EU Digital Skills Indicator

Cozmina Secula

EU Digital Skills Indicator: 2023 compared to 2021

Outline

1. Install and load packages
2. Load data
3. Data wrangling
4. Data visualization
5. Final visualization
6. Packages used in the project

Install and Load Packages

packages <- c("tidyverse", "ggalt", "ggtext", "glue", "DT")
installed <- packages %in% rownames(installed.packages())

if (any(!installed)) {
  install.packages(packages[!installed])
}

library(tidyverse)
library(ggalt)
library(ggtext)
library(glue)
library(DT)

Load data

Define paths

data_path <- "20240830.csv"

Load data

if (file.exists(data_path)) {
  df <- read.csv(data_path)
} else {
  stop("Data file is not available.")
}

Data wrangling

Clean and transform the data

digital_skills_summary <- df |>
  # Keep only "at least basic skills" for Overall Digital Skills
  filter(!indicator %in% c("Individuals with above basic overall digital skills (all five component indicators are at above basic level)",
                           "Individuals with basic overall digital skills (all five component indicators are at basic or above basic level, without being all above basic)"
  )) |>
  # Extract `digital skills` and `indicator level` from the `indicator` column
  mutate(
    digital_skills = case_when(
      str_detect(indicator, "communication and collaboration") ~ "Communication and Collaboration Skills",
      str_detect(indicator, "digital content creation") ~ "Digital Content Creation Skills",
      str_detect(indicator, "information and data literacy") ~ "Information and Data Literacy Skills",
      str_detect(indicator, "overall digital skills") ~ "Overall Digital Skills",
      str_detect(indicator, "problem solving skills") ~ "Problem Solving Skills",
      str_detect(indicator, "safety skills") ~ "Safety Skills"
    ),
    indicator_level = case_when(
      # Special case for overall digital skills with at least basic level
      str_detect(indicator, "basic overall digital skills \\(all five component indicators are at basic or above basic level\\)") ~
        "At least basic skills",

      # General cases
      str_detect(indicator, "basic or above basic") ~ "Basic or above basic skills",
      str_detect(indicator, "above basic") ~ "Above basic skills",
      str_detect(indicator, "basic") ~ "Basic skills")
  )

country_codes <- data.frame(
  Country = c("Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus", "Czechia", "Denmark",
              "Estonia", "Finland", "France", "Germany", "Greece", "Hungary", "Ireland",
              "Italy", "Latvia", "Lithuania", "Luxembourg", "Malta", "Netherlands",
              "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden", "EU_27"),
  ISO_Code = c("AT", "BE", "BG", "HR", "CY", "CZ", "DK", "EE", "FI", "FR", "DE",
               "GR", "HU", "IE", "IT", "LV", "LT", "LU", "MT", "NL", "PL", "PT", "RO",
               "SK", "SI", "ES", "SE", "EU")
)

Prepare data for visualization

dsi <- digital_skills_summary |>
  filter(digital_skills == "Overall Digital Skills") |>
  select(year, country, value) |>
  pivot_wider(names_from = year, values_from = value) |>
  rename(
    `2021` = `2021`,
    `2023` = `2023`,
    Country = country
  ) |>
  mutate(Change = round((`2023` - `2021`), 2),
         `2021` = round(`2021`, 1),
         `2023` = round(`2023`, 1)) |>
  left_join(country_codes, by = c("Country" = "Country"))

Preview the data

Data Visualization

Base Plot

dsi |>
  mutate(Country = fct_reorder(Country, `2023`)) |>
  ggplot(aes(x = `2021`,
             xend = `2023`,
             y = Country,
             group = Country)) +
  geom_dumbbell(colour = "grey80",
                size = 3,
                colour_xend = "#9D1B1FFF",
                colour_x = "#33645FFF",
                alpha = 0.7,
                dot_guide = TRUE,
                dot_guide_size = 0.15)

