,分为三个部分，此篇为Part1，推荐学习一些基础知识后阅读~
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Part 1: Introduction to ggplot2, 覆盖构建简单图表并进行修饰的基础知识
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Part 2: Customizing the Look and Feel, 更高级的自定义图形
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Part 3: Top 50 Ggplot2 Visualizations - The Master List, 应用Part1、2部分知识创建进阶图形
1 理解ggplot语法
（1）对数据框类型数据进行可视化
（2）可以叠加层来不断丰富图形信息
让我们基于midwest数据集来初始化一个基本的图形：
# Setup
options(scipen=999) # 关闭科学计数表示法 1e+06
library(ggplot2)
data("midwest", package = "ggplot2") # 加载数据
# midwest <- read.csv("http://goo.gl/G1K41K") # alt source
# 初始化 Ggplot
ggplot(midwest, aes(x=area, y=poptotal)) # area 和 poptotal 是'midwest'中的列
aes()函数用来专门指定x和y轴，源数据框的任何信息都需要在这个函数中特意指定。
2 线性模型拟合散点图
library(ggplot2) g <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() + geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands plot(g)
?geom_smooth 查询该函数帮助文档
3 调整x y轴范围
#Method 1: By deleting the points outside the range library(ggplot2) g <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() + geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands # Delete the points outside the limits g + xlim(c(0, 0.1)) + ylim(c(0, 1000000)) # deletes points #Method 2: Zooming In library(ggplot2) g <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() + geom_smooth(method="lm") # set se=FALSE to turnoff confidence bands # Zoom in without deleting the points outside the limits. # As a result, the line of best fit is the same as the original plot. g1 <- g + coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000)) # zooms in plot(g1)
4 改变标题
# Full Plot call
library(ggplot2)
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point() +
geom_smooth(method="lm") +
coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# or
g1 + ggtitle("Area Vs Population", subtitle="From midwest dataset") + xlab("Area") + ylab("Population")
5 改变点的颜色和大小
library(ggplot2) ggplot(midwest, aes(x=area, y=poptotal)) + geom_point(col="steelblue", size=3) + # Set static color and size for points geom_smooth(method="lm", col="firebrick") + # change the color of line coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
改变颜色以反应另一列变量的类型
library(ggplot2)
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
plot(gg)
color, size, shape, stroke (thickness of boundary) and fill (fill color) 均可指定
也可以改变调色板
gg + scale_colour_brewer(palette = "Set1") # change color palette
更多调色板可以在 RColorBrewer 包中找到
library(RColorBrewer) head(brewer.pal.info, 10) # show 10 palettes #> maxcolors category colorblind #> BrBG 11 div TRUE #> PiYG 11 div TRUE #> PRGn 11 div TRUE #> PuOr 11 div TRUE #> RdBu 11 div TRUE #> RdGy 11 div FALSE #> RdYlBu 11 div TRUE #> RdYlGn 11 div FALSE #> Spectral 11 div FALSE #> Accent 8 qual FALSE
6 改变x轴文本和刻度位置
breaks and labels
Step 1: Set the breaks
scale_x_continuous —— X 轴变量是连续变量
scale_x_date ——日期变量
library(ggplot2)
# Base plot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# Change breaks
gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01))
Step 2: Change the labels
改变 labels at the axis ticks. labels 需要和 breaks向量长度保持一致
library(ggplots)
# Base Plot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state), size=3) + # Set color to vary based on state categories.
geom_smooth(method="lm", col="firebrick", size=2) +
coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) +
labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics")
# Change breaks + label
gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = letters[1:11])
# Reverse X Axis Scale
gg + scale_x_reverse()
为轴标签自定义文本
Method 1: Using sprintf(). (Have formatted it as % in below example)
Method 2: Using a custom user defined function. (Formatted 1000’s to 1K scale)
library(ggplot2) # Base Plot gg <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point(aes(col=state), size=3) + # Set color to vary based on state categories. geom_smooth(method="lm", col="firebrick", size=2) + coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics") # Change Axis Texts gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = sprintf("%1.2f%%", seq(0, 0.1, 0.01))) + scale_y_continuous(breaks=seq(0, 1000000, 200000), labels = function(x){paste0(x/1000, 'K')})
使用内置主题一次性自定义整个主题
?theme_bw
theme_set() to set the theme before drawing the ggplot. Note that this setting will affect all future plots. *
Draw the ggplot and then add the overall theme setting (eg. theme_bw())
library(ggplot2) # Base plot gg <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point(aes(col=state), size=3) + # Set color to vary based on state categories. geom_smooth(method="lm", col="firebrick", size=2) + coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + labs(title="Area Vs Population", subtitle="From midwest dataset", y="Population", x="Area", caption="Midwest Demographics") gg <- gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01)) # method 1: Using theme_set() theme_set(theme_classic()) # not run gg # method 2: Adding theme Layer itself. gg + theme_bw() + labs(subtitle="BW Theme") gg + theme_classic() + labs(subtitle="Classic Theme")
更多主题可以看看 the ggthemes package and the ggthemr package.
参考：
英文教程：http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html