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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6604bd798c523d15564c8ce65b8a76b779bee978 | 7afbb148ec11b3105aaead6bdd900f847e49eb18 | /tests/testthat/test-stringsAsFactors.R | 6a3ca57d2505b0393493bbf32bffea235959da18 | [
"MIT"
] | permissive | tidymodels/recipes | 88135cc131b4ff538a670d956cf6622fa8440639 | eb12d1818397ad8780fdfd13ea14d0839fbb44bd | refs/heads/main | 2023-08-15T18:12:46.038289 | 2023-08-11T12:32:05 | 2023-08-11T12:32:05 | 76,614,863 | 383 | 123 | NOASSERTION | 2023-08-26T13:43:51 | 2016-12-16T02:40:24 | R | UTF-8 | R | false | false | 1,356 | r | test-stringsAsFactors.R | library(testthat)
library(recipes)
n <- 20
set.seed(752)
as_fact <- data.frame(
numbers = rnorm(n),
fact = factor(sample(letters[1:3], n, replace = TRUE)),
ord = factor(sample(LETTERS[22:26], n, replace = TRUE),
ordered = TRUE
)
)
as_str <- as_fact
as_str$fact <- as.character(as_str$fact)
as_str$ord <- a... |
c00bbdf80b57623ede3a38899e7aeacfbbda3f8c | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/2161_2/rinput.R | 90268212ecbae1bb085e738eea85f3c3360e93bd | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("2161_2.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2161_2_unrooted.txt") |
5755827f3d0d4a28000eaca07c16ffb170c11ad1 | 008f618a0c800605f6cdc664aa40c36abc211a67 | /R code/Introductory tutorials/test.R | ba20b397b3bfa8e8bfaab22feab32155e47023a8 | [] | no_license | ThomasKraft/Dartmouth-EEES-Modeling-Group | 2b4e509ffec61650ea4f3ee295ea533123c23cfc | 7144974d4b4ec896cb46e3b96aa7531dd90009bb | refs/heads/master | 2021-01-10T15:15:04.661372 | 2015-11-20T19:06:43 | 2015-11-20T19:06:43 | 45,926,068 | 1 | 1 | null | 2015-11-20T19:06:43 | 2015-11-10T16:49:26 | R | UTF-8 | R | false | false | 478 | r | test.R | ### Test to help everyone get started!
#new code
install.packages("ggplot2")
install.packages("RCurl")
install.packages("foreign")
library(ggplot2)
library(RCurl)
library(foreign)
url1 <- getURL("https://raw.githubusercontent.com/ThomasKraft/Dartmouth-EEES-Modeling-Group/master/Data/serotiny.csv")
cdat <- read.csv(te... |
f3c1f90ae4befad8de8aa1b97886780209589899 | d9b8cc600e0bc75224f170988dff4891e10d492a | /cachematrix.R | 3b79fe099984a6aee0c7b178f9bd3b48f0d0d8de | [] | no_license | skom2308/ProgrammingAssignment2 | 20e9794116277386e7d5bfeea8e7dd77a787d144 | c705a826d15171f6411ffc5c68499c1d27b6ffde | refs/heads/master | 2020-12-11T01:52:17.021994 | 2015-05-09T18:26:55 | 2015-05-09T18:26:55 | 35,337,630 | 0 | 0 | null | 2015-05-09T17:15:05 | 2015-05-09T17:15:04 | null | UTF-8 | R | false | false | 1,237 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## The first function, makeCacheMatrix creates a special "marix", which is really a list containing a function to:
## 1. set the value of the matrix
## 2. get the value of the matrix
## 3. set the value of the inverse matrix
## 4. get t... |
b3c961c3cdeb5cba51a59efe0684f2620e33415d | 4f27aabc6080cc95e79dfb3d20730a6b0d1552ed | /plot2.R | caedfe95b0b1ccb58a1dc0c4d40345bdb64d07ba | [] | no_license | eminnett/exploratory-data-analysis-2 | a3a401acfd2ff6db8f9165b442cb3eb2251151ea | aceac74fced6a044426374e48c71e797ecf72b3b | refs/heads/master | 2020-12-03T03:51:17.532266 | 2015-11-22T21:19:22 | 2015-11-22T21:19:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,693 | r | plot2.R | # Have total emissions from PM2.5 decreased in the Baltimore City, Maryland
# (fips == "24510") from 1999 to 2008? Use the base plotting system to make
# a plot answering this question.
library(dplyr)
# This function assumes https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip has been downloaded and ex... |
c9a514fadc4d5505068a79006a84de4f78f43e65 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/weibulltools/examples/kaplan_method.Rd.R | 5b1ea8d5380be3e8eb9048af591690c3f04be541 | [] | 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 | 805 | r | kaplan_method.Rd.R | library(weibulltools)
### Name: kaplan_method
### Title: Estimation of Failure Probabilities using Kaplan-Meier
### Aliases: kaplan_method
### ** Examples
# Example 1
obs <- seq(10000, 100000, 10000)
state <- c(0, 1, 1, 0, 0, 0, 1, 0, 1, 0)
uic <- c("3435", "1203", "958X", "XX71", "abcd", "tz46",
"fl... |
c308b8352e4847236b9cd72c0d484d9462fef61b | 87fdffc82b36e9c907506b60cbbb979a264b1fad | /EDAProject2/plot2.r | 376c52798f8cd29687261f852977516cc15e185d | [] | no_license | Whitchurch/datasciencecoursera | 5aacc924b93b413bf3743f93e7eba5e74b0c747a | aa99702d13fc33de9f9bd1981d428c60164ad33a | refs/heads/master | 2020-11-29T06:45:55.222830 | 2020-06-05T17:23:20 | 2020-06-05T17:23:20 | 230,049,998 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,912 | r | plot2.r | #load dplyr
library(dplyr)
library(tidyr)
#Function to read and return the files
readfiles <- function(filenametoreplace, filenametosubstitute)
{
currentpath <- rstudioapi::getSourceEditorContext()$path
currentpath <- gsub(filenametoreplace,filenametosubstitute, currentpath)
if(file.exists(currentpath))
{
... |
b9b17e0f43f5d14d34104b6336bd6bb233231555 | 5a531782c95a3a258058d90210171895ab0abc43 | /man/plot_close.Rd | 8e0f166263252499caffad08be84b0e03ad12435 | [
"MIT"
] | permissive | colin-fraser/questradeR | 8d74b9e6a21053a625208882382bce93f161645a | d5f73b99ce2dd8a75faa9076fada9aac4dc0da46 | refs/heads/master | 2023-03-04T05:04:09.371340 | 2020-12-23T17:44:38 | 2020-12-23T17:44:38 | 322,346,721 | 0 | 0 | NOASSERTION | 2021-02-16T16:01:50 | 2020-12-17T16:10:43 | R | UTF-8 | R | false | true | 486 | rd | plot_close.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extra.R
\name{plot_close}
\alias{plot_close}
\title{Plot close prices from candle df}
\usage{
plot_close(candle_df, yaxis = c("price", "return"), facet = FALSE)
}
\arguments{
\item{candle_df}{data frame returned from candles()}
\item{yaxis}{... |
a49e0f48f4dbd5b1ad84a441bff19de5f9546e3b | 97d7c3a59494aaefd41e97197d1896c1ca267096 | /R/StripRepetitions.R | f7cc17eb7c681e1e709021d0d28b96d38808954a | [] | no_license | cran/TauP.R | 052e5b006ffa792116ea66507bef6a34391be699 | d520d562eb5f6b0fd268727babad86f8f87b5902 | refs/heads/master | 2021-06-02T15:03:44.398055 | 2018-08-31T07:09:07 | 2018-08-31T07:09:07 | 17,693,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 713 | r | StripRepetitions.R | StripRepetitions <-
function(phase)
{
phase=strsplit(phase,'')[[1]]
phaseout=phase
repetitions=NULL
w=getOption('warn')
options(warn=-1)
if(!is.na(as.numeric(phase[length(phase)]))){
repstring=NULL
done=0
len=length(phase)
indy=len
while( done==0){
if(!is.na(as.numeric(phase[indy]))){
... |
b6efd6e2f026008b16d7782ff3b2105d7527fe91 | 0f188217a4a2b3955ebb33ec753c0f27bab62ec9 | /R/logprimedown.R | 7bfa9ea41fbee9bc0736813a557c7e8c348be6c4 | [] | no_license | Monneret/MarginalCausality | f15ace3619494eef9502b0498649ce3d07622f0a | 4cd442f52284cfa302e1f5e17d7dbc15294c22ea | refs/heads/master | 2021-01-12T16:24:30.354511 | 2016-10-05T09:39:50 | 2016-10-05T09:39:50 | 70,046,341 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,530 | r | logprimedown.R | #' Compute the gradient of the log-likelihood for the downstream model.
#'
#' @export
#' @param wt Observationnal data, matrix of dimension N*2, where N stand for the number of replicate.
#' The first column correspond to the knocked-out gene.
#' @param ko Interventionnal data, matrix of dimension N*2, where N stand fo... |
c7e20b1af682db06192854a055aeeccc05b0612c | df10b7fb39449e70b92d28c8ce791d9fcd772c8a | /make_clembets.R | 7579ee3b8e8b9d8504aeb1ad0ddcd2ac743f5cea | [] | no_license | adndebanane/zdsbets | 3848c7a39090ff7644ab20f03d13af2e3a59baa8 | 95a0d149384468eb9e7652e1ffd53881f336a4a6 | refs/heads/master | 2020-04-17T10:12:48.460050 | 2017-06-08T07:52:04 | 2017-06-08T07:52:04 | 67,291,486 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,054 | r | make_clembets.R | load("results20162017.rda")
cur.season <- season20162017
bets <- read.table(file = "00-datasets/20162017/players_bets.csv", header = TRUE, sep = ",",
stringsAsFactors = F)
load("players.rda")
##List of games to play with
list.betted.games <- unique(bets$idmatch)
nb.betted.games <- length(list.bett... |
de7c821fd150c971c8cc6c6a0c1cc03d0390e08e | 1febe6b946637c6b7a946976d281a8352899e2dc | /man/anyOf.Rd | 17e9024fbc49bb73a2f0aa87313cd0a9177cab92 | [
"CC0-1.0"
] | permissive | rmflight/vRbalExpressions | 136b446889e9641c196a2c2de55b18f6663c210c | 19042e1254d0092157112b2ddcb70ba7ca5ddfdd | refs/heads/master | 2021-01-15T12:26:03.255398 | 2013-08-27T20:11:18 | 2013-08-27T20:11:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 119 | rd | anyOf.Rd | \name{anyOf}
\alias{anyOf}
\title{Any given character}
\usage{
anyOf(value)
}
\description{
Any given character
}
|
8c3e1e0c5593853cdda8a2a91a88f3bb8e8abdaa | 272c346ee4a331272851866cf2395c0ae8d6e508 | /RaghuInterview (1).R | 915239e10e42cc3eb07775f3edae15d43e234495 | [] | no_license | SRV17/Analysis-on-LAPD-Crime-Data | f9e770b61bf661c533d2213a81e227f8436b74d4 | 64c10a7704390d227d4ea0b5d242e8ece517cd96 | refs/heads/master | 2021-01-22T22:58:01.262464 | 2017-03-28T22:02:53 | 2017-03-28T22:02:53 | 85,598,224 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,900 | r | RaghuInterview (1).R | library(lubridate)
#install.packages("lubridate")
library(stringr)
#install.packages("stringr")
library(dplyr)
#install.packages("dplyr")
library(tidyr)
#install.packages("tidyr")
library(ggplot2)
#Load Data
RawData <- as.data.frame(read.csv("C:/users/Shwetha Hara/Downloads/Crimes_2012-2015.csv", stringsA... |
ee0a2b90cbc254594b40a43f1e8fc017bd53ad77 | 781615c49118f65be9286f1339067a88c8111b46 | /R/psmc2history.R | 169de95cdaa17e475af24008ed2cd3eb82a02331 | [
"MIT"
] | permissive | P2C2M/P2C2M_PSMC | b0512c611cc5e18e96dc406a51287562c2abe77f | 0f872cbaa867cf43a0d7ad1cd8ebcf5f0487e4c1 | refs/heads/master | 2022-09-04T12:17:08.115460 | 2020-05-19T12:51:59 | 2020-05-19T12:51:59 | 265,243,240 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,734 | r | psmc2history.R | ##### Function for converting psmc output file to history file #####
### Testing ###
# file_name = "French_upper.psmc" # name of psmc output file
# n_iterations = 20
# u = 1.1e-08
psmc2history <- function(file_name, n_iterations, gen, u, from_file, save_output){ # file_name = name of PSMC output file or list of line... |
3d96dcb740a73a92d1faaf12a6be4e8184a9c409 | fbb65f366b801846471ecd30c5a3c03f29618027 | /man/ExampleAnswers.Rd | eab4ee4e7a656a376903544e5ae5549e103c6742 | [] | no_license | ReneMayer/questionnaire | 55cb8cc13cf31e6725aeb0dd256a5049d722e9ec | a5ff66455285572e317818fe775f5ae5bea60347 | refs/heads/master | 2021-01-21T13:08:39.552796 | 2014-04-11T05:02:12 | 2014-04-11T05:02:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 481 | rd | ExampleAnswers.Rd | \docType{data}
\name{ExampleAnswers}
\alias{ExampleAnswers}
\title{norm: items aggregated within scales}
\format{a \code{data.frame} instance, 1 row per item (115 total).}
\source{
simulated data set
}
\description{
This data set contains an example responses to a launched
questionnaire object. The data would be ... |
cf43bd97d45a621ba9c327f8d4df0561d08606cc | 983c9935444c604a1634eb98b8ab3d4e3fc188cf | /pca/in_class_pca.r | 5dc367ee86771b1e02b74961ea82acb274a66be0 | [] | no_license | 37chandler/applied-data-analytics | 8929b36e617ea40ea2228cd12c4809b87230599d | d771c42e29aeb13d7ad2ffe042b45eff75f1fdf7 | refs/heads/master | 2021-01-25T17:10:36.974393 | 2019-11-20T22:07:31 | 2019-11-20T22:07:31 | 203,245,280 | 0 | 2 | null | 2019-09-02T20:03:25 | 2019-08-19T20:26:28 | HTML | UTF-8 | R | false | false | 3,156 | r | in_class_pca.r |
