Just as ggplot2 provides a layered implementation of a grammar of graphics, loon.ggplot provides a layered implementation of a grammar of interactive graphics.

With loon.ggplot, data analysts can easily switch between the elegant and beautiful static graphics of ggplot2 and the powerful direct manipulation interactive graphics of loon, using each where it is most natural.

A working dataset – airquality

To provide a working example, consider the airquality dataset:

#>      Ozone           Solar.R           Wind             Temp      
#>  Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00  
#>  1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00  
#>  Median : 31.50   Median :205.0   Median : 9.700   Median :79.00  
#>  Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88  
#>  3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00  
#>  Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00  
#>  NA's   :37       NA's   :7                                       
#>      Month            Day      
#>  Min.   :5.000   Min.   : 1.0  
#>  1st Qu.:6.000   1st Qu.: 8.0  
#>  Median :7.000   Median :16.0  
#>  Mean   :6.993   Mean   :15.8  
#>  3rd Qu.:8.000   3rd Qu.:23.0  
#>  Max.   :9.000   Max.   :31.0  

It has missing data (in Ozone and Solar.R) and the variable Month appears as numerical. The date information can easily be made more meaningful:

airquality$Date <- with(airquality,
                        as.Date(paste("1973", Month, Day, sep = "-")))
# Could also look up the day of the week for each date and add it
airquality$Weekday <- factor(weekdays(airquality$Date),
                             levels = c("Monday", "Tuesday", "Wednesday",
                                        "Thursday", "Friday",
                                        "Saturday", "Sunday"))

Month can also be turned it into a factor so that its levels are three character month abbreviations (arranged to match calendar order):

airquality$Month <- factor(month.abb[airquality$Month], 
                           levels = month.abb[unique(airquality$Month)])

The data now look like

head(airquality, n = 3)
#>   Ozone Solar.R Wind Temp Month Day       Date   Weekday
#> 1    41     190  7.4   67   May   1 1973-05-01   Tuesday
#> 2    36     118  8.0   72   May   2 1973-05-02 Wednesday
#> 3    12     149 12.6   74   May   3 1973-05-03  Thursday

With its mix of continuous and categorical variables (some with missing data) this transformed data will be used to illustrate loon.ggplot’s grammar of interactive graphics.

The trick: ggplot() becomes l_ggplot()

The interactive grammar begins by simply replacing ggplot() by l_ggplot(), wherever it appears in the layered grammar. The same arguments (e.g., data, mapping, etc.) and clauses (e.g. geoms, scales, coordinates, etc.) are used, but now to create an interactive plot.

For example,

lgp <- l_ggplot(airquality, 
                mapping = aes(x = Wind, y = Ozone)) +
  ggtitle("Air quality in New York (1973)") +
  geom_point(size = 3) 

Like ggplot(), l_ggplot() produces a data structure containing the information needed to create a plot. No plot is actually yet displayed; rather lgp has the potential to produce a plot on demand.

A look at its class

#> [1] "l_ggplot" "gg"       "ggplot"

suggests that it is both an l_ggplot and a ggplot (and gg).

In ggplot2 identical methods have been written for both the plot() and the print() function to show the ggplot on the current device.
In loon.ggplot these functions are different for an l_ggplot:

  • plot(lgp) displays the lgp structure as a static ggplot on the current device
  • print(lgp) displays it as an interactive loon plot in the R session.

For example, to see the ggplot:


And to see the interactive loon plot, simply print it:

lgp                   # or print(lgp)

which will look something like (as rendered in grid graphics).

Each plot (the static ggplot and the interactive loon plot) presents the same information, but in slightly different form (e.g., different choices on title placement, white space padding, etc.)

Though the data information is identical in both plots, the loon plot appears a little more spartan. This is because an interactive plot is dynamic and can change in real time by direct interaction; it is enough that the analyst appreciates the data content of the plot without too much concern over display details. In contrast, the ggplot is more often meant to be shared in print and so demands more flexibility to lay out its plot elements in an elegant display.

