05_incarceration.Rmd

# Incarceration

This visualization is a sobering one --- showing incarceration rates across different demographics over time.  The data comes via #TidyTuesday via the Vera Institute of Justice.  My strategy is pretty simple - to show changes in rates over time for different groups using facet grid. 

```{r, include = F}
knitr::opts_chunk$set(cache = F)
```


```{r, echo = F, cache = F}
dir <- "raw_data"
file <- paste0(dir, "/", "prison_summary.csv")
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-22/prison_summary.csv"

# create raw_data folder
if (!dir.exists(dir)) { dir.create(dir) }

# download data from internet and save
if (!file.exists(file)) { download.file(url = url, destfile = file) }

# read in downloaded data
df <- readr::read_csv(file)
```


```{r, echo = F}
data <- df %>% 
  filter(pop_category %in% c("Female", "Male")) 
```

A random sample from the data set: 
```{r, echo = F}
knitr::kable(head(data, 5), format = "html")
```

---

```{r incarceration_01, eval=F, echo=F}
  ggplot(data = data) + 
  aes(x = year, y = rate_per_100000) +
  geom_line() +
  facet_grid(urbanicity ~ pop_category) +
  theme_minimal(base_family = "Times") +
  labs(x = "", y = "Incarcerated per 100000") +
  labs(subtitle = "Data source: Vera Institute of Justice") +
  labs(title = "Incarceration rates by sex and urbanicity in the US, 1983-2015")
```


`r apply_reveal("incarceration_01")`


---

```{r, echo =F}
dat <- df %>% 
  filter(!(pop_category %in% c("Female", "Male", "Total", "Other"))) 
```

A random sample from the data set: 
```{r, echo = F}
knitr::kable(head(dat, 5), format = "html")
```
---

```{r incarceration_02, eval=F, echo=F}
  ggplot(data = dat) + 
  aes(x = year, y = rate_per_100000) +
  geom_line() +
  facet_grid(urbanicity ~ pop_category) +
  theme_minimal(base_family = "Times") +
  labs(x = "", y = "Incarcerated per 100000") +
  labs(subtitle = "Data source: Vera Institute of Justice") +
  labs(title = "Incarceration rates by race/ethnicity and urbanicity in the US, 1983-2015") 
```

`r apply_reveal("incarceration_02")`

---