id stringlengths 40 40 | repo_name stringlengths 5 110 | path stringlengths 2 233 | content stringlengths 0 1.03M ⌀ | size int32 0 60M ⌀ | license stringclasses 15
values |
|---|---|---|---|---|---|
ed6b1326f83e57cb58b45df35a4897a76e7b99ed | bilakhiaricky/hub_turkr | Hub_Miner/R/repositories.R | ################################################################################
# General repo information
#' Get list of repositories of current user
#'
#' @param ... extra parameters, see http://developer.github.com/v3/repos/
#'
#' @param ctx the github context object
#'
#' @return list of repositories
get.my.repos... | 22,179 | gpl-2.0 |
91a45f2793b7b1b8f8bda250180a4e4ae5356554 | franeviso/pva-gisera | processing_data.R | setwd("~/Documents/Programming/C++/Population_viability_simulations")
seedbank_data <- read.table('demo_sb_fire.dat', skip = 10, header = TRUE, sep ='\t', stringsAsFactors = FALSE)
View(seedbank_data)
length(names(seedbank_data))
colnames(seedbank_data) <- c("Gen", "Nrun", "Veg","Veg_err", "Repro", "Repro_err", "Age"... | 1,360 | apache-2.0 |
e88862c705ebf9097892e04b8651050360015bc7 | seqcloud/seqcloudR | R/makeTx2GeneFromFASTA.R | #' Make a Tx2Gene object from transcriptome FASTA
#'
#' @export
#' @note RefSeq transcript FASTA (e.g. "GRCh38_latest_rna.fna.gz") doesn't
#' contain gene identifiers, and is not supported.
#' @note Updated 2019-11-06.
#'
#' @inheritParams acidroxygen::params
#' @param source `character(1)`.
#' FASTA file source:
#... | 3,999 | mit |
ed6b1326f83e57cb58b45df35a4897a76e7b99ed | akhmed1/rgithub | R/repositories.R | ################################################################################
# General repo information
#' Get list of repositories of current user
#'
#' @param ... extra parameters, see http://developer.github.com/v3/repos/
#'
#' @param ctx the github context object
#'
#' @return list of repositories
get.my.repos... | 22,179 | mit |
ed6b1326f83e57cb58b45df35a4897a76e7b99ed | aronlindberg/hub_turkr | Hub_Miner/R/repositories.R | ################################################################################
# General repo information
#' Get list of repositories of current user
#'
#' @param ... extra parameters, see http://developer.github.com/v3/repos/
#'
#' @param ctx the github context object
#'
#' @return list of repositories
get.my.repos... | 22,179 | gpl-2.0 |
162137d4b0d28387959df6f66b6cbb45c4687282 | ChristosChristofidis/h2o-3 | h2o-r/tests/testdir_hdfs/runit_HDFS_basic.R | #----------------------------------------------------------------------
# Purpose: This test exercises HDFS operations from R.
#----------------------------------------------------------------------
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source('../h2o-runit.R')
#---------------------... | 2,083 | apache-2.0 |
f23d6646f43813307127c7b20adbe97db0c3fc3b | BuddyVolly/OpenSARKit | shiny/ui/MS_fusion_LS_S1_KC_srtm_ui.R | #-----------------------------------------------------------------------------
# Multi-sensor fusion
tabItem(tabName = "ms_ls_s1_kc_srtm",
fluidRow(
# Include the line below in ui.R so you can send messages
tags$head(tags$script(HTML('Shiny.addCustomMessageHandler("jsCode",function(message) ... | 4,627 | mit |
d7e0c9dae65f0d62e7c73412027dd83cd1964b9a | realviacauchy/shiny-court-grapher | reports/eMag_TDOJ_Monthly_FY10-FY15_condensed_PLOT.R |
# Load libraries
library(dplyr)
library(stringr)
library(ggplot2)
# read in emagistrate data (emags)
source("reports/emagistrate_prep.R")
TDOJ <-
emags %>%
filter(Type=="TDOJ")
#this is where we start using summary counts per month (FYMonthAbbrev factor is ordered by Fiscal Calendar)
TDOJmonthly <-
group_b... | 1,212 | mit |
f3103fc1e9232fde5b06ae5f1c9a86b36fd56060 | CuppenResearch/MutationalPatterns | tests/testthat/test-get_indel_context.R | context("test-get_indel_context")
## Get a GRangesList object with only indels.
indel_grl <- readRDS(system.file("states/blood_grl_indel.rds",
package = "MutationalPatterns"
))
