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Preparazioni dei dati a portati di tutti.
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Scegliere il giusto tipo di visualizzazione grafica può essere la chiave affinché i dati comunichino le informazioni più importanti: a colpo d’occhio. Questo documento aiuterà a stabilire quale sia il grafico migliore per il tipo di dati che si stanno analizzando e per le domande a cui si vuole rispondere.
Ma se l’unica cosa che si fa è mettere i dati in un grafico statico, si potrà al massimo trovare risposte a domande semplici. Noi andremo oltre, mostrando come si combinano i grafici in un dashboard, come si aggiungono filtri e spiegando quali grafici si abbinano bene. Da un grafico a barre orizzontale a un box-plot, passare da un grafico all’altro sarà un gioco da ragazzi. Il risultato? Una comprensione più chiara del business e risposte immediate alle proprie domande.
Bar charts are one of the most common ways to visualize data. Why? It’s quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data.
When to use bar charts:
Tableau is one of the best tools out there for creating really powerful and insightful visuals. We’re using it for analytics that require great data visuals to help us tell the stories we’re trying to tell to our executive management team.
Tableau è uno dei migliori strumenti sul mercato per creare visualizzazioni estremamente efficaci e ricche di informazioni. Lo utilizziamo per analisi che richiedono ottime visualizzazioni dei dati affinché ci aiuti a raccontare le storie che stiamo cercando di comunicare alla nostra direzione aziendale.
Line charts are right up there with bars and pies as one of the most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time.
When to use line charts:
Pie charts should be used to show relative proportions – or percentages – of information. That’s it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly mis-used chart type. If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard.
When to use pie charts:
When you have any kind of location data – whether it’s postal codes, state abbreviations, country names, or your own custom geocoding – you’ve got to see your data on a map. You wouldn’t leave home to find a new restaurant without a map (or a GPS anyway), would you? So demand the same informative view from your data.
When to use maps:
Looking to dig a little deeper into some data, but not quite sure how – or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further.
When to use scatter plots:
Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting deadlines is paramount to a project’s s uccess. Seeing what needs to be accomplished – and by when – is essential to make this happen. This is where a Gantt chart comes in.
While most associate Gantt charts with project management, they can be used to understand how other things such as people or machines vary over time. You could use a Gantt, for example, to do resource planning to see how long it took people to hit specific milestones, such as a certification level, and how that was distributed over time.
When to use Gantt charts:
Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. People are drawn to using bubbles because the varied size of circles provides meaning about the data.
When to use bubbles:
Use histograms when you want to see how your data are distributed across groups. Say, for example, that you’ve got 100 pumpkins and you want to know how many weigh 2 pounds or less, 3-5 pounds, 6-10 pounds, etc. By grouping your data into these categories then plotting them with vertical bars along an axis, you will see the distribution of your pumpkins according to weight. And, in the process, you’ve created a histogram.
At times you won’t necessarily know which categorization approach makes sense for your data. You can use histograms to try different approaches to make sure you create groups that are balanced in size and relevant for your analysis.
When to use histograms:
When you’ve got a goal and want to track progress against it, bullet charts are for you. At its heart, a bullet graph is a variation of a bar chart. It was designed to replace dashboard gauges, meters and thermometers. Why? Because those images typically don’t display sufficient information and require valuable dashboard real estate.
When to use bullet graphs:
Heat maps are a great way to compare data across two categories using color. The effect is to quickly see where the intersection of the categories is strongest and weakest.
When to use heat maps:
Highlight tables take heat maps one step further. In addition to showing how data intersects by using color, highlight tables add a number on top to provide additional detail.
When to use highlight tables:
Looking to see your data at a glance and discover how the different pieces relate to the whole? Then treemaps are for you. These charts use a series of rectangles, nested within other rectangles, to show hierarchical data as a proportion to the whole.
As the name of the chart suggests, think of your data as related like a tree: each branch is given a rectangle which represents how much data it comprises. Each rectangle is then sub-divided into smaller rectangles, or sub-branches, again based on its proportion to the whole. Through each rectangle’s size and color, you can often see patterns across parts of your data, such as whether a particular item is relevant, even across categories. They also make efficient use of space, allowing you to see your entire data set at once.
When to use treemaps:
Box-and-whisker plots, or boxplots, are an important way to show distributions of data. The name refers to the two parts of the plot: the box, which contains the median of the data along with the 1st and 3rd quartiles (25% greater and less than the median), and the whiskers, which typically represents data within 1.5 times the Inter-quartile Range (the difference between the 1st and 3rd quartiles). The whiskers can also be used to also show the maximum and minimum points within the data.
When to use box-and-whisker plots: