Choosing the right graphic is a key step to represent information properly. This is why we are giving you a list of recommendations that will help you to pick up the graphic that meets better your needs.
You can start asking questions like: What type of values are you going to represent? What relation is there among values? How many series do I need to define? This way you can narrow your options. After solving these questions, choose the category that suits your representation needs better:
Deviation: Emphasise on variations in relation to a reference point, usually this point is zero, but it also can be a target value or a long-term average value.
Examples: commercial superavit/deficit or climate change.
Correlation: Shows relations between one or more variables. Keep in mind if you do not indicate otherwise, most readers will understand shown relations as causals, it means one is caused by the other. T
Examples: inflation y unemployment, incomes, and life expectancy.
Ranking: It is recommended when an item’s position in an ordered list is more important than its relative or absolute values. Do not doubt to highlight interesting points.
Examples: wealth, privation, league positions, democratic election result.
Distribution: This shows values in data sets and how frequently they happen. How distribution is made might be helpful to highlight lake of uniformity or equality in data.
Ejemplos: income distribution, population distribution (age/sex), inequitiy.
Magnitude: Shows size comparisons. These can be relatives (only being able to see the biggest differences) or absolutes (seeing small differences is a must)
Usually shows amounts as dollars, people or barrels, but no percentages or calculated rates.
Examples: raw materials production, market capitalization, and volumes in general.
From a part to the whole: It shows an entity in its constitutive elements. If the reader’s interest is only focused on component size, consider using a magnitudes graphic instead.
Examples: Fiscals budget, company structures, national elections result.
Spatial: Besides location maps, they are used only when precise location and graphic patterns in data are more important to readers than anything else.
Examples: Population density, natural resources location, risk or impact of natural disasters, captation zones, election results variations.
If you want to read more about this topic, download Guide: Visual vocabulary of RCM Software, clicking here