Regular Expressions II

Jo Hardin

2024-01-12

Lookaround

A lookaround specifies a place in the regular expression that will anchor the string you’d like to match.

  • “x(?=y)” – positive lookahead (matches ‘x’ when it is followed by ‘y’)
  • “x(?!y)” – negative lookahead (matches ‘x’ when it is not followed by ‘y’)
  • “(?<=y)x” – positive lookbehind (matches ‘x’ when it is preceded by ‘y’)
  • “(?<!y)x” – negative lookbehind (matches ‘x’ when it is not preceded by ‘y’)

Lookaround

The four different lookaround options: positive and negative lookahead and lookbehind. Each lookaround provides an anchor for where to start the regular expression matching.

Figure 1: Image credit: Stefan Judis https://www.stefanjudis.com/blog/a-regular-expression-lookahead-lookbehind-cheat-sheet/

Example - Taskmaster

Data scraped from the wiki site for the TV series, Taskmaster.

Screenshot of the wiki page for the Taskmaster TV series.

Figure 2: Taskmaster Wiki https://taskmaster.fandom.com/wiki/Series_11

Scraping and wrangling Taskmaster

Goal: to scrape the Taskmaster wiki into a dataframe including task, description, episode, episode name, air date, contestant, score, and series.1

results <- read_html("https://taskmaster.fandom.com/wiki/Series_11") |>
  html_element(".tmtable") |> 
  html_table() |>
  mutate(episode = ifelse(startsWith(Task, "Episode"), Task, NA)) |>
  fill(episode, .direction = "down") |>
  filter(!startsWith(Task, "Episode"), 
         !(Task %in% c("Total", "Grand Total"))) |>
  pivot_longer(cols = -c(Task, Description, episode),
               names_to = "contestant",
               values_to = "score") |>
  mutate(series = 11)

Scraping and wrangling Taskmaster data - results

results |> 
  select(Task, Description, episode, contestant, score, series) |>
  head(10)
# A tibble: 10 × 6
  Task  Description                              episode contestant score series
  <chr> <chr>                                    <chr>   <chr>      <chr>  <dbl>
1 1     Prize: Best thing you can carry, but on… Episod… Charlotte… 1         11
2 1     Prize: Best thing you can carry, but on… Episod… Jamali Ma… 2         11
3 1     Prize: Best thing you can carry, but on… Episod… Lee Mack   4         11
4 1     Prize: Best thing you can carry, but on… Episod… Mike Wozn… 5         11
5 1     Prize: Best thing you can carry, but on… Episod… Sarah Ken… 3         11
6 2     Do the most impressive thing under the … Episod… Charlotte… 2         11
# ℹ 4 more rows

more succinct results

   Task  Description         episode   contestant score series
  1     Prize: Best thing…  Episode 1… Charlotte… 1         11
  1     Prize: Best thing…  Episode 1… Jamali Ma… 2         11
  1     Prize: Best thing…  Episode 1… Lee Mack   4         11
  1     Prize: Best thing…  Episode 1… Mike Wozn… 5         11
  1     Prize: Best thing…  Episode 1… Sarah Ken… 3         11
  2     Do the most…        Episode 1… Charlotte… 2         11
  2     Do the most…        Episode 1… Jamali Ma… 3[1]      11
  2     Do the most…        Episode 1… Lee Mack   3         11
  2     Do the most…        Episode 1… Mike Wozn… 5         11
  2     Do the most…        Episode 1… Sarah Ken… 4         11

Currently, the episode column contains entries like

"Episode 1: It's not your fault. (18 March 2021)"

Cleaning the score column

table(results$score)

   –    ✔    ✘    0    1    2    3 3[1] 3[2]    4 4[2]    5   DQ 
   7    1    1   11   37   42   48    1    3   50    1   55   13

How should the scores be stored? What is the cleaning task?

Screenshot of the scores for each contestand on each task. Note that many of the scores have footnotes which are recorded in the results table from scraping the wiki.

