Manipulating strings

STAT1003 – Statistical Techniques

Dr. Emi Tanaka

Australian National University

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What are strings?

  • Strings are sequences of characters enclosed in quotes
  • In R, strings can use single ' or double " quotes
  • What if you want to add quotes in the string then?

Special strings

  • How to specify both single and double quote then?
  • What if you want to write backslash then?
  • Or just use the raw string (from R version 4.0.0)

Manipulating strings

  • The string may be manipulated using Base R functions, e.g. paste0(), strsplit()

  • But instead we use the stringr package from the Tidyverse.

  • stringr package is powered by the stringi package, which in turn uses the ICU C library to provide fast performance for string manipulation.

  • Main functions in stringr prefix with str_ (stringi prefix with stri_) and the first argument is a string (or a vector of strings)

  • What do you think str_trim and str_squish do?

Why use stringr?

  • Let’s consider combining multiple strings into one:
  • stringr ensures consistency in syntax and user expectation
  • E.g. missing values in the data can have different expected results

Basic string operations

Pattern matching

  • Suppose we have a variable address that is comprised of street number, street name, suburb, state (or territory), and postcode.
  • You can see the pattern is like:
<street number> <street name>, <suburb> <state> <postcode>

[digits] [alphabets], [alphabets] [NSW|VIC|WA|ACT|QLD|SA|NT|TAS] [4 digits]

Regular expressions

  • Regular expressions, or regex, is a string of characters that define a search pattern.
  • [:digit:] or [0-9] matches any digit (0-9)
  • . matches any single character
  • [:alpha:] or [A-Za-z] matches any alphabetic character (a-z, A-Z)
  • + matches 1 or more of the preceding character
  • ( and ) are used to create capture groups
  • | acts as a logical OR
  • {n} matches exactly n occurrences of the preceding character

Named capture groups

But in the context of data, it may be better to use the separate_wider_regex() from tidyr package.

Case study Australian Local Government Area

  • The LGA names include the LGA status in the bracket:
    • C = Cities
    • B = Boroughs
    • M = Municipalities
    • RegC = Regional Councils
    • A = Areas
    • S = Shires
    • T = Towns
    • RC = Rural Cities
    • DC = District Councils
    • AC = Aboriginal Councils


🎯 Extract the LGA status from the data

Checking the pattern

  • If regex is difficult for you, AI can help you with refining your pattern.
  • But be sure to check if the pattern works for your data!

String interpolation

Recall: paste0(), paste() or stringr::str_c() can combine strings:

  • Above works, but it can be more convenient to interpolate strings with {}:

stringr cheatsheet