Day5 - R(5). 22.08.29.월

[문제 복습 8/25일.금]

1. 히스토그램은 자료가 연속적이여야 한다.

2. shape=cty에서만 에러남. (숫자라서)연속형

• ggplot(mpg,aes(displ,cty,shape=cty))+geom_point()

3. 산점도 색상 그레이인가? -> (x)

• ggplot(data=mpg,aes(x=manufacturer, y=hwy, color="Gray"))+geom_point()
  : aes(안에 들어가야한다.)

4.+labs 'gplot'라고 제목 추가하고 x,y축 라벨명 변경하고 싶다.
:xlabs( ), ylabs( ) / labs(title = 'gplot', x = , y = )

5.y:count , x:차종,
: ggplot(mpg,aes(x=manufacturer))+geom_bar()

6. 
   

• ggplot(mpg, aes(manufacturer)) + geom_bar() + facet_wrap(vars(class)): class별로 따로따로 보겠다.

7. ggplot(data=mpg,aes(x=year))+geom_bar()

• unique(mpg$year) #[1] 1999 2008 *year:int형

• ggplot(mpg, aes(x = as.factor(year))) + geom_bar()

:mpg $ year<-as.factor(mpg $ year)

• ggplot(data=mpg,aes(x=year,fill=year))+geom_bar()

8. ggplot(mpg,aes(x=class))+geom_histogram() <- 오류남

• ggplot(mpg,aes(x=class))+geom_histogram(stat = "count")

9. ggplot(drugs,aes(x=drug,y=effect)) + geom_bar() <-오류남

• ggplot(drugs,aes(x=drug,y=effect)) + geom_bar(s`tat='identity')
• ggplot(drugs,aes(x=drug,y=effect)) + geom_col()

10. ggplot(mpg, aes(x= displ, y= hwy, color= drv))+ geom_point() + labs(title= "배기량에 따른 고속도로 연비 비교", x= '배기량', y= '연비')

• ggplot(mpg, aes(x= displ, y= hwy, color= drv))+ geom_point() + labs(title= "배기량에 따른 고속도로 연비 비교", x= '배기량', y= '연비')+facet_grid(drv~.)

• ggplot(mpg, aes(x= displ, y= hwy, color= drv))+ geom_point() + labs(title= "배기량에 따른 고속도로 연비 비교", x= '배기량', y= '연비')+facet_wrap(.~ drv)

---

<진도>

• install.packages("gapminder")

• library(gapminder)

• data(package="gapminder")

• names(gapminder)
  [1] "country" "continent"
  [3] "year" "lifeExp"
  [5] "pop" "gdpPercap"

• glimpse(gapminder)
  Rows: 1,704
  Columns: 6
  $ country "Afghanist…
  $ continent Asia, Asia…
  $ year 1952, 1957…
  $ lifeExp 28.801, 30…
  $ pop 8425333, 9…
  $ gdpPercap 779.4453, …

---

• g <- gapminder %>%
  filter(year == 2007) %>%
  group_by(continent) %>%
  summarise(lifeExp = median(lifeExp))

• g

• ggplot(g, aes(x=continent, y=lifeExp, fill=continent))+ geom_col()

#범례 안보이게

ggplot(g, aes(x=continent, y=lifeExp, fill=continent))+ geom_col() + theme(legend.position = "none")

• gapminder |> ggplot( aes(x=continent, y=lifeExp, fill=continent))+ geom_boxplot()

---

• install.packages("plotly")

• library(plotly)

• plotly

• mpg |> ggplot(aes(x=displ,y=cty,color=drv))+geom_point()
  

• ggplotly(mpg |> ggplot(aes(x=displ,y=cty,color=drv))+geom_point()) :정보를 보여줌

---

• Hue : 색을 다양하게

---

• mtcars $ cly= as.factor(mtcars$cly)
• mtcars $ cly

:> mtcars $ cyl
[1] 6 6 4 6 8 6 8 4 4 6 6 8 8
[14] 8 8 8 8 4 4 4 4 8 8 8 8 4
[27] 4 4 8 6 8 4
Levels: 4 6 8

• ggplot(mtcars ,aes(x = cyl,fill=cyl)) + geom_bar()+ scale_color_hue(h= c(0,360)+5,c=50,l=65)

---

#FFFFFF
포토샵.색지정. hex.

---

<기본값>

• ggplot(mtcars, aes(x=cyl,fill=cyl))+geom_bar()

  

<hcl 각도조정>

• ggplot(mtcars, aes(x=cyl,fill=cyl))+geom_bar()+scale_fill_hue(h=c(180,300))

  

• ggplot(mtcars, aes(x=cyl,fill=cyl))+geom_bar()+scale_fill_hue(c=50)
  

<palette set이용>

• ggplot(mtcars, aes(x=cyl,fill=cyl))+geom_bar()+scale_fill_brewer(palette = "Set1")

  

• ggplot(mtcars, aes(x=cyl,fill=cyl))+geom_bar()+scale_fill_brewer(palette = "Set2")
  

<직접지정>

ggplot(mtcars, aes(x=cyl,fill=cyl))+geom_bar()+scale_fill_manual(values = c("red","green","blue"))

---

<기본값>

• ggplot(mtcars,aes(wt,mpg))+geom_point()
• ggplot(mtcars,aes(wt,mpg))+
  geom_point(color='red')
• ggplot(mtcars,aes(wt,mpg))+
  geom_point(color='red')+
  geom_text_repel(aes(label=rowname))
  :텍스트를 label=rowname 값으로 만들겠다.

