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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
67fe7fb23c3d662e5826ce53ed891426af3cbc26 | 2f0cbb9303747f445da1c9faf4bf75b055d725a1 | /R/detectFaces.R | 0238e7047e61fb13ca4c8e29881ee61ca776c183 | [] | no_license | peoplecure/r_facepp | 77a6e8528a6a06e93a8d71525dee91f9c004df8a | 01bbb385c741d53cc7ff30837b0f8fbc3ddb263c | refs/heads/master | 2020-05-30T13:45:29.323971 | 2017-09-17T14:33:18 | 2017-09-17T14:33:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 337 | r | detectFaces.R | detectFaces <-
function(proxy, image_file){
r <- POST(url='https://api-cn.faceplusplus.com/facepp/v3/detect',
body=list(api_key=proxy$api_key,
api_secret=proxy$api_secret,
image_file=upload_file(path=image_file)),
encode='multipart')
cont... |
83ec27ce4279daf25136337983bd1708e400e508 | 592bf5bfffd630f6372a710f12cbdc6ad71e0b07 | /R/predictionTheta.R | aa26b13a21a25833089114ff3d274ae134d31008 | [] | no_license | cran/warpMix | 306043d19671cb9fc02cffd49c8f5c65946c5192 | 156fe1cb94375f903d905765ad6fab10f1b24130 | refs/heads/master | 2021-01-19T08:03:39.362417 | 2017-02-15T14:11:39 | 2017-02-15T14:11:39 | 82,067,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,015 | r | predictionTheta.R | #' Predict the warping parameters.
#'
#' This function predict the warping parameters, using the estimations of those parameters,
#' and fitting a linear mixed effect model on them.
#'
#' @param thetaObs A matrix (size: n * T) corresponding of the estimations of the warping parameters.
#' @param sigmaEpsilon A number, ... |
1db870fc65f0d9ced210c8c40e993a55d9af6866 | fac69dc12b6607d5a1b08f694453164ad5c61326 | /ps_user_stuttgart_part2.R | b6ed58720377f30dac7a2f755a4be3831112766a | [] | no_license | Japhilko/ps_2017_11_user_stuttgart | 154cc8def1d45280ddac4384c5eeece1210291bd | afd53942b362242c12f2599a96ab778463a16be9 | refs/heads/master | 2021-09-03T08:42:20.864002 | 2018-01-07T17:07:55 | 2018-01-07T17:07:55 | 111,187,978 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,975 | r | ps_user_stuttgart_part2.R | ## ---- include=FALSE------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,cache=T,warning=F,message=FALSE)
par(mai=c(0,0,0,0))
log_gesis=F
log_home=!log_gesis
internet=F
noint = !internet
## ----echo=F,eval=F-------------------------------------------------------
## install.packa... |
2c782765b6e83db86b7e497e9587cb80df403c41 | 69bd4458ed69408391c7f1876e2d156885433b43 | /R/robust-lmrob-tidiers.R | 47a90fc7c01bcf046ee928112106da8ba34a72e9 | [] | no_license | sjewo/broom | 51f52249069ad0e29063609129ffe14996e7fd16 | e10e7598e33b675cc804a5d3871089c8fc7d5a93 | refs/heads/master | 2020-03-09T18:59:29.549589 | 2018-09-07T11:33:22 | 2018-09-07T11:33:22 | 128,946,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,595 | r | robust-lmrob-tidiers.R | #' @templateVar class lmRob
#' @template title_desc_tidy_lm_wrapper
#'
#' @param x A `lmRob` object returned from [robust::lmRob()].
#'
#' @details For tidiers for robust models from the \pkg{MASS} package see
#' [tidy.rlm()].
#'
#' @examples
#'
#' library(robust)
#' m <- lmRob(mpg ~ wt, data = mtcars)
#'
#' tidy(m)... |
42ae0ebe76d1fb8bd0ffc7c0611a7679598049d6 | e653cd6ae50f5b178a25253423a9e09f8efb8790 | /man/checkpnr.Rd | 834c3b1b970a96ce208b152eb769a102dfca8c72 | [
"MIT"
] | permissive | chrk623/cooccurExtra | c44621496dafa56aa1dfdec6383668cc525b637e | bea970034f7ef24940282d4e9dc43fc773abba10 | refs/heads/master | 2020-08-28T19:19:09.714150 | 2019-10-28T10:43:32 | 2019-10-28T10:43:32 | 217,797,075 | 0 | 0 | null | 2019-10-27T02:52:25 | 2019-10-27T02:52:25 | null | UTF-8 | R | false | true | 1,595 | rd | checkpnr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/checkpnr.R
\name{checkpnr}
\alias{checkpnr}
\title{A function to distinct the cooccurence of species pairs from a "cooccur" ouput model object.}
\usage{
checkpnr(cooccur.mod)
}
\arguments{
\item{cooccur.mod}{An model object generated by "cooc... |
dd7882cc20397f6bbb310956c2385ab46007c5fb | 58f7e798793e68a9b22d767782d1e5e0bdde7755 | /src/01_pipeline/00_industry_code_change_scraper.R | 4db216c3df53daf173452fbabba187b5aed71e77 | [] | no_license | tjvananne/dataoftheunion | b661e1fb654738ddc5c6cdc8af3ad5928525abb7 | 6dd67de84532dcefdc8a5dd43c821164d2f6e3bb | refs/heads/master | 2022-01-20T05:53:11.141499 | 2021-12-30T19:39:31 | 2021-12-30T19:39:31 | 173,947,402 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 783 | r | 00_industry_code_change_scraper.R |
# NAICS industry code change mapping tables
# Script config -------
FILE_NAME_2012_TO_2017 <- "proc_data/NAICS_2012_to_2017_map.csv"
# Load libs -----
library(dplyr)
library(rvest)
library(xml2)
# Scrape changes -----
url <- "https://www.naics.com/naics-resources/2017-naics-changes-preview/"
urldata <- xml2:... |
4bd22cbec0ca272f78444fdb007cd3fd374e93d8 | d08e69198fbd60086aa35d765c7675006d06cf3f | /R/RidgeOrdinalLogistic.R | be7e0d3350ff6f4d93721286dbc7a6a834cbdd44 | [] | no_license | villardon/MultBiplotR | 7d2e1b3b25fb5a1971b52fa2674df714f14176ca | 9ac841d0402e0fb4ac93dbff078170188b25b291 | refs/heads/master | 2023-01-22T12:37:03.318282 | 2021-05-31T09:18:20 | 2021-05-31T09:18:20 | 97,450,677 | 3 | 2 | null | 2023-01-13T13:34:51 | 2017-07-17T08:02:54 | R | UTF-8 | R | false | false | 1,031 | r | RidgeOrdinalLogistic.R |
RidgeOrdinalLogistic <- function(y, x, penalization = 0.1, tol = 1e-04, maxiter = 200, show = FALSE) {
if (!is.ordered(y)) stop("The dependent variable must be ordinal")
if (is.matrix(x)) {
n <- nrow(x)
}
else {
n <- length(x)
}
Y=y
Niveles=levels(y)
y=as.numeric(y)
model=Ordi... |
8e54b39b39363f19460198f486d74dd1b365e307 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ashr/examples/lik_normal.Rd.R | 8d99a33a2b62e7032accc0d4c381a43dcaec164e | [] | 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 | 240 | r | lik_normal.Rd.R | library(ashr)
### Name: lik_normal
### Title: Likelihood object for normal error distribution
### Aliases: lik_normal
### ** Examples
z = rnorm(100) + rnorm(100) # simulate some data with normal error
ash(z,1,lik=lik_normal())
|
88a933506be578da6cccc27b75330108a388eb71 | 82ce9573daab73ac52534e9baddbdf6244abd5d3 | /pgm-r/stacking_20171108r1.R | 252cf61d5a2c48c8e17f8a5b3fe8d5248bddabf0 | [] | no_license | zoe3/bank | be2fa37e00123941a56a633fa63e0bf53e49473d | 7db841a3fec4256083291355e9f51597ec5837e9 | refs/heads/master | 2021-08-18T22:06:41.613722 | 2017-11-24T03:10:32 | 2017-11-24T03:10:32 | 110,041,227 | 0 | 0 | null | null | null | null | SHIFT_JIS | R | false | false | 8,634 | r | stacking_20171108r1.R | ##スタッキングの実装例(投稿用)
#使用ライブラリ
library(dplyr)
library(rpart)
library(pROC)
library(ggplot2)
library(partykit)
#データ読込
train<-read.csv("../motodata/train.csv", header=TRUE)
test<-read.csv("../motodata/test.csv", header=TRUE)
#### データ加工
## Job
test$y <- 9
combi <- rbind(train,test)
combi <- combi %>%
... |
1357ebc6636d1198e1d5aae8e909fd208bb65ba5 | b81b84fe38fd6e7580f07818a09e900566a55c5c | /R/training.R | 2e8a630a19d50589bf339ad11aaabeef34940306 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | Moonerss/violentSurv | ee4569b0abddb6b12ee9d80f330b8068b726fc78 | 39cce5e200ac35c339bce10b7c7db269c88c3675 | refs/heads/main | 2023-04-06T20:08:12.149462 | 2021-04-22T11:33:09 | 2021-04-22T11:33:09 | 316,680,883 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,451 | r | training.R | #' @name train_signature
#' @title Train the signature model in a data sets
#' @description this function will filter the signature are not significant in the logrank test
#' @param surv_data a `data.frame` containing variables, ids, time and event.
#' @param id column name specifying samples ID , default is 'ids'.
#' ... |
51f1d4a7fb22de7292038b6ab6c72e63db57344f | 175e45e8344a1d2a8fac50e12fca4a9bfb6b5e18 | /man/position-methods.Rd | c1797bd85cd526a4ca19544d3614079eace0c37b | [] | no_license | reidt03/MassArray | 99b3c0c303b1df2d47dbc212e1e440a848824b57 | 186fad2e1bc09670566fc6f1c0c6398f44f4a66e | refs/heads/master | 2020-04-29T18:41:16.530471 | 2019-03-18T21:56:09 | 2019-03-18T21:56:09 | 176,330,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 769 | rd | position-methods.Rd | \name{position-methods}
\docType{methods}
\alias{position-methods}
\alias{position,MassArrayData-method}
\alias{position<-,MassArrayData,missing-method}
\alias{position<-,MassArrayData,character-method}
\title{ Operate on positional information (methods)}
\description{
Methods to access (and/or assign) positional info... |
b40c69239b078dbeb3a0dd067d9c53e799b063f1 | 1ea35aa8adc3131f178d873800c1c818343b9dec | /src/R/shiny/ROMOPOmics/src/applyFilters.R | e65298ab75e93061451b871e66975cc50f61c57e | [
"MIT"
] | permissive | NCBI-Codeathons/OMOPOmics | 9afa7abd4f59baa48248b73a823d5e50d0197663 | c6f0293f99189cc682d04aef9f40e43a8878ca8b | refs/heads/master | 2020-12-06T04:54:42.723704 | 2020-06-04T16:45:14 | 2020-06-04T16:45:14 | 232,348,286 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 851 | r | applyFilters.R | #!/bin/Rscript
