18.1 Introduction

We encountered missing values in previous chapters.

You first saw them in Chapter 1 where they resulted in a warning when making a plot

ggplot2::ggplot(
  data = palmerpenguins::penguins,
  mapping = ggplot2::aes(
      x = .data[["flipper_length_mm"]], 
      y = .data[["body_mass_g"]]
      )
) + 
ggplot2::geom_point()

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).

palmerpenguins::penguins |> 
  dplyr::filter(
    is.na(flipper_length_mm) | is.na(body_mass_g)
  ) |> 
  reactable::reactable(
    theme = reactablefmtr::dark(),
  )

nycflights13::flights |> 
  dplyr::group_by(.data[["month"]]) |> 
  dplyr::summarize(
    avg_delay = mean(.data[["dep_delay"]])
  ) |> 
  reactable::reactable(
    theme = reactablefmtr::dark(),
    defaultPageSize = 5
  )

In Section 3.5.2 where they interfered with computing summary statistics

nycflights13::flights |> 
  dplyr::group_by(.data[["month"]]) |> 
  dplyr::summarize(
    avg_delay = mean(.data[["dep_delay"]], 
                     na.rm = FALSE),
    avg_delay_corrected = mean(.data[["dep_delay"]], 
                     na.rm = TRUE)
  ) |> 
  reactable::reactable(
    theme = reactablefmtr::dark(),
    defaultPageSize = 5
  )

Their infectious nature and how to check for their presence in Section 12.2.2

NA > 5

## [1] NA

10 == NA

## [1] NA

NA == NA

## [1] NA

is.na(NA)

## [1] TRUE

We learn more of the details in this chapter, covering additional tools (besides is.na and na.rm argument) for working with missing values

• Explicit missing values
• Implicit missing values
• Empty groups