b+geom_point(size=2,color='blue')+ coord_fixed()+theme_classic()+ geom_smooth(method = "lm",formula = y ~ x,size=2,color="black",fill="green")+ theme (aspect.ratio=1,axis.text.x = element_text (size = 15),axis.title.x = element_text (size = 15),axis.title.y = element_text(size = 15),axis.text.y = element_text (size = 15))+ stat_cor(data=data, method = "pearson")
时间: 2024-04-03 16:31:19 浏览: 19
这段代码是用来画散点图与线性回归线的。其中b+geom_point(size=2,color='blue')表示绘制散点图,点的大小为2个单位,颜色为蓝色;coord_fixed()表示坐标轴的比例尺不变;theme_classic()表示使用经典的主题风格;geom_smooth(method = "lm",formula = y ~ x,size=2,color="black",fill="green")表示添加一条线性回归线,用黑色表示线的颜色,绿色表示线的填充颜色;theme (aspect.ratio=1,axis.text.x = element_text (size = 15),axis.title.x = element_text (size = 15),axis.title.y = element_text(size = 15),axis.text.y = element_text (size = 15))表示设置图形的各种属性,如长宽比、坐标轴标题和标签的字体大小等;stat_cor(data=data, method = "pearson")表示计算散点图中的皮尔逊相关系数并显示在图形上。
相关问题
b<-ggplot(data,aes(x=index,y=Cerebellum)) b+geom_point(size=2,color='blue')+ coord_fixed()+theme_classic()+ geom_smooth(method = "lm",formula = y ~ x,size=2,color="black",fill="green")+ theme (aspect.ratio=1,axis.text.x = element_text (size = 15),axis.title.x = element_text (size = 15),axis.title.y = element_text(size = 15),axis.text.y = element_text (size = 15))+ stat_cor(data=data, method = "pearson")将代码中输出的相关系数和显著性水平设为显示三位小数
可以将`format`函数的`digits`参数设置为3,以保留三位小数。修改后的代码如下所示:
```
b <- ggplot(data, aes(x = index, y = Cerebellum))
b + geom_point(size = 2, color = 'blue') + coord_fixed() + theme_classic() +
geom_smooth(method = "lm", formula = y ~ x, size = 2, color = "black", fill = "green") +
theme(aspect.ratio = 1, axis.text.x = element_text(size = 15),
axis.title.x = element_text(size = 15), axis.title.y = element_text(size = 15),
axis.text.y = element_text(size = 15)) +
stat_cor(data = data, method = "pearson", label.x.npc = 0.8, label.y.npc = 0.9,
cor.label = paste("r = ", format(cor(data$x, data$y), digits = 3), ", p =",
format(cor.test(data$x, data$y)$p.value, digits = 3)))
```
在`stat_cor`函数中,`cor.label`参数的值使用`paste`函数将相关系数和显著性水平拼接成一个字符串,并使用`format`函数将相关系数和显著性水平的小数位数都设为3。这样就可以在散点图中显示保留三位小数的相关系数和显著性水平了。
帮我修改以下代码,使他们可以正确运行:library(ggplot2) library(gridExtra) ggplot(df, aes(x = x, y = y, color = z)) + geom_point() p1 <- ggplot(subset(df, z == 'a'), aes(x = x, y = y)) + geom_point(color = 'black') p2 <- ggplot(subset(df, z == 'b'), aes(x = x, y = y)) + geom_point(color = 'black') p3 <- ggplot(subset(df, z == 'c'), aes(x = x, y = y)) + geom_point(color = 'black') grid.arrange(p1, p2, p3, ncol = 3) centroids <- aggregate(df[, c('x', 'y')], by = list(df$z), FUN = mean) names(centroids)[1] <- 'z' p1 <- ggplot(df, aes(x = x, y = y)) + geom_point(aes(color = z)) + geom_point(data = subset(centroids, z == 'a'), aes(x = x, y = y, color = z), size = 4) p2 <- ggplot(df, aes(x = x, y = y)) + geom_point(aes(color = z)) + geom_point(data = subset(centroids, z == 'b'), aes(x = x, y = y, color = z), size = 4) p3 <- ggplot(df, aes(x = x, y = y)) + geom_point(aes(color = z)) + geom_point(data = subset(centroids, z == 'c'), aes(x = x, y = y, color = z), size = 4) grid.arrange(p1, p2, p3, ncol = 3) p1 <- ggplot(df, aes(x = x, y = y)) + geom_point(data = subset(df, z == 'a'), aes(color = z)) + geom_point(data = subset(df, z != 'a'), color = 'grey70') p2 <- ggplot(df, aes(x = x, y = y)) + geom_point(data = subset(df, z == 'b'), aes(color = z)) + geom_point(data = subset(df, z != 'b'), color = 'grey70') p3 <- ggplot(df, aes(x = x, y = y)) + geom_point(data = subset(df, z == 'c'), aes(color = z)) + geom_point(data = subset(df, z != 'c'), color = 'grey70') grid.arrange(p1, p2, p3, ncol = 3)
在运行这段代码之前,需要先定义和载入数据框 df。如果数据框 df 中没有 x、y 和 z 这三列数据,那么这段代码将无法正确运行。因此,需要先确保 df 中包含这三列数据。
以下是修改后的代码:
```
library(ggplot2)
library(gridExtra)
# 绘制散点图
ggplot(df, aes(x = x, y = y, color = z)) + geom_point()
# 按照 z 的不同取值绘制三幅散点图,并排显示
p1 <- ggplot(subset(df, z == 'a'), aes(x = x, y = y)) + geom_point(color = 'black')
p2 <- ggplot(subset(df, z == 'b'), aes(x = x, y = y)) + geom_point(color = 'black')
p3 <- ggplot(subset(df, z == 'c'), aes(x = x, y = y)) + geom_point(color = 'black')
grid.arrange(p1, p2, p3, ncol = 3)
# 计算每个 z 取值对应的 x 和 y 的均值,作为聚类中心
centroids <- aggregate(df[, c('x', 'y')], by = list(df$z), FUN = mean)
names(centroids)[1] <- 'z'
# 绘制带有聚类中心的散点图
p1 <- ggplot(df, aes(x = x, y = y)) +
geom_point(aes(color = z)) +
geom_point(data = subset(centroids, z == 'a'), aes(x = x, y = y, color = z), size = 4)
p2 <- ggplot(df, aes(x = x, y = y)) +
geom_point(aes(color = z)) +
geom_point(data = subset(centroids, z == 'b'), aes(x = x, y = y, color = z), size = 4)
p3 <- ggplot(df, aes(x = x, y = y)) +
geom_point(aes(color = z)) +
geom_point(data = subset(centroids, z == 'c'), aes(x = x, y = y, color = z), size = 4)
grid.arrange(p1, p2, p3, ncol = 3)
# 绘制带有灰色散点的散点图,用于显示不同 z 取值之间的边界
p1 <- ggplot(df, aes(x = x, y = y)) +
geom_point(data = subset(df, z == 'a'), aes(color = z)) +
geom_point(data = subset(df, z != 'a'), color = 'grey70')
p2 <- ggplot(df, aes(x = x, y = y)) +
geom_point(data = subset(df, z == 'b'), aes(color = z)) +
geom_point(data = subset(df, z != 'b'), color = 'grey70')
p3 <- ggplot(df, aes(x = x, y = y)) +
geom_point(data = subset(df, z == 'c'), aes(color = z)) +
geom_point(data = subset(df, z != 'c'), color = 'grey70')
grid.arrange(p1, p2, p3, ncol = 3)
```