Tools to help to create tidy data, where each
column is a variable, each row is an observation, and each cell
contains a single value.  'tidyr' contains tools for changing the
shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset,
turning deeply nested lists into rectangular data frames
('rectangling'), and extracting values out of string columns. It also
includes tools for working with missing values (both implicit and
explicit).

Tidy Messy Data

package-ubuntu-20.04-r-4.0.5#cran#tidyr-1.1.3

<p>Drop rows containing missing values
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>data</code></td>
<td>
<p>A data frame.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>&lt;<code><a href="tidyr_tidy_select.html">tidy-select</a></code>&gt; Columns to inspect for
missing values.</p>
</td>
</tr>
</table>

library(dplyr)
df <- tibble(x = c(1, 2, NA), y = c("a", NA, "b"))
df %>% drop_na()
df %>% drop_na(x)

vars <- "y"
df %>% drop_na(x, any_of(vars))

package-ubuntu-20.04-r-4.0.5#cran#tidyr-1.1.3#docs#drop_na

`data`	A data frame.
`...`	<`tidy-select`> Columns to inspect for missing values.

drop_na

Description

Usage

Arguments

Examples

tidyr

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