OECD LTC plot
David Henderson
2021-09-29T21:21:25-05:00
Intro
Packages
library(tidyverse)
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## ✓ tibble 3.1.3 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(lubridate)
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## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union
library(ggthemes)
library(countrycode)
library(OECD)
## Warning: package 'OECD' was built under R version 4.1.1
theme_set(theme_minimal(base_size = 16))Data
Use OECD package to get data
dataset <- "HEALTH_LTCR"
dstruc <- get_data_structure(dataset)
str(dstruc)
## List of 7
## $ VAR_DESC :'data.frame': 7 obs. of 2 variables:
## ..$ id : chr [1:7] "VAR" "UNIT" "COU" "YEA" ...
## ..$ description: chr [1:7] "Variable" "Measure" "Country" "Year" ...
## $ VAR :'data.frame': 13 obs. of 2 variables:
## ..$ id : chr [1:13] "LTCW" "LTWF" "LTWFFWHC" "LTWFFWHO" ...
## ..$ label: chr [1:13] "Long-term care workers" "Long-term care workers: formal sector" "Formal LTC workers (Head counts)" "Formal LTC workers at home (Head counts)" ...
## $ UNIT :'data.frame': 29 obs. of 2 variables:
## ..$ id : chr [1:29] "NURPCAMT" "IWP65N" "PCFORFPF" "TOTNURNB" ...
## ..$ label: chr [1:29] "Total (nurses and personal carers)" "Per 100 population aged 65 years old and over" "Females per 100 population aged 65 years old and over" "Total nurses (at home and in institutions)" ...
## $ COU :'data.frame': 45 obs. of 2 variables:
## ..$ id : chr [1:45] "AUS" "AUT" "BEL" "CAN" ...
## ..$ label: chr [1:45] "Australia" "Austria" "Belgium" "Canada" ...
## $ YEA :'data.frame': 61 obs. of 2 variables:
## ..$ id : chr [1:61] "1960" "1961" "1962" "1963" ...
## ..$ label: chr [1:61] "1960" "1961" "1962" "1963" ...
## $ OBS_STATUS :'data.frame': 19 obs. of 2 variables:
## ..$ id : chr [1:19] "b" "d" "e" "B" ...
## ..$ label: chr [1:19] "Break in series" "Deviation from OECD definition" "Estimate" "Break" ...
## $ TIME_FORMAT:'data.frame': 5 obs. of 2 variables:
## ..$ id : chr [1:5] "P1Y" "P1M" "P3M" "P6M" ...
## ..$ label: chr [1:5] "Annual" "Monthly" "Quarterly" "Half-yearly" ...dstruc$UNITdstruc$VARltc <- get_dataset("HEALTH_LTCR")Plot 1
ltc %>%
mutate(obsTime = str_c(obsTime, "0101") %>% ymd(.)) %>%
filter(obsTime == ymd(20190101)) %>%
filter(UNIT == "PRTT65QT" & VAR %in% c("LTCILTIT", "LTCILTHT")) %>%
mutate(VAR = factor(VAR, levels = c("LTCILTIT", "LTCILTHT"),
labels = c("Institution\n(Non-hospital)", "Home")),
COU = countrycode(COU, origin = "iso3c", destination = "country.name")) %>%
group_by(COU) %>%
mutate(totVal = sum(obsValue)) %>%
ungroup %>%
ggplot(aes(reorder(COU, -totVal), obsValue, fill = VAR)) +
geom_col() +
#geom_hline(yintercept = 7.6) +
scale_y_continuous(limits = c(0, 40),
labels = scales::percent_format(scale = 1),
expand = c(0,0.1)) +
scale_fill_ptol() +
theme(legend.position = "top",
plot.caption = element_text(size = 10),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
axis.ticks.x = element_line(),
axis.text.x = element_text(angle = 25, hjust = 1, vjust = 1)) +
labs(y = "",
x = "",
fill = "",
title = "Percentage of people aged 65+ in receipt of long-term care, 2019",
subtitle = "OECD countries for which data is available",
caption = "Source:https://stats.oecd.org/") -> ltc_plot
ltc_plot
ggsave("ltc_plot.png", ltc_plot, height =9, width = 12)