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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b748819aa6494b1593f6fdb654177cf7f1f55297 | a58b45b89139bf17831f51890021131f84cf1f04 | /script/rep_info.R | 39f01b6a2ed2d8c0e22e3d0dfc65f735ba397d39 | [] | no_license | BETAPANDERETA/rocket-engine-data-analysis | c0ee07cb19cb8c84e96ebf6c55cdff7e3205c92d | a187c561f1d326a492ae26f75407f16d3f4e104c | refs/heads/master | 2023-01-20T21:55:48.929723 | 2020-11-25T03:01:49 | 2020-11-25T03:01:49 | 305,259,434 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,357 | r | rep_info.R | # ---------------------------------------------
# AUTOR: Leonardo Betancur A.K.A BETAPANDERETA|
# CONTACTO: <lbetancurd@unal.edu.co> |
# ---------------------------------------------
library(extrafont) # Cargando fonts --> Revisar disponibles con windosFonts()
library(ggplot2) # Gráficos
library(mgcv) ... |
d688aef5e830e7fab6d5f93194e416b2dfdb12fa | 87f20607fc468c291f31f46c2718ddbbc69d3a75 | /man/preprocess_secuences.Rd | 472e83e6d47e3a7c6b05fed67d4be38d6bc91481 | [
"MIT"
] | permissive | santiago1234/iCodon | ffce61abc8b69b38b138861e5c098454311c4c83 | c6739dde6b03e3516e8a464aea3e64449c0f980d | refs/heads/master | 2023-04-07T20:43:43.191022 | 2022-07-30T19:10:45 | 2022-07-30T19:10:45 | 238,309,734 | 13 | 2 | null | null | null | null | UTF-8 | R | false | true | 741 | rd | preprocess_secuences.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ml-preprocessing.R
\name{preprocess_secuences}
\alias{preprocess_secuences}
\title{Preprocess sequence for prediction mRNA stability}
\usage{
preprocess_secuences(secuencias, specie_ = "human")
}
\arguments{
\item{secuencias}{character, a vec... |
361b5a14d31fdf0fa02d207ae8a48c19d85b0486 | d8ff92d904d21c1ab52d056fe6e624abfad939b4 | /R/prVis.R | 2624c6bb833db20a8920a34f24e8ded244e5a4cf | [] | no_license | guhjy/prVis | 4c1ea6cc85b7843808725bdce331a5116026fba2 | 587582d463f66a87e91a84ec0b4ff50b554ff045 | refs/heads/master | 2020-04-11T12:00:30.488013 | 2018-12-13T21:16:27 | 2018-12-13T21:16:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,952 | r | prVis.R | # two-dimensional visualization of the X data in classification problems,
# similar in spirit to ordinary PCA and t-sne, color coded by Y values
# (class IDs in classification case, subinternvals of Y in regression
# case)
# t-sne, e.g. in the Rtsne package, applied in dimension k, attempts to
# find a k-dimensional m... |
2a87270d132e197c8a76039782220e6ce2f3df94 | c7d84bf201e560096c7925b9586343fac438d74a | /lectuer2-2.R | 2f202e5fb2e3a0c9e9317b48bdc75557fe5dad02 | [] | no_license | narendrameena/diffrentRScripts | 029301f875d97c1ec5cea5c706e27fef9995f55a | e83942da0021a36284bea4124a3b6930032d9f63 | refs/heads/master | 2021-01-01T16:47:24.060418 | 2017-07-21T07:56:24 | 2017-07-21T07:56:24 | 97,921,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,528 | r | lectuer2-2.R | library(devtools)
install_github("jennybc/gapminder")
library(gapminder)
data(gapminder)
head(gapminder)
s =gapminder$year[[1952]]
table(gapminder$year ==1952)
hist(gapminder$lifeExp[which(gapminder$year==1952)])
hist(log10(gapminder$lifeExp[which(gapminder$year==1952)]))
mean(gapminder$lifeExp[which(gapminder$year==19... |
61dbe80fa10793473067f427c00f7c4a87269cf7 | 4daca2e1978ed5fb927db5af6e6e36a372608c16 | /R/functions/descriptorImportancePlots.R | 916a972555bc04db265ad988ba791c6f84027ee3 | [
"CC-BY-4.0"
] | permissive | jasenfinch/Index_measures_for_oak_decline_severity_using_phenotypic_descriptors | 518ac57f78d23bad929e26284e4dbbaa30fa8ca8 | c4928890a4993dcca5797d4f854027f1f6d70404 | refs/heads/master | 2023-03-17T11:57:03.033059 | 2021-03-09T20:31:14 | 2021-03-09T20:31:14 | 165,732,718 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,419 | r | descriptorImportancePlots.R |
descriptorImportancePlots <- function(PDI_descriptor_importance,DAI_descriptor_importance){
importance_plots <- list(
a = {
dat <- PDI_descriptor_importance %>%
arrange(`%IncMSE`) %>%
mutate(Feature = factor(Feature,levels = Feature),
rev_Rank = Feature %>%
s... |
5525d708ae110e18d54f817be141e08c67eda71c | a4bf8ea2ca052a6ebaa8d32ea427eb4f747e3b67 | /inst/doc/edl.R | 1da1a0657ee594f760b1f6a5de31c290b7f21d7f | [] | no_license | cran/edl | 941498a8f2b8d8df00ffa7c317d707ff25c189f9 | c40160df056e31bfafda4197405bddf89158d4b4 | refs/heads/master | 2023-08-11T09:11:13.818275 | 2021-09-20T06:40:05 | 2021-09-20T06:40:05 | 357,249,841 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,970 | r | edl.R | ## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, fig.width = 4, fig.height = 4)
## ----load-packages,message=FALSE----------------------------------------------
library(edl)
## --------------------------------------------------------------------------... |
f9e45ef6b8af83ad12427af9b37ece404d0e5608 | 2fb32969061efc165421d571e688848240e537a5 | /man/plostitle.Rd | 60d20941deb0ddf912ad83625b881df69757ceec | [] | no_license | phillord/rplos | cb740bb57eb9f249e94aa360ca11c6d36144f439 | a0e75c4399d85fd98709fcccdc28bf7e5ff32471 | refs/heads/master | 2021-01-15T18:41:41.776122 | 2012-10-23T15:18:37 | 2012-10-23T15:18:37 | 8,477,914 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 985 | rd | plostitle.Rd | \name{plostitle}
\alias{plostitle}
\title{Search PLoS Journals titles.}
\usage{
plostitle(terms, fields = NULL, limit = NULL,
url = "http://api.plos.org/search",
key = getOption("PlosApiKey", stop("need an API key for PLoS Journals")))
}
\arguments{
\item{terms}{search terms for article titles (character)}
... |
a2bcbe3fce797b854786d6827d60bca3ba8d7bfc | 64257a0e57cf928b0ae7676a108a3688001181bd | /tests/testthat/test-count_events.R | 5b81b9c4419bcb0c048109af32c7c2d9dea6ff8e | [
"BSD-3-Clause"
] | permissive | marcpaterno/artsupport | 9842a678c8070468dd93a258810b84067fe22f32 | 803310561741c4aa54bdd44e393da9ae8551bfa0 | refs/heads/master | 2020-06-30T06:49:04.780687 | 2020-04-20T23:14:48 | 2020-04-20T23:14:48 | 74,387,093 | 0 | 1 | NOASSERTION | 2019-02-07T07:11:33 | 2016-11-21T17:15:33 | R | UTF-8 | R | false | false | 468 | r | test-count_events.R | context("count_events")
test_that("counting (distinct) events works for unlabeled data", {
a <- load_module_timing("timing-a.db")
expect_equal(count_events(a), 120)
b <- load_module_timing("timing-b.db")
expect_equal(count_events(b), 240)
})
test_that("counting (distinct) events works for labeled data", {
a... |
3fa9eeef2f9df5e8b2e60f6fc9ffc80b2ce0ca47 | 9713c2a057cb71e8641e6f40fea276ac4e722a34 | /Paper/Descriptive_Analysis.R | 90eadbb3b234cd39d7934c70a16555d4a75ac330 | [] | no_license | Insper-Data/Data_Macro | 4da269c71d2af6d72a1965659083a547e2403688 | c805895f45ddc162d614829e8ca1b70677a3b4f7 | refs/heads/master | 2023-07-12T02:30:39.424353 | 2021-08-25T22:11:04 | 2021-08-25T22:11:04 | 296,466,536 | 4 | 4 | null | 2020-11-02T21:52:55 | 2020-09-17T23:44:59 | HTML | UTF-8 | R | false | false | 20,078 | r | Descriptive_Analysis.R |
# JANUARY 2021
# Script to generate figures used in our paper
# Authors: Augusto Netto, Gabriela Garcia, Maria Clara Drzeviechi and Victor H. Alexandrino
#--------------------------------------------------------------------------------------------
# Libraries
library(scales)
library(dplyr)
library(ggthemes)
libra... |
c99243c954f65096e4ddcc8921b21bf586d24444 | b7e84b97452dde338b7bc8f9d8e9df33168a61d1 | /project/man/wrangle.TablesList.Rd | 569b91539d302080b4d12078709192366639ed17 | [
"MIT"
] | permissive | melissachamary/RDataWrangler | 338bf153e55bc25cb954312cdab61223413a73e8 | 5337ae7793738c6d2bd698a5559757cb65101a10 | refs/heads/master | 2020-04-27T06:42:39.176883 | 2019-05-07T07:08:58 | 2019-05-07T07:08:58 | 173,745,360 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 782 | rd | wrangle.TablesList.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DataWrangle.R
\name{wrangle.TablesList}
\alias{wrangle.TablesList}
\title{wrangle.TablesList
Import data table list into R (according source_file description) and wrangle into new data table list following wrangle_parameter_file description.}... |
25a665f012d90ee8bf88d2282d58d94de73fe12a | 45a5b0036c075cf4ecb71d0018443b1b125e1fac | /man/print.predict.ocm.Rd | 084ab1f4f4bb34e540b5dceed8a627fa77fb0218 | [] | no_license | SvetiStefan/ordinalCont | 54a45adfc0b6638a904529e569e773a2277d4d27 | fc94c4c90a71f920103f877a7d3abb0a8c77c0f8 | refs/heads/master | 2021-05-28T20:07:44.093122 | 2015-05-26T05:46:23 | 2015-05-26T05:46:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 867 | rd | print.predict.ocm.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/ocm.methods.R
\name{print.predict.ocm}
\alias{print.predict.ocm}
\title{Print the output of predict method}
\usage{
\method{print}{predict.ocm}(x, ...)
