install.packages("readxl")
library(readxl)
df_csv_exam<-read.csv("C:/Users/Admin/Desktop/study/csv_exam.csv")
df_csv_exam

df_midterm<-data.frame(english = c(90,80,50,70),
                       math = c(50,60,70,100),
                       class = c(1,1,2,2))
df_midterm

write.csv(df_midterm, file= "df_midterm.csv")
df_midterm

exam<-read.csv("C:/Users/Admin/Desktop/study/csv_exam.csv")
exam

head(exam)
head(exam, 10)

tail(exam)
tail(exam, 10)

View(exam)

dim(exam)

str(exam)

str(exam$id)
str(exam$science)

summary(exam)

## 1st Qu : 1사분위 수 - 하위 25 지점에 위치하는 값
## 3rd Qu : 3사분위 수 - 하위 75% 지점에 위치한 값

summary(exam$english)

## 1. 56점 2. 98점 3.

install.packages("ggplot2")

mpg<-as.data.frame(ggplot2::mpg)

head(mpg)

View(mpg)

str(mpg)

summary(mpg)

df_raw<-data.frame(var1=c(1,2,1),
                   var2=c(2,3,2))

df_raw

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

df_new<-df_raw
df_new

df_new<-rename(df_new,v2=vw)
df_new

df_raw

df_raw

mpg_new<-mpg

mpg_new<-rename(mpg_new, city=cty)
mpg_new<-rename(mpg_new, highway=hwy)

head(mpg_new)

mpg$total<-(mpg$cty+mpg$hwy)/2

head(mpg)

mean(mpg$total)

summary(mpg$total)

hist(mpg$total)

ifelse(mpg$total>=20, "pass","fail")

mpg$test<-ifelse(mpg$total>=20, "pass","fail")
mpg$test

table(mpg$test)
library(ggplot2)
qplot(mpg$test)

mpg$grade<-ifelse(mpg$total>=30,"A",
                  ifelse(mpg$total>=20,"B",
                         ifelse(mpg$total>=15,"C","D")))
mpg$grade

table(mpg$grade)
qplot(mpg$grade)









