R语言使用ggplot2绘制好看的分组散点图

这篇文章主要介绍了R语言使用ggplot2绘制好看的分组散点图操作，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

我们以iris数据集为例，该数据集包括花萼的长度和宽度，花瓣的长度和宽度，以及物种，如下图：

本文我们要绘制不同物种下花萼的长度和宽度的分布情况，以及二者之间的相关性关系。

1. 首先载入ggplot2包，

 library(ggplot2)

2. 然后进行ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())绘制，在绘制中第一个参数是数据，第二个参数是数据映射，是绘制的全局变量，其中包含的参数有x,y,color,size,alpha,shape等。

例如：ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species))，然后通过快捷散点绘制

+geom_point(size = 2.0, shape = 16)，颜色代表不同的物种，如下图：

3. 上面显示的是最原始的散点绘制，通过颜色区分不同的物种，那么如何进行效果的提升呢？

首先是可以进行分面，使得不同物种的对比效果更为显著，这里使用+facet_wrap( ~ Species)，效果如下：

4. 通过分面后对比效果好了不少，如果想看下不同物种下花萼长度与宽度的关系呢？可以使用+geom_smooth(method = "loess")，效果图如下：

5. 通过上面的操作效果好了很多，但是还是感觉不够高大上，那我们可以使用library(ggthemes)这个包进行精修一下，通过修改theme，使用+theme_solarized()，效果如下：

还有更多的theme选择，例如+theme_wsj()，效果如下：

这样我们的图是不是高大上了很多呢，所以其实数据可视化也没有多难。最后给下源码：

 library(ggthemes) library(ggplot2) ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) + geom_point(size = 2.0, shape = 16) + facet_wrap( ~ Species) + geom_smooth(method = "loess")+ theme_wsj()

补充：R语言画图神器ggplot2包

ggplot2

R语言里画图最好用的包啦。感觉图都挺清晰的，就懒得加文字了（或者以后回来补吧>.）前面几个图挺基础的，后面也许会有没见过的ggplot用法哦。

Install Package

 install.packages("ggplot2") library(ggplot2)

Scatter Plot

为了方便展示，用gapminder的数据

 if(!require(gapminder)) install.packages("gapminder"） library(gapminder) gapminder

数据大概是这样的

假设我们现在想要知道2007年lifeExp和人均GDP之间的关系。

先筛选数据

 library(dplyr) gapminder_2007 <- gapminder %>% filter(year == 2007)

画lifeExp和gdpPercap关系的散点图，x为gdpPercap，y为lifeExp。

 ggplot(gapminder_2007,aes(x = gdpPercap, y = lifeExp))+geom_point()

看的出来lifeExp与gdpPercap存在近似lifeExp=log(gdpPercap)的关系，对x轴的数值进行log值处理。另外，为了呈现更多信息，用颜色标记国家所在的洲，并用点的大小表示人口数量。

 ggplot(gapminder_2007,aes(x = gdpPercap, y = lifeExp, color = continent, size = pop))+ geom_point()+scale_x_log10()+theme_minimal()+ labs(x = "GDP per capita", y = "Life expectancy", title = "Life expectancy increases as GDP per capita increases", caption = "Data source: gapminder")

另外一种呈现方式如下：

加入了回归线和坐标轴的histogram。

 plot <- ggplot(gapminder_2007, aes(x = gdpPercap, y = lifeExp)) + geom_point()+geom_smooth(method="lm")+scale_x_log10()+ labs(x = "GDP per capita", y = "Life expectancy", title = "Life expectancy increases as GDP per capita increases", caption = "Data source: gapminder") ggMarginal(plot, type = "histogram", fill="transparent") #ggMarginal(plot, type = "boxplot", fill="transparent")

Histogram

 gapminder_gdp2007 <- gapminder %>% filter(year == 2007, continent == "Americas") %>% mutate(country = fct_reorder(country,gdpPercap,last)) ggplot(gapminder_gdp2007, aes(x=country, y = gdpPercap))+ geom_col(fill="skyblue", color="black")+ labs(x = "Country", y = "GDP per capita", title = "GDP per capita in North America and South America, 2007", caption = "Data source: gapminder")+ coord_flip()+theme_minimal()

Line Plot

 gapminder_pop <- gapminder %>% filter(country %in% c("United States","China")) ggplot(gapminder_pop,aes(x = year, y = pop, color = country))+ geom_line(lwd = 0.8)+theme_light()+ labs(x = "Year", y = "Population", title = "Population in China and United States, 1953-2007", caption = "Data source: gapminder")

Facet Plot

 gapminder_gdp <- gapminder %>% group_by(year, continent) %>% summarize(avg_gdp = mean(gdpPercap)) ggplot(gapminder_gdp,aes(x = year, y = avg_gdp, color = continent))+ geom_line(lwd = 0.8)+theme_light()+facet_wrap(~continent)+ labs(x = "Year", y = "Average GDP per capita", title = "Average GDP per capita change in different continent", caption = "Data source: gapminder")+ scale_x_continuous(breaks=c(1955,1970,1985,2000))

Path Plot

 gapminder_lifeexp <- gapminder %>% filter(year %in% c(1957,2007), continent == "Europe") %>% arrange(year) %>% mutate(country = fct_reorder(country,lifeExp,last)) ggplot(gapminder_lifeexp) +geom_path(aes(x = lifeExp, y = country), arrow = arrow(length = unit(1.5, "mm"), type = "closed")) + geom_text( aes(x = lifeExp, y = country, label = round(lifeExp, 1), hjust = ifelse(year == 2007,-0.2,1.2)), size =3, family = "Bookman", color = "gray25")+ scale_x_continuous(limits=c(45, 85))+ labs( x = "Life expectancy", y = "Country", title = "People live longer in 2007 compared to 1957", subtitle = "Life expectancy in European countries", caption = "Data source: gapminder" )

Density Plot

 gapminder_1992 <- gapminder %>% filter(year == 1992) ggplot(gapminder_1992, aes(lifeExp))+theme_classic()+ geom_density(aes(fill=factor(continent)), alpha=0.8) + labs( x="Life expectancy", title="Life expectancy group by continent, 1992", caption="Data source: gapminder", fill="Continent")

Slope Chart

 gapminder_lifeexp2 <- gapminder %>% filter(year %in% c(1977,1987,1997,2007), country %in% c("Canada", "United States","Mexico","Haiti","El Salvador", "Guatemala","Jamaica")) %>% mutate(lifeExp = round(lifeExp)) ylabs <- subset(gapminder_lifeexp2, year==head(year,1))$country yvals <- subset(gapminder_lifeexp2, year==head(year,1))$lifeExp ggplot(gapminder_lifeexp2, aes(x=as.factor(year),y=lifeExp)) + geom_line(aes(group=country),colour="grey80") + geom_point(colour="white",size=8) + geom_text(aes(label=lifeExp), size=3, color = "black") + scale_y_continuous(name="", breaks=yvals, labels=ylabs)+ theme_classic()+ labs(title="Life Expectancy of some North America countries change from 1977 to 2007") + theme(axis.title=element_blank(), axis.ticks = element_blank(), plot.title = element_text(hjust=0.5))