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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ec7aafd7632a3984ac481ec47fea0b8977c672c5 | 306e3e5c3afeb3af6a5ed7171f6cc886b7df00b4 | /run_analysis.R | 7abe3be55be648e84133fb35095788314aabf33b | [] | no_license | lsablake/GettingCleaningData | 0577076dc288d048b178351e6fd846be2d15102a | c9ea16be0fc3536c5203ace32de98a090323566c | refs/heads/master | 2021-01-14T07:55:10.982049 | 2017-03-21T22:52:19 | 2017-03-21T22:52:19 | 81,877,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,100 | r | run_analysis.R | #setwd("C:/Users/Logan/MyRProgram")
#---------------------------------------------------------------------------------------------------
#1. Download and extract raw datasets from source (unnecessary to repeat if datasets are unchanged).
if(!file.exists("./data")) {
dir.create("./data")}
fileURL <- "https:... |
3d86cd41ffaed7b5e82e001333fbdcaac678d2f5 | 719500684fceaf0a7a80ce663e9cf07802e10b9a | /R/write-fwf.r | 0f10fd40d536240263ecea7a97b3752eba4d30f6 | [] | no_license | pbreheny/breheny | 964baf0670a9eb4975946eae66772d47d9affd11 | 3e15bb78a769616adb49ea50c800d026f48ef8e7 | refs/heads/master | 2023-08-09T18:34:35.865871 | 2023-08-01T15:38:54 | 2023-08-01T15:38:54 | 82,972,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,610 | r | write-fwf.r | #' Generate fixed width file in R
#'
#' https://gist.github.com/haozhu233/28d1309b58431f4929f78243054f1f58
#'
#' @param dt The data to be printed
#' @param width Either a single number or a vector of widths
#' @param con Connection, as in `writeLines()`; default is `stdout()`.
#' @param align ... |
c2f4cef290a0d413f44a2a23806e5c00bcad2a88 | 4b77c231c94281c8b111ba762ca60693b460c278 | /lab1/ex3.R | 3a7b11cb62954b8a6ddb089de045a62d53ae88b5 | [] | no_license | kkosiorowska/statistical-lab | 657a6c019ea2186b949f6bcbdad8dd0be0766af6 | 1ae367377087ea38144b3407cda204db7094b344 | refs/heads/master | 2022-04-06T20:37:38.400913 | 2020-03-06T10:39:05 | 2020-03-06T10:39:05 | 244,583,632 | 0 | 0 | null | null | null | null | IBM852 | R | false | false | 1,012 | r | ex3.R | r <- 0.05
rr <- 1 + r / 12
K <- 300000
L <- 20
N <- 12*L
n <- 1:N
rataKredytu <- K * rr ^ N * (rr - 1) / (rr ^ N - 1)
zadluzenie <- K * (rr ^ N - rr ^ n) / (rr ^ N - 1)
odsetki <- K * (rr ^ N - rr ^ ( n - 1)) / (rr ^ N - 1) * (rr - 1)
rataKapitalu <- rataKredytu - odsetki
kredyt <- cbind(rataKapitalu, odsetki, rataKre... |
0784d3c597d65cc0cfc77cf66b6ca187dd621a3f | 16beab4e9d61e113858cdb1fad1c09cc2ad03d26 | /community_based_features.R | 4f348f0d1711f5b6e7dfb68ae926322fc6cb09b8 | [] | no_license | jiunnguo/rstyle | ddced1de69eecdce7699db1fe4cd0054e57f8a27 | 48557a9d1a7fbc006b5608f7df1f1781eccf9bce | refs/heads/master | 2020-09-11T10:49:52.683371 | 2019-07-12T08:48:23 | 2019-07-12T08:48:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,921 | r | community_based_features.R | require(tidyverse)
require(igraph)
require(rex)
require(datasets)
require(dplyr)
require(purrr)
require(stringr)
require(iterators)
pkgs <- readRDS("pkgs_functions_with_syntax_feature.RDS")
comm <- readRDS("cran_community_20190518.RDS")
style_regexes <- list(
"alllowercase" = rex(start, one_or_more(rex(one_of(l... |
5e5e6e25aec773f41d0fb071a8ab283abff96b71 | 256d3f44b60010812de16c9cfc8d361e8a7c14ed | /plot2.R | d116958f057d84e588337b980d9523b68c090770 | [] | no_license | mrcherve/ExData_Plotting1 | b0a9f486c87d5a09c70fe27b08378bf937fe586f | 2b3df09db4b1eee77d78fcb591ca08a598925e11 | refs/heads/master | 2020-09-10T20:29:17.754545 | 2019-11-15T02:34:45 | 2019-11-15T02:34:45 | 221,826,861 | 0 | 0 | null | 2019-11-15T02:24:45 | 2019-11-15T02:24:43 | null | UTF-8 | R | false | false | 939 | r | plot2.R |
data_url <- "C:/Users/mikael.herve/Documents/R/household_power_consumption.txt"
class <- c(Voltage="numeric", Global_active_power="numeric",Global_intensity="numeric",Sub_metering_1="numeric",Sub_metering_2="numeric",Sub_metering_3="numeric",Global_reactive_power="numeric")
origin <-read.table(data_url, header=TRUE... |
3b72cf4bd289fb9199385c4b17f86e38be1ae54f | 81a2fa3228451179b12779bb0149398cbfc8e9b1 | /man/dot-insp1dimByClustering.Rd | 161a1f587972d0e7ddeb358e0345acdd1f2e5cd2 | [] | no_license | cran/wrMisc | c91af4f8d93ad081acef04877fb7558d7de3ffa2 | 22edd90bd9c2e320e7c2302460266a81d1961e31 | refs/heads/master | 2023-08-16T21:47:39.481176 | 2023-08-10T18:00:02 | 2023-08-10T19:30:33 | 236,959,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,210 | rd | dot-insp1dimByClustering.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/searchLinesAtGivenSlope.R
\name{.insp1dimByClustering}
\alias{.insp1dimByClustering}
\title{Segment (1-dim vector) 'dat' into clusters}
\usage{
.insp1dimByClustering(
dat,
automClu = TRUE,
cluChar = TRUE,
silent = FALSE,
debug = FAL... |
6a14412a082ab32593dc501b392d8ffde5bd9e47 | 74d8c1f83aa5cc608eecb91e5282b1c93cfb87db | /ShinyApps/teamStats/server.R | 24d8c9f45d734168e04af1a2fd68f4b654cf7179 | [] | no_license | rjmorgan4/585-project | 6eb053e213537944797cbd68fb6c18018c064360 | 23cd32eb882d824040bb8eae1094bb37cd71e3f8 | refs/heads/master | 2021-01-18T23:48:37.882000 | 2017-05-01T16:03:59 | 2017-05-01T16:03:59 | 87,127,431 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 985 | r | server.R | ###Team Stats
## Server
library(shiny)
library(ggplot2)
library(plotly)
##Read in a data set that is saved to the same folder
teamStats <- read.csv("All_Schools_Team_Stats_Post2000.csv")
shinyServer(function(input, output) {
#Put reactive something here for manipulating the data
filte... |
86b780d9d3316d35053495e47d7f4d5c53ba1e7a | 58d06ff7d5c1e12e4033f2024e837b723951a7f7 | /TM_Swades.R | c97652df5fdc698f4b6a1dd35f09d76c17396888 | [] | no_license | karthiknr2/Karthik | a7471280d31164db19afc0343cf448eebf22cb1e | ddca7b72fe4074992686f78e4781df07b7855dbd | refs/heads/master | 2023-01-07T08:54:12.702385 | 2020-10-19T19:03:29 | 2020-10-19T19:03:29 | 257,606,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,937 | r | TM_Swades.R | library(rvest)
library(XML)
library(magrittr)
library(rJava)
library(tm)
library(SnowballC)
library(scales)
library(wordcloud)
library(RWeka)
library(textir)
library(data.table)
library(stringr)
library(slam)
library(ggplot2)
library(syuzhet)
library(reshape2)
library(dplyr)
library(lubridate)
librar... |
e2b5b9205320a06702e50a2295468a49aa42a44c | 366397e9b2bf247a1f2be266b6ec3ccc092ed288 | /man/cc_severe.Rd | 69023239a185c27a602a7fedeca3bd83712e6e4d | [] | no_license | cran/edgedata | c7f5356b20551f899a0990062ce5df570390e872 | 25157bf66a805e34014cbf0e400f3aa7ba1f92c0 | refs/heads/master | 2023-03-09T02:34:20.016139 | 2021-02-26T21:00:09 | 2021-02-26T21:00:09 | 258,765,469 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 852 | rd | cc_severe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cc_int.R
\docType{data}
\name{cc_severe}
\alias{cc_severe}
\title{HCC to severity group mapping - Table 6}
\format{
An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with
8 rows and 3 columns
\describe{
... |
4e385e3d52ab74ee044f4b95bcf0e21128531f44 | ab85697ca3f211c4bd5e2b4f0086426fa3839298 | /drills/plot-drills/ggplots/ggplots.r | f1d9c835ab244b859f245761c3b39c0e424ae665 | [] | no_license | hadley/stat405-resources | f248dc99e0b523f340ff3eb6f56a5e9ba39524ad | 35fed042554e8fb29b49bf28144e98c563b565ab | refs/heads/master | 2016-09-06T04:09:32.598802 | 2011-01-12T13:20:35 | 2011-01-12T13:20:35 | 288,798 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 9,172 | r | ggplots.r | library(maps)
library(ggplot2)
feb13 <- read.csv("delays/delays-feb-13-2007.csv", header = T, stringsAsFactors = F)
# 1. Texas Plane flights
texas <- map_data("state", "texas")
texmap <- c(
geom_polygon(data = texas, colour = "grey70", fill = NA),
scale_x_continuous("", limits = c(-107, -93)),
scale_y_continu... |
200e42635c81a23c0a24278b9a1a81b00d104f04 | e04c0d423fde5be2567111b6983cc91e63c93232 | /R/databricks_execute.R | 93400929ae27a198074efd53dc641999065c1d08 | [] | no_license | RafiKurlansik/bricksteR | b42b3b3556ef3394b7e7801568a8e228083ad336 | 9199ab34dda462601186c25cf8655483f0bbe408 | refs/heads/master | 2022-10-28T14:35:21.875280 | 2022-10-06T15:36:30 | 2022-10-06T15:36:30 | 227,508,502 | 25 | 6 | null | 2021-07-15T11:59:22 | 2019-12-12T03:04:36 | R | UTF-8 | R | false | false | 5,679 | r | databricks_execute.R | #'
#' Remote execution of commands on a Databricks cluster.
