TidierStrings.jl

What is TidierStrings.jl

TidierStrings.jl is a 100% Julia implementation of the R stringr package.

TidierStrings.jl has one main goal: to implement stringr's straightforward syntax and of ease of use for Julia users. While this package was develeoped to work seamelessly with Tidier.jl fucntions and macros, it can also work as a indepentenly as a standalone package.

Installation

For the development version:

using Pkg
Pkg.add(url = "https://github.com/TidierOrg/TidierStrings.jl.git")

What functions does TidierStrings.jl support?

TidierStrings.jl currently supports:

• str_detect()
• str_replace()
• str_replace_all()
• str_removal_all()
• str_remove()
• str_count()
• str_squish()
• str_equal()
• str_to_upper()
• str_to_lower()
• str_subset()

Examples

using Tidier
using TidierStrings
df = DataFrame(
  Names = ["Alice", "Bob", "Charlie", "Dave", "Eve", "Frank", "Grace"],
  City = ["New York        2019-20", "Los    \n\n\n\n\n\n    Angeles 2007-12 2020-21", "San Antonio 1234567890         ", "       New York City", "LA         2022-23", "Philadelphia            2023-24", "San Jose               9876543210"],
  Occupation = ["Doctor", "Engineer", "Final Artist", "Scientist", "Physician", "Lawyer", "Teacher"],
  Description = ["Alice is a doctor in New York",
                 "Bob is is is an engineer in Los Angeles",
                 "Charlie is an artist in Chicago",
                 "Dave is a scientist in Houston",
                 "Eve is a physician  in Phoenix",
                 "Frank is a lawyer in Philadelphia",
                 "Grace is a teacher in San Antonio"]
)

7×4 DataFrame
 Row │ Names    City                               Occupation    Description                       
     │ String   String                             String        String                            
─────┼─────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York        2019-20            Doctor        Alice is a doctor in New York
   2 │ Bob      Los    \n\n\n\n\n\n    Angeles 2…  Engineer      Bob is is is an engineer in Los …
   3 │ Charlie  San Antonio 1234567890             Final Artist  Charlie is an artist in Chicago
   4 │ Dave            New York City               Scientist     Dave is a scientist in Houston
   5 │ Eve      LA         2022-23                 Physician     Eve is a physician  in Phoenix
   6 │ Frank    Philadelphia            2023-24    Lawyer        Frank is a lawyer in Philadelphia
   7 │ Grace    San Jose               9876543210  Teacher       Grace is a teacher in San Antonio

str_squish(): Removes leading and trailing white spaces from a string and also replaces consecutive white spaces in between words with a single space. It will also remove new lines.

df = @chain df begin
    @mutate(City = str_squish(City))
end

7×4 DataFrame
 Row │ Names    City                         Occupation    Description                       
     │ String   String                       String        String                            
─────┼───────────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York 2019-20             Doctor        Alice is a doctor in New York
   2 │ Bob      Los Angeles 2007-12 2020-21  Engineer      Bob is is is an engineer in Los …
   3 │ Charlie  San Antonio 1234567890       Final Artist  Charlie is an artist in Chicago
   4 │ Dave     New York City                Scientist     Dave is a scientist in Houston
   5 │ Eve      LA 2022-23                   Physician     Eve is a physician  in Phoenix
   6 │ Frank    Philadelphia 2023-24         Lawyer        Frank is a lawyer in Philadelphia
   7 │ Grace    San Jose 9876543210          Teacher       Grace is a teacher in San Antonio

str_detect, str_replace, str_replace_all, str_count, str_equal, and str_subset support regex use

str_detect()

'str_detect()' checks if a pattern exists in a string. It takes a string and a pattern as arguments and returns a boolean indicating the presence of the pattern in the string. This can be used inside of @filter, @mutate, if_else() and case_when(). str_detect supports logical operators | and &. case_when() with filter() and str_detect()

@chain df begin
    @mutate(Occupation = if_else(str_detect(Occupation, "Doctor | Physician"), "Physician", Occupation))
    @filter(str_detect(Description, "artist | doctor"))
end

 Row │ Names    City                    Occupation    Description                     
     │ String   String                  String        String                          
─────┼────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York 2019-20        Physician     Alice is a doctor in New York
   2 │ Charlie  San Antonio 1234567890  Final Artist  Charlie is an artist in Chicago

@chain df begin
    @mutate(state = case_when(str_detect(City, "NYC | New York") => "NY", 
                              str_detect(City, "LA | Los Angeles | San & Jose") => "CA", 
                              true => "other"))
end

