Using `stringr` to Work with Strings

Tuesday, October 14

Today we will…

Layout for Week 5
New material
- String variables
- Functions for working with strings
- Regular expressions
PA 5.1: Scrambled Message

Week 5 Layout

Today: Strings with stringr
- Practice Activity: Decoding a Message

Thursday: Dates with lubridate
- Practice Activity: Jewel Heist

Lab Assignment Solving a Murder Mystery
- Using dplyr + stringr + ludridate

String Variables

What is a string?

A string is a bunch of characters.

There is a difference between…

…a string (many characters, one object)…

and

…a character vector (vector of strings).

my_string <- "Hi, my name is Bond!"
my_string

my_vector <- c("Hi", "my", "name", "is", "Bond")
my_vector

Strings in a Data Frame

We’ve encountered a lot of strings before in the datasets we’ve worked with.

penguins
- species
- island
- sex

colleges
- STABBR
- INSTNM
military
- Country

Until now, we’ve taken for granted the values of these string variables, but today we’re going to learn how to use expressions to look for and / or modify specific values!

Strings in a Data Frame

For the colleges dataset:

a string is:

colleges$INSTNM[214]

a character vector is:

colleges$INSTNM

`stringr`

Common tasks

Identify strings containing a particular pattern.
Remove or replace a pattern.
Edit a string (e.g., make it lowercase).

Note

The stringr package loads with tidyverse.
All functions are of the form str_xxx().

`string =`

None of the stringr functions have a .data = argument! These functions only accept a character vector (string =) as an input.

str_detect(data = colleges, 
           string = INSTNM, 
           pattern = "California")

So, these functions will need to be combined with functions from dplyr to work with a dataset!

`pattern =`

The pattern argument appears in many stringr functions.

The pattern must be supplied inside quotes.

str_detect(colleges$INSTNM, 
           pattern = "Polytechnic")

str_remove(colleges$INSTNM, 
           pattern = "(University|College)")

str_replace(colleges$INSTNM, 
            pattern = "$u", 
            replacement = "U")

Let’s talk more about what some of these symbols mean.

Regular Expressions

“Regular expressions are a very terse language that allow you to describe patterns in strings.”

R for Data Science

Regular Expressions

…are tricky!

There are lots of new symbols to keep straight.
There are a lot of cases to think through.

We’re going to focus on:

anchors
quantifiers

character classes
groups

Anchor Characters: `^ $`

^ – looks at the beginning of a string.

str_subset(colleges$INSTNM, 
           pattern = "^California State")

$ – looks at the end of a string.

str_subset(colleges$INSTNM, 
           pattern = "State University$")

Quantifier Characters: `+ *`

+ – occurs 1 or more times

str_subset(colleges$INSTNM, 
           pattern = "St\\.+")

* – occurs 0 or more times

str_subset(colleges$INSTNM, 
           pattern = "\\s*-\\s*")

Quantifier Characters: `{}`

{n} – occurs exactly n times

str_subset(colleges$INSTNM, 
           pattern = "[A-Z]{4}")

{n,m} – occurs between n and m times

str_subset(colleges$INSTNM, 
           pattern = "[A-Z]{4,6}")

Want at least 4? {4,}

Character Groups: `()`

() creates a group of characters
We can specify “either” / “or” within a group using |.

str_subset(colleges$INSTNM, 
           pattern = "(T|t)ech")

Character Classes: `[]`

[ ] – specifies a range of characters.

[A-Z] or [:upper:] matches any capital letter.
[a-z] or [:lower:] matches any lowercase letter.
[A-z] or [:alpha:] matches any letter
[0-9] or [:digit:] matches any number
[:alnum:] matches any alpha numeric character
[:punct:] matches any punctuation character

str_subset(colleges$INSTNM, 
           pattern = "[A-Z]{4}")

str_subset(colleges$INSTNM, 
           pattern = "[:digit:]+")

str_subset(colleges$INSTNM, 
           pattern = "[:punct:]{1,}")

Excluding Characters

[^ ] – specifies characters not to match on

str_subset(colleges$INSTNM, 
           pattern = "[^y]$")

Beware: a ^ doesn’t always mean “not”

Starts with “University”

str_subset(colleges$INSTNM, 
           pattern = "^University")

Does not starts with “University”

str_subset(colleges$INSTNM, 
           pattern = "^[^University]")

Let’s use these expressions!

Detecting Patterns

`str_detect()`

Returns a logical vector indicating whether the pattern was found in each element of the supplied vector.

my_vector <- c("Hello,", 
               "my name is", 
               "Bond", 
               "James Bond")


str_detect(my_vector, pattern = "Bond")

Pairs well with filter()

Works with summarise()
- sum (to get total matches)
- mean (to get proportion of matches)

`str_detect()` with `filter()`

Which colleges in the dataset have “Polytechnic” in their name?

colleges |> 
  filter(str_detect(INSTNM, pattern = "Polytechnic"))

`str_detect()` with `summarize()`

How many colleges in the colleges dataset have “Polytechnic” in their name?

colleges |> 
  summarize(
    count_polytech = sum(
      str_detect(INSTNM, pattern = "Polytechnic")
      ) 
    )

Replace / Remove Patterns

`str_replace()`

replace the first matched pattern in each string

str_replace(my_vector, 
            pattern = "Bond", 
            replace = "Franco")

`str_replace()` with `mutate()`

Make capitalization of “University” consistent

colleges |> 
  mutate(INSTNM = str_replace(INSTNM, 
                              pattern = "$u", 
                              replacement = "U")
         )

`str_remove()`

remove the first matched pattern in each string

str_remove(my_vector, 
           pattern = "Bond")

Related Functions

This is a special case of str_replace(x, pattern, replacement = "").

