---
title: "Getting Started with canvasXpress in R"
author: "Isaac Neuhaus"
date: "`r Sys.Date()`"
output:
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---
## Overview
<a href="https://www.canvasxpress.org"><img src="images/hexagon.jpg" align="left" width="175"></a>
***canvasXpress*** was developed as the core visualization component for bioinformatics and systems biology analysis
at Bristol-Myers Squibb. It supports a large number of [visualizations](https://canvasxpress.org/examples.html) to display scientific and non-scientific
data. ***canvasXpress*** also includes a simple and unobtrusive [user interface](https://canvasxpress.org/docs/interface.html) to explore complex data sets, a sophisticated and unique mechanism to keep track of all user customization for [Reproducible Research](https://canvasxpress.org/docs/audit.html) purposes, as well as an 'out of the box'
broadcasting capability to synchronize selected data points in all ***canvasXpress*** plots in a page. Data can
be easily sorted, grouped, transposed, transformed or clustered dynamically. The fully customizable mouse events
as well as the zooming, panning and drag'n drop capabilities are features that make this library unique in its
class.
***canvasXpress*** can now be used within R at the console to generate conventional plots in R-Studio or seamlessly embedded in [Shiny](https://shiny.posit.co) web applications. Full-fledged examples of the ***canvasXpress*** library including the mouse events, zooming, and broadcasting capabilities are included in this package in several examples that can be accessed using the cxShinyExample function. This ***canvasXpress*** R library was created with the [htmlwidgets](https://github.com/ramnathv/htmlwidgets) package.
## Examples
### Scatter 3D Plot
```r
y <- read.table("https://canvasxpress.org/data/r/cX-irist-dat.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z <- read.table("https://canvasxpress.org/data/r/cX-irist-var.txt", header=TRUE, sep= "\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(data = y,
varAnnot = z,
graphType ="Scatter3D",
colorBy = "Species",
ellipseBy = "Species",
xAxis = list("Sepal.Length"),
yAxis = list("Petal.Width"),
zAxis = list("Petal.Length"),
theme = "CanvasXpress",
title = "Iris Data Set",
axisTickScaleFontFactor = 0.5,
axisTitleScaleFontFactor = 0.5)
```
### Scatter 2D Matrix Plot
```r
y <- read.table("https://canvasxpress.org/data/r/cX-irist-dat.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z <- read.table("https://canvasxpress.org/data/r/cX-irist-var.txt", header=TRUE, sep= "\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(data = y,
varAnnot = z,
graphType = "Scatter2D",
colorBy = "Species",
layoutAdjust = TRUE,
scatterPlotMatrix = TRUE,
theme = "CanvasXpress")
```
### Boxplot
```r
y <- read.table("https://www.canvasxpress.org/data/r/cX-toothgrowth-dat.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
x <- read.table("https://www.canvasxpress.org/data/r/cX-toothgrowth-smp.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(data = y,
smpAnnot = x,
graphType = "Boxplot",
groupingFactors = list("dose", "supp"),
stringSampleFactors = list("dose"),
graphOrientation = "vertical",
colorBy = "dose",
title = "The Effect of Vitamin C on Tooth Growth in Guinea Pigs",
smpTitle = "dose",
xAxisTitle = "len",
smpLabelRotate = 90,
xAxisMinorTicks = FALSE,
xAxis2Show = FALSE,
legendScaleFontFactor = 1.8)
```
### Heatmap
```r
y <- read.table("https://canvasxpress.org/data/r/cX-multidimensionalheatmap-dat.txt",
header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE,
check.names=FALSE, stringsAsFactors=FALSE)
y2 <- read.table("https://canvasxpress.org/data/r/cX-multidimensionalheatmap-dat2.txt",
header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE,
check.names=FALSE, stringsAsFactors=FALSE)
y3 <- read.table("https://canvasxpress.org/data/r/cX-multidimensionalheatmap-dat3.txt",
header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE,
check.names=FALSE, stringsAsFactors=FALSE)
y4 <- read.table("https://canvasxpress.org/data/r/cX-multidimensionalheatmap-dat4.txt",
header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE,
check.names=FALSE, stringsAsFactors=FALSE)
x <- read.table("https://canvasxpress.org/data/r/cX-multidimensionalheatmap-smp.txt",
header=TRUE, sep= "\t", quote="", row.names=1, fill=TRUE,
check.names=FALSE, stringsAsFactors=FALSE)
z <- read.table("https://canvasxpress.org/data/r/cX-multidimensionalheatmap-var.txt",
header=TRUE, sep= "\t", quote="", row.names=1, fill=TRUE,
check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(data = list(y = y, data2 = y2, data3 = y3, data4 = y4),
smpAnnot = x,
varAnnot = z,
graphType = "Heatmap",
guides = TRUE,
outlineBy = "Outline",
outlineByData = "data2",
shapeBy = "Shape",
shapeByData = "data3",
sizeBy = "Size",
sizeByData = "data4",
showHeatmapIndicator = FALSE,
afterRender = list(list("clusterSamples")))
```
### Four-way Venn Diagram
```r
canvasXpress(vennData = data.frame(AC=456, A=340, ABC=552, ABCD=148, BC=915,
ACD=298, BCD=613, B=562, CD=143, ABD=578,
C=620, D=592, AB=639, BD=354, AD=257),
graphType = "Venn",
vennLegend = list(A="List 1", D="List 4", C="List 3", B="List 2"),
vennGroups = 4)
```
## Additional Information
For the use of canvasXpress plots in shiny there are interactive examples available through the
package function *cxShinyExample*
```r
#List example names
cxShinyExample()
#Run an interactive shiny example
cxShinyExample(example = "example1")
```
There is also a wealth of additional information including full API documentation
and extensive R and JavaScript examples at [https://www.canvasxpress.org](https://www.canvasxpress.org).