Laboratory 3
ADVANCED R PROGRAMMING, SUMMER 2026 EDITION
Built-in functions for processing text data
String is an appropriately encoded sequence of bytes, i.e. small integers in the range {0,1….255}.
text <- "Advanced R programming"
hex <- charToRaw (text) # in the hexadecimal system (HEX)
as.integer (hex) # in the decimal system (DEC)## [1] 65 100 118 97 110 99 101 100 32 82 32 112 114 111 103 114 97 109 109
## [20] 105 110 103## [1] "Advanced R programming"The R language has many built-in functions for working with text
data, e.g. paste(), substr(),
tolower(), toupper().
## [1] "Advanced R programming"## [1] "pro"## [1] "Advanced R PROgramming"## [1] "advanced r programming"## [1] "ADVANCED R PROGRAMMING"The sprintf() function, well known to C users, is used
to conveniently combine text and numeric values.
## [1] "Test #1 of 3" "Test #2 of 3" "Test #3 of 3"## [1] "data science 3.1416"## [1] "SELECT * FROM myTable WHERE id IN (6, 3, 9)"The functions match (x,table) and
pmatch (x,table) return the positions of the elements of
the vector x in the vector table. The
pmatch() function is especially useful for strings because
it allows partial matching. The match() function has a
%in% operator associated with it, which returns a logical
value instead of an index.
veggies <- c ("carrot", "cauliflower", "potato", "broccoli")
v <- c ("carrot", "car", "broccoli")
match (v, veggies)## [1] 1 NA 4## [1] TRUE FALSE TRUE## [1] 1The stringr package
Some of the built-in text processing functions are not vectorized
correctly or do not handle the NA values.
## [1] "Advanced" "R" "programming" "NA"The above problems were solved in the ‘stringi’ package, co-authored
by Marek Gągolewski from the Faculty of MiNI, Warsaw University of
Technology. This package has gained huge popularity around the world and
has been appreciated by the creators of tidyverse, who have
written functions that simplify the use of the package. These functions,
called wrappers, are placed in a package called
stringr.
Combining strings
str_c() combines multiple character vectors into a
single character vector. It’s very similar to paste0() but uses
tidyverse recycling and NA rules.
## [1] "AdvancedRprogramming2025"## [1] "Advanced R programming 2025"text <- str_c (c ("Advanced", "programming"), c("R", "2025"), sep = " ", collapse = " ")
print (text)## [1] "Advanced R programming 2025"## [1] 1## [1] "a,1;b,2"str_flatten() reduces a character vector to a single
string. This is a summary function because regardless of the length of
the input x, it always returns a single string.
## [1] "Advanced R programming 2025"## [1] 1Substrings
## [1] "abc"## [1] "ąęś"## [1] "ąę"## [1] "abc123AES"Simple text search
The stringr package offers the following types (engines)
of text searches:
regex- regular expressions, default mode,fixed- very fast, coding-independent substring search,coll- coding-dependent (specific language) pattern search (for natural language processing, works slowly),boundary- searches for fragments constituting boundaries along which the text can be divided into smaller units.
Basic search functions in the stringr package:
str_detect,str_count,str_extract,str_extract_all,str_locate,str_locate_all,str_replace,str_replace_all,str_match,str_match_all.
The pattern of using the above functions is very similar. The first
argument is the character vector to be searched, and the second is the
pattern (pattern), which is a regular expression by default
(regex() function), but can be changed to
fixed, coll or boundary.
The str_detect() function checks whether a given pattern
occurs in a given string:
## [1] TRUE## [1] FALSE TRUE## [1] TRUE FALSE NA## [1] TRUE TRUEThe str_count() function calculates the number of times
a given pattern appears in a given string:
## [1] 3## [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"## [1] 1 1 2 3 0 0 1 2 1 0## [1] 0 0 0 1 1 2 2 1 1 2## [1] 0 1 1 0 0 0 1 2 0 0## [1] 1 0 0 0 3 1 1 0 1 2## [1] 2 1 2 3The str_extract() function searches for substrings
matching a given pattern:
## [1] "berry"## [[1]]
## [1] "berry" "berry" "berry" "berry" "berry" "berry" "berry" "berry" "berry"
## [10] "berry" "berry" "berry" "berry" "berry"The str_locate() function returns an integer matrix
containing the location of the pattern in the searched string. The first
column contains the location of the start of the pattern and the second
column contains the location of the end.
