Push the `knit` button!


```{r setup, child="exercise-setup.Rmd", eval = file.exists("exercise-setup.Rmd")}
```

```{r, include = FALSE}
knitr::opts_chunk$set(
  fig.path = "images/part2-exercise-01/",
  class.source = "solution",
  message = FALSE,
  error = FALSE,
  warning = FALSE,
  fig.height = 3,
  fig.width = 10
)
```

```{r pkgs, message = FALSE, warning = FALSE}
library(tidyverse) # contains ggplot2, dplyr, tidyr, etc
```

## `tuberculosis` dataset

```{r tb-data}
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: Stacked bar chart

```{r part2-exercise-01, echo = T, class = "target"}
ggplot(tb_oz, aes(x=year, y=count, fill=sex)) +
  geom_bar(stat="identity") + 
  facet_wrap(~age_group, ncol=6) +
  scale_fill_brewer("Sex", palette="Dark2") 
```

### Exercise 1.2: Side-by-side bar chart

```{r part2-exercise-02, echo = T, class = "target"}
ggplot(tb_oz, aes(x=year, y=count, fill=sex)) +
  geom_bar(stat="identity", position="dodge") + 
  facet_wrap(~age_group, ncol=6) +
  scale_fill_brewer("Sex", palette="Dark2") 
```

### Exercise 1.3: Facetted bar chart

```{r part2-exercise-03, echo = T, class = "target", fig.height = 4}
tb_oz %>% 
  ggplot(aes(x=year, y=count, fill=sex)) +
  geom_bar(stat="identity") + 
  facet_grid(sex~age_group) +
  scale_fill_brewer("Sex", palette="Dark2") 
```

### Exercise 1.4: Facetted bar chart for 2012

```{r part2-exercise-04, echo = T, class = "target"}
tb_oz %>% 
  filter(year == 2012) %>%
  ggplot(aes(x=sex, y=count, fill=sex)) +
  geom_bar(stat="identity") + 
  facet_wrap(~age_group, ncol=6) +
  scale_fill_brewer("Sex", palette="Dark2") 
```

### Exercise 1.5: Reverse facetted bar chart for 2012

```{r part2-exercise-05, echo = T, class = "target"}
tb_oz %>% 
  filter(year == 2012) %>%
  ggplot(aes(x=age_group, y=count, fill=age_group)) +
  geom_bar(stat="identity") + 
  facet_wrap(~sex, ncol=6) +
  scale_fill_brewer("Age", palette="Dark2") +
  xlab("Age")
```

### Exercise 1.6: Facetted pie chart for 2012

```{r part2-exercise-06, echo = T, class = "target"}
tb_oz %>% 
  filter(year == 2012) %>%
  ggplot(aes(x=1, y=count, fill=sex)) +
  geom_bar(stat="identity", position="fill") + 
  facet_wrap(~age_group, ncol=6) +
  scale_fill_brewer("", palette="Dark2") +
  ggtitle("Angle") + xlab("") + ylab("") +
  coord_polar(theta = "y")
```

### Exercise 1.7: Reverse facetted pie chart for 2012

```{r part2-exercise-07, echo = T, class = "target"}
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") +
  ggtitle("Angle") + xlab("") + ylab("") +
  coord_polar(theta = "y")
```


### Exercise 1.8: Aggregated count bar chart

```{r part2-exercise-08, echo = T, class = "target"}
tb_oz %>%
  group_by(year) %>%
  summarise(count = sum(count)) %>%
  ggplot(aes(x=year, y=count)) +
    geom_bar(stat="identity")
```


### Exercise 1.9: Showing proportions

```{r part2-exercise-09, echo = T, class = "target"}
tb_oz %>% group_by(year, age_group) %>% 
  summarise(p = count[sex=="m"]/sum(count)) %>%
  ggplot(aes(x=year, y=p)) +
    geom_hline(yintercept = 0.50, colour="white", size=2) +
    geom_point() +
    facet_wrap(~age_group, ncol=6) +
    ylab("proportion of males")
```

### Exercise 1.10: Overlaying a model

```{r part2-exercise-10, echo = T, class = "target"}
tb_oz %>% group_by(year, age_group) %>% 
  summarise(p = count[sex=="m"]/sum(count)) %>%
  ggplot(aes(x=year, y=p)) +
    geom_hline(yintercept = 0.50, colour="white", size=2) +
    geom_point() +
    geom_smooth(se=F) +
    facet_wrap(~age_group, ncol=6) +
    ylab("proportion of males")
```