}
\arguments{
\item{x}{an object of class \code{predict.ocm}}
\item{...}{further ... |
e97a560a3485fa2044fbac55b58fb294c3b6f61e | f7da4c0a8eabae8bcc23f15095cb534204d5794d | /scripts/lake_meta.r | 3cc9bbbad67c24513278a243dc1d4d0fe7dbcc95 | [] | no_license | calandryll/coursey_pond | 9e94e8640cfedba2d68326029329408c36fc4728 | a35baa50c17397e31d6a760017c8b2b123c874a4 | refs/heads/master | 2021-04-28T13:27:54.662225 | 2018-02-19T19:06:43 | 2018-02-19T19:06:43 | 122,106,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,039 | r | lake_meta.r | source('scripts/fun.prod.new.r')
coursey = read.csv("csv/CP_cleaned.csv")
coursey = coursey %>% mutate(Date = ymd_hms(Date, tz = 'EST'))
coursey = coursey %>% mutate(Wind_cor = WSpd * (10 / 4.5)^0.15, k600 = k.crusius.base(Wind_cor, method = 'bilinear'), K_wind = k600.2.kGAS.base(k600, Temp, gas = 'O2'))
pr... |
a29967e0d692bbfd5135abb7b83729a2750c2c18 | 6b39480379545b6a50d863f8263bb0c0a5e56cfb | /scripts/1_getting_started.R | d0aa1dbbf8bbdfe3b0038f7b89082bc9b73600b4 | [] | no_license | loremarchi/hands-on-machine-learning-R-module-3 | 6b9dbe6aba6d871a41eb83361bb2aa9e100674b8 | 4077d3fd4cc84ad3dc507d66203a2ceae0e90454 | refs/heads/main | 2023-07-13T00:33:08.777046 | 2021-08-29T09:24:41 | 2021-08-29T09:24:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 715 | r | 1_getting_started.R | ##############################################
# Getting started
##############################################
## --------------------------------------------------------------------------------------------------------------------------------------------------
library(keras)
x <- k_constant(c(1, 2, 3, 4, 5, 6),
... |
4e885f06bc322551fa1319b45232de972c58055e | 38a5a35e74e487f400fccb327749a1a97e0309a8 | /code/create_biccn_signac.R | 365498d9d962a05482908d66b0d55aa5d72137c4 | [] | no_license | timoast/signac-paper | 1d0f303f20ab018aa69e8929f6a66cc110e1c81f | 1cdbb6dd6a5ad817bd23bb7d65319d5bf802455f | refs/heads/master | 2023-08-25T10:59:33.951761 | 2021-11-01T21:05:01 | 2021-11-01T21:05:01 | 309,495,686 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,453 | r | create_biccn_signac.R | library(Signac)
library(Seurat)
library(GenomicRanges)
library(future)
plan("multicore", workers = 8)
options(future.globals.maxSize = +Inf)
annot <- readRDS("data/biccn/annotations.rds")
# load metadata
metadata <- read.table("data/biccn/Supplementary Table 2 - Metatable of nuclei.tsv", sep="\t", skip=1)
rownames(me... |
cdf869e8520eb555d7a0c6126c5d8f298072709f | 7870272b64347ed7e88f26be3d4082a9ba4c5ba6 | /IJCAJ/dividetest.R | 51e44eff2bb2e5afb1865a0736ddf0c901bc0d07 | [] | no_license | jiahui-qin/MachineLearning | 6ded04dca27f2024f0bd386fc78cd3ef1d844e3c | e80f48ca7946cd6baee3a9939099fd118b1831b2 | refs/heads/master | 2021-09-16T03:25:37.483320 | 2018-06-15T16:02:43 | 2018-06-15T16:02:43 | 104,068,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 252 | r | dividetest.R | #划分测试集
#先用item_id 举例子
train_list <- fct_count(subset(train,train$context_timestamp != 6)$item_id)$f
ifelse(train[which(train$context_timestamp == 6),]$item_id %in% train_list, train[which(train$context_timestamp == 6),]$item_id ,-1) |
355e985731c26e8b954fb0926e646dc8ae3ebf77 | bb660ae1c6194d054248ca508475493ee264a6ae | /man/near_channel_stats.Rd | e10bb2ffbf2108ac2813235ef5d48d3e7f9887c4 | [
"MIT"
] | permissive | hrvg/RiverML | bdfdd6d47f2bb7a7a26c255fa4bf268b11f59c43 | 22b3223f31f310e00313096b7f1fb3a9ab267990 | refs/heads/master | 2023-04-10T05:02:17.788230 | 2020-10-08T16:13:21 | 2020-10-08T16:13:21 | 288,584,365 | 0 | 0 | NOASSERTION | 2020-12-04T01:19:50 | 2020-08-18T23:18:50 | R | UTF-8 | R | false | true | 798 | rd | near_channel_stats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tam-dm.R
\name{near_channel_stats}
\alias{near_channel_stats}
\title{Derive near-channel statistics: "median","mean", "min", "max", "sd", "skew"}
\usage{
near_channel_stats(i, .ls, .lines, .stat, bf = 100)
}
\arguments{
\item{i}{numeric, indi... |
34157f936498a529391bf1d5a936f500837e0638 | eb469ba06f74d3fc458b5daa94fec2a78b339bc9 | /man/nysr.Rd | 4091cf01298e891ec5378d392b44e3ed341b49d1 | [] | no_license | cran/thickmatch | a4eb817c8c7af842826b14b12bd855dce108e41d | 061dad4bca821e1a2bef6fdade4a6f94c2a0a17a | refs/heads/master | 2020-07-21T23:44:36.260401 | 2020-04-11T12:30:02 | 2020-04-11T12:30:02 | 207,004,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,271 | rd | nysr.Rd | \name{nysr}
\alias{nysr}
\docType{data}
\title{
Adolescent Work Intensity and Substance Use
}
\description{
NYSR data on adolescent work intensity and substance Use.
}
\usage{data("nysr")}
\format{
A data frame with 2816 observations on the following 18 variables.
\describe{
\item{\code{IDS}}{NYSR ... |
3fcacdd933e4f58403a5e1a6c0efa60f05e8aea3 | eb4883c3d4c45d719c16109f9113869059488c22 | /plot2.R | 97b970dfb952ee84d7991db8f19509cbf836911c | [] | no_license | maridelf/ExData_Plotting1 | f2f949417385bbb56a253ba2c606e85478e46d99 | 3d141b798d29ea186628731710feb98ba867aa27 | refs/heads/master | 2021-01-23T10:34:53.847674 | 2017-06-01T21:08:20 | 2017-06-01T21:08:20 | 93,072,555 | 0 | 0 | null | 2017-06-01T15:33:18 | 2017-06-01T15:33:17 | null | UTF-8 | R | false | false | 555 | r | plot2.R | plot2 <- function(feb1_2 = data.frame(), readdata = TRUE) {
library(dplyr)
library(lubridate)
source("getDataFeb1_2.R")
if(readdata) {feb1_2 <- getDataFeb1_2()}
with(feb1_2, plot(dt, Global_active_power,
main = "",
... |
678c2616a061e8fd8d62d8c28f7ea5793841c8c8 | 485ad4123816e24842bf7c388d084b24e772b926 | /R/tif.R | 9854b5e2609addefd49925d37f936fe2d662a6ea | [] | no_license | cran/cacIRT | 57c446a880fb52552b231a19e666765f96260df8 | 8fde3135204cb26982e22ad7ca5842a5b744d3cd | refs/heads/master | 2021-01-01T18:34:41.707166 | 2015-08-28T01:08:33 | 2015-08-28T01:08:33 | 17,694,912 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 230 | r | tif.R | tif <-
function (ip, x, D = 1.7)
{
i = iif(ip, x, D)
if (is.null(dim(i$f)))
dim(i$f) = c(length(i$x), length(i$f))
f = apply(i$f, 1, sum)
r = list(x = i$x, f = f, ni = ncol(i$f))
return(r)
}
|
d03ab76db827a6db4c789028eaa6de5a13eee5bf | 83cd2dd744e10494b4461b70b8804b21b2b78341 | /file2tokens.R | 6eebf6cd61842b6da6168c17ceb10306cfb911f8 | [
"MIT"
] | permissive | dmitrytoda/SwiftPredict | ece6ead11282ebff935656019ccf145d9c2afcfb | 9693bac0dd52ce07ca23461e35d866014ee6a7d8 | refs/heads/main | 2023-02-12T04:26:16.378593 | 2021-01-08T18:03:35 | 2021-01-08T18:03:35 | 315,111,716 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 746 | r | file2tokens.R | library(tm)
file2tokens <- function (infile) {
# Reads ALL lines from infile and converts to a list of
# char vectors, each element of a vector representing a token
con <- file(infile, 'r')
text <- readLines(con)
close(con)
# remove empty strings (arti... |
4606d642408a9d76bcd5165f9848c015965f02b3 | d5cd873bd84c6294df226984346fdf68e0f395aa | /CellChat/AnalyseCellCellCommunicationInIntestinalData.R | 15585ea1661ca2fde0c51177e665a947af19581b | [] | no_license | Wenxue-PKU/scRNASeqAnalysisAndModelling | 9056cb0820ea4fba15b05154083d453a8b35c865 | 69a249dce6862a7ab359b9570596997da61d84c3 | refs/heads/master | 2023-01-06T14:00:08.560215 | 2020-10-10T03:43:42 | 2020-10-10T03:43:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 716 | r | AnalyseCellCellCommunicationInIntestinalData.R | ### Application of Suoqin Jin's CellChat package to various datas on intestinal tissue (healthy and UC)
# Load the relevant packages
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(CellChat)
library(ggalluvial)
### Set the ligand-receptor database. Here we will use the "Secreted signalling"... |
c9d546a3e4eb8d527c2cc57ebc954a8926754d16 | c1034eb8f34b18105acf3244bf9a0b0339d6ca8d | /man/jm.prob.Rd | 0d19d20ce1e950e6b2ad20f41126294cf5f37806 | [
"MIT"
] | permissive | svkucheryavski/mdatools | f8d4eafbb34d57283ee753eceea1584aed6da3b9 | 2e3d262e8ac272c254325a0a56e067ebf02beb59 | refs/heads/master | 2023-08-17T16:11:14.122769 | 2023-08-12T16:58:49 | 2023-08-12T16:58:49 | 11,718,739 | 31 | 11 | NOASSERTION | 2020-07-23T18:50:22 | 2013-07-28T11:10:36 | R | UTF-8 | R | false | true | 520 | rd | jm.prob.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ldecomp.R
\name{jm.prob}
\alias{jm.prob}
\title{Calculate probabilities for distance values and given parameters using Hotelling T2 distribution}
\usage{
jm.prob(u, eigenvals, ncomp)
}
\arguments{
\item{u}{vector with distances}
\item{eigenv... |
0b629146921d411a1ff2c0c676ecbf9769bd38f4 | be2e286124b952221135f0ac736423b3032f0906 | /dist/main.js | de702e8022a7f2270884a693fce2f52256cf2aed | [
"MIT"
] | permissive | moelders/react-windowed-select | 9529ce177c3b97d489f0ad64ac9115de7890c408 | ca52057ff5315f23c6b2f7ceb3b0f05fe427cf63 | refs/heads/master | 2023-05-14T16:07:33.515525 | 2021-06-02T15:04:03 | 2021-06-02T15:04:03 | 373,188,020 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 15,650 | js | main.js | module.exports=function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}return r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.... |
6d52c1d40090e7e2bb8c9efdad66750417587f39 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/gasper/R/GVN.R | 416e22e407722ebd6ead7588d845b502a9f3c7cf | [] | 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 | 864 | r | GVN.R | #' Graph Von Neumann Estimator.
#'
#' Graph equivalent of the Von Neummann variance estimator.
#'
#' @export GVN
#' @param y Noisy data.
#' @param A Adjacency matrix.
#' @param L Laplacian matrix.
