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DRDASD2021.R
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DRDASD2021.R
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rm(list=ls())
library(tidyverse)
library(curl)
library(sf)
library(readxl)
library(ragg)
library(cowplot)
library(extrafont)
library(ggtext)
library(ggrepel)
library(scales)
library(readODS)
theme_custom <- function() {
theme_classic() %+replace%
theme(plot.title.position="plot", plot.caption.position="plot",
strip.background=element_blank(), strip.text=element_text(face="bold", size=rel(1)),
plot.title=element_text(face="bold", size=rel(1.5), hjust=0,
margin=margin(0,0,5.5,0)),
text=element_text(family="Lato"))
}
#Drug-related deaths Scotland
temp <- tempfile()
source <- "https://www.nrscotland.gov.uk/files//statistics/drug-related-deaths/20/drug-related-deaths-20-tabs-figs.xlsx"
rawdata <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
#Read in DRDs by age
DRD.s <- read_excel(rawdata, sheet="4 - sex and age", range="G20:M36", col_names=FALSE) %>%
mutate(year=2004:2020, cause="DRD") %>%
gather(age, deaths, c(1:7)) %>%
mutate(age=case_when(
age=="...1" ~ "under 15",
age=="...2" ~ "15-24",
age=="...3" ~ "25-34",
age=="...4" ~ "35-44",
age=="...5" ~ "45-54",
age=="...6" ~ "55-64",
age=="...7" ~ "65+"),
age=factor(age, levels=c("under 15", "15-24", "25-34", "35-44",
"45-54", "55-64", "65+")))
#Alcohol-specific deaths by age for Scotland (only up to 2019 ATM)
temp <- tempfile()
source <- "https://www.nrscotland.gov.uk/files//statistics/alcohol-deaths/2019/alcohol-specific-deaths-19-all-tabs.xlsx"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
ASD.s <- read_excel(temp, sheet="3 - Age-group", range="C33:U48", col_names=FALSE) %>%
mutate(year=2004:2019, cause="ASD") %>%
gather(age, deaths, c(1:19)) %>%
mutate(age=case_when(
age %in% c("...1", "...2", "...3") ~ "under 15",
age %in% c("...4", "...5") ~ "15-24",
age %in% c("...6", "...7") ~ "25-34",
age %in% c("...8", "...9") ~ "35-44",
age %in% c("...10", "...11") ~ "45-54",
age %in% c("...12", "...13") ~ "55-64",
age %in% c("...14", "...15", "...16", "...17", "...18", "...19") ~ "65+"),
age=factor(age, levels=c("under 15", "15-24", "25-34", "35-44",
"45-54", "55-64", "65+"))) %>%
group_by(year, age, cause) %>%
summarise(deaths=sum(deaths)) %>%
ungroup()
#Read in population data
temp <- tempfile()
temp2 <- tempfile()
source <- "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fpopulationestimatesforukenglandandwalesscotlandandnorthernireland%2fmid2001tomid2020detailedtimeseries/ukdetailedtimeseries2001to2020.zip"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=temp2)
rawpop <- read.csv(file.path(temp2, "MYEB1_detailed_population_estimates_series_UK_(2020_geog20).csv")) %>%
mutate(age=case_when(
age<15 ~ "under 15",
age<25 ~ "15-24",
age<35 ~ "25-34",
age<45 ~ "35-44",
age<55 ~ "45-54",
age<65 ~ "55-64",
TRUE ~ "65+")) %>%
rename(ladcode20=ï..ladcode20) %>%
gather(year, pop, c(6:25)) %>%
group_by(ladcode20, laname20, country, age, year) %>%
summarise(pop=sum(pop)) %>%
ungroup() %>%
mutate(year=as.numeric(substr(year, 12,16)))
natpop <- rawpop %>%
group_by(country, age, year) %>%
summarise(pop=sum(pop)) %>%
ungroup()
data.s <- bind_rows(DRD.s, ASD.s) %>%
mutate(country="S") %>%
merge(natpop) %>%
mutate(mortrate=deaths*100000/pop) %>%
arrange(cause, age, year) %>%
mutate(index=c(1:112, 1:119),
index2=c(1:16, 18:33, 35:50, 52:67, 69:84, 86:101, 103:118, 1:119))
data_drd <- data.s %>% filter(cause=="DRD")
#grouped path of DRD
x1 <- c(0, data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="under 15" & data_drd$year==2020],
0)
x2 <- c(0, data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="15-24" & data_drd$year==2020],0)
x3 <- c(0, data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="25-34" & data_drd$year==2020],0)
x4 <- c(0, data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="35-44" & data_drd$year==2020],0)
x5 <- c(0, data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="45-54" & data_drd$year==2020],0)
x6 <- c(0, data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="55-64" & data_drd$year==2020],0)
x7 <- c(0, data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2004],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2005],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2006],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2007],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2008],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2009],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2010],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2011],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2012],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2013],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2014],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2015],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2016],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2017],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2018],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2019],
