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 developed to work seamlessly with TidierData.jl functions and macros, it also works independently as a standalone package.
For the stable version:
] add TidierStrings
The ] character starts the Julia package manager. Press the backspace key to return to the Julia prompt.
or
For the development version:
using Pkg
Pkg.add(url = "https://github.com/TidierOrg/TidierStrings.jl.git")TidierStrings.jl currently supports:
| Category | Function |
|---|---|
| Matching | str_count, str_detect, str_locate, str_locate_all, str_replace, str_replace_all, |
str_remove, str_remove_all, str_split, str_starts, str_ends, str_subset, str_which |
|
| Concatenation | str_c, str_flatten, str_flatten_comma |
| Characters | str_dup, str_length, str_width, str_trim, str_squish, str_wrap, str_pad |
| Locale | str_equal, str_to_upper, str_to_lower, str_to_title, str_to_sentence, str_unique |
| Other | str_conv, str_like, str_replace_missing, word |
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))
end7×4 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
Support Regex: str_detect, str_replace, str_replace_all, str_remove, str_remove_all, str_count, str_equal, and str_subset
'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"))
end7×5 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
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 "))
end7×4 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…
These remove the first match occurrence or all occurences, respectively.
@chain df begin
@mutate(split = str_remove_all(Description, "is"))
end7×5 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
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
These will take a string and convert it to all uppercase or lowercase.
@chain df begin
@mutate(Names = str_to_upper(Names))
@select(Names)
end7×1 DataFrame
Row │ Names
│ String
─────┼─────────
1 │ ALICE
2 │ BOB
3 │ CHARLIE
4 │ DAVE
5 │ EVE
6 │ FRANK
7 │ GRACE