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R绘图_ggplot2绘制qplot

R绘图_ggplot2绘制qplot

作者: 谢俊飞 | 来源:发表于2020-02-11 20:27 被阅读0次

    ggplot2在线学习:STHDA :Statistical tools for high-throughput data analysis.
    ggplot2使用说明:https://ggplot2.tidyverse.org/reference/

    • 火狐截图_2020-02-11T08-36-22.554Z.png

    qplot

    英文:http://www.sthda.com/english/wiki/qplot-quick-plot-with-ggplot2-r-software-and-data-visualization

    根据说明文档,运行代码……

    #The data set mtcars is used in the examples below:
    data(mtcars)
    head(mtcars)
    df <- mtcars[, c("mpg","cyl","wt")]
    head(df)
    
    #The plot can be created using data from either numeric vectors or a data frame:
    # Use data from numeric vectors
    x <- 1:10; y = x*x
    # Basic plot
    qplot(x,y)
    # Add line
    qplot(x, y, geom=c("point", "line"))
    # Use data from a data frame
    qplot(mpg, wt, data = mtcars)
    
    #Scatter plots with smoothed line
    # Smoothing
    qplot(mpg, wt, data = mtcars, geom = c("point", "smooth"))
    
    #The argument color is used to tell R that we want to color the points by groups:
    # Linear fits by group
    qplot(mpg, wt, data = mtcars, color = factor(cyl), geom = c("point","smooth"))
      
    # Change the color by a continuous numeric variable
    qplot(mpg, wt, data = mtcars, color = cyl)
    # Change the color by groups (factor)
    qplot(mpg, wt, data = mtcars, color = factor(cyl))
    qplot(mpg, wt, data = mtcars, color = factor(cyl), geom = c("point","line"))
    
    # Change the size of points according to 
    # the values of a continuous variable
    qplot(mpg, wt, data = mtcars, size = mpg)
    # Change point shapes by groups
    qplot(mpg, wt, data = mtcars, shape = factor(cyl))
    
    #Scatter plot with texts
    qplot(mpg, wt, data = mtcars, label = rownames(mtcars), 
          geom = c("point", "text"),)
    
    #hjust 和 vjust 的值仅定义在0和1之间:#0表示左对齐 #1表示右对齐
    qplot(mpg, wt, data = mtcars, label = rownames(mtcars), 
          geom=c("point", "text"),
          hjust=0, vjust=0)
    
    #Box plot, dot plot and violin plot
    head(PlantGrowth)
    x <- 1
    y <- rnorm(100)
    qplot(x, y, geom = "boxplot")
    
    qplot(group, weight, data = PlantGrowth, geom = "boxplot")
    #Dot plot
    qplot(group, weight, data = PlantGrowth)
    qplot(group, weight, data = PlantGrowth, geom = "dotplot", stackdir = "center", binaxis = "y")
    #violin plot
    qplot(group, weight, data = PlantGrowth, geom = "violin", trim = FALSE)
    #add jitter and change fill color by group
    qplot(group, weight, data = PlantGrowth, geom = c("boxplot", "jitter"), fill = group)
    #dot plot
    qplot(group, weight, data = PlantGrowth, 
          geom = "dotplot", stackdir = "center", binaxis = "y",
          color = group, fill = group)
    
    #The histogram and density plots are used to display the distribution of data.
    set.seed(1234)
    mydata <- data.frame(
      sex = factor(rep(c("F","M"), each = 200)),
      weight = c(rnorm(200, 55), rnorm(200, 58))
    )
    #notice the difference : rep(c("1","2"),each = 5) / rep(c("1","2"), 5)
    head(mydata)
    #basic histogram 
    #the histogram and density plots are used to display the distributin of data
    qplot(weight, data = mydata, geom = "histogram")
    qplot(weight, data = mydata, geom = "histogram", color = sex)
    qplot(weight, data = mydata, geom = "histogram", fill = sex)
    
    #basic density plot
    qplot(weight, data = mydata, geom = "density")
    #change the density plot line color by group(sex) \ change the line type
    qplot(weight, data = mydata, geom = "density", color = sex, linetype = sex)
    
    #main title and axis labels
    qplot(weight, data = mydata, geom = "density",
          xlab = "Weight(kg)", ylab = "Density",
          main = "Density plot of Weight(kg)")
    

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