# Here's a PCA on some Wedge Data. Give
# this a look. Can you explain what's going
# on here?
library(dplyr)
library(ggplot2)
library(reshape2)
library(scales)
d <- readr::read_tsv("owner_level_top_prod_sales.txt")
d <- d %>%
filter(owner != 3)
# To make our code run a bit faster and get rid of some extreme... |
761bbddc3e6ecb3c86c456030b0886dfceb3e321 | e1cbbf8791b0ac6d40f6d5b397785560105441d9 | /man/partri.Rd | b295705447fa22195fe6c7f89e0d7fc36a875123 | [] | no_license | wasquith/lmomco | 96a783dc88b67017a315e51da3326dfc8af0c831 | 8d7cc8497702536f162d7114a4b0a4ad88f72048 | refs/heads/master | 2023-09-02T07:48:53.169644 | 2023-08-30T02:40:09 | 2023-08-30T02:40:09 | 108,880,810 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,236 | rd | partri.Rd | \name{partri}
\alias{partri}
\title{Estimate the Parameters of the Asymmetric Triangular Distribution}
\description{
This function estimates the parameters of the Asymmetric Triangular distribution given the L-moments of the data in an L-moment object such as that returned by \code{\link{lmoms}}. The relations between ... |
abc5b3a233d4b4e5d5b2446c9aaa1877480c4253 | 50eda1d64a7a0dc2c12ecaf1149868e91498fd64 | /R/AllGenerics.R | 35c94f66933d2e45adbcb61792580af1fafac747 | [
"MIT"
] | permissive | hliu2016/bcbioRNASeq | 19af39216ed640b77a44866429650c7eff9e9b77 | ebca75ae614d4f757dd164d33760e35f44e3eff5 | refs/heads/master | 2021-05-06T10:20:07.691114 | 2017-12-11T23:57:36 | 2017-12-11T23:57:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,854 | r | AllGenerics.R | #' S4 Generics
#'
#' @rdname AllGenerics
#' @name AllGenerics
#' @keywords internal
#'
#' @param object Object.
#' @param x Object.
#' @param i An integer or numeric scalar.
#' @param value Value to assign.
#' @param withDimnames A `logical`, indicating whether dimnames should be
#' applied to extracted assay element... |
b77bdc9515e68ddb5a924a735026f3ba5ab7574d | 5cf9bcc6478d95b3a8d15c398cea4f77c00c9307 | /Loading data.R | 611a717f2d3ba69c2e91a1803886cd41e0b79b7c | [] | no_license | cianlarkin/Final-Project | e4f10b9439f8d0ce0ad83cb60a4b58568e9b61d8 | 75cc55a5dd6cc623fe0ddc75070defeb42114333 | refs/heads/main | 2023-04-20T15:59:16.419155 | 2021-05-16T13:33:49 | 2021-05-16T13:33:49 | 367,888,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,072 | r | Loading data.R | library(tidyverse)
#LOADING DATA
PremierLeague2020 <- read.csv("english_premier_league_2020.csv", header = TRUE, sep = ",")
PremierLeague2020PlayersIn <- read.csv("Prem2020PlayersIn.csv", header = TRUE, sep = ",")
PremierLeague2019 <- read.csv("english_premier_league_2019.csv", header = TRUE, sep = ",")
PremierL... |
06d86120b3953e7a94d3486ddeec84e8cce7774b | d62b2953237ecf2d42e15582a8eec47443fd8ff1 | /R/calculate_coverage.R | c583d68912bcd5a2ca77d5b704dc522ca6c517b2 | [] | no_license | MHi-C/MHiC | 2b9c440c5bd3792c4db1cfba9fb7d7401efee472 | 06a1e7176b106e33570c59b17c113d4f118bff5f | refs/heads/master | 2020-06-25T18:59:54.351246 | 2019-10-01T02:51:25 | 2019-10-01T02:51:25 | 199,396,268 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,716 | r | calculate_coverage.R | calculate_coverage<-function(interactions,flag = TRUE){
#
##########################################################
if(nrow(interactions)>1e8){
t <- ceiling(nrow(interactions)/1e8)
IList <- list()
IList[[1]] <- interactions[1:1e8,]
for(i in 2:t){
IList[[i]] <- interactions[(((i... |
51da781e0f748ac21349807fb5f6fa737825d50e | f3d1e8504b45792eb5efea527e253dccd338c2fe | /crossfit_prep/experimentation.R | 908217190b059ca3b579d45088057bb82a15e640 | [] | no_license | vbernardes/crossfit-2015-eda | eb0e604cba2e4ed455df9560fad72cd005eff07c | a9aceeafd2739253189a6108a6614f4c6b763d39 | refs/heads/master | 2020-05-25T12:50:24.691173 | 2019-05-21T15:49:52 | 2019-05-23T19:37:26 | 187,806,820 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,017 | r | experimentation.R | library(data.world)
library(tidyverse)
library(GGally)
athletes <- read.csv('bgadoci-crossfit-data/data/athletes.csv')
leaderboard <- read.csv('bgadoci-crossfit-data/data/leaderboard_15.csv')
#Using an inner join, match on athlete ids that are in both dataframes
cf <- leaderboard %>%
inner_join(athletes, by="athlet... |
6735295ef6815bf92364d60bf628ed21c1971630 | ef6681b93278cbba78b89d5679c88517cdf70305 | /init_script.R | a71f0a149916b303e7d9faa78f08f6529cd9b18b | [] | no_license | lehmkudc/mtg-database | 966f48f28023b828a738c81b065356ea934e59e3 | 43cb3a51df48cac8f2ba7e55ad1fd55ec1ad5ea2 | refs/heads/master | 2020-03-09T06:39:04.878364 | 2018-09-01T19:08:31 | 2018-09-01T19:08:31 | 128,599,306 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 799 | r | init_script.R | rm( list = ls() )
options(stringsAsFactors = FALSE)
library(magrittr)
library(shiny)
library(RMySQL)
library(rhandsontable)
library(rlist)
library(rjson)
empty <- data.frame( QTY=as.integer(0), CardName=rep('',20),
SetName = rep('',20), CNumber=rep('',20),
Notes = rep('',20)... |
5d85693bdfd2890e1aede80d9c118d087d4c5037 | efe3bdc6afd1f111ece86b830fc92cc4bec8910e | /man/age_df_countries.Rd | 218631be170a17d080dcd29d010e6d4bd8b7c6fc | [
"MIT"
] | permissive | Bisaloo/contactdata | cd2b35db31ae2d32b52721bc13e01cc80a5e4484 | 444ba7569703863092ed488f8f4b572a6453c5e6 | refs/heads/main | 2023-04-11T15:34:34.543076 | 2023-03-22T11:19:38 | 2023-03-22T11:19:48 | 293,047,665 | 6 | 2 | NOASSERTION | 2023-09-05T11:32:09 | 2020-09-05T09:52:13 | R | UTF-8 | R | false | true | 866 | rd | age_df_countries.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/age_countries.R
\name{age_df_countries}
\alias{age_df_countries}
\title{Get a data.frame (in long format) of population by age for multiple countries}
\usage{
age_df_countries(countries)
}
\arguments{
\item{countries}{A character string or a ... |
b7449fda2f83bbeb3dcbcf58a362104ec8577865 | 49e2a0b28e9398f788ad8e481fbbe4c223e6a196 | /projektMOW/man/winequality-white.csv.Rd | 7ec14cdf72c4c367d4d1afb432c308b20ec4d222 | [] | no_license | eatrunner/MOW | 850e5d7d1e2f0f0c031c7a764eebf0fd6c870ef7 | d80b306bf9d6d24b26c81c93a2fa9bb34cf77f97 | refs/heads/master | 2021-09-04T22:18:50.641509 | 2018-01-22T16:47:44 | 2018-01-22T16:47:44 | 112,388,294 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,253 | rd | winequality-white.csv.Rd | \name{winequality-white.csv}
\alias{winequality-white.csv}
\docType{data}
\title{
White wine dataset.
}
\description{
Dataset was created, using white wine samples.
The inputs include objective tests (e.g. PH values)
and the output is based on sensory data
(median of at least 3 evaluations made by wine experts).
