TOC

  • Rpcop: Principal Curves of Oriented Points
  • panelView: Visualizing Panel Data
  • ggvoronoi: Voronoi Diagrams and Heatmaps with 'ggplot2'
  • VIM: Visualization and Imputation of Missing Values
  • replacer: A Value Replacement Utility
  • charlatan: Make Fake Data
  • FactoMineR: Multivariate Exploratory Data Analysis and Data Mining
  • ggh4x: Hacks for 'ggplot2'
  • corrr: Correlations in R
  • cfda: Categorical Functional Data Analysis
  • visR: Clinical Graphs and Tables Adhering to Graphical Principles

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

Rpcop: Principal Curves of Oriented Points

mytags: #statistics #data analysis links
[cran package link] https://CRAN.R-project.org/package=Rpcop
[vignette link]
[github link]

description from the author/vignette

Principal curves generalize the notion of a first principal component to the case in which it is a non linear smooth curve. This package provides a function pcop(X) to compute principal curves with the algorithm defined in Delicado (2001) doi:10.1006/jmva.2000.1917 from a data matrix X. mynotes

panelView: Visualizing Panel Data

mytags: #statistics #data analysis links
[cran package link] https://CRAN.R-project.org/package=panelView
[vignette link]
[github link]

description from the author/vignette

Visualizes panel data. It has three main functionalities:(1) it visualizes treatment status and missing values in a panel dataset; (2) it plots an outcome variable (or any variable) in a time-series fashion; (3) it visualizes bivariate relationships of two variables by unit or in aggregate. mynotes

ggvoronoi: Voronoi Diagrams and Heatmaps with 'ggplot2'

mytags: #ggplot #tidyverse #voronoi links
[cran package link] https://CRAN.R-project.org/package=ggvoronoi
[vignette link] https://cran.r-project.org/web/packages/ggvoronoi/vignettes/ggvoronoi.html [github link]

description from the author/vignette

Easy creation and manipulation of Voronoi diagrams using 'deldir' with visualization in 'ggplot2'. Convenient functions are provided to create nearest neighbor diagrams and heatmaps. Diagrams are computed with 'deldir' and processed to work with the 'sp' framework. Results are provided in a convenient spatial data structure and displayed with 'ggplot2'. An outline can be provided by the user to specify the spatial domain of interest.

mynotes

VIM: Visualization and Imputation of Missing Values

mytags: #data #missing values links
[cran package link] https://CRAN.R-project.org/package=VIM
[vignette link] https://cran.r-project.org/web/packages/VIM/vignettes/VIM.html
https://cran.r-project.org/web/packages/VIM/vignettes/donorImp.html
https://cran.r-project.org/web/packages/VIM/vignettes/irmi.html
https://cran.r-project.org/web/packages/VIM/vignettes/modelImp.html

[github link] https://github.com/statistikat/VIM

description from the author/vignette

New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.

mynotes

replacer: A Value Replacement Utility

mytags: #data #replace #missing values links
[cran package link] https://CRAN.R-project.org/package=Replacer
[vignette link] https://cran.r-project.org/web/packages/replacer/vignettes/readmefirst.html

description from the author/vignette

New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.

mynotes

charlatan: Make Fake Data

mytags: #data #simulation links
[cran package link] https://CRAN.R-project.org/package=charlatan
[vignette link] https://cran.r-project.org/web/packages/charlatan/vignettes/charlatan.html
https://cran.r-project.org/web/packages/charlatan/vignettes/contributing.html

description from the author/vignette

Make fake data, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers from distributions and within a range.

mynotes

FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

mytags: #pca #clustering #multivariate #data links
[cran package link] https://CRAN.R-project.org/package=FactoMineR
[vignette link] https://cran.r-project.org/web/packages/FactoMineR/vignettes/FactoMineR.pdf https://cran.r-project.org/web/packages/FactoMineR/vignettes/clustering.html

description from the author/vignette

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications:principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017). mynotes

ggh4x: Hacks for 'ggplot2'

mytags: #ggplot #tidyverse links
[cran package link] https://CRAN.R-project.org/package=ggh4x
[vignette link] https://cran.r-project.org/web/packages/ggh4x/vignettes/Facets.html https://cran.r-project.org/web/packages/ggh4x/vignettes/Miscellaneous.html https://cran.r-project.org/web/packages/ggh4x/vignettes/PositionGuides.html https://cran.r-project.org/web/packages/ggh4x/vignettes/Statistics.html https://cran.r-project.org/web/packages/ggh4x/vignettes/ggh4x.html

description from the author/vignette

A 'ggplot2' extension that does a variety of little helpful things. The package extends 'ggplot2' facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. Also allows multiple colour and fill scales per plot. Also hosts a smaller collection of stats, geoms and axis guides.

mynotes

corrr: Correlations in R

mytags: #data #correlation #calculus links
[cran package link] https://CRAN.R-project.org/package=corrr
[vignette link] https://cran.r-project.org/web/packages/corrr/vignettes/databases.html
https://cran.r-project.org/web/packages/corrr/vignettes/using-corrr.html

description from the author/vignette

A 'ggplot2' extension that does a variety of little helpful things. The package extends 'ggplot2' facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. Also allows multiple colour and fill scales per plot. Also hosts a smaller collection of stats, geoms and axis guides.

mynotes

cfda: Categorical Functional Data Analysis

mytags: #data #categorical links
[cran package link] https://CRAN.R-project.org/package=cfda
[vignette link] https://cran.r-project.org/web/packages/cfda/vignettes/cfda.html

description from the author/vignette

Package for the analysis of categorical functional data. The main purpose is to compute an encoding (real functional variable) for each state doi:10.3390/math9233074. It also provides functions to perform basic statistical analysis on categorical functional data. mynotes

visR: Clinical Graphs and Tables Adhering to Graphical Principles

mytags: #data #clinical links
[cran package link] https://CRAN.R-project.org/package=cfda
[vignette link] https://cran.r-project.org/web/packages/visR/vignettes/CDISC_ADaM.html https://cran.r-project.org/web/packages/visR/vignettes/Consort_flow_diagram.html https://cran.r-project.org/web/packages/visR/vignettes/Styling_KM_plots.html https://cran.r-project.org/web/packages/visR/vignettes/Time_to_event_analysis.html

description from the author/vignette

To enable fit-for-purpose, reusable clinical and medical research focused visualizations and tables with sensible defaults and based on graphical principles as described in:"Vandemeulebroecke et al. (2018)" doi:10.1002/pst.1912, "Vandemeulebroecke et al. (2019)" doi:10.1002/psp4.12455, and "Morris et al. (2019)" doi:10.1136/bmjopen-2019-030215. mynotes