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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
95ceb73042a4f2fe6da6b944f2757ab3fbc1da90 | eb7d452b52b530796cba8fa13b405b650b8df4a7 | /find_hist_stat.R | ecc00bf6c66f3712a9546c560d896605b92f0e16 | [] | no_license | MehliyarSadiq/TEMIR | 62730c3a9f1b62a2855385582fff87432b66eef3 | c4546f8b0084be03ec56f19c8ee8019c43354e8d | refs/heads/master | 2020-06-05T01:23:37.520711 | 2019-06-17T03:02:11 | 2019-06-17T03:02:11 | 192,264,948 | 0 | 0 | null | 2019-06-17T02:55:20 | 2019-06-17T02:55:20 | null | UTF-8 | R | false | false | 7,030 | r | find_hist_stat.R | ################################################################################
### Module for calculating statistics of default hourly output data in nc files
################################################################################
# Function to find daily statistics (e.g., mean, max, min) from default hourl... |
e2caceb1631f13034df3fb2c24072de569cbc925 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ROC632/examples/ROC.Rd.R | 8982c98b4f6435439fd0250ee09d5edf1887e352 | [] | 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 | 487 | r | ROC.Rd.R | library(ROC632)
### Name: ROC
### Title: Estimation of the traditional ROC curves (without censoring)
### Aliases: ROC
### Keywords: ROC curve
### ** Examples
# import and attach the data example
X <- c(1, 2, 3, 4, 5, 6, 7, 8) # The value of the marker
Y <- c(0, 0, 0, 1, 0, 1, 1, 1) # The value of the binary outc... |
3030ead456f623c034c9d16c4fbe51751d861d0a | 4b06cc5da85d381921c1ffedd44e08d9d6839a03 | /Data Science/corr.R | 08aaa3498e136501fbbcbe6a2c82d23918760dde | [] | no_license | muaoran/R | 2fe8881b95a71b0cdc68f952c942824c20e631b5 | 2fdf31ad33666d18c7c9500434f3f129bf9c2321 | refs/heads/master | 2021-01-10T06:51:50.553052 | 2015-12-26T10:31:34 | 2015-12-26T10:31:34 | 50,765,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 514 | r | corr.R | corr<-function(directory, threshold=0){
dirt<-list.files(directory,full.name=TRUE)
data_source<-vector(mode="numeric",length=0)
for (i in 1:length(dirt)){
monitor_i<-read.csv(dirt[i])
corr_sum<-sum((!is.na(monitor_i$sulfate))&(!is.na(monitor_i$nitrate)))
monitor_i_1<-monitor_i[which(!is.na(monitor_... |
69c5c2e341a06c3a11de375f7c3a89449b606a7f | 0ab233b9f40236e52ad2bb43dadd2ffca739aa8b | /R/parse_post.R | b63267de4cd1ecfbec2d4abdf1123bbd96b184cf | [
"Apache-2.0"
] | permissive | opencpu/opencpu | 49cada256c9a67a8ea8514d848986b5305f36172 | b8e9c840b90afb33abeae5c2a353339217cfdee2 | refs/heads/master | 2023-08-30T08:24:19.756598 | 2023-08-06T13:35:23 | 2023-08-06T13:35:23 | 10,206,132 | 384 | 62 | NOASSERTION | 2023-08-06T13:35:24 | 2013-05-21T21:45:12 | R | UTF-8 | R | false | false | 2,141 | r | parse_post.R | parse_post <- function(reqbody, contenttype){
#check for no data
if(!length(reqbody)){
return(list())
}
#strip title form header
contenttype <- sub("Content-Type: ?", "", contenttype, ignore.case=TRUE);
#invalid content type
if(!length(contenttype) || !nchar(contenttype)){
stop("No Content-Type ... |
1c1256cf3737c73a727335395bc2b13c85a6688a | 8b885a8159c2a4cabd1555bb971fe7ceffb895f0 | /ui.R | ca9293dc8b45ade30344382c4dc1032698d7500a | [] | no_license | Frikster/mouseActionGrapher | c6459a941849e250d874a3c6ba99d3b4bcdc2eef | 2001d0bb39da53d865dfc278609dc0810159fe31 | refs/heads/master | 2020-12-28T21:28:42.949756 | 2015-09-08T19:06:06 | 2015-09-08T19:06:06 | 39,651,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,834 | r | ui.R | rm(list = ls())
# Immediately enter the browser/some function when an error occurs
# options(error = some funcion)
library(shiny)
library(DT)
shinyUI(fluidPage(
titlePanel("MurphyLab"),
sidebarLayout(
sidebarPanel(
# fileInput('file1', 'Choose CSV File',
# accept=c('text/csv',
# ... |
074bde3403025868ce16eaa4c07c1d308887c6b2 | b75cdbee114168b86f64a51e3a8ca16433e30792 | /code/renaissance_palette.R | 7a34bdaa50cbb099be1d1e41f2112fc35cbd00d9 | [] | no_license | AndreaCirilloAC/dataviz | 1ccdee0c474260ec986c550193e3a6348238f2d0 | daffd43170f4ef3c44b8c6ded14d26323e184c38 | refs/heads/master | 2021-01-20T06:25:05.930740 | 2017-06-01T20:36:36 | 2017-06-01T20:36:36 | 89,876,329 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | renaissance_palette.R | library(pixmap)
library(dplyr)
library(scales)
#convert aminadab_rgb.jpg aminadab_rgb.ppm
painting_michelangelo <- read.pnm("images/profeta_daniele.ppm")
str(painting_michelangelo)
red_vector <- as.vector(painting_michelangelo@red)
green_vector <- as.vector(painting_michelangelo@green)
blue_vector ... |
258fc3ffa283dbfe3a7917209b716d6b7c7c7300 | 19499542a5d57031d3dc1f496ea0f80b14bc4a5f | /Discrete2/Output/2DUnitSquare/Rscript for UnitSquare.R | 21c3fc7342830d1a1ac151157ffc2fb12db59b88 | [
"Apache-2.0"
] | permissive | jakent4498/IDS6938-SimulationTechniques | 51168eb6cb05f9e33a843042961d9f6ff5121f52 | 2ecdbe57a51f8139839f0c22baf4c5bb34447e83 | refs/heads/master | 2021-01-09T06:16:11.443537 | 2017-04-24T02:03:06 | 2017-04-24T02:03:06 | 80,947,128 | 0 | 0 | null | 2017-02-04T20:42:02 | 2017-02-04T20:42:02 | null | UTF-8 | R | false | false | 1,255 | r | Rscript for UnitSquare.R | setwd("C:/Users/jaken/OneDrive/Documents/Spring 2017 SimTech/IDS6938-SimulationTechniques/Discrete2/Output/2DUnitSquare")
library(ggplot2)
library(grid)
library(gridExtra)
jakdf1 <- read.csv("raw_results_ranlux48_2D-uniform2.txt", header=FALSE)
p1 <- ggplot(jakdf1) + geom_point( aes(x=jakdf1$V1, y=jakdf1$V2)) + xlab("... |
0bc976e2fa5f45ccf0921f69e747b49b624d449a | 02e16d94c252fdcba74cd8bd397bdaae9d7758c7 | /man/faConfInt.Rd | 67ab62ccc5be4f32df950fa268c2a3a9739ffbbd | [] | no_license | Matherion/ufs | be53b463262e47a2a5c4bcbc47827f85aa0c4eb2 | 9138cab0994d6b9ac0cea327a572243d66487afb | refs/heads/master | 2020-03-24T21:11:05.053939 | 2019-02-12T10:33:48 | 2019-02-12T10:33:48 | 143,017,526 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,737 | rd | faConfInt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/faConfInt.R
\name{faConfInt}
\alias{faConfInt}
\title{Extract confidence bounds from psych's factor analysis object}
\usage{
faConfInt(fa)
}
\arguments{
\item{fa}{The object produced by the \code{\link[psych:fa]{psych::fa()}} function from th... |
a1e76c911cf449adad8700f177fbe84c36c4dd25 | 2a04df4844316bcc181587008414b4a23277360f | /run_tests.R | 2f612b313fd73515ea305345645206c3093c740d | [] | no_license | jpalowitch/bmd | 633822e6866811ef51fe6c63f41053b20f127155 | 3888df7847ac7f9fbfb976c3ac656bb867a5bde4 | refs/heads/master | 2021-01-11T18:10:03.477170 | 2017-12-04T07:31:48 | 2017-12-04T07:31:48 | 79,506,895 | 0 | 1 | null | 2017-07-05T09:33:57 | 2017-01-19T23:50:43 | R | UTF-8 | R | false | false | 77 | r | run_tests.R | library(testthat)
test_results <- test_dir("./tests", reporter = "summary")
|
a0745952576b743eb1caebe88422ec6a1389e150 | 7ae8b04333b69534a08cd8af2d6a27229af73a3a | /ui.R | 4b97b1009c2860ecea2bdc925b7a99f20ea69890 | [] | no_license | Parkyuyoung/capstone2_F | 953cccefa6084b05411113cc4845fc0a6cd70d6d | 297828ed21089b844456382ea1e3fcff8eb2f380 | refs/heads/master | 2020-07-27T18:24:48.528306 | 2019-12-03T06:00:37 | 2019-12-03T06:00:37 | 209,185,562 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,615 | r | ui.R |
source("common.R")
jscode <- '
$(function() {
var $els = $("[data-proxy-click]");
$.each(
$els,
function(idx, el) {
var $el = $(el);
var $proxy = $("#" + $el.data("proxyClick"));
$el.keydown(function (e) {
if (e.keyCode == 13) {
$proxy.click();
}
});
}... |
984e00f1273c12d9ff3249db07fa84c9da89bbec | 40bd7bdcd28e05e842c77749b381eb78cbd459cc | /plot3.R | fde4c4ca32a381cecdeae9c2f629232ccae23b22 | [] | no_license | jackman1224/ExData_Plotting1 | f0f1d3610b4f1c5b9267c2b8f1063f733efab160 | 8a480d18306cfd63552abb88253aaafa5654eee9 | refs/heads/master | 2021-01-25T07:44:14.867667 | 2017-06-08T21:04:38 | 2017-06-08T21:04:38 | 93,657,387 | 0 | 0 | null | 2017-06-07T16:39:28 | 2017-06-07T16:39:28 | null | UTF-8 | R | false | false | 1,330 | r | plot3.R | fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileURL, destfile = "./Dataset.zip")
unzip(zipfile = "./Dataset.zip", exdir = "./")
hpc <- read.table("C:/Users/jackman/Desktop/R Files/Exploratory Data Analysis/Week 1/Electric Power Consumption Exercise/ho... |
1ca37e4769138d306464607fdbece955ec5a32e7 | 57ce4924de86c96cf663737ae5f0291fc616d0a4 | /Utils.R | c64b05cd46c31e3b14beac9a6b91aab706376971 | [] | no_license | lfmingo/GramAnt | a482fd49f7e9c4bdfabed9b7128ac813dba680f5 | 5525a8d0af863bf5bef90d9692f4aa503c829b2d | refs/heads/master | 2021-07-15T19:42:06.096865 | 2017-10-19T08:02:47 | 2017-10-19T08:02:47 | 107,306,274 | 0 | 1 | null | 2017-10-19T08:02:48 | 2017-10-17T18:10:43 | R | UTF-8 | R | false | false | 2,270 | r | Utils.R | ReadBNFFile <- function(filename) {
