rugplot: Heatmaps

A heatmap is typically a rectangular tiling of a color-shaded data matrix. Additionally, this technique is one of the most space-efficient ways to visualize data. Here some examples are given to show how to create heatmaps using the rugplot container. The exergaming dataset from Soancatl-Aguilar et al. [SAvandGrondeL+17] will be used to create such examples. The dataset can be downloaded by running the following command

LinuxPowerShell

wget https://raw.githubusercontent.com/rijksuniversiteit-groningen/rugplot/master/tests/testthat/data/exergamelf2.csv

or by adding the link in the previous command to the JSON template that will be created in the second step to create the heatmap.

Creating a heatmap using the rugplot container

For simplicity it is better to create an alias, see the Docker commands section.

1. Step 1, create a rugplot histogram template

   aliasraw commandPowerShell

   rugplot template -p heatmap
   

   A heatmap_params.json file will be created including some of the name/value pairs listed below:

   {
       "description": "Parameters to create a heatmap plot using the 'rugplot' R package",
       "filename": "<filename path>",
       "method": "raster",
       "aesthetics": {
           "y_variable": "<Y required column name>",
           "x_variable": "<X required column name>",
           "fill": "<required variable>",
       },
   }
   

2. Step 2, adding 'data file', 'x, y and fill variables', and some save properties:

   {
       "filename": "exergamelf2.csv",
       "aesthetics": {
           "y_variable": "myVars",
           "x_variable": "idrow",
           "fill": "normVal",
       },
       "save": {
           "save": true,
           "outputfilename": "exergaming_heatmap",
           "width": 17,
           "height": 7,
           "dpi": 300,
           "device": "png",
       }
   }
   

3. Step 3, create the heatmap

   aliasraw commandPowerShell

   rugplot plot -p heatmap --file heatmap_params.json
   

   The result will be stored in the exergamin_heatmap.png file.

   

4. Step 4, customizing color, label and LaTeX format

   LaTeX breaks

   {
     "labels": {
         "x": "Trials per participant",
         "y": "Balance measures",
         "fill": "Normalized\nmeasures",
     },
     "colour_scales": {
         "fill_gradient": {
             "method": "gradient",
             "low": "#fee0d2",
             "high": "#67000d",
          }
     },
     "axes_scales": {
         "y_discrete": {
             "labels": ["medTIms51 = I`", "medTI51 = I",
             "medSpeed = Speed", "medLsdD51 = SD`",
             "medLsd51 = SD", "medLrmslD51 = RMS`",
             "medLrms51 = RMS", "medLcovD51 = CoV`",
             "medLcov51 = CoV", "medK = $\\\\kappa$",
             "meanK = $\\\\overline{\\\\kappa}$"]
         }
     },
     "save": {
         "device": "tikz",
         "sanitize": false
     }
   }
   

   

References

SAvandGrondeL+17

Venustiano Soancatl-Aguilar, Jasper J. van de Gronde, Claudine J. C. Lamoth, Mike van Diest, Natasha M. Maurits, and Jos B. T. M. Roerdink. Visual data exploration for balance quantification in real-time during exergaming. PLOS-One, January 2017. doi:10.1371/journal.pone.0170906.