TOC

  • anscombiser: Create Datasets with Identical Summary Statistics
  • Groundhog: Addressing The Threat That R Poses To Reproducible Research
  • optimx: Expanded Replacement and Extension of the 'optim' Function
  • gsignal: Signal Processing in R
  • compareGroups 4.0: Descriptives by groups
  • NGLVieweR: load a PDB in R in order to view it
  • vivid: variable importance and variable interaction displays
  • vbp: Blood Pressure Analysis in R
  • PDE: Extract Tables and Sentences from PDFs with User Interface
  • tidydice: simulates rolling a dice and flipping a coin
  • tiling:Polygon Tiling Examples

Introduction

Each month I will describe the package that I've discovered or rediscovered and the ones that I've used the most of my time. I will start with the package used in my work and the the one that I would like to try/did not had time to use for work and also fun

Each card is organised as this

Name of the package: short description

mytags: #example tag

links
[cran package link]
[cran vignette link]
[github link]

description from the author/vignette

mynotes

anscombiser: Create Datasets with Identical Summary Statistics

mytags: #statistics

links
[cran package link] https://cran.r-project.org/web/packages/anscombiser/index.html
[cran vignette link] https://cran.r-project.org/web/packages/anscombiser/vignettes/intro-to-anscombiser.html
[github link] https://github.com/paulnorthrop/anscombiser

description from the author/vignette

The anscombiser package takes a simpler and quicker approach to the same problem, using Anscombe’s statistics. It uses shifting, scaling and rotating to transform the observations in an input dataset to achieve a target set of Anscombe’s statistics"

mynotes

I was wandering around searching for new packages to have fun in the R-art sector I've come accross the review of anscombiser and I was very happy of this serendipitous discover (even If I was and still am interested in Anscombe's dataset I did not find this before)

Groundhog: Addressing The Threat That R Poses To Reproducible Research

mytags: #reproducibility

links
[cran package link] https://cran.r-project.org/web/packages/groundhog/index.html
[cran vignette link]
[github link] https://github.com/CredibilityLab/groundhog

description from the author/vignette

Make R scripts that rely on packages reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the new command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available on CRAN at that date).

mynotes I've found this great post from datacolada https://datacolada.org/95?subscribe=success#blog_subscription-4 that focus on reproducible research and patch R problem with different versions of packages

optimx: Expanded Replacement and Extension of the 'optim' Function

mytags: #optim #

links
[cran package link] https://cran.r-project.org/web/packages/tidydice/index.html [cran vignette link] https://cran.r-project.org/web/packages/optimx/vignettes/Extend-optimx.pdf [github link] https://github.com/cran/optimx

description from the author/vignette

Provides a replacement and extension of the optim() function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that function 'optimr()' was prepared to simplify the incorporation of minimization codes going forward. Also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.

mynotes

For an article I'm using several function minimization and optimx is saving really a lot of time. As soon as I've finished the article I will prepare a blog post with the calculations performed

gsignal: Signal Processing in R

mytags: #signal processing #filters #boxcar

links
[cran package link] https://cran.r-project.org/web/packages/gsignal/index.html [cran vignette link] https://cran.r-project.org/web/packages/gsignal/vignettes/gsignal.html [github link] https://github.com/cran/gsignal

description from the author/vignette

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

mynotes

This month, I needed to filter hundreds of signals using boxcar method and since I did not want to reinvent the wheel get the help of gsignal. I'm sure I've just scratched the surface of what it can do. Since you probably don't want to get crazy pay attention to what it masks ! (I don't remember if gsignal or signal masked a function that I was using findpeaks)

compareGroups 4.0: Descriptives by groups

mytags: #statistics #clinical data

links
[cran package link] https://cran.r-project.org/web/packages/compareGroups/index.html [cran vignette link] https://cran.r-project.org/web/packages/compareGroups/vignettes/compareGroups_vignette.html [github link] https://github.com/isubirana/compareGroups

description from the author/vignette

compareGroups is an R package available on CRAN which performs descriptive tables displaying means, standard deviation, quantiles or frequencies of several variables. Also, p-value to test equality between groups is computed using the appropiate test. With a very simple code, nice, compact and ready-to-publish descriptives table are displayed on R console. They can also be exported to different formats, such as Word, Excel, PDF or inserted in a R-Sweave or R-markdown document

mynotes I'm performing statistical analysis for an article involving clinical data and I've found this package a timesaver

