,分為三個部分，此篇為Part1，推薦學習一些基礎知識后閱讀~
-
Part 1: Introduction to ggplot2, 覆蓋構建簡單圖表並進行修飾的基礎知識
-
Part 2: Customizing the Look and Feel, 更高級的自定義圖形
-
Part 3: Top 50 Ggplot2 Visualizations - The Master List, 應用Part1、2部分知識創建進階圖形
1 理解ggplot語法
（1）對數據框類型數據進行可視化
（2）可以疊加層來不斷豐富圖形信息
讓我們基於midwest數據集來初始化一個基本的圖形：
# Setup
options(scipen=999) # 關閉科學計數表示法 1e+06
library(ggplot2)
data("midwest", package = "ggplot2") # 加載數據
# 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()函數用來專門指定x和y軸，源數據框的任何信息都需要在這個函數中特意指定。
2 線性模型擬合散點圖
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 查詢該函數幫助文檔
3 調整x y軸范圍
#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 改變標題
# 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 改變點的顏色和大小
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")
改變顏色以反應另一列變量的類型
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
也可以改變調色板
gg + scale_colour_brewer(palette = "Set1") # change color palette
更多調色板可以在 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 改變x軸文本和刻度位置
breaks and labels
Step 1: Set the breaks
scale_x_continuous —— X 軸變量是連續變量
scale_x_date ——日期變量
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
改變 labels at the axis ticks. labels 需要和 breaks向量長度保持一致
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()
為軸標簽自定義文本
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')})
使用內置主題一次性自定義整個主題
?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")
更多主題可以看看 the ggthemes package and the ggthemr package.
參考：
英文教程：http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html