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c3084f99c581b9c7d5aa95b40ec4cdef6ade78b5 | 29585dff702209dd446c0ab52ceea046c58e384e | /npbr/inst/doc/ex-npbr.R | b3a6a5d6c021530482a41d86181c060ab8f598fb | [] | 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 | 57,423 | r | ex-npbr.R | ### R code from vignette source 'ex-npbr.rnw'
### Encoding: ISO8859-1
###################################################
### code chunk number 1: ex-npbr.rnw:60-65
###################################################
owidth <- getOption("width")
options("width"=70)
ow <- getOption("warn")
options("warn"=-1)
... |
a35264ff77005b5ec66b6c89a845a738c92ce45b | 7cc5f6f1879edcc0ee241c7f47ae024d2caac606 | /man/fftN.Rd | 70069fb91d46c7a3d3765899c2098b12233bf234 | [] | no_license | cran/FIACH | 20fe0bfedbe5959e4c737a13cb7546df7a660ede | 32c477e755dbe28f0d03cd19346e3e62d67ce3b5 | refs/heads/master | 2021-01-10T13:17:11.910515 | 2015-10-09T10:28:48 | 2015-10-09T10:28:48 | 48,080,097 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 639 | rd | fftN.Rd | \name{fftN}
\alias{fftN}
\title{
Zero Padded 1D Fourier transform
}
\description{
This function is a simple wrapper of Armadillo's fft function. It allows for fast and easy zero padding of a signal.
}
\usage{
fftN(X,N=NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{X}{... |
fb258e650a156f86e4cf99ca0c11c818c32d0a70 | 1d7c7adfe4190456c0bce74a55f8842f113b54e1 | /R/wilkie_sd.R | b26ea7abf4785e52a09a2c0498871723b8785386 | [
"MIT"
] | permissive | dmoseev/j0j0r | dd56cfa4e9d80324c2e39872a0f9ab480e0c3ba6 | 38e61ba17d1e8395738379adcb4af3f6ef33824b | refs/heads/master | 2023-07-31T13:57:54.235793 | 2020-05-18T20:52:57 | 2020-05-18T20:52:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,170 | r | wilkie_sd.R | #' @title slowdown_setup
#'
#' @description Function to return a setup for a slowdown momentum
#' distribution, based on Wilkie 2018, https://arxiv.org/abs/1808.01934v2, eq.
#' 2.9.
#'
#' @param b \code{numeric} b-parameter in Wilkie 2018 eq. 2.9. Quantifies
#' importance of transport. Suggested in the range 0 (n... |
60a22eda04b68f849b0fc582444c4712fa446bf8 | b32dd1f1c3b674c1c558570dd0319590694dee34 | /R/me.R | 1dba7ffd3ebe759706ab4cfca200c47c916f59ec | [] | no_license | cran/valmetrics | 1595ca14df527d868302c7105861b94a49599986 | 9964419ce0f640ce71fe2ff7dbe8d0c1048350be | refs/heads/master | 2023-02-21T04:20:10.619811 | 2021-01-13T14:30:02 | 2021-01-13T14:30:02 | 334,226,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 483 | r | me.R | #' @title me
#' @description Calculates the Mean error (ME) from observed and
#' predicted values.
#' @inherit mae return author
#' @inheritParams mae
#' @return Mean error (ME).
#' @details Interpretation: smaller is better. Sometimes called bias.
#' @inherit mae return references
#' @example... |
e777c7e6b1c944988fbbed90d1200ff96046d975 | 8c2e5408ad2acac0f38cd43bf56bfa52768a3417 | /Apriori.R | f097ede9a666172def774e98160314186ea0bd77 | [] | no_license | fall2018-wallace/data_science_project | 964b92e7c82b79dc5c7ddde52ab96ef2788c6fe3 | 44b12d17e6ef76d1307335cc5ae74b536fa5662a | refs/heads/master | 2020-03-30T02:14:25.360575 | 2018-12-10T01:42:32 | 2018-12-10T01:42:32 | 150,621,038 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,341 | r | Apriori.R |
library(lubridate)
library(arules)
library(dplyr)
library(arulesViz)
cleanData$Satisfaction <- as.numeric(as.character(cleanData$Satisfaction))
satisfied <- ifelse(cleanData$Satisfaction < 3.5,"no","yes")
pricesensitivity <- ifelse(cleanData$Price_sensitivity<3,"low","high")
Flightspa <- ifelse(cleanData$... |
23686c99c44f6ee184506dded61e2f0a7efc7e82 | 4905bd421b07d09c583c765d97a277095bbe85c7 | /inst/ggraptR/test/system-rc.R | 6c72470b945ac7d53de0ff123a1d6774f55687e1 | [] | no_license | cargomoose/ggraptR1 | dbe876f92e76ff5843194ae1b6c649eb8990316f | 418835e4c6de486fbc82d800b4c6a5e8856d41b1 | refs/heads/master | 2020-05-31T06:01:09.100490 | 2016-09-19T11:01:13 | 2016-09-19T11:01:13 | 69,013,446 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 883 | r | system-rc.R | # http://adv-r.had.co.nz/OO-essentials.html#picking-a-system
# http://www.cyclismo.org/tutorial/R/s3Classes.html
# http://www.agapow.net/programming/r/reference-classes/
Rappy.ggplot <- setRefClass("Rappy.ggplot",
fields = c('gg'), # dataset, x
methods = list(
# initialize <- function(dataset, x) { # doe... |
d406d64f0646c59b0eb60b3329a2047636eabed7 | 7950d582ff90f0b616bc84cf14d3c52cf3132a4c | /Lab and Lecture tasks/Lab_7/Lab 7.R | 0fc2c92dca32233cceef9a141be743f94e461a54 | [] | no_license | bilalhoda1/Statistics | ae62d765c30174ac8f14a1ee56cd3450899aea10 | 6a98494e497d72b26635895beef80f386ebbfb6a | refs/heads/main | 2023-01-04T18:45:42.798762 | 2020-11-01T19:27:37 | 2020-11-01T19:27:37 | 309,116,380 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,551 | r | Lab 7.R | #In this session, we will explore different probability distributions
#This tutorial was adapted on R-bloggers session on probability distributions
#Please note that for this week's assignment, you are expected to answer questions in this script
#Please submit your answers as a .doc or .pdf file with the correspondi... |
d79b1147ed7aa86881c53282aded46c4f610591c | adf13968c14ecb2f547c8afc96842ffd1b2efa39 | /man/returnsDistribution.Rd | 1758e4d6d79615824ee9005999957e803bdf7976 | [] | no_license | runiaruni/Meucci | 10ed5635d3a756b743a9f75956e247dadb7489ff | 9f0a946631eebe65837c15d05b53e22c5333c25d | refs/heads/master | 2021-05-31T18:07:39.024992 | 2016-05-05T17:17:59 | 2016-05-05T17:17:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | rd | returnsDistribution.Rd | \docType{data}
\name{returnsDistribution}
\alias{returnsDistribution}
\title{Panel X of joint returns realizations and vector p of respective probabilities for returns}
\description{
Panel X of joint returns realizations and vector p of
respective probabilities for returns
}
\author{
Xavier Valls\email{flamejat@g... |
f3e404889f58f624372294295f4572d62626f1d6 | 588ad4f33dc2119680b4078355c8c170243330a8 | /inst/App/server.R | abdd8e04dc6b6ae6b8c8343c1aff53ba80c2f5b5 | [] | no_license | gfsarmanho/Outliers.App | f0b5d48ea2d9c5e0e3f2248d70cc35e02d72d037 | 2a01202a9ba2be49c46a716b4b5fe0a893e7c7da | refs/heads/master | 2020-04-05T05:29:56.710079 | 2018-12-17T16:58:52 | 2018-12-17T16:58:52 | 156,598,272 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,552 | r | server.R |
#==============#
# Begin server #
#==============#
shinyServer(function(input, output, session){
# Add report file to temporary directory
temp_dir_report <- file.path(tempdir(), "logo.png")
file.copy("www/logo.png", temp_dir_report, overwrite=TRUE)
# Reactive variables
RV <- reactiveValues(
dados = NUL... |
59b7773a4090f63d309cf435e8a27d3f91706dae | 2fcf1d9d4c98ced6de0784f941f6d318b79e6d6e | /man/area_triangle.Rd | 3222b1eef8cd6b9cdc884ed0e60b9705a2ba1f85 | [] | no_license | katiesocolow/Week_2_Project_Package | 9921e607c3f2799c996ed68289089d00d522e787 | d825b69e7378ea161018e67de191de86c999d52e | refs/heads/master | 2020-03-19T14:02:05.183081 | 2018-06-08T10:57:30 | 2018-06-08T10:57:30 | 136,605,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 427 | rd | area_triangle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/area_triangle.R
\name{area_triangle}
\alias{area_triangle}
\title{Calculate the area of a triangle.}
\usage{
area_triangle(b, h)
}
\arguments{
\item{b}{base of a triangle.}
\item{h}{height of a triangle.}
}
\value{
The area of the triangle.
... |
f6aa8c21a363aa8c8991743085b63b56d6999c57 | d03924f56c9f09371d9e381421a2c3ce002eb92c | /R/Lattice.R | 0975bec532fbfac8871b03e5d8c8b4a41b7ffda2 | [] | no_license | cran/distr | 0b0396bbd5661eb117ca54026afc801afaf25251 | c6565f7fef060f0e7e7a46320a8fef415d35910f | refs/heads/master | 2023-05-25T00:55:19.097550 | 2023-05-08T07:10:06 | 2023-05-08T07:10:06 | 17,695,561 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 625 | r | Lattice.R | setMethod("width", signature(object="Lattice"), function(object) object@width)
setMethod("Length", signature(object="Lattice"), function(object) object@Length)
setMethod("pivot", signature(object="Lattice"), function(object) object@pivot)
setReplaceMethod("width", signature(object="Lattice"),
function... |
961473f9c65a8ad3c4c253c933411b2f75c5fcb2 | 2760271256e3f035f97fae8c6c697f0e8ddb79ca | /Week3/Quiz3.R | 8f7bf449b8b034370c7bbf8fd073e28cf32299eb | [] | no_license | hd1812/Getting_And_Cleaning_Data | 1a276d62972fcd15b20819b85e10308e850dd633 | 9a5437730fe888290518ca246242ec7ac850cad7 | refs/heads/master | 2020-07-02T02:13:05.837901 | 2015-10-11T12:06:13 | 2015-10-11T12:06:13 | 38,585,776 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 875 | r | Quiz3.R | ####Quiz3
##Getting data from web
restData <- read.csv("getdata-data-ss06hid.csv")
##Q1
agricultureLogical<-(restData$ACR==3 & restData$AGS==6)
x<-restData[which(agricultureLogical==TRUE),]
head(x)
##Q2
if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fjeff.... |
00b08f3846754f68d2b81f18a90e50e39208d758 | d2ba50e01559fca07314d41432f30c411b773f87 | /TAXRATE.R | 651962db2e0d012a1e9b7c3eefcee10dabf859ee | [] | no_license | Eikonomics/TwitterOptimalTax | 6791afa0aeb2455f70d201ec0ca8446a023d1952 | c680411851eac02e6d21256896b661d69206271d | refs/heads/master | 2021-01-04T14:59:29.973190 | 2020-02-14T22:17:27 | 2020-02-14T22:17:27 | 240,601,453 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,748 | r | TAXRATE.R | ## ## Boring old set-up
library(tidyverse)
library(scales)
rm(list = ls())
### Fun set-up for calculation
## Baseline data (assumptions)
M <- 6641000 # Soruce: Hagstofan, gross total income 2018
SD <- 7982000 # Soruce: Hagstofan, SD of Total income 2018
MinW <- 50000 * 12 # Min annual income ... |
200d58a94e0544ab6baf1462129fc6233925c8db | 0d1685a2218c0c37bfc700fcb8008dda69625ede | /man/EpivizGenesTrack-class.Rd | 3cc2b4b0f4dae160bdf163c3141d4e6a34a08685 | [] | no_license | epiviz/epivizrChart | 75f41609bd6d82517e374126102a8c32c0c7a060 | 445ac18b7da77581616e0b94785336c53c40c046 | refs/heads/master | 2021-11-26T01:26:00.082587 | 2021-07-30T07:35:15 | 2021-07-30T07:35:15 | 89,830,859 | 3 | 1 | null | 2021-01-22T13:06:22 | 2017-04-30T05:15:47 | HTML | UTF-8 | R | false | true | 611 | rd | EpivizGenesTrack-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EpivizCharts-classes.R
\docType{class}
\name{EpivizGenesTrack-class}
\alias{EpivizGenesTrack-class}
\alias{EpivizGenesTrack}
\title{Data container for an Epiviz Genes Track.}
\description{
Data container for an Epiviz Genes Track.
