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!
There are countries where they do, with notable examples being Kuwait, Puerto Rico, Djibouti, Mongolia and Uruguay.
Since rural populations are declining everywhere, relative weights of urban populations, as well as those in the largest cities, are increasing, so largest cities now are more dominating than they were in the ’60s.
The most concentrated region seems to be East Asia, but that is because they have Hong Kong and Macao with almost 100% of people living in the only city.
What: Median proportion of the population in the largest city, the urban area without the largest city, and the rural population. When: 1960 and 2019 Where: The whole world, with some exceptions having a total of 26 million inhabitants, the biggest being Botswana, Lesotho and Slovenia. Many of those exceptions are small, possibly one-city countries which do not have needed statistics in the Worldbank database. Source: Worldbank
I tracked down the time when I start my working day, what percentage of weekly goals I do achieve and whether I do many other daily routine things (meditation, exercise, proper meal, not checking social media after 6 p.m. etc.) which I aggregated into “Level of discipline”.
During the time of self-observation I began to wake up and start working earlier, I started to achieve more goals, but this “discipline” thing did not improve. I guess that trying to do many “useful” things during the day is not as useful and productive as it may seem.
What: Weekly average time of starting to work, the proportion of weekly goals achieved, and “level of discipline” measured in points. Moving averages are calculated using 5-week intervals. There are omissions in the data, as one may see. When: 38 weeks during 2019-2020 Source: self-observation
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
We spend more and more money on movies even in times of television and the internet. So, not a big surprise, that more and more movies are being made.
Median rating of all movies in the IMDB database is declining. Is it a sign of movies getting worse? No, its a sign of more movies being made, when this art is accessible to not only exceptional talents but mediocre talents as well. Anyway, who watches 2000 movies a year? Let’s become picky.
Let’s pick the most popular 200 movies each year and calculate their median. Now there is a stable trend – median popular movie usually has a rating of just around 6.4. (Movie popularity is measured in the number of ratings on IMDB). That’s good.
Now let’s take 200 top-rated movies each year with at least 25 000 ratings. A sharp decline becomes visible! And before the 80s not many such movies existed at all. So, if you like only top-rated popular movies, you might experience deterioration in their quality. Sequels of Transformers are getting worse and worse.
However there are great lesser-known movies, especially those made in non-English speaking countries, so they get less attention and therefore fewer ratings. I’d subjectively say that a good lesser-known movie has at least 4000 ratings. If we pick 200 best such movies, we get a different view – often a median movie will have a rating higher than 7, and no permanent downward trend is present. Great!
What: Movie earnings in USD billion adjusted for inflation. The number of movies – with more than 100, 4000 and 25 000 ratings. IMDB movie ratings indicate the median rating calculated for every year, and a trend line fitted using a generalized additive model. All movies – those which have at least 100 ratings Most popular movies – 200 most rated but having at least 100 ratings (because there were times when less than 200 movies were made). Top-rated popular movies – 200 top-rated movies from those having at least 25 000 ratings. Top-rated movies for cinephiles – 200 top-rated movies from those having at least 4000 ratings. If less than 40 movies left remaining after filtering – no rating calculated for that year. When: 1968 – 2019 Where: Movie earnings represent earnings around the globe. Source: IMDB for ratings and number of movies the-numbers.com for movie earnings. WB and IMF for inflation used to adjust earnings.
The good news is that we are really reducing poverty. Fewer and fewer people live for 1,90 dollars a day, fewer and fewer experience hunger, fewer and fewer experience struggle to get water.
The sad news is the realization that behind nice world trends there are still countries where one-third of the total population are poor, experience hunger and struggle to get water. And even worse – while over 40% of people in low-income countries don’t have access to BASIC drinking water service, 73% don’t have access to SAFE drinking water service.
But we’re reducing. There is still much to achieve and let’s hope for the best.
What: Very poor people = Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population), Hungry people = Prevalence of undernourishment (% of population), People not having water = People not using at least basic drinking water services (% of population) When: Improvements by income class are shown between 2000 and 2017 with possible deviations a year or two for different category combinations. Where: Total world + the world divided into 4 income groups by the World Bank. Source: WB
Yes, but only if the market has crashed very seriously. The same is with selling – historically it is good to sell only when markets have skyrocketed unprecedently.
