KGSS 08, 13, 18년도 Data Cleaning
HeeYoung
2021 3 14
1. Raw Data 준비
Raw Data 다운 받은 뒤
08, 13, 18년 3개년 데이터만 따로 빼기: Spss -> data -> 케이스 선택: 08, 13, 18년
2. sav 파일을 csv 파일로 변환한 뒤 R로 불러오기
library(haven)#sav 파일 읽어오기
kgssdata <- read_sav("C:/RRR/kgss/kgss_raw_data/kgss_08_13_18_rawdata.sav") #읽어온 file1.sav를 file1.csv로 새롭게 저장
write.table(kgssdata, file = "C:/RRR/kgss/kgss_raw_data/kgss_08_13_18_rawdata.csv"
, sep = "," #새 데이터의 열 구분은 ","(csv파일이니깐)
, row.names = FALSE)# R로 csv파일 불러오기
kgssdata <- read.csv("C:/RRR/kgss/kgss_raw_data/kgss_08_13_18_rawdata.csv"
, header = T #첫 행 변수명으로
, sep = "," #csv에서 자료 구분은 ","
, stringsAsFactors = F #문자열을 factor로 읽지 않기
, na.strings = "") 3. 쓸 변수만 추려내기
KG1 <- subset(kgssdata
, select = c(SEXATT1, SEXATT2, SEXATT3
, SEX, AGE, EDUC, GRADUATE
, EMPLY, URBRURAL, PARTYLR
, PRTYID08, PRTYID13, PRTYID18
, MARITAL, INCOME, NORTHWHO
, KRPROUD, UNIFI
, ABORT1, ABORT2, HBBYWK08
, TRTASSB, TRTFIRM, TRTRELI
, TRTJURI, TRTEDUC, RELIG
, RELLEAD1, RELSCI2, RELFUN1
, RELFUN2, RELPOWR, RELDIFF1
, RELDIFF2, RELEXTM1, GODCONC
, GODBELI, RELNW1, RELNW2
, RELNW3, RELNW4, RELNW5
, RELNW6, RELNW7, SPIRIT1
, SPIRIT2, SPIRIT3, SPIRIT4
, SPIRIT5, SPIRIT6, RELMA
, RELDENMA, RELFA, RELDENFA
, RELUP, RELDENUP, RELATNMA
, RELATNFA, RELATNUP, PRAYFREQ
, RELACT, RELOBJT, RELVIST
, RELIGOUS, SPIRITUA, RELGSTY2
, RELGSTY3, REGGRP1, REGGRP2
, REGGRP3, REGGRP5, REGGRP6
, REGGRP7, REGGRP8, REGGRP9
, SEXROLE1, PATRACH1,SD1JP,
SD2JP, SD3JP, SD1TW, SD2TW,
SD3TW,SD1CN,SD2CN,SD3CN,
SD1SEA, SD2SEA, SD3SEA, SD1NAM,
SD2NAM, SD3NAM, SD1EUR, SD2EUR,
SD3EUR, FORNWKER,FORNBRID,
CONTACT3,ENGPROF1, ENGPROF2,
ENGPROF3,IMPLIMI0, SELFINT0,
FORCUL0))4. 변수 재구성하기 전에
일단 모든 애들을 다 numeric으로 바꾼다
library(purrr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionKG1 <- KG1 %>% map_if(is.factor, as.numeric)
KG1 <- KG1 %>% map_if(is.character, as.numeric)## Warning in .f(.x[[i]], ...): 강제형변환에 의해 생성된 NA 입니다KG1 <- KG1 %>% map_if(is.integer, as.numeric)변수 재구성 전에는 항상 새로운 object로 만든 뒤 시작하기!
KG2 <- data.frame(KG1)5. 변수 재구성 시작!
library("descr")변수 재구성 전에는 빈도분석을 통해서 항상 결측치 혹은 이상값이 있는지 확인하고 시작할 것!
혼전성교 반대(역코딩)
descr::freq(KG2$SEXATT1) #빈도 확인
## KG2$SEXATT1
## Frequency Percent
## -8 25 0.6522
## 1 863 22.5150
## 2 695 18.1320
## 3 1321 34.4639
## 4 929 24.2369
## Total 3833 100.0000KG2$SEXATT1_n <- ifelse(KG2$SEXATT1 == 1, 4,
ifelse(KG2$SEXATT1 == 2, 3,
ifelse(KG2$SEXATT1 == 3, 2,
ifelse(KG2$SEXATT1 == 4, 1, NA))))descr::freq(KG2$SEXATT1_n)
## KG2$SEXATT1_n
## Frequency Percent Valid Percent
## 1 929 24.2369 24.40
## 2 1321 34.4639 34.69
## 3 695 18.1320 18.25
## 4 863 22.5150 22.66
## NA's 25 0.6522
## Total 3833 100.0000 100.00혼외성교 반대(역코딩)
descr::freq(KG2$SEXATT2)
## KG2$SEXATT2
## Frequency Percent
## -8 16 0.4174
## 1 2800 73.0498
## 2 636 16.5927
## 3 277 7.2267
## 4 104 2.7133
## Total 3833 100.0000KG2$SEXATT2_n <- ifelse(KG2$SEXATT2 == 1, 4,
ifelse(KG2$SEXATT2 == 2, 3,
ifelse(KG2$SEXATT2 == 3, 2,
ifelse(KG2$SEXATT2 == 4, 1, NA))))descr::freq(KG2$SEXATT2_n) 
## KG2$SEXATT2_n
## Frequency Percent Valid Percent
## 1 104 2.7133 2.725
## 2 277 7.2267 7.257
## 3 636 16.5927 16.662
## 4 2800 73.0498 73.356
## NA's 16 0.4174
## Total 3833 100.0000 100.000동성애 반대(역코딩)
descr::freq(KG2$SEXATT3)
## KG2$SEXATT3
## Frequency Percent
## -8 61 1.591
## 1 2354 61.414
## 2 519 13.540
## 3 450 11.740
## 4 449 11.714
## Total 3833 100.000KG2$SEXATT3_n <- ifelse(KG2$SEXATT3 == 1, 4,
ifelse(KG2$SEXATT3 == 2, 3,
ifelse(KG2$SEXATT3 == 3, 2,
ifelse(KG2$SEXATT3 == 4, 1, NA))))descr::freq(KG2$SEXATT3_n)
## KG2$SEXATT3_n
## Frequency Percent Valid Percent
## 1 449 11.714 11.90
## 2 450 11.740 11.93
## 3 519 13.540 13.76
## 4 2354 61.414 62.41
## NA's 61 1.591
## Total 3833 100.000 100.00성별
freq(KG2$SEX)
## KG2$SEX
## Frequency Percent
## 1 1812 47.27
## 2 2021 52.73
## Total 3833 100.00KG2$SEX_n <- factor(kgssdata$SEX, levels = c(1, 2), labels = c("Male", "Female"))freq(KG2$SEX_n)
## KG2$SEX_n
## Frequency Percent
## Male 1812 47.27
## Female 2021 52.73
## Total 3833 100.00연령
freq(KG2$AGE)
## KG2$AGE
## Frequency Percent
## -8 3 0.07827
## 18 45 1.17402
## 19 64 1.66971
## 20 73 1.90451
## 21 60 1.56535
## 22 81 2.11323
## 23 73 1.90451
## 24 72 1.87842
## 25 65 1.69580
## 26 66 1.72189
## 27 44 1.14793
## 28 54 1.40882
## 29 59 1.53926
## 30 56 1.46100
## 31 67 1.74798
## 32 64 1.66971
## 33 72 1.87842
## 34 78 2.03496
## 35 79 2.06105
## 36 69 1.80016
## 37 88 2.29585
## 38 92 2.40021
## 39 83 2.16541
## 40 75 1.95669
## 41 71 1.85233
## 42 86 2.24367
## 43 98 2.55674
## 44 86 2.24367
## 45 87 2.26976
## 46 94 2.45239
## 47 75 1.95669
## 48 75 1.95669
## 49 69 1.80016
## 50 91 2.37412
## 51 57 1.48709
## 52 82 2.13932
## 53 68 1.77407
## 54 60 1.56535
## 55 40 1.04357
## 56 61 1.59144
## 57 36 0.93921
## 58 54 1.40882
## 59 51 1.33055
## 60 53 1.38273
## 61 37 0.96530
## 62 42 1.09575
## 63 40 1.04357
## 64 43 1.12184
## 65 54 1.40882
## 66 44 1.14793
## 67 40 1.04357
## 68 38 0.99139
## 69 29 0.75659
## 70 45 1.17402
## 71 52 1.35664
## 72 30 0.78268
## 73 36 0.93921
## 74 28 0.73050
## 75 35 0.91312
## 76 29 0.75659
## 77 35 0.91312
## 78 28 0.73050
## 79 35 0.91312
## 80 27 0.70441
## 81 18 0.46961
## 82 17 0.44352
## 83 15 0.39134
## 84 19 0.49570
## 85 11 0.28698
## 86 8 0.20871
## 87 7 0.18262
## 88 7 0.18262
## 89 1 0.02609
## 90 3 0.07827
## 91 3 0.07827
## 97 1 0.02609
## Total 3833 100.00000KG2$AGE_n <- ifelse(KG2$AGE < 0, NA, KG2$AGE)freq(KG2$AGE_n)
## KG2$AGE_n
## Frequency Percent Valid Percent
## 18 45 1.17402 1.17493
## 19 64 1.66971 1.67102
## 20 73 1.90451 1.90601
## 21 60 1.56535 1.56658
## 22 81 2.11323 2.11488
## 23 73 1.90451 1.90601
## 24 72 1.87842 1.87990
## 25 65 1.69580 1.69713
## 26 66 1.72189 1.72324
## 27 44 1.14793 1.14883
## 28 54 1.40882 1.40992
## 29 59 1.53926 1.54047
## 30 56 1.46100 1.46214
## 31 67 1.74798 1.74935
## 32 64 1.66971 1.67102
## 33 72 1.87842 1.87990
## 34 78 2.03496 2.03655
## 35 79 2.06105 2.06266
## 36 69 1.80016 1.80157
## 37 88 2.29585 2.29765
## 38 92 2.40021 2.40209
## 39 83 2.16541 2.16710
## 40 75 1.95669 1.95822
## 41 71 1.85233 1.85379
## 42 86 2.24367 2.24543
## 43 98 2.55674 2.55875
## 44 86 2.24367 2.24543
## 45 87 2.26976 2.27154
## 46 94 2.45239 2.45431
## 47 75 1.95669 1.95822
## 48 75 1.95669 1.95822
## 49 69 1.80016 1.80157
## 50 91 2.37412 2.37598
## 51 57 1.48709 1.48825
## 52 82 2.13932 2.14099
## 53 68 1.77407 1.77546
## 54 60 1.56535 1.56658
## 55 40 1.04357 1.04439
## 56 61 1.59144 1.59269
## 57 36 0.93921 0.93995
## 58 54 1.40882 1.40992
## 59 51 1.33055 1.33159
## 60 53 1.38273 1.38381
## 61 37 0.96530 0.96606
## 62 42 1.09575 1.09661
## 63 40 1.04357 1.04439
## 64 43 1.12184 1.12272
## 65 54 1.40882 1.40992
## 66 44 1.14793 1.14883
## 67 40 1.04357 1.04439
## 68 38 0.99139 0.99217
## 69 29 0.75659 0.75718
## 70 45 1.17402 1.17493
## 71 52 1.35664 1.35770
## 72 30 0.78268 0.78329
## 73 36 0.93921 0.93995
## 74 28 0.73050 0.73107
## 75 35 0.91312 0.91384
## 76 29 0.75659 0.75718
## 77 35 0.91312 0.91384
## 78 28 0.73050 0.73107
## 79 35 0.91312 0.91384
## 80 27 0.70441 0.70496
## 81 18 0.46961 0.46997
## 82 17 0.44352 0.44386
## 83 15 0.39134 0.39164
## 84 19 0.49570 0.49608
## 85 11 0.28698 0.28721
## 86 8 0.20871 0.20888
## 87 7 0.18262 0.18277
## 88 7 0.18262 0.18277
## 89 1 0.02609 0.02611
## 90 3 0.07827 0.07833
## 91 3 0.07827 0.07833
## 97 1 0.02609 0.02611
## NA's 3 0.07827
## Total 3833 100.00000 100.00000연령 범주형
KG2$AGE_n_c <- ifelse(KG2$AGE_n <= 29, 1,
