Showing 200 of total 376 results (show query)

tidyverse

dplyr:A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

Maintained by Hadley Wickham. Last updated 13 days ago.

data-manipulationgrammarcpp

10.1 match 4.8k stars 24.68 score 659k scripts 7.8k dependents

markfairbanks

tidytable:Tidy Interface to 'data.table'

A tidy interface to 'data.table', giving users the speed of 'data.table' while using tidyverse-like syntax.

Maintained by Mark Fairbanks. Last updated 2 months ago.

7.0 match 458 stars 11.41 score 732 scripts 10 dependents

yihui

xfun:Supporting Functions for Packages Maintained by 'Yihui Xie'

Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.

Maintained by Yihui Xie. Last updated 3 days ago.

3.8 match 145 stars 18.18 score 916 scripts 4.4k dependents

hadley

reshape:Flexibly Reshape Data

Flexibly restructure and aggregate data using just two functions: melt and cast.

Maintained by Hadley Wickham. Last updated 3 years ago.

6.0 match 9.83 score 21k scripts 231 dependents

nathaneastwood

poorman:A Poor Man's Dependency Free Recreation of 'dplyr'

A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.

Maintained by Nathan Eastwood. Last updated 1 years ago.

base-rdata-manipulationgrammar

5.3 match 341 stars 10.79 score 156 scripts 27 dependents

elbersb

tidylog:Logging for 'dplyr' and 'tidyr' Functions

Provides feedback about 'dplyr' and 'tidyr' operations.

Maintained by Benjamin Elbers. Last updated 9 months ago.

dplyrtidyrtidyversewrapper-functions

4.7 match 593 stars 10.23 score 1.7k scripts

usdaforestservice

gdalraster:Bindings to the 'Geospatial Data Abstraction Library' Raster API

Interface to the Raster API of the 'Geospatial Data Abstraction Library' ('GDAL', <https://gdal.org>). Bindings are implemented in an exposed C++ class encapsulating a 'GDALDataset' and its raster band objects, along with several stand-alone functions. These support manual creation of uninitialized datasets, creation from existing raster as template, read/set dataset parameters, low level I/O, color tables, raster attribute tables, virtual raster (VRT), and 'gdalwarp' wrapper for reprojection and mosaicing. Includes 'GDAL' algorithms ('dem_proc()', 'polygonize()', 'rasterize()', etc.), and functions for coordinate transformation and spatial reference systems. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Includes raster 'calc()' to evaluate a given R expression on a layer or stack of layers, with pixel x/y available as variables in the expression; and raster 'combine()' to identify and count unique pixel combinations across multiple input layers, with optional output of the pixel-level combination IDs. Provides raster display using base 'graphics'. Bindings to a subset of the 'OGR' API are also included for managing vector data sources. Bindings to a subset of the Virtual Systems Interface ('VSI') are also included to support operations on 'GDAL' virtual file systems. These are general utility functions that abstract file system operations on URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR' archives, and in-memory files. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API.

Maintained by Chris Toney. Last updated 6 hours ago.

gdalgeospatialrastervectorcpp

4.5 match 42 stars 9.52 score 32 scripts 3 dependents

r-lib

fs:Cross-Platform File System Operations Based on 'libuv'

A cross-platform interface to file system operations, built on top of the 'libuv' C library.

Maintained by Gábor Csárdi. Last updated 4 months ago.

filesystemlibuvcpp

2.0 match 370 stars 20.26 score 8.1k scripts 5.2k dependents

bioc

SNPhood:SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data

To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest.

Maintained by Christian Arnold. Last updated 5 months ago.

software

8.8 match 3.90 score 1 scripts

mitchelloharawild

vitae:Curriculum Vitae for R Markdown

Provides templates and functions to simplify the production and maintenance of curriculum vitae.

Maintained by Mitchell OHara-Wild. Last updated 9 months ago.

cvozunconf18resumeunconf

3.0 match 1.2k stars 10.78 score 556 scripts

tidyverse

googledrive:An Interface to Google Drive

Manage Google Drive files from R.

Maintained by Jennifer Bryan. Last updated 7 months ago.

google-drive

2.0 match 329 stars 14.97 score 2.1k scripts 164 dependents

nepem-ufsc

metan:Multi Environment Trials Analysis

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

Maintained by Tiago Olivoto. Last updated 9 days ago.

3.0 match 2 stars 9.48 score 1.3k scripts 2 dependents

miraisolutions

XLConnect:Excel Connector for R

Provides comprehensive functionality to read, write and format Excel data.

