R의 데이터 프레임에서 작업 시에 데이터 프레임의 열의 순서가 구조 때문에 데이터의 전체적인 구조나 처리가 어려움을 겪는 때가 있다. 이런 경우 열의 순서를 바꾸어 새로운 데이터 프레임을 만들거나 기존의 데이터 프레임에 엎어 쓰고 작업한다. 열이 몇개 되지 않는 경우에는 dplyr의 select를 이용하여 간단히 열의 순서를 바꿀 수 있지만 열의 수가 많은 데이터 프레임의 경우에는 작업이 수월하지 않다.
이런 경우 사용할 수 있는 함수가 relocate이다.
샘플 데이터 셋은 우리나라 코로나 데이터를 사용하도록 하겠다. COVID19 패키지는 전세계 코로나 확진자, 사망자, 백신 접종자등 데이터를 제공하는 패키지이다. 사용 방법은 다음의 패키지 설명서를 참조하라.(https://cran.r-project.org/web/packages/COVID19/COVID19.pdf)
## COVID19 패키지 설치
if(!require(COVID19)) {
install.packages('COVID19')
## COVID19 패키지 로딩
library(COVID19)
}
## 한국의 코로나 데이터를 data.covid19 데이터 프레임에 저장
data.covid19 <- covid19('KOR')## We have invested a lot of time and effort in creating COVID-19 Data Hub, please cite the following when using it:
##
## Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open
## Source Software 5(51):2376, doi: 10.21105/joss.02376.
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {COVID-19 Data Hub},
## year = {2020},
## doi = {10.21105/joss.02376},
## author = {Emanuele Guidotti and David Ardia},
## journal = {Journal of Open Source Software},
## volume = {5},
## number = {51},
## pages = {2376},
## }
##
## To retrieve citation and metadata of the data sources see ?covid19cite. To hide this message use 'verbose = FALSE'.전체 데이터 셋 중에 2021년 데이터만 필터링하여 사용하도록 한다. 이를 위해 dplyr패키지를 로딩해야하고 날짜(data.covid19$date)에서 연도만 추출한 후 결과가 2021인 행만 남긴다.
library(dplyr)
## 2021년 데이터만 필터링하여 저장
data.covid19 <- data.covid19 %>% filter(lubridate::year(data.covid19$date) == '2021')샘플 데이터 셋의 행 수와 열 수를 구해본다. 행의 수가 130개, 열의 수가 36개이다.
dim(data.covid19)## [1] 130 36head(data.covid19)## # A tibble: 6 x 36
## # Groups: id [1]
## id date vaccines tests confirmed recovered deaths hosp vent icu
## <chr> <date> <dbl> <int> <int> <int> <int> <dbl> <int> <int>
## 1 KOR 2021-01-01 NA 4092391 62593 43578 942 NA NA NA
## 2 KOR 2021-01-02 NA 4123412 63244 44507 962 NA NA NA
## 3 KOR 2021-01-03 NA 4154366 64264 45240 981 NA NA NA
## 4 KOR 2021-01-04 NA 4185876 64979 46172 1007 NA NA NA
## 5 KOR 2021-01-05 NA 4245610 65818 46995 1027 NA NA NA
## 6 KOR 2021-01-06 NA 4312786 66686 47649 1046 NA NA NA
## # ... with 26 more variables: population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # latitude <dbl>, longitude <dbl>, key <lgl>, key_apple_mobility <chr>,
## # key_google_mobility <chr>데이터 중에 국가의 위, 경도를 나타내는 latitude, longitude 열을 id 열 뒤에 나타내려면 어떻게 해야 할까?
dplyr의 select는 데이터 프레임의 열에 대한 필터링 함수이다. 이 함수를 필터링에 사용하지 않고 열이름을 배치하고 싶은 순서대로 나열해 주면 나열된 열의 순서대로 데이터 프레임이 생성된다. 열 이름 벡터를 사용할 수도 있고 열 인덱스 순서 벡터를 사용할 수도 있다.
