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etl - Extract-Transform-Load Framework for Medium Data

A predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system.

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7.82 score 134 stars 1 dependents 41 scripts 413 downloads

mdsr - Complement to 'Modern Data Science with R'

A complement to all editions of *Modern Data Science with R* (ISBN: 978-0367191498, publisher URL: <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>). This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package.

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6.85 score 41 stars 870 scripts 900 downloads

teamcolors - Color Palettes for Pro Sports Teams

Provides color palettes corresponding to professional and amateur, sports teams. These can be useful in creating data graphics that are themed for particular teams.

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6.63 score 49 stars 217 scripts 319 downloads

macleish - Retrieve Data from MacLeish Field Station

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/discover-smith/smith-action/sustainable-smith/macleish-field-station> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.

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5.70 score 2 stars 84 scripts 342 downloads

tidychangepoint - A Tidy Framework for Changepoint Detection Analysis

Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.

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5.18 score 3 stars 10 scripts 408 downloads