About

This chart is about highlighting specific lines in a bump chart. The chart shows the ranking of European countries according to their share of gross fixed capital formation (GFCF) in % of GDP.

This bump chart is the work of Matthias Schnetzer. Thanks to him for sharing his work!

Libraries

This chart requires a few libraries, especially the ggbump package. This package provides a geom_bump() function that allows to build ggbump charts easily.

Install it with install.packages("ggbump").

# Library
library(tidyverse)
library(countrycode)
library(rdbnomics)
library(ggbump)
library(MetBrewer)
library(scales)

Dataset

The dataset is retrieved from the rdbnomics package and is from the AMECO database.

It contains the share of gross fixed capital formation (GFCF) in % of GDP for a selection of European countries. The data is available for the years 2000, 2010 and 2020.

This pre-processing step is necessary to rank the countries according to their share of GFCF in % of GDP. The rank() function is used to create a ranking variable for each year.

The countrycode() function is used to convert the ISO3 country codes to country names in German.

allcty <- codelist |> filter(!is.na(eu28)) |> pull(iso3c) |> tolower()

gfcf_private <- rdb(provider_code = "AMECO", dataset_code = "UIGP", 
             dimensions = list(geo = allcty, unit = "mrd-ecu-eur"))
gdp <- rdb(provider_code = "AMECO", dataset_code = "UVGD",
           dimensions = list(geo = allcty, unit = "mrd-ecu-eur"))

data <- bind_rows(gfcf_private, gdp) |> 
  mutate(variable = case_when(
    str_detect(dataset_name, "private") ~ "GFCF_p",
    TRUE ~ "GDP")) |> 
  select(geo, variable, period, value) |> 
  pivot_wider(names_from = variable, values_from = value) |> 
  mutate(share_p = GFCF_p/GDP*100, year = year(period), geo = toupper(geo))

ranked <- data |> 
  filter(year %in% c(2000, 2010, 2020)) |> 
  mutate(ranking = rank(desc(share_p), ties.method = "first"), .by = year) |> 
  mutate(ctry = countrycode(geo, "iso3c", "country.name.de"),
         ctry = ifelse(geo == "CZE", "Tschechien", ctry))

selected <- c("AUT", "DEU", "IRL", "FRA")

Simple bump plot

Let’s start by creating a simple bump plot. The geom_bump() function is used to create the chart. The smooth argument is used to control the smoothness of the lines.

ranked |>
  ggplot(aes(x = year, y = ranking, group = geo)) +
  geom_bump(linewidth = 0.6, color = "gray90", smooth = 6) +
  geom_bump(aes(color = geo), linewidth = 0.8, smooth = 6,
                    data = ~. |> filter(geo %in% selected))

Change order and add individual points

The geom_point() function is utilized to add individual points to the chart, while the geom_text() function is employed to add labels to the chart.

The slice_max() function is used to select the top country for each year. The filter() function is used to select the countries that are highlighted in the chart.

ranked |>
  ggplot(aes(x = year, y = ranking, group = geo)) +
  geom_bump(linewidth = 0.6, color = "gray90", smooth = 6) +
  geom_bump(aes(color = geo), linewidth = 0.8, smooth = 6,
                    data = ~. |> filter(geo %in% selected)) +
  geom_point(color = "white", size = 4) +
  geom_point(color = "gray90", size = 2) +
  geom_point(aes(color = geo), size = 2, 
             data = ~. |> filter(geo %in% selected)) +
  geom_text(aes(label = ctry), x = 2021, hjust = 0,
            color = "gray50", family = "Roboto Condensed", size = 3.5,
            data = ranked |> slice_max(year, by = geo) |> 
              filter(!geo %in% selected)) +
  geom_text(aes(label = ctry), x = 2021, hjust = 0,
            color = "black", family = "Roboto Condensed", size = 3.5,
            data = ranked |> slice_max(year, by = geo) |> 
              filter(geo %in% selected)) +
  scale_color_manual(values = met.brewer("Juarez")) +
  scale_x_continuous(limits = c(1999.8, 2027), expand = c(0.01,0),
                     breaks = c(2000, 2010, 2020)) +
  scale_y_reverse(breaks = c(25,20,15,10,5,1), expand = c(0.02,0),
                  labels = number_format(suffix = "."))

Change theme and add annotations

The theme_minimal() function is employed to alter the chart’s theme. Conversely, the theme() function is utilized to eliminate grid lines and the legend.

The labs() function is designed to incorporate a title, subtitle, and caption into the chart.

plot <- ranked |>
  ggplot(aes(x = year, y = ranking, group = geo)) +
  geom_bump(linewidth = 0.6, color = "gray90", smooth = 6) +
  geom_bump(aes(color = geo), linewidth = 0.8, smooth = 6,
                    data = ~. |> filter(geo %in% selected)) +
  geom_point(color = "white", size = 4) +
  geom_point(color = "gray90", size = 2) +
  geom_point(aes(color = geo), size = 2, 
             data = ~. |> filter(geo %in% selected)) +
  geom_text(aes(label = ctry), x = 2021, hjust = 0,
            color = "gray50", family = "Roboto Condensed", size = 3.5,
            data = ranked |> slice_max(year, by = geo) |> 
              filter(!geo %in% selected)) +
  geom_text(aes(label = ctry), x = 2021, hjust = 0,
            color = "black", family = "Roboto Condensed", size = 3.5,
            data = ranked |> slice_max(year, by = geo) |> 
              filter(geo %in% selected)) +
  scale_color_manual(values = met.brewer("Juarez")) +
  scale_x_continuous(limits = c(1999.8, 2027), expand = c(0.01,0),
                     breaks = c(2000, 2010, 2020)) +
  scale_y_reverse(breaks = c(25,20,15,10,5,1), expand = c(0.02,0),
                  labels = number_format(suffix = ".")) +
  labs(x = NULL, y = NULL,
       title = toupper("Investitionstätigkeit in der EU"),
       subtitle = "Ranking nach Bruttoanlageinvestitionen in % des BIP",
       caption = "Anm.: Investionen des privaten Sektors. Daten: AMECO Grafik: @matschnetzer") +
  theme_minimal(base_family = "Roboto Condensed", base_size = 12) +
  theme(legend.position = "none",
        panel.grid = element_blank(),
        plot.title.position = "plot",
        plot.title = element_text(size = 14, hjust = .5),
        plot.subtitle = element_text(size = 10, hjust = .5),
        plot.caption = element_text(size = 8))

ggsave("img/graph/web-bump-plot-with-highlights.png", width = 4, height = 8, dpi = 320, bg = "white")

Bump plot with highlights