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7183aa956fbdc46bb74df160d32db26f88140a84 | f61064bb7d0013f111123206b230482514141d9e | /R/sir_xx_initialize.R | a183f94bebedb5b399784299f780fe214c55ebf2 | [] | no_license | nianqiaoju/agents | 6e6cd331d36f0603b9442994e08797effae43fcc | bcdab14b85122a7a0d63838bf38f77666ce882d1 | refs/heads/main | 2023-08-17T05:10:49.800553 | 2021-02-18T23:01:47 | 2021-02-18T23:01:47 | 332,890,396 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,164 | r | sir_xx_initialize.R | #' @title Gibbs sampler for SIR model
#' @description initialize agent states for SIR model given y such that the complete likelihood is not zero.
#' @param y population observations
#' @param model_config a list containinng parameters, features, and network structure
#' @export
#' @return agent_states
sir_xx_initial... |
54b9b264c53952c191effde604720958fd3d7fbb | 1689120410245895c81e873c902c3b889198be70 | /man/chk_clm_rdr.Rd | 55f6705fa20365bfa910e7f4e2c3e92fa518e813 | [] | no_license | seokhoonj/underwriter | 9d4f0ff7ae35b7c9fdc529fc44a92d841654148b | 347bbe69cb54136789d69abaf329962c46973ac1 | refs/heads/master | 2023-03-26T21:58:57.729018 | 2021-03-26T06:00:14 | 2021-03-26T06:00:14 | 293,969,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 512 | rd | chk_clm_rdr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chk_clm_rdr.R
\name{chk_clm_rdr}
\alias{chk_clm_rdr}
\title{Create claim rider vector}
\usage{
chk_clm_rdr(rider, code, target)
}
\arguments{
\item{rider}{is a rider vector}
\item{code}{is a kcd code regular expression vector}
\item{target}... |
425ed7055652bc3286cd70ffad9020c3d7170caa | 6fc4c3537514c05034823015f386a92887a5a8b5 | /R/F.GEV.R | 4c89fddb327522b888edb50436488b395bb20404 | [] | no_license | cran/PRSim | 956adacd883a34cc94d94f0a846e481cc4b4ff96 | 56a2625a8a79f510a54ff6a87935ee9362607116 | refs/heads/master | 2023-06-22T01:23:53.243445 | 2023-06-13T13:40:05 | 2023-06-13T13:40:05 | 236,875,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 186 | r | F.GEV.R | F.GEV <- function (x, xi, alfa, k)
{
if (k == 0) {
y <- (x - xi)/alfa
}
else {
y <- -k^(-1) * log(1 - k * (x - xi)/alfa)
}
F <- exp(-exp(-y))
return(F)
}
|
7e0ea8142172d6f7970e194a1e71b318bdc8acf9 | 83bd4ab4313515d2aefd13d96701267e9efc1018 | /A4_Q2.R | 183c157dd34932273addcc22ccb76e839f487d00 | [] | no_license | AkshayGovindaraj/Computational-Statistics | 3a1e7f3bf323c3915e526d17b153d89e54300a7d | c73a71e2bbc474c18b9393e86cec4bccc811b67b | refs/heads/master | 2020-04-17T05:21:39.182246 | 2019-09-17T20:03:04 | 2019-09-17T20:03:04 | 166,274,997 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | r | A4_Q2.R | #Facial Recognition - Computational Statistics
library(imager)
filename <- 'yalefaces/subject14.gif'
file <- system.file(filename,package='imager')
im <- load.image(file)
|
880e733c69e670c4c9860df59e615d3873ec262b | 66f658595b4fd87c0c58486c389c2260605d2b71 | /man/Ar1_sd.Rd | 3e497346abe39ea783347dddae5611b5ab2788be | [
"MIT"
] | permissive | cbuelo/tvsews | 2f05ad1fd3f6b6eada639d2c43d4c12e68ba3906 | 8d7a0fbbee3d8033b42d1e50b629c72af9571c6c | refs/heads/master | 2023-04-09T18:05:16.062047 | 2022-01-18T23:00:19 | 2022-01-18T23:00:19 | 332,109,603 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 563 | rd | Ar1_sd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rolling_window.R
\name{Ar1_sd}
\alias{Ar1_sd}
\title{SD of lag-1 autocorrelation with detrending}
\usage{
Ar1_sd(x, detrend = FALSE, prop_req = 0.99)
}
\arguments{
\item{x}{numeric}
\item{detrend}{TRUE or FALSE, default = FALSE}
\item{prop_... |
4dacdd4998d194a1bb2935b2961a24caf80b3226 | 85c526147ee8eb8976be4429cc719deb8077209c | /run_analysis.R | 5cba664f813d550209d086034d86ddf017a56156 | [] | no_license | jjvornov/DataCleaning | b09b56d141661831fa6b2a28b2a0a92eea61f002 | ec0805747efadb1ca62626017fc5a9373dd9594f | refs/heads/master | 2020-06-05T18:36:34.396223 | 2015-05-20T16:30:38 | 2015-05-20T16:30:38 | 35,955,395 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,077 | r | run_analysis.R | ##First read the Samsung data in the working directory
##subject_test are the subject ids, activity the activity ids,
##features are the actual datapoints for subjects and activities
subject_test<-read.table("test/subject_test.txt")
activity_test<-read.table("test/y_test.txt")
features_test<-read.table("test/x_test.t... |
4f5bd3ce26345952bfca0679791aa44547f2344c | 53a9ea36ab32e5768f0c6e1f4c4f0135c1b65e46 | /salmon_merge_table.R | b6ae26091c9e577358555d3e63b076c7c257b917 | [] | no_license | barrantesisrael/Dual_RNAseq_review_analysis | 220bea938ddc154f9ad8fa6243bacfc678f194d1 | 468fa5e4f1fecdd2f5bd8f084d59bba2a45ff798 | refs/heads/main | 2023-03-28T11:23:38.473519 | 2021-03-30T10:06:58 | 2021-03-30T10:06:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,765 | r | salmon_merge_table.R | #!/salmon_merge_table.R
#The script merges the Salmon reference-free alignments outputs into one file for both gene and transcript level.
#The script has 2 inputs:
#1)fileDirectory -> list of directories where the salmon's outputs are saved.
#2)gene_tx -> path to a file which has atleast two columns called "GeneID" a... |
c72fa58c6251d355dd2ea5652dacdd8fc80db12d | fbc824546d61ae83ff70b714b7540c27fa215970 | /WGCNA.R | 490b9108549bb378481baf041716793b442dc766 | [
"MIT"
] | permissive | ben-laufer/cffDNA-and-Brain-Manuscript | d997dc95d923a904160abd7a6752caeb8d0ee0a8 | 2fffac8d10b707fab8ae80d53c8522793d10c4b9 | refs/heads/main | 2023-04-17T01:56:31.093401 | 2021-08-31T19:50:22 | 2021-08-31T19:50:22 | 386,458,928 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 46,623 | r | WGCNA.R | # WGCNA for DMRichR results
# Modified from: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html
# Ben Laufer
rm(list=ls())
options(scipen=999)
cat("\n[DM.R] Loading packages \t\t\t", format(Sys.time(), "%d-%m-%Y %X"), "\n")
.libPaths("/share/lasallelab/programs/DMRichR/R_... |
c18f3bcc3d1d60de6fb9dd413c89498cc884869b | 8bb16a139c8dda84505596c4528f71f4b440a924 | /LG1/LabGuide1- Presentation/FirstRscript.R | c374e9ed5745e1072a118dfeee307bc2452d9961 | [] | no_license | berkozdoruk/Rstudio | 4afe89247bccc1df20b8cf295e3fd199c6e52b73 | c08cc1ddd0df9495cc512e51628157398d83fe34 | refs/heads/master | 2020-09-10T19:03:18.797693 | 2019-11-14T23:55:45 | 2019-11-14T23:55:45 | 221,807,783 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 201 | r | FirstRscript.R | # this is my first R script
# do some things
x <- 34
y <- 16
z <- x + y # addition
w <- y/x # division
# display the results
x
y
z
w
# change x
x <- "some text"
# display the results
x
y
z
w |
f8886b7bf533c9e2b7835b9c8f9f8f3033c9294f | 68d91392e0d0274ce502db0f612cbd7a9f1cd155 | /scripts/gem_emodel.R | dbcf49954bc44a9b3e770fd1e0037c4d6e991ca5 | [] | no_license | kwesterman/diet-methylome-catalog | b994d38069ce4d2ba828969bd94531d36ab39508 | 2985bcefbbddd93de1bb0500d5febf45eadd15f6 | refs/heads/master | 2020-06-19T17:38:00.111057 | 2018-12-06T23:03:07 | 2018-12-06T23:03:07 | 74,843,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,810 | r | gem_emodel.R | library(data.table)
library(dplyr)
args <- commandArgs(trailingOnly=TRUE)
if (!(length(args)==2)) { # Check for exactly 2 arguments
stop("2 arguments needed: exposure_var and cov_suffix", call.=FALSE)
}
envVar <- args[1] # First argument is the dietary exposure of interest
cov_suffix <- args[2] # Second argument ... |
ba032a06250aab83bdf890bcaf3cc1a0afdc13da | 29585dff702209dd446c0ab52ceea046c58e384e | /localdepth/R/normal.R | 9857dbe5583f60f91d1e46211098a97fe552d9b5 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,247 | r | normal.R | lsdnorm <- function(x, mean=0, sd=1, tau=0.2) {
if (tau >= 1 | tau <= 0)
stop("quantile order of 'tau' must be in the range (0,1). For order equal to 1 use function 'sdnorm'")
tau <- 1-(1-tau)/2
tau <- qnorm(tau, 0, sqrt(2)*sd)
ldnorm.int <- function(x, tau, mean, sd) {
pdf <- function(x) dnorm(x,... |
58554fc444bf0fb30fc711a62f2b44dffa282e53 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/arsenal/examples/write2specific.Rd.R | 86ef299c08709d41ee662a086725dfe721e5fe08 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | write2specific.Rd.R | library(arsenal)
### Name: write2specific
### Title: write2word, write2html, write2pdf
### Aliases: write2specific write2word write2pdf write2html
### ** Examples
## Not run:
##D data(mockstudy)
##D # tableby example
##D tab1 <- tableby(arm ~ sex + age, data=mockstudy)
##D write2html(tab1, "~/trash.html")
##D
##D... |
16e15dc70f38477913f48116568cab59b438d1a1 | 734951b8582b89a3336ab0244a2f55addb233a0a | /FactorAnalysis.R | 089a9a84a3e1f91d15213d35a5ccf03d516d4151 | [] | no_license | gerardloquet/WP4pilot | 222f84255704b1776b41a60dae81ab7d7c5a752a | 4e5568c6b72225d39f264a380c2e1a73bf693b07 | refs/heads/master | 2023-07-13T20:15:41.185346 | 2021-08-27T12:19:13 | 2021-08-27T12:19:13 | 300,050,885 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,139 | r | FactorAnalysis.R | # Load Packages
library(tidyverse)
library(here)
library(xlsx)
library(caret)
library(psych)
# Data path
data.path <- "C:/Users/loquetg/Documents/Data/"
# Load data
data.right <- read.xlsx(paste0(data.path,"DATA_All_Norm_R_V6.xlsx"),1)
#data.left <- read.xlsx(paste0(data.path,"DATA_All_Norm_L_V6.xlsx"),... |
712b5994662e99c0c450f009b2af5386dfcae9c6 | a05d526d7092349652c96f5318194cf1735f1cc3 | /man/import.bedmolecule.Rd | 6fce45e1e6a6bd43dc744f60229f0c35dc9c27d2 | [] | no_license | charles-plessy/CAGEr | 220fb5b044df118a505905896af8ce2efcfdbed1 | 0c79a3a592c1c8f6b5da086082d6369736dfa4ff | refs/heads/devel | 2023-08-30T20:52:40.435814 | 2023-07-27T23:55:10 | 2023-07-27T23:55:10 | 113,113,541 | 7 | 5 | null | 2023-07-18T01:58:27 | 2017-12-05T01:03:04 | HTML | UTF-8 | R | false | true | 876 | rd | import.bedmolecule.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ImportMethods.R
\name{import.bedmolecule}
\alias{import.bedmolecule}
\title{import.bedmolecule}
\usage{
import.bedmolecule(filepath)
}
\arguments{
\item{filepath}{The path to the BED file.}
}
\value{
Returns a \code{\link{CTSS}} object.
