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f5aaf3b3a5e061180f96f91f23fc2a6bd42ca6b9 | 08b7728c2120413ad2266a5e7ae3403e6b67471b | /cryptoJNS/R/hashtools.R | 572bdb1e4c335a3757da30798257d95d91f215de | [] | no_license | JamesSolum/Codes-and-Encryption | 8241438fa8fa6c1e36ecfc293d6c1bf02cc3f36b | 59df5d2ca7505795bd876db2b532351a28a9a122 | refs/heads/master | 2020-05-29T21:04:36.541728 | 2017-02-21T00:09:00 | 2017-02-21T00:09:00 | 82,615,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,051 | r | hashtools.R | #' Miniature SHA hash
#'
#' Implements a 32-bit hash function by taking the first 32 bits of the SHA-1 hash.
#'
#' @param x A character string.
#'
#' @return A four-character hash string.
#' @export
#' @import openssl
#'
#' @examples
#' miniSHA("Corned Beef and Haggis") # should be "de0d"
miniSHA <- function(x) {
x <... |
e734ef8ccd20a4ecb250e4ad1ced3ca4534c09c4 | d24f41fc305f63fb86132cd18cab8907be132d7e | /cachematrix.R | c78c77610fc52fea9a832a5c73d9293c98b1d757 | [] | no_license | a-d-m/ProgrammingAssignment2 | 9782a9798b1f0801b411227c3b1490c754684f52 | 07cad30a526dadd37cb66a45349ae51000993ac5 | refs/heads/master | 2021-01-18T10:45:04.489270 | 2016-02-07T10:09:50 | 2016-02-07T10:09:50 | 51,242,937 | 0 | 0 | null | 2016-02-07T09:57:22 | 2016-02-07T09:57:21 | null | UTF-8 | R | false | false | 1,313 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Entering a comment to notate the severe disconnect between
## the material studied, depth of swirl exercises, and the actual
## assignments expected to be completed. This should also break up
## the monotony for the viewers of the We... |
058f303e1282987a556805128446e491e40e29dd | d2129c74ed601e23dc34ab451f2f9337af3dc588 | /man/grapes-greater-than-grapes.Rd | 7d09daf50d85fb5cbc06fc4a03204a243cef456c | [
"MIT"
] | permissive | kferris10/rcdimple | 9aa69f530c0958043c34f57b281478f0b1d0604e | 9dfc80320c745d45b522528432e78e013eb055de | refs/heads/master | 2021-01-23T20:55:51.743348 | 2015-10-02T16:35:51 | 2015-10-02T16:35:51 | 32,642,581 | 1 | 0 | null | 2015-03-21T17:37:58 | 2015-03-21T17:37:57 | null | UTF-8 | R | false | false | 274 | rd | grapes-greater-than-grapes.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/utils.R
\name{\%>\%}
\alias{\%>\%}
\title{pipe with magrittr importFrom; uses dplyr technique}
\usage{
lhs \%>\% rhs
}
\description{
pipe with magrittr importFrom; uses dplyr technique
}
|
5e1e5100c6b0e62cc223935205e0a2afba06d2fc | 38d59286ad65e9fbb89182bb058e648be936828f | /FinalResearchProject/archive/old.ema.test.R | e401b6722f967fd2220c1fd8cad8b6cd1023d388 | [] | no_license | navdeepsingh8/BirkbeckStats | c40ed7e250545bde3cbd09a264aaf66d790da4b3 | c1d2f2dcfc6cae6961ee359ef88930f52644ca80 | refs/heads/master | 2021-01-20T19:34:54.202876 | 2016-07-01T18:17:47 | 2016-07-01T18:17:47 | 61,050,071 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,208 | r | old.ema.test.R | #Tests in this script:
# Compute PL for multiple parameterisations of the 2-EMA crossover rule
# Calculate Sharpe ratio and drawdown statistics for each one
#load packages
source("preamble.R")
source("functions.R")
#load data
load("FedData.Rdata")
#two ema crossover rule
#parameters
params <- vector("li... |
bab7e531406145dea572bdbd93bd0036ea63eb94 | 4590ff8f7115ac49c657a863df78d042611359d8 | /savio/pi.r | 72903e9a0818afea6f312ad8f3e636823bdcaa73 | [] | no_license | BerkeleyBiostats/tl-app-experiments | c36b28e3c414bdb1357502c9bf84e1c4175b1a37 | 59b53021f45aefbee1a9fb09d16fe0f988170cc8 | refs/heads/master | 2021-01-01T16:09:08.550126 | 2017-07-30T08:00:15 | 2017-07-30T08:00:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 692 | r | pi.r | library(batchtools)
reg = makeRegistry(file.dir = '/global/home/users/marcpare/pi', seed = 1)
reg$cluster.functions = makeClusterFunctionsSlurm(template="/global/home/users/marcpare/batchtools.slurm.tmpl",
clusters = NULL, array.jobs = TRUE, scheduler.latency = 1,
fs.latency = 65)
saveRegistry(reg=reg)
piApprox ... |
f9790325ea06a4dd6f545f0b5c4f2f704cfe9e07 | 8fd3836f4292a8a3d917e9737f037afabb5db502 | /man/six_hourly_precip.Rd | 5766deca2c8ce6f65269ca8ff0e581814ca63ad3 | [] | no_license | everydayduffy/climvars | beb8202b60db00012273a6fac50c8548274fcc1e | 886a5d2642cc132642563ab3fffcc6b9994706cf | refs/heads/master | 2023-06-12T15:42:48.936770 | 2021-07-09T08:59:09 | 2021-07-09T08:59:09 | 256,446,786 | 0 | 0 | null | 2020-04-17T08:32:12 | 2020-04-17T08:32:11 | null | UTF-8 | R | false | true | 511 | rd | six_hourly_precip.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{six_hourly_precip}
\alias{six_hourly_precip}
\title{Six-hourly precipitation for 2019.}
\format{A numeric vector of precipitation (mm/six-hour).}
\source{
\url{https://www.ecmwf.int/en/era5-land}
}
\usage{
six_hour... |
d80bfdd683ede461eb3cecb487e2d0bc7d442de0 | 3ec118f35ddf7983639c3b3f3345689b85241729 | /plot6.R | b7756d14f85c92472966a5e1d48b8497214891fc | [] | no_license | ryanniemann/Exploratory-Data-Analysis-Project-2 | 4a368c5d0318490e226efeca31d9c93dc0e2eb41 | 95877d557a8c11163bf922275cb829081641093a | refs/heads/master | 2020-04-15T14:31:51.790910 | 2015-12-18T04:38:22 | 2015-12-18T04:38:22 | 48,208,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,384 | r | plot6.R | #6.Compare emissions from motor vehicle sources in Baltimore City
#with emissions from motor vehicle sources in Los Angeles County,
#California (fips == "06037"). Which city has seen greater
#changes over time in motor vehicle emissions?
#Read the data
NEI <- readRDS("data/summarySCC_PM25.rds")
SCC <- readRDS("data... |
9564fe181976ac72ad953f102104f334f896dae2 | a63a05179f9d4494356cd43b58f632d80b21dabd | /R/internals.R | 9df45287fc1a636bd57c8ed83162b667c170cb14 | [] | no_license | cran/rasciidoc | 6d524dc8b1fdb44759a2c560dfe8d9b18cf174df | 4fa4e1ed55487ca8735a2346017c2438359eecfc | refs/heads/master | 2023-06-28T06:53:38.425600 | 2023-06-14T07:20:02 | 2023-06-14T07:20:02 | 173,755,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,131 | r | internals.R | .onAttach <- function(...) { # Exclude Linting
if (!isTRUE(getOption("write_to_disk")))
packageStartupMessage("\n", hint_writing())
}
write_default_output <- function(msg, adoc_file) {
lines <- readLines(system.file("files", "default.html",
package = "rasciido... |
8d99b94c54b9a99520aaed98d25d8e1190006567 | a72b056bec6bd2fc62e9bdd77c4ce45652fb1b28 | /run_analysis.R | a3428fbb5d333fa1bcc97710a93f1686453b2ba6 | [] | no_license | gbprime/GetAndCleanDataP2 | dad548ce4ecd6723c5848c87a54951b85677356d | 756985f195bda68f3ff980b1f5c990ccbaa8d703 | refs/heads/master | 2021-01-01T05:32:37.991174 | 2015-07-26T15:58:05 | 2015-07-26T15:58:05 | 39,521,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,293 | r | run_analysis.R | # *********************************************************************************************************
# The purpose of this project is to collect, work with, and clean a data set. The goal is to prepare tidy
# data that can be used for later analysis.
#
# This R script gets, cleans and performs certain operatio... |
9a364174aec6656384373b827099195560fa2f2d | 6be70ffdb95ed626d05b5ef598b842c5864bac4d | /old/tests/make_senate_party_calls_keep_very_lopsided_new_match.R | a79b9d912351f840ccfa590ed4a9040e5038ddfe | [] | no_license | Hershberger/partycalls | c4f7a539cacd3120bf6b0bfade327f269898105a | 8d9dc31dd3136eae384a8503ba71832c78139870 | refs/heads/master | 2021-09-22T17:54:29.106667 | 2018-09-12T21:16:56 | 2018-09-12T21:16:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 404 | r | make_senate_party_calls_keep_very_lopsided_new_match.R | library(partycalls)
set.seed(347611200, kind = "L'Ecuyer")
senate_party_calls <- lapply(93:112, code_party_calls_by_congress_number,
chamber = "senate", pval_threshold = 0.05, sim_annealing = FALSE,
use_new_match_check = TRUE, drop_very_lopsided_votes = FALSE,
hybrid = FALSE, reassign_flip_flop = FALSE)
save(se... |
bab5196665ee3cc05cab6d9f4425971d51373b4e | 6052995864c5362b030ec1f418518e57b565ba29 | /cachematrix.R | d568fa369d187bbf76c9eec6c8a021325731ebaf | [] | no_license | robuchowski/ProgrammingAssignment2 | cd1309e1ddfdc61280bcc02505e2371e39dfd68a | 810b0cc4bb0ae4dedb6f830512c931cc0a63ac61 | refs/heads/master | 2020-08-30T07:11:42.505166 | 2019-10-29T15:21:43 | 2019-10-29T15:21:43 | 218,301,856 | 0 | 0 | null | 2019-10-29T14:06:09 | 2019-10-29T14:06:08 | null | UTF-8 | R | false | false | 1,715 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
##The purpose of the functions in this assignment is to write a pair of functions named
##"makeCacheMatrix" and cacheSolve" that cache the inverse of a matrix. Matrix inversion is usually
## a costly computation and therefore can be b... |
78be9fc44a99401b3db7c4b5d399f4f1e60341d9 | 5c4bff6c12024a093130e925d8240aa1b426b312 | /plyr_to_purrr1.R | 5dd9a667caa0e7fc707604ac4cfbd625a8b00d60 | [] | no_license | mrtnj/rstuff | 81ef05fb813c19abfede0ea18ef447c9bf9d1081 | 2f8e1939b7009fb4f01dfa17fc8d09aa7550f875 | refs/heads/master | 2021-11-23T05:09:17.874029 | 2021-11-13T16:31:56 | 2021-11-13T16:31:56 | 7,859,226 | 6 | 6 | null | null | null | null | UTF-8 | R | false | false | 2,781 | r | plyr_to_purrr1.R |
## Demonstrate different ways to do plyr-like operations with modern tidyverse
## Needs to be loaded first to not conflict with dplyr
library(plyr)
library(broom)
library(dplyr)
library(magrittr)
library(purrr)
library(tidyr)
set.seed(20210807)
n_groups <- 10
group_sizes <- rpois(n_groups, 10)
n <- sum(group_siz... |
51c24e34a201429c9ecb532ce6456e6c12cacef1 | f5cbb7ed7532f5fee7e51d9187e6ab5e5d30b624 | /plot2.R | 74bd17924d2ecbe5643e87915f515ae4cbb5121d | [] | no_license | cdesouza2/ExData_Plotting1 | 886e774aa6e9a73ed2dcad62c20ace7d6bc7a737 | 57ccf9cdf145019f80989370febd157c5a7ffadc | refs/heads/master | 2020-04-18T06:54:19.910044 | 2019-01-24T11:54:26 | 2019-01-24T11:54:26 | 167,341,517 | 0 | 0 | null | 2019-01-24T09:35:05 | 2019-01-24T09:35:04 | null | UTF-8 | R | false | false | 517 | r | plot2.R | # Contains the code to produce plot2.png