data_drd$mortrate[data_drd$age=="65+" & data_drd$year==2020],0)
DRDplot <- ggplot()+
geom_polygon(aes(x=c(1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17), y=x1), fill="Tomato")+
geom_polygon(aes(x=c(18,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,34), y=x2), fill="Tomato")+
geom_polygon(aes(x=c(35,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,51), y=x3), fill="Tomato")+
geom_polygon(aes(x=c(52,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,68), y=x4), fill="Tomato")+
geom_polygon(aes(x=c(69,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,85), y=x5), fill="Tomato")+
geom_polygon(aes(x=c(86,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,102), y=x6), fill="Tomato")+
geom_polygon(aes(x=c(103,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,119), y=x7), fill="Tomato")+
geom_path(data=data_drd,aes(x=index, y=mortrate, group=age), arrow=arrow(angle=25, type="closed", length=unit(0.2, "cm")))+
theme_custom()+
scale_x_continuous(breaks=c(9,26,43,60,77,94,111),
labels=c("under 15", "15-24", "25-34", "35-44", "45-54",
"55-64", "65+"),name="Age")+
scale_y_continuous(name="Annual drug-related deaths per 100,000")+
labs(title="Scotland's drug death epidemic has hit 35-54 year olds hardest",
subtitle="Annual rates of drug-related deaths by age group in Scotland 2014-2020",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
DRDinset <- ggplot()+
geom_polygon(aes(x=c(1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17),
y=c(0,6,4,3,9,10,12,13,10,16,15,17,11,14,18,16,20,18,0)),
fill="Tomato")+
geom_line(aes(x=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17),
y=c(6,4,3,9,10,12,13,10,16,15,17,11,14,18,16,20,18)),
arrow=arrow(angle=25, type="closed", length=unit(0.2, "cm")))+
theme_classic()+
theme(axis.line=element_blank(), axis.text=element_blank(),axis.ticks=element_blank(),
axis.title=element_blank())
DRDfull <- ggdraw()+
draw_plot(DRDplot)+
draw_plot(DRDinset, x=0.82, y=0.75, width=0.13, height=0.2)+
draw_label("2004", x=0.84, y=0.76, size=10)+
draw_label("2020", x=0.94, y=0.76, size=10)+
draw_label("Key", x=0.88, y=0.95, size=10, fontface="bold")
tiff("Outputs/DRDScot2021.tiff", units="in", width=9, height=6.6, res=500)
ggdraw(DRDfull)
dev.off()
data_asd <- data.s %>% filter(cause=="ASD")
#grouped path of ASD
y1 <- c(0, data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="under 15" & data_asd$year==2019],0)
y2 <- c(0, data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="15-24" & data_asd$year==2019],0)
y3 <- c(0, data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="25-34" & data_asd$year==2019],0)
y4 <- c(0, data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="35-44" & data_asd$year==2019],0)
y5 <- c(0, data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="45-54" & data_asd$year==2019],0)
y6 <- c(0, data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="55-64" & data_asd$year==2019],0)
y7 <- c(0, data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2004],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2005],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2006],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2007],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2008],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2009],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2010],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2011],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2012],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2013],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2014],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2015],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2016],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2017],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2018],
data_asd$mortrate[data_asd$age=="65+" & data_asd$year==2019],0)
ASDplot <- ggplot()+
geom_polygon(aes(x=c(1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,16), y=y1), fill="SkyBlue")+
geom_polygon(aes(x=c(17,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,32), y=y2), fill="SkyBlue")+
geom_polygon(aes(x=c(33,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,48), y=y3), fill="SkyBlue")+
geom_polygon(aes(x=c(49,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,64), y=y4), fill="SkyBlue")+
geom_polygon(aes(x=c(65,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,80), y=y5), fill="SkyBlue")+
