Category Archives: Experiments

Visualizations, tools, charts developed for FINLWD.

Quantifying and Visualizing “Deep Work” (reposted)

A few days ago I posted my first article on Medium on “Quantifying and Visualizing “Deep Work”“. This is a little personal visualization project I developed over the holidays.

I have been collecting personal data for the whole year on what I call “deep work sessions”: times when I deliberately decide to work with maximum focus and no interruptions or distractions on something.

At the end of the year, I decided to take a look at the data and build the story of my “deep work” in 2016. In the article you’ll also find some reflections on the process and what I have learned by doing it.

I hope you’ll enjoy it! Feel free to comment here or on Medium if you wish.

p.s. My first experience with Medium was so good that I am considering switching entirely. I still have to figure out what the advantages and disadvantages may be but I for sure loved the writing experience and the feedback I received.

Chart: The Story of K2 Ascents with Tableau – Part 2

Last week I presented the first part of my chart on the story of K2 ascents, that is, the ascents of one of the toughest and most respected mountains in the world.

I promised a part 2 and here it is. In this second part I focus more on the temporal trends I could find in the data.

In these charts the routes are organized row by row and time is on the horizontal axis as usual.

Additional Encoding

  • Color = Nation
  • Size = Number of climbers

Little Tricks
The first little trick here is to sort the routes according to the time when the route was used with success for the first time. I’ve also added a line that connects all the first ascents to ease the eye in following all the first ascents for each track.

The second trick is to layer a soft grey band on top of each route that starts from the first time a route has been climbed up to the last. In this way it is easier to spot how much activity has been going on in a given route.

Facts and Trends

In this chart you can easily see that the traditional SE Ridge (Abruzzi Spur) is again by far the most used and has a steady set of climbers every year.

Oh well … of course you can also see the Italians climbed it first in 1954 and that it took quite some time to any other nation to repeat it (I am sorry I couldn’t resist highlighting this).

But we have other interesting facts. NE Ridge has been climbed only once by the Americans. SSE Ridge has not been climbed until mid-nineties but then it turned into a classic like the Abruzzi Spur. The North Ridge has also been climbed quite frequently, followed by the West Ridge and the Magic Line. The other routes have all been climbed only once.

Note the West Face! It’s been climbed only once by a good number of Russian climbers (11 people) all in 2007.

You can also notice that many firts ascents have been made by the Japanese which as we saw already in the first part are by far the biggest lovers of this mountain.

And if you give a look to the slope of the line that connects the first ascents you can see that almost every 5 years about a couple of new routes have been climbed, starting from the seventies up to mid-nineties.

And what about the oxygen?

As usual segmenting the data according to an interesting parameter always brings new stuff to light. Here is the same data as before, organized in the same way but color is mapped to oxygen use (blue with, orange without).

Of course the Italians are still the first ones (did I say that?) but the Americans hold an important record too: they were the first to summit K2 without oxygen (by the way, there is a very long debate on whether the Italians climbed with or without oxygen, but this is too long to discuss here).

Isn’t surprising to see how many ascents have been done without oxygen? This is something I didn’t really expect. More surprising though, is that many of the first ascents are in fact ascents without oxygen! Almost all the new routes climbed after mid-eighties for the first time are without oxygen.

Do you know anyone of these climbers? I’d love to speak with one of them for at least half an hour. How does it feel like climbing a new K2 route without a mask and succeeding?

Ok folks that’s all for now. I hope you enjoyed the K2 story. Of course during the design of these charts I’ve learned a lot about Tableau and as I said I am in a love and hate relationship with it (more love recently).

I plan to write something about it soon. If there is anything you would like to know on a technical level on how I built these charts let me know. I’d be happy to share it.

Take care. Have fun. Tweet me if you like it or have questions.



