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c24cda1c87a77191b759292fb18bf91969e7988f | b8fa00b408af080b5e25363c7fdde05e5d869be1 | /Task5_0867117/Analytics5_7.r | a7899d252a2f5b1454d4da05ca01b5f7887d8ef5 | [] | no_license | anurag199/r-studio | bc89f0c18a8d44164cb4ede8df79321ea965bc77 | e42909505fbe709f476081be97f89cc945a2745d | refs/heads/master | 2020-04-26T23:04:25.061780 | 2019-03-05T06:56:26 | 2019-03-05T06:56:26 | 173,891,322 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 132 | r | Analytics5_7.r | library(tm)
library(SnowballC)
# creating dtm for cleanemail_corpus
email_dtm <- DocumentTermMatrix(cleanemail_corpus)
email_dtm |
ebbe9de9274a0502ae3006ad979299f5fa6de5e4 | 5693d3497d8d4e61f5ffa0becca905a1074de569 | /PRA/18-08-2020/ArimaXpm2.5.R | 483cbc887b6278894ab257e2ae726918c622083f | [] | no_license | souviksamanta95/R_Personal | 65a6272253792839c3b44cfab3cf934227609ae8 | 56597d51b620a587e097c8b669d014a807481f39 | refs/heads/master | 2023-02-16T11:10:15.966371 | 2021-01-20T11:12:20 | 2021-01-20T11:12:20 | 260,390,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,147 | r | ArimaXpm2.5.R | ## Time Series ARIMA and MSARIMA Models in R: Box Jenkin's Methodology
rm(list=ls())
cat("\014")
dev.off()
install.packages("tseries")
install.packages("forecast")
install.packages("plm")
install.packages("Formula")
install.packages("tcltk")
install.packages("uroot")
install.packages("pdR")
install.packages("stats")
i... |
24af2af275e54fca1e43a21879d33e3796907880 | b4d28b7cb7b25f05687a027e24d70b3c1116408b | /Resources/AlertDlogUtils.r | eade5c3d6c06d6c623364e5c6831ba1c443f2a2a | [] | no_license | fruitsamples/Bitblitz | af494e92834306c0e5002da03da390fa4ff748b8 | 4708515a9fe06052a42df257025729a35cdf0ca3 | refs/heads/master | 2021-01-10T11:47:18.098116 | 2015-11-25T21:22:37 | 2015-11-25T21:22:37 | 46,887,726 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,149 | r | AlertDlogUtils.r |
/*======================================================================================*/
/* File: AlertDlogUtils.r */
/* */
/* By: George Delaney */
/* Mac CPU Software Quality */
/* Date: 5/14/90 */
/* */
/* Con... |
e7bc7f154526ada715f009fb8d95e724dfe762d1 | 303ee8c30e03e6bf734e69e1e00f43fefaf3bda4 | /heatmap.R | 7244d0e9c069152651d9d2092ec6e8b804ff06c8 | [] | 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 | 681 | r | heatmap.R | x <- as.matrix(eurodist)
countries <- c("Greece", "Spain", "Belgium", "France", "France",
"Germany", "Denmark", "Switzerland", "Gibraltar", "Germany",
"Holland", "Portugal", "France", "Spain", "France", "Italy",
"Germany", "France", "Italy", "Sweden", "Austria")
... |
2377d908f6c82be4c896dfc70099bd0d05b4414a | ab1985a8774796e33ff3aea3bab6ecf45b700101 | /R/coordinate_conv.R | 072c5619b01a2747f805fa740a5715e2631e23b7 | [] | no_license | dindiarto/coordinate_converter | 161dc47639ce45d08087de94a8a360fbcfded673 | 26bea7490343a1522b3601caa58eec35aa7ed872 | refs/heads/master | 2020-07-16T14:57:38.842452 | 2019-09-02T08:35:46 | 2019-09-02T08:35:46 | 205,810,609 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,570 | r | coordinate_conv.R | library(measurements)
library(stringr)
library(magrittr)
library(dplyr)
library(readr)
# Geographic coordinates converter
#
#' DMS to decimal degree
#'
#' @param df df is a dataframe with latitude and longitude columns in gms format
#' @param lat latitude in gms format
#' @param lon longitude in gms format
#'
#' @retu... |
a0ddc23ea1df8678eb297f77a5ca8841f636cabe | 9d3e3c3950c4101bc863a90e69606d7c7d03a4e9 | /Lagoon/999_laptop/51_analysis_plots/three_in_one/0_AW_Precip_3_in_1.R | c6230f2c28a3fc0b293620a277cbdb664107da12 | [
"MIT"
] | permissive | HNoorazar/Ag | ca6eb5a72ac7ea74e4fe982e70e148d5ad6c6fee | 24fea71e9740de7eb01782fa102ad79491257b58 | refs/heads/main | 2023-09-03T18:14:12.241300 | 2023-08-23T00:03:40 | 2023-08-23T00:03:40 | 146,382,473 | 3 | 6 | null | 2019-09-23T16:45:37 | 2018-08-28T02:44:37 | R | UTF-8 | R | false | false | 9,135 | r | 0_AW_Precip_3_in_1.R | rm(list=ls())
library(lubridate)
library(ggpubr)
library(purrr)
library(tidyverse)
library(data.table)
library(dplyr)
library(ggplot2)
options(digit=9)
options(digits=9)
source_path_1 = "/Users/hn/Documents/GitHub/Ag/Lagoon/core_lagoon.R"
source_path_2 = "/Users/hn/Documents/GitHub/Ag/Lagoon/core_plot_lagoon.R"
source... |
d38bcc79cb5d8c641300479cf6cf76040466dbff | 107f84efa479feb92f1a0b8ab2ff2193ff2d38ba | /man/timer.Rd | cd0db1b54a00520794ab71640de9c7837f98a2b3 | [] | no_license | data-steve/holstr | f7cc7a01a0686fa37c522c344d83d53adb786261 | 5a3176d7f213ffee1897a7d11ec38044f2e7af44 | refs/heads/master | 2021-07-16T14:32:54.556193 | 2016-09-16T19:17:22 | 2016-09-16T19:17:22 | 57,908,552 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 871 | rd | timer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/timer.R
\name{tic}
\alias{tic}
\alias{toc}
\title{Crude Timing}
\usage{
tic(pos = 1, envir = as.environment(pos))
toc(pos = 1, envir = as.environment(pos))
}
\arguments{
\item{pos}{Where to do the assignment. By default, assigns into the glo... |
87e9c31b200eabe9e96500866b9ad0646be832a4 | 18ff0dc93ac08d5e6a7e3f774d659012092a9a8b | /render_site.R | e372f84455e97b1ff4bfa7cc1990a222a8405112 | [] | no_license | jpowerj/tad-workshop | b492aa726c9ef064e8fb0c7bdd323acc41f7b659 | 30f900a217f76d6c167b747b5d36e8d8c2413b02 | refs/heads/master | 2020-04-23T05:18:49.342473 | 2019-02-20T22:41:42 | 2019-02-20T22:41:42 | 150,912,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 231 | r | render_site.R | # Used to be cool. Now it just runs render_site()
library(rmarkdown)
# Delete tad_old
#file.remove("../../git_io/tad_old/")
# Rename the old version tad_old
#file.rename("../../git_io/tad/","../../git_io/tad_old/")
render_site()
|
766fc46ae28d868ffba418aa374186d3f0025a0d | e95c8b8e2b5aa04d24030818efe28cc92dc0937e | /R/covyw.R | 7790522e56fbf8abd3f5c77adfc0f48b272f4326 | [] | no_license | cran/touchard | 42ad60a56f704c6fed7a221ed644987a5642eaeb | 4c7b4f62f616c75052ce7296ce32e4281ab663d4 | refs/heads/master | 2020-03-27T04:05:37.158323 | 2019-05-31T11:40:03 | 2019-05-31T11:40:03 | 145,911,224 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 516 | r | covyw.R | ### E[ Y x W ] and COV(Y,W), W=log(Y+1)
cov_yw <- function(lambda,delta,N=100){
c0 <- function(lambda, delta, N){
B = 0
j=1
B[j] = lambda * 2^delta * log(2)
eps = sqrt(.Machine$double.eps)
while(B[j] > eps){
B[j+1] = (lambda/j) * (log(j+2)/log(j+1)) * ((j+2)/(j+1))^delta * B[j]
j=j+1
... |
ecedb512a776c3114eea26e9ca091385e0add719 | c4c1c6cf56b94961369e1f8f665580b09aa38b8e | /ScrapingWebsitesData.R | 92fd2e43d06145edde00b4e103f5876d6e5c03bc | [] | no_license | tadevosyana/Home | 5561b79b11a8fa18c646140d9ef5b146cb19b57e | 0105fb38d96889ea26099061f3097c0b996f3247 | refs/heads/main | 2023-06-18T16:10:31.055972 | 2021-07-15T14:06:46 | 2021-07-15T14:06:46 | 386,302,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,258 | r | ScrapingWebsitesData.R | #Cleans everything (any previously datasets, functions created,..)
rm(list = ls())
pkgs <- c('lubridate', "rvest", "readr","sjmisc","dplyr","stringr","qdapRegex","gsubfn","tidyverse", "httr", "jsonlite","readxl","yesno")
for (i in pkgs) {
if (!i %in% installed.packages()) {
install.packages(i)
}
li... |
a03d8e8d1bb24f62ab5a063e67d85ef915c29612 | e823078b011f333dcd28cd43b2e523d963558359 | /001_ingest.R | cab417add76672b77ecd064a0a9f7eb80079d493 | [] | no_license | EvanT33/baseball | e254178c54c22ebbab2e41ad3e1082b822e1270e | 4d046799fb6701fb2e579aff3d10cc6a2d934c63 | refs/heads/master | 2020-03-27T05:18:56.786069 | 2018-08-23T21:38:26 | 2018-08-23T21:38:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,466 | r | 001_ingest.R | #--------------------------
# This script produces play-by-play data for a particular year and team. Later we want it to ingest data for
# all teams and all years iteratively.
# Download this folder: https://www.retrosheet.org/events/2017eve.zip
# in command prompt, navigate to 2017eve folder you downloaded using "cd"... |
d9452fe3770a9a5b6d7d708c03841d0928944daa | 0a60c49b4647d110b63f9ac38219f1ca826be340 | /tests/testthat/test-compat-cli.R | 7a097e068e76180ea91a56cdf7871be08ef22394 | [
"MIT",
"BSD-2-Clause"
] | permissive | pkq/rlang | 2c7653d2575b4af006d26aadc894ede59de340cd | b501cf763097f796f90982dead5e646bdd9dd3f0 | refs/heads/master | 2021-08-06T11:03:50.944095 | 2021-06-08T15:33:59 | 2021-06-08T15:33:59 | 135,239,116 | 0 | 0 | null | 2018-05-29T03:58:27 | 2018-05-29T03:58:27 | null | UTF-8 | R | false | false | 674 | r | test-compat-cli.R | skip_if_not_installed("cli")
cli::test_that_cli(configs = c("plain", "ansi"), "can style strings with cli", {
expect_snapshot({
style_emph("foo")
style_strong("foo")
style_code("foo")
style_q("foo")
style_pkg("foo")
style_fn("foo")
style_arg("foo")
style_kbd("foo")
style_key("foo"... |
6a4dfc575aed9c8b9700f09934d79e55070d1003 | 44b083e16634d27e18553082786c46749493cf4d | /R/panoids.R | 66a2705e4ad5e5d4f5ef2934e0bcbdf530167996 | [] | no_license | DrRoad/streetview-1 | 069755458b17238c0aaf0d21de9c53c792797228 | 94af03ac8acd81b817897336240cab0239ce91c5 | refs/heads/master | 2020-04-08T06:32:35.540083 | 2017-08-18T16:31:15 | 2017-08-18T16:31:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,669 | r | panoids.R | #' \code{streetview} package
#'
#' panoids
#'
#' See the README on
#'
#' @docType package
#' @name streetview
NULL
## quiets concerns of R CMD check re: the .'s that appear in pipelines
if(getRversion() >= "2.15.1") utils::globalVariables(c("."))