}
\usa... |
417bf497f97e170476bd036ac294c4d527da6af9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Mqrcm/examples/predict.iMqr.Rd.R | 3190ddc5632051f3d292a1daf9b3253e5c2de629 | [] | 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 | 1,317 | r | predict.iMqr.Rd.R | library(Mqrcm)
### Name: predict.iMqr
### Title: Prediction After M-Quantile Regression Coefficients Modeling
### Aliases: predict.iMqr
### Keywords: methods
### ** Examples
# using simulated data
n <- 250
x <- runif(n)
y <- rlogis(n, 1 + x, 1 + x)
# true quantile function: Q(p | x) = beta0(p) + beta1... |
13624921f5b9ba81a60e428fa8883022ff97117b | 9d1166d3ef82256211f215979fa60bef1b4b511f | /R/server.R | 2c205d36bcd6f0ef23f7eed2521ef94ebdfcdb09 | [] | no_license | antonioricardojr/vereador | cfccbcf467ab72bae09b41440913a422727c447c | e10fb41b24e9f0c32da9958a6044936903bce250 | refs/heads/master | 2020-09-14T07:25:53.182817 | 2016-09-12T22:17:51 | 2016-09-12T22:17:51 | 66,152,429 | 1 | 0 | null | 2016-08-28T22:12:15 | 2016-08-20T15:00:13 | HTML | UTF-8 | R | false | false | 5,227 | r | server.R | library(dplyr, warn.conflicts = F)
library(stringi, warn.conflicts = F)
library(RPostgreSQL)
library(lubridate, warn.conflicts = F)
library(purrr, warn.conflicts = F)
library(futile.logger)
source("data_access.R")
source("vereadores_logic.R")
camara_db = start_camara_db()
#* @get /ementas/contagem
get_theme_count = f... |
77611d5e777a3efc840a1017bf70048320f7dce0 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/mnis/tests/testthat/test_extra.R | 3933cce0bb75b5b053c5ae5232a8323c4c3e518a | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 286 | r | test_extra.R | library(mnis)
context("mnis_extra")
test_that("mnis_extra returns expected format", {
skip_on_cran()
xmnise <- mnis_extra(4019)
expect_length(xmnise, 188)
expect_type(xmnise, "list")
expect_true(nrow(xmnise)==1)
expect_true(tibble::is_tibble(xmnise))
})
|
4bba16aa9127ac229b367e2c498000241917beb2 | 304b2c50ecbc07dfb68521690381ea908dda638f | /master.R | c5a1eaddbe01e1bbbebf83e26b005544623dd184 | [] | no_license | haachicanoy/climate-risk-profiles | 73af330cc3dcc7bf1111fbc262db56d8ee30dd67 | 4e8d883c06c6cd6177383c883dac0c9c97e17df4 | refs/heads/master | 2023-03-09T21:49:28.791901 | 2021-02-24T18:45:40 | 2021-02-24T18:45:40 | 299,702,287 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,876 | r | master.R | # -------------------------------------------------- #
# Climate Risk Profiles -- Master code
# A. Esquivel, H. Achicanoy, and J. Ramirez-Villegas
# Alliance Bioversity-CIAT, 2021
# -------------------------------------------------- #
# R options and load packages
options(warn = -1, scipen = 999)
suppressMessages(if(!... |
fba20d5f361099c1c4f474c8f422677f55e96f1f | ec6ec7b40992d2db8b3d6fed6a04980d32ffeae6 | /test/support/pfacat.R | 49b5db2d16712a7ac118f1996972c40b316f9a7f | [
"MIT"
] | permissive | jimmy05/eps | 49b66c75420a1b5c971ac35104317b84ae29e6cf | fe476bca27ede37ffb70603bc6f78fe2a6995911 | refs/heads/master | 2020-04-19T03:18:29.459096 | 2019-02-04T18:11:22 | 2019-02-04T18:11:22 | 167,926,964 | 0 | 0 | MIT | 2019-01-28T08:35:48 | 2019-01-28T08:35:47 | null | UTF-8 | R | false | false | 198 | r | pfacat.R | library(aurelius)
x <- 1:4
weekday <- c("Sunday", "Sunday", "Monday", "Monday")
y <- c(12, 14, 22, 24)
data <- data.frame(x=x, weekday=weekday, y=y)
model <- lm(y ~ ., data)
write_pfa(pfa(model))
|
e93b5acab45fcabca2e61c4a49fe191fe222ae86 | a6185dbfaa3349971f10972df80379a11fde2159 | /preprocessData.R | 45c04e19a889034adebed185448212c1510b0690 | [
"MIT"
] | permissive | sarikayamehmet/CryptoPortfolio | 37d6add39df49175b011dc486496570bbb0a15ef | b88937bc56926d129531de8f72b7328a81b2a690 | refs/heads/master | 2020-05-18T07:39:32.829256 | 2019-11-12T06:32:18 | 2019-11-12T06:32:18 | 184,271,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,754 | r | preprocessData.R | library(highcharter)
library(lubridate) # ymd
library(dplyr) # group_by
library(tidyr) #complete
#Read market data
df <- read.csv("marketPrices.csv")
df$T <- as.Date(df$T)
#Calculate the period-over-period percentage change at each point in time
percentageCalc <- function(marketName){
marketData <- df[df$M... |
61ed1b2ab28cd2cf041d3f52f9c839e5ec5b3d0e | 0a230e74f7343a500cf667c725a798d42ec12539 | /man/post_html_from_Rmd_addin.Rd | 9f6add1647384c418276b87e49df830936833083 | [
"MIT"
] | permissive | kbttik/posteR | 2793a0495a226e254a598756e8cc6a4098fdc86b | fedbf6d12ad924be9421a0bc12e010be6065eae0 | refs/heads/master | 2022-12-11T15:00:10.896933 | 2020-09-10T08:56:31 | 2020-09-10T08:56:31 | 285,782,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 445 | rd | post_html_from_Rmd_addin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/post_html.R
\name{post_html_from_Rmd_addin}
\alias{post_html_from_Rmd_addin}
\title{htmlレポート転送用アドイン関数}
\usage{
post_html_from_Rmd_addin()
}
\value{
\code{post_html_from_Rmd()} returns the URL of the html page.
}
\description{
RstudioのAddinsで関... |
d40234a2555633409d35f8475d27ba8f56374f30 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/markovchain/examples/markovchainListFit.Rd.R | 4c369a840e9ba946fd131c3ec4bcb8c6d1d0e2d5 | [] | 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 | 358 | r | markovchainListFit.Rd.R | library(markovchain)
### Name: markovchainListFit
### Title: markovchainListFit
### Aliases: markovchainListFit
### ** Examples
# using holson dataset
data(holson)
# fitting a single markovchain
singleMc <- markovchainFit(data = holson[,2:12])
# fitting a markovchainList
mclistFit <- markovchainListFit(data = hols... |
c2174639b6ca17c79a314ee1c91174039a639252 | 8615a79ededec2faaf0fee8ddf047280d3f9cad9 | /R/PredictoR.xgboost.R | f702b80a2be16d73af3edf4614a9ed505079339d | [
"Apache-2.0"
] | permissive | htssouza/predictoR | 5923720ce35b8d57216a14ad2b2c2910e9c3e501 | dd920ca1edda92816859300caf100d0dc7987ffc | refs/heads/master | 2021-01-11T15:28:16.498822 | 2018-01-08T23:33:00 | 2018-01-08T23:33:00 | 80,353,247 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,341 | r | PredictoR.xgboost.R | ################################################################################
# PredictoR.xgboost
################################################################################
################################################################################
# External dependencies
################################... |
53d9acf7b4606051f6d4954dbf2643aaf7338b68 | 510bc25ad2b6e67e4a3c13043cacd4424b75552e | /R/print_commodity.r | 5194f81a55dcc2ca4700a5ad4747fa46dec3b334 | [] | no_license | orangeluc/energyRt | ff7423a2010d8edc3915034c396f079662ea4315 | c72d1a528a95ef8fada215e0abef45d523383758 | refs/heads/master | 2020-04-24T06:04:06.819280 | 2019-02-20T13:26:26 | 2019-02-20T13:26:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,100 | r | print_commodity.r | #---------------------------------------------------------------------------------------------------------
#! print.commodity < -function(x) : print commodity
#---------------------------------------------------------------------------------------------------------
print.commodity <- function(x) {
# print commodity... |
f1d6b151bc587ad152c892c9967559e7e68d68d0 | ae0d624abcfa7033b97fdef60642594d5448e29e | /Plot2.R | 0dcb9f5b1106464815a434bab74ec11aa1800c5b | [] | no_license | kcbaskar/ExData_Plotting1 | 709b70999326fe724bda47f7f7160aca54c46c10 | 64be72db0ae2410bd53caba815bd825884429ee5 | refs/heads/master | 2021-01-09T07:03:33.392057 | 2015-01-11T21:00:17 | 2015-01-11T21:00:17 | 29,097,005 | 0 | 0 | null | 2015-01-11T16:02:47 | 2015-01-11T16:02:46 | null | UTF-8 | R | false | false | 306 | r | Plot2.R | # Load data
source('load_data.R')
#open png file
png(filename="plot2.png", width=480, height=480)
#plot 2 now
plot(data$DateTime,
data$Global_active_power,
type="l",
col="black",
xlab="",
ylab="Global Active Power (kilowatts)",
main="")
#close the png file
dev.off()
|
d08976b6c912e73488c352f91b8cdd0f38bb0c91 | 0e5804e1c61ebfe908e0b6a116538ba21587c1c7 | /cachematrix.R | 613d299cfe05d27825f8945f70649257a8645b84 | [] | no_license | kpokk/ProgrammingAssignment2 | 13ce29d2148ebbd7274a299c87bb8ba4817004fd | 74429888579597c4c246be280e8367a0d47a6221 | refs/heads/master | 2021-01-24T14:47:24.216664 | 2015-09-25T14:37:34 | 2015-09-25T14:37:34 | 43,149,079 | 0 | 0 | null | 2015-09-25T14:26:12 | 2015-09-25T14:26:12 | null | UTF-8 | R | false | false | 1,221 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Functions makeCacheMatrix and cacheSolve are used to create a
## special object that stores a matrix and cache's its inverse
## Write a short comment describing this function
## Function makeCacheMatrix makes list that has functi... |
0b7743dca02e0c42e66de25cba0ad02cf9f829de | 08457a9bcd1fa7bf10d1f22116ce42f8696dad32 | /R/grd.box.ts.R | eaf593b1c6d071354b59457361d41b3e833ec654 | [] | no_license | ogutu/clim.pact | 8a249acc5d4e3c68c0293fca0f3ec6effbfef910 | dd4a01bb38f0f8d92fcdc443ab38ecec16f95523 | refs/heads/master | 2021-01-18T16:30:56.801697 | 2011-11-22T00:00:00 | 2011-11-22T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,846 | r | grd.box.ts.R | grd.box.ts <- function(x,lon,lat,lev=NULL,what="abs",greenwich=TRUE,mon=NULL,
col="grey10",lwd=1,lty=1,pch=".",add=FALSE,
filter=NULL,type="l",main=NULL,sub=NULL,xlab=NULL,ylab=NULL,
xlim=NULL,ylim=NULL) {
library(akima)
# library(ts)
if ((cl... |
7a8a3043ba9b8ac52d3feb1f4e70773feb6f994e | f0b1bc9d37d67113311f9b09dd559b9abbec166f | /inst/tests/tInfo1.R | d5d5f2bc6cabd4ae479fc4007ac02123ef261c36 | [] | no_license | GAMS-dev/gdxrrw-miro | b0a8f28c730eaa02fb63887a6832d861f48914c5 | 91486406f60986429b385cf37b2741648ac5b2e2 | refs/heads/master | 2023-04-03T17:53:31.082850 | 2023-03-15T17:24:04 | 2023-03-15T17:24:04 | 219,473,354 | 2 | 1 | null | 2021-01-28T09:19:28 | 2019-11-04T10:19:39 | C | UTF-8 | R | false | false | 1,335 | r | tInfo1.R | ### Test gdxInfo
# We get a list of symbol names from the transport data
if (! require(gdxrrwMIRO)) stop ("gdxrrw package is not available")
if (0 == igdx(silent=TRUE)) stop ("the gdx shared library has not been loaded")
source ("chkSame.R")
tryCatch({
fn <- "trnsport.gdx"
s <- gdxInfo (fn, dump=FALSE, ret... |
29ad1933366a2945371bd90bcbefa4e9ce20f605 | 04ad3ce63344d5a050f76111db01eb9e7d47e05c | /NNforMNIST_ori.r | 19b6e59bf13372fd9c078097d4a70cc6917392d2 | [] | no_license | Saw-Aung/Making-Neural-network-with-R-Programming-language | bd40ca9e4ebea735bb13678d77350203058fa49c | 1af20c8f7e9fd4bc0b52b3f418340c4a8525f7d2 | refs/heads/main | 2023-05-12T05:18:53.955111 | 2021-06-04T07:01:43 | 2021-06-04T07:01:43 | 373,751,134 | 0 | 0 | null | null | null | null | ISO-8859-7 | R | false | false | 3,469 | r | NNforMNIST_ori.r | rm(list=ls())
setwd("c:/usr/doc/")
## ³tf[^Ctestf[^ΜΗέέ
dt.tr <- read.csv("mnist_train.csv",header=F)
res.tr <- dt.tr[,1]; let.tr <- dt.tr[,2:(28*28+1)]
ns.tr <- 60000
dt.te <- read.csv("mnist_test.csv",header=F)
res.te <- dt.te[,1]; let.te <- dt.te[,2:(28*28+1)]
ns.te <- 10000
## O[XP[... |
aabe44c304131117f163462113065ad6306ceb2f | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diffrprojects/inst/testfiles/dist_mat_absolute/libFuzzer_dist_mat_absolute/dist_mat_absolute_valgrind_files/1609960684-test.R | 667e5457a4a72674c013e6ec158ee7fe0f781e88 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 234 | r | 1609960684-test.R | testlist <- list(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 65535L, -1L, -10726L, 805306367L, -1L, -16777216L ), y = integer(0))
result <- do.call(diffrprojects:::dist_mat_absolute,testlist)
str(result) |
4f76ff827492198117438042070c09eb3cb7fb1c | f8485998852a260211eabfcb54a51fcb0878581f | /man/Ocgrtools.Rd | af143bed5e565ed180bfcdcff375319606221f89 | [] | no_license | aposacka/Ocgrtools | 1ee047998e9f2831cb0f201add5fa163692dfb64 | ff2f998bdad77fb71e93dbd5a77f9a221c55f188 | refs/heads/master | 2021-06-11T10:44:36.448973 | 2017-02-23T06:36:03 | 2017-02-23T06:36:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 452 | rd | Ocgrtools.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Ocgrtools-package.r
\docType{package}
\name{Ocgrtools}
\alias{Ocgrtools}
\alias{Ocgrtools-package}
\title{Ocgrtools: Visualize oceanographic datasets}
\description{
Readlily plot depth profiles of ocean properties and utilize a dataset of var... |
5893d4d3218677aa52d553d09b077befcac3962e | 6006b35ac1272e6808f813171747783019a6b0a1 | /Question # 5.R | 0fbebf61fa3bafc1bed28f6bd66aa6e8ebc2a9fc | [] | no_license | Ory-Data-Science/r-dataframes-assignment-dylan-ryan-daniel | 1532c2c378fd0559a85536f9bf1137c77ef4f2f1 | 43ab16b0dfa101d226d79bb2790a59005cd86b55 | refs/heads/master | 2021-07-20T00:57:12.579203 | 2017-10-25T17:49:56 | 2017-10-25T17:49:56 | 108,028,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 423 | r | Question # 5.R | # Our code here is mean to find the mean volume of the shrubs by a given group, site and experiment, respectively.
