5.气泡图Bubble Plot 6.边际直方图/箱线图Marginal Histogram / Boxplot 7.相关图Correlogram 1.散点图 Scatterplot 数据分析最常用的图无疑是散点图。每当你想了解两个变量之间关系的性质时,第一选择总是散点图。它可以使用geom_point()绘制。此外,默认情况下绘制平滑线的geom_smooth可以通过设置
2、边缘箱图/直方图(Marginal Histogram / Boxplot) 这类图,python的seaborn和R中的ggstatsplot都能很便利的绘制,参考: R可视化17|ggstatsplot几行code终结SCI级图表统计+画图 (上) Python可视化24|seaborn绘制多变量分布图(jointplot|JointGrid) library(ggplot2) library(ggExtra) data(mpg, package="ggplot2") ...
Marginal Histogram / Boxplot 边缘直方图 箱形图 可以在一张图中展示关系和分布 在散点图周围有 X and Y 变量的直方图 使用ggMarginal() 函数 来自‘ggExtra’包 除去 histogram 也可以选则 boxplot or density图 通过设置 type 选项 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # load package and...
1.5 气泡图(Bubble Plot) 1.6 边际直方图/箱线图(Marginal Histogram / Boxplot) 1.7 相关图(Correlogram) 2 偏差(Deviation) 2.1 发散条形图(Diverging bars) 2.2 发散棒棒糖图(Diverging Lollipop Chart) 2.3 ...
1.6. Marginal histogram boxplot If you want to show the relationship as well as the distribution in the same chart, use the marginal histogram. It has a histogram of the X and Y variables at the margins of the scatterplot. This can be implemented using the ggMarginal() function from the...
边际直方图/箱线图(Marginal Histogram / Boxplot) 相关图(Correlogram) 1.1 散点图(Scatterplot) 数据分析中最常用的图无疑是散点图。每当您想了解两个变量之间关系的性质时,首选始终是散点图。 它可以使用geom_point()绘制。此外,geom_smooth默认情况下会绘制一条平滑线(基于损失),可以通过设置method='lm’来...
选择"histogram"参数绘制统计直方图; 选择""boxplot"参数绘制箱形图。 绘制核密度估计图 library(ggpubr) # Basic scatter plot with marginal density plot ggscatterhist(iris, x = "Sepal.Length", y = "Sepal.Width", color = "#00AFBB", margin.params = list(fill = "lightgray"), ...
# Marginal histogram plot ggMarginal(sp2 + theme_gray(), type = "histogram", fill = "steelblue", col = "darkblue") Insert an external graphical element inside a ggplot The function annotation_custom() [in ggplot2] can be used for adding tables, plots or other grid-based elements. The...
gghistogram(df,x="weight",add = "mean",rug = TRUE,color = "sex",fill = "sex",palette = c("#00AFBB", "#E7B800")) 1 2 3 4 5 6 # Create some data format set.seed(1234) wdata = data.frame(sex = factor(rep(c("...
p1:就是上一个笔记的代码,因为有字数限制,这边就不重复输出了。p2 <- p1 + geom_rug(show.legend = F) #添加添加边缘轴须图,不显示图例 p3 <- ggMarginal(p1,type = "histogram", groupColour = TRUE, groupFill = TRUE)p4 <- ggscatterstats(data = sample_data,x = carat,y = price,...