library(dplyr) #2
##
## 다음의 패키지를 부착합니다: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(psych)
library(ggplot2)
##
## 다음의 패키지를 부착합니다: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
data("diamonds")
diamonds %>% filter(price!=1000&cut=="Ideal")%>%head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.23 Ideal J VS1 62.8 56 340 3.93 3.9 2.46
## 3 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
diamonds %>% filter(price!=1000&color=="E") %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
diamonds %>% filter(carat<1|carat>5) %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
diamonds %>% filter(cut%in%c("Ideal","Good")) %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 3 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
diamonds %>% select(carat,depth,price) %>%
filter(depth==max(depth)|price==min(price))
## # A tibble: 4 × 3
## carat depth price
## <dbl> <dbl> <int>
## 1 0.23 61.5 326
## 2 0.21 59.8 326
## 3 0.5 79 2579
## 4 0.5 79 2579
diamonds %>% summarise(AvgPrice=mean(price),
MedianPrice=median(price),
AvgCarat=mean(carat))
## # A tibble: 1 × 3
## AvgPrice MedianPrice AvgCarat
## <dbl> <dbl> <dbl>
## 1 3933. 2401 0.798
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ✔ readr 2.1.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
diamonds %>% mutate(Ratio=price/carat,Double=Ratio*2) %>%
head(3)
## # A tibble: 3 × 12
## carat cut color clarity depth table price x y z Ratio Double
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 1417. 2835.
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 1552. 3105.
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 1422. 2843.
diamonds %>% group_by(cut) %>%
summarise(n=n()) %>%
mutate(total=sum(n),pct=n/total*100)
## # A tibble: 5 × 4
## cut n total pct
## <ord> <int> <int> <dbl>
## 1 Fair 1610 53940 2.98
## 2 Good 4906 53940 9.10
## 3 Very Good 12082 53940 22.4
## 4 Premium 13791 53940 25.6
## 5 Ideal 21551 53940 40.0
quantile(diamonds$price)
## 0% 25% 50% 75% 100%
## 326.00 950.00 2401.00 5324.25 18823.00
diamonds <-diamonds %>% mutate(price_class=ifelse(price>=5324.25,"best",
ifelse(price>=2401,"good",
ifelse(price>=950,"normal","bad"))))
table(diamonds$price_class)