}
\de... |
30b7aa2549e770896c6752b1db8d15ab9a789e67 | 24851be32893bfb1027b2a33164ef515fc4fb76b | /code/OLD/binning/totalproduction.r | ea8017ab88a0c76391be527942f247e173a5518d | [] | no_license | qdread/forestlight | acce22a6add7ab4b84957d3e17d739158e79e9ab | 540b7f0a93e2b7f5cd21d79b8c8874935d3adff0 | refs/heads/master | 2022-12-14T03:27:57.914726 | 2022-12-01T23:43:10 | 2022-12-01T23:43:10 | 73,484,133 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,763 | r | totalproduction.r | # Correction factor for total production
prodtotals <- obs_totalprod %>%
group_by(fg, year) %>%
summarize(total = sum(bin_value * (bin_max-bin_min)))
dbh_pred <- exp(seq(log(1.2), log(315), length.out = 101))
mids <- function(a) a[-length(a)] + diff(a)/2
dbh_binwidth <- diff(dbh_pred)
dbh_df <- data.frame(dbh =... |
bc8d90dd053ad23c75d0dd36bdb6a7dc5645365e | f0a34c8e93e8aec9220ccc865b27d43f357f4646 | /scripts/process-baby-names.R | 2038116aa0ad5c72c776cb1cbc213681fb62b8f7 | [] | no_license | organisciak/names | 4867c1512319d7333ed58145f4cd34b78471c94f | 7f0632c442fff5fb3d73633ceb959ded2605d05f | refs/heads/master | 2016-09-06T14:38:58.384023 | 2014-12-03T20:36:50 | 2014-12-03T20:51:50 | 26,832,651 | 45 | 38 | null | null | null | null | UTF-8 | R | false | false | 3,973 | r | process-baby-names.R | library("data.table")
# Import name counts by state, sex, year
names <- fread("raw/us-names-by-gender-state-year.csv")
## Save name counts by sex
tmp <- names[, list(count=sum(count)), by=list(sex, name)][order(-count)]
write.csv(tmp, "data/us-names-by-gender.csv", quote=F, row.names=F)
## Smaller data:top ... |
7059d7d6d79f6c7d5e51ff27f7e52359840851ca | a82065b9c6b3313294bb57c1cdee55c454cd3de4 | /MBE_Code/networkmodels/unrestrictedmodel_stochasticmigrations/migstep3_runno1.R | 0dd4c7a5383a67732e34cc22886180370428db55 | [] | no_license | khanna7/circularmigrations | 88e49661ee0fa46c4580a517f2f8b89d3026447f | 536da8f01680a6d99e1b1733eb0a2504d8810a53 | refs/heads/master | 2020-05-20T13:11:16.020880 | 2015-10-03T15:07:06 | 2015-10-03T15:07:10 | 38,516,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,975 | r | migstep3_runno1.R | #####################################################
### Progression of versions
#####################################################
## Goal: put all pieces together for one simulation file
## control file -- just modify the mig_step --
## all output is sent to an external csv file
## 20 May 2012: Modified mig-ste... |
e4cadd9634d2b1cda4b980aa6a06ee067cd2d6ca | 5ead2261701abdad8a99016e46f5c0ebeb7f99c4 | /chenb.r | ad1a3cbb6a5569c4f455061ab2a75edb19bcd736 | [] | no_license | liuzh811/NEchina | b833027d68a4a34046cb16e0b46ca54fae711444 | 7f1eefa21b5513df4384522f10890fb63dbb26c7 | refs/heads/master | 2021-01-21T14:24:34.588130 | 2017-10-07T02:55:53 | 2017-10-07T02:55:53 | 57,230,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,149 | r | chenb.r | #download MODIS brdf to VI data for BAED
# load libraries
library(rgdal)
library(raster)
#################### Data pre-processing #######################
# list files
Work.Dir <- "D:/users/Zhihua/Landsat/p122024"
setwd(Work.Dir)
# read a polygon shapefile
testr1 = readOGR("D:\\users\\Zhihua\\Landsat\\p122024\\tes... |
97d6c842e3495900b985e2851d78cf17a1222fc1 | 5426b7385d33b4b218a823df29347bba654a5c30 | /nanopore_qc.R | 9d384d72f1bf97bc6b8948216ac27b1bb2cf1bec | [] | no_license | timplab/moth | 9b5fe32da79d6c486f276f56cfa7073c757b5efb | 19f6ce262357acb2abc8dab6412f533564f7feba | refs/heads/master | 2022-12-07T12:14:28.340198 | 2020-09-01T00:44:11 | 2020-09-01T00:44:11 | 198,861,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,231 | r | nanopore_qc.R | ##Ok - this is an R to do qc from nanopore fastq
##Default input variable
sampdir="/kyber/Data/Nanopore/projects/moth/SRA_data"
modif="blah"
##Plots go here:
#plotdir=file.path(sampdir)
##Load libraries and sources
library(Biostrings)
library(ShortRead)
library(ggplot2)
library(reshape2)
library(plyr)
library(ti... |
89f2f4ecd818521b41caf8cd1a2cc78fefe31b28 | d160c0b746059bc1c516e32cc5971c5cc5d5d9d4 | /R/OpencgaFiles-methods.R | 6ce83f1cb1e6a3057e66390600ae10afe8b15dd0 | [
"Apache-2.0"
] | permissive | melsiddieg/opencgaR | 093b4806f5077a6bab5162ac47ecebd18f40810b | 037f1bdd41fdfcccad1da47f0adf4d5251c0575b | refs/heads/master | 2021-01-15T22:51:36.714130 | 2017-08-10T12:29:08 | 2017-08-10T12:29:08 | 99,919,869 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | r | OpencgaFiles-methods.R | #' @title A method to query Opencga Files
#' @aliases OpencgaFiles
#' @description This method allow the user to create, update and explore
#' files data and metadta
#' @export
setMethod("OpencgaFiles", "Opencga", definition = function(object,
id=NULL, ac... |
b0a9d7f062a22beee8e9af2f01c96e4b8190fb59 | 004bd8664f1e19040c71442d01ad1847c535ff38 | /data/test_cases/Step1b_build_data_cuba.R | 494956d77f9c1c8485a2c0faf4a49b8412502d6f | [] | no_license | MS-COM/mscom | ef2ddf27f49d169c3df3b650347df3d6aa5bebe4 | 45043a4f505b74972284778c3772bc5a8d68b14e | refs/heads/master | 2021-05-11T13:05:24.821468 | 2018-11-28T06:22:11 | 2018-11-28T06:22:11 | 117,671,098 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,609 | r | Step1b_build_data_cuba.R |
# Setup
################################################################################
# Clear workspace
rm(list = ls())
# Turn off sci notation
options(scipen=999)
# Packages
library(rio)
library(plyr)
library(dplyr)
library(tidyr)
library(reshape2)
library(RColorBrewer)
# Directories
datadir <- "data/test_case... |
a4e8e408941cbac82f05cb238f54c04e7beb7d33 | 473b8a4300845f256b2eacfde73be8620cfd5ac0 | /Week3/code/TreeHeight.R | fb49e052dfde8d0ff0ad179dfc951b3b0ecadf68 | [] | no_license | ee-jackson/QMEEbootcamp | cf0771bf94980ed420449659cd6faff01c125d55 | 51b43b6cdfb0b6ec00d8b5fc453e22c1a28a5528 | refs/heads/master | 2021-07-17T04:58:14.494742 | 2021-07-05T11:48:10 | 2021-07-05T11:48:10 | 212,303,611 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 707 | r | TreeHeight.R | #!/usr/bin/env Rscript
# This function calculates heights of trees given distance of each tree
# from its base and angle to its top, using the trigonometric formula
#
# height = distance * tan(radians)
#
# ARGUMENTS
# degrees: The angle of elevation of tree
# distance: The distance from base of tree (e.g., meter... |
363c549a79bd25d9a4daffa2825c67b25872a896 | 8f0431de29762061acb57e06f492d22d5ce2604f | /R/gt_theme_guardian.R | 2ccb7f403b23ca8260910b647ce96c46a2d53475 | [
"MIT"
] | permissive | adamkemberling/gtExtras | 2c3e1a81d5dd97666dedab710d49377a2a7572dd | 40d1e5a006fa67833a702733055c94606f8cffb7 | refs/heads/master | 2023-08-17T11:12:00.431133 | 2021-10-13T16:28:10 | 2021-10-13T16:28:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,691 | r | gt_theme_guardian.R | #' Apply Guardian theme to a `gt` table
#'
#' @param gt_object An existing gt table object of class `gt_tbl`
#' @param ... Optional additional arguments to `gt::table_options()`
#' @return An object of class `gt_tbl`.
