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78a93a0aaa63a2c0e84e493173b250de472bc420 | 2e8a0f83c5a27cfd1977eb7b94863d12bee7bc5f | /barplotPercentClasse.R | 76a4030610c7335b58677a5547a6fb93d442bd2b | [] | no_license | ABorrel/saltbridges | 611036cfa101da4c0e390de3c12b9cae04e3b5b0 | 5b8a0bb15ab6876082891f2afc2d3ce0b4c03c7a | refs/heads/master | 2020-06-17T21:51:16.843534 | 2016-11-28T10:54:37 | 2016-11-28T10:54:37 | 74,966,556 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,534 | r | barplotPercentClasse.R | #!/usr/bin/env Rscript
source("tool.R")
source("AFC.R")
openFile = function(path, type, line){
file = paste(path,"proportionType", type, sep = "")
#print (file)
data = read.csv(file , sep = "\t", header = TRUE)
#print (data[line,])
return (data[line,])
}
frequencyMatrix = function (data){
nbCol = dim(d... |
7b23730a01e65740bcf89dad88391ccb218efdf1 | 268e84cedc0d48cc1b59d153ec093729b323cb90 | /Exercise 2.R | e39a60131d2f55e2732bf3cae0ff38c650bbf43d | [] | no_license | dashdalvi/Data-Analysis-Decision-Making-Coursework | 33e738e06d5a73956310dc2ae40f8651cd26c486 | 1a81c5d967833316010079d830d9652301fc8666 | refs/heads/master | 2020-04-24T12:46:21.403455 | 2019-02-22T00:24:06 | 2019-02-22T00:24:06 | 171,966,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,075 | r | Exercise 2.R | #Exercise 2
library(ggplot2)
library(grid)
library(gridExtra)
# Set the state for random number generation in R
set.seed(123)
# Pick the number of observations
n <- 100
# Pick the values for the intercept and the slope
beta0 <- 10
beta1 <- 2
# Assume the error has a normal distribution
# Pick the me... |
330b666b08b71639bbe39dd71b3821c89ada716c | d43ac1bb61f96e970fb96d1f52cd2afef79b73be | /man/users.Rd | 0dbf4a69cc4a6af8a08ae8691b6563bf90248eeb | [] | no_license | debruine/demopsydata | faf3079561bbb7d36964e84a1257485ca618d8d8 | 1b5d2a179edd873162a54dec11faff2163afe6c0 | refs/heads/master | 2021-04-06T03:57:48.554015 | 2018-03-08T16:32:41 | 2018-03-08T16:32:41 | 124,418,673 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 484 | rd | users.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{users}
\alias{users}
\title{Participant data.}
\format{A data frame with 53999 observations and 3 variables:
\code{user_id}, \code{sex}, and \code{birthday}.}
\usage{
users
}
\description{
A dataset containing th... |
283f7c58e135cb192978e9163141dd7ba716478e | 357c61695c0b2885916745226b5d0dc7408766c0 | /BAMMtools/man/testTimeVariableBranches.Rd | 88c960355462af86c287d7f8af88c4c72a805ff4 | [] | no_license | macroevolution/bammtools | 62b4f9c6dd20ea37d1df6b7dd75d10967a8f3e75 | 07a17d8260a9e17419ca4bbc27687b4b6a7164be | refs/heads/master | 2022-11-22T15:11:11.336582 | 2022-11-11T17:08:43 | 2022-11-11T17:08:43 | 17,520,404 | 7 | 7 | null | 2016-05-05T21:09:28 | 2014-03-07T16:42:06 | R | UTF-8 | R | false | true | 5,228 | rd | testTimeVariableBranches.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/testTimeVariableBranches.R
\name{testTimeVariableBranches}
\alias{testTimeVariableBranches}
\title{Evaluate evidence for temporal rate variation across tree}
\usage{
testTimeVariableBranches(ephy, prior_tv = 0.5, return.type = "posterior")
}
... |
01833603ea95c4e8518e82702eee17b092830169 | c3eb3703ce4dd401cc37b112f751aa49b6b1e0ef | /man/wiggleplotr.Rd | ac281a11e892b9cf17ffb13737ce895c5f83b329 | [
"Apache-2.0"
] | permissive | js29/wiggleplotr | 6a2b17e3e424d9bae0f06dbb13807a3b8fe0e4e0 | 24b5a5af6ade2bf6ff91903f4496ea3d49d50f98 | refs/heads/master | 2020-12-24T23:55:05.900753 | 2016-09-08T12:12:51 | 2016-09-08T12:12:51 | 67,697,987 | 0 | 0 | null | 2016-09-08T11:40:17 | 2016-09-08T11:40:17 | null | UTF-8 | R | false | true | 236 | rd | wiggleplotr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wiggleplotr-package.r
\docType{package}
\name{wiggleplotr}
\alias{wiggleplotr}
\alias{wiggleplotr-package}
\title{wiggleplotr.}
\description{
wiggleplotr.
}
|
b177b9c070b4887430176f5df8796a1e8c2f6177 | 1017ba10076f8b16baf35156427ab046d7077743 | /Analysis/toy_model.R | 18526f7c09a1e425713f4a6ad69e7abac8e4a7c4 | [] | no_license | sumitsrv/metaphors | 254bfb1ca404e2160b983b9117e4c38e28a037a0 | e2abdc134a035844cf7f4f03ec035c10d7998639 | refs/heads/master | 2021-05-19T12:58:34.629277 | 2016-07-25T15:34:53 | 2016-07-25T15:34:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,406 | r | toy_model.R | d1 <- read.csv("../Model/Output/lion-1feature.csv", header=FALSE)
colnames(d1) <- c("category", "feature1", "prob")
ggplot(d1, aes(x=category, y=prob, fill=feature1)) +
geom_bar(stat="identity", color="black", position=position_dodge()) +
theme_bw() +
scale_fill_brewer(palette="Accent")
d2 <- read.csv("../Model... |
d2b1aae5133a4412b8c62890f57a1b3f07f4e959 | a83cf31dc97cb34639e9962e43cc8e40dd79dfa3 | /bobby1.R | 35b55843ec00287376f10f1525e78e7f4d53afec | [] | no_license | jnownes/ds202_project | 9cf0d5c78701ad4ed428b8c34654d003d96f2059 | f7b4ec97a794d9f7036e7df45b3df00e6f4c8c13 | refs/heads/master | 2021-05-23T16:57:45.958877 | 2020-05-05T16:59:22 | 2020-05-05T16:59:22 | 253,391,008 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 954 | r | bobby1.R | library(lubridate)
library(dplyr)
library(ggplot2)
library(plotly)
dat = read.csv("us_states_covid19_daily.csv", stringsAsFactors = FALSE)
#View(dat)
str(dat)
dat$date = ymd(dat$date)
trend <- dat %>%
group_by(date)
ggplot(trend,aes(x= date,y = positiveIncrease,fill = state)) +geom_bar(stat= 'ide... |
dd0038cad0af312afe17744150dfdccfc70997ee | fa31080db4ae795e124286a54da1468428cde439 | /man/fracMake.Rd | 806f816f6493298180b2946286a4aca5c3396107 | [] | no_license | cran/Rfractran | 59d2d46f11d1ed911a87001300c71a0adab2c621 | 271ea36f804b3b09f0e0dc3208eef3fc1f5893f0 | refs/heads/master | 2022-11-09T23:51:48.719714 | 2020-06-25T15:20:11 | 2020-06-25T15:20:11 | 276,704,632 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 796 | rd | fracMake.Rd | \name{fracMake}
\alias{fracMake}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Function to Create a Sequence of \code{bigq} Fractions
}
\description{
Feed this function a collection of numerator and denominator values;
get a vector of \code{bigq} fractions suitable for use as a FRACTRAN progra... |
1bba0047efcbf7a870a98297551710399cd6f32d | ce7a1122d6b1e21733f6ac0ae572336c7063134f | /Capstone_Two_Script.r | 234eafe011c0ab474da1a49964dc542e291228f0 | [] | no_license | Thom-J-H/Capstone2_Harvard_edX | 88700ace0cc58e6f0d2cd80ddff8f8a944354b57 | 0cad8d90843254a78b162998c16e2510b8be7924 | refs/heads/master | 2020-04-28T07:31:52.205830 | 2019-11-19T02:04:45 | 2019-11-19T02:04:45 | 175,096,029 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 52,178 | r | Capstone_Two_Script.r | # Capstone Project Two: 21 models tested on the WDBC data
# R Script: All Code in RMD and more
# Submitted on 2019-03-12, by Thomas J. Haslam
# In partial fulfillment of the requirements for the Harvard edX:
# Data Science Professional Certificate
# Revised (after graded) on 2019-03-14, based on peer-review ... |
83c95a4c923cfa3be73dde5e52576869cc546064 | 3fe3c4c2e05c1f60ebba273d53e413607684ff92 | /man/print.nb.test.Rd | aa37dec75fbb1b6da8db57ca7ceff108956204c8 | [] | no_license | cran/NBPSeq | 60282cf216bd83793780d5f276a4ef7df88c08d1 | 0b30098f016d10c514e7942402dd405c1e18a363 | refs/heads/master | 2022-06-29T05:26:20.184281 | 2022-06-09T10:02:06 | 2022-06-09T10:02:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 370 | rd | print.nb.test.Rd | \name{print.nb.test}
\alias{print.nb.test}
\title{Print output from \code{\link{test.coefficient}}}
\usage{
\method{print}{nb.test}(x, ...)