##
## bad best good normal
## 13483 13485 13496 13476
diamonds %>% group_by(cut) %>%
summarize(AvgPrice=mean(price)) %>%
arrange(desc(AvgPrice))
## # A tibble: 5 × 2
## cut AvgPrice
## <ord> <dbl>
## 1 Premium 4584.
## 2 Fair 4359.
## 3 Very Good 3982.
## 4 Good 3929.
## 5 Ideal 3458.
data<-read.csv('C:/data/Data1.csv',header=TRUE,sep=",")
library(dplyr)
glimpse(data) %>% head(5)
## Rows: 1,925
## Columns: 26
## $ Q1 <int> 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
## $ Q2 <int> 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 2, 2, …
## $ Q3 <int> 2, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 2, 4, 4, 4, 4, 4, 3, 2, 3, …
## $ Q4 <int> 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 2, 4, 4, 4, 2, 2, 4, …
## $ Q5 <int> 4, 4, 2, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 4, 4, 4, 4, 3, 1, 2, …
## $ Q6 <int> 2, 3, 4, 4, 4, 4, 4, 4, 1, 2, 2, 2, 4, 4, 3, 5, 2, 2, 1, 4, …
## $ Q7 <int> 2, 2, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 5, 4, 4, 5, 4, 3, 4, 4, …
## $ Q8 <int> 4, 4, 4, 4, 4, 4, 5, 5, 2, 2, 4, 4, 4, 4, 3, 5, 4, 2, 4, 4, …
## $ Q9 <int> 4, 4, 4, 4, 2, 4, 5, 5, 3, 4, 4, 4, 2, 2, 4, 5, 2, 4, 2, 4, …
## $ Q10 <int> 4, 4, 2, 4, 4, 4, 5, 5, 2, 4, 2, 4, 4, 4, 3, 4, 4, 3, 2, 3, …
## $ Q11 <int> 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 3, 4, 4, 4, 4, 5, 4, 3, 3, …
## $ Q12 <int> 4, 4, 4, 4, 4, 4, 5, 5, 3, 4, 4, 3, 4, 3, 3, 4, 5, 4, 4, 2, …
## $ Q13 <int> 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 2, 4, 4, 4, 5, 4, 4, 4, …
## $ Q14 <int> 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 4, 3, 4, 5, 4, 5, 4, 4, 4, …
## $ Q15 <int> 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 4, 3, 1, 4, 4, 4, 5, 4, 4, 4, …
## $ Q16 <int> 4, 4, 4, 4, 4, 4, 5, 2, 4, 4, 4, 4, 4, 4, 5, 4, 5, 4, 4, 4, …
## $ Q17 <int> 4, 3, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 3, 2, 4, 5, 4, 4, 3, 4, …
## $ Q18 <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 4, 4, 4, …
## $ Q19 <int> 4, 2, 4, 4, 4, 4, 4, 2, 4, 2, 4, 4, 1, 4, 4, 4, 5, 4, 2, 3, …
## $ Q20 <int> 4, 1, 3, 4, 4, 4, 4, 2, 4, 2, 4, 4, 4, 2, 4, 5, 5, 4, 2, 4, …
## $ Gender1 <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, …
## $ EDU1 <int> 1, 1, 2, 1, 2, 1, 1, 1, 4, 3, 2, 1, 1, 3, 