Giving a Thematic Touch to your Interactive Chart

Adding thematic context and visual character to interactive charts.
data-visualization
highcharter
Author

Joshua Kunst

Published

March 3, 2017

Modified

August 13, 2026

Preliminars

Usually (mainly at work) I made a chart and when I present it nobody cares about the style, if the chart comes from an excel spreadsheet, paint or intercative chart, or colors, labels, font, or things I like to care. That’s sad for me but it’s fine: the data/history behind and how you present it is what matters. And surely I’m overreacting.

But hey! That’s not implies you only must do always clean chart or tufte style plots. Sometimes you can play with the topic of your chart and give some thematic touch.

The first example that come to my mind is the Iraq’s bloody toll visualization:

Iraq’s bloody toll

So. We’ll use some resources to try:

  • Add some context of the topic before the viewer read something.
  • Hopefully keep in the viewer’s memory :) in a gooood way.

Keeping the message intact, ie, don’t abuse adding many element so the user don’t lose the main point of the chart.

Example I: Oil Spills

We can reuse the bloody toll effect, using with Oil Spills data.

The ourworldindata.org website have a descriptive study Max Roser.

Max Roser (2016) - ‘Oil Spills’. Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/oil-spills/ [Online Resource]

They start with:

Over the past 4 decades - the time for which we have data - oil spills decreased dramatically. Although oil spills also happen on land, marine oil spills are considered more serious as the spilled oil is less containable

Let’s load the data and make the basic chart.

library(tidyverse) 
library(jsonlite)

json <- read_lines("https://ourworldindata.org/wp-content/uploads/nvd3/nvd3_multiBarChart_Oil/multiBarChart_Oil.html")
json <- json[seq(
  which(str_detect(json, "var xxx")),
  first(which(str_detect(json, "\\}\\]\\;")))
)]

json <- fromJSON(str_replace_all(json, "var xxx = |;$", ""))
json <- transpose(json)

str(json)
List of 2
 $ :List of 2
  ..$ values:'data.frame':  43 obs. of  2 variables:
  .. ..$ x: num [1:43] 0.00 3.16e+10 6.31e+10 9.47e+10 1.26e+11 ...
  .. ..$ y: int [1:43] 30 14 27 31 27 20 26 16 23 32 ...
  ..$ key   : chr ">700 Tonnes"
 $ :List of 2
  ..$ values:'data.frame':  43 obs. of  2 variables:
  .. ..$ x: num [1:43] 0.00 3.16e+10 6.31e+10 9.47e+10 1.26e+11 ...
  .. ..$ y: int [1:43] 7 18 48 28 90 96 67 69 59 60 ...
  ..$ key   : chr "7-700 Tonnes"
dspills <- map_df(json, function(x) {
  df <- as.data.frame(x[["values"]])
  df$key <- x[["key"]]
  tibble::as_tibble(df)
  df
})

glimpse(dspills)
Rows: 86
Columns: 3
$ x   <dbl> 0.00000e+00, 3.15569e+10, 6.31138e+10, 9.46707e+10, 1.26228e+11, 1…
$ y   <int> 30, 14, 27, 31, 27, 20, 26, 16, 23, 32, 13, 7, 4, 13, 8, 8, 7, 10,…
$ key <chr> ">700 Tonnes", ">700 Tonnes", ">700 Tonnes", ">700 Tonnes", ">700 …

The data is ready. So we can make an staked area chart. I used areaspline here to make a liquid effect.

library(highcharter)

hcspills <- hchart(dspills, "areaspline", hcaes(x, y, group = "key")) %>% 
  hc_plotOptions(series = list(stacking = "normal")) %>% 
  hc_xAxis(type = "datetime") %>% 
  hc_title(text = "Number of Oil Spills Over the Past 4 Decades")

hcspills

Yay, the spills are decreasing over time. So we can do:

  • Add a deep sea background.
  • Reverse the yAxis to the give the fall effect.
  • Add a dark colors to simulate the oil.
  • Add the credits for give the serious (? ;) ) touch.
hcspills2 <- hcspills %>% 
  hc_colors(c("#000000", "#222222")) %>% 
  hc_title(align = "left", style = list(color = "black")) %>% 
  hc_plotOptions(series = list(marker = list(enabled = FALSE))) %>% 
  hc_tooltip(sort = TRUE, table = TRUE) %>% 
  hc_legend(align = "right", verticalAlign = "top", layout = "horizontal") %>% 
  hc_credits(
    enabled = TRUE,
    text = "Data from ITOPF.com",
    href = "http://www.itopf.com/knowledge-resources/data-statistics/statistics/"
  ) %>% 
  hc_chart(
    divBackgroundImage = "https://images-na.ssl-images-amazon.com/images/I/71EUEG8orVL._SL1500_.jpg",
    backgroundColor = hex_to_rgba("white", 0.50)
  ) %>% 
  hc_xAxis(
    opposite = TRUE,
    gridLineWidth = 0,
    title = list(text = "Time", style = list(color = "black")),
    lineColor = "black", tickColor = "black",
    labels = list(style = list(color = "black"))
    ) %>% 
  hc_yAxis(
    reversed = TRUE, 
    gridLineWidth = 0, 
    lineWidth = 1,
    lineColor = "black",
    tickWidth = 1,
    tickLength = 10, 
    tickColor = "black",
    title = list(text = "Oil Spills", style = list(color = "black")),
    labels = list(style = list(color = "black"))
    ) %>% 
  hc_add_theme(hc_theme_elementary())
hcspills2

Example II: Winter Olympic Games

Here we will take the data and chart the participating nations over the years.

library(rvest)

tables <- read_html("https://en.wikipedia.org/wiki/Winter_Olympic_Games") %>% 
  html_table(fill = TRUE)

dgames <- tables |>
  purrr::map(janitor::clean_names) |>
  purrr::keep(
    ~ "year" %in% names(.x) &&
      any(c("host", "host_city") %in% names(.x)) &&
      any(c("nations", "countries") %in% names(.x))
  ) |>
  purrr::pluck(1) |>
  tibble::as_tibble()

if ("host_city" %in% names(dgames)) {
  dgames <- dplyr::rename(dgames, host = host_city)
}

if ("countries" %in% names(dgames)) {
  dgames <- dplyr::rename(dgames, nations = countries)
}

dgames <- mutate_if(dgames, is.character, str_trim)

dgames <- dgames |> 
  mutate(year = as.numeric(str_extract(year, "[0-9]{4}"))) |> 
  filter(
    !is.na(year),
    !year %in% c(1940, 1944),
    !year %in% seq(2018, by = 4, length.out = 4)
  )

Not sure how re-read data to get the right column types. So a dirty trick.

tf <- tempfile(fileext = ".csv")

write_csv(dgames, tf)

dgames <- read_csv(tf)

dgames <- mutate(dgames,
                 # nations = n,
                 nations = str_extract(nations, "\\d+"),
                 nations = as.numeric(nations))

glimpse(dgames)
Rows: 23
Columns: 12
$ year                  <dbl> 1924, 1928, 1932, 1936, 1948, 1952, 1956, 1960, …
$ no                    <chr> "I", "II", "III", "IV", "V", "VI", "VII", "VIII"…
$ host                  <chr> "Chamonix", "St. Moritz", "Lake Placid", "Garmis…
$ host_2                <chr> "France", "Switzerland", "United States", "Germa…
$ games_dates_opened_by <chr> "25 January – 5 February 1924Undersecretary of S…
$ sports_disciplines    <chr> "6 (9)", "4 (8)", "4 (7)", "4 (8)", "4 (9)", "4 …
$ competitors           <chr> "258", "464", "252", "646", "669", "694", "821",…
$ competitors_2         <chr> "247", "438", "231", "566", "592", "585", "687",…
$ competitors_3         <chr> "11", "26", "21", "80", "77", "109", "134", "144…
$ events                <chr> "16", "14", "14", "17", "22", "22", "24", "27", …
$ nations               <dbl> 16, 25, 17, 28, 28, 30, 32, 30, 36, 37, 35, 37, …
$ top_nation            <chr> "Norway", "Norway", "United States", "Norway", "…

Let’s see the first chart:

hcgames <- hchart(dgames, "areaspline", hcaes(year, nations, name = host), name = "Nations") %>% 
  hc_title(text = "Number of Participating Nations in every Winter Olympic Games") %>%
  hc_xAxis(
    title = list(text = "Time"),
    max = 2014,
    endOnTick = FALSE,
    maxPadding = 0.01
  ) %>% 
  hc_yAxis(title = list(text = "Nations"))

hcgames

With that increase of nations in 1980 we can:

