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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ec0d0a4ccdcbbfe6fcdec5e7f77ec506446ad117 | cab6be4f5004f4c9106e77623dfc85aec4fbeeec | /ballerDepHeterogenScripts/Sunny_matching_script.R | 6a18d533a4aec13fb8b970164ee1f6942cdd9de3 | [] | no_license | PennBBL/ballerDepHeterogenScripts | fab351c54bb5263e1aac4d133a8f92b66df4b8f4 | 90d112fd734e41ae93ec4bd3a591885c53915359 | refs/heads/master | 2021-06-04T11:55:28.632889 | 2020-02-16T03:09:25 | 2020-02-16T03:09:25 | 112,241,167 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,119 | r | Sunny_matching_script.R | library(MASS)
library(Matching)
library(tidyr)
library(readr)
library(effsize)
library(dplyr)
###Loading Data
alldata<-read.csv("OlfactionFromStata_2017_06-19.csv") %>%
group_by(group) %>%
arrange(group,bblid)
View(alldata)
str(alldata)
colnames(alldata)
#Cleaning up variables
alldata$anyspectrum<-as.factor(alld... |
9c71f0fb29de6138b94ebabfdc69e6e140d7bd6d | 895bdd32974b8b6f1a61fec16b3c555325e7782e | /Greenhouse 2017 2018/PLANT_UTILIZATION.R | e300c683829deab3d2c7fa17887bdef62f157495 | [] | no_license | kelseyefisher/larvalmonarchmovementandcompetition | 9060641aff73f7c595aee2d5113934b8336fd727 | b57117ecd42b1ef62710f1b3106dc27a319b8c86 | refs/heads/main | 2023-02-13T05:30:45.902043 | 2021-01-09T16:22:58 | 2021-01-09T16:22:58 | 328,189,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,139 | r | PLANT_UTILIZATION.R | setwd("C:/Users/kefisher/Box Sync/Publications/Monarch Larval Biomass Consumption - GH 2017 & 2018/Analysis_022019")
##Leaves in first 24 hours
firstobs<-read.csv("012119_GH2018_FirstObservations_ForAnalysis.csv")
head(firstobs)
#average by number of plants
library(plyr)
ddply(firstobs,~NumPlants,summarise,... |
6dd414b4e1b127ff95dc244537ddabe8891b7895 | d7f68113ba841857d68f2ac452bcda91fe373cf0 | /Insight/Scripts/Reimbursement.R | ae816ce2dcb9f1b1945767fcee891ae15c4649b1 | [] | no_license | ramyead/Insight-Project-kNOw-Care | bc731398d3a49ac803af08d70ca58beb9a38d09d | 654c0203bc51036f671a14c17e259c8fa4f17b47 | refs/heads/master | 2021-01-17T11:58:33.172780 | 2017-06-26T00:45:01 | 2017-06-26T00:45:01 | 95,390,630 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 812 | r | Reimbursement.R | pacman::p_load(stringr, ggplot2, car, effects, lme4, lmerTest, dplyr, reshape2, tidyr, sjPlot, nlme)
reimbursement = read.csv("Data/Hospital_Revised_Flatfiles/Payment and Value of Care - Hospital.csv") %>%
select(Hospital.Name = Hospital.name, State, Payment.measure.name, Payment.measure.ID, Payment)
reimbursemen... |
fc23f686958698f56b867e1256794b0534be48ef | 4bd57b8501d4326ecc06c1d1ea499935e1668d95 | /MASH-dev/JohnHenry/Pf_Analysis/MT_TE.R | 419abed1af2d4f19bb6b5bdb3d6b43bd9631723d | [] | no_license | aucarter/MASH-Main | 0a97eac24df1f7e6c4e01ceb4778088b2f00c194 | d4ea6e89a9f00aa6327bed4762cba66298bb6027 | refs/heads/master | 2020-12-07T09:05:52.814249 | 2019-12-12T19:53:24 | 2019-12-12T19:53:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,857 | r | MT_TE.R | library(readxl)
library(fitdistrplus)
library(zoib)
library(gamlss)
Mosquito_Transmission <- read_excel("~/Malaria Data Files/Mosquito_Transmission.xlsx")
MT_Days <- read_excel("~/Malaria Data Files/MT_Days.xlsx")
Gt_Col <- read_excel("~/Malaria Data Files/Gt_Col.xlsx")
#MT_GT_NP <- read_excel("~/Malaria Data Files/MT... |
641bf4c68b058c9ce29a60ae16adf731b824b844 | 2ab25ee2419091aec254ff0a1919e252f2d176fa | /assignment4.R | 1481a298cc0cfd0abfb9f607ccb1bbcf24ab5a6c | [] | no_license | GeorgeAaronG/WallStreetBets-Reddit-Analysis | f3d14eac512590f04421bd117d299ea7806ad12a | 7ae9c3fa507e0f5242d5dbe8ee2ee853162a3e8b | refs/heads/master | 2020-09-11T17:50:16.148655 | 2019-11-20T05:12:22 | 2019-11-20T05:12:22 | 222,143,523 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,029 | r | assignment4.R | # George Garcia | 11.16.19
#
# "assignment4.r": Collects Reddit forum posts from the 'WallStreetBets' community to create
# user network graphs with 'dplyr' and identify entities such as persons, organizations,
# locations, dates, monies, and percentages with 'openNLP'.
# To prevent java.lang.OutOfMemoryError, set... |
eee2adb0ad019e60b3cca78a8d48d88c9058c184 | 2aa473e524c173313ebbfc757b6a91f7c7b24f00 | /man/mungepiece.Rd | 0f03663284fa85b637a1fd9e4eacba6c0461b1cd | [
"MIT"
] | permissive | syberia/mungebits2 | 64052c3756828cef6bc1139106d4929ba02c8e75 | 1b4f0cd2a360856769ccb2b11dfc42f36fa5d84c | refs/heads/master | 2020-04-06T04:16:54.292474 | 2017-09-19T21:27:27 | 2017-09-19T21:27:27 | 29,579,712 | 1 | 2 | null | 2017-09-19T21:27:28 | 2015-01-21T08:32:15 | R | UTF-8 | R | false | true | 299 | rd | mungepiece.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mungepiece.R
\docType{class}
\name{mungepiece}
\alias{mungepiece}
\title{Mungepiece.}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
mungepiece
}
\description{
Mungepiece.
}
\keyword{datasets}
|
95e6f9749049a89f5f5a10f5871f176bf460baa3 | cddaaa6370390e142ef8c05cfe6fb9dae79685d0 | /funcs-common.r | c464719dd6faf1175fef8ba72a5d8b0e3d50b3f7 | [
"MIT"
] | permissive | matthewgthomas/ami-summarise | 9d51c9fb0e265eb85d308c3ca72d1d040129e218 | f5c522c622c4df235b2a0fda9abc20df955a2b99 | refs/heads/master | 2020-06-26T19:48:29.079484 | 2015-04-22T21:07:50 | 2015-04-22T21:07:50 | 34,285,216 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,715 | r | funcs-common.r | ##
## Process Content Mine output XML files into node/edge lists that can be visualised in network graphs.
##
## This file contains the main input, processing and output functions.
##
## By: Matthew Gwynfryn Thomas
##
## {------- email --------}
## {-- twitter --}
## mgt@matthewgthomas.co.uk
## ... |
d059f2606e1b6b5922c13a36db20dbd43456881a | 0e3d395211cc8e6c9c2e5739ca3b6d3111683530 | /man/baSimuError.Rd | 58800a602bf7b02ce71621922599fecdee459160 | [] | no_license | olssol/cava | 759a63d24a652d49faf4bc0c9c43d7aa3e434ac4 | 29a52ccf9beb6b3e32598e4369a7b3df34c668b0 | refs/heads/master | 2023-09-04T01:43:31.902245 | 2023-08-22T17:59:52 | 2023-08-22T17:59:52 | 240,382,223 | 0 | 2 | null | 2023-08-22T17:59:53 | 2020-02-13T22:44:17 | R | UTF-8 | R | false | true | 825 | rd | baSimuError.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/basim_simu.R
\name{baSimuError}
\alias{baSimuError}
\title{Simulate random error}
\usage{
baSimuError(
n,
error.type = c("normal", "skewed"),
sig = 1,
mu = 0,
skew.mu = NULL,
skew.phi = NULL,
skew.noise = 0.001
)
}
\arguments{
\... |
01490d9754c3b85cee87dc1a47347b1da8864f87 | ed4eaf6ab9dc7bc7d952589ddbf1cb0191e35bff | /R/kayvan.R | 5b5a782811e3cf38f6addf46c41182ee1285a08b | [] | no_license | cran/ggm | 6c9cb51c79aebe7f91800afa543b538ae42f3c1a | 809e5625d5e8f1c6f2bb3eda5c02ba18e3dddcec | refs/heads/master | 2021-01-15T11:29:01.365096 | 2020-02-16T13:00:02 | 2020-02-16T13:00:02 | 17,696,375 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 75,875 | r | kayvan.R | #### Functions by Kayvan Sadeghi 2011-2012
## May 2012 Changed the return values of some functions to TRUE FALSE
require(graph)
require(igraph)
######################################################################
######################################################################
rem<-function(a,r){ # this is set... |
0c6e377187d488679c425e0426f69b7e36afc740 | aa5a0d32a413a832e2cf6a68d2003185246ee3ae | /man/genIndex.Rd | cb85bd1b62aeb7594aa6c91b7b8e180a94ef32f6 | [] | no_license | lvclark/polysat | 62fb4ddcad15db6886d1b1d38e04c6892998dd9f | ab6f69af3310e102846faa1c8ea4242eae3e87d8 | refs/heads/master | 2022-09-19T04:23:37.375464 | 2022-08-23T12:52:08 | 2022-08-23T12:52:08 | 39,743,630 | 10 | 7 | null | 2018-09-10T15:24:09 | 2015-07-26T21:58:14 | R | UTF-8 | R | false | false | 1,793 | rd | genIndex.Rd | \name{genIndex}
\alias{genIndex}
\alias{genIndex,genambig-method}
\alias{genIndex,array-method}
\title{
Find All Unique Genotypes for a Locus
}
\description{
This function will return all unique genotypes for a given locus (ignoring allele
order, but taking copy number into account) and return those genotypes as well
... |
2267d68f37a372658c3afce5793ff1606a1eab8c | 05af69e8ba746112a1682d183950d24a592ddae7 | /man/forecast_NWP.Rd | 839f48c339ed9220d8ce8f86153e509eaa7401f1 | [
"MIT"
] | permissive | mhdella/solarbenchmarks | ccbceea99c33baf8210883821b2c5262090dc15c | 00c13a4b7729e82e2796282b4a690b7244ff2da5 | refs/heads/master | 2022-07-25T03:30:45.608815 | 2020-05-18T18:49:45 | 2020-05-18T18:49:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,113 | rd | forecast_NWP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/forecast_methods.R
\name{forecast_NWP}
\alias{forecast_NWP}
\title{Do raw numerical weather prediction ensemble forecast}
\usage{
forecast_NWP(nwp, percentiles, sun_up)
}
\arguments{
\item{nwp}{A [day x issue time x lead time x member] matrix... |
be96b02081b5146be96ad4b7e013388dadb107c6 | 1675291d2a606bd1de7fcd677aea9bf112fd0300 | /R/github.R | e626735254dfa51279fc651dd1d2c50c8818de36 | [] | no_license | rtobar/remotes | b78931ef435e7e90efb40d0c339e1efa55d457e6 | faaae2da7d3371c7be9d0e4aaff8f9fa585d6beb | refs/heads/master | 2021-06-20T07:19:21.535220 | 2017-08-03T03:51:55 | 2017-08-03T03:51:55 | 99,062,373 | 1 | 0 | null | 2017-08-02T02:18:33 | 2017-08-02T02:18:33 | null | UTF-8 | R | false | false | 763 | r | github.R |
github_GET <- function(path, ..., pat = github_pat()) {
url <- paste0("https://api.github.com/", path)
tmp <- tempfile()
download(tmp, url, auth_token = pat)
fromJSONFile(tmp)
}
github_commit <- function(username, repo, ref = "master") {
url <- file.path("https://api.github.com",
"rep... |
2a62b921fc7f4f5e9c7d55f0790b72bbb4aab764 | 842d1b35c31962b75ad8f3d61481570bc53904e9 | /knn_zoo.R | c889547ede349a142afb64d0dbe8c4261a0bab20 | [] | no_license | MGPraveen07/K-NN | 5d76a307b2a8fd2559ef523a59135a4b4600ade0 | 586e91400c2593973774a0576e7a81b7d10d1fe8 | refs/heads/master | 2020-12-09T06:22:58.321384 | 2020-01-11T11:31:38 | 2020-01-11T11:31:38 | 233,221,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,206 | r | knn_zoo.R |