#' @importFrom gt %>%
#' @export
#' @import gt
#' @examples
#'
#' library(gt)
#' themed_tab <- head(mt... |
6a350cd206ef11e72a8385aedd766e43974ba523 | 752b5cf8419142d8b4c1bff2f97149d00a694d48 | /Excel_pipe_v1.01.R | 5eaa88cf026940665eb121140527922aed600d23 | [] | no_license | cprabucki/R | fdfc854b8323ef7257a00c8db4c5038bfc5dcffc | 33fa57eeeb20dd36cf51bfe0f2c231081e6f52ad | refs/heads/master | 2021-01-01T05:03:24.462242 | 2016-12-30T00:12:38 | 2016-12-30T00:12:38 | 77,395,338 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,084 | r | Excel_pipe_v1.01.R | # Carga la excel
library(readxl)
W01 <- read_excel("C:/Users/a212857/Downloads/W01.xlsx", 1)
# Obtiene el vector lógico para consultar las oportunidades con Closing date 2016
lfecha.closing <- as.Date.numeric(W01$`Closing Date`, origin="1899-12-30") < as.Date("2017/01/01") & as.Date.numeric(W01$`Closing Date`, origin=... |
48e4aae7ddeae48c4a870837a076955459f180cf | 6c1cebe424c5ffed3295cc108fc36ff044cfb260 | /man/Mapper.Rd | af26a26487a7d32df76b5e0c69414d61e427cac9 | [] | no_license | corybrunson/Mapper | fec7b778b8cea0557984b95b1219d79169cad568 | 76b861b1a74a99fa9bb43e0beeeba7434b8ca5f8 | refs/heads/master | 2022-06-18T00:59:43.087056 | 2022-05-22T15:53:06 | 2022-05-22T15:53:06 | 190,648,752 | 0 | 0 | null | 2019-06-06T20:56:49 | 2019-06-06T20:56:48 | null | UTF-8 | R | false | true | 2,954 | rd | Mapper.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mapper.R
\encoding{UTF-8}
\name{mapper}
\alias{mapper}
\title{mapper}
\usage{
mapper(
X,
filter,
cover = c(cover = "fixed interval", number_intervals = 10L, percent_overlap = 35),
distance_measure = "euclidean",
clustering_algorithm... |
658ec0944ac0210593bcf797e2d1a0c124bc62ea | 7e5f89d948abbdc3ee5314ef561e7229763ca225 | /R/ui.R | d400e7b01721633a6ad0460bb8b494fa3cf5954e | [] | no_license | GiulioGenova/SMCcalibration | bb798adfc34100ac7575d798788a92c79a145aad | 21fbee9c3374ef422515f6e747b16bb1d258036d | refs/heads/master | 2021-08-08T17:12:08.981088 | 2018-07-10T06:58:46 | 2018-07-10T06:58:46 | 135,586,313 | 0 | 2 | null | 2018-05-31T13:18:25 | 2018-05-31T13:18:25 | null | UTF-8 | R | false | false | 8,696 | r | ui.R |
if (!require("Cairo")) install.packages("Cairo")
if (!require("robustbase")) install.packages("robustbase")
if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyr")) install.packages("tidyr")
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("leaflet")) install.packages("leaflet")
if (!... |
607f9249afb07987de5b885113b0975fb845dcdf | e3ce3ad557ebd51429ed7acfea936723149a8d4c | /R/mof.dent.R | 003e88eaa6499c18a3e9bfbfed327c4dc41b15e5 | [] | permissive | jakobbossek/smoof | 87512da9d488acfe3a7cc62aa3539a99e82d52ba | d65247258fab57d08a5a76df858329a25c0bb1b8 | refs/heads/master | 2023-03-20T02:05:12.632661 | 2023-03-08T13:59:27 | 2023-03-08T13:59:27 | 22,465,741 | 32 | 27 | BSD-2-Clause | 2022-01-21T10:02:19 | 2014-07-31T10:39:43 | R | UTF-8 | R | false | false | 1,619 | r | mof.dent.R | #' @title
#' Dent Function
#'
#' @description
#' Builds and returns the bi-objective Dent test problem, which is defined as
#' follows:
#' \deqn{f(\mathbf{x}) = \left(f_1(\mathbf{x}_1), f_2(\mathbf{x})\right)}
#' with
#' \deqn{f_1(\mathbf{x}_1) = 0.5 \left( \sqrt(1 + (x_1 + x_2)^2) + \sqrt(1 + (x_1 - x_2)^2) + x_1 - x_... |
cb31e17e4dcb9e18072ece96fc34ebcc386db78b | 768c8adb2b2dfc7809201819c9a6f162d0749a4e | /Pizza.R | 24fb100dc7854e93e3789492dc38a9873e99d9b2 | [] | no_license | ohmpatthanay/R-language-Basic-Program- | badbc52a34e31be1534894f098c09471aad55cc1 | 71787744b4831ddfc622f1a36c992affcb8ff4af | refs/heads/master | 2020-08-23T14:28:54.567253 | 2019-10-21T18:45:09 | 2019-10-21T18:45:09 | 216,639,603 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,792 | r | Pizza.R | pizza <- function(y = 0.8){
opar <- par()$mar
on.exit(par(mar = opar))
par(mar = rep(0, 4))
plot.new()
circle(0.5, 0.5, 4, "cm", , 4)
circle(0.5, 0.5, 3.5, "cm", , 4)
circle(0.42, 0.62, 0.3, "cm", , 4)
circle(0.47, 0.4, 0.3, "cm", , 4)
circle(0.60, 0.45, 0.3, "cm", , 4)
circle(0.66, 0.54, 0.3, "cm",... |
bca573d06c506c3272074cb38dd08191b85e55f9 | 075610f48fb5314e016574e246f486f1ab04ce3e | /R/03_calculation-C.R | d43f9b8950dfe968fb6a1af133a987ed31e918a2 | [
"MIT"
] | permissive | asri2/icr | aed09b5a39f7dbf640003eb310c36f2173074beb | ab6afaeeaef587b18186cb6e270857034167d63d | refs/heads/master | 2022-09-13T20:55:11.001629 | 2020-06-04T07:11:49 | 2020-06-04T07:11:49 | 265,541,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,006 | r | 03_calculation-C.R | # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Series :01
# Name: Day of the week and Volatility
# Description: This program estimate return and asset's volatility
# by using GARCH and Modified-GARCH
# Ticker: IDX; JKSE
# Author: Asri Surya
# Date: March 2020
#++++++++++++++++++++++... |
8f1a971864a348d9715bac001e7974ca59f5083e | 3c0531db5f30de38f06b8dffd741c3e13a94eeb5 | /code/01_wrangle-data.R | 20e71228ad613f2463a9d1c64f6e08e3f7d3548e | [] | no_license | spoicts/ccweedmeta-analysis | df206efcbfd9d88f378e91b2aaf45eaeea28b2ba | 63abef61a9a66d92fb604fc73626f798f4ae85ee | refs/heads/master | 2023-05-08T23:19:39.195889 | 2021-06-01T20:42:55 | 2021-06-01T20:42:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,810 | r | 01_wrangle-data.R | #############################
#
# Author: Gina Nichols
#
# Date Created: March 5 2020
#
# Date last modified:
#
# Purpose: prepare data for analysis
#
# Inputs: ccweeddat (from the ccweedmetapkg)
#
# Outputs: wd_wide and wd_long in working_data folder
#
# Steps: 1) calculate termination-to-planting gaps
# 2) F... |
c6cc10c5229f6a53c8614dc59082fae381631608 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/celestial/examples/deg2dms.Rd.R | 06628f9f20b089ae1e86df77c07f5d884d5127d1 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 306 | r | deg2dms.Rd.R | library(celestial)
### Name: deg2dms
### Title: Convert decimal degrees to dms format.
### Aliases: deg2dms
### Keywords: convert
### ** Examples
print(deg2dms(12.345))
print(deg2dms(12.345,type='cat',sep=':'))
print(deg2dms(12.345,type='cat',sep='dms'))
print(deg2dms(12.345,type='cat',sep='DMS'))
|
16a0293b34482c3d4adc063a70560ff7ec950942 | 47a8dff9177da5f79cc602c6d7842c0ec0854484 | /man/AugmentPlot.Rd | 103643f7a41508f1705fe25a75bea88f6ec45d0f | [
"MIT"
] | permissive | satijalab/seurat | 8949973cc7026d3115ebece016fca16b4f67b06c | 763259d05991d40721dee99c9919ec6d4491d15e | refs/heads/master | 2023-09-01T07:58:33.052836 | 2022-12-05T22:49:37 | 2022-12-05T22:49:37 | 35,927,665 | 2,057 | 1,049 | NOASSERTION | 2023-09-01T19:26:02 | 2015-05-20T05:23:02 | R | UTF-8 | R | false | true | 846 | rd | AugmentPlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{AugmentPlot}
\alias{AugmentPlot}
\title{Augments ggplot2-based plot with a PNG image.}
\usage{
AugmentPlot(plot, width = 10, height = 10, dpi = 100)
}
\arguments{
\item{plot}{A ggplot object}
\item{width, height}{Width ... |
2d74cd55522b34bb776d69bdb4930afd86ce5d93 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MetaheuristicFPA/examples/rcpp_MetaheuristicFPA.Rd.R | f435514d81046f806363619a4bdb333d68a42561 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 714 | r | rcpp_MetaheuristicFPA.Rd.R | library(MetaheuristicFPA)
### Name: fpa_optim
### Title: Metaheuristic - Flower Pollination Algorithm
### Aliases: fpa_optim
### ** Examples
# find the x-value that gives the minimum of the dejong benchmark function
# y = sum(x[i]^2), i=1:n, -5.12 <= x[i] <= 5.12
# global minimum is 0 when each x = 0
deJong<-func... |
be9e76954e79136e135e672e40acb31fdf45cbb0 | 9e4da4d8a7640baf68b334212eac19701a0ecbb5 | /demean_age.R | 1a21563d3b02ce397e55b054de2a2b35f546c70f | [] | no_license | poldrack/r_testing | 6427c8f7a038dbdb6b2c39a5c0950952e4c14de1 | ada0a58bb90242d4ff248a1996572b21e019df78 | refs/heads/master | 2021-01-01T05:46:28.656150 | 2016-05-10T20:06:03 | 2016-05-10T20:06:03 | 58,487,735 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 340 | r | demean_age.R | args=commandArgs(trailingOnly = TRUE)
demographics <- read.csv(file=args[1], head=TRUE, sep="\t")
age <- demographics$age
mean_age=sum(age)/length(age)
demean_age <- age - mean_age
stopifnot(mean_age<100)
stopifnot(mean_age>10)
write.table(demean_age, file="age_demeaned.tsv", row.names=FALSE, col.names=FALSE, sep="\t"... |
93253544af7982bf013654e4c203279879c6692b | 85cb470f1dfc89dd7b355cb0b4c9451e9f811a44 | /model-scripts/Pre-Expiry-Implementation-Archive/clinical-engagement.R | 93c389cd733c141a8681d306cc17d5e3614a5a43 | [] | no_license | khanna7/bc-navigation | 541ad187d45bec8c79043e4ed7f181cb29ac6656 | b2dd636ea5cb05c8e74b29dcc42447f87e416a0b | refs/heads/master | 2021-12-01T07:30:09.250506 | 2021-11-14T18:01:46 | 2021-11-14T18:01:46 | 133,086,609 | 1 | 0 | null | 2020-09-21T17:16:22 | 2018-05-11T20:39:52 | R | UTF-8 | R | false | false | 5,650 | r | clinical-engagement.R | ## module for clinical engagement
#baseline screening
# Load libraries ---------------------------
library(ergm)
#Initialize function
clinical_engagement <- function(net.f, institutional, social, control, time_step){
#this function simulates clinic visits
## get individual attributes
pop_size <- 5000
ag... |
81489b25639238341b5b0a6201af8db5150a8ed7 | 3fb74b4fb8458a4328b90511a25d71778e71f107 | /scripts/utils.R | 7a5ccaa4bcae33445ec40fd7252d02ce2524235f | [] | no_license | chblanc/zillow | 52b5d5096ccc054467c477a4c9063e44cb29eedf | 982872aaf46d95ac5dfa37da686dd55bdd3daebd | refs/heads/master | 2021-01-25T10:55:58.939291 | 2017-06-18T21:09:21 | 2017-06-18T21:09:21 | 93,894,240 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,140 | r | utils.R | # =========================================================================== #
# Utility Functions
# =========================================================================== #
# =================================== #
# plotting
# =================================== #
ggplotMissing <- function(x) {
#' plots mis... |
f2b83805fedec580bcbabc5fa7e2954fff5c3b84 | b79956f25c9cc130ef7bf41629ed5909467bd4de | /man/whoconnected.Rd | c204a5543973f4802c2610f68e5105dbcb6e9bff | [] | no_license | mbtyers/riverdist | 652f6ca7153722741c5fa450834fe5d6e6be938e | 160e9368d420b960f776a3e93f1b84b716a19e23 | refs/heads/master | 2023-08-09T18:23:30.990174 | 2023-08-07T17:11:08 | 2023-08-07T17:11:08 | 47,280,222 | 21 | 1 | null | 2023-08-02T18:37:35 | 2015-12-02T18:32:05 | R | UTF-8 | R | false | true | 627 | rd | whoconnected.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/riverdist_1.R
\name{whoconnected}
\alias{whoconnected}
\title{Check Which Segments are Connected to a Given Segment.}
\usage{
whoconnected(seg, rivers)
}
\arguments{
\item{seg}{The segment to check}
\item{rivers}{The river network... |
96525f828b17422043dfe647de46c053aec9c8ad | 01835557bc01c93e3927b05d7dcb86ac5f6f32d9 | /man/getVideoMetrics.Rd | 9aca1d54199139be99adddc04495edf5908f905c | [
"MIT"
] | permissive | EricGoldsmith/rYouTube | ea414e2b3e3a9f5d0eff37f9f0db7316b586e953 | 50dbdc6b10875a19761ced733f3ca38014d937a5 | refs/heads/main | 2023-04-18T15:34:03.485125 | 2021-04-30T22:12:19 | 2021-04-30T22:12:19 | 363,250,475 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,017 | rd | getVideoMetrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getVideoMetrics.R
\name{getVideoMetrics}
\alias{getVideoMetrics}
\title{Get metrics for a list of videos}
\usage{
getVideoMetrics(token, contentOwner, from, to, videos, metrics = NULL)
}
\arguments{
\item{token}{Access token}
\item{contentOw... |
809c7fffb2b424aba294deeb756c78722bf996c7 | f8078eb0b79e56424915ceb1921a8ab11e5f12f7 | /man/tradeOffTable.Rd | 12a3d94c8a0168ec780dcf5f0c454a761eb8b54e | [] | no_license | cran/pid | 274b1a98e714a8635f9bfed0c77754f7488729bd | 08e7af35733ea942ce048ab6fdd229306c66a7fb | refs/heads/master | 2020-04-06T04:10:07.360728 | 2018-11-23T16:30:03 | 2018-11-23T16:30:03 | 38,597,979 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,323 | rd | tradeOffTable.Rd | % http://cran.r-project.org/doc/manuals/r-release/R-exts.html
\name{tradeOffTable}
%name typically is the basename of the Rd file containing the documentation. It is the "name" of the Rd object represented by the file and has to be unique in a package. To avoid problems with indexing the package manual, it may not con... |
19be00e5ac1ca7cb740d1138127748acf6f55454 | baf88771e84f099c939564f6afe9efd2d330b984 | /inst/script/pathwaysTXT.R | c9543888ed9a745a683102774dedd86c74d5ba75 | [
"MIT"
] | permissive | rosscm/fedup | 0ba6e27f4f0fb66336bfc24690da5a0796a9a4d6 | dab35971ae2cab7d0734d00c80d1de5275493c3e | refs/heads/main | 2023-05-31T11:02:17.429245 | 2021-07-12T21:34:08 | 2021-07-12T21:34:08 | 302,429,834 | 7 | 0 | MIT | 2021-05-25T18:26:23 | 2020-10-08T18:32:57 | R | UTF-8 | R | false | false | 1,303 | r | pathwaysTXT.R | # Download raw data from https://boonelab.ccbr.utoronto.ca/supplement/costanzo2016/
# Data file S5 (sheet 3)
library(openxlsx)
library(tibble)
library(biomaRt)
library(dplyr)
# Use biomaRt to get gene members per SAFE term
#pathwayFile <- system.file("extdata", "Data_File_S5_SAFE_analysis_Gene_cluster_identity_and_fun... |
cccd365af2825552450002210f0067660af20c12 | 2c38fc71287efd16e70eb69cf44127a5f5604a81 | /R/tar_built.R | d0af9e05c0028745935e135ce50e7ba6e9de9043 | [
"MIT",
"Apache-2.0"
] | permissive | ropensci/targets | 4ceef4b2a3cf7305972c171227852338dd4f7a09 | a906886874bc891cfb71700397eb9c29a2e1859c | refs/heads/main | 2023-09-04T02:27:37.366455 | 2023-09-01T15:18:21 | 2023-09-01T15:18:21 | 200,093,430 | 612 | 57 | NOASSERTION | 2023-08-28T16:24:07 | 2019-08-01T17:33:25 | R | UTF-8 | R | false | false | 1,276 | r | tar_built.R | #' @title List built targets.