data.covid19 %>% select("id", "latitude", "longitude", "date", "vaccines", "tests", "confirmed", "recovered", "deaths", "hosp", "vent", "icu", "population", "school_closing", "workplace_closing", "cancel_events", "gatherings_restrictions", "transport_closing", "stay_home_restrictions", "internal_movement_restrictions", "international_movement_restrictions", "information_campaigns", "testing_policy", "contact_tracing", "stringency_index", "iso_alpha_3", "iso_alpha_2", "iso_numeric", "currency", "administrative_area_level", "administrative_area_level_1", "administrative_area_level_2", "administrative_area_level_3", "latitude", "longitude", "key", "key_apple_mobility", "key_google_mobility") ## # A tibble: 130 x 36
## # Groups: id [1]
## id latitude longitude date vaccines tests confirmed recovered
## <chr> <dbl> <dbl> <date> <dbl> <int> <int> <int>
## 1 KOR 37 128. 2021-01-01 NA 4092391 62593 43578
## 2 KOR 37 128. 2021-01-02 NA 4123412 63244 44507
## 3 KOR 37 128. 2021-01-03 NA 4154366 64264 45240
## 4 KOR 37 128. 2021-01-04 NA 4185876 64979 46172
## 5 KOR 37 128. 2021-01-05 NA 4245610 65818 46995
## 6 KOR 37 128. 2021-01-06 NA 4312786 66686 47649
## 7 KOR 37 128. 2021-01-07 NA 4378047 67358 48369
## 8 KOR 37 128. 2021-01-08 NA 4439552 67999 49324
## 9 KOR 37 128. 2021-01-09 NA 4503693 68664 50409
## 10 KOR 37 128. 2021-01-10 NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>data.covid19 %>% select(1, 32, 33, 2:31, 34:36)## # A tibble: 130 x 36
## # Groups: id [1]
## id latitude longitude date vaccines tests confirmed recovered
## <chr> <dbl> <dbl> <date> <dbl> <int> <int> <int>
## 1 KOR 37 128. 2021-01-01 NA 4092391 62593 43578
## 2 KOR 37 128. 2021-01-02 NA 4123412 63244 44507
## 3 KOR 37 128. 2021-01-03 NA 4154366 64264 45240
## 4 KOR 37 128. 2021-01-04 NA 4185876 64979 46172
## 5 KOR 37 128. 2021-01-05 NA 4245610 65818 46995
## 6 KOR 37 128. 2021-01-06 NA 4312786 66686 47649
## 7 KOR 37 128. 2021-01-07 NA 4378047 67358 48369
## 8 KOR 37 128. 2021-01-08 NA 4439552 67999 49324
## 9 KOR 37 128. 2021-01-09 NA 4503693 68664 50409
## 10 KOR 37 128. 2021-01-10 NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>그런데 위와 같이 사용하는건 너무 어렵고 자칫 오류를 발생하기 쉽다. 이렇게 열이 많은 데이터 프레임은 relocate를 사용하면 쉽게 열의 구조를 바꿀 수 있다.
- 특정 열을 맨앞으로 이동
data.covid19 %>% relocate(c(latitude, longitude))## # A tibble: 130 x 36
## # Groups: id [1]
## latitude longitude id date vaccines tests confirmed recovered
## <dbl> <dbl> <chr> <date> <dbl> <int> <int> <int>
## 1 37 128. KOR 2021-01-01 NA 4092391 62593 43578
## 2 37 128. KOR 2021-01-02 NA 4123412 63244 44507
## 3 37 128. KOR 2021-01-03 NA 4154366 64264 45240
## 4 37 128. KOR 2021-01-04 NA 4185876 64979 46172
## 5 37 128. KOR 2021-01-05 NA 4245610 65818 46995
## 6 37 128. KOR 2021-01-06 NA 4312786 66686 47649
## 7 37 128. KOR 2021-01-07 NA 4378047 67358 48369
## 8 37 128. KOR 2021-01-08 NA 4439552 67999 49324
## 9 37 128. KOR 2021-01-09 NA 4503693 68664 50409
## 10 37 128. KOR 2021-01-10 NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>- 특정 열을 특정 열 뒤로 이동
data.covid19 %>% relocate(c(latitude, longitude), .after = id)## # A tibble: 130 x 36
