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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6e81f7850a4886b57ddb2c4b1ca5c929818bc134 | 3cebb834ac72ecf720c9a0a229a2ede27bb1ed3a | /cleaning/merge.r | 745dd298ed05cde9b6e0f53d09416d1cc2e14f46 | [] | no_license | mpsanchis/airquality-alpha | e1e9ff5220dc5312c35686f822892cf734d6b9bf | fe4a7296d045462548508b7041b1681fb2d30a91 | refs/heads/master | 2021-05-11T14:32:02.394943 | 2018-01-24T11:32:02 | 2018-01-24T11:32:02 | 117,703,615 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,522 | r | merge.r | # FUNCTION: this code joined the data from the two files in "join.R" to one file: "full_data.csv"
#
# ORIGINAL FILES: 'obs_data.csv' and 'mod_data.csv'
# WORKING DIRECTORY ----------------------------------
setwd("~/Documents/R/airquality-alpha")
# LOAD PACKAGES --------------------------------------
library(data.tab... |
afd15789468955729d9c35e38dda88514c511048 | bc43cfc66bf4508f26682b1d9bf0bd29f219a3cc | /r_code/results10k005.R | c54a5b50a384cdf07a413de187aa7ef480133706 | [] | no_license | fernote7/thesis | 0a7b856477de9c5beeb2fedbd98af926d10ef719 | c79684f4a4c1443ddb79aaa89f151066b99b30cc | refs/heads/master | 2021-03-23T11:35:57.911382 | 2017-12-08T18:20:29 | 2017-12-08T18:20:29 | 81,347,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 674 | r | results10k005.R | load("./r_code/dados/resulteadi10k.Rda")
resulteadi <-result
load("./r_code/dados/resulteuler10k.Rda")
resulteuler <-result
load("./r_code/dados/resultkj10k.Rda")
resultkj <-result
rm(result)
cfhest = c(14.176)
par(mfrow= c(3,1), oma=c(0,0,0,0), mar=c(3,4,1,2))
hist(resulteuler, breaks = 100, main = "", col = "grey", ... |
e9fa838609d8bcaced272a547cfecaeaa0920f58 | a23b0cca20c9d0d6f0def3c8d4a51d486b690af4 | /man/RFsim.Rd | 6c4eaf9b6fe08dc034c39e04ce6bb89e19a4ae41 | [] | no_license | cran/CompRandFld | 7cb6c7cd9fdb696f089d019bec0003ad07ec2703 | 6705a1498c0a23fff027e0394d35b152e3e9ddd4 | refs/heads/master | 2021-05-15T01:38:00.698452 | 2020-01-10T04:30:45 | 2020-01-10T04:30:45 | 17,678,514 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,809 | rd | RFsim.Rd | \name{RFsim}
\alias{RFsim}
\alias{print.RFsim}
\title{Simulation of Gaussian, Binary and Max-stable Random Fields}
\description{
Simulation of spatial and spatio-temporal Gaussian, binary and max-stable random fields.
The function returns one or more replications of a random field for a given covariance model and ... |
7b9bb860497bd8f1567ebaebcd4df192b8708c00 | af3c3bef72e78cbee524814229539ed9c4b81c68 | /R/symptoms_scripts/sympt_positive_age_band.R | 06d11d3afe7a26e1c00f48417c1b0c6130fd4eb4 | [] | no_license | gabrielburcea/cvindia | fbb2092fc3b1d3131064cb085f1b7754d5fe24c5 | 3307704674e4bd407b5fea3bc3d7141b90ee0a80 | refs/heads/master | 2023-03-02T15:09:34.665456 | 2021-02-15T10:51:18 | 2021-02-15T10:51:18 | 261,289,679 | 0 | 1 | null | 2020-05-15T15:30:05 | 2020-05-04T20:34:14 | R | UTF-8 | R | false | false | 3,190 | r | sympt_positive_age_band.R | #' sympt_positive_age_band
#'
#' @param data a dataset that contains ids, showing symptoms of covid, and symptoms
#' @param start_date a min date passed in as.Date
#' @param end_date a max data passed as.Date
#' @param plot_chart if TRUE then give me a chart, if FALSE then give me a table with symptoms accross age, wit... |
e1a87ca5267c6ec253456beea78a8a9644b27905 | c454bb95b38915faf2fdb4bf55b8bf50b88b251f | /man/process_eset.Rd | d9fbe473886549a0aa101e48387e0ad712675ea9 | [] | no_license | frankRuehle/systemsbio | 3faceeead1f8def591316dc63b7f2dca54461d9e | 533341be6c314b1216d8ab5920360961af44aa82 | refs/heads/master | 2021-07-03T12:41:05.583964 | 2020-09-10T06:43:53 | 2020-09-10T06:43:53 | 61,125,514 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,226 | rd | process_eset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GEX_process_eset.R
\name{process_eset}
\alias{process_eset}
\title{Processing of expression data}
\usage{
process_eset(eset, method_norm = "quantile", transform = "none")
}
\arguments{
\item{eset}{ExpressionSet or ExpressionSetIllumina}
\ite... |
ad25cbea2737b67062978d49d6f3e124842ea20b | b39ce967048b4c8846b7d7bfe179780f0860a942 | /HW2/lan_tian_ps2_task5.R | 5dc781fc21af34131a99e969c6c2d9177bc05fa9 | [] | no_license | lantian2012/STAT221 | dd824ad7471d7a5b94de0b7ed47821b3a138dbaf | 9a671ee2ba77dc3634832827ff32c8a2586107e5 | refs/heads/master | 2016-09-05T15:12:43.952807 | 2014-11-21T18:43:51 | 2014-11-21T18:43:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,573 | r | lan_tian_ps2_task5.R | #PSet 2 Task 4
args <- as.numeric(commandArgs(trailingOnly = TRUE))
if(length(args) != 1) {
args[1] = 1
}
job.id = args[1]
source("poissonLogN_MCMC.R")
source("Lan_Tian_ps2_functions.R")
source("rASL.R")
w = read.table('weights.txt')[[1]]
select = job.id%%4
if (select == 0)
select = 4
x0 = c(1.6, 1.6, 1.6, 1.6)... |
b4bf040db4b2b44d2fe0e42e9d714b89fabb7fe5 | dffc96de2021301fd93ccdd91bcfd912f95be448 | /Atividades_1,2/Script_atv1,2.R | a3e7a278674a064e79bc0bb1d5a64a4e9ec33d64 | [] | no_license | CarlosFilgueira/ciencia_colab | fe25db0e0c7b752562f23fce8c2275a7a29b1d19 | 40aa3457cce1dae1d0a08ae69d916aa59972d3f3 | refs/heads/master | 2023-08-20T10:31:45.789958 | 2021-10-14T05:17:55 | 2021-10-14T05:17:55 | 416,744,453 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,622 | r | Script_atv1,2.R | ###Disciplina de Ciencia Colaborativa###
##Atividades 01 e 02##
## ATIVIDADE 01 - Planilhamento de Dados ##
#Carregando Pacotes
library(tidyverse)
library(taxize)
library(vegan)
library(rgbif)
library(dplyr)
#Montar planilha de dados
data.frame(
participante = seq(1, 10, 1),
fichas = c(read.csv("~/github/cursod... |
491652508ccaaa1730abd0cfaee68aa69fba0255 | a55a9fe01bc8db5ac8ab4242a5a27a5946d20181 | /ui.R | 96984072b8357faeb8f6e77ea0043888e0c897e3 | [] | no_license | azeroz/ddp1 | ca42912d5fc91ee62d46010a90a5d5a4db742a12 | b0de163f7d57048604cee2d49802a5f8715fb726 | refs/heads/master | 2021-05-28T21:04:32.936594 | 2015-06-21T23:29:14 | 2015-06-21T23:29:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,108 | r | ui.R | shinyUI(pageWithSidebar(
headerPanel("Linear Regression with mtcars"),
sidebarPanel(
h6(paste("This application uses the mtcars dataset to investigate the effects of",
"multiple variables on the linear regression model. Selected variables",
"below will be included in the formula used t... |
001d14d5b28e5f65ca21cf755629fbdd614c8d26 | fb717eec1432650b78e989ad1b6969ba37e9a6b6 | /Rpack/man/profiles.Rd | 3d4da50d579061d5bbad8f8ad287d6dcac33bc20 | [] | no_license | traets/IASB | 116a4c60f3d1213f383d95739448e75dcbe038d4 | bbba998a358c2a3690585f1fe78d2fe436f8a053 | refs/heads/master | 2021-06-16T07:49:17.388140 | 2017-04-24T12:39:35 | 2017-04-24T12:39:35 | 62,038,893 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 746 | rd | profiles.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generation.R
\name{profiles}
\alias{profiles}
\title{Profiles generation}
\usage{
profiles(lvls, coding, intercept = FALSE)
}
\arguments{
\item{lvls}{A vector which contains for each attribute, the number of levels.}
\item{coding}{Type op co... |
fc653280f02653c88da5b35329cfe5d266e3fb3c | 54ff210f8313184137e68f4a337c1b0bfd74af56 | /man/r6_extract_methods.Rd | ce6eb77c9e0d998dc2bbc740cee6888b0b2dd849 | [
"MIT"
] | permissive | petermeissner/db6 | fdcf3fac8ffe29d36bf983ba14b67f406a86e352 | fbff81c8c7a319efc8d84127c18d8952d4aa39ac | refs/heads/master | 2020-03-27T01:54:39.952645 | 2018-10-31T15:34:32 | 2018-10-31T15:34:32 | 145,753,015 | 5 | 1 | null | null | null | null | UTF-8 | R | false | true | 334 | rd | r6_extract_methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/r6_extract_methods.R
\name{r6_extract_methods}
\alias{r6_extract_methods}
\title{r6_extract_methods}
\usage{
r6_extract_methods(r6)
}
\arguments{
\item{r6}{R6 object instance}
}
\description{
Extract public methods from R6 object ... |
de2dcbabdce8ccd88c7f77b961af712b3881756a | b22595c1f5903877cf6d6c13ece977f246aa6bdd | /Plot2.R | d81992c24f2efee48d9af0bc0491e9936123a9d0 | [] | no_license | mw179/ExData_Plotting1 | 99952db216a4963478c67cdb5a60d5ff5a7b4f9e | 1dff15eccbe1e5e1f13805e5bd88f6b8641c5ed9 | refs/heads/master | 2021-01-22T15:11:21.426085 | 2015-06-07T23:36:38 | 2015-06-07T23:36:38 | 37,034,540 | 0 | 0 | null | 2015-06-07T22:22:48 | 2015-06-07T22:22:48 | null | UTF-8 | R | false | false | 584 | r | Plot2.R | #this is a large dataset, and may take a long time to load
dat <- read.table("household_power_consumption.txt", header=TRUE, sep=";")
#isolate date range
dates <- as.Date(dat$Date, "%d/%m/%Y")
start_date <- as.Date("2007-02-01", "%Y-%m-%d")
end_date <- as.Date("2007-02-02", "%Y-%m-%d")
date_range <- dates >= start_da... |
08fcc40a5e9433af5a8c9943b46a808863644033 | 592a15eb25edb66fff4a938d3a20969aa8d2e3aa | /man/NIRpaarorDataSQL.Rd | 0ceaf2810fb719012403dc900417a9f26de2e3ca | [] | no_license | Rapporteket/intensiv | a6accf24600e47cded5065a988f1192e5ba62c14 | 544b6b120efa17da041eba1f53a986712f648ec3 | refs/heads/rel | 2023-07-19T03:01:43.656550 | 2023-07-12T09:38:42 | 2023-07-12T09:38:42 | 60,067,715 | 0 | 0 | null | 2023-07-06T05:53:31 | 2016-05-31T07:30:48 | R | UTF-8 | R | false | true | 731 | rd | NIRpaarorDataSQL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NIRpaarorDataSQL.R
\name{NIRpaarorDataSQL}
\alias{NIRpaarorDataSQL}
\title{Henter data fra pårørendeskjema registrert for Intensiv og kobler til
noen variabler fra hovedskjema.}
\usage{
NIRpaarorDataSQL(datoFra = "2015-12-01", datoTil = Sys.... |
e6111ff3a599ebe915040c4da4ba2011b83ef6f2 | 2b7562a55a2a7176402d832795db563ded09d80d | /cachematrix.R | 370894e0464735b22c1d8bdf10d978f4dad7a94f | [] | no_license | Vital95/ProgrammingAssignment2 | e8369f75fdc5726e260fc5c6f7354391760e9197 | d3eda4a9c18a03ac057c6c2a65261783db903158 | refs/heads/master | 2021-01-19T01:15:16.907475 | 2017-04-04T21:51:26 | 2017-04-04T21:51:26 | 87,233,785 | 0 | 0 | null | 2017-04-04T20:49:52 | 2017-04-04T20:49:52 | null | UTF-8 | R | false | false | 1,384 | r | cachematrix.R | ## This couple of functions allows to efficiently compute inverse matrix
## with ability to retrieve cached data if input is the same
## How to use:
## > matrix
## [,1] [,2]
## [1,] 5 1
## [2,] 4 8
## > newMatrix <- makeCacheMatrix(matrix)
## > solve<- cacheSolve(newMatrix)
## > solve
## [,... |
a01360f53011c90d1602ef198c0097cc5a6282fb | c680fe4504ef675532f088c610b0437bfdb7cf6a | /cachematrix.R | 63a2705042d3a47e217acd3c52d1dc9f2cc4ba94 | [] | no_license | angelalegaspi/ProgrammingAssignment2 | 92397c128e28dce322444b48d1a68f384bf0deb7 | ad64c0d79910dcecd70c259375212ba0e1defc67 | refs/heads/master | 2021-08-29T12:21:44.013715 | 2017-12-13T23:55:38 | 2017-12-14T00:17:05 | 113,756,072 | 0 | 0 | null | 2017-12-10T14:21:10 | 2017-12-10T14:21:10 | null | UTF-8 | R | false | false | 885 | r | cachematrix.R |
##This is a list of functions
makeInverse <- function(x = matrix()) {
i <- NULL #Sets the inverse to NULL
set <- function(y) {
x <<- y ##Saves called matrix outside present environment
i <<- NULL #Reset inverse to null every time called matrix is changed
}
get <- function() {x} ##Returns called matri... |
d9449745b66a7e0685dc4f53b70bf6912ef3bd51 | edf49cec9bf1872d8855113c106b4fc5900bdfed | /cachematrix.R | 3e30e048222fbb37e2e155cd9403e009dd41ca1b | [] | no_license | lc19940813/ProgrammingAssignment2 | 5e5ab0290b47512f1d22ec67657a24ccff9c9351 | a802a64ad36d40a717b807c0dad8a5ab083873f0 | refs/heads/master | 2021-01-15T22:00:33.287124 | 2016-02-28T08:02:53 | 2016-02-28T08:02:53 | 52,659,126 | 0 | 0 | null | 2016-02-27T09:19:44 | 2016-02-27T09:19:44 | null | UTF-8 | R | false | false | 1,514 | r | cachematrix.R | ## In this R file, we can reduce the cost of computing the inversion of a matrix repeatedly by storing
