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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e80df917a09fa4cbbe7d269cab8a30a7615bd6e1 | 29585dff702209dd446c0ab52ceea046c58e384e | /BatchJobs/tests/testthat/test_loadResults.R | d83a189aee1753218c7d4832390694da7521a6e2 | [] | 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 | 1,317 | r | test_loadResults.R | context("loadResults")
test_that("loadResults", {
reg = makeTestRegistry()
ids = 1:2
batchMap(reg, identity, ids)
submitJobs(reg)
waitForJobs(reg)
ys1 = 1:2
ys2 = loadResults(reg, simplify=TRUE, use.names="none")
expect_equal(ys1, ys2)
ys1 = as.list(ys1)
ys2 = loadResults(reg, simplify=FALSE, use.n... |
39f057a626d953dfc9008db7e03461cc9561f2cd | ab9676fc34ff7ddd49f4cc59ee4d04f79dc9bc77 | /pdsi/data/raster/script_pdsi.R | a8689c3d56ab1bce7fbe0bdb298e52188fcc803b | [] | no_license | mossypanga/Drought_CA | 8d8aad89f70bdc00dabd9b166a1427de0223041a | e44b340b8f935ddcc4f595f6328d83f89b89ef4f | refs/heads/master | 2021-05-28T21:29:56.057731 | 2015-09-07T11:42:35 | 2015-09-07T11:42:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,433 | r | script_pdsi.R | # Load packages
library(RCurl)
library(raster)
library(rgdal)
library(maptools)
library(rasterVis)
# Set the directory
di <- '/Users/ajpeluLap/myrepos/pdsiSpatial/'
setwd(di)
#### run only a time!!!!! ########
## Raster data about scPDSI
## http://www.wrcc.dri.edu/wwdt/archive.php?folder=scpdsi
## Loop to downloa... |
644d40c31f7cf9acca5ee92603acb97616502266 | 2465a334c91859bf40acfaa03af86fb48492df68 | /machine_learning/caret_package/question_one.R | 74c65234cfabc7f6bd572ec3a6eb9a053edc9974 | [] | no_license | dunagan5887/datasciencecoursera | 987415f477e7d580902a2936414c607c8983e77c | be76a8117d5a715abd093482d6f991b8df560652 | refs/heads/master | 2021-01-21T05:00:25.808625 | 2016-07-15T21:17:09 | 2016-07-15T21:17:09 | 42,409,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 246 | r | question_one.R | library(AppliedPredictiveModeling)
library(caret)
data(AlzheimerDisease)
adData = data.frame(diagnosis,predictors)
trainIndex = createDataPartition(diagnosis, p = 0.50,list=FALSE)
training = adData[trainIndex,]
testing = adData[-trainIndex,]
|
6543f8095159610e4fbac0c4118c9f2a343d446b | f5d7971c957884f67ada8002df185ab51fbdaa45 | /scripts/visualize/visualize-doy.R | 0729bdac060d3d7d3ec49dc3a52e74c8789c3bba | [
"CC0-1.0"
] | permissive | mwernimont/gages-through-ages | f196decc52c03efafadd0a258be02212ac070d2b | 51e216a09a1775120f74437f271cabbaccc7e719 | refs/heads/master | 2021-01-24T18:27:16.895006 | 2017-03-10T22:36:51 | 2017-03-10T22:36:51 | 84,447,844 | 0 | 0 | null | 2017-03-09T13:56:53 | 2017-03-09T13:56:53 | null | UTF-8 | R | false | false | 2,190 | r | visualize-doy.R | visualize.doy <- function(viz = getContentInfo(viz.id = "doy-NM")){
library(svglite)
library(dplyr)
library(xml2)
daily <- readData(viz[["depends"]][["daily"]])
zoomer <- readData(viz[["depends"]][["zoomer"]])
zoomer.xml <- read_xml(zoomer)
doy_svg = svglite::xmlSVG({
par(omi=c(0,0,0,0), mai=... |
56361dfd591d357bae9d3c3e27e751cbc5a73cf3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rnaseqWrapper/examples/determineSynonymous.Rd.R | d55297e4bf8dd6d017f75b64545ce660db653416 | [] | 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 | 456 | r | determineSynonymous.Rd.R | library(rnaseqWrapper)
### Name: determineSynonymous
### Title: Determine whether or not variants are synonomous
### Aliases: determineSynonymous
### ** Examples
## Load needed data
data(varScanExample)
data(fastaExample)
determineSynonymous(varTable=varScanExample,
refPosCol=2,
... |
1f31cdc3fe34e100e3bb7bb3d17bbd61f1f96650 | c8328a268e6c2993a1396d6d8c20dbce963c1e0d | /R_DS_ML/Kmeans_after_PCA.R | 5a4f2400fd0a46d43e7a36f388d62592333ce5e7 | [] | no_license | battyone/MLProjects | 9781e83af0ec6f943c9f53d4e9baa1d1fa25e1c8 | f36fa6f26b100107b04f6fa477a24a0d94a3e98c | refs/heads/master | 2020-09-22T12:49:45.672878 | 2019-11-04T00:43:44 | 2019-11-04T00:43:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,628 | r | Kmeans_after_PCA.R | ---
title: "K-means Clustering after PCA for wine dataset"
author: "Amol Deshmukh"
date: "April 7, 2019"
output: html_document
---
```{r}
library(dplyr)
library(ggplot2)
```
```{r}
wine_kaka %>%
ggplot(aes(fixed.acidity, alcohol)) +
geom_point()
wine_kaka %>% summary()
```
```{r}
wine_kaka %>%
ggplot(aes(ci... |
8a088e3ec13abede87b372746772e51dd27001d5 | 49767c4f6942985f5f6ae4a5b30b0c1f0e619a83 | /fresh/app.R | 940899259b64f3e90486c2aeaab4e948471c2aba | [] | no_license | GreatLakesSIIM/shiny-fhir | 8cf22623d7e1c1ee44c967879ba66e50ade20020 | 1ce5ec615b858ab146b9bb5cae5e31a5a8085a1b | refs/heads/master | 2020-06-10T02:01:34.734196 | 2019-12-04T16:00:04 | 2019-12-04T16:00:04 | 193,551,147 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,138 | r | app.R |
library(shiny)
library(shinyFiles)
library(jsonlite)
library(tidyverse)
library(DT)
library(rjson)
library(httr)
library(stringi)
library(RJSONIO)
post_data <- function(resourceType, data){
POST(paste0('http://hackathon.siim.org/fhir/',resourceType),
add_headers('apikey' = Sys.getenv(x='SiimApiKey'),
... |
96450787e4a6ae562d5c08384d35ddd944074529 | 61decf7eddaafd1356342d8601455760c82541b7 | /man/read_fb.Rd | 57f9f4fb49999dbc77d60a2b754c7a75f133095f | [] | no_license | grice2/GPATOOLS | f26ce237d095b4b410696ff9e11142bab9efcd3f | bb3a68a1707b03a03ffc1f247c3101e53abbc8d4 | refs/heads/master | 2023-03-24T05:20:55.725319 | 2021-03-19T16:56:33 | 2021-03-19T16:56:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 856 | rd | read_fb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_fb.R
\name{read_fb}
\alias{read_fb}
\title{Read and Format GPA Facebook Data}
\usage{
read_fb(df_reactions_dir, df_engagement_dir)
}
\arguments{
\item{df_reactions_dir}{Directory to GPA Facebook post reaction types data (must contains 'p... |
42e0c7ce6c4cd5688a747f54815346db977ac775 | 3d171996a631a1df6bd7b04cc1f5776d4715273f | /man/varExp_Gaussian.Rd | 9d688328098eccb6ab99441cd765d6532d920b7b | [] | no_license | YipengUva/RpESCA | 5c562bd0433afdafdff891f2ba7fe586658c87c8 | d4f49f23015c2641353906f9152d25fbec209078 | refs/heads/master | 2020-05-09T11:13:49.574061 | 2019-07-01T13:54:11 | 2019-07-01T13:54:11 | 181,071,934 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 956 | rd | varExp_Gaussian.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/supplementary_functions.R
\name{varExp_Gaussian}
\alias{varExp_Gaussian}
\title{Compute the variation expalined raitios when Gaussian data is used}
\usage{
varExp_Gaussian(X, mu, A, B, Q)
}
\arguments{
\item{X}{a \eqn{n*d} quantitative data s... |
107e26d0bae0b627065e1ad9a7d07c3a15ddadf5 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/MTS/man/Vpmiss.Rd | 987166ad6b8f8589f3a2df4bca0693117ac7f526 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,172 | rd | Vpmiss.Rd | \name{Vpmiss}
\alias{Vpmiss}
\title{Partial Missing Value of a VARMA Series
}
\description{Assuming that the data is only partially missing,
this program estimates those missing values. The model is assumed to be
known.
}
\usage{
Vpmiss(zt, piwgt, sigma, tmiss, mdx, cnst = NULL, output = T)
}
\arguments{
\item{zt}{... |
fff112686de779eaee8a3daaaa3b3223cab21f1e | 0ee7f4115cfbbcbd58814b90c7b3f629c4314a75 | /Exercise1/ui.R | 499c42640fbed52f7edb62ae80c74192b925d0e9 | [] | no_license | EdinbR/mango-shiny-workshop | d4c28f2410eea41d86fa3afffe0f3360c82f9722 | 7c4ed2508c4f4179eb31fea2f34c49185cb58b38 | refs/heads/master | 2021-01-19T03:24:11.501842 | 2015-06-18T10:45:36 | 2015-06-18T10:45:36 | 37,653,338 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 540 | r | ui.R | library(shiny)
shinyUI(fluidPage(
# Define the header for the page
titlePanel("An Exciting Shiny App to Print Dates!"),
# Set up the page to have a sidebar
sidebarLayout(
# Define the contents of the sidebar
sidebarPanel(
dateInput(inputId = "dateInput",
label... |
b84719535b984e06a43d5c70bb65f3aa22e7b029 | 584a01c7f6cace0bb42e4c1e37b8957d24a6825d | /nanopore_qc/server.R | a0a6cf5e0dc0593c7ebf49b1d6003946c7f9dbbb | [] | no_license | ambro01/nanopore_qc_mbi | 15c5f1ad0fa2c10e64b4ec2375d91c93de86c44c | ab5358cd5cd67eaf741bfa7975f6a4f91496b970 | refs/heads/master | 2021-09-06T22:42:19.139377 | 2018-02-12T20:11:41 | 2018-02-12T20:11:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,248 | r | server.R | library(IONiseR)
library(minionSummaryData)
library(ggplot2)
library(gridExtra)
library(poRe)
source('global.R')
source('ui.R')
source('server_ioniser.R')
source('server_pore.R')
source('server_poretools.R')
server <- function(input, output, session) {
# wszystko co laczy sie z odczytywanie stanu kontrolek na wido... |
36f889d60fb333c973fe614258dcfb0d81f0075a | 2a86e10f76fcbd693038479a9f13c1c4b7717b8d | /code/boosting/lr-ud-predictor.R | 2b21b7bf216ddf7744b6b8259ef163336813466b | [] | no_license | naturegirl/EavesDroid | a2bec34186841da6608e240666c463f7fd82612f | 02f32e0f7a39aad86573e5b0c3e5332d350a02f0 | refs/heads/master | 2020-12-24T13:29:25.717850 | 2014-05-16T19:27:05 | 2014-05-16T19:27:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,171 | r | lr-ud-predictor.R | library(RWeka)
library(rJava)
args <- commandArgs(trailingOnly = TRUE)
file_lr <- "./../../data/dataset/training.lr.arff"
data_lr <- read.arff(file_lr)
file_ud <- "./../../data/dataset/training.ud.arff"
data_ud <- read.arff(file_ud)
# load the saved models for LR and UD predictors
load(file="./adaboost.rf.lr.... |
1970791912bdce7bacc181ef93e3f1c9038d050e | 486deb2a88424a9dd6e4761af999263a6fa316b8 | /inst/doc/v2.R | b87433476a17446ebd44f7bda978bdc63d2c1156 | [] | no_license | cran/eRTG3D | 4ba5c89aba6d9f2a2500b6514de7a8fd157b7561 | 785c339e125caad743cc40502d58bfe15d53d24c | refs/heads/master | 2022-03-24T18:26:26.296102 | 2022-02-25T11:10:05 | 2022-02-25T11:10:05 | 209,614,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,114 | r | v2.R | ## ---- echo = FALSE, eval=TRUE, include=FALSE----------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
options(knitr.table.format = "html", rmarkdown.html_vignette.check_title = FALSE)
library(eRTG3D)
set.seed(123)
## ----eval=FALSE-----------------------------------------------------... |
ae13aeeab3cc855e5d7ac98c63f743eb93cf6f69 | 474b9c0dd798418894fd47cea3251da1239cc027 | /iteration.R | 2b5811054a4eb7a7317b260dd963792ba27f3e77 | [] | no_license | abessudnov/dataanalysis3 | d80af34487aca7685787fa8cf7a79a789e8d2402 | b9689b7442526d96ca6f1d9ce1a874cac900c8b2 | refs/heads/master | 2021-05-12T00:09:24.271244 | 2019-01-17T21:19:35 | 2019-01-17T21:19:35 | 117,527,612 | 3 | 35 | null | 2018-08-21T16:34:34 | 2018-01-15T09:53:42 | R | UTF-8 | R | false | false | 7,745 | r | iteration.R | # 5 Feb 2017
