Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits.
Rewrite underlying javascript to use new version of d3panels.
Add a new chart, scat2scat. The idea is to summarize each of a
long series of scatterplots with a pair of numbers. Then a
scatterplot of those summary statistics is linked to the underlying
details: click on a point in the main scatterplot and have the
underlying scatterplot be shown.
Add a new chart, itriplot, for plotting trinomial probabilities,
represented as points in an equilateral triangle.
Refactor iplotPXG and idotplot so that idotplot is the main
function, and iplotPXG calls it.
Add some additional options, such as horizontal for iplotMap
and iplotPXG.
Change the name of some options, such as linecolor and linewidth
in iplotCurves (in place of strokecolor and strokewidth).
Add a digits argument for all plot functions, with the aim to
reduce the size of the datasets included in the resulting charts.
Removed the vignettes from the package (for complicated reasons); they're available at the R/qtlcharts website.
Fix proliferation of tool tips
For use with Shiny, clear SVG before drawing
Changed license and author list in order to post the package on CRAN, http://cran.r-project.org
idotplot and iplot now use the names of the input data as
individual IDs if indID is missing or NULL.
iplotScanone with pxgtype="ci". In the case of
phenotypes with missing values, the confidence intervals were
incorrect.iplotCorr has argument scatterplots that controls whether
scatterplots will be shown when clicking on pixels in the heatmap.
(If scatterplots=FALSE, we skip the scatterplots.)iplotMScanone can plot just points (rather than curves) for the
LOD scores and QTL effects in the lower and right-hand panels.
Fix a bug in iplotMScanone (x-axis labels in right-hand plot
weren't being shown)
setScreenSize function for controlling the default sizes of
charts.Added idotplot function for plotting a quantitative variable in
different categories. (It's just like iplotPXG, but with data
values not connected to a cross object.)
Reorganized the d3panels code: just
using d3panels.min.js and d3panels.min.css rather than linking
to js code for individual panels.
Refactored the entire package to use htmlwidgets. A big advantage is that the charts now work nicely within RStudio.
To save a plot to a file, you now need to assign the result of a plot
function to an object and then use the
htmlwidgets function saveWidget.
library(qtlcharts)library(qtl)data(hyper)hyper <- calc.genoprob(hyper, step=1)out <- scanone(hyper)chart <- iplotScanone(out, hyper)htmlwidgets::saveWidget(chart, file="hyper_scanone.html")
It's now simpler to include interactive charts within an R Markdown
document. You simply make the plots in a code chunk in the way that
you would at the R prompt. There's no longer a need to worry about
print_qtlcharts_resources() or results="asis".
Separated out the basic panel functions as a separate repository, d3panels, to make it easier for them to be used separately from R/qtlcharts.