## Load the corresponding reference genome.
ref_genome <- "BSgenome.Hsapiens.UCSC.hg19"
library(ref_genome, character.only = TRUE)
## Get ... | 692 | mit |
d7e0c9dae65f0d62e7c73412027dd83cd1964b9a | zhuoaprilfu/demo_fork | reports/eMag_TDOJ_Monthly_FY10-FY15_condensed_PLOT.R |
# Load libraries
library(dplyr)
library(stringr)
library(ggplot2)
# read in emagistrate data (emags)
source("reports/emagistrate_prep.R")
TDOJ <-
emags %>%
filter(Type=="TDOJ")
#this is where we start using summary counts per month (FYMonthAbbrev factor is ordered by Fiscal Calendar)
TDOJmonthly <-
group_b... | 1,212 | mit |
d7e0c9dae65f0d62e7c73412027dd83cd1964b9a | zhuoaprilfu/shiny-court-grapher | reports/eMag_TDOJ_Monthly_FY10-FY15_condensed_PLOT.R |
# Load libraries
library(dplyr)
library(stringr)
library(ggplot2)
# read in emagistrate data (emags)
source("reports/emagistrate_prep.R")
TDOJ <-
emags %>%
filter(Type=="TDOJ")
#this is where we start using summary counts per month (FYMonthAbbrev factor is ordered by Fiscal Calendar)
TDOJmonthly <-
group_b... | 1,212 | mit |
b783869eba82d9c747aed4a576496d46d6e3b8ca | polarise/Traffic-Modelling | plot_timeseries.R | library( ggplot2 )
d <- read.table( "cumul_time.txt", stringsAsFactors=F )
p <- ggplot( d, aes( x=V1, y=V2, colour="Forward" )) + geom_line()
p <- p + geom_line( data=d, mapping=aes( x=V1, y=V3, colour="Reverse" ))
p <- p + scale_colour_hue( "Direction" )
p <- p + ggtitle( "Traffic Time Series" ) + xlab( "Time" ) + y... | 425 | gpl-2.0 |
6111506f076b714f8b647ef93ec9c7c63c042435 | paul-shannon/projects | priceLab/alison/trenaDB/install.R | source("http://bioconductor.org/biocLite.R")
biocLite("DT")
| 60 | mit |
ef51057dc8200788d7a1687d4371142e70b5cb1e | distributions-io/laplace-cdf | test/fixtures/test.typedarray.R | options( digits = 16 )
library( jsonlite )
library( bda )
mu = 300
b = 20
x = seq( -300, 300, 0.5 )
y = plap( x, mu, 1/b )
cat( y, sep = ",\n" )
data = list(
mu = mu,
b = b,
data = x,
expected = y
)
write( toJSON( data, digits = 16, auto_unbox = TRUE ), "./test/fixtures/typedarray.json" )
| 298 | mit |
6f7ccd5d3516c4e57008653cccd87e23a51aedab | nimble-dev/nimble-demos | blog_posts/LM_comparisons_Beraha_etal/LM_C_comparisons_Beraha_etal.R | # This file contains code adapted from
# https://github.com/daniele-falco/software_comparison/tree/main/linear_models
#
# In this file we fix the way the model is written to allow nimble to
# detect conjugacy.
library(nimble)
# library(extraDistr) # This package from Beraha et al's code conflicts with nimble's dinvga... | 6,697 | bsd-3-clause |
d3eea8d0fc3e681756a11f3b9facfa56742a8603 | jukiewiczm/renjin | tests/src/test/R/test.graphics.hist.default.0a716ec9afefbe37657034da70f80e22.R | library(hamcrest)
expected <- structure(list(breaks = c(0x1.9p+6, 0x1.2cp+8, 0x1.f4p+8, 0x1.5ep+9
), counts = c(82L, 55L, 7L), density = c(0x1.7530eca8641fep-9,
0x1.f49f49f49f49fp-10, 0x1.fdb97530eca86p-13), mids = c(0x1.9p+7,
0x1.9p+8, 0x1.2cp+9), xname = "structure(c(112, 118, 132, 129, 121, 135, 148, 148, 136, 1... | 2,101 | gpl-3.0 |
f7610e68956f0c7276c7da9014685bb0a798cf9a | cowboysmall/jhudatascience | rprog/project1/cachematrix.R | ## This file contains two functions intended to be used to cache the
## relatively expensive operation of finding the inverse of a square
## invertible matrix (O(n^2))
##
## makeCacheMatrix: creates a special matrix object that can cache its
## inverse.
##
## cacheSolve: computes the inverse of the special matrix r... | 1,395 | mit |
8d4941ed6ff2d2b0df26a32e1b3d6170b198cb2d | diego-plan9/citation-analysis-in-R | citenet/R/writeAttributesCna.R | writeAttributesCna <- function( rcnadata, fileName="CNA.csv" )
{
if( fileName=="CNA.csv" )
{
fileName <- paste( "CNA ", date(), ".csv", sep="" )
fileName <- gsub( ":", "-", fileName )
}
tbl <- getAttributes( rcnadata )
write.csv( tbl, fileName, row.names=F)
}
| 319 | gpl-2.0 |
8d4941ed6ff2d2b0df26a32e1b3d6170b198cb2d | lecy/citation-analysis-in-R | citenet/R/writeAttributesCna.R | writeAttributesCna <- function( rcnadata, fileName="CNA.csv" )
{
if( fileName=="CNA.csv" )
{
fileName <- paste( "CNA ", date(), ".csv", sep="" )
fileName <- gsub( ":", "-", fileName )
}
tbl <- getAttributes( rcnadata )
write.csv( tbl, fileName, row.names=F)
}
| 319 | gpl-2.0 |
182acd1205f35e149ca616ce793d585cfcce904e | mhunter1/OpenMx | demo/OneFactorModel_LikelihoodVector.R | #
# Copyright 2007-2017 The OpenMx Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 4,467 | apache-2.0 |
1c2e9139ac0807bc62d36f0e7ced04dd7c07bf42 | SwissTPH/TBRU_TBHIV | scripts/SNPS_table_by_HIV.R | ####R version: 3.2.2 (2015-08-14) -- "Fire Safety"####
####Copyright (C) 2015 The R Foundation for Statistical Computing###
####Platform: x86_64-pc-linux-gnu (64-bit)###
####Daniela Brites###
####Date:1.12.2016####
####content:This scripts extracts SFS of Mtb/HIV+ and Mtb/HIV- mutations and performs chi-square tes... | 11,868 | mit |
87aeefc51ad9716b8294fe694e17c322fa92d3b2 | bertcarnell/rational | R/rational-add.R | # include the rational-class.R so that it is loaded first
#' @include rational-class.R
#' @title Rational Number Arithmetic
#'
#' @param e1 rational numbers, integers, or numerics
#' @param e2 rational numbers, integers, or numerics
#' @field add for R6 classes, using the \code{$add(e1)} to do addition is the fastest ... | 6,442 | gpl-2.0 |
6a788fed0c80ccffe59692e379326879c42993d9 | xluo11/xxIRT | R/module1_model_gpcm.R | #' Generalized Partial Credit Model
#' @description Routine functions for the GPCM
#' @name model_gpcm
NULL
#' @rdname model_gpcm
#' @param t ability parameters, 1d vector
#' @param a discrimination parameters, 1d vector
#' @param b item location parameters, 1d vector
#' @param d item category parameters, 2d vector
#'... | 7,298 | gpl-2.0 |
1f894793c3517e15da4f9fc805338b097d8f1ceb | stan-dev/rstanarm | tests/testthat/test_stan_polr.R | # Part of the rstanarm package for estimating model parameters
# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version ... | 4,042 | gpl-3.0 |
f136f4d10ccc3528777271a083cfebe83fd12690 | chezou/sparkavro | R/sparkavro.R | #' Reads a Avro File into Apache Spark
#'
#' Reads a Avro file into Apache Spark using sparklyr.
