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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1e117f645dcf4f9da084b8b8cd497060a7e18f9e | bad132f51935944a52a00e20e90395990afd378a | /R/ISODistributor.R | 2f77cf8820e4dc3e929905e4376052d67d33a1d4 | [] | no_license | cran/geometa | 9612ad75b72956cfd4225b764ed8f048804deff1 | b87c8291df8ddd6d526aa27d78211e1b8bd0bb9f | refs/heads/master | 2022-11-10T21:10:25.899335 | 2022-10-27T22:45:13 | 2022-10-27T22:45:13 | 92,486,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,328 | r | ISODistributor.R | #' ISODistributor
#'
#' @docType class
#' @importFrom R6 R6Class
#' @export
#' @keywords ISO distributor
#' @return Object of \code{\link{R6Class}} for modelling an ISODistributor
#' @format \code{\link{R6Class}} object.
#'
#' @examples
#' md <- ISODistributor$new()
#' rp <- ISOResponsibleParty$new()... |
e3ed66211465e0c56a22142e985bad610b9b4046 | d53ad1327c7481e52f9cfc4644b836a6754f89a0 | /man/dist_extract.Rd | c6c42f68723e9c9a74a37cd52cac1889119dc2ef | [] | no_license | talegari/forager | 5aa152f65c4596c7d1161694a8aab40225950973 | f3963444886afac85d252d6c0b5455426361a7f3 | refs/heads/master | 2020-03-19T20:40:45.641914 | 2019-03-09T19:25:26 | 2019-03-09T19:25:26 | 136,911,591 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,000 | rd | dist_extract.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dist_extract.R
\name{dist_extract}
\alias{dist_extract}
\title{Extract distances between specified indexes from a 'dist' object}
\usage{
dist_extract(object, i, j, product = "inner")
}
\arguments{
\item{object}{distance object}
\item{i}{inde... |
c65cd649129a55ad38e1f8530385457f71f71658 | 7ebb7e41627e33a28534308c24e654cafeb5e787 | /Generalized Linear Models - 411/Bonus Assignments/Insurance/Insurance.R | 477b27447580cc4aa9188ad3681c72c21ab56e98 | [] | no_license | tamtwill/NU_MSDS_Courses | 9ab150484d8e6eb2b58e4f7a8b9c16b6db3175d6 | 141b34d002e4fde6d15e762a35d4435d997b960a | refs/heads/master | 2021-06-06T07:08:05.950402 | 2020-04-21T22:40:21 | 2020-04-21T22:40:21 | 140,891,149 | 0 | 0 | null | 2018-07-13T21:42:05 | 2018-07-13T20:41:03 | HTML | UTF-8 | R | false | false | 2,552 | r | Insurance.R | # Tamara Williams extra credit, Insurance
# include required packages
#---------------------------
library(readr)
library(pbkrtest)
library(car)
library(leaps)
library(MASS)
library(data.table)
library(ggplot2)
library(reshape2)
#####
# Set working directory
#####
setwd("~/NorthwesternU_MSPA/Classes/Generalized Line... |
7a4ae293602ced55950287e732c35a4366a05e8d | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.apigateway/man/flush_stage_authorizers_cache.Rd | ab4e1f6e8f6442fa0c95e43fda48024d25f5ef70 | [
"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 | 649 | rd | flush_stage_authorizers_cache.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.apigateway_operations.R
\name{flush_stage_authorizers_cache}
\alias{flush_stage_authorizers_cache}
\title{Flushes all authorizer cache entries on a stage}
\usage{
flush_stage_authorizers_cache(restApiId, stageName)
}
\arguments{
\item{re... |
2827d6774aab4a2bce7389c35f937e23860f1eac | 573e3623cd6d65c46e1771c2869c12d7ace68e0d | /R/outlier_detect.R | 6ef762e775445ab271a6074b021aca99d22f77d6 | [] | no_license | mellyxx/xmsPANDA | 14cda7624d99d4433fbced4fcb95222634be1cee | 006831474e6c9181118a21922c040183a0e49b78 | refs/heads/master | 2023-03-12T21:07:50.886053 | 2021-03-02T16:40:24 | 2021-03-02T16:40:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,186 | r | outlier_detect.R | outlier_detect <-
function(data_matrix,ncomp=2,pthresh=0.005,outlier.method="sumtukey"){
cnames<-{}
p1<-mixOmics::pca(t(data_matrix[,-c(1:2)]),center=TRUE,scale=TRUE,ncomp=ncomp)
cnames<-colnames(data_matrix[,-c(1:2)])
U3=p1$variates$X
##save(p1,U3,file="pcaoutlier.Rda")
if(outlier.method=="pcac... |
3325c789aa2310e94d6d2374bd7a8957405aaf49 | c4f6ca47ebe3c7ce07590fb04eb6801f120564a8 | /man/locate_data.Rd | 3f53dfa00b4c2d39cfa98adc05c1f6bb754a71cc | [
"MIT"
] | permissive | jimsforks/locatr | 516f843d2a7afd8d380f40bf63f94e24fe0fa3a1 | dd4bfecbe0b4de75f82ac4b2827737d4d5033267 | refs/heads/master | 2022-10-07T21:52:52.834301 | 2020-06-08T08:17:17 | 2020-06-08T08:17:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 887 | rd | locate_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/locate_data.R
\name{locate_data}
\alias{locate_data}
\title{Locates data cells}
\usage{
locate_data(sheet = NULL, ...)
}
\arguments{
\item{sheet}{a data frame produced by xlsx_cells}
\item{...}{a filter expression that identifies data cells.... |
331bb8494bcc19b195e9cf1f6501015ebe36454b | 63ec49bc7d1bbe18efd040ad0f616f91b195dfb4 | /Relatório/roteiro.R | 2a68e966f42d359619f6883b73e5347bb4f9e5e2 | [] | no_license | ddeniel1/EP-IA | b29b32b310c459fce713732966cb9ceaeedd4ea0 | 6c475d0f5afb6b533bd838cf892be6f1cb9b8cb9 | refs/heads/master | 2020-05-02T02:25:43.249101 | 2019-04-29T01:27:44 | 2019-04-29T01:27:44 | 177,704,385 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,930 | r | roteiro.R | library(plyr)
library(tidyverse)
library(readr)
#Automatico
automatico <- read_csv("~/Documentos/IA/EP-IA/res/automatico/182_pop_4847_mut_20_ger/0.csv")
pop118mut7724 <- read_csv("~/Documentos/IA/EP-IA/res/automatico/118_pop_7724_mut_20_ger/0.csv")
pop18mut5976 <- read_csv("~/Documentos/IA/EP-IA/res/automatico/18_pop_5... |
ab307d3ef033b53cf659dfadad844ac63e6ace8a | 1e820fe644a039a60bfbee354e50c775af675f6b | /ProbStatsR/E7_2_10.R | 842f6da0a0df97982edcc13ec0fe5ba712c666dc | [] | no_license | PyRPy/stats_r | a334a58fca0e335b9b8b30720f91919b7b43d7bc | 26a3f47977773044d39f6d8ad0ac8dafb01cce3f | refs/heads/master | 2023-08-17T00:07:38.819861 | 2023-08-16T14:27:16 | 2023-08-16T14:27:16 | 171,056,838 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 362 | r | E7_2_10.R |
# 7.2-10-------------------------------------------------------------------
dat <- read.table("E7_2-10.txt", quote="\"", comment.char="")
race <- dat$V1
# mean of race time difference
mu = mean(race) # 0.07875
# lower bond for mu
mu + qt(0.05, length(race)-1) * sd(race) / sqrt(length(race)) # -0.01043263
# no effe... |
d7d226c0adb66dd5cf53887dfc96d6d1090b3436 | 6e5b5050ff779266b83be384e0372fd507044b4d | /utils/to_csv.R | 5ab2c6b55b30349b65d4ecf151e91255c87aa5f9 | [] | no_license | cmmid/covidm_reports | b3968b096d40a830d775a0fa3eac7afee1e3e56e | b87f48cb81c4ef3bbbd221295ea47fad17c27b13 | refs/heads/master | 2022-12-05T23:36:42.314743 | 2020-08-10T06:38:15 | 2020-08-10T06:38:15 | 254,046,734 | 9 | 6 | null | 2020-08-05T20:14:54 | 2020-04-08T09:43:35 | R | UTF-8 | R | false | false | 379 | r | to_csv.R | suppressPackageStartupMessages({
require(data.table)
require(qs)
})
.args <- if (interactive()) c(
"~/Dropbox/covidm_hpc_output"
) else commandArgs(trailingOnly = TRUE)
fls <- grep(
"old",
sort(list.files(.args[1], "qs$", full.names = TRUE, recursive = TRUE)),
invert = T, value = T
)
ls <- lapply(fls, fu... |
b3976477d215df7e9b600084d2e5dc2d99f84ffc | e5c43a31a082bbfec5ebbc20b34d373896721579 | /R/functions/most.freq.R | 5c358d2fda2c807f46ed24262ff97618bbea5df8 | [] | no_license | geryan/rfst | 3dde3a499651f3a1ccc736f8c6597c5972f0e17c | 0aac1f0c3b17096af0c5b0b06e1ad80ac6d709ca | refs/heads/master | 2023-05-02T12:32:51.743467 | 2021-04-27T01:26:47 | 2021-04-27T01:26:47 | 164,573,310 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 497 | r | most.freq.R | most.freq <- function(x, na.rm = TRUE){
if(na.rm == FALSE){
if(any(is.na(x))){
return(NA_integer_)
}
}
if(all(is.na(x))){
return(NA_integer_)
}
ux <- unique(x)
if(length(ux) == 1){
return(as.integer(x[1]))
}
tx <- table(x)
mx <- max(tx)
wx <- which(tx==mx)
... |
7a86625785fbe2fddc0b7a55e79bd1f2c966a3b3 | 817c6a53e081e008cb771ae2beb8bb7cb438bd9a | /man/example2.Rd | d046bcde26783a235ba94c0cd23e15b382a0eff8 | [] | no_license | nfultz/learnSampling | 4cfcde2d34229dbdffef87e8a5292433eb7ae9c5 | a7130da3f57d6f43cb32d7a682461a9afe0ea840 | refs/heads/master | 2021-08-30T22:53:34.769019 | 2017-09-07T20:35:32 | 2017-09-07T20:35:32 | 114,797,849 | 2 | 1 | null | 2017-12-19T18:23:58 | 2017-12-19T18:23:57 | null | UTF-8 | R | false | false | 410 | rd | example2.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/data-example2.R
\docType{data}
\name{example2}
\alias{example2}
\title{Example 2 Data (n=75)}
\format{A data frame with 75 rows and 2 variables:
\describe{
\item{SelfEsteem}{Self-Esteem scores }
\item{Height}{Heights of participan... |
6691b860b5bf5153b2cf7509bf733d2d1f81a7dc | 7352fd6b28ff208887681eb87cafae0e3177a0da | /R/find_resourcesPT.R | 03fe0a1df2e8a4615e751f6453645bf78f311e40 | [] | no_license | p1981thompson/HBM_Rmarkdown_template | 2e9d3634997b9c00fe8164b8e53c93654c4ea521 | b0cd5fa24ea8b7366e535d3e0c796fa3771898c9 | refs/heads/master | 2020-09-23T02:38:01.088500 | 2019-12-02T14:12:49 | 2019-12-02T14:12:49 | 225,381,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 311 | r | find_resourcesPT.R | find_resourcesPT<-function (template, file = "template.tex")
{
res <- system.file("rmarkdown", "templates", template, "resources",
file, package = "WileyHBMtemplate")
if (res == "")
stop("Couldn't find template file ", template, "/resources/",
file, call. = FALSE)
res
}
|
34d8f2328bffd3fb67f37ccaf76633998ebfcd60 | da6909e677947ef96408927ad91af4c3a4821637 | /man/HRV_vars_domain.Rd | c627cabf77f63d5fd0bbebfb3a01eae31a565870 | [
"MIT"
] | permissive | Lightbridge-KS/labChartHRV | 57bae5e0821c37bace7683843abf0a62bb5c7a31 | 204136c40a64943c87d21d4cbadd40c6d3b78a78 | refs/heads/main | 2023-04-19T05:32:07.776771 | 2022-06-08T07:56:54 | 2022-06-08T07:56:54 | 480,112,543 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 682 | rd | HRV_vars_domain.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{HRV_vars_domain}
