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8a037c47d4e6822a6068c31ce9d0098d47df8eb2 | b7cc95efa28c2f230c2ff27b369e9de5ccc67eec | /dashboardCovid/acumulados/Japan.R | 82ef25f4129c8f47853a6aaaabcb442fe98836e4 | [] | no_license | carlosal1015/R | 9e273346796e9d786ec013bb07956d79ce13701f | 14ba296d3dc8627a858009023e62625824766894 | refs/heads/master | 2022-11-03T17:46:58.197806 | 2020-06-13T05:24:06 | 2020-06-13T05:24:06 | 272,256,011 | 0 | 0 | null | 2020-06-14T18:00:29 | 2020-06-14T18:00:00 | null | UTF-8 | R | false | false | 2,127 | r | Japan.R | #------------------ Packages ------------------
library(flexdashboard)
library(coronavirus)
data(coronavirus)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "f... |
830f386594a16545df19a9464185102a68173b00 | e322c90299a454dc197321ffa8325df3c9d1c761 | /sub_code/process_facebook_embeddings.R | 54229f7ec4cef57cbcdc75dc889047e4b141d123 | [
"Apache-2.0"
] | permissive | JonnoB/SETSe_assortativity_and_clusters | e6cbcd7c36f28511f5b696dcadf08dbb543620f6 | 46af943c19ccebba2110b7c85383412457abf97e | refs/heads/master | 2021-04-20T12:40:28.144222 | 2020-10-27T14:11:52 | 2020-10-27T14:11:52 | 249,684,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,172 | r | process_facebook_embeddings.R |
#this loads the facebook embeddings then saves each list element as a separate file so as not to clog up the memory
if(!(length(list.files(file.path("/home/jonno/setse_1_data/facebook_embeddings",
"processed_embeddings")))==7)){
file_paths <- list.files("/home/jonno/setse_1_da... |
52ba90ec30f1664cb410d951f008097ad7023521 | 1e45d64203edd6d5125980bf23db3daedc9da89d | /sources/framework/visioneval/tests/models/StagedModel/Stage-2/run_model.R | 3c769686af37054047c8b12f3314f839ed957cb0 | [
"Apache-2.0"
] | permissive | VisionEval/VisionEval-Dev | 5c1600032307c729b96470355c40ef6cbbb9f05b | 701bf7f68d94bf1b4b73a0dfd622672a93d4af5f | refs/heads/development | 2023-08-19T17:53:55.037761 | 2023-08-15T12:33:50 | 2023-08-15T12:33:50 | 144,179,471 | 6 | 34 | Apache-2.0 | 2023-09-07T20:39:13 | 2018-08-09T16:44:22 | R | UTF-8 | R | false | false | 1,671 | r | run_model.R | #===========
#run_model.R
#===========
#This script tests the VisionEval staged model with the second half of the model run
#Load libraries
#--------------
library(visioneval)
writeLog('Running Stage 2')
#Initialize model
#----------------
initializeModel(
ModelScriptFile = "run_model.R",
ParamDir = "defs",
Ru... |
ce793813daf0bffec220381eb7442023c7d78e3f | ab1b963df9e33c2c9601b9df8a7cb525de408ebb | /R/describe.R | 27279703c2a09d1a2830723beb39d26fa69907f8 | [] | no_license | javierluraschi/sparkhail | 1e707047690523347bec2ea6cc35ca05e7055942 | 39598e78582f38520a0b152069026589aa65ca53 | refs/heads/master | 2020-06-18T20:50:48.750330 | 2019-07-03T16:31:29 | 2019-07-03T16:31:29 | 196,443,127 | 0 | 0 | null | 2019-07-11T18:02:34 | 2019-07-11T18:02:34 | null | UTF-8 | R | false | false | 2,298 | r | describe.R | #' Describe a matrixTable object
#'
#' @param jobj
#'
#'
#' @examples
#'
#' @export
describe <- function(jobj){
cat(" Global Fields:", "\n",
paste0(" ", mt_globals_fields(jobj), "\n"),
"Column Fields:", "\n",
paste0(" ", mt_col_fields(jobj), "\n"),
"Row Fields:", "\n",
paste0(" ... |
a26660222d7784d81d13ee1284c595a5f06f332f | d92043c1e880559f479d25a7b50cfedcee3f48df | /man/f_plot_profit_bars_plus_area.Rd | 194a4815a7ba0f526dcc7cebf18e42abbd17d7fe | [] | no_license | erblast/oetteR | f759d69121361007136499706687fae6cd9274ef | 02f59f2c0562ae224b798a2115c2061608e8e6f7 | refs/heads/master | 2021-06-21T13:36:58.536874 | 2019-05-07T08:39:10 | 2019-05-07T08:39:10 | 104,708,489 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,346 | rd | f_plot_profit_bars_plus_area.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/f_plot_profit.R
\name{f_plot_profit_bars_plus_area}
\alias{f_plot_profit_bars_plus_area}
\title{plot revenues cost and profit development over time with bars for
revenue and costs and an area chart for profit.}
\usage{
f_plot_profit_bars_pl... |
03a6018d878c27187913ccdba5bcbe6b5661e67a | d0613e3f380dd0c051200a503f1330abb20c83d8 | /misc/installPackage.R | dae4121c53a681715b9e5f4bf70aa640d2401631 | [] | no_license | JasonGregory/dlearn | db560214165124e911a60fbe33fced59cd7131bd | 5716785c23064d799dd309d56e44372b871d3977 | refs/heads/master | 2021-07-08T19:52:26.084207 | 2017-09-30T14:11:24 | 2017-09-30T14:11:24 | 102,974,849 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 272 | r | installPackage.R | library(roxygen2)
devtools::document()
# install package from computer
#devtools::install("~/Documents/R/Packages/dataFun")
#devtools::install("//Co.ihc.com/sh/User/jgregor1/GitHub/dataFun")
#install package from Github
devtools::install_github("JasonGregory/dlearn")
|
1518c27741d89180ee11932ee42d28f36892bc78 | ae46a28c8eb6c7ac2214fcd0cda47dc0e5840607 | /graphics_R_scripts_code/correlation_plot/t_stat_gene_expression/quad_colored_scatter_plot_RINT.R | ce9e4e9a52544825d4fc813ad72a90497052df72 | [] | no_license | sariya/CUMC_taub | a8c7079fe6f73e3c259767489472864056a91550 | d3b83adc97ea3fb31b5223d00e2b898989554c56 | refs/heads/master | 2021-11-20T08:45:00.951631 | 2021-08-26T19:15:20 | 2021-08-26T19:15:20 | 135,483,729 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,131 | r | quad_colored_scatter_plot_RINT.R |
#
#Sanjeev Sariya
#06 14 2021
.libPaths(c( "/home/ss5505/libraries_R/R_LIB4.0",.libPaths()))
library(dplyr)
library(ggplot2)
library(ggpubr)
df_zf_human_orthologues<-read.table("/mnt/mfs/hgrcgrid/shared/GT_ADMIX/model_organisms/zebra_fish/DEG_files_ZF/human2zebrafish.txt", header=TRUE)
load(file = "/mnt/mfs/hgrcgr... |
38e60777d4e61fa26b588fa288830bcca7b235b8 | a63301c6573cf86c0d2b59ff32616efea115616d | /code/rstat/functions.R | 5a1ae5742fd76a0098c0c76c2cc79316c5fc795a | [] | no_license | skasberger/grazwahl2012 | 307936642ef586091bc08089c1a8acb4da2b01d6 | a9bde45cc2120d05d5c6a59e41adc5799ec1bbd8 | refs/heads/master | 2021-01-10T19:37:59.279834 | 2013-05-20T20:15:41 | 2013-05-20T20:15:41 | 6,854,066 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,636 | r | functions.R | ######################################################
#
# Title: functions_grw2012.R
# Description: Functions of the analyses of the
# Grazer Gemeinderatswahlen 2012
#
#
# Author: Stefan Kasberger
# Date: 02.12.2012
# Version: 1.0
# Language: 2.15.2
# Software: RStudi... |
5da5dc8b5a766e86af331fb62539548c04e87d9f | dfbd727cb3a08510b13bad71744a0d4121a686c0 | /man/add_tags.Rd | ef3367b1cc4a79cf6de33aedb76931cf7352923b | [
"MIT"
] | permissive | pommedeterresautee/fastrtext | e6fa126b61f243a58bc69e0e9ac8c14733369b9b | b63c5de9a5168378e8e1abfc4a50be7292002bfb | refs/heads/master | 2021-01-02T08:23:41.922483 | 2019-10-28T08:35:17 | 2019-10-28T08:35:17 | 99,001,176 | 77 | 17 | NOASSERTION | 2019-03-12T07:17:59 | 2017-08-01T12:54:04 | C++ | UTF-8 | R | false | true | 1,074 | rd | add_tags.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/API.R
\name{add_tags}
\alias{add_tags}
\title{Add tags to documents}
\usage{
add_tags(documents, tags, prefix = "__label__", new_lines = " ")
}
\arguments{
\item{documents}{texts to learn}
\item{tags}{labels provided as a \link{list} or a \l... |
a46643329bc27d8fbedcc7b15d1f60cb0225fb3f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/funrar/examples/distinctiveness.Rd.R | 2421dd23466f6a24f6bf6d759de051f9c527de34 | [] | 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 | 847 | r | distinctiveness.Rd.R | library(funrar)
### Name: distinctiveness
### Title: Functional Distinctiveness on site-species matrix
### Aliases: distinctiveness
### ** Examples
data("aravo", package = "ade4")
# Site-species matrix
mat = as.matrix(aravo$spe)
# Compute relative abundances
mat = make_relative(mat)
# Example of trait table
tra =... |
f1fd6643077da3eb24a2d9c6c2bfa8d3ff197977 | eda751fd8916aafb27e6a7ec01287615f0a6b220 | /Scripts/ML_ReinforcementLearning.R | f9d41552b0c037036d407907e84f7b943818682b | [
"MIT"
] | permissive | Miyake-Diogo/Artificial_Inteligence_and_MachineLearning_Formation-Udemy | fb9df3cfa64b79d9b8e871a1625213fdadd0ac4e | bc715e831e7d07bc72c01d4d2b4f01a8063992a6 | refs/heads/master | 2020-03-22T03:51:33.989826 | 2018-07-06T12:06:09 | 2018-07-06T12:06:09 | 139,456,460 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,064 | r | ML_ReinforcementLearning.R | # Formação IA e ML - UDEMY
# Reinforcement Learning
# instalação e importação do pacote
install.packages("ReinforcementLearning")
library(ReinforcementLearning)
# Criação do ambiente, usando a função gridworldEnvironment, do próprio pacote
ambiente <- gridworldEnvironment
# Visualização do ambiente
print(ambiente)
#... |
b04cb1045aff17a0fdb3ca324aebf1f7003a9c8b | 54cd1abbd80c5193d9f3227e9af7083bff965d35 | /R/xxirt_ic.R | ead106d6b8bcc2f7d90234aa8b2b9b37febbd8ae | [] | no_license | isoyturk/sirt | fdc049b008e9e8779179f59474ac490448778afd | 6f7804c61ffb8c6d7fc3ad26bfcfefdd0811d822 | refs/heads/master | 2022-04-17T09:35:44.016913 | 2020-04-18T14:06:31 | 2020-04-18T14:06:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 423 | r | xxirt_ic.R | ## File Name: xxirt_ic.R
## File Version: 0.184
#-- information criteria xxirt
xxirt_ic <- function( dev, N, par_items, par_Theta, I,
par_items_bounds )
{
# Information criteria
ic <- list( "deviance"=dev, "n"=N, "I"=I )
# ic$np.item <- length(par_items)
ic$np.items <- sum(par_items_bounds$act... |
f1def545445eed907adbaf77324590923021f314 | d5430fd76d18d78bc27de2f510f4ed5256396a81 | /Gas_Station.R | 217df0afc660e189b46ee59ad1dfc728169b310b | [] | no_license | Raseshgarg/Queuing-Model | 34498ff92c35d17005d29812f0befbdfa968d696 | 38589dd975899b364ddffa6c4a9911adfdd18b32 | refs/heads/master | 2022-06-21T15:19:02.480414 | 2020-05-06T03:52:57 | 2020-05-06T03:52:57 | 260,820,993 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,939 | r | Gas_Station.R | #install.packages('simmer')
#install.packages('simmer.plot')
#install.packages('xlsx')
#install.packages('parallel')
library(simmer.plot)
library(simmer)
library(dplyr)
library(tidyr)
library(parallel)
library(xlsx)
### Setting initial parameters of the model
max_wait_time = 15
simulation_time = 60*7 # 7 hours, say f... |
401fb7b8fc53bf88f71d2e0d741d2c8a13e7d015 | 6e08f0dd4d56945e4dea19cfbf4217d93c6dd101 | /scripts/04_eda.R | f588df86830254ad55d5928e53f2e4340b197c51 | [] | no_license | dhicks/chlorpyrifos | bb9f3c22c4da615960254302df7119403865af3d | 5d05831c702364315354995e8ec618f3838ea193 | refs/heads/master | 2021-08-07T16:35:36.623445 | 2020-04-01T17:04:08 | 2020-04-01T17:04:08 | 135,846,776 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,510 | r | 04_eda.R | #' This document conducts an EDA on census data only, first at the tract level and then at the places level.