#'
#' @param sc An active \code{spark_connection}.
#' @param name The name to assign to the newly generated table.
#' @param path The path to the file. Needs to be accessible from the cluster.
#' Supports the \samp{"hdfs:... | 4,208 | apache-2.0 |
8791eb56d171210218892d83e6a44ff079b1170c | cran/icd9 | R/parse-rtf.R | # Copyright (C) 2014 - 2015 Jack O. Wasey
#
# This file is part of icd9.
#
# icd9 is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ... | 18,090 | gpl-3.0 |
8791eb56d171210218892d83e6a44ff079b1170c | jackwasey/icd9 | R/parse-rtf.R | # Copyright (C) 2014 - 2015 Jack O. Wasey
#
# This file is part of icd9.
#
# icd9 is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ... | 18,090 | gpl-3.0 |
31ee2f7c4c8b00e015ffa95d4a12c6185cf44c6b | sjbeckett/weighted-modularity-LPAwbPLUS | paper/papercode/MaximumMedianQ.R |
QQ = read.csv("output/summary/summaryQuaQBM.csv")
QL = read.csv("output/summary/summaryQuaLPAwb+.csv")
QE = read.csv("output/summary/summaryQuaEXLPAwb+.csv")
BQ = read.csv("output/summary/summaryBinQBM.csv")
BL = read.csv("output/summary/summaryBinLPAwb+.csv")
BE = read.csv("output/summary/summaryBinEXLPAwb+.csv")
... | 1,644 | mit |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | aviralg/R-dyntrace | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | bedatadriven/renjin | packages/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | krlmlr/r-source | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | allr/timeR | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | minux/R | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | SensePlatform/R | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | reactorlabs/gnur | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | allr/r-instrumented | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
d7ac6f8ced30c863b69545bf92b7adcb864e6317 | sarahvanhala/nomscheck | R/compare_differences.R | #' Calculate Differences
#'
#' Calculate differences in H-data between current and most previous assessment
#'
#' @param noms_data dataframe created by read_noms_data
#' @return Dataframe with a column of differences
#' @examples
#' calc_diffs(noms_data)
#' @export
calc_diffs <- function(noms_data) {
noms_data %>%
... | 3,588 | gpl-3.0 |
71c8b5da9ca4e5a20d04261e8c7a0bd75a774dd2 | jeroenooms/r-source | src/library/datasets/data/precip.R | "precip" <-
structure(c(67, 54.7, 7, 48.5, 14, 17.2, 20.7, 13, 43.4, 40.2, 38.9, 54.5,
59.8, 48.3, 22.9, 11.5, 34.4, 35.1, 38.7, 30.8, 30.6, 43.1, 56.8, 40.8,
41.8, 42.5, 31, 31.7, 30.2, 25.9, 49.2, 37, 35.9, 15, 30.2, 7.2, 36.2,
45.5, 7.8, 33.4, 36.1, 40.2, 42.7, 42.5, 16.2, 39, 35, 37, 31.4, 37.6,
39.9, 36.2, 42.8, 4... | 1,330 | gpl-2.0 |
76ef8ee5b41ec0abe68cbafddd38ed4d81ecf3b4 | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/abbreviate/tc_abbreviate_11.R | expected <- eval(parse(text="c(\"Svnst\", \"N.462\", \"Mnchr\", \"N.475\", \"Velvt\", \"Ptlnd\", \"Glbrn\", \"N.457\", \"WN.38\", \"Trebi\")"));
test(id=0, code={
argv <- eval(parse(text="list(c(\"Svansota\", \"No. 462\", \"Manchuria\", \"No. 475\", \"Velvet\", \"Peatland\", \"Glabron\", \"No. 457\", \"Wiscon... | 447 | gpl-2.0 |
76ef8ee5b41ec0abe68cbafddd38ed4d81ecf3b4 | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/abbreviate/tc_abbreviate_11.R | expected <- eval(parse(text="c(\"Svnst\", \"N.462\", \"Mnchr\", \"N.475\", \"Velvt\", \"Ptlnd\", \"Glbrn\", \"N.457\", \"WN.38\", \"Trebi\")"));
test(id=0, code={
argv <- eval(parse(text="list(c(\"Svansota\", \"No. 462\", \"Manchuria\", \"No. 475\", \"Velvet\", \"Peatland\", \"Glabron\", \"No. 457\", \"Wiscon... | 447 | gpl-2.0 |
76ef8ee5b41ec0abe68cbafddd38ed4d81ecf3b4 | rho-devel/rho | src/extra/testr/filtered-test-suite/abbreviate/tc_abbreviate_11.R | expected <- eval(parse(text="c(\"Svnst\", \"N.462\", \"Mnchr\", \"N.475\", \"Velvt\", \"Ptlnd\", \"Glbrn\", \"N.457\", \"WN.38\", \"Trebi\")"));
test(id=0, code={
argv <- eval(parse(text="list(c(\"Svansota\", \"No. 462\", \"Manchuria\", \"No. 475\", \"Velvet\", \"Peatland\", \"Glabron\", \"No. 457\", \"Wiscon... | 447 | gpl-2.0 |
