R语言可视化之案例收集篇

作者: 思考问题的熊 | 来源:发表于2018-02-11 16:44 被阅读49次

R语言可视化之案例收集篇
R语言之可视化①④一页多图（1）
R语言之可视化⑥R图形系统续
R语言之可视化⑩坐标系统
R语言之可视化⑨火山图
R语言之可视化⑦easyGgplot2散点图
R语言之可视化⑧easyGgplot2散点图续
R语言之可视化①③散点图+拟合曲线
R语言之可视化①⑥一页多图（2）
R语言之可视化④韦恩图upsetR

qplot 使用

导入所需数据格式为data.frame

data(mtcars)
df <- mtcars[, c("mpg", "cyl", "wt")]
head(df)

qplot()基本用法

qplot(x, y=NULL, data, geom="auto",
      xlim = c(NA, NA), ylim =c(NA, NA))

# geom 画什么图；main 题目；xlab,ylab xy轴标签
# color 颜色；size 点大小；shape 点形状

Scatter plots 散点图

library(ggplot2)
# 基本款
qplot(mpg, wt, data=mtcars)
# 增加standard error和 smoothed line
qplot(mpg, wt, data = mtcars, geom = c("point", "smooth"))
# 分组增加smoothed line
qplot(mpg, wt, data = mtcars, color = factor(cyl),
      geom=c("point", "smooth"))

image.png

boxplot violin plot

boxplot
geom="boxplot"

dotplot
geom="dotplot"

violin
geom="violin"

# Basic box plot from data frame
qplot(group, weight, data = PlantGrowth,
      geom=c("boxplot"), fill = group)
# fill 填充颜色

# Dot plot
qplot(group, weight, data = PlantGrowth,
      geom=c("dotplot"),
      stackdir = "center", binaxis = "y",color = group, fill = group)

# Violin plot
qplot(group, weight, data = PlantGrowth,
      geom=c("violin"), trim = FALSE, fill = group)

histogram density plot

qplot(Sepal.Length, data = iris, geom = "histogram", binwidth=0.1)
qplot(Sepal.Length, data = iris, geom = "density", color = Species,
      main = "test", xlab = "x_test", ylab = "y_test")

ggplot2 box plot

ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# notched box plot
ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_boxplot(notch=TRUE, outlier.color = "red")

改变线颜色

三种方法

p<-ggplot(ToothGrowth, aes(x=dose, y=len, color=dose)) +
  geom_boxplot()
p
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
p+scale_color_brewer(palette="Dark2")

改变箱颜色

p<-ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) +
  geom_boxplot()
p
# Use custom color palettes
p+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# use brewer color palettes
p+scale_fill_brewer(palette="Dark2")

# 改变横坐标展示顺序
p + scale_x_discrete(limits=c("2", "0.5", "1"))

多组展示

ggplot(ToothGrowth, aes(x=dose, y=len, fill=supp)) +
  geom_boxplot()

定制


bp <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) +
  geom_boxplot()+
  labs(title="Plot of length  per dose",x="Dose (mg)", y = "Length")
bp + theme_classic()
bp + scale_fill_brewer(palette="Blues") + theme_classic()
bp + scale_fill_brewer(palette="Dark2") + theme_minimal()

violin plots

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

# trim
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_violin()
p
# no trim
p2<-ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_violin(trim=FALSE)
p2

显示范围

# 显示范围
p + scale_x_discrete(limits=c("0.5", "2"))

添加描述统计量

使用stat_summary()

# violin plot with mean points
p + stat_summary(fun.y=mean, geom="point", shape=23, size=2)

# 加箱线图
p + geom_boxplot(width=0.1)

修改颜色

和box plot 类似


p<-ggplot(ToothGrowth, aes(x=dose, y=len, color=dose)) +
  geom_violin(trim=FALSE)
p
# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")

定制

dp <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) +
  geom_violin(trim=FALSE)+
  geom_boxplot(width=0.1, fill="white")+
  labs(title="Plot of length  by dose",x="Dose (mg)", y = "Length")
dp
dp + scale_fill_brewer(palette="Blues") + theme_classic()

histogram 直方图

基础

#构造数据
set.seed(1234)
df <- data.frame(
  sex=factor(rep(c("F", "M"), each=200)),
  weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5)))
  )

# 基础
ggplot(df, aes(x=weight)) + geom_histogram()

# 加density
ggplot(df, aes(x=weight)) +
 geom_histogram(aes(y=..density..), colour="black", fill="white")+
 geom_density(alpha=.2, fill="#FF6666")

修改颜色并分组

# Change histogram plot line colors by groups
ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white")
# Overlaid histograms
ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", alpha=0.7, position="identity")

ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", alpha=.7, position="dodge")

library(plyr)
mu <- ddply(df, "sex", summarise, grp.mean=mean(weight))
p<-ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", position="dodge")+
  geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")+
  theme(legend.position="top")
p
# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")

填充

# Use semi-transparent fill
p<-ggplot(df, aes(x=weight, fill=sex, color=sex)) +
  geom_histogram(position="identity", alpha=0.5)
p
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))

定制

ggplot(df, aes(x=weight, color=sex, fill=sex)) +
geom_histogram(aes(y=..density..), position="identity", alpha=0.5)+
geom_density(alpha=0.6)+
geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
           linetype="dashed")+
scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
labs(title="Weight histogram plot",x="Weight(kg)", y = "Density")+
theme_classic()

desnsity plot

p<-ggplot(df, aes(x=weight, fill=sex)) +
  geom_density(alpha=0.4)
p
# Add mean lines
p+geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")

scatter plots

mtcars$cyl <- as.factor(mtcars$cyl)

# Change the point size
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point(aes(size=qsec))

# Add the regression line
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point()+
  geom_smooth(method=lm)
# Remove the confidence interval
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point()+
  geom_smooth(method=lm, se=FALSE)
# Loess method
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point()+
  geom_smooth()

添加 Error bar


#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of a column containing the variable
  #to be summariezed
# groupnames : vector of column names to be used as
  # grouping variables
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}

df2 <- data_summary(ToothGrowth, varname="len",
                    groupnames=c("supp", "dose"))
# Convert dose to a factor variable
df2$dose=as.factor(df2$dose)

library(ggplot2)
# Default bar plot
p<- ggplot(df2, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity", color="black",
           position=position_dodge()) +
  geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.2,
                 position=position_dodge(.9))
print(p)
# Finished bar plot
p+labs(title="Tooth length per dose", x="Dose (mg)", y = "Length")+
   theme_classic() +
   scale_fill_manual(values=c('#999999','#E69F00'))

# Default line plot
p<- ggplot(df2, aes(x=dose, y=len, group=supp, color=supp)) +
  geom_line() +
  geom_point()+
  geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.2,
                 position=position_dodge(0.05))
print(p)
# Finished line plot
p+labs(title="Tooth length per dose", x="Dose (mg)", y = "Length")+
   theme_classic() +
   scale_color_manual(values=c('#999999','#E69F00'))