3, 2, 1, 1, 1, 4, …
## $ BF <dbl> 3.4, 4.0, 3.6, 4.2, 4.0, 4.0, 3.6, 3.6, 3.6, 3.2, 4.0, 3.2, …
## $ BM <dbl> 3.2, 3.4, 3.6, 4.0, 3.6, 4.0, 4.6, 4.6, 2.2, 3.2, 3.2, 3.6, …
## $ Happiness <dbl> 4.0, 4.0, 3.8, 4.0, 4.0, 4.0, 4.8, 4.4, 3.8, 4.0, 4.0, 3.4, …
## $ Peace <dbl> 4.0, 2.8, 3.8, 4.0, 4.0, 4.0, 3.8, 2.4, 4.0, 3.2, 4.0, 3.9, …
## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20
## 1 4 4 2 3 4 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4
## 2 4 4 4 4 4 3 2 4 4 4 4 4 4 4 4 4 3 4 2 1
## 3 4 4 4 4 2 4 4 4 4 2 4 4 4 4 3 4 4 4 4 3
## 4 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 5 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4
## Gender1 EDU1 BF BM Happiness Peace
## 1 0 1 3.4 3.2 4.0 4.0
## 2 0 1 4.0 3.4 4.0 2.8
## 3 0 2 3.6 3.6 3.8 3.8
## 4 0 1 4.2 4.0 4.0 4.0
## 5 0 2 4.0 3.6 4.0 4.0
names(data)
## [1] "Q1" "Q2" "Q3" "Q4" "Q5" "Q6"
## [7] "Q7" "Q8" "Q9" "Q10" "Q11" "Q12"
## [13] "Q13" "Q14" "Q15" "Q16" "Q17" "Q18"
## [19] "Q19" "Q20" "Gender1" "EDU1" "BF" "BM"
## [25] "Happiness" "Peace"
data %>% select(21:26) %>% head(10)
## Gender1 EDU1 BF BM Happiness Peace
## 1 0 1 3.4 3.2 4.0 4.0
## 2 0 1 4.0 3.4 4.0 2.8
## 3 0 2 3.6 3.6 3.8 3.8
## 4 0 1 4.2 4.0 4.0 4.0
## 5 0 2 4.0 3.6 4.0 4.0
## 6 0 1 4.0 4.0 4.0 4.0
## 7 0 1 3.6 4.6 4.8 3.8
## 8 0 1 3.6 4.6 4.4 2.4
## 9 1 4 3.6 2.2 3.8 4.0
## 10 0 3 3.2 3.2 4.0 3.2
data[,c(21:26)] %>% head(10)
## Gender1 EDU1 BF BM Happiness Peace
## 1 0 1 3.4 3.2 4.0 4.0
## 2 0 1 4.0 3.4 4.0 2.8
## 3 0 2 3.6 3.6 3.8 3.8
## 4 0 1 4.2 4.0 4.0 4.0
## 5 0 2 4.0 3.6 4.0 4.0
## 6 0 1 4.0 4.0 4.0 4.0
## 7 0 1 3.6 4.6 4.8 3.8
## 8 0 1 3.6 4.6 4.4 2.4
## 9 1 4 3.6 2.2 3.8 4.0
## 10 0 3 3.2 3.2 4.0 3.2
data$Q1+data$Q2
## [1] 8 8 8 9 8 8 6 6 8 8 8 8 8 6 8 8 6 8 6 6 7 5 10 8
## [25] 8 8 8 5 8 8 7 8 8 8 8 8 8 8 8 8 7 8 8 4 8 8 8 3
## [49] 8 7 4 8 7 5 2 6 8 8 6 6 8 8 8 5 8 9 7 9 8 3 8 8
## [73] 8 6 7 8 8 6 2 5 3 6 6 10 3 8 8 5 8 8 6 7 8 9 8 4
## [97] 8 10 8 6 8 6 7 6 8 8 4 7 8 7 8 6 7 7 6 7 8 5 8 8
## [121] 5 10 6 8 7 6 8 6 6 4 8 6 6 9 7 8 9 8 8 4 8 8 6 5
## [145] 7 9 7 4 6 8 8 4 6 7 8 8 7 6 8 8 8 6 5 7 8 4 7 8
## [169] 8 8 8 8 8 10 9 6 7 8 8 6 7 8 8 8 6 8 8 7 7 8 8 6
## [193] 6 8 8 10 8 5 8 7 8 7 6 7 6 8 6 4 6 8 8 6 8 9 8 5
## [217] 6 8 