library(readr)
Zoo <- read_csv("C:/Users/Admin/Desktop/Assignments/k_nn/Zoo.csv")
view(Zoo)
str(Zoo)
summary(Zoo)
#Create a function to normalize the data
Zoo$type <- factor(Zoo$type, levels = c("1","2","3","4","5","6","7"), labels = c("Animal 1","Animal 2","Animal 3","Animal 4","Animal 5","Animal 6","Animal 7"... |
fd508f1289458ac0e15e0233151a7cb56c92c3a9 | 7124b867ea78c31a56cae27bb491ed89ae5baf01 | /plot3.r | 57ed44629fc98f4c7ecfe4839351a6f38a931dab | [] | no_license | cnik343/exploratoryDataAnalysis-Assgn02 | a7b7228f4f60a6daaa037bf216d294d2240edba0 | 172c8090196a9a5da9490529ed6a7abf61dbc1a1 | refs/heads/master | 2021-01-18T20:10:30.079631 | 2016-06-22T19:29:16 | 2016-06-22T19:29:16 | 61,240,907 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,892 | r | plot3.r | # Exploratory Data Analysis - Assignment 02
# -----------------------------------------------------------------------------
# Assignment
# The overall goal of this assignment is to explore the National Emissions
# Inventory database and see what it say about fine particulate matter pollution
# in the United state... |
756229b151c052bdc1c8995af82ee0db5a87bedf | 2eadca37495f2fb1a1c9c594bf67bb50ce9bf029 | /man/harrypotter.Rd | d6d9b1c2104c2b9620fd8848412a1f3816a03254 | [] | no_license | bradleyboehmke/harrypotter | 34e52612bc262fa6581c4c969d92c6520f85c3bf | 51f714619350e6685534c8a9c892ff1870e56558 | refs/heads/master | 2022-11-01T18:02:51.868320 | 2016-12-30T13:56:50 | 2016-12-30T13:56:50 | 77,556,514 | 71 | 18 | null | 2022-10-26T13:45:15 | 2016-12-28T19:34:28 | R | UTF-8 | R | false | true | 368 | rd | harrypotter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/books.R
\docType{package}
\name{harrypotter}
\alias{harrypotter}
\alias{harrypotter-package}
\title{J.K. Rowling's Harry Potter Series (Books 1-7)}
\description{
This package contains the complete text of the first seven Harry Potter books,
f... |
0b8dbefd8439046fb82a6ffbea4351814b6f1e02 | 75cf6a9fd035883b64ca2309382e0178cf370b43 | /Empirical/r/doc-R/Social/ergm/divers/spark.r | 25d4634b4d9ffe033881c8ff6ccb5de65c5b6837 | [] | no_license | ygtfrdes/Program | 171b95b9f32a105185a7bf8ec6c8c1ca9d1eda9d | 1c1e30230f0df50733b160ca73510c41d777edb9 | refs/heads/master | 2022-10-08T13:13:17.861152 | 2019-11-06T04:53:27 | 2019-11-06T04:53:27 | 219,560,170 | 1 | 2 | null | 2022-09-30T19:51:17 | 2019-11-04T17:39:52 | HTML | UTF-8 | R | false | false | 2,983 | r | spark.r | library(sparklyr)
library(dplyr)
sc <- spark_connect(master = "local")
# Using dplyr #############################################################################"
iris_tbl <- copy_to(sc, iris)
flights_tbl <- copy_to(sc, nycflights13::flights, "flights")
batting_tbl <- copy_to(sc, Lahman::Batting, "batting")
src_tb... |
b3d7dca3a2dae570045b22712f6d1f3fe207d6c0 | 0878fefd2602360c2b08f77b36d7fc713c055e62 | /man/traitextract.Rd | 09a58a97f92a031aa1d44447f4e755a521f422e6 | [] | no_license | Koalha/sidtraits | 8f340051b4c5eda915b7095e4953a25f49308061 | 24594f0c57d5e93806ea5c60753886f849e37e38 | refs/heads/master | 2021-01-19T08:48:55.461754 | 2015-02-10T11:08:05 | 2015-02-10T11:08:05 | 19,442,693 | 1 | 0 | null | 2015-02-10T11:08:05 | 2014-05-05T03:25:30 | R | UTF-8 | R | false | false | 435 | rd | traitextract.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{traitextract}
\alias{traitextract}
\title{Extract seed traits from SID}
\usage{
traitextract(queryresult)
}
\arguments{
\item{queryresult}{A single row result from the function sidspecurl or sidspecurls}
}
\description{
This function checks a sidspecurl result ... |
bc210d53d12497966cd57aa5392edd97e01e99b4 | c8639992711286c4f3313345049a4fceceb1f072 | /LPmerge_concensus_map.R | 5bbc42a3c672ab4a250cd30e8ae3402aee3732e8 | [] | no_license | rabbit-tooth/R | aacf954a13ad016009aa38a962f9b36e1587d1c2 | 0a52e3da74efd6ce1b0ccb651311cfc105a0524b | refs/heads/master | 2021-01-19T16:37:19.159223 | 2019-04-26T14:26:27 | 2019-04-26T14:26:27 | 101,012,953 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 855 | r | LPmerge_concensus_map.R | #Author by 2017/9/1 R --vanilla --slave --args chr < LPmerge_concensusmap.R
library("LPmerge")
args <- commandArgs()
chr <- args[5]
getMap <- function(chromosome,population){
inp <- paste0(chromosome,population)
map <- read.delim(inp, header=T, sep=",", comment.char="", fill=T, stringsAsFactors=F)#no retu... |
67902da13360164e82bb62ee2b8b3a7f89e6ff2e | 1c3cb8bed8c0aa9837e3f1e6bf0cd3a9e23faef1 | /R/3SelectRunFromFolder.R | 63de8802cbccdb415ebf949635cd38cc9394c454 | [] | no_license | Peccary-PMX/PeccaResult | 2c2b08665bdc7f59c02e2ee64f6ca09cda395efd | 726deec236ac1a6b9ee5b10056f9c1cb8d3ed0c4 | refs/heads/main | 2023-04-15T08:21:11.764143 | 2022-01-29T00:16:51 | 2022-01-29T00:16:51 | 453,237,110 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,873 | r | 3SelectRunFromFolder.R | #' @export
setGeneric("select_run",
function(object, value = 0){standardGeneric("select_run")}
)
#' From a folder give the run
#' Author: Thibaud Derippe
#' Arguments: object, the dossier object
#' Arguments: value, the run you want to create from
#' Output: a run object
#' @export
# object <- createFolde... |
65466a4a3b7f1fb12952c55b66deb538c91a17f3 | 0812d024c1cbe7a0429ad4de5767a6046db8acad | /ALY6015_FinalProject_GroupEpsilon.R | 83f8bed990ee50bf7d2a2f33e27e04a26d466eca | [] | no_license | binarynex/NEUProjects | 2ba9f7281783fb662d8cab48ac611db2b4c6f6a8 | 49056a504ff5ffd25a09393868ea55d6bdbdf774 | refs/heads/main | 2023-05-26T21:15:33.779712 | 2021-06-04T19:05:37 | 2021-06-04T19:05:37 | 347,789,436 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,834 | r | ALY6015_FinalProject_GroupEpsilon.R | ###-------------------------------------------------------------------
### Group EPSILON:
### Nicole Castonguay, Ryan Goebel, Tim Przybylowicz, Brad Viles
###
### ALY6015.23546
### Final Project
###-------------------------------------------------------------------
###--------------------------------------------------... |
f04a621f2e3add441495ed657d7a584ac7ad91a6 | c2cc9e7bde76f30f0c63f7068bdde39cf91aa0f8 | /Unit 2 - Linear Regression/Unit_2_homework.R | 65519ed01a6f1a85774f2b83e8da77841814b9ad | [] | no_license | arubino322/The_Analytics_Edge | 5319e3d538c682ace9c8c077792935581841cdfb | a6137fe80a8023eaab63a77700fb274f0785d4b6 | refs/heads/master | 2016-09-06T10:55:51.039815 | 2015-08-26T22:06:50 | 2015-08-26T22:06:50 | 41,329,691 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,202 | r | Unit_2_homework.R | ##CLIMATE CHANGE
climate <- read.csv("climate_change.csv")
summary(climate)
str(climate)
training <- subset(climate, Year < 2007)
testing <- subset(climate, Year > 2006)
model1 <- lm(Temp ~ MEI + CO2 + CH4 + N2O + CFC.11 + CFC.12 + TSI + Aerosols, data = training)
summary(model1)
model2 <- lm(Temp ~ M... |
1446a932f64bfd2a52895d75c01224fa0197095c | 90fe73abb06f5bec5aef3f1f5d41af328f5e359c | /complete.R | df4b8200d50edabce05e89f47807d644727aaf0d | [] | no_license | mcfornier/Rprogramming | 7e108d395ee5f83a03388ba61650cf6c560b5a09 | dc21d4fd6725c53cc1adb2e988a30273d066af6e | refs/heads/master | 2021-01-01T18:37:55.214773 | 2015-02-22T12:19:29 | 2015-02-22T12:19:29 | 31,159,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,185 | r | complete.R | complete <- function(directory, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'id' is an integer vector indicating the monitor ID numbers
## to be used
## Return a data frame of the form:
## id nobs
... |
9cf21e983641d0e311b0c9b9b2e9de50883b1895 | 48e3c473d637b9eef3592471acdf6ee9d37650f8 | /10.02_CalcSimilarityMatrices.Recon.r | 3f1a815dfc24a36584ccdb5301bf1ee0d0a40da2 | [] | no_license | bishopsqueeze/k_soc | a82906c652791db044e8134f86722a8fe38b3f70 | 916e7e1ef5f1f47daeb7fa8fe693afeae493cb12 | refs/heads/master | 2021-01-10T00:53:58.127310 | 2014-06-21T19:00:19 | 2014-06-21T19:00:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,948 | r | 10.02_CalcSimilarityMatrices.Recon.r | ##------------------------------------------------------------------
## This script is used to compute similarity matrices for the
## et of "reconstructed" egonets that were based on the original
## data provided by kaggle. See "06_CalcReconstructedEdgeNetwork"
## for details of the reconstruction process.
##
## This ... |
bb4a27729f78595ee13d04151dfed776c8648581 | b7dbc8fa280edb6215a6260e1401e0f83b9954b0 | /Optiver/Optd_l2.R | 23808d8b6dcce85c692cacf25f82638de87c429f | [] | no_license | cwcomiskey/Misc | 071c290dad38e2c2e6a5523d366ea9602c4c4e44 | 1fad457c3a93a5429a96dede88ee8b70ea916132 | refs/heads/master | 2021-05-14T18:10:35.612035 | 2020-02-17T15:09:51 | 2020-02-17T15:09:51 | 116,065,072 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 621 | r | Optd_l2.R | Optd_l2 <- function(winner, p1, p2, p3){
if("I1" %in% winner & "I2" %in% winner){
d <- (p1 + p2)/2
} else if("I1" %in% winner & "I3" %in% winner){
d <- (p2 + p3)/2
} else if("I1" %in% winner & "I4" %in% winner){
d <- c(p1 - .Machine$double.eps, p3 + .Machine$double.eps)
} else if("I2"... |
ac13034aa6065de584a398f8048021562c45d948 | 902037115141ead7b315e7b63e437ec61c01c2c1 | /man/rowHWEs.Rd | 7487b0081164e2fe4fca4185d3ae1a0510274daf | [] | no_license | cran/scrime | 4bdc7e989ba9e648d004ca47cd2d10bb5e78a717 | cf0033dbfe2a6fa807593a460ef4bcb0931db96a | refs/heads/master | 2021-06-02T21:50:17.706604 | 2018-12-01T10:00:03 | 2018-12-01T10:00:03 | 17,699,500 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,571 | rd | rowHWEs.Rd | \name{rowHWEs}
\alias{rowHWEs}
\title{Rowwise Test for Hardy-Weinberg Equilibrium}
\description{
Tests for each row of a matrix whether the Hardy-Weinberg Equilibrium holds for the SNP
represented by the row.