shrub_data <- read.csv("shrub-volume-experiment.csv")
shrub_data %>%
mutate(volume = length * width * height) %>%
group_by(site) %>%
summarize(mean_volume = mean(volume))
shrub_data %>%
mutate(v... |
0c1b15cac6b5dbd507f011cb30ef18b4d41ea6a0 | b609b0a398c7d2b1ae74b05704600ef872d673d3 | /R/poissonDisc.R | 07b3aa7e32f36cb5433cd413bb3b274e4e8b6d80 | [] | no_license | anyanyany/WAE | e96f63ddbce2a2faac3946b296dbf003cd6395d9 | 8287c6b504d7ed1120e9b3a7b6f742b850560eb3 | refs/heads/master | 2016-09-14T05:54:09.119468 | 2016-06-03T15:30:56 | 2016-06-03T15:30:56 | 58,619,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,281 | r | poissonDisc.R | A = -100
B = 100
x = list()
y = list()
plotPoints = function(accuracy)
{
plot(A:B, A:B, type="n", ylim=c(A,B))
grid = generatePoints(accuracy, 2)
x = list()
y = list()
for(i in c(1:length(grid[1,,1])))
{
for(j in c(1:length(grid[1,,1])))
{
if(all(grid[i, j,] == c(-1,... |
4026713414dcb05c0ac55d9a72372eba50b763ea | 051eb2a9b907e5a1e5aa834b93972862cc0703c6 | /04_CalculateSpearmans.R | 9a814998935719fa86e5757caff32676837cf0dc | [] | no_license | ddiannae/tcga-rawcounts-comparison | 69b12241e2e9a4f1998bce21644161abbc770d05 | 9afc6c11d37a5254c8041ce78f770147379bcfd8 | refs/heads/master | 2020-03-30T14:37:39.501434 | 2018-10-02T22:46:52 | 2018-10-02T22:46:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,495 | r | 04_CalculateSpearmans.R |
setwd("~/Workspace/rnapipeline/tcga-rawcounts-comparison")
load(file = "cancerRawCleanLegacy.RData")
load(file = "healthyRawCleanLegacy.RData")
load(file = "cancerRawClean.RData")
load(file = "healthyRawClean.RData")
### Define Spearmans Function
spearmanCorr <- function(newGenes, legacyGenes){
dim(newGenes)
dim(... |
1cbff098d34d4f590761456e436b07c21ac6a4c4 | 57d4fbdc1a124c16794952be5c4ff02e0c9c1d97 | /man/plot.haplo.score.Rd | 3fa0ab2ae7ff57a2d137f04097b67f476545de20 | [] | no_license | cran/haplo.score | 60e30209f513758169fb88d088af8ac06a3f6a71 | 69ff52bf74f28e5141a6234d092e92e539ae0584 | refs/heads/master | 2021-01-10T19:19:50.096127 | 2002-09-13T00:00:00 | 2002-09-13T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,965 | rd | plot.haplo.score.Rd | %
% Copyright 2001 Mayo Foundation for Medical Education and Research.
%
% 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 2
% of the License, or (at your option) any later versi... |
90653ee3bd821ff357e177ffe08ddad5d81faf12 | 486e749753ee0cb1f984d64c39370fb20ca95e27 | /R files/Data Preprocessing Script.R | 57485e459dae1919a99afe0efe3ffb656886f8c5 | [] | no_license | 54heart/OceanCleanup | 44ee6b1276df481a4f2170ea8327318af7491e8e | f097cf69f135f019f4d85b538640566297374d89 | refs/heads/master | 2021-01-09T15:51:24.747340 | 2020-03-03T05:04:36 | 2020-03-03T05:04:36 | 242,361,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,182 | r | Data Preprocessing Script.R | library(readxl)
raw_data <- read_excel("~/Downloads/Data_Level5_BAH_OceanCleanup.xlsx")
library(plotly)
library(dplyr)
library(tidyverse)
library(ggplot2)
plot(x = raw_data$State, y = )
# df = raw_data[, -1] # remove the ID column
# df = unique(df) # get unique rows
# write.csv(df, "~/Documents/GitHub/OceanCleanup/... |
e1a46a076bac9b556c0b66855c2253e2f97dbc9a | 37e2a536296418edb66bbef82a65b02831b86ea7 | /Weekly Best Practice Figures/3D Fix Cars.R | 89497dd12aa978291b34053a8e7c07e9706c9138 | [] | no_license | frankmuzio/datavisualization | 54bdc0da361a34969424c241955557b0a9725f3d | bbc182bf08c05c400a83af53a2eb095c0588f3b5 | refs/heads/main | 2023-04-14T03:45:22.503198 | 2021-04-21T12:13:48 | 2021-04-21T12:13:48 | 335,643,100 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,684 | r | 3D Fix Cars.R | ### Fixing a 3D plot####
## March 24, 2021
# Frank Muzio
#------------------------------
library(ggplot2)
library(ggpubr)
head(mtcars)
p1 <- ggplot(mtcars, aes(x = mpg, y = qsec))
p2 <- ggplot(mtcars, aes(x = mpg, y = hp))
p3 <- ggplot(mtcars, aes(x = qsec, y = hp))
p4 <- ggplot(mtcars, aes(x = disp, y ... |
ab0b57df5815afcf778b34e602fda48be50b446d | 3b0d1151da42d9ebf2baa30983dcb273dc2ab180 | /pRRophetic/man/predictionAccuracyByCv.Rd | 4983851ccef63193b8bcbe883cfd76e72dbe8e2e | [] | no_license | SiYangming/pRRophetic2 | d0c127737fceae7b548ff8a270959335ca091750 | f3ad0a78f1b1afec1b314a4354ad570300861edd | refs/heads/master | 2023-03-19T04:08:28.036196 | 2017-05-12T14:45:59 | 2017-05-12T14:45:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,585 | rd | predictionAccuracyByCv.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/compute_phenotype_function.R
\name{predictionAccuracyByCv}
\alias{predictionAccuracyByCv}
\title{Cross validation on training dataset}
\usage{
predictionAccuracyByCv(trainingExprData, trainingPtype, testExprData = -1,
cvFold = -1, p... |
f534b43f1d133d13536601514ed9fa035600ef4f | 66e5fcdae6a48a6ca4e5290a0fde540d3fa3e992 | /src/kosovo-population-animation-v2.r | 433af0f5b7ac23766873a2a653aa83dd4c00f3f5 | [] | no_license | gentrexha/kosovo-population-animation | 7bee7b4dc304301b7914d4654bfa7ed763441c2d | 2e8878e05069c08951d14694afe62853304440a4 | refs/heads/master | 2021-07-19T08:17:49.237768 | 2020-08-07T14:51:20 | 2020-08-07T14:51:20 | 203,448,693 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,458 | r | kosovo-population-animation-v2.r | library(readr)
library(dplyr)
library(rgdal)
library(ggplot2)
library(maps)
library(ggthemes)
library(gganimate)
library(hablar)
library(mapproj)
library(scales)
library(RColorBrewer)
library(ggmap)
library(transformr)
library(magick)
# set wd
setwd("C:/Projects/Personal/kosovo-covid-data/src")
# Load data
url_csv <-... |
d39cc0a0075693d2e3ab42d01971461cce05c6d2 | a87b9e6961a0e99bb8a0f51d2b117cf6bfec014a | /r/image_processing_aux/VIs_Calculation.R | f78fdd258a92afb5e1d69c51a447808e1e814792 | [] | no_license | Lorenagzp/Scripts | 9e12785c512f5de70370ea9e75b8f89c02ea5c38 | 5d7007dd59287cf98e9fa3067af23f31fbe1837b | refs/heads/master | 2021-07-07T18:30:07.610185 | 2020-07-14T17:29:34 | 2020-07-14T17:29:34 | 141,351,391 | 0 | 1 | null | 2019-10-16T21:20:35 | 2018-07-17T22:22:16 | Python | UTF-8 | R | false | false | 12,449 | r | VIs_Calculation.R | #####################################################################
# Title: ENVI_Indices
# Name: Francisco Manuel Rodriguez Huerta
# Date: 9 Abril 2014
# Description: Compute Indices
#####################################################################
#Load required libraries
require(raster)
require(rgdal)
### no... |
cab831026d4c64bb8fb103f36e23a5af3ccab7d5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/climatol/examples/dd2m.Rd.R | 2da327a2446c3f76391c289ab5ea8ce09ab27057 | [] | 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 | 561 | r | dd2m.Rd.R | library(climatol)
### Name: dd2m
### Title: Compute monthly data from daily series
### Aliases: dd2m
### Keywords: datagen
### ** Examples
#Set a temporal working directory and write input files:
wd <- tempdir()
wd0 <- setwd(wd)
data(Ttest)
write(dat,'Ttest_1981-2000.dat')
write.table(est.c,'Ttest_1981-2000.est',ro... |
3637df55a1b7f5b03b1b6cc8a16e2e165aa9ea3a | 337e914fb4383becb3694d1bb0b453b6a1b01dd2 | /Shiny_server/ui.R | 1788cde892b6fd6925611bfb3fc1d886539c3253 | [] | no_license | cguillamet/Shiny-App-Cancer | 0c14db5f55a449b08b498c2fda01809396680133 | 121351f23c572713ddb950396232a7095b9686a7 | refs/heads/master | 2023-04-17T20:39:39.080814 | 2021-04-26T15:45:38 | 2021-04-26T15:45:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,759 | r | ui.R | library(shiny)
library(ggplot2)
library(dplyr)
library(shinydashboard)
datos <- read.csv("base2015_2020.csv")
datos$año <- substr(datos$FECDIAG, start = 1, stop = 4)
datos <- filter(datos, año %in% c("2015", "2016", "2017", "2018", "2019"))
datos2 <- datos %>%
group_by(TOP..cat.) %>%
tally() %>%
mutate(po... |
f360e10052dd89e2c0356af06be6ca14092c97e1 | c252ef51fd38429f60f1674bfbf9bcdefe1484e5 | /plot6.R | da08aaa18d2d4e9ee3fb67a5d5eaa42d777799cd | [] | no_license | albiondervishi/Exploratory-Data-Analysis-Course-Project-2 | b777eb91fdb1b790741a153f6656efa4b5ae298f | 475515497284dce72c4ef9017513a9c5388ff12f | refs/heads/master | 2016-09-06T09:12:05.414120 | 2015-02-22T19:00:38 | 2015-02-22T19:00:38 | 31,154,079 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,395 | r | plot6.R | # install.packages("ggplot2")
