一、散点图

一) 基本

1. plot (x,y)

plot(mtcars$wt,mtcars$mpg)

2. 使用qplot

library (ggplot2)
qplot(mtcars$wt,mtcars$mpg) 
qplot(wt, mpg, data = mtcars)  ###X,Y来自同一个数据框

3. 使用ggplot2

ggplot(mtcars, aes(x = wt, y = mpg)) +  geom_point()
               ##指定x,y                ##指定图的类型,point=散点图

二) 美化

ggplot(data, aes(names , Activated.B.cell)) +
  geom_point(aes(color = group1)) +
  scale_color_manual(values = c(COLON = "#8ac53e" , LIVER = "#ff00ff"))+   ##改颜色
  theme_classic()+   ##经典背景
  theme(axis.text.x=element_blank(), ##删除X轴标签
        axis.ticks.x=element_blank())+  ##删除X轴刻度
  xlab("names") +  ##x轴命名
  ylab("Activated B cell") +  ##y轴命名
  scale_y_continuous(breaks = seq(0.2,1.2,0.1))  ##改y轴刻度值

散点图

二、折线图、曲线图

1. plot函数: type参数可以指定绘图的类型

plot(pressure$temperature, pressure$pressure, type = "l") 
points (pressure$temperature, pressure$pressure) ##points:将点在线上表示出来

2. lines函数

lines(pressure$temperature, pressure$pressure/2, col ="red") 
points(pressure$temperature, pressure$pressure/2, col = "red")

3. qplot

library (ggplot2)
qplot (pressure$temperature, pressure$pressure, geom = "line")
qplot (pressure$temperature, pressure$pressure, geom = c("line","point")) ###同时画出线和点
qplot(temperature, pressure,data = pressure, geom = "line") #变量x和y来自于同一个数据框

4. ggplot2

ggplot(pressure, aes(x = temperature, y = pressure)) +  geom_line() + geom_point()

三、柱状图

一) 基本画图

1. 使用barplot

barplot(BOD$demand,names.arg = BOD$Time)
           ##Y轴      ##x轴

2. 使用qplot()

library(ggplot2)
qplot(mtcars$cyl) #连续变量
qplot(factor(mtcars$cyl)) #分类变量

3. ggplot

##单一个X
ggplot(mtcars,aes(cyl))+geom_bar()  #连续变量
ggplot(mtcars,aes(factor(cyl)))+geom_bar()  #分类变量


##指定X和Y时
stat = "count"(默认)：表示一个x对应落到该x的样本数
stat = "identity": 表示一个x对应一个y,
即identity提取横坐标x对应的y值，count提取横坐标的频数

ggplot(BOD,aes(Time,demand)) + geom_bar(stat = "identity") #连续变量
ggplot(BOD,aes(factor(Time),demand)) + geom_bar(stat = "identity")#分类变量

二) 美化

1. 柱状图调色补充和分组

library(ggplot2)
library(gcookbook) ##加载教程表格的R包
ggplot(pg_mean,aes(group, weight)) + geom_bar(stat = "identity") #基本图 

1.1 修改柱状图的填充（fill）和描边（color）：

ggplot(pg_mean,aes(group, weight)) +
    geom_bar(stat = "identity", fill = "lightblue", color = "black")

1.2 通过分组变量设置颜色：

ggplot(cabbage_exp, aes(Date, fill = Cultivar)) +
    geom_bar(position = "dodge")

postition参数： 主要是指对图像的微调，最常见的应用是在分组的柱形图（bar）中，因为分组的柱形图会产生组内堆积和不堆积两种主要的效果

position常用参数值：其中stack和dodge最为常用。
"identity": 不调整，组内前后重叠；
"stack": 堆积， 默认；
"fill": 按比例堆积；
"dodge": 分散

1.3 通过scale_fill_brewer() scale_fill_brewer()修改颜色模式：

ggplot(cabbage_exp, aes(Date, Weight, fill = Cultivar)) +
  geom_bar(position = "dodge", stat = "identity", color = "black") +
  scale_fill_brewer(palette = "Pastel1")