## # Groups: id [1]
## id latitude longitude date vaccines tests confirmed recovered
## <chr> <dbl> <dbl> <date> <dbl> <int> <int> <int>
## 1 KOR 37 128. 2021-01-01 NA 4092391 62593 43578
## 2 KOR 37 128. 2021-01-02 NA 4123412 63244 44507
## 3 KOR 37 128. 2021-01-03 NA 4154366 64264 45240
## 4 KOR 37 128. 2021-01-04 NA 4185876 64979 46172
## 5 KOR 37 128. 2021-01-05 NA 4245610 65818 46995
## 6 KOR 37 128. 2021-01-06 NA 4312786 66686 47649
## 7 KOR 37 128. 2021-01-07 NA 4378047 67358 48369
## 8 KOR 37 128. 2021-01-08 NA 4439552 67999 49324
## 9 KOR 37 128. 2021-01-09 NA 4503693 68664 50409
## 10 KOR 37 128. 2021-01-10 NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>- 특정 열을 특정 열 앞으로 이동
data.covid19 %>% relocate(c(latitude, longitude), .after = date)## # A tibble: 130 x 36
## # Groups: id [1]
## id date latitude longitude vaccines tests confirmed recovered
## <chr> <date> <dbl> <dbl> <dbl> <int> <int> <int>
## 1 KOR 2021-01-01 37 128. NA 4092391 62593 43578
## 2 KOR 2021-01-02 37 128. NA 4123412 63244 44507
## 3 KOR 2021-01-03 37 128. NA 4154366 64264 45240
## 4 KOR 2021-01-04 37 128. NA 4185876 64979 46172
## 5 KOR 2021-01-05 37 128. NA 4245610 65818 46995
## 6 KOR 2021-01-06 37 128. NA 4312786 66686 47649
## 7 KOR 2021-01-07 37 128. NA 4378047 67358 48369
## 8 KOR 2021-01-08 37 128. NA 4439552 67999 49324
## 9 KOR 2021-01-09 37 128. NA 4503693 68664 50409
## 10 KOR 2021-01-10 37 128. NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>- 특정 열을 맨 뒤로 이동
data.covid19 %>% relocate(c(latitude, longitude), .after = last_col())## # A tibble: 130 x 36
## # Groups: id [1]
## id date vaccines tests confirmed recovered deaths hosp vent icu
## <chr> <date> <dbl> <int> <int> <int> <int> <dbl> <int> <int>
## 1 KOR 2021-01-01 NA 4.09e6 62593 43578 942 NA NA NA
## 2 KOR 2021-01-02 NA 4.12e6 63244 44507 962 NA NA NA
## 3 KOR 2021-01-03 NA 4.15e6 64264 45240 981 NA NA NA
## 4 KOR 2021-01-04 NA 4.19e6 64979 46172 1007 NA NA NA
## 5 KOR 2021-01-05 NA 4.25e6 65818 46995 1027 NA NA NA
## 6 KOR 2021-01-06 NA 4.31e6 66686 47649 1046 NA NA NA
## 7 KOR 2021-01-07 NA 4.38e6 67358 48369 1081 NA NA NA
## 8 KOR 2021-01-08 NA 4.44e6 67999 49324 1100 NA NA NA
## 9 KOR 2021-01-09 NA 4.50e6 68664 50409 1125 NA NA NA
## 10 KOR 2021-01-10 NA 4.53e6 69114 52552 1140 NA NA NA
## # ... with 120 more rows, and 26 more variables: population <int>,
## # school_closing <int>, workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>,
## # latitude <dbl>, longitude <dbl>- 특정 데이터 타입의 열을 모두 맨 앞으로 이동
## 문자형 열을 맨 앞으로 이동
data.covid19 %>% relocate(where(is.character))## # A tibble: 130 x 36
## # Groups: id [1]
## id iso_alpha_3 iso_alpha_2 currency administrative_area_~ key_apple_mobil~
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 KOR KOR KR KRW Korea, South <NA>
## 2 KOR KOR KR KRW Korea, South <NA>
## 3 KOR KOR KR KRW Korea, South <NA>
## 4 KOR KOR KR KRW Korea, South <NA>
## 5 KOR KOR KR KRW Korea, South <NA>
## 6 KOR KOR KR KRW Korea, South <NA>
## 7 KOR KOR KR KRW Korea, South <NA>
## 8 KOR KOR KR KRW Korea, South <NA>
## 9 KOR KOR KR KRW Korea, South <NA>
## 10 KOR KOR KR KRW Korea, South <NA>
## # ... with 120 more rows, and 30 more variables: key_google_mobility <chr>,
## # date <date>, vaccines <dbl>, tests <int>, confirmed <int>, recovered <int>,
## # deaths <int>, hosp <dbl>, vent <int>, icu <int>, population <int>,
## # school_closing <int>, workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_numeric <int>, administrative_area_level <int>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # latitude <dbl>, longitude <dbl>, key <lgl>## 논리형 열을 맨 앞으로 이동
data.covid19 %>% relocate(where(is.logical))## # A tibble: 130 x 36