# Iteration
# *********************
# Iteration in R
# Sometimes we need to repeat the same sequence of commands seeral times.
# Whenever you need to repeat the same action more than twice try to automate it and
# apply iteration.
# Loops in R
# 1. For loops
for (i in 1:5) {
print(i)
}
# (i in 1:5... |
211fb149d5ea0f8d5c5d0c5762bcc3e8b2ae8b0d | 7b72be0cf5fa9fcab13d04b4424ec2d8c4f56a65 | /man/head.rdeque.Rd | 97a7f803ec548f0aacb91a5e06b5ea5f8aa61f30 | [] | no_license | cran/rstackdeque | 88722be9cbd257a9dccef79a223f91338fa49bc5 | 1ee75225901b90b2a674255952068f7588225d6a | refs/heads/master | 2021-01-23T19:46:24.826995 | 2014-12-01T00:00:00 | 2014-12-01T00:00:00 | 27,569,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 730 | rd | head.rdeque.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{head.rdeque}
\alias{head.rdeque}
\title{Return the first n elements of an rdeque as an rdeque}
\usage{
\method{head}{rdeque}(x, n = 6L, ...)
}
\arguments{
\item{x}{rdeque to get the head of.}
\item{n}{number of elements to get.}
\item{...}{arguments to be pas... |
8d4b07e3f29e30b27341266ac48caa5f3211b39a | 311e68e04863ccf23a8d689f8183bd2dce4108d0 | /man/plotOUProcessPriors.Rd | 99472698d933b118bf104c776e5a2245fcb8544f | [] | no_license | carlopacioni/bdskytools | 93f812ab3b889862264fbc994864fdac70c321da | d6b3faefe3c70357e081742fcf6f35192f054ac7 | refs/heads/master | 2021-01-19T21:00:10.721556 | 2017-02-04T20:49:08 | 2017-02-04T20:49:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 406 | rd | plotOUProcessPriors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PlotPrior.R
\name{plotOUProcessPriors}
\alias{plotOUProcessPriors}
\title{Parameters may be a prior or a constant
Use getPrior to quote the prior function to be passed with parameters}
\usage{
plotOUProcessPriors(x0_prior, t, mu_prior, sigma_... |
1c60456c61532e27db28b8044fbc9131c11f300b | 0f5f50d2b69ca8054531355005ec759d015d3911 | /man/stock.price.Rd | f0615e3708453495ac04ca37b80f6a342ae3739b | [] | no_license | cran/RcmdrPlugin.RiskDemo | 933d1f238271c80f1f9919cf38c1653c4d0c1eaf | 69f613fcee6b3845c9877fd8b61699bdaf3ac1d0 | refs/heads/master | 2021-07-11T11:33:10.099976 | 2021-04-06T10:30:02 | 2021-04-06T10:30:02 | 99,853,362 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,674 | rd | stock.price.Rd | \name{stock.price}
\alias{stock.price}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Computing stock prices
}
\description{
This function computes the intrinsic stock price using the constant growth dividend discount model.
}
\usage{
stock.price(dividend, k = NULL, g = NULL, ROE = NULL, b = NUL... |
1d9c4f1a009cddacf732cf40e2616e7fbb128785 | 63903e164b919a329c9b551b3384c6dfa28fb563 | /simulated_genefamilies_analysis.R | 27e5bf8b1218cdf883a3fb35e880c845312e53d6 | [] | no_license | dhbrand/drought_metagen | 86c3dc2a8bd93600e7f02761ca1ad6a7ba69eb30 | 4f8a71eb40630b5e8d9168ecb897bb0fbee2de35 | refs/heads/master | 2020-03-11T10:12:34.984491 | 2018-07-03T02:03:46 | 2018-07-03T02:03:46 | 129,936,100 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,101 | r | simulated_genefamilies_analysis.R | library(tidyverse)
library(magrittr)
library(rlang)
source("TwoStage_Package_Code.R")
dat_calls <- read_tsv("DNApolymAIII/Calls_genefamilies-cpm.tsv")[-1,] %>% dplyr::select(1, Annotation = "EstID", label = "EstEC", 4:34) %>% replace_na(list(label = "unknown")) %>% as.data.frame
factors <- cbind(names(dat_calls)[4:... |
f77656f7946092a2a7477b004249bbb854818ffb | 4ba442619cf976909ac303ae6abd96eb3a451fd9 | /SCperf_v.1.0/R/bullwhip.R | bbc531ac736436d43b0374ae548a9ee1ee56db83 | [] | no_license | lulzzz/SCperf | 11e96291cb037f14a7ebdc12a66661be81930aa5 | cf7ffdef4664c0eedd5f4c6a2bc3f6e6c81a46ed | refs/heads/master | 2021-05-10T23:27:19.314669 | 2018-01-09T12:50:30 | 2018-01-09T12:50:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 663 | r | bullwhip.R | bullwhip=function(method=c("MMSE","SMA","ES"),phi,L,p,alpha)
{ method <- match.arg(method)
if (L==0){cat("L is at least one, the review period, ...\n")
}
else
{
if (method=="MMSE"){r<-1+2*phi*(1-phi^L)*(1-phi^(L+1))/(1-phi)
}
else{ if (method=="SMA"){r<-1+2*(1-phi^p)*((L/p)^2... |
04e636e9dddfc0236764c3b462986c0d5c642d23 | 78014f186b458c0dfbd4dd4dc660adfe858d4c62 | /man/droplinks.Rd | 8bb64f5aa22a189a9345f64e4e70d27664e21b4e | [] | no_license | r-spatial/spdep | a1a8cebe6c86724bc4431afefc2ba2f06a3eb60c | a02d7aa6be8a42e62ba37d2999a35aff06a0ea59 | refs/heads/main | 2023-08-21T19:27:09.562669 | 2023-08-07T12:08:51 | 2023-08-07T12:08:51 | 109,153,369 | 109 | 30 | null | 2022-12-10T12:31:36 | 2017-11-01T16:10:35 | R | UTF-8 | R | false | false | 1,787 | rd | droplinks.Rd | % Copyright 2001 by Roger S. Bivand
\name{droplinks}
\alias{droplinks}
\title{Drop links in a neighbours list}
\description{
Drops links to and from or just to a region from a neighbours list. The example corresponds to Fingleton's Table 1, p. 6, for lattices 5 to 19.
}
\usage{
droplinks(nb, drop, sym=TRUE)
}
\argume... |
98ba4751010cef61bbc6956160441a1e27843794 | 0c93e201530984c1dfd41db112457c017fd55a83 | /tinyplyr/R/util.R | 5824bec829c917071a5227f6e3c0fef26c2ea230 | [] | no_license | Rindrics/reformr | a763e4a0885f2913d24e687cc2e5ab8ceb7b14db | 5f1994f9febe8d9adeb3f0003e93af6bb444b9c3 | refs/heads/master | 2021-10-20T00:11:22.350559 | 2019-02-25T01:05:50 | 2019-02-25T01:05:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,157 | r | util.R | # ----------------------------------------------------------------------------.
# This file is tangled from tinyplyr.org. |
# (https://github.com/smxshxishxad/tinyplyr/blob/master/tinyplyrjmdatar.org) |
# Do not edit by hand. |
... |
6c4f3312c4ac3567d59773331790436a208539a4 | b613b9e3ef1ebff3d8a6ad1a552bc3742353914e | /module_12/problem_set_12/.Rproj.user/FA5B6B16/sources/per/t/D4AA0A11-contents | e8530b1bb8501f013e22054299733099dc2063b0 | [
"MIT"
] | permissive | SHASHANK992/computational-statistics-jhu-spring-20 | 044bff959b7ada946f156c95d292c369ae2d2293 | 75e4341c1adbf3be9df71fe967fcb7ed6f2cc7b2 | refs/heads/master | 2022-07-18T10:51:52.486357 | 2020-05-15T03:15:45 | 2020-05-15T03:15:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,827 | D4AA0A11-contents | ### Q1
xs <- scan(file.choose()) # Load data
ys <- scan(file.choose())
# plot(xs,ys)
df <- data.frame(x = xs, y = ys)
df_sort <- df[order(df$x),] # sort values by x_i's
rownames(df_sort) <- NULL # reset indices
# part (a)
### Plot the CVRSS_k(s_hat_k) versus k and an explanation
### of the chosen span.
odd_values <- ... | |
282fd8f2e4785c9eec80a535954a72c27fcb259b | 8615590c2f6976ceb62b7a3ec5bb2ea3329c0003 | /man/elevation.Rd | b9b1915a802f064322b403dab9740662c015cdb9 | [
"MIT"
] | permissive | JanLauGe/rgbif | 859fc81e7b81adb187c63ba1844f0a0ee1f81a7f | 5f39a2f1ee4c4e00cdf1bf22ae76d7d600ed542d | refs/heads/master | 2021-01-13T09:45:45.895135 | 2016-11-10T18:28:23 | 2016-11-10T18:28:23 | 69,766,790 | 1 | 0 | null | 2016-10-01T22:16:31 | 2016-10-01T22:16:31 | null | UTF-8 | R | false | true | 2,288 | rd | elevation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/elevation.r
\name{elevation}
\alias{elevation}
\title{Get elevation for lat/long points from a data.frame or list of points.}
\usage{
elevation(input = NULL, latitude = NULL, longitude = NULL,
latlong = NULL, key, ...)
