10.19 Replace Missing Values
20201026 See Section 10.13 to replace missing vallues with an imputed (or guessed at) values, and Section 10.9 to drop rows in a dataset that contain missing values.
To replace missing values (NA) in a data set with a specific
default value, like 0 for numeric data, we can use
tidyr::replace_na() within a pipeline. In the following
example only the numeric columns of the dataset are considered
dplyr::across() the dataset, by checking
tidyselect::where() the data base::is.numeric().
ds %>%
mutate(across(where(is.numeric), ~replace_na(.x, 0)))## # A tibble: 226,868 × 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 4.8 8.5
## 2 2008-12-02 Albury 7.4 25.1 0 4.8 8.5
## 3 2008-12-03 Albury 12.9 25.7 0 4.8 8.5
## 4 2008-12-04 Albury 9.2 28 0 4.8 8.5
## 5 2008-12-05 Albury 17.5 32.3 1 4.8 8.5
## 6 2008-12-06 Albury 14.6 29.7 0.2 4.8 8.5
## 7 2008-12-07 Albury 14.3 25 0 4.8 8.5
## 8 2008-12-08 Albury 7.7 26.7 0 4.8 8.5
## 9 2008-12-09 Albury 9.7 31.9 0 4.8 8.5
## 10 2008-12-10 Albury 13.1 30.1 1.4 4.8 8.5
## # ℹ 226,858 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 <dbl>, 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