library(tidyverse)
library(sf)
library(spdep)
library(tidycensus)
data_dir = '~/Google Drive/Coding/EJ datasets/CA pesticide/'
load_sf = function(rds_file) {
rds_file %>%
str_c(data_dir, .) %>%
... |
5759b39176326283217f5d5647a1266c8f7d46d6 | b03601e864642b89c448c529447fff6d0f853ace | /R/tsht.R | 15a51222433b12439adef2555be206bf917396a2 | [] | no_license | shearer/simboot | db813dd25c3b1d1a38b6c3d925a1f516a5134717 | cb540d814b5356ee6fcc671f27d7979e27f0c23e | refs/heads/master | 2021-01-13T01:53:50.982124 | 2018-08-22T20:04:44 | 2018-08-22T20:04:44 | 11,773,008 | 3 | 1 | null | 2017-03-08T22:16:28 | 2013-07-30T19:01:54 | R | UTF-8 | R | false | false | 3,980 | r | tsht.R | tsht <-
function(X, f, theta, cmat, conf.level, alternative, R, args)
{
bargs <- args
XOBS <- as.data.frame(X)
estindsum <- function(X, f, cmat, theta)
{
estsum <- theta(X = X, f = f)
SE <- sqrt(estsum$varest)
estC <- (cmat %*% estsum$estimate)
varC <- (cmat^2) %*% (estsum$varest)... |
a5be4ebc194588e2e65540c47d69a6f7ec5742bd | d17f6fda2c41536d3eb7e60c3429f9630cb7697f | /man/getItem.Rd | af33065a372f51ff548f1c8e9b87f5006445bccc | [] | no_license | klmedeiros-ag/DGEobj | 049ba274ab76b31eaf2c178467e18b3af39871fc | fd68063215058b50ab0c12e7dd018abf15b5aa73 | refs/heads/develop | 2023-01-10T07:09:38.089778 | 2020-11-03T03:25:42 | 2020-11-03T03:25:42 | 304,167,313 | 0 | 0 | null | 2020-11-03T03:25:43 | 2020-10-15T00:25:22 | R | UTF-8 | R | false | true | 519 | rd | getItem.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get.R
\name{getItem}
\alias{getItem}
\title{Function getItem}
\usage{
getItem(dgeObj, itemName)
}
\arguments{
\item{dgeObj}{A class DGEobj created by function initDGEobj()}
\item{itemName}{Name of item to retrieve}
}
\value{
The requested da... |
e28b9eea13fe1b83177e67a515be33fc80488619 | 38d64d099cfef6f39fa08aa6364b0464a988102d | /bipartite/man/moduleWeb-class.Rd | 612c284398fb1eab1ad75593aa59ae064f666670 | [] | no_license | biometry/bipartite | 004b458f73c25f64de5bda3c4c9e2c861aec983a | 2fb52577d297480a3a1c1c707a3549ac97e5d08c | refs/heads/master | 2023-06-23T12:37:01.423686 | 2023-03-01T15:22:14 | 2023-03-01T15:22:14 | 24,846,853 | 37 | 16 | null | 2020-05-27T11:07:11 | 2014-10-06T13:26:44 | R | UTF-8 | R | false | false | 2,728 | rd | moduleWeb-class.Rd | \encoding{UTF-8}
\name{moduleWeb-class}
\docType{class}
\alias{moduleWeb-class}
\title{Class "moduleWeb"}
\description{
This class is the output of an application of the function \code{computeModules} to a graph. It consists of the matrix representing the original graph which has been passed to \code{computeModules}... |
fd3c7284887b90ae60ac2efb3263e362a052e2c9 | a88e78c568076609192341a7428d2379668f5a15 | /Code/Data Collection.R | 4e051e2a99509a1618d568cf24a6e13ee43fa49c | [] | no_license | almutaz12/Honors-calculations | c03e933a77312d8e19af59391e61dfc65a0e3179 | 5b598093e6aaf5c4f1100bc0a506f881212e9f98 | refs/heads/master | 2021-01-10T23:49:00.340310 | 2017-02-01T18:23:45 | 2017-02-01T18:23:45 | 70,094,145 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,339 | r | Data Collection.R | # This file is using the honors_stocks code to download stock data
honors_stocks <- function(symbols='F',
what=c("prices","daily","weekly", "monthly", "dividends"),
start_year=1986,end_year=2016) {
if (! require(RCurl)) stop("Must install RCurl package.")
if (... |
5c4cdef472bf69cded18fc0523f478fbdfd6a2d2 | f488f38e7d64ca808c18414c4a40c87d4d2e4e3d | /wordcloud/src/main_wordcloud.R | 48a7a9c758b0df4f4c3329b7edc8e3f16a349d9c | [] | no_license | maite828/Text_Mining | c73dcb78cb422a57feb4317ba623b1c7b849fec7 | 8a1b98722e11d02c107ca84efee55fb15a201cde | refs/heads/master | 2021-06-18T19:17:57.841630 | 2017-06-30T20:54:14 | 2017-06-30T20:54:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,702 | r | main_wordcloud.R | ###############################################################################
## Wordcloud generator from tweets ##
###############################################################################
#
#Copyright Telefonica Digital
#Author: User Modelling Analytics Team
#Mantaine... |
bc8f54fa04e2c6af2bc906d5a6b9d0f5a41dd92c | 229ca63ae86118ac32365a5320a008a0c073110e | /subfunctie_df_gebied.R | c21b18015c242d113110d17b52e21aa616294b67 | [] | no_license | Jolien-GGD/Uitdraai_Tabellen | 4a2050ed746372be2d1f5582c75b8bb246152ad4 | fbe4e282dbe5dde45abcf9283bcd816c10423d01 | refs/heads/master | 2021-06-22T16:57:22.182763 | 2021-06-08T17:36:42 | 2021-06-08T17:36:42 | 223,177,918 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,781 | r | subfunctie_df_gebied.R |
subgebied <- function(df_gebied, gebiedskolom, ci_level, n_vraag_afkapwaarde, data, var_df, survey_design){
# Initialiseer regelnummer op 1, voor het wegschrijven van de uitgerekende data
regelnummer <- 1
for (gebied in unique(data[[gebiedskolom]])) {
# survey_design_sub <- subset(survey_design, c... |
681595bde7de42e06fbb74a5630d4241c1ab1523 | 9acd1e1d8d00bfb9c0e256bd5b5f6b690dea0d07 | /tests/testthat.R | d8adbe45436d9a4e5b9577ebb1f02e0de48b50f9 | [
"MIT"
] | permissive | Reckziegel/DynamicStrategies | 712bcdac026d879466feebfaa3775089387823f7 | a088f9ec4e3845af9e6bc39cd25aa01f45c202d5 | refs/heads/main | 2023-08-22T02:37:26.071589 | 2021-09-21T18:33:20 | 2021-09-21T18:33:20 | 399,301,405 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 78 | r | testthat.R | library(testthat)
library(DynamicStrategies)
test_check("DynamicStrategies")
|
7be0143de9c7219a0b1c7ea76421b3ee5af36fc6 | 3bef70f4b3d6283f2b2bfb44ccdfbf9b28c6429d | /inst/extdata/scripts/plot_data.R | fbb626cbf0ad9c336350e8a20274756385dad89b | [
"MIT"
] | permissive | KWB-R/dwc.wells | 4c1594ea66b1792c6c955b98418982edf80675c1 | 45e8670647c4771fe70d59db0f7cfd1e80242361 | refs/heads/main | 2023-04-10T01:24:40.973815 | 2022-07-12T13:42:20 | 2022-07-12T13:42:20 | 351,021,733 | 0 | 0 | MIT | 2022-10-16T09:17:19 | 2021-03-24T09:35:15 | R | WINDOWS-1252 | R | false | false | 16,051 | r | plot_data.R | # load package, paths and variable sets from global.R --------------------------
source("inst/extdata/scripts/global.R")
# MAIN 0: histogram of static water level measurements and data origin ---------
if (FALSE) {
# plot histogram
plot_histogram <- function(df) {
ggplot2::ggplot(df, ggplot2::aes(
x = W... |
92fc82329d1e53ae4fa2c60c6c5acba5d5c7630c | 9b34b2250d39c1b05a9d44392d7fed4711d26d30 | /man/pairs_lower.Rd | c858bbbb36251d30af93044face43f000b5fb831 | [] | no_license | lbraglia/lbstat | 11bbd806dfb74e46ce332cac23c33da726541205 | f8dc128b507bc1b1cb2741af49c171971abe658c | refs/heads/master | 2023-05-11T00:24:32.746694 | 2023-04-28T12:18:40 | 2023-04-28T12:18:40 | 51,751,382 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 232 | rd | pairs_lower.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pairs2.R
\name{pairs_lower}
\alias{pairs_lower}
\title{lower panel for stats::pairs}
\usage{
pairs_lower(x, y)
}
\description{
correlation coefficients
}
|
a1fc4a90a41f52d063fb21b109f628bcda27dca1 | 96cf6b7c28944616697b5efb2a0cf06ec00dcc3c | /cerealsClustering.R | 7059d2e6d3cd3235660e4421872f00a102b14559 | [] | no_license | alondraSanchezM/clustering | 82301cff806548e6ca35a7722a9838286bc4b06a | 3f257249cb04083fd29816af8b97d9821332f1ea | refs/heads/main | 2023-03-18T17:43:28.222916 | 2021-03-04T19:28:44 | 2021-03-04T19:28:44 | 344,296,061 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,194 | r | cerealsClustering.R | #install.packages("clustertend")
library(cluster)
library(ggplot2)
library(factoextra)
#A. Calificación de clientes de cereales para el desayuno
dataCereales<-read.csv("RFiles/Cereals.csv",header = TRUE, sep = ",")
#---Exploración inicial de los datos
str(dataCereales)
View(dataCereales)
summary(dataCereales)
#---... |
676d4493bd18ac9a81e0453e187a151c56f1828b | 51c04b7e4481afa63a44bcb07835a18adffed8e2 | /merge-fasta.R | dbe62a3490ac2d5950a8cb20dab844095aa940bb | [] | no_license | DanielleQuinn/skate-code | 18e1736390e2eeff6ff958d77beb20fdad176231 | 32b8d4b93f0fac1c512c554c327ed0053a06a888 | refs/heads/master | 2021-04-09T17:36:15.363109 | 2018-03-19T17:05:22 | 2018-03-19T17:05:22 | 125,892,201 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 841 | r | merge-fasta.R | # Load Packages
library(tidyr)
library(seqinr)
library(dplyr)
# Read in Working Data
data<-read.delim("data-working.txt")
data$finclip<-as.character(data$finclip)
data$species_confirmed<-as.character(data$species_confirmed)
# Read in Fasta File
ffile<-data.frame(id=names(read.fasta("data-genetics.fasta")))
fdata<-se... |
0b11c57748d271e92d2116c0bf38845f9f16364e | b38e79b60909104a069f3f9a8a0fab64adf07665 | /scripts/process-cxr | 0c3ea03ccb5a6301dbb17e9e56bff12f94bcdcd2 | [] | no_license | pyrrhicPachyderm/interaction-partitioning | ec8b157195f5fa853092c97b4390504425355699 | 8d08ccd69159b5b2970ddbf48d5f85df4ee10dbb | refs/heads/master | 2023-04-18T19:29:47.347692 | 2023-02-25T08:32:20 | 2023-02-25T08:32:20 | 398,422,947 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,920 | process-cxr | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library(optparse))
suppressPackageStartupMessages(library(magrittr))
##################
#Argument parsing.