# reads a bnf grammar file and returns a list structure
# read the file line by line
con=file(filename, open="r")
lines=readLines(con)
close(con)
# parse the lines
rule_list = list()
for (l in lines) {
l = trim_space(l)
gram_line = strsplit(l, "::=")[[1... |
62624169c1036c65cf0af803203b40b21811bd65 | 51f14fb4b19eb9e5fd6b26552d128f1cc0ff9875 | /R/distributions.R | 018eee21246b0a737e2566ea88731196ce2fb095 | [] | no_license | zhaoxiaohe/greta | 62222ad5d73ae1328f7708d67e39890a2191c2fd | 1489a7272d041f97b780d844b889f1124d7a0726 | refs/heads/master | 2021-01-20T07:22:55.088857 | 2017-05-01T13:01:39 | 2017-05-01T13:01:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,698 | r | distributions.R | flat_distribution <- R6Class (
'flat_distribution',
inherit = distribution,
public = list(
to_free = function (y) {
upper <- self$parameters$upper$value()
lower <- self$parameters$lower$value()
qlogis((y - lower) / (upper - lower))
},
tf_from_free = function (x, env) {
# can... |
fe7c72b46691dbc28ceb0408374f6b325a0330a2 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Wintersteiger/RankingFunctions/rankfunc39_signed_32/rankfunc39_signed_32.R | 4e3785e8fb841422477867282dffbb522a165911 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 835 | r | rankfunc39_signed_32.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 7401
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 7383
c
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 7383
c
c Input Parameter (command line, fil... |
17b8609592345a64d455705ccfeb88bdb347b4d5 | 51f891721c5ad00748780d3bf5df9018c7537277 | /other/R/server.R | 17474d2e7b009d1421855f27bef48ad3db723d5e | [] | no_license | rmutalik/VisualAnalytics | a6e573db02260f86bdb7972e5f2c67ac4a9b43de | ed1a1ffd71cf57004f9f1a76e8b8bf8000eed78a | refs/heads/master | 2022-11-27T09:02:20.005811 | 2020-08-04T02:29:33 | 2020-08-04T02:29:33 | 276,780,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 364 | r | server.R | server <- function(input, output, session) {
output$plot <- renderLeaflet({
leaflet() %>%
addTiles() %>%
setView(-30, 30, zoom = 2)
})
output$map <- renderPlotly({
plot_geo(geo_ports, lat = ~lat, lon = ~lng) %>%
add_markers(
text = ~paste(paste("Slaves: ", n_slaves_arrived)... |
334fd0bf63295a58764acc2929ff247b0a64e65b | f663a843dcd66b1d4e15bfe6b9a6f618a169c3f7 | /fluoro/R/helper_functs.R | c27e23af7ef32e9b24c17eb351615db755d6483c | [
"MIT"
] | permissive | rhlee12/Fluoro-Package | 44556f53aaf7a455aa9229138b11367143e90903 | 07d6f88df2a56ad9220d12de96ee53b9e2cfedae | refs/heads/master | 2021-03-30T17:56:33.852014 | 2018-05-30T22:18:51 | 2018-05-30T22:18:51 | 118,687,653 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,147 | r | helper_functs.R | gen.seq=function(raw.eem){
em=as.numeric(raw.eem[3:length(raw.eem[,1]), 1]) # HEADER IS NEEEDED
ex=as.numeric(raw.eem[1, 2:length(raw.eem)]) # HEADER IS NEEEDED
return(list(em=em,ex=ex))
}
raman.correct=function(raman){
raman.begin=as.numeric(raman[3,1]) #raman start wavelenth
raman.end=as.numeric... |
98e1380143aae6609fb8837e1a0e51d3f5f4f318 | 697e3ac9cbe9010ed9b50f356a2cddf5ed8cc8a0 | /R/vreq_classic_methods.R | e64c75562690afe93db1db17eda9d15ccb5b54cf | [] | no_license | reumandc/tsvr | 4c2b2b0c9bbbb191ae55058648da87589bc25e01 | f8f7a72d4f8ba40e881e78a1a2fb53791d227d21 | refs/heads/master | 2021-06-01T14:27:34.757125 | 2021-01-08T17:09:11 | 2021-01-08T17:09:11 | 132,662,500 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,609 | r | vreq_classic_methods.R | #' Basic methods for the \code{vreq_classic} class
#'
#' Set, get, summary, and print methods for the \code{vreq_classic} class.
#'
#' @param object,x,obj An object of class \code{vreq_classic}
#' @param newval A new value, for the \code{set_*} methods
#' @param ... Not currently used. Included for argument consisten... |
f58c4397653f21c4808387d9a0b1e3b0c1657cf8 | eaa977b7723a7ea9d54f0d2fff0a250b702445d1 | /tools/analysisTools.r | efe0a0a9a5544f1bd5594b72f721d3b840801d76 | [
"BSD-3-Clause"
] | permissive | hyunjimoon/laplace_manuscript | 894e5620870814b9bb43ddaeabac5001428b6716 | f0e967a38d8562e3d1d2b646dfcc0387c0912ace | refs/heads/master | 2022-11-07T08:51:59.245244 | 2020-04-28T21:22:44 | 2020-04-28T21:22:44 | 275,398,155 | 0 | 0 | BSD-3-Clause | 2020-06-27T15:17:43 | 2020-06-27T15:17:42 | null | UTF-8 | R | false | false | 4,527 | r | analysisTools.r |
#########################################################################
## Tools to analyze results from the cluster
select_lambda <- function(parm, quant, n_select) {
p <- ncol(parm)
n <- nrow(parm)
quantile_parm <- rep(NA, p)
for (i in 1:p) quantile_parm[i] <- sort(parm[, i])[quant * n]
selected <- sort... |
e9f780038ed35a03c7e13d0a03c0887d1ed9dfec | 5cdbcc53194772da16c7af453dd6ebb6e108c4b1 | /metrics/report/report_dockerfile/test.R | 8c9dabe01e588d2ed3abe2e18215e89b77ddb72b | [
"Apache-2.0"
] | permissive | clearlinux/cloud-native-setup | 591b2ba543db9bb76f19fc4d2e28d535490d0f83 | 9e3697308ee3555aec1b6ee44cd5fb7ecc026946 | refs/heads/master | 2023-06-08T15:55:00.573561 | 2022-09-21T16:49:34 | 2022-11-15T21:39:32 | 160,404,934 | 60 | 82 | Apache-2.0 | 2022-09-21T16:57:42 | 2018-12-04T18:58:55 | Shell | UTF-8 | R | false | false | 190 | r | test.R |
suppressMessages(library(jsonlite)) # to load the data.
options(digits=22)
x=fromJSON('{"ns": 1567002188374607769}')
print(x)
print(fromJSON('{"ns": 1567002188374607769}'), digits=22)
|
70ac8ccd49c776155d9a2e73701ab110b9f894f5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ssdtools/examples/is.fitdists.Rd.R | 80abb0c2489d40a8424e752f52372b7a6584fd1c | [] | 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 | 161 | r | is.fitdists.Rd.R | library(ssdtools)
### Name: is.fitdists
### Title: Is fitdists
### Aliases: is.fitdists
### ** Examples
is.fitdists(boron_lnorm)
is.fitdists(boron_dists)
|
c9b2108344fba1718729ca31d29d6c6cc63439de | ecf1aa864dfc40840f5b0c98965f7d55875e135f | /MODULES/ESTIMATE/areasPlot.R | 491c4c25067039e56beaba5dd3e4a9bc1672b246 | [] | no_license | VeenDuco/shinyforcmdstan | 78b03ec5cd2378ab594ab1a7552a655f70ca3462 | 74da0751f7958d08a969d05c17d168e91a2ecd18 | refs/heads/main | 2023-02-09T22:56:25.189305 | 2021-01-04T19:16:23 | 2021-01-04T19:16:23 | 325,535,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,657 | r | areasPlot.R | areasPlotUI <- function(id){
ns <- NS(id)
tagList(
wellPanel(
fluidRow(
column(width = 6,
selectizeInput(
inputId = ns("diagnostic_param"),
label = h5("Parameter"),
multiple = TRUE,
choices = .make_param_list_with_g... |
165fe120eeabeab0d68bbec1649a45a5e9c4c315 | 9531bf05292a40e21835d3e63de124846635fdd0 | /dataScience_sujet2.4.R | 2ba2189f0f2255f59b30c643efd0445aa53272be | [] | no_license | florinePrat/Projet-data-science | 865eb8f6ec1a1bd0fac765a2f0f9216ea12c53a9 | 888edcc16f1ffb24b8a9651829295ff1fdd2b95b | refs/heads/main | 2023-02-16T20:49:31.293057 | 2021-01-11T15:17:02 | 2021-01-11T15:17:02 | 315,313,576 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,665 | r | dataScience_sujet2.4.R | # Title : Projet data science
# Objective : TODO
# Created by: Florine | Timi | Axel
# Created on: 23/11/2020
##### test with CSV
dfGlobal <- read.csv2("C:/Users/Axel/Desktop/projetDS/Projet-data-science-main/BddBruteConfinement.csv", header = TRUE, encoding = 'UTF-8')
## Get the stucture of dataframe... |
da5e1778d02cb60c8df49ec30336a1ad851ff8df | 941bcfc6469da42eec98fd10ad1f3da4236ec697 | /man/track_bearing.Rd | ea6f83467fd7f9912547444aeff8480ca7824abd | [] | no_license | cran/traipse | 29c3fd65e98f65049da98b1d878512bfdd93940f | 01635fd40512f2144e1ce712e0f5912143214e49 | refs/heads/master | 2022-10-22T02:59:19.828085 | 2022-10-10T06:40:02 | 2022-10-10T06:40:02 | 236,953,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,045 | rd | track_bearing.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/track_bearing.R
\name{track_bearing}
\alias{track_bearing}
\title{Track bearing}
\usage{
track_bearing(x, y)
}
\arguments{
\item{x}{longitude}
\item{y}{latitude}
}
\value{
a numeric vector of absolute bearing in degrees, see Details
}
\descr... |
29460efddd88558f396e14b091654ceef27fc3cb | 5a698b4cf5e86426da354a51c5c1582a99b1450a | /man/print.occdat.Rd | 41ad15ba9ceba4b8c79516869f552031f0fa51a7 | [
"MIT"
] | permissive | jarioksa/spocc | 721edf2bc0bf5070fcca5dcbea5bb2c558bab5f1 | 1f18a697abdd3d634a1b10e180b9bbab68bcc197 | refs/heads/master | 2021-01-15T20:08:46.390092 | 2014-09-10T20:44:51 | 2014-09-10T20:44:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 600 | rd | print.occdat.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{print.occdat}
\alias{print.occdat}
\title{Print brief summary of occ function output}
\usage{
\method{print}{occdat}(x, ...)
