Category Archives: Books

Blink

Este libro fue escrito por Malcolm Gladwell, periodista y divulgador cientifico.

Trata sobre el contenido y el origen de esas impresiones y conclusiones instantáneas que surgen de forma espontánea cuando conocemos a alguien, cuando afrontamos una situación difícil o cuando tenemos que decidir algo en condiciones de estrés.

El libro se divide en 6 capitulos, en cada uno de ellos se presenta un concepto y distintas situaciones que sirven para mostrar dicha idea:

  • Capitulo 1, La estatua que tenia algo raro, trata no solo acerca del Kuros del Museo X, sino tambien acerca del matrimonio y las relaciones, asi como tambien acerca de las demandas por negligencia.
  • Capitulo 2,  La puerta cerrada, trata acerca de la necesidad humana de explicar lo inexplicable a traves de tres casos
  • Capitulo 3, El error de Warren Harding,
  • Capitulo 4, La gran victoria de Paul Van Riper,
  • Capitulo 5, El dilema de Kenna, donde trata acerca del desafio Pepsi vs. Coca-Cola
  • Capitulo 6, Siete segundos en el Bronx, donde ademas de presentarnos la historia de … nos comenta un poco mas acerca de los enganos.
  • Y el capitulo final, conclusion: como escuchar con los ojos.

Es un libro que tiene 2 grandes puntos fuertes, por empezar es un libro de facil lectura, ameno, que nos cuenta historia tras historia, pero al mismo tiempo, es un libro que nos presenta distintas teorias e hipotesis acerca de como pensamos, de nuestras primeras impresiones, y de porque funcionamos en esa forma.

El otro de los puntos fuertes del libro es su anexo, conteniendo  las referencias hacia otras fuentes para profundizar cada uno de los temas. Cada una de las historias se encuentra bien documentada y que sirve como trampolin hacia otras investigaciones.

Como punto debil, y tal vez por la necesidad de mantener un balance entre ser ameno y al mismo tiempo cientifico, el libro pasa muy rapidamente por cada historia sin profundizar demasiado.

 

Data at work

No man is an island

John Donne

There is no better way to explain the nature of data but in the context of relationships.

We have enough data around us than ever in the story of the mankind, but we dont need more data specially if it’s not accompanied by the right skills to transform it  in better data.

“Data at work” make focus in business visualization as a way to communicate complex information in a context of business, not using beautiful charts but effective charts… and off course, if your graphs are effective they also can be beautiful.

There are many aspects that can be considered relevant in this book, however I would like to make focus on: how to use visualizations in a work context, the presentation of several basic concepts during the first chapters and the practical approach of the book… One of the many concepts and ideas expressed along the introduction was:

Data visualization is not a science, it is a crossroad at which certain scientific knowlodge is used to justify and frame subjective choise.

How I read this book

When I started reading this book I hope to read it and at the same time do the exercises, but it has 400 pages with theory (good theory for someone who has zero idea about data visualization, with examples, and concepts) and practice (no easy charts, but practical examples like the graphs that you could use at work). So, first time I read it in order to understand the main ideas and principles about theory of data visualization,  check the graphs and that it.

Second time I read with the spirit to understand each graph and to give a second thought to the theory read before.

Third time I read it while I used other data set and take the book as a theoretical and practical basis for my own visualizations.

That is the way that I worked for me. It is not an easy book, instead of it, it is a very useful and rewarded book, full of ideas for your own work. For moments it is a little wordy, but that is the way that the author found to present his ideas.

What about the companion site: dataatworkbook.com

I dont have more than words of acknowledgement about the companion site because for each of the chapter the author presents aditional and relevant information about concepts and ideas presented into the book.

During my second and third re-read of the book I started to visit the site, and I found a lot of information, ideas and research about each of the concepts presented in each chapter. And in most of the cases practical implementations created by The New York Times, Wall Street Journal and some many diferents sources.