}
\arguments{
\item{x}{output from \code{\link{test.coefficient}}}
\item{...}{currenty not used}
}
\description{
We simply print out the structure of \code{x}. (Currenlty
the ... |
e2910a1b45ccdea607c890486e3b81f883bdd29d | cad7ac48b067c7f10c4890dccb41c68f12368c2c | /1_4.R | 7a4ec6bdd3780b11c0e8bb582c3397b6a3d04cf1 | [] | no_license | AnguillaJaponica/rctr | 2a595d16c29501021b9ffad7d92d596897717210 | 37d1bdda97d8d58ecccc15dee8e8fd359c37faa8 | refs/heads/master | 2023-04-15T22:45:45.531240 | 2021-04-29T13:23:30 | 2021-04-29T13:23:30 | 362,815,054 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,219 | r | 1_4.R | install.packages("tidyverse")
library("tidyverse")
email_data <- read.csv("http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv")
male_df <- email_data %>%
filter(segment != "Womens E-Mail") %>%
mutate(treatment = ifelse(segment == "Mens E-Mail", 1, 0))
summ... |
d5aece887083d6864876e5ccf169296f2b2f0983 | 59aa5552391ef722b9a7fea03b42d7d588c00688 | /Intervals_PMRF.R | b45abb7ea47b85894ccf6aef4bb72360d594a2a9 | [] | no_license | cascadiaresearch/PMRF_Feb2020_Report | a0255ae347b35bc682ab0b13606eabc0cfd5488c | 53efa5d504bd2a88e933396f5b2cf88a54298be7 | refs/heads/main | 2023-01-04T04:23:16.450276 | 2020-11-02T18:29:13 | 2020-11-02T18:29:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,411 | r | Intervals_PMRF.R | ## PMRF Feb 2020 Report:
## Calculate intervals b/t locations
## Michaela A. Kratofil
## 05 OCT 2020
#######################################################
# load packages
library(tidyverse)
library(lubridate)
# read in location data
locs <- read.csv('Douglas Filtered/TtTag002-035_DouglasFiltered_KS_r... |
eaa64ad29e19141ee9c8ac6ccbe6b55ce105884a | 1ccb6df12f53fe2e387bf624b65da4f6af78f203 | /notes/shiny/tutorialUI/lesson2/App-2a/ui.R | e19a751ffc738d27c87eceb4fc59f132c1f7baf9 | [] | no_license | raffled/DSCI504 | 960963720cad8c5b4f6c4bea449beaec7ef7ff91 | 0aebb9331b0cfabe40db4977c117df4c58c6297b | refs/heads/master | 2016-09-05T09:52:07.153963 | 2015-05-04T03:01:34 | 2015-05-04T03:01:34 | 34,693,548 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 185 | r | ui.R | # ui.R
shinyUI(fluidPage(#position = "right",
titlePanel("title panel"),
sidebarLayout(position = "right",
sidebarPanel( "sidebar panel"),
mainPanel("main panel")
)
)) |
99d79b9222b86b326e7a5d8793a8afa35b1865c7 | f72562681d88b12b1e0c76e2f41878a44729650e | /D3playing.R | ff94e548924d5cdee72f94e820bf5fddd2d6da91 | [] | no_license | vargovargo/CHVIr | 1706cfd35e0779cfebfd56f759bd1ded2a503dbb | 56e2b295a78a733f4fcf5f140097327f314d107b | refs/heads/master | 2018-09-17T18:09:44.056721 | 2018-08-13T21:31:49 | 2018-08-13T21:31:49 | 106,874,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,704 | r | D3playing.R |
library(shiny)
library(tidyverse)
library(leaflet)
library(shinythemes)
library(ggthemes)
library(sf)
library(DT)
library(plotly)
CHVIdata <- readRDS("~/GitHub/CHVIr/CHVIz/chviCountyTidyRace.RDS")
tri <- {CHVIdata %>%
filter(def == "Projected number of extreme heat days" & strata %in% c("2085", 2085, "none") ... |
d1bc560336ed0394bd81773c52721683c63e3bdc | 044d3102f385d400d921ef094f32622037bc8f85 | /Basic Programs/Input_Output/app.R | 556c209bbb6672c7f429aa1289dceccf99e1e5bb | [] | no_license | chidcrushev/RShiny | 6d2ce453d73b46b518166463010567595ef11ec4 | b6a7010d71aa0126ec33c0f08a9afd2de12f41d3 | refs/heads/master | 2020-05-07T11:27:35.423219 | 2019-04-09T23:05:52 | 2019-04-09T23:05:52 | 180,462,378 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,599 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
? sliderInput()
# Define UI for application that draws a histogram
ui <- fluidPage(
#textInput... |
59846157cd4a5dae5ea0cec6d7a2831281dc6ce8 | 38a5a35e74e487f400fccb327749a1a97e0309a8 | /code/downsampling_code/run_archr_geneactivity.R | 1e3e882bfa44c3c4056b84f3ab71aa0b10f4c6b0 | [] | no_license | timoast/signac-paper | 1d0f303f20ab018aa69e8929f6a66cc110e1c81f | 1cdbb6dd6a5ad817bd23bb7d65319d5bf802455f | refs/heads/master | 2023-08-25T10:59:33.951761 | 2021-11-01T21:05:01 | 2021-11-01T21:05:01 | 309,495,686 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 739 | r | run_archr_geneactivity.R | library(ArchR)
args = commandArgs(trailingOnly = TRUE)
ncore <- as.numeric(args[1])
arrowfile <- args[2]
nrep <- args[3]
timefile <- args[4]
genome <- args[5]
# load archr project for downsampling level
proj <- loadArchRProject(path = arrowfile)
# set threads
addArchRThreads(threads = ncore)
addArchRGenome(genome)
... |
8dbc259a69df6f34f8694ccc66c831c94e216558 | fcebca7c5725c44796d90a7158350e52aa61cc72 | /man/RSiena-package.Rd | 8e6b8ae0a0f8d977c8b290c80e71ac741102b698 | [] | no_license | kkc-krish/RSiena | c082a0e1c3698bffd68734387347c4de7981698f | 4f9d65392367703150e6285291a9b41d23e647c6 | refs/heads/master | 2020-12-24T19:59:58.649070 | 2013-06-18T00:00:00 | 2013-06-18T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,757 | rd | RSiena-package.Rd | \name{RSiena-package}
\alias{RSiena-package}
\alias{RSiena}
\docType{package}
\title{
Simulation Investigation for Empirical Network Analysis
}
\description{
Fits statistical models to longitudinal sets of networks, and to
longitudinal sets of networks and behavioral variables.
Not only one-mode networks but ... |
3876639b7f2bee6aa8ced406e1d6bb97634cdc2c | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/exuber/R/tidiers-radf.R | 1927943cbbbda6688cf6ff89da3c583f142105ed | [] | 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 | 3,246 | r | tidiers-radf.R | #' Tidy a `radf_obj` object
#'
#' Summarizes information about `radf_obj` object.
#'
#' @param x An object of class `radf_obj`.
#' @param format Long or wide format (default = "wide").
#' @param panel If TRUE then returns the panel statistics
#' @param ... Further arguments passed to methods. Not used.
#'
#' @... |
8f8c4d3874a2c07f77e39fb9c31da9036004d4c3 | 90b3ff028fc6fb3729933bac757d999454be96d9 | /06_data.R | 1b2db78b5947dff0e457cc57758b4788ae4dc62e | [] | no_license | bacoon23/polyreg_build_tmp | bd986aa658b4c2c6d9427413114a7549c7d98640 | 3aed0fe604ff134bddf08f8d868c03614c214ccf | refs/heads/master | 2020-03-16T08:54:44.795896 | 2018-05-08T12:26:52 | 2018-05-08T12:26:52 | 132,604,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 798 | r | 06_data.R | # Data Workflow, same.
install.packages("triangle")
library("triangle")
gen_beerbowl <- function() {
gen_dat <- data.frame(x1=c(rtriangle(250,0,7,01.2),rep(0,250)))
gen_dat$x2 <- gen_dat$x1^2
gen_dat$x3 <- gen_dat$x1^3
gen_dat$y <- 191.6 + 11.55*gen_dat$x1 + -6*gen_dat$x2 + 0.4*gen_dat$x3 + rnorm(500,0,18)
... |
fd4705d5b6345d738e2fad5685fc7eaa1c9ffa79 | aea96af9c73c07791e509a65c28fe0f76053007d | /describe/R/missingness_functions.R | 2350dce67801fe524af8d055eab407108044c5e1 | [] | no_license | jgutman/eduanalytics | cb0f4e933575c3cc4eec7fc69b75aa36a3846a97 | d4cc37bd80fb820350f72a93511b33218c1393b6 | refs/heads/master | 2021-11-23T15:37:59.928954 | 2017-10-06T19:50:58 | 2017-10-06T19:50:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,678 | r | missingness_functions.R | ## FUNCTIONS EXPLORING MISSINGNESS
#' Function to create a matrix with the percent of observations missing in each variable
#' in a data frame or tibble grouped by another variable (generally appl_year)
#'
#' @param dat tibble or data frame
#' @param varname grouping variable
#' @param round_digits number of digits f... |
aafb9282d30ffe5078f5885a441c6f5aea946fca | 2f5ed17ace2ae9c7a1102617ca1dcc91ae1f2466 | /R/calc_streak.R | 1eaba07cf855746523b7004c78b30138be4ab2f2 | [] | no_license | jbryer/DATA606 | 0b9f79590d257040e997b48a78c3b0c9ce0b006c | 3c702d4b08af2e2258d54dc31b13ae61a8e29bcd | refs/heads/master | 2023-08-17T04:27:03.710532 | 2023-08-11T14:59:38 | 2023-08-11T14:59:38 | 39,025,976 | 6 | 15 | null | 2022-11-11T22:27:03 | 2015-07-13T17:09:52 | HTML | UTF-8 | R | false | false | 193 | r | calc_streak.R | #' Calculate a streak.
#'
#' @export
calc_streak <- function(x) {
y <- rep(0,length(x))
y[x == "H"] <- 1
y <- c(0, y, 0)
wz <- which(y == 0)
streak <- diff(wz) - 1
return(streak)
}
|
aad2a3692145f63c1fef0790d5af0b2072c5dc8a | 7b82068433efacf8840c57e2c05b613dbe13d31c | /man/get_execution_role.Rd | 0f51182a93345b976f68f99354089dcc6376b5dc | [
"Apache-2.0"
] | permissive | OwenGarrity/sagemaker-r-sdk | d25f0d264dcddcb6e0fa248af22d47fc22c159ce | 3598b789af41ed21bb0bf65bd1b4dfe1469673c9 | refs/heads/master | 2022-12-09T04:50:07.412057 | 2020-09-19T13:02:38 | 2020-09-19T13:02:38 | 285,834,692 | 0 | 0 | NOASSERTION | 2020-09-19T13:02:39 | 2020-08-07T13:23:16 | R | UTF-8 | R | false | true | 455 | rd | get_execution_role.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/session.R
\name{get_execution_role}
\alias{get_execution_role}
\title{Return the role ARN whose credentials are used to call the API.}
\usage{
get_execution_role(sagemaker_session = NULL)
}
\arguments{
\item{sagemaker_session}{(Session): Curr... |
4c4409748da7458b0627019ccfaa8c4e4fad4944 | 742987e658baec8f280792b07253b8e1d7d00bf4 | /R/LNM.Sim.R | 276c686bbd74be96f558fa0d84d403228b95f7e7 | [] | no_license | ZRChao/LRTT | ab083de0a8d64f688ac68ff7e2cd2cff39ff10ae | 47ea5c46adf326e101b86b80a95019182980f178 | refs/heads/master | 2020-03-06T14:47:49.748541 | 2018-09-12T11:52:33 | 2018-09-12T11:52:33 | 126,942,320 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,292 | r | LNM.Sim.R | #############################################################################
### logistical normal multinomial distribution simulation #####
### Given one tree structure and sample depth, on leafs it will follow#####
### logistical normal multinomial distribution. Correspond to the tree, #####
### choo... |
3d835198de9540570017c85ab70fdf5c5d06d214 | 56a6b27413fc7e5d04c584675a9fbf599467428c | /CSV2GIFT.R | 6dfb9b159afb6fd349648825a84ecc784ce4a39f | [] | no_license | dominikfroehlich/CSV2GIFT.R | b3336c544d3ba371a26393c8d1a746d1192b4108 | b2a75e06fb950e9bf0ba3defa2fafa61cf00621e | refs/heads/master | 2020-04-08T19:25:06.252688 | 2018-11-29T11:18:36 | 2018-11-29T11:18:36 | 159,654,387 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,709 | r | CSV2GIFT.R |
full <- read.csv2(file = file.choose(new = FALSE))
cat <- "StudentMC1"
output <- paste0(cat,".gift")
write("\n",file=paste(output),append=FALSE) #deletes file!
for (i in 1:dim(full)[1]){
df <- full[i, ]
attach(df)
subcat <- ifelse(is.na(Einheit),"uncategorized",Einheit)
numT <- sum... |
55f77afc012c3159596fccd911257816fb71d192 | df1e8f192926f8a38ce24948bc5297d380466d2c | /RGCCA/R/define_M_regularisation.R | 4de92b603b32c2ef13ae9b140e7ce3086335f484 | [] | no_license | AGloaguen/-MGCCA---Reproducible-Code | 0f622230e3b0869ab390a9d8a3e8200509e7bdfe | 2ed68908d416a83aa9aa741477eba737bfcb587f | refs/heads/master | 2020-05-17T08:29:28.548106 | 2019-04-26T12:59:09 | 2019-04-26T12:59:09 | 183,607,206 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,470 | r | define_M_regularisation.R | define_M_regularisation <- function(M_regularisation, n_way, tau, A, A_m, n, p = NULL, K = NULL, J = NULL, M_K = NULL, M_J = NULL, Proj_K = NULL, Proj_J = NULL) {
if (n_way){
switch(M_regularisation,
###############################
## non_kronecker_RGCCA ##
###############################
... |
1ef0b2093d7b4ac224ae06aca3d42d591def3cb0 | 9a808268700a7ddf02c3b11b3820eed269acd9b9 | /functions.R | 2b46c025d1c55102c450677f958cdfc4df2951df | [] | no_license | wmattbrown/dndhelper | 92c532b9e2fbbe5d4351df401ea8e18ca9a67de0 | 1ab4b25d83dc90939c9bbb9dc2bd03f6e5a2900c | refs/heads/master | 2022-05-29T12:41:48.356959 | 2020-05-02T20:36:36 | 2020-05-02T20:36:36 | 259,116,249 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,205 | r | functions.R | # functions!