geom_polygon(aes(x=c(81,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,96), y=y6), fill="SkyBlue")+
geom_polygon(aes(x=c(97,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,112), y=y7), fill="SkyBlue")+
geom_path(data=data_asd,aes(x=index, y=mortrate, group=age), arrow=arrow(angle=25, type="closed", length=unit(0.2, "cm")))+
theme_custom()+
theme(plot.title=element_text(face="bold", size=rel(1.2)))+
scale_x_continuous(breaks=c(8,25,41,57,73,90,106),
labels=c("under 15", "15-24", "25-34", "35-44", "45-54",
"55-64", "65+"),name="Age")+
scale_y_continuous(name="Annual alcohol-specific deaths per 100,000")+
labs(title="Scotland has made significant progress in reducing alcohol-specific deaths in middle ages",
subtitle="Annual rates of alcohol-specific deaths by age group in Scotland 2014-2019",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
ASDinset <- ggplot()+
geom_polygon(aes(x=c(1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,16),
y=c(0,15,18,16,20,16,13,11,10,15,12,9,8,10,7,9,6,0)),
fill="SkyBlue")+
geom_line(aes(x=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16),
y=c(15,18,16,20,16,13,11,10,15,12,9,8,10,7,9,6)),
arrow=arrow(angle=25, type="closed", length=unit(0.2, "cm")))+
theme_classic()+
theme(axis.line=element_blank(), axis.text=element_blank(),axis.ticks=element_blank(),
axis.title=element_blank())
ASDfull <- ggdraw()+
draw_plot(ASDplot)+
draw_plot(ASDinset, x=0.15, y=0.65, width=0.1, height=0.2)+
draw_label("2004", x=0.17, y=0.66, size=10)+
draw_label("2019", x=0.24, y=0.66, size=10)+
draw_label("Key", x=0.18, y=0.85, size=10, fontface="bold")
tiff("Outputs/ASDScot2020.tiff", units="in", width=9, height=6.6, res=500)
ggdraw(ASDfull)
dev.off()
#Combined plot
combplot <- ggplot()+
geom_polygon(aes(x=c(1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,17), y=x1), fill="Tomato", alpha=0.8)+
geom_polygon(aes(x=c(18,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,34), y=x2), fill="Tomato", alpha=0.8)+
geom_polygon(aes(x=c(35,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,51), y=x3), fill="Tomato", alpha=0.8)+
geom_polygon(aes(x=c(52,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,68), y=x4), fill="Tomato", alpha=0.8)+
geom_polygon(aes(x=c(69,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,85), y=x5), fill="Tomato", alpha=0.8)+
geom_polygon(aes(x=c(86,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,102), y=x6), fill="Tomato", alpha=0.8)+
geom_polygon(aes(x=c(103,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,119), y=x7), fill="Tomato", alpha=0.8)+
geom_path(data=data_drd,aes(x=index, y=mortrate, group=age), arrow=arrow(angle=25, type="closed", length=unit(0.2, "cm")))+
geom_polygon(aes(x=c(1,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,16), y=y1), fill="SkyBlue", alpha=0.5)+
geom_polygon(aes(x=c(18,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,33), y=y2), fill="SkyBlue", alpha=0.6)+
geom_polygon(aes(x=c(35,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,50), y=y3), fill="SkyBlue", alpha=0.6)+
geom_polygon(aes(x=c(52,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,67), y=y4), fill="SkyBlue", alpha=0.6)+
geom_polygon(aes(x=c(69,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,84), y=y5), fill="SkyBlue", alpha=0.6)+
geom_polygon(aes(x=c(86,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,101), y=y6), fill="SkyBlue", alpha=0.6)+
geom_polygon(aes(x=c(103,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,118), y=y7), fill="SkyBlue", alpha=0.6)+
geom_path(data=data_asd,aes(x=index2, y=mortrate, group=age), arrow=arrow(angle=25, type="closed", length=unit(0.2, "cm")))+
theme_custom()+
theme(plot.subtitle=element_markdown())+
scale_x_continuous(breaks=c(9,26,43,60,77,94,111),
labels=c("under 15", "15-24", "25-34", "35-44", "45-54",
"55-64", "65+"),name="Age")+
scale_y_continuous(name="Annual deaths per 100,000")+
labs(title="The opposing trajectories of drug and alcohol deaths in Scotland",
subtitle="Annual rates of <span style='color:tomato3;'>drug-related</span> and <span style='color:skyblue3;'>alcohol-specific</span> deaths between 2004 and 2020<br>(Alcohol-specific deaths data for 2020 will be published in mid-August)",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
combfull <- ggdraw()+
draw_plot(combplot)+
draw_plot(ASDinset, x=0.1, y=0.58, width=0.13, height=0.15)+
draw_plot(DRDinset, x=0.1, y=0.72, width=0.13, height=0.15)+
draw_label("2004", x=0.12, y=0.58, size=10)+
draw_label("2020", x=0.21, y=0.58, size=10)+
draw_label("Key", x=0.15, y=0.88, size=10, fontface="bold")+
draw_label("Alcohol", x=0.17, y=0.625, size=10)+
draw_label("Drugs", x=0.17, y=0.77, size=10)
tiff("Outputs/ASDDRDScot2021.tiff", units="in", width=9, height=6.6, res=500)