##################
usage = "%prog SPECIES_DATA_INPUT_FILE FOCAL_OUTPUT_FILE DATA_TYPE RESPONSE_OUTPUT_FILE DESIGN_OUTPUT_FILE SPECIES_DATA_OUTPUT_FILE MIN_OB... | |
e4667a271cced2d053ba077527f7b03a76c80a63 | 4a06e5ed7573355e478d13a7dacdb101a1016999 | /R/cptSlopeplot.R | e8628e6101cadb9d1725f86983881a1c75c3eeb4 | [] | no_license | BhaktiDwivedi/GISPA | 38f5ab0b6b0c2c5f6fb8229f6bcf4cce297fef11 | 364b9605cafac1f6a5778700d3a70328cd860312 | refs/heads/master | 2021-06-03T20:51:54.985446 | 2020-06-20T20:51:34 | 2020-06-20T20:51:34 | 39,084,820 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,407 | r | cptSlopeplot.R | #'@name cptSlopeplot
#'@aliases cptSlopeplot
#'@title Scatterplot representation of identified change points gene set slopes
#'@description This function will plot the average slopes estimated over all gene sets within each change point by data types
#'@usage cptSlopeplot(gispa.output,feature,type,cpt)
#'@param gispa.o... |
3f86a4f3faf513ea749cb2caccda7fbfa03fbaad | 1868d1380a70a8d6d3be239a553a214817a3f4fa | /R/dataset_documentation.R | 56c10b69619041dbf902bcf69f31be06be310a85 | [] | no_license | MalteThodberg/coRe | 6f8bae1c1970cb21a7c3782849748b16ae4eb744 | fa0cddbce9d46035e97c4639dfe35a92024cb2a0 | refs/heads/master | 2021-01-21T04:31:39.120875 | 2016-07-04T09:29:21 | 2016-07-04T09:29:21 | 38,311,979 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,187 | r | dataset_documentation.R | #' Common Packages
#'
#' Names of basic packages that should always be loaded.
#'
#' @format Character vector
#' @author Malte Thodberg
#' @details
#' The list of packages includes:
#'
#' Data manipulation: magrittr, readr, tidyr, dplyr
#'
#' Special data formats: stringr, lubridate
#'
#' Plotting: grid, gridExtra, ggp... |
6023b1c354b3c485c67b18b236d3d2b25030253b | 71db4a78c8a989b58a0d839a77d58d1774dbec5f | /Code/R/Likelihood_Function.R | dd52206882be31a29317bce8b7f7fff68d771284 | [] | no_license | saulmoore1/MSc_CMEE | 906a7bdf09528c39c0daf6e37f2d722b8ad7bd3d | 5bfd0a5f696c59a092aa9df5536169d905d7ab69 | refs/heads/master | 2022-04-30T20:14:59.660442 | 2022-03-30T11:28:15 | 2022-03-30T11:28:15 | 158,312,708 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,790 | r | Likelihood_Function.R | #!/usr/bin/env R
binomial.likelihood <- function(p){
choose(10,7)*p^7*(1-p)^3
}
binomial.likelihood(p=0.1) # Reutrns: 8.748e-06
p <- seq(0,1,0.01)
likelihood.values <- binomial.likelihood(p)
plot(p, likelihood.values, type="l")
abline(h=max(likelihood.values), lty=4)
abline(v=p[likelihood.values==max(likelihood.va... |
7bcfd9e55ecef260bad38f40654075e8ba513df0 | c6e7f411826df81b26253e89ea67b0da7ef01bf9 | /R/GeoDistPSU.R | ef86a3e44a1bacab9052979eeed8ea023f6cb01b | [] | no_license | cran/PracTools | 2572c63a788e6cb46258de844ae30bf732f733d0 | c3c4a5626e2458d533f4d184fe2afb17180ab201 | refs/heads/master | 2023-05-27T15:15:10.763794 | 2023-05-23T06:10:16 | 2023-05-23T06:10:16 | 17,681,595 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,258 | r | GeoDistPSU.R | GeoDistPSU <- function(lat, ## Latitude variable. Must be in decimal
long, ## Longitude variable. Must be in decimal
dist.sw, ## Distance: miles or kilometers (kms)
max.dist, ## Maximum distance within PSU
... |
c20681776322524c760e79f91b92668aeb1530fa | c3bd69562a080767188a3df5c202a5b600e2c81a | /R/phase.R | 9ba5681b7f9383ee3ef6ba2dcf0dae23e2dff379 | [] | no_license | EricArcher/strataG | 007bde64c4f999ddea609e0e18333c9bfe87d155 | d89348cb390379522202beed20be49fa77cd5eae | refs/heads/master | 2023-02-27T16:01:24.665136 | 2023-02-09T21:11:11 | 2023-02-09T21:11:11 | 34,808,785 | 25 | 16 | null | 2020-04-21T16:36:11 | 2015-04-29T17:46:44 | HTML | UTF-8 | R | false | false | 13,035 | r | phase.R | #' @name phase
#' @title PHASE
#' @description Run PHASE to estimate the phase of loci in diploid data.
#'
#' @param g a \linkS4class{gtypes} object.
#' @param loci vector or data.frame of loci in 'g' that are to be phased. If a
#' data.frame, it should have columns named
#' \code{locus} (name of locus in 'g'),... |
f42ac8f3af487508497f6b820103546bcee34742 | 4f88f602a464420e278482f4b1036645f9fce164 | /R/DF0_COUNTY_DATA.R | 96ecd7715010a51fda8421673beeae357e567fa1 | [] | no_license | DavidSoSiZoch/SitRep | 61d95b127e748be557e5b52007ccd8d6024a0cc2 | 4083dcf9de99fc5c2b884d96f8818743980e2bf9 | refs/heads/main | 2023-09-03T00:51:03.189970 | 2021-10-21T07:17:01 | 2021-10-21T07:17:01 | 393,357,742 | 0 | 0 | null | 2021-10-15T12:09:15 | 2021-08-06T11:34:45 | R | UTF-8 | R | false | false | 1,868 | r | DF0_COUNTY_DATA.R | DF0_COUNTY_DATA <- function(matching_key){
# This function depends on the matching key, for this test application
# specifically the healthauthority_county_key.
# This function creates a dataframe, with the first 3 columns equal to
# the three columns of the matching key. The rest of the dataframe is fil... |
cf65a0346ee2373a138360f7f31a2ed2027ab74b | 5154a4f1cf8569e007604f40737473477e20ddc9 | /plot2.R | 9f459b6f430ade2cf95e2a6248a926af3a424a98 | [] | no_license | Williambrunzell/ExData_Plotting1 | 73c799322df3421682cf316ae1fee22a9eceec87 | fc7fd411e9f87dc2705fa13cf6c42cac4d3f260a | refs/heads/master | 2022-11-12T18:37:06.889234 | 2020-06-29T15:40:44 | 2020-06-29T15:40:44 | 274,715,230 | 0 | 0 | null | 2020-06-24T16:21:37 | 2020-06-24T16:21:36 | null | UTF-8 | R | false | false | 177 | r | plot2.R | #Code for Plot 2
plot(subdata$datetime, subdata$Global_active_power,
type="l", ylab = "Global Active Power (kilowatts)",
xlab=NA)
dev.copy(png,'plot2.png')
dev.off() |
fae3bed65805ca7e78e1d331e0da747d98d547e4 | 3fce68c7d6f45822e4a3294de46cd6935d392d06 | /man/bernieGrob.Rd | 0009f797167a52fd93c882497c0c77a2786a44d4 | [
"MIT"
] | permissive | murrayjw/ggbernie | 388867ccb5812333aecc6df34fd558ddf9a842f4 | f97915109ae3f9fa233795b418e29fe18aa50e0d | refs/heads/master | 2023-02-21T11:07:43.432905 | 2021-01-21T17:01:53 | 2021-01-21T17:01:53 | 331,683,214 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 217 | rd | bernieGrob.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_bernie.R
\name{bernieGrob}
\alias{bernieGrob}
\title{bernie grob}
\usage{
bernieGrob(x, y, size, theme)
}
\description{
bernie grob
}
|
346aa06dd187a5c31d0ec8c6115e0fb4134f857f | 48cbb955ea27365c1266b6bedd1f2f56288615d1 | /R/prepare_ecologist_r_code.R | 53fb4a57042475208f95312b35f3cf3873e84ec5 | [
"CC0-1.0"
] | permissive | fschirr/sampling_r_package | 015dc1ed44834c043d57f742998133735d789d9c | 4703c8c2dc8b2cdd7d5d1f773fa96eb3444081e1 | refs/heads/master | 2021-01-17T04:48:23.200150 | 2016-06-13T13:38:07 | 2016-06-13T13:38:07 | 38,422,415 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,245 | r | prepare_ecologist_r_code.R | #' Rewrite the dataset into a usable format.
#'
#' \code{PrepareDataset} rewrites the dataset to make it useable for the
#' Sampling function of this package. The function comibines the different
#' columns and creates a new dataset. If there are now data fro a column the
#' Column will be filled with zeros.
#'
#' ... |
59b45707edaa7654d639000a1684526259683cf0 | e541db64d64f43a9a21dcbfa2d190b02118ec0ff | /analysis/s2_analysis.R | 80b4e3f0378a69b7e2ff32b0451e8197e067c3c6 | [] | no_license | amyxli/kidbandit_revision | 1e9b38719523c9584b6a002c4dafc235391a9abb | 21b5f076752c171f69b4b42beb580bf4500c1264 | refs/heads/main | 2023-09-05T21:29:14.364998 | 2021-10-27T06:03:56 | 2021-10-27T06:03:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,753 | r | s2_analysis.R | ## Last edited 19/10/21 AXL
