Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

CLICK HERE TO VISIT THE UPDATED SURVIVAL GUIDE

Replacing Missing Values

20201026 See Section 9.11 to impute (guess) values to replace 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: 176,747 x 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
## # ... with 176,737 more rows, and 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 <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>