simplePlot: Simple Plot of 2 columns (Plot)
In mariytu/RegressionLibs: What the package does (short line)

Description Usage Arguments See Also Examples

Generate a plot of 2 columns of data set using ggplot. You must indicate which columns you want in the graph.

1 2	simplePlot(data, DependentVariable, x_axis, y_axis, dependentVariableName, pointSize, alphaPoint, colours)

`data`	an object of class data frame with the data.
`DependentVariable`	an object of class "numeric", "factor" or "integer" is a list of values containig the dependent variable.
`x_axis`	an integer that represent the number of the column that you want in your x axis.
`y_axis`	an integer that represent the number of the column that you want in your y axis.
`dependentVariableName`	is an optional parameter. It's an string that contains the name of your dependent variable.
`pointSize`	is an optional parameter of class numeric with a single value that represent the point size of plot.
`alphaPoint`	is an optional parameter of class numeric with a single value that represent the alpha of points in the plot.
`colours`	is an optional parameter of class character with a list of colours to use in the plot. The default value for continuos dependent variable is c("darkred", "yellow", "darkgreen") and for categorical dependent variable are the default colours defined by ggplot.

elbowPlot

#Example 1
iris.x <- iris[,1:4] # These are the independent variables
Species <- iris[,5] # This is the dependent variable

# Plot of first 2 columns of data set
simplePlot(iris.x, Species, 1, 2, "Species", 2, 0.9)
# A plot with a different colours palette
myPalette <- c("darkolivegreen4", "goldenrod1", "dodgerblue4")
simplePlot(iris.x, Species, 1, 2, "Species", 2, 0.9, colours = myPalette)


#Example 2
iris.x <- iris[,1:4] # These are the independent variables
Species <- iris[,5] # This is the dependent variable

ir.pca <- prcomp(iris.x, center = TRUE, scale. = TRUE) #performing prcomp

# Plot of first 2 columns of principal components
simplePlot(as.data.frame(ir.pca$x), Species, 1, 2, "Species", 2, 0.9)
# A plot with a different colours palette
myPalette <- c("darkolivegreen4", "goldenrod1", "dodgerblue4")
simplePlot(as.data.frame(ir.pca$x), Species, 1, 2, "Species", 2, 0.9, colours = myPalette)


#Example 3
# Getting a clean data set (without missing values)
cars <- read.csv("https://dl.dropboxusercontent.com/u/12599702/autosclean.csv", sep = ";", dec = ",")
cars.x <- cars[,1:16] # These are the independent variables
cars.y <- cars[,17] # This is the dependent variable

cars.pca <- prcomp(cars.x, center = TRUE, scale. = TRUE) #performing prcomp

# Plot of first 2 columns of principal components
simplePlot(as.data.frame(cars.pca$x), cars.y, 1, 2, "Price", 2, 0.9)
# A plot with a different colours palette
myPalette <- c("darkolivegreen4", "goldenrod1", "dodgerblue4")
simplePlot(as.data.frame(cars.pca$x), cars.y, 1, 2, "Price", 2, 0.9, colours = myPalette)