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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d4dfcdf9443cd638520b08a2ddf2056a0858fc23 | 709c16710d7cae612de6c779cafb7199813e0f24 | /AhasHfBkleAmputation/extras/EvidenceExplorer/global.R | 10e36d1ac7e347070212f1b43cf9b324fd235059 | [
"Apache-2.0"
] | permissive | OHDSI/StudyProtocols | 87a17fc3c00488b350f9416c584a1d0334d8dfcb | 8de0454c6be4c120ba97d7376907d651327573a4 | refs/heads/master | 2023-04-27T18:59:35.785026 | 2020-02-16T00:32:52 | 2020-02-16T00:32:52 | 27,415,586 | 37 | 41 | null | 2023-04-25T19:55:45 | 2014-12-02T04:49:53 | R | UTF-8 | R | false | false | 2,937 | r | global.R | blind <- FALSE
fileNames <- list.files(path = "data", pattern = "resultsHois_.*.rds", full.names = TRUE)
resultsHois <- lapply(fileNames, readRDS)
allColumns <- unique(unlist(lapply(resultsHois, colnames)))
addMissingColumns <- function(results) {
presentCols <- colnames(results)
missingCols <- allColumns[!(allCol... |
3fbcf8f46c48480902b6f2a849fa52863c465451 | a8130957d7af1c4f3d60c06fa9f7840181135606 | /class work/2-4-15.R | 7798a181d3a9b3b7e07adefef067c2dc657c3f76 | [] | no_license | fehercm/STT-3851 | fb99fb189ae5eff0a8cdd4923be18aa6658468a0 | a2db792b53a4a0764c5d96947ab9933e0f8f01ca | refs/heads/master | 2021-01-02T09:19:41.594847 | 2015-04-28T15:51:05 | 2015-04-28T15:51:05 | 29,276,097 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 427 | r | 2-4-15.R | personality <- read.csv(file=url("http://www1.appstate.edu/~thomleyje/R-datafiles/PersonalitySTT1810.csv"))
table(personality$Gender, personality$EorI)
addmargins(table(personality$Gender, personality$EorI),2)
prop.table(table(personality$Gender, personality$EorI),2)
addmargins(prop.table(table(personality$Gender, per... |
fb5033c03009268f52d76e78a35cecadc4933f5a | 60d6cb128057cd61aa17812fa61fdfc96f8cf1d9 | /plot1.R | 5749a733186085f139a4f378b11178786435847f | [] | no_license | jackgidding/ExData_Plotting1 | 211a7e817fa3a25c4e2dac6ccfb94f8729c58add | 321fd6a3a6760e66a359fbacbb79c3cac76c1307 | refs/heads/master | 2020-12-24T10:10:31.577383 | 2015-02-08T03:32:39 | 2015-02-08T03:32:39 | 30,461,272 | 0 | 0 | null | 2015-02-07T16:17:40 | 2015-02-07T16:17:40 | null | UTF-8 | R | false | false | 930 | r | plot1.R | ## plot1.R
## Author: Jack Gidding
##
## Purpose: Generate the 1st chart for the Exploratory Data Analysis course
## Project 1.
##
## The chart is a histogram of the UCI data, Global Active Power,
## minute-by-minute samples during January and February 2007.
## The x-label and t... |
03e3d03147b9e446b0b445eef939c440ec0b958a | 9b3cff0dd9a6e0402747cb68083f71bd3705ebe1 | /man/correctGeno.Rd | 01b1d745386c336eb7edede4638606dbd3587032 | [] | no_license | cran/MPR.genotyping | f3656d7e298f5999b80e62ac15f2ac29c25c65d7 | 9d4112d6ddf825f9701d5631b3123b19ef39b67f | refs/heads/master | 2021-05-05T06:34:14.060691 | 2018-01-24T17:24:42 | 2018-01-24T17:24:42 | 118,804,856 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 161 | rd | correctGeno.Rd | \name{correctGeno}
\alias{correctGeno}
\title{
Correct Geno
}
\description{
correct Geno
}
\seealso{
\code{\link{hmm.vitFUN.rils}}
}
\keyword{ HMM }
|
52595ab012ef1d367e1d9410d84d9c7c877c861b | 684aba486bcaef860a928fe6c0e9a53bdcb41cbc | /plot3.R | 59aef363195f3584f3ea08b9015503eee0f51c90 | [] | no_license | understructure/ExData_Plotting1 | a6b8e926b789f79a4bd000e2dbfee60236632be8 | 4052545bb481ef1fd4b7060dd10bc6661237e716 | refs/heads/master | 2021-01-16T21:40:49.146060 | 2015-04-13T23:35:37 | 2015-04-13T23:35:37 | 33,484,632 | 0 | 0 | null | 2015-04-06T13:56:49 | 2015-04-06T13:56:46 | null | UTF-8 | R | false | false | 1,800 | r | plot3.R |
# download the file and get it into a variable
# NOTE : If you're on Windows or an OS without this directory,
# you must change this to an existing directory in order to
# run this code successfully!
myDir <-"~/Downloads"
setwd(myDir)
# get the file, you may need the rCurl library installed for this to work
www <- "... |
40854394c6958d17cf56cfa2efdf1b039c8d5cc5 | 088e1a000955bd0725db1739ffa986edc09877c2 | /4_Exploratory_Data_Analysis/Household_Power_Consumption/getData.R | b9aa5d9e45198086dc47a7786101d8f445fed952 | [] | no_license | maxgaz59/Coursera_Data_Science_Specialisation | 950e3b4a42e2e7920f0f4d2f0decc275bf8f21c0 | 4f36bb79a2163f1a0b98a5b4355e5898f54b6c10 | refs/heads/master | 2021-04-30T16:32:58.775497 | 2017-01-26T03:20:07 | 2017-01-26T03:20:07 | 80,057,440 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,289 | r | getData.R |
library(dplyr)
setwd("~")
setwd("./[coursera]DataSciences/DataScienceSpecialisation/4_Exploratory/ProgAss1_ExploratoryData")
#rm(list=ls())
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
fileName <- "household_power_consumption.zip"
if (!file.exists(fileName)){
... |
8ffd33576ea66a172fd3b6408d3a1e55e6dbf36a | 3d52bb75ea458b44c7e2935f818a25117bc4370d | /chap1.r | 724335621948eb0f886bbf67e6f53d348144da1d | [] | no_license | omelhoro/r-nlp-baayen | 1b068853125d9a39872cb400074b839308ed4a98 | 5c71cb96a2a9be715d66e5a14246d717611b4bb0 | refs/heads/master | 2020-04-02T12:39:56.776704 | 2016-06-02T18:50:55 | 2016-06-02T18:50:55 | 60,289,180 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 812 | r | chap1.r | # TODO: Add comment
#
# Author: igor
###############################################################################
install.packages("languageR")
library("languageR")
#task1
spanishMeta
colnames(spanishMeta)
#task2
xtabs(~FullName,data=spanishMeta)
tapply(spanishMeta$PubDate,spanishMeta$Author, mean)... |
b9115f9a3cd4ecbb0482cc733636f12a7d4c26eb | 60627dc5c9f23a9bafcf942c5a083629da786785 | /man/power_eeg_bands.Rd | 516efd2528972b929b6856f4d88382a30c4c2e02 | [] | no_license | adigherman/EEGSpectralAnalysis | 6375fc44e8dd7864c0f1aa39c427a1369de1ddde | dadc57bcd0fb1ec39db0b0a9ab3ac1667e695184 | refs/heads/master | 2022-12-03T13:00:27.162666 | 2020-08-18T19:28:24 | 2020-08-18T19:28:24 | 234,624,034 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,159 | rd | power_eeg_bands.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/power_eeg_bands.R
\name{power_eeg_bands}
\alias{power_eeg_bands}
\title{Get power values for EEG bands}
\usage{
power_eeg_bands(
eeg_signal,
sampling_frequency = 125,
max_frequency = 32,
num_sec_w = 5,
aggreg_level = 6
)
}
\argument... |
275bb6713d484de58321afe024875651b1195c6b | bc5430fe73aa5e66981e65ae8c843dd5ba632c6d | /Code/korea_data_cleaning.R | 865fd61fec4ac7fb046125e52f73e1c92fe9b8fc | [] | no_license | cdbale/Hackathon | 085c90c00bbe9da7326efa61c8af169e661ecc04 | a5e1762009a59cb6b094f18062d79569cc7c0348 | refs/heads/master | 2023-04-18T04:23:30.390315 | 2023-04-15T12:13:54 | 2023-04-15T12:13:54 | 265,055,467 | 0 | 1 | null | 2020-05-22T05:54:21 | 2020-05-18T20:35:10 | R | UTF-8 | R | false | false | 2,627 | r | korea_data_cleaning.R | #############################################################################
########### Data Cleaning for South Korea COVID-19 Patients Data ############
### Project Contributors: Matthew Schneider, Jordan Fischer, Cameron Bale ###
#############################################################################
# See d... |
0db3171b44357c84ec23c2bfdd2abde0e55ed40a | 8452f6a438a5505e1a2517dbebe85bce148b636f | /plot1.R | b00a4a88a1351271a9a1490c2dc4d208d1624da6 | [] | no_license | minyili/ExData_Plotting1 | 66945fb400a48e9ae0d836e4d8cfcd234042145e | 4bb0b2831cf6197345ed157ba07c6f5b15d94abe | refs/heads/master | 2021-01-18T06:30:32.884453 | 2014-07-09T05:37:08 | 2014-07-09T05:37:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,114 | r | plot1.R | #setting working directory
#setwd("/Users/myli/Desktop/Data scientist specialization/Propractise/IndividualElectricityPowerConsumption/ExData_Plotting1")
#specify which column to read, columns with type 'NULL' are disregarded
readCol<-c(rep('character',2),'numeric',rep('NULL',6))
#reading the first three columns (Dat... |
20ef3928e342fcd6a441ead516a0ec247248b6ee | 1c1ac604314d3c8785a8f3d14f2df1afc7429ad3 | /tests/testthat/test_pdist.R | d24996dc18619c0b6af3d630e570e1114b058815 | [
"MIT"
] | permissive | jokergoo/cola | 7abd6dfd0bb487ce601a045f021c0a61359486df | 8376b71ab216f69fd77b7af9f898048c5dfc6070 | refs/heads/master | 2023-06-07T08:34:15.370800 | 2023-06-06T07:45:08 | 2023-06-06T07:45:08 | 91,272,219 | 58 | 14 | null | 2018-03-01T09:50:37 | 2017-05-14T21:21:56 | R | UTF-8 | R | false | false | 256 | r | test_pdist.R |
m1 = matrix(rnorm(10*2), nrow = 10)
m2 = matrix(rnorm(10*2), nrow = 10)
d1 = cola:::pdist(t(m1), t(m2), 1)
d2 = as.matrix(dist(t(cbind(m1, m2))))[1:2, 3:4]
dimnames(d2) = NULL
test_that("test pdist", {
expect_equal(all(abs(d1 - d2) < 1e-6), TRUE)
})
|
510dc04b27a3a9b05d3de34b997091f108a198a4 | db0b537705b0671f527a8d406f13fb1de5b49616 | /data/test_dataset/dataframe/Data Visualization and Statistics/R/plotting_players_by_clusters.R | d2aa17df501dfb4ba81db95d9052bd24d089c7af | [] | no_license | rakeshamireddy/Automatic-Code-Translation | 7e43d9232b1af2f9e1c62e76c1720f9469bdd842 | 9c4d0b040ee2eaccdcec8d8321f262d748c5c4b0 | refs/heads/master | 2023-04-22T23:29:55.205930 | 2021-05-15T03:06:03 | 2021-05-15T03:06:03 | 299,511,652 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 103 | r | plotting_players_by_clusters.R | nba2d <- prcomp(nba[,goodCols], center=TRUE)
twoColumns <- nba2d$x[,1:2]
clusplot(twoColumns, labels) |
