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Benchmark of CSV parse

This is CSV parse benchmark for Ruby.

Main article is written in ipynb below.

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Rover inspect example

This is some examples of Rover::DataFrame#inspect in ruby.

This feature is experimental, not impremented in official release.

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Rover summary method example

This is a experimental code and usage of Rover::DataFrame#summary of Ruby.

This method is not implemented in official release.

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# ==== Prefix ====
# fix
# feat
# docs
# style
# refactor
# test
# chore
 
# ==== Emojis ====

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Package 'datasets'

[1/84] ability.cov

Ability and Intelligence Tests

• name: Rdatasets: datasets: ability.cov
• licenses: GPL-3

RedAmber::DataFrame : 6 x 8 Vectors
Vectors : 8 numeric

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red-data-tools / red-datasets #140

Fix Rdatasets#each to change String datatype for mixed data of Numeric and String.

• This is the data preview after this fix applied.
• 55 datasets are fixed.

# [package, dataset] pairs
[["AER", "ResumeNames"], ["boot", "tau"], ["carData", "Blackmore"], ["causaldata", "restaurant_inspections"], ["causaldata", "ri"], ["causaldata", "training_example"], ["DAAG", "biomass"], ["DAAG", "hotspots"], ["DAAG", "rockArt"], ["datasets", "attenu"], ["dragracer", "rpdr_ep"], ["drc", "germination"], ["Ecdat", "Mathlevel"], ["Ecdat", "Schooling"], ["gap", "aldh2"], ["gap", "cnv"], ["gap", "mao"], ["ggplot2", "mpg"], ["ggplot2movies", "movies"], ["gt", "gtcars"], ["HistData", "Pyx"], ["MASS", "Cars93"], ["mosaicData", "Galton"], ["mosaicData", "HELPfull"], ["nycflights13", "airports"], ["nycflights13", "planes"], ["openintro", "birds"], ["openintro", "cards"], ["openintro", "cpu"], ["openintro", "epa2012"], ["openintro", "epa2021"], ["openintro", "kobe_basket"], ["openintro",

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red-data-tools / red-datasets #139

Fix Rdatasets#each to change "NA" to nil.

• This is the data preview after this fix applied.
• 265 datasets are fixed.

# [package, dataset] pairs
[["AER", "BenderlyZwick"], ["AER", "GrowthDJ"], ["AER", "GSS7402"], ["AER", "MASchools"], ["AER", "STAR"], ["AER", "USMacroG"], ["AER", "USMacroSWM"], ["AER", "USSeatBelts"], ["asaur", "hepatoCellular"], ["boot", "neuro"], ["boot", "urine"], ["carData", "Chile"], ["carData", "Davis"], ["carData", "Freedman"], ["carData", "GSSvocab"], ["carData", "Hartnagel"], ["carData", "UN"], ["carData", "UN98"], ["causaldata", "abortion"], ["causaldata", "adult_services"], ["causaldata", "auto"], ["causaldata", "close_college"], ["causaldata", "nhefs"], ["causaldata", "nhefs_complete"], ["causaldata", "scorecard"], ["causaldata", "social_insure"], ["causaldata", "thornton_hiv"], ["cluster", "animals"], ["cluster", "plantTraits"], ["cluster", "votes.repub"], ["COUNT", "loomis"], ["COUNT", "ships"], ["DAAG", "bomregions"], ["DAAG", "

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Answering @zverok 's amazing expectation for DataFrame

This is an answering comments for @zverok 's amazing expectation for DataFrame. I was very impressed to know how my DataFrame library matched his expectations.

Foreword: My RedAmber's DataFrame

Class RedAmber::DataFrame represents 2D-data.

A DataFrame consists with:

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Comparing between RedAmber, dplyr/tidyr and pandas (tentative)

This is the comparison of basic feature between RedAmber and other major DataFrame libraries, comparing only for the method 'verbs' ignoring parameters and options.

Remarks:

1. dataframe represents 2D data containers such as DataFrame, tibble or Table.
2. vector represents 1D data containers such as Vector, Series or Column.

Select columns (variables)