advanced ggplot

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Author

Harlow Malloc

Published

February 15, 2023

df <- data.frame(
  x = c(3, 1, 5), 
  y = c(2, 4, 6), 
  label = c("a","b","c")
)
df
A data.frame: 3 × 3
x y label
<dbl> <dbl> <chr>
3 2 a
1 4 b
5 6 c 
p <- ggplot(df, aes(x, y, label = label)) + 
  labs(x = NULL, y = NULL) + # Hide axis label
  theme(plot.title = element_text(size = 12)) # Shrink plot title
p + geom_point() + ggtitle("point")

p + geom_text() + ggtitle("text")

p + geom_bar(stat = "identity") + ggtitle("bar")

p + geom_tile() + ggtitle("raster")

p + geom_line() + ggtitle("line")

p + geom_area()+ggtitle("area")

p + geom_path() + ggtitle("path")

collective geoms

Geoms can be roughly divided into individual and collective geoms.

different groups on different layers

we want to plot summaries that use different levels of aggregation.

data(Oxboys, package = "nlme")
head(Oxboys)
A nfnGroupedData: 6 × 4
Subject age height Occasion
<ord> <dbl> <dbl> <ord>
1 1 -1.0000 140.5 1
2 1 -0.7479 143.4 2
3 1 -0.4630 144.8 3
4 1 -0.1643 147.1 4
5 1 -0.0027 147.7 5
6 1 0.2466 150.2 6 
ggplot(Oxboys, aes(age, height, group = Subject)) + 
  geom_line() + 
  geom_smooth(method = "lm", se = FALSE)
#> `geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'

ggplot(Oxboys, aes(age, height)) + 
  geom_line(aes(group = Subject)) + 
  geom_smooth(method = "lm", size = 2, se = FALSE)
Warning message:
“Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.”
`geom_smooth()` using formula = 'y ~ x'

head(Oxboys)
A nfnGroupedData: 6 × 4
Subject age height Occasion
<ord> <dbl> <dbl> <ord>
1 1 -1.0000 140.5 1
2 1 -0.7479 143.4 2
3 1 -0.4630 144.8 3
4 1 -0.1643 147.1 4
5 1 -0.0027 147.7 5
6 1 0.2466 150.2 6 
ggplot(Oxboys,aes(Occasion,height))+
    geom_boxplot()

ggplot(Oxboys, aes(Occasion, height)) + 
  geom_boxplot() +
  geom_line(aes(group = Subject), colour = "#3366FF", alpha = 0.5)

matching aesthetics to graphic objects

  • lines and paths operate on the first value principle: each segment is defined by two observations
  • ggplot2 applies the aesthetic value associated with the first observation when drawing the segment.
df <- data.frame(x = 1:3, y = 1:3, colour = c(1,3,5))

ggplot(df, aes(x, y, colour = factor(colour))) + 
  geom_line( aes(group = 1),size = 2) +
  geom_point(size = 5)

ggplot(df, aes(x, y, colour = colour)) + 
  geom_line(aes(group = 1),size = 2) +
  geom_point(size = 5)

在左手边的颜色是离散的，右手边是连续的，即使颜色变量是连续的，ggplot不会平滑

library(ggplot2)
ggplot(data = diamonds) +
    geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))
Warning message:
“The dot-dot notation (`..prop..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(prop)` instead.”

group="whatever" 是一个”虚拟”分组来覆盖默认行为，(这里)是按 cut 分组，通常是按x 变量.geom_bar的默认值是按 x 变量分组，以便分别计算 x 变量的每个级别中的行数.例如，在这里，geom_bar默认返回cut 等于"Fair"、"Good"等的行数.

但是，如果我们想要比例，那么我们需要将所有级别的cut一起考虑.在第二个图中，数据首先按cut分组，因此分别考虑 cut的每个级别.Fair in Fair 的比例是 100%，Good in Good 等的比例也是如此.group=1(或 group="x" 等)阻止了这一点，因此每个级别的削减比例将相对于所有削减水平.

xgrid <- with(df, seq(min(x), max(x), length = 50))
interp <- data.frame(
  x = xgrid,
  y = approx(df$x, df$y, xout = xgrid)$y,
  colour = approx(df$x, df$colour, xout = xgrid)$y  
)
ggplot(interp, aes(x, y, colour = colour)) + 
  geom_line(size = 2) +
  geom_point(data = df, size = 5)

若我们要进行混合渐变形式，

ggplot(mpg, aes(class)) + 
  geom_bar()
ggplot(mpg, aes(class, fill = drv)) + 
  geom_bar()