## its inverse matrix so the next time we use it by withdrawing it from the cache matrix object we created
## makeCacheMatrix: This function creates a special "matrix" object that can cache its inverse.
makeCacheMa... |
bad914194f50aff92da31a4a13f9b81ea0e2d0a2 | a6b5611ffa3310eb97b3e7e9c51ea8118f53adb4 | /Functions/f_predicting_links_HSM.R | 092655bf288d2abe0de2daad163d47e226ba9592 | [] | no_license | SalomeLang/link-prediction-social-networks | f3b6032a70f1ef9ef57ed8c68911f74250c1897a | 837dc519d8329aa6d798022cc1d046e363ca2593 | refs/heads/master | 2022-07-11T02:34:17.389020 | 2020-05-21T11:30:32 | 2020-05-21T11:30:32 | 265,830,467 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,331 | r | f_predicting_links_HSM.R | # Copyright (c) 2015. All rights reserved.
####################################################################################################
# Proj: Link Prediction
# Desc: Predicting links using Hierarchical Structure Model
# Auth: Bublitz Stefan, Gaegauf Luca, Lang Salome, Sutter Pascal
# Date: 2015/12/07
hsm.num... |
a40b3c59470779c705d75d6afd676c774a1b5457 | a9098f4badaf8222a47fd3864b9adf193de880a2 | /ui.R | 60886d9a6c10bb1044fa9f79afb9f77c4451c8f4 | [] | no_license | majisomnath/DevelopingDataProduct | f03a7cfe7cdba014bbb3bb08fc5a891a5809f2ac | a2a1edf0022f5b90e691513ba42765bc928f3c06 | refs/heads/master | 2021-05-03T16:36:53.254291 | 2018-02-06T10:42:34 | 2018-02-06T10:42:34 | 120,439,611 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,069 | r | ui.R | # Call required libraries
library(shiny)
library(ggplot2)
dataset <- diamonds
pageWithSidebar(
# Add header panel with name of the application
headerPanel("Diamond Dataset Analysis"),
sidebarPanel(
# Add text to explain data attributes
h4(' The variables are as follows:'),
h6... |
a6dd085da43d0806a794bcbd86bc6954bf8311c4 | 16c6a136123cefe07cbd1cac3bb893a6a1521f93 | /generate_trajectories/retrieve_NARR_data.R | 00b1f0965913fec9216f756d4f91dd66710788c0 | [] | no_license | bendabel/SEPPR_moisture_source | 6de600584bcdf7ec2a508e59b9e1731a1168b134 | 180fc503877ca8747d0a633af53ec755495a6746 | refs/heads/main | 2023-07-02T19:07:04.833622 | 2021-08-10T22:04:32 | 2021-08-10T22:04:32 | 394,783,707 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 239 | r | retrieve_NARR_data.R | #Retrieve desired NARR files from Hysplit ftp server
source("getMet.R")
#Choose years, months, and save location and place in vectors below
years <- 1979:1980
months <- 5:9
path <- "C:/hysplit/NARR/"
getMet(years, months, path)
|
272835eccb4379b93b115e29d2460ace59e834f3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/simecol/examples/pcuseries.Rd.R | 15b73378505c5acb47b74c9f205e46eef01b6882 | [] | 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 | 312 | r | pcuseries.Rd.R | library(simecol)
### Name: pcuseries
### Title: Generate Plackett Bivariate Random Numbers
### Aliases: pcuseries pcu rho2alpha alpha2rho
### Keywords: distribution
### ** Examples
x <- runif(100)
y <- pcu(x, rho = 0.8)
plot(x, y)
cor(x, y)
x <- pcuseries(1000, rho=0.8)
plot(x, type="l")
acf(x)
pacf(x)
|
d0d3afdea4bf66469f7bdd736fa76b01e59431ec | 50d5966803e3a2bb9b1d3be0a21a53725dac6547 | /old_files/SR_model_Run_brood_switch_Bayesian.R | 121faef6bd30f063162efe5f6da772f84f029efe | [] | no_license | hhamazaki/Shiny-Apps | 9a474cbc562001e4427ef8dcc04effd4a753ed5f | 23bcc89208079bfe264016136b868f196b8c0ee2 | refs/heads/develop | 2023-01-20T11:00:15.488378 | 2023-01-18T04:49:15 | 2023-01-18T04:49:15 | 235,636,206 | 0 | 2 | null | 2023-01-18T04:54:08 | 2020-01-22T18:19:00 | R | UTF-8 | R | false | false | 87,071 | r | SR_model_Run_brood_switch_Bayesian.R | #initialize
library(shiny)
library(shinythemes)
library(datasets)
#library(lmtest)
library(reshape2)
#library(mgcv)
library(MCMCpack)
#library(maptools)
#library(nlme)
#library(AICcmodavg)
library(bsplus)
library(coda)
library(R2jags)
library(openxlsx)
#==================================================================... |
1d4db04e9c8136a0336d195dc0ea8651e0ae9dc2 | 103b59cc7623bfc4f974a7ee310a2cfe30c205bb | /statistic-data-analysis/Project.R | 3c3376be0f15e6020921d087a59e7138404c28ef | [] | no_license | bmiddag/ugent-projects | 3ef392ab863ff1b4335d327b91e16a48fb09cdde | 4eee35670a8e9911c58b0562a6b65583565d2e64 | refs/heads/master | 2021-01-11T00:09:22.926996 | 2016-10-12T22:58:48 | 2016-10-12T22:58:48 | 70,746,814 | 2 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 7,766 | r | Project.R | #########################################
# Project: Statistische Gegevensanalyse #
# Naam: Bart Middag #
# Richting: 3de bachelor informatica #
# Academiejaar: 2013-2014 #
#########################################
# Voorbereiding project
nsim <- 10000 # aantal simulaties
set.seed(... |
18a30c20463cae60576f6c5aeaf26a0d9ba46068 | ac746dd6f2e113bc6587bc2fe975a858448fc30f | /R/mapper.R | cfe7be52426da04b632a9cbf1e1328563aeb235e | [] | no_license | emcramer/covid19project | e750de1d9304a01dba035ea25db17adffb159333 | 0e709b62e90d2b6f11f7b6bc97370993f59b6e8c | refs/heads/master | 2021-12-14T14:52:32.753957 | 2021-12-11T18:58:48 | 2021-12-11T18:58:48 | 253,441,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,730 | r | mapper.R |
libs <- c("tidyverse", "dplyr", "ggplot2", "lubridate")
easypackages::libraries(libs)
# get the data
countyData <- read.csv("https://static.usafacts.org/public/data/covid-19/covid_confirmed_usafacts.csv")
# pivot the data to be longer along the date dimension, and remove appended "X"
convDate <- function(x) {
prin... |
5d692b98f890374384b16de9e1f0d49f3a03a760 | a94308678716ab60f03956e503f554a767e73733 | /R/DIF.Logistic.R | c50d2696f0d0f0f4257fc9adb861cec1de2e9ac1 | [] | no_license | cswells1/MeasInv | 9f9cb20da68b695cc1f65fc5c80f92ea31b030e7 | b74acffcf8ec0d6886f7081882aa3965306eb4af | refs/heads/master | 2023-07-14T21:35:32.915150 | 2021-09-12T22:50:49 | 2021-09-12T22:50:49 | 405,707,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,769 | r | DIF.Logistic.R | #' Logistic regression DIF method
#'
#' @param data numeric: either the data matrix only, or the data matrix plus the vector of group membership.
#' @param group factor: the vector of group membership
#' @param sig.level numeric: the significance level
#' @param purify logical: Default is FALSE.