# Assumes the file loadData.R has previously been run in order to load the data
# from the file "household_power_consumption.txt" into the dat variable,
# and where the type converted columns are prefixed with "N",
# and only the rows of interest have been included.
# sou... |
8a3a108d77069939382384ea137c2bde05b72231 | 148ebcd8b769e2e6b1d779dbce0512381a0bfde8 | /bin/sequence_url_update_nextseq.R | d19d9c4cb27040861a43b5af2ca5574e5a163a4e | [] | no_license | jdhayes/hts_pipeline | f99bd3c3f09d812ec499f37aa19dd5546af9397a | 72d57fe2bca9ed6a9fea95f4282935bc97aa96aa | refs/heads/master | 2023-04-10T14:54:37.956716 | 2019-12-20T21:39:57 | 2019-12-20T21:39:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 53 | r | sequence_url_update_nextseq.R | ../post_demultiplex/bin/sequence_url_update_nextseq.R |
b95e8f40465518d1f095d1b0d98d23502fb06e29 | b7d91b7c0b71597dff66d10d9852fb5ec42979fe | /a.R | 82a731fdef1a2171ebb76072ee3eb10ea2db7c9f | [] | no_license | maidh126/Facial-Expression-Recognition | 824ccb3ceb3f4859cb3476e5e944e1cb7fe256b3 | cc0d45dd90dd926caf7c0eb0f5af990969d65794 | refs/heads/main | 2023-05-19T23:06:12.432889 | 2021-06-08T17:33:43 | 2021-06-08T17:33:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,803 | r | a.R | library(caTools)
library(tidyverse)
library(kernlab)
library(e1071)
library(RColorBrewer)
library(ISLR)
library(caret)
library(ROCR)
library(pROC)
# Set the working directory
setwd("/Users/maido/OneDrive - Oxford Brookes University/Advanced Machine Learning DALT7012/Assessments/grammatical_facial_expression")
# Load... |
ba1e8ebd2fd1a7c74a3b8c36c34c19433136ab7f | 1f579a6cd3a03ff52b2d54f96baaad5714aa3f1a | /server.R | 3e03309d0ce1a2811d7648d5cdd1def509aecc29 | [] | no_license | chuagh74/COVID19SG | 31edb9691b7f571e81f1ef0e45df020325b0e349 | c127dafbd98c3d209a7777381d1755bf1086fc3c | refs/heads/master | 2021-04-23T18:07:02.702151 | 2020-03-25T11:45:40 | 2020-03-25T11:45:40 | 249,960,898 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70,587 | r | server.R | shinyServer(function(input, output, session) {
source("global.R")
######################################################################
# Main View
######################################################################
output$colorMsg=renderUI({
msg=sprintf("Color codes used for icons. <br />Color is... |
85e6bbf3fb6675c8fa064760ac2c4d8a2fcfa128 | db12b990924703cd74748d8585cd9c11fafa6746 | /h2o-r/tests/testdir_algos/isofor/runit_isofor_creditfraud_large.R | d5c850819e60fe79eb773ead6a1961c130c85fdb | [
"Apache-2.0"
] | permissive | h2oai/h2o-3 | 919019a8f297eec676011a9cfd2cc2d97891ce14 | d817ab90c8c47f6787604a0b9639b66234158228 | refs/heads/master | 2023-08-17T18:50:17.732191 | 2023-08-17T16:44:42 | 2023-08-17T16:44:42 | 17,371,412 | 6,872 | 2,345 | Apache-2.0 | 2023-09-14T18:05:40 | 2014-03-03T16:08:07 | Jupyter Notebook | UTF-8 | R | false | false | 2,785 | r | runit_isofor_creditfraud_large.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
# Note: this test uses a Credit Card Fraud dataset licensed under ODbL v1.0
# full ODvL: https://opendatacommons.org/licenses/odbl/1.0/
# additional dataset details: https://www.kaggle.com/mlg-ulb/credi... |
d7c5ba80ad187b3e646558a5f10db6202106a732 | d64c2f2ed23d9929951eb3cecc0fc0f456985005 | /data/os3_data/TabTextToCsv.R | 51ebd39efa7ecf80fd493349c5a0d748eaf735ca | [] | no_license | jbryer/DATA606Fall2018 | 77609a8b9da6403638f2cb00dd850adfd18e7787 | 33abdb03bf34db8730a94d9ecb59813cc0cf1e9b | refs/heads/master | 2020-03-25T02:41:26.441585 | 2019-01-15T22:21:49 | 2019-01-15T22:21:49 | 143,302,830 | 8 | 5 | null | null | null | null | UTF-8 | R | false | false | 483 | r | TabTextToCsv.R | # Copyright 2014, OpenIntro
# This code is released under a Creative Commons Attribution 3.0 license
# The TabTextToCsv() function can be used to convert
# tab-delimited text file to a CSV file
TabTextToCsv <-
function (file.in, file.out) {
x <- read.delim(file.in)
write.table(x, file.out,
quote = FA... |
8755af5e92733f407aaab3041499a911b4d25793 | 4ce0a8e66ad3694a60840ab3cd3c34ea82de94ea | /rdev/R/knobs.R | 59c89977cdce05f35e662323e37a1668be9415ee | [] | no_license | curtisKJ/mrgsolve | 5549fe81796e5dcd7172462624d016abe58bedc5 | 05818cc861e467db08a04b65af59d783bdd16a53 | refs/heads/master | 2021-01-17T11:33:21.588950 | 2016-05-17T06:12:23 | 2016-05-17T06:12:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,365 | r | knobs.R | ## This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
## To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ or send a letter to
## Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
##' @include mrgsims.R
... |
2fe9c1f68c45ee33df7b4eb9ce8cbd149ff93eaa | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.sagemaker/man/list_tags.Rd | 89cec9062453d2191f35d9a0646056c292f61919 | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 863 | rd | list_tags.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.sagemaker_operations.R
\name{list_tags}
\alias{list_tags}
\title{Returns the tags for the specified Amazon SageMaker resource}
\usage{
list_tags(ResourceArn, NextToken = NULL, MaxResults = NULL)
}
\arguments{
\item{ResourceArn}{[required... |
b77f46e3af3f54027dd11df44e068de65dec6003 | 63be42f1b75b95d6a7e42410f32b260e319b5e10 | /code/Differential_Expression/123_combined/DE_Village_123_combined_noTreat_batchCorrectionUsingLMandBloodAsCovar_withBlocking_all_villages.R | 1ae7d4f7e6d6f0fabbf24764d6e22eec9fe98b32 | [] | no_license | ireneg/SEA_Regulatory_Variation | 2d2107f21d18715a208cf25123521477e2a1273f | 499aa1af77a90451688623149147497790b5609a | refs/heads/master | 2020-05-04T13:50:03.358863 | 2019-12-23T06:16:55 | 2019-12-23T06:16:55 | 179,175,671 | 0 | 0 | null | 2019-04-02T23:44:36 | 2019-04-02T23:44:35 | null | UTF-8 | R | false | false | 17,229 | r | DE_Village_123_combined_noTreat_batchCorrectionUsingLMandBloodAsCovar_withBlocking_all_villages.R | # script created by KSB, 08.08.18
# Perform DE analysing relationship between islands
### Last edit: IGR 2019.10.19
### Changed paths to deal with removal of MPI-296
### 0. Load dependencies and functions and set input paths --------------------------
### 1. Begin analyses and initial QC ----------------------------... |
15230aabe7bc928a98185fc05824d586d042e29b | aba010bbf8165acc06349b629ddf4593628325de | /Summary_NetworkRevolution.R | 64c3d459934c6517509e58ade67b35182cdb2468 | [] | no_license | karafede/Energy | f2557d0e7e6b5ca1141177c836b2f2f2dc610355 | 9b3f8a0b344dd01fb9944ac85e326d28e1fa615e | refs/heads/master | 2021-01-10T09:40:27.074294 | 2016-03-22T11:32:35 | 2016-03-22T11:32:35 | 54,454,862 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 58,771 | r | Summary_NetworkRevolution.R |
library(threadr)
library(readr)
library(openair)
library(ggplot2)
library(plotly)
library(dplyr)
#### Load Residential Customer data ################################################
Basic_Profile_ID <- read_csv("C:/NetworkRevolution/TC1a/Electricity_data_TC1a.csv")
Basic_Profile_hour <- read_csv("C:/Netwo... |
40b606245b9b85a88fd51afd39f7cb41c02d32dc | c79d865438aaa879e97f1082a3d36e4f61172e44 | /complete.R | 022f7eb74ead4276e134df61c74d7fe71b7abdd0 | [] | no_license | srinivasmurthyps/datasciencecoursera | 8f77ffc5b25ff441f41bfba50ace9bb6b049e231 | 6497e4add2a61a4aecaf1b292a7e0cefc48069b9 | refs/heads/master | 2022-12-15T18:14:01.181098 | 2020-09-19T23:06:56 | 2020-09-19T23:06:56 | 293,680,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 503 | r | complete.R | complete <- function (directory, id) {
output_df <- data.frame("id" = numeric(0), "nobs"=numeric(0))
count <- 1
for (i in id) {
i_modified <- formatC(i, width=3, flag="0")
i_modified <- paste(i_modified, ".csv", sep = "")
file_name <- paste(directory, i_modified, sep = "")
df <- read.csv(fi... |
1f783a67e359c0e4f85ee7b9595edd1d03481880 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610131652-test.R | cbf72e7f317a9b025be8581d254243ae22c9fb82 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 888 | r | 1610131652-test.R | testlist <- list(a = -65025L, b = -15007745L, x = c(-1L, -65281L, -48641L, -65281L, -16777216L, 255L, 100925439L, 116916223L, 926365495L, 939523881L, -250L, -1L, -230L, -1L, -250L, 50331647L, -14804225L, -16711680L, 63996L, -114819298L, -2145510657L, -62721L, -1L, -58880L, -604029440L, 16777211L, -16835046L, -16711... |
fb52131c02beaeb1bc8489618040f7bfa941c2a2 | 371064d7042c999784982112a77a2e6aa3b7da40 | /pollutantmean.R | 63b7e7f10c463e65470822b215df44ace3bc9c20 | [] | no_license | atenasadeghi/datasciencecoursera | 81906ae6752f30fe53754bdf0ac3a6ec35033021 | f89126799e4fa7a26bd6672a4c958ff58d2c3c16 | refs/heads/master | 2020-05-30T04:05:36.865684 | 2017-11-28T01:24:30 | 2017-11-28T01:24:30 | 40,325,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 581 | r | pollutantmean.R | pollutantmean<-function(directory, pollutant, Id=1:332){
summation<-0
numberofrows<-0
for (n in Id){
if (n <10) {
filepath<-paste(directory, "/" ,"00" ,n, ".csv", sep="")
}
else{
if (n<100) {filepath<-paste(directory,"/","0",n, ".csv", sep="")}
else {
... |
acba85f2a907e42c488584e7da18552eb1c71395 | 912ca5b887910e9f8f449a5aaa54688dc4ec9e54 | /Bond prices/R script Bonds prices - tree-based methods.R | ffac48ccae6ea6dd26aa0734e512f00b55f8a428 | [] | no_license | bwalwyn/CV | a7bb60aa0f09517cbdd72640a45546e993ccb077 | e12dc24c3bcaf3f3e9b7f20710a87b3a6f0022b1 | refs/heads/master | 2021-05-31T21:23:02.813951 | 2016-06-09T20:58:17 | 2016-06-09T20:58:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,784 | r | R script Bonds prices - tree-based methods.R | #Ben Walwyn
