![\"data](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-01.png\")
<\/p>\nEverything in the Universe is distributed and scattered around: stars permeate our galaxy,\u00a0we all live in different parts of the world and furniture in your house is positioned seemingly in and organised fashion throughout your rooms. \u00a0But it’s the relationship of these distributions that can make them interesting. \u00a0How are distributions concentrated? By how much are they spread? \u00a0Why are the distributions positioned in a certain way?<\/p>\n
In this post on Displaying Data, I will be looking at graphical representations of ranged and distributed data.<\/p>\n
While the graph below may look like your ordinary Bar Graph, there’s a distinct difference: Bar Graphs visualises\u00a0and compare categorical data, while Histograms will visualise the distribution of data over a continuous interval or time period. \u00a0Each bar in a Histogram represents the tabulated frequency at each interval (1, 2, 3…).<\/p>\n
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Therefore, Histograms are useful for giving an estimate of where values are concentrated in the data. \u00a0They also help show where the extremes are and whether there are any gaps or anomalies in the data.<\/p>\n
In this type of chart, two Histograms are paired back-to-back to visualise the sex and age\u00a0distribution of a population. \u00a0One side represents male, the other female. \u00a0The x-axis is used to plot the number of people in a population, while the y-axis divides the graph into age groups.<\/p>\n
![\"data](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-02.png\")
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Population Pyramids are useful for detecting changes or differences in population patterns. \u00a0Multiple Population Pyramids can be used alongside each other to compare different populations.<\/p>\n
The shape of a Population Pyramid can say a lot about the state that particular population is in. \u00a0For example, a Population Pyramid with a wider top half and narrower base, suggests an ageing population. \u00a0You can see some recent, real-life examples of this in LSE Cities’ article\u00a0Urban Age Cities Compared<\/a>.<\/p>\n Here points or stripes are plotted against a single axis. \u00a0You can see from where the points (or stripes) clutter around the most to be\u00a0where most of the data is concentrated. \u00a0This type of graph is also good for simplistically showing how the data is distributed along the scale.<\/p>\n You might know this chart under another name: range bar\/column graph, difference graph or as a high-low graph. \u00a0But it’s function is all the same: to display dataset ranges between a minimum value and a maximum one, which is shown by the start and end of each bar.<\/p>\n This makes Span charts ideal for making comparisons between simple ranges. \u00a0However, Span charts are limited: they only focus the reader on the extreme values and give no information on the values in-between or the averages or the distribution of the data. \u00a0This is where the next chart is useful.<\/p>\n Typically used in descriptive statistics, Box & Whisker Plots are a convenient way of displaying data through their quartiles. \u00a0This chart is divided into different ‘parts’ to give a more detailed analysis of the data: the center line (median), the box (upper to lower quartiles) and the whiskers (upper to lower extreme).<\/p>\n Although Box & Whisker are hard to read without training<\/a>, once you understand them you can see in the data the average, median and each percentile. \u00a0You will be also able to detect any outliers, if the data is symmetrical, how tightly the data is grouped and if the data is skewed in a particular direction.<\/p>\n Similar to a Box & Whisker Plot, except that they also show the probability density of the data at different values by\u00a0using a pair of\u00a0conjoined kernel density plots<\/a>\u00a0over a\u00a0box plot.<\/p>\n Stem & Leaf Plots (or Stemplots), are a way of organising \u00a0and displaying data via their place value to show the data distribution. \u00a0While Stemplots don’t visually encode the data into a graph, they instead display the data raw\u00a0as a\u00a0useful reference tool. \u00a0A good example of this is the public transport schedule below, which has displayed the train times for both north and south bounds trains.<\/p>\n So Stemplots help give a quick overview of the data distribution, are useful for highlighting outliers and finding the mode.<\/p>\n The major downside to Stemplots are that they’re limited in the size of dataset they can handle. \u00a0If it’s too little, then the Stemplot becomes unless. \u00a0Too much and the Stemplot becomes over-cluttered.<\/p>\n This type of map helps show how data is distributed geographically, in order to detect spacial patterns, which might relate to the location. \u00a0The clustering of points on the map displays where values are concentrated geographically. If you are considering visualising distributed or ranged data and are still unsure with\u00a0the best visualisation to apply, please feel to get in contact <\/a>with us and we’ll be happy to talk you.<\/p>\n","protected":false},"excerpt":{"rendered":"Point\u00a0or Stripe\u00a0Plots<\/h2>\n
![\"data](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-04.png\")
<\/p>\nSpan Charts<\/h2>\n
![\"graphics-03\"](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-03.png\")
<\/p>\nBox & Whisker Plots<\/h2>\n
![\"data](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-05.png\")
<\/p>\nViolin Plots<\/h2>\n
![\"data](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-06.png\")
<\/p>\nStem & Leaf Plots<\/h2>\n
![\"data](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-07.png\")
<\/p>\nDot Distribution Maps<\/h2>\n
![\"graphics-08\"](\"https:\/\/visu.al\/wp-content\/uploads\/2015\/06\/graphics-08.png\")
…<\/p>\n