ggplot2学习总结

最近画图比较多，对ggplot2做一个总结，以后会慢慢更新

ggplot2颜色设置及使用

https://blog.csdn.net/qq_45759229/article/details/125422964

主题theme使用

https://zhuanlan.zhihu.com/p/115639331

library(ggplot2)
library(gcookbook)
hw_plot <- ggplot(heightweight, aes(x = ageYear, y = heightIn)) +
geom_point()
hw_plot

hw_plot + theme_bw()

hw_plot + theme_grey()

hw_plot + theme_minimal()

hw_plot + theme_classic()

hw_plot + theme_void()

hw_plot + theme_void()+ggtitle(" theme setting") # 设置标题

hw_plot + theme_void(base_size = 20,base_family = "Times")+ggtitle(" theme setting") # 设置字体

library(extrafont)
hw_plot + theme_void(base_size = 30,base_family = "mono")+ggtitle(" theme setting") # 有问题，不知道怎么回事

Warning message:
“程辑包‘ggplot2’是用R版本4.1.2 来建造的”

标题居中

## 注意标签
df <- data.frame(A = rnorm(10), B = rnorm(10))
p = ggplot(data = df, aes(x = A, y = B)) + geom_point()
p = p + xlab("A long x-string so we can see the effect of the font switch")
p = p + ylab("Likewise up the ordinate")
p+labs(title="datadagtat sdfas",x="X axis",y="Y axis")+theme(plot.title = element_text(hjust = 0.5))
# xlab和labs(x="?")是等价的

​

theme(strip.background)参数

https://zhuanlan.zhihu.com/p/225852640

# Load data and convert dose to a factor variable
data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

# Box plot, facet accordding to the variable dose and supp
p <- ggplot(ToothGrowth, aes(x = dose, y = len)) + 
  geom_boxplot(aes(fill = supp), position = position_dodge(0.9)) +
  scale_fill_viridis_d()  # 离散颜色
p + facet_grid(dose ~ supp)

p + facet_grid(dose ~ supp, labeller = label_both) 
# 给labeller传递一个lable_both就可以将分面的标签加上这个分面的label

p + facet_grid(dose ~ supp)+
    theme(
      strip.text.x = element_text(
        size = 12, color = "red", face = "bold.italic"
        ), # 这里设置x轴方向的字体类型，
      strip.text.y = element_text(
        size = 12, color = "red", face = "bold.italic"
        ) # 这里设置y轴方向的字体类型，
      ) # 修改字体和颜色

p + facet_grid(dose ~ supp)+
 theme(
   strip.background = element_rect(
     color="black", fill="#FC4E07", size=1.5, linetype="solid"
     )
   )
#分面label的背景设置是theme里面的strip.background调整


p + facet_grid(dose ~ supp)+
 theme(
   strip.background.x = element_rect(
     color="black", fill="#FC4E07", size=1.5, linetype="solid"
     ),
   strip.background.y = element_rect(
     color="black", fill="green", size=1.5, linetype="solid"
     )
   )
# 设置x轴和y轴strip的颜色不同


p + facet_grid(dose ~ supp)+
 theme(
   strip.background.x = element_rect(
     color="black", fill="#FC4E07", size=1.5, linetype="solid"
     ),
   strip.background.y = element_rect(
     color="black", fill="green", size=1.5, linetype="solid"
     ),
   panel.border = element_rect(color = 'red')
   )
# 设置各个分面的panel的边框的颜色还是panel.border,对应的输入格式是element_rect。


p + facet_grid(dose ~ supp)+
 theme(
   strip.background.x = element_rect(
     color="black", fill="#FC4E07", size=1.5, linetype="solid"
     ),
   strip.background.y = element_rect(
     color="black", fill="green", size=1.5, linetype="solid"
     ),
   strip.placement = "outside",
   )


p + facet_grid(dose ~ supp)+
 theme(
   strip.background.x = element_rect(
     color="black", fill="#FC4E07", size=1.5, linetype="solid"
     ),
   strip.background.y = element_rect(
     color="black", fill="green", size=1.5, linetype="solid"
     ),
   strip.placement = "inside",
   strip.switch.pad.grid = unit(2, "cm"),
   strip.switch.pad.wrap = unit(2, "cm")
   )

ggplot(ToothGrowth, aes(x = dose, y = len)) + geom_boxplot(aes(fill = supp),position = position_dodge(0.9))

position_dodge(柱形图柱宽度)

ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) +
　　geom_bar(stat="identity", width=0.9, position="dodge")

ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) +
　　geom_bar(stat="identity", width=0.5, position="dodge")
##
ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) +
   geom_bar(stat="identity",position = position_dodge(0.8))

facet_grid()分面

#----------分面作图----------
library(ggplot2)
library(patchwork)
p1<-ggplot(mtcars,aes(mpg,wt))+geom_point()+facet_grid(vs~.)+
  labs(title = "by row")+
  theme(plot.title = element_text(hjust = 0.5))
p2<-ggplot(mtcars,aes(mpg,wt))+geom_point()+facet_grid(.~vs)+
  labs(title = "by column")+
  theme(plot.title = element_text(hjust = 0.5))
p1|p2

df=mtcars[mtcars$vs==0,]
p1<-ggplot(df,aes(mpg,wt))+geom_point()
p1 ## 放大

p1<-ggplot(mtcars,aes(mpg,wt))+geom_point()+facet_grid(vs~cyl)+
  labs(title = "by row and column")+
  theme(plot.title = element_text(hjust = 0.5))
p1

​

​

element_blank()

http://events.jianshu.io/p/528d5da54c2e

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(palmerpenguins))

p <- penguins %>%
  drop_na() %>%
  ggplot(aes(x=flipper_length_mm,
             y=bill_length_mm, 
             color=species,# 设置颜色
             shape=sex))+ # 设置形状
  geom_point()+
  labs(title="Flipper length vs Bill length",
       subtitle="Palmer Penguins", # 设置子title
       caption="cmdlinetips.com", # 设置caption
       tag="A")
 
p

# 删除主要网格
p + theme(panel.grid.major=element_blank())

# 删除次要网格
p + theme(panel.grid.minor=element_blank())

# 删除图像背景
p + theme(panel.background = element_blank())

# 删除轴刻度
p + theme(axis.ticks = element_blank())

# 删除轴文本
p + theme(axis.text = element_blank())

# 删除图例键
p + theme(legend.key=element_blank())

# 删除多个主题元素
p + theme(panel.background = element_blank(),
          axis.ticks = element_blank(),
          axis.text = element_blank(),
          legend.key=element_blank(),
          plot.tag=element_blank(),
          plot.caption=element_blank())

Warning message:
“程辑包‘tibble’是用R版本4.1.2 来建造的”
Warning message:
“程辑包‘tidyr’是用R版本4.1.2 来建造的”
Warning message:
“程辑包‘readr’是用R版本4.1.2 来建造的”
Warning message:
“程辑包‘dplyr’是用R版本4.1.2 来建造的”

ggplot2显示中文

rm(list=ls())
library(ggplot2)
## 显示中文
library(showtext) # 安装这个包
showtext_auto()
data=data.frame(投入时间=c(1:50),结果=c(1:50)*1.25+rnorm(50))
#plot(data$投入时间,data$结果,family="STKaiti")#这句也可以

#plot(data$投入时间,data$结果,main="中文题目")
print(ggplot(data=data,aes(x=投入时间,y=结果))+geom_point())

Warning message:
“程辑包‘showtext’是用R版本4.1.2 来建造的”
载入需要的程辑包：sysfonts

Warning message:
“程辑包‘sysfonts’是用R版本4.1.2 来建造的”
载入需要的程辑包：showtextdb

​

载入程辑包：‘showtextdb’

​

The following object is masked from ‘package:extrafont’:

    font_install

​

ggplot2显示导入的png图像

library(ggplot2)
library(cowplot)
library(magick)
library(patchwork)
# Update 2020-04-15: 
# As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore.
# So, either we add theme_cowplot() when we build the graph 
# (commented out in the example below), 
# or we set theme_set(theme_cowplot()) at the beginning of our script:
theme_set(theme_cowplot())