ggdraw(combfull)
dev.off()
#############################################################################
#DRDs by sex
DRD.sex <- read_excel(rawdata, sheet="4 - sex and age", range="D20:E36", col_names=FALSE) %>%
mutate(year=2004:2020) %>%
gather(sex, deaths, c(1,2)) %>%
mutate(sex=if_else(sex=="...1", "Male", "Female"))
lab.f <- DRD.sex$deaths[DRD.sex$sex=="Female" & DRD.sex$year==2020]/DRD.sex$deaths[DRD.sex$sex=="Female" & DRD.sex$year==2004]
lab.m <- DRD.sex$deaths[DRD.sex$sex=="Male" & DRD.sex$year==2020]/DRD.sex$deaths[DRD.sex$sex=="Male" & DRD.sex$year==2004]
lab.f <- paste0("+",round(lab.f*100,0),"%")
lab.m <- paste0("+",round(lab.m*100,0),"%")
tiff("Outputs/DRDScotxSex.tiff", units="in", width=9, height=6.6, res=500)
ggplot(DRD.sex)+
geom_line(aes(x=year, y=deaths, colour=sex), show.legend=FALSE)+
scale_x_continuous(name="", breaks=c(2004:2020))+
scale_y_continuous(name="Annual drug-related deaths", limits=c(0,NA))+
scale_colour_manual(name="", values=c("#00cc99", "#6600cc"))+
theme_custom()+
theme(plot.subtitle=element_markdown())+
labs(title="Women have seen a bigger relative increase in drug-related deaths in Scotland",
subtitle="Since 2004, drug-related deaths <span style='color:#00cc99;'>in women</span> have increased more than fivefold, while they have trebled <span style='color:#6600cc;'>in men</span>",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")+
annotate("text", x=2020, y=900, label=lab.m)+
annotate("text", x=2020, y=410, label=lab.f)
dev.off()
###############################################################################
#DRDs by drug type
DRD.drg <- read_excel(rawdata, sheet="3 - drugs reported", range="A26:R38", col_names=FALSE) %>%
gather(drug, deaths, c(3:18)) %>%
rename(year=`...1`, total=`...2`) %>%
mutate(drug=case_when(
drug=="...3" ~ "Heroin/morphine",
drug=="...4" ~ "Methadone",
drug %in% c("...6", "...7") ~ "Codeine/Dihydrocodeine",
drug=="...10" ~ "'Prescribable' benzodiazepine",
drug=="...12" ~ "'Street' benzodiazepine",
drug=="...14" ~ "Gabapentin/Pregabalin",
drug=="...15" ~ "Cocaine")) %>%
group_by(year, drug, total) %>%
summarise(deaths=sum(deaths)) %>%
ungroup() %>%
filter(!is.na(drug)) %>%
mutate(deathprop=deaths/total,
drug=factor(drug, levels=c("Heroin/morphine", "Methadone", "'Prescribable' benzodiazepine",
"'Street' benzodiazepine", "Codeine/Dihydrocodeine",
"Gabapentin/Pregabalin", "Cocaine")))
#Plot of totals
tiff("Outputs/DRDScotxDrugAbs.tiff", units="in", width=9, height=6, res=500)
ggplot()+
geom_line(data=DRD.drg, aes(x=year, y=deaths, colour=drug), show.legend=FALSE)+
geom_text_repel(data=DRD.drg %>% filter(year==2020),aes(color=drug, label=drug, x=year, y=deaths),
show.legend=FALSE, family="Lato", xlim=(c(2020.1, NA)), segment.color = NA)+
scale_x_continuous(name="", breaks=c(2008:2020), limits=c(2008, 2020))+
scale_y_continuous(name="Deaths reported as involving...")+
scale_colour_paletteer_d("LaCroixColoR::paired")+
coord_cartesian(clip = 'off') +
theme_custom()+
theme(plot.margin = unit(c(1,10,1,1), "lines"),
plot.subtitle=element_markdown())+
labs(title="Most drug types are involved in an increasing number of deaths",
subtitle="Deaths involving <span style='color:#C70E7B;'>opiates</span>/<span style='color:#FC6882;'>opiods</span>, <span style='color:#54BCD1;'>'street' benzodiazepine</span>, <span style='color:#F4B95A;'>Gapanentin/Pregablin</span> and <span style='color:#009F3F;'>cocaine</span> are all rising",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
#Plot of proportions
tiff("Outputs/DRDScotxDrugProp.tiff", units="in", width=9, height=6.6, res=500)
ggplot()+
geom_line(data=DRD.drg, aes(x=year, y=deathprop, colour=drug), show.legend=FALSE)+
geom_text_repel(data=DRD.drg %>% filter(year==2020),aes(color=drug, label=drug, x=year, y=deathprop),
show.legend=FALSE, family="Lato", xlim=(c(2020.1, NA)), segment.color = NA)+
scale_x_continuous(name="", breaks=c(2008:2020))+
scale_y_continuous(name="Proportion of all drug-related deaths which involve...",
labels = scales::percent_format(accuracy = 2))+
scale_colour_paletteer_d("LaCroixColoR::paired")+
coord_cartesian(clip = 'off') +
theme_custom()+
theme(plot.margin = unit(c(1,10,1,1), "lines"),
plot.title=element_markdown(),
plot.subtitle=element_markdown())+
labs(title="Deaths involving <span style='color:#54BCD1;'>'street' benzodiazepine</span> have exploded since 2015",
subtitle="While the proportion involving <span style='color:#F4B95A;'>Gabapentin/Pregablin</span> or <span style='color:#009F3F;'>cocaine</span> is also rising, but more slowly",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
#And by age
DRD.drg.age <- read_excel(rawdata, sheet="6 - sex, age and drugs", range="A19:R23", col_names=FALSE) %>%