# install.packages("plyr")
library(plyr)
library(ggplot2)
NEI <- readRDS("./exdata-data-NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("./exdata-data-NEI_data/Source_Classification_Code.rds")
# subset the data to contain only LA informations
losangelesdata <- subset(NEI, NEI$fips == "06037... |
e5b961bc44d0c707ec527032a1fe07011865132f | df8133c5f01ffd0ed4d3fe816f681b8242058b1c | /man/arguments.Rd | 0a80bfd71976354d16ef742edba46dc1788ec1f9 | [] | no_license | rauschenberger/semisup | ea6c7c54e0356bb879eaa944ddc69ea215d83595 | 0c92f0fb28047183bdb11f83f1769027128f55cd | refs/heads/master | 2021-06-20T00:34:44.182268 | 2020-05-08T12:42:24 | 2020-05-08T12:42:24 | 113,054,516 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,462 | rd | arguments.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pkgname.R
\name{arguments}
\alias{arguments}
\title{Documentation}
\arguments{
\item{y}{\strong{observations:}
numeric vector of length \code{n}}
\item{Y}{\strong{observations:}
numeric vector of length \code{n},
or numeric matrix with \code... |
4bd5fc1a5a5c12195466e8aa436cfd4c4de7313b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/uptimeRobot/tests/test-account.details.R | 38d0fa5c84d2203fcfc0b21bf6780f236d83d9a2 | [] | 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 | 1,089 | r | test-account.details.R | test_that("uptimerobot.account.details", {
skip_on_cran()
api.key <- Sys.getenv("KEY", "")
account.details.list <- uptimerobot.account.details(api.key)
# Output is a list
expect_is(account.details.list, "list")
# Output list has 5 elements
expect_equal(length(account.details.lis... |
09b7b83993eb7996364849a76a8e3f11910f9f6d | 78d4bb19db2d2baaeeca5c3ea13106bd83410904 | /R/trainer.R | 2ca224ccb51be3ae196b60f5993529eefd9e19a7 | [] | no_license | IshmaelBelghazi/bigoptim | 77964dcbc8a14aed1a53d82bd67e6fbea3449686 | 720e80b0c2b935a2d0c814c159b8b971ad7d7e73 | refs/heads/master | 2016-09-05T16:34:10.796934 | 2015-12-09T14:45:15 | 2015-12-09T14:45:15 | 37,093,936 | 12 | 4 | null | 2015-09-03T17:13:12 | 2015-06-08T21:30:13 | C | UTF-8 | R | false | false | 10,613 | r | trainer.R | ##' @title Stochastic Average Gradient
##' @param X Matrix, possibly sparse of features.
##' @param y Matrix of targets.
##' @param lambda Scalar. L2 regularization parameter.
##' @param maxiter Maximum number of iterations.
##' @param w Matrix of weights.
##' @param alpha constant step-size. Used only when fit_alg == ... |
8982a3acc05a483e3bc3d1943eeac3541bf38e92 | b7a86e371a0c3ec4516ecc471c9bb96a1c77c071 | /R/04_sensitivity_nimble_bym2.R | d96ab913bc3584e1db6ea62631fd7b44c2ae0fd5 | [] | no_license | sophie-a-lee/spatial_smooth_framework | 7a81d4333be83d4f7b3b635d853c5c65db3c0be2 | c43ea298bbf220a0f6890e8d4efe66cdbb736c59 | refs/heads/main | 2023-04-18T19:30:02.164885 | 2022-09-06T14:42:33 | 2022-09-06T14:42:33 | 496,954,346 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,534 | r | 04_sensitivity_nimble_bym2.R | ###################################################################
#### ####
#### Sensitivity study 1: a distance-based spatial structure ####
#### NIMBLE CAR model comparison ####
#### ... |
3f487be18c54d6af5c5f7344defc7c4c1924b2c3 | 338f244da7ed149b69d7365102eabbb6db30c674 | /man/check_pedig_parent.Rd | 99f6aaa1861e391299fd70ae62602bec0b10e87e | [
"MIT"
] | permissive | fbzwsqualitasag/qpdt | 4103572a71a149b4cff79643228dbf0c164501de | 736d6e45d1240affbf9d9f3cfaaf6a957bf562a6 | refs/heads/main | 2023-04-06T22:04:40.170526 | 2021-04-01T06:24:44 | 2021-04-01T06:24:44 | 344,161,630 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,916 | rd | check_pedig_parent.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check_pedigree_parent.R
\name{check_pedig_parent}
\alias{check_pedig_parent}
\title{Check Properties of Parents in a Pedigree}
\usage{
check_pedig_parent(
ps_pedig_path,
ps_delim = "|",
ps_id_col = "#IDTier",
ps_sire_col = "IDVater",
... |
8a3382ebcceea2b744f59d18ea6496509582d081 | 29585dff702209dd446c0ab52ceea046c58e384e | /BayesCR/R/UtilitariosSim.r | 2c178cdb45b1606bd6e739c22776ef981152f033 | [] | 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 | 7,894 | r | UtilitariosSim.r | PearsonVII <- function(y,mu,sigma2,nu,delta)
{
Acum <- z <- vector(mode = "numeric", length = length(y))
sigma2a <- sigma2*(delta/nu)
z <- (y-mu)/sqrt(sigma2a)
Acum <- pt(z,df=nu)
return(Acum)
}
AcumSlash <- function(y,mu,sigma2,nu)
{
Acum <- z <- vector(mode = "numeric", length = length(y))
... |
36d574471b63f06db020b56dec5f125887308875 | d01ec57d0f07840b1844fa2e10701904fec1a693 | /step1.R | 96215a400b013a052f35e2c1a6b73327f4fc1578 | [] | no_license | OpiDataScientists/textmining | 81e693264407d0af95507c2d4cd7507660eefeaf | 5e7eec1307e623da86e35292ab51ef0dea6b16be | refs/heads/master | 2021-05-02T03:00:38.207865 | 2018-03-01T09:58:20 | 2018-03-01T09:58:20 | 120,890,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,697 | r | step1.R | library(XML)
xml = xmlTreeParse("FR_BCKST66_TO_2017-34_0015.xml", useInternalNodes=TRUE,encoding = "UTF-8") # faut supprimer la balise Transaction et la remplacer
app_number=xpathApply(xml, "//ApplicationNumber", xmlValue)
app_d=xpathApply(xml, "//ApplicationDate", xmlValue)
app_filling_place=xpathApply(xml, "//Fili... |
fd6fd01c4c215fce39d9a47aff032dd7aa56406b | b5d37b88584dda24636216703bef79ef270e4ba6 | /drawingSimple3TipTrees.R | ba78193d0f4b836bb33214d9e59cc783517238da | [] | no_license | NikVetr/dissertation_work | 7aadd278a30dcf20a6d1783e7fadf417037c13d1 | 6f0f4ba5bed36c351eca135ce4c4f45195266d87 | refs/heads/master | 2022-01-16T19:46:32.809960 | 2022-01-06T01:05:08 | 2022-01-06T01:05:08 | 216,916,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,805 | r | drawingSimple3TipTrees.R | library(latex2exp)
library(pBrackets)
rootStates <- rbind(c(30,100), c(100,70), c(30,1), c(100,1), c(150,1))
rootStates <- rbind(c(20,5), c(70,5), c(100,5), c(170,5), c(210,5))
plot(0:270, 0:270*.5, col = 0, axes = F, xlab = "", ylab = "")
text2 <- function(label, coordinates, cex, font) {text(label = label, x = coord... |
6987967f27614e1f599baecfe2f42e4d02656e18 | f883fb9434f63f7ef62a154832e27de407969630 | /dashboard/config_utils.R | 0fc040b63b8a4fe235ba48b5766a50d3c40ad603 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rowanlimb/Clew | 06756069829a51d0c785b36da364293e2ee15df7 | b34e0c5364c9be7059f5c58cd8bb51a0893700ac | refs/heads/master | 2021-01-23T02:10:42.881213 | 2017-03-23T16:31:37 | 2017-03-23T16:31:37 | 85,972,007 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,992 | r | config_utils.R | #Copyright (c) 2017 BT Plc (www.btplc.com)
#
#You should have received a copy of the MIT license along with Clew.
#If not, see https://opensource.org/licenses/MIT
config_utils = new.env()
#Functions for checking validity of config.yml
#Eventually this script will be generated by a build script
#so that it can be buil... |
b3bc67b67b5d34b2d147842a4f6040355d7af098 | 9f039abf455336e8f8b56f576999e9514c8bad5b | /plot3.R | 14bcb4a2a903372ead5833fd5e350a3cd472e31a | [] | no_license | jordicuadrado/ExData_Plotting1 | d5ccdcd9aba1646498576d9ad3ebc4cfd12f09b1 | 2314165bb96d061156451a8ca2f686f693270cf1 | refs/heads/master | 2021-01-14T12:47:15.087707 | 2015-10-11T17:10:53 | 2015-10-11T17:10:53 | 43,952,338 | 0 | 0 | null | 2015-10-09T11:50:13 | 2015-10-09T11:50:13 | null | UTF-8 | R | false | false | 2,945 | r | plot3.R | # Project 1 from coursera's Data Science Specialization: 4 - Exploratory Data Analysis course.
# Reconstruct Plot 3: Global Active Power (kilowatts) over the period of of time of two days of the three submeters (1: kitchen, 2: laundry room, 3: HVAC) in the same graph.
# The code in this file is intended to be easily us... |
22d96175d472aa11bb75c24c4dd54b3c37fca039 | a156e30406427ce722bb61916d5d0aa2e0b2d71b | /R/match_thrC.R | 6bf7d14d040eec9f425ec804de2bab90e6245d75 | [] | no_license | krmclaughlin/RCPS | c0fbde59528cc3d9519a32a61077836fb187c3de | 9244d8bc1f7150e8c9e0804720e96b536fd7e23f | refs/heads/main | 2023-07-09T10:22:07.014209 | 2021-08-13T03:45:31 | 2021-08-13T03:45:31 | 395,510,615 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,276 | r | match_thrC.R | ## Generate documentation using roxygen
#' Generate recruitment chain based on utilities.
