R:ggplot2数据可视化——进阶(2)

Part 2: Customizing the Look and Feel,

更高级的自定义化，比如说操作图例、注记、多图布局等 

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# 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

1. element_text(): Since the title, subtitle and captions are textual items
2. element_line(): Likewise element_line() is use to modify line based components such as the axis lines, major and minor grid lines, etc.
3. element_rect(): Modifies rectangle components such as plot and panel background.
4. 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

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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”)

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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()

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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()

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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

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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

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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() 

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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

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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

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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

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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

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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()

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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

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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

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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

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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

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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

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# 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

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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

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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

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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