7 4 6 7 8 8 8 7 7 7 7 6 6 6 4 4 6 8 4 6 6 8
## [241] 9 7 7 4 6 7 5 8 5 8 6 8 2 8 8 7 8 7 7 7 7 5 8 4
## [265] 7 9 6 8 4 9 8 8 7 8 7 6 3 3 8 8 8 8 8 7 5 8 7 8
## [289] 8 7 5 5 9 6 7 9 8 6 8 7 8 8 8 9 4 8 6 6 4 4 8 5
## [313] 8 7 8 6 7 3 6 4 7 6 7 7 6 8 8 8 7 10 8 6 8 5 9 8
## [337] 4 8 8 8 8 9 6 8 10 7 8 6 8 8 8 8 8 7 7 5 7 8 4 6
## [361] 8 8 8 8 4 5 6 6 7 8 7 8 6 6 6 6 8 8 7 6 4 4 8 8
## [385] 8 8 7 8 7 8 5 6 6 4 8 3 8 8 10 6 8 6 7 8 4 3 7 8
## [409] 7 3 4 6 7 8 8 6 8 8 8 8 7 7 10 8 8 6 8 7 4 6 8 7
## [433] 8 6 6 8 10 8 4 9 8 7 8 8 6 4 8 5 8 3 6 8 7 7 7 7
## [457] 7 8 8 7 8 6 8 8 5 7 6 6 10 10 8 6 7 8 4 8 7 6 8 8
## [481] 6 7 4 8 8 9 5 6 4 8 8 8 8 8 8 7 8 8 5 10 7 4 8 6
## [505] 8 8 8 7 5 7 7 5 8 4 7 9 10 8 10 7 6 8 8 8 9 4 4 8
## [529] 5 5 3 7 8 8 8 3 7 4 5 8 8 9 4 8 8 6 6 8 5 8 4 5
## [553] 5 5 4 4 5 8 8 7 5 9 7 8 7 8 7 4 8 4 6 8 4 8 3 7
## [577] 6 8 5 8 7 7 7 3 9 8 6 8 8 8 6 8 6 4 6 5 5 6 8 6
## [601] 7 9 4 7 10 7 8 6 8 6 4 9 7 9 8 8 7 9 8 6 8 7 7 8
## [625] 6 7 6 4 8 7 7 8 7 7 6 6 8 7 7 6 7 7 4 5 4 6 5 7
## [649] 7 7 8 8 7 8 4 5 7 2 5 5 5 4 4 5 7 2 5 5 4 4 4 2
## [673] 7 8 4 8 6 7 6 8 6 7 5 9 8 6 10 7 7 8 7 8 5 6 5 8
## [697] 7 4 6 9 9 7 5 8 6 7 8 8 5 8 7 9 7 8 8 8 9 8 7 8
## [721] 5 7 5 5 4 4 5 4 4 4 5 7 8 6 8 8 8 6 3 7 9 8 8 4
## [745] 8 7 8 8 6 7 5 6 5 8 10 7 8 7 5 5 8 6 6 6 6 4 6 3
## [769] 2 6 5 4 5 4 6 3 2 6 3 7 6 10 7 8 6 7 7 8 8 4 5 8
## [793] 6 6 7 8 8 6 6 10 5 8 4 7 8 6 8 8 7 8 9 8 7 2 5 4
## [817] 8 8 4 4 4 4 8 7 10 8 7 4 7 7 8 7 6 7 7 10 4 4 7 8
## [841] 6 7 5 5 7 10 6 4 8 5 5 5 2 4 5 4 6 4 10 9 8 8 7 6
## [865] 8 8 8 8 8 6 8 9 7 7 5 7 7 8 10 8 7 8 5 8 4 7 5 8
## [889] 5 6 2 6 5 6 2 6 2 6 5 4 6 2 7 6 8 10 4 8 8 5 7 8
## [913] 6 7 6 5 8 7 8 8 8 7 6 7 6 4 8 5 5 7 8 6 7 7 6 10
## [937] 5 2 5 4 6 2 4 5 8 7 7 8 7 4 8 4 4 8 8 8 8 7 8 6
## [961] 6 8 8 9 4 5 4 5 5 8 8 4 9 8 7 6 6 7 8 9 9 5 6 6
## [985] 7 7 8 6 8 7 7 7 8 7 5 8 6 7 6 6 8 6 8 7 7 6 8 6
## [1009] 8 8 4 7 7 3 7 7 8 8 8 8 8 8 4 6 8 8 7 6 8 6 8 8
## [1033] 4 8 6 10 8 8 9 8 9 7 8 7 8 8 8 6 8 8 6 8 6 9 5 7
## [1057] 6 8 10 5 8 6 7 6 6 4 6 7 8 6 8 4 8 5 8 7 7 6 7 6
## [1081] 4 7 10 8 5 10 9 5 7 8 6 8 7 7 8 6 7 8 8 8 6 7 7 7
## [1105] 9 8 4 7 9 8 8 7 6 8 4 4 8 6 8 10 2 2 7 10 8 5 5 6
## [1129] 8 8 7 8 6 5 5 4 9 8 3 7 7 8 8 