#Analytics project R
#04/09/14
#----------------------Part 1------------------------#
setwd("F:/Analytics")
install.packages("tree")
install.packages("MASS")
install.packages("randomForest")
install.packages("doParallel")
install.packages("foreach")
install.packages("ipred")
install.packages("gbm")
install... |
2ac8556d55bc5f5ac128c350b4a57624e117d3bc | 7fb057028dfc91b81f98bbef71f4fb62223d5520 | /cars_plot.R | 5882c04dbbad6a2effc872a38bdc0952fcfaae6d | [] | no_license | Defra-Data-Science-Centre-of-Excellence/rmarkdown-plots | adea58fd702bafda92b8f593d529884e5cbfa065 | 4ffcc17974d7bab6c5e4ec168d2ef7c133b6a3ed | refs/heads/main | 2023-02-23T07:35:02.803618 | 2021-01-29T15:20:10 | 2021-01-29T15:20:10 | 334,132,569 | 0 | 0 | null | 2021-01-29T15:20:11 | 2021-01-29T12:02:28 | R | UTF-8 | R | false | false | 140 | r | cars_plot.R |
#' Basic function to plot mtcars data
cars_plot <- function(){
plot(mtcars$mpg, mtcars$cyl, main = "Car fuel efficiency (MPG Vs CYL)")
} |
7e67347ed8ae8ca828f73a849a6c38ded0bc0fc5 | 0186f97cba792fafa20dffe0a3dc431838495c30 | /R/session.R | c267c7712d9d6325c762778ffa51517c1f9def0a | [
"Apache-2.0"
] | permissive | nagyistge/opencpu | 8f78d6015ad7217aa1b2a6c6450be93de997d08c | 0d664a5e2e934e184022f7c6a4cd4a373a253250 | refs/heads/master | 2021-01-20T03:17:01.826815 | 2017-04-15T14:23:49 | 2017-04-15T14:23:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,503 | r | session.R | #create the regex to identify session keys
session_regex <- function(){
paste0("^x[0-9a-f]{", config("key.length") + 1, "}$")
}
#' @importFrom openssl rand_bytes
session <- local({
#generates a random session hash
generate <- function(){
while(file.exists(sessiondir(
hash <- paste0("x0", substring(pa... |
d002205ecb210a33b0fbb8d523acf1d6905e0f89 | 1343b3fa694cecdb438a341c729230f35d6095ca | /test script.R | 5b1ef3ec3cb7a9d6a6823c679999e6ac0e583595 | [] | no_license | aldosterone/IntroToGit | cf365fbb6ccd56275c69d8a7efaa33dbc0365f09 | 2c496696799f6f81ee3fdf96b74009040a9ac33c | refs/heads/master | 2022-11-21T07:22:05.373983 | 2020-07-27T17:22:49 | 2020-07-27T17:22:49 | 282,958,216 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 27 | r | test script.R | # simple script
x = 9
x - 7 |
aa1edd2d697b8d6a832bd0cfc75aa5b290ef3dcc | 35d97acdeb97180f1bb4c3bdbd232a7bcbe17692 | /examples/stLL.R | 804ed2e8129f4c6e7516140097ce8d61dcbcb26d | [] | no_license | jmhewitt/telefit | 1b9d7e80a423689a83b584c5611035f874895485 | 6815ca6f472cf4d7d933b55823ad5349ad471073 | refs/heads/master | 2021-03-22T03:29:12.284545 | 2020-02-03T19:15:28 | 2020-02-03T19:15:28 | 62,270,043 | 1 | 2 | null | 2019-02-19T22:32:22 | 2016-06-30T01:41:06 | R | UTF-8 | R | false | false | 450 | r | stLL.R | library(dplyr)
library(foreach)
library(itertools)
set.seed(2018)
data("coprecip")
data("coprecip.fit")
attach(coprecip)
ests = coef(coprecip.fit, burn = 50)
ll = stLL(stData = coprecip, stFit = coprecip.fit,
beta = matrix(ests$beta, ncol = 2),
sigmasq_y = ests$sigmasq_y, sigmasq_r = ests$sigm... |
a564bb36ab2c6515d482ae009b7adac72414b8d5 | d3c76f9d023e644faaa78e96930265adb39891c3 | /AodToPm_Offline.R | 1deb403d6834d76b7204c3f88882a749bba88b96 | [] | no_license | JieLuoybfq/Data-Merging-and-Interpolation-Methods-MODIS-Terra-and-Aqua-Daily-Aerosol-Case | 32d797882860c1e2dc0fd76c582336c94adc776d | c6e975abfef92e765a1a29c4466ee46415ecb0b1 | refs/heads/master | 2021-06-23T07:26:51.533089 | 2017-08-30T14:53:17 | 2017-08-30T14:53:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,427 | r | AodToPm_Offline.R | # TODO: create PM image from aod and temp (PM regression)
#
# Author: phamha
###############################################################################
#import library
library(gstat)
library(base)
library(RPostgreSQL)
library(stringr)
library(raster)
library(gdalUtils)
library(rgdal)
library(rPython)
host_name... |
9a009d1a2b4ec464ea4b86e1bec9f27a4824e520 | 89c966c31c6fee422bcf63cbd38675a41b910ada | /model/select_best.R | 795cdab4ee79f0478bcce5f394b96f283eba7636 | [] | no_license | Yixf-Education/course_Statistics_Story | ad17b0373d35f506131af209b94e41c4956c4d42 | 5a16791517169d657d00e5470f2cf1eb0e722e6b | refs/heads/master | 2023-04-03T02:45:53.099737 | 2023-04-01T08:13:43 | 2023-04-01T08:13:43 | 81,038,740 | 34 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,847 | r | select_best.R | #!/usr/bin/Rscript
library(tidyverse)
dfa <- data.frame(cutoff=numeric(), result=numeric())
for (cutoff in seq(from=10, to=90, by=10)){
for (i in 1:10000){
x <- sample(1:100, size=100)
x_pass <- x[1:cutoff]
max_pass <- max(x_pass)
cutoff1 <- cutoff + 1
x_keep <- x[cutoff1:100]
x_select <- he... |
730fb4f1e745104ba40d3be89aeb476a8b9a32ac | 76dd1344fcb157f5f557232b473890eaae3a06f2 | /ceratodon_area_v_biomass.R | 15663ac1d50e8727d5d75a42839e6013c1e4a867 | [] | no_license | sarahcarey/area_v_biomass_Ceratodon | 956a1277db8128742f23953ae0c72e224728cca1 | 0a0f6a535e27039117ea0db90a5b3b33c6ec6a0d | refs/heads/master | 2021-04-08T15:07:29.785421 | 2020-03-20T17:58:15 | 2020-03-20T17:58:15 | 248,785,791 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,187 | r | ceratodon_area_v_biomass.R |
## R script for Area from image analyses accurately estimates dry-weight biomass of juvenile moss tissue
## Burtscher and List et al.
## Script by W. Burtscher and S. Carey
## R version 3.5.3
#### import data with averages for clonal replicates excluding dead replicates for stats and scatterplot ####
# these data c... |
fdbe2ed456d6286744911814caae707512045230 | 0df8bf87849196facb24125cbc3d455010c34265 | /01_data_import.R | 10b18455778829ccadbdc955db0b6935fe5b2360 | [] | no_license | datahoundz/Springboard_Data_Science | 427768ce34917dea4b72841c600bad88126a45ee | da230ea3d160bc40c57a1fbde6602be6bd4e42fe | refs/heads/master | 2021-04-28T01:25:40.472836 | 2018-04-26T17:50:59 | 2018-04-26T17:50:59 | 122,276,605 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,630 | r | 01_data_import.R | library(xml2)
library(readxl)
library(readr)
library(tidyr)
library(dplyr)
library(purrr)
library(ggplot2)
library(lubridate)
# Set options to limit sci notation and decimal places
options(scipen = 999, digits = 3)
# =======================================================================
#
# Data Import - CDC Suicid... |
645acbc99eea7811908bd13ed71096f33035187f | 6c3f506898473dfbbabe0d8544c46b1a5b6cde19 | /Plot1R.R | 74a918057b542ed64a4a4322611936b268f27ab5 | [] | no_license | nickhitt/DataScienceCourseProject4 | d0b543475e201faf1d7a6d06a1831adae1f840e2 | c0b695e248caad4069a332283887b2fe73190258 | refs/heads/main | 2023-01-09T22:47:16.676634 | 2020-11-09T04:10:14 | 2020-11-09T04:10:14 | 311,218,225 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,106 | r | Plot1R.R | ## Exploratory Data Analysis Course Project Code Problem 1
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
nei_pm_1999 <- subset(NEI$Emissions, NEI$year == 1999)
nei_pm_2002 <- subset(NEI$Emissions, NEI$year == 2002)... |
70d5f9b9d0c7fb3632bbf30dbb84a2bf3b636c1c | a41da845203a8756c0c014ac89ca96c7f0565bd1 | /databases/reproject_r_file.R | 13d19f66fcd7d72adbeeff827599747f49809ba9 | [] | no_license | bailsofhay/downscale_code | 121e40242770f2f1306241a60d6ebec7e0c4c82d | 42655c510fa66916c2b87adf9817dc93a22572c2 | refs/heads/master | 2020-04-10T07:06:49.725784 | 2018-12-17T15:14:36 | 2018-12-17T15:14:36 | 160,870,602 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 606 | r | reproject_r_file.R | library(raster)
library(gtools)
slurm_id = as.numeric(Sys.getenv('SLURM_ARRAY_TASK_ID'))
ref = raster('/data/gpfs/assoc/gears/tree_vel/dem/raw/ca_nv_dem_utm.tif')
files = list.files(path = "/data/gpfs/assoc/gears/tree_vel/climate/renamed/v", pattern = ".tif", include.dirs = T, full.names = T)
files = mixedso... |
8a4231c10e78f010e7e240bc4286b072549755f5 | 89555af2f9fb4bb05c9b7da9ba37cd735a2588a9 | /cachematrix.R | 674b3c65a40d840a620db95cedc203909c06c65a | [] | no_license | nancyirisarri/ProgrammingAssignment2 | ebb0fbadbcf1e9fdd85a468fe50d83a284eb3eac | abe4c1ac1be68d8ec98d0a4c4ebbe48aae9b19af | refs/heads/master | 2021-01-19T02:47:44.343418 | 2017-04-05T10:44:28 | 2017-04-05T10:44:28 | 87,293,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 914 | r | cachematrix.R | ## Return cached inverse of a matrix or, if not cached,
## compute if.
## Create a matrix object and cache its inverse.
makeCacheMatrix <- function(mat = matrix()) {
inverse <- NULL
set <- function(y) {
mat <<- y
inverse <<- NULL
}
get <- function() mat
setinverse <- function... |
e5d0d438070b437f09aa29e65933c90112fae140 | 1211bb4bac6accd22ba5252f083bb1efd0d97ba9 | /man/read_uka.Rd | 4275acb1c893847b3cd16bd66bc1dbc5c47bb5fd | [
"MIT"
] | permissive | CogDisResLab/creedenzymatic | dd72766af0914760ced56916518ece6d76387e6f | dff08cf325576f9fd2833c3801235a57c12475c7 | refs/heads/main | 2023-08-31T09:14:54.478650 | 2023-08-17T21:03:59 | 2023-08-17T21:03:59 | 350,755,295 | 1 | 0 | NOASSERTION | 2022-03-17T00:56:51 | 2021-03-23T15:04:41 | R | UTF-8 | R | false | true | 558 | rd | read_uka.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_uka.R
\name{read_uka}
\alias{read_uka}
\title{Reads and Rank UKA table}
\usage{
read_uka(df, ...)