76ef8ee5b41ec0abe68cbafddd38ed4d81ecf3b4 | kmillar/rho | src/extra/testr/filtered-test-suite/abbreviate/tc_abbreviate_11.R | expected <- eval(parse(text="c(\"Svnst\", \"N.462\", \"Mnchr\", \"N.475\", \"Velvt\", \"Ptlnd\", \"Glbrn\", \"N.457\", \"WN.38\", \"Trebi\")"));
test(id=0, code={
argv <- eval(parse(text="list(c(\"Svansota\", \"No. 462\", \"Manchuria\", \"No. 475\", \"Velvet\", \"Peatland\", \"Glabron\", \"No. 457\", \"Wiscon... | 447 | gpl-2.0 |
76ef8ee5b41ec0abe68cbafddd38ed4d81ecf3b4 | kmillar/cxxr | src/extra/testr/filtered-test-suite/abbreviate/tc_abbreviate_11.R | expected <- eval(parse(text="c(\"Svnst\", \"N.462\", \"Mnchr\", \"N.475\", \"Velvt\", \"Ptlnd\", \"Glbrn\", \"N.457\", \"WN.38\", \"Trebi\")"));
test(id=0, code={
argv <- eval(parse(text="list(c(\"Svansota\", \"No. 462\", \"Manchuria\", \"No. 475\", \"Velvet\", \"Peatland\", \"Glabron\", \"No. 457\", \"Wiscon... | 447 | gpl-2.0 |
937440239dfa980aefd413d009c4fbb4e085c2e8 | CtheDataIO-sdpenaloza/Kaggle-Titanic-Machine-Learning-from-Disaster | ML- SVR - Support Vector Regression/SVM_TRAIN.R | # Regression Template
# Importing the dataset
dataset = read.csv('train.csv')
#Cleaning (Removing Columns that wont be use for this model)
dataset <- subset( dataset, select = -Ticket )
dataset <- subset( dataset, select = -Cabin )
dataset <- subset( dataset, select = -Name )
dataset <- subset( dataset, select = -P... | 2,833 | gpl-3.0 |
76ef8ee5b41ec0abe68cbafddd38ed4d81ecf3b4 | krlmlr/cxxr | src/extra/testr/filtered-test-suite/abbreviate/tc_abbreviate_11.R | expected <- eval(parse(text="c(\"Svnst\", \"N.462\", \"Mnchr\", \"N.475\", \"Velvt\", \"Ptlnd\", \"Glbrn\", \"N.457\", \"WN.38\", \"Trebi\")"));
test(id=0, code={
argv <- eval(parse(text="list(c(\"Svansota\", \"No. 462\", \"Manchuria\", \"No. 475\", \"Velvet\", \"Peatland\", \"Glabron\", \"No. 457\", \"Wiscon... | 447 | gpl-2.0 |
07513f770e481eca10b6ff47aa682252ac705fd3 | Swaathik/cellbase | clients/R/R/AllGenerics.R |
# CellBaseR methods
########################################################################################################################
#' The generic method for getCellbase. This method allows the user to query the cellbase web services without any
#' predefined categories, subcategries, or resources. Please, ... | 16,575 | apache-2.0 |
dd6d89468b5005f85c557e8c6c6bce229850da25 | kllloyd/Thesis | toSource/GenerateData.R | GenerateData <- function(dataOptionsStructure,outerFolder,nReps){
#-----------------------------------------------------------------------------------------------------#
# K Lloyd 2016_09_16
#-----------------------------------------------------------------------------------------------------#
# Function applies Ma... | 9,523 | apache-2.0 |
dd6d89468b5005f85c557e8c6c6bce229850da25 | kllloyd/GPSurvival | code/GenerateData.R | GenerateData <- function(dataOptionsStructure,outerFolder,nReps){
#-----------------------------------------------------------------------------------------------------#
# K Lloyd 2016_09_16
#-----------------------------------------------------------------------------------------------------#
# Function applies Ma... | 9,523 | mit |
9a8f8b40b2187c50f9938b47fb05c2a9fbc19d22 | rivolli/utiml | tests/testthat/test_ensemble.R | context("Ensemble tests")
test_that("Majority votes", {
probs <- matrix(
c(1, 1, 1, 1, 0.6, 0.1, 0.8, 0.2, 0.8, 0.3, 0.4, 0.1),
ncol = 3
)
preds <- matrix(
unlist(as.numeric(probs > 0.5)),
ncol = 3
)
# probs preds
# [,1] [,2] [,3] [,1] [,2] [,3]
# [1,] 1... | 7,101 | gpl-2.0 |
8e835509df00607c3e041120d4c82db2cdc2bfb0 | jbkunst/r-posts | 030-lego/readme.R | rm(list = ls())
library("dplyr")
library("rvest")
dfcolors <- read_html("http://lego.wikia.com/wiki/Colour_Palette") %>%
html_nodes("table") %>%
html_table(fill = TRUE) %>%
.[[3]] %>%
tbl_df()
dfcolors2 <- read_html("http://www.peeron.com/cgi-bin/invcgis/colorguide.cgi") %>%
html_nodes("... | 395 | apache-2.0 |
cd0791e231ddbcf3bdad75ff0737281477fab939 | SMHendryx/quantifyBiomassFromPointClouds | R/plots/plotOutlierClusters.R | # Making and plotting catalog of las tiles on local machine
# Clear workspace:
rm(list=ls())
# Load packages:
library(lidR)
library(data.table)
library(ggplot2)
library(feather)
library(rgl)