}
\section{... |
12bb0c478092dcf0478d0006c193936497b12c09 | cecced4835b4f960141b85e25eabd8756f1702ea | /R/sc_aligning.R | aaca3f55277a3cf12ab09356508d7701b50ad676 | [] | no_license | LuyiTian/scPipe | 13dab9bea3b424d1a196ff2fba39dec8788c2ea8 | d90f45117bf85e4a738e19adc3354e6d88d67426 | refs/heads/master | 2023-06-23T01:44:20.197982 | 2023-04-17T13:26:42 | 2023-04-17T13:26:42 | 71,699,710 | 61 | 26 | null | 2023-06-12T11:04:49 | 2016-10-23T11:53:40 | HTML | UTF-8 | R | false | false | 12,818 | r | sc_aligning.R | ##########################################################
# Aligning Demultiplxed FASTQ Reads to a Known Reference
##########################################################
#' @name sc_aligning
#' @title aligning the demultiplexed FASTQ reads using the Rsubread:align()
#' @description after we run the \code{sc_trim_... |
a0ab934e9f15434aeb15d2f2eeb495d2c442a30b | 5ca77e6f4a0f5803be717464bad720b3b2e2a1ba | /hypermap/hyper_embed.R | d4e06aafdd0fd3f1aa17e35a049eca98fec7f585 | [] | no_license | mananshah99/hyperbolic | 4e591daefc228438e3d520aa3c01ca902cdd6402 | 8cb12c30aceb7890b39de90a702d2c237bea4237 | refs/heads/master | 2020-04-04T20:57:49.548332 | 2019-03-01T23:16:20 | 2019-03-01T23:16:20 | 156,267,115 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 476 | r | hyper_embed.R | library(igraph)
load_graph <- function(filename) {
edgelist <- read.table(filename, sep = "", header = F)
return(edgelist)
}
convert_graph <- function(graph_df) {
e <- c()
for(i in 1:nrow(graph_df)) {
row <- graph_df[i,]
e <- c(e, row[[1]] + 1)
e <- c(e, row[[2]] + 1)
}
return(graph(edges = e, n = max(gra... |
7d6d9620c5bad41fb8796a7c4f6b4a8bb4be0f5f | 8a270978e710878945f37852d0be9f73cfa75078 | /scrape_bundesliga_tables/R/scrape_dfb_bundesliga_results.R | cc293ccdbc502605cbb61d26e6b3984ad4f16df8 | [] | no_license | bydata/football_data | bdcacdfff7d8d099aaf93637a0f131c48462ae01 | 44e59cd8349f2a02df983b0d16eafc37fbed0e4e | refs/heads/master | 2023-07-08T02:20:20.089361 | 2023-06-30T15:22:04 | 2023-06-30T15:22:04 | 145,601,237 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,622 | r | scrape_dfb_bundesliga_results.R | library(tidyverse)
library(rvest)
library(tictoc)
# Scrape page IDs for season final tables from drop-down selection
# The value of the options contains the season id and matchday id (last matchday of season) combined
url <- "https://www.dfb.de/bundesliga/spieltagtabelle/"
page <- read_html(url)
ddoptions <- html_no... |
a3a35deb6d48589052f7bd3bc87bb68bbb34ba9c | dc7d3873fd7896fd4a81329a7aa24d4704a8bd90 | /scripts/BcBOTnet/03_bigRR_cistrans.R | a73d77dbc6d6ae490249766c31a10072d2901f61 | [] | no_license | nicolise/BcAt_RNAGWAS | 4cd4cf169c06f46057e10ab1773779c8eaf77ab1 | 64f15ad85186718295c6a44146befa3ca57b7efc | refs/heads/master | 2021-01-12T11:40:59.400854 | 2019-10-21T19:54:53 | 2019-10-21T19:54:53 | 72,249,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,341 | r | 03_bigRR_cistrans.R | #Nicole E Soltis
#06/08/18
#03_bigRR_cistrans
#--------------------------------------------------------------------
#extract BotBoaNet5 bigRR data to subfolder
# try post-hoc meta-analysis across phenotypes
#first approach: MANTEL in perl? beta-value scaling
#check cis vs. trans SNP effect estimates
#later repeat thi... |
64b45a96c680b567de80e1b2926d93f5d7b40af5 | baabef082db1a4504983d24f783e5da0a39ec54f | /cachematrix.R | 86e2902e29d4bbaa27961978e192283152e6847d | [] | no_license | alextan2468/ProgrammingAssignment2 | 88de416669815d476515731d4d449734d50af2fa | 63e78df6cb883c5232c894de146e2b4e0b56bbb1 | refs/heads/master | 2020-12-25T06:36:49.445054 | 2015-02-08T13:43:39 | 2015-02-08T13:43:39 | 30,461,817 | 0 | 0 | null | 2015-02-07T16:35:54 | 2015-02-07T16:35:51 | null | UTF-8 | R | false | false | 1,390 | r | cachematrix.R | ## The makeCacheMatrix function will be used to create a matrix
## that can allow the storage of the matrix information itself
## as well as retrieving the matrix and the inverse of the matrix
## The inverse of the matrix would be solved by the cacheSolve function
## makeCacheMatrix creates a storage structure for a m... |
f6fd2704f6e729f8212bc450d5eb14af02841a19 | 7ce53616b1e41c8376bd802e7569fc414094ef9d | /lab 15.R | 53742c288b3afa235931a77ad1a108a4b87032b1 | [] | no_license | qingze-lan/PSTAT-10 | 693d7c8006ef5fa56996d6746cf958b52339478f | 6cc4754f43c164af3101d3a7d33534a10c82f97e | refs/heads/main | 2023-03-23T10:36:30.617443 | 2021-03-14T23:36:13 | 2021-03-14T23:36:13 | 347,781,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,332 | r | lab 15.R | PSTAT10db <- dbConnect(RSQLite::SQLite(), "PSTAT-db.sqlite")
EMPLOYEE <- read.csv("~/Desktop/pstat 10/EMPLOYEE.txt")
DEPARTMENT <- read.csv("~/Desktop/pstat 10/DEPARTMENT.txt")
CUSTOMER <- read.csv("~/Desktop/pstat 10/CUSTOMER.txt")
EMPOLYEE_PHONE <- read.csv("~/Desktop/pstat 10/EMPLOYEE_PHONE.txt")
INVOICES <- read.cs... |
fb2a893591c8d6edf05e5230a678b49b5f900394 | 8293856ff3bd5d9eec1dbd76e8370682ef6d3802 | /tests/testthat/test_connector.R | 6ac91d640c4ce76c46d2ce503d2bb674bcf38109 | [] | no_license | 14Gus/wdcconnector | cabdce0728b69dda70b8bb5d03197359a085b232 | 695eefa6f9a9fc6f40d1f649b7ceccff5612f9ca | refs/heads/master | 2020-03-11T03:16:01.166492 | 2018-04-24T07:21:18 | 2018-04-24T07:21:18 | 129,742,112 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 325 | r | test_connector.R | context("connector")
test_that("Generate WDC connector works",{
expected <- "$(document).ready(function () {\n $(\"#submitButton\").click(function () {\n tableau.connectionName =\"iris Table Feed\";\n tableau.submit();\n });\n});"
actual <- generateWDCConnectorJS("iris")
expect_equal(expected, actual)... |
70d75b1a2eef83aa59d7d4fc6607e544f2734947 | b3bf7b8c56b2f3e8d8594cccce6f65981c9514e5 | /R/efficacy_aggregate.R | 455868f8c8c289f81643b7f957bdc9285ad6d64c | [] | no_license | faustovrz/bugcount | 055ee388bcf9049e5d01cf3ad19898220f7787a2 | f3fbb7e9ed5cecae78fdfaa1035e2a87e072be2d | refs/heads/master | 2021-03-27T15:43:12.992541 | 2018-05-04T22:17:49 | 2018-05-04T22:17:49 | 104,142,648 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,149 | r | efficacy_aggregate.R | #' Efficacy calculations as ratio of count means, geometric means, or medians
#' @param wf A whitefly count dataframe.
#' @param control Control genotype string.
#' Genotype Denominator in efficacy: e = u1/u0.
#' @return A dataframe with efficacy calculated per clone
#'
#' @examples
#' # efficacy.df <- efficacy... |
988639821c05fcfc7dd70676a9ce975193a61115 | bf5435204ec8de8afae96e3695c4e2c8b5d86f62 | /man/setOutputSubdir.Rd | 308f18a3015028820736d998f53390d5436cacbb | [] | no_license | vreuter/projectInit | 289e7521bb1071b69e367a6aafd9e55edf662563 | 3d3a905f4c623d649a13b102539893b7802e4888 | refs/heads/master | 2020-04-10T10:16:35.994057 | 2018-01-19T18:37:17 | 2018-01-19T18:37:17 | 89,945,482 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 253 | rd | setOutputSubdir.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dirAliases.R
\name{setOutputSubdir}
\alias{setOutputSubdir}
\title{Creates and sets outputSubdir}
\usage{
setOutputSubdir(...)
}
\description{
Creates and sets outputSubdir
}
|
06ac95e07df9fb44569be3c4f25813f58613f2dd | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Kronegger-Pfandler-Pichler/bomb/p20-5.pddl_planlen=3/p20-5.pddl_planlen=3.R | d1e78cd693e1ca1a35e2f364b0fd0b9c3e69276f | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,970 | r | p20-5.pddl_planlen=3.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 17763
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 17433
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 17433
c
c Input Parameter (command line, ... |
d997a24dc4e4eb838456a577632845ff926572c4 | e8327d77350b80110fb20a5506b180155a108e7b | /ED_Workflow/2_SAS/compile_SAS_runs.R | ec15ebb98620c35d09cc1d4f43a2f80640764a6a | [] | no_license | MortonArb-ForestEcology/URF2018-Butkiewicz | c537fe28c2eeb886d324b9b8e565d100187fb9ff | d5f3f630045e24bd165bc2a35885a5a6e3d0c2c4 | refs/heads/master | 2021-06-23T12:27:08.987348 | 2019-06-20T17:49:56 | 2019-06-20T17:49:56 | 136,949,391 | 0 | 0 | null | 2018-07-12T18:37:05 | 2018-06-11T16:00:18 | R | UTF-8 | R | false | false | 4,565 | r | compile_SAS_runs.R | # ------------------------------------------------------------------------------------
# This file compiles the steady-state approximation for an accelerated model spinup
# at individual points (this will need to be modified to work efficiently with spatially
# files)
#
# References:
# 1. Xia, J.Y., Y.Q. Luo, Y.-P.... |
d3f648975c32c44738d6a31ca5ab13ddde5d3d37 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/credule/examples/credule-package.Rd.R | 8104eacfa62a94f8f0acdceb5ea5ad9129d1be42 | [] | 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 | 1,328 | r | credule-package.Rd.R | library(credule)
### Name: credule-package
### Title: Credit Default Swap pricing and Credit Curve bootstrapping
### Aliases: credule-package
### Keywords: Credit Default Swap, Credit Default Swaps, CDS, spread,
### survival probability, survival probabilities, default probability,
### default probabilities, pric... |
194f0f14dd2289ed2fb2a01cadaea1a38374da2b | 1fd16ea95286ec5a99eeaed2cae20699bc5fb108 | /Explore relative sample sizes with different variances - add scenarios for VaryR.R | d983bf2e9e3d3d4204c02f56858de84beda4fc28 | [] | no_license | JiananH/Profile-Bayesian | 4b8ffb9c22a8d80f2d89fbcab794aa4395109a00 | 5fa8aaecf16a953f8acc904f17caa29b37e66634 | refs/heads/master | 2020-05-09T10:47:41.722918 | 2020-04-08T14:13:24 | 2020-04-08T14:13:24 | 181,053,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,309 | r | Explore relative sample sizes with different variances - add scenarios for VaryR.R | ######################################################################
#### New simulation settings to accomondate reviewer's comments ####
#### Jianan Hui, Apr 7, 2020 ####
######################################################################
# Varying relative sample sizes (ra... |
ee48239f94fe9e53f39119452fcee62fca66132e | 73c273fdf85a99b3d6156986537cf82b0876fc5f | /R/accessions_by_spp.R | 81b7b12a059f563b5b49bf251d6a71dddbbb08cc | [
"MIT"
] | permissive | NCBI-Hackathons/GeneHummus | e55ce7d1fd231db5516ffac039a329c255a68316 | 1fb36181760e0c1b91e65dd3cbd05af27010d8c4 | refs/heads/master | 2021-06-03T15:49:38.606418 | 2020-09-02T21:10:25 | 2020-09-02T21:10:25 | 131,613,965 | 8 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,324 | r | accessions_by_spp.R | #' Compute the total number of accession proteins per species
#'
#' Summarizes a dataframe of protein ids and return the total number of accessions
#' per organism.