b00c21cbbc7f28ac409a69c467f2e717691236d6 | 9803cb750744ab39e22ade6864a48246c957d8f9 | /tests/testthat/test_is_zero_factor.R | 13c64c69c841a69ea66117526a20003d321144f2 | [] | no_license | kkdey/flashr | 19da364375b7bdf23515c9c0fc387383c4701eb4 | 4bc27f39dec59486dfd56c71e610d7032b606b30 | refs/heads/master | 2020-04-12T08:47:33.952487 | 2016-11-27T20:42:58 | 2016-11-27T20:42:58 | 65,643,575 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 247 | r | test_is_zero_factor.R | library(flashr)
context("Zero Factors")
test_that("is_zero_factor returns correct result",{
l=matrix(rnorm(500),nrow=100,ncol=5)
l[,1]=rep(0,100)
l[,3]=rep(0,100)
expect_equal(is_zero_factor(l),c(TRUE,FALSE,TRUE,FALSE,FALSE))
}
)
|
933324b16877af7968f0d8aa5726eef1571c9e0d | f1f38d1f92133aaa0ee5c3df6b0048aaf0dd9054 | /man/occ_count.Rd | 3c12dff256a18ffb287f9d40924e8db0412d8aad | [
"CC0-1.0"
] | permissive | imclab/rgbif | acda8ae9828d4cb281deab6016e1741192e8756b | c62edb8ecd0f89796dd18a38cfb8cd327e25584e | refs/heads/master | 2021-01-11T05:02:47.481188 | 2013-11-29T05:40:54 | 2013-11-29T05:40:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,238 | rd | occ_count.Rd | \name{occ_count}
\alias{occ_count}
\title{Get number of occurrence records.}
\usage{
occ_count(nubKey = NULL, georeferenced = NULL,
basisOfRecord = NULL, datasetKey = NULL, date = NULL,
catalogNumber = NULL, country = NULL,
hostCountry = NULL, year = NULL, from = 2000,
to = 2012, type = "count", publi... |
a25758b1cc6cd41e250fe49046d3e946631938e7 | 97fa19dc9569076f5830faf93b1346c8ba43b3d8 | /geo_submission_files.r | a8c448b35d8270963492f03d5b86a8c8f054a8f9 | [] | no_license | CGSbioinfo/GX-Illumina | 9c4876547ea74d3a3b4280ae271540aac49df78f | 883f16dec35dc25620d7886da907a35d14385e70 | refs/heads/master | 2016-09-01T03:43:56.458499 | 2015-11-29T16:06:56 | 2015-11-29T16:06:56 | 47,068,559 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,995 | r | geo_submission_files.r | library(lumi)
library(limma)
library(beadarray)
### Generate raw and normalized data to submit to geo
setwd('Z:/SERVICES/Microarrays/GX-Nugen/Angela Riedel/Sept2014/AR_Sep14_Results/')
raw_data<-lumiR("raw_data.txt")
norm_data<-lumiN(raw_data)
detection_pvals_raw_data=detection(raw_data)
detection_pvals_norm_data=de... |
a22ff341d087922b1744a0625fc6f6ebc4e821f9 | d9d4c5f99898d63552201cc30adc873c71042d20 | /man/create_graph.Rd | f098a63a21bf6afad9938e64af4c99347a85cdf0 | [] | no_license | UweBlock/DiagrammeR | f010857b5e95943c0ae5de458afc63a2daf83d8d | 203fc1d8a64bf518d4cb5b8339df57c7a1f4ece0 | refs/heads/master | 2020-05-29T11:45:30.027097 | 2015-11-11T10:22:01 | 2015-11-11T10:22:01 | 46,001,828 | 1 | 0 | null | 2015-11-11T18:38:58 | 2015-11-11T18:38:57 | null | UTF-8 | R | false | true | 3,327 | rd | create_graph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_graph.R
\name{create_graph}
\alias{create_graph}
\title{Create a graph object using data frames representative of nodes and edges}
\usage{
create_graph(nodes_df = NULL, edges_df = NULL, graph_attrs = NULL,
node_attrs = NULL, edge_att... |
5649960d5c08bb940c9b1d748b8ecfef36716e78 | 3fe30961b6b2d54597ff38d5bf02359a4b6f859a | /R/REV.R | b0e46f0bf3306305d922a81fe561f66fcd0b7114 | [] | no_license | cran/mcmcabn | 83d978ac43e0da88b740b7166c1cc53811f4dbdd | ffe2262400c2d1771270977720a068a83b3b067e | refs/heads/master | 2022-11-30T01:14:27.043816 | 2022-11-18T22:30:02 | 2022-11-18T22:30:02 | 174,589,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,845 | r | REV.R | REV <- function(n.var, dag.tmp, retain, ban, max.parents, sc, score.cache, score, verbose, heating) {
rejection <- 1
A <- 0
# stor number of edges
n.edges <- sum(dag.tmp)
# randomly select one
if (sum(dag.tmp*(1-retain)*(1-t(ban))) != 0){
#i->j
selected.edge <- whic... |
2522146c6db04a569de0050d051de0f91573468d | dab398fd11e87204187cebad9771b5d09f87faac | /man/specificity.Rd | 4be728f587fbb023e86ba718617ab622e64198e3 | [] | no_license | mdlincoln/modeltests | ca4c8fd4a9af82efad32fe28dfee27f2f910fd17 | d6268f425fb4a2d646f708475227c5a4c3b60f6f | refs/heads/master | 2021-01-21T17:46:22.194647 | 2015-01-25T22:55:50 | 2015-01-25T22:55:50 | 29,830,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | rd | specificity.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/modeltests.R
\name{specificity}
\alias{specificity}
\title{Calculate specificity}
\usage{
specificity(truth, pred)
}
\arguments{
\item{truth}{A logical vector of true values}
\item{pred}{A logical vector of values predicted by the mo... |
532b4eeb42aaa3db77d949c091c92103191da6b9 | b005168d2b1cb99440d9ca936a1d284e4a1a84ae | /human.R | 840e64275f447f482f46d83920bb0c1214b1426c | [] | no_license | miljavon/IODS-project | 56940feeebe4e52c5dcf3d398ebf3ace86a84b04 | 8bc66d640e8bf1ccb8cdab2c7eef7b2a1f5f00af | refs/heads/master | 2021-05-01T17:17:56.028794 | 2017-02-24T21:18:14 | 2017-02-24T21:18:14 | 79,423,939 | 0 | 0 | null | 2017-01-19T06:43:39 | 2017-01-19T06:43:39 | null | UTF-8 | R | false | false | 2,316 | r | human.R | hd <- read.csv("http://s3.amazonaws.com/assets.datacamp.com/production/course_2218/datasets/human_development.csv", stringsAsFactors = F)
gii <- read.csv("http://s3.amazonaws.com/assets.datacamp.com/production/course_2218/datasets/gender_inequality.csv", stringsAsFactors = F, na.strings = "..")
str(hd)
#195 obs of... |
641ae5d949187407884bc4eac41728f5aae14581 | 59f00bf769f88c9c0bfd91f3091d4a7e49517b73 | /R/z_test.R | 787860966d28c6becdf571e30736c66d516d93b1 | [
"MIT"
] | permissive | RachelQueen1/YorkPackage | be2c335d40bd22c25c4e9c06ec640903d7afebee | 754b74f8df56b65b797000af96afac0404d4dd8b | refs/heads/master | 2020-12-06T03:19:28.544440 | 2020-01-07T13:33:44 | 2020-01-07T13:33:44 | 232,316,884 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26 | r | z_test.R | z_test <- function(v){
}
|
34a7e442668c81428612213f4db7a1da52cdf9b1 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /B_analysts_sources_github/bcaffo/brisk/display.R | ed6a565a41eb278d5193e6a742c307074bfa4679 | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,842 | r | display.R | display <- function(file, type = "analyze", imageCenter = c(0, 0, 0),
roi = TRUE,
positive = TRUE,
negative = TRUE,
plevels = 1,
nlevels = 1
){
if (type == "analyze") img2 <- extract.data(read.ANAL... |
e6901f22aeb0c56a7b8d59400619adb1ac8b0d93 | c1bf288727b9ff4a63b3ad82b86153848bc194aa | /app/ui.R | d51b3fc89d4def1fdc40ae99bf360f63803100eb | [
"BSD-3-Clause"
] | permissive | HimesGroup/pargasite | fa0df418ca70551e46e970197b761d7d4a301daa | b5e7f8adee320372bcb404a0dd6570184dd27336 | refs/heads/master | 2023-08-23T05:24:16.080244 | 2023-08-08T16:02:49 | 2023-08-08T16:02:49 | 139,889,958 | 3 | 4 | BSD-3-Clause | 2023-08-08T16:02:51 | 2018-07-05T19:05:32 | HTML | UTF-8 | R | false | false | 8,908 | r | ui.R | #.libPaths("/home/rebecca/R/x86_64-pc-linux-gnu-library/3.4/")
#.libPaths("/home/maya/R/x86_64-pc-linux-gnu-library/3.4/")
library(leaflet)
library(shinyWidgets)
shinyUI(fluidPage(theme = "bootstrap.css",
setBackgroundColor("ghostwhite"),
tags$style(HTML("body {line-height: 1.75}")... |
78464aef203b9b93476bf33eb524c586a0f75165 | 15d195cb63018eae33fb6cb95e5ce51c6c155e6a | /scripts/04_analyse3.R | a4831c74952e58e70dcd171fa742f7dbc807b9d5 | [] | no_license | tsimonso/Coursera_reprodResearch_courseProject1 | 70149ca8b0694fa3ba0a23a8697f3bcf339908c3 | e303e4d2fe5e476cf126aafd9fbe0367d9173ebd | refs/heads/master | 2022-11-06T06:35:16.367088 | 2020-06-20T00:56:46 | 2020-06-20T00:56:46 | 272,913,367 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,196 | r | 04_analyse3.R | ## IMPUTING MISSING VALUES
## =======================
## Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
## ===============================================================================================================
sum(!complete.cases(activity))
##... |
e2bff590b5ff4c6c9ecaceb3f605ae5980033b20 | 687807152165cc49493a40b8c02ebbcc3d98aec2 | /code/highLevelSearchAlgos.R | f440b1ea7cdbabe2068148bb162adfab71d0cb1d | [
"LicenseRef-scancode-unknown-license-reference",
"CC-BY-4.0",
"CC-BY-2.0"
] | permissive | chirimacha/simple_bandit | 48cfed8ddd9b4097fd362ec39a49ccb90f1aba6f | 5fb095914d4cfc1bfa79f6e72d84e8e39b6a5f4b | refs/heads/master | 2021-03-24T13:16:31.121768 | 2017-06-30T22:29:36 | 2017-06-30T22:29:36 | 95,824,071 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,098 | r | highLevelSearchAlgos.R | '
################################
Bandit Simulations to use to compare global and bandit searching
This code:
-Calls Bandit Code
-[Optionally] Simulates infestations
-Simulates Searches: Provides mechanism for both a random search and a ring search
-Simulates a search using a bandit based on the parameters ... |
88dc2fe9de31c28a7ef0fd2f83ced819800f9b23 | 8c13beebb7ca53ef301ebc93076a4dbf9b9b9ca9 | /R Codes/BIM_Rcode_ORDER.R | c4025f2fa4cca71e5d2083f1335d3f189f00b62b | [] | no_license | petertea96/Genetic-Association-Methodology-Research | 933aeb00583f352c159969758d67f94930a42335 | 03943856723a534455a1fcdacb56faa2705f290a | refs/heads/master | 2020-04-08T22:39:45.135568 | 2019-04-10T19:48:48 | 2019-04-10T19:48:48 | 159,796,100 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 522 | r | BIM_Rcode_ORDER.R | #Today is July 25th, 2018
#The order() function just takes the (200x200) haplotype distance matrix and reorders the elements
#such that the columns and rows are ordered from haplotype 1 to haplotype 200.