## This script analyzes data from replication adult and child participants.
## ESS added analysis on post-tests 1/24/2020.
library(tidyverse)
library(ggthemes)
library(ggplot2)
library(ggpubr)
library(here)
library(effsize)
data_sum <- read_csv(here("data_tidy", "study2_data_sum.csv")) ... |
bf5d5e55f61588a34324df3ff53eff0c2093129b | d08284a960a69fe39e7f46ce82097d537300db26 | /R/ml.est.R | 84e229ea569157fb30fae67b4696e80289e1c94d | [] | no_license | cran/SeleMix | 01978514d14c599f439cee2359b5547b71d3d86a | dca94e4ba9b3a16c34051a326ab5be56042e91cd | refs/heads/master | 2021-01-25T08:55:23.315314 | 2020-11-29T00:30:03 | 2020-11-29T00:30:03 | 17,693,652 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,664 | r | ml.est.R | ml.est <- function (y, x=NULL, model = "LN", lambda=3, w=0.05, lambda.fix=FALSE, w.fix=FALSE, eps=1e-7, max.iter=500, t.outl=0.5, graph=FALSE)
{
#------------------------------------------------------------------------------
# Individuazione degli outlier basata su un modello mistura di 2 gaussiane
#----... |
803dd784b1f18d7e462abe80b0eb9dde80dd5108 | c28bdbe50f95ce0d7ba0b313d33246ab4d1e62ec | /simpleMean.R | 3fed0811cdb07cc778b5204521ab7b21625a3ae0 | [] | no_license | wangbinzjcc/bayesian00 | 626253cdefef20d3e7d4bcd772dc4768845a94b1 | 21218253b8fd9d683b2e048c8e0ceae6fad70886 | refs/heads/master | 2021-01-22T23:54:25.798740 | 2014-01-20T16:03:25 | 2014-01-20T16:03:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,848 | r | simpleMean.R | # Simple normal mean model in LaplacesDemon
# Generate two samples of body mass measurements of male peregrines
y1000 <- rnorm(n = 1000, mean = 100, sd = 10) # Sample of 1000 birds
###==========================================================
mean(y1000)
###==========================================================
lm... |
9f6ee031ede80d5b0c9d709d023ca741e5327ce4 | 91ffdf0cea4024d9368c7599a19f852e3bb3e2c1 | /man/gcol.Rd | 7e83d3d3e6ff2b7f4764a418dcb91f39bfaacb55 | [] | no_license | qianlin-qz/AnalyseDD | a7c4e21c491402fb2b7e018c96ff3d458a66f281 | 2795cfb2f256cb32e6f5ea4e787b0507b2ac7462 | refs/heads/master | 2021-01-19T19:12:38.835024 | 2017-04-16T10:22:50 | 2017-04-16T10:22:50 | 88,406,021 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | true | 351 | rd | gcol.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analysed.R
\name{gcol}
\alias{gcol}
\title{Centre gravite de colonnes}
\usage{
gcol(data)
}
\arguments{
\item{data:}{valeur d'origine}
}
\value{
un vector de centre de gravite
}
\description{
Le centre de gravite de profils-lignes affect¨¦s a... |
368aa5164f80e002c157c2abf5bcb6676e3084f6 | 2bb142f602ad3f563818b272cfa90c98bf59f4b0 | /bayesian statistics/01-beta.R | be2bf7e50569b2b032c1051e15ff45fcf7afe33f | [] | no_license | sercandogan/lessons | 253ba4c1da7db8900c8e266caf636424f6bc56cb | f497a50ffa9c58274f5489a69681c992c5245282 | refs/heads/master | 2021-09-05T03:15:14.570580 | 2018-01-23T23:33:46 | 2018-01-23T23:33:46 | 107,280,290 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 952 | r | 01-beta.R | # 90% positive of 10 ratings
o1 <- 9
o0 <- 1
M <- 100
N <- 100000
m <- sapply(0:M/M,function(prob)rbinom(N,o1+o0,prob))
v <- colSums(m==o1)
df_sim1 <- data.frame(p=rep(0:M/M,v))
df_beta1 <- data.frame(p=0:M/M, y=dbeta(0:M/M,o1+1,o0+1))
# 80% positive of 500 ratings
o1 <- 400
o0 <- 100
M <- 100
N <- 100000
m <- sappl... |
ff26280eb50595570e7bdcac08076ed4e6505936 | e6eadf086af79f7ccea1b3c765a909157c77255d | /man/errorGen.Rd | 9a0697914a2c17cbe8309921d65db2b6a97ae5a2 | [] | no_license | jsta/ipdw | dc76bc00c32725c953b9870edcf40dffe836fe49 | 630a20a635b9a21b3838270c1f5ec02b1399fc0d | refs/heads/master | 2023-03-16T07:09:08.043013 | 2023-03-09T19:11:17 | 2023-03-09T19:11:17 | 20,983,236 | 12 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,281 | rd | errorGen.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/errorGen.R
\name{errorGen}
\alias{errorGen}
\title{Generate interpolation error stats from validation datasets}
\usage{
errorGen(
finalraster,
validation.sf_ob,
validation.data,
plot = FALSE,
title = ""
)
}
\arguments{
\item{finalra... |
4f624bc8f27708d14dd8257d9641769fb054fb5d | d56cff14262b0c58733898164659a27e2739d97d | /R/rstan_generics.R | a7f271565093c09ac6c761a3ec73a741ffe0e98c | [] | no_license | cran/idealstan | 6aeffb800be1490c1f2f969313e3f79d57eb5c5d | daa29ce7e203c63fbba916aa258d53b48ea430b2 | refs/heads/master | 2021-05-02T03:26:00.009381 | 2019-07-10T14:00:03 | 2019-07-10T14:00:03 | 120,898,012 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,545 | r | rstan_generics.R | # These functions are implemented for compatibility with the
# rstantools package (and rstanarm)
#' Generic Method for Obtaining Posterior Predictive Distribution from Stan Objects
#'
#' This function is a generic that is used to match the functions used with \code{\link[bayesplot]{ppc_bars}} to calculate
#' the pos... |
d705f7d7ef096c97472fb22981d95fe2eea28e37 | 030e413aebffc20fe1243ebe264755d7f8d5cee5 | /Census NAICS Trade.R | b10203aef90434a0f807b0cb77dd7f85f2866db0 | [] | no_license | szmsp/bilateral-trade-in-goods-naics | 63e2d79663996620fb319a0ff7c95465a677d4b9 | dbad0737fe78c0d77f08c227b2e94f245ad0e4d5 | refs/heads/master | 2020-04-28T01:44:14.120594 | 2019-03-24T16:58:22 | 2019-03-24T16:58:22 | 174,869,220 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,681 | r | Census NAICS Trade.R | ###############################################################################################################################
# README
# Project: Goods trade by three-digit NAICS code between the US and other countries
# Objective: From the Census API, download two years of the US Census Bureau bilateral tra... |
dde988f8f181b4bc4cf9be888dc141982f1507f8 | 300ba207fa8ce6bde43e7b7a5c9232b3868fc7cd | /plot4.R | 9b10c1f6fbfc0fdbe80600968a726da46c61539a | [] | no_license | ShrutiVij/Coursera_ExploratoryDataAnalysis | 5de23afe14bce658408773440bef04cebe6221b3 | a0a98369b021c5c15e8e93d1372e5351ed7f83d8 | refs/heads/master | 2021-01-13T07:57:23.853206 | 2016-10-23T03:59:26 | 2016-10-23T03:59:26 | 69,839,127 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 705 | r | plot4.R | setwd("./Desktop/Coursera/ExploratoryDataAnalysis/Assignment")
library(ggplot2)
library(dplyr)
# Read the RDS Summary and Source Classification code RDS files
sData <- readRDS("summarySCC_PM25.rds")
scCode <- readRDS("Source_Classification_Code.rds")
coalSource <- subset(scCode, EI.Sector %in% c("Fuel Comb - Electric... |
b0b18608c34f6247d994c0c4eaf8b3683684b211 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spind/examples/GEE.Rd.R | 78edb6712d46137fc1c4d07fa0ab70812e3ebaf0 | [] | 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 | 731 | r | GEE.Rd.R | library(spind)
### Name: GEE
### Title: GEE (Generalized Estimating Equations)
### Aliases: GEE plot.GEE predict.GEE summary.GEE
### ** Examples
data(musdata)
coords<- musdata[,4:5]
## Not run:
##D mgee <- GEE(musculus ~ pollution + exposure,
##D family = "poisson",
##D data = musdata,
##... |
2f6d5b058ef6eac30d0ee7575e8b0f2b20f3b4ed | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BosonSampling/examples/permanents.Rd.R | 27a336b03877abfbc9d2cf707572c9ec61503418 | [] | 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 | 398 | r | permanents.Rd.R | library(BosonSampling)
### Name: Permanent-functions
### Title: Functions for evaluating matrix permanents
### Aliases: Permanent-functions cxPerm rePerm cxPermMinors
### ** Examples
set.seed(7)
n <- 20
A <- randomUnitary(n)
cxPerm(A)
#
B <- Re(A)
rePerm(B)
#
C <- A[,-n]
v <- cxPermMinors(C)
#... |
e6fae73f65ff79a832cc73756f17e9af23a6776a | e05051fb108db2ab20c430c74f95fac6a893dc29 | /R/ons-ts-collision.R | fcea50222aa4b957b045dd127b933459f217e114 | [] | no_license | mhoehle/naming | 7d6baef51150be4350014adcca1a33b229e09bbd | df7a59ceceac1964f66832df5f808bb3b92370db | refs/heads/master | 2023-01-29T05:30:27.153488 | 2017-09-20T22:02:47 | 2017-09-20T22:02:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,533 | r | ons-ts-collision.R | ######################################################################
## Author: Michael Höhle <http://www.math.su.se/~hoehle>
## Date: 2017-04-23, modified 2017-09-20 to include 2016 data.
##
## Description:
## Create bonus material plot containing the time series of the UK baby
## name collision probability. All... |
2980af67f77a4a56b9e914bce8aae05680cde989 | a1a1661d8f42f8005f4a41de6ba333eec0b096a7 | /ERA5_grib_data_extraction.R | ac0c9afc55e4e41294f232c38f6371467dc98cb9 | [] | no_license | jihadrashid/UsedRcodeForMyWork | 5db49e15b245f29d1f7ea2b76ba0494536e8b1ab | 1b37b9c524464e0fb1b9e87b4a9a4444d494003a | refs/heads/main | 2023-01-31T05:57:38.302766 | 2020-12-15T06:05:05 | 2020-12-15T06:05:05 | 321,569,013 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 826 | r | ERA5_grib_data_extraction.R | library(rNOMADS)
library(ra/ster)
x=raster("D:/Downloads/cape.grib")
grib=brick("D:/Downloads/cape.grib")
shp=shapefile("D:/Downloads/shp.shp")
shp=spTransform(shp, CRSobj = "+proj=longlat +a=6367470 +b=6367470 +no_defs")
grib=crop(grib,shp)
grib_array= as.array(grib)
pointCoordinates=read.csv("D:/Downloads/s... |
13e6fe8a8734f3c4dc6f652fcc47991f9b8d4b0c | 547898da61b4dae81aaa0636e5db1bc964c1651f | /test/inst/shiny_app/server.R | e7adaacdaaa0c70b3feb2fadd40f1e5da9341b86 | [] | no_license | sergeitarasov/ontoFAST | 2e48156d3196033ae4e8b5e48db6b1cbf04cc9d5 | c4e12584fde3ea4ccb4928374066f954a56a65d2 | refs/heads/master | 2022-09-02T00:54:23.513397 | 2022-08-04T13:02:00 | 2022-08-04T13:02:00 | 96,386,296 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,102 | r | server.R | server <- function(input, output, session) {
########### Network
output$network <- renderVisNetwork({
withProgress(message = "Creating Network", value = 0.9, {
# minimal example
nodes <- data.frame(id = 1:3)
edges <- data.frame(from = c(1,2,3), to = c(2,3,1))
visNetwork(nodes, edges... |