#' @examples
#' data(minnesota)
#' A <- minnesota$A
#' L <- laplacian_mat(A)
#' x <- minnesota$xy[ ,1]
#' n <- length(x)
#'... |
dd8df6c4d5742a2146b7416cfdaca749da281e55 | 4697a51bdf1a57dae9559774b2b20624257881bf | /lib/helpers.R | df36bb2ae31486a36bb32410f0b1e310c143eeeb | [] | no_license | Castdeath97/future-learn-analysis | f24eaf5f10e7c8ab25f6191a3886843a9aee4f44 | 1f3508d0ec97faa6df0a5f1b4b84c521ea05fa2a | refs/heads/master | 2021-10-26T17:32:06.990862 | 2021-10-07T13:17:40 | 2021-10-07T13:17:40 | 156,971,824 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,966 | r | helpers.R |
## @knitr archetypes-clean
idIndex = 1
responseIndex = 3
archetypesClean = function(df){
# Remove fields that won't be used (id, responded_at)
df = df[-responseIndex]
df = df[-idIndex]
# fix id format
df$learner_id = as.character(df$learner_id)
return(df)
}
## @knitr questions-clean
clozeIndex =... |
f7a6e2412e0f1c4844100ce20bebd53c0c611a58 | 2f18e19c490e350fc4a0f93579dfb8fffa2813f9 | /SuddenlyExpandingPopulation/Simulated_densities_of_T_Total.R | 67e46b2f841faf09b28a1490208ebbbfd649e469 | [] | no_license | JetteS/Inhomogeneous_phase-type_distributions_in_population_genetics- | 276f574506a1f78558b3a7a095b7a9ee5a7168fe | ce38663bc84b9e5f618beb9c4a26a30f27cdd979 | refs/heads/main | 2023-02-16T18:20:51.427009 | 2021-01-09T16:50:21 | 2021-01-09T16:50:21 | 313,293,604 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,533 | r | Simulated_densities_of_T_Total.R | ## Creating plots of the simulated density of T_Total
## under a suddenly expanding population growth model
##################################################
## Simulating the time to the most recent
## common ancestor and the total branch length
## under a model with variable population size
#######################... |
0668cffc350c5d1d3527bcb45ff6f0ff61e546f5 | 570e3adb41c325d7b3357b27fb39c7ea2b346f60 | /Plot1.R | b721a06f5342b4b44afc3987991ae0b4ab9430ac | [] | no_license | kathy0305/ExData_Project1 | 89e79a75d0b9b82616108f3917d0d50d0fd5a7ac | f9bf09e94c3be4f55dd310f92a86b4b445d75e02 | refs/heads/master | 2020-12-31T04:17:40.199973 | 2016-05-13T19:13:26 | 2016-05-13T19:13:26 | 58,765,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,182 | r | Plot1.R | ## Assignment: Course Project 1
## Plot1.R
## Checking if the file has laready been downloaded, if it hasn't then create a directory
if(!file.exists('Week1Project.zip')){
dir.create('Week1Project')
## extracting data set from website
fileUrl<-"http://archive.ics.uci.edu/ml/machine-learning-databases... |
c872bd9829746f81a7a9e8a52b918b2b1130a80a | 5027d65a10985981f7e275b0c9da8e68329b87b5 | /scripts/plot_PS.R | 7084b3459180980d575cb1d30f2cfd2084ffe348 | [] | no_license | DPCscience/GenomicPrediction_2018 | 3f52a7247dd4153591d327d2ff52ea94841434b1 | 507bf5e8ddad6dccec56b6e73910a52017772e79 | refs/heads/master | 2021-09-10T21:40:55.990635 | 2018-04-02T18:37:57 | 2018-04-02T18:37:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,203 | r | plot_PS.R | ### Comparison of model performace across the Grid Search
ids <- c('rice_DP_Spindel','sorgh_DP_Fernan','soy_NAM_xavier','spruce_Dial_beaulieu','swgrs_DP_Lipka')
setwd('/Volumes/azodichr/03_GenomicSelection/')
cls <- c(ID="character", MSE="numeric",Model="character",Trait="character",params="character")
data <- read.cs... |
7c36ac38c98d39e6381278fadaada1c51947feeb | 563c6cb8816345083ec54fffef8e10148d3642e9 | /run_analysis.R | feb507ce04b6e69a91c9239dfd77baf7b4694eaa | [] | no_license | rtomasc/GettingCleaningCourseProject | fddda9541ab3da11a801979888fce24f5de3fa78 | d70da385cdcd3a75b4568ccc30fbf4c1fa815e32 | refs/heads/master | 2021-01-10T04:29:10.104014 | 2015-12-27T23:02:50 | 2015-12-27T23:02:50 | 46,667,885 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,779 | r | run_analysis.R | #PART 0. Downloading the zip file, loading libraries and getting data
FileUrl<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(FileUrl, destfile="Dataset.zip")
downloaded<-date()
# Look at the file names in Dataset.zip. Get the file names to extract them.
unzip("... |
5de1eb315522ba5a1fa36a1e1f3328b45bf9e61b | 29585dff702209dd446c0ab52ceea046c58e384e | /ParamHelpers/R/OptPath_getter.R | 1e6eec00950b480f8758232d7fd0363d09e4b392 | [] | 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 | 8,250 | r | OptPath_getter.R | #' Get the length of the optimization path.
#'
#' Dependent parameters whose requirements are not satisfied are represented by a scalar
#' NA in the output.
#'
#' @template arg_op
#' @return [\code{integer(1)}]
#' @export
#' @family optpath
getOptPathLength = function(op) {
UseMethod("getOptPathLength")
}
#' Get an ... |
a965be02387d68721ca7be86329bd3f425f15834 | c944ff7ec610960d106391311ceb27869c979741 | /station_density_walking.R | e3f5e09cba87b1a9c64c7aa6d579cc79a51f27e2 | [] | no_license | sjrodahl/BDA17-Bike-Share-Analysis | 924686722d4528ef2ced7ba6c646f60225791b3a | 4f53c7c9be3023ce69dafbbc056f5727379c0d51 | refs/heads/master | 2021-01-20T12:44:53.686341 | 2017-06-13T05:33:58 | 2017-06-13T05:33:58 | 90,402,869 | 0 | 0 | null | 2017-06-13T05:33:59 | 2017-05-05T17:56:15 | R | UTF-8 | R | false | false | 1,789 | r | station_density_walking.R | #Station density - stations closer than a five minute walk to the next one
setwd("D:/Sondre/Dokumenter/UCSD/IRGN452 BigDataAnalytics/Project/BDA17-Bike-Share-Analysis/")
stations <- read.csv("data/station.csv" , stringsAsFactors=FALSE)
distance.matrix<-read.csv('gmapsWalkTimeMatrix.csv')
distance.matrix[distance.mat... |
03f8fd923e23343a9174ec1d9964780dbbad1a98 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Cashmore-Fox-Giunchiglia/Planning-CTE/pipesnotankage04_6/pipesnotankage04_6.R | b4fd2e50e7deea7b984e4c994c586a9ee893fe7d | [] | 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 | 69 | r | pipesnotankage04_6.R | c17a968585a5d9c0c35855d2b0671a55 pipesnotankage04_6.qdimacs 839 21727 |
e63e4ae47da8de1a00c6a4713d4b59adf3c93175 | 79f9f86aa6cc9fdee3aa152c326a08b08c4b9533 | /man/prefs_reset_to_defaults.Rd | 377d1a33c0585dcc9ecf151e375ef7484f4f694f | [] | no_license | charliejhadley/rstudioprefswitcher | bb888a96e6fb7ec51834dc2e8a3fde26f5264cca | ee10a3f3cbc2665e1a15be2270cff0a9c3af1239 | refs/heads/master | 2023-04-13T16:06:35.066160 | 2021-04-16T09:38:06 | 2021-04-16T09:38:06 | 331,698,487 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 472 | rd | prefs_reset_to_defaults.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set_prefs.R
\name{prefs_reset_to_defaults}
\alias{prefs_reset_to_defaults}
\title{Restore default preferences}
\usage{
prefs_reset_to_defaults(improve_code_reproducibility = TRUE)
}
\description{
\code{prefs_reset_to_defaults()} resets RStudi... |
957dbc7fa0fbf2c48601a29c8e57b0f8e69360d3 | 9279ca3701e7a6ae465c3e482f8deae70fb2daa5 | /variableDistributions_WK3.R | 25bf0363ee05171d32c6fccc711778c11515f83d | [] | no_license | zliu008/dataManageVisualization | 4fb16ba88b54c251338d70e6b505f0c008ad8a56 | 89716bc0801fdedb9649b677d6d011bcf253a92e | refs/heads/master | 2021-01-10T01:51:05.014230 | 2015-10-23T00:09:26 | 2015-10-23T00:09:26 | 43,625,093 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,099 | r | variableDistributions_WK3.R | rm(list=ls(all=TRUE))
#function to calculate distribution
caculateDist <- function(data_in, varName) {
freq <- data.frame(table((data_in), exclude = NULL));
colnames(freq)[1] = varName;
freq$Perc <- freq$Freq/sum(freq$Freq);
print(sprintf("the distribution of %s:", varName));
print(freq);
freq;
... |
be321676a1c3a7ae5b3650615f65f75b7d05e960 | 2f66f60715bf67758d1f93b650886500b04ed431 | /man/cyan2yellow.Rd | 76cf2e3b11a40e4ac9f363867cc7fc4d6127384a | [] | no_license | SymbolixAU/colourvalues | 6e2e32b29c3ce2a0a0699e36e86cb6dc77ccf5db | dfc75685389ebcfcbac15dccbbd0c024c2854117 | refs/heads/master | 2023-04-11T13:42:57.481895 | 2023-04-08T22:43:28 | 2023-04-08T22:43:28 | 147,190,723 | 34 | 9 | null | 2022-12-30T02:33:38 | 2018-09-03T10:44:56 | C++ | UTF-8 | R | false | true | 226 | rd | cyan2yellow.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/palettes.R
\name{cyan2yellow}
\alias{cyan2yellow}
\title{Cyan2yellow}
\usage{
cyan2yellow()
}
\description{
Data Frame of the cyan2yellow palette
}
|
1be1ecaf861f1c44e7c27e6dd9d002d56641ca92 | e462ad72e1db20ece2432b27e266a8dee927bf7f | /讨论班/分位数回归/ADMMRQ.r | 880e4303ce3212e27decf82504f53677e9018453 | [] | no_license | dujiangbjut/dujiangbjut.github.io | 1c4ca37dd8f9b7c0d63cff600a679f2ba2a13ee8 | 6a9ce1bebd054023bd6e4c431f2bdf291093569c | refs/heads/master | 2020-04-19T10:18:32.461941 | 2019-09-07T14:12:03 | 2019-09-07T14:12:03 | 168,131,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,066 | r | ADMMRQ.r | ADMMRQ <- function(X,y,tau,iter=200,esp=1e-03){
n=length(y)
e.new=e.old=rep(0,n)
X=as.matrix(X)
p=ncol(X)
beta.new=beta.old=rep(0,p)
u.new=u.old=rep(0,n)
step=0
error=1
while(step<iter&error>esp){
beta.old=beta.new
e.old=e.new
u.old=u.new
step... |
8111ebac8eb6a8fae9f0c41aa1b19681fc356059 | 62cfdb440c9f81b63514c9e545add414dc4d5f63 | /man/qat_plot_roc_rule_dynamic_2d.Rd | de0569163f6d029d860bf68c1ac4bd436f44cdc8 | [] | no_license | cran/qat | 7155052a40947f6e45ba216e8fd64a9da2926be4 | 92975a7e642997eac7b514210423eba2e099680c | refs/heads/master | 2020-04-15T16:53:45.041112 | 2016-07-24T01:26:59 | 2016-07-24T01:26:59 | 17,698,828 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,700 | rd | qat_plot_roc_rule_dynamic_2d.Rd | \name{qat_plot_roc_rule_dynamic_2d}
\alias{qat_plot_roc_rule_dynamic_2d}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Plot a dynamic ROC rule result}
\description{
A plot of the result of a dynamic ROC rule check will be produced.