#applyFilters
# Given the query table and a compiled filter table, this function iteratively
# applies each filter (one per row of the table) based on the filter's type
# indicated in the "type" column. For instance, a filter of type "txt" is
# applied using the characterFilter() function.
applyFilters ... |
0af73d27b3d19481d275e28124e881399e1f3a8c | 7c3b1b37f1986d00ef740e0185db4e24b5ca4cb4 | /man/gimage.Rd | 95b00bc39792e6ddfea12e8c7b3358383e911bb8 | [] | no_license | jverzani/gWidgetsWWW2.rapache | 2b9ea2402b334d9b57cc434ef81d8169d5a88f54 | f0678d800d0e824f15f0098212271caac71bb67c | refs/heads/master | 2020-04-06T07:02:06.600687 | 2014-02-01T03:47:41 | 2014-02-01T03:47:41 | 5,430,063 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,512 | rd | gimage.Rd | \name{gimage}
\alias{gimage}
\title{Container for an image}
\usage{
gimage(filename = "", dirname = "", size = NULL,
handler = NULL, action = NULL, container = NULL, ...,
width = NULL, height = NULL, ext.args = NULL)
}
\arguments{
\item{filename}{an image file.}
\item{dirname}{ignored.}
\item{size}{A ... |
44ac495125f014ab6c6473677fb1cef61d9ff074 | 72fd0ce524135aad3de7a54fb8a6d6be72e76c6a | /ANNUncomplicatedMalAug2020.r | 2f38b9d9aebe7fcd17bacd8b726eb973584ba083 | [] | no_license | winfrednyoroka/Machine-Learning-in-Clinical-Malaria | 2d955ad1890feb64b8209c6bf1e0c7d8c1faf7c0 | d2108043a8b94ed0748801ae9e0cf07ec9f1f9b0 | refs/heads/master | 2022-12-17T10:18:48.325575 | 2020-09-28T09:04:10 | 2020-09-28T09:04:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,117 | r | ANNUncomplicatedMalAug2020.r | #Script for ANN for UM vs nMI
####################################@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
library(pacman)
pacman::p_load(ggplot2, reshape2, gplots, grid, spatstat, raster, sp, dplyr,
klaR, ggfortify, stringr, cluster, Rtsne, readr, RColorBrewer, Hmisc, mice, tidyr,
... |
b2b169f1dc9a4280baf86f14929ff4f0c0a5394c | bb3c6821ebd76a7f6d6f87478007a82baa59352c | /Actividad_0/Zyrus/Practicas.r | 18fe521d43368e27535339cf74f42f56e64a8332 | [] | no_license | franciscosucre/Estadistica-2016 | 3c8e8910c79f788f74f26d3001eed637155ebdd6 | 8c4eebd316c388d4661a17956b1b6242b175f03b | refs/heads/master | 2021-01-20T19:49:51.245454 | 2016-08-20T01:40:34 | 2016-08-20T01:40:34 | 63,622,287 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,955 | r | Practicas.r | #Practice 1
#1
esc <- 11:15
#2
vec <- seq(1,19,2)
#3
x <- c(esc,vec)
#4
x[c(2,3,5)] <- x[c(2,3,5)] * -1
#5
x <- x[-c(4,8)]
#6
length(x)
#7
Nombres <- c('A','D','X','Z','Y','M','L','B','V','E','R','A','B','T','Z','Z','U')
#8
which(Nombres == 'A') #This function wasn't described anywhere in the class, was in the documen... |
97009c761f128a36e21ba5ee77388748497233ed | fced4b5a08001c0a186c49a1bcc60031349521a1 | /R/scoringTools.R | aff5e36972cdd2223ced05bf84058154267ec5ab | [] | no_license | adimajo/scoringTools | 470577a9adafced24fc364264bb298c31d49a49e | 2bc2c29b0ecebecaf1b5a69f4a515d0e833111a7 | refs/heads/master | 2023-02-13T03:37:41.735293 | 2021-01-10T14:42:41 | 2021-01-10T14:42:41 | 84,586,749 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 75 | r | scoringTools.R | #' Credit Scoring Tools.
#'
#' Refer to the package's vignette.
"_PACKAGE"
|
3ca0927d6812bf980e339ac72ed90e6d962af46c | dcf54728279ae9b361a1830c5573b50773542292 | /man/decomposer.Rd | e36082aef3fa94318b4f17409b367108ce70f155 | [] | no_license | CGnal/EnergyPricingModel | 02d4da8636372cff8cc66aea1c2852cffaae7226 | c692845fefc7872710ca6a4ea36b4fbdaa260615 | refs/heads/master | 2021-03-16T10:23:46.632419 | 2016-12-22T14:59:52 | 2016-12-22T14:59:52 | 77,153,704 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,830 | rd | decomposer.Rd | \name{decomposer}
\alias{decomposer}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Function to decompose complex price series
}
\description{
This function implements different possible decomposition methods
}
\usage{
decomposer(TS, method = c("emd","eemd","ceemdan"), plot = FALSE, ...)
}
%- ma... |
27416da58964fc46c6a0efdcee4f5acde3f4c2b6 | 902037115141ead7b315e7b63e437ec61c01c2c1 | /man/rowTables.Rd | 83109558f4f489ac6f915e6153480588c9504ce7 | [] | no_license | cran/scrime | 4bdc7e989ba9e648d004ca47cd2d10bb5e78a717 | cf0033dbfe2a6fa807593a460ef4bcb0931db96a | refs/heads/master | 2021-06-02T21:50:17.706604 | 2018-12-01T10:00:03 | 2018-12-01T10:00:03 | 17,699,500 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,211 | rd | rowTables.Rd | \name{rowTables}
\alias{rowTables}
\title{Rowwise Tables}
\description{
Computes a one-dimensional table for each row of a matrix that summarizes
the values of the categorical variables represented by the rows of the matrix.
}
\usage{
rowTables(x, levels = 1:3, affy = FALSE, includeNA = FALSE,
useNN = c... |
5a8fe6234d527e76ffc964485f1eeab470e80ffb | 11f79671651f5b2ebfed0adb91728e66c4d7eaea | /man/mp_update_rgmp_offc_id.Rd | 4920f1bb229108647e4672a25bd65b4bc6802e26 | [] | no_license | gyang274/route | aabb4302a9f8f841d3e6818ff94ecb3c39871bb6 | 94ea662006f7aafa1435269ce121f60e2a288290 | refs/heads/master | 2020-05-29T15:11:53.086200 | 2016-08-30T20:03:54 | 2016-08-30T20:03:54 | 65,648,339 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 466 | rd | mp_update_rgmp_offc_id.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/route_mp.r
\name{mp_update_rgmp_offc_id}
\alias{mp_update_rgmp_offc_id}
\title{mp_update_rgmp_offc_id (animation)}
\usage{
mp_update_rgmp_offc_id(offc_id, zoom = 12L)
}
\value{
1. rgmp (side effect): updated global rgmp with new offc_id
2. r... |
39d913f606ee403b5164d41bbe106900b7b394d8 | a0bedd98b914e7d410d26978fdde987bc8cec426 | /POS+WEB Model.R | ddc357e78352ebc191ff303c41f074a912143afc | [] | no_license | ruthvik07071995/New-Product-Performance-Prediction-in-Fashion-Retailing | 5c0022031d4c1abc2c0ad8ba0b14bfeeef26f4c0 | d20102420a2c4b7bbf8f003ce5440c151377b1ac | refs/heads/master | 2022-09-15T17:57:58.137892 | 2020-06-01T01:46:53 | 2020-06-01T01:46:53 | 256,284,821 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 47,836 | r | POS+WEB Model.R | options(java.parameters = "-Xmx64048m") # 64048 is 64 GB
#install.packages("odbc")
#install.packages("RMariaDB")
library(RMariaDB)
# Connect to a MariaDB version of a MySQL database
con <- dbConnect(RMariaDB::MariaDB(), host="datamine.rcac.purdue.edu", port=3306
, dbname="***********"
... |
cf54d4bae9971809808cd5a8af4b50683331ab43 | e5604981a0ae5102f33e58218946e625e1e25fd3 | /tests/testthat/test-matrix.R | c713eb0554b2e19aaa3cd716de6a7e572ad86ac2 | [] | no_license | talgalili/broom | d77633d58ba81ddae2e65328fc487b1943e91020 | 8bb9902b62a566ec2b7a4c37a36c32ef4a6ecfb6 | refs/heads/master | 2021-01-12T09:19:56.804074 | 2018-06-14T18:40:33 | 2018-06-14T18:40:33 | 81,334,167 | 0 | 1 | null | 2017-02-08T13:44:59 | 2017-02-08T13:44:59 | null | UTF-8 | R | false | false | 234 | r | test-matrix.R | context("matrix tidiers")
test_that("matrix tidiers work", {
skip("Deprecating soon")
mat <- as.matrix(mtcars)
td <- tidy(mat)
check_tidy(td, exp.row = 32, exp.col = 12)
gl <- glance(mat)
check_tidy(gl, exp.col = 4)
})
|
88156732a0e6fe0306c5ab6d58e666da13b0088f | 7f241bd79a339ff7922a5b1b32a75ea3fb490ce4 | /Inclass13June.R | 449278679290bcd665321ded3830d11a1ffd99c0 | [] | no_license | n1tk/nonparametrics | 1edb684e09f2e5dbf01395dca574e2a56557d250 | fa3e8409b182f202784def8bc3580ab041f934ef | refs/heads/master | 2021-06-04T12:15:26.650976 | 2016-07-27T23:07:44 | 2016-07-27T23:07:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,486 | r | Inclass13June.R | ### In class Assignment
library(MASS)
library(perm)
attach(birthwt)
RMD.test <- function(samp1,samp2,direction=c('two.sided','less','greater')[1],nsamp=10000){
devs1 <- samp1-median(samp1)
devs2 <- samp2-median(samp2)
devs <- c(devs1,devs2)
RMD <- mean(abs(devs1))/mean(abs(devs2))
if (direction[1]=='two.sid... |
5827306b30e9ef2b6229d52f8f948236d9b3b654 | 488c2cdfd06b9f7be1f5f20dd7c3e8c42492d189 | /man/create_ET_trial_data.Rd | 9d1fb26105039f6fddbc1b3e12bf34ad6a67f61c | [
"MIT"
] | permissive | samhforbes/DDLab | c7061383d5190718d3328ac89a322aafe0c2faea | 167b1ac6902b98f9206a12c72309f8c01efdc988 | refs/heads/master | 2023-07-19T20:17:31.761831 | 2023-07-17T15:20:33 | 2023-07-17T15:20:33 | 170,550,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,041 | rd | create_ET_trial_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_ET_trial_data.R
\name{create_ET_trial_data}
\alias{create_ET_trial_data}
\title{create a trial report from a fixation eyetracking data}
\usage{
create_ET_trial_data(data, task, write = F, show_all_missing = F)
}
\arguments{
\item{data}... |
de655d3fe481bf0f9875d43d37076163f2b803dd | a8be61e1b71cfb146baa08412b06ec0bf91a551e | /plot1.R | 2e1a55b33bf05fda381f0eb55370c7a7d26e807b | [] | no_license | lenin-grib/ExData_Plotting1 | af2ee3bc6c6155dfa41b579cada1c1299af1aa42 | f2746449920e032abbdc0f206633bf0482bbd79b | refs/heads/master | 2020-04-08T16:59:39.472027 | 2018-11-28T19:02:57 | 2018-11-28T19:02:57 | 159,545,400 | 0 | 0 | null | 2018-11-28T18:16:45 | 2018-11-28T18:16:44 | null | UTF-8 | R | false | false | 715 | r | plot1.R | ## read the data assuming file is saved to the working directory
full <- read.table("household_power_consumption.txt", header = T, sep = ";",
colClasses = c("character", "character", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric"),
... |
1d29cef97338d1bb692b285230d95e5301125a3d | 48197dba4bc931c8f5bfa27014b282d704f2336c | /inst/tinytest/test_wand.R | 7b8cda8700bdee330fc4bce34c5676c0229d2997 | [
"MIT"
] | permissive | hrbrmstr/wand | c5dd3049ef9a96a4864cb79894cfae6c58962ebf | 1f89bed4a5aba659376ab7f626dc077ee148df39 | refs/heads/master | 2021-01-09T20:33:25.668124 | 2019-09-26T10:10:56 | 2019-09-26T10:10:56 | 65,586,565 | 21 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,359 | r | test_wand.R | library(wand)
list(
actions.csv = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
actions.txt = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
actions.xlsx = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
test_128_44_jstereo.mp3 = "audio/mp3", te... |
cdd561062b52ce5f4415c624a4b84b03d46a4b30 | 22540d050618fa7c69c40c89d1397609e2f39936 | /man/opts.Rd | 6a85c3263723d70e652e782560d6aa280cda7893 | [] | no_license | cran/psyverse | 8d3e6723d66c292f02a4d0b8978d85f868ca52b9 | d1e2dc7f6be23f674f7b6cc1d21089995a331ba0 | refs/heads/master | 2023-03-17T00:04:47.391838 | 2023-03-05T21:00:07 | 2023-03-05T21:00:07 | 250,514,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,708 | rd | opts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/opts.R
\docType{data}
\name{opts}
\alias{opts}
\alias{set}
\alias{get}
\alias{reset}
\title{Options for the psyverse package}
\format{
An object of class \code{list} of length 4.