# roll <number> <sided> dice
# e.g. roll 3 8 sided dice: roll(8, 3)
# e.g. roll 1 20 sided die: roll(20)
roll <- function(sided = 20, number = 1){
stopifnot(is.numeric(number))
stopifnot(number == round(number))
stopifnot(sided %in% c(4, 6, 8, 10, 12, 20, 100))
stopifnot(number > 0)
# roll th... |
678f8df82b9b093326c2aea4c515ac79cf564b8c | 435ee8ff8c0f06de1f77edb787d4bd019f4c4b98 | /Watson-CONVERSATION-Code-Snippet.R | bff0940da7a91e371814710c028785727ad51196 | [
"Apache-2.0"
] | permissive | RudyMartin/R_Scripts_for_Watson | 8e01734037d404c8f9228146a739723605fda249 | 50c7fdd643e51ecc5461f75a29e57512d8938968 | refs/heads/master | 2021-01-14T13:21:30.958126 | 2016-08-27T20:56:59 | 2016-08-27T20:56:59 | 67,254,718 | 1 | 0 | null | 2016-09-02T21:05:05 | 2016-09-02T21:05:04 | null | UTF-8 | R | false | false | 3,400 | r | Watson-CONVERSATION-Code-Snippet.R | ######################################################
### IBM Watson - Code Snippet --- VERY EARLY CODE - API"S NOT FULLY AVAILALBLE YET
### Experimental Code. R Interface for IBM Watson Services
### DOCS: http://www.ibm.com/watson/developercloud/doc/conversation/
### Before you begin you will need (1) An IBM Bluemix ... |
ee1188a33b3cbe7a68011de86ceb432fbb8590f4 | fd194cce7c398cddba8dfc7dfaf75dc3a10f1bb7 | /data-raw/process_data_sets_maggie.R | 693cf3ee1a90ab8fc54a6177ee6e6cc3c0098c5a | [
"MIT",
"CC-BY-4.0"
] | permissive | Starryz/fivethirtyeight | 16241e7f3fc5939ebb01b0676e89a05c1bb08f5c | 1c1cb1f92caa9802fc6086bd1af0d45bebb5a10f | refs/heads/master | 2020-05-31T12:23:33.192568 | 2019-09-20T07:06:10 | 2019-09-20T07:06:10 | 190,279,423 | 1 | 0 | NOASSERTION | 2019-09-20T07:06:12 | 2019-06-04T21:07:20 | R | UTF-8 | R | false | false | 11,415 | r | process_data_sets_maggie.R | library(tidyverse)
library(janitor)
library(usethis)
# nba-carmelo---------------------------------------------------------------------
nba_carmelo <- read_csv("https://projects.fivethirtyeight.com/nba-model/nba_elo.csv") %>%
clean_names() %>%
mutate(
team1 = as.factor(team1),
team2 = as.factor(team2),
... |
c56386485865ca3d193ebb5b00f83d6cab2280b7 | 54054ff32cea5b78942ec2e259a630067c1aa2cd | /cachematrix.R | 30c3d5cebc6a4703d10bf1b056d7aacc75cc0dff | [] | no_license | JLovr/ProgrammingAssignment2 | 4fd9a5315dfa90a31b0e3d7887fd069ac692c8c7 | ab6f87198dfe2d604b935524bc3d3e506975f9eb | refs/heads/master | 2021-01-21T06:02:23.094706 | 2015-01-16T06:07:57 | 2015-01-16T06:07:57 | 29,264,092 | 0 | 0 | null | 2015-01-14T20:25:37 | 2015-01-14T20:25:37 | null | UTF-8 | R | false | false | 997 | r | cachematrix.R | ## creates a cached list that contains a matrix and its inverse
## which are employed by cachSolve.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinverse <- function(inverse) m <<- inver... |
0b9156fe7d6baafc586178b4ac6f8e288695ed9b | 863aa7e71911423a9096c82a03ef755d1cf34654 | /man/get_type.Rd | 927144ef983a9012d244dda896c811fb9f031092 | [] | no_license | BioSystemsUM/specmine | 8bd2d2b0ee1b1db9133251b80724966a5ee71040 | 13b5cbb73989e1f84e726dab90ff4ff34fed68df | refs/heads/master | 2023-08-18T05:51:53.650469 | 2021-09-21T13:35:11 | 2021-09-21T13:35:11 | 313,974,923 | 1 | 1 | null | 2021-09-21T13:35:12 | 2020-11-18T15:22:49 | R | UTF-8 | R | false | false | 721 | rd | get_type.Rd | \name{get_type}
\alias{get_type}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Get type of data
}
\description{
Get the type of the data from the dataset
}
\usage{
get_type(dataset)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{dataset}{
list representin... |
a9632fac1518ecf88f06826d0f47868900a90dc1 | db72f30a7e160279bda10af408ce82d7bb905d0e | /R/ALLOCATION.R | 2bbbb3021c483cae56613a68b846d640ffa18e5d | [] | no_license | NeveTong/mySIDES | 3fe563cc73f4903eb4f5a4e6fefce26b30b74360 | ab3c4acf6bebcfd9db03bb1cc0f2a010eb2c4c56 | refs/heads/main | 2023-05-28T21:34:09.971860 | 2021-06-14T02:36:39 | 2021-06-14T02:36:39 | 376,684,419 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,979 | r | ALLOCATION.R | library(nnet)
allocation_procedure = function(H, pct_random, Xcov, type_var, prop_gpe, alloc_hp=TRUE, overall_imb=FALSE, seed=NA){
if(is.na(seed)==FALSE){
set.seed(seed)
}
nb_patients = nrow(Xcov)
if(H > 1){
X = Xcov
ind_cont = which(type_var=="continuous")
nb_... |
f94e86bb2b92d27f6ae09c8c83e565d7a1addabe | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ptstem/examples/stem_modified_hunspell.Rd.R | 551090fb49a50f4ac2828635e13b547d31c0f890 | [] | 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 | 278 | r | stem_modified_hunspell.Rd.R | library(ptstem)
### Name: stem_modified_hunspell
### Title: Stemming with small modification of Hunspell
### Aliases: stem_modified_hunspell
### ** Examples
words <- c("balões", "aviões", "avião", "gostou", "gosto", "gostaram")
ptstem:::stem_modified_hunspell(words)
|
a6af62a8298d9395f8ed85fba5ba3b884a9cd53d | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Numerical_Methods_In_Finance_And_Economics:_A_Matlab-Based_Introduction_by_Paolo_Brandimarte/CH8/EX8.8/Page_445_ExchangeMC.R | b3b170bc90200695664986fdf3ea489135bf16d0 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 907 | r | Page_445_ExchangeMC.R | require(fBasics)
norm.interval = function(data, variance = var(data), conf.level = 0.95) {
z = qnorm((1 - conf.level)/2, lower.tail = FALSE)
xbar = mean(data)
sdx = sqrt(variance/length(data))
c(xbar - z * sdx, xbar + z * sdx)
}
f <- function(r,T,VT,UT) {
exp(-r*T)*max(VT-UT, 0)
}
ExchangeMC <- func... |
1762d759f2b4153784cdb8af0b69520ac3c76842 | 685adb82b0ef76319c7d0e5fe4cb9aabae82367a | /man/MLD.Rd | f3facf0b25d36b23d463b2da479444c5d073f2ed | [] | no_license | scarpino/binequality | 6474cc7a520b414dd622437582fe1d8e8fcbc3b7 | c810d3e5f066bfa8e1b67edbe8b06bc289f380b0 | refs/heads/master | 2021-01-19T02:19:01.663501 | 2018-11-05T14:04:13 | 2018-11-05T14:04:13 | 37,625,137 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 486 | rd | MLD.Rd | \name{MLD}
\alias{MLD}
\title{
A function to calculate the MLD
}
\description{
This fuction calculates MLD
}
\usage{
MLD(samps)
}
\arguments{
\item{samps}{
a (non-empty) numeric vector of values to calculate MLD over, for example, bin mid points or samples take from a fitted distribution.
}
}
\details{
FIXME - equa... |
97b990f2837350a31df32ae61bead64e2b5e8222 | 15fe81660db70c06d112157ae21fed7303ea9194 | /dataGeneration/Pic_Selector.R | 1475dc6628c10518d068ad73062eae93eb324997 | [] | no_license | bStrangerman/ie332group11 | d7dcf3ed7cc694b20545a9cd31560f9787165667 | 4febd1e850b2fa09f437df6f76008cc9444e4209 | refs/heads/master | 2020-03-29T16:59:25.070299 | 2018-12-18T18:01:27 | 2018-12-18T18:01:27 | 150,137,731 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 405 | r | Pic_Selector.R | # This script assigns a random number of pictures to each warehouse in the database
num_of_warehouse <- 932
num_of_pictures <- 150
warehouse <- 1:num_of_warehouse
pictures <- 1:num_of_pictures
warehouse_pic <- matrix(0,1,200)
for (i in 1:length(warehouse)){
z <- sample(seq(1,150),1, replace = TRUE)
warehouse_pic[i] <-... |
992987c297f874f7b91d35080a1f73466a81e0e8 | eb6eaba44c1d54cb301ec351898ecb1a71591a11 | /man/webchem-deprecated.Rd | a53248e48584cf418cda379b266ef68c8650e032 | [
"MIT"
] | permissive | ropensci/webchem | b93a02272b134c2e22586ba04a9be4941cc33039 | 7cb91fd7ca653a176f5acf0e127eb27dc55bc7ab | refs/heads/master | 2023-08-25T17:47:31.939888 | 2023-08-15T16:49:20 | 2023-08-15T16:49:20 | 31,718,339 | 150 | 62 | NOASSERTION | 2023-08-03T20:01:00 | 2015-03-05T14:41:50 | R | UTF-8 | R | false | true | 708 | rd | webchem-deprecated.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/webchem-deprecated.R
\name{webchem-deprecated}
\alias{webchem-deprecated}
\alias{cid_compinfo}
\alias{aw_query}
\title{Deprecated function(s) in the webchem package}
\usage{
cid_compinfo(...)
aw_query(...)