#Example: Individual 1 has haplotype 1 and haplotype 2. Individual 4 has haplotype 8 and haplotype 9.
order = fun... |
747577dc8c34fcefdb8d754542d18eab6fb66548 | 40bfd1969152253992498197f8b2b36e136e3a7b | /run_Analysis.R | aad7582e84b742328bf7cb2c78fed4e9b5e02be5 | [] | no_license | lemurey/cleaning-data-project | 8fa7303a193855c582012a4b4d8edb176f696cb2 | aca043ca903d5f444d2f70e4ae1424a4d9a7d04e | refs/heads/master | 2020-06-03T14:32:59.966380 | 2015-03-22T16:33:04 | 2015-03-22T16:33:04 | 32,684,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,336 | r | run_Analysis.R |
## Change this to the working directory that contains the UCI HAR dataset folder
setwd('valid path to UCI HAR dataset')
## This gets the list of files (with path) that need to be imported.
train_files <- paste0('./train/',
list.files(path = './train',
pattern='... |
1f5f7c59077595b97b70efea5c0c84d4660e4075 | 032bfab14ff7d77349273c52ac18a181a9996192 | /cachematrix.R | 7c341c4cdd210fa8527be08f46da53c4fca5f335 | [] | no_license | davidcarnahan/ProgrammingAssignment2 | 56de8ce9d614fb82df33f09a2dd52602c5e1390e | c99e662555332fb9813a5baa8d09d14879290cc2 | refs/heads/master | 2021-01-15T13:00:37.712046 | 2016-08-25T05:25:21 | 2016-08-25T05:25:21 | 66,516,805 | 0 | 0 | null | 2016-08-25T02:25:38 | 2016-08-25T02:25:37 | null | UTF-8 | R | false | false | 1,076 | r | cachematrix.R | ## Matrix inversion can be a costly compution -- so caching may be of signficant benefit.
## The two functions below will 1) create a special matrix object that can cache its inverse, and
## 2) either compute/retrieve the inversion depending on whether it has been calculated already.
## This function creates a specia... |
22b6423b17af34884eaad388bf62436773c15c40 | 007a5459cc41c25d6450fdb2d6ac331587b6b439 | /R/data.R | 537b9ac509ccee0a0645c18fa90ebc3e0f4c88f7 | [] | no_license | Edouard-Legoupil/APLA_Dataset | 7637fa1f6b29def80ee3b9e9a8c28478a7106575 | 338c79e20ceb9be38b77de685ba76bfbe90658f4 | refs/heads/main | 2022-12-31T12:11:27.220906 | 2020-10-12T20:29:28 | 2020-10-12T20:29:28 | 303,468,494 | 0 | 0 | null | 2020-10-12T17:44:37 | 2020-10-12T17:44:36 | null | UTF-8 | R | false | false | 34,664 | r | data.R | #' @title APLA_Database
#' @description APLA Database is the original APLA Dataset
#' @format A data frame with 551 rows and 262 variables:
#' \describe{
#' \item{\code{Q1}}{character COLUMN_DESCRIPTION}
#' \item{\code{Q2}}{character COLUMN_DESCRIPTION}
#' \item{\code{Q5#1_1}}{character COLUMN_DESCRIPTION}
#' \... |
cd5776c80df4128d96cc3635f0c1da5b5fa73293 | ec7dd158dd44c0be69dc2f7d1466b21fa70a563b | /R/zzz.R | f7770aed6c999a5a976f94997bd0b7b5adbab422 | [
"Apache-2.0"
] | permissive | snp/LFQ | 1089f9bf1c72fb6e404a4a084be7a81d7fb0f91f | 404f714c5e065253e9051341aced6be56e6d1245 | refs/heads/master | 2020-05-27T22:04:24.391185 | 2018-06-29T08:17:57 | 2018-06-29T08:17:57 | 82,574,979 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 104 | r | zzz.R | .onAttach <- function(libname, pkgname) {
packageStartupMessage("Welcome to LFQ Proteomics package")
} |
cbf12d035652a15180b37bab711ba6d898174f58 | 0059bd90bf026c9ea589e0a227f1db415d453864 | /baseline/aknn.R | dfaf2bdb3f3560d860b954421fa4dddec94056cf | [] | no_license | stavnycha/master_thesis | cd1f221170c89fa5e0d896ae0db7b604e853da70 | eabf9360c86eaed098cc962b452fbbacef947281 | refs/heads/master | 2020-03-25T12:14:42.759870 | 2015-08-03T18:47:11 | 2015-08-03T18:47:11 | 40,141,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,280 | r | aknn.R |
library(ggplot2)
library(tm)
library(SnowballC)
library("clue")
library("parallel")
library(gsubfn)
library("XLConnect")
library(kohonen)
library(e1071)
library(kknn)
library(clv)
library(stringr)
library(lsa)
folder.root <- "."
setwd("D:/tartuUniversity/for_ms/baseline/scripts")
source('./aknn_cosin... |
e40d978faf2891904e82f6e0af19e80e3f917bd1 | 995182d67072061d54b02947055d1fb66e319e7a | /compare_tef2.R | 788193e3d1ec52cdb8d01a6553686485ce39d79e | [] | no_license | healyke/FestR_replacement_tests | 4e75caa6e916217806e74f44198a31c1cd74cf66 | 70dc0cab15464a9ca38cbd175d5680d942618fc9 | refs/heads/master | 2016-08-11T00:33:52.867069 | 2016-04-01T13:36:01 | 2016-04-01T13:36:01 | 53,622,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 728 | r | compare_tef2.R | compare_tef <- function(Tef.data = c(),
isotope = c(carbon, nitrogen),
Tef.output = c()){
obv_delta <- unlist(plot_list(Tef.data, isotope = isotope)$delta.plot.list)
######Carbon = 1, Nitrogen = 3.5 this doesnt work
#standard_diff <- vector()
#if(isotope == "carbon"){
#... |
018915cbd817b04a93a0a892b970c7f0244215b4 | f1dd2f4825547b6880b2f3efa2727137ba532a8d | /transferAndylab/automaticRules/experimentQualitativeRules.R | 269e115948397f9e703609bd38dc39dd44609abf | [] | no_license | mkirchhof/rslAppl | 38b21f17456140012f890c30a14ccceba81caa2e | c6b9e66c790a3d879dfdfa28e2decc8dfb5e6f33 | refs/heads/main | 2023-06-18T15:30:26.809778 | 2021-07-23T07:32:46 | 2021-07-23T07:32:46 | 371,723,181 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,393 | r | experimentQualitativeRules.R | # Get the ID that SLURM hands over to the script as argument
folds <- as.integer(Sys.getenv("PBS_ARRAYID"))
if(is.na(folds)){
folds <- 1
}
cat("ID = ", folds, "\n")
nRules <- 90
# Dependencies:
library("parallel")
source("rsl_qualitative.R")
library(microbenchmark)
# Initialize cluster
ntasks <- 10
... |
97df6e8e3baf91e800dc920dda0e2539cf78a330 | 1bacdd09c333e1fd459c35c405fa83e46e04349e | /revisions/model predictions.R | 9babdfd505f4fd7dbb21e98c54a858a25601b831 | [] | no_license | robertlynch66/Migrations-revisions-NHB | ec135458e70d5b90701372cbda29752e795eddb1 | 7256ecde5f2c93b960289baac3ec4c5fa37a07f5 | refs/heads/master | 2020-04-01T03:55:41.552160 | 2019-01-30T11:40:53 | 2019-01-30T11:40:53 | 152,841,960 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,993 | r | model predictions.R | #### Final models for OSR project
data <- readRDS("full_pop_data.rds")
data <- readRDS("C:/Users/rofrly/Dropbox/Github/Populism_ms/full_pop_data.rds")
# scale social capital index
data$soc_cap_indx_14 <- data$soc_cap_indx_14 -min(data$soc_cap_indx_14,na.rm=T)
data$soc_cap_indx_14 <- data$soc_cap_indx_14/(max(data$soc_... |
9d534b25cec598cfbca52771cba02bed7b441580 | 2a3f6b843354fb60f5ca97d3580e512aa5771a0e | /R scripts/Statistical_tests.R | e26f41eaf1d899422d83b082881dc95107e90d67 | [] | no_license | anmako/Rstats | b954415e4988b0d8b5adfd8ba4a495173b9fa3a4 | 6145570551f14695b9eae83e9841b244a84350e2 | refs/heads/master | 2020-03-30T14:20:25.031099 | 2020-01-03T12:15:05 | 2020-01-03T12:15:05 | 151,313,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,070 | r | Statistical_tests.R | ## Basic Statistical Tests Explained with R
## (1) Shapiro Test: Testing for normality
normaly_disb <- rnorm(100, mean = 5, sd = 1)
shapiro.test(normaly_disb)
not_normally_dist <- runif(100)
shapiro.test(not_normally_dist)
## If p-Value is less than the significance level (0.05), the null-hypothesis that it is norm... |
ddd29c4a66073e637d9bb021d8a8c6d4e011b6be | f109648c4a8a5a663bda80b3b3dd43421771978e | /Assignment_Hospital Quality/rankhospital.R | 73b24acc0a70cabecbab7886a549fa609d6af409 | [] | no_license | s1g9/R-Programming | 0e0f58bcdc75aaee8bce137384dfc24772c4758c | 76fa5e9337beee114b7f5f88c6208e30ffc2ca6b | refs/heads/master | 2020-12-31T07:54:52.672727 | 2015-11-20T20:14:06 | 2015-11-20T20:14:06 | 46,577,603 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,023 | r | rankhospital.R | rankhospital <- function(state, outcome, num = "best"){
data <- read.csv("rprog-data-ProgAssignment3-data/outcome-of-care-measures.csv")
subsetstate <- data[data$State==state,]
if (!nrow(subsetstate)){
stop("invalid state")
}
cause<-NA
if(outcome=="heart attack") {
cause <- "Heart.Attack"
}
if(o... |
8b59b0a8df6308aab351767c06afbfa0fa8557d7 | cbaa250faba198eb548d83e39967ed2057cc8daa | /DMRegressionFreq2/man/DM_fit_Freq.Rd | 50135ca954f303a85fdf20fc4464401e3ad1c9ac | [] | no_license | carriegu/STAT-840 | 3418718f0b0c8bab2812270cdc281af66190c1aa | d6447ec79b07c09f457bd9bd5631f530aa362dca | refs/heads/master | 2022-04-18T13:28:32.649076 | 2020-04-12T20:34:06 | 2020-04-12T20:34:06 | 255,138,685 | 0 | 0 | null | 2020-04-13T03:09:13 | 2020-04-12T17:51:12 | R | UTF-8 | R | false | true | 596 | rd | DM_fit_Freq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DM_fit_Freq.R
\name{DM_fit_Freq}
\alias{DM_fit_Freq}
\title{Estimate beta coefficients of the Dirichlet-Multinomial regression model
using Frequentist approach.}
\usage{
DM_fit_Freq(Y, X)
}
\arguments{
\item{Y}{Matrix of \verb{n x J} response... |
55177f0b93aaf7d267fc35f0f32587468f536ce2 | 812720f93b43704a1bb00c16716c74e2e637fd4f | /man/depart.LDL.Rd | 8b270f6ee504ee685f60e72f0ffc491ffb3b6cb1 | [] | no_license | cran/HAPim | 9df01704bb002f166674d189790fc19a59ecc789 | 8b189df9b1547d74bfbad541ed2c1af88b18054f | refs/heads/master | 2020-05-17T15:48:30.314265 | 2009-10-10T00:00:00 | 2009-10-10T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,626 | rd | depart.LDL.Rd | \name{depart.LDL}
\alias{depart.LDL}
\title{starting values for the optimization of HAPimLDL method}
\description{
The function calculates the starting value of the error variance and the starting value of the QTL effect for the optimization of HAPimLDL method.