First part of the K2 chart (you find the data there too)

Wikipedia entry for K2 (good overview on the history of ascents) (lots of stats on adventures)

Chart: The Story of K2 Ascents with Tableau – Part 1

K2 Mountain

(Note: I had originally planned to write one single post out of this, but I realized it was too long to digest. So here is the first part. The rest will come soon. Stay tuned!)

K2 is a mighty mountain. Attractive, respected, and dreadful. Normally people think of Everest as the toughest mountain just because it’s the highest peak in the World but it suffices to say that only around 280 people summited K2 so far, whereas more than 2000 people climbed to the top of Mount Everest. has some nice data describing the ascents of K2 (plus many others I suggest you to give a look to), so I thought it would be great to have a chart telling the story of the ascents. I actually did this already some time ago with Excel but I was not very satisfied with the results. So, here I propose a redesign with Tableau.

Tableau is a great tool, even if I have a love & hate relationship with it. And this short series is also a chance to talk a bit about my experience with it.

But first things first. Here is the data if you want to try out your own charts: k2-ascents.csv (please do it … and let me know how it goes!)

Data and Questions

The dataset contains several information but I decided to focus on what can help tell a compelling story. Namely:

  • Nationality of the expedition
  • Route used to climb up to the peak
  • Oxygen if used or not used to climb it up
  • Date of ascent

After several hours spent asking questions and charting I came to the conclusion that the most interesting aspects relate to:

  1. How the nations compare in their quest to reach the peak.
  2. How this relates to the alternative routes attempted (note that conquering a peak from a new route is a big achievement in climbing history).
  3. How the story changes when we consider climbers who used oxygen vs. those who didn’t (by the way, going to 8000m without a mask is totally mad!)


Here is a first set of stories I have been able to build around this data. They mainly concern: the nationality of the expeditions and the routes used to summit the mountain.


This first one shows the number of successful ascents by nation. As you can see the Japanese are by far the best team, even if they seem to have not the best lungs in the world since most of their ascents are with oxygen.

International expeditions hold the record of ascents without oxygen, followed by the Italians, and both seem to prefer tough expeditions without oxygen more than any other (you read my satisfaction between the lines?). The same preference is hold by all the other teams below Italy, with the exception of the Chinese, which together with South-Koreans prefer oxygen.

One open question is whether the nations preferring not to use oxygen have been able to do so because they always take traditional or simpler routes or just because they are “tougher”. But we’ll check that later.

In the next chart you can see the number of distinct routes attempted by each nation. As you can see the Japanese are again on the top. Chinese and South-Korean didn’t attempt too many different routes so this probably means that they really prefer going with oxygen. Or is there another explanation? Let’s give a look to another chart that relates nations with the routes they climbed.

I have sorted the nations according to the number of different routes the nation used (by the way, the sorting capabilities of Tableau are simply amazing). As you can see the Japanese have climber several different routes, many non-conventional ones, and this may explain the reason they have used oxygen so often.

International and Italian expeditions normally take the traditional route, and this may explain why they have been able to do so many ascents without oxygen (can you see my slight disappointment?). The Chines and South-Korean have no excuses by the way: they have been climbing with oxygen mostly on conventional routes!

Several Russian climber did the West Face without oxygen. American, International and French also summited non-conventional routes without oxygen.


Now let’s give a closer look to the routes. K2 has 11 alternative routes. Some of them a really mythical and described by intimidating names like “The Shoulder”, “The Black Pyramid”, “The Bottleneck” “The Magic Line”. In the picture you can get the feeling of what a route is and how they are distributed around the mountain. Please keep in mind how difficult this is: here climbers reach prohibitive altitudes where one single step feels like death.

This first chart shows the distribution of ascents. As you can see the classic “South-East Ridge, Abruzzi Spur” is the most traditional route and by far the most used, followed by the SSE Ridge and the North Ridge.

Again, segmenting the data according to oxygen use reveals interesting facts: the North Ridge and the West Face have been climbed almost exclusively without oxygen. Does it mean they are easier routes, I don’t think so. On the contrary, the West Ridge has been climbed only with oxygen. The 3 major and more common routes are frequently climbed without oxygen with success.

