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HLEbyLAv2.R
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HLEbyLAv2.R
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rm(list=ls())
library(tidyverse)
library(readxl)
library(curl)
library(sf)
library(paletteer)
library(forcats)
#Download data
temp <- tempfile()
source <- "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/healthandsocialcare/healthandlifeexpectancies/datasets/healthstatelifeexpectancyatbirthandatage65bylocalareasuk/current/hsleatbirthandatage65byukla201618.xlsx"
temp <- curl_download(url=source, destfile=temp, quiet=FALSE, mode="wb")
maledata <- read_excel(temp, sheet="HE - Male at birth", range="A4:Q490") %>%
mutate(name=coalesce(`Area Names`, `...3`, `...4`),
sex="Male") %>%
rename(ctyua17cd=`Area Codes`) %>%
select(ctyua17cd, name, sex, LE, HLE, DfLE) %>%
na.omit()
femaledata <- read_excel(temp, sheet="HE - Female at birth", range="A4:Q490") %>%
mutate(name=coalesce(`Area Names`, `...3`, `...4`),
sex="Female") %>%
rename(ctyua17cd=`Area Codes`) %>%
select(ctyua17cd, name, sex, LE, HLE, DfLE) %>%
na.omit()
data <- bind_rows(maledata, femaledata) %>%
mutate(ctyua17cd=case_when(
ctyua17cd=="S12000048" ~ "S12000024",
ctyua17cd=="E06000059" ~ "E10000009",
ctyua17cd=="S12000047" ~ "S12000015",
ctyua17cd=="E06000058" ~ "E06000028",
TRUE ~ ctyua17cd))
data <- data %>%
filter(ctyua17cd=="E06000028") %>%
mutate(ctyua17cd="E06000029") %>%
bind_rows(data)
#Plot national level figures
tiff("Outputs/HLELEBarsxcountry.tiff", units="in", width=8, height=6, res=500)
data %>% filter(name %in% c("ENGLAND", "SCOTLAND", "NORTHERN IRELAND", "WALES")) %>%
mutate(ULE=LE-HLE) %>%
gather(metric, value, c("HLE", "ULE")) %>%
mutate(metric=factor(metric, levels=c("ULE", "HLE"))) %>%
ggplot(aes(x=value, y=name, fill=metric))+
geom_col()+
scale_x_continuous(name="Years of life")+
scale_y_discrete(labels=c("England", "Northern Ireland", "Scotland", "Wales"),
name="")+
scale_fill_paletteer_d("rcartocolor::Safe", name="Years lived in",
labels=c("Poor Health", "Good Health"))+
facet_wrap(~sex)+
theme_classic()+
theme(strip.background=element_blank(), strip.text=element_text(face="bold", size=rel(1)),
plot.title=element_text(face="bold", size=rel(1.2)))+
labs(title="We live a substantial proportion of our lives in poor health",
subtitle="Years of life lived by self-reported health status across the UK in 2016-18",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
#Read in shapefile of LA boundaries
#Download shapefile of LA boundaries
temp <- tempfile()
temp2 <- tempfile()
source <- "https://opendata.arcgis.com/datasets/6638c31a8e9842f98a037748f72258ed_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))
#Bring in data
map.data <- full_join(shapefile, data, by="ctyua17cd")
tiff("Outputs/HLEbyLA.tiff", units="in", width=12, height=7, res=500)
ggplot(data=subset(map.data, !is.na(HLE)), aes(fill=HLE, geometry=geometry))+
geom_sf(colour=NA)+
scale_fill_paletteer_c("pals::ocean.haline", name="Healthy Life\nExpectancy", direction=-1)+
facet_wrap(~sex)+
theme_classic()+
theme(plot.title=element_text(face="bold", size=rel(1.2)),
axis.line=element_blank(), axis.ticks=element_blank(), axis.text=element_blank(),
axis.title=element_blank(), strip.background=element_blank(),
strip.text=element_text(face="bold"))+
labs(title="Healthy Life Expectancy varies hugely across the UK",
subtitle="Average years of life lived in 'good' or 'very good' health in UK Local Authorities",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
tiff("Outputs/LEbyLA.tiff", units="in", width=12, height=7, res=500)
ggplot(data=subset(map.data, !is.na(LE)), aes(fill=LE, geometry=geometry))+
geom_sf(colour=NA)+
scale_fill_paletteer_c("pals::ocean.matter", name="Life Expectancy", direction=-1)+
facet_wrap(~sex)+
theme_classic()+
theme(plot.title=element_text(face="bold", size=rel(1.2)),
axis.line=element_blank(), axis.ticks=element_blank(), axis.text=element_blank(),
axis.title=element_blank(), strip.background=element_blank(),
strip.text=element_text(face="bold"))+
labs(title="Women live longer than men across the UK",
