10.18 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: 217,049 × 24
## date location min_temp max_temp rainf…¹ evapo…² sunsh…³ wind_…⁴ wind_…⁵
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <ord> <dbl>
## 1 2008-12-01 Albury 13.4 22.9 0.6 4.8 8.5 W 44
## 2 2008-12-02 Albury 7.4 25.1 0 4.8 8.5 WNW 44
## 3 2008-12-03 Albury 12.9 25.7 0 4.8 8.5 WSW 46
## 4 2008-12-04 Albury 9.2 28 0 4.8 8.5 NE 24
## 5 2008-12-05 Albury 17.5 32.3 1 4.8 8.5 W 41
## 6 2008-12-06 Albury 14.6 29.7 0.2 4.8 8.5 WNW 56
## 7 2008-12-07 Albury 14.3 25 0 4.8 8.5 W 50
## 8 2008-12-08 Albury 7.7 26.7 0 4.8 8.5 W 35
## 9 2008-12-09 Albury 9.7 31.9 0 4.8 8.5 NNW 80
## 10 2008-12-10 Albury 13.1 30.1 1.4 4.8 8.5 W 28
## # … with 217,039 more rows, 15 more variables: wind_dir_9am <ord>,
## # wind_dir_3pm <ord>, wind_speed_9am <dbl>, wind_speed_3pm <dbl>,
## # humidity_9am <dbl>, humidity_3pm <dbl>, pressure_9am <dbl>,
## # pressure_3pm <dbl>, cloud_9am <dbl>, cloud_3pm <dbl>, temp_9am <dbl>,
## # temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>, rain_tomorrow <fct>, and
## # abbreviated variable names ¹rainfall, ²evaporation, ³sunshine,
## # ⁴wind_gust_dir, ⁵wind_gust_speed
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