#'
#' This function sends commands to an execution context on an existing
#' Databricks cluster via REST API. It requires a context_id from
#' \code{create_execution_context}. Commands must be compatible with the
#' language of the execution context - 'r', '... |
5fc225f17f574c0305f91ff2bc53cefe1c75c9e1 | 7577bceb20befd9f54f24f1779abb7b04e3b2190 | /R-scripts/cuffdiff.R | 45e2fdea191e511826f0ff5a99b9aef1ba3886b3 | [] | no_license | vitaly001/G_RNASeq | f9be8637871a35d7b21cdf4f1eafd05374dc644c | 1f6a0b629e083d52f60845fbef4d90b76e22bbf8 | refs/heads/master | 2021-08-20T00:14:38.095296 | 2017-11-27T19:15:06 | 2017-11-27T19:15:06 | 112,236,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,009 | r | cuffdiff.R | setwd("/Volumes/HD3/NGS/G_RNASeq")
samples = read.csv("samples.csv", stringsAsFactors=FALSE)
genome = "/Volumes/HD3/UCSC/mm10/Sequence/WholeGenomeFasta/genome.fa"
gf = "/Volumes/HD3/NGS/G_RNASeq/Cuffmerge/merged.gtf"
rm(testfiles)
testfiles = ''
cuff_labels = ''
for(i in seq_len(nrow(samples))) {
lib = samples... |
f2659cd88e992844ec0272c7e52943fef82fa956 | dd6e07d255641d3a33e305ffca842c4a149921a2 | /teste_variablen_in_tibble.R | 7d97b08db25bf17fc67fb8014615617398f8e85d | [] | no_license | W-Ing/SolarPV | c507dd77702644066bf4af8e6070af32a61bbfdf | dba19c25ca5fefdf506830f714b77668bd485bfc | refs/heads/master | 2021-09-03T14:18:10.433302 | 2018-01-09T18:53:03 | 2018-01-09T18:53:03 | 111,313,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 584 | r | teste_variablen_in_tibble.R | library(tidyverse)
# library(tibbletime)
# library(lubridate)
# library(reshape2)
#require(stringr)
myfunc <- function(par){
result <- ifelse(par=="JA", 1, 0 )
return(result)
}
myfunc("N")
# get first observation for each Species in iris data -- base R
mini_iris <- iris[c(1, 51, 101), ]
# gather Sepal.Length, S... |
f153840c7b2f111f6ba55eaa5427e33d8540d989 | 57d3aae331ff9f9907800a36eaff6c0f689b4217 | /Script files/plot_sfs.R | f01c51b6c3fe8bd527f953e0ff540b154b0cccdc | [] | no_license | carolinelennartsson/PopulationGeneticsGroup6 | ceba82ae9cb08e144355833960ed9d0173874449 | 39bcc430f1dd5701f4b98d79b9136e39649a1497 | refs/heads/main | 2023-03-29T20:12:25.027102 | 2021-04-09T11:54:30 | 2021-04-09T11:54:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,943 | r | plot_sfs.R | .libPaths("~/groupdirs/SCIENCE-BIO-popgen_course-project/Group6_Simulations1/software/Rlib")
library(optparse)
# Input arguements
option_list <- list(
make_option("--path", type="character", help="path to .obs file"),
make_option("--id", type="character",
help="Id for output figures. Will return sfs_id.png"))
... |
96bc650e6a18d53eec410bb11614c75bb958a860 | 30babe66ab1ea3648dffce92aa6973f5a18ab8f6 | /Going_through_chapter_4.R | c7365cf34ed473319654f64f13d26eb59fbf530b | [] | no_license | annelinethomsen/ExperimentalMethods | a6093b6690e6fc37e126204d0ea7a49202223e10 | d3bd69b89be8b891dfc0f7b92d75b1f6eb1cb538 | refs/heads/master | 2020-07-28T04:46:51.924232 | 2019-11-19T16:07:56 | 2019-11-19T16:07:56 | 209,313,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,061 | r | Going_through_chapter_4.R | setwd("/Users/anne-linethomsen/Documents/R/ExperimentalMethods/Chapter 5")
install.packages("car")
install.packages("ggplot2")
install.packages("pastecs")
install.packages("psych")
library(tidyverse)
#I import the data:
festivalData <- read.delim("DownloadFestival.dat", header = TRUE)
#I tell ggplot to use my data wi... |
f790d5f85ecb1a1ebdd22855205525d8bbe5ea36 | d83cf027f2836a2e8b8a4c91a14f617b32d02016 | /Plot2.R | 4e704feec0d68ed1e3bf2e8635e451e8ee606f60 | [] | no_license | ProgramLearner7/ExData_Plotting1 | 3e7792bb7cf503f418a520db61388ab36bb864ea | 9ef64c94227728f10de8f1df8d5fd3dfb301c29e | refs/heads/master | 2020-03-28T19:19:12.216308 | 2018-09-16T16:41:53 | 2018-09-16T16:41:53 | 148,964,767 | 0 | 0 | null | 2018-09-16T04:58:11 | 2018-09-16T04:58:11 | null | UTF-8 | R | false | false | 951 | r | Plot2.R | library(dplyr)
library(readr)
library(lubridate)
household_power_consumption = read_delim("household_power_consumption.txt",
";", escape_double = FALSE, col_types = cols(Date = col_date(format = "%d/%m/%Y"),
... |
8cbaf6f27b4d97176186f575874bcc36215186d2 | a1c59394a2b42d6756c2b9564697db714b27fe49 | /R/CST_Calibration.R | e973c4d84dae5973e4f02653ea92747d13c98c1d | [] | no_license | cran/CSTools | e06a58f876e86e6140af5106a6abb9a6afa7282e | 6c68758da7a0dadc020b48cf99bf211c86498d12 | refs/heads/master | 2023-06-26T01:20:08.946781 | 2023-06-06T13:10:05 | 2023-06-06T13:10:05 | 183,258,656 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 41,046 | r | CST_Calibration.R | #'Forecast Calibration
#'
#'@author Verónica Torralba, \email{veronica.torralba@bsc.es}
#'@author Bert Van Schaeybroeck, \email{bertvs@meteo.be}
#'@description Five types of member-by-member bias correction can be performed.
#'The \code{"bias"} method corrects the bias only, the \code{"evmos"} method
#'applies a va... |
fb65f82f1acb43410e3a4e9f9a89ea3a33aafa07 | 73744a740941b13641c0175c8e583b20cfd023a1 | /analysis/words/10_IAT_analyses/scripts/04_get_iat_by_model.R | 7854bce47bb9d2d24b47bab37e44e79718ecd473 | [] | no_license | mllewis/WCBC_GENDER | 8afe092a60852283fd2aa7aea52b613f7b909203 | ed2d96361f7ad09ba70b564281a733da187573ca | refs/heads/master | 2021-12-25T22:41:21.914309 | 2021-12-22T19:08:36 | 2021-12-22T19:08:36 | 248,584,454 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,840 | r | 04_get_iat_by_model.R | # get IAT for each model
# load packages etc
library(tidyverse)
library(here)
library(data.table)
library(glue)
source(here("analysis/words/10_IAT_analyses/scripts/IAT_utils.R"))
# Outfile
ES_OUTFILE <- here("data/processed/iat/other/iat_es_by_model.csv")
# Model paths
KIDBOOK_FULL_PATH <- here("data/processed/iat/... |
85218e12a8d5b5c2b71f06b4794a43cecfe80102 | 852b46209a2bb6839078ae48f28f24a8fbf6bfa5 | /R/alphaPowCon.R | 4cda47c3be16226a993b5e07db09b4169024fa3a | [] | no_license | AKitsche/poco | 24fd28ab0e517a8e5f1acf02bc72a38906d90cd6 | a07c7ce7b19e38597904dab7bbfb4728fe6c4b47 | refs/heads/master | 2021-01-16T18:40:32.051653 | 2015-10-07T17:18:38 | 2015-10-07T17:18:38 | 18,356,832 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,328 | r | alphaPowCon.R | alphaPowCon <- function(power, n, mu, sd, n.sub=2, TreatMat = "Tukey", SubMat = "GrandMean", thetas = 1, alternative = c("two.sided", "less", "greater")){
Alpha <- function(alpha){
alpha <- as.numeric(alpha)
PowCon(mu=mu,
sd=sd,
n = n,
n.sub=n.sub,
TreatMat= Tre... |
78a707e153f5002f08431ba5aa065076549694be | f72f364b54e40f0ccac7f0c44c96e326a5a0e2d9 | /man/EasyUpliftTree-package.Rd | c22e25f4d4228416476a18174ac457206759c7ce | [] | no_license | cran/EasyUpliftTree | 937bbb394b945d931ce3c56668cd9ac16c281abf | 9dccbf87f8714dbd43cd7dbca57f9b8632ce12ed | refs/heads/master | 2021-01-23T06:26:36.687184 | 2013-03-24T00:00:00 | 2013-03-24T00:00:00 | 17,717,451 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 837 | rd | EasyUpliftTree-package.Rd | \name{EasyUpliftTree-package}
\alias{EasyUpliftTree-package}
\alias{EasyUpliftTree}
\docType{package}
\title{
Easy Uplift Tree Model for R
}
\description{
Easy Uplift Tree Model for R
}
\details{
\tabular{ll}{
Package: \tab EasyUpliftTree\cr
Type: \tab Package\cr
Version: \tab 0.0.2\cr
Date: \tab 2013-02-24\cr
License:... |
78f158891d91f9138c943b7bfb649dd75eb8f828 | dfee2e61441a20ba3101a67ae8c5479169d8f086 | /man/installHumanGenomeAnnotation.Rd | 413450746ffedc8593070a97bddc11f003d21bf6 | [
"MIT"
] | permissive | hyginn/BCB420.2019.ESA | 58e8045e063aab83acc2e22738f943c4051e5630 | cd56c0445ddc31551839e759657bc019ccd8f5b5 | refs/heads/master | 2020-04-29T06:07:39.178498 | 2019-04-06T04:35:25 | 2019-04-06T04:35:25 | 175,906,118 | 0 | 30 | MIT | 2019-04-06T05:07:32 | 2019-03-16T00:00:12 | R | UTF-8 | R | false | true | 509 | rd | installHumanGenomeAnnotation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GeneCorVSgotermEDA.R
\name{installHumanGenomeAnnotation}
\alias{installHumanGenomeAnnotation}
\title{\code{installHumanGenomeAnnotation} install Human genome Annotation when necessary.}
\usage{
installHumanGenomeAnnotation()
}
\value{
(NULL)
... |
70be4f44f2f58b78474da92575476e264e902c8a | 74bf92385b7b328d4304301fa542c814098c89bd | /Reading_PreparaingData.R | 771291f2a4c8a49c8fa92c0f39e2a6d680c7d0b9 | [] | no_license | aaizenm/ExData_Plotting1 | 982418acefe95304a6016aab5655e70a22923c4f | 6161d830cd1fa024e35a33a1cebe1737fbd43a53 | refs/heads/master | 2021-01-17T21:55:21.326339 | 2014-07-10T07:00:07 | 2014-07-10T07:00:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 454 | r | Reading_PreparaingData.R | ## Reading the Data
hpc=read.table("./data/household_power_consumption.txt",header=TRUE,check.names=FALSE,as.is=TRUE, sep=";")
## Selecting only the Days that are of our interest
hpc1<-hpc[which(hpc$Date =="1/2/2007" | hpc$Date =="2/2/2007"), ]
## concatenate the dates and time
hpc1$NewDate<-apply(hpc1[,c('Date', 'Ti... |
c9d21dbeecec4dc92dae33f67a52b149e75d4ce4 | 8a2b0cab64ac5f28bedfb06684774b2464bfa87c | /functions/combined_rr_pa_pa.R | a55678bb8fd8c07ceedbcecbfadace5f710d7db7 | [] | no_license | walkabillylab/ITHIM-R | 037264528ffef905a8c9b32c4f9500d8601bae63 | d6809907950af715a68d03c4a4dcd6851170994e | refs/heads/master | 2020-04-02T06:13:15.504959 | 2018-11-06T15:55:35 | 2018-11-06T15:55:35 | 154,136,680 | 0 | 0 | null | 2018-11-06T15:55:37 | 2018-10-22T12:06:04 | HTML | UTF-8 | R | false | false | 866 | r | combined_rr_pa_pa.R | combined_rr_pa_pa <- function(ind_pa,ind_ap){
# Replace NaNs with 1
ind_ap[is.na(ind_ap)] <- 1
# Replace Na with 1
ind_pa[is.na(ind_pa)] <- 1
# remove common columns from ap
ind_ap <- dplyr::select(ind_ap, -c(sex, age, age_cat))
# join pa and ap ind datasets
ind <- left_join(ind_pa, ind_ap, ... |
d2a7c023d32141c90fe8afbc82c4c5ffa7791e09 | 1ef693a6f51d5b66d72f29670e8318e57b4ade53 | /R/apply.r | f258c8c554a29924c7c08536462f56247e456805 | [] | no_license | cran/DynamicGP | 20f649672d3a9235d98a26f192ac51d9cb068232 | 1eb19d2fd1719137086fc19599bd48be76475c94 | refs/heads/master | 2022-11-11T05:05:24.731474 | 2022-11-08T09:10:09 | 2022-11-08T09:10:09 | 129,434,322 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 665 | r | apply.r | genericApply <- function(mat, margin, func, ..., nthread=1, clutype="PSOCK")
{
if(nthread <= 1)
return(apply(mat,margin,func,...))
