Our Standard Libraries

As a team of coders — it’s very healthy and good practice to use the same set of libraries. This improves our ability to collaborate and the readability of our code.

This document showcases the libraries that we picked out as a team, and should always be used over their alternatives if the same results can be achieved. Find the CRAN link by clicking the package name. If you need examples, look for the vignettes or install the package and use the function browseVignettes("package_name")

Preferred / go-to packages

We first try to solve most of our analytical problems with the packages below. This is not an exhaustive list, so there will be cases where we’ll need additional or different packages – which is fine, as long as it is necessary. Also have a look at the noteworthy packages section where you might find what it is you are looking for.

Category	Package	What it does	Discussion
Reading / Writing Data	readr	Reads and writes Comma Seperated Values (.csv)	We are avid fans of the tidyverse and will thus be using readr over data_table. Only if absolutely necessary, we can use data_table for the added speed.
Reading / Writing Data	haven	Reads and writes S, SAS, SPSS and Stata files	Again, a tidyverse classic which has our preffered piping system.
Reading / Writing Data	readxl	Reads and writes Excel files (.xls,.xlsx)	Again, a tidyverse classic which has our preffered piping system.
Data Manipulation	tidyverse	This collection of packages allows us to manipulate data frames, datetime, factors, lists, strings and tidying object outputs	This package contains most of our data wrangling needs that work tightly with our reading and writing packages.
Data Manipulation	zoo	Working with timeseries.	The zoo package is state of the art for time-series objects and will serve it's purpose for most modeling packages.
Data Visualisation	ggplot2	Create extremely customizable plots	Easily the most-used package for R and there is no better alternative.
Document generation	knitr	Report generation & tables	Knitr will be our go-to packagewhen we want to create reports and tables. It changed reproducible workflows immensely.
Interactive tables	DT	Interactive tables	DT has a great interactable andcustomizable interface for HTML generated tables which we can include in any online application that we create. Also incorperated into Shiny
Interactive Data Vizualisations	echarts4r	Interactive plots	Includes a sophisticated API
Modeling \| Clustering	clValid	Run & compare multiple clustering techniques	This package runs and compares multiple clustering techniques such as kmeans, diana, agnes, hierarchical and more.
Modeling \| Classification	randomForest	Classification based on a forest of trees using random inputs
Modeling \| Regressions	caret		The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions that attempt to streamline the process for creating predictive models.
Feature Selection	Boruta	Tree based features selection that uses shadow of original variables	An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).
Package/Project documentation	roxygen2		Generate your Rd documentation, 'NAMESPACE' file, and collation field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation upto-date as your requirements change

Other noteworthy packages

Category	Package	What does it do?	Extensive description
Reading / Writing Java/Debian Data	foreign	Also reads formats like DBF, WEKA and others.	Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.
Reading / Writing ML Data	XML	Reads and writes XML files	Many approaches for both reading and creating XML (and HTML) documents (including DTDs),both local and accessible via HTTP or FTP. Also offers access to an 'XPath' interpreter.
Reading / Writing JSON Data	jsonlite	Reads and writes JSON files	A fast JSON parser and generator optimized for statistical data and the web. Started outas a fork of 'RJSONIO', but has been completely rewritten in recent versions. The package offers flexible, robust, high performance tools for workingwith JSON in R and is particularly powerful for building pipelines and interacting with a web API.
Working with APIs	httr	Interacting / obtaining data from API's	Useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on).
Webscraping	rvest	Read data from the web	Web scraping: Extract data from HTML pages. Inspired by Python's Beautiful Soup. Works well with Selectorgadget.
Data Visualisation	widgetframe	Writes interactive HTML widgets for easy embadding on wordpress	This package allows for easier embedding of 'htmlwidgets' in content management systems such as 'wordpress', 'blogger' etc.
Data Visualisation	flexdashboard	Creating HTML Dashboards	If Shiny is too complex and involved for your needs, this package offers asimpler (if somewhat less robust) solution based on R Markdown.
Modeling \| Classification \| Regression with SVM	e1071	Package for classification & regression	Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier.
Modeling \| Classification \| Regression with Trees	rpart	Package for classification & regression	Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Data Visualisation	tables	Formula-based tables construction
Data Visualisation	stargazer	Pretty regression outputs	Produces LaTeX code, HTML/CSS codeand ASCII text for well-formatted tables that hold regression analysis results from several models side-by-side, as well as summary statistics.
Modeling \| Classification \| Regression with regularization	glmnet	LASSO and Ridge Regression	Extremely efficient procedures for fitting the entire lasso or elastic-netregularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model
Modeling \| Classification \| Regression	lme4	Hierarchical models	Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods.
Modeling \| Classification \| Regression with Bayesian approach	brms	Interface to STANfor highly sophisticated regressions	Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context.
Parallel computing	foreach	A better for loop that allows for error handling, parallelization, etc.	Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function. Using foreach without sideeffects also facilitates executing the loop in parallel.
Modeling \| Forecasting	prophet	Library from Facebook for univariate time series forecasting	Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Modeling \| Classification \| Regression	vars	vector autoregression models	Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Modeling \| Classification \| Regression with neural networks	keras	Package to create arbitrarily complex neural networks	Interface to Keras, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly onboth 'CPU' and 'GPU' devices.
Tables Presentation	kableExtra	styling of kable tables in rmarkdown	Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' and the piping syntax from 'magrittr'. Function 'kable()' is a light weight table generator coming from 'knitr'. This package simplifies the way to manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows users to construct complex tables and customize styles using a readable syntax.
Modeling \| Classification \| Regression	bnlearn	Package to learn causal structuresand estimate parameters of bayesian networks	Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHCand RSMAX2) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests
Modeling \| Classification \| Regression	BTYDplus	Package to estimate BTYD models on transactional data	Provides advanced statistical methods to describe and predict customers' purchase behavior in a non-contractual setting. It uses historic transaction records to fit a probabilistic model, which then allows to compute quantities of managerial interest on a cohort- as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.).
Data Visualisation	ggplot extentions	Egg, ggrepel, ggthemes,	Egg: align multiple plots ggrepel: add repel labels ggthemes: more geoms/themes
Interactive applications			Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuiltwidgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.