Suppose I have invested USD 100 in stock two years ago. It has gained some positive or negative amount of dollars since then. What gains could I expect after one year if I invest USD 100 today? After analyzing historical data I see that future gains are almost certainly positive only when markets have had dropped more than 35% in two years from their original value. Also, future gains are almost certainly negative when markets have had grown more than 80% from their original value.
What: “Gain” is the amount in dollars one’s investment would change from the initial USD 100 invested. Investment is divided equally between Dow Jones Industrial Average and Nasdaq Composite indices. When: Monthly data between February 1985 and April 2020 Source: investing.com
Sure. OECD Composite Leading Indicator (CLI) indicates it. It is surprising how sudden a drop in March 2020 is. Obviously, the only reason for it is the virus.
Will the recession become a crisis of a wider scope? Sadly, CLI does not reach that far into the future.
What: Composite Leading Indicator of OECD countries, amplitude adjusted, percentage deviations from long term average. GDP growth rate of OECD countries comparing month to the same month of the previous year. Both indicators are seasonally adjusted. When: February 1961 – March 2020. Where: OECD total, ~40 countries. Source: OECD
After seeing that military spending has no visible relation to deaths in most regions I decided to investigate the special relation the USA has with the wars in the East.
Some major wars in the East are indicated on the chart. During those, the military expenditure of the USA seems to climb up. Data for deaths are available only from 1980, and I filtered out only the relevant region.
Is the USA the reason for those deaths? Or are those people just fighting among themselves, and the USA just gets involved? Does it worsens the situation, or improves? I’ll leave those questions open for now.
What: Military expenditure in constant USD + Deaths due to Conflict and Terrorism. When: Expenditure: 1949-2018, Deaths: 1980-2017 Where: Expenditure: USA only, Deaths: Middle East, North Africa, and Afghanistan Source: Institute for Health Metrics and Evaluation for deaths + Stockholm International Peace Research Institute for expenditure.
That seems not to be the case. There are many wars in Africa, but not so much money spent. Also, China and countries in Western Europe spend a lot, however, they’re not actively fighting (some missions to some hot spots do not count).
In the Middle East – the boiling point of wars – the biggest spenders are Saudi Arabia, which fights satellite war in Yemen, and Israel which fights against Palestine (as I understand it). But many more casualties come from Iraq, Syria, and Afghanistan, all of which seem to be a mixture of civil wars and satellite wars.
The special case is the USA which will have its separate chart.
This topic is so geopolitical, that I refrain from diving into deeper conclusions, but it’s tempting to say, that more weapons do not make more deaths. More stupidity does.
What: > Military expenditure in constant USD – some data for USSR and UAE were interpolated using very rough methods – just to fill the gaps and avoid fake jumps. This data have lots of gaps. > Deaths due to Conflict and Terrorism. When: 1980-2018 (Data for deaths until 2017) Where: Probably the whole world Source: Institute for Health Metrics and Evaluation for deaths + Stockholm International Peace Research Institute for expenditure
Mostly in Africa. It was quite difficult to find the exact diseases behind particular spikes of deaths, but some of them like Ebola got a lot of attention. Surprising enough, but it seems that in 2017 people suddenly stopped dying from diseases there.
Sadly I could not find what caused so many deaths in China in 1984. Just imagine the results I got when I searched for “china 1994 epidemics” – the overwhelming majority of articles about the current outbreak of coronavirus with some articles claiming that Chinese government is the one from Orwell’s novel.
What: Deaths due to epidemics When: 1980-2017 Where: 194 countries and regions Source: Institute for Health Metrics and Evaluation
As expected, the most dangerous regions are Africa and the Middle East. However, a single event in Rwanda pushed Sub-Saharan Africa to the first place. Also, half of the deaths from 1980 till 2017 occurred in only five countries (we hear a lot about them in the news).
The rest of the world is more or less peaceful.
What: Deaths due to war and terrorism. When: 1980-2017 Where: 194 countries and regions Source: Institute for Health Metrics and Evaluation
Natural disasters seem to be quite concentrated, more than half of all deaths during 1980-2017 have occurred in the same five countries and four of those five are in Asia. It is not possible to differentiate to smaller regions (like S.E. Asia, East Asia etc.) because a single tsunami or earthquake affects several regions at once.
The earthquake in Haiti seem to be the most deadly disaster for a single country, which makes up more than half of total deaths in Americas.
What: Deaths due to natural disasters When: 1980-2017 Where: 194 countries and regions Source: Institute for Health Metrics and Evaluation