## start end
## [1,] 12 14## [[1]]
## start end
## [1,] 14 17
## [2,] 26 29
## [3,] 37 40
## [4,] 54 57str_replace search for a piece of text and replace it
with another.
## [1] "I love dogs! I think cats are the best pets!"## [1] "I love dogs! I think dogs are the best pets!"The str_split() function splits the text into smaller
pieces. It is useful to use the boundary() pattern for this
function.
text <- "First sentence. Second sentence. Third sentence. Fourth sentence."
str_split (text, boundary (type = "sentence"))## [[1]]
## [1] "First sentence. " "Second sentence. " "Third sentence. "
## [4] "Fourth sentence."## [[1]]
## [1] "First" "sentence" "Second" "sentence" "Third" "sentence" "Fourth"
## [8] "sentence"## [[1]]
## [1] "F" "i" "r" "s" "t" " " "s" "e" "n" "t" "e" "n" "c" "e" "." " " "S" "e" "c"
## [20] "o" "n" "d" " " "s" "e" "n" "t" "e" "n" "c" "e" "." " " "T" "h" "i" "r" "d"
## [39] " " "s" "e" "n" "t" "e" "n" "c" "e" "." " " "F" "o" "u" "r" "t" "h" " " "s"
## [58] "e" "n" "t" "e" "n" "c" "e" "."text <- c ("apples and oranges and pears and bananas", "pineapples and mangos and guavas")
str_split (text, " and ")## [[1]]
## [1] "apples" "oranges" "pears" "bananas"
##
## [[2]]
## [1] "pineapples" "mangos" "guavas"## [,1] [,2] [,3] [,4]
## [1,] "apples" "oranges" "pears" "bananas"
## [2,] "pineapples" "mangos" "guavas" ""Regular expressions
Regular expressions are used to encode patterns in text. They are universal, that is, they have the same syntax regardless of the programming language.
## [1] 2## [1] 4## [1] 4## [1] 4## [1] 4## [1] 6## [[1]]
## [1] "a" "d" "z"## [[1]]
## [1] "a" "b" "d" "w" "z"## [[1]]
## [1] "zA"shopping_list <- c("apples x4", "bag of flour", "bag of sugar", "milk x2")
str_extract_all(shopping_list, "[a-z]+")## [[1]]
## [1] "apples" "x"
##
## [[2]]
## [1] "bag" "of" "flour"
##
## [[3]]
## [1] "bag" "of" "sugar"
##
## [[4]]
## [1] "milk" "x"## [[1]]
## [1] "apples"
##
## [[2]]
## [1] "bag" "of" "flour"
##
## [[3]]
## [1] "bag" "of" "sugar"
##
## [[4]]
## [1] "milk"## [[1]]
## [1] "4"
##
## [[2]]
## character(0)
##
## [[3]]
## character(0)
##
## [[4]]
## [1] "2"strings <- c(" 219 733 8965", "329-293-8753 ", "banana", "595 794 7569",
"387 287 6718", "apple", "233.398.9187 ", "482 952 3315",
"239 923 8115 and 842 566 4692", "Work: 579-499-7527", "$1000",
"Home: 543.355.3679")
phone <- "([2-9][0-9]{2})[- .]([0-9]{3})[- .]([0-9]{4})"
str_match (strings, phone)## [,1] [,2] [,3] [,4]
## [1,] "219 733 8965" "219" "733" "8965"
## [2,] "329-293-8753" "329" "293" "8753"
## [3,] NA NA NA NA
## [4,] "595 794 7569" "595" "794" "7569"
## [5,] "387 287 6718" "387" "287" "6718"
## [6,] NA NA NA NA
## [7,] "233.398.9187" "233" "398" "9187"
## [8,] "482 952 3315" "482" "952" "3315"
## [9,] "239 923 8115" "239" "923" "8115"
## [10,] "579-499-7527" "579" "499" "7527"
## [11,] NA NA NA NA
## [12,] "543.355.3679" "543" "355" "3679"Web scraping
Web scraping extracts information from HTML, CSS, and Javascript code lines. The term usually refers to an automated process, which is less error-prone and faster than gathering data by hand.