}
\arguments{
\item{i... |
314aea46fe9768a8650c6a740094cc3178ebe423 | d8aaf8a83ce8dff401aa5fd22aa0c5be086d4d59 | /dev/RScripts/plotFunction.R | fdd07dc6b26fae3665b8299373fdc7e03ed63601 | [] | no_license | ainaollevila/Lichen-s-project | 2b3022bdfb41a08641996c41a0b281efb002a076 | cd34e064688aa76c1b57df9d97818b498dede578 | refs/heads/master | 2020-04-15T12:45:21.009289 | 2017-04-26T22:40:18 | 2017-04-26T22:40:18 | 61,504,423 | 3 | 0 | null | 2017-05-25T23:15:34 | 2016-06-19T23:05:24 | Matlab | UTF-8 | R | false | false | 3,483 | r | plotFunction.R | plotOneStuff <- function(){
dev.off()
par(mar=rep(1,4))
plot(m29all$x[m29all$A != maxAlgae],m29all$y[m29all$A != maxAlgae],axes=F,pch=20,col="grey",xlab="lat",ylab="long",cex=2)
box()
points(m29all$x[m29all$A == maxAlgae],m29all$y[m29all$A == maxAlgae],col="dark red",pch=20,cex=2)
text(m29all$x[m29all$A == maxAlg... |
f8b9e4297cb7de2ecf4efdbcd1771f8662a75f06 | af142fbc7a5c36557015e59f32c6fc1369e85b1d | /man/n2w.Rd | 0d3306250557edcfc137a47b2b0f9f78f7c9f434 | [] | no_license | TheoMichelot/localGibbs | fdbd5dbab06c98aa02b2979d4a06ee8fc6db8359 | e1dd16b41e2e6c03ac7a1aedc92cba47b923d2a0 | refs/heads/master | 2022-05-08T05:10:22.329007 | 2022-03-09T15:41:47 | 2022-03-09T15:41:47 | 135,612,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,124 | rd | n2w.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/n2w.R
\name{n2w}
\alias{n2w}
\title{Transform parameters from natural to working scale}
\usage{
n2w(beta, rdist = c("fixed", "multistate", "exp", "gamma", "weibull"),
r = NULL, sigma = NULL, shape = NULL, rate = NULL, gamma = NULL,
xy = N... |
c6e2abe1bf0cc4050651249661b14884eed254d9 | bb3b3bfc7fb7144176ed9a1f322e8515bbfdf8f5 | /CAS Datenanalyse/Graphische Datenexploration/4 Plot-Techniken-drei-Variablen.R | a9f9d48b91e35bf7f93747f8a93156c85f00556f | [] | no_license | huli/mas-data-science | c0d7e9fd14e67ea8fa81f8787e8a4ed8da642f74 | 0ca11a9e9534ce43b0bf291e1b47b21571b60091 | refs/heads/master | 2021-01-12T10:54:31.418764 | 2017-07-28T15:31:11 | 2017-07-28T15:31:11 | 72,750,778 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,950 | r | 4 Plot-Techniken-drei-Variablen.R | ################################################################
## Skript: 4 Plot-Techniken-drei-Variablen
## Studiengang: CAS Datenanalyse 16/17
## Modul: Graphische Datenexploration und Datenvisualisierung
## Lernziele: Techniken der Datenexploration mit R - drei Variablen
##
###################... |
d9b389a098e2cd7b69508b581446c3713a0c1156 | c395a92aee7a688cc6374c447ee76eda5e082723 | /danboard/test.distribute.r | 73fc233dcdb97360da9ef5d48d4d4fafaf09f2cf | [] | no_license | sasakihajime/devasi | ebdc0378ab37be45bfe07ec3484e27264bcff5f8 | 81d5c84fcdfce9faf7e6907ee0e0e09c88a52966 | refs/heads/master | 2021-05-28T03:55:23.484054 | 2014-12-22T00:28:29 | 2014-12-22T00:28:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 230 | r | test.distribute.r | ##正規性の検定
#シャピロ-ウィルク検定を採用
for (i in 2:length(att)){
att.norm<-shapiro.test(att[,i])
print(att.norm)
}
#以下p-valueが0.05以上の変数をTRUEそうでないものをFALSEとする。
|
63a74989a8c77f51ba0d6e6893c1ba6fc9b61eda | 8b7b4a096cabade05415edc75f8a5adce62b1576 | /man/timeline_specs.Rd | 9f6282dbbbfa7901c729b2da943a54d416826a50 | [
"MIT"
] | permissive | 2DegreesInvesting/r2dii.plot.static | 0350b9fe0952be30d14bf8868ba3816adde6f63b | fcb89716c1668774cda5f0bf1ba10af9a0443057 | refs/heads/master | 2023-05-05T22:37:08.046611 | 2021-05-28T15:03:22 | 2021-05-28T15:03:22 | 327,862,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 686 | rd | timeline_specs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/timeline_specs.R
\name{timeline_specs}
\alias{timeline_specs}
\title{Creates the default specs data frame for \code{\link[=plot_timelineA]{plot_timelineA()}}}
\usage{
timeline_specs(data)
}
\arguments{
\item{data}{Pre-processed data for the c... |
1459fe70d7692f57c38e50627e7377181a9b75ed | f87ec62962540a29ba3c879caeeec77bd601c88c | /Examples/Styles.R | 59d28b5e9c406d2106658acb728c9e58a05b1c82 | [] | no_license | RobDregmans/R_Project_No_Poverty | 4f16580043873d47e9f5537c7ac4cb4822aad4f0 | 5522aa11c41788327dfcb6a529e4a578bd470132 | refs/heads/master | 2020-04-01T01:11:15.712060 | 2018-11-09T19:25:03 | 2018-11-09T19:25:03 | 152,729,417 | 5 | 1 | null | 2018-10-12T10:40:09 | 2018-10-12T09:46:15 | R | UTF-8 | R | false | false | 11,703 | r | Styles.R | #' @export
style_base <- function(textsize=7) {
list(
gg_text_size = grid::convertX(grid::unit(textsize, "points"), "mm", valueOnly = TRUE),
gg_max_point_size = grid::convertX(grid::unit(0.1, "npc"), "mm", valueOnly = TRUE),
theme_map = function(aspect_ratio = 1) {
t <- theme(
panel.grid = e... |
93e1642a000124cca6e1f53ad03e72fd53feaa2e | 1ec77d38d3100edd84b092d76de0f5557ab613f4 | /man/Dataset.Rd | 5ff9c3f3c8e8e6d255272795a2d6df14d7a742f2 | [
"Apache-2.0"
] | permissive | MicroStrategy/mstrio | 63cfe68995a7072a6a6013e386033d363c815229 | b656000adf913bf0fc66dc0960021463fcf3214f | refs/heads/master | 2023-07-20T05:14:27.985866 | 2023-07-06T10:08:35 | 2023-07-06T10:08:35 | 138,627,534 | 21 | 11 | Apache-2.0 | 2023-07-06T10:08:37 | 2018-06-25T17:23:25 | R | UTF-8 | R | false | true | 10,878 | rd | Dataset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset.R
\docType{class}
\name{Dataset}
\alias{Dataset}
\title{Create, update, delete and certify MicroStrategy datasets}
\description{
When creating a new dataset, provide a dataset name and an optional description.
When updating a pre-exis... |
e88a5107d3620f1ec9ccad73fc2dc5ca6b3bdc60 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.business.applications/man/alexaforbusiness_disassociate_device_from_room.Rd | 67e685facf4b0836ba6df30a8f7b16b10a14f42d | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 798 | rd | alexaforbusiness_disassociate_device_from_room.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/alexaforbusiness_operations.R
\name{alexaforbusiness_disassociate_device_from_room}
\alias{alexaforbusiness_disassociate_device_from_room}
\title{Disassociates a device from its current room}
\usage{
alexaforbusiness_disassociate_device_from_... |
eb6ff8d7bf1d96792337ebedff2c1e89fb07155f | 4baecdaa52eca140342b4b84055cf70a71b5c1e1 | /plot_mate.R | 1836226bdd06afcfd5c652b66bd3fa66973cddcb | [] | no_license | baibaidj/VisualData | aa68624e0217e7a723ae25fa3558ec464c10dee6 | c206aee27bb530079c7d1031547f844b418b37e4 | refs/heads/master | 2021-10-23T09:03:22.695954 | 2019-03-16T10:46:22 | 2019-03-16T10:46:22 | 123,928,367 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,817 | r | plot_mate.R | # function: install new packages and their dependencies
ipak <- function (pkg) {
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
## new packages can be added here
packages <- c("re... |
b49421182f3d244d9256b89f777f4d0978c43efd | 6ba63d79afa36c706583045654f262afd57ec3ea | /run_analysis.r | e778da5117f88193993d7ad632dc4718b2b54161 | [] | no_license | sandeepsingh2406/Coursera-Getting-and-Cleaning-Data | 2ae960fe08a28a0535dda4791e899913cafe7c3b | 794e2e6cd1bfee67079cf19d1ecec75d0c70d335 | refs/heads/master | 2020-07-22T01:58:03.041321 | 2019-09-11T04:52:50 | 2019-09-11T04:52:50 | 207,039,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,826 | r | run_analysis.r | library(dplyr);
library(data.table)
#download the zip file if it doesn't exist
file_name<-"getdata_projectfiles.zip"
if(!file.exists(file_name)){
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip",file_name,method="curl")
}
#unzip the file if not already done
if(... |
b4e85b3c70497e500f9ff98f653ceff34c887e7f | 1bedc58d6ad7d98194c986c6f47ad6c9691ebb73 | /src/03-logistic_regression.R | 9e7de4c0c9491dd047ed7b8d748a8f111b8e220e | [] | no_license | bgulbis/NMBA_Enteral_Feeding | a1533cabbdf21eee450ed8299651f54b052f588b | 6419350388e47a1468005ee3cf76b48f2ba3aadf | refs/heads/master | 2021-01-11T21:29:12.152815 | 2017-04-10T16:17:27 | 2017-04-10T16:17:27 | 78,790,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 374 | r | 03-logistic_regression.R | library(tidyverse)
library(readxl)
library(broom)
nm <- c("goal", "pressor_avg", "osmolality", "motility_agent")
df <- read_excel("data/external/regression_data.xls", col_names = nm, skip = 1) %>%
dmap_at(c("goal", "motility_agent"), ~ .x == 1)
mod <- glm(goal ~ pressor_avg + osmolality + motility_agent, df, fam... |
75363e151daaf438a21bf5caa11eeefe9226a6bd | c0316bffea019bcd9d895124c8cf0cd5523b6352 | /Clases.R | a669563b3aa539e48a187f7e4cbb509ee128f166 | [] | no_license | IsmaelSPL/Mis_Clases_R | 87ea41172800e94260ea76189acd87b0d0e21bc8 | 06f55b8fabeb4a9b2a97d3906f79eb5d3f1ed812 | refs/heads/main | 2023-04-28T13:07:16.695072 | 2021-05-13T05:52:26 | 2021-05-13T05:52:26 | 356,630,283 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 50,601 | r | Clases.R | #(Clase 1) Comandos básicos, Asignación de variables,
#tipo de datos y operaciones en Rstudio
#Print
print("Hola mundo")
"hola mundo"
#Help
help(summary)
mean() #Sombreas + FI
#Instalación de paquete y activamos el paquete
install.packages("car")
library(car)
#Para saber donde está el directorio de trabajo
getwd()... |
af2236b69d322d81475e8a86315969ab1fb6d6ca | 520f1f550b555247e5eeec2b43459f826f446fd5 | /R/difMH.r | 58311a61c3d22602d70b8e4fd8a8b5e5f1ec4115 | [] | no_license | cran/difR | d11a3dc2bd1c6c07e8a5b28cf55aa30abe15e311 | c6a14ca0d0f72777881387c87314ebe8c884c1bd | refs/heads/master | 2021-01-01T05:37:28.999542 | 2020-05-13T07:10:02 | 2020-05-13T07:10:02 | 17,713,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,645 | r | difMH.r | # DIF MANTEL-HAENZEL
difMH<-function (Data, group, focal.name, anchor=NULL, match="score", MHstat = "MHChisq", correct = TRUE, exact=FALSE,
alpha = 0.05, purify = FALSE, nrIter = 10, p.adjust.method=NULL, save.output = FALSE,
output = c("out", "default"))
{
if (purify & match[1] != "score")
... |
4e015047cfb0e6fda2ccf9488bf3ab39f53c8954 | e1f093f20200ed2bd820d4ee0884c87c73e41d66 | /man/mantel.Rd | aee19050c2e0785ef81cc8491d159c999228709f | [] | no_license | cran/ecodist | 8431a5659f02211c3131e282fbd2c90765285aa0 | a34b199c4d70d5ee21e2d6abbd54d2a9729d7dd0 | refs/heads/master | 2022-05-13T06:14:42.563254 | 2022-05-05T05:50:08 | 2022-05-05T05:50:08 | 17,695,709 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,465 | rd | mantel.Rd | \name{mantel}
\alias{mantel}
\title{ Mantel test }
\description{
Simple and partial Mantel tests, with options for ranked data, permutation tests, and bootstrapped confidence limits.