ee1b44e815b06e3fbc87a8b7211012fb4ef0c5dc | d1a9b4bcf7b3c71c8ba79eb481f343bf2eef9850 | /man/aggiungiRisultatiModulo.Rd | 7ccd601001c3f2b5d41f590ec1aa07ba62059c53 | [] | no_license | kendomaniac/BBBMGU | 1be5aa2af0a703b5747297e4f704ec3c23953d0a | 59d19ad075ad4c2d2db6d40163559d2048e0fb68 | refs/heads/master | 2022-02-26T06:33:08.160285 | 2022-02-03T05:13:12 | 2022-02-03T05:13:12 | 170,837,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,600 | rd | aggiungiRisultatiModulo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aggiungiRisultatiModulo.R
\name{aggiungiRisultatiModulo}
\alias{aggiungiRisultatiModulo}
\title{Una funzione che aggrega output di aggiornaStudentiVoti.}
\usage{
aggiungiRisultatiModulo(
excel.esame,
input.voti,
output.voti,
modulo = ... |
f15a3972c135800e05c57c1bc0bfa738063d8bfe | ee07a8960aa9623207d1f0b592d5438d33a99864 | /2_comparison_DHvsBL/2.06_FreqFocusHaps_inBL.r | 4e4e2896aaddc9914ad895919fb7f3007af2fb10 | [] | no_license | DilanSarange/GWAS_DHs_landraces | 4896e4acf58604f8a80b8abfb24c049c3f2ff3c1 | dc9b765edbcc3c0e1e68e4a5223c2cf6eebc5597 | refs/heads/master | 2022-12-30T16:41:46.394929 | 2020-10-15T06:29:41 | 2020-10-15T06:29:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 38,999 | r | 2.06_FreqFocusHaps_inBL.r | ###################################################################
###################################################################
####
#### assess frequencies of focus haplotypes (identified with GWAS in DHs)
#### in breeding lines
#### Plot frequencies, distinguishing favorable, unfavorable, random haplotypes
##... |
1ef05e0dd2fa3b81667ac0c29980bb6715efda25 | 1c0a7c18e1dbe868f5ef11592384c8cb62f22bc4 | /man/AllPreds_E.Rd | 0e9c726e78076b1f30bcdf681fbd19ccdc0e9f04 | [
"MIT"
] | permissive | SimonDedman/gbm.auto | de1851a0729800fc01713dc43d85b926b69bfd81 | ee6217a76f6ace8b250108daca5ed2ddc3cb59a1 | refs/heads/master | 2023-09-03T23:32:34.257690 | 2023-09-01T16:01:21 | 2023-09-01T16:01:21 | 23,468,620 | 13 | 5 | null | null | null | null | UTF-8 | R | false | true | 1,060 | rd | AllPreds_E.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{AllPreds_E}
\alias{AllPreds_E}
\title{Data: Predicted abundances of 4 ray species generated using gbm.auto}
\format{
A data frame with 378570 rows and 7 variables:
\describe{
\item{Latitude}{Decimal latitudes in th... |
3add8f1ce6ed5ddafd9b360f2aa54b335b630b05 | e08feba647b37a30417755c52c8cb7971d92036d | /R/yao_utils.r | 68d193c6d5241095e0d4a43ed14a4801ccac3404 | [] | no_license | skranz/YamlObjects | 17e984850a7c0acba003916503c211d3e3c9014d | 295eb4fa8db053df10e29362535f629e006cc309 | refs/heads/master | 2021-01-10T18:58:09.827847 | 2015-10-17T03:45:59 | 2015-10-17T03:45:59 | 26,432,913 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,554 | r | yao_utils.r |
# mark the encoding of character vectors as UTF-8
mark_utf8 <- function(x) {
if (is.character(x)) {
Encoding(x) <- 'UTF-8'
return(x)
}
if (!is.list(x)) return(x)
attrs <- attributes(x)
res <- lapply(x, mark_utf8)
attributes(res) <- attrs
res
}
#' Compute the depth of a nested list
list.depth <... |
0c8fe3d8e2211fb48bd1d04be44ead7698dc633b | 0a333b063b6275ca1c278db7d3bf1be69e91bcff | /man/rotation.Rd | 3817731d866dde798a4c784be63ed1bcbdd90133 | [] | no_license | md0u80c9/huxtable | 6e82ee1b09e9b6477da7da2077dc7ef0731a0218 | 8e595d9a3fb64f7c2b1bd5a53b814b250a863ac8 | refs/heads/master | 2020-03-26T12:51:52.863480 | 2018-08-15T23:07:05 | 2018-08-15T23:07:05 | 144,829,038 | 0 | 0 | null | 2018-08-15T08:48:05 | 2018-08-15T08:48:04 | null | UTF-8 | R | false | true | 1,401 | rd | rotation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/properties.R
\name{rotation}
\alias{rotation}
\alias{rotation<-}
\alias{set_rotation}
\title{Text rotation}
\usage{
rotation(ht)
rotation(ht) <- value
set_rotation(ht, row, col, value, byrow = FALSE)
}
\arguments{
\item{ht}{A huxtable.}
\ite... |
ef7da63f5e167094b0dd2b42e660bd8ba936a8dd | a0ab4687753a2d8ff741b054a323c6e401d51a2f | /man/gr95Resid.Rd | 8e628a0c2e313be3c57f294aaa4e61053b850f11 | [] | no_license | lakin-p/respsurf | 1e3e170d37ff5e7d54992c0ba3f35ffa4e7113ea | 8849d1e165a540e06d9ecf14e29d9b239916f113 | refs/heads/master | 2016-09-01T19:58:33.536933 | 2013-06-03T21:11:53 | 2013-06-03T21:11:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 811 | rd | gr95Resid.Rd | \name{gr95Resid}
\alias{gr95Resid}
\title{Returns the residuals of the Greco 1995 model for a given
set of parameters versus concentration data and observed
endpoint data.}
\usage{
gr95Resid(param, dlist, evec, mpos)
}
\arguments{
\item{mpos}{A logical variable indicating whether the
dose slopes are positive. Usu... |
f24275e0abd0dd1c090ea9cc95251c9679e82c3c | 8924983f788d5cdce430505b523f1be6b2a2a00d | /1lesson.R | 4c96d1440fd9acce44c91795b9f162e7b78d373a | [] | no_license | Maxim1488/matmod | 778f422bf08f913e0f446d651af27a443755b3b5 | 392e5b9d0287fa126aa1713afa3b9aaf38bdd6cd | refs/heads/master | 2021-01-10T16:19:27.770387 | 2016-02-05T13:14:34 | 2016-02-05T13:14:34 | 51,139,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 304 | r | 1lesson.R | t=c(T,TRUE)
f=c(F,FALSE)
# commentary as numeric log()
?as `logical-class`()
?as logi
# tip peremens
# func (a,b,c)
plot(density(rnorm(1:100)),col="blue")
# operator (=)
t=25^.5
# log operatory "==" logical oper
z==4
z>4=4
z<4=4
z=c(1,4,10)
# seqi(from=
)
A=c("A","B","C")
B=1:5
C=c(T,F)
#names func
#f |
73aa3b2adfc940409ab0907fcf33ea2a48ba84fd | e6294d8e2099ac9bc6efbb803e7032e4d6c922db | /man/helperpeak.Rd | ace2351f5ee351142922188f31a93585d5083a07 | [] | no_license | cran/peakPick | 670a26602eb50a763ab2b48a6d37f1f1e571ca7a | 1737a1884e0eb08238cd253b1224b025929675c9 | refs/heads/master | 2021-01-10T13:15:04.134978 | 2015-12-04T15:40:34 | 2015-12-04T15:40:34 | 48,085,628 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 745 | rd | helperpeak.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/peakpicking.R
\name{helperpeak}
\alias{helperpeak}
\title{helper function for small peak elimination}
\usage{
helperpeak(thepos, vec, nsd, npos)
}
\arguments{
\item{thepos}{integer position of peak in vector vec}
\item{vec}{vector of values ... |
c1d868b98da46890d676f2332677d49b75f3b9d8 | 081c62f36f7703d7987218c1c22931e083198e73 | /myelo/inst/doc/papers/craigGCSF/Qdb.R | 0fd37d330ab518eb50fe6db324e2457779467662 | [] | no_license | radivot/myelo | be7ed23a6d1772e55310ced91270aa1d09da6735 | 2498bed404c98f096fcda4075c34a2881265e24b | refs/heads/master | 2022-12-15T00:11:22.751773 | 2022-12-04T14:24:36 | 2022-12-04T14:24:36 | 6,070,078 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,451 | r | Qdb.R | rm(list=ls())
library(tidyverse)
library(deSolve)
library(myelo)
(x0=craigIC[c(1,8)])
(parsQ=craigPars[c("Qss","Aqss","tauS","fQ","the2","s2")])
parsQ["kapDel"]=craigPars["kapss"]+craigPars["kapDel"]
parsQ
attach(as.list(parsQ))
fbeta=function(Q) fQ/(1+(Q/the2)^s2)
betaSS=fbeta(Qss)
(kapDel=(Aqss-1)*betaSS)
detach(as... |
8095626306e34eac87156351bf6f25b4e4797bf0 | f32dbf645fa99d7348210951818da2275f9c3602 | /man/Zdate.Rd | 21a19080183246cfc7725a0bff70107f076dfd27 | [] | no_license | cran/RSEIS | 68f9b760cde47cb5dc40f52c71f302cf43c56286 | 877a512c8d450ab381de51bbb405da4507e19227 | refs/heads/master | 2023-08-25T02:13:28.165769 | 2023-08-19T12:32:32 | 2023-08-19T14:30:39 | 17,713,884 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,193 | rd | Zdate.Rd | \name{Zdate}
\alias{Zdate}
\alias{dateList}
\alias{dateStamp}
\title{Date functions}
\description{
Make character vector from dates
}
\usage{
Zdate(info, sel=1, t1=0, sep=':')
dateList(datevec)
dateStamp(datelist, sep=':')
}
\arguments{
\item{info}{info structure from trace structure}
\item{sel}{selection of wh... |
f05ebde65b61aa141dac26c372c09b85db8c4db3 | f77708703a51a8ff8a15504d6c8d7e6340d815bb | /man/make.quartiles.Rd | d9a58d59c2cd8bdde720d443e9800753880d3379 | [] | no_license | syyang93/yangR | 7533fa60814ce7e9917464728455b3499c3a9640 | dfe4cc8f9037024ff25a862e653c5630bc591ae4 | refs/heads/master | 2021-06-14T21:12:42.469432 | 2021-02-19T14:45:07 | 2021-02-19T14:45:07 | 143,451,657 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 573 | rd | make.quartiles.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make.quartiles.R
\name{make.quartiles}
\alias{make.quartiles}
\title{Function to make quartiles from a column within a dataframe --> taken from fashaR}
\usage{
make.quartiles(test)
}
\arguments{
\item{test}{data that needs to be divided into ... |
fd2e99b5d274eaa3bd66729e9cb6338662695cae | 117936196834fbda370de297d6f5a77846bf45e9 | /old/update1_jan18/newFigures/testplot.R | 23e5cc8d738acefbab5f995f9c6c17d5e0b403b9 | [] | no_license | javirudolph/testingHMSC | a79dc2ffcdec967ed45d23e46151044d1365ab51 | 61c3e1b035b8095c45755833d2ab0ebc1179a6fb | refs/heads/master | 2021-06-16T04:27:22.878177 | 2021-03-11T18:46:51 | 2021-03-11T18:46:51 | 170,368,566 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,404 | r | testplot.R | datFig2 %>% filter(., scenario == "Fig2a") %>%
arrange(desc(r2)) %>%
ggtern(aes(x = env, z = spa, y = codist)) +
scale_T_continuous(limits=c(0,1.0),
breaks=seq(0,1,by=0.1),
labels=seq(0,1,by=0.1)) +
scale_L_continuous(limits=c(0.0,1),
breaks=seq(... |
7bf9317f1afb852bae998b10c3ff3fe030841683 | bd808eb9a1c233ba0723120a636c1b3bfa057ef3 | /R/getGenbank.R | 3a2df413384b4a5ec636eb289f97ecbc76114f28 | [] | no_license | wind22zhu/rDNAse | 1165dcdcdc0840f8148c0da41647cf2221450f98 | 829fe7ebc5abe710bde3539f9e164cdd9091aa47 | refs/heads/master | 2021-01-17T06:33:09.818121 | 2016-07-14T00:51:17 | 2016-07-14T00:51:17 | 63,228,252 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,322 | r | getGenbank.R | #' Get DNA/RNA Sequences from Genbank by GI ID
#'
#' Get DNA/RNA Sequences from Genbank by GI ID
#'
#' This function get DNA/RNA sequences from Genbank by GI ID(s).
#'
#' @param id A character vector, as the GI ID(s).
#'
#' @return A list, each component contains one of the DNA/RNA sequences.
