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Stringi is THE R package for fast, correct, consistent and convenient string/text processing in each locale and any native character encoding


The stringr package is a wrapper for the R stringi package that provides consistent function names and error handling for string manipulation



Stringr provides more human-readable wrappers around the base r functions though as of dec 2014 the development version has a branch built on top of stringi mentioned below

from question  

How to remove all whitespace from a string?

If speed is your concern base r can be faster than stringr stringi the only consequence is that the na is turned into a literal nana here so the workaround is which makes base r about as fast as stringi and slightly faster than stringr in this case. i m mildly annoyed that paste converts na to na though that s already been addressed here on so .

from question  

R string operation : How could i optimize this one?

Stringr is newer based on stringi internally and is often even faster

from question  

How to speed up this string split operation within data.table

Since stringi runs slightly faster and has a larger set of functionalities i prefer to stick with stringi . see stringi vs stringr blog by hrbrmstr i tried to use base r data.frame syntax above where possible to avoid any confusion but if i were doing this for my self i d stick to the full data.table syntax as follows

from question  

Replace multiple values using a reference table

Stringr is based on stringi but stringi has more options

from question  

R POS tagging and tokenizing in one go

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