}
\arguments{
\item{...}{Parameters... |
8a1023b5bfe304e23acb159d2f556a157950e7c7 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/genepop/inst/genepop-shiny/opts/opt3ct.R | a37dcdbfcfcc40d34f9d6f06102e26456238addd | [] | 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 | 610 | r | opt3ct.R | opt33 <- eventReactive(input$RunOpt33, {
cat("Opt33\n")
dem = input$Dememo33
nbatchs = input$Nbatches33
niters = input$Niters33
ficout = tempfile()
ficin = GenepopFile()$datapath
cat(ficin)
setRandomSeed(getSeed(input$randomSeed))
RPDGenotypicAllPopulationDifferentiation(fic... |
30e5d41ae54dc246d2ed654fc3fb112f00fda3c1 | 2bc59a2d2a9b7562e66b1108b7ff87c2aee1a506 | /ch08/ch08_1_적합도.R | 910a55ba225fdc0f22c4a602f870344ea6062d13 | [] | no_license | ckiekim/R-Statistics | 4bb78296b9e59761bdfac63433a44abf19c4e386 | d7b6f1bb32a15b310254e524ab4cf277a124a6f0 | refs/heads/master | 2020-06-05T03:35:42.388304 | 2019-07-05T08:35:39 | 2019-07-05T08:35:39 | 192,299,631 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,190 | r | ch08_1_적합도.R | # 8장. 범주형 자료분석
# 8-1. 적합도 검정
# 그림 8-1
x <- seq(0, 15, by=0.01)
dc <- dchisq(x, df=3)
alpha <- 0.05
tol <- qchisq(0.95, df=3)
par(mar=c(0,1,1,1))
plot(x, dc, type="l", axes=F, ylim=c(-0.03, 0.25), xlab="", ylab="")
abline(h=0)
tol.g <- round(tol, 2)
polygon(c(tol.g, x[x>tol.g], 15), c(0, dc[x>tol.g], 0), col="red")
te... |
a97f10a71a5e5259752aa03b7376e6efb5a80280 | cdbdfa2809213938a9fefd8bdd304a2cb5ad6278 | /tests/testthat/test-alma-in.R | 1fa02dfdc0eff798d5c39c9290b890f1e3c3de7e | [
"MIT"
] | permissive | DavisVaughan/almanac | 49491a478e3bcdfae801111e5263efc86c33a3fb | 7b14f6e8f1e685975231e5dadb40bb5bb8f2a9c8 | refs/heads/main | 2023-04-27T20:31:58.281595 | 2023-04-14T17:29:53 | 2023-04-14T17:29:53 | 208,673,066 | 74 | 4 | NOASSERTION | 2023-04-19T19:08:04 | 2019-09-15T23:45:27 | R | UTF-8 | R | false | false | 1,424 | r | test-alma-in.R | test_that("can check if a date is in a runion", {
rrule <- monthly(since = "2019-01-01")
expect_true(alma_in("2019-01-01", rrule))
expect_false(alma_in("2019-01-02", rrule))
})
test_that("is vectorized", {
rrule <- monthly(since = "2019-01-01")
expect_identical(alma_in(c("2019-01-01", "2019-01-02"), rrule)... |
ab1fbdbb1c7d8d9ed6b89456f83d5cbb17f4263e | fce53c0e4a1d45c9c4d3470684f6b0fde6c939eb | /Chapter_5/xtendShinyjs/ui.R | e3d59952fa7fe07b6fe46efe40e9210b1203756c | [] | no_license | himynameismarcel/Web-Application-Development-with-R-Using-Shiny | 579ff710e977519cf1b33256f8165f586aa68d35 | 7dff0ddcb992725c892c65c44edafd94c4458faa | refs/heads/master | 2020-06-02T03:52:12.919588 | 2019-06-16T12:03:07 | 2019-06-16T12:03:07 | 191,026,674 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,009 | r | ui.R | ## Marcel Kropp
## 09.06.2019
## Shiny Application, Gapminder
## Following the book: Web Application with Shiny R (Breeley, 2018)
library(leaflet)
library(DT)
library(shinyjs)
fluidPage(
# we need to define the CSS in the head of the HTML using tabs$head:
tags$head(
tags$style(HTML(".redText {
... |
ff43c0d91a260f88afe8db0d9aaaad1df3421a7f | 4247ac14240060b5f1ea8897e69e2dbfa56b6656 | /man/extractData.Rd | 86bf552302e338d900aafcb15107927b87c65aaa | [] | no_license | bussejoh/eatGADS | 980f4d12aefa63315e6c7265e604a1d39ca65008 | dfaa7108069be4480ec2ceff44c589a0333364c7 | refs/heads/master | 2022-12-04T17:18:11.162390 | 2020-08-11T12:12:43 | 2020-08-11T12:12:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,497 | rd | extractData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extractData.R
\name{extractData}
\alias{extractData}
\title{Extract Data}
\usage{
extractData(
GADSdat,
convertMiss = TRUE,
convertLabels = "character",
dropPartialLabels = TRUE,
convertVariables
)
}
\arguments{
\item{GADSdat}{A \co... |
716e07166c8d72d5785973d18266d174ff8f07cd | d12216651e80028f584efce9e87a9e6ee9ac8d71 | /man/grapes-tail_while-grapes.Rd | 210340d9c5d6ca9837568ade8b427d5420a010d5 | [
"MIT"
] | permissive | d0rj/pido | e2774a727719143747c2c4fa0468bb5c1cd7b2e2 | 4da06b8b70a17f0dc55a7cd538702ba80bdd0bd4 | refs/heads/main | 2023-06-30T06:40:56.643640 | 2021-07-14T12:08:03 | 2021-07-14T12:08:03 | 354,776,985 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 455 | rd | grapes-tail_while-grapes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functional.R
\name{\%tail_while\%}
\alias{\%tail_while\%}
\title{tail_while combinator which returns tail elements while predicate is TRUE (infix version)}
\usage{
x \%tail_while\% p
}
\arguments{
\item{x}{vector to filter}
\item{p}{predicat... |
c624b3f36ade75ab34fbadc9719937e3de6b571d | 1005478508bac2fe3b259c0bfc9270c85328c5d4 | /man/mzrtsim.Rd | b25ffba4ddcb5817e6de685b7674bfd53b8b9969 | [] | no_license | yufree/mzrtsim | bdf288efe7ccb2b257c692eb41472887505f8d5d | 07918656844c42cb9f84932dccc443c457ebf45e | refs/heads/master | 2023-08-31T03:54:31.689131 | 2023-08-29T02:12:06 | 2023-08-29T02:12:06 | 129,319,854 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,751 | rd | mzrtsim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mzrtsim.R
\name{mzrtsim}
\alias{mzrtsim}
\title{Generate simulated count data with batch effects for npeaks}
\usage{
mzrtsim(
ncomp = 100,
fc = NULL,
ncond = 2,
ncpeaks = 0.1,
nbatch = 3,
nbpeaks = 0.1,
npercond = 10,
nperbatc... |
450764220f8dc6fb805769a1a1976573c904cdb9 | c6b0c684b665af00cd61b2c5e72d595f917ca25a | /functions/old/calc_parents_TGV.R | 5f133049ffbcf1aa14d8878559b2a3f2069dfb65 | [
"MIT"
] | permissive | arfesta/SimBreeder_Project | 861acb0678bce76a49f08f90c38b709934b3692f | 000337e18be4501d49839b1b213e1025d3ef6fa2 | refs/heads/master | 2023-07-09T04:56:59.737463 | 2023-06-26T20:10:39 | 2023-06-26T20:10:39 | 64,675,861 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,520 | r | calc_parents_TGV.R | #' Cacluate Parents Total Genetic Value
#'
#' This function esimates the total genetic value of parents produced from the create_parents function
#' @param parents The object that is returned from the create_parents function
#' @param map.info The object returned from create_map function
#' @param A Value assigned to t... |
2ca84c95982462c3e023713f64880e04a07fa887 | 32ed69ea8721f9913b704a1223e13a6fe398374b | /tests/testthat/test-mNIX_NLL.R | 513f268eeb6cf9ed1024a64c18d7968b061624c8 | [] | no_license | mlysy/losmix | 1957a388845bafba673c41c609ef1aee32aaefc9 | 16de5dbeb18d89a45f658e93e3c6b276e9ac1f13 | refs/heads/master | 2021-06-22T23:05:57.787587 | 2021-01-13T14:38:26 | 2021-01-13T17:35:15 | 180,701,572 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,929 | r | test-mNIX_NLL.R | ## require(losmix)
## require(testthat)
## require(numDeriv)
source("losmix-testfunctions.R")
context("Single mNIX")
test_that("Sufficient statistics are the same in R and TMB", {
ntests <- 10
suff_names <- c("yy", "Xy", "XX", "N")
for(ii in 1:ntests) {
N <- sample(10:20, 1)
p <- sample(3:5, 1)
X <-... |
32b3d3ae6a98ccd1d38587de1c3c5bbb9653337a | a4a5aa44832be7058fb253eb0e1fe57f1c796c6b | /R/bootstats.R | 759a0bbc68b9c9cab530cf3792690f65a91fba54 | [] | no_license | AkselA/R-confintplot | 03bcbcab7edebbb6fb429a8d58b5d5768f8ef5a3 | ff21fe40b25f51d0a7ff93d9805b92819703f364 | refs/heads/master | 2021-01-01T17:38:40.638186 | 2018-01-05T22:09:14 | 2018-01-05T22:09:14 | 98,118,494 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,654 | r | bootstats.R | #' Calculate mean and median with bootstrapped confidence intervals.
#'
#' This function returns the bootstrapped mean and median,
#' and the respective confidence intervals, of the supplied numeric vector.
#' @param x A vector of numerical values.
#' @param p.level A number giving the level of confidence for the inte... |
2ca642e5a090ff1fb51c74427bdd360a44272ef1 | ed67c7d9b3860901a9b2feb68914fcee3c3de736 | /Scripts/4_Figures_betadiversity_S_ENSPIE_All_Native_Scen2_10plots.R | 37398904fae7171c7b3d0c820b2b04c88cea046a | [
"CC-BY-2.0"
] | permissive | dylancraven/Hawaii_diversity | f5ebaef8ebba6bd652aef33efa6d668374107d61 | 270a61cd7723b662f395eeaf04f4bc7b124fa79d | refs/heads/master | 2022-01-23T15:37:17.950429 | 2019-07-15T12:12:29 | 2019-07-15T12:12:29 | 192,880,471 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,259 | r | 4_Figures_betadiversity_S_ENSPIE_All_Native_Scen2_10plots.R | ################
## beta_S_PIE #
################
# 10 plots #####
###############
# only Scenario II: "Het + Age"
# just Beta S
require(tidyr)
require(dplyr)
require(ggplot2)
#require(ggridges)
#require(grid)
require(reshape2)
#############
# Data #####
#############
load("Cleaned_Data/Scen2_All_Native_BetaS_anov... |
dcb742a42ce26ed40e3fac04e2c0175686cc42f8 | 84ff0aa8aed2eab8635c3823e8f4c2f364e0e192 | /Examan_2.R | 70a12011d4c5cd89b79a2b2d07f6ee701c5500fd | [] | no_license | pmtempone/AID | 1843415711abcad8d1aee4704291dc78643e58cb | b6292e97c31ae4a9abf281fe7f06e25a1e7fd271 | refs/heads/master | 2021-01-20T19:15:55.635664 | 2016-07-28T15:18:59 | 2016-07-28T15:18:59 | 64,361,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,674 | r | Examan_2.R | library(readxl)
suppressPackageStartupMessages(library(xlsx))
library(ggplot2)
#library(Stuff)
library(knitr)
library(reshape)
library(biotools)
suppressPackageStartupMessages(library(psych))
suppressPackageStartupMessages(library(caret))
library(MVN)
library(klaR)
library(Rmisc)
library(Hotelling)
library... |
5ca6bdb4e0f71ef6a1f707d740843fc992b39ca6 | 6fb04083c9d4ee38349fc04f499a4bf83f6b32c9 | /R/utilities.R | 574d02a23ada6aea2cb40cbb802bb6765b8736ad | [] | no_license | phani-srikar/AdapteR | 39c6995853198f01d17a85ac60f319de47637f89 | 81c481df487f3cbb3d5d8b3787441ba1f8a96580 | refs/heads/master | 2020-08-09T10:33:28.096123 | 2017-09-07T09:39:25 | 2017-09-07T09:39:25 | 214,069,176 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 24,516 | r | utilities.R | # Contains the support functions
#' @include platforms.R
NULL
setOldClass("RODBC")
setOldClass("FLConnection")
cleanNames <- function(x){
##change factors to strings
if(is.factor(x) || class(x)=="Date")
x <- as.character(x)
if(is.character(x))
x <- gsub("^ +| +$","",x)
x
}
sqlError ... |
978fe00482b0d953bde367ecba9d261cab4ad6c1 | 51c56c6dd891f2f5899bc8ddb0082b8dc56e0230 | /cachematrix.R | f298529610b790b41b57e0a96d9362813578f9d2 | [] | no_license | nsamady/ProgrammingAssignment2 | 6f5f03f07388459d5a42dc2b46d5bb9342f42a60 | 17d6b1228931792862477a4a3447ad7c17cf9f53 | refs/heads/master | 2021-04-29T11:33:08.756903 | 2017-01-02T08:49:02 | 2017-01-02T08:49:02 | 77,819,390 | 0 | 0 | null | 2017-01-02T08:08:19 | 2017-01-02T08:08:18 | null | UTF-8 | R | false | false | 1,153 | r | cachematrix.R | ## makeCacheMatrix creates a special matrix object, and then cacheSolve