}
\usage{
qat_plot_roc_rule_dynamic_2d(flagvector, filename, mea... |
c698dd575392abe5da295a989a4c965a26faa256 | 382df78024f588acea08039a0b0a9e24f297b6a3 | /r/math/Pi.R | 9c351ab133daaa8d23ff33c150cd852013cde2f9 | [] | no_license | id774/sandbox | c365e013654790bfa3cda137b0a64d009866d19b | aef67399893988628e0a18d53e71e2038992b158 | refs/heads/master | 2023-08-03T05:04:20.111543 | 2023-07-31T14:01:55 | 2023-07-31T14:01:55 | 863,038 | 4 | 1 | null | 2020-03-05T06:18:03 | 2010-08-26T01:05:11 | TeX | UTF-8 | R | false | false | 64 | r | Pi.R | s <- 10000000
x <- runif(s)
y <- runif(s)
sum(x^2+y^2 <= 1)*4/s
|
c2caf6918238f15fda90158a45d7682db05142af | 51fb651cdd636bf2b0f08c77ab6aa9d1341920a3 | /R/ahn_gen.R | c64149fd73acb4ca50195bb77ee6aa32c1a25fad | [] | no_license | ecopeng/AnimalHabitatNetwork | 4ee45cce56c298d5b47d926c932a3ca41e464a12 | ff4fc3a80477a222742222b1c5615b5db4b2699a | refs/heads/master | 2023-02-03T03:49:42.038460 | 2020-12-22T10:20:51 | 2020-12-22T10:20:51 | 272,998,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,296 | r | ahn_gen.R | #' @title Generate networks characterising habitat physical configurations
#' @description Generate undirected networks (weighted or unweighted, connected or disconnected) characterising the physical attributes and spatial organizations (or distributions) of habitat components (i.e. habitat configurations).
#'
#' @para... |
473919ffbcd2258f11093304ff4b9cb9883814b3 | 95bb91fd4c03527735c4eb4ac273f17ace035042 | /R/formatting.R | bb6c9392dfb8f351aeeb253df155ebfa2ce4b041 | [
"MIT"
] | permissive | garborg/clean.dw | f69344d82e5486bb5f2d1defc8b3d5aae2d6a86a | bba685ad7f94b93d1847b91f620a9a5fd777aa6e | refs/heads/master | 2021-01-06T20:38:30.506900 | 2014-05-02T21:56:33 | 2014-05-02T21:56:33 | 12,885,336 | 1 | 0 | null | 2014-05-02T21:56:33 | 2013-09-17T03:25:20 | R | UTF-8 | R | false | false | 567 | r | formatting.R | options(scipen=20)
enquote = function(names) {
if (length(names)) {
ifelse(nzchar(names), paste0('"', names, '"'), "")
} else
names
}
enquoteNames = function(x) {
names(x) = enquote(names(x))
x
}
chrEscape = function(chr) {
if (length(chr)) {
paste0("'", gsub("'", "''", ch... |
2e85ceadc25a610fbca401c6c609e603744a4e7f | 3bf3c24531fdf87ca1d5e03fb0a591d650f062b5 | /man/ad_setup.Rd | 4acd27b9aa7596a0dee86c0dc752b249a19a18c6 | [] | no_license | hiendn/autodiffr | c8eba7519a8cee22d6c53fabcef53bc23b3038a9 | d59407ce1289358afc4fadb1501ad743f5280e28 | refs/heads/master | 2020-06-19T17:19:43.480408 | 2018-12-18T23:41:57 | 2018-12-18T23:41:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 931 | rd | ad_setup.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autodiff.R
\name{ad_setup}
\alias{ad_setup}
\title{Do initial setup for package autodiffr.}
\usage{
ad_setup(JULIA_HOME = NULL, reverse = TRUE, forward = TRUE,
verbose = TRUE, ...)
}
\arguments{
\item{JULIA_HOME}{the file folder which conta... |
9e9bb32e15010f9469836a2f755101b6c349efd5 | 8124be31adfe738983227380f28b6694b62efdea | /man/data2HTML.Rd | 6984c87421482f90fb74eac509394c5bdf6033f2 | [] | no_license | guhjy/rrtable | 7a1b849bca2a9157972add2507ae5ee4ed849698 | 587eeee021768a2dc34639dd8c422b3c2c622c21 | refs/heads/master | 2020-04-08T19:49:43.445657 | 2018-10-16T04:26:30 | 2018-10-16T04:26:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,051 | rd | data2HTML.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data2HTML.R
\name{data2HTML}
\alias{data2HTML}
\title{Make a HTML5 file with a data.frame}
\usage{
data2HTML(data, preprocessing = "", path = NULL,
filename = "report.HTML", rawDataName = NULL,
rawDataFile = "rawData.RDS", vanilla = FALSE... |
2749d6f5d4a2a3a7170bf1c3a0447a8289d583a2 | 66332bb30c8d14f824af71a9d418c5d6345f58d1 | /source/get.R | 508fcd28addd774bc40462aa3f72d1a4a204c93f | [] | no_license | moggces/ActivityProfilingGUI | f2c2f073733e6c3feba6679e8620e81cf4d78c1e | f2bb6533cceba979e31aa91a3a42cfe538c9052e | refs/heads/master | 2020-04-15T23:48:14.361354 | 2017-10-06T15:50:04 | 2017-10-06T15:50:04 | 17,751,883 | 1 | 0 | null | 2017-03-27T15:21:38 | 2014-03-14T16:08:17 | R | UTF-8 | R | false | false | 14,497 | r | get.R | # specific to activity file generated from KNIME
get_property_name <- function (master)
{
col_list <- strsplit(colnames(master), '.', fixed=TRUE)
#unique(unlist(lapply(col_list, function (x) x[[length(x)]]))) # get the unique assay name
names <- lapply(col_list, function (x)
{
if (length(x) == 3)
{... |
53fd0350f89cb1c6a03fb4ce4baf7f8c407272a3 | 40962c524801fb9738e3b450dbb8129bb54924e1 | /Day - 1/Assignment/Q2 - UserInput.R | 36edb2808efbb9da098ea49c577a1751118cee21 | [] | no_license | klmsathish/R_Programming | 628febe334d5d388c3dc51560d53f223585a0843 | 93450028134d4a9834740922ff55737276f62961 | refs/heads/master | 2023-01-14T12:08:59.068741 | 2020-11-15T13:23:31 | 2020-11-15T13:23:31 | 309,288,498 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 270 | r | Q2 - UserInput.R | #UserInput
Id = readline("Enter your UserID:") #Prompts user for input(id)
Branch = readline(prompt="Enter you Branch/Group:") #Prompts user for input(branch)
cat("Your UserID is",Id,"and you belong to",Branch,"group") #Displaying Values |
112a58fb89bdf1037197144d51edca7fb80b92ff | b32a5bca6aeac9ec9c0761079164540b21103dfa | /visualization-project-shiny.R | a53004252a0fc8b8c4ef79b921db911878765340 | [] | no_license | chelseypaulsen/Fall2Orange2 | 5533f479bf91c1262bc0b6b8d8b696d5c61c7b30 | f807c5ca9d548b72c75a3d791dd67c69bfb8e8f3 | refs/heads/master | 2020-03-30T20:38:44.468347 | 2018-10-25T04:53:41 | 2018-10-25T04:53:41 | 151,597,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,257 | r | visualization-project-shiny.R | rm(list=ls())
library(shiny)
library(ggplot2)
#install.packages(c('maps','mapproj','caschrono'))
library(maps)
library(mapproj)
library(tidyverse)
library(readxl)
library(zoo)
library(forecast)
library(haven)
library(fma)
library(expsmooth)
library(lmtest)
library(seasonal)
library(lubridate)
library(tseries)
library(... |
4ed7443a61480e153b81d20832617c75ce2987b3 | e54a704f3197440a47f987a4e080716e8db55cc7 | /tests/testthat/test-RE-stan_data.R | 560a898fba345acfc22d115580d66e2e4330327e | [] | no_license | dimbage/epidemia | ccb2b13c25b0dcb8a4857590cf6dc6d2494af3b2 | c68720cabbc3856fa903038f603e9af6f6afa799 | refs/heads/master | 2023-07-13T08:51:39.922234 | 2021-06-23T10:59:35 | 2021-06-23T10:59:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,090 | r | test-RE-stan_data.R | context("Random effects stan data")
load(file = "../data/NYWA.RData")
args <- list()
args$data <- NYWA$data
args$inf <- epiinf(gen=NYWA$si)
expect_warning(args$obs <- epiobs(deaths ~ 1, i2o = NYWA$inf2death * 0.02))
args$chains <- 0
test_that("Correct dimensions and values for quantities t, p, q and l in the stan dat... |
6254930780e33489118c6548709de47705a7aaca | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pmxTools/examples/calc_sd_2cmt_linear_oral_1_lag.Rd.R | 7a65f618132392e356e05ed6782b799fb1f06bce | [] | 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 | 370 | r | calc_sd_2cmt_linear_oral_1_lag.Rd.R | library(pmxTools)
### Name: calc_sd_2cmt_linear_oral_1_lag
### Title: Calculate C(t) for a 2-compartment linear model after a single
### first-order oral dose with a lag time
### Aliases: calc_sd_2cmt_linear_oral_1_lag
### ** Examples
Ctrough <- calc_sd_2cmt_linear_oral_1_lag(t = 11.75, CL = 7.5, V1 = 20, V2 = 30... |
440ac7a6cd985447816c5de8fec889be81ee6d08 | f30cc1c33978ca5a708a7e0a493403ea88550160 | /R/neuronlist_interactive_3d.R | be1488ecb8e9b486c865769c0d07514d2a5cfdae | [] | no_license | natverse/nat | 044384a04a17fd0c9d895e14979ce43e43a283ba | 1d161fa463086a2d03e7db3d2a55cf4d653dcc1b | refs/heads/master | 2023-08-30T21:34:36.623787 | 2023-08-25T07:23:44 | 2023-08-26T19:02:50 | 15,578,625 | 35 | 10 | null | 2023-01-28T19:03:03 | 2014-01-02T07:54:01 | R | UTF-8 | R | false | false | 8,757 | r | neuronlist_interactive_3d.R | #' Find neurons within a 3D selection box (usually drawn in rgl window)
#'
#' @details Uses \code{\link{subset.neuronlist}}, so can work on dotprops or
#' neuron lists.
#' @param sel3dfun A \code{\link{select3d}} style function to indicate if points
#' are within region
#' @param indices Names of neurons to searc... |
486ae5a58b059238b5689733158f2627f6da5f7d | 01f3d2abd6a99a98d3a26441c6508e8994f310d8 | /Project.r | 848aaa79f1f61680f1d37249409d8f3420cc8fe6 | [] | no_license | gauthamsaimr/TimeSeriesAnalysisPanelData | 1b453d06b3d0d75b98f1d305df7b3ea0767cf153 | 83a094fb270cade8b3664cfcc2a78d9f11df77ba | refs/heads/master | 2020-06-04T03:18:32.071659 | 2019-06-14T01:22:22 | 2019-06-14T01:22:22 | 191,852,443 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,419 | r | Project.r | library(haven)
library(data.table)
library(ggplot2)
library(broom)
library(forecast)
library(plm)
library(margins)
library(sandwich)
library(lmtest)
library(tseries)
library(DBI)
library(RSQLite)
library(tidyverse)
library(car)
library(TSA)
library(deeplr)
library(dplyr)
library(partykit)
mydata<- ... |
2ae390c37aa9798c221c6971d85c73aea786e4db | bf8802140cc5e08b9e0a9abcb060f2e15eb83b55 | /man/cvglasso.Rd | 2edbbd6f2095553001f72326739c702ee35d6a82 | [] | no_license | cran/robustcov | b2415fa2d141a74ef54d49471dd26983f6276b46 | 35d25b8a3df67f09172ee1dbb74ffa1a3a0af669 | refs/heads/master | 2023-07-08T16:52:39.466320 | 2021-08-04T09:00:05 | 2021-08-04T09:00:05 | 392,744,128 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,035 | rd | cvglasso.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/glasso_CV.R
\name{cvglasso}
\alias{cvglasso}
\title{Cross validation to chose tuning parameter of glasso}
\usage{
cvglasso(
data,
k = 10,
covest = cov,
rhos = seq(0.1, 1, 0.1),
evaluation = negLLrobOmega,
...
