3 Web scraping
3.1 Getting Started
Important tool
Our approach to web scraping relies on the Chrome browser and an extension called a Selector Gadget. Download them here:
Data Acquisition
Reading The Student Life
How often do you read The Student Life?
a. Every day
b. 3-5 times a week
c. Once a week
d. Rarely
What do you think is the most common word in the titles of The Student Life opinion pieces?
Analyzing The Student Life
Using the titles of the opinion pieces from The Student Life website, we can figure out the most common words.
How do you think the sentiments in opinion pieces in The Student Life compare across authors?
Roughly the same?
Wildly different?
Somewhere in between?
Using the first paragraph of each opinion article, we can see which others have the most positive and negative sentiment articles.
All of this analysis is done in R! {.centered}
(mostly) with tools you already know!
Common words in The Student Life titles {.smaller}
Code for the earlier plot:
data(stop_words) # from tidytext
tsl_opinion_titles |>
tidytext::unnest_tokens(word, title) |>
anti_join(stop_words) |>
count(word, sort = TRUE) |>
slice_head(n = 20) |>
mutate(word = fct_reorder(word, n)) |>
ggplot(aes(y = word, x = n, fill = log(n))) +
geom_col(show.legend = FALSE) +
theme_minimal(base_size = 16) +
labs(
x = "Number of mentions",
y = "Word",
title = "The Student Life - Opinion pieces",
subtitle = "Common words in the 500 most recent opinion pieces",
caption = "Source: Data scraped from The Student Life on Nov 4, 2024"
) +
theme(
plot.title.position = "plot",
plot.caption = element_text(color = "gray30")
)Avg sentiment scores of first paragraph {.smaller}
Code for the earlier plot:
afinn_sentiments <- get_sentiments("afinn") # need tidytext and textdata
tsl_opinion_titles |>
tidytext::unnest_tokens(word, first_p) |>
anti_join(stop_words) |>
left_join(afinn_sentiments) |>
group_by(authors, title) |>
summarize(total_sentiment = sum(value, na.rm = TRUE), .groups = "drop") |>
group_by(authors) |>
summarize(
n_articles = n(),
avg_sentiment = mean(total_sentiment, na.rm = TRUE),
) |>
filter(n_articles > 1 & !is.na(authors)) |>
arrange(desc(avg_sentiment)) |>
slice(c(1:10, 69:78)) |>
mutate(
authors = fct_reorder(authors, avg_sentiment),
neg_pos = if_else(avg_sentiment < 0, "neg", "pos"),
label_position = if_else(neg_pos == "neg", 0.25, -0.25)
) |>
ggplot(aes(y = authors, x = avg_sentiment)) +
geom_col(aes(fill = neg_pos), show.legend = FALSE) +
geom_text(
aes(x = label_position, label = authors, color = neg_pos),
hjust = c(rep(1,10), rep(0, 10)),
show.legend = FALSE,
fontface = "bold"
) +
geom_text(
aes(label = round(avg_sentiment, 1)),
hjust = c(rep(1.25,10), rep(-0.25, 10)),
color = "white",
fontface = "bold"
) +
scale_fill_manual(values = c("neg" = "#4d4009", "pos" = "#FF4B91")) +
scale_color_manual(values = c("neg" = "#4d4009", "pos" = "#FF4B91")) +
scale_x_continuous(breaks = -5:5, minor_breaks = NULL) +
scale_y_discrete(breaks = NULL) +
coord_cartesian(xlim = c(-5, 5)) +
labs(
x = "negative ← Average sentiment score (AFINN) → positive",
y = NULL,
title = "The Student Life - Opinion pieces\nAverage sentiment scores of first paragraph by author",
subtitle = "Top 10 average positive and negative scores",
caption = "Source: Data scraped from The Student Life on Nov 4, 2024"
) +
theme_void(base_size = 16) +
theme(
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5, margin = unit(c(0.5, 0, 1, 0), "lines")),
axis.text.y = element_blank(),
plot.caption = element_text(color = "gray30")
)3.2 Where is the data coming from?