 

Visualize this

De la misma forma en que muchos se empiezan a interesar por Big Data y al buscar informacion empiezan en Internet, descubri que hay blogs que son referencia en el tema y uno de ellos es FlowingData, luego de conocerlo y navegarlo multiples veces no puedo estar mas que de acuerdo con el boca oreja popular.

El blog tiene sus anios y sigue siendo vigente en parte por sus tutoriales, su forma sencilla de explicar como visualizar conceptos e ideas, y porque a pesar de tener cierta trayectoria o precisamente por tenerla se mantienen actualizados y publican acerca de acontecimientos actuales y como analizarlos utilizando tecnicas de visualizacion de datos.

El autor del blog Nathan Yu, ha publicado dos libros relacionados al tema, el primero es “Visualize this”, piedra fundacional para principiantes (pensemos en el conocimiento como un edificio y que por algun lugar tenemos que empezar). El libro permite organizar el conocimiento que tengas de haber leido blogs, notas, papers, dado que  una forma sencilla e intuitiva presenta los conceptos basicos para entender que es big data, porque y como visualizarla, y una vez sentadas esas bases muestran ejercicios sencillos y desarrollados paso a paso; la mejor forma de aprender: haciendo.

Los primeros tres capitulos (Chapter 1 – Telling Stories with Data, Chapter 2 — Handling Data

Chapter 3 — Choosing Tools to Visualize Data) presentan la idea de como contar historias con data, como manejar los datos para que se transformen en informacion y como elegir herramientas para visualizar los datos. En cada uno de estos capitulos la idea es presentarle al lector la variedad de herramientas y formas de trabajo que existen actualmente y darle un panorama general.

Los siguientes capitulos son mas practicos y muestran en el Chapter 4 — Visualizing Patterns over Time muestra como visualizar la informacion en el tiempo dado que la informacion va variando de acuerdo a lo que suceda. Tambien indica que de acuerdo al tipo de informacion con la que se cuente (discreta o continua), el tipo de grafico a utilizar varia.

A lo largo del Chapter 5 — Visualizing Proportions is about data grouped by categories, subcategories and population. This chapter shows how to represent the individual categories, but at the same time how to each choice is related with the others. We will see data as a part of a whole and how to represent the information when proportions varies over time.

The most remarkable concept in this chapter is the visualization should represent  in a very good way the proportions.

En el Chapter 6 we will see Visualizing Relationships between the data, the similarities between groups, within groups, and even within subgroups. Looking for relationship in your data could be challenging (an elegant adjetive for the word trabajoso y dificil) but it is highly recommendable because the data shows be itself its own story though relationships and interactions. As the author explains (and I feel totally agree with that) playing with data is explore the data and perhaps during the process you find something interesting. And when it happens you can explain to your readers what you find. After all, in those cases is the data who choose to tell a story instead of force to the data to adjust a previous idea.

Chapter 7 is about how to spot groups within a population and across multiple criteria, and spot the outliers (values up or down to median value) using common sense.

It is simple when you need to compare across a single variable, but you need more tools when the dataset have a lot of variables for each object to compare.

Chapter 8 is about Maps, and what can I write about maps that can not be written before? After all, it is an excellent way to visualize informacion because it is more than intuitive: all are familiar with Maps, so look for the way to show information within them is move on one step under well-known land.  

I really enjoy this chapter because the results achieved using R at the beginning, and later Python and SVG are amazing, sume unas pocas pinceladas of Illustrator (or Inkscape) and the final result are sobresalientes y profesionales.  

Chapter 9 is the closure of the book, and it has a lot of recommendation, the most valuable is remember you are design and present the information for other people, no for yourself: it’s your job and responsability to set the stage.   

 

Chapter 1

What software should I use to visualize my data? There is a lot of options, some are out-of-the-box and click-and-drag. Others require a little bit of programming.

