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📜 R Scatterplots

📅 最后修改于: 2021-01-08 10:01:22 🧑 作者: Mango

R散点图

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	It is the dataset whose values are the horizontal coordinates.
2.	y	It is the dataset whose values are the vertical coordinates.
3.	main	It is the title of the graph.
4.	xlab	It is the label on the horizontal axis.
5.	ylab	It is the label on the vertical axis.
6.	xlim	It is the limits of the x values which is used for plotting.
7.	ylim	It is the limits of the values of y, which is used for plotting.
8.	axes	It indicates whether both axes should be drawn on the plot.

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

#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：

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()函数接受一系列输入项。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

#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：

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。以下是一些添加了不同参数的示例。

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：

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：

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：

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：

示例5：添加具有动态名称的标题

#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：

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：

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：

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：