tsl_opinion_titles# A tibble: 500 × 4
title authors date first_p
<chr> <chr> <dttm> <chr>
1 Elon Musk’s million-dollar-a-day rewards … Celest… 2024-11-01 16:27:00 have y…
2 The politics behind apolitical acts Eric Lu 2024-11-01 16:21:00 while …
3 In Defense of the Pomona College Judicial… Henri … 2024-11-01 16:15:00 former…
4 ‘Yakking’ isn’t a canon event, party resp… Kabir … 2024-11-01 16:10:00 whirri…
5 The ‘if he wanted to, he would’ mentality… Tess M… 2024-11-01 16:01:00 ladies…
6 You can’t silence us: A united front agai… Outbac… 2024-10-25 11:23:00 in the…
# ℹ 494 more rows3.3 Web scraping
3.3.1 Scraping the web: what? why?
Increasing amount of data is available on the web
These data are provided in an unstructured format: you can always copy & paste, but it’s time-consuming and prone to errors
Web scraping is the process of extracting this information automatically and transform it into a structured dataset
-
Two different scenarios:
Screen scraping: extract data from source code of website, with html parser (easy) or regular expression matching (less easy).
Web APIs (application programming interface): website offers a set of structured http requests that return JSON or XML files.
3.3.2 Hypertext Markup Language
Most of the data on the web is largely available as HTML - while it is structured (hierarchical) it often is not available in a form useful for analysis (flat / tidy).
<html>
<head>
<title>This is a title</title>
</head>
<body>
<p align="center">Hello world!</p>
<br/>
<div class="name" id="first">John</div>
<div class="name" id="last">Doe</div>
<div class="contact">
<div class="home">555-555-1234</div>
<div class="home">555-555-2345</div>
<div class="work">555-555-9999</div>
<div class="fax">555-555-8888</div>
</div>
</body>
</html>Some HTML elements
-
<html>: start of the HTML page -
<head>: header information (metadata about the page) -
<body>: everything that is on the page -
<p>: paragraphs -
<b>: bold -
<table>: table -
<div>: a container to group content together
3.3.3 rvest
- The rvest package makes basic processing and manipulation of HTML data straight forward
- It is designed to work with pipelines built with
|> - rvest.tidyverse.org
Core functions:
read_html()- read HTML data from a url or character string.html_elements()- select specified elements/tags from the HTML document using CSS selectors.html_element()- select a single element/tag from the HTML document using CSS selectors.html_table()- parse an HTML table into a data frame.html_text()/html_text2()- extract element’s/tag’s text content.html_name- extract an element’s/tag’s name(s).html_attrs- extract all attributes.html_attr- extract attribute value(s) by name.
html, rvest, & xml2 {.smaller}
html <-
'<html>
<head>
<title>This is a title</title>
</head>
<body>
<p align="center">Hello world!</p>
<br/>
<div class="name" id="first">John</div>
<div class="name" id="last">Doe</div>
<div class="contact">
<div class="home">555-555-1234</div>
<div class="home">555-555-2345</div>
<div class="work">555-555-9999</div>
<div class="fax">555-555-8888</div>
</div>
</body>
</html>'read_html(html){html_document}
<html>
[1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
[2] <body>\n <p align="center">Hello world!</p>\n <br><div class="name" ...3.3.4 Selecting elements
read_html(html) |> html_elements("p"){xml_nodeset (1)}
[1] <p align="center">Hello world!</p>read_html(html) |> html_elements("p") |> html_text()[1] "Hello world!"read_html(html) |> html_elements("p") |> html_name()[1] "p"read_html(html) |> html_elements("p") |> html_attrs()[[1]]
align
"center" read_html(html) |> html_elements("p") |> html_attr("align")[1] "center"read_html(html) |> html_elements("div"){xml_nodeset (7)}
[1] <div class="name" id="first">John</div>
[2] <div class="name" id="last">Doe</div>
[3] <div class="contact">\n <div class="home">555-555-1234</div>\n ...
[4] <div class="home">555-555-1234</div>
[5] <div class="home">555-555-2345</div>
[6] <div class="work">555-555-9999</div>
[7] <div class="fax">555-555-8888</div>read_html(html) |> html_elements("div") |> html_text()[1] "John"
[2] "Doe"
[3] "\n 555-555-1234\n 555-555-2345\n 555-555-9999\n 555-555-8888\n "
[4] "555-555-1234"
[5] "555-555-2345"
[6] "555-555-9999"
[7] "555-555-8888" 3.3.5 CSS selectors
We will use a tool called SelectorGadget to help us identify the HTML elements of interest by constructing a CSS selector which can be used to subset the HTML document.
