urlchecker::url_check()

Author: hadley

DESCRIPTION

@@ -14,7 +14,7 @@ Description: A consistent, simple and easy to use set of wrappers around
     length vectors in the same way, and the output from one function is
     easy to feed into the input of another.
 License: MIT + file LICENSE
-URL: http://stringr.tidyverse.org, https://github.com/tidyverse/stringr
+URL: https://stringr.tidyverse.org, https://github.com/tidyverse/stringr
 BugReports: https://github.com/tidyverse/stringr/issues
 Depends: 
     R (>= 3.3)

NEWS.md

@@ -142,7 +142,7 @@ Hot patch release to resolve R CMD check failures.
 ## New features
 
 * `str_glue()` and `str_glue_data()` provide convenient wrappers around
-  `glue` and `glue_data()` from the [glue](http://glue.tidyverse.org/) package
+  `glue` and `glue_data()` from the [glue](https://glue.tidyverse.org/) package
   (#157).
 
 * `str_flatten()` is a wrapper around `stri_flatten()` and clearly
@@ -236,7 +236,7 @@ Hot patch release to resolve R CMD check failures.
 
 # stringr 1.0.0
 
-* stringr is now powered by [stringi](https://github.com/Rexamine/stringi) 
+* stringr is now powered by [stringi](https://github.com/gagolews/stringi) 
   instead of base R regular expressions. This improves unicode and support, and 
   makes most operations considerably faster.  If you find stringr inadequate for
   your string processing needs, I highly recommend looking at stringi in more

README.Rmd

@@ -18,15 +18,15 @@ library(stringr)
 <!-- badges: start -->
 [![CRAN status](https://www.r-pkg.org/badges/version/stringr)](https://cran.r-project.org/package=stringr)
 [![R-CMD-check](https://github.com/tidyverse/stringr/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverse/stringr/actions)
-[![Codecov test coverage](https://codecov.io/gh/tidyverse/stringr/branch/main/graph/badge.svg)](https://codecov.io/gh/tidyverse/stringr?branch=main)
-[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
+[![Codecov test coverage](https://codecov.io/gh/tidyverse/stringr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/stringr?branch=main)
+[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 <!-- badges: end -->
 
 ## Overview
 
-Strings are not glamorous, high-profile components of R, but they do play a big role in many data cleaning and preparation tasks. The stringr package provides a cohesive set of functions designed to make working with strings as easy as possible. If you're not familiar with strings, the best place to start is the [chapter on strings](http://r4ds.had.co.nz/strings.html) in R for Data Science.
+Strings are not glamorous, high-profile components of R, but they do play a big role in many data cleaning and preparation tasks. The stringr package provides a cohesive set of functions designed to make working with strings as easy as possible. If you're not familiar with strings, the best place to start is the [chapter on strings](https://r4ds.had.co.nz/strings.html) in R for Data Science.
 
-stringr is built on top of [stringi](https://github.com/gagolews/stringi), which uses the [ICU](http://site.icu-project.org) C library to provide fast, correct implementations of common string manipulations. stringr focusses on the most important and commonly used string manipulation functions whereas stringi provides a comprehensive set covering almost anything you can imagine. If you find that stringr is missing a function that you need, try looking in stringi. Both packages share similar conventions, so once you've mastered stringr, you should find stringi similarly easy to use.
+stringr is built on top of [stringi](https://github.com/gagolews/stringi), which uses the [ICU](https://icu.unicode.org) C library to provide fast, correct implementations of common string manipulations. stringr focusses on the most important and commonly used string manipulation functions whereas stringi provides a comprehensive set covering almost anything you can imagine. If you find that stringr is missing a function that you need, try looking in stringi. Both packages share similar conventions, so once you've mastered stringr, you should find stringi similarly easy to use.
 
 ## Installation
 

README.md

@@ -9,9 +9,9 @@
 status](https://www.r-pkg.org/badges/version/stringr)](https://cran.r-project.org/package=stringr)
 [![R-CMD-check](https://github.com/tidyverse/stringr/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverse/stringr/actions)
 [![Codecov test
-coverage](https://codecov.io/gh/tidyverse/stringr/branch/main/graph/badge.svg)](https://codecov.io/gh/tidyverse/stringr?branch=main)
+coverage](https://codecov.io/gh/tidyverse/stringr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/stringr?branch=main)
 [![Lifecycle:
-stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
+stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 <!-- badges: end -->
 
 ## Overview
@@ -21,11 +21,11 @@ play a big role in many data cleaning and preparation tasks. The stringr
 package provides a cohesive set of functions designed to make working
 with strings as easy as possible. If you’re not familiar with strings,
 the best place to start is the [chapter on
-strings](http://r4ds.had.co.nz/strings.html) in R for Data Science.
+strings](https://r4ds.had.co.nz/strings.html) in R for Data Science.
 