It can be viewed as an internal function.
The user do... |
acd7c6c5ee65cfde3e08df75181c4422672e6a5b | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/5606_0/rinput.R | 16233b13d48a12b13af277f60da8b08982c370cf | [] | 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("5606_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="5606_0_unrooted.txt") |
e5c05bca280e5db442ca2d7fbf80a8207282b611 | 7a3410528d2ce4cf0aaa53f01a2486d705543283 | /R/plot_tf_idf.R | 1fbac5f0caf3cc1b903e8e2bae4e38aacd0c6d0c | [
"MIT"
] | permissive | deandevl/RtextminerPkg | 85907057a4cb259f22b6677a7c8547b32b3d7f59 | da0893ef95c52baed1c333927addee20fc13f317 | refs/heads/main | 2023-03-19T20:25:57.510189 | 2022-10-25T13:28:01 | 2022-10-25T13:28:01 | 238,649,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,385 | r | plot_tf_idf.R | # Title : plot_tf_idf
# Objective : plot the top tf_idf values in a bar chart
# Created by: Rick Dean
# Created on: 2021-01-07 8:43 AM
#
#' Function plots the top \code{N} ordered tf_idf values
#'
#' @description Function brings together RtextminerPkg::get_tf_idf() and
#' RplotterPkg::create_bar_plot() to plot the... |
fcde577d3b49518788774f79250e32e4771e9388 | c96fb047660d57547921e01de547ffcdfc2af4f8 | /man/setupGSEArun.Rd | b4f8141d29cca4dba7c4fae643b4b219803ee6e0 | [
"MIT"
] | permissive | BaderLab/POPPATHR | d5a3acf04fdda8ce3e9ad6ef41ade62dee7f8052 | 19290bfdaaa3ff06c9cfcad72f04b3f3e789007b | refs/heads/master | 2023-06-23T21:30:03.556374 | 2021-06-09T20:35:43 | 2021-06-09T20:35:43 | 201,321,105 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,292 | rd | setupGSEArun.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/setupGSEArun.R
\name{setupGSEArun}
\alias{setupGSEArun}
\title{Sets up and runs GSEA using population-based FST values}
\usage{
setupGSEArun(
fst_file,
annotation_file,
snp2gene_file,
SET_PERM = 10000,
SNP2GENE_DIST = 5e+05,
MIN_G... |
9051bcb1c9d560b126e4e0c93b0a7780e8901382 | 6c7783c0da4511ea88f1d334849a41f288d157b7 | /03_scripts/10_cooccurence.R | 1432e875fde7badba95d2636f2bde3037d44d3ed | [] | no_license | skraftarcher/LA_FW_Sponges | fd59296b245edbd92a1aedfe326692e5ce41e30e | 39c6d325144bf9af40152fe6a41b7f291559e1fd | refs/heads/main | 2023-07-11T13:39:14.195450 | 2021-08-17T18:31:23 | 2021-08-17T18:31:23 | 311,411,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 873 | r | 10_cooccurence.R | install.packages("cooccur")
library(cooccur)
library(tidyverse)
library(vegan)
sp1<-read_rds("02_wdata/sponges_pa_Nov162020.rds")
sp2<-data.frame(sp1 %>%
select(Site,sponges,ab)%>%
mutate(ab = ifelse(is.na(ab),0,ab))%>%
pivot_wider(names_from = Site,values_from = ab))
rownames(sp2)<-sp2$sponges
sp2<-sp2[,-... |
4e3bae4d58b280b3b21fe37ea6ce6387e29f8724 | 799468ce526db6f14f2aa5003c601e259e5f0d62 | /man/stage.vector.plot.Rd | a6abff5dca6f9d3362a027a15541cd64fcc49416 | [] | no_license | kostask84/popbio | 6aa45015bfc1659bd97f2ce51ad5246b8d434fac | 682d3ffb922dfab4fd2c7fc7179af2b0d926edfd | refs/heads/master | 2021-05-09T02:01:42.050755 | 2017-02-09T21:44:20 | 2017-02-09T21:44:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,849 | rd | stage.vector.plot.Rd | \name{stage.vector.plot}
\alias{stage.vector.plot}
\title{ Plot stage vector projections }
\description{
Plots short-term dynamics and convergence to stage stage distribution using stage vector projections.
}
\usage{
stage.vector.plot(stage.vectors, proportions=TRUE, legend.coords="topright",
ylim=NULL, xl... |
4f492d57cd3bc46fe4c53279a6bb88496e78eec6 | 7ada85f56845751f5636ed7918114a20d52ddaaa | /Ch4.R | ea80dc1c42c14ac52a9b944c1a945296446d4540 | [] | no_license | nravishanker/FCILM-2 | 8e67225c274ae92b55fe2cc69dde2d20489a2a5e | 1901d54e1d3650955a6c5683aa41bbe8f6d27b97 | refs/heads/master | 2023-08-29T09:12:20.704051 | 2021-10-28T22:22:34 | 2021-10-28T22:22:34 | 375,077,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,472 | r | Ch4.R | ## Simple Linear Regression (SLR) Model
# Dataset: cars in the R library "car"
library(car)
data("cars")
attach(cars)
plot(dist, speed)
# Simple correlation
cor(dist, speed)
corr.test <- cor(dist, speed)
cor.test
# OLS estimation in an SLR model
mod.slr <- lm(speed ~ dist, data = cars)
smod <- summary(mod.slr)
# Pull o... |
3cc27b3389d36330d87b72e10987118010830c57 | 5350036523d7a905429605a828865e5ddd95d3c5 | /emoji_scrape.R | dcd0ae6d4c308020575d593e31de84e566c121f6 | [] | no_license | Mvondoivan/Text_mining_sauce | e5adb8b34627966260a8964c0eded70ee4fafde0 | 664ae99e73e8b92d9e6d8fff3e7d41da15633d75 | refs/heads/master | 2020-03-22T08:16:16.866897 | 2018-11-27T05:55:00 | 2018-11-27T05:55:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,395 | r | emoji_scrape.R | scrap_all_emoji=function(){
str_url=c('https://emojipedia.org/apple/', 'https://emojipedia.org/emojidex/', 'https://emojipedia.org/emojione/',
'https://emojipedia.org/emojipedia/11.1/', 'https://emojipedia.org/facebook/', 'https://emojipedia.org/google/',
... |
5c07748291211e15444a103c93173054e09cde06 | 66d59625dd1c08762976ff2fb2917659abb60a3b | /code/process/testCNDRtoABAprint.R | 46e16e57e35f2d2b0fba3f02418f141e90cb31cf | [
"MIT"
] | permissive | ejcorn/tau-spread | 37811e2b5ba600c4903d02f2d30eaa69fe69cca0 | eda26928b726370ffad8ba57772e85f84e547bc0 | refs/heads/master | 2023-06-30T04:39:32.402466 | 2021-07-20T22:18:40 | 2021-07-20T22:18:40 | 242,880,836 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,106 | r | testCNDRtoABAprint.R | # print which regions match and don't match between ABA and CNDR mappings
#################
### Load data ###
#################
grp <- 'IgG1'
rm(list=setdiff(ls(),c('params','grp')))
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'diffmodel/',sep='')
dir.create(savedir,recursive=T)
source('cod... |
c98eeead10babd1ec62a8a2d14d9afca0d8ec8df | d6ebc7fc723fe6280478a2c4e6c303adbdc0e364 | /R/shinydashboardPlusGallery.R | a22d9c5b00d94f0471bb3fda5528355af610efc6 | [] | no_license | pvictor/shinydashboardPlus | 19cafc572b53077f1ad5aa79752b73b6d17e9872 | df71208b1489f5e8e9b8da0c51dbd8a6a94367e7 | refs/heads/master | 2020-03-14T23:15:57.456885 | 2018-05-02T11:37:08 | 2018-05-02T11:37:08 | 131,840,991 | 0 | 0 | null | 2018-05-02T11:27:31 | 2018-05-02T11:27:31 | null | UTF-8 | R | false | false | 978 | r | shinydashboardPlusGallery.R | #' @title Launch the shinydashboardPlus Gallery
#'
#' @description
#' A gallery of all components available in shinydashboardPlus.
#'
#' @export
#'
#' @examples
#'
#' if (interactive()) {
#'
#' shinydashboardPlusGallery()
#'
#' }
shinydashboardPlusGallery <- function() { # nocov start
if (!requireNamespace(package =... |
800b2c1194581a0046bda71d49c30c8b96cd2cfa | 86706fbbece1928081502b6ad124fde660e9a40f | /spotify_app.R | 6aa6edb231f2af59cd28de1cba803ef426e00233 | [] | no_license | ntsherm2/spotify | 8530c6d1853f9f9a4ede764a33f1f8e71279d23e | 93c251036b0270a3ec89571646b932d501af9860 | refs/heads/master | 2021-01-05T08:23:21.654562 | 2020-02-16T20:08:40 | 2020-02-16T20:08:40 | 240,951,408 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,363 | r | spotify_app.R | library(shiny)
library(ggplot2)
library(dplyr)
library(mlr)
library(DT)
chill3.0 = readRDS('chill3.0.rds', refhook = NULL)
ui = pageWithSidebar(
# App title ----
headerPanel('Spotify Playlist Analysis'),
# Sidebar panel for inputs ----
sidebarPanel(
selectInput('var_x', 'X Varia... |
fb651139a413a0680493bc96de55c6412d64dfec | e36e8d5859f764ffa3e6f18d2b5dcd6bbd4e80f0 | /man/df.gis_wsp.Rd | 089127d1571ca55c0ee5a5dfebdc542ddbe9e5a3 | [
"MIT"
] | permissive | ropensci/rrricanes | 23855df40a5cc598b94ec90ac9e32c70b291e2a8 | 533454c8e4d3b7dff6dc2a6592a7b304fef41fdb | refs/heads/main | 2023-01-07T17:56:01.118103 | 2022-12-31T18:29:58 | 2022-12-31T18:29:58 | 74,975,357 | 19 | 9 | NOASSERTION | 2022-12-31T18:29:59 | 2016-11-28T13:25:12 | R | UTF-8 | R | false | true | 437 | rd | df.gis_wsp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{df.gis_wsp}
\alias{df.gis_wsp}
\title{GIS wind speed probabilities for Hurricane Sandy (AL182012)}
\format{
An object of class \code{list} of length 3.