ColorBrewer 配色: 使用的是scale_colour_brewer()
可以使用RColorBrewer::display.brewer.all()查看所有的调色板

通过scale_fill_manual修改颜色

自定义颜色：输入的变量长度与分组变量的长度一致

scale_fill_manual(..., values, aesthetics = "fill", breaks = waiver(), na.value = "grey50")

1.4除了fill可以指定分组变量，color, linestyle也可以指定；

ggplot(cabbage_exp, aes(Date, Weight, color = Cultivar)) + 
   geom_bar(position = "dodge", stat = "identity")

1.5如果类别变量的组合有任何缺失，则该栏将缺失，相邻的栏将扩展以填充该空间。

ce = cabbage_exp[1:5,]
ggplot(ce, aes(Date, Weight, fill = Cultivar)) +
 geom_bar(position = "dodge", stat = "identity", color = "black") +
 scale_fill_brewer(palette = "Pastel1")

实际情况下确实存在有一种类别没有对应的y值，此时可以使用NA或者0代替

ce_NA <- cabbage_exp
ce_NA$Weight[6] <- 0

ggplot(ce_NA, aes(Date, Weight, fill = Cultivar)) +
 geom_bar(position = "dodge", stat = "identity", color = "black") +
 scale_fill_brewer(palette = "Pastel1")

1.6 修改颜色的技巧

upc <- subset(uspopchange, rank(Change) > 40)
ggplot(upc, aes(x = Abb, y = Change, fill = Region)) + 
 geom_bar(stat = "identity")

1.6.1使用reorder()函数，将条形按其高度进行排序：

ggplot(upc, aes(x = reorder(Abb, change), y = Change, fill = Region)) +  
 geom_bar(stat = "identity", color = "black") +
 scale_fill_manual(values = c("#669933", "#FFCC66")) + 
 xlab("State")

reorder()函数：
reorder(x, X, FUN = mean, ..., order = is.ordered(x), decreasing = FALSE)
x: x是要排序的数据，排序后的结果作用于x上
X: 要根据X进行排序

1.6.2 正负两极不同的颜色

新建一个字段；该字段描述来正和负

csub <- subset(climate, Source == "Berkeley" & Year >= 1900)
csub$pos <- csub$Anomaly10y >= 0 #指定fill为该字段
ggplot(csub, aes(x = Year, y = Anomaly10y, fill = pos)) +
    geom_bar(stat = "identity", position = "identity")

2. 调整条形的宽度和间距

2.1 width调整间距

library(gcookbook)
# width默认是0.9 , 最大值只能设置为1:
ggplot(pg_mean, aes(x = group, y = weight)) + 
 geom_bar(stat = "identity", width = 0.5)

2.2 position_dodge调节分组条形图之间的间距：

默认的同一分组之间的条形是没有间距的

ggplot(cabbage_exp, aes(x = Date, y = Weight, fill = Cultivar)) +
   geom_bar(stat = "identity", width = 0.5, position = 
   position_dodge(0.7))
##当position_dodge=0的时候,两个柱子会重合

2.3 堆积柱状图：

position的默认值为stack；
即如果不设置position，并且设置了分组变量，就是画堆积图；

2.3.1 'guides()'修改图例堆积的顺序

ggplot(cabbage_exp, aes(x = Date, y = Weight, fill = Cultivar)) +
 geom_bar(stat = "identity") +
 guides(fill = guide_legend(reverse = T))

2.3.2 修改图形堆积顺序：修改因子水平

cabbage_exp$Cultivar <- factor(cabbage_exp$Cultivar, levels = c("c52", "c39"))
ggplot(cabbage_exp, aes(x = Date, y = Weight, fill = Cultivar)) +
  geom_bar(stat = "identity", color = "black") + 
  guides(fill = guide_legend(reverse = T)) 

3. 修改标签 geom_text：

3.1 vjust参数设定标签位置的设定

ggplot(cabbage_exp, aes(x = interaction(Date, Cultivar), y = Weight)) +
 geom_bar(stat = "identity") +
 geom_text(aes(label = Weight), vjust = 1.5, colour = "white")
###vjust正值在水平线下,负值在水平线上

3.2 为了防止标签跑出图形，调整y轴的范围：

##1. ylim()函数
ggplot(cabbage_exp, aes(x = interaction(Date, Cultivar), y = Weight)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = Weight), vjust = -0.2) +
 ylim(0, max(cabbage_exp$Weight) * 1.05)

##2. 以weight为基准，调节y值，图形高度会自动适配：
ggplot(cabbage_exp, aes(x = interaction(Date, Cultivar), y = Weight)) + 
  geom_bar(stat = "identity") +
  geom_text(aes(y = Weight + 0.1, label = Weight))

3.3分组柱状图加标签

需要设定position_dodge(),以调整字体适合位置

ggplot(cabbage_exp, aes(x = interaction(Date, Cultivar), y = Weight, fill = Cultivar)) + 
  geom_bar(stat = "identity", position = "dodge") + 
  geom_text(aes(label = Weight), vjust = 1.5, color = "white", position = position_dodge(0.9), size = 3) 
 # width参数默认是0.9，position_dodge(0.9)，让标签位于柱子中间

3.4 堆积柱状图添加label

library(plyr)
ce <- arrange(cabbage_exp, Date, Cultivar)
ce <- ddply(ce, "Date", transform, label_y = cumsum(Weight))
ce$Cultivar <- factor(ce$Cultivar, levels = c("c52", "c39"))
ggplot(ce, aes(x = Date, y = Weight, fill = Cultivar)) + 
  geom_bar(stat = "identity") + 
  geom_text(aes(y = label_y, label = Weight), vjust = 1.5, colour = "white")

3.5 position_stack修改标签至中央

ggplot(ce, aes(x = Date, y = Weight, fill = Cultivar)) + 
  geom_bar(stat = "identity") + 
  geom_text(aes(label = Weight), position = position_stack(vjust = 0.5),colour = "white")

3.6 添加标签单位，并修改颜色模式

ggplot(ce, aes(x = Date, y = Weight, fill = Cultivar)) +
  geom_bar(stat = "identity", colour = "black") +
  geom_text(aes(y = label_y, label = paste(format(Weight,nsmall = 2), "kg")), size = 4) 

4. 柱状图的拓展：饼图

###1.1 扭曲y轴方向,柱子变弯
ggplot(mpg , aes(class)) +  geom_bar() + coord_polar(theta = "y")

###1.2 扭曲X轴 , 柱子从一个点出发
ggplot(mpg , aes(class)) + geom_bar() + coord_polar(theta = "x")

###1.3 加颜色分组
ggplot(mpg , aes(class)) +  geom_bar(aes(fill = drv)) + 
   coord_polar(theta = "y")

###2.1 正常饼图
ggplot(mpg , aes(1 , fill = class)) + ##只有一个柱子的堆积柱状图,按class填充
  geom_bar() +
  coord_polar(theta = "y")  ##扭曲Y轴

###2.2 加标签
ggplot(mpg , aes(1 , fill = class)) + 
  geom_bar(width = 0.5) +
  coord_polar(theta = "y") + 
  geom_text(stat = "count" , aes(label = scales::percent(..count../100)) , 
            size =3 , position = position_stack(vjust = 0.5) )

5. 柱状图拓展: 克里夫兰点图绘制

library(gcookbook)
tophit <- tophitters2001[1:25,]
 
# 从最基本的散点图出发
ggplot(tophit, aes(x = avg, y = name)) + geom_point()

# reorder排序
ggplot(tophit, aes(x = avg, y = reorder(name, avg))) + 
 geom_point(size = 3) +   ##修改点的大小
 theme_bw() +   ##去掉灰色背景
 theme(panel.grid.major.x = element_blank(),  #设置纵向网格线为空
    panel.grid.minor.x = element_blank(),     #设置纵向网格线为空
    panel.grid.major.y = element_line(colour = "grey60", linetype = "dashed"))  
                                                          #设置横向网格线为虚线 
# 若颠倒图形的x轴和y轴,网格线设置x,y对调
ggplot(tophit, aes(x = reorder(name, avg), y = avg))+ 
 geom_point(size = 3) + #修改点的大小
 theme_bw() + #修改背景
 theme(panel.grid.major.y = element_blank(), 
    panel.grid.minor.y = element_blank(),  
    panel.grid.major.x = element_line(colour = "grey60", linetype = "dashed"))

