下面是对每个人来说最简单的代码

注:显示皮尔森的Rho，而不是R^2。

library(ggplot2)
library(ggpubr)

df <- data.frame(x = c(1:100)
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)
p <- ggplot(data = df, aes(x = x, y = y)) +
        geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
        geom_point()+
        stat_cor(label.y = 35)+ #this means at 35th unit in the y axis, the r squared and p value will be shown
        stat_regline_equation(label.y = 30) #this means at 30th unit regresion line equation will be shown

p

使用ggpubr:

library(ggpubr)

# reproducible data
set.seed(1)
df <- data.frame(x = c(1:100))
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)

# By default showing Pearson R
ggscatter(df, x = "x", y = "y", add = "reg.line") +
  stat_cor(label.y = 300) +
  stat_regline_equation(label.y = 280)

# Use R2 instead of R
ggscatter(df, x = "x", y = "y", add = "reg.line") +
  stat_cor(label.y = 300, 
           aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~"))) +
  stat_regline_equation(label.y = 280)

## compare R2 with accepted answer
# m <- lm(y ~ x, df)
# round(summary(m)$r.squared, 2)
# [1] 0.85

下面是对每个人来说最简单的代码

注:显示皮尔森的Rho，而不是R^2。

library(ggplot2)
library(ggpubr)

df <- data.frame(x = c(1:100)
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)
p <- ggplot(data = df, aes(x = x, y = y)) +
        geom_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
        geom_point()+
        stat_cor(label.y = 35)+ #this means at 35th unit in the y axis, the r squared and p value will be shown
        stat_regline_equation(label.y = 30) #this means at 30th unit regresion line equation will be shown

p

类似于@zx8754和@kdauria的答案，只是使用了ggplot2和ggpubr。我更喜欢使用ggpubr，因为它不需要自定义函数，比如这个问题的顶部答案。

library(ggplot2)
library(ggpubr)

df <- data.frame(x = c(1:100))
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)

ggplot(data = df, aes(x = x, y = y)) +
  stat_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
  geom_point() +
  stat_cor(aes(label = paste(..rr.label..)), # adds R^2 value
           r.accuracy = 0.01,
           label.x = 0, label.y = 375, size = 4) +
  stat_regline_equation(aes(label = ..eq.label..), # adds equation to linear regression
                        label.x = 0, label.y = 400, size = 4)

也可以把p值加到上图中吗

ggplot(data = df, aes(x = x, y = y)) +
  stat_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
  geom_point() +
  stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), # adds R^2 and p-value
           r.accuracy = 0.01,
           p.accuracy = 0.001,
           label.x = 0, label.y = 375, size = 4) +
  stat_regline_equation(aes(label = ..eq.label..), # adds equation to linear regression
                        label.x = 0, label.y = 400, size = 4)

当您有多个组时，也可以很好地使用facet_wrap()

df$group <- rep(1:2,50)

ggplot(data = df, aes(x = x, y = y)) +
  stat_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
  geom_point() +
  stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")),
           r.accuracy = 0.01,
           p.accuracy = 0.001,
           label.x = 0, label.y = 375, size = 4) +
  stat_regline_equation(aes(label = ..eq.label..),
                        label.x = 0, label.y = 400, size = 4) +
  theme_bw() +
  facet_wrap(~group)

真的很喜欢@Ramnath的解决方案。为了允许使用自定义回归公式(而不是固定为y和x作为字面变量名)，并将p值添加到打印输出中(正如@Jerry T评论的那样)，下面是mod:

lm_eqn <- function(df, y, x){
    formula = as.formula(sprintf('%s ~ %s', y, x))
    m <- lm(formula, data=df);
    # formating the values into a summary string to print out
    # ~ give some space, but equal size and comma need to be quoted
    eq <- substitute(italic(target) == a + b %.% italic(input)*","~~italic(r)^2~"="~r2*","~~p~"="~italic(pvalue), 
         list(target = y,
              input = x,
              a = format(as.vector(coef(m)[1]), digits = 2), 
              b = format(as.vector(coef(m)[2]), digits = 2), 
             r2 = format(summary(m)$r.squared, digits = 3),
             # getting the pvalue is painful
             pvalue = format(summary(m)$coefficients[2,'Pr(>|t|)'], digits=1)
            )
          )
    as.character(as.expression(eq));                 
}

geom_point() +
  ggrepel::geom_text_repel(label=rownames(mtcars)) +
  geom_text(x=3,y=300,label=lm_eqn(mtcars, 'hp','wt'),color='red',parse=T) +
  geom_smooth(method='lm')

不幸的是，这对facet_wrap或facet_grid不起作用。

我修改了stat_smooth和相关函数的源代码中的几行代码，以创建一个添加了拟合方程和R平方值的新函数。这也适用于小面图!

library(devtools)
source_gist("524eade46135f6348140")
df = data.frame(x = c(1:100))
df$y = 2 + 5 * df$x + rnorm(100, sd = 40)
df$class = rep(1:2,50)
ggplot(data = df, aes(x = x, y = y, label=y)) +
  stat_smooth_func(geom="text",method="lm",hjust=0,parse=TRUE) +
  geom_smooth(method="lm",se=FALSE) +
  geom_point() + facet_wrap(~class)

我使用了@Ramnath回答中的代码来格式化方程。stat_smooth_func函数不是很健壮，但是使用它应该不难。

https://gist.github.com/kdauria/524eade46135f6348140。如果出现错误，请尝试更新ggplot2。