cl <- parallel::makeCluster(nthread,type=clutype)
ret <- tryCatch(parallel::parApply(cl,mat,margin,func,...),
finally=parallel::stopCluster(cl)... |
d0a6f673a996b14ec5fd795c4b3316d643aac78f | 09df45040befbcb4634a3a62c3b9fa7dea7a5742 | /man/boxplot_cov.Rd | 35af7cb32a3006e8a2c9b3e8cb0a1ace330a3714 | [] | no_license | YanruiYang/design143 | 61f18a0b0251188536b849bda330718b6d047c18 | 544112293ff0601c52117d9ccf329a7a825d0b17 | refs/heads/main | 2023-05-28T23:59:25.133001 | 2021-06-09T07:06:37 | 2021-06-09T07:06:37 | 375,220,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 737 | rd | boxplot_cov.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/testing.R
\name{boxplot_cov}
\alias{boxplot_cov}
\title{Create rmse boxplots to compare covairance models}
\usage{
boxplot_cov(function_name, k, design_type, n, cov = "all", m = 20)
}
\arguments{
\item{function_name}{name of the testing funct... |
a1e0de2db27cccfd7cce7935191c6cc6f93db7f7 | c8ced99c717b29c3f4eff59ded1d92f4179bf0fb | /4. Exploratory Data Analysis/week1.R | 0695a00ed4a4bc10ee31c4637b33bf7694eb1f50 | [] | no_license | salus0324/datasciencecoursera | a20f6c7dc98d1516262b701efed15903412db9a5 | 7e53a50ef89c48faf4240febbc65b274bbb8d26b | refs/heads/gh-pages | 2020-03-12T08:46:16.127192 | 2019-02-02T14:36:10 | 2019-02-02T14:36:10 | 130,535,533 | 0 | 0 | null | 2019-02-02T14:36:11 | 2018-04-22T04:36:35 | HTML | UTF-8 | R | false | false | 2,268 | r | week1.R | pollution <- read.csv("./data/avgpm25.csv", colClasses = c("numeric", "character", "factor", "numeric","numeric"))
head(pollution)
summary(pollution$pm25)
boxplot(pollution$pm25, col ="blue")
abline(h=12)
hist(pollution$pm25, col="green", breaks=100)
rug(pollution$pm25)
abline(v=12, lwd=2)
abline(v=median(pollution$pm... |
146be8b6c5efebd176aa3404cbd8a5b59bd4bcd8 | e59452676887ae6d4052ad6bcaf6ae74c3b94aa8 | /run_analysis.R | c493726ffac01dc447809491c5b7948f97c45c84 | [] | no_license | Everymans-ai/Getting-and-Cleaning-Data-Course-Project | 9d3d815db23034d2da3f4cb37abdd935cc34e128 | 41e0b634753411bd6695690b3898e5b4b94bf267 | refs/heads/master | 2021-05-30T21:17:12.831326 | 2016-04-07T16:48:43 | 2016-04-07T16:48:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,679 | r | run_analysis.R | filesPath <- "C:\\Users\\msunkpal\\Desktop\\R_Final\\UCI HAR Dataset"
# packages loaded are: dplyr and tidyr
#Reading trainings files
trainX <- read.table(file.path(filesPath, "train","X_train.txt"))
XtrainData <- tbl_df(trainX)
trainy <- read.table(file.path(filesPath, "train","y_train.txt"))
ytrainData <- t... |
f19bdbd90fe11fbade8ec1e1d640ffbb59b94c56 | b1eca3d685a89eb0fa20e0619e1f98aee971d231 | /fractal-dimension/regression.r | 2b0e8746b247845874271b3750658a5828d62d6a | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | tomwhite/how-far-away-is-the-sea | 36255ec4323362871e086933e55baa8f69f0c98b | c1ccd2f8a2fff23b92376e6bad171b7dac4f3946 | refs/heads/master | 2020-12-24T06:42:19.927199 | 2013-07-28T19:53:46 | 2013-07-28T19:53:46 | 7,388,142 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 656 | r | regression.r | # Enter columns as calculated by DumpCoast program
x=c(40.02177855743041,20.926887304365344,11.364379535864254,6.435809731800157,3.7523206497028117,2.268029441545984,1.4504876804847864,0.8834338574652224,0.6043891952733216)
y=c(92.62456926247125,88.59415989750448,81.57066534732749,72.09660001403037,61.82840478155156,51... |
ca8fae47c9b3e700f094aa39dc2a61ddcbe808a5 | 6c2f655cd45c3a8f01f84c6024ce7b3087271749 | /Restaurant project.R | 18c77f1fc2a7973666b8c75e0cab102f263d7d1f | [] | no_license | varun331/R-Code | d8a039d91f157c8f3b9af1df2dc0892df3260414 | d8ec7abfcdf9464c3beda8a330b65040aca80035 | refs/heads/master | 2021-06-08T14:02:53.468970 | 2016-11-22T04:30:26 | 2016-11-22T04:30:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,756 | r | Restaurant project.R | Project1 = read.csv(file="~/Documents/R Projects/train.csv",head=TRUE)
test=read.csv(file="~/Documents/R Projects/test.csv",head=TRUE)
train <- Project1
attach(train)
plot(revenue,P1,col='1',pch=16)
points(revenue,P2,col='2',pch=16)
points(revenue,P3,col='3',pch=15)
points(revenue,P4,col='4',pch=15)
points(revenue,P5,c... |
2c29aca910e444c490aaab66c25a5d1691c48153 | 6a475ba8392918c4837f41ceee4e3c9015ca56a5 | /Death Vizualization in United States/server.R | f3b343e3c24e7096360e4adec969d9c943c366e1 | [] | no_license | dipteshnath/R | d2cd51cd3ffad87e0a3cc8c75a56287a27bd42d4 | 338261b15d630f0cbeff7271bf868c1d61ffd628 | refs/heads/master | 2021-06-29T00:30:04.120255 | 2017-09-19T00:04:30 | 2017-09-19T00:04:30 | 104,002,743 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,181 | r | server.R | # By default, the file size limit is 5MB. It can be changed by
# setting this option. Here we'll raise limit to 9MB.
require(googleVis)
require(shiny)
require(plotly)
## Prepare data to be displayed
library(RCurl)
options(shiny.maxRequestSize = 9*1024^2)
shinyServer(function(input, output) {
output$contents <- rende... |
2f1512877d3c27bc83c7a98c36d283c2816b4818 | 91ac969835c4460ef590bf74e61b9f8379e6efe8 | /R/closest.R | 54bb70ad5c9a930ceb6d90c2926800d01f18bbd6 | [] | no_license | prestevez/crimeineq | abd3336332aec8e46336f045afa36099e970fcb9 | 0672c79f9040b8a3d8c5208bde9ac1988ca61dcb | refs/heads/master | 2021-05-15T15:15:44.520436 | 2017-12-22T23:05:12 | 2017-12-22T23:05:12 | 107,298,694 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 204 | r | closest.R | #' A function to get the closest value from a vector
#' @export
closest <- function(x, y)
{
xy <- abs(x - y)
closest <- which(xy == min(xy))
names(closest) <- names(y)
return(closest)
}
|
50047109d1608c5a405209c59479694ab4555f09 | 24cf4c59481802f340e4efa527103804f7687ae9 | /RcodeIntegration/bin/Debug/hello.R | 08ef461d2ee5f3f9c91d5107f9b518722317b873 | [] | no_license | sachinbabladi/MyBackupRepo | e133adcd9bbe25bdaf6a800378495b6319c94918 | 31e00d43a5efc881b1d241b040b7c42ee72dc64c | refs/heads/master | 2020-12-31T07:55:32.480446 | 2015-12-04T15:32:56 | 2015-12-04T15:32:56 | 47,407,351 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,564 | 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: 'Ctrl + Shift + B'
# Check Package: ... |
37afaf27ff6eae8b67f82b3668aa13d4de7bd5b8 | 6d6ee3156d44f079df9712753a9f4de77f806a24 | /functions/fn_execution_coordinator.R | 228f41e0a932ebaee70815b0b42b254783cd2efa | [
"Apache-2.0"
] | permissive | chowagiken-hubacz/website-classification | 1dc2d55bb2c4f0f8f2392d56d9d843498bbf13fe | 99805da874eadf53e5584a7f223d6d6fc8202279 | refs/heads/master | 2023-03-20T09:29:22.285088 | 2020-06-11T13:50:50 | 2020-06-11T13:50:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,292 | r | fn_execution_coordinator.R | # Die Intelligenz des Programms - der Ausführer.
# Entscheidet anhand der Inputtabelle, welche Modelle mit welchen Daten aufgerufen werden.
if (exists("master") == F) {
master <- new.env()
}
# FUNKTION 1: Entscheidung über Modell
# !!!! Unbedingt mit TRY aufrufen !!!!
# IN: Eine Zeile des Steuerungs-Dataframes
mast... |
7cf100ecc1520c7933cb0dcd3b3037744b2f1bc0 | 4a2d5b3331bfcf892aecc61c52d35fb0ef4584d2 | /tests/testthat/test_server_getOMLDataSetQualities.R | ce25cce26f0e057f5f1ae22c389a14177fa37664 | [
"BSD-3-Clause"
] | permissive | openml/openml-r | ede296748ae1b9bcf22d661f4e25f495283402dd | 530b00d9bde9895d5ba9224dbc812aeb3095e0f3 | refs/heads/master | 2022-11-09T02:51:47.329148 | 2022-10-19T19:50:09 | 2022-10-19T19:50:09 | 12,809,430 | 78 | 32 | NOASSERTION | 2019-11-19T16:00:48 | 2013-09-13T12:52:44 | Jupyter Notebook | UTF-8 | R | false | false | 332 | r | test_server_getOMLDataSetQualities.R | test_that("getOMLDataSetQualities", {
with_test_server({
qual = getOMLDataSetQualities(1)
expect_data_frame(qual, min.rows = 1L, ncol = 2L)
expect_set_equal(names(qual), c("name", "value"))
expect_character(qual$name, unique = TRUE, any.missing = FALSE)
expect_numeric(qual$value, any.missing = FAL... |
9a49e57201be413334ea8e3c5c55c13a6f6ad8c7 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.end.user.computing/man/workdocs_describe_document_versions.Rd | c30f80a59f3ab7cd638d3c2a11e31bc1d0c8c525 | [
"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 | 1,219 | rd | workdocs_describe_document_versions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/workdocs_operations.R
\name{workdocs_describe_document_versions}
\alias{workdocs_describe_document_versions}
\title{Retrieves the document versions for the specified document}
\usage{
workdocs_describe_document_versions(
AuthenticationToken... |
262958f1e3d744e3a6617dd2650987bc91f654c4 | c81f7ac57ac005ea5b7dc058715d62aaad5e6aa4 | /plot4.R | e2942b5cc7b97ebd0b91103da3cbd02c38e7637f | [] | no_license | KareemGamgoum/ExData_Plotting1 | 331faa7afef46df4022f81171c69c0bec6f51515 | 62e69534cb6e2eb7a8adb6406f05284f488ae3a7 | refs/heads/master | 2021-01-21T14:33:03.968391 | 2017-06-24T15:55:25 | 2017-06-24T15:55:25 | 95,298,572 | 0 | 0 | null | 2017-06-24T13:15:42 | 2017-06-24T13:15:42 | null | UTF-8 | R | false | false | 1,508 | r | plot4.R |
# This script creates plot4
# Set Working Directory
setwd("C:/Users/kareem.gamgoum/Desktop/DataScience/Course 4 - Exploratory Data Analysis/Course Project 1")
# Load in the data
rawdata <- read.table("household_power_consumption.txt", header=TRUE, sep=";", na.strings = "?")