)
}
\arguments{
\item... |
254f0ae649cd1c98d1dc54b97372a965bd7a7334 | 9b62eff47bbe5b29e362778d855bd7eac1ee7595 | /man/launch_shinytmb.Rd | e442c979c2e755dfd88ab066fe31aa45f133a2a7 | [] | no_license | jmannseth/adnuts | 2876ff9d3993b68235de3d2b01e136160e1b4610 | 055fa3e5899c0d521607c0aa05a3d673e2af8cd1 | refs/heads/master | 2021-06-21T03:33:59.678604 | 2017-08-18T21:26:48 | 2017-08-18T21:26:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 465 | rd | launch_shinytmb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helper.R
\name{launch_shinytmb}
\alias{launch_shinytmb}
\title{A high level wrapper to launch shinystan for a TMB fit.}
\usage{
launch_shinytmb(fit)
}
\arguments{
\item{fit}{A named list returned by \code{sample_tmb}.}
}
\description{
A high ... |
a07c60aa60b6a23a55f6b7ee9b9fca472ed20891 | 538e7a274b5e7442b61d03df954e1355d2ebe683 | /R/CI_script_transform.R | 2af996a40697fffb6227e7156b1ef2957a35e763 | [] | no_license | WilliamsPaleoLab/Farley_Optimizing_Computing_SDMs | 7fdbe26c620dcadc136f01e4826234ffabd27e38 | 0613a8d2274078d9c7de422c0cdb0441bf11db9e | refs/heads/master | 2021-06-18T20:47:28.320997 | 2017-05-12T17:50:32 | 2017-05-12T17:50:32 | 58,968,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,325 | r | CI_script_transform.R | library(plyr)
con <- dbConnect(dbDriver("MySQL"), host='104.154.235.236', password = 'Thesis-Scripting123!', dbname='timeSDM', username='Scripting')
## get results from database
r <- dbGetQuery(con, "Select * From Experiments Inner Join Results on Results.experimentID = Experiments.experimentID
WHERE... |
8fee2ebed7a3fde56c2128d9e702db09d45fd52e | bb3fd8e814b3210022371974c95684066034ec39 | /man/slide2.Rd | 953808050518272a24646247ada7e2070fd0a955 | [] | no_license | xtmgah/tsibble | 34f9990f5db6c39552e446ebee7b399d05fa12b2 | 1c5ec296036d86cae1a4c24bc01fa9e676cd63ed | refs/heads/master | 2020-03-23T00:40:20.332753 | 2018-07-13T13:06:38 | 2018-07-13T13:06:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,917 | rd | slide2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/slide.R
\name{slide2}
\alias{slide2}
\alias{slide2}
\alias{slide2_dfr}
\alias{slide2_dfc}
\alias{pslide}
\alias{pslide}
\alias{pslide_dfr}
\alias{pslide_dfc}
\title{Sliding window calculation over multiple inputs simultaneously}
\usage{
slide... |
71ffa13e50e7599dbd37d0e8ea2f5b0474792412 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/flux/examples/plot.reco.Rd.R | b6b22eac0898e96bd7b000a297a8220e26abe86a | [] | 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 | 173 | r | plot.reco.Rd.R | library(flux)
### Name: plot.reco
### Title: Plot diagnostic plots for Reco models.
### Aliases: plot.reco
### Keywords: hplot
### ** Examples
## see examples at gpp
|
525010b2e0ac02ed11cca4340dd52417a339d99e | 510e69ed40a0db235877d7f5e531ddbd2e118af6 | /Copy_MySQL_2_CBIPMySQL.R | 61db7a2bde4c9310945d97661417f95504d2d36b | [] | no_license | predsci/Climate_Data-BSVE | fd2afcf2c6f79cdc9d0527f7a49db742d2d7b0e0 | 656050ad249849c0a34bdfc42c7102c71364a259 | refs/heads/master | 2020-04-17T09:20:34.404417 | 2019-06-24T18:33:46 | 2019-06-24T18:33:46 | 166,454,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,164 | r | Copy_MySQL_2_CBIPMySQL.R |
rm(list=ls())
# require(DICE)
require(dplyr)
require(lubridate)
require(RMySQL)
# require(RPostgreSQL)
# load functions for opening connections
# source("~/Dropbox/MyLEPR/SQL/BSVE/containers/docker-NOAA_proc/source/NOAA_Funs.R")
source("/home/source/NOAA_Funs.R")
MySQLdb = OpenCon("predsci")
# CBIP_db = OpenCon("dc... |
09362ea0de0e86f189b4f02723b5b50897347b61 | 124e3a9cd70c301798fa4201a6ccd27e3c15e7dc | /Data Cleaning/clean_subnational_ihme.R | 9f6c7f79a24c1f1c29b4774ee19c15f0be04f587 | [] | no_license | shinnakayama/aquatic_food_justice_model | 540b195b6f54dacac51917eb876d7351ebeddaf5 | 86d3bbc886e6fab3fbaf90880dcb10f0a2027453 | refs/heads/master | 2023-01-25T03:22:22.722033 | 2020-12-07T03:00:49 | 2020-12-07T03:00:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,591 | r | clean_subnational_ihme.R | #######################
# Extracting and cleaning harmonized nighttime lights data
# Zach Koehn
# zkoehn@stanford.edu
#######################
library(tidyverse)
library(pbapply)
library(countrycode)
library(raster)
library(sf)
library(spData)
library(exactextractr)
library(viridis)
library(readxl)
library(rnaturalea... |
39bdc4d6331bc5d46c2ced51b0fbd44f590cf035 | 1fb22fa3ac1f5a78e0cce797d6d7bdcf78d8236f | /man/plot_adjmatrix.Rd | 4b4e18fdc1df3753dbd2252036ed199a0f2163c2 | [] | no_license | scramblingbalam/graphclass | 2fb0a77d6fa984a9dd6c56b39487ea43a8cbda34 | 930d2ff47319e5fd5c70c6d73afdea2763eee3ab | refs/heads/master | 2021-04-12T04:21:09.096338 | 2018-02-23T23:01:18 | 2018-02-23T23:01:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 577 | rd | plot_adjmatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Plots.R
\name{plot_adjmatrix}
\alias{plot_adjmatrix}
\title{Plot a vectorized adjacency matrix.}
\usage{
plot_adjmatrix(beta, type = "intersection")
}
\arguments{
\item{beta}{Vectorized adjacency matrix. For undirected networks use o... |
aea40445e4ec4dd508273349b5c80bcd8610811e | 431719d48e8567140216bdfdcd27c76cc335a490 | /man/TagResource.Rd | 887428e23b31150bf861c719b6dac20455af2198 | [
"BSD-3-Clause"
] | permissive | agaveplatform/r-sdk | 4f32526da4889b4c6d72905e188ccdbb3452b840 | b09f33d150103e7ef25945e742b8d0e8e9bb640d | refs/heads/master | 2018-10-15T08:34:11.607171 | 2018-09-21T23:40:19 | 2018-09-21T23:40:19 | 118,783,778 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 416 | rd | TagResource.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TagResource.r
\docType{data}
\name{TagResource}
\alias{TagResource}
\title{TagResource Class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
TagResource
}
\description{
Resource to which a tag has been associated
}
... |
561abbf77b0c3d3a863283db47b1796ab65da59a | 9a3ed38b823f090cd9826def42fb77f50c2f8492 | /USAspending/cfda_extraction_datatable.r | 09f6e7813a0a1b296aaf685e8a59fd1daf6aa17a | [
"MIT"
] | permissive | cenuno/Spatial_Visualizations | fded362213663882103d0dea10ed73ab0fe4231c | 7a4776dd5b87449497b7615887c42f05a3d2e694 | refs/heads/master | 2021-01-02T08:44:56.313875 | 2017-12-23T03:47:02 | 2017-12-23T03:47:02 | 99,060,719 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,106 | r | cfda_extraction_datatable.r | #
# Author: Cristian Nuno
# Date: August 20, 2017
# Purpose: To write an API function with USA Spending data
# regarding CFDA inqueries
#
#
#
# Example URL: https://api.usaspending.gov/api/v1/references/cfda/
# Summary: Returns information about CFDA programs
#
require(compiler)
# compilePKGS()
set... |
a5ac1487ea95831fbe0ec9498ea97a9f17d6be7e | 478e2bacd8621f8846fca224134b8b121ea6f193 | /graphics/drawGraph.r | 3212fef06d93104c6d11688e914b119d0b58b59f | [] | no_license | krprls/Variant-graph-analysis | aae8c419bfca91a9bb69f2fa21c4764c1ed11c8a | 981de650670baf25bea19659b2e5e9b1668bd044 | refs/heads/master | 2020-06-03T05:53:02.225539 | 2017-06-01T07:51:12 | 2017-06-01T07:51:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,216 | r | drawGraph.r | ##############################################################################
# drawGraph.r
#
# Analyses for master thesis
# ======================================
#
# Feeds the edgeList from BuildGraph.java into igraph for visualization
###############################################################################
... |
510c7fc4e951aff2c10c7800e4276a2b9d12fe47 | 31fbbb9f41d6540933a9dd69f43a3b7f97577423 | /user_jc/overlap_between_disease_sets.R | 5f5ddedaac099133c6dd57cbcfc351317ae69a9f | [] | no_license | potentece/ses | ecd75f5705344443d30d2560003ebf57f5416871 | 3fb7cd5d6c1a8e413f4ebd929b68ceef31b9bc38 | refs/heads/master | 2022-12-24T02:27:57.515238 | 2020-10-01T10:05:20 | 2020-10-01T10:05:20 | 280,169,302 | 0 | 0 | null | 2020-09-01T11:44:02 | 2020-07-16T14:02:43 | null | UTF-8 | R | false | false | 2,671 | r | overlap_between_disease_sets.R | #' ---
#' title: Examples
#' output:
#' html_document:
#' toc: true
#' highlight: zenburn
#' ---
#' <!-- rmarkdown::render("supervised_play/nice_code.R") -->
#' <!-- [See here.](http://brooksandrew.github.io/simpleblog/articles/render-reports-directly-from-R-scripts/) -->
#' Set global options
#+ setup, w... |
3083de1ff392fb9a7049d66edf09ee1613544a16 | df02a3254469994aee94ba9783ae5823a138d6a8 | /scRNA_operation.R | ff432147fada5b794c583034ee52aecc0416845f | [] | no_license | liuzhe93/FengLab-PCNSL | 57022228e177b018135696d8b44f03027d28240d | 77c3eac98be00ed2c5ec804888e2c61d996a8eae | refs/heads/main | 2023-07-03T21:24:13.972032 | 2021-08-07T13:49:00 | 2021-08-07T13:49:00 | 386,863,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 63,014 | r | scRNA_operation.R | # set workpath and load R packages
# conda activate Renv
setwd("/ifs1/Grp8/liuzhe/scRNA/")
rm(list = ls())
#remotes::install_github("satijalab/seurat", ref = "release/4.0.0")
#remotes::install_github("jlmelville/uwot")
#install.packages('Seurat')
library("Seurat")
#install.packages('tidyverse')
library("tidyve... |
4fef26fd9dbc9ca88ab2da0f41a37e8076bcd20a | 5d6ac20f8e20d5c7739c68e7d44542f3bc5f8ba7 | /R/grid2.R | a55f90ad65b8ebd026c426a240307f5d844da0ee | [] | no_license | kl-lab/fformpp | d4585c8a8359e1d0a15bbcea379ffa54488e0992 | 1ec46c507e0c4781a8d274fb69354f7b778df92e | refs/heads/master | 2022-11-16T14:36:59.297031 | 2020-07-14T14:51:23 | 2020-07-14T14:51:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 200 | r | grid2.R | ##' Add grids on plot
##'
##' @export
grid2 <- function(x.at = NA, y.at = NA, col = "black", lty="dotted", lwd = 0.5, ...)
{
abline(h = y.at, v = x.at, col = col, lty = lty, lwd = lwd, ...)
