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:
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()drops rows containing missing values:
df <- tibble(x = c(1, 2, NA), y = c("a", NA, "b")) df #> # A tibble: 3 × 2 #> x y #> <dbl> <chr> #> 1 1 a #> 2 2 <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 #> <dbl> <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 #> <dbl> <chr> #> 1 1 a #> 2 2 <NA>
Please see the release notes for a complete list of changes.
We are excited to announce real-time collaborative editing on RStudio Cloud. Users can join the same project, edit code, and immediately see each other’s changes.
In this series, we walk through lesser-known tips and tricks to help you work more effectively and efficiently in R Markdown. This third post focuses on features that save you time and trouble.