#'
#' @importFrom dplyr %>% count rename
#'
#' @param my_accessions A data frame with accession protein ids and organisms
#'
#' @usage accessions_b... |
39af362b28a94ce7703d5c6f086abf8ce58a3ea6 | 180d8eb6821307e854d43e93c556eb72af82fcac | /R_code/Length_Distribution.R | abcc0fc9740b63a7f5d1104a0268665c97e61dd0 | [
"Apache-2.0"
] | permissive | NCEAS/oss-fishteam | 9b4864ff0ce63a237086f3f92a9c2a7d134e7d38 | 651fd1b0f8874ea2ab85c67409520192b5074b8e | refs/heads/master | 2021-01-01T16:01:44.082589 | 2018-06-07T17:35:39 | 2018-06-07T17:35:39 | 97,758,520 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,652 | r | Length_Distribution.R | #Using datatables GLFREC and INVREC, determine what the length frequency of red snapper is caught with gear (GEAR_TYPE) shrimp trawl (ST)
#Not looking at sex of fish because "male and female red snapper grow rapidly and at about the same rate until about 8 years old and about 28 inches in length."- Seagrant
rm(list=l... |
eeb4a2a8945f762e9734842b5e5d24d6c5292c02 | 28e13e398df9e2b7310b369b4536e8bfdedd9778 | /inst/examples/ex8.R | 61d1c4037b3945957461c42e35f2e8bce0b81b71 | [] | no_license | davids99us/whywhere | 497274cfc0a8a3c93d85d117ce9e0b3f7f3db2d9 | e51a00206c495d697cae2530bfb7d498edf13c72 | refs/heads/master | 2021-01-18T23:40:37.149298 | 2016-05-22T13:15:40 | 2016-05-22T13:15:40 | 33,341,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 539 | r | ex8.R | #Compare some of the top variables
par(mfrow=c(2,2), mar=c(2, 2, 2, 2) + 0.1)
path="/home/davids99us/data/Terrestrial"
Tfiles=list.files(path,pattern='pgm')
file <- paste(system.file(package="dismo"), '/ex/bradypus.csv',sep='')
Pres <- fread(file, header=T,sep=",")
Pres$species=NULL
o =ww(Pres,c("lccld08.pgm"),dirnam... |
5882f76e7142012017215b3499c78d203710fbe0 | 660b33ebda363b8508bb430c13b664718b34704f | /man/seaice.Rd | bb71b56bf9ef34c0d49c3b2ca50207e7e7c3bf29 | [] | no_license | dis-organization/seaice | f47692ec6d49217e694288e9affa82ecc9aedd86 | 8116c2f76a283337a15c4a3ad7f99d468e283e96 | refs/heads/master | 2021-06-14T17:25:24.658179 | 2017-03-06T22:16:08 | 2017-03-06T22:16:08 | 84,126,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 205 | rd | seaice.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/seaice-package.r
\docType{package}
\name{seaice}
\alias{seaice}
\alias{seaice-package}
\title{seaice.}
\description{
seaice.
}
|
093e0f0c1a564049efcfe0c8f416b491a7b0b338 | 2875548a66e0e411567acb689df5bbd3a183e12d | /man/derivatives.Rd | 9ead9f554f33beccc6efc01241df2eb23c46ab20 | [] | no_license | cran/KSPM | 6fb1f3d2b73ec4e6e565c19ad8cc690d65137243 | 8a6566f83eced36c4d536a8a02cde7bc88b379d6 | refs/heads/master | 2021-07-08T01:02:18.398063 | 2020-08-10T12:32:11 | 2020-08-10T12:32:11 | 164,908,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,523 | rd | derivatives.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/derivatives.R
\name{derivatives}
\alias{derivatives}
\title{Computing kernel function derivatives}
\usage{
derivatives(object)
}
\arguments{
\item{object}{an object of class "kspm", usually, a result of a call to \code{kspm}.}
}
\v... |
bec7e5196f2412b1d59549cf57d601849f7ea221 | 8340317041a7f6aded928bc61237c78d32e059ee | /man/Cb.logistic.Rd | 75682a5db1a5fe4583f90a5a706809c092a4312f | [] | no_license | msadatsafavi/txBenefit | c2b2051168db0e0b0ef4a6015136c60f7a4f6b30 | 7342099f8cadadb7090eb7557c330d04f27e0520 | refs/heads/master | 2020-12-02T23:35:00.883225 | 2020-02-01T00:57:58 | 2020-02-01T00:57:58 | 231,154,313 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,236 | rd | Cb.logistic.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Cb.R
\name{Cb.logistic}
\alias{Cb.logistic}
\title{Cb calculations for a logistic regression model.}
\usage{
Cb.logistic(reg_object, tx_var, semi_parametric = FALSE)
}
\arguments{
\item{reg_object}{An object of class 'glm' that contains the r... |
dc8d14e371c332718b529cfd35f3a91f8d0eacaa | 6a1ffcaf3fe6081859849f82ea3d8784cd87bb94 | /man/freshdesk_api_call.Rd | 000217530b17937f52d35f1069404c01875b0c6a | [
"MIT"
] | permissive | jjanuszczak/freshdeskr | 870cae5da26ec5743ec3efdb779e868111e5672d | 4cf49becd0bf0d9c4fdecdaf9fa2824d1dd0f7c7 | refs/heads/master | 2020-03-14T06:51:55.201309 | 2018-07-22T10:20:17 | 2018-07-22T10:20:17 | 131,492,092 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,251 | rd | freshdesk_api_call.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api.R
\name{freshdesk_api_call}
\alias{freshdesk_api_call}
\title{Calls Freshdesk API}
\usage{
freshdesk_api_call(client, path, query = NULL)
}
\arguments{
\item{client}{The Freshdesk API client object (see \code{\link{freshdesk_client}}).}
... |
fc02f73aa27937e643ea4d4669bdee755023eeea | 2b01f6be3f3a4a043effeab2f7bfaa7e6f24e87f | /utils/man/read.output.Rd | feb875c16ade768e47a74e9e2a2a2a406e6b3333 | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | serbinsh/pecan | bebd2b6eb3ce1587783afa987ee94f9a7202fa33 | 3d48860eaf7cd481a67dba3e16eec994f817990b | refs/heads/master | 2023-05-11T09:07:25.415892 | 2017-07-13T22:31:36 | 2017-07-13T22:31:36 | 19,829,867 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,450 | rd | read.output.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read.output.R
\name{read.output}
\alias{read.output}
\title{Read model output}
\usage{
read.output(runid, outdir, start.year = NA, end.year = NA,
variables = "GPP", dataframe = FALSE)
}
\arguments{
\item{runid}{the id distiguishing the mode... |
5998247e1b047a0c0d2c07ea25f41401dbced087 | b89bebbde0659a8c9ae84a5956c8af0e6e9575de | /app_ui.R | 844adbe6e8bd35908638f9b868d075fdbd2a6bdc | [
"MIT"
] | permissive | chloewlee/sp21-lab07 | a96ab8ed722ad8f1767025381e989ac91fa99c6a | 689155de6db68abb818ab1ea4b3efb9b17b9e39c | refs/heads/main | 2023-05-01T01:09:48.555807 | 2021-05-12T18:27:34 | 2021-05-12T18:27:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 988 | r | app_ui.R | #install packages
library(shiny)
library(ggplot2)
library(plotly)
# read dataset
immunizations <- read.csv("immunizations.csv")
# --------- CREATE WIDGETS ----------
# choose county widget (this widget allows you to
# choose which county you want to have the plot focus on)
# enrollment size widget (this widge... |
871e34794c626c58eebd6820362eb0570ca173f6 | dc08edafa5740fd34e85da39b4cf46e1a26815f7 | /r/source_rfcv.r | b2cbbafc43641aacae14aae6bbc3a81170bad001 | [] | no_license | jtresko/GBM_reversion | 8a6848b47ae67d9049d62eaedd0164cbe9b5d3a2 | df5291e0a24bd016e5acbc100fe985cacec65c2d | refs/heads/master | 2022-10-03T15:08:55.605348 | 2017-05-02T01:22:13 | 2017-05-02T01:22:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 181 | r | source_rfcv.r | A single object matching ‘round’ was found
It was found in the following places
package:base
namespace:base
with value
function (x, digits = 0) .Primitive("round")
|
25d382a9c663a6d3011a1584b93b1ffc3811d62a | 169a6494a475f42d0452d3ade4622bde1eb939cc | /tests/testthat/test-ncbi_downstream.R | 3c6749b2f663a0578103f2466bfa69c2657eeff8 | [
"MIT"
] | permissive | ropensci/taxize | d205379bc0369d9dcdb48a8e42f3f34e7c546b9b | 269095008f4d07bfdb76c51b0601be55d4941597 | refs/heads/master | 2023-05-25T04:00:46.760165 | 2023-05-02T20:02:50 | 2023-05-02T20:02:50 | 1,771,790 | 224 | 75 | NOASSERTION | 2023-05-02T20:02:51 | 2011-05-19T15:05:33 | R | UTF-8 | R | false | false | 2,006 | r | test-ncbi_downstream.R | context("ncbi_downstream")
test_that("ncbi_downstream returns correct structure", {
skip_on_cran() # uses secrets
vcr::use_cassette("ncbi_downstream", {
tt <- ncbi_downstream(id = 7459, downto="species")
})
expect_is(tt, "data.frame")
expect_equal(NCOL(tt), 3)
for (i in seq_along(tt)) expect_is(tt[[i]... |
80a586e036897d3b1d8e8a4242fd29560886fffb | bd33b34437e80d51fbdca7e703a4b0e505c2ebb1 | /power_ml.R | 4b1744a7f7c205b7bbbf5d29116026938a566358 | [] | no_license | RobbievanAert/power_ml | 08c7d83599dc4d2aa1766c2ce67a590e3ac97a58 | db1e946512ae56fab77b0543d398630a576c7b50 | refs/heads/master | 2020-05-14T01:23:27.186600 | 2019-04-16T12:46:45 | 2019-04-16T12:46:45 | 181,684,248 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,948 | r | power_ml.R | ################################################################################
##### STATISTICAL POWER FOR TESTING NULL HYPOTHESIS OF NO EFFECT AND NO #####
##### BETWEEN-STUDY VARIANCE IN A META-ANALYSIS #####
##### Author: Robbie C.M. van Aert ##... |
78b59b0473c4f0517518f8f792342ecefb54ddde | c05e0de22f5699d1c2b2921480be68c8e8b8943f | /R/utils_pipe.R | e3d536e8d8fa4a4d632b8b34d1621aba4a111d93 | [
"MIT"
] | permissive | rstudio/gt | 36ed1a3d5d9a1717dfe71ed61e5c005bc17e0dce | c73eeceaa8494180eaf2f0ad981056c53659409b | refs/heads/master | 2023-09-04T06:58:18.903630 | 2023-09-01T02:06:05 | 2023-09-01T02:06:05 | 126,038,547 | 1,812 | 225 | NOASSERTION | 2023-09-08T00:21:34 | 2018-03-20T15:18:51 | R | UTF-8 | R | false | false | 791 | r | utils_pipe.R | #------------------------------------------------------------------------------#
#
# /$$
# | $$
# /$$$$$$ /$$$$$$
# /$$__ $$|_ $$_/
# | $$ \ $$ | $$
# | $$ | $$ | $$ /$$
# | $$$$$$$ | $$$$/
# \____ $$ \___/
# /$$ \ $$
# | $$$$$$/
# \______/
#
# This f... |
3da84f9276966b6d836cfea72cc761220f196fa3 | 250c51be2dbb89a73fcc0e4acabe7aa83ce2a199 | /pipe/lib/megadaph.mtdna/man/compute_strand_bias.Rd | eafe561ccc4c9089253477dbf4d03a58b5ffa1d6 | [
"MIT"
] | permissive | fennerm/daphnia-mtdna-ma | 744d1530ff9a5aa363e31b127c2de0ac43f2d95a | 1f257838883924289b16344be877af55383b4f65 | refs/heads/master | 2021-03-27T19:53:19.899696 | 2019-03-18T01:11:49 | 2019-03-18T01:11:49 | 92,858,584 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 685 | rd | compute_strand_bias.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multinomial_variant_calling.R
\name{compute_strand_bias}
\alias{compute_strand_bias}
\title{Calculate strand bias for a single genome position using fisher exact test}
\usage{
compute_strand_bias(counts, wild_type_allele, mutant_allele)
}
\ar... |
7bc860a6fd318ff65cf49933ff4e14e7a87edb51 | b0ee96a1b50dd537b080e59bb1a8e786f22f26cb | /R/RFLPcombine.R | 3849e645c0345f8e8bf81b208a97d85452e5ed30 | [] | no_license | cran/RFLPtools | a989cfaf23994bbeaf7604e0ce65435ddf78ad37 | 9f048d015d8df7459ce26c7d5b8ac7ecd1e86b0e | refs/heads/master | 2022-03-05T22:27:27.183989 | 2022-02-08T08:40:02 | 2022-02-08T08:40:02 | 17,681,978 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 978 | r | RFLPcombine.R | ###############################################################################
## Combine data sets
###############################################################################
RFLPcombine <- function(...){
x <- list(...)