Adding Custom X Axis Labels

dsi |>
  mutate(Country = fct_reorder(Country, `2023`)) |>
  ggplot(aes(x = `2021`,
             xend = `2023`,
             y = Country,
             group = Country)) +
  geom_dumbbell(colour = "grey80",
                size = 3,
                colour_xend = "#9D1B1FFF",
                colour_x = "#33645FFF",
                alpha = 0.7,
                dot_guide = TRUE,
                dot_guide_size = 0.15) +
  scale_x_continuous(labels = scales::percent_format(scale = 1),
                     limits = c(0, 100),
                     breaks = seq(0, 100, by = 20))

Adding Title, Subtitle and Caption

title <- glue::glue("56% of people in the EU have at least basic digital skills, <br>with a goal of 80% by 2030")
subtitle <- glue::glue("Digital Skills: <span style='color:#9D1B1FB3'><b>2023</b></span> compared to <span style='color:#33645FB3'><b>2021</b></span>")
caption <- paste0("**Graphic**: Cozmina Secula<br>**Data**: Eurostat, EU survey on the use of ICT in households and by individuals (2023)")

dsi |>
  mutate(Country = fct_reorder(Country, `2023`)) |>
  ggplot(aes(x = `2021`,
             xend = `2023`,
             y = Country,
             group = Country)) +
  geom_dumbbell(colour = "grey80",
                size = 3,
                colour_xend = "#9D1B1FFF",
                colour_x = "#33645FFF",
                alpha = 0.7,
                dot_guide = TRUE,
                dot_guide_size = 0.15) +
  scale_x_continuous(labels = scales::percent_format(scale = 1),
                     limits = c(0, 100),
                     breaks = seq(0, 100, by = 20)) +
  theme_minimal() +
  labs(
    title = title,
    subtitle = subtitle,
    caption = caption
  )

Adding Custom Theme

dsi |>
  mutate(Country = fct_reorder(Country, `2023`)) |>
  ggplot(aes(x = `2021`,
             xend = `2023`,
             y = Country,
             group = Country)) +
  geom_dumbbell(colour = "grey80",
                size = 3,
                colour_xend = "#9D1B1FFF",
                colour_x = "#33645FFF",
                alpha = 0.7,
                dot_guide = TRUE,
                dot_guide_size = 0.15) +
  scale_x_continuous(labels = scales::percent_format(scale = 1),
                     limits = c(0, 100),
                     breaks = seq(0, 100, by = 20)) +
  theme_minimal() +
  labs(
    title = title,
    subtitle = subtitle,
    caption = caption
  ) +
  theme(
    axis.title = element_blank(),
    panel.grid.major.y = element_blank(),
    axis.ticks.x = element_line(linewidth = 0.5, color = "#414040"),
    axis.ticks.length = unit(.25, "cm"),
    plot.title = element_markdown(color = "#414040",
                                  size = 28,
                                  family = "Georgia",
                                  face = "bold",
                                  margin = margin(0, 0, 12, 0)),
    plot.title.position = "plot",
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    axis.line.y.left = element_line(linewidth = 1),
    axis.line.x.bottom = element_line(linewidth = 1),
    axis.text = element_text(family = "Georgia",
                             color = "#414040",
                             size = 15,
                             face = "bold",
                             hjust = 0.5),
    plot.subtitle = element_textbox_simple(size = 24,
                                           vjust = 1,
                                           margin = margin(0, 0, 12, 0),
                                           color = "#646369",
                                           family = "Georgia"),
    plot.caption = element_markdown(color = "#828282",
                                    size = 13,
                                    hjust = 0,
                                    family = "Georgia",
                                    margin = margin(12, 0, 0, 0)),
    plot.caption.position = "plot"
  )

Adding Custom Text

callout_1 <- paste0("While some countries <br> (Hungary, Czechia) <br> have made substantial progress, <br> others have not improved <br>as much or at all (Romania, Greece), <br>and the gap between higher- and <br>lower-performing countries <br> may widen.")
callout_2 <- paste0("In 2023, only 55.6% of EU citizens <br> had at least basic digital skills,<br> up from 53.9% in 2021. <br>The EU goal for 2030 is <br>at least 80% of individuals aged 16-74 have <br>at least basic digital skills.")