#' @param output.filena... |
2884752a0a4d6914223ef12d780051d440daf2f8 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.networking/R/backupgateway_operations.R | 437a51e66030a462b494fb1dc03604ce05e50e79 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | false | 34,693 | r | backupgateway_operations.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common get_config new_operation new_request send_request
#' @include backupgateway_service.R
NULL
#' Associates a backup gateway with your server
#'
#' @description
#' Associates a backup gateway with your server. After you complete th... |
764c66551bb88a2d3edd50f52781783e6428ce43 | 277b330d9b385fd1e6ff507acd83d233dfc05543 | /Intro_CRSP_Kenneth_French.R | 98764ce37ae6b5a9162eefef1ea3a1db87575a6d | [] | no_license | nikaash123/Quantitative-Asset-Management | 828cf521f92a079829c73e996856fc2752d5f20d | 33f298f4ba40cfafa98b8fe556c812a7de832cb4 | refs/heads/master | 2020-03-31T19:52:58.845547 | 2018-10-11T04:10:32 | 2018-10-11T04:10:32 | 152,516,078 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,326 | r | Intro_CRSP_Kenneth_French.R |
### Problem Set 1 by Nikhil Gupta ###
#Question 1
library(lubridate)
library(data.table)
library(tidyr)
library(xts)
library(moments)
data_1 = read.csv("C:/Users/nikaa/Desktop/Lectures/Spring_2018/Quantitative_Asset_Management/Home_Works/HW1/Full_Data.csv", header=TRUE,
stringsAsFactors = FALSE,n... |
e6f5580844ebe500ef8c9105493cd6ea4f721a77 | 57222f96e553dd2802316928f2f2c7825ef05197 | /032-client-data-and-query-string/server.r | 53405a3bcabb5361d62300b865f49e71b99762f2 | [
"MIT"
] | permissive | rstudio/shiny-examples | 6815bb4d8198e4b90765926a4865fdef1d1dc935 | c7bf00db4a8a68e579e39ed07d516a33661a853e | refs/heads/main | 2023-08-17T16:35:03.411795 | 2023-08-03T19:51:30 | 2023-08-03T19:51:30 | 13,722,949 | 2,046 | 4,576 | NOASSERTION | 2023-08-03T19:51:31 | 2013-10-20T17:05:23 | JavaScript | UTF-8 | R | false | false | 1,106 | r | server.r | list_to_string <- function(obj, listname) {
if (is.null(names(obj))) {
paste(listname, "[[", seq_along(obj), "]] = ", obj,
sep = "", collapse = "\n")
} else {
paste(listname, "$", names(obj), " = ", obj,
sep = "", collapse = "\n")
}
}
function(input, output, session) {
# Print out ... |
3d648ab07d2b8592920de003973cefb2cf272cad | f21c8b03b3c4d882fd7709b9aaec5cb347bf7e0e | /man/som_lcp.Rd | 2c73a69d541d4f9f61a8134b8f01fcfa050a46fc | [] | no_license | mschmidt000/oposSOM.PT | a2e67e7da2cb70887d884c65020ba8a46787167a | e1c49dc2c849eb46dba4c7389adb21ea43508938 | refs/heads/main | 2022-12-27T13:30:21.171303 | 2020-10-05T11:15:01 | 2020-10-05T11:15:01 | 301,334,606 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 737 | rd | som_lcp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/som_lcp.R
\name{som_lcp}
\alias{som_lcp}
\title{SOM Least Cost Path (LCP) function}
\usage{
som_lcp(env, root = c(), tips = c(), lineages = c())
}
\arguments{
\item{env}{An enviroment produced by the oposSOM pipeline.}
\item{root}{A single g... |
86cf13485981b25db1310f6d3ee7ff49a64b5c25 | ae2434d333a9a945af6724c0d764fe26c22221af | /tests/testthat/test_standardCols.R | ab2bba6b629215cd195d71f21a8672c14f4ae716 | [] | permissive | neckl004/NecklenJacobTools | 33949a300dc9165185db34e35b96de284c9add4f | f9940973c91f2a88c41988e58d1982590a43af7a | refs/heads/master | 2021-01-25T10:00:16.981959 | 2018-03-06T22:01:32 | 2018-03-06T22:01:32 | 122,780,726 | 0 | 0 | MIT | 2018-02-24T21:25:51 | 2018-02-24T21:19:51 | null | UTF-8 | R | false | false | 313 | r | test_standardCols.R |
context("Standardize the columns of a matrix")
test_that("standardCols standardizes columns and returns a matrix of identical size", {
a = matrix(rnorm(15),nrow=3,ncol=5)
b = standardCols(a)
nrow(a) == nrow(b) && ncol(a) == ncol(b)
})
|
03e8c2a0f3fe190d9221e60b022ec7f7c37fe802 | 216b84b5193584bd70ec8ba00945939b461f375a | /Joel_MEAR_Issue.R | 17788990fd0ad970fa6adfce99caefd721d2e35b | [] | no_license | dc-larson/Multi-Level_Modeling_Water_Customers | 501235c8dc45c7ded1f6277a817c4d0ed559647d | 3f90ff15e406521ab81738d39aa19aa58bf6756e | refs/heads/master | 2020-12-10T02:54:02.331992 | 2020-01-13T21:53:43 | 2020-01-13T21:53:43 | 233,486,239 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,846 | r | Joel_MEAR_Issue.R | load(file = "MEAR_issue.rda")
## Replicating my analysis so you can see where I get tripped up.##
##Had to remove NA values in order to compute means. The lme4 model just handles them for you
###This could be a potential issue.
wtp = mean(mydata$participation_willingness, na.rm = TRUE)
pc = mean(mydata$participation... |
6404ae1c3d8c1e0b07e9393270d4e97e6712a42e | 16d1b8d98710bbdfe15113d1c869bae56f16eded | /Code/Twitter Descriptive/old/term_freq_alternative.R | e26cc2ed74e52bcee48663cbdd843382b5e1353c | [] | no_license | lubrunn/DSP_sonstigerCode_Abgabe | 4651cd7ac893971c035f7a6a509529880d6133b8 | be8885287c5edbc37b22ddf5e80c590e1ba7f9bb | refs/heads/main | 2023-03-22T07:39:29.852517 | 2021-03-19T14:00:32 | 2021-03-19T14:00:32 | 349,191,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,094 | r | term_freq_alternative.R | string_error <- "current state affair chump"
error_row <- df %>% filter(str_detect(text, string_error))
b <- df %>% filter(date_variable == "2018-11-30")
threshold_single <- 830
####### old
b %>% group_by(date_variable) %>%
table(unlist(strsplit(tolower(text), " ")))
b %>%
separate_rows(text,... |
08aa04215e0b80531be689f619744d35d0c6570b | 7513790d15902b75b9ff193e1c341d13362fdf70 | /SuperFarmerDA/tests/testthat/testystrategia_DKA.R | 6cd551d6a4ff06f3b1d197fc87b29b75d8c65e1f | [] | no_license | ambroziakd/KursRprojekt2 | 2ae5f046a16b0c710beb3233ba66ebf41a2230a0 | ca96ab8d9292bdd52c90d2deb16e89ed60225400 | refs/heads/master | 2020-06-21T18:50:26.422000 | 2017-03-17T18:02:39 | 2017-03-17T18:02:39 | 74,774,696 | 0 | 1 | null | 2017-03-17T18:02:39 | 2016-11-25T16:49:44 | HTML | UTF-8 | R | false | false | 3,687 | r | testystrategia_DKA.R |
## plik strategia_DKA.R
test_that("strategia_DKA zwraca wektor",{
expect_true(is.vector(strategia_DKA(c(7, 0, 0, 0, 0, 0, 0))))
})
test_that("strategy_DKA dokonuje wymiany",{
expect_equal(unname(strategia_DKA(c(7, 0, 0, 0, 0, 0, 0)))[2], 1)
})
test_that("wymiana_na_tansze zwraca ramke danych",{
... |
8b29ed71f75b0b9bc17f925f027e5081d94975b3 | a0e3e006caf18ff920ea1cfe91c9f13c253dfc09 | /simulation/tests/testthat.R | cbe8c71dae591ea60d6a934b5dc04acefa2bb6d2 | [
"MIT"
] | permissive | linnykos/simulation | a9bb151de04e6f57067778e96e008be2c279da5b | f6598a14bc762e63558584327c306834be104538 | refs/heads/master | 2022-04-13T08:45:58.033611 | 2020-03-24T19:01:21 | 2020-03-24T19:01:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 64 | r | testthat.R | library(testthat)
library(simulation)
test_check("simulation")
|
d6f176b08c71b4febe7bef46f14965381f566a95 | cd298aa036d3c974c18c22623ad1a58d120463df | /man/dhillon_emb.Rd | f7f618402b45a964f43961069a82712c4a47ac59 | [
"MIT"
] | permissive | YutongWangUMich/corgi | eaecf19836c27985f7e4129e6b2cb40f6b8bd08b | d7d6f2fab63065268ddc5f463d45e74d9f121a64 | refs/heads/master | 2020-04-18T21:20:00.199195 | 2019-10-24T20:29:02 | 2019-10-24T20:29:02 | 167,762,558 | 0 | 0 | MIT | 2019-04-08T06:30:03 | 2019-01-27T02:47:21 | R | UTF-8 | R | false | true | 849 | rd | dhillon_emb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/run_corgi.R
\name{dhillon_emb}
\alias{dhillon_emb}
\title{The embedding as defined in Dhillon, Inderjit S. "Co-clustering documents and words using bipartite spectral graph partitioning." Proceedings of the seventh ACM SIGKDD international co... |
8ac1882ddfe3e2fcab327fa0c202c2cbff114981 | 711e35a8a54f04234725d9b97501b94279c0379f | /R/use_files.R | b0aebfa800a41833ebe0a2a468cb3616e5c8e2ad | [
"MIT"
] | permissive | AartGoossens/golem | 70f461df103bba8955e35455a4e779aa95ac1be7 | cdc25bbfd7d1fd9ad55043d689b8afcb3410dd26 | refs/heads/dev | 2020-09-20T22:36:05.438064 | 2019-11-27T20:45:21 | 2019-11-27T20:45:21 | 224,607,705 | 0 | 0 | NOASSERTION | 2019-11-28T08:41:00 | 2019-11-28T08:40:59 | null | UTF-8 | R | false | false | 2,768 | r | use_files.R | #' Use Files
#'
#' These functions download files from external sources and install them inside the appropriate directory.
#'
#' @inheritParams add_module
#' @param url String representation of URL for the file to be downloaded
#' @param dir Path to the dir where the file while be created.
#' @export
#' @rdname use... |
8404f289e8e9d1f32234acd0e41aebe847777a80 | bb9504bfabd84b5c2b662095b33188cf35264343 | /man/external_crossval.Rd | 02e21c7cdde230a82f97681a6f2e4666dc511a5c | [] | no_license | bbuchsbaum/rMVPA | 5f34425859cc6f0a69f223992b43e12a2e9c7f11 | bd178d4967a70c8766a606810f5de6f3e60b9c22 | refs/heads/master | 2023-05-25T16:08:02.323323 | 2023-05-01T01:43:15 | 2023-05-01T01:43:15 | 18,340,070 | 14 | 11 | null | null | null | null | UTF-8 | R | false | true | 3,835 | rd | external_crossval.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mvpa_iterate.R
\name{external_crossval}
\alias{external_crossval}
\title{#' external_crossval
#' @keywords internal
#' @importFrom stats predict
#' external_crossval
#' @keywords internal
#' @importFrom stats predict
external_crossval <- func... |
17b57266437f567576769b5549a3983b4a354d00 | 4201e9b754760dc35fc0aeef9df5a8b9d801c47f | /bin/R-3.5.1/tests/Pkgs/exNSS4/R/nss4.R | b63081b7385918bff1e864944c8e577d340d8adc | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only"
] | permissive | lifebit-ai/exomedepth | cbe59cb7fcf2f9183d187f8d466c6620fb1a0c2e | 5a775ae5e2a247aeadc5208a34e8717c7855d080 | refs/heads/master | 2020-03-27T12:55:56.400581 | 2018-10-11T10:00:07 | 2018-10-11T10:00:07 | 146,578,924 | 0 | 0 | MIT | 2018-08-29T09:43:52 | 2018-08-29T09:43:51 | null | UTF-8 | R | false | false | 1,824 | r | nss4.R | setClass("pubClass", representation("numeric", id = "integer"))
setClass("privCl", representation(x = "numeric", id = "integer"))
.showMe <- function(object)
cat("show()ing object of class ", class(object),
" and slots named\n\t",
paste(slotNames(object), collapse=", "), "\n")
setMethod("show", "pubClass", .sh... |
9ccc486be164166902a45493e2b9b80dcd0eac0e | e3cad415435dd7f7f7f1be653272c4655a24f219 | /tests/testthat.R | 268852200634f44d64423fde230d7839800a98e4 | [
"MIT"
] | permissive | katerine-dev/queridodiario | f55ea5a7db817f402f8d79c468818c7517bf2e4f | 34b40bd2b71030e7ccc19b3fbe338ec5602aead4 | refs/heads/master | 2023-07-20T11:10:11.585563 | 2021-08-24T13:03:43 | 2021-08-24T13:03:43 | 346,433,278 | 15 | 1 | NOASSERTION | 2021-08-24T13:03:44 | 2021-03-10T17:12:20 | R | UTF-8 | R | false | false | 80 | r | testthat.R | library(testthat)
library(queridodiario)
testthat::test_check("queridodiario")
|
49e24528ba7a29a921a05c3468d6ef6a66655668 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/textreg/man/tm_gregexpr.Rd | eb8bd8c33efb85fbd17481ea93ae93046c93425b | [] | 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 | true | 923 | rd | tm_gregexpr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/text_searching.R
\name{tm_gregexpr}
\alias{tm_gregexpr}
\title{Call gregexpr on the content of a tm Corpus.}
\usage{
tm_gregexpr(pattern, corpus, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
}
\arguments{
\item{patter... |
6aad7b3f3d992844295cf7ab213f6cdc7d56e863 | 986e46d230dab8ed82d560dc533193fee32e993e | /scripts/circuit.r | 392b22b4d66d72cfeb55562dbd1659a961626c55 | [
"MIT"
] | permissive | XieConnect/SecureMA | adc58d66627f1cc801243946fdf62811fba4b616 | 3ebfe07bae68446707cb19839532a8a6069eef08 | refs/heads/master | 2020-05-26T18:19:42.517279 | 2014-05-29T16:00:25 | 2014-05-29T16:15:56 | 16,626,910 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 977 | r | circuit.r | #!/usr/bin/Rscript
# simulate the binary circuit for estimating n and epsilon
args <- commandArgs(TRUE)
# Input two shares for X
x1 <- as.integer(args[1])
x2 <- as.integer(args[2])
# to customize inputs
maxN <- 80
bitLength <- 80
# input randomization parameters
randa <- 0
randb <- 0
## init
est <- 1
n <- 0
x <- x... |
99c1a355fbcdd623beeaf8b9c467cea8683984cf | f044402735a52fa040c5cbc76737c7950406f8b2 | /BrCa_Age_Associated_TMA/Packages/biostatUtil/R/utils.R | 52e7f3c0b29e7b2e329388d3e16e694bd94bfcd3 | [] | no_license | BCCRCMO/BrCa_AgeAssociations | 5cf34f3b2370c0d5381c34f8e0d2463354c4af5d | 48a11c828a38a871f751c996b76b77bc33d5a3c3 | refs/heads/master | 2023-03-17T14:49:56.817589 | 2020-03-19T02:18:21 | 2020-03-19T02:18:21 | 247,175,174 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,657 | r | utils.R | # magrittr placeholder
globalVariables(".")