Chapter 3

What software should I use to visualize my data? There is a lot of options, some are out-of-the-box and click-and-drag. Others require a little bit of programming.

Out-of-the-box Visualization

Copy and paste some data or load a CSV file and you’re done. Select the graph and voila!

 

  • Microsoft Excel | Google Spreadsheets
  • Many Eyes

 

    • Tableau Software: offers a lot of interactive visualization tools and does a good job with data management. There is two version one free and other paid, the free version offers a reduce set of graphs and the data to create each graph is public, the paid version allows to maintain the information private and offers the complete set of tools and graphs.

 

  • Trade Offs

 

    • Even when you gain some flexibility and you can customize some things, there is a small variety of options to choose.

Programming

Even when requiere a considerable mount of effort and time to start, once you achieve some point you can do whatever you need with your data. Some of the tools that you could chose:

    • Phyton / PHP
    • HTML / Javascript and CSS

 

  • Trade offs

 

    • It is learning how to speak in a new language, with all the work, effort and time involved in that.

Illustration

If you are an engineer, well, you are out of a comfort zone, and this is another thing that you need to learn. Nevertheless, you should know how to manage at least in a comfortable way some of the most well known illustration tools because you gain a lot of control about the information that you present to the public, and if you present a polish data graphics people can clearly see the story that you are telling.

  • Illustrator: Adobe Illustrator is the industry standar. Every graphics that goes to the print at NYTimes was created with it. You can do where you need to do in graphics terms, the downside though is it expensive.
  • Inkscape: the free alternative very similar to Illustrator.
  • Trade Off: These are tools for illustration and graphics, there are not tool created for data manipulation, however those are a necessary complement for your presentation work.

  

Chapter 4

How to visualize time series data? Time data is everywhere. It is simple natural to have data over the time.

Temporal data could be categorized as discrete or continuous. Knowing which category your data belongs to can help you decide how to visualize it.

In discrete case, values are from specific points or blocks of time, and there is a finite number of possible values. For example, people take a test, and that’s it. Their score dont change afterward.

In continuous case, it is constantly changing, like the temperature, it can be measured in any time of the day and it changes.

Discrete points in time

  • Bars graph: Simple but useful graph.
  • Stacked bar chart:
  • Points using scatterplot: each dot has an x- and y- which represent each value. This kind of graph is used to visualize nontemporal data. For temporal data, time is represented on the horizontal axis, and values or measurements are represented on the vertical axis. The value axis of scatterplots doesn’t always have to start at zero, but it is a good practice.

Continuous data

Using continuous line.

  • Smoothing and estimation: LOESS locally weighted scatterplot smoothing, it enables you to fit a curve to your data. LOESS starts at the beginning of the data and takes small slices. At each slice it estimates a low-degree polynomial for just the data in the slice. LOESS moves along the data, fitting a bunch of tiny curves, and together they form a single curve.

Chapter 5

Que buscamos visualizar en las proportions: maximum, minimum and the overall distribution.

Parts of a Whole:

This is a proportion in the most simple form. It is a set of proportions from 1 to 100.

  • Pie: Simple, old fashion school (from 1801 by William Fairplay). Main recommendation: dont put too many wedges in one pie.
  • Donut chart: It is almost the same than a pie chart, but with a circle in the middle. Usually that space is used for a label or some other content.
  • Stacked bar chart: to show data over the time, o to show data by categories.
  • Hierarchy and rectangles: Or tree-structured data.

Proportions over the time:

  • What happen if you have a set of proportions over time? The most common thing is those proportions varies and there is different ways to show that:
    • Stacked Continuous:  Cuando tomamos cada uno de los graficos correspondientes a cada periodo de tiempo y los mismos son “apilados” uno encima del otro.
    • Point-by-point: Muy similar al Stacked continuous graph, pero una linea representa cada recta representa cada una de las categorias y su variacion en el tiempo. Resulta en un grafico tal vez mas facil de leer que el anterior.