}
\usage{
rowHWEs(x, levels = 1:3, affy = FALSE, check = TRUE)
}
\arguments{
\item{x}{a matrix... |
70ef5fda02ec086aa3dbe8601ad99db1dccfb4c1 | 324e429f6047f9adb9c197f05c7677040c9275a8 | /scripts/Geochemical-water_conditions/molecule_amount.R | 78bad7f1488eb9fe708c5332bf43175982635b12 | [] | no_license | amunzur/Microbial_Diversity_of_the_Saanich_Inlet | f0dc9a7e20e3a064e8aae0f4eab7d7913bd8c4a5 | 5b5a7cb7ba1b201411c2da1ab5e4f85da3ea3d1f | refs/heads/master | 2023-01-29T08:43:44.223217 | 2020-12-12T02:14:02 | 2020-12-12T02:14:02 | 320,462,384 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,638 | r | molecule_amount.R | Saanich_Data <- read_csv("~/Desktop/MICB_405/MICB405_proj/Saanich_Data.csv")
df <- Saanich_Data %>% filter(Cruise == 72, Depth == 0.100 | Depth == 0.120 | Depth == 0.200)
# subset data
idx <- c(5, 6, 7, 9, 10, 12, 14, 23, 25)
df <- df[, idx]
element_list <- c("O2", "PO4", "NO3", "NH4", "NO2", "H2S", "N20", "CH4")
el... |
511afbcd2957cc5757c71618187cf56074e43801 | f73694b79bdebb1e686c4abe8164282453ea7ce2 | /pkg/virta/R/init_virtualtables.R | 8941a85fd3560c4d23ce8b77279e3dff5789dbbb | [] | no_license | rsund/SurvoR | c664c64c14f830a6480074ae49628a75e8ea51a3 | bc1fe2fe8cdc184bc2fd2981612454faf27043c6 | refs/heads/master | 2023-03-02T14:54:10.147124 | 2023-02-27T23:49:04 | 2023-02-27T23:49:04 | 190,526,434 | 4 | 0 | null | 2022-06-29T11:42:43 | 2019-06-06T06:25:32 | C | UTF-8 | R | false | false | 1,092 | r | init_virtualtables.R | # Borrowed from RSqlite.extfuns-package by Seth Falcon
#.allows_extensions <- function(db)
#{
# v <- dbGetInfo(db)[["loadableExtensions"]]
# isTRUE(v) || (v == "on")
#}
.lib_path <- function()
{
## this is a bit of a trick, but the NAMESPACE code
## puts .packageName in the package environment and this
... |
cef30d6e19982e9a55ba40de65bc4e3e1e53a0bf | fa42803a6d4079f67d989885a024ab66b3e13a03 | /R_Programming/multivariate-dataviz/m5/5-2 - Quantiatiative Trivariate Analysis (Lattice).R | 35050a648e852d9eb735c0ee4e35b1506ebc6d31 | [] | no_license | AnanyaPandey/QuarantineUpskill | 1a5c75e9b57273be7f73f0e2bb4f8e227c36af68 | 77e51cb698848820ff069bb2c98a34eb210ec282 | refs/heads/master | 2023-02-24T06:45:57.006282 | 2021-01-21T13:38:48 | 2021-01-21T13:38:48 | 254,535,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,708 | r | 5-2 - Quantiatiative Trivariate Analysis (Lattice).R | # Three numerical variables
# Create a gradient color-scale scatterplot
xyplot(
x = Critic.Score ~ Runtime,
data = movies2014,
col = gradient[cut(movies2014$Box.Office, 5)],
pch = 16,
main = "Runtime, Critic Score, and Box Office Revenue",
xlab = "Runtime (min)",
ylab = "Critic Sc... |
e24b0123f627b272c30c8ba9650162d49dc9248f | dfc914acd4a06aa611be1d39d5ca7a1751405eaf | /plot4.R | f41745b7bc817dce70b17e3d8df7a579127ee0c7 | [] | no_license | evdy13/ExData_Plotting1 | ebe1dcbde239938d856e709671c076484446ade6 | f1d750183d179b608e2efc262813bc87a6702e6d | refs/heads/master | 2021-01-18T11:52:42.901923 | 2014-10-12T23:36:45 | 2014-10-12T23:36:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,071 | r | plot4.R | ## create a 2 by 2 matrix for plots
## assign margin size
par(mfrow = c(2, 2), mar= c(2, 2, 1, 1))
## create the four plots
with(hpc, {
plot(hpc$Time_Date, hpc$Global_active_power, type = "n", xlab = "", ylab="Global Active Power")
lines(hpc$Time_Date, hpc$Global_active_power, type ="l")
plot(hpc$Time_Dat... |
5904d7879591f65fbc5ef9e686d580865ecb78be | c37f1410153d7704ec13a4abaaf144214feec863 | /app.R | e7afb48343091e0a73766df3145db974f5985a4a | [] | no_license | MayaGans/testthat_example | 15eaf78f7b433a0738c3f8fa6daa2ceee175ae95 | cadf0a3b6da9d843c54ca286225d799a1c5412d2 | refs/heads/master | 2021-01-04T13:48:20.589273 | 2020-02-17T01:13:21 | 2020-02-17T01:13:21 | 240,582,265 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 451 | r | app.R | library(shiny)
library(tidyverse)
ui <- fluidRow(numericInput("num_input", "Sepal Length Greater Than", min = 4.3, value = 6, max = 7.9),
textOutput("text_test"),
tableOutput("summary"))
server <- function(input, output) {
output$summary <- renderTable(iris %>% filter(Sepal.Length >... |
421268b7234bf3888f086ec62d1a4d884c7ade99 | b9a7317a4f83ec4d51f00cc574c7e492e5e5659f | /R/np.svar.R | 34a1c82c245da9129289af7f0c2cae213e8b9284 | [] | no_license | rubenfcasal/npsp | 98120f2d1196e1f96941d2a874b41fcbf5fd9694 | 9655e881102c642219cb792607de93062bf138a2 | refs/heads/master | 2023-05-02T01:14:01.909236 | 2023-04-22T09:59:19 | 2023-04-22T09:59:19 | 64,307,277 | 5 | 6 | null | null | null | null | UTF-8 | R | false | false | 16,825 | r | np.svar.R | #····································································
# np.svar.R (npsp package)
#····································································
# np.svar S3 class and methods
# np.svar() S3 generic
# np.svar.default(x, y, h, maxlag, nlags, minlag, degree,
# ... |
254a82d10adb9a8cb7927cf5f7370a085d5f0645 | a6fd85360b899ee98d79a4080c05bcefc01f009b | /SourceCode/Analysis/Old/ExploreCrisis.R | cd05cfc2c818a54f59586393261df36ed21cdb5d | [] | no_license | FGCH/amcData | 951f4aeb9ab217e40509a9657efc1f5a934443a4 | 2209173c3011aa17aac95a36006f1a1d22304048 | refs/heads/master | 2020-05-15T01:17:35.288389 | 2014-05-12T09:41:56 | 2014-05-12T09:41:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,719 | r | ExploreCrisis.R | #########
# Exploritory AMC Analysis with amcCrisisYear dataset
# Christopher Gandrud
# Updated 30 July 2012
#########
# Load required packages
library(RCurl)
library(ggplot2)
library(Zelig)
# Load data from the GitHub site
url <- "https://raw.github.com/christophergandrud/amcData/master/MainData/amcCrisisYear.csv"
... |
2ff72bb2ee61eead8ef5540e8ab12306512282d8 | 3b801d00b90dee6d58f4f2c68fb7b4a242a16071 | /pre_made_scripts/ant_code/1_data_post_processing/source/3_apply_rotation_to_datfiles.R | 969cde8b14c72def3262f5b0a032b2f8beba5ce7 | [] | no_license | connor-klopfer/animal_response_nets | 421fe4319ad8524fd42432eab5967162dd7df032 | 31f9a561196f46a2a71224f77bf52c0deeb6e76e | refs/heads/master | 2023-06-14T17:21:42.976099 | 2021-06-29T18:18:51 | 2021-06-29T18:18:51 | 327,428,275 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,117 | r | 3_apply_rotation_to_datfiles.R | ####1_apply_rotation_to_datfiles.R#####
### Calls C++ functions datcorr created when compiling anttrackingUNIL/Antorient (https://github.com/laurentkeller/anttrackingUNIL)
### Takes tracking datfile and tagfile as an input and returns an oriented datfile
### Note that the tag files used as an input must have been or... |
a4bf7b90b27962169492da290e33508c069addaf | 8d6d6c66658ad8cd4b72d69d24b895a8cdb9a09c | /Using-R/Affair (Logi_Reg).R | 8be8307d945f82c51603bca408ca38058da48676 | [] | no_license | hvgollar/Logistic_Regression | edac4d020a32a5651686143e064b6373bae83b38 | 03ccb84271952d26ac7f3a999f79093ed1981e1d | refs/heads/master | 2022-04-11T18:04:07.724773 | 2020-04-03T08:39:37 | 2020-04-03T08:39:37 | 250,188,744 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,713 | r | Affair (Logi_Reg).R |
affair <- read.csv(file.choose()) # Choose the claimants Data set
str(affair)
summary(affair)
View(affair)
attach(affair)
table(affair$affairs) ## 0.625
#sum(is.na(affair))
#affair <- na.omit(affair) # Omitting NA values from the Data
# na.omit => will omit the rows which has atleast 1 NA value
dim(affair)
... |
a25396986e6def6df7977e1317b6b48cc7041ea7 | 880ab4f16d1eabf34d9b728fe673a90402f5f052 | /assets/docs/EDA-Student-Alcohol-Consumption.R | 26b474e8608ec5b182f090a781ae07af3ad1732d | [
"MIT"
] | permissive | TiewKH/TiewKH.github.io | 6a08fd867142afaff794535a94e6dc9c8af3b0a1 | a089b3120313ea6847a277618896727ebbfeabce | refs/heads/master | 2023-05-03T10:37:59.911886 | 2022-01-07T06:51:14 | 2022-01-07T06:51:14 | 76,365,100 | 0 | 0 | MIT | 2023-04-17T18:29:59 | 2016-12-13T14:17:20 | HTML | UTF-8 | R | false | false | 2,833 | r | EDA-Student-Alcohol-Consumption.R | library(ggplot2)
library(plotly)
library(gridExtra)
library(reshape2)
library(plyr)
library(dplyr)
df <- read.csv("student-mat.csv", header = TRUE)
is.special <- function(x){
if (is.numeric(x)) !is.finite(x) else is.na(x)
}
(data.frame(sapply(df, is.special)))
boxplot1 <- ggplot(df, aes(x=Dalc, y=G1, fill=Dalc))+... |
5ae479dfcc911678bf11550cf0dcb1ae248b5ded | 3284004194ec74cb7c6d646306a94e487d38068f | /data-analysis/4_compute_cand_scores.r | f20bd9e04e82e4b01b785047cb500ba6779c24d1 | [] | no_license | florence-nocca/mps-autonomy | 8f3a943adab30125f4a08391da95e0459139514f | 21a705177d37d9b361593886aef4fabcd3fe7ece | refs/heads/master | 2021-01-22T22:13:43.164703 | 2017-10-03T13:16:46 | 2017-10-03T13:16:46 | 92,764,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,016 | r | 4_compute_cand_scores.r | ## 4_compute_cand_scores.r transforms the corpuses into dfms and performs different models on them to compute candidates and parties similarity
## It needs to be executed again when a new score is computed to append it to cand_scores.csv
rm(list = ls(all = TRUE))
library(quanteda)
library(readtext)
library(stringi)
l... |
c1e1c786f7aec62ea28512de7fc6b404329e93ed | 8388995e45cde141163d52c461d1aaeeb01aded4 | /app.R | 997659c5ed77e73abe53f2576446b489d8c082c0 | [] | no_license | kearney-stats/kearney-stats.github.io | e3db3be44a7cfad97dd5e003e8233e141252fc9f | 8bbae84a52dc3f816aae4347caa7afc4e50d2f61 | refs/heads/main | 2023-03-01T14:11:04.697215 | 2021-01-21T15:08:28 | 2021-01-21T15:08:28 | 331,662,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,455 | 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)
library(locfit)
library(knitr)
library(DT)
library(tidyverse)
library(dplyr)
data(penny)
# Define... |
a222a0aca8fefa593c9ef6e5835c779eb96d0ee4 | 388934fe38dffcff8770d29633cf4725dc80890b | /Primer_sesion.R | 18922cd1f10e85f0705bbec597190f4a853bdaf4 | [] | no_license | GerarLDz/RInicio | be0236c518794dae799ec69fe5a49af49ef6e10c | 8d07891c041cbcad994423e6e016a1bf884c5c0f | refs/heads/master | 2022-10-23T09:12:43.409791 | 2020-06-17T02:39:28 | 2020-06-17T02:39:28 | 272,860,941 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 13,531 | r | Primer_sesion.R |
variable.char <- 'Hola R'
variable_num <- 3.1416
variable.int <- 149213L
variable.logical <- TRUE
typeof(variable_num)
typeof(variable.char)
class(variable_num)
print(class(variable.char))
print(class(variable.num))
print(class(variable.int))
print(class(variable.logical))
x.1 <- 1
print((x.1*0.582... |
17b30daf53b303c525924a95bc97bc454cdef0af | d4bee912d31490aaa7a35eb2afa5bbab22305a6d | /man/theme_cb.Rd | 0d74f980dfde43e2fe8039c56aa7ae28aa5bbe51 | [] | no_license | cole-brokamp/CB | 8fec717343d4a67516514e06a9d8cfa88376a4c1 | 7ea91855c70c9586ee9546ecbdb10b044f3ef100 | refs/heads/master | 2022-11-06T08:09:11.511057 | 2019-02-26T22:29:04 | 2019-02-26T22:29:04 | 39,470,699 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 419 | rd | theme_cb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/theme_cb.R
\name{theme_cb}
\alias{theme_cb}
\title{Custom CB ggplot2 theme}
\usage{
theme_cb(base_size = 11, ...)