8 4 6 7 7 7 6 8 5
## [1153] 7 8 5 8 6 4 4 8 6 6 6 6 8 10 10 10 9 8 7 6 7 7 8 5
## [1177] 8 7 6 7 6 5 9 7 9 4 6 8 8 7 7 4 6 8 9 4 6 8 6 7
## [1201] 8 7 7 6 7 8 6 7 5 7 7 5 6 7 8 6 8 7 7 10 9 10 6 8
## [1225] 8 8 6 9 6 7 5 8 6 7 6 6 8 8 8 4 5 7 4 6 9 6 5 8
## [1249] 6 6 5 8 7 8 7 8 8 5 7 7 4 7 4 6 8 5 10 8 6 8 4 8
## [1273] 8 8 10 8 6 6 8 6 6 7 8 7 6 7 5 5 8 10 8 7 8 8 7 8
## [1297] 4 6 8 7 7 8 4 7 10 8 7 8 6 5 6 5 7 8 8 8 8 8 8 8
## [1321] 8 4 8 10 6 8 10 6 4 7 9 7 8 4 5 7 6 4 8 8 7 8 6 8
## [1345] 6 6 5 6 7 5 4 7 5 6 8 8 5 6 8 6 6 8 4 7 8 8 7 7
## [1369] 7 7 4 4 5 6 5 6 9 8 5 8 7 7 7 3 8 6 7 7 6 6 4 5
## [1393] 8 7 7 8 7 7 7 8 8 8 7 6 8 7 8 7 6 7 5 5 7 8 8 6
## [1417] 9 8 7 7 8 7 7 9 8 5 6 8 8 4 4 6 8 8 8 8 7 4 9 5
## [1441] 10 8 7 8 8 4 4 4 8 8 4 9 4 7 8 7 6 6 4 6 7 8 7 8
## [1465] 8 8 6 8 7 7 6 8 7 7 7 5 8 8 4 7 7 8 5 6 6 6 8 8
## [1489] 7 6 8 4 8 7 4 6 7 9 8 6 8 7 4 8 6 6 7 4 8 4 7 7
## [1513] 4 7 7 7 6 8 6 8 6 8 7 8 8 8 5 8 7 4 8 8 6 8 8 8
## [1537] 5 6 8 9 7 5 8 7 5 8 5 7 8 7 9 6 7 7 8 7 7 8 5 3
## [1561] 5 6 5 6 7 6 6 8 8 8 4 6 6 7 7 9 5 8 9 9 9 5 5 8
## [1585] 6 5 9 7 7 6 6 5 7 5 7 7 8 6 9 8 7 8 7 5 6 7 9 9
## [1609] 8 6 8 7 7 9 7 8 8 8 8 8 7 6 8 7 8 7 6 6 5 8 7 8
## [1633] 7 6 6 7 6 5 6 9 6 8 7 8 5 5 9 6 3 6 8 4 6 6 8 5
## [1657] 6 6 10 6 9 2 7 5 8 5 9 8 8 8 10 8 8 4 7 10 8 6 6 10
## [1681] 10 7 7 8 8 4 6 8 8 8 7 7 7 7 7 7 9 4 6 7 6 4 8 6
## [1705] 10 10 8 6 9 7 5 6 5 7 10 8 8 6 7 9 8 9 6 2 8 10 5 7
## [1729] 7 8 9 8 6 8 8 6 7 7 8 8 7 8 6 7 7 4 7 6 5 8 7 9
## [1753] 9 10 8 9 6 6 6 8 7 10 6 10 6 6 7 8 6 8 8 8 6 6 9 7
## [1777] 9 7 7 7 9 9 8 5 10 9 6 8 8 5 6 5 5 7 7 5 9 8 7 7
## [1801] 7 8 7 8 10 9 8 6 8 8 4 4 9 7 5 8 4 8 10 5 7 10 9 10
## [1825] 6 8 7 7 6 7 8 4 6 7 7 5 7 4 7 8 7 7 5 9 7 8 7 8
## [1849] 8 8 7 8 6 6 8 7 4 6 6 6 8 7 3 8 8 4 7 8 10 7 10 8
## [1873] 5 6 5 6 7 5 9 8 10 8 8 8 6 4 10 8 10 7 5 8 7 9 6 8
## [1897] 6 10 4 7 8 8 8 10 10 8 6 6 8 7 8 8 8 7 5 7 8 8 6 8
## [1921] 4 5 9 8 6
data %>% count(Gender1)
## Gender1 n
## 1 0 1136
## 2 1 789
data$Gender1<-ifelse(data$Gender1==1,0,1)
glimpse(data)