if(length(x) < 2)
stop("You have to specify at least two data sets!")
nam... |
17452b042b665d6ce44ff95cb5e7e06f30349568 | 361469edf71a80776e71c2c1b6da64c7a83d27db | /R연습2.R | ad3f3ce506c29f89c0911abad1a016f09784f4ba | [] | no_license | HanseamChung/prac_R | d7d06fb811dabcc64cf0c5f116fa5f68c5e7f4f9 | 71d533430ef3d34c82fc503b3241e1b82ed31967 | refs/heads/master | 2021-04-30T14:22:51.025394 | 2018-02-27T07:12:46 | 2018-02-27T07:12:46 | 121,216,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 984 | r | R연습2.R | a<-11
b<-22
setwd('c:\\easy_r')
getwd()
var1 <- c(1, 2, 5, 7, 8)
var2 <- c(1:5)
var3 <- seq(1, 5)
var4 <- seq(1, 10, by =2)
var1+2
var1-var2
str1 <- 'a'
str1
str3 <- 'Hello World!'
str4 <- c('a','b','c')
x <- c(1,2,3)
x
mean(x)
max(x)
min(x)
str5 <- c('Hello!', 'World', 'is', 'good!')
str5
paste(st... |
3b849553f6a24ca9888e8b337ae8319a9c3347ef | 67e26dbc19937477f768935005c200bfaae11471 | /R_practice/Ggplot2_prac.R | 40e30ad8ebd3921856da7bdbee09c627af4f2a64 | [] | no_license | skickham/brainteasers | f34f9eacd5c7c80adabe7a4b4268c7e0f6a8a6af | a707a4b7dbff1ffedb86ae99684f152c1d622da3 | refs/heads/master | 2021-04-03T09:20:57.473616 | 2018-05-03T18:21:58 | 2018-05-03T18:21:58 | 125,241,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,885 | r | Ggplot2_prac.R | setwd(dir = '/Users/skick/Desktop/NYC Data Science Academy/Class_R/')
library(dplyr)
library(ggplot2)
#Question 1
#1
Champions = read.csv('Champions.csv', stringsAsFactors = FALSE)
#View(Champions)
tbl_df = filter(Champions, HomeGoal > AwayGoal)
filter(Champions, HomeTeam == 'Barcelona' | HomeTeam == 'Real Madrid')
... |
3a328d0d0897fe20af3a81ee55754f8513303b3a | f7408683a4b9f3ea36e6c56588f257eba9761e12 | /R/f_sum2.R | 6b751c6d0907d99f554d8cbd9c1580e2a36ed5cd | [] | no_license | refunders/refund | a12ad139bc56f4c637ec142f07a78657727cc367 | 93cb2e44106f794491c7008970760efbfc8a744f | refs/heads/master | 2023-07-21T21:00:06.028918 | 2023-07-17T20:52:08 | 2023-07-17T20:52:08 | 30,697,953 | 42 | 22 | null | 2023-06-27T15:17:47 | 2015-02-12T10:41:27 | R | UTF-8 | R | false | false | 683 | r | f_sum2.R | #' Sum computation 2
#'
#' Internal function used compute a sum in FPCA-based covariance updates
#'
#' @param y outcome matrix
#' @param fixef current estimate of fixed effects
#' @param mu.q.c current value of mu.q.c
#' @param kt number of basis functions
#' @param theta spline basis
#'
#' @author Jeff Goldsmith \e... |
f274cd1ce57c9aa316bd26586bc5438662cced42 | 333c0b5c43c56475c0c885b07d58817ae0cd0430 | /análisis/03_visualización.R | 537186e138eb77cc6bca5dbc444705a248bea5c2 | [
"MIT"
] | permissive | RMedina19/Intersecta-PJCDMX | 6d2172962236ca748176b960fd53f4cc3d8884bc | f717781e705fa10d7ea8648d5655ce41b4ba1587 | refs/heads/main | 2023-03-03T02:34:41.080298 | 2021-02-10T17:21:06 | 2021-02-10T17:21:06 | 329,800,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,930 | r | 03_visualización.R | #------------------------------------------------------------------------------#
# Proyecto: TRIBUNAL SUPERIOR DE JUSTICIA DE CIUDAD DE MÉXICO
# Objetivo: Procesar datos de la PJCDMX
# Encargadas: Estefanía Vela y Regina I. Medina
# Correo: rmedina... |
c6eced4e71e9c64e5f786743267a8b1714b1a081 | e49fb88b541ac83a3dadb10deaff64e1772dabac | /03_getting_and_cleaning_data/quiz02.R | 230a9bdf3a726a45c89c5d1d47334d639f06c80b | [] | no_license | Cardosaum/data_science_specialization_jhu | 61652b9e4a27a0f716b1f822de28650912e33d29 | 7268ddee814ff1afc90c5e88b382a1afb196d172 | refs/heads/master | 2022-12-21T11:14:57.892640 | 2020-09-06T23:22:22 | 2020-09-06T23:22:22 | 257,573,104 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,414 | r | quiz02.R | # Quiz, week 2
## Let's set the cwd correctly first. (Using as reference the directory containing the .Rproj file)
setwd("./03_getting_and_cleaning_data")
## Now we load the libraries
library(data.table, quietly = TRUE)
library(tidyverse, quietly = TRUE)
library(RMariaDB, quietly = TRUE)
library(xml2, quietly = TRUE)
... |
9e7f9f341e00f68944e0df029b9ac524e8ee5446 | efbdac03439077f3d3059c4ad0aef4e67a63d108 | /Sport Analytics/Project/R/get_discrete_plot.R | 1e7cb176f4654acfd89adc6fa5600e561b9ab6d6 | [] | no_license | anhnguyendepocen/LiU-Statistics-and-Machine-Learning | afc8811dacad80ca4e0e7948cbfb4f455f7e5148 | 47067c552a8071753526971dd105730a8683f735 | refs/heads/master | 2023-01-20T11:03:16.186847 | 2020-11-13T11:58:11 | 2020-11-13T11:58:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,194 | r | get_discrete_plot.R | get_discrete_plot <- function(game_shots = choose_data(), orientation) {
#----------------------------get_colours---------------------
den_color <- c("#C2FFC8","#03FF03")
colors_points <- c("goal" = "#eb1010",
"successful" = "#10EA10",
"failed" = "#1010EA")
g... |
fa51befefa4b9e454ec53103df6c1852f942770f | 07a74984bf59ce4486e1bcaefafb8ce692b50d5a | /tests/testthat/test-layer_path.R | 8ad78dc3fd4dd079dde557f163442b4a7739b34c | [] | no_license | SymbolixAU/mapdeck | c3bc3a61b8d8ade69b9b67fa69a00f9294281630 | 6138c6845e37ab3479e4ff65d9b0fff29e20f070 | refs/heads/master | 2023-09-03T22:34:43.418728 | 2023-08-24T22:14:59 | 2023-08-24T22:14:59 | 141,350,341 | 344 | 50 | null | 2023-08-09T22:22:59 | 2018-07-17T22:06:34 | HTML | UTF-8 | R | false | false | 2,906 | r | test-layer_path.R | context("path")
test_that("add_path accepts multiple objects", {
# testthat::skip_on_cran()
#
# geo <- '[{"type":"Feature","properties":{"stroke_colour":"#440154FF","stroke_width":1.0,"dash_size":0.0,"dash_gap":0.0,"offset":0.0},"geometry":{"geometry":{"type":"LineString","coordinates":[[145.014291,-37.830458],[14... |
c01f33e852e8c36ed15b94a4e51c7c3ac8bfec05 | b5f93df7ebaaa7e326dec711c55cd9519ce63a71 | /enetLTS_UPDATED/subsample_sharing.R | fd1b2feb8e7a0cfc6a2563c27febb2c899deaaba | [] | no_license | VincentWtrs/Sparse_Robust_Logistic_Regression | 822a700b387a4d66e3c6e0f5a2bdf43eed37c49e | ad0212cb3e0a6ac7a245c482c66058edd9cdc603 | refs/heads/master | 2021-03-22T03:25:31.737217 | 2019-08-13T17:49:40 | 2019-08-13T17:49:40 | 123,120,927 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,573 | r | subsample_sharing.R | index_comparison <- function(indexall){
### index_comparison() FUNCTION: takes the indexall object from the warmCsteps() function and compares them to see if there is a difference
# Extracting dimension sizes
h <- dim(indexall)[1]
length_lambda <- dim(indexall)[2]
length_alpha <- dim(indexall)[3]
# So... |
6c399ca0414d89a2f2beaf1fd6b92ff4b150138e | c182fa25d27f0f292332620f8240bd26e014a07a | /analysis/occupancy/src/setup.R | 7c8cc92aa6c77704f61fbe32ef762d9b86f4c382 | [] | no_license | Magaiarsa/intFlex | 14978e361120d22788ffe5d2f631933e32353481 | 41d18084cb8dfbb92cde416cddbd020c567d01a2 | refs/heads/main | 2023-02-24T03:00:01.292007 | 2021-02-01T10:29:11 | 2021-02-01T10:29:11 | 325,310,689 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,665 | r | setup.R | ## add making data null for each model type
setup <- function(type, input) {
if (type == "partner") {
print("partner setup")
input$monitors <- c("phi.0",
"gam.0",
"phi.fra",
"gam.fra",
"phi.var",
... |
7a2d558e2132c21a301418700f21793c03221bba | ba01ed5947c6ab6c988097d07fa37ee4ad7bf533 | /man/rhone.Rd | f0af1513d541e5e8d5de36548240451ee97387cb | [] | no_license | aursiber/adedata | 63b3380454add630df69afe14967c708949405b0 | 20147d003784425b2a20fd42e3b1f21c41b375e1 | refs/heads/master | 2021-12-13T13:26:44.390094 | 2017-03-21T15:24:27 | 2017-03-21T15:24:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,293 | rd | rhone.Rd | \name{rhone}
\alias{rhone}
\docType{data}
\title{Physico-Chemistry Data}
\description{
This data set gives for 39 water samples a physico-chemical description
with the number of sample date and the flows of three tributaries.
