p <- ggplot(mtcars, aes(mpg, wt)) +
geom_point()
p
p + theme(panel.background = element_rect(colour = "pink"))
p + theme_bw()
# Scatter plot of gas mileage by vehicle weight
p <- ggplot(mtcars, aes(wt, mpg)) +
geom_point()
# Calculate slope and intercept of line of best fit
coef(lm(mpg ~ wt, data = mtcars))
p + geom_abline(intercept = 37, slope = -5)
# Calculate correlation coefficient
with(mtcars, cor(wt, mpg, use = "everything", method = "pearson"))
#annotate the plot
p + geom_abline(intercept = 37, slope = -5) +
geom_text(data = data.frame(), aes(4.5, 30, label = "Pearson-R = -.87"))
# Change the axis labels
# Original plot
p
p + labs(x = "Vehicle Weight", y = "Miles per Gallon")
# Or
p + labs(x = "Vehicle Weight", y = "Miles per Gallon")
# Change title appearance
p <- p + labs(title = "Vehicle Weight-Gas Mileage Relationship")
# Set title to twice the base font size
p + theme(plot.title = element_text(size = rel(2)))
p + theme(plot.title = element_text(size = rel(2), colour = "blue"))
# Changing plot look with themes
DF <- data.frame(x = rnorm(400))
m <- ggplot(DF, aes(x = x)) +
geom_histogram()
# Default is theme_grey()
m
# Compare with
m + theme_bw()
# Manipulate Axis Attributes
m + theme(axis.line = element_line(size = 3, colour = "red", linetype = "dotted"))
m + theme(axis.text = element_text(colour = "blue"))
m + theme(axis.text.y = element_blank())
m + theme(axis.ticks = element_line(size = 2))
m + theme(axis.title.y = element_text(size = rel(1.5), angle = 90))
m + theme(axis.title.x = element_blank())
m + theme(axis.ticks.length = unit(.85, "cm"))
# Legend Attributes
z <- ggplot(mtcars, aes(wt, mpg)) +
geom_point(aes(colour = factor(cyl)))
z
z + theme(legend.position = "none")
z + theme(legend.position = "bottom")
# Or use relative coordinates between 0 and 1
z + theme(legend.position = c(.5, .5))
# Add a border to the whole legend
z + theme(legend.background = element_rect(colour = "black"))
# Legend margin controls extra space around outside of legend:
z + theme(legend.background = element_rect(),
legend.margin = unit(1, "cm"))
z + theme(legend.background = element_rect(),
legend.margin = unit(0, "cm"))
# Or to just the keys
z + theme(legend.key = element_rect(colour = "black"))
z + theme(legend.key = element_rect(fill = "yellow"))
z + theme(legend.key.size = unit(2.5, "cm"))
z + theme(legend.text = element_text(size = 20, colour = "red", angle = 45))
z + theme(legend.title = element_text(face = "italic"))
# To change the title of the legend use the name argument
# in one of the scale options
z + scale_colour_brewer(name = "My Legend")
z + scale_colour_grey(name = "Number of \nCylinders")
# Panel and Plot Attributes
z + theme(panel.background = element_rect(fill = "black"))
z + theme(panel.border = element_rect(linetype = "dashed", colour = "black"))
z + theme(panel.grid.major = element_line(colour = "blue"))
z + theme(panel.grid.minor = element_line(colour = "red", linetype = "dotted"))
z + theme(panel.grid.major = element_line(size = 2))
z + theme(panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank())
z + theme(plot.background = element_rect())
z + theme(plot.background = element_rect(fill = "green"))
# Faceting Attributes
set.seed(4940)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
k <- ggplot(dsmall, aes(carat, ..density..)) +
geom_histogram(binwidth = 0.2) +
facet_grid(. ~ cut)
k + theme(strip.background = element_rect(colour = "purple", fill = "pink",
size = 3, linetype = "dashed"))
k + theme(strip.text.x = element_text(colour = "red", angle = 45, size = 10,
hjust = 0.5, vjust = 0.5))
k + theme(panel.margin = unit(5, "lines"))
k + theme(panel.margin.y = unit(0, "lines"))
# Put gridlines on top
meanprice <- tapply(diamonds$price, diamonds$cut, mean)
cut <- factor(levels(diamonds$cut), levels = levels(diamonds$cut))
df <- data.frame(meanprice, cut)
g <- ggplot(df, aes(cut, meanprice)) + geom_bar(stat = "identity")
g + geom_bar(stat = "identity") +
theme(panel.background = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.ontop = TRUE)
# Modify a theme and save it
mytheme <- theme_grey() + theme(plot.title = element_text(colour = "red"))
p + mytheme
## Run this to generate a graph of the element inheritance tree
build_element_graph <- function(tree) {
require(igraph)
require(plyr)
inheritdf <- function(name, item) {
if (length(item$inherit) == 0)
data.frame()
else
data.frame(child = name, parent = item$inherit)
}
edges <- plyr::rbind.fill(mapply(inheritdf, names(tree), tree))
# Explicitly add vertices (since not all are in edge list)
vertices <- data.frame(name = names(tree))
graph.data.frame(edges, vertices = vertices)
}
g <- build_element_graph(ggplot2:::.element_tree)
V(g)$label <- V(g)$name
set.seed(324)
par(mar=c(0,0,0,0)) # Remove unnecessary margins
plot(g, layout=layout.fruchterman.reingold, vertex.size=4, vertex.label.dist=.25)Run the code above in your browser using DataLab