}
\arguments{
\item{df}{dataframe, UKA table output (requires at least Kinase and Z columns)}
\item{..., }{arguments passed to rank_kinase... |
de06992fa2ce22a28b7bc72db5c6a00f228aa014 | 6a8660cca771bd781cdf09a81c07c5f9bf7e75b5 | /R/support-functions.R | ce7f8b5b2d78b18090963ccfb48c5c769a976cbd | [
"MIT"
] | permissive | berkorbay/ibb | 10455d82a3a07281473ed05bf9729599236993e0 | 848c34cdad9cd33746427d15d88ab066da1e7d02 | refs/heads/master | 2023-04-06T08:35:57.183568 | 2021-04-24T09:02:47 | 2021-04-24T09:02:47 | 262,798,514 | 8 | 1 | NOASSERTION | 2020-11-20T11:20:58 | 2020-05-10T13:56:00 | R | UTF-8 | R | false | false | 2,368 | r | support-functions.R | #' @title Change Language to English
#' @description Changes API language to English
#' @export
change_language_to_english <- function() {
Sys.setenv(IBB_LANGUAGE = "en")
}
#' @title Set Query Limit
#' @description Sets maximum number of returned results
#' @param limit Limit of the maximum results
#' @export
set_qu... |
2493e1d98b9e59470a2dc2864e3378de621e37b2 | c196e54c5dbf539f0055ad55b5c46abe344c3517 | /whitefly.R | 5f86a249aba36f044b0fa249f225f6829b25f504 | [] | no_license | kien300/whitefly | 452559cfbb06dd428f9de0bffda6047945b56ce3 | 5060b034e3a24876c739a2901fdf511975488261 | refs/heads/master | 2021-05-02T07:16:12.893364 | 2018-02-26T09:24:07 | 2018-02-26T09:24:07 | 120,872,426 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,571 | r | whitefly.R | library(tidyverse) #general data
library(readxl) #import Excel files
library(zoo) #fill in data
library(vcd) #Association Plort
library(fifer) #post-hoc Chi Squared test
library(knitr) #for table format
setwd("D:/OneDrive - CGIAR/CIAT/OTA/Nami'sData_2.9.18")
cm <- read_excel("for analysis/Cambodia.xls")
vn <- read_... |
28ba65ea969bbbb06a90e9cd614abd241626e8b1 | 5094ff788262bd7d83741b4404e226f541e1da51 | /GeneradorOrdenes.R | fadd0328f18a0fbfee81408fb368e94e21d81752 | [
"MIT"
] | permissive | CIAssetManagement/FundSeries | 7fb4aa3bb3d8baea5600e79ea4a8297adeff6a1d | 9c233449860158e941833cc9aad962c5a1e3bebd | refs/heads/master | 2021-01-18T17:41:57.100162 | 2018-08-02T16:54:04 | 2018-08-02T16:54:04 | 100,494,469 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,448 | r | GeneradorOrdenes.R | ###########################################################################################################
#
# Paso 1, Verificación de la serie
#
###########################################################################################################
#Funciones
options(scipen=999)
source("fondos.R",local=FALSE)
... |
44d9a712494df5074a9cd9a02f2767112c8521ae | c6c9b0c0ee14337e2d46176bba525c0d85decae6 | /shinymodule.R | b6488df19c9f4a6162402219e590cfbabea33155 | [] | no_license | benearnthof/FFFT | dcde3cc0727a9d6a896c2575433750473bffb142 | 4881f744bb83f9187299caf75e9d09f2ad06bbae | refs/heads/master | 2023-02-09T20:27:04.229070 | 2021-01-04T15:57:14 | 2021-01-04T15:57:14 | 261,377,588 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,761 | r | shinymodule.R | library(shiny)
counterButton <- function(id, label = "Counter") {
ns <- NS(id)
tagList(
actionButton(ns("button"), label = label),
verbatimTextOutput(ns("out"))
)
}
counter <- function(input, output, session) {
count <- reactiveVal(0)
observeEvent(input$button, {
count(count() + 1)
})
output... |
7ee5a46a819252586920fe4be5e1bb1043e1f20b | a6e4f9bfde9278133f702383542b0148307ec2dc | /finalmodel/residmodel.R | a92dd95f206c44a66d19005db477220ac7bd7ec9 | [] | no_license | dill/giam-italy | 024682da7c61f10d7ae917cf7000c37a86294981 | 7457e445f675ae8d90f8ed3593959e9362dce121 | refs/heads/master | 2021-01-01T05:36:51.123893 | 2011-09-12T11:59:38 | 2011-09-12T11:59:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,267 | r | residmodel.R | # fit a model to the residuals to see what's left
# relies on you having run model.R FIRST
# load soem useful libraries
library(maps)
library(mapdata)
library(soap)
library(dillhandy)
#load("fullmod-Gamma.RData")
load("fullmod-Tweedie(1.2).RData") # load the data
# Italy boundary
it<-list(x=fixdat$italy$map$km.e,... |
dd538f646fef05b26af9924272210a959c2b1f3e | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googleautomlv1beta1.auto/man/TextExtractionModelMetadata.Rd | 8b68866439835a310fa713bd873373f964ee2ff8 | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 611 | rd | TextExtractionModelMetadata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/automl_objects.R
\name{TextExtractionModelMetadata}
\alias{TextExtractionModelMetadata}
\title{TextExtractionModelMetadata Object}
\usage{
TextExtractionModelMetadata(modelHint = NULL)
}
\arguments{
\item{modelHint}{Indicates the scope of mod... |
61b5111ef5e5b999f227029ba06b373f267165fe | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613125609-test.R | dc187d91635c2321fcdec0dc58a0e10d31537382 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | 1613125609-test.R | testlist <- list(A = structure(c(1.08768969560471e-43, 1.26371023742382e+225, 2.19450232779207e+294, 2.44323852959515e-308, 9.69722668877813e-232, 2.40225282744071e-169), .Dim = c(6L, 1L)), B = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(10L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(r... |
b85c73b2b8117c13984e84bbb088d0f4b61909ef | bd2122f8c31442514f5a87dc4bab42a535825c4f | /plot4.R | 0a72e4d66e145f25a95322369213269fe1b509fc | [] | no_license | RamanChoudhury/ExData_Plotting1 | d94e5fdcdb7a1798e0150619010a8abf7823e65b | 743bd2fb8b1c2c81de215747173cab1c3839590f | refs/heads/master | 2021-01-14T12:40:08.915020 | 2016-07-17T14:04:09 | 2016-07-17T14:04:09 | 63,393,147 | 0 | 0 | null | 2016-07-15T05:04:08 | 2016-07-15T05:04:08 | null | UTF-8 | R | false | false | 2,703 | r | plot4.R | library(lubridate)
library(dplyr)
##reading the whole data into a data frame with character colClasses
total_data<-read.table("./household_power_consumption.txt",sep=";",header=T,stringsAsFactors = FALSE)
#total_data<-transform(total_data,Date=dmy(Date),Time=hms(Time),Global_active_power=as.numeric(Global_active_... |
b9bc3f960988f97bf90aba7cf4ac134e15f1c0b5 | 6034d565642a30876b7b7a025b74a31580c44613 | /R/datasets.R | e71c10bf9610ecdd8995a177285298757fa6a258 | [] | no_license | cran/parameters | a95beba8c8bd820a88b74ca407609cc08a62fcab | f19575ccdbbd303a1896a13d8b4b8210563cabfa | refs/heads/master | 2023-06-08T08:58:24.080762 | 2023-05-26T09:20:02 | 2023-05-26T09:20:02 | 211,083,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 921 | r | datasets.R | #' @docType data
#' @title Sample data set
#' @name fish
#' @keywords data
#'
#' @description A sample data set, used in tests and some examples.
NULL
#' @docType data
#' @title Sample data set
#' @name qol_cancer
#' @keywords data
#'
#' @description A sample data set with longitudinal data, used in the... |
bb8be5d14f0bc731bab9efdaca950df5d52a9adb | da20f463f5425a6540c6613ea280c03220414b6c | /plot4.R | 321f0438a8d39a223269a0489cfe02b24a664fe3 | [] | no_license | samyeager/ExData_Plotting1 | 7c374cb04157e9b135594a01262e812a482b3b20 | 133ae903bd548b52ba067702d4beb0d208773f2f | refs/heads/master | 2020-12-30T18:57:51.091224 | 2014-08-10T21:35:16 | 2014-08-10T21:35:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,021 | r | plot4.R | ## Plots a 2x2 plotting grid for the "household_power_consumption.txt" dataset.
## The grid plots are:
## top-left: date+time v. Global Active Power
## bottom-left: date+time v. active energy by sub-meter
## top-right: date+time v. Voltage
## bottom-right: date+time v. Global Reactive Power
## Assumes "ho... |
66bdccca6594f579f4b5ffa2228b37086f6a4af2 | 5b532b0284f115cb9b96800d92f35d76283c827f | /R/ICM-methods.r | e64249e471da9b26bf7d88e39f7b488127d5845e | [] | no_license | ge11232002/TFBSTools | cc5dc41216e951a3ac4904f8fd5a3b5ef47ed601 | 1bda142f74c546c9c58242686e5d52f4d9a0bbbd | refs/heads/master | 2021-09-15T23:23:47.428928 | 2021-09-10T21:18:42 | 2021-09-10T21:18:42 | 46,871,450 | 22 | 10 | null | 2021-09-10T20:40:06 | 2015-11-25T15:53:42 | R | UTF-8 | R | false | false | 7,948 | r | ICM-methods.r |
### ------------------------------------------------------------------------
### The "ICM" generic and methods
setMethod("toICM", "character",
function(x, pseudocounts=0.8, schneider=FALSE,
bg=c(A=0.25, C=0.25, G=0.25, T=0.25)){
dnaset = DNAStringSet(x)
toICM(dnaset... |
e819202a1280cb13d8eb6f37469213a22b5d20ab | e35092ffd25f20bdd5f52948f9ff276088ed2836 | /class_script.R | 6462b560d15716bef5a3f86a68c64cdec45ae6bf | [] | no_license | tisem-digital-marketing/smwa-networks-inter | a5825b43fdfc6653e51292b8ba8cc4c51aae4830 | 382f641ca38538ad271c725a0a1de614788f1542 | refs/heads/main | 2023-05-09T11:53:49.587725 | 2021-05-27T12:25:02 | 2021-05-27T12:25:02 | 371,324,716 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,478 | r | class_script.R | library(readr)
library(tidygraph)
library(ggraph)
library(dplyr)
library(tidyr)
library(tibble)
library(igraph)
# --- Download the Data --- #
url <- "https://github.com/rfordatascience/tidytuesday/raw/master/tidytuesday_tweets/data.rds"
out_file <- "data/tt_tweets.rds"
download.file(url, destfile = out_file, mode = "... |
ff29d5131498bc4b60a5371192664bd0c7da2d52 | ad0f62efae3e4660fd46d0678e71d52dfbbf74ff | /R/salinity-model-functions.R | 9612f333b5f10a2503da46fe939da0328ff6a709 | [
"MIT"
] | permissive | gopalpenny/deltasalinity | 8b781acaa0cac772cb9be9b40f03f742484067fc | 926034e7668b58fc5142934a9fec307fb2ef1728 | refs/heads/main | 2023-01-22T21:06:26.357754 | 2020-12-03T19:12:09 | 2020-12-03T19:12:09 | 317,027,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,449 | r | salinity-model-functions.R | # salinity_model_functions.R
#' Simulate salinity, re-initializing each year
#'
#' @param Q_df A \code{data.frame} containing \code{Q_cumec} and \code{year} columns. The rows must be in order of consecutive days.