dsi |>
  mutate(Country = fct_reorder(Country, `2023`)) |>
  ggplot(aes(x = `2021`,
             xend = `2023`,
             y = Country,
             group = Country)) +
  geom_dumbbell(colour = "grey80",
                size = 3,
                colour_xend = "#9D1B1FFF",
                colour_x = "#33645FFF",
                alpha = 0.7,
                dot_guide = TRUE,
                dot_guide_size = 0.15) +
  scale_x_continuous(labels = scales::percent_format(scale = 1),
                     limits = c(0, 100),
                     breaks = seq(0, 100, by = 20)) +
  theme_minimal() +
  labs(
    title = title,
    subtitle = subtitle,
    caption = caption
  ) +
  theme(
    axis.title = element_blank(),
    panel.grid.major.y = element_blank(),
    axis.ticks.x = element_line(linewidth = 0.5, color = "#414040"),
    axis.ticks.length = unit(.25, "cm"),
    plot.title = element_markdown(color = "#414040",
                                  size = 28,
                                  family = "Georgia",
                                  face = "bold",
                                  margin = margin(0, 0, 12, 0)),
    plot.title.position = "plot",
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    axis.line.y.left = element_line(linewidth = 1),
    axis.line.x.bottom = element_line(linewidth = 1),
    axis.text = element_text(family = "Georgia",
                             color = "#414040",
                             size = 15,
                             face = "bold",
                             hjust = 0.5),
    plot.subtitle = element_textbox_simple(size = 24,
                                           vjust = 1,
                                           margin = margin(0, 0, 12, 0),
                                           color = "#646369",
                                           family = "Georgia"),
    plot.caption = element_markdown(color = "#828282",
                                    size = 13,
                                    hjust = 0,
                                    family = "Georgia",
                                    margin = margin(12, 0, 0, 0)),
    plot.caption.position = "plot"
  ) +
  geom_richtext(
    aes(x = 94, y = 1, label = "% of individuals"),
    size = 5.5, lineheight = 1.2, family = "Georgia",
    color = "#414040", label.colour = NA, fill = NA) +

  geom_richtext(
    aes(x = 70, y = 21, label = callout_1),
    family = "Georgia", size = 5.5, lineheight = 1.2,
    color = "#414040", hjust = 0, vjust = 1.03,
    label.color = NA, fill = NA) +

  geom_richtext(
    aes(x = 60, y = 10, label = callout_2),
    family = "Georgia", size = 5.5, lineheight = 1.2,
    color = "#414040", hjust = 0, vjust = 1.03,
    label.color = NA, fill = NA) +

  annotate("curve", x = 76, xend = 58, y = 9.7, yend = 12,
           curvature = 0.35, angle = 60, color = "grey60",
           linewidth = .4, arrow = arrow(type = "closed", length = unit(.08, "inches"))) +

  # Highlight countries with significant improvements
  geom_point(data = dsi |>
               filter(Country %in% c("Hungary", "Czechia", "Romania", "Greece")),
             aes(x = `2023`, y = Country),
             color = "#9D1B1FFF", size = 7.5, alpha = 0.4)

Final Visualization

Packaged used in this project

• tidyverse: A collection of R packages designed for data science, making it easy to manipulate and visualize data. Learn more

• ggalt: Extends ggplot2 with additional geoms for enhanced data visualization, including dumbbell charts and lollipop charts. Learn more

• ggtext: Provides tools for improved text rendering in ggplot2 visuals, including markdown, HTML, and custom formatting. Learn more

• glue: A package for combining strings and variables in R efficiently. Learn more

• DT: A package for creating interactive tables in R using the DataTables library, providing easy-to-use and flexible tables. Learn more