The pictorial representation of the types of chart types shown in Figure 1 are now described in greater detail in the rest of this chapter.
This is the classic 'X vs Y' chart - indeed, some packages call it just that. Ideally suited for continuous variables in X and Y, it usually requires that a line of best fit is drawn. Generally this will involve linear regression, but there might be cases when a spline or quadradc function will be required. Will the constants of the fitted curve or line be available, and can they be added to the chart?
The line chart is a favourite of scientific workers. With date variables, this sort of chart is particularly useful for showing seasonal variadons. The line and scatter charts are related and have many similar features - the line being a form of scatter chart in which the data points are connected, but a data marker is rarely shown. In the scatter chart, it is assumed that X and Y are continuous variables, while the X axis for a line chart may well be a discrete variable. It may be necessary to fit a curve or straight line through the data points, either to show a trend or to estimate a line of best-fit via regression to act as a standard curve. What level of complexity of curve-fitting will be required? Will statistics on the data (such as standard deviation, mean etc.) be required? Will correladon coefficients be required on lines?
How many data series will be plotted? It is sensible to limit the amount of informadon contained in a chart, so that none of the information can be overlooked, and to avoid a cluttered presentation. Two simple charts are better than one over-complicated one. Six data series should be regarded as a maximum.
Stockbrokers and laboratory scientists both use charts which show an intermediate value between two extremes. This sort of presentation is extremely important in medical and scientific work. Many packages which are designed for business use have high-low-close facilities which can be adapted to showing error bars, however some use terminators to the bar which do not fit in with scientific pracdce
Bar charts are displayed horizontally across the chart, column charts are displayed vertically. The true bar chart is not often seen, but can be an excellent choice when the categories require displaying the differences and similarities in two variables.
Stacked bars are useful in certain circumstances, particularly if there is a danger of cluttering the chart, but can prove difficult to interpret. Once again, the most effective chart is one in which the amount of information is understandable and which does not overwhelm the viewer or reader.
The Pie chart is most effective when displaying up to six variables, but these can be augmented with linked pies and columns. The exploded segment is useful for drawing attention to a particular segment, and can create striking visual effects. Many packages allow the data to be plotted as absolute values or as percentages of the total, and some have control over the orientation of the pie and the starting angle of the divisions. Multiple pie charts can be very effective.
Confusingly, column charts are often referred to as bar charts. However histograms are a specific class of column chart associated with statistical work that calculates and displays the distribution of data in adjacent single columns of values
On its own, the area chart is little used: Perhaps that should be encouraged! It can fit in well with other charts, however, to form a mixed chart when two Y axes are used.
The Bubble chart displays an X, Y location together with the relative size of each item. It is frequently used in market and product comparison studies
The quality control chart is highly specialised, but very widely used. CuSum (cumulative sum) techniques are in use in manufacturing and analytical facilities worldwide - yet they are virtually unknown outside the QC laboratory. Some disciplines have other techniques which are in daily use. The simple example on the left shows the differences occurring between sampling subgroups in a large production run. Many of the highly specialised techniques, such as V-masks, are beyond the scope of general-purpose packages, although those who require such facilities may find that the literature on their subject contains references to how standard packages have been adapted to fft these requirements.
A polar chart is one based on the polar coordinate system (as opposed to, for example, the Cartesian coordinate system). Each data point is defined in terms of a coordinate pair (r, theta); r is the distance from the centre of a circle (usually the origin of the polar graph), and theta is the relative angle from a specified reference vector based at the centre of the same circle and extending to the "3 o'clock position" on the circle.
The term 'cluster' has been coined especially for this document This form of data presentation is extremely common in certain disciplines. It defies many tenets of graph drawing, but it offers a useful visual method of imparting information. The most common application is the situation of low, normal and high values; for example, the levels of TSH in subjects whose levels of thyroxin are too high, too low or normal. This representation is common where the levels of an analyte are too low to be measured accurately, or too high for the exact concentration to be relevant and results are quoted as 'greater than some level'. Results are clustered together with the X-axis being descriptive rather than numeric.
This sort of chart is often seen in medical and biological work, but the facility is not offered by many packages. The requirement for this sort of presentation should always be discussed early in an analysis of requirements: it cuts down the choice of packages dramatically and can save a lot of time!
A vector chart is used to display the location, direction and magnitude of XY data pairs.
Organisation charts are useful for displaying management structures. The number of levels required must always be defined.
The text chart is perhaps the most widely used chart of all. Important limitations may cover subscripts and superscripts, foreign characters, chemical symbols and mathematical symbols. The most basic word processor can generally be of some use, although an early definition of the requirements for coloured text, gradient-filled backgrounds, imported bitmaps etc. will ascertain whether a word processor or a sophisticated presentation package will be needed. There is now considerable overlap in the facilities offered by word-processing, desktop publishing and presentation packages; the deciding factor maybe one of the choice and availability of output devices, or it may depend simply on personal preference and familiarity. In any event, it is essential that the package has all the facilities required; some DTP packages, for example, have very poor drawing facilities, while some modern WP packages have quite sophisticated drawing facilities. For the purposes of this document, text is considered to be a special case of graphics data.
It is often necessary to be able to represent certain data as a 2-dimensional surface in 3-dimensional space. In general, we wish to plot a function of the form z=f(x,y), where, typically, the x and y values represent the 2-D location of a point, and z represents the variable to be visualised. One method is to make use of contour lines, as in, for example, an Ordnance Survey contour map. In the case of a relief map, the altitude at points within the area represented by the map is the function concerned, and the grid reference coordinates of these points are the independent variables. Data visualised via contouring are typically measurements of the height of a particular landscape, but in fact, anything which is a function of two independent variables, and which can be measured in some sense, can be visualised via contouring.
Similar to 2D scatter charts, except that the addidon of a third Z co-ordinate allows the data to be represented in 3 dimensional space.
A grid chart is effectively a 3 dimensional column chart of regular data Many packages give support for converting irregularly space data to this regular grid form for analysis.
Similar to 2D histogram except that it calculates and displays the distribution of X-Y pairs from two columns of data thereby giving a 3D distribution of data pairs. It may also be considered a special form of 3D grid.
A 3D surface plot graphs a matrix of X,Y and Z values as a 3 dimensional; grid or mesh.
This is a combination of laying a contour plot over a 3D surface plot.
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