R-forcats-0.4.0.tgz


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Description

R-forcats - Tools for working with categorical variables (factors)

Property Value
Distribution NetBSD 7.2
Repository NetBSD amd64
Package filename R-forcats-0.4.0.tgz
Package name R-forcats
Package version 0.4.0
Package release -
Package architecture amd64
Package type tgz
Category R math
Homepage https://CRAN.R-project.org/package=forcats
License gnu-gpl-v3
Maintainer -
Download size 326.51 KB
Installed size 396.36 KB
Helpers for reordering factor levels (including moving specified
levels to front, ordering by first appearance, reversing, and randomly
shuffling), and tools for modifying factor levels (including
collapsing rare levels into other, 'anonymising', and manually
'recoding').

Alternatives

Package Version Architecture Repository
R-forcats-0.4.0.tgz 0.4.0 i386 NetBSD
R-forcats - - -

Requires

Name Value
R >= 2.2.1nb2
R-ellipsis >= 0.2.0.1
R-magrittr >= 1.5
R-rlang >= 0.4.0
R-tibble >= 2.1.3

Download

Type URL
Mirror ftp.netbsd.org
Binary Package R-forcats-0.4.0.tgz
Source Package R-forcats

Install Howto

Install R-forcats tgz package:

# pkg_add R-forcats

See Also

Package Description
R-forecast-8.7.tgz Forecasting functions for time series and linear models
R-formatR-1.7.tgz Format R code automatically
R-fracdiff-1.4.2.tgz Fractionally differenced ARIMA aka ARFIMA(p,d,q) models
R-gdata-2.18.0nb1.tgz Various R programming tools for data manipulation
R-geoR-1.7.5.2.1.tgz Analysis of geostatistical data
R-geoRglm-0.9.11.tgz Package for generalised linear spatial models
R-ggExtra-0.8.tgz Add marginal histograms to 'ggplot2', and more 'ggplot2' enhancements
R-ggplot2-3.2.0.tgz Create elegant data visualisations using the grammar of graphics
R-ggtern-3.1.0.tgz Extension to 'ggplot2', for the creation of ternary diagrams
R-gh-1.0.1.tgz GitHub API
R-git2r-0.26.1.tgz Provides access to Git repositories
R-glue-1.3.1.tgz Interpreted String Literals
R-gridExtra-2.3.tgz Miscellaneous Functions for "Grid" Graphics
R-gss-2.1.10.tgz General smoothing splines
R-gstat-2.0.2.tgz Spatial and spatio-temporal geostatistical modelling, prediction and simulation
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