gather(drug, deaths, c(3:18)) %>%
rename(age=`...1`, total=`...2`) %>%
mutate(drug=case_when(
drug=="...3" ~ "Heroin/morphine",
drug=="...4" ~ "Methadone",
drug %in% c("...6", "...7") ~ "Codeine/Dihydrocodeine",
drug=="...10" ~ "'Prescribable' benzodiazepine",
drug=="...12" ~ "'Street' benzodiazepine",
drug=="...14" ~ "Gabapentin/Pregabalin",
drug=="...15" ~ "Cocaine")) %>%
group_by(age, drug, total) %>%
summarise(deaths=sum(deaths)) %>%
ungroup() %>%
filter(!is.na(drug)) %>%
mutate(deathprop=deaths/total,
drug=factor(drug, levels=c("Heroin/morphine", "Methadone", "'Prescribable' benzodiazepine",
"'Street' benzodiazepine", "Codeine/Dihydrocodeine",
"Gabapentin/Pregabalin", "Cocaine")),
age=factor(age, levels=c("Under 25", "25-34", "35-44", "45-54", "55 and over"))) %>%
group_by(drug) %>%
mutate(drugtot=sum(deaths)) %>%
ungroup() %>%
mutate(deathprop2=deaths/drugtot)
tiff("Outputs/DRDScotxAgexDrugProp.tiff", units="in", width=9, height=6.6, res=500)
ggplot()+
geom_line(data=DRD.drg.age, aes(x=age, y=deathprop, group=drug, colour=drug), show.legend=FALSE)+
geom_text_repel(data=DRD.drg.age %>% filter(age=="55 and over"),
aes(color=drug, label=drug, x=age, y=deathprop),
show.legend=FALSE, family="Lato",segment.color = NA, direction="y",
hjust=0)+
scale_x_discrete(name="",)+
scale_y_continuous(name="Proportion of all drug-related deaths which involve...",
labels = scales::percent_format(accuracy = 2))+
scale_colour_paletteer_d("LaCroixColoR::paired")+
coord_cartesian(clip = 'off') +
theme_custom()+
theme(plot.margin = unit(c(1,10,1,1), "lines"),
plot.title=element_markdown())+
labs(title="<span style='color:#009F3F;'>Cocaine</span> is implicated in a greater proportion of deaths in younger age groups",
subtitle="Other drugs are more likely to be involved in deaths at older ages",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
ggplot(DRD.drg.age, aes(x=deathprop2, y=drug, fill=age))+
geom_col()+
theme_custom()+
scale_fill_paletteer_d("LaCroixColoR::paired", name="Age")+
scale_x_continuous(name="Proportion of all drug-related deaths involving...",label=label_percent(accuracy=1))
tiff("Outputs/DRDScotxAgexDrugProp2.tiff", units="in", width=9, height=6.6, res=500)
ggplot(DRD.drg.age, aes(x=deathprop, y=drug, fill=drug))+
geom_col(position="dodge", show.legend=FALSE)+
theme_custom()+
scale_fill_paletteer_d("LaCroixColoR::paired")+
scale_x_continuous(name="Proportion of all drug-related deaths involving...",
label=label_percent(accuracy=1))+
scale_y_discrete(name="")+
facet_wrap(~age)+
theme(plot.title=element_markdown(), plot.subtitle=element_markdown())+
labs(title="In all age groups in Scotland, <span style='color:#54BCD1;'>'street' benzos</span> are involved in the most deaths",
subtitle="But in younger age groups, <span style='color:#009F3F;'>Cocaine</span> is also involved in a substantial proportion of drug-related deaths.<br>Deaths can (and often do) involve multiple drugs, so proportions within each age group sum to more than one.")
dev.off()
####################################################################
#DRDs in Scotland by ICD-10 codes
DRD.cause <- read_excel(rawdata, sheet="2 - causes", range="C39:E48", col_names=FALSE) %>%
mutate(year=c(2011:2020)) %>%
gather(cause, deaths, c(1:3)) %>%
mutate(cause=case_when(
cause=="...1" ~ "Drug abuse",
cause=="...2" ~ "Accidental poisoning",
TRUE ~ "Intentional self-poisoning"))
tiff("Outputs/DRDScotxCause.tiff", units="in", width=9, height=6.6, res=500)
ggplot(DRD.cause)+
geom_line(aes(x=year, y=deaths, colour=cause), show.legend=FALSE)+
scale_x_continuous(name="", breaks=c(2011:2020))+
scale_y_continuous(name="Annual drug-related deaths")+
scale_colour_paletteer_d("colorblindr::OkabeIto")+
theme_custom()+
theme(plot.subtitle=element_markdown())+
labs(title="The rise in drug-related deaths is entirely driven by accidental overdoses",
subtitle="Drug-related deaths in Scotland from <span style='color:#E69F00;'>accidental poisoning</span>, <span style='color:#56B4E9;'>drug abuse</span> and <span style='color:#009E73;'>intentional self-poisoning",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
#################################################################################
#DRDs in Scotland by deprivation
DRD.SIMD <- read_excel(rawdata, sheet="12 - SIMD Deciles", range="F9:AS28", col_names=FALSE) %>%
select(`...1`, `...5`, `...9`, `...13`, `...17`, `...21`, `...25`, `...29`, `...33`, `...37`) %>%
set_names(c("SIMD10 (most deprived)", "SIMD9", "SIMD8", "SIMD7", "SIMD6", "SIMD5", "SIMD4", "SIMD3",
"SIMD2", "SIMD1 (least deprived)")) %>%
mutate(year=2001:2020,
SIMD2=as.numeric(SIMD2), `SIMD1 (least deprived)`=as.numeric(`SIMD1 (least deprived)`)) %>%
gather(SIMD, DRD.rate, c(1:10)) %>%
