README.Rmd

---
output:
  md_document:
    variant: markdown_github
    toc: false
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[![Coverage status](https://codecov.io/gh/HughParsonage/grattan/branch/master/graph/badge.svg)](https://app.codecov.io/github/HughParsonage/grattan?branch=master)

# grattan

Utilities for costing and evaluating Australian tax policy, including high-performance tax and transfer calculators, a fast method of projecting tax collections from ATO sample files, and an interface to common indices from the Australian Bureau of Statistics.  Written to support Grattan Institute's Australian Perspectives program.


```{r knitrOpts, include=FALSE}
library(knitr)
opts_chunk$set(dev = 'svg',
               fig.path = "man/figures/README_")
```

# Overview
```{r eval=FALSE}
install.packages("grattan")
```

```{r loadPackages}
library(grattan)
```

## `income_tax`
Calculates the income tax for a given taxable income and financial year:

```{r income_tax_1}
income_tax(50e3, "2015-16")
```

### With sample files
`income_tax` is designed to work well with the ATO's sample files. You can obtain the sample files from my repo:

```{r install_taxstats}
# install.packages("taxstats", repos = "https://hughparsonage.github.io/tax-drat")
library(taxstats)

library(hutils)
library(data.table) 
library(magrittr)
library(ggplot2)
```

Simply pass the sample file to `.dots.ATO` and the complexities of things like Medicare levy and the Seniors and Pensioners Tax Offset are handled for you. For example:

```{r income_tax_s1314}
s1314 <- as.data.table(sample_file_1314)
s1314 %>%
  .[, tax := income_tax(Taxable_Income, "2013-14", .dots.ATO = s1314)] %>%
  .[, .(Taxable_Income, tax)]
```

## `model_income_tax`: modelling changes to personal income tax
While `income_tax` is designed to inflexibly return the tax payable as legislated, 
`model_income_tax` is designed to calculate income tax when changes are made. For example,

```{r model_income_tax}
s1314 %>%
  # reduce top threshold from 180,000 to 150,000
  model_income_tax(ordinary_tax_thresholds = c(0, 18200, 37000, 80000, 
                                               150e3), 
                   baseline_fy = "2013-14") %>%
  .[, .(Taxable_Income, baseline_tax, new_tax)]
```


## `project`
Given a sample file, we can project forward a number of years

```{r s1617}
s1617 <- project(s1314, h = 3L)
```

or to a particular financial year

```{r s1718}
s1718 <- project_to(s1314, "2017-18")
```

Together with `model_income_tax`, this allows us to make point-predictions of future years. The function `revenue_foregone` prettily prints the resultant revenue:

```{r model1819}
sample_file_1314 %>%
  project_to("2018-19") %>%
  model_income_tax(baseline_fy = "2017-18",
                   ordinary_tax_thresholds = c(0, 18200, 37000, 87000, 
                                               150e3)) %>%
  revenue_foregone
```

### `compare_avg_tax_rates`:

Create comparison of average tax rates:

```{r compare-30-37-42-thresholds}
lapply(list("30k" = 30e3,
            "36k" = 36e3,
            "42k" = 42e3),
       function(T2) {
         model_income_tax(s1718,
                          baseline_fy = "2017-18",
                          ordinary_tax_thresholds = c(0, 
                                                      18200,
                                                      T2,
                                                      87000, 
                                                      180e3))
       }) %>%
  rbindlist(idcol = "id",
            use.names = TRUE,
            fill = TRUE) %>%
  compare_avg_tax_rates(baseDT = .[id %ein% "36k"]) %>%
  ggplot(aes(x = Taxable_Income_percentile,
             y = delta_avgTaxRate,
             color = id,
             group = id)) +
  geom_hline(yintercept = 0) +
  geom_line()
```

# NEWS
```{r NEWS, results='asis', echo=FALSE}
cat(readLines("NEWS.md"), sep = "\n")
```

# CRAN Notes
```{r CRAN-comments, results='asis', echo=FALSE}
cat(readLines("cran-comments.md"), sep = "\n")
```