#' @title get_panoids
#'
#' @description Retrieve list of panoids an... |
634ec282834bac4625f82d49586191ae2ba1682b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/event/examples/hgextval.Rd.R | 2bfae7a6392a28479e349d3808dc5c79a2617ea8 | [] | 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 | 187 | r | hgextval.Rd.R | library(event)
### Name: hgextval
### Title: Log Hazard Function for an Extreme Value Process
### Aliases: hgextval
### Keywords: distribution
### ** Examples
hgextval(1, 2, 1, 2)
|
a09a0908a607559c469d166e02585f4d2831be89 | 8468b7047206beb226a84ad7d3a9b67612c9b7e0 | /0.4.LST_ViewAngle_data.processing.R | 32c09d1120774c68914a87427dd82d1564b8ef29 | [
"Apache-2.0"
] | permissive | GeoscienceAustralia/lst-gde | 6038b19aac99f204a11f184b12b5e7e33fd6c9c5 | bc94df556128f6abebb8a52bf219083d6057254a | refs/heads/master | 2021-05-15T12:02:05.073656 | 2017-10-23T07:46:23 | 2017-10-23T07:46:23 | 106,478,479 | 0 | 2 | null | 2017-11-14T11:44:35 | 2017-10-10T22:31:40 | R | UTF-8 | R | false | false | 1,624 | r | 0.4.LST_ViewAngle_data.processing.R | rm(list=ls())
setwd('C:/Local Data/')
X = read.csv('C:/Local Data/Code/CB_Date_Range.csv')
Date = strptime(X[,1],'%Y-%m-%d')
CURRENT = as.POSIXlt(Date)
CURRENT.YMD = format(CURRENT,'%Y%m%d')
#---------------------------------
#--- Define Area of Interest -----
#---------------------------------
### 2... |
b422a135f0da2814f72741ba63ac695f24486e0f | c23c8ded85d46acfed25cadba5738380dc1d7ee7 | /static/expr_med.r | 0d9d3b93b13ab2032844247a836d15953dabe354 | [
"MIT"
] | permissive | raysinensis/tcgaAPP | c6a67b2abd2423cd45b01c7dff4841e8865a4941 | 96efc39a6d1e049579ff2765ba6450ca2ad886e0 | refs/heads/master | 2020-12-31T00:10:52.145391 | 2017-06-26T21:30:16 | 2017-06-26T21:30:16 | 86,538,455 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,745 | r | expr_med.r | ##adjust p values for multiple comparisons
mainpath=getwd()
filepath=paste(getwd(),'/static/OUT',sep='')
setwd(filepath)
mainpath=filepath
csvfiles <- list.files(path=mainpath, pattern="output.csv")
cancerlist=c()
for (singlecsv in csvfiles) {
fullfilename <-paste(mainpath,"/",singlecsv,sep="")
generesult <- read.csv... |
7209cf34381df301c54732e0380a05d7a03e4a99 | 91ef1638c42ad7e3584b56ed32970b12c66b6c14 | /cachematrix.R | 8192614142877efcb4c2f85b6002cf339d09a649 | [] | no_license | tradewind/ProgrammingAssignment2 | 2055a0a784d931807907032cb9aba153d9fcee11 | 5a319317d5831dbeec6abb7cc56a41c94e43d570 | refs/heads/master | 2020-12-24T23:54:11.652204 | 2015-02-19T15:55:13 | 2015-02-19T15:55:13 | 31,003,348 | 0 | 0 | null | 2015-02-19T05:57:10 | 2015-02-19T05:57:10 | null | UTF-8 | R | false | false | 1,093 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix takes a matrix as argument and has the following functions,
## get(): return the matrix
## set(): reset the matrix and nullify the previously cached inverse
## getInverse(): return the inverse of the matrix
## setInve... |
b70d450589c58e615c2b1d8ca0ed4081ae5dc6b5 | fa4b331d6804c877eb62fc9566c3a652bccd08f1 | /man/ConnectionAttributes.Rd | c2521d4d37c02fd98392d39aca802b50854f16f1 | [
"MIT"
] | permissive | r-dbi/odbc | 0091c72371abfe95f6d2e5ea940ab06c134e2063 | 56eef6949b4c63468015cd533bd6539f952877cd | refs/heads/main | 2023-08-31T15:19:29.556401 | 2023-08-04T00:49:58 | 2023-08-04T00:49:58 | 63,273,973 | 252 | 98 | NOASSERTION | 2023-09-04T18:48:42 | 2016-07-13T19:32:07 | C++ | UTF-8 | R | false | true | 833 | rd | ConnectionAttributes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Connection.R
\docType{data}
\name{SUPPORTED_CONNECTION_ATTRIBUTES}
\alias{SUPPORTED_CONNECTION_ATTRIBUTES}
\alias{ConnectionAttributes}
\title{Supported Connection Attributes}
\description{
These (pre) connection attributes are supported and ... |
ab85683657dc803737d7463fef14271b6849d102 | 9671527e351255e4b2a412d5c57118e369d82a68 | /man/geom_rocci.Rd | 3bae2d3767838e3cbdb22192ccf39d787c418269 | [
"MIT"
] | permissive | sachsmc/plotROC | 1e80902e2f588698d3c620c654e9851c57e7848a | c8664ed9ba3677f5008a4b47b1e310e733ea2ea7 | refs/heads/master | 2022-06-20T17:32:29.866861 | 2022-05-27T09:34:16 | 2022-05-27T09:34:16 | 24,857,963 | 83 | 13 | NOASSERTION | 2020-03-16T15:18:49 | 2014-10-06T18:12:40 | HTML | UTF-8 | R | false | true | 4,976 | rd | geom_rocci.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_rocci.R
\docType{data}
\name{geom_rocci}
\alias{geom_rocci}
\alias{GeomRocci}
\title{Confidence regions for the ROC curve}
\format{
An object of class \code{GeomRocci} (inherits from \code{Geom}, \code{ggproto}, \code{gg}) of length 6.
}... |
344e03b65194cea506f14ac08179433fd608f7b5 | 09e20b6464db79866dd68eb5eed1fadef97e8b35 | /man/balance_errors.Rd | c1ad5da49a14cd9f5c77c5f52266499064ed5c50 | [] | no_license | tdienlin/td | 8f18604d8b990ec59263c8c83c6dd32ba88f18cd | 4e970efb92dd28367d7326a780322bf57baabca3 | refs/heads/master | 2021-08-17T21:20:33.222171 | 2021-06-09T15:25:14 | 2021-06-09T15:25:14 | 129,587,660 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 299 | rd | balance_errors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/balance_errors.R
\name{balance_errors}
\alias{balance_errors}
\title{Balance Alpha and Beta Error}
\usage{
balance_errors(sesoi, n, one_tailed)
}
\description{
Find alpha-value for which 1-power and alpha are balanced.
}
|
da2a897166fe7c1d5ca8e22a123461c1a6f1c29f | 21cd74bf56e9b101dc885e45cb05c219b8a5d211 | /Exercise11 - [unsupervised learning] - K mean clustering.R | 06ee4f4b707a913cbb3ad4f7042dcdbe21615b59 | [] | no_license | ariannalangwang/R-Exercises | 242aeea98ec38adb1b0641d7278ffcb192e39b24 | f1b8f79e9118d40487c3fe9a6a0e87337c01aaf2 | refs/heads/master | 2021-09-13T18:17:59.909694 | 2018-05-02T19:38:03 | 2018-05-02T19:38:03 | 106,767,559 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,537 | r | Exercise11 - [unsupervised learning] - K mean clustering.R | ####################################################
#### Unsupervised Learning - K Means Clustering ####
####################################################
# Methodology:
# K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity.
# Unsupervised learning means ... |
82a6a910081ad2b7820ed41f04af23fedb97a034 | 2d47450c41c23f6d008bfca5bf08d3161bb13491 | /vignettes/tutorial.R | 76b5afa8db3803da7a1066de5be0859bb1f3bbc7 | [] | no_license | khaled-alshamaa/brapi | 2c14727d65fc82a77d243bdc40c10b67955a04d5 | 5f2a5caa48d72e2412ead128b9143cc1882a060c | refs/heads/master | 2022-03-21T20:19:07.470329 | 2019-10-16T15:51:00 | 2019-10-16T15:51:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,190 | r | tutorial.R | ## ---- message=TRUE, warning=TRUE-----------------------------------------
library(brapi)
library(magrittr)
white_list <- ba_db()
# print names of databases from whitelist
white_list
sp_base <- ba_db()$sweetpotatobase
# print summary of sp_base object
sp_base
## -------------------------------------------------... |
6280ca7a050b7aab8551243d90b69261a9acba1a | 10e6d3b9a993e1b861559bb9a9527af842296d15 | /r/day04.R | 544c704a5f914aef52273e9ae64b331b2c7fe5b4 | [] | no_license | mayrop/adventofcode | f968c1716d9df16f7c9e9177d6c08344aecc0feb | 0b9d38440111f9cc8f2b37d7db8d5949c966d261 | refs/heads/master | 2023-02-08T23:48:45.366892 | 2021-01-04T02:17:07 | 2021-01-04T02:17:07 | 318,456,771 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,873 | r | day04.R | library(tidyverse)
################################################################
# Read Data
input.prod <- read_file("../data/day04/prod.txt")
input.test <- read_file("../data/day04/dev.txt")
input <- input.prod
input <- input.test
# Transform Data
df <- strsplit(input, "\n\n")[[1]] %>%
as_tibble() %>%
dplyr... |
44baa36772c4bcd0b05597abacada0aba8aeb9a2 | 66242c594c285bdacfa37dd7cf8c7a351670bd2c | /rgujja14.r | 26dd925263860a3bd77e4f7287231843335f7b6b | [] | no_license | rithinrao/QuantManagement | 11b47d09b95a0ac698a99e9d5f0d914c1611ee96 | 2167edd3d5c48a31237e5367dca66633c03632d9 | refs/heads/master | 2020-07-22T09:18:45.262141 | 2019-11-04T18:40:22 | 2019-11-04T18:40:22 | 207,147,593 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 580 | r | rgujja14.r | getwd()
library(lpSolveAPI)
lprec<-make.lp(0,8)
lp.control(lprec,sense='min')
#objective function
set.objfn(lprec,c(622,614,630,0,641,645,649,0))
#constraints
add.constraint(lprec,rep(1,4),"=",100,indices =c(1,2,3,4))
add.constraint(lprec,rep(1,4),"=",120,indices =c(5,6,7,8))
add.constraint(lprec,rep(1,2),"=",80,indice... |
58f8bfbc560cf62516e603207e1021c1a9bed7a6 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/distrMod/examples/fiHampel.Rd.R | 8175c1ef940b4de126ca95bd473b2c2e4940d483 | [] | 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 | 261 | r | fiHampel.Rd.R | library(distrMod)
### Name: fiHampel
### Title: Generating function for fiHampel-class
### Aliases: fiHampel
### Keywords: robust
### ** Examples
fiHampel()
## The function is currently defined as
function(bound = Inf){ new("fiHampel", bound = bound) }
|
bc1d2afdd16145c536c95d6c27394985de418fe2 | 754ad848531df93b28b8c4320b4e3df4103a5347 | /R/geo.R | a7f6d95f3b631fbe4c8e61a7057475cf33ee9aa4 | [] | no_license | pokyah/agrometeoR-mlr | 0fa95a3266c75e8951283e39328d3951ea8f5912 | 8288d1616a3f235a84856def4e2def64cccce8ed | refs/heads/master | 2020-03-21T16:04:26.784576 | 2018-09-07T07:31:29 | 2018-09-07T07:31:29 | 138,749,018 | 0 | 1 | null | 2018-07-12T13:12:18 | 2018-06-26T14:21:23 | HTML | UTF-8 | R | false | false | 25,207 | r | geo.R | #' Build a topographic rasters stack (elevation, slope, aspect)
#'
#' This function builds a topographic rasters stack for Belgium (elevation, slope, aspect). The rasters are projected in the
#' 3812 EPSG code. No input parameters are required.