#' @export
#' @family progress
#' @description List targets whose progress is `"built"`.
#' @return A character vector of built targets.
#' @inheritParams tar_progress
#' @param names Optional, names of the targets. If supplied, the
#' function restricts its output to these targets.
#' ... |
3ad9bae6f9703be54d472d6260d09b4b1c6747a5 | bc6bb93c2b160be814a0b95d58a63663e834ca35 | /distrplots/ui.R | 715a7c91a35aa672890418bc5691ceb30653f3ba | [] | no_license | MaciekNowak/Developing-Data-Products | f91e9dac3a626c3848fbd240c86940f669a19945 | 92a4574ca49fdc30fbcfd497d65e465a515d0875 | refs/heads/master | 2021-01-10T17:01:51.894601 | 2015-11-12T03:56:53 | 2015-11-12T03:56:53 | 45,958,135 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,011 | r | ui.R | #
# The UI part of the Developing Data Products Coursera Course.
#
library(shiny)
shinyUI(pageWithSidebar(
# The header panel with a title
#
headerPanel("Various distributions plots"),
# The side bar panel with controls that help adjust plots' parameters
#
sidebarPanel(
... |
6078d6b4e181a06f5874d8dcb6a37db08e6389a5 | f0fbf8f001e103c50309d68cbcc3cd88266c9833 | /R/recode_values.R | 5a2738d13c2a8d78fc5af2f1cfc4676655dbfc9d | [] | no_license | DaanNieboer/DCTFmisc | 13bebc942105563c4c6b583517623d3915358800 | 5dbffb1d11dfc515a4b899af9c1c3bde20948363 | refs/heads/master | 2021-04-29T17:57:54.045871 | 2018-03-06T11:17:26 | 2018-03-06T11:17:26 | 121,683,011 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 468 | r | recode_values.R | #' Recode the values in a vector
#'
#' @param x vector containing values
#' @param from vector containing the unique elements of x
#' @param to values in which the corresponging elements of from needs to be changed in
#' @return Returns a vector where the values of x are changed from the values in the vector from to t... |
7893c2e63433b804816706fb19f4cdee9965148c | 10e2f579a7e84ef8f7186265fb1fc12c9db62bde | /demo/plotKML.R | c00108bd29cc781060689460853edf2c7630b8e0 | [] | no_license | cran/plotKML | bddd88464e2fa5b0c981086a4f8a33a4fdbeac37 | 068aaaf06a1976d202222142a95f2e951da0f604 | refs/heads/master | 2022-06-30T19:58:42.092133 | 2022-06-07T13:00:02 | 2022-06-07T13:00:02 | 17,698,575 | 8 | 8 | null | null | null | null | UTF-8 | R | false | false | 11,927 | r | plotKML.R | ## Complete tutorial available at: [http://plotkml.r-forge.r-project.org]
plotKML.env(kmz = FALSE)
## -------------- SpatialPointsDataFrame --------- ##
library(sp)
library(rgdal)
data(eberg)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## subset to 20 percent:
eberg <- eberg[runi... |
74947b52d645b4aa10029fd4b831118900b0c315 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ggspectra/examples/scale_x_wl_continuous.Rd.R | ad9a46ce036e0dd7edadbfa617b80910078090c1 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 533 | r | scale_x_wl_continuous.Rd.R | library(ggspectra)
### Name: scale_x_wl_continuous
### Title: Wavelength x-scale
### Aliases: scale_x_wl_continuous
### ** Examples
library(ggplot2)
library(photobiology)
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous()
ggplot(sun.spct) +
geom_line() +
scale_x_wl_continuous(-6)
ggplot(sun.spct) +... |
51d84833a89f8340c24fda8d93e541629da1b526 | dd726f4f83fdb6ef8c4a2b7486795da27b1b4fc2 | /r/2_19/data_processing/data_processing/Script.R | 8ec910dd06d23b04ba18660b2a0fc76bbbb2d164 | [] | no_license | mgh3326/big_data_web | 84890dc72cd0aa1dd49be736ab1c6963611ee4a5 | f5cae3c710414697a1190ad57469f26dd9c87d8a | refs/heads/master | 2023-02-20T07:28:32.024292 | 2019-09-04T15:49:13 | 2019-09-04T15:49:13 | 119,160,730 | 0 | 1 | null | 2023-02-15T21:30:18 | 2018-01-27T12:02:38 | HTML | UHC | R | false | false | 5,001 | r | Script.R | install.packages("dplyr")
library(dplyr)
setwd("c:\\easy_r")
exam <- read.csv("csv_exam.csv")
exam
exam %>% filter(class == 1)
exam %>% filter(class == 2)
exam %>% filter(class != 1)
exam %>% filter(class != 3)
exam %>% filter(math > 50)
exam %>% filter(math < 50)
exam %>% filter(math >= 80)
exam %>% filter(math <= 80)... |
062d9874dbe74565fa312258d5128336491c5cdc | 0a42295e49af92434972d44674872ffa0d233db0 | /man/TEIdy.Rd | 7bd61559975d5675860d8361c9281e8a536a1eb9 | [] | no_license | HumanitiesDataAnalysis/TEIdy | 130ee30914db62367dcbc7bfcfc7d536381fda49 | facc5dabee401523a98ee46a7a00e9ecf631e8c6 | refs/heads/master | 2020-04-22T23:45:37.743425 | 2019-03-20T04:23:20 | 2019-03-20T04:23:20 | 170,752,248 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,032 | rd | TEIdy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/framer.R
\name{TEIdy}
\alias{TEIdy}
\title{Load an XML document as a data.frame}
\usage{
TEIdy(fname, ignore = c(), discard = c(), keep = c())
}
\arguments{
\item{fname}{A filename}
\item{ignore}{Tags that, when encountered, will not be adde... |
af411ee9c320fa30bec04acdc88a73cfafd574f7 | 4485cc6d9a2a089660ec7c5de835031c7719d031 | /man/fill.f.Rd | 0546be6c1e0b636b248b14cd78fa623ef605c7d0 | [] | no_license | theoldfather/KaggleR | 02dbed9af4d210eee3d36cc5da73adfe4729ccec | df308ae6df89096c204c70343975d46ec55009e0 | refs/heads/master | 2021-01-20T17:02:35.249248 | 2016-06-29T02:48:30 | 2016-06-29T02:48:30 | 61,461,213 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 320 | rd | fill.f.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{fill.f}
\alias{fill.f}
\title{Replace conditional on given function}
\usage{
fill.f(x, value, f = is.na)
}
\arguments{
\item{x}{values}
\item{value}{replacement value}
}
\description{
Replace conditional on given function
}
|
47f915b68ad70f0a544c190c5968398f946d1a8d | e6afe03209c6f0f522450857b1105a15d1cc0fb7 | /R/potential-details.R | b6e8b950736155938faa29c8423f431ebb83065d | [] | no_license | antiphon/PenGE | d5d73b032aead05bcd6aa19f23b169b69c0cf70a | 020334fcedd3aac457b241bc3f8bca17afecf848 | refs/heads/master | 2021-06-28T16:02:49.054730 | 2019-07-30T08:44:03 | 2019-07-30T08:44:03 | 95,548,050 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,112 | r | potential-details.R | #' Estimate Potential
#'
#' Details of the estimated interaction potential between two types
#'
#' @param fit Model fit returned by fitGlbin_CV
#' @param i Type 1 index, one of 1, ..., p
#' @param j Type 2 index, one of 1, ..., p
#'
#' @details The lasso-path coefficients of the interaction parameters between types i a... |
4d299c4dbc5e6e73e9b62af50cc0757161db5a0c | d4937db239ca48f728ab45eeed730e38b31a23fe | /R/help.R | a32cdc5b5197890253d37a5d697f6ade598a19bc | [] | no_license | JiangXD/ggmap | 8d6a4c11ac114cc0fa29670e56d2d47cc75df696 | cee35370572b9011eadac7faeba8376274743a6c | refs/heads/master | 2021-01-15T20:57:05.536219 | 2013-09-29T12:04:42 | 2013-09-29T12:04:42 | 13,191,594 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 144 | r | help.R | #' @import proto scales RgoogleMaps png plyr reshape2 grid rjson mapproj
#' @docType package
#' @name ggmap
#' @aliases ggmap package-ggmap
NULL |
d12ec3d26e32ebf47c004daa242d1d679a817127 | 5ec06dab1409d790496ce082dacb321392b32fe9 | /clients/r/generated/R/ComDayCqReplicationImplContentDurboBinaryLessContentBuilderInfo.r | eb827c07bc3e999196e3da2230809817ae495a7b | [
"Apache-2.0"
] | permissive | shinesolutions/swagger-aem-osgi | e9d2385f44bee70e5bbdc0d577e99a9f2525266f | c2f6e076971d2592c1cbd3f70695c679e807396b | refs/heads/master | 2022-10-29T13:07:40.422092 | 2021-04-09T07:46:03 | 2021-04-09T07:46:03 | 190,217,155 | 3 | 3 | Apache-2.0 | 2022-10-05T03:26:20 | 2019-06-04T14:23:28 | null | UTF-8 | R | false | false | 4,837 | r | ComDayCqReplicationImplContentDurboBinaryLessContentBuilderInfo.r | # Adobe Experience Manager OSGI config (AEM) API
#
# Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API
#
# OpenAPI spec version: 1.0.0-pre.0
# Contact: opensource@shinesolutions.com
# Generated by: https://openapi-generator.tech
#' ComDayCqReplicationImplContentDu... |
05035250b7f09e59d7bb529697da44c4799b6972 | 3b62fb6cfb2a9de90945a98849fd9354751d4432 | /results/assignments/Navigator_Chapt_5.R | f5ccbd79d33830dab7b296c470e2715680524120 | [] | no_license | Rajani462/eda_rhine | 490b2e9e55eaabff28103b91285f81d1979edf3e | 95cdefc003f26c49e4d948d0716845d4fd527f67 | refs/heads/master | 2022-05-10T11:57:15.604207 | 2022-03-23T08:53:54 | 2022-03-23T08:53:54 | 253,453,477 | 0 | 0 | null | 2020-04-06T09:36:45 | 2020-04-06T09:36:44 | null | UTF-8 | R | false | false | 2,105 | r | Navigator_Chapt_5.R | ##Navigator question-----------------
library(data.table)
library(ggplot2)
precip_raw <- readRDS('./data/raw/precip_day.rds')
runoff_summary <- readRDS('data/runoff_summary.rds')
runoff_summary_key <- readRDS('data/runoff_summary_key.rds')
runoff_stats <- readRDS('data/runoff_stats.rds')
runoff_month_key <- readRDS('d... |
3c080520cacfb995db586bb014a75187c3b6024f | da725622bc962b639e1eb6df535b433e4366bcc5 | /opportunityYouth/getDataFromMySQL.R | f35580c38efa3174d57c9dec0b7aff8f7ffcb383 | [] | no_license | bekahdevore/rKW | 5649a24e803b88aa51a3e64020b232a23bd459fa | 970dcf8dc93d4ec0e5e6a79552e27ddc0f850b91 | refs/heads/master | 2020-04-15T12:41:49.567456 | 2017-07-25T16:29:31 | 2017-07-25T16:29:31 | 63,880,311 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 959 | r | getDataFromMySQL.R | library(RMySQL)
con <- dbConnect(MySQL(), group = "kwlmi", dbname = "kwlmi")
## Variables
kentucky <- "kentuckyPUMS"
peerCities <- "peerCityPUMS"
louisville <- "louisvilleMSA_PUMS"
statement <- function(place) {
paste("SELECT *", "FROM", place, ";")
}
# Pull data from MySQL Database, change place argument in stat... |
b86d4ea0421fba01474b6d8e5329f48fe7db2eab | 83278d193bf24349883f51a40860f04dfba23f9b | /data-raw/create_top_tracks.R | 3959cc17b031044762f4a879e486313c202cbae7 | [] | no_license | sbudai/spotify-recommendations | 6c93c6330bf5e74d45cc6c7035f374f9fd0c21f3 | 966306a98294f59126d6249f9c5e7d4760868054 | refs/heads/main | 2023-01-28T12:31:54.558736 | 2020-12-06T19:16:24 | 2020-12-06T19:16:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,175 | r | create_top_tracks.R | ## code to prepare `DATASET` dataset goes here
library(tidyverse)
library(spotifyrecommendations)
sk_top_tracks <- read.csv(file.path(
'data-raw', 'SK_Artist_Top_Tracks_Table.csv'
)) %>%
setNames(., c("spotify_artist_id", names(.)[2:ncol(.)]))