#'
#' Generates a recruitment chain based on the given utilities \code{U} and \code{V}. Uses the multiple matching process I developed. This is the matching algorithm for the UNRANKED process (i.e., if a person recruits three p... |
eba87a73a734c26550613f1deeae2af04cbd7809 | 5989a671b320818c74d546ea3ca119ca57d82984 | /man/SESOI_upper_independent_func.Rd | 461e4ef4932a9ee9a15e745d5218a6f66b603c71 | [
"MIT"
] | permissive | mladenjovanovic/bmbstats | 5ea699af6884bbc6a3dbc936a4e1ac5c04494b79 | ab62109bad903b65000eeac5539c36774f5675f3 | refs/heads/master | 2022-11-23T08:47:16.514748 | 2020-08-03T07:22:28 | 2020-08-03T07:22:28 | 274,726,285 | 4 | 5 | null | null | null | null | UTF-8 | R | false | true | 932 | rd | SESOI_upper_independent_func.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compare_independent_groups.R
\name{SESOI_upper_independent_func}
\alias{SESOI_upper_independent_func}
\title{SESOI upper threshold for \code{\link{compare_independent_groups}}}
\usage{
SESOI_upper_independent_func(group_a, group_b, na.rm = FA... |
b4f51493de4672f8b8741ebd9f2944f84f3b217f | 7712fbde5f04f94655680a20131abfd77131f5b0 | /q431/helloworld.R | b1a32c24740919a8f269587da8488e82244b11d7 | [] | no_license | cielavenir/codeiq_problems | 5bc7fd972c56ff0f9e534555c3b955b3e76664ce | ac111010aef44e2ac0eb69bf33ed6307544720c6 | refs/heads/master | 2020-05-21T03:24:14.739998 | 2019-01-11T03:56:58 | 2019-01-11T03:56:58 | 12,939,073 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 489 | r | helloworld.R | #!/usr/bin/Rscript
i01<-length(c(TRUE))
i02<-i01+i01
i04<-i02+i02
i08<-i04+i04
i10<-i08+i08
i20<-i10+i10
i40<-i20+i20
cat(intToUtf8(i40+i08))
cat(intToUtf8(i40+i20+i04+i01))
cat(intToUtf8(i40+i20+i08+i04))
cat(intToUtf8(i40+i20+i08+i04))
cat(intToUtf8(i40+i20+i08+i04+i02+i01))
cat(intToUtf8(i20))
cat(intToUtf8(i40+i10+... |
14a87e5af984d588de102ca01ef60055d62eeec8 | d7dbc2ffe4da4d8b132ea2d92f7233ca8326bbfb | /man/naver_keyword.Rd | 91ebc7ae46a0e09804f18daa2393e90c79affff9 | [] | no_license | statkclee/suggestK | 4853fff110adfa0201f14e504bfac4b910146e19 | 7eb8fe4ac9e37b7196155ba2d49d6632c949b5d1 | refs/heads/master | 2020-03-15T15:32:43.117836 | 2018-05-05T14:39:05 | 2018-05-05T14:39:05 | 132,214,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 779 | rd | naver_keyword.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/naver_keyword.R
\name{naver_keyword}
\alias{naver_keyword}
\title{Naver Suggest Search Terms - Search Level 2}
\usage{
naver_keyword(keyword)
}
\arguments{
\item{keyword}{search terms, keywords which supports Korean as well.}
}
\description{
... |
342968ebfede18424d4fea9860cb6de948600609 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/RobustGaSP/man/Sample.rgasp.Rd | 3511110df5419b367ecfc7e0427d680b8d02219f | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,626 | rd | Sample.rgasp.Rd | \name{Sample}
\alias{Sample}
\alias{Sample.rgasp}
\alias{Sample.rgasp-class}
\alias{Sample,rgasp-method}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
Sample for Robust GaSP model
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
... |
4e92de774fdda81e71fae40878085085a1a7aec2 | 01a458f4aecf432967314c418251dc64ca73f95c | /session_data_prep.r | 6783aedd2eb33c9dda30fe4592a4e0a9b5b05e98 | [] | no_license | vdpappu/kaggle-Airbnb | 0e0c8d70c270ade0a607089a62c55c4bc63ff641 | 997934c34a47cbf9f03d7459d9db81839de34b1e | refs/heads/master | 2021-01-15T10:42:01.305875 | 2017-08-07T15:43:02 | 2017-08-07T15:43:02 | 99,591,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,819 | r | session_data_prep.r | rm(list=ls())
library(readr)
library(data.table)
library(dplyr)
library(reshape)
library(reshape2)
setwd("/Users/vdpappu/Documents/Kaggle/In-Progress/AIR_BNB")
df_sessions <- read_csv("./input/sessions.csv")
user_sess <- subset(df_sessions,select=c("user_id","secs_elapsed"))
df_sessionTime <- user_sess %>%
... |
eac49badf9ad5cf3c3bfa2320d2ca8f3c39213b5 | c73d44c98456527625aeb0c4d23d25d417c3e72e | /plot2.R | 3b6376f2e7cdd8229165d53e3918915da8e1a9e3 | [] | no_license | rgrivasp71/ExploratoryDataAnalysis | 47d7c00570c4da6d09aa98be65516677d752f5f7 | 5dadc471f55f3f000ac5dfae391f957152db08d4 | refs/heads/master | 2022-11-28T06:17:21.432046 | 2020-08-10T13:52:50 | 2020-08-10T13:52:50 | 282,776,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 682 | r | plot2.R | #opening the data set
data<-read.table("/cloud/project/household_power_consumption.txt",sep = ";", header = TRUE)
#formating date type columns
data$Date<-as.Date(data$Date, "%d/%m/%Y")
# subsetting dates 2007-02-01 and 2007-02-02
newdata<-subset(data, Date=="2007-02-01"|Date=="2007-02-02")
#Creating a new variable wit... |
13c73a41cc9f0a5c9c399559f6cbe12c96bbc79a | 785c2a9c0a772048e3bc48e6ee07046ab902ff71 | /R/setup.R | 879ee5583bc4f29e28c75ab74a4654ec28640abe | [
"MIT"
] | permissive | bartongroup/MG_GlycoTreg | c4fa57e58ff176061bc0fff33af36a258657ad33 | 1dc5eddcce62907542c90d00063b259b4dcc127f | refs/heads/master | 2023-04-14T20:49:24.494690 | 2023-02-17T07:56:57 | 2023-02-17T07:56:57 | 186,639,498 | 1 | 0 | null | 2021-04-29T06:50:27 | 2019-05-14T14:28:01 | R | UTF-8 | R | false | false | 840 | r | setup.R | N <- function(n) prettyNum(n, big.mark = ",")
# top dir for the project
topDir <- "../glycotreg/"
# Public HTML for file downloads
public_html <- "http://www.compbio.dundee.ac.uk/user/mgierlinski/glycotreg/"
makeDirs <- function(topDir) {
lapply(subDirs, function(d) paste0(topDir, d))
}
# Sub-directories
subDirs ... |
4e460cadbcce9f84e95b0e77e57c761dc18cec67 | 14b4098e27706f1cbae4e558391c7c8f2a1ba61e | /man/vecYears.Rd | 1b4a5a34c5d1a16a5ad8d9845549215da8b5eed7 | [] | no_license | cran/qualypsoss | b0f1a247ae39f21c21d6537e320d97522afd8c98 | f69214faa637b89596081521c3fb135e17c5440d | refs/heads/master | 2022-09-19T01:29:12.112426 | 2022-08-31T10:50:02 | 2022-08-31T10:50:02 | 236,877,195 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 439 | rd | vecYears.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/description-data.r
\docType{data}
\name{vecYears}
\alias{vecYears}
\title{vecYears gives the years corr. to Y, i.e. from 1971 to 2099}
\format{
vectors of length 129
}
\usage{
data(vecYears)
}
\description{
vecYears gives the yea... |
7d98c7e7f9d993c92f4b202c37f53dd67ce23144 | 557cda9a1cb3fd04da7ef15c9adec69bb3df9888 | /man/profresp.Rd | 595ffeae4b9943eb95f2de6bdb0919cc67123a7a | [] | no_license | cran/SDAResources | 7e4cb27a87fa4e8e334f641c419fcc6e912e33a2 | addafccfb82d962f234606fc6fcb2386fc8f60f3 | refs/heads/master | 2023-08-22T23:38:51.589732 | 2021-10-22T08:20:13 | 2021-10-22T08:20:13 | 368,240,812 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,764 | rd | profresp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/profresp.R
\docType{data}
\name{profresp}
\alias{profresp}
\title{profresp data}
\format{
This data frame contains the following columns:
\describe{
\item{prof_cat:}{Level of professionalism
1 = novice
2 = average
3 = professional}
\ite... |
d154a13d12b55253c06fbb908af04541313c7146 | ace27a97e2012c2c3c275e78f3394fc5ca11b736 | /R/IRR.R | 8985bc5b73d3d4daf1f7803b4983e8e37a66b5e1 | [] | no_license | pem725/MRES | 6a27ed4884d2ea31524307b0703ee0d097097f14 | 01b0d92577a344614b04a357a820357082896e84 | refs/heads/master | 2016-09-05T10:40:03.284385 | 2013-09-16T15:42:34 | 2013-09-16T15:42:34 | 4,679,282 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,946 | r | IRR.R | ## This is the beginning code to test Bob Holt's agreement indices
## Latest Revision Date: 11/04/2011
## generate example data
dat <- data.frame(id=1:10,rater1=c(0,1,2,3,4,5,6,7,8,9),rater2=c(1,2,3,4,5,6,7,8,9,9),rater3=c(9,8,7,6,5,4,3,2,1,0),rater4=c(3,2,5,6,9,8,1,2,4,0),group=gl(2,5))
dat.l <- reshape(IRRdatEx,va... |
6a15b86fe7eebaa5f81741682176dee5343c028b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ssh.utils/examples/ps.grep.remote.Rd.R | f68eb56f1c3b4da8ae8e018e159f6af1014dc965 | [] | 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 | 287 | r | ps.grep.remote.Rd.R | library(ssh.utils)
### Name: ps.grep.remote
### Title: Checks for processes running on a local or remote machine.
### Aliases: ps.grep.remote
### ** Examples
## Not run:
##D # Check if Eclipse is running.
##D ps.grep.remote("Eclipse", remote = "")
##D # [1] TRUE
## End(Not run)
|
a43133fd133eaca2cf07a517dbfe336d8fa1a0e1 | c1d359cdf0281885744cdcd85d41a21e91218b43 | /man/controls.Rd | 3151dad3474f472726ebd2b1c324b788ef460ca7 | [
"MIT"
] | permissive | zwdzwd/sesame | 20b2d29578661487db53432c8991d3c4478aa2c1 | 62fe6ef99a02e7f94b121fb601c3f368b8a4c1a8 | refs/heads/master | 2023-08-08T01:45:02.112492 | 2023-07-26T13:23:03 | 2023-07-26T13:23:03 | 122,086,019 | 37 | 26 | MIT | 2023-01-05T16:02:38 | 2018-02-19T16:00:34 | R | UTF-8 | R | false | true | 465 | rd | controls.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SigDFMethods.R
\name{controls}
\alias{controls}
\title{get the controls attributes}
\usage{
controls(sdf, verbose = FALSE)
}
\arguments{
\item{sdf}{a \code{SigDF}}
\item{verbose}{print more messages}
}
\value{
the controls data frame
}
\desc... |
80787b7569fe587ce32a380f0607930fb4866809 | 78c6fb7a90dfd4ce5240032408811660358dfbd7 | /R/search-better.r | 7aff16b30124d28234526ad9f20a512456aa24fe | [] | no_license | haleyjeppson/tourr | b335178bcb9c155be194afbe4d20ee662581578c | 272a2f88d9981b4bc7d5c86e2f8cde24183037e5 | refs/heads/master | 2021-12-07T17:54:33.348101 | 2021-05-30T00:39:47 | 2021-05-30T00:39:47 | 167,255,934 | 0 | 0 | null | 2019-01-23T21:20:03 | 2019-01-23T21:20:03 | null | UTF-8 | R | false | false | 6,595 | r | search-better.r | #' Generate nearby bases, e.g. for simulated annealing.