## will calculate the inverse of the matrix.
## If the matrix inverse has already been calculated, it will instead
## find it in the cache and return it instead of calculating it again
makeCacheMatrix <- function(x = matrix()) {
inv = NULL
... |
8ac6c470bfe01da99396d8e3f42a56ef8b600040 | 29585dff702209dd446c0ab52ceea046c58e384e | /StatDA/R/treesold.R | f5630f124811010c7fdccd573f95b09d7cd91080 | [] | 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 | ISO-8859-1 | R | false | false | 15,258 | r | treesold.R | # plot trees as multivariate graphics
#
setClass("branch",representation(LR="numeric",
w="numeric",h="numeric",El="numeric",LeafL="branch",LeafR="branch",Bole="numeric"),
prototype(LR=0,w=0,h=1,El=0,LeafL=NULL,LeafR=NULL,Bole=2))
setMethod("show", "branch",function(object){
cat("Tree with ",length(object@El... |
f2aff405ac96995b204519acae92b9722a51e1a8 | 55e042f05ee3da0db86ecfb806c0e695382a843d | /R/pkgdepends.R | cd5fa65ce2aee438362a3e9f68a3a47b21311ea8 | [
"MIT"
] | permissive | r-lib/pkgdepends | f507dfe031e34c994311ca9a139dda9a6d7e016a | a0f5132320498780c8b87ce8eb4f66e754906376 | refs/heads/main | 2023-08-03T15:56:48.339228 | 2023-07-19T09:13:42 | 2023-07-19T09:13:42 | 102,942,545 | 86 | 23 | NOASSERTION | 2023-09-11T20:44:59 | 2017-09-09T09:17:38 | R | UTF-8 | R | false | false | 378 | r | pkgdepends.R |
# nocov start
#' @description
#' pkgdepends is a toolkit for package dependencies, downloads and
#' installations, to be used in other packages. If you are looking for a
#' package manager, see [pak](https://github.com/r-lib/pak).
#'
#' @includeRmd tools/doc/README-body.Rmd
"_PACKAGE"
fix_check <- function() {
R6:... |
caea8843209fc602297f1fb0fc0f8043d194f624 | 823829085704d2c5fa88be0b2a1092eee4678b8c | /scripts/Rscripts/utilities.R | 87f0385a96c4edecdc5b143a5bdb28c18e755003 | [] | no_license | amunzur/CHIP_project | a90e0ec5bc8741a44f318d89f2672badfb64eb70 | 8401f2a59b91a0bf7548c09db0d24337e4acf245 | refs/heads/main | 2023-07-18T05:51:15.959496 | 2021-09-07T21:26:53 | 2021-09-07T21:26:53 | 379,702,512 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 852 | r | utilities.R | cool_theme <-
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black", size = 1),
axis.ticks = element_line(colour = "black", size = 2),
axis.text = element_text(size=10),
... |
84d5ef490b0ba93967625c71cf36466507b0d049 | 4e335d85c9210de018e0c4c057b4f00b2e9246a4 | /Day03/Session01.R | eeeb3cbbba43ea1a026f773001546ef6315f3bd3 | [] | no_license | isrt09/Statistical_Analysis_with_R | 0b226de7da9b06a2235d59a7de98d7edf2a75daf | 65047800890fa29130e111373234d8c3247aa615 | refs/heads/main | 2023-06-24T06:45:15.707458 | 2021-07-24T14:55:42 | 2021-07-24T14:55:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 640 | r | Session01.R | # Sequence numbers in R
x <- seq(1,10)
x
x<- seq(1,10,length=100)
x
x<- c(seq(1,10,length=5),30,40)
x
x<- rep(seq(1,3,length=.5),3)
x
x<-rep(seq(1,3,0.5),each=3)
x
# Random number
x <- sample(100,12)
x
y<- 1:100
n<- sample(y,5)
n
m<- sample(10,50, replace = TRUE)
m
n<- sample(10,50, replace = TRUE)
n
p<- s... |
86e5a6014449820d751752936fd4f24a9472e07e | 86ab62ed514326b49a142335a1f0a88dbcf1d962 | /rt-crowd-forecast/update-rt.R | 2d0698fb6c8bf38d9d8bffec8d4e3a420aeb1f9b | [
"MIT"
] | permissive | 5l1v3r1/covid.german.forecasts | f29b53ac135eab90772f20d4960a19f76a59bdeb | fb93ec15fadb08350a6b508a4ee960d8210c600e | refs/heads/master | 2023-05-08T17:47:04.222681 | 2021-05-24T05:48:45 | 2021-05-24T05:48:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 682 | r | update-rt.R | # update-rt-data by copying it from the Rt folder
library(here)
library(covid.german.forecasts)
locations <- c("Germany", "Poland")
date <- latest_weekday()
for (location in locations) {
file_names <- c("summarised_estimates.rds", "estimate_samples.rds",
"model_fit.rds", "model_args.rds", "repor... |
fe4b03a48974e7e7a48ea9d7b11a427d37b5f458 | eff7ef52692c5fc92667f9627ff621ce43843aa2 | /man/ec.theme.Rd | 4e80499d9a7a03921db69b9b53face3ddacef7d7 | [
"Apache-2.0"
] | permissive | statunizaga/echarty | 9e718e18dd74bab0fde39932a9a92255a30547ec | 7e5f794e8e86c3363b8df2cf455022d9d99b6f57 | refs/heads/main | 2023-06-24T02:45:40.669501 | 2021-07-29T17:57:18 | 2021-07-29T17:57:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,066 | rd | ec.theme.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/echarty.R
\name{ec.theme}
\alias{ec.theme}
\title{Themes}
\usage{
ec.theme(wt, name, code = NULL)
}
\arguments{
\item{wt}{An \code{echarty} widget as returned by \link{ec.init}}
\item{name}{Name of existing theme file (without extension), or... |
9971ef9eea486312587a0e10d0eae13b5155f18a | 45079c348da4eac1f6916b1a082c801e14fea0fe | /ch4.R | 337695b798ce18a17aaf040a1b114cad1defc9f7 | [] | no_license | guhjy/ggplot2_book | e5780d4c0314d231835cb840508e6abc1ea5d1bd | 65c5e1d3bdcd32f58a2d78ac229c927d4bdc4c5d | refs/heads/master | 2021-04-28T20:11:17.711546 | 2012-04-12T02:49:14 | 2012-04-12T02:49:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,161 | r | ch4.R | require(ggplot2)
data(diamonds)
#*************************************************************************
# 4.2 Creating a Plot *
#*************************************************************************
# when you use qplot(), it does a lot of things automatically... |
604c2b3af2fb5803bb41f079a34c711485d2a8a2 | 6151cde12944c1a41b263be4fe2745ba15fa0137 | /R/zzz.R | b2c797e079ef0720b51c21e0f6b454e82557e890 | [] | no_license | cran/crq | 31b8009817c95cd03a765926d478bda585eb66c2 | 4c61f0c4e6e8849e8c098a531638b4639f2b33f0 | refs/heads/master | 2016-09-06T14:43:09.870751 | 2007-10-04T00:00:00 | 2007-10-04T00:00:00 | 17,718,605 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 82 | r | zzz.R | .First.lib<-function(lib,pkg) {
require(survival)
library.dynam("crq",pkg,lib)
}
|
4f38732c7f155055090edd324fd1588cedf06521 | 898a10dfdfa3b065e33d2b2557439cb94ffd3e1d | /parse_sql.R | 4c561d8c0b97358003ce6fbc27661060a4945787 | [] | no_license | fmalmeida/rscripts | e9a783a9eac6dbf3691b2a789f4e9c3ba9dbf8e1 | fd26423fccdc40c26da42d412db6e9cdb9dd9d7f | refs/heads/master | 2021-07-21T02:30:33.166493 | 2021-07-14T13:21:39 | 2021-07-14T13:21:39 | 144,742,354 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,030 | r | parse_sql.R | #!/usr/bin/Rscript
suppressMessages(library(RSQLite))
suppressMessages(library(glue))
suppressMessages(library(stringr))
suppressMessages(library(DataCombine))
# Setting Help
'usage: parse_sql.R [--input=<file> --start=<int> --end=<int> --fofn=<file> --regex=<chr> --type=<chr> --prefix=<chr> --outdir=<chr>]
options:
... |
8abccccf4950947ad2a2bb29b4d843ebaf6a2e25 | dc9aaff85df4cb5bc681454a96cd6bac3a236732 | /R-Derivative.r | eb69e04434f9a45303500bccf2b35c4d1fe3e57d | [] | no_license | 17523185/R_Function | d3fb4b7cd4013540511533d799452e91ca34ae78 | 6a96394e41c9af07a47b0b8981de1cfd485ca638 | refs/heads/master | 2020-03-30T08:28:09.240988 | 2018-12-18T16:47:12 | 2018-12-18T16:47:12 | 151,017,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 216 | r | R-Derivative.r |
#Nomor 1
rule11 <- function(x){
return(1 / (2*sqrt(x)))
}
rule11(4)
#Nomor 2
library(Ryacas)
x <- Sym("x")
#2.1
Simplify(deriv(2*x^5, x))
#2.2
Simplify(deriv(x^2 + 4, x))
#2.3
Simplify(deriv(x^5 - 6*x^7, x))
|
9293b00885b1e93acf94c79834525f8c11453006 | deb24e0574d7833c16ead96596de1451c76194bf | /MSD_scaled_g1_revised.R | 83c874a2ca1f77901324349dba9eac4c02db2965 | [] | no_license | edwardcooper/lammps | e5b371b239e74fe05853d0b6a1d8ac0b764c703d | 3c9f3f932a19243a042f14892943a0d8c2968a1e | refs/heads/master | 2021-01-25T15:43:54.419938 | 2019-04-18T03:58:44 | 2019-04-18T03:58:44 | 100,535,812 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,781 | r | MSD_scaled_g1_revised.R | # MSD calculation for scaled position coordinates xs, ix .
### First define a function to do the calculation for one temeprature.
MSD_scaled_g1_one_temp=function(path="~/Dropbox/lammps/PMMA_long/atom300",filename="atom.300_long2",polymer="PMMA_long"
,num_mol=64,molecule_atoms=602,molec... |
edb7760299d52e3f1d768382a1aafbe2288f860f | 6eebf6c5eb3700a0f7ff463aea4083511e8ca0e1 | /Project_Prob2.R | b87b1e4901e47b4a9af06b99a0b88b034e446cea | [] | no_license | jingtian808/Machine-Learning-in-Stock-Price-Prediction | 70e53d6f60b7a4c5ce619bd3cdb11d885e0fe78e | c12ec799108435fafba5baf0a24340b3657aa817 | refs/heads/master | 2020-04-22T08:28:10.073728 | 2019-02-12T04:43:14 | 2019-02-12T04:43:14 | 170,243,280 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,244 | r | Project_Prob2.R | library(randomForest)
library(ranger)
library(caret)
#---------------- Set location--------------------
setwd("C:/Users/xieli/Desktop/850 project")
# read our sector CVS
data_sectors <- read.csv('GICS.csv',header=TRUE,stringsAsFactors=FALSE)
sector_names_list <- unique(data_sectors$Sector)
#------ load all ... |
a8cfa0b8e70b2846905269e54f581399b79a54ab | de310f0eda436964b08550e6f6de59ae186eec5c | /R/modify.R | 1ce1bb7c4762afbb2a4cb44c5893e8b227f9fc80 | [] | no_license | jimsforks/dplyrExtras | 582c9c1c3eed5aa857a5830fb1ba2d54b6a58dd3 | 8b05cf2a3957167484ec31add14fdb3b534b6995 | refs/heads/master | 2022-07-17T19:56:30.044058 | 2020-05-18T19:58:27 | 2020-05-18T19:58:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,851 | r | modify.R |
examples.modify = function(a=0,K=0,n=0,x=0,y=0) {
library(microbenchmark)
K = 3
n = 10
dt = data.table(a= sample(1:3,n,replace=TRUE),
b= sample(1:100,n,replace=TRUE),
x=rnorm(n))
df = as.data.frame(dt)
# Set x to 100 where a==2
modify(dt,a==2, y=x+100, z=y+K)
m... |
f0e254d4e7abf5ebcab4ebb318d29c6128a43822 | caf356fd6c1fda3935d492763639e4d80485b02f | /man/simulate.mpt.Rd | 738cfa1328b1e12cfbaaa8911cdb044f3ba5cc8a | [] | no_license | cran/mpt | 2bddd9800c22c31c6364d583e5edb8b209b07439 | 700e976dff2ede16f170dfcfcf828c42830a7ccc | refs/heads/master | 2022-05-11T11:46:00.202450 | 2022-03-23T06:50:02 | 2022-03-23T06:50:02 | 17,697,663 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,464 | rd | simulate.mpt.Rd | \name{simulate.mpt}
\alias{simulate.mpt}
\title{Simulate Responses from MPT Models}
\description{
Simulates responses from the distribution corresponding to a fitted
\code{mpt} model object.