#'
#' @keywords Genbank
#'... |
c029f1eab3af1df2e69b0aa9b8a5576a2ce49fe6 | 1fdce84d0fadf95c5908553ac84efee4ea1aafe0 | /R/permKS.R | e9c3c54eee9a9a89adc0c71bbe9a4898a8ced73a | [] | no_license | cran/perm | 351a16efefd17be5340873d1b0487b0210f6d976 | 4b6d9b252ebd7e18f022271a1d5c55180d1081d3 | refs/heads/master | 2023-09-03T05:22:50.930937 | 2023-08-24T21:00:02 | 2023-08-24T23:30:45 | 17,698,470 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,558 | r | permKS.R | `permKS` <-
function (x, ...){
UseMethod("permKS")
}
`permKS.formula` <-
function(formula, data, subset, na.action, ...){
## mostly copied from wilcox.test.formula
if (missing(formula) || (length(formula) != 3) || (length(attr(terms(formula[-2]),
"term.labels")) != 1))
stop("'fo... |
853d4d1c63e87061ffb0f3d2840f167433ee0e75 | fc3ef1e0f0fcc246981349c65a88300c8918bab2 | /kmeans-old.R | a201a566dd32de13a0898052fe7adf97b1f03abd | [] | no_license | CuriousPICTians/lifematters | bac1d52cd3f55cb3f23d486c28b08d228d818453 | ef2bb3faf265596a73c6e1b81944cb80ba848a9e | refs/heads/master | 2021-01-19T07:20:47.449293 | 2017-06-06T03:58:29 | 2017-06-06T03:58:29 | 87,537,594 | 1 | 4 | null | 2017-06-06T03:58:30 | 2017-04-07T11:09:54 | PHP | UTF-8 | R | false | false | 1,670 | r | kmeans-old.R | #!/usr/bin/env Rscript
#Usage :
# 1) From Command line : $ cd /var/www/html/lifematters; Rscript kmeans.R '<email_id>'
# 2) From PHP : exec("Rscript kmeans.R <email_id>", $out);
library(rJava)
library(RMongo)
i <- commandArgs(TRUE)
#i <- '1018@hotmail.com'
rootkea <- mongoDbConnect('organ')
donors <- dbGetQuery(roo... |
c3599ffdf1219b4f5e724d786a239d20bdd81e62 | 9c59572fe0a298f89a54221d68ee1728524db215 | /motCorr_topUp/motionCorrect.afni.blip_singleVolume.R | 79bc77a5043ab92790878ebe55808e1f67ac9ed7 | [] | no_license | AlessioPsych/AnalysisAfni | f2f2f9a3117ccff12b97bd014a9e869c7f6b5197 | 735678954d517031804ee42d92fe43ce1b7dd1a6 | refs/heads/master | 2023-06-28T07:00:06.160686 | 2023-06-23T08:50:17 | 2023-06-23T08:50:17 | 156,407,302 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,457 | r | motionCorrect.afni.blip_singleVolume.R | args <- commandArgs(T)
print( args )
#args <- c( 'EPI/', 'TOPUP/', '2', '2' )
#setwd('/media/alessiofracasso/storage2/SpinozaTest/HighRes/AF_HighRes_04112016/topUpDataset2')
# get actual dir
mainDir <- getwd()
# get EPIs
setwd( args[1] )
epiFiles <- dir( pattern=sprintf('*.nii') )
# get TOPUPs
setwd( mainDir )
setw... |
ace3b64fb701211ca6fdde426d16ccd93887b349 | 0d10da12b402ed6b1af64c1992f48f4bd553719e | /predictions_on_na.R | 9750ccd490b28c406bdbe1573c0f713565ea7610 | [] | no_license | edples/Predictions-of-8-levels | 6ea504c564f54cbf583db6440bca364628a7c065 | fe5ceb6a9a8be31f6954368d2240ef8a87920f61 | refs/heads/master | 2020-06-17T01:46:45.622859 | 2020-01-06T12:30:43 | 2020-01-06T12:30:43 | 195,759,105 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,152 | r | predictions_on_na.R |
setwd("C:/comply")
m_data <- read.csv("M.csv")
profile_data <- read.csv("ProfileMetadata.csv")
library(tidyverse)
class(m_data)
class(profile_data)
str(m_data)
str(profile_data)
glimpse(m_data)
glimpse(profile_data)
head(m_data)
colSums(is.na(m_data)) #no NA's, the n.a.'s are characters
colSums(i... |
c6254221570f2e29495aa4bbb8bb3307a9e05bad | 2cb2bc953975540de8dfe3aee256fb3daa852bfb | /thisweek_masuipeo/q711/tyama_codeiq711_next.R | 57225b9e753eb6ec5127c5d1d9789c6ca8da342d | [] | no_license | cielavenir/codeiq_solutions | db0c2001f9a837716aee1effbd92071e4033d7e0 | 750a22c937db0a5d94bfa5b6ee5ae7f1a2c06d57 | refs/heads/master | 2023-04-27T14:20:09.251817 | 2023-04-17T03:22:57 | 2023-04-17T03:22:57 | 19,687,315 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,043 | r | tyama_codeiq711_next.R | #!/usr/bin/Rscript
next_permutation<-function(env,name,n=NA){
a=get(name,env)
if(is.na(n))n<-length(a)
if(n<0||length(a)<n)return(FALSE)
i<-0
a<-c(a[1:n],rev(a[-n:0]))
for(i in rev(1:(length(a)-1)))if(a[i]<a[i+1])break # r doesn't go beyond the range
if(a[i]>=a[i+1]){
assign(name,rev(a),env)
return(FALSE)
}... |
c144dc18a12a94f55208bb609cdb8f315f7f05ea | 5b1c24cc6be830fa9a8b084acb40929d26122166 | /man/getStartingData.Rd | 47ec377f5eb7164cff044682429b78ede7b8fc87 | [
"MIT"
] | permissive | mathewroy/ynabr | bf4e995daa16940bbb67aabee85b93967c70e68c | 098a4c13db4cf1b1f0191ae03bac8b9c7e73420b | refs/heads/master | 2023-01-28T11:27:08.043989 | 2023-01-19T23:30:33 | 2023-01-19T23:30:33 | 159,608,851 | 14 | 1 | null | null | null | null | UTF-8 | R | false | true | 589 | rd | getStartingData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getStartingData.R
\name{getStartingData}
\alias{getStartingData}
\title{Retrieves user or budget names}
\usage{
getStartingData(i, param.token.code, param.token.env)
}
\arguments{
\item{i}{name of endpoint}
\item{param.token}{Your YNAB API p... |
1e3ef57c8c7dd700f96d4da7d75aa9326d03e833 | 34cc9bb4242a1aa4d873b726230a1c1b82203fcb | /merge_ele_data.R | b0eb6a42b3f957a536ee533f3b857ce6ca49b1b4 | [] | no_license | secs-lab/hackathon-2020 | d820db7440b518a41dacd3b42895bfad352e47bc | 69a478a10642221674c55e27949e46dbe45cfac6 | refs/heads/master | 2020-11-26T10:42:52.075188 | 2020-01-15T15:51:19 | 2020-01-15T15:51:19 | 229,047,729 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,908 | r | merge_ele_data.R | library(reshape2)
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
#read in caluclated elephant poaching and population datasets
load("data/raw/SH_AnnualModelPreds.Rdata")
ele_sums <- readRDS("data/raw/ele_sums.Rdata")
#read in raw populaion survery data, select relevant columns
#and filter to relevant year... |
8f9c039e4d0dc04b00418bc594b39f3252993d31 | ff10ad9933ad8d63bb824ddd0dc7527a279325f5 | /cachematrix.R | bf302f732273e2c5df93c602fb11c92d3226278d | [] | no_license | lemenendez/ProgrammingAssignment2 | b2dc40efa6b3c5ea672308d22b61c8dbd6be5f1f | 60979011e7537cfdbb459ec7675faec64a0de5ce | refs/heads/master | 2021-07-13T20:57:09.172316 | 2017-10-19T04:44:33 | 2017-10-19T04:44:33 | 107,472,738 | 0 | 0 | null | 2017-10-18T23:06:10 | 2017-10-18T23:06:09 | null | UTF-8 | R | false | false | 933 | r | cachematrix.R | ## source file contains two functions to handle the mechanims
## to calculate the inverse of a matrix in a very efficient way by caching
## the result if does not change
## object for working whit a cacheable inverse of a matrix
makeCacheMatrix <- function(x = matrix()) {
i <- NULL # inver... |
a3660517e7b95412a5768b9fb6a6df1231864ff8 | df6279f728136d1201b18c940ce7f16c9c2bfcf7 | /man/plot.fitcurve.Rd | 23d84462c859387106d0b12dcb741c80704a2a6a | [] | no_license | cran/WindCurves | b298f70c08252c37428657e957461aa892ea1425 | d6b14115982540accdd7370db73783fcf0ed77ad | refs/heads/master | 2022-06-04T15:25:06.495082 | 2022-05-01T03:50:02 | 2022-05-01T03:50:02 | 120,623,905 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 656 | rd | plot.fitcurve.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fitcurve_plot.R
\name{plot.fitcurve}
\alias{plot.fitcurve}
\title{A function to plot the curves fitted with fitcurve() function}
\usage{
\method{plot}{fitcurve}(x, ...)
}
\arguments{
\item{x}{is object returned by fitcurve() function... |
c097000f69908e415f72157d1bf94aba40c7c534 | bcfc2d522327afe96f503871df7892fbbac94697 | /www/lib/ionic/js/ionic-angular.min-compiled.js.map | 3c493c83607051bf628a0b2059731bb560833767 | [] | no_license | derrickwilliams/phymoo | 15b674b507d289f119bf4049434ec84cd6ebc06c | 88d3053056aa9d10506cb037b403c43625b4426c | refs/heads/master | 2021-01-10T11:56:25.237743 | 2015-06-03T13:12:01 | 2015-06-03T13:12:01 | 36,804,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 341,594 | map | ionic-angular.min-compiled.js.map | {"version":3,"sources":["/Users/derwilliams/workspace/me/projects/phymoo/www/lib/ionic/js/ionic-angular.min.js"],"names":[],"mappings":"aAcA,CAAC,CAAA,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC... |
aa4aed2d8e631e234d68ae38463a6c52b2d2f0b3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bedr/examples/bedr.sort.region.Rd.R | c8c5f23ed31a8783e87c09982ace3edf159e57af | [] | 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 | 254 | r | bedr.sort.region.Rd.R | library(bedr)
### Name: bedr.sort.region
### Title: sort a region file
### Aliases: bedr.sort.region
### Keywords: sort
### ** Examples
if (check.binary("bedtools")) {
index <- get.example.regions();
a <- index[[1]];
b <- bedr.sort.region(a);
}
|
d17d101a986468f864914b1cdeb7a4c06fefb85c | f9376bb4d345ec552ac295d4098f523f18eaacba | /R/Lecture2/Lecture2/OldMaterial/SQLandR.R | 67e7122fe91ec649a788db18a54e5f3b5df6b710 | [] | no_license | StephenElston/DataScience410 | 1c201792c8c7084e699cf9397daaa658ea40ef73 | 21855687724240192592d0d4f72674f5f21f6895 | refs/heads/master | 2023-01-24T21:19:47.038382 | 2020-12-04T03:01:01 | 2020-12-04T03:01:01 | 115,932,652 | 10 | 15 | null | 2020-01-29T03:03:53 | 2018-01-01T16:56:19 | Jupyter Notebook | UTF-8 | R | false | false | 4,535 | r | SQLandR.R | ##--------------------------------------------
##
## Using SQL from R
##
## Class: PCE Data Science Methods Class
##
##--------------------------------------------
getwd()
#setwd('C:/Users/Steve/Dropbox/UW/DataSci350/Lecture 2')
##-----Getting/Storing Data-----
# txt files
?read.table
# csv files. Is wraper on read.... |
cef6ed6ef2c4e861101ee07c03081dfc0d544f2a | 1137633080330c3cf316af30b8bcf6a625310447 | /06 - Predicting with unlabel dataset.R | aae09f01a8a9cd91f76c7f1f16ace772c01e3c3b | [] | no_license | gloria2691/WiDS-Datathon-2021 | a78fd6693740d3d684808fc749a3397a5e3df12a | c185482b07defd5b8fccb0aff2145530f87f7cd5 | refs/heads/main | 2023-03-11T20:06:51.022257 | 2021-03-02T16:11:36 | 2021-03-02T16:11:36 | 339,709,882 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 435 | r | 06 - Predicting with unlabel dataset.R | # Doing prediction with the unlabel dataset
require(randomForest)
#Load Data unlabel
data_unlabel <- read.csv(file.path(data_dir, "UnlabeledWiDS2021.csv"))
View(data_unlabel)
sum(is.na(data_unlabel))
# Predicting values
?predict
predictions_unlabel <- predict(modelo, newdata = data_unlabel, type = ... |
37412a1843e0493f6149e4a5fa1ddd5c0674be15 | 2cbfbfe385329c7c3768522af14ad383e2414c1a | /pollutantmean.R | 5ad0f7490984c880922fe4308af34e8421830f46 | [] | no_license | vcerveron/datasciencecoursera | 7e440a801091e963408bc20d953bebdfc2b94f41 | 5e40a492517bba1c09a5fcf50ef3e2f3125c7d64 | refs/heads/master | 2021-01-19T00:08:47.806107 | 2015-05-28T17:59:02 | 2015-05-28T17:59:02 | 33,619,445 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 249 | r | pollutantmean.R | pollutantmean <- function(directory, pollutant, id=1:332) {
data <- NULL
for(idn in id) {
datap <- read.csv(paste0(directory,"/",formatC(idn,width=3,flag="0"),".csv"))
data <- rbind(data, datap)
}
mean(data[,pollutant], na.rm=TRUE)
} |
0080cff0760cac0fc5089d2a08b215824025fd5d | 073e4e7c9c2f4822e798f4a56e4ff90b11b5a85c | /Code/impact_CNA_pipeline.R | 80a208816e2967c68d7f0ebbd303c6cec61208da | [] | 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 | 5,623 | r | impact_CNA_pipeline.R | library(data.table)
library(dplyr)
library(tidyr)
master.ref <- fread('/ifs/work/bergerm1/zhengy1/RET_all/Sample_mapping/master_ref_080719.csv')
cna.dir <- paste0('/ifs/work/bergerm1/zhengy1/RET_all/Analysis_files/cna_',format(Sys.time(),'%m%d%y'))
# cna.dir <- paste0('/ifs/work/bergerm1/zhengy1/RET_all/Analysis_files... |
20c086a77a47f1b11cd92c38d7188108f4cbb7cc | c3826e89c7c78acdcc4596820d03fa96c8710b38 | /R/zzz.R | d9c48acd4955ef78045ace43d0d7f82139456bd4 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | chen496/SomaDataIO | 7b393fad010774e17e086555a026c2a38de06415 | b8f00329aaa283f8243d1064a7bda19b873fdd67 | refs/heads/master | 2023-06-24T21:22:02.222540 | 2021-07-27T20:45:52 | 2021-07-27T20:45:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,835 | r | zzz.R |
#' @importFrom stringr str_glue
#' @noRd
.onAttach <- function(libname, pkgname) {
packageStartupMessage(
cli::rule(right = "Legal", line = 2, col = crayon::magenta),
"\n",
stringr::str_glue(
"
SomaDataIO\u2122
Copyright \u00A9 2021 SomaLogic, Inc.
