Regular Expressions

Kieran Healy

Duke University

February 7, 2024

A brief introduction to regular expressions

Load the packages, as always

library(here)      # manage file paths
library(socviz)    # data and some useful functions
library(tidyverse) # your friend and mine
library(gapminder) # gapminder data
library(stringr) # Loaded automatically but just highlighting it here

Regular
Expressions

  • Or, waiter, there appears to be a language inside my language

stringr is your gateway to regexps

library(stringr) # It's loaded by default with library(tidyverse)

regexps are their own whole world

This book is a thing of beauty.

Searching for patterns

A regular expression is a way of searching for a piece of text, or pattern, inside some larger body of text, called a string.

The simplest sort of search is like the “Find” functionality in a Word Processor. The pattern is a literal letter, number, punctuation mark, word or series of words; the text is a document searched one line at a time. The next step up is “Find and Replace”.

Every pattern-searching function in stringr has the same basic form:

str_view(<STRING>, <PATTERN>, [...]) # where [...] means "maybe some options"

Functions that replace as well as detect strings all have this form:

str_replace(<STRING>, <PATTERN>, <REPLACEMENT>)

(If you think about it, <STRING>, <PATTERN> and <REPLACEMENT> above are all kinds of pattern: they are meant to “stand for” all kinds of text, not be taken literally.)

Searching for patterns

  • Here I’ll follow the exposition in Wickham & Grolemund (2017).
x <- c("apple", "banana", "pear")

str_view(x, "an", html=FALSE)
[2] │ b<an><an>a

Searching for patterns

  • Regular expressions get their real power from wildcards, i.e. tokens that match more than just literal strings, but also more general and more complex patterns.
  • The most general pattern-matching token is, “Match everything!” This is represented by the period, or .
  • But … if . matches any character, how do you specifically match the literal character .?

Escaping

  • You have to “escape” the period to tell the regex you want to match it exactly, rather than interpret it as meaning “match anything”.
  • regexs use the backslash, \, to signal “escape the next character”.
  • To match a ., you need the regex \.

Hang on, I see a further problem

  • We use strings to represent regular expressions. \ is also used as an escape symbol in strings. So to create the regular expression \. we need the string \\.
# To create the regular expression, we need \\
dot <- "\\."

# But the expression itself only contains one:
writeLines(dot)
\.
# And this tells R to look for an explicit .
str_view(c("abc", "a.c", "bef"), "a\\.c")
[2] │ <a.c>

But … how do you match a literal \?

x <- "a\\b"
writeLines(x)
a\b
#> a\b

str_view(x, "\\\\") # you need four!
[1] │ a<\>b

But … how do you match a literal \?

This is the price we pay for having to express searches for patterns using a language containing these same characters, which we may also want to search for.

I promise this will pay off

Matching start and end

  • Use ^ to match the start of a string.
x <- c("apple", "banana", "pear")
str_view(x, "^a")
[1] │ <a>pple

Matching start and end

  • Use ^ to match the start of a string.
x <- c("apple", "banana", "pear")
str_view(x, "^a")
[1] │ <a>pple
  • Use $ to match the end of a string.
str_view(x, "a$")
[2] │ banan<a>

Matching start and end

  • To force a regular expression to only match a complete string, anchor it with both ^ and $
x <- c("apple pie", "apple", "apple cake")
str_view(x, "apple")
[1] │ <apple> pie
[2] │ <apple>
[3] │ <apple> cake
str_view(x, "^apple$")
[2] │ <apple>

Matching character classes

\d matches any digit.

\s matches any whitespace (e.g. space, tab, newline).

abc matches a, b, or c.

^abc matches anything except a, b, or c.