}
\source{
\url{http://www.nhc.noaa.gov/gis/archive_wsp.php}
}... |
7bb2cbf3ecc0fc7f2ebd44953eeade33c88a91d8 | ec43bdacb37a1e923b27a85b96be8774f7e3e722 | /man/securities_returns.Rd | aebab61bb9381c7344a95fa4b497c7ac21a09c90 | [] | no_license | irudnyts/estudy2 | 32e9ffcf3d9b0053bb5330556a6e3233fbf752fb | 4824af33ab3c8a7f2a60f2e412355e7510c87b26 | refs/heads/master | 2022-11-13T03:36:30.018707 | 2022-04-12T12:36:33 | 2022-04-12T12:36:33 | 38,372,777 | 13 | 4 | null | 2022-10-21T07:52:05 | 2015-07-01T13:32:16 | R | UTF-8 | R | false | true | 722 | rd | securities_returns.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{securities_returns}
\alias{securities_returns}
\title{Returns of seven companies from 2019-04-01 to 2020-04-01}
\format{
A list of eight \code{zoo} elements:
\itemize{
\item AMZN
\item ZM
\item UBER
\item N... |
50f16279b649a17ccd300f9c43359899fdb1ae98 | 28169cfd99b95aeb5ad2391881ac994d40614fcf | /coup_exploration.R | 5582e7b730cc0b292c075314cfb950f5e269ca7c | [] | no_license | pscharfe/Predicting-Coup-Outcomes-in-R | 0ca477f1a7a7b9d819687d567cfeedb1d7baf0dc | 10ff1ac007d0513dce93323690ca15563e85b6b5 | refs/heads/master | 2023-02-02T08:42:42.457324 | 2020-12-20T03:55:51 | 2020-12-20T03:55:51 | 231,047,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,410 | r | coup_exploration.R | library(tidyverse)
require(stargazer)
library(stats)
library(dplyr)
library(janitor)
library(boot)
library(knitr)
library(rpart)
library(rpart.plot)
library(ggplot2)
library(randomForest)
library(gbm)
library(Hmisc)
# Introduction: The goal of this script is to explore the basic trends in the data, espec... |
5fac535ad8cf422dbcd7c1482d1d78cff6d20d2f | 1a953ee20468612ccf2c0fe05e3189593e8488de | /IBM_full_Likelihood.R | e01be287a1dba60fb518332de6f544ddd3af1d22 | [] | no_license | MarieAugerMethe/Babybou_IBM | 96528350263212a4591311560db124374fce613e | 86f9088d7d98a18bef21a9f1dbd7cffeabc7367d | refs/heads/master | 2016-09-05T23:28:27.982712 | 2014-08-29T20:03:48 | 2014-08-29T20:03:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,566 | r | IBM_full_Likelihood.R | ##
# Date created: September 25th, 2013
# Authors: Marie Auger-Methe, Ulrike Schlaegel, Craig DeMars
# Please cite our paper if you use our script:
# DeMars, C., M. Auger-Methe, U. Schlaegel, S. Boutin, (Published online)
# Inferring Parturition and Neonate Survival from Movement Patterns of Female Ungulates.
# ... |
76741a59db412fcf6755a34da0bb297fcc04c2db | a77ada7874b9f355fc43edfb8ed0ead40afa6165 | /analysis/beats_to_blocks.R | 1212309cc2fb9c929cb794c32e6de93ed9615959 | [] | no_license | johnson-shuffle/rsji | bab734aac9cf6008eeaaae7426573c8a4b555028 | 2adc2c75db415ef4c8d4a1379cd6c14c13d45630 | refs/heads/master | 2018-09-01T10:09:32.896051 | 2018-08-21T04:33:05 | 2018-08-21T04:33:05 | 107,724,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,076 | r | beats_to_blocks.R | # ----- preample ----------------------------------------------------------
library(rgdal)
library(rgeos)
library(sp)
library(maptools)
# ----- spatial data ------------------------------------------------------
# seattle bg's
download.file('https://tinyurl.com/y9933p2x', destfile = 'raw/tmp.xlsx')
geo <- read_exce... |
9a3eb7ebf523a0fbbbc8ecc4ab5b98aab8c83227 | 2dbaaeda91fc8c894d3ab498cde5bf2eafe2b564 | /code/R/NEW_supplemental_figures.R | ef2f2523c495d558211f333d1f98c12f86dfd9f2 | [] | no_license | jlleslie/AdaptiveImmunity_and_Clearance | f19756b8a5f1c74031bd5fbd868d002ed2763b1c | bed19c78a1759da752d384b8156d1a5331ed25a5 | refs/heads/master | 2021-07-31T21:45:03.870499 | 2021-07-20T18:20:39 | 2021-07-20T18:20:39 | 62,152,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,471 | r | NEW_supplemental_figures.R | ###Supplmental Figures for Clearance Paper
#setworking directory
setwd("~/Desktop/AdaptiveImmunity_and_Clearance/data")
library(ggplot2)
library(grid)
library(scales)
library(vegan)
library(gtable)
# Modified from soure of function: http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/
## Gives c... |
3ec6226f2981ea78333fdf00086dfcd3e6040a3a | 2367f790fd23832252d4453bb4a83ee75ea71f04 | /man/calc.FXtF2.Rd | 1b4fcaef6587969b651118d9c731c6c702012705 | [] | no_license | cran/SpatioTemporal | 41857dce535b45735c3711786b0294d5b8951f69 | 3149f4a6ba0359d5b9c1a8fd599ce1bcdb855b1b | refs/heads/master | 2021-05-16T01:45:33.120006 | 2019-02-09T15:31:02 | 2019-02-09T15:31:02 | 17,693,753 | 0 | 4 | null | 2021-04-20T21:51:01 | 2014-03-13T03:40:40 | R | UTF-8 | R | false | true | 2,519 | rd | calc.FXtF2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/c_F_mult.R
\name{calc.FXtF2}
\alias{calc.FXtF2}
\title{Compute Quadratic Form Bewteen Temporal Trends and Sigma B}
\usage{
calc.FXtF2(F, mat, loc.ind, F2 = F, loc.ind2 = loc.ind)
}
\arguments{
\item{F, F2}{(number of obs.) - by - (nu... |
1774f936fa486894d8de52be64433a70153848c0 | 13af81ebab307021042b3820f80f9ab71f6fd9d7 | /man/RentCalculatoR.Rd | b9e25deeed2e547e5d1a886cd231c06473cc103f | [] | no_license | rp6921/RentCalculatoR | 39d9f142b48b97f9ea6026c5f198e81d969de5eb | 18a6dc5828288a0c13be5afe895bcfc679331edd | refs/heads/master | 2022-10-16T07:29:23.640188 | 2020-06-12T22:04:48 | 2020-06-12T22:04:48 | 270,312,991 | 1 | 0 | null | 2020-06-07T13:42:34 | 2020-06-07T13:20:45 | R | UTF-8 | R | false | false | 4,990 | rd | RentCalculatoR.Rd | \name{RentCalculatoR}
\alias{RentCalculatoR}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{RentCalculatoR
%% ~~function to do ... ~~
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
With the package RentCalculatoR you can calculate the change in rent of re... |
561b7521c9b6d50d7511470d2b8356652141b7ea | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dse/examples/checkResiduals.Rd.R | 5d1fde381ff155b8f1355e9502d248cc0265b532 | [] | 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 | 336 | r | checkResiduals.Rd.R | library(dse)
### Name: checkResiduals
### Title: Autocorrelations Diagnostics
### Aliases: checkResiduals checkResiduals.default checkResiduals.TSdata
### checkResiduals.TSestModel
### Keywords: ts
### ** Examples
data("eg1.DSE.data.diff", package="dse")
model <- estVARXls(eg1.DSE.data.diff)
checkResi... |
58a53eb58fef88ac7f877431bd9112b57149c69a | 6eed4337c1a918c2e615198699b8271ac8d25ffc | /R_basics/13_LogisticRegression.R | b6f250e3e6b983db39500a6f5774f5e1ff675310 | [] | no_license | Niks056/R_basics | b1653d6d0cb0d6f31033fa1c822a513272c5d43d | 67fb11246ebb5757a0f3d19543361bae23586064 | refs/heads/master | 2022-12-12T17:18:54.513429 | 2020-09-10T10:00:21 | 2020-09-10T10:00:21 | 294,370,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,108 | r | 13_LogisticRegression.R | setwd("C:\\Users\\91869\\Documents\\r pROGRAMS\\R")
data1<-read.csv("loan predictionLog.csv",na.strings = c("NA","NaN",""),stringsAsFactors = FALSE)
str(data1)
data1$Loan_Status<- ifelse(data1$Loan_Status=='Y',1,0)
data1$Loan_Status<- as.factor(data1$Loan_Status)
#Excluding LoanId column
col<-ncol(data1)
d... |
01128c59b4e24a8941ca08e42f6dc6de62b2dda5 | afdabf3f753220a8341d99fd45849ab84e768a5a | /man/CkVarType.Rd | 6ee728973aec51b4e60fda8972fde2683c8c0217 | [] | no_license | vmoprojs/GeoModels | 92b4292cc87c1e4eeed674010f4d214783d7546e | 55b885c10288a9d3cf5132807b80cca143869f7a | refs/heads/master | 2023-08-30T23:28:00.066551 | 2023-08-29T03:45:09 | 2023-08-29T03:45:09 | 122,672,674 | 4 | 13 | null | 2021-03-28T20:13:41 | 2018-02-23T21:05:54 | C | UTF-8 | R | false | false | 1,046 | rd | CkVarType.Rd | \name{CkVarType}
\alias{CkVarType}
\encoding{UTF-8}
\title{Checking Variance Estimates Type}
\description{
Subroutine called by InitParam.
The procedure controls the method used to compute the estimates' variances.