mutate(SIMD=factor(SIMD, levels=c("SIMD10 (most deprived)", "SIMD9", "SIMD8", "SIMD7", "SIMD6", "SIMD5", "SIMD4", "SIMD3",
"SIMD2", "SIMD1 (least deprived)")),
label=case_when(
SIMD=="SIMD10 (most deprived)" ~ "Most deprived decile",
SIMD=="SIMD1 (least deprived)" ~ "Least deprived decile",
TRUE ~ ""))
tiff("Outputs/DRDScotxSIMD.tiff", units="in", width=9, height=6.6, res=500)
ggplot()+
geom_line(data=DRD.SIMD, aes(x=year, y=DRD.rate, group=SIMD, colour=SIMD), show.legend=FALSE)+
geom_text_repel(data=DRD.SIMD %>% filter(year==2020), aes(x=year, y=DRD.rate, label=label, colour=SIMD),
family="Lato", xlim=(c(2020.1, NA)), segment.color = NA, show.legend=FALSE)+
scale_x_continuous(name="")+
scale_y_continuous(name="Age-standardised rate of drug-related deaths per 100,000")+
scale_colour_manual(values=c("#2d004b", "#542788", "#8073ac", "#b2abd2", "#d8daeb",
"#fee0b6", "#fdb863", "#e08214", "#b35806", "#7f3b08"))+
theme_custom()+
coord_cartesian(clip = 'off')+
theme(plot.margin = unit(c(1,10,1,1), "lines"))+
labs(title="The huge rise in Scottish drug-related deaths is largely in the most deprived groups",
subtitle="Age-standardised rates of drug-related deaths by decile of the Scottish Index of Multiple Deprivation.\nValues based on fewer than 10 deaths are censored.",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
#################################################################################
#DRDs in Scotland by HB
DRD.HB <- read_excel(rawdata, sheet="HB1 - summary", range="A14:L27", col_names=FALSE) %>%
gather(year, deaths, c(2:12)) %>%
rename(HB="...1") %>%
mutate(year=as.numeric(substr(year, 4,5))+2008,
HB=str_replace(HB, "&", "and"),
HB=str_replace(HB, " 3", ""))
#Bring in populations
temp <- tempfile()
source <- "https://www.nrscotland.gov.uk/files//statistics/population-estimates/mid-20/mid-year-pop-est-20-time-series-4.xlsx"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
HBpop <- read_excel(temp, range=c("B6:AP19"), col_names=FALSE) %>%
gather(year, pop, c(2:41)) %>%
rename(HB="...1") %>%
mutate(year=as.numeric(substr(year, 4,5))+1979)
DRD.HB <- merge(DRD.HB, HBpop, all.x=TRUE)
DRD.HB <- DRD.HB %>%
mutate(mortrate=deaths*100000/pop) %>%
group_by(HB)
tiff("Outputs/DRDScotxHB.tiff", units="in", width=9, height=6.6, res=500)
ggplot(DRD.HB)+
geom_line(aes(x=year, y=mortrate, colour=HB), show.legend=FALSE)+
scale_x_continuous(name="", breaks=c(2010:2020))+
scale_y_continuous(name="Drug-related deaths per 100,000")+
scale_colour_manual(values=c(rep("Grey70", 6), "#c51b8a", rep("Grey70", 7)))+
theme_custom()+
theme(plot.subtitle=element_markdown())+
labs(title="Scotland's drug death epidemic is centred on Glasgow",
subtitle="Drug-related death rates in <span style='color:#c51b8a;'>Greater Glasgow & Clyde</span> compared to <span style='color:Grey70;'>other Health Board areas",
caption="Data from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
#DRDs in Scotland by HB and drug
DRD.HB.drg <- read_excel(rawdata, sheet="HB3 - drugs implicated", range="A17:R30", col_names=FALSE) %>%
gather(drug, deaths, c(3:18)) %>%
rename(HB="...1", total="...2") %>%
filter(total>=20) %>%
mutate(drug=case_when(
drug=="...3" ~ "Heroin/morphine",
drug=="...4" ~ "Methadone",
drug %in% c("...6", "...7") ~ "Codeine/Dihydrocodeine",
drug=="...10" ~ "'Prescribable' benzodiazepine",
drug=="...12" ~ "'Street' benzodiazepine",
drug=="...14" ~ "Gabapentin/Pregabalin",
drug=="...15" ~ "Cocaine")) %>%
group_by(HB, drug, total) %>%
summarise(deaths=sum(deaths)) %>%
ungroup() %>%
filter(!is.na(drug)) %>%
mutate(deathprop=deaths/total,
drug=factor(drug, levels=c("Heroin/morphine", "Methadone", "'Prescribable' benzodiazepine",
"'Street' benzodiazepine", "Codeine/Dihydrocodeine",
"Gabapentin/Pregabalin", "Cocaine")))
tiff("Outputs/DRDScotxHBxdrug.tiff", units="in", width=10, height=8, res=500)
ggplot(DRD.HB.drg)+
geom_col(aes(x=deathprop, y=fct_rev(HB), fill=HB), show.legend=FALSE)+
scale_x_continuous(name="Proportion of drug-related deaths involving...",
labels=scales::percent_format(accuracy=2))+
scale_y_discrete(name="")+
scale_fill_manual(values=c(rep("Grey70", 4), "#c51b8a", rep("Grey70", 5)))+
facet_wrap(~drug)+
theme_custom()+
labs(title="There's something different about Grampian",
subtitle="A larger proportion of deaths there are linked to cocaine or 'prescribable' benzodiazepine and a much smaller proportion to 'street' diazepine",
caption="\n\nHealth boards with fewer than 20 deaths are excluded\nData from National Records of Scotland | Plot by @VictimOfMaths")
dev.off()
####################################################################################################
#Bivariate map
#Read in Scottish DRD data at Council Level 2018-2020
DRD.s <- read_excel(rawdata, sheet="C1 - summary", range="A10:L41", col_names=FALSE) %>%