}
\usage{
opts
}
\description{
The \code{psyver... |
5e97166f40eb201eb013e70df0d0f7b3f42afc2c | 74fe29da37e54fb5e49a1ae7d4cf5051428202eb | /R/output_visualise_cells.R | 9bf087340c53b7ae410d415ab8485f57779593a0 | [] | no_license | CRAFTY-ABM/craftyr | 7fd8e63f85f4ddc13fbb0a79b67710a7b5a818f2 | 5630d1f0e4a1b1c34e3d10740640d414346f1af4 | refs/heads/master | 2022-08-11T13:20:13.579266 | 2018-06-16T06:55:19 | 2018-06-16T06:55:19 | 266,212,786 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,972 | r | output_visualise_cells.R | library(ggplot2) # correct (see stack exchange question) for %+replace%
#' Prints a list of data.frames as ggplot2 facet plot.
#'
#' @param simp SIMulation Properties
#' @param celldata (list of) data.frames contain info and X and X coordinates. If a list of data.frames,
#' elements must be named differently
#' @par... |
74396aa95ae5380d95c3c0ab50f62271871c15d7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/acebayes/examples/aceglm.Rd.R | 787705234389a0331bbb41e7a4f107ea0ea1559f | [] | 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 | 3,202 | r | aceglm.Rd.R | library(acebayes)
### Name: aceglm
### Title: Approximate Coordinate Exchange (ACE) Algorithm for Generalised
### Linear Models
### Aliases: aceglm paceglm
### ** Examples
## This example uses aceglm to find a Bayesian D-optimal design for a
## first order logistic regression model with 6 runs 4 factors. The pri... |
4e9ff07749022ca7b3df9e27cdc06a3966086a3e | c9fb5b8c15fc82fe19f1f8d339bb1472de18e51c | /Data/make_onet_score.R | 6e6c6d52985ddd5c0a519a66dc288c9b88190931 | [] | no_license | kota-tagami/J-Onet_EDA | 92ff9d2300f23f3c9d0fbb17ae11e56de9cb1d9a | bd5576ab0ecef3027de7b1a74e1b50ea99ca6147 | refs/heads/master | 2023-01-13T07:05:12.263174 | 2020-11-18T06:22:56 | 2020-11-18T06:22:56 | 289,420,822 | 0 | 0 | null | 2020-11-18T06:22:57 | 2020-08-22T04:45:52 | R | UTF-8 | R | false | false | 2,379 | r | make_onet_score.R | library(tidyverse)
library(readxl)
## Onetウェブサイトからダウンロードしたデータを読み込む
onet_score_00 <-
"IPD_DL_numeric_1_8.xlsx" %>%
str_c("Data", ., sep = "/") %>%
read_excel(
sheet = 1,
col_names = T,
.name_repair = "unique",
skip = 19
) %>%
select(
- `20`,
id_row = `...2`,
everything()
) %... |
76d79a0d607379baa0de4110ac98f35719bc61f8 | 52b84546a64b4f31245eb0bfaa68bfa489c90534 | /sta141a/2016/discussion06.R | bca384f326d0b0b1cd95a3550d6d96e062c2a42c | [
"CC-BY-NC-SA-4.0"
] | permissive | nick-ulle/teaching-notes | 6cb48d874ef4c8c99402b9987e58b2958adff056 | 12e388f626f415bd39543bfed99c44e4130a065b | refs/heads/master | 2023-02-20T12:55:06.521649 | 2023-02-05T02:53:22 | 2023-02-05T02:53:22 | 86,759,329 | 31 | 33 | BSD-3-Clause-Clear | 2019-01-15T15:44:11 | 2017-03-30T23:49:53 | Jupyter Notebook | UTF-8 | R | false | false | 5,193 | r | discussion06.R | # discussion06.R
# Week 5
# ------
# Linear Models
# -------------
# "All models are wrong, but some are useful." -- G. Box
library(tidyverse)
# ### Example: Elmhurst College 2011 Financial Aid
# The Elmhurst data set has three variables.
#
# * family_income: total family income
# * gift_aid: total gift aid f... |
e5aef92d14ab22c5ca7c2a9098c84b331f85dcc9 | 34d6b8a8648cec16a214278169e993eca182b344 | /simulations/Exp3_AsyNormality/Exp3_AsyNormality_run.R | 88d31db7131d7139f209589c182d6803143e69dd | [] | no_license | predt/regsynth | 9817ecf3f7dfd377af876bf975e09e12e1bd1dae | ace6c9d5b6c7b341e53595c94b98922852d97816 | refs/heads/master | 2021-03-13T18:04:55.200460 | 2019-10-16T13:40:24 | 2019-10-16T13:40:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,841 | r | Exp3_AsyNormality_run.R | ### Exp3: Asymproric Normality, Run file
### Jeremy L Hour
### 21/02/2018
setwd("//ulysse/users/JL.HOUR/1A_These/A. Research/RegSynthProject/regsynth")
rm(list=ls())
### 0. Settings
### Load packages
library("MASS")
library("ggplot2")
library("gtable")
library("grid")
library("reshape2")
library("LowRankQP")
library... |
9ce6fec94475b0c7c6b5943c0e2b9d913c49daff | f62736da11b1818af73866a6c5da7c5b8b75b980 | /2018/05-facebook.R | fe5018b4338af5edd9ab2bdaedd7d39c8e5fb1ea | [] | no_license | erikgahner/posts | 95b108dccea199a81656fd207857ba7afc7cf92a | 38293e4f7d5a02ef87f9ae4cf36af0fefa209b86 | refs/heads/master | 2023-08-30T17:36:37.503975 | 2023-08-27T08:33:32 | 2023-08-27T08:33:32 | 25,849,217 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,046 | r | 05-facebook.R | # R script to "Why you should not trust the Facebook experiment"
# Link: http://erikgahner.dk/2018/why-you-should-not-trust-the-facebook-experiment/
library("ggplot2")
respondents <- 1095
df_fb <- data.frame(
time = c(0, 0, 1, 1),
tr = c("Treatment", "Control", "Treatment", "Control"),
res = c(rep(respondents/... |
da22a100c4d021270df3cff0dd72461623949f39 | 092e6cb5e99b3dfbb089696b748c819f98fc861c | /scripts/doASTSAEMlearnCircleWithEstimatedInitialCondFA.R | 44811cb812a533b6aa8fcf4a7e03d50f834e9978 | [] | no_license | joacorapela/kalmanFilter | 522c1fbd85301871cc88101a9591dea5a2e9bc49 | c0fb1a454ab9d9f9a238fa65b28c5f6150e1c1cd | refs/heads/master | 2023-04-16T09:03:35.683914 | 2023-04-10T16:36:32 | 2023-04-10T16:36:32 | 242,138,106 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,486 | r | doASTSAEMlearnCircleWithEstimatedInitialCondFA.R |
require(astsa)
require(MASS)
require(ramcmc)
require(plotly)
require(mvtnorm)
require(gridExtra)
require(reshape2)
source("../src/squareRootKF.R")
source("../src/smoothLDS_R.R")
source("../src/estimateKFInitialCondFA.R")
source("../src/plotTrueInitialAndEstimatedMatrices.R")
source("../src/plotTrueInitialAndEstimatedV... |
1be4d4298ae6f6aed3dbf4a95347588008201565 | 66ae31e851638ad20305409b99df93d8ce2f8133 | /R/snlRigidNodeAbsorption.R | 9246dfb1cd28d1486803259575a682abf316d715 | [] | no_license | rwoldford/edmcr | 150e1702ceb451d154223ff5e9ded10defeda9e6 | ee322d7dcc0bf3f497576c31a87a4886bc17d8a8 | refs/heads/main | 2021-12-06T06:09:38.997297 | 2021-09-08T17:59:47 | 2021-09-08T17:59:47 | 142,780,936 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,202 | r | snlRigidNodeAbsorption.R | snlRigidNodeAbsorption <- function(ic,jc,Dpartial,Dcq,eigvs,grow,Dcqinit,condtolerscaling,r,n,csizesinit){
flagred <- 0
e22 <- Dpartial[,jc] & Dcq[,ic]
ne22 <- sum(e22)
if(ne22 == sum(Dcq[,ic]) & (length(eigvs) < ic || is.na(eigvs[[ic]]))){
Dcq[jc,ic] <- 1
grow <- 1
}else{
temp <- ... |
9132c31a474e0146f8767abcbccaa69162bb6c24 | 86151a6ecec532ac065621a1ffdfd827504176a3 | /R/aggregate_brick.R | 8f1e5d459e31b6b8015594c612d405e88dc91b9b | [] | no_license | imarkonis/pRecipe | 3454f5ce32e6915a6caef1dbc041d12c411c9ae5 | 07c6b1da653221a0baeeb2aa81b8744393ff587e | refs/heads/master | 2022-11-02T20:27:40.979144 | 2022-10-28T10:52:04 | 2022-10-28T10:52:04 | 237,580,540 | 0 | 0 | null | 2020-02-01T07:44:23 | 2020-02-01T07:44:23 | null | UTF-8 | R | false | false | 1,698 | r | aggregate_brick.R | #' Parallel aggregate
#'
#' Function to aggregate a raster brick
#'
#' @import parallel
#' @importFrom methods as
#' @importFrom raster aggregate as.list brick setZ
#' @param dummie_nc a character string
#' @param new_res numeric
#' @return raster brick
#' @keywords internal
aggregate_brick <- function(dummie_nc, new... |
5da0e6ab2ba34f000f1e119a987623203944babb | 774b77ad325d4268d86162f030130132bff9adac | /Politwitter_URL_Scrape.R | 973ac44b9d2b7bec0ca650fdff70703c16d34219 | [] | no_license | adamingwersen/CA | f0923c6c43f210d72bf0627127f8e5604b07edcb | 7deb3a8a66edfb3efe87ca5a7be2fe836f4ba3be | refs/heads/master | 2021-01-17T14:25:32.384749 | 2016-07-14T22:26:56 | 2016-07-14T22:26:56 | 45,525,168 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,177 | r | Politwitter_URL_Scrape.R | ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### #### ### ####
#Politwitter scrape
library("rvest")
library("dplyr")
# De... |
a202791deb1e2ce301fba7c1d75b661c307a2e68 | 4ec101ac9e7fdc57510182243ace54747b5c404e | /scripts/mean_chip_raw_data_plot.R | 26491e3335c9c1ff507117a8f43f055aae368123 | [] | no_license | satyanarayan-rao/tf_nucleosome_dynamics | e2b7ee560091b7a03fa16559096c1199d03362de | 00bdaa23906460a3e5d95ac354830120c9dd108e | refs/heads/main | 2023-04-07T17:29:40.644432 | 2021-04-12T14:16:20 | 2021-04-12T14:16:20 | 356,676,912 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 785 | r | mean_chip_raw_data_plot.R | library(data.table)
library(dplyr)
library(R.utils)
library(ggplot2)
library(ggthemes)
library(stringr)
library(reshape2)
library(Cairo)
# args[1]: combined data
# args[2]: hist pdf file
options(error=traceback)
args = commandArgs(trailingOnly = T)
dt = read.table(args[1], sep = "\t", header = T, stringsAsFactors = ... |
840e0346a133b2df0e6c38d2d41cfdd3e515fc34 | 0f380dcb3509961dbbcf59f8b2dfb1d70f92e993 | /R/exonsAsSummarizedExperiment.R | f370cffea0d3a55c2bb9c000b7774dd6cf2f9bac | [] | no_license | ttriche/regulatoR | dced0aa8c0f60b191c38d106b333f3dda84317fa | d7e6b00ef1514423fdf8ca32a73eebc715642161 | refs/heads/master | 2016-09-10T00:04:25.637227 | 2013-02-26T23:14:05 | 2013-02-26T23:14:05 | 4,615,162 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,644 | r | exonsAsSummarizedExperiment.R | # processing (here I am assuming TCGA patient IDs as names)
#
# for i in *exon*; do
# j=`echo $i | cut -f1,3 -d'-' | tr - _`
# cat $i | cut -f1,4 | gzip > $j.rpkm.gz
# done
#
## FIXME: tabulate raw counts as well: FIXED! 2/22
#
# setwd("/where/you/keep/your/processed/RPKM/files")
exonsAsSummarizedExperiment <- fun... |