}
\arguments{
\item{x}{Input...}
\item{...}{Ignored.}
}
\description{
Print brief summary of occ function output
}
\examples{
\dontrun{... |
3bfa6e22f5054283363e8c352720616459912c7b | c5a08892d45ce23f54771eafe379ed843363f27e | /man/changejoint.Rd | 08a26f057a59ebf4dcb37af471d1e2594a83fa5d | [] | no_license | cran/StratigrapheR | 9a995ea399e97a449bb94a5c8bb239935b108da0 | aff0937f9ee8d0976fc67a46768b32379cf0274b | refs/heads/master | 2023-07-26T21:02:30.211546 | 2023-07-05T23:14:06 | 2023-07-05T23:14:06 | 163,700,147 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,448 | rd | changejoint.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/changejoint.R
\name{changejoint}
\alias{changejoint}
\title{Change the dimensions of bedding joints}
\usage{
changejoint(
joint,
yinv = F,
xinv = F,
yleft = NA,
yright = NA,
ymin = NA,
ymax = NA,
xmin = NA,
xmax = NA
)
}
\ar... |
bccd1ad9a488c7e4849d956dc13861ffb9ad3112 | 42fd9b059f4ee5e9a0c043d8813db9b240f53ba0 | /tests/testthat/test-fpShapesGp.R | 288fe5521cd584d60e2ed85db10a37de4350f717 | [] | no_license | X-FLOWERRR/forestplot | 85db51ccdafcb4a0eccd43276ffd9ad7b02ad0cc | b0b25f2c5db6f7ba68de759d7ea275ea0d2886ac | refs/heads/master | 2023-07-15T13:57:25.991719 | 2021-08-25T20:01:16 | 2021-08-25T20:01:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,553 | r | test-fpShapesGp.R | library('testthat')
context('fpShapesGp')
test_that("Check fpShapesGp can be used as shapes_gp parameter",
{
expect_silent(
forestplot(labeltext = cbind(Author=c("Smith et al","Smooth et al", "al et al")),
mean=cbind(1:3, 1.5:3.5), lower=cbind(0:2, 0.5:2.5), upper=cbind(4:6,5.5:7.5),
is.summary=c(FALSE,TRUE,FA... |
cb8fc58f14f4f13c25639e4d70aa38a72538d491 | 34a991f4b3ecbfcb5b55bf3f6be91b20646863dd | /man/digits.Rd | 41fdb2fe46cbe9c20309a9f026a75d22e2d5724d | [] | no_license | cran/RnavGraphImageData | fb0cfc9b922c6715ba0f318ad566ad8864a6caeb | efebb5f84ba4820f62e5d87ceb991d28c9b3756b | refs/heads/master | 2020-05-17T12:14:59.706496 | 2018-05-15T20:09:03 | 2018-05-15T20:09:03 | 17,693,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 318 | rd | digits.Rd | \name{digits}
\docType{data}
\alias{digits}
\title{USPS Handwritten Digits}
\description{
8-bit 16x16 grayscale images of "0" through "9"; 1100 examples of each class.
}
\usage{digits}
\format{Data frame with one image per column.}
\source{\url{http://www.cs.nyu.edu/~roweis/data.html}}
\keyword{datasets}
|
1bedc93e2ffa79540cf597276909a4e4d1ffa3f5 | 930a64ae51ba9c4052bcd2b6d4392ff70f98bde8 | /UniPennState_GeneralizedLinearModels/TwoWayTable/VitaminC_high.R | 8e64e9886305144e64824ddab738d719f169f516 | [] | no_license | statisticallyfit/RStatistics | 1e9f59a1ebef597d4c73f3cf10bed5170126d83b | 93915cc141c4cb2b465d301d44695b8ce0ad35f8 | refs/heads/master | 2020-06-26T02:29:51.113577 | 2019-10-18T18:00:14 | 2019-10-18T18:00:14 | 74,606,344 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,716 | r | VitaminC_high.R | source('/datascience/projects/statisticallyfit/github/learningstatistics/RStatistics/StatsFormulas.R')
ski <- matrix(c(310, 170, 1090, 1220), ncol=2,
dimnames=list(Treatment=c("Placebo", "VitaminC"),
Cold=c("Cold", "NoCold")))
ski
# Percentage of Row and Col and of Total o... |
489467328a20eae0ec7119bd108f55bf0ed0a88d | 8ea8dd82beb390c5ae59d32acaf854067e2f310a | /tests/testthat/test-4-execution.R | 77979a6442b79e11b8091f10a08365f8019f31bd | [
"MIT"
] | permissive | hongooi73/AzureDSVM | 91d9f69e8ad30f8d589f49f734422a5d8496e319 | 3553b5581dd640513a37101bb71a8170498f1809 | refs/heads/master | 2021-07-06T12:24:35.772109 | 2017-10-02T17:00:31 | 2017-10-02T17:00:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,617 | r | test-4-execution.R | # test remote execution on a Linux DSVM with specified computing context.
if(interactive()) library("testthat")
library(AzureSMR)
settingsfile <- getOption("AzureSMR.config")
config <- read.AzureSMR.config()
timestamp <- format(Sys.time(), format="%y%m%d%H%M")
context("Remote execution")
asc <- createAzureContext... |
eb661256ca930ee65b532b3018d090f0533c665c | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /B_analysts_sources_github/jeroen/rgdal/sp_gdal.R | 6023bb3916c746d58eac8e8a5c58dd4df98d858a | [] | 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 | 21,199 | r | sp_gdal.R | GDALinfo <- function(fname, silent=FALSE, returnRAT=FALSE, returnCategoryNames=FALSE, returnStats=TRUE, returnColorTable=FALSE, OVERRIDE_PROJ_DATUM_WITH_TOWGS84=NULL, returnScaleOffset=TRUE, allowedDrivers=NULL, options=NULL) {
if (nchar(fname) == 0) stop("empty file name")
x <- GDAL.open(fname, silent=silent,
... |
a03e53f1d3f7152397d0d1ada1deda17800253d8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pracma/examples/flipdim.Rd.R | cdfc1a6f53df2b5dafb42b88473ab4b8d99d3f07 | [] | 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 | 280 | r | flipdim.Rd.R | library(pracma)
### Name: flipdim
### Title: Matrix Flipping (Matlab Style)
### Aliases: flipdim flipud fliplr circshift
### Keywords: manip
### ** Examples
a <- matrix(1:12, nrow=3, ncol=4, byrow=TRUE)
flipud(a)
fliplr(a)
circshift(a, c(1, -1))
v <- 1:10
circshift(v, 5)
|
28627916f3878743df6fb3e175e80f9b172dd09a | 14553970249fcf633c25e13d84259ad608220233 | /man/logregtree.Rd | 6c57bc5e64e5662520565175ef3ccf16a1ad09f2 | [] | no_license | cran/LogicReg | c1d0cbd785af6cf95a73a796f561d60fdd872a73 | 73bb7739987b1884d21f976d7e534cef6cebb8e4 | refs/heads/master | 2023-08-30T21:07:25.420747 | 2023-08-08T23:10:10 | 2023-08-09T01:42:24 | 17,691,856 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,444 | rd | logregtree.Rd | \name{logregtree}
\alias{logregtree}
\title{Format of class logregtree}
\description{This help file contains a description of the format of
class logregtree. }
\usage{logregtree()}
\value{
An object of class logregtree is typically a substructure of an object
of the class \code{logregmodel}. It will typically be ... |
ae79ba080d7bbdcd43364754d4f0461901c080e1 | 5f82d1bc22e4ef72a63c58852a2d035e124f1a37 | /tests/testthat/test_last_n.R | 5675633bcb39362f933bce31771597594992b26d | [] | no_license | cran/bupaR | 75608804ef045f678821740aaff123991d5d36b5 | ef020af22301e7aa8c82d62e4d01dd5aebaea99e | refs/heads/master | 2023-04-20T17:49:49.645967 | 2023-04-02T21:00:06 | 2023-04-02T21:00:06 | 86,215,725 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,932 | r | test_last_n.R |
#### eventlog ####
test_that("test last_n on eventlog", {
load("./testdata/patients.rda")
last <- patients %>%
last_n(n = 2)
instances <- patients %>%
filter(!!activity_instance_id_(.) %in% c("11", "12")) %>%
nrow()
expect_s3_class(last, "eventlog")
expect_equal(dim(last), c(i... |
6826dc0135ec978abb9fe3f1c40872739b97c7fa | 646f4be0623653e8e9cf4d701a15fa318eda9824 | /tests/testthat/test-recently-added.R | 13dab52667c5345fd069898086ee6a5463aca9e3 | [
"MIT"
] | permissive | jemus42/tauturri | dc24b37136cd88c97ad79babf38c71b2043b84a0 | 2f23895985d962f18b1d9ea3977fefdfbca714f0 | refs/heads/master | 2022-09-24T09:02:36.203045 | 2022-09-18T16:44:40 | 2022-09-18T16:44:40 | 121,064,812 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 259 | r | test-recently-added.R | context("test-recently-added.R")
test_that("get_recently_added works", {
count <- 5
res <- get_recently_added(count = count)
expect_is(res, "tbl")
expect_length(res, 42)
expect_equal(nrow(res), count)
expect_error(get_recently_added("", ""))
})
|
4880cc79284c1f24f2477ad6f06e8217e45b5325 | d741d22e89b3c036276cc75378f25ab4d5df2f67 | /code/exploration.R | 7370d1b69f1b4610fc54020d6fef86b7b2c90211 | [] | no_license | andyhoegh/NCAA | f58e98602d434ec98c7304f69d224ceddf88863c | 4d110fdd45fa029cea3edf59a53521293f92bb34 | refs/heads/master | 2021-01-01T16:49:43.200349 | 2015-01-07T18:04:43 | 2015-01-07T18:04:43 | 16,755,168 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,822 | r | exploration.R | source("scoring_code.R")
# This gets the win rate for all teams over all given seasons
id = 501:856
nteams = length(id)
regular_season_results = read.csv("~/regular_season_results.csv")
nwins = numeric(length(id))
nlosses = numeric(length(id))
for(i in 1:nteams){
nwins[i] = sum(regular_season_results$wteam == id[... |
79066c87346372b53ae19826e9ba73275d8d8d94 | 528f00fe5ccc8d13132caf0e38cfe362b0ae20e4 | /R function/pml.R | 9bacdbe691b13c3f1cdf718d8f2cadb13a2bc861 | [] | no_license | MingchenInSZ/practicalmachinelearning | 72c805e6d7e90e60699c988cc380e65a14985da4 | a14227e5fc060ceae7b227545adacb44c8116a6c | refs/heads/master | 2016-09-06T10:35:19.836091 | 2014-06-22T15:46:39 | 2014-06-22T15:46:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,262 | r | pml.R | pml<-function()
{
library(caret)
curdir<-getwd()
trainfile<-paste(curdir,"/pml-training.csv",sep="")
testfile<-paste(curdir,"/pml-testing.csv",sep="")
training <-read.table(trainfile,header=T,sep=",")
testing <- read.table(testfile,header=T,sep=",")
rs<-c()
rsc<-c()
for(name in names(training))
... |
0527d29e945830bcabc7cf8d5cf473d82d2f2fb4 | 2b66528ea70115d88464fb90179365542e7313be | /auc_functions.R | a55280e7538767abcfad27cc6ce21fbaede9e4f8 | [] | no_license | pavanjuturu/Numerai | bd92f5a82fd60a75e8b1629f9089eb78672429af | 0b66770d83838b78767376c23703eab4c0f76ad5 | refs/heads/master | 2020-05-22T19:04:50.872296 | 2016-02-10T20:50:53 | 2016-02-10T20:50:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 737 | r | auc_functions.R | auc <- function(outcome, proba){
outcome <- as.vector(outcome)
proba <- as.vector(proba)
N <- length(proba)
N_pos <- sum(outcome)
df <- data.frame(out = outcome, prob = proba)
df <- df[order(-df$prob),]
df$above <- (1:N) - cumsum(df$out)
return( 1- sum( df$above * df$out ) / (N_pos * (N-N_pos) )... |
905ef0f1d9699be896f50e88046c9530a11bae8e | b0670f8484d05498938b7a9770318857e2de5527 | /plot2.R | 1aaa317cf318b485874ea4930fdc1ec9a6d6cef5 | [] | no_license | akolchin/ExData_Plotting1 | eeaa36befc6a7ffda584e0e38a16eaa90608beb8 | b0a24c30d0834533ca576ca3d288331621444e73 | refs/heads/master | 2021-01-18T02:52:33.055350 | 2014-05-10T12:37:59 | 2014-05-10T12:37:59 | 19,465,353 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 948 | r | plot2.R | plot2 <- function() {
## load and unzip
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileUrl, destfile="data.zip")
unzip("data.zip")
## load and prepare datas
data <- read.table(... |
cb172ae89877e4ae97a8ea122391f1e9a651ae3c | 3d1ec18944e584c2f00e2b9902dcaaccb79c8c41 | /R/qmosaic.R | 41bbf2660192c9bfa8dd6d89177e75a28e74a107 | [] | no_license | tudou2015/cranvas | a84ffebf61fac235959cefb8acbd4c7bdb0d7d58 | af082a2a1cb09d42ca95c0021f8046df5054240f | refs/heads/master | 2020-06-10T18:52:07.441557 | 2015-03-13T17:03:09 | 2015-03-13T17:03:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,862 | r | qmosaic.R |
constructCondition <- function (hdata) {
library(reshape2)
library(plyr)
hdata$ID <- 1:nrow(hdata)
res.melt <- melt(hdata,id.var="ID")
res.melt$cond <- with(res.melt, sprintf("(%s == '%s')", variable, value))
NAs <- which(is.na(res.melt$value))
res.melt$cond[NAs] <- sprintf("is.na(%s)", res.melt$variabl... |
8eb13bbe65be0e8944964e1a88e4e0c8fda85d73 | a6253060e42e9bb8393f2dae6a0ecf395c873c19 | /R_scripts_and_data/fig3.R | 96ce676783b77b7f435458089e2eaef80e8bddcf | [] | no_license | idopen/asymmetry_and_ageing | 25af63061553b484a3167c523cda443c08fb1026 | 9e1001366a0f638f5d1cd899a79e931d80a1e13f | refs/heads/master | 2022-11-27T10:22:59.807279 | 2020-08-03T12:02:11 | 2020-08-03T12:02:11 | 284,663,122 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,231 | r | fig3.R | ## figure 2:
## panel of plots
## A: dynamics r=1
## B: dynamics r=0
## C: damage r=0,1
library(tidyverse)
library(cowplot)
library(mgcv)
## fig2A: dynamics for m0=1
## read data (dynamics)
dA <- read.table("evol_m0_1.txt",header = F)
names(dA) <- c("generation","popsize","age","rep0","rep1","rpr0",
... |
5150f285b87a634e8568c5070aeadd4f726fe1d3 | 4a2f9a190e08ce4f60156c5b56094ab48c5fa295 | /Simple Linear Regression/emp_data.R | 05d0b361158b5c9426b4842d8cc14899d0100afd | [] | no_license | Tusharbagul/Machine-Learning-With-R | 2b5faa76b195895d084f8452dc76b428d6d9cd58 | 99c935e63d12463cc2861d4417fb0cb9a5eeb0bf | refs/heads/master | 2022-12-01T15:02:13.666455 | 2020-08-13T12:27:52 | 2020-08-13T12:27:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,025 | r | emp_data.R | #Read Data
emp <- read.csv('C:/Users/Tushar Bagul/Desktop/Data_Science/Assignments/simpleLinearregression/emp_data.csv')
colnames(emp)
#correlation matrix
cor(emp)
#Regression model and summary
model1 <- lm(Churn_out_rate~Salary_hike, data = emp)
summary(model1)
#New Data Frame With New Data
churn_rate =... |
8bc19fe3f8a99bd83f9be71a0bd14c6e48f415b0 | 185eb75246acc598d15d43a6a487ef2ee0b3d231 | /R/mousebrain.org/preprocess-loom.R | 8948b3747b4f6d508965a62fabea083da09d213c | [] | no_license | suzannejin/SCT-MoA | 4cd295da2252475d482905bbdfffa48aa9ca4c2d | bfd455479d94db92d30153b763d06f5732879606 | refs/heads/master | 2023-05-30T01:18:39.043455 | 2019-02-25T18:20:10 | 2019-02-25T18:20:10 | 362,417,400 | 0 | 0 | null | 2021-04-28T09:50:38 | 2021-04-28T09:50:37 | null | UTF-8 | R | false | false | 1,505 | r | preprocess-loom.R | # Preprocess loom files.