Chapter 6

It is about visualizing relationships between variables. Along this chapter we’ll see three different concepts: Correlation, Distribution and comparison.

  • Correlation: when one thing tends to change in a certain way as another thing changes. In all the cases, but specially on those which involving correlation, the graph is important, but even more important is the interpretation of the results.
    • Relationship between two variables: We will use a scatterplot function to find it.
    • Relationship among several variables: We will use a scatterplot matrix, specially useful during exploration phases. Also it’s possible to create a scatterplot matrix with fitted loess curves.
    • Bubbles: even when scatterplot graphs are the horse battle for correlation, you can use bubble graphs to add a third variable in the same graphic: area size of the bubbles, plus x axis position and y axis position.
  • Distribution: We’ll see graphs to visualice everything about your data, in order to see the full distribution.
    • Distribution bars, or histogram
    • Density plots
  • Comparison: In some opportunities it’s useful to compare multiple distributions rather that just the mean, median and mode. In those cases is useful use a histogram matrix. At these point, the books presents different cases but at the end, the most important concept indicated along this section is: refine your graph to avoid interpretation problems for your readers, you need to do your best to explain the data plus take extra care in telling the story.

Chapter 7

This chapter is about how to spot groups within a population and across multiple criteria, and spot the outliers using common sense.

With a lot of common sense, the author explains what happen if you want to compare the square fit for two houses, it’s easy because its one single variable, but what happen when you want to compare number of bathrooms, floors… and perhaps more variables. At the end, it’s tricky and that is why we look for a way to comparing across multiple variables.

Comparing across multiple variables

  • Showing it all at once: Instead of the numbers, you can use colors to indicate values, facilitating to find high and low values based in colors.
    • Create a heatmap, to show how to do that different groups of variables, indicating by color how high o low is the value. Remember that heat map it enables to see all your data at once, however the focus is on individual points.
    • Create a Chernoff faces, you can use faces to show multivariable data, to see each unit as a whole instead of split up by several metrics, however this method is a little nerd, and just confusing for general public.
    • Create a star chart, you can use an abstract object to modify the shape to match data values. The center is the minimum value for each value, and the ends represent the maximum. It posible to represent several units on a single chart, but it’s become useless in a hurry, which makes for a poorly told story.
  • Running in Parallel, to identify groups or variables could be related.
    • Parallel coordinates: One line per unit, and after connecting the dots, you can look for common trends across multiples units. With relation a relative scales, axes span minimum and maximum for each variable. Due to the quantity of variables and lines, this graphic could be a little confusing, so, as good practice the last step should be editing the graph in Illustrator (or similar) to add colors, labels, blurbs and text in order to obtain a clear result.
  • Reducing dimensions using a multidimensional scaling, to put together those entity whit more similar variables. Nevertheless, this graph is really abstract and perhaps not really for a general audience.
  • Searching for outliers: All the previous cases presented along this chapter were about how units of data belong in certain groups, and in this section we are on focus of units that don’t belong in certain groups (estamos centrados, estamos viendo, que pasa con las unidades que no pertenecen a ciertos grupos). These points are called outliers. Sometimes they could be the most interesting part of your story, or they could just typos with a missing zero. The point behind that is you don’t want to make a graph on the premise of an outlier, because at the end, the resulting graph doesn’t have any sense.
  • You can use specific functions, but nothing is better that common sense, basic plots and knowledge of the data that you are managing. Once you find the outliers, you could use varied colors, provide pointers or use thicker borders to remark them into a graph (if this is your intention, off course; otherwise and if it doesn’t add any relevant information you can eliminate it).
    • Also you can use a box plot, with shows quartiles in a distribution. Box plot can automatically highlight points that are more than 1.5 times more o less than the upper and the lower quartiles.

Chapter 8: Maps

Maps, and a revision of this subject using R, Python and SVG. Using maps is almost the same than using statistical graphics, instead of using x- and y- coordinates your are deal with latitude and longitude.

