EU Salary Race: Average Full-Time Adjusted Wages

What is this?

Eurostat publishes yearly average full-time adjusted salaries (nama_10_fte) for every EU/EFTA country. This vignette pulls that series directly from the Eurostat API and turns it into three progressively more polished visualisations: an interactive time series, an animated bar chart, and a “bar chart race” — with Greece highlighted in red against the rest of Europe in gray.

Packages

Code

# install.packages(c("dplyr", "tidyr", "countrycode", "plotly",
#                     "eurostat", "gganimate", "gifski", "scales", "ragg"))

library(dplyr)
library(tidyr)
library(countrycode)  # geo codes -> country names / ISO codes
library(plotly)       # interactive charts
library(eurostat)     # Eurostat data API
library(gganimate)    # animation
library(gifski)       # GIF renderer
library(scales)       # axis formatting

The data

get_eurostat() downloads the raw dataset table by its Eurostat code. We keep only country-level rows (dropping the EU27_2020 and EA20 aggregates), derive a plain-language country name and ISO code with countrycode, and rank countries by salary within each year.

Code

df_eusalary <- get_eurostat("nama_10_fte", filters = list(unit = "EUR"))
df_eusalary <- df_eusalary[!df_eusalary$geo %in% c("EU27_2020", "EA20"), ]

df_eusalary$year    <- lubridate::year(df_eusalary$time)
df_eusalary$country <- countrycode(df_eusalary$geo, origin = "eurostat", destination = "country.name")
df_eusalary$iso     <- countrycode(df_eusalary$geo, origin = "eurostat", destination = "iso2c")

df_eusalary$rank  <- NA
df_eusalary$color <- NA
for (i in unique(df_eusalary$year)) {
  df_eusalary[df_eusalary$year %in% i, "rank"] <-
    rank(-df_eusalary[df_eusalary$year %in% i, "values"], ties.method = "min")
}

df_eusalary[df_eusalary$country %in% "Greece", "color"]  <- "#E74C3C"
df_eusalary[!df_eusalary$country %in% "Greece", "color"] <- "gray70"

title <- "Average full time adjusted salary [nama_10_fte]"

head(df_eusalary[, c("country", "year", "values", "rank")], 12)

# A tibble: 12 × 4
   country  year values  rank
   <chr>   <dbl>  <int> <int>
 1 Belgium  1995  27855     3
 2 Belgium  1996  27875     3
 3 Belgium  1997  27774     3
 4 Belgium  1998  28200     3
 5 Belgium  1999  29691     3
 6 Belgium  2000  30506     4
 7 Belgium  2001  31777     3
 8 Belgium  2002  32985     3
 9 Belgium  2003  33678     3
10 Belgium  2004  34447     4
11 Belgium  2005  35009     4
12 Belgium  2006  36327     4

Each row is one country in one year, with:

Column	Description
`country`	Country name (derived from Eurostat’s `geo` code)
`year`	Year of observation
`values`	Average full-time adjusted salary, in EUR
`rank`	Country’s salary rank within that year (1 = highest)
`color`	Highlight color — red for Greece, gray for everyone else

Building the visualisations

Step 1 — interactive time series

A line per country lets you trace how each economy moved over time, and hovering reveals the exact value.

Code

colors <- c("#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231", "#911eb4", "#46f0f0",
            "#f032e6", "#d2f53c", "#fabebe", "#008080", "#e6beff", "#aa6e28", "#fffac8",
            "#800000", "#aaffc3", "#808000", "#ffd8b1", "#000080", "#808080", "#ffffff",
            "#000000")

plot_ly(df_eusalary[df_eusalary$values > 0, ],
        x = ~year,
        y = ~values,
        color = ~country,
        mode = "lines+markers",
        type = "scatter",
        text = ~paste("Year:", year,
                      "<br>Value:", values,
                      "<br>Country Name:", country),
        hoverinfo = "text",
        colors = colors,
        showlegend = TRUE,
        marker = list(size = 8)) %>%
  plotly::layout(title = "",
                 autosize = TRUE,
                 margin = list(l = 50, r = 50, b = 50, t = 50, pad = 0),
                 legend = list(orientation = "vertical", xanchor = "right", x = 30, y = 1),
                 xaxis = list(title = "Year", tickangle = -90),
                 yaxis = list(title = ""),
                 font = list(size = 20, color = "gray25", weight = "bold"))

Step 2 — animated bar chart

Sliding a frame argument to plot_ly turns a static bar chart into a play/pause animation stepping through each year.

