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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cee17c33c8a2ef5a0bf5284c4ff6f75eb7391352 | 5b345b8a1c60a40853dc67543b4b23635ca52af8 | /R/oblicz_stale_czasowe.R | 5b616cb38dbba6bddcdd8b4d73f86c63eff44d66 | [] | no_license | tzoltak/MLAKdane | 9dd280e628a1434ef3e0433a7adab8ee6653e258 | 3ff0567b98729648cd54cbb118d55d6bcd5d7bd3 | refs/heads/master | 2021-01-12T08:24:27.554590 | 2016-11-14T15:39:47 | 2016-11-14T15:39:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 654 | r | oblicz_stale_czasowe.R | #' oblicza stałe czasowe i konwertuje istniejące stałe czasowe z powrotem na daty
#' @param dane ramka danych ZDAU (lub pochodna)
#' @param data_badania data konca badanego okresu
#' @return data.frame
#' @export
#' @import dplyr
oblicz_stale_czasowe = function(dane, data_badania){
dane = dane %>%
mutate_(
... |
7237504a8f57d137d42244c49b3206bbd79f3bda | acc4881b822ffa781e47e55a2c8c56df0100440d | /man/model.fake.par.Rd | 08c30f974bd5ba746132bc7d996b189fb4215a9d | [] | no_license | cran/sgr | 693368d448056bb68616c471f69fc06292226829 | d1df9176782321241b0c5718a010ce87130b6892 | refs/heads/master | 2023-04-06T05:36:43.974635 | 2022-04-14T13:30:02 | 2022-04-14T13:30:02 | 17,699,634 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,643 | rd | model.fake.par.Rd | \name{model.fake.par}
\alias{model.fake.par}
\title{
Internal function.
}
\description{
Set different instances of the conditional replacement distribution.
}
\usage{
model.fake.par(fake.model = c("uninformative", "average", "slight", "extreme"))
}
%- maybe also 'usage' for other objects documented here.
\arguments{
... |
2d5db523dd65751720114850542158dd2324595e | 7a10e3e78d2e6f276ce8358b0b196363b880b902 | /ggplotGraphics2.R | 1ba4cda638677ea3460606a4c15b837418d0f0ff | [] | no_license | dhackenburg/Bio381_2018 | 508792c1afd5073656060d6d02ced00bad620974 | f609f3cabb1c925215cd09662b9882169b76aceb | refs/heads/master | 2021-05-05T09:54:08.286930 | 2018-07-21T15:15:39 | 2018-07-21T15:15:39 | 117,878,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,997 | r | ggplotGraphics2.R | # ggplot graphics
#5 April 2018
#DMH
# preliminaries
library(ggplot2)
library(ggthemes)
library(patchwork)
library(TeachingDemos)
char2seed("10th Avenue Freeze-Out")
d <- mpg
str(d)
#create 4 individual graphs
#graph 1
g1 <- ggplot(data=d, mapping=aes(x=displ,y=cty)) +
geom_point() +
geom_smooth()
print(g1)
#g... |
0da61db862ade69365c6e0b969e65646c7f2a85b | d816b9a672e7fcd18f34d9f41426b0678715da41 | /man/hdi.Rd | cf17a5989ef4c26c5f46d4b4926dc2a232883ee0 | [] | no_license | cran/hdi | dd85bac2a284df87cf54ecc670101de626fe212e | 3f51705e4e07701de8cc58fa12c2dab62cd2cf9d | refs/heads/master | 2021-07-05T10:34:05.280375 | 2021-05-27T12:10:02 | 2021-05-27T12:10:02 | 17,696,611 | 2 | 5 | null | null | null | null | UTF-8 | R | false | false | 4,134 | rd | hdi.Rd | \name{hdi}
\alias{hdi}
\title{Function to perform inference in high-dimensional (generalized) linear models}
\description{Perform inference in high-dimensional (generalized) linear
models using various approaches.}
\usage{
hdi(x, y, method = "multi.split", B = NULL, fraction = 0.5,
model.selector = NULL, EV = NUL... |
95eb4fffe2b4441f642b4779a6d41e72d14f91ea | 2a2d771ab408218a642f8639c5c2bfc683aece21 | /man/splitDateTime.Rd | eb5a441977516d503912ae3a3d52b2c78e275a8e | [] | no_license | mmiche/esmprep | 7a525f6d3dfc5365f3c1ef4040c28225bef89e0f | 8cd3330d9621ba6e69b2d9aa8df62d97eb988a95 | refs/heads/master | 2021-01-20T10:28:47.764859 | 2019-07-05T11:15:49 | 2019-07-05T11:15:49 | 101,635,503 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,270 | rd | splitDateTime.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/splitDateTime.R
\name{splitDateTime}
\alias{splitDateTime}
\title{splitDateTime}
\usage{
splitDateTime(refOrEsDf = NULL, refOrEs = NULL,
RELEVANTINFO_ES = NULL, RELEVANTVN_ES = NULL,
RELEVANTVN_REF = NULL, dateTimeFormat = "ymd_HMS")
}
\a... |
fcdd8f863da69fc17f308f0517cf031cabf39a5a | 369fd863417f6a3bade3e0b7f90302e0fde76815 | /sbtest5download/PdfDownload/ui.R | cd10aba8746a02291629acf132f5e8c76a8d5059 | [] | no_license | jeffnorville/shinysb1 | f4338e3e0c4020694cc305cea2ff4507eee143a4 | f02f3b78a232903254e41aa6dbbfebaefed1e18e | refs/heads/master | 2020-04-06T03:33:24.348633 | 2016-09-15T14:21:59 | 2016-09-15T14:21:59 | 57,279,961 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 557 | r | ui.R | # IMPREX download doc test
require(shiny)
pageWithSidebar(
headerPanel("Output to PDF"),
sidebarPanel(
checkboxInput('returnpdf', 'output pdf?', FALSE),
conditionalPanel(
condition = "input.returnpdf == true",
strong("PDF size (inches):"),
sliderInput(inputId="w", label = "width:", min=3,... |
986b1f35b620e18588ad92c01ad14f0e1fbc189b | caf56f313d6e34f4da4c5a0a29d31ff86262533a | /R/tibble.R | 1760626ce8b1e356c531ec38b0dcb2b917290a89 | [] | no_license | bhive01/tibble | c00b4894e4067a2d6443a33808649bf327367b3a | 7c0aca252cba66ff02e48e9a9dffea816ffe4d4f | refs/heads/master | 2021-01-17T04:56:06.995980 | 2016-03-19T00:43:30 | 2016-03-19T00:43:30 | 54,232,754 | 0 | 1 | null | 2016-03-19T00:43:30 | 2016-03-18T21:34:01 | R | UTF-8 | R | false | false | 1,329 | r | tibble.R | #' @useDynLib tibble
#' @importFrom Rcpp sourceCpp
#' @import assertthat
#' @importFrom utils head tail
#' @aliases NULL
#' @section Getting started:
#' See \code{\link{tbl_df}} for an introduction,
#' \code{\link{data_frame}} and \code{\link{frame_data}} for construction,
#' \code{\link{as_data_frame}} for coercion,
#... |
46dd347ddd7677618e260e62c71d3a9a2c8f8ece | d60a4a66919a8c54d29a4677574b418107b4131d | /man/REDWINE.Rd | f9a1be2bfc6e4cddb90510ca4c62211303bc8146 | [] | no_license | cran/tsapp | 65203e21a255e832f0ad9471f9ee308793eb7983 | f2679a3d5ee0e3956a4ba013b7879324f77cf95f | refs/heads/master | 2021-11-12T21:18:18.835475 | 2021-10-30T10:30:02 | 2021-10-30T10:30:02 | 248,760,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 576 | rd | REDWINE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/series.r
\docType{data}
\name{REDWINE}
\alias{REDWINE}
\title{Monthly sales of Australian red wine (1000 l)}
\format{
REDWINE is a univariate time series of length 187; start January 1980, frequency =12
\describe{
\item{REDWINE}{Monthly sales... |
0b9633a20d1737b24b3945a52dab43f5bb9ac7dc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hmm.discnp/examples/predict.hmm.discnp.Rd.R | 99d389070612427e2694054c4cc54de047658a3c | [] | 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 | 620 | r | predict.hmm.discnp.Rd.R | library(hmm.discnp)
### Name: predict.hmm.discnp
### Title: Predicted values of a discrete non-parametric hidden Markov
### model.
### Aliases: predict.hmm.discnp
### Keywords: models
### ** Examples
P <- matrix(c(0.7,0.3,0.1,0.9),2,2,byrow=TRUE)
R <- matrix(c(0.5,0,0.1,0.1,0.3,
0.1,0.1,0,0.3,0.5),5... |
e0dc5bf4982dae091d10dc3cc1434adb71df355d | 303ee8c30e03e6bf734e69e1e00f43fefaf3bda4 | /AllCharts/PieChart.R | 7ba06dbb05f8b63ccd2f7f998832d6c7c3220d27 | [] | no_license | zt2730/Rplot | d2d57c331283d309dd8ae1d41425874ee432e291 | a4979f63029b26912c43eb4d631e04c489ca7328 | refs/heads/master | 2021-01-01T03:33:35.002731 | 2016-05-24T21:37:27 | 2016-05-24T21:37:27 | 59,609,059 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 460 | r | PieChart.R | library(ggplot2)
#Pie chart
data <- textConnection("Category,Value
Category A,5
Category B,4
Category C,3
Category D,2
Category E,1
")
data <- read.csv(data, h=T)
p <- ggplot(aes(x=factor(1), fill=Category, weight=Value), data=data)
p + geom_bar(width = 1) +
coord_polar(theta="y") +
scale_fill_discrete("Legend Ti... |
4e4444437ff4f03407d8c1fb769b8c98627aaa1e | e786517480475f327d99a4638e1b787004166d77 | /handwriting/rf_and_bagging.R | 6221bca611331a64b7335d92d664f3f72c92d3a8 | [] | no_license | asterix135/kaggle | 217d8c41ba0832698e90d4730b95437d94f47d22 | 45971047b42b8120e756b1608a6154946156e6d2 | refs/heads/master | 2021-01-10T14:00:54.158976 | 2015-12-11T01:32:26 | 2015-12-11T01:32:29 | 46,736,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,476 | r | rf_and_bagging.R | #####
# Building some alternate models
#####
# Make sure working directory is where the data is saved
if (getwd() != "/Users/christophergraham/Documents/Code/kaggle/handwriting") {
setwd("/Users/christophergraham/Documents/Code/kaggle/handwriting")
}
require(caret)
require(e1071)
num_data <- read.csv('train.csv'... |
d4779a89c08c174d3aec3d0c3cbee6499a7bfd35 | 8244df3775912c290eaf3df9a457019dc2d7b6a9 | /kmz/move_kmz_destination.R | 0f1d4c2a8f5cf2f336a1220eab1b1d141633db78 | [] | no_license | CIAT-DAPA/cwr_verticaltask | 6e247e3ec32a600d13c4beb3986154d68a37f5b2 | 63da126832048383cea0a8b5b62641ef105fc5b5 | refs/heads/master | 2021-01-21T21:38:58.859729 | 2016-05-31T15:01:29 | 2016-05-31T15:01:29 | 34,528,073 | 1 | 1 | null | 2015-11-26T14:46:42 | 2015-04-24T16:01:07 | Java | UTF-8 | R | false | false | 4,820 | r | move_kmz_destination.R | ###################### Dependencies #######################
library(parallel)
###################### Configuration #######################
work_home <- ""
work_destination <- ""
work_force <- FALSE
work_cores <- 10
###################### Internal Variables #######################
root_dirs <- NULL
file_extension ... |
8fc82be2d4b7509a587f2ba4afa454b5148aa555 | ab9cfa948b2b005aab7c00f72b3a461e9252a5d4 | /plot5.R | 41b717b299e6db8ffd3f7514ae603316b649a3c4 | [] | no_license | doctapp/ExData_Plotting2 | 136ee5fde0756ce69fc9762ee328d9dbd0c0a529 | 7997921fffba5df755b0cea17d67b871dce454de | refs/heads/master | 2016-09-01T20:38:47.872972 | 2014-11-23T16:54:56 | 2014-11-23T16:54:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,637 | r | plot5.R | require(ggplot2)
require(plyr)
get_data <- function() {
# Download the data if not already downloaded
zipfile <- "exdata-data-NEI_data.zip"
if (!file.exists(zipfile)) {
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.... |
849f34730b4936ba01935470b54217e74e90efff | 98a06c9667a439fa92ddf393bfe685156121327b | /R/profile.dataset.R | 92ac43149abe27b40d5f520a43e8774872ed04d2 | [
"Apache-2.0"
] | permissive | mjfii/Profile-Dataset | f11b0f7169462de28fab13eddf6c2ba928081652 | 378d3276ac5976e91081a47fd8046e0b14bb912e | refs/heads/master | 2021-01-22T08:02:32.186668 | 2017-02-13T22:11:54 | 2017-02-13T22:11:54 | 81,870,272 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,083 | r | profile.dataset.R | library(reshape2)
library(ggplot2)
single.class <- function(x) {
y <- class(x)
return(y[length(y)])
}
profile.data.frame <- function (pdf) {
density <- sapply(pdf, function(y) sum(length(which(!is.na(y)))))
sparsity <- sapply(pdf, function(y) sum(length(which(is.na(y)))))
unique.vals <- sapply(pdf, functio... |
bd6f4b48025030381704017a57c134818cc7fdd0 | 3d6d4f7e6c2213e43eeb206d23f74bc38c604e19 | /R_Functions/Functions Folder V2/Old Functions/import_4C.R | c552490c076be85e0f6950f25bdd96c5445d21e4 | [] | no_license | dmcmill/4C_QuantWritingAnalysis | 64fa2d0f91f84237f73de313bf986e8662a61bc1 | e3fc6644fab293b50a708e4d8a5f921bf0abd101 | refs/heads/master | 2021-06-17T00:16:24.002550 | 2017-06-05T20:41:10 | 2017-06-05T20:41:10 | 75,655,899 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,999 | r | import_4C.R | ##This script will read a worksheet in 4C format from the Master Excel Data Sheet (MEDS) into a local dataframe. The dataframe will be modified so that each student is associated with a single matrix containing his/her 4C score for a specific assignment. The dataframe will then be added to the master 4C dataframe.