Figure 3: Taskmaster Wiki https://taskmaster.fandom.com/wiki/Series_11

Extracting numeric information

Suppose we have the following string:

"3[1]"

And we want to extract just the number “3”:

str_extract("3[1]", "3")
[1] "3"

Extracting numeric information

What if we don’t know which number to extract?

str_extract("3[1]", "\\d")
[1] "3"
str_extract("4[1]", "\\d")
[1] "4"
str_extract("10[1]", "\\d")
[1] "1"
str_extract("10[1]", "\\d+")
[1] "10"
str_extract("DQ", "\\d")
[1] NA

str_extract()

str_extract() is an R function in the stringr package which finds regular expressions in strings of text.

str_extract("My cat is 3 years old", "cat")
[1] "cat"
str_extract("My cat is 3 years old", "3")
[1] "3"

Matching multiple options

str_extract() returns the first match; str_extract_all() allows more than one match.

str_extract("My cat is 3 years old", "cat|dog")
[1] "cat"
str_extract("My dog is 10 years old", "cat|dog")
[1] "dog"
str_extract("My dog is 10 years old, my cat is 3 years old", 
            "cat|dog")
[1] "dog"
str_extract_all("My dog is 10 years old, my cat is 3 years old", 
                "cat|dog")
[[1]]
[1] "dog" "cat"

Matching groups of characters

What if I want to extract a number?

str_extract("My cat is 3 years old", "\\d")
[1] "3"

What will the result be for the following code?

str_extract("My dog is 10 years old", "\\d")

Matching groups of characters

What if I want to extract a number?

str_extract("My cat is 3 years old", "\\d")
[1] "3"

What will the result be for the following code?

str_extract("My dog is 10 years old", "\\d")
[1] "1"

Matching groups of characters

What if I want to extract a number?

str_extract("My cat is 3 years old", "\\d")
[1] "3"

What will the result be for the following code?

str_extract("My dog is 10 years old", "\\d")
[1] "1"

The + symbol in a regular expression means “repeated one or more times”

str_extract("My dog is 10 years old", "\\d+")
[1] "10"

Extracting from multiple strings

strings <- c("My cat is 3 years old", "My dog is 10 years old")
str_extract(strings, "\\d+")
[1] "3"  "10"

Extracting from multiple strings

What if we have multiple instances across multiple strings? We need to be careful working with lists (instead of vectors).

strings <- c("My cat is 3 years old", "My dog is 10 years old")
str_extract(strings, "\\w+")
[1] "My" "My"
str_extract_all(strings, "\\w+")
[[1]]
[1] "My"    "cat"   "is"    "3"     "years" "old"  

[[2]]
[1] "My"    "dog"   "is"    "10"    "years" "old"

Extracting episode information

Currently, the episode column contains entries like:

"Episode 2: The pie whisperer. (4 August 2015)"

How would I extract just the episode number?

Extracting episode information

Currently, the episode column contains entries like:

"Episode 2: The pie whisperer. (4 August 2015)"

How would I extract just the episode number?

str_extract("Episode 2: The pie whisperer. (4 August 2015)", "\\d+")
[1] "2"

Extracting episode information

Currently, the episode column contains entries like:

"Episode 2: The pie whisperer. (4 August 2015)"

How would I extract the episode name?

Goal: find a pattern to match: anything that starts with a :, ends with a .

Let’s break down that task into pieces.

Extracting episode information

How can we find the period at the end of the sentence? What does each of these lines of code return?

str_extract("Episode 2: The pie whisperer. (4 August 2015)", ".")
str_extract("Episode 2: The pie whisperer. (4 August 2015)", ".+")
str_extract("Episode 2: The pie whisperer. (4 August 2015)", "\\.")

Extracting episode information - solution

str_extract("Episode 2: The pie whisperer. (4 August 2015)", ".")
[1] "E"
str_extract("Episode 2: The pie whisperer. (4 August 2015)", ".+")
[1] "Episode 2: The pie whisperer. (4 August 2015)"

We use an escape character when we actually want to choose a period:

str_extract("Episode 2: The pie whisperer. (4 August 2015)", "\\.")
[1] "."

Extracting episode information

Goal: find a pattern to match: anything that starts with a :, ends with a .

str_extract("Episode 2: The pie whisperer. (4 August 2015)",
            ":.+\\.")
[1] ": The pie whisperer."

Lookaround (again)

The four different lookaround options: positive and negative lookahead and lookbehind. Each lookaround provides an anchor for where to start the regular expression matching.