• ggplot(mtcars,aes(wt,mpg))+
  geom_point(color='red')+
  geom_text_repel(aes(label=rowname))+
  theme_economist()
  :배경 지정 값

• ggplot(mtcars,aes(wt,mpg))+
  geom_point(color='red')+
  geom_text_repel(aes(label=rowname))+
  theme_economist_white()
  :배경 흰색으로 지정 값

---

<한국복지패널 데이터 분석>

<성별에 따른 월급 차이>

(1). foregin 패키지 설치

• install.packages("fofreign")
• library(foreign)
• foreign
• library(dplyr)
• library(gglot2)
  

-[칼럼의 갯수, 변수의 갯수]

• colnames(welfare)
• ncol(welfare)
• dim(welfare)[2]
  *length(names(welfare))
  

-[데이터 파악]

str(): 데이터 속성 출력
summary(): 요약통계량
names(): 이름만 추출
glimpse():
plot():

#spss 데이터 읽어오고 살펴보기

• raw_welfare <- read.spss("Koweps_hpc10_2015_beta1.sav")
  

#복사본 만들기

• welfare <- as.data.frame(raw_welfare)
  

#변수 이름바꾸기

• welfare <- welfare %>%
  rename( gender= h10_g3, birth= h10_g4,
  marriage= h10_g10, religion= h10_g11,
  income= p1002_8aq1, job= h10_eco9,
  region= h10_reg7) %>%
  select(gender, birth, marriage,
  religion, income, job, region)

• welfare$income <- ifelse(
  is.na (welfare $ income), 0, welfare $ income)
  

• table(welfare $ gender)

• plot(welfare)
  #유용한 데이터.

  
  

---

#[데이터 살펴보기]

• pairs(job~ income+gender+region, data=welfare)

• boxplot(welfare)
  

• boxplot(welfare $ income)
  

• plot(welfare$income)
  
  

• boxplot(welfare $ income, welfare $ job)
  
  

• sum(is.na(welfare))
  
  

• is.na(welfare)
  

• summary(welfare $ income)
  
  

• mean(welfare $ income)
  :[1] 67.1905

• mean(welfare $ income, na.rm=T)
  :[1] 67.1905

• range(welfare $ income)
  :[1] 0 2400

• min(welfare $ income,na.rm=T)
  :[1] 0

• max(welfare $ income,na.rm=T)
  :[1] 2400
  

#[성별로 이름 붙이기]

• welfare$gender <- ifelse(welfare$gender==1,"male","female")
• welfare$gender

---

• ggplot(data=welfare,aes(x=income))+geom_density()

#남자따로 여자따로 구하기

• ggplot(data=welfare,aes(x=income))+geom_density()
  

• welfare |> select(income,gender) |> head(10)
  
  

• ggplot(data=welfare, aes(x=income,color= factor(gender)))+geom_density()
  
  

#빈도수 출력

• ggplot(data=welfare, aes(x=income,color= factor(gender)))+geom_freqpoly()
  

---

<데이터프레임으로 만들어 달라.>

• table(welfare $ gender)
  

• mode(table(welfare $ gender))
  

• typeof(table(welfare $ gender))
  

• as.data.frame(table(welfare $ gender)) <- 데이터베이스 만드는법
  
  

• gender <- as.data.frame(table(welfare $ gender))

• gender
  

<막대그래프 그려주세요>

• gender |> ggplot(aes(x=Var1,y=Freq ,fill=Var1))+geom_col()
  

• gender %>% ggplot(data= gander, aes(x=Var1,y=Freq ,fill=Var1))+geom_bar() <-오류임
  -> x,y를 둘다 지정하면 col로 해야함.
  -> 파이프를 넣을려면 data를 빼주어야 한다.
  

<칼럼이름바꾸기(성별,수입)>

• gender %>% ggplot(aes(x = Var1, y = Freq, fill = Var1)) + geom_col() +
  labs(x = "성별", y = "수입")
  

• names (gender) <- c("성별","수입")

*gender

• gender %>% ggplot(aes(x = 성별, y = 수입, fill = 성별)) + geom_col()

  

<색추가해라>

• ggplot(data=welfare, aes(x=gender))+geom_bar()

• ggplot(data=welfare, aes(x=gender,fill=as.factor(gender)))+geom_bar()

• ggplot(data=welfare, aes(x=gender,fill=gender))+geom_bar() <- 오류임.

-> gender가 숫자형이라 백터로 형 변환함.
-> as.는 변환하겠다는 의미!

• welfare %>% group_by(gender) %>% summarise(평균 = mean(income, na.rm = T))

  : -> na 값들은 뺴고 칼럼의 값을 평균내주어라.

• table(welfare $ income)

• welfare %>% group_by(gender) %>%
  summarise(수입평균 = mean(income,na.rm=T)) %>%
  ggplot(aes(x=gender, y=수입평균, fill=gender)) + geom_col()

---

<na를 0으로 대체합시다>

• welfare $ income<- ifelse(welfare $ income==NA,0,welfare $ income) <- 오류남
  -> na는 결측치 특정상 비어있어서 물음 자체가 안됨!!
  -> 무조건 할당되어서 == na로 하면 안됨!! na 값 찾을때 is.na() 써야함.

• welfare $ income <- ifelse(is.na (welfare $ income), 0, welfare $ income)
  :함수로 빈값을 찾아라 라고 알려줌.

• is.na()
  :사용할 수 없음 / 결측값
  

---

<0을 na로 대체합시다>

• welfare $ income <- ifelse(welfare $ income == 0), NA, welfare $ income)

• welfare $ income