#' @author Thomas Goossens - pokyah.github.io
#' @return A stack of topogra... |
1fd426cd98507fc598dad2ead7553ee69ea9250c | de58bb051901977ca144e4d27f155b4ac8a5c46e | /resultados/resultsJoin.R | f99406beddbd4a28d85426e082305d486963d799 | [] | no_license | MiguelGubern/TFG_Hadoop_Spark_GraphX | a9c53be3781418dd49ac51f94cf77a3c27fb90a2 | c0407c60e47a4f1cb0a6e3458af0aa2d8e0dc1a6 | refs/heads/master | 2020-04-14T15:09:35.918128 | 2019-01-03T03:34:36 | 2019-01-03T03:34:36 | 163,917,288 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,796 | r | resultsJoin.R | library(dplyr)
library(purrr)
library(tidyr)
library(lubridate)
library(plyr)
library(bit64)
library(data.table)
library(matrixStats)
####################### JOINING ALL BETWEENNESS RESULTS ###########################
path <- "c:/Users/migue/Documents/results/betweenness/"
allFiles <- list.files(path = ... |
5e61b5b3659afe4e657a91b11c1f1eb773b3a2c2 | a74a1820fcc5538848ec4b90c7569452ba15a5f0 | /R/githug-package.r | 2de58aa5cdabda829fb8020b624dfed59162eb61 | [] | no_license | jrnold/githug | cfa5fa23c0fcfccb2d260c64014f603329d1aa93 | 675269266c06335365b8410a898fe3c1d08979f8 | refs/heads/master | 2021-01-21T18:50:41.565967 | 2016-09-23T15:30:22 | 2016-09-23T15:30:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 81 | r | githug-package.r | #' githug.
#'
#' @importFrom purrr %||%
#' @name githug
#' @docType package
NULL
|
d9b90cfa2bdeffb23df3dfb9c7736331b6871f56 | 066278fee756b36693832e5572d3c4c346972170 | /R/mt.ttdetect.R | d2d38935f497979ef1f3f267663ecb12777bae77 | [] | no_license | jmbh/mt.analysis | 53a5303d12fd05e8ba82fef55a6fa6e807219c7f | f1cb6528b314cec86bb69eb1c1740e45fbbe7853 | refs/heads/master | 2016-09-09T21:22:41.476501 | 2015-04-14T09:47:28 | 2015-04-14T09:47:28 | 29,824,603 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 887 | r | mt.ttdetect.R |
#setwd("G:\\MPI\\__trajtypes_paper_2015\\RawData")
#data <- readRDS("koop_processed.RDS")
#library(ggplot2)
## input:
# data matrix with MAD column labeled "MAD"
# number of clusters: nclust
# vector of column names of variables used for clustering
## output:
# data matrix with one additional column = cluster-member... |
ce9fd753b965e5832843d9a97f3c43cf4c3a0848 | 3ac99ff4e4c9f54adfd1227df92a39123cef9cdf | /R/monthly_plot.R | 0f37cb4f5fbe7eb32092e1e5b5e6e2bddcf3c870 | [] | no_license | sukhyun23/wage | d84f7caa8e90013401b128167dc93a36dd80b2c1 | 86f2eb65ca41c2a5141214c8b284313d1f14ec49 | refs/heads/master | 2020-12-04T19:07:31.508869 | 2020-04-11T13:45:25 | 2020-04-11T13:45:25 | 231,876,228 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,771 | r | monthly_plot.R | monthly_plot <- function(data) {
data <- data.table::data.table(data)
gdat <- data
gdat$x <- lubridate::day(gdat$start)
gdat$y <- -as.numeric(gdat$id)
gdat$base_hour <- gdat$base_hour_day + gdat$base_hour_night
gdat$y <- ifelse(gdat$base_hour <= 0, NA, gdat$y)
ydat <- unique(gdat[!is.na(y), .(y, 이름)... |
e99ffb13786d675315d2cd6c10b45839ca8c96f6 | 428dad6718179a377e250c40d0adf0c5070e7d51 | /functions/plot_tools.R | a972745a882d5ebb225bab2674c08d8e1db70bc4 | [] | no_license | michbur/malarial_signal_peptides | 5156ea01192768cc92c0580b0e7e7c9db860fa23 | aa6651cacfa39970963f60d605f59e185b115967 | refs/heads/master | 2020-04-10T03:56:10.127236 | 2016-09-28T04:43:41 | 2016-09-28T04:43:41 | 40,606,471 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,140 | r | plot_tools.R | size_mod <- -5
my_theme <- theme(plot.background=element_rect(fill = "transparent",
colour = "transparent"),
panel.grid.major = element_line(colour="grey", linetype = "dashed", size = 0.5),
panel.grid.major = element_line(colour="lightg... |
e591092949a3b2ca2fe1d90b1b54211a8c1f5c3b | fcf6a44685bc68e2dc31ef88b5bd3b74ef158c52 | /sparseWtime.R | bd348fc3d53c37c5489e086f17fa4bec9060cd56 | [] | no_license | scwatson812/BayesianSpaTemQuantileRegression | ca584630d6835904ffdb62497332eb2f43f99a3f | 50426fefd115d71291ebdbe5142956ae6fa62af0 | refs/heads/master | 2022-11-23T15:01:41.793825 | 2020-07-17T16:43:19 | 2020-07-17T16:43:19 | 274,753,118 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 489 | r | sparseWtime.R |
sparseWtime<-function(g.s,tm){
W.base = sparseW(g.s)
W.time = Matrix(matrix(0,g.s*g.s*tm,g.s*g.s*tm),sparse = TRUE)
for(i in 1:tm){
W.time[((i-1)*g.s*g.s+1):(i*g.s*g.s),((i-1)*g.s*g.s+1):(i*g.s*g.s)] = W.base
}
for(i in 1:(g.s^2)){
W.time[i,(i+g.s^2)] = 1
}
for(i in (g.s^2 +1):((tm-1)... |
90a973d4c544b628c3e067a2725fed599db12a5b | efa0fb0cc58bb692e60d324caa87bc89c5eadcb7 | /Boston practice.R | 5ad4e2192871d836c9670ff5ed5595657af3794d | [] | no_license | chetanbommu/RStudioProject | 0621dc39d5482b054929617f7fe5b766770be8d8 | b17fa09089459128196f0274837b90c147d5e450 | refs/heads/master | 2020-04-11T15:45:12.475266 | 2018-12-06T01:44:11 | 2018-12-06T01:44:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 608 | r | Boston practice.R |
library(MASS)
data("Boston")
?Boston
head(Boston)
summary(Boston)
str(Boston)
Boston$chas=as.factor(Boston$chas)
Boston$rad=as.factor(Boston$rad)
hist(Boston$medv)
hist(sqrt(Boston$medv))
Boston$medv_sqrt=sqrt(Boston$medv)
## simpletest linear regression
plot(Boston$lstat,Boston$medv)
cor(Boston$lstat,Boston$m... |
c31423eafe86e13671de6708c939608d702b7283 | d510eba7bbfeff11bc2498ddbdf91a21ef05c2ff | /Course04_Exploratory_Data_Analysis/project_1/plot4.R | 1d5aaa88be243643e7927ef29fd9684dbabada43 | [] | no_license | git-comp/datasciencecoursera | 5d0200073fe44e0a125f837f70a6332952ac7ebd | 69cdf910c0ad5b3cf31f2f721279f4347fc45fc4 | refs/heads/main | 2023-05-09T05:03:58.247752 | 2021-05-25T08:58:39 | 2021-05-25T08:58:39 | 362,227,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,201 | r | plot4.R | # Read data to powerDT
powerDT <- data.table::fread(input = "household_power_consumption.txt", na.strings="?")
# Adjust date format
powerDT[, Date := lapply(.SD, as.Date, "%d/%m/%Y"), .SDcols = c("Date")]
# Restrict to period of 2007-02-01 and 2007-02-02
powerDT <- powerDT[(Date >= "2007-02-01") & (Date <= "2007-02-0... |
4498cd9c98ba034b9eb20dcc1cda262b31e8d8c5 | 411aab55a0cc48e2fefadd20da10edeb5922a945 | /src/000_moving_window_test.R | 1f2a9306f791a277930942e1bbff4936c14d8dd6 | [] | no_license | GeoMOER-Students-Space/Envimaster-Geomorph | 3ed0d65692a9bd71fea22d035452d49b7868f425 | 8171383f5681b25bc36c8bfc1d3db083a3d934af | refs/heads/master | 2020-06-02T11:09:51.322486 | 2019-09-27T22:06:56 | 2019-09-27T22:06:56 | 191,135,581 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,693 | r | 000_moving_window_test.R | #############################################################################################
###--- Setup Environment -------------------------------------------------------------------#
############################################### #
# require libs for setup #EEE... |
29539c0f0cdfa945035a12be9d79200c7ebf29d5 | dc1f17859c4d14d2d18e34a377a474b7d955c09f | /PEPATACr/man/narrowPeakToBigBed.Rd | 8ea4118417fd83447492c8861743f8de821aeec6 | [
"BSD-2-Clause"
] | permissive | databio/pepatac | 55f4b7947333c3543f892e19e60803d04003eba5 | 9ee0b6c1251b1addae8265c12f16cbfeae76d489 | refs/heads/master | 2023-08-08T22:02:23.327668 | 2023-07-31T21:32:28 | 2023-07-31T21:32:28 | 58,678,230 | 46 | 11 | BSD-2-Clause | 2023-07-31T21:32:30 | 2016-05-12T21:29:13 | R | UTF-8 | R | false | true | 633 | rd | narrowPeakToBigBed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PEPATACr.R
\name{narrowPeakToBigBed}
\alias{narrowPeakToBigBed}
\title{Convert a narrowPeak file to a bigBED format file.}
\usage{
narrowPeakToBigBed(
input = input,
chr_sizes = chr_sizes,
ucsc_tool = ucsc_tool,
keep = FALSE
)
}
\argu... |
876eca34f456d3b315db50e018d0ac542a89627d | ca019df5543ac6378cca22e974a6ee8ac711ebaf | /week 1 quiz.R | a0de5582c96745d12b7bde008e5f2ec39c1bf75a | [] | no_license | arkhamknight1234/courseracapstone | 8ea231a8351f9038f2e7116e26370be5f976a4fa | 914faa7c0e3cac09bf71c3fadf1f84a4321d0511 | refs/heads/master | 2020-05-05T06:45:31.322293 | 2019-04-06T07:34:48 | 2019-04-06T07:34:48 | 178,858,448 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,199 | r | week 1 quiz.R |
#Question - 2
twitter <- readLines(con <- file("en_US.twitter.txt"), encoding = "UTF-8", skipNul = TRUE)
length(twitter)
#Question 3
#What is the length of the longest line seen in any of the three en_US data sets?