setwd("~/git/SCT-MoA")
options(stringsAsFactors = F)
# usage: preprocess-loom.R <input_dir> <output_dir>
args = commandArgs(trailingOnly = T)
if (length(args) < 2)
stop("must provide input and output directories")
input_dir = args[1]
output_dir = args[2]
if (!dir.exists(input_dir))
stop("i... |
934464cf4438e8090390a3798e889665d6f17072 | 7f141116154eed50968bddd35c9a47b7194e9b88 | /R/richness_objective_bayes.R | b5e3ebfe2915c215c8a600a43b8b9fae583bc869 | [] | no_license | adw96/breakaway | 36a9d2416db21172f7623c1810d2c6c7271785ed | d81b1799f9b224113a58026199a849c2ec147524 | refs/heads/main | 2022-12-22T06:20:56.466849 | 2022-11-22T22:35:57 | 2022-11-22T22:35:57 | 62,469,870 | 65 | 22 | null | 2022-11-22T22:35:58 | 2016-07-02T21:10:56 | R | UTF-8 | R | false | false | 36,028 | r | richness_objective_bayes.R | #' Objective Bayes species richness estimate with the Negative Binomial model
#'
#' @param data TODO(Kathryn)
#' @param output TODO(Kathryn)
#' @param plot TODO(Kathryn)
#' @param answers TODO(Kathryn)
#' @param tau TODO(Kathryn)
#' @param burn.in TODO(Kathryn)
#' @param iterations TODO(Kathryn)
#' @param Metropolis.... |
a75795d0f97520cf6dddd622b5beff2aadbf2110 | 1b7a6d3cb7abe17ffbee71262cf3771f98a1323c | /MoreConcepts.R | 859f2cefc587cabb42e3419551f4e89dde3f417f | [] | no_license | johnpilbeam/r-datasci-work | 33057f9d9d9987011b150619306093dcb86e9d42 | 9ee0fe2a10ecc58f6079752a8f80c2cd1cc3c9fb | refs/heads/master | 2020-04-26T04:06:06.302690 | 2019-03-28T16:20:06 | 2019-03-28T16:20:06 | 173,290,055 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,239 | r | MoreConcepts.R | # Download each of the data sets for 2006, 2007, 2008
df1 <- read.csv("~/Documents/r-datasci/2006.csv")
df2 <- read.csv("~/Documents/r-datasci/2007.csv")
df3 <- read.csv("~/Documents/r-datasci/2008.csv")
myDF <- rbind(df1,df2,df3)
dim(myDF)
rm(df1,df2,df3)
head(myDF)
tail(myDF)
unique(myDF$Year)
# Quiz #17 - Answer: 6... |
e5de1c105a78728c5ac16947c204cfad7a42bc22 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/banter/examples/addBanterDetector.Rd.R | d2119eca247fcc64fd1760276faf4fa7a9011f0a | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 552 | r | addBanterDetector.Rd.R | library(banter)
### Name: addBanterDetector
### Title: Add a BANTER Detector Model
### Aliases: addBanterDetector removeBanterDetector
### ** Examples
data(train.data)
# initialize BANTER model with event data
bant.mdl <- initBanterModel(train.data$events)
# add the 'bp' (burst pulse) detector model
bant.mdl <- add... |
bde4c56c8368ffab5ba2b7bc32778a18d58a1093 | 6f56fdd53e87575377b95b95280f21fe215cab0d | /man/create_empty_rtweet_tbl.Rd | 8e20cc93919bbc71ee125b8725783d4e63bbfe2d | [
"MIT"
] | permissive | urswilke/rtweettree | ab9603adb1801cf622789d3eef820b83e199dbbf | cfabadf5b38d2946f917b71fa6d499cf3d70108b | refs/heads/master | 2023-08-11T14:22:24.597334 | 2021-10-07T23:58:12 | 2021-10-07T23:58:12 | 284,338,760 | 6 | 0 | NOASSERTION | 2021-10-02T19:14:58 | 2020-08-01T21:06:18 | R | UTF-8 | R | false | true | 446 | rd | create_empty_rtweet_tbl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{create_empty_rtweet_tbl}
\alias{create_empty_rtweet_tbl}
\title{Create an empty rtweet tibble}
\usage{
create_empty_rtweet_tbl()
}
\value{
An empty tibble with columns of the type that, e.g. rtweet::lookup_statuses() produces
}
... |
9086f2b3fc56092ae632f5718e503c6905654f52 | af31c9e40581eb197adc156f5524a0d2bdc22b78 | /plot3.R | 33de91bde7b181ad5116bfb3a6261fd5c5070a22 | [] | no_license | sammarten/ExData_Plotting1 | fbd27570684e3d7f23b74e5e64627793e9631691 | 75fbed27ea33bf982638a348ed9fd9df843205e8 | refs/heads/master | 2021-01-21T00:48:06.177217 | 2014-08-08T23:06:06 | 2014-08-08T23:06:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,213 | r | plot3.R | # Assumption that "household_power_consumption.txt" is in working directory
data <- read.csv("household_power_consumption.txt",
sep=";",
stringsAsFactors=FALSE,
colClasses=c("character", "character", "numeric",
"numeric", "numeric", "nu... |
e1e1e64bf5b07d9f0c5e9e01ec8b8b0dfb955d4e | 332eb3e452b905363ffa89745d772f43e658a1cd | /R/msgfParTda.R | ed21d434e03e8ddbac4cef0dfc70a4641f610958 | [] | no_license | thomasp85/MSGFplus-release | cff4dc347d3b260a16ee9236bd3397b909078f1f | f3dd2a93ff0cc05aabb7dc0cdacdf12a1e3d5d59 | refs/heads/master | 2016-08-04T20:24:37.233950 | 2015-02-03T08:52:12 | 2015-02-03T08:52:12 | 25,248,232 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,747 | r | msgfParTda.R | #' A class handling use of target-decoy approach for FDR estimation
#'
#' This class defines whether to use target-decoy approach and provides methods
#' to get correct system call parameters.
#'
#' @slot tda A boolean defining whether to use tda or not
#'
#' @examples
#' tda <- msgfParTda(TRUE)
#'
#' @family msgf... |
c7e2ea0e6c1948bba0f812a0dd39cd2890224fe7 | caad99a2eb7e431beefb3a04c2a31350e64951c7 | /decision tree.R | 5c46fdcc14e2fc696ae8540687d446f7277c667b | [] | no_license | tonyk7440/kaggle_titanic_dataset | 48ec388eb0cc036576350b209565b710bad522d8 | 4c5c72635cfc4362c067ce2d89cf8d3688d29ea5 | refs/heads/master | 2021-01-10T01:55:52.196467 | 2016-02-01T21:11:43 | 2016-02-01T21:11:43 | 50,435,308 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,070 | r | decision tree.R | #Decision Trees
# train and test set are still loaded in
str(train)
str(test)
library(rpart)
# Build the decision tree
my_tree_two <- rpart(Survived ~ Pclass + Sex + Age + SibSp + Parch + Fare + Embarked,
data = train,
method ="class")
# Visualize the decision tree using p... |
e1e0b08f7f9afde79b56b450ac1ef5ef29278a7f | 4c699cae4a32824d90d3363302838c5e4db101c9 | /06_Regressao_com_R/03-FeatureSelection.R | 896ca026789c8762a25ab4417019cd9bd9c397c9 | [
"MIT"
] | permissive | janes/BigData_Analytics_com_R | 470fa6d758351a5fc6006933eb5f4e3f05c0a187 | 431c76b326e155715c60ae6bd8ffe7f248cd558a | refs/heads/master | 2020-04-27T19:39:10.436271 | 2019-02-06T11:29:36 | 2019-02-06T11:29:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,181 | r | 03-FeatureSelection.R | # Feature Selection
# ... cont do script 02
dim(bikes)
any(is.na(bikes))
# Criando um modelo para identificar os atributos com maior importancia para o modelo preditivo
require(randomForest)
# Avaliando a importanci de todas as variaveis
modelo <- randomForest(cnt ~ .,
data = bikes,
... |
046245c4e30e7d8d803206a1035eaaeed725b1f7 | ea1c371421755474c644854cfec37962d9be468e | /scripts/code1.r | e6d638d0484d6126dd424ed969f5b2b753ef5711 | [] | no_license | verm0nter21/testgit | f835b8ee1a4d2478f07c5684ddfbd273e3a0a2db | f9babcb53fa81ec5c5ca335fb0ed3ef9793c8083 | refs/heads/master | 2020-03-21T23:07:27.675802 | 2018-06-29T15:56:06 | 2018-06-29T15:56:06 | 139,167,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 85 | r | code1.r | # this is not really an R script but it is a comment
# this is version 1, the master
|
f53feba589776c8a79ade80fdeefcbb9c6412117 | e9b8841424aff6f0a47f61d3a7f64796c8b1e4b4 | /Rscripts/getESCCfitGenes.R | 7ff74b054404445221691849ae285d1c64b132ec | [] | no_license | 2waybene/MustARD | e0b3361c59d1262b331ff06d09f244bb732f2b8a | e856eff2945f873a32ba09c8e666caeef8d1e769 | refs/heads/master | 2021-07-15T17:24:20.772322 | 2021-02-22T21:29:43 | 2021-02-22T21:29:43 | 240,051,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | getESCCfitGenes.R | setwd("X:/project2020/MustARD/learningFromBigWigs/Behan_nature_CRISPR-Cas9/")
ESCC.cell.lines <- read.csv("esophagus_CellLines.csv")
fit.genes <- read.csv("fitness_genes_all.csv")
dim(fit.genes)
ESCC <- ESCC.cell.lines$CMP_id[ ESCC.cell.lines$CancerType %in% "Esophageal Squamous Cell Carcinoma"]
length(which... |
5522b004505b1317d03309e91b74af5c2eb96a06 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mason/examples/polish.Rd.R | 1e31e7a68a00cbb0f72ae46920b961e08c683ad0 | [] | 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 | 698 | r | polish.Rd.R | library(mason)