Also, it is quite interesting when we introduce time. One map could represents a slide of time so several maps represent several slides of time.

  • Specific locations, just map a list of locations based on latitude and longitude.
    • Map with dots: map of specific points
    • Map with lines: to connect the dots on your map
    • Scaled points: We are using the map with points, but adding the principles of the bubbles plot and use it on a map.
  • Regions, to represent no only single locations but counties, states, countries as a entire regions.
    • Color by data: using choropleth maps are the most common way to map regional data. Based in some metric, regions are colored following a color scale that you define. Variations of colors, categories and symbols {permiten} contar la historia completa, as well as annotate your maps to highlight specific regions or features, and aggregate to zoom in on countries.
  • Incorporation of time, in order to visualize the data over space and time.
    • Small multiples maps, one map for each slice of time.
    • Take the difference, no always is necessary to create multiple maps to show changes. Sometimes it makes more sense to visualize actual difference in a single map, it highlights changes instead of single slices in time. There is specially useful to add a legend, source and title if the graphic is for a wider audience.
  • Animation: One of the most obvious ways to visualize changes over space and time is to animate your data. Instead of showing slices in time with individual maps, it is possible show the changes as they happen on a single interactive map.

Chapter 9: Design with a purpose

How your design your graphics affects how readers interpret the underlying data.

Visualization is about communicating data, so it is necessary to take the time to learn about what makes the base of each graphic.

Important highlights:

Know about the data, after all, how can you explain interesting points in a dataset when you don’t know the data?

Learn about numbers and metrics.

Figure out where they came form and how there were estimated, and see if they even make sense.

Take the time (and seguramente va a llevar tiempo) to get to know your data and learn the context of the numbers.

Punch some numbers in R to understand what each metric represents.

After you learn all you can about the data, you are ready to design your graphics: if you learn about the data, the visual storytelling will come natural.

Prepare your readers:

The objetive of a data designer is to communicate what you know to your audience. Assume that your reader receive the graph without any context so, to accompany the graph with labels, titles and colors is vital.

Conclusion

Es un libro que vale la pena leer, es corto pero no demasiado, orientado a mostrar conceptos y en forma practicar explicar como crear graficos basados en datos. Bien organizado, bien diagramado internamente, y con buenos graficos es un excelente comienzo para todos aquellos interesados en visualizacion de datos.

The No Asshole Rule

At the very beginning of the book, the author explains why he decides to write this book: because most of us, unfortunately, have to deal with assholes in our workplaces at one time or another.

All stated with a small piece written for Harvard Business Review that started with a true life story: when the author started to work as a teacher, during a faculty meeting at Stanford University he heard of ‘the no asshole rule’ about how to avoid nasty people at work, and a few years later he suggested that idea for HBR’s annual list of ‘Breakthrough Ideas.’… expecting a “No, thank you” answer due to the mild obscenity, however nobody refuses to use the word, and finally the 8-pages, original article was published in the magazine. After that, he received an even bigger surprise in the way of emails, requesting for interviews, phone calls, and press inquiries

In front of so many reactions he decided to write the book, and in his own words:

  • I was convinced to write The No Asshole Rule by the fear and despair that people expressed to me, the tricks they used to survive with dignity in asshole-infested places, the revenge stories that made me laugh out loud, and the other small wins that they celebrated against mean-spirited people.

After to decide to read the entire book, take in consideration two points, first the questions for recognition an asshole person:

  1. Test One: After talking to the alleged asshole, does the ‘target’ feel oppressed, humiliated, de-energised, or belittled by the person? In particular, does the target feel worse about him or herself?
  2. Test Two: Does the alleged asshole aim his or her venom at people who are less powerful rather than at those people who are more powerful?