 stringr is built on top of
 [stringi](https://github.com/gagolews/stringi), which uses the
-[ICU](http://site.icu-project.org) C library to provide fast, correct
+[ICU](https://icu.unicode.org) C library to provide fast, correct
 implementations of common string manipulations. stringr focusses on the
 most important and commonly used string manipulation functions whereas
 stringi provides a comprehensive set covering almost anything you can
@@ -75,88 +75,88 @@ str_count(x, "[aeiou]")
 
 There are seven main verbs that work with patterns:
 
--   `str_detect(x, pattern)` tells you if there’s any match to the
-    pattern:
-
-    ``` r
-    str_detect(x, "[aeiou]")
-    #> [1] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
-    ```
-
--   `str_count(x, pattern)` counts the number of patterns:
-
-    ``` r
-    str_count(x, "[aeiou]")
-    #> [1] 0 3 1 2 2 4
-    ```
-
--   `str_subset(x, pattern)` extracts the matching components:
-
-    ``` r
-    str_subset(x, "[aeiou]")
-    #> [1] "video"     "cross"     "extra"     "deal"      "authority"
-    ```
-
--   `str_locate(x, pattern)` gives the position of the match:
-
-    ``` r
-    str_locate(x, "[aeiou]")
-    #>      start end
-    #> [1,]    NA  NA
-    #> [2,]     2   2
-    #> [3,]     3   3
-    #> [4,]     1   1
-    #> [5,]     2   2
-    #> [6,]     1   1
-    ```
-
--   `str_extract(x, pattern)` extracts the text of the match:
-
-    ``` r
-    str_extract(x, "[aeiou]")
-    #> [1] NA  "i" "o" "e" "e" "a"
-    ```
-
--   `str_match(x, pattern)` extracts parts of the match defined by
-    parentheses:
-
-    ``` r
-    # extract the characters on either side of the vowel
-    str_match(x, "(.)[aeiou](.)")
-    #>      [,1]  [,2] [,3]
-    #> [1,] NA    NA   NA  
-    #> [2,] "vid" "v"  "d" 
-    #> [3,] "ros" "r"  "s" 
-    #> [4,] NA    NA   NA  
-    #> [5,] "dea" "d"  "a" 
-    #> [6,] "aut" "a"  "t"
-    ```
-
--   `str_replace(x, pattern, replacement)` replaces the matches with new
-    text:
-
-    ``` r
-    str_replace(x, "[aeiou]", "?")
-    #> [1] "why"       "v?deo"     "cr?ss"     "?xtra"     "d?al"      "?uthority"
-    ```
-
--   `str_split(x, pattern)` splits up a string into multiple pieces:
-
-    ``` r
-    str_split(c("a,b", "c,d,e"), ",")
-    #> [[1]]
-    #> [1] "a" "b"
-    #> 
-    #> [[2]]
-    #> [1] "c" "d" "e"
-    ```
+- `str_detect(x, pattern)` tells you if there’s any match to the
+  pattern:
+
+  ``` r
+  str_detect(x, "[aeiou]")
+  #> [1] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
+  ```
+
+- `str_count(x, pattern)` counts the number of patterns:
+
+  ``` r
+  str_count(x, "[aeiou]")
+  #> [1] 0 3 1 2 2 4
+  ```
+
+- `str_subset(x, pattern)` extracts the matching components:
+
+  ``` r
+  str_subset(x, "[aeiou]")
+  #> [1] "video"     "cross"     "extra"     "deal"      "authority"
+  ```
+
+- `str_locate(x, pattern)` gives the position of the match:
+
+  ``` r
+  str_locate(x, "[aeiou]")
+  #>      start end
+  #> [1,]    NA  NA
+  #> [2,]     2   2
+  #> [3,]     3   3
+  #> [4,]     1   1
+  #> [5,]     2   2
+  #> [6,]     1   1
+  ```
+
+- `str_extract(x, pattern)` extracts the text of the match:
+
+  ``` r
+  str_extract(x, "[aeiou]")
+  #> [1] NA  "i" "o" "e" "e" "a"
+  ```
+
+- `str_match(x, pattern)` extracts parts of the match defined by
+  parentheses:
+
+  ``` r
+  # extract the characters on either side of the vowel
+  str_match(x, "(.)[aeiou](.)")
+  #>      [,1]  [,2] [,3]
+  #> [1,] NA    NA   NA  
+  #> [2,] "vid" "v"  "d" 
+  #> [3,] "ros" "r"  "s" 
+  #> [4,] NA    NA   NA  
+  #> [5,] "dea" "d"  "a" 
+  #> [6,] "aut" "a"  "t"
+  ```
+
+- `str_replace(x, pattern, replacement)` replaces the matches with new
+  text:
+
+  ``` r
+  str_replace(x, "[aeiou]", "?")
+  #> [1] "why"       "v?deo"     "cr?ss"     "?xtra"     "d?al"      "?uthority"
+  ```
+
+- `str_split(x, pattern)` splits up a string into multiple pieces:
+
+  ``` r
+  str_split(c("a,b", "c,d,e"), ",")
+  #> [[1]]
+  #> [1] "a" "b"
+  #> 
+  #> [[2]]
+  #> [1] "c" "d" "e"
+  ```
 