Permission is hereby ... |
0f5d5d950d82e530a8fe9ce52307ba4943656096 | 56f809d92798dc2cdb1ffc47fdfcb306fdba014e | /R/temp_risks.R | 1ed987e8f024dc7aa044d753c76a93f6c9bc1a76 | [] | no_license | gclawson1/computingpackage | 1fcfe94fa9d1262a4d7df752d726b1f279f7d22f | e0d13be4b612f24a92f9df2c6538d4a6380c7342 | refs/heads/master | 2020-06-01T11:55:47.375459 | 2019-06-13T21:45:05 | 2019-06-13T21:45:05 | 190,771,358 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,391 | r | temp_risks.R | #' temp_risks
#'
#' Compute the number of days per each location over the span of the data set where there is risk of heat stroke, comfortable weather, and freezing at 3 PM.
#' @param data data frame with columns Date, Location, Temp3pm
#' @author Gage Clawson
#' @example temp_risks(data)
#' @return Returns a table con... |
9b5d7b60782069e33e58f1472f3c51320f0005e7 | 46eeb7254223e1a4c7a08ac33b50f0689d6e3024 | /algo/lcs4.R | 4874fea4747399ed6dc3cd7e6627d2203c53bf26 | [] | no_license | moshahmed/R | 4712f5021aa0c59665375ee3a6004feac2923413 | 2d79c7af4bab63cdbafd3776fc9f8bc6c29d6392 | refs/heads/master | 2022-10-15T03:58:00.930511 | 2022-10-10T17:30:40 | 2022-10-10T17:30:40 | 28,472,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,074 | r | lcs4.R | # What: LCS in R
# Changes GPL(C) moshahmed@gmail.com
# See c:/doc3/algo/malgo/dynamic/lcs/lcs.htm
# See lcs3.R 2016-10-15
lcs4p <- function(B, X, i, j) {
# cat("lcs4p",i,j,"\n")
if ( i==0 || j==0 ) return()
if (B[i+1, j+1] == '/') {
lcs4p(B, X, i-1, j-1)
print (X[i])
} else if (B[i+1, j+1] == '^') {
... |
9c3e10d0974b93f415dcaea32cc01ffd49a4a77e | 59e30e3e196df56abca3a58f44d6fb1fd67d098c | /2-R Programming/A3/Hospital.R | f846419bddf7d600d3837765ccc15755cf28ae9e | [] | no_license | yiapplege/Data_JHU_Cousera | 9afdaf38ad61990e05efbe896e562d433c4119d5 | 860824f79393772f155f1bc9a131a9b03553cc98 | refs/heads/master | 2020-04-04T08:03:34.659730 | 2019-02-05T04:26:11 | 2019-02-05T04:26:11 | 155,769,760 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,968 | r | Hospital.R | #R Programming
#Assignment for Week 4
#Author: Yige
#Date: Nov.6, 2018
rm(list=ls())
setwd("~/Desktop/Data Science @Coursera/Assignments/2_3")
#1 plot the 30-day mortality rates for heart attack
outcome <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
names(outcome)
ncol(outcome)
nrow(outcome)
de... |
d2231521ef11a416d1a6ee118d631cee08f9641a | a297edbe0a8b9895cc685fbd94cf1e49d58db807 | /tests/testthat/test-tri-graticule.R | 101a173d40542b8590fb0e8947bc7e7b9d1de297 | [] | no_license | mdsumner/bluegum | 146aaa1091ad8a0f8932fdbd306028752c6c8223 | 06e9115edd0e118d87d28608c8a0815d1927321e | refs/heads/master | 2020-08-01T02:59:37.781664 | 2019-10-26T03:18:26 | 2019-10-26T03:18:26 | 210,838,144 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,123 | r | test-tri-graticule.R | test_that("tri_graticule works", {
mesh <- tri_graticule() %>% expect_s3_class( "mesh3d")
expect_s3_class(tri_graticule(xlim = c(10, 20)), "mesh3d")
expect_s3_class(tri_graticule(ylim = c(-80, -70)), "mesh3d")
expect_s3_class(tri_graticule(xlim = c(100, 150), ylim = c(-80, -70)), "mesh3d")
expect_s3_class(t... |
345486522ddbffed0a5dbd1572845e585e321e04 | 7b264bf1eabfaa4615bd53045397d8951de1c1f4 | /R/gitlink-package.R | eec02bbc85be92ab3663c2eb53a310c59a71acf6 | [
"MIT"
] | permissive | colearendt/gitlink | 062bf27ddd54bb693e741e2f76adadbd5bf9adf9 | bf5f9ab2018934ba841f8b9809bf01d7938c88e8 | refs/heads/master | 2020-04-10T10:46:28.196527 | 2019-11-28T05:32:55 | 2019-11-28T05:32:55 | 160,975,384 | 16 | 0 | NOASSERTION | 2019-11-28T05:32:24 | 2018-12-08T20:20:01 | R | UTF-8 | R | false | false | 237 | r | gitlink-package.R | #' @importFrom htmltools a
#' @importFrom htmltools css
#' @importFrom htmltools div
#' @importFrom htmltools img
#' @importFrom htmltools tags
#' @importFrom htmltools tagList
#' @importFrom rlang list2
#' @keywords internal
"_PACKAGE"
|
20a5cf4830c6ea9656ce6faae361fed48a92a32c | fdb0f6ac3ca332d07b3a088ca53f6a7b0edc9b4c | /Code/Temporal_ordering_of_TF_programs.R | d051037269945b777514e64feed4f9e6d539c8a4 | [] | no_license | gaoweiwang/SCislet | f98966f4dcc63e9c0a9ee2784f5c1a48bc1d9d8d | dbff1869624743d4297f7d404fe3bc43cfe3729a | refs/heads/main | 2023-04-16T17:45:22.847727 | 2023-03-02T21:44:34 | 2023-03-02T21:44:34 | 490,007,552 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,996 | r | Temporal_ordering_of_TF_programs.R | library(Seurat)
library(Signac)
library(monocle3)
library(SeuratWrappers)
library(ggplot2)
library(patchwork)
library(reshape2)
library(cicero)
library(gplots)
library(dplyr)
library(plyr)
blank_theme <- theme_minimal()+
theme(
panel.border = element_blank(),
panel.grid=element_blank(),
axis.ticks = eleme... |
87d5d035970e001fc28b36209a7d7c6eec898505 | 092393a8e01f95e26abb175fccfe13f509c25404 | /man/shifts.Rd | 21abe14ac641475815fa49dd50b03b3e42840765 | [] | no_license | bdemeshev/torro | 8a74f3db6a5468fc9c65e76706e748d4b599e7a4 | 69c02a714e5b9afb4948a58a691fbdd20aa873a0 | refs/heads/master | 2021-01-19T15:16:50.032726 | 2018-03-06T06:11:09 | 2018-03-06T06:11:09 | 100,956,656 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 570 | rd | shifts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/torro.R
\docType{data}
\name{shifts}
\alias{shifts}
\title{Shifts}
\format{tibble}
\usage{
data(shifts)
}
\description{
Tibble with the following columns
}
\details{
\itemize{
\item shift_name name of the shift
\item shift_T_start starting ti... |
4ea8ad027df86d3ad2bd0713756e6e7c66af39cd | b492ada9c3472a01aa6e6deab9551f233a0174c9 | /gtexR_GO.R | d677c7552ddad1e13651a806f7f3b86120dd48d6 | [] | no_license | Parks-Laboratory/GTEX_liver_WGCNA_Human | f1e8ed6dabfcb25dd374836bd7ce0df09a2f3734 | b53cd27743cf270332f4f12c711744821a4e0725 | refs/heads/master | 2020-05-30T00:11:03.509497 | 2019-07-24T15:08:51 | 2019-07-24T15:08:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,105 | r | gtexR_GO.R |
BiocManager::install('org.Mm.eg.db')
BiocManager::install('org.Hs.eg.db')
install.packages ("xml2")
BiocManager::install("biomaRt")
install.packages("sqldf")
library (org.Mm.eg.db)
library (org.Hs.eg.db)
library(biomaRt)
library (WGCNA)
library(sqldf)
load (file = "2-gtex-InfoWithModule.RData")
annot = read.csv ("G... |
1076eb47d748efffca4664c1774133d81bb1091f | 093f4979b58388700d670906ddb4f9e839675299 | /plotExons-old.R | 60763222cb18561ae4f2a636ebcff17379763e34 | [] | no_license | sgschneider01/R_code | 6ac0c871ad619635f238a655dde7c5f6e5d1a106 | 39d1db3d91673655cf799343eb884ee4caded5ad | refs/heads/master | 2020-06-20T12:44:19.672021 | 2016-11-27T02:50:22 | 2016-11-27T02:50:22 | 74,863,072 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,365 | r | plotExons-old.R | source("~/Desktop/research/notes/R_code/getExons.R")
source("~/Desktop/research/notes/R_code/getExonsIntrons.R")
source("~/Desktop/research/notes/R_code/parseExonTable.R")
plotExons <- function (egid,sp) {
exons <- getExons(egid,sp)
if (!is.null(exons)) {
sap <- apply(exons[seq(1,nrow(exons),by=2),], 1,
... |
773bb41fc4d040c9d1fc8f740bab32204a67d882 | 4d7504edc5e0242f5f7fdb0e2735b2f99947eca5 | /man/UScpiqs.Rd | 17db9c45d637df1f1114ac7a6fad188ca4563310 | [] | no_license | ccrostirolla/midasr | 45b08c3d79b405599975adf9714d6cbdd35d1185 | bd06bbd98c9b0fa7831c37d9edf7fa39b647aa1e | refs/heads/master | 2021-10-21T21:11:49.410418 | 2019-03-06T14:13:27 | 2019-03-06T14:13:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 387 | rd | UScpiqs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/midasr-package.R
\docType{data}
\name{UScpiqs}
\alias{UScpiqs}
\title{US quartely seasonaly adjusted consumer price index}
\format{A \code{\link{data.frame}} object.}
\source{
\href{http://www.bea.gov/national/xls/gdplev.xls}{FRED}
}
\descrip... |
2a9ddf83c9f1653fa6738957174f38ddfb7bee02 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ridge/examples/logisticRidge.Rd.R | e5f90884c3c83dc0156931533425c6477eb4140c | [] | 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 | 362 | r | logisticRidge.Rd.R | library(ridge)