Some examples of basic selector syntax is below,
| Selector | Example | Description |
|---|---|---|
| .class | .title |
Select all elements with class=“title” |
| #id | #name |
Select all elements with id=“name” |
| element | p |
Select all <p> elements |
| element element | div p |
Select all <p> elements inside a <div> element |
| element>element | div > p |
Select all <p> elements with <div> as a parent |
| [attribute] | [class] |
Select all elements with a class attribute |
| [attribute=value] | [class=title] |
Select all elements with class=“title” |
CSS classes and ids
classandidare used to style elements (e.g., change their color!)classcan be applied to multiple different elementsidis unique to each element
read_html(html) |> html_elements(".name"){xml_nodeset (2)}
[1] <div class="name" id="first">John</div>
[2] <div class="name" id="last">Doe</div>read_html(html) |> html_elements("div.name"){xml_nodeset (2)}
[1] <div class="name" id="first">John</div>
[2] <div class="name" id="last">Doe</div>read_html(html) |> html_elements("#first"){xml_nodeset (1)}
[1] <div class="name" id="first">John</div>
3.3.6 Text with html_text() vs. html_text2()
html = read_html(
"<p>
This is the first sentence in the paragraph.
This is the second sentence that should be on the same line as the first sentence.<br>This third sentence should start on a new line.
</p>"
)html |> html_text()[1] " \n This is the first sentence in the paragraph.\n This is the second sentence that should be on the same line as the first sentence.This third sentence should start on a new line.\n "html |> html_text2()[1] "This is the first sentence in the paragraph. This is the second sentence that should be on the same line as the first sentence.\nThis third sentence should start on a new line."
3.3.7 HTML tables with html_table()
html_table =
'<html>
<head>
<title>This is a title</title>
</head>
<body>
<table>
<tr> <th>a</th> <th>b</th> <th>c</th> </tr>
<tr> <td>1</td> <td>2</td> <td>3</td> </tr>
<tr> <td>2</td> <td>3</td> <td>4</td> </tr>
<tr> <td>3</td> <td>4</td> <td>5</td> </tr>
</table>
</body>
</html>'read_html(html_table) |>
html_elements("table") |>
html_table()[[1]]
# A tibble: 3 × 3
a b c
<int> <int> <int>
1 1 2 3
2 2 3 4
3 3 4 53.3.8 SelectorGadget
SelectorGadget (selectorgadget.com) is a javascript based tool that helps you interactively build an appropriate CSS selector for the content you are interested in.
3.3.9 Recap
- Use the SelectorGadget identify tags for elements you want to grab
- Use the rvest R package to first read in the entire page (into R) and then parse the object you’ve read in to the elements you’re interested in
- Put the components together in a data frame (a tibble) and analyze it like you analyze any other data
3.4 Plan for webscraping
- Read in the entire page
- Scrape opinion title and save as
title - Scrape author and save as
author - Scrape date and save as
date - Create a new data frame called
tsl_opinionwith variablestitle,author, anddate
3.4.1 Read in the entire page
tsl_page <- read_html("https://tsl.news/category/opinions/")
tsl_page{html_document}
<html lang="en-US">
[1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
[2] <body class="archive category category-opinions category-2244 custom-back ...- we need to convert into something more familiar, like a data frame
3.4.2 Scrape title and save as title
tsl_page |>
html_elements(".entry-title a") {xml_nodeset (10)}
[1] <a href="https://tsl.news/opinion-democrats-need-to-let-their-voters-cho ...
[2] <a href="https://tsl.news/opinion-the-case-for-vaping/" title="OPINION: ...
[3] <a href="https://tsl.news/opinion-linkedin-is-not-our-end-all-be-all/" t ...
[4] <a href="https://tsl.news/opinion-stop-using-chatgpt/" title="OPINION: S ...
[5] <a href="https://tsl.news/opinion-more-than-a-body-challenging-the-dehum ...
[6] <a href="https://tsl.news/opinions-we-stand-in-solidarity-with-our-stude ...
[7] <a href="https://tsl.news/opinion-western-fashion-deserves-cultural-appr ...
[8] <a href="https://tsl.news/opinion-why-kamala-harris-lost/" title="OPINIO ...
[9] <a href="https://tsl.news/opinion-what-my-messy-breakup-with-social-medi ...
