3  geom()

3.1 geom_point()

library(plyr)
library(tidyverse)

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✔ readr   2.1.3      ✔ forcats 0.5.2 
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ggplot(mpg, aes(cty, hwy))+geom_point() 

3.2 geom_smooth()

ggplot(mpg, aes(cty, hwy))+geom_smooth() 

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

3.3 geom_bar()

library(ggplot2)

ggplot()+geom_bar(data=mpg,mapping=aes(manufacturer))

ggplot()+geom_bar(data=mpg,mapping=aes(class))

3.4 geom_line()

df <- data.frame(x = 1:10, y = c(4, 1, 9, 5, 8, 7, 3, 8, 3, 5))
df

    x y
1   1 4
2   2 1
3   3 9
4   4 5
5   5 8
6   6 7
7   7 3
8   8 8
9   9 3
10 10 5

ggplot(df, aes(x, y))+
  geom_line(size = 10)

ggplot(df, aes(x, y))+
  geom_step(size = 10)

3.5 实例

group_cyl<-group_by(mpg,cyl)
data_sumr<- summarise(group_cyl,avg=mean(hwy),se=sd(hwy)/sqrt(n()))
data_sumr

# A tibble: 4 × 3
    cyl   avg    se
  <int> <dbl> <dbl>
1     4  28.8 0.502
2     5  28.8 0.25 
3     6  22.8 0.415
4     8  17.6 0.390

p<-ggplot(data_sumr,aes(cyl, avg)) + geom_line()+geom_point(size=4) 
p+geom_errorbar(aes(ymin=avg-se, ymax=avg+se), width=0.1)