# Create DateTime variable
raw... |
fab21558668fb6f1f32b597b42b6a669d1934105 | 6e04a59a255f1ea4e684c76f0f09123fa37a3fc5 | /man/find_lag_time.Rd | 91e174531adb3c2b60528a5f7b5a2d38f0a3eeac | [
"MIT"
] | permissive | Ryan-Lima/gRandcanyonsand | 12c19946588d41385cbb93e81a1be4702768ce19 | 1482d59120211ee4f34c6b142b31837acfb0dbea | refs/heads/main | 2023-06-28T11:45:16.328664 | 2021-08-06T18:53:24 | 2021-08-06T18:53:24 | 326,823,645 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 603 | rd | find_lag_time.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/02-near_gage_lag.R
\name{find_lag_time}
\alias{find_lag_time}
\title{Find lag time to the nearest gage}
\usage{
find_lag_time(rm, print = F)
}
\arguments{
\item{rm}{--numeric-- river mile, miles downstream from Lees Ferry}
\item{print}{print... |
f5ecef57514a32c84e18130fbd0c640370c0995d | efc016d1345168cae64db251731fbb1b309e3483 | /plot1.R | c80c8204873fcec93fc565e78c441999d941a780 | [] | no_license | secastro/ExData_Plotting1 | c8410048823a48df89044c1e4e6938b6d540bf48 | 87bc1fcd61318d74995cbfb9d9d251a5e86a86cd | refs/heads/master | 2021-01-20T23:32:30.975263 | 2014-08-09T07:26:52 | 2014-08-09T07:26:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 265 | r | plot1.R | # Load the data
source("load-data.R")
data_subset <- load_data()
# Creates figure 1
png(filename="figure1.png", width=480, height=480)
hist(data_subset$Global_active_power, col="red",
xlab="Global Active Power (kilowatts)",
main="Global Active Power")
dev.off()
|
342ee185bbfc3497e83f35998b61ffdb923bebc2 | c6286a95d80085cd0ca3d1081c31cdb217eb916e | /R/genelist_specific_profile.R | 0fad74f75ff943cbc65d277799ebc2fe792d52c1 | [] | no_license | sethiyap/wangfangscripts | 9751c84d1a9039c7deaa42aab069c55ca1c66c58 | c40821b6636303553072090f2770c265da3429a5 | refs/heads/master | 2020-04-22T13:59:27.379301 | 2019-10-30T08:51:34 | 2019-10-30T08:51:34 | 170,428,191 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,539 | r | genelist_specific_profile.R | #--- with TBP and Pol2
gff_file <- "/Users/Pooja/Documents/Data-Analysis/Others/Reference_Annotation/An/A_nidulans_FGSC_A4_version_s10-m04-r07_features.gff"
list_1 <- read_delim(pipe("pbpaste"), delim="\t", col_names =FALSE) #-- plot sorted according to this list, provide expression value as well
list_2 <- read_delim(... |
94ba51ac219c9c0afa1018308270b2a3f0786e25 | a6c370c5411e5c9f78dc0009595305eb83e26b82 | /GradientBoosting_Data_Modelling.R | 4e43d61f9816ef75408f4b44b8dec98e33a084ba | [] | no_license | Sanjanarajagopal/employee-attrition-analysis | 09760bc42ddd0afeb6b3c728a50cab098539e312 | 5a88c789b3778db3dfaeb77692d5735fcae6a010 | refs/heads/master | 2020-04-05T05:19:13.342633 | 2018-12-15T16:37:55 | 2018-12-15T16:37:55 | 156,590,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,393 | r | GradientBoosting_Data_Modelling.R | #Title : 707 Project - Gradient Boosting Algorithm
#@Author : Sanjana Rajagopala
#Start Date : November 19, 2018
#Load the required libraries
library(caret)
library(dplyr)
library(arules)
library(klaR)
library(tictoc)
library(mlbench)
library(pROC)
library(gbm)
library(ROSE)
library(rpart)
#############... |
6a44de178932cff06c7a5a56892bf9e6df5b744b | e9ed3aaa01ba50bd57d88b9d918960e2010a7351 | /plot1.R | a920b170f7747548ab2a94c26f17e54852a234b0 | [] | no_license | cordlepe/ExData_Plotting1 | 902ccd5a42c80afa6c23ec3541d819ae0144106a | 276064ba51729f5fa98f0173403914c3adc131a9 | refs/heads/master | 2020-04-24T11:55:18.540429 | 2019-02-22T14:15:39 | 2019-02-22T14:15:39 | 171,941,410 | 0 | 0 | null | 2019-02-21T20:36:36 | 2019-02-21T20:36:35 | null | UTF-8 | R | false | false | 507 | r | plot1.R | rm(list=ls())
file <- "./data/household_power_consumption.txt"
#read in specified file
df <- read.table(file, header = TRUE, sep = ";", na.strings = "?")
#convert date to date type
df$Date <- as.Date(df$Date, format = "%d/%m/%Y")
#keep only data for specific dates
df <- subset(df, Date == "2007-02-01" | Date == "200... |
a9aa286507dd2ab924540a2b439cd63f48007e98 | d01f116539c9ec88b1327f61093b715f41f88e87 | /w2_lec05_preprocess.R | 69fd092c46c4aa4a7fe96fdaf29fa46b458c14ec | [] | no_license | jlnguyen/08-practical-ml | 6e8a00305791a132f4f4227cf5128d11f76f7bef | 40b0e87c1e67fe9c4fd1f309fa10d01be2ad6fc9 | refs/heads/master | 2021-01-10T09:49:05.704187 | 2015-11-24T01:38:44 | 2015-11-24T01:38:44 | 46,034,993 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,376 | r | w2_lec05_preprocess.R | # Coursera JHPH Data Science
# 08 - Pratical Machine Learning
# Week 2 | Lecture 5 - Basic preprocessing
#
# Joe Nguyen | 13 Nov, 2015
# Change working directory
dirBase <- "/home/joe/Documents/01-coursera/01-data-science"
dirWorking <- "/08-practical-ml"
setwd(file.path(dirBase, dirWorking))
rm(list = ls())
library... |
2812a0247d54d20a7f88bf93bf491ecd3eff63a0 | 307b0f73161701e48e24192aea10713c4c76db13 | /man/index.cell_label.Rd | 88257fff3f54d53067960e8018a1e3c0adfdba42 | [] | no_license | spgarbet/tangram | aef70355a5aa28cc39015bb270a7a5fd9ab4333c | bd3fc4b47018ba47982f2cfbe25b0b93d1023d4f | refs/heads/master | 2023-02-21T03:07:43.695509 | 2023-02-09T17:47:22 | 2023-02-09T17:47:22 | 65,498,245 | 58 | 3 | null | 2020-03-24T15:28:05 | 2016-08-11T20:07:01 | R | UTF-8 | R | false | true | 617 | rd | index.cell_label.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/render-index.R
\name{index.cell_label}
\alias{index.cell_label}
\title{Generate an index from a label object}
\usage{
\method{index}{cell_label}(object, id = "tangram", key.len = 4, ...)
}
\arguments{
\item{object}{cell; The cell for indexing... |
b88a1364c02172988d39c3db8715c27be6cbafc5 | 2c1805e79d915c88faa0f6c258fc41e95937dba5 | /R/Unity/player_log_quest.R | aa8bc165cb3250c679874a75897266fc86f0da4e | [] | no_license | hejtmy/VR_City_Analysis | b85c14ddc7aad5db8aeeb353ae02462986b20e59 | b149d3f52d76fc8fb0104fa42ec7b38ae7470ba0 | refs/heads/master | 2021-01-18T16:16:53.962471 | 2017-05-21T22:01:26 | 2017-05-21T22:01:34 | 49,779,651 | 0 | 0 | null | 2017-02-18T17:35:16 | 2016-01-16T15:48:50 | R | UTF-8 | R | false | false | 949 | r | player_log_quest.R | #' Extracts playuer log information only for the duration of the quest
#'
#' @param quest_set Important because of the information about the set in which quest took place
#'
player_log_quest = function(quest_set, trial_sets = NULL, quest = NULL, quest_order_session = NULL, include_teleport = T){
if(is.null(trial_se... |
815756c90414e275b55f12aec863d44c87bca54b | c04075b8045b8412f8fe3aeb25e02cee2821cc05 | /coursera/c2week1 PartialMatching.R | 61e6693c59bbe8b78dc3f2e094b0c6182c2da018 | [] | no_license | tmPolla/R | f2e174b1cd75ce9cd3a191e51f403205834a8ad1 | e6780f0baaf855075bd271fea87d781a7b996bb5 | refs/heads/master | 2021-01-12T05:24:06.333057 | 2018-07-21T05:15:00 | 2018-07-21T05:15:00 | 77,921,688 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 252 | r | c2week1 PartialMatching.R | ##Data Science - Johns Hopkins University
##coursera
## course 2 - R
##week1
x<- list(aardvark=1:5)
# print where name start with a
x$a
# print where the name exactly equal to a
x[["a"]]
#print where name start with a
x[["a",exact=FALSE]]
|
8baf6917b6e41f5c58d1ac61f9f8ca7eb4530949 | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.autoscaling/man/record_lifecycle_action_heartbeat.Rd | 960633fa421a921815670fcc3b140338be9358e0 | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,605 | rd | record_lifecycle_action_heartbeat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.autoscaling_operations.R
\name{record_lifecycle_action_heartbeat}
\alias{record_lifecycle_action_heartbeat}
\title{Records a heartbeat for the lifecycle action associated with the specified token or instance}
\usage{
record_lifecycle_act... |
89cc18ebc117b798db319d58e7dd719d73c1d02d | bbd803cd4fe2623ae8f41f46586684691a2e7f92 | /tests/testthat/test_clusterSingle.R | 5fee6e838950b4ea2dae7aac46ae4e965759b454 | [] | no_license | 12379Monty/clusterExperiment | 96d3359aefe60a65bfdfd3eb4f05a647347c020d | a26d494a9a23d467269d85c69348c4904a08bb56 | refs/heads/master | 2021-01-21T15:21:53.986787 | 2017-06-14T23:30:20 | 2017-06-14T23:30:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,536 | r | test_clusterSingle.R | context("clusterSingle")
source("create_objects.R")
test_that("`clusterSingle` works with matrix, ClusterExperiment objects, and
SummarizedExperiments", {
clustNothing <- clusterSingle(mat, clusterFunction="pam",
subsample=FALSE, sequential=FALSE,
... |
d62a67b41e53afe0be8a7963db452bf8d338e4a5 | 420827a0e5b5283493e4e624063d83699a1e692b | /scripts/diagrams.R | d74491c30d778444b1ab33b7666dcd9babbc1d23 | [] | no_license | szymonm/CGMethodsForInfluence | 7ce6d8a316b25a377e3e58c6d3d61dad895b1225 | ac43a08ea1db3189c6f4f397fddffaab8760f7c6 | refs/heads/master | 2021-01-21T11:23:59.411979 | 2014-11-23T11:29:35 | 2014-11-23T11:45:51 | 16,474,066 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 248 | r | diagrams.R | # Args: filename, name
args <- commandArgs(trailingOnly=T)
filename <- args[1]
print(paste("Reading from:", filename))
data <- read.table(args[1])
source("plotDiagram.R")
pdf(paste(filename,".pdf", sep=""))
plotDiagram(args[2], data)
dev.off()
|
33784d83fa64c936d61a3fa861e4a6cf55422062 | f12b660880582889b0b09df015dd54ce09805a92 | /label_summary.R | dda12eee2fd153002f2d039a1c6c33db443438be | [] | no_license | KenHBS/LDA_adventures | 833bbce62d6cc5a7d6c62b1cc41ae7419ab301bd | aaade1632362c47d4c17eb9786f27d2d90ff4817 | refs/heads/master | 2020-12-10T03:13:56.471203 | 2017-06-26T09:14:19 | 2017-06-26T09:14:19 | 95,428,836 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,738 | r | label_summary.R | library(RMySQL)
library(stringr)
## Get the data in R:
con <- dbConnect(MySQL(), host = "localhost", port = 3306, user = "root",
password = "qwertz", dbname = "basecamp")
quer <- dbSendQuery(con, "SELECT * FROM abstract_set")
df <- fetch(quer, n = -1)
dbClearResult(quer)
... |
f41e666cdf7bd7d69bc581ac64c4d120a39e21a2 | c132e78b8002ceb0ce7f06c2e2cb21e4b458e049 | /ESPmap_ggplot_Arabidopsis.R | 0b4317ee380d3f2db609ce33b261e7df71d9f866 | [] | no_license | abj1x/ESPmap_ggplot_Arabidopsis | ae0e64e85aa9713a9055e3356ab86640f1ccfa49 | 45fb5e94214d53673e5c3956b3f1ad3a7c020182 | refs/heads/master | 2021-05-26T02:56:21.054444 | 2020-04-08T08:03:42 | 2020-04-08T08:03:42 | 254,024,141 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,151 | r | ESPmap_ggplot_Arabidopsis.R | ## after https://www.rpubs.com/spoonerf/countrymapggplot2
library(maptools)
library(raster)
library(ggplot2)
library(rgdal)
library(plyr)
## download Spain shapefile data
spain<-getData("GADM",country="ES",level=0)
## make base map
esp<-ggplot()+geom_polygon(data=spain,aes(x=long,y=lat,group=group))+coord_fixed(1.... |
60139cbea704e26b1c79b47affa1ac13eb318d10 | 43419401c9bf60ba8650c5f79dfdd2e224c9943e | /hacker_assignment.R | 79910cdf3eb681c32f88cfd790a995ec210aed98 | [] | no_license | p4r1t05h/Patient-Adherence | 793daafe93e6223e2d6464bc8d8b86bb327678da | 41099f156899a372b09985ca1602080b0822a517 | refs/heads/master | 2020-03-28T16:56:25.795268 | 2018-09-14T06:05:27 | 2018-09-14T06:05:27 | 148,743,010 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,139 | r | hacker_assignment.R | rm(list=ls())
#setting working directory
setwd("D:/Data Science/Abzooba")
getwd()
#Loading important libraries
library(rpart)
library(C50)
library(randomForest)
library(class)
library(e1071)
library(caret)
library(boot)
#Loading data sets
train<-read.csv("Training Data.csv", header = T)
test<-read.csv("Test Data.cs... |
fe84980c39b0d6daa86b9fa4255f4bad48efb04d | 8101cce3db89cabfb1ab278d6e8a4cc5148d007c | /analysis.R | 12e3ea9d3a5c69f2640359b88439884e8d8242d2 | [] | no_license | hedgef0g/jb_es_2020 | 0ae11349eb7259a59b44184dd0fb6644bab99139 | fc44c27bfb2c72a1a67ea86284a866a0394b57c0 | refs/heads/main | 2023-01-02T08:44:13.892845 | 2020-10-23T06:49:30 | 2020-10-23T06:49:30 | 301,535,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,125 | r | analysis.R | install.packages("tidyverse")
install.packages("foreign")
library(tidyverse)
library(foreign)
data <- read_csv("./DevEcosystem 20 external data sharing/2020_sharing_data_outside.csv")
qre <- read_csv("./DevEcosystem 20 external data sharing/DevEcosystem 2020 questions_outside.csv")
data <- data %>%
mutate(years_exp... |
f8101123e8b935f4fc8c1f327c25ce2ec30d92fa | 6125f56ef5651c81bfbe85eebccc05c81f49398c | /R/TADCompare.R | e4eef5955c640bb312f06734564a884156b90f87 | [
"MIT"
] | permissive | dozmorovlab/TADCompare | 87612572160b43d754b1444f0357135d2d3965ed | f1b61b789eb6717ce9397cbe059b9f0f721d2bb1 | refs/heads/master | 2022-05-13T20:31:58.255606 | 2022-04-25T01:13:55 | 2022-04-25T01:13:55 | 207,209,435 | 19 | 2 | null | null | null | null | UTF-8 | R | false | false | 18,064 | r | TADCompare.R | #' Differential TAD boundary detection
#'
#' @import dplyr
#' @import magrittr
#' @import PRIMME
#' @import ggplot2
#' @param cont_mat1 Contact matrix in either sparse 3 column, n x n or
#' n x (n+3) form where the first three columns are coordinates in BED format.
