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R散点图示例图解

本文概述

散点图用于比较变量。当我们需要定义一个变量受另一变量影响的程度时, 需要对变量进行比较。在散点图中, 数据表示为点的集合。散点图上的每个点都定义了两个变量的值。垂直轴选择一个变量, 水平轴选择另一个变量。在R中, 有两种创建散点图的方法, 即使用plot()函数和使用ggplot2包的函数。

在R中创建散点图有以下语法:

plot(x, y, main, xlab, ylab, xlim, ylim, axes)

这里,

S.No Parameters Description
1. x 它是数据集, 其值是水平坐标。
2. y 它是数据集, 其值是垂直坐标。
3. main 它是图形的标题。
4. xlab 它是横轴上的标签。
5. ylab 它是纵轴上的标签。
6. xlim x值的极限用于绘图。
7. ylim 是用于绘制的y值的极限。
8. axes 它指示是否应在绘图上绘制两个轴。

让我们看一个示例, 以了解如何使用plot函数构造散点图。在我们的示例中, 我们将使用数据集” mtcars”, 它是R环境中可用的预定义数据集。

例子

#Fetching two columns from mtcars
data <-mtcars[, c('wt', 'mpg')]
# Giving a name to the chart file.
png(file = "scatterplot.png")
# Plotting the chart for cars with weight between 2.5 to 5 and mileage between 15 and 30.
plot(x = data$wt, y = data$mpg, xlab = "Weight", ylab = "Milage", xlim = c(2.5, 5), ylim = c(15, 30), main = "Weight v/sMilage")
# Saving the file.
dev.off()

输出

R散点图示例图解

使用ggplot2的散点图

在R中, 还有另一种创建散点图的方法, 即借助ggplot2包。

ggplot2包提供了用于创建散点图的ggplot()和geom_point()函数。 ggplot()函数采用一系列输入项。第一个参数是输入向量, 第二个参数是aes()函数, 我们在其中添加x轴和y轴。

让我们在一个使用熟悉的数据集” mtcars”的示例的帮助下开始了解如何使用ggplot2软件包。

例子

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot_ggplot.png")
# Plotting the chart using ggplot() and geom_point() functions.
ggplot(mtcars, aes(x = drat, y = mpg)) +geom_point()
# Saving the file.
dev.off()

输出

R散点图示例图解

我们可以添加更多功能, 也可以绘制出更具吸引力的散点图。下面是一些示例, 其中添加了不同的参数。

示例1:具有组的散点图

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot1.png")
# Plotting the chart using ggplot() and geom_point() functions.
#The aes() function inside the geom_point() function controls the color of the group.
ggplot(mtcars, aes(x = drat, y = mpg)) +
geom_point(aes(color=factor(gear)))
# Saving the file.
dev.off()

输出

R散点图示例图解

示例2:轴的变化

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot2.png")
# Plotting the chart using ggplot() and geom_point() functions.
#The aes() function inside the geom_point() function controls the color of the group.
ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color=factor(gear)))
# Saving the file.
dev.off()

输出

R散点图示例图解

示例3:具有拟合值的散点图

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot3.png")
#Creating scatterplot with fitted values.
# An additional function stst_smooth is used for linear regression.
ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color = factor(gear))) + stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
#in above example lm is used for linear regression and se stands for standard error.
# Saving the file.
dev.off()

输出

R散点图示例图解

向图表添加信息

示例4:添加标题

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot4.png")
#Creating scatterplot with fitted values.
# An additional function stst_smooth is used for linear regression.
new_graph<-ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
#in above example lm is used for linear regression and se stands for standard error.
new_graph+
labs(
        title = "Scatterplot with more information"
)
# Saving the file.
dev.off()

输出

R散点图示例图解

示例5:添加带有动态名称的标题

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot5.png")
#Creating scatterplot with fitted values.
# An additional function stst_smooth is used for linear regression.
new_graph<-ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
#in above example lm is used for linear regression and se stands for standard error.
#Finding mean of mpg
mean_mpg<- mean(mtcars$mpg)
#Adding title with dynamic name
new_graph + labs(
        title = paste("Adding additiona information. Average mpg is", mean_mpg)
)
# Saving the file.
dev.off()

输出

R散点图示例图解

示例6:添加字幕

#Loading ggplot2 package
library(ggplot2)
# Giving a name to the chart file.
png(file = "scatterplot6.png")
#Creating scatterplot with fitted values.
# An additional function stst_smooth is used for linear regression.
new_graph<-ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
#in above example lm is used for linear regression and se stands for standard error.
#Adding title with dynamic name
new_graph + labs(
        title =
                "Relation between Mile per hours and drat", subtitle =
                "Relationship break down by gear class", caption = "Authors own computation"
)
# Saving the file.
dev.off()

输出

R散点图示例图解

示例7:更改x轴和y轴的名称

#Loading ggplot2 package
library(ggplot2
# Giving a name to the chart file.
png(file = "scatterplot7.png")
#Creating scatterplot with fitted values.
# An additional function stst_smooth is used for linear regression.
new_graph<-ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
#in above example lm is used for linear regression and se stands for standard error.
#Adding title with dynamic name
new_graph + labs(
        x = "Drat definition", y = "Mile per hours", color = "Gear", title = "Relation between Mile per hours and drat", subtitle = "Relationship break down by gear class", caption = "Authors own computation"
)
# Saving the file.
dev.off()

输出

R散点图示例图解

示例8:添加主题

#Loading ggplot2 package
library(ggplot2
# Giving a name to the chart file.
png(file = "scatterplot8.png")
#Creating scatterplot with fitted values.
# An additional function stst_smooth is used for linear regression.
new_graph<-ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
#in above example lm is used for linear regression and se stands for standard error.
#Adding title with dynamic name
new_graph+
theme_dark() +
                labs(
                        x = "Drat definition, in log", y = "Mile per hours, in log", color = "Gear", title = "Relation between Mile per hours and drat", subtitle = "Relationship break down by gear class", caption = "Authors own computation"
                )
# Saving the file.
dev.off()

输出

R散点图示例图解

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