## # Groups: id [1]
## administrative_are~ administrative_ar~ key id date vaccines tests
## <lgl> <lgl> <lgl> <chr> <date> <dbl> <int>
## 1 NA NA NA KOR 2021-01-01 NA 4.09e6
## 2 NA NA NA KOR 2021-01-02 NA 4.12e6
## 3 NA NA NA KOR 2021-01-03 NA 4.15e6
## 4 NA NA NA KOR 2021-01-04 NA 4.19e6
## 5 NA NA NA KOR 2021-01-05 NA 4.25e6
## 6 NA NA NA KOR 2021-01-06 NA 4.31e6
## 7 NA NA NA KOR 2021-01-07 NA 4.38e6
## 8 NA NA NA KOR 2021-01-08 NA 4.44e6
## 9 NA NA NA KOR 2021-01-09 NA 4.50e6
## 10 NA NA NA KOR 2021-01-10 NA 4.53e6
## # ... with 120 more rows, and 29 more variables: confirmed <int>,
## # recovered <int>, deaths <int>, hosp <dbl>, vent <int>, icu <int>,
## # population <int>, school_closing <int>, workplace_closing <int>,
## # cancel_events <int>, gatherings_restrictions <int>,
## # transport_closing <int>, stay_home_restrictions <int>,
## # internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # latitude <dbl>, longitude <dbl>, key_apple_mobility <chr>,
## # key_google_mobility <chr>- 특정 데이터 타입의 열을 모두 특정 데이터 타입의 뒤나 앞으로 이동
## 문자형 열을 마지막 정수형 열 뒤로 이동
data.covid19 %>% relocate(where(is.character), .after = where(is.integer))## # A tibble: 130 x 36
## # Groups: id [1]
## date vaccines tests confirmed recovered deaths hosp vent icu
## <date> <dbl> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2021-01-01 NA 4092391 62593 43578 942 NA NA NA
## 2 2021-01-02 NA 4123412 63244 44507 962 NA NA NA
## 3 2021-01-03 NA 4154366 64264 45240 981 NA NA NA
## 4 2021-01-04 NA 4185876 64979 46172 1007 NA NA NA
## 5 2021-01-05 NA 4245610 65818 46995 1027 NA NA NA
## 6 2021-01-06 NA 4312786 66686 47649 1046 NA NA NA
## 7 2021-01-07 NA 4378047 67358 48369 1081 NA NA NA
## 8 2021-01-08 NA 4439552 67999 49324 1100 NA NA NA
## 9 2021-01-09 NA 4503693 68664 50409 1125 NA NA NA
## 10 2021-01-10 NA 4532010 69114 52552 1140 NA NA NA
## # ... with 120 more rows, and 27 more variables: population <int>,
## # school_closing <int>, workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_numeric <int>, administrative_area_level <int>, id <chr>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, currency <chr>,
## # administrative_area_level_1 <chr>, key_apple_mobility <chr>,
## # key_google_mobility <chr>, administrative_area_level_2 <lgl>,
## # administrative_area_level_3 <lgl>, latitude <dbl>, longitude <dbl>,
## # key <lgl>## 논리형 열을 첫번째 숫자열 앞으로 이동
data.covid19 %>% relocate(where(is.logical), .before = where(is.numeric))## # A tibble: 130 x 36
## # Groups: id [1]
## id date administrative_are~ administrative_ar~ key vaccines tests
## <chr> <date> <lgl> <lgl> <lgl> <dbl> <int>
## 1 KOR 2021-01-01 NA NA NA NA 4.09e6
## 2 KOR 2021-01-02 NA NA NA NA 4.12e6
## 3 KOR 2021-01-03 NA NA NA NA 4.15e6
## 4 KOR 2021-01-04 NA NA NA NA 4.19e6
## 5 KOR 2021-01-05 NA NA NA NA 4.25e6
## 6 KOR 2021-01-06 NA NA NA NA 4.31e6
## 7 KOR 2021-01-07 NA NA NA NA 4.38e6
## 8 KOR 2021-01-08 NA NA NA NA 4.44e6
## 9 KOR 2021-01-09 NA NA NA NA 4.50e6
## 10 KOR 2021-01-10 NA NA NA NA 4.53e6
## # ... with 120 more rows, and 29 more variables: confirmed <int>,
## # recovered <int>, deaths <int>, hosp <dbl>, vent <int>, icu <int>,
## # population <int>, school_closing <int>, workplace_closing <int>,
## # cancel_events <int>, gatherings_restrictions <int>,
## # transport_closing <int>, stay_home_restrictions <int>,
## # internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # latitude <dbl>, longitude <dbl>, key_apple_mobility <chr>,
## # key_google_mobility <chr>- 특정 문자로 시작하거나 끝나는 열을 이동
## 'lat'으로 시작하는 열 이름을 가진 열을 맨 앞으로 이동
data.covid19 %>% relocate(starts_with('lat'))## # A tibble: 130 x 36
