README.md
In lgleadershipacademy/dataset: Sample Dataset in LG Leadership Academy
dataset
dataset 패키지는 LG 인화원의 교육을 위해서 작성한 데이터 패키지입니다.
설치
dataset 패키지는 아래 코드를 이용해 설치할 수 있습니다. remotes 패키지는 설치되어 있지 않다면 설치해야
github내의 패키지를 설치할 수 있습니다.
install.packages("remotes")
remotes::install_github("lgleadershipacademy/dataset")
사용할 수 있는 데이터 리스트
library(dataset)
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
list_of_dataset()
#> [1] "avocado" "churn" "emp_tmnt" "emp_attr" "mall"
#> [6] "credit_train" "credit_test" "games" "retail" "mining_samples"
데이터 사용
리스트내에 mining_sample과 같이 데이터셋 이름의 뒤에 _sample 이 붙은 데이터가 아니라면 바로 이름으로
사용할 수 있습니다.
avocado
#> # A tibble: 18,249 x 13
#> Date AveragePrice `Total Volume` `4046` `4225` `4770` `Total Bags` `Small Bags`
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2015-12-27 00:00:00 1.33 64237. 1037. 5.45e4 48.2 8697. 8604.
#> 2 2015-12-20 00:00:00 1.35 54877. 674. 4.46e4 58.3 9506. 9408.
#> 3 2015-12-13 00:00:00 0.93 118220. 795. 1.09e5 130. 8145. 8042.
#> 4 2015-12-06 00:00:00 1.08 78992. 1132 7.20e4 72.6 5811. 5677.
#> 5 2015-11-29 00:00:00 1.28 51040. 941. 4.38e4 75.8 6184. 5986.
#> 6 2015-11-22 00:00:00 1.26 55980. 1184. 4.81e4 43.6 6684. 6556.
#> 7 2015-11-15 00:00:00 0.99 83454. 1369. 7.37e4 93.3 8319. 8197.
#> 8 2015-11-08 00:00:00 0.98 109428. 704. 1.02e5 80 6829. 6267.
#> 9 2015-11-01 00:00:00 1.02 99811. 1022. 8.73e4 85.3 11388. 11105.
#> 10 2015-10-25 00:00:00 1.07 74339. 842. 6.48e4 113 8626. 8061.
#> # ... with 18,239 more rows, and 5 more variables: `Large Bags` <dbl>, `XLarge Bags` <dbl>,
#> # type <chr>, year <dbl>, region <chr>
glimpse(avocado)
#> Observations: 18,249
#> Variables: 13
#> $ Date <dttm> 2015-12-27, 2015-12-20, 2015-12-13, 2015-12-06, 2015-11-29, 2015-11-22,...
#> $ AveragePrice <dbl> 1.33, 1.35, 0.93, 1.08, 1.28, 1.26, 0.99, 0.98, 1.02, 1.07, 1.12, 1.28, ...
#> $ `Total Volume` <dbl> 64236.62, 54876.98, 118220.22, 78992.15, 51039.60, 55979.78, 83453.76, 1...
#> $ `4046` <dbl> 1036.74, 674.28, 794.70, 1132.00, 941.48, 1184.27, 1368.92, 703.75, 1022...
#> $ `4225` <dbl> 54454.85, 44638.81, 109149.67, 71976.41, 43838.39, 48067.99, 73672.72, 1...
#> $ `4770` <dbl> 48.16, 58.33, 130.50, 72.58, 75.78, 43.61, 93.26, 80.00, 85.34, 113.00, ...
#> $ `Total Bags` <dbl> 8696.87, 9505.56, 8145.35, 5811.16, 6183.95, 6683.91, 8318.86, 6829.22, ...
#> $ `Small Bags` <dbl> 8603.62, 9408.07, 8042.21, 5677.40, 5986.26, 6556.47, 8196.81, 6266.85, ...
#> $ `Large Bags` <dbl> 93.25, 97.49, 103.14, 133.76, 197.69, 127.44, 122.05, 562.37, 283.83, 56...