source("~/githublocal/quantifyBiomassFromPointClouds/R/utils_colors.R")
# Run:
setwd("/Users/seanhendryx/DATA/Lidar/SRER/max... | 1,802 | gpl-3.0 |
291f48f471df18fab0bc5e43f9b34fd880c88678 | grishagin/RIGconvertbiopax | R/internal_MAIN_step3_combine_write_biopax.R | internal_MAIN_step3_combine_write_biopax<-
function(file_dir=NULL
,output_dir=NULL){
#prepare directories if not supplied
if(is.null(file_dir)){
file_dir<-
getwd()
}
if(is.null(output_dir)){
output_dir<-
... | 3,660 | gpl-2.0 |
956114046dac8ef8ec3cbda28320cac151c89c58 | HyuksuRyu/mixedLM_tutorial | tutorial_LM.R | require(dplyr)
# model 1
# pitch ~ sex
pitch = c(233,204,242,130,112,142)
sex = c(rep("female",3),rep("male",3))
my.df = data.frame(sex, pitch)
my.df
## building lm
xmdl = lm(pitch ~ sex, my.df)
xmdl
summary(xmdl)
coef(summary(xmdl))
# pitch ~ age
age = c(14,23,35,48,52,67)
pitch = c(252,244,240,233,212,204)
my.df... | 869 | gpl-3.0 |
dbd4dc4ee3bcbd3072d603b38b66dfd38274611e | SchlossLab/Sze_FollowUps_Microbiome_2017 | code/srn/srn_run_41_RF.R | ### Build the best lesion model possible
### Try XG-Boost, RF, Logit (GLM), C5.0, SVM
### Find the best based on Jenna Wiens suggestions on test and training
## Marc Sze
#Load needed libraries
source('code/functions.R')
loadLibs(c("dplyr", "caret","scales", "doMC"))
load("exploratory/srn_RF_model_setup.RDa... | 1,758 | mit |
52f72bfb621826591eca793511f183e1b1b2298c | joesoftheart/Metagenomic | R_Script/AbundancebarplotModibar_new.R | # Bar plot for number of samples in 4-10 samples
#rm(list=ls())
args <- commandArgs(TRUE)
library(reshape2)
library(ggplot2)
library(scales)
# library(randomcoloR)
data=read.table(args[1], sep = "\t", header=T)
head(data)
data1=as.data.frame(t(data[,2:ncol(data)]))
colnames(data1)=data$taxonomy
data2=cbind(taxonomy=ro... | 4,400 | mit |
a6c110da6100b8f8511444d965143cc38a6f3d38 | francescojm/OT_15_libraries_and_pipelines | Pipelines/previous pipelines/ToRearrange/OT15.PL_09.SingleTestANOVA.Bench.R | # source('Libraries/ANOVA/ANOVA.SingleTestVerification.R')
# source('Libraries/ANOVA/ANOVA_vis_library.R')
# source('Libraries/ANOVA/ANOVA_stats_library.R')
#
load('../../DATA/R/Pathways/miniPathwayEvents.rdata')
load('../../DATA/R/Pathways/miniPathwayList.rdata')
ANOVA_individualANOVA<-function(DEP_GENE,cellLineSe... | 19,847 | mit |
fc5eabd0cc5588b60457a6d488234499140c2e13 | mlhim/ichi2015_fhir_semantics | models/CarePlan/ccdCarePlana96c76ed433b/R/CarePlanparticipantrole.R | # Copyright 2015, Timothy W. Cook <tim@mlhim.org>
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to ... | 2,813 | apache-2.0 |
c3ac3f7cc652b9bf8c101f3b671ab27aa1db884f | Shians/Glimma | tests/testthat/test-gllink.R | context("Chart linking functions")
test_that("Linking functions are correct", {
expect_error(gllink(0, 1, src="none", dest="none", flag="none"),
"'src', 'dest' and 'flag' cannot simultaneously be 'none'")
expect_error(gllink(0, 1, src="click", dest="none"),
"src cannot be defined while dest is ... | 728 | lgpl-3.0 |
ba07b9ab33f3e2827abc375e52e25473e5fb5e9c | mknapper1/Income-Prediction-Model | Source Code/linear_regression_model (Autosaved).R | # linear regression using random variable selected by me
adult.poly = lm(income~poly(age,12)+education+poly(capitalGain,12)+poly(capitalLoss,12)+poly(hoursPerWeek,16)+sex+race+workclass+maritalStatus+relationship+occupation, data=adult.data)
#summary(adult.poly)
# do the prediction
yhat = predict(adult.poly,adult.tes... | 467 | mit |
618d12ceddc3703a377c262e46b4621a1d60ca53 | vguillemot/multiblox | data_scripts/load_pHGG.R | load.pHGG.data <-
function(cgh_mode=c("seg", "norm"), n=92, pathtofile="./"){
if (is.null(n)) { n <- 92 }
if (is.null(cgh_mode)) { cgh_mode <- "seg" }
# if (cgh_mode != "norm") {
# cgh_mode <- ""
# }
load(paste(pathtofile, "pHGG_multiblox_data.Rdata", sep=""))
### X, y, clinic, CGH_annot
... | 947 | mit |
3d5a5bd093cfbb75398788f55cfbc75cfbea803f | grishagin/RIGessentials | R/split_cols_lengthen_df.R | split_cols_lengthen_df <-
function(dFrame
,colsToSplit
,patternToSplit="\\|"
,at_once=TRUE){
#' @export
#' @title
#' Split Column(s) Based on Pattern
#' @description
#' Wrapper for a \code{strsplit} function.
