Quilt: A patchwork of efficient and tidy multidimensional data operations.
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DESCRIPTION
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README.md

quilt - A patchwork of efficient and tidy multidimensional data operations

Colby T. Ford, Ph.D.

quilt icon

Installation

You can install the latest stable version from GitHub using the following command:

library(devtools)
install_github("colbyford/quilt")
library(quilt)

To Do List

  • n-dimensional Matrix Pattern Search
  • Multidimensional Run Length Encoding
    • 2-dimensional RLE rle_2()
    • n-dimensional RLE rle_n(., n)
  • Class-specific Transpose transpose()
  • Enhanced Dataset Operations
    • not in %notin%
    • shuffle order of a vector shuffle()
    • drillable hierarchical lists group_by() %>% set_hierarchy() %>% as.list()
    • enhance tibbles/dataframes with pre-computed summary values (mean, sum, sd, etc.)
    • easily digestible information about a variable/object about()
    • rename_columns, rename_rows, and reset_rownames functions for pipes
    • reorder_columns and reorder_rows functions for pipes
    • unlist_all function to unlist each column in a data frame.
    • Split dataset into n parts (training, testing, and validation sets, etc.) using split_2 and split_n
  • New set operations
    • Create new set type using as.set()
    • Introduce subset/superset logic
  • Fancy loops
    • automatically-binding loop bloop(i in 1:100, method = "cbind", parmethod = "doParallel")
    • returns multiple objects from loop returns()
  • ID'd Directional Pipe %>%(1,2) for object<1> and function()<2>
  • Automatic Parallelism
    • inherently set up local parallel parkour() or parcore() using .onLoad or .onAttach
    • recognize loop operations to parallelize for(){}
  • Specialized Imputation Methods for NAs.
    • Statistics-based methods: distribution, mean, mode, etc.
    • Directional-based methods
    • Time series methods
  • Replacement of Slow Functions
    • TBD

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details