- 종교 변수 검토
class(welfare$religion)
# "numeric"
table(welfare$religion)
# 1 2
# 8047 8617- 전처리, 종교 유무 이름 부여
welfare$religion <- ifelse(welfare$religion == 1, "yes", "no")
table(welfare$religion)
# no yes
# 8617 8047
qplot(welfare$religion)
- 혼인 상태 변수 검토
class(welfare$marriage)
# "numeric"
table(welfare$marriage)
# 0 1 2 3 4 5 6
# 2861 8431 2117 712 84 2433 26 - 이혼 여부 변수 만들기
welfare$group_marriage <- ifelse(welfare$marriage == 1, "marriage",
ifelse(welfare$marriage == 3, "divorce", NA))
table(welfare$group_marriage)
# divorce marriage
# 712 8431
table(is.na(welfare$group_marriage))
# FALSE TRUE
# 9143 7521
qplot(welfare$group_marriage)
- 종교 유무에 따른 이혼율 표 만들기
religion_marriage <- welfare %>%
filter(!is.na(group_marriage)) %>%
group_by(religion, group_marriage) %>%
summarise(n = n()) %>%
mutate(tot_group = sum(n),
pct = round(n/tot_group*100, 1))
religion_marriage
# A tibble: 4 × 5
# Groups: religion [2]
# religion group_marriage n tot_group pct
# <chr> <chr> <int> <int> <dbl>
# 1 no divorce 384 4602 8.3
# 2 no marriage 4218 4602 91.7
# 3 yes divorce 328 4541 7.2
# 4 yes marriage 4213 4541 92.8
# count()를 이용해 집단별 빈도를 구하고 비율로 만들기
religion_marriage <- welfare %>%
filter(!is.na(group_marriage)) %>%
count(religion, group_marriage) %>%
group_by(religion) %>%
mutate(pct = round(n/sum(n)*100, 1))- 이혼 추출해 이혼율 표 만들기
divorce <- religion_marriage %>%
filter(group_marriage == "divorce") %>%
select(religion, pct)
divorce
# A tibble: 2 × 2
# Groups: religion [2]
# religion pct
# <chr> <dbl>
# 1 no 8.3
# 2 yes 7.2
ggplot(data = divorce, aes(x = religion, y = pct)) + geom_col()
- 연령대, 종교 유무, 결혼 상태별 비율표 만들기
<!-- -->
ageg_marriage <- welfare %>%
filter(!is.na(group_marriage)) %>%
group_by(ageg, group_marriage) %>%
summarise(n = n()) %>%
mutate(tot_group = sum(n),
pct = round(n/tot_group*100, 1))
ageg_marriage
# A tibble: 6 × 5
# Groups: ageg [3]
# ageg group_marriage n tot_group pct
# <chr> <chr> <int> <int> <dbl>
# 1 middle divorce 437 4918 8.9
# 2 middle marriage 4481 4918 91.1
# 3 old divorce 273 4165 6.6
# 4 old marriage 3892 4165 93.4
# 5 young divorce 2 60 3.3
# 6 young marriage 58 60 96.7
# count()를 이용해 집단별 빈도를 구하고 비율로 만들기
ageg_marriage <- welfare %>%
filter(!is.na(group_marriage)) %>%
count(ageg, group_marriage) %>%
group_by(ageg) %>%
mutate(pct = round(n/sum(n)*100, 1))
- 연령대별 이혼율 그래프 만들기
ageg_divorce <- ageg_marriage %>%
filter(ageg != "young" & group_marriage == "divorce") %>%
select(ageg, pct)
ageg_divorce
# A tibble: 2 × 2
# Groups: ageg [2]
# ageg pct
# <chr> <dbl>
# 1 middle 8.9
# 2 old 6.6
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),
pct = round(n/tot_group*100, 1))
ageg_religion_marriage