#' See "Input_Data" vignette for more information.
#... |
29fc3358e7f2ac224c4dd2e9c8fe6bafdf7f2bf2 | 9ac06a307c5449ae56b8dfffd3abaa6303e0feab | /R/lambda.R | 878ba0ecc9302659e2e7c3860b59e7f2adb70275 | [] | no_license | Rapporter/rapportools | 5b9d69a707e289aff34d484e2cde2668dd740dac | f45730af9cbdf147cafbd7c030602bae2fe915d5 | refs/heads/master | 2022-05-10T16:09:34.816759 | 2022-03-21T23:20:00 | 2022-03-21T23:20:00 | 15,641,997 | 5 | 1 | null | 2017-11-01T10:57:46 | 2014-01-05T00:01:26 | R | UTF-8 | R | false | false | 2,005 | r | lambda.R | #' Goodman and Kruskal's lambda
#'
#' Computes Goodman and Kruskal's lambda for given table.
#' @param table a \code{table} of two variables or a \code{data.frame} representation of the cross-table of the two variables without marginals
#' @param direction numeric value of \code{c(0, 1, 2)} where 1 means the lambda val... |
354847dac8236f3922f5ef6bbff22be63551d8b2 | 1ab2d3219a33e1902d9f9c8f66893e2d0e30892c | /source/sensitivity-analysis.R | 172ef51fad0fe7b1dccbfc112d7b6c3e1d2577ab | [] | no_license | MiljanaM94/hw-git | 1cbb0606d31441cfc0b8ad1fcdcf1e68b44f11e2 | 7594cb8f96528babb54da62f3d31c895937fcc33 | refs/heads/master | 2023-04-13T14:38:39.283356 | 2020-03-25T17:00:46 | 2020-03-25T17:00:46 | 248,203,605 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,162 | r | sensitivity-analysis.R | library(tidyverse)
library(scales)
forecast <- read_rds("results/forecast.RDS")
cpi <- read_rds("results/cpi.RDS")
gdp <- read_rds("results/gdp.RDS")
res <- list()
cpi <- forecast %>%
filter(indicator == "cpi") %>%
select(date, year, quarter, base) %>%
arrange(year, quarter, date) %>%
group_by(year, quarter) ... |
481867d0fad83b6360c56a8b8e85c3cbec8efb49 | 0a92b4ff5d70a6473dce1bf93116227e3c8586c0 | /man/plot_map_wqis.Rd | f4b77d8723b50f17bbd674c282035541c6b71dea | [
"Apache-2.0"
] | permissive | bcgov/wqindex | a27524eea5d92d13d4ad781ceea234c01c57a06e | 804ced921cda5e0301b8c743e5e8ee30f8428526 | refs/heads/master | 2021-04-27T09:49:51.477556 | 2020-12-16T23:09:55 | 2020-12-16T23:09:55 | 122,523,615 | 7 | 5 | Apache-2.0 | 2020-04-15T00:34:19 | 2018-02-22T19:17:11 | R | UTF-8 | R | false | true | 1,283 | rd | plot_map_wqis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot_map_wqis}
\alias{plot_map_wqis}
\title{Plot Map of Water Quality Index Categories.}
\usage{
plot_map_wqis(data, x = "Long", y = "Lat", size = 3, shape = 21,
keep = NULL, input_proj = NULL)
}
\arguments{
\item{data}{A data.... |
281a8efb0ea825803ae65bd6e8c19b0c163b8aca | c547df6e3849fafb39062335578e6f52d10780e6 | /src/20221206/20221206_challenges.R | 2350d8c2819ba41fe4ae68de97aff4483957146d | [
"CC-BY-4.0"
] | permissive | inbo/coding-club | 974bec58103744c023d6a8da55880b9cc4f5a183 | f2165b98fa495c9393dd3143638e08fbbb4889e6 | refs/heads/main | 2023-08-31T07:49:53.272744 | 2023-08-30T14:18:58 | 2023-08-30T14:18:58 | 168,415,968 | 7 | 12 | null | 2023-04-12T08:58:49 | 2019-01-30T21:11:16 | HTML | UTF-8 | R | false | false | 2,025 | r | 20221206_challenges.R | library(tidyverse)
library(sf)
library(terra)
library(maptiles)
library(mapview)
library(leaflet)
library(htmltools)
library(leafem)
library(crosstalk)
library(DT)
## CHALLENGE 1 - Plots
# Plotting is still important. Let's warm-up by plotting some geospatial data.
# 1. GIS data (continuous variable)
natura2000 <- ... |
7e48dbe3c4f37abda96baa0d51f7df75a1e2aae8 | 8ff1d9fbd53f6337ffeade2edce27d0c057a6c17 | /figures/Fig1PREOverlap.R | cc2df2bdb3f4faacda99839927ae47547be7b1d3 | [
"Apache-2.0"
] | permissive | robertstreeck/PolycombPaperR | 39cd632d642c37de97e093e75ad67af3515acb36 | fe37b8e0fd6b21ca50fd59a0d5d9292b2f416a8a | refs/heads/main | 2023-06-25T08:20:58.208166 | 2021-07-20T10:39:12 | 2021-07-20T10:39:12 | 382,376,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,351 | r | Fig1PREOverlap.R | library(GenomicRanges)
library(rtracklayer)
library(fmsb)
library(tidyverse)
library(rcartocolor)
load("data/fig1/SevenClassGenomeModel.Rdata")
genome = read.delim("/Users/streeck/Genomes/DmelBDGP6.91/chrNameLength.txt", header = F, stringsAsFactors = F)
genome = genome[1:7,]
gr = GRanges(genome[,1], IRanges(1, as.in... |
2d92ef845a13d8faaf6e10fc0a474a0bb79cd076 | b2eba40bbf555f706ad16b693e644fa09ce80222 | /assets/R/optimization.R | ba32559c2fe6f3076d8e1e03c96121cb3414a50e | [] | no_license | mlqmlq/STAT628 | 14a4da2295006a5267265231dca6d200b3d00487 | 3befc2329f05cbc18197d984b7708ce74b391e58 | refs/heads/master | 2023-08-05T07:40:42.325897 | 2021-07-13T17:30:29 | 2021-07-13T17:30:29 | 277,651,847 | 8 | 1 | null | 2021-09-28T05:55:00 | 2020-07-06T21:24:18 | Jupyter Notebook | UTF-8 | R | false | false | 1,759 | r | optimization.R | set.seed(0)
mu = 0.01; L = 1; kappa = L/mu
n = 100
D = runif(n); D = 10^D; Dmin = min(D); Dmax = max(D)
D = (D-Dmin) / (Dmax-Dmin)
D = mu + D*(L-mu)
A = diag(D)
x0 = runif(n, 0, 1)
x_star = rep(0, 100)
f <- function(x) {
0.5*t(x) %*% A %*% x
}
df <- function(x) {
A %*% x
}
GradientDescent <- function(x0, x_star... |
56199c3bcd474de80fbb034491f2e1cb2e896416 | e3ca1bec4bcaf4582f8dab32f0d58b13fb30c8df | /global.R | 0fa1a1e71aca113e2792c6805e00944f1b4f46ca | [] | no_license | matt-dray/dehex-challenge | 9cdc6014df1f3ed042c434fe40eeb7d60c9daa27 | 331e3be4c13cb5fd4acdbe744588997ad8c1f0cc | refs/heads/main | 2023-07-23T05:18:52.069418 | 2021-08-26T12:00:27 | 2021-08-26T12:00:27 | 395,112,733 | 1 | 0 | null | 2021-08-24T16:04:06 | 2021-08-11T20:46:51 | R | UTF-8 | R | false | false | 46 | r | global.R |
library(shiny)
library(dehex)
library(bslib)
|
7b262de89dbbc97fc5196f3450187930af7416c4 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /RNOmni/R/BAT.R | 314c44a7f85307ff250732be3853d8be5556b92d | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,795 | r | BAT.R | # Purpose: Basic score test
# Updated: 2020/10/03
#' Partition Data
#'
#' Partition y and X according to the missingness pattern of g.
#'
#' @param e Numeric residual vector.
#' @param g Genotype vector.
#' @param X Model matrix of covariates.