}
\arguments{
\item{base_size}{base font size}
\item{...}{other arguments passed to \code{ggplot2::theme_*()}}
}
\description{
... |
cb6ce2fd9c7692e181066a4bd6d90db41b6e5798 | 0cd4ff55a44c27ffd2ec1a3a9cdec8e3d0a4e3f9 | /lesson5/Main.R | 59ecdc6ac71099e3a724e78a0f6c79ae0f9c7c87 | [] | no_license | agex5/GeoScripting | 572d54572d5a359fcd9077824de5ffdc68446377 | 42b23d5c5371a4edac211fbab8cffd4264488596 | refs/heads/master | 2021-01-11T23:40:57.301225 | 2017-02-02T18:06:40 | 2017-02-02T18:06:40 | 78,621,847 | 0 | 5 | null | 2017-01-17T19:02:04 | 2017-01-11T09:09:18 | R | UTF-8 | R | false | false | 1,161 | r | Main.R | # TScriptLadies Nadine Drigo and Amy Gex
# 13 January 2017
#import libraries
library(raster)
library(rgdal)
#Source to run fuctions
source('R/NDVIFun.R')
#
#import datasets, both urls are defined as variables to be used as arguments in the following function
#if needed to change urls, make sure you change the last n... |
b902c32203236550858456c43d38c1effc650b60 | 6e38b67c8f57b114f11fde7ad1192084d1b37ad0 | /MiscLectureMaterials/ml_lecture_r_demo_1.R | aab1154c31dc48f9fe2c6ff62da6a55a5ebb8356 | [] | no_license | dcolley99/public-repo | 0a6c0a03d6b171ad545fd217b371bd64bfba3813 | 5cc6cc57dfc1613945fa3b5861e1c2d40efb54f8 | refs/heads/master | 2023-01-07T15:02:25.713989 | 2023-01-03T10:16:37 | 2023-01-03T10:16:37 | 155,693,466 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,811 | r | ml_lecture_r_demo_1.R | # Pre-requisite: Install the following packages using the Packages menu in R.
# ODBC (provides database connectivity)
# nnet (neural network functions)
# arules (associative rule functions)
# e1071 (mixed ML functions)
# plotly (graphing)
library(odbc)
library(plotly)
# Set up the connection to the CottageIndustries... |
1de17706b0df65332e8c5e2ffc42bb9391d0eaf3 | fd2a324a9505ed29e6136a06216edce999fa97a1 | /R/rMVNmixture2.R | 8450ad46ee45e102cebbfd98dd6df50cf51c7221 | [] | no_license | cran/mixAK | 995c88ac9b1f70ab2dac51b4fc1347b9b1356eed | adc4c2229d8ad3573e560fd598158e53e5d1da76 | refs/heads/master | 2022-09-27T10:45:02.953514 | 2022-09-19T13:46:13 | 2022-09-19T13:46:13 | 17,697,529 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,981 | r | rMVNmixture2.R | ##
## PURPOSE: Random number generation from the mixture of the multivariate normal distributions
## * mixing performed in C++ code
##
## AUTHOR: Arnost Komarek (LaTeX: Arno\v{s}t Kom\'arek)
## arnost.komarek[AT]mff.cuni.cz
##
## CREATED: 07/11/2008
## 15/03/2017 .C call u... |
171b6b5efac764377659c6a7d70ea0346429c19d | 35d453ab1756e5b648e9d8d7d89cdfd33c4f8f38 | /R/compareTwoPlayerAves.r | 1cd79cc7a9ba76c3a37a018544fa024561414cc6 | [] | no_license | nickzani/Cricinfo | 1c4b099aa4e67347a6d8ce5ecac606677bd7c989 | 6d6bb3489f6690c8d57ae38bbae11614ae6019bf | refs/heads/master | 2021-01-10T06:28:02.987967 | 2016-02-29T19:34:33 | 2016-02-29T19:34:33 | 36,493,623 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,335 | r | compareTwoPlayerAves.r | #' compareTwoPlayerAves
#'
#' Uses the getPlayerInfo function to return a dataframe with the two player averages
#' @param Player1 Name of the first player
#' @param Player2 Name of the second player
#' @param Player1Country Country of the first player
#' @param Player2Country Country of the second player
#' @export
#'... |
9edd335c01674c1a95d215847270f1b10b721a8f | 7fe3e5d85e046a4777b741484bd9a372387218f3 | /knn.r | e310403005caaffe1d1a3da9d60fcb8de6f6658a | [
"MIT"
] | permissive | CodeMySky/KDD99 | 3485bf6b6e6efc78165162f73f9b564fae8f2192 | 3c1bd6f0cb967a877bad7c7ea9fb05c555c03ed6 | refs/heads/master | 2016-08-07T08:01:11.772513 | 2015-05-17T04:33:20 | 2015-05-17T04:33:20 | 33,835,421 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 384 | r | knn.r | library('MASS')
x_train_n = data.matrix(x_train)
x_test_n = data.matrix(x_test)
# u [44461 * 41]
u = svd(x_train_n)$u
# p_c = [20 * 41]
prominent_component = u[1:20,]
# invp [41 * 20]
invp = ginv(prominent_component)
# x_train_n = [44461 * 41]
x_train_n_pca = x_train_n %*% invp
x_test_n_pca = x_test_n %*% invp
y_hat =... |
3377f2bc5a8acdeeefc0ad85a2691e9a0b3351b1 | be5d6168799188c437cf5448d9d606bad24fa199 | /src/Update_Tankbeurt.R | 410d18d7526ef3928f65a81fd97c06c2c7b48abd | [
"MIT"
] | permissive | SanderDevisscher/monies | 61f4a16ed99036e677855142efa5b0c8732f802e | a29492a13f79c277b884078c798241e8642c807c | refs/heads/master | 2022-11-25T10:14:47.918610 | 2022-02-20T13:40:33 | 2022-02-20T13:40:33 | 246,482,305 | 0 | 0 | MIT | 2020-03-11T06:01:03 | 2020-03-11T05:25:01 | R | UTF-8 | R | false | false | 2,840 | r | Update_Tankbeurt.R | library(tidyverse)
library(googlesheets4)
bo_email <- Sys.getenv("bo_email")
gs4_auth(email = bo_email)
source("./src/Update_Level.r")
update_level()
Data <- read_sheet("1YLYWYwPsXXAeTEFz1Mpi2tV6J13sXUveIIKY8HBDncI", sheet = "Mazout", range = "V10:V11")
avg <- read_sheet("1YLYWYwPsXXAeTEFz1Mpi2tV6J13sXUveIIKY8HBDnc... |
5a8545f13b95ca89d6b4ea5744137b8e8047456c | 569a4a4f753c2a77b54d57a471a39158862139c0 | /matchupSummary.R | c79977fc470ef05d097d1bbf451aa70cc185789b | [] | no_license | karigunnarsson/midMatchup | e75684a2ab9efc8d4982ffab5057673b16fe518a | 05345cc0963199735bd2c970d51bae686b056737 | refs/heads/master | 2021-06-13T13:47:36.093903 | 2017-04-14T00:59:28 | 2017-04-14T00:59:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,192 | r | matchupSummary.R | #######
# In this file we'll create the final datamart, where we'll calculate the win ratio, gold and XP
# difference between the heroes, the output file here should contain 113x113 lines, and could
# be used as input for a website.
#######
library(plyr)
library(dplyr)
combinedFinal <- readRDS("combinedFinal.RDS")
h... |
6a88e92d5f2c7a56f895c26507a62958e9f82ecb | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/labstatR/examples/gen.vc.Rd.R | d254c0c6abc35e8f5e0c249e7717860b4af24f94 | [] | 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 | 276 | r | gen.vc.Rd.R | library(labstatR)
### Name: gen.vc
### Title: Simula una variabile casuale discreta
### Aliases: gen.vc
### Keywords: distribution
### ** Examples
x <- c(-2,3,7,10,12)
p <- c(0.2, 0.1, 0.4, 0.2, 0.1)
y <- NULL
for(i in 1:1000) y <- c(y,gen.vc(x,p))
table(y)/length(y)
|
111ffb46f94173738d1a64246af693897fa0dec0 | 6cc6a8f3cfae2e25a86b7a85b78896357f1bba0f | /Code/dataooopen.R | 92c9e4875d16567234426a16708de69ab0dd402b | [] | no_license | RyanYaNg7/dataopencitadel | dc2abe859bb4ada07b089ff67b29609c2c94e5e9 | ae91d217bf0bc45fc1d696f8c08a47041218ce0f | refs/heads/master | 2020-04-02T08:46:09.341935 | 2018-10-23T04:04:11 | 2018-10-23T04:04:11 | 154,260,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,098 | r | dataooopen.R | # load data
library(readr)
library(dplyr)
library(stringr)
library(data.table)
chemicals <- read_csv("Desktop/dataopen/chemicals.csv") %>% as.data.table()
industry_occupation <- read_csv("Desktop/dataopen/industry_occupation.csv") %>% as.data.table()
water_usage <- read_csv("Desktop/dataopen/water_usage.csv") %>% as.d... |
59425a5c682f32c8c6f25f567d3c3d510752a246 | d1f039f7a355c4a004037de196106a2798c53aee | /Big5VarianceAnalysis/VarianceAnalysis.R | a4dbfee7aacf18332015d72683a25fe9c67e3287 | [] | no_license | PetraBurdejova/SPL-1 | b7d59f9c70fa9032e2afc7be80563efbae3b3b4d | 09e68aa0a8c7e89819bfae6c1a30ea4bf7834d8e | refs/heads/master | 2020-03-28T14:26:16.105598 | 2018-08-10T15:12:02 | 2018-08-10T15:12:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,470 | r | VarianceAnalysis.R | source("SPL_Big5GritDataPreparation/SPL_Big5GritDataPreparation.R")
DATA = getCombinedData(FALSE)
#Do the analysis of variance
aov1f = aov(Agree~age,data=DATA) #an example of analysis of variance for Agree ~age
summary(aov1f) #show the summary table
#creating ANOVA for Personality traits against independent... |
520a04d15e7476f898b011316a82012ab8d3ce4b | de7d600029c0a14195b033df58ea6506ba2012f1 | /plot1.R | 6c2c1fad202b8d59a3b1f1ed8b6b853e1b6f6874 | [] | no_license | mmottahedi/ExData_Plotting1 | 76e781f5340efabce15a73c83cbda881053e2d84 | cca9f65df1eb45b280938b075fc4d5b148c071de | refs/heads/master | 2021-01-17T15:50:25.295753 | 2015-05-10T03:27:16 | 2015-05-10T03:27:16 | 35,355,114 | 0 | 0 | null | 2015-05-10T03:14:09 | 2015-05-10T03:14:09 | null | UTF-8 | R | false | false | 468 | r | plot1.R | setwd("/home/mfc/cwd/data.science/EDA/Project1")
library(data.table)
data <- read.table("/home/mfc/cwd/data.science/EDA/Project1/data.txt",sep = ";",header = T,na.strings = "?")
