9.3 Random Sample
20200317
A common task is to randomly sample rows from a dataset. The dplyr::sample_frac() function will randomly choose a specified fraction (e.g. 20%) of the rows of the dataset:
## # A tibble: 55,082 × 24
## date location min_temp max_temp rainfall evaporation sunshine
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-12-07 Darwin 28.2 34.5 0 8 9.9
## 2 2015-11-23 Bendigo 11 25.4 0 NA NA
## 3 2021-10-16 Wollongong 13.2 21.9 NA NA NA
## 4 2024-01-13 Wollongong 21 26.4 0 NA NA
## 5 2020-04-15 Brisbane 16.7 27.4 0 NA 10
## 6 2020-11-26 Sale 14.1 23.7 0 NA NA
## 7 2009-01-30 Nuriootpa 22 41.9 0 14.8 12.7
## 8 2013-06-11 Williamtown 9.9 20.5 0.6 1.4 9.5
## 9 2018-12-01 Sale 9.6 26.8 0 NA NA
## 10 2012-03-10 Townsville 24.2 30.7 0 5.8 3.5
## # ℹ 55,072 more rows
## # ℹ 17 more variables: wind_gust_dir <ord>, wind_gust_speed <dbl>,
## # wind_dir_9am <ord>, wind_dir_3pm <ord>, wind_speed_9am <dbl>,
## # wind_speed_3pm <dbl>, humidity_9am <int>, humidity_3pm <int>,
## # pressure_9am <dbl>, pressure_3pm <dbl>, cloud_9am <int>, cloud_3pm <int>,
## # temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## # rain_tomorrow <fct>The next time you randomly sample the dataset the resulting sample will be different:
## # A tibble: 55,082 × 24
## date location min_temp max_temp rainfall evaporation sunshine
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013-11-07 Tuggeranong 4.7 31.1 0 NA NA
## 2 2015-04-15 Ballarat 13.3 24.3 1.2 NA NA
## 3 2020-11-01 Darwin 25.5 36.3 0 8 12
## 4 2013-05-25 Richmond 6.2 20.7 1.6 NA NA
## 5 2011-11-22 Albury 11.9 22.7 2.6 NA NA
## 6 2023-07-08 Woomera 7.1 18.5 0 NA NA
## 7 2020-10-04 SalmonGums 4.5 15.9 0.6 NA NA
## 8 2025-01-19 Ballarat 12 33 0 NA NA
## 9 2018-06-28 Richmond 8.1 16.4 2.8 0.4 NA
## 10 2010-12-07 Adelaide 21.1 32.5 3.4 5.8 6
## # ℹ 55,072 more rows
## # ℹ 17 more variables: wind_gust_dir <ord>, wind_gust_speed <dbl>,
## # wind_dir_9am <ord>, wind_dir_3pm <ord>, wind_speed_9am <dbl>,
## # wind_speed_3pm <dbl>, humidity_9am <int>, humidity_3pm <int>,
## # pressure_9am <dbl>, pressure_3pm <dbl>, cloud_9am <int>, cloud_3pm <int>,
## # temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## # rain_tomorrow <fct>To ensure the sample random sample each time use base::set.seed():
## # A tibble: 55,082 × 24
## date location min_temp max_temp rainfall evaporation sunshine
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2026-01-11 MountGinini 7.4 17.1 0 NA NA
## 2 2013-04-08 Nuriootpa 12.6 25.3 0 8 10.2
## 3 2017-11-03 Dartmoor 3.5 15 0.8 NA NA
## 4 2018-01-11 BadgerysCreek 17.3 27 0.2 NA NA
## 5 2011-10-27 Launceston 5.1 19.1 0 NA NA
## 6 2024-01-27 Nuriootpa 5.9 23.8 0 8 NA
## 7 2021-04-11 Wollongong 15.1 19 0 NA NA
## 8 2022-01-15 Penrith 19.9 32.5 1.4 NA NA
## 9 2022-03-06 Ballarat 11 19.1 2 NA NA
## 10 2010-06-22 Dartmoor 5 14.8 0 0.6 4.8
## # ℹ 55,072 more rows
## # ℹ 17 more variables: wind_gust_dir <ord>, wind_gust_speed <dbl>,
## # wind_dir_9am <ord>, wind_dir_3pm <ord>, wind_speed_9am <dbl>,
## # wind_speed_3pm <dbl>, humidity_9am <int>, humidity_3pm <int>,
## # pressure_9am <dbl>, pressure_3pm <dbl>, cloud_9am <int>, cloud_3pm <int>,
## # temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## # rain_tomorrow <fct>## # A tibble: 55,082 × 24
## date location min_temp max_temp rainfall evaporation sunshine
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2026-01-11 MountGinini 7.4 17.1 0 NA NA
## 2 2013-04-08 Nuriootpa 12.6 25.3 0 8 10.2
## 3 2017-11-03 Dartmoor 3.5 15 0.8 NA NA
## 4 2018-01-11 BadgerysCreek 17.3 27 0.2 NA NA
## 5 2011-10-27 Launceston 5.1 19.1 0 NA NA
## 6 2024-01-27 Nuriootpa 5.9 23.8 0 8 NA
## 7 2021-04-11 Wollongong 15.1 19 0 NA NA
## 8 2022-01-15 Penrith 19.9 32.5 1.4 NA NA
## 9 2022-03-06 Ballarat 11 19.1 2 NA NA
## 10 2010-06-22 Dartmoor 5 14.8 0 0.6 4.8
## # ℹ 55,072 more rows
## # ℹ 17 more variables: wind_gust_dir <ord>, wind_gust_speed <dbl>,
## # wind_dir_9am <ord>, wind_dir_3pm <ord>, wind_speed_9am <dbl>,
## # wind_speed_3pm <dbl>, humidity_9am <int>, humidity_3pm <int>,
## # pressure_9am <dbl>, pressure_3pm <dbl>, cloud_9am <int>, cloud_3pm <int>,
## # temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## # rain_tomorrow <fct>Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