Maintained by Martin Studer. Last updated 18 days ago.

cross-platformexcelr-languagexlconnectopenjdk

2.0 match 130 stars 12.28 score 1.2k scripts 1 dependents

matthewheun

matsbyname:An Implementation of Matrix Mathematics that Respects Row and Column Names

An implementation of matrix mathematics wherein operations are performed "by name."

Maintained by Matthew Heun. Last updated 10 days ago.

3.4 match 2 stars 6.65 score 150 scripts 1 dependents

ropensci

git2rdata:Store and Retrieve Data.frames in a Git Repository

The git2rdata package is an R package for writing and reading dataframes as plain text files. A metadata file stores important information. 1) Storing metadata allows to maintain the classes of variables. By default, git2rdata optimizes the data for file storage. The optimization is most effective on data containing factors. The optimization makes the data less human readable. The user can turn this off when they prefer a human readable format over smaller files. Details on the implementation are available in vignette("plain_text", package = "git2rdata"). 2) Storing metadata also allows smaller row based diffs between two consecutive commits. This is a useful feature when storing data as plain text files under version control. Details on this part of the implementation are available in vignette("version_control", package = "git2rdata"). Although we envisioned git2rdata with a git workflow in mind, you can use it in combination with other version control systems like subversion or mercurial. 3) git2rdata is a useful tool in a reproducible and traceable workflow. vignette("workflow", package = "git2rdata") gives a toy example. 4) vignette("efficiency", package = "git2rdata") provides some insight into the efficiency of file storage, git repository size and speed for writing and reading.

Maintained by Thierry Onkelinx. Last updated 2 months ago.

reproducible-researchversion-control

2.3 match 99 stars 10.03 score 216 scripts 4 dependents

repboxr

repboxUtils:Utility functions shared by several repbox packages

Utility functions shared by several repbox packages

Maintained by Sebastian Kranz. Last updated 30 days ago.

4.5 match 4.21 score 9 dependents

olgaviedma

LadderFuelsR:Automated Tool for Vertical Fuel Continuity Analysis using Airborne Laser Scanning Data

Set of tools for analyzing vertical fuel continuity at the tree level using Airborne Laser Scanning data. The workflow consisted of: 1) calculating the vertical height profiles of each segmented tree; 2) identifying gaps and fuel layers; 3) estimating the distance between fuel layers; and 4) retrieving the fuel layers base height and depth. Additionally, other functions recalculate previous metrics after considering distances greater than certain threshold. Moreover, the package calculates: i) the percentage of Leaf Area Density comprised in each fuel layer, ii) remove fuel layers with Leaf Area Density (LAD) percentage less than 10, and iii) recalculate the distances among the reminder ones. On the other hand, it identifies the crown base height (CBH) based on different criteria: the fuel layer with the highest LAD percentage and the fuel layers located at the largest- and at the last-distance. When there is only one fuel layer, it also identifies the CBH performing a segmented linear regression (breaking points) on the cumulative sum of LAD as a function of height. Finally, a collection of plotting functions is developed to represent: i) the initial gaps and fuel layers; ii) the fuels base height, depths and gaps with distances greater than certain threshold and, iii) the CBH based on different criteria. The methods implemented in this package are original and have not been published elsewhere.

Maintained by Olga Viedma. Last updated 5 months ago.

ladderfuelsr

3.9 match 7 stars 4.87 score 4 scripts

poissonconsulting

mcmcdata:Manipulate MCMC Samples and Data Frames

Manipulates Monte Carlo Markov Chain samples and associated data frames.

Maintained by Joe Thorley. Last updated 2 months ago.

5.0 match 1 stars 3.56 score 4 scripts 4 dependents

insileco

inSilecoMisc:inSileco Miscellaneous Functions

A set of miscellaneous R functions written by our inSileco group.

Maintained by Kevin Cazelles. Last updated 3 years ago.

5.3 match 4 stars 3.30 score 4 scripts

wraff

wrMisc:Analyze Experimental High-Throughput (Omics) Data

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Maintained by Wolfgang Raffelsberger. Last updated 7 months ago.

3.9 match 4.44 score 33 scripts 4 dependents

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 28 days ago.

asremlmixed-models

1.8 match 19 stars 9.34 score 200 scripts

branchlab

metasnf:Meta Clustering with Similarity Network Fusion

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

Maintained by Prashanth S Velayudhan. Last updated 5 days ago.

bioinformaticsclusteringmetaclusteringsnf

1.9 match 8 stars 8.21 score 30 scripts

bioc

PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit

Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.

Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.

geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction

3.5 match 1 stars 4.31 score 17 scripts