}
\usage{
\method{simulate}{mpt}(object, nsim, seed, pool = TRUE, \dots)
}
\arguments{
\item{object}{an object of class \co... |
826f9b58fa745ccb4fb5edb48d4c8183a436072b | 47d61e0421b5ee045f40f9b174d0cfb6819a7849 | /R/hcrPlot.r | 634fe64ee03e7239aedb8f9752e1bd23fe165064 | [] | no_license | gomezcatalina/bio.lobster | 912844380f8c122f97190cb0eb2b00c93d32f56c | 586e11a58319925a65d0639509a1e23603947c17 | refs/heads/master | 2021-01-09T06:50:18.206316 | 2017-01-20T16:27:30 | 2017-01-20T16:27:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,260 | r | hcrPlot.r | #' @export
hcrPlot <- function(B,mF,USR,LRP,RR,yrs,ylims=NULL,xlims=NULL) {
if(is.null(ylims)) ylims = c(0, (max(mF,RR)*1.05))
if(is.null(xlims)) xlims = c(0, (max(B,USR)*1.05))
plot( B, mF, type="b", xlim=xlims, ylim=ylims, col="darkorange", cex=0.8, lwd=2, xlab="", ylab="", pch=20,... |
92ac3f69cbdefe18b72f02e64b71986cd6c59365 | a9dcc6d36e928267e6ac9b3d8de324afd7030a72 | /WoodchesterPark/PhylogeneticTree/PlotMLTreeIncPoorAndRef_13-10-16.R | 2dfb12f6be59b974b49718c83f5348c6643565df | [] | no_license | xulijunji/GeneralTools | e5778d2da6e64264a26027a713e577d88391007e | 758c769ba10cde1c02e74d5dec70d978d9b6675d | refs/heads/master | 2021-08-23T20:14:33.238693 | 2017-12-06T10:47:49 | 2017-12-06T10:47:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,899 | r | PlotMLTreeIncPoorAndRef_13-10-16.R | # Load the ape package
library(ape)
library(geiger) # For the tips function
library(plotrix)
# Set the path
path <- "C:/Users/Joseph Crisp/Desktop/UbuntuSharedFolder/Woodchester_CattleAndBadgers/NewAnalyses_13-07-17/"
###################################
# Get the Maximum Likelihood Tree #
##################... |
810d28b02aa5d34efc56d9c5bd9cdcb487de78d2 | 34131c61655635da412ea7474ba22a172f17f6ff | /man/ov_video_player.Rd | 69147cc130752059cc28fa51121fedcd38982a1e | [
"MIT"
] | permissive | openvolley/ovideo | 8cfbe226050de88dbec2c3e7016e419708f37ea6 | c9380b9dcc4be3669dda086949aab421164d14ff | refs/heads/master | 2023-05-11T23:44:39.775596 | 2023-05-02T22:17:54 | 2023-05-02T22:17:54 | 244,466,765 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,597 | rd | ov_video_player.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/player.R
\name{ov_video_player}
\alias{ov_video_player}
\title{Video player tag element}
\usage{
ov_video_player(
id,
type,
controls = FALSE,
version = 1,
controller_var = paste0(id, "_controller"),
with_js = FALSE,
...
)
}
\arg... |
8e2e3c858aa6c3957c34ada401b1f86a65fe0d9f | 7f4976dc84dc1a97237a52f2ed4fb1fd42f1980d | /Analysis/testing_ideas_about_analysis.R | 8ed14542515e82d5caeea09c8706f9ae0dbe73f1 | [] | no_license | punctuationmarks/IMPD-Data-Analysis | 3b8d997b4a0bfeb322b6cfed6a7e9bef5f95cb34 | 2bc2f806fdd2e219f5920d9d3ee094671e5c1362 | refs/heads/master | 2021-07-03T23:25:24.470081 | 2020-12-28T00:26:06 | 2020-12-28T00:26:06 | 210,629,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,234 | r | testing_ideas_about_analysis.R | # testing the idea of having unique individual UOF on citizens
library(tidyverse)
library(ggrepel) # for some ggplot visual improvements
testing_uof <- read_csv("../CleanData/UOF/cleanedUOF_withGeoLocation_andFormattedDate.csv")
View(testing_uof)
# colnames(testing_uof)
uof_ <- testing_uof %>%
group_by(
lo... |
658078ddbc06e8e10db59572dfdec1a24d71631e | f2eadee083a58efffd3928f7feffae3d76f99b99 | /plot3.R | 1ab3223b818c27a71b3665b225046ac86ba5e8bb | [] | no_license | tianwenlan/4.Exploratory_Data_Analysis_Week1 | fe9900342f21621871c1e9f31f410e38f0d8ece4 | e4f12e7452f17354e54e92d7a6f30e118dd310b6 | refs/heads/master | 2020-12-26T23:44:40.939320 | 2020-02-02T01:10:19 | 2020-02-02T01:10:19 | 237,692,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,341 | r | plot3.R | library(dplyr)
#step 0: downlaod files
zipUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
zipFile <- "household_power_consumption.zip"
if (!file.exists(zipFile)) {
download.file(zipUrl, zipFile, mode = "wb")
}
# unzip zip file containing data if data directory doesn't... |
28a921680a013453e90e0d41c469aeabe94783f4 | ad89addeb61f00bdf8a18275055c99eb22cfc0b2 | /graphs/citation.R | abe27683ecb4033df36085024fb4fe2d95b35529 | [] | no_license | techtronics/ESOF522 | 850d9f987713dc6f18aba013edf27360caeb4c59 | 7b7b03165c12f6142a9958d5e09b04df928457ac | refs/heads/master | 2017-12-07T11:23:15.938041 | 2015-05-21T23:54:31 | 2015-05-21T23:54:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,678 | r | citation.R | myDF <- data.frame(rank = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3),
year = c(1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1,2,3,4,5,1... |
97f3df05c2edb441d7f937ce6d7a002d303d5caf | e42721b2bf31675a294e14b2d59111a1b83de58b | /man/GSE33335.Rd | 2f5121f16123262092ecde9d86d5f91c0766ddce | [] | no_license | szymczak-lab/DataPathwayGuidedRF | d60fdd2a07cf69359f147b7956a27b03d1b9dd02 | 8af8869378e6aae27b727f1417e51c30564f4a34 | refs/heads/master | 2020-12-11T06:42:48.292639 | 2020-01-27T09:16:50 | 2020-01-27T09:16:50 | 233,790,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 842 | rd | GSE33335.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GSE33335.R
\docType{data}
\name{GSE33335}
\alias{GSE33335}
\title{GSE33335}
\format{A Summarized Experiment object with 17025 genes and 50 samples (25 cases and 25 controls).
The column outcome in the colData corresponds to the outcome that w... |
ae274cbba57598b559630d43223cad69d4fe2b5a | 0a1172cc3ee12f60bf998d79bee902131d45e58e | /How to join a shapefile with a csv/join_csv_with_shapefile.R | ebd7ca126ce91be5a5f528aa5d25154af4d2d8ca | [] | no_license | ft-interactive/R-tutorials | 2ae76472e1d18cb61ff97fdb3f456c8c16da5775 | 5ccc42d67ba211e43649df0e25c1eb1271edf788 | refs/heads/master | 2021-01-21T14:40:50.954977 | 2017-07-10T17:01:35 | 2017-07-10T17:01:35 | 95,441,282 | 20 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,615 | r | join_csv_with_shapefile.R | library(rgdal)
library(dplyr)
library(rgeos)
library(ggplot2)
#you neeed to make sure the merge/join column in your csv has the same name as the column in your shapefile.
# Looking at at the attribute table in QGis will give you this info.
#at present this works for .shp files only.