}
|
ec40df02cfe540c751e8221ad834516f07f72228 | ccf77c83c0c4a328b7d6e353ad85ce4d2bf199d6 | /R/summarize_model_results.R | c11e8fe0f879fcab213c8cdaeb52c35a79938c0a | [] | no_license | FerreiraAM/CyTOFpower | 6c314ad5e842dcc3ac0c5481168f38b5f6c0fe01 | 0ba3553ad1c288fc35c89bbd25e46c4f1cf76f66 | refs/heads/master | 2023-08-29T10:09:27.221017 | 2021-10-18T20:40:35 | 2021-10-18T20:40:35 | 355,366,603 | 0 | 0 | null | 2021-10-13T01:51:48 | 2021-04-07T00:32:49 | R | UTF-8 | R | false | false | 1,323 | r | summarize_model_results.R | # Functions to summarize results from the different models
#' Summarize data and results.
#'
#' @details Function to do a summary of the tested data and model's results
#' for the CytoGLMM and diffcyt packages.
#'
#' @param summary_from_model list, output from the functions running the models.
#' @param package char... |
1cbd46b39aa98746196511a1c9563d6ad7ef39be | 265fd47d1e5471091acf34c02769f0cf87a931c3 | /ml02/ej_e_plot.R | c29aaffc73e38a3f3273d2310d8f0e381e02e9d7 | [] | no_license | hgurmendi/machine-learning | 1646e4e2d96841afa02f025a101130ae997a844b | 72181f5a27dba1f25f498ecfc4f0b66899d16c59 | refs/heads/master | 2018-10-19T19:04:57.322637 | 2018-07-23T00:53:56 | 2018-07-23T00:53:56 | 86,375,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,438 | r | ej_e_plot.R | #!/usr/bin/Rscript
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 3) {
message("USAGE: ./ej07_plot.R parallel_errors diagonal_errors anns_errors")
quit()
}
message("parallel")
message(args[1])
message("diagonal")
message(args[2])
parallel <- read.csv(args[1], header = TRUE)
diagonal <- read.c... |
71d8c9f8d30402dbee3c95359cd7b00143d50376 | 18fb84b6b827113aea196209b55b34fc846f718c | /man/print.callfest.Rd | 84fb24f08fdd3fc427191df2d84c622217d49c92 | [] | no_license | vst/callfest | b291ce49598884ff4eecbeb3f43aa28bfca4949b | 177c348cd2401b252c574ae07c43bf4a72766d39 | refs/heads/master | 2021-01-01T05:49:10.127641 | 2014-12-13T10:03:06 | 2014-12-13T10:03:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 376 | rd | print.callfest.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{print.callfest}
\alias{print.callfest}
\title{Pretty prints the \code{callfest} instance}
\usage{
\method{print}{callfest}(x, ...)
}
\arguments{
\item{x}{A \code{callfest} instance to be pretty printed.}
\item{...}{Additional arguments to print method.}
}
\des... |
537b03771687fdd357f02cf471e6393c35b695ea | 10fe70854c82345f8bd8669330306c0e5fdcffb4 | /function/test_what.R | 2608019cbc74e8cde1b3cfbf2def124466c67ada | [] | no_license | yahcong/travel | 724f7c40ff91c89cf1580df223af52b241cf55c7 | 1863d1d89476b81b4dc42e9ded65c4479535982b | refs/heads/master | 2021-05-13T15:27:12.628805 | 2018-01-14T13:16:57 | 2018-01-14T13:16:57 | 116,768,809 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 361 | r | test_what.R | rm(list=ls())
library(data.table)
setwd("F:/DataMining/R/travel")
load("model/randomForest2.rda")
library(randomForest)
test_action=new_aciton_test_hour
str(test_action)
test_action$predict_type=predict(fit2,test_action)
table(test_action$predict_type)
#-------------------predict----------------------#
load("data/o... |
46f8960aed6fbe78dc4dcf1b7d25dcc5bb6f2270 | efde884c9fe091891cc24f765a5d3dfe08eed653 | /tests/testthat/test-normalisation.R | 6beb66efa0681dce82e029ff2b35b8d637de3529 | [] | no_license | koenvandenberge/dynnormaliser | 7cd07ff17e4f4903491034dfa4f73fc9940d47a7 | 41e08d28a0ab83781ed550f3eb3f0f204b4d0752 | refs/heads/master | 2020-03-09T23:50:26.359309 | 2018-04-11T09:20:36 | 2018-04-11T09:20:36 | 129,066,110 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 502 | r | test-normalisation.R | context("Normalisation")
test_that("Testing normalise function", {
counts <- matrix(round(2^rnorm(30*50, mean = 6, sd = 2)), ncol=50)
counts[sample(c(T, F), length(counts), prob = c("T"=.1, "F"=.9), replace = TRUE)] <- 0
rownames(counts) <- paste0("Cell", seq_len(nrow(counts)))
colnames(counts) <- paste0("Gene... |
3e5a61973c8b1dde5140b8114ec5f4e3694cd209 | 297dd41b2457e3d2b2fd0ea9f4fd6dc91ccc0217 | /cimis/get_cimis_et.R | b102526ab2c94f1dfc2ccc436f0a84ae0bfd2774 | [] | no_license | richpauloo/junkyard | 51ee1bdbca9fa343872f03e753dddba08af5cc11 | 9a38f823805cc86d43a2a2b2523573062ef7ce56 | refs/heads/master | 2022-08-29T04:31:47.949705 | 2022-08-15T21:34:33 | 2022-08-15T21:34:33 | 164,265,351 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,927 | r | get_cimis_et.R | ##########################################################################
# This script scrapes the last month's ET data from active stations in
# California's Central Valley from CIMIS:
# * web: https://cimis.water.ca.gov/
# * API: https://et.water.ca.gov/Rest/Index
#############################################... |
01ab62b2f986b9af96721ee0548763e576bacfe0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/StreamMetabolism/examples/simp.Rd.R | a27fdc87b2958605ceba56358e97f39857997dd2 | [] | 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 | 324 | r | simp.Rd.R | library(StreamMetabolism)
### Name: simp
### Title: Numeric Integration Using Simpson's method
### Aliases: simp
### Keywords: math
### ** Examples
# 4-x^2-y^2
fun <- function(x, y){
a <- 4
b <- x^2
d <- y^2
z <- a-b-d
return(z)
}
a <- fun(seq(-1000,1000,1), seq(-1000,1000,1))
simp(a, x=-1000:1000, n=1000) ... |
b9df6252ef6cba18186a808cde95fe4a07d7df8f | 5ac57449f8a0cfbc0e9c8f716ab0a578d8606806 | /man/temax.Rd | 0053701f48ac16c9094b1c71dff69acf00b2b25c | [] | no_license | hugaped/MBNMAtime | bfb6913e25cacd148ed82de5456eb9c5d4f93eab | 04de8baa16bf1be4ad7010787a1feb9c7f1b84fd | refs/heads/master | 2023-06-09T01:23:14.240105 | 2023-06-01T12:51:48 | 2023-06-01T12:51:48 | 213,945,629 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 5,233 | rd | temax.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/time.functions.R
\name{temax}
\alias{temax}
\title{Emax time-course function}
\usage{
temax(
pool.emax = "rel",
method.emax = "common",
pool.et50 = "rel",
method.et50 = "common",
pool.hill = NULL,
method.hill = NULL,
p.expon = F... |
defeb0db1217403fc2a13f825d911f416947f194 | 9dd936c7b849d368e9ef2a450ebd32f09ed924da | /man/BrainIQ.Rd | 97990df82d6b07288239dd7c64a5fbcb0bf0978e | [] | no_license | cran/MPsychoR | 0d12d19c6044ebc9207ae27a41061301f217ae0c | 717f2223eecdd97ad55ed9d073410512b1e94077 | refs/heads/master | 2021-04-03T09:19:37.646636 | 2020-06-18T05:17:05 | 2020-06-18T05:17:05 | 124,736,769 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,591 | rd | BrainIQ.Rd | \name{BrainIQ}
\alias{BrainIQ}
\docType{data}
\title{
Brain Size and Intelligence
}
\description{
Willerman et al. (1991) conducted their study at a large southwestern university. They selected a sample of 40 right-handed Anglo introductory psychology students who had indicated no history of alcoholism, unconsciousness... |
eb16e09400d638de2b4fd5ba1692d738f7ef6aab | 8cbfb091e9444261b9a957bbb745ac04a5ef2c98 | /PCAWG_timing.R | c7cbf2eb6e0b30f1841f6562f2c76abc8c8ad225 | [] | no_license | smm19900210/PCAWG11-Timing_and_Signatures | 2ee400c7828e463a7486aa42126be144cc723c89 | 015bc714d8e9d45b61fb829b2e66bb634a2e8b43 | refs/heads/master | 2022-02-11T15:27:11.941919 | 2019-08-25T16:46:27 | 2019-08-25T16:46:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,157 | r | PCAWG_timing.R | # Mutational timing of gains in single samples
# Libraries and additional code
library(VariantAnnotation)
library(cancerTiming)
source("cancerTiming.WGD.R")
# each VCF file is input as argument to script
args = commandArgs(TRUE)
vcf_filename = args[1]
# clustering results
clustering_output_path = args[2]
# copy n... |
7b952bc6613fcf35c106048469c0de4e36a5c52c | 7f83f684b76b225e21f00ef721f846d371025521 | /join.R | 61f56e1c0a0f43156fedb22a595ec83132484d2a | [] | no_license | vladmonteiro/eletiva_analise_de_dados | 2225771ba7c145742f478d1835fc3852605f6c7d | 650d283a68148ece6fda730f2fda237ea6c67d04 | refs/heads/master | 2023-06-10T13:56:19.802780 | 2021-07-05T02:33:37 | 2021-07-05T02:33:37 | 356,731,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 413 | r | join.R | library(tidyverse)
library(dplyr)
library(foreign)
conflicts <- read.csv2('base_original/MIDB4.3.CSV', sep = ',')
politics <- read.csv2('base_original/institutions.csv', sep = ',')
banco <- inner_join(conflicts, politics,
by = c("styear" = "year", "stabb" ="ifs"))
banco2... |
6d5221b595a9e71b9791d0c46e2642665ad47d20 | 83840bba98c2ed74d2256f9bddc436a4c59a8126 | /tests/testthat/test_common_artifact_part.R | 03ace8f4bdad5b6f93409aba1fac7346498c5607 | [] | no_license | botchkoAI/VertaRegistryService | 4290e1d5fbb67c979ead090f4c042f2d3dedf5f7 | 0bc53050d6ddc4ab9ff540e8116d571cd9eb5699 | refs/heads/main | 2023-06-03T08:37:22.340430 | 2021-06-16T23:01:05 | 2021-06-16T23:01:05 | 377,648,143 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 563 | r | test_common_artifact_part.R | # Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test CommonArtifactPart")
model.instance <- CommonArtifactPart$new()
test_that("part_number", {
# tests for the property `part_number` (character)
# uncomment below to test the propert... |
5cfac08d714fb53f0f9dd3f073442e503afdf481 | 5a952d0bc4237afd8c732186f263f6016f99d508 | /run_analysis.R | d00627e6408c968d290006c0e2c91c0a4cac40f4 | [] | no_license | pjohnston/gettingandcleaningdata | cd2f4fb24024b2b1046c2f141aa56cbe870572d1 | c276bbf732f60d9551af09aa145d0dcd89f10aad | refs/heads/master | 2020-05-07T05:46:37.075361 | 2014-12-19T16:51:04 | 2014-12-19T16:51:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,962 | r | run_analysis.R | # Getting and Cleaning Data - Coursera Course. Course Project
#
# Paul Johnston
# 12/8/2014
# One of the most exciting areas in all of data science right now is wearable computing
# Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract
# new users. The data linked ... |
5fa60a8d85d6a4f56250c88bacd14a554e764b0a | 161940a51deb66a640489adcd953caa85317e107 | /R/import.R | 0ecf9f5cc6111d35386f2ec3ca392983ff8f64c0 | [] | no_license | baifengbai/modules | f8d00c9ac573efb01b78343189c437981c2c3849 | 9873a6ada76f7d1f24d7dc6293459498f3e59f3c | refs/heads/master | 2020-04-02T12:03:38.991021 | 2018-06-24T20:50:46 | 2018-06-24T20:50:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,124 | r | import.R | #' @rdname module
#' @export