[10] <a href="https://tsl.news/opinion-when-looks-kill-the-issue-with-attract ...title <- tsl_page |>
html_elements(".entry-title a") |>
html_text()
title [1] "OPINION: Democrats need to let their voters choose"
[2] "OPINION: The Case for Vaping"
[3] "OPINION: LinkedIn is not our end-all be-all"
[4] "OPINION: Stop using ChatGPT"
[5] "OPINION: More than a body: Challenging the dehumanization of Black women in sports"
[6] "OPINION: We stand in solidarity with our students"
[7] "OPINION: Western fashion deserves cultural appreciation"
[8] "OPINION: Why Kamala Harris lost"
[9] "OPINION: What my messy breakup with social media taught me"
[10] "OPINION: When Looks Kill: The issue with attractive depictions of murderers on screen"title <- title |>
str_remove("OPINION: ")
title [1] "Democrats need to let their voters choose"
[2] "The Case for Vaping"
[3] "LinkedIn is not our end-all be-all"
[4] "Stop using ChatGPT"
[5] "More than a body: Challenging the dehumanization of Black women in sports"
[6] "We stand in solidarity with our students"
[7] "Western fashion deserves cultural appreciation"
[8] "Why Kamala Harris lost"
[9] "What my messy breakup with social media taught me"
[10] "When Looks Kill: The issue with attractive depictions of murderers on screen"
3.4.4 Scrape date and save as date
date <- tsl_page |>
html_elements(".published") |>
html_text()
date [1] "November 22, 2024 2:12 am" "November 22, 2024 2:02 am"
[3] "November 22, 2024 1:44 am" "November 22, 2024 1:35 am"
[5] "November 22, 2024 1:25 am" "November 15, 2024 1:19 am"
[7] "November 15, 2024 1:11 am" "November 15, 2024 1:03 am"
[9] "November 15, 2024 12:55 am" "November 15, 2024 12:45 am"date <- date |>
lubridate::mdy_hm(tz = "America/Los_Angeles")
date [1] "2024-11-22 02:12:00 PST" "2024-11-22 02:02:00 PST"
[3] "2024-11-22 01:44:00 PST" "2024-11-22 01:35:00 PST"
[5] "2024-11-22 01:25:00 PST" "2024-11-15 01:19:00 PST"
[7] "2024-11-15 01:11:00 PST" "2024-11-15 01:03:00 PST"
[9] "2024-11-15 00:55:00 PST" "2024-11-15 00:45:00 PST"3.4.5 Create a new data frame
tsl_opinion <- tibble(
title,
author,
date
)
tsl_opinion# A tibble: 10 × 3
title authors date
<chr> <chr> <dttm>
1 Democrats need to let their voters choose Adam A… 2024-11-22 02:12:00
2 The Case for Vaping Parker… 2024-11-22 02:02:00
3 LinkedIn is not our end-all be-all Kabir … 2024-11-22 01:44:00
4 Stop using ChatGPT Alex B… 2024-11-22 01:35:00
5 More than a body: Challenging the dehumanization … Naomi … 2024-11-22 01:25:00
6 We stand in solidarity with our students Interc… 2024-11-15 01:19:00
# ℹ 4 more rows
3.4.6 map() over multiple pages
tsl_opinions <- function(i){
tsl_page <- read_html(paste0("https://tsl.news/category/opinions/page/",i))
title <- tsl_page |>
html_elements(".entry-title a") |>
html_text() |>
str_remove("OPINION: ")
author <- tsl_page |>
html_elements(".author") |>
html_text() |>
tibble() |>
set_names(nm = "authors") |>
filter(str_detect(authors, "By ")) |>
mutate(authors = str_replace(authors, "By ", ""))
date <- tsl_page |>
html_elements(".published") |>
html_text() |>
lubridate::mdy_hm(tz = "America/Los_Angeles")
first_p <- tsl_page |>
html_elements(".entry-content p") |>
html_text() |>
tolower()
tibble(
title,
author,
date,
first_p
)
}
tsl_opinion_titles <- 1:50 |> purrr::map(tsl_opinions) |>
list_rbind()3.5 Web scraping considerations
3.5.1 Check if you are allowed!
library(robotstxt)
paths_allowed("https://tsl.news/category/opinions/")[1] TRUEpaths_allowed("http://www.facebook.com")[1] FALSE3.5.2 Ethics: “Can you?” vs “Should you?”
Source: Brian Resnick, Researchers just released profile data on 70,000 OkCupid users without permission, Vox.
3.5.3 Challenges: Unreliable formatting
3.5.4 Challenges: Data broken into many pages