}
\usage{data(rhone)}
\format{
\code{rhone} is a list of 3 components.
\describe{
\i... |
65b553cbdf214a7ad2c46f6fdd02224d28a1a5d2 | 2e697124393b5e2a22272a2bf87d77c73f0aeb90 | /man/dataf.tecator.Rd | f36e78e2b1f02b0e69bd5b24abaa11eae509f4f2 | [] | no_license | cran/ddalpha | f083aedb09e1f010b14930761226121aeae3d79a | 3755274a6ee666258351a01063ebfb311efbfbdb | refs/heads/master | 2022-05-11T10:20:58.026649 | 2022-03-23T06:50:16 | 2022-03-23T06:50:16 | 17,695,418 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,966 | rd | dataf.tecator.Rd | \name{dataf.tecator}
\alias{dataf.tecator}
\alias{tecator}
\docType{data}
\title{
Functional Data Set Spectrometric Data (Tecator)
}
\description{
This dataset is a part of the original one which can be found at
\url{https://www.stat.cmu.edu/}. For each peace of finely chopped meat we observe one spectrometric curve wh... |
4a48a35032675bc76608972ba3a32669acad0838 | fdc9f8c5273f456c82a29b62878eb160c95a8789 | /plot1.R | 1e2ca817c3638b85e31e3f8eab9e9d2ea699666c | [] | no_license | cris1403/EDA-project | ea61743d7bb2c3c7c20f50c78f08a644de8d36fa | 92aa06f2bfb7f7dd3c7d6493d4f210cec8f7f08c | refs/heads/master | 2016-09-05T21:15:40.295918 | 2014-10-28T14:13:38 | 2014-10-28T14:13:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,286 | r | plot1.R | #################################################################################################
# Course Project 2 - Question 1
# Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
# Using the base plotting system, make a plot showing the total PM2.5 emission from all sources
# for ea... |
466c26f93382c8e78cecd2765fc11e47e18d95d1 | 035d7e1721cc68aaa80111de35bfd7c47f48ce80 | /run_analysis.R | 8dd638d8e357724e7dd9d8ea08bdeee28e5f6cfe | [] | no_license | Maca944/GetData-Assignment | 4af277859a87601ab33dfcb8009829a4b7920ee8 | e244e45e628acdf07c4d3a9fd1d9b383669eee4c | refs/heads/master | 2020-05-25T11:38:28.590067 | 2014-09-17T14:31:31 | 2014-09-17T14:31:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,597 | r | run_analysis.R | ###############################################################################################################################
## Coursera Getting and Cleaning Data
## Course Project
## Maarten Caminada
##
## Note: I'm happy with the outcome, although I realize there are more ways of achievin... |
fa0d300695a09170d5e7a7a15a450e79a15ff826 | 5c5c2ca1037fcebb3cd48008c18d543b96c9e66a | /vizualization_scripts/plot_TE_ACR_perms.R | cda1f1530083921a49463ebbb2d03b9a3b36ce45 | [] | no_license | plantformatics/multispecies_TE_CRE_analysis | 7ea05ffdf1cbed9b5059ef88a709fcfbe654845c | b1e6c163a3c4f467303d3f2cd93838b756bc5e58 | refs/heads/master | 2020-03-26T12:28:35.574746 | 2018-08-16T18:36:03 | 2018-08-16T18:36:03 | 144,894,427 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 865 | r | plot_TE_ACR_perms.R | rm(list=ls())
setwd("~/Desktop/sapelo2_mnt/reference_genomes/Athaliana/")
library(scales)
ol <- 2328
a <- read.table("At.TE.perm.txt")
# plot parameters
den <- density(a$V1, from=min(a$V1), to=max(a$V1))
par(xaxp = c(min(ol,den$x),max(den$x,ol) , 4),
yaxp = c(min(den$y),max(den$y), 4))
plot(den, col=alpha("grey... |
1a3d33c03154d1992a26de60a50738910d9b73c9 | a3c78700a65f10714471a0d307ab984e8a71644d | /base/utils/man/status.Rd | 5e5aace2ca38daa8ea9d4bced65a59d960540fb3 | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | PecanProject/pecan | e42a8a6a0fc9c0bb624e0743ab891f6cf131ed3f | ce327b92bf14498fa32fcf4ef500a7a5db5c9c6c | refs/heads/develop | 2023-08-31T23:30:32.388665 | 2023-08-28T13:53:32 | 2023-08-28T13:53:32 | 6,857,384 | 187 | 217 | NOASSERTION | 2023-09-14T01:40:24 | 2012-11-25T23:48:26 | R | UTF-8 | R | false | true | 1,961 | rd | status.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/status.R
\name{status}
\alias{status}
\alias{status.start}
\alias{status.end}
\alias{status.skip}
\alias{status.check}
\title{PEcAn workflow status tracking}
\usage{
status.start(name, file = NULL)
status.end(status = "DONE", file = NULL)
s... |
5f8dbfeda81fb8b5266a8e194fe6be657933513e | 100412ee06fe63606e3c7b8c6b6ca73dd04170b0 | /tests/testthat.R | 1daf8f2e6aafab59ab80cbcdf76d32683860954b | [] | no_license | gudaleon/nhdR | bdaf3b1eeaaa5a2a87cffe0be8ae972e8c91b123 | acd4eaea025bc2a2b7d6d5f63785f3bdcfe0708f | refs/heads/master | 2021-09-03T02:27:18.698124 | 2018-01-04T22:31:52 | 2018-01-04T22:31:52 | 118,405,164 | 1 | 1 | null | 2018-01-22T04:15:53 | 2018-01-22T04:15:53 | null | UTF-8 | R | false | false | 52 | r | testthat.R | library(testthat)
library(nhdR)
test_check("nhdR")
|
f0852f5e7fa7c6ffeccf60c77e2e5c87ccf6f167 | c72cbc1e01cb8a7e25266bc2d0a18bfd197612ca | /my working files/r_built_in_functions.R | d9534c56547e38132f1b655ee750876ddedd4f88 | [] | no_license | psefton/r_portilla | e05ec0c8a22e6f286d49da926d6927ab981bd256 | 2391d93670d625319a3c9732730f34d803e4425b | refs/heads/master | 2021-01-12T10:49:17.657241 | 2016-11-03T07:22:01 | 2016-11-03T07:22:01 | 72,718,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 809 | r | r_built_in_functions.R | ##built in R functions
#seq()
#sort()
#rev() -- reverse
#str() -- structure
#append() -- merge objects together
seq(0,100,2)
seq(0,100,10)
v<- c(1,4,7,2,13,3,11)
sort(v, decreasing = TRUE)
cv <- c('b', 'd', 'a')
sort(cv)
v<- 1:10
rev(v)
str(v)
v <- 1:10
v2 <- 35:40
v
v2
append(v,v2)
#check data type
# is.
# as.... |
38247292a1c476f2a48a156c9f677b07c5516640 | 1326e8d034496b76d9d5902b1b5aacca3aa58879 | /sim_annealing_reheating_2opt_tsp.R | 9a7dc66554f9c73d2de4612357e6554e473a4a31 | [] | no_license | phabee/tsp_scripts | 1215c170422954adfe9ae97dc28dc823051f0f71 | cb756ddccc2e4a33bec2fd7be97a8200d4963598 | refs/heads/main | 2023-01-23T06:47:14.101158 | 2020-12-09T22:37:55 | 2020-12-09T22:37:55 | 316,303,331 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,034 | r | sim_annealing_reheating_2opt_tsp.R | #' apply simulated annealing to 2opt algorithm.
#'
#' @param tsp the tsp instance
#' @param dima the distance matrix
#' @param T0 initial temperature
#' @param alpha the temperature reduction factor per iteration
#'
#' @return the solution
#' @export
simulatedAnnealing <- function(tsp, dima, T0 = 1e2, alpha = 0.9) {
... |
3b6db77a9259ccacaa3eea0c439bf2813bf9f520 | 74d8df7e5a0fd61394fd0494f35ce82dfaa30c96 | /man/zero_range.Rd | 84ed2e4469d8f5313c02271e3e9e38086b11c317 | [
"MIT"
] | permissive | immunogenomics/scpost | 8bde0fff6be217aa92e5b2cb48d145cd35031343 | 9e6ce336addc7e0d50e266299e8b46bed7df78d0 | refs/heads/main | 2023-04-13T13:15:08.708526 | 2021-07-22T14:14:36 | 2021-07-22T14:14:36 | 312,683,900 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 602 | rd | zero_range.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{zero_range}
\alias{zero_range}
\title{Simple implementation from Hadley Wickham that checks if all elements
of a vector are equal}
\usage{
zero_range(x, tol = .Machine$double.eps^0.5)
}
\arguments{
\item{x}{Vector containing num... |
d44d08328cfbdc7f39dd234a59a76f98cefe195c | 53f7d6caae267524bfd6084ec622a2f56777bd34 | /r_scripts/r-analysis.R | 730947ee14b971ed0ca685e4bdf684b8992592cb | [] | no_license | stredger/lind_benchmark | e016c651e4d7568c4582904d58e21d8fa4746767 | 371e9a7a6ccdf3f545be94ca103bb5a97da84bcd | refs/heads/master | 2021-01-23T17:31:16.891385 | 2012-02-28T22:38:02 | 2012-02-28T22:38:02 | 3,576,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,698 | r | r-analysis.R | # Lind benchmarking R script
#
# Stephen Tredger :)
#
#Args <- commandArgs(); # retrieve args
#x <- c(1:as.real(Args[4])); # get the 4th argument
# lind timing files
lind_file_path = "~/Documents/DS/lind_benchmarking/lind_results"
# c timing files
c_file_path = "~/Documents/DS/lind_benchmarking/c_results"
# plac... |
57c3ccf131259a4772a2e2981a8fcbde0941b4ec | d481473c7bf59ef07fb2f0f7f6353e0beff5fa48 | /man/voluminous2.Rd | 686fe2888bab02fcaee3ef812fd99a3344ba46b8 | [] | no_license | crumplecup/muddier | 92e1d4845db3d13e1297060d50d0244d5b00064f | c4d67a17377e45a35426cbb11ace342afaed6806 | refs/heads/master | 2021-11-28T03:41:33.262356 | 2021-08-13T03:11:39 | 2021-08-13T03:11:39 | 175,301,894 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 738 | rd | voluminous2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mod.R
\name{voluminous2}
\alias{voluminous2}
\title{voluminous2}
\usage{
voluminous2(nodes, n, vi, vo, tp = 0.1, lp = 0.5)
}
\arguments{
\item{nodes}{is a spatial object (creek nodes)}
\item{n}{is the number of years to simulate accumulation... |
bb807fa3b09a23a8df1682b3780f4eb94a8ca5ce | 2ae048da4fce01231f9b85796b5c927c10ac1742 | /plot4.R | 7cc9ecc9674a541e29260dbccac3bc0447048f6f | [] | no_license | owamo/ExData_Plotting1 | 71d613c4124e16c66bab16e99be6f2e129945e0c | 1e449f85ddc7f06492776a86d5dee09511d8f4a3 | refs/heads/master | 2021-01-15T09:14:31.333239 | 2014-09-09T15:38:52 | 2014-09-09T15:38:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,848 | r | plot4.R | ## Download the data if does not exist on working directory
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
fileZIP <- "exdata_data_household_power_consumption.zip"
if (!file.exists(fileZIP)){
download.file(fileURL, destfile = fileZIP)
}
## Read the data from ... |
4f918692f536c7fb38a154033b1fc3ee71eb3d8d | b201f1f182b1828a66a2d97baf28224b39d70564 | /R/modules/ui/submodules/cellimage_main_ui.R | 899e544a3e47d19b369dccb66d0b47ec7b5bcaf5 | [
"MIT"
] | permissive | Drinchai/iatlas-app | 147294b54f64925fb4ee997da98f485965284744 | 261b31224d9949055fc8cbac53cad1c96a6a04de | refs/heads/master | 2023-02-08T08:17:45.384581 | 2020-07-20T23:27:08 | 2020-07-20T23:27:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,949 | r | cellimage_main_ui.R | cellimage_main_ui <- function(id){
ns <- shiny::NS(id)
shiny::tagList(
iatlas.app::messageBox(
width = 12,
shiny::includeMarkdown("markdown/cellimage.markdown"),
shiny::actionLink(ns("method_link"), "Click to view method.")
