ggplot2_参数简介（一）

1. aes Construct aesthetic mappings

aes用于定义x, y，并且可以对响应的数据进行计算后绘图

aes(x = mpg, y = wt)
aes(mpg, wt)
# You can also map aesthetics to functions of variables
aes(x = mpg ^ 2, y = wt / cyl)
# aes is almost always used with ggplot() or a layer
ggplot(mpg, aes(displ, hwy)) + geom_point()
ggplot(mpg) + geom_point(aes(displ, hwy))

2. aes_colour_fill_alpha Colour related aesthetics: colour, fill and alpha

该命令定义颜色，填充和希腊字母

# Bar chart example
c <- ggplot(mtcars, aes(factor(cyl)))
# Default plotting
c + geom_bar()
# Combining both, you can see the changes more clearly
c + geom_bar(fill = "white", colour = "red")
k <- ggplot(mtcars, aes(factor(cyl), fill = factor(vs)))
k + geom_bar()
# Fill aesthetic can also be used with a continuous variable，连续变量
m <- ggplot(faithfuld, aes(waiting, eruptions))
m + geom_raster()
m + geom_raster(aes(fill = density))
# Some geoms don't use both aesthetics (i.e. geom_point or geom_line)
layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))
b <- ggplot(economics, aes(x = date, y = unemploy))
b + geom_line()
b + geom_line(colour = "green")
b + geom_point()
b + geom_point(colour = "red")
# For large datasets with overplotting the alpha
# aesthetic will make the points more transparent
df <- data.frame(x = rnorm(5000), y = rnorm(5000))
h <- ggplot(df, aes(x,y))
h + geom_point()
h + geom_point(alpha = 0.5)
h + geom_point(alpha = 1/10)
# Alpha can also be used to add shading
j <- b + geom_line()
j
yrng <- range(economics$unemploy)
j <- j + geom_rect(aes(NULL, NULL, xmin = start, xmax = end, fill = party),
ymin = yrng[1], ymax = yrng[2], data = presidential)
j
j + scale_fill_manual(values = alpha(c("blue", "red"), .3))

3. aes_group_order Aesthetics: grouping，分组

#柱状图
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point(aes(colour = factor(cyl)), size = 4)
# Or you can use shape to distinguish the data
p + geom_point(aes(shape = factor(cyl)), size = 4)
# Using fill
a <- ggplot(mtcars, aes(factor(cyl)))
a + geom_bar()
a + geom_bar(aes(fill = factor(cyl)))
a + geom_bar(aes(fill = factor(vs)))

# Using facets
k <- ggplot(diamonds, aes(carat, ..density..)) + geom_histogram(binwidth = 0.2)
k + facet_grid(. ~ cut)
# There are three common cases where the default is not enough, and we
# will consider each one below. In the following examples, we will use a simple
# longitudinal dataset, Oxboys, from the nlme package. It records the heights
# (height) and centered ages (age) of 26 boys (Subject), measured on nine
# occasions (Occasion).
# Multiple groups with one aesthetic
h <- ggplot(nlme::Oxboys, aes(age, height))
# A single line tries to connect all the observations
h + geom_line()
# The group aesthetic maps a different line for each subject
h + geom_line(aes(group = Subject))
# Different groups on different layers
h <- h + geom_line(aes(group = Subject))
# Using the group aesthetic with both geom_line() and geom_smooth()
# groups the data the same way for both layers
h + geom_smooth(aes(group = Subject), method = "lm", se = FALSE)
# Changing the group aesthetic for the smoother layer
# fits a single line of best fit across all boys
h + geom_smooth(aes(group = 1), size = 2, method = "lm", se = FALSE)
# Overriding the default grouping
# The plot has a discrete scale but you want to draw lines that connect across
# groups. This is the strategy used in interaction plots, profile plots, and parallel
# coordinate plots, among others. For example, we draw boxplots of height at
# each measurement occasion

# 箱线图
boysbox <- ggplot(nlme::Oxboys, aes(Occasion, height))
boysbox + geom_boxplot()
# There is no need to specify the group aesthetic here; the default grouping
# works because occasion is a discrete variable. To overlay individual trajectories
# we again need to override the default grouping for that layer with aes(group = Subject)
boysbox <- boysbox + geom_boxplot()
boysbox + geom_line(aes(group = Subject), colour = "blue")

4. aes_linetype_size_shape，线的类型

# Line types should be specified with either an integer, a name, or with a string of
# an even number (up to eight) of hexadecimal digits which give the lengths in
# consecutive positions in the string.
# 0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash
# Data
df <- data.frame(x = 1:10 , y = 1:10)
f <- ggplot(df, aes(x, y))
f + geom_line(linetype = 2)
f + geom_line(linetype = "dotdash")
# An example with hex strings, the string "33" specifies three units on followed
# by three off and "3313" specifies three units on followed by three off followed
# by one on and finally three off.
f + geom_line(linetype = "3313")
# Mapping line type from a variable
ggplot(economics_long, aes(date, value01)) +
geom_line(aes(linetype = variable))
# Size examples
# Should be specified with a numerical value (in millimetres),
# or from a variable source
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point(size = 4)
p + geom_point(aes(size = qsec))
p + geom_point(size = 2.5) +
geom_hline(yintercept = 25, size = 3.5)
# Shape examples
# Shape takes four types of values: an integer in [0, 25],
# a single character-- which uses that character as the plotting symbol,
# a . to draw the smallest rectangle that is visible (i.e., about one pixel)
# an NA to draw nothing
p + geom_point()
p + geom_point(shape = 5)
p + geom_point(shape = "k", size = 3)
p + geom_point(shape = ".")
p + geom_point(shape = NA)
# Shape can also be mapped from a variable
p + geom_point(aes(shape = factor(cyl)))
# A look at all 25 symbols
df2 <- data.frame(x = 1:5 , y = 1:25, z = 1:25)
s <- ggplot(df2, aes(x, y))
s + geom_point(aes(shape = z), size = 4) +
scale_shape_identity()
# While all symbols have a foreground colour, symbols 19-25 also take a
# background colour (fill)
s + geom_point(aes(shape = z), size = 4, colour = "Red") +
scale_shape_identity()
s + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") +
scale_shape_identity()

5. aes_position 坐标轴位置

# Generate data: means and standard errors of means for prices
# for each type of cut
dmod <- lm(price ~ cut, data = diamonds)
cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut =
unique(diamonds$cut)), se = TRUE)[c("fit", "se.fit")])
se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit,
ymax = fit + se.fit, colour = cut))
se + geom_pointrange()
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
p + annotate("rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25,
fill = "dark grey", alpha = .5)
# Geom_segment examples
p + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25),
arrow = arrow(length = unit(0.5, "cm")))
p + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15),
arrow = arrow(length = unit(0.5, "cm")))
p + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
arrow = arrow(length = unit(0.5, "cm")))
# You can also use geom_segment to recreate plot(type = "h") :
counts <- as.data.frame(table(x = rpois(100, 5)))
counts$x <- as.numeric(as.character(counts$x))
with(counts, plot(x, Freq, type = "h", lwd = 10))
ggplot(counts, aes(x, Freq)) +
geom_segment(aes(yend = 0, xend = x), size = 10)