#> $ `XLarge Bags` <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, ...
#> $ type <chr> "conventional", "conventional", "conventional", "conventional", "convent...
#> $ year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
#> $ region <chr> "Albany", "Albany", "Albany", "Albany", "Albany", "Albany", "Albany", "A...
mining_sample
mining 데이터셋은 용량이 커서 다운로드로 제공하며, 패키지 내에는 mining_sample로 첫 100줄의 데이터만
확인할 수 있습니다. 전체 데이터를 불러오려면 get_mining_data() 함수를 사용하시면 됩니다.
mining <- get_mining_data()
glimpse(mining)
#> Observations: 737,453
#> Variables: 24
#> $ date <dttm> 2017-03-10 01:00:00, 2017-03-10 01:00:00, 2017-03-10 01...
#> $ `% Iron Feed` <dbl> 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55...
#> $ `% Silica Feed` <dbl> 16.98, 16.98, 16.98, 16.98, 16.98, 16.98, 16.98, 16.98, ...
#> $ `Starch Flow` <dbl> 3019.53, 3024.41, 3043.46, 3047.36, 3033.69, 3079.10, 31...
#> $ `Amina Flow` <dbl> 557.434, 563.965, 568.054, 568.665, 558.167, 564.697, 56...
#> $ `Ore Pulp Flow` <dbl> 395.713, 397.383, 399.668, 397.939, 400.254, 396.533, 39...
#> $ `Ore Pulp pH` <dbl> 10.0664, 10.0672, 10.0680, 10.0689, 10.0697, 10.0705, 10...
#> $ `Ore Pulp Density` <dbl> 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1....
#> $ `Flotation Column 01 Air Flow` <dbl> 249.214, 249.719, 249.741, 249.917, 250.203, 250.730, 25...
#> $ `Flotation Column 02 Air Flow` <dbl> 253.235, 250.532, 247.874, 254.487, 252.136, 248.906, 25...
#> $ `Flotation Column 03 Air Flow` <dbl> 250.576, 250.862, 250.313, 250.049, 249.895, 249.521, 24...
#> $ `Flotation Column 04 Air Flow` <dbl> 295.096, 295.096, 295.096, 295.096, 295.096, 295.096, 29...
#> $ `Flotation Column 05 Air Flow` <dbl> 306.4, 306.4, 306.4, 306.4, 306.4, 306.4, 306.4, 306.4, ...
#> $ `Flotation Column 06 Air Flow` <dbl> 250.225, 250.137, 251.345, 250.422, 249.983, 250.356, 25...
#> $ `Flotation Column 07 Air Flow` <dbl> 250.884, 248.994, 248.071, 251.147, 248.928, 251.873, 25...
#> $ `Flotation Column 01 Level` <dbl> 457.396, 451.891, 451.240, 452.441, 452.441, 444.384, 44...
#> $ `Flotation Column 02 Level` <dbl> 432.962, 429.560, 468.927, 458.165, 452.900, 443.269, 44...
#> $ `Flotation Column 03 Level` <dbl> 424.954, 432.939, 434.610, 442.865, 450.523, 460.449, 45...
#> $ `Flotation Column 04 Level` <dbl> 443.558, 448.086, 449.688, 446.210, 453.670, 439.920, 43...
#> $ `Flotation Column 05 Level` <dbl> 502.255, 496.363, 484.411, 471.411, 462.598, 451.588, 44...
#> $ `Flotation Column 06 Level` <dbl> 446.370, 445.922, 447.826, 437.690, 443.682, 433.539, 44...
#> $ `Flotation Column 07 Level` <dbl> 523.344, 498.075, 458.567, 427.669, 425.679, 425.458, 43...
#> $ `% Iron Concentrate` <dbl> 66.91, 66.91, 66.91, 66.91, 66.91, 66.91, 66.91, 66.91, ...