#' @param v Vector of length 4 containing log parameter values: \code{log(a), log(b), log(d), and log(C_d)... |
ceb27c0baadce070ef181fd48f1f2d59ef1107f7 | 619c0ba0282a4c2cb9a1b20a14536ef82dc46e8f | /R/ShannonDiversity.R | ab4dcba72d308618dcfc225b518751848e6d8bb1 | [] | no_license | SEELab/enaR | 796b51159ca43d2338ef441022e2077db516bc7f | 281a0c71f83fb4659c9300801e41d09729dbd261 | refs/heads/develop | 2023-04-26T01:58:20.788858 | 2023-04-22T20:24:54 | 2023-04-22T20:24:54 | 12,623,293 | 14 | 8 | null | 2018-05-17T22:34:51 | 2013-09-05T16:52:53 | R | UTF-8 | R | false | false | 2,046 | r | ShannonDiversity.R | #' ShannonDiversity Shannon Diversity Metrics
#'
#' Calculates a number of metrics based on the Shannon information entropy
#' measure of diversity in a vector, x.
#'
#' @param x 1 x n vector.
#' @return \item{H}{Shannon entropy-based metric of diversity. This captures
#' the effects of both richnes (the length of t... |
f53daef2d72ebcca6945a94043474009feef0448 | 63f42fd8e71f9cb58882dbc80bbb5fd1ae9a7e45 | /src/h2o-automl-demo/quanteda_lab.R | a19f1164a3d9079efd6ebcdfc6d1c6a706b79593 | [
"MIT"
] | permissive | codez0mb1e/donald-trump-tweets | 3273f4d293a0964c849fb35cc188a778be515dba | cd2fad84ffe6de8734ac077f0e207b1355195457 | refs/heads/master | 2020-07-16T23:56:34.206734 | 2020-01-09T15:45:58 | 2020-01-09T15:45:58 | 205,895,648 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,806 | r | quanteda_lab.R |
# Import dependencies and read config ----
options(max.print = 1e3, scipen = 999, width = 1e2)
options(stringsAsFactors = F)
suppressPackageStartupMessages({
library(dplyr)
library(tidyr)
library(purrr)
library(magrittr)
library(lubridate)
library(quanteda)
})
secrets <- config::get(file = "secret... |
dd8327251e595e04bf91328be91e07d1aa940e12 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/PedCNV/R/cnvlmm_plot.R | 2ac5bf293288c50507a08332e02e1ef6ed0b66e4 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,723 | r | cnvlmm_plot.R | ##' Makes formatted plots from the clustering result returned from \code{\link{ClusProc}}.
##'
##' @title Plots clustering result
##' @param x The clustering results obtained from \code{\link{ClusProc}}.
##' @param type Factor. For specifying the plot type. It must be one of 'histo', 'scat' and 'sil'. If it is 'histo',... |
0f1c28c4263b40afe2230f002735678fecc608ee | a9bce7ec992cfbc6713c01afc947fe4a3296ffdb | /man/test_coverage.Rd | 12a3477b02985d00e5860bd2973a9a08c48b0a8a | [] | no_license | jimsforks/testthis | 059e2aec4080a05e56c0d15cb372247a58104d7e | 2f22b34d3e6fe851a09517e93d891237063ad8a9 | refs/heads/master | 2022-11-20T19:25:33.146038 | 2020-07-21T05:22:37 | 2020-07-21T05:22:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,583 | rd | test_coverage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/test_coverage.R
\name{test_coverage}
\alias{test_coverage}
\title{Test coverage of package}
\usage{
test_coverage(from_tags = TRUE, from_desc = TRUE)
}
\arguments{
\item{from_tags}{\code{logical} scalar. Checks the files if your test director... |
8b3658c10de9c35105561de228cb39b2f85424e0 | 00be44c6e49e7f0e948bb202457240467665480e | /R_scripts/buildByteBIgrams.R | ad4264e262de26135c50958836345f394a8613d4 | [] | no_license | AkiraKane/scharf-personal | b9469d76e026255283f99d66c5cb0e17456bf8b5 | b6b6560bc8ac5033871e6e64cb2920b6b14f30bd | refs/heads/master | 2021-01-19T20:58:07.745222 | 2016-09-22T15:12:26 | 2016-09-22T15:12:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 692 | r | buildByteBIgrams.R | require(data.table)
require(doMC)
registerDoMC(cores = 8)
files = sort(list.files('/media/tim-ssd/backup/malware_data/byte',full.names = T))
alpha = c(0:9,'A','B','C','D','E','F')
hex = c('??',as.character(outer(alpha,alpha,FUN = 'paste0')))
bigram_lev = as.character(outer(hex,hex,FUN='paste0'))
start = proc.time(... |
cd76468c121cd25eda4c97dbf55e47e2e6a44bd2 | a97332504666f71b63b093bb8dfbb3ba7d670928 | /NMA_barplot.R | 5991072a3cccafeaebd8e8806294dcd6a990490e | [] | no_license | JiayiJessieTong/NMA_data_analysis | 5fb67a58456807d2e62fd95b8bb19dbb5e0e542b | d8fe7ab0e43628c8bb5ea15007f04a42c5f0b487 | refs/heads/master | 2020-07-08T13:00:49.388996 | 2019-10-09T01:15:58 | 2019-10-09T01:15:58 | 203,680,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,474 | r | NMA_barplot.R | # this code is used to draw the barplot to show the ranking of different drugs
# Aim1: barplot for the following six drugs (selective serotonin reuptake inhibitor (SSRI))
# "Paroxetine, Fluvoxamine, Escitalopram, Sertraline, Fluoxetine, Citalopram"
# Aim2: barplot for the following five drugs (serotonin–norepinephrin... |
f8e2bb2cd7861e83fef3a20c7af8097fbc74f87a | a9d15a277c938a64baff2c8aa0237aa749e019ff | /DS501/ds50112/project/run.r | 02cdccd7f7369edf51296964510613cb7dc34833 | [] | no_license | dgallup2020/College | 5f71314063264284dec2ec012fe9e193a0135568 | ab10fc050bea85053c0bd7d5b2315a1603c8a211 | refs/heads/master | 2022-12-10T23:50:25.106600 | 2020-09-11T05:03:34 | 2020-09-11T05:03:34 | 294,594,512 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,570 | r | run.r | #
# a plot I didn't finish in class
# to run it login, cd to /u1/junk/cs459/feb04, then run R
# and finally source('run.r')
# check out the values in the variables u and t
options(stringsAsFactors=FALSE)
# read the list, add names for each item, convert to df
li <- scan('data',what=list('','','','','','','',''))
nam... |
0c03a8255faa02cbb7edbd5239ae1004c1a19f3f | c02b1b6252a59c992a0f3ebb542f08fb0cf261a4 | /man/get_player_master_acs.Rd | 690aaa6938b467523aa931c36366f5ba28006858 | [] | no_license | systats/lolR | d57b04d592b40906b70f0da1acc9a332b965aa23 | f2b38453460cac1c9fe24861603e75bebf549669 | refs/heads/master | 2020-03-18T07:13:38.225502 | 2018-06-02T17:13:56 | 2018-06-02T17:13:56 | 134,439,850 | 0 | 2 | null | 2018-05-31T01:11:19 | 2018-05-22T15:58:05 | HTML | UTF-8 | R | false | true | 314 | rd | get_player_master_acs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_player_master_acs.R
\name{get_player_master_acs}
\alias{get_player_master_acs}
\title{get_player_master_acs}
\usage{
get_player_master_acs(x)
}
\arguments{
\item{x}{html_node}
}
\value{
data
}
\description{
get players from match
}
|
fa5dadae4dd5dd2c3300160be7ee0751e97762fa | 7bc0759b1a3f1a8a4c48e02f1c5e393a13e94eff | /man/extractSequence.Rd | f2a1c432a8d4225a2be984bb76a9880fa46ff29c | [] | no_license | kvittingseerup/IsoformSwitchAnalyzeR | b7e4a249572b3d487ffcdea62a409e800ac23fe3 | 5f471360da38101777d37d9eb91a99c3ac81eda4 | refs/heads/master | 2023-07-21T19:52:53.966098 | 2023-06-30T11:04:50 | 2023-06-30T11:04:50 | 88,636,530 | 66 | 20 | null | 2023-01-31T12:23:30 | 2017-04-18T14:46:47 | R | UTF-8 | R | false | false | 9,847 | rd | extractSequence.Rd | \name{extractSequence}
\alias{extractSequence}
\title{
Extract nucloetide (and amino acid) sequence of transcripts.
}
\description{
This function extracts the nucleotide (NT) sequence of transcripts by extracting and concatenating the sequences of a reference genome corresponding to the genomic coordinates of the isofo... |
525e4466edf584f927484682948c022c1cefcd40 | d5e9909f7f6cc74db31298236d331392e8202a9f | /man/mRNA_matrix.Rd | 20be4d70d23d782e89c8d1f8ab89f2971f9cea77 | [
"Artistic-2.0"
] | permissive | weiliu123/RLassoCox | fbfd97494b39f62ceadaf449737e1d32d24b2670 | 9c4148900f96f89125a224da6036edbf22d47b82 | refs/heads/master | 2023-01-14T00:28:22.782283 | 2020-11-20T07:26:05 | 2020-11-20T07:26:05 | 308,259,734 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 555 | rd | mRNA_matrix.Rd | \name{mRNA_matrix}
\alias{mRNA_matrix}
\docType{data}
\title{
The expression data
}
\description{
An example of GBM expression data. We acknowledge the TCGA Research Network
for generating the GBM datasets.
