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。

我的包ggpmisc中的统计数据stat_poly_eq()使它可以根据线性模型拟合添加文本标签。

“ggpmisc”(>= 0.4.0)和“ggplot2”(>= 3.3.0)的答案已于2022-06-02更新。 在示例中，我使用stat_poly_line()而不是stat_smooth()，因为它的方法和公式的默认值与stat_poly_eq()相同。在所有代码示例中，我都省略了stat_poly_line()的附加参数，因为它们与添加标签的问题无关。

library(ggplot2)
library(ggpmisc)
#> Loading required package: ggpp
#> 
#> Attaching package: 'ggpp'
#> The following object is masked from 'package:ggplot2':
#> 
#>     annotate
# artificial data
df <- data.frame(x = c(1:100))
df$y <- 2 + 3 * df$x + rnorm(100, sd = 40)
df$yy <- 2 + 3 * df$x + 0.1 * df$x^2 + rnorm(100, sd = 40)

# using default formula, label and methods
ggplot(data = df, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq() +
  geom_point()

# assembling a single label with equation and R2
ggplot(data = df, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*"))) +
  geom_point()

# adding separate labels with equation and R2
ggplot(data = df, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq(aes(label = after_stat(eq.label))) +
  stat_poly_eq(label.y = 0.9) +
  geom_point()

# regression through the origin
ggplot(data = df, aes(x = x, y = y)) +
  stat_poly_line(formula = y ~ x + 0) +
  stat_poly_eq(formula = y ~ x + 0, aes(label = after_stat(eq.label))) +
  geom_point()

# fitting a polynomial
ggplot(data = df, aes(x = x, y = yy)) +
  stat_poly_line(formula = y ~ poly(x, 2, raw = TRUE)) +
  stat_poly_eq(formula = y ~ poly(x, 2, raw = TRUE),
               aes(label = after_stat(eq.label))) +
  geom_point()

# adding a hat as asked by @MYaseen208 and @elarry
ggplot(data = df, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq(eq.with.lhs = "italic(hat(y))~`=`~",
               aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*"))) +
  geom_point()

# variable substitution as asked by @shabbychef
# same labels in equation and axes
ggplot(data = df, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq(eq.with.lhs = "italic(h)~`=`~",
               eq.x.rhs = "~italic(z)",
               aes(label = after_stat(eq.label))) +
  labs(x = expression(italic(z)), y = expression(italic(h))) +
  geom_point()

# grouping as asked by @helen.h
dfg <- data.frame(x = c(1:100))
dfg$y <- 20 * c(0, 1) + 3 * df$x + rnorm(100, sd = 40)
dfg$group <- factor(rep(c("A", "B"), 50))

ggplot(data = dfg, aes(x = x, y = y, colour = group)) +
  stat_poly_line() +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*"))) +
  geom_point()

ggplot(data = dfg, aes(x = x, y = y, linetype = group, grp.label = group)) +
  stat_poly_line() +
  stat_poly_eq(aes(label = paste(after_stat(grp.label), "*\": \"*",
                                 after_stat(eq.label), "*\", \"*",
                                 after_stat(rr.label), sep = ""))) +
  geom_point()

# a single fit with grouped data as asked by @Herman
ggplot(data = dfg, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*"))) +
  geom_point(aes(colour = group))

# facets
ggplot(data = dfg, aes(x = x, y = y)) +
  stat_poly_line() +
  stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                 after_stat(rr.label), sep = "*\", \"*"))) +
  geom_point() +
  facet_wrap(~group)

由reprex包于2022-06-02创建(v2.0.1)

这里有一个解决方案

# GET EQUATION AND R-SQUARED AS STRING
# SOURCE: https://groups.google.com/forum/#!topic/ggplot2/1TgH-kG5XMA

lm_eqn <- function(df){
    m <- lm(y ~ x, df);
    eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2, 
         list(a = format(unname(coef(m)[1]), digits = 2),
              b = format(unname(coef(m)[2]), digits = 2),
             r2 = format(summary(m)$r.squared, digits = 3)))
    as.character(as.expression(eq));
}

p1 <- p + geom_text(x = 25, y = 300, label = lm_eqn(df), parse = TRUE)

编辑。我找到了这个代码的来源。下面是ggplot2谷歌组中原始帖子的链接

另一种选择是创建一个自定义函数，使用dplyr和broom库生成方程:

get_formula <- function(model) {
  
  broom::tidy(model)[, 1:2] %>%
    mutate(sign = ifelse(sign(estimate) == 1, ' + ', ' - ')) %>% #coeff signs
    mutate_if(is.numeric, ~ abs(round(., 2))) %>% #for improving formatting
    mutate(a = ifelse(term == '(Intercept)', paste0('y ~ ', estimate), paste0(sign, estimate, ' * ', term))) %>%
    summarise(formula = paste(a, collapse = '')) %>%
    as.character
  
}

lm(y ~ x, data = df) -> model
get_formula(model)
#"y ~ 6.22 + 3.16 * x"

scales::percent(summary(model)$r.squared, accuracy = 0.01) -> r_squared

现在我们需要将文本添加到情节中:

p + 
  geom_text(x = 20, y = 300,
            label = get_formula(model),
            color = 'red') +
  geom_text(x = 20, y = 285,
            label = r_squared,
            color = 'blue')

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)