Do you see anything else interesting here? If yes, please let me know. I am looking forward to hear you.

One key lessons learned

I’ll give it straight away: the question and the story you tell are the only thing that matters, not the fanciness of your chart (provided it is not junkchart).

One surprising fact of this first part of the post, after having worked long hours on it, is that the charts I included are relatively simple, just few barcharts. And this would be even more surprising if you had a look to the tens of different and intricate designs I explored with Tableau!

But at some point I refocused my attention and asked myself: “what kind of story do I really want to tell here?“, “where are the interesting questions?“. And as soon as I started answering what was really interesting I discovered that many of these questions could be answered with simple charts like those shown above.

Don’t worry, in the next post you will get some fancier stuff. But if there is one rule I learned out of it (other than realizing how amazing Tableau is) is the one I stated above. Ask your questions first. This is what really counts. Wandering mindlessly around the huge space of possible designs has little value.

Preview of Part 2

In my next post I will provide some temporal analysis of this data. You will see how the ascents distribute over time and how oxygen played a role. Finally, we will see which nations dominated in the struggle to conquer the top by climbing a new route.

Here below a couple of  small teaser :-)

Stay tuned and send me your feedback! Thanks for reading and retweet below if you like it!

Charting the Index of Economic Freedom: a call to action

The Heritage Foundation together with the Wall Street Journal has recently published the last report on the Index of Economic Freedom. The index captures in a series of factors like: “freedoms of movement for labor, capital, and goods, and an absolute absence of coercion or constraint of economic liberty, etc.” the degree of economic freedom of a given country.
Apart for the intrinsic interest such data has, in that it measures freedom, the case is very challenging in terms of graphical representation. Here is just an example of what I’ve been able to do so far, sincerely with little success.



The dataset (which can be downloaded directly from the website) contains for each country and year, form 1995 to 2009, the overall score and the individual factors (e.g., business freedom, trade freedom, fiscal freedom, etc.) that compose the score. Technically speaking it is, in fact, a multivariate time series, a quite tough object to handle indeed.


In my proposed solution I focus on the representation of the states that experienced the highest positive or negative changes in the whole time range. Beyond the obvious reading of best and worse countries in the overall score, which can be easily obtained from the website, I think representing measures of change is a lot more interesting.
I’ve created the chart with MicroCharts a wonderful little Excel add-on. Each sparkline represents the time variation of the overall score, so that it is possible to see ups and downs in the considered time span. Since the variation is represented in terms of the individual maximum and minimum values, the timelines cannot be compared in terms of their absolute values. But this is ok as long as the main goal is to covey messages like: “hey this country has significantly and steadily improved its index over the course of the years!”. The absolute values can be read on the right side where min and max are color-coded the same way the small dots are coded in the sparkline. The size of the dot represents the value and the bar chart the amount of variation.


I am by no means satisfied with my design, but I think it sheds some interesting light on the data. We can see that Armenia had an impressive improvement from 42.2 to 70.6. We can also see that many Eastern Europe countries like Moldova, Bosnia and Herzegovina, Lithuania, and Romania, had a great improvement as well, as highlighted in the report. Sad examples are Argentina, which experienced a sudden decrease, probably concomitant with the country economic breakdown, and Zimbawe which went from the already low 48.7 to 22.7.

A call to action!

The real challenge for these data is to represent the single factors together with the overall score and to represent the whole dataset, which I’ve not done. These factors can help explain for any major variation, if it is due to a specific sector or an overall change. I’m also convinced the same data can be seen under a myriad of other lenses different to mine. It is for this reason that I propose a “call to action“, inviting you to create a chart of this intriguing dataset.
In order to facilitate your task I have attached here a processed version of the file that contains the overall score organized by time in a single Excel sheet (the original data has one sheet for each year). If you go into some preprocessing too pay attention to some data inconsistencies the original file may have. Especially, note that Somalia in some years is removed from the dataset.
Good Luck!