Note: In ggplot2, every time a ggplot is printed, a new plot is produced on the current device. Similarly, in loon.ggplot, every time an l_ggplot is printed, a new (interactive) loon plot is produced; every time it is plotted, a new (static) ggplot is produced.

Programmatic access of the interactive plot

At times, as when creating this document, it will be handy to have programmatic access to the loon plot. This can be done by assigning the loon plot to a variable, say lp, in any one of several different ways:

When it was first printed it, the interactive plot would have returned a string. For example, it was

#> [1] ".l0.ggplot.plot"

which is of the form ".lXX.ggplot.plot" where XX is a non-negative integer.

This is the tcltk “path” to the loon plot and uniquely identifies the loon structure. The data structure is now accessed through l_getFromPath(pathname) as below.

lp <- l_getFromPath(".lXX.ggplot.plot")  # replace XX by whatever number appeared

Of course, this requires you to have noticed and recovered the string pathname for that plot when it first appeared. Fortunately, that is not necessary. In the title bar of the window containing the loon plot, the string following "path: " can also be used. In the present case, this will be of the form ".lXX.ggplot" (as before but without the additional ".plot" suffix). The call

lp <- l_getFromPath(".lXX.ggplot")  # replace XX by whatever number appeared

will return the interactive plot as before.

Finally, if you have the foresight to know that you would like to have programmatic access to the interactive plot from the start, you could assign it to a variable when it was first created.
There are two ways to do this.

One is

lp <- print(lgp)

The other uses a powerful function called loon.ggplot() (more on this below):

lp <- loon.ggplot(lgp)

Note: that unlike l_getFromPath(), either of the above calls will produce a new interactive plot from lgp and assign it to lp.

The loon-ggplot duality

Using l_ggplot() in place of ggplot() extends the graphics grammar of ggplot2 to produce interactive loon plots.

  • use l_ggplot() in place of ggplot()

    • plotting an l_ggplot produces a static ggplot display

    • printing an l_ggplot produces an interactive loon display.

  • the l_ggplot is not the interactive loon plot; it is an enhanced ggplot and can be augmented just as any other ggplot.

  • Changes to the ggplot have no effect on the interactive plot

    For example,

    plot(lgp + geom_smooth())

    behaves like any other ggplot with no effect on the interactive plot.

  • Changes to the interactive loon plot have no effect on the static l_ggplot

    Make whatever changes you like interactively to the loon plot lp, or programmatically as below:

    # Change glyph aesthetics of ALL points
    lp["color"] <- "lightgrey"
    lp["glyph"] <- "ctriangle"  # closed triangle
    lp["size"]  <- 10           # proportional to area in loon
    # Dynamically change the scaling (magnify or zoom in and out)
    for (mag in rep(c(0.8, 1, 1.2), times = 5)){
       lp["zoomX"] <- mag
       lp["zoomY"] <- mag
       Sys.sleep(0.1) # slow down to see effect
    # Settle on
    lp["zoomX"] <- 1.2
    lp["zoomY"] <- 1.2
    # Or, similarly, change the location/origin of the plot
    xlocs <- seq(min(lp["x"]),
                 length.out = 10)
    ylocs <- seq(min(lp["y"]),
                 length.out = 10)
    # Dynamically change the origin
    for (i in 1:length(xlocs)){
      lp["panX"] <- xlocs[i]
      lp["panY"] <- ylocs[i]
      Sys.sleep(0.1) # slow down to see effect
    # and back
    xlocs <- rev(xlocs)
    ylocs <- rev(ylocs)
    # dynamically
    for (i in 1:length(xlocs)){
      lp["panX"] <- xlocs[i]
      lp["panY"] <- ylocs[i]
      Sys.sleep(0.1) # slow down to see effect
    # Perhaps settle on
    lp["panX"] <- 7
    lp["panY"] <- 0

    And now observe the effect on each of the plots:

    # First the l_ggplot 

    No change. It is unaffected by any change to the interactive plot.

    # Now the l_plot 

    Reflects the changes made on the interactive plot.

The function loon.ggplot()

Earlier, loon.ggplot() was recommended as a means to produce an interactive plot from an l_ggplot and to assign it to a variable, as in

lp <- loon.ggplot(lgp)

This makes it seem essentially equivalent to print(lgp), but it is not.