Figure 4: Image credit: Stefan Judis https://www.stefanjudis.com/blog/a-regular-expression-lookahead-lookbehind-cheat-sheet/

Lookbehinds

(?<=) is a positive lookbehind. It is used to identify expressions which are preceded by a particular expression.

str_extract("Episode 2: The pie whisperer. (4 August 2015)", 
            "(?<=: ).+")
[1] "The pie whisperer. (4 August 2015)"
str_extract("Episode 2: The pie whisperer. (4 August 2015)", 
            "(?<=\\. ).+")
[1] "(4 August 2015)"

Lookaheads

(?=) is a positive lookahead. It is used to identify expressions which are followed by a particular expression.

str_extract("Episode 2: The pie whisperer. (4 August 2015)", 
            ".+(?=\\.)")
[1] "Episode 2: The pie whisperer"
str_extract("Episode 2: The pie whisperer. (4 August 2015)", 
            ".+(?=:)")
[1] "Episode 2"

Extracting episode information

Getting everything between the : and the .

str_extract("Episode 2: The pie whisperer. (4 August 2015)", 
            "(?<=: ).+(?=\\.)")
[1] "The pie whisperer"

Extracting air date

I want to extract just the air date. What pattern do I want to match?

str_extract("Episode 2: The pie whisperer. (4 August 2015)", ...)

Extracting air date

str_extract("Episode 2: The pie whisperer. (4 August 2015)", 
            "(?<=\\().+(?=\\))")
[1] "4 August 2015"

Wrangling the episode info

Currently:

# A tibble: 270 × 1
  episode                                        
  <chr>                                          
1 Episode 1: It's not your fault. (18 March 2021)
2 Episode 1: It's not your fault. (18 March 2021)
3 Episode 1: It's not your fault. (18 March 2021)
4 Episode 1: It's not your fault. (18 March 2021)
5 Episode 1: It's not your fault. (18 March 2021)
6 Episode 1: It's not your fault. (18 March 2021)
# ℹ 264 more rows

Wrangling the episode info

One option:

results |>
  select(episode) |>
  mutate(episode_name = str_extract(episode, "(?<=: ).+(?=\\.)"),
         air_date = str_extract(episode, "(?<=\\().+(?=\\))"),
         episode = str_extract(episode, "\\d+"))
# A tibble: 270 × 3
  episode episode_name        air_date     
  <chr>   <chr>               <chr>        
1 1       It's not your fault 18 March 2021
2 1       It's not your fault 18 March 2021
3 1       It's not your fault 18 March 2021
4 1       It's not your fault 18 March 2021
5 1       It's not your fault 18 March 2021
6 1       It's not your fault 18 March 2021
# ℹ 264 more rows

Wrangling the episode info

Another option:

results |>
  separate_wider_regex(episode, 
                       patterns = c(".+ ", 
                                    episode = "\\d+", 
                                    ": ", 
                                    episode_name = ".+", 
                                    "\\. \\(", 
                                    air_date = ".+", 
                                    "\\)"))
# A tibble: 270 × 3
  episode episode_name        air_date     
  <chr>   <chr>               <chr>        
1 1       It's not your fault 18 March 2021
2 1       It's not your fault 18 March 2021
3 1       It's not your fault 18 March 2021
4 1       It's not your fault 18 March 2021
5 1       It's not your fault 18 March 2021
6 1       It's not your fault 18 March 2021
# ℹ 264 more rows

Regular expressions and SQL

Back to the IMDb database…

SELECT production_year, title
  FROM title
  WHERE kind_id = 1 AND
        title REGEXP '(?i)star'
  LIMIT 0, 20;
Displaying records 1 - 10
production_year title
2005 "Dancing with the Stars" (I)
2005 "Dancing with the Stars" (II)
2005 "Dancing with the Stars" (III)
2017 "Girl Starter" (II)
2001 "Popstars" (I)
2001 "Popstars" (II)
2002 "Popstars" (I)
2000 "Popstars" (I)
1959 "Startime" (II)
1959 "Startime" (I)

Course project

Don’t forget, next week, each person will be working on their own mini project!

  1. Using SQL queries and joins to wrangle complicated data tables.
  2. Writing regular expressions to parse observations.
  3. Creating a SQL database.
  • email jo.hardin@pomona.edu by Tuesday, Jan 16 with an idea of what you plan to do.
    • Question of interest that you hope to address.
    • Holistic description of the dataset(s) (a few sentences).
    • Description of the observational units and columns in each data table.
    • Full reference for data citation.
    • Link to the resources.