R - 데이터 분석 프로젝트

# 9장 '한국복지패널데이터' 분석 준비하기 209p

install.packages("foreign")

library(foreign)  # spss 파일 불러오기
library(dplyr)  # 전처리
library(ggplot2)  # 시각화
library(readxl)  # 엑셀파일불러오기

#데이터 불러오기
raw_welfare <- read.spss(file="D:/rclass/Koweps_hpc10_2015_beta1.sav",
                         to.data.frame = T)

#복사본 만들기
welfare <- raw_welfare

#데이터 검토하기
head(welfare)
tail(welfare)
View(welfare)
dim(welfare)
str(welfare)
summary(welfare)

# 변수명 바꾸기  
welfare <- rename(welfare,
                  sex = h10_g3, # 성별
                  birth = h10_g4, # 태어난 연도
                  marriage = h10_g10, # 혼인상태
                  religion = h10_g11, # 종교
                  income = p1002_8aq1, # 월급
                  code_job = h10_eco9, # 직업코드
                  code_region = h10_reg7) # 지역코드
head(welfare)
                  
# 성별에 따른 월급 차이 _ " 성별에 따라 월급이 다를까? "
class(welfare$sex) # 변수의 타입 확인

table(welfare$sex)

# 이상치 확인
table(welfare$sex)

#이상치 결측 처리
welfare$sex <- ifelse(welfare$sex == 9, NA, welfare$sex)

# 결측치 확인
table(is.na(welfare$sex))

#성별 항목 이름 부여
welfare$sex <- ifelse(welfare$sex == 1 , "male", "female")
table(welfare$sex)

qplot(welfare$sex)  # 빈도 확인 

# 월급 변수 검토
class(welfare$income)

summary(welfare$income)

qplot(welfare$income)

qplot(welfare$income) + xlim(0, 1000)

# 이상치 확인
summary(welfare$income)
# 이상치가 9999라고 되어있고, 1~9998 사이의 값을 갖는다고 되어있는데,
# 최솟값이 0인걸보면 0도 결측치로 처리해야한다는 것을 알 수 있다.

# 이상치 결측 처리
welfare$income <- ifelse(welfare$income %in% c(0,9999), NA, welfare$income)

# 결측치 확인
table(is.na(welfare$income))

# 성별 월급 평균표 만들기
sex_income <- welfare %>% 
  filter(!is.na(income)) %>% 
  group_by(sex) %>% 
  summarise(mean_income = mean(income))

# 그래프 만들기
ggplot(data = sex_income, aes(x=sex, y=mean_income)) + geom_col()

#### 09-3 ####

## ---------------------------------------------------------- ##
#변수 검토하기
class(welfare$birth)
summary(welfare$birth)
qplot(welfare$birth)


# 전처리
summary(welfare$birth)  # 이상치 확인

table(is.na(welfare$birth))  # 결측치 확인

welfare$birth <- ifelse(welfare$birth == 9999, NA, welfare$birth)
table(is.na(welfare$birth)) # 이상치 결측 처리


## ----------------------------------------------------------- ##
welfare$age <- 2015 - welfare$birth + 1  # 파생변수 만들기 - 나이
summary(welfare$age) 
qplot(welfare$age)


## ----------------------------------------------------------- ##

# 나이에 따른 월급 평균표 만들기

age_income <- welfare %>%
  filter(!is.na(income)) %>%
  group_by(age) %>%
  summarise(mean_income = mean(income)) # 나이별 평균 표 생성

head(age_income)

# x축은 나이, y축은 평균표로 설정 한 선그래프 생성
ggplot(data = age_income, aes(x = age, y = mean_income)) + geom_line()

#### 09-4 ####

## -------------------------------------------------------------------- ##
welfare <- welfare %>%
  mutate(ageg = ifelse(age < 30, "young",
                       ifelse(age <= 59, "middle", "old")))

table(welfare$ageg)
qplot(welfare$ageg)


## -------------------------------------------------------------------- ##
ageg_income <- welfare %>%
  filter(!is.na(income)) %>%
  group_by(ageg) %>%
  summarise(mean_income = mean(income))

ageg_income

ggplot(data = ageg_income, aes(x = ageg, y = mean_income)) + geom_col()


## -------------------------------------------------------------------- ##
ggplot(data = ageg_income, aes(x = ageg, y = mean_income)) +
  geom_col() +
  scale_x_discrete(limits = c("young", "middle", "old"))