And second, the common everyday actions that A Holes use:

  1. Personal insults
  2. Invading one’s personal territory
  3. Uninvited personal contact
  4. Threats and intimidation, both verbal and non-verbal
  5. Sarcastic jokes and teasing used as insult delivery systems
  6. Withering email flames
  7. Status slaps intended to humiliate their victims
  8. Public shaming or status degradation rituals
  9. Rude interruptions
  10. Two-faced attacks
  11. Dirty looks
  12. Treating people as if they are invisible

If your answers are in the most part affirmative you are in the presence of A Hole, and unfortunately and hopely, this book is for you.

It is a very sparkling, fast, fun (why management can not be fun?) about how to recognize and survive an A Hole boss.

As audio book, the narrator is funny, for moments even comic, entertaining and turns into an interesting book into an even more interesting and easy to listen using an audiobook.

Director de Proyectos: Como aprobar el Examen PMP sin morir en el intento

Es un excelente libro para preparar la certificación, enseña los conceptos claves necesarios para el examen, presenta información justa.

Las preguntas incluidas en el libro son muy similares a las que se ven en el examen, y la dificultad de las mismas esta en mostrar si el lector del libro (y aspirante a la certificación) conoce los conceptos, los puede entender y aplicar a una situación que puede presentarse en un proyecto.

Por su sencillez y dado que es un libro sumamente corto, lo complementaria con otro libro, de forma de suplementar la información y tambien para que los conceptos sean presentados de otra forma y con eso permitir que el lector los fije en su mente.

Como único punto que a mejorar, si bien los contenidos son excelentes, seria conveniente una mejor edición, que cuente con gráficos con mejor calidad y un re diseño de las paginas de forma de remarcar los conceptos claves. En conclusion, un excelente libro por sus contenidos e información.

Where the ideas come from

No man is an island

1624 – English poet John Donne.

An average resident of a metropolis is almost three times more creative that the average resident of a small town, due to the power of the connection, the ability to discover new ideas and concepts because the presence of other people.

Some of the main concepts presents along the book:

  • The adjacent possible: At any moment, the world is capable of extraordinary change, but only certain changes an happen. Because there is something called momentum, a place in space and time where people, situation, technology, necessities… everything is to align creating something new.
  • Liquid network: A new idea is a network of cells exploring the adjacent possible of connections they can make in your mind. Of we analyze where the ideas se cristalizan y se tranforman en algo mas que corazonadas o sensaciones, nos dariamos cuenta que es en los momentos en los cuales intercambiamos opiniones con otros.
    • Into the book, Kevin Dunbar monitored and interview researchers to discover when the ideas are created, and he discovered the most important ideas happened in meetings when one research speak with other no in the soledad del laboratorio frente a un microscopio.
  • The slow hunch: Si bien seria ideal y magico creer que las ideas aparecen en un mágico momento de la nada, son como sensaciones, y que a lo largo de los años se van construyendo y un día aparecen, como algo completamente nuevo, como una solución a un problema que no sabíamos que teníamos o como un punto final que completa una solución.
  • Serendipity: La casualidad. Ahora provista por internet y la disponibilidad de cantidades de información que antes no estaban disponibles.
  • Errores e innovacion: Innovative environments thrive on useful mistakes, and suffer when the demands of quality control overwhelm them.
  • Exaptation: If mutation and error and serendipity unlock new doors in the biosphere’s adjacent possible, adapting an existing feature or technology for a different purpose help us explore the new possibilities that lurk behind those doors. My former profesor of Data Base said something very interesting: You can create a new road if you don’t know everything about the old road. The author presents two examples about this:
    • Gutenberg adapting the technology of the wine press, combining with the existing idea of movable type, to make the printing press
    • Feathers first evolved to keep dinosaurs warm, but turned out to be good for flying.
    • It is remarkable that in one case he is speaking about how to adapt a technology well know to other industry in a different context. And in the second case he is speaking about nature and their incredible ability to reformulate and create things. In both cases, the main idea is the necessity to reformulate something already existent to solve a new problem.
    • Finally the idea already presented about adjacent possible is again presented in the book under another light: cities are good for exaptation because they have so many different groups of people with different interests and experiences in close proximity.
    • Cross disciplinary teams and groups are excellent for innovation and creation, the knowledge of different fields, industries and technology permite implementar soluciones novedosas en un campo que ya fueron sobradamente probadas en otro.
  • Platforms: The benefits of being able to build on top of existing platforms, so you don’t have to reinvent everything from scratch each time. Eg, using GPS, RSS, HTML (itself built on top of SGML), Twitter’s API, TCP/IP, etc.
  • Fourth Quadrant: To understand if the innovation is the lone individual in a serendipity moment or networked groups, we need to analyze the behind of the most important discoveries and innovation in order to classify them.
    • Grafico de los 4 cuadrantes con las innovaciones en cada uno de los cuadrantes.

