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#TidyTuesday 2023 – Week 35

Jonathan Kitt
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Thanls to Dan Oehm for sharing his tips on addind icons in the script titles!

< section id="introduction" class="level1">

Introduction


The #TidyTuesday weekly challenge is organised by the R4DS (R for Data Science) Online Learning Community.

Every tuesday throughout the year, participants work on a common dataset and share the plots they create.

The dataset for this challenge comes from the U.S. Copyright Office Fair Use Index.


< section id="getting-the-data" class="level1">

Getting the data


First of all, let’s load the packages we’ll be using :

If you don’t have these packages installed, simply use the install.packages() function.

# 📦 Load packages ----

library(tidyverse)
library(showtext)
library(ggtext)


We also load the s we will use in the plots: Bebas Neue for the text and Londrina Shadow for the title.

# 🔤 Import s ----

_add_google("Roboto Condensed", "Roboto Condensed")
showtext_auto()


We can now download the dataset :

# ⬇️ Import the dataset ----

fair_use_cases <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-08-29/fair_use_cases.csv")


The dataset has 251 observations (rows) and 7 variables (columns).


< section id="cleaning-the-data" class="level1">

Cleaning the data


We use the following code to clean the data:

# 🧹  Clean the data ----

d <- fair_use_cases |>
  # keep years 2013-2022
  filter(year >= 2013) |>
  # count number of found/not found cases per year
  count(year, fair_use_found) |>
  # repeat each row n times (n = nb of occurences)
  uncount(n) |>
  # create a row index
  mutate(case_id = 1:n(), .by = c(year, fair_use_found)) |>
  # use negative values for "not found" cases
  mutate(y = case_when(fair_use_found == TRUE ~ case_id,
                       TRUE ~ -case_id))


< section id="creating-the-plot" class="level1">

Creating the plot


We use the following code to create the plot:

p <- ggplot(data = d) +
  geom_point(aes(x = year, y = y,
                 colour = fair_use_found),
             shape = 21, size = 6,
             show.legend = FALSE) +
  geom_text(aes(x = year, y = y,
                colour = fair_use_found),
            label = "C",
            family = "Roboto Condensed", size = 12,
            show.legend = FALSE) +
  geom_text(aes(x = year, y = 0, label = year),
            family = "Roboto Condensed", size = 15,
            colour = "white") +
  scale_colour_manual(values = c("#fd574a", "#2eed91")) +
  labs(title = "Number of fair use court cases in the U.S. (2013-2022)",
       subtitle = "Colours indicate whether fair use was<span style='color:#2eed91;'> found</span> or <span style='color:#fd574a;'>not found</span> by the court",
       caption = "#TidyTuesday 2023 week 35 | Data from the U.S. Copyright Office Fair Use Index | Jonathan Kitt") +
  theme_void() +
  theme(panel.background = element_rect(fill = "black", colour = "black"),
        plot.background = element_rect(fill = "black", colour = "black"),
        plot.title = element_markdown(family = "Roboto Condensed",
                                      colour = "white", size = 75,
                                      margin = margin(t = 20, l = 20)),
        plot.subtitle = element_markdown(family = "Roboto Condensed",
                                         colour = "white", size = 50,
                                         margin = margin(t= 5, l = 20)),
        plot.caption = element_text(family = "Roboto Condensed",
                                    colour = "white", size = 30,
                                    hjust = 0.5, margin = margin(t = 10, b = 10)))


We now create the second plot:

# ✏️ Create the plot ----

p2 <- ggplot() +
  geom_rect(data = p2_scores,
             aes(xmin = x - 0.85, xmax = x + 0.85,
                 ymin = 0, ymax = total_thsd),
            fill = "#edeb00") +
  geom_text(data = p2_scores,
            aes(x = x, y = total_thsd - 160, label = total_thsd),
            family = "Bebas Neue", colour = "black", size = 18) +
  geom_text(data = p2_x_labels,
            aes(x = x, y = y, label = label),
            family = "Bebas Neue", colour = "white", size = 18) +
  geom_text(data = p2_text,
            aes(x = x, y = y, label = label),
            family = "Bebas Neue", colour = "white", size = 20,
            hjust = 0) +
  xlim(-1, 46) +
  theme_void() +
  theme(panel.background = element_rect(fill = "black"),
        plot.background = element_rect(fill = "black"))

# 💾 Export plot ----

ggsave("figs/tt_2023_w35_fair_use.png", p, dpi = 320, width = 12, height = 6)


And here’s the result!

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