Matching the special characters

Look for a literal character that normally has special meaning in a regex:

str_view(c("abc", "a.c", "a*c", "a c"), "a[.]c")
[2] │ <a.c>
str_view(c("abc", "a.c", "a*c", "a c"), ".[*]c")
[3] │ <a*c>

This works for most (but not all) regex metacharacters: $ . | ? * + ( ) [ {. Unfortunately, a few characters have special meaning even inside a character class and must be handled with backslash escapes. These are ] \ ^ and -

Alternation

Use parentheses to make the precedence of the ‘or’ operator | clear:

str_view(c("groy", "grey", "griy", "gray"), "gr(e|a)y")
[2] │ <grey>
[4] │ <gray>

Repeated patterns

  • ? is previous token 0 or 1 times
  • + is previous token 1 or more times
  • * is previous token 0 or more times
x <- "1888 is the longest year in Roman numerals: MDCCCLXXXVIII"
str_view(x, "CC?")
[1] │ 1888 is the longest year in Roman numerals: MD<CC><C>LXXXVIII

Repeated patterns

  • ? is previous token 0 or 1 times
  • + is previous token 1 or more times
  • * is previous token 0 or more times
str_view(x, "CC+")
[1] │ 1888 is the longest year in Roman numerals: MD<CCC>LXXXVIII

Repeated patterns

  • ? is previous token 0 or 1 times
  • + is previous token 1 or more times
  • * is previous token 0 or more times
x <- "1888 is the longest year in Roman numerals: MDCCCLXXXVIII"
str_view(x, 'C[LX]+')
[1] │ 1888 is the longest year in Roman numerals: MDCC<CLXXX>VIII

Exact numbers of repetitions

  • {n} is previous token exactly n times
  • {n,} is previous token n or more times
  • {,m} is previous token at most m times
  • {n,m} is previous token between n and m times
str_view(x, "C{2}")
[1] │ 1888 is the longest year in Roman numerals: MD<CC>CLXXXVIII

Exact numbers of repetitions

  • {n} is previous token exactly n times
  • {n,} is previous token n or more times
  • {,m} is previous token at most m times
  • {n,m} is previous token between n and m times
str_view(x, "C{2,}")
[1] │ 1888 is the longest year in Roman numerals: MD<CCC>LXXXVIII

Exact numbers of repetitions

  • {n} is previous token exactly n times
  • {n,} is previous token n or more times
  • {,m} is previous token at most m times
  • {n,m} is previous token between n and m times
str_view(x, "C{2,3}")
[1] │ 1888 is the longest year in Roman numerals: MD<CCC>LXXXVIII

Exact numbers of repetitions

  • {n} is previous token exactly n times
  • {n,} is n previous token or more times
  • {,m} is previous token at most m times
  • {n,m} is previous token between n and m times

By default these are greedy matches. You can make them “lazy”, matching the shortest string possible by putting a ? after them. This is often very useful!

str_view(x, 'C{2,3}?')
[1] │ 1888 is the longest year in Roman numerals: MD<CC>CLXXXVIII

Exact numbers of repetitions

  • {n} is previous token exactly n times
  • {n,} is previous token n or more times
  • {,m} is previous token at most m times
  • {n,m} is previous token between n and m times

By default these are greedy matches. You can make them “lazy”, matching the shortest string possible by putting a ? after them. This is often very useful!

str_view(x, 'C[LX]+?')
[1] │ 1888 is the longest year in Roman numerals: MDCC<CL>XXXVIII