}
\usage{
CkVarType(type)
}
\arguments{
\item{type}{String; the method used to compute the estimate... |
efb93dbeb830163419403f85259a1496aaf6c59e | e5e9a24cddbe5af33623dda455051b1e67646ff7 | /project(1).R | 590d07190d1ff0ef7cd86fac1f4e50cc51fc79e5 | [] | no_license | bielusha/hahaha | 94adbd2fa8d81c325f07a80d6cae8c809762a97d | 6c4674589691cc931f74842fc6396cdf13689c99 | refs/heads/master | 2021-01-19T16:26:12.116879 | 2017-04-15T00:38:06 | 2017-04-15T00:38:06 | 88,264,290 | 0 | 0 | null | 2017-04-15T00:04:00 | 2017-04-14T12:04:40 | R | UTF-8 | R | false | false | 1,862 | r | project(1).R | rm(list=ls())
library(quantmod)
library(fGarch)
#(a)
getSymbols('VIIIX',from='1998-1-1',to='2017-4-7')
getSymbols('VGTSX',from='1998-1-1',to='2017-4-7')
priceviiix<-VIIIX$VIIIX.Close
pricevgtsx<-VGTSX$VGTSX.Close
retviiix <- diff(log(priceviiix))
retvgtsx <- diff(log(pricevgtsx))
retviiix <- retviiix [-1,]
retvg... |
eb4e4ef19b11d059839b3e72aa3f4a7415a063fd | fe50588a00b21024546902728bc6ae66ae4ec846 | /run_analysis.r | 55d9d7e0421493b07940465fb0bcb2a3832a04e7 | [] | no_license | willysousa/datasciencecoursera | 2353208f3f2ae1e7d829e8578d199c001825dc86 | 33fed3939f05148ebdab824f60e0cc17f5d6611c | refs/heads/master | 2016-09-05T13:24:46.491722 | 2014-09-21T04:04:19 | 2014-09-21T04:04:19 | 22,669,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,441 | r | run_analysis.r | library("data.table")
library("reshape2")
table1 <- read.table("./Unit2/project/UCI HAR Dataset/activity_labels.txt")[,2]
table2 <- read.table("./Unit2/project/UCI HAR Dataset//features.txt")[,2]
table3 <- grepl("mean|std", table2)
table4 <- read.table("./Unit2/project/UCI HAR Dataset/test/X_test.txt")
table5 <- read.t... |
b8c4b74ba0e7ac63e928ee30bb0ba422da00a450 | 648bae9ec2bd795413f067ce43d966eb8902939b | /man/weighted_sum_ga.Rd | 36c386c803228da6c70edad0fc200217e2f37a44 | [
"Apache-2.0"
] | permissive | jiripetrlik/r-multiobjective-evolutionary-algorithms | e1b129ad710bcd9a5a6fd45095dba916c357fe37 | dcbdcb943f9c6ebd53b7522cb0762de69c52d591 | refs/heads/master | 2020-06-12T19:29:11.397358 | 2020-04-26T19:58:46 | 2020-04-26T19:58:46 | 194,402,707 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,032 | rd | weighted_sum_ga.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/weigted_sum_ga.R
\name{weighted_sum_ga}
\alias{weighted_sum_ga}
\title{Weighted sum genetic algorithm}
\usage{
weighted_sum_ga(objective_functions_list, weights, chromosome_size,
chromosome_type = "binary", population_size = 100,
number_o... |
4195792b113eb2edd68af817206843f16b8ca4ab | 1744354b7b694860f0b2e4b7a2646bbde216fbd3 | /maria_sebastian.R | 67a7667a2a380f53d3810dff9f8a404f8b8a27da | [] | no_license | relund/mdpClass2020 | 9f4be5933a0c1ed87f2075adeaa94c5a9ca993a8 | a0a6fb1f7082cf565691962c4a8aabcefcece32d | refs/heads/master | 2022-06-16T04:44:51.923979 | 2020-05-07T13:32:10 | 2020-05-07T13:32:10 | 259,257,266 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,617 | r | maria_sebastian.R | library(MDP2)
h= matrix(c( 0.000, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000, 0.770, 0.176, 0.000, 0.020,
0.034, 0.000, 0.117, 0.677, 0.058, 0.075, 0.073, 0.000, 0.067, 0.200, 0.279, 0.233, 0.221,
0.000, 0.000, 0.167, 0.167, 0.240, 0.426, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000), # the data el... |
3d5df443e102f9d674c870940e7158720e05636b | 645bfd42abf0a53f0194819697ec54e32b033652 | /tests/testthat/test-error.R | 086a70bd5eae7381e03aedfcdba2cc8df2adcc01 | [
"MIT"
] | permissive | r-lib/callr | a0b319f93588f5e1e377882735118b7d836fffdb | e3e0acffdd817bb1c0f7fc5054f93b1d65ed8a45 | refs/heads/main | 2023-04-14T00:17:09.895755 | 2023-04-05T19:59:37 | 2023-04-05T19:59:37 | 58,728,879 | 249 | 37 | NOASSERTION | 2023-04-05T19:59:39 | 2016-05-13T10:26:09 | R | UTF-8 | R | false | false | 5,737 | r | test-error.R |
test_that("error is propagated, .Last.error is set", {
expect_r_process_snapshot(
callr::r(function() 1 + "A", error = "error"),
.Last.error,
transform = redact_srcref
)
})
test_that("error is propagated, printed if non-interactive mode", {
expect_r_process_snapshot(
callr::r(function() 1 + "A",... |
11badc89ce7582fb614902ff26fda60ace315394 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /stream/man/DSO_Sampling.Rd | a2e5954be339445eb0226032e0a56c6f0534791b | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,989 | rd | DSO_Sampling.Rd | \name{DSO_Sample}
\alias{DSO_Sample}
\title{Sampling from a Data Stream (Data Stream Operator)}
\description{
Extracts a sample form a data stream using Reservoir Sampling.
}
\usage{
DSO_Sample(k = 100, biased = FALSE)
}
\arguments{
\item{k}{the number of points to be sampled from the stream.}
\item{biased}{if... |
18f4d72a4e57cdf262fb61a5ade52767bb2195ed | d42e07aa8a9f17049b361e399c7bcd592cd02a09 | /slide4.R | 14464bee047704965013c29a88d934205f63a1ee | [] | no_license | thoughtfulbloke/digitalhumans | 2e67d58077789964c1f448cf80a6473d4974abdb | 5f87bab67cd9474434656990c7c4d8e524628232 | refs/heads/master | 2020-08-14T16:40:03.758457 | 2019-10-15T03:48:19 | 2019-10-15T03:48:19 | 215,201,107 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | slide4.R | library(atus)
library(dplyr)
library(ggplot2)
library(ggthemes)
data(atusact)
data(atusresp)
slide <- atusresp %>% filter(tuyear==2016) %>%
left_join(atusact %>% filter(tiercode >= 180000, tiercode < 190000), by="tucaseid") %>%
count(dur) %>% filter(dur<=120) %>%
ggplot(aes(x=dur, y=n, xend=dur)) + ylab("\nCount... |
d223c24400e22678a58364a88cf9c4ee3f515eb6 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.database/man/redshiftdataapiservice_cancel_statement.Rd | fcad69887e66068df44e1ec9cebdfcf356b3b177 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 839 | rd | redshiftdataapiservice_cancel_statement.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/redshiftdataapiservice_operations.R
\name{redshiftdataapiservice_cancel_statement}
\alias{redshiftdataapiservice_cancel_statement}
\title{Cancels a running query}
\usage{
redshiftdataapiservice_cancel_statement(Id)
}
\arguments{
\item{Id}{[re... |
47fdcdeded7bd2ef30e4b513ddc0c285699aea9f | 29585dff702209dd446c0ab52ceea046c58e384e | /plsgenomics/R/rirls.spls.aux.R | d05e3f99adfc1cff2240b5a8ffc9dd4fc0abc910 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,284 | r | rirls.spls.aux.R | ### rirls.spls.aux.R (2014-10)
###
### Ridge Iteratively Reweighted Least Squares followed by Adaptive Sparse PLS regression for binary responser
### Short version for multiple call in cross-validation procedure
###
### Copyright 2014-10 Ghislain DURIF
###
### Adapted from rpls function in plsgenomics package, c... |
2cd2ffaff108956d0d67a50b43783a2433ab39f7 | c5afc1376eb22fbf791423fa8040fded3242d171 | /R_Scripts/1_master_file.R | 4db82abc7eb434e82b0b2a1025a3be1f70c414eb | [] | no_license | sjkiss/class_voting_canada_2019 | a1f69bbfdaf51c96a588cb21cd6806d4bb24d1af | 346c88babdf5f6ac4affc9938faf855985713706 | refs/heads/main | 2023-03-24T17:12:11.676816 | 2021-03-19T14:00:47 | 2021-03-19T14:00:47 | 349,434,189 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,699 | r | 1_master_file.R | ##load recoded ces files
load("Data/recoded_cesdata.Rdata")
library(tidyverse)
library(labelled)
library(here)
library(car)
### Checks
nrow(ces74)==2562 #TRUE
table(ces68$var323, ces68$var379)
table(ces68$var379, ces68$union_both)
table(ces74$size)
table(ces68$var379)
ces19phone$immigration
look_for(ces68, "marital")... |
34d372a047a165443dba59917ddc8bea22b10ed7 | d3410af0856f5ed552896a2bcd51548e5dd312eb | /man/social.Rd | 03926b6cdf8ba1469a36f3540213c5aec161fff3 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | lnsongxf/experimentdatar | 4f0810c49d29656a2757771eb843480a8bda7867 | f71a9d072aabadf4da95e71baa757842a2d295c9 | refs/heads/master | 2021-01-01T09:48:14.429333 | 2019-02-11T12:15:59 | 2019-02-11T12:15:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,944 | rd | social.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ExperimentData.R
\docType{data}
\name{social}
\alias{social}
\title{social data}
\format{A tibble with variables:
\describe{
\item{outcome_voted}{Response variable: Indicator variable where =1 indicates voted in the August 2006 primary}
\item... |
f2935cb296ef346687675570000c085eac100987 | 29585dff702209dd446c0ab52ceea046c58e384e | /cIRT/R/survey_data.R | 1402764e1c61ee55d40824fa627a70e9d6aa8bb4 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 583 | r | survey_data.R | #' @name survey_data
#' @title Survey Data
#' @description This data set contains the subject's responses survey questions administered using Choice38.
#' @docType data
#' @usage data(survey_data)
#' @format A data frame with 102 observations on the following 2 variables.
