R-universe search: exports:cols

Showing 10 of total 10 results (show query)

tidyverse

readr:Read Rectangular Text Data

The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.

Maintained by Jennifer Bryan. Last updated 8 months ago.

csv fwf parsing cpp

1.0k stars 20.06 score 132k scripts 2.1k dependents

tidyverse

vroom:Read and Write Rectangular Text Data Quickly

The goal of 'vroom' is to read and write data (like 'csv', 'tsv' and 'fwf') quickly. When reading it uses a quick initial indexing step, then reads the values lazily , so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.

Maintained by Jennifer Bryan. Last updated 7 months ago.

csv csv-parser fixed-width-text tsv tsv-parser cpp

625 stars 17.82 score 4.5k scripts 2.1k dependents

gdemin

maditr:Fast Data Aggregation, Modification, and Filtering with Pipes and 'data.table'

Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' conversion to 'data.table'.

Maintained by Gregory Demin. Last updated 5 months ago.

data-table magrittr pipes

61 stars 8.98 score 248 scripts 7 dependents

gesistsa

minty:Minimal Type Guesser

Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom').

Maintained by Chung-hong Chan. Last updated 3 months ago.

cpp

5 stars 7.16 score 5 scripts 26 dependents

gisler

DTSg:A Class for Working with Time Series Data Based on 'data.table' and 'R6' with Largely Optional Reference Semantics

Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling (asymmetric) window functions and automatic detection of periodicity. As it is mainly based on 'data.table', it is fast and (in combination with the 'R6' package) offers reference semantics. In addition to its native R6 interface, it provides an S3 interface for those who prefer the latter. Finally yet importantly, its functional approach allows for incorporating functionalities from many other packages.

Maintained by Gerold Hepp. Last updated 6 days ago.

class reference-semantics time-series-data

5 stars 6.03 score 24 scripts

spgarbet

tangram:The Grammar of Tables

Provides an extensible formula system to quickly and easily create production quality tables. The processing steps are a formula parser, statistical content generation from data as defined by formula, followed by rendering into a table. Each step of the processing is separate and user definable thus creating a set of composable building blocks for highly customizable table generation. A user is not limited by any of the choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package, or possibly one would rather have Gini coefficients for their statistical content in a resulting table. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided.

Maintained by Shawn Garbett. Last updated 2 years ago.

68 stars 5.93 score 62 scripts

bioc

plyxp:Data masks for SummarizedExperiment enabling dplyr-like manipulation

The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame.

Maintained by Justin Landis. Last updated 12 days ago.

annotation genomeannotation transcriptomics

4 stars 5.88 score 6 scripts

cmann3

eList:List Comprehension and Tools

Create list comprehensions (and other types of comprehension) similar to those in 'python', 'haskell', and other languages. List comprehension in 'R' converts a regular for() loop into a vectorized lapply() function. Support for looping with multiple variables, parallelization, and across non-standard objects included. Package also contains a variety of functions to help with list comprehension.

Maintained by Chris Mann. Last updated 4 years ago.

2 stars 4.48 score 9 scripts 1 dependents

bioc

RNAdecay:Maximum Likelihood Decay Modeling of RNA Degradation Data

RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.

Maintained by Reed Sorenson. Last updated 5 months ago.

immunooncology software geneexpression generegulation differentialexpression transcription transcriptomics timecourse regression rnaseq normalization workflowstep

4.18 score 2 scripts

fabsig

spate:Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach

Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.

Maintained by Fabio Sigrist. Last updated 1 years ago.

fftw3

1 stars 4.16 score 29 scripts