10.8 Combine Rows
20201026 We can utilise dplyr::bind_rows() to combine the rows of two datasets with the same columns:
## # A tibble: 12 × 24
## date location min_temp max_temp rainfall evaporation sunshine
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2008-12-01 Albury 13.4 22.9 0.6 NA NA
## 2 2008-12-02 Albury 7.4 25.1 0 NA NA
## 3 2008-12-03 Albury 12.9 25.7 0 NA NA
## 4 2008-12-04 Albury 9.2 28 0 NA NA
## 5 2008-12-05 Albury 17.5 32.3 1 NA NA
## 6 2008-12-06 Albury 14.6 29.7 0.2 NA NA
## 7 2026-01-25 Uluru 29 44.1 0 NA NA
## 8 2026-01-26 Uluru 30 41.6 0 NA NA
## 9 2026-01-27 Uluru 30.6 41.6 0 NA NA
## 10 2026-01-28 Uluru 30 39.4 0 NA NA
## 11 2026-01-29 Uluru 26.8 44.5 0 NA NA
## 12 2026-01-30 Uluru 26.5 42.4 0.4 NA NA
## # 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 operation performed by dplyr::bind_rows() is also useful to convert a named list (or vector if all values are of the same type) into a tibble:
ds %>%
head() %>%
select(2:5) %>%
bind_rows(list(location="Somewhere", min_temp=-10,
max_temp=30, rainfall=10))## # A tibble: 7 × 4
## location min_temp max_temp rainfall
## <chr> <dbl> <dbl> <dbl>
## 1 Albury 13.4 22.9 0.6
## 2 Albury 7.4 25.1 0
## 3 Albury 12.9 25.7 0
## 4 Albury 9.2 28 0
## 5 Albury 17.5 32.3 1
## 6 Albury 14.6 29.7 0.2
## 7 Somewhere -10 30 10If you find this curated material useful then you can consider a donation to support it's ongoing availability and give you access to the PDF version of this book. The material has been scoped up by Generative AI without permission or any kind of recompense so do consider a donation if you can afford it. Unlike Generative AI your access to this materials is freely given. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Togaware has a 30 year tradition of making popular open source software which includes sold privacy preserving productivity apps, 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