#' Collapse unique levels of variable into one string
#' @noRd
collapse_var <- function(x, collapse = " || ") {
return(paste(unique(x), collapse = collapse))
}
#' Print confidence interval wrapper
#' @noRd
printCI <- function(z) {
paste0(z[1], " (", z[2], " - ", z[3], "... |
843698a79ff535cd732d9a0d14fef301b6d04216 | 20964628865adf294fa4dace057d8f8657bada4a | /R/present.R | 3120d2d86d248c58c6193604cf72d6466279f74f | [] | no_license | asrenninger/rgs | 24dfadfb5880b59f5de0778255ecebca156446b5 | 8df2dc1a52190d3c0da000db1fc6d4bd87b50288 | refs/heads/main | 2023-07-31T17:01:30.289116 | 2021-09-07T23:38:20 | 2021-09-07T23:38:20 | 398,044,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,087 | r | present.R | ##################################
### HOUSE HUNTERS
# 2 processing
##################################
## packages
library(tidyverse)
library(gt)
library(tidytext)
library(tm)
library(widyr)
library(sf)
library(glue)
## palette
pal <- scico::scico(n = 9, palette = 'hawaii')
## table 1
tibble(activity = c("sex", "s... |
ed8eed52bf76241820cd4c63d296f52f0ddada13 | 31b12b7aeb512e246008b24cb25c3cae01284822 | /scripts/rcourse_lesson1.R | 447068e551375044ddb83bbf4902abf2950ca4b3 | [] | no_license | Efsilvaa/ModeloProjetoML | fc3fe5aae7715f06c42b24cccba0e62b8100df59 | a51e9d35c22dbc8fe05f69b5a90d9ac1d3e29095 | refs/heads/master | 2020-03-07T06:33:32.822407 | 2018-04-02T22:58:39 | 2018-04-02T22:58:39 | 127,325,969 | 0 | 5 | null | null | null | null | UTF-8 | R | false | false | 4,203 | r | rcourse_lesson1.R | ## LOAD PACKAGES ####
library(dplyr)
library(ggplot2)
## READ IN DATA AND ORGANIZE ####
### Read in data
data = read.table("data/rcourse_lesson1_data.txt", header=T, sep="\t")
# Look at dimension of data
dim(data)
# Look at first few rows of data
head(data)
# Look at final few rows of data
tail(data)
# Look at numbe... |
c2e9ded66862f6c3fba0380d7c20281131f7c411 | 89c6b11ff16630bb9eaae1dc83034739f3cba846 | /notes.R | 094e297ca60db142ef8947f82be16af2452c0a8b | [] | no_license | Data8021/LOLGameDataProcessing | 34918ba7cd9ac5e587f537d43b20c3a116426681 | ecc9fe87ad7867244b7d04d13acd65b8b486c531 | refs/heads/master | 2021-01-10T18:01:46.982509 | 2016-01-30T03:33:18 | 2016-01-30T03:33:18 | 50,451,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,853 | r | notes.R | library(dplyr)
library(lubridate)
## Load functions
fromSeconds <- function(x){
if (!is.numeric(x)) stop("x must be numeric")
if (length(x)<=0)return(x)
unlist(
lapply(x,
function(i){
if (i >= 3600) {
y <- seconds_to_period(i)
sprintf('%02d:%02d:%02d',... |
3bc7cd058c5058f09055c723fa53d7c041ab9007 | f9055f2e316129ec21665a10200fda9ad856f975 | /R/data_selection.R | e9ffdb30ea35f385239ccc733fdc912fadc77bfd | [
"MIT"
] | permissive | bnicenboim/eegUtils | 879b4a8173acc29de17b1dc0ca7ec8453dbc2522 | 09eb5fcc983d24d058bfa4575d5e8a2537fcfe21 | refs/heads/master | 2020-03-11T03:16:30.714963 | 2018-05-03T18:53:24 | 2018-05-03T18:53:24 | 129,742,381 | 0 | 0 | null | 2018-04-16T12:47:20 | 2018-04-16T12:47:19 | null | UTF-8 | R | false | false | 8,749 | r | data_selection.R | #' Select timerange
#'
#' Generic function for selecting specific time ranges from a given dataset.
#' Input can be a dataframe, or an object of class \code{eeg_data} or
#' \code{eeg_epochs}. Note this finds the closest times to those specified, so
#' times out may not correspond exactly to requested times.
#'
#' @auth... |
07a2d78aa3eacba4e4adcdc589c3a68d820033a7 | 372ad64ae50d9a2b4b45f16ee0a549169e3c66fb | /00-misc-tests/tmp06-nostalgia.R | 0a5bf85f449a4be6ee97dce3fd1cf73cb7081871 | [] | no_license | btmonier/shiny-tests | 10b2a805e8600533d33cd7193356d373e2ad9bd0 | 4afe16e258bb54e4b0933853565ffd7b173904cd | refs/heads/master | 2021-09-14T20:09:18.369859 | 2018-05-18T17:22:16 | 2018-05-18T17:22:16 | 105,826,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 786 | r | tmp06-nostalgia.R | # Text plots?
# Load packages
require(txtplot)
# Load test data
datobj <- read.csv("deseq-test.csv", header = TRUE, row.names = 1)
# MA
txtplot(
x = log10(datobj[, 1]),
y = datobj[, 2],
xlab = "log 2 fold change",
ylab = "normalized mean",
ylim = c(-4, 4)
)
# Volcano
txtplot(
x = datobj[, 2],
y = -log10... |
6b6d3ad1f28356fa6cdcdfce77444263e4bf392b | da1dc5317dd8723f6fc549492cae6f6f1a6e57fd | /loaddata/mergeData.R | 7184f8fad39c3988803e5a43568858feadc9a848 | [] | no_license | quace/BusinessIntelligence | cfe402ad31ebf094ea0f524db0b2a5e052290e9a | f170becfdc5ff6479872da661d08287dda806322 | refs/heads/master | 2021-08-29T19:26:46.327734 | 2017-12-14T18:53:14 | 2017-12-14T18:53:14 | 108,522,007 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,243 | r | mergeData.R | #transform(fullData2,Name = sub("^\\S+\\s","",Name))
#fullData2 <- rename(fullData2, c("Name"="Name2"))
#testfullData <- transform(fullData,Name2 = (paste(substring(Name,1,1),". ",sub("^\\S+\\s","",Name),sep="")))
#mergedData <- merge(x = testfullData, y = fullData2[,c("Name2","Photo","Flag","Club.Logo","Value","Wage")... |
b0dae1b5bef02fc8cc1795a6b61000f07f129e11 | e6a9d96db33e6cd819e4f1dbd60c80b0eb53a615 | /R/agTrendTMB-package.R | 2cc3293c286e37ddd83cc61f400a8886ecb1fcda | [] | no_license | dsjohnson/agTrendTMB | 42384a16831a7f21b4bfe61ad61075b6555cac8f | 403c55da56e891c8126b912ab8958346d7f1d3c8 | refs/heads/master | 2023-01-10T19:30:07.323254 | 2020-11-10T01:19:36 | 2020-11-10T01:19:36 | 307,476,720 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 629 | r | agTrendTMB-package.R | #' @rawNamespace useDynLib(agTrendTMB, .registration=TRUE); useDynLib(agTrendTMB_TMBExports)
#' @importFrom stats coef cov.wt dnorm lm model.matrix optim pnorm qnorm sd
#' @keywords internal
"_PACKAGE"
# The following block is used by usethis to automatically manage
# roxygen namespace tags. Modify with care!
## uset... |
8db5cf667db9fcf774b8567d1ca759025ebe1153 | dbd36e59bc4495c18212fa4f69d7de90a80417e2 | /man/alpha_ellipsoid.Rd | ff59f814b839290a792d2096db23e5e036d0ea68 | [] | no_license | cran/cda | 5b595698cd7d0f402341e7f950370fbcb984e73e | 7b3515d0d109ed30d5fd7bae7ed0f78aa85da59b | refs/heads/master | 2020-12-25T17:37:35.997436 | 2016-08-16T23:58:00 | 2016-08-16T23:58:00 | 17,695,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,105 | rd | alpha_ellipsoid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/polarizability.R
\name{alpha_ellipsoid}
\alias{alpha_ellipsoid}
\title{alpha_ellipsoid}
\usage{
alpha_ellipsoid(sizes, material, medium)
}
\arguments{
\item{sizes}{matrix of cluster sizes in nm}
\item{material}{data.frame with wavelength and... |
9ceea8b4056a8aa10a1d7e7dad0f21b91bf6c72b | bc8bbd84cc7e88c7ce0f5849d3fb6660e6cab82a | /R/fts.plot.covariance.R | f5840c79340ca1ebf65391d35493340deb3820c6 | [] | no_license | kidzik/freqdom.fda | 82e6c1c75e121c6ad9b3573f7f148edc1dd16cc4 | 722f2d86113ec7d1d3ddc08822a7ebb8e2695593 | refs/heads/master | 2022-05-20T11:48:58.626572 | 2022-04-18T16:27:32 | 2022-04-18T16:27:32 | 93,265,117 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,312 | r | fts.plot.covariance.R | #' Contour plot for the kernels of cross-covariance operators.
#'
#' @title Contour plot for the kernels of cross-covariance operators.
#'
#' @param X an object of class \code{\link[fda]{fd}} representing a functional data sample.