##a... |
e671cc454249b1ad6bb0e6adca75603c2f2f3d7a | 8b3cd7ee200564b65db2d76ca8ab953466e091e2 | /man/cull.backfaces.Rd | d63f76ec0633d954205d76c143b8187f8b18e2b4 | [] | no_license | alicejenny/project011 | 3df759dfb96e5a7276bde4dd315bbc81f812a98c | 7de1339bc4c148bfe41264acb3da9307a605e863 | refs/heads/master | 2021-01-10T14:30:09.381069 | 2015-07-15T20:17:40 | 2015-07-15T20:17:40 | 36,937,861 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 305 | rd | cull.backfaces.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/cullbackfaces.R
\name{cull.backfaces}
\alias{cull.backfaces}
\title{Cull Backfaces}
\usage{
cull.backfaces()
}
\description{
Cull the backfaces of a point cloud based on vertex normals.
}
\examples{
cull.backfaces()
}
|
ca074c3ca420ed10956248a2fb4fa196ee161ab4 | 26e26aca4102f40bc848120c4ebc99bb40d4a3c1 | /R/Archive/Other Codes/62-FEI-Urban.R | bcf25db1d58506000e8de8747b762ccbe06260ab | [] | no_license | IPRCIRI/IRHEIS | ee6c00dd44e1e4c2090c5ef4cf1286bcc37c84a1 | 1be8fa815d6a4b2aa5ad10d0a815c80a104c9d12 | refs/heads/master | 2023-07-13T01:27:19.954174 | 2023-07-04T09:14:58 | 2023-07-04T09:14:58 | 90,146,792 | 13 | 6 | null | 2021-12-09T12:08:58 | 2017-05-03T12:31:57 | R | UTF-8 | R | false | false | 8,333 | r | 62-FEI-Urban.R | # FEI method
#
#
#
# Copyright © 2018:Arin Shahbazian
# Licence: GPL-3
#
rm(list=ls())
starttime <- proc.time()
cat("\n\n================ FEI method =====================================\n")
library(yaml)
Settings <- yaml.load_file("Settings.yaml")
library(readxl)
library(reldist)
library(Hmisc)
library(dplyr... |
69e192cd3eb2867c07d9b73c5d295f84e7265732 | ce6c631c021813b99eacddec65155777ca125703 | /R/mdlKM.R | 00b0564db3f4f7893d3b1488a944aae284c2f638 | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer"
] | permissive | Zhenglei-BCS/smwrQW | fdae2b1cf65854ca2af9cd9917b89790287e3eb6 | 9a5020aa3a5762025fa651517dbd05566a09c280 | refs/heads/master | 2023-09-03T04:04:55.153230 | 2020-05-24T15:57:06 | 2020-05-24T15:57:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,004 | r | mdlKM.R | #' @title Estimate Statistics
#'
#' @description Support function for computing statistics for left-censored data.
#'
#' @importFrom survival survfit Surv
#' @param x an object of "lcens" to compute.
#' @param group the group variable.
#' @param conf.int the confidence interval .
#' @return An object of class "survfit.... |
ca6ee109db6f7ee7d2f7fbd1d07745824a0f244f | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/tensorBSS/R/tJADERotate.R | 698ce754b32f0642504f362a8b2c79e5cdca0391 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 912 | r | tJADERotate.R | tJADERotate <-
function(x, k = NULL, maxiter, eps){
r <- length(dim(x)) - 1
rotateStack <- vector("list", r)
for(m in 1:r){
pm <- dim(x)[m]
if(is.null(k)){
this_k <- pm
}
else{
this_k <- k[m]
}
if(this_k > 0){
ijStack <- NULL
for(i in 1:pm){
... |
75d9f52c0737f31600e561ad52f7818eb22e4a05 | 9d2996ee9ca0f2d7cbacedc163fede388a937d59 | /R/app.R | fe7b36e37ad17d727db5281f2e8f4f9cf0a503b0 | [] | no_license | KaitlanM/MemoryMeasurer-App | 66f88412b52b0ec7d111f41b6d41d0a3998e8354 | f3570faeb0289c1ada7b5b2a56c365c1ba5a8a2c | refs/heads/master | 2020-05-25T18:12:25.961430 | 2019-05-31T21:22:01 | 2019-05-31T21:22:01 | 187,924,419 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,100 | r | app.R | #' @import shiny plotrix lubridate
#'
source("~/MemoryMeasurer/R/scoring-words.R")
source("~/MemoryMeasurer/R/load-words.R")
source("~/MemoryMeasurer/R/draw_circle_plot.R")
ui <- navbarPage(title = "Memory Measurer",
tabPanel("Instructions",
tags$h1("This is the Memory Measurer!"),
tags... |
e24c5aedf75c5c78ac4295ea0ed303a3e3d828e9 | 7c96b6eb387314abde40c3998b76784097c06092 | /Governers.R | a8a7b2c8596ce2c7b2851b625c8e345ec6536a4b | [] | no_license | SanjayPJ/RBI-Governers- | 960c467a0965d72c2d4ed09b7d7680753dbdb746 | e944d6495d3025e3d1f67cad6c3449d1ffc69f28 | refs/heads/master | 2020-08-10T12:37:48.402045 | 2019-10-10T17:46:56 | 2019-10-10T17:46:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,400 | r | Governers.R | library(robotstxt)
library(curl)
library(rvest)
paths_allowed(
paths = c("https://en.wikipedia.org/wiki/List_of_Governors_of_Reserve_Bank_of_India")
)
# Since the o/p is TRUE we can go ahead with the extraction of data
rbi_guv <- read_html("https://en.wikipedia.org/wiki/List_of_Governors_of_Reserve_Bank_... |
4940bd58ea065cc6578fde729bd5388f8807cdaf | 311fad25897b2153154a7e2bc92325a7dde4eb98 | /app.R | 32064616cb382386832c0f6b43f6391d146e0146 | [
"MIT"
] | permissive | debruine/bfrr | b1a83056a01c4ea7db05bf14c7c08ff0fa6308b2 | 9b80a9933b38b2f1afaa340fad978fa6818b8c90 | refs/heads/master | 2020-12-20T05:18:06.639719 | 2020-03-06T20:39:59 | 2020-03-06T20:39:59 | 235,974,846 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,491 | r | app.R | ## app.R ##
library(shiny)
library(shinyjs)
library(shinydashboard)
library(dplyr)
library(ggplot2)
## Functions ----
source("R/Bf.R")
source("R/bfrr.R")
source("R/plot.bfrr.R")
source("R/summary.bfrr.R")
source("R/default.R")
source("R/likelihood.R")
source("R/utils-pipe.R")
ggplot2::theme_set(theme_bw(base_size = ... |
4b48749fa5a0014acc2bd7b0097ac09ef5eb1754 | 7206275c2c45d8dd8c2bd35e74802452c14066c7 | /alphaimpute/3_Imputed_GWAS_Run_log_Lambs.R | a5227f6a0d23eeb4aad50264e9aad6b44a7d9cd4 | [] | no_license | sejlab/Soay_Immune_GWAS | bdf4a994f7ed1e7e0a27cf1a559096a0133478f4 | cc8025817218f11d944c16d7b45fc392a58df6df | refs/heads/master | 2020-05-17T09:44:59.090210 | 2019-10-16T12:30:27 | 2019-10-16T12:30:27 | 183,641,012 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,971 | r | 3_Imputed_GWAS_Run_log_Lambs.R |
library(asreml)
library(reshape)
library(GenABEL)
library(plyr)
setwd("alphaimpute/")
load("Imputed_GWAS_log_Lambs.RData", verbose = T)
load("BEAST_GWAS.RData")
models <- subset(models, LambAdult == "Lambs" & Response == "IgEmp")
BEASTX <- subset(BEASTX, IgEmp != 0)
BEASTX$IgEmp <- log10(BEASTX$IgE... |
c4318321671167bd1fae320cd49d8c3346e1fd09 | 1b676b2d613bf67d8bec3079b3e9c0c4abb2213b | /R/rotation2d.R | 5ee82c8c1546f912f1fdcea12d6575ffb17b36e4 | [] | no_license | cran/denpro | 995a97a3eb39a8a75d75b1fc5b17ab8d497675a0 | 503a536c5b2963f0615a9eacf65aa5a84765d6c6 | refs/heads/master | 2016-09-06T13:58:43.857283 | 2015-04-24T00:00:00 | 2015-04-24T00:00:00 | 17,695,458 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 193 | r | rotation2d.R | rotation2d<-function(dendat,alpha){
Rx<-matrix(0,2,2)
Rx[1,]<-c(cos(alpha),-sin(alpha))
Rx[2,]<-c(sin(alpha),cos(alpha))
detdat<-Rx%*%t(dendat)
detdat<-t(detdat)
return(detdat)
}
|
78c490f71d402ce08ec683eefa27c4e3647a49b2 | 71df6d25207ba173a45b29484d5a0594737cb48f | /auctions/send_update.R | ac2c60478f0b00f85cef8b46cfd4c13e782ac6eb | [] | no_license | filipstachura/home-scripts | d7ffcf2f94199042df65a8da9ae8249e472e3d76 | db0e10f04281320bd4ebf23a584a571610a8ff33 | refs/heads/master | 2021-08-30T12:06:36.621560 | 2017-12-17T21:31:36 | 2017-12-17T21:31:36 | 113,757,814 | 0 | 0 | null | 2017-12-17T12:30:54 | 2017-12-10T14:44:38 | JavaScript | UTF-8 | R | false | false | 796 | r | send_update.R | library(lubridate)
library(purrr)
library(purrrlyr)
library(dplyr)
source('../mails/send_mail.R', chdir = TRUE)
parse_price <- function(price) {
price %>%
gsub(';', '.', ., fixed = TRUE) %>%
gsub(' zł', '', ., fixed = TRUE) %>%
as.numeric() %>%
round(2)
}
prepare_content <- function() {
data <- r... |
90f2b663bd6863e5ff3956f62fcc5baf60465e7f | dab3c57e18228e58418fadea86362f366fa0d3ee | /R/imports.R | 3704a8574642a0f705dcbcda2903af475e8e921c | [] | no_license | gravesee/binnr2 | 01446f0a43d8cce1c8aa9b09e36bd492fff70d1c | 02050367d2e1893a0bd6987291463f9d63bce7e9 | refs/heads/master | 2021-06-10T05:37:18.777779 | 2016-01-22T23:19:27 | 2016-01-22T23:19:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 50 | r | imports.R | #' @useDynLib binnr2
