#합격율이 높았던 시험
a <- Atest2 %>% filter(필기합격률 == max(필기합격률))
View(a)
#실기
a <- Atest2 %>% filter(실기합격률 == max(실기합격률))
View(a)
#합격율이 낮았던 시험
#필기
a <- Atest2 %>% filter(필기합격률 == min(필기합격률))
View(a)
#실기
a <- Atest2 %>% filter(실기합격률 == min(실기합격률))
View(a)
#합격율이 높았던 시험
#필기
a <- Atest2 %>% filter(필기합격률 == max(필기합격률))
View(a)
#합격율이 높았던 시험
#필기
a <- Atest2 %>% filter(필기합격률 == max(필기합격률))
#합격율이 높았던 시험
#필기
a <- Atest2 %>% filter(필기합격률 == max(필기합격률))
View(a)
#실기
a <- Atest2 %>% filter(실기합격률 == max(실기합격률))
View(a)
#합격율이 낮았던 시험
#필기
a <- Atest2 %>% filter(필기합격률 == min(필기합격률))
View(a)
#실기
a <- Atest2 %>% filter(실기합격률 == min(실기합격률))
View(a)
# 가장 높은 필기접수 응시율
a <- Atest2 %>% filter(필기접수 == max(필기접수))
View(a)
View(Atest2)
Atest2 <- Gtest[grep("20", Gtest$연도),]
View(Atest2)
#전체 합격률
test %>% filter(필기응시 ==  sum(필기응시))
View(a)
#전체 합격률
a <- test %>% filter(필기응시 ==  sum(필기응시))
View(a)
#전체 합격률
sum(Atest$필기응시)
sum(Atest$필기합격)
#전체 합격률
sum(Atest$필기응시) / sum(Atest$필기합격)
#전체 합격률
sum(Atest$필기합격) / sum(Atest$필기응시)
#실시
sum(Atest$실기합격) / sum(Atest$실기응시)
#필기
sum(Atest$필기합격) / sum(Atest$필기응시)
#필기
sum(Gtest$필기합격) / sum(Gtest$필기응시)
#실기
sum(Gtest$실기합격) / sum(Gtest$실기응시)
#남성
(sum(Atest$필기합격) - sum(Gtest$필기합격)) / (sum(Atest$필기응시) - sum(Atest$실기응시))
#남성
(sum(Atest$필기합격) - sum(Gtest$필기합격)) / (sum(Atest$필기응시) - sum(Atest$필기응시))
#남성
(sum(Atest$필기합격) - sum(Gtest$필기합격)) / (sum(Atest$필기응시) - sum(Atest$필기응시))
실기
#남성
(sum(Atest$필기합격) - sum(Gtest$필기합격)) / sum(Atest$필기응시) - sum(Atest$실기응시))
#남성
(sum(Atest$필기합격) - sum(Gtest$필기합격)) / (sum(Atest$필기응시) - sum(Gtest$필기응시))
#테스트
a <- Atest %>% filter(연도 == '2016')
View(a)
#테스트
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('필기응시')
View(a)
a <- ok %>% select('종목','필기응시')
View(a)
ggplot(data = a, aes(x= reorder(종목), y= 필기응시)) + geom_col() + coord_flip()
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 필기응시)) + geom_col() + coord_flip()
a <- ok %>% select('종목','필기응시')
View(a)
ggplot(data = a, aes(x= reorder(종목), y= 필기응시)) + geom_col() + coord_flip()
ggplot(data = a, aes(x= reorder(종목), y= 필기응시)) + geom_col() + coord_flip()
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 종목)) + geom_col() + coord_flip()
a <- ok %>% select('종목','필기응시')
View(a)
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 필기응시)) + geom_col() + coord_flip()
a <- ok %>% select(arrange('종목'),'필기응시')
install.packages("dplyr")
install.packages("dplyr")
install.packages("plyr")
install.packages("dplyr")
install.packages("dplyr")
install.packages("dplyr")
install.packages("dplyr")
install.packages("dplyr")
install.packages("dplyr")
a <- ok %>% select(order('종목')),'필기응시')
a <- ok %>% select(('종목')),'필기응시')
a <- ok %>% select('종목'),'필기응시')
a <- ok %>% select(order('종목'),'필기응시')
a <- ok %>% select(('종목'),'필기응시')
#테스트
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select(('종목'),'필기응시')
View(a)
#테스트
ok <- Atest %>% filter(연도 == '2016')
library(dplyr)
library(ggplot2)
library(readxl)
a <- ok %>% select(('종목'),'필기응시')
#테스트
ok <- Atest %>% filter(연도 == '2016')
test <- read.csv("123.csv")
View(test)
test <- test %>% filter(등급 == '기사')
# 전체만 표현된 데이터
Atest <- test %>% filter(성별 == '전체')
#테스트
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select(('종목'),'필기응시')
View(a)
a <- ok %>% select(order('종목'),'필기응시')
View(a)
a <- ok %>% select(('종목'),'필기응시')
View(a)
a <- ok %>% select('종목','필기응시')
