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

```{r setup, include=FALSE}
library(m61r)

```

# Introduction

The R-package `m61r` gathers functions similar to the ones present in `dplyr` and `tidyr`, but written entirely in Base R without any external dependencies. All functions are designed to work directly with standard `data.frames`.

# filter

The `filter_` function subsets a data frame, retaining all rows that satisfy your specific conditions.

```{r filter_eg}
tmp <- filter_(CO2, ~Plant == "Qn1")
head(tmp)

```

# select

The `select_` function allows you to zoom in on specific columns of interest.

```{r select_eg}
tmp <- select_(CO2, ~c(Plant, Type))
head(tmp, 2)

```

# Transformative selections of a data.frame

`mutate_` adds new variables while preserving existing ones, whereas `transmutate_` keeps only the newly created variables.

```{r mutate_eg}
tmp <- mutate_(CO2, z = ~conc / uptake)
head(tmp, 2)

```

# summarise

`summarise_` creates a new data frame with aggregated statistics. It can be used on the whole data frame or on specific groups.

```{r summarise_eg}
# Global summary
summarise_(CO2, mean = ~mean(uptake), sd = ~sd(uptake))

# Grouped summary
g_info <- get_group_indices_(CO2, ~c(Type, Treatment))
summarise_(CO2, group_info = g_info, mean = ~mean(uptake))

```

# join

`m61r` provides a full suite of join functions. Here is an example of an inner join between two datasets.

```{r data_join, echo=FALSE}
authors <- data.frame(
    surname = c("Tukey", "Venables", "Tierney", "Ripley", "McNeil"),
    nationality = c("US", "Australia", "US", "UK", "Australia"))

books <- data.frame(
    name = c("Tukey", "Venables", "Tierney", "Ripley", "McNeil"),
    title = c("EDA", "MASS", "LISP-STAT", "Spatial", "Interactive"))

```

```{r join_eg}
inner_join_(authors, books, by.x = "surname", by.y = "name")

```

# reshape

The `gather_` function transforms data from a "wide" format to a "long" format, making it easier to analyse certain types of data.

```{r reshape_eg}
df3 <- data.frame(id = 1:2, age = c(40, 50), dose.a1 = c(1, 2), dose.a2 = c(2, 1))
df4 <- gather_(df3, pivot = c("id", "age"))
df4

```