#' @param Y an object of class\code{\link[fda]{fd}} representing a functional data samp... |
d356996471061b10513f39c5a4cfbb21be438b83 | 461559b761bd4b1a8cc61131a2e57704db929f53 | /Hand in/TestingBCa.R | b6872fde968b2e10a5bb3e4cbdea0480d7714de7 | [] | no_license | penguin-coding/asmt3 | 012148df0ca7de93e34d80b81d4066254cce2594 | 0f358cba59ce1bdec37e6b48abf613b7e13f12d7 | refs/heads/master | 2021-08-19T07:55:17.248936 | 2017-11-25T09:39:54 | 2017-11-25T09:39:54 | 108,715,297 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,718 | r | TestingBCa.R | library(boot)
source('simulation.r')
# given the strange BCa results, I got very worried that my BCa code was
# wrong. Not finding an issue with the formulas being used to retrieve the
# alpha values, I decided a comparison with the boot package's own BCa would be
# the next reasonable point of call as a means of test... |
b4a7be0bce0719cfcf7f0861661d74e8d4c5e65c | 60d9971e3f2c2456c874d017eff9527e86368c99 | /R_codefile.R | 9a82f954c41caaca4f544425c3b82fac7e329bc9 | [] | no_license | Anok-swarupnarain/Santander-Customer-Prediction | 7c2555512b7113da244832f31e196a9aec666f8c | 0f3f809898b1011eb3dba8e878fe3fe618337f9c | refs/heads/master | 2022-04-23T05:24:41.083474 | 2020-04-19T01:06:40 | 2020-04-19T01:06:40 | 256,885,765 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,214 | r | R_codefile.R | #set working directory
library(readr)
setwd("C:/Users/Anok/Downloads/Edwisor Santander Project")
#get working directory
getwd
x <- c("ggplot2", "corrgram", "DMwR", "caret", "randomForest", "unbalanced", "C50", "dummies", "e1071", "Information",
"MASS", "rpart", "gbm", "ROSE", 'sampling',"scales" ,'DataCom... |
87c35fdf26d0dc7c8e144b42a084bab878c088f1 | f24ec4dcd2ce1e35b35a17b1b22994a3c7677ff2 | /man/addcds.Rd | 25a685f14cb9c062367768590f0f8d21f6aa27c5 | [] | no_license | fiksdala/fiksdal | 2fbdd4256091b9cca4d57107c214aa8c30c762f4 | 873fab35441560664a26869113ff420165f570d5 | refs/heads/master | 2022-01-10T17:49:07.608249 | 2022-01-07T00:00:16 | 2022-01-07T00:00:16 | 118,020,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 539 | rd | addcds.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/addcds.R
\name{addcds}
\alias{addcds}
\title{addcds}
\usage{
addcds(df, influence.object, ID)
}
\arguments{
\item{df}{dataframe}
\item{influence.object}{An object returned from influence.ME's influence()}
\item{ID}{ID name of df}
}
\descrip... |
efcf37fad6b06ebb0f2d08e70308caa15209e186 | 0d701209e55f554ad3bf2250e83ef68297a98aa4 | /VeraCode.R | f2c751caea5403c4c65dfc1c46ce44f09464189c | [] | no_license | ClaudiaVeraArias/IBC_Exercise_08 | be06d0cd319490d748886e433f3077d6368ec607 | 7ed4ad8ddd9793c5fec81ae2aa438ac010480a19 | refs/heads/master | 2020-09-11T09:06:51.479617 | 2019-11-22T00:23:12 | 2019-11-22T00:23:12 | 222,015,615 | 0 | 0 | null | 2019-11-15T22:48:54 | 2019-11-15T22:48:54 | null | UTF-8 | R | false | false | 2,961 | r | VeraCode.R | # EXERCISE 8
# select the work directory
# instal the packages ggplot2 and select it
setwd('/Users/claudiaveraarias/Documents/ND_Classes/Fall_Semester_2019/Biocomputing/R/W11_BC/Tutorial/')
install.packages("ggplot2")
library("ggplot2")
#1) Using the score-by-score information from this game summarized in
#“UWvMS... |
bc7d4f40de81792ad27a2a0cb83516f38e0db609 | c8decce1d7dce60b3dcd9982c9b48bbaa632bcca | /RQA1.R | 878ebb8ced6f1c0692bb961c26a71de80aacde1f | [] | no_license | skylight0306/R-Design-Project | 188a2e13cdeed3af0211639dac5965a1090abdc0 | ed9e32796ceac72f84ccaeb245424a1bd952c9a0 | refs/heads/master | 2023-02-10T20:32:07.121558 | 2021-01-11T16:39:14 | 2021-01-11T16:39:14 | 265,524,105 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,418 | r | RQA1.R | ################################
rm(list=ls())
#
# ============================================================================== #
# Q&A 1.
# Modify the following function to produce the same out as shown below.
HanoiTower <- function(n,A,B,C) {
timee <<-0
hanoi(n,A,B,C)
cat("It takes ", timee, " ... |
ff94cf6ecfc6f7cd6e400b6b6cd8b607fdde4c99 | c6f1c615abb4f9c8a9a3ebe17f14a7c67d01d0bd | /04-data-tables-and-the-tidyverse/reshaping-to-wide-with-tidyr.R | be6e949923b08970c8f348d11364e95c1a8016ce | [] | no_license | melff/dataman-r | 05b903ace706d41ef91e7a113adcedc92c0be879 | 4e919fd9be46acc243f702889fdb88ed8c4429d7 | refs/heads/main | 2023-06-23T03:24:28.475559 | 2021-07-15T19:53:32 | 2021-07-15T19:53:32 | 324,203,840 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,246 | r | reshaping-to-wide-with-tidyr.R | #' # Reshaping data to wide format with *tidyr*
#' The following makes use of the packages *tidyr* and *readr*. You may need to
#' install them from [CRAN](https://cran.r-project.org) using the code
#' `install.packages(c("tidyr","readr"))` if you want to run this on your computer. (The packages are already installed
... |
cd5d522f4733c55ea7b1b8f723850890dba96ff7 | f97c0ca971a7696cc7524d86019478adec9b84f0 | /exploratoryDataAnalysis/exploringNumericalData.R | 62c99fd79db568483689661b8a3b9539e5405a36 | [] | no_license | muhammadali229/r_data_analyst | e20d7a6f1dac6459ea0bd3b223d987b1dcdd1396 | 464bd36e5746036cb3f60b76cba88adb543fe5c4 | refs/heads/main | 2023-08-07T00:22:51.369649 | 2021-09-16T08:14:04 | 2021-09-16T08:14:04 | 377,105,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,702 | r | exploringNumericalData.R | library(readr)
library(dplyr)
library(ggplot2)
# Load comics dataset
cars <- read_csv("D:/R_data_analyst/exploratoryDataAnalysis/cars04.csv")
# Learn data structure
str(cars)
# Create faceted histogram
ggplot(cars, aes(x = city_mpg)) +
geom_histogram() +
facet_wrap(~ suv)
###########################################... |
90e5e8030e050dc2cb06d129027e97e892f1a1b9 | 118cf005c146919c38e22e88f24b903353df30f0 | /Datahantering, r.R | 14e35f16524931e516b0c9e0c195a98dfa66798c | [] | no_license | Tondar7/Uppsats | 964188c2332eaa17e23d7ee8e9382c7f4140efc7 | 9b4912475a93715a178af87bd3c3179a2cd7e821 | refs/heads/master | 2022-12-28T23:10:10.713017 | 2020-09-22T01:00:56 | 2020-09-22T01:00:56 | 297,378,542 | 0 | 0 | null | null | null | null | ISO-8859-15 | R | false | false | 3,399 | r | Datahantering, r.R | ### Datahantering NEK uppsats
#install.packages("readxl")
library("readxl")
#install.packages("tidyverse")
library("tidyverse")
#library("selectr")
#library("xml2")
#library("rvest")
library("stringr")
#library("jsonlite")
data_kommun <- as.data.frame(read_excel("raw_data.xlsx"))
treatment_data <- ... |
3e8be864994b764c5a4b7ae475809501d325d906 | bac12b8b3542bc01b0616de3e70802e2e7e9cc46 | /ExData_Plotting1.R | 91c0e42c168fa13b1cdf94b29d5d83ebf2f98ddf | [] | no_license | Viditya/ExData_Plotting1 | c005a3554666191c74d7478b8f7ce88e7f6a275d | 4f56e2a2d52fd820f55794d50fd9f3c7fef469ab | refs/heads/master | 2021-05-01T11:03:27.959566 | 2018-02-11T18:29:15 | 2018-02-11T18:29:15 | 121,111,975 | 0 | 0 | null | 2018-02-11T10:27:33 | 2018-02-11T10:27:32 | null | UTF-8 | R | false | false | 8,917 | r | ExData_Plotting1.R | #If the device is on at the starting, switch it off(not required now,as written the same in exit part)
#dev.off()
#1.Download the file using the URL and store it in a zip file in the directory
#download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", dest= "Household_Pow... |
dc1b9ad98d476904a16fe4cf258538654ff6f325 | 388d7a62bbbd144f243438f9e6a5a456eb2cce3c | /man/leastCostMap.Rd | 66bba8ee7a869f42f1f4f3c698d783455211533c | [] | no_license | aspillaga/fishtrack3d | 64c7dcb2a97a833ef830d845e8bfbc3aaf387827 | 2be695e0f88d97e095f074acd17240cb8878dbbc | refs/heads/master | 2022-01-18T10:50:53.776454 | 2019-05-23T15:09:18 | 2019-05-23T15:09:18 | 118,634,135 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,054 | rd | leastCostMap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/syntPath.R
\name{leastCostMap}
\alias{leastCostMap}
\title{Generate a least cost transition matrix from a topographical raster}
\usage{
leastCostMap(topo, min.depth = 0, max.depth = NULL)
}
\arguments{
\item{topo}{raster dataset (\code{Raster... |
94350aed6f866c4fdb9aaf1aae7b12556ad21381 | 97bb2f60ac0b5da9f2f0d4e6a4c2b35b857227cc | /packrat/src/packrat/packrat/R/github.R | 49da66098e8be48487daec6c46c57078d9a3ea80 | [
"MIT"
] | permissive | rachjone/iapsr | 153158167a6c2c12bee5a27fd6de5de85fa19f24 | b7f2c2e8b83a67c2a39d380da830337f9b11ef2e | refs/heads/master | 2022-12-02T02:39:46.956892 | 2020-07-27T21:13:12 | 2020-07-27T21:13:12 | 282,985,045 | 0 | 0 | NOASSERTION | 2020-07-27T18:39:50 | 2020-07-27T18:39:49 | null | UTF-8 | R | false | false | 762 | r | github.R | isGitHubURL <- function(url) {
is.string(url) && grepl("^http(?:s)?://(?:www|api).github.com", url, perl = TRUE)
}
canUseGitHubDownloader <- function() {
all(packageVersionInstalled(devtools = "1.9.1", httr = "1.0.0"))
}
githubDownload <- function(url, destfile, ...) {
onError(1, {
github_pat <- yoink(... |
a7e0694c7bfc9c9838705bf63494c56a93fc508c | a21350122ae4dbbb6d852ee14a9f4407338393dd | /pgm/ch3.r | 5205e29ece99bb98cd03f2e5f446fe1f0bc4a76b | [] | no_license | DataEconomyLab/forecast | d40516f5837732c2286163d21993ac64dc44c591 | 197aef8804ce94b964d57f40e32e922a11fa31dc | refs/heads/main | 2023-05-02T08:22:56.168067 | 2021-05-13T23:51:09 | 2021-05-13T23:51:09 | 350,149,589 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,137 | r | ch3.r | # Figure 3-1
setwd("c:/work/data")
gdp <- read.csv("gdpq.csv", header=TRUE)
gdp_o <- ts(gdp[,1]/1000, start=1970, frequency=4)
gdp_sa <- ts(gdp[,2]/1000, start=1970, frequency=4)
gdp_gr <- ts((gdp_sa - lag(gdp_sa, -1)) / lag(gdp_sa, -1)*100, start=c(1970, 2), frequency=4)
plot(gdp_gr, ylab="경제성장률(전기대비)", xlab="연도", co... |
160d75d331f28b19f7cf667da4d84bb93974dccb | cb7c00e1dcc28b2b04e13e6248a2fce1fb6d61bc | /R/fit-lm.R | 36674bd503e4dd4204569719b8e0a13d92d442d4 | [
"MIT"
] | permissive | DavisVaughan/tidyversity | bd5fe9f3991999d39ff891077b078f36a09f4f44 | 589379d0126a5d1b40c40fceaca932db9cd04633 | refs/heads/master | 2020-03-15T12:44:43.800276 | 2018-05-04T16:03:16 | 2018-05-04T16:03:16 | 132,151,121 | 0 | 0 | null | 2018-05-04T14:32:31 | 2018-05-04T14:32:31 | null | UTF-8 | R | false | false | 1,064 | r | fit-lm.R |
ols_fit <- function(m) {
s <- summary.lm(m)
## f and its p value
f <- s$fstatistic[1]
fp <- do.call("pf", as.list(c(unname(s$fstatistic), lower.tail = FALSE)))
## root mean square error
rmse <- rmse(m)
## deviance
#ll <- -2 * logLik(m)
#lln <- as.integer(attr(ll, "df")
# AIC/BIC
aic <- AIC(m)
b... |
a75f3130203e8927dc473394a7eafd804d5326eb | 9eb69d839ca649884d060a677b2a621a5fbf931e | /man/OneR.Rd | b2cee434d96e7471b4b0274c7962049ed648ac7a | [] | no_license | cran/OneR | 739bda58b0f3f15e4096de79b5348cca09a027f0 | e4a1e0414d5ca36fff51554a0241cb1bfc46b7fd | refs/heads/master | 2021-01-20T21:00:57.853733 | 2017-05-05T17:20:25 | 2017-05-05T17:20:25 | 61,529,527 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,951 | rd | OneR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/OneR_main.R
\name{OneR}
\alias{OneR}
\alias{OneR.formula}
\alias{OneR.data.frame}
\title{One Rule function}
\usage{
OneR(x, ...)