60e7a88717bc8507c49fd0c1fe9fedfbc58d4f7c | 339364322e830270c930521da6edefa78b8b3bd3 | /R/plot_all_inv_v0.R | 797cc85c44774095994145d525209d5aed9db839 | [] | no_license | adsteen/subspec | a2cbbf304467e0e7faace06f7f713a393c62435f | d4803015a9075ad1b1d810fc1bbb982593f9624c | refs/heads/master | 2016-09-11T00:42:10.532585 | 2015-07-12T23:20:06 | 2015-07-12T23:20:06 | 38,911,564 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,217 | r | plot_all_inv_v0.R | ##' Makes fig 4
##'
##' @param d data frame
##' @export
plot_all_inv_v0 <- function(d, print_plot=TRUE, save_plot=FALSE, fn=NA, height=4, width=7, dpi=300, ...) {
# Make a plot of all inverse v0 vs inhibitor concentration
# browser()
p_all_inv_v0 <- ggplot(d, aes(x=conc.pNA, y=1/nM.per.hr)) +
geom_point(s... |
fe007152c3d85317740f7b2c3aa224b750a42eee | 8f5dd342a8630748449eb50e3f9462d448663350 | /R/convertToTime.r | b7764160544fcddb3ec3db53288c3e5b62ada84d | [] | no_license | gleday/ShrinkNet | 87e484f997185d331a1e486b3e6921adfdc314c1 | cf1513cd86cfb4db4ee0973304841a2cf92169cc | refs/heads/master | 2020-05-21T03:26:08.766503 | 2018-04-06T19:36:57 | 2018-04-06T19:36:57 | 43,956,548 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 353 | r | convertToTime.r | # Internal function
# Convert seconds into a "HH:MM:SS" format
# Author: Gwenael G.R. Leday
.convertToTime <- function(x){
h <- as.character(x%/%3600)
m <- as.character((x%%3600)%/%60)
s <- as.character(round((x%%3600)%%60))
if(nchar(m)==1) m <- paste(0,m,sep="")
if(nchar(s)==1) s <- paste(0,s,sep="")
... |
9b6ec18a0407660513844b49527edd3319bfbbc3 | 5c033f7e6c842882d11ccadd2e110e19d7cb42f9 | /predictive-model/text2vec_impl/create-dtm.R | b56578c798f8da13d63585384afb744b356b1e43 | [] | no_license | natereed/coursera-data-science-capstone-old | b6f70d0738f3a6be0af04518a28490dfcf8ddc1a | 7b0466008a7b1fd2d5c536605e8959e081f0d4ee | refs/heads/master | 2021-01-17T18:09:30.029996 | 2016-08-23T11:54:52 | 2016-08-23T11:54:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 988 | r | create-dtm.R | # Install the first time: devtools::install_github('dselivanov/text2vec')
library(text2vec)
dir <- file.path("~", "Coursera", "Capstone", "final", "en_US")
blogs_data <- readLines(file.path(dir, "en_US.blogs.txt"))
length(blogs_data);
# [1] 899288
it <- itoken(blogs_data,
preprocess_function = tolower, ... |
d25db5e6796d0d02f9d847d18da9ee977652daa8 | 8b61baaf434ac01887c7de451078d4d618db77e2 | /R/readLine.R | 3fef13a65c22fd7a549fbdea8fc3ea9b6f7a9296 | [] | no_license | drmjc/mjcbase | d5c6100b6f2586f179ad3fc0acb07e2f26f5f517 | 96f707d07c0a473f97fd70ff1ff8053f34fa6488 | refs/heads/master | 2020-05-29T19:36:53.961692 | 2017-01-17T10:54:00 | 2017-01-17T10:54:00 | 12,447,080 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 800 | r | readLine.R | #' readLine, and split on character
#'
#' read a single line from a file, or connection, and then
#' split the result on a separator. eg tab, comma, or spaces
#'
#' @param file a file, or open connection
#' @param split the character to split on. eg tab, comma, or default=spaces
#' @param ok unused
#'
#' @return a c... |
19665fe9ef85fff97b1ef5c33f5b249222c40cc9 | af34ab9351b7e004b501dff4c5bb78f523e0d345 | /Script/9_EGSL_compile.r | 2859db78e142b0a691c56d5cfe5f4b6fecc97fe5 | [
"MIT"
] | permissive | david-beauchesne/Interaction_catalog | 6aca5d257fcaf426bc0363a97bfe134b471bc3b4 | 4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612 | refs/heads/master | 2021-01-20T19:05:11.705068 | 2018-06-12T19:53:32 | 2018-06-12T19:53:32 | 65,501,183 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,960 | r | 9_EGSL_compile.r | # Compiling available data for EGSL species
load("RData/Biotic_inter.RData")
EGSL_inter <- matrix(nrow = nrow(Biotic_inter[[4]]), ncol = 6, dimnames = list(Biotic_inter[[4]][, 'taxon'], c('species','genus','family','order','class','phylum')))
pb <- txtProgressBar(min = 0,max = nrow(Biotic_inter[[4]]), style = 3)
for(i... |
b725bea968fd3d40270eff51c2d93d67386be04d | 5054535a86ac34f6ee92fab3a0c7178c6657303b | /src/scripts/r/transduce.r | 43f77352cb045930d2ded4c83977a8654473355a | [
"Apache-2.0"
] | permissive | palisades-lakes/collection-experiments | b46907a02b436e6cbf9ffb9f796ff99d97ce6bae | bd96ca2c58afc5f18d301a8259a26540748c75df | refs/heads/master | 2023-08-25T11:58:24.927470 | 2023-08-01T19:11:13 | 2023-08-01T19:11:13 | 113,613,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,077 | r | transduce.r | # filter-map-reduce experiments
# palisades dot lakes at gmail dot com
# version 2021-02-01
#-----------------------------------------------------------------
if (file.exists('e:/porta/projects/collection-experiments')) {
setwd('e:/porta/projects/collection-experiments')
} else {
setwd('c:/porta/projects/collection... |
e0c53a09d380dde8d80821c23bb8ba5d649b77db | 251df421cec78612cbf56db7a0cbf2078b205dcd | /debug_na.R | b33c0d2df708ff8585577273c0247ea389e7df2b | [
"MIT"
] | permissive | deponent-verb/popgen.analysis.pipeline | 7987d6e12f3b57ea70ce62dc0d3987e6d02eeb05 | ae482e915c7b2baca87242717cb6a0f19ca08792 | refs/heads/master | 2021-08-19T04:57:27.080926 | 2021-07-03T04:04:17 | 2021-07-03T04:04:17 | 213,124,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 341 | r | debug_na.R | #debugging NA values script
pacman::p_load(tidyverse)
df<-read_csv("./data/toy_df.csv")
#extract H values
temp<-df[,1:14]
temp2<-temp %>% filter_all(any_vars(is.na(.)))
#extract D values
temp<-df[,15:24]
temp1<-temp %>% filter_all(any_vars(is.na(.)))
#D is not outputting NAs.
#new_data <- data %>% filter_all(... |
7a385463f3c81e6a878ea7b1b4970af63d08bc9b | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Managerial_Statistics_by_Gerald_Keller/CH8/EX8.2/Ex8_2.R | edd5584605338b485c95028f2463c8cd8fa153b7 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 72 | r | Ex8_2.R | ###page_no_261###
rm(list=ls())
m=1000; s=100; n=1100
pnorm(1100,m,s) |
ed0314db9bc839a808c4396488e0e77a1d7f10ac | 7a5927014872451f3a79438a11da07d8ba22c982 | /k-Means Clustering.R | d94eb3dde98e3c716df311400a21cc3dccda9a34 | [] | no_license | ank234/k-Means-Clustering-on-Dungaree-Data-Set | f4df9944c10097d8e2858e5ce6bf868c2e4be21a | 046d6cf6057f1bd1f1711eafe3af1fb2f896e6e9 | refs/heads/master | 2020-04-22T14:22:53.295159 | 2018-09-03T19:13:54 | 2018-09-03T19:13:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,623 | r | k-Means Clustering.R | # read csv file
dungree <- read.csv("E:/GitHub Projects/K-Means Clustering/dungaree.csv")
#View(dungree)
# normalize data
dungree.norm<- sapply(dungree[,2:6],scale)
#View(dungree.norm)
colnames(dungree.norm) <- c('z_fashion','z_leisure','z_stretch','z_original','z_salestot')
df <- cbind(dungree,dungree.norm)... |
6023942daa25de81983ed6ae6a3379807adeba2e | 597fb95d3edf6c8904874d065db7f2623db23848 | /src/deliveries.R | 47a105e90708a9f88e8d730dfd86d26b1d630745 | [] | no_license | danyx23/covid_vaccinations | ac66375932e2a9d080b0509c240592b8701d0a48 | 231d42eae99e132f00fc1456236b0f33be654e87 | refs/heads/main | 2023-02-18T00:02:24.305060 | 2021-01-08T12:19:43 | 2021-01-08T12:19:43 | 327,906,705 | 0 | 0 | null | 2021-01-08T13:11:49 | 2021-01-08T13:11:48 | null | UTF-8 | R | false | false | 1,312 | r | deliveries.R | library(dplyr)
library(readr)
# break deliveries down by state in proportion to population
# from https://twitter.com/BMG_Bund/status/1345012835252887552
deliveries <- tibble(
doses = c(
rep(1.3e6/3, 3),
rep(2.8e6/4, 4)
),
delivery_date = lubridate::dmy(c(
paste0(c('26.12.', '28.12.', '30.12.'), '20... |
1019215e5c914525ff90dba387b5fc2f0dd9b2f9 | 4c78bb06198a510622640f4052d1abf770a28fbb | /server.R | 5ee7262c82f2daf3764bee3cddde88e11da1a80f | [] | no_license | qg0/options | c07ba8aa057b437ecaf1a2f836fba996000cc141 | dcc901c9703ffa462e346bc8711792b98135aeac | refs/heads/master | 2021-05-29T06:31:00.410873 | 2015-09-27T16:00:59 | 2015-09-27T16:00:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,558 | r | server.R | library(shiny)
# Black-Scholes Function
BS <-
function(S, K, T, r, sig, type="C"){
d1 <- (log(S/K) + (r + sig^2/2)*T) / (sig*sqrt(T))
d2 <- d1 - sig*sqrt(T)
if(type=="C"){
value <- S*pnorm(d1) - K*exp(-r*T)*pnorm(d2)
}
if(type=="P"){
value <- K*exp(-r*T)*pnorm(-d2) - S*pnorm(-d1... |
a13590ee48c9c6014b741f7a2b7971875d153932 | a6ba30aa49badda9be0507045bd66edc354db15f | /R/models__taildependence__funinv2d.R | b3e274473217eaee930ec19043775ab8b0798b25 | [] | no_license | ayotoasset/cdcopula | fdecbd663a31985bac90369db93e10db24e52ae8 | b0a93b0008b19b8e2f2f3157e2e4e2cdc0297c60 | refs/heads/master | 2022-12-26T18:01:40.918493 | 2020-09-28T10:48:30 | 2020-09-28T10:48:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,018 | r | models__taildependence__funinv2d.R | #' @export
funinv2d <- function(FUN, x1, y, x1lim, x2lim,...,
method = c("tabular", "iterative")[1], tol = 1e-02)
{
## y = f(x1, x2) -> x2
if(tolower(method) == "tabular")
{
## If the FUNNAME does not exist, create it
set.seed(object.size(FUN))
FUNNAME.prefix <- runif(1)
tabul... |
6992f72dd72ee1f87799c8a3f97da64328c62fe4 | b8ebc5db1b08ed2bfd3e001cf01c360d840d5b1e | /april_19_23/Exercise 4.R | 9e1c7cb5bf90003cd292db055adb8601b3031977 | [] | no_license | MichalSalach/RR_classes | 8bc55224e6504ec0fd0df7d5a27a75af3f8175b8 | 6a9a928b97cef134d9ff0db0e9abe55eb42f2711 | refs/heads/main | 2023-04-12T08:47:20.362292 | 2021-05-13T14:59:56 | 2021-05-13T14:59:56 | 355,957,857 | 0 | 0 | null | 2021-04-08T15:19:42 | 2021-04-08T15:19:41 | null | UTF-8 | R | false | false | 6,661 | r | Exercise 4.R | #### Path ####
setwd("april_19_23")
#### Libraries ####
library(readxl)
library(Hmisc)
library(stringr)
library(dplyr)