And finally … backreferences

fruit # built into stringr
 [1] "apple"             "apricot"           "avocado"          
 [4] "banana"            "bell pepper"       "bilberry"         
 [7] "blackberry"        "blackcurrant"      "blood orange"     
[10] "blueberry"         "boysenberry"       "breadfruit"       
[13] "canary melon"      "cantaloupe"        "cherimoya"        
[16] "cherry"            "chili pepper"      "clementine"       
[19] "cloudberry"        "coconut"           "cranberry"        
[22] "cucumber"          "currant"           "damson"           
[25] "date"              "dragonfruit"       "durian"           
[28] "eggplant"          "elderberry"        "feijoa"           
[31] "fig"               "goji berry"        "gooseberry"       
[34] "grape"             "grapefruit"        "guava"            
[37] "honeydew"          "huckleberry"       "jackfruit"        
[40] "jambul"            "jujube"            "kiwi fruit"       
[43] "kumquat"           "lemon"             "lime"             
[46] "loquat"            "lychee"            "mandarine"        
[49] "mango"             "mulberry"          "nectarine"        
[52] "nut"               "olive"             "orange"           
[55] "pamelo"            "papaya"            "passionfruit"     
[58] "peach"             "pear"              "persimmon"        
[61] "physalis"          "pineapple"         "plum"             
[64] "pomegranate"       "pomelo"            "purple mangosteen"
[67] "quince"            "raisin"            "rambutan"         
[70] "raspberry"         "redcurrant"        "rock melon"       
[73] "salal berry"       "satsuma"           "star fruit"       
[76] "strawberry"        "tamarillo"         "tangerine"        
[79] "ugli fruit"        "watermelon"

Grouping and backreferences

  • Find all fruits that have a repeated pair of letters:
str_view(fruit, "(..)\\1", match = TRUE)
 [4] │ b<anan>a
[20] │ <coco>nut
[22] │ <cucu>mber
[41] │ <juju>be
[56] │ <papa>ya
[73] │ s<alal> berry

Grouping and backreferences

  • Backreferences and grouping will be very useful for string replacements.

OK that was a lot

Learning and testing regexps

  • Practice with a tester like https://regexr.com
  • Or an app like Patterns
  • The regex engine or “flavor” used by stringr is Perl- or PCRE-like.

Regexps in practice

Example: Politics and Placenames

library(ukelection2019)

Example: Politics and Placenames

library(ukelection2019)

ukvote2019
# A tibble: 3,320 × 13
   cid     constituency electorate party_name candidate votes vote_share_percent
   <chr>   <chr>             <int> <chr>      <chr>     <int>              <dbl>
 1 W07000… Aberavon          50747 Labour     Stephen … 17008               53.8
 2 W07000… Aberavon          50747 Conservat… Charlott…  6518               20.6
 3 W07000… Aberavon          50747 The Brexi… Glenda D…  3108                9.8
 4 W07000… Aberavon          50747 Plaid Cym… Nigel Hu…  2711                8.6
 5 W07000… Aberavon          50747 Liberal D… Sheila K…  1072                3.4
 6 W07000… Aberavon          50747 Independe… Captain …   731                2.3
 7 W07000… Aberavon          50747 Green      Giorgia …   450                1.4
 8 W07000… Aberconwy         44699 Conservat… Robin Mi… 14687               46.1
 9 W07000… Aberconwy         44699 Labour     Emily Ow… 12653               39.7
10 W07000… Aberconwy         44699 Plaid Cym… Lisa Goo…  2704                8.5
# ℹ 3,310 more rows
# ℹ 6 more variables: vote_share_change <dbl>, total_votes_cast <int>,
#   vrank <int>, turnout <dbl>, fname <chr>, lname <chr>

Example: Politics and Placenames

library(ukelection2019)

ukvote2019 |>
  group_by(constituency)
# A tibble: 3,320 × 13
# Groups:   constituency [650]
   cid     constituency electorate party_name candidate votes vote_share_percent
   <chr>   <chr>             <int> <chr>      <chr>     <int>              <dbl>
 1 W07000… Aberavon          50747 Labour     Stephen … 17008               53.8
 2 W07000… Aberavon          50747 Conservat… Charlott…  6518               20.6
 3 W07000… Aberavon          50747 The Brexi… Glenda D…  3108                9.8
 4 W07000… Aberavon          50747 Plaid Cym… Nigel Hu…  2711                8.6
 5 W07000… Aberavon          50747 Liberal D… Sheila K…  1072                3.4
 6 W07000… Aberavon          50747 Independe… Captain …   731                2.3
 7 W07000… Aberavon          50747 Green      Giorgia …   450                1.4
 8 W07000… Aberconwy         44699 Conservat… Robin Mi… 14687               46.1
 9 W07000… Aberconwy         44699 Labour     Emily Ow… 12653               39.7
10 W07000… Aberconwy         44699 Plaid Cym… Lisa Goo…  2704                8.5
# ℹ 3,310 more rows
# ℹ 6 more variables: vote_share_change <dbl>, total_votes_cast <int>,
#   vrank <int>, turnout <dbl>, fname <chr>, lname <chr>

