SMHS SciVisualization

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Questions

• How and why should we “look” at data?

• What data characteristics are important for exploratory data analytics (EDAs)?

Scientific Data-driven or Simulation-driven visualization methods may be classified in many alternative ways. Visualization techniques can be classified according to many criteria:

SMHS SciVisualization1.png

• Data Type: structured/unstructured, small/large, complete/incomplete, time/space, ascii/binary, Euclidean/non-Euclidean, etc.

• Task type: Task type is one of the aspects considered in classification of visualization techniques, which provides means of interaction between the researcher, the data and the display software/platform

• Scalability: Visualization techniques are subject to some limitations, such as the amount of data that a particular technique can exhibit

• Dimensionality: Visualization techniques can also be classified according to the number of attributes

• Positioning and Attributes: the distribution of attributes on the chart may affect the interpretation of the display representation, e.g., correlation analysis, where the relative distance among the plotted attributes is relevant for observation

• Investigative Need: the specific scientific question or exploratory interest may also determine the type of visualization:

o Examining the composition of the data

o Exploring the distribution of the data

o Contrasting or comparing several data elements, relations, association

o Unsupervised exploratory data mining

http://www.socr.umich.edu/CSCD/html/Cores/Macore2/SciViz.html

SOCR Charts

• URL: http://socr.umich.edu/html/cha/ (Java applet)

• About/List: http://wiki.stat.ucla.edu/socr/index.php/About_pages_for_SOCR_Chart_List

• Activities: http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_ChartsActivities

• Data: http://wiki.socr.umich.edu/index.php/SOCR_Data

SMHS SciVisualization2.png

Excel Charts

MS Excel provides a large number of charts that can be used to quickly and effectively render complex multivariate data. For instance, the table below contains the principal component analysis (PCA) of 50 derived neuroimaging biomarkers (region of interest (ROI) by shape morphometry metric). The loadings of these 50 variables on the top 5 (most significant) principal component directions are shown in the table. Experiment with effective visualizations of these data.

Hemi ROI measure ROI_Measure Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
L insular AvgMeanCurvature L_insular_cortex_AvgMeanCurvature 0.72 0 0.06 0.06 0
L insular ComputeArea L_insular_cortex_ComputeArea 0.77 0.06 0.04 0.01 0
L insular Volume L_insular_cortex_Volume 0.72 0.09 0.04 0.03 0.01
L insular ShapeIndex L_insular_cortex_ShapeIndex 0.46 0.06 0.01 0.02 0.01
L insular Curvedness L_insular_cortex_Curvedness 0.78 0 0.05 0.03 0.01
R insular AvgMeanCurvature R_insular_cortex_AvgMeanCurvature 0.79 0 0.03 0.08 0
R insular ComputeArea R_insular_cortex_ComputeArea 0.79 0.09 0.03 0.01 0
R insular Volume R_insular_cortex_Volume 0.73 0.11 0.03 0.03 0
R insular ShapeIndex R_insular_cortex_ShapeIndex 0.27 0.17 0 0.07 0
R insular Curvedness R_insular_cortex_Curvedness 0.84 0.02 0.03 0.01 0
L cingulate AvgMeanCurvature L_cingulate_gyrus_AvgMeanCurvature 0.72 0 0.05 0.02 0.02
L cingulate ComputeArea L_cingulate_gyrus_ComputeArea 0.74 0.05 0.06 0.04 0.01
L cingulate Volume L_cingulate_gyrus_Volume 0.69 0.08 0.05 0.05 0.01
L cingulate ShapeIndex L_cingulate_gyrus_ShapeIndex 0.53 0 0.05 0 0.03
L cingulate Curvedness L_cingulate_gyrus_Curvedness 0.7 0.01 0.05 0.04 0.03
R cingulate AvgMeanCurvature R_cingulate_gyrus_AvgMeanCurvature 0.6 0 0.02 0.03 0.01
R cingulate ComputeArea R_cingulate_gyrus_ComputeArea 0.73 0.06 0.04 0.03 0.01
R cingulate Volume R_cingulate_gyrus_Volume 0.68 0.09 0.04 0.04 0.01
R cingulate ShapeIndex R_cingulate_gyrus_ShapeIndex 0.56 0.01 0.05 0 0.01
R cingulate Curvedness R_cingulate_gyrus_Curvedness 0.25 0 0.01 0.04 0
L caudate AvgMeanCurvature L_caudate_AvgMeanCurvature 0.52 0 0.05 0 0.01
L caudate ComputeArea L_caudate_ComputeArea 0.51 0.09 0.03 0.04 0.02
L caudate Volume L_caudate_Volume 0.44 0.09 0.03 0.06 0.03
L caudate ShapeIndex L_caudate_ShapeIndex 0.2 0.03 0.04 0.04 0
L caudate Curvedness L_caudate_Curvedness 0.51 0.12 0.02 0.01 0.01
R caudate AvgMeanCurvature R_caudate_AvgMeanCurvature 0.68 0.04 0.04 0.02 0
R caudate ComputeArea R_caudate_ComputeArea 0.67 0.17 0.03 0.02 0.01
R caudate Volume R_caudate_Volume 0.61 0.16 0.02 0.03 0.01
R caudate ShapeIndex R_caudate_ShapeIndex 0.18 0.02 0.03 0.11 0
R caudate Curvedness R_caudate_Curvedness 0.65 0.19 0.01 0 0
L putamen AvgMeanCurvature L_putamen_AvgMeanCurvature 0.62 0 0.04 0.03 0.02
L putamen ComputeArea L_putamen_ComputeArea 0.56 0.05 0.04 0.03 0.05
L putamen Volume L_putamen_Volume 0.52 0.07 0.04 0.05 0.05
L putamen ShapeIndex L_putamen_ShapeIndex 0.06 0.13 0 0.15 0
L putamen Curvedness L_putamen_Curvedness 0.64 0.11 0.03 0.01 0.03
R putamen AvgMeanCurvature R_putamen_AvgMeanCurvature 0.62 0 0.07 0.04 0.01
R putamen ComputeArea R_putamen_ComputeArea 0.66 0.08 0.03 0.01 0.03
R putamen Volume R_putamen_Volume 0.64 0.12 0.03 0.02 0.03
R putamen ShapeIndex R_putamen_ShapeIndex 0.15 0.24 0 0.08 0.03
R putamen Curvedness R_putamen_Curvedness 0.65 0.05 0.05 0 0.02
L hippocampus AvgMeanCurvature L_hippocampus_AvgMeanCurvature 0.78 0 0.01 0.04 0
L hippocampus ComputeArea L_hippocampus_ComputeArea 0.75 0.07 0.01 0 0.02
L hippocampus Volume L_hippocampus_Volume 0.72 0.09 0.01 0.01 0.01
L hippocampus ShapeIndex L_hippocampus_ShapeIndex 0.45 0.17 0 0.04 0.02
L hippocampus Curvedness L_hippocampus_Curvedness 0.79 0.03 0.01 0 0.02
R hippocampus AvgMeanCurvature R_hippocampus_AvgMeanCurvature 0.72 0 0 0.1 0.01
R hippocampus ComputeArea R_hippocampus_ComputeArea 0.71 0.09 0 0 0.05
R hippocampus Volume R_hippocampus_Volume 0.68 0.1 0 0 0.04
R hippocampus ShapeIndex R_hippocampus_ShapeIndex 0.37 0.18 0 0.02 0.03
R hippocampus Curvedness R_hippocampus_Curvedness 0.77 0.03 0 0.02 0.04
SMHS SciVisualization3.png
SMHS SciVisualization4.png
SMHS SciVisualization5.png