NULL
#' @import glmnet
NULL
|
91661b9c545a88d32ecc7162368da10ade8a5788 | f44fd21032067475ce3e61651ee8ad4dd60d6300 | /Machine Learning/Markov Chains/RentalCar/RentalCar.R | 67d2121657dc18baa0f48337590bb9ff3e803d92 | [] | no_license | ribartra/alexhwoods.com | a52d1bb814cf33374d69b439c2c860915437b5e5 | 7bb8c084e6592f89a16aa1372c241c5a2300c196 | refs/heads/master | 2021-10-28T18:32:18.698430 | 2019-04-24T13:47:57 | 2019-04-24T13:47:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,261 | r | RentalCar.R | # Suppose a car rental agency has three locations in Ottawa: Downtown location (labeled A), East end location (labeled B) and a West end location (labeled C). The agency has a group of delivery drivers to serve all three locations. The agency's statistician has determined the following:
#
# 1. Of the calls to the... |
396bcd446c028aa02544a7dc409805ed736130e0 | 70fe269c7eed2af3a23402a2031e3d2e549170d5 | /Json practice.R | 8c20d79e8ba84577107f7b5bb71f4d2dcfde03c3 | [] | no_license | VetMomen/Getting-and-cleaning-data | 1f08e116d1dbeb2a84d6e2b055e81f4f3d9908dc | 27e87ade198b9da236fe39cbe6301950d342cb98 | refs/heads/master | 2020-04-02T14:12:16.231607 | 2018-11-13T21:46:02 | 2018-11-13T21:46:02 | 154,515,160 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 141 | r | Json practice.R |
#converting df to json and reverse it
mtcarJ<-toJSON(mtcars,pretty = T)
mtcar<-fromJSON(mtcarJ)
#######################################
|
73bc0e3dd387c2d20fa1ecbcb4888a3b95d8faf4 | ada7b6a9c28e9c1f4f7eff6f9f04b7b0775eb50b | /man/OEFPIL.Rd | db5cd02cd60a3bf03878152b0607b9e6d72058f7 | [] | no_license | stazam/OEFPIL- | b68d57895e26fb99b74019ce6f91b0b8c944c7c9 | 8667a4509df875e6c5cdc9b96b95087ec3a8dc21 | refs/heads/main | 2023-07-03T12:11:40.204486 | 2021-08-18T17:01:43 | 2021-08-18T17:01:43 | 342,588,412 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 6,400 | rd | OEFPIL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/OEFPIL.R
\name{OEFPIL}
\alias{OEFPIL}
\title{Optimal Estimation of Parameters by Iterated Linearization}
\usage{
OEFPIL(data, form, start.val, CM, max.iter = 100, see.iter.val = FALSE,
save.file.name, th, signif.level, useNLS = TRUE)
}... |
a877e3eb0f99bc5405e8694b74c5cb1d5ea6c9af | a8dae99358913f006416494901fb13ce88071020 | /files for github/df_intronCounts_genEx.R | 46e10a8cd7e145657d4353a8e1dadb076a99671b | [] | no_license | nishika/SRA_Annotation | b31570ece50ab90a3eb8ea35c3e25320669f3eb2 | fce1fe1d347fe4fa9ae5fb0b032e937f2283ba23 | refs/heads/master | 2020-05-17T21:20:49.008274 | 2015-07-29T20:08:07 | 2015-07-29T20:08:07 | 39,899,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,730 | r | df_intronCounts_genEx.R | #This script creates a data frame that includes expression and total intron counts of a particular gene.
#'sumcall' will be used to measure gene expression. see 'sumcall' script (found in this package);'sumcall' yields 'df_gene', which contains the 500 summed gene expression values.
doc <- read.table("numbers_of_in... |
63050c63182c990df5a99b72a383cfc781434425 | cef2a9c5c283d31cabb2afec875a4912ebed2a5a | /scripts/r_scripts/4.1_fourth_site.R | 7bf582b0cd531fb2e9929558eb0f9c977ba57e29 | [] | no_license | la466/fourth_site | a15620c08be57e90cedf62cc4b9470b302107a70 | 8eecac178f26cb43f4086d4da862d810e428f4a7 | refs/heads/master | 2020-12-02T17:48:48.660463 | 2017-09-05T08:42:27 | 2017-09-05T08:42:27 | 96,431,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,697 | r | 4.1_fourth_site.R |
# Remove all variables
closeAllConnections()
rm(list=ls(all=TRUE))
# Create directory if not already created
dir.create(file.path("outputs/", "r_outputs/"), showWarnings = FALSE)
dir.create(file.path("outputs/", "graphs/"), showWarnings = FALSE)
dir.create(file.path("outputs/", "graphs/tiff/"), showWarnings = FALSE)... |
80cd820c97a390386379aa101ca4b8a8d3c07860 | e91d3e01663cea7314679cad9d7fae8e4387253a | /Cross_validation_CDA.R | 183b932f59bbd3b5771c9e035225cde0bf46eb94 | [] | no_license | HelloFloor/CaseStudyLineair2018 | 736a95aec14240e19027ac04bb7d724adc4693de | 37b8d1600e45a688f504a30e4a7d58e733f7ea46 | refs/heads/master | 2020-04-07T08:50:36.713519 | 2019-01-21T12:38:03 | 2019-01-21T12:38:03 | 158,229,938 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,851 | r | Cross_validation_CDA.R | ################################################################################
# Run-Time Environment: R version 3.4.2
# Author: Ilse van Beelen
# Script: Model_final.R
# Purpose of script: Cross-validation of final model CDA
# Datafiles used: Clean_data_CDA_2... |
968696118d0ae3d6bf60c99585f700132661aa52 | 5dd398427794e8b4df1096460c66412696bef039 | /man/iowaSW97_06small.Rd | 05cbff5d1c624cfd84e21dc7ccdb83c29b656f59 | [] | no_license | cran/CARrampsOcl | f2dd8d9f1df5f58f50c5e7601b7bafe331c2a49e | 83866543a9ed921ba23e39ce606783eb0b58ba54 | refs/heads/master | 2021-01-19T08:07:44.348036 | 2013-08-18T00:00:00 | 2013-08-18T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,355 | rd | iowaSW97_06small.Rd | \name{iowaSW97_06small}
\alias{iowaSW97_06small}
\docType{data}
\title{Southwest Iowa 10-year normalized difference vegetation index NDVI values}
%% ~~ data name/kind ... ~~
\description{
Normalized difference vegetation index (NDVI) values derived from satellite
image data from southwest Iowa and eastern Nebraska i... |
9311f0900fd423b9ca01e4a27038728e79e915b9 | 7e83da9f8716e394e68d82229d486a43f83cad4e | /01-data_cleaning-post-strat1.R | d21b19f9ea02d2f8b1ea22a2767607bedbe65348 | [] | no_license | Juntonglin/problemset-3 | 599c779d67270bd5f78f48ba27ec820356f36baf | ff8a8e0222b7ba17805fb7f81c657bfa5fcb1a5b | refs/heads/main | 2023-01-03T20:02:10.674300 | 2020-11-03T04:54:39 | 2020-11-03T04:54:39 | 308,939,145 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,445 | r | 01-data_cleaning-post-strat1.R | #### Preamble ####
# Purpose: Prepare and clean the survey data downloaded from census data
# Author: Juntong Lin
# Data: 22 October 2020
# Contact: juntong.lin@mail.utoronto.ca
# License: MIT
# Pre-requisites:
# - Need to have downloaded the ACS data and saved it to inputs/data
# - Don't forget to gitignore it!
###... |
22446fc9a6866d0cbec4850e801944bdeea08c32 | 3159605ba0ef744fe785a747340dea82d95d56dd | /Project_2/helper_functions.R | c88afdd6526b59f0b88ca4a89168ca668bc97eaf | [] | no_license | datasci611/bios611-projects-fall-2019-mlfoste1 | cf3df1b49fbc542d6ba0132f8c922c386b3a81de | 87eede980d17e8e9d0d08d80362909c4b58b9590 | refs/heads/master | 2022-02-15T08:48:57.578492 | 2022-01-12T01:43:38 | 2022-01-12T01:43:38 | 207,409,446 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,004 | r | helper_functions.R | library(tidyverse)
library(dplyr)
library(stringr)
#Create dataset-----------------------------------------
#load data file
UMD_df = read_tsv("https://raw.githubusercontent.com/biodatascience/datasci611/gh-pages/data/project1_2019/UMD_Services_Provided_20190719.tsv", na = '**')
#visuals
#Replace spaces in field name... |
721e423f5d94de51a42d9482c5641c4b932ea03e | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/FDX/R/print_fun.R | 5c8a24aac074bb40f7391d55bc92f23dae1f3d63 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,121 | r | print_fun.R | #'@title Printing FDX results
#'
#'@description
#'Prints the results of discrete FDX analysis, stored in a \code{FDX}
#'S3 class object.
#'
#'@return
#'The respective input object is invisibly returned via \code{invisible(x)}.
#'
#'@param x an object of class "\code{FDX}".