}
\usage{
mantel(formula = formula(data), data, nperm = 1000,
mrank = FALSE, nboot = 500, pboot = 0.9, cboot = 0.95)
}
\arguments{... |
c610e66433f87a9eaee7f4cfd03d1b5b79c2723e | 5aee1f7922b6ffb6a3aec7a0a73d22429957fc4a | /plot2.r | 8c4e29ff63ae9193aa488cd951aa4f6c087bc42a | [] | no_license | rodrigo-m/ExData_Plotting1 | 81b355b5b979deefca010f157a74d1ec117c3b3b | 891e6bcb796e0c949300958418b8444fc7aaf93c | refs/heads/master | 2020-12-25T21:34:57.203333 | 2015-06-07T07:49:40 | 2015-06-07T07:49:40 | 37,008,589 | 0 | 0 | null | 2015-06-07T07:45:07 | 2015-06-07T07:45:07 | null | UTF-8 | R | false | false | 661 | r | plot2.r | # Make sure the library for read.csv.sql is loaded
library(sqldf)
# Set working folder
setwd ("c:/R/ExploratoryA1")
# Load the two days that we are interested in
hpc <- read.csv.sql("household_power_consumption.txt", sql = "select * from file where Date in ('1/2/2007', '2/2/2007')", header = TRUE, sep =";",stringsAsF... |
9041124258cfd2a071e4c7de507e2bdb5ffb5ebe | 629d07722bb2dee9a6aaa9701df9b921e3f232f2 | /MCPcounter.Score.R | fd4da5e98fbf38f7fda398f8a1ea58151b67a799 | [] | no_license | trial-lab/TCGA | 58db4b796421d8f0931790cd14234f7945a3dabe | f5e68eb92a87e4e0f62fb4210dd326060729920f | refs/heads/master | 2020-04-28T23:39:05.100028 | 2019-04-03T06:09:32 | 2019-04-03T06:09:32 | 175,663,987 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 318 | r | MCPcounter.Score.R | library(devtools)
library(MCPcounter)
df<-read.csv("",header=TRUE,row.names=1)
#replace the first gene_id column to HUGO_symbols
mcpcounter_estimates <- MCPcounter.estimate(df,"HUGO_symbols")
heatmap(as.matrix(mcpcounter_estimates),col=colorRampPalette(c("blue","white","red"))(100))
write.csv(mcpcounter_estimates,"") |
290920f26fc6d6e6f367f0e4e8a4f85e2eb640d4 | 4f64eb6bf672ddc53e10ce3da70e2f4d4b88adad | /run_analysis.R | 9d07ba2649182d9c79dfc2b4008b9cd98f9bdf09 | [] | no_license | Swarno1984/Getting-and-Cleaning-Data-Project | f3bc137daddb73b8703970521b39290d46df7d36 | b83e973f85c68e6e79d248d9688d4db58e4273cf | refs/heads/master | 2021-01-23T01:21:05.864120 | 2017-05-30T19:49:10 | 2017-05-30T19:49:10 | 92,867,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,427 | r | run_analysis.R | library(reshape2)
# Set the working Directory and unzip and extract data
setwd('C:/Users/ss45360/Documents/Coursera/UCI/');
filename <- "getdata_dataset.zip"
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip "
download.f... |
4fa37a581c478f7d7b4a0bb81697cae0f9eafaa2 | 0f811c3e6c6bac8fcab04175516d085c26834ee1 | /man/mutation_gene_data_solution.Rd | b6fa7df79adf77e9fb50599f32d7a7de2cc2021f | [
"MIT"
] | permissive | lukatrkla/lanpAnalysis | 7674b1b5a137b9456b956d2b30eee41c08aa6bb7 | b4a747df063103d7c11ef4e2bd288cd2d4eb6a26 | refs/heads/master | 2023-02-04T17:00:58.641015 | 2020-12-10T01:32:52 | 2020-12-10T01:32:52 | 307,875,938 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 387 | rd | mutation_gene_data_solution.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{mutation_gene_data_solution}
\alias{mutation_gene_data_solution}
\title{Produced mutation_gene_data_solution}
\format{
NULL
@examples
}
\usage{
mutation_gene_data_solution
}
\description{
The solution to running ... |
dc4534efd5533449c18c0ca719aa543e06177b7c | f5d517ac83c8860abdf8a7db2223d58886e24155 | /R/dReport.r | 7cb9e93e902c069ee0a3863a621217722a46d24e | [] | no_license | dupontct/greport | 8efe567313486de2c1c51cb5e93c4f50057f0b57 | 7cc99883f6ff27ba682c8d207d7d0e112a9018a4 | refs/heads/master | 2021-09-05T08:46:28.937827 | 2018-01-25T19:06:21 | 2018-01-25T19:06:21 | 118,954,658 | 0 | 0 | null | 2018-01-25T18:58:32 | 2018-01-25T18:58:31 | null | UTF-8 | R | false | false | 23,192 | r | dReport.r | #' Descriptive Statistics Report
#'
#' Generate graphics and LaTeX with descriptive statistics
#'
#' \code{dReport} generates multi-panel charts, separately for categorical analysis variables and continuous ones. The Hmisc \code{summaryP} function and its plot method are used for categorical variables, and \code{bppl... |
9aefeb5cd2743eda2c512db3f8f9219b2edfe491 | 6f83ecfc6399fb31f10f74e8bf819f1e7042e80e | /R/02-coercion.R | 836e4188ac543eda2cb35b693f19ffdb64a5e824 | [
"MIT"
] | permissive | jessesadler/debvctrs | d510edfd21d968d5df7ba237c7503552154cec8a | d3a04b446c5f20b34a5044b5b52530d4adcb2d23 | refs/heads/main | 2022-05-04T02:22:29.272112 | 2020-06-29T19:38:15 | 2020-06-29T19:38:15 | 207,156,132 | 27 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,070 | r | 02-coercion.R | ## Coercion for deb_decimal and deb_lsd prototypes ##
# Create coercion hierarchy: numeric() -> deb_decimal() -> deb_lsd()
# A) deb_decimal ----------------------------------------------------------
# 1. deb_decimal and deb_decimal ------------------------------------------
#' Coercion hierarchy for deb_decimal uni... |
d7413345dd7e2112ef39954841cdff5a44cb899b | 88c20b9bac5999fa1cc73d63ffb124958df2dc0d | /man/mariadbClientLibraryVersions.Rd | 1124ca709ac5d7671db89d79d8155d0a926fb81b | [
"MIT"
] | permissive | r-dbi/RMariaDB | 2fb2656ba0e36391856943aa07d6391102802ebd | b5a54aecde60621ec600471ae894ed89e6eaeefe | refs/heads/main | 2023-07-06T08:40:50.295370 | 2023-04-02T02:07:20 | 2023-04-02T02:07:20 | 96,334,875 | 103 | 38 | NOASSERTION | 2023-06-14T04:16:58 | 2017-07-05T15:35:16 | R | UTF-8 | R | false | true | 594 | rd | mariadbClientLibraryVersions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MariaDBDriver.R
\name{mariadbClientLibraryVersions}
\alias{mariadbClientLibraryVersions}
\title{MariaDB Check for Compiled Versus Loaded Client Library Versions}
\usage{
mariadbClientLibraryVersions()
}
\value{
A named integer vector of lengt... |
ce33c89ab14ca5646485b4199aded315333e3dc6 | 29585dff702209dd446c0ab52ceea046c58e384e | /dataRetrieval/R/readNWISunit.r | ffb0962d8431e5406edb43b5f84b78b3e4b09a52 | [] | 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 | 20,734 | r | readNWISunit.r | #' Instantaneous value data retrieval from USGS (NWIS)
#'
#' Imports data from NWIS web service. This function gets the data from here: \url{http://waterservices.usgs.gov/}
#' A list of parameter codes can be found here: \url{http://nwis.waterdata.usgs.gov/nwis/pmcodes/}
#' A list of statistic codes can be found he... |
4da9acce35ee291174a5721af8ea1508bd3f21c2 | 58b943c940514e5c08106b97da156404ba6345cf | /man/simulate.R.K.Rd | db14e19b35914e8424eb3c00d2e82fde0c38e0d0 | [] | no_license | bcm-uga/apTreeshape | 1dd209ae8e4a0a30d22b906a99f98f459ef992f7 | 369236069399b25f462bc109db305cbdb4a6113c | refs/heads/master | 2020-04-09T18:54:00.858207 | 2019-09-25T12:08:34 | 2019-09-25T12:08:34 | 124,239,795 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 654 | rd | simulate.R.K.Rd | \name{simulate.R.K}
\alias{simulate.R.K}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Simulate (R,K)
}
\description{
Simulate a split and the number of marks in each resulting subintervals
}
\usage{
simulate.R.K(beta, n)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
... |
b3abe67e1c3f0d25358280362fa7043a37de1dea | a2fabf34e4bb74729e274c2d1c3f2f45a7536588 | /Plot5.R | 4e80a6cc4967c8aa36e820163892bd60f337304d | [] | no_license | jsennator/ExData_CourseProject2 | d609b7763b556d39a94e874dc35335bbfec2cc4f | 6ebd99e07f87dd68c50df6ed0780f8f6434a05b3 | refs/heads/master | 2016-08-10T14:22:37.972053 | 2015-09-24T21:26:45 | 2015-09-24T21:26:45 | 43,093,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 666 | r | Plot5.R | NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#subset for Baltimore & On Road
baltimore <- NEI[NEI$fips=="24510" & NEI$type=="ON-ROAD",]
# Aggregate by sum Baltimore emissions by year
aggtotalyearBaltimore <- aggregate(Emissions ~ year, baltimore, sum)
png('plot5.png')
g <- g... |
3550186b8e97f0a27fe9a5c455565f79b84d7f03 | 71af4c97f1dd3ad115d7fc230b50b535886ef641 | /missing together.R | ef8de9dc79734a1effafa120895a92e235261636 | [
"MIT"
] | permissive | 610311101/Home-Value-Prediction | dc211126d1f2cfbec9ecf52e0f63d20e3f259d0b | c90f0ff9aba7b4a076362bd7d09836985f60ce5a | refs/heads/master | 2021-06-27T23:37:54.127108 | 2017-09-19T20:57:01 | 2017-09-19T20:57:01 | 103,949,521 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 553 | r | missing together.R | library("dplyr");
library("magrittr");
setwd("C:/Users/Hou/Desktop/Home-Value-Prediction");
rm(list = ls());
Data <- read.csv("properties_2016.csv");
##
## fips, latitude, longitude,
## propertylandusetypeid, rawcensustractandblock,
## regionidcounty,
## are the same missing 11437# data.
index <- 1:nrow(Data);
Dat... |
1c500c0d6c0236ec5b780d1bf251cf31bdae8ce8 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Sauer-Reimer/ISCAS89/s09234_PR_4_10/s09234_PR_4_10.R | 5caaba6008b7f3d6e5d10a8887aa7fcb2180805b | [] | 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 | 66 | r | s09234_PR_4_10.R | 5bb48a03c11c155192a156dbe320a4c2 s09234_PR_4_10.qdimacs 5464 15583 |
899bdc3f9539e140cf2663f2f878f9660ad4af5e | 55190b9854fd714e7bac146a836a7bba8435adc5 | /src/helper.R | 262b423024019b294a19c0e5857d7358c5f35741 | [] | no_license | danielns/2019-01-max-milhas | 5853b319157668f7a70701720d60ac01a472f2a8 | 369855915da196756a1ee014ec7f5ad274895600 | refs/heads/master | 2020-04-15T12:52:37.926457 | 2019-01-08T16:56:14 | 2019-01-08T16:56:14 | 164,687,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,959 | r | helper.R |
# Funções para auxiliar a análise
removeAcentos = function(vetor){
#===================================================================================