\alias{HRV_vars_domain}
\title{HRV Time & Frequency Domain Variable}
\format{
A list with 2 elements:
\describe{
\item{time}{contain character vector of time-domain variables}
\item{freq}{contain c... |
98b779ec099096ab857733e05d6f4757ecb750d7 | f1a7ab41ba3ce33c01a30e7283339c6432f9745f | /man/proteinInteraction.Rd | fee67d867a9c4eea478e32a9fd8a76703a7546bf | [] | no_license | vishalbelsare/xnet | 3ccba84442ebbf411fd96dc5c02dfedfc794adbf | 4093905ae81281b6cf81c6a3425bdaf884e78fb4 | refs/heads/main | 2023-05-30T09:50:59.545097 | 2021-06-03T13:04:23 | 2021-06-03T13:04:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,364 | rd | proteinInteraction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_proteinInteraction.R
\docType{data}
\name{proteinInteraction}
\alias{proteinInteraction}
\alias{Kmat_y2h_sc}
\title{Protein interaction for yeast}
\format{\itemize{
\item proteinInteraction: a numeric square matrix with 150 rows/column... |
17ada2f1909f6a4a498c4118564b3873b0e62100 | a45a5a8e3e0b85a3fe2ed73cba9997d216267bff | /scripts/films2.R | 5bc55c9d5d6d0b32216efbb2b6fd65b33dcd0a20 | [] | no_license | sbtr1/tidytuesday | 66c9c273739c5114b23c63de7a4576639534881f | 49716d1a9097f9b5af62c14727edd21fd24ee73d | refs/heads/master | 2020-06-05T01:44:19.468384 | 2019-07-09T08:28:38 | 2019-07-09T08:28:38 | 192,269,115 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,097 | r | films2.R | #### Tidy Tuesday week 30 - Movie revenues
## Load libraries
library(tidyverse)
library(lubridate)
library(ggrepel)
## Load data
movies = read_csv("../data/movie_profit.csv") %>%
select(-X1) %>% # remove X1 column
distinct(release_date, movie, production_... |
39dc060a46337affe303f5a2f3ed08568fe431e3 | bc0cb9110b38234b17b77110af228f7fadd9503d | /_tests/test-error.r | f984e881e1a39657e72b6239e88912e083b6aa06 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | klmr/sys | e079754d0afa157e1fcaedcc7fe7cb1f242e2bde | ea12f5e2f61e7efde7395c7ab7ecfe6cb86de507 | refs/heads/master | 2021-06-17T20:04:20.613476 | 2016-08-01T17:36:10 | 2016-08-01T17:36:10 | 38,907,303 | 14 | 1 | null | 2017-09-26T14:37:47 | 2015-07-11T00:09:13 | R | UTF-8 | R | false | false | 1,314 | r | test-error.r | context('Error messages')
test_that('wrong arguments cause errors', {
expect_that(xc(character(0), arg('filename', 'the input file')),
shows_error('filename'))
expect_that(xc('foo', arg('filename', 'the input file')),
shows_no_error('filename'))
expect_that(xc('--extra', a... |
9ff1fc00be0786e866c9c05cc4fb69359dbc3b1c | f908123c6cf4362373810d61bd9b1c1e4a72d0c3 | /man/render_pmap_file.Rd | 8c3ecaa87a6b9550ed9f6fac2a1903f2ae29992e | [
"MIT"
] | permissive | yingza/pmap | eecbd0a39dce192d0b68baa8e094f627e09de2ba | 9287304bd855aecb74cb2200a9cd4e1289897498 | refs/heads/master | 2021-05-11T23:38:34.182677 | 2018-01-15T02:48:14 | 2018-01-15T02:48:14 | 117,515,029 | 0 | 0 | null | 2018-01-15T07:59:30 | 2018-01-15T07:59:30 | null | UTF-8 | R | false | true | 1,151 | rd | render_pmap_file.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/render_pmap_file.R
\name{render_pmap_file}
\alias{render_pmap_file}
\title{Render the process map as a file}
\usage{
render_pmap_file(p, file_name, format = c("png", "pdf", "svg", "ps"),
width = NULL, height = NULL)
}
\arguments{
\item{p}{t... |
3ef9f0a955d7264d9fb27b63a7f6cf640384220f | 5e55d9058c2efca8fff0147404ad105d9cac6bd1 | /iocovid animation.R | b6bcfe4691c55621f8f4eb7e65d18926836e4629 | [
"CC0-1.0"
] | permissive | higgi13425/rmrwr-book | 0a67e7beff1a498565b68a0c2e5cb13d34d6e171 | 22a57b162714d5189302ec295c6e66925ef293ec | refs/heads/master | 2023-04-28T05:47:20.129120 | 2023-04-17T02:05:34 | 2023-04-17T02:05:34 | 249,053,179 | 14 | 10 | CC0-1.0 | 2021-01-10T19:49:02 | 2020-03-21T20:14:33 | HTML | UTF-8 | R | false | false | 3,409 | r | iocovid animation.R | library(tidyverse)
library(shadowtext)
library(gganimate)
library(gifski)
options(scipen = 20)
read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv") %>%
gather(date, cases, 5:ncol(.)) %>%
mutate(date = as... |
aec1e57ec94a3b0539e186adef9ae1e02da6a27d | 16ff96e4318d7a0a9eae27eb8900a28874dc8407 | /R/ocpu_print.R | 176f89ff1ac81daa3a5dcaa9c9d47f83ed78afb7 | [] | no_license | bfatemi/ocputils | 3f72bab3d687e5af1d51cde60de7d43fc4ffaddb | bc811c6cd3fe948f4165e39ebf1cc1ce9d3877b2 | refs/heads/master | 2020-05-16T21:41:49.825792 | 2019-05-15T12:09:29 | 2019-05-15T12:09:29 | 183,313,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,247 | r | ocpu_print.R |
#' OCPU PRINT UTILITY FUNCTIONS
#'
#' @param msg TBD
#' @param sym TBD
#' @param slim TBD
#' @param content TBD
#' @param addtime TBD
#'
#' @importFrom stringr str_split str_c str_length str_count str_trunc
#' @importFrom crayon bgWhite bold magenta bgMagenta white make_style combine_styles bgCyan
#'
#' @name ocpu_... |
c5eb0fdce8ea3cdcc741ae93b79125c752890d6c | 722737ad902763e6687e4f9b5f31a10b9ec044fe | /man/getUpperBounds.Rd | 86428c3ced08049376f1ce3f21f6ad352107e1d9 | [] | no_license | cran/editrules | 7e82f60f78173b8476bc9c92ce565f9ca335c540 | a84ffadd8103298883b59d425b3ccca4eefb8ee3 | refs/heads/master | 2021-01-01T15:51:26.200390 | 2018-07-01T20:30:03 | 2018-07-01T20:30:03 | 17,695,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 553 | rd | getUpperBounds.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getUpperBounds.R
\name{getUpperBounds}
\alias{getUpperBounds}
\title{Get upperbounds of edits, given the boundaries of all variables}
\usage{
getUpperBounds(E, xlim)
}
\arguments{
\item{E}{\code{editmatrix}}
\item{xlim}{\code{matrix} with co... |
37e87f1dde20cf5d880819dc3643d5e01ec89289 | 5f368369467d46cdc73cabdd04eb439c856caccd | /R/p.R | e7e367cc4c810554486ee253d25036921b3a4ef6 | [] | no_license | geogugal/quickmapr | a3dfca514377c749b1ff46c64745b793867c1e5a | 37410d105e1c80f55f81564fd8b2721f471a68c0 | refs/heads/master | 2023-08-16T17:09:02.787781 | 2023-08-14T19:04:58 | 2023-08-14T19:04:58 | 23,543,371 | 0 | 1 | null | 2023-09-11T16:21:36 | 2014-09-01T13:26:09 | R | UTF-8 | R | false | false | 1,286 | r | p.R | #' Pan the current plot
#'
#' Interactively reposition the current plot. Works on an existing
#' \code{qmap} object. Simply pass that object to \code{p()}. A single
#' repositioning results and the extent of the \code{qmap} object is changed.
#'
#' @param qmap_obj A qmap object. Optional, but performs better... |
cad7982db6f054ce298cb3af48937ba430069d9f | 604687e6abdbc28a02ca74a8e78cebeeef022689 | /ElectreI/R/ElectreI.R | 13cf1a4581bedd2be593fb089a0fec8f2c51860b | [] | no_license | MrSpejn/MCDA-R-modules | 3c0577e013572734b0c92e575b72ea7af8dc7e78 | 099505805250310b190baf16fe19efed64639be8 | refs/heads/master | 2020-04-09T04:33:54.620340 | 2019-01-18T17:53:53 | 2019-01-18T17:53:53 | 160,028,445 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 809 | r | ElectreI.R | ElectreI <- function(performanceMatrix, criteriaWeights, directions, indifferenceThresholds, preferenceTreshholds, vetoThresholds) {
concorndanceCoefficients <- calculateConcordanceCoefficients(
calculatePartialConcordanceCoefficients(
performanceMatrix,
directions,
indif... |
035f1c5af975f084d3a052b89259de6a6295d3e8 | 7be984ab2843a06b4e0b9fd8a09dce8d2a00cfba | /run_analysis.R | 3f830d53dc9f604b73ad7137c9d9f4cfa819f6d7 | [] | no_license | opticalcloaking/GetCleanData_CourseProject | ecf8ce9fb3f3164646da30f5a009b25ebd7548b5 | f8f1fb78673fc920bd4199cd015351e8b6bba8b7 | refs/heads/master | 2020-05-17T08:27:03.242172 | 2015-07-26T20:02:05 | 2015-07-26T20:02:05 | 39,738,229 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,390 | r | run_analysis.R | # read in features names
features <- read.csv("./features.txt", header=FALSE, sep=" ",
col.names=c("num", "feature"))
features$feature <- as.character(features$feature)
# identify features that are either a mean or a standard deviation
logicalMeanStd <- (grepl("mean()", features$feature, fixed=TRU... |
7d246e22caa57a0ed9cdba3df173c483c18a38eb | fd7113534a112f1cbbaf715087638de7b1916e42 | /religious texts.R | 739ef49df7bc2d72f6e753393eb6a72b11f374c8 | [] | no_license | sabreenaabedin/ds4001 | 9827cd546088fff59e08dd8d16a76d18c56eaf94 | 151f1a49ffaec9325c44a849e2bf513f9a0dcb13 | refs/heads/master | 2020-03-23T13:25:12.053988 | 2018-07-19T18:35:14 | 2018-07-19T18:35:14 | 141,616,975 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,845 | r | religious texts.R | ## load libraries
library(tm)
library(wordcloud)
library(SnowballC)
library(ggplot2)
## Create Corpus
{
dox <- Corpus(DirSource("C:/Users/Sabreena/Dropbox/DS/final"),readerControl = list(language="eng"))
quran <- Corpus(DirSource("C:/Users/Sabreena/Dropbox/DS/quran"),readerControl = list(language="eng"))
bi... |
50f5f3ab55e180135d8fcae636d381c940adb63b | f8ddb60bbd550d0c18104e09ee62d5ac0e295d5b | /man/heightToPeak.Rd | 5434432c2f8de23b083ce46f913717238ad3abbc | [] | no_license | OskarHansson/strvalidator | 1a52ae233b1458b01c020d6089b877a0c978b271 | 734d17dda259271ee180bd876e80152e208085f9 | refs/heads/master | 2023-07-19T01:53:57.239456 | 2023-07-16T13:25:45 | 2023-07-16T13:25:45 | 8,653,176 | 5 | 3 | null | 2020-05-04T06:35:10 | 2013-03-08T15:05:30 | R | UTF-8 | R | false | true | 845 | rd | heightToPeak.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/heightToPeak.r
\name{heightToPeak}
\alias{heightToPeak}
\title{Height To Peak.}
\usage{
heightToPeak(data, width = 1, keep.na = TRUE, debug = FALSE)
}
\arguments{
\item{data}{data frame containing at least columns 'Height' and 'Size'.}
\item... |
3012631bd716cc775c766f80693e640dfbc6c2ff | 02731a4437feddd63aff72dac76679be1c4c1e6a | /vif.R | a96e1f2a7149825e4a88ac87cadb8ac3487a4e0d | [
"MIT"