),
shiny::fluidRow(
... |
55749b2b03895d229d450e7270bd1690a450c3f9 | 46124bc5042dded971e14c09440951a5b12392a9 | /R/BGData.R | 01462a6fd713ef7ac051c82a21b04c04ebb8414e | [] | no_license | minghao2016/BGData | 38cc55889c0687819955b86656674d4b123b688b | 028f609d846c8084b06c1b989c378682bc0d29c3 | refs/heads/master | 2020-12-30T14:45:29.203046 | 2017-05-12T10:53:12 | 2017-05-12T10:53:12 | 91,086,007 | 1 | 0 | null | 2017-05-12T11:45:56 | 2017-05-12T11:45:56 | null | UTF-8 | R | false | false | 28,045 | r | BGData.R | # Convert ff_matrix into an S4 class
setOldClass("ff_matrix")
#' An Abstract S4 Class Union of Matrix-Like Types.
#'
#' [geno-class] is a class union of several matrix-like types, many of them
#' suitable for very large datasets. Currently supported are
#' [LinkedMatrix::LinkedMatrix-class], [BEDMatrix::BEDMatrix-cla... |
c765eaebe08b4077758db0253ff13f276db917b1 | bf86fb3091905ddecfbcc7c7047f17f82ceffe88 | /man/pathInterpolate.Rd | 81a1310eceaa798f34eed66d84595a8208ea4806 | [] | no_license | cbhurley/condvis2 | 0580842f55b2ee7e4ea449eb2b5cba763d565bca | 60c370cb279fa29337ac0ad7af403ef633274b68 | refs/heads/master | 2022-09-23T03:22:43.790073 | 2022-09-13T16:34:31 | 2022-09-13T16:34:31 | 160,338,346 | 6 | 1 | null | null | null | null | UTF-8 | R | false | true | 896 | rd | pathInterpolate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/condpath.R
\name{pathInterpolate}
\alias{pathInterpolate}
\alias{pathInterpolate.default}
\alias{pathInterpolate.factor}
\alias{pathInterpolate.data.frame}
\title{Interpolation}
\usage{
pathInterpolate(x, ninterp = 4)
\method{pathInterpolate... |
e59322f54eeaef017203a6f6effcd03b8bcf0847 | 0b89292d1fbcd390a0c0c964e46bc54620ffb706 | /code/functions/lvrates.R | 498bc1e652cb6948898debd5bb4bb76f8adfa41f | [] | no_license | JSHuisman/Recorder | 90c008eb733bf642e00d5a368b514f1c19fd7693 | 06766477df5656ca54345d5a8ed1b8416dd19a77 | refs/heads/main | 2023-08-05T20:58:34.636453 | 2021-11-24T14:30:30 | 2021-11-24T14:30:30 | 344,049,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,805 | r | lvrates.R | ###########################################
## lvrates.R
##
## Returns a function that encodes the transition
## rates of migration, birth, death, and plasmid transmission
## processes for use in the adaptive-tau simulation
##
## This function includes a carrying capacity.
##
## Author: Jana S. Huisman
## Last update: ... |
9c5e5ebf9f66099ac42dfdae58b13d6f21a89b20 | aa8a256304ebfcdb556269a5bef26f56940f031c | /R/getWaterSpeedRecordFromWiki.R | 25a4ad8b3d5f83a05903fb3ea689ca5be429316f | [] | no_license | canardRapide/speedRecords | 2aad42409ab87df5206a91d2d16486b2e9fb9f9c | 7c35334c683d5c143f6708f111ed96104ff6bb15 | refs/heads/master | 2021-01-10T13:26:37.221851 | 2016-02-22T05:20:16 | 2016-02-22T05:20:16 | 51,705,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 859 | r | getWaterSpeedRecordFromWiki.R | getWaterSpeedRecordFromWiki <- function() {
# Water Speed Record (Prop-driven and Jet Hydroplane)
library(rvest)
nHeaderLines <- 0
url <- "https://en.wikipedia.org/wiki/Water_speed_record"
tables <- html(url) %>% html_nodes(".wikitable") %>% html_table(fill = TRUE)
table <- tables[[1]]
date <- as.vector(tab... |
02571e4232357a192bf719a90c46150c37d87ec8 | ed28666d9201bf050c305f0740756f7730a66ef3 | /NatureEE-data-archive/Run203071/JAFSdata/JAFSnumPerPatch30360.R | 4cacb45bed7220882d8e2e6ec6aaede0209f7090 | [] | no_license | flaxmans/NatureEE2017 | 7ee3531b08d50b3022d5c23dbcf177156c599f10 | b3183abe6bb70f34b400d0f5ec990cce45e10b33 | refs/heads/master | 2021-01-12T08:02:37.153781 | 2017-01-27T15:48:55 | 2017-01-27T15:48:55 | 77,110,421 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33 | r | JAFSnumPerPatch30360.R | numPerPatch30360 <- c(2444,2556)
|
ccb041f267a0d51a7f3e9add27ca94bb4e567a31 | bff50b46f43920f28d23fdf01478985fb1abe085 | /AnalisisDEF2_new_frailty.R | b8199d471c2ef5465fe0c55d52d20175a65ea025 | [] | no_license | dmorinya/miRecSurv | 352970253e38ea64eee0cbd617f599458e273700 | b65ab48f655ef56e98dc3158fcb57af80df3ddbb | refs/heads/main | 2023-08-23T02:15:34.992998 | 2021-11-02T11:00:03 | 2021-11-02T11:00:03 | 420,038,521 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 168,282 | r | AnalisisDEF2_new_frailty.R | library(survsim)
library(data.table)
library(COMPoissonReg)
library(compoisson)
library(survival)
library(doParallel)
library(MASS)
library(WriteXLS)
nCores <- detectCores()
registerDoParallel(nCores)
#setwd("/home/dmorina/Documents/Docència/Tesis/Gilma/Articles/3 Recurrent events")
#source("scripts/genResultsDEF1... |
ce5a0f6fb5c5834f04c2ab3d8063d55507953d7d | 17f6825befaa193b78eb585851e8215121285481 | /1_Generate_FRK_L3/4-plot.R | 49cf1ae39cea8950e4907c47a9f26176db27bef5 | [] | no_license | andrewzm/oco2-frk | 58cde159b41c03ed943132d0939120db69cdabe0 | 641778dd11bc2ecd068fca48a098af7709d44427 | refs/heads/master | 2018-10-31T16:23:27.053989 | 2018-01-23T12:43:26 | 2018-01-23T12:43:26 | 109,343,849 | 4 | 2 | null | 2017-11-10T18:00:54 | 2017-11-03T02:43:57 | R | UTF-8 | R | false | false | 5,987 | r | 4-plot.R | ## Produces daily map plots from the Fixed Rank Kriging results.
# Change this to "oco2v7" or "oco2v8"
data_version <- "oco2v7"
library(FRK)
library(dplyr)
library(ggplot2)
theme_set(theme_grey(base_size = 20))
my_colours <- c("#03006d","#02008f","#0000b6","#0001ef","#0000f6","#0428f6","#0b53f7","#0f81f3",
... |
eb6b61755465ba84754f7b6a4bdf963694d8abdf | c1c3cda3cd900f8ec3a3c7dfa9d8ab5e5aeaffa7 | /day11.R | 1e9cba98e56427e6e7a7f7d57b430393d01d3e2d | [] | no_license | sethmcg/advent-2015 | 75c7434e0784822da604396de7f1a98b78170b48 | 9ff5aff58e126317c6de11911f2e1afa25503767 | refs/heads/master | 2021-05-31T02:30:57.595405 | 2016-03-19T23:31:12 | 2016-03-19T23:31:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 832 | r | day11.R | pw <- "hxbxwxba"
pw <- match(unlist(strsplit(pw,"")),letters)
inc <- function(v){
v[8] <- v[8]+1
while(any(v > 26)){
i <- max(which(v > 26))
v[i-1] <- v[i-1]+1
v[i] <- 1
}
return(v)
}
bad <- c("l","i","o")
doubles <- paste0(letters,letters)
runs <- paste0(lette... |
1e74229b9595bf7dfb07cd53bc57df0ca9396820 | dc7c1016493af2179bd6834614be0902a0133754 | /boxplotex.R | 62b793ea2b163d50bfeb0d8dc75eb5c50d171d80 | [] | no_license | ashishjsharda/R | 5f9dc17fe33e22be9a6031f2688229e436ffc35c | fc6f76740a78d85c50eaf6519cec5c0206b2910c | refs/heads/master | 2023-08-08T13:57:05.868593 | 2023-07-30T13:51:56 | 2023-07-30T13:51:56 | 208,248,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | boxplotex.R | png(file="boxplotex.png")
boxplot(mpg~hp,data = mtcars,xlab="Number of Cylinders",ylab="Miles per Gallon",main="Mileage Data")
dev.off()
|
f6e4011b4ebdf53f15c3b754631e5225cf2325c7 | 7b4ec05acf034f52643945b46fb069ab51613af3 | /tutorial_R/R_rainclouds.R | a5b9ffcde938863c3ee7a92ba61e735cdb63fe11 | [
"MIT"
] | permissive | RainCloudPlots/RainCloudPlots | 489f91bb4f5c44d012f7ae27b8fa9047dc2c722a | 4ceeb06dc5bd5b9911e7147d1b0b452c4c2f9b1d | refs/heads/master | 2023-05-29T03:23:31.551781 | 2023-03-27T09:40:01 | 2023-03-27T09:40:01 | 144,041,501 | 732 | 235 | MIT | 2023-03-27T09:40:02 | 2018-08-08T16:40:34 | HTML | UTF-8 | R | false | false | 2,715 | r | R_rainclouds.R | ### This script creates an R function to generate raincloud plots, then simulates