import <- function(from, ..., attach = TRUE, where = parent.frame()) {
where <- importCheckAttach(where, attach)
pkg <- importGetPkgName(match.call())
objectsToImport <- importGetSelection(match.call(), pkg)
addDependency(pkg, objectsToImport, where, makeDelayedAssignment, pkg)
in... |
8caae52bfd7dc0f9566c438460b74d4b554c954c | 414754f98514fec8c8c8230e064f742f2a4ae6b3 | /machine_learning/matrix_factorization.R | 71f694e59b892a49dbb31e70cc25cf05f3f87784 | [] | no_license | brambloemen/HarvardX_GenomicsClass | 38da74d6eb56c5704ee4af7bfc052c614af7baf9 | 2a0a2357b21866832d7ccef5b7a2dd37b8561562 | refs/heads/main | 2023-05-08T15:00:38.847068 | 2021-05-29T12:01:58 | 2021-05-29T12:01:58 | 371,959,067 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,119 | r | matrix_factorization.R | .libPaths('C:/Users/BrBl1834/R/win-library')
library(tidyverse)
# set.seed(1987)
#if using R 3.6 or later, use `set.seed(1987, sample.kind="Rounding")` instead
set.seed(1987, sample.kind="Rounding")
n <- 100
k <- 8
Sigma <- 64 * matrix(c(1, .75, .5, .75, 1, .5, .5, .5, 1), 3, 3)
m <- MASS::mvrnorm(n, rep(0, 3), Sigm... |
acb19f34fa4d702a781b02ed18ed50be8a7b58f0 | 9c0aa0ced21d27e9dd0618fef4785943d59c420d | /ui.R | 457d1085d47fd7381d8c5c1206c06d83592ae9dc | [] | no_license | travisfell/10---Capstone | d7107c1cf341ca0dc2ee30f06241226d208d6031 | cf74dd63b9c308e66d27085f0ccda9e90fdcad68 | refs/heads/master | 2021-01-21T13:07:26.466000 | 2016-04-21T04:13:25 | 2016-04-21T04:13:25 | 53,469,004 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 833 | r | ui.R | #ui.R
# This is the UI definition script for the
#Capstone final project
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Text Prediction Cloud"),
sidebarPanel(
h3('Enter your text.'),
p("This app predicts the next word based on the words you enter."),
p("Type your text in the ... |
aa7880fa2d6a93677a5efe2ab1b6b72cdd0a221a | e3495708f48030176093019ba38a08b0afc6fe5a | /waterfall.R | bc4621e4bcf45ae6ab6f943d429972f86fddd4ca | [] | no_license | plus4u/Data-Analytics | 32ae849aeb0c40484e733d02a4edc6e5b64b1c5c | 811ea7ac651019d6cb03f71d47d008f7074beb25 | refs/heads/master | 2022-07-07T09:47:05.876879 | 2022-07-03T23:01:36 | 2022-07-03T23:01:36 | 146,960,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,834 | r | waterfall.R | ### waterfall
### 2 methods : simple ,
###################################
### @ 1
install.packages("waterfalls")
library(waterfalls)
group <- LETTERS[1:6]
value <- c(2000, 4000, 2000,
-1500, -1000, -2500)
df <- data.frame(x = group, y = value)
waterfall(values = value, labels = g... |
fcecc45d7a099f97e517c1aea7be4e7e059828e6 | 6984fd7eca0e35087b50c245a2c4f55b3b1c0964 | /WIP/Evaluating Variational Inference/Figure_2_linear_reg.R | 939bf09637210d7b610a9dae11d56003a592cc15 | [
"MIT"
] | permissive | junpenglao/Planet_Sakaar_Data_Science | 20620b5b0de0705fd7f7dcf0a477745325fa43c1 | 4366de036cc608c942fdebb930e96f2cc8b83d71 | refs/heads/main | 2023-04-13T15:06:08.128262 | 2023-04-05T08:56:32 | 2023-04-05T08:56:32 | 127,780,588 | 55 | 14 | null | null | null | null | UTF-8 | R | false | false | 7,526 | r | Figure_2_linear_reg.R | ###### Figure_2, a bayesian linear regression, ##############################
#############the result is sensitive to the tolorance, which scales with the problem complexity. K hat gives a convergence diagnoistic
library(rstan)
library(loo) ### loo is used for PSIS
setwd("")
rstan_options(auto_write = TRUE)
options(... |
b3c4cbdd4e4d8e035e9fec1277cfbb92f2adbe1a | 644cf30f84ed01751811c124c9c3594f62a25de0 | /Part 2/Prediction/Random Forest/RF_FULL.R | 34c5708d9247077cb5b7261e81f547ee7b03d302 | [] | no_license | ahmeduncc/Team_1_Energy_Consumption_modeling | f35c369cb1e9d4668c2cc71190b75f9c9e6ddb6f | a9593549c582042719fabb1b545a2bc295ad883d | refs/heads/master | 2021-06-08T23:19:12.311840 | 2016-11-19T05:24:59 | 2016-11-19T05:24:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,929 | r | RF_FULL.R | install.packages("hydroGOF")
install.packages("randomForest")
library("hydroGOF")
library("randomForest")
install.packages("rjson")
library("rjson")
config_json<- fromJSON(file="config_RF.json")
n_tree_value <- as.integer(config_json$ntree)
ab <- read.csv("final_sample_format_Part1.csv")
#View(ab)
final_file <- data.... |
c14778450a5d4b477f0bc797481fb3eb8ad2bf18 | 8ced7c6508be6fbc0b3c3ba6a84440a6353f33b9 | /bsem_analyses/process_models.R | b0cb55dd74676b74853b751ef016ee2d9d4f214a | [] | no_license | UNCDEPENdLab/attachment_physio_coreg | ae7bff7c288874204a6900addff101cd005d5d37 | 5a73c065e5b82f168a93dceebdad46adf02c7643 | refs/heads/master | 2022-12-16T08:44:25.337138 | 2020-09-25T17:46:43 | 2020-09-25T17:46:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 46,652 | r | process_models.R |
# Set up paths ------------------------------------------------------------
output_dir <- "../output"
bsem_process_res_dir <- "../output/bsem_process"
bsem_personalitymod_res_dir <- "../output/bsem_personalitymod"
if(!dir.exists(output_dir)) {dir.create(output_dir)}
if(!dir.exists(bsem_process_res_dir)) {dir.create(bs... |
566202ed1c8769f5ad66f66c9118f800f26a6830 | ef572bd2b0515892d1f59a073b8bf99f81d6a734 | /data-raw/update_cached_map_datapack_cogs.R | 3bdd2f4eda1c99d04a387d4149aa3829bb28f290 | [
"CC0-1.0"
] | permissive | pepfar-datim/datapackr | 5bc604caa1ae001b6c04e1d934c0c613c59df1e6 | 9275632673e45948db6846513a53c1436cfc0e47 | refs/heads/master | 2023-08-30T23:26:48.454382 | 2023-08-11T13:01:57 | 2023-08-11T13:01:57 | 170,350,211 | 9 | 7 | CC0-1.0 | 2023-09-11T21:53:24 | 2019-02-12T16:19:47 | R | UTF-8 | R | false | false | 1,229 | r | update_cached_map_datapack_cogs.R | # Load PSNUs into package from DATIM ####
library(magrittr)
library(datapackr)
# Point to DATIM login secrets ####
secrets <- Sys.getenv("SECRETS_FOLDER") %>% paste0(., "cop-test.json")
datimutils::loginToDATIM(secrets)
# Processing
datapack_cogs <- datapackr::datapack_cogs
datapack_cogs$COP22 <-
datimutils::get... |
91d889ea878a9b28d6c06ecc0e1c0abec2e7add0 | 6be70ffdb95ed626d05b5ef598b842c5864bac4d | /old/senate_party_calls_replicate_figures_emIRT_only.R | 2c458e2313bd3927b81feb88c1111310d53d268b | [] | no_license | Hershberger/partycalls | c4f7a539cacd3120bf6b0bfade327f269898105a | 8d9dc31dd3136eae384a8503ba71832c78139870 | refs/heads/master | 2021-09-22T17:54:29.106667 | 2018-09-12T21:16:56 | 2018-09-12T21:16:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,906 | r | senate_party_calls_replicate_figures_emIRT_only.R | library(partycalls)
library(data.table)
options(stringsAsFactors = FALSE)
# load data for analysis
load("data/senator_year_data_emIRT_only.RData")
senator_year_data <- senator_year_data[senator_year_data$drop == 0]
# whoheeds13 <- readstata13::read.dta13(
# "inst/extdata/who-heeds-replication-archive.dta")
# setDT(n... |
7722403a336a399bd19c2208149d259764073cb8 | 727e491d1d19394960d52f732e367f37be038956 | /dieroller/dieroller.Rcheck/00_pkg_src/dieroller/R/hello.R | 35e2345e0f7a968fc2f454eb8a51cb0acd9431d7 | [] | no_license | JustinRiverNg/Computing-Data | a0a3ddd9a0ae11699bdf3efe34cda7dc45b369fd | 4ff7415cb09b5148fadb51310b27dbdd1746603b | refs/heads/master | 2020-03-30T10:34:10.423755 | 2018-04-27T18:12:36 | 2018-04-27T18:12:36 | 151,125,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,881 | r | hello.R | # Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Build and Reload Package: 'Cmd + Shift + B'
# Check Package: ... |
334141a545dd8c654fe3bee70856ff9fb2a18ba0 | 0b5a137ce9998b30efd1a2b3061712951602f4c0 | /analysis/fit_model.R | 10329eb427475f05efe3e618fad22b753f043fe9 | [] | no_license | jsphillips2/midge_pi | 0c272ba3acdef5faddb999240251382ea917d41b | 33e28ff1a250620f201299feadb67a6a33ea8b9e | refs/heads/master | 2022-12-01T13:36:36.001157 | 2020-07-28T13:54:10 | 2020-07-28T13:54:10 | 277,593,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,578 | r | fit_model.R | #==========
#========== Preliminaries
#==========
# load packages
library(tidyverse)
library(rstan)
source("analysis/model_fn.R")
# stan settings
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores()-2)
# import data
data <- read_csv("data/metabolism_2015.csv")
# process data
dd <- data %>%
... |
7cc5954d2799faec76769c7ae25ca5dd11bfc3db | 1dc421a198c86a888f2029ce96d09924aac6cab2 | /R/testIndSpeedglm.R | f79b939f052f93648e27825b5a645c5a13fa5487 | [] | no_license | JokerWhy233/MXM | 519b8216a1a2a965a8e43531fd52fb0a7c460f86 | 035673338ed6647239a4859981918ddf3b8ce38e | refs/heads/master | 2021-08-22T14:54:05.597447 | 2017-11-30T10:41:38 | 2017-11-30T10:41:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,790 | r | testIndSpeedglm.R | testIndSpeedglm = function(target, dataset, xIndex, csIndex, wei = NULL, dataInfo = NULL, univariateModels = NULL, hash = FALSE,
stat_hash = NULL, pvalue_hash = NULL, target_type = 0, robust = FALSE) {
# TESTINDSPEEDGLM Conditional independence test for large sample sized data (tens an... |
94e5857e3b152851644083bb8737371274f5fe63 | aff8c05c8d8c6f7ac6b95d535aa3a00576c14798 | /fuzzycmeans/fuzzycmeans.R | 8d928cf9bcbd094afbb4f2a258832bcef0a0dffb | [] | no_license | coppolalab/Chandran_pipeline | 6905263d043bd34c82e4d5a0deda9744d6d2f16a | 84fbf25398deb9e2e47ad2df7bdbdcc6f87d988e | refs/heads/master | 2021-01-22T22:13:22.137333 | 2017-03-20T00:52:41 | 2017-03-20T00:52:41 | 85,520,556 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,717 | r | fuzzycmeans.R | #String operations
library(stringr)
#Reading and writing tables
library(readr)
library(openxlsx)
#For plotting
library(ggplot2)
#For microarray stuff
library(Biobase)
library(matrixStats)
library(abind)
library(lumi)
library(lumiHumanAll.db)
library(annotate)
library(sva)
library(peer)
library(limma)
#Longitudinal ... |
23e9d58f3ada650c4cc1d8e9f3b3b1b73e70850d | b6e7d568d3c01b5fb2ee63a8c4594efc995e37c3 | /R/msrep.R | e9da95c6fef6ad34a9feb034ebb1472ec6f2917c | [] | no_license | cran/longit | 4b87d6851d600a900f59b5e4ebd09f1d1e725a7e | 2aa442df666057b016cc191b5da3e182dda3eacc | refs/heads/master | 2023-04-08T05:09:29.130163 | 2021-04-15T07:00:05 | 2021-04-15T07:00:05 | 358,312,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 580 | r | msrep.R | #' @title longitudinal data
#'
#' @description Longitudinal observation on single variable at different timepoints. Observations arranged in a column as the patient with corresponding column of ID.
