# code

For replication code, see the research tab.

My other code can be accessed on GitHub, including the packages I’ve written (described below).

### Python packages

`get_files`

automates downloading files from a website using web scraping when you provide it with a url and the file extensions to scrape.

`word2pdf`

automates Microsoft Word document to pdf conversion.

`crop_eps`

crops .eps files; useful when you can’t get the cropping of a graph just right in your statistical software.

### R packages

`tabulator`

efficiently tabulates and produces Stata `tabulate`

-like output.

To install `tabulator`

directly through R:

```
devtools::install_github("skhiggins/tabulator")
```

`tabulator`

includes the following functions:

`tab()`

efficiently tabulates based on a categorical variable, sorts from most common to least common, and displays the proportion of observations with each value, as well as the cumulative proportion.`tabcount()`

counts the unique number of categories of a categorical variable or formed by a combination of categorical variables.`quantiles()`

produces quantiles of a variable. It is a wrapper for`base::quantile()`

but is easier to use, especially within`data.table`

s or`tibble`

s.

### Stata packages

To install directly through Stata:

```
ssc install <package_name>, replace
```

`ceq`

is a suite of commands to estimate fiscal incidence following the Commitment to Equity framework.

`exampleobs`

prints (randomly selected) example observations and optionally stores the values in a local macro. This is useful to explore possible values of a variable in your data set without being biased by the ordering of the data.

`fiscal_impoverishment`

includes commands to estimate fiscal impoverishment (FI) and fiscal gains to the poor (FGP), which are measures of how much the poor benefit from or are hurt by the tax and transfer system from Higgins and Lustig (2016). Additional commands graph FI and FGP curves.

`head`

prints the head observations (first observations in data set) and mimics the `head()`

function in R and `head`

command in Linux.

`randomselect`

randomly selects observations and marks them with a dummy variable. It differs from `sample`

in that it does not drop the non-selected observations from the data set, and that either individual observations or other units, defined by a variable in the data set, can be randomly selected.

`tail`

prints the tail observations (last observations in data set) and mimics the `tail()`

function in R and `tail`

command in Linux.