\method{OneR}{formula}(formula, data, ties.method = c("first", "chisq"),
verbose = FALSE, ...)
\... |
af6cc83376081e9e3edb51c4bc752239db3a1c1b | 9ff1c5bb2148e0a9782bf3084817878f95f191d1 | /scripts/ejercicio_011.r | 37e8e454d54a2ea6111a8dcc7018b5fea49b4c29 | [] | no_license | mar71n/cursoR | 95a1045f89e77cc6406bd834f3e1a5ff5e38a00c | fad97fccc114bb5b8cf367952eb64bc397ba6431 | refs/heads/master | 2021-01-16T18:30:01.702666 | 2013-07-18T17:19:35 | 2013-07-18T17:19:35 | 31,655,439 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 5,169 | r | ejercicio_011.r | # Un trigrama es una sucesión de tres palabras. Los lingüistas computacionales construyen bases de datos de ellos a partir de textos y los usan para,
# por ejemplo, crear motores de traducción automáticos.
#
# En este enlace se ve la evolución temporal del uso de de dos trigramas en los libros que Google tiene escane... |
66daa1ea0871f325b8b5c7ec2f6dba7e3b09edc8 | 7c3001f7a2cc3d0f3597f8facd1dbd441f9c7bc9 | /PortBath.R | 9ea3bc1fcdad2cbae09e5bfcdc3ca706803775fa | [] | no_license | gilaum/Running_HS_Track_Guide | 27fd706683fe369b752afd3d03a67a08b3137c02 | dc80f5aab6012d36cf1335b052ba0aa26dec8eb2 | refs/heads/master | 2023-02-28T03:28:01.772960 | 2021-02-10T01:32:41 | 2021-02-10T01:32:41 | 334,561,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,354 | r | PortBath.R | # Tom Alig
# Oct 11, 2019
# Are Bath times or Portage times faster?
#library(rvest)
#library(plyr)
library(tidyverse)
library(lubridate)
library(scales)
library(hms)
library(reshape2)
library(data.table)
library(Hmisc)
library(tools)
source("RunningProjectR/scripts/my_track_scripts.R")
# Took the girls xc data from 2... |
b15ddd37fb67370b32b13caa035b923e6380f98e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/wql/examples/wqData.Rd.R | 6b3c8d75c64caf8562de519b2d9016ddbee8bc42 | [] | 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 | 793 | r | wqData.Rd.R | library(wql)
### Name: wqData
### Title: Construct an object of class "WqData"
### Aliases: wqData
### Keywords: classes data
### ** Examples
## Not run:
##D
##D
##D # Create new WqData object from sfbay data. First combine date and time
##D # into a single string after making sure that all times have 4 digits.... |
6a82fe9ec74de17b87718cd2ee653260f21794d7 | 0362e9207d5e9848bda3b76a47b4529c3fcfaec3 | /r_files/ENG_CORR.r | 8028c90ecc6fe2d249f408082dbfe55c2abd7e24 | [] | no_license | chelseaz117/SelfPacedReading_PsychoPy | 7dccf87d6d2ed3100fc99d08e1b69b1baa169e6d | 5072a6be34a2f3ff1ae75719f5010fdd6788ebdf | refs/heads/master | 2022-05-12T10:41:41.613665 | 2017-03-07T03:19:31 | 2017-03-07T03:19:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,436 | r | ENG_CORR.r | library(lme4)
wholeENG_CORR = read.csv("/Users/chongzhang/Desktop/ENG_CORR.csv")
summary(wholeENG_CORR)
lR34 = lmer (log_R34 ~ RC1 * RC2 + (1|Participant)+(1|Item), wholeENG_CORR)
summary(lR34)
SS_R78_rt = subset(wholeENG_CORR, RC1 == "S" & RC2 == "S")$log_R78
SO_R78_rt = subset(wholeENG_CORR, RC1 == "S" & RC2 == "O... |
a38df708cedc20f9fd4602a9124a139cc2ddca5e | 6fb04083c9d4ee38349fc04f499a4bf83f6b32c9 | /tests/benchmarking_functions.R | 704ff3c2284987fc4d46babf607c9068a35ec0e6 | [] | 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 | 16,316 | r | benchmarking_functions.R |
#benchmarking for solve.
FL_benchmarking_generic(specs = list(list(n =5,isSquare =TRUE)),classes = c("FLMatrix"),operator = "solve")
#benchmarking for ginv
FL_benchmarking_generic(specs = list(list(n=5,isSquare = FALSE)),classes = c("FLMatrix"),operator = "ginv")
#benchmarking for dim
... |
b0831aaf56c046df7bc4155169b73f7c55857d96 | 84597ca9950c4205e3c172b8c05de45fb80a5676 | /R/character-utils.R | d1c8e42613778f30b70469272856174839fb33f5 | [] | no_license | Bioconductor/S4Vectors | 6590230a62f7bbcd48c024f5e4ac952ad21df8c8 | 5cb9c73f6ece6f3a2f1b29b8eb364fc1610657d0 | refs/heads/devel | 2023-08-08T21:26:55.079510 | 2023-05-03T04:40:11 | 2023-05-03T04:40:11 | 101,237,056 | 17 | 23 | null | 2023-07-25T13:44:44 | 2017-08-24T00:37:11 | R | UTF-8 | R | false | false | 2,007 | r | character-utils.R | ### =========================================================================
### Some utility functions to operate on strings
### -------------------------------------------------------------------------
### NOT exported
capitalize <- function(x)
{
substring(x, 1L, 1L) <- toupper(substring(x, 1L, 1L))
x
}
#... |
89921d308679ad453f7921692193060a2648c075 | 8a476df8f065da042e23ea00a9d1cd86ae63ad4e | /man/merluzaChile.Rd | 9f3b66199a8f68cb485de2d28b825d970b909eae | [] | no_license | cran/skewtools | b40ab0ce0f1a3a1dd210105c0a8659ccebcd6881 | 91c35886083fd03f4af94ff3ad5d86b483a2bb88 | refs/heads/master | 2021-01-18T14:24:05.220898 | 2012-07-21T00:00:00 | 2012-07-21T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 803 | rd | merluzaChile.Rd | \name{merluzaChile}
\alias{merluzaChile}
\docType{data}
\title{
Merluccius gayi gayi Data
}
\description{
Length at age database of Common Hake (Merluccius gayi gayi, males fished off Chilean coast during 2005)
}
\usage{data(merluzaChile)}
\format{
A data frame containing 1638 observations on the followi... |
3f1c865f2f97a08d811e6e0274862b8e3afa353a | f70af54a59c09e64d8697b53fac6213f92108e58 | /man/emv_gamma.Rd | 76421f902e769ff5989dcc4eaeb3d1d78a0c3405 | [] | no_license | cran/noisySBM | ce4c1cc38623108f6afa9621b3933364b151368a | 2ae46a0440befeeddbd909a4251bcfa02da03cc3 | refs/heads/master | 2023-02-01T19:05:05.020325 | 2020-12-16T09:40:06 | 2020-12-16T09:40:06 | 323,347,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 661 | rd | emv_gamma.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/VEMalgorithm.R
\name{emv_gamma}
\alias{emv_gamma}
\title{compute the MLE in the Gamma model using the Newton-Raphson method}
\usage{
emv_gamma(L, M, param.old, epsilon = 0.001, nb.iter.max = 10)
}
\arguments{
\item{L}{weighted mean of log(dat... |
6b7d764d4f48ab4ca680bcd6bb1a35b8c5a0e626 | 75747bff0565bfc31b3de6bdeb226157bb364241 | /SHEAF_climate_access.R | 1c24757cfd6d57dca0b0755b48b3e60051cc4932 | [] | no_license | soilhealthfeedback/SHEAF_EDA | 53b0eed2c549d5a51fdc19d10370973ff5511948 | 0b8a6299619b1e7bd66c1e27da99bc8e16b4f8ff | refs/heads/master | 2020-04-09T09:34:20.613120 | 2019-11-18T21:35:05 | 2019-11-18T21:35:05 | 160,238,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,116 | r | SHEAF_climate_access.R | #------------------------------------------------------------------------#
# TITLE: netcdf_access.R
#
# AUTHOR: Erich Seamon
#
# INSTITUITON: College of Natural Resources
# University of Idaho
#
# DATE: Feb 1, 2019
#
# STAGE: netcdf access
#
# COMMENTS: This script opens a... |
f1b34e77d5b91f69b8520d8b593d5a994b36fc1d | 8eac35d80030bcc4f9154aa5636ae581aa4eb256 | /posterPlots.R | 0c33f96ab7c60dfbc61c786516a19a7fea895585 | [] | no_license | jsnoke/altman_HD | 4f9d2904806a4ad577e52200e881f9edbe1828a0 | e996ac433c633a37726258f9e2f38da57cb64bc5 | refs/heads/master | 2021-01-10T09:26:24.221583 | 2016-04-11T15:55:00 | 2016-04-11T15:55:00 | 50,198,373 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,493 | r | posterPlots.R | #####
## risk plots
#####
load("~/Documents/Altman_HD/R/miNoise4.RData")
load("~/Documents/Altman_HD/R/partNoise4.RData")
load("~/Documents/Altman_HD/R/noErr4.RData")
library(ggplot2)
library(grid)
plotDF = data.frame(matrix(NA, nrow = 3*10*6, ncol = 5))
colnames(plotDF) = c("Factor1", "Factor2", "Noise", "p", "epsil... |
c453c84cf98d9e9c516c549810e8d377de5b3bee | 3a122c36f2b8e5e39cc49819ee92c4902851d762 | /plot1.R | 376ba47dfa2e5147d6663f5ecb0add29bd784604 | [] | no_license | vijeshm/ExData_Plotting1 | 0350a233ea78895a1fc8eb5abdb7491134a7bad5 | a88f2fd48e6bb7e3bcef931427819a4464c66afa | refs/heads/master | 2020-12-27T12:02:38.101199 | 2014-09-08T10:53:56 | 2014-09-08T10:53:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,382 | r | plot1.R | # Author: mv.vijesh@gmail.com
# Dated: 8th Sept, 2014
# Description: This code plots the histogram of global active power, averaged hourly, for households during the time period 1-2-2007 and 2-2-2007, onto a PNG.