View(a)
a <- ok %>% select('종목',order('필기응시'))
View(a)
a <- ok %>% select('종목',('필기응시'))
View(a)
a <- ok %>% select('종목',('필기응시')) head(5)
a <- ok %>% select('종목',('필기응시')) %>% head(5)
View(a)
a <- ok %>% select(arrange('종목'),('필기응시')) %>% head(5)
arrange('종목'%ok)
arrange('종목'$ok)
arrange(ok,종목)
a <- ok %>% select('종목',arrange(ok,필기응시)) %>% head(5)
a <- ok %>% select('종목',arrange(ok,'필기응시')) %>% head(5)
View(ok)
ok <- arrange(ok,필기응시)
View(ok)
ok <- arrange(ok,desc(필기응시))
View(ok)
ok <- arrange(ok,desc(필기응시)) head(10)
a <- test %>% select('종목','필기응시')
View(a)
a <- test %>% select('종목',arrange(ok,desc(필기응시)))
a <- test %>% select('종목','필기응시')
View(a)
a <- arrange(ok,desc(필기응시))
View(a)
a <- test %>% select('종목','필기응시')
a <- arrange(a,desc(필기응시))
View(a)
a <- arrange(a,desc(필기응시)) %>% hard(10)
a <- arrange(a,desc(필기응시)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 필기응시)) + geom_col() + coord_flip()
#여성이 가장 많이 응시한 시험
Gtest <- test %>% filter(성별== '여성')
# 여성 ~ 제거버전
Gtest2 <- Gtest[grep("20", Gtest$연도),]
ok <-Gtest2 %>% filter(연도 == '2016')
View(a)
View(ok)
a <- Atest %>% select('종목','필기응시')
a <- arrange(a,desc(필기응시)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 필기응시)) + geom_col() + coord_flip()
a <- Gtest2 %>% select('종목','필기응시')
a <- arrange(a,desc(필기응시)) %>% head(10)
View(a)
a <- ok %>% select('종목','필기응시')
a <- arrange(a,desc(필기응시)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 필기응시)) + geom_col() + coord_flip()
#그래프 합격율
ok <- Atest %>% filter(연도 == '2016')
a <- Atest %>% select('종목','실기합격률')
View(a)
#그래프 합격율
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('종목','실기합격률')
View(a)
a <- arrange(a,desc(실기합격률)) %>% head(10)
View(a)
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
a <- arrange(a,(실기합격률))
View(a)
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률))
View(a)
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
e
ggplot(data = a, aes(x= reorder(종목, 필기응시), y= 필기응시)) + geom_col() + coord_flip()
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
View(a)
#그래프 합격율
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
#가장 합격률이 높은 시험 2016
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
View(Gtest2)
## 테스트
성별 <- c(남자, 여자)
## 테스트
성별 <- c('남자', '여자')
## 테스트
성별 <- c('남성', '여성')
## 테스트
s <- c('남성', '여성')
s
## 테스트
성별 <- c('남성', '여성')
응시률 <-c(sum(AStest %>% select(필기접수)) - sum(GStest %>% select(필기접수)),sum(GStest %>% select(필기접수)))
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
test <- test %>% filter(등급 == '기사')
# 전체만 표현된 데이터
Atest <- test %>% filter(성별 == '전체')
# 남성 여성 응시율을 측정하기위해 요약
Stest <- test %>% select(성별, 필기접수, 실기접수)
응시률 <-c(sum(AStest %>% select(필기접수)) - sum(GStest %>% select(필기접수)),sum(GStest %>% select(필기접수)))
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
응시률 <-c(sum(AStest %>% select(필기접수)) - sum(GStest %>% select(필기접수)),sum(GStest %>% select(필기접수)))
# 여성 필기 접수, 실기 접수
GStest <- Stest %>% filter(성별== '여성')
응시률 <-c(sum(AStest %>% select(필기접수)) - sum(GStest %>% select(필기접수)),sum(GStest %>% select(필기접수)))
sc <- data.frame(성별, 응시률)
View(sc)
gplot(sc)
qplot(sc)
qplot(sc)
ggplot(data=sc, aes(x= 성별, y= 응시률)) + geom_col()
#가장 합격률이 낮은 시험 2016
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
#가장 합격률이 높은 시험 2016
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
#가장 합격률이 낮은 시험 2016
