R:ggplot2数据可视化—

更高级的自定义化，比如说操作图例、注记、多图布局等

# Setup

options(scipen=999)

library(ggplot2)

data("midwest", package = "ggplot2")

theme_set(theme_bw())

# midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source

# Add plot components --------------------------------

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

# Call plot ------------------------------------------

plot(gg)

传递给 theme() 的参数要求使用特定的 element_type() 函数来设置. 主要有四种类型

element_text(): Since the title, subtitle and captions are textual items
element_line(): Likewise element_line() is use to modify line based components such as the axis lines, major and minor grid lines, etc.
element_rect(): Modifies rectangle components such as plot and panel background.
element_blank(): Turns off displaying the theme item.清除主题展示

1 添加图形和轴标题

theme() 函数接受四个 element_type() 函数之一作为实参由于标题是文本使用element_text()修饰

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

# Modify theme components -------------------------------------------

gg + theme(plot.title=element_text(size=20,

                                    face="bold",

                                    family="American Typewriter",

                                    color="tomato",

                                    hjust=0.5,

                                    lineheight=1.2),  # title

            plot.subtitle=element_text(size=15,

                                       family="American Typewriter",

                                       face="bold",

                                       hjust=0.5),  # subtitle

            plot.caption=element_text(size=15),  # caption

            axis.title.x=element_text(vjust=10,

                                      size=15),  # X axis title

            axis.title.y=element_text(size=15),  # Y axis title

            axis.text.x=element_text(size=10,

                                     angle = 30,

                                     vjust=.5),  # X axis text

            axis.text.y=element_text(size=10))  # Y axis text

vjust, controls the vertical spacing between title (or label) and plot. 行距
hjust, controls the horizontal spacing. Setting it to 0.5 centers the title. 间距
family, 字体
face, sets the font face (“plain”, “italic”, “bold”, “bold.italic”)

?theme

2 修改图例

aesthetics are static, a legend is not drawn by default.

aesthetics (fill, size, col, shape or stroke) base on another column, as in geom_point(aes(col=state, size=popdensity)), a legend is automatically drawn.

改变标题

Method 1: Using `labs()`

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

gg + labs(color="State", size="Density")  # modify legend title

Method 2: Using `guides()`

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

gg <- gg + guides(color=guide_legend("State"), size=guide_legend("Density"))  # modify legend title

plot(gg)

Method 3: Using `scale_aesthetic_vartype()` format

scale_aestheic_vartype() 可以关闭指定变量的图例，其余保持不变通过设置 guide=FALSE

基于连续变量的点的大小的图例, 使用 scale_size_continuous() 函数

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

# Modify Legend

gg + scale_color_discrete(name="State") + scale_size_continuous(name = "Density", guide = FALSE)  # turn off legend for size

改变图例标签和点的颜色（针对不同类型）

使用对应的 scale_aesthetic_manual() 函数新的图例标签作为一个字符向量（labels argument）

通过 values 实参改变颜色

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

gg + scale_color_manual(name="State",

                        labels = c("Illinois",

                                   "Indiana",

                                   "Michigan",

                                   "Ohio",

                                   "Wisconsin"), #可以改变图例标签的顺序

                        values = c("IL"="blue",

                                   "IN"="red",

                                   "MI"="green",

                                   "OH"="brown",

                                   "WI"="orange"))

改变图例的顺序

guides()

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

gg + guides(colour = guide_legend(order = 1),

            size = guide_legend(order = 2))

改变图例标题文本背景的样式

图例的 key 是一个像元素的图形 , 使用 element_rect() 函数设置

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

gg + theme(legend.title = element_text(size=12, color = "firebrick"),

           legend.text = element_text(size=10),

           legend.key=element_rect(fill='springgreen')) +

  guides(colour = guide_legend(override.aes = list(size=2, stroke=1.5)))

删除图例和改变图例位置

可以使用 theme()函数设置图例的位置如果想把图例放在图形内部，可以使用 legend.justification 调整图例的铰接点

legend.position 是图例在图形中的坐标其中(0,0)是左下 (1,1)是右上 legend.justification 指图例内的铰接点

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

# No legend --------------------------------------------------

gg + theme(legend.position="None") + labs(subtitle="No Legend")

# Legend to the left -----------------------------------------

gg + theme(legend.position="left") + labs(subtitle="Legend on the Left")