ifelse(KG2$AGE_n <= 39, 2,
ifelse(KG2$AGE_n <= 49, 3,
ifelse(KG2$AGE_n <= 59, 4,
ifelse(KG2$AGE_n <= 69, 5,
ifelse(KG2$AGE_n >=70, 6, NA))))))KG2$AGE_n_c <- factor(KG2$AGE_n_c
, levels = c(1:6)
, labels = c("20s 이하", "30s", "40s", "50s", "60s", "70s 이상"))freq(KG2$AGE_n_c)
## KG2$AGE_n_c
## Frequency Percent Valid Percent
## 20s 이하 756 19.72345 19.74
## 30s 748 19.51474 19.53
## 40s 816 21.28881 21.31
## 50s 600 15.65354 15.67
## 60s 420 10.95747 10.97
## 70s 이상 490 12.78372 12.79
## NA's 3 0.07827
## Total 3833 100.00000 100.00교육수준
freq(KG2$EDUC)
## KG2$EDUC
## Frequency Percent
## -8 1 0.02609
## 0 130 3.39160
## 1 384 10.01826
## 2 280 7.30498
## 3 1095 28.56770
## 4 460 12.00104
## 5 1260 32.87242
## 6 168 4.38299
## 7 50 1.30446
## 8 5 0.13045
## Total 3833 100.00000KG2$EDUC_n <- ifelse(KG2$EDUC == c(-8), NA,
ifelse(KG2$EDUC == 8, NA, KG2$EDUC))freq(KG2$EDUC_n)
## KG2$EDUC_n
## Frequency Percent Valid Percent
## 0 130 3.3916 3.397
## 1 384 10.0183 10.034
## 2 280 7.3050 7.316
## 3 1095 28.5677 28.612
## 4 460 12.0010 12.020
## 5 1260 32.8724 32.924
## 6 168 4.3830 4.390
## 7 50 1.3045 1.307
## NA's 6 0.1565
## Total 3833 100.0000 100.000현재 취업
freq(KG2$EMPLY)
## KG2$EMPLY
## Frequency Percent
## 1 2153 56.17
## 2 1680 43.83
## Total 3833 100.00KG2$EMPLY_n <- factor(kgssdata$SEX, levels = c(1, 2), labels = c("취업", "미취업"))freq(KG2$EMPLY_n)
## KG2$EMPLY_n
## Frequency Percent
## 취업 1812 47.27
## 미취업 2021 52.73
## Total 3833 100.00거주 도시 규모
freq(KG2$URBRURAL)
## KG2$URBRURAL
## Frequency Percent
## -8 5 0.1304
## 1 1094 28.5416
## 2 991 25.8544
## 3 1202 31.3592
## 4 501 13.0707
## 5 40 1.0436
## Total 3833 100.0000KG2$URBRURAL_n <- ifelse(KG2$URBRURAL < 0, NA, KG2$URBRURAL)KG2$URBRURAL_n <- factor(KG2$URBRURAL_n
, levels = c(1:5)
, labels = c("큰 도시", "큰 도시 주변", "작은 도시", "시골마을", "외딴 곳"))freq(KG2$URBRURAL_n)
## KG2$URBRURAL_n
## Frequency Percent Valid Percent
## 큰 도시 1094 28.5416 28.579
## 큰 도시 주변 991 25.8544 25.888
## 작은 도시 1202 31.3592 31.400
## 시골마을 501 13.0707 13.088
## 외딴 곳 40 1.0436 1.045
## NA's 5 0.1304
## Total 3833 100.0000 100.000정치 성향
freq(KG2$PARTYLR)
## KG2$PARTYLR
## Frequency Percent
## -8 86 2.244
## 1 204 5.322
## 2 1081 28.202
## 3 1257 32.794
## 4 1015 26.481
## 5 190 4.957
## Total 3833 100.000KG2$PARTYLR_n <- ifelse(KG2$PARTYLR < 0, NA, KG2$PARTYLR)freq(KG2$PARTYLR_n)
## KG2$PARTYLR_n
## Frequency Percent Valid Percent
## 1 204 5.322 5.444
## 2 1081 28.202 28.850
## 3 1257 32.794 33.547
## 4 1015 26.481 27.088
## 5 190 4.957 5.071
## NA's 86 2.244
## Total 3833 100.000 100.000자한당 지지 = 1
KG2$party_n1 <- ifelse(KG2$PRTYID08 == 1, 1
, ifelse(KG2$PRTYID08 ==2, 1
, ifelse(KG2$PRTYID08 ==3, 1
, ifelse(KG2$PRTYID13 == 1, 1
, ifelse(KG2$PRTYID18 == 2, 1
, ifelse(KG2$PRTYID18==3, 1
, 0))))))freq(KG2$party_n1)
## KG2$party_n1
## Frequency Percent
## 0 2441 63.68
## 1 1392 36.32
## Total 3833 100.00민주당 지지 = 1
KG2$party_n2 <- ifelse(KG2$PRTYID08 == 4, 1
, ifelse(KG2$PRTYID13 == 2, 1
, ifelse(KG2$PRTYID18 == 1, 1
, 0)))freq(KG2$party_n2)
## KG2$party_n2
## Frequency Percent
## 0 2734 71.33
## 1 1099 28.67
## Total 3833 100.00미혼1 기혼2 그외3
freq(KG2$MARITAL)
## KG2$MARITAL
## Frequency Percent
## -8 7 0.1826
## 1 2337 60.9705
## 2 364 9.4965
## 3 149 3.8873
## 4 33 0.8609
## 5 925 24.1325
## 6 18 0.4696
## Total 3833 100.0000KG2$MARITAL_n <- ifelse(KG2$MARITAL == 5, 1
, ifelse(KG2$MARITAL == 1, 2, 3))KG2$MARITAL_n <- factor(KG2$MARITAL_n
, levels = c(1, 2, 3)
, labels = c("미혼", "기혼", "사별, 이혼, 별거, 동거"))freq(KG2$MARITAL_n)
## KG2$MARITAL_n
## Frequency Percent
## 미혼 925 24.13
## 기혼 2337 60.97
## 사별, 이혼, 별거, 동거 571 14.90
## Total 3833 100.00임금
freq(KG2$INCOME)
## KG2$INCOME
## Frequency Percent
## -8 224 5.8440
## 0 32 0.8349
## 1 225 5.8701
## 2 223 5.8179
## 3 222 5.7918
## 4 213 5.5570
## 5 307 8.0094
## 6 231 6.0266
## 7 373 9.7313
## 8 217 5.6614
## 9 294 7.6702
## 10 160 4.1743
## 11 298 7.7746
## 12 98 2.5567
## 13 134 3.4960
## 14 60 1.5654
## 15 93 2.4263
## 16 41 1.0697
## 17 90 2.3480
## 18 30 0.7827
## 19 37 0.9653
## 20 12 0.3131
## 21 219 5.7135
## Total 3833 100.0000북한 인식
freq(KG2$NORTHWHO)
## KG2$NORTHWHO
## Frequency Percent
## -8 74 1.931
## 1 609 15.888
## 2 1263 32.951
## 3 1381 36.029
## 4 506 13.201
## Total 3833 100.000KG2$NORTHWHO_n <- ifelse(KG2$NORTHWHO == -8, NA, KG2$NORTHWHO)KG2$NORTHWHO_n <- factor(KG2$NORTHWHO_n
, levels = c(1:4)
, labels = c("지원대상", "협력대상", "경계대상", "적대대상"))freq(KG2$NORTHWHO_n)
## KG2$NORTHWHO_n
## Frequency Percent Valid Percent
## 지원대상 609 15.888 16.20
## 협력대상 1263 32.951 33.60
## 경계대상 1381 36.029 36.74
## 적대대상 506 13.201 13.46
## NA's 74 1.931
## Total 3833 100.000 100.00북한 지원, 협력 = 1, 경계, 적대 = 2
freq(KG2$NORTHWHO_n)
## KG2$NORTHWHO_n
## Frequency Percent Valid Percent
## 지원대상 609 15.888 16.20
## 협력대상 1263 32.951 33.60
## 경계대상 1381 36.029 36.74
## 적대대상 506 13.201 13.46
## NA's 74 1.931
## Total 3833 100.000 100.00KG2$NORTHWHO_n <- as.numeric(KG2$NORTHWHO_n)KG2$NORTHWHO_n2 <- ifelse(KG2$NORTHWHO_n <= 2, 1
, 2)KG2$NORTHWHO_n2 <- factor(KG2$NORTHWHO_n2
, levels = c(1:2)
, labels = c("지원,협력", "경계,적대"))freq(KG2$NORTHWHO_n2)
## KG2$NORTHWHO_n2
## Frequency Percent Valid Percent
## 지원,협력 1872 48.839 49.8
## 경계,적대 1887 49.230 50.2
## NA's 74 1.931
## Total 3833 100.000 100.0한국인 자긍심_높을수록 자긍
freq(KG2$KRPROUD)
## KG2$KRPROUD
## Frequency Percent
## -8 48 1.252
## 1 1226 31.985
## 2 1955 51.004
## 3 562 14.662
## 4 42 1.096
## Total 3833 100.000KG2$KRPROUD_n <- ifelse(KG2$KRPROUD == 4, 1
, ifelse(KG2$KRPROUD == 3, 2
, ifelse(KG2$KRPROUD == 2, 3
, ifelse(KG2$KRPROUD == 1, 4, NA))))freq(KG2$KRPROUD_n)
## KG2$KRPROUD_n
## Frequency Percent Valid Percent
## 1 42 1.096 1.11
## 2 562 14.662 14.85
## 3 1955 51.004 51.65
## 4 1226 31.985 32.39
## NA's 48 1.252
## Total 3833 100.000 100.00인공유산 _ 높을수록 반대
freq(KG2$ABORT2)
## KG2$ABORT2
## Frequency Percent
## -8 33 0.8609
## -1 1294 33.7595
## 1 667 17.4015
## 2 460 12.0010
## 3 777 20.2713
## 4 602 15.7057
## Total 3833 100.0000KG2$ABORT2_n <- ifelse(KG2$ABORT2 == 4, 1
, ifelse(KG2$ABORT2 == 3, 2
, ifelse(KG2$ABORT2 == 2, 3
, ifelse(KG2$ABORT2 == 1, 4, NA))))freq(KG2$ABORT2_n)
## KG2$ABORT2_n
## Frequency Percent Valid Percent
## 1 602 15.71 24.02
## 2 777 20.27 31.01
## 3 460 12.00 18.36
## 4 667 17.40 26.62
## NA's 1327 34.62
## Total 3833 100.00 100.00종교
freq(KG2$RELIG)
## KG2$RELIG
## Frequency Percent
## -8 5 0.13045
## 1 873 22.77589
## 2 869 22.67154
## 3 362 9.44430
## 4 1672 43.62118
## 5 4 0.10436
## 6 12 0.31307
## 7 4 0.10436
## 8 1 0.02609
## 9 5 0.13045
## 11 2 0.05218
## 77 24 0.62614
## Total 3833 100.00000KG2$RELIG_n <- ifelse(KG2$RELIG == 4, 1
, ifelse(KG2$RELIG == 1, 2
, ifelse(KG2$RELIG ==2, 3
, ifelse(KG2$RELIG ==3, 4
, NA))))KG2$RELIG_n <- factor(KG2$RELIG_n
, levels = c(1:4)
