ggplot2-绘制分布图（转载）

本文更新地址: http://blog.csdn.net/tanzuozhev/article/details/51106291

本文在 http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/ 的基础上加入了自己的理解

生成绘图数据

set.seed(1234)dat <- data.frame(cond = factor(rep(c("A","B"), each=200)),                    rating = c(rnorm(200),rnorm(200, mean=.8)))# View first few rowshead(dat)

##   cond     rating## 1    A -1.2070657## 2    A  0.2774292## 3    A  1.0844412## 4    A -2.3456977## 5    A  0.4291247## 6    A  0.5060559

library(ggplot2)

直方图和概率密度图

## Basic histogram from the vector "rating". Each bin is .5 wide.## These both result in the same output:ggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5) # rating作为横轴

# ggplot(dat, aes(x=rating)) +    geom_histogram(binwidth=.5,     colour="black", # 边框颜色     fill="white" #填充颜色 )

ggplot(dat, aes(x=rating)) + geom_density() # 添加密度曲线

# Histogram overlaid with kernel density curveggplot(dat, aes(x=rating)) +     geom_histogram(aes(y=..density..),      # 这一步很重要,使用density代替y轴                   binwidth=.5,                   colour="black", fill="white") +    geom_density(alpha=.2, fill="#FF6666")  # 重叠部分采用透明设置

添加一条均值线(红色部分)

ggplot(dat, aes(x=rating)) +    geom_histogram(binwidth=.5, colour="black", fill="white") +    geom_vline(aes(xintercept=mean(rating, na.rm=T)),   # Ignore NA values for mean               color="red", linetype="dashed", size=1)

多组数据的直方图和密度图

# cond作为各组的分类,以颜色填充作为区别# position的处理很重要,决定数据存在重叠是的处理方式 "identity" 不做处理,但是设置了透明ggplot(dat, aes(x=rating, fill=cond)) +    geom_histogram(binwidth=.5, alpha=.5, position="identity")

# Interleaved histogramsggplot(dat, aes(x=rating, fill=cond)) +    geom_histogram(binwidth=.5, position="dodge")

# dodge 表示重叠部分进行偏离# 密度图ggplot(dat, aes(x=rating, colour=cond)) + geom_density()

# 半透明的填充ggplot(dat, aes(x=rating, fill=cond)) + geom_density(alpha=.3)

Add lines for each mean requires first creating a separate data frame with the means:

# Find the mean of each grouplibrary(plyr)# 以 cond 作为分组, 计算每组的rating的均值cdat <- ddply(dat, "cond", summarise, rating.mean=mean(rating))cdat

##   cond rating.mean## 1    A -0.05775928## 2    B  0.87324927

# 绘制两组数据的均值ggplot(dat, aes(x=rating, fill=cond)) +    geom_histogram(binwidth=.5, alpha=.5, position="identity") +    geom_vline(data=cdat, aes(xintercept=rating.mean,  colour=cond),               linetype="dashed", size=1)

# 密度图ggplot(dat, aes(x=rating, colour=cond)) +    geom_density() +    geom_vline(data=cdat, aes(xintercept=rating.mean,  colour=cond),               linetype="dashed", size=1)

使用分面

按照 cond 进行分面处理, 上图为A,下图为B

# 按照 cond 进行分面处理, 上图为A,下图为Bggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") +     facet_grid(cond ~ .)

# 添加均值线ggplot(dat, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white") +     facet_grid(cond ~ .) +    geom_vline(data=cdat, aes(xintercept=rating.mean),               linetype="dashed", size=1, colour="red")

箱线图

# A basic box plotggplot(dat, aes(x=cond, y=rating)) + geom_boxplot()

# cond作为填充颜色的分类ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()

# The above adds a redundant legend. With the legend removed:ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() +    guides(fill=FALSE) # 关闭图例

# With flipped axesggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot() +     guides(fill=FALSE) +   coord_flip() # x轴 y轴翻转

使用 `stat_summary’ 添加均值

# Add a diamond at the mean, and make it largerggplot(dat, aes(x=cond, y=rating)) + geom_boxplot() +    stat_summary(fun.y=mean, geom="point", shape=5, size=4)