#' @keywords internal
basis_nearby <- function(current, alpha = 0.5, method = "linear") {
method <- match.arg(method, c("linear", "geodesic"))
new <- basis_random(nrow(current), ncol(current))
switch(method,
linear = orthonormalise((1 - alpha) * current... |
43088bfe2e603569af4804f598626be9963897d8 | 1fbd0362678e5198511a6f9f970d57fa71123039 | /figures/supplementary/sfigure_5/sfigure_5_panels_A_and_B_nucleosome_density_diff.R | a3d5f4b9fe57ecb86aebdbbf3d9943fd67236a78 | [] | no_license | jbelsky/2015_genes_and_dev_belsky | 179ba4628b340414427e729b937e7e91c85e710f | cba346bb78e01f766a7f31eb796c027bd0fe897b | refs/heads/master | 2021-01-10T19:38:49.741327 | 2018-10-06T20:34:42 | 2018-10-06T20:34:42 | 34,973,600 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,951 | r | sfigure_5_panels_A_and_B_nucleosome_density_diff.R | #############################################
# Set the Filenames
feature.fn = paste("/data/data2/jab112/2014_mnase_manuscript/datasets/jab112_yeast_feature_files/replication_origins/",
"oridb_acs_feature_file_curated_798_sites_timing_whitehouse_raw_oem_acs_seq.csv", sep = ""
)
oridb_subnuc_density.fn = paste... |
874847bffdc60f86d096a04d54d064d268b207b4 | 321ee8033d9154f903fb70b381b940a1d1c6e9b9 | /Storable-Goods-Demand.R | 70ea163c605dd7b41487747996b5c47dfd9adb00 | [] | no_license | Allisterh/Choice-Model-Consumer-Stockpiling-Behaviour | 2a34067125c5a5840420106423bd7e1fdbb68a24 | a0f60180893222cbb54256db572f710cc198ac6a | refs/heads/master | 2022-01-22T08:03:52.444462 | 2018-02-25T08:01:15 | 2018-02-25T08:01:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,897 | r | Storable-Goods-Demand.R | # Parameters
settings = list(K = 2, # number of pack sizes
n = c(2, 6, 0), # number of units in size k
I = 20, # maximum inventory
T = 200000, # periods of simulations
T0 = 200, # burn-in period
tol = 1e-8, # convergence tolerance
... |
35b62c60109af323cabb3f8f4b8e2f2699cf5cc8 | c8cde1ea449bbd1c26454124a96e97df954ed319 | /R/e06-engineAct.R | 31e36ddf73127944a8c2c64e6d3434748b1f6342 | [] | no_license | cran/Umpire | b5785399ca942ecdf80cb369aa973b0df38230e0 | 46c5342920eda3414b38f3a1c46658db11a4c4fb | refs/heads/master | 2021-07-03T21:09:38.015302 | 2020-11-10T20:10:06 | 2020-11-10T20:10:06 | 96,940,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,799 | r | e06-engineAct.R | ###############################################################
# An ENGINE WITH ACTIVITY allows for the possibility that some
# components (or genes) in an expression engine (or tissue) might
# be transcriptionally inactive. Thus, the true biological signal
# described previously should really be viewed as a mixt... |
428675c6a24651d5122a1861fabc9bb99ca8efcc | 7bf73de2e26caf7765125fc351c4b9a7c68d9fe7 | /man/R/sin_cerosDF.R | 959adaff018b50c4431fe0eef10d26a8d714072e | [
"MIT"
] | permissive | mariosandovalmx/tlamatini | dcd816b451aff9eba66f4e105a982383fdea78f7 | 5b9fb29713bb6e3f88a9096a00faf27514485401 | refs/heads/master | 2023-07-06T19:56:11.124336 | 2023-07-03T18:48:02 | 2023-07-03T18:48:02 | 411,068,930 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 606 | r | sin_cerosDF.R | #' Quitar todos los ceros de un dataframe, para quedarnos con observaciones completas
#'
#' Quitar todos los ceros del dataframe y quedarse solo con las filas con observaciones completas.
#' @param dataframe Un dataframe variables.
#'
#' @return quitar ceros del vector.
#' @export
#'
#' @examples
#' df<-sin_cerosDF(i... |
dcc7d21e2e5d90d7ded3cd952c50e6d78490b470 | a0448d4b83075c557d179b7a9e4ba870a6998a20 | /R/returnlevelplot.R | 99f061b9deba2ce4f558096f7853836597d938fb | [] | no_license | cran/distrMod | 920b64674d2f1f25d1659096e05472aaee0bd8a2 | 8c7d22f2fcabc2b2a04980091307132611e7d511 | refs/heads/master | 2022-11-27T17:04:46.155165 | 2022-11-16T02:50:18 | 2022-11-16T02:50:18 | 17,695,551 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 22,647 | r | returnlevelplot.R | ################################################################
# return level - Plot functions in package distrMod
################################################################
setMethod("returnlevelplot", signature(x = "ANY",
y = "UnivariateDistribution"),
function(x, ... |
4cf6a6f907d8d8f92bde01d2641734f06d077524 | 1018cb5b9b71e476a2e26a13b874145de5eb2304 | /SQL in R.R | 76c23fe178f838bc4adc892e4d27d1d0c23ed14c | [] | no_license | lilatsopelakou/Rental-Car-SQL | 71c987eb404fe7e2493236e67e50f761b39f171e | 6a5adfc30d66cad5104fecf26cee67d22cbad38a | refs/heads/master | 2020-04-05T16:02:52.908988 | 2018-11-10T15:44:55 | 2018-11-10T15:44:55 | 156,994,840 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 896 | r | SQL in R.R | library(RMySQL)
#Load csv file to R
customer <-read.csv(file = "C:/Users/User/Desktop/Assignment_1_Customers.csv",
header=TRUE,sep=",",stringsAsFactors = FALSE)
#Connect to the DataBase
mydb <- dbConnect(MySQL(),user='root',password='Lt2091992!',
dbname='dmbi_assignment1'... |
93cd2efe2281d48c6e99854653219e17e8cc2429 | cf145b6637e0b8f031c775ccb57b374c7e4ba14f | /man/cforest_control.Rd | 1bada8f48b9033999907253e54e95d68aa02b1ce | [] | no_license | cran/party | 67e36fa4051a5961e255c2aadc3918abfe71cc66 | 5d781906fc192ea41d12b5dbc2cfd563fb30769c | refs/heads/master | 2023-04-10T06:32:27.376616 | 2023-03-17T11:10:09 | 2023-03-17T11:10:09 | 17,698,357 | 4 | 8 | null | null | null | null | UTF-8 | R | false | false | 4,886 | rd | cforest_control.Rd | \name{Control Forest Hyper Parameters}
\alias{cforest_control}
\alias{cforest_classical}
\alias{cforest_unbiased}
\title{ Control for Conditional Tree Forests }
\description{
Various parameters that control aspects of the `cforest' fit via
its `control' argument.
}
\usage{
cforest_unbiased(\dots)
cforest_classica... |
d4460a67d63c51a54de61211e7fcd263986479a2 | 9c80e6fb2840bc832983d506b53a92d60c7e8e79 | /Stat/R/Example-9-1-1.r | b6b9dbf83371a9763800e36c9ce8a6f74f204bd2 | [] | no_license | AppliedStat/class | fd08dd570705634d42b3412e08679ce37aec9389 | 22da5f30b13a2726efab7e137dbe17d31323529b | refs/heads/master | 2023-07-23T17:04:08.986749 | 2023-07-15T04:14:18 | 2023-07-15T04:14:18 | 212,270,114 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,344 | r | Example-9-1-1.r |
#======================================================
# Example 9.1.1 on Page 425
# Test H0: random versus H1: not random
#------------------------------------------------------
data = c(5,8,3,1,9,4,6,7,9,2,6,3,0,
8,7,5,1,3,6,2,1,9,5,4,8,0,
3,7,1,4,6,0,4,3,8,2,7,3,9,
8,5,6,1,8,7,... |
d5f0380cf9fc00e6de6060314bdc2adbadb13c38 | 62ba22745ba13b3afaface326d217480980bbaed | /R/umlca.r | d0015d4837dcefc26f76227740877dab9be627b5 | [] | no_license | cran/iv | 6fa670f0c8de03330352b7e1442f2a0d7292ff48 | fbf5f635935a9997e895843150ac16e57a82b655 | refs/heads/master | 2016-09-16T14:16:12.453192 | 2011-10-26T00:00:00 | 2011-10-26T00:00:00 | 17,718,922 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 915 | r | umlca.r | #need more attributes, col etc
umlmerge = function (...)
{ m = extend (list (...), "umlca")
for (con in m) con$m = 0
m$n = length (m)
m$col = m [[1]]$col
m$fill = m [[1]]$fill
m [[1]]$m = m
invisible (m)
}
plot.umlca = function (ca, ...)
