install.packages("dplyr")
library(dplyr)

exam <-read.csv("csv_exam.csv")
exam

exam %>% filter(class==1)

exam %>% filter(class==2)

exam %>% filter(class==3)

exam %>% filter(class!=2)

exam %>% filter(math<50)

exam %>% filter(class==1 & math >=50)

exam %>% filter(class==2 & math>=60)

exam %>% filter(math>=90 | english >= 90)

exam %>% filter(english<90 | science < 50)

exam %>% filter(class==1 | class==2 | class==3)

class1 <- exam %>% filter(class==1)

class2 <- exam %>% filter(class==2)

mean(class1$math)

mean(class2$math)

sum(class1$math)

class5 <-exam %>% filter(class==5)
mean(class5$math)

## 3반의 영어 평균
class3 <- exam %>% filter(class==3)
mean(class3$english)

install.packages("ggplot2")
library(ggplot2)
mpg
mpg1 <- mpg %>% filter(displ<=4)
mpg2 <- mpg %>% filter(displ>=5)
mean(mpg1$hwy)
mean(mpg2$hwy)

audi<-mpg %>% filter(manufacturer=="audi")
audi

toyota <-mpg %>% filter(manufacturer=="toyota")
toyota

mean(audi$cty)
mean(toyota$cty)

cfh <- mpg %>% filter(manufacturer=="chevrolet" | manufacturer=="ford" | manufacturer=="honda")
cfh

mean(cfh$hwy)

exam %>% select(math)
exam %>% select(math,english)
exam %>% select(-math)

exam %>% filter(class==2) %>% select(math)
exam %>%  filter(class==2) %>% select(english)
exam %>% select(id,math) %>% head(10)