#'@param ... furthe... |
4965697b72e8907cb4468e46745ce42e9e2d096b | 9bb6b5a33eb6f6fed4022a36d49e64d7b9879389 | /code/old/XX_try-diff-stats.R | c23ac261e64e4787b9cace714d374badd184297b | [] | no_license | vanichols/ghproj_pfiweeds | 1a4d9f396a9785342cabbe385e51953bfccdbabe | 5a6eedd35606d67d289d16bd64b2f83b47a62453 | refs/heads/master | 2023-02-18T23:28:20.165748 | 2021-01-08T22:52:09 | 2021-01-08T22:52:09 | 247,970,512 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,080 | r | XX_try-diff-stats.R | ##################################
# Author: Gina Nichols (vnichols@iastate.edu)
# Created: Dec 30 2019
# Last modified: march 23 2020 (trying to recreate old analysis where things were sig)
#
# Purpose: do 'official' stats for manuscript
#
# Inputs: td_GHspecies, td_GHsum, td-all-ryebm2008-2019
#
# Outputs:
#
# Notes... |
03846b897597d814858f53f746b5f10e158e25d3 | 4951e7c534f334c22d498bbc7035c5e93c5b928d | /developers/sdarticle.R | f9bb7fa77cfad3286f9981f143abb9dc367c0460 | [] | no_license | Derek-Jones/ESEUR-code-data | 140f9cf41b2bcc512bbb2e04bcd81b5f82eef3e1 | 2f42f3fb6e46d273a3803db21e7e70eed2c8c09c | refs/heads/master | 2023-04-04T21:32:13.160607 | 2023-03-20T19:19:51 | 2023-03-20T19:19:51 | 49,327,508 | 420 | 50 | null | null | null | null | UTF-8 | R | false | false | 1,026 | r | sdarticle.R | #
# sdarticle.R, 8 Jan 17
# Data from:
# Knowledge Organization and Skill Differences in Computer Programmers
# Katherine B. McKeithen and Judith S. Reitman and Henry H. Ruster and Stephen C. Hirtle
#
# Example from:
# Empirical Software Engineering using R
# Derek M. Jones
source("ESEUR_config.r")
library("plyr")
... |
b0d9d9d649d47d3ef4f93ae0f5356ecb87e8426e | 1b8eedf870f07fd6316154c09241ecd7c9089943 | /analysis/features.R | dc064f50da94210240b3b513d46349cdff9517c2 | [] | no_license | dtkaczyk/dark-or-light | 24b604f8e44fb733f97df6bac399520d1003e8ec | ecf83d08601fe117420536017062522d14e3fc55 | refs/heads/master | 2021-01-09T08:03:02.898751 | 2017-02-02T05:48:06 | 2017-02-02T05:48:06 | 65,752,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,652 | r | features.R | basicFeatures <- c("Red", "Green", "Blue")
multFeatures <- c("MultRedGreen", "MultGreenBlue", "MultRedBlue", "MultRedGreenBlue")
ratioFeatures <- c("RatioRedGreen", "RatioGreenRed", "RatioRedBlue", "RatioBlueRed",
"RatioBlueGreen", "RatioGreenBlue", "RatioRedGreenBlue")
sqFeatures <- c("SqRed", "SqGr... |
003f3330a6c5871ef3f60b9bbd7f424a2f967317 | 3fefe890b546e1b9cbdc6daeed56f9ee121bbfd1 | /man/fetch_all_deputados.Rd | e945f32c4d9269d320f509d0cf02a5ddd4c90db5 | [] | no_license | analytics-ufcg/rcongresso | 0cc0078aebbdd57047e1d21c93e56b60128d2fd0 | d34877d8f7e7ef4da1ad9053d5391f9be02c2828 | refs/heads/master | 2021-12-24T07:32:39.539758 | 2021-10-18T14:45:43 | 2021-10-18T14:45:43 | 100,041,012 | 53 | 12 | null | 2021-06-28T18:06:16 | 2017-08-11T14:37:06 | R | UTF-8 | R | false | true | 707 | rd | fetch_all_deputados.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deputados.R
\name{fetch_all_deputados}
\alias{fetch_all_deputados}
\alias{fetch_ids_deputados}
\title{Fetches details abaout all deputys}
\usage{
fetch_all_deputados(ids_dep)
fetch_ids_deputados(legislatura_base = .LEGISLATURA_INICIAL)
}
\ar... |
1baa5b654950a121c52f1fc73e7b364bacb3b254 | f7794399168afc3d4a16f0514e04b7e1e9c09202 | /R/imports.R | 74a25dcaba4d3dec62f8825e624f99b61f82b7b5 | [] | no_license | ryapric/fcf | b90d8942f654406ed5ef773506270232fd0c9f00 | ceee2aa566151fc28bf373a17f8a701c5419be93 | refs/heads/master | 2020-04-07T22:21:09.092856 | 2018-11-26T22:04:33 | 2018-11-26T22:04:33 | 158,766,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 55 | r | imports.R | #' @import dplyr
#' @import rvest
#' @import xml2
NULL
|
d789470e18af9a0e5c0c09102e0095c19d0c9237 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/haploR/vignettes/haplor-vignette.R | dc32d111bc278e7d4392476b8460a033e949d4dd | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,546 | r | haplor-vignette.R | ## ---- message=FALSE, echo=FALSE------------------------------------------
#library(knitcitations)
#cleanbib()
#options("citation_format" = "pandoc")
#r<-citep("10.1093/nar/gkr917")
#r<-citep("10.1101/gr.137323.112")
#r<-citep("10.1093/bioinformatics/btv402")
#write.bibtex(file="references.bib")
## ---- echo=TRUE, e... |
bd16f2ecc95b8db1e477392be7f2f90476279724 | da240952753caf3a3b79e777b1bfe24140aaba86 | /ZAnc/make_rf_outliers_by_pop.R | 77e643c990d952cc5e77a8f9dec028835a934c58 | [] | no_license | cooplab/hilo | ea5ea9d472ee7cf2cab17aa83e8f568c54fce34c | 64483aaf0abd40d25846969b8732e07abf9b7667 | refs/heads/master | 2023-08-18T13:03:07.458675 | 2021-09-20T20:12:10 | 2021-09-20T20:12:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,521 | r | make_rf_outliers_by_pop.R | #!/usr/bin/env Rscript
library(dplyr)
library(tidyr)
library(bedr)
# this script identifies high introgression ancestry outlier regions
# in an individual population and shared across pops
# and outputs regions files for outliers
# from focal pop only (1pop)
# and from focal pop + at least 3 other pops (4pop)
# to be ... |
ff5dcd3ea68ccee2f35b8712ba610a45c85eee0e | da316d00f89f9481e7f3381326651594328c5061 | /functions/getCryptoHistoricalPrice.R | fe3c304034578cbeae123ee123f308d8a6639e1e | [] | no_license | strebuh/crypto_currencies_models | ab7a796459953dbfc2a65aed2cb4a24d75058ec4 | ee5b0f54c86b9279dd8436f2a836d51cf8142875 | refs/heads/master | 2023-08-12T17:23:01.771012 | 2021-10-17T00:27:16 | 2021-10-17T00:27:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 633 | r | getCryptoHistoricalPrice.R | getCryptoHistoricalPrice <- function(x){
# this function scraps the OHLC historical crypto prices from www.coinmarketcap.com
library(tidyverse)
paste0("https://coinmarketcap.com/currencies/",
x,
"/historical-data/?start=20130428&end=21000101") %>%
xml2::read_html() %>%
rvest::html_table(... |
bd178d4aadd8583fb787baf94d30390da8d729c8 | 2d4b32b315ef275119df1be0ea7daa350bb3e3f4 | /fxScrap/loopedScrapingFunction.R | 8a405955fc8c1e6dee48074e94055429e3d8e731 | [] | no_license | muchDS/FX-TS | ae190efc7ed1df5ccd8325ec4f8a0a9732c1b3fd | 2ae13f3cd88cf490178d3c88d60f1ecd3913be27 | refs/heads/master | 2021-01-22T07:57:00.315744 | 2017-09-10T12:40:03 | 2017-09-10T12:40:03 | 92,585,590 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,267 | r | loopedScrapingFunction.R | loopedScrapingFunction <- function(connectionObject, FXCssTagsVector, MICssTagsVector, rowId, localProxyDF, localInitProxyIP, localInitProxyPort){
source("readPage.R")
startTime <- Sys.time()
tryCatch(dbGetInfo(connectionObject), error = function(e){print("reconnecting");Sys.time();connectionObject <<- conn... |
b31cd474e1630e857ff97fc136a7edef250d3088 | 9b3cff0dd9a6e0402747cb68083f71bd3705ebe1 | /man/checkData.Rd | f4b120dc1318aed46ffb159545c886dc693352e5 | [] | no_license | cran/MPR.genotyping | f3656d7e298f5999b80e62ac15f2ac29c25c65d7 | 9d4112d6ddf825f9701d5631b3123b19ef39b67f | refs/heads/master | 2021-05-05T06:34:14.060691 | 2018-01-24T17:24:42 | 2018-01-24T17:24:42 | 118,804,856 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 623 | rd | checkData.Rd | \name{checkData}
\alias{checkData}
\docType{data}
\title{
Data for check
}
\description{
this is used to check up the genotype results in my example.
}
\usage{data("checkData")}
\format{
The format is:
chr [1:11948, 1:2] "A" "A" "C" "T" "A" ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:11948] "05... |
b4d8264d747c35ec655d103774ce2bf6e6869009 | 0e7a9c1aad4673d965f406accab79d887ce67843 | /app/data/2016-01-14/format/consistancy_check_V2.R | 24902d77002aa5ac55347f35406adee6558836bb | [] | no_license | anandgavai/ANDI | 6207d0442eebdf4d149c2bcb2c2f3a4be29920c5 | 6e4555f14cd45767c54ef4d700bf907aed8d0a34 | refs/heads/master | 2020-12-15T17:03:08.632888 | 2016-05-23T20:11:28 | 2016-05-23T20:11:28 | 39,182,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,531 | r | consistancy_check_V2.R | library (gdata)
library (dplyr)
require(RJSONIO)
library (jsonlite)
#df = read.xls ("//home//anandgavai//ANDI//app//data//2016-01-14//format//ANDI_betaTemplate_0303.xlsx", sheet = 1, header = TRUE)
df = read.csv ("//home//anandgavai//ANDI//app//data//2016-01-14//format//ANDI_betaTemplate_11_03_16.csv", header = TRUE)... |
9f3a4d505d52485edd993e6401b20ab32c899089 | 1e76886c729c7e0ae15cf18102fe0f614f9297e0 | /R/threshold_perf.R | 9ea652b3ae92059d02ea8cda7a27d8da5c342d1e | [
"MIT"
] | permissive | tidymodels/probably | 2abe267ef49a3595d29dd7fdbdf7c836b3103c8d | c46326651109fb2ebd1b3762b3cb086cfb96ac88 | refs/heads/main | 2023-07-10T13:09:55.973010 | 2023-06-27T17:11:22 | 2023-06-27T17:11:22 | 148,365,953 | 87 | 12 | NOASSERTION | 2023-06-27T17:11:24 | 2018-09-11T19:02:58 | R | UTF-8 | R | false | false | 7,151 | r | threshold_perf.R | #' Generate performance metrics across probability thresholds
#'
#' `threshold_perf()` can take a set of class probability predictions
#' and determine performance characteristics across different values
#' of the probability threshold and any existing groups.