# Blogs file
blogs<-file("en_US.blogs.txt","r")
blogs_lines<-readLines(blogs)
close(blogs)
summary(nc... |
479c46554030f953ddab16feaa896381b768fab9 | 9afc1ed9b218051b1b531539a49ebe65b661d861 | /download_Prelictum_GOterms.R | d354814d68074c773a6a70b5e823518fd38e0c99 | [] | no_license | vincenzoaellis/sequence_capture_code | 52eeed07f232d8d76284059bb7a7dfb2a83b459a | 9f674ab9459241a3f58fe2c2689f0fda5f774a5c | refs/heads/master | 2020-03-29T17:36:51.404612 | 2018-10-30T12:54:07 | 2018-10-30T12:54:07 | 150,173,135 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 683 | r | download_Prelictum_GOterms.R | #### Download GO terms for P. relictum from PlasmoDB website
#### Vincenzo A. Ellis
#### 25 September 2018
prel_go <- function(){
gaf.colnames <- c("DB", "DB Object ID", "DB Object Symbol", "Qualifier", "GO_ID",
"DB_Reference", "Evidence_Code", "With_or_From", "Aspect", "DB_Object_Name",
... |
488b95eb25c7c91a6cdba62aee2b31f15d4d2e7e | 7f928e44ff33be967054c302d4007f92f895929e | /Statistics/Assignments/assignment3/assignment3.R | 506b156d32f03cde34c2b06612cf6c7722c3061d | [] | no_license | pranjalijambhule/GreyCampus-DS3 | 9f28c78dfdd70b2226e4898c7d1c5d954d01430a | 1d02112ed953d0fa32c003b89536878b8e906eab | refs/heads/main | 2023-05-18T03:34:29.401981 | 2021-06-04T11:32:58 | 2021-06-04T11:32:58 | 353,991,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,550 | r | assignment3.R | library(dplyr)
library(manipulate)
library(ggplot2)
library(moments)
getwd()
setwd("C:/sovi/Data Science/Stats/week3/assignment")
covidFile <- read.csv("COVID19.csv",
na.strings = c("", "NA"),
stringsAsFactors = FALSE)
str(covidFile)
head(covidFile,10)
colSums(is.na(covid... |
08d711612e16849d7a73f69cc51476dd82a9aa5e | 748d8aa1622b35e27454f25893de6229134eea21 | /Segundo-encontro/predicao-de-despesas-medicas.r | 68519d9d60ac75592a94dfed3bd9fdbdd1f8aafe | [] | no_license | luizaalves/CursoR | b16cf854a30c166d99c395665cee29e531b13559 | 1629a07d42011d620cdddfb04ae537065b6fcd3e | refs/heads/master | 2020-07-23T02:20:20.900434 | 2019-09-12T01:13:14 | 2019-09-12T01:13:14 | 207,415,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,130 | r | predicao-de-despesas-medicas.r | insurance <-read.csv("insurance.csv", stringsAsFactors = TRUE)
#variaveis vão impactar no valor das despesas medicas
str(insurance)
#ver o minimo e o máximo;
summary(insurance$expenses)
#ver o histograma, nota-se que quanto maior os gastos, menos pessoas estão nesses dados
hist(insurance$expenses)
table(insurance$... |
2b5c11efd1da3a8da0759dcf0bf7996f8c7b83ca | 1fcaaafb1f597ec8ec80fd6a8e0ce46b518436a3 | /cachematrix.R | b2bf0d1e84dd4ac307f2da71621e351a50d28c42 | [] | no_license | gcrowder/ProgrammingAssignment2 | 4db11bb50f7841c5008703d117104936d183b9a9 | 11a37fcabde4d96779d97cc11d9948d897c1285c | refs/heads/master | 2021-01-22T00:24:32.230603 | 2016-07-17T20:15:30 | 2016-07-17T20:15:30 | 63,441,111 | 0 | 0 | null | 2016-07-15T17:57:30 | 2016-07-15T17:57:29 | null | UTF-8 | R | false | false | 1,044 | r | cachematrix.R | ## Together, these two functions find the
## inverse of a matrix and caches the result.
## makeCacheMatrix takes an matrix and returns a list of functions to set or get
## the matrix and its inverse.
makeCacheMatrix <- function(x = matrix()) {
inverse <- NULL
set <- function(y) {
x <<- y
inver... |
77eee7fa6f0a3fdee1bd9922fae15d4673b8d350 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/NlsyLinks/examples/ExtraOutcomes79.Rd.R | a12855bd08dda605b0872a9e4ce69147336578e9 | [] | 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 | 687 | r | ExtraOutcomes79.Rd.R | library(NlsyLinks)
### Name: ExtraOutcomes79
### Title: Extra outcome variables in the NLSY79
### Aliases: ExtraOutcomes79
### Keywords: datasets
### ** Examples
library(NlsyLinks) #Load the package into the current R session.
gen2Outcomes <- subset(ExtraOutcomes79, Generation==2) #Create a dataset of only Gen2 sub... |
a9d353ca5682e352fa18740457504477734df7cd | 8fe6731c8cca05a9d9989fb178b63e6297303312 | /man/add_column.Rd | d4bf961386e2956ec1f8105eade19ec7ec4bc1d8 | [
"MIT"
] | permissive | bradleyboehmke/tibble | a375eb7ce4cf9430782ddae001ecdce4ffa7bb4b | 2b3ab6e56e7c0aef24c665552a37513659468dc7 | refs/heads/master | 2020-09-15T10:45:23.342089 | 2019-11-18T18:13:44 | 2019-11-18T18:15:19 | 223,424,494 | 0 | 1 | NOASSERTION | 2019-11-22T14:49:16 | 2019-11-22T14:49:15 | null | UTF-8 | R | false | true | 1,346 | rd | add_column.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add.R
\name{add_column}
\alias{add_column}
\title{Add columns to a data frame}
\usage{
add_column(.data, ..., .before = NULL, .after = NULL)
}
\arguments{
\item{.data}{Data frame to append to.}
\item{...}{Name-value pairs, passed on to \code... |
f00ceba337776e7df43b0cfa0b8f102d5990eaf3 | e649b8474ab3c5f5367abf806c6c750580ebbdfa | /ui.R | 08b48d40b7bc2dabebb45e623d40884763512caa | [] | no_license | joelpolanco/Coursera-Shiny-Project | 4951b0857819343fca4159d78b84eac39df797bf | f207d5d22294fe71eba1e8ecc6697e460b2324fd | refs/heads/master | 2021-01-10T09:56:20.293030 | 2016-04-01T22:13:20 | 2016-04-01T22:13:20 | 55,262,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 263 | r | ui.R | library(shiny)
shinyUI(pageWithSidebar(
headerPanel("CRM Analytics Single Purchaser Dashboard"),
sidebarPanel(
sliderInput('mu', 'Guess at the mu',value = 70, min = 0, max = 500, step = 10,) ),
mainPanel(
plotOutput('myHist')
)
)) |
ce0c3b2964aba9af9a1d684b15139b39d463e1fc | 983b875a39f510b9ac134b3a592165be0f260090 | /analysis/1_data_cleaning.R | f03abecc036c92a7d7ddc259448a9ff952585c5e | [
"MIT"
] | permissive | adelaidetovar/ozone-strain-survey | 25f3a19f23d9d2d83409609166a34105eb1814f9 | d5f1f964c37646f981ae5e299d8b339a8c9abe0a | refs/heads/main | 2023-08-12T05:39:41.682131 | 2021-10-08T19:24:13 | 2021-10-08T19:24:13 | 404,029,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,356 | r | 1_data_cleaning.R | # contributed by Wes Crouse and Adelaide Tovar
source("0_functions.R")
setwd("../data")
##############################
#### Phenotype Data Input ####
##############################
raw_data <- read.xlsx('raw_data.xlsx', sheet = 2, startRow = 2)
raw_data[,c(2:4)] <- sapply(raw_data[,c(2:4)], as.factor)
raw_data$stra... |
a27fe8fa19eca19cdea9e246940829797aff243e | 7c022651a9545e83efbe2fcc0054beb2d268b36c | /plot4.R | c0cda6add3dee179a7b3890e10665f226ae42355 | [] | no_license | debrakesner/ExData_Plotting1 | 653152e5879b579c0b280033ea0a43c39b587a61 | 8bc09d6e0e0794fa6279c400b8aa5b638c5bf8f4 | refs/heads/master | 2021-08-26T08:32:48.181008 | 2017-11-22T13:55:27 | 2017-11-22T13:55:27 | 111,604,186 | 0 | 0 | null | 2017-11-21T21:39:39 | 2017-11-21T21:39:39 | null | UTF-8 | R | false | false | 1,882 | r | plot4.R | ## creates 4 plots
#read file
setwd("C:/Users/Debra/datasciencecoursera/explorProject1")
fileLoc <- "C:/Users/Debra/datasciencecoursera/explorProject1/household_power_consumption.txt"
data <- read.table(fileLoc,header = TRUE,sep = ";")
#rename columns
names(data) <- c("date","time","globalactivepower","globalreactive... |
8c746c4c6a5f02b5484cca6319f0eabcc49ad133 | 4df9da5cbe5af504e5f668929006e5a423bfeb77 | /R/convertRows.R | 9f08ee4c8a441ea8cca2903a3adbf78a28d0b5c1 | [] | no_license | UBod/msa | b1e57917d7f405f320fda4a549b0f4dbd62962c3 | 7688d0547e209ee1fc19a692a4713ea6621d1d39 | refs/heads/master | 2023-07-20T16:20:08.111418 | 2023-07-11T10:35:17 | 2023-07-11T10:35:17 | 133,512,829 | 12 | 8 | null | 2023-02-20T12:25:05 | 2018-05-15T12:27:12 | C | UTF-8 | R | false | false | 1,534 | r | convertRows.R | convertAlnRows <- function(rows, type)
{
version <- rows[1]
if (length(rows) < 3 ||
##!identical(grep("^CLUSTAL", rows[1L]), 1L) ||
!identical(sub("^\\s+$", "", rows[2:3]), c("", "")))
stop("There is an invalid aln file!")
rows <- tail(rows, -3)
rows <- sub("^(\\S+\\s+\\S+)\\s*... |
9c4fafeaa10fbdc7f3bb083eb4cef4d4bbd98394 | 3748be5371ba854978d2c88ad72044319ca9d528 | /man/sle.Rd | 225715aec402e63924dc80c16297419fcd39575d | [] | no_license | rushkin/dla | 0c93e0f90d3233021cc5590f4d20ae6574f3cfdb | 5f22ab13276a91b683b904c9ce786330a3f5d4c8 | refs/heads/master | 2020-03-23T08:46:45.750688 | 2019-01-31T18:42:13 | 2019-01-31T18:42:13 | 141,344,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,489 | rd | sle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sle.R
\name{sle}
\alias{sle}
\title{Stochastic Loewner Evolution}
\usage{
sle(kappa = 4, tmax = 1, a = 1, kappaL = 0, nsteps = 2000,
p_timescaling = 0.5, verbose = TRUE, forcing = NULL)
}
\arguments{
\item{kappa}{strength of the 1d Brownian... |
fe42393c7354d44af0c7fce99a910cc8ce52bd7d | 625b520f0e6390bf2a756008fc5a04fe81b76c1f | /scripts/lat_fe_stderr_qc.R | cd8739702a54dc47073a52c9ecb08aab7c9392fe | [] | no_license | harrymengpku/trans_ethnic_ma | 982f8fa88cde18828a744065e434139eebfd5796 | 927c8894d2874e03ae33ad9b003cbffa79139783 | refs/heads/master | 2023-08-27T07:10:22.966883 | 2021-10-24T11:09:18 | 2021-10-24T11:09:18 | 367,319,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,769 | r | lat_fe_stderr_qc.R | # QC for fixed effect MA implemented by METAL,
setwd("/SAN/ugi/mdd/trans_ethnic_ma/results/")
library(data.table)
df <- fread("METAANALYSIS_HIS_1.tbl")
# Read 1000G all bim files and create a FileName column to store filenames
list_of_files <- list.files(path = "/SAN/ugi/ukhls/1000G/1KGP3_bim", recursive = TRUE,
... |
db836bf610528b9761538f9110fc62b08c6bfaa3 | 396ec8eba748b30f8d7134761ff80fa291b5d63b | /R/06_modelling_ANN2.R | c22c94018a5caefd68c66cd81f7e5e975c36b7ad | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rforbiodatascience/2020_group03 | 589e5a9b90def83e84ae84e393ba19b6ee202127 | eb9bc3b18be68596bf6462b0d0c41406125390f2 | refs/heads/master | 2021-04-05T22:48:58.222615 | 2020-05-13T21:34:23 | 2020-05-13T21:34:23 | 249,718,909 | 0 | 2 | null | 2020-05-05T09:14:54 | 2020-03-24T13:43:35 | R | UTF-8 | R | false | false | 3,059 | r | 06_modelling_ANN2.R | # Clear workspace
# ------------------------------------------------------------------------------
rm(list = ls())
# Load libraries
# ------------------------------------------------------------------------------
library("tidyverse")
library("caret")
library("UniprotR")
library("ANN2")
library("yardstick")
# Define... |
b3eb131f548fb322244bda48a9865220f11c15a2 | 5095ee2491ad9d5129802954e11681b21acfc499 | /Functions/resultsConcatenator-20161020.R | 3c8ec5f78bad7c34d4ff8a4b384da76a4834a8c6 | [] | no_license | IssieWinney/Antirrhinum-ImageAnalysis | 2069a68e3566a9c0db4b67ddd102038b0087615b | db160c3cc0bd2ac936d451ed99daf71569470a04 | refs/heads/master | 2020-09-21T16:57:29.104027 | 2016-12-12T13:20:16 | 2016-12-12T13:20:16 | 66,936,806 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | r | resultsConcatenator-20161020.R | # Isabel Winney