#' \describe{
#' \item{\code{id}}{Subject's A... |
5d967582fe9caa4e7e92ba9f221f419a3ae1dd50 | 90b7b861e03f62e160891de6288d0b774c413a9d | /Network Analysis in R/Network analysis in R.R | 397a26725bd1244204355b89e7bc4263e135ced0 | [] | no_license | arjun-1102/R-Programming | 7b1927f79ec97fd6b715859156aeea0b42d56c26 | fd0ad6399bc344bf329839477f5084f29cad9f85 | refs/heads/master | 2021-04-03T01:23:46.647059 | 2018-05-13T17:37:33 | 2018-05-13T17:37:33 | 124,435,537 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,494 | r | Network analysis in R.R | ###---- Network Analysis in R
# Load igraph
library(igraph)
# Inspect the first few rows of the dataframe 'friends'
head(friends)
# Convert friends dataframe to a matrix
friends.mat <- as.matrix(friends)
# Convert friends matrix to an igraph object
g <- graph.edgelist(friends.mat, directed = FALSE)
# Make a very b... |
46f6caf3801e88e159835be37fdca699f5c8aac3 | 0d3011640e72586db9b3b7c8533fd6b94a59e6ce | /PieBarViz/man/hello.Rd | 2651b7271be0f815b5c3063034bbf62d3190bc98 | [] | no_license | GenevieveRichards/Pie_Bar_Viz | 31dc5ff120725a9972642878a58f0ef4e8d10f8d | cc77a3df4cdb63cd5de014e9c9dc0c50e8627e02 | refs/heads/master | 2020-04-13T18:10:01.368301 | 2018-12-28T23:17:55 | 2018-12-28T23:17:55 | 163,366,594 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 405 | rd | hello.Rd | \name{pieBarViz}
\alias{pieBarViz}
\title{Pie Bar Vizualisation}
\usage{
pieBar(Dataset,Stacked = FALSE )
}
\description{
}
\examples{
chart2 <-
read.table(text = "Name No_Mutation ATM ATR BRCA2 NBN PALB2 PMS2 POLD1 PRSS1 RAD51D SLX4 XRCC2
'Aggressive Cases' 85 2 1 2 2 1 1 1 1 1 1 1",
... |
068124baaf71ac2ecd8ef3bcd4a2a847ddde36de | ded24dcc44f53ec1adcf336ed211e4b8c4076c70 | /library/graph_level.R | ceba741b9104490c134f1b259d664f667428e493 | [] | no_license | hying99/capstry | 76ecd155d3a3a11a1e9e512249a34b3835235140 | e7288ab030e72cdee53bc882f9727d798aa5d2a7 | refs/heads/master | 2023-07-04T07:46:47.850022 | 2021-08-08T08:42:09 | 2021-08-08T08:42:09 | 393,864,850 | 1 | 0 | null | 2021-08-08T08:42:10 | 2021-08-08T05:15:42 | Java | GB18030 | R | false | false | 1,118 | r | graph_level.R | #按照路径最长原则得到每个GO标签所属于的层级
GraphLevel<-function (graph.var,onto="BP")
{
graph.new.nodes=graph.var@nodes
graph.new.edgeL=graph.var@edgeL
for(i in 1:length(graph.var@edgeL))
{
if(length(graph.var@edgeL[[i]][[1]])>0)
{
graph.new.edgeL[i][[1]]=list(edges=graph.var@edgeL[[i]][[1]],weights=rep(-1,leng... |
bb802ccac838debb1a85409fcb12da502685d3c7 | 92742af6fbcb1d9fdd2e54360c6a5afdbf3bc94d | /man/boxGet.Rd | ebaa891de735ffffbb80fc8c1bc386a2e6767f25 | [
"MIT"
] | permissive | alexbrodersen/boxr | 93f7405828ac445acb6a65ce347cf49c82bd0b99 | 14d2ae61c5142edc4dc8ff1c1c96c64b1b5bf650 | refs/heads/master | 2022-12-29T08:07:18.782372 | 2020-09-06T18:05:56 | 2020-09-06T18:05:56 | 295,472,641 | 0 | 0 | NOASSERTION | 2020-09-14T16:20:04 | 2020-09-14T16:20:03 | null | UTF-8 | R | false | true | 609 | rd | boxGet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/boxr__internal_get.R
\name{boxGet}
\alias{boxGet}
\title{Issue a get request for a file stored on box.com}
\usage{
boxGet(
file_id,
local_file,
version_id = NULL,
version_no = NULL,
download = FALSE,
pb = FALSE
)
}
\description{
T... |
62d6e4c1d1b3a03550725f154491e7e10483201a | 9b07a5ebd6eeceeda384cfdcb4012763769d9455 | /code/SelectSlidingwindow.R | 388b629ede62c636b2355faa4b08ab47a27e96ba | [] | no_license | qlcm/tmp | 4a16aebd06581bb738c137bedf3566b780c218e6 | fc33d28354cad3863b9743617ebf5d65b7488e5d | refs/heads/master | 2021-01-10T04:52:28.976116 | 2018-05-05T13:55:13 | 2018-05-05T13:55:13 | 54,164,883 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,107 | r | SelectSlidingwindow.R | GetDensity <- function(ref.fa, kWinSize) {
posi <- matchPattern("CG",ref.fa)
rt <- logical(ref.length)
rt[start(posi)] <- TRUE
win <- list(L = kWinSize, R = kWinSize)
return(swsCalc(rt, win))
}
GetScore <- function(cg.mtbr, kWinSize, ref.length) {
colnames(cg.mtbr) <- c("chr", "posi", "rC_n", "rC_p", "r... |
8cdbf7b29f1a3065e5c756c90cf891fde6805d79 | 5d371965180b1e28876554175f15ad7ba289795a | /Plot2.R | abab8041659661702444432b4356cbc449a99542 | [] | no_license | emclass/MyPlotsProj1 | 148a943be9528c4a96b62ffe7c3ddd99606d20f2 | 7677c8c72ad9b29fa0b13b8886c2c98e53382ff4 | refs/heads/master | 2020-12-26T08:15:05.840526 | 2014-10-14T01:58:43 | 2014-10-14T01:58:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 798 | r | Plot2.R | initial <- read.table("household_power_consumption.txt", sep= ";", nrows=5000,
header = TRUE)
classes <- sapply(initial, class)
data <- read.table("household_power_consumption.txt", sep = ";", colClasses=classes,
na.strings = "?",header=TRUE)
library(data.table)
data<-data.tabl... |
9e972efad7e3f5c3cc957c1d4c18dda2628ea6c4 | ecfc59b69d7c55c7e9cf14475b45fcb128cc5705 | /man/is_date.Rd | d27f543caff129a8281b686ae6c548b989dd9b1b | [] | no_license | cran/assertive | 2c49d5e8197c3eaa844212dbf54ba2c999c08666 | 3fa4a529eeaa4560c065c2043ae122105f1469f8 | refs/heads/master | 2020-12-29T02:44:25.998287 | 2020-08-01T00:00:02 | 2020-08-01T00:00:02 | 17,694,504 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 327 | rd | is_date.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exports-types.R
\name{is_date}
\alias{is_date}
\alias{is_posixct}
\alias{is_posixlt}
\alias{assert_is_date}
\alias{assert_is_posixct}
\alias{assert_is_posixlt}
\title{Is the input a date?}
\description{
See \code{\link[assertive.types]{is_dat... |
5000ac46cdfd4a902c1c004fccf4f45598b92691 | 9ec02f3f906d9b5d5e8641de721ededa1b50831f | /man/partial.Rd | 3561311c98bd111ae2ac8e2a566526c6d71287dc | [] | no_license | thomasp85/curry | c84ae13ba02724373ff5ee863903dc240e8d4f66 | e68ed51ec823f81986774ce855da278559d79f13 | refs/heads/master | 2020-05-23T08:09:11.205811 | 2016-09-29T13:49:04 | 2016-09-29T13:49:04 | 69,344,879 | 29 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,419 | rd | partial.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/partial.R
\name{partial}
\alias{\%><\%}
\alias{partial}
\title{Apply arguments partially to a function}
\usage{
fun \%><\% args
partial(fun, args)
}
\arguments{
\item{fun}{A function to be partially applied. Can be any function (normal,
alre... |
594b946102efd0ab17888a4f1f55bba085606c2d | 50711e687a44aeb126149528f2e5deec6e261cfb | /complexHeatMap.R | 175dee70bbf2786014c1fbd3c91badbefb8feb96 | [] | no_license | BlackburnLab/immuneCellEnrichment | 83ba534213e7a7de6a21b6854149b182b3a56545 | 3f23106995dca2a7037b8f21f1499f438ef194a3 | refs/heads/master | 2023-04-02T13:22:44.139834 | 2021-03-14T18:58:49 | 2021-03-14T18:58:49 | 352,647,806 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,233 | r | complexHeatMap.R | library(devtools)
##install_github("jokergoo/ComplexHeatmap")
library(ComplexHeatmap)
library(UpSetR)
cellPop = read.csv("C:/Users/Javan_Okendo/Desktop/cybersort/cybersort_source_code/TB_hart_deconPDF/complete_sample_group_immune_deconvolution.csv",
header = TRUE, sep = ",")
head(cellPop)
... |
358db25c1bdd9b30bf71d94ffc8459712e477bba | 35edf35bebc25df564887a2e107b13f5ba47a605 | /Homework1/man/dmvnorm.Rd | d6ae22e47e521ccc6c5689e3126e863370299df1 | [] | no_license | dengdetian0603/Biostat778_HW1 | 0c8241bb7f67127271527516a523d43c1c65b3c2 | 9694bc444ffd26567f12cb14977a62ddaab676a9 | refs/heads/master | 2021-01-21T03:51:40.111609 | 2013-11-13T16:22:31 | 2013-11-13T16:22:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,864 | rd | dmvnorm.Rd | \name{dmvnorm}
\alias{dmvnorm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Fast Multivariate Normal Density
}
\description{
This function evaluates the k-dimensional multivariate Normal density with mean mu and covariance S.
}
\usage{
dmvnorm(x, mu, S, log = TRUE)
}
%- maybe also 'usage' for ... |
3be4953efaadb1543cc3bcc3e4fee5b645eae7e3 | 060e6cb5651dea54d531a2bf43790c50f89756f4 | /man/getSoreDurations.Rd | b0d5183c80a1af5aa768e12484560a57079fb888 | [] | no_license | MikeLydeamore/TMI | fdadc800417119fef939cf10938703d9fb015b9d | 87e2c0db71122f248a8131b5a55e58a8ef810e24 | refs/heads/master | 2020-03-06T21:36:28.878958 | 2019-10-20T05:25:26 | 2019-10-20T05:25:26 | 127,082,021 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 676 | rd | getSoreDurations.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tmi-functions.R
\name{getSoreDurations}
\alias{getSoreDurations}
\title{Get the per-individual sore durations}
\usage{
getSoreDurations(panel_data, formula, id = ID)
}
\arguments{
\item{panel_data}{Panel data to fit for}
\item{formula}{A for... |
e80628bef4462c6c3733d2850ccec7ab427c72fe | a4a658d367ddf2cf2ad2f2c381605573cc0228fb | /man/get_version.Rd | 2fa1586b5c4095dc3acef4dd960e78a5a3200e37 | [
"MIT"
] | permissive | Dschaykib/newsmd | 1054d016e48f25490906149a51b756f1b4501ffc | 1614d02eca9c35af7360de86ca1a5ce85251fd9a | refs/heads/master | 2023-04-19T19:56:02.042189 | 2023-04-19T09:54:56 | 2023-04-19T09:54:56 | 141,821,455 | 7 | 2 | NOASSERTION | 2023-04-19T09:55:52 | 2018-07-21T14:44:57 | R | UTF-8 | R | false | true | 826 | rd | get_version.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_version.R
\name{get_version}
\alias{get_version}
\title{Retrieve the version number of a NEWS.md file}
\usage{
get_version(file, latest = TRUE)
}
\arguments{
\item{file}{a path to a file}
\item{latest}{a Boolean, if TRUE (default) only t... |
0fdacb2abefc75924618b6670d340ff76d8480c2 | 766e8ee63386380e3c02e9254ba05b1a43870bd7 | /01_LF data wrangle.R | ec39f3b6398a1f3db14f7ccaa88be95231138337 | [] | no_license | stephenresearch/Local_Freezeup_Churchill | c97813e45d76823760c7e2761aba48313a92564d | a6850d8fdec8c9b3c02985dfe8b59cef63071269 | refs/heads/master | 2023-07-13T15:36:33.452676 | 2021-08-20T22:25:54 | 2021-08-20T22:25:54 | 398,411,800 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,545 | r | 01_LF data wrangle.R | library(tidyverse)
library(lubridate)
#library(reshape)
library(weathercan)
# Palette -----------------------------------------------------------------
#just for fun palette
library(wesanderson)
pal <- wes_palette(name = "Zissou1", type = "discrete")
pal2 <- c(pal[1], pal[3])
scale_color_manual(values=wes_palette(n=3... |
b17e2bce591b183e8bbb1c05aebe79357b3ec7af | fc36112ec2687ee3a56086fc121a8e8101c5d62c | /man/vocab_lter_scope.Rd | a4c09e1b1c3d675adeabd3aad51185ec475715c9 | [
"MIT"
] | permissive | EDIorg/EMLassemblyline | ade696d59147699ffd6c151770943a697056e7c2 | 994f7efdcaacd641bbf626f70f0d7a52477c12ed | refs/heads/main | 2023-05-24T01:52:01.251503 | 2022-11-01T01:20:31 | 2022-11-01T01:20:31 | 84,467,795 | 36 | 17 | MIT | 2023-01-10T01:20:56 | 2017-03-09T17:04:28 | R | UTF-8 | R | false | true | 576 | rd | vocab_lter_scope.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{vocab_lter_scope}
\alias{vocab_lter_scope}
\title{Get the scope of an LTER Controlled Vocabulary term}
\usage{
vocab_lter_scope(id)
}
\arguments{
\item{id}{(numeric) An identification number of a valid term in the LTER
Cont... |
67d7e921f15c3847383321a59fac25befcda156d | 8fe55d803ff5b0567cae81c5f5c996b22d0be251 | /06_Literature Reanalysis (Mouse)/06_Zeisel et al Reanalysis.R | fc4f0cf88ec2fed37cfde4a352f4bdbf2b6e2ad8 | [
"CC-BY-4.0"
] | permissive | samuel-marsh/Marsh_et-al_2022_scRNAseq_Dissociation_Artifacts | 3493e6f1c54536262178f979f8bdd8909bac7e73 | aeb696544d8cb2d026ca96899f706537ec296657 | refs/heads/master | 2023-04-18T08:27:44.228473 | 2022-03-08T21:43:09 | 2022-03-08T21:43:09 | 449,016,962 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,055 | r | 06_Zeisel et al Reanalysis.R | # Load Data & Create Object -------------------------------------------
zeisel <- connect(filename = "~/Desktop/Literature Reanalysis/Zeisel et al (Papain 10X)/l6_r4_microglia.loom", mode = "r")
zeisel
zeisel_seurat <- as.Seurat(zeisel)
zeisel$close_all()
# save raw seurat object after conversion from loom
write_rd... |
508e31d8cfb5d294a2f4572e84e387e37abbaa28 | f2cc6cedcabacbd3700bb46ddcefce0bc1215300 | /one/AnalyzeGDS2771_RollNumber.R | 5d032a409b9df5a7fe2d67a3d2015befe055f361 | [] | no_license | hrushikesht/ee622-Assignments | e438200cb58207af8d4045531de3b214ba6b6cef | 8bb53c26178fcd85e4bee4a0d770e999769b4108 | refs/heads/master | 2020-12-02T09:28:29.624147 | 2016-08-24T14:56:20 | 2016-08-24T14:56:20 | 66,467,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,177 | r | AnalyzeGDS2771_RollNumber.R | # 1. Install packages to read the NCBI's GEO microarray SOFT files in R
# 1.Ref. http://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/r/geo/
# 1.1. Uncomment only once to install stuff
#source("https://bioconductor.org/biocLite.R")
#biocLite("GEOquery")
#biocLite("Affyhgu133aExpr")
# 1.2. Use... |
f5e45149faf75995d0b82a2d57d47ceaf0641341 | 145d82d84702fc8794f9db674e041c5bc4205a75 | /1a parte/flex_files/flex_cart.R | 95dff56c025617e240c35344927e6dbee7477a7f | [] | no_license | sanchezvivi/instacart | d1ccc2e86a0b456e19f8e11979320e8a6317b594 | d52cc176aff44f559e29d6808161b0f8a54c4cf2 | refs/heads/master | 2022-12-23T00:55:48.132426 | 2020-09-05T21:55:48 | 2020-09-05T21:55:48 | 285,945,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,120 | r | flex_cart.R |
# Bibliotecas -------------------------------------------------------------
# biblios <- c('tidyverse','dplyr', 'ggplot2', 'lubridate', 'stringr',
# 'inspectdf', 'skimr', 'naniar', 'visdat', 'tidymodels',
# 'klaR', 'corrplot', 'NetCluster', 'factoextra', 'maptree', 'treemap', 'DT','patchwo... |
0eaacb4a208ff2fd53ddac28cece45ef90a2ddd6 | 5f93a26137b2e5f09eb970f2ea610963eec8d642 | /R/gradientPickerD3_example.R | 5e8c056e976252a8f3e8a070e7c9ae9cbadb231d | [] | no_license | peikert/gradientPickerD3 | 82e91a11dc083107426db0ba755838ef07ce7365 | 23cc649bc03bc34bf55838b90f0f03215580b7cf | refs/heads/master | 2021-03-27T09:56:13.250000 | 2017-10-11T13:49:46 | 2017-10-11T13:49:46 | 95,441,859 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,370 | r | gradientPickerD3_example.R | #' gradientPickerD3_example
#'
#' Creates an example shiny app which include the gradientPickerD3 and a rendered table for gradientPickerD3 return value. By clicking the reload button new random ticks will be generated.