7×5 DataFrame
 Row │ Names    City                         Occupation    Description                        state  
     │ String   String                       String        String                             String 
─────┼───────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York 2019-20             Doctor        Alice is a doctor in New York      NY
   2 │ Bob      Los Angeles 2007-12 2020-21  Engineer      Bob is is is an engineer in Los …  CA
   3 │ Charlie  San Antonio 1234567890       Final Artist  Charlie is an artist in Chicago    other
   4 │ Dave     New York City                Scientist     Dave is a scientist in Houston     NY
   5 │ Eve      LA 2022-23                   Physician     Eve is a physician  in Phoenix     CA
   6 │ Frank    Philadelphia 2023-24         Lawyer        Frank is a lawyer in Philadelphia  other
   7 │ Grace    San Jose 9876543210          Teacher       Grace is a teacher in San Antonio  CA

str_replace()

Replaces the first occurrence of a pattern in a string with a specified text. Takes a string, pattern to search for, and the replacement text as arguments. It also supports the use of regex and logical operator | . This is in contrast to str_replace_all() which will replace each occurence of a match within a string.

@chain df begin
  @mutate(City = str_replace(City, r"\s*20\d{2}-\d{2,4}\s*", " ####-## "))
  @mutate(Description = str_replace(Description, "is | a", "will become "))
end

7×4 DataFrame
 Row │ Names    City                         Occupation    Description                       
     │ String   String                       String        String                            
─────┼───────────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York ####-##             Doctor        Alice will become a doctor in Ne…
   2 │ Bob      Los Angeles ####-## 2020-21  Engineer      Bob will become is is an enginee…
   3 │ Charlie  San Antonio 1234567890       Final Artist  Charlie will become an artist in…
   4 │ Dave     New York City                Scientist     Dave will become a scientist in …
   5 │ Eve      LA ####-##                   Physician     Eve will become a physician in P…
   6 │ Frank    Philadelphia ####-##         Lawyer        Frank will become a lawyer in Ph…
   7 │ Grace    San Jose 9876543210          Teacher       Grace will become a teacher in S…

str_remove and str_remove_all

These remove the first match occurrence or all occurences, respectively.

@chain df begin
    @mutate(split = str_remove_all(Description, "is"))
end

7×5 DataFrame
 Row │ Names    City                         Occupation    Description                        split                          
     │ String   String                       String        String                             String                         
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York 2019-20             Doctor        Alice is a doctor in New York      Alice a doctor in New York
   2 │ Bob      Los Angeles 2007-12 2020-21  Engineer      Bob is is is an engineer in Los …  Bob an engineer in Los Angeles
   3 │ Charlie  San Antonio 1234567890       Final Artist  Charlie is an artist in Chicago    Charlie an artist in Chicago
   4 │ Dave     New York City                Scientist     Dave is a scientist in Houston     Dave a scientist in Houston
   5 │ Eve      LA 2022-23                   Physician     Eve is a physician  in Phoenix     Eve a physician in Phoenix
   6 │ Frank    Philadelphia 2023-24         Lawyer        Frank is a lawyer in Philadelphia  Frank a lawyer in Philadelphia
   7 │ Grace    San Jose 9876543210          Teacher       Grace is a teacher in San Antonio  Grace a teacher in San Antonio

str_equal()

Checks if two strings are exactly the same. Takes two strings as arguments and returns a boolean indicating whether the strings are identical.

@chain df begin
    @mutate(Same_City = case_when(str_equal(City, Occupation) => "Yes", 
                                  true => "No"))
end

 Row │ Names    City                         Occupation    Description                        Same_City 
     │ String   String                       String        String                             String    
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ Alice    New York 2019-20             Doctor        Alice is a doctor in New York      No
   2 │ Bob      Los Angeles 2007-12 2020-21  Engineer      Bob is is is an engineer in Los …  No
   3 │ Charlie  San Antonio 1234567890       Final Artist  Charlie is an artist in Chicago    No
   4 │ Dave     New York City                Scientist     Dave is a scientist in Houston     No
   5 │ Eve      LA 2022-23                   Physician     Eve is a physician  in Phoenix     No
   6 │ Frank    Philadelphia 2023-24         Lawyer        Frank is a lawyer in Philadelphia  No
   7 │ Grace    San Jose 9876543210          Teacher       Grace is a teacher in San Antonio  No

str_to_upper and str_to_lower

These will take a string and convert it to all uppercase or lowercase.

@chain df begin
    @mutate(Names = str_to_upper(Names))
    @select(Names)
end

7×1 DataFrame
 Row │ Names   
     │ String  
─────┼─────────
   1 │ ALICE
   2 │ BOB
   3 │ CHARLIE
   4 │ DAVE
   5 │ EVE
   6 │ FRANK
   7 │ GRACE