# 2016-10-20
# Results concatenator
# Apparently adding continuously to a results file is
# a hard task in imageJ/Fiji. This script is to take the
# .txt files produced by imagej and turn them in to a single file.
resultsConcatenator <- function(filepath, output){
# INPUT filepat... |
2ebceb05dc1e9aba24b84c9efe881acb811c4d33 | f7575b705a341ed4c808e8e2ebfa9e055395275f | /annotateCoords_visualization.R | c1ea67ffdd979850395d2d9388cd64ae05b1c4f2 | [] | no_license | canderson44/ProfileHMM_scriptsForServer | 97084900d058b8c64becc9eddebeb4bbfbcb2560 | 120b2c3bc28a3909c5074460b6e22ff6c8b490a4 | refs/heads/master | 2020-06-04T09:30:44.115100 | 2019-08-09T01:36:24 | 2019-08-09T01:36:24 | 191,966,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,252 | r | annotateCoords_visualization.R | #!/usr/bin/env Rscript
#### Description ####
# Counts combinations of regions identified per zmw. Outputs a csv of format RegionCombo, count
# Goal: determine where Adapter, 3' barcode, 5' barcode, and their reverse complements
# lie within a given CCS
# The pipe taken from script by Colin Dewey
#### load packages and... |
3bb55d829d1a66ec7ffa992dfc93fd882c79c659 | cbc51337357b46d5f159eaa5a820c1fc0b9f7cdc | /MachineLearning/caretPackage.R | 729888ffd503ac54a84a4c11116c347c8d261eeb | [] | no_license | jvaldivial/coursera | b559629e7013f186b1a76465f65dfec52a5f563a | 93a38268c9eba077f0c44684012fcde140eccc40 | refs/heads/master | 2021-04-25T04:01:49.383821 | 2018-10-04T11:00:50 | 2018-10-04T11:00:50 | 115,521,610 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,021 | r | caretPackage.R | # Sample Code for Machine Learning Course @ Coursera
setwd("~/GitHub/coursera/MachineLearning")
library(caret)
library(kernlab)
library(e1071)
data("spam")
# Subsetting the data set for Trainig and Testing
inTrain <- createDataPartition(y=spam$type, p=0.75, list = FALSE)
training <- spam[inTrain, ]
testing <- spam[-i... |
49ff1cbeda3684aee503663973875f81e80a02f0 | 3fa1b23746232975b3b014db2f525007a3b49991 | /anna_code/qc/real_or_dup_data_after_nov2019/out/plot_survey_qc.R | 565e391571af73ff2ef9445ef0a75a6a74a09884 | [] | no_license | AshleyLab/myheartcounts | ba879e10abbde085b5c9550f0c13ab3f730d7d03 | 0f80492f7d3fc53d25bdb2c69f14961326450edf | refs/heads/master | 2021-06-17T05:41:58.405061 | 2021-02-28T05:33:08 | 2021-02-28T05:33:08 | 32,551,526 | 7 | 1 | null | 2020-08-17T22:37:43 | 2015-03-19T23:25:01 | OpenEdge ABL | UTF-8 | R | false | false | 1,235 | r | plot_survey_qc.R | rm(list=ls())
library(ggplot2)
source("~/helpers.R")
fnames=dir()
for (fname in fnames){
if (endsWith(fname,'.tsv')){
print(fname)
data=read.table(fname,header=TRUE,sep='\t')
data$Date=as.Date(data$Date)
data=data[order(data$Date),]
p1=ggplot(data=data,
aes(x=data$Date,
y=data$Uploads))+
ge... |
a475f29c509dcacb18075ee3b8546c4b4de8d17d | 5e45d9217574199d680f21c9d5651d06c63c9377 | /man/continuous.SuperLearner.Rd | 7bb890871abb45ab020fa81c18f1c1420b77f05c | [] | no_license | tiantiy/superMICE | c358924a3e8c2607daaffe5305753c5b4da80a89 | e7dc81015f046dcec15893522212573ef94ab1f5 | refs/heads/master | 2022-12-02T11:55:16.338673 | 2020-08-12T06:36:13 | 2020-08-12T06:36:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,731 | rd | continuous.SuperLearner.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/continuous_SuperLearner_regression.R
\name{continuous.SuperLearner}
\alias{continuous.SuperLearner}
\title{Function to generate imputations using regression and SuperLearner for data with a continuous outcome}
\usage{
continuous.SuperLearner(... |
23d0ccef850c677e19457b1c322f6b71d8703428 | 29585dff702209dd446c0ab52ceea046c58e384e | /SPIn/R/bootSPIn.R | fa8a607293c7ade434b653e887407c7477cf89e4 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,268 | r | bootSPIn.R | bootSPIn <-
function(x, n.boot=50, conf = 0.95, bw = 0, lb = -Inf, ub = Inf, l=NA, u=NA){
n.sims <- length(x)
x <- sort(x)
gaps <- x[2:n.sims] - x[1:(n.sims-1)]
gaps <- c (gaps[1], gaps, gaps[n.sims-1])
gap.bandwidth <- 2
mean.gap <- rep (NA, n.sims)
for (j in 1:n.sims){
mean.gap[j] <- mean (gaps[max (1, j-(ga... |
2f00b1dfa6f1308ceec234f4bf54c121941fc2da | fd570307c637f9101ab25a223356ec32dacbff0a | /src-local/specpr/src.specpr/fcn48-51/getpt.r | 942df6e7f81a0deabff5f58c722988c6b59e92e7 | [] | no_license | ns-bak/tetracorder-tutorial | 3ab4dd14950eff0d63429291c648820fb14bb4cb | fd07c008100f6021c293ce3c1f69584cc35de98a | refs/heads/master | 2022-07-30T06:04:07.138507 | 2021-01-03T22:19:09 | 2021-01-03T22:49:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 990 | r | getpt.r | subroutine getpt(ichk,x,y,xmin,ymin)
implicit integer*4 (i-n)
#ccc version date: 06/01/83
#ccc author(s): Roger Clark & Jeff Hoover
#ccc language: Ratfor
#ccc
#ccc short description:
#ccc This subroutine
#ccc algorithm description: none
#ccc system requirements: none
#ccc subroutines call... |
afcf40cafca39a3ccb4087c3eed46089ff90a378 | bd454c45d38cc48f6247d9dec829de0533793549 | /man/piat.feedback.simple_score.Rd | 0883b57e6673381a63451e81edc3f1981715887d | [
"MIT"
] | permissive | pmcharrison/piat | f445431e6d59cbf63228619547ad4e078af58c2f | 73c77acf379c233480819738214187cd9b1ba3f7 | refs/heads/master | 2023-08-14T17:02:04.665315 | 2023-07-26T21:27:39 | 2023-07-26T21:27:39 | 131,727,383 | 2 | 3 | NOASSERTION | 2022-12-21T10:09:03 | 2018-05-01T15:09:06 | R | UTF-8 | R | false | true | 421 | rd | piat.feedback.simple_score.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/feedback.R
\name{piat.feedback.simple_score}
\alias{piat.feedback.simple_score}
\title{PIAT feedback (simple score)}
\usage{
piat.feedback.simple_score(dict = piat::piat_dict)
}
\arguments{
\item{dict}{The psychTestR dictionary used for inter... |
39b4127396f9a61719d4fea13e9383b7ed5356ea | 625c6620f117f50ab79f5fd3296e9576a0910187 | /man/blackgrass.Rd | 1e2d5556ac855bfbb7c6f5620be1ee200b4f181e | [] | no_license | DoseResponse/drcData | 378c850587d3332caa076192e480b4efb6904ba9 | 09f9da308aeea62322b0a7b67946435a87c36589 | refs/heads/master | 2023-02-24T02:20:00.374757 | 2021-01-28T12:04:31 | 2021-01-28T12:04:31 | 108,513,898 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,313 | rd | blackgrass.Rd | \name{blackgrass}
\alias{blackgrass}
\docType{data}
\title{Seedling Emergence of Blackgrass (Alopecurus myosuroides)}
\description{Seedling emergence of herbicide susceptible (S) and resistant (R) Alopecurus myosuroides in reponse to sowing depth and suboptimal temperature regimes (10/5C) and optimal temperature regim... |
c4a735d95aad6ac9e58e11e1d9c2c0ccbb2402a1 | 257ad4d98f21db8c9930f18efd6ce7e47c0a1ff1 | /FPKMGCLM.R | 68bcff5a87a27448f32f4b9946d3530940308208 | [] | no_license | SethMagnusJarvis/QuantSeqComparison | b37357a62757cbd1b517c1776e0bdeddb44a88f3 | 9e1c5b9e6d253f58ece2b2e5bffba013acd51599 | refs/heads/master | 2022-02-16T06:30:10.912423 | 2019-09-13T11:16:29 | 2019-09-13T11:16:29 | 208,249,672 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,583 | r | FPKMGCLM.R | library(tidyverse)
library(DESeq2)
GetStats <- function(RPKM){
RPKM <- RPKM %>%
column_to_rownames("ensemblID")
WT <- select(RPKM, contains("WT"))
WT$WTMean <- rowMeans(WT)
WT <- WT %>%
rownames_to_column("ensemblID")
HOM <- select(RPKM, -contains("WT"))
HOM$HOMMean <- rowMeans(HOM)
HOM <- HOM %>... |
9794bb648ec1230a243d32b7a3f261c947938ce9 | a608046e295e1030abe6977a725e3b1cb129a482 | /plot2.R | f765865d8836770b4829cd1c4989fa9834b9b5d6 | [] | no_license | Halcyonhx/ExData_Plotting1 | 98e489c04644f799cc5d8b886f5ddcf1fe5fbb22 | 34aaeeecc2b0f5dfa8da2afe0fc8af07a19c79f2 | refs/heads/master | 2020-03-14T07:13:59.662053 | 2018-04-29T14:37:52 | 2018-04-29T14:37:52 | 131,500,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 253 | r | plot2.R | subData$Time <- strptime(paste(subData$Date, subData$Time), format = '%d/%m/%Y %H:%M:%S')
png(filename = "plot2.png", width = 480)
with(subData, plot(Time, Global_active_power, type = 'l', xlab = "", ylab = "Global Active Power (kilowatts)"))
dev.off()
|
4a2b664e19e6fad33e282da3dc64ed70ee76336e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rminer/examples/imputation.Rd.R | 89abc688e3ff5ae92d7a892675e55d67c8e02174 | [] | 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 | 1,003 | r | imputation.Rd.R | library(rminer)
### Name: imputation
### Title: Missing data imputation (e.g. substitution by value or hotdeck
### method).
### Aliases: imputation
### Keywords: manip
### ** Examples
d=matrix(ncol=5,nrow=5)
d[1,]=c(5,4,3,2,1)
d[2,]=c(4,3,4,3,4)
d[3,]=c(1,1,1,1,1)
d[4,]=c(4,NA,3,4,4)
d[5,]=c(5,NA,NA,2,1)
d=data.f... |
25757fa8afe5e2686003397f254260391c355b93 | a8148b19c2675fc14901bdb29654fba677693f56 | /man/projections_accessors.Rd | 3717803657fd0a6ce2be34118e1b0c3b8fd15cff | [] | no_license | sangeetabhatia03/projections | 50277da32c9ee7c3aedb28778866e046f6ffbd6d | f99b4d87ecdd1bb877129e98c3bb66cac5b68ffd | refs/heads/master | 2023-01-10T23:06:02.357274 | 2021-04-22T09:17:47 | 2021-04-22T09:17:47 | 133,662,783 | 0 | 0 | null | 2018-05-16T12:31:45 | 2018-05-16T12:31:45 | null | UTF-8 | R | false | true | 1,105 | rd | projections_accessors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/accessors.R
\name{get_dates}
\alias{get_dates}
\alias{get_dates.projections}
\title{Access content projections objects}
\usage{
\method{get_dates}{projections}(x, ...)