, labels = c("무교", "불교", "개신교", "천주교"))freq(KG2$RELIG_n)
## KG2$RELIG_n
## Frequency Percent Valid Percent
## 무교 1672 43.621 44.280
## 불교 873 22.776 23.120
## 개신교 869 22.672 23.014
## 천주교 362 9.444 9.587
## NA's 57 1.487
## Total 3833 100.000 100.000종교지도자: 선거시 투표 영향 _ 높을수록 영향 미쳐도 되
freq(KG2$RELLEAD1)
## KG2$RELLEAD1
## Frequency Percent
## -8 22 0.574
## -1 1294 33.759
## 1 1181 30.811
## 2 565 14.740
## 3 376 9.810
## 4 199 5.192
## 5 196 5.113
## Total 3833 100.000KG2$RELLEAD1_n <- ifelse(KG2$RELLEAD1 < 0, NA, KG2$RELLEAD1)freq(KG2$RELLEAD1_n)
## KG2$RELLEAD1_n
## Frequency Percent Valid Percent
## 1 1181 30.811 46.921
## 2 565 14.740 22.447
## 3 376 9.810 14.938
## 4 199 5.192 7.906
## 5 196 5.113 7.787
## NA's 1316 34.333
## Total 3833 100.000 100.000종교와 과학: 사람들은 과학 지나치게 신뢰, 종교는 신뢰안함_높을수록 동의
freq(KG2$RELSCI2)
## KG2$RELSCI2
## Frequency Percent
## -8 31 0.8088
## -1 1294 33.7595
## 1 303 7.9050
## 2 868 22.6454
## 3 745 19.4365
## 4 475 12.3924
## 5 117 3.0524
## Total 3833 100.0000KG2$RELSCI2_n <- ifelse(KG2$RELSCI2 == 5, 1
, ifelse(KG2$RELSCI2 == 4, 2
, ifelse(KG2$RELSCI2 ==3, 3
, ifelse(KG2$RELSCI2 ==2, 4
, ifelse(KG2$RELSCI2 ==1, 5
, NA)))))freq(KG2$RELSCI2_n)
## KG2$RELSCI2_n
## Frequency Percent Valid Percent
## 1 117 3.052 4.665
## 2 475 12.392 18.939
## 3 745 19.436 29.705
## 4 868 22.645 34.609
## 5 303 7.905 12.081
## NA's 1325 34.568
## Total 3833 100.000 100.000종교는 평화보다 갈등 유발_높을수록 반대
freq(KG2$RELFUN1)
## KG2$RELFUN1
## Frequency Percent
## -8 26 0.6783
## -1 1294 33.7595
## 1 330 8.6094
## 2 791 20.6366
## 3 591 15.4187
## 4 570 14.8709
## 5 231 6.0266
## Total 3833 100.0000KG2$RELFUN1_n <- ifelse(KG2$RELFUN1<0, NA, KG2$RELFUN1)freq(KG2$RELFUN1_n)
## KG2$RELFUN1_n
## Frequency Percent Valid Percent
## 1 330 8.609 13.132
## 2 791 20.637 31.476
## 3 591 15.419 23.518
## 4 570 14.871 22.682
## 5 231 6.027 9.192
## NA's 1320 34.438
## Total 3833 100.000 100.000종교 신념 강한 사람은 배타적 _ 높을수록 반대
freq(KG2$RELFUN2)
## KG2$RELFUN2
## Frequency Percent
## -8 34 0.887
## -1 1294 33.759
## 1 517 13.488
## 2 882 23.011
## 3 556 14.506
## 4 376 9.810
## 5 174 4.540
## Total 3833 100.000KG2$RELFUN2_n <- ifelse(KG2$RELFUN2<0, NA, KG2$RELFUN2)freq(KG2$RELFUN2_n)
## KG2$RELFUN2_n
## Frequency Percent Valid Percent
## 1 517 13.49 20.639
## 2 882 23.01 35.210
## 3 556 14.51 22.196
## 4 376 9.81 15.010
## 5 174 4.54 6.946
## NA's 1328 34.65
## Total 3833 100.00 100.000한국사회 종교조직이 권력 많이 갖고 있다 _ 높을수록 반대 즉 권력 적게 갖는다
freq(KG2$RELPOWR)
## KG2$RELPOWR
## Frequency Percent
## -8 77 2.009
## -1 1294 33.759
## 1 606 15.810
## 2 947 24.706
## 3 686 17.897
## 4 171 4.461
## 5 52 1.357
## Total 3833 100.000KG2$RELPOWR_n <- ifelse(KG2$RELPOWR<0, NA, KG2$RELPOWR)freq(KG2$RELPOWR_n)
## KG2$RELPOWR_n
## Frequency Percent Valid Percent
## 1 606 15.810 24.614
## 2 947 24.706 38.465
## 3 686 17.897 27.864
## 4 171 4.461 6.946
## 5 52 1.357 2.112
## NA's 1371 35.768
## Total 3833 100.000 100.000다른 종교 가진 사람이 친척 가족과 결혼하는 것 허용? _ 높을수록 허용 안함
freq(KG2$RELDIFF1)
## KG2$RELDIFF1
## Frequency Percent
## -8 27 0.7044
## -1 1294 33.7595
## 1 990 25.8283
## 2 1042 27.1850
## 3 298 7.7746
## 4 182 4.7482
## Total 3833 100.0000KG2$RELDIFF1_n <- ifelse(KG2$RELDIFF1<0, NA, KG2$RELDIFF1)freq(KG2$RELDIFF1_n)
## KG2$RELDIFF1_n
## Frequency Percent Valid Percent
## 1 990 25.828 39.411
## 2 1042 27.185 41.481
## 3 298 7.775 11.863
## 4 182 4.748 7.245
## NA's 1321 34.464
## Total 3833 100.000 100.000극단주의자들의 공개집회 _ 높을수록 허용해야 한다
freq(KG2$RELEXTM1)
## KG2$RELEXTM1
## Frequency Percent
## -8 46 1.200
## -1 1294 33.759
## 1 218 5.687
## 2 746 19.463
## 3 827 21.576
## 4 702 18.315
## Total 3833 100.000KG2$RELEXTM1_n <- ifelse(KG2$RELEXTM1 == 4, 1
, ifelse(KG2$RELEXTM1 == 3, 2
, ifelse(KG2$RELEXTM1 == 2, 3
, ifelse(KG2$RELEXTM1 == 1, 4, NA))))freq(KG2$RELEXTM1_n)
## KG2$RELEXTM1_n
## Frequency Percent Valid Percent
## 1 702 18.315 28.159
## 2 827 21.576 33.173
## 3 746 19.463 29.924
## 4 218 5.687 8.744
## NA's 1340 34.960
## Total 3833 100.000 100.000어릴때 어머니 종교
KG2$YEAR_n <- kgssdata$YEARfreq(KG2$RELMA)
## KG2$RELMA
## Frequency Percent
## -8 70 1.82625
## -1 1999 52.15236
## 1 408 10.64440
## 2 179 4.66997
## 3 1105 28.82859
## 6 1 0.02609
## 7 7 0.18262
## 8 9 0.23480
## 9 19 0.49570
## 11 32 0.83486
## 13 1 0.02609
## 14 1 0.02609
## 15 1 0.02609
## 77 1 0.02609
## Total 3833 100.00000KG2$RELMA_n <- ifelse(KG2$RELMA == -1 & KG2$YEAR_n==2013, 99, KG2$RELMA)KG2$RELMA_n <- ifelse(KG2$RELMA_n == -1, 1,
ifelse(KG2$RELMA_n == 3, 2,
ifelse(KG2$RELMA_n == 1, 3,
ifelse(KG2$RELMA_n == 2, 4, NA))))KG2$RELMA_n <- factor(KG2$RELMA_n
, levels = c(1:4)
, labels = c("무교", "불교", "개신교", "천주교"))freq(KG2$RELMA_n)
## KG2$RELMA_n
## Frequency Percent Valid Percent
## 무교 705 18.39 29.412
## 불교 1105 28.83 46.099
## 개신교 408 10.64 17.021
## 천주교 179 4.67 7.468
## NA's 1436 37.46
## Total 3833 100.00 100.000어릴때 아버지 종교
freq(KG2$RELFA)
## KG2$RELFA
## Frequency Percent
## -8 117 3.05244
## -1 2692 70.23219
## 1 231 6.02661
## 2 99 2.58283
## 3 642 16.74928
## 6 2 0.05218
## 7 3 0.07827
## 8 2 0.05218
## 9 34 0.88703
## 11 10 0.26089
## 77 1 0.02609
## Total 3833 100.00000KG2$RELFA_n <- ifelse(KG2$RELFA == -1 & KG2$YEAR_n==2013, 99, KG2$RELFA)KG2$RELFA_n <- ifelse(KG2$RELFA_n == -1, 1,
ifelse(KG2$RELFA_n == 3, 2,
ifelse(KG2$RELFA_n == 1, 3,
ifelse(KG2$RELFA_n == 2, 4, NA))))KG2$RELFA_n <- factor(KG2$RELFA_n
, levels = c(1:4)
, labels = c("무교", "불교", "개신교", "천주교"))freq(KG2$RELFA_n)
## KG2$RELFA_n
## Frequency Percent Valid Percent
## 무교 1398 36.473 58.987
## 불교 642 16.749 27.089
## 개신교 231 6.027 9.747
## 천주교 99 2.583 4.177
## NA's 1463 38.169
## Total 3833 100.000 100.000종교적 분위기 속에서 자랐나?
freq(KG2$RELUP)
## KG2$RELUP
## Frequency Percent
## -8 55 1.43491
## -1 2292 59.79650
## 1 411 10.72267
## 2 157 4.09601
## 3 859 22.41064
## 6 5 0.13045
## 7 5 0.13045
## 8 6 0.15654
## 9 17 0.44352
## 11 14 0.36525
## 14 1 0.02609
## 15 1 0.02609
## 77 10 0.26089
## Total 3833 100.00000KG2$RELUP_n <- ifelse(KG2$RELUP == -1 & KG2$YEAR_n==2013, 99, KG2$RELUP)KG2$RELUP_n <- ifelse(KG2$RELUP_n == -1, 1,
ifelse(KG2$RELUP_n == 3, 2,
ifelse(KG2$RELUP_n == 1, 3,
ifelse(KG2$RELUP_n == 2, 4, NA))))KG2$RELUP_n <- factor(KG2$RELUP_n
, levels = c(1:4)
, labels = c("무교", "불교", "개신교", "천주교"))freq(KG2$RELUP_n)
## KG2$RELUP_n
## Frequency Percent Valid Percent
## 무교 998 26.037 41.155
## 불교 859 22.411 35.423
## 개신교 411 10.723 16.948
## 천주교 157 4.096 6.474
## NA's 1408 36.734
## Total 3833 100.000 100.000얼마나 자주 예배, 미사?
descr::freq(kgssdata$ATTEND)
## kgssdata$ATTEND
## Frequency Percent
## -8 56 1.461
## 1 347 9.053
## 2 552 14.401
## 3 144 3.757
## 4 180 4.696
## 5 497 12.966
## 6 170 4.435
## 7 114 2.974
## 8 1773 46.256
## Total 3833 100.000KG2$ATTEND_n <- ifelse(kgssdata$ATTEND == 1, 8,
ifelse(kgssdata$ATTEND ==2, 7,
ifelse(kgssdata$ATTEND==3, 6,
ifelse(kgssdata$ATTEND==4, 5,
ifelse(kgssdata$ATTEND==5, 4,
ifelse(kgssdata$ATTEND==6,3,
ifelse(kgssdata$ATTEND==7,2,
ifelse(kgssdata$ATTEND==8,1, NA))))))))summary(KG2$ATTEND_n)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 3.000 3.416 6.000 8.000 56descr::freq(KG2$ATTEND_n)