#' In a given dataframe, split speci... | 7,888 | gpl-2.0 |
960401120662e4a44ca5db2e4287d2eb5af045ea | swanderz/xis_mining | scratch/2017-01-07.R | GetYearReport <- function() {
#load MB reports
t1.report <- GetReportsDFfromMBcsv("data/t1 comments.csv")
t2.report <- GetReportsDFfromMBcsv("data/t2 comments.csv")
t3.report <- GetReportsDFfromMBcsv("data/t3 comments.csv")
#create vector of columnnames to counteract ugl... | 5,610 | mit |
1496bf474879e59b229594a931069553e4f22085 | jeremyrcoyle/sl3 | inst/examples/delayed_sl3.R | library(sl3)
library(shiny)
library(future)
data(cpp_imputed)
cpp_imputed <- cpp_imputed[sample(nrow(cpp_imputed), 10000, replace = T), ]
covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
outcome <- "haz"
options(sl3.save.training = TRUE)
task <- sl3_Task$new(cpp_imputed, covariates =... | 1,009 | gpl-3.0 |
74791717a131cd42f78f7f9c9f7eb0214f214659 | VijayKrishna/sleep-work-relax | analyze2.R | library(tm)
library(wordcloud)
timedData <- read.csv("/home/vijay/hackdata/timebasedData2.csv", header=TRUE)
day_wordcloud <- function(day) {
lords <- readLines(paste("/home/vijay/hackdata/titles_",day, sep = ""))
txt <- VectorSource(lords);
txt.corpus <- Corpus(txt);
lords <- tm_map(txt.corpus, stripWhitespa... | 1,615 | mit |
362419dd24fbd40e236da59ae474e921b282b421 | sdgroopkund/Adherence_in_VIRAHEP_C | combined2_2stageest.R | homdir<-"C:/material/coursework old/coursework/coursework/tracs/data"
#homdir <- "H:/codes/survival/additive_model"
setwd(homdir)
source(file="fun_general.R")
################################################################
######## Data management #######################################
##############################... | 12,938 | gpl-2.0 |
045c15cafa0b1087c707c9205d685edf9d6e3145 | rinze/estudio-voto-blanco2011 | functions.R | # All D'Hont functions are versions of the code proposed by Carlos Gil Bellosta
# on http://r.789695.n4.nabble.com/D-Hondt-method-td879362.html
# Version 1: according to current Spanish law:
# 1. Blank votes are valid.
# 2. Parties with less than 5 % of valid vote are not taken into account.
computeDHontCurrent <-... | 4,759 | gpl-2.0 |
6a5303a76bf1dcd1f9ae644dbef89bf40592fcc3 | clarkfitzg/STA137 | project/207/fit2lmer.R | library(lme4)
load('../fastrak.Rda')
# The zeros are most likely not valid readings.
fastrak = fastrak[fastrak$count != 0, ]
# The date range where the counts inexplicably doubled.
a = as.POSIXct('2010-06-23')
b = as.POSIXct('2010-08-04')
toobig = with(fastrak, (a < time) & (time < b))
fastrak = fastrak[!toobig, ]
... | 700 | mit |
aee59fac6e76c7008ad89a1605791a8f83e6c21d | MazamaScience/MazamaSpatialUtils | tests/testthat/test-US_stateConversion.R | # ----- US_stateCodeToName ----------------------------------------------------------------
testthat::context("US_stateCodeToName()")
test_that("Returns expected output", {
expect_equal(US_stateCodeToName("VT"), "Vermont")
expect_equal(US_stateCodeToName(c("SD", "NY", "WA", "CA")),
c("South Dakota"... | 2,109 | gpl-2.0 |
35c03bfe890b3ad95e40c239bcab48a9be24e358 | prem-pandian/algos | ensemble.R | +-+-+-+-+ +-+-+-+-+-+-+-+
|P|r|e|m| |P|a|n|d|i|a|n|
+-+-+-+-+ +-+-+-+-+-+-+-+
::::::::::::::::::::::::::::::::::::::::
Ensemble Model
Models:Random Forest, SVM, GBM, BayesGLM
::::::::::::::::::::::::::::::::::::::::
# Install Libraries
install.packages(c("arm","caret","gbm","randomForest","caTools","foreach","doMC"... | 9,198 | mit |
1496bf474879e59b229594a931069553e4f22085 | tlverse/sl3 | inst/examples/delayed_sl3.R | library(sl3)
library(shiny)
library(future)
data(cpp_imputed)
cpp_imputed <- cpp_imputed[sample(nrow(cpp_imputed), 10000, replace = T), ]
covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn")
outcome <- "haz"
options(sl3.save.training = TRUE)
task <- sl3_Task$new(cpp_imputed, covariates =... | 1,009 | gpl-3.0 |
6a5303a76bf1dcd1f9ae644dbef89bf40592fcc3 | clarkfitzg/STA137 | project/207/fit4.R | library(lme4)
load('../fastrak.Rda')
# The zeros are most likely not valid readings.
fastrak = fastrak[fastrak$count != 0, ]