## Rows: 1,925
## Columns: 26
## $ Q1 <int> 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
## $ Q2 <int> 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 2, 2, …
## $ Q3 <int> 2, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 2, 4, 4, 4, 4, 4, 3, 2, 3, …
## $ Q4 <int> 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 2, 4, 4, 4, 2, 2, 4, …
## $ Q5 <int> 4, 4, 2, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 4, 4, 4, 4, 3, 1, 2, …
## $ Q6 <int> 2, 3, 4, 4, 4, 4, 4, 4, 1, 2, 2, 2, 4, 4, 3, 5, 2, 2, 1, 4, …
## $ Q7 <int> 2, 2, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 5, 4, 4, 5, 4, 3, 4, 4, …
## $ Q8 <int> 4, 4, 4, 4, 4, 4, 5, 5, 2, 2, 4, 4, 4, 4, 3, 5, 4, 2, 4, 4, …
## $ Q9 <int> 4, 4, 4, 4, 2, 4, 5, 5, 3, 4, 4, 4, 2, 2, 4, 5, 2, 4, 2, 4, …
## $ Q10 <int> 4, 4, 2, 4, 4, 4, 5, 5, 2, 4, 2, 4, 4, 4, 3, 4, 4, 3, 2, 3, …
## $ Q11 <int> 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 3, 4, 4, 4, 4, 5, 4, 3, 3, …
## $ Q12 <int> 4, 4, 4, 4, 4, 4, 5, 5, 3, 4, 4, 3, 4, 3, 3, 4, 5, 4, 4, 2, …
## $ Q13 <int> 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 2, 4, 4, 4, 5, 4, 4, 4, …
## $ Q14 <int> 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 4, 3, 4, 5, 4, 5, 4, 4, 4, …
## $ Q15 <int> 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 4, 3, 1, 4, 4, 4, 5, 4, 4, 4, …
## $ Q16 <int> 4, 4, 4, 4, 4, 4, 5, 2, 4, 4, 4, 4, 4, 4, 5, 4, 5, 4, 4, 4, …
## $ Q17 <int> 4, 3, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 3, 2, 4, 5, 4, 4, 3, 4, …
## $ Q18 <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 4, 4, 4, …
## $ Q19 <int> 4, 2, 4, 4, 4, 4, 4, 2, 4, 2, 4, 4, 1, 4, 4, 4, 5, 4, 2, 3, …
## $ Q20 <int> 4, 1, 3, 4, 4, 4, 4, 2, 4, 2, 4, 4, 4, 2, 4, 5, 5, 4, 2, 4, …
## $ Gender1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, …
## $ EDU1 <int> 1, 1, 2, 1, 2, 1, 1, 1, 4, 3, 2, 1, 1, 3, 3, 2, 1, 1, 1, 4, …
## $ BF <dbl> 3.4, 4.0, 3.6, 4.2, 4.0, 4.0, 3.6, 3.6, 3.6, 3.2, 4.0, 3.2, …
## $ BM <dbl> 3.2, 3.4, 3.6, 4.0, 3.6, 4.0, 4.6, 4.6, 2.2, 3.2, 3.2, 3.6, …
## $ Happiness <dbl> 4.0, 4.0, 3.8, 4.0, 4.0, 4.0, 4.8, 4.4, 3.8, 4.0, 4.0, 3.4, …
## $ Peace <dbl> 4.0, 2.8, 3.8, 4.0, 4.0, 4.0, 3.8, 2.4, 4.0, 3.2, 4.0, 3.9, …
data %>% count(EDU1)
## EDU1 n
## 1 1 233
## 2 2 472
## 3 3 1022
## 4 4 198
attach(data) # vector에 직접 접근 가능 근데 잘 안쓰임.
EDU1[EDU1 ==4]<-1
EDU1[EDU1 ==3]<-2
EDU1[EDU1 ==2]<-3
EDU1[EDU1 ==1]<-4
detach(data)