dColorPlot {DepecheR} | R Documentation

## Display third variable as color on a 2D plot

### Description

Function to overlay one variable for a set of observations on a field created by two other variables known for the same observations. The plot is constructed primarily for displaying variables on 2D-stochastic neighbour embedding fields, but can be used for any sets of (two or) three variables known for the same observations. As the number of datapoints is often very high, the files would, if saved as pdf of another vector based file type become extremely big. For this reason, the plots are saved as jpeg and no axes or anything alike are added, to simplify usage in publications.

### Usage

    dColorPlot(colorData, controlData, xYData, colorScale = "rich_colors",
      plotName = "default", densContour = TRUE, title = FALSE,
      plotDir = "default", truncate = TRUE, bandColor = "black",
      dotSize = 500/sqrt(nrow(xYData)), multiCore = "default",
      nCores = "default", createOutput = TRUE)

### Arguments

`colorData` | A numeric matrix or dataframe or a vector, be it numeric, charater or factor, that should be used to define the colors on the plot.
`controlData` | Optional. A numeric/integer vector or dataframe of values that could be used to define the range of the colorData. If no control data is present, the function defaults to using the colorData as control data.
`xYData` | These variables create the field on which the colorData will be displayed. It needs to be a matrix or dataframe with two columns and the same number of rows as the colorData object.
`colorScale` | This argument controls the colors in the plot. See `dColorVector` for alternatives.
`plotName` | The name(s) for the plot(s). 'default' returns the column names of the colorData object in the case this is a dataframe and otherwise returns the somewhat generic name 'testVariable'. It can be substituted with a string (in the case colorData is a vector) or vector of strings, as long as it has the same length as the number of columns in colorData.
`densContour` | If density contours should be created for the plot(s) or not. Defaults to TRUE. If a density object, as generated by dContours, is included, this will be used instead.
`title` | If there should be a title displayed on the plotting field. As the plotting field is saved a jpeg, this title cannot be removed as an object afterwards, as it is saved as coloured pixels. To simplify usage for publication, the default is FALSE, as the files are still named, eventhough no title appears on the plot.
`plotDir` | If different from the current directory. If specified and non-existent, the function creates it. If "." is specified, the plots will be saved at the current directory. By default, a new directory is added if the created plots will be more than 1.
`truncate` | If truncation of the most extreme values should be performed for the visualizations. Three possible values: TRUE, FALSE, and a vector with two values indicating the low and high threshold quantiles for truncation.
`bandColor` | The color of the contour bands. Defaults to black.
`dotSize` | Simply the size of the dots. The default makes the dots maller the more observations that are included.
`multiCore` | If the algorithm should be performed on multiple cores. This increases the speed if the dataset is medium-large (>100000 rows) and has at least 5 columns. Default is TRUE when the rows exceed 100000 rows and FALSE otherwise.
`nCores` | If multiCore is TRUE, then this sets the number of parallel processes. The default is currently 87.5 percent with a cap on 10 cores, as no speed increase is generally seen above 10 cores for normal computers.
`createOutput` | For testing purposes. Defaults to TRUE. If FALSE, no plots are generated.

### Value

Plots showing the colorData displayed as color on the field created by xYData.

### See Also

`dDensityPlot`, `dResidualPlot`, `dWilcox`, `dColorVector`

### Examples

    # Load some data
    data(testData)

    ## Not run:
    # Run Barnes Hut tSNE on this. For more rapid example execution, a pre-run
    # SNE is inluded
    # library(Rtsne)
    # testDataSNE <- Rtsne(testData[,2:15], pca=FALSE)
    data(testDataSNE)

    # Run the function for two of the variables
    dColorPlot(colorData = testData[2:3], xYData = testDataSNE$Y)

    # Now each depeche cluster is plotted separately and together.

    # Run the clustering function. For more rapid example execution,
    # a depeche clustering of the data is included
    # testDataDepeche <- depeche(testData[,2:15])
    data(testDataDepeche)

    dColorPlot(colorData = testDataDepeche$clusterVector,
        xYData = testDataSNE$Y, plotName = 'clusters')

    ## End(Not run)

[Package DepecheR version 1.0.3 Index]