The question is it easy to replicate the default settings of one charting software in another charting software bothered me for some time. Are the default settings more universal or less universal? Do different vendors have different attitudes towards what should be the default setting?
I chose to work with a line chart because different software interprets differently how to arrange multiple series in a bar chart – some tools stack them, some not. By adjusting this arrangement I would lose the defaultness, while without any corrections the charts would be less comparable. I made all charts squared, so they fit better in the grid.
Insights about the defaults:
All have horizontal gridlines, ggplot2 and Tableau have even vertical ones.
Only Google Data Studio and Tableau have highlighted the zero line, although Tableau highlight is barely noticeable.
Blue and red or orange are within the first three choices in every palette.
ggplot2 looks exceptional with its grey panel.
The default settings of Tableau make the least sense because they are configured for more charts with more legends. One chart with one legend looks a bit weird.
Grey squares at the top right of Google Data Studio charts are how the control buttons are rendered as an image.
Insights about the comparisons:
Of course, ggplot2 manages to replicate even the most complex cases. The biggest challenge was using Google font from Google Data Studio because the library”showtext” which seemingly allows achieving this does not work well with ggplot2.
Settings of ggplot2 itself were the most difficult to replicate.
Tableau was the only software that could not replicate the exact colours of lines, because a colour must be chosen from a predefined palette there.
It was quite annoying that Power BI and Google Data Studio could only export to PDF, however they are not meant to make pretty pictures after all.
Somehow square charts from Excel lost the squareness after saved as images.
Google Data Studio insisted on a black line indicating zero and refused to show vertical gridlines. Maybe I just don’t know this tool well enough or maybe these are the limits.
Adjusting the limits of the x-axis was always a challenge, the y-axis is often allowed for way more freedom.
Adjusting legends was always the most difficult part. Legend is what distinguishes one tool from another.
I believe this exercise is of little use, but it was fun to do it!
Different freedom indices measure different fields of freedom – press, economy or general human freedom. Are they consistent within a country?
As we see from the 2nd segment of this chart – no, freedoms are not consistent. Economic freedom index is quite often much higher than indices of other freedoms, especially in those countries at the low end. I did my best to adjust the indices so their ranges are uniform, but their averages are still quite different – most countries love economic freedom, while many of them do not care much about democracy or moral freedom.
What: Freedom indices recalculated to fit the range from 0 to 1, where 1 means the best index and 0 means the worst (in 3 out of 5 cases – it’s North Korea). Country average is a simple average of all 5 indices. Index, When, Source: Democracy Index, 2019, EIU Human Freedom Index, 2017, The Human Freedom Index 2018: A Global Measurement of Personal, Civil, and Economic Freedom Economic Freedom Index, 2020, The Heritage Foundation Moral Freedom Index, 2020, The Foundation for the Advancement of Liberty Press Freedom Index, 2020, Reporters Without Borders Where: 172 countries were ranked on at least 3 of these indices.
I’ve found five freedom indices measuring various fields of freedom. The question is whether all those measures are consistent within countries, or do they vary a lot?
The answer is in the chart below the chart below – more often they’re consistent than not. Exceptions are in some Muslim countries which do not like moral freedom or democracy – those ratings are low, but they want high economic freedom – so this one particular rating is often high.
What: Freedom indices recalculated to fit the range from 0 to 1, where 1 means best index and 0 means the worst (in 3 out of 5 cases – it’s North Korea) Index, When, Source: Democracy Index, 2019, EIU Human Freedom Index, 2017, The Human Freedom Index 2018: A Global Measurement of Personal, Civil, and Economic Freedom Economic Freedom Index, 2020, The Heritage Foundation Moral Freedom Index, 2020, The Foundation for the Advancement of Liberty Press Freedom Index, 2020, Reporters Without Borders Where: 172 countries were ranked on at least 3 of these indices.
The Stacked Bar chart is one of my favourites, I even made the same stacked bar chart with 9 online tools – but it has one major weakness, it’s difficult to compare changes of its segments over time. I tried finding a way to improve it and here let me introduce the Comparative Stacked Bars:
Triangles show the absolute increase or decrease of each segment. They are colour-coded to make it even easier to read.
Too many is too many – if there are too many categories the triangles will make the chart look messy and difficult to read. But even more difficult it would be without triangles.
Too small is too small – if the change is too small the triangle might become invisible. But without them, the changes would get invisible much sooner – just observe the top segment in the above chart.
The example above was made in R, and the example below was made in Tableau. Unfortunately I have no not-overly-complicated solution for Excel. If anyone knows how to implement it properly, please let me know!
The pie chart faces a tremendous amount of criticism for attempts to show part to the whole relations. Of course – it is easily the single most misused chart! However more and more data visualizations practitioners are writing articles to defend it with Robert Kosara being the most thorough and methodical in my opinion.
Here I will offer an alternative which is something like a mix of square pie chart, marimekko and packed bars. Let me introduce the Cake Chart:
Always a regular square, total area = 100%.
Each segment is a bar for easy comparison, but its area represents the percentage.
The height of the chart is distributed evenly among bars.
Only the selected number of largest categories are shown separately.
All other categories are aggregated into the irregular steppy gray area.
If there are 5 categories shown separately, then the largest cannot be larger than 100% / 5 = 20%. For X categories to be shown – the maximum value cannot be larger than 100% / X.
Aggregated other categories are difficult to compare to bars.
There are limitations how long the longest bar can be without destroying the squariness of the square.
Square is not as intuitive to be 100% as circle.
Bars are super easy to compare.
It’s still a regular shape.
It’s quite obvious how to make it – make the panel square, make its background grey. However, making the Others category selectable in Tableau is a challenge and here is the result:
To see nominal numbers of debt increasing won’t tell us much, so it is better to look at debt expressed as percentage of GDP. Also we need to compare current growth to something, so I am comparing growth in 10 years until the most recent data (2008-2018) with growth in 10 years until the Great Recession (1998-2008).
One thing is seen at once – the governments in most countries are getting more debt than before.
Corporations and household are increasing their debt in more countries than decreasing, but the rate of increase is slower now and more countries are decreasing than before. We’re less crazy than in those crazy times.
What: Debt made of loans and debt securities expressed in % of GDP. When: 1998 – 2018 Where: 103 countries of the world. Iceland was removed from the chart and they know why. (Because of extreme numbers, debt levels reaching over 700%). Also, there might be some bias in the data, because not all countries have data for all periods and all debt receivers. Source: BIS