1.nycflights13 라이브러리에 담겨 있는 nycflights 데이터 불러와서 구조확인
library(nycflights13)
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, union
c(str(flights), dim(flights))
## Classes 'tbl_df', 'tbl' and 'data.frame': 336776 obs. of 19 variables:
## $ year : int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ month : int 1 1 1 1 1 1 1 1 1 1 ...
## $ day : int 1 1 1 1 1 1 1 1 1 1 ...
## $ dep_time : int 517 533 542 544 554 554 555 557 557 558 ...
## $ sched_dep_time: int 515 529 540 545 600 558 600 600 600 600 ...
## $ dep_delay : num 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
## $ arr_time : int 830 850 923 1004 812 740 913 709 838 753 ...
## $ sched_arr_time: int 819 830 850 1022 837 728 854 723 846 745 ...
## $ arr_delay : num 11 20 33 -18 -25 12 19 -14 -8 8 ...
## $ carrier : chr "UA" "UA" "AA" "B6" ...
## $ flight : int 1545 1714 1141 725 461 1696 507 5708 79 301 ...
## $ tailnum : chr "N14228" "N24211" "N619AA" "N804JB" ...
## $ origin : chr "EWR" "LGA" "JFK" "JFK" ...
## $ dest : chr "IAH" "IAH" "MIA" "BQN" ...
## $ air_time : num 227 227 160 183 116 150 158 53 140 138 ...
## $ distance : num 1400 1416 1089 1576 762 ...
## $ hour : num 5 5 5 5 6 5 6 6 6 6 ...
## $ minute : num 15 29 40 45 0 58 0 0 0 0 ...
## $ time_hour : POSIXct, format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...
## [1] 336776 19
2. year, month, day 컬럼 선택하기
select(flights, year, month, day)
## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # ... with 336,766 more rows
3. 년도와 일 컬럼선택하기(inclusive)
select(flights, year:day)
## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # ... with 336,766 more rows
4.년도와 일 컬럼 제외하고 선택하기(exclusive)
select(flights, -(year:day))
## # A tibble: 336,776 x 16
## dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
## <int> <int> <dbl> <int> <int> <dbl>
## 1 517 515 2.00 830 819 11.0
## 2 533 529 4.00 850 830 20.0
## 3 542 540 2.00 923 850 33.0
## 4 544 545 -1.00 1004 1022 -18.0
## 5 554 600 -6.00 812 837 -25.0
## 6 554 558 -4.00 740 728 12.0
## 7 555 600 -5.00 913 854 19.0
## 8 557 600 -3.00 709 723 -14.0
## 9 557 600 -3.00 838 846 - 8.00
## 10 558 600 -2.00 753 745 8.00
## # ... with 336,766 more rows, and 10 more variables: carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>
5.select 함수와 everything() 함수를 결합하면 원하는 컬럼을 맨앞으로 두고 정렬 가능
select(flights, time_hour, air_time, everything())
## # A tibble: 336,776 x 19
## time_hour air_time year month day dep_time sched_dep_time
## <dttm> <dbl> <int> <int> <int> <int> <int>
## 1 2013-01-01 05:00:00 227 2013 1 1 517 515
## 2 2013-01-01 05:00:00 227 2013 1 1 533 529
## 3 2013-01-01 05:00:00 160 2013 1 1 542 540
## 4 2013-01-01 05:00:00 183 2013 1 1 544 545
## 5 2013-01-01 06:00:00 116 2013 1 1 554 600
## 6 2013-01-01 05:00:00 150 2013 1 1 554 558
## 7 2013-01-01 06:00:00 158 2013 1 1 555 600
## 8 2013-01-01 06:00:00 53.0 2013 1 1 557 600
## 9 2013-01-01 06:00:00 140 2013 1 1 557 600
## 10 2013-01-01 06:00:00 138 2013 1 1 558 600
## # ... with 336,766 more rows, and 12 more variables: dep_delay <dbl>,
## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
## # hour <dbl>, minute <dbl>
6.select 함수와 contain 함수를 같이 사용하여 특정 값이 포함된 컬럼 선택
select(flights, contains("TIME"))
## # A tibble: 336,776 x 6
## dep_time sched_dep_time arr_time sched_arr_time air_time
## <int> <int> <int> <int> <dbl>
## 1 517 515 830 819 227
## 2 533 529 850 830 227
## 3 542 540 923 850 160
## 4 544 545 1004 1022 183
## 5 554 600 812 837 116
## 6 554 558 740 728 150
## 7 555 600 913 854 158
## 8 557 600 709 723 53.0
## 9 557 600 838 846 140
## 10 558 600 753 745 138
## # ... with 336,766 more rows, and 1 more variable: time_hour <dttm>
ref: R for data Scientist pp.51~54