select(`...1`, `...10`, `...11`, `...12`) %>%
rename(LA=`...1`) %>%
mutate(DRD=(`...10`+`...11`+`...12`)/3) %>%
select(LA, DRD)
#Read in Scottish ASD data at Council Level 2017-2019
temp <- tempfile()
source <- "https://www.nrscotland.gov.uk/files//statistics/alcohol-deaths/2019/alcohol-specific-deaths-19-all-tabs.xlsx"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
ASD.s <- as.data.frame(t(read_excel(temp, sheet="5 - Local Authority", range=c("C44:AH46"), col_names=FALSE))) %>%
mutate(ASD=(V1+V2+V3)/3) %>%
select(ASD)
#The columes in the ASD data match the DRD data, so don't bother faffing about with names
DRDASD.s <- cbind(DRD.s, ASD.s)
#Read in English & Welsh data at LTLA level
#DRDs 2018-20
temp <- tempfile()
source <- "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fbirthsdeathsandmarriages%2fdeaths%2fdatasets%2fdrugmisusedeathsbylocalauthority%2fcurrent/2020localauthorities.xls"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
DRD.ew <- read_excel(temp, sheet="Table 6", range="A7:E438", col_names=FALSE) %>%
mutate(LA=coalesce(`...3`, `...4`)) %>%
select(`...1`, `...5`, LA) %>%
rename(code=`...1`, DRD=`...5`) %>%
#adjust for the fact that the number of deaths is ths cumulative 3 year total
mutate(DRD=DRD/3) %>%
#fix names that don't align with ASD data
mutate(LA=case_when(
LA=="Kingston upon Hull, City of" ~ "Kingston upon Hull",
LA=="Herefordshire, County of" ~ "Herefordshire",
LA=="Bristol, City of" ~ "Bristol",
TRUE ~ as.character(LA)),
code=if_else(LA=="Buckinghamshire", "E10000002", as.character(code)),
#Tidy up Welsh LA names
LA=if_else(substr(code, 1, 1)=="W", substr(LA, 1, regexpr("/", LA)-2), as.character(LA)))
#ASDs for England 2017-19
temp <- tempfile()
source <- "https://fingertipsws.phe.org.uk/api/all_data/csv/by_profile_id?parent_area_code=E92000001&parent_area_type_id=6&child_area_type_id=102&profile_id=87&category_area_code="
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
ASD.e <- read.csv(temp) %>%
filter(Indicator.ID=="91380" & Sex=="Persons" & Area.Type=="County & UA (pre 4/19)") %>%
select(Area.Code, Area.Name, Value, Time.period) %>%
rename(code=Area.Code, LA=Area.Name, rate=Value)
#Bring in population (based on 2017 data to cope with Dorset)
temp <- tempfile()
source <- "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fpopulationestimatesforukenglandandwalesscotlandandnorthernireland%2fmid2017/ukmidyearestimates2017finalversion.xls"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
LApop <- read_excel(temp, sheet="MYE2 - All", range="A6:D445", col_names=FALSE) %>%
select(-`...3`) %>%
rename(code=`...1`, LA=`...2`, pop=`...4`)
ASD.e <- merge(ASD.e, LApop, by="code", all.x=TRUE) %>%
select(-LA.y) %>%
rename(LA=LA.x) %>%
mutate(ASD=rate*pop/100000)
#Faff about with Dorset & Bournemouth, which are missing from the latest data
temp <- subset(ASD.e, code %in% c("E06000028", "E06000029", "E10000009") &
Time.period=="2015 - 17") %>%
mutate(code=case_when(
code %in% c("E06000028", "E06000029") ~ "E06000058",
TRUE ~ "E06000059"),
LA=case_when(
code=="E06000058" ~ "Bournemouth, Christchurch and Poole",
TRUE ~ "Dorset"))
ASD.e <- ASD.e %>%
filter(Time.period=="2017 - 19") %>%
bind_rows(temp) %>%
select(-Time.period) %>%
group_by(code, LA) %>%
summarise(ASD=sum(ASD)) %>%
ungroup()
#ASDs for Wales 2015-17
temp <- tempfile()
source <- "https://www.healthmapswales.wales.nhs.uk/IAS/data/csv?viewId=155&geoId=108&subsetId=&viewer=CSV"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
ASD.w <- read.csv(temp)[c(1:22),c(2,161)] %>%
rename(LA=Name, ASD=Numerator.27) %>%
mutate(LA=if_else(LA=="The Vale of Glamorgan", "Vale of Glamorgan", as.character(LA)))
DRDASD.ew <- merge(bind_rows(ASD.e, ASD.w), DRD.ew, by="LA", all.x=TRUE) %>%
mutate(code=coalesce(code.x, code.y)) %>%
select(-code.x, -code.y)
#Read in NI DRD by LA 2017-19
temp <- tempfile()
source <- "https://www.ninis2.nisra.gov.uk/Download/Population/Drug%20Related%20Deaths%20and%20Deaths%20due%20to%20Drug%20Misuse%20(administrative%20geographies).ods"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
DRD.ni <- read_ods(temp, sheet="LGD2014", range="A5:H15", col_names=FALSE) %>%
select(-C, -E, -G) %>%
mutate(DRD=(D+`F`+H)/3) %>%
rename(LA=A, code=B) %>%
select(LA, code, DRD)
#Read in NI ASD by LA 2017-19
temp <- tempfile()
source <- "https://www.ninis2.nisra.gov.uk/Download/Population/Alcohol%20Specific%20Deaths%20(administrative%20geographies).ods"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
ASD.ni <- read_ods(temp, sheet="LGD2014", range="A5:E15", col_names=FALSE) %>%
mutate(ASD=(C+D+E)/3) %>%
rename(LA=A, code=B) %>%
select(LA, code, ASD)
DRDASD.ni <- merge(DRD.ni, ASD.ni)
#Bring in populations (2019)
temp <- tempfile()
