To learn more, see the ggplot reference site, and Winston Chang's excellentĬookbook for R site. P + theme(axis.title=element_text(face="alic", P <- qplot(hp, mpg, data=mtcars, shape=am, color=am,įacets=gear~cyl, main="Scatterplots of MPG vs. Note that ggplot2 functions can be chained with "+" signs to generate the final plot. For greater control, use ggplot() and other functions provided by the package. They can be modified using the theme() function, and by adding graphic parameters within the qplot() function. Unlike base R graphs, the ggplot2 graphs are not effected by many of the options set in the par( ) function. Qplot(gear, mpg, data=mtcars, geom=c("boxplot", "jitter"),įill=gear, main="Mileage by Gear Number", # observations (points) are overlayed and jittered Qplot(wt, mpg, data=mtcars, geom=c("point", "smooth"), # Separate regressions of mpg on weight for each number of cylinders Xlab="Horsepower", ylab="Miles per Gallon") Qplot(hp, mpg, data=mtcars, shape=am, color=am, # in each facet, transmittion type is represented by shape and color hp for each combination of gears and cylinders Main="Distribution of Gas Milage", xlab="Miles Per Gallon", Qplot(mpg, data=mtcars, geom="density", fill=gear, alpha=I(.5), # grouped by number of gears (indicated by color) Mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8), Mtcars$am <- factor(mtcars$am,levels=c(0,1), Mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5), Here are some examples using automotive data (car mileage, weight, number of gears, number of cylinders, etc.) contained in the mtcars data frame. In contrast, size=I(3) sets each point or line to three times the default size. For example size=z makes the size of the plotted points or lines proporational to the values of a variable z. Use I( value ) to indicate a specific value.At present, ggplot2 cannot be used to create 3D graphs or mosaic plots.Two-element numeric vectors giving the minimum and maximum values for the horizontal and vertical axes, respectively For univariate plots (for example, histograms), omit yĬharacter vectors specifying horizontal and vertical axis labels Specifies the variables placed on the horizontal and vertical axis. Note that the formula uses the letters x and y, not the names of the variables.įor method="gam", be sure to load the mgcv package. Changing the formula to y~poly(x,2) would produce a quadratic fit. The formula parameter gives the form of the fit.įor example, to add simple linear regression lines, you'd specify geom="smooth", method="lm", formula=y~x. Methods include "lm" for regression, "gam" for generalized additive models, and "rlm" for robust regression. When the number of observations is greater than 1,000, a more efficient smoothing algorithm is employed. If geom="smooth", a loess fit line and confidence limits are added by default. geom values include "point", "smooth", "boxplot", "line", "histogram", "density", "bar", and "jitter".Ĭharacter vectors specifying the title and subtitle The geom option is expressed as a character vector with one or more entries. Specifies the geometric objects that define the graph type. To create trellis graphs based on a single conditioning variable, use rowvar~. Its value is expressed as rowvar ~ colvar. Legends are drawn automatically.Ĭreates a trellis graph by specifying conditioning variables. For density and box plots, fill associates fill colors with a variable. For line plots, color associates levels of a variable with line color. Qplot( x, y, data=, color=, shape=, size=, alpha=, geom=, method=, formula=, facets=, xlim=, ylim= xlab=, ylab=, main=, sub=)Īlpha transparency for overlapping elements expressed as a fraction between 0 (complete transparency) and 1 (complete opacity)Īssociates the levels of variable with symbol color, shape, or size. #R STUDIO GGPLOT FULL#While it does not expose ggplot's full power, it can create a very wide range of useful plots. The qplot() function can be used to create the most common graph types. There is a helper function called qplot() (for quick plot) that can hide much of this complexity when creating standard graphs. Mastering the ggplot2 language can be challenging (see the Going Further section below for helpful resources). The creation of trellis plots (i.e., conditioning) is relatively simple. Grouping can be represented by color, symbol, size, and transparency. Origianlly based on Leland Wilkinson's The Grammar of Graphics, ggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner. Its popularity in the R community has exploded in recent years. The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots.
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