Instead, loon.ggplot() is a powerful two way bridge between ggplots and loon plots.

When called on

  • an l_ggplot (an extended ggplot) loon.ggplot(lgp) produces an interactive loon plot.

  • an interactive loon plot, it produces a static ggplot

    This is a ggplot with parameters to make it look like the loon plot it was built from (e.g. the title is centred in this ggplot).

    loon.ggplot() gives us a second way to produce a static version of an interactive loon plot.

    • plot(lp) produces a grid graphic object (or grob)

    • loon.ggplot(lp) produces a ggplot graphic object.

    Either reproduces the current loon plot as a static snapshot of its present appearence (ideally wysiwyg).

    Which is preferred depends on the use intended for the static plot .

    The ggplot version is easier to work with and trivial to adapt using the grammar.

    gp <- loon.ggplot(lp)  # loon to ggplot
    #> [1] "gg"     "ggplot"
    gp + geom_smooth()

    Alternatively, a new interactive plot can be generated from an ordinary ggplot as

    new_lp <- loon.ggplot(gp + geom_smooth())

    and this interactive plot is turned into a new static ggplot with the same function, as shown below


    Note that the grey polygon emphasizing the confidence region does not appear. It is there, but as a hidden layer of the loon plot that can be revealed at any time (via the loon inspector or programmatically). The polygon is not shown by default because colours do not (yet) have an alpha channel (for transparency) in tcltk.

The bridge: loon.ggplot() turns ggplots to interactive loon plots and loon plots to ggplots.

The interactive grammar

loon.ggplot extends the grammar of graphics by adding several new clauses.

+ linking()

+ linking(linkingGroup = NULL, linkingKey = NULL, linkedStates = NULL, sync = NULL)

loon implements a group-key-state linking model.

Interactive plots having the same linkingGroup are linked, in that each plot changes its display in response to display changes of the linkedStates of any plot in the same linkingGroup. Only those linkedStates in common are changed and display elements are matched by values of the linkingKey.

  • linkingGroup

    A string naming the group or NULL for no linking.

  • linkedStates

    A character vector of the states to be linked.

    By default these include selected, active, and others peculiar to each type of plot such as color and size.

  • linkingKey

    A length n character vector of unique strings, one for each observation; default is "0", "1", …, "n-1".

  • sync

    Specifies the direction of synchronizing the linked states at the time the plot is created.
    The value "pull" pulls the values of the linked states from plots in the linking group and assigns them to the new plot; value "push" pushes the newly created plot’s linked states values out to the others in the linking group.

    Default is "pull" unless some aesthetics matching the linked states are specified in the plot creation; then the default will be "push".

Each plot will propogate, and respond to, only changes in those states named in its linkedStates. Display elements associated with the unique set of keys in each plot’s linkingKey will change together.

+ hover()

+ hover(itemLabel = NULL, showItemLabels = NULL)

Provides a pop up display as the mouse hovers over a plot element in the interactive plot.

  • itemLabel

    A character vector of length n with a string to be used to pop up when the mouse hovers above that element.

  • showItemLabels

    A single logical value: TRUE if pop up labels are to appear on hover, FALSE (the default) if they are not.

+ selection()

+ selection(selected = NULL, selectBy = NULL, selectionLogic = NULL)

Set which elements (i.e., observations) are "selected". These are shown highlighted in the plot.

  • selected

    a logical or a logical vector of length n that determines which observations are selected (TRUE and hence appear highlighted in the plot) and which are not. Default is FALSE and no points are highlighted.

  • selectBy

    A string determining how selection will occur in the interactive plot. Default is "sweeping" where a rectangular region is reshaped or “swept” out to select observations.; alternately "brushing" will indicate that a fixed rectangular region is moved about the display to select observations.

  • selectionLogic

    One of "select" (the default), "deselect", and "invert". The first highlights observations as selected, the second downlights them, and the third inverts them (downlighting highlighted observations and highlighting downlighted ones).