# Example with PNG (for fun, the OP's avatar - I love the raccoon)
p1=ggdraw() +
  draw_image("./gg_test1.png") 

p2=ggdraw() +
  draw_image("./gg_test1.png") 

p1+p2

载入程辑包：‘cowplot’

​

The following object is masked from ‘package:patchwork’:

    align_plots

​

Linking to ImageMagick 6.9.12.3
Enabled features: cairo, fontconfig, freetype, heic, lcms, pango, raw, rsvg, webp
Disabled features: fftw, ghostscript, x11

ggplot2调整图例大小

# 调整图例大小(不使用guides)
library(ggplot2)
# http://www.360doc.com/content/12/0121/07/77506210_1001626628.shtml
#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each=3),
                 position=rep(c('Guard', 'Forward', 'Center'), times=3),
                 points=c(14, 8, 8, 16, 3, 7, 17, 22, 26))

#create grouped barplot
ggplot(df, aes(fill=position, y=points, x=team)) +
  geom_bar(position='dodge', stat='identity')

ggplot(df, aes(fill=position, y=points, x=team)) +
  geom_bar(position='dodge', stat='identity') +
  theme(legend.title = element_text(size=30))+
  theme(legend.text = element_text(size=30))+ 
   theme(legend.key.height= unit(1, 'cm'),legend.key.width= unit(2, 'cm'))

ggplot2设置图片大小(jupyter)

options(repr.plot.width = 18, repr.plot.height = 10)## 这个点其实有点大
# 调整图例大小(不使用guides)
library(ggplot2)
# http://www.360doc.com/content/12/0121/07/77506210_1001626628.shtml
#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each=3),
                 position=rep(c('Guard', 'Forward', 'Center'), times=3),
                 points=c(14, 8, 8, 16, 3, 7, 17, 22, 26))

#create grouped barplot
ggplot(df, aes(fill=position, y=points, x=team)) +
  geom_bar(position='dodge', stat='identity')

options(repr.plot.width = 5, repr.plot.height = 5)## 这个点其实有点大
library(ggplot2)
# http://www.360doc.com/content/12/0121/07/77506210_1001626628.shtml
#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each=3),
                 position=rep(c('Guard', 'Forward', 'Center'), times=3),
                 points=c(14, 8, 8, 16, 3, 7, 17, 22, 26))

#create grouped barplot
ggplot(df, aes(fill=position, y=points, x=team)) +
  geom_bar(position='dodge', stat='identity')

ggplot2保存图片

#Figure 4
library(ggplot2)
# http://www.360doc.com/content/12/0121/07/77506210_1001626628.shtml
#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each=3),
                 position=rep(c('Guard', 'Forward', 'Center'), times=3),
                 points=c(14, 8, 8, 16, 3, 7, 17, 22, 26))

#create grouped barplot
p=ggplot(df, aes(fill=position, y=points, x=team)) +
  geom_bar(position='dodge', stat='identity')
ggsave("./gg_test1.tiff",p,width=24,height = 26,dpi=300,compression="lzw")
ggsave("./gg_test1.pdf",p,width=24,height = 26,dpi=300)
ggsave("./gg_test1.png",p,width=24,height = 26,dpi=300)

ggarange 排列子图

http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/

library(ggpubr)
# ToothGrowth
data("ToothGrowth")
#head(ToothGrowth)
# mtcars 
data("mtcars")
mtcars$name <- rownames(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
#head(mtcars[, c("name", "wt", "mpg", "cyl")])

# Box plot (bp)
bxp <- ggboxplot(ToothGrowth, x = "dose", y = "len",
                 color = "dose", palette = "jco")
bxp
# Dot plot (dp)
dp <- ggdotplot(ToothGrowth, x = "dose", y = "len",
                 color = "dose", palette = "jco", binwidth = 1)
dp

# Bar plot (bp)
bp <- ggbarplot(mtcars, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in ascending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
bp + font("x.text", size = 8)
# Scatter plots (sp)
sp <- ggscatter(mtcars, x = "wt", y = "mpg",
                add = "reg.line",               # Add regression line
                conf.int = TRUE,                # Add confidence interval
                color = "cyl", palette = "jco", # Color by groups "cyl"
                shape = "cyl"                   # Change point shape by groups "cyl"
                )+
  stat_cor(aes(color = cyl), label.x = 3)       # Add correlation coefficient
sp