#'
#' Note that that the global option `yardstick.event_fir... |
54b0f4054259c0d9a17180e0693092722171a9ee | 55654e444839976992cc3556ed54ae8f911fb413 | /plot1.R | 4d316203e5f32333c00e5e1dace6122f1df38c71 | [] | no_license | justin-price/ExData_Plotting1 | e66ad2835cd3f19a02299c2867b59999704f6da0 | ce7fcb38880d567301ca1f0391af27cffb213318 | refs/heads/master | 2020-12-15T10:11:19.787049 | 2020-01-27T01:55:53 | 2020-01-27T01:55:53 | 235,071,384 | 0 | 0 | null | 2020-01-20T10:06:20 | 2020-01-20T10:06:18 | null | UTF-8 | R | false | false | 631 | r | plot1.R | headers = read.table("household_power_consumption.txt",
sep=";",header = F, nrows = 1, as.is = T)
# reading index 2007-02-01 to 2007-02-02 only
power_consumption <- read.table("household_power_consumption.txt",
sep = ";", header = F, skip = 66637, nrows = 2880)
col... |
36fd66b8515b2e58405d49323ef13f56590cf476 | 6a8d76365adc20d81fd8016da8f2fc2e6635273d | /script.R | fe2394398052012a49973879656ebf7cea187efd | [] | no_license | davidbiol/Missing-data-talk | 94644b193978a8e2d67bd2e03c34c18339f7f6df | 955b8f54a1cebdfd8dfda385edd4a7eef6256f0d | refs/heads/master | 2023-08-29T08:52:15.707643 | 2021-09-22T21:54:00 | 2021-09-22T21:54:00 | 406,864,311 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,022 | r | script.R | ## Instalar paquetes de visualización de datos faltantes
install.packages("visdat")
install.packages("VIM")
## Instalar paquete en desarrollo de estimación de datos faltantes
# install.packages("remotes")
remotes::install_github("davidbiol/empire")
# library(visdat)
# library(VIM)
# Library(empire)
#----------------... |
082cdbfafeff4a5cbbc62a338c45e48a8fe36b1a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/runner/tests/length_run.R | 81ce2d5ca45599b0431b785bed93337a712bf148 | [] | 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 | 522 | r | length_run.R | context("Running length")
k <- sample(1:5,10, replace=T)
idx <- cumsum(sample(c(1,2,3), 10, replace=T))
test_that("length_run constant k",{
x1 <- rep(NA, 10)
x2 <- rep(NA, 10)
for(i in 1:10)
for(j in i:1)
if(idx[j] <= (idx[i]-3)){
x1[i] <- i - j
break
}
for(i in 1:10)
fo... |
35f2dbde8c7b8fc1e53c6f68c4dd7d14dde0e299 | cafa52c05f020af31985cfd1b8e2c676ea6e3baa | /lib/SmallRNA/proportionTest.R | 62acb75973675329a786eb6e5c66a0160a7d8dfc | [
"Apache-2.0"
] | permissive | shengqh/ngsperl | cd83cb158392bd809de5cbbeacbcfec2c6592cf6 | 9e418f5c4acff6de6f1f5e0f6eac7ead71661dc1 | refs/heads/master | 2023-07-10T22:51:46.530101 | 2023-06-30T14:53:50 | 2023-06-30T14:53:50 | 13,927,559 | 10 | 9 | Apache-2.0 | 2018-09-07T15:52:27 | 2013-10-28T14:07:29 | Perl | UTF-8 | R | false | false | 3,368 | r | proportionTest.R | # rm(list=ls())
# outFile='output'
# parSampleFile1='fileList2.txt'
# parSampleFile2=""
# parSampleFile3=''
# parSampleFile4=''
# parFile1='RA_4949_mouse.Category.Table.csv'
# parFile2=''
# parFile3=''
#setwd("C:/projects/composition_test")
library(reshape2)
library(ggplot2)
library(DirichletReg)
library(pheatmap)
... |
6daac75a8189f31ba614f5d73e21bbc0f7b57f9e | 37fb539825eb513562fd580e4c3573c141e774fd | /Plot3.R | 7a5eacca50ceabc528aa5abe2061c5bf153ee9d1 | [] | no_license | DonMof/ExData_Plotting1 | 4d68053f0a392a907292fb824acf2e2f0a84e704 | 7752a532f0fd88a0290fec79f9f5b5a74a7f6331 | refs/heads/master | 2020-03-23T06:00:06.350065 | 2018-07-30T06:04:31 | 2018-07-30T06:04:31 | 141,182,372 | 0 | 0 | null | 2018-07-16T19:11:13 | 2018-07-16T19:11:12 | null | UTF-8 | R | false | false | 1,675 | r | Plot3.R | plot3week1 <- function () {
#install.packages("dplyr")
#install.packages("data.table") # install it
library(dplyr)
library(data.table)
library(lubridate)
# Week 1 plot 1 file
# Read in the zip file
power_data <- read.table("household_power_consumption.txt",sep=";",header=T)
... |
d3a4c367f89e23bbdbf9b333abc8d559f1779adb | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /output/sources/authors/992/colbycol/ncol.colbycol.R | 392954923e4c00889b56de74d128192e6e5c4eb2 | [] | 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 | 477 | r | ncol.colbycol.R | #################################################################
#
# File: ncol.colbycol.r
# Purpose: Gets the number of columns in a colbycol object
#
# Created: 20090509
# Author: Carlos J. Gil Bellosta
#
# Modifications:
#
#################################################################
... |
b430e5b6d502cb5ef96b73492f444e472fba1972 | c504360a5e3127560c9c3038610664bacf431e33 | /R_code/stab_results.R | 87ea0b1c5f4beb624c599dd7ca15274e4e1dff17 | [] | no_license | gauzens/Intertidal_food_webs | 3c2342d79da9e54a88d2f575d0dd227a35f9f92b | 800d0363354e2b70ebde14d5e34c3d24476bfa93 | refs/heads/master | 2020-11-24T23:42:57.532551 | 2020-04-25T11:22:58 | 2020-04-25T11:22:58 | 228,392,473 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 26,071 | r | stab_results.R | rm(list = ls())
library(nlme)
detach(tab)
library(ggplot2)
library(measurements)
library(RColorBrewer)
library(viridis)
library("ggsci")
library(car)
error.bars<-function(x,y,xbar,ybar, coul)
{arrows(x,y-ybar,x,y+ybar,code=3,angle=90,length=0.05, col=coul)
#arrows(x-xbar,y,x+xbar,y,code=3,angle=90,length=0.05, col=c... |
50300c3b36dc32e8626d3a89c1ba8275c44ba57e | 5f66de9c67ebbf11de219b15663f631335584914 | /Estatítica/BasePaises_Discritivas.R | 5401ee521934a9c47b776eac5128db9062362fd9 | [] | no_license | ZecaRueda/FIAP4IA | da319cab3a7cbad3198d6f897530257c0bd7bab4 | 6edd82e08d64a55d9bf3ac193ce996a35f58ef90 | refs/heads/master | 2020-04-11T10:18:17.156568 | 2018-12-06T23:30:09 | 2018-12-06T23:30:09 | null | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 3,444 | r | BasePaises_Discritivas.R | # limpar memória do R
rm(list=ls(all=TRUE))
# mostrar até 2 casas decimais
options("scipen" = 2)
# Ler arquivo csv
paises <- read.csv("C:/Users/logonrmlocal/Documents/paulofranco/FIAP4IA/DADOS_Papercsv_1.csv", row.names=1, sep=";")
fix(paises)
#Verificando o formato das variáveis
str(paises)
#Estatísticas descr... |
f3419a78c4a0b6f18ef6074ccadac39ab54a9673 | ce787bd5433526b83f1ea4e0912aca346f181ae7 | /man/chi.mle.Rd | ce87c81d931f430bcf2b2da4078f4f9e26b0d979 | [] | no_license | wangyf/rseismNet | d43cc77382276cbba8ff225d4e748a5c3a93c65b | 34264097f3c1fe3ca5f78d8ae673599903418e1c | refs/heads/master | 2023-03-17T11:42:02.864201 | 2019-07-06T07:26:21 | 2019-07-06T07:26:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,899 | rd | chi.mle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fmd.R
\name{chi.mle}
\alias{chi.mle}
\title{\eqn{\chi}-value}
\usage{
chi.mle(m, mc, mbin = 0.1)
}
\arguments{
\item{m}{a numeric vector of earthquake magnitudes}
\item{mc}{the completeness magnitude value}
\item{mbin}{the magnitude binning... |
7af35a42f56f3af082d12f99c3437d84ba4fb7d4 | 2c9cb01e8fee85a5d4c1184bb9f7db1eda5bbaa5 | /R/greenampt.R | 35b086019cb3d2279c7e1ee20f9b15d974ef19fe | [] | no_license | Mactavish11/vadose | 8cc191528d148caee32b42aee9e45c7d1cc34945 | d8466273720822c8c1a6d6313175a20652bd5892 | refs/heads/master | 2023-03-17T19:45:15.855983 | 2018-02-19T14:04:57 | 2018-02-19T14:04:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,218 | r | greenampt.R | #' Green and Ampt infiltration parameter optmisation in R
#'
#' @description This function optimises Green and Ampt (1911)
#' infiltration parameters: Ks and G. It also predicts infiltration.
#'
#' @inheritParams philip
#' @inheritParams OFEST
#' @inheritParams BEST
#' @inheritParams lass3
#' @inheritParams ksat
#' @... |
5682758afd7ffa83e426a0596119f8c929dcc7ff | 24de8feb7a5c21c5b32536f5c0e048945ca522e5 | /script.R | 74107acb932d18e87db68754b3d1fd36f06deb77 | [] | no_license | thomasantonakis/Practical-Machine-Learning-CP | cec430fbc9034fadde509330acc43c1c7f4d3eab | 3cd2debe41142c4671bfe92b7c3e3f6a59a3ce05 | refs/heads/master | 2021-01-16T20:42:17.890661 | 2014-11-22T17:52:59 | 2014-11-22T17:52:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,842 | r | script.R | # Libraries
library(caret)
library(randomForest)
# Downloading the data
if(!file.exists("data")){
dir.create("data")
}
trainUrl<-"https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
testUrl<-"https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
if(!file.exists("data/train.csv"... |
97ddc08a0cf60a412dbddce85b9b030d97ddf881 | f0167ebc6323c601e75de50252c62f44d306ab2e | /R/survey_prep/r2f_score.R | 33acf2afdd78ea868712eff5e9c400619ae4e38a | [] | no_license | mattdblanchard/PhD-study-2 | bfd21386c2d84948953fb3555de400e94b71a5f9 | fa2c8eb2cddb6aaf40b0e65959cf67fae480e437 | refs/heads/master | 2021-07-05T14:14:07.137015 | 2021-05-18T01:29:10 | 2021-05-18T01:29:10 | 242,681,922 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,083 | r | r2f_score.R | source("R/survey_prep/r2f_neg.R")
source("R/survey_prep/r2f_pos.R")
# Need to create a new itemnum variable that is uniform across both frames
# some participants did not complete both frames of R2F
# need to remove these teams and calculate vars using those that compelted both Pos & Neg
# used the following code to i... |
9c1c9f1df51bdafc9cbfec8c60fe7dc99ab705c3 | 9beb6005d6581bb534b6eef49ed82296499518a7 | /16_Modelo_Estadistico_Regresion_R.R | 4442e9e0e81d51e4ff5ff06b2a064ad1129e5d95 | [] | no_license | BidartMG/R-Mas-Scripts-Practicas | 68ca1c635d235cfcbe932afdba4e3235299cc6e8 | af53bff823d372206cfcc6b51867b1d25a6ef980 | refs/heads/master | 2022-12-25T06:52:48.642663 | 2020-09-29T00:22:18 | 2020-09-29T00:22:18 | 297,231,643 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 837 | r | 16_Modelo_Estadistico_Regresion_R.R | # modelos
# cargando paquete para analizar datos
library(tidyverse)
# cargando datos a entorno
data("Orange")
# cargando datos a entorno
head(Orange)
# problema/pregunta
# Cuanto medirá la circunsferencia, en promedio, de
# un árbol de naranjas a los 800 días de plantarlo
Orange %>%
ggplot(aes(x = age,
... |
7d1345693da56c131213a25c6ba38f502550f5d4 | fb508866590bcd29193226f8100a3dc77f923c93 | /R/dm.bc.script.R | 3dc77357c128c0e21ccbadcae0c8be3bc3674942 | [
"MIT"
] | permissive | bostasie/WFCTSI-Public | 45f8ebcf4c15e810c27f31d72a176e0cf7fa6482 | 8d4c13f0c077af5ec8cf69ce5098500a2e76b71d | refs/heads/master | 2020-05-26T04:54:51.213043 | 2018-05-10T14:43:41 | 2018-05-10T14:43:41 | 84,992,771 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,583 | r | dm.bc.script.R | ######################
#MERGE DATA FROM I2B2#
######################
#import i2b2 file, merge and clean data in preparation for model
# data setpredefined as patients with diabetes type II between ages of 40 and 90 on any medication
#outcome- bladder cancer
#exposure - tzd
#covariates - gender, race, age, bmi, sm... |
39e7c012762e8e7ba6d70a5a4a69fe90bb136ae1 | 5debcf7061d78d9cfd29372e9d6cb505c166a1d3 | /statsSensitivity.R | 604b9d771373e33cc451133dc79de641544149e3 | [
"MIT"
] | permissive | NadineJac/gaitEEGfootprint | 481222df5438d49a302b12acf51cf02dabe72a30 | 8dcad94adf409968274342a1f4553db19724e4b6 | refs/heads/master | 2023-06-11T10:58:11.958004 | 2023-05-30T09:07:30 | 2023-05-30T09:07:30 | 241,641,737 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,478 | r | statsSensitivity.R | # statistical comparisons of the footprint sensitivity analysis
#
# directory: */derivates/footprint/group/results
#
# developed in R (v4.0.1) by Nadine Jacobsen, nadine.jacobsen@uol.de
# June 2020, last revision July 1, 2020
#PATH = file.path("E:", "nadine", "test_footprint_BIDS") # add your path to the BIDS ... |
d61164eac98089bb1d4238e2849a96d1a5723c37 | 2f60f4273dcf277a9579cc0cb10a5182f59280c6 | /WEEKS-8_9_R/02-read_data.R | 1c20c184bddaf0387efb2a3dd91335eeb0f431d0 | [] | no_license | chirlas24/Master_Data_Science | b82bb40c3d51f2a288ec7dc76721a2e4dbaf1f20 | 719fc2c4328478a1ad8fa51232a338ad35b65829 | refs/heads/master | 2020-04-08T00:54:28.541418 | 2019-04-28T17:27:30 | 2019-04-28T17:27:30 | 158,873,183 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,743 | r | 02-read_data.R | ##########################################################################
# Jose Cajide - @jrcajide
# Master Data Science: Reading data
##########################################################################
list.of.packages <- c("R.utils", "tidyverse", "doParallel", "foreach", "sqldf")
new.packages <- list.of.pac... |
9dc2dcaa871234e2b2f7498dce636cf93039e6a0 | 0438fa2503105ab4ac26171bb5c018120007d386 | /R/oolong_intro.R | f780bc63f3bdaa0f5dccf4892648cb3d0f08ca78 | [] | no_license | bachl/workshop_topicmodels | be611105bca4beef63c77c3d64e1e9e511cb19d1 | 7819121d81bc034961cc0ba73471ebb864c84b0b | refs/heads/master | 2022-11-21T08:18:51.878185 | 2020-07-21T09:12:41 | 2020-07-21T09:12:41 | 273,678,089 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,302 | r | oolong_intro.R | ## ---- oolong-intro
m37 = read_rds("R/data/model37.rds")
out = read_rds("R/data/out.rds")
# Erstellen eines Tests
m37_oolong = create_oolong(
input_model = m37, # Modell, das wir testen wollen
input_corpus = out$meta$txt, # Korpus, auf dem Modell basiert; können wir aus "out" für stminsights nehmen
use_frex_wo... |
1e713b0a0c5595ef1d02126e2920cc9984e2a4bc | 3b6b122a29011054de8dfd7e4fd2b2087be4407c | /man/mle_foot.Rd | 06ba71fc0c254b28e2c5e1bb15971e9973784d12 | [] | no_license | LeoEgidi/footBayes | e0845ec52d934e848af595af87200043391062c1 | c3c9b3dd49fe2aa75ab379d60f9ac1d8bbbfa3be | refs/heads/master | 2022-12-16T18:45:46.955707 | 2022-12-13T14:36:37 | 2022-12-13T14:36:37 | 219,478,427 | 34 | 5 | null | 2022-11-11T13:24:32 | 2019-11-04T10:47:59 | R | UTF-8 | R | false | true | 2,647 | rd | mle_foot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mle_foot.R
\name{mle_foot}
\alias{mle_foot}
\title{Fit football models with Maximum Likelihood}
\usage{
mle_foot(data, model, predict, ...)
