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tsuomela : r   219

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tidyverts
"Tidy tools for time series."
r  statistics  time-series  package 
yesterday by tsuomela
DDAR
"Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results."
book  r  statistics  discrete  data-science 
11 days ago by tsuomela
text2vec
"text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP)."
r  package  text-mining  text-analysis 
4 weeks ago by tsuomela
CRAN - Package rlist
"Provides a set of functions for data manipulation with list objects, including mapping, filtering, grouping, sorting, updating, searching, and other useful functions. Most functions are designed to be pipeline friendly so that data processing with lists can be chained."
r  package 
6 weeks ago by tsuomela
CRAN - Package jsonlite
"A fast JSON parser and generator optimized for statistical data and the web. Started out as a fork of 'RJSONIO', but has been completely rewritten in recent versions. The package offers flexible, robust, high performance tools for working with JSON in R and is particularly powerful for building pipelines and interacting with a web API. The implementation is based on the mapping described in the vignette (Ooms, 2014). In addition to converting JSON data from/to R objects, 'jsonlite' contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications."
r  package  json  data-science 
6 weeks ago by tsuomela
Automate Package and Project Setup • usethis
"usethis is a workflow package: it automates repetitive tasks that arise during project setup and development, both for R packages and non-package projects."
r  package 
september 2018 by tsuomela
Teaching Data for Statistics and Data Science • testDriveR
"The goal of testDriveR is to provide data sets for teaching statistics and data science courses. This package includes a sample of data from John Edmund Kerrich’s famous coinflip experiment. These are data that I use for teaching SOC 4015 / SOC 5050 at Saint Louis University. "
teaching  statistics  data-sources  r  package 
september 2018 by tsuomela
How to Make Better-Looking, More Readable Charts in R | FlowingData
"Defaults are generalized settings to work with many datasets. This is fine for analysis, but data graphics for presentation benefit from context-specific design."
data-science  visualization  r  tutorial 
august 2018 by tsuomela
All the fake data that's fit to print
charlatan is an R package for simulating / creating fake data
r  statistics  package  data  teaching 
june 2017 by tsuomela
An Introduction to Spatial Data Analysis and Visualisation in R - CDRC Data
"This tutorial series is designed to provide an accessible introduction to techniques for handling, analysing and visualising spatial data in R. R is an open source software environment for statistical computing and graphics. It has a range of bespoke packages which provide additional functionality for handling spatial data and performing complex spatial analysis operations. The practical series uses open data which has been made readily available and demonstrates a range of techniques useful in social sciences including multivariate analysis, mapping and spatial interpolation. "
r  statistics  tutorial  geospatial  mapping  gis 
may 2017 by tsuomela
Book Memo: “Categorical Data Analysis by Example” | Data Analytics & R
"Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject. Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations."
book  recommendations  r  statistics  categories 
may 2017 by tsuomela
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