day 0 challenge

Author

Jen Richmond

Published

March 31, 2025

I have some time on my hands at the moment and thought the #30DayChartChallenge might be a good way to refresh my ggplot skills.

The Our World in Data is a fun place to explore data visualisations. They always make really interesting plots out of all kinds of different data, but they use an in-house data viz platform called “Grapher” so it isn’t easy to find code that can be used to reproduce the charts.

My goal in April is use the 30 day chart challenge prompt to find a Our World in Data plot that looks interesting, and then to try to reproduce the plot using ggplot code.

I will post the final code for each plot in a tabset below, but if you want to see how each chart came about (i.e. my troubleshooting process), there will be a blog post associated with each one.

The owidapi package is an easy way to get access to the data sources that the Our World in Data site uses.

Load the package and then use the owid_get_catalog() function to get a dataframe of all the different plots that have been made.
Find one on the site that fits the 30 Day Chart Challenge prompt and then find the slug in your catalog dataframe and use that with the owid_get to read the data from the API.

library(owidapi)

catalog <- owid_get_catalog()

willingness <- owid_get("willingness-climate-action")

Day 1 fractions

Willingness climate action

my plot
r code

willingness2024 %>%
  filter(entity_name != "World") %>%
  ggplot(aes(y =  prediction_others_willingness, x = self_willingness, colour = region)) +
  geom_point() +
  scale_y_continuous(labels = percent_format(accuracy = 1, scale = 1), 
                      limits = c(0,100), expand = c(0,0), breaks = seq(0,100,20)) +
    scale_x_continuous(labels = percent_format(accuracy = 1, scale = 1), 
                       limits = c(0,100), expand = c(0,0), breaks = seq(0,100,20)) +
  labs(y = "Predicted share willing to give", 
       x = "Actual share who said they were willing to give 1% of their income", 
       title = "People underestimate others' willingness to take climate action", 
       subtitle = "Participants were asked if they would contribute 1% of their income to tackle \nclimate change. \nThe share that answered 'yes' is shown on the horizontal axis. \nThe share of the population in their country that people think would be willing \nis shown on the vertical axis.") +
  theme_bw()  +
  easy_remove_gridlines(axis = "both", minor = TRUE, major = FALSE) +
  geom_abline(
    slope = 1, 
    intercept = 0, 
    color = "grey", 
    linetype = "dotted") +
  scale_colour_manual(values = c("#a2559b", "#00847d", "#4b6a9c", "#e56e59", "#38aaba", "#883039")) +
  easy_remove_legend_title() +
  geom_text_repel(aes(label = entity_name), size = 3, max.overlaps = 20)

Day 2 slope

Time spent alone

my plot
r code

time %>%
  filter(age < 80) %>%
  filter(group == "All people") %>%
  ggplot(aes(x = age, y = hours, colour = category)) +
  geom_point(size = 1) + 
  geom_line() +
  scale_colour_manual(values = c("#496899", "#6b3d8d", "#2b8465", "#986d39", "#b03508", "#883039")) +
  theme_minimal() +
  scale_y_continuous(expand = c(0,0), limits = c(-0.05,8.1), breaks = seq(0,9,1)) +
  scale_x_continuous(breaks=c(15,30,40,50,60,70,80)) +
  easy_remove_gridlines(axis = "x") +
  easy_remove_gridlines(axis = "y", major = FALSE, minor = TRUE) +
  theme(panel.grid = element_line(linewidth =  0.4, linetype = 2)) +
  theme(axis.ticks.x =  element_line(linewidth = 0.5, color="darkgrey") , 
        axis.line.x = element_line(linewidth = 0.2, colour = "darkgrey", linetype=1)) +
  easy_remove_legend() +
  ### the geom_text code below are created using the ggannotate package
  geom_text(data = data.frame(x = 82, y = 7.8, 
    label = "Alone"), aes(x = x, y = y, label = label), size = 3, colour = "#496899") +
  geom_text(data = data.frame(x = 82, y = 4.4, 
    label = "With \npartner"), aes(x = x, y = y, label = label), size = 3,colour = "#6b3d8d") +
  geom_text(data = data.frame(x = 80, y = 1.3, 
    label = "With family"),aes(x = x, y = y, label = label), size = 3, colour = "#2b8465") +
  geom_text(data = data.frame(x = 83.5, y = 0.7, 
    label = "With children"), aes(x = x, y = y, label = label), size = 2.5,colour = "#986d39") +
 geom_text(data = data.frame(x = 83.5, y = 0.5, 
    label = "With friends"), aes(x = x, y = y, label = label), size = 2.5,colour = "#b03508") +
  geom_text(data = data.frame(x = 83.5, y = 0.1, 
    label = "With coworkers"), aes(x = x, y = y, label = label), size = 2.5, colour = "#883039") +
  labs(title = "Who Americans spend their time with, by age, All people", 
       subtitle = "Measured in hours per day, based on averages from surveys in the United States \nbetween 2010 and 2023", 
       x = "Age", 
       y = "Hours",
       caption = "Data source: U.S. Bureau of Labor Statistics (2023). \nNote: Activities such as sleeping, grooming, and personal care are not included in the data. \nRelationships used to categorize people are not exhaustive and time spent with multiple people counts toward all \n(e.g., attending a party with friends and partner counts toward both friends and partner)") +
  theme(plot.caption = element_text(hjust = 0)) # make the caption appear on the left

Day 3 circular

Women in government

my plot
r code

share2024_regions %>%
  ggplot(aes(x = "", y = percent, fill = percent_women)) +
  geom_col(colour = "white") +
 coord_polar("y", start = 0) +
  facet_wrap(~entity_name) +
    scale_fill_manual(values = c("#a2559b", "#00847d", "#4b6a9c", "#e56e59", "#38aaba", "#6b3d8d",  "#986d39" )) +
    theme_void() +
   easy_add_legend_title("Percent seats") +
labs(title = "Percent of countries by share of women in parliament in 2024, Regions", 
       subtitle = "Percent of seats in lower or single chamber of the legislature held by women", 
       caption = "Data source: V-Dem (2025) via OurWorldInData. \nNote: Only countries with at least one legislative chamber included.")

Day 4 big small

Time spent in unpaid work

my plot
r code

dom_recent %>%
  ggplot(aes(x = male, y = female, colour = country)) +
  geom_point(size = 2) +
  theme_classic() +
  easy_remove_legend() +
  theme(panel.grid.major = element_line(color = "lightgray", linetype = 2)) +
   scale_y_continuous(labels = percent_format(accuracy = 1, scale = 1), 
                     expand = c(0,0), breaks = seq(0,25, 5), limits = c(0,30)) +
   scale_x_continuous(labels = percent_format(accuracy = 1, scale = 1), 
                      expand = c(0,0), breaks = seq(0,12, 2), limits = c(0,14)) +
  geom_abline(intercept = 0, slope = 1, colour = "gray", linetype = 2) +
  geom_abline(intercept = 0, slope = 2, colour = "gray", linetype = 2) +
  geom_abline(intercept = 0, slope = 4, colour = "gray", linetype = 2) +
  geom_text(data = data.frame(x = 10, y = 3.5, label = "Women and men spend \nequal time"), 
            mapping = aes(x = x, y = y, label = label), size = 2.5, inherit.aes = FALSE) +
  geom_text(data = data.frame(x = 12.5, y = 28, label = "Women spend 2x \nmore time"), 
            mapping = aes(x = x, y = y, label = label), size = 2.5,  inherit.aes = FALSE) +
  geom_text(data = data.frame(x = 5, y = 26.8, label = "Women spend 4x \nmore time"), 
            mapping = aes(x = x, y = y, label = label), size = 2.5, inherit.aes = FALSE) +
  geom_text_repel(aes(label = country), size = 2.5, max.overlaps = 5) +
  labs(title = "Time women and men spend on unpaid care and domestic work, 2022",
       subtitle = "The average share of each day that women and men aged 15 and older spend on unpaid care \nand domestic work.",
       x = "Men (% of 24 hour day)", 
       y = "Women (% of 24 hour day)", 
       caption = "Data source: UN Statistics Division and UN WOMEN.
Note: Unpaid care and domestic work includes: food preparation,
dishwashing, upkeep of a dwelling, laundry, ironing, gardening, caring
for pets, shopping, servicing and repair of personal and household
goods, childcare, and care of the sick, elderly or disabled household
members, among others.") +
   theme(plot.caption = element_text(hjust = 0), 
         plot.subtitle = element_text(margin=margin(0,0,20,0))) +
  geom_curve(data = data.frame(x = 5.5, y = 28.5, xend = 6.4, yend = 26),
          mapping = aes(x = x, y = y, xend = xend, yend = yend), curvature = -0.515, 
          arrow = arrow(20L, unit(0.1, "inches"), "last", "closed"), alpha = 1, inherit.aes = FALSE) +
  geom_curve(data = data.frame(x = 11.8, y = 28, xend = 12.2, yend = 26),
          mapping = aes(x = x, y = y, xend = xend, yend = yend), curvature = 0.515, 
          arrow = arrow(20L, unit(0.1, "inches"), "last", "closed"), alpha = 1, inherit.aes = FALSE) +
  geom_curve(data = data.frame(x = 8.8, y =4, xend = 8.2, yend = 7),
          mapping = aes(x = x, y = y, xend = xend, yend = yend), curvature = -0.515, 
          arrow = arrow(20L, unit(0.1, "inches"), "last", "closed"), alpha = 1, inherit.aes = FALSE)

# insert ggplot code

Day 6 florence

my plot
r code

# insert ggplot code

Day 7 outliers

my plot
r code

# insert ggplot code

Day 8 histogram

my plot
r code

# insert ggplot code

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