# Ensure that the text file, household_power_consumption, is in the working directory.
#The values ar... |
5746a92334dca7fed9835c5d5f545a90311246b0 | 6cf9a94de51479dc65dad3608a4b315ba289a36f | /man/get_single_run-HierarchicalPartition-method.rd | 17d58b6523e57472c8a74539dc571e07111d6970 | [] | no_license | NagaComBio/cola | 818c3afdab7e140d549ab9ebf6995a882c967cf5 | 304b3cf771e97ced7f4b20388815b882202cdd84 | refs/heads/master | 2021-04-27T08:31:19.184145 | 2018-02-26T10:00:07 | 2018-02-26T10:00:07 | 122,491,685 | 0 | 0 | null | 2018-02-22T14:45:23 | 2018-02-22T14:45:23 | null | UTF-8 | R | false | false | 635 | rd | get_single_run-HierarchicalPartition-method.rd | \name{get_single_run-HierarchicalPartition-method}
\alias{get_single_run,HierarchicalPartition-method}
\title{
Get result for a specified level in the partition hierarchy
}
\description{
Get result for a specified level in the partition hierarchy
}
\usage{
\S4method{get_single_run}{HierarchicalPartition}(object, node =... |
000f4f4dc12c9452c26dcceeb085f10f7eb92a81 | 4ae34bc5607632a1c771a52e66a56d2cb049e050 | /plot2.R | d64ae4b680757b753e41040b52ea72bfa64ee4c0 | [] | no_license | christina-zhong/R-Exercise2 | a7ebbf37edf4c9988ac26635430e911944762304 | 23b9a9ca12065bf4a579382aac50b846a8498638 | refs/heads/master | 2020-12-25T12:57:39.262669 | 2014-08-10T19:52:56 | 2014-08-10T19:52:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 485 | r | plot2.R | data<-read.table("household_power_consumption.txt", header=TRUE,sep=";", na.strings="?",colClasses=c(rep("character",2),"numeric",rep("NULL",6)))
gap <- subset(data,Date %in% c("2/1/2007","2/2/2007"))
gap$datetime <- strptime(paste(gap$Date,gap$Time,sep=" "),format='%m/%d/%Y %H:%M:%S')
par(mar=c(5.1,5.1,4.1,2.1))
with(... |
9ce23ef11b237d604e04bf35f265312a57fc0d29 | 44fe4a9f6d9630d0a80f987ace8474ba48b858f7 | /man/shiny_rdf_spec_curve.Rd | f7272a367438e93b283c5e3235b94ba8c2ba16ee | [
"MIT"
] | permissive | iMarcello/rdfanalysis | ee8be635d38554f81abc21f87673fd48aef06041 | 5c43ee48ff2d7f82b921419d9dbb9b2663e57d7c | refs/heads/master | 2020-09-27T22:15:35.705406 | 2019-12-07T13:12:16 | 2019-12-07T13:12:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,853 | rd | shiny_rdf_spec_curve.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shiny_rdf_spec_curve.R
\name{shiny_rdf_spec_curve}
\alias{shiny_rdf_spec_curve}
\title{An Interactive Specification Curve}
\usage{
shiny_rdf_spec_curve(ests, spec_curve_parms, design = NULL,
rel_dir = NULL, start_input = NULL, regression_cu... |
33728b39ed42a170890d6998f0d9eb4a7f6d89b0 | 6e632fa2255635d340bff97ae0dc31e860baed54 | /R/mt_plotpairs.R | 895cbb413e0e706313c46af3ac022a0da1a595a5 | [] | no_license | TYMichaelsen/mmtravis | 01a2546da1af40c7ebe42276e6a27d7fb35637db | 0bb0cb651be6737b01aa4c9b7776e177d55cb7eb | refs/heads/master | 2020-03-14T23:15:04.587733 | 2019-08-03T05:35:17 | 2019-08-03T05:35:17 | 131,840,635 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,069 | r | mt_plotpairs.R | #' @title Plot multiple combinations of sample gene expressions in a pairs plot
#'
#' @description Plots the gene expression from multiple samples in a \code{mmt} object in a grid plot with all pairs of variables.
#'
#' @param mmt (\emph{required}) A \code{mmt} list loaded with \code{\link{mt_load}}.
#' @param samples ... |
df1eeb5e418aa9e79a9c0a12881534090b2a078d | d11e3c96cbec4c4ce02d8c663a51835d4b213278 | /documentation/esm237examplesS18/R/clim.R | 022ae6e384089da054a02bd8604efca4ef515364 | [] | no_license | molly-williams/esm262-assignments-MW | b48d8c1d2356e299c5ce426bbf2e19351ba0e6c7 | 15bff91481c6c19b3a879b874ad7eaf3b938cfb1 | refs/heads/master | 2021-06-17T15:38:58.697157 | 2021-03-16T20:22:47 | 2021-03-16T20:22:47 | 184,777,904 | 0 | 1 | null | 2019-06-01T02:54:44 | 2019-05-03T15:19:40 | HTML | UTF-8 | R | false | false | 439 | r | clim.R | #' Climate Data from Santa Barbara
#'
#' Data from SB-LTER meterology station
#'
#' @format A data frame with 27274 rows and 8 colums
#' \itemize{
#' \item date day/month/year
#' \item tmin minimum daily temperature (C)
#' \item tmax maximum daily temperature (C)
#' \item rain daily rainfall (mm)
#' \item year
#' \item... |
b678101b5eb34525942526668fee26b50e94ad06 | 15107b515d45e60c7ea59cfcb63b758984c52272 | /man/getTicks.Rd | 93c5b6edba2856b59e16a24d7b78bec569c3aac4 | [] | no_license | gforge/forestplot | e00e700b727758c30a530d077168d26b86c63f4b | b26b33561d2664933fc7a9b8258e26dffa8fe2e5 | refs/heads/master | 2023-09-01T12:16:44.558022 | 2023-08-27T19:52:18 | 2023-08-27T19:52:18 | 28,350,997 | 38 | 16 | null | 2022-11-23T20:55:20 | 2014-12-22T17:53:03 | R | UTF-8 | R | false | true | 1,557 | rd | getTicks.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getTicks.R
\name{getTicks}
\alias{getTicks}
\title{Ticks for plot axis}
\usage{
getTicks(low, high = low, clip = c(-Inf, Inf), exp = FALSE, digits = 0)
}
\arguments{
\item{low}{lower bound, can be a single number or a vector}
\item{high}{upp... |
880e88392272c1796805aab2d60e77bbb2b7804c | b2041dcde785d4ac86387fc48960e53d0c9af848 | /hw3/rankhospital.R | 31e8412cad268b8797a3970b59907303deba91d6 | [] | no_license | YeLibrarian/cdata-analysis | 53ea05f408b36af598fdb95a65db7630cdc30692 | f171d961cf2b2e08a9fa71409079e420eea31c9f | refs/heads/master | 2021-04-15T12:12:54.480545 | 2014-02-18T06:32:23 | 2014-02-18T06:32:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,579 | r | rankhospital.R | rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
outcomeData <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
#this is being harcoded because it's kind of a hell
labels=list("heart attack"=11, "heart failure"=17, "pneumonia"=23)
dataCols=sapply(labels, "... |
e13c6a7d1498ad170be7b70924857eea7b58e61d | 5c5e9ddcff88fe03d6249fc25935574608a2abed | /R/kge_nse.r | b4422043dbbed2532b320939679c50d448d44cac | [] | no_license | freysimon/PestR | 5864e5a596f57a4bcfa4d9fe40577c899a00f9db | 66416dd406aa59ede65647104b6fc29dc0492705 | refs/heads/master | 2021-01-18T22:22:10.554621 | 2017-05-30T14:39:09 | 2017-05-30T14:39:09 | 87,049,633 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 914 | r | kge_nse.r | # Designed to read a COSERO outputfile
kge_nse <- function(x,simcol=2,
OUT = "kge_nse.txt",
from = NULL, to = NULL){
# libraries laden
require(hydroGOF)
require(xts)
if(!any(is.null(c(from,to)))){
x <- x[paste(from,"/",to,sep="")]
}
if(is.null(from) & !is.null(t... |
4dbe51a0f4d8dcc04ec13d0c7821c8073814a797 | 34fab1c1c94231367e92f67e05762b73137df8d7 | /RNA-Seq/Quantifying-RNA-Expression/Gene-Level-Analysis/Differential-Expression-Analysis/DESeq2/simpleDESeq2Analysis.R | f13f260547acc260f69a506c0968c27acb0c2ab0 | [] | no_license | aiminy/tutorials | ac35ce4bf1fca3625cb84fba9b8d3b99387c16d4 | 511af0ed6535b59bab80099096ed35ce808f6661 | refs/heads/master | 2021-01-21T01:38:55.693437 | 2015-10-25T13:52:10 | 2015-10-25T13:52:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,325 | r | simpleDESeq2Analysis.R | library(DESeq2)
directory = 'C:/RNASeq/ReadCounts/'
setwd(directory)
# Load data directly from HTSeq Count
sampleFiles=grep('.geneIDCounts.txt',list.files(directory),value=TRUE)
sampleNames=sub('.geneIDCounts.txt','',sampleFiles)
sampleCondition=sub('[1-3]','',sampleNames)
sampleTable=data.frame(sampleName=sampleNames... |
bf7bc818abe24a9b4bcf039b9219566060d007e0 | a59627d4b4d1dd1daa6bbee94f56b1caba590675 | /FB.R | 9ef9563ad21bf6e5c16013092eb2d7c73ba5366c | [] | no_license | AshleshaSampathi/Exploring-Data-Science-Market-through-Glassdoor | 10b4406e2712ff34712fff8ddbfe95f374430c84 | 53c3a310a98b389adfb395708f04ec48e01d3883 | refs/heads/master | 2021-04-02T17:48:26.755765 | 2020-03-18T17:53:56 | 2020-03-18T17:53:56 | 248,302,826 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,077 | r | FB.R | library(tidyverse)
library(tidytext)
library(tm)
library(wordcloud)
library(udpipe)
library(lattice)
#Stemming packages
library(SnowballC)
#library(hunspell)
#library(proustr)
fb = read.csv("C:\\Users\\ashle\\Downloads\\fb_reviews_csv.csv", header = TRUE)
colnames(fb)
fb= fb %>%
gather(pros, con... |
86d831853dd3673cabd28851f39afc3ab701b962 | b23e8b8fa4f70d915f0c8d3c6ed57ba2d2ae4ee6 | /GDM_Workflow.R | a46687813e5705a703fc0117a13493895d9d84c5 | [] | no_license | cwarecsiro/gdmEngine | 99195eefc1369165051ce8985f5954369358664c | 4fd57e609f7411ebb989b0987e6b10d33af63ae4 | refs/heads/master | 2021-07-15T23:33:37.415368 | 2018-11-23T00:06:05 | 2018-11-23T00:06:05 | 137,005,799 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,100 | r | GDM_Workflow.R | ###########################################################################################
##
## Generailsed Dissimilarity Modelling workflow
##
## Karel Mokany et al, (CSIRO) - Draft 30 August 2017
##
###########################################################################################
# Install libraries
# ... |
1933de62d50fa0b8c6efdd94543f9b25e36374c0 | f185c837110671db3e2632110d14d4d160d0eae5 | /ScriptLoadData.r | 4d073a3330667f34c39fe7747c767d8624fdb70e | [] | no_license | irycisBioinfo/ResCap | eca0df674a21db126d74565ddecd8298b242b403 | 4fcffccb308849d6f871f6589e03f09337ff0a28 | refs/heads/master | 2020-04-08T12:37:40.659752 | 2017-05-08T13:34:23 | 2017-05-08T13:34:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 550 | r | ScriptLoadData.r | #####
#
# R script to load count data using tidyverse package.
# All count files must be in the R working directory
#
####
library("tidyverse")
lista = dir()[grep("csv",dir())]
j=0
for(i in lista)
{
tabla = read_delim(i, delim = "\t", col_names = FALSE)
colnames(tabla) = c("Gene","Reads","RPK","Uniq","Coverage")
... |
12a75a47ef2ea9debaed4fe8d50030a672e903f6 | 982e2e9fd6cc6415196b34454eec2ab3edd00785 | /man/trackgroupout.Rd | 0fcc1fe4ad63e6ed6ece65b2b77f5ab2bf1bdde4 | [] | no_license | allen-chen-noaa-gov/sur.kmeans | 810f930ef3ef7be53c14590ba695e3e2a473d08d | 04ab91c08d4110ccefd733eb74a93c4c58736f69 | refs/heads/master | 2021-07-15T18:30:09.058362 | 2020-09-22T06:17:37 | 2020-09-22T06:17:37 | 212,443,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 463 | rd | trackgroupout.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trackgroupout.r
\name{trackgroupout}
\alias{trackgroupout}
\title{Algorithm to move into closest group}
\usage{
trackgroupout(FullTableFull, pooledvcov, testvars, nontestvars, sigmai,
betai, betaWFE, TrackGroup, YYlist, XXlist)
}
\... |
4f1339919eb7cb0e243446a103d2d0d4388d97ce | def0ab934ebe0d3b8593253d02e591ab428d199b | /cachematrix.R | cd139fe5424f0683a158c68a627bc8d8f6f2f0ed | [] | no_license | JakeNel/ProgrammingAssignment2 | 7271ccd1cb3db47ec2420cff28d1890826ade4b6 | 3fd00486712adb00919eac63a75c461288e55187 | refs/heads/master | 2021-01-21T06:10:59.871212 | 2015-10-23T17:13:57 | 2015-10-23T17:13:57 | 44,654,277 | 0 | 0 | null | 2015-10-21T05:17:29 | 2015-10-21T05:17:29 | null | UTF-8 | R | false | false | 1,131 | r | cachematrix.R | ##makeCacheMatrix works to both store the value of a matrix
##as well as it's inverse. cacheSolve calculates the inverse of a matrix and
##stores the value in makeCacheMatrix.
