Export Data Frames to Excel 'xlsx' Format

Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter' < https://libxlsxwriter.github.io>. Fast and no Java or Excel required.

A Simpler Way to Find Your Files

Constructs paths to your project's files. Declare the relative path of a file within your project with 'i_am()'. Use the 'here()' function as a drop-in replacement for 'file.path()', it will always locate the files relative to your project root.

Read, Write and Edit xlsx Files

Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of 'Rcpp', read/write times are comparable to the 'xlsx' and 'XLConnect' packages with the added benefit of removing the dependency on Java.

Create, Stow, and Read Data Packages

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a 'parquet' file or released to a 'GitHub' repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

Download Datasets from the Swiss National Science Foundation (SNF, FNS, SNSF)

Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; < https://snf.ch>). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries.

'Memoisation' of Functions

Cache the results of a function so that when you call it again with the same arguments it returns the previously computed value.

Download Data from the Swiss National Bank (SNB)

Download data (tables and datasets) from the Swiss National Bank (SNB; < https://www.snb.ch/en>), the Swiss central bank. The package is lightweight and comes with few dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into 'zoo' objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

Tools for Storing, Restoring and Searching for R Objects

Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. The more projects we work with the more artifacts are produced and the harder it is to manage these artifacts. Archivist helps to store and manage artifacts created in R. Archivist allows you to store selected artifacts as a binary files together with their metadata and relations. Archivist allows to share artifacts with others, either through shared folder or github. Archivist allows to look for already created artifacts by using it's class, name, date of the creation or other properties. Makes it easy to restore such artifacts. Archivist allows to check if new artifact is the exact copy that was produced some time ago. That might be useful either for testing or caching.

Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Nonlinear Root Finding, Equilibrium and Steady-State Analysis of Ordinary Differential Equations

Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method, (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code.