---
title: "Advanced Data Manipulation with m61r in Pure Base R"
author: "pv71u98h1"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Advanced Data Manipulation with m61r in Pure Base R}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
library(m61r)
set.seed(42)

```

# Introduction

The `m61r` package provides a grammar for data manipulation prioritising speed and memory efficiency.

# The Pipeline Interface

The `m61r` object allows for seamless chaining of operations.

```{r example_pipeline}
p <- m61r(mtcars)
p$filter(~mpg > 20)
p$select(~c(mpg, cyl, hp))
p$mutate(hp_norm = ~hp / max(hp))
p$head()

```

# Advanced Scoped Operations

## With Subset Data (.SD)

You can use the `.SD()` method within a formula to access the current data subset for advanced transformations.

```{r scoped_mutate_SD}
p <- m61r(iris)
p$mutate(scaled = ~lapply(.SD()[, vapply(.SD(), is.numeric, logical(1))], 
                          function(x) x * 100))
p$head(3)

```

## With `select_cols`

The `.select_cols` helper allows for clean, functional column selection based on predicates.

```{r scoped_mutate_select_cols}
tmp <- m61r(mtcars)
tmp$mutate(all_num = ~lapply(.SD()[, .select_cols(.SD(), is.numeric)], 
                             function(x) x * 10))
tmp$head(3)

```

## Conversions

Easily convert multiple columns at once using standard R functions inside the `m61r` environment.

```{r conversions_numeric}
p <- m61r(mtcars)
# Convert all columns to numeric using lapply for Base R purity
p$mutate(~lapply(.SD(), as.numeric))

```

## Pattern Matching with `Grep`

Use `grep` to apply transformations to columns matching specific naming patterns.

```{r normalization}
p <- m61r(mtcars)
# Normalize only columns that contain the word "mpg" or "hp"
p$mutate(~lapply(.SD()[, grep("mpg|hp", names(.SD()))], function(x) x / max(x)))
p$head()

```

# Complex Conditional Logic

The `case_when` function provides a vectorised way to handle complex conditional assignments.

```{r case_when}
p <- m61r(mtcars)
p$mutate(category = ~case_when(
  mpg > 25, "Eco",
  mpg > 18, "Standard",
  "High-Perf"
))
head(p[, c("mpg", "category")], 5)

```

# Across

The `across()` function provides a familiar syntax for applying functions across a selection of columns during summarisation.

```{r across}
p <- m61r(mtcars)
p$summarise(
  avg = ~across(c("mpg", "disp", "hp"), mean)
)

```

# Conclusion

By staying true to Base R, `m61r` ensures that your code remains portable, fast, and light.