---
title: "Introduction to 'scatterPlotMatrix'"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to 'scatterPlotMatrix'}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 7
)
```

```{r setup}
library(scatterPlotMatrix)
```

## Basic usage (dataset uses `factor` type)

```{r}
scatterPlotMatrix(iris)
```

'Species' column is of factor type and has box representation for its categories.

## `slidersPosition` argument

```{r}
scatterPlotMatrix(iris, slidersPosition = list(
  dimCount = 3, # Number of columns to draw
  xStartingDimIndex = 2, # Index of first drawn column horizontally
  yStartingDimIndex = 2 # Index of first drawn column vertically
))
```

Set initial position of sliders, specifying which columns intervals are visible.
Here, visible columns starts at second column in x direction, second column in y direction, and three columns are represented.

## `zAxisDim` argument (referenced column is categorical)

```{r}
scatterPlotMatrix(iris, zAxisDim = "Species")
```

Each point has a color depending of its 'Species' value.

## `categoricalCS` argument

```{r}
scatterPlotMatrix(iris, zAxisDim = "Species", categoricalCS = "Set1")
```

Colors used for categories are not the same as previously (supported values: `Category10`, `Accent`, `Dark2`, `Paired`, `Set1`).

## `zAxisDim` argument (referenced column is continuous)

```{r}
scatterPlotMatrix(iris, zAxisDim = "Sepal.Length")
```

Each point has a color depending of its `Sepal.Length` value.

## `continuousCS` argument

```{r}
scatterPlotMatrix(iris, zAxisDim = "Sepal.Length", continuousCS = "YlOrRd")
```

Colors used for points are not the same as previously (supported values: `Blues`, `RdBu`, `YlGnBu`, `YlOrRd`, `Reds`).

## `corrPlotType` argument

Supported values: `Empty`, `Circles`, `Text`, `AbsText`

```{r}
scatterPlotMatrix(iris, corrPlotType = "Text")
```

Correlation plots use simple texts instead of circle tree maps as previously;
Value of correlations is used to attribute the color, using a color scale with a domain [-1; 1] and the palette `RdBu`.

```{r}
scatterPlotMatrix(iris, corrPlotType = "AbsText")
```

Absolute value of correlations is used to attribute the color, using a color scale with a domain [0; 1] and palette 'Blues'.

## Basic usage (dataset doesn't use `factor` type)

```{r}
scatterPlotMatrix(mtcars)
```

Several columns are of numerical type but should be of factor type (for example `cyl`).

## `categorical` argument

```{r}
categorical <- list(cyl = c(4, 6, 8), vs = c(0, 1), am = c(0, 1), gear = 3:5, carb = 1:8)
scatterPlotMatrix(mtcars, categorical = categorical, zAxisDim = "cyl")
```

`cyl` and four last columns have a box representation for categories (use top slider to see the last three columns).

## `distribType` argument

```{r}
scatterPlotMatrix(iris, zAxisDim = "Species", distribType = 1)
```

Distribution plots are of type 'density plot' (instead of histogram).

## `regressionType` argument

```{r}
scatterPlotMatrix(iris, zAxisDim = "Species", regressionType = 1)
```

Add linear regression plots.

## `cutoffs` argument

```{r}
cutoffs <- list(
  list(
    xDim = "Sepal.Length",
    yDim = "Species",
    xyCutoffs = list(
      list(c(4, 8), c(-0.1, 0.1)),
      list(c(4, 8), c(1.9, 2.1))
    )
  )
)
scatterPlotMatrix(iris, zAxisDim = "Species", cutoffs = cutoffs)
```

Traces which are not kept by cutoffs are greyed; only kept traces are used for histograms.

## `rotateTitle` argument

```{r}
scatterPlotMatrix(iris, zAxisDim = "Species", rotateTitle = TRUE)
```

Column names are rotated (can be useful for long column names).

## `columnLabels` argument

```{r}
columnLabels <- gsub("\\.", "<br>", colnames(iris))
scatterPlotMatrix(iris, zAxisDim = "Species", columnLabels = columnLabels)
```

Given names are displayed in place of column names found in dataset; `<br>` is used to insert line breaks.

## `cssRules` argument

```{r}
scatterPlotMatrix(iris, cssRules = list(
  ".jitterZone" = "fill: pink", # Set background of plot to pink
  ".tick text" = c("fill: red", "font-size: 1.8em") # Set text of axes ticks red and greater
))
```

Apply CSS to the plot. CSS is a simple way to describe how elements on a web page should be displayed (position, colour, size, etc.). 
You can learn the basics at [W3Schools](https://www.w3schools.com/css/).
You can learn how to examine and edit css at [MDN Web Docs](https://firefox-source-docs.mozilla.org/devtools-user/page_inspector/how_to/open_the_inspector/index.html) for Firefox or [Chrome devtools](https://developer.chrome.com/docs/devtools/css/) for Chrome.

## `plotProperties` argument

```{r}
scatterPlotMatrix(iris, plotProperties = list(
  noCatColor = "DarkCyan", # Color used when categories coloring is not applied
  point = list(
    alpha = 0.3, # Opacity value used for points
    radius = 4 # Radius used to draw points as circles
  )
))
```

Adjust some properties which can not be set through CSS (mainly size, color and opacity of points).
Here, points of plot are customised: two times greater, with opacity reduced from 0.5 to 0.3, and a `DarkCyan` color.

## `controlWidgets` argument

```{r}
scatterPlotMatrix(iris, controlWidgets = TRUE, width = 770, height = 770)
```

Widgets are added above the graph, allowing to modify some of its attributes.