Example: Politics and Placenames

library(ukelection2019)

ukvote2019 |>
  group_by(constituency) |>
  slice_max(votes)
# A tibble: 650 × 13
# Groups:   constituency [650]
   cid     constituency electorate party_name candidate votes vote_share_percent
   <chr>   <chr>             <int> <chr>      <chr>     <int>              <dbl>
 1 W07000… Aberavon          50747 Labour     Stephen … 17008               53.8
 2 W07000… Aberconwy         44699 Conservat… Robin Mi… 14687               46.1
 3 S14000… Aberdeen No…      62489 Scottish … Kirsty B… 20205               54  
 4 S14000… Aberdeen So…      65719 Scottish … Stephen … 20388               44.7
 5 S14000… Aberdeenshi…      72640 Conservat… Andrew B… 22752               42.7
 6 S14000… Airdrie & S…      64008 Scottish … Neil Gray 17929               45.1
 7 E14000… Aldershot         72617 Conservat… Leo Doch… 27980               58.4
 8 E14000… Aldridge-Br…      60138 Conservat… Wendy Mo… 27850               70.8
 9 E14000… Altrincham …      73096 Conservat… Graham B… 26311               48  
10 W07000… Alyn & Dees…      62783 Labour     Mark Tami 18271               42.5
# ℹ 640 more rows
# ℹ 6 more variables: vote_share_change <dbl>, total_votes_cast <int>,
#   vrank <int>, turnout <dbl>, fname <chr>, lname <chr>

Example: Politics and Placenames

library(ukelection2019)

ukvote2019 |>
  group_by(constituency) |>
  slice_max(votes) |>
  ungroup()
# A tibble: 650 × 13
   cid     constituency electorate party_name candidate votes vote_share_percent
   <chr>   <chr>             <int> <chr>      <chr>     <int>              <dbl>
 1 W07000… Aberavon          50747 Labour     Stephen … 17008               53.8
 2 W07000… Aberconwy         44699 Conservat… Robin Mi… 14687               46.1
 3 S14000… Aberdeen No…      62489 Scottish … Kirsty B… 20205               54  
 4 S14000… Aberdeen So…      65719 Scottish … Stephen … 20388               44.7
 5 S14000… Aberdeenshi…      72640 Conservat… Andrew B… 22752               42.7
 6 S14000… Airdrie & S…      64008 Scottish … Neil Gray 17929               45.1
 7 E14000… Aldershot         72617 Conservat… Leo Doch… 27980               58.4
 8 E14000… Aldridge-Br…      60138 Conservat… Wendy Mo… 27850               70.8
 9 E14000… Altrincham …      73096 Conservat… Graham B… 26311               48  
10 W07000… Alyn & Dees…      62783 Labour     Mark Tami 18271               42.5
# ℹ 640 more rows
# ℹ 6 more variables: vote_share_change <dbl>, total_votes_cast <int>,
#   vrank <int>, turnout <dbl>, fname <chr>, lname <chr>

Example: Politics and Placenames

library(ukelection2019)

ukvote2019 |>
  group_by(constituency) |>
  slice_max(votes) |>
  ungroup() |>
  select(constituency, party_name)
# A tibble: 650 × 2
   constituency                    party_name             
   <chr>                           <chr>                  
 1 Aberavon                        Labour                 
 2 Aberconwy                       Conservative           
 3 Aberdeen North                  Scottish National Party
 4 Aberdeen South                  Scottish National Party
 5 Aberdeenshire West & Kincardine Conservative           
 6 Airdrie & Shotts                Scottish National Party
 7 Aldershot                       Conservative           
 8 Aldridge-Brownhills             Conservative           
 9 Altrincham & Sale West          Conservative           
10 Alyn & Deeside                  Labour                 
# ℹ 640 more rows