names ( sk_top_tracks )
listen_local_artists <- sk_top_tracks %>%
s... |
7169b28e7fb228814eb0b9d71afe63a285efee5a | d5ea85feed4c01ce9db8c019d9142f30a0c68a0e | /R/20_bifd.R | 937957eaa808262f1a8c5bb7aae564835766c2ab | [] | no_license | yixuan/fdaplus | 4b59d15d4a0501a4b66f21d0f6adab407107cc98 | 51abb6d5d6a0060a8117060135a8167642eb4b56 | refs/heads/master | 2016-09-06T14:19:30.570338 | 2015-05-16T00:59:17 | 2015-05-16T00:59:17 | 24,311,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,358 | r | 20_bifd.R | #' Bivariate Function Using Basis Expansion
#'
#' This class defines bivariate functions that can be expressed by two sets
#' of basis functions and the associated coefficient matrix. It takes the following
#' form:
#'
#' \deqn{K(s,t)=\sum_{i,j} a_{ij}f_i(s)g_j(t)}{K(s, t) = sum_{i,j} a_{ij} * f_i(s) * g_j(t)}
#'
#' He... |
3e3e77acfaffd2efc5296664dbb76e109baee66b | ae5fb8c8cba912eb62290e2b124fb7f999cc824a | /DESEq2 Analysis.R | f9faedfc26bd99a3fbe4921de8d492e275098321 | [] | no_license | jessedunnack/LoTurco-RNASeq | cdccd5a67dfa40607717529301a4c9419b9536a3 | 22f5b9bfb4913d4908d9ddb3e64475395eee4c3d | refs/heads/master | 2020-04-01T18:00:46.681004 | 2018-10-17T15:09:56 | 2018-10-17T15:09:56 | 153,465,175 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,264 | r | DESEq2 Analysis.R | # WORKFLOW FOR PROCESSING RNASEQ USING DESEQ2
library(DESeq2)
library(biomaRt)
library(pheatmap)
#CHANGE THIS DEPENDING ON WHAT FILES YOU'RE PROCESSING
directory <- "/Volumes/NO NAME/RNASeq/FUS1 RNASeq/LoTurco/Counts/Name_Sorted/"
setwd(directory)
#ESTABLISH BIOMART OBJECT TO RENAME ENSEMBL IDs
mart <- useDataset("mm... |
aca3e237c07c996565f612fc0d1d405f99b448b4 | 29585dff702209dd446c0ab52ceea046c58e384e | /magclass/R/getRegions.R | 308e65943a562d978c2621e2167346ced352b804 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 857 | r | getRegions.R | getRegions <- function(x) {
if(sum(substr(dimnames(x)[[1]],4,4)!=".")>0) { #not all regions have 3-character names (need to use slow method)
output <- unique(as.vector(as.matrix(cbind.data.frame(strsplit(dimnames(x)[[1]],'\\.'))[1,])))
} else { #region names all have 3 characters -> fast method
output ... |
94d375d79042e5d6c21eb872d85c4619945d9cfc | 693e19181178a2ebeeb77c78eae958655d8d81f5 | /Graphs/Graphs.R | a9344730b8fb255c64af2c1593341c1dafbdb7c3 | [] | no_license | adityaraj52/ConvNet | 76bdd9d0689d0d7a25fb1cf8a0f80ed5f0f5f3fe | a3cb37293f1f4006b1ed69f27a532293e62929d5 | refs/heads/master | 2021-01-22T19:31:04.716095 | 2017-03-16T15:04:18 | 2017-03-16T15:04:18 | 85,207,921 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,598 | r | Graphs.R | ################################################################################
# Instructions to export the files
################################################################################
# Export files in landscape format and as A5 format
######################################################################... |
742a1fb4ef7a962cf4693a189c0828dfd896ac70 | 2f94bc0d7c4c991297e294ce4afe672d3ab715da | /tests/testthat/test-geojson_properties.R | 0362b535b404f91cca93424b021d076fa0f0be7d | [] | no_license | techisdead/geojsonsf | 4100b8c686962a2317dec66b97a8b2922fd41f9c | d5e757c3e60f969f6b369dab3f41e756351b1ce7 | refs/heads/master | 2020-03-10T21:40:23.692782 | 2018-04-15T09:21:18 | 2018-04-15T09:21:18 | 129,599,801 | 0 | 0 | null | 2018-04-15T10:33:33 | 2018-04-15T10:33:33 | null | UTF-8 | R | false | false | 2,871 | r | test-geojson_properties.R | context("properties")
test_that("properties captured correctly", {
f <- '{
"type": "Feature",
"properties": { "id" : 1, "name" : "foo" },
"geometry": {"type": "LineString", "coordinates": [[101.0, 0.0], [102.0, 1.0]]}
}'
sf <- geojson_sf(f)
wkt <- geojson_wkt(f)
expect_true(
all(names(sf) == c("geomet... |
8692ed3d416a0006fe8c958371250146207dca4b | 75db022357f0aaff30d419c13eafb9dddfce885a | /inst/IP/LobsterFisheryAttributes/LicenceCharacteristics.r | f8299130fc9c8207c77476715f433ed9b3986fbc | [] | no_license | LobsterScience/bio.lobster | d4c553f0f55f561bb9f9cd4fac52c585e9cd16f8 | b2af955291cb70c2d994e58fd99d68c6d7907181 | refs/heads/master | 2023-09-01T00:12:23.064363 | 2023-08-23T16:34:12 | 2023-08-23T16:34:12 | 60,636,005 | 11 | 5 | null | 2017-01-20T14:35:09 | 2016-06-07T18:18:28 | R | UTF-8 | R | false | false | 4,251 | r | LicenceCharacteristics.r | ## licences by port
require(ggplot2)
require(bio.lobster)
require(bio.utilities)
require(devtools)
load_all('~/git/bio.utilities')
a = lobster.db('process.logs.unfiltered')
b = lobster.db('community_code')
d = lobster.db('vessels.by.port')
d = na.zero(d)
d1 = aggregate(cbind(GROSS_TONNAGE, BHP, LOA, BREADTH, DEPTH,YE... |
8ac02305a26885c252bd318232884263c7c4c1b6 | 5c21ffa379f009e9c8eff32a7ff67821f7e1638b | /sim_time_variation.R | 64be43c50cb3be957cddb64f806c4ae7bd90956b | [] | no_license | cfaustus/core_matrix_publish | cf8460356f4304c8ca323b290b1ab27902246335 | 548158c12c35af34bd0484bf29bdee3e06b0db0b | refs/heads/master | 2022-07-10T14:09:19.685412 | 2022-07-06T12:31:04 | 2022-07-06T12:31:04 | 99,240,559 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,695 | r | sim_time_variation.R | ################################################################################
## script to look at how rate & amount of deforestation affect disease over time
## script made by christina.faust@gmail.com 14 april 16
################################################################################
rm(list = ls())
s... |
618f04d640b1386c61f437cf8b1d4ed5a847fba7 | 2d6f5821fca8e1d5ef62b4bf643af2c56e58bfbe | /9thMar18.R | 3a10208e3954d54cfa0c4e7af498b52b806efb8b | [] | no_license | shrishtripathi/RCodelatest | 0d5b83ad2884a25f435187e320e6078b3828c3d3 | bab3862da454e77bb1ef84fce0f66391eff839b6 | refs/heads/master | 2020-03-31T12:08:10.441464 | 2018-10-09T07:20:23 | 2018-10-09T07:20:23 | 152,204,431 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 967 | r | 9thMar18.R | vec1<-c('Shrish','Satya')
vec8<-c(36,29)
mat2<-cbind(vec1,as.character(vec8))
mat2
t(mat2)
vec9<-vec8<-vec1
vec9
vec10<-vec9
vec10
vec8
vec11<-c('shrish'=36,'satya'=29)
vec11
c
z
z<-c(11,12,13,14)
y<-c(12)
z>=y
z<-c(2,6,4,'k')
class(z)
z[4]
z[1]
y<-c(1,1)
sum(y) >1
count(y<=1)
sum(2<1)
y<-c(1,7,4,2)
y
sum(y[4])
sum(y[2... |
865c9597c008daed8bcce85fd38e9a266f4a4f97 | 8de68f566e3ca78a368a207e600c39ac35599ed3 | /app.R | d000d2d27f5d30113307bf79a9d5c77affd1a55b | [] | no_license | onohayat/Japan-Trade | 5c7f38953ad18dde3265e7740a46207c94462f3f | ef3d0ba1c62d0717c49a2a40b89023b8f898bba1 | refs/heads/master | 2020-08-10T11:30:23.482961 | 2019-10-11T03:34:06 | 2019-10-11T03:34:06 | 214,333,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,916 | r | app.R |
ui <- fluidPage(
# Create a title for app
column(3, titlePanel("Japanese Trade")),
column(12,
# Create page for export map with slider input
tabsetPanel( tabPanel("Export Map",
sliderInput(inputId = "Years", h3("Choose the Year"),
... |
c93ea9894ed3ba9ba44a82667893667d37051cf0 | 90e772dfeb9fc441424dcf5e5beaa545af606f1c | /man/calJSI.Rd | d1aa778256cb3462029ad3b5dfd6051a812a84b7 | [
"GPL-3.0-only"