source <- "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fpopulationestimatesforukenglandandwalesscotlandandnorthernireland%2fmid2019april2020localauthoritydistrictcodes/ukmidyearestimates20192020ladcodes.xls"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
LApop2 <- read_excel(temp, sheet="MYE2 - Persons", range="A6:D431", col_names=FALSE) %>%
select(-`...3`) %>%
rename(code=`...1`, LA=`...2`, pop=`...4`) %>%
mutate(code=if_else(code=="E06000060", "E10000002", as.character(code)))
#Scotland
DRDASD.s <- DRDASD.s %>%
mutate(LA=str_replace(LA, "&", "and")) %>%
merge(LApop2, by="LA", all.x=TRUE)
#NI
DRDASD.ni <- merge(DRDASD.ni, LApop2, all.x=TRUE)
#England
DRDASD.ew <- merge(DRDASD.ew, LApop2, all.x=TRUE, by="code") %>%
select(-LA.y) %>%
rename(LA=LA.x)
#Merge
DRDASD <- bind_rows(DRDASD.s, DRDASD.ew, DRDASD.ni) %>%
gather(cause, deaths, c("DRD", "ASD")) %>%
mutate(mortrate=deaths*100000/pop) %>%
mutate(country=case_when(
substr(code, 1, 1)=="E" ~ "England",
substr(code, 1, 1)=="W" ~ "Wales",
substr(code, 1, 1)=="S" ~ "Scotland",
substr(code, 1, 1)=="N" ~ "Northern Ireland"))
#Save
write.csv(DRDASD, "Data/DRDASD.csv")
#Download shapefile
temp <- tempfile()
temp2 <- tempfile()
source <- "https://opendata.arcgis.com/datasets/43b324dc1da74f418261378a9a73227f_0.zip?outSR=%7B%22latestWkid%22%3A27700%2C%22wkid%22%3A27700%7D"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
unzip(zipfile=temp, exdir=temp2)
#The actual shapefile has a different name each time you download it, so need to fish the name out of the unzipped file
name <- list.files(temp2, pattern=".shp")
shapefile <- st_read(file.path(temp2, name))
names(shapefile)[names(shapefile) == "ctyua19cd"] <- "code"
map.data <- full_join(shapefile, DRDASD, by="code")
#Download Carl Baker's lovely cartogram
utla <- tempfile()
source <- ("https://github.com/houseofcommonslibrary/uk-hex-cartograms-noncontiguous/raw/main/geopackages/LocalAuthorities-uppertier.gpkg")
utla <- curl_download(url=source, destfile=utla, quiet=FALSE, mode="wb")
Background <- st_read(utla, layer="7 Background")
utlacases <- st_read(utla, layer="4 UTLA-2019") %>%
mutate(cua.code=case_when(
cua.code=="S12000015" ~ "S12000047",
cua.code=="S12000046" ~ "S12000049",
cua.code=="S12000044" ~ "S12000050",
cua.code=="S12000024" ~ "S12000048",
TRUE ~ cua.code)) %>%
left_join(DRDASD, by=c("cua.code"="code"))
Groups <- st_read(utla, layer="2 Group")
Group_labels <- st_read(utla, layer="1 Group labels") %>%
mutate(just=if_else(LabelPosit=="Left", 0, 1))
#ASD map only
ASDUK <- ggplot()+
geom_sf(data=subset(map.data, cause=="ASD"), aes(geometry=geometry, fill=mortrate),
colour=NA)+
scale_fill_paletteer_c("pals::ocean.ice", direction=-1, name="Deaths\nper 100,000",
na.value="White")+
theme_classic()+
theme(axis.line=element_blank(), axis.ticks=element_blank(), axis.text=element_blank(),
axis.title=element_blank(), text=element_text(family="Roboto"))
tiff("Outputs/ASD2021UK.tiff", units="in", width=8.5, height=14, res=500)
ASDUK+labs(title="In spite of recent progress, alcohol-specific deaths remain highest in Scotland",
subtitle="Rates of mortality from alcohol-specific causes in UK Local Authorities",
caption="Data from ONS, NRS, NISRA & PHE | Plot by @VictimOfMaths\nData reflects the most recently-available figures for each jurisdiction")+
theme(plot.title=element_text(face="bold", size=rel(1.4)),
plot.subtitle=element_text(size=rel(1.2)))
dev.off()
plot1 <- ggplot()+
geom_sf(data=Background, aes(geometry=geom))+
geom_sf(data=utlacases %>% filter(cause=="ASD"),
aes(geometry=geom, fill=mortrate), colour="Black", size=0.1)+
geom_sf(data=Groups, aes(geometry=geom), fill=NA, colour="Black")+
geom_sf_text(data=Group_labels, aes(geometry=geom, label=Group.labe, hjust=just),
size=rel(2.4), colour="Black")+
scale_fill_paletteer_c("pals::ocean.ice", direction=-1, name="Deaths\nper 100,000",
na.value="White")+
theme_void()+
theme(plot.title=element_text(face="bold", size=rel(1.2)),
text=element_text(family="Lato"))
agg_tiff("Outputs/ASD2021UKCartogram.tiff", units="in", width=9, height=10, res=800)
plot1+
labs(title="In spite of recent progress, alcohol-specific deaths remain highest in Scotland",
subtitle="Rates of mortality from alcohol-specific causes in UK Local Authorities",
caption="Data from ONS, NRS, NISRA & PHE | Cartogram from @carlbaker/House of Commons Library\nPlot by @VictimOfMaths\nData reflects the most recently-available figures for each jurisdiction")
dev.off()
#DRD map only
DRDUK <- ggplot()+
geom_sf(data=subset(map.data, cause=="DRD"), aes(geometry=geometry, fill=mortrate),
colour=NA)+
scale_fill_paletteer_c("pals::ocean.amp", name="Deaths\nper 100,000",
na.value="White")+
theme_classic()+
theme(axis.line=element_blank(), axis.ticks=element_blank(), axis.text=element_blank(),