### Name: polish
### Title: Do some final polishing of the scrubbed mason analysis data.
### Aliases: polish polish_renaming polish_filter
### polish_transform_estimates polish_adjust_pvalue
### ** Examples
library(magrittr)
ds <- swiss %>%
design('glm') %>%
add_settings() %>%
add_variables('yva... |
47023a1e30559af42bee9cd4e02e95454dc07dd6 | 9f727ce9fded2d1082f8473bf9c353c4dc524eca | /partsm/man/acf.ext1.Rd | 52336da6db3b445faadc96a13fa7155f19ec50ec | [] | no_license | MatthieuStigler/partsm | 7043eabf882eecda2cef25ef3de92af4d06f7d02 | f342e1966083b6f5d7ce520af0395eab1d4a6a54 | refs/heads/master | 2021-05-16T02:57:35.563197 | 2020-11-25T03:48:30 | 2020-11-25T03:48:30 | 18,262,949 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,738 | rd | acf.ext1.Rd | \name{acf.ext1}
\alias{acf.ext1}
\title{Autocorrelation function for several transformations of the original data}
\description{
This function is based on the \link[stats]{acf} function and extends it by allowing for some transformations of the data before computing the autocovariance or autocorrelation func... |
503e746356e1b743942863575fe1ea12ea40f51e | aaf8222e2e7c1ca3480092387472ed539e79985a | /man/SplitAuthor.Rd | 56620539822c62ac42c710a2fd55a2f0e93db4c9 | [] | no_license | M3SOulu/MozillaApacheDataset-Rpackage | 57e7028f2d2ee9a6a672a9775f20bf40af9e4f4a | 3644dbd266325309be4bfdf1ac926ae8859ebd19 | refs/heads/master | 2022-06-23T11:56:58.580415 | 2022-06-20T11:03:39 | 2022-06-20T11:03:39 | 238,914,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 339 | rd | SplitAuthor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/identities.R
\name{SplitAuthor}
\alias{SplitAuthor}
\title{Split author}
\usage{
SplitAuthor(author.key)
}
\arguments{
\item{author.key}{The author fields.}
}
\value{
A list of splitted authors.
}
\description{
Split Git author fields based o... |
8d981cf091b46aeca9032202901c856e5fdbf1d7 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googlesheetsv4.auto/man/BatchUpdateValuesRequest.Rd | 09788ea44a689dcc9ff123dd8e9ac34a6448b3cc | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,052 | rd | BatchUpdateValuesRequest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sheets_objects.R
\name{BatchUpdateValuesRequest}
\alias{BatchUpdateValuesRequest}
\title{BatchUpdateValuesRequest Object}
\usage{
BatchUpdateValuesRequest(valueInputOption = NULL, data = NULL,
responseDateTimeRenderOption = NULL, responseVa... |
2d99b0143c6c337080dd742409052f4446ec03df | c6683226b4317a677e43475b31a743b7ad273410 | /LML_Tidy_Helpers.R | 7c5385159399d155bc2a3ec6cfc406c9f13b420f | [] | no_license | henwood-dev/logmylife | a3f754ad41f61c27d9fc977cb86eb018198c356b | 425f75b78c2bc3a69f1aff1320aec3373f30184b | refs/heads/master | 2020-03-29T23:28:50.285911 | 2020-03-21T01:32:15 | 2020-03-21T01:32:15 | 125,543,473 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,487 | r | LML_Tidy_Helpers.R | library(sjlabelled)
library(splitstackshape)
library(data.table)
library(doParallel)
library(parallel)
library(iterators)
library(foreach)
library(haven)
library(tidyverse)
library(bit64)
select <- dplyr::select
write_prompt_responses <- function(data_dirname, wockets_dirname, manual_dirname = NULL, skip_manual = TR... |
cf083925135c08c0f0c28a61528b2c8597fe7215 | 1f96642b72c65546393c7fe9d201f52737885c02 | /Rexam/lab11.R | 440186f19b1d2eeff1b982976fe0273bdd60550a | [] | no_license | kdragonkorea/R-data-analysis | 3051881bcc9984e20092ba65feda712b7aa76427 | 0625fea1d1b4842ed6d7554e5a6aaf20d2b4d43a | refs/heads/master | 2023-03-23T00:24:06.549611 | 2021-03-15T00:14:10 | 2021-03-15T00:14:10 | 341,584,046 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,533 | r | lab11.R | library(RSelenium)
remDr <- remoteDriver(remoteServerAddr = "localhost" , port = 4445, browserName = "chrome")
remDr$open()
remDr$navigate("http://gs25.gsretail.com/gscvs/ko/products/event-goods")
goodsname <- NULL; goodsprice <- NULL; nextpage <- NULL
# 첫 페이지에서 2+1 메뉴로 이동
two_to_one <- remDr$findElement(using='css ... |
249778eced85499440f2f50c681c8a7de099b64b | bed0fbea3a7dce73838418e15e2516b1a16a490b | /man/linspace.Rd | 2ddc848c72b3c3519e30ccd3e7b2f9415385435d | [] | no_license | bgreenwell/ramify | af5fc93c73844869ab5de44d0dd126496e8e4b79 | 7dbadcb773f6d9b7e910e97bc57b2d17b4df7927 | refs/heads/master | 2021-01-17T04:11:44.564375 | 2017-01-04T13:02:16 | 2017-01-04T13:02:16 | 31,129,928 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 652 | rd | linspace.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convenience.R
\name{linspace}
\alias{linspace}
\title{Linearly-spaced Elements}
\usage{
linspace(a, b, n = 50)
}
\arguments{
\item{a}{The starting value of the sequence.}
\item{b}{The final value of the sequence.}
\item{n}{The number of sam... |
bfdde574377bea7707e59a7a6e90e8efedc4f5c6 | 715f2721c5f9c69876c75957694cef3ceea86e0e | /man/tsa.Rd | 130852006e1b149f8dd94f7414be84ff8e44f0ae | [
"Apache-2.0"
] | permissive | YongLuo007/bcmaps | f59a35edc8a5dd63c1be8aa90db5b6323b28aad4 | bf71d9f0f9ab8292f68e0852f655a37982e59eab | refs/heads/master | 2021-05-05T11:38:28.271150 | 2020-01-20T19:00:23 | 2020-01-20T19:00:23 | 118,187,826 | 0 | 1 | null | 2018-01-19T22:55:03 | 2018-01-19T22:55:03 | null | UTF-8 | R | false | true | 1,069 | rd | tsa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/download_data.R
\name{tsa}
\alias{tsa}
\title{British Columbia Timber Supply Areas and TSA Blocks}
\format{An \code{sf} or \code{Spatial} polygons object with B.C.'s Timber Supply
Areas and TSA Blocks}
\source{
Original data from the
\href{ht... |
22e7bbc03ba9b976607ab22a467d5504223ba012 | 8dc79304ecd803c5a9bce0dd62e4b25d4523649d | /man/getFunctionEnvelopeCat.Rd | c45c53b92fcc29dcf35f3a2d5489789ce2b79211 | [] | no_license | jonotuke/catenary | 2fb519be7ef9cafcee1255902971f12de383c81d | f2e64e4dabe69af8b74fae028ff179f72efae4b5 | refs/heads/master | 2020-04-02T04:05:23.024278 | 2018-05-04T07:47:44 | 2018-05-04T07:47:44 | 60,389,251 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 889 | rd | getFunctionEnvelopeCat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getFunctionEnvelopeCat.R
\name{getFunctionEnvelopeCat}
\alias{getFunctionEnvelopeCat}
\title{Function to return function envelope for catenary}
\usage{
getFunctionEnvelopeCat(data, R = 1000, initial, x)
}
\arguments{
\item{data}{data frame wi... |
b9dc257fb85e457f6c4cac9ee769bcddda46250d | cb5d3ff3ab8e30c7c14215d1d3a64a05d82b02c3 | /plot3.R | 7d868db40c5a60123473a659461b48328c9e7ccb | [] | no_license | dwaynedreakford/ExData_Plotting1 | d2abc89062ad48c43dc83dab2f98b02631aca104 | fe382136aa5467e3a26a2f4b04242b14fe648121 | refs/heads/master | 2021-01-11T03:05:58.774256 | 2016-10-17T06:08:59 | 2016-10-17T06:08:59 | 71,093,291 | 0 | 0 | null | 2016-10-17T02:34:56 | 2016-10-17T02:34:55 | null | UTF-8 | R | false | false | 1,724 | r | plot3.R |
makePlot3 <- function() {
# Read the dataset
powerData <- read.delim("household_power_consumption.txt", sep = ";", na.strings = "?", colClasses = "character")
# Filter the observations for the dates "2007-02-01" and "2007-02-02"
powerData["DateTime"] <- strptime(paste(powerData$Date, powerDa... |
351be250c035122edcb0eafeb362b4a417676fe8 | 7b7a28198b4948db5ce5040ed6ded340cda2d1cb | /R/DNbuilder-lm.R | b9e636b22ce28457a2ef18482bfd6ac536a1e57a | [] | no_license | amirjll/DynNom-V4.1.1 | 5061dcdb7ed27addd20c704ce54be74d7167bbb1 | 8c7afbd08321241cc5c41905d666489b6059ddd4 | refs/heads/master | 2020-03-23T22:24:24.185296 | 2018-07-24T14:56:10 | 2018-07-24T14:56:10 | 142,168,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,323 | r | DNbuilder-lm.R |
DNbuilder.lm <- function(model, data, clevel = 0.95, m.summary = c("raw", "formatted"),
covariate = c("slider", "numeric")) {
if (length(dim(data)) > 2 & sum(class(data)=="data.frame")==0)
stop("Error in data format: dataframe format required")
if (attr(model$terms, "dataClasses")[[1]... |
46c881b22f0647f5c701e3b32071fd55e01a1c13 | dc359f8017e0d3d8b89585b012d6ddfa92d0336e | /R/make_csv.R | 3ed9fc414bc0cb6d422e339bb10e5490b1f7f2b9 | [] | no_license | fmichonneau/impatiens | ab50245429f1c87b6e8791d99750e20faa1dc158 | d48d31d5fb6143ffcc770c92459575297a509f24 | refs/heads/master | 2021-03-16T08:29:46.843419 | 2015-05-28T19:10:54 | 2015-05-28T19:10:54 | 26,188,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 691 | r | make_csv.R | check_file <- function(file, verbose) {
if (!file.exists(file)) {
stop(file, " wasn't created.")