## KG2$ATTEND_n
## Frequency Percent Valid Percent
## 1 1773 46.256 46.942
## 2 114 2.974 3.018
## 3 170 4.435 4.501
## 4 497 12.966 13.159
## 5 180 4.696 4.766
## 6 144 3.757 3.813
## 7 552 14.401 14.615
## 8 347 9.053 9.187
## NA's 56 1.461
## Total 3833 100.000 100.000스스로 믿음이 강하다고 보는가?
freq(kgssdata$RELITEN)
## kgssdata$RELITEN
## Frequency Percent
## -8 37 0.9653
## -1 1677 43.7516
## 1 428 11.1662
## 2 657 17.1406
## 3 1034 26.9763
## Total 3833 100.0000KG2$RELITEN_n <- ifelse(kgssdata$RELITEN == 3, 1,
ifelse(kgssdata$RELITEN ==2,2,
ifelse(kgssdata$RELITEN==1,3,NA)))freq(KG2$RELITEN_n)
## KG2$RELITEN_n
## Frequency Percent Valid Percent
## 1 1034 26.98 48.80
## 2 657 17.14 31.01
## 3 428 11.17 20.20
## NA's 1714 44.72
## Total 3833 100.00 100.00얼마나 자주 기도?
freq(KG2$PRAYFREQ)
## KG2$PRAYFREQ
## Frequency Percent
## -8 2 0.05218
## -1 1294 33.75946
## 1 1122 29.27211
## 2 67 1.74798
## 3 78 2.03496
## 4 279 7.27889
## 5 88 2.29585
## 6 93 2.42630
## 7 63 1.64362
## 8 135 3.52205
## 9 308 8.03548
## 10 114 2.97417
## 11 171 4.46126
## 98 19 0.49570
## Total 3833 100.00000KG2$PRAYFREQ_n <- ifelse(KG2$PRAYFREQ <0, NA,
ifelse(KG2$PRAYFREQ == 98, NA,
ifelse(KG2$PRAYFREQ==10, NA, KG2$PRAYFREQ)))freq(KG2$PRAYFREQ_n)
## KG2$PRAYFREQ_n
## Frequency Percent Valid Percent
## 1 1122 29.272 46.672
## 2 67 1.748 2.787
## 3 78 2.035 3.245
## 4 279 7.279 11.606
## 5 88 2.296 3.661
## 6 93 2.426 3.869
## 7 63 1.644 2.621
## 8 135 3.522 5.616
## 9 308 8.035 12.812
## 11 171 4.461 7.113
## NA's 1429 37.282
## Total 3833 100.000 100.000종교의식 외 모임, 활동 참여
freq(KG2$RELACT)
## KG2$RELACT
## Frequency Percent
## -8 6 0.1565
## -1 1294 33.7595
## 1 657 17.1406
## 2 51 1.3306
## 3 74 1.9306
## 4 203 5.2961
## 5 69 1.8002
## 6 71 1.8523
## 7 28 0.7305
## 8 158 4.1221
## 9 135 3.5220
## 10 1078 28.1242
## 98 9 0.2348
## Total 3833 100.0000KG2$RELACT_n <- ifelse(KG2$RELACT <0, NA,
ifelse(KG2$RELACT == 98, NA,
ifelse(KG2$RELACT==10, NA, KG2$RELACT)))freq(KG2$RELACT_n)
## KG2$RELACT_n
## Frequency Percent Valid Percent
## 1 657 17.1406 45.436
## 2 51 1.3306 3.527
## 3 74 1.9306 5.118
## 4 203 5.2961 14.039
## 5 69 1.8002 4.772
## 6 71 1.8523 4.910
## 7 28 0.7305 1.936
## 8 158 4.1221 10.927
## 9 135 3.5220 9.336
## NA's 2387 62.2750
## Total 3833 100.0000 100.000자신이 얼마나 종교적인가? 높을수록 종교적
freq(KG2$RELIGOUS)
## KG2$RELIGOUS
## Frequency Percent
## -8 19 0.4957
## -1 1294 33.7595
## 1 172 4.4873
## 2 204 5.3222
## 3 677 17.6624
## 4 508 13.2533
## 5 245 6.3919
## 6 223 5.8179
## 7 491 12.8098
## Total 3833 100.0000KG2$RELIGOUS_n <- ifelse(KG2$RELIGOUS ==7, 1
, ifelse(KG2$RELIGOUS==6, 2
, ifelse(KG2$RELIGOUS ==5, 3
, ifelse(KG2$RELIGOUS == 4, 4
, ifelse(KG2$RELIGOUS == 3, 5
, ifelse(KG2$RELIGOUS ==2, 6
, ifelse(KG2$RELIGOUS==1, 7, NA)))))))freq(KG2$RELIGOUS_n)
## KG2$RELIGOUS_n
## Frequency Percent Valid Percent
## 1 491 12.810 19.484
## 2 223 5.818 8.849
## 3 245 6.392 9.722
## 4 508 13.253 20.159
## 5 677 17.662 26.865
## 6 204 5.322 8.095
## 7 172 4.487 6.825
## NA's 1313 34.255
## Total 3833 100.000 100.000종교 목적(외적) 친구 사귀는게 도움_ 높을수록 찬성
freq(KG2$RELGSTY2)
## KG2$RELGSTY2
## Frequency Percent
## -8 27 0.7044
## -1 1294 33.7595
## 1 427 11.1401
## 2 1198 31.2549
## 3 562 14.6621
## 4 210 5.4787
## 5 115 3.0003
## Total 3833 100.0000KG2$RELGSTY2_n <- ifelse(KG2$RELGSTY2 == 1, 5,
ifelse(KG2$RELGSTY2 == 2, 4,
ifelse(KG2$RELGSTY2 == 3, 3,
ifelse(KG2$RELGSTY2 == 4, 2,
ifelse(KG2$RELGSTY2 ==5, 1, NA)))))freq(KG2$RELGSTY2_n)
## KG2$RELGSTY2_n
## Frequency Percent Valid Percent
## 1 115 3.000 4.578
## 2 210 5.479 8.360
## 3 562 14.662 22.373
## 4 1198 31.255 47.691
## 5 427 11.140 16.998
## NA's 1321 34.464
## Total 3833 100.000 100.000종교 목적(외적) 어렵거나 슬플 때 위안_ 높을수록 찬성
freq(KG2$RELGSTY3)
## KG2$RELGSTY3
## Frequency Percent
## -8 24 0.6261
## -1 1294 33.7595
## 1 823 21.4714
## 2 1268 33.0811
## 3 284 7.4093
## 4 78 2.0350
## 5 62 1.6175
## Total 3833 100.0000KG2$RELGSTY3_n <- ifelse(KG2$RELGSTY3 == 1, 5,
ifelse(KG2$RELGSTY3 == 2, 4,
ifelse(KG2$RELGSTY3 == 3, 3,
ifelse(KG2$RELGSTY3 == 4, 2,
ifelse(KG2$RELGSTY3 ==5, 1, NA)))))freq(KG2$RELGSTY3_n)
## KG2$RELGSTY3_n
## Frequency Percent Valid Percent
## 1 62 1.618 2.465
## 2 78 2.035 3.101
## 3 284 7.409 11.292
## 4 1268 33.081 50.417
## 5 823 21.471 32.724
## NA's 1318 34.386
## Total 3833 100.000 100.000종교 목적(내적) 나에겐 오직 신이 존재하기에 삶이 의미가 있다
summary(kgssdata$SPIRIT3)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.000 -1.000 3.000 2.108 5.000 5.000KG2$SPIRIT3_n <- ifelse(kgssdata$SPIRIT3 == 1, 5,
ifelse(kgssdata$SPIRIT3 == 2, 4,
ifelse(kgssdata$SPIRIT3 == 3, 3,
ifelse(kgssdata$SPIRIT3 == 4, 2,
ifelse(kgssdata$SPIRIT3 ==5, 1, NA)))))summary(KG2$SPIRIT3_n)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 2.201 3.000 5.000 1317####다음 종교집단에 대한 의견은? 개신교 _ 높을수록 부정적
freq(KG2$REGGRP1)
## KG2$REGGRP1
## Frequency Percent
## -8 38 0.9914
## -1 1294 33.7595
## 1 332 8.6616
## 2 494 12.8881
## 3 894 23.3238
## 4 485 12.6533
## 5 296 7.7224
## Total 3833 100.0000KG2$REGGRP1_n <- ifelse(KG2$REGGRP1 <0, NA, KG2$REGGRP1)freq(KG2$REGGRP1_n) 
## KG2$REGGRP1_n
## Frequency Percent Valid Percent
## 1 332 8.662 13.27
## 2 494 12.888 19.75
## 3 894 23.324 35.75
## 4 485 12.653 19.39
## 5 296 7.722 11.84
## NA's 1332 34.751
## Total 3833 100.000 100.00다음 종교집단에 대한 의견은? 천주교 _ 높을수록 부정적
freq(KG2$REGGRP2)
## KG2$REGGRP2
## Frequency Percent
## -8 32 0.8349
## -1 1294 33.7595
## 1 290 7.5659
## 2 944 24.6282
## 3 957 24.9674
## 4 197 5.1396
## 5 119 3.1046
## Total 3833 100.0000KG2$REGGRP2_n <- ifelse(KG2$REGGRP2 <0, NA, KG2$REGGRP2)freq(KG2$REGGRP2_n) 
## KG2$REGGRP2_n
## Frequency Percent Valid Percent
## 1 290 7.566 11.568
## 2 944 24.628 37.655
## 3 957 24.967 38.173
## 4 197 5.140 7.858
## 5 119 3.105 4.747
## NA's 1326 34.594
## Total 3833 100.000 100.000다음 종교집단에 대한 의견은? 불교 _ 높을수록 부정적
freq(KG2$REGGRP3)
## KG2$REGGRP3
## Frequency Percent
## -8 28 0.7305
## -1 1294 33.7595
## 1 288 7.5137
## 2 962 25.0978
## 3 944 24.6282
## 4 180 4.6961
## 5 137 3.5742
## Total 3833 100.0000KG2$REGGRP3_n <- ifelse(KG2$REGGRP3 <0, NA, KG2$REGGRP3)freq(KG2$REGGRP3_n)
## KG2$REGGRP3_n
## Frequency Percent Valid Percent
## 1 288 7.514 11.470
## 2 962 25.098 38.311
## 3 944 24.628 37.595
## 4 180 4.696 7.168
## 5 137 3.574 5.456
## NA's 1322 34.490
## Total 3833 100.000 100.000다음 종교집단에 대한 의견은? 원불교 _ 높을수록 부정적
freq(kgssdata$REGGRP4)
## kgssdata$REGGRP4
## Frequency Percent
## -8 99 2.583
## -1 1294 33.759
## 1 79 2.061
## 2 412 10.749
## 3 1361 35.507
## 4 361 9.418
## 5 227 5.922
## Total 3833 100.000KG2$REGGRP4_n <- ifelse(kgssdata$REGGRP4 <0, NA, kgssdata$REGGRP4)
freq(KG2$REGGRP4_n) 
## KG2$REGGRP4_n
## Frequency Percent Valid Percent
## 1 79 2.061 3.238
## 2 412 10.749 16.885
## 3 1361 35.507 55.779
## 4 361 9.418 14.795
## 5 227 5.922 9.303
## NA's 1393 36.342
## Total 3833 100.000 100.000다음 종교집단에 대한 의견은? 천도교 _ 높을수록 부정적
freq(KG2$REGGRP5)
## KG2$REGGRP5
## Frequency Percent
## -8 133 3.4699
## -1 1294 33.7595
## 1 36 0.9392
## 2 256 6.6788
## 3 1407 36.7075
## 4 412 10.7488
## 5 295 7.6963
## Total 3833 100.0000KG2$REGGRP5_n <- ifelse(KG2$REGGRP5 <0, NA, KG2$REGGRP5)
freq(KG2$REGGRP5_n)
## KG2$REGGRP5_n
## Frequency Percent Valid Percent
## 1 36 0.9392 1.496
## 2 256 6.6788 10.640
## 3 1407 36.7075 58.479
## 4 412 10.7488 17.124
## 5 295 7.6963 12.261
## NA's 1427 37.2293
## Total 3833 100.0000 100.000다음 종교집단에 대한 의견은? 이슬람교 _ 높을수록 부정적
freq(KG2$REGGRP6)
## KG2$REGGRP6
## Frequency Percent
## -8 132 3.4438
## -1 1294 33.7595
## 1 35 0.9131
## 2 187 4.8787
## 3 1234 32.1941
## 4 488 12.7315
## 5 463 12.0793
## Total 3833 100.0000KG2$REGGRP6_n <- ifelse(KG2$REGGRP6 <0, NA, KG2$REGGRP6)
freq(KG2$REGGRP6_n) 
## KG2$REGGRP6_n
## Frequency Percent Valid Percent
## 1 35 0.9131 1.454
## 2 187 4.8787 7.769
## 3 1234 32.1941 51.267
## 4 488 12.7315 20.274
## 5 463 12.0793 19.236
## NA's 1426 37.2032
## Total 3833 100.0000 100.000다음 종교집단에 대한 의견은? 힌두교 _ 높을수록 부정적
freq(KG2$REGGRP7)
## KG2$REGGRP7
## Frequency Percent
## -8 143 3.7308
## -1 1294 33.7595
## 1 33 0.8609
## 2 176 4.5917
## 3 1326 34.5943
## 4 437 11.4010
## 5 424 11.0618
## Total 3833 100.0000KG2$REGGRP7_n <- ifelse(KG2$REGGRP7 <0, NA, KG2$REGGRP7)
freq(KG2$REGGRP7_n) 
## KG2$REGGRP7_n
## Frequency Percent Valid Percent
## 1 33 0.8609 1.377
## 2 176 4.5917 7.346
## 3 1326 34.5943 55.342
## 4 437 11.4010 18.239
## 5 424 11.0618 17.696
## NA's 1437 37.4902
## Total 3833 100.0000 100.000다음 종교집단에 대한 의견은? 유대교 _ 높을수록 부정적
freq(KG2$REGGRP8)
## KG2$REGGRP8
## Frequency Percent
## -8 143 3.7308
## -1 1294 33.7595
## 1 32 0.8349
## 2 214 5.5831
## 3 1359 35.4553
## 4 443 11.5575
## 5 348 9.0791
## Total 3833 100.0000KG2$REGGRP8_n <- ifelse(KG2$REGGRP8 <0, NA, KG2$REGGRP8)
freq(KG2$REGGRP8_n) 
## KG2$REGGRP8_n
## Frequency Percent Valid Percent
## 1 32 0.8349 1.336
## 2 214 5.5831 8.932
## 3 1359 35.4553 56.720
## 4 443 11.5575 18.489
## 5 348 9.0791 14.524
## NA's 1437 37.4902
## Total 3833 100.0000 100.000다음 종교집단에 대한 의견은? 무교 _ 높을수록 부정적
freq(KG2$REGGRP9)
## KG2$REGGRP9
## Frequency Percent
## -8 69 1.800
## -1 1294 33.759
## 1 205 5.348
## 2 430 11.218
## 3 1502 39.186
## 4 180 4.696
## 5 153 3.992
## Total 3833 100.000KG2$REGGRP9_n <- ifelse(KG2$REGGRP9 <0, NA, KG2$REGGRP9)
freq(KG2$REGGRP9_n) 
## KG2$REGGRP9_n
## Frequency Percent Valid Percent
## 1 205 5.348 8.300
## 2 430 11.218 17.409
## 3 1502 39.186 60.810
## 4 180 4.696 7.287
## 5 153 3.992 6.194
## NA's 1363 35.560
## Total 3833 100.000 100.000다음 종교집단에 대한 의견(개신교, 무교 제외)
KG2$REGGRP_t1_n <- (
+KG2$REGGRP2_n
+KG2$REGGRP3_n
+KG2$REGGRP4_n
+KG2$REGGRP5_n
+KG2$REGGRP6_n
+KG2$REGGRP7_n
+KG2$REGGRP8_n)/7
freq(KG2$REGGRP_t1_n)
## KG2$REGGRP_t1_n
## Frequency Percent Valid Percent
## 1 25 0.65223 1.05977
## 1.14285714285714 2 0.05218 0.08478
## 1.28571428571429 1 0.02609 0.04239
## 1.42857142857143 2 0.05218 0.08478
## 1.57142857142857 4 0.10436 0.16956
## 1.71428571428571 3 0.07827 0.12717
## 1.85714285714286 24 0.62614 1.01738
## 2 100 2.60892 4.23908
## 2.14285714285714 24 0.62614 1.01738
## 2.28571428571429 39 1.01748 1.65324
## 2.42857142857143 66 1.72189 2.79780
## 2.57142857142857 105 2.73937 4.45104
## 2.71428571428571 250 6.52231 10.59771
## 2.85714285714286 282 7.35716 11.95422
## 3 564 14.71432 23.90844
## 3.14285714285714 113 2.94808 4.79017
## 3.28571428571429 108 2.81764 4.57821
## 3.42857142857143 92 2.40021 3.89996
## 3.57142857142857 89 2.32194 3.77279
## 3.71428571428571 71 1.85233 3.00975
## 3.85714285714286 53 1.38273 2.24671
## 4 90 2.34803 3.81518
## 4.14285714285714 58 1.51318 2.45867
## 4.28571428571429 33 0.86094 1.39890
## 4.42857142857143 30 0.78268 1.27173
## 4.57142857142857 28 0.73050 1.18694
## 4.71428571428571 25 0.65223 1.05977
## 4.85714285714286 15 0.39134 0.63586
## 5 63 1.64362 2.67062
## NA's 1474 38.45552
## Total 3833 100.00000 100.00000다음 종교집단에 대한 의견(천주교, 불교, 무교 제외)
KG2$REGGRP_t2_n <- (
+KG2$REGGRP4_n
+KG2$REGGRP5_n
+KG2$REGGRP6_n
+KG2$REGGRP7_n
+KG2$REGGRP8_n)/6
freq(KG2$REGGRP_t2_n)
## KG2$REGGRP_t2_n
## Frequency Percent Valid Percent
## 0.833333333333333 26 0.67832 1.10123
## 1 2 0.05218 0.08471
## 1.16666666666667 2 0.05218 0.08471
## 1.5 5 0.13045 0.21177
## 1.66666666666667 117 3.05244 4.95553
## 1.83333333333333 16 0.41743 0.67768
## 2 34 0.88703 1.44007
## 2.16666666666667 60 1.56535 2.54130
## 2.33333333333333 108 2.81764 4.57433
## 2.5 981 25.59353 41.55019
## 2.66666666666667 122 3.18289 5.16730
## 2.83333333333333 128 3.33942 5.42143
## 3 117 3.05244 4.95553
## 3.16666666666667 85 2.21758 3.60017
## 3.33333333333333 186 4.85260 7.87802
## 3.5 67 1.74798 2.83778
## 3.66666666666667 43 1.12184 1.82126
## 3.83333333333333 45 1.17402 1.90597
## 4 34 0.88703 1.44007
## 4.16666666666667 183 4.77433 7.75095
## NA's 1472 38.40334
## Total 3833 100.00000 100.00000직장에서 함께 일하는 동료로 받아들일 것인가? 1이 그렇다 2가 아니다
일본인
freq(KG2$SD1JP)
## KG2$SD1JP
## Frequency Percent
## -8 9 0.2348
## -1 1294 33.7595
## 1 2025 52.8307
## 2 505 13.1751
## Total 3833 100.0000KG2$SD1JP_n <- ifelse(KG2$SD1JP <0, NA, KG2$SD1JP)
KG2$SD1JP_n <- factor(KG2$SD1JP_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD1JP_n)
## KG2$SD1JP_n
## Frequency Percent Valid Percent
## 그렇다 2025 52.83 80.04
## 아니다 505 13.18 19.96
## NA's 1303 33.99
## Total 3833 100.00 100.00중국인
freq(KG2$SD1CN)
## KG2$SD1CN
## Frequency Percent
## -8 15 0.3913
## -1 1294 33.7595
## 1 1912 49.8826
## 2 612 15.9666
## Total 3833 100.0000KG2$SD1CN_n <- ifelse(KG2$SD1CN <0, NA, KG2$SD1CN)
KG2$SD1CN_n <- factor(KG2$SD1CN_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD1CN_n)
## KG2$SD1CN_n
## Frequency Percent Valid Percent
## 그렇다 1912 49.88 75.75
## 아니다 612 15.97 24.25
## NA's 1309 34.15
## Total 3833 100.00 100.00동남아인
freq(KG2$SD1SEA)
## KG2$SD1SEA
## Frequency Percent
## -8 15 0.3913
## -1 1294 33.7595
## 1 1948 50.8218
## 2 576 15.0274
## Total 3833 100.0000KG2$SD1SEA_n <- ifelse(KG2$SD1SEA <0, NA, KG2$SD1SEA)
KG2$SD1SEA_n <- factor(KG2$SD1SEA_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD1SEA_n)
## KG2$SD1SEA_n
## Frequency Percent Valid Percent
## 그렇다 1948 50.82 77.18
## 아니다 576 15.03 22.82
## NA's 1309 34.15
## Total 3833 100.00 100.00미국/캐나다인
freq(KG2$SD1NAM)
## KG2$SD1NAM
## Frequency Percent
## -8 13 0.3392
## -1 1294 33.7595
## 1 2085 54.3960
## 2 441 11.5053
## Total 3833 100.0000KG2$SD1NAM_n <- ifelse(KG2$SD1NAM <0, NA, KG2$SD1NAM)
KG2$SD1NAM_n <- factor(KG2$SD1NAM_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD1NAM_n)
## KG2$SD1NAM_n
## Frequency Percent Valid Percent
## 그렇다 2085 54.40 82.54
## 아니다 441 11.51 17.46
## NA's 1307 34.10
## Total 3833 100.00 100.00유럽인
freq(KG2$SD1EUR)
## KG2$SD1EUR
## Frequency Percent
## -8 15 0.3913
## -1 1294 33.7595
## 1 2050 53.4829
## 2 474 12.3663
## Total 3833 100.0000KG2$SD1EUR_n <- ifelse(KG2$SD1EUR <0, NA, KG2$SD1EUR)
KG2$SD1EUR_n <- factor(KG2$SD1EUR_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD1EUR_n)
## KG2$SD1EUR_n
## Frequency Percent Valid Percent
## 그렇다 2050 53.48 81.22
## 아니다 474 12.37 18.78
## NA's 1309 34.15
## Total 3833 100.00 100.00동네주민으로 받아들일 것인가? 1이 그렇다 2가 아니다
일본인
freq(KG2$SD2JP)
## KG2$SD2JP
## Frequency Percent
## -8 23 0.6001
## -1 1294 33.7595
## 1 2142 55.8831
## 2 374 9.7574
## Total 3833 100.0000KG2$SD2JP_n <- ifelse(KG2$SD2JP <0, NA, KG2$SD2JP)
KG2$SD2JP_n <- factor(KG2$SD2JP_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD2JP_n)
## KG2$SD2JP_n
## Frequency Percent Valid Percent
## 그렇다 2142 55.883 85.14
## 아니다 374 9.757 14.86
## NA's 1317 34.360
## Total 3833 100.000 100.00중국인
freq(KG2$SD2CN)
## KG2$SD2CN
## Frequency Percent
## -8 29 0.7566
## -1 1294 33.7595
## 1 2001 52.2045
## 2 509 13.2794
## Total 3833 100.0000KG2$SD2CN_n <- ifelse(KG2$SD2CN <0, NA, KG2$SD2CN)
KG2$SD2CN_n <- factor(KG2$SD2CN_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD2CN_n)
## KG2$SD2CN_n
## Frequency Percent Valid Percent
## 그렇다 2001 52.20 79.72
## 아니다 509 13.28 20.28
## NA's 1323 34.52
## Total 3833 100.00 100.00동남아인
freq(KG2$SD2SEA)
## KG2$SD2SEA
## Frequency Percent
## -8 33 0.8609
## -1 1294 33.7595
## 1 2026 52.8568
## 2 480 12.5228
## Total 3833 100.0000KG2$SD2SEA_n <- ifelse(KG2$SD2SEA <0, NA, KG2$SD2SEA)
KG2$SD2SEA_n <- factor(KG2$SD2SEA_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD2SEA_n)
## KG2$SD2SEA_n
## Frequency Percent Valid Percent
## 그렇다 2026 52.86 80.85
## 아니다 480 12.52 19.15
## NA's 1327 34.62
## Total 3833 100.00 100.00미국/캐나다인
freq(KG2$SD2NAM)
## KG2$SD2NAM
## Frequency Percent
## -8 31 0.8088
## -1 1294 33.7595
## 1 2150 56.0918
## 2 358 9.3399
## Total 3833 100.0000KG2$SD2NAM_n <- ifelse(KG2$SD2NAM <0, NA, KG2$SD2NAM)
KG2$SD2NAM_n <- factor(KG2$SD2NAM_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD2NAM_n)