{ #note, no validation
#plus for now, assume all composite arrows are nort... |
565fecff94dcd5036bb186147fcbe97229c992db | d7cfe7ae0d92f3071640bbb2a19aaf0ca567b154 | /ch04-flowingdata_subscribers/book.R | 2a7aa3cd4221cc08b6980ded1b414273ec83ed8f | [
"MIT"
] | permissive | yuanyangwu/note-on-visualize-this | 1b414c698ed8cb35845967b5fa9e98ce75b9bbcd | e090ecfcbfd70759ba5278883436e1586e42a6b0 | refs/heads/master | 2020-04-06T04:30:56.186268 | 2014-02-18T13:33:47 | 2014-02-18T13:33:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 373 | r | book.R | setwd("C:/Users/wuf2/Documents/GitHub/note-on-visualize-this/ch04-flowingdata_subscribers")
subscribers <- read.csv("flowingdata_subscribers.csv", sep=",", header=TRUE)
plot(subscribers$Subscribers, type="p", ylim=c(0, 30000))
plot(subscribers$Subscribers, type="h", ylim=c(0, 30000),
xlab="Day", ylab="Subscribers")... |
3a0f0320e2f28b2d9343695238f5abb3eaf96f71 | 126443f109bff3a83ee287262c76c7cb4e6d811e | /rmse_count.R | 24577c4b0c43d16554c28d790b2c3255cb88d3a8 | [] | no_license | sumeetkr13/SlopeOne | 33e9cb616f1530e8ff40dd51a4baae89e344fea4 | 448c445a3a00ec9193bf2db462652ea774d626f9 | refs/heads/master | 2020-03-11T06:50:23.746524 | 2016-04-04T14:41:30 | 2016-04-04T14:41:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 410 | r | rmse_count.R | #
#author vedraiyani
#
# @return vector of rms and rms error percentage
#
rmse_count<-function(){
#read dataset
data <- read.csv("./ml-100k/u2.test",FALSE,"\t");
#data=testSet;
count=length(data[,1]);
sum=0
for(i in 1:count){
predictedRating=predict_rating(data[i,1],data[i,2]);
sum=sum+(data[i,3... |
ce0355b78c797a881099082ab18475b95df7e038 | 6921b02c3c01f24641ebea68151743b6c5bdde2b | /download_data.R | 8f7aa13b257dd16115dde9d7eb35830daacb432f | [] | no_license | dexter11235813/UCSD-bus-route | 50b53e7e35923cb706ca854e476c91f36f3f9ef0 | 5cfabf3b474a815b766baca332a0cb873e1f23bf | refs/heads/master | 2021-01-12T16:14:26.134976 | 2016-10-27T04:12:56 | 2016-10-27T04:12:56 | 71,954,318 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,297 | r | download_data.R | library(rvest)
library(stringr)
source("~/UCSD-bus\ map/get_text.R")
route_id = c('2092','312','314','1263','1264','1114','1113','3442','3440','3159','1098','2399','1434','313','3849')
route = route_id[8]
##### Data Scraping
get_data = function(route_id)
{
url = paste0('https://ucsdbus.com/Route/',route_id[1],"/Vehic... |
4e4a46c1f266c6797fb0e826975a23829dbd84d8 | 086b3d93a0d22a0beadea74150404a7919a28e66 | /QE_Functions/priming/M_constraint_prim.R | 0535e7f6699c82429d0af0abe0529d5ad6dc0e25 | [] | no_license | mingkaijiang/QEframework | 443a9b0c662f44843c8395f0090be8c78363565d | 16a3860877bf8c4815b4ad0bce6e460ab3aec36a | refs/heads/master | 2021-06-24T05:13:25.654618 | 2019-04-26T04:03:36 | 2019-04-26T04:03:36 | 118,394,762 | 4 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,417 | r | M_constraint_prim.R |
### Function for nutrient N constraint in medium term
# considering priming effect on slow SOM
M_constraint_prim <- function(df, a, C_pass, C_slow, Nin_L) {
# passed are df and a, the allocation and plant N:C ratios
# parameters :
# Nin is fixed N inputs (N deposition annd fixation) in g m-2 yr-1 (could ... |
2a707132045cc89c8b671eade47ff6903a5573db | bcfc8846696469235da486d23f9151600fb2d81b | /tests/testthat/test_methods.R | 9dfafa4db27aa02bc6bd41fe535346157a563aa9 | [
"Apache-2.0"
] | permissive | jdyen/growmodr | 6e187b5d56d03e21a03be91440e24bf05846f3a8 | 93ed6108d4d78b66946fc95730e56ec9b6e8cf07 | refs/heads/rename-package | 2021-01-16T07:25:39.208954 | 2017-08-11T05:49:46 | 2017-08-11T05:49:46 | 99,996,129 | 3 | 0 | null | 2017-08-11T05:55:40 | 2017-08-11T05:51:48 | C++ | UTF-8 | R | false | false | 5,366 | r | test_methods.R | # test methods for growmod S3 class
library(growmod)
SEED <- 12345
set.seed(SEED)
ITER <- 10
CHAINS <- 2
SW <- suppressWarnings
data_test <- growmod_sim()
capture.output(
SW(mod1 <- growmod(size ~ (index | block / predictors),
data = data_test,
model = 'hillslope',
... |
01d62a517e88eab2b64f9725eee20c127a7851e0 | fd4e0630829d4fb77f483b8257bd31e060aaaa0e | /Exp2_Choice_Modelling.R | c7be33b92432be1242a2c33004f03fa01b37ba20 | [] | no_license | carpio-ucv/incentives-repo | f5598a29dcd1402a7322f0853279a15318c0804d | 01a1106c0d19227d3a3c4de3ce2e1c996b47823c | refs/heads/master | 2021-01-10T13:30:48.377914 | 2016-10-16T22:30:51 | 2016-10-16T22:30:51 | 49,686,868 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,986 | r | Exp2_Choice_Modelling.R |
##libraries
library("prefmod", lib.loc="~/R/win-library/3.2")
##DATA SETTING
setwd("C:/Users/K56CA/Dropbox/Big Data/Robust Methods R")#cambiar
exp <- read.csv("bltexp2.csv") # cambiar...
exp2 <- llbt.design(exp, nitems = 5, cat.scovs = "cond")
gr <- factor(exp2$cond)
names(exp2)[5:9] <- c("GIFT50", "GIFT75", "GIFT25... |
ffc4c1da43fcd70bd327b1068a577c9f6cdf5754 | 991d72b16c087afb9835502757fa69f38e5ce79a | /R/bal.tab.designmatch.R | bb5a54b650927d5cc506fe32a1719a29ddf844bd | [] | no_license | ngreifer/cobalt | a1862b212efb254a55a8913a814d4971aaa43ea2 | 42c1ac803a8bae3916833d669f193a7f06c4d89e | refs/heads/master | 2023-08-03T18:58:45.744235 | 2023-07-28T03:44:14 | 2023-07-28T03:44:14 | 63,369,821 | 63 | 13 | null | 2022-10-13T07:20:51 | 2016-07-14T21:07:03 | R | UTF-8 | R | false | false | 2,013 | r | bal.tab.designmatch.R | #' @title Balance Statistics for `designmatch` Objects
#' @description Generates balance statistics for output objects from \pkg{designmatch}.
#'
#' @inheritParams bal.tab.Match
#' @param x the output of a call to \pkgfun{designmatch}{bmatch} or related wrapper functions from the \pkg{designmatch} package.
#' @param s... |
0051fa87f133f7b15aa5f878f8d2a2484a00190c | b74ccf99aeb3fdb371705237fbb0978ce9affb44 | /run_analysis.R | 012c9e47499e5f9c8f7b3cbdb72cd476760b516c | [] | no_license | sawasakakazuma/GettingAndCleaningData_Pre-trial | b51a9a2513f1ff26c72197a94ed7eca9d9081d04 | 51c663875feb613da637ab38ab3a14268991bf4e | refs/heads/master | 2021-01-18T04:04:03.690883 | 2014-11-24T01:23:13 | 2014-11-24T01:23:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 874 | r | run_analysis.R | train <- read.table("./UCI HAR Dataset/train/X_train.txt")
test <- read.table("./UCI HAR Dataset/test/X_test.txt")
concat <- rbind(train,test)
nameTable <- read.table("./UCI HAR Dataset/features.txt")
names(concat) <- nameTable[,2]
selected <- concat[,grep("(mean()|std())",names(concat))]
subject_train <- read.table(... |
632584b00569a6f435d8a8a3bd79f15fbcd6bf19 | fd932417cdd3bc27033a0d4dbec0782e51efb599 | /dna_methylation/correlation.R | fa0df2b743b4589e264793a82c5f7bf1c1d31be2 | [] | no_license | usegalaxy-eu/temporary-tools | 2282b851f70f30236ee5c902d455cdb1d51ebfcc | aa603c1def53c7402fdcee9273eaa31b75e66f9e | refs/heads/master | 2023-08-09T18:23:56.031577 | 2023-07-24T23:58:17 | 2023-07-24T23:58:17 | 156,679,078 | 0 | 9 | null | 2023-07-24T23:58:19 | 2018-11-08T09:05:23 | Python | UTF-8 | R | false | false | 59 | r | correlation.R | /data/0/bgruening_tools/galaxytools/methtools/correlation.R |
5c2606547bc05cc0cf756293ee7f71e3d10678e9 | 8ad548bd9153ba2464925beb148a72a5dea7548b | /man/chemometrics.Rd | 5a26c18248711de2eb7807e2cf8b139facfe76d3 | [] | no_license | cran/qut | df53d55ec51b9cbb480792b935003fac071d35ce | 985776919059aa2586361d4da915213bd5917a4c | refs/heads/master | 2021-07-25T02:59:46.209559 | 2021-01-19T09:00:02 | 2021-01-19T09:00:02 | 48,086,667 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 586 | rd | chemometrics.Rd | \name{chemometrics}
\alias{chemometrics}
\docType{data}
\title{
Chemometrics data set from Sardy (2008)
}
\description{
Fuel octane level measurements with sample size N = 434 and P = 351 spectrometer measurements.
}
\usage{data(chemometrics)}
\format{
A data frame with 434 observations on the following 2 ... |
7b712e0b03a8620c49682abae4a243af1fbfbc70 | 79f1746216379b959f7d12c6467f77ac14e1d16b | /Shiny/BeersAndBreweries/server.R | 12f48cc4d90c43eae802fa1e2c87476e3402afe1 | [] | no_license | DHLaurel/DHLaurel.github.io | e5cb9cb774498ffb7f0001c6d8b436d8c637dd0f | 32cc5fbaba3ac0cbb103b88d6d5a7adfef2290ac | refs/heads/master | 2023-04-14T12:09:18.445668 | 2023-04-09T04:06:08 | 2023-04-09T04:06:08 | 147,439,684 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,944 | r | server.R | #
# The skeleton code for the below was largely generated by ChatGPT. Thanks ChatGPT!
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
libr... |
39d33ff62f0507398802aa8b9c29afaedd8ba62e | be76c59c9cf0196af18969a47ec00e8a54f5ade9 | /strategies/team9.r | 7f0d9ac7c254dced8ca645bca306c1333189a19e | [] | no_license | happy369300/R-project-1 | 0dfb4ac9d33ef5f4bdb9444f83f6c1e43f8aa919 | 56028608a847d3cd5351080a845208a430201ec5 | refs/heads/master | 2021-01-13T09:21:13.828344 | 2016-10-04T03:28:06 | 2016-10-04T03:28:06 | 69,930,889 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,980 | r | team9.r | #params <-list(series=1:10,lookback1=26,lookback2=12,lookback3=40, #params for pairs trading
#spreadPercentage=0.8,maxPositionSize=70, #params for limit order
#lookbackB=11,sdParam=1.2,seriesB=1:10, #params for bbands
#lookbackM=37... |
a7b02b03cc63c558d584b1e4f5967c00375c4092 | 4a589cb2ff5ccc42eeec1741d2407b18f0346192 | /R/parscensims.R | db80157ddb5a6c45a7b82cbc182ad4fed9d39bc1 | [] | no_license | openfields/Ambcitsci | 633f636fdd56b4d8392e3a539ace555f8ed0c565 | 0d8385a59b56a8437f365355a8243c4ab8cfd8c8 | refs/heads/master | 2020-04-15T14:05:02.179703 | 2017-01-03T21:16:42 | 2017-01-03T21:16:42 | 58,056,418 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,093 | r | parscensims.R | # scenario simulations - first run
#out <- array(dim=c(50,2,100), data=NA)
#system.time(for(i in 1:100){
#out[,,i] <- popsim(nyears=50, init_pop=c(100,100), dphi_sa = .9986, pr_b= .4230, nb_dphi = .9992, a_in_night_prob = c(0.333,0.333,0.334),
# a_rmort = 0.0407, a_in_num_guards = c(0,0,0), guard_limit = 10, a_... |
ae866749ac3edf96246bbed73785bbbed2ff8440 | 1820722c3c8a37ee2b052db65d658085ab786630 | /scripts-libro/Code/Berkeley.R | 1615678a38ae50ab577a87ed22a1e8f2a5afb251 | [] | no_license | cristianhernan/austral-mcd-aid | 8f0efcb76fc90bf24d92f210ae7fa3636899636c | 99d49f1562624de3525abeaba8783f942d8f910d | refs/heads/main | 2023-06-24T05:33:16.219278 | 2021-07-22T01:14:21 | 2021-07-22T01:14:21 | 376,146,006 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 1,471 | r | Berkeley.R | library(readxl) # Permite leer archivos xlsx
Berkeley=read_excel("C:/.../Berkeley.xlsx")
# Importa la base con la cual se va a trabajar
attach(Berkeley) # Fija la base de datos donde se trabaja
tabla.sexo=table(Sexo,Admisión) # Calcula la frecuencias por Sexo y Admisión
colnames(tabla.sexo)=c("NO","SI") # Cambia de ... |
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