README.Rmd

---
output: rmarkdown::github_document
---

# qldyjcost

Cost of Offending Tools for Youth Justice in Queensland

```{r, include = FALSE}
knitr::opts_chunk$set(
comment = NA, fig.width = 12, fig.height = 8, cache = FALSE)
```

```{r set-options, echo=FALSE, cache=FALSE}
options(width = 400)
```

## Purpose

This is a simple, lightweight R package purely for convenience for my day job as a Senior Data Scientist at the consulting firm [Nous Group](https://nousgroup.com/) where I frequently conduct evaluations for Queensland Youth Justice.

## Installation

You can install the development version of `qldyjcost` from GitHub using the following:

```{r eval = FALSE}
devtools::install_github("hendersontrent/qldyjcost")
```

## Usage

The core of `qldyjcost` is a data frame of historical costs from peer-reviewed sources called `yjcosts`. It can be accessed easily via:

```{r, message = FALSE, warning = FALSE}
library(qldyjcost)
head(yjcosts)
```

`yjcosts` contains historical offending costs broken down by offence seriousness for court costs, police costs, wider social costs, and custody costs.

`qldyjcost` also contains functionality for adjusting the historical offending costs to values for a given financial year using inflation. The `get_cpi` function webscrapes inflation rates from the [Australian Bureau of Statistics](https://www.abs.gov.au/statistics/economy/price-indexes-and-inflation/consumer-price-index-australia/) and automatically cleans it up ready for use:

```{r, message = FALSE, warning = FALSE}
cpi <- get_cpi(location = "Australia")
head(cpi)
```

Instead of grabbing CPI for the country, we can instead get it for any capital city:

```{r, message = FALSE, warning = FALSE}
cpi_bris <- get_cpi(location = "Brisbane")
head(cpi_bris)
```

While this function is useful in isolation, it forms a key component of the workhorse function of `yjcosts` which is `adjust_costs`. `adjust_costs` applies inflation adjustments to the offending cost data to any given financial year. Specifically, it does this by averaging CPI values for each FY associated with the baseline costs in `yjcosts` as well as the user-specified FY to adjust to. The interface is incredible simple. Here is how to adjust costs to FY 2023-24 dollars:

```{r, message = FALSE, warning = FALSE}
adj <- adjust_costs(fy = "2023-24", location = "Australia")
head(adj)
```