### Name: logisticRidge
### Title: Logistic ridge regression.
### Aliases: logisticRidge coef.ridgeLogistic plot.ridgeLogistic
### predict.ridgeLogistic print.ridgeLogistic summary.ridgeLogistic
### print.summary.ridgeLogistic
### ** Examples
data(GenBin)
mod <- logisticRidge(Phenotypes ~ ., data... |
9315f043d9aad8f61416ae32f05d485a3447197f | 8a8e37a05bd1810e0c6c46bdf3e63a8ff0a79e86 | /r/SCEUtils/man/run_umap.Rd | 494a29a07f19d2ea7e8e6a16ac4e5d49f68cc349 | [] | no_license | nathancfox/tools | 3209208c45226988273af4a9a69267a49f9bfcf1 | 3f7db30b380a8fbf569d2016795e42cb853fa15e | refs/heads/master | 2021-12-10T04:45:38.955354 | 2021-09-29T14:19:56 | 2021-09-29T14:19:56 | 240,432,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 454 | rd | run_umap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dim_red.R
\name{run_umap}
\alias{run_umap}
\title{Runs UMAP on an scRNA-seq PCA matrix.}
\usage{
run_umap(mat, n_pcs = NULL)
}
\arguments{
\item{mat}{A cells x PCs matrix.}
\item{n_pcs}{The number of PCs to include.}
}
\value{
A cells x 2 ma... |
e981a60da9344529803346fc9965a9e29962bed6 | 06ea54d8727bf5be3c51ce50c98f7c6ea0320d90 | /man/setBibliography-easyreporting-method.Rd | 63f3c6f2e8a189cac2440304be1ac1c1303ead20 | [] | no_license | drighelli/easyreporting | 198f76976150f07a81eaa8ea2cae86ccf96879dc | 4b9b7dd26950557516ca6de98e4f691128a0c06b | refs/heads/master | 2021-06-12T15:17:19.120986 | 2021-03-15T18:34:35 | 2021-03-15T18:34:35 | 153,299,157 | 1 | 0 | null | 2021-03-08T16:57:21 | 2018-10-16T14:24:01 | R | UTF-8 | R | false | true | 571 | rd | setBibliography-easyreporting-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{setBibliography,easyreporting-method}
\alias{setBibliography,easyreporting-method}
\title{setBibliography}
\usage{
\S4method{setBibliography}{easyreporting}(object, bibfile = NULL)
}
\arguments{
\item{object}{an easyreporting ... |
61cdf0c111f3ad9f7079fb46639602b58841082c | 8de3017b43a354005bbd2af4d7ea86603eb760e4 | /man/GetLines.Rd | d2c38d66a714bb358e74f2b6a8e97798f0c98970 | [] | no_license | ccagrawal/sportsTools | de30d024a3b1e5cd3aaf0a89e3182792cf8b0d1d | 748a6e88a4cf5922496668bcb1b9bfa757e96b24 | refs/heads/master | 2020-04-12T07:30:10.042890 | 2017-05-11T01:05:09 | 2017-05-11T01:05:09 | 42,074,685 | 22 | 6 | null | null | null | null | UTF-8 | R | false | true | 595 | rd | GetLines.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GetLines.R
\name{GetLines}
\alias{GetLines}
\title{Betting lines.}
\usage{
GetLines(sport = "NBA", year, type = "Both")
}
\arguments{
\item{sport}{either "NBA", "NFL", or "WNBA"}
\item{year}{season (e.g. 2008 for 2007-08 season)}
\item{type... |
6793797cd2c097bf8e9b590fd3bb643400a0598d | 824cdb464cb7e28622532a965328ee4a2a49f986 | /data-raw/comext_data.r | 068ab61584d1d31aee67ed3bf3807e0817f915b5 | [] | no_license | trialsolution/ceta | 516be3ed6cd1024ef3443ffb87cfff956ec85cce | 69f817907e5a3c0e46401cb71231e08454c89749 | refs/heads/master | 2020-04-24T09:35:23.547612 | 2019-02-21T12:52:34 | 2019-02-21T12:52:34 | 171,867,062 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,295 | r | comext_data.r | library(tidyverse)
#------
# CANADIAN EXPORTS (FOB)
canexport <- read.csv(file = "csv/canexport.csv", header = TRUE, colClasses = c(rep("character",3),"numeric",rep("character",2),"numeric"), sep = ";")
canexport <- as.tibble(canexport)
canexport$DECLARANT <- "CAN"
# rename FLOW
canexport <- canexport %>% select(-F... |
ddc7106aa1bf828dce5500cc1a7734fdf57c787a | 4e5f8a57cc9e4bca1711d4d32a89cbaa691ee57f | /R/install.R | df3b85997af16ef21bfb604cf1be35a98957e587 | [
"MIT"
] | permissive | Hong-Sung-Hyun/multilinguer | 1db6704266b91011f20c31d54acf2ad0dfa9a38c | 6068e6377e493ad60ec95e874d5cfcee9d619207 | refs/heads/master | 2021-06-21T14:37:57.385129 | 2020-02-02T16:52:25 | 2020-02-02T16:52:25 | 254,499,426 | 1 | 0 | null | 2020-04-09T23:27:02 | 2020-04-09T23:27:01 | null | UTF-8 | R | false | false | 1,220 | r | install.R | #' Install conda
#'
#' @details
#' Download the [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
#' installer, and use it to install Miniconda.
#' All function and descriptions from [reticulate package](https://github.com/rstudio/reticulate/blob/master/R/miniconda.R)
#'
#' @examples
#' \dontrun{
#' install... |
9feac120390c8894211955b651156687cec19df8 | db377b98ae482c97a225d8532ffedff88010aabb | /tests/testthat/helper_objects.R | c2db4934013abf5ca5cb7909b7bf9012122a88b3 | [
"BSD-2-Clause"
] | permissive | JiaHaobo/mlr | d0a568480d6495c506c2dc72bd89618281fed3ce | 17d7eac68433b5e37bc4c118d1a9056c5e4cc497 | refs/heads/master | 2021-01-19T11:20:27.365236 | 2017-04-11T15:27:00 | 2017-04-11T15:27:00 | 87,954,613 | 1 | 0 | null | 2017-04-11T16:10:10 | 2017-04-11T16:10:10 | null | UTF-8 | R | false | false | 5,947 | r | helper_objects.R | data(Sonar, package = "mlbench", envir = environment())
data(BreastCancer, package = "mlbench", envir = environment())
binaryclass.df = Sonar
binaryclass.formula = Class~.
binaryclass.target = "Class"
binaryclass.train.inds = c(1:50, 100:150)
binaryclass.test.inds = setdiff(seq_len(nrow(binaryclass.df)), binaryclass.... |
f5959fd1d91cb138f38ea5fe0cdabe0ec6094fc8 | 987177740c7f263151f2dd928a726300a8653857 | /plot2.R | 4ed35aea81ea3759ea14c2b2c5589439ed9e0d97 | [] | no_license | rajashrip/GettingCleaningData | 5cf1ee544250944b0aa2a56f413c9325d4f768dc | 89a13a20d4d699f5841b6ec7fed28c12d2dedffe | refs/heads/master | 2021-01-01T17:21:26.165806 | 2015-03-05T05:19:21 | 2015-03-05T05:19:21 | 31,028,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,100 | r | plot2.R | require(sqldf)
# set your working directory to the folder where you have the raw data file stored
# If you don't have the file,get it from here https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
#read the file
file <- c("household_power_consumption.txt")
a2 <- re... |
50670b04614a38f2d7ae7d4cbc88d4b369ff58ed | da006494be33a8f6b9cd623bf4fb506e17d8c439 | /R/vcf2eqtl.R | 9ba5f40b8dda0d9a895bd7ce9e3a6e0d91c1e390 | [] | no_license | noahrose/vcf2eqtl | 7b1b18223ab5cd14ac1c2e88b0f311e406b3ec49 | e424d82310a5b09e364cb05747a3cd882eefb16d | refs/heads/master | 2021-01-11T02:09:17.057783 | 2017-08-20T23:36:05 | 2017-08-20T23:36:05 | 70,826,485 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,666 | r | vcf2eqtl.R | vcf2eqtl <-
function(vcf,
expr,
pops=NULL,
minHet=3,
minHetDP=10,
mc.cores=1,
alpha=0.05,
calculateFst=T,
outliers=T,
testDE=F,
all3=F,
hweFilter=T,
hweAlpha=0.05,
covariates=NULL,
propExplained=T,
withinPop=T,
format='bcftools',
transcripts=NULL){
if(is.null(pops)){
calculateFst=F
testDE=F
propExplained=F
w... |
f095d70f3dafa694a94a1105a47e85db7f97a602 | 919359f24635c22a844a8ef3ddeb1181a72759dd | /lib/analysis/functions.R | 74908fab49e0621e4d57ebedeeccf56e45cb4c8a | [] | no_license | weswigham/chromium-history | 1aa9c23ca9fcc2f3f74e1c13bdf8dabff7f76761 | 091e7e4f9dafdfac961b3ce6b594f2200453cacb | refs/heads/master | 2021-01-11T18:11:48.052839 | 2016-04-05T19:36:16 | 2016-04-05T19:36:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,675 | r | functions.R | # Define the functions
Dsquared <-function(obs = NULL, pred = NULL, model = NULL, adjust = FALSE) {
# version 1.3 (3 Jan 2015)
model.provided <- ifelse(is.null(model), FALSE, TRUE)
if (model.provided) {
if (!("glm" %in% class(model))) stop ("'model' must be of class 'glm'.")
if (!is.null(pred)) mess... |
624e32bc1ed7557545a6ead8bb0f4c5a63803b89 | 74923b9335356d7ddea1264932bad0d4851181a9 | /R/tagtools/man/block_rms.Rd | a831cdb429f7dc1b29f00029d31b43452cdc8427 | [] | no_license | FlukeAndFeather/TagTools | 29e491898266bf82edf436674beb7d82f78724c2 | a6f3fd4eab0ddaef79cc9f1718f2801b30c67971 | refs/heads/master | 2020-08-04T03:07:00.111174 | 2019-07-17T18:37:20 | 2019-07-17T18:37:20 | 211,981,790 | 1 | 0 | null | 2019-10-01T00:15:49 | 2019-10-01T00:15:49 | null | UTF-8 | R | false | true | 1,537 | rd | block_rms.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/block_rms.R
\name{block_rms}
\alias{block_rms}
\title{Compute RMS of sample blocks}
\usage{
block_rms(X, n, nov = NULL)
}
\arguments{
\item{X}{A vector or a matrix containing samples of a signal in each column.}
\item{n}{The number of sample... |
0879b96cb33c6ef6df3605311833e77e0692e060 | f439a076bc3fcac2c8d7eb72e69dc8d24a00b263 | /Unit 5 Text Analytics/Assignment5_Spam1.R | 6923bc23e45126e5dec60e5409c60083d43cd07b | [] | no_license | jakehawk34/MIT-Analytics | 73f9afb0cbfbbd8202e415f0c50c8e638aa76db1 | daa2ca2eca44ba6c74ba5773d992f68e8c775b90 | refs/heads/main | 2023-05-07T13:54:40.796512 | 2021-05-21T00:31:11 | 2021-05-21T00:31:11 | 344,290,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,941 | r | Assignment5_Spam1.R | # Assignment 5
# Separating Spam from Ham (Part 1)
emails = read.csv("emails.csv", stringsAsFactors = FALSE)
str(emails)
summary(emails)
table(emails$spam)
emails$text[1]
emails$text[2]
# How many characters are in the longest email in the dataset?
max(nchar(emails$text))
# Which row contains the shortest email in... |
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