NGLVieweR: load a PDB in R in order to view it

mytags: #chemistrys #visualization #molecular

links
[cran package link] https://cran.r-project.org/web/packages/NGLVieweR/index.html [cran vignette link] https://cran.r-project.org/web/packages/NGLVieweR/vignettes/NGLVieweR.html [github link] https://github.com/nglviewer/ngl

description from the author/vignette

Provides an 'htmlwidgets' https://www.htmlwidgets.org/ interface to 'NGL.js' http://nglviewer.org/ngl/api/. 'NGLvieweR' can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications. It includes a set of API functions to manipulate the viewer after creation in Shiny. mynotes I need to work a bit on pdb file this month and so I needed a visualizer (try to spin and rotate the molecule !That is the structure of the imunoglobulin

vivid: variable importance and variable interaction displays

mytags: #statistics #clinical data

links
[cran package link] https://cran.r-project.org/web/packages/vivid/ [cran vignette link] https://cran.r-project.org/web/packages/vivid/vignettes/vividQStart.html [github link] https://github.com/AlanInglis/vivid

description from the author/vignette

Variable importance, interaction measures and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In our R package vivid (variable importance and variable interaction displays) we create new visualisation techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We also construct a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualisations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large and the interaction structure complex.

mynotes Hope to find the time to use it asap

vbp: Blood Pressure Analysis in R

mytags: #statistics #clinical data

links
[cran package link] https://cran.r-project.org/web/packages/bp/index.html [cran vignette link] https://cran.r-project.org/web/packages/bp/vignettes/bp.html [github link] https://github.com/cran/bp

description from the author/vignette

Cardiovascular disease (CVD) is the leading cause of death worldwide with Hypertension, specifically, affecting over 1.1 billion people annually. The goal of the package is to provide a comprehensive toolbox for analyzing blood pressure data using a variety of statistical metrics and visualizations to bring more clarity to CVD.

mynotes I needed to analyse medical data and searching for a way to better plot them I discovered this very very useful package. and BTW if you need to check your BP pressure like me and like R, this is a great tool to impress your physician

PDE: Extract Tables and Sentences from PDFs with User Interface

mytags: #pdf #scraping

links
[cran package link] https://cran.r-project.org/web/packages/PDE/vignettes/PDE.html [cran vignette link] https://cran.r-project.org/web/packages/PDE/vignettes/PDE.html [github link]

description from the author/vignette

PDE is a R package that easily extracts information and tables from PDF files. The PDE_analyzer_i() performs the sentence and table extraction while the included PDE_reader_i() allows the user-friendly visualization and quick-processing of the obtained results.

mynotes I will need soon to try a meta analysis and I guess it will be quite useful

tidydice: simulates rolling a dice and flipping a coin

mytags: #teaching #fun

links
[cran package link] https://cran.r-project.org/web/packages/tidydice/index.html [cran vignette link] https://cran.r-project.org/web/packages/tidydice/vignettes/tidydice.html [github link] https://github.com/rolkra/tidydice/

description from the author/vignette

This package simulates rolling a dice and flipping a coin. Each experiment generates a tibble. Dice rolls and coin flips are simulated using sample(). The properties of the dice can be changed, like the number of sides. A coin flip is simulated using a two sided dice. Experiments can be combined with the pipe-operator.

mynotes Fun and useful for teaching probabilities. I wish I had this one while studing statistical thermodinamics

tiling:Polygon Tiling Examples

mytags: #arts #fun

links
[cran package link] https://cran.rstudio.com/web/packages/gridpattern/ [cran vignette link] https://cran.rstudio.com/web/packages/gridpattern/vignettes/tiling.html [github link]

description from the author/vignette

Several uniform regular polygon tiling patterns can be achieved by use of grid.pattern_regular_polygon() plus occasionally grid.polygon() to set a background color. This vignette highlights several such tiling patterns plus a couple notable non-uniform tiling patterns.

mynotes I've search for procedural art and R and I've found this very useful and fun package. I hope I have a bit of time to test a few ideas I've in mind