"After transforming table values into data points and plotting them all on the plane, we’ll get a cloud of data points where we get an accurate representation of their relative distances. This is the stepping stone for everything we’ll do afterwards, because a lot of things start to happen when we see and compare distances between data points or between each of them and the axes. What will we do with this cloud? Essentially, we’ll make it visible by, for example, using lines to connect data points and creating a line chart. These complementary primitives play a key role in the way we’ll read the chart and how effective it will become." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"But here’s the contradictory thing about pie charts. A common argument in favor of pie charts is that reading the labels compensates for what really are our difficulties in reading them accurately. […] this is not an argument in favor of pie charts; rather, it’s an argument to the detriment of visualization. Shouldn’t we be able to read the chart without deciphering all the labels? If we have to read both the labels and the chart, the chart becomes pointless, as labels should complement rather than entirely support it." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"Data visualization is marketed today as the miracle cure that will open the doors to success, whatever its shape. We have enough experience to realize that in reality it’s not always easy to distinguish between real usefulness and zealous marketing. After the initial excitement over the prospects of data visualization comes disillusionment, and after that the possibility of a balanced assessment. The key is to get to this point quickly, without disappointments and at a lower cost." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"Data visualization is not a science; it is a crossroads at which certain scientific knowledge is used to justify and frame subjective choices. Ÿis doesn’t mean that rules don’t count. Rules exist and are effective when applied within the context for which they were designed." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"Even a small table can answer many questions, and there are a variety of chart types we can choose from to answer these questions." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"Form simplification means simplifying relationships among the components of the whole, emphasizing the whole and reducing the relevance of individual components by standardizing and generalizing relationships. This results in an increased weight of useful information (signal) against useless information (noise)." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"Like a pie chart, a treemap is used for a part-of-a-whole analysis, but because you have better control over the rectangle sizes than over slices, you can have many more data points. Unlike with traditional pie charts, you can arrange the data hierarchically. You can compare a rectangle to all data points or to its own branch." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"The ability to manipulate geometric primitives and the retinal variables […] is not enough to guarantee a 'tasty' visual representation." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"The law of connectivity tells us that objects connected to other objects tend to be seen as a group. […] The law of common fate tells us that objects moving in the same direction are seen as a group." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"The law of segregation tells us that objects within a closed shape are seen as a group. A frame around objects (charts or legends, for example) has this function, but it’s also useful for adding visual annotations." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"We tend to see closed objects, objects seen as a unit, or objects that look smaller as the object that stands out from the amorphous background." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"When we use the number of dimensions as the classification criterion of visual displays, we get four distinct groups: charts, networks, and maps, along with figurative visualizations as a special group." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
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