#> $ `% Silica Concentrate` <dbl> 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1....
lgleadershipacademy/dataset documentation built on May 7, 2019, 6:58 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
dataset
dataset 패키지는 LG 인화원의 교육을 위해서 작성한 데이터 패키지입니다.
설치
dataset 패키지는 아래 코드를 이용해 설치할 수 있습니다. remotes 패키지는 설치되어 있지 않다면 설치해야
github내의 패키지를 설치할 수 있습니다.
install.packages("remotes")
remotes::install_github("lgleadershipacademy/dataset")
사용할 수 있는 데이터 리스트
library(dataset)
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
list_of_dataset()
#> [1] "avocado" "churn" "emp_tmnt" "emp_attr" "mall"
#> [6] "credit_train" "credit_test" "games" "retail" "mining_samples"
데이터 사용
리스트내에 mining_sample과 같이 데이터셋 이름의 뒤에 _sample 이 붙은 데이터가 아니라면 바로 이름으로
사용할 수 있습니다.
avocado
#> # A tibble: 18,249 x 13
#> Date AveragePrice `Total Volume` `4046` `4225` `4770` `Total Bags` `Small Bags`
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2015-12-27 00:00:00 1.33 64237. 1037. 5.45e4 48.2 8697. 8604.
#> 2 2015-12-20 00:00:00 1.35 54877. 674. 4.46e4 58.3 9506. 9408.
#> 3 2015-12-13 00:00:00 0.93 118220. 795. 1.09e5 130. 8145. 8042.
#> 4 2015-12-06 00:00:00 1.08 78992. 1132 7.20e4 72.6 5811. 5677.
#> 5 2015-11-29 00:00:00 1.28 51040. 941. 4.38e4 75.8 6184. 5986.
#> 6 2015-11-22 00:00:00 1.26 55980. 1184. 4.81e4 43.6 6684. 6556.
#> 7 2015-11-15 00:00:00 0.99 83454. 1369. 7.37e4 93.3 8319. 8197.
#> 8 2015-11-08 00:00:00 0.98 109428. 704. 1.02e5 80 6829. 6267.
#> 9 2015-11-01 00:00:00 1.02 99811. 1022. 8.73e4 85.3 11388. 11105.
#> 10 2015-10-25 00:00:00 1.07 74339. 842. 6.48e4 113 8626. 8061.
#> # ... with 18,239 more rows, and 5 more variables: `Large Bags` <dbl>, `XLarge Bags` <dbl>,
#> # type <chr>, year <dbl>, region <chr>
glimpse(avocado)
#> Observations: 18,249
#> Variables: 13
#> $ Date <dttm> 2015-12-27, 2015-12-20, 2015-12-13, 2015-12-06, 2015-11-29, 2015-11-22,...
#> $ AveragePrice <dbl> 1.33, 1.35, 0.93, 1.08, 1.28, 1.26, 0.99, 0.98, 1.02, 1.07, 1.12, 1.28, ...
#> $ `Total Volume` <dbl> 64236.62, 54876.98, 118220.22, 78992.15, 51039.60, 55979.78, 83453.76, 1...
#> $ `4046` <dbl> 1036.74, 674.28, 794.70, 1132.00, 941.48, 1184.27, 1368.92, 703.75, 1022...
#> $ `4225` <dbl> 54454.85, 44638.81, 109149.67, 71976.41, 43838.39, 48067.99, 73672.72, 1...
#> $ `4770` <dbl> 48.16, 58.33, 130.50, 72.58, 75.78, 43.61, 93.26, 80.00, 85.34, 113.00, ...
#> $ `Total Bags` <dbl> 8696.87, 9505.56, 8145.35, 5811.16, 6183.95, 6683.91, 8318.86, 6829.22, ...
#> $ `Small Bags` <dbl> 8603.62, 9408.07, 8042.21, 5677.40, 5986.26, 6556.47, 8196.81, 6266.85, ...
#> $ `Large Bags` <dbl> 93.25, 97.49, 103.14, 133.76, 197.69, 127.44, 122.05, 562.37, 283.83, 56...