date <- strptime(paste(data$Date,data$Time),"%d/%m/%Y %H:%M:%S")
data <- cbind(date,data)
png(filename="plot1.png",width =480 , height =... |
074c9a9c4562b18fd295429591a2c41ebcccdbdd | 791c7e3f6a37d11f73c5b125f693e4bccc0d76cf | /src/BrowsingHistoryAnalysis/feature_extraction_for_model_generation.r | d683f13652baf1e9b02d4fbc3ad02109470acf32 | [] | no_license | akshaynayak/Behavioural-Targeting-Tool | 64757a9f458a933a2eabcf628e94d2c81c651a53 | fa94b1b90501ac4703398d301e76f9919007d1ce | refs/heads/master | 2016-08-12T06:45:02.702486 | 2016-01-24T21:05:10 | 2016-01-24T21:05:10 | 50,301,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,023 | r | feature_extraction_for_model_generation.r | library(tm)
colnames(unskewed_data)<-c("title","no","label","url")
colnames(unskewed_data)
myCorpus = Corpus(VectorSource(unskewed_data$title))
#class(myCorpus)
#myCorpus = Corpus(VectorSource(sample))
myCorpus=tm_map(myCorpus,stripWhitespace)
myCorpus = tm_map(myCorpus, content_transformer(tolower))
myCorpus = tm_map... |
9eb4b3c1a655f93c6c3902144cdb04b1686dba6d | 34c1b039e379665053ec1b439c531d130731b0ba | /plot4.R | 001ef677dc20558d91cd1c9ff2c338a2be31a18c | [] | no_license | iambritishdaniel/ExploratoryDataAnalysis_Assignment2 | 6839df0fc5ae2b6c6cf4fca974567ff090b4fe94 | b39c544b1f99a5369717fcbd073599f456615894 | refs/heads/master | 2021-01-16T19:16:53.387278 | 2014-06-19T22:56:09 | 2014-06-19T22:56:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,419 | r | plot4.R | require(ggplot2)
# check for data directory and create if necessary
if (!file.exists("data")) {
dir.create("data")
}
# check for data file else download and extract archive to data directory
dataFile1 = "data/Source_Classification_Code.rds"
dataFile2 = "data/summarySCC_PM25.rds"
if (!file.exists(dataFile1) ||... |
ed436979c017c02aa9fa4e66aba3681ee1d12fbd | 29585dff702209dd446c0ab52ceea046c58e384e | /tclust/R/R_restr.eigen.R | 73359c2ec5346ca6d9fa4930f4f4c38514269e07 | [] | 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 | WINDOWS-1252 | R | false | false | 10,225 | r | R_restr.eigen.R |
.restr2_eigenv <- function (autovalues, ni.ini, restr.fact, zero.tol)
{
ev <- autovalues
###### function parameters:
###### ev: matrix containin eigenvalues #n proper naming - changed autovalue to ev (eigenvalue)
###### ni.ini: current sample size of the clusters #n proper naming - changed ni.ini ... |
0ced84bca21eca9c267304a5b362c5d6c0ca1c64 | ed157f4d1f9f309b50c228bbeb58900ca276116d | /man/trim.Rd | 97064cf64cbaa6478e5dfe8c70eb9ffaef3b222f | [] | no_license | ugenschel/rotations | fad38b96a98de9d811b51912f399d2d2faaa91e0 | d52a5c1063962f19e48389287baf21f5c97ba3b6 | refs/heads/master | 2021-01-24T01:30:11.660323 | 2012-08-17T16:35:32 | 2012-08-17T16:35:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 599 | rd | trim.Rd | \name{trim}
\alias{trim}
\title{Function to Trim the Sample}
\usage{
trim(Rs, alpha)
}
\arguments{
\item{Rs}{The sample of random rotations}
\item{alpha}{The percent of observations to be trimeed}
}
\value{
S3 \code{trim} object; a sample of size n-(n*alpha) of
random roatations
}
\description{
This functi... |
e0faa49d2c89a1c35f3de5cd3bfb6f711711b767 | 1bd67a3f0b937d3e76fc8fce02c06dc73884ecc4 | /Exercise-2.R | 327f4b128c32f736bc9e864362379bb952a6590d | [] | no_license | kvipin16/R-Exercise | 6068de1a0bb89de6edea39ecf4bebaa19cc9c7ab | 0dc80109baa40133a90567dec5c9ee72c5ecd949 | refs/heads/master | 2023-04-04T04:52:53.194342 | 2021-04-25T18:30:43 | 2021-04-25T18:30:43 | 361,378,026 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 148 | r | Exercise-2.R | #2. Write a R program to get the details of the objects in memory
name<-"vipin"
int1 <- 1.5
int2 <- 6
nums <- c(1,2,3,5)
print(ls())
print(ls.str()) |
189deaf01f41cca37b5c0938213ab75ed2460d41 | 46906266c769d91a63bb13f39d5bfef1a0c1d4a6 | /global.R | 4a8b285f95696bdcb970708595a871bb3f3ae78a | [] | no_license | Mihobsine/Workshop_COVID19_tracker | e1cc5e2d1ae5c19de032483abbdb51582c17d6cb | fdc87a8c6cd371011b38fac537ce4fa6fbea1ae8 | refs/heads/master | 2022-09-27T07:41:42.743895 | 2020-06-05T15:32:22 | 2020-06-05T15:32:22 | 267,881,197 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 334 | r | global.R | #Files for all global values of the app
library(dplyr)
library(geojsonio)
library(lubridate)
# Step 1 - Open your files here and prepare the dataframe to merge it with geojson here
# Step 4 - Prepare your datas for plot here
# Use this line to set up the format of the day column
#your_data_frame$jour = ymd(your_da... |
d898ed8ee04afcd77829703dfac8704b38ffd6ce | ef88c5d7857b9d8f3a7b7f0c2a11475774f83ab2 | /R/kaps.R | ec103ec1dd490a4df88177307cc6ac78c9202696 | [] | no_license | cran/kaps | bb8e5695ff48704172802d86cf13d71350088761 | 130a0db675e6293cb7d159f76e4fd0e57bf52668 | refs/heads/master | 2021-01-21T12:21:25.733266 | 2014-11-01T00:00:00 | 2014-11-01T00:00:00 | 17,696,879 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,305 | r | kaps.R | ##########################################################################
####
#### Multiway-splits adaptive partitioning with distribution free approach
####
#### Soo-Heang EO and HyungJun CHO
####
#### Version 1.0.0
####
#### 30 Dec, 2013
####
##########################################################... |
783433a1925aec9afb3c28517752838a9a700b8d | 6fa7c44eefa97557cde6c6daefa91959f9ff2264 | /carmen/src/calc_pwilcox.r | 3b27c3370fdb31e27f9761febb2f6dad5f0f627f | [] | no_license | DavidQuigley/QuantitativeGenetics | a8b24019f0233f70be468c17175c7fe3b3ae9139 | 31c19fef62901f823b1d329298f72428d190abd0 | refs/heads/master | 2020-12-22T08:59:28.749267 | 2017-12-01T16:37:41 | 2017-12-01T16:37:41 | 3,517,532 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,441 | r | calc_pwilcox.r | write_to_max_p = function(max_val, max_p){
for( i in (1:max_val) ){
if( i < j ){
for( j in ( i:(max_val)) ){
legal = F
for( test_val in (3: 10000) ){
pval = pwilcox(test_val, i, j) * 2 # 2 tailed
if( legal==F && pval < max_p ){
legal = T
}
if( legal && pval > max_p ){
... |
348e24dc03249593feae3e5e6a0edcce1cef0440 | 3db42fef7b85bdd3d51a220deb261193365d4d3b | /man/weight.Rd | 350082e38d6d83e69b618798916123fe77184f7b | [] | no_license | mwheymans/psfmi | db30501dd207a057803e0f57e9003f96a8fe3d01 | 6afb51f1f1d9d7df11e91e5ffc64b14c3a663e33 | refs/heads/master | 2023-06-23T14:23:27.086994 | 2023-06-17T12:57:35 | 2023-06-17T12:57:35 | 129,861,191 | 15 | 6 | null | 2022-10-19T17:12:03 | 2018-04-17T07:05:18 | R | UTF-8 | R | false | false | 753 | rd | weight.Rd | \name{weight}
\alias{weight}
\docType{data}
\title{Dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS)}
\description{
Dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS)
}
\usage{data(weight)}
\format{
A data frame with 450 observations on the foll... |
81d0fde6e6820bd286e4fd4b248ded933a8d4eb6 | bee49fb2eb33c04541e372ef0142f7b763824319 | /session1_tasks.R | 2a437f4968692b7cb6442bc189c25f31a9355543 | [] | no_license | mrzhsp/session_1 | 4b20a300412cfd997ad25c2f33340f120fdceeb1 | e67aa14632d5714ab328c4f185212e9b60460d3b | refs/heads/master | 2020-06-13T10:15:49.284079 | 2019-07-01T14:39:17 | 2019-07-01T14:39:17 | 194,624,728 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,740 | r | session1_tasks.R | # Task 1
library(tidyverse)
mpg %>% tbl_df()
ggplot(data = mpg, aes(x = displ, y = hwy, color = trans)) +
geom_point(color = "red") +
geom_smooth()
# Task 2
ggplot(data = mpg, aes(x = displ, y = hwy)) +
geom_point(color = "red") +
geom_smooth()
# Task 3
ggplot(data = mpg, aes(x = displ, y = hwy, color = drv))... |
df2ad9feadc3b0f8f375ef478111e6defd5becd9 | 4ec548c762474908e50080adb57410063899f85a | /man/isSubsetArbitrary.Rd | 715d90e0caf98c0b25b57af6ca2f212eaf919418 | [] | no_license | jaspeir/NIJ_Tabitha | d6e37ae02ba14620967ca2eb0530fed1478c7337 | 37ec34c0a446d19c5c4413b2e340555adc373b10 | refs/heads/master | 2022-10-01T11:05:41.720909 | 2020-04-01T21:56:33 | 2020-04-01T21:56:33 | 268,837,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 472 | rd | isSubsetArbitrary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helperFunctions.R
\name{isSubsetArbitrary}
\alias{isSubsetArbitrary}
\title{Function to determine whether A is a subset of B - arbitrary sets.}
\usage{
isSubsetArbitrary(A, B)
}
\arguments{
\item{A}{the left operand - vector}
\item{B}{the ri... |
daecbcaf4e31d1225412c94ffa18df306fe0cc81 | 1bf4bc16ae1e516b82788227118778f9fa008dda | /Hmsc_CD/oregon_ada/code_GIS/Pred4_3_clamped_plots.r | 1a015a7f74e44bf3e0809b609ff5bb1bf409e3d3 | [
"MIT"
] | permissive | dougwyu/HJA_analyses_Kelpie | 93b6b165e4d2de41f46bacd04c266afa7af0541e | 4ab9de1072e954eac854a7e3eac38ada568e924b | refs/heads/master | 2021-11-03T19:45:17.811298 | 2021-10-26T09:45:15 | 2021-10-26T09:45:15 | 245,229,951 | 1 | 1 | MIT | 2021-09-10T07:19:04 | 2020-03-05T17:44:55 | HTML | UTF-8 | R | false | false | 7,804 | r | Pred4_3_clamped_plots.r |
library(dplyr)
library(ggplot2)
library(raster)
setwd("J:/UEA/gitHRepos/HJA_analyses_Kelpie/Hmsc_CD/oregon_ada")
# setwd("D:/CD/UEA/gitHRepos/HJA_analyses_Kelpie/Hmsc_CD/oregon_ada")
gis_in <- "J:/UEA/Oregon/gis/raw_gis_data"
gis_out <- "J:/UEA/Oregon/gis/processed_gis_data"
# gis_in <- "D:/CD/UEA/Oregon/gis/raw_gis... |
818eeb83a699c39a208371652f27a02c63dfc905 | b81875d1dc66033329e6e82914cd08727dffc8bf | /R/var.R | 5837d792bede8870244e621e635e4756bfc30b97 | [] | no_license | cran/Bolstad | b4cb3d49c8edca8ebcc51fe89539a3d144e8de32 | 3dc15d83e44e4e5e120e91465ae7ca213ba4e699 | refs/heads/master | 2021-01-21T00:52:47.450624 | 2020-10-05T05:50:02 | 2020-10-05T05:50:02 | 17,678,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 529 | r | var.R | #' Variance generic
#'
#' @param x an object for which we want to compute the variance
#' @param \dots Any additional arguments to be passed to \code{var}.