It is important to note that web scraping can raise ethical concerns, as it involves accessing and using data from websites without the explicit permission of the website owner. It is a good practice to respect a website’s terms of use and seek written permission before scraping large amounts of data.
HTML and CSS
Before starting it is important to have a basic knowledge of HTML and CSS. This section aims to briefly explain how HTML and CSS work.
Starting from HTML, an HTML file looks like the following piece of code.
<!DOCTYPE html>
<html lang="en">
<body>
<h1 href="https://en.wikipedia.org/wiki/Carl_Friedrich_Gauss"> Carl Friedrich Gauss</h1>
<h2> Biography </h2>
<p> Johann Carl Friedrich Gauss was born on 30 April 1777 in Brunswick. </p>
<h2> Profession </h2>
<p> Gauss is considered as one of the greatest mathematician, statistician and physicist of all time. </p>
</body>
</html>Those instructions produce the following:
Carl Friedrich Gauss
Biography
Johann Carl Friedrich Gauss was born on 30 April 1777 in Brunswick.
Profession
Gauss is considered as one of the greatest mathematician, statistician and physicist of all time.
As you read above, HTML is used to describe the infrastructure of a web page, for example we may want to define the headings, the paragraphs, etc.
This infrastructure is represented by what are called tags (for
example <h1>...<\h1> or
<p>...<\p> are tags). Tags are the core of an
HTML document as they represent the nature of what is inside the tag
(for example h1 stands for heading 1). It is important to
observe that there are two types of tags:
- starting tags (e.g.
<h1>) - ending tags (e.g.
<\h1>)
This is what allows to nest different tags. Tags can also have
attributes, for example in
<h1 href="https://en.wikipedia.org/wiki/Carl_Friedrich_Gauss"Carl Friedrich Gauss</h1>,
href is an attribute of the tag h1 that
specifies an URL.
As the output of the above HTML code is not super elegant, CSS is used to style the final website. For example CSS is used to define the font, the color, the size, the spacing and many more features of a website.
What is important for this article are CSS
selectors, which are patterns used to select elements. The most
important is the .class selector, which selects all
elements with the same class. For example the .xyz selector
selects all elements with class="xyz".
The rvest package
Inspired by beautiful soup and RoboBrowser
(two Python libraries for web scraping), rvest has a
similar syntax, making it the most eligible package for those from
Python.
The rvest package provides functions to access a web
page and specific elements using CSS selectors and XPath. The library is
a part of the tidyverse collection of packages, i.e., it shares some
coding conventions (e.g., the pipes) with other libraries such as tibble
and ggplot2.
The web scraping operation is usually made in 3 steps:
- HTTP GET request
- Parsing HTML content
- Getting HTML element attributes
Step 1: HTTP GET request
The HTTP GET method is a method used to send a server a question to get specific data and information. It is essential to notice that this method does not change the server’s state. To send a GET request, we need the link (as a character) to the page we want to scrape:
Sending the request to the page is simple, rvest provides the read_html function, which returns an object of html_document type:
## {html_document}
## <html lang="en" class=" nytapp-vi-homepage " xmlns:og="http://opengraphprotocol.org/schema/">
## [1] <head>\n<meta charset="UTF-8">\n<title data-rh="true">The New York Times ...
## [2] <body>\n \n <div id="app">\n<a class="css-kgn7zc" href="#site-conte ...Step 2: Parsing HTML content
As we saw in the last chunk of code, NYT_page contains
the raw HTML code, which is not so easily readable. In order to make it
readable from R it has to be parsed, which means generating a
Document Object Model (DOM) from the raw HTML. DOM is
what connects scripts and web pages by representing the structure of a
document in memory. The rvest package provides 2 ways to
select HTML elements:
- XPath
- CSS selectors
Selecting elements with rvest is simple, for XPath we
use the following syntax:
while for CSS selector we need:
Suppose that for a project you need the summaries of the articles of the NYT (note that what is in the following picture is not what you see now in the New York Times web page).