# legend at the bottom and horizontal ------------------------

gg + theme(legend.position="bottom", legend.box = "horizontal") + labs(subtitle="Legend at Bottom")

# legend at bottom-right, inside the plot --------------------

gg + theme(legend.title = element_text(size=12, color = "salmon", face="bold"),

           legend.justification=c(1,0),

           legend.position=c(0.95, 0.05),

           legend.background = element_blank(),

           legend.key = element_blank()) +

  labs(subtitle="Legend: Bottom-Right Inside the Plot")

# legend at top-left, inside the plot -------------------------

gg + theme(legend.title = element_text(size=12, color = "salmon", face="bold"),

           legend.justification=c(0,1),

           legend.position=c(0.05, 0.95),

           legend.background = element_blank(),

           legend.key = element_blank()) +

  labs(subtitle="Legend: Top-Left Inside the Plot")

添加文本标签

对人口数超过 300K的县标记首先创建一个切出来符合条件的数据框 (midwest_sub)

然后用这个数据框作为数据源去画 geom_text 和 geom_label

推荐使用ggrepel包为点添加文本或者标签因为不会遮盖点

library(ggplot2)

# Filter required rows.

midwest_sub <- midwest[midwest$poptotal > 300000, ]

midwest_sub$large_county <- ifelse(midwest_sub$poptotal > 300000, midwest_sub$county, "")

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

# Plot text and label ------------------------------------------------------

gg + geom_text(aes(label=large_county), size=2, data=midwest_sub) + labs(subtitle="With ggplot2::geom_text") + theme(legend.position = "None")   # text 设置data参量

gg + geom_label(aes(label=large_county), size=2, data=midwest_sub, alpha=0.25) + labs(subtitle="With ggplot2::geom_label") + theme(legend.position = "None")  # label 是有外边框的

# Plot text and label that REPELS eachother (using ggrepel pkg) ------------

library(ggrepel)

gg + geom_text_repel(aes(label=large_county), size=2, data=midwest_sub) + labs(subtitle="With ggrepel::geom_text_repel") + theme(legend.position = "None")   # text

gg + geom_label_repel(aes(label=large_county), size=2, data=midwest_sub) + labs(subtitle="With ggrepel::geom_label_repel") + theme(legend.position = "None")   # label

添加注记

使用 annotation_custom() 函数需要一个 grob 作为参数

创建一个包含你想展示的文本的grob

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest")

# Define and add annotation -------------------------------------

library(grid)

my_text <- "This text is at x=0.7 and y=0.8!" #文本

my_grob = grid.text(my_text, x=0.7,  y=0.8, gp=gpar(col="firebrick", fontsize=14, fontface="bold")) #文本位置 样式

gg + annotation_custom(my_grob)

4 翻转x y轴

交换x y轴

coord_flip()

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest", subtitle="X and Y axis Flipped") + theme(legend.position = "None")

# Flip the X and Y axis -------------------------------------------------

gg + coord_flip()

逆转坐标轴的范围顺序

library(ggplot2)

# Base Plot

gg <- ggplot(midwest, aes(x=area, y=poptotal)) +

  geom_point(aes(col=state, size=popdensity)) +

  geom_smooth(method="loess", se=F) + xlim(c(0, 0.1)) + ylim(c(0, 500000)) +

  labs(title="Area Vs Population", y="Population", x="Area", caption="Source: midwest", subtitle="Axis Scales Reversed") + theme(legend.position = "None")

# Reverse the X and Y Axis ---------------------------

gg + scale_x_reverse() + scale_y_reverse()

5 切面：在一个图形中画多图

library(ggplot2)

data(mpg, package="ggplot2")  # load data

# mpg <- read.csv("http://goo.gl/uEeRGu")  # alt data source

g <- ggplot(mpg, aes(x=displ, y=hwy)) +

      geom_point() +

      labs(title="hwy vs displ", caption = "Source: mpg") +

      geom_smooth(method="lm", se=FALSE) +

      theme_bw()  # apply bw theme

plot(g)

我们得到一个简单的 highway mileage (hwy) 和 engine displacement (displ) 的图，但是如果想研究不同类型车辆这两个变量之间的关系呢？　

Facet Wrap

所有的图形在x和y轴的缩放比例默认相同可以通过设置 scales='free' 解除约束但是这样难以比较不同组之间的差异

library(ggplot2)

