Prerequisites
# install.packages("tidyverse") |
library("ggplot2") |
The ggplot2 packages are included in a popular collection of packages called “the tidyverse”. |
Basics
ggplot2 is based on the grammar of graphics, the idea that you can build every graph from the same components: a data set, a coordinate system, and geoms—visual marks that represent data points. |
To display data values, map variables in the data set to aesthetic properties of the geom like size, color, and x and y locations. |
Create a graph using ggplot() or qplot()
qplot(x = cty, y = hwy, color = cyl, data = mpg, geom = "point") Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults. |
ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer. |
Add a new layer to a plot with a geom_() or stat_() function. Each provides a geom, a set of aesthetic mappings, and a default stat and position adjustment. |
Labels
t + labs( x = "New x axis label", y = "New y axis label", title ="Add a title above the plot", subtitle = "Add a subtitle below title", caption = "Add a caption below plot", <aes> = "New <aes> legend title") t + annotate(geom = "text", x = 8, y = 9, label = "A") |
Red Greeb Blue (RGB) Color Space
RGB is the built-in colour space. Instead of "manually" creating a #RRGGBB colour string, a colour can be specified using R's rgb() function that takes three arguments: red, green, and blue (which, by default, all have a range of [0, 1]). |
RGB Color Space
> ggplot() + + facet_wrap(~b) + + scale_x_continuous(name="red", breaks=seq(0.05, 1.05, 0.2), labels=seq(0, 1, 0.2)) + + scale_y_continuous(name="green", breaks=seq(0.05, 1.05, 0.2), labels=seq(0, 1, 0.2)) + + scale_fill_identity() + + geom_rect(data=d, mapping=aes(xmin=r, xmax=r+resolution(r), ymin=g, ymax=g+resolution(g), fill=rgb(r,g,b)), color="white", size=0.1)
Legends:
n + theme(legend.position = "bottom") Place legend at "bottom", "top", "lef", or "right" |
n + guides(fill = "none") Set legend type for each aesthetic: colorbar, legend, or none (no legend) |
n + scale_fill_discrete(name = "Title", labels = c("A", "B", "C", "D", "E")) Set legend title and labels with a scale function. |
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One Variable graphs
ggplot(df, aes(x=weight, fill=sex)) + + geom_area(stat ="bin")
geom_density
ggplot(diamonds, aes(depth, fill = cut, colour = cut)) + + geom_density(alpha = 0.1) xlim(55, 70)
geom_bar
> g <- ggplot(mpg, aes(class)) > g + geom_bar(aes(fill = drv))
geom_dotplot
ggplot(mtcars, aes(x = mpg, fill = factor(cyl))) + geom_dotplot(stackgroups = TRUE, binwidth = 1, method = "histodot")
Color Blind Friendly brewer palettes:
library(RColorBrewer)
display.brewer.all(colorblindFriendly = TRUE)
Line Types:
ggpubr::show_line_types()+
theme_gray()
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Two Variable graphs
ggplot(iris, aes(Sepal.Length, Sepal.Width))+ + geom_point(aes(color = Species)) + + geom_smooth(aes(color = Species, fill = Species), method = "lm") + + scale_color_viridis(discrete = TRUE, option = "D")+ + scale_fill_viridis(discrete = TRUE)
geom_boxplot
bp <- bp + geom_boxplot(aes(fill = Species)) > bp + scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
geom_bind2d
> d <- ggplot(diamonds, aes(x, y)) + xlim(4, 10) + ylim(4, 10) > d + geom_bin2d(bins = 10)
Three Variable graphs
df <- melt(volcano) > > p <- ggplot(df, aes(Var1, Var2)) + + geom_raster(aes(fill=value)) + + scale_fill_distiller(palette = "Spectral", direction = -1) + + labs(x="West to East", + y="North to South", + title = "Elevation map of Maunga Whau", + fill = "Elevation") + + theme(text = element_text(family = 'Fira Sans'), + plot.title = element_text(hjust = 0.5)) > > ggplotly(p)
Shapes:
> ggplot() + scale_y_continuous(name="") + scale_x_continuous(name="") + scale_shape_identity() +
geom_point(data=d, mapping=aes(x=p%%16, y=p%/%16, shape=p), size=5, fill="red")+geom_text(data=d, mapping=aes(x=p%%16, y=p%/%16+0.25, label=p), size=3)
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