AE 10: Opinion articles in The Chronicle

Part 1 - Data scraping

See chronicle-scrape.R for suggested scraping code.

Part 2 - Data analysis

Let’s start by loading the packages we will need:

library(tidyverse)

Load the data you saved into the data folder and name it chronicle.

chronicle <- read_csv("data/chronicle.csv")

Who are the most prolific authors of the 500 most recent opinion articles in The Chronicle?

chronicle |>
  count(author, sort = TRUE)

# A tibble: 191 × 2
   author             n
   <chr>          <int>
 1 Luke A. Powery    32
 2 Advikaa Anand     26
 3 Heidi Smith       26
 4 Nik Narain        23
 5 Monday Monday     20
 6 Aaron Siegle      15
 7 Anna Garziera     15
 8 Sonia Green       12
 9 Linda Cao         10
10 Angikar Ghosal     9
# ℹ 181 more rows

Draw a line plot of the number of opinion articles published per day in The Chronicle.

chronicle |>
  count(date) |>
  ggplot(aes(x = date, y = n, group = 1)) +
  geom_line()

What percent of the most recent 100 opinion articles in The Chronicle mention “climate” in their title?

chronicle |>
  mutate(
    title = str_to_lower(title),
    climate = if_else(str_detect(title, "climate"), "mentioned", "not mentioned")
    ) |>
  count(climate) |>
  mutate(prop = n / sum(n))

# A tibble: 2 × 3
  climate           n  prop
  <chr>         <int> <dbl>
1 mentioned        11 0.022
2 not mentioned   489 0.978

What percent of the most recent 100 opinion articles in The Chronicle mention “climate” in their title or abstract?

chronicle |>
  mutate(
    title = str_to_lower(title),
    abstract = str_to_lower(abstract),
    climate = if_else(
      str_detect(title, "climate") | str_detect(abstract, "climate"), 
      "mentioned", 
      "not mentioned"
      )
    ) |>
  count(climate) |>
  mutate(prop = n / sum(n))

# A tibble: 2 × 3
  climate           n  prop
  <chr>         <int> <dbl>
1 mentioned        16 0.032
2 not mentioned   484 0.968

Come up with another question and try to answer it using the data.

# add code here