常用绘图R包
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1.基础包 略显陈旧 了解一下
plot(iris[,1],iris[,3],col = iris[,5])
text(6.5,4, labels = 'hello')
boxplot(iris[,1]~iris[,5])
dev.off()
2.ggplot2 中坚力量 学起来有点难
test = iris
if(!require(ggplot2))install.packages('ggplot2')
library(ggplot2)
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length,
color = Species))
3.ggpubr 江湖救急 ggplot2简化和美化 褒贬不一
if(!require(ggpubr))install.packages('ggpubr')
library(ggpubr)
ggscatter(iris,
x="Sepal.Length",
y="Petal.Length",
color="Species")
STHDA美图中心：www.sthda.com
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1.入门级绘图模板：作图数据，横纵坐标
library(ggplot2)
test = iris
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length))
2.属性设置（颜色、大小、透明度、点的形状，线型等）
2.1 手动设置，需要设置为有意义的值
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy),
color = "blue")#目前手动只能设置1种颜色，注意color在aes（）外！
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy,color=class),
size = 5, # 点的大小5mm
alpha = 0.5, # 透明度 50%
shape = 8) # 点的形状
show_point_shapes() +
theme_classic()#显示点的形状(ggpubr包内的函数）
image.png
2.2 映射：按照数据框的某一列来定义图的某个属性
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length,
color = Species))
Q1 能不能自行指定映射的具体颜色？
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length,
color = Species))+
scale_color_manual(values = c("blue","grey","red"))
十六进制颜色
Q2 区分color和fill两个属性
Q2-1 空心形状和实心形状都用color设置颜色
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length,
color = Species),
shape = 17) #17号，实心的例子
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length,
color = Species),
shape = 2) #2号，空心的例子
Q2-2 既有边框又有内心的，才需要color和fill两个参数
ggplot(data = test)+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length,
color = Species),
shape = 24,
fill = "black") #22号，双色的例子
3.分面
ggplot(data = test) +
geom_point(mapping = aes(x = Sepal.Length, y = Petal.Length)) +
facet_wrap(~ Species)
双分面
test$Group = sample(letters[1:5],150,replace = T)#新增了1列
ggplot(data = test) +
geom_point(mapping = aes(x = Sepal.Length, y = Petal.Length)) +
facet_grid(Group ~ Species) #前行后列
练习6-1
示例数据：ggplot2中的数据集mpg
liberary(ggplot2)
a <- mpg
mpg#直接显示ggplot2显示也很优化
1.分别以mpg的displ和hwy两列作为横纵坐标，画点图。
ggplot(data = mpg) +
geom_point(mapping = aes(x =displ, y =hwy))
2.尝试修改颜色或大小，从mpg数据框中任选可以用来分类的列。
length(unique(mpg$manufacturer))#看某1列有几个取值
length(unique(mpg$displ))
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy,color=displ),
size =4, # 点的大小5mm
alpha = 0.8, # 透明度 50%
shape = 18) # 点的形状
分类变量和连续变量 颜色分配方式不同，连续变量的调色自行进一步搜索学习函数
3.根据class列来分面
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy,color=fl)) +
facet_wrap(~ class)
4.根据drv和cyl两个变量来分面
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy,color=fl)) +
facet_grid(drv~ cyl)
配色方案可以搜索教程
4.几何对象
局部设置和全局设置
ggplot(data = test) +
geom_smooth(mapping = aes(x = Sepal.Length,
y = Petal.Length))+
geom_point(mapping = aes(x = Sepal.Length,
y = Petal.Length))
ggplot(data = test,mapping = aes(x = Sepal.Length, y = Petal.Length))+
geom_smooth()+
geom_point()
练习6-2
1.尝试写出下图的代码
数据是iris
X轴是Species
y轴是Sepal.Width
图是箱线图，试一试搜一搜，ggplot2箱线图函数是哪个
ggplot(data=iris,mapping = aes(x=Species,y=Sepal.Width))+
geom_boxplot()+
geom_point()
- 尝试在此图上叠加点图，
能发现什么问题？
3.用下列代码作图，观察结果
ggplot(test,aes(x = Sepal.Length,y = Petal.Length,color = Species)) +
geom_point()+
geom_smooth(color = "black")
请问，当局部设置和全局设置冲突，谁说了算？局部！
5.统计变换-直方图
View(diamonds)
table(diamonds$cut)
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut))
ggplot(data = diamonds) +
stat_count(mapping = aes(x = cut))
统计变换使用场景
5.1.不统计，数据直接做图
fre = as.data.frame(table(diamonds$cut))
fre
ggplot(data = fre) +
geom_bar(mapping = aes(x = Var1, y = Freq), stat = "identity")#指定Y轴数据
5.2count改为prop
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))#Y轴按各组比例显示
6.位置关系
6.1抖动的点图
ggplot(data = mpg,mapping = aes(x = class,
y = hwy,
group = class)) +
geom_boxplot()+
geom_point()
ggplot(data = mpg,mapping = aes(x = class,
y = hwy,
group = class)) +
geom_boxplot()+
geom_jitter()
补充dotplot，点不重合，也不奔放
ggplot(data = mpg,mapping = aes(x = class,
y = hwy,
group = class)) +
geom_boxplot()+
geom_dotplot(binaxis = "y",binwidth = .5,stackdir = "center")
6.2堆叠直方图
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut,fill=clarity))
6.3 并列直方图
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")
7.坐标系
横纵坐标翻转coord_flip()
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
geom_boxplot() +
coord_flip()
极坐标系coord_polar()
bar <- ggplot(data = diamonds) +
geom_bar(
mapping = aes(x = cut, fill = cut),
show.legend = FALSE,
width = 1
) +
theme(aspect.ratio = 1) +
labs(x = NULL, y = NULL)
bar + coord_flip()
bar + coord_polar()
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#老师简化版
colnames(iris)#显示列名，直接复制。。
ggplot(data = iris,aes(Sepal.Width,Species))+ #简化函数
geom_violin(aes(fill=Species))+ #代码先后顺序，决定图层上下,不能直接写Specie，因为是映射不是violin单独参数
geom_boxplot()+
geom_point()+
geom_jitter(aes(shape=Species))+
theme_bw()#主题背景选择
ggpubr 搜代码直接用，基本不需要系统学习
sthda上有大量ggpubr出的图
library(ggpubr)
ggscatter(iris,x="Sepal.Length",
y="Petal.Length",
color="Species")
p <- ggboxplot(iris, x = "Species",
y = "Sepal.Length",
color = "Species",
shape = "Species",
add = "jitter")
p
my_comparisons <- list( c("setosa", "versicolor"),
c("setosa", "virginica"),
c("versicolor", "virginica") )
p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 9) #箱线图加了组间比较！
image.png
图片保存的三种方法
1.基础包作图的保存
#三段论：保存格式+代码+关闭图片
pdf("iris_box_ggpubr.pdf")
boxplot(iris[,1]~iris[,5])
text(6.5,4, labels = 'hello')
dev.off()
2.ggplot系列图(包括ggpubr)通用的简便保存 ggsave
p <- ggboxplot(iris, x = "Species",
y = "Sepal.Length",
color = "Species",
shape = "Species",
add = "jitter")
ggsave(p,filename = "iris_box_ggpubr.png")
3.eoffice包 导出为ppt,全部元素都是可编辑模式
library(eoffice)
topptx(p,"iris_box_ggpubr.pptx")
可探索export包
拼图
R包par基础包 patchwork 兼容
可探索入门
https://mp.weixin.qq.com/s/p7LLLvzR5LPgHhuRGhYQBQ