Swing States Visualization

Some weeks ago Robert Kosara posted in his EagerEyes a visualization made with Tableau with the intent to understand the swing states in the US elections. His post generated quite a lot of reactions. Many people tried to propose alternative designs and suggested potential improvements. After few days, I tried to create my own interactive version and here is the result I have put together so far.
The original data provided by Robert contains for each pair of year and state, the winning party. I have added to this data the information about who was the president elected and the winning party.
Here is a screenshot.
Click on this link to launch the applet (on a new window).

Visualization Design

The visualization is a simple interactive matrix where the rows represent the states and the columns the years. Since the focus of the visualization is to see which states swing, graphical marks are added only when they in fact swing, and the color is the one of the winning party (I originally used a slightly different design where a shade of colors from the previous to the actual winner was used, but the result was too noisy). On the top of the visualization an additional row is used to depict which party was finally the winner in the elections. In this way it is possible to see which states where determinant for the final result.
The visualization has some few interactive features. Hovering is used to focus on a specific row-column pair and to dynamically show which presidents was elected in a given year. On the bottom there are some few filtering tools:

  • From/To: to focus on specific swings from one party to another.
  • #Swings: to filter out the states for which the total number of swings is below a threshold.

By using these tools it is possible to focus on specific patterns.


I must admit I did not spend much time analyzing the result (I hope you would do it for me! :-)). Anyway some few things soon hit the eye:

  • It is not evident which states swing or do not swing but it is quite clear that they tend to swing all to the same direction. Every column in fact contains entries almost all of the same color.
  • Some years have had very large scale swings: 1912, 1916, 1932, 1952, 1963, etc.
  • Luisiana (LA) had an impressive number of consecutive swings between 1948-1980, changing from one party to another in every election.

I am pretty sure there are hundreds of interesting patterns to discover yet . I hope you would find some of them.

Potential Improvements

There are obviously a very large number of potential improvements that might be included in the visualization, it is by no means a finished product, rather a toy.
One interesting feature I have seen proposed that I did not include, is the ordering of the states (rows) to bring together the states that tends to behave similarly. In this way it would be possible to cluster them visually and gain some additional insights. So far, however, I didn’t have enough time to implement it.
Another filter could be added to isolate not only states with big sweeps but also years with big sweeps. Again, this is not yet implemented.


I really hope you would like to critique this visualization and suggest potential improvement. At the same time it would be nice to know if you have found some additional interesting patterns.
The additional data about presidents and winning party can be found here: presidents_mod.csv.

Charts: OECD Education at a Glance

Inspired by some recent government interventions on the Italian public school and the consequent large development of protests all around the country I have designed few charts to see if I can better understand the issue from the data and communicate some results.


I have used the data from the Organization for Economic Co-operation and Development (OECD), which is often referred to as one of the main trusted authority for whatever concerns the education systems of a country. More precisely the data comes from the OECD report: “Education at a Glance“.
At the origin of the protest there is the reduction of the number of main teachers per class from 3 to 1, with a consequent reduction of the public personnel. The government says that having less teachers will not influence the quality of the studies and that quite a lot of public money will be saved. The protesters believe that the opposite is true and that the savings should not come from these cuts.
The goal of these charts is not to provide a solution to the debate, rather it is a very small and focused view on the problem. I just tried to find some hints on two related questions that came to my mind:

  • How efficiently does the Italian system spend its money?
  • Is proportion of students to teachers the cause of poor performance?

How efficiently does the Italian system spend its money?

The first chart replies to the first question. At least partially. The chart is a scatter plot of the OECD data on efficiency of school systems based on the following data:

Scientific performance: called PISA (Programme for International Student Assessment) and defined as “an international study conducted by the OECD which measures how well young adults, at age 15 and therefore approaching the end of compulsory schooling, are prepared to meet the challenges of today’s knowledge societies.” It is supposed to be a good indication of how well our schools do.
Expenditure per Student: It is defined as the equivalent US dollars expended per student.