#' @export
var = function(x, ...){
UseMethod("var")
}
#' @export
var.default = function(x, ...){
stats::var(x, ...)
}
#' @export
var.Bolstad = function(x, ...){... |
6fcc1524cee74b1a54fa927e9749059975d2b5c7 | 9e1efc0746759ecfb9f9d4cd56490f2dfe50ed48 | /R code/population and line chart.R | 2ce6d121c5aaeec6ba650d4eb69a53e71c9b432a | [] | no_license | yungclee/Dialysis-Analysis | ee84f3af67b25b816ff0685825f3a7b7124ef262 | 29955cefc8cc8e4f47356b56ae577885a61b0516 | refs/heads/master | 2022-03-06T04:25:31.490496 | 2019-10-21T00:07:47 | 2019-10-21T00:07:47 | 105,085,300 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 3,317 | r | population and line chart.R | total.id.harea=read.csv("D:/data1/temp/total_id_hosparea.csv")
total.id.harea
summary(total.id.harea)
sort(total.id.harea$id_birthday)
subset(total.id.harea,by='id_birthday'==18000101)
cd=read.csv("D:/data1/temp/20141227/corrected data(total_cd_list_hosparea_2002).csv")
total.id.harea=cd
total.id.harea.id=... |
b34ff74777d74cbda0ae7aeb0ee6d2e662408a0c | 30eb33fa62cd3f2a8b4b4de48d156647e79df700 | /R/iViewDatasetControls.R | 15d1af8260aee447a68d8bc5ccbd676eb4c31024 | [] | no_license | MindscapeNexus/CardinaliView | 857d205ba9ad27c35c8108a8b0c50adfa7542915 | 7bd5ff5c92a57b28e1aa84048342ee5755d83327 | refs/heads/master | 2021-01-18T10:30:55.814124 | 2014-06-16T10:21:31 | 2014-06-16T10:21:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,219 | r | iViewDatasetControls.R |
#### Class selecting a dataset ####
## --------------------------------
.iViewDatasetControls <- setRefClass("iViewDatasetControls",
contains = "iViewControls",
methods = list(
initialize = function(...) {
uuid <<- Cardinal:::uuid()
interface <<- ggroup(...)
plist$dataset <<- "(no dataset)"
widgets$dat... |
9840fdb5830e84f790d97a3f343f9457b1bf0f16 | ac1368f9fd5ef76bec2ee541c91f3b0bc2d975ef | /man/list_body.Rd | 81e932e78ff401346a944d92c275c50c55c436bc | [] | no_license | cran/frite | 9bcf904b11118e32a485ca5f0d28eb26d1609e5d | 493efb605501751d91f8e841bf3b62c6d9543214 | refs/heads/master | 2020-03-27T03:51:56.846778 | 2018-07-01T14:00:06 | 2018-07-01T14:00:06 | 145,894,726 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 432 | rd | list_body.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/list_body.R
\name{list_body}
\alias{list_body}
\title{Converts the body of a function to a list}
\usage{
list_body(.f)
}
\arguments{
\item{.f}{A non-primitive function}
}
\value{
A list
}
\description{
This will help you see wh... |
b412651ec5fc6eedabb898b233354c9a51fb9775 | 4c4450be5daa591b195ebb9517c822fd2ab898fb | /Data_Deep_Dive_EP.R | a27f362dbbb3e43cb9c9930cb313e7ea307591ec | [] | no_license | aweisberg44/nflscrapR_EPA_AW | f065eb9573dafe0c1f146d55efcf88ad99b4a531 | 87c33e93fd257c8366eeb5b63a94483db5dc7aad | refs/heads/master | 2020-04-29T01:12:02.356786 | 2019-03-15T01:19:41 | 2019-03-15T01:19:41 | 175,722,571 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,861 | r | Data_Deep_Dive_EP.R | # Title: Data_Deep_Dive_EP.R
# Author: Aaron Weisberg
# Created: Feb 04, 2017
# Last Edit: Feb 09, 2017
# Description: This script takes NFL play by play data
# from 2009 to 2016 and explores the EP training data for relationhips
# between points added and key scoring drivers in football.
# Examples of suc... |
838448ce4d3f6ccab21189588b44e82e32104499 | a177b4eb653bbd30224bc02b61eccfe37f17b073 | /r-for-data-science/Chapter16_Vectors/vectors.R | 635cd0f71f98659eadc946988aec2f73a92c3993 | [] | no_license | cholzkorn/data-science | 566e8a40080622c0810ddd5e2812736dabdf3e82 | f7e2c2898132a63e30ea632604d1ec97724cfbd2 | refs/heads/master | 2021-06-08T23:32:41.494590 | 2020-01-03T20:25:09 | 2020-01-03T20:25:09 | 148,336,430 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,767 | r | vectors.R | rm(list=ls())
# We will use functions from the purr package, which is included in the tidyverse
library(tidyverse)
# There are two types of vectors:
# Atmomic vectors: logical, integer, double, character, complex and raw
# integer and double are also called numeric vectors
# Lists, which are sometimes called... |
17731a750bb19418062b395e2fe812da4160aa4c | 72329732fb914e85559b59109bcdd7b609367051 | /file_selector.R | f8ceda089a9be320e7ff52a18e242f6b9ab26768 | [] | no_license | Altanastor/Momoshop | 7e94a22f0ea78b264399b1e66f5ecebd42046579 | 04f3cb519fee1bf69e42d9e67ac07c8f24f54b6e | refs/heads/master | 2020-04-19T06:56:18.698362 | 2018-02-14T16:19:12 | 2018-02-14T16:19:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,180 | r | file_selector.R | # Dependencies -----
library(tidyverse)
library(magick)
# shiny related
library(shiny)
library(shinydashboard)
library(shinyFiles)
library(DT)
# domestic functions
# remove path, keep filename
trim_path <- function(x){
x %>% strsplit("/") %>% sapply(function(.x) .x[length(.x)])
}
# remove extension, keep filename
... |
f1c9af4f60ec72ebac2b3b2b062e13122aec8c5d | 3e508d7cd0798f70f70242a837f99123397fc1c4 | /tests/sim/20210919-sim/util.R | 66f674b52e742dd87f0f3983f498a657d32ac623 | [] | no_license | jlivsey/Dvar | f0d142df9866a4232947d114891111097c4c5851 | 824cdcc822829d655b905bfeca54a40b82f16e36 | refs/heads/master | 2023-07-26T19:17:28.531305 | 2023-07-06T13:55:28 | 2023-07-06T13:55:28 | 237,249,747 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,182 | r | util.R | printf = function(msg, ...)
{
cat(sprintf(msg, ...))
}
logger = function(msg, ...)
{
sys.time = as.character(Sys.time())
cat(sys.time, "-", sprintf(msg, ...))
}
print_vector = function(x)
{
sprintf("c(%s)", paste(x, collapse = ","))
}
#' Convert valid alpha array to probabilities under log-linear formulat... |
31ddf554ee4f91790de1ca46c9e4bf1a80442e7b | 9b893f56477a12e7634cf2ed0cb6d16d981c9ed4 | /notes/bglmer_runs.R | 59d9880f728b519b8456489dd334ec62325e61a8 | [] | no_license | bbolker/mixedmodels-misc | 32ac8b2d8bc86e841fdc102078daef63dcd86ec8 | b75c0f548a7f02e10b2cea31d92dbc4dc6fa60d3 | refs/heads/master | 2023-06-26T11:39:35.474015 | 2023-04-07T21:31:22 | 2023-04-07T21:31:22 | 32,484,880 | 110 | 44 | null | 2022-09-05T15:57:32 | 2015-03-18T21:17:36 | HTML | UTF-8 | R | false | false | 2,128 | r | bglmer_runs.R | library(lme4)
library(blme)
library(glmmTMB)
library(brms)
library(MCMCglmm)
## @knitr setup_runs
form <- contrast~c.con.tr*c.type.tr*c.diff.tr+(1|id)+(1|item.new)
mydata <- expand.grid(c.con.tr=factor(1:3),
c.type.tr=factor(1:2),
c.diff.tr=factor(1:2),
... |
6f1c3b5b769b0fe7bab15ccea15a11ea24fdaad6 | 1e3df770a5a917c22ee0a53666cb5ef2c9083361 | /R/confSim.R | 3de390c9ab26904136502694afb6bee2b1a056cc | [] | no_license | cran/visualizationTools | 1c7d75ef5535381498ec154b1880e24ee090c8ff | 7a58ca10d2e406eab889f3e36d1d8cb498801239 | refs/heads/master | 2020-05-18T18:04:21.695412 | 2011-08-01T00:00:00 | 2011-08-01T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,736 | r | confSim.R | confSim <-
function(fun=mean,conf.level=0.95,mu=0,stdev=1,sleep=0.2,trials=100,n=8,N=20,xlim,sim=TRUE)
{
if(identical(fun,mean)==FALSE && identical(fun,sd)==FALSE)
stop("function is not yet supported! ")
if(missing(xlim))
{if(identical(fun,mean))
xlim=c(mu-7*stdev/sqrt(n),mu+10*stdev/sqrt(n))
if(ident... |
04ac5d38aaf3eef8cf75bad0c5ad1d19a9cbb3bd | c39efa69fd31f46c64ce9bda5b1f2b88229592d5 | /autotable/man/offender_characteristics_relationship_to_victim.Rd | 6576f0c54352d0c107833661a3751303fb9c0451 | [] | no_license | ONS-centre-for-crime-and-justice/nature-of-crime-r | e0126bb48df8fae64a5f7000a8df40b43af121fa | 117cb5a56b34795421b8574395b0b1adf86a1c89 | refs/heads/master | 2022-12-04T18:50:38.816978 | 2020-09-01T13:23:17 | 2020-09-01T13:23:17 | 288,486,934 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 569 | rd | offender_characteristics_relationship_to_victim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in
% R/offender_characteristics_relationship_to_victim.R
\name{offender_characteristics_relationship_to_victim}
\alias{offender_characteristics_relationship_to_victim}
\title{offender_characteristics_relationship_to_victim}
\usage{
offender_char... |
1b488f6a5f4599d5f999817bb79f8e6b7383b5a0 | fae9abe8a466ef7db9d124032b9b7254391ea68b | /plot1.R | 9a9767d6146c30fa70e69828a3bd206b953a2358 | [] | no_license | sankaraju/ExData_Plotting1 | 58bc9adaa0a3262503a7b5d2ac016259d0da5b69 | d25a238b969876a263f6720801f3468d976548f0 | refs/heads/master | 2020-12-27T10:32:52.998852 | 2015-01-12T01:16:15 | 2015-01-12T01:16:15 | 29,109,429 | 0 | 0 | null | 2015-01-11T23:04:08 | 2015-01-11T23:04:08 | null | UTF-8 | R | false | false | 753 | r | plot1.R | ## Getting full dataset
power <- read.csv("./household_power_consumption.txt", header=T, sep=';', na.strings="?",
nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"')
power$Date <- as.Date(power$Date, format="%d/%m/%Y")
## Subsetting the data
powerFiltered <- subset(pow... |