#### Data ####
# Import data from the O*NET database, at ISCO-08 occupation level.
# The original data uses a version of SOC classification, but the data we load here
# are already cross-walked to I... |
6863457286604ff104551c9f8ddf60984cd215a7 | b38df3e8ae84be340fe8fda161b64fa8909adec2 | /Polarity.R | 16f2b9151327699047e61262d0613aa13b97f958 | [] | no_license | Tanay0510/Geo-Political-Multipolarity | 61aba383a367bd31e5471108ded763350de53f81 | a96339737fd0aa0d797f515648ade8974779a6e3 | refs/heads/master | 2023-02-18T18:36:17.245202 | 2021-01-22T03:25:40 | 2021-01-22T03:25:40 | 274,976,771 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,540 | r | Polarity.R | # --- [~] Stepwise Regression --- [~] #
# http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/
# --- Regression in R --- #
# Set Working Directory [Mac = ~/Desktop | PC = C:/Users/Default/Desktop]
setwd("~/Desktop")
# Get working directory
getwd()
# Get l... |
aef10430bb47d0b4ccbc1d14e5e5d0b9aa29592f | 1754113fcf2b24c711ceb1d4b43513cb908cfd32 | /feature_extraction.R | f6791aea3e8225385e2c34ad3d9742c677f1eeeb | [] | no_license | wuandtan/userInteractivity | 86bc8e26ce9bbe36f2bf95c12e69540b325c3bac | a22b8ba7fcd497942941279050e20614f776c4be | refs/heads/master | 2016-09-06T18:56:13.961695 | 2015-02-26T10:46:30 | 2015-02-26T10:46:30 | 31,362,664 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,651 | r | feature_extraction.R | feature_extraction <- function (single_episode,segmentLen = 2,Num_place_for_pause = 2,Num_place_for_freeze = 5, chosen_window_size = 15)
{
#here i am thinking to (1) see if there is any re-positioning. if yes, then start to consider the period between each re-positioning
#(2) among the whole episode, ch... |
5ad0e05171fc2b6e43846a269e11af2f3aacaad0 | 3d80000fb79a94180d14cd085130ccacce3dd6a4 | /1_helpers/1_helpers_generic.R | 32606a510e93ae9323fb6f6e70d1d452010216df | [
"MIT"
] | permissive | boyercb/ueda-replication | 0dec1c2c74e5460238c595a9a84fd454fe64ac3b | 4abb20ab6b0d4131556911bac5c97647460c24bf | refs/heads/master | 2022-11-20T06:44:25.774427 | 2020-07-24T19:23:30 | 2020-07-24T19:23:30 | 271,377,856 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 299 | r | 1_helpers_generic.R | # Load helper functions ---------------------------------------------------
get_data <- function(path) {
if(!is.null(CSV_ROOT_DIR)) {
paste0(CSV_ROOT_DIR, path)
} else {
stop("Must specify location of CSV directory!")
}
}
specd <- function(x, k) trimws(format(round(x, k), nsmall=k)) |
eb61768ec8fcf538b59fcc492f22881cdaa3e916 | d40484c9232a01a0b4daf29622e18d841c0ad841 | /Test_app/server.R | f86dbf3bd14c6253b2328aa7b60df9ef47480ee0 | [] | no_license | GM-AI/R-Markdown-and-Leaflet | 3319c19600628e1989cec144430f2480599f3d87 | 8b6ae12e34f7b8e0efeb60624def08e393eb7070 | refs/heads/master | 2022-12-07T02:28:13.236200 | 2020-08-13T20:15:15 | 2020-08-13T20:15:15 | 286,549,214 | 0 | 0 | null | 2020-08-13T20:20:59 | 2020-08-10T18:21:51 | HTML | UTF-8 | R | false | false | 1,038 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define server logic required to draw a histogram
vln<-as.data.frame(read.csv("vilnius... |
178d380d9aaae2263a07f2091758239df743feaf | 6528e839f7b6adecc76f052c0eb5e6e627776529 | /run_analysis.R | b82f5768798cb0bfc1e9c5185cf639f76fc67fe7 | [] | no_license | srholt/Getting-and-Cleaning-Data-Course-Project | 69e6b31ae70c2ea6b6886912e4be85d5f4e5b238 | e42da16879928e08387502b51a7c0a69fd4b722f | refs/heads/master | 2020-12-31T04:56:43.794346 | 2016-05-08T18:16:03 | 2016-05-08T18:16:03 | 58,324,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,572 | r | run_analysis.R | #run_analysis.R
#download data and move to working directory
setwd("/Users/shaunholt1/datasciencecoursera/week5")
library(downloader)
download("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip", dest="dataset.zip", mode="wb")
unzip ("dataset.zip")
dir()
setwd("/Users/shaunholt1/da... |
a6836d7e81e92f391cde02d39b54b800f9a3d05d | a61f3d918215f5e7f7dbc0c1b51295226f1f67d0 | /man/dmatnorm.Rd | e72a71ce1409ba6da088731c48d042e6373335f5 | [] | no_license | bdemeshev/vectordf | 150f185c45e0de9b908e7f0cd359193ca2cab061 | 0d11673956b7f22aaaca41f8dada9e451965be98 | refs/heads/master | 2021-01-19T07:56:44.367710 | 2015-01-03T12:47:01 | 2015-01-03T12:47:01 | 28,709,229 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 645 | rd | dmatnorm.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{dmatnorm}
\alias{dmatnorm}
\title{Matrix Normal density function}
\usage{
dmatnorm(X, M = matrix(0), U = diag(nrow(M)), V = diag(ncol(M)))
}
\arguments{
\item{X}{matrix-point, argument for density function}
\item{M}{matrix of expected values (r x s)}
\item{U}... |
a396f238a3c148ef0dd3c745ab685d61b0548b34 | 02bd0187bfa29b8ba18721dd010c7916f9a8dff4 | /Part A/complete.R | 2a525a637851fcf29dba7f66ef1dce72f2e69d6b | [] | no_license | anbarisker/datascienceunitec | c4aa2ca985a7eeab5de0f1d02bee09cc99237dfe | 79ff377b55ad2539d5c22c636e742fc3c7bd6ecd | refs/heads/master | 2020-04-27T19:31:02.137875 | 2019-04-12T22:46:27 | 2019-04-12T22:46:27 | 174,622,123 | 0 | 0 | null | 2019-04-12T22:46:28 | 2019-03-08T22:57:05 | null | UTF-8 | R | false | false | 1,166 | r | complete.R | #Name: Anbarasan
#StudentID: 1508153
complete <- function(directory, id=1:332)
{
## directory is location of the csv files
## id is the montior ID number to be used
## Return a data frame of the form:
## id nobs
## 1 117
## 2 1047
## ..
## Where 'id' is the monitor ID number and 'nobs' is the no. of c... |
579a746528d4f663a3f27c0c9a1154d97f54e581 | d6e943fe1e8884d2048ee9b08a28c89204e6f924 | /man/colNormalization.Rd | 3a90d595f5941bf58d57b2e4c2627555b7797ea4 | [
"MIT"
] | permissive | YosefLab/VISION | c9b08b358d56d9cb8121c3da02da62a0da8079fa | 8dc5c4e886ddfeb8412ef3a82cead1c794f0e43b | refs/heads/master | 2023-02-21T05:10:06.549965 | 2023-02-08T19:05:14 | 2023-02-08T19:05:14 | 79,424,615 | 123 | 27 | MIT | 2022-04-26T21:30:04 | 2017-01-19T06:51:45 | R | UTF-8 | R | false | true | 401 | rd | colNormalization.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NormalizationMethods.R
\name{colNormalization}
\alias{colNormalization}
\title{Performs z-normalization on all columns}
\usage{
colNormalization(data)
}
\arguments{
\item{data}{data matrix}
}
\value{
Data matrix with same dimensions, with eac... |
aaedd29e9bb39adb4ddef55ef6deff1f7ad5d253 | 5d3121e7e42bfb2cc8ae76062a83df2791a45b95 | /man/sbs1.Rd | 9ee918ec7e5130c33a5dbef6fcabc7c855d0089d | [] | no_license | neslon/dprep | 3b872a3cbfe3492a27314d4d68c427a949cd538a | bedc64837b72919f0a249d716b6cecbb23923ad0 | refs/heads/master | 2021-01-11T14:49:55.339753 | 2017-01-27T23:09:36 | 2017-01-27T23:09:36 | 80,226,293 | 0 | 0 | null | 2017-01-27T16:51:27 | 2017-01-27T16:51:26 | null | UTF-8 | R | false | false | 756 | rd | sbs1.Rd | \name{sbs1}
\alias{sbs1}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{One-step sequential backward selection}
\description{
This functions performs one-step of the sequential backward selection
procedure.}
\usage{
sbs1(data, indic, correct0, kvec, method = c("lda", "knn", "rpart"))
}
... |
9a80139a50d859cc346a8f2aeaf6e2ede8a9d474 | 9219831ac8e54247e850803474333e4fff531e60 | /R/localUtils.R | 5e9844ab5dee65c2ab3ee7a7bb1c483265a63e91 | [] | no_license | WTaoUMC/RegEnrich | 3a81aa3dd18a7dd5cd7a47f49009ecfd2f11bd37 | 2e41cd739d3d49674604bd3d0a5ba338422593f2 | refs/heads/master | 2021-08-01T12:00:16.199392 | 2021-07-31T06:42:10 | 2021-07-31T06:42:10 | 245,637,870 | 4 | 1 | null | 2021-07-31T06:19:40 | 2020-03-07T13:29:03 | R | UTF-8 | R | false | false | 8,141 | r | localUtils.R | #' @importFrom magrittr %>%
#' @export
#' @examples
#' \donttest{
#' # library(RegEnrich)
#' data("Lyme_GSE63085")
#' data("TFs")
#'
#' data = log2(Lyme_GSE63085$FPKM + 1)
#' colData = Lyme_GSE63085$sampleInfo
#' data1 = data[seq(2000), ]
#'
#' design = model.matrix(~0 + patientID + week, data = colData)
#'
#' # Ini... |
2d6cea79c47b96f143f35e1b54d4e490bbedb62a | e56da52eb0eaccad038b8027c0a753d9eb2ff19e | /man/LabelSplits.Rd | 3f04f388259d43e118b8651445258d9260d1f892 | [] | no_license | ms609/TreeTools | fb1b656968aba57ab975ba1b88a3ddf465155235 | 3a2dfdef2e01d98bf1b58c8ee057350238a02b06 | refs/heads/master | 2023-08-31T10:02:01.031912 | 2023-08-18T12:21:10 | 2023-08-18T12:21:10 | 215,972,277 | 16 | 5 | null | 2023-08-16T16:04:19 | 2019-10-18T08:02:40 | R | UTF-8 | R | false | true | 2,880 | rd | LabelSplits.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Support.R
\name{LabelSplits}
\alias{LabelSplits}
\title{Label splits}
\usage{
LabelSplits(tree, labels = NULL, unit = "", ...)