`str_remove()` with `mutate()`

Remove “College” or “University” at the end of each name so only the main institution name remains.

colleges |> 
  mutate(INSTNM = str_remove(INSTNM, 
                             pattern = "(College|University)"
                             )
         )

Find the Length of a String

`str_length()`

returns number of elements (characters) of a string

colleges |> 
  mutate(
    name_length = str_length(INSTNM)
         ) |> 
  select(INSTNM, name_length)

Change the Length

shorten or lengthen a string to a specified length

str_sub()
str_pad()

Extract values of a string based on a starting and ending location.

colleges |> 
  mutate(short_name = str_sub(INSTNM, 
                                  start = 1,
                                  end = 8)
         )

Make every string have a fixed length (width)

colleges |> 
  mutate(long_name = str_pad(INSTNM, 
                             width = 20, 
                             pad = "_", 
                             side = "both")
         )

Modify Characters

Edit Capitalization of Strings

Convert letters in a string to a specific capitalization format.

str_to_lower()
str_to_upper()
str_to_title()

converts all letters in a string to lowercase.

colleges |> 
  mutate(INSTNM = str_to_lower(INSTNM))

converts all letters in a string to uppercase.

colleges |> 
  mutate(INSTNM = str_to_upper(INSTNM))

converts the first letter of each word to uppercase.

colleges |> 
  mutate(INSTNM = str_to_title(INSTNM))

Handling Whitespace

`str_trim()`

removes whitespace from start and end of string

colleges |> 
  mutate(
    INSTNM = str_trim(INSTNM, side = "both")
         )

Joining Strings

`str_c()`

join multiple strings into a single character vector

colleges |> 
  mutate(
    address = str_c(CITY, STABBR, ZIP, sep = ", ")
         )

Note

Similar to paste() and paste0() but with more precision.

Tips for String Success

Refer to the stringr cheatsheet
Refer to your handout!
Remember that str_xxx functions need the first argument to be a vector of strings, not a dataset!
- You will use these functions inside dplyr verbs like filter() or mutate().

PA 5.1: Scrambled Message

In this activity, you will use functions from the stringr package and regular expressions to decode a message.

A pile of tiles from the game of Scrabble.

This activity will require knowledge of:

functions from dplyr
stringr functions for previewing string contents
regular expressions for locating patterns
stringr functions for removing whitespace
stringr functions for truncating strings
stringr functions for replacing patterns
stringr functions for combining multiple strings

None of us have all these abilities. Each of us has some of these abilities.

Pair Programming Expectations

External Resources

During the Practice Activity, you are not permitted to use Google or ChatGPT for help. . . .

You are permitted to use:

the handout,
the stringr cheatsheet,
the course textbook, and
the course slides.

Submission

Submit the name of the movie the quote is from.

Each person will input the full name of the movie the scrambled message is from into the PA5 quiz.
The person who last occupied the role of Typer will submit the link for your Colab notebook (don’t forget to use the share link!)
- Only one submission per group!

5-minute break

Team Assignments - 9am

The person who has the most pets starts as the Typer (listening to explanations from the Talker)!

Team Assignments - 12pm

The person who has the most pets starts as the Typer (listening to explanations from the Talker)!

Using stringr to Work with Strings

Tuesday, October 14

Week 5 Layout

Week 5 Layout

String Variables

What is a string?

Strings in a Data Frame

Strings in a Data Frame

stringr

string =

pattern =

Regular Expressions

Regular Expressions

Regular Expressions

Anchor Characters: ^ $

Quantifier Characters: + *

Quantifier Characters: {}

Character Groups: ()

Character Classes: []

Excluding Characters

Let’s use these expressions!

Detecting Patterns

str_detect()

str_detect() with filter()

str_detect() with summarize()

Replace / Remove Patterns

str_replace()

str_replace() with mutate()

str_remove()

str_remove() with mutate()

Find the Length of a String

str_length()

Change the Length

Modify Characters

Edit Capitalization of Strings

Handling Whitespace

str_trim()

Joining Strings

str_c()

Tips for String Success

PA 5.1: Scrambled Message

This activity will require knowledge of:

Pair Programming Expectations

External Resources

Submission

5-minute break

Team Assignments - 9am

Team Assignments - 12pm

Exit Ticket

Using `stringr` to Work with Strings

`stringr`

`string =`

`pattern =`

Anchor Characters: `^ $`

Quantifier Characters: `+ *`

Quantifier Characters: `{}`

Character Groups: `()`

Character Classes: `[]`

`str_detect()`

`str_detect()` with `filter()`

`str_detect()` with `summarize()`

`str_replace()`

`str_replace()` with `mutate()`

`str_remove()`

`str_remove()` with `mutate()`

`str_length()`

`str_trim()`

`str_c()`