Some interesting new R packages

Some of the packages that appeared in CRAN during June seem interesting. I haven’t tried them yet but have been listed at the R Views blog in Joseph Rickert’s regular column, which I recommend to anyone wanting to keep up to date on new packages.

  • genesysr: Genesys PGR Client. Access data on plant genetic resources from genebanks around the world published on Genesys (<>).
  • rppo: Access the Global Plant Phenology Data Portal. An R interface to the Global Plant Phenology Data Portal, which is accessible online at <>.
  • spectralAnalysis: Pre-Process, Visualize and Analyse Process Analytical Data, by Spectral Data Measurements Made During a Chemical Process. Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified.
  • BiocManager: Access the Bioconductor Project Package Repository. A convenient tool to install and update Bioconductor packages.
  • pkgbuild: Find Tools Needed to Build R Packages. Provides functions used to build R packages. Locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately so R can use them.
  • ssh: Secure Shell (SSH) Client for R. Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client.

ggpmisc 0.3.0

A major update to ‘ggpmisc’ (0.3.0) follows availability of the update of ‘ggplot2’ (3.0.0) (see release announcement) and the release of the new package ‘gginnards’ (0.0.1) (see announcement) through CRAN. Package documentation is available at as a web site.

Functions earlier included in ‘ggpmisc’ (< 0.3.0) related to debugging and low level manipulation of ggplot objects have been moved to package ‘gginnards’. The functions that remain in ‘ggpmisc’ (>= 0.3.0) are related to annotation of plots with fitted-model equations, fit diagnosis, ANOVA and summary tables, highlighting and labelling of peaks and valleys in curves, and local density-based highlighting or labelling in scatter plots.  Additionally specializations of the ggplot() constructor allow on-the-fly conversion of time-series and geom_table() makes it possible to add tables to plots respecting the the Grammar of Graphics paradigm.

The enhancements in this update to ‘ggpmisc’ are made possible by changes to ‘ggplot2’ (>= 3.0.0) made while adding support for sf (simple features). Not all extensions to ‘ggplot2’ have been updated yet to be compatible with the new version of ‘ggplot2’ so if you need them, you may need to wait before updating to ‘ggplot2’ (3.0.0) and consequently also before updating to this version of ‘ggpmisc’.

This time I include some example plots taken from the documentation of the package. In all cases annotations are generated automatically, but formatting is flexible. Larger plots are linked to the thumbnails.

Main changes since ‘ggpmisc’ (0.2.17) the previous CRAN release.

  • Add geom_table() a geom for adding a layer containing one or more tables to a plot panel.
  • Add stat_fit_tb() a stat that computes a tidy tabular version of the summary table or ANOVA table from a model fit.
  • Add support for "weight" aesthetic in stat_poly_eq() so that weights can be passed to lm() (fixing bug reported by S.Al-Khalidi).
  • Add support for column selection and renaming to stat_fit_tb().
  • Add new statistic stat_fmt_tb() for formatting of tibbles for addition to plots as tables.
  • Rename stat_quadrat_count() into stat_quadrant_count() (fix miss-spelling).
  • Move debug stats and geoms to package ‘gginnards’.
  • Move plot layer manipulation functions to package ‘gginnards’.
  • Revise vignettes.

NOTE: The new version of ‘ggpmisc’ is on its way to CRAN and should be available for download later in the week.

from Data to Viz (external link)

from data to Viz is a new web site related to data analysis and R. Its aim is to make it easier to choose among different types of data visualisations. It looks beautiful, is easy to navigate, includes “trees” displaying a classification of visualizations and multiple individual examples with the corresponding R code.  Highly recommended!

To access the website and/or to buy the printed poster visit from Data to Viz.