subtitle="Life Expectancy at birth in UK Local Authorities",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
tiff("Outputs/DFLEbyLA.tiff", units="in", width=12, height=7, res=500)
ggplot(data=subset(map.data, !is.na(DfLE)), aes(fill=DfLE, geometry=geometry))+
geom_sf(colour=NA)+
theme_classic()+
scale_fill_paletteer_c("pals::ocean.speed", name="Life Expectancy", direction=-1)+
theme(axis.line=element_blank(), axis.ticks=element_blank(), axis.text=element_blank(),
axis.title=element_blank(), strip.background=element_blank(),
strip.text=element_text(face="bold"))+
facet_wrap(~sex)+
labs(title="Disease free life expectancy by Local Authority in England",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
#Analysis of data
analysis.data <- map.data %>%
st_drop_geometry() %>%
select(ctyua17cd, ctyua17nm, sex, LE, HLE, DfLE) %>%
mutate(country=case_when(
substr(ctyua17cd,1,1)=="S" ~ "Scotland",
substr(ctyua17cd,1,1)=="E" ~ "England",
substr(ctyua17cd,1,1)=="N" ~ "Northern Ireland",
substr(ctyua17cd,1,1)=="W" ~ "Wales"))
tiff("Outputs/LEbyLAScatter.tiff", units="in", width=8, height=6, res=500)
analysis.data %>%
select(ctyua17cd, country, sex, LE) %>%
filter(!is.na(country)) %>%
spread(sex, LE) %>%
ggplot(aes(x=Male, y=Female, colour=country))+
geom_point()+
geom_abline()+
scale_x_continuous(name="Male Life Expectancy at birth", limits=c(72, 87))+
scale_y_continuous(name="Female Life Expectancy at Birth", limits=c(72, 87))+
scale_colour_paletteer_d("fishualize::Scarus_quoyi", name="Country")+
theme_classic()+
theme(plot.title=element_text(face="bold", size=rel(1.2)))+
labs(title="Life Expectancy is higher for women in every UK Local Authority",
subtitle="Life Expectancy at Birth based on 2016-18 data",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
tiff("Outputs/HLEbyLAScatter.tiff", units="in", width=8, height=6, res=500)
analysis.data %>%
select(ctyua17cd, country, sex, HLE) %>%
filter(!is.na(country)) %>%
spread(sex, HLE) %>%
ggplot(aes(x=Male, y=Female, colour=country))+
geom_point()+
geom_abline()+
scale_x_continuous(name="Male Healthy Life Expectancy at birth", limits=c(50, 75))+
scale_y_continuous(name="Female Healthy Life Expectancy at Birth", limits=c(50, 75))+
scale_colour_paletteer_d("fishualize::Scarus_quoyi", name="Country")+
theme_classic()+
theme(plot.title=element_text(face="bold", size=rel(1.2)))+
labs(title="Healthy Life Expectancy is broadly similar for men and women",
subtitle="Healthy Life Expectancy at Birth based on 2016-18 data",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
tiff("Outputs/HLEGap.tiff", units="in", width=8, height=6, res=500)
analysis.data %>%
filter(!is.na(sex) & !is.na(country)) %>%
mutate(flag=case_when(
ctyua17nm=="Orkney Islands" & sex=="Female" ~ "a",
ctyua17nm=="Nottingham" & sex=="Female" ~ "a",
ctyua17nm=="Richmond upon Thames" & sex=="Male" ~ "a",
ctyua17nm=="Blackpool" & sex=="Male" ~ "a",
TRUE ~ "b"
)) %>%
ggplot(aes(x=sex, y=HLE, colour=flag))+
geom_jitter(width=0.05, show.legend=FALSE)+
scale_x_discrete(name="")+
scale_y_continuous(name="Healthy Life Expectancy")+
scale_colour_manual(values=c("Tomato", "Grey70"))+
theme_classic()+
theme(plot.title=element_text(face="bold", size=rel(1.2)))+
annotate("text", x=1.05, y=73.3, label="Orkney", colour="Tomato", hjust=0)+
annotate("text", x=1.05, y=54.2, label="Nottingham", colour="Tomato", hjust=0)+
annotate("text", x=2.05, y=71.9, label="Richmond upon Thames", colour="Tomato", hjust=0)+
annotate("text", x=2.05, y=53.3, label="Blackpool", colour="Tomato", hjust=0)+
annotate("text", x=1.2, y=(73.3+54.2)/2, label="Gap of 19.1 years", angle=90,
colour="Tomato2", fontface=2)+
annotate("text", x=2.2, y=(71.9+53.3)/2, label="Gap of 18.6 years", angle=90,
colour="Tomato2", fontface=2)+
labs(title="There is huge inequality in Healthy Life Expectancy across the UK",
subtitle="Average years of life lived in 'good' or 'very good' health by Local Authority",
caption="Data from ONS | Plot by @VictimOfMaths")
dev.off()
analysis.data %>%
filter(sex=="Male") %>%
mutate(ULE=LE-HLE) %>%
gather(metric, outcome, c("HLE", "ULE")) %>%
ggplot(aes(x=fct_reorder(ctyua17cd, outcome), y=outcome, fill=metric))+
geom_col()