# The date range where the counts inexplicably doubled.
a = as.POSIXct('2010-06-23')
b = as.POSIXct('2010-08-04')
toobig = with(fastrak, (a < time) & (time < b))
fastrak = fastrak[!toobig, ]
... | 700 | mit |
0474674aec3b1c6c88cc1f8a444fbb467fd60b8e | fsotoc/grtools | R/econdc.R | econdc <- function(m, use_kadlec=T) {
#compute proportion-matrix from input matrix
P <- pmatrix(m)
#----------------------------------------
# Test c for A conditional on B1
# get data
h1 <- P[1,1] / (P[1,1] + P[1,2])
fa1 <- P[2,1] / (P[2,1] + P[2,2])
h2 <- P[1,3] / (P[1,3] + P[1,4])
fa2 <- ... | 3,477 | gpl-2.0 |
2009aac83d1e28a48ea2585d79c811daffa9ed9f | DianeBeldame/AppVelib | my_function.R | data_par_station <- function(m,num_station){
require(mongolite)
my_aggregate <- paste0('[
{"$match":{"number":',num_station,'}},
{"$unwind":"$serie"},
{"$project":{"_id":0,
"number" : 1,
"add... | 9,187 | gpl-3.0 |
05b58b02803995d58f821449f89ce52d7593b7a7 | kbrannan/ODEQ-Bacteria-Model-R | R_scripts/sub-models/Wildlife-Duck/Wildlife_Duck_Sub_Model (KMB 01162014).R | wildlifeDuck <- function(chr.input="wildlifeDuckxx.txt",chr.wrkdir=getwd()) {
## read input file
SubModelFile <- paste0(chr.wrkdir,"/",chr.input)
SubModelData <- read.delim(SubModelFile, sep=":",comment.char="*",stringsAsFactors=FALSE, header=FALSE)
names(SubModelData) <- c("parameter","value(s)")
##
### G... | 4,555 | gpl-2.0 |
ff5924a457050ab161f335ccc25da82803d22680 | irintch3/BALVM | 3vex_estim.R |
D.ex<-read.table(file="DavidEx.txt", header=FALSE)
p=ncol(D.ex)
n=nrow(D.ex)
ord=4
m=10
S <- grid.size <- 300
grid.eta<-seq(0.00000001,0.999999,length.out=S)
eta.seq<-seq(0.00000001,0.999999,length.out=n)
Bj1<-bs(u1,knots=AknotsI[(ord+1):(length(AknotsI)-ord)], intercept = TRUE, df=m,
Boundary.knots = c(1e-... | 2,954 | gpl-2.0 |
ed92ca268bac2c3b19f520ff679b5f1f8e55aac5 | jpritikin/OpenMx | R/MxRAMModel.R | #
# Copyright 2007-2019 by the individuals mentioned in the source code history
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 28,370 | apache-2.0 |
8316820d4a497f962574e438728ee275e87c5416 | bedatadriven/renjin | tests/src/test/R/by.R | #
# Renjin : JVM-based interpreter for the R language for the statistical analysis
# Copyright © 2010-2019 BeDataDriven Groep B.V. and contributors
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundati... | 1,022 | gpl-2.0 |
11de521a9e4ad621b962329621ba7a1b3d6d30c4 | francojc/dsfl-swirl | Data manipulation/initLesson.R | # Code placed in this file fill be executed every time the
# lesson is started. Any variables created here will show up in
# the user's working directory and thus be accessible to them
# throughout the lesson.
swirl_options(swirl_logging = TRUE) # allow logging for submission to Google Form
.get_cou... | 717 | apache-2.0 |
2597d82749296b4e91db447d0e1477ea4cf307f5 | michalkurka/h2o-3 | h2o-r/tests/testdir_algos/xgboost/runit_xgboost_feature_interactions.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.XGBoost.feature_interactions <- function() {
prostate.hex <- h2o.uploadFile(locate("smalldata/logreg/prostate.csv"), destination_frame="prostate.hex")
response <- "RACE"
ignored_colum... | 781 | apache-2.0 |
2597d82749296b4e91db447d0e1477ea4cf307f5 | h2oai/h2o-3 | h2o-r/tests/testdir_algos/xgboost/runit_xgboost_feature_interactions.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.XGBoost.feature_interactions <- function() {
prostate.hex <- h2o.uploadFile(locate("smalldata/logreg/prostate.csv"), destination_frame="prostate.hex")
response <- "RACE"
ignored_colum... | 781 | apache-2.0 |
674a49aaef5a755751a08714893fe31ba4952ede | gavinsimpson/coenocliner | R/response-functions.R | ##' @title Species response models for coenocline simulation
##'
##' @description Parameterise species response curves along one or two gradients according to a Gaussian or generalised beta response model.
##'
##' @details \code{Gaussian()} and \code{Beta()} return values from appropriately parameterised Gaussian or ge... | 10,451 | gpl-2.0 |
869a3e7c696e4a698b268bc6a1138aa6972e66a0 | jolars/eulerr | tests/testthat/test-geometry.R | test_that("check that disc separation optimization works", {
set.seed(1)
r1 <- 5
r2 <- 5
tot <- r1^2*pi + r2^2*pi
tol <- 1e-6
expect_equal(eulerr:::separate_two_discs(r1, r2, 0), 10, tolerance = tol)
expect_equal(eulerr:::separate_two_discs(r1, r2, tot), 0, tolerance = tol)
expect_equal(eulerr:::separ... | 1,576 | gpl-3.0 |
85d8a20fb0c46f8cfc41983fd78e8a3d594f4422 | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_2.R | expected <- eval(parse(text="\"\\\\ab\\\\c\""));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\ab\\\\c\", FALSE, NULL, 0L, NULL, 3L, FALSE, NA)"));
.Internal(`format`(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]], argv[[8]]));
}, o=expected);
| 312 | gpl-2.0 |
85d8a20fb0c46f8cfc41983fd78e8a3d594f4422 | kmillar/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_2.R | expected <- eval(parse(text="\"\\\\ab\\\\c\""));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\ab\\\\c\", FALSE, NULL, 0L, NULL, 3L, FALSE, NA)"));
.Internal(`format`(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]], argv[[8]]));
}, o=expected);
| 312 | gpl-2.0 |
68ee422f3287b11ea65d86769c0d514334fb4113 | oganm/neuroexpresso | server.R |
print('starting server')
# beginning of server -----------
shinyServer(function(input, output, session) {
lb = linked_brush2(keys = NULL, "red")
vals = reactiveValues(fingerprint = '', # will become user's fingerprint hash
ipid = '', # will become user's ip address
... | 21,196 | gpl-2.0 |
cff2c4d0e0ce86d5930b317712cea5396e84a0fb | setempler/miscset | R/RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' @title Return Triangular Numbers
#'
#' @description
#' Return the series of triangular (/triangle) numbers up to a number of
#' \code{n} rows of a triangle. The series has the entry number ... | 800 | gpl-3.0 |
85d8a20fb0c46f8cfc41983fd78e8a3d594f4422 | rho-devel/rho | src/extra/testr/filtered-test-suite/format/tc_format_2.R | expected <- eval(parse(text="\"\\\\ab\\\\c\""));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\ab\\\\c\", FALSE, NULL, 0L, NULL, 3L, FALSE, NA)"));
.Internal(`format`(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]], argv[[8]]));
}, o=expected);
| 312 | gpl-2.0 |
85d8a20fb0c46f8cfc41983fd78e8a3d594f4422 | kmillar/rho | src/extra/testr/filtered-test-suite/format/tc_format_2.R | expected <- eval(parse(text="\"\\\\ab\\\\c\""));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\ab\\\\c\", FALSE, NULL, 0L, NULL, 3L, FALSE, NA)"));
.Internal(`format`(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]], argv[[8]]));
}, o=expected);
| 312 | gpl-2.0 |
68ee422f3287b11ea65d86769c0d514334fb4113 | oganm/cellTypeExpression | server.R |
print('starting server')
# beginning of server -----------
shinyServer(function(input, output, session) {
lb = linked_brush2(keys = NULL, "red")
vals = reactiveValues(fingerprint = '', # will become user's fingerprint hash
ipid = '', # will become user's ip address
... | 21,196 | gpl-2.0 |
e49d8610d3c34b31ad4c975fa7432d9300df4121 | stan-dev/bayesplot | man-roxygen/args-density-controls.R | #' @param bw,adjust,kernel,n_dens Optional arguments passed to
#' [stats::density()] to override default kernel density estimation
#' parameters. `n_dens` defaults to `1024`.
