---
title: "FakeDataR: Getting started"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{FakeDataR: Getting started}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE)
set.seed(1)  # ensure RNG is initialized for clean vignette sessions
library(FakeDataR)
```

This vignette shows how to mirror the structure of real data with fully synthetic values, verify the structure, and produce an LLM-ready bundle.

## Quick start

```{r tiny-end-to-end}
# tiny input with a few likely sensitive fields
df <- data.frame(
  id = sprintf("id%03d", 1:10),
  email = paste0("a", 1:10, "@x.com"),
  Progress = paste0(sample(80:100, 10, TRUE), "%"),
  check.names = FALSE
)

orig <- prepare_input_data(df)

fake_priv <- generate_fake_with_privacy(
  data = orig, n = 10, level = "low", seed = 1,
  sensitive = c("id", "email"),
  sensitive_detect = TRUE,
  sensitive_strategy = "fake",
  normalize = TRUE
)

# quick validation sample
head(validate_fake(orig, fake_priv), 5)

```


```{r}
library(FakeDataR)

# Basic fake from a data.frame
fake_mtc <- generate_fake_data(mtcars, n = 200, seed = 1)
validate_fake(mtcars, fake_mtc)

```


## Factors, characters, and numerics

```{r}
fake_co2 <- generate_fake_data(as.data.frame(CO2), n = 200, seed = 2)
validate_fake(as.data.frame(CO2), fake_co2)

```

```{r}
fake_tg <- generate_fake_data(ToothGrowth, n = 120, seed = 3)
validate_fake(ToothGrowth, fake_tg)
```

## Dates and POSIXct (time zones preserved)

```{r}
df_date <- data.frame(d = seq(as.Date("2020-01-01"), by = "day", length.out = 50))
fake_date <- generate_fake_data(df_date, n = 80, seed = 4)
str(fake_date$d)

```

```{r}
dt <- data.frame(
  when = seq.POSIXt(as.POSIXct("2023-05-01 00:00:00", tz = "America/New_York"),
                    by = "hour", length.out = 200)
)
fake_dt <- generate_fake_data(dt, n = 50, seed = 5)
str(fake_dt$when)
range(fake_dt$when)

```

## Public datasets - wrap in guards, trim sizes

These chunks run only if the packages are installed.

```{r flights-demo, message=FALSE}

if (requireNamespace("nycflights13", quietly = TRUE)) {
  fl <- nycflights13::flights
  set.seed(10)
  fl_small <- fl[sample.int(nrow(fl), 2000), ]  # smaller
  fake_fl <- generate_fake_data(
    fl_small, n = 500, seed = 10,
    numeric_mode = "distribution"
  )
  head(validate_fake(fl_small, fake_fl), 5)
} else {
  message("nycflights13 not installed - skipping.")
}
```


```{r penguins-demo, message=FALSE}
if (requireNamespace("palmerpenguins", quietly = TRUE)) {
  peng <- na.omit(palmerpenguins::penguins[, c("species","island","bill_length_mm","sex")])
  fake_peng <- generate_fake_data(
    peng, n = 400, seed = 11,
    category_mode = "preserve"
  )
  head(validate_fake(peng, fake_peng), 5)
} else {
  message("palmerpenguins not installed - skipping.")
}

```

## Gapminder demo

```{r gapminder-demo, message=FALSE}
# Optional package; make the chunk robust
if (requireNamespace("gapminder", quietly = TRUE)) {
  set.seed(21)
  gm <- gapminder::gapminder
  # Keep it light if you want: gm <- gm[sample.int(nrow(gm), 2000), ]

  fake_gm <- generate_fake_data(
    gm, n = 800, seed = 21,
    numeric_mode = "distribution",  # nicer numeric spread
    category_mode = "preserve"      # keep factor levels
  )

  validate_fake(gm, fake_gm)
} else {
  message("gapminder not installed; skipping demo.")
}
```



## Sensitive columns: fake vs drop

```{r pii-demo, message=FALSE}
set.seed(12)
df_pii <- data.frame(
  id    = 1:100,
  email = sprintf("user%03d@corp.com", 1:100),
  phone = sprintf("(415) 555-%04d", 1:100),
  spend = runif(100, 10, 500)
)

fake_keep <- generate_fake_data(
  df_pii, n = 120,
  sensitive_detect   = TRUE,
  sensitive_strategy = "fake"
)
fake_drop <- generate_fake_data(
  df_pii, n = 120,
  sensitive_detect   = TRUE,
  sensitive_strategy = "drop"
)

names(fake_keep)        # expect id/email/phone present but synthetic
names(fake_drop)        # expect only "spend"

```


## LLM bundle: data + schema + README (+ optional ZIP)

```{r}
b1 <- llm_bundle(
  data = ToothGrowth, n = 150, level = "high", seed = 10,
  formats = c("csv","rds"),
  path = tempdir(), filename = "toothgrowth_fake",
  write_prompt = TRUE, zip = TRUE
)
b1$schema_path
b1$readme_path
b1$zip_path

```

## Parquet export (optional)

```{r parquet-export, message=FALSE}
if (requireNamespace("arrow", quietly = TRUE)) {
  fake_air <- generate_fake_data(airquality, n = 400, seed = 20)
  export_fake(fake_air, file.path(tempdir(), "air.parquet"))
} else {
  message("arrow not installed - skipping Parquet export.")
}

```

## Reproducibility

```{r}
a1 <- generate_fake_data(CO2, n = 123, seed = 42)
a2 <- generate_fake_data(CO2, n = 123, seed = 42)
identical(a1, a2)

```

```{r big-benchmark, eval=FALSE}
big <- data.frame(
  a = runif(2e5),
  b = sample(letters, 2e5, TRUE),
  c = as.Date("2020-01-01") + sample.int(3000, 2e5, TRUE)
)
system.time({
  fake_big <- generate_fake_data(big, n = 2e5, seed = 99)
})

```