#' @return List containing:
#' \itemize{
#' \item "g_obs", observed ge... |
a4ff468d86a1edc0e77f27dc1a2ffd2fa22694e3 | 03dcfc60d68155db4be09639174c1e73dc0340de | /cachematrix.R | 4823d65d9329658ae049bd56423ae7a3bbedd1fe | [] | no_license | yleporcher/ProgrammingAssignment2 | 0e62fee1e1c479095b2947a08bfc499349c64e9b | 824b6bc084d9fb931280e3f5644bfb4e3ce57487 | refs/heads/master | 2021-05-29T02:14:27.422744 | 2015-06-21T16:27:49 | 2015-06-21T16:27:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix() and cacheSolve() are designed to compute
## the inverse of a matrix and store it in a cache so as to prevent
## un-necessary re-computation
## Write a short comment describing this function
## This function initil... |
9518d78ceaeed2207addaa42942f22a66df41933 | 4b3688af9ed5dfe92ccd51c1c7c851d9bdcce4a1 | /plot1.R | caefb7e7595a5bc22a6f33257fc8cb75f863fe35 | [] | no_license | stevejburr/ExData_Plotting1 | b0636e44a0a091e8c434c7cf89fbf1b17337dc3e | 8f661e7dd8b6daf0408761b63499c82fab1e77f3 | refs/heads/master | 2020-12-25T11:32:39.308809 | 2014-06-05T08:45:59 | 2014-06-05T08:45:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 800 | r | plot1.R |
#Read in all data from WD
#colClass doesn't seem to like numeric for this data so using character (don't want any factors)
Data<-read.csv2("./household_power_consumption.txt", na.strings = "?",colClasses=c("character"))
#subset data and replace large file in memory with filtered set
DataDay1<-subset(Data, Date=="1/2... |
fb8ffcf7704d67235a124abccc942214056245f1 | 01e6f98609708ebdfd6d1db5fda9cb443f9f7856 | /man/date-zone.Rd | 04c4237b6670233a13dd43220a334ea38e117382 | [
"MIT"
] | permissive | isabella232/clock-2 | 3258459fe4fc5697ce4fb8b54d773c5d17cd4a71 | 1770a69af374bd654438a1d2fa8bdad3b6a479e4 | refs/heads/master | 2023-07-18T16:09:11.571297 | 2021-07-22T19:18:14 | 2021-07-22T19:18:14 | 404,323,315 | 0 | 0 | NOASSERTION | 2021-09-08T13:28:17 | 2021-09-08T11:34:49 | null | UTF-8 | R | false | true | 2,180 | rd | date-zone.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posixt.R
\name{date-zone}
\alias{date-zone}
\alias{date_zone}
\alias{date_set_zone}
\title{Get or set the time zone}
\usage{
date_zone(x)
date_set_zone(x, zone)
}
\arguments{
\item{x}{\verb{[POSIXct / POSIXlt]}
A date-time vector.}
\item{z... |
db6f70814c37fa8fca0fb9ad53c601a98c1c6241 | b31c65dca75018c34c3846a4fa197c9893ec113d | /man/CovMat.Design.Rd | 95588ee2f6307697e9a48d46ac077cac212fa043 | [] | no_license | cran/samplingDataCRT | 5211ff75c8b3c843b72f6ea4f5bf40eb4203b33a | a0b6ea5a69c848ff0ebfebb1863587893b0a909b | refs/heads/master | 2021-01-09T06:46:45.937336 | 2017-02-06T13:28:31 | 2017-02-06T13:28:31 | 81,090,234 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,244 | rd | CovMat.Design.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CovarianceMatrix.R
\name{CovMat.Design}
\alias{CovMat.Design}
\title{covariance matrix for the multivariate normal distributed variables}
\usage{
CovMat.Design(K, J, I, sigma.1.q, sigma.2.q = NULL, sigma.3.q)
}
\arguments{
\item{K}{n... |
5752f72eb61da8be722f8b183800423f8b8ab61a | 51349029aa0676a8e31c638465469dc9cd33afe9 | /R/species.R | 9c4b424192994845742811edb5f2310d9b9679c7 | [] | no_license | weecology/ratdat | 07327836b89e0ce5312f02e89b39054e91461c88 | d7599f3a44d7a338b06d677dc5b789f59385bb91 | refs/heads/main | 2022-05-24T04:18:54.643356 | 2022-04-05T14:17:04 | 2022-04-05T14:17:04 | 122,650,282 | 2 | 4 | null | 2023-08-29T14:17:12 | 2018-02-23T17:12:34 | R | UTF-8 | R | false | false | 405 | r | species.R | #' Species data.
#'
#' Data on species captured at the Portal Project
#'
#' @source Portal Project Teaching Database,
#' \doi{10.6084/m9.figshare.1314459}
#' @format A data frame with columns:
#' \describe{
#' \item{species_id}{Species identifier}
#' \item{genus}{The genus of the species}
#' \item{species}{The latin s... |
4b31e3ea9ca1f010d4bbdb6d035690edbc9485db | d3968caa658b72c858fb0765418c63d517d73de7 | /scripts/visualization_ramon_y_cajal.R | eeba963e15077b1e9406bff14763ae032ee412e8 | [] | no_license | dernapo/ramon_y_cajal | 50f5b5ee194ad67d9a5e7a59bb55cdc3b3479a38 | 284bbf162f2e5b692086a93d6a52101695c1ddbc | refs/heads/master | 2022-12-19T19:04:39.982617 | 2020-10-09T07:33:04 | 2020-10-09T07:33:04 | 302,559,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,532 | r | visualization_ramon_y_cajal.R | #####################################################
## Vamos a preparar varias gráficas con los datos
#####################################################
## Cargar librerias #####
pacman::p_load(data.table, here, ggplot2, hrbrthemes,
patchwork, ggtext)
## Set theme ####
theme_set(theme_ipsum_rc())
... |
1204ba990166dc84b9516cde03f3cd15fbc928e2 | e4a5ffbf0b6d567b9c9dc38f3664a995e98db579 | /R/functions.R | ff7abd376c4c63eb9ceb631e0ec70ada24039f0c | [
"CC-BY-4.0",
"LicenseRef-scancode-public-domain"
] | permissive | RJManners/ClimMob-analysis | be659caa521c39088bedcffc2968296679206cf8 | c2d5c8481a73302ea747f2c54df8424cfccbae8b | refs/heads/master | 2023-08-05T15:58:16.926883 | 2021-10-05T10:28:45 | 2021-10-05T10:28:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,082 | r | functions.R | ###Functions for Climmob Reporting Analysis
#' Validate the class of objects generated in the tryCatch(s)
any_error <- function(x){
isTRUE("error" %in% class(x))
}
#' Runs specific lines of the code
source2 <- function(file, start, end, ...) {
file.lines <- scan(file, what=character(), skip=start-1, nlines=end-s... |
43feedd8be6da193c961c4332496865ae4d7604d | dfaf36782928084c27c955e60592baffe214510f | /R/Plot.R | 48f2b2e3b8435ef40630de1743fa368868c74554 | [] | no_license | SimonGrund/blm | 05aa4c9d7f31078ea0d50c1585468d758c6ad386 | 31fb8fe2b623b612cdaaa6d7656aa6456843e1b5 | refs/heads/master | 2021-06-10T18:30:28.476092 | 2017-01-16T15:01:35 | 2017-01-16T15:01:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,879 | r | Plot.R | #' Plot
#'
#' This function plots blm models
#'
#' @param x An objct of class blm
#' @param fit Should target variables (y axis) be plotted from fitted or data values
#' @param newData for plot of new predicted target variable values, add new Data and assign fit = TRUE
#' @param ... Additifonal data,
#'
#' @return... |
e051235df4375e72802720c5234c9a1999b05110 | 3c0a74cde5a48df98e1a362e109e82c31ef2ad21 | /app/server.R | bce01711c31208a82af8b54b47491302bf85d936 | [] | no_license | tomsb459/rtu-neighborhood-map | be499ef51fbbb47ae2f8e32f12cac6e6806672c4 | c0f88071914c69a593c01d42944493927925a8ea | refs/heads/master | 2023-01-04T01:53:00.014728 | 2020-10-19T13:38:11 | 2020-10-19T13:38:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,188 | r | server.R | server <- function(input, output, session) {
# Login -------------------------------------------------------------------
# Call login module supplying data frame, user and password cols
# and reactive trigger
credentials <- callModule(
shinyauthr::login,
id = "login",
data = user_base,
us... |
1aa455d6a6ed3e90fa36879e9e3bfe1fa20588a8 | f8ce1034cef41685ab2387fa42084ac1ee5c96cf | /chapter18/rook.R | 52c18a980028e2572b095c94529f4db0ad118ef9 | [] | no_license | AnguillaJaponica/RProgramming | ab7e15d99153ebe585745289490e6e3df118a35c | ae1401920b5a52795cffc95bd83922f15761e531 | refs/heads/master | 2020-11-28T10:07:02.579925 | 2019-12-23T15:28:23 | 2019-12-23T15:28:23 | 229,777,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 373 | r | rook.R | library(Rook)
hello_fun <- function(env) {
res <- Rook::Response$new()
res$write("<html>ヮ<head><title>HelloWorld</title></head>・n<body>・n")
res$write("<h1>Hello World</h1>・n")
res$write("</body>・n</html>・n")
res$finish()
}
#
rk <- Rhttpd$new()
rk$start(quiet = TRUE)
rk$add(app = hello_fun, nam... |
1930091d640f83a281aec0af554f05e49f7201b2 | dd4eedb2d9b20284b5be5f72eb8c9d3f86208855 | /stage_18/analysis.R | 44c98c81c25e984daff74f77a2f67ed0362c3685 | [] | no_license | hasmitapatel18/Master_thesis | d3c0b6a456edb11cffc144e8138b57b519458ef1 | 43e0d68efca749901a51f2462821892edb05905a | refs/heads/master | 2020-03-27T11:37:58.390429 | 2018-08-28T20:50:46 | 2018-08-28T20:50:46 | 146,498,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,800 | r | analysis.R |
rm(list=ls()) # clean up the workspace
# *******************************************************************
# Constructing priors on 'g' (genartion time) and
# 'u' (per-generation mutation rate)
# *******************************************************************
# generation prior construction
m <- 29.5; v <- (... |
ef873d924be15686528ff1314636679423fd5af1 | da67e60cc58adb0fe9e02b1edbf365d558cc35eb | /R/s3.R | 372a356858bc2de946fa928a5a08991b1eeaaba4 | [] | no_license | brunaw/music21 | ef506e88fb8e576b622fc62a4d0ae0c7f0b7ad56 | b67f5d82735cf05bed3b5c366c0f74d9e6906060 | refs/heads/master | 2021-07-12T17:57:22.951629 | 2017-10-13T18:47:16 | 2017-10-13T18:47:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,294 | r | s3.R | plot.music21.base.Music21Object <- function(x, ...) {
env <- reticulate::import("music21.environment")
env$set('graphicsPath', Sys.which("lilypond")[1])
img <- x$write("lily.png")
print(magick::image_read(img))
invisible(img)
}
#' Prints music21 generic object
#'
#' Uses show method from music21
#'
#' @param... |
f5f6baeb463b4d3c3d4d2cb083ee021220573feb | fa853f13add91b485908ac7ffec0275cbb458b0c | /Day_3.R | b3e712873b5cf1876910fffce33e3c285d5b604e | [] | no_license | MpumalangaMnyekemfu/Biostats_2021 | 60a8bff9043297811bdfd2c3236b9a074dea1b60 | 5efc173fd3594bce5a08fde7420634430ab518a5 | refs/heads/master | 2023-04-05T16:46:02.478318 | 2021-04-27T12:15:51 | 2021-04-27T12:15:51 | 359,588,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,147 | r | Day_3.R | #Day_3
head(faithful)
eruption.lm <- lm(eruptions ~ waiting, data = faithful)
summary(eruption.lm)
slope <- round(eruption.lm$coef[2], 3)
p.val = 0.001
r2 <- round(summary(eruption.lm)$r.squared, 3)
ggplot(data = faithful, aes(x = waiting, y = eruptions)) +
geom_point() +
annotate("text", x = 45, y = 5, label... |
b716ebc7aebec2b91e37aa1f6d7acc78c852607f | 4606b7fb6bec2053fa493d6a828bbf34bdb30f69 | /tests/testthat/test-util.R | e2d33506bba8bc202b4b25572a22ff99e054e400 | [] | no_license | manisahni/icd9 | 705ed3fa16d3c21bb96baa7ed6a88cc2c1861e73 | 2eddaa4ae22c7a2cf76e05b949193ddd55d05d96 | refs/heads/master | 2021-01-18T18:56:54.207249 | 2014-07-21T15:54:18 | 2014-07-21T15:54:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 870 | r | test-util.R | context("test utility functions")
test_that('strMultiMatch with and without dropping empties', {
expect_equal(strMultiMatch(pattern="jack", text=c("not", "here")), list(character(), character()))
expect_equal(strMultiMatch(pattern="(jack)", text=c("not", "here")), list(character(), character()))
expect_equal(st... |
e57173b1dd057b12ae1d3dcb8dcc13f01873dc8b | 1a9536036975eee9d8d7b8f1475a9bdbc36b8806 | /man/curve_it.Rd | cadac1e1f580a5d97b4c2a23c64ec418b3b32a7c | [] | no_license | chrisbrunsdon/caricRture | 10781aa2b83678a0751acf6eebfdb5e5722bde0a | 48acb95a7a07ea3155e1c8c625c6d5f3609feb67 | refs/heads/master | 2021-01-10T19:27:32.209393 | 2016-04-12T09:26:54 | 2016-04-12T09:26:54 | 39,564,788 | 7 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,134 | rd | curve_it.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/caricRture.R
\name{curve_it}
\alias{curve_it}
\title{'Curvify' polygon-based objects.}
\usage{
curve_it(spdf,s)
spdf %>% curve_it(s)
}
\arguments{