}
\arguments{
\item{x}{A \code{projections} object.}
\item{...}{Further a... |
7abec5ee9b939016ca7e2c85292c6c7f3707951a | 12ea178f7c8dda5267269f31b3b02fdab29e5bee | /man/format.mondate.rd | 78e079d968f1724b47ac2d5e5d6bd4ceabb75316 | [] | no_license | chiefmurph/mondate | 0696c656c89843caba98122db2c621bbb89d23dd | 67d2d11a5abdf94bbd7579f1f91d326f28da9276 | refs/heads/master | 2022-09-14T20:27:33.599641 | 2022-08-29T08:54:38 | 2022-08-29T08:54:38 | 42,559,910 | 1 | 2 | null | 2015-10-21T17:56:13 | 2015-09-16T02:34:33 | R | UTF-8 | R | false | false | 738 | rd | format.mondate.rd | \name{format.mondate}
\alias{format.mondate}
\title{Format a mondate}
\description{
Function to format a \code{mondate} into its character
representation according to the \code{displayFormat} property.
}
\usage{
\method{format}{mondate}(x, \dots)
}
\arguments{
\item{x}{
a \code{mondate}.
}
\item{\dots}{
further argumen... |
4815af80ad875048fc5e06c5149abd32e4270ce8 | 2579ef45fce30a693d90b8c83338cc8f107125c0 | /inst/shiny-examples/firstShiny/app.R | f0fcb832a0f4d2609b5a00910255de5976cf3ea9 | [
"MIT"
] | permissive | yut4916/MATH5793YUT | c5473980b8da01192303fc14b9fc7a99757f3bd0 | 3c14a832d9e350de5b47457bc4f2c80f9d0ca97f | refs/heads/master | 2023-03-26T09:04:54.416150 | 2021-03-30T02:28:59 | 2021-03-30T02:28:59 | 335,433,993 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,994 | r | app.R | # First Shiny App
# Katy Yut
# Feb 22, 2021
# Load necessary packages
library(shiny)
library(shinydashboard)
library(ggplot2)
library(dplyr)
library(latex2exp)
library(rootSolve)
# Read in data
# data <- read.csv("/Users/Katy/Desktop/07_school/MATH5793/06_data/fourmeasure.csv", header=TRUE)
data("T4.3")
data <- T4.3
... |
055c69ed1210c0d6b56c180ee9a5026a53237a40 | 3466fe41d18e0c76cec8220d9e49019b1fd8be66 | /April20_in_class_work.R | 8f806dbb8d491633614a02e3a75598c1741b3255 | [] | no_license | pantp/DataAnalyticsSpring2020 | 868cc1bd21fe5a20a86ef64d6976488fbaa4c53a | 3fa036779928a0597f70da8a8d41971f58103fa0 | refs/heads/master | 2020-12-19T22:14:00.976263 | 2020-05-06T17:24:44 | 2020-05-06T17:24:44 | 235,868,063 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,678 | r | April20_in_class_work.R | # LOESS Example 1:
data(economics, package="ggplot2") # load data
economics$index <- 1:nrow(economics) # create index variable
economics <- economics[1:80, ] # retail 80rows for better graphical understanding
loessMod10 <- loess(uempmed ~ index, data=economics, span=0.10) # 10% smoothing span
loessMod25 <- loess(uempme... |
2a8d8e795f20b96b1628646beaf0639ed300ecb0 | 5d7740f555e642a7679cc91ffa31193993fd911b | /R/wlogr2.R | 0691146c45522369f0c812e289652112cce0a385 | [] | no_license | cran/WWR | c75c0ea6b965f15502c5a616525b3de5a87c55fe | 827c382cd8e4834d87dcd236619e883a81de74c4 | refs/heads/master | 2021-01-22T05:47:06.752627 | 2017-10-25T02:40:18 | 2017-10-25T02:40:18 | 81,703,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 472 | r | wlogr2.R |
wlogr2<-function(y,d,z,wty=1)
{
n<-length(y)
ay<-rank(y,ties.method="min")
da<-cbind(ay,d,z)
db<-da[order(ay,d,z),]
stat<-0.0
vstat<-1.0
abc2<-.Fortran("logrank2",as.integer(n),as.integer(db[,1]),as.integer(db[,2]),as.integer(db[,3]),as.integer(wty),stat=as.double(stat),vstat=as.double(vsta... |
b1c702dbfb3f5a7ab872463c38d7e177edd77db7 | fcb5767cd47bf52162a781d5db9b3dfbc37d535f | /app.R | d75e26a03670a1f5954c42f8291c0bc8ceae270a | [] | no_license | crimpfjodde/daoc | 54211637a442d6de756e056aab899d7c6ecdab37 | 1efd1c520383664697cd21df4d3e748d4fc54426 | refs/heads/main | 2023-01-19T07:46:32.119535 | 2020-11-24T13:06:06 | 2020-11-24T13:06:06 | 315,639,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,148 | r | app.R |
ui <- dashboardPage(
dashboardHeader(title = 'DAoC'),
dashboardSidebar(
sidebarMenu(
menuItem("Herald",
tabName = 'herald',
icon = icon("broom")),
menuItem("Castspeed/buff",
tabName = "castspeed",
icon = icon("dashboard")),
menuIt... |
ffd07f45ce96af7c27b9b0bc8764c7ff4b25caa4 | a06dc5d2c6a50c626d90978e0939b01be96163d9 | /scripts/buildSampleTemplate.R | 153007bcf4915bc1740c9c6d82ecbc3d41c573b6 | [] | no_license | a-lud/sample-management | f398a0d506f7dee77f9a9bd61db030e664db50bb | 90ef51fe574695b949a8cd6e7dcb805412ebe977 | refs/heads/main | 2023-08-31T04:06:21.455239 | 2021-09-21T09:01:38 | 2021-09-21T09:01:38 | 408,292,160 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 362 | r | buildSampleTemplate.R | # Generate template for sample upload ---
# - Get the column names from the col_types object
# - When the user uploads some new data, check the types against the col_types
buildSampleTemplate <- function(file, rds_coltypes) {
col_spec <- readr::read_rds(rds_coltypes)
write(
x = paste(names(col_spec$cols... |
6d367acb2a6234455d4073bd9477f3bdcc9c9dd7 | 388e05b9ad100c9310b1dc4a207a33d8797862da | /LinearRegression_Rcode.R | 9680560ba38b0a7482e5753dca5613b850da9922 | [] | no_license | nithingautham/Linear-regression-1 | c0bd33b42028389ca6c82f97d0526730193b27b8 | ca5eaeb3973e3dcb4fd58d26371f13b59006dac6 | refs/heads/master | 2021-05-04T07:24:08.003413 | 2016-10-11T19:06:38 | 2016-10-11T19:06:38 | 70,626,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,790 | r | LinearRegression_Rcode.R | setwd("E:\\Nitin")
## Rcode
library(moments)
library(ggplot2)
library(GGally)
library(MASS)
library(car)
carmileage <- read.csv("carMPG.csv" )
# Checking data redundancy using de duplication
sum( duplicated( carmileage[,] ))
# Data prepration
# Converting all car names to lower case
carmileag... |
8914e1a4467bcb746212b27344378cfbcd8898d0 | e82c2b2cee78f6a599e432b41921ebd72e7dfd03 | /inputs.R | c1c34bc96e6201336a558c4768819c37df3abb6a | [] | no_license | BU-IE-360/spring20-berkayakbas | 1449cf9b24fb0f71090df1fd2262738af70634af | 8415ed61223754147dc1eb3175d1041177eadbd4 | refs/heads/master | 2021-01-06T14:04:45.200979 | 2020-07-05T17:13:41 | 2020-07-05T17:13:41 | 241,353,284 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 152 | r | inputs.R | forecasted_product_number = 2
filter_last_n_days = as.numeric(as.Date(Sys.time()) - as.Date('2020-03-13'))
test_period = 2
parallel_processing_on = TRUE |
c97280a8e05c77f330d22224fa1b447d0abafaf3 | f84ad3a13ef2c0e60e0a0cca0c9b9795efd3c80a | /man/Plot.vars.Rd | 0ca634641327cb86ea9b55bf9ffc69e84649a66c | [] | no_license | baccione-eawag/EawagSchoolTools | 4906194eeb8e83dacb1f44ed66ed379427553d39 | af79a45a72ca4c1052ac9f2977cffc764fba95a8 | refs/heads/master | 2023-08-04T20:53:57.532002 | 2021-09-27T15:24:28 | 2021-09-27T15:24:28 | 409,323,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,404 | rd | Plot.vars.Rd | \name{Plot.vars}
\alias{Plot.vars}
\title{Plotting functions (eawagSummerSchoolTools)}
\description{
Function to plot several sets of results.
}
\usage{
Plot.vars(vars,L,ncol=NA,mar=NA,ylim=list(),markers=F,headers=list(),
xlab="",ylab="",pos="topright")
}
\arguments{
\item{vars}{matrix... |
af07943b37fb65842c641d062a431535aedc9c69 | 7cdc5ce9fe9a7cff542d495d21955891b7042b3f | /summary.mars.R | b0f4cf015c9f678aaeae945454db38cfeaa1b1e6 | [] | no_license | utoor1705/Multivariate-Adaptive-Regression-Splines | 7ba7a8d7d945de2f0ffd02df2943177416d99370 | b31dbab0620ef76be0bdab7dcdeddc7f6128dfc1 | refs/heads/main | 2023-05-12T13:33:21.002885 | 2021-05-14T17:48:16 | 2021-05-14T17:48:16 | 361,565,149 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 500 | r | summary.mars.R | summary.mars <- function(object) {
xn <- object$x_names
bn <- names(object$coefficients)
ss <- object$splits
cat("Basis functions:\nB0:\n Intercept\n")
for(i in 2:length(ss)) {
cat(paste0(bn[i],":\n"))
for(j in 2:nrow(ss[[i]])) {
cat(paste0(" Component ",j-1,": variable ",xn[ss[[i]][j,"v"]],"... |
89989499c3cce31c98fd308a3c84241a4e9bd557 | d6284537be05b1835e6e81d50872f93d9c7e68dc | /CO2_extract_graph.R | 83e769c00a3a9fca637b7f48663fed1da20c0d80 | [] | no_license | ArminUCSD/CO2detect-Rcode | 7662a8acd5c601eb01fbad20a95e11cd2039e611 | 5057909306528903d48570237bbb1abaad35d71d | refs/heads/master | 2020-12-09T01:15:44.423857 | 2020-02-12T06:11:52 | 2020-02-12T06:11:52 | 233,148,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,270 | r | CO2_extract_graph.R | # Extract graph from Peters 2017 - Fig 2
library(digitize)
file.name = 'Peters2017_Fig2_edited.png'
#---------------------------------------------------------------------
# Past time series
cal = ReadAndCal(file.name)
series1 = DigitData(col = 'red')
series2 = DigitData(col = 'blue')
series1.cal = Calibrate(series1, ... |
260c86a05b96f2318b3b348a24bc47c865b5f3bf | 9b7dea6c59b2ca3cb68d73e4af65783d20314810 | /cx2scratch2.2.R | e232ed737f447b4de792e3a908a2aca10c826c23 | [] | no_license | chriseshleman/cx2 | 57213ba60c644ed4745e9a0e4c59c01297029986 | 55ae0bda43b8b4eed4104de8d302ad3c3949768e | refs/heads/master | 2020-09-04T12:36:00.233652 | 2019-12-30T02:37:25 | 2019-12-30T02:37:25 | 219,733,347 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,322 | r | cx2scratch2.2.R |
setwd("~/Dropbox/Work and research/Port Authority/cx2")
library(dplyr)
library(ggplot2)
library(beepr)
library(tidyr)
library(tidyverse)
library(tree)
library(randomForest)
library(rpart)
library(rpart.plot)
library(rsample)
library(caret)
rm(list = ls()) # clear global environment
cat("\014") # clear the... |
b4e090155a9728994e724d2e0fcf505dbbfdd914 | 9549d0226a9ef21c86d450519c114db595889f9c | /R/get_data.R | e0e1455261310271ac4fc6afe6df570f3949745b | [
"Apache-2.0"
] | permissive | jimtyhurst/stackR | 08f671f7908f39416fd7b4f1f942514e34d6aa36 | e1ef0cd047f1b556711b97679f9fbaf36e874b9c | refs/heads/master | 2020-05-05T10:16:34.158581 | 2019-04-28T21:54:58 | 2019-04-28T21:54:58 | 179,937,734 | 0 | 0 | NOASSERTION | 2019-04-28T21:54:59 | 2019-04-07T08:31:52 | R | UTF-8 | R | false | false | 474 | r | get_data.R | # Functions to load Stack Exchange data.