c1fb9bfeae3c4e116a80b5c7be00969b2886ff36 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Letombe/renHorn/renHorn_400CNF1800_2aQBF_24/renHorn_400CNF1800_2aQBF_24.R | 273438a9d29132f8ecf69c33a6d0474160b53082 | [] | 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 | 77 | r | renHorn_400CNF1800_2aQBF_24.R | 9a71c1613431d45efa6d438254c146e8 renHorn_400CNF1800_2aQBF_24.qdimacs 400 1800 |
27f947a5e792350fec595f0a7edd91f446ed393c | 49ff0bc7c07087584b907d08e68d398e7293d910 | /mbg/mbg_core_code/mbg_central/LBDCore/R/get_location_code_mapping_GAUL.R | 3bd74076546ea6986a127da00f44e7acded5c508 | [] | no_license | The-Oxford-GBD-group/typhi_paratyphi_modelling_code | db7963836c9ce9cec3ca8da3a4645c4203bf1352 | 4219ee6b1fb122c9706078e03dd1831f24bdaa04 | refs/heads/master | 2023-07-30T07:05:28.802523 | 2021-09-27T12:11:17 | 2021-09-27T12:11:17 | 297,317,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,404 | r | get_location_code_mapping_GAUL.R | #' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param remove_diacritics PARAM_DESCRIPTION
#' @return OUTPUT_DESCRIPTION
#' @details DETAILS
#' @examples
#' \dontrun{
#' if (interactive()) {
#' # EXAMPLE1
#' }
#' }
#' @seealso
#' \code{\link[DBI]{dbDisconnect}}
#' @rdname get_location_code_mapping_G... |
07bd707689ea179c40cc755f6bca05441379865b | 325d076c5fcdba87e8bad019a147b37eeb677e90 | /man/train.Rd | 65b2166dae2fc2863dabce27a6852b1d357f9e3f | [
"CC-BY-4.0"
] | permissive | iiasa/ibis.iSDM | 8491b587b6ccc849477febb4f164706b89c5fa3c | e910e26c3fdcc21c9e51476ad3ba8fffd672d95e | refs/heads/master | 2023-08-26T12:38:35.848008 | 2023-08-19T21:21:27 | 2023-08-19T21:21:27 | 331,746,283 | 11 | 1 | CC-BY-4.0 | 2023-08-22T15:09:37 | 2021-01-21T20:27:17 | R | UTF-8 | R | false | true | 9,085 | rd | train.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/train.R
\name{train}
\alias{train}
\alias{train,}
\alias{train-method}
\title{Train the model from a given engine}
\usage{
train(
x,
runname,
filter_predictors = "none",
optim_hyperparam = FALSE,
inference_only = FALSE,
only_linea... |
d14b2dd8ba5b51ccfdea2683ead1e3ece169914e | 87a01a4adfb9bb4b6bd46210b76763c486e15049 | /man/getb0.biseq.Rd | 8d210728e4c327e63acc9f0b383c40018f48f4b6 | [] | no_license | Shicheng-Guo/deconvSeq | 14ac09738dae750d59f8372b6d7b9f531d3fd4d6 | b4de0d17545b09f7f2235814454805761ff75fe6 | refs/heads/master | 2020-11-26T07:14:05.522586 | 2019-08-30T23:10:39 | 2019-08-30T23:10:39 | 228,999,883 | 0 | 1 | null | 2019-12-19T07:31:17 | 2019-12-19T07:31:17 | null | UTF-8 | R | false | true | 777 | rd | getb0.biseq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/withDoc.R
\name{getb0.biseq}
\alias{getb0.biseq}
\title{compute b0, projection matrix, given methylation counts}
\usage{
getb0.biseq(methmat, design, sigg = NULL)
}
\arguments{
\item{methmat}{a matrix of counts with rows corresponding to meth... |
d17c9256c584c537b8112c38bbc4186f56e41b02 | 5edf3ebc52f12c8b7ed4dbc1aa5f97a8e8929605 | /models/openml_lupus/regression_TIME/eddc80da42940ee05ed92bea2fa85b15/code.R | 29875ba33701c18fcc07f00496b143a7595036e1 | [] | no_license | lukaszbrzozowski/CaseStudies2019S | 15507fa459f195d485dd8a6cef944a4c073a92b6 | 2e840b9ddcc2ba1784c8aba7f8d2e85f5e503232 | refs/heads/master | 2020-04-24T04:22:28.141582 | 2019-06-12T17:23:17 | 2019-06-12T17:23:17 | 171,700,054 | 1 | 0 | null | 2019-02-20T15:39:02 | 2019-02-20T15:39:02 | null | UTF-8 | R | false | false | 1,324 | r | code.R | #:# libraries
library(caret)
library(digest)
library(OpenML)
#:# config
set.seed(1)
#:# data
data <- getOMLDataSet(data.id = 472L)
head(df)
df <- data$data
#:# preprocessing
head(df)
#:# model
regr_rf <- train(TIME ~ ., data = df, method = "ranger", tuneGrid = expand.grid(
mtry = 3,
splitrule = "variance",
m... |
bd43994cd8f0096c3db0885e93bf9ad1e368e1e8 | e2b0f1bac85bf906766e69d65d187228bdaf7e8c | /R/gfunction.the.alp.cc.R | 632168aa0e85b2ba77e704cb38553a7344b10a55 | [] | no_license | zhangh12/gim | 0741ecd98fdf0e8d841a7a3316b16025f6b49489 | f8ebca8e670882928f73a1cbdad3b085e75f82fd | refs/heads/master | 2021-11-16T00:14:08.944377 | 2021-08-26T05:39:20 | 2021-08-26T05:39:20 | 122,691,034 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,202 | r | gfunction.the.alp.cc.R |
gfunction.the.alp.cc <- function(para, map, ref, Delta, delta, ncase, nctrl, xi, pr){
nmodel <- length(map$bet)
the <- para[map$the]
g.the.alp <- list()
nthe <- length(the)
n <- nrow(ref)
const <- list()
for(i in 1:nmodel){
id <- c(alp.index.cc(map, i), map$bet[[i]])
gam <- para[i... |
207d79923d613cb8b03b4e3e9b5ac6e6ea0da057 | 14c467c1f779dc3f5139d91bacb845ab05b9e3cc | /R/segHT v1.4.0/man/run_scenario.Rd | 7e018d0b82dacf5f7070e4800f15743731b7dad2 | [] | no_license | milleratotago/Independent_Segments_R | 3706ba6117b04621d2f57056e8919558c6887c89 | c54ea4936ef97e794e2d636c10173ec0737512f1 | refs/heads/master | 2023-08-20T14:55:33.606406 | 2021-10-28T22:19:36 | 2021-10-28T22:19:36 | 275,279,467 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,350 | rd | run_scenario.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SegmentedHypTestEngine.R
\name{run_scenario}
\alias{run_scenario}
\title{run_scenario.SegmentedHypTestEngine}
\usage{
run_scenario(
segmented_hyp_test_engine,
segmented_researcher,
stat_procedure,
effects_list
)
}
\arguments{
\item{se... |
746b73a0804decb840835938845b152a9552ffb5 | 5fec6f72ea804f2c48046c8d9bb48f4586169b8d | /man/auditWorkers.Rd | 4e285832d62f72a1ba0dc7b1bd8fc860d2db1cad | [] | no_license | agencer/labelR | 1d8d81ee64a88c49413f0732ef888217b8bc5d02 | 0dfd92afcce706f31436615c83d1b5bdcebe7db2 | refs/heads/master | 2022-11-10T02:14:45.624475 | 2020-06-19T05:10:55 | 2020-06-19T05:10:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 966 | rd | auditWorkers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auditWorkers.R
\name{auditWorkers}
\alias{auditWorkers}
\title{Audit Mechanical Turk workers}
\usage{
auditWorkers(current_experiment_results, reference_results = NULL)
}
\arguments{
\item{current_experiment_results}{An object returned from \... |
256ad3a7656c01c8fd9c5fa7fd03b332b5a5ff31 | 62d085c276575b6a6a86d3e0957074db890efb4e | /man/appalmswts.Rd | a49229a8cf9476bf7638b01957ed0b03dc28bb30 | [] | no_license | PrecisionLivestockManagement/DMApp | d72fbd1561e0f6e559d2ac1a69f83635ac56e976 | 989590e395f529925020b309e6454d974164607e | refs/heads/master | 2023-08-07T22:49:10.413284 | 2023-08-01T03:44:42 | 2023-08-01T03:44:42 | 228,276,174 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,816 | rd | appalmswts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/appalmswts.R
\name{appalmswts}
\alias{appalmswts}
\title{Retrieves data for the ALMS Weights graph from the DataMuster database}
\usage{
appalmswts(
property,
sex,
category,
alms,
zoom,
start,
rangewt1,
rangewt2,
timezone,
... |
555c4511062f14308ef42fcc01c6e9ffb4aefc4a | 44598c891266cd295188326f2bb8d7755481e66b | /DbtTools/pareto/R/PDEscatterApprox.R | 2a44dbbbf554ae276dd7725c7a738dc40f52ce18 | [] | no_license | markus-flicke/KD_Projekt_1 | 09a66f5e2ef06447d4b0408f54487b146d21f1e9 | 1958c81a92711fb9cd4ccb0ea16ffc6b02a50fe4 | refs/heads/master | 2020-03-13T23:12:31.501130 | 2018-05-21T22:25:37 | 2018-05-21T22:25:37 | 131,330,787 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,867 | r | PDEscatterApprox.R | `PDEscatterApprox` <- function(x,y,paretoRadius=0,drawTopView=TRUE,nrOfContourLines=20,dataPointLineSpec='b',nInPspheres=NaN){
# function [AnzInPspheres,ParetoRadius] = PDEscatterApprox(x,y,ParetoRadius,DrawTopView,NrOfContourLines,DataPointLineSpec,AnzInPspheres);
# % schnelle Approximation fuer PDEScatter
# % [A... |
2af08ebc1f4c7ac3ae8938aea237d5ab7bfebf8a | 6bbed7494fe345f49c6ab2743280110e640015be | /man/fill_treatment_selection.Rd | d27488cb38ab521b936321fe6a1b2575d67cb7c6 | [
"MIT"
] | permissive | tpmp-inra/tpmp_shiny_common | 43912398f92e420e27c9bc97322b7ffa17091192 | 06bb71932e10ef26662ed2f7df7e33489e60fdbe | refs/heads/master | 2021-05-19T09:53:39.758148 | 2020-04-01T10:09:44 | 2020-04-01T10:09:44 | 251,639,231 | 0 | 0 | null | 2020-04-01T10:09:45 | 2020-03-31T15:05:06 | R | UTF-8 | R | false | true | 585 | rd | fill_treatment_selection.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shiny_common_all.R
\name{fill_treatment_selection}
\alias{fill_treatment_selection}
\title{fill_treatment_selection}
\usage{
fill_treatment_selection(
df,
input_id = "cbTreatmentSelection",
label_id = "Select treatments to be displayed"... |
86f998bda2fa849b2e38423add23cd42d6b0392f | 17a46d56660cd2a48ca1505b6ede37653f63c0f5 | /run_model_and_decomp.R | 49590f07f1b097f630be069334aff81d3151e468 | [] | no_license | ngraetz/rwjf_counties | 9b4f21fae2705caa6330f6adeb915b23e500da7e | 8f205d59150fbb19f93e72716d3ee47af79cbc0f | refs/heads/master | 2020-03-24T14:12:58.477014 | 2019-06-26T13:53:30 | 2019-06-26T13:53:30 | 142,762,592 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 25,471 | r | run_model_and_decomp.R | ## Set location of repo.