ok <- Atest %>% filter(연도 == '2016')
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
# 합격률 그래프
필기합격률 <- c((sum(Atest$필기합격) - sum(Gtest$필기합격)) / (sum(Atest$필기응시) - sum(Gtest$필기응시)), sum(Gtest$필기합격) / sum(Gtest$필기응시))
tc1 <- data.frame(성별, 응시률)
View(tc1)
tc1 <- data.frame(성별, 필기합격률)
View(tc1)
ggplot(data=sc, aes(x= 성별, y= 필기합격률)) + geom_col()
#실기
실기합격률 <- c((sum(Atest$실기합격) - sum(Gtest$실기합격)) / (sum(Atest$실기응시) - sum(Gtest$실기응시)), sum(Gtest$실기합격) / sum(Gtest$실기응시))
tc2 <- data.frame(성별, 실기합격률)
View(tc2)
ggplot(data=sc, aes(x= 성별, y= 필기합격률)) + geom_col()
ggplot(data=sc, aes(x= 성별, y= 실기합격률)) + geom_col()
#필기
필기합격률 <- c((sum(Atest$필기합격) - sum(Gtest$필기합격))
/ (sum(Atest$필기응시) - sum(Gtest$필기응시)), sum(Gtest$필기합격) / sum(Gtest$필기응시))
tc1 <- data.frame(성별, 필기합격률)
View(tc1)
ggplot(data=sc, aes(x= 성별, y= 필기합격률)) + geom_col()
#실기
실기합격률 <- c((sum(Atest$실기합격) - sum(Gtest$실기합격))
/ (sum(Atest$실기응시) - sum(Gtest$실기응시)), sum(Gtest$실기합격) / sum(Gtest$실기응시))
tc2 <- data.frame(성별, 실기합격률)
View(tc2)
ggplot(data=sc, aes(x= 성별, y= 실기합격률)) + geom_col()
test <- read.csv("123.csv")
View(test)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
View(Atest)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
View(Atest)
# 남성 여성 응시율을 측정하기위해 요약
Stest <- test %>% select(성별, 필기접수, 실기접수)
View(Stest)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
View(Atest)
library(readxl)
library(dplyr)
library(ggplot2)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
View(Atest)
# 전체만 표현된 데이터
Atest <- test %>% filter(성별 == '전체')
# 남성 여성 응시율을 측정하기위해 요약
Stest <- test %>% select(성별, 필기접수, 실기접수)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
View(Atest)
# 남성 여성 응시율을 측정하기위해 요약
Stest <- test %>% select(성별, 필기접수, 실기접수)
View(Stest)
# 남성 여성 응시율을 측정하기위해 요약
Stest <- test %>% select(성별, 필기접수, 실기접수)
View(Stest)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
View(AStest)
View(AStest)
Atest2
Atest2 <- Gtest[grep("20", Gtest$연도),]
View(Atest2)
# 가장 높은 필기접수 응시율
a <- Atest2 %>% filter(필기접수 == max(필기접수))
View(a)
# 가장 높은 필기접수 응시율
a <- Atest2 %>% filter(필기접수 == max(필기접수))
View(a)
# 가장 높은 필기접수 응시율
a <- Atest %>% filter(필기접수 == max(필기접수))
View(a)
#필기
sum(Gtest$필기합격) / sum(Gtest$필기응시)
#실기
sum(Gtest$실기합격) / sum(Gtest$실기응시)
#남성
(sum(Atest$필기합격) - sum(Gtest$필기합격)) / (sum(Atest$필기응시) - sum(Gtest$필기응시))
#실기
(sum(Atest$실기합격) - sum(Gtest$실기합격)) / (sum(Atest$실기응시) - sum(Gtest$실기응시))
ggplot(data=sc, aes(x= 성별, y= 실기합격률)) + geom_col()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_col()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_line()
ggplot(data=Atest2, aes(x=필기합격률, y=연도)) + geom_line()
연도
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_line()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_point()
ggplot(data=Atest2, aes(x=연도,필기합격률, y=필기합격률)) + geom_point()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_point()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_linet()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_linet()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_line()
ggplot(economics, aes(x=date, y=unemploy)) + geom_line()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_line()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_col()
ggplot(data=Atest2, aes(x=연도, y=필기합격률)) + geom_col()
ggplot(data=Atest2, aes(x=연도, y=실기합격률)) + geom_col()