}
\arguments{
\item{tree}{A tree of class \code{\link[ape:read.tree]{phylo}}.}
\item{labels}{Named vector listing ... |
783d47e448ba1a9bc22bc4839f2c5bc95b66db4d | 97e50001a42a6fdebaf083700bb167c08eef6676 | /plot1.R | efe12b76c2cd9a4d54b3ceb5a6e4eed44740819f | [] | no_license | PJGreen/ExData_Plotting1 | e0460c60bfaa31c2a38b0c4f2be785d58d768cc6 | 9dba4b3c8d44db7f41d559360eeaa4916416b4b0 | refs/heads/master | 2021-05-14T09:04:14.587832 | 2018-01-06T22:51:53 | 2018-01-06T22:51:53 | 116,318,412 | 0 | 0 | null | 2018-01-04T23:39:26 | 2018-01-04T23:39:25 | null | UTF-8 | R | false | false | 171 | r | plot1.R | hist(dt_sub$Global_active_power, col="red", main="Global Active Power", ylab="Frequency", xlab="Global Active Power (kilowatts)")
dev.copy(png, file="Plot1.png")
dev.off() |
1cfec71d6c5184c2e626739c2a728956d240d60d | e082728a5557b4584812addfac6c91c266f29994 | /spls/discriminant_analysis.R | 115e5ff62004d6d243cd23cbf090041b48b4e85f | [] | no_license | eprdz/pipelines_git | c5eb34df6add8a3f7f97e1868dbed4d712799630 | bd000a3f4d07e3025cbf58dfa832c18c1bf1c97d | refs/heads/main | 2023-08-10T18:48:07.545777 | 2021-10-07T13:29:09 | 2021-10-07T13:29:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,670 | r | discriminant_analysis.R | #!/usr/bin/env Rscript
#######################################
#### Discriminant analysis ####
#######################################
pacman::p_load(mixOmics, ggplot2, gplots, DiscriMiner, clusterProfiler)
parameters <- commandArgs(trailingOnly=TRUE)
counts.file <- as.character(parameters[1]) # Omic datase... |
db0e8bcf251306da04efbb9771483d923569ec85 | 567f2d42ca081c76732ecde1becfb2df212ceec4 | /man/elexonURL.Rd | 56a34e15701291732c0258a1b126bb78524b2fda | [] | no_license | p-hunter/Relexon | 1d2e90cb36802f58f8385b21a3221e9c70f789e9 | 1dd8b3008095a4c4d3a7b22aba629a1da829422d | refs/heads/master | 2023-08-24T21:23:43.764762 | 2021-10-23T23:37:29 | 2021-10-23T23:37:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,142 | rd | elexonURL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/elexonURL.R
\name{elexonURL}
\alias{elexonURL}
\title{elexonURL}
\usage{
elexonURL(dataset = "ROLSYSDEM", key, from = Sys.Date() - 2,
to = Sys.Date() - 1, test = FALSE)
}
\arguments{
\item{dataset}{The dataset you are pulling from... |
8d05cde28e41a5c880003c7708426c8c3326090b | d28508911e5a2f5c3d8d849d7d2a97c687dbffd9 | /Chapter03/neural_network_with_neuralnet.R | 7c6c9940546d7042546c4f5bebd70d798e9b467c | [
"MIT"
] | permissive | PacktPublishing/Hands-on-Deep-Learning-with-R | 10032fb0aceed0b315cf7bb399f53e07885df8f7 | 6e3766377395d4e2a853f787d1f595e4d8d28fa5 | refs/heads/master | 2023-02-11T11:05:47.140350 | 2023-01-30T09:37:44 | 2023-01-30T09:37:44 | 124,351,189 | 21 | 15 | MIT | 2020-04-09T06:29:03 | 2018-03-08T07:03:57 | R | UTF-8 | R | false | false | 2,761 | r | neural_network_with_neuralnet.R | # load libraries
library(tidyverse)
library(caret)
library(Metrics)
# load data
wbdc <- readr::read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data", col_names = FALSE)
# convert the target variable to 1 and 0 and relabel
wbdc <- wbdc %>%
dplyr::mutate(target = dplyr... |
599f11c550f4e8f32e581be41db74541c5325215 | 510bc25ad2b6e67e4a3c13043cacd4424b75552e | /R/print_demand.r | 8a632d30205b9e69085c4bb55546f8b38b6a36a6 | [] | no_license | orangeluc/energyRt | ff7423a2010d8edc3915034c396f079662ea4315 | c72d1a528a95ef8fada215e0abef45d523383758 | refs/heads/master | 2020-04-24T06:04:06.819280 | 2019-02-20T13:26:26 | 2019-02-20T13:26:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 794 | r | print_demand.r | #---------------------------------------------------------------------------------------------------------
#! print.demand < -function(x) : print demand
#---------------------------------------------------------------------------------------------------------
print.demand <- function(x) {
# print demand
if_prin... |
5b3d988c4eefa02929a634acdba5a36bd42ced7b | a91f8efbfc949026cc36e85760336cfaeb37477f | /8 XtremeGradientBoost.r | 68bb1b3d49d20233eab9bc91828848d0b783f28a | [] | no_license | azankhanyari/SurveyLevel_Disease_detection_ML | 2757b840be20e2a76595468a00e1807ef61e898c | e383d58df36d3003d13d65c4ab60f685e88f8295 | refs/heads/master | 2022-03-31T07:13:05.448347 | 2019-12-22T19:07:30 | 2019-12-22T19:07:30 | 229,615,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,682 | r | 8 XtremeGradientBoost.r |
library(xgboost)
data_xbg <- model_train_tomek
data_xbg$Status <- as.numeric(data_xbg$Status)
table(data_xbg$Status)
test_x <- test
test_x$Status <- as.numeric(test_x$Status)
str(data_xbg$Status)
table(data_xbg$Status)
data_xbg$Status <- ifelse(data_xbg$Status == 2,0,data_xbg$Status)
train_xgb <- data_xbg[,-23]
te... |
9a75de7631b4d160f0655ebf4f957ec1df782103 | eb9b5a5b759b10bfbf8421f3a67a025a9ff7c069 | /results_in_paper/10_PWAS_cor_coloc.R | 1875e69244a5ffb060308688579be8d66e350871 | [] | no_license | Jingning-Zhang/PlasmaProtein | fc42790f4eaea03e5b0285dbcc5ca7bc929ecc3e | 1a3fd772782bf2b599f8c81054e4bf899ca41bd1 | refs/heads/main | 2023-04-15T22:55:54.912402 | 2022-11-29T06:42:31 | 2022-11-29T06:42:31 | 465,238,572 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,608 | r | 10_PWAS_cor_coloc.R | library(readxl)
library(dplyr)
library(readr)
urateid <- c("SeqId_13676_46","SeqId_7955_195","SeqId_17692_2","SeqId_19622_7","SeqId_6897_38","SeqId_8307_47","SeqId_15686_49","SeqId_17765_3","SeqId_8900_28","SeqId_8403_18")
urategene <- c("INHBB","ITIH1","BTN3A3","INHBA","B3GAT3","C11orf68","INHBC","SNUPN","NEO1","FASN... |
6408aef46d1c63883ac03bd365489b353440f764 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/echarts4r/examples/formatters.Rd.R | 210f85851499f0ed0d6159d0351752955551b683 | [] | 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 | 345 | r | formatters.Rd.R | library(echarts4r)
### Name: e_format_axis
### Title: Formatters
### Aliases: e_format_axis e_format_x_axis e_format_y_axis
### ** Examples
# Y = %
df <- data.frame(
x = 1:10,
y = round(
runif(10, 1, 100), 2
)
)
df %>%
e_charts(x) %>%
e_line(y) %>%
e_format_y_axis(suffix = "%") %>%
e_format_x... |
817291ba5baf8aa0dfe59eb560cef2f943884c22 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/7602/dprint/tbl.struct.R | 4abfbecaf645ea5f90838af31b513ba24450b360 | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,742 | r | tbl.struct.R | #' Table Structure
#'
#' Generalization of table structure
#'
#' @param fmla Formula interface to define table structure
#' @param data data.frame
#' @param label name of column containing row labels
#' @param group name of column containing hieriarchy labels for the row names
#' @param regx regular expression to be re... |
9d1425defc0df06dd62668bddbf8f01c6ff65173 | b3afc44d91b7e1a84c7b04e4f715fc4ed8dd3320 | /src/Script5_Sept 10 reshaping data.R | 390fe2a2706835fee48b21e02045dd940cbd68f0 | [] | no_license | AChase44/FISH-504 | 99d3f103b4e45d6799d74ab5e6890b202d60f1f7 | 872d2b3032f20b36a70cfa317cb59f337b06e664 | refs/heads/main | 2023-02-04T15:56:54.771052 | 2020-12-12T20:29:44 | 2020-12-12T20:29:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,311 | r | Script5_Sept 10 reshaping data.R |
# Day 1 Data Wrangleing ---------------------------------------------------
#data wrangling day 1
download.file(url = "https://ndownloader.figshare.com/files/2292169",
destfile = "C:/Users/Student Account/Documents/FISH504RProjects/src/504_Sept3_Live_Code/sept3.csv")
surveys <- read.csv("C:/Users/Stu... |
5ecb8d3a1f6e74bf983fd63b2c746d052a20036d | 4958fcfba9cf8bd5ef2840a3d1ba89119932a4b8 | /man/importGtf.Rd | 75cba714c1fa56eaf5df643732168a46fccca0b8 | [] | no_license | BIMSBbioinfo/RCAS | 25375c1b62a2624a6b21190e79ac2a6b5b890756 | d6dc8f86cc650df287deceefa8aeead5670db4d9 | refs/heads/master | 2021-07-23T10:11:46.557463 | 2021-05-19T16:21:54 | 2021-05-19T16:21:54 | 43,009,681 | 4 | 4 | null | 2017-10-19T23:28:43 | 2015-09-23T15:29:13 | R | UTF-8 | R | false | true | 1,805 | rd | importGtf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/report_functions.R
\name{importGtf}
\alias{importGtf}
\title{importGtf}
\usage{
importGtf(
filePath,
saveObjectAsRds = TRUE,
readFromRds = TRUE,
overwriteObjectAsRds = FALSE,
keepStandardChr = TRUE,
...