## KG2$SD2NAM_n
## Frequency Percent Valid Percent
## 그렇다 2150 56.09 85.73
## 아니다 358 9.34 14.27
## NA's 1325 34.57
## Total 3833 100.00 100.00유럽인
freq(KG2$SD2EUR)
## KG2$SD2EUR
## Frequency Percent
## -8 34 0.887
## -1 1294 33.759
## 1 2122 55.361
## 2 383 9.992
## Total 3833 100.000KG2$SD2EUR_n <- ifelse(KG2$SD2EUR <0, NA, KG2$SD2EUR)
KG2$SD2EUR_n <- factor(KG2$SD2EUR_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD2EUR_n)
## KG2$SD2EUR_n
## Frequency Percent Valid Percent
## 그렇다 2122 55.361 84.71
## 아니다 383 9.992 15.29
## NA's 1328 34.646
## Total 3833 100.000 100.00가까운 친척으로 받아들일 것인가? 1이 그렇다 2가 아니다
일본인
freq(KG2$SD3JP)
## KG2$SD3JP
## Frequency Percent
## -8 14 0.3652
## -1 1294 33.7595
## 1 1704 44.4560
## 2 821 21.4193
## Total 3833 100.0000KG2$SD3JP_n <- ifelse(KG2$SD3JP <0, NA, KG2$SD3JP)
KG2$SD3JP_n <- factor(KG2$SD3JP_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD3JP_n)
## KG2$SD3JP_n
## Frequency Percent Valid Percent
## 그렇다 1704 44.46 67.49
## 아니다 821 21.42 32.51
## NA's 1308 34.12
## Total 3833 100.00 100.00중국인
freq(KG2$SD3CN)
## KG2$SD3CN
## Frequency Percent
## -8 24 0.6261
## -1 1294 33.7595
## 1 1568 40.9079
## 2 947 24.7065
## Total 3833 100.0000KG2$SD3CN_n <- ifelse(KG2$SD3CN <0, NA, KG2$SD3CN)
KG2$SD3CN_n <- factor(KG2$SD3CN_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD3CN_n)
## KG2$SD3CN_n
## Frequency Percent Valid Percent
## 그렇다 1568 40.91 62.35
## 아니다 947 24.71 37.65
## NA's 1318 34.39
## Total 3833 100.00 100.00동남아인
freq(KG2$SD3SEA)
## KG2$SD3SEA
## Frequency Percent
## -8 29 0.7566
## -1 1294 33.7595
## 1 1559 40.6731
## 2 951 24.8109
## Total 3833 100.0000KG2$SD3SEA_n <- ifelse(KG2$SD3SEA <0, NA, KG2$SD3SEA)
KG2$SD3SEA_n <- factor(KG2$SD3SEA_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD3SEA_n)
## KG2$SD3SEA_n
## Frequency Percent Valid Percent
## 그렇다 1559 40.67 62.11
## 아니다 951 24.81 37.89
## NA's 1323 34.52
## Total 3833 100.00 100.00미국/캐나다인
freq(KG2$SD3NAM)
## KG2$SD3NAM
## Frequency Percent
## -8 25 0.6522
## -1 1294 33.7595
## 1 1729 45.1083
## 2 785 20.4800
## Total 3833 100.0000KG2$SD3NAM_n <- ifelse(KG2$SD3NAM <0, NA, KG2$SD3NAM)
KG2$SD3NAM_n <- factor(KG2$SD3NAM_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD3NAM_n)
## KG2$SD3NAM_n
## Frequency Percent Valid Percent
## 그렇다 1729 45.11 68.77
## 아니다 785 20.48 31.23
## NA's 1319 34.41
## Total 3833 100.00 100.00유럽인
freq(KG2$SD3EUR)
## KG2$SD3EUR
## Frequency Percent
## -8 27 0.7044
## -1 1294 33.7595
## 1 1701 44.3778
## 2 811 21.1584
## Total 3833 100.0000KG2$SD3EUR_n <- ifelse(KG2$SD3EUR <0, NA, KG2$SD3EUR)
KG2$SD3EUR_n <- factor(KG2$SD3EUR_n
, levels = c(1:2)
, labels = c("그렇다", "아니다"))
freq(KG2$SD3EUR_n)
## KG2$SD3EUR_n
## Frequency Percent Valid Percent
## 그렇다 1701 44.38 67.71
## 아니다 811 21.16 32.29
## NA's 1321 34.46
## Total 3833 100.00 100.00외국인 노동자가 한국에 감소하기를 바라는가? 높을수록 감소하길 바람
freq(KG2$FORNWKER)
## KG2$FORNWKER
## Frequency Percent
## -8 69 1.800
## -1 1294 33.759
## 1 52 1.357
## 2 421 10.984
## 3 776 20.245
## 4 928 24.211
## 5 293 7.644
## Total 3833 100.000KG2$FORNWKER_n <- ifelse(KG2$FORNWKER <0, NA, KG2$FORNWKER)
freq(KG2$FORNWKER_n)
## KG2$FORNWKER_n
## Frequency Percent Valid Percent
## 1 52 1.357 2.105
## 2 421 10.984 17.045
## 3 776 20.245 31.417
## 4 928 24.211 37.571
## 5 293 7.644 11.862
## NA's 1363 35.560
## Total 3833 100.000 100.000결혼이주 여성이 한국에 감소하기를 바라는가? 높을수록 감소하길 바람
freq(KG2$FORNBRID)
## KG2$FORNBRID
## Frequency Percent
## -8 91 2.374
## -1 1294 33.759
## 1 57 1.487
## 2 487 12.705
## 3 863 22.515
## 4 801 20.897
## 5 240 6.261
## Total 3833 100.000KG2$FORNBRID_n <- ifelse(KG2$FORNBRID <0, NA, KG2$FORNBRID)
freq(KG2$FORNBRID_n)
## KG2$FORNBRID_n
## Frequency Percent Valid Percent
## 1 57 1.487 2.328
## 2 487 12.705 19.894
## 3 863 22.515 35.253
## 4 801 20.897 32.721
## 5 240 6.261 9.804
## NA's 1385 36.134
## Total 3833 100.000 100.000하루 평균 몇 명과 연락하거나 만나는가(가족, 친척 제외)
freq(KG2$CONTACT3)
## KG2$CONTACT3
## Frequency Percent
## -8 8 0.2087
## -1 1294 33.7595
## 1 132 3.4438
## 2 1116 29.1156
## 3 615 16.0449
## 4 378 9.8617
## 5 207 5.4005
## 6 56 1.4610
## 7 27 0.7044
## Total 3833 100.0000KG2$CONTACT3_n <- ifelse(KG2$CONTACT3 <0, NA, KG2$CONTACT3)
freq(KG2$CONTACT3_n)
## KG2$CONTACT3_n
## Frequency Percent Valid Percent
## 1 132 3.4438 5.215
## 2 1116 29.1156 44.093
## 3 615 16.0449 24.299
## 4 378 9.8617 14.935
## 5 207 5.4005 8.179
## 6 56 1.4610 2.213
## 7 27 0.7044 1.067
## NA's 1302 33.9682
## Total 3833 100.0000 100.000국가경제 보호 위해 외국 상품 수입 제한해야 한다_높을수록 동의
freq(KG2$IMPLIMI0)
## KG2$IMPLIMI0
## Frequency Percent
## -8 6 0.1565
## -1 1294 33.7595
## 1 141 3.6786
## 2 292 7.6181
## 3 600 15.6535
## 4 583 15.2100
## 5 572 14.9230
## 6 268 6.9919
## 7 77 2.0089
## Total 3833 100.0000KG2$IMPLIMI0_n <- ifelse(KG2$IMPLIMI0 ==7, 1
, ifelse(KG2$IMPLIMI0==6, 2
, ifelse(KG2$IMPLIMI0 ==5, 3
, ifelse(KG2$IMPLIMI0 == 4, 4
, ifelse(KG2$IMPLIMI0 == 3, 5
, ifelse(KG2$IMPLIMI0 ==2, 6
, ifelse(KG2$IMPLIMI0==1, 7, NA)))))))
freq(KG2$IMPLIMI0_n)
## KG2$IMPLIMI0_n
## Frequency Percent Valid Percent
## 1 77 2.009 3.040
## 2 268 6.992 10.580
## 3 572 14.923 22.582
## 4 583 15.210 23.016
## 5 600 15.654 23.687
## 6 292 7.618 11.528
## 7 141 3.679 5.567
## NA's 1300 33.916
## Total 3833 100.000 100.000타국가와 갈등 있더라도 국가 이익을 추구해야 함_높을수록 동의
freq(KG2$SELFINT0)
## KG2$SELFINT0
## Frequency Percent
## -8 6 0.1565
## -1 1294 33.7595
## 1 251 6.5484
## 2 505 13.1751
## 3 747 19.4887
## 4 453 11.8184
## 5 432 11.2705
## 6 119 3.1046
## 7 26 0.6783
## Total 3833 100.0000KG2$SELFINT0_n <- ifelse(KG2$SELFINT0 ==7, 1
, ifelse(KG2$SELFINT0==6, 2
, ifelse(KG2$SELFINT0 ==5, 3
, ifelse(KG2$SELFINT0 == 4, 4
, ifelse(KG2$SELFINT0 == 3, 5
, ifelse(KG2$SELFINT0 ==2, 6
, ifelse(KG2$SELFINT0==1, 7, NA)))))))
freq(KG2$SELFINT0_n)
## KG2$SELFINT0_n
## Frequency Percent Valid Percent
## 1 26 0.6783 1.026
## 2 119 3.1046 4.698
## 3 432 11.2705 17.055
## 4 453 11.8184 17.884
## 5 747 19.4887 29.491
## 6 505 13.1751 19.937
## 7 251 6.5484 9.909
## NA's 1300 33.9160
## Total 3833 100.0000 100.000외국 영화, 음악, 책 등에 노출되면 한국문화는 손상됨_높을수록 동의
freq(KG2$FORCUL0)
## KG2$FORCUL0
## Frequency Percent
## -8 6 0.1565
## -1 1294 33.7595
## 1 128 3.3394
## 2 241 6.2875
## 3 454 11.8445
## 4 548 14.2969
## 5 650 16.9580
## 6 380 9.9139
## 7 132 3.4438
## Total 3833 100.0000KG2$FORCUL0_n <- ifelse(KG2$FORCUL0 ==7, 1
, ifelse(KG2$FORCUL0==6, 2
, ifelse(KG2$FORCUL0 ==5, 3
, ifelse(KG2$FORCUL0 == 4, 4
, ifelse(KG2$FORCUL0 == 3, 5
, ifelse(KG2$FORCUL0 ==2, 6
, ifelse(KG2$FORCUL0==1, 7, NA)))))))
freq(KG2$FORCUL0_n)
## KG2$FORCUL0_n
## Frequency Percent Valid Percent
## 1 132 3.444 5.211
## 2 380 9.914 15.002
## 3 650 16.958 25.661
## 4 548 14.297 21.634
## 5 454 11.845 17.923
## 6 241 6.288 9.514
## 7 128 3.339 5.053
## NA's 1300 33.916
## Total 3833 100.000 100.000아내는 경력 쌓기보다는 남편 경력 쌓게 도와야
freq(KG2$SEXROLE1)
## KG2$SEXROLE1
## Frequency Percent Valid Percent
## -8 3 0.07827 0.08034
## -1 1294 33.75946 34.65453
## 1 376 9.80955 10.06963
## 2 406 10.59223 10.87306
## 3 534 13.93165 14.30102
## 4 435 11.34881 11.64971
## 5 404 10.54005 10.81950
## 6 152 3.96556 4.07070
## 7 130 3.39160 3.48152
## NA's 99 2.58283
## Total 3833 100.00000 100.00000KG2$SEXROLE1_n <- ifelse(KG2$SEXROLE1 ==7, 1
, ifelse(KG2$SEXROLE1==6, 2
, ifelse(KG2$SEXROLE1 ==5, 3
, ifelse(KG2$SEXROLE1 == 4, 4
, ifelse(KG2$SEXROLE1 == 3, 5
, ifelse(KG2$SEXROLE1 ==2, 6
, ifelse(KG2$SEXROLE1==1, 7, NA)))))))
freq(KG2$SEXROLE1_n)
## KG2$SEXROLE1_n
## Frequency Percent Valid Percent
## 1 130 3.392 5.334
## 2 152 3.966 6.237
## 3 404 10.540 16.578
## 4 435 11.349 17.850
## 5 534 13.932 21.912
## 6 406 10.592 16.660
## 7 376 9.810 15.429
## NA's 1396 36.421
## Total 3833 100.000 100.000가정에서 아버지의 권위는 어떠한 경우에도 존중되어야 함
freq(KG2$PATRACH1)