}
\arguments{
\item{data}{A data frame, or a matrix containing the following mandatory items: season, ... |
3bc9ed253a3e13d368e8e245a9faa05a6fa66e44 | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.serverlessapplicationrepository/man/list_application_dependencies.Rd | fcd125a21b0cdadfa483802855b0a25782c042d0 | [
"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 | 965 | rd | list_application_dependencies.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in
% R/paws.serverlessapplicationrepository_operations.R
\name{list_application_dependencies}
\alias{list_application_dependencies}
\title{Retrieves the list of applications nested in the containing application}
\usage{
list_application_dependen... |
2bbc0877e0a0a1505f60515b6d54c8e0f11fe684 | 30d2ed023fed988d04dbb83e66edba3df96dad74 | /redd_substrate/redd_substrate_srv.R | 5de37f43da10c2a6ece9ef03cf80905355266c6f | [
"MIT"
] | permissive | arestrom/Chehalis | 7949a449a0e4ec603b98db72b9fbdefd0c39529a | c4d8bfd5c56c2b0b4b58eee3af7eb1a6b47b2695 | refs/heads/master | 2023-05-31T05:58:51.247660 | 2021-06-29T17:26:19 | 2021-06-29T17:26:19 | 295,434,189 | 0 | 0 | MIT | 2021-05-26T22:11:54 | 2020-09-14T14:03:02 | HTML | UTF-8 | R | false | false | 20,901 | r | redd_substrate_srv.R | #========================================================
# Generate lut select ui's
#========================================================
# Substrate level
output$substrate_level_select = renderUI({
req(valid_connection == TRUE)
substrate_level_list = get_substrate_level(pool)$substrate_level
substrate_leve... |
ddbf033063698068e96205662514c93bac74c2a5 | 158782c06de5cf63cb4ded26991acdea475894da | /R/ClassificationTreeScript.R | 6c59814d9f30d7a5680a907e53cda18e7e98023e | [] | no_license | jackmoorer/Project | 459bda911bd6a46c637f6935f09469f11ab00264 | 9d48f93fdd113c35a6d79f3422ff5b024f51d800 | refs/heads/master | 2021-03-24T09:33:16.049563 | 2017-12-12T05:29:21 | 2017-12-12T05:29:21 | 112,793,017 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,993 | r | ClassificationTreeScript.R | #Title: Classification Tree Script
#Discription: Run code for Classification Tree part of project
#load packages
library(ggplot2)
library(tree)
library(ROCR)
#read in clean data
train <- read.csv("../data/clean_train.csv", header = TRUE)
#remove numeric predictor
train_tree <- train[,-ncol(train)]
#intialize basic ... |
474dc4784cf5f772e624f4615062980d0c203341 | dcaf3f2986f96a68f9f7f351a9be2ff31f37acbf | /server.r | 183863babf347f9259e6a2508b999e0d5a19d8dd | [] | no_license | glesica/exploring-phillips | de2aa9dab834dda48490220441fecbf43a2a26f9 | 61671d2b336892a954c597006cf0e767a4dae4aa | refs/heads/master | 2016-09-06T03:10:46.463617 | 2013-09-08T18:19:09 | 2013-09-08T18:19:09 | 12,630,982 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 777 | r | server.r | shinyServer(function(input, output) {
customDataset <- reactive({
load.phillips(input$yrs[1], input$yrs[2],
clusters=input$clusters, df=full.df)
})
output$phillipsPlot <- renderPlot({
plot.phillips(customDataset(), lag=input$lag, labs=input$labs)
})
output$inflationHist <- renderPlot... |
b1421a96d778c95be069dc880aee81620b79473b | 47a8dff9177da5f79cc602c6d7842c0ec0854484 | /man/CellSelector.Rd | 23713204f4c7c14afa46740ad1f59cd82bddef79 | [
"MIT"
] | permissive | satijalab/seurat | 8949973cc7026d3115ebece016fca16b4f67b06c | 763259d05991d40721dee99c9919ec6d4491d15e | refs/heads/master | 2023-09-01T07:58:33.052836 | 2022-12-05T22:49:37 | 2022-12-05T22:49:37 | 35,927,665 | 2,057 | 1,049 | NOASSERTION | 2023-09-01T19:26:02 | 2015-05-20T05:23:02 | R | UTF-8 | R | false | true | 1,248 | rd | CellSelector.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{CellSelector}
\alias{CellSelector}
\alias{FeatureLocator}
\title{Cell Selector}
\usage{
CellSelector(plot, object = NULL, ident = "SelectedCells", ...)
FeatureLocator(plot, ...)
}
\arguments{
\item{plot}{A ggplot2 plot}... |
b453fd14cea1a83d348976a8f298fc324b06284f | d474efb74fd5268fd908a2a5394a8ecc97e28f3b | /R/git/medips.R | a62420c4ad5e703e4e4cdcd5aa72d88670af2681 | [] | no_license | bradleycolquitt/seqAnalysis | e1f2fbefa867ee11a51801aeeaf57ebc357a0057 | d2c37fb0609754a0ec4e263dda27681717087523 | refs/heads/master | 2021-01-01T05:42:03.060788 | 2017-05-28T02:47:58 | 2017-05-28T02:47:58 | 2,284,790 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,776 | r | medips.R | library(MEDIPS)
library(BSgenome.Mmusculus.UCSC.mm9)
medipsToGRanges <- function(medips.object) {
chrs <- c(paste("chr", 1:18, sep=""), "chrX", "chrY")
which.idx <- genome_chr(medips.object) %in% chrs
genome.chr <- Rle(genome_chr(medips.object)[which.idx])
genome.rng <- IRanges(start=genome_pos(medips.object)[... |
7133fb58df1f068714037a5db997f2339c2fee8a | f997b825b89a191ef89709870065d375dd84358d | /man/datastamp-package.Rd | a4947ff72e2e853d457afc7c48075a799eb62daf | [
"MIT"
] | permissive | teunbrand/datastamp | 1557b85d423c7a892696a4900c3e239870d1bf72 | ebd6c3bc3a3bd9efe08bc615cc7d9e38ea252ebd | refs/heads/master | 2022-12-28T19:44:54.396093 | 2020-10-19T08:49:09 | 2020-10-19T08:49:09 | 304,337,082 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 834 | rd | datastamp-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datastamp-package.R
\docType{package}
\name{datastamp-package}
\alias{datastamp}
\alias{datastamp-package}
\title{datastamp: Stamping data with metadata}
\description{
The goal of datastamp is to make it easy to attach some metadata to
R... |
f7a3d2f4b9a229f64cc55523b41f5ba968758eba | 633f3cf081277fcc3fad31e19b6717dfe2b11915 | /Bayesian_MS/BEDms/OptimDataFilesGenerator.R | 52e2fddd67c18475bc6f4ffa5cd46ac25ab67367 | [] | no_license | csynbiosysIBioEUoE/ODMSiSY_2020_SI | a26620cf8cd908ec9b988c2908c4c2d1a0433cf3 | 5f9353a64fa7a0ca92bfd007d1c201c0aaf90fa2 | refs/heads/master | 2022-12-24T16:13:20.689940 | 2020-09-08T12:10:04 | 2020-09-08T12:10:04 | 276,348,122 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,680 | r | OptimDataFilesGenerator.R | ################################# Generate Data Files for Optimum Experiments #################################
# Function used to generate the necessary CSV Data Files (Events_Inputs, Inputs and Observables) in order to generate plots
# and simulate the three different models to a specific set of inputs using the fun... |
c3115927d43618480a381dc69050a8b2377726c6 | 73613f0527f130ed04c641b8408620fca49cd8e8 | /R/tabulate_chemo_effects.R | 785c2c2cb86ae154727b133e8cbdea90b59344da | [] | no_license | cobriniklab/rb_exome | 86b7be48dbc518059ffdb9e715cb6c782cdb475e | 8ffe630d119fac1ba7fea0816bbcd7420d41dc7d | refs/heads/main | 2022-12-23T10:38:22.642801 | 2022-03-19T00:17:39 | 2022-03-19T00:17:39 | 151,138,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 861 | r | tabulate_chemo_effects.R | ##' .. content for \description{} (no empty lines) ..
##'
##' .. content for \details{} ..