] | permissive | chenjy327/MesKit | 97d356c8c8ac73493ba6f60488d5a0c6aae23092 | c9eb589fca6471e30e45cb9e03030af5ade69f83 | refs/heads/master | 2021-08-17T07:48:53.618404 | 2021-06-24T06:19:08 | 2021-06-24T06:19:08 | 304,196,319 | 0 | 0 | MIT | 2020-10-15T03:10:38 | 2020-10-15T03:10:37 | null | UTF-8 | R | false | true | 1,943 | rd | calJSI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calJSI.R
\name{calJSI}
\alias{calJSI}
\title{compareJSI}
\usage{
calJSI(
maf,
patient.id = NULL,
pairByTumor = FALSE,
min.ccf = 0,
plot = FALSE,
use.circle = TRUE,
title = NULL,
number.cex = 8,
number.col = "#C77960",
use.... |
df48a9ca491ffb6028bc55ef812b4020a73f35f0 | c0007ad0ff4aeb8ffae123b098e2f2a60c2c4a3a | /kmeans.R | 0686fdb55bd79b1cacfd6f8e0f809c29a61cd22b | [] | no_license | chetan015/DataMining_R | cc53ce32c2f9af759f3de3034375769b2d880801 | feb04a6b1c8f84fcec6027da75cef276bdd9f028 | refs/heads/master | 2020-05-26T15:31:47.145264 | 2019-05-23T18:37:25 | 2019-05-23T18:37:25 | 188,287,320 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,560 | r | kmeans.R | apr14<-read.csv("dataset/uber-raw-data-apr14.csv")
may14<-read.csv("dataset/uber-raw-data-may14.csv")
jun14<-read.csv("dataset/uber-raw-data-jun14.csv")
jul14<-read.csv("dataset/uber-raw-data-jul14.csv")
aug14<-read.csv("dataset/uber-raw-data-aug14.csv")
sep14<-read.csv("dataset/uber-raw-data-sep14.csv")
library(dplyr... |
da3c1d494141113f111f8db36a3c13a2edb72dec | d669e19a5a9f8f3517578cd3411c3d2d4415d2d2 | /data-raw/clean-charleston1.R | e6757a4629ee2fc081aab5214fa349b1176741d7 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | spatialanalysis/geodaData | d44fc25392fe91ed437a97cca7e33506861e2c5a | 1e27c7e77b14cbdadd9f43fd9ba56753930350f2 | refs/heads/master | 2021-07-13T11:17:45.096357 | 2020-10-05T19:06:42 | 2020-10-05T19:06:42 | 213,948,169 | 16 | 7 | NOASSERTION | 2020-10-05T19:06:43 | 2019-10-09T15:00:00 | R | UTF-8 | R | false | false | 212 | r | clean-charleston1.R | library(sf)
library(usethis)
charleston1 <- st_read("data-raw/sc_final_census2.shp",
quiet = TRUE,
stringsAsFactors = FALSE)
usethis::use_data(charleston1, overwrite = TRUE)
|
2b717b4253fc4f9befb2421ef9229f45247af544 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tidyhydat/examples/hy_daily_flows.Rd.R | 7b4723a7233d972b6e6ccb2626c2c622e3f8e9d9 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 390 | r | hy_daily_flows.Rd.R | library(tidyhydat)
### Name: hy_daily_flows
### Title: Extract daily flows information from the HYDAT database
### Aliases: hy_daily_flows
### ** Examples
## Not run:
##D #download_hydat()
##D hy_daily_flows(station_number = c("08MF005"),
##D start_date = "1996-01-01", end_date = "2000-01-01")
##D
##D hy_daily... |
94a40ca49408579f915fb6aa473717bffdd1860f | 1bc8ecb3c03577de895908534d849b687adc551d | /R/guide_axis_genomic.R | db40f3ac096f704e548a057de062121ba8b73e02 | [
"MIT"
] | permissive | teunbrand/ggnomics | ce30f3f302391b600e814b8844b7e6ae676e3c17 | 30568bef426d87b42a652619582f6beea5dd52aa | refs/heads/master | 2021-09-11T10:17:02.129116 | 2020-07-29T21:12:08 | 2020-07-29T21:12:08 | 181,505,723 | 88 | 7 | null | 2020-06-19T19:36:06 | 2019-04-15T14:37:23 | null | UTF-8 | R | false | false | 13,896 | r | guide_axis_genomic.R | # Constructor -------------------------------------------------------------
#' @name guide_genomic_axis
#' @title Axis for genomic positions
#'
#' @description This axis guide is the genomic equivalent of
#' \code{\link[ggplot2]{guide_axis}} to pair with the genomic position scales
#' \code{\link[=scale_genomic]{scale... |
3b715054304a06863a0bafb0f6eb19794f4280b1 | 13285d0f589987bfa5a4bde109be596ae6fba0ac | /kaggle/predict-wordpress-likes/carter_s/r/models.r | 920b5ca20b1d9310a3d353733a4a4e71989388b0 | [] | no_license | raziakram/solutions | 46508666b32f08baa998e95bf8b1748c390d9258 | eeffe73300941b7768d3a062a0ee296e3bcd56e6 | refs/heads/master | 2020-12-25T15:30:39.395463 | 2014-05-20T05:32:02 | 2014-05-20T05:32:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,993 | r | models.r | Sys.time()
require(biglm)
require(randomForest)
# load functions in here
source("./r/genfunc.r")
source("./r/datafunc.r")
# define dataset alteration functions
alter.dataset <- function()
{
dataset$segment <<- with(dataset,
ifelse(
(user_blog_hist_like_ct > 0) | (user_blog_all_user_like_blog_post_share > ... |
27fb110c0d46d48946b9e1b498ecab0aefd8353f | 724f59e3c9449ba8ffd56bf06512b3d2802babe8 | /data-raw/HarvardOxford/ho_ctab.R | 0df46d9ee4f26a8019549b84c9bb7ba078bf22d8 | [
"MIT"
] | permissive | bbuchsbaum/ggsegExtra | 74088c50fa544f9b1032326de593fd1e05421b96 | e1409c8453d6268cd13bf39bc2cb6a269ee847c3 | refs/heads/master | 2020-08-26T20:47:26.562016 | 2019-10-23T20:42:08 | 2019-10-23T20:42:08 | 217,143,613 | 0 | 0 | NOASSERTION | 2019-10-23T20:05:08 | 2019-10-23T20:05:07 | null | UTF-8 | R | false | false | 929 | r | ho_ctab.R | ## make an annotation file for the harvard-oxford cortical atlas
library(tidyverse)
library(xml2)
ho <- xml2::read_xml(file.path(Sys.getenv("FSLDIR"), "/data/atlases/HarvardOxford-Cortical.xml"))
ll <- as_list(ho)
labels <- map_chr(ll$atlas$data, 1)
names(labels) <- NULL
labels <- c("unknown", labels)
HO <- tibble... |
73d85bf99e76a5fc1a5c9384e1c8fdde9b1203c6 | 2e2b4e090626d27adf065735751efe31248dcbbd | /lcebchk.R | d70006aec1fbe62553cbe77d88ff78a8810672fa | [] | no_license | P-R-McWhirter/skycamT_variable_classification_functions | 62742e89da01dee71bd690119f6079389721c7c4 | ae32976f882a8fc999843571a4d0b82c5aed6538 | refs/heads/master | 2021-02-18T21:16:55.109810 | 2020-03-05T18:26:20 | 2020-03-05T18:26:20 | 245,238,114 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,766 | r | lcebchk.R | lcebchk <- function(ts, per = 1.0, ep = 0.01, bins = 100, quiet = FALSE) {
library(e1071)
start <- Sys.time()
lowper <- per * (1 - ep)
highper <- per * (1 + ep)
pers <- seq(lowper, highper, length.out = 1000)
perf <- rep(0, length(pers))
res <- matrix(0, nrow = length(pe... |
896ab74ee1483461356d052b2b92a95891f7c924 | a361f14c000fc1c153eaeb5bf9f4419951c7e3aa | /man/data_BTm_bms.Rd | 7685b58bf7ad2f00454dd443be514419470695f6 | [] | no_license | kbenoit/sophistication | 0813f3d08c56c1ce420c7d056c10c0d8db4c420e | 7ab1c2a59b41bd9d916383342b6ace23df2b1906 | refs/heads/master | 2021-06-04T23:19:22.944076 | 2021-05-08T13:40:26 | 2021-05-08T13:40:26 | 101,664,780 | 43 | 7 | null | 2020-08-25T11:19:22 | 2017-08-28T16:40:21 | R | UTF-8 | R | false | true | 732 | rd | data_BTm_bms.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{data_BTm_bms}
\alias{data_BTm_bms}
\title{Fitted Bradley-Terry model from Benoit, Munger, and Spirling (2018)}
\format{
Fitted model of type \link[BradleyTerry2:BTm]{BTm}
}
\usage{
data_BTm_bms
}
\description{
Fitt... |
c80577e6523537e4693ca9d958fa1f080b7221b9 | ef443d64d07775335795d28502cf5ed3990e3b76 | /r-package/chebInterp/R/calculateChebyshevPolynomials.R | df2743edc83292dcdfa38d64864d2f5f16640cc5 | [] | no_license | walterwzhang/Chebyshev-Interpolation | b7fb68ffce07791d18af60c4c84c6846eca40918 | 7266c2783f2b88815042e4b66270de77620502eb | refs/heads/master | 2021-07-09T20:48:24.899193 | 2020-07-20T20:51:14 | 2020-07-20T20:51:14 | 169,814,173 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 921 | r | calculateChebyshevPolynomials.R | # calculateChebyshevPolynomials -------------------------------------------------------------------
#' Computes the polynomials for a given degree and vector of values.