Searching in the HTML code, it is not that complex to find
<p class="summary-class">, which is the markup of
what we are looking for. To parse the HTML using this selector we use
the html_element() function:
## {xml_nodeset (6)}
## [1] <p class="summary-class css-crclbt">The Pentagon used missiles untested i ...
## [2] <p class="summary-class css-1vqq9lj">Two Chinese ships successfully passe ...
## [3] <p class="summary-class css-1vqq9lj">The tanker full of crude oil could r ...
## [4] <p class="summary-class css-1vqq9lj">Markwayne Mullin, the incoming D.H.S ...
## [5] <p class="summary-class css-1vqq9lj">Members of President Trump’s circle ...
## [6] <p class="summary-class css-crclbt">Super Mario Bros. and Minecraft becam ...The easiest way to obtain a CSS selector is opening the inspect mode in your browser.
Step 3: Getting HTML element attributes
Since the chunk of code above collect all the elements p
with the class summary, we render all the elements of
NYT_summary as a text using the html_text()
function:
## [1] "The Pentagon used missiles untested in combat in an attack that struck civilian sites near a military compound on Feb. 28, according to video examined by The Times and weapons experts."
## [2] "Two Chinese ships successfully passed through the waterway, according to a ship-tracking platform. President Trump said that “great progress” was being made toward a deal."
## [3] "The tanker full of crude oil could reach its expected destination today, providing a lifeline to the island amid intense U.S. pressure."
## [4] "Markwayne Mullin, the incoming D.H.S. secretary, must balance a public reset of the agency while delivering on President Trump’s deportation agenda."
## [5] "Members of President Trump’s circle have caught the eye of Denmark’s intelligence services for trying to make friends and cut deals on the Danish territory."
## [6] "Super Mario Bros. and Minecraft became movie blockbusters, and Call of Duty and Legend of Zelda adaptations are on the way. Fans are watching closely."Another important functions to extract data from a HTML elements are
html_text2() and html_table(). The first one
works similar to html_text() but it simulates how text
looks in a browser. Roughly speaking, it converts
<br /> to \n, add blank lines around
<p> tags, and lightly formats tabular data. However,
it is much slower than html_text().
The html_table() function parses an HTML table into a
data frame. As an example we will scrape data from the Wikipedia’s page
of Formula 1 drivers.
link <- "https://en.wikipedia.org/wiki/List_of_Formula_One_drivers"
page <- read_html(link)
drivers_F1_raw <- html_element(page, "table.sortable")
drivers_F1_raw## {html_node}
## <table class="wikitable sortable sticky-header" style="font-size: 85%; text-align:center">
## [1] <tbody>\n<tr>\n<th scope="col">Driver name\n</th>\n<th scope="col">\n<a h ...## # A tibble: 6 × 11
## `Driver name` Nationality `Seasons competed` `Drivers' Championships`
## <chr> <chr> <chr> <chr>
## 1 Carlo Abate Italy 1962–1963 0
## 2 George Abecassis United Kingdom 1951–1952 0
## 3 Kenny Acheson United Kingdom 1983, 1985 0
## 4 Andrea de Adamich Italy 1968, 1970–1973 0
## 5 Philippe Adams Belgium 1994 0
## 6 Walt Ader United States 1950 0
## # ℹ 7 more variables: `Race entries` <chr>, `Race starts` <chr>,
## # `Pole positions` <chr>, `Race wins` <chr>, Podiums <chr>,
## # `Fastest laps` <chr>, `Points[a]` <chr>## tibble [878 × 11] (S3: tbl_df/tbl/data.frame)
## $ Driver name : chr [1:878] "Carlo Abate" "George Abecassis" "Kenny Acheson" "Andrea de Adamich" ...
## $ Nationality : chr [1:878] "Italy" "United Kingdom" "United Kingdom" "Italy" ...
## $ Seasons competed : chr [1:878] "1962–1963" "1951–1952" "1983, 1985" "1968, 1970–1973" ...
## $ Drivers' Championships: chr [1:878] "0" "0" "0" "0" ...
## $ Race entries : chr [1:878] "3" "2" "10" "36" ...
## $ Race starts : chr [1:878] "0" "2" "3" "30" ...
## $ Pole positions : chr [1:878] "0" "0" "0" "0" ...
## $ Race wins : chr [1:878] "0" "0" "0" "0" ...
## $ Podiums : chr [1:878] "0" "0" "0" "0" ...
## $ Fastest laps : chr [1:878] "0" "0" "0" "0" ...
## $ Points[a] : chr [1:878] "0" "0" "0" "6" ...