Look at the gramm cheat sheet

Type doc gramm to find links to the documentation of each method.

• Accepts X Y and Z data as arrays, matrices or cells of arrays

• Accepts grouping data as arrays or cellstr.

• Multiple ways of separating groups of data:

  • Colors, lightness, point markers, line styles, and point/line size ('color', 'lightness', 'marker', 'linestyle', 'size')
  • Subplots by row and/or columns, or wrapping columns (facet_grid() and facet_wrap()). Multiple options for consistent axis limits across facets, rows, columns, etc. (using 'scale' and 'space')

• Multiple ways of directly plotting the data:

  • scatter plots (geom_point()) and jittered scatter plot (geom_jitter())
  • lines (geom_line())
  • confidence intervals (geom_interval())
  • bars plots (geom_bar())
  • raster plots (geom_raster())
  • point counts (point_count())

• Multiple ways of plotting statistics on the data:

  • y data summarized by x values (uniques or binned) with confidence intervals (stat_summary())
  • histograms and density plots of x values (stat_bin() and stat_density())
  • box and whisker plots (stat_boxplot())
  • violin plots (stat_violin())
  • quantile-quantile plots (stat_qq()) of x data distribution against theoretical distribution or y data distribution.
  • spline-smoothed y data with optional confidence interval (stat_smooth())
  • 2D binning (stat_bin2d())
  • GLM fits (stat_glm(), requires statistics toolbox)
  • Custom fits with user-provided anonymous function (stat_fit())
  • Ellipses of confidence (stat_ellipse())

• When Z data is provided in the call to gramm(), geom_point() and geom_line() generate 3D plots

• Subplots are created without too much empty space in between (and resize properly !)

• Polar coordinates (set_polar())

• Color data can also be displayed as a continous variable, not as a grouping factor (set_continuous_color())

• Possibility to customize color generations in the LCH color space, chose alternative colormaps (Matlab's default, colorbrewer2), or provide a custom colormap (set_color_options())

• Possibility to customize marker shapes and sizes with (set_point_options())

• Possibility to customize line styles and width with (set_line_options())

• Possibility to customize text elements with (set_text_options())

• Possibility to change ordering of grouping variables between native, sorted, or custom (set_order_options)

• Confidence intervals as shaded areas, error bars or thin lines

• Set the width and dodging of graphical elements in geom_ functions, stat_bin(), stat_summary(), and stat_boxplot(), with 'width' and 'dodge' arguments

• The member structure results contains the results of computations from stat_ plots as well as graphic handles for all plotted elements

• Figure title (set_title())

• Multiple gramm plots can be combined in the same figure by creating a matrix of gramm objects and calling the draw() method on the whole matrix. An overarching title can be added by calling set_title() on the whole matrix.

• Different groupings can be used for different stat_ and geom_ layers with the update() method

• Matlabs axes properties are acessible through the method axe_property()

• Custom legend labels with set_names()

• Plot reference line on the plots with geom_abline(), geom_vline(),geom_hline()

• Date ticks with set_datetick()

• Gramm works best with table-like data: separate variables / structure fields / table columns for the variables of interest, with each variable having as many elements as observations.

The code for the following figures and numerous others is in examples.m.

All the mappings presented below can be combined.

###Relationship between categorical and continuous variables

###Distribution of a continuous variable Note that we by using Origin as a faceting variable, we visualize exactly the same quantities as in the figure above.

###Relationship between two continous variables

###Repeated trajectories Here the variable given as Y is a Nx1 cell of 1D arrays containing the individual trajectories. Color is given as a Nx1 cellstr.

###Spike trains This example highlights the potential use of gramm for neuroscientific data. Here X is a Nx1 cell containing spike trains collected over N trials. Color is given as a Nx1 cellstr. Using stat_bin() it is possible to construct peristimulus time histograms.

With set_color_options()

###Reordering of categorical variables With set_order_options()

By making calling the update() method after a first draw, the same axes can be reused for another gramm plot. Here this allows to use different groupings for the points and for the glm fit.

gramm was inspired and/or used code from:

• ggplot2
• Panda for color conversion
• subtightplot for subplot creation
• colorbrewer2