##'
##' @title
##' @param reynolds_snv
##' @return
##' @author whtns
##' @export
tabulate_chemo_effects <- function(reynolds_snv) {
reynolds_post_chemo_samples <- c("194-CL", "196-CL", "203-CL")
reynolds_mean_var <-
... |
e93ef53a2f02071149996d7f9075c84dca335320 | 3e3ab1934554bb4dd1ba876f69fa636ce17f21f7 | /Biostatistics-with-R/Intermediate Linear Regression - Predictors of Body Fat.R | 84514ae0597ef5dfafdf985d35caf29159f794c4 | [] | no_license | MadzivaDuane/Academic-Projects | 12f4d87e72dcb9885188f533b4cd82b26f3a4b64 | 78fa58467236ca23ea5364ba399dea3f27a467ab | refs/heads/master | 2023-04-20T18:40:27.423875 | 2023-04-04T22:52:24 | 2023-04-04T22:52:24 | 267,905,746 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,878 | r | Intermediate Linear Regression - Predictors of Body Fat.R | #Predictors of Body Fat
data <- read.csv("~/Documents/Academics/Other/BIS 505 Biostats for PH II/Datasets/body_fat.csv")
body_fat <- data; head(body_fat); dim(body_fat)
#website on using cook's distance or dffts to identify outliers:
#https://cran.r-project.org/web/packages/olsrr/vignettes/influence_measures.html
#... |
43078230d0aa0c7082cacd10e47821e57347064f | 90d339192c3d427dfbc9363e7b1bb637fe831b55 | /man/sam.gen.ncpen.Rd | 048d0183aa6baa4442437666801de231b8caad8b | [] | no_license | zeemkr/ncpen | acd4c57fb3d78a8063ca2473306097488c298039 | e17a0f5f2869d3993a3323e7a269dbb1819201ac | refs/heads/master | 2021-03-16T09:22:17.260173 | 2018-11-19T00:20:49 | 2018-11-19T00:20:49 | 107,593,275 | 9 | 0 | null | 2018-11-19T00:17:27 | 2017-10-19T20:09:23 | C++ | UTF-8 | R | false | true | 2,127 | rd | sam.gen.ncpen.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ncpen_cpp_wrap.R
\name{sam.gen.ncpen}
\alias{sam.gen.ncpen}
\title{sam.gen.ncpen: generate a simulated dataset.}
\usage{
sam.gen.ncpen(n = 100, p = 50, q = 10, k = 3, r = 0.3,
cf.min = 0.5, cf.max = 1, corr = 0.5, seed = NULL,
family = c(... |
d2160b3efd7816d7457bdf0abd490b644710c70f | 1b88a1b82041a657fe526f4d670bab12dfce4b14 | /Assignment_two/Plot.1.R | 1f87c4f8543baf0a9d7150119c9e9327a8388e0e | [] | no_license | 3Dan3/Coursera-Exploratory-Data-Analysis | 05746f046429aef88c14748140086bb4930166f3 | 9c4737fef0ddd5eb965184df5140697ec6885139 | refs/heads/master | 2021-01-21T01:52:36.162793 | 2017-07-07T18:27:21 | 2017-07-07T18:27:21 | 96,565,351 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 909 | r | Plot.1.R | ### Data ###
#Load required packages
library(downloader)
suppressPackageStartupMessages(library(dplyr))
#Download and store data into R
dataset_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download(dataset_url, dest = "data.zip", mode = "wb")
unzip("data.zip", exdir = ".")
NEI <- re... |
4c4a125ae0642ac3b427db965bf26ccf1f6ba9a0 | 499a9e4122dd4524b5fdb7a262ad64ae1e072314 | /Script/Old/data_combineUGA-VL.R | f89a5f06906fc225c7163eee0d26a33865b2b173 | [] | no_license | vincentlinderhof/NutritionUGA | 9dbda98e8f59c7f72ee55885054ca21188873465 | 78d0a32c4b753428ef0c76db282c437e670d1b1a | refs/heads/master | 2020-09-10T04:51:51.614791 | 2016-11-28T11:17:50 | 2016-11-28T11:17:50 | 67,415,327 | 0 | 1 | null | 2016-11-28T10:06:17 | 2016-09-05T11:30:11 | R | UTF-8 | R | false | false | 2,643 | r | data_combineUGA-VL.R | # -------------------------------------
# creating a panel dataset and a
# balanced panel dataset with the waves
# of the UGA data (three waves)
# -------------------------------------
#Tom
#dataPath <- "C:/Users/Tomas/Documents/LEI/"
# LEI server dataPath
#Vincent at home
dataPath <- "D:/Models/CIMMYT/UGA/Data"
set... |
276ba4b2edc8cd152bf05b22b38c57595d633757 | dc3665fa074c42cd25d3eca313b90f4ae4482520 | /weight_from_string_list.R | b7d8a330a9765512fee11048cabe88041814b2f4 | [] | no_license | andfdiazrod/darkweb_functions | 5f6a350e6902bfbb9a9ce8886425ed62c48dbf3e | b8f20f47c916494103a9f7f2f418ed2a39f80b6d | refs/heads/master | 2022-05-16T02:01:45.786947 | 2019-11-29T16:53:37 | 2019-11-29T16:53:37 | 216,660,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,962 | r | weight_from_string_list.R | weight_from_string_list <- function(string_list){
weight_words_1 <- c('g\'s','gr','g.','g ','gs','gz','g','gm','gram','grams','gramme','oz','ounce','ounces','mg', 'kg','kilo')
conversion <- c(1,1,1,1,1,1,1,1,1,1,1,28.3495,28.3495,28.3495,1/1000,1000,1000)[order(nchar(weight_words_1),decreasing = TRUE)]
weight_... |
f1aa9ec68bbf684850cb386961b9ba6bafc62e8f | d0b099dca80322316a1dd5083bb0bad993d9c206 | /scripts/preprocess.R | 4b18b1b24e94a3c046cd4c3382150d9e90c4ba6b | [
"BSD-3-Clause"
] | permissive | lmsac/BacteriaMS-mixture | c84c4e189a826ad6ebcf1fbd5f342f1efcb9bdb1 | 0b1379ad4ccda74386f8b8f27d09b12447cb0250 | refs/heads/master | 2021-07-13T11:30:28.183455 | 2020-05-25T08:22:51 | 2020-05-25T08:22:51 | 142,287,626 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 956 | r | preprocess.R | #' setwd(...)
local({
mz.lower = 4000
mz.upper = 12000
window = 0.015
offset = 0.5
dir.create('raw')
# dir.create('normalized')
dir.create('peaklists')
lapply(list.files(pattern = '.txt'), function(file) {
raw = read.table(file)
raw = crop.mz(raw, mz.lower = mz.lower, mz.upper = mz.u... |
212bfd3d92cf46fdc021cabfb14f3f06c61a0386 | 936617f15596e0cebec03c21457d3182c79c45ba | /datanalysis/.Rproj.user/8E5AEE1C/sources/per/t/DDA697CC-contents | eb0a7c57aa3f561a888dc9f07b5e8969c330c8c2 | [] | no_license | raizaoliveira/dados-mestrado | 2967925f0902aa4cdff1e233894dd2edc8045cfd | e446303d04b084c07f0f87c82fca60328197d1cd | refs/heads/master | 2022-01-12T03:27:39.205961 | 2019-06-12T19:35:00 | 2019-06-12T19:35:00 | 190,767,552 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,629 | DDA697CC-contents | read_files <- function() {
require(miscTools)
folder <- "D:\\Camila\\Documentos\\IMPACTO\\datanalysis\\CAP3\\"
file_list <- list.files(path=folder, pattern="*.csv")
for (l in 1:length(file_list)){
variabilities <- read.csv(paste(folder, file_list[l], sep=''), na.strings = c("","NA"), header=FALSE, ... | |
d4746bfc61c0d7859fa3437bf85282baf15ba05a | f90071514fd6defd84cbba44946b51a1c23f8f76 | /plot4.R | 9067cfcf2d522d616e13678fada25ba9cba665e3 | [] | no_license | nokka09/exploringdataanalysiswk1 | 8fccb88647f4dcd127c1c8ed311ad34345243cb9 | caae38cd281860d50bcf6eb3f76c8f2ccd41b879 | refs/heads/master | 2020-11-25T06:37:11.346154 | 2019-12-17T05:46:16 | 2019-12-17T05:46:16 | 228,541,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,117 | r | plot4.R | # First we create a directory for our dataset
if(!file.exists("./dataset")){dir.create("./dataset")}
# Then we download the file for the dataseth
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileUrl,destfile = "./dataset/sourcedata.zip")
# Next we unzi... |
73077a73bdeac115c0eb0d6c5974ef4fdfdaa040 | 77c10327c17b9f60397f91a7259d3e0ec45f1fbe | /integration/integrate.R | e807a6cdaec2848b3da67dd9d1f7470d91cec44c | [] | no_license | jasminalbert/Albert_ReumanRepo | 5c435dbf897871d6850ba9bdd295c256085a253d | e00f75a49568b122dd04e4a5e7de7bbaeb72cce5 | refs/heads/master | 2023-07-17T21:55:01.727810 | 2021-09-01T17:28:00 | 2021-09-01T17:28:00 | 243,134,004 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,933 | r | integrate.R | ? integrate
install.packages("cubature")
library(cubature)
pdf1 <- function(x, mu=0, sigma=1){
pdf1 <- 1/(sigma*sqrt(2*pi))*exp((-1/2)*((x-mu)/sigma)^2)
return(pdf1)
}
gr <- function(u, delta=0.5){
gr <- log(1-delta+delta*exp(u))
return(gr)
}
integrand_rsharp1 <- function(u, mu=0, sigma=1, delta=0.5){
pdf1 <- ... |
fc9a11ba6db25d05c57f8f050a8cc0127886fd0b | 4848ca8518dc0d2b62c27abf5635952e6c7d7d67 | /R/V_STL_sh_3si.R | b46ba8ebaa38126b1c01d117494cf5a5795a30a7 | [] | no_license | regenesis90/KHCMinR | ede72486081c87f5e18f5038e6126cb033f9bf67 | 895ca40e4f9953e4fb69407461c9758dc6c02cb4 | refs/heads/master | 2023-06-28T00:29:04.365990 | 2021-07-22T04:44:03 | 2021-07-22T04:44:03 | 369,752,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,674 | r | V_STL_sh_3si.R | #' Straight-through Traffic Using a Shared Left-turn Lane on Access Road with Only Straight and Left-turn Shared Lanes at 3-way Signalized Intersection
#'
#' On an access road with only straight and left turns at a three-way signal intersection,
#' if there is a public lane for turning left,
#' the amount of st... |
312699eede8bf053875356fff6771046707ef565 | 7d105c9c74252ed1005f6cd3af441960eb888287 | /man/download_schellens_et_al_2015_sup_1.Rd | 0303faa714c364bf24b5c4db1d7b047e98c69d8a | [
"MIT"
] | permissive | richelbilderbeek/bianchi_et_al_2017 | 8a892d50b1a8f92c6e5b8fc2e1d2a6ac82ee3872 | 2e1460fe84dd3650108755273942e96201d21206 | refs/heads/master | 2022-12-26T12:40:37.420782 | 2022-12-13T09:14:18 | 2022-12-13T09:14:18 | 253,762,495 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,022 | rd | download_schellens_et_al_2015_sup_1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/download_schellens_et_al_2015_sup_1.R
\name{download_schellens_et_al_2015_sup_1}
\alias{download_schellens_et_al_2015_sup_1}
\title{Downloads the XLSX file by Schellens et al., 2015}
\usage{
download_schellens_et_al_2015_sup_1(
url = "http:... |
6c83f23d22c0e74e1c6037aa2d00cb810ee26dd9 | 71c4400f7cd574bf38c8a0ec1fe355e3648d62f5 | /Pierre_Casco_HW9.R | fa3040d877d78cea2d974a6e56a7ba1ee864b7c3 | [] | no_license | PierreCasco/SVM-lab | 35b35f37e5f0c426a0dce4d14d174663271215f2 | a84a11a39d1dc95cfa80659452feaa684ff92e0c | refs/heads/master | 2020-04-27T15:17:10.963868 | 2019-03-14T00:52:00 | 2019-03-14T00:52:00 | 174,440,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,937 | r | Pierre_Casco_HW9.R | library('arules')
library('kernlab')
#Load the air quality dataset
aq <- airquality
aq[is.na(aq)] <- 0
#Study data set
str(aq)
#Prepare train and test data sets
numrows <- nrow(aq)
cutoff <- (numrows/3*2)
randIndex <- sample(1:numrows[1])
aq.train <- aq[randIndex[1:cutoff],]
aq.test <- aq[randIndex[(cutoff+1):numro... |
f52805b6ececdb8967e14c72a238b023a5f6c50f | d5e085247744171e340504e0bf8720e2c7f82b1b | /R/fars_summarize_years.R | f3fd442076fff19b92cc7c5807c90b31640c06e6 | [] | no_license | jcpsantiago/FARSr | 9a1bfd0588585e8b1247c63a0ca055f81421e886 | 908bb763e6abe85d7f7d39b9429df4b54472bcc8 | refs/heads/master | 2020-05-22T03:16:57.059092 | 2017-03-11T17:46:21 | 2017-03-11T17:46:21 | 84,594,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 675 | r | fars_summarize_years.R | #' Number of accidents per year and month.
