R geom_直方图数据的线性模型

我正在使用数据集，其中有连续变量x和分类变量y和z。大概是这样的：

set.seed(222)
df = data.frame(x = c(0, c(1:99) + rnorm(99, mean = 0, sd = 0.5), 100),
           y = rep(50, times = 101)-(seq(0, 50, by = 0.5))+rnorm(101, mean = 30, sd = 20),
           z = rnorm(101, mean = 50, sd= 10))

df$positive.y = sapply(df$y,
                         function(x){
                           if (x >= 50){"Yes"} else {"No"}
                         })

df$positive.z = sapply(df$z,
                       function(x){
                         if (x >= 50){"Yes"} else {"No"}
                       })

df$bin = sapply(df$x,
                function(x){
                  if (x <= 10){1}
                  else if (x > 10 & <= 20) {20}
                  else if .......
                })

然后使用这个数据集，我可以创建直方图来查看变量x和正.y（z）之间是否存在相关性。对于10个箱子，很明显x与正.y相关，但与正.z不相关：

ggplot(df,
       aes(x = x, fill = positive.y))+
  geom_histogram(position = "fill", bins = 10)

ggplot(df,
       aes(x = x, fill = positive.z))+
  geom_histogram(position = "fill", bins = 10)

现在我想要两件事：

提取实际数据点，将其提供给corr.test（）函数或类似的函数

将geom_smooth（method=“lm”）添加到我的绘图中

我尝试将“bin”列添加到df中，如下所示：

set.seed(222)
df = data.frame(x = c(0, c(1:99) + rnorm(99, mean = 0, sd = 0.5), 100),
           y = rep(50, times = 101)-(seq(0, 50, by = 0.5))+rnorm(101, mean = 30, sd = 20),
           z = rnorm(101, mean = 50, sd= 10))

df$positive.y = sapply(df$y,
                         function(x){
                           if (x >= 50){"Yes"} else {"No"}
                         })

df$positive.z = sapply(df$z,
                       function(x){
                         if (x >= 50){"Yes"} else {"No"}
                       })

df$bin = sapply(df$x,
                function(x){
                  if (x <= 10){1}
                  else if (x > 10 & <= 20) {20}
                  else if .......
                })

df$bin=sapply（df$x，
功能（x）{
如果（x 10&我看不到添加
lm
行的好理由。逻辑回归是合适的模型，不需要装箱：

df$positive.y <- factor(df$positive.y)
mod <- glm(positive.y ~ x, data = df, family = "binomial")
summary(mod)
anova(mod)

library(ggplot2)
ggplot(df,
       aes(x = x, fill = positive.y))+
  geom_histogram(position = "fill", bins = 10) +
  stat_function(fun = function(x) predict(mod, newdata = data.frame(x = x), 
                                    type = "response"), 
    size = 2)

使用
hist
函数？我不会添加
lm
行。逻辑回归似乎是一个更好、更合适的模型。你不需要任何分类。