Untitled

我们首先尝试使用Rmarkdown进行统计图形的绘制。

library(ggplot2)
data(mpg)
head(mpg)

## # A tibble: 6 × 11
##   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
##   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
## 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
## 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
## 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

看完了统计图，我们再来尝试进行回归分析。

fit<- lm(cty ~ hwy, data = mpg)
fit

## 
## Call:
## lm(formula = cty ~ hwy, data = mpg)
## 
## Coefficients:
## (Intercept)          hwy  
##      0.8442       0.6832

summary(fit)

## 
## Call:
## lm(formula = cty ~ hwy, data = mpg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9247 -0.7757 -0.0428  0.6965  4.6096 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.84420    0.33319   2.534   0.0119 *  
## hwy          0.68322    0.01378  49.585   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.252 on 232 degrees of freedom
## Multiple R-squared:  0.9138, Adjusted R-squared:  0.9134 
## F-statistic:  2459 on 1 and 232 DF,  p-value: < 2.2e-16

plot(fit)