显示多种颜色，需要多种的bars对于每一个class

statistical summaries

A layer combines data, aesthetic mapping, a geom (geometric object), a stat (statistical transformation), and a position adjustment. Typically, you will create layers using a geom_ function, overriding the default position and stat if needed.

revealing uncertainty

having the infomation about the uncertainty present in your idea

  • discrete x,range:geom_errorbar(),geom)linerange()
  • discrete x,range&center:geom_crossbar(),geom_pointrange()
  • continuous x,range:geom_ribbon()
  • continuous x,range&center:geom_smooth(stat="identity")
y <- c(18,11,16)
df <- data.frame(x=1:3,y=y,se=c(1.2,0.5,1.0))

base <- ggplot(df,aes(x,y,ymin=y-se,ymax=y+se))
箱线图
base+geom_crossbar()

base+geom_pointrange()

base+geom_smooth(stat="identity")

base+geom_errorbar()
base+geom_linerange()
base+geom_ribbon()

weighted data

dealing with overplotting

constructing a bi-gauss distribution

df <- data.frame(x = rnorm(2000), y = rnorm(2000))
norm <- ggplot(df, aes(x, y)) + xlab(NULL) + ylab(NULL)
norm + geom_point()
norm + geom_point(shape = 1) # Hollow circles "1"is circle,"2"is rectangle
norm + geom_point(shape = ".") # Pixel sized

adjust the opacity
norm + geom_point(alpha = 1 / 3)
norm + geom_point(alpha = 1 / 5)
norm + geom_point(alpha = 1 / 10)

norm
norm +geom_bin2d()
norm + geom_bin2d(bins=10)

  • Estimate the 2d density with stat_density2d()

Statistical summaries

bar
ggplot(diamonds, aes(color)) + 
  geom_bar()

ggplot(diamonds, aes(color, price)) + 
  geom_bar(stat = "summary_bin", fun = mean)

surfaces

we are considered two classes of geoms: - simple geoms where there’s a one-on-one correspondence between rows in the data - statistical geoms where introduce a layer of statistical summaries in between the raw data and the fault - we will consider cases where a visualization of a three dimensional surface

data(faithfuld)
ggplot(faithfuld,aes(eruptions,waiting))+
    geom_contour(aes(z=density,color=..level..))

..level.. 变量

..意味着一个内部计算的变量

演示相同的分布做一个热力图

ggplot(faithfuld, aes(eruptions, waiting)) + 
  geom_raster(aes(fill = density))

generated variables

a stat takes a data frame as input and returns a data frame as output, and so a stat can add new variables to the original dataset

Geoms

geometric objects or geoms for short,perform the actual rendering of the layer, controlling the type of plot that you create.

  • graphical primitives:
    • geom_blank()：啥也没有
    • geom_point()points
    • geom_path()
    • geom_rect() rectangles.
    • geom_ploygon() filled polygons.
    • geom_text()
  • One variable:
    • discrete
    • continuous
  • two variables:
    • both continuous:
      • geom_point()
      • geom_smooth()
  • three variables:
    • geom_contour()
    • geom_tile()
    • geom_raster(): fast version of geom_tile() for equal sized tiles

Stats

统计变换，或统计转换数据，通常是用某种方式来总结它

  • stat_bin()
  • stat_bin2d()
  • stat_bindot()
  • stat_binplot()

other stats can’t be created with a geom_ function

ggplot(mpg,aes(trans,cty))+
    geom_point()+
    stat_summary(geom="point",fun="mean",color="red",size=4)

ggplot(mpg,aes(trans,cty))+
    geom_point()+
    geom_point(stat="summary",fun="mean",color="red",size=4)

the way to use these functions. you can either add a stat_() function and override the default geom or add a geom_() function and override the default stat:

generated variables

a stat takes a data frame as input and returns a data frame as output, and so a stat can add new variables to the original dataset. it is possible to map aesthetics to these new variables. example: stat_bin 用于构建histogram 产生一系列的变量： - count,the number of observation in each bin - density the density of observation in each bin - x the centre of the bin

ggplot(diamonds,aes(price))+
    geom_histogram(binwidth = 500)

the after_stat() must wrap the name, preventing the confusion in case the original dataset includes a variables with the same name as a generated variable

ggplot(diamonds,aes(price))+
    geom_histogram(aes(y = after_stat(density)),binwidth=500)

scale and guides

ggplot(mpg, aes(displ, hwy)) + 
  geom_point(aes(colour = class))