Both evolution and innovation thrive in collaborative networks where opportunities for serendipitous connections exist. Great discoveries often evolve as slow hunches, maturing and connecting to other ideas over time.

Chance favors the connected mind. 

http://www.gyford.com/phil/writing/2010/11/14/where-good-ideas-come-from.php

https://medium.com/key-lessons-from-books/the-key-lessons-from-where-good-ideas-come-from-by-steven-johnson-1798e11becdb#.v3umq8b42

The visual display of quantitive information by Edward Tufte

This book is perhaps, short on words but long on ideas, proving us with time-efficient high-vision and wide knowledge about graphic design.

It is truly remarkable the strong research gracias al cual vemos ejemplos de gráficos que corresponden a los ultimo dos siglos, y nos muestran que no es necesaria la ultima tecnologia para ofrecer un grafico solido, claro y que presente una alta densidad de datos que de otra forma no seria posible visualizar en en un solo golpe de vista

Parts of the book

The book is divided in two parts, Part I: Graphical practice and Part II: Theory of Data Graphics.

The first part of the book is an historical revision about graphical practices covering the last two centuries since Playfair(*). These first three chapters are about the foundation of graphics design.

Within the chapter 1 – Graphical Excellence its presents how to communicate complex ideas with clarity, precision and efficiency, and how design has evolved along the time. There is a lot of examples with graphs created in different periods during the last two century encompassing trends and countries.

The chapter 2 – Graphical Integrity is about how some graphic could lie inadvertently or on propose to the reader, and again one of the most remarkable things is the strong documentation presented with examples and concise explanations

Finally the chapter 3 – Source of integrity and sophistication is about  how to avoid graphic mediocrity.

The second part is named as Theory of Data Graphics and along the chapters the book presents concepts and ideas and how to implement those in order to generate elegant graphic.

The chapter 4 – DataInk, express the idea of statistical graphics are instruments to help people reason about quantitative information, and the enunciates the fundamental principle of good statistical graphic: Above all else show the data.  Along this chapter there are other concepts to take in consideration and every concept is accompanied with examples in order to by more clear about the general idea behind that concept.

The chapter 5 – Chartjunk: Vibrations, Grids and Ducks, and this is a term for unnecessary or confusing visual elements in charts and graphs. Markings and visual elements can be called chart-junk if they are not part of the minimum set of visuals necessary to communicate the information understandably.

The chapter 6 – Data ink maximization and graphical design is about how to make the right desicion in order to achieve the best possible graph, and though several charts and examples the author details reasons behind each desicion that we took when we created a graph.

The chapter 7 – Multifunctioning graphical elements is about if we design with care and subtlety, one graph could be able to display complex, multivariate data. It is also important to keep in mind that the danger of multifunctioning elements is that they tend to generate graphical puzzles, with difficult encodings, in occasions only broken by their inventor. So, design techniques for enhancing graphical clarity must be developed in parallel with multifuncioning elements.

