(This article was first published on

**RStudio Blog**, and kindly contributed to R-bloggers)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 tidy data vignette. Install it with:

`install.packages("tidyr")`

I mostly released this version to bundle up a number of small tweaks needed for R for Data Science. But there’s one nice new feature, contributed by Jan Schulz: `drop_na()`

. `drop_na()`

drops rows containing missing values:

```
df <- tibble(x = c(1, 2, NA), y = c("a", NA, "b"))
df
#> # A tibble: 3 × 2
#> x y
#>
```
#> 1 1 a
#> 2 2
#> 3 NA b
# Called without arguments, it drops rows containing
# missing values in any variable:
df %>% drop_na()
#> # A tibble: 1 × 2
#> x y
#>
#> 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
#>
#> 1 1 a
#> 2 2

Please see the release notes for a complete list of changes.

To

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