The tale of two charts combined

…Or when two half chart are more than one, or maybe ‘The love between the streamgraph and stack column chart’… In this post we’ll review some ideas in the chart used by Financial Times in it’s Coronavirus tracker, and how we can replicate it in R using highcharter package.

Contents

• Introducction
• Data
• The streamgraph
• The stacked column chart
• The final chart

Post updated on Mar 26, 2024

Introducction

Week ago I see a tweet from Steven Bernard @sdbernard from Financial Times showing a streamgraph on the top of a stacked column chart. I take a look some seconds and then boom: What a combination! Why?

One of them is the complement of the other.

The link for the original source is here.

I like the streamgraph but it is hard to see the change the distribution between categories when the total change sudden. So have this auxiliar chart is a nice add to don’t loose from sigth the distribution.

Data

In this post we will use the Our Workd In Data Covid deaths (link here) because I’m not sure what is the data used by Financial Times team.

We’ll load the data and check the structure and get only what we need to replitcate the chart:

# A tibble: 7 × 2
  continent         n
  <chr>         <int>
1 Africa        86641
2 Asia          75850
3 Europe        82907
4 North America 62373
5 Oceania       36492
6 South America 21284
7 <NA>          18423

Rows: 365,547
Columns: 5
$ continent  <chr> "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "As…
$ iso_code   <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AF…
$ date       <date> 2020-01-05, 2020-01-06, 2020-01-07, 2020-01-08, 2020-01-09…
$ location   <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan",…
$ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…

FT used the 7-day rolling average in the chart so we’ll use the {RcppRoll} package to get that series for each contienent. Check the next code to see how the function roll_meanr works.

 [1] NA NA  2  3  4  5  6  7  8  9

Now we need to group the data to calculate the roll mean for every country/location and then filter to reduce some noise.

The chart show continent so we’ll group by date and continent.

Rows: 876
Columns: 3
Groups: date [146]
$ date       <date> 2020-03-16, 2020-03-16, 2020-03-16, 2020-03-16, 2020-03-16…
$ continent  <chr> "Africa", "Asia", "Europe", "North America", "Oceania", "So…
$ new_deaths <dbl> 0, 91, 277, 7, 0, 1, 2, 165, 1158, 47, 0, 8, 15, 194, 2676,…

The streamgraph

Before combine two charts we need to know how to get every chart independently. Let’s start with the main one:

A good start, but we can do it better. So, some considerations:

• The yAxis don’t have a meaning in the streamgraph so we’ll remove it.
• We can set endOnTick and startOnTick en yAxis to gain some extra vertical space.
• Remove the vertical lines to get a more clear chart.
• Get a better tooltip (table = TRUE).
• In this case but we can try adding labels to each series instead of using legend, same as the FT chart.
• This is not associate to the chart itself but what is representing: In the original FT chart some countries like UK, US are separated for their continent because are relevant, and then the color used is similar to their continent to get the visual association.

To separate the information for some coutries from theirs continent we’ll create a grp variable:

# A tibble: 13 × 3
   continent     grp                n
   <chr>         <chr>          <int>
 1 Africa        Africa          8322
 2 Asia          Asia            7205
 3 Asia          India            146
 4 Europe        Europe          7734
 5 Europe        Russia           146
 6 Europe        United Kingdom   146
 7 North America Mexico           146
 8 North America North America   5694
 9 North America United States    146
10 Oceania       Oceania         3504
11 South America Brazil           146
12 South America Chile            146
13 South America South America   1752

Fun part #1: To the continent which have separated countries will add the "Rest of " to be specific this is no the total continent.

# A tibble: 13 × 3
   continent     grp                       n
   <chr>         <chr>                 <int>
 1 Africa        Africa                 8322
 2 Asia          India                   146
 3 Asia          Rest of Asia           7205
 4 Europe        Rest of Europe         7734
 5 Europe        Russia                  146
 6 Europe        United Kingdom          146
 7 North America Mexico                  146
 8 North America Rest of North America  5694
 9 North America United States           146
10 Oceania       Oceania                3504
11 South America Brazil                  146
12 South America Chile                   146
13 South America Rest of South America  1752

Fun part #2: We’ll use a specific color for each continent, and a brighten variation for the the separated countries. For this task the {shades} package offer the brightness function.

continent	grp	n	aux	continent_color	fct	grp_color	continent_cln
Africa	Africa	36659	TRUE	#f1c40f	0.00	#F1C40F	africa
Asia	India	75811	FALSE	#d35400	0.10	#EC5E00	asia
Asia	Rest of Asia	140569	TRUE	#d35400	0.00	#D35400	asia
Europe	Russia	56198	FALSE	#2980b9	0.05	#2C89C6	europe
Europe	United Kingdom	30761	FALSE	#2980b9	0.10	#2F92D2	europe
Europe	Rest of Europe	199806	TRUE	#2980b9	0.00	#2980B9	europe
North America	United States	154283	FALSE	#2c3e50	0.05	#33485D	north_america
North America	Mexico	47339	FALSE	#2c3e50	0.10	#3A526A	north_america
North America	Rest of North America	19174	TRUE	#2c3e50	0.00	#2C3E50	north_america
Oceania	Oceania	3304	TRUE	#7f8c8d	0.00	#7F8C8D	oceania
South America	Brazil	98961	FALSE	#2ecc71	0.05	#31D978	south_america
South America	Chile	9002	FALSE	#2ecc71	0.10	#34E67F	south_america
South America	Rest of South America	83471	TRUE	#2ecc71	0.00	#2ECC71	south_america

Then exctract some vectors:

Before continuing let’s see the original colors and the finishes obtained with the {shades} package.

Original palette

Colors considering variations

The colors and levels are ready so let’s regroup the data using this new grp variable:

Then plot the previous chart but now considering all the comments made before.

The stacked column chart

For the stacked column chart we’ll use the data which have deaths by continent (no grp). This is a simple chart so the only important part is set borderWidth, groupPadding, pointPadding to 0 to remove the space between columns.

The final chart

There are some important things to do before code the final chart:

• Create and add two yAxis using hc_yAxis_multiples and create_yaxis functions. One for each type of series. The two series will share the same xAxis.
• For the column series we’ll use the id parameter with the unique(cont) value, then in the streamgraph use the linkedTo parameter to link the series. With this the Russia, UK and Rest of Europe series from the streamgraph are link with the Europe series from the stacked column chart, so if the user click the Europa legend all those series will hide.

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The tale of two charts combined – Data, Code and Visualization The tale of two charts combined – Data, Code and Visualization The tale of two charts combined – Data, Code and Visualization Data, Code and Visualization …Or when two half chart are more than one, or maybe ‘The love between the streamgraph and stack column chart’… In this post we’ll review some ideas in the chart used by Financial Times in it’s Coronavirus tracker, and how we can replicate it in R using highcharter package. …Or when two half chart are more than one, or maybe ‘The love between the streamgraph and stack column chart’… In this post we’ll review some ideas in the chart used by Financial Times in it’s Coronavirus tracker, and how we can replicate it in R using highcharter package.