 0 0
Field 1 0 1
Forest, Wooded Area 0 0 1
Friend’s Residence 1 0 0
Garage 0 0 0
Halfway House 1 0 1
homeless shelter 1 0 0
Hospital 43 1 13
Hospital or Emergency Room 2 0 2
Hotel or Motel 51 0 13
House 3 0 1
In Vehicle 13 0 4
Multiple Locations 1 0 0
Nursing Home 0 0 3
Office Building 1 0 1
Other 122 3 44
Other (unknown) 5 0 2
Other indoor Area 4 0 3
Other, Farm or Ranch 1 0 0
Other, Industrial Area 1 0 0
Other, Other Outdoor Area 7 0 7
Other, Park or Recreational Area 2 0 2
Other, Public Buildings 1 0 0
Other, Residential Institution 1 0 0
Parking Lot 15 0 6
Porch 1 0 1
Public Park 1 0 0
Public Park, Urban or Suburban 1 0 0
Rehab House 1 0 0
Residence 1308 30 446
Residential Building 58 1 22
Rest Home 0 0 0
Restaurant 6 0 2
Roadway 3 0 0
School, Primary or Secondary 1 0 0
Sidewalk 1 0 1
Store or Shopping Area 2 0 0
Street 2 0 2
Train or Subway Station 2 0 0
Unknown 43 1 17 
drug_deaths_mega$Location2 <- drug_deaths_mega$Location
drug_deaths_mega$Location2 <- gsub("Convalescent Home", "Other", drug_deaths_mega$Location2)
drug_deaths_mega$Location2[is.na(drug_deaths_mega$Location2)] <- "Other"

kable(table(drug_deaths_mega$Location2, drug_deaths_mega$town_type))
Mixed Rural Urban
13 1 1
Hospital 582 13 217
Other 206 2 72
Residence 968 21 326 
percent_type <- drug_deaths_mega %>%
  group_by(Location2, town_type) %>%
  summarise(total=n()) %>%
  mutate(percent=round(total/sum(total, na.rm=T)*100,2)) %>%
  select(Location2, town_type, percent) %>%
  spread(town_type, percent)
kable(percent_type)
Location2 Mixed Rural Urban
72.22 5.56 5.56 16.67
Hospital 67.36 1.50 25.12 6.02
Other 56.28 0.55 19.67 23.50
Residence 73.11 1.59 24.62 0.68 
percent_type2 <- drug_deaths_mega %>%
  group_by(town_type, Location2) %>%
  summarise(total=n()) %>%
  mutate(percent=round(total/sum(total, na.rm=T)*100,2)) %>%
  select(town_type, Location2, percent) %>%
  spread(town_type, percent)
kable(percent_type2)
Location2 Mixed Rural Urban
0.73 2.70 0.16 2.00
Hospital 32.90 35.14 35.23 34.67
Other 11.64 5.41 11.69 57.33
Residence 54.72 56.76 52.92 6.00 
location_info <- drug_deaths_mega %>%
  group_by(residence.and.death) %>%
  summarise(deaths=n())
  
gg <- ggplot(location_info, aes(residence.and.death, deaths)) 
gg <- gg + geom_bar(stat="identity")
gg <- gg + labs(x=NULL, y="Overdoses", title="Overdoses by location type",
                subtitle="",
                caption="SOURCE: Department of Mental Health and Addiction Services, Medical Examiner's Office\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg

pharm <- read.csv("https://data.ct.gov/api/views/2vby-9bet/rows.csv?accessType=DOWNLOAD", stringsAsFactors=F)
pharm <- ctnamecleaner(City, pharm)
## Joining, by = "name2"
## [1] "...All names matched. That's a rare thing."
pharm <- pharm %>%
  group_by(real.town.name) %>%
  summarise(pharmacies=n())
colnames(pharm) <- c("town.of.residence", "pharm")

counselors <- read.csv("https://data.ct.gov/api/views/jixj-wkjd/rows.csv?accessType=DOWNLOAD", stringsAsFactors=F)

counselors <- subset(counselors, State=="CT")
counselors <- ctnamecleaner(City, counselors)
## Joining, by = "name2"
## Your file with fixed town names has been exported. 
## Unfortunately, no matches were found for  1  They can be found in your folder. The file is called no_matches.csv
counselors <- counselors %>%
  group_by(real.town.name) %>%
  summarise(counselors=n())
colnames(counselors) <- c("town.of.residence", "couns")


drop <- read.csv("https://data.ct.gov/api/views/h7ba-v8cg/rows.csv?accessType=DOWNLOAD", stringsAsFactors = F)
drop <- ctnamecleaner(City, drop)
## Joining, by = "name2"
## [1] "...All names matched. That's a rare thing."
drop <- drop %>%
  group_by(real.town.name) %>%
  summarise(drop=n())
colnames(drop) <- c("town.of.residence", "drop.offs")

combined2 <- left_join(combined, pharm)
## Joining, by = "town.of.residence"
combined2 <- left_join(combined2, counselors)
## Joining, by = "town.of.residence"
combined2 <- left_join(combined2, drop)
## Joining, by = "town.of.residence"
combined2$per_capita_p <- round(combined2$pharm/combined2$pop*10000,2)
combined2$per_capita_c <- round(combined2$couns/combined2$pop*10000,2)
combined2$per_capita_d <- round(combined2$drop.offs/combined2$pop*10000,2)

combined3 <- combined2 %>%
  select(town.of.residence, town_type, per_capita_a, per_capita_o, per_capita_p, per_capita_c, per_capita_d) %>%
  gather("type", "per_capita", 3:7)

combined3$type <- ifelse(combined3$type=="per_capita_a", "admissions", combined3$type )
combined3$type <- ifelse(combined3$type=="per_capita_o", "overdoses", combined3$type )
combined3$type <- ifelse(combined3$type=="per_capita_p", "pharmacies", combined3$type )
combined3$type <- ifelse(combined3$type=="per_capita_c", "counselors", combined3$type )
combined3$type <- ifelse(combined3$type=="per_capita_d", "drop.offs", combined3$type )




gg <- ggplot(combined3, aes(x=town_type, y=per_capita, group=town_type, fill=town_type))
gg <- gg +  geom_boxplot()
gg <- gg +  facet_wrap(~type, ncol=2, scales="free")
gg <- gg + labs(x=NULL, y=NULL, title="Connecticut",
                subtitle="Per 1,000 residents",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
## Warning: Removed 176 rows containing non-finite values (stat_boxplot).

combined4 <- combined2 %>%
  select(town.of.residence, town_type, admissions, overdose.deaths, pharm, couns, drop.offs) %>%
  gather("type", "total", 3:7)


gg <- ggplot(combined4, aes(x=town_type, y=total, group=town_type, fill=town_type))
gg <- gg +  geom_boxplot()
gg <- gg +  facet_wrap(~type, ncol=2, scales="free")
gg <- gg + labs(x=NULL, y=NULL, title="Connecticut",
                subtitle="",
                caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
## Warning: Removed 176 rows containing non-finite values (stat_boxplot).