  • Use a white color to simulate a big snowed mountain.
  • Put a relevant background.
  • Put some flags for each host.
  • And work on the tooltip to show more information.
Code
library(countrycode)

urlico <- "url(https://raw.githubusercontent.com/tugmaks/flags/2d15d1870266cf5baefb912378ecfba418826a79/flags/flags-iso/flat/24/%s.png)"

# dgames <- dgames %>% 
#   mutate(country = tn,
#     country = str_extract(host, ", .*$"),
#          country = str_replace(country, ", ", ""),
#          country = str_trim(country)) %>% 
#   mutate(countrycode = countrycode(country, origin = "country.name", destination = "iso2c")) %>% 
#   mutate(marker = sprintf(urlico, countrycode),
#          marker = map(marker, function(x) list(symbol = x)),
#          flagicon = sprintf(urlico, countrycode),
#          flagicon = str_replace_all(flagicon, "url\\(|\\)", "")) %>% 
#   rename(men = c_2, women = c_3)

glimpse(dgames)
Rows: 23
Columns: 12
$ year                  <dbl> 1924, 1928, 1932, 1936, 1948, 1952, 1956, 1960, …
$ no                    <chr> "I", "II", "III", "IV", "V", "VI", "VII", "VIII"…
$ host                  <chr> "Chamonix", "St. Moritz", "Lake Placid", "Garmis…
$ host_2                <chr> "France", "Switzerland", "United States", "Germa…
$ games_dates_opened_by <chr> "25 January – 5 February 1924Undersecretary of S…
$ sports_disciplines    <chr> "6 (9)", "4 (8)", "4 (7)", "4 (8)", "4 (9)", "4 …
$ competitors           <chr> "258", "464", "252", "646", "669", "694", "821",…
$ competitors_2         <chr> "247", "438", "231", "566", "592", "585", "687",…
$ competitors_3         <chr> "11", "26", "21", "80", "77", "109", "134", "144…
$ events                <chr> "16", "14", "14", "17", "22", "22", "24", "27", …
$ nations               <dbl> 16, 25, 17, 28, 28, 30, 32, 30, 36, 37, 35, 37, …
$ top_nation            <chr> "Norway", "Norway", "United States", "Norway", "…
Code
urlimg <- "https://i.pinimg.com/originals/1a/ad/f5/1aadf58a23d4928945e1dd793f129261.jpg"
ttvars <- c("year", "nations", "sports", "competitors", "women", "men", "events")
tt <- tooltip_table(
  ttvars,
  sprintf("{point.%s}", ttvars), img = tags$img(src="{point.flagicon}", style = "text-align: center;")
)

hcgames2 <- hchart(dgames, "areaspline", hcaes(year, nations, name = host), name = "Nations") %>% 
  hc_colors(hex_to_rgba("white", 0.8)) %>% 
  hc_title(
    text = "Number of Participating Nations in every Winter Olympic Games",
    align = "left",
    style = list(color = "white")
  ) %>% 
  hc_credits(
    enabled = TRUE,
    text = "Data from Wipiedia",
    href = "https://en.wikipedia.org/wiki/Winter_Olympic_Games"
  ) %>% 
  hc_xAxis(
    title = list(text = "Time", style = list(color = "white")),
    max = 2014,
    endOnTick = FALSE,
    maxPadding = 0.01,
    gridLineWidth = 0,
    labels = list(style = list(color = "white"))
  ) %>% 
  hc_yAxis(
    lineWidth = 1,
    tickWidth = 1,
    tickLength = 10,
    title = list(text = "Nations", style = list(color = "white")),
    gridLineWidth = 0,
    labels = list(style = list(color = "white"))
  ) %>% 
  hc_chart(
    divBackgroundImage = urlimg,
    backgroundColor = hex_to_rgba("black", 0.10)
    ) %>% 
  hc_tooltip(
    headerFormat = as.character(tags$h4("{point.key}", tags$br())),
    pointFormat = tt,
    useHTML = TRUE,
    backgroundColor = "transparent",
    borderColor = "transparent",
    shadow = FALSE,
    style = list(color = "white", fontSize = "0.8em", fontWeight = "normal"),
    positioner = JS("function () { return { x: this.chart.plotLeft + 15, y: this.chart.plotTop + 0 }; }"),
    shape = "square"
  ) %>% 
  hc_plotOptions(
    series = list(
      states = list(hover = list(halo = list(size  = 30)))
    )
  ) %>% 
  hc_add_theme(hc_theme_elementary())
hcgames2

What do you think? I had fun, so for me this worth every background I used.