I have drawn two lines to divide the space into 4 quadrants with respect to where Italy is placed. Of these quadrants I have highlighted the bottom right because it represents all countries who can perform better in terms of the PISA index and spend less. In other words all the countries in the quadrant not only are able to spend less but they also use this money more efficiently because they produce better students.
The sad truth is that my lovable country performs very bad. Greece and Portugal are valid companions but at least they spend less.
In oder to be sure that these results are not affected by the economic level of countries, I have also produced a second chart where the expenditure is normalized with respect to GDP (gross domestic product).


Unfortunately the result is even worse: Greece and Portugal perform worse but almost all the other countries are better. From the chart we can also see (in the bottom right) that Finland performs exceptionally well and that New Zealand, Netherlands and Australia performs very well too but spending less money.

Is proportion of students to teachers the cause of poor performance?

Since at the center of the debate there is the question of whether more or less teachers affect the quality of an education system, I created a bar chart comparing the ratio of students to teachers for the countries shown in the scatter plot.
Here are two bar charts, one for primary school and one for secondary school. Again I have highlighted Italy in the chart to make the comparison with it easy.



As you can see Italy has one of the lowest ratios both in primary and secondary school, meaning that there are quite a few students for each teacher or, in other word, that teachers are not very overloaded compared to other countries. The comparison with other countries is quite interesting. Finland, Netherlands and New Zealand (Australia is missing in the data) which are very efficient, as we have seen in the scatter plots above, have quite higher values compared to Italy. Can we say then that at the root of the poor Italian performance there is the number of teachers? Or can we say that a small number of students per teacher is necessary to produce a school of high quality? I don’t know … but at least the graphics instill some doubts.

Technical Notes

The charts have all been done with Excel. After all it is always the best and most readily available tool. There is always a bit of a hassle in doing certain things, especially the defaults are crazy (like strong dark backgrounds), but in the end it works great.
I have used the XY Chart Labeller to reduce label overlaps on the bar charts. This is also a bit cranky but in the end it does its job well.
The annotations on the charts have been done with the graphic tools in Excel and externally within SnagIt, which I use to screencapture the charts. Yes I’ve used screen capture! I know I could use VBScript stuff or similar things to save the charts into images but it’s always a kind of pain and less flexible than just press PrintScrn and edit the image.


With these charts I don’t pretend to demonstrate anything, it’s more an interesting exercise for me to create data graphics and to show how easily we can reason about data that pertains to facts related to our social life.
The charts might show and evident bias towards judging the government interventions appropriate, but this is not my intent. Rather I would be very curious to see other charts that better clarify the issue and show with data and graphics arguments opposite to mine.

Final Reflection

In order to build these charts I have invested very very few time (I invested a lot more time to write this post though!). I was able in a few clicks to clarify to myself some things on an issue which is quite hot during these days in my home country and which I dare about. The same thing might be done by millions of citizens if only instructed appropriately. And that would mean having a population of informed people, able to ground their protests on hard data and to communicate their arguments with the vividness of well done data graphics.
Unfortunately this is very far to come. Simple techniques like these are never used by politicians or protesters, they prefer to use thousands and thousands of words in place of few well done charts. It’s a pity for us and it’s a pity for them.

Chart on K2 Succesful Ascents

Some time ago I watched on TV an extraordinary documentary about the last Italian expedition on summer 2007 to the marvelous K2, the famous Himalayan mountain, and amazed by the extraordinary stories around it I started searching for some additional info about it.
For those of you who know nothing about it will suffice to say this:

“Its height is eclipsed only by Mt. Everest, but its level of difficulty is eclipsed by none. One of the Himalayas’ fourteen 8,000-meter peaks, K2 has earned the nickname “The Savage Mountain” due to its violent storms and catastrophic avalanches. Less than 200 climbers have conquered the mountain (compared to 1,700 at Mt. Everest), and more than 50 have died trying.” [Source: Women of K2]

To my surprise, not only I could find plenty of sources about the mythical history of its ascents, but very soon I stumbled upon a whole set of excel sheets with plenty of data to dig into. I was soon thrilled by the idea of visualizing these data in some way and started playing with Excel to find a proper visualization; one that could tell a story.
The chart that I present here is the first result of my exploration, after having tested tens of different visual solutions. To be frank, I’m not totally satisfied yet but this is the best I could create given my experience with Excel and I hope you can provide some useful feedback on how to do it better.


