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#TidyTuesday 2023 – Week 37

Jonathan Kitt
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< 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 Human Chronome Project.


< 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(patchwork)


We also load the s we will use in the plots.

# 🔤 Import s ----

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


We can now download the dataset :

# ⬇️ Import the dataset ----
all_countries <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-12/all_countries.csv")
global_human_day <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-12/global_human_day.csv")


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

Cleaning the data


We use the following code to clean the data:

# 🧹  Clean the data ----

categories <- all_countries |>
  # extract unique values
  distinct(Category, Subcategory)

d <- global_human_day |>
  # join the categories
  left_join(categories) |>
  # select columns
  select(Category, hoursPerDay) |>
  # add hours per category
  summarise(total = sum(hoursPerDay), .by = Category) |>
  # arrange by decreasing amount of time
  arrange(-total) |>
  # split the total column into two values
  separate(col = total, into = c("h", "m"), remove = F) |>
  # transform the trailing hours value into minutes
  mutate(m = round(as.numeric(paste0("0.", m)) * 60),
         h = as.numeric(h)) |>
  # create labels for plots
  mutate(duration = case_when(h == 0 ~ paste0(m, "m"),
                              TRUE ~ paste0(h, "h ", m, "m"))) |>
  # select columns
  select(Category, total, duration)


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

Creating the plot


First we create a custom function to generate one plot per category :

plot_hm <- function(data, row) {
  ggplot() +
    geom_rect(aes(xmin = 3, xmax = 4,
                  ymin = 0, ymax = 24),
              colour = "#7e38b7", fill = "#7e38b7") +
    geom_rect(data = slice(data, row),
              aes(xmin = 3, xmax = 4,
                  ymin = 0, ymax = total),
              colour = "#9c89ff", fill = "#9c89ff") +
    coord_polar(theta = "y") +
    xlim(c(0.05, 4)) +
    labs(title = d$Category[row]) +
    annotate("text", x = 0.05, y = 0,
             label = d$duration[row],
             family = "Roboto Condensed",
             colour = "#c4feff",
             size = 25) +
    theme_void() +
    theme(panel.background = element_rect(fill = "#541675",
                                          colour = NA),
          plot.background = element_rect(fill = "#541675",
                                         colour = NA),
          plot.title = element_text(family = "Roboto Condensed",
                                    colour = "#c4feff", size = 40,
                                    hjust = 0.5,
                                    margin = margin(b = -10)))
}


We use the following code to create and assemble the plots and export the final figure :

# Create the plots
p1 <- plot_hm(d, 1)
p2 <- plot_hm(d, 2)
p3 <- plot_hm(d, 3)
p4 <- plot_hm(d, 4)
p5 <- plot_hm(d, 5)
p6 <- plot_hm(d, 6)
p7 <- plot_hm(d, 7)
p8 <- plot_hm(d, 8)

# Assemble the plots
p <- (p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8) +
  plot_layout(ncol = 4) +
  plot_annotation(title = "How humans spend their day time",
                  caption = "#TidyTuesday 2023 week 37 | Data from the Human Chronome Project | Jonathan Kitt",
                  theme = theme(panel.background = element_rect(fill = "#541675", colour = NA),
                                plot.background = element_rect(fill = "#541675", colour = NA),
                                plot.title = element_text(family = "Roboto Condensed",
                                                            colour = "#c4feff", size = 100,
                                                            hjust = 0.5, margin = margin(t = 5, b = 25)),
                                plot.caption = element_text(family = "Roboto Condensed",
                                                            colour = "white", size = 30, hjust = 0.5)))

# Export the plot
ggsave("figs/tt_2023_w37_global_human_day.png", p, dpi = 320, width = 12, height = 6)


We now create the second plot:


And here’s the result!

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