---
title: "package mycor"
author: "Keon-Woong Moon <cardiomoon@gmail.com>"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{package mycor}
  %\VignetteEngine{knitr::rmarkdown}
  %\usepackage[utf8]{inputenc}
---
```{r,echo=FALSE}
knitr::opts_chunk$set(collapse=TRUE, comment=NA)
```

## Motivation

### For correlation analysis : `cor`, `cor.test` and `lm`

When I began to study R software, I was really impressed with the function `cor`. As you knows, the 'cor' function returns all `r` values of every possible pairs of matrix or data.frame provided that it consists of numeric data only. For example, `cor(mtcars[1:5])` acts just as expected. 
```{r}
cor(mtcars[1:5])
```

But `cor(iris)` returns error because the data.frame consist of both numeric and factor variable.

```{r,error=TRUE,purl=FALSE}
cor(iris)
```

If you wanted to get `p` values as well as `r` values, you should use `cor.test` instead of `cor`. But `cor.test` can deal with only one pair of numeric vectors of the same length, neither a matrix nor a data.frame. Furthermore, if you wanted to get the `slope` and `intercept` of simple linear regression line of xyplot, you had to perform `lm` test for every pairs of numeric variables of the data.frame.  

## Solution ; Do not repeat yourself !! 

My idea is that a single function deals with data.frame of mixed numeric, logical and factor variables, select numeric variables, perform `cor.test` and `lm` to get `r`,`p`,`slope` and `intercept` of every pairs of the variables for exploratory analysis. It can save my time and effort.   

## Function "mycor" and Class "mycor"

Use of mycor function is simple. Just call mycor with a data.frame. For example, just call `cor(iris)`. Unlikely `cor`, it does not result in an error.
```{r,message=FALSE}
require(lattice)
require(mycor)
mycor(iris)
```

The mycor function reurns an object of class "mycor". This can be saved for print, summarize and plot. A S3 method for class `formula` can be used to function `mycor`. Function `print.mycor` shows the r values and th p values similar to the function `cor`. A `mycor` class object can be summarized with summary function, summary().

```{r}
out=mycor(iris,alternative="greater", method="kendall",digits=2)
out1=mycor(~mpg+disp+hp+wt,data=mtcars)
summary(out1)
```

The `mycor` function uses cor.test internally, so you can use all options of `cor.test` - namely `alternatives`, `method`, `conf.level`,... .

## Plot "mycor" object

Probably most valuable function is plot. It is not a new function. It uses internally one of two popular function : graphics::pairs() and lattice::parallelplot(). In fact, plot.mycor function have four types of plot : Three variants of pairs and parallelplot. Call function `plot` with no option makes pairs().  

```{r,fig.width=6, fig.height=6}
plot(out)
```

But if you specify the groups, you can get more pretty plot. You can use extra arguments which can used in pairs() or parallelplot().

```{r,fig.width=6, fig.height=6}
plot(out,groups=species,main="Test of mycor::plot")
```

With `type=2` option, you can get histogram at diagonal panel. 

```{r,fig.width=6, fig.height=6}
plot(out,type=2,groups=spe)
```

With `type=3` option, you can get correlation plot at upper panels.

```{r,fig.width=6, fig.height=6}
plot(out1,type=3)
```

With `type=4` option, you can get parallelplot.

```{r,fig.width=6, fig.height=6}
plot(out,type=4,groups=spe)
plot(out1,type=4,groups=cyl)
```