##This function stores four other functions in a list, all of which set or retrieve
##either the matrix or its inverse
makeCacheMatrix <- f... |
6471f30d9666ba49f1ec4d3a4aebfd813072af6d | 773217982af4a02563a259ef2087c3a412c18741 | /global.R | fc52db978faa34f66f603a2b6e45139167bb2bd7 | [] | no_license | cndesantana/DashboardCorreria | afb71fa87b27f8f1d690101b5847f36004c3b1a3 | a5c1a9d9c29c3c2b8fe99072cfa9301ee2672179 | refs/heads/master | 2021-10-10T10:59:37.332773 | 2019-01-09T22:57:03 | 2019-01-09T22:57:03 | 113,132,380 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,876 | r | global.R | library(shiny)
library(shinySignals)
library(dplyr)
library(bubbles)
library(stringr)
library(quanteda)
library(readtext)
library(ggplot2)
library(tidyr)
library(shinydashboard)
library(shinyFiles)
library(devtools)
library(Rfacebook)
library(lubridate)
library(stylo)
library(tidytext)
library(tm)
#library(wordcloud)
l... |
0118a31c2065272373ff979e97d246f5a4bffb49 | dff4be894fcfabd2b7a201a0372170bd865a9e3e | /R/wine.R | 45d3add11764f3913b30cb68c4552eb107b0fc43 | [] | no_license | zhaozhg81/AIXZ | ba106545a1aae22984dcbb4453318e63b7262f1a | 49f835bf9d2ca9c9642ea3a40eb36ec925172f57 | refs/heads/master | 2023-05-04T20:19:10.771017 | 2023-04-17T14:51:10 | 2023-04-17T14:51:10 | 213,441,232 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 3,123 | r | wine.R | library(fields)
library(LassoSIR)
library(Rdimtools)
wine <- read.table("./data/wine.data",sep=",")
## Pairwise scatterplot
pairs(~., data=wine[,2:14])
## Correlation
wine.cor <- cor( wine[,2:14] )
breaks <- seq(from=-0.9,to=0.9,length=65)
hmcols <- tim.colors(64)
image.plot(x=1:13, y=1:13, z = wine.cor, zlim = c(-... |
509e972f44db2ec78e1bd12db43766edabb89992 | 11394cd22cea3b4e644d20564ff4b500018d943e | /scripts/separateAnalysis/gen_cellphonedb_input.R | f25521f035a69c587dcec6018ceeedd3d570d1dd | [
"MIT"
] | permissive | shunsunsun/single_cell_rna_seq_snakemake | 3d153c9cb7db9988917aff38991217a35940aa64 | f275546eb3bd63d5d535a13407ce47ee36e94cae | refs/heads/master | 2023-04-17T08:44:13.954986 | 2021-04-27T08:13:50 | 2021-04-27T08:13:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,134 | r | gen_cellphonedb_input.R | ##https://www.cellphonedb.org/explore-sc-rna-seq
library(Seurat)
library("biomaRt")
library(data.table)
args <- commandArgs(trailingOnly=T)
infile <- args[1]
outprefix <- args[2]
assay <- args[3] #SCT or RNA
se <- readRDS(infile)
counts <- GetAssayData(se, assay=assay, slot="data") #normalized counts
meta <- se@met... |
1b76d5cccefa70ba29bd97dc255267c3f041d5ef | 4a0b1ae93f3f67fd1b499a4dd7dcc58659a7151a | /inst/examples/example_read_rra.R | 993600bcefb961ceb81ae8dc4c41a724aa829cb4 | [
"MIT"
] | permissive | pldimitrov/Rrd | 3481fa87353dbbe5874ac28c760f267a65e84b75 | 261f3865d1897e2162078efc99f45c3580782381 | refs/heads/master | 2021-05-04T09:38:30.689885 | 2018-05-13T22:31:23 | 2018-05-13T22:31:23 | 20,504,007 | 11 | 2 | null | 2018-05-13T22:29:16 | 2014-06-04T22:57:56 | C | UTF-8 | R | false | false | 765 | r | example_read_rra.R | rrd_cpu_0 <- system.file("extdata/cpu-0.rrd", package = "rrd")
# Note that the default end time is the current time (Sys.time())
# However, since the sample data is historic, specify the end time
start_time <- as.POSIXct("2018-05-01") # timestamp with data in example
end_time <- as.POSIXct("2018-05-02") # timestamp... |
89333cdc3d1d298efa415ee4cac23762acb6185b | f39b96268d1379e951ed772147962c7dc109c305 | /MalloryLaRusso_PCA10.R | affa78a8ffa7bc05bb05eb52c858df566c57f31b | [] | no_license | mhlarusso/R-class-projects | 98c8c5544feb9dbd96b764bf7334e53d6143d8d8 | 31810a656a738d69e2ca2c179d6d768c99355b3f | refs/heads/master | 2020-07-30T12:33:29.680100 | 2019-12-10T20:39:27 | 2019-12-10T20:39:27 | 210,236,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 772 | r | MalloryLaRusso_PCA10.R | ####################################
##Pre-Class Activity 10
##Name: Mallory LaRusso
####################################
#Create an R script that will do the following:
library(readr)
library(dplyr)
riverData <- read_csv("https://raw.githubusercontent.com/jbpost2/IntermediateR/master/datasets/river.csv")
riverData... |
35af6c5baa391071f4c813275b8ae2365bc16697 | 50f66f58e47bc44ca599fc46826ed70288ac8439 | /ecoregionMapInStudyAreaRaster.R | 916834034db307f326a23046d54d82ea7e788948 | [] | no_license | ianmseddy/landwebNRV | a6c400acc0b1b4994afb68e720c594593ce4fbb0 | 88cf31e128c6543bfe8f72d63372f7a247fad8d1 | refs/heads/master | 2020-03-31T22:36:04.433059 | 2017-03-20T02:45:17 | 2017-03-20T02:45:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 920 | r | ecoregionMapInStudyAreaRaster.R | rm(list=ls())
source('~/GitHub/landwebNRV/landwebNRV/R/ecoregionClassification.R')
studyarea <- readRDS("C:/Users/yonluo/Documents/LandWeb/landwebsimplePoly.rds")
ecoregionMap <- rgdal::readOGR("M:/data/Ecozones/ecozones.shp",
layer = "ecozones")
# this is the first function
dd <- ecor... |
f67f763ee08fe09c30499b3fd631d17a15e25e7e | cbffed995d684f470a02d1af779f34f7dfef340c | /PredictingActivity_SamsungSensor.R | cc83c108b33f50e54b70fac907ddc6314e606d11 | [] | no_license | kjy/Data-Analysis-stuff | 92a4637087b669c7158e8cd9246ac691769133d7 | b95889e52e42a0d292056bf90f7c8fe8f82098d0 | refs/heads/master | 2021-01-01T17:37:01.448919 | 2013-11-08T18:26:32 | 2013-11-08T18:26:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,939 | r | PredictingActivity_SamsungSensor.R |
load("/Users/karenyang/Downloads/samsungData.rda")
str(samsungData)
names(samsungData) <- tolower(names(samsungData))
names(samsungData) <- gsub("-", ".", names(samsungData))
names(samsungData) <- gsub(",", ".", names(samsungData))
names(samsungData) <- gsub("\\()", "", names(samsungData))
names(samsungData) <- gsub(... |
3b68cd61261c2a18ab1fc47ec87b5b4dc5cdb641 | 1e330c7d604dd28b1f79507ec5a5d4797ff5fa62 | /Analisis/MosquitoReview.R | 48bfa8a82e434ec216765ac08052ef08f375bcf6 | [] | no_license | Damian-VA/MosquitoReview | e87d76466849fad8723f4c4e82d7fbb9daa5daed | bfa0b26f49f6929172e499322520a306aa33b09d | refs/heads/main | 2023-03-28T23:25:23.245235 | 2021-03-31T14:57:46 | 2021-03-31T14:57:46 | 311,420,366 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 63,210 | r | MosquitoReview.R | # Analyses of "Mosquito bloodmeal preference
# in disturbed vs. wild landscapes (a review)"
# Guadalupe López-Nava
# Damián Villaseñor-Amador
# César A. Sandoval-Ruiz
#R map tutorial----
#By Kim Gilbert
#https://www.molecularecologist.com/2012/09/making-maps-with-r/
#Packages
library(maps) #map functions
libra... |
92c35bd56bb412d7ac6e19dcc5e0d40d4c75d7d8 | 55efbadb0cc8e772639aa3e2aa89317385615711 | /encounter.metro_s_meanonly_sim.R | 56ed56ae2037ab1dd783dd0018ed860f2ed0afe7 | [
"MIT"
] | permissive | Sandy4321/hierarchical-association-rules | ee53e2a4c22e58b01e806e4d25dd0abf11cbf9f2 | 820adbe222c50584e40f9868cc1533f6a57133ff | refs/heads/master | 2020-04-25T17:35:59.707778 | 2018-02-02T00:14:08 | 2018-02-02T00:14:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,072 | r | encounter.metro_s_meanonly_sim.R | #only keep the mean to try to use all rules
#######this version only keeps probabilities.
pt.tmp.fcn<-function(beta.tmp,g,x.matrix=x.mat){
out=exp((x.matrix%*%beta.tmp)+g)
return(out)}
f.metro<-function(y,n, x.mat=NULL, n.iter=5000,n.thin=10,n.burn=1000,n.chain=3){
#beta is n.rules*ncol(x)+1
#gamma is n.us... |
c8b7e9cea247f87849889c7e3fe28cf9bacab709 | c750c1991c8d0ed18b174dc72f3014fd35e5bd8c | /pkgs/bayesm/man/bank.Rd | 3155e72f47c60aa219a170c5346f9d3692a3c5df | [] | no_license | vaguiar/EDAV_Project_2017 | 4b190e66fe7a6b4078cfe1b875bccd9b5a594b25 | 288ffaeec1cfdd873fe7439c0fa0c46a90a16a4f | refs/heads/base | 2021-01-23T02:39:36.272851 | 2017-05-01T23:21:03 | 2017-05-01T23:21:03 | 86,010,131 | 1 | 0 | null | 2017-05-01T23:43:04 | 2017-03-24T00:21:20 | HTML | UTF-8 | R | false | false | 4,021 | rd | bank.Rd | \name{bank}
\alias{bank}
\docType{data}
\title{ Bank Card Conjoint Data of Allenby and Ginter (1995)}
\description{
Data from a conjoint experiment in which two partial profiles of
credit cards were presented to 946 respondents. The variable
bank$choiceAtt$choice indicates which profile was chosen. The
profi... |
2acf26e6b3e405253680b65580da9cc9461af8a9 | bac2ab705e25265df85dade5af77b501267abe0d | /R-Programming for Absolute Beginers-Udemy/24-The ifelse() Function.R | 6d7b239875f5ba88853f17d5f0018a1953b141a8 | [] | no_license | manasohara/R-Programming | ee7ec51ab3d2b8019f90c5869b228687e77eea12 | 869212761fcc5da751f9631723951a35b9ac5885 | refs/heads/main | 2023-08-18T08:10:01.410635 | 2021-09-20T06:39:53 | 2021-09-20T06:39:53 | 399,898,235 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 626 | r | 24-The ifelse() Function.R | #ifelse(condition,action if true,action if false)
x<-c(6,10,9,5,20,7,16)
y<-ifelse(x%%2 == 0,x/2,x)
y
#explanation
#if x%%2==0
#true condition given is x/2 which means
#divide the value by 2 so 6 became 3
#else x
#false condition is x only
#so the x value copied as it is
## see 9
#ex2
x<-c(2,6,15,10,20... |
5366cd52d4e5c4c4f9a1f245ed8853603efa5b3a | c211370f32539ab7a746d0ce69b3b39098f604cf | /Scripts/ANOVA.R | f0411dbfbb494f40e77bfe2538b2121b55798837 | [
"MIT"
] | permissive | grvsrm/My-Tidy-Stats | 3f038e741fffeef93f7162f8c27b0d5db8e56ab3 | 580765febc48602de75ba38e568e2127dff85f5c | refs/heads/main | 2023-01-24T08:00:38.319485 | 2020-12-07T10:19:12 | 2020-12-07T10:19:12 | 318,995,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,790 | r | ANOVA.R | # Pre-requisites
library(palmerpenguins)
library(tidyverse)
library(rstatix)
library(car)
# Data
dat <- penguins %>%
select(species, flipper_length_mm) %>%
drop_na()
dat %>%
ggplot(aes(species, flipper_length_mm, color = species)) +
geom_jitter()
# Test Independence
durbinWatsonTest(dat$flippe... |
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