10.14 Filter Rows Having Missing Values

20201202 To select the rows from a dataset which have missing values in any of the columns across the dataset we dplyr::filter() dplyr::across() tidyselect::everything() that base::is.na() and reduce it within the dplyr::filter() using the or operator. In the example we randomly sample a few rows and columns to show the result.

ds %>%
  filter(across(everything(), is.na) %>% reduce(`|`)) %>%
  sample_frac() %>%
  select(date, location, sample(3:length(vars), 4))

## # A tibble: 196,937 × 6
##    date       location      wind_dir_3pm temp_9am risk_mm wind_speed_3pm
##    <date>     <chr>         <ord>           <dbl>   <dbl>          <dbl>
##  1 2022-05-11 Dartmoor      NNE               9.3     2                6
##  2 2019-08-15 Cairns        SE               21.1     0               26
##  3 2022-09-13 Mildura       N                10.6     0                9
##  4 2025-10-31 Williamtown   ESE              20       3.2             22
##  5 2013-06-06 Albany        SSW              14.4     1.6              6
##  6 2023-11-10 NorfolkIsland SE               18.9     0               20
##  7 2025-12-06 NorfolkIsland SE               21.2     0               20
##  8 2020-03-12 Newcastle     <NA>             20.4     0               NA
##  9 2023-03-14 Nhil          NW               17.4     0               11
## 10 2014-11-13 Witchcliffe   SE               13.5     0.2             30
## # ℹ 196,927 more rows

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