The MATLAB® data analysis and graphics tools for visual data exploration leverage its Handle Graphics® capabilities. In addition to the presentation techniques described in the following section, they include:
Highlighting and editing observations on graphs with data brushing
Connecting data graphs with variables with data linking
Finding, adding, removing, and changing data values with the Data Brushing with the Variables Editor
Describing observations on graphs with data tips
Used alone or together, these tools help you to perceive trends, noise, and relationships in data sets, and understand aspects of the phenomena you model.
Finding patterns in numbers is a mathematical and an intuitive undertaking. When people collect data to analyze, they often want to see how models, variables, and constants explain hypotheses. Sometimes they see patterns by scanning tables or sets of statistics, other times by contemplating graphical representations of models and data. An analyst's powers of pattern recognition can lead to insights into data's distribution, outliers, curvilinearity, associations between variables, goodness-of-fit to models, and more. Computers amplify those powers greatly.
Graphically exploring digital data interactively generally requires:
Data displays for charts, graphs, and maps
A graphical user interface (UI) capable of directly manipulating the displays
Software that categorizes selected data performs operations on the categories, and then updates or creates new data displays
This approach to understanding is often called exploratory data analysis (EDA), a term coined during the infancy of computer graphics in the 1970s and generally attributed to statistician John Tukey (who also invented the box plot). EDA complements statistical methods and tools to help analysts check hypotheses and validate models. An EDA UI usually lets analysts divide observations of variables on data plots into subsets using mouse gestures, and then analyze further or eliminate selected observations.
Part of EDA is simply looking at data graphics with an informed eye to observe patterns or lack of them. What makes EDA especially powerful, however, are interactive tools that let analysts probe, drill down, map, and spin data sets around, and select observations and trace them through plots, tables, and models.
Well before digital tool sets like the MATLAB environment developed, curious quantitative types plotted graphs, maps, and other data diagrams to trigger insights into what their collections of numbers might mean. If you are curious about what data might mean and like to reflect on data graphics, MATLAB provides many options:
Plotting data — scatter, line, area, bar, histogram and other types of graphs
Plotting thematic maps to show spatial relationships of point, lines and area data
Plotting N-D point, vector, contour, surface, and volume shapes
Overlaying other variables on points, lines, and surfaces (e.g. texture-maps)
Rendering portions of a 3-D display with transparency
Animating any of the above
All of these options generate static or dynamic displays that may reveal meaning in data. In many environments, however, users cannot interact with them; they can only change data or parameters and redisplay the same or different data graphics. MATLAB tools enable users to directly manipulate data displays to explore correlations and anomalies in data sets, as the following sections explain.