#> $ `XLarge Bags` <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, ...
#> $ type <chr> "conventional", "conventional", "conventional", "conventional", "convent...
#> $ year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, ...
#> $ region <chr> "Albany", "Albany", "Albany", "Albany", "Albany", "Albany", "Albany", "A...
mining_sample
mining 데이터셋은 용량이 커서 다운로드로 제공하며, 패키지 내에는 mining_sample로 첫 100줄의 데이터만
확인할 수 있습니다. 전체 데이터를 불러오려면 get_mining_data() 함수를 사용하시면 됩니다.
mining <- get_mining_data()
glimpse(mining)
#> Observations: 737,453
#> Variables: 24
#> $ date <dttm> 2017-03-10 01:00:00, 2017-03-10 01:00:00, 2017-03-10 01...
#> $ `% Iron Feed` <dbl> 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55.2, 55...
#> $ `% Silica Feed` <dbl> 16.98, 16.98, 16.98, 16.98, 16.98, 16.98, 16.98, 16.98, ...
#> $ `Starch Flow` <dbl> 3019.53, 3024.41, 3043.46, 3047.36, 3033.69, 3079.10, 31...
#> $ `Amina Flow` <dbl> 557.434, 563.965, 568.054, 568.665, 558.167, 564.697, 56...
#> $ `Ore Pulp Flow` <dbl> 395.713, 397.383, 399.668, 397.939, 400.254, 396.533, 39...
#> $ `Ore Pulp pH` <dbl> 10.0664, 10.0672, 10.0680, 10.0689, 10.0697, 10.0705, 10...
#> $ `Ore Pulp Density` <dbl> 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1.74, 1....
#> $ `Flotation Column 01 Air Flow` <dbl> 249.214, 249.719, 249.741, 249.917, 250.203, 250.730, 25...
#> $ `Flotation Column 02 Air Flow` <dbl> 253.235, 250.532, 247.874, 254.487, 252.136, 248.906, 25...
#> $ `Flotation Column 03 Air Flow` <dbl> 250.576, 250.862, 250.313, 250.049, 249.895, 249.521, 24...
#> $ `Flotation Column 04 Air Flow` <dbl> 295.096, 295.096, 295.096, 295.096, 295.096, 295.096, 29...
#> $ `Flotation Column 05 Air Flow` <dbl> 306.4, 306.4, 306.4, 306.4, 306.4, 306.4, 306.4, 306.4, ...
#> $ `Flotation Column 06 Air Flow` <dbl> 250.225, 250.137, 251.345, 250.422, 249.983, 250.356, 25...
#> $ `Flotation Column 07 Air Flow` <dbl> 250.884, 248.994, 248.071, 251.147, 248.928, 251.873, 25...
#> $ `Flotation Column 01 Level` <dbl> 457.396, 451.891, 451.240, 452.441, 452.441, 444.384, 44...
#> $ `Flotation Column 02 Level` <dbl> 432.962, 429.560, 468.927, 458.165, 452.900, 443.269, 44...
#> $ `Flotation Column 03 Level` <dbl> 424.954, 432.939, 434.610, 442.865, 450.523, 460.449, 45...
#> $ `Flotation Column 04 Level` <dbl> 443.558, 448.086, 449.688, 446.210, 453.670, 439.920, 43...
#> $ `Flotation Column 05 Level` <dbl> 502.255, 496.363, 484.411, 471.411, 462.598, 451.588, 44...
#> $ `Flotation Column 06 Level` <dbl> 446.370, 445.922, 447.826, 437.690, 443.682, 433.539, 44...
#> $ `Flotation Column 07 Level` <dbl> 523.344, 498.075, 458.567, 427.669, 425.679, 425.458, 43...
#> $ `% Iron Concentrate` <dbl> 66.91, 66.91, 66.91, 66.91, 66.91, 66.91, 66.91, 66.91, ...
#> $ `% Silica Concentrate` <dbl> 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1....
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