Code

plot_ly(df_eusalary,
        x = ~country,
        y = ~values,
        frame = ~year,
        ids = ~country,
        type = "bar",
        text = ~paste("Year:", year,
                      "<br>Value:", values,
                      "<br>Country:", country),
        hoverinfo = "text",
        colors = colors,
        showlegend = FALSE) %>%
  layout(
    title  = title,
    xaxis  = list(title = "", tickangle = -90),
    yaxis  = list(title = "Euro"),
    font   = list(size = 20, color = "gray25", weight = "bold"),
    margin = list(l = 50, r = 50, b = 200, t = 100)
  ) %>%
  animation_opts(
    frame = 500,
    transition = 500,
    easing = "linear",
    redraw = FALSE,
    mode = "immediate"
  )

Step 3 — bar chart race, ranked

Instead of plotting by country name, we plot by rank, so the bars physically reorder every year as economies overtake each other. Greece is layered on top in red so it’s always visible against the gray field.

Code

plot_ly() %>%
  add_bars(
    data         = df_eusalary[df_eusalary$color == "gray70", ],
    x            = ~rank,
    y            = ~values,
    frame        = ~year,
    ids          = ~rank,
    marker       = list(color = "gray70"),
    text         = ~country,
    hoverinfo    = "text",
    textposition = "outside",
    textangle    = 90,
    showlegend   = FALSE
  ) %>%
  add_bars(
    data         = df_eusalary[df_eusalary$color %in% "#E74C3C", ],
    x            = ~rank,
    y            = ~values,
    frame        = ~year,
    ids          = ~rank,
    marker       = list(color = "#E74C3C"),
    text         = ~country,
    hoverinfo    = "text",
    textposition = "outside",
    textangle    = 90,
    showlegend   = FALSE
  ) %>%
  layout(
    xaxis  = list(title = "Rank", tickmode = "array"),
    yaxis  = list(title = "Euro"),
    title  = title,
    margin = list(l = 50, r = 50, b = 200, t = 100),
    font   = list(size = 20, color = "gray25", weight = "bold")
  ) %>%
  animation_opts(frame = 1000, transition = 0, easing = "linear", redraw = TRUE)

Step 4 — dark themed GIF with `gganimate`

The interactive versions above are great for exploring in a browser, but a rendered GIF is easier to share. ggplot2 + gganimate recreates the same ranked race as a horizontal bar chart with a dark background, country labels, and a large year watermark.

Code

p <- ggplot(df_eusalary, aes(x = rank, y = values, fill = color, group = country)) +
  geom_col(width = 0.85, show.legend = FALSE) +
  geom_text(aes(y = 0, label = country, color = color),
            hjust = 1, nudge_y = -400,
            size = 3.8, fontface = "bold", show.legend = FALSE) +
  geom_text(aes(label = comma(values, accuracy = 1)),
            hjust = 0, nudge_y = 400,
            color = "gray80", size = 3.2) +
  scale_fill_identity() +
  scale_color_identity() +
  scale_x_reverse(breaks = NULL) +
  scale_y_continuous(
    labels = comma,
    breaks = seq(0, 100000, by = 20000),
    limits = c(-8000, NA),
    expand = expansion(mult = c(0, 0.08))
  ) +
  geom_text(
    data = df_eusalary %>% dplyr::distinct(year),
    aes(x = 23, y = 85000, label = year),
    inherit.aes = FALSE,
    hjust = 1, vjust = 1,
    size = 20, color = "white", alpha = 0.15,
    fontface = "bold"
  ) +
  coord_flip(clip = "off") +
  labs(title   = title,
       x       = NULL,
       y       = "Euros",
       caption = "Source: Eurostat  |  Greece highlighted in red") +
  theme_minimal(base_size = 10) +
  theme(
    plot.background    = element_rect(fill = "#0F1923", color = NA),
    panel.background   = element_rect(fill = "#0F1923", color = NA),
    panel.grid.major.x = element_line(color = "#1E2D3D", linewidth = 0.4),
    panel.grid.major.y = element_blank(),
    panel.grid.minor   = element_blank(),
    panel.grid.minor.x = element_line(color = "#1E2D3D", linewidth = 0.4),
    axis.text.x   = element_text(color = "#90A4AE", size = 10),
    axis.text.y   = element_blank(),
    plot.title    = element_text(color = "white", face = "bold", size = 10,
                                  margin = margin(12, 0, 2, 0)),
    plot.subtitle = element_text(color = "gray80", face = "bold", size = 10,
                                  hjust = 0.98,
                                  margin = margin(0, 0, 6, 0)),
    plot.caption  = element_text(color = "#546E7A", size = 8, hjust = 1,
                                  margin = margin(8, 0, 8, 0)),
    plot.margin   = margin(10, 30, 10, 120)) +
  transition_states(year, transition_length = 0, state_length = 5) +
  ease_aes("cubic-in-out")