Example: Politics and Placenames

library(ukelection2019)

ukvote2019 |>
  group_by(constituency) |>
  slice_max(votes) |>
  ungroup() |>
  select(constituency, party_name) |>
  mutate(shire = str_detect(constituency, "shire"),
         field = str_detect(constituency, "field"),
         dale = str_detect(constituency, "dale"),
         pool = str_detect(constituency, "pool"),
         ton = str_detect(constituency, "(ton$)|(ton )"),
         wood = str_detect(constituency, "(wood$)|(wood )"),
         saint = str_detect(constituency, "(St )|(Saint)"),
         port = str_detect(constituency, "(Port)|(port)"),
         ford = str_detect(constituency, "(ford$)|(ford )"),
         by = str_detect(constituency, "(by$)|(by )"),
         boro = str_detect(constituency, "(boro$)|(boro )|(borough$)|(borough )"),
         ley = str_detect(constituency, "(ley$)|(ley )|(leigh$)|(leigh )"))
# A tibble: 650 × 14
   constituency party_name shire field dale  pool  ton   wood  saint port  ford 
   <chr>        <chr>      <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
 1 Aberavon     Labour     FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 2 Aberconwy    Conservat… FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 3 Aberdeen No… Scottish … FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 4 Aberdeen So… Scottish … FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 5 Aberdeenshi… Conservat… TRUE  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 6 Airdrie & S… Scottish … FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 7 Aldershot    Conservat… FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 8 Aldridge-Br… Conservat… FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 9 Altrincham … Conservat… FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
10 Alyn & Dees… Labour     FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# ℹ 640 more rows
# ℹ 3 more variables: by <lgl>, boro <lgl>, ley <lgl>

Example: Politics and Placenames

library(ukelection2019)

ukvote2019 |>
  group_by(constituency) |>
  slice_max(votes) |>
  ungroup() |>
  select(constituency, party_name) |>
  mutate(shire = str_detect(constituency, "shire"),
         field = str_detect(constituency, "field"),
         dale = str_detect(constituency, "dale"),
         pool = str_detect(constituency, "pool"),
         ton = str_detect(constituency, "(ton$)|(ton )"),
         wood = str_detect(constituency, "(wood$)|(wood )"),
         saint = str_detect(constituency, "(St )|(Saint)"),
         port = str_detect(constituency, "(Port)|(port)"),
         ford = str_detect(constituency, "(ford$)|(ford )"),
         by = str_detect(constituency, "(by$)|(by )"),
         boro = str_detect(constituency, "(boro$)|(boro )|(borough$)|(borough )"),
         ley = str_detect(constituency, "(ley$)|(ley )|(leigh$)|(leigh )")) |>
  pivot_longer(shire:ley, names_to = "toponym")
# A tibble: 7,800 × 4
   constituency party_name toponym value
   <chr>        <chr>      <chr>   <lgl>
 1 Aberavon     Labour     shire   FALSE
 2 Aberavon     Labour     field   FALSE
 3 Aberavon     Labour     dale    FALSE
 4 Aberavon     Labour     pool    FALSE
 5 Aberavon     Labour     ton     FALSE
 6 Aberavon     Labour     wood    FALSE
 7 Aberavon     Labour     saint   FALSE
 8 Aberavon     Labour     port    FALSE
 9 Aberavon     Labour     ford    FALSE
10 Aberavon     Labour     by      FALSE
# ℹ 7,790 more rows