##################### 以下三种格式都是可以的
## type1
ggarrange(bxp, dp, bp + rremove("x.text"), 
          labels = c("A", "B", "C"),
          ncol = 2, nrow = 2)

## type2
library("cowplot")
plot_grid(bxp, dp, bp + rremove("x.text"), 
          labels = c("A", "B", "C"),
          ncol = 2, nrow = 2)
## type3
library("gridExtra")
grid.arrange(bxp, dp, bp + rremove("x.text"), 
             ncol = 2, nrow = 2)

ggplot2标注图片

options(repr.plot.width=12,repr.plot.height=12)
library(ggpubr)
# ToothGrowth
data("ToothGrowth")
#head(ToothGrowth)
# mtcars 
data("mtcars")
mtcars$name <- rownames(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
#head(mtcars[, c("name", "wt", "mpg", "cyl")])

# Bar plot (bp)
bp <- ggbarplot(mtcars, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in ascending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
# Scatter plots (sp)
sp <- ggscatter(mtcars, x = "wt", y = "mpg",
                add = "reg.line",               # Add regression line
                conf.int = TRUE,                # Add confidence interval
                color = "cyl", palette = "jco", # Color by groups "cyl"
                shape = "cyl"                   # Change point shape by groups "cyl"
                )+
  stat_cor(aes(color = cyl), label.x = 3)       # Add correlation coefficient


figure <- ggarrange(sp, bp + font("x.text", size = 10),
                    ncol = 1, nrow = 2)
annotate_figure(figure,
                top = text_grob("Visualizing mpg", color = "red", face = "bold", size = 14),
                bottom = text_grob("Data source: \n mtcars data set", color = "blue",
                                   hjust = 1, x = 1, face = "italic", size = 10),
                left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),
                right = "I'm done, thanks :-)!",
                fig.lab = "Figure 1", fig.lab.face = "bold"
                )

`geom_smooth()` using formula 'y ~ x'

ggplot画密度图并设置图例

rm(list=ls())
library(tidyverse)

set.seed(2)
dat = data.frame(dv = rnorm(100, 10, 3),
                 sim = rnorm(100, 11, 2))

print(ggplot(dat) +
  geom_density(aes(dv, colour="dv")) +
  geom_density(aes(sim, colour="sim")) +
  labs(colour="Type", 
       x="Concerta Peak1 Cmax Distribution",
       y="Density") +
  scale_colour_manual(values=c("green", "red")) +
  theme(legend.position=c(0.9, 0.9)))

# ggplot2确实是挺好用的，但是问题是一定得注意colour的位置和拼写，首先colour的拼写是colour而不是color，而且colour的设置是在aes()的括号内，而不是在ggplot2()的括号内，如果写错了，很容易犯错误

​

​

散点图和拟合图叠加(设置x轴和y轴范围）

#load package and data
options(scipen=999)  # turn-off scientific notation like 1e+48，关闭科学记数法
library(ggplot2)
theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")
# midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source

# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.05)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)
#ggsave(filename = "test2.pdf", gg, width = 8, height = 5, dpi = 200)

options(scipen=999)  # turn-off scientific notation like 1e+48
library(ggplot2)
theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")


# Scatterplot 
# 设置x轴和y轴范围
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.10)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)

`geom_smooth()` using formula 'y ~ x'

Warning message:
“Removed 66 rows containing non-finite values (stat_smooth).”
Warning message:
“Removed 66 rows containing missing values (geom_point).”
`geom_smooth()` using formula 'y ~ x'

Warning message:
“Removed 15 rows containing non-finite values (stat_smooth).”
Warning message:
“Removed 15 rows containing missing values (geom_point).”

