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https://github.com/neuhausi/canvasXpress
CanvasXpress: A JavaScript Library for Data Analytics with Full Audit Trail Capabilities.
https://github.com/neuhausi/canvasXpress
analytics bioinformatics chart charting cran dash dashboard data-analytics data-science data-visualization genomics graphs javascript network network-visualization python r reproducible-research shiny visualization
Last synced: 3 months ago
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CanvasXpress: A JavaScript Library for Data Analytics with Full Audit Trail Capabilities.
- Host: GitHub
- URL: https://github.com/neuhausi/canvasXpress
- Owner: neuhausi
- Created: 2016-02-03T04:48:37.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2024-12-02T01:27:07.000Z (3 months ago)
- Last Synced: 2024-12-02T02:28:28.902Z (3 months ago)
- Topics: analytics, bioinformatics, chart, charting, cran, dash, dashboard, data-analytics, data-science, data-visualization, genomics, graphs, javascript, network, network-visualization, python, r, reproducible-research, shiny, visualization
- Language: R
- Homepage: http://www.canvasXpress.org
- Size: 111 MB
- Stars: 290
- Watchers: 23
- Forks: 44
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
Awesome Lists containing this project
- jimsghstars - neuhausi/canvasXpress - CanvasXpress: A JavaScript Library for Data Analytics with Full Audit Trail Capabilities. (R)
README
---
title: "CanvasXpress R Library"
output:
html_document:
self_contained: no
---
[](https://cran.r-project.org/package=canvasXpress)
[](https://cran.r-project.org/package=canvasXpress)
[](https://cdnjs.com/libraries/canvasXpress)
[](https://app.codecov.io/gh/cb4ds/canvasXpress?branch=master)
***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://www.canvasxpress.org/examples.html) to display scientific and non-scientific
data. ***canvasXpress*** also includes a simple and unobtrusive [user interface](https://www.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://www.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-and-drop capabilities are features that make this library unique in its
class.
***canvasXpress*** can be now simply 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.
### Installation
canvasXpress is available for installation from
[CRAN](https://CRAN.R-project.org/package=canvasXpress) or you can install the
latest version of ***canvasXpress*** from GitHub as follows:
```r
devtools::install_github('neuhausi/canvasXpress')
```
### Examples
These are included to get you started on basic charting - there are many more
examples (including complex and compound visualizations) with R code available
in the Examples section of the main website at
[https://www.canvasxpress.org](https://www.canvasxpress.org)
#### Scatter 3D Plot
```r
y <- read.table("https://www.canvasxpress.org/data/cX-irist-dat.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z <- read.table("https://www.canvasxpress.org/data/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://www.canvasxpress.org/data/cX-irist-dat.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z <- read.table("https://www.canvasxpress.org/data/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/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/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 (Multi-dimensional)
```r
y <- read.table("https://www.canvasxpress.org/data/cX-multidimensionalheatmap-dat.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
y2 <- read.table("https://www.canvasxpress.org/data/cX-multidimensionalheatmap-dat2.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
y3 <- read.table("https://www.canvasxpress.org/data/cX-multidimensionalheatmap-dat3.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
y4 <- read.table("https://www.canvasxpress.org/data/cX-multidimensionalheatmap-dat4.txt", header=TRUE, sep="\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
x <- read.table("https://www.canvasxpress.org/data/cX-multidimensionalheatmap-smp.txt", header=TRUE, sep= "\t",
quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z <- read.table("https://www.canvasxpress.org/data/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)
```
### More Examples and Resources
In addition to the built-in package documentation there are vignettes with
more information on getting started and additional examples:
```r
#List all package vignettes
vignette(package = "canvasXpress")
#View a specific vignette
vignette("getting_started", package = "canvasXpress")
vignette("additional_examples", package = "canvasXpress")
```
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).