| 179 | gpl-3.0 |
baddd30d898812d38daaccea35fdbbd4c0782d89 | ofurkusi/limestats | R/fetchQuestionVariables.R | fetchQuestionVariables <- function(data, question) {
regex <- paste("^", question, "(_[a-zA-Z0-9]{1,}){0,}$", sep="")
variables <- grep(regex, colnames(data))
#variables <- grep("^Q2_1(_[a-zA-Z0-9]{1,}){0,}$", colnames(data))
thisQuestion <- subset(data, select=variables)
# Fetch question labels
quest... | 628 | lgpl-3.0 |
85d8a20fb0c46f8cfc41983fd78e8a3d594f4422 | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_2.R | expected <- eval(parse(text="\"\\\\ab\\\\c\""));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\ab\\\\c\", FALSE, NULL, 0L, NULL, 3L, FALSE, NA)"));
.Internal(`format`(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]], argv[[8]]));
}, o=expected);
| 312 | gpl-2.0 |
85d8a20fb0c46f8cfc41983fd78e8a3d594f4422 | krlmlr/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_2.R | expected <- eval(parse(text="\"\\\\ab\\\\c\""));
test(id=0, code={
argv <- eval(parse(text="list(\"\\\\ab\\\\c\", FALSE, NULL, 0L, NULL, 3L, FALSE, NA)"));
.Internal(`format`(argv[[1]], argv[[2]], argv[[3]], argv[[4]], argv[[5]], argv[[6]], argv[[7]], argv[[8]]));
}, o=expected);
| 312 | gpl-2.0 |
9868ca423f41500f677aa79953ceb25af19dff25 | SMRUCC/R-sharp | REnv/R/utils.R | imports ["Html", "http", "graphquery"] from "webKit";
#' Run graphquery on html document
#'
#' @param url the url or local filepath of the target html document
#' @param graphquery the script text of a required given graphquery
#'
#' @return A ``R#`` object that parsed from the target html web
#' page with given qu... | 744 | gpl-3.0 |
3ed65407ffb42254b8155cec72eac10e1370da9f | CIAT-DAPA/cwr_interdependence | r_script/_interactive/_code/get_statistics.R | # Get statistics from region to country
# H. Achicanoy & C. Khoury
# CIAT, 2016
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= #
# Food supplies
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= #
work_dir <- 'C:/Users/haachicanoy/Documents/GitHub/interdependence_circos'
fs_region_coun... | 3,542 | gpl-3.0 |
77740a43a590d8ff490a9688eed3387224266a61 | jennybc/purrr | R/utils.R | #' Pipe operator
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
NULL
maybe_as_data_frame <- function(out, x) {
if (is.data.frame(x)) {
tibble::as_tibble(out)
} else {
out
}
}
recycle_args <- function(args) {
lengths <- map_int(args,... | 2,251 | gpl-3.0 |
6ca42000a762307d0b95927c8757129d0f94c94e | GDSL-UL/housing-indicators | build_db/pull_data.R | # Code to download all csv and merge them into `LR_Transactions_GEOREF.csv`
# Land Registry Price Paid Data Input
# Download files into a [Data] folder, or run line below
dir.create("Data")
# Files that must be manually downloaded into 'Data':
# 'NSPL_AUG_2016_UK.csv', available at: http://ons.maps.arcgis.com/home/it... | 5,001 | bsd-3-clause |
2eb0bca8c78925e1c9c7ed0f1d0dde33d2e3f20b | whitneyburrow/HighDim2Means | R/hotellingTest.R | #' Performs Hotelling T2 Test
#'
#' @param x Data set 1.
#' @param y Data set 2.
#'
#' @return
#' @export
hotellingT2 <- function(x, y) {
x <- as.matrix(x)
y <- as.matrix(y)
n1 <- nrow(x)
n2 <- nrow(y)
n <- n1 + n2 - 2
p <- ncol(x)
dbar <- colMeans(x) - colMeans(y)
sPool <- ((n1 - 1) * cov(x) + (n2 - 1)... | 393 | mit |
751275573a408d3758e34ec9332f1a064ba9e18a | liquidSVM/liquidSVM | bindings/R/liquidSVM/R/liquidData.R | # Copyright 2015-2017 Philipp Thomann
#
# This file is part of liquidSVM.
#
# liquidSVM is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any late... | 17,819 | agpl-3.0 |
21bf8c6fe59e7826b1788174e9eb51287884ed78 | Beirnaert/speaq | R/Winedata.R | #' Wine dataset
#'
#' 1H-NMR data of 40 wines, different origins and colors are included.
#'
#' @docType data
#'
#' @usage data(Winedata)
#'
#' @format A list with the spectra, ppm values, color and origin as list entries.
#'
#' @keywords datasets
#'
#' @references Larsen et al. (2006) An exploratory chemometric study... | 805 | apache-2.0 |
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