ggplot2自定义统计函数

# 我今天画直方图和拟合的密度图发现一个问题，就是使用ggplot2画图时，画的直方图时离散的点，例如样本数为20，
#但是我想画密度图，这个时候相当于x是连续的，画的点也不会再是20个，
# 也就是说这两个x的维度大小不一样，不可能放入一个dataframe,所以是使用ggplot2画图时，
# 需要使用stat_function()函数，这个函数就特别的好用，可以画出上述效果的图
# 首先给一个stat_function的用法案例
library(ggplot2)
#这个函数是可以自定义的
MyFun <- function(x, p) {
  res <- x^(1 / p)
  return(res)
}

my.df <-data.frame(x = c(0,1))
ggplot(my.df, aes(x=x)) +
  stat_function(fun = MyFun, n = 1000, args = list(p = 10), aes(colour = "line1")) +
  stat_function(fun = MyFun, n = 1000, args = list(p = 3), aes(colour = "line2")) +
  stat_function(fun = MyFun, n = 1000, args = list(p = 2), aes(colour = "line3")) +
  stat_function(fun = MyFun, n = 1000, args = list(p = 1), aes(colour = "line4")) +
  scale_colour_manual("Lgend title", values = c("red", "blue", "green", "orange"))

​

​

额外库1 easyGgplot2

library(easyGgplot2)
# data.frame
df <- ToothGrowth
# 自定义框图与中心点图
plot1<-ggplot2.boxplot(data=df, xName='dose',yName='len', groupName='dose', addDot=TRUE, dotSize=1, showLegend=FALSE)
# 带中心点图的自定义点图
plot2<-ggplot2.dotplot(data=df, xName='dose',yName='len', groupName='dose',showLegend=FALSE)
# 带有中心点图的自定义带状图
plot3<-ggplot2.stripchart(data=df, xName='dose',yName='len', groupName='dose', showLegend=FALSE)
# Notched box plot
plot4<-ggplot2.boxplot(data=df, xName='dose',yName='len', notch=TRUE)
#在同一页上的多个图表
ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2)

Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

​
​

额外库2(patchwork)

额外库3(egg)

额外库(ggpubr)

http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/

library(ggpubr)
# ToothGrowth
data("ToothGrowth")
#head(ToothGrowth)
# mtcars 
data("mtcars")
mtcars$name <- rownames(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
#head(mtcars[, c("name", "wt", "mpg", "cyl")])

# Box plot (bp)
bxp <- ggboxplot(ToothGrowth, x = "dose", y = "len",
                 color = "dose", palette = "jco")
bxp
# Dot plot (dp)
dp <- ggdotplot(ToothGrowth, x = "dose", y = "len",
                 color = "dose", palette = "jco", binwidth = 1)
dp

# Bar plot (bp)
bp <- ggbarplot(mtcars, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in ascending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
bp + font("x.text", size = 8)
# Scatter plots (sp)
sp <- ggscatter(mtcars, x = "wt", y = "mpg",
                add = "reg.line",               # Add regression line
                conf.int = TRUE,                # Add confidence interval
                color = "cyl", palette = "jco", # Color by groups "cyl"
                shape = "cyl"                   # Change point shape by groups "cyl"
                )+
  stat_cor(aes(color = cyl), label.x = 3)       # Add correlation coefficient
sp

​

​

`geom_smooth()` using formula 'y ~ x'

​

## type1
ggarrange(bxp, dp, bp + rremove("x.text"), 
          labels = c("A", "B", "C"),
          ncol = 2, nrow = 2)

## type2
library("cowplot")
plot_grid(bxp, dp, bp + rremove("x.text"), 
          labels = c("A", "B", "C"),
          ncol = 2, nrow = 2)
## type3
library("gridExtra")
grid.arrange(bxp, dp, bp + rremove("x.text"), 
             ncol = 2, nrow = 2)

options(repr.plot.width=12,repr.plot.height=12)
figure <- ggarrange(sp, bp + font("x.text", size = 10),
                    ncol = 1, nrow = 2)
annotate_figure(figure,
                top = text_grob("Visualizing mpg", color = "red", face = "bold", size = 14),
                bottom = text_grob("Data source: \n mtcars data set", color = "blue",
                                   hjust = 1, x = 1, face = "italic", size = 10),
                left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),
                right = "I'm done, thanks :-)!",
                fig.lab = "Figure 1", fig.lab.face = "bold"
                )

`geom_smooth()` using formula 'y ~ x'

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