10.27 Replace Missing Values

20201026 See Section 10.22 to replace missing vallues with an imputed (or guessed at) values, and Section ?? 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: 275,410 × 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 275,400 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 <dbl>,
## #   temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## #   rain_tomorrow <fct>

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