#'
#' This function will summarize the number of accidents for each month in each year of \code{years}.
#' If the user provides an invalid year, an error message will be printed.
#'
#' @param years A vector with the years of interest.
#'
#' @return A tibble with the number of ... |
23e6037c4464e1b1bc8c7d8793101501fa2c0f61 | de17eb3f7b45bb9ea5e2db8a2bcd1f13fe3689b9 | /server.R | 6016f06f9656c8d5abc1f5f651f4e2c0c419df96 | [] | no_license | kuzmenkov111/Shiny-login-page | 5c04781d3e55863ded64d9d56636a91638d536c9 | 9c8a83e60397b811e43df6231963074eb185b563 | refs/heads/master | 2020-08-22T13:50:28.379179 | 2019-08-01T13:14:44 | 2019-08-01T13:14:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,652 | r | server.R | library(shiny)
library(V8)
library(sodium)
library(openssl)
library(rJava) #For sending an email from R
library(mailR) #For sending an email from R
library(DBI)
library(pool)
library(RSQLite)
#Database
sqlite_path = "www/sqlite/users"
pool <- dbPool(drv = RSQLite::SQLite(), dbname=sqlite_path)
onStop(function() {
... |
57882f06763f502adbbf96f49ee961e1dc510298 | 1e48c563b2b9c723ed2a234df90f1dcd9338e6c3 | /R/request.R | 131b13ecd6843f1bcdaac1edfb5e6f3619de8240 | [] | no_license | bobjansen/mattR | e87f254d9bc54022a83ea4dc7eb1561528e3c956 | 9dfba5bd5436e0954b5dab77ba283f1adc94c13d | refs/heads/master | 2021-01-20T06:04:58.701879 | 2018-04-03T19:47:09 | 2018-04-03T19:49:20 | 101,481,868 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,240 | r | request.R | #' Extract Parameters from a request
#'
#' Extract the parameters from a request (GET, POST and from the URL).
#'
#' @param request The request object from which to extract the parameters.
#' @return A list of extracted parameters.
#'
#' @import shiny
#' @importFrom utils modifyList hasName
#' @export
extractParameters... |
9a6b18666c4a98fbeb8f231f260ab02c653eb8cb | b5cad150733fd310e8bf8d8802a6aa94cf735ff3 | /R/do.transform.R | a86d2828d11061a3613123bbf990fbf177d11bee | [] | no_license | yannabraham/cytoCore | 187242e0d24c53275da9c203e2cb43a2affb75a2 | 6cd18d96ec5aa52c6c4308e8e12109e6309fad00 | refs/heads/master | 2021-01-10T01:12:50.951843 | 2015-11-26T22:05:28 | 2015-11-26T22:05:28 | 46,948,430 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | r | do.transform.R | do.transform <-
function(ff,cols=NULL,type=NULL,fun=arcsinhTransform()) {
if(!is.null(cols)) {
if(!all(cols %in% parameters(ff)$name)) {
warning("Some column names are not found")
}
cls <- which(parameters(ff)$name %in% cols)
} else if(!is.null(type)) {
if(!all(type %in% parameters(ff)$type)) {
warning(... |
6b6ca1607ff4b3b4525b10bac8063c891f0c3b2e | bc1665e1cbe713412e707020da4bf4b46b755796 | /fleschkincaid/esda_flesch.R | c34888c21c9f33443178a51e84137b2e9302c166 | [] | no_license | rheimann/UMBC | 421480eef9cbe7106f5e5b0f7743d567616747a2 | 51e4aa8537e0a24d1c72ceff4c34d814d1a868be | refs/heads/master | 2020-12-24T14:27:03.890648 | 2014-05-09T00:54:40 | 2014-05-09T00:54:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,669 | r | esda_flesch.R | #### ESDA Example Fletch Kincaid ####
install.packages("spdep", dependencies=TRUE)
require(spdep)
require(maptools)
geo.fk <- readShapePoly("/Users/heimannrichard/Google Drive/GIS Data/flesch_kincaid/TwitterReadingCNTYJoin.shp",
proj4string=CRS('+proj=longlat +datum=NAD83'))
# colnames(geo.f... |
0b5b41519a1d64a16ff1e86540b32e86e671ed87 | 0be6957e9e66f84aa906f351a0a8c48260cb15ba | /meansd.R | 38ddb50622b9f7456065a7a1ceff344a77e31f93 | [] | no_license | nate-koser/Yoruba-project | 426e57be3a85572137b04a5465ff3fff8919ceb0 | a7401c1fabc660fa75f23dd96d0d11a07377296c | refs/heads/master | 2022-05-08T23:15:23.411763 | 2019-06-23T19:37:56 | 2019-06-23T19:37:56 | 173,513,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,947 | r | meansd.R | source("datatidy.R")
#CV----------------------------------------------------------------------------------------
#means, sd, various ------------------------------------------------------------------------
#mean + sd vowel durations
mean(Ltones$target_dur, na.rm = T)
sd(Ltones$f0_2, na.rm = T)
mean(Mtones$target_dur... |
d9c288578ee9bb346608fe3bb6c31d4ee11d3592 | edecc93bdb59672ef2ae77cd859ef0056e1f1ffc | /ui.R | 7919de43dbc7fc0e22c6e654e55498c463c7717f | [] | no_license | Lakshmi-Kovvuri/Data-Science-Capstone-final-project | 240be42d6124a9e1297abdd3a3b680f1007c0691 | 6205bf03c224bc1d7a12f4084ead63da13e2f83e | refs/heads/main | 2023-01-07T23:42:58.811323 | 2020-11-02T05:23:45 | 2020-11-02T05:23:45 | 308,567,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,889 | r | ui.R | suppressWarnings(library(shiny))
suppressWarnings(library(markdown))
shinyUI(navbarPage("Coursera's Data Science Capstone: Final Project",
tabPanel("Next Word Predictor",
HTML("<strong>Author: Lakshmi Kovvuri </strong>"),
br(),
... |
5038f4d8aff5397eed3dc2269153346a16f3c9a6 | 9cc58a8eb35ba76bfac93e44722d859a10b1f064 | /pipeline/getDist.R | 7d1d57e57d57df9af15e7ad2d09ee5ee09bef5c9 | [] | no_license | gradjitta/WorkingCode | 8400a3a7c8cd7b1145d382d381cc5ff8c4b0b289 | 64e07620b5894889b0b6a15d12c311839895ca86 | refs/heads/master | 2020-06-05T12:15:29.651940 | 2014-12-31T20:51:19 | 2014-12-31T20:51:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 319 | r | getDist.R | getDist <- function(g1,g2,gn, cSize) {
source("getBoundingBoxc.R")
ln <- getBoundingBoxc(gn, cSize)
l1 <- getBoundingBoxc(g1, cSize)
l2 <- getBoundingBoxc(g2, cSize)
dist1 <- sqrt( ((ln-l1)[1])^2 + ((ln-l1)[3] )^2)
dist2 <- sqrt( ((ln-l2)[1])^2 + ((ln-l2)[3] )^2)
ret <- c(dist1, dist2)
ret
}
|
ab927bdeb5258fea8fed63ca1922b9fee2899bb7 | b9fb1d757a4faed32cd5b7a8572c45c442ca44ed | /report_util.r | 8a22cb3256674d937fbbd762f069e835c08784c7 | [] | no_license | nesl/gprstest | 6518db01b909f6f98c6e1ac2a96eb72a55101557 | 326347b85d01f95e0552f6ecdcfd5de23d28933c | refs/heads/master | 2021-01-22T04:34:30.419109 | 2007-06-14T22:30:47 | 2007-06-14T22:30:47 | 12,227,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,711 | r | report_util.r | library(chron)
library(quantreg)
get.table <- function(table.name) {
table <- read.table(table.name, header = TRUE,sep = ",")
date.and.time <-
matrix(unlist(strsplit(as.character(table$time_date), " ")),
ncol = 2, byrow = TRUE)
chrons <- chron(date.and.time[,1],
date.and.time[,2]... |
23b60af539cad5cd5be840ebb6aa723c4e6604bf | 94bacf8ae33f625e602140d254c11b6fe9edfbbc | /man/group.vect.Rd | 8a819acb0d07902551a8febbb1be59256cfb47a5 | [] | no_license | cran/varmixt | 7deb71f4f40c3fccc8fbd6e84a1327e31cfcb6a2 | 3a4d2d30de189eab78e3cdcec090f91c955a7e08 | refs/heads/master | 2021-01-22T01:28:33.623587 | 2005-06-17T00:00:00 | 2005-06-17T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 588 | rd | group.vect.Rd | \name{group.vect}
\alias{group.vect}
\title{Extraction of the vector of the variance group of each gene}
\description{This function extracts the vector of the variance group of each gene.
A variance group is determined by the variance mixture model.
}
\usage{
group.vect(data)
}
\arguments{
\item{data... |
88886f60a43746da1f8b7b35203558dfc4023395 | 5d690f159266b2c0f163e26fcfb9f9e17a0dc541 | /envi/R/globals.R | b8ac621780280b22f26ba400df5555599e49a4e7 | [] | no_license | albrizre/spatstat.revdep | 3a83ab87085895712d7109c813dcc8acb55493e9 | b6fc1e73985b0b7ed57d21cbebb9ca4627183108 | refs/heads/main | 2023-03-05T14:47:16.628700 | 2021-02-20T01:05:54 | 2021-02-20T01:05:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20 | r | globals.R | globalVariables("k") |
b99156fdb115673c76850f97f7b41a051da6b4d4 | 71aaa0ee806fc83cc5fa7ddd136511bb4b117bfb | /first.R | 6c266f2d175aa56567d0b17b599b93a1bb4c4906 | [] | no_license | IgorP17/R-folder | fe7317a51ea070ca7879fb86467dc127798b7f5a | 948f23034dc7259c24627103cd6db6a06ec35d9e | refs/heads/master | 2021-09-18T11:51:44.197522 | 2018-07-13T19:12:03 | 2018-07-13T19:12:03 | 125,160,632 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 51 | r | first.R | my <- c(20,30,40)
barplot(my)
#source("first.R")
|
75a8101e7bfb8e2653bc5171fe03e1a3b0188426 | 75a69dfdfd593dfecf931d497d48fcba90caf356 | /R/normalise.R | 99a884b34aee19b9618fe7414dcee830ffa9fc03 | [] | no_license | ashley-williams/phenoScreen | e05ee774ffe2fa4ae5eca1d23566610be6e00f7c | 431c0e04aeca27ff76fbc603a51c68f5ee87dbf0 | refs/heads/master | 2020-04-25T18:07:40.122452 | 2018-11-22T15:26:39 | 2018-11-22T15:26:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,466 | r | normalise.R | #' normalise against negative control
#'
#' description
#'
#' @param data dataframe, can be a grouped dataframe
#' @param compound_col name of column containing compound information
#' @param neg_control name of the negative control compound in `compound_col`
#' @param method how to normalise, either "subtract" or "div... |
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