)
}
\arguments{
\item{fileP... |
2f7c390c1dfb41f9c9c8e181f41d55d8e653730f | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googledataflowv1b3.auto/man/GetDebugConfigRequest.Rd | 4e513a309ac5feaab6e5667f7f92960ed36f25ea | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 612 | rd | GetDebugConfigRequest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataflow_objects.R
\name{GetDebugConfigRequest}
\alias{GetDebugConfigRequest}
\title{GetDebugConfigRequest Object}
\usage{
GetDebugConfigRequest(componentId = NULL, workerId = NULL)
}
\arguments{
\item{componentId}{The internal component id f... |
8345c8576ead077faaef9c214ed780313e040a60 | cc33f833ba275ea5421f7a83ec623b85f77400f6 | /acogarchSimulationAppHelpers.R | 2ede97d589052eec30dd0ce0fc15cfb6d6a715ba | [] | no_license | JonasKir97/aparch_app | 12641451b010354da03019d7c2c496999ba2f57b | 13ada0f8a7a769dba1a56109583ae976e9884df0 | refs/heads/master | 2023-04-15T06:41:38.427690 | 2021-04-29T18:19:18 | 2021-04-29T18:19:18 | 359,941,448 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,183 | r | acogarchSimulationAppHelpers.R | #' helper to validate the inputs for the discrete simulation of an APARCH(1,1)-process
parseDiscreteSimulationInput <- function(shinyInputObject, maxSteps = NULL) {
deltas <- shinyInputObject$deltaDiscrete #Zeichenkette, ggf kommagetrennt für mehrere Simulationen mit variierenden Delta
deltas <- as.numeric(strsplit... |
8ad264c4fddbbdfa00643b9f58d22de157cd7b98 | 6b4fe2baa84e74af637f319ea5d887cb2fd6f9a2 | /kevin/rimod-analysis/kegg_pathway_view.R | 0a97f5a43ef0f9024caa6f856c100f6f6bff4a9b | [] | no_license | dznetubingen/analysis_scripts | 1e27ca43a89e7ad6f8c222507549f72b1c4efc20 | 4fcac8a3851414c390e88b4ef4ac461887e47096 | refs/heads/master | 2021-06-25T10:47:40.562438 | 2021-01-04T16:02:34 | 2021-01-04T16:02:34 | 187,789,014 | 1 | 0 | null | 2020-09-03T11:37:25 | 2019-05-21T07:55:17 | Jupyter Notebook | UTF-8 | R | false | false | 4,580 | r | kegg_pathway_view.R | library(pathview)
library(biomaRt)
setwd("~/rimod/integrative_analysis/immune_system_pathway_analysis/")
ensembl <- useMart("ensembl", dataset="hsapiens_gene_ensembl")
data(paths.hsa)
for (i in 264:length(paths.hsa)) {
print(i)
pw <- gsub("hsa", "", names(paths.hsa)[i])
pname <- paths.hsa[i]
pname <- gsub(" ... |
2ea21942905bea8576dba5faa3a47daa73215a11 | eaaf41d49afd7cb9bf24e0c1f77f60c23acdbdd4 | /R/customerHistory.R | 5d617c087cdc3451c2547db5c952a69b21dd394b | [] | no_license | fhirschmann/ml_dmc2014 | 7fd23165dc7fcbcee38267699ea245b7e123ca4f | 253c8223891d167565137994676b4a556ae21e64 | refs/heads/master | 2016-09-10T08:29:14.312845 | 2014-11-07T23:37:47 | 2014-11-07T23:37:47 | null | 0 | 0 | null | null | null | null | ISO-8859-3 | R | false | false | 2,325 | r | customerHistory.R |
source("r/data.r")
library(data.table)
library(plyr)
x <- dt.dmc$M30$train[sample(nrow(dt.dmc$M30$train), 2)]
#dt.from <- data.table(x[x$deliveryDateMissing == "no", ])
#orderDates <- unique(dt.from[, c("customerID", "orderDate", "itemID"), with=F])
#setkeyv(orderDates, c("customerID", "orderDate", "i... |
ad7c414801f701e99b5d0458fa75461e5aecefe7 | d6e4cae0c1f3968ddd1a57797a00de47c96a01fa | /Simulation Study 1/Large Missingness/R Parallel LC - LatentGOLD.r | 5bd642e6cf503bb80f7816091f6e27a2a9a763bd | [] | no_license | davidevdt/BLCMforMI | f87db89d61f02a712d0de2bbe510edbe503c4b01 | f9a30d21b593a826f0b588782aba4f5ba3e0878c | refs/heads/master | 2021-08-29T04:58:15.973700 | 2017-12-13T13:01:39 | 2017-12-13T13:01:39 | 114,115,025 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,919 | r | R Parallel LC - LatentGOLD.r | #Before Running the following code, perform model selection with BLC-model scripts (file "R Parallel - BLC [with model selection].r")
#LG set-up
library(foreach)
library(doParallel)
library(plyr)
no_cores <- detectCores()
#Function for combining results obtained in different PC cores
comb <- function(x,... |
e5c2ed31042599e49903c8c45a0d9cd40e17c2b5 | 9c7c2ca8700a1751fa6f66094295cd34e13fc484 | /quantfin_ropen_p1quandl.R | d9cb27cd718a55a6094205a74a15b9ecf94a8613 | [] | no_license | jrottersman/Rcode | 0e9a756f006eba9c330ff39909bea0b0639bcf9b | 4c29d79ac9cde3c63999e425d37c86defe54a2d0 | refs/heads/master | 2021-01-10T22:05:31.861269 | 2015-04-29T23:42:11 | 2015-04-29T23:42:11 | 25,662,055 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 469 | r | quantfin_ropen_p1quandl.R | library(Quandl)
gold <- Quandl("OFDP/FUTURE_GC2", collapse = "monthly")
#20 years of monthly gold prices
gold20 <- Quandl("OFDP/FUTURE_GC2", collapse= "monthly", start_date = "1992-06-01", end_date = "2012-05-01")
#calculating log prices again monthly this time
#shocking I know
gold.settle <- gold20[, "Settle"]
go... |
4a2f018e40603ec77ed67772f971efd6bb5f020d | 907054819ef2b22288814b42a855c42406a06585 | /man/arkdb-package.Rd | f619130b146bec981a02b009ea61739250d6cfb9 | [
"MIT"
] | permissive | ropensci/arkdb | f723b334523a3f3474c4eb8776d55b05178dffcf | 18ec931cba15925afd3905a921c7a73b05db5031 | refs/heads/master | 2023-05-23T20:00:18.113506 | 2022-11-18T06:32:21 | 2022-11-18T06:32:21 | 136,522,042 | 62 | 5 | NOASSERTION | 2022-11-18T06:32:22 | 2018-06-07T19:29:36 | R | UTF-8 | R | false | true | 1,386 | rd | arkdb-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/arkdb.R
\docType{package}
\name{arkdb-package}
\alias{arkdb}
\alias{arkdb-package}
\title{arkdb: Archive and Unarchive Databases Using Flat Files}
\description{
Flat text files provide a more robust, compressible,
and portable way to store ta... |
5e18492bce7a9450b0fa4bf4f280fd90340e9759 | 45d7455b79bdf23be24e81bcf91396b941ce3f53 | /R/plotSelectedEUCases.R | 953cca42d53c38abd735d1159d76953d4943f0fb | [
"CC-BY-4.0"
] | permissive | lgreski/COVID-19 | 525e188120de11d228dbfd821d686d7a64829b4d | b66e0ce3d38bc6ed4a5ea7b79c6088dfeb6d2c2a | refs/heads/master | 2023-06-12T18:02:24.736309 | 2023-06-10T18:35:30 | 2023-06-10T18:35:30 | 249,577,621 | 1 | 1 | null | 2023-02-12T20:43:45 | 2020-03-24T00:48:05 | R | UTF-8 | R | false | false | 1,238 | r | plotSelectedEUCases.R | #
# plot covid-19 cases for selected countries in Europe
#
# (c) 2020 - 2023 Leonard Greski
# copying permitted with attribution
data$Country_Region[data$Country_Region == "UK"] <- "United Kingdom"
require(dplyr)
require(ggplot2)
require(ggeasy)
countryList <- c("United Kingdom", "Ireland", "France","Germany",
... |
91f66d57ebbcb70c69f4adfb397cf745811e2956 | 722d32d39d2906b3f24eb8ac2172059700021ecb | /R/sdev.R | f30ce0bfa3f2c4bd0f959fe607b2616a14ba4a1e | [] | no_license | einarhjorleifsson/husky | a69f9820b4d0634a77ec031f2d972c55879e45ec | 7219dec18579308bb941015e45282f11e21f83cf | refs/heads/master | 2020-07-26T05:25:16.353936 | 2016-12-02T16:51:26 | 2016-12-02T16:51:26 | 73,732,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 182 | r | sdev.R | #' sdev
#'
#' @description
#'
#' location: /net/hafkaldi/export/u2/reikn/Splus5/SMB/GEOMETRY.NEW/.RData
#' @param x XXX
#'
#' @export
sdev <- function (x) {
return(sqrt(var(x)))
}
|
b450acbf8d4a1179fffd55019b6e0c25d28db1f5 | b9ee02abf87564a92883d1a03e7ff6a0da5f621d | /man/important_gene.Rd | f881e236508fcc94002ef7d2c35746439102c40e | [] | no_license | fparyani/DeepDeconv | e69888e8357992035285f9b46a821f5bf331ecd0 | e3151f95c1364b2954daafc9a73201829f7561de | refs/heads/master | 2023-06-07T04:50:37.113013 | 2021-07-05T18:05:51 | 2021-07-05T18:05:51 | 382,455,047 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,321 | rd | important_gene.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/important_gene.R
\name{important_gene}
\alias{important_gene}
\title{Find Important Gene}
\usage{
important_gene(
quant_mat,
factor_group,
cell_type,
st_gene = NA,
num_gene = 500
)
}
\arguments{
\item{quant_mat}{A gene expression ma... |
887173763f06dd220f02d3a85c847320ab50d385 | cd3772c8fa26937675aba85c21146dd6b99de2a2 | /partials/income_map_pop_contour.R | e12cefec5ca1e9611cc4e42b3d7d319b3d8fa52a | [] | no_license | jimjh/315-project | 8c7fbec209bd369cfa8cabb07a07d2623496ed9f | dd85d5d71fd94789d5c270191369172f57fa679a | refs/heads/master | 2021-01-10T21:04:40.975842 | 2013-05-07T01:29:36 | 2013-05-07T01:29:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,243 | r | income_map_pop_contour.R | incomepop.data <- list('male' = louisiana.blkgrp10$income.male,
'female' = louisiana.blkgrp10$income.female)
output$income_vs_pop <- renderPlot({
par(mfrow=c(1,2))
if (input$income_contour == TRUE) {
# plot the contour overlay on the map showing pop density
plot(louisiana.bl... |
77a9e35522eb4438f3e019158a3eb05f3fe7d29a | 6943ec72c033da6fdc0e6f001adf74b5b1098287 | /R/transform.R | 380f9f62b3b1ba61d20fb943674555a979037911 | [
"MIT"
] | permissive | mjmm13/BCB420.2019.COSMIC | 4f33d2c052234b662b3db486e5cb2b52fdf72ae6 | a448dfb6437f2f459bb77db08886ebb8516beab1 | refs/heads/master | 2020-04-20T16:49:39.317169 | 2019-02-11T06:40:46 | 2019-02-11T06:40:46 | 168,969,821 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 800 | r | transform.R | # transform.R
#' MutationTransform
#'
#' This function will transform the data into mutation rate data allowing
#' us to understand the prevalence of mutation by genes and tissue types
#'
#' @param <mut> <Table of all targetted screens by genes, including negatives>.