# Remove acentos de um vetor, considerando se o Encoding deste é UTF-8 ou unknown.
# Outros tipos de enconding não foram considerados
#====================... |
2983e260591cc452e556a602c1a223b745b1e07d | a0ceb8a810553581850def0d17638c3fd7003895 | /scripts/scripts_analysis/debugging_rerun/check_LDA.R | 99d2c5fda938420b1bbcee4895fe0d3b8154396e | [] | no_license | jakeyeung/sortchicAllScripts | 9e624762ca07c40d23e16dbd793ef9569c962473 | ecf27415e4e92680488b6f228c813467617e7ee5 | refs/heads/master | 2023-04-15T22:48:52.272410 | 2022-10-24T10:45:24 | 2022-10-24T10:45:24 | 556,698,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,194 | r | check_LDA.R | # Jake Yeung
# Date of Creation: 2019-12-20
# File: ~/projects/scchic/scripts/scripts_analysis/debugging_rerun/check_LDA.R
# Check LDA output
rm(list=ls())
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(topicmodels)
library(hash)
library(igraph)
library(umap)
library(Rts... |
076e097c2189caaece1007c1dd0aed45991fe918 | dfb2a56f64c2c206cd0b5c67e708b3ba030b4984 | /week_10/case_study_10.R | 032fb9a73ff4c953942ef5abf133e6979aa675c4 | [] | no_license | geo511-2020/geo511-2020-tasks-shruti8297 | 982ba288d63b045a1ab29ebebec3192af30f7891 | b0c92b30200315199525dd07abdb50994861d5aa | refs/heads/master | 2023-02-02T07:17:31.909382 | 2020-12-18T21:13:01 | 2020-12-18T21:13:01 | 296,927,787 | 0 | 0 | null | 2020-10-11T01:13:36 | 2020-09-19T18:24:31 | R | UTF-8 | R | false | false | 4,195 | r | case_study_10.R |
# performed the task with groups help
library(raster)
library(rasterVis)
library(rgdal)
library(ggmap)
library(tidyverse)
library(knitr)
# used groups help for performing the task
# New Packages
library(ncdf4) # to import data from netcdf format
# Land use Land cover
# Create afolder to hold the downloaded dat... |
099dd4e99abf0f52c07531182be1d8d558b7649f | 50197791ff29706c824e3b3d0a893a870ad2d70e | /Week 2/complete.R | 26984209392b905f908c9445e4d1a84fab0d36f3 | [] | no_license | alittlec/datasciencecoursera | 771e16c7e3f5a0b349178ad9549fb722f7d5490d | 46ad2c7bfbf7ac55030cc711d5599b5331879b8e | refs/heads/master | 2020-12-25T15:17:29.028952 | 2016-09-23T08:43:25 | 2016-09-23T08:43:25 | 67,360,857 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 617 | r | complete.R | readfiles <- function(directory, id){
filenames <- paste(directory, "/", formatC(id, width=3, flag="0"), ".csv", sep="")
specdata <- list(length = length(filenames))
for (i in seq_along(filenames)){
specdata[[i]] <- read.csv(filenames[i])
}
specdata
}
complete <- function(directory,id=1:332){
specda... |
b9e9ee0a67f0f7660251bb3c844496da5deb9e7f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gear/tests/test-mle-geolmStd.R | 926d470063eede217e94a10054808221eeb68ae3 | [] | 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 | 2,723 | r | test-mle-geolmStd.R | set.seed(1)
library(geoR)
fields <- grf(100, cov.pars = c(1, 1), nugget = 0.1, cov.model = "exponential",
kappa = 0.5, messages = FALSE)
geoR_fit = likfit(fields, trend = "1st", ini.cov.pars = c(1, 1),
nugget = 0.1, lik.method = "ML")
cmod1 = cmod.std("exponential", psill = 1, r = 1, ... |
71a0bdd9a7ff227c4e3b3aecc4ee9187ecc91453 | 9735953ba24ca7f7d25d8bf7da409c1115544446 | /man/RearrangeEdges.Rd | a4a09129fdc266cf82c191504d3d285dca4b7cf6 | [] | no_license | cran/TreeSearch | 38dd910f725f6782363bc8bc6f8cfba3fc6f2611 | 4c5db5e620f3bc0a253d5ba24117ad7062e2f4dd | refs/heads/master | 2023-09-01T04:22:59.096778 | 2023-08-26T04:50:02 | 2023-08-26T05:30:29 | 109,836,458 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,758 | rd | RearrangeEdges.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tree_rearrangement.R
\name{RearrangeEdges}
\alias{RearrangeEdges}
\title{Rearrange edges of a phylogenetic tree}
\usage{
RearrangeEdges(
parent,
child,
dataset,
TreeScorer = MorphyLength,
EdgeSwapper,
scoreToBeat = Tre... |
6360e7696f85cf8f41b817e8dbfe1db3644dace0 | 49ff4bbc5ee1a548a1b3c9543693a496acf4a7bc | /cachematrix.R | 0eb8c4b62d5b69d915040468934114128d41dcb0 | [] | no_license | christinegaribian/ProgrammingAssignment2 | 8096cb430e8ea4ccc8eb03e320e8daaa6a2fd559 | 1f30df15f572ebf6753164ef7cf9a84c0b5c8ef1 | refs/heads/master | 2020-12-28T19:11:47.951226 | 2015-04-26T02:26:14 | 2015-04-26T02:26:14 | 34,594,008 | 0 | 0 | null | 2015-04-26T01:59:02 | 2015-04-26T01:59:00 | null | UTF-8 | R | false | false | 1,165 | r | cachematrix.R | ## makeCacheMatrix and cacheSolve cache the value of the inverse matrix so that when needed, the inverse
## matrix can be looked up in the cahce rather than recomputed, saving time.
## makeCacheMatrix returns a list containing functions to:
## set value of matrix
## get value of matrix
## set value of inverse
## get v... |
1ea5b9afdaf41a4d482721d403dfae9a6a2452fe | 2b23caf2784021a3b21dddf73985a043b850206e | /Run_Miscellaneous.R | ac3fdf8838bc23d0cd7f9be329c34d6cf6e491b2 | [] | no_license | HaBi2018/code_msis_project | 8ab59a7781faf6504ca65a36f590ca5f7011a7fa | 90ef669d080d92e0074de9e1711c25a5a5f2211c | refs/heads/master | 2021-07-16T05:08:03.052042 | 2018-12-04T12:37:32 | 2018-12-04T12:37:32 | 139,113,174 | 0 | 0 | null | null | null | null | ISO-8859-15 | R | false | false | 5,503 | r | Run_Miscellaneous.R | # Aggregate/count outbreak signals pr system, pr year
results<-fullDataSVM[,.(
numS=sum(s_status!="Normal"),
numM=sum(msis_outbreak==T),
numV=sum(vesuv_outbreak==1)
),by=.(year)]
results
openxlsx::write.xlsx(results,file=file.path(SHARED_FOLDER_TODAY,"signaler.xlsx"))
## Graph signals total ######
toPlot <... |
53596f2544cd897d213ce3503f1b0a4c58e8f3a4 | ecb1d037e50203f2e5e79da0d845598c5b6cdd99 | /man/logistic_regression_multiclasses.Rd | c52238ef62abec3083c0daa4ddaba15660e87099 | [] | no_license | Z1chenZhao/bis557 | 59669f343f9764ba6aa4c132dfefe1d4eba0b8ab | b9c1f1d2fc60ec23d2ec18f274fe743769477ab3 | refs/heads/master | 2023-02-01T00:36:18.885873 | 2020-12-18T19:45:21 | 2020-12-18T19:45:21 | 296,169,783 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 847 | rd | logistic_regression_multiclasses.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/logistic_regression_multiclasses.R
\name{logistic_regression_multiclasses}
\alias{logistic_regression_multiclasses}
\title{logistic_regression_multiclasses}
\usage{
logistic_regression_multiclasses(X, Y, mu_fun, maxit = 1e+05, tol = 1e-05)
}
... |
69d282f83d4175b5fc76649bad58aad28c8fc8a3 | 16cb7477604cea01c8dc1d5c850415c007acc1fd | /man/df_list2csv.Rd | 856edaa4c2341fc2e1af3bdee27e6766b26bcd33 | [] | no_license | rdinnager/r-personal-utility | 9c14ee79e81d264a72d4da0c8533fe4c0e6a3091 | e113af97a2dc887ce49469ae28cd579123720d81 | refs/heads/master | 2020-06-01T10:07:49.173099 | 2014-06-27T06:14:12 | 2014-06-27T06:14:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 886 | rd | df_list2csv.Rd | % Generated by roxygen2 (4.0.0): do not edit by hand
\name{df_list2csv}
\alias{df_list2csv}
\title{Save a list of data.frames as seperate csv's}
\usage{
df_list2csv(df_list, filename, digits = nchar(as.character(length(df_list))))
}
\arguments{
\item{df_list}{A list of data.frame objects}
\item{filename}{The base file... |
2ec8b326532cea6e9246f92a3db97c4da51a99cb | b6ec7e998306e610899c6866daaa4a95b63b300c | /MS_server.R | 67f02ce463057ff18bc5fea677124bbe32403d00 | [] | no_license | taiyunkim/QCMAP | 4acf498383242f56aa54ef79fd232da32ff9d2eb | 30468836fbe00b42140f8132b47a9ac2ac6f806d | refs/heads/master | 2020-03-22T08:47:20.543554 | 2019-04-30T07:42:25 | 2019-04-30T07:42:25 | 139,790,920 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 65,002 | r | MS_server.R | # author - Taiyun Kim
options(shiny.maxRequestSize=5*1024^3)
Sys.setlocale("LC_ALL","C")
library(progress)
library(htmlwidgets)
library(colourpicker)
library(shinyTime)
library(rhandsontable)
library(D3TableFilter)
library(DT)
library(glmnet)
# source the necessary functions
source("./functions/summariseFile.R")
sour... |
7f6ef2c75d217b014d8093d4704b1995bb3c56cb | 036a2a87b291db062719a8db83b88e1fba01306d | /R/hello.R | badedb301540e0bfedd1dde532a240a8982b4064 | [] | no_license | KellenX/RPackageHelloWorld | 2522ee598686e4475fe4caabb210a1a570250cb5 | a748bd01fac694368503d5b961107f468df25d5f | refs/heads/main | 2023-02-06T10:59:58.451205 | 2021-01-01T02:11:35 | 2021-01-01T02:11:35 | 325,891,547 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 327 | r | hello.R | #' Prints "Hello, world!"