The data represents all the successful ascents of K2 from 1954 (the first ascent) to 2006. Each data item is a single climber with the following associated data:

  • Year -> mapped to the x-axis
  • Order of arrival -> mapped to the y-axis
  • Expedition -> mapped to color (only the first 8 in terms of total number of ascents)
  • With oxygen -> marked with an “o”
  • Died on descend -> marked with a “x”
  • Sex -> females marked with an “F”

Some interesting facts can be extracted from the chart, here are some examples:
– A big gap between the first ascent in 1954 by the Italians and the second in 1977 by the Japaneses … and then some other small gaps. I tried to investigate to find the reason about it but it looks like it is just that despite many attempts, in some years nobody succeeded.
– There are few female climbers in the history of K2 and they are in fact quite famous. Legendary is the “curse on women”: the first 6 female climbers are all dead either on descent or later in other circumstances. In the chart it is easy to spot the three who are dead while descending.
– Oxygen has been sparingly used by a small proportion of climbers. Notably the first ascent without oxygen was on 1978 during the first successful American expedition (From K2 Timeline: “there is some debate about who the first climber to reach the top of K2 without supplemental oxygen was“).
I am sure there are many others that can be spot on the Chart. A timeline of relevant events can be found on

One of the interesting things which are not shown in the map is how different routes have been tried during all these years. I plan to draw another chart where the successful ascents through new routes are shown.

Few reflections

1) An interesting part of the journey that took me from the idea of visualizing the K2 data to a final picture is what I learned by using the Excel chart tools. Before starting I was barely able to create a simple scatter plot with the wizard, at the end I was able to customize every chart I wanted to draw and to create some fairly complex VB scripts. I have contrasting feelings about Excel because there are very good and very bad things about it. For instance, it’s crazy how the default settings for each chart seems to have been designed purposely to create junk. I had to overwrite almost all the default settings to have a neat picture (e.g., changing background from gray to white). At the same time however I am amazed by how flexible Excel is and how it is open to any possibility if a bit of programming is learned.
2) Being a person trained on interactive visualization I had never experienced the process necessary to transform data into a still picture. It’s amazing how a different mindset is necessary to design a visualization in this way (and yet how much basic knowledge on visual perception is needed). Programming a visualization, knowing that a user will be able to interact with it to disambiguate certain information, is easier then when you know that everything must be conveyed in a single image. Related to that is my surprise once again on how limited the visual features are when you want to map data dimensions to visual dimensions. Here in this chart I had to struggle a lot with myself to decide which dimension I wanted to map to which feature. And changing even only one of these mappings can change the whole story.
3) Even if I agree with many on saying that Excel is a mess and that obtaining the desired results is a pain, I had a very bad experience trying with other software. I’m totally shocked about it! Before I was convinced that Excel was the best way to go, I downloaded tens of little and big applications full of features and all promising to be a piece of cake when creating charts. To my experience this is plain false. The best alternatives are represented by complex software like SPSS or Illustrator which in many ways are better than Excel (e.g., Illustrator is great if the final result must be manipulated by hand and it’s meant to be printed) but still very hard to use and to learn (and very expensive too!).
4) I think there is a full potential for interesting investigations out there about sport and expeditions datasets. The main data set I have used here was extracted from a web site full of data on many kinds of adventures. See Adventure Stats for more details. I am sure that visualization can help telling many interesting stories about exploration in compact and well designed charts.