} else {
if (verbose) message(file, " succesfully created.")
}
}
create_dir <- function(file, verbose) {
if (!file.exists(dirname(file))) {
if (verbose) {
message("Create... |
5c4be76e277d1200ec689dd164f4e8e41fdfbf94 | b2cb3b4ad1b581a59448552b0d0911e6a58c3dae | /chapter3_simulations/code/generate_prior_predictive_distributions.R | 4122008f09c7bd2dcdffb5b5a1aba08e853e529f | [
"MIT"
] | permissive | vasishth/RetrievalModels | 1c3c6a9b915cdae7073c7895653cdf089cb6f693 | 40eb268da11cd3adb7287ec32435cfc32c7de724 | refs/heads/master | 2022-01-01T11:49:06.842924 | 2021-12-16T10:11:43 | 2021-12-16T10:11:43 | 242,512,415 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,825 | r | generate_prior_predictive_distributions.R | library(dplyr)
library(tidyr)
library(ggplot2)
source("interACT.R")
rmsd <- function (obs, pred) {
sqrt(mean((obs - pred)^2, na.rm = TRUE))
}
compute_int_means <- function(d){
int <- select(filter(d, Distractor=="Match"), -Condition, -Distractor)
dim(int)
int$int <- filter(d, Distractor=="Match")$latency - filte... |
f40441b5e7e797a77326d675c1c114aec6e09584 | 154f590295a74e1ca8cdde49ecbb9cbb0992147e | /man/cv.Rd | f2133c31d4cb0fea71e295bf80d907e6917a4b7e | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer",
"CC0-1.0"
] | permissive | klingerf2/EflowStats | 2e57df72e154581de2df3d5de3ebd94c3da0dedf | 73891ea7da73a274227212a2ca829084149a2906 | refs/heads/master | 2017-12-07T10:47:25.943426 | 2016-12-28T20:52:42 | 2016-12-28T20:52:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 522 | rd | cv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cv.R
\name{cv}
\alias{cv}
\title{Function to return the coefficient of variation for a given data series}
\usage{
cv(x)
}
\arguments{
\item{x}{data frame containing value data for the chosen timeseries}
}
\value{
cv coefficient of variation f... |
73a31bc41376ae3769ecfc570edb030cb98f4b7b | 8a081c8fb7584d2ecd3dd68479923d8dbf0345e1 | /Generate_InputFiles.R | 8fcdbf0f540bfd7200541cd7c0926c8254532c93 | [] | no_license | lfuess/TagSeqMS | a895d140475f8b6b8e7cf027ab18389c2cd4d61c | 6da10a68f3d92047ba7b36e6d09a908bc6da6492 | refs/heads/master | 2022-09-07T06:13:12.101637 | 2020-06-03T21:30:21 | 2020-06-03T21:30:21 | 257,366,869 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,141 | r | Generate_InputFiles.R | ##This script will be used to prepare data for DESEQ2##
library(dplyr)
library(janitor)
library(data.table)
initialdata = read.csv("Masterdata_TagSeq_SDH_23July2018.csv", check.names= FALSE)
dim(initialdata);
names(initialdata);
##Now to select the data we want##
data = as.data.frame(initialdata[c(2,4,27:28,30:31,3... |
afe0e04e34439c975a8f9ffbe731d5f7ac08424a | 9053fe0a4613ceb51475071215358acffc4c4976 | /assignment1/R_Python_Session/a.r | 4a134f5d714fa1a0b8db973a5800fc6123b5c946 | [] | no_license | arunv3rma/CS-725 | fbf1c38a6a1ab15276eae487a137ba72ffb27d3c | 171d879041eb02a43fafc9a19982776f8199b412 | refs/heads/master | 2021-06-04T07:19:02.966467 | 2016-10-25T20:11:05 | 2016-10-25T20:11:05 | 69,154,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,317 | r | a.r | ###############################################################################
##Part-1
setwd("/home/raju/Downloads/kaggle")
train <- read.csv("~/Downloads/kaggle/train.csv")
test <- read.csv("~/Downloads/kaggle/test.csv")
str(train)
head(train,100)
t1<-train[1:100,]
t2<-train[100:891,]
str(t1)
str(t2)
train <- t2
Vi... |
e155298e813aa55785553a57a8a638a81969b3c0 | eb2a963e50d6954cdc73a4d0d6ab9a5dcfa35008 | /jules/ASMap/man/exmap.Rd | 0cfe2d9a949f100ad3d06c819b2674bdcae7e40a | [
"CC-BY-3.0"
] | permissive | dpastoor/R-workshop | e02a3e27ef40fb780d6ac210c1e8d0c49c5eb7f6 | 41f5b8257cf532128d934216bdd32c2dedcdaac1 | refs/heads/master | 2021-01-24T03:08:14.742841 | 2014-07-11T00:17:02 | 2014-07-11T00:17:02 | 21,742,907 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 895 | rd | exmap.Rd | \name{exmap}
\alias{exmap}
\docType{data}
\title{Genotypic marker data for a doubled haploid wheat population in R/qtl format}
\description{Linkage map marker data for a doubled
haploid population in the form of a constructed R/qtl object.
}
\usage{data(exmap)}
\format{This data relates to a linkage map of 59... |
49f005a6581c89c56d85b6cbc19a603b86b9af97 | 269e5ad7a6255e2de06a3512858b6a1151992eaa | /R/Align.Concat.R | 74c6234bc3df4db8d47b4e94925cf2c5d40f6162 | [] | no_license | dvdeme/regPhylo | 0c3b158283e9a7eaa72f7d8597e65eb2a4b478a0 | 56017f10b5ac7c2f54972572739b509175360a6f | refs/heads/master | 2023-06-13T02:42:38.686275 | 2023-05-26T10:07:29 | 2023-05-26T10:07:29 | 165,783,636 | 5 | 1 | null | 2020-06-12T04:45:59 | 2019-01-15T04:10:34 | R | UTF-8 | R | false | false | 12,903 | r | Align.Concat.R | #' @title Concatenate alignments from different gene regions into a supermatrix at the species level
#' @description This function concatenates the alignments from different gene regions into a
#' single supermatrix in nexus and fasta formats, at the species level. The function also allows the
#' inclusion of species... |
f1507a4bd6840fb41fc6b70b240e9d8c8f319b58 | c673605e54dd80c63433796bed3e71e74a4409ca | /svd/svd_i.R | c128c385d10767fab222ce33600bb83f01473e35 | [
"BSD-3-Clause"
] | permissive | hu17889/R_ALGO | fca739aa2fdb02077e81f02c1ec2506343f012bc | 944010d874e027c20acfb218947735152c4421cf | refs/heads/master | 2020-05-16T22:44:15.310226 | 2014-10-23T09:50:52 | 2014-10-23T09:50:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,252 | r | svd_i.R | #!/usr/bin/env Rscript
# 非完全增量学习算法，非稀疏矩阵
r = 4 # 隐式特征数
nr = 6 # 用户数
nc = 4 # 物品数
# 真实值矩阵
inputdata = matrix(c(5,5,0,5,5,0,3,4,3,4,0,3,0,0,5,3,5,4,4,5,5,4,5,5), nrow = nr, ncol = nc, byrow = TRUE)
# 初始化分解矩阵
U = matrix(seq(1,24),nrow=r,ncol=nr)
M = matrix(2,nrow=r,ncol=nc)
# 初始化正则化系数与迭代步长
ku = 0.05
km = 0.05
u = 0.0... |
beb20afdae7219906b561e105d7825065df88f0e | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/5976_6/rinput.R | 105728d07d81490d552f0ba75325d541e4bb9e35 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("5976_6.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="5976_6_unrooted.txt") |
79c998eff8d20edab3c23a600fa7d9060b041bcd | 2d8189185b86d69b097565649f18df45945717f5 | /SLA Scripts/PApr 2007_SLA.R | 20a277f697bef65c6cf6762adc1bea742fa50e54 | [] | no_license | eherdter/r-work | e2b9035e6098c06983b198de3674e535cbc04458 | 8f429408d89d9b7dfe8146b1d3b22a65af5e2780 | refs/heads/master | 2021-01-16T18:27:47.789159 | 2015-03-18T17:18:33 | 2015-03-18T17:18:33 | 30,160,696 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,427 | r | PApr 2007_SLA.R | library(maps)
library(spam)
library(fields)
library(chron)
library(ncdf)
SSH_4_07 = open.ncdf("dt_global_allsat_msla_h_y2007_m04.nc")
lats = get.var.ncdf(SSH_4_07, "lat")
## the latsU correspond to the sla lats and longs
lons = get.var.ncdf(SSH_4_07, "lon")
###### for June 2006 ####
# for stations 31, 10-40, PC1... |
56602d168e793500d254e414ed2a4afd1219dfc4 | d6bd873a9b74236be1b016a496acaec69c0ee066 | /man/modelList.Rd | c16827f6b8025781a23adf31c3660a2bcb3d4fd9 | [] | no_license | BenRollert/ensembler | 4bd6be615f83546f3cf67d2ecd3210bd5117b36a | cda6a8e12dcfb6b68750044d71e4806ac2da1acc | refs/heads/master | 2020-07-14T10:20:56.287335 | 2015-05-22T22:12:35 | 2015-05-22T22:12:35 | 35,344,162 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 789 | rd | modelList.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/model_func.R
\name{modelList}
\alias{modelList}
\title{Create a list of caret train objects trained on multiple Domino instances.}
\usage{
modelList(dataset, models)
}
\arguments{
\item{dataset}{A character string specifying the name ... |
bc634b4dd1fceb29146b38fde92cbda776f9a766 | 98c29220391a8fc864ba394536c6cde766dc8ecd | /standard_eqtl_calling/visualize_banovich_chrom_hmm_enrichment_analysis.R | 105872cab79925b0d03cfe5be1fed09d64d78b46 | [] | no_license | BennyStrobes/ipsc_cardiomyocyte_differentiation | 175d2a86b07e6027a343b79376a07eba7941607a | 6f6ac227df5f7ea2cc9e89563447d429aae2eeb5 | refs/heads/master | 2021-07-11T07:09:15.169745 | 2020-07-02T15:37:54 | 2020-07-02T15:37:54 | 156,638,838 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,105 | r | visualize_banovich_chrom_hmm_enrichment_analysis.R | args = commandArgs(trailingOnly=TRUE)
library(ggplot2)
library(ggthemes)
library(cowplot)
library(reshape)
load_in_odds_ratios <- function(file_name, adding_constant) {
aa <- read.table(file_name,header=TRUE)
real_overlaps <- as.numeric(aa$real_overlaps) + adding_constant
real_misses <- as.numeric(aa$r... |
0617135564a1868fd5455e7e73ea8cd74f4a06f0 | 9e835c1f388bfbb3cdfbacf7a99ac54ba1215857 | /PEGASUS/analysis code.R | d4c900b7fb555108838c2849dbe39e86ba78c5f5 | [] | no_license | yizhenxu/Reinforcement-Learning | efcf1da09a13c8b623dfd34b61c6ee369f8ed9c4 | 9751e0dd8dfb01ba80513cad72e667e22bbc3ba1 | refs/heads/master | 2021-08-23T12:26:41.276211 | 2017-12-04T22:30:01 | 2017-12-04T22:30:01 | 113,102,399 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,028 | r | analysis code.R | # change the probability of each direction to 0.225, so it is 0.9 prob for g(s,a,p) to use a
library("Rcpp")
library("RcppArmadillo")
library("parallel")
sourceCpp("~/codefoo.cpp")
map = matrix(0,5,5)
map[5,1] = 1 # start S
map[1,5] = 2 # end G
# 1up: i-1,j
# 2left: i,j-1
# 3down: i+1,j
# 4right: i,j+1
action.va... |
29cbdc218e8f45ffcca5c662113aa38bf2fec367 | 7ca4419b9a542ec7cd796db8b9ccf2828eaec062 | /man/Cytosine.Rd | 689e0be2e80f3d57a6deb9e6989c2ab182da181b | [] | no_license | danielbraas/ShinyMetab | 7d5f3688f3a2f3b5337d4c265104fd4f94ccf001 | bd4767d912697f63a29324ec7a7ee981ff109f07 | refs/heads/main | 2023-02-05T02:59:48.169527 | 2020-12-29T05:47:58 | 2020-12-29T05:47:58 | 325,178,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 338 | rd | Cytosine.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{Cytosine}
\alias{Cytosine}
\title{A character vector of cytosine-related compounds.}
\format{
A character vector of length 9.