#' @title Some text
#' @description Some other text
#'
#'
#' @param x The name of the person to say hi to
#' @return The output from \code{\link{print}}
#' @examples
#' hello('Foo')
#' \dontrun{hello('Bar')
#' }
#' @export
hello <- function(x){
print(paste0("Hello,",x ,", this is the world!... |
cee2253be8d5481af89da1ceca1a4db9d602291f | c1832bdea9445795c9a216f97dcdb6e3bbc02c2a | /R/tam_linking_extract_list.R | f66ea36d6e768df89ff7f351f73f2335d55bc684 | [] | no_license | alexanderrobitzsch/TAM | c5b4a04094336adb198916d87590917f9f7f4c51 | 7fd74230a55c8296fd8865db1700009445e4e54f | refs/heads/master | 2022-09-04T10:52:37.928736 | 2022-08-29T09:31:35 | 2022-08-29T09:31:35 | 95,301,463 | 16 | 10 | null | 2020-06-28T07:59:48 | 2017-06-24T14:08:58 | R | UTF-8 | R | false | false | 343 | r | tam_linking_extract_list.R | ## File Name: tam_linking_extract_list.R
## File Version: 0.05
tam_linking_extract_list <- function( input, entries, names=NULL )
{
if ( is.null(names) ){
names <- entries
}
NE <- length(entries)
output <- list()
for (ee in 1:NE){
output[[ names[ee] ]] <- input[[ entries[ee] ]]
... |
e670f5a0695e8980e3d754f4d03e3807f6305ea1 | 77dc8a24ea79fa40e4e22d8a8e7d9f10fca3fae3 | /code/generate_drug_data.R | ce7565020b362d18b50dbd79c9c940eff62789e1 | [] | no_license | Duke-GCB/ddh | e6801c0b83c4a722e227e20b1b365fc0e3bb65c4 | 372f7de176d3a4d001dabea9bf868e3920274352 | refs/heads/master | 2021-06-28T14:29:35.002158 | 2021-01-15T01:25:30 | 2021-01-15T01:25:30 | 210,918,432 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,853 | r | generate_drug_data.R | #load libraries
library(tidyverse)
library(here)
library(janitor)
library(corrr)
library(moderndive)
library(purrr)
library(webchem)
#rm(list=ls())
#read current release information to set parameters for download
source(here::here("code", "current_release.R"))
time_begin_data <- Sys.time()
#These are log-fold chan... |
bb052a5005fca5c7de90b10155038a92b0f8ce8c | 8f5056ddfcf185da545eab99728b545af931c015 | /causalA16/man/make_data_for_figure_4_1.Rd | 063d705856c11127de6d712c9eca08f86054f5da | [] | no_license | asrosenberg/Quant3_A16 | 2bb9590d0c8370309555b8f6150efd2c48c86c79 | 11f8ef8c069d3d864635818fce285fd5bd797e12 | refs/heads/master | 2020-04-12T03:51:07.734178 | 2017-01-14T17:20:57 | 2017-01-14T17:20:57 | 68,039,960 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 443 | rd | make_data_for_figure_4_1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/causal_graphs.R
\name{make_data_for_figure_4_1}
\alias{make_data_for_figure_4_1}
\title{Simulate data that corresponds to Figure 4.1 from MgW}
\usage{
make_data_for_figure_4_1(n, seed = sample.int(.Machine$integer.max, 1))
}
\arguments{
\item... |
806830532d5533a271eea7a6e9527c9bdd405785 | 9f568e53d15e4fd26286ef2eec7ea8a5de222c35 | /NBA/pullDataWeeklySB.R | 57a538e9b985f3930303a5aba5c5f7e1c0183b0e | [] | no_license | tcash21/sports_2016 | d9c239d76387ba558e983acbac28c9df3b9ddb84 | 881fc8d9b2cb7a6ff79353d00e4ceaad0612eca9 | refs/heads/master | 2021-06-14T07:27:59.429216 | 2017-03-08T20:59:59 | 2017-03-08T20:59:59 | 45,196,476 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,432 | r | pullDataWeeklySB.R | library(plyr)
library(RSQLite)
library(sendmailR)
drv <- dbDriver("SQLite")
con <- dbConnect(drv, "/home/ec2-user/sports2015/NBA/sports.db")
tables <- dbListTables(con)
lDataFrames <- vector("list", length=length(tables))
## create a data.frame for each table
for (i in seq(along=tables)) {
if(tables[[i]] == 'NBAH... |
087d7cf57d065a7275dfcdb8cbf1625fa1c27cdf | 705fc2f3c1f1d01775655a78024f6f4a0f824515 | /multiple_linear_regressionTest.R | b6fcb480e41083b4cda8a0409176e6c200f1dbc3 | [] | no_license | sajiasd/hello-world | ccc73d2d87fa519fb47dbdecc598235ca4bdaa34 | bebf29c322b3e31befc10973f3a8a71879742d93 | refs/heads/master | 2020-04-16T19:55:57.265428 | 2019-01-15T15:57:32 | 2019-01-15T15:57:32 | 165,879,414 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,318 | r | multiple_linear_regressionTest.R | # Multileaner regression
dataset = read.csv('50_Startups.csv')
# Encoding categorical data
dataset$State = factor(dataset$State,
levels = c('New York', 'California', 'Florida'),
labels = c(1, 2, 3))
#Spliting data in to training and test set
# install package... |
9d165c25f90dc844c4f00fc03b4c8ecf68c4307e | 8340317041a7f6aded928bc61237c78d32e059ee | /exclude/data_creator.R | 1cc5cfecc72947f8b41759dcd07fd41cf9b6fc74 | [] | no_license | msadatsafavi/txBenefit | c2b2051168db0e0b0ef4a6015136c60f7a4f6b30 | 7342099f8cadadb7090eb7557c330d04f27e0520 | refs/heads/master | 2020-12-02T23:35:00.883225 | 2020-02-01T00:57:58 | 2020-02-01T00:57:58 | 231,154,313 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 841 | r | data_creator.R | rct_data_raw<-readRDS(file="M:\\Projects\\2018\\Project.RCTSubGroup\\Output\\1.StatMed\\R2\\Code&DataForReview\\macro_data.RDS")
n<-dim(rct_data_raw)[1]
time<-1.1-rexp(n,10)
time[which(time>1)]<-1
time[which(time<0.5)]<-0.5
rct_data_raw[,'time']<-time
rct_data_raw[,'tte']<-NA
for(i in 1:n)
{
message(i)
n_exac<... |
25909908de82abec45968600c9d2762c192b93a2 | 880a3d1e88a31a36cc76669ed4b7e70db5c02e54 | /Admission Prediction Model.R | 6ff3174456ed4bf588c060432ce4804fc615b5a2 | [] | no_license | Suggestions-Only/Logistic-Regression--Grad-School-Admit | a22cae23769277d7ff8c475ff2bc759d3fde9279 | 89f5e50874d92a449aca6af801f1bb317377d596 | refs/heads/master | 2022-07-25T12:11:57.628440 | 2020-05-14T05:08:54 | 2020-05-14T05:08:54 | 263,820,279 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 976 | r | Admission Prediction Model.R | library(mlbench)
library(caret)
library(ModelMetrics)
#load in data, change admit to factor
mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
mydata$admit <- as.factor(mydata$admit)
#fit logistic regression model
admit.model <- glm(admit ~ gre + gpa + rank, family = binomial(link = "logi... |
5b769718891dd9c39f2d0408ac23ec4cae7f162c | 17120f21cf39f3e90cb5d8fea73dd18b8e1256db | /PatternCausality.R | 9f11dbd05b177d2cf7fa0711a072d4e34d171dac | [] | no_license | DingYinghui/pattern_causality | beafc5beac439118ae385b0e2a3601516572a764 | d931c164d57735a752fb018ba1661ff2f8b85495 | refs/heads/master | 2022-03-27T22:28:08.633676 | 2020-01-21T18:52:46 | 2020-01-21T18:52:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,279 | r | PatternCausality.R | patternCausality <- function(X,Y,E,tau,metric,h,weighted) {
###################################
### STEP 0: PREPARATORY ACTIONS ###
###################################
NNSPAN = E+1 # Former NN | Reserves a minimum number of nearest neighbors
CCSPAN = (E-1)*tau # This will remove the common coordinate NNs
ha... |
3ac25ded95bb75f2235ca0178c5b03eb9a5439ea | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GWEX/examples/simGwexModel.Rd.R | 245dda607118bca21cee40df65a5003c0ecb0078 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,353 | r | simGwexModel.Rd.R | library(GWEX)
### Name: simGwexModel
### Title: simGwexModel
### Aliases: simGwexModel
### ** Examples
# vector of dates
vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"),
to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day')
###############################################################
# ... |
96f3cb4355bc29465cf9f65e11b3cde61b6102bd | f80a6370ca97c230dc2dd84ec4d9c90f91c9d38f | /man/order_docs.Rd | 36b994ff726a69efdd0a8399ea8d91140615d585 | [] | no_license | zhanglipku/rainette | 936fea76a91b307618a9229e4be6fde89810a341 | 7229e7321dae120b9f13f709817fcd86bac83e02 | refs/heads/master | 2023-04-20T00:33:59.403453 | 2021-05-10T12:46:44 | 2021-05-10T12:46:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 504 | rd | order_docs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rainette.R
\name{order_docs}
\alias{order_docs}
\title{return documents indices ordered by CA first axis coordinates}
\usage{
order_docs(m)
}
\arguments{
\item{m}{dtm on which to compute the CA and order documents, converted to an integer mat... |
d8c98f5546026514eb0bd72ca21a9802ac8db387 | a01a8e73ede70072aa7a71f41ac72821243cac60 | /cachematrix.R | d4bd725d4226ba4376eb656c76e769cc60949b8a | [] | no_license | giconi/ProgrammingAssignment2 | 5804d173bf11ad0914544244bb16820be4a493e6 | 80ce1c94045fa8a0686ab780fd90b8f0991a5dd7 | refs/heads/master | 2020-12-25T08:38:21.254879 | 2015-02-20T20:31:15 | 2015-02-20T20:31:15 | 30,846,719 | 0 | 0 | null | 2015-02-15T23:44:44 | 2015-02-15T23:44:44 | null | UTF-8 | R | false | false | 2,559 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix - caches the inverse of a matrix
## get - returns the matrix
## set - sets env var x (i.e. the matrix)
## setInverse - sets env variable inv (i.e. the inverse)
## getInverse - returns contents of var inv
##
## cacheSol... |
4593dba19db3453ff29da7972ca913ceb1474ebd | e7c8d9ff9fa91af61dba9aafe952c0b111a6eaaf | /references/2. SVM.R | b4a4bb690d215f7d2465d7ed80d8c61222229ee9 | [] | no_license | devonwalshe/havamal | ed1672b934526f13f3e7120219f3267f3427297d | 1a1ea234c041139f6030b9fe3571250f69eace32 | refs/heads/master | 2021-06-17T16:04:22.520283 | 2017-05-23T15:05:32 | 2017-05-23T15:05:32 | 59,772,121 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,073 | r | 2. SVM.R | ### SVM model testing
### Data
cc = read.csv("./data/creditcard.csv")
mtsvr = mtcars[order(mtcars$mpg),]
mtsvr_factor = mtsvr
mtsvr_factor[,c("vs", "am", "cyl", "gear", "carb")] = lapply(mtsvr_factor[,c("vs", "am", "cyl", "gear", "carb")], as.factor)
mtsvr_norm = data.frame(scale(mtsvr))
library(e1071)
### Fit mode... |
6dbcfd3093728402617a0a9bbb54d812ddae21ad | 75f1f683951e8da74dc4e225df6e6b0bf47cd095 | /code/2_sampling.R | d8330d0a1c6c7fde86a820ff057f1e1a41c6540b | [] | no_license | MonikaKonarska/Binary-Classification-Models | d36f9c5480dd8b8ac9789040a219b801b0e6fa3e | 1d256085063e332a8daa78f3662aa1cb538958eb | refs/heads/master | 2020-05-18T03:01:11.208477 | 2020-01-06T11:29:55 | 2020-01-06T11:29:55 | 184,133,825 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 796 | r | 2_sampling.R |