}
\usage{data("mRNA_matrix")}
\format{
The format is:
num [1:314, 1:4853] 0.562167 0.022435 -0.000102 -0... |
9cb3244e9fc79821e9c69221a35ffb82b1404736 | 61c188bba8f228b0f14f4bae7c2fa3dcd1f7b3a2 | /man/beta.summary.Rd | 97898736c1dfdd51f092e490f948e2f2fd980cc6 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | covid19br/now_fcts | 24cb3b7bcbf47b827e50fec43f0dd9647c89dde4 | 44479971618513ef23e82ac277c749b8384e12f8 | refs/heads/master | 2023-02-27T01:34:07.757658 | 2021-02-05T20:41:10 | 2021-02-05T20:41:10 | 273,057,036 | 0 | 1 | CC0-1.0 | 2020-07-07T00:27:17 | 2020-06-17T19:04:42 | R | UTF-8 | R | false | true | 1,108 | rd | beta.summary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/beta.summary.R
\name{beta.summary}
\alias{beta.summary}
\title{Médias e ICs das probabilidades de notificação a cada dia}
\usage{
beta.summary(NobBS.output, NobBS.params.post)
}
\arguments{
\item{NobBS.output}{objeto retornado pela função Nob... |
845b71c37138a1e8d259fcd75b140a452f46c195 | a74cf5656f928862307733948c604c16d1a8fa04 | /R scripts/voles.R | 8c56b5627e5d72b3279c27b37be5b126d7d6160c | [] | no_license | bgreenwell/STT6300 | 82b08da731356d73b7bba4739c9a6c99f3d5646c | f254e2185daa437312b41d2c83e368f5ca681142 | refs/heads/master | 2021-01-20T06:36:06.998033 | 2017-12-05T21:24:08 | 2017-12-05T21:24:08 | 101,509,145 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,304 | r | voles.R | ################################################################################
# Voles data
################################################################################
# A study was conducted to differentiate between two different species of voles
# found in Europe. Several morphometric measurements were obtain... |
5b0ad268c756f554dddf928b338adb24c29c4f40 | 9d3e3c3950c4101bc863a90e69606d7c7d03a4e9 | /analogy/03_find_analogs/R/before_biofix/wareHouse/MatchIt/mathchit/matt_style/d_matt_style.R | 60476c3de1091fdc86136b25d24efbd9fc71e1b5 | [
"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 | 3,805 | r | d_matt_style.R | .libPaths("/data/hydro/R_libs35")
.libPaths()
library(data.table)
library(dplyr)
library(MESS) # has the auc function in it.
library(geepack)
library(chron)
library(MatchIt)
source_path = "/home/hnoorazar/analog_codes/core_analog.R"
source(source_path)
options(digit=9)
options(digits=9)
###############################... |
aacfc34a1108b7cef50bc35565efdeeffcf7d9aa | d6eeab02f0268cec50488b03b796da5c1ec42292 | /man/regression_cph.Rd | 548a67dea5fdaf105ec7a1cba8728f9d2aa77b21 | [] | no_license | Ifitrains/AirBnBCopenhagen | 7d857ab10691e9bad17c7a1f58fabaca3c972d64 | 8b046566753c9b6760130ef3caffd8fd067313c3 | refs/heads/master | 2020-04-20T03:25:08.524591 | 2019-01-31T22:54:31 | 2019-01-31T22:54:31 | 168,597,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 363 | rd | regression_cph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/regression_cph.R
\name{regression_cph}
\alias{regression_cph}
\title{Estimate models with robust standard errors}
\usage{
regression_cph(Data)
}
\arguments{
\item{Data}{data frame}
}
\value{
4 regressions and 2 latex ouput
}
\description{
Est... |
b37c2b10e7db5ebe87be82a3eb4d23ae93360e34 | 184180d341d2928ab7c5a626d94f2a9863726c65 | /issuestests/AGread/inst/testfiles/get_VM_C/get_VM_C_output/log_a008f34ddf7b2c13c4131860f85d5ba0ca8bdd9b/get_VM_C-test.R | ab3d60929fc6a1545f0f6b2825ead642deb6de22 | [
"MIT"
] | permissive | akhikolla/RcppDeepStateTest | f102ddf03a22b0fc05e02239d53405c8977cbc2b | 97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5 | refs/heads/master | 2023-03-03T12:19:31.725234 | 2021-02-12T21:50:12 | 2021-02-12T21:50:12 | 254,214,504 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,961 | r | get_VM_C-test.R | testlist <- list(x = c(-9.55582196017401e+29, -4.90459010763407e-29, 4.90985848662137e-132, 1.34087923799627e-99, -2.83758126022851e-214, -3.86582708286114e+118, 1.56442933387274e-40, 7.80171305823656e-14, -1.83590112917186e-143, -2.12225393135038e-27, -2.39035558481144e+94, 1.39381080421907e+78, 8.09020071287152e+... |
37b166c69fb5c625105fbeeb71e9c9f57b169dce | 164d2fe8bea6f6bc6e9f8397ae0a63c43b2a0768 | /indicators/emergency_admissions.R | 20bb772e10a3a037859c6dd8a4453775b8b29ea0 | [] | no_license | Public-Health-Scotland/covid-vulnerability | fab5753c64efa92cc50909da54a2c0437726af65 | 9385de13200613aea5baf5cd61053c21fe18bc95 | refs/heads/master | 2023-04-27T03:47:24.105962 | 2021-05-13T14:13:57 | 2021-05-13T14:13:57 | 249,380,562 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,148 | r | emergency_admissions.R | # Emergency admissions and multiple emergency admissions(+65).
# Parts 1 and 2 take about ~20 minutes to run
# Part 1 - Extract data from SMRA
# Part 2 - Create the different geographies basefiles
# Part 3 - Run analysis functions
###############################################.
## Packages/Filepaths/Function... |
4ccb591d4e735e4fa9016c1acb2951710212ff66 | 9698bc9a642e549505c211078a5066e3d6a945cf | /Relectoral/man/Agregado_Prov_MIR.Rd | 0fcc4666c48fdf90ed499c2c6715eb480b72b6f2 | [] | no_license | Miguelro/Electoral | 9924eb77325feb6840db06ca0b8a2978fba3e0ad | b8c502bfcdd55d9042447d10379f096eb6b844e3 | refs/heads/master | 2020-12-07T22:36:18.297678 | 2020-06-24T08:24:54 | 2020-06-24T08:24:54 | 232,817,651 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,211 | rd | Agregado_Prov_MIR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Leer_Datos.R
\name{Agregado_Prov_MIR}
\alias{Agregado_Prov_MIR}
\title{Download. Datos Agregado a nivel Provincial obtenidos del MIR (Ministerio Interior)}
\usage{
Agregado_Prov_MIR(Ano, Mes, Tipo, Ruta, Borrar = T)
}
\arguments{
\item{Ano}{E... |
88d5fd436d3c853a36e2b646480418bc981d4246 | 1233bd68fa715c898ea416f1945235bd1ee341ac | /scripts/cell_agg_fxn.R | d5ed2843067e694495d1d2cc90b87cb83550d09c | [] | no_license | grapp1/mb_sensitivity | 98a3ef97e989b99f945e452b2859efb77c0a05fe | 783531044cd8877a21e32803543a0eb8bd4d8453 | refs/heads/master | 2021-06-14T06:36:09.843400 | 2020-09-01T00:56:39 | 2020-09-01T00:56:39 | 254,479,809 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,530 | r | cell_agg_fxn.R | ### 20191107 cell_agg_fxn
# function to aggregate particle data by cell (from XY data)
# returns data frame with X_cell, Y_cell, and desired statistic
cell_agg_fxn <- function(input_df,x_colname = "init_X",y_colname = "init_Y",agg_colname, funct = mean){
nx <- 91
ny <- 70
df_agg <- input_df
col1 <- which(coln... |
0ceb0aec84814c507955c66ec8c4e121f5c47165 | 012c3d8e5a9bb435b0273687481f8c7f5b3e5175 | /scripts/R/pres_abs_dendo.R | 21558c2af388ffd4539381e88c60a6422940ee4b | [] | no_license | BjornWouters/internship_uu | 084ec1ee967790f20a9f78fea74c7f7dc6fcc676 | 2aa33fb64c172639de4e0f410833ba306ac4f55b | refs/heads/master | 2020-03-23T00:08:57.847298 | 2018-12-10T14:05:12 | 2018-12-10T14:05:12 | 140,847,123 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,216 | r | pres_abs_dendo.R |
# load libraries
library(pvclust)
# biocLite("ComplexHeatmap")
# Set the working directory.
setwd('Documents/school/internship/repo/internship_uu/data/')
cov_file_pfs <- read.table('prepared_dataset.csv', sep = ',', row.names = 'mrna_id', header = 1)
cov_file_non_pfs <- read.table('prepared_test_dataset.csv', sep =... |
ad93563b79010497107ac9540c764ddf64b2234d | 28c3f73a6d70c2fed4b2d2011bd1d9416a293b0e | /R/sequester.R | dbeab77718e158388d809ea05e442d1abad4fa6e | [] | no_license | cdeterman/OmicsMarkeR | a8cbe69bc26f98db69b89c02949a3a4d0ab2d8a1 | f9a0f3dfd067c0a0beb9ad421982ad86e63914cf | refs/heads/master | 2021-01-10T20:58:11.111636 | 2017-01-10T15:14:14 | 2017-01-10T15:14:14 | 13,659,839 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,056 | r | sequester.R |
#' @title Sequester Additional Parameters
#' @description When the user provides additional arguments to either
#' \code{fs.stability} or \code{fs.ensembl.stability} this function will
#' extract the parameters to be fit if optimization is not used i.e.
#' \code{optimize = FALSE}.
#' @param theDots List of ad... |
c012f72a8d39fcc623415c8429a6ddcdf6269fc0 | 7d632db47db7c86fe8b94d9808d3454ad9fe8b8e | /assignment_3/assignment_gruber.R | 925a844c9a14d59cdbf7a0a21e03034bc27de25c | [] | no_license | corneliagru/comp_stat | 83aca222491bcf0be04513e8370fdd219bbb89f6 | 71faa124f368df871218ab95ca379696842f8671 | refs/heads/main | 2023-02-10T14:53:15.934679 | 2021-01-12T12:56:57 | 2021-01-12T12:56:57 | 326,626,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,913 | r | assignment_gruber.R |
# import ------------------------------------------------------------------
data <- read.table("data.txt")
x <- data$V1
source("preparation.R")
# description -------------------------------------------------------------
#overview data
round(summary(x), digits = 2 )
# find best parameter settings. apparently ... |
a8adf7778baf721ef4fbdae00e4a42b10e7b3403 | fc7c8dc1c418f4ef10370e64d4d0fa957283885c | /man/embed_var.Rd | 8e7a91a5532d2120f591200300c175962bee6427 | [] | no_license | cran/i2dash | 195727ba7eeeb763a8e89558cd05991752597689 | 5848c079c871c384b02f2ef524f0b2af7f83f26f | refs/heads/master | 2023-03-28T12:33:52.674409 | 2021-03-29T14:20:02 | 2021-03-29T14:20:02 | 267,008,053 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 538 | rd | embed_var.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/components.R
\name{embed_var}
\alias{embed_var}
\title{A method to embed tabular data into an HTML link for download.}
\usage{
embed_var(x, ...)