library(readr)
library(tibble)
#' @export
get_stack_exchange_data <- function() {
as_tibble(list(x = "not", y = "implemented", z = "yet"))
}
download_data <- function() {
# temp <- tempfile()
# download.file("https://archive.org/download/stackexchange/stackoverflow.com-... |
a720cd396691986da8b00c50273a3b4c7569b5be | 12e66d45837f4ab8e505e6b6adce840e70063356 | /titanic_test_v2.R | 30699c26d234adbf2b1631fe0cef508949e4c20f | [] | no_license | lukezheng2/Titanic | 81a6aaeab973a995f21eea1b9194916c81c87c2a | a062e6f6128e91ff1ac2ee63c5795c0fd52ec028 | refs/heads/master | 2020-05-02T17:26:52.044762 | 2019-03-28T00:59:17 | 2019-03-28T00:59:17 | 178,098,595 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,884 | r | titanic_test_v2.R | library(tidyverse)
library(magrittr)
library(data.table)
library(MLmetrics)
library(stringr)
library(glmnet)
# tratando a base -------------------------------------------------------------------
train <- fread("../train.csv")
test <- fread("../test.csv")
test <- test %>% mutate(Survived = 0, Embarked = fa... |
544d400067388177650d97c0f04cc710fbf226a1 | 073e4e7c9c2f4822e798f4a56e4ff90b11b5a85c | /Code/table_to_maf.R | 6599fe8438f406b4fcdbda513b7a0015741148fc | [] | no_license | peteryzheng/RET_ACCESS | 2cff82bd261beff926affd24798ac02ef2b8775a | ac4e3544d85c90ef723aa3dc433d468515020133 | refs/heads/master | 2022-12-13T08:56:32.229201 | 2020-08-06T04:19:45 | 2020-08-06T04:19:45 | 285,464,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,469 | r | table_to_maf.R | library(data.table)
library(tidyverse)
table_to_maf = function(tmp.table,sample.table){
# tmp.table = fillouts.dt
# sample.table = sample.sheet
# tmp.table = ret.006.table
# sample.table = ret.006.sample.sheet
# extract information for plasma and tumor
tmp.table = data.table(tmp.table)
lapply(sample.tabl... |
f6055a3f565c7a600fd923bf69c44b95228c83a4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Aoptbdtvc/examples/aoptgdtd.Rd.R | 5c215efeae1a2379c5e4fc37c6950e232f67a953 | [] | 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 | 542 | r | aoptgdtd.Rd.R | library(Aoptbdtvc)
### Name: aoptgdtd
### Title: A-optimal group divisible treatment designs
### Aliases: aoptgdtd
### Keywords: group divisible treatment design A-optimal
### ** Examples
## construct an A-optimal GDT design with 12 (= 4 x 3) test treatments
##in 12 blocks each of size 6
aoptgdtd(m=4,n=3,b=12,k=6)... |
f0082f1f7ca569cd6a03ad872ef8d1c1c99760a0 | e0d533a9f4d79ee01fee5e638ddd56ef04535dcd | /R/runmodelscript.R | a170eb13b003befd29d1acbf0a395693f3c8a092 | [] | no_license | Sandy4321/Tree-Death-Physiological-Models | d54f83485974156c5ec1872fe5da45bb2c5fce32 | 5202b5800124a19cdd294dd5d0fb2caaeaa8b2ab | refs/heads/master | 2021-01-11T10:05:45.097326 | 2011-07-29T19:02:14 | 2011-07-29T19:02:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,564 | r | runmodelscript.R | #This is a run file file the tree physiology model
require(deSolve) #load relevant packages and function files
require(rootSolve)
require(gdata)
source("R/treephys_ODEs.r")
source("R/weibullcurves.R")
source("R/modelplots.R")
source("R/parameterfile.R") ... |
1bb1e3e7982409ef54359f2e56fc069e934c9bba | f0f91ff5dee7a1d7f1ff10fa47445df3e23cb97a | /Read_data_16s.R | 3fd8fa3d40f203395d4557cbb72d99e9ae5999f8 | [] | no_license | mariofajardo/Trans_NSW | 5a471c16e9553ede7274f33ca17b8121d0d7b1fd | 1ad12cd78ff0ea030b8ea972ffb858327b49d376 | refs/heads/master | 2021-01-10T07:14:41.308088 | 2015-12-15T00:23:49 | 2015-12-15T00:23:49 | 48,009,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,965 | r | Read_data_16s.R | require(phyloseq)
###Import BIOM table data###
OTUS_16S<-import_biom(BIOMfilename = 'Y:/VPino_hpc/ubuntu_qiime/16SvpNS_output/Diversity/7otus_16SvpNS/json_biom.biom',
treefilename ='Y:/VPino_hpc/ubuntu_qiime/16SvpNS_output/Diversity/7otus_16SvpNS/rep_set.tre',
# ... |
627718623ba0afc16b14cad6c757f8ed8dc4dcb8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gPCA/examples/gDist.Rd.R | 4e1d87b4c0996a21db1408c14ff1b1ff73326802 | [] | 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 | 157 | r | gDist.Rd.R | library(gPCA)
### Name: gDist
### Title: Density/Distribution Plot for gPCA
### Aliases: gDist
### Keywords: ~kwd1 ~kwd2
### ** Examples
# gDist(out)
|
03afc043e334a56d518a69f3661a58fc50f1e3bd | 405cfc8d0a48719214ea3f216a9ff87c24a2460c | /Script cats and dogs table.R | 2821004e4b010ea8c684e134a05ec73c6272d3e7 | [] | no_license | Anavoron/Rstudio-table-contest-2020 | ddc5f72584dc63a71c2db64b28a4d189b8a5c0d0 | 84cdf2fdb9a44ae88852a428a842615ed28ba746 | refs/heads/main | 2023-01-09T01:07:31.244081 | 2020-11-12T15:22:33 | 2020-11-12T15:22:33 | 312,292,247 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,639 | r | Script cats and dogs table.R | # loading the libraries
library(tidyverse)
library(gt)
library(showtext)
library(extrafont)
library(extrafontdb)
library(webshot)
### loading and organising the data
# uploading cat density
postcode_c <- read_csv("APHA0372-Cat_Density_Postcode_District.csv")
# uploading dog density
postcode_d <- read... |
b16ad748423f7f785a3dac6813fa480aa3214668 | 5f4696ef6b9ece4dc7efa0e72a9138dacfbb5fca | /tests/testthat/test_ICTpower.R | db5d417bbfd99ef4e9be54e47700d0405a9490c5 | [] | no_license | ICTatRTI/PersonAlyticsPower | fb37bbbb5c6a68c577774059fef19ffdb21bdb0a | 507ad4bded6bb01493040dbb029aecc2f92cd03c | refs/heads/master | 2022-12-04T20:35:24.718140 | 2022-06-10T22:40:06 | 2022-06-10T22:40:06 | 215,357,407 | 0 | 0 | null | 2021-12-02T14:29:20 | 2019-10-15T17:26:48 | R | UTF-8 | R | false | false | 1,465 | r | test_ICTpower.R | context("ICTpower")
library(PersonAlyticsPower)
test_that("piecewise",
{
myPolyICT <- polyICT$new(
groups = c(group1=10, group2=10) ,
phases = makePhase() ,
propErrVar = c(randFx=.5, res=.25, mserr=.25) ,
randFxOrder = 1 ... |
ed0eb66e60c16ca1769fb76871b8a381edff102b | a8b6bfc9e1e34d5d260b833587b9fc7ff84215da | /R/nbss.R | 11ae8930156aaf2fcbc0e3a0acf604c9873819a1 | [] | no_license | jiho/nbssr | d93d92efaa92fe3099187c74527eed22aa77844d | 838ddfe8829829aed2a0023d63798d377e5090b1 | refs/heads/master | 2022-07-26T15:18:55.354762 | 2020-05-14T23:40:21 | 2020-05-14T23:40:21 | 262,024,579 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,300 | r | nbss.R | #' Normalised Biomass Size Spectrum
#'
#' @param x vector of biomasses, biovolumes, or lengths.
#' @param w vector of weights, typically concentrations associated with individual measurements in `x`.
#' @param type whether to compute a biomass/biovolume or abundance spectrum.
#' @param base base of the logarithm for th... |
55cfc78977dc6a2d5dcf4b1c07643366ac781f29 | f2c77a19bf0c7532363cd5f521d1f674b88f1b8d | /Plot2.R | 25b8996f9df9daf3bdebde3465089485c83bb315 | [] | no_license | Yambcn/ExData_Plotting1 | ec3a273d75da1e112d1ad8d8849c695fad5d0c72 | 2d537e802cae8fc151e9b0121a05c9b4f83c578f | refs/heads/master | 2020-12-30T23:22:04.191273 | 2014-05-09T19:23:28 | 2014-05-09T19:23:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 891 | r | Plot2.R | ##Process the data
##I have created a tiny data file with the data of the selected days, to avoid the use of a txt file of 100MB
project<-read.csv("Dataproject.txt", sep=";", stringsAsFactor=FALSE) ## Read the Tiny Data, avoid columns as class Factor
project$Time<-paste(project$Date, project$Time, sep=" ") ## Merge... |
d52bbc007a5efd09446799886bd0860774baa012 | e68e99f52f3869c60d6488f0492905af4165aa64 | /man/cuda_get_device_capability.Rd | 89594e151d37eeb15fca579d50ca1359af544d78 | [
"MIT"
] | permissive | mlverse/torch | a6a47e1defe44b9c041bc66504125ad6ee9c6db3 | f957d601c0295d31df96f8be7732b95917371acd | refs/heads/main | 2023-09-01T00:06:13.550381 | 2023-08-30T17:44:46 | 2023-08-30T17:44:46 | 232,347,878 | 448 | 86 | NOASSERTION | 2023-09-11T15:22:22 | 2020-01-07T14:56:32 | C++ | UTF-8 | R | false | true | 458 | rd | cuda_get_device_capability.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cuda.R
\name{cuda_get_device_capability}
\alias{cuda_get_device_capability}
\title{Returns the major and minor CUDA capability of \code{device}}
\usage{
cuda_get_device_capability(device = cuda_current_device())
}
\arguments{
\item{device}{In... |
24e5ec147aff087bc7051ff883a37f179b48db87 | 9b62a8e5ba50b9f424e14dbb56f299285aa1b03b | /inf_prj.R | bedc5b71b84e4b519491374a710738813c963a0d | [] | no_license | sj-choi/inf_prj | b5fba9ecfae497183029d15835ee76226d7f3353 | f799ecf8466bdf0f710a6b62f5ac97b3ef6e2791 | refs/heads/master | 2021-01-10T03:19:11.078399 | 2015-05-26T14:42:42 | 2015-05-26T14:42:42 | 36,045,464 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,455 | r | inf_prj.R | ## Distribution of 40 random exponentials (with rate = 2)
set.seed(2) # This line is intended to perform a reproducible simulation.