#### 09-5 ####

## -------------------------------------------------------------------- ##
sex_income <- welfare %>%
  filter(!is.na(income)) %>%
  group_by(ageg, sex) %>%
  summarise(mean_income = mean(income))

sex_income

ggplot(data = sex_income, aes(x = ageg, y = mean_income, fill = sex)) +
  geom_col() +
  scale_x_discrete(limits = c("young", "middle", "old"))

ggplot(data = sex_income, aes(x = ageg, y = mean_income, fill = sex)) +
  geom_col(position = "dodge") +
  scale_x_discrete(limits = c("young", "middle", "old"))


## -------------------------------------------------------------------- ##
# 성별 연령별 월급 평균표 만들기
sex_age <- welfare %>%
  filter(!is.na(income)) %>%
  group_by(age, sex) %>%
  summarise(mean_income = mean(income))

head(sex_age)

# 그래프 만들기
ggplot(data = sex_age, aes(x = age, y = mean_income, col = sex)) + geom_line()

#### 09-6 ####

## -------------------------------------------------------------------- ##
class(welfare$code_job)
table(welfare$code_job)

library(readxl)
list_job <- read_excel("Koweps_Codebook.xlsx", col_names = T, sheet = 2)
head(list_job)
dim(list_job)

welfare <- left_join(welfare, list_job, id = "code_job")

welfare %>%
  filter(!is.na(code_job)) %>%
  select(code_job, job) %>%
  head(10)


## --------------------------------------------------------- ##
job_income <- welfare %>%
  filter(!is.na(job) & !is.na(income)) %>%
  group_by(job) %>%
  summarise(mean_income = mean(income))

head(job_income)

top10 <- job_income %>%
  arrange(desc(mean_income)) %>%
  head(10)

top10

ggplot(data = top10, aes(x = reorder(job, mean_income), y = mean_income)) +
  geom_col() +
  coord_flip()


# 하위 10위 추출
bottom10 <- job_income %>%
  arrange(mean_income) %>%
  head(10)

bottom10

# 그래프 만들기
ggplot(data = bottom10, aes(x = reorder(job, -mean_income), 
                            y = mean_income)) +
  geom_col() + 
  coord_flip() + 
  ylim(0, 850)

#### 09-7 ####

## ------------------------------------------------------ ##
# 남성 직업 빈도 상위 10개 추출
job_male <- welfare %>%
  filter(!is.na(job) & sex == "male") %>%
  group_by(job) %>%
  summarise(n = n()) %>%
  arrange(desc(n)) %>%
  head(10)

job_male

# 여성 직업 빈도 상위 10개 추출
job_female <- welfare %>%
  filter(!is.na(job) & sex == "female") %>%
  group_by(job) %>%
  summarise(n = n()) %>%
  arrange(desc(n)) %>%
  head(10)

job_female

# 남성 직업 빈도 상위 10개 직업
ggplot(data = job_male, aes(x = reorder(job, n), y = n)) +
  geom_col() +
  coord_flip()

# 여성 직업 빈도 상위 10개 직업
ggplot(data = job_female, aes(x = reorder(job, n), y = n)) +
  geom_col() +
  coord_flip()

#### 09-8 ####

## -------------------------------------------------------- ##
class(welfare$religion)
table(welfare$religion)

# 종교 유무 이름 부여
welfare$religion <- ifelse(welfare$religion == 1, "yes", "no")
table(welfare$religion)
qplot(welfare$religion)


## ---------------------------------------------------------- ##
class(welfare$marriage)
table(welfare$marriage)

# 이혼 여부 변수 만들기
welfare$group_marriage <- ifelse(welfare$marriage == 1, "marriage",
                                 ifelse(welfare$marriage == 3, "divorce", NA))

table(welfare$group_marriage)
table(is.na(welfare$group_marriage))
qplot(welfare$group_marriage)


## -------------------------------------------------------- ##
religion_marriage <- welfare %>%
  filter(!is.na(group_marriage)) %>%
  group_by(religion, group_marriage) %>%
  summarise(n = n()) %>%
  mutate(tot_group = sum(n)) %>%
  mutate(pct = round(n/tot_group*100, 1))

religion_marriage

religion_marriage <- welfare %>%
  filter(!is.na(group_marriage)) %>%
  count(religion, group_marriage) %>% 
  group_by(religion) %>% 
  mutate(pct = round(n/sum(n)*100, 1))

