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.

Getting the data

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

{tidyverse} to clean the data and create the plots
{showtext} to change the fonts used
{patchwork } to assemble the plots

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 fonts we will use in the plots.

# 🔤 Import fonts ----

font_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")

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)

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!