+ active()

+ active(active = NULL, activeGeomLayers = NULL)

Set active and/or activeGeomLayers

  • active

    a logical, or a logical vector of length n, determining which observations are active (hence appear in the plot) and which are inactive (FALSE and hence do not appear). Default is TRUE.

  • activeGeomLayers

    determine which geom layer is interactive by its geom_... position in the grammar of the expression. Currently, only geom_point() and geom_histogram() can be set as the active geom layer(s) so far. (N.B. more than one geom_point() layer can be set as an active layer, but only one geom_histogram() can be set as an active geom layer and it can be the only active layer)

+ zoom()

+ zoom(layerId = NULL, scaleToFun = NULL)

Change the visible plot region by scaling to different elements of the display.

  • layerId

    numerical; which layer to scale the plot by. If the layerId is set as NULL (default), the region of the interactive graphics loon will be determined by the ggplot object (i.e. coord_cartesian, xlim, etc); else one can use scaleToFun to modify the region of the layer.

  • scaleToFun

    scale function to be used. If NULL (default), based on different layers, different scale functions will be applied. For example, if the layer is the main graphic model, i.e. l_plot l_hist, then the default scaleToFun is l_scaleto_plot; else if the layer is a general l_layer widget, the default scaleToFun would be loon::l_scaleto_layer.

    If it is not NULL, any of the following scaleToFun functions could be used

    scale to Subfunction
    plot l_scaleto_plot
    world l_scaleto_world
    active l_scaleto_active
    selected l_scaleto_selected
    layer l_scaleto_layer

    Alternatively, scaleToFun can be any function whose arguments match those of the functions above.

+ interactivity()

+ interactivity(linkingGroup, linkingKey, linkedStates, sync,  # linking
                active, activeGeomLayers,                      # active
                selected, selectBy, selectionLogic,            # selection
                layerId, scaleToFun,                           # zoom
                itemLabel,  showItemLabels,                    # hover
                ... )

Set interactive components (e.g. linking, selection, etc) in one clause. All named arguments are as described in the other clauses.

  • ... other named arguments to modify loon plot states. See l_info_states().

The trick: l_ggplot() becomes ggplot()

We began with the recommendation that to have interactive ggplots, all we need do is replace ggplot() in the grammar by l_ggplot() with all the usual arguments. All clauses of the ggplot grammar can be used as before plus the new interactive clauses. The result is an l_ggplot that prints as an interactive loon plot and plots as a ggplot2 plot. The advantage is that using l_ggplot() makes it clear that an ordinary ggplot is not being produced and emphasizes the loon-ggplot duality.

The final trick is that, with loon.ggplot, one does not actually need to use l_ggplot(), simply use ggplot(). If no clauses are interactive, then an ordinary ggplot will be produced; if an interactive clause is added, an l_ggplot will be produced.

For example, the following produces an ordinary ggplot.

ggp <- ggplot(airquality, mapping = aes(Solar.R, Temp)) +
  geom_point(size = 3) +
  ggtitle("Air quality in New York (1973)")
# which is an ordinary ggplot and prints as one

Adding an interactive clause, turns the result into an l_ggplot.

lggp <- ggp + 
  linking(linkingGroup = "airquwqality") +
  selection(selected = airquality$Solar.R < 100) +
  zoom(layerId = 1, scaleToFun = l_scaleto_selected) +
# which is an interactive loon and prints as one
#> [1] ".l3.ggplot.plot"
#> attr(,"class")
#> [1] "l_plot" "loon"
# but plots as a ggplot

Note that the interactive effects do not appear in plot(l_ggp); this is because this is still an l_ggplot. To print the interactive plot, a handle to it must be found, for example using l_getFromPath(".l3.ggplot")

l_ggp <- l_getFromPath(".l3.ggplot")
# Alternatively, the loon plot could have been captured when first
# created by using loon.ggplot(lggp) in stead of print(lggp) as follows
# l_ggp <- loon.ggplot(lggp)
# Either way, it will look like the following as a grid graphics plot
# and as below when presented as a ggplot

The final trick is that either l_ggplot() or ggplot() cam be produce interactive plots using the ggplot grammar.

It’s largely a matter of taste.