}
\usage{
Cytosine
}
\description{
A character vector of cytosine-related compounds.
}
\... |
d886387d2cb6f02eb5cfce33b90170158dff920e | 6ebc3e12c3bfdd8c34b63c1da3cb10442cf70c3b | /R/commonplot.R | b66565ef47dee3116ab7b239855f427466ae1bcf | [] | no_license | epicentre-msf/rosm | 6f8900fcfed4f29bf5a6c532e6c991cce9bc6040 | 8f417038ccc09d9b00020f673ec6a1ad99ea224a | refs/heads/master | 2021-01-22T21:57:53.840997 | 2017-04-07T15:43:24 | 2017-04-07T15:43:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,250 | r | commonplot.R | #functions used by both google and osm
tile.cachedir <- function(type, cachedir=NULL) {
if(is.null(cachedir)) {
cachedir <- get_default_cachedir()
}
safename <- gsub("[^a-zA-z0-9]", "", type$name)
folder <- file.path(cachedir, safename)
created <- dir.create(folder, showWarnings=FALSE, recursive=TRUE)
... |
8fb8e16577c0a926adc30279c9b2557b606d49e8 | 3f436064cd2299140e328117a2c0611281c9691e | /Chapter 2/0-setup.R | 5edd89887efb619869e9d54ef50cab5cbd99305d | [] | no_license | ZhangWS/dissertation | deb9e7f7bd1fd945c0266c1db27073e02f93e7bb | be9761f1c0ed5d05e8c377438c9407eeffee3102 | refs/heads/master | 2020-03-19T15:35:50.447168 | 2019-02-05T23:54:53 | 2019-02-05T23:54:53 | 136,677,561 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,141 | r | 0-setup.R | ##################################
# CHAPTER ONE preliminary setup
##################################
#Make sure you run this before embarking on analyses for Sections 1-3
library(foreign)
library(dplyr)
library(ggplot2)
library(reshape2)
library(DescTools)
library(tidyr)
library(lsr)
#Read in initial data
setwd("."... |
e0f8dbe28bd54d2d06f9e19432c1706036e7da59 | 1291bf249bff01814610befd45c512580beb9f2f | /man/dyCandlestick.Rd | fc3fedfe6d32e3b7009ed66a5036a89aee3eba17 | [] | no_license | pz10/dygraphs | 058875bcb7126e1f5056564ae96b225a270d4fb2 | a4e3553005a021fbf597b97ed5b9170f37bb611c | refs/heads/master | 2021-01-19T14:28:08.771038 | 2017-03-19T14:37:07 | 2017-03-19T14:37:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 643 | rd | dyCandlestick.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/candlestick.R
\name{dyCandlestick}
\alias{dyCandlestick}
\title{Candlestick plotter for dygraph chart}
\usage{
dyCandlestick(dygraph, compress = FALSE)
}
\arguments{
\item{dygraph}{Dygraph to draw chart on}
\item{compress}{If true, compress ... |
9da201db0c28c1d76251f17233c8da93f5e8c019 | fe254ef6be0bd316d41b6796ef28f1c9e1d5551e | /R/CubeCoord.R | d1af468de3d9c94e9bb315117f5e388284931de0 | [] | no_license | matthias-da/robCompositions | 89b26d1242b5370d78ceb5b99f3792f0b406289f | a8da6576a50b5bac4446310d7b0e7c109307ddd8 | refs/heads/master | 2023-09-02T15:49:40.315508 | 2023-08-23T12:54:36 | 2023-08-23T12:54:36 | 14,552,562 | 8 | 6 | null | 2019-12-12T15:20:57 | 2013-11-20T09:44:25 | C++ | UTF-8 | R | false | false | 22,497 | r | CubeCoord.R | #' cubeCoord
#'
#' @name cubeCoord
#' @rdname cubeCoord
#' @importFrom tidyr unite
#' @importFrom tidyr spread
#' @importFrom graphics boxplot
#' @title Coordinate representation of a compositional cube and of a sample of compositional cubes
#' @aliases cubeCoord
#' @aliases cubeCoordWrapper
#' @importFrom tidyr unit... |
d8cef0c611b78ed8bcc99e3851080f9a5141daf3 | 2b5728585d67ad9f0210a21189459a1515faa72f | /R/fullFact.R | ea42c23f8f6e2b1f7ea8ad2ccba5ed27a4f1c21a | [] | no_license | Matherion/userfriendlyscience | 9fb8dd5992dcc86b84ab81ca98d97b9b65cc5133 | 46acf718d692a42aeebdbe9a6e559a7a5cb50c77 | refs/heads/master | 2020-12-24T16:35:32.356423 | 2018-09-25T06:41:14 | 2018-09-25T06:41:14 | 49,939,242 | 15 | 9 | null | 2018-11-17T10:34:37 | 2016-01-19T08:50:54 | R | UTF-8 | R | false | false | 1,324 | r | fullFact.R | #' fullFact
#'
#' This function provides a userfriendly interface to a number of advanced
#' factor analysis functions in the \code{\link{psych}} package.
#'
#'
#' @param dat Datafile to analyse; if NULL, a pop-up is provided to select a
#' file.
#' @param items Which variables (items) to factor-analyse. If NULL, al... |
79b0227f06ba17135b49df2df53ffe9cab2b34e9 | 3838084df843d65746fcdd9a7eb274cd2087aece | /Examples/tracking_debugg.R | 1ed2bd42eca82ebbc815bfd7689b1b719b5303a2 | [] | no_license | jie108/FOD_Needlets_codes | 772b2ff5bbb537725dcafa2b5be8887d6a626ff9 | 76223d7598941ad1f8e7989715a26fb295ad56e5 | refs/heads/master | 2020-08-15T21:50:02.140122 | 2019-10-15T23:10:58 | 2019-10-15T23:10:58 | 215,412,811 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,025 | r | tracking_debugg.R | rm(list=ls())
library(R.matlab)
library(rgl)
library(compositions)
source("dwi_fit.R")
source("dwi_track.R")
path_load = '/Users/hao/Dropbox/stats_project/FOD_codes_simulation/Real_data/S110933/fitting/space_indexx108-123y124-139z37-42/'
num_fib_cut = 4
temp = readMat(paste0(path_load,'for_tracking_cut',toString(num_... |
2ff2ee23ad9f3b8c77ab3985fcfff6fceddce0b0 | 3a5ae60a34608840ef484a901b61a363b1167756 | /vignettes/general_processing.R | c73797128ad456b1d9e694537df20b6442797cd8 | [] | no_license | SWS-Methodology/hsfclmap | 3da8ca59a1ceb90564ec70a448a6f0340ca86420 | eb2bc552fcce321b3dd7bc8655b092bc7a428e1e | refs/heads/master | 2021-01-17T17:35:59.484994 | 2016-12-19T17:47:53 | 2016-12-19T17:47:53 | 70,464,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,679 | r | general_processing.R | library(magrittr)
library(stringr)
library(futile.logger)
library(dplyr, warn.conflicts = FALSE)
library(hsfclmap)
cores <- parallel::detectCores(all.tests = TRUE)
if(cores > 1) {
library(foreach)
library(doParallel)
doParallel::registerDoParallel(cores = cores)
}
trade <- esdata13
tariffline <- FALSE
reportdir... |
df4673ad0c6156a5b48a3304388ac65fa4963a91 | 90df0cb421dc4221bfce0929054d8067a50af72a | /Rscripts/old_fig_scripts/fig_mixture_model_demo.R | 860068e77338bdadd6b1802a9054e6f88b5328be | [
"MIT"
] | permissive | SlavovLab/DART-ID_2018 | c1c7de6cd03690e70cf1c27a9bac92d977b96599 | 84e73bc66e9e9a64d848d06463255db92561bfb7 | refs/heads/master | 2020-04-13T03:21:46.341466 | 2019-05-15T05:47:18 | 2019-05-15T05:47:18 | 162,929,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,226 | r | fig_mixture_model_demo.R | ## mixture model demo ------
x <- seq(0,60,by=0.1)
#y1 <- dlnorm(x, meanlog=4.663, sdlog=0.5089)
y1 <- dnorm(x, mean=38, sd=17)
y2 <- dnorm(x, mean=20, sd=1.78)
#y3 <- dnorm(x, mean=80, sd=2.3)
#y4 <- dnorm(x, mean=120, sd=2)
#plot(x, y2, 'l', col='red')
#lines(x,y1,'l', col='black')
#p <- ggplot(data.frame(x,y1,y2,... |
f3a56af15a2c4e5f138c081c9f93eac1fcb80d28 | 4160ec1f770aa1124aeefe44cca5b97be3b368a5 | /Cleaning_Featuring/Katz_Back-off_2.2.R | 632d0144acfc8d0f9206bea5239f46c6f67891d5 | [] | no_license | jordiac/Capstone_DSS | 831e4f9c0081cbf5a0e569b2d7b393e04d718aed | a99f80b82de2994af5102b864a429a26b4426d0d | refs/heads/master | 2020-12-30T16:42:17.682853 | 2017-07-22T13:57:06 | 2017-07-22T13:57:06 | 91,016,685 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,223 | r | Katz_Back-off_2.2.R |
## -------------------------------------------------------
## Katz's Back-off implementation
## -------------------------------------------------------
## ******************* Notes ***************************
## This implementation considers only 2-grams and 3-grams
## ----------------... |
7a04a1c4d57be2bb2404019c38e7898be4539955 | e248c9ff1d03ac10216bb9e86611491ec16c1fdd | /R/functions.R | 46d09c59e833899cff67d448bb4fb69c68614dfb | [
"Apache-2.0"
] | permissive | lolow/ENGAGE-overshoot-impacts | 25a1b56395c0a8198046d82c6ba4c815ca450d63 | 2bd2a81eae63e5dcf114b108842ded55a07df867 | refs/heads/main | 2023-04-07T14:16:59.671132 | 2021-11-09T08:28:12 | 2021-11-09T08:28:12 | 414,539,627 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 559 | r | functions.R |
source('R/data_ssp_db.R')
source('R/data_engage_db.R')
source('R/data_climate.R')
source('R/impact.R')
source('R/impact_bhm.R')
source('R/impact_levels.R')
source('R/impact_arnell.R')
source('R/impact_slr.R')
source('R/impact_tail.R')
source('R/compute_net_benefits.R')
source('R/compute_cmit.R')
source('R/compute_ad... |
e3a46fe90e94d385b403d2aed5fdda4ec970fd3b | 8eccf1b9d13564b48d6936fc7e8878bca2a757f7 | /Amazon reviews scraping.R | 30db1823ee5ad5ac9810eab1c36ca55e40d36345 | [] | no_license | MohitKedia/Web-Scraping | a9b2b37dec0cbf3ffd4b8005cba249960d405bc3 | d4154faf1ffd09b72aeaee6156ca12cb10db3ab0 | refs/heads/master | 2020-03-10T04:48:24.023858 | 2018-07-09T13:40:12 | 2018-07-09T13:40:12 | 129,201,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,656 | r | Amazon reviews scraping.R | library(rvest)
install.packages("RCrawler")
##############################
#NOKIA8_Reviews#
#METHOD1#
url <- "https://www.amazon.in/Nokia-8-Polished-Blue-64GB/product-reviews/B0714DP3BJ/ref=cm_cr_getr_d_show_all?showViewpoints=1&pageNumber=1&reviewerType=all_reviews"
webpage <- read_html(url)
reviews_dat... |