R-Charts

There are 100’s of packages and 1,000 of different charts, plots and graphs that can be generated using R. Such interactive visualizations enable deeper exploration of data, models and results. JavaScript libraries, e.g., D3, provide advantages for data visualization as these involve HTML5 and are easily shareable online. The R community is developing R interfaces to some popular JavaScript libraries to allow users to create interactive visualizations without detailed knowledge of JavaScript.

Examples of powerful R interactive visualization packages

• ggplot2 – http://ggplot2.org

• ggvis – interactive plots extending the static ggplot2 charts, http://ggvis.rstudio.com

• rCharts – R interface to multiple JavaScript charting libraries, http://rcharts.io

• plotly – transforming ggplot2 charts into interactive plots, https://plot.ly/r

• googleVis – Google Charts using R, http://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html

• HTMLWidgets

o leaflet – library for creating dynamic maps, supports panning and zooming, annotations, markers, polygons, etc. http://www.htmlwidgets.org/showcase_leaflet.html

o dygraphs – provides mechanism for charting time-series data, supports interactive navigation features including series/point highlighting, zooming, and panning, http://www.htmlwidgets.org/showcase_dygraphs.html

o networkD3 – library for creating D3 network graphs including force directed networks, Sankey diagrams, and Reingold-Tilford tree networks, http://www.htmlwidgets.org/showcase_networkD3.html

o DataTables – displays R matrices or data frames as interactive HTML tables that support filtering, pagination, and sorting, http://www.htmlwidgets.org/showcase_datatables.html

o Rthreejs – features 3D scatterplots and globes based on WebGL, http://www.htmlwidgets.org/showcase_threejs.html

• Other R graphic examples

o To write out plots out to file use:

# pdf() command all graphs are redirected to test.pdf.  Also works with other common formats:  jpeg, png, ps, tiff.
pdf("C:\\Users\\Dinov\\Desktop\\test.pdf"); plot(1:100, 1:100); dev.off()
# Generates Scalable Vector Graphics (SVG) that can be edited by vector graphics software
svg("test.svg"); plot(1:100, 1:100); dev.off()
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