Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , Leave a comment on WVPlots 1.1.2 on CRAN

WVPlots 1.1.2 on CRAN

I have put a new release of the WVPlots package up on CRAN. This release adds palette and/or color controls to most of the plotting functions in the package.

WVPlots was originally a catch-all package of ggplot2 visualizations that we at Win-Vector tended to use repeatedly, and wanted to turn into “one-liners.” A consequence of this is that the older visualizations had our preferred color schemes hard-coded in. More recent additions to the package sometimes had palette or color controls, but not in a consistent way. Making color controls more consistent has been a “todo” for a while—one that I’d been putting off. A recent request from user Brice Richard (thanks Brice!) has pushed me to finally make the changes.

Most visualizations in the package that color-code by group now have a palette argument that takes the name of a Brewer palette for the graph; Dark2 is usually the default. To use the ggplot2 default palette, or to set an alternative palette, such as viridis or a manually specified color scheme, set palette=NULL. Here’s some examples:

library(WVPlots)
library(ggplot2)

mpg = ggplot2::mpg
mpg$trans = gsub("\\(.*$", '', mpg$trans)
 
# default palette: Dark2 
DoubleDensityPlot(mpg, "cty", "trans", "City driving mpg by transmission type")

Unnamed chunk 1 1

# set a different Brewer color palette
DoubleDensityPlot(mpg, "cty", "trans", 
                  "City driving mpg by transmission type",
                  palette = "Accent")

Unnamed chunk 1 2

# set a custom palette
cmap = c("auto" = "#7b3294", "manual" = "#008837")

DoubleDensityPlot(mpg, "cty", "trans", 
                  "City driving mpg by transmission type",
                  palette=NULL) + 
  scale_color_manual(values=cmap) + 
  scale_fill_manual(values=cmap)

Unnamed chunk 1 3

For other plots, the user can now specify the desired color for different elements of the graph.

title = "Count of cars by number of carburetors and cylinders"

# default fill: darkblue
ShadowPlot(mtcars, "carb", "cyl",
           title = title)

Unnamed chunk 2 1

# specify fill
ShadowPlot(mtcars, "carb", "cyl",
           title = title,
           fillcolor = "#a6611a")

Unnamed chunk 2 2

We hope that these changes make WVPlots even more useful to our users. For examples of several of the visualizations in WVPlots, see this example vignette. For the complete list of visualizations, see the reference page.

Posted on Categories data science, Opinion, Programming, Statistics, TutorialsTags , , 2 Comments on WVPlots now at version 1.0.0 on CRAN!

WVPlots now at version 1.0.0 on CRAN!

Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. We are excited to announce the WVPlots is now at version 1.0.0 on CRAN!

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Posted on Categories Opinion, Statistics, TutorialsTags , ,

Ready Made Plots make Work Easier

A while back Simon Jackson and Kara Woo shared some great ideas and graphs on grouped bar charts and density plots (link). Win-Vector LLC‘s Nina Zumel just added a graph of this type to the development version of WVPlots.

NewImage

Nina has, as usual, some great documentation here.

Continue reading Ready Made Plots make Work Easier

Posted on Categories data science, Opinion, Statistics, TutorialsTags , , , , ,

We Want to be Playing with a Moderate Number of Powerful Blocks

Many data scientists (and even statisticians) often suffer under one of the following misapprehensions:

  • They believe a technique doesn’t work in their current situation (when in fact it does), leading to useless precautions and missed opportunities.
  • They believe a technique does work in their current situation (when in fact it does not), leading to failed experiments or incorrect results.

I feel this happens less often if you are working with observable and composable tools of the proper scale. Somewhere between monolithic all in one systems, and ad-hoc one-off coding is a cognitive sweet spot where great work can be done.

Continue reading We Want to be Playing with a Moderate Number of Powerful Blocks

Posted on Categories Administrativia, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , , , , 1 Comment on More documentation for Win-Vector R packages

More documentation for Win-Vector R packages

The Win-Vector public R packages now all have new pkgdown documentation sites! (And, a thank-you to Hadley Wickham for developing the pkgdown tool.)

Please check them out (hint: vtreat is our favorite).

NewImage Continue reading More documentation for Win-Vector R packages

Posted on Categories Administrativia, Programming, StatisticsTags , , , , 5 Comments on Announcing the wrapr packge for R

Announcing the wrapr packge for R

Recently Dirk Eddelbuettel pointed out that our R function debugging wrappers would be more convenient if they were available in a low-dependency micro package dedicated to little else. Dirk is a very smart person, and like most R users we are deeply in his debt; so we (Nina Zumel and myself) listened and immediately moved the wrappers into a new micro-package: wrapr.


WrapperImage: Friedensreich Hundertwasser
Continue reading Announcing the wrapr packge for R