# A tibble: 8 × 6
# Groups: ageg, religion [4]
# ageg religion group_marriage n tot_group pct
# <chr> <chr> <chr> <int> <int> <dbl>
# 1 middle no divorce 260 2681 9.7
# 2 middle no marriage 2421 2681 90.3
# 3 middle yes divorce 177 2237 7.9
# 4 middle yes marriage 2060 2237 92.1
# 5 old no divorce 123 1884 6.5
# 6 old no marriage 1761 1884 93.5
# 7 old yes divorce 150 2281 6.6
# 8 old yes marriage 2131 2281 93.4
# count()를 이용해 집단별 빈도를 구하고 비율로 만들기
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
# A tibble: 4 × 3
# Groups: ageg, religion [4]
# ageg religion pct
# <chr> <chr> <dbl>
# 1 middle no 9.7
# 2 middle yes 7.9
# 3 old no 6.5
# 4 old yes 6.6
ggplot(data = df_divorce, aes(x = ageg, y = pct, fill = religion )) +
geom_col(position = "dodge")
- 지역 변수 검토
class(welfare$code_region)
# "numeric"
table(welfare$code_region)
# 1 2 3 4 5 6 7
# 2486 3711 2785 2036 1467 1257 2922-
지역 변수 전처리
- 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 # code_region region # 1 1 서울 # 2 2 수도권(인천/경기) # 3 3 부산/경남/울산 # 4 4 대구/경북 # 5 5 대전/충남 # 6 6 강원/충북 # 7 7 광주/전남/전북/제주도- 지역명 변수 추가
<!-- -->welfare <- left_join(welfare, list_region, id = "code_region") welfare %>% select(code_region, region) %>% head # code_region region # 1 1 서울 # 2 1 서울 # 3 1 서울 # 4 1 서울 # 5 1 서울 # 6 1 서울 -
지역별 연령대 비율 분석하기
<!-- -->
region_ageg <- welfare %>%
group_by(region, ageg) %>%
summarise(n = n()) %>%
mutate(tot_group = sum(n),
pct = round(n/tot_group*100, 2))
head(region_ageg)
# A tibble: 6 × 5
# Groups: region [2]
region ageg n tot_group pct
<chr> <chr> <int> <int> <dbl>
1 강원/충북 middle 417 1257 33.2
2 강원/충북 old 555 1257 44.2
3 강원/충북 young 285 1257 22.7
4 광주/전남/전북/제주도 middle 947 2922 32.4
5 광주/전남/전북/제주도 old 1233 2922 42.2
6 광주/전남/전북/제주도 young 742 2922 25.4
# count()를 이용해 집단별 빈도를 구하고 비율로 만들기
region_ageg <- welfare %>%
count(region, ageg) %>%
group_by(region) %>%
mutate(pct = round(n/sum(n)*100, 2))
-
그래프 만들기
- 연령대별 비율 그래프
<!-- -->ggplot(data = region_ageg, aes(x = region, y = pct, fill = ageg)) + geom_col() + coord_flip()
- 노년층 비율 높은 순으로 막대 정렬
# 노년층 비율 내림차순 정렬 list_order_old <- region_ageg %>% filter(ageg == "old") %>% arrange(pct) list_order_old # A tibble: 7 × 4 # Groups: region [7] # region ageg n pct # <chr> <fct> <int> <dbl> # 1 수도권(인천/경기) old 1109 29.9 # 2 서울 old 805 32.4 # 3 대전/충남 old 527 35.9 # 4 부산/경남/울산 old 1124 40.4 # 5 광주/전남/전북/제주도 old 1233 42.2 # 6 강원/충북 old 555 44.2 # 7 대구/경북 old 928 45.6 # 지역명 순서 변수 만들기 order <- list_order_old$region order # [1] "수도권(인천/경기)" "서울" # [3] "대전/충남" "부산/경남/울산" # [5] "광주/전남/전북/제주도" "강원/충북" # [7] "대구/경북" ggplot(data = region_ageg, aes(x = region, y = pct, fill = ageg)) + geom_col() + coord_flip() + scale_x_discrete(limits = order)
- 연령대 순으로 막대 색깔 나열하기
class(region_ageg$ageg) # "factor" levels(region_ageg$ageg) # [1] "old" "middle" "young" region_ageg$ageg <- factor(region_ageg$ageg, level = c("old", "middle", "young")) ggplot(data = region_ageg, aes(x = region, y = pct, fill = ageg)) + geom_col() + coord_flip() + scale_x_discrete(limits = order)
