ls()
## character(0)
rm(list = ls())
getwd()
## [1] "D:/data/paper"
setwd("D:/data/paper")
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.0 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
#년도별, gt별, 월별 기동횟수
start<-read.csv("start count.csv")
repair<-read.csv("repair1.csv")
str(repair)
## 'data.frame': 103 obs. of 12 variables:
## $ no : int 1 2 3 4 5 6 7 8 9 10 ...
## $ date : int 20220415 20201231 20200717 20190919 20181005 20180205 20170731 20160831 20160909 20160930 ...
## $ gt : chr "1gt" "1gt" "1gt" "1gt" ...
## $ ma1 : chr "Spring" "Exciter" "Ignitor" "Exciter" ...
## $ ma2 : chr "" "" "" "" ...
## $ ma3 : chr "" "" "" "" ...
## $ mat1 : chr "4" "2" "1" "2" ...
## $ mat2 : chr "-" "-" "-" "-" ...
## $ mat3 : chr "-" "-" "-" "-" ...
## $ cause : chr "경년열화" "부품손상" "부품손상" "부품손상" ...
## $ 증상 : chr "손상" "손상" "손상" "손상" ...
## $ action: chr "점화기 리턴 불량으로 인한 Spring 교체 " "Exciter 동작불량으로 교체" "5번 Ignitor 교체 " "Exciter 동작불량으로 교체" ...
repair$year<-substr(repair$date,1,4)
repair$mon<-substr(repair$date,5,6)
str(repair)
## 'data.frame': 103 obs. of 14 variables:
## $ no : int 1 2 3 4 5 6 7 8 9 10 ...
## $ date : int 20220415 20201231 20200717 20190919 20181005 20180205 20170731 20160831 20160909 20160930 ...
## $ gt : chr "1gt" "1gt" "1gt" "1gt" ...
## $ ma1 : chr "Spring" "Exciter" "Ignitor" "Exciter" ...
## $ ma2 : chr "" "" "" "" ...
## $ ma3 : chr "" "" "" "" ...
## $ mat1 : chr "4" "2" "1" "2" ...
## $ mat2 : chr "-" "-" "-" "-" ...
## $ mat3 : chr "-" "-" "-" "-" ...
## $ cause : chr "경년열화" "부품손상" "부품손상" "부품손상" ...
## $ 증상 : chr "손상" "손상" "손상" "손상" ...
## $ action: chr "점화기 리턴 불량으로 인한 Spring 교체 " "Exciter 동작불량으로 교체" "5번 Ignitor 교체 " "Exciter 동작불량으로 교체" ...
## $ year : chr "2022" "2020" "2020" "2019" ...
## $ mon : chr "04" "12" "07" "09" ...
reyear<-repair %>% group_by(year) %>% summarise(count=n())
reyear
## # A tibble: 13 x 2
## year count
## <chr> <int>
## 1 2009 18
## 2 2010 17
## 3 2011 7
## 4 2012 10
## 5 2014 2
## 6 2015 2
## 7 2016 11
## 8 2017 5
## 9 2018 9
## 10 2019 8
## 11 2020 8
## 12 2021 5
## 13 2022 1
reyear<-reyear %>% slice(1,2,3,4,5,6,7,8,9,10,11)
reyear
## # A tibble: 11 x 2
## year count
## <chr> <int>
## 1 2009 18
## 2 2010 17
## 3 2011 7
## 4 2012 10
## 5 2014 2
## 6 2015 2
## 7 2016 11
## 8 2017 5
## 9 2018 9
## 10 2019 8
## 11 2020 8
stryear<-start %>% group_by(year) %>% summarise(count=sum(count))
stryear
## # A tibble: 12 x 2
## year count
## <int> <int>
## 1 2009 1205
## 2 2010 1346
## 3 2011 1401
## 4 2012 1427
## 5 2013 1400
## 6 2014 867
## 7 2015 255
## 8 2016 326
## 9 2017 161
## 10 2018 287
## 11 2019 333
## 12 2020 209
reyear$year<-as.numeric(reyear$year)
year<-full_join(reyear,stryear,by="year")
year
## # A tibble: 12 x 3
## year count.x count.y
## <dbl> <int> <int>
## 1 2009 18 1205
## 2 2010 17 1346
## 3 2011 7 1401
## 4 2012 10 1427
## 5 2014 2 867
## 6 2015 2 255
## 7 2016 11 326
## 8 2017 5 161
## 9 2018 9 287
## 10 2019 8 333
## 11 2020 8 209
## 12 2013 NA 1400
mean(year$count.x,na.rm=T)
## [1] 8.818182
year$count.x<-ifelse(is.na(year$count.x),8.818,year$count.x)
ggplot(data=year,aes(x=year,y=count.x,z=count.y))+geom_point()
str(start)
## 'data.frame': 864 obs. of 4 variables:
## $ year : int 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 ...
## $ month: int 1 2 3 4 5 6 7 8 9 10 ...
## $ gt : chr "1gt" "1gt" "1gt" "1gt" ...
## $ count: int 2 7 21 26 7 15 28 25 24 19 ...
strgt<-start %>% group_by(gt) %>% summarise(count=sum(count))
strgt
## # A tibble: 6 x 2
## gt count
## <chr> <int>
## 1 1gt 1554
## 2 2gt 1490
## 3 3gt 1488
## 4 4gt 1426
## 5 5gt 1622
## 6 6gt 1637