] | permissive | rnaimehaom/ToxicityModel | 6738665974ca7dcefec1db92882a7a13a894089d | d194e6c5ddf3c981171296dda4d5972bbd45199f | refs/heads/master | 2023-04-27T20:25:29.194101 | 2021-05-15T10:07:56 | 2021-05-15T10:07:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 202 | r | vif.R | vif <-function(x,df) {
y = names(df)
y_new = y[!(y %in% x)]
eqn = paste(y_new, collapse = '+')
form = paste(x,"~", eqn)
vif = 1/(1-summary(lm(as.formula(form), data = df))$r.squared)
vif
}
|
49800e0ac23cbcb94ff366271de372ae36d16b34 | 3e5d8d362b3367e4ff0e152b0242b7a285d8484f | /man/resamp_area_Param.Rd | c7bc2aae9b70a95f62f99e5727b854d2e0d6df5f | [] | no_license | mandymejia/ciftiTools | d591a6e8732dd9df17dd62d959a7a808eee16bef | 7becc99a6301c47541c883739f7fb2f0f3413e60 | refs/heads/master | 2023-08-17T06:07:35.229385 | 2023-01-23T20:00:17 | 2023-01-23T20:00:17 | 241,136,369 | 30 | 10 | null | 2023-08-21T21:47:22 | 2020-02-17T15:06:01 | HTML | UTF-8 | R | false | true | 773 | rd | resamp_area_Param.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rox_args_docs.R
\name{resamp_area_Param}
\alias{resamp_area_Param}
\title{resamp_area_Param}
\arguments{
\item{areaL_original_fname, areaR_original_fname}{File paths to the surfaces
to use for vertex area correction during adaptive resampling... |
df852795c6a1fb9adbe6e76066b83198985997cc | e6549edacf38351730ca91ead2456d50ba20f1cd | /man/InvBasis.wst.rd | ccbdfc4de068a89e2aafa9b8652eba5c0b147d6f | [] | no_license | cran/wavethresh | 96f92574f59f62f77b9b5fe5c318e27011de585c | 433dac8d2b5f3bf806530a29b5fe022fd2fe9087 | refs/heads/master | 2022-11-29T22:37:39.292801 | 2022-11-16T14:20:02 | 2022-11-16T14:20:02 | 17,700,852 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,913 | rd | InvBasis.wst.rd | \name{InvBasis.wst}
\alias{InvBasis.wst}
\title{Invert a wst library representation with a basis specification}
\usage{
\method{InvBasis}{wst}(wst, nv, \dots)
}
\arguments{
\item{wst}{The wst object that you wish to invert}
\item{nv}{The node vector, basis spec, that you want to pick out}
\item{...}{Other arguments, th... |
f26790dfb9fe59e99eee512b66ab3c72a0c96bbc | 0419c49a00967c2eae4c577a9fac79e7464b675b | /commonDEGs_test.R | 9b791789d7ed70b2950d746b23ed43f8f4b40ab3 | [] | no_license | zerland/PhD_Code | 1a6348f89e98da387dffd5fdfd9c2a104d116213 | 51d702adf900117d64a8f250879820e6d89e91de | refs/heads/master | 2023-03-18T18:59:56.892139 | 2019-03-08T11:50:15 | 2019-03-08T11:50:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,658 | r | commonDEGs_test.R | setwd("/users/clairegreen/Documents/PhD/TDP-43/TDP-43_Code/Results/GeneExpression/noMedian/")
C9 <- read.csv("C9_unique.csv")
C9 <- C9[order(C9$P.Value),]
CH <- read.csv("CH_unique.csv")
CH <- CH[order(CH$P.Value),]
sals <- read.csv("sals_unique.csv")
sals <- sals[order(sals$P.Value),]
ftld <- read.csv("ftld_unique.cs... |
ec70381fc1754ac7eaca2f87bfa62dad0c3f8bd2 | 3d87b099f242e6fb9d8b594537ee7c972691de42 | /run_analysis.R | b9a024a3a866b523df5137f0a8b8da1d83429116 | [] | no_license | fsmunoz/cleaningdatacoursera | 53dc21de08af119ef75b293bd3bdcde9a9a41268 | a507e171121eab4469c53577b86aa98c39aa775d | refs/heads/master | 2021-01-22T22:35:45.173122 | 2014-07-25T07:47:36 | 2014-07-25T07:47:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,247 | r | run_analysis.R | ### Coursera - Getting and Cleaning Data
### Course Project
###
### 2014, Frederico Munoz <fsmunoz@gmail.com>
###
### Here are the data for the project:
### https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
### You should create one R script called run_analysis.R that does the foll... |
4ca08bc815f6c6557c66cd75f698ad1e78dcb560 | 118bc327b85a3ac1b40649dd4559d5f75b913a43 | /man/gcDist.Rd | 4102bbcefbd4e12da66b90da71ada52e9483fa7e | [
"LicenseRef-scancode-warranty-disclaimer",
"MIT"
] | permissive | bobverity/bobFunctions | 2c613da2db8a3c2eaaffab0a8a5df170240891d4 | 3bce3fd92e4c30710413b02ea338ad1d987e5782 | refs/heads/master | 2021-01-17T12:36:57.566104 | 2018-10-05T10:57:10 | 2018-10-05T10:57:10 | 59,672,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 566 | rd | gcDist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main.R
\name{gcDist}
\alias{gcDist}
\title{great circle distance}
\usage{
gcDist(origin_lat, origin_lon, dest_lat, dest_lon)
}
\arguments{
\item{origin_lat}{scalar latitude of origin in degrees}
\item{origin_lon}{scalar longitude of origin i... |
588f4ea36067e1ae5323c0474b6f318fbc0e13fd | c9f3369c749e5a3cfebaa96dc1b484e72a3dd7d0 | /R/nomle.R | bcde1b5033f450711357cc84541ab6ae3c020bdf | [] | no_license | amoloudi/R-PKG-Distributions | 128ff992a10a0da915f27aa941d2f9d476ad9e9a | 20daa9657d6833cb7aff2ac9194340504c1f0270 | refs/heads/master | 2021-09-02T19:18:04.772144 | 2018-01-03T19:13:48 | 2018-01-03T19:13:48 | 115,752,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 272 | r | nomle.R | nomle <- function(x){
u = 0
v = 0
n = dim(x)
out <- rep(0, 2)
for(i in 1:n[1]){
u = u + x[i,1]
}
u = u / n[1]
out[1] = u - 1
for(i in 1:n[1]){
v = v + (x[i,1]-u)^2
}
v = v / (n[1]-1)
out[2] = v
return(out)
}
|
03c6efb21e21596f0be8e3f0d8f1608fb4797c83 | 136eb41b20d1869345c052ffea61e43f0b4f4840 | /man/plot_hazard1.Rd | 0a2f3e1a52c355b7391359145764813cd78c64b6 | [] | no_license | andybega/spduration | d24964b65492c56242061392405a3872a8467e99 | 6154bfcff9d172a3598d7c73058c1d08f1b65d18 | refs/heads/master | 2023-06-21T18:37:24.942725 | 2023-06-21T07:16:28 | 2023-06-21T07:16:28 | 6,479,313 | 4 | 2 | null | null | null | null | UTF-8 | R | false | true | 426 | rd | plot_hazard1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.spdur.R
\name{plot_hazard1}
\alias{plot_hazard1}
\title{Plot conditional hazard rate}
\usage{
plot_hazard1(x, ...)
}
\arguments{
\item{x}{class "spdur" object}
\item{...}{passed to \code{plot_hazard}}
}
\value{
NULL, plots.
}
\descripti... |
59d2cdf4d96db9a9b39837df613d5278d135f38e | f550d59dfeb0e70fe46102e2b09cedaeffe08a90 | /Advanced-R_exercises/Exercise_page_213.R | 7e8f62aeb69c8c7d3ea17656291cd753532862b2 | [] | no_license | cimentadaj/random-stuff | f877ed9223583565f0d338850a1d04e79a7a3686 | 2f883a7c5fcb2001d0195a760615049bfb96279a | refs/heads/master | 2021-03-08T16:53:27.168732 | 2018-02-01T10:11:54 | 2018-02-01T10:11:54 | 55,167,526 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,912 | r | Exercise_page_213.R | # 1.
vapply(mtcars, sd, numeric(1))
num <- vapply(iris, is.numeric, logical(1))
vapply(iris[num], sd, numeric(1))
# 2
# Because certain objects might have two classes and sapply returns a vector. In a case
# like this one, you will obtain a list instead of a a vector.
df2 <- data.frame(x = 1:10, y = Sys.time() + 1:... |
4acdc3896dcd2fbcf38989a9948bba0edb6ab4db | 51553e75514bbcc59310f612a10b0e9c47f5b210 | /scripts/ldshrink_ld.R | 7adccfd70f95caba5a3ca9aa3c3c6449c8dd9161 | [] | no_license | CreRecombinase/ptb_workflowr | b5a63ff13037893c627e96233097332f322d50f2 | d4c05d1b04888f397370a3172c57a15a2f867250 | refs/heads/master | 2020-05-18T15:05:04.640004 | 2020-05-08T18:24:46 | 2020-05-08T18:24:46 | 184,487,007 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,007 | r | ldshrink_ld.R | library(dplyr)
library(ldmap)
library(ldshrink)
library(EigenH5)
shrink <- snakemake@params[["shrink"]]
if(is.null(shrink)){
doshrink <- TRUE
}else{
doshrink <- shrink=="shrink"
}
bim_df <- read_plink_bim(snakemake@input[["bimf"]]) %>%
mutate(snp_id = 1:n(),
ldmr = snp_overlap_region(snp_struct, ldetect... |
d385a6eb2bfcb1e487d86a1fa0df5c9dc094f67e | 05e321568f7d4f3b5bee75a23d4c5c2378a72022 | /semtiment analysis.R | d75462c81a7779b11f81d6bb34e1de14ca1e0f6a | [] | no_license | Wisienkas/Datascience | 5c154ae75d7d9e0b7cc074d4b963c37458a8a193 | 2f77a6bfadd746de3270c2e2079af4e31c36d406 | refs/heads/master | 2016-08-12T09:42:50.763329 | 2016-01-14T07:38:38 | 2016-01-14T07:38:38 | 49,534,793 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,256 | r | semtiment analysis.R | # Load data
data <- read.csv2("testdata.manual.2009.06.14.csv", sep = ",", quote = '"')
names(data) <- c("fromid", "toid", "date", "userfrom", "userto", "msg")
# Ectract text msg and write to file
text <- data[, "msg"]
write.csv(text, file = "temp/data.txt")
install.packages("tm")
install.packages("wordcloud")
libra... |
9d0689009c8450a397c66fc8440b5a06410aa413 | bb8dbb7667c71f5c2376b01412becbf2bee4fc6a | /cachematrix.R | f968ee05c9bf10c69a8947e7e3056ac7b15c243b | [] | no_license | BobCDell/ProgrammingAssignment2 | 96d702947cb0dc946cfffdc2dd6911b1fa848dcc | ba45b6362cf3c0a9d82693436a5e7f65fa4bd9c9 | refs/heads/master | 2021-01-21T08:01:52.682877 | 2016-04-24T19:16:25 | 2016-04-24T19:16:25 | 56,986,782 | 0 | 0 | null | 2016-04-24T18:11:12 | 2016-04-24T18:11:10 | null | UTF-8 | R | false | false | 2,119 | r | cachematrix.R | ## The functions makeCacheMatrix and cacheSolve create a special "matrix" object (makeCacheMatrix) that can
## cache its inverse which is then retrieved by cacheSolve.
## makeCacheMatrix: This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(mat=matrix()){
... |
58dbdd70340186821821d6d631df0f6055ff559c | 82d5e4e6fdf1969172bafaf4f66c39d8458eba26 | /man/Deltarho.Rd | 51a1587a9d8f5d845f1b841a5551907b652f94e3 | [] | no_license | victormesquita40/DFA | b9de8f44354d2efb396586adc97e8bd0253b4816 | 01b2848b3b9be8905dee11795d6ae9b7c81c948d | refs/heads/master | 2023-01-07T11:19:30.393913 | 2020-11-11T18:52:58 | 2020-11-11T18:52:58 | 271,644,851 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,298 | rd | Deltarho.Rd | \name{Deltarho}
\alias{Deltarho}
\title{
Delta Amplitude Detrended Cross-Correlation Coefficient (DeltarhoDCCA)
}
\description{
Applies the Detrended Cross-Correlation Coefficient Difference (Deltarho) to nonstationary time series.