#'
#' Resultant matrix of polynomials is of size length(x) by N + 1
#'
#' @param x Vector of values to compute the polynomials at (numeric)
#' @param N... |
753830f2cdf0a8891548011b7a4296a2641ffd76 | 31da9633913672a623a1635b9691a09e8dee52da | /man/MorphoLink.Rd | 7efb5b7145af0f0bea292bfd5e31deb2f1f80f9d | [] | no_license | ms609/MorphoBank | eecdeaf3de338a8e62c369cdbfbb7948a843c88e | 6dd019a5be3d93b1e3301a5837f4f93966642db6 | refs/heads/master | 2023-04-28T10:01:49.483968 | 2023-04-17T10:01:03 | 2023-04-17T10:01:03 | 141,433,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 800 | rd | MorphoLink.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/text.R
\name{MorphoLink}
\alias{MorphoLink}
\title{Link to MorphoBank project}
\usage{
MorphoLink(id = getOption("MorphoBankProject"),
linkText = paste("project", id), checkAccess = TRUE)
}
\arguments{
\item{id}{Integer corresponding to the... |
321af7e707acf199c9bc98102ef0225806491489 | 74bc48ba64859a63855d204f1efd31eca47a223f | /Corporacion/100.Prav_arima01.R | ff2a295ab8defdbb91ba8d5d5becf7b71922c6cb | [] | no_license | PraveenAdepu/kaggle_competitions | 4c53d71af12a615d5ee5f34e5857cbd0fac7bc3c | ed0111bcecbe5be4529a2a5be2ce4c6912729770 | refs/heads/master | 2020-09-02T15:29:51.885013 | 2020-04-09T01:50:55 | 2020-04-09T01:50:55 | 219,248,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,140 | r | 100.Prav_arima01.R |
library(dplyr)
library(forecast)
library(reshape2)
library(data.table)
library(foreach)
library(date)
library(lubridate)
#library(doMC)
library(doParallel)
print(expm1(0))
print(expm1(1))
print(log1p(0))
print(log1p(1))
train <- fread('./input/train.csv')
test <-fread('./input/test.csv')
train$date <- as.Date(par... |
0a5016060f4603fc0b8518b243c13e26f58eafa5 | 02ba97c94a3293951417e4e9b8a94230b9c0d11e | /run_analysis.R | b5c903f322f6e48e8e0a05069819cb86688aa7f1 | [] | no_license | winstonyma/Datasciencecoursera-Course-3-Week-4-Assignment | 6818cae9eeacac546047895d3c99d219b5206d56 | 78ffbc8249f7d66f6e294265020f18a09c8899be | refs/heads/master | 2021-01-22T19:04:23.778004 | 2017-03-18T03:16:28 | 2017-03-18T03:16:28 | 85,156,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,833 | r | run_analysis.R | # download and unzip data
## download data
if(!file.exists("C:/Users/v-wima/Desktop/Coursera")){dir.create("C:/Users/v-wima/Desktop/Coursera")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileUrl,destfile="C:/Users/v-wima/Desktop/Coursera/UCI H... |
ba3ac3527f62672bcc3de7fa82d26d6a5289ee7e | 82b1c5655856b660c053d18ec7ad94f3aa30a964 | /man/get_package_function_usage.Rd | e26ad19d16fcd0aab56df32b8ce41d7e5087257d | [
"MIT"
] | permissive | KWB-R/kwb.fakin | 9792dfa732a8dd1aaa8d2634630411119604757f | 17ab0e6e9a63a03c6cb40ef29ee3899c2b2724a0 | refs/heads/master | 2022-06-09T22:25:09.633343 | 2022-06-08T21:24:14 | 2022-06-08T21:24:14 | 136,065,795 | 1 | 0 | MIT | 2021-03-15T10:55:17 | 2018-06-04T18:21:30 | R | UTF-8 | R | false | true | 1,676 | rd | get_package_function_usage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_package_function_usage.R
\name{get_package_function_usage}
\alias{get_package_function_usage}
\title{How Often Are the Functions of a Package Used?}
\usage{
get_package_function_usage(tree, package, simple = FALSE, by_script = FALSE)
}
\a... |
8c18ce8012a0f3016131e1f629a8dcf7f1a8face | 4b39182868496103501ca43b0871fb69483d6927 | /run_analysis.R | 4ab5569ac8a3af8d153f123c538be53bd4ff82be | [] | no_license | Nmoheby/GettingandCleaningData | 32e8d3b6ba25331aafdd0de105afcd85fab56846 | b7f2c9526a6c8757e7e30abf78583654b09de6b9 | refs/heads/master | 2020-03-15T17:47:47.679687 | 2018-05-05T17:34:49 | 2018-05-05T17:34:49 | 132,269,768 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,124 | r | run_analysis.R | library(dplyr)
#---------------------------
# read train data
pathdata = file.path("./")
X_train <- read.table(file.path(pathdata, "train", "X_train.txt"),header = FALSE)
Y_train <- read.table(file.path(pathdata, "train", "y_train.txt"),header = FALSE)
Sub_train <- read.table(file.path(pathdata, "train", "subject_train... |
b53c27d50543805db322023b889b1e7eb6b81598 | bf9bb0d310731e70ec2164fea0d192b663ebef3e | /man/read.survey-package.Rd | 8be973d6a7f4e5a9f5a3e8379f89ada52ef93d95 | [] | no_license | DIRKMJK/read.survey | 334f1fe7b3f50f7af7af4de774f0f03e35bd1cbc | 50a57e76bcc2fe945295ddd45a157af1933bc4eb | refs/heads/master | 2020-04-07T05:32:52.764714 | 2015-02-10T10:03:44 | 2015-02-10T10:03:44 | 30,558,716 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 578 | rd | read.survey-package.Rd | \name{read.survey-package}
\alias{read.survey-package}
\alias{read.survey}
\docType{package}
\title{
Read Surveymonkey file
}
\description{
Prepare data from Surveymonkey xlsx or csv file for analysis.
}
\details{
\tabular{ll}{
Package: \tab read.survey\cr
Type: \tab Package\cr
Version: \tab 1.0\cr
Date: \tab 2015-02-0... |
271469f945c182a5648939660bf464923e6e1f41 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dsa/examples/xtsplot.Rd.R | 4978d55ef67716108615d545db5eeb965958ced4 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 562 | r | xtsplot.Rd.R | library(dsa)
### Name: xtsplot
### Title: Create a plot for xts series
### Aliases: xtsplot
### ** Examples
x <- xts::xts(rnorm(100), seq.Date(as.Date("2010-01-01"), length.out=100, by="months"))
y <- xts::xts(runif(100), seq.Date(as.Date("2010-01-01"), length.out=100, by="months"))
xtsplot(x, font="sans")
xtsplot(... |
fb47c642bbe8a6b189d27720db3daf99295c8e54 | 79c50b22f90863654139e18731cac46ba046cb3c | /create_output_for_gene_list.R | 183cd6bb7290d987392841bb3470ef9fc05c97ac | [] | no_license | micktusker/ensembl_rest_api | 5383344b4f8a7580f8bc1f6822a3a75adc5d1e8a | 5a4384f3ae3abc2b6aa948cf5953fe3b3a49f8fd | refs/heads/master | 2021-01-17T14:30:41.548493 | 2016-10-11T16:02:54 | 2016-10-11T16:02:54 | 70,240,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,204 | r | create_output_for_gene_list.R | source('./ensembl_rest_api_functions.R')
source('./file_functions.R')
options(stringsAsFactors = FALSE)
# Returns the first column in a given file. Use this to turn
# a single column of gene names into a vector.
getGeneNamesFromFileAsVector <- function(gene.names.file) {
return(getColumnAsVector(gene.names.file))
}... |
9cc9459a9d93b34e1e63a5c93734ff643e248dd7 | 0a7308e8330385a6e42c04f7fa3b750c76708eb5 | /Materials 2018/Week 1. Basic R 1/R-Wizardry 2018 week 1 (skeleton).R | f1b0cbbc6b1b2c4b63260d945c6c92d1a4d38270 | [] | no_license | imstatsbee/R-Wizardry | 49852bc32fa79fabe22426c3bdd4749675b3fbf8 | bbc291d7fe93d21dc309bdae00e9b68a77b2408b | refs/heads/master | 2021-09-10T06:18:50.003815 | 2018-03-21T11:52:38 | 2018-03-21T11:52:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,917 | r | R-Wizardry 2018 week 1 (skeleton).R |
# R-Wizardry 2018
* R-Studio and environment: getting around and setting it up
* Data management and spreadsheet organization before starting.
* Plan well before starting to work.
## Day 1
1.1 R as a calculator
1.2 Commenting your code
1.3 Assigning variables
1.4 Data structure
### R as a calculator
R can do ... |
088f82df263777f53db14cfd344c7cc30a36be91 | 09111a2b17b76ed68ae161291a230b05369584d6 | /meetings/answers/ch3_supplementalQs_rkc.R | 6ec4956ee2d0fbebfe9004470b49cacc761f47c0 | [
"CC-BY-4.0",
"Apache-2.0"
] | permissive | rkclement/studyGroup | ba6f492b37085d568efeefd9b7d336d99d3bee4f | 6f5ecd0fe042b1fd37aeb8c945e5a8bd22f5921e | refs/heads/gh-pages | 2021-01-12T03:35:15.559713 | 2017-09-28T18:51:43 | 2017-09-28T18:51:43 | 78,233,449 | 0 | 1 | null | 2017-01-06T19:40:00 | 2017-01-06T19:39:59 | null | UTF-8 | R | false | false | 8,971 | r | ch3_supplementalQs_rkc.R | # Excerpt From: Taylor Arnold and Lauren Tilton, _Humanities Data in R_
# Chapter 3 Supplemental Questions
# 26. The dataset iris is a very well-known statistical data from the 1930s. It
# gives several measurements of iris sepal and petal lengths (in centimeters)
# for three species. Construct a table of sepal lengt... |
4db851bf10a2e13b414292ee3de7b1063c027e64 | 394597e53309453248426a7fa82c2c01457af122 | /Log.R | 58736fa8795dca8753522cd3eea0796fa6d6a248 | [] | no_license | dhruvchandralohani/Image-Processing-Code | a6b360ce7fca57cd2da8352b8bdb057b890a8ad1 | 1b8fb63d54b20e63eecc2991b97b7f0fdb6facc6 | refs/heads/main | 2023-06-06T07:39:22.842798 | 2021-06-26T03:50:54 | 2021-06-26T03:50:54 | 380,223,734 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 415 | r | Log.R | img1 <- image_read("/home/dhruv/Desktop/doc.png")
plot(img1)
img1gray <- image_convert(img1,type = "grayscale")
plot(img1gray)
img1gray
img2gray <- magick2cimg(img1gray,alpha = "rm")
img2gray
plot(img2gray,rescale = FALSE)
img2_mat <- as.matrix(img2gray)
img_log <- matrix(0,220,445)
for(i in 1:220){
for(j in 1:445... |
22b1e4620cc0f648c013817258877136b6f0c5f5 | 6e506411d591fd71e20730f89c497213b195c81f | /src/phageMain.R | 344cbfb9164da47320556a3099c21e14e1ea978c | [
"MIT"
] | permissive | pablocarderam/PhageModel | 6aaff3b282cdb6619863d334afd6f0ab6d4cd7f8 | a792bad36247165ce906904458c6d5165ff7313b | refs/heads/master | 2021-01-19T13:41:36.593397 | 2017-09-01T12:34:40 | 2017-09-01T12:34:40 | 100,855,812 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,463 | r | phageMain.R |
# MAIN
# Clear workspace
rm(list = ls(all = TRUE))
# Imports
source("dat/phageParams.R") # needed for ODE
source("src/phageSolveODE.R") # needed for ODE
source("src/phagePlots.R") # needed for plots
# Run everything
param = getParameters()
setup_list = setup(param) # returns list with [[1]] list of starting vectors... |
4514ea942cec10598ddb08d61518a659cf3f6f4b | 6d3fd06f9dfcb1e971c9a97607e0b04420ac8829 | /PPT_scatter.R | a6b0364cf6e26e8ed6c467eef48d5d1311b02edd | [] | no_license | mtnorton/CBPChangeDetection | 5d941734774bc713b86ea7431fcbeb0971d7debd | c5484717b3514014a6456910629d56125a2a86ba | refs/heads/master | 2021-04-12T12:53:15.429201 | 2017-09-11T14:42:56 | 2017-09-11T14:42:56 | 94,553,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 385 | r | PPT_scatter.R | # sample scatterplot for PPT
# Michael Norton 6.27.17
data <- matrix(c(100,31.3,0,4.4,0,35.7,0,11.7,0,16.9),ncol = 2, byrow = TRUE)
plot(data[,1],data[,2],col=c("#008000", "#00FF00", "#FFAA00", "#FF0000", "#000000"), cex=2, pch=19,main="Land Cover Changes",xlim=c(0,100),ylim=c(0,100),ylab="Percentage of land cover 2... |
858bddc89471704cf6aabc59d562afa2041f1823 | f17b379365b21a6ecaed501228f3cafabf42bd62 | /man/tpm4plot-class.Rd | d371b84bfbb0e8293cab3de6da24175943357991 | [] | no_license | ericaenjoy3/CHIPRNA | 649efda9e6f6461e10e3d134653553e1599c18ff | 1392c5f1c620d8ac421f4a99014debc20cefad3e | refs/heads/master | 2021-07-19T03:05:33.745582 | 2017-10-25T20:09:27 | 2017-10-25T20:09:27 | 104,156,786 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,297 | rd | tpm4plot-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CHIPRNAclass.R
\docType{class}
\name{tpm4plot}
\alias{tpm4plot}
\alias{tpm4plot-class}
\title{Store TPM and related information for plotting}
\description{
Store TPM and related information for plotting
}
\section{Slots}{
\describe{
\item{\c... |
c73815f24b7a4120ad6902e2281d69d853e911a2 | bafdfcab4680d5208d451021f833f51b50ad2700 | /R/word_classification_data.R | dbcc6c5275cb8ef9573a1806c5c0c9aa57d027bf | [] | no_license | denis-arnold/AcousticNDLCodeR | b973c79c06efd3a3614e0b681a977b0a74d44f09 | 488f9a6300b23cb0aa93bbe51eeee9d764a982fd | refs/heads/master | 2021-01-24T20:58:16.335997 | 2018-07-06T15:42:16 | 2018-07-06T15:42:16 | 123,262,982 | 0 | 2 | null | 2018-02-28T15:19:04 | 2018-02-28T09:37:05 | R | UTF-8 | R | false | false | 1,759 | r | word_classification_data.R | #' Data of PLoS ONE paper
#'
#' Dataset of a subject and modeling data for an auditory word identification task.