# 이혼 추출
divorce <- religion_marriage %>%
  filter(group_marriage == "divorce") %>% 
  select(religion, pct)

divorce

ggplot(data = divorce, aes(x = religion, y = pct)) + geom_col()


## ----------------------------------------------------------- ##
ageg_marriage <- welfare %>%
  filter(!is.na(group_marriage)) %>%
  group_by(ageg, group_marriage) %>%
  summarise(n = n()) %>%
  mutate(tot_group = sum(n)) %>%
  mutate(pct = round(n/tot_group*100, 1))

ageg_marriage

ageg_marriage <- welfare %>%
  filter(!is.na(group_marriage)) %>%
  count(ageg, group_marriage) %>% 
  group_by(ageg) %>% 
  mutate(pct = round(n/sum(n)*100, 1))

# 초년 제외, 이혼 추출
ageg_divorce <- ageg_marriage %>% 
  filter(ageg != "young" & group_marriage == "divorce") %>% 
  select(ageg, pct)

ageg_divorce

# 그래프 만들기
ggplot(data = ageg_divorce, aes(x = ageg, y = pct)) + geom_col()


## ----------------------------------------------------------- ##
# 연령대, 종교유무, 결혼상태별 비율표 만들기
ageg_religion_marriage <- welfare %>%
  filter(!is.na(group_marriage) & ageg != "young") %>%
  group_by(ageg, religion, group_marriage) %>%
  summarise(n = n()) %>%
  mutate(tot_group = sum(n)) %>%
  mutate(pct = round(n/tot_group*100, 1))

ageg_religion_marriage

ageg_religion_marriage <- welfare %>%
  filter(!is.na(group_marriage) & ageg != "young") %>%
  count(ageg, religion, group_marriage) %>%
  group_by(ageg, religion) %>% 
  mutate(pct = round(n/sum(n)*100, 1))

# 연령대 및 종교 유무별 이혼율 표 만들기
df_divorce <- ageg_religion_marriage %>%
  filter(group_marriage == "divorce") %>% 
  select(ageg, religion, pct)

df_divorce

ggplot(data = df_divorce, aes(x = ageg, y = pct, fill = religion )) +
  geom_col(position = "dodge")

#### 09-9 ####

## ------------------------------------------------------------ ##
class(welfare$code_region)
table(welfare$code_region)

# 지역 코드 목록 만들기
list_region <- data.frame(code_region = c(1:7),
                          region = c("서울",
                                     "수도권(인천/경기)",
                                     "부산/경남/울산",
                                     "대구/경북",
                                     "대전/충남",
                                     "강원/충북",
                                     "광주/전남/전북/제주도"))
list_region

# 지역명 변수 추가 
welfare <- left_join(welfare, list_region, id = "code_region")

welfare %>%
  select(code_region, region) %>%
  head


## --------------------------------------------------------- ##

region_ageg <- welfare %>%
  group_by(region, ageg) %>%
  summarise(n = n()) %>%
  mutate(tot_group = sum(n)) %>% 
  mutate(pct = round(n/tot_group*100, 2))

head(region_ageg)

region_ageg <- welfare %>%
  count(region, ageg) %>%
  group_by(region) %>% 
  mutate(pct = round(n/sum(n)*100, 2))

ggplot(data = region_ageg, aes(x = region, y = pct, fill = ageg)) +
  geom_col() +
  coord_flip()


## -------------------------------------------------------- ##
# 노년층 비율 내림차순 정렬
list_order_old <- region_ageg %>%
  filter(ageg == "old") %>%
  arrange(pct)

list_order_old

# 지역명 순서 변수 만들기
order <- list_order_old$region
order

ggplot(data = region_ageg, aes(x = region,  y = pct, fill = ageg)) +
  geom_col() +
  coord_flip() +
  scale_x_discrete(limits = order)


class(region_ageg$ageg)
levels(region_ageg$ageg)

region_ageg$ageg <- factor(region_ageg$ageg,
                           level = c("old", "middle", "young"))
class(region_ageg$ageg)
levels(region_ageg$ageg)

ggplot(data = region_ageg, aes(x = region,  y = pct, fill = ageg)) +
  geom_col() +
  coord_flip() +
  scale_x_discrete(limits = order)