# Base Plot

g <- ggplot(mpg, aes(x=displ, y=hwy)) +

      geom_point() +

      geom_smooth(method="lm", se=FALSE) +

      theme_bw()  # apply bw theme

# Facet wrap with common scales

g + facet_wrap( ~ class, nrow=3) + labs(title="hwy vs displ", caption = "Source: mpg", subtitle="Ggplot2 - Faceting - Multiple plots in one figure")  # Shared scales

# Facet wrap with free scales

g + facet_wrap( ~ class, scales = "free") + labs(title="hwy vs displ", caption = "Source: mpg", subtitle="Ggplot2 - Faceting - Multiple plots in one figure with free scales")  # Scales free

Facet Grid

facet_grid() 用来把一个大图按照不同种类拆分成许多小图将一个 formula作为主要参数 ~ 左边构成行而~右边构成列

标题行会占用很多空间 facet_grid() 会清理这些标题 facet_grid 不能选择行数与列数

library(ggplot2)

# Base Plot

g <- ggplot(mpg, aes(x=displ, y=hwy)) +

      geom_point() +

      labs(title="hwy vs displ", caption = "Source: mpg", subtitle="Ggplot2 - Faceting - Multiple plots in one figure") +

      geom_smooth(method="lm", se=FALSE) +

      theme_bw()  # apply bw theme

# Add Facet Grid

g1 <- g + facet_grid(manufacturer ~ class)  # manufacturer in rows and class in columns

plot(g1)

把这些图放到一个面板中

# Draw Multiple plots in same figure.

library(gridExtra)

gridExtra::grid.arrange(g1, g2, ncol=2)

6 修改背景主要次要坐标轴

改变背景

library(ggplot2)

# Base Plot

g <- ggplot(mpg, aes(x=displ, y=hwy)) +

      geom_point() +

      geom_smooth(method="lm", se=FALSE) +

      theme_bw()  # apply bw theme

# Change Plot Background elements -----------------------------------

g + theme(panel.background = element_rect(fill = 'khaki'),

          panel.grid.major = element_line(colour = "burlywood", size=1.5),

          panel.grid.minor = element_line(colour = "tomato",

                                          size=.25,

                                          linetype = "dashed"),

          panel.border = element_blank(),

          axis.line.x = element_line(colour = "darkorange",

                                     size=1.5,

                                     lineend = "butt"),

          axis.line.y = element_line(colour = "darkorange",

                                     size=1.5)) +

    labs(title="Modified Background",

         subtitle="How to Change Major and Minor grid, Axis Lines, No Border")

# Change Plot Margins -----------------------------------------------

g + theme(plot.background=element_rect(fill="salmon"),

          plot.margin = unit(c(2, 2, 1, 1), "cm")) +  # top, right, bottom, left

    labs(title="Modified Background", subtitle="How to Change Plot Margin")

删除主要次要格网改变边界轴标题文本和刻度

library(ggplot2)

# Base Plot

g <- ggplot(mpg, aes(x=displ, y=hwy)) +

      geom_point() +

      geom_smooth(method="lm", se=FALSE) +

      theme_bw()  # apply bw theme

g + theme(panel.grid.major = element_blank(),

          panel.grid.minor = element_blank(),

          panel.border = element_blank(),

          axis.title = element_blank(),

          axis.text = element_blank(),

          axis.ticks = element_blank()) +

  labs(title="Modified Background", subtitle="How to remove major and minor axis grid, border, axis title, text and ticks")

在背景中添加图片

library(ggplot2)

library(grid)

library(png)

img <- png::readPNG("screenshots/Rlogo.png")  # source: https://www.r-project.org/

g_pic <- rasterGrob(img, interpolate=TRUE)

# Base Plot

g <- ggplot(mpg, aes(x=displ, y=hwy)) +

      geom_point() +

      geom_smooth(method="lm", se=FALSE) +

      theme_bw()  # apply bw theme

g + theme(panel.grid.major = element_blank(),

          panel.grid.minor = element_blank(),

          plot.title = element_text(size = rel(1.5), face = "bold"),

          axis.ticks = element_blank()) +

  annotation_custom(g_pic, xmin=5, xmax=7, ymin=30, ymax=45)

Inheritance Structure of Theme Components

主题组分的继承结构

参考：

http://r-statistics.co/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html