Fix more links

Author: hadley

NEWS.md

@@ -165,7 +165,7 @@ See `vignette("in-packages")` for a detailed transition guide.
 
 * The first argument of `nest()` has changed from `data` to `.data`.
 
-* `unnest()` uses the [emerging tidyverse standard](https://www.tidyverse.org/articles/2019/01/tibble-2.0.1/#name-repair)
+* `unnest()` uses the [emerging tidyverse standard](https://www.tidyverse.org/blog/2019/01/tibble-2.0.1/#name-repair)
   to disambiguate unique names. Use `names_repair = tidyr_legacy` to 
   request the previous approach.
 

README.Rmd

@@ -28,7 +28,7 @@ The goal of tidyr is to help you create __tidy data__. Tidy data is data where:
 1. Every row is an observation.
 1. Every cell is a single value.
 
-Tidy data describes a standard way of storing data that is used wherever possible throughout the [tidyverse](https://tidyverse.org). If you ensure that your data is tidy, you'll spend less time fighting with the tools and more time working on your analysis. Learn more about tidy data in `vignette("tidy-data")`.
+Tidy data describes a standard way of storing data that is used wherever possible throughout the [tidyverse](https://tidyverse.org/). If you ensure that your data is tidy, you'll spend less time fighting with the tools and more time working on your analysis. Learn more about tidy data in `vignette("tidy-data")`.
 
 ## Installation
 
@@ -81,7 +81,7 @@ tidyr functions fall into five main categories:
 
 tidyr [supersedes](https://www.tidyverse.org/lifecycle/#superseded) reshape2 (2010-2014) and reshape (2005-2010). Somewhat counterintuitively, each iteration of the package has done less. tidyr is designed specifically for tidying data, not general reshaping (reshape2), or the general aggregation (reshape). 
 
-[data.table](http://r-datatable.com/) provides high-performance implementations of `melt()` and `dcast()`
+[data.table](https://rdatatable.gitlab.io/data.table) provides high-performance implementations of `melt()` and `dcast()`
 
 If you'd like to read more about data reshaping from a CS perspective, I'd recommend the following three papers:
 

README.md

@@ -23,7 +23,7 @@ where:
 3.  Every cell is a single value.
 
 Tidy data describes a standard way of storing data that is used wherever
-possible throughout the [tidyverse](https://tidyverse.org). If you
+possible throughout the [tidyverse](https://tidyverse.org/). If you
 ensure that your data is tidy, you’ll spend less time fighting with the
 tools and more time working on your analysis. Learn more about tidy data
 in `vignette("tidy-data")`.
@@ -86,8 +86,8 @@ counterintuitively, each iteration of the package has done less. tidyr
 is designed specifically for tidying data, not general reshaping
 (reshape2), or the general aggregation (reshape).
 
-[data.table](http://r-datatable.com/) provides high-performance
-implementations of `melt()` and `dcast()`
+[data.table](https://rdatatable.gitlab.io/data.table) provides
+high-performance implementations of `melt()` and `dcast()`
 
 If you’d like to read more about data reshaping from a CS perspective,
 I’d recommend the following three papers:

cran-comments.md

@@ -1,3 +1,7 @@
+This is a resubmission with corrected links
+
+---
+
 ## Test environments
 
 * OS X install: R 3.5

vignettes/in-packages.Rmd

@@ -365,4 +365,4 @@ nested %>% unnest(any_of("my_data"))
 ```
 
 [ggplot2-packages]: https://ggplot2.tidyverse.org/dev/articles/ggplot2-in-packages.html
-[name-repair]: https://www.tidyverse.org/articles/2019/01/tibble-2.0.1/#name-repair
+[name-repair]: https://www.tidyverse.org/blog/2019/01/tibble-2.0.1/#name-repair

vignettes/nest.Rmd

@@ -98,4 +98,4 @@ mtcars_nested <- mtcars_nested %>%
 mtcars_nested  
 ```
 
-This workflow works particularly well in conjunction with [broom](https://broom.tidyverse.org/), which makes it easy to turn models into tidy data frames which can then be `unnest()`ed to get back to flat data frames. You can see a bigger example in the [broom and dplyr vignette](https://broom.tidyverse.org/articles/broom_and_dplyr.html).
+This workflow works particularly well in conjunction with [broom](https://broom.tidymodels.org/), which makes it easy to turn models into tidy data frames which can then be `unnest()`ed to get back to flat data frames. You can see a bigger example in the [broom and dplyr vignette](https://broom.tidymodels.org/articles/broom_and_dplyr.html).

vignettes/rectangle.Rmd

@@ -282,7 +282,7 @@ loc %>%
 
 ## Sharla Gelfand's discography
 
-We'll finish off with the most complex list, from [Sharla Gelfand's](https://sharla.party/posts/discog-purrr/) discography. We'll start the usual way: putting the list into a single column data frame, and then widening so each component is a column. I also parse the `date_added` column into a real date-time[^readr].
+We'll finish off with the most complex list, from [Sharla Gelfand's](https://sharla.party/post/discog-purrr/) discography. We'll start the usual way: putting the list into a single column data frame, and then widening so each component is a column. I also parse the `date_added` column into a real date-time[^readr].
 
 [^readr]: I'd normally use `readr::parse_datetime()` or `lubridate::ymd_hms()`, but I can't here because it's a vignette and I don't want to add a dependency to tidyr just to simplify one example.