R:ggplot2数据可视化——进阶(1)

,分为三个部分，此篇为Part1，推荐学习一些基础知识后阅读~

Part 1: Introduction to ggplot2, 覆盖构建简单图表并进行修饰的基础知识
Part 2: Customizing the Look and Feel, 更高级的自定义图形
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

1	`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])

1 2	`# 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")