## 绘制彩色克利夫兰点图：
ggplot(tophit, aes(x = avg, y = name)) +
 geom_segment(aes(yend = name), xend = 0, colour = "grey50") +
 geom_point(size = 3, aes(color = lg)) + # 设置分组变量
 scale_colour_brewer(palette = "Set1",limits = c("NL", "AL"))+  #limits限定颜色先后顺序：
 theme_bw() + 
 theme(panel.grid.major.y = element_blank(), ##去除横线网格线
    legend.position = c(1, 0.55), ##设置图例的位置：这里的1指的是与x轴的比例；
    legend.justification = c(1, 0.5))  ##表示图例右边缘中点；(1,0)表示右下角，(0, 1) 表示左上角，以此类推；

#按照lg和avg对name进行排序，先按lg排，再按avg排
nameorder <- tophit$name[order(tophit$lg, tophit$avg)]

#将name变量转化为因子，因子水平设定位nameorder：
tophit$name <- factor(tophit$name, levels = nameorder)

分面绘制：facet_grid()函数：

 ggplot(tophit, aes(x = avg, y = name)) +
  geom_segment(aes(yend = name), xend = 0, colour = "grey50") +
  geom_point(size = 3, aes(color = lg)) + # 设置分组变量
  scale_colour_brewer(palette = "Set1", limits = c("NL", "AL"), 
                     guide = F) +  # guides去除图例：
  theme_bw() +
  theme(panel.grid.major.y = element_blank()) +
  # 一列多行：lg~ 行数等于lg的种类数目；
  # scales设置每个分块的单位宽度；space设置每个分块的宽度；
  facet_grid(lg~.,scales = "free_y", space = "free_y")

四、直方图

x是连续变量

1. hist函数

hist(mtcars$mpg)
hist(mtcars$mpg, breaks = 10)  #break参数指定组数

2. qplot()

qplot(mpg,data = mtcars, binwidth = 1 ) #binwidth参数指定组距

3. ggplot2

ggplot(data = mtcars, aes(x = mpg)) + geom_histogram( binwidth = 1)

五、箱线图

1. 使用plot（）函数

plot(ToothGrowth$supp, ToothGrowth$len)
##当x为因子变量（与数值变量对应时），默认绘制箱线图

2. boxplot()函数

##1. 
boxplot(len  ~  supp, data = ToothGrowth)
        #y轴值  #x轴值
#2. 或者
boxplot(ToothGrowth$len~ToothGrowth$supp)

#3. 在x轴引入两变量的交互，可以绘制分组箱线图
boxplot(len ~ supp + dose, data = ToothGrowth)
                   ##分组变量

3. qplot()函数

##1. 
qplot(supp , len,data = ToothGrowth, geom = "boxplot")
     ##x轴  ##y轴
##2. 
qplot(interaction(supp,dose),len, data = ToothGrowth,geom = "boxplot")
 ##使用interaction连接x与分组变量

4. ggplot2

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

六、绘制函数图像

1. 使用curve()函数绘制

##传入一个关于变量x的表达式
curve(x^3 - 5*x, from = -4, to =4)

2. 自定义函数图像

my_fun <- function(xvar){
 1/(1 + exp(-xvar + 10))
   }
curve(my_fun(x), from = 0, to =20)

3. 原有的基础上添加一条线

curve(1 - my_fun(x),add = T, col = "red")

4. ggplot2

##1. 
ggplot(data.frame(x= c(0,20)),aes(x=x)) + stat_function(fun = my_fun, geom ="line")
                                                                        ##线图
##2. 再加一条线
my_fun2 <- function(xvar){
 1- 1/(1 + exp(-xvar + 10))
}
ggplot(data.frame(x = c(0,20)),aes(x =x)) + stat_function(fun = my_fun, geom ="line") + stat_function(fun = my_fun2, geom ="line",color="red")