}
\usage{
Deltarho(file,file2,file3,file4,scale = 2^(1/8),box_size = 4,m=1)
}
... |
d633598a5f44ee25ba82aaeec5db9f95a45378bb | bbf2e61a2a1a9932012fbf260883ba517fb0001c | /cachematrix.R | 2bd6da3fe2d46f7f1ce2294b8179b5534f2e9e1f | [] | no_license | orfalchechor/ProgrammingAssignment2 | 30340ae80468d1dbb5ef7c47f855eb856cb2050f | 04c9351c1b401495437666acc160bfcf4fb3cc08 | refs/heads/master | 2021-01-18T10:32:23.351439 | 2016-06-04T17:45:04 | 2016-06-04T17:45:04 | 60,422,029 | 0 | 0 | null | 2016-06-04T17:26:17 | 2016-06-04T17:26:16 | null | UTF-8 | R | false | false | 1,561 | r | cachematrix.R | ## Description:
## makeCacheMatrix create an object contain a matrix, which cacheSolve
## can consume and calculate its inverse (provided the matrix invertible).
## Made possible by example code provided by course.org from repo rdpeng.
##
## Example:
## z <- makeCacheMatrix()
## z$set( c(1,-1,4,6), 2 )
## ... |
34703995d63b64493b079540213f3e63da8fb6f1 | aa13ffbd51383f778dc7fe135af9615ffeae17b0 | /assignment/HW01/HW01_63130500100.R | da8f831263abfbd7536418f58128019ec0d142f0 | [
"MIT"
] | permissive | jirasin-c/020-Video-Game-Sales | d961cedaebe9c61fc50cce1583a068b605a2c1e7 | 5912fecb33cb1d255253d463433a4356be62396d | refs/heads/main | 2023-08-23T13:17:44.087765 | 2021-10-22T11:00:31 | 2021-10-22T11:00:31 | null | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 1,599 | r | HW01_63130500100.R | # Example 0
x <- 1
y <- 2
print(x+y) #3
#Exercise 1
num = c(10.4, 5.6, 3.1, 6.4, 21.7)
avr = mean(num) #average
avr #9.44
sum <- sum(num) #sum
sum #47.2
med <- median(num) #median
med
sd <- sd(num) #sd
sd
var <- var(num) #variance
var
#Exercise2
# List of Marvel movies (Order by Marvel Phase released)
names <- c... |
869af6750fb241483e7addff802e2f5d78a37324 | c2a3647efa1093e8a8d72b8ec65f2ead24060336 | /inst/unitTests/test_TarSeqQC.R | 002cb6b38497567f56f54f5f43706324d560769e | [] | no_license | gamerino/TarSeqQC | 2363d1ffe1dadcc6f14e552840f90fd79b4a1a30 | ebdf8ca544f5d5073966cf0a16c912ceeac7202b | refs/heads/master | 2021-07-15T03:28:58.735473 | 2020-01-31T11:24:13 | 2020-01-31T11:24:13 | 101,313,918 | 1 | 0 | null | 2017-08-24T16:06:36 | 2017-08-24T16:06:36 | null | UTF-8 | R | false | false | 27,574 | r | test_TarSeqQC.R | # rm(list=ls())
# library("RUnit")
# library("TarSeqQC")
# library("Rsamtools")
library(BiocParallel)
##-----------------------------------------------------------------------------
##TargetExperiment-class Tests
##-----------------------------------------------------------------------------
##Empty object test: Does... |
9002d320e88ebebee319e615486db8c0ee080634 | 1b427294f22048b45ef77636d91152a6012eb95c | /assists/01-scrape.R | b3fe57b40fe577f65f50933874fcded8e7250a9b | [] | no_license | willhua03/d3 | 4fd35815b9ce863e3dfd2498d4005be00f1953b1 | f0e5b977c82fe7752348e225b4d15f8ffd702649 | refs/heads/master | 2020-03-26T14:54:06.311959 | 2017-06-22T18:59:17 | 2017-06-22T18:59:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,705 | r | 01-scrape.R | library(mysportsfeedsR)
library(rvest)
library(stringr)
# authentication ----------------------------------------------------------
# authenticate_v1_0('username', 'password')
# functions ---------------------------------------------------------------
create_gameid <- function(entry) {
paste0(str_replace_all(... |
043360b6edf6666423cbb0bf29551f94535b90e3 | e2b778b98bd747aa48ac247b09b324bd4a7cae00 | /Definitive Data Generator.R | 821a769985a1659bd9abea525fd54bce402454cc | [] | no_license | Mait22/TQuant2017_G2 | 9f3ed0b779d6f1fcefed8799420083434eb49d36 | d64ba771ddc0210c29b8efdd65b6867a2bde9c18 | refs/heads/master | 2021-01-18T15:55:38.713932 | 2017-03-31T22:35:43 | 2017-03-31T22:35:43 | 86,695,595 | 0 | 3 | null | 2017-03-30T14:26:15 | 2017-03-30T11:36:21 | R | UTF-8 | R | false | false | 1,120 | r | Definitive Data Generator.R | gen4 = function(x1,x2,x3,x4){
n = length(x1)
return( 0
+3*x1
+ 3*x1^2
+ 1*x2
+ 1*x1*x2
+ 1*x1*x3
+ 1*x2*x3
+ 0.008*x4
+ 0.0001*rnorm(length(x1),0,2*n/10)*sin(x1)*n^2
+ 0.0001*sin(x2)*runif(n... |
807237ac11e88bdb5940229bc223800585db3558 | 53beba2f6c2bf43378da27f51bd11f14f06efc24 | /plot4.R | fafb9d33812d178fc14524c36605a7e02014d3ca | [] | no_license | ahmedelgendycoursera/ExData_Plotting1 | 92b4e282f5edf7337b4f4ccdeb7ba99013b71226 | 6c296ba5f1185c699f84db34096748c614397df3 | refs/heads/master | 2021-01-20T17:23:32.098343 | 2016-01-10T22:06:08 | 2016-01-10T22:06:08 | 49,340,006 | 0 | 0 | null | 2016-01-09T20:24:34 | 2016-01-09T20:24:33 | null | UTF-8 | R | false | false | 1,446 | r | plot4.R | #Read all data but save in the workspace part of based on a condition
data <- subset(read.table(file="household_power_consumption.txt",header = TRUE,na.strings = "?",sep=';'),(Date=="2/2/2007"|Date=="1/2/2007"))
#Format Date column from d/m/y format into Date class format to be mergable with Time column.
data$x1 <- as.... |
553363be574a20c2d4720d08731f9ffb09d74cd4 | 0d92a20f2f35dcfcd572c52f5e3b4184279bfa04 | /seprcp26_posneg.R | aca125d221aeaf6da00f0d55419e9c8183856b48 | [] | no_license | richardcode/cmip5_anal | a578158209a67e2cae796a1d12d71d5afc8f1661 | 1d5588f177ac4d1eeb92d2d958a86b819724e1b0 | refs/heads/master | 2020-06-16T14:31:59.559643 | 2017-04-12T14:31:03 | 2017-04-12T14:31:03 | 75,091,699 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,546 | r | seprcp26_posneg.R | source("tcre_var_functs.R")
#Load in the data from the CMIP5 ensembles
cmip5_data <- read.csv('./Data/ESM_cmip5_tempems.csv')
#Get models that are both in RCP2.6 and 4xCO2
rcp26_data <- cmip5_data[cmip5_data$Scenario=='RCP26',-c(6:(2004-1850+6))]
warm_threshs <- c(0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)
for (warm_thre... |
1cee29b8622ea5b404a6efa7e00a1bb172a7ebcc | f8e2de8eac41b7049c161240c16907ae7f3f6000 | /linearRegression/linearRegression.R | f3f0fe337f13b1bb09fdf522c14c98beca576f88 | [] | no_license | moriano/machineLearningExamples | a3394df27e43b2dc96101916ad0e7b4764bac649 | 7fb5d16bd30cdc122cd01832ee964ed7a23f13a5 | refs/heads/master | 2021-01-14T11:35:07.265197 | 2016-08-14T00:02:17 | 2016-08-14T00:02:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,605 | r | linearRegression.R | all_data <- read.csv("sampleData1.csv")
# Quick exploration of the dataset
print(paste("Number of rows", nrow(all_data)))
head(all_data)
# First, lets split the set, 70% is for traing, 30% for testing
train_data <- all_data[0:70, ]
test_data <- all_data[71:100, ]
# Train a simple model
model <- lm(price ~ x + y, trai... |
87a34bf1fa368635585cb73d295773cc3e2b8b4c | 3830551a6c5213a309e9d4aa40fd54131c5fdcbb | /tests/testthat/test-datatable.R | bea46a341f1bb7c99edf69052799c3b8bfea5c97 | [
"MIT"
] | permissive | b-rodrigues/paint | 765df57b266bdc078c9be3da19a44548f566a630 | 163e333d0ce785b797ea57389176e037817fa24f | refs/heads/master | 2023-07-09T22:00:49.768581 | 2021-08-08T10:56:54 | 2021-08-08T10:58:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 466 | r | test-datatable.R | test_that("data.table", {
rlang::with_options(
cli.num_colors = 256,
paint_n_rows = NULL,
paint_max_width = NULL,
paint_palette = NULL,
paint_align_row_head = NULL,
paint_dark_mode = NULL,
.expr = {
pp_dt <- data.table::as.data.table(palmerpenguins::penguins)
expect_snapshot(pa... |
f59f1cd01e2b3fbd780775810285c86b8a00f257 | 2ab1525d7aaccd52b3d6a205310d7f9a31e9af48 | /R/compileTimeVaryTransProbs.R | f8496ac8ccd2497cf582ccc9c4a221c82d4e147a | [
"CC-BY-4.0",
"CC0-1.0"
] | permissive | KevinSee/DABOM | bbb3e8f03f3d8179c225fcd35588b8c90c1ba927 | 6c868732b7350be82600f50f9a9ccdb99e047f23 | refs/heads/main | 2023-08-27T03:57:23.327826 | 2023-08-25T19:21:54 | 2023-08-25T19:21:54 | 103,584,269 | 1 | 6 | NOASSERTION | 2023-08-25T19:21:55 | 2017-09-14T21:38:53 | R | UTF-8 | R | false | false | 2,010 | r | compileTimeVaryTransProbs.R | #' @title Compile Time-Varying Transition Probabilities
#'
#' @description Extracts the MCMC posteriors of time-varying transition probabilities for a DABOM model. The time-varying parameters should be set up so that they are organized as an array with the first dimension corresponding to the fish origin, the second to... |
939635b2b92465bcbf29e46dd1a3ccfde3723fda | e02d833b8e59bc008dda8bb42be31aa89ee7255a | /man/Franken.Rd | b31d2eee4dad9489819aad2606c2b1c8b0bf710b | [] | no_license | brian-j-smith/MRMCaov | 1d437daf62f2619f72a4ab5bb0c3fa71137f5a18 | d79e49bcef42ef2a17195aae84ca84ae569f8702 | refs/heads/master | 2023-01-29T10:40:59.924662 | 2023-01-18T21:05:53 | 2023-01-18T21:05:53 | 196,109,727 | 10 | 2 | null | null | null | null | UTF-8 | R | false | true | 864 | rd | Franken.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{Franken}
\alias{Franken}
\title{Multi-reader multi-case dataset}
\format{
A data frame with 800 rows and 5 variables:
\describe{
\item{reader}{reader identifier}
\item{treatment}{treatment identifier}
\item{c... |
872582ab4d598a2517fd27de2effe73b64936f0e | 5213fc5250aeb751d6dbefcd35aa9b836d0c7c66 | /run_analysis.R | cb1b39f7ac2f433b323b84e43963aad43f24a4f6 | [] | no_license | skallinen/Coursera-Getting-And-Cleaning-Data | 62045baa38f2aea103f5b1ed3fd3feded3020352 | 8771a19c3fd538f0bb1c69a455bfc2a27bd5030c | refs/heads/master | 2020-04-06T07:10:06.800402 | 2014-10-27T04:42:06 | 2014-10-27T04:42:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,990 | r | run_analysis.R | ## Create one R script called run_analysis.R that does the following:
## 1. Merges the training and the test sets to create one data set.
## 2. Extracts only the measurements on the mean and standard deviation for each measurement.
## 3. Uses descriptive activity names to name the activities in the data set
## 4. Appr... |
7ae863f67f878d4f5e880411483e34b044a3ea30 | f3da1980b138389d08e5e5b0c3cb76dacbc0e1b6 | /workflow.R | 98128d63e1a29850f473fdd02b37a064b38f24d8 | [] | no_license | brianlle/PSPG_245B | 64cdc05281d141c2a3463c68ed23ad336e32a7c0 | 1b25afbc7d17d1f7540091569de4e6e62564807b | refs/heads/master | 2020-04-28T07:17:56.745296 | 2019-03-12T20:36:43 | 2019-03-12T20:36:43 | 175,086,816 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,237 | r | workflow.R | #R3.5.2
#Adaped from Dr. Bin Chen's drug repositioning pipeline
#This is a simplified version of a pipeline used to predict drug hits for a given disease
#Depending on your context, you can also compare different phenotypes, responders vs. non-responders, mutation vs. wild type, etc
setwd("~/PSPG_245_Test/")
#install... |
d6becde3e81440a9d32ac864b5d131a5361109be | 35dd3dd5dec0f7ae8e0fb99adf95f9784294f58c | /createMaps/Erdbeben/Tiefe.R | 7f4f625479a1f342f9d88921fe09f15195955be0 | [] | no_license | ReneNyffenegger/Hydroplattentheorie | cdb76a165fdaf5372643919503c520fa076b1fb7 | 4a52f5e48b15bf31c0b7b792b9fd12c0d4b024c1 | refs/heads/master | 2021-01-17T01:14:21.933398 | 2018-01-13T08:07:55 | 2018-01-13T08:07:55 | 59,203,104 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 370 | r | Tiefe.R | quakes <- read.csv("2015_Magnitude-groesser-gleich-5.csv", stringsAsFactors = FALSE)
depths <- quakes[c('depth')]$depth
x11()
hist(depths, main='Erdbebentiefen (2015, Mag >= 5)', xlab='Tiefe (km)', ylab='')
z <- locator(1)
depths <- depths[depths > 150]
hist(depths, main='Erdbebentiefen (2015, Mag >= 5, Tiefe>150 ... |
f02ed6cc1be75518858f1f8a92e9e62df5e78441 | 61e67ee6b59b4649a8b3d1643edc98350a822d17 | /R/get_recipe_nutrition.R | a2b5708be14f206336ead9102e01b33b30296ae6 | [] | no_license | cynthiawang315/SpoonacularAPI | fd7ac3f5b230cf86da398f5f1bd3464acf870475 | 3483dc9628ee5e098e21ea9ca9059095bd18b685 | refs/heads/master | 2021-01-06T01:17:02.024747 | 2020-02-17T19:26:19 | 2020-02-17T19:26:19 | 241,187,235 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,335 | r | get_recipe_nutrition.R | #' Get the nutritional information of the recipes.
#'
#' This functions gets nutritional information of one recipe.
#' @param key API key
#' @param recipe_id The recipe ID.
#' @return The nutritional information of the recipe entered.
#' @examples
#' get_recipe_nutrition(key = Sys.getenv("SPOON_KEY"), recipe_id = "7536... |
b5306ec97ef34062153536b51c79ea0c830189a4 | 9bd22c31db29eec72a209c9cc2a5c721ce03b20a | /admissions/Kelvin/script2 (Autosaved).R | 0c896a604618905d1192fd1e98eccd2549e292e7 | [] | no_license | kelvinabrokwa/wm-stats-blog | 9fdb08939e5529e33781100cb2b95125cd482f89 | 5fbfe494bd916d8e5afa96686af2e618d57c3de4 | refs/heads/master | 2021-01-16T18:02:53.775614 | 2014-12-05T14:34:23 | 2014-12-05T14:34:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 249 | r | script2 (Autosaved).R | setwd('/users/kelvinabrokwa/documents/repositories/wm-stats-blog/kelvin')
library(googleVis)
data.long <- read.csv('agg_data_long.csv', header=TRUE)
View(data.long)
motion = gvisMotionChart(data.long, idvar="variable", timevar="Year")
plot(motion) |
641960003e87013d942d2a065438876331aa3c1b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/comtradr/examples/ct_commodity_lookup.Rd.R | 199ad2b36e08e7a12bacdf14c6ade4edc6ff13f1 | [] | 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 | 552 | r | ct_commodity_lookup.Rd.R | library(comtradr)
### Name: ct_commodity_lookup
### Title: UN Comtrade commodities database query
### Aliases: ct_commodity_lookup
### ** Examples
# Look up commodity descriptions related to "halibut"
ct_commodity_lookup("halibut",
return_code = FALSE,
return_char = FALSE,
... |
e1e991052e86d023a51b33096bb88ff7ffab230f | fc266ad2e073b99aff20b6fc718c0e9b27d1c617 | /man/rowMax.units.Rd | 5a370eef5a08431d3cecea436593613e3920490f | [] | no_license | ttriche/oldGridExtra | 59676ed24529f7fc4ae7ca768cc660e01476eb72 | c28db2479f9e9d07afbbf51d508a60570eb8d55c | refs/heads/master | 2016-09-06T14:39:49.324675 | 2015-07-16T19:49:42 | 2015-07-16T19:49:42 | 39,217,023 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 329 | rd | rowMax.units.Rd | \name{rowMax.units}
\alias{colMax.units}
\alias{rowMax.units}
\title{rowMax.units}
\usage{
rowMax.units(u, nrow)
}
\arguments{
\item{u}{list of units}
\item{nrow}{nrow}
}
\value{
a vector of units
}
\description{
calculates the max of a list of units arranged in a
matrix
}
\seealso{
\code{unit.c}, \code{... |
1485b5bf8f4d1f4c05b14de710a23c0201a002ee | 0414e310bf964d10dfe0ca2c5fe0c487f04693c1 | /figures/scripts/fig4-tp53-telomerase-panels.R | 48c974c2a70e61413d2b12709f0fff3a1b3b1342 | [
"CC-BY-4.0",
"CC-BY-3.0",
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | permissive | jharenza/OpenPBTA-analysis | 90838d0af9fa438a00148d6641fb7d65497ca2fd | 489d729cde4318654a26b0a9ef0dee6d706352cd | refs/heads/master | 2022-05-04T14:55:22.800848 | 2022-04-17T18:26:40 | 2022-04-17T18:26:40 | 305,783,266 | 0 | 0 | NOASSERTION | 2022-04-04T22:28:24 | 2020-10-20T17:25:03 | HTML | UTF-8 | R | false | false | 14,329 | r | fig4-tp53-telomerase-panels.R | # S. Spielman for ALSF CCDL 2022
#
# Makes pdf panels for reporting TP53 and telomerase results in main text
library(tidyverse)
# Establish base dir
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
# Declare output directory
output_dir <- file.path(root_dir, "figures", "pdfs", "fig4", "panels")
if (!dir.... |
3cb49dca579542fc2cc370256964ee9e39b0ff60 | 84526595f5bb52ad787c5bcd4eac5ee6af937ffb | /R/enrichment_clustperturb.R | 1722b27e1c3a7043cd5c0a0d409be7018e56ff75 | [] | no_license | GregStacey/ppicluster | d2b9cb378603c627436d1d9fd454a3d6b110bd5f | 912c31dcc74e2bc9f8ed5c5d248b1f9cab54d019 | refs/heads/master | 2021-07-03T16:31:37.156136 | 2020-09-01T00:34:12 | 2020-09-01T00:34:12 | 160,438,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,329 | r | enrichment_clustperturb.R | source("functions.R")
source("clust-perturb-tool/clust-perturb.R")
source("clust-perturb-tool/functions.R")
# binarize corum
fn = "../data/allComplexes.txt"
corum = as.data.frame(read_tsv(fn))
corum = corum[corum$Organism=="Human",]
ints.corum = as.data.frame(read_tsv("../data/interactomes/corum_pairwise.txt"))
unqpr... |
7a431d1498831efc108b599bb5de897f6e3aff7e | 3ccae4dc240fd39478a93f2c8d58d1895b2c35f5 | /R/Main.R | dc93c6cc7f8e67d6d42fcf5a0f0a2fc210d3e6ee | [] | no_license | ohdsi-studies/CovariateImbalanceDiagnosticsEvaluation | 849d5fab37069d2d2e6d6729e0642df3b1b6b787 | 8141a7d36dd11d6c2f117e12f322ea0ade9935da | refs/heads/master | 2023-06-04T08:35:01.602840 | 2021-06-15T05:11:07 | 2021-06-15T05:11:07 | 346,760,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,936 | r | Main.R | # Copyright 2021 Observational Health Data Sciences and Informatics
#
# This file is part of CovarBalDiagEval
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... |
e234100119f6cdf31dd5794cb592ca6872e553b1 | a77b7389641ff5078d1c5bd4bb2404c100e84203 | /cachematrix.R | 4403801442eaeb7af485bdce330d3561e5b85b40 | [] | no_license | jaydoc/ProgrammingAssignment2 | 6e9ae423fc178a2b6646e2c310fa43910854cf61 | dbe9badf0d37951aaf2002903cf8a4bff4432f1d | refs/heads/master | 2020-12-03T09:19:53.053861 | 2014-04-27T01:20:43 | 2014-04-27T01:20:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 928 | r | cachematrix.R | ## The following function creates a list (in this context, a matrix) containing a function to