# your .shp (and accompanying fi... |
72197b2394426150780498c60d4867b6b210cb7f | e621d01f3584196d7977f25ebe57af5549ca7600 | /man/create_NMEA_files.Rd | 1e477f54bf90e350f4395792e45f4dc765dbfd7f | [] | no_license | Lewis-Barnett-NOAA/gapctd | 0b739094971a7b29f5973faffc17ebc7ba465550 | 1868f5cc0e583a0ab68387da2f8f51c0200b528f | refs/heads/main | 2023-04-16T23:41:49.621428 | 2021-04-23T23:14:25 | 2021-04-23T23:14:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,016 | rd | create_NMEA_files.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_NMEA_files.R
\name{create_NMEA_files}
\alias{create_NMEA_files}
\title{Create NMEA files with LAT/LON for Derive TEOS-10}
\usage{
create_NMEA_files(rodbc_channel = NA, haul_csv = NA, vessel, region, year)
}
\arguments{
\item{rodbc_chan... |
c15b44711411f398d65fb8edb315a226fe97db06 | 1c01ed7a5e79c5e281c0ede3406f702f79766882 | /man/l.cap.Rd | a7c56991d902d0624870fe6a6e0b5447f0c4efff | [] | no_license | christiantillich/AnaliTools | 19e738e4084be1678ff7aeda45aa9f146de5ac1d | cab56ef7729f1d9692af5241ac5eca60060c3045 | refs/heads/master | 2020-04-06T05:12:48.950283 | 2019-02-25T22:09:03 | 2019-02-25T22:09:03 | 47,645,937 | 0 | 1 | null | 2019-02-25T22:09:04 | 2015-12-08T19:53:20 | R | UTF-8 | R | false | true | 453 | rd | l.cap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.tools.R
\name{l.cap}
\alias{l.cap}
\title{l.cap}
\usage{
l.cap(v, lbnd = -Inf, ubnd = Inf)
}
\arguments{
\item{v}{- A list input.}
\item{lbnd}{- The lower bound. Defaults to -Inf.}
\item{ubnd}{- The upper bound. Defaults to Inf}
}
\val... |
34d36b63ff59b34959ef217bdc3254131a3ef819 | 1061216c2c33c1ed4ffb33e6211565575957e48f | /r/tests/testthat/test_bot.R | 3c037460377a909b8e662b4d3a56153e1ab87b7e | [] | no_license | MSurfer20/test2 | be9532f54839e8f58b60a8e4587348c2810ecdb9 | 13b35d72f33302fa532aea189e8f532272f1f799 | refs/heads/main | 2023-07-03T04:19:57.548080 | 2021-08-11T19:16:42 | 2021-08-11T19:16:42 | 393,920,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,859 | r | test_bot.R | # Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test Bot")
model.instance <- Bot$new()
test_that("user_id", {
# tests for the property `user_id` (AnyType)
# uncomment below to test the property
#expect_equal(model.instance$`user_... |
b1bd9cacca27441864e4ab789b3b3af85ae69142 | 3f476a051eb22af77130ee485d0cbac40ccde03c | /lynx.vitals.r | 4d95e12542e4f0ffc6c9933fa89201c44f732195 | [] | no_license | ranalut/Scripts | a62d44f809c460319cdd1e2ad249f32aec9ce4b3 | d863122f53e3a22d23c87a1105c6de46280e2ad0 | refs/heads/master | 2020-12-29T02:38:27.728016 | 2017-01-23T07:00:16 | 2017-01-23T07:00:16 | 8,935,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 735 | r | lynx.vitals.r |
# Vital rates taken from Carroll 2007
# Multiplier for demographic cycling
fecund <- c(1,0.8,0.2,0.25,0.25,0.25,0.25,0.4,0.6)
surv <- c(1,0.89,0.67,0.56,0.56,0.56,0.56,0.67,0.89)
mean.fecund <- mean(fecund)
mean.surv <- mean(surv)
# Max values
surv.max <- c(0.77,0.77,0.99,0.99,0.99,0.44)
fecund.max <- c(0,2.4,2.4,... |
f961727bb4c84ed9a621da30a57c7003357dcf8d | 37ff9d489e5663fba507851294d1c5161b9482bc | /r-misc.R | 285b5018a778b83a3b630b4c4ccb2339f826c11d | [] | no_license | artyomovlab/seurat_docker | c820ed83c810175e217d16d0e05043e7a6b919f7 | f2419a3180a6033391fca3b5f805d5109a777f4b | refs/heads/main | 2023-05-07T23:31:17.001027 | 2021-05-21T17:29:42 | 2021-05-21T17:29:42 | 369,606,307 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 298 | r | r-misc.R | source("https://bioconductor.org/biocLite.R")
install.packages(c("tidyverse","ggplot2","Hmisc","plotrix","png", "Matrix", "RJSONIO", "cowplot", "devtools","foreign","latticeExtra","car","rstatix", "ggpubr"),repo=paste0("https://mran.microsoft.com/snapshot/",format(Sys.Date(), format="%Y-%m-%d")))
|
5425003fd4a61d2e4d4338c5c1e333609a523f1f | 01f8a1aadcd95197914d7fcf496667ee756a29be | /data preprocess.R | d8529d8fceeeafb77fbf302a0c50b1cff961895c | [] | no_license | sahilsingh2110/Prediction-for-VC-investment-in-startups-Python-R | 9996b55fd13f067245557d38686a9d26a488e62b | 2321c431ee4be205a1771ee91fc0ee468302763f | refs/heads/master | 2020-03-23T02:19:34.157334 | 2018-08-22T17:03:18 | 2018-08-22T17:03:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 925 | r | data preprocess.R | dataset = read.csv('Data.csv')
dataset
#taking care of missing dataset
dataset$Age = ifelse(is.na(dataset$Age),ave(dataset$Age, FUN= function(x) mean(x,na.rm = TRUE)),dataset$Age)
dataset$Salary = ifelse(is.na(dataset$Salary),ave(dataset$Salary, FUN= function(x) mean(x,na.rm = TRUE)),dataset$Salary)
# Encodi... |
46ed009f14273d2b50ea6c6dc654de39f677bf15 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /MixMatrix/man/rmatrixinvt.Rd | 0dcdc039bbc07b38924ba02f62f9f4e3c07612d1 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,817 | rd | rmatrixinvt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/matrixt.R
\name{rmatrixinvt}
\alias{rmatrixinvt}
\alias{dmatrixinvt}
\title{Distribution functions for matrix variate inverted t distributions}
\usage{
rmatrixinvt(n, df, mean, L = diag(dim(as.matrix(mean))[1]),
R = diag(dim(as.matrix(mean)... |
733906e313f753189982de7afbad7c8fa35f58bc | 53528ae8feec89209d99660d4bc6cd0d7f4c79d9 | /run_analysis.R | e7a33af625ed55d4c4b69e6f569ac9af6d13a726 | [] | no_license | fenom/GetData_PeerAssessment | 5ee99e47f470de57295d7f593a0d09910e6d1e24 | 20959f0bad92d6efcb00c47c0b25e9af2c48502d | refs/heads/master | 2016-08-11T06:19:37.909032 | 2015-12-26T04:20:54 | 2015-12-26T04:20:54 | 48,567,984 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,174 | r | run_analysis.R | features <- read.table("UCI HAR Dataset/features.txt")
labels <- read.table("UCI HAR Dataset/activity_labels.txt")
train <- read.table("UCI HAR Dataset/train/X_train.txt")
train.labels <- read.table("UCI HAR Dataset/train/y_train.txt")
train.subjects <- read.table("UCI HAR Dataset/train/subject_train.txt")
names(train... |
bcd96798cdb33eb7dedc238681a34049a9ba3082 | 086b3d93a0d22a0beadea74150404a7919a28e66 | /QE_Functions/n_uptake_root_biomass/M_constraint_root_ocn.R | ce24da3838859ed0a03fe6ba4c441565c6f77ec2 | [] | 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 | 1,371 | r | M_constraint_root_ocn.R | ### Function for nutrient N constraint in medium term ie passive, leaching, wood considered
# specifically for uptake as a function of root - O-CN approach
# i.e. N uptake as a saturating function of mineral N
M_constraint_root_ocn <- function(df, a, C_pass, C_slow, Nin_L) {
# passive pool burial
s_coef ... |
1ecc029cc37620844c56b3fa0d136b41294c3c2f | 825074ec285e262936790f313648a2844876b179 | /data-raw/ML-Desafio.R | f26c722328422ee4cf29efe35d130bd51acbd63d | [] | no_license | ViniciusJacobs/CreditoXGBoost | 0b341361fcdac067302953af9b720cb473f6e165 | 207ae71f1bba17e413cbc3ee47c2894866a47e7a | refs/heads/master | 2023-07-17T02:55:29.436825 | 2021-08-31T23:45:43 | 2021-08-31T23:45:43 | 305,163,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,139 | r | ML-Desafio.R | library(tidyverse)
library(tidymodels)
library(ggplot2)
library(skimr)
library(stats)
library(doParallel)
cores = 5
base_adult <- read_rds("data/adult.rds")
glimpse(base_adult)
base_adult$workclass <- as.factor(base_adult$workclass)
base_adult$education <- as.factor(base_adult$education)
base_adult$marital_status ... |
5785b9b277b7e5f37359734e1861df17698d3efb | d56f0073431b98da11187c472b60e5951fb727ed | /R/Meta_Permutation.R | 435b012ecf6d0134c050207c0f8f51ab5e8f433e | [] | no_license | cran/MetaSKAT | b5e68aa2f115449d915552cfb7eef8965fc5d9a5 | 43921edb6bbbfa27bb59d34669653bfc5ccfae43 | refs/heads/master | 2022-08-05T01:59:38.838570 | 2022-07-21T08:20:02 | 2022-07-21T08:20:02 | 17,680,913 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,717 | r | Meta_Permutation.R |
ReadPermu_Header<-function(File.MPermu){
con = file(File.MPermu, "rb")
header<-readBin(con, integer(), n = 10, size = 8)
# n.permu, n.all, n, nSets, nSNPs, nSNPs.unique
re = list(con=con, n.permu=header[2], n.all=header[3], n=header[4], nSets=header[5], nSNPs.unique=header[6])
if(header[1] != 1){
... |
43976dec67fada5d3e9b0c2991c9a9a66718f27e | 65b2d8f88199970ca6b83e658760dfb66c5d951d | /GenAlgVarSelection/R/select.R | 7a3174acd1a9885041fbbfc77ce08d16e8beaea8 | [] | no_license | ClayCampaigne/stat243_Project | 8776a1572701f9fb261b3ab1b81186fbd11fddcb | b3483b486a39b66c00f3dac96495265335f7dea9 | refs/heads/master | 2021-12-04T01:24:46.989193 | 2014-12-11T10:15:11 | 2014-12-11T10:15:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,295 | r | select.R | ############################ Project: Main Code #################################
##### Description: #####
### This is the main code of the generic algorithm that attempts to solve the variable selection problem.
##### Input: #####
### Training Set: (X, y) (User Defined) #####
### genePool: size/# of individuals of th... |
a7c1489dede567d70dcdb08ad16d701df94ef03c | 6e520bb1eec1e3cfd543f994949e28565cdd6c54 | /server.R | 567fa070106d0b8f0c9cce979afd3bf8da8aae53 | [] | no_license | yusagi0603/Service-Time-Series-Analysis | 653413f1b7ac47b6710c29f187d276e5e9ce8cef | dcef8489bad5a63f40eb5097a8feef73ef4d3cca | refs/heads/main | 2023-04-04T01:01:34.032865 | 2021-04-04T12:56:06 | 2021-04-04T12:56:06 | 354,541,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,735 | r | server.R | ## load packages
library(zoo)
library(xts)
library(ggplot2)
library(reshape2)
library(forecast)
library(DT)
library(plotly)
library(lubridate)
library(dplyr)
library(tidyr)
# library(highcharter)
# input$[variable] to use variable in ui.R
# EX: input$store -> to use a variable "store" in ui.R
###############
source(".... |
5797752824337e3785d0737870bc8fbdffae96e7 | 373f2abd88834acc0d1ab21ba13ce335600ceb0f | /R/debut.r | a2eb548f263d589bb09a74f88bc07535894cd97d | [] | no_license | ClementCalenge/adehabitat | fa26e43fba432c29a5757fcd4b5f9ffd972bdd44 | 23ba023d5a57eec861fb6d3d07772cb9d2db6968 | refs/heads/master | 2021-01-22T05:20:32.012511 | 2018-01-28T12:27:32 | 2018-01-28T12:27:32 | 81,652,118 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,185 | r | debut.r | ##### Chargement de base
.onAttach <- function(libname, pkgname)
{
msg <- paste("\n************************************************\n",
"************************************************\n",
"THE PACKAGE adehabitat IS NOW DEPRECATED!!!!!!!\n It is dangerous to use it, as bugs will n... |
3f2ff8fea96ddcdc294b36ef8a6749ac64403993 | 0afd4c95ea233454ad43437ccc6478f0f4c7e27e | /run_analysis.R | c8edb65a0793a0722b5dbb9872443303d7b6c5b0 | [] | no_license | tristanmarkwell/GettingCleaningProject | 6df6dd5af0e3784f459ae8b348d232bbab6c46a5 | 9adbf4c2fcf9f89dda51d2e141a047897e9b1fdb | refs/heads/master | 2020-05-17T10:39:22.749155 | 2014-05-25T05:06:27 | 2014-05-25T05:06:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,795 | r | run_analysis.R | ## Part 0 - This code downloads the data if you're starting from scratch
# if (!file.exists('data')) dir.create('data')
# download.file('https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip',
# 'rawDatasets.zip')
# dateDownloaded <- date()
#
# ## unzip files
# unzip('.\\... |
423a30cf006afa52115c1ac4aefcaefcf78fe1e3 | ad9a4aee8d97fcfcfc3345afb03fe3b64d4cc48d | /Code/2-MultipleLinearRegression.R | e4790bf108caf5e70e77a31d595c14a49ba200a6 | [
"MIT"
] | permissive | ErisonBarros/Demanda-de-Transporte | 23e86f7482c829c349892f4dbb9407c3bedf970f | a5bc469a69865c3f36845b8cf49dd7239cd8f186 | refs/heads/master | 2023-03-23T10:35:08.669996 | 2021-03-14T19:24:48 | 2021-03-14T19:24:48 | 348,381,022 | 0 | 0 | MIT | 2021-03-16T14:37:05 | 2021-03-16T14:35:47 | Jupyter Notebook | UTF-8 | R | false | false | 7,790 | r | 2-MultipleLinearRegression.R |
#'
#' #### Example exercise: Trip production of 57 Traffic Assignment Zones of Chicago in 1960's.
