Push the `knit` button!

``library(tidyverse) # contains ggplot2, dplyr, tidyr, etc``

## `tuberculosis` dataset

``````tb <- read_csv(here::here("data/TB_notifications_2020-07-01.csv")) %>%
dplyr::select(country, iso3, year, new_sp_m04:new_sp_fu) %>%
pivot_longer(cols=new_sp_m04:new_sp_fu, names_to="sexage", values_to="count") %>%
mutate(sexage = str_replace(sexage, "new_sp_", "")) %>%
mutate(sex=substr(sexage, 1, 1),
age=substr(sexage, 2, length(sexage))) %>%
dplyr::select(-sexage) %>%
filter(!(age %in% c("04", "014", "514", "u"))) %>%
filter(year > 1996, year < 2013) %>%
mutate(age_group = factor(age,
labels = c("15-24", "25-34", "35-44",
"45-54", "55-64", "65-"))) %>%
dplyr::select(country, year, age_group, sex, count)

# Filter Australia
tb_oz <- tb %>%
filter(country == "Australia") ``````

### Exercise 1.1: Which plot arrangement better reveals the spike in counts?

``# add your answer here!``

### Exercise 1.2: Rearrange this plot to better answer

“Is the proportion of TB incidence in males relative to females increasing with age?”

``````tb_oz %>%
filter(year == 2012) %>%
ggplot(aes(x=1, y=count, fill=age_group)) +
geom_bar(stat="identity", position="fill") +
facet_wrap(~sex, ncol=6) +
scale_fill_brewer("", palette="Dark2") +
xlab("") + ylab("") +
coord_polar(theta = "y")``````