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#ggplot

4 posts4 participants1 post today
Public
Michael Friendly

#dataviz #rstats
A new release, v. 1.7.4 of my {heplots} pkg just dropped to CRAN. It features a new utility `noteworthy(x,y,method)` to identify
unusual points in a 2D plot, by a variety of criteria. Working on a `stat_noteworthy()` to bring this to #ggplot,
labeling those points.
friendly.github.io/heplots/

friendly.github.ioVisualizing Hypothesis Tests in Multivariate Linear ModelsProvides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related candisc package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Public
Different Than

Listen, #ggplot: Iove you, I do. But 90% of the time when I want to set fill or color manually I don't want to have to look up and figure out an entire fucking palette of colors. I want 2 or 3 colors, the end. I want to specify them easily.

#ItsNotYouItsMe I realize this

I just want firebrick and navy. That's all. It always takes me 10 minutes of searching to remember that scale_color_manual exists; I always end up seeing pages for scale_color_discrete first.

#personalIssues#rstats
Public
Cosmin Saveanu

The truly important reason to try the #positron R/Python editor is that it can work in parallel with another R session open in #RStudio for a different project...

Also amazed by the #Pluto notebook for #Julia scientific work. One of its nice features is that the associated file is in text format and the code can be easily extracted. Not yet ready to switch from #ggplot to #VegaLite though.

positron.posit.co/start.html
plutojl.org/
#rstat

positron.posit.coGet Started – Positron
Public
Aleksandra Lazić

Played around with labeling a line plot. Ended up using:

📦{directlabels} for the two curved lines
📦{geomtextpath} for the horizontal line

Felt like an hour well spent. 😀

Removed actual labels to avoid starting a totally different conversation, but a keen eye might spot the topic!

A code snippet written in R using the ggplot2 package to create a line graph.
A line graph with two curves representing different groups and a horizontal reference line. The x-axis is labeled 'X Label,' and the y-axis is labeled 'Y Label.' The solid blue line, labeled 'Group one label here,' starts at a high value on the y-axis and gradually declines before dropping steeply near the right side of the graph. The dashed maroon line, labeled 'Group two label here,' starts at a lower y-value and decreases steadily, reaching near zero around 70% on the x-axis. A black dotted horizontal line is positioned around 0.3 on the y-axis and is labeled 'Labelled horizontal reference line.' The chart has a grid background and uses direct labeling instead of a legend.
#RStats#ggplot
Public
Data Science

{ggdist}: Visualizations of distributions and uncertainty mjskay.github.io/ggdist/ #rstats #ggplot

mjskay.github.ioVisualizations of Distributions and UncertaintyProvides primitives for visualizing distributions using ggplot2 that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.
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