 As well as regular expressions (the default), there are three other
 pattern matching engines:
 
--   `fixed()`: match exact bytes
--   `coll()`: match human letters
--   `boundary()`: match boundaries
+- `fixed()`: match exact bytes
+- `coll()`: match human letters
+- `boundary()`: match boundaries
 
 ## RStudio Addin
 
@@ -183,21 +183,21 @@ learn. Additionally, they lag behind the string operations in other
 programming languages, so that some things that are easy to do in
 languages like Ruby or Python are rather hard to do in R.
 
--   Uses consistent function and argument names. The first argument is
-    always the vector of strings to modify, which makes stringr work
-    particularly well in conjunction with the pipe:
+- Uses consistent function and argument names. The first argument is
+  always the vector of strings to modify, which makes stringr work
+  particularly well in conjunction with the pipe:
 
-    ``` r
-    letters %>%
-      .[1:10] %>% 
-      str_pad(3, "right") %>%
-      str_c(letters[2:11])
-    #>  [1] "a  b" "b  c" "c  d" "d  e" "e  f" "f  g" "g  h" "h  i" "i  j" "j  k"
-    ```
+  ``` r
+  letters %>%
+    .[1:10] %>% 
+    str_pad(3, "right") %>%
+    str_c(letters[2:11])
+  #>  [1] "a  b" "b  c" "c  d" "d  e" "e  f" "f  g" "g  h" "h  i" "i  j" "j  k"
+  ```
 
--   Simplifies string operations by eliminating options that you don’t
-    need 95% of the time.
+- Simplifies string operations by eliminating options that you don’t
+  need 95% of the time.
 
--   Produces outputs than can easily be used as inputs. This includes
-    ensuring that missing inputs result in missing outputs, and zero
-    length inputs result in zero length outputs.
+- Produces outputs than can easily be used as inputs. This includes
+  ensuring that missing inputs result in missing outputs, and zero
+  length inputs result in zero length outputs.

man/stringr-package.Rd

@@ -66,7 +66,7 @@ Overall the main differences between base R and stringr are:
     arguments, stringr instead uses helper functions (like `fixed()`, 
     `regexp()`, and `coll()`).
 
-Next we'll walk through each of the functions, noting the similarities and important differences. These examples come from the [stringr documentation](https://cran.r-project.org/web/packages/stringr/stringr.pdf) and here, they are contrasted with the analogous base R operation(s).
+Next we'll walk through each of the functions, noting the similarities and important differences. These examples are adapted from the stringr documentation and here they are contrasted with the analogous base R operations.
 
 ```{r setup}
 library(stringr)

vignettes/from-base.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 library(stringr)
 ```
 
-Regular expressions are a concise and flexible tool for describing patterns in strings. This vignette describes the key features of stringr's regular expressions, as implemented by [stringi](https://github.com/gagolews/stringi). It is not a tutorial, so if you're unfamiliar regular expressions, I'd recommend starting at <http://r4ds.had.co.nz/strings.html>. If you want to master the details, I'd recommend reading the classic [_Mastering Regular Expressions_](https://www.amazon.com/Mastering-Regular-Expressions-Jeffrey-Friedl/dp/0596528124) by Jeffrey E. F. Friedl. 
+Regular expressions are a concise and flexible tool for describing patterns in strings. This vignette describes the key features of stringr's regular expressions, as implemented by [stringi](https://github.com/gagolews/stringi). It is not a tutorial, so if you're unfamiliar regular expressions, I'd recommend starting at <https://r4ds.had.co.nz/strings.html>. If you want to master the details, I'd recommend reading the classic [_Mastering Regular Expressions_](https://www.amazon.com/Mastering-Regular-Expressions-Jeffrey-Friedl/dp/0596528124) by Jeffrey E. F. Friedl. 
 
 Regular expressions are the default pattern engine in stringr. That means when you use a pattern matching function with a bare string, it's equivalent to wrapping it in a call to `regex()`: