Brazilian Companies

business
finance
brazil
What types of firms control the most capital?
Author

Cole Baril

Published

January 27, 2026

Load Packages

require(pacman)
p_load(tidytuesdayR, magick, tidyverse, janitor, here, trashpanda, glue, ggtext, ggside, scales, ggridges, ggtext, ggview)

Load Data

tuesdata <- tidytuesdayR::tt_load('2026-01-27')
companies <- tuesdata$companies
legal_nature <- tuesdata$legal_nature
qualifications <- tuesdata$qualifications
size <- tuesdata$size

Data Cleaning

lorenz_df <- companies |> 
  filter(!is.na(capital_stock),
         capital_stock > 0,
         !is.na(owner_qualification)) |> 
  group_by(owner_qualification) |> 
  ungroup() |> 
  
  group_by(owner_qualification) |> 
  arrange(capital_stock, .by_group = TRUE) |> 
  mutate(
    firm_rank   = row_number(),
    firm_share  = firm_rank / n(),
    capital_cum = cumsum(capital_stock),
    capital_share = capital_cum / sum(capital_stock)
  ) |> 
  ungroup()

owner_order <- lorenz_df %>%
  group_by(owner_qualification) %>%
  summarise(
    capital_top_1pct = capital_share[which.min(abs(firm_share - 0.99))]
  ) %>%
  arrange(capital_top_1pct) %>%
  pull(owner_qualification)

lorenz_df <- lorenz_df %>%
  mutate(
    owner_qualification = factor(owner_qualification,
                                 levels = owner_order)
  )

Share of Capital

Lorenz Plot

ggplot(
  lorenz_df,
  aes(firm_share, capital_share, colour = owner_qualification)) +
  geom_line(linewidth = 1.3, alpha = 0.95) +
  geom_abline(
    slope = 1,
    intercept = 0,
    linetype = "dashed",
    colour = "grey60",
    linewidth = 0.8) +
  cwb::scale_cwb("discrete_21", type = "d", aesthetics = "colour", name = "Owner qualification") +
  scale_x_continuous(
    labels = percent_format(),
    expand = c(0, 0)) +
  scale_y_continuous(
    labels = percent_format(),
    expand = c(0, 0)) +
  labs(
    title = "A Small Share of Firms Controls Most Declared Capital",
    subtitle = "Cumulative share capital by leadership role for Brazilian companies",
    x = "Cumulative share of companies",
    y = "Cumulative share of total declared capital") +
    theme_cole(remove_grid = TRUE) +
    add_caption_cwb()

Ridgeline Plot

companies_long <- companies |> 
  select(legal_nature, owner_qualification, company_size, capital_stock) |> 
  pivot_longer(!capital_stock, names_to = "group", values_to = "category") |> 
  mutate(
    group = str_replace(group, "_", " "),
    group = str_to_sentence(group),
    category = str_remove_all(category, "\\(.*?\\)"),
    category = str_trim(category),
    category = str_replace(category, "-", " "),
    category = str_to_sentence(category),
    category = str_replace(category, "llc", "LLC"),
    category = str_replace(category, "brazil", "Brazil")
  ) |> 
  mutate(capital_stock = capital_stock / 1000000000) |> 
  mutate(n = n(), .by = c(group, category)) |> 
  filter(n >= 5) |> 
  mutate(y = n_distinct(category), .by = group)

summary_data <- companies_long |> 
  summarise(med = median(capital_stock),
            .by = c(group, category)) |> 
  arrange(desc(med))

companies_long$category <- factor(companies_long$category, levels = summary_data$category)


plot2 <- ggplot() +
  geom_density_ridges(
    data = companies_long,
    mapping = aes(x = capital_stock, y = category),
    fill = "cadetblue",
    colour = "cadetblue",
    alpha = 0.6) +
  facet_wrap(~group, scales = "free_y", space = "free_y") +
  scale_x_log10(limits = c(0.00005, 1000),
                breaks = c(0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000),
                labels = function(x) format(x, scientific = FALSE, drop0trailing = TRUE)) +
      scale_y_discrete(
        limits = rev
      ) +
  labs(
    x = "Declared share capital (billions, BRL)", y = NULL,
    title = "Publicly traded Brazilian corporations\nreport highest share of capitol") +
  coord_cartesian(expand = FALSE, clip = "off") +
  theme_cole(base_size = 20, dark = FALSE, remove_grid = TRUE, show_axis_lines = "none") +
  theme(
    plot.margin = margin(10, 20, 10, 10),
    axis.title.x = element_text(margin = margin(t = 5, b = 10)),
    axis.text.y = element_text(margin = margin(r = 0)),
    strip.text = element_text(size = 15 * 1.3)) +
    add_caption_cwb()

# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "brazil_ridgeline.png"

ggsave(plot = plot2, 
       dpi = "screen",
       width = 16,
       height = 18,
       device = ragg::agg_png,
       filename = file.path(current_dir, plot_name))


img <- image_read(file.path(current_dir, plot_name))

img_card <- image_scale(img, "1200x675")           
img_card <- image_extent(
  img_card,
  geometry = "1200x675",
  gravity = "center"
)

# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))

knitr::include_graphics(
  file.path(current_dir, plot_name)
)

References

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