Example: Politics and Placenames

place_tab <- ukvote2019 |> 
  group_by(constituency) |> 
  slice_max(votes) |> 
  ungroup() |> 
  select(constituency, party_name) |> 
  mutate(shire = str_detect(constituency, "shire"),
         field = str_detect(constituency, "field"),
         dale = str_detect(constituency, "dale"),
         pool = str_detect(constituency, "pool"),
         ton = str_detect(constituency, "(ton$)|(ton )"),
         wood = str_detect(constituency, "(wood$)|(wood )"),
         saint = str_detect(constituency, "(St )|(Saint)"),
         port = str_detect(constituency, "(Port)|(port)"),
         ford = str_detect(constituency, "(ford$)|(ford )"),
         by = str_detect(constituency, "(by$)|(by )"),
         boro = str_detect(constituency, "(boro$)|(boro )|(borough$)|(borough )"),
         ley = str_detect(constituency, "(ley$)|(ley )|(leigh$)|(leigh )")) |> 
  pivot_longer(shire:ley, names_to = "toponym")

Example: Politics and Placenames

place_tab
# A tibble: 7,800 × 4
   constituency party_name toponym value
   <chr>        <chr>      <chr>   <lgl>
 1 Aberavon     Labour     shire   FALSE
 2 Aberavon     Labour     field   FALSE
 3 Aberavon     Labour     dale    FALSE
 4 Aberavon     Labour     pool    FALSE
 5 Aberavon     Labour     ton     FALSE
 6 Aberavon     Labour     wood    FALSE
 7 Aberavon     Labour     saint   FALSE
 8 Aberavon     Labour     port    FALSE
 9 Aberavon     Labour     ford    FALSE
10 Aberavon     Labour     by      FALSE
# ℹ 7,790 more rows

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym)
# A tibble: 7,800 × 4
# Groups:   party_name, toponym [120]
   constituency party_name toponym value
   <chr>        <chr>      <chr>   <lgl>
 1 Aberavon     Labour     shire   FALSE
 2 Aberavon     Labour     field   FALSE
 3 Aberavon     Labour     dale    FALSE
 4 Aberavon     Labour     pool    FALSE
 5 Aberavon     Labour     ton     FALSE
 6 Aberavon     Labour     wood    FALSE
 7 Aberavon     Labour     saint   FALSE
 8 Aberavon     Labour     port    FALSE
 9 Aberavon     Labour     ford    FALSE
10 Aberavon     Labour     by      FALSE
# ℹ 7,790 more rows

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour"))
# A tibble: 6,816 × 4
# Groups:   party_name, toponym [24]
   constituency party_name toponym value
   <chr>        <chr>      <chr>   <lgl>
 1 Aberavon     Labour     shire   FALSE
 2 Aberavon     Labour     field   FALSE
 3 Aberavon     Labour     dale    FALSE
 4 Aberavon     Labour     pool    FALSE
 5 Aberavon     Labour     ton     FALSE
 6 Aberavon     Labour     wood    FALSE
 7 Aberavon     Labour     saint   FALSE
 8 Aberavon     Labour     port    FALSE
 9 Aberavon     Labour     ford    FALSE
10 Aberavon     Labour     by      FALSE
# ℹ 6,806 more rows

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour")) |>
  group_by(toponym, party_name)
# A tibble: 6,816 × 4
# Groups:   toponym, party_name [24]
   constituency party_name toponym value
   <chr>        <chr>      <chr>   <lgl>
 1 Aberavon     Labour     shire   FALSE
 2 Aberavon     Labour     field   FALSE
 3 Aberavon     Labour     dale    FALSE
 4 Aberavon     Labour     pool    FALSE
 5 Aberavon     Labour     ton     FALSE
 6 Aberavon     Labour     wood    FALSE
 7 Aberavon     Labour     saint   FALSE
 8 Aberavon     Labour     port    FALSE
 9 Aberavon     Labour     ford    FALSE
10 Aberavon     Labour     by      FALSE
# ℹ 6,806 more rows