animate(p,
        nframes  = 100,
        fps      = 10,
        duration = 20,
        width    = 900,
        height   = 800,
        units    = "px",
        res      = 100,   # set explicitly: when knitting, gganimate otherwise uses the chunk dpi (300 x retina 2)
        renderer = gifski_renderer(paste0(directory,"eu_salary_race.gif")))

Note: rendering the GIF takes a couple of minutes and requires an internet connection to reach the Eurostat API. The GIF is written to the working directory (getwd()).

Code

# knitr::include_graphics(paste0(directory,"eu_salary_race.gif"))

What the chart shows

Northern and Western European economies (e.g. Denmark, Luxembourg, Ireland) consistently occupy the top ranks across the whole period.
Greece sits in the lower half of the ranking throughout, and its relative position (highlighted in red) makes the post-2010 economic crisis visible as a plateau while other countries continue climbing.
Ranks shuffle more among mid-table countries than at the extremes — the top and bottom of the distribution are comparatively stable year to year.

Customising it

Highlight a different country
Codedf_eusalary[df_eusalary$country %in% "Portugal", "color"]  <- "#E74C3C"
df_eusalary[!df_eusalary$country %in% "Portugal", "color"] <- "gray70"

Change the animation speed

Code

animate(p, nframes = 50, fps = 5, duration = 10, ...)   # faster / shorter
animate(p, nframes = 200, fps = 20, duration = 30, ...) # smoother / longer