## set and get the values of the matrix and its inverse.
## function that creates a special matrix which can then cache its inverse
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
... |
93deb8f088ea8971ed7f6565c5b6816eea9a1b1c | dd9f94ac6181401f0ea48e78f9731f1c337b85ca | /tests/testthat/test_est_V.R | c7badd73fcde6663402981a507d06df44954e670 | [
"MIT"
] | permissive | unagpal/susieR | 67de44e321ec21599d56342df409fa2c3dc8eef1 | 46d37a49ccd680b1ff5fa9dfecf8928ca09018cf | refs/heads/master | 2023-07-02T20:50:26.853819 | 2021-07-22T21:13:29 | 2021-07-22T21:13:29 | 276,458,113 | 0 | 0 | MIT | 2020-07-01T18:52:45 | 2020-07-01T18:52:44 | null | UTF-8 | R | false | false | 405 | r | test_est_V.R | context("test_est_V.R")
test_that("est_V has V nonzero for the debug case", {
debug = readRDS('est_V_debug.rds')
betahat = debug$b
shat2 = debug$s2
prior_weights = debug$pw
V_debug = est_V_uniroot(betahat, shat2, prior_weights)
betahat_V0 = rep(0, 512)
shat2_V0 = rep(1, 512)
V_0 = est_V_uniroot(betah... |
ce23515471e457a84f501e2aa0cc9c36332fe4f3 | 852b1aae6ad8138dc164eafbf4045e3ea5de87c8 | /man/add_constant_metadata_resource.Rd | 996eb4e31a78a914fe0b390f3d0a82e5c447cab0 | [] | no_license | PinkDiamond1/fin2ddh | 35bafcb1a3de815d11554d87f85fb45b3d6262d0 | 40ffcf8f79ff3bb9845b742e57ddb1fbf5f82e66 | refs/heads/master | 2023-03-17T00:52:19.799023 | 2019-05-08T01:24:10 | 2019-05-08T01:24:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 478 | rd | add_constant_metadata_resource.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_constant_metadata_resource.R
\name{add_constant_metadata_resource}
\alias{add_constant_metadata_resource}
\title{add_constant_metadata_resource}
\usage{
add_constant_metadata_resource(metadata_list)
}
\arguments{
\item{metadata_list}{list... |
a4f4de784380f6a96b0e061248ec8a56577c82a8 | 4201e9b754760dc35fc0aeef9df5a8b9d801c47f | /bin/R-3.5.1/src/library/stats/man/family.Rd | 62b7765caa0cdc30b81545d6518f00d8a9f3528b | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only"
] | permissive | lifebit-ai/exomedepth | cbe59cb7fcf2f9183d187f8d466c6620fb1a0c2e | 5a775ae5e2a247aeadc5208a34e8717c7855d080 | refs/heads/master | 2020-03-27T12:55:56.400581 | 2018-10-11T10:00:07 | 2018-10-11T10:00:07 | 146,578,924 | 0 | 0 | MIT | 2018-08-29T09:43:52 | 2018-08-29T09:43:51 | null | UTF-8 | R | false | false | 9,595 | rd | family.Rd | % File src/library/stats/man/family.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2015 R Core Team
% Distributed under GPL 2 or later
\name{family}
\alias{family}
\alias{binomial}
\alias{gaussian}
\alias{Gamma}
\alias{inverse.gaussian}
\alias{poisson}
\alias{quasi}
\alias{quasibinomial}
\alias... |
26d6eef4838ff9ad6e18a38c0edc625794e18347 | aa734a9629d3e0e942bdf08fb1bed6a5b2d1031b | /cachematrix.R | 8eedaf265b3ccf4f41f6f72b01929fc8c6f74e54 | [] | no_license | acgadala/ProgrammingAssignment2 | 126fae138a15105da6ad530c36d0aa1b6608b5d3 | 3545cc56cf515c4f2ce91fc99759dfdba1407df6 | refs/heads/master | 2021-01-15T23:35:03.538902 | 2014-08-24T17:55:01 | 2014-08-24T17:55:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 758 | r | cachematrix.R | ## These functions will calculate and cache the inverse of a matrix.
## Creates a special "matrix" object to cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
i<-NULL
set<-function(y){
x<<-y
i<<-NULL
}
get<-function() x
setInverse<-function(inverse) i<<-inverse
get... |
9f5d2ba133f754d95d1f322349ebcca658d882be | 6eb980a9312f50491782a92875a52618dfbcffc6 | /tests/testthat/setup-vcr.R | 41522a15773edf52a377250650d1dd01b7e0047c | [] | no_license | cran/deepdep | d9636bb8dd22b64e86b893adb2c0873ea87068c4 | 74b4aafcb30d8d1bde5e212c6187d052180a7e94 | refs/heads/master | 2023-03-05T18:45:35.804669 | 2023-02-20T23:10:05 | 2023-02-20T23:10:05 | 245,601,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 250 | r | setup-vcr.R | if (requireNamespace("vcr", quietly = TRUE)) {
library("vcr") # *Required* as vcr is set up on loading
invisible(vcr::vcr_configure(
dir = vcr::vcr_test_path("fixtures"),
verbose_errors = TRUE
))
vcr::check_cassette_names()
}
|
a2abaef24b9999ebedd1fcc30e14159b9bee0fc5 | 6aec1a0100c3d9f09f3874ed43c72d254c8459f1 | /Scripts_Booth_et_al_2018/batchReadLengthDistribution.R | 40d8a52446145e7980c1d15a936f07bef3de4175 | [] | no_license | summer-yangqin/Pombe_PROseq | 81050f8a0d5dbca4ebc3217143e511e4e2725dea | 9d38b91cb2c03f615a78b1f41f1bdc765de11c04 | refs/heads/master | 2023-03-19T18:53:09.115451 | 2018-04-24T19:17:49 | 2018-04-24T19:17:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,064 | r | batchReadLengthDistribution.R | ## to use this script, just change the logpath to the directory where all of the clipLengthCounts.txt files are for an alignment.
## also change the name of the file to be output (last line of the script, while running plotting function)
## the functions will not be screwed up by other files present as it looks for " ... |
95d04397990152141e151a5cb34ee4eafb6ee78e | 0a906cf8b1b7da2aea87de958e3662870df49727 | /distr6/inst/testfiles/C_EmpiricalMVPdf/libFuzzer_C_EmpiricalMVPdf/C_EmpiricalMVPdf_valgrind_files/1610036452-test.R | 095431a7c324c0d66c6605f394b093749e04cc7b | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 322 | r | 1610036452-test.R | testlist <- list(data = structure(c(7.40700664744977e-304, 0, 1.24499602095536e-319, Inf), .Dim = c(4L, 1L)), x = structure(c(1.23802521124132e-308, -Inf, 5.79391781278816e-307, -Inf, 6.14293298952177e-183, NA, 6.14293298947794e-183), .Dim = c(7L, 1L)))
result <- do.call(distr6:::C_EmpiricalMVPdf,testlist)
str(resul... |
48c6b8ca82b704a1420fff5d125250cb0aa7f599 | 9397a453a4b9c4ddd9988235fe4a8ee6720358c1 | /Paper2/test_droppoints.R | fb1089cd755a36b4294c046a0c89bc72266643c1 | [] | no_license | komazsofi/myPhD_escience_analysis | 33e61a145a1e1c13c646ecb092081182113dbce3 | 5f0ecdd05e7eaeb7fce30f0c28e0728642164dbc | refs/heads/master | 2021-06-04T21:39:58.115874 | 2020-06-18T12:59:53 | 2020-06-18T12:59:53 | 119,659,750 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,465 | r | test_droppoints.R | "
@author: Zsofia Koma, UvA
Aim: Modelling drop out points
"
library("lidR")
library("rgdal")
library("dplyr")
# Set working dirctory
workingdirectory="D:/Sync/_Amsterdam/03_Paper2_bird_lidar_sdm/DataProcess_Paper2_1/"
setwd(workingdirectory)
#Import
las=readLAS("tile_61.laz")
options(digits = 20)
head(las$gpstim... |
5be6ea4b83eb1258e5d060f21321dfcdfeb2198b | 71129b1c03eed2abdd67fc2b52b57874bae49f45 | /collapsibleTree/R/collapsibleTree.data.frame.R | 41af5c2381880244df54015dae65b84ca7931a83 | [] | no_license | Bastiaanspanjaard/LINNAEUS | 0eb880d8e581f870b58d69cea7060822baf8564a | 6c86288e8e684d77f5499249023e7157d0c440dc | refs/heads/master | 2022-11-13T10:48:40.584477 | 2020-07-03T14:56:07 | 2020-07-03T14:56:07 | 255,870,978 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,805 | r | collapsibleTree.data.frame.R | #' @rdname collapsibleTree
#' @method collapsibleTree data.frame
#' @export
collapsibleTree.data.frame <- function(df, hierarchy, root = deparse(substitute(df)),
inputId = NULL, attribute = "leafCount",
aggFun = sum, fill = "lightsteelblue",
... |
5df665d82fa00c33987a8349a6ec20f0720b132c | d3e67d0e9d5c399e26f8f485fb219799aabe6e93 | /clasificador_som.r | 9e377f035e657d3eb80d4c6a8c09988379338c24 | [] | no_license | igmalta/algoritmo_som | ee58c4ff72532fec2d8a9d9a53071b7cdd6afb2d | 11bc6f5c85fc0ddd2d1940c2894294374e9ee977 | refs/heads/master | 2023-04-22T18:11:53.780664 | 2021-05-04T22:46:11 | 2021-05-04T22:46:11 | 364,394,622 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 15,685 | r | clasificador_som.r | rm(list=ls())
gc()
# Librerías
# ==============================================================================
if (!require("data.table")) install.packages("data.table"); library("data.table")
if (!require("ggplot2")) install.packages("ggplot2") ; library("ggplot2")
if (!require("dplyr")) install.packages("... |
ccacb4745aa3b912ce4255cce7e0aaabdeb91ddd | 17fad5a66a5335387480a1767d742e2213085dd1 | /R/5. Logistic Regression/Case Study 1/Credit Card Prediction.R | c251f15ca6a1826c029f8c9a48fc2f1ca2f20a4e | [] | no_license | abhijitvp/DS | e3c2bd36d5974653d75bb74b525f7b93ede81b87 | 320ba0b9b8aeff6cb91f1579a72acfd2959ad907 | refs/heads/master | 2020-04-01T23:13:32.861798 | 2018-12-13T06:26:32 | 2018-12-13T06:26:32 | 153,749,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,285 | r | Credit Card Prediction.R | # Logistic Regression
#Problem Statement:
#==================
----------------------------------------------
#Packages Needed
#dplyr
#car
#Install if not exists
#dplyr
if("dplyr" %in% rownames(installed.packages()) == FALSE) {
install.packages("dplyr")
}
#car
if("car" %in% rownames(installed.packages()) == ... |
f7b499a38007d8e9d2cbb1db4ca6d740d22a223c | e10648f044122f75e85e49f0d25141ccf218e276 | /Package_Niels/man/lab2.Rd | b3bff4a851482f897a7dc19081e7b32a962dac43 | [] | no_license | rainbowniels/Advanced-Models-in-R | 7b2aa089ed475741122f3f4a648c630c40f11079 | 128568a875aeefa38f413e38acb41b3e7161cf28 | refs/heads/master | 2021-07-15T13:20:19.465057 | 2017-10-23T02:37:00 | 2017-10-23T02:37:00 | 107,921,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 525 | rd | lab2.Rd | \name{lab2}
\alias{lab2}
\title{
Exercise 2 in advanced modeling in r
}
\description{
function named lab2 which produces log likelihoood scores done as an exercise for advanced R modeling.