#' @usage data(msrep)
#' @format A \code{tibble} with 7 columns which are :
#' \describe{
#' \item{Subject}{Patient ... |
9c281d9e44c2e9f52f9bd5dc3b10f26c7be5d005 | f2bcc38a5ece9b14a3d8b68c5d4a09edff06314d | /man/Seq.Rd | c42e2c811247151c7ec36bbc3170080006a3d33d | [
"Apache-2.0"
] | permissive | charlieccarey/monarchr.biolink | 8eb618c9c61091ac3371e7d67636520a07d3db6b | 5b488a06cedcafc9df44368e2d665555bc5cd53d | refs/heads/master | 2020-03-09T08:40:56.748770 | 2018-04-11T04:33:54 | 2018-04-11T04:33:54 | 128,694,973 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 681 | rd | Seq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Seq.r
\docType{data}
\name{Seq}
\alias{Seq}
\title{Seq Class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
Seq
}
\description{
Seq Class
}
\section{Fields}{
\describe{
\item{\code{id}}{}
\item{\code{label}}{}
\... |
b5de92a0fa9796eadd12ad1f26e2e2eb30b67176 | 72341bedc0e8951ced5f1afa53239d7ee02b03b1 | /split.R | e0a3517e2d629fc9744ecdcb3fd830846ef0c7c7 | [] | no_license | vippro169/Load-profile-simulator | e9cba74dc2d9bb4172836fdd697f7bdfbf2e9174 | c80aeb38fb67059206955307d9db78b125db874b | refs/heads/master | 2021-09-13T11:35:26.326489 | 2017-11-26T04:51:35 | 2017-11-26T04:51:35 | 111,104,055 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,122 | r | split.R | setwd('~/Documents/Project 1/household 1/01_plugs_csv/01/06')
filename <- list.files(pattern = "*.csv")
for(i in 1:length(filename)){
power <- read.csv(filename[i])
time <- seq(ISOdate(2017,11,7), by='sec', length.out = 86399)
smr <- data.frame(unclass(summary(power)))
preference <- power[,1]
preference[pr... |
65b345974ac63d9d915815a535be763133073b8a | abedec3e252d97868aea5b3385a77313ae26a92f | /baby_names02.R | 6128e229d267fd910e6d688ec033a35353f83f13 | [] | no_license | dk81/R_projects | b4129a88ab48060727cde75810d69baa2e50e5f3 | f55ba52f4864593bc3970bc9de22f9b96f02ec43 | refs/heads/master | 2022-03-13T23:45:35.582464 | 2022-03-06T16:33:59 | 2022-03-06T16:33:59 | 140,731,173 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,831 | r | baby_names02.R | # Sideways Bar Graph
# Analyzing Baby Names Part 2:
# A Bar Graph Approach
library(babynames) # Baby Names dataset:
library(ggplot2) # Data visualization
library(data.table) # For data wrangling and manipulation
# Save the babynames data into baby_data:
baby_data <- data.table(babynames)
# Preview the data:
head(... |
da428483de4c57b4a9763d492de1b4cd5154eb9c | 074d650201fbf5c15482e7a733e5f2a0e6330dcd | /NetLogo R Analyses/Spacial Statistics.R | 160fd885d986aa5e0bf9c4be03c3bcd780d18539 | [] | no_license | smwoodman/Bee-Lab-Thesis | b2944b0bbda8fa05c1ba1abb616577695213a3c3 | 87c4219bcc68f2c4ac1859715142aaed5654dc4f | refs/heads/master | 2023-05-01T21:21:08.467938 | 2021-05-21T00:09:20 | 2021-05-21T00:09:20 | 50,375,952 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,117 | r | Spacial Statistics.R | # Sam Woodman
# Analysis of spacial statistic R
# General, Desne, and Sparse Specific Testing
library(dplyr)
setwd("~/Google Drive/Semester 8/Thesis/NetLogo+R GitHub/BehaviorSpace Data/R Value/")
### General Testing
g.data <- read.csv("Bees R extra_sparse testing1-table.csv", header = TRUE, skip = 6)
## Filter data ... |
47426d3f488d8a71d89632133776d65fab14ecc2 | 63ce3e48f2217972353de20d58bf2a56d58a31b0 | /R/some.R | 54c8ecf46ed1846ff16b6441974e253ae0bcaed1 | [] | no_license | cran/car | 6c8c284a58be9798daa0407f65cd5dd1fc808d34 | d67a2f5ed4013c8766e8d3e827dcaf6aaaf7f0fa | refs/heads/master | 2023-04-06T03:29:23.872258 | 2023-03-30T09:40:02 | 2023-03-30T09:40:02 | 17,694,945 | 8 | 20 | null | 2022-06-06T07:18:20 | 2014-03-13T04:11:37 | R | UTF-8 | R | false | false | 776 | r | some.R | # adapted from head() and tail()
# 3/10/2017: S. Weisberg modified to add an argument 'cols'
# cols = num will display only the first num cols
some <- function(x, ...) UseMethod("some")
some.default <- function(x, n=10, ...){
len <- length(x)
ans <- x[sort(sample(len, min(n, len)))]
if (leng... |
dc56a85d7d36a36a3e0f32f4da2f80b6b2d23301 | 55a2bdd215a66d17bf5e170019cb8de960de760d | /mymain.R | 488bff2c1ad9a98eea927780d9dbec93588202ba | [] | no_license | yvonnechanlove97/Movie-Review | 8569f102f690a5f5ed03efa4354b3d8ee350ef91 | 89c3568044b903dea7679139adff0b54b71716aa | refs/heads/master | 2020-12-13T12:44:03.257227 | 2020-02-18T19:52:46 | 2020-02-18T19:52:46 | 234,420,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,870 | r | mymain.R | rm(list=ls())
library(text2vec)
library(magrittr)
library(glmnet)
#get data
all = read.table("data.tsv",stringsAsFactors = F,header = T)
all$review = gsub('<.*?>', ' ', all$review)
splits = read.table("splits.csv", header = T)
s = 3 # Here we get the 3rd training/test split.
myvocab=as.matrix(read.table("myV... |
87a7b183c2f42c7f07258dd02b328c7415a690e6 | 2e7fcffe2b532125bdc6662db16363ee36696df8 | /RepRes_Peer_Assessment_1.r | 7802225a7cdb113fd4a49c628e7ddecfb0bfde18 | [] | no_license | megerex/RepData_PeerAssessment1 | 802ef20c7c64d8a32821cbcb7dea9f1c94cbf5e7 | 5ecb62b522354e694a8c24ac6702ebbbf6edba5a | refs/heads/master | 2020-05-29T11:55:16.073065 | 2015-05-14T13:59:49 | 2015-05-14T13:59:49 | 35,497,221 | 0 | 0 | null | 2015-05-12T15:43:04 | 2015-05-12T15:43:04 | null | UTF-8 | R | false | false | 1,521 | r | RepRes_Peer_Assessment_1.r | my.data <- read.csv(file="activity.csv", header=TRUE, sep=",", na.string="NA")
my.data$date <- as.Date(my.data$date)
my.data.aveSteps <- aggregate(formula = steps ~ date, data = my.data, FUN = mean)
require(ggplot2)
ggplot(data=my.data.aveSteps, aes(x=date, y=steps)) +
geom_histogram(stat="identity", fill="lightblue... |
e24aeebc182541bed949e7761d51b1a101b05a44 | ca26b58313dc16a137f31f45e39aefe0a1a8f1ba | /rprog-data-ProgAssignment3-data/rankhospital.R | 1f121f24ff966b30b20401dd6dad4ed95fa50850 | [] | no_license | ChuckChekuri/datasciencecoursera | 06c4a95ef37c4fde39ab55dc98de615be98f56cf | 8e81a42c3a1a43372390aa44fdb35bafa21c08fd | refs/heads/master | 2020-05-23T10:06:24.413977 | 2017-04-08T14:18:33 | 2017-04-08T14:19:12 | 80,387,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,040 | r | rankhospital.R |
rankhospital = function(state, outcome, rank) {
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
states <- unique(data$State)
if (!state %in% states) stop("invalid state")
outcomes <- c("heart attack", "heart failure", "pneumonia")
if (!outcome %in% outcomes ) stop("invalid outcome")
#... |
006fe86376259fba85bd2d7d9e6a2dc9db7fa9a3 | cd0e51bf6d007e8f540f22833d8e6ce2d4bba92f | /man/poisson.process.Rd | 1652fd76a39d1d55edb379c39732072d06c292a2 | [
"MIT"
] | permissive | David-Statistics/Clancy-Functions | 07daef7b20f2064e25f42bf987bba6243855cd27 | 2921e42a14f1cd39c5208d5772ebdbb915bb6d03 | refs/heads/master | 2021-05-04T06:46:31.702577 | 2016-11-30T19:21:19 | 2016-11-30T19:21:19 | 70,514,224 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 819 | rd | poisson.process.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/poisson.process.r
\name{poisson.process}
\alias{poisson.process}
\title{Interarrival times and arrival times of a poisson process}
\usage{
poisson.process(rate = 1, events = NULL, time.running = NULL)
}
\arguments{
\item{rate}{The rate/intens... |
fdbc7be148ef51f37de157586c2a330f54ba5f31 | 03d20ec52ea429d2bffdefa849044ab6d0ad7481 | /03_stop_frisk/scripts/stop_and_frisk.R | 4e000dd92f4633cb09c0abbd2cd7809b2f2d1f92 | [] | no_license | GWarrenn/dc_data | 3f679b28aa02f1cec7b9e887d66087d44ed40d7c | 15b358d77210644dcdd908ef05d6e95930fbf62e | refs/heads/master | 2021-11-17T07:36:55.337352 | 2021-09-30T21:44:56 | 2021-09-30T21:44:56 | 98,127,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 58,317 | r | stop_and_frisk.R | ## Author: August Warren
## Description: Analysis of DC Stop and Frisk Data
## Date: 9/6/2018
## Status: Published
## Specs: R version 3.3.2 (2016-10-31)
###################################
##
## Load packages
##
###################################
library(ggplot2)
library(dplyr)
library(gdata)
library(rgdal)
library... |
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