1125c9ecf1eb5c2d6f4b018c509ddf761ce7b2b4 | 4e77858f348a7081e6d9bc4fa5b0296bfa3b4291 | /Assignment_1/assignment_1.R | 484dd74e2718e2d7f1676509a7566bc22f888c09 | [] | no_license | bazzim/Coding-2-web-scraping | 8da2f278e5048e173e08683b7d09ddccafda3407 | bb89715aeb0d2c11b6471028b4c14b7d0258bd1e | refs/heads/main | 2023-01-18T18:07:38.268269 | 2020-11-22T20:19:08 | 2020-11-22T20:19:08 | 315,120,911 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,462 | r | assignment_1.R | library(rvest)
library(data.table)
rm(list=ls())
# website of interest: https://www.sciencenews.org/
## create a function which downloads information from a url to dataframe (from sciencenews.org)
get_sciencenews_page <- function(my_url){
print(my_url)
t <- read_html(my_url)
boxes <- t %>% html_nodes('.pos... |
b1f8bdbcd39741a77a03898e41dfc3ae9fefc80f | 85da7f67f9fd656b39f16a7cf0e63424636b706a | /ExData_Plotting1/plot4.R | 10bec9d74cbdc970741150627911e2edc28b64e1 | [] | no_license | pivezhandi/ExData_Plotting1 | cfe011fd20734a06779d767f43ac0b37b21758e7 | 53c7857bb451ee7184d43851ad94e83dd54cd234 | refs/heads/master | 2021-01-25T03:50:00.394834 | 2015-09-13T22:35:14 | 2015-09-13T22:35:14 | 42,414,640 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,420 | r | plot4.R | setwd(file.path("D:", "sbu", "RLearning", "exploratory data analysis","project1"))
EPC <- read.csv("household_power_consumption.txt",
header=T, sep=';', na.strings="?",
nrows=2075259, check.names=F,
stringsAsFactors=F, comment.char="",
quote='\"')
EPC$D... |
9717621be715bfc0c43746752df16c0f4fbd3f77 | 8e8abb1b8f31b1cad68e1e4534be0489555ad59e | /lovelyanalytics_kmeans_R.R | 89a8230677770ceb71abf7cbbffa15bc458ed1b5 | [] | no_license | mjvieille/lovelyanalytics-kmeans | 8d29f5f3c825a45a3dadf21ec5ae2b1bf1da0a04 | 49fc526754b56cf6188ffcf41154635b632536d5 | refs/heads/master | 2021-01-17T07:38:51.436410 | 2017-03-05T09:34:48 | 2017-03-05T09:34:48 | 83,783,186 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 390 | r | lovelyanalytics_kmeans_R.R | #***** lovelyanalytics.com *****
#***** k-means *****
# Chargement des données
data<-read_excel("~/lovelyanalytics/k-means/data/data.xlsx")
# Algorithme k-means pour créer 3 clusters
resultat_kmeans<- kmeans(data[,2:3],3)
# Anciennete moyenne et panier moyen par cluster
resultat_kmeans[2]
... |
40f83bfa8baf6d72ea6ac442aa5176a7947b80d7 | c8b609bf58dab1a383bbea8b43a7bc2708adcb38 | /man/circle_line_intersections.Rd | 8bcae8c3d75d06e52181a7fe4b85371b31944bca | [] | no_license | holaanna/contactsimulator | ce788627c12323c4ab6b3aa902da26bf3e2e4cf5 | 8bcd3f01e0bbe5fb7328d9f6beb27eb907779bdd | refs/heads/master | 2022-03-17T03:25:18.841897 | 2019-11-26T18:33:29 | 2019-11-26T18:33:29 | 111,702,061 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,590 | rd | circle_line_intersections.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{circle_line_intersections}
\alias{circle_line_intersections}
\title{Generates a set of intersection points between the cirlce and the grid lines.}
\usage{
circle_line_intersections(circle_x, circle_y, r, n_line, grid_lines... |
2a8f73f7423e6d1c969c5dfe7ded2095b0c0e78c | a5b6e45f613c45691b9f8b9811791637fe40b378 | /OldScripts/old_uORFome/DataBaseGetters.R | 8a79aa9e7e3040a9c5c65674452573c48e3bb0d9 | [] | no_license | Roleren/RCode | e0bb86ca02fc5c7eb66943be028d11982782799b | db8c65ee9d15b0576f1269fcce81915bb38b6b31 | refs/heads/master | 2023-01-08T16:04:34.751910 | 2020-11-12T16:44:42 | 2020-11-12T16:44:42 | 312,334,583 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,501 | r | DataBaseGetters.R |
#' Get orf names from the orf data-base
#'
#' This is the primary key for most tables in the data-base
#' @param with.transcript a logical(F), should the transcript be included, this makes the
#' list have duplicated orfs
#' @param only.transcripts a logical(F), should only the transcript and not orfId be included
#... |
ed75b474ac649b8e04bf4ae1a42114250d10b7f1 | 6b0acbabf78b41cb2bf79128ec0cc47a19704488 | /assignment3&4bySaurabhBidwai.R | 8048c52f317063f832edd28652f77f6931550a77 | [] | no_license | wejay28/R_basics | edd757d973dbc37de1486afe17c4df7773eb30f2 | a58d1d7de3fff2b2d74d22946c81b850ebd18f67 | refs/heads/master | 2021-06-14T12:54:43.279637 | 2017-05-21T06:46:14 | 2017-05-21T06:46:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,624 | r | assignment3&4bySaurabhBidwai.R | #Q.1 print no from 1 to 5 using 'repeat' and 'break'
f=1
repeat
{
print(f)
f=f+1
if(f==6){
break()
}
}
#Q.2 Identify the no. is positive or negative
neg=function(a){
if(a>0){
print("PositiveNumber")
}
else if(a<0){
print("NegativeNumber")
}else{
print("ZeroNumb... |
019be191a59ff96209446bd77c37f6749875976d | cb45abba22cc632e19661516ad16d60793103495 | /Talleres/Problema_FormulaCuadratica.r | dd64f8e2ac33d6ca719d4a6f956b3097c6f5eabe | [] | no_license | Estebanmc2912/An-lisis-Num-rico | b48ee29bbf79588697c88dbc16eb9a1db0be42cc | aa6658eefeb799e317181818199548d25f58b2b9 | refs/heads/master | 2020-06-23T13:38:39.838320 | 2019-11-12T01:46:35 | 2019-11-12T01:46:35 | 198,640,070 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 315 | r | Problema_FormulaCuadratica.r | #Pablo Veintemilla & Esteban Moreno:
#SIMULACION CUADRATICA ax2 + bx + c = 0.
options(digits=8)
a=3
b=9^12
c=-3
#Método suma
x1=-(b+sqrt(b^2-4*a*c))/(2*a)
#Fórmula racionalizada
x2=-(2*c)/(b+sqrt(b^2-4*a*c))
cat("Solución \n")
cat("Raíz 1: ",x1, " Raíz 2: ",x2,"\n")
|
393ae3009035dc7489263fcddef268b1bc3b4421 | c830d7ecdd2739c356242a3141beb38960fc44e2 | /R/freorder.R | 10c6f6d714b25cc1ccf7529ec833d39fcce24941 | [] | no_license | STAT545-UBC-hw-2018-19/hw07-janehuang1647 | 8812c99a7eba9b0fd576f2c42ece25cb828fd068 | 84749400b004c6f0cccb02b0c7c7d3f1650db13c | refs/heads/master | 2020-04-05T09:11:51.538674 | 2018-11-13T20:54:22 | 2018-11-13T20:54:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 623 | r | freorder.R | #' Reorder Levels of a Factor
#'
#' @param x a factor, an atomic vector. The vector is treated as a categorical variable whose levels will be reordered.
#'
#' @usage freorder(x)
#' @return By default, the function will return the factor in the descending order.
#' @export
#' @examples
#' @seealso \link{reorder}
#' fre... |
24c8067e2da1115d5e3759c1257fcbd88921c5b2 | b31298d41ca6b8aaf52c01bf69ec6f9f577341bd | /creating_covariates_dataset.R | 1c2815b1a3172b398359b6deaf5adc32b01d7b12 | [] | no_license | nskaff/CORE | b3a73740c6b91b90fcd286ee95b780304cc6a101 | 494041528b0b24cafb2d242050979d700c9b20c5 | refs/heads/master | 2021-01-19T17:38:31.117378 | 2017-10-27T14:59:26 | 2017-10-27T14:59:26 | 101,078,656 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,554 | r | creating_covariates_dataset.R | #creating a useful dataset for covariates
library(dplyr)
library(lubridate)
library(tidyr)
library(googlesheets)
library(mapview)
#loading in data with z-score
my_sheets <- gs_ls() #Will have to authenticate Google here (in browser, very easy)
core <- gs_title("z_score_data") #get whole document
z_scores <- core %>% g... |
f46802d5fd4d7c7aa6dab382107bc177309905d9 | 96380c781c896f2731e301c9fe17bbb1303b3344 | /svm_analysis_final.R | d5c2b6c50bd1a98b0a80ecfb4288811324c8e530 | [] | no_license | tborrman/DNA-rep | e5c6b955059d48dd5f8fd8dfee5db9ab300481bf | d0a311b82029de5537d377550b31cba5c66cb763 | refs/heads/master | 2020-04-22T10:15:38.496413 | 2017-10-18T00:21:51 | 2017-10-18T00:21:51 | 42,599,464 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,195 | r | svm_analysis_final.R | # A script to analyze DNA replication origin data by support vector machines
library("e1071");
library("ROCR");
# Set paths
begPath <- "/Users/User/Research/DNArep";
wkDir <- paste(begPath, "/Data", sep="");
# Read in ori_data_1.6.txt
full_ori_data <- read.table(paste(wkDir, "/ori_data_1.8.txt", sep=""), h... |
64cc51a1ef8d48d11033c35dc7f7224035e74330 | d373be2775975e19c92321809900453645663fc9 | /R/landmarks.R | 2cf2c3a128d84c3158d2e7252bf0aae51ce2b01c | [] | no_license | spencerbell/tigris | 84b4f532d957fca402df375da30fcaa2ec7f4f6d | 8779398dc2203ad917c7c78035c99ea92f237195 | refs/heads/master | 2021-01-19T13:04:07.240755 | 2017-04-12T14:17:41 | 2017-04-12T14:17:41 | 88,059,990 | 0 | 0 | null | 2017-04-12T14:16:50 | 2017-04-12T14:16:49 | null | UTF-8 | R | false | false | 4,472 | r | landmarks.R | #' Download the Military Installation National Shapefile into R
#'
#' Description from the US Census Bureau: "The Census Bureau includes landmarks
#' such as military installations in the MAF/TIGER database for
#' locating special features and to help enumerators during field operations. The Census Bureau adds
#' landm... |
71a1fc393ad2dd6db4ef4e5c470b88d3c4f4a6ef | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.servicecatalog/man/associate_service_action_with_provisioning_artifact.Rd | be3064ef5c4d3326864d452a6cf70f3c53b011e7 | [
"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 | 1,230 | rd | associate_service_action_with_provisioning_artifact.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.servicecatalog_operations.R
\name{associate_service_action_with_provisioning_artifact}
\alias{associate_service_action_with_provisioning_artifact}
\title{Associates a self-service action with a provisioning artifact}
\usage{
associate_se... |
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