---
title: "Introduction to the charlatan package"
author: "Scott Chamberlain"
date: "`r Sys.Date()`"
output:
  html_document:
    toc: true
    toc_float: true
    theme: readable
vignette: >
  %\VignetteIndexEntry{Introduction to the charlatan package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r echo=FALSE}
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE
)
```

`charlatan` makes fake data, inspired from and borrowing some code from Python's [faker](https://github.com/joke2k/faker)

Why would you want to make fake data? Here's some possible use cases to
give you a sense for what you can do with this package:

* Students in a classroom setting learning any task that needs a dataset.
* People doing simulations/modeling that need some fake data
* Generate fake dataset of users for a database before actual users exist
* Complete missing spots in a dataset
* Generate fake data to replace sensitive real data with before public release
* Create a random set of colors for visualization
* Generate random coordinates for a map
* Get a set of randomly generated DOIs (Digial Object Identifiers) to
assign to fake scholarly artifacts
* Generate fake taxonomic names for a biological dataset
* Get a set of fake sequences to use to test code/software that uses
sequence data

## Package API

* Low level interfaces: All of these are `R6` objects that a user can
initialize and then call methods on. These contain all the logic that
the below interfaces use.
* High level interfaces: There are high level functions prefixed with
`ch_*()` that wrap low level interfaces, and are meant to be easier
to use and provide an easy way to make many instances of a thing.
* `ch_generate()` - generate a data.frame with fake data, choosing which columns to include from the data types provided in `charlatan`
* `fraudster()` - single interface to all fake data methods, - returns
vectors/lists of data - this function wraps the `ch_*()` functions described above

## Install

Stable version from CRAN

```{r eval=FALSE}
install.packages("charlatan")
```

Development version from Github

```{r eval=FALSE}
devtools::install_github("ropensci/charlatan")
```

```{r}
library("charlatan")
```

## high level function

... for all fake data operations

```{r}
x <- fraudster()
x$job()
x$name()
x$job()
x$color_name()
```

## locale support

Adding more locales through time, e.g.,

Locale support for job data

```{r}
ch_job(locale = "en_US", n = 3)
ch_job(locale = "fr_FR", n = 3)
ch_job(locale = "hr_HR", n = 3)
ch_job(locale = "uk_UA", n = 3)
ch_job(locale = "zh_TW", n = 3)
```

For colors:

```{r}
ch_color_name(locale = "en_US", n = 3)
ch_color_name(locale = "uk_UA", n = 3)
```

More coming soon ...

## generate a dataset

```{r}
ch_generate()
```

```{r}
ch_generate('job', 'phone_number', n = 30)
```

## Data types

### person name

```{r}
ch_name()
```

```{r}
ch_name(10)
```


### phone number

```{r}
ch_phone_number()
```

```{r}
ch_phone_number(10)
```

### job

```{r}
ch_job()
```

```{r}
ch_job(10)
```

### credit cards

```{r}
ch_credit_card_provider()
ch_credit_card_provider(n = 4)
```

```{r}
ch_credit_card_number()
ch_credit_card_number(n = 10)
```

```{r}
ch_credit_card_security_code()
ch_credit_card_security_code(10)
```


## Messy data

Real data is messy, right?  `charlatan` makes it easy to create
messy data. This is still in the early stages so is not available
across most data types and languages, but we're working on it.

For example, create messy names:

```{r}
ch_name(50, messy = TRUE)
```

Right now only suffixes and prefixes for names in `en_US` locale
are supported. Notice above some variation in prefixes and suffixes.
