---
title: "Convert a continuous variable into groups"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Convert a continuous variable into groups}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

The `egen` function is used to convert a continuous variable into groups by discretizing it into intervals. It is a deprecated function that has been replaced by the `cut` function from the `mStats` package. The main difference between `egen` and `cut` is the input they accept.

- `egen` works with data frames or tibbles, allowing variable grouping within the context of the entire dataset.
- `cut` operates on a vector, performing grouping directly on that vector.

The `egen` function is deprecated and serves as a wrapper around the cut function. It issues a deprecation warning indicating that the recommended approach is to use cut directly.

```{r setup}
library(mStats)

data <- data.frame(x = 1:10)

egen(data, x, at = c(3, 7), label = c("low", "medium", "high"))

```

## `egen` versus `mutate` + `cut`

```{r}
library(dplyr)

# Example 1: Using egen() function
data <- data.frame(x = 1:10)
data <- egen(data, var = x, at = c(3, 7), label = c("low", "medium", "high"))

# Example 2: Using mutate() and cut() functions
data2 <- data.frame(x = 1:10)
data2 <- mutate(data2, x = cut(x, at = c(-Inf, 3, 7, Inf), label = c("low", "medium", "high")))

# Check if the results are the same
identical(data, data2)  # Should be TRUE
```

In both examples, a data frame `data` and `data2` with a single variable `x` is created. The goal is to group the values of `x` into three categories: "low", "medium", and "high", based on the specified breakpoints.