Save as MP4 instead of GIF

Code

# install.packages("av")
animate(p, renderer = av_renderer("eu_salary_race.mp4"), fps = 24, width = 1280, height = 720)

--- title: "EU Salary Race: Average Full-Time Adjusted Wages" author: "" output: html_document: css: styles.css theme: flatly highlight: tango toc: true toc_float: true code_folding: show self_contained: true vignette: > %\VignetteIndexEntry{EU Salary Race} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} directory<-if (rstudioapi::isAvailable()) paste0(dirname(rstudioapi::getActiveDocumentContext()$path),"/") else "" source(paste0(directory, "R/code_options.R")) source(paste0(directory, "R/code_rwf.R")) ``` ## What is this? Eurostat publishes yearly **average full-time adjusted salaries** (`nama_10_fte`) for every EU/EFTA country. This vignette pulls that series directly from the Eurostat API and turns it into three progressively more polished visualisations: an interactive time series, an animated bar chart, and a "bar chart race" — with Greece highlighted in red against the rest of Europe in gray. --- ## Packages ```{r packages} # install.packages(c("dplyr", "tidyr", "countrycode", "plotly", # "eurostat", "gganimate", "gifski", "scales", "ragg")) library(dplyr) library(tidyr) library(countrycode) # geo codes -> country names / ISO codes library(plotly) # interactive charts library(eurostat) # Eurostat data API library(gganimate) # animation library(gifski) # GIF renderer library(scales) # axis formatting ``` --- ## The data `get_eurostat()` downloads the raw dataset table by its Eurostat code. We keep only country-level rows (dropping the `EU27_2020` and `EA20` aggregates), derive a plain-language country name and ISO code with `countrycode`, and rank countries by salary within each year. ```{r data} df_eusalary <- get_eurostat("nama_10_fte", filters = list(unit = "EUR")) df_eusalary <- df_eusalary[!df_eusalary$geo %in% c("EU27_2020", "EA20"), ] df_eusalary$year <- lubridate::year(df_eusalary$time) df_eusalary$country <- countrycode(df_eusalary$geo, origin = "eurostat", destination = "country.name") df_eusalary$iso <- countrycode(df_eusalary$geo, origin = "eurostat", destination = "iso2c") df_eusalary$rank <- NA df_eusalary$color <- NA for (i in unique(df_eusalary$year)) { df_eusalary[df_eusalary$year %in% i, "rank"] <- rank(-df_eusalary[df_eusalary$year %in% i, "values"], ties.method = "min") } df_eusalary[df_eusalary$country %in% "Greece", "color"] <- "#E74C3C" df_eusalary[!df_eusalary$country %in% "Greece", "color"] <- "gray70" title <- "Average full time adjusted salary [nama_10_fte]" head(df_eusalary[, c("country", "year", "values", "rank")], 12) ``` Each row is one country in one year, with: | Column | Description | |--------|-------------| | `country` | Country name (derived from Eurostat's `geo` code) | | `year` | Year of observation | | `values` | Average full-time adjusted salary, in EUR | | `rank` | Country's salary rank within that year (1 = highest) | | `color` | Highlight color — red for Greece, gray for everyone else | --- ## Building the visualisations ### Step 1 — interactive time series A line per country lets you trace how each economy moved over time, and hovering reveals the exact value. ```{r timeseries} colors <- c("#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231", "#911eb4", "#46f0f0", "#f032e6", "#d2f53c", "#fabebe", "#008080", "#e6beff", "#aa6e28", "#fffac8", "#800000", "#aaffc3", "#808000", "#ffd8b1", "#000080", "#808080", "#ffffff", "#000000") plot_ly(df_eusalary[df_eusalary$values > 0, ], x = ~year, y = ~values, color = ~country, mode = "lines+markers", type = "scatter", text = ~paste("Year:", year, "<br>Value:", values, "<br>Country Name:", country), hoverinfo = "text", colors = colors, showlegend = TRUE, marker = list(size = 8)) %>% plotly::layout(title = "", autosize = TRUE, margin = list(l = 50, r = 50, b = 50, t = 50, pad = 0), legend = list(orientation = "vertical", xanchor = "right", x = 30, y = 1), xaxis = list(title = "Year", tickangle = -90), yaxis = list(title = ""), font = list(size = 20, color = "gray25", weight = "bold")) ``` ### Step 2 — animated bar chart Sliding a `frame` argument to `plot_ly` turns a static bar chart into a play/pause animation stepping through each year. ```{r animated_bar} plot_ly(df_eusalary, x = ~country, y = ~values, frame = ~year, ids = ~country, type = "bar", text = ~paste("Year:", year, "<br>Value:", values, "<br>Country:", country), hoverinfo = "text", colors = colors, showlegend = FALSE) %>% layout( title = title, xaxis = list(title = "", tickangle = -90), yaxis = list(title = "Euro"), font = list(size = 20, color = "gray25", weight = "bold"), margin = list(l = 50, r = 50, b = 200, t = 100) ) %>% animation_opts( frame = 500, transition = 500, easing = "linear", redraw = FALSE, mode = "immediate" ) ``` ### Step 3 — bar chart race, ranked Instead of plotting by country name, we plot by `rank`, so the bars physically reorder every year as economies overtake each other. Greece is layered on top in red so it's always visible against the gray field. ```{r bar_race} plot_ly() %>% add_bars( data = df_eusalary[df_eusalary$color == "gray70", ], x = ~rank, y = ~values, frame = ~year, ids = ~rank, marker = list(color = "gray70"), text = ~country, hoverinfo = "text", textposition = "outside", textangle = 90, showlegend = FALSE ) %>% add_bars( data = df_eusalary[df_eusalary$color %in% "#E74C3C", ], x = ~rank, y = ~values, frame = ~year, ids = ~rank, marker = list(color = "#E74C3C"), text = ~country, hoverinfo = "text", textposition = "outside", textangle = 90, showlegend = FALSE ) %>% layout( xaxis = list(title = "Rank", tickmode = "array"), yaxis = list(title = "Euro"), title = title, margin = list(l = 50, r = 50, b = 200, t = 100), font = list(size = 20, color = "gray25", weight = "bold") ) %>% animation_opts(frame = 1000, transition = 0, easing = "linear", redraw = TRUE) ``` ### Step 4 — dark themed GIF with `gganimate` The interactive versions above are great for exploring in a browser, but a rendered GIF is easier to share. `ggplot2` + `gganimate` recreates the same ranked race as a horizontal bar chart with a dark background, country labels, and a large year watermark. ```{r gif, eval=TRUE} p <- ggplot(df_eusalary, aes(x = rank, y = values, fill = color, group = country)) + geom_col(width = 0.85, show.legend = FALSE) + geom_text(aes(y = 0, label = country, color = color), hjust = 1, nudge_y = -400, size = 3.8, fontface = "bold", show.legend = FALSE) + geom_text(aes(label = comma(values, accuracy = 1)), hjust = 0, nudge_y = 400, color = "gray80", size = 3.2) + scale_fill_identity() + scale_color_identity() + scale_x_reverse(breaks = NULL) + scale_y_continuous( labels = comma, breaks = seq(0, 100000, by = 20000), limits = c(-8000, NA), expand = expansion(mult = c(0, 0.08)) ) + geom_text( data = df_eusalary %>% dplyr::distinct(year), aes(x = 23, y = 85000, label = year), inherit.aes = FALSE, hjust = 1, vjust = 1, size = 20, color = "white", alpha = 0.15, fontface = "bold" ) + coord_flip(clip = "off") + labs(title = title, x = NULL, y = "Euros", caption = "Source: Eurostat | Greece highlighted in red") + theme_minimal(base_size = 10) + theme( plot.background = element_rect(fill = "#0F1923", color = NA), panel.background = element_rect(fill = "#0F1923", color = NA), panel.grid.major.x = element_line(color = "#1E2D3D", linewidth = 0.4), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), panel.grid.minor.x = element_line(color = "#1E2D3D", linewidth = 0.4), axis.text.x = element_text(color = "#90A4AE", size = 10), axis.text.y = element_blank(), plot.title = element_text(color = "white", face = "bold", size = 10, margin = margin(12, 0, 2, 0)), plot.subtitle = element_text(color = "gray80", face = "bold", size = 10, hjust = 0.98, margin = margin(0, 0, 6, 0)), plot.caption = element_text(color = "#546E7A", size = 8, hjust = 1, margin = margin(8, 0, 8, 0)), plot.margin = margin(10, 30, 10, 120)) + transition_states(year, transition_length = 0, state_length = 5) + ease_aes("cubic-in-out") animate(p, nframes = 100, fps = 10, duration = 20, width = 900, height = 800, units = "px", res = 100, # set explicitly: when knitting, gganimate otherwise uses the chunk dpi (300 x retina 2) renderer = gifski_renderer(paste0(directory,"eu_salary_race.gif"))) ``` > **Note:** rendering the GIF takes a couple of minutes and requires an internet connection to reach the Eurostat API. The GIF is written to the working directory (`getwd()`). ```{r show_gif, echo=TRUE} # knitr::include_graphics(paste0(directory,"eu_salary_race.gif")) ``` --- ## What the chart shows - Northern and Western European economies (e.g. Denmark, Luxembourg, Ireland) consistently occupy the top ranks across the whole period. - Greece sits in the lower half of the ranking throughout, and its relative position (highlighted in red) makes the post-2010 economic crisis visible as a plateau while other countries continue climbing. - Ranks shuffle more among mid-table countries than at the extremes — the top and bottom of the distribution are comparatively stable year to year. --- ## Customising it ### Highlight a different country ```{r custom_highlight, eval=FALSE} df_eusalary[df_eusalary$country %in% "Portugal", "color"] <- "#E74C3C" df_eusalary[!df_eusalary$country %in% "Portugal", "color"] <- "gray70" ``` ### Change the animation speed ```{r custom_speed, eval=FALSE} animate(p, nframes = 50, fps = 5, duration = 10, ...) # faster / shorter animate(p, nframes = 200, fps = 20, duration = 30, ...) # smoother / longer ``` ### Save as MP4 instead of GIF ```{r custom_mp4, eval=FALSE} # install.packages("av") animate(p, renderer = av_renderer("eu_salary_race.mp4"), fps = 24, width = 1280, height = 720) ``` --- ## Further reading - [Eurostat `nama_10_fte` dataset](https://ec.europa.eu/eurostat/databrowser/view/nama_10_fte/default/table) — average full-time adjusted salary series - [`eurostat` R package](https://ropengov.github.io/eurostat/) — programmatic access to Eurostat data - [gganimate documentation](https://gganimate.com/) — all transition types and rendering options --- *Rendered with R `r getRversion()` · plotly · ggplot2 · gganimate · eurostat*

What is this?

Packages

The data

Building the visualisations

Step 1 — interactive time series

Step 2 — animated bar chart

Step 3 — bar chart race, ranked

Step 4 — dark themed GIF with gganimate

What the chart shows

Customising it

Highlight a different country

Change the animation speed

Save as MP4 instead of GIF

Further reading

Step 4 — dark themed GIF with `gganimate`