}
\usage{
lab2(theta,y,X,Z)
}
\arguments{
\item{theta}{
value of theta}
\item{Z}... |
95b3ae68cff52b217517c6c9644dda691c509c2f | 60dffdc12c12478b469d78cf4cfe5ee148107dd4 | /R/calculateGLR.R | 8147045303bf2de6c629ae57eec88dc92006b74c | [] | no_license | cran/evian | dab851281636703b01c062b148327ceed16b7c28 | e65295408da03281978691febd50c92657d972ea | refs/heads/master | 2020-03-27T03:58:07.273598 | 2019-05-23T15:30:03 | 2019-05-23T15:30:03 | 145,902,356 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,179 | r | calculateGLR.R | calculateGLR=function(snp,formula_tofit,model,data,bim,lolim,hilim,m,bse,family,c,plinkCC){
#same as evian linear/logit in cleaning data, picking models, and calculate likelihood
# GLR does not have robust correction
data_subset=subsetData(snp,formula_tofit,data,plinkCC) # subset so that only contains 1 snp
af=... |
f08b410b1b6b4a0fbc9735dbd458be8fdcf4ad6f | a82d76818477a635dfa49cd7da7694703e0973c3 | /app.R | b2016a2d8249349bd6f9c456949e8a021cc135fc | [] | no_license | WarrenSink/Election2020-shiny | b38e0e0732edab01e909f718d632063133a71b51 | 5b71d30a91eedd4224044eb013fca7fa5601132b | refs/heads/main | 2023-01-12T13:22:27.995653 | 2020-11-14T22:36:10 | 2020-11-14T22:36:10 | 312,733,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 726 | r | app.R | library(shiny)
library(spData)
library(tmap) # for static and interactive maps
library(leaflet) # for interactive maps
library(tidyverse) # tidyverse data visualization package
library(sf)
get_congress_map <- function(cong=113) {
tmp_file <- tempfile()
tmp_dir <- tempdir()
zp <- sprintf("http://cdmaps.polisci.u... |
5aa8d924bd709d02aa5ecd0a2e4bc4d75b5a042a | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /lwgeom/R/split.R | 4b524dff1b4242dd11ec678cd51855efe383d6e2 | [] | 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 | 2,696 | r | split.R | #' Return a collection of geometries resulting by splitting a geometry
#'
#' @name st_split
#' @param x object with geometries to be splitted
#' @param y object split with (blade); if \code{y} contains more than one feature geometry, the geometries are \link[sf:geos_combine]{st_combine} 'd
#' @return object of the sam... |
a2bf41ac9bd2d9dfc6686c402c6c42a94e5d92c4 | 85d70ac0202118ab478c49daead8a3f6c272cdda | /cachematrix.R | 943de2d0cada1a2c64e8734864026da5846341e0 | [] | no_license | preeti-d/ProgrammingAssignment2 | 5e178dfab1a8623e5e8827eb01959d23da97000c | b1618cd5e0d8d22f5dc21b87ea0d54d51ac1ee2b | refs/heads/master | 2021-01-14T13:06:28.777534 | 2015-04-25T23:23:29 | 2015-04-25T23:23:29 | 34,301,116 | 0 | 0 | null | 2015-04-21T03:10:52 | 2015-04-21T03:10:51 | null | UTF-8 | R | false | false | 2,014 | r | cachematrix.R | ## This function makeCacheMatrix requires an input argument of a matrix.
## The function computes the invert of the matrix.
## Matrix conversion can be a costly operation.
## This function uses the "<<-" operator that can be used to cache the value of a variable.
## This function is is used to create a special object ... |
e1257f54af036fd8df3f6f69e8205126eb28b3e5 | 003b71cd1c3e6468089fd97b9cfe0696f241986d | /man/plot.cv.bridge.Rd | d3ffb9f4c41bad8b626e298ce5cde265971e9b1b | [] | no_license | cran/rbridge | 80c66c90f0d805376ed9d212785ce85e7bdc38be | 3c9d0364d3731b7cc5f263eb2f3141c9b45ef28a | refs/heads/master | 2020-08-23T05:18:35.298287 | 2020-02-29T10:40:03 | 2020-02-29T10:40:03 | 216,552,135 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 693 | rd | plot.cv.bridge.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{plot.cv.bridge}
\alias{plot.cv.bridge}
\title{Plot a 'cv.bridge' object function}
\usage{
\method{plot}{cv.bridge}(x, sign.lambda = 1, ...)
}
\arguments{
\item{x}{Design matrix.}
\item{sign.lambda}{Either plot against \code{l... |
ba0ff4671c861d1a3eac9c9a42de1053a282ebd2 | 2cb6a06d171e3d5d4a5f092acdaf1f642d11f944 | /Questionnaire.R | d80664e9dc6d0d7ba8fd23b5af3a2aa5b8a3e831 | [] | no_license | SNaGLab/Risky-health-choices-and-the-Balloon-Economic-Risk-Protocol | 9e48d4759d31b545a74f7f2fef5e57e496ba9cf1 | 03717703af778d75cb83b45df09a66c3c6caa4b7 | refs/heads/master | 2020-04-10T18:15:14.571146 | 2019-04-08T09:39:36 | 2019-04-08T09:39:36 | 161,198,463 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,669 | r | Questionnaire.R |
# Questionnaire data analyses
cd <- "/DataFiles/" ### Put your own current directory here.
q1="Questionnaire.csv" #leave name intact
### Upload file: stored in R as Q.
file1=paste(cd, q1, sep = "")
Q=read.csv(file1)
Q$Participant <- Q$What.is.your.subject.ID.
## Initialize count variable
count=0
## Code impulsivi... |
9c3ec8337cd346273b24c00a6e015eeb897bf2fd | bf610e1f34c1ab03178d1ad8f15bcff5e72ab522 | /man/effect_N_continuous.Rd | a9a788066a1f5f7b2fd79a64cc8365c3ea00437f | [] | no_license | caroliver/mobr | bd1d5cd9d5d41be5b62a9801e49a37ae615b001d | 033569e1a2b571661505c5240d3bb8b65e9f6e21 | refs/heads/master | 2021-04-29T22:21:43.653097 | 2019-02-17T17:44:55 | 2019-02-17T17:44:55 | 121,636,007 | 1 | 0 | null | 2019-02-17T19:09:23 | 2018-02-15T13:59:32 | R | UTF-8 | R | false | true | 474 | rd | effect_N_continuous.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mobr.R
\name{effect_N_continuous}
\alias{effect_N_continuous}
\title{Auxiliary function for get_delta_stats()
Effect of N when type is "continuous"}
\usage{
effect_N_continuous(mob_in, S, group_levels, env_levels, group_data, plot_dens,
plo... |
706a2f614c6f158a99e1a52385f2a87bf2d4b857 | 184a5b70c5bf8642501c82610e8ea5562445029b | /R/addmod2.R | 3f4e92e8bd7657f2bc08f4fbfbf7a8678b1be3fd | [] | no_license | cran/QuantumOps | 4910e53dda44803981801cbe1545e4ab63154538 | 35e2a8be5a6bbefbdc53a732eb6145a04dcd9e8e | refs/heads/master | 2020-04-07T09:57:13.065211 | 2020-02-03T08:20:18 | 2020-02-03T08:20:18 | 158,270,510 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 273 | r | addmod2.R | #bit-wise mod 2 add two integers
#' @export
addmod2 <- function(x,a){
bx <- as.character(intToBits(x))
ba <- as.character(intToBits(a))
s <- 0
for(j in 1:length(bx)){
if( (bx[j] == "00" & ba[j] == "01") | (bx[j] == "01" & ba[j] == "00") )
s <- s + 2^(j-1)
}
s
}
|
bdec900a1c8b649301e45f2651002ac424002cf9 | 755af7368270bc41b4025f49a094e48d421edcc3 | /run_analysis.R | 6d5c99aac2259eea98bfa7739102fad4e7d9ee2e | [] | no_license | clarcombe/DSassignments | 096c4e592210aea610efde72f63bcef988a4428d | 9056b481a0404873c9801812327156825c8bb5a9 | refs/heads/master | 2021-01-21T12:06:31.275456 | 2015-08-19T10:07:32 | 2015-08-19T10:07:32 | 41,026,842 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,476 | r | run_analysis.R | #Load Relevan libraries
library(tidyr)
library(dplyr)
library(rapport)
# Initialise all of the variables for the filenames
xtrainfile = file.path("train","X_train.txt")
ytrainfile = file.path("train","y_train.txt")
xtestfile = file.path("test","X_test.txt")
ytestfile = file.path("test","y_test.txt")
activitiesfile = f... |
5b357ec2fb086c2876a31c304440a18bc785e8f6 | 4ba27be09d1e637028ebd20fb1a648a7b98ad515 | /shiny/ui.R | 743ee7a1c190d361ae2bf6510de97aca66425bfb | [
"MIT"
] | permissive | nezakrzan/Analiza-bruto-mesecnih-plac-v-Sloveniji | 8afe9d87a91edd4c66198ca8f168e027e0c8271d | 729ca1a394cfac5767f52b289b461674965334c6 | refs/heads/master | 2023-02-25T00:39:32.559880 | 2021-01-28T21:17:06 | 2021-01-28T21:17:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 324 | r | ui.R | library(shiny)
fluidPage(
titlePanel(""),
tabPanel("Graf",
sidebarPanel(
selectInput("Leto", label = "Izberi leto",
choices = unique(gospodarskadejavnost$leto))),
mainPanel(plotOutput("graf_dejavnosti"),
tableOutput("legenda")))
... |
892ec82e526039ea73aefd9232618017ca581725 | 2acf0eaf8524b8a3a3a7d56f48d32f89d4529dd6 | /plot1.R | 1328e5bd1a22582b39c16031d06078db62c2cf7e | [] | no_license | kishmk/ExData_Plotting1 | c9d2f3f6e0f59eca950b687584da5a572563574c | 933d90735cc301160001aa737fc77ec482f9c14c | refs/heads/master | 2021-01-17T10:27:38.655905 | 2015-09-13T20:45:19 | 2015-09-13T20:45:19 | 42,238,873 | 0 | 0 | null | 2015-09-10T10:48:50 | 2015-09-10T10:48:50 | null | UTF-8 | R | false | false | 432 | r | plot1.R | plot1 <- function()
{
data <- read.table(pipe('grep "^[1-2]/2/2007" "../household_power_consumption.txt"'),sep=";",colClasses="character",header=T)
data
datah <- read.table("../household_power_consumption.txt",sep=";",nrows=1,header=T)
names(data)<-names(datah)
png(file="plot1.png")
hist(as.numeric(data$... |
41fa46cdc39177bc4da84cfaabf0d2877497581f | 9b7d1879ea46e49138469b275dc5fa8afc30c00e | /scripts/TCL.R | 91f5ed02da87104ec3c7084da25dd65945085d84 | [] | no_license | lbelzile/MATH60604-diapos | 84f9c182e56ba0ae97d7b2392ecc0897d7edf4d1 | 31947d266e6c5589e0f98a381094d498b02dbbf2 | refs/heads/master | 2023-04-23T07:45:51.660337 | 2021-05-04T23:23:43 | 2021-05-04T23:23:43 | 285,688,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,530 | r | TCL.R | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
setwd("../img")
library(gganimate)
#> Loading required package:
library(ggplot2)
library(viridisLite)
set.seed(1234)
pmix <- 0.6
sampmixt <- function(n){
ifelse(rbinom(n = n, size = 1, pmix),
TruncatedNormal::rtnorm(n = n, mu = 2, sd = 3, lb = ... |
1c2762581de61e88bd1a96b3ff23db64a25213e7 | ed0738a868e64399ff12b23b830be0582eae509a | /hw2.5.R | d4e075a690a6e4dcbdbccddbb62619c036d4131a | [] | no_license | xkuang/internationalEducationData | 4fadba2a53ceea2960482a0f3ce226e0eeb9a10f | 9515a36cdb30d7cf99125d170cd9a71b7067ab1c | refs/heads/master | 2021-01-11T21:33:35.171266 | 2017-02-13T13:30:00 | 2017-02-13T13:30:00 | 78,805,708 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,394 | r | hw2.5.R | library(cluster)
library(Matrix)
P <- matrix(c(97,4,4,7,2,
9,87,8,37,9,
8,16,93,12,12,
11,59,17,96,12,
9,15,26,12,86),nrow=5,byrow=TRUE)
# the following code puts elements of the lowerhalf vector P into the full matrix D
#symmetry the matrix
#method 1
S <- forceSymmetric(P)
#method 2
P[lower.tri(P)] =... |
5dc650c5bdd9bf717e1c9a913f3f22eee340b368 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Pan/k_d4_n/k_d4_n-6/k_d4_n-6.R | eff98737262e7eec0501af6e0d2e7f61d6bfef42 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 58 | r | k_d4_n-6.R | 0e42fc16fc805cd9bf64377ae76476bd k_d4_n-6.qdimacs 567 1950 |
c7bde0d0ff6a1ecdb14a094593f295e6082c55ba | 13cb16a2eff4bae3ddd5f4826edfd879a03001a0 | /man/createDistMat.Rd | 4e7093ce2de67914809aad11027ea53dcc9906ac | [] | no_license | nkurzaw/Rtpca | 2a3471ff14756699883f57a6be7590bdd05cd940 | c1922eb6c195263cae2fe941584f32b8035b3df1 | refs/heads/master | 2023-05-29T19:50:58.078565 | 2023-04-25T15:20:19 | 2023-04-25T15:20:19 | 227,799,428 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,553 | rd | createDistMat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{createDistMat}
\alias{createDistMat}
\title{Create distance matrix of all vs all protein
melting profiles}
\usage{
createDistMat(
objList,
rownameCol = NULL,
summaryMethodStr = "median",
distMethodStr = "euclidean"
... |
5aa31780c8843116686fd5a0f970ff0950591a5c | 8d319d56425bae6aace33374d41280ded36e00c6 | /man/simeans.binormal.Rd | 451d07ce4b0fa2391470503827df2f02cfa31ad1 | [] | no_license | cran/asd | 6a8733a815ca87deb186809ac26a55f0b23dd31b | 64a2e65f0c0d460810f02494eb4ca22115434dac | refs/heads/master | 2021-01-21T13:48:45.488898 | 2016-05-23T10:14:13 | 2016-05-23T10:14:13 | 17,694,497 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,121 | rd | simeans.binormal.Rd | \name{simeans.binormal}
\alias{simeans.binormal}
\title{
Simulate Bivariate Normal Means
}
\description{
Simulates bivariate normal means; for use with \code{asd.sim} and \code{gasd.sim} in ASD.