repo <- '/share/code/geospatial/ngraetz/rwjf_counties/'
## Load all libraries and functions.
library(INLA)
library(data.table)
library(ggplot2)
library(raster)
library(rgdal)
library(rgeos)
library(gridExtra)
library(grid)
library(plyr)
library(RColorBrewer)
library(Hmisc)
library(spdep)
sourc... |
473611599907db895eeb9d1fb79e3539fdc575bf | 74d94399398d71541b02452697500fb8bed86d6b | /docs/FunctionToAddLeadingZero.R | 69aae69dd2d74f4fa57045e12a88f1df9306991e | [] | no_license | xiehanding/Heart-Failure-Readmission | 4be518abc625c5df487bda969144aa6d18a168d5 | d7bd6384353d237f1b3d62a3ab34df5ba29f6dd1 | refs/heads/master | 2020-04-17T14:19:31.637680 | 2017-01-06T17:28:23 | 2017-01-06T17:28:23 | 67,823,660 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,500 | r | FunctionToAddLeadingZero.R | ###############################
# Now we wanna write a function to add zero to every code so that they have equal length, say five digits
# nchar function needs a character variable
is.character(pridgns)
st1 <- as.character(pridgns)
is.character(pridgns)
table(pridgns)
table(st1)
dim(pridgns); dim(st1)
nchar(st1)[1:3]... |
bc13ae5b13706bcd48e497f0eca9d540363c4b39 | ab6350fe9c40847991ff657c67996a96517d58d5 | /R/plot_results.R | 7bfd7524f3f8b97eaab6c61f5ab8f565953f5a55 | [] | no_license | sonthuybacha/benchmarkme | 544d02ec0e01bb8daba31bcea952a1401dacea2c | 3b4674e49e773c56eba089a8ccd6f02b7fc5af07 | refs/heads/master | 2020-09-24T01:08:49.051130 | 2017-11-06T21:54:41 | 2017-11-06T21:54:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,974 | r | plot_results.R | nice_palette = function(){
alpha =150
palette(c(rgb(85,130,169, alpha=alpha, maxColorValue=255),
rgb(200,79,178, alpha=alpha,maxColorValue=255),
rgb(105,147,45, alpha=alpha, maxColorValue=255),
rgb(204,74,83, alpha=alpha, maxColorValue=255),
rgb(183,110,39, alpha=alp... |
d57084e0e5dff462d008c6152f5b0f939695649d | 1d6be29caa42ddd1c0087a1959db1a9f56fb2eed | /src/onh_variant_annotation.R | b5e0cf4e1c9323feaefbe6bb6d8e07aad82221b5 | [] | no_license | cobriniklab/onh_pipeline | 2c332bfcbb882f82d40e301ae11c3db934f63ff2 | afb8daf26b69d8c0f47d9283a8ee5d33611c706a | refs/heads/master | 2021-07-14T21:12:04.086940 | 2017-10-19T19:12:29 | 2017-10-19T19:12:29 | 107,584,949 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,327 | r | onh_variant_annotation.R | #!/usr/bin/Rsript
# load required libraries -------------------------------------------------
library(VariantAnnotation)
library(biobroom)
library(BSgenome.Hsapiens.UCSC.hg19)
library(org.Hs.eg.db)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(tibble)
library(dplyr)
library(data.table)
library(purrr)
# load i... |
541d0a00950170f18a8dba5d2ebb33da6d1f18d8 | 314a08065adb696b530306a41fdc65b9d49da1b3 | /01_top_frequented_nearby.R | b14503cf07accba8adc793b2fb166f3380d1ec5b | [] | no_license | DrPav/Kaggle-FBV | 4b628082791f03003554f462e618b582d5581e20 | 0db959223b6eeccb0a9e43508ee609dedbd319d0 | refs/heads/master | 2016-09-14T19:11:39.514213 | 2016-06-09T00:39:22 | 2016-06-09T00:39:22 | 59,562,125 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,896 | r | 01_top_frequented_nearby.R | #Find the top 3 visited places in range of the xy co-ords
require(data.table)
require(dplyr)
require(magrittr)
require(bit64)
raw_train = fread("data/train.csv", integer64 = "character")
# Get errors later on when joining on placeID that is integer64.
#Use "Character" instead as it is only a key
#Use as.data.frame to... |
9bc0e6bba975a1663d17ecd41de65c2565a0eb19 | 17e0f15ea9dd36e747d9102d96e414bc5c6acbc8 | /clase 02_04.R | 067723f25121de7b75364784b871b6b178e2e06d | [] | no_license | noelzap10/Programacion_Actuarial_lll | b1066c059cebbefddc1b72252663da5fbba4db9e | 3b36367f1a676a29176870eaf23dce16ab2ef397 | refs/heads/master | 2021-09-14T23:12:45.457302 | 2018-05-22T01:22:38 | 2018-05-22T01:22:38 | 119,411,273 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 910 | r | clase 02_04.R | #Scoping Rules
setwd("~/GitHub/Programacion_Actuarial_lll")
^
lm
lm <- function(x){x*x}
lm
rm(lm)
lm
search() #te muestra la lista de busqueda
library(swirl)
search()
hacer.potencia <- function(n) {
potencia <- function(x) {
x^n
}
potencia
}
cubica <- hacer.potencia(3)
cubica (3)
cuadrada <- hacer.potenc... |
fde72d3183a7baa1c6be940e5bcb441bd009cb22 | 50cf2f989dea08b97b43e80e9a334619e4909dc4 | /scripts/loaddata.R | 6cc909d4296f4d2e8ed2bea51e52961684f7c5b2 | [] | no_license | seanspicer/ExData_Plotting1 | 925325faa647f5d836b06772dd7d26bbf1d79195 | c95282a350de154be8ff63a8c8389c5a9833865a | refs/heads/master | 2020-04-10T17:21:39.320030 | 2015-07-11T13:20:14 | 2015-07-11T13:20:14 | 38,724,045 | 0 | 0 | null | 2015-07-08T01:18:11 | 2015-07-08T01:18:11 | null | UTF-8 | R | false | false | 1,674 | r | loaddata.R | #
# loaddata.R
#
# Author: Sean Spicer (sean.spicer@gmail.com)
# Date: 11-Jul-2015
#
#
# If the raw data has not been processed, download- unzip- and load- it.
# Save it to a native R object file for subsequent runs.
#
# Source Location: https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption... |
cb53b61e97d5e07d3d44e846faacae3dbde91753 | 864b08c705ff5cd6947cea43f06781f2793f0b83 | /R/linear.R | 6c9ecf03a2e77cdfaa56bfbf4dd0a2c1da03dae7 | [] | no_license | data-cleaning/errorlocate | 4a90594c2f779cf3eff0e22e8d28c48ff92fe260 | db111b170db30101586cf1b15521450952eed340 | refs/heads/master | 2022-07-09T21:16:40.482706 | 2022-06-29T07:32:52 | 2022-06-29T07:32:52 | 38,886,469 | 19 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,758 | r | linear.R | LOGS <- c("log", "log1p", "log10", "log2")
# code is mainly copied from validate, but needed for linear sub expressions in
# conditional statements.
#' Check which rules are linear rules.
#'
#' Check which rules are linear rules.
#'
#' @note `errorlocate` supports linear,
#' categorical and conditional rules to be use... |
fa04df4d897b609ab717707257f58671e6bd412a | 2ccc18877d7cfcec3b35fe38f6cee88f4eb30ee0 | /Lecture 3/Lecture3_looping_and_functions.R | 6ced113be20dfefd6b4d3cd841cd2ef50298994a | [] | no_license | srodrb/Visualization_MSE | 442625f7fe272b5079e1953cf0a4630f5b509a90 | 08a27c88a200ca53d80c24895d81e55fea3d62aa | refs/heads/master | 2020-12-24T15:05:33.118872 | 2014-10-08T14:48:25 | 2014-10-08T14:48:45 | 24,328,669 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,070 | r | Lecture3_looping_and_functions.R | # The script works on the current directory
#
# Samuel Rodriguez Bernabeu
# Assignment: lecture 3
# deliver date: 06/10/14
#
#
# Exercise 1: For each of the following code sequences, predict the result.
# Then do the computation:
answer <- 0
for(j in 3:5) { answer <- j + answer }
cat("Answer ", answer)
... |
7fbbcee0a67e21ca30b12ab3d0f80eb4829a8ce8 | e4564823bda709beb5ac12295401be810372ed6d | /R/Hpoints.R | ecb23a7aba11c075a263dcaf3e9156196ab4699a | [] | no_license | cran/Renext | ee3a3a53bd4ee7473c6d38c59726f0a3556473e8 | c7d43fcd7c0aeff7bc632d72b247131b53fbb923 | refs/heads/master | 2022-12-05T08:20:08.238641 | 2022-11-24T14:20:02 | 2022-11-24T14:20:02 | 17,693,289 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 881 | r | Hpoints.R | ##' Plotting positions for exponential retun level plots.
##'
##' The plotting positions are numeric values to use as the abscissae
##' corresponding to the order statistics in an exponential return
##' level plot. They range from 1 to about \eqn{\log n}{log(n)}.
##' They can be related to the plotting positions given... |
68ebef2bde0c9ed148d9b7b2937373f9c71a390c | d635576d8e4823765313f3015735ebe0017ba18a | /website/static/slides/05-data-wrangling/met-datatable.R | db99b67b91a9daca5ed27d566f4c968ed296ee66 | [] | no_license | USCbiostats/PM566 | 4590498e4e0ec495da689c7d38832fe41e00069c | 5d6f69ade27326042965dde4317db7db012531d1 | refs/heads/master | 2023-08-18T00:39:21.873340 | 2023-08-07T17:47:52 | 2023-08-07T17:47:52 | 252,278,014 | 20 | 33 | null | 2022-08-04T19:17:12 | 2020-04-01T20:19:30 | HTML | UTF-8 | R | false | false | 4,726 | r | met-datatable.R |
# Tailor to download a subset of the data for the lab
library(data.table)
# 1. Download the data
stations <- fread(
"ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-history.csv",
check.names = TRUE
)
# continental US only, remove AK, HI, territories and weather stations with missing WBAN numbers
st_us <- stations[
... |
cf4f8a9e4f62f6aa973eb9cef51e59c39bc51102 | 29585dff702209dd446c0ab52ceea046c58e384e | /mtk/R/mtkParsor.R | 2d882126b5a974e4f71a3a91c83e53a5864953ef | [] | 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 | 12,097 | r | mtkParsor.R | # Mexico Toolkit
#
# version : 0.01
# date : 30 nov 2009
# MAJ : 10 dec 2009
# licence : GPL
# Author(s) : Juhui Wang, MIA-Jouy en Josas, INRA, 78352
# repository: https://mulcyber.toulouse.inra.fr/projects/baomexico/
# Web : http://www.reseau-mexico.fr/
#
#' $Rev:: 240 $: revision number of the ... |
186838a40fc3646dca967ca7ba36d161b236f5c5 | 4863435653da8649080259a7bb151c436782be6d | /s_gwet.R | 4ddb8e11edd17c790f3ea521506f9680c5a595b1 | [] | no_license | Carnuntum/agree | 842e5e8898deb3fae7775e03edcff7cb95aa0f5e | 2ca81550cf8512075515c62d08b96da746d2f1ce | refs/heads/master | 2023-05-30T04:00:05.889149 | 2021-06-23T18:15:08 | 2021-06-23T18:15:08 | 288,434,397 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,968 | r | s_gwet.R |
kappa_gwet <- tabItem(
tabName = 'kappa_gwet',
fluidRow(column(width = 10,
offset = 1,
style = 'padding-left: 0px; padding-right: -5px;',
box(
id = 'gwetDocum',
width = NULL,
style = measure_title_sty... |
7f6f0daf83eac46971fe9da1d06469740aeca10a | 8bd435b8937f6b490a6eca74e03164dba64ec98f | /csubref.r | 220f8eea14316ae36208e16123ef4f3c61c052e3 | [] | no_license | KnaveAndVarlet/ADASS2019 | df2ad5840cafc55a3145a112ac36cbd9e7ee641f | 58751de6942a2cdeeef5f860aedf40388859a438 | refs/heads/master | 2020-07-19T17:10:53.020159 | 2019-11-22T03:50:01 | 2019-11-22T03:50:01 | 206,484,934 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,878 | r | csubref.r | #! /usr/bin/env Rscript
# c s u b r e f . r
#
# Summary:
# 2D array access test in R, using a reference class containing an array.
#
# Introduction:
# This is a test program written as part of a study into how well different
# languages handle accessing elements of 2D rectangular... |
d818f624f711b01ed847f31ee4c9e31feb834cb6 | 6ca2a0b93c96b986a36cc0280844d0f21dffa4d7 | /plots.R | a85005c4cf5635820f757155c05f2d24c4ae684e | [] | no_license | emmalouiser/Data_Visualisation | 4405f3c0424e4ac4dba75a7fe1af76f2266ff71a | a5019f20fedcaf6560c137e3a593d34f03e2db68 | refs/heads/master | 2020-04-04T17:02:36.467611 | 2018-12-01T22:58:26 | 2018-12-01T22:58:26 | 156,104,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,367 | r | plots.R | library(plotly)
library(quantmod)
## A simple example with a couple of actors
## The typical case is that these tables are read in from files....
actors <- c("Alice", "Bob", "Cecil", "David", "Esmeralda")
g <- make_graph(edges=c("Bob", "Cecil", "Cecil", "David","David", "Esmeralda", "Alice", "Bob", "Alice", "Alice", "... |
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