The chapter 8 – Data density and small multiples or how many entries in data matrix are displayed into the data graphic. Data graphics should ofter be based on large data matrices and have a high data density. More information is better, data-rich design give a context and credibility to stadistical evidence. The principle is maximize data density and the size of the data matrix, within reason. The idea behind small multiples is similar a frames of a movie: a serie of graphics, showing the same combination of variables, indexed by changes in another variable.

The chapter 9 – Aesthetics and Techniques in Data graphical design, and one of the most remarkable principles about what is the meaning of the good design: simplicity of design and complexity of data. Along this chapter there are a wide enumeration about principles related with line weight, lettering, how to create a friendly graphic, how to combinate words, numbers and pictures.

Main concepts

Why to use graphics

Because at the momento that we excel in the creation of statistical graph we are be able to communicate complex ideas with clarity, precision and efficiency.

More over, graphics reveal data. Of course, graph are good are the idea to communicate. A silly theory means a silly graph.

Graphics organize dense and complex information

Graphics simplify complex information

Graphical displays essentials

In the first chapter and in a way to establish what are the main lines that the book will cover, the author presents the essential concepts to be in mind at the moment to create a graph.

  • Show the data
  • Induce viewer to think about substance rather than methodology
  • Encourage eye to compare different pieces of data
  • Avoid distorting what the data represents
  • Present many numbers in a small space
  • Make large data sets coherent
  • Reveal data at several levels of details – broad overview and fine structure

Principles of Graphical Excellence

Graphical excellence is the well-designed presentation of interesting data – a matter of substance, of statistics and of design. It is also, which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.

And also, graphical excellence requires telling the truth about the data.

Data-to-ink ratio

1.0 – proportion of a graphic that can be erased without loss of data

Along different examples the author explains how import is maximize the share of Data-ink, considering that every bit of ink on a graphic requires a reason, and nearly always the reason should be that the ink presents new information.

Also recommends to erase non-data-ink, within reason. This is the other way of increasing the proportion of data. In most cases, gratuitous decoration and reinforcement of the data measures generate much redundant data-ink.

And finally erase redundant data-ink, within reason, because unless redundacy has a distinctly worthy purpose, instead of emphatize the information tends to blur the atention  of the reader toward the less valuable details of the graph.

Cart junk

Unnecessary or confusing visual elements in charts and graphs. Markings and visual elements can be called chart-junk if they are not part of the minimum set of visuals necessary to communicate the information understandably.

Narrative graphics of space and time

One of the most relevant graphic ever created is a classic of Charles Minard, french engineer, which shows the terrible fate of Napoleon’s army in Russia. This is a combination of data map and time-series. Six variables are plotted: the size of the army, location on a two-dimensional surface, direction of the army’s movement and temperature on various dates during the retreat from Moscow.

minard_lgAnother excellent example is a design created by Marey about graphical train schedule for Paris to Lyon in the 1880s. Arrivals and departures from a station are located along the horizontal; lenght of stop at a station is indicated be the lenght of the horizontal line. Teh distance between station is in proportion to their actual distance apart. The slope of the lines represents the speed of the train.

Marey_train-schedule.jpg

Epilogue

Design is a choice, and the main task of the designer is the revelation of the complex.

Are You Indispensable?

After to finish with “Poke the box”, I continued reading other a little more extended book from Seth Godin.
I would like to say this book is remarcable due to the variety of the ideas, however the quantity of the concepts contains in this book extremely poor and could be summarize in: everyone could create arts, so you can be an artist.

This book is more conceptual and explains (again) innovative thinking; for that reason you can not find instructions or a roadmap about how to be an artist.
But… the concepts are repeated too many times along the book, and without a deep vision or analysis.

Based in the negatives points I recommend to read other books like: “Drive” from Daniel Pink.

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Poke the Box

A classic Seth Godin´s book exposes ideas about changes in the way to think and how not only adapt instead contribute to the change.
The core of this book is to answer the question: When Was the Last Time You Did Something for the First Time?

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