#'
#' **Your task**: Estimate a linear regression model that predicts trips per occupied dwelling unit.
#'
#' #### Variables:
#'
#' * `TODU`: Motorized Trips (private car or Public Transportation) per occupied dwell... |
f7396f84a0e7f5dae73f13251a92065dc072ab66 | 84a6e98edb10a596cd2a08b6e5ea6b8d707e1045 | /R/read_Boolean_functions_c.R | ddcb0bc8366bcf3d99b3d05cda24af4b428c4bf1 | [] | no_license | SPIDDOR/SPIDDOR | 66af1c6995f9c87e66468c83eba1f09bde324b34 | 770f5b35baf9385d36b209f02b2153bcba2573cb | refs/heads/master | 2021-07-04T06:07:03.650996 | 2019-04-24T14:51:55 | 2019-04-24T14:51:55 | 72,274,593 | 6 | 2 | null | null | null | null | UTF-8 | R | false | false | 12,314 | r | read_Boolean_functions_c.R | read.Boolean.functions.C<-function(file=NULL,Lines=NULL){
if(length(Lines)==0) Lines <- readLines(file, -1)
Lines <- gsub("#.*", "", Lines) #remove comments
Lines <- Lines[nchar(Lines) > 0]
#Create .cpp:
write("","Boolean_func_C.cpp")
add_header("Boolean_func_C.cpp")
nodes<-unlist(lappl... |
b3884fae99b8970e6a517018ae8fbffe4f5a1845 | b1f207db195eb035198ef612602c0adde867bb06 | /shizhan002.R | 1511ab84d1d892130f7990583f193eb9a6cec7f9 | [] | no_license | kmustzjq/StudyR | 4782c68883b9f86e2b15928914a279e3441054f8 | 2785ab410691889d030cff21896f4fe1b0ff12ab | refs/heads/master | 2021-08-27T20:48:20.986278 | 2021-08-09T00:14:06 | 2021-08-09T00:14:06 | 164,197,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 670 | r | shizhan002.R | vars<-c("mpg","hp","wt")
head(mtcars[vars])
summary(mtcars)
var(mtcars)
sapply(mtcars, var)
sapply(mtcars, sd)
library(Hmisc)
describe(mtcars)
mtcars$am
library(vcd)
attach(Arthritis)
mytable<-xtabs(~Treatment+Improved,data=Arthritis)
chisq.test(mytable)
library(randomForest)
set.seed(... |
c0aa34aa3bf75053af4701fbe599b94acae8a084 | ddc8c8f96c348abeccba5fc31ed01844d093b33b | /Rummy/Rank_order_analysis.R | 642bd175202a8ff1ee6f30ad8eba243492d9dced | [
"MIT"
] | permissive | hypdoctor/Lawson2020 | b47c4fa8c67ccfe5f2ccf7d55cc12ee464943fa2 | 82662ff8183307ec09439dc001834537ec00bda3 | refs/heads/master | 2023-03-16T04:18:55.757725 | 2020-09-23T16:45:18 | 2020-09-23T16:45:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,363 | r | Rank_order_analysis.R | setwd("C:/Users/rumi/Desktop/")
## reading in coreCTLs
#Cores_daisy <- read.table("Screen_recent/Screens/drugZ_V1/Output/core_drugZV1.txt", header = T, stringsAsFactors = F)
## selecting mid timepoint data for all screen, except OVA_QR
col_index=which(!grepl("OVA_QR",colnames(gene_data_table.combined)) &
... |
a2df71979ed2f7c8102328b0afd429d9f85c9719 | d9d66c4db287172c6bf40526501d8ce4ab36645a | /R/MacroPCApredict.R | 66b21d55d334c697ceb333e36d85d540a4b80ec2 | [] | no_license | cran/cellWise | fea14ee5b9e6e5f5b086e7528051818fb7c6c79f | d08e57e682651001645c94d429778839daea79b0 | refs/heads/master | 2023-04-30T13:02:06.532853 | 2023-04-20T21:22:30 | 2023-04-20T21:22:30 | 75,787,435 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 6,371 | r | MacroPCApredict.R |
MacroPCApredict <- function(Xnew, InitialMacroPCA, MacroPCApars = NULL)
{ # Added a check whether the number of columns of Xnew
# matches that of InitialMacroPCA, and cleaned up code.
if (is.null(MacroPCApars)) {
MacroPCApars = InitialMacroPCA$MacroPCApars
}
else {
if (!is.list(MacroPCApars)) ... |
50924afc48a2a313dcf659f44938e7104e9768cb | 3f680c621d68cd817097e1a83915ceaead162e12 | /man/combineKeepRF.Rd | 0ce48939e28bf17dff31093b8ca0e12acf6dd656 | [] | no_license | rohan-shah/mpMap2 | 46273875750e7a564a17156f34439a4d93260d6c | c43bb51b348bdf6937e1b11298b9cdfe7a85e001 | refs/heads/master | 2021-05-23T20:34:59.327670 | 2020-07-19T10:24:09 | 2020-07-19T10:24:09 | 32,772,885 | 10 | 6 | null | null | null | null | UTF-8 | R | false | true | 1,680 | rd | combineKeepRF.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/combineKeepRF.R
\name{combineKeepRF}
\alias{combineKeepRF}
\title{Combine mpcross objects, keeping recombination fraction data}
\usage{
combineKeepRF(
object1,
object2,
verbose = TRUE,
gbLimit = -1,
callEstimateRF = TRUE,
skipVali... |
4cffcf60bfa4b9454939efb4bf9a819560c40d5a | e3c3dbb97047f287cbeab9880539eaa5dfb988d7 | /SE/Network Construction - WGCNA.R | 7c3e45ae9adb240115261c61e34723da05cfba18 | [] | no_license | desilvakithmee/Research_GRN | 3c07950a4e5edcda9f993df821ba56374f4de886 | 3e2c036219a2a6799e532bbd22c12b7d4acea46b | refs/heads/master | 2022-04-06T11:49:30.706229 | 2020-02-28T02:02:38 | 2020-02-28T02:02:38 | 213,803,973 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 19,363 | r | Network Construction - WGCNA.R | ### ----------------------------- STEP I --------------------------------------------
##----------------------- Data Input and Preprocessing ------------------------------
setwd("D:/UNI/4TH YEAR/RESEARCH/Codes/Arabidopsis")
library(WGCNA)
options(stringsAsFactors = FALSE)
#Reading data
exp = read.csv('data_pr... |
037979c8929b436d3a3c92766d32b3b93476e31f | c00b9a984cb8c8c9ae7a9e62d76eaeeac9863ab8 | /firstRfunction.R | c6337a1961fb13aaec5bd2bde7c84490fc6e4853 | [] | no_license | stdare/R-EnrolViz | db7fecf422ecad17a34b81c4b2b097aad13a051e | 2ef73fe99ef1ff63b5e4dd9ad82e8f1910d2eeec | refs/heads/master | 2021-01-01T19:28:19.342143 | 2014-06-01T05:23:50 | 2014-06-01T05:23:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 382 | r | firstRfunction.R | setwd("~/GitHub/R-EnrolViz/data")
seeplot <- function(x) {
rawdataset <- read.csv("trial.csv")
dataset <-na.omit(rawdataset)
enrolments <- c(sum(dataset$YearLeft=="2011"),sum(dataset$YearLeft=="2012"),sum(dataset$YearLeft=="2013"),sum(dataset$YearLeft=="2014"))
colors<-c("red","green","yellow","blue")
barplot(enrolment... |
516f534a266c912a85cd589baf60bfe6b73fd4a4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/AlphaSimR/examples/meanP.Rd.R | 40016b85a41441cb19583c10533bd19d69f647d2 | [] | 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 | 356 | r | meanP.Rd.R | library(AlphaSimR)
### Name: meanP
### Title: Mean phenotypic values
### Aliases: meanP
### ** Examples
#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)
#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)
#Create population
pop = newPop(foun... |
5db9b5c15887255ac267891a7d2b8b2f5e8cb299 | 221ec6024ee196f0f9305687ec5fab20e25df291 | /new_objects.R | 7bc710824a519b30d52b75f4509670061e506a32 | [] | no_license | dannemil/comt_expression | 2f6dd8e4f664ea3ede73cbab1a96fb14ae58eafb | c8e3c2779e99e78ace471fa22ed2c086bf823a08 | refs/heads/master | 2020-03-07T05:32:47.971796 | 2018-07-16T17:06:08 | 2018-07-16T17:06:08 | 127,298,861 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,720 | r | new_objects.R | # Program to determine which new objects were created in an R session so that the ones that are temporary can be removed.
work.path <- c('/Volumes/Macintosh_HD_3/genetics/genenetwork2/')
setwd(work.path)
# Include sourced programs here.
source.prog <- data.frame(rcode='func_cbind_na',
'fun... |
9dca9b03f4ba0b33acf6f7ae0c846d2a8a340934 | 90d74d03513e588f1f0161846dfd9657c78feae8 | /R/add_light.R | d2e2a9d580c78a9357dafe3e4bc396b9978faa69 | [
"MIT"
] | permissive | ropensci/unifir | 859fe03f09e7f2a96cc0785d02f1153c465f24c1 | e5c1df562b43751775e04777e204777646390c42 | refs/heads/main | 2023-05-23T12:33:33.104020 | 2022-12-04T15:15:46 | 2022-12-04T15:15:46 | 373,628,173 | 22 | 0 | NOASSERTION | 2022-12-02T17:12:52 | 2021-06-03T20:06:53 | R | UTF-8 | R | false | false | 2,973 | r | add_light.R | #' Add a light to a Unity scene
#'
#' This function creates light objects within a Unity scene. This function can
#' only add one light at a time -- call the function multiple times to add
#' more than one light.
#'
#' @param light_type One of "Directional", "Point", "Spot", or "Area". See
#' <https://docs.unity3d.com/... |
f65b751c388c377019716f586cec67dc7c31ad96 | b7cc80a80bd647649e5d828a0f95dfa0abcb4368 | /cvvm201310_MDS.R | be51128b14565c7226af3a9be38fec53d1861901 | [] | no_license | krojtous/CzechParliamentaryElections | de9b4262d73b09f89e4f72ffd10badaf582560fc | ac87b44c32339042ee9238b3d1c2b51681ff6777 | refs/heads/master | 2021-01-11T22:41:53.306363 | 2017-01-26T17:05:25 | 2017-01-26T17:05:25 | 79,015,826 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,025 | r | cvvm201310_MDS.R | #Date: 18.1.2017
#Author: Matouš Pilnáček - Public Opinion Research Centre, Czech Academy of Science
#E-mail: matous.pilnacek@soc.cas.cz
#Description: Multidimensinal scaling of voters by party and clusters of undicided voters
#--------------Load data----------------
library(foreign)
cvvm = read.spss(file = "./data/V... |
68d00f14555c8ac165a3bb14f5f6f36b519176d1 | 6d3fb21b34d50c70c0525bba1bcf40b0d4008c21 | /man/student_equity.Rd | 45f6655e91bf9db500f39712dfe33b5d0898a5b8 | [] | no_license | vinhdizzo/DisImpact | 2df6051da147dbf8d3bd7292ac7a5439e1d6d269 | 9d0cc5ccd79e06acd3854365b7cf8edfb6991c9c | refs/heads/master | 2022-10-14T10:13:45.066043 | 2022-10-10T17:37:21 | 2022-10-10T17:37:21 | 134,330,630 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,550 | rd | student_equity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_student_equity.R
\docType{data}
\name{student_equity}
\alias{student_equity}
\title{Fake data on student equity}
\format{
A data frame with 20,000 rows:
\describe{
\item{Ethnicity}{ethnicity (one of: \code{Asian}, \code{Black}... |
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