Add ref to @AmeliaMN paper

Author: hadley

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 
 ## Overview
 
-R uses __factors__ to handle categorical variables, variables that have a fixed and known set of possible values. Historically, factors were much easier to work with than character vectors, so many base R functions automatically convert character vectors to factors. (For more historical context, I recommend [_stringsAsFactors: An unauthorized biography_](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) by Roger Peng, and [_stringsAsFactors = \<sigh\>_](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh) by Thomas Lumley.) These days, making factors automatically is no longer so helpful, so packages in the [tidyverse](http://tidyverse.org) never create them automatically.
+R uses __factors__ to handle categorical variables, variables that have a fixed and known set of possible values. Historically, factors were much easier to work with than character vectors, so many base R functions automatically convert character vectors to factors. (For historical context, I recommend [_stringsAsFactors: An unauthorized biography_](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) by Roger Peng, and [_stringsAsFactors = \<sigh\>_](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh) by Thomas Lumley.  If you want to learn more about other approaches to working with factors and categorical data, I recommend [_Wrangling categorical data in R_](https://peerj.com/preprints/3163/), by Amelia McNamara and Nicholas Horton.) These days, making factors automatically is no longer so helpful, so packages in the [tidyverse](http://tidyverse.org) never create them automatically.
 
 However, factors are still useful when you have true categorical data, and when you want to override the ordering of character vectors to improve display. The goal of the __forcats__ package is to provide a suite of useful tools that solve common problems with factors. If you're not familiar with strings, the best place to start is the [chapter on factors](http://r4ds.had.co.nz/factors.html) in R for Data Science.
 

README.md

@@ -8,7 +8,7 @@ forcats <img src="logo.png" align="right" />
 Overview
 --------
 
-R uses **factors** to handle categorical variables, variables that have a fixed and known set of possible values. Historically, factors were much easier to work with than character vectors, so many base R functions automatically convert character vectors to factors. (For more historical context, I recommend [*stringsAsFactors: An unauthorized biography*](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) by Roger Peng, and [*stringsAsFactors = &lt;sigh&gt;*](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh) by Thomas Lumley.) These days, making factors automatically is no longer so helpful, so packages in the [tidyverse](http://tidyverse.org) never create them automatically.
+R uses **factors** to handle categorical variables, variables that have a fixed and known set of possible values. Historically, factors were much easier to work with than character vectors, so many base R functions automatically convert character vectors to factors. (For historical context, I recommend [*stringsAsFactors: An unauthorized biography*](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) by Roger Peng, and [*stringsAsFactors = &lt;sigh&gt;*](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh) by Thomas Lumley. If you want to learn more about other approaches to working with factors and categorical data, I recommend [*Wrangling categorical data in R*](https://peerj.com/preprints/3163/), by Amelia McNamara and Nicholas Horton.) These days, making factors automatically is no longer so helpful, so packages in the [tidyverse](http://tidyverse.org) never create them automatically.
 
 However, factors are still useful when you have true categorical data, and when you want to override the ordering of character vectors to improve display. The goal of the **forcats** package is to provide a suite of useful tools that solve common problems with factors. If you're not familiar with strings, the best place to start is the [chapter on factors](http://r4ds.had.co.nz/factors.html) in R for Data Science.
 
@@ -66,11 +66,10 @@ factor(x2, month_levels)
 
 parse_factor(x2, month_levels)
 #> Warning: 1 parsing failure.
-#> row col           expected actual
-#>   3  -- value in level set    Jam
+#> row # A tibble: 1 x 4 col     row   col           expected actual expected   <int> <int>              <chr>  <chr> actual 1     3    NA value in level set    Jam
 #> [1] Dec  Apr  <NA> Mar 
 #> attr(,"problems")
-#> # A tibble: 1 × 4
+#> # A tibble: 1 x 4
 #>     row   col           expected actual
 #>   <int> <int>              <chr>  <chr>
 #> 1     3    NA value in level set    Jam