## # Groups: id [1]
## latitude id date vaccines tests confirmed recovered deaths hosp
## <dbl> <chr> <date> <dbl> <int> <int> <int> <int> <dbl>
## 1 37 KOR 2021-01-01 NA 4092391 62593 43578 942 NA
## 2 37 KOR 2021-01-02 NA 4123412 63244 44507 962 NA
## 3 37 KOR 2021-01-03 NA 4154366 64264 45240 981 NA
## 4 37 KOR 2021-01-04 NA 4185876 64979 46172 1007 NA
## 5 37 KOR 2021-01-05 NA 4245610 65818 46995 1027 NA
## 6 37 KOR 2021-01-06 NA 4312786 66686 47649 1046 NA
## 7 37 KOR 2021-01-07 NA 4378047 67358 48369 1081 NA
## 8 37 KOR 2021-01-08 NA 4439552 67999 49324 1100 NA
## 9 37 KOR 2021-01-09 NA 4503693 68664 50409 1125 NA
## 10 37 KOR 2021-01-10 NA 4532010 69114 52552 1140 NA
## # ... with 120 more rows, and 27 more variables: vent <int>, icu <int>,
## # population <int>, school_closing <int>, workplace_closing <int>,
## # cancel_events <int>, gatherings_restrictions <int>,
## # transport_closing <int>, stay_home_restrictions <int>,
## # internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # longitude <dbl>, key <lgl>, key_apple_mobility <chr>,
## # key_google_mobility <chr>## 'tude'으로 끝나는 열 이름을 가진 열을 맨 앞으로 이동
data.covid19 %>% relocate(ends_with('tude'))## # A tibble: 130 x 36
## # Groups: id [1]
## latitude longitude id date vaccines tests confirmed recovered
## <dbl> <dbl> <chr> <date> <dbl> <int> <int> <int>
## 1 37 128. KOR 2021-01-01 NA 4092391 62593 43578
## 2 37 128. KOR 2021-01-02 NA 4123412 63244 44507
## 3 37 128. KOR 2021-01-03 NA 4154366 64264 45240
## 4 37 128. KOR 2021-01-04 NA 4185876 64979 46172
## 5 37 128. KOR 2021-01-05 NA 4245610 65818 46995
## 6 37 128. KOR 2021-01-06 NA 4312786 66686 47649
## 7 37 128. KOR 2021-01-07 NA 4378047 67358 48369
## 8 37 128. KOR 2021-01-08 NA 4439552 67999 49324
## 9 37 128. KOR 2021-01-09 NA 4503693 68664 50409
## 10 37 128. KOR 2021-01-10 NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>## 'tude'를 포함하는 열 이름을 가진 열을 맨 앞으로 이동
data.covid19 %>% relocate(contains('tude'))## # A tibble: 130 x 36
## # Groups: id [1]
## latitude longitude id date vaccines tests confirmed recovered
## <dbl> <dbl> <chr> <date> <dbl> <int> <int> <int>
## 1 37 128. KOR 2021-01-01 NA 4092391 62593 43578
## 2 37 128. KOR 2021-01-02 NA 4123412 63244 44507
## 3 37 128. KOR 2021-01-03 NA 4154366 64264 45240
## 4 37 128. KOR 2021-01-04 NA 4185876 64979 46172
## 5 37 128. KOR 2021-01-05 NA 4245610 65818 46995
## 6 37 128. KOR 2021-01-06 NA 4312786 66686 47649
## 7 37 128. KOR 2021-01-07 NA 4378047 67358 48369
## 8 37 128. KOR 2021-01-08 NA 4439552 67999 49324
## 9 37 128. KOR 2021-01-09 NA 4503693 68664 50409
## 10 37 128. KOR 2021-01-10 NA 4532010 69114 52552
## # ... with 120 more rows, and 28 more variables: deaths <int>, hosp <dbl>,
## # vent <int>, icu <int>, population <int>, school_closing <int>,
## # workplace_closing <int>, cancel_events <int>,
## # gatherings_restrictions <int>, transport_closing <int>,
## # stay_home_restrictions <int>, internal_movement_restrictions <int>,
## # international_movement_restrictions <int>, information_campaigns <int>,
## # testing_policy <int>, contact_tracing <int>, stringency_index <dbl>,
## # iso_alpha_3 <chr>, iso_alpha_2 <chr>, iso_numeric <int>, currency <chr>,
## # administrative_area_level <int>, administrative_area_level_1 <chr>,
## # administrative_area_level_2 <lgl>, administrative_area_level_3 <lgl>,
## # key <lgl>, key_apple_mobility <chr>, key_google_mobility <chr>'데이터 전처리' 카테고리의 다른 글
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