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(This article was first published on <strong><a href="https://blog.rstudio.org/2016/08/15/tidyr-0-6-0/"> RStudio Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I’m pleased to announce tidyr 0.6.0. tidyr makes it easy to “tidy” your data, storing it in a consistent form so that it’s easy to manipulate, visualise and model. Tidy data has a simple convention: put variables in the columns and observations in the rows. You can learn more about it in the <a target="_blank" href="http://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html">tidy data</a> vignette. Install it with:</p>
<pre><code>install.packages(&quot;tidyr&quot;)</code></pre>
<p>I mostly released this version to bundle up a number of small tweaks needed for <a target="_blank" href="http://r4ds.had.co.nz/tidy-data.html">R for Data Science</a>. But there’s one nice new feature, contributed by <a target="_blank" href="https://github.com/janschulz">Jan Schulz</a>: <code>drop_na()</code>. <code>drop_na()</code>drops rows containing missing values:</p>
<pre><code>df &lt;- tibble(x = c(1, 2, NA), y = c(&quot;a&quot;, NA, &quot;b&quot;))
df
#> # A tibble: 3 × 2
#>       x     y
#>   &lt;dbl> &lt;chr>
#> 1     1     a
#> 2     2  &lt;NA>
#> 3    NA     b

# Called without arguments, it drops rows containing
# missing values in any variable:
df %>% drop_na()
#> # A tibble: 1 × 2
#>       x     y
#>   &lt;dbl> &lt;chr>
#> 1     1     a

# Or you can restrict the variables it looks at, 
# using select() style syntax:
df %>% drop_na(x)
#> # A tibble: 2 × 2
#>       x     y
#>   &lt;dbl> &lt;chr>
#> 1     1     a
#> 2     2  &lt;NA></code></pre>
<p>Please see the <a target="_blank" href="https://github.com/hadley/tidyr/releases/tag/v0.6.0">release notes</a> for a complete list of changes.</p>
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