}
\arguments{
\item{x}{Data, which will be written to the embedded file.}
\item{...}{... |
6c3b0dd5de6202602cbc2e03e2930cc5b71b676b | c3e6148a7a86fa46cc6aa3a80efadcf42a56cb06 | /Old/gamlss_no_bccg_v3_cole.R | 96bde37ea58b9e81b3aa4122c8da630c40e6f1bb | [] | no_license | ljwright/gamlss-tutorial | 3a90468cf3c433b4829eaa0deffb2a8a29a1cfc2 | c8a3054c9ccac38d0cceb99c0f2ebdacf209e028 | refs/heads/master | 2023-08-27T11:11:23.217067 | 2021-11-12T17:38:04 | 2021-11-12T17:38:04 | 427,446,287 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,075 | r | gamlss_no_bccg_v3_cole.R | #load the libaries
library(gamlss)
library(sitar)
library(tidyverse)
library(haven)
library(psych) #for the tabstat equivilent describeby to check sub group means, sd
#set wd
setwd("C:/Users/DAB/OneDrive - University College London/ideas/quantile 2/analysis/output")
#load all data
dat <- read_dta('cleaned_datafor_r.d... |
6191a555b442ec80dcf5e3a8f5e1a94ba651e020 | d434ec91242aad694c4e2d78580b60a9da3ce29a | /man/get_function_names.Rd | 1217c4d1309acd339213757145fc3ebfc1ae4f7b | [
"MIT"
] | permissive | rmsharp/rmsutilityr | 01abcdbc77cb82eb4f07f6f5d8a340809625a1c5 | d5a95e44663e2e51e6d8b0b62a984c269629f76c | refs/heads/master | 2021-11-20T08:45:23.483242 | 2021-09-07T17:28:22 | 2021-09-07T17:28:22 | 97,284,042 | 0 | 2 | MIT | 2021-09-07T17:28:22 | 2017-07-15T01:17:14 | R | UTF-8 | R | false | true | 586 | rd | get_function_names.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_function_names.R
\name{get_function_names}
\alias{get_function_names}
\title{Get functions defined in a vector of lines}
\usage{
get_function_names(lines)
}
\arguments{
\item{lines}{character vector with text from file}
}
\value{
A charac... |
c7af591e1bdc843ca60943650cdf2cf543ffbb2e | 2eebaf7f9e3246d2453df9289574fba9e7f8151a | /tests/testthat/test_flowfinder_functions.R | 51ab3b97c1271e21ed6b25d030b9aa535b6c2cee | [
"MIT"
] | permissive | mikejohnson51/FlowFinder | b47f2e726c452770bfa755c0da90ff7d13a1da92 | 617610cb3d53229de23a43775892223f8f854162 | refs/heads/master | 2021-06-07T20:26:34.622266 | 2021-03-15T00:05:05 | 2021-03-15T00:05:05 | 136,057,097 | 6 | 1 | MIT | 2021-03-15T00:05:05 | 2018-06-04T17:00:17 | R | UTF-8 | R | false | false | 279 | r | test_flowfinder_functions.R | context("server functions")
test_that("check get_nomads_filelist routings",{
fileList = try(get_nomads_filelist(num = 40))
# Make sure 3 vals
expect_equal(length(fileList), 3)
# Right number of urls are returned
expect_equal(length(fileList$urls), 40)
}) |
b722653b5a25143084a118913520bbbc9c904aef | ad1f387f3030032f5b2c38c0bc70e80e5f2baa0a | /R/zzz.R | 7fe2be5f94a2516faf8b39ad45fd4e91bfc49aee | [] | no_license | YeLibrarian/RMassBank | 89fde716ed662d872ff7b73d0e848bed196a28cb | c579455cff45d1971c029a9af9189a9e65429e16 | refs/heads/master | 2020-07-11T10:56:53.391534 | 2019-06-25T12:21:49 | 2019-06-25T12:21:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 433 | r | zzz.R | .onLoad <- function(libname, pkgname) {
RMassBank.env <<- new.env()
RMassBank.env$ReadAnnotation <- FALSE
RMassBank.env$testnumber <- 1
## new variables
RMassBank.env$verbose.output <- FALSE
RMassBank.env$export.invalid <- FALSE
RMassBank.env$export.molfiles <- TRUE
RMassBank.env$strictMsMsSpectraSelect... |
2bd461d8d25613a9cc5cde56a50265bde9490513 | 37efc02db536821a09cd3592009ae89993bfe2fa | /R/dummy_cols_rows.R | 4617b54775bcababbb4b3b51c98bd95d6c1bb520 | [] | no_license | ChandlerLutz/CLmisc | 7466ed60a13a6bf6b5971fc19753859497492da6 | 29a636ee291012da035c143378231dc2977f877d | refs/heads/master | 2022-12-13T09:52:56.782097 | 2022-11-29T18:57:51 | 2022-11-29T18:57:51 | 121,792,858 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,580 | r | dummy_cols_rows.R | ## c:/Dropbox/Rpackages/CLmisc/R/dummy_cols_rows.R
## Chandler Lutz
## Questions/comments: cl.eco@cbs.dk
## $Revisions: 1.0.0 $Date: 2019-11-03
## Shamelessly stolen from
## https://github.com/jacobkap/fastDummies
check_type <- function(.data) {
if (data.table::is.data.table(.data)) {
data_t... |
9bbf60b5e6fb3972e299857d6d83f518745c8e5d | 667812a66716b979b7208a0e033e54e1558b8f59 | /man/gpw4_deu2010.Rd | 6e4443ede47858a0de2790ad6b923819fe5dbe3d | [] | no_license | zhukovyuri/SUNGEO | 686e4afb398257e3bc183ba1a695f6d253f406dc | 7780b6e19e971d2a2f17a52c6c2906e65749dc23 | refs/heads/master | 2023-04-01T11:55:00.849451 | 2023-03-20T21:45:06 | 2023-03-20T21:45:06 | 254,211,865 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 966 | rd | gpw4_deu2010.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{gpw4_deu2010}
\alias{gpw4_deu2010}
\title{Population count raster for Germany, 2010.}
\format{
class : SpatRaster
dimensions : 186, 220, 1 (nrow, ncol, nlyr)
resolution : 0.04166667, 0.04166667 (x, y)
ext... |
5441b0a2c831ee6a3cb4334dd330981c6b9d9be4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/maptools/examples/gcDestination.Rd.R | fa81c763222bb1cda9a54e0f77bfa14a045cf8de | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 634 | r | gcDestination.Rd.R | library(maptools)
### Name: gcDestination
### Title: Find destination in geographical coordinates
### Aliases: gcDestination
### Keywords: spatial
### ** Examples
data(state)
res <- gcDestination(state.center$x, state.center$y, 45, 250, "km")
plot(state.center$x, state.center$y, asp=1, pch=16)
arrows(state.center$x... |
6da1fb407d60687c45dfa8328505c55c375e9647 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googleappenginev1alpha.auto/man/StaticFilesHandler.httpHeaders.Rd | 590c50a42fc22badb93a534a8d4ea97be7993271 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 582 | rd | StaticFilesHandler.httpHeaders.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/appengine_objects.R
\name{StaticFilesHandler.httpHeaders}
\alias{StaticFilesHandler.httpHeaders}
\title{StaticFilesHandler.httpHeaders Object}
\usage{
StaticFilesHandler.httpHeaders()
}
\value{
StaticFilesHandler.httpHeaders object
}
\descrip... |
1011d314d8c9e766b6b6b8592744609fcaac59de | e6c64291c457aec015f548a67572554c8f69269a | /man/parse_pattern.Rd | 9f9d6f6b52d13051ece92145f26e9cd1b28733e6 | [] | no_license | MatteoLacki/LFQBench2 | ba0d32b6d13f032df9fc528f5674fab466ce021e | ed5e175716b3ccfd3979466a569c2150dd7b9e52 | refs/heads/master | 2020-04-21T12:50:04.356863 | 2020-03-30T16:20:24 | 2020-03-30T16:20:24 | 169,577,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 669 | rd | parse_pattern.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wide2long.R
\name{parse_pattern}
\alias{parse_pattern}
\title{Parse column pattern.}
\usage{
parse_pattern(s)
}
\arguments{
\item{s}{Teh pattern to match}
}
\value{
a list with the pattern without group names, and the found names.
}
\descript... |
0477f5890a991ae0fe271c6df4e7bfbe086a822a | 82e6137d5d2a0e9114f76c7e427514bba62aaaf3 | /shinylego-master1/R/mod_display_lego_2d.R | 6f206314ab33e7a4f4619550cd5ecb57c98f47ee | [
"MIT"
] | permissive | lukuiR/Rpublic | 3a0c25519d10457bc08d6d3a8510865212943a37 | daa067ca078ddce54bb4d822666d4e9f9335c6a5 | refs/heads/master | 2022-06-28T16:10:50.713523 | 2022-06-21T14:48:48 | 2022-06-21T14:48:48 | 119,683,495 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,092 | r | mod_display_lego_2d.R | # Module UI
#' @title mod_display_lego_2dui and mod_display_lego_2d
#' @description A shiny Module that ...
#'
#' @param id shiny id
#'
#' @export
#' @importFrom shiny NS tagList
#' @examples
mod_display_lego_2dui <- function(id, height_window = 500){
ns <- NS(id)
tagList(
withLoader(
plotOutput(
... |
0723eb8061996ae421ef119a18dbff754af6a834 | 40845868e6803507d19d8b7ae63dea0ec0a109dd | /lab6/fibonacci_bonus.r | 74ce2b7c3b54b858d12f5cf36844867b2c60c912 | [] | no_license | srravula1/NCTU-R-Programming-2018 | 42f3ddcc24fdabc78bbb0029791e35e497f446fd | 0cbf426132db8848c97ab5fa4811e4d35d97e82b | refs/heads/master | 2021-03-14T03:34:45.708922 | 2019-03-04T18:13:46 | 2019-03-04T18:13:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 401 | r | fibonacci_bonus.r | data.file = "hw6_input.csv"
ans.file = "hw6_answer.csv"
data<-read.csv(data.file, header=FALSE, sep=",")
ans <- read.csv(ans.file, header=FALSE, sep=",")
len<-30
f<-numeric(len)
r_fibonacci <- function(n){
if(n<=1){
return(n)
}
else{
return(r_fibonacci(n-1)+r_fibonacci(n-2))
}
}
for(n in data){
my... |
a7b080d81b1251c9cc877b9fba2d3e141163a159 | 2cc56a6341f179923977128ad90bb31419e033d0 | /R/get_deviance.R | 5df0380c157fcd0d217096c2771086b3e8ccd7c8 | [] | no_license | cran/insight | 5e1d2d1c46478c603b491f53aa80de57bc8f54b4 | 247206683ad374a1ba179356410d095f6861aede | refs/heads/master | 2023-07-19T11:33:37.490704 | 2023-06-29T13:30:02 | 2023-06-29T13:30:02 | 174,554,249 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,441 | r | get_deviance.R | #' Model Deviance
#'
#' Returns model deviance (see `stats::deviance()`).
#'
#' @param ... Not used.
#' @inheritParams get_residuals
#'
#' @return The model deviance.
#'
#' @details For GLMMs of class `glmerMod`, `glmmTMB` or `MixMod`,
#' the *absolute unconditional* deviance is returned (see 'Details' in
#'... |
9c55e4ea39f34c343f9fde76f4d115455d0e1544 | 600fe48edbaaaa9e66baba760cd3a79bec023fc9 | /project.r | 595d50728414427162cf45f9bd4f36a7632ff23c | [] | no_license | justrite/bumblebees | a2bdf599af96cec55b32323bebd8dd5f4dd74cb1 | 2dd05be242d821050a15ac22e575ac92b7b49b8b | refs/heads/master | 2022-11-17T18:03:50.790945 | 2020-07-21T17:40:03 | 2020-07-21T17:40:03 | 281,457,070 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 19,022 | r | project.r | sink("output.txt")
data = read.csv("DryadSubmission/data_bombclimate.csv")
data$period = ''
data[which(data$periodFrom == 1901),]$period = "historic"
data[which(data$periodFrom == 1975),]$period = "early"
data[which(data$periodFrom == 1987),]$period = "middle"
data[which(data$periodFrom == 1999),]$period = "late"... |
9d616272865eed8f5976c8db257a14324f36c6c4 | 38878da75f684d6a701d910e747cba37d93bf36c | /run_analysis.R | 27428cf6acf31ea3458c63ba55a383845ac1da96 | [] | no_license | joshuaburkhow/PersonalRepository | 867d49e42a6b763eecee479abca05fc14859b523 | bd6b95c28124b68add7cd2d3b9cdebc0a45e13ba | refs/heads/master | 2021-05-27T21:57:57.455618 | 2014-05-25T20:05:36 | 2014-05-25T20:05:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,090 | r | run_analysis.R | # You should create one R script called run_analysis.R that does the following.