ggplot(data=Atest2, aes(x=연도, y=실기합격률)) + geom_line()
dd <- Atest2 %>% filter(과목 == '정보처리기사')
View(Atest2)
dd <- Atest2 %>% filter(종목 == '정보처리기사')
View(dd)
ggplot(data=dd, aes(x=연도, y=실기합격률)) + geom_line()
View(dd)
ggplot(data=dd, aes(x=연도, y=실기합격률)) + geom_line()
View(dd)
group=1
ggplot(data=dd, aes(x=연도, y=실기합격률)) + geom_line()
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line()
View(dd)
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(10,30)
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.2,0.6)
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.2,0.8)
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.25,0.85)
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.2,0.8)
View(dd)
# // 전체 필기, 실기 접수
AStest <- Stest %>% filter(성별== '전체')
dd <- Atest2 %>% filter(종목 == '정보처리기사')
View(dd)
View(dd)
View(AStest)
# 전체만 표현된 데이터
Atest <- test %>% filter(성별 == '전체')
View(Atest)
Atest22 <- Atest[grep("20", Gtest$연도),]
View(Atest22)
# 전체만 표현된 데이터
Atest <- test %>% filter(성별 == '전체')
View(Atest)
Atest22 <- Atest[grep("20", Gtest$연도),]
View(Atest22)
View(Gtest2)
Atest22 <- Atest[grep("20", Atest$연도),]
View(Atest22)
ggplot(data=Atest22, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.2,0.8)
dd
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.2,0.8)
View(dd)
dd <- Atest22 %>% filter(종목 == '정보처리기사')
View(dd)
ggplot(data=dd, aes(x=연도, y=실기합격률, group=1)) + geom_line() + ylim(0.2,0.8)
ggplot(data=dd, aes(x=연도, y=필기합격률, group=1)) + geom_line() + ylim(0.2,0.8)
Atest3 <- Atest[grep("11", Atest$연도),]
View(Atest3)
Atest3 <- Atest[grep("09~11", Atest$연도),]
View(Atest3)
a <- Atest22 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
View(Atest22)
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
#그래프 합격률 09~11
Atest22 <- Atest[grep("20", Atest$연도),]
View(Atest22)
View(ok)
a <- ok %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,(필기합격률)) %>% head(10)
View(a)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
View(a)
a <- arrange(a,desc(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- arrange(a,(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- arrange(a,desc(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,desc(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
View(a)
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
View(a)
View(Atest3)
View(Atest3)
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,(필기합격률)) %>% head(10)
View(Atest3)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
a <- arrange(a,desc(필기합격률)) %>% head(10)
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,desc(필기합격률)) %>% head(10)
View(a)
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
View(a)
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,desc(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,desc(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
Atest3 <- Atest[grep("20", Atest$연도),]
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
Atest3 <- Atest[grep("09~11", Atest$연도),]
a <- Atest3 %>% select('종목','필기합격률')
a <- arrange(a,(필기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 필기합격률), y= 필기합격률)) + geom_col() + coord_flip()
a <- Atest3 %>% select('종목','실기합격률')
a <- arrange(a,(실기합격률)) %>% head(10)
ggplot(data = a, aes(x= reorder(종목, 실기합격률), y= 실기합격률)) + geom_col() + coord_flip()