samplingTrainTestValid <- function() {
set.seed(1234)
load(file.path(dataPath, "dataToTranTestValid_cleaned.RData"))
dataToTranTestValid_cleaned$group <- sample(c("train", "test", "valid"),
size = nrow(dataToTranTestValid_cleaned),
... |
27c5eabb690286460e0664e70f7e91c004156acb | 5c2dcf913088ef4671fa2bd07f9fbcd4ad564e71 | /tests/testthat/test-ops.R | a6b1e81a48f6bd225dfa6da7df33fd3a152b41c7 | [] | no_license | GRSEB9S/errors | 9c93724ddbcb2056eede1ac65f1242076afcc290 | 68d28a5dab9c69065d0d7a7f6adaa1eac7586304 | refs/heads/master | 2021-08-22T17:02:45.003708 | 2017-11-30T18:27:04 | 2017-11-30T18:27:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,552 | r | test-ops.R | context("ops")
detach("package:errors", unload = TRUE)
library(errors)
test_that("bolean ops throw a warning once", {
xval <- 1
x <- set_errors(xval, 1)
expect_warning(expect_equal(!x, !xval))
expect_silent(expect_equal(!x, !xval))
options(errors.warn.bool = TRUE)
expect_warning(expect_equal(x & x, xval ... |
4f618abc4f74c3b774b89fc82695fe2393bab7b2 | a5093ed082403fd13679a5be7635113d280f7824 | /ggparallel.R | 70f483919db20bb3578f7d105f70164a186a2170 | [] | no_license | jironghuang/ggparallel_test | b65996c62d6ce1431725d7ad32e1f0d06a16697d | 1f8cbad8904b41687fb405a6bcc5ac0712ae0f8d | refs/heads/master | 2020-04-19T11:57:34.353499 | 2019-01-29T15:57:24 | 2019-01-29T15:57:24 | 168,180,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 950 | r | ggparallel.R | library("triangle")
set.seed(0)
q1_d1 <- round(rtriangle(1000, 1, 7, 5))
q1_d2 <- round(rtriangle(1000, 1, 7, 6))
q1_d3 <- round(rtriangle(1000, 1, 7, 2))
df <- data.frame(q1_d1 = factor(q1_d1),
q1_d2 = factor(q1_d2),
q1_d3 = factor(q1_d3))
library(dplyr)
# group by combinations ... |
a2b906b0e591eedd06113012c2fa8fa014f70f97 | 9136f3f10181bff473701a43697c6961415c4e6d | /man/get_instruments.Rd | 52664e274acd8309cbbb4276eafdef0f0e324911 | [] | no_license | MRCIEU/mrever | c51ccb3de431822a9b16edf0a1817ec1be056f18 | 497aa311b03441d0fe3df832217ab9abd15e0063 | refs/heads/master | 2022-12-21T07:39:39.327660 | 2022-12-12T13:29:18 | 2022-12-12T13:29:18 | 172,141,911 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 371 | rd | get_instruments.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/query.r
\name{get_instruments}
\alias{get_instruments}
\title{<brief desc>}
\usage{
get_instruments(id, graph = options()$eveneo4jg)
}
\arguments{
\item{graph}{= options()$eveneo4jg <what param does>}
\item{id=NULL}{<what param does>}
}
\val... |
270878228c6344025ded8f1de537cf57317a863e | 3a4c8fc4e09edb9be762949b266192fb4abbf22e | /app.R | bc92ffb01987ddba75ef904b8f6d6c05ed4dc276 | [] | no_license | kreitmew/mpp | 4a195f0fa92f9ba9172336d80859a7edbcc8cd01 | a5e8313c559dbe91dc741d78a954a46b48121b8d | refs/heads/master | 2021-05-08T15:04:41.111065 | 2018-03-02T18:44:15 | 2018-03-02T18:44:15 | 120,103,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,952 | r | app.R | library(shiny)
library(shinydashboard)
library(plotrix)
library(rlist)
library(xlsx)
library(markdown)
library(rmarkdown)
library(grDevices)
library(rgl)
library(htmlwidgets)
library(GenSA)
library(shinyjs)
library(ggplot2)
library(plotly)
library(Bessel)
options(shiny.sanitize.errors = FALSE)
source('air_parameters... |
418f9fa10deafcf11f6e91e8d9eb7791dd83bdf4 | de76bcdc1b54d52d8a9308cd4ae34290adb3754c | /inst/examples/Shakespeare_data.R | 21f1b8fd824a0996cf889a0fbfcffbc8c968ab15 | [] | no_license | COMHIS/estc | d0e3f249e23d76140f50cc5040e067cc2207a615 | 44a1befe88e4cfe2909d40364a246d946485e7cc | refs/heads/master | 2022-05-01T05:46:41.347913 | 2022-04-02T11:27:48 | 2022-04-02T11:27:48 | 107,229,188 | 5 | 3 | null | null | null | null | UTF-8 | R | false | false | 665 | r | Shakespeare_data.R | # Pick info sheet for Shakespeare & Cervantes
my.authors <- c("Shakespeare, William (1564-1616)", "Cervantes Saavedra, Miguel De (1547-1616)")
for (my.author in my.authors) {
df <- df.preprocessed %>%
dplyr::filter(author %in% my.author) %>%
dplyr::select(estc_control_number = control_number, title, publica... |
7e4d029b2331ffe8017be47c6e43d72026393f4b | 8d74e1620f99fea978e9b2e1473eebec8af1785f | /1 - Basic R/scripts/7_plots.R | 16776a4b91e7d6eaf949db56478f82e5208cd0a7 | [] | no_license | anhnguyendepocen/workshop-R | bf18292a348b3bc51c51ab127b8b7d849a4e8bfc | 0c44940475817d57e56087e55e90a6a3dfae8258 | refs/heads/master | 2022-01-08T19:02:22.953694 | 2019-06-06T11:27:39 | 2019-06-06T11:27:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,999 | r | 7_plots.R | ## ----eval=FALSE----------------------------------------------------------
plot(log(subs), log(citeprice), data = Journals)
rug(log(Journals$subs))
rug(log(Journals$citeprice), side = 2)
## ----eval=FALSE----------------------------------------------------------
plot(log(citeprice) ~ log(subs), data = Journals, pch =... |
0277db73c1cd43b042b8d3a77ad34a1550329aa1 | 882f43bcf93a8b35864dbcd20fdc1c4d2690106d | /run_analysis.R | ea7b588d815d2c11cdba59f78b8ef6ce754bc7a4 | [] | no_license | andreacrescini/GettingandCleaningData | 8dd273ad5082caed41432e3bef9950c1a0a3e5a8 | 845a69e4f98ebfdf780898ec87585c7fdae3fb6f | refs/heads/master | 2016-09-03T06:41:11.350731 | 2015-05-20T11:31:33 | 2015-05-20T11:31:33 | 35,942,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,786 | r | run_analysis.R | if (!getwd() == "./temp_temp") {
dir.create("./temp_temp")
}
library(plyr)
library(data.table)
library(dplyr)
temp <- tempfile()
download.file("http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip",temp)
unzip(temp, list = TRUE) #This provides the list of variables and I choose th... |
faf42e720736c6d7958d67f74099d287a4761193 | fcb7e4a6ed15054450abf708e044b1cb45b428f0 | /R/lds.R | e34d87e343377ce5a012e8aef89435e6ea74f9ad | [] | no_license | eshilts/handysmurf | c4dd7071490c05d3c2386924d70871811ad450d1 | 952e9fefae256678eefc3be3722a20ef53ce9fae | refs/heads/master | 2020-04-26T12:29:21.333818 | 2012-06-13T15:58:18 | 2012-06-13T15:58:18 | 3,317,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 360 | r | lds.R | #' Easy Dubug Logging
#' Streamlined debug logging using sprintf. Unfortunately,
#' this function is dependent on the logging package and setting
#' the log level to debug.
#'
#' @param \dots arguments for logging using \code{sprintf}
#' @author Erik Shilts
#' @note Part of a suite of Opower packages
#' @export
lds <-... |
509e78f1d5c6701fc028265c7799fbd53aadd225 | 58e8b007f00ff8868b53772844f3b0888b6f2876 | /tests/testthat/test-kin.R | e4d4f120b8dccee871884fdfa813caa3f9b1387b | [] | no_license | ckenaley/trackter | 650989f740faefa715c4d322082b07eac9b70f99 | a1e8d02bd921814ea71d1f6257fe0bfc3da619cc | refs/heads/master | 2022-02-08T16:51:52.706637 | 2022-02-07T21:52:21 | 2022-02-07T21:52:21 | 120,318,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,049 | r | test-kin.R |
context("kin functions")
test_that("kin.simple works fine", {
y <- EBImage::readImage(system.file("extdata/img", "sunfish_BCF.jpg", package = "trackter"))
t <-tempdir()
ti <-paste0(tempdir(),"/images")
tp <- paste0(tempdir(),"/processed_images")
dir.create(ti)
dir.create(tp)
EBImage::writeImage(y,pa... |
dc52dafa96cd9fd664125a9eae7d4f233e619371 | 09269ff66e0ce0874858425d7ca5f3b8a72a889f | /inst/shiny_interfaces/agro/server.R | 5ce9d1a95383ff306b9f8d93ff60becf5405dae6 | [] | no_license | gaelleVF/PPBstats-PPBmelange | 13bae3ee4646036ae46a7862b3611eca809802e4 | 0a68aa3fd37e12210e8a75e577cdec8b93a62797 | refs/heads/master | 2023-05-10T23:33:10.101992 | 2021-05-21T15:16:01 | 2021-05-21T15:16:01 | 371,670,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,649 | r | server.R | # PPBstats_interface
# Agro server
server <- function(input, output) {
data("data_GxE")
data_GxE = format_data_PPBstats(data_GxE, type = "data_agro")
# Design experiment ----------
# out_design_experiment = reactive(
# design_experiment(
# expe.type = input$design_experiment_expe.type,
# ... |
34a1f47888e86a500cab6a42a67859b4ca306448 | 9fe1127bad83f9181c9148190953cde2c31985cc | /R/transitorio/transitorio.R | 3b76a32790e6fd5ae60c72bf3bdd81b07fee8495 | [] | no_license | bendeivide/statscience | 6ab66d101848d08d3bd411ffa4f9918005a9f4dc | b934b5b38f27b113d698ec950b4cafa366ac984d | refs/heads/main | 2023-01-21T21:45:08.504973 | 2020-11-27T07:15:32 | 2020-11-27T07:15:32 | 316,344,148 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,774 | r | transitorio.R | # tt <- tktoplevel()
# tkpack(quadroinicial <- tkframe(tt), fill = "both", expand = TRUE)
# telainicial <- ttknotebook(quadroinicial)
# ##
# paned1 <- ttkpanedwindow(telainicial, orient = "horizontal", style = "Toolbar.TPanedwindow")
# paned2 <- ttkpanedwindow(telainicial, orient = "horizontal", style = "Toolbar.TPaned... |
9859088364656cab9c4b41e873affd5f66c92734 | 34cc9dcaba027079ae08e441f6b1ee0c46432a93 | /man/municipios_2014.Rd | 61b2dc705e8e2466e8c1f13fe5607832a98132b2 | [
"MIT"
] | permissive | rOpenSpain/LAU2boundaries4spain | d471d1356e5a43125b0ac93fdf14a105ef00a815 | e07fdc690186b63d1792485054d295c17a1679c7 | refs/heads/master | 2023-04-02T06:37:04.917731 | 2021-04-14T21:01:20 | 2021-04-14T21:01:20 | 125,485,567 | 3 | 1 | MIT | 2021-04-14T20:36:48 | 2018-03-16T08:19:50 | null | UTF-8 | R | false | true | 910 | rd | municipios_2014.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/document_data.R
\docType{data}
\name{municipios_2014}
\alias{municipios_2014}
\title{LINDES-2014 del IGN para los municipios (LAU2-SPAIN)}
\format{
A sf-data frame con 8.201 filas y 7 variables
\itemize{
\item INECodMuni: Codigo INE para el ... |
d4809ade61e55306ab8900e9d866e685be83077d | be741ee891f309778069ba92f4232e9ef18f038a | /R/crime.R | d08c2fa738e713feb93c7f5cd719337966c96ec7 | [
"OGL-UK-3.0"