#' @param <tissue> <boolean, returns mutation rates... |
5f25075b0c9c16af0d14d2bb2b709f3eadf5a783 | b0630daa7219ac30bd41d49f8535c4d3d8afff0b | /Standard_Models/Random_Forest_Regressor.R | 4e6711b026ba94fbd2e5e1557c5278e6c86e1cb7 | [] | no_license | oscarm524/Machine-Learning | c861d2ef501405d2d0507c2e931b16073567b1f5 | ccca40bcd09e19e29a51237f9b169f7d62d09f27 | refs/heads/master | 2023-05-08T06:55:49.437633 | 2021-05-28T22:39:49 | 2021-05-28T22:39:49 | 261,507,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 490 | r | Random_Forest_Regressor.R | #############################
## Random Forest Regressor ##
#############################
Random_Forest_Regressor <- function(X, Y){
## Checking for randomforest package
if (!require(randomForest, character.only = T, quietly = T)) {
install.packages(randomForest)
library(randomForest, character.o... |
9437b9defe665b4a05ce7ecc79fd3417640e061e | da63137ed3cbeccff8fd7c7aea6fc4403829ce4d | /run_analysis.R | 24f62b907b30c5968838b17ac7f42d70af2c7bcb | [] | no_license | gvillemsr/getcleandata_Courseproject | ed961471f6da8b215c53a890411c3281089c9a4f | 7126ad66ffec764952b4069cfff4bc9713b612d6 | refs/heads/master | 2021-01-23T06:49:25.701064 | 2014-06-21T03:56:44 | 2014-06-21T03:56:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,911 | r | run_analysis.R | test<-read.table("UCI HAR Dataset/test/X_test.txt", sep="",header=FALSE)
train<-read.table("UCI HAR Dataset/train/X_train.txt", sep="",header=FALSE)
extcols<-c(1:6,41:46,81:86,121:126,161:166,201:202,214,215,227,228,240,241,253,254)
extcols2<-c(266:271,345:350,424:429,503,504,516,517,529,530,542,543)
extract<-c(extcols... |
84c754bf02e6b2271f7f6f518aeba17d8f432e23 | 2058b23e90178e75d154081642a1c2fb38abc446 | /app.R | 39b2b37205d2978d6ad71693862fe31e81601ff8 | [] | no_license | cmartini86/Developing_Data_Products | 8ec58ab82617fb58e0ffecd146cf884be3c42283 | 648dbd9dd23e4635de486ac1eae3097b4504fb01 | refs/heads/main | 2023-01-07T20:36:59.056799 | 2020-11-13T22:12:15 | 2020-11-13T22:12:15 | 306,761,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,173 | r | app.R | library(shiny)
setwd("C:/DevDataProd")
data <- read.csv("QB_STATS.csv", header=TRUE)
dat <- read.csv("QB_STATS.csv", header=TRUE, row.names="NAME")
server <- function(input, output) {
# Fill in the spot we created for a plot
output$statPlot <- renderPlot({
par(mar=c(11,4,4,4))
# Render... |
e562d6cc64f1841f4bc41e65bcc5cba5ae201f2f | bca52aeca6a6db6bb675ebdb1906a2b78f9b89df | /misc scripts/three dimensional array.R | ec5a297f583aed575a199992a0feb7a66cd39489 | [] | no_license | ammeir2/selective-fmri | 1b6402e3f007c82a73bb92f18024d88c936c3739 | 4c2274257ba46c1c744a1da764e8f92fd60294b0 | refs/heads/master | 2021-01-11T13:29:16.942383 | 2017-06-20T22:58:27 | 2017-06-20T22:58:27 | 81,491,192 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,872 | r | three dimensional array.R |
plotBRAIN <- function(coordinates, column, col = NULL) {
for(ind in 1:K) {
temp <- subset(coordinates, k == ind)
signal <- temp[, column]
imagemat <- matrix(nrow = I, ncol = J)
for(l in 1:nrow(temp)) {
imagemat[temp$i[l], temp$j[l]] <- signal[l]
}
if(is.null(col)) {
(image(1:I, 1... |
186ed8098d8a4e68b06d8fd65116bd45d651b50f | 9f8a04acadbd7ab8e0aa5f223a572216f2de11d3 | /BCB_Practical2.R | dfe70093548545a5a65ea9f7c5ea970b42257bbb | [] | no_license | jonchan2003/Uni-Work | 0c5857f80223da5a352a8eb295ab517ec42716eb | 4821ae63a9b559d58098ba392dd67d70e6a1d4c4 | refs/heads/master | 2020-04-26T18:37:31.149129 | 2019-03-04T17:57:34 | 2019-03-04T17:57:34 | 173,750,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,309 | r | BCB_Practical2.R | library(ape)
library(caper)
library(geiger)
setwd("downloads/Practical 2")
mammal.orders <- read.delim("MammalOrderS.txt")
source("hcd.functions.R")
mammal.hcd <- hcd.fit(mammal.orders$richness, reps = 1)
plot.hcd(mammal.hcd)
# Red line shows mammal richness, black line shows equal rates Markov model
m... |
eb2d88fa2c7aad3f1a42cfdd562e34219f4accb9 | e639760af64558ff1cefa03362d8c5fa5139119e | /nvd3/examples_json.R | 98099e747b9ecfbd70c9a88842596ddeddbe6a0e | [] | no_license | timelyportfolio/docs | b22dad53e1da9e21802c1c4713965f1c437c3ead | 19c7e4f841eb06eb7e57d32042386c605e177b42 | refs/heads/master | 2021-01-18T16:42:53.631088 | 2014-04-01T15:05:07 | 2014-04-01T15:05:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 904 | r | examples_json.R | dat <- paste(readLines('nvd3/charts.R'), collapse = '\n')
examples <- strsplit(dat, '\n## ----')[[1]]
examples2 <- lapply(Filter(function(x) x!= "", examples), function(example){
ex = strsplit(example, '-+\n')[[1]]
ex_nm = strsplit(ex, ",")[[1]][1]
c(ex[2], ex_nm)
})
names(examples2) = sapply(examples2, '[[', 2)... |
20eb90631304968fc018af8197963f0f4e0b955f | 6ff24bc1f35410c47d2662d1b8e5a2f34e65b1b7 | /man/cv.knn.Rd | 3846d1b0cc4fe5a70e246797e3b085c822065375 | [] | no_license | ablanda/Esame | 5d3d7c1408e5ed0e9771ea015855db0788036d8e | b43749d3fc4214e878d93b4e2b7c073c64cb7610 | refs/heads/master | 2020-12-30T11:39:37.681842 | 2018-08-11T12:42:47 | 2018-08-11T12:42:47 | 91,511,654 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 360 | rd | cv.knn.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cv.knn.R
\name{cv.knn}
\alias{cv.knn}
\title{cross validation leave one out knn}
\usage{
cv.knn(K, x, y, folds = NULL)
}
\arguments{
\item{K}{}
\item{x}{}
\item{y}{}
\item{folds}{}
}
\value{
errore totale per un determinato k
}
\descriptio... |
1a57bb317e18168ba35437335ea960aa2f3e12f8 | 68e96e54f6dabbfa92d30adffaab0ef6a7bc7a63 | /RJSDMX/man/RJSDMX-package.Rd | 897e1f4f9671735052e4d24a1f36777e37d9d864 | [] | no_license | darthbeeblebrox/WorldBankData | f047bd2a7c361af19d59916ba663db1c2525d0f3 | 5465617cb982d619120008c71ea67328142e1fe1 | refs/heads/master | 2021-01-19T13:49:39.233912 | 2017-02-02T13:00:36 | 2017-02-02T13:00:36 | 82,421,540 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,986 | rd | RJSDMX-package.Rd | % Copyright 2010,2014 Bank Of Italy
%
% Licensed under the EUPL, Version 1.1 or as soon they
% will be approved by the European Commission - subsequent
% versions of the EUPL (the "Licence");
% You may not use this work except in compliance with the
% Licence.
% You may obtain a copy of the Licence at:
%
%
% http://ec.... |
891751c082c0322359cb7f1d5295a0b073d53873 | 98e3f5ba9fdf45b20ae26172827da72002a0e248 | /R/status.R | 2ab72baa778ca281e23198432e5186468e35587d | [
"MIT"
] | permissive | eddelbuettel/rhub | c167b0140ce6b6c5bb362afd38619c7d423d65ca | d5a495450aba062861b8c774f0cee389b672156a | refs/heads/master | 2021-01-13T09:22:17.666224 | 2016-10-15T14:44:36 | 2016-10-15T14:44:36 | 70,002,510 | 0 | 0 | null | 2016-10-04T20:16:03 | 2016-10-04T20:16:02 | null | UTF-8 | R | false | false | 3,035 | r | status.R |
#' Query the status of an r-hub check
#'
#' @param id The check id, an r-hub status URL, or the object retured
#' by [check()].
#' @return A list with the status of the check. It has entries:
#' `status`, `submitted` and `duration`. Currently the duration is
#' only filled when the build has finished.
#'
#' @exp... |
4daa38c2e2d6c59ba0e885c4139a0b430d8f377b | 21d49a6e91b2546255c66d514a7f7842c6721475 | /Shiney_App_Next_Word/ui.R | 52340924de016fc1ca93a723fc448c5b50395ecd | [] | no_license | DScontrol/shiny_app_next_word_prediction | 94f0a397f0e1e362fb7346aaee32c40b58be0752 | 694f42b2d00a0f9572cd9755929a465231ae7d71 | refs/heads/master | 2022-01-26T15:25:59.338697 | 2018-09-06T03:44:53 | 2018-09-06T03:44:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,365 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(dplyr)
library(shinythemes)
library(DT)
library(ggplot2... |
78b4063fc451eb125bb950826bf91db64efd8941 | 41cff625d6d1352aac02d1f206279d16e86685a1 | /R/MTuplesList-class.R | 24ce3ee000f36a4fab2f94a8a455a8957744a4f0 | [] | no_license | PeteHaitch/MethylationTuples | dae3cf80085d58f57ac633d99f3be44e6fb84daa | 4e127d2ad1ff90dbe8371e8eeba4babcb96e86f2 | refs/heads/master | 2020-12-11T22:52:16.651509 | 2015-04-24T13:26:56 | 2015-04-24T13:27:12 | 24,593,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,491 | r | MTuplesList-class.R | ### =========================================================================
### GTuplesList objects
### -------------------------------------------------------------------------
###
# TODO: unit tests
# TODO: Base documentation on GTuplesList
#' MTuplesList objects
#'
#' @description
#' The \code{MTuplesList} class... |
d53f156072e805e8dd9852bc905bbd95359f80b4 | 9b40d9d2a1a525ef69f989518b64259feb51e684 | /02_ini_simulation_simple_reg.R | 775c9c07cbf90506a7b481670649579636d7a425 | [] | no_license | CaroHaensch/IPD_MA_Survey_Data | 5c144f9c127fc5a5b3aadb6a623194f3e2ccdb95 | 32c15a3d28bc17074542cce3c5a183023560dab9 | refs/heads/master | 2020-04-28T09:35:11.085620 | 2019-03-12T08:59:45 | 2019-03-12T08:59:45 | 175,172,121 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,150 | r | 02_ini_simulation_simple_reg.R |
## Filename: 02_ini_simulation_simple_reg.R
## Description: Ini file for the whole simulation
## Author: Anna-Carolina Haensch
## Maintainer: Anna-Carolina Haensch (anna-carolina.haensch@gesis.org)
## Software version: R 3.3.3
## Creation: 2017-11-20
## Last updated on: 2018-05-09
###
# ATTENTION: Takes about 10 h... |
69b8092189f6b2f206b28ab39e6fb6716bceed5f | d62ed0b5061ba4e025635162076245871baabff6 | /ui.R | 381c9368692bd03020a1d637b758b9b3d422246f | [] | no_license | NJBongithub/course_DSJH_DataProducts | 64305fcfb26aec1d8988534f39a87c29866560bc | 0e3df7e6bf630fe80743b4fba2641e5e0dde5b41 | refs/heads/master | 2021-01-20T23:32:23.438286 | 2015-03-21T09:27:59 | 2015-03-21T09:27:59 | 32,625,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,307 | r | ui.R | shinyUI(pageWithSidebar(
headerPanel("Deciding to Reject a Null Hypothesis"),
sidebarPanel(
p('You measure the mean amount of Substance X per gram of soil for several soil samples.'),
h4('Your Observation'),
numericInput('t_observed', 'Enter current sample mean.', 0.06, min=0, max=0.20, step=0.01),
... |
e84bcab1210f49687f2c2ee385b9da3a0e227ad1 | fae0770ad0cd10b81a641d8bfcd61ffbcb32f142 | /MODELOS/ComparingMethods.R | 946c72bf5bc78c6f5f771b01bb21d399d0fc386b | [] | no_license | jorgeramirezcarrasco/l3p3_Titanic | 8f7edc78e63c6dea3ecc9814ff1eab2ad9c13eb6 | 0376f4a66c46834fdec476ffce9ae9bf1568f197 | refs/heads/master | 2022-12-05T15:47:57.843079 | 2014-07-15T07:56:26 | 2014-07-15T07:56:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,295 | r | ComparingMethods.R | #Logistic regression model no.1#
ctab.test1 <- table(pred=titanic$pred1>0.44, Survived=titanic$Survived)
precision <- ctab.test[2,2]/sum(ctab.test[2,])
recall <- ctab.test[2,2]/sum(ctab.test[,2])
#Logistic regression model no.2#
ctab.test2 <- table(pred=titanic$pred1>0.44, Survived=titanic$Survived)
precision <... |
a3174d9fbcb393ab9dbf277876167aceb5f68602 | fc8cf5aa32e4c08cf6f2542b4c87c158659c8c0a | /man/writeNanoStringRccSet.Rd | 82a98ff1369414c94d030d1c1474f378fb7eea3e | [] | no_license | amarinderthind/NanoStringNCTools | c4848828cca752991e068bf613afc286b9539bdd | 4ea743e7dff21ffe8d96ea34c72092dbc74f1948 | refs/heads/master | 2023-03-01T22:33:40.170820 | 2021-02-02T19:51:55 | 2021-02-02T19:51:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,308 | rd | writeNanoStringRccSet.Rd | \name{writeNanoStringRccSet}
\alias{writeNanoStringRccSet}
\concept{NanoStringRccSet}
\title{Write NanoString Reporter Code Count (RCC) files}
\description{
Write NanoString Reporter Code Count (RCC) files from an instance of class
\code{\linkS4class{NanoStringRccSet}}.
}
\usage{
writeNanoStringRccSet(x, dir = g... |
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