### data for plots. If using for your own data, you only need lines 1-80.
### It relies largely on code previously written by David Robinson
### (https://gist.github.com/dgrtwo/eb7750e74997891d7c20)
### and the package ggplot2 by Hadley W... |
9bb2af8fde92517cf0258b78949e5f7763f1fdf3 | 7e1cd4641569868113092e90721b8c88ec58c853 | /stages2.R | 17af4261ebc21e1c3aed8f11428d3aa31fe0bc39 | [
"MIT"
] | permissive | quevedomario/eco3r | ef9f38996bb991eaf70b6ef5ee0dfa5d18a8ddea | e358a173b5e876869a4379c12db09a8cc77e21fa | refs/heads/master | 2022-05-09T05:04:22.482384 | 2022-04-06T07:28:36 | 2022-04-06T07:28:36 | 173,913,221 | 0 | 0 | null | 2019-05-01T11:11:35 | 2019-03-05T09:11:26 | null | UTF-8 | R | false | false | 1,715 | r | stages2.R | ## ----setup, include=FALSE----------------------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
options(warn=-1)
## ----message=FALSE, warning=FALSE--------------------------------------------------------------
library (popbio)
## ------------------------------------------------... |
8f9b203f1bfcd9724827ae00e12d7310ba4510d5 | c90ed7da05ae61c51b752b7086a74f4e35053755 | /R/pwrEWAS.shiny_v1.7.R | e5d164ca5214228c454b4fec1a79fba3853b90f3 | [] | no_license | stefangraw/pwrEWAS | 9e382c7cd3cf3674cca3666be720be8235256f67 | 945c77d69f5c7e80240f91b5319623efd012ceb0 | refs/heads/master | 2021-12-01T23:46:32.454505 | 2019-10-25T12:42:00 | 2019-10-25T12:42:00 | 133,530,231 | 6 | 5 | null | 2021-11-12T14:39:57 | 2018-05-15T14:42:52 | R | UTF-8 | R | false | false | 15,269 | r | pwrEWAS.shiny_v1.7.R |
#' @title Shiny pwrEWAS
#'
#' @description pwrEWAS_shiny provides a user-friendly point-and-click interface for pwrEWAS
#'
#' @keywords DNAm microarray power Shiny
#'
#' @return pwrEWAS_shiny initializes pwrEWAS's user-interface
#'
#' @export
#'
#' @examples
#'
#' if(interactive()) {
#' pwrEWAS_shiny()
#' }
... |
3efcf8fefa4836b04ee4324d35d467f4c0db1afe | df6638f57ede542680cfd4634effcc1e205ae07f | /DAY 0.R | 7c83cb7b088d99277a12eda6e90e69ca5160f953 | [] | no_license | BioinformaticsDeepLearning/Learn_R_and_Use_R | 1d4804b90e262ffffa9a656a3beec5ea2e206b61 | 8d987ee4afbb2e415ee8cd6732a6369d71413bf8 | refs/heads/master | 2022-12-04T19:20:39.877479 | 2020-08-11T08:04:48 | 2020-08-11T08:04:48 | 273,900,570 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,627 | r | DAY 0.R | #What is R programming?
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
a) an effective data handling and storage facility,
b) a suite of operators for calculations on arrays, in particular matrices,
c) a large, coherent, integrated collection of int... |
d2694ecc9631ac5b424b5cd181c7bd6e799e0a34 | ccd2ba51797d860fe7e7b69ff1d14301d74493a1 | /examples.r | ebe8c0dc8b3181d4365f2e0177f1e7a9a71bb800 | [] | no_license | moone009/wec-preprocess | 841959f2cac105e115a97fd15677a802d0e52608 | cdcd5a8e22dd0c0c14faa79bdc329516048007be | refs/heads/master | 2021-01-21T04:54:00.900386 | 2016-07-25T15:51:13 | 2016-07-25T15:51:13 | 54,666,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,398 | r | examples.r |
##_____________________________________________________________________________________________________________________________
# setup test data
mtcars$carb = as.factor(mtcars$carb)
mtcars$am = as.character(mtcars$am)
##________________________________________________________________________________________________... |
50d7dc354eaecd476f66547b6b424c3290eb35f5 | 7b8da296768b3586d71ff5b9808d36fed89c98fc | /plotOutlierClines.R | ac8b44984e661490ff14b4ed95d38f81ec6c8a10 | [] | no_license | raonyguimaraes/exomeAnalysis | 6e7e9a19dc46979f2f03e1594bba7b2e574ee4bb | b21deb76e8425fa009d137eb62dbbd08bbdf2264 | refs/heads/master | 2020-04-14T14:53:47.675052 | 2013-04-11T16:57:10 | 2013-04-11T16:57:10 | 10,248,781 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,312 | r | plotOutlierClines.R | clines <- 5
fit <- read.table("/Users/singhal/thesisWork/introgression/clineAndSummaryStats.out",header=T)
fit$outtype <- factor(fit$outtype, levels = c(levels(fit$outtype), "sweep"))
indices <- which(fit$type == "widerange")
fit$outtype = replace(fit$outtype,indices,"sweep")
contact <- "gillies"
fit <- fit[complete... |
bb8a1ddd0e53a0531f00ce447d39b735e1221345 | b033ba5c86bbccca8f33a17a91d7d8ba1fc41976 | /R/regionList.R | 2d3cea5de51c4f9392c0e62ab23c7d9708791ea1 | [] | no_license | neuroconductor/brainKCCA | 889419ba83967592cc5f70cddaf8a23d4abbe27f | e8e08788b4ec395cfe5ba670d13332e03a35814f | refs/heads/master | 2021-07-19T05:44:31.800018 | 2021-05-17T13:38:42 | 2021-05-17T13:38:44 | 126,418,981 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,209 | r | regionList.R | #read nii region file provided by user and transform it into RData.
#oh<-regionList("AAL_MNI_2mm.nii","RegionList.txt")
#example<-regionList("AAL_MNI_2mm.nii", "RegionList.txt")
regionList<-function(regionData, regionCode, resolution="2mm"){
cat("reading and manipulating the regionData...", "\n")
largeM... |
f077bf0f2b4da19e022c93b8c03ce9954916a97d | b67bef2e6295b68a6ba404e78505258a1ac2f95f | /man/gdirmn.Rd | 93c3dbf89d00a8abd1ebabf6c2c799fd88dc8e3f | [] | no_license | cran/MGLM | beda91fe76a43884434647620d2bf4aebedc1a59 | e0b8d5d6dec9b3b0dcc74514b0b68438276513d4 | refs/heads/master | 2022-05-01T07:22:15.450258 | 2022-04-13T22:32:32 | 2022-04-13T22:32:32 | 17,680,602 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 4,411 | rd | gdirmn.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MGLMgen.R, R/pdfln.R
\name{rgdirmn}
\alias{rgdirmn}
\alias{gdirmn}
\alias{dgdirmn}
\title{The Generalized Dirichlet Multinomial Distribution}
\usage{
rgdirmn(n, size, alpha, beta)
dgdirmn(Y, alpha, beta)
}
\arguments{
\item{n}{the number of ... |
95c7a0f3f8d11421c6148731d97fe861fe29104c | 5aaa165524c0f6a1cb3c91906275e04fd40bbe85 | /JE_Visualize/Supporting_Functions/Reduce_size_burdenmap.R | a4a59adf9b6b38e76f468abe65b1442017cbf47a | [] | no_license | m2man/SIYOUCRU | 8ad8c6250b63673360314243d8991a463615495c | 6b1db34c0cb7476aab4f85707ecb2bb53cf1c7a8 | refs/heads/master | 2020-07-05T00:34:59.076099 | 2019-08-30T09:50:07 | 2019-08-30T09:50:07 | 202,470,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,235 | r | Reduce_size_burdenmap.R | # --- NOTE ---
# Use this script to generate Burden_Cases_Map.Rds (which will be used in Tab 4 Shiny)
# Also use this script to generate Incidence Rate or Deaths (instead of Cases) to be used in Tab 4 Shiny
# ---------- #
# Get directory of the script (this part only work if source the code, wont work if run directly ... |
130a941435cfa409f0cd5fa1887b201ba9dad4d2 | 83ae358d90cb1c54c8be380bc7bd628a2f6ed530 | /man/bread.Rd | 5cbe237086023e011f8e6b7e82cc3206a32f3a4e | [] | no_license | cran/Rlab | c7963e1210e2140fc6d397ff6a2cf289f0dd3bd2 | c72e630626f6df15cf75ffd8b9ee7c85322aeda8 | refs/heads/master | 2022-05-28T16:35:40.306539 | 2022-05-04T22:10:02 | 2022-05-04T22:10:02 | 17,693,343 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 755 | rd | bread.Rd | \name{bread}
\alias{bread}
\title{Bread rising experiment}
\description{
The data set bread contains height measurements of 48 cupcakes.
A batch of Hodgson Mill Wholesome White Bread mix was divided into three parts
and mixed with 0.75, 1.0, and 1.25 teaspoons of yeast, respectively.
Each part was made into 8 di... |
f72733b4889db966c3c00faa47489a31558d723d | 41e6440cae2f89175d5c1b356402c47cc581dd62 | /R/app.R | d338d96098d4c6ec6b458bda7fb881bf94ee9446 | [
"MIT"
] | permissive | derryleng/metar | 00f963d1ecb47f02b118c190f64fd5b5d2978617 | 3683a3aef41d71c3270a2e15d4e7de76e9559f5b | refs/heads/main | 2023-06-20T15:08:20.205025 | 2021-07-21T06:04:40 | 2021-07-21T06:04:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,058 | r | app.R | metarShinyApp <- function() {
if (!requireNamespace("shiny", quietly = T) | !requireNamespace("shinyFiles", quietly = T)) {
stop("Packages \"shiny\" and \"shinyFiles\" are not installed.", call. = F)
}
require(data.table)
require(shiny)
require(shinyjs)
require(shinyWidgets)
require(shinyFiles)
re... |
04f665d0f92abed3c13618fa5d5c95b362674b44 | bd74dfc8aecca440ca0f94627c85118ecca19a3d | /Figure6/iba1-new.R | 3fd6101d3669c46f12e1b0acf5ca142b88cddeda | [] | no_license | ErbB4/LMM-GLMM-R-plasticity-paper | 74c94427ef1e7067eb65c8bd9b0cda8bbf15c67d | 0d8c3c7d95d18f11a5476bb953885e6c17f6a832 | refs/heads/master | 2023-04-17T06:11:44.508678 | 2021-04-28T20:02:11 | 2021-04-28T20:02:11 | 269,365,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,763 | r | iba1-new.R | #analyses for IBA1 protein expression
library(lme4)
# importin data
data = read.csv("iba1.csv")
# specifiy certain variables as factors
str(data)
head(data)
data$Time <- as.factor(data$Time)
data$Sham <- as.factor(data$Sham)
data$Ipsi <- as.factor(data$Ipsi)
data$Distance2 <- as.factor(data$Distance2)
da... |
3859dd8f221bb31bb9171739be62a26ab734acbe | adcfd5d1a21ca60bb6d34324fddc0e63c7794f60 | /Scripts/Scripts_Alfonso/8_observed_VS_expected_probabilities.R | 8db3532ea0e0ac529b215b2ea470a0e989097c6c | [] | no_license | JoseBSL/FunctionalMotifs | 349c81472347230ac6fe9466f8fe8939fcdf7c96 | 33e827c120bc7efb94dd60299d98a4e1bde2a0fe | refs/heads/main | 2023-04-16T09:31:19.110654 | 2023-02-16T12:46:22 | 2023-02-16T12:46:22 | 339,147,455 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,384 | r | 8_observed_VS_expected_probabilities.R |
library(tidyverse)
# https://bartomeuslab.com/2014/12/17/preferring-a-preference-index/
chi_pref <- function(obs, exp, alpha = 0.05){
chi <- chisq.test(obs, p = exp, rescale.p = TRUE, simulate.p.value = F, B = 2000)
print(chi) #tells you if there is an overall preference. (sig = pref)
res <- chi$residuals
#r... |
5a2c40f1c5bc568bb353a00fe0e2915a834d8228 | ef35717b113233dc1a9122df61cf1c06645ceaec | /man/autoSpec.Rd | 06424cb4560ae1e5717c31cb01d4dd1e191ccb2a | [] | no_license | cran/astsa | d33ba640a0edda0dd9e112ed011bb05ac5c36fb3 | 1e597fa74efc437eb247787fcf7d26e0fe0c6b17 | refs/heads/master | 2023-04-10T07:36:18.196423 | 2023-01-09T21:50:14 | 2023-01-09T21:50:14 | 17,694,513 | 7 | 14 | null | 2016-03-21T15:10:46 | 2014-03-13T04:00:06 | R | UTF-8 | R | false | false | 4,608 | rd | autoSpec.Rd | \name{autoSpec}
\alias{autoSpec}
\title{
autoSpec - Changepoint Detection of Narrowband Frequency Changes
}
\description{
Uses changepoint detection to discover if there have been slight changes in frequency
in a time series. The autoSpec procedure uses minimum description length (MDL)
to do nonparametric sp... |
7e0108a8f2b40323e519375bc5b1e304e9d6ae08 | d5d5ebf85c43614cc5d5d10cde2e729e546ba1cc | /dataR.R | 46b0c9ce55cdeecf2aa8e2886d9d799f23d6304d | [] | no_license | gaurav1988007/Jigsaw-Assignment | de22f4e7764089b9d2e5ed96f67a7705f48f6eb8 | 0e76a4cc6bf117326411b87ad42d8068d203c25f | refs/heads/master | 2020-12-24T09:30:53.641293 | 2016-11-21T15:55:08 | 2016-11-21T15:55:08 | 73,287,772 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 78 | r | dataR.R | # Creating git R file
pp <- print("Gaurav")
pp
library(datasets)
data(mtcars)
|
c89b52afc4e50aedb9a95f671b634d36ffc10bdf | db8a43ce4e4d58a57a0a2bb29b63acf6c30b5092 | /R/diff.R | c5acb10c214207970bd856b64da7fe5b80906456 | [] | no_license | zhaoxiaohe/MachineShop | ca6fa7d6e7f00ac7d6f8522d50faeec2f4735b2d | 85b1ff6a9d7df425d041289856861e75ce596621 | refs/heads/master | 2020-04-11T06:30:08.059577 | 2018-12-13T00:45:43 | 2018-12-13T00:45:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,489 | r | diff.R | #' Model Performance Differences
#'
#' Pairwise model differences in resampled performance metrics.