# 1. Merges the training and the test sets to create one data set.
# 2. Extracts only the measurements on the mean and standard deviation for each
# measurement.
# 3. Uses descriptive activity names to name the activities in the data set... |
4c064bb2b7757b461b2ddb78bd12745b726c31d6 | 5787f475dc6fedd99056018a2221ad546bef576a | /calcJMI.R | 081a95c1fd8e543693ae00dd47aa8c676932aed3 | [] | no_license | mmaitenat/ideafix-behind | e4c5d1faa3acf72465f19691d81035a58fe2207c | 361694544437c2f366ba641bfa954dfeb8bf334e | refs/heads/master | 2023-08-01T04:21:35.549217 | 2021-09-13T11:33:31 | 2021-09-13T11:33:31 | 311,494,235 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,851 | r | calcJMI.R | #!/usr/bin/env Rscript
library(tidyverse)
library(infotheo)
DIR <- "~/data/ENA_SRP044740/tidydata/"
Y.deam.filenames <- list.files(path = DIR, pattern = "_deaminations_Y.rds", full.names = TRUE)
Y.deam <- Y.deam.filenames %>%
set_names(sub("_[^_]+$", "", sub("_[^_]+$", "", basename(.)))) %>%
map_dfr(readRDS, .id =... |
c718bbc17467803c9503df7e51a05f390c6571e9 | 4dfa999678e9faff7957ef086d7ab0567dfca216 | /data_frame.R | b0c5481abfaccad0709ad3aba4ae188db412281a | [] | no_license | F-Cruz/R | 506796ca7ec7605e59649e59408e4b1bb13eae67 | 7a85688693c84eac1ea1e0810c73f7652f8341eb | refs/heads/master | 2020-05-23T19:25:27.426977 | 2019-05-15T22:48:08 | 2019-05-15T22:48:08 | 186,912,068 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 156 | r | data_frame.R | # Data Frame
d1 <- data.frame(x = 1:10, y = c(51,54,61,67,68,75,77,75,80,82))
d1
names(d1)
class(d1)
d1$x
d1$y
plot(d1)
|
638bf86e462c13a51d4082ce328aafc1162c301a | 470c71add81cdf8e9076059dc471c72932bd5aef | /functions/getDataFunctions.R | 9e0f7a6f39c0f8d26d99738e669f9a1a9e3749d8 | [] | no_license | erichhuang/CRC-RASness | 26fdc06704807e91f9b22f077fecf34414fca980 | cb584147b245c9be45244c2b221db63c4bdc5648 | refs/heads/master | 2021-01-18T06:24:43.752181 | 2013-02-13T17:09:02 | 2013-02-13T17:09:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,494 | r | getDataFunctions.R | ## FUNCTIONS TO EXTRACT RAW DATA OBJECTS FROM SYNAPSE
#####
## ANALYST: BRIAN M. BOT
#####
#####
## KFSYSCC DATA
#####
getKFSYSCCdata <- function(){
require(synapseClient)
require(affy)
## GRAB THE ARCHIVE FILE FROM KOO - UNTAR IN TEMP DIRECTORY
kfEnt <- downloadEntity("syn1528362")
kfDir <- tempfile(... |
8dd74cc077576970912728f371c506ad54b14a15 | e8bd1221d5edf301183e222ae215afa7f3a4c166 | /man/sample.lgcp.Rd | 27656d2510a2a2794014425318109fcd46a77025 | [] | no_license | dill/inlabru | 1b9a581ae5b56246fcd748db8df051ae4ff8bfa8 | e2c38a34d591f712b57cbe430c24bb0a82f03ae4 | refs/heads/master | 2021-01-22T22:53:21.963501 | 2017-03-18T09:30:08 | 2017-03-18T09:30:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,297 | rd | sample.lgcp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sampling.R
\name{sample.lgcp}
\alias{sample.lgcp}
\title{Sample from a log Gaussian Cox process (LGCP)}
\usage{
sample.lgcp(mesh, loglambda, strategy = "rectangle", R = 6371,
samplers = NULL)
}
\arguments{
\item{mesh}{An \link{inla.mesh} ob... |
37ca87f0d8a35fda90e9121c5ec92682b91bfc63 | 836b133c87bce43ae20673cc1842ab20e9decc87 | /paper_code/Figure_codes/SFigures/FigureS9_distinct_module/FigureS9C_mutability_score_environment_bin.R | e63d92ac44f5e45652d36c8b8165e68c7686d512 | [
"MIT"
] | permissive | sashaflevy/PPiSeq | b98836b99e1e78ccb54e02be46792c700cb7fa32 | 646dbe151e7b6044e762fff1cf36b185dffe3bdc | refs/heads/master | 2021-07-01T14:47:49.241826 | 2020-10-05T15:29:52 | 2020-10-05T15:29:52 | 177,872,906 | 0 | 4 | null | 2020-10-05T15:29:53 | 2019-03-26T21:41:35 | HTML | UTF-8 | R | false | false | 3,737 | r | FigureS9C_mutability_score_environment_bin.R | setwd("~/Desktop/PPiSeq_additional_data/")
source("function.R") # load commonly used functions
#Commonly used colors
apple_colors = c("#5AC8FA", "#FFCC00", "#FF9500", "#FF2D55", "#007AFF", "#4CD964", "#FF3B30",
"#8E8E93", "#EFEFF4", "#CECED2", "#000000", "007AFF")
vScore_PPI = csvReader_T("Datasets_gen... |
819592c3b3a59440c3e56443d0aad9bed1459c80 | 63bab589b6d666684a490d11320621b3c41e66cd | /scrape_parler.R | f95edaee2d689b13fa21a46b8d163b1406f1d272 | [] | no_license | inh2102/scrape-parler-R | 626fac07045692a404746555ba2767b96f8c8ad0 | 150562c79d09312d5e43d9364c3171c998d494ed | refs/heads/main | 2023-02-10T09:04:47.348211 | 2021-01-05T20:29:44 | 2021-01-05T20:29:44 | 316,870,699 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 124 | r | scrape_parler.R | source("functions.R")
packages()
df <- scrape_parler(scrolls=10)
posts <- df$posts
trending_hashtags <- df$trending_hashtags |
43710dccc76cfdf0b034f4a5ac09905571ae1e85 | bb7cc2b21d4337ca8f0889e29b01a4fdfd301d8c | /data-raw/umcsent_example.R | ed45ddb9767f06a84a82d88464b63749114fefa8 | [] | no_license | cbergmeir/Rlgt | 2d8ecb948c3c26cb28b1c4108ed82b3a9b4e0f22 | 652ba908205ca49a92e166000fea24843a33f02c | refs/heads/master | 2023-08-31T13:17:37.877023 | 2023-08-31T07:46:53 | 2023-08-31T07:46:53 | 154,053,696 | 21 | 13 | null | 2023-09-11T05:46:22 | 2018-10-21T21:25:08 | R | UTF-8 | R | false | false | 2,319 | r | umcsent_example.R | library(gtrendsR)
library(dplyr)
library(lubridate)
umscent <- read.csv('../data-raw/UMCSENT.csv', stringsAsFactors = FALSE)
umscent$DATE <- as.Date(umscent$DATE)
# search engine
search.engine <- gtrends(keyword = NA, geo = "US",
category = 485, time = "all")$interest_over_time
# financial pla... |
782448ccc07a4cba6576a80e632705bb8a47b44a | 43b6f5fce442d4eb963ee6cfc74f33662602ed61 | /man/tds_list_datasets.Rd | 05c99e420d11a7f2e9615a641415d28e2fd44cd3 | [] | no_license | mt-climate-office/thredds | 92e6cb1ebe8c8b4d7d794c664d5e4c91cfe9437c | 2d95d0a6d66213d0877db25697e6e4fd79288299 | refs/heads/master | 2021-09-13T17:26:46.345417 | 2018-05-02T14:59:25 | 2018-05-02T14:59:25 | 94,267,113 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 806 | rd | tds_list_datasets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tds_list_datasets.R
\name{tds_list_datasets}
\alias{tds_list_datasets}
\title{Get a list of available datasets on a THREDDS data server.}
\usage{
tds_list_datasets(thredds_url, recursive = FALSE)
}
\arguments{
\item{thredds_url}{A string prov... |
44392d6f2a9d2771890ad6018c10e6d8c0eb108e | 725a33f27fce430ee481a3542aae5bb81a94dfc0 | /R/qcMetric_EVD.R | 0d86173fa67ddb8b2e986c04bcbc0634f49a17be | [
"BSD-3-Clause"
] | permissive | cbielow/PTXQC | fac47ecfa381737fa0cc36d5ffe7c772400fb24e | f4dc4627e199088c83fdc91a1f4c5d91f381da6c | refs/heads/master | 2023-07-20T00:39:45.918617 | 2023-05-17T14:23:03 | 2023-05-17T14:23:03 | 20,481,452 | 41 | 30 | NOASSERTION | 2023-05-17T14:23:04 | 2014-06-04T11:53:49 | HTML | UTF-8 | R | false | false | 71,991 | r | qcMetric_EVD.R | #####################################################################
qcMetric_EVD_UserContaminant = setRefClass(
"qcMetric_EVD_UserContaminant",
contains = "qcMetric",
methods = list(initialize=function() { callSuper(
helpTextTemplate =
"User defined contaminant plot based on peptide intensities ... |
aec36a979348594c9b47db1593a39c9666dfd2a2 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/3865_0/rinput.R | 4d0063f38b5f688a17a83b4b4c113aae30012c66 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("3865_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3865_0_unrooted.txt") |
e6bf053a2948a65f2e83554669f939c07d71c23d | 0a8007a2a68bdcd0bcb3a84be4422ea25e42c806 | /TranscriptionFactorBinding.R | d6c186d78566747ae46287dee536fc17f2f59b50 | [] | no_license | mbanf/CNS_BindingPrediction | 44bb14f16b6b3bb9c0077695e27ce81627659e72 | 855c6035c4d2ec320567da3ce0ff0e57b977deac | refs/heads/master | 2022-05-18T14:10:35.092179 | 2022-05-01T13:48:00 | 2022-05-01T13:48:00 | 81,061,652 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 77,496 | r | TranscriptionFactorBinding.R | # TODO: Add comment
#
# Author: michaelbanf
###############################################################################
# load a multiset of binding motifs - last curated spring 2016
get_cell_and_pnas_Paper_PWMs <- function(){
print("prepare PWM motifs and mappings")
library(fume)
## create motif - gene ... |
6f4c4d2569f35b5b7098ac2a0571b2dda9e30306 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dockerfiler/examples/Dockerfile.Rd.R | 72775a3641b1b96173b45a858c7b65e6ccd68fe8 | [] | 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 | 173 | r | Dockerfile.Rd.R | library(dockerfiler)
### Name: Dockerfile
### Title: A Dockerfile template
### Aliases: Dockerfile
### Keywords: datasets
### ** Examples
my_dock <- Dockerfile$new()
|
a892536cf2a1406ce81add7754d58f7a4d31240c | d36beb31d88895dae08898b9c5526aad2c0ef6bb | /R/list_all_upstream.R | 39f51f2029349a4170ff34923e3859e8616c1903 | [] | no_license | lhmet-forks/catchstats | 7e3b9dc1791df303059b1b5d49bd98b4aedcff35 | 4a7f497b81fe3b4ea50fa93975bb7747f3e3b3ac | refs/heads/master | 2022-03-18T06:12:18.797350 | 2019-11-19T20:51:05 | 2019-11-19T20:51:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,184 | r | list_all_upstream.R | #' Identify all the subcatchments upstream of a list of specified catchments
#'
#' @param hierarchy a dataframe containing catchment id and next downstream (nextds) id fields
#' @param catchnames a vector of catchment ids for which a a list of upstream catchment
#' ids will be returned.
#' @return a list of upstream ca... |
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