## KG2$PATRACH1
## Frequency Percent
## -8 6 0.1565
## -1 1294 33.7595
## 1 571 14.8969
## 2 593 15.4709
## 3 683 17.8189
## 4 278 7.2528
## 5 245 6.3919
## 6 108 2.8176
## 7 55 1.4349
## Total 3833 100.0000KG2$PATRACH1_n <- ifelse(KG2$PATRACH1 ==7, 1
, ifelse(KG2$PATRACH1==6, 2
, ifelse(KG2$PATRACH1 ==5, 3
, ifelse(KG2$PATRACH1 == 4, 4
, ifelse(KG2$PATRACH1 == 3, 5
, ifelse(KG2$PATRACH1 ==2, 6
, ifelse(KG2$PATRACH1==1, 7, NA)))))))
freq(KG2$PATRACH1_n)
## KG2$PATRACH1_n
## Frequency Percent Valid Percent
## 1 55 1.435 2.171
## 2 108 2.818 4.264
## 3 245 6.392 9.672
## 4 278 7.253 10.975
## 5 683 17.819 26.964
## 6 593 15.471 23.411
## 7 571 14.897 22.542
## NA's 1300 33.916
## Total 3833 100.000 100.000가정 내 성역할(08_18)_남편은 돈벌고 아내는 가정_높을수록 찬성
freq(KG2$HBBYWK08)
## KG2$HBBYWK08
## Frequency Percent
## -8 8 0.2087
## -1 1294 33.7595
## 1 309 8.0616
## 2 535 13.9577
## 3 566 14.7665
## 4 598 15.6014
## 5 523 13.6447
## Total 3833 100.0000KG2$HBBYWK08_n <- ifelse(KG2$HBBYWK08 ==5, 1,
ifelse(KG2$HBBYWK08 ==4, 2,
ifelse(KG2$HBBYWK08 ==3, 3,
ifelse(KG2$HBBYWK08 ==2, 4
, ifelse(KG2$HBBYWK08==1, 5
, NA)))))
freq(KG2$HBBYWK08_n)
## KG2$HBBYWK08_n
## Frequency Percent Valid Percent
## 1 523 13.645 20.66
## 2 598 15.601 23.63
## 3 566 14.767 22.36
## 4 535 13.958 21.14
## 5 309 8.062 12.21
## NA's 1302 33.968
## Total 3833 100.000 100.00가부장주의 변수 만들기
KG2$FAMILY_n1 <- KG2$SEXROLE1_n + KG2$PATRACH1_n + KG2$HBBYWK08_n
summary(KG2$FAMILY_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.00 10.00 13.00 12.58 15.00 19.00 1407KG2$FAMILY_n1 <- ifelse(KG2$FAMILY_n1<=10, 1,
ifelse(KG2$FAMILY_n1<=13, 2,
ifelse(KG2$FAMILY_n1<=15, 3,
ifelse(KG2$FAMILY_n1<=19, 4, NA))))
freq(KG2$FAMILY_n1)
## KG2$FAMILY_n1
## Frequency Percent Valid Percent
## 1 675 17.61 27.82
## 2 694 18.11 28.61
## 3 508 13.25 20.94
## 4 549 14.32 22.63
## NA's 1407 36.71
## Total 3833 100.00 100.00비본질적(외적) 종교성 2개 합치기
KG2$RELGSTY_t_n <- KG2$RELGSTY2_n + KG2$RELGSTY3_n
summary(KG2$RELGSTY_t_n)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.000 7.000 8.000 7.722 9.000 10.000 1327KG2$RELGSTY_t_n <- ifelse(KG2$RELGSTY_t_n<=7, 1,
ifelse(KG2$RELGSTY_t_n<=8, 2,
ifelse(KG2$RELGSTY_t_n<=9, 3,
ifelse(KG2$RELGSTY_t_n<=10, 4, NA))))
freq(KG2$RELGSTY_t_n)
## KG2$RELGSTY_t_n
## Frequency Percent Valid Percent
## 1 903 23.559 36.03
## 2 885 23.089 35.32
## 3 372 9.705 14.84
## 4 346 9.027 13.81
## NA's 1327 34.620
## Total 3833 100.000 100.00년도 1,2,3으로
freq(KG2$YEAR_n)
## KG2$YEAR_n
## Frequency Percent
## 2008 1508 39.34
## 2013 1294 33.76
## 2018 1031 26.90
## Total 3833 100.00KG2$YEAR_n1 <- ifelse(KG2$YEAR_n == 2008, 1,
ifelse(KG2$YEAR_n ==2013, 2,
ifelse(KG2$YEAR_n ==2018, 3, NA)))6. 변수 재구성 하기 전 원래 변수들 제거하기
* 재구성한 변수들은 공통적으로 "_n"을 포함하고 있으니, 이게 없는 애들만 삭제하면 된다.
KG3 <- dplyr::select(KG2, contains("_n"))*새롭게 만들어진 KG3를 csv 파일로 저장해놓기
write.table(KG3, file = "C:/RRR/kgss/kgss_raw_data/kgss_08_13_18_main.csv" #읽어온 file1.sav를 file1.csv로 새롭게 저장하되
, sep = "," #새 데이터의 열 구분은 ","(csv파일이니깐)
, row.names = FALSE)7. 결측값들 Imputation 하기
library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindmice1 <- mice(KG3, 1)##
## iter imp variable
## 1 1 SEXATT1_n SEXATT2_n SEXATT3_n AGE_n AGE_n_c EDUC_n URBRURAL_n PARTYLR_n NORTHWHO_n NORTHWHO_n2 KRPROUD_n ABORT2_n RELIG_n RELLEAD1_n RELSCI2_n RELFUN1_n RELFUN2_n RELPOWR_n RELDIFF1_n RELEXTM1_n RELMA_n RELFA_n RELUP_n ATTEND_n RELITEN_n PRAYFREQ_n RELACT_n RELIGOUS_n RELGSTY2_n RELGSTY3_n SPIRIT3_n REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n REGGRP5_n REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n REGGRP_t1_n REGGRP_t2_n SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n SD3SEA_n SD3NAM_n SD3EUR_n FORNWKER_n FORNBRID_n CONTACT3_n IMPLIMI0_n SELFINT0_n FORCUL0_n SEXROLE1_n PATRACH1_n HBBYWK08_n FAMILY_n1 RELGSTY_t_n
## 2 1 SEXATT1_n SEXATT2_n SEXATT3_n AGE_n AGE_n_c EDUC_n URBRURAL_n PARTYLR_n NORTHWHO_n NORTHWHO_n2 KRPROUD_n ABORT2_n RELIG_n RELLEAD1_n RELSCI2_n RELFUN1_n RELFUN2_n RELPOWR_n RELDIFF1_n RELEXTM1_n RELMA_n RELFA_n RELUP_n ATTEND_n RELITEN_n PRAYFREQ_n RELACT_n RELIGOUS_n RELGSTY2_n RELGSTY3_n SPIRIT3_n REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n REGGRP5_n REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n REGGRP_t1_n REGGRP_t2_n SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n SD3SEA_n SD3NAM_n SD3EUR_n FORNWKER_n* FORNBRID_n* CONTACT3_n* IMPLIMI0_n SELFINT0_n* FORCUL0_n SEXROLE1_n* PATRACH1_n* HBBYWK08_n FAMILY_n1 RELGSTY_t_n
## 3 1 SEXATT1_n SEXATT2_n SEXATT3_n AGE_n AGE_n_c EDUC_n URBRURAL_n PARTYLR_n NORTHWHO_n NORTHWHO_n2 KRPROUD_n ABORT2_n* RELIG_n RELLEAD1_n* RELSCI2_n RELFUN1_n RELFUN2_n RELPOWR_n RELDIFF1_n* RELEXTM1_n* RELMA_n RELFA_n RELUP_n ATTEND_n RELITEN_n PRAYFREQ_n RELACT_n RELIGOUS_n* RELGSTY2_n* RELGSTY3_n* SPIRIT3_n* REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n REGGRP5_n REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n REGGRP_t1_n REGGRP_t2_n SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n SD3SEA_n SD3NAM_n SD3EUR_n FORNWKER_n* FORNBRID_n CONTACT3_n* IMPLIMI0_n SELFINT0_n* FORCUL0_n SEXROLE1_n PATRACH1_n HBBYWK08_n* FAMILY_n1 RELGSTY_t_n
## 4 1 SEXATT1_n SEXATT2_n SEXATT3_n AGE_n AGE_n_c EDUC_n URBRURAL_n PARTYLR_n NORTHWHO_n NORTHWHO_n2 KRPROUD_n ABORT2_n RELIG_n RELLEAD1_n* RELSCI2_n* RELFUN1_n* RELFUN2_n* RELPOWR_n RELDIFF1_n RELEXTM1_n* RELMA_n RELFA_n RELUP_n ATTEND_n RELITEN_n PRAYFREQ_n RELACT_n* RELIGOUS_n* RELGSTY2_n RELGSTY3_n SPIRIT3_n REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n REGGRP5_n REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n REGGRP_t1_n REGGRP_t2_n SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n SD3SEA_n SD3NAM_n SD3EUR_n FORNWKER_n FORNBRID_n CONTACT3_n IMPLIMI0_n SELFINT0_n FORCUL0_n SEXROLE1_n PATRACH1_n HBBYWK08_n FAMILY_n1 RELGSTY_t_n
## 5 1 SEXATT1_n SEXATT2_n SEXATT3_n AGE_n AGE_n_c EDUC_n URBRURAL_n PARTYLR_n NORTHWHO_n NORTHWHO_n2 KRPROUD_n ABORT2_n RELIG_n RELLEAD1_n RELSCI2_n RELFUN1_n RELFUN2_n RELPOWR_n RELDIFF1_n RELEXTM1_n RELMA_n RELFA_n RELUP_n ATTEND_n RELITEN_n PRAYFREQ_n RELACT_n RELIGOUS_n RELGSTY2_n RELGSTY3_n SPIRIT3_n REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n REGGRP5_n REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n* REGGRP_t1_n REGGRP_t2_n SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n SD3SEA_n SD3NAM_n SD3EUR_n FORNWKER_n* FORNBRID_n CONTACT3_n* IMPLIMI0_n SELFINT0_n* FORCUL0_n* SEXROLE1_n PATRACH1_n HBBYWK08_n FAMILY_n1* RELGSTY_t_n*## Warning: Number of logged events: 334summary(mice1)## Class: mids
## Number of multiple imputations: 1
## Imputation methods:
## SEXATT1_n SEXATT2_n SEXATT3_n SEX_n AGE_n AGE_n_c
## "pmm" "pmm" "pmm" "" "pmm" "polyreg"
## EDUC_n EMPLY_n URBRURAL_n PARTYLR_n party_n1 party_n2
## "pmm" "" "polyreg" "pmm" "" ""
## MARITAL_n NORTHWHO_n NORTHWHO_n2 KRPROUD_n ABORT2_n RELIG_n
## "" "pmm" "logreg" "pmm" "pmm" "polyreg"
## RELLEAD1_n RELSCI2_n RELFUN1_n RELFUN2_n RELPOWR_n RELDIFF1_n
## "pmm" "pmm" "pmm" "pmm" "pmm" "pmm"
## RELEXTM1_n YEAR_n RELMA_n RELFA_n RELUP_n ATTEND_n
## "pmm" "" "polyreg" "polyreg" "polyreg" "pmm"
## RELITEN_n PRAYFREQ_n RELACT_n RELIGOUS_n RELGSTY2_n RELGSTY3_n
## "pmm" "pmm" "pmm" "pmm" "pmm" "pmm"
## SPIRIT3_n REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n REGGRP5_n
## "pmm" "pmm" "pmm" "pmm" "pmm" "pmm"
## REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n REGGRP_t1_n REGGRP_t2_n
## "pmm" "pmm" "pmm" "pmm" "pmm" "pmm"
## SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n
## "logreg" "logreg" "logreg" "logreg" "logreg" "logreg"
## SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n
## "logreg" "logreg" "logreg" "logreg" "logreg" "logreg"
## SD3SEA_n SD3NAM_n SD3EUR_n FORNWKER_n FORNBRID_n CONTACT3_n
## "logreg" "logreg" "logreg" "pmm" "pmm" "pmm"