\item{s}{\code{shape} parameter as in \link[graphics]{xspline}}
\item{spdf}{a \link[sp]{Spatia... |
aef5e590d0761ac021e5d028b5626b1d6bf19f67 | 3ad73d74e1323aa0e3992912bf8704cfe6f58e6c | /data_visualisation.R | dd41ecfcb6c3edf1936682dc9bfa181bd7b533d7 | [] | no_license | alyomahoney/Monopoly | 323c539d4a67ac069739c0618031879af4f48064 | 8ada658acd7ba986aaf2e4bbade1da863850a5ab | refs/heads/master | 2022-11-05T02:56:00.713170 | 2020-06-22T09:04:32 | 2020-06-22T09:04:32 | 269,175,499 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,133 | r | data_visualisation.R | set1 <- "hsl(29, 69%, 34%)"
set2 <- "hsl(205, 82%, 73%)"
set3 <- "hsl(297, 77%, 47%)"
set4 <- "hsl(35, 100%, 54%)"
set5 <- "hsl(0, 76%, 49%)"
set6 <- "hsl(60, 88%, 60%)"
set7 <- "hsl(111, 74%, 39%)"
set8 <- "hsl(218, 62%, 33%)"
colour_sets <- c(set1,set2,set3,set4,set5,set6,set7,set8)
colour_label <- c("Brown","Light b... |
50f5e532aa31d554d9be09bd1f28d5db4a9ee781 | f4e504d84c935accb29cf0394729372413152b93 | /man/bootstrap_C.Rd | c575ae3f4aba20a15c522c8ee6e2755198a6b711 | [] | no_license | cshannum/unequalgroupoutlier | 8fd8c2f00628358f809842a3037fef19daa71fdc | c1ea76a3dca80a1f2ceee7c7e37aaab155b1af8b | refs/heads/master | 2020-03-31T22:54:00.884508 | 2019-02-28T23:32:18 | 2019-02-28T23:32:18 | 152,635,181 | 0 | 0 | null | 2018-11-09T18:48:07 | 2018-10-11T18:11:29 | R | UTF-8 | R | false | true | 2,240 | rd | bootstrap_C.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Depth_Outlier_Funcs.R
\name{bootstrap_C}
\alias{bootstrap_C}
\title{Bootstrap a cutoff value to identify anomalies}
\usage{
bootstrap_C(coeff, d.method, c.method, alpha, B)
}
\arguments{
\item{coeff}{A dataframe of coefficients of interest. T... |
8738938395202e127ad9561e6ec4cac2cc27f0da | e77b87fc6aca13fe63b75bcee7ea56554c39963b | /man/playlist_demographics.Rd | 14cc6433d700c7bcbf95e700e0a807df09e73a41 | [
"MIT"
] | permissive | davisj95/YTAnalytics | e5dacebd4fc8cdfb9e4f6dcc3b061e297225cc0a | 8a52248e8750701c8e5ad1ea814d8f6e40e4fd03 | refs/heads/main | 2023-09-01T15:00:20.373935 | 2023-08-31T15:52:02 | 2023-08-31T15:52:02 | 387,549,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 553 | rd | playlist_demographics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/demographics.R
\name{playlist_demographics}
\alias{playlist_demographics}
\title{Playlist Demographic Data}
\usage{
playlist_demographics(playlistId = NULL, ...)
}
\arguments{
\item{playlistId}{Required. String. Id of YouTube playlist.}
\ite... |
65f6b1860aefc196636ad9dad7ff74c84b45b972 | 9b93b2e65b95236b1d939179d314ca49acab0d39 | /docs/concrete-ml.R | fc51dbbc0fa1e158aeddc32af26366ec9976035e | [] | no_license | anguswg-ucsb/176c-project | b2d701cb569004f74c086939a1cafd4d09124e1f | a3c3aac6a10457ec0786168e058d0f4c77e8ba94 | refs/heads/main | 2023-03-29T09:52:55.594129 | 2021-04-05T16:07:26 | 2021-04-05T16:07:26 | 352,705,065 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,602 | r | concrete-ml.R | # Decision trees example
library(tidymodels)
library(baguette)
library(rules)
library(workflowsets)
data(concrete, package = "modeldata")
glimpse(concrete)
concrete <- concrete %>%
group_by(cement, blast_furnace_slag, fly_ash, water, superplasticizer,
coarse_aggregate, fine_aggregate, age) %>%
summari... |
26e90c78d88d0401156e67125a26b2e2dfdaaadd | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/saeSim/examples/sim_gen.Rd.R | 745c192979c3233f2309a67383a6fded4e32d282 | [] | 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 | 618 | r | sim_gen.Rd.R | library(saeSim)
### Name: sim_gen
### Title: Generation component
### Aliases: sim_gen sim_gen_generic
### ** Examples
# Data setup for a mixed model
sim_base() %>% sim_gen_x() %>% sim_gen_v() %>% sim_gen_e()
# Adding contamination in the model error
sim_base() %>% sim_gen_x() %>% sim_gen_v() %>% sim_gen_e() %>% si... |
4653b5950464d6dc83443803009064599c0a30a0 | af0df2be1822e2ed328f8bc1fffd0410d0e954ac | /montecarlo.R | 12b9e496e5e7901b3aa8e6e2d84e8431378a01de | [] | no_license | dabaja/StatFinDat | 8a08d729595f0a77e693acfb44bd33a259c8157d | 7537bcf39d0c53aaec202d66557dd9483fe2976b | refs/heads/master | 2021-07-04T04:30:34.762642 | 2017-09-28T18:06:57 | 2017-09-28T18:06:57 | 103,696,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,440 | r | montecarlo.R | library(Rsafd)
# Generating random samples
GWN <- rnorm(1024)
CWN <- rcauchy(1024)
par(mfrow=c(2,1))
plot(GWN,type="l")
title("Sequential plot of a standard Gaussian sample")
plot(CWN,type="l")
title("Sequential plot of a standard Cauchy sample")
par(mfrow=c(1,1))
# Notice that the relative size of ex... |
ed4d3670f193b4c19447f9b74647424902732ff3 | c85471f60e9d5c462de6c60c880d05898ec81411 | /cache/dallinwebb|tidy_tuesday|2019__06_house_morgage__code.R | c6e61e3190b69d6c7085a15084c19189c7e9ec66 | [
"CC-BY-4.0",
"MIT"
] | permissive | a-rosenberg/github-content-scraper | 2416d644ea58403beacba33349ee127e4eb42afe | ed3340610a20bb3bd569f5e19db56008365e7ffa | refs/heads/master | 2020-09-06T08:34:58.186945 | 2019-11-15T05:14:37 | 2019-11-15T05:14:37 | 220,376,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,389 | r | dallinwebb|tidy_tuesday|2019__06_house_morgage__code.R | library(tidyverse)
library(USAboundaries)
library(sf)
hpi <- read_csv("https://github.com/rfordatascience/tidytuesday/raw/master/data/2019/2019-02-05/state_hpi.csv")
now <- hpi %>%
filter(year == 2001,
month == 11) %>%
select(state,
year_now = year,
price_index_now = price_index)
joi... |
50c472553eafcc236b5358774ce167c8200341d9 | e535d498001519774956adcc5b0106a5f4e555ac | /simulations/ASE_scripts/plot_data.r | 70f160138b161ca652efaa58ae86e6a745a2f569 | [] | no_license | kraigrs/thesis_work | f73c6f130a0cf33ed079acb35208bff9cb85d4d1 | bcc8e46b5c65f08c61d5beb8e29ac7e4df101cff | refs/heads/master | 2021-01-22T16:18:29.372793 | 2015-09-10T18:48:11 | 2015-09-10T18:48:11 | 34,088,947 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,887 | r | plot_data.r | exons <- read.table("/Users/kraigrs/Wittkopp/Simulations/SNPs_in_const.txt",header=FALSE,sep="\t");
lengths <- exons[,3]-exons[,2];
exons <- cbind(exons,lengths);
exprn <- read.table("/Users/kraigrs/Wittkopp/Simulations/zhr_z30_exons_expression.txt",sep="\t");
exons <- merge(exons,exprn,by.x="V8",by.y="V1");
tiled_e... |
fa929553b09838e470b9b9049557c5250c0c68ba | 1ff5773280731e9de136b796d3102cd942977e7c | /man/SelectControls.Rd | 30109481d307cbb537386fafc08797523113136c | [] | no_license | na89/SVDFunctions | 014dc99608f4ba304e26437e3f410c34640ebab5 | e9af744ba684fbdda85a4c0d658222b2983a5c6f | refs/heads/master | 2020-05-18T14:23:21.487558 | 2019-03-01T19:11:49 | 2019-03-01T19:11:49 | 184,469,099 | 0 | 0 | null | 2019-05-01T19:22:34 | 2019-05-01T19:22:34 | null | UTF-8 | R | false | true | 1,424 | rd | SelectControls.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/selector.R
\name{SelectControls}
\alias{SelectControls}
\title{Selection of the optimal set of controls}
\usage{
SelectControls(genotypeMatrix, SVDReference, caseCounts,
minLambda = 0.75, softMinLambda = 0.9, softMaxLambda = 1.05,
maxLamb... |
5b06cfe4a60c6f6dcfef2eca037c148774bfd2e3 | ee49a71e821e06bdda7a8d59486a5070cee68fa3 | /inst/rstudio/templates/project/proj_fls/data/02_read.R | ac3162da0b163ba766cfc03cd67d5ee67e3beef9 | [
"MIT"
] | permissive | scholaempirica/reschola | 728b42ba5acb7eb32c712c2ab404ab5546f09700 | 16f7d64889950cb7fe183d26ed7da1f7d8d6283e | refs/heads/master | 2023-04-28T08:21:25.166619 | 2023-04-13T12:20:00 | 2023-04-13T12:20:00 | 245,384,211 | 4 | 1 | MIT | 2021-02-24T01:08:23 | 2020-03-06T09:53:26 | R | UTF-8 | R | false | false | 51 | r | 02_read.R | library(reschola)
library(tidyverse)
library(here)
|
cc528eff1db049f478aa0c98954dd1d87c222cb5 | b201f1f182b1828a66a2d97baf28224b39d70564 | /man/build_tm_distplot_tbl.Rd | f98c0ddb944f2d7657182835220950b64b31f94e | [
"MIT"
] | permissive | Drinchai/iatlas-app | 147294b54f64925fb4ee997da98f485965284744 | 261b31224d9949055fc8cbac53cad1c96a6a04de | refs/heads/master | 2023-02-08T08:17:45.384581 | 2020-07-20T23:27:08 | 2020-07-20T23:27:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 513 | rd | build_tm_distplot_tbl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/til_map_distributions_functions.R
\name{build_tm_distplot_tbl}
\alias{build_tm_distplot_tbl}
\title{Build Tilmap Distplot Tibble}
\usage{
build_tm_distplot_tbl(tbl, id, scale_method)
}
\arguments{
\item{tbl}{A tibble with columns sample_id, s... |
b3e7a651272f645222b0a3325ff9d192e1032e7a | 4e5e4dd54801402c93bb5909bab70ec3bc5a09e6 | /script/source scrape Big3.R | 8cca372b9ffcf3d12e0d4476400ef4c8e3b5a3c9 | [] | no_license | mguideng/text-mining-big3-reviews | ada2e96356e02cd15da9809a7031b49f8e3de520 | 28ff8df8afa554055be9b5b3a9360cb8ca7198d2 | refs/heads/master | 2020-03-23T15:23:17.583972 | 2018-08-03T00:28:55 | 2018-08-03T00:28:55 | 141,744,132 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,258 | r | source scrape Big3.R | ## Set URL
baseurl <- "https://www.glassdoor.com/Reviews/"
sort <- ".htm?sort.sortType=RD&sort.ascending=true"
totalreviews <- read_html(paste(baseurl, company, sort, sep="")) %>%
html_nodes(".margBot.minor") %>%
html_text() %>%
sub(" reviews", "", .) %>%
sub(",", "", .) %>%
as.integer()
maxresu... |
46319a13a724b2b2cf1a227b37c1d4f88152540b | 9e4df408b72687493cc23144408868a975971f68 | /SMS_r_prog/r_prog_less_frequently_used/short-term_sensitivity.r | efb8c675b9e3dc97cca808e86584364bcd6a55ce | [
"MIT"
] | permissive | ices-eg/wg_WGSAM | 7402ed21ae3e4a5437da2a6edf98125d0d0e47a9 | 54181317b0aa2cae2b4815c6d520ece6b3a9f177 | refs/heads/master | 2023-05-12T01:38:30.580056 | 2023-05-04T15:42:28 | 2023-05-04T15:42:28 | 111,518,540 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,615 | r | short-term_sensitivity.r | # stochastic, short term forecast,
# lines begining with # are comments and can be put anywhere
#
#
# Specification of year range
# 1. first year for inclusion in mean
# 2. second year for inclusion in mean
# 3. include variance. 1=TRUE, 0=FALSE
###################################################
# Input
options<-c(... |
b54b95369790a519b944aa004bf0d3b2bd765e3e | 992a8fd483f1b800f3ccac44692a3dd3cef1217c | /Rstudy/tidyr and dplyr.r | 111e728b3ef9b1ba6127be753d6930871e80c877 | [] | no_license | xinshuaiqi/My_Scripts | c776444db3c1f083824edd7cc9a3fd732764b869 | ff9d5e38d1c2a96d116e2026a88639df0f8298d2 | refs/heads/master | 2020-03-17T02:44:40.183425 | 2018-10-29T16:07:29 | 2018-10-29T16:07:29 | 133,203,411 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 577 | r | tidyr and dplyr.r | # tidyr and dplyr、
install.packages("tidyr") # install the package
library(tidyr)
install.packages("dplyr") # install the package
library(dplyr)
germination <- read.csv("Germination.csv", sep = ";")
head(germination)
# subset rows
germinSR <- filter(germination, Species == 'SR')
# select columns
germin_clean ... |
2fb0a60c402cb4a6a0c29d7b7dfefa3a8fd38ce7 | 26fc0711f31ec6dcce1f1c3960a271eaa8457548 | /Stepik4/data_table.r | 2a64465ec948dc52b1922f0bcb188837d8d0a705 | [] | no_license | venkaDaria/rlang-demo | bdeee1621c7a506c7f1f520333550a7e130e34ac | 1ba8ee3904541a86c11e9d8ace8f528c569d6b48 | refs/heads/master | 2022-04-13T13:39:54.597495 | 2020-04-11T16:15:43 | 2020-04-11T16:18:12 | 254,906,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,011 | r | data_table.r | # Напишите функцию filter.expensive.available, которая принимает на вход products (объект типа data.table) и вектор названий брендов,