ss40 <- rexp(n = 40, rate = 0.2)
hist(ss40)
mean(ss40)
var(ss40)
## Sample mean vs. Theoretical mean
n <- 1000
set.seed(3)
means <- cumsum(rexp(n, rate = 0.2))/(1:n)
plot(means ~ c(1:n),... |
3754cdcc1b0973642fb496b683526866bfa5f1f0 | 053257d525da78078c77c624649f8c3c7aab7cb8 | /R/BLasso_DL_Joo.R | a33133c6210cf99f988eb8616db4cd92b954c80c | [] | no_license | guhjy/BLasso | 8febfd926c8ba41927daaa2986494cd29a5c7dc4 | 5efa658d9fc6e3f143c64570c8a93fd0f580dd85 | refs/heads/master | 2020-06-21T00:33:33.421257 | 2017-01-01T14:49:11 | 2017-01-01T14:49:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,215 | r | BLasso_DL_Joo.R | #' Bayesian Lasso by Variational Bayes + Dirichlet-Laplace Priors
#'
#' @description Extended Bayesian Lasso (Park \& Casella (2008)) with Dirichlet-Laplace Priors (Bhattacharya et al. 2015)
#'
#' @references Bhattacharya, Anirban, et al. "Dirichlet–Laplace priors for optimal shrinkage." Journal of the American Statist... |
10742fbb4ae6b0e472d98530ce97253847420612 | 3e74b2d423d7b4d472ffce4ead1605621fb2d401 | /thirdparty/R_FindAllMarkers/require.R | fb1e3fb888d15cd98aa5d4542577756e315cb554 | [] | no_license | jamesjcai/My_Code_Collection | 954988ee24c7bd34139d35c880a2093b01cef8d1 | 99905cc5d063918cbe6c4126b5d7708a4ddffc90 | refs/heads/master | 2023-07-06T07:43:00.956813 | 2023-07-03T22:17:32 | 2023-07-03T22:17:32 | 79,670,576 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 32 | r | require.R | require(Seurat)
require(Matrix)
|
6dae97eae133f6c9567a40ebfba37ee7d51435be | 810ea69a7d07656d7f4956ad1698c4be524e007f | /Taller2_Octubre18/solucion/Taller2/Solucion.R | dd2a0ae058a7e54d8d7cdc49be91a6ea0440c8c9 | [] | no_license | Jegomezre/EstadisticaII | 72ae2ec70e71492c9b4b051082b3980699af6481 | c08db34e139aff2c6b6cfd994dc642b0c95132b0 | refs/heads/main | 2023-08-30T04:37:04.962853 | 2021-10-25T22:21:35 | 2021-10-25T22:21:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,208 | r | Solucion.R | library(tidyverse) #cargando el tidyverse
#Leyendo la base de datos
#Ejercicio 1
datos <- read.csv("Ecommerce_Customers.csv")
str(datos) #esto nos muestra la estructura de la base de datos
datos.modelo <- datos %>%
select(Avg..Session.Length:Length.of.Membership, Yearly.Amount.Spent)
#Ejercicio 2
plot(datos.modelo... |
26f6612c0b535e3894b08caf9a4ca9f39ff95a55 | d7c06c71c00235be14b06ef74857ae34fa88a213 | /R/shinyTurkTools-package.R | d6b53259597edff1e71d603fe4b39c7423089f00 | [] | no_license | trinker/shinyTurkTools | af78a841bf5a53ccd9848dbed9ffde842a453b7c | bbdfa0ebe51e43b325ce100b41e974537a7651ab | refs/heads/master | 2021-01-17T20:50:54.075141 | 2016-08-05T13:48:25 | 2016-08-05T13:48:25 | 62,141,989 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 179 | r | shinyTurkTools-package.R | #' Tools to accompany the shinyTurk.
#'
#' Tools to accompany the shinyTurk..
#' @docType package
#' @name shinyTurkTools
#' @aliases shinyTurkTools package-shinyTurkTools
NULL
|
858e1e8c818985e75e2ca27393e3cb4fe6da0bb2 | 2ede61c76a368a328c5e490d630125b5f272b3e0 | /man/iv.replace.woe.Rd | 818f19a69ce809b1898b35d675f50e828dd713f2 | [] | no_license | l0o0/woe | 289d6a422e7f8765545d1d9426ff7da2e5d6f7ef | 678be360a3517b2c902bd3bdc7dba137aa4c8a03 | refs/heads/master | 2020-03-18T23:44:59.670437 | 2018-11-14T03:59:04 | 2018-11-14T03:59:04 | 135,425,130 | 1 | 0 | null | 2018-05-30T10:10:26 | 2018-05-30T10:10:25 | null | UTF-8 | R | false | false | 1,016 | rd | iv.replace.woe.Rd | \name{iv.replace.woe}
\alias{iv.replace.woe}
\title{Replace raw variables with Weight of Evidence}
\usage{
iv.replace.woe(df, iv, verbose = FALSE)
}
\arguments{
\item{df}{data frame with original data}
\item{iv}{list of information values for variables -
output from \code{\link{iv.mult}} with
\code{summary=F... |
da6a9f6d8d8dd58e073123036660cd143a0171b5 | 22540d050618fa7c69c40c89d1397609e2f39936 | /man/dct_object.Rd | 5099e2ddd1ef04964f89e9927b9ae15c4c237f89 | [] | no_license | cran/psyverse | 8d3e6723d66c292f02a4d0b8978d85f868ca52b9 | d1e2dc7f6be23f674f7b6cc1d21089995a331ba0 | refs/heads/master | 2023-03-17T00:04:47.391838 | 2023-03-05T21:00:07 | 2023-03-05T21:00:07 | 250,514,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,231 | rd | dct_object.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dct_object.R
\name{dct_object}
\alias{dct_object}
\title{Create a DCT object}
\usage{
dct_object(
version = as.character(utils::packageVersion("psyverse")),
id = NULL,
prefix = paste(sample(letters, 4), collapse = ""),
label... |
06a1898c1323cce72592d02e81dd52e38c9c160c | c4d4329c7cae09599d1b07f6486d7e530b052654 | /plot1.R | 32c9f2fb5c87b54844adc3c1b66d3daa07a4e6f9 | [] | no_license | LukaSlov/ExData_Plotting1 | ead5d8b8199428ff7bc56889a6a30ed971e9bbb2 | 0ef096fa5ea56de0575834f2222367c0a399a739 | refs/heads/master | 2020-12-30T20:43:25.637915 | 2014-12-07T09:17:39 | 2014-12-07T09:17:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 514 | r | plot1.R | install.packages('sqldf')
library(sqldf)
#Separator is ';'
data <- read.csv.sql('household_power_consumption.txt', sql="select * from file where Date = '1/2/2007' or Date='2/2/2007'", sep=';')
#Use datetime format
time <- paste(data$Date, data$Time)
data$Time <- strptime(time, "%d/%m/%Y %H:%M:%S")
#check if loaded d... |
98e164a8c10c706458edb2c0eea1f046d064debb | 29585dff702209dd446c0ab52ceea046c58e384e | /DiscreteWeibull/R/Edweibull.R | ed9bf061e8d79d3dfd72532700b6016af36637d1 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,555 | r | Edweibull.R | Edweibull<-function (q, beta, eps = 1e-04, nmax = 1000, zero = FALSE)
{
if (beta == 1 & !zero)
e <- 1/(1 - q)
else if (beta == 1 & zero)
e <- q/(1 - q)
else
{
xmax <- min(2 * qdweibull(1 - eps, q, beta, zero), nmax)
if(xmax < nmax)
{
x <- 1:xmax
e<... |
f51679aea474b2f7841b607607585228a1997b48 | e6ba7aa1d351004a7a816e0f131c3f1740cb0984 | /plot2.R | 5a6e24db4b2b9a63bb3fc4cd02c80dc5be9aa0f0 | [] | no_license | jerry-ban/coursera_rexploratory | b21ae716ca42cd3c5bcfe918a1c2a94a837eb63c | a791ed23ced88724efcf119818a9e174ad79fbb5 | refs/heads/master | 2021-05-09T09:33:51.965499 | 2018-02-12T23:42:22 | 2018-02-12T23:42:22 | 119,446,986 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | plot2.R | getwd()
setwd("C:/_research/homeworks/hw001/ExData_Plotting1")
raw_data <- read.table("./household_power_consumption.txt", header = TRUE, sep = ";", stringsAsFactors = FALSE)
col_names <-names(raw_data)
raw_data$Timestamp = strptime(paste(raw_data$Date, raw_data$Time), "%d/%m/%Y %H:%M:%S")
raw_data["Date"]=as.Date(raw... |
14acb1367fb49ec6229f8bc43b7ce3c9ebc18ef3 | a528173483407425c55cbbdf278a2b724830a01e | /man/load.images.Rd | eb9f6ac67d60d25dcc59f359e5529fb2bfb4092b | [
"MIT"
] | permissive | gmke/zernike | 7ea52f89dc353f7d72a8385078e03bc2853a22c1 | 397a5d2f316e2f95cc1a1209007780855da16b13 | refs/heads/master | 2023-05-28T21:58:50.075555 | 2023-05-10T15:07:23 | 2023-05-10T15:07:23 | 166,230,701 | 0 | 0 | MIT | 2021-06-18T12:00:04 | 2019-01-17T13:30:49 | R | UTF-8 | R | false | false | 1,018 | rd | load.images.Rd | \name{load.images}
\alias{load.images}
\alias{load.pgm}
\title{Read images}
\description{
Loads image files in jpeg, tiff or raw format.
\code{load.pgm} provides legacy support for reading
files in pgm format.
}
\usage{
load.images(files, channels=c(1,0,0), scale=1, FLIP=FALSE)
load.pgm(files, imdiff=NULL)
}
\arg... |
6754b0ceb471e0eb23d32c3b1e113e4c92fc5253 | 2657eac7e42b17c815bee869a860254d6eb4640e | /deathstar.worker | 5e94cf4f6451a5df4fd5c5a73b01ecf99f29ef8a | [] | no_license | armstrtw/deathstar.core | 5773edbd49ea303ab9c2aec8a1ce9d4189aa1ecf | d480bc63a960ff13f5a4ce5e28a3d4be51170a56 | refs/heads/master | 2016-08-04T18:44:26.320823 | 2012-05-04T15:43:52 | 2012-05-04T15:43:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 868 | worker | deathstar.worker | #!/usr/bin/env Rscript
library(rzmq)
worker.id <- paste(Sys.info()["nodename"],Sys.getpid(),sep=":")
cmd.args <- commandArgs(trailingOnly=TRUE)
print(cmd.args)
work.endpoint <- cmd.args[1]
ready.endpoint <- cmd.args[2]
log.file <- cmd.args[3]
sink(log.file)
context = init.context()
ready.socket = init.socket(co... |
d724485bc43455e880be9abf50c105531f7fc03b | 7a5fd9fb60ee6e1715e111b8dc46f60bad2ee524 | /ui.R | b9616214d735f411175e831c085a98c3a877337a | [] | no_license | filippomiramonti/developingDataProducts | b14a22da233420c9145c4cbfc26b56019332d855 | 778d4df246589b3d23bed0ffb83e1a46c773e450 | refs/heads/master | 2022-12-05T04:00:10.743697 | 2020-08-26T12:30:13 | 2020-08-26T12:30:13 | 290,468,030 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,342 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
shinyUI(fluidPage(
tags$style(HTML(".js-irs-0 .irs-single, .... |
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