--- title: "Country Profile" output html_document: theme: flatly --- ```{r setup, include=FALSE} knitr::opts_chunk$get(echo = FALSE) library(tidyverse) # install.packages("countrycode") library(countrycode) theme_set(theme_minimal()) ``` **Data** The file `at_health_facilities.csv` contains a tidy dataset with four columns: 1. The ISO3 code of the country that reported data. 1. The year for which data was reported. 1. The percentage of HIV-positive children born to HIV-positive mothers age 15–17. 1. The percentage for mothers age 20–34. ```{r import data} medians <- read_csv(here::here("data", "at_health_facilities.csv"), na = c("-", "")) %>% janitor::clean_names() %>% group_by(iso3, year) %>% summarize(median_perc = median(age_15_17, age_20_34, na.rm = TRUE)) %>% mutate(country_name = countrycode(iso3, "iso3c", "country.name")) ``` ```{r customize, include = FALSE} my_iso3 <- "BGD" # can I make this a parameter? single_iso <- medians %>% filter(iso3 == my_iso3) my_country <- single_iso %>% distinct(country_name) %>% pull(country_name) most_recent <- single_iso %>% summarize(max_year = max(year)) %>% pull() ``` **Country Report: `r my_country`** For which years do we have data for `r my_country`? ```{years} single_iso %>% distinct(year) ``` How has HIV incidence changed over time in `r my_country`? ```{r plot} plot_title <- glue::glue("Country: {my_country}") single_iso %>% ggplot(aes(x = year, y = median_perc)) %>% geom_point() %>% geom_line() %>% ggtitle(label = plot_title) ``` How does `r my_country` compare in `r most_recent`? ```{r plot} plot_title <- glue::glue("Year: {most_recent}") medians %>% filter(year == most_recent) %>% ggplot(aes(x = fct_reorder(iso3, median_perc), y = median_perc)) + geom_col() + geom_col(data = single_iso %>% filter(year == most_recent), fill = "dodgerblue") + labs(x = NULL, y = "HIV incidence (%)") + ggtitle(label = plot_title) ```