## IMPLIMI0_n SELFINT0_n FORCUL0_n SEXROLE1_n PATRACH1_n HBBYWK08_n
## "pmm" "pmm" "pmm" "pmm" "pmm" "pmm"
## FAMILY_n1 RELGSTY_t_n YEAR_n1
## "pmm" "pmm" ""
## PredictorMatrix:
## SEXATT1_n SEXATT2_n SEXATT3_n SEX_n AGE_n AGE_n_c EDUC_n EMPLY_n
## SEXATT1_n 0 1 1 1 1 1 1 0
## SEXATT2_n 1 0 1 1 1 1 1 0
## SEXATT3_n 1 1 0 1 1 1 1 0
## SEX_n 1 1 1 0 1 1 1 0
## AGE_n 1 1 1 1 0 1 1 0
## AGE_n_c 1 1 1 1 1 0 1 0
## URBRURAL_n PARTYLR_n party_n1 party_n2 MARITAL_n NORTHWHO_n
## SEXATT1_n 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1
## NORTHWHO_n2 KRPROUD_n ABORT2_n RELIG_n RELLEAD1_n RELSCI2_n RELFUN1_n
## SEXATT1_n 1 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1 1
## RELFUN2_n RELPOWR_n RELDIFF1_n RELEXTM1_n YEAR_n RELMA_n RELFA_n
## SEXATT1_n 1 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1 1
## RELUP_n ATTEND_n RELITEN_n PRAYFREQ_n RELACT_n RELIGOUS_n RELGSTY2_n
## SEXATT1_n 1 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1 1
## RELGSTY3_n SPIRIT3_n REGGRP1_n REGGRP2_n REGGRP3_n REGGRP4_n
## SEXATT1_n 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1
## REGGRP5_n REGGRP6_n REGGRP7_n REGGRP8_n REGGRP9_n REGGRP_t1_n
## SEXATT1_n 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1
## REGGRP_t2_n SD1JP_n SD1CN_n SD1SEA_n SD1NAM_n SD1EUR_n SD2JP_n
## SEXATT1_n 1 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1 1
## SD2CN_n SD2SEA_n SD2NAM_n SD2EUR_n SD3JP_n SD3CN_n SD3SEA_n SD3NAM_n
## SEXATT1_n 1 1 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1 1 1
## SD3EUR_n FORNWKER_n FORNBRID_n CONTACT3_n IMPLIMI0_n SELFINT0_n
## SEXATT1_n 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1
## FORCUL0_n SEXROLE1_n PATRACH1_n HBBYWK08_n FAMILY_n1 RELGSTY_t_n
## SEXATT1_n 1 1 1 1 1 1
## SEXATT2_n 1 1 1 1 1 1
## SEXATT3_n 1 1 1 1 1 1
## SEX_n 1 1 1 1 1 1
## AGE_n 1 1 1 1 1 1
## AGE_n_c 1 1 1 1 1 1
## YEAR_n1
## SEXATT1_n 0
## SEXATT2_n 0
## SEXATT3_n 0
## SEX_n 0
## AGE_n 0
## AGE_n_c 0
## Number of logged events: 334
## it im dep meth out
## 1 0 0 collinear EMPLY_n
## 2 0 0 collinear YEAR_n1
## 3 1 1 REGGRP_t1_n pmm REGGRP_t2_n
## 4 1 1 REGGRP_t2_n pmm REGGRP_t1_n
## 5 1 1 SD1JP_n logreg REGGRP_t1_n, REGGRP_t2_n
## 6 1 1 SD1CN_n logreg REGGRP_t1_n, REGGRP_t2_nKG4 <- complete(mice1) #imputed data8. 변수 중심화하기
반드시 missing value imputation을 한 뒤에 변수 중심화 진행하기
종교성 중심화
summary(KG4$RELIGOUS_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 3.688 5.000 7.000KG4$RELIGOUS_n1 <- KG4$RELIGOUS_n - 3.777
summary(KG4$RELIGOUS_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.77700 -1.77700 0.22300 -0.08903 1.22300 3.22300예배 빈도 중심화
summary(KG4$ATTEND_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 3.402 6.000 8.000KG4$ATTEND_n1 <- KG4$ATTEND_n - 3.416
summary(KG4$ATTEND_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.4160 -2.4160 -1.4160 -0.0137 2.5840 4.5840가부장 중심화
summary(KG4$FAMILY_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.309 3.000 4.000KG4$FAMILY_n2 <- KG4$FAMILY_n1 - 2.384
summary(KG4$FAMILY_n2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.3840 -1.3840 -0.3840 -0.0751 0.6160 1.6160다른 종교 가진 사람이 친척, 가족과 결혼하는 것 허용 중심화
summary(KG4$RELDIFF1_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 1.957 2.000 4.000KG4$RELDIFF1_n1 <- KG4$RELDIFF1_n - 1.869
summary(KG4$RELDIFF1_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.86900 -0.86900 0.13100 0.08769 0.13100 2.13100얼마나 자주 기도? 중심화
summary(KG4$PRAYFREQ_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 1.00 3.00 4.08 8.00 11.00KG4$PRAYFREQ_n1 <- KG4$PRAYFREQ_n - 4.067
summary(KG4$PRAYFREQ_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.06700 -3.06700 -1.06700 0.01336 3.93300 6.93300종교 모임 외 모임 빈도
summary(KG4$RELACT_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 2.916 4.000 9.000KG4$RELACT_n1 <- KG4$RELACT_n - 3.623
summary(KG4$RELACT_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.6230 -2.6230 -2.6230 -0.7073 0.3770 5.3770종교 친구 사귀는데 도움 중심화
summary(KG4$RELGSTY2_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 3.00 4.00 3.64 4.00 5.00KG4$RELGSTY2_n1 <- KG4$RELGSTY2_n - 3.642
summary(KG4$RELGSTY2_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.642000 -0.642000 0.358000 -0.001509 0.358000 1.358000종교 어렵거나 힘들 때 위안 중심화
summary(KG4$RELGSTY3_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 3.995 5.000 5.000KG4$RELGSTY3_n1 <- KG4$RELGSTY3_n - 4.078
summary(KG4$RELGSTY3_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.07800 -0.07800 -0.07800 -0.08348 0.92200 0.92200나에겐 오직 신이 존재하기에 삶이 의미
summary(KG4$SPIRIT3_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.187 3.000 5.000KG4$SPIRIT3_n1 <- KG4$SPIRIT3_n - 2.201
summary(KG4$SPIRIT3_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.2010 -1.2010 -0.2010 -0.0142 0.7990 2.7990이슬람교에 대한 생각 중심화
summary(KG4$REGGRP6_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 3.000 3.486 4.000 5.000KG4$REGGRP6_n1 <- KG4$REGGRP6_n - 3.481
summary(KG4$REGGRP6_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.481000 -0.481000 -0.481000 0.004781 0.519000 1.519000개신교, 무교 제외 타종교에 대한 생각 중심화
summary(KG4$REGGRP_t1_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.714 3.000 3.102 3.429 5.000KG4$REGGRP_t1_n1 <- KG4$REGGRP_t1_n - 3.118
summary(KG4$REGGRP_t1_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.1180 -0.4037 -0.1180 -0.0164 0.3106 1.8820천주교, 불교, 무교 제외 타종교에 대한 생각 중심화
summary(KG4$REGGRP_t2_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8333 2.5000 2.5000 2.7632 3.1667 4.1667KG4$REGGRP_t2_n1 <- KG4$REGGRP_t2_n - 2.775
summary(KG4$REGGRP_t2_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.9417 -0.2750 -0.2750 -0.0118 0.3917 1.3917외국 문화가 한국 문화 손상 중심화
summary(KG4$FORCUL0_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 4.005 5.000 7.000KG4$FORCUL0_n1 <- KG4$FORCUL0_n - 3.808
summary(KG4$FORCUL0_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.8080 -0.8080 0.1920 0.1972 1.1920 3.1920한국인 자긍심 중심화
summary(KG4$KRPROUD_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 3.000 3.151 4.000 4.000KG4$KRPROUD_n1 <- KG4$KRPROUD_n - 3.153
summary(KG4$KRPROUD_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.153000 -0.153000 -0.153000 -0.001943 0.847000 0.847000종교 지도자 투표시 영향 중심화
summary(KG4$RELLEAD1_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.223 3.000 5.000KG4$RELLEAD1_n1 <- KG4$RELLEAD1_n - 2.072
summary(KG4$RELLEAD1_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.0720 -1.0720 -0.0720 0.1513 0.9280 2.9280혼전섹스 중심화
summary(KG4$SEXATT1_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.393 3.000 4.000KG4$SEXATT1_n1 <- KG4$SEXATT1_n - 2.392
summary(KG4$SEXATT1_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.3920000 -0.3920000 -0.3920000 0.0006428 0.6080000 1.6080000혼외섹스 중심화
summary(KG4$SEXATT2_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 3.000 4.000 3.606 4.000 4.000KG4$SEXATT2_n1 <- KG4$SEXATT2_n - 3.606
summary(KG4$SEXATT2_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.6060000 -0.6060000 0.3940000 0.0000527 0.3940000 0.3940000비본질적 종교성 중심화
summary(KG4$RELGSTY_t_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 2.065 3.000 4.000KG4$RELGSTY_t_n1 <- KG4$RELGSTY_t_n - 2.064
summary(KG4$RELGSTY_t_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.0640000 -1.0640000 -0.0640000 0.0009622 0.9360000 1.9360000타종교 인식 합 중심화
summary(KG4$REGGRP_t1_n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.714 3.000 3.102 3.429 5.000KG4$REGGRP_t1_n1 <- KG4$REGGRP_t1_n - 3.33
summary(KG4$REGGRP_t1_n1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.33000 -0.61571 -0.33000 -0.22840 0.09857 1.670009. 만들어진 최종 객체(KG4)를 csv 파일로 저장하기
write.table(KG4, file = "C:/RRR/kgss/kgss_raw_data/kgss_08_13_18_main2.csv"
, sep = ","
, row.names = FALSE)9. 저장!
save.image("C:/RRR/kgss/kgssdata_210303.RData")