#'
#' @name diff
#' @rdname diff-methods
#'
#' @param x object containing resampled metrics.
#' @param ... arguments to be passed to other methods.
#'
#' @return \code{ModelMetricsDiff} class object that inherits fro... |
34d4796923b4a0eccc536b028102caaa40fcb902 | 0084280ad5d1400c280c110c402d3018b7a129af | /R/preprocess/aliquots-coverage-metrics.R | cdd9fb8613eba8bb6ad5f5eb3799871a45018432 | [
"MIT"
] | permissive | fpbarthel/GLASS | 457626861206a5b6a6f1c9541a5a7c032a55987a | 333d5d01477e49bb2cf87be459d4161d4cde4483 | refs/heads/master | 2022-09-22T00:45:41.045137 | 2020-06-01T19:12:30 | 2020-06-01T19:12:47 | 131,726,642 | 24 | 10 | null | null | null | null | UTF-8 | R | false | false | 2,474 | r | aliquots-coverage-metrics.R | #######################################################
# Enumerate cumulative coverage per aliquot for WGS/WXS
# Date: 2018.11.06
# Author: Kevin J.
#######################################################
# Directory for GLASS analysis.
mybasedir = '/Volumes/verhaak-lab/GLASS-analysis/'
datadir = 'results/align/wgs... |
438edd113a771e49fb836006418a47e2e2d4875c | efbff8ea44ac87a421dedb6b8e87182f93292e75 | /app.R | 8e52bcbc83394965ea7894026d28f213ff922355 | [
"MIT"
] | permissive | smsaladi/em_data_requirements | acdc0410c409c0241dd50e808469bbc25d3242da | 20cd3f423b5156d18a8231bb398dc3eb00aee2f9 | refs/heads/master | 2021-01-23T05:29:15.462726 | 2018-10-15T14:54:52 | 2018-10-15T14:54:52 | 86,308,659 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,039 | r | app.R | #
# EM Data Requirements
#
# A back of the envelope calculation for your bigger-than-an-envelope
# microscope and data
#
# Shyam Saladi (saladi@caltech.edu)
# October 2017
#
library(gdata)
library(tidyverse)
library(shiny)
ui <- fluidPage(
tags$head(includeScript("google_analytics.js")),
includeHTML("github_co... |
5c0df0926716cc0a98280ebcf18100e91e014d92 | f367801e7c4f24560f607a5aaa95187a0f963def | /cachematrix.R | e793878da85aa12b316af2163910508f36598488 | [] | no_license | MichaelChoudhury/ProgrammingAssignment2 | 6daf6b112982c12e35a75b787f00e712860b44c6 | c291323e82cdbf4c4b32ab71c2f4cc1e649dce89 | refs/heads/master | 2021-01-24T21:03:30.839872 | 2014-11-23T08:51:42 | 2014-11-23T08:51:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,300 | r | cachematrix.R | ## The two functions below will calculate the inverse of a matrix x.
## If the inverse has been cached as a result of a previous calculation for the same matrix,
## the result is simply returned and no further calculation takes place.
##
## The "makeCacheMatrix" function creates an object that:
## - Initializes a va... |
a3516f24d9e5410616b4ee35fbad627a75450da6 | 4f9aac69cfacaf3605cafaef44412c666f8b83f8 | /economics/upgrade-languages.R | 0fd69235aeb96a9d7e3dde70814dd9bfb95f7883 | [] | no_license | gopinathsubbegowda/ESEUR-code-data | b7c11113892d840295baec19c8bc14817a6ad6eb | d576dad762e8551272a6ac302eb4ef1de1153158 | refs/heads/master | 2021-05-06T18:50:54.604461 | 2017-11-26T00:33:15 | 2017-11-26T00:33:15 | 112,064,865 | 1 | 0 | null | 2017-11-26T07:42:13 | 2017-11-26T07:42:13 | null | UTF-8 | R | false | false | 1,765 | r | upgrade-languages.R | #
# upgrade-languages.R, 2 Mar 17
# Data from:
# Information Goods Upgrades: {Theory} and Evidence
# V. Brian Viard
#
# Example from:
# Empirical Software Engineering using R
# Derek M. Jones
source("ESEUR_config.r")
pal_col=rainbow(2)
library("plyr")
full_price=function(df)
{
lines(df$Date, df$Full.Price, col="b... |
87b0cc25bdbebd31f318ca0bbeccf689001567c6 | dd0d26163c4a0498de5b25e4ee57c4ce70b2676d | /R/autoplotECRResult.R | cc1bb3aa455ae805c1774ccb23a5df3cb062f8ff | [] | no_license | jakobbossek/ecr | a1f97be9b4cb3b2538becebb38c9a5085b8464c9 | f9954f5b1374cc70776f8b7e780f906e57ca50b7 | refs/heads/master | 2020-04-04T07:26:32.216427 | 2017-06-06T11:05:27 | 2017-06-06T11:05:27 | 17,904,690 | 13 | 5 | null | 2016-09-27T10:30:10 | 2014-03-19T13:15:56 | R | UTF-8 | R | false | false | 4,714 | r | autoplotECRResult.R | #' @title
#' Plot optimization trace.
#'
#' @description
#' Call this function on the result object of an \code{\link{doTheEvolution}}
#' function call to visualize the optimization trace.
#'
#' @param object [\code{ecr_result}]\cr
#' ecr result object.
#' @param show.process [\code{logical(1)}]\cr
#' Should the fu... |
16a6c5ee7d7941d846fe6b9d8b188556f72f75ad | 39396f1d2c1ddea904ff24f2e15efdf4470906e7 | /R/amino_acid_pal.R | b818c716b5555ae61ec964ee3f2aa39193ec3cb0 | [] | no_license | smsaladi/heliquest | 40a2a7f17964227c6e4113cf0b481b6373851375 | 45da6bd24f5f2f20ca0bc2b0903d29b500d5288f | refs/heads/master | 2021-09-21T19:17:34.042903 | 2018-08-30T15:02:21 | 2018-08-30T15:02:21 | 61,735,718 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,545 | r | amino_acid_pal.R | #' Provide common color palettes for amino acids
#'
#' @name name of palette to retrieve
#' @keywords color protein amino-acid
#' @export
#' @examples
#' amino_acid_pal('shapely')
#' #' @export
amino_acid_pal <- function(name) {
clustal <- c(rep("orange", 4),
rep("red", 3),
rep("b... |
ec320b6ec3abb0221a1e7a509fc7e55e218088b2 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.security.identity/man/ram.Rd | 97c838debbf9111d3532cdcd361d609db561e91b | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 9,737 | rd | ram.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ram_service.R
\name{ram}
\alias{ram}
\title{AWS Resource Access Manager}
\usage{
ram(config = list(), credentials = list(), endpoint = NULL, region = NULL)
}
\arguments{
\item{config}{Optional configuration of credentials, endpoint, and/or re... |
2cd8917d49306b36cc1e3f67fcaa7b168fbdb99c | bca10cf62a15c32150d9276d520a4f527ce3db23 | /script.r | eebc653086c81cdb1a7973cc1c926669efbf50c3 | [] | no_license | JonMinton/human_fertility_database | f198de9418172b45660f364ceac4cd6a91f142ed | a9c396c6616f39d2feee8e8eac76a4941b4cb8f1 | refs/heads/master | 2020-05-18T11:31:37.807970 | 2015-04-06T22:02:27 | 2015-04-06T22:02:27 | 25,087,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,920 | r | script.r | rm(list=ls())
### To do :
# Combine with female population size estimates from hmd
source("scripts/LoadPackages.R")
RequiredPackages(
c(
"r2stl",
"ggplot2",
"reshape2",
"plyr",
"lattice",
"stringr"
)
)
data <- read.csv("data/tidy/lexis_square_combined.csv")
data$X <- NULL
data$cod... |
63d55060bd0ad8f4c255ffc5cfbdd48e790a8767 | 3b1c82ecb7622ce0030470c19732c17f6fda89ff | /SC2019Lab-3-王哲涵-16081043.R | f5cd20e9cb4912bef1bd8a53a06e6d27c42f9b97 | [] | no_license | anhnguyendepocen/SC2019-assignments | 64bbb7a8d82afba4cc636122ed89268db8aca25e | a4cc348c40c4dc4cb373cbbde2cf92acb71cd69b | refs/heads/master | 2020-09-02T03:41:35.656473 | 2019-04-12T12:48:48 | 2019-04-12T12:48:48 | null | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 811 | r | SC2019Lab-3-王哲涵-16081043.R | x<-readLines('C:/BABAnews.txt',encoding="UTF-8") #读取文件 用encoding消除乱码
x #打印
str(x) #确定文章的段落数
library(glue)
trim(x)
nchar(x) #计算字符数
ee<-as.matrix(x) #将x转换为矩阵形式
y<-paste(ee,collapse = " ") #用paste函数将段落合并并且打印
regexpr("技术架构", x) #根据输出结果可知文章中含有技术架构
r <- regexpr("双11", x[1:5])
m <- regmatches(x[1:5], r)
d <- gsub(... |
9d8021f7df5e17f98d514a70d426cf51c01d7f82 | 09486238326c1adcb80b29bdb0023ca65155ccb7 | /SC reuslts/alltogather.NSGAII.R | 9144498478518b825316e8720c200911f70608d5 | [] | no_license | shaikatcse/EnergyPLANDomainKnowledgeEAStep1 | 127b58e0727d19a2ed999f7f24a8921b79d5a6d9 | 649cd38e0ca5e53105f9f6a051831da9cf401646 | refs/heads/master | 2022-09-28T20:03:56.327477 | 2022-09-20T15:47:03 | 2022-09-20T15:47:03 | 16,055,790 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,271 | r | alltogather.NSGAII.R | #This is a R file that generate a boxplot for six different problem for NSGA-II.
#There are 6 rows and three columns. Each row presents a problem and 1st column present stop generation, 2nd column presents HVd and third column presents epsd.
#Someone needs to select appropriate path to work with.
postscript("ALL_NSGA... |
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