Update urls

Author: hadley

DESCRIPTION

@@ -14,7 +14,7 @@ Description: Helpers for reordering factor levels (including
     levels (including collapsing rare levels into other, 'anonymising',
     and manually 'recoding').
 License: MIT + file LICENSE
-URL: http://forcats.tidyverse.org,
+URL: https://forcats.tidyverse.org,
     https://github.com/tidyverse/forcats
 BugReports: https://github.com/tidyverse/forcats/issues
 Depends: 

README.Rmd

@@ -29,7 +29,7 @@ R uses __factors__ to handle categorical variables, variables that have a fixed
 * `fct_relevel()`: Changing the order of a factor by hand.
 * `fct_lump()`: Collapsing the least/most frequent values of a factor into "other".
 
-You can learn more about each of these in `vignette("forcats")`. If you're new to factors, the best place to start is the [chapter on factors](http://r4ds.had.co.nz/factors.html) in R for Data Science.
+You can learn more about each of these in `vignette("forcats")`. If you're new to factors, the best place to start is the [chapter on factors](https://r4ds.had.co.nz/factors.html) in R for Data Science.
 
 ## Installation
 
@@ -88,7 +88,7 @@ starwars %>%
 
 ## More resources 
 
-For a history of factors, 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. 
+For a history of factors, I recommend [_stringsAsFactors: An unauthorized biography_](https://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) by Roger Peng and [_stringsAsFactors = \<sigh\>_](https://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. 
 
 ## Getting help
 

README.md

@@ -29,7 +29,7 @@ values. Some examples include:
 
 You can learn more about each of these in `vignette("forcats")`. If
 you’re new to factors, the best place to start is the [chapter on
-factors](http://r4ds.had.co.nz/factors.html) in R for Data Science.
+factors](https://r4ds.had.co.nz/factors.html) in R for Data Science.
 
 ## Installation
 
@@ -85,7 +85,7 @@ starwars %>%
   count(species)
 #> # A tibble: 4 x 2
 #>   species     n
-#>   <fct>   <int>
+#> * <fct>   <int>
 #> 1 Droid       6
 #> 2 Gungan      3
 #> 3 Human      35
@@ -114,9 +114,9 @@ starwars %>%
 
 For a history of factors, I recommend [*stringsAsFactors: An
 unauthorized
-biography*](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/)
+biography*](https://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)
+&lt;sigh&gt;*](https://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

man/forcats-package.Rd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-The goal of the __forcats__ package is to provide a suite of useful tools that solve common problems with factors. Factors are useful when you have categorical data, variables that have a fixed and known set of values, and when you want to display character vectors in non-alphabetical order. If you want to learn more, the best place to start is the [chapter on factors](http://r4ds.had.co.nz/factors.html) in R for Data Science.  
+The goal of the __forcats__ package is to provide a suite of useful tools that solve common problems with factors. Factors are useful when you have categorical data, variables that have a fixed and known set of values, and when you want to display character vectors in non-alphabetical order. If you want to learn more, the best place to start is the [chapter on factors](https://r4ds.had.co.nz/factors.html) in R for Data Science.  
 
 ## Ordering by frequency