#'
#' @name word_classification_data
#' @docType data
#' @usage data(word_classification_data)
#'
#'
#' @format Data from the four experiments and model estimates
#' \describe{
#' \item{\code{Experi... |
874e4a2746aee5071bd5907068a6ff3b1df7087d | 51ccbfa4c644057b992d08630d20ce59a5521c4a | /man/savings_summary.Rd | ec6ff3f04554fa65ba6f1933dc0d6d5fb78668be | [
"BSD-3-Clause-LBNL",
"BSD-3-Clause"
] | permissive | LBNL-ETA/RMV2.0 | 25e0000f5ec68c28c015e92e73b98038d8fb254b | 979a96332fb8566063f25cd1d5d24e44b285221b | refs/heads/master | 2023-03-01T20:21:07.746272 | 2020-10-28T00:50:58 | 2020-10-28T00:50:58 | 110,508,552 | 34 | 14 | null | null | null | null | UTF-8 | R | false | true | 566 | rd | savings_summary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/savings.R
\name{savings_summary}
\alias{savings_summary}
\title{Savings summary}
\usage{
savings_summary(sav_out)
}
\arguments{
\item{sav_out}{a shiny reactiveValues object where the baseline object and the pre/post data are stored}
\item{in... |
96416dcd8e62e88c0a6c19f70cb1a5bc7c60a7f6 | 29585dff702209dd446c0ab52ceea046c58e384e | /b6e6rl/R/nahvyb_expt.R | 226b15499e16872e3183ccfb090d60ae9c3299df | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 282 | r | nahvyb_expt.R | nahvyb_expt <-
function(N,k,expt){
opora <- 1:N
nargin <- length(as.list(match.call())) -1
if (nargin==3)
opora <- opora[-expt]
vyb <- rep(0,k)
for (i in 1:k){
index <- 1+trunc(runif(1)*length(opora))
vyb[i] <- opora[index]
opora <- opora[-index]
}
return(vyb)
}
|
6cab27124eeff88b381481371f8aeec920f7da8f | 5e3d48e48438e540f0cabefa4572830d23479d5e | /TidyGEO/server/all_data/join_dfs.R | 282cbd4c809dded660d9e8ec2fbef40bc56cb0d4 | [
"Apache-2.0"
] | permissive | srp33/TidyGEO | 3b9ece9700873ebb5dfb1aa9cd000a8c5d77963e | 7a31d49578a78e2375a2e960371db36e5a930b85 | refs/heads/master | 2023-02-26T22:08:48.537762 | 2023-02-18T06:10:18 | 2023-02-18T06:10:18 | 171,913,236 | 5 | 3 | Apache-2.0 | 2023-02-18T06:10:19 | 2019-02-21T17:14:26 | HTML | UTF-8 | R | false | false | 9,582 | r | join_dfs.R |
# side panel --------------------------------------------------------------
output$col_to_join1_selector <- renderUI({
col_selector_ui("data_to_join1", "col_to_join1")
})
output$col_to_join2_selector <- renderUI({
col_selector_ui("data_to_join2", "col_to_join2")
})
output$col_to_join3_selector <- renderUI({
... |
8ef0656f6df9f35b5caab99f0df42a34f14eab26 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/medicare/examples/price_deflate.Rd.R | 4a8a4e4061b080bfa66fdf9827c8310b87880b2f | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 266 | r | price_deflate.Rd.R | library(medicare)
### Name: price_deflate
### Title: Deflate prices within a sector, relative to a base period.
### Aliases: price_deflate
### ** Examples
# convert $100 in current inpatient spending to year 2007 dollars
price_deflate(100, "ip", 2014, 2007)
|
caa446b6710b99e1cc890f2efcec6b55e5093ffb | 1c8a1e0041124a546f13dc5e98997571111745f6 | /prep/ICO/ICO.R | 8c58bddb9befe6089543b644c06aa26512ca02a0 | [] | no_license | OHI-Science/chl | bdbf046f10fbcfd617747572f6b636238d443649 | 3699fd7b5d0ef08df3159beaa4e6b4314f6f5419 | refs/heads/master | 2023-08-17T10:31:53.577099 | 2023-08-10T19:14:12 | 2023-08-10T19:14:12 | 25,446,924 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,504 | r | ICO.R | library(plyr)
library(maditr)
lyr1 = c('ico_spp_extinction_status' = 'risk_category')
lyr2 = c( 'ico_spp_popn_trend' = 'popn_trend')
# cast data ----
l_data1 = SelectLayersData(layers, layers=names(lyr1))
l_data2 = SelectLayersData(layers, layers=names(lyr2))
l_data1 <- select(l_data1, c("id_num", "catego... |
270ebdfa0888aca6cc4a294510e3493303695e6e | af1895e041c8ccde1b333c4f0334bb88b3cca493 | /fireSense_NWT_DataPrep.R | 18ab57833bc9000997b7b4c67a82e8f57fbfaa84 | [] | no_license | PredictiveEcology/fireSense_NWT_DataPrep | 2ffc85c9a78ff1a8a7da4fb7b505a495668275f6 | 99b21c4883e520eefb7f091a41c12828fc9bc9f0 | refs/heads/master | 2022-03-28T04:36:48.828324 | 2019-12-04T09:08:25 | 2019-12-04T09:08:25 | 168,408,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,001 | r | fireSense_NWT_DataPrep.R | # Everything in this file gets sourced during simInit, and all functions and objects
# are put into the simList. To use objects, use sim$xxx, and are thus globally available
# to all modules. Functions can be used without sim$ as they are namespaced, like functions
# in R packages. If exact location is required, functi... |
88a9a149360c24f3791b5589c595ad782131343e | 0c8ea2fb4d959cd1734cf0b9a17e0107250d696b | /Neural net with nnet.R | a8ab8d4d9483eb421af983b746409e57e8e8675b | [] | no_license | aubhik-mazumdar/CS513-Project | d5601c3576d0e8472c0c1acfac04abec6bbc6586 | c6f6cff6a60e671c60197ea48b33503991483adb | refs/heads/master | 2020-04-03T23:47:19.167355 | 2018-12-04T15:35:30 | 2018-12-04T15:35:30 | 155,630,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,498 | r | Neural net with nnet.R | Car_data <- read.csv("data.csv")
Car_data<- na.omit(Car_data)
Car_data <- Car_data[,-10]
c <- as.matrix(sapply(Car_data,as.numeric))
c1 <- scale(c)
index <- sample(nrow(c),as.integer(.75*nrow(c)))
Test_dataset<- c[-index,]
Training_dataset<- c[index, ]
library("neuralnet")
net_bc<- neuralnet(MSRP~M... |
51020ed0110eb9d925641564f2887b59558f2196 | e39cd762e483cb80774aec5c89a333e3ff4cfa36 | /maitri_ref.R | 833bb00e9a736b6e5022fefa8bd16f8bf7673bf2 | [] | no_license | CodeFire98/impCodes | eaad7171bd77f489822f8998b5bed35c7fa947ca | 9740a3d53eedd71718e60d8b6f0e969f15afcdcb | refs/heads/master | 2020-03-21T07:02:42.985096 | 2018-06-22T05:10:38 | 2018-06-22T05:10:38 | 138,257,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,947 | r | maitri_ref.R | library(ggplot2)
library(reshape)
library(scales)
library(reshape2)
maitri = Final_File
#setwd("/root/R-project/data/polar data/Aws/IIG/Bharati")
#maitri<-read.csv("imd_maitri_2015.csv")
head(maitri)
maitri$obstime<-strptime(maitri$obstime,format="%d/%m/%Y %H:%M")
#maitri$obstime<-strptime(maitri$obstime,for... |
b5d9949e43c3977c30ea4abe918eaeb7c58f8bae | 38261102ce03c8e67f96f72f2a87ac0378a3f607 | /cytoscapeJsSimpleNetwork.R | 6e47e1b96d0be6b8b8949738a37b8e8d44f597e7 | [
"Apache-2.0"
] | permissive | abhik1368/r-cytoscape.js | 118c3e0d64b619263b7fabbed5db51ebb7851e0a | 5e22fae5dc16bd0efb0eb5484a229bc0e003d2bc | refs/heads/master | 2021-01-17T13:03:55.990784 | 2014-11-22T01:31:13 | 2014-11-22T01:31:13 | 30,137,434 | 0 | 1 | null | 2015-02-01T05:35:43 | 2015-02-01T05:35:43 | null | UTF-8 | R | false | false | 9,326 | r | cytoscapeJsSimpleNetwork.R | #' Generate a CytoscapeJS compatible network
#'
#' @param nodeData a data.frame with at least two columns: id and name
#' @param edgeData a data.frame with at least two columns: source and target
#' @param nodeColor a hex color for nodes (default: #666666)
#' @param nodeShape a shape for nodes (default: ellipse)
#' @... |
28e183dc36baf6a2ef47426a889289118c80a258 | 560bac9c5ab4c6d5bcc5266c07be3b574f3ede7d | /cachematrix.R | 2ee8b48bfa03cf75a23ccedc67b8b3b96bb9bed4 | [] | no_license | Zeunouq/ProgrammingAssignment2 | bacff9edc45adf0ef3ef3af9126bd65e7e48c9c9 | cf936ce980f729ce7188791de49c02a9910e7562 | refs/heads/master | 2020-03-07T01:12:53.824862 | 2018-03-29T08:34:47 | 2018-03-29T08:34:47 | 127,177,813 | 0 | 0 | null | 2018-03-28T17:52:42 | 2018-03-28T17:52:41 | null | UTF-8 | R | false | false | 1,534 | r | cachematrix.R | ## The function cachematrix.R is a set of two functions 1- makeCacheMatrix and 2- cacheSolve
## that allow to calculate the inverse of a matrix. If the contents of the matrix are not changing,
## the function will cache the value of the inverse so that when we need it again, it can
## be looked up in the cache rather... |
d9b877cf6194a91d981a7d640a15f8e47abd1a1e | 33bbdfdef10d809d4f0b4101fd89c6ad84980183 | /R/methods_config.R | ba4ef0959a37e5afe84b17b81da00b07dc7c8522 | [] | no_license | Shelly-Lin-97/scRNAIdent | fdde83e50d9f1f898d0a533e93996fad6040516d | b5fb9090ca0a024dcee44f178d8168c733b31593 | refs/heads/master | 2023-01-13T17:58:15.310405 | 2020-11-17T17:02:56 | 2020-11-17T17:02:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 328 | r | methods_config.R | methods.config.scmap <- list(nfeatures=500,threshold=0.5,seed=1)
methods.config.seurat <- list(nfeatures=2000,pc_dims=10,resolution=0.5)
methods.config.tscan <- list(cvcutoff=0.01,k=8)
methods.config.sc3 <- list(nfeatures=500,k=8)
methods.config.cellassign <- list(learning_rate=1e-2,shrinkage=TRUE,marker_gene_method='s... |
037521473cc9d06024295aabf5415e86ca0c8367 | 4951e7c534f334c22d498bbc7035c5e93c5b928d | /statistics/misc/dist-overlap.R | b63476cd3cfa8e6be931211245966291d1986c1e | [] | no_license | Derek-Jones/ESEUR-code-data | 140f9cf41b2bcc512bbb2e04bcd81b5f82eef3e1 | 2f42f3fb6e46d273a3803db21e7e70eed2c8c09c | refs/heads/master | 2023-04-04T21:32:13.160607 | 2023-03-20T19:19:51 | 2023-03-20T19:19:51 | 49,327,508 | 420 | 50 | null | null | null | null | UTF-8 | R | false | false | 718 | r | dist-overlap.R | #
# dist-overlap.R, 2 Dec 15
#
# Example from:
# Empirical Software Engineering using R
# Derek M. Jones
source("ESEUR_config.r")
pal_col=diverge_hcl(2)
y1_sd=1
xpoints=seq(-0.96, 1+2*1.96, by=0.01)
y1_points=dnorm(xpoints, mean=0, sd=y1_sd)
y2_points=dnorm(xpoints, mean=2*y1_sd*1.96, sd=1)
plot(xpoints, y1_point... |
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