# и возвращает только те строчки, которые соответствуют товарам, цена которых больше или равна 5000 рублей,
# которые доступны на складе и принадлежат одному из переданных брендов.
fil... |
5ca7033e059d1a56f8a59bc07f19d770cba9760e | e93365ff9ea828bb82bb691b8e88037280f26a36 | /src/visualization/plot_saleprice_waterfront.r | cd422e29b88b7d15c34284f069fd6a7e3aa79d6a | [] | no_license | YufenLin/housing_prices_project | 42ae66e2244218e055c5263f29f0425cc6478165 | a1d31b84d51ab39f1b7e6dd3651570837cc9f3f8 | refs/heads/master | 2020-09-23T08:44:13.599004 | 2019-12-06T23:25:07 | 2019-12-06T23:25:07 | 225,455,807 | 0 | 1 | null | 2019-12-06T21:25:00 | 2019-12-02T19:48:33 | Jupyter Notebook | UTF-8 | R | false | false | 1,285 | r | plot_saleprice_waterfront.r | #
# Author: Yu Fen
# Date: Decemeber 6, 2019
# Purpose: Visualize the distribution of sale price and waterfront
#
# load necessary libraries ----
# install.packages("tidyverse")
library(tidyverse)
# set working directory ----
setwd("~/flatiron/project/housing_prices_project/")
# load necessary data ----
pr... |
f44fb9916af8d26f0622f826cde0a80e73b62d59 | ba53c61c1916301ec353def6c857d3af9c17a284 | /man/expand_matrix.Rd | fd793d58c8db3ab8788481c50c316d3ff8d90cba | [] | no_license | ivaughan/econullnetr | 7921b08fe0b16c771afee2f7320a137c0359b36e | e2227492df82cef54936eb89815f6bf207b26b70 | refs/heads/master | 2023-06-17T11:39:19.704109 | 2021-05-28T15:46:56 | 2021-05-28T15:46:56 | 104,395,786 | 10 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,699 | rd | expand_matrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expanding_interaction_matrix.R
\name{expand_matrix}
\alias{expand_matrix}
\title{Expand a summarised interaction matrix}
\usage{
expand_matrix(X, r.names = rownames(X), MARGIN = 1)
}
\arguments{
\item{X}{A matrix or data frame representing th... |
39a66ed058b86ec1fa206acb87cd9c12695f4f80 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/urca/examples/plot-methods.Rd.R | f26e3e138b0c1fc60b1d8723bb6fc1e593a9e539 | [] | 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 | 968 | r | plot-methods.Rd.R | library(urca)
### Name: plot-methods
### Title: Methods for Function plot in Package urca
### Aliases: plot plot-methods plot,ur.ers,missing-method
### plot,ur.kpss,missing-method plot,ca.jo,missing-method
### plot,ca.po,missing-method plot,ur.pp,missing-method
### plot,ur.sp,missing-method plot,ur.za,missing-m... |
d067de495732c6aa6ec587b3eaf380d9f7e6b7fd | 97fd888949808a0ed1734bab1c602eb8ca0fbaa2 | /R/param_network.R | 3f848b09b2cf39601591915f36f12acaab789f10 | [
"MIT"
] | permissive | tidymodels/dials | 31850316efdb13c97944130a93f845a035cf88e8 | 55763e0cbd49a16a3f5a532dc92b9069258d54e7 | refs/heads/main | 2023-07-20T16:14:58.149708 | 2023-04-03T18:10:23 | 2023-04-03T18:10:23 | 141,954,544 | 111 | 33 | NOASSERTION | 2023-07-14T16:03:15 | 2018-07-23T03:07:49 | R | UTF-8 | R | false | false | 1,576 | r | param_network.R | #' Neural network parameters
#'
#' These functions generate parameters that are useful for neural network models.
#' @inheritParams Laplace
#' @details
#' * `dropout()`: The parameter dropout rate. (See `parsnip:::mlp()`).
#'
#' * `epochs()`: The number of iterations of training. (See `parsnip:::mlp()`).
#'
#' * `hidde... |
4e29cafe8ff043bb453da9a1605de6c969dd6f5a | ba71ea3bd22182e6733a3b4132d18f20ed681b7d | /man/BatchUpdateValuesByDataFilterRequest.Rd | fe109d10e81d22223e78a9da6efbff2b709abb5b | [] | no_license | key-Mustang/googleSheetsR | 4ef61ef15e944825746bcb2ae1427f3c2850ed50 | c904a53fccddb3dc332655f645ed2dc465eac434 | refs/heads/master | 2020-03-28T12:40:56.857407 | 2018-07-22T05:39:03 | 2018-07-22T05:39:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,248 | rd | BatchUpdateValuesByDataFilterRequest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sheets_objects.R
\name{BatchUpdateValuesByDataFilterRequest}
\alias{BatchUpdateValuesByDataFilterRequest}
\title{BatchUpdateValuesByDataFilterRequest Object}
\usage{
BatchUpdateValuesByDataFilterRequest(valueInputOption = NULL, data = NULL,
... |
ce54235e90c1d4b05ddf22c2ec6aa74230d6755e | a63298e74cb572c76046f4585e49ee5f6327c75e | /R/deltafunc.R | ce580f236a1e2bfba8b228a79a6acf6e6b686f46 | [] | no_license | leminhthien2011/CONETTravel | 65b24f58830c0e347d938719862b8043603d88ae | 5fbc75cfe4225c36dacb057e9e04b1122d5b9c94 | refs/heads/main | 2023-04-05T13:25:12.253787 | 2020-11-02T15:44:22 | 2020-11-02T15:44:22 | 308,343,455 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 532 | r | deltafunc.R | #' This gives estimation for delta
#' @param data data of A,R,D sequence
#' @export
deltafunc = function(data){
ddeath_sim = data[,3] - c(0,data[,3][-length(data[,3])])
active_sim = c(1,data[,1][-length(data[,1])])
index7 = which(active_sim==0)
index8 = c(1,index7)
ddeath_sim = ddeath_sim[-index8]
... |
52acb7f2d81849e9a7b1d22b3549d3527a911397 | 34072d4e8efe0531b20dbb9d57a930ae5d85e9d9 | /classic-bugs-vol1/pump.R | 1df2d0dc6b39198ef94d4e8f8918abe20339f036 | [] | no_license | datacloning/dcexamples | 489aa223bb7e76851958ab93918ac02d575bfcde | c3774eda0766cc5a8061885daef634bdb9ef6289 | refs/heads/master | 2021-01-01T19:06:46.632020 | 2016-01-13T00:03:11 | 2016-01-13T00:03:11 | 25,499,601 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 610 | r | pump.R | ## pump: conjugate gamma-Poisson hierarchical model (BUGS Examples Vol. 1)
library(dcmle)
pump <- makeDcFit(
multiply = "N",
data = list(
"N" = 10,
"t" =
c(94.3, 15.7, 62.9, 126, 5.24, 31.4, 1.05, 1.05, 2.1, 10.5),
"x" =
c(5, 1, 5, 14, 3, 19, 1, 1, 4, 22)),
mo... |
8253a50472d8ae1d255cd242875af45f1907820f | 124bf41d015e2d72b5757c7912ff49040f93827c | /man/mnlogit.Rd | bd0e2fc395eeafdc77bd173717f8cee8ed703d67 | [] | no_license | floswald/mnlogit | 70e5cbdaefbd062df771f8acece5a39d0baedbd0 | 40b878c4ffa69b87c1f9a6dc98600fee9511a1d2 | refs/heads/master | 2020-05-28T04:08:36.198242 | 2019-12-12T16:00:45 | 2019-12-12T16:00:45 | 188,875,438 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,213 | rd | mnlogit.Rd | \name{mnlogit}
\alias{mnlogit}
\alias{print.mnlogit}
\alias{summary.mnlogit}
\alias{predict.mnlogit}
\alias{coef.mnlogit}
\alias{print.est.stats}
\alias{print.model.size}
\alias{print.summary.mnlogit}
\title{Fast estimation of multinomial logit models}
\description{
Time and memory efficient estimation of multinomial... |
1f70ac803c2e96558677193de2ae41688542806b | dfb5bf243b895ee58b8b8dea5f11fc3f5472dcae | /man/equation_9_7.Rd | 14852a9ac473720db4dbecd39014cb4c15726735 | [] | no_license | trollock/respiratoR | d6301459d80caf996e268e3676cf4838e879c86a | a86fd4bccfcf5f37f2756a7304500739498c8104 | refs/heads/master | 2020-09-01T16:39:13.359005 | 2019-11-25T15:25:08 | 2019-11-25T15:25:08 | 218,963,440 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 872 | rd | equation_9_7.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/equation_9_7.R
\name{equation_9_7}
\alias{equation_9_7}
\title{Baseline drift correction inspetion plot}
\usage{
equation_9_7(dat, val1, val2)
}
\arguments{
\item{dat}{dataframe containing spline fits of respirometry data, this is the output ... |
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