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour")) |>
  group_by(toponym, party_name) |>
  summarize(freq = sum(value))
# A tibble: 24 × 3
# Groups:   toponym [12]
   toponym party_name    freq
   <chr>   <chr>        <int>
 1 boro    Conservative     7
 2 boro    Labour           1
 3 by      Conservative     6
 4 by      Labour           2
 5 dale    Conservative     3
 6 dale    Labour           1
 7 field   Conservative    10
 8 field   Labour          10
 9 ford    Conservative    17
10 ford    Labour          12
# ℹ 14 more rows

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour")) |>
  group_by(toponym, party_name) |>
  summarize(freq = sum(value)) |>
  mutate(pct = freq/sum(freq))
# A tibble: 24 × 4
# Groups:   toponym [12]
   toponym party_name    freq   pct
   <chr>   <chr>        <int> <dbl>
 1 boro    Conservative     7 0.875
 2 boro    Labour           1 0.125
 3 by      Conservative     6 0.75 
 4 by      Labour           2 0.25 
 5 dale    Conservative     3 0.75 
 6 dale    Labour           1 0.25 
 7 field   Conservative    10 0.5  
 8 field   Labour          10 0.5  
 9 ford    Conservative    17 0.586
10 ford    Labour          12 0.414
# ℹ 14 more rows

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour")) |>
  group_by(toponym, party_name) |>
  summarize(freq = sum(value)) |>
  mutate(pct = freq/sum(freq)) |>
  filter(party_name == "Conservative")
# A tibble: 12 × 4
# Groups:   toponym [12]
   toponym party_name    freq   pct
   <chr>   <chr>        <int> <dbl>
 1 boro    Conservative     7 0.875
 2 by      Conservative     6 0.75 
 3 dale    Conservative     3 0.75 
 4 field   Conservative    10 0.5  
 5 ford    Conservative    17 0.586
 6 ley     Conservative    26 0.722
 7 pool    Conservative     2 0.286
 8 port    Conservative     3 0.333
 9 saint   Conservative     3 0.5  
10 shire   Conservative    37 0.974
11 ton     Conservative    37 0.507
12 wood    Conservative     7 0.636

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour")) |>
  group_by(toponym, party_name) |>
  summarize(freq = sum(value)) |>
  mutate(pct = freq/sum(freq)) |>
  filter(party_name == "Conservative") |>
  arrange(desc(pct))
# A tibble: 12 × 4
# Groups:   toponym [12]
   toponym party_name    freq   pct
   <chr>   <chr>        <int> <dbl>
 1 shire   Conservative    37 0.974
 2 boro    Conservative     7 0.875
 3 by      Conservative     6 0.75 
 4 dale    Conservative     3 0.75 
 5 ley     Conservative    26 0.722
 6 wood    Conservative     7 0.636
 7 ford    Conservative    17 0.586
 8 ton     Conservative    37 0.507
 9 field   Conservative    10 0.5  
10 saint   Conservative     3 0.5  
11 port    Conservative     3 0.333
12 pool    Conservative     2 0.286

Example: Politics and Placenames

place_tab |>
  group_by(party_name, toponym) |>
  filter(party_name %in% c("Conservative", "Labour")) |>
  group_by(toponym, party_name) |>
  summarize(freq = sum(value)) |>
  mutate(pct = freq/sum(freq)) |>
  filter(party_name == "Conservative") |>
  arrange(desc(pct))
# A tibble: 12 × 4
# Groups:   toponym [12]
   toponym party_name    freq   pct
   <chr>   <chr>        <int> <dbl>
 1 shire   Conservative    37 0.974
 2 boro    Conservative     7 0.875
 3 by      Conservative     6 0.75 
 4 dale    Conservative     3 0.75 
 5 ley     Conservative    26 0.722
 6 wood    Conservative     7 0.636
 7 ford    Conservative    17 0.586
 8 ton     Conservative    37 0.507
 9 field   Conservative    10 0.5  
10 saint   Conservative     3 0.5  
11 port    Conservative     3 0.333
12 pool    Conservative     2 0.286