] | permissive | xmr17/ukpolice | 14822407bf181e8b927b8dbecf33537d47a288bb | e2d2705bead920c8fa1d16f5a795269456da89bc | refs/heads/master | 2020-06-10T21:59:45.097493 | 2019-04-17T02:00:34 | 2019-04-17T02:00:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,531 | r | crime.R | #' Find street level crime within a specified distance or area
#'
#' Crimes at street-level; either within a 1 mile radius of a single point, or
#' within a custom area. The street-level crimes returned in the API are
#' only an approximation of where the actual crimes occurred, they are not
#' the exact lo... |
f41f31a2d07f329377a2d2e453385f8723c388cd | ed155a50ba7cc4cbad11b17cc8a2b85d7ecc95df | /Heatmaps.R | 9057b2357521ab51cfcc33c2d0b73a22789adfda | [] | no_license | Abdulrahims/Climate-Change-Survey-Heatmaps | 29d0d1efcfec5039cd6efff223cdf325254b81c6 | 385a7ffbac9d1640bf889dbce421027b7c029ffc | refs/heads/master | 2021-01-01T05:45:29.168056 | 2016-05-06T02:27:58 | 2016-05-06T02:27:58 | 58,097,068 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,214 | r | Heatmaps.R | library(ggplot2)
library(ggmap)
climate_change <- read.csv("Climate_change.csv", stringsAsFactors = FALSE)
#for loop to remove columns that aren't US. Or use dplyr, one of those R/SQL packages
#13866 is the magic number
#try subsetting by = "US"
for(i in 1:nrow(climate_change)){
if(climate_change$countryCode[i] !... |
be77f8420b55221122fc202a2d82762494039461 | 8b9c4825565521d67b192990016b5a174f9650d6 | /man/burr_plt.Rd | 3de02ce317c234a975800e5297e692cdb7d84388 | [] | no_license | cran/distributionsrd | d6c0efa50e5ad60999b6ffa3cee94dff50047973 | e380c274007c99e2ccaa6b34775f6cfb4fefe1c9 | refs/heads/master | 2022-09-07T03:41:19.652239 | 2020-05-25T17:50:03 | 2020-05-25T17:50:03 | 267,007,404 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,097 | rd | burr_plt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/burr.R
\name{burr_plt}
\alias{burr_plt}
\title{Burr coefficients after power-law transformation}
\usage{
burr_plt(shape1 = 2, shape2 = 1, scale = 0.5, a = 1, b = 1, inv = FALSE)
}
\arguments{
\item{shape1, shape2, scale}{Shape1, shape2 and sc... |
86ac5dc5038f82488e238fb5116b4d8ece4aad31 | b6cb8530baaf46f4907713acae0c36bb301903bd | /2_content/Unit_4/practical/exercise_milk.R | 75d8d20ff8869b0b2e69cf54564f230bec7483cc | [] | no_license | hokirv/BIO144 | 10581ba7fc2123d43027ae96dd2588e574c20130 | e103e3dd9218819a7e63ae5d03ab98145e683287 | refs/heads/master | 2021-01-26T06:58:56.273474 | 2020-02-21T19:53:07 | 2020-02-21T19:53:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,243 | r | exercise_milk.R | ##
## Clear R
rm(list=ls())
## load some libraries
library(rethinking)
library(tidyverse)
library(ggfortify)
library(cowplot)
## load the dataset
milk <- read_csv("https://github.com/opetchey/BIO144/raw/master/3_datasets/milk_rethinking.csv")
## look at the data and we see there are NAs in the neocortex.perc var... |
0a4daebf388291401743a3dd9bd226ff4060ade2 | fe7bd0af52c925054508955ca6679bd23aefb91c | /myfirstscript.R | 646262832ec0b03bb2192c0523066e1c17eeff73 | [] | no_license | ChrisDStats/MyFirstProject | 5b960c281ea5ec0748bf5443f1530333e5eaf2f8 | d499d2dcba45663d56247f0a472fe12de39ff796 | refs/heads/master | 2021-06-25T18:52:47.214669 | 2017-09-07T15:19:15 | 2017-09-07T15:19:15 | 102,742,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,405 | r | myfirstscript.R | #Thisis a comment
5+ #this is the famous five
209+ #I could note that this is the CCGs in 2016
211+ #while this is the number of CCGs in 2015
6
/
3
mean
3<4
"2017-09-04"
as.Date("2017-09-04")
"what?"
2^2
sqrt(-1)
1:5
pi
pi^2
1/0
#floating point errors
0.2<0.2
0.2==0.2
(0.8-0.6)==(0.7-0.5)
all.equal(0.8-... |
dabc51a5b93ec928b80f8fc4216688d8d7ad8fe9 | 31cff46884118639496db381186e6156ca45fa7e | /R/Stability.R | 080e87f80f7e21850cfe56d91d8ce04abf07a76d | [
"MIT"
] | permissive | meyer-lab-cshl/drStable | aeb908d251cb1b4363c688b6e3a848b5e9d3b72d | 692d56aa4ddb7b3d924dad4cd0931c6a2d1bd96c | refs/heads/master | 2023-02-22T11:37:22.179948 | 2021-01-27T19:24:55 | 2021-01-27T19:24:55 | 136,906,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,592 | r | Stability.R | #' Estimate stability
#'
#' Takes the output of \code{\link{subsetDimReduction}} and finds
#' the stable low-dimension components across dimensionality reduced
#' subsets of the original data.
#' @param dr [list] either output of \code{\link{subsetDimReduction}} or
#' manually generated list with dimensionality reduct... |
fb8ba646ab1f8d8754ef8524f2a548c7e09c4fc5 | 72f23d77494b391dfae650a46f3d563d25bcf049 | /analyses/Vieux/anacom3.R | d163023c26f68c29befa3fd5fbb345c701f75b8b | [
"MIT"
] | permissive | JehanneRiv/SeineMSP | dfa4034c41b04cab59ccd8f0f65c7840119c436b | 1e85bd87d9f8aea5937b2b8d5773e137df9417b6 | refs/heads/master | 2022-12-30T13:06:08.401014 | 2020-10-11T14:43:44 | 2020-10-11T14:43:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,449 | r | anacom3.R | library(ggplot2)
library(dplyr)
library(FactoMineR) # pour MCA
library(missMDA)
library(cluster) # pour agnes
library(RColorBrewer)
library(fastcluster)
library(NbClust)
library(sp)
library(raster)
library(cowplot)
setwd("../")
{
load(file="data/ICES/Tabfin1.Rdata")
Tabfin1<- titi
names(Tabfin1)[5]<- "Pred1"
Tabfin... |
34ecc88bfc58c8733d248c05bce7248f123593eb | bb767f6a07340c0c313c79587ea6c96ce5e17f33 | /man/tCorpus-cash-fold_rsyntax.Rd | c83925a4b1ba12a99d8af0489307cc6618c7160c | [] | no_license | psychobas/corpustools | 82694086aa0b3d861e38624a5cf17a53ce61e23e | e9c1ac2011234a62b2fc2c7b46ab01dd9159e4ac | refs/heads/master | 2023-05-04T01:35:30.735603 | 2021-05-25T10:41:30 | 2021-05-25T10:41:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,660 | rd | tCorpus-cash-fold_rsyntax.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rsyntax.r
\name{tCorpus$fold_rsyntax}
\alias{tCorpus$fold_rsyntax}
\title{Fold rsyntax annotations}
\arguments{
\item{annotation}{The name of an rsyntax annotation column}
\item{by_label}{The labels in this column for which you want to aggre... |
eaf865a13e93c0a53682137d537fc35d0f3951db | 80f6dd459811091ed7cafd02968241f23ea206da | /jennysFlower.R | bd241e7b591f6ded5e9fcf6a016a0ea41ffce678 | [] | no_license | betanaught/R | 68afa5f450823161bbd5d294581ec4a12df40f9c | 29c60aef9c6d45ae1be22c7e5526b9b54e66dedf | refs/heads/master | 2021-09-15T15:28:28.767401 | 2018-06-05T19:52:57 | 2018-06-05T19:52:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,997 | r | jennysFlower.R | options(repr.plot.width = 4, repr.plot.height = 4)
library("ggplot2")
# Make a dandelion
angle <- pi*(3-(sqrt(5)))
points <- 500
t <- (1:points)*angle
x <- sin(t)
y <- cos(t)
df <- data.frame(t, x, y)
p <- ggplot(df, aes(x*t, y*t))
p + geom_point(aes(size=t), alpha = 0.5, color = "black", shape=8) +
theme(
plot.... |
5f6d31a1e4b50d477ed78573f113092d91bba37b | 5d9f8ec0e042104a57b4af446f71ec9f2a5d60bc | /R/rbm_train.R | 59bd2253c7ead1254ca83fa88e91a666bcd7e309 | [] | no_license | cran/deepnet | 93155a5f01a778e4340fa01c2f669cf111d0431f | 6e1efd18aed8971c1c398b296e4c2b81533bee9f | refs/heads/master | 2022-07-20T20:09:48.283817 | 2022-06-24T11:29:27 | 2022-06-24T11:29:27 | 17,942,921 | 24 | 23 | null | 2015-12-05T19:09:32 | 2014-03-20T13:11:10 | R | UTF-8 | R | false | false | 4,984 | r | rbm_train.R | ##' Training a RBM(restricted Boltzmann Machine)
##'
##' Training a RBM(restricted Boltzmann Machine)
##' @param x matrix of x values for examples
##' @param hidden number of hidden units
##' @param visible_type activation function of input unit.Only support "sigm" now
##' @param hidden_type activation function o... |
179b1c94b1e134f81f835f018e117173821477d7 | 9f7b4256915d26c5221bedc20af3278879f46fa5 | /Suduroy_fannar/main.R | e7036b6760165498b9cc615713350c0971cf49cf | [] | no_license | KasperEinarson/Faroe-Islands | c53468f910c04cf39f36134ace38b130476608fa | 833e09848dd53aaaebeccc08dc55673d779f4fd5 | refs/heads/master | 2021-01-20T09:26:07.890153 | 2017-05-04T10:58:24 | 2017-05-04T10:58:24 | 90,253,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 668 | r | main.R | rm(list = ls())
graphics.off()
source("libs/initR.R") # initiating the R session
source("libs/getData.R") # getting basic data files
# --------------------------------------------------------------------------- #
# Dataanalyse
# source("libs/Analyse_vindhastighed.R")
source("libs/Analyse_vindretning.R")
s... |
57eb525ec09b72283d03b998a5e668fc9ab3d7ed | 4250e06e89ad4b7042d798691d2a17b258a51e21 | /Calculate Position In Race from Telemetry Segments.R | 7e6cc463ad9856fec718d7ccbda7777b5ec159e5 | [] | no_license | jalnichols/p-c | b143bd210e222d6f755a7034d688db31b10f1720 | 1ea6587e28a1c20ce8a2f6870eb4ece7aeaa05e3 | refs/heads/master | 2022-06-15T22:03:40.823212 | 2022-06-11T20:25:31 | 2022-06-11T20:25:31 | 199,725,985 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,036 | r | Calculate Position In Race from Telemetry Segments.R |
library(tidyverse)
library(rvest)
library(RMySQL)
dbDisconnect(con)
con <- dbConnect(MySQL(),
host='localhost',
dbname='cycling',
user='jalnichols',
password='braves')
#
# Bring in Strava Activities
#
all_race_activities <- dbGetQuery(con, "SELECT... |
84e992c6f10d0a21d389c1c9917b7f0b31cbf2fb | 875d72407d0de9e8c597a09c2792fb76a0521416 | /2_amplitude_CIs.R | 8252f3ad2ee7f22cbbed6b0e1fca5c99cb4582e0 | [] | no_license | agbarnett/weekends | 1071af8d5562d153f68ead4ad675cc6683a66dbf | 6a4897261841081c5d42b62c80c2043ffcdcc751 | refs/heads/master | 2020-07-23T03:12:01.538292 | 2020-01-03T11:53:47 | 2020-01-03T11:53:47 | 207,429,869 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,360 | r | 2_amplitude_CIs.R | # 2_amplitude_CIs.R
# make credible intervals for amplitude for hour of day analysis
# October 2019
# get the chain estimates
index = grep('^cosine.c|^sine.c', rownames(bugs.results$summary))
cnames = rownames(bugs.results$summary)[index]
to.use1 = as.matrix(bugs.results$sims.array[,1,index]) # first chain
to.... |
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