Scatter plot with linear regression line of best fit 图1,显示不同类别 df <- ggplot2::mpg %>% setDT() df_select <- df[cyl %in% c(4,8),] %>% .[,cyl:=as.factor(cyl)] cyl_color <- c("#1f77b4", "#ff983e") # geom_smooth的填充范围,只有数据和全图可选,而且se只会按垂直方向...
2. 简单散点图 library(ggplot2)library(tidyr)df<-iris#修改表头colnames(df)<-c('SepalL','SepalW','PetalL','PetalW','Species')#宽数据变长数据df<-gather(df,Index,Value,-Species)#将Index和Value变量映射x轴和y轴,同时将Species分类变量映射颜色和形状ggplot(df,aes(Index,Value,color=Species,sh...
1、边界散点图(Scatterplot With Encircling) options(scipen = 999) library(ggplot2) library(ggalt) midwest_select <- midwest[midwest$poptotal > 350000 & midwest$poptotal <= 500000 & midwest$area > 0.01 & midwest$area < 0.1, ] # Plot ggplot(midwest, aes(x=area, y=poptotal)) + geom_p...
1.2 带边界的散点图(Scatterplot With Encircling) 1.3 抖动图(Jitter Plot) 1.4 计数图(Counts Chart) 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) ...
1.2 带边界的散点图(Scatterplot With Encircling) 在介绍结果时,有时我会在图表中加上某些特殊的点或区域组,以便引起人们对那些特殊情况的注意。使用ggalt包中的geom_encircle()可以方便地完成此操作。在geom_encircle()中,将数据设置为仅包含点(行)或兴趣点的新数据帧。此外,还可以展开曲线,以便仅在点之外通过...
library(hexbin) bin <- hexbin(mydatay, xbins=50)plot(bin, main=“Hexagonal Binning with 10000 Observations”) 1. 2. hexbin包中的hexbin()函数 IDPmisc包中的iplot()函数也可以通过颜色来展示点的密度。 library(IDPmisc) iplot(mydatay, main=“Image Scatter Plot with Color Indicating Density”)...
title="Scatterplot with overlapping points", caption="Source: midwest") 上图中其实有很多点是重合的 原始数据是整数 1 dim(mpg) 用jitter_geom()画抖动图 重合的点在原先的位置基于一定阈值范围(width)随机抖动 1 2 3 4 5 6 7 8 9 10 11 12 library(ggplot2) data(mpg, package="ggplot2")...
散点图是数据分析中非常常用的一种形式(The most frequently used plot for data analysis is undoubtedly the scatterplot);如果你想初步了解两个变量之间的关系,第一选择一定是散点图(Whenever you want to understand the nature of relationship between two variables, invariably the first choice is the scatter...
Scatter plot 散点图Line plot 线图Histogram plot 直方图Box plot 箱形图 Part 1 从一个默认的ggplot开始 | Preparation 1）需要安装的 R 包 tidyverse 包，同样出自 Hadley Wickham 大神之手，包含了 ggplot2，dplyr，tidyr，readr，purrr，tibble，stringr, forcats 等多个包：ggplot2, for data visualisation ...
#Createsomenoisy exponentially-distributeddataset.seed(201)n<-100dat<-data.frame(xval=(1:n+rnorm(n,sd=5))/20,yval=2*2^((1:n+rnorm(n,sd=5))/20))# A scatterplotwithregular (linear) axis scalingsp<-ggplot(dat,aes(xval,yval))+geom_point()sp#log2scalingofthe y axis (withvisual...