}
\usage{
simeans.binormal(n = n, means = means, vars = vars, corr = corr)
}
\arguments{
\item{n}{
Number of r... |
d41a7b5acf6690be9dea1c8e08278c3dd8afd641 | e6e8e6339083a42d8fd6ce6ea22d17dd6fa92ea8 | /cachematrix.R | 2d41c5c503c56b5ef0e34ebf40de493fc658527c | [] | no_license | chris451/ProgrammingAssignment2 | 7e1183f6f99b3c2311a8dca04a763a02aea470f2 | 86fc6a74e9912998366431b2f3817a0a90af3e5a | refs/heads/master | 2020-05-29T11:56:37.499258 | 2014-04-27T11:50:09 | 2014-04-27T11:50:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,487 | r | cachematrix.R | # This code contains 2 functions:
# - makeCacheMatrix: create an advanced ("special") matrix which also contains its inverse
# - cacheSolve: compute the inverse of a matrix if the inverse has not been calculated already
#
# usage:
# source("cachematrix.R")
# m <- makeCacheMatrix(matrix(5:8,2))
# m$get()
# ... |
63dbe3c045b69e5c9574e0f35bd6a0d9c798a6ed | 43be21b1b2063184e3f6877fa08233bce701cc2e | /Dashboard2/test_graph_general.R | e555c33ddd679d181122f1112fd7b2c8c29deed7 | [] | no_license | escandethomas/Dashboard_V_Dem | 0a4a94ac42e653fa589049a0554f999acf254f1c | 261644146e762800a9b10ead9e2c75ac75a05af3 | refs/heads/main | 2023-06-19T05:43:57.465606 | 2021-07-19T13:14:18 | 2021-07-19T13:14:18 | 326,505,915 | 0 | 0 | null | 2021-03-17T21:56:28 | 2021-01-03T21:41:12 | R | UTF-8 | R | false | false | 815 | r | test_graph_general.R | library(ggplot2)
democracies <-readRDS("data/data_dashboard.rds")
# ui.R
ui <- fluidPage(
titlePanel("Investigating Seedling Traits across Elevation"),
sidebarLayout(
sidebarPanel(
selectInput(inputId = "var_y",
label = 'Trait',
choices = c("Height (cm)... |
77c37c10a253df66474915a9ba84758514bf1991 | a9cd617502cc40c385cb138b14b476511f12de45 | /2 Getting and Cleaning Data/Assignments/run_analysis.R | cb941c5b7c2e97dee14ddb69371e4ac50c61c0ea | [] | no_license | ssmolenski/CourseraDataScience | 3a3a26225428889c4a508501cba3c08e7cfd9e57 | aa785ae237aa6eae376911ee81e40f365327f7d3 | refs/heads/master | 2020-04-01T02:32:22.908081 | 2019-05-03T18:09:26 | 2019-05-03T18:09:26 | 152,783,384 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,056 | r | run_analysis.R | library(dplyr)
library(tidyr)
library(devtools)
library(stringr)
setwd("C:/Users/Sarah/Downloads/Dataset/UCI HAR Dataset")
#Read in the features and split into a vector, then subset only mean/std values
readLines("features.txt") %>% strsplit(" ") -> features
features<-features[grepl("(mean|std)",features)]
features<-... |
5c0ea1298319357f6b640ca75bf7670828b40543 | 7b838911abcf1d892c507c3b97c285b76525cf8f | /man/grapes-IfNull-grapes.Rd | 6b48d7ba25d164f9a28cf0a2a066e06e00898bc6 | [
"MIT"
] | permissive | Mehranmzn/LMMELSM | d6412ef299df70ae906fc75ac636f6b2b9bdc8eb | b4c05f9d3e4be0fb3585217a02821f218da61473 | refs/heads/master | 2023-03-17T23:44:43.930882 | 2021-03-18T00:14:17 | 2021-03-18T00:14:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 532 | rd | grapes-IfNull-grapes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{\%IfNull\%}
\alias{\%IfNull\%}
\title{Operator for testing NULL and returning expr if NULL}
\usage{
object \%IfNull\% expr
}
\arguments{
\item{object}{Object to test for NULL}
\item{expr}{Expression to evaluate and return if ... |
6db40089d12c97d75b2a610cd20f4d8d68b9719a | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /MGMM/man/tr.Rd | fe815bfcdd9f71cee41f8ed3d544640cbd90fb49 | [] | 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 | true | 266 | rd | tr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{tr}
\alias{tr}
\title{Matrix Trace}
\usage{
tr(A)
}
\arguments{
\item{A}{Numeric matrix.}
}
\value{
Scalar.
}
\description{
Calculates the trace of a matrix \eqn{A}.
}
|
25e96ad2c7223d8fa4da833420bbdde885ccfef7 | 9d016ff8a3482452bd280a0a73797eb65f3ab83b | /R/get_bootstrap_matrix.r | ad52d2d0858754107c0742b3658dd0b23f27da74 | [] | no_license | NathanSkene/ALS_Human_EWCE | dd199b78530224b118ea51514d598b6df922d785 | 551c4478cbf0b97d938632522de88946428c679a | refs/heads/master | 2022-01-13T03:55:42.947202 | 2019-06-25T10:31:30 | 2019-06-25T10:31:30 | 176,797,486 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 783 | r | get_bootstrap_matrix.r | get_bootstrap_matrix <- function(nReps,mouse.hits,mouse.bg,cell_types,sct_data,annotLevel){
combinedGenes = unique(c(mouse.hits, mouse.bg))
exp_mats = list()
for(cc in cell_types){ # For each celltype...
exp_mats[[cc]] = matrix(0,nrow=nReps,ncol=length(mouse.hits)) # Create an empty matrix, with a row for eac... |
f58462954085696d42a9a37a3685ac900f4ab17c | b0255d4e54415b6fb1519b8fc0e4d1ca6717b080 | /R/completeVecs.R | ded679da0961a308006eaf67d58c2d1f1d1912ec | [] | no_license | mrdwab/SOfun | a94b37d9c052ed32f1f53372a164d854537fcb4a | e41fa6220871b68be928dfe57866992181dc4e1d | refs/heads/master | 2021-01-17T10:22:12.384534 | 2020-06-19T22:10:29 | 2020-06-19T22:10:29 | 16,669,874 | 30 | 3 | null | null | null | null | UTF-8 | R | false | false | 990 | r | completeVecs.R | #' Extract the `complete.cases` for a Set of Vectors
#'
#' Takes vectors as input and outputs a matrix of the complete cases across
#' these vectors.
#'
#'
#' @param \dots The vectors that need to be combined.
#' @return A matrix with the same number of columns as there are input vectors.
#' @note Short vectors are ... |
b3ac58d802779c5190af25d2398b6c8a407bfd26 | ae0bb1ca6a4cad154695d5145e14496c336b131a | /pctcurves/pctcurves/man/pct_vs_t_fun_logit_quad.Rd | 78ec772538f068e31fe08fced16f5fa3f0a445ef | [] | no_license | Sempa/assaypctcurves | fee70b13c3ebe1eb113a5d31a9b2324d7c4fa98b | 5f74d48be3e405a210fb4fa2ac86690b42865047 | refs/heads/main | 2023-07-26T22:08:27.231553 | 2021-09-10T14:43:15 | 2021-09-10T14:43:15 | 404,289,420 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,799 | rd | pct_vs_t_fun_logit_quad.Rd | \name{pct_vs_t_fun_logit_quad}
\alias{pct_vs_t_fun_logit_quad}
\title{
Percentiles generated from P_r(t), with glm and logit link gml funtion
%% ~~function to do ... ~~
}
\description{
A function to create percentile curves using a glm function and logit link function, with time since infection as a quad funtion
}
\us... |
4b3d9dfd5af1baaafeb1c87932a065feb471cac9 | 9eac9f8e7495d916f7596c4444461521b1a39086 | /scripts/plot_Gviz.R | ad8e9a293f9beeb21b93b36421d556ecd2dca112 | [
"Apache-2.0"
] | permissive | uniqueg/scripts | bbb42d455196f8e047df2681661a02d38e4a762f | 9fdcb93f740c0d353b8f9c0fe3ceab6a941af87d | refs/heads/master | 2023-04-08T17:08:00.911197 | 2023-03-16T08:46:40 | 2023-03-16T08:46:40 | 211,389,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,070 | r | plot_Gviz.R | ## PLOT YH2AX CLUSTERS AND ASISI SITES
# Import .bed file as GRanges object
library(rtracklayer)
yH2Ax <- import("clusters_100000_z_cutoff_induced.bed", genome="hg19", asRangedData=FALSE)
AsiSI <- import("AsiSI_orig.bed", genome="hg19", asRangedData=FALSE)
# Extract genome and chromosome information
gen <- as.chara... |
1e8eeed3d057f147e70dcd4acb45ab408f500c68 | 10ddc648602995325c6a467d0b17595cb8766fdb | /R/pipe.R | f591f1876359017573e396469555a40a42b1c120 | [
"MIT"
] | permissive | rich-iannone/hyper | cfd4479ee232aadee89477872e3e814061ce3b34 | ef5f6e4672a4fe6b8d5d9d4875b04d299159c97a | refs/heads/master | 2021-04-29T17:20:31.200263 | 2018-05-14T04:14:13 | 2018-05-14T04:14:13 | 121,667,337 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 209 | r | pipe.R | #' The magrittr pipe
#'
#' hyper uses the pipe function [%>%] to turn
#' function composition into a series of
#' imperative statements
#' @importFrom magrittr %>%
#' @name %>%
#' @rdname pipe
#' @export
NULL
|
04d653c835f480269c1d4fe24398b2ce5413be69 | 248182be042384a92a64acbe7052e4c0bae54c84 | /plot2.R | 4a13cf159d25cb33ffca3fb7c6f23d3f2954054f | [] | no_license | ptconroy/ExData_Plotting1 | ef8b5e574d8f6ace5398c5f127192bb4234d6612 | 9b6cb52023084f2775717e605621b93114ec3afd | refs/heads/master | 2021-01-16T18:17:45.859636 | 2016-07-06T05:30:05 | 2016-07-06T05:30:05 | 62,682,009 | 0 | 0 | null | 2016-07-06T01:40:08 | 2016-07-06T01:40:06 | null | UTF-8 | R | false | false | 610 | r | plot2.R | library(lubridate)
library(dplyr)
plot2 <- function() {
din <- read.table("household_power_consumption.txt", header = TRUE, na.strings = "?", sep = ";")
first_day <- ymd("2007-02-01")
second_day <- ymd("2007-02-02")
our_days <- filter(din, dmy(Date) == second_day | dmy(Date) == first_day)
temp_dt <-... |
8a5b2603d6b9ed7a7301c117726d8214717eb7a2 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610125465-test.R | beb14046fb3b187ab5a44770b6a583b59bdf33ed | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 155 | r | 1610125465-test.R | testlist <- list(rates = numeric(0), thresholds = numeric(0), x = c(8.22753060281186e+62, Inf))
result <- do.call(grattan::IncomeTax,testlist)
str(result) |
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