#'
#' @import shiny stats
#' @export
gradientPickerD3_example = function() {
shinyApp(ui <- fluidP... |
f1145e3ec730070a3ec48d40ac39f1b1cd94cb5f | b406befbab9bcf0ea7c0eac21d6e163c3888ef9a | /example/ex.chisqstretch.pow.R | 9de0f545599679953d33c581f67f894528bbeb0d | [] | no_license | olli0601/abc.star | 0f5fc8a3d1d4ba7edb3719dc46b688454bab9cfa | dbda96d2e52b096e74a2fbdef32f3443b45da7a7 | refs/heads/master | 2016-09-15T03:35:43.924846 | 2016-04-14T20:11:38 | 2016-04-14T20:11:38 | 8,214,145 | 0 | 1 | null | 2016-04-03T13:43:53 | 2013-02-15T07:11:58 | R | UTF-8 | R | false | false | 1,104 | r | ex.chisqstretch.pow.R | n.of.x <- 60
n.of.y <- 60
# compute ABC tolerances
cali <- vartest.calibrate(n.of.x=n.of.x, n.of.y=n.of.y, tau.l=1/2, tau.u=2, what='CR', alpha=0.01)
# problematic ABC tolerances for ABC inference:
# although power is not zero, does not plateau around 1, and still high around rho=1 (desirable),
# the power is no... |
4b428f84fc830f3b8bf8d690da33017918177369 | d6c0595084b6f9f3a541df39d7e54ad2cdd29d8e | /man/resizeImage.Rd | 7a8032b4a912bf1057f3367a43e6d14f14881991 | [] | no_license | cran/phenopix | 2b6e5b2ea601de51c312e692e04ec050529bf5e8 | 9220b65ba06c6e08e1df76a365db0b78364ed684 | refs/heads/master | 2023-08-19T07:31:53.401802 | 2023-08-09T13:50:02 | 2023-08-09T15:30:47 | 94,452,244 | 7 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,731 | rd | resizeImage.Rd | \name{resizeImage}
\alias{resizeImage}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Resize an image (and a tROI) to a given pixel resolution
%% ~~function to do ... ~~
}
\description{
This function allows to resize a sample image and a correspondent ROI to a given pixel resolution to be used ... |
17b595c09a90ad2012e7edc9659e19dc4c2c1c38 | 623b144df283c68b8fe833f975a4068a98fed59e | /run_analysis.R | 9f66e375996c1018059f023fb18cbdaf11e3fb2e | [] | no_license | sougatabh/getting_and_cleaning_data | e463f74baa79cd7e37b8b6c8101f33fafc4bb57d | 8e20ced9f8b7976e99388410fb86d1fd7cf2e188 | refs/heads/master | 2020-05-04T23:54:17.116325 | 2019-04-05T02:57:44 | 2019-04-05T02:57:44 | 179,558,847 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,738 | r | run_analysis.R | library(data.table)
library("reshape2")
#### Download the Data ###
fileName <- "UCIdata.zip"
url <- "http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
dir <- "UCI HAR Dataset"
# File download verification. If file does not exist, download to working directory.
if(!file.exists(file... |
08e0ba37245d80d4ed3b3526b4714d35f698aaaf | dcaef87c4a61606e623560a924fba0b97bb50f20 | /decimals.R | d131e66fb56c1a7ceee57b1d515477ae2031a34e | [] | no_license | nmolanog/refous | bcbb9b3b9029bea1a1546de6f228275afe4f0860 | 6c519c068f53bf2b2cf5d1884d44461dd86655ac | refs/heads/master | 2020-03-22T22:03:15.367335 | 2018-10-29T23:53:00 | 2018-10-29T23:54:41 | 140,730,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,034 | r | decimals.R | rm(list=ls())
library(tidyverse)
div_int<-function(bse,x){
div_int<-x%/%bse
res<-x-div_int*bse
return(c(div_int,res))
}
fromx_to_10_v2<-function(bse,x){
if(x%>%str_detect("\\.")){
x%>%as.character()%>%strsplit("\\.")%>%unlist->break_x
break_x%>%map(~as.character(.)%>%strsplit("")%>%unlist%>%as.numeric(... |
aa7fadfd6c5043411333b512c12fd3e4f6538026 | d0717c9f78e4da0be6e8150640082a92c8bd10ae | /RegressionTests/move_files.R | 96acc617a62a5d157c741ed243b9ec005a5b5a7e | [] | no_license | bleutner/caret | b230f73042744b9ce22340b5e21bc74e9f6a2242 | b317b14d8f47d0995b5c09a3ec7f96297d7ff07e | refs/heads/master | 2021-01-18T11:42:32.834582 | 2015-06-15T15:56:33 | 2015-06-15T15:56:33 | 37,474,793 | 1 | 0 | null | 2015-06-15T15:46:38 | 2015-06-15T15:46:38 | null | UTF-8 | R | false | false | 2,275 | r | move_files.R | setwd("/Users/kuhna03/Code/github/caret/RegressionTests")
#############################################################################
newPath <- paste(format(Sys.time(), "%Y_%m_%d_%H"),
packageDescription("caret")$Version,
sep = "__")
dir.create(paste0("~/tmp/", ... |
1d011a576f381537c75c57ac3b8b160b3c570d76 | a8f01b9017964981d97f3720e50908c11ca0f99b | /R/03_formalize.R | 8b415d7c1d50be52d10dfa9a77780ad38a81d5d8 | [] | no_license | jeffnorville/map | eda5f12ffe952054cffee896aa4d28f9ddbdf6df | 75eb41e501b3a9b7f3dcb8b5742ebe68a00ed7ba | refs/heads/master | 2021-07-04T08:27:41.832563 | 2020-09-02T08:35:00 | 2020-09-02T08:35:00 | 167,517,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,401 | r | 03_formalize.R | #spatialisation only to DB
# format of form commun
# working directory
wd <- list()
# commonly used paths in my working directory
wd$data <- "C:/Users/Jeff Norville/Documents/R/map/data/"
wd$output <- "C:/Users/Jeff Norville/Documents/R/map/output/"
#rm(list=ls())
# appel packages
require(foreign)
require(dplyr)
r... |
6f869cf9fc15516d7453596ea43ab9e453e35a10 | ef9ba3e4ece365ddbc6c67fcf69d5d6a3457de1e | /R/plot functions.R | b2a394308fbf0205dbd7fbc63c8a7d718d117e2b | [] | no_license | gheger11/eigenangles | 573616bf40cffc3a58beea0570796bf1386c45d7 | 17248d7601e57f52318ab383b4fd9478dd6d58db | refs/heads/master | 2022-05-25T23:02:54.988694 | 2020-04-18T17:56:19 | 2020-04-18T17:56:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,414 | r | plot functions.R | plot.eigenangles<-function(tbl,component=1, colour='blue'){
ggplot(tbl %>% extract_component(component))+
geom_hline(aes(yintercept = integration_angles), colour=colour)+
geom_hline(aes(yintercept = -conservation_angles), colour=colour)+
geom_hline(yintercept=0, colour='black')+
coord_polar(theta='y',... |
a3c6bc4ae40048a06d2f5c1286a8f19e5d9b11c2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/cyphr/examples/key_sodium.Rd.R | 1fc4ba0cac261d97025713c22a32c984ad46501a | [] | 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 | 343 | r | key_sodium.Rd.R | library(cyphr)
### Name: key_sodium
### Title: Symmetric encryption with sodium
### Aliases: key_sodium
### ** Examples
# Create a new key
key <- cyphr::key_sodium(sodium::keygen())
key
# With this key encrypt a string
secret <- cyphr::encrypt_string("my secret string", key)
# And decrypt it again:
cyphr::decrypt_... |
88fc5dcaf09323ddaa32b1ce8271f192dbd16026 | a226f4b4cf54dd0e8164a727d24dca99e79e1354 | /tests/testthat/test_createGADS.R | 7b6c690b7eecd28fe63fae6a7b5f1a4c2e637ee8 | [] | no_license | beckerbenj/eatGADS | 5ef0bdc3ce52b1895aaaf40349cbac4adcaa293a | e16b423bd085f703f5a548c5252da61703bfc9bb | refs/heads/master | 2023-09-04T07:06:12.720324 | 2023-08-25T11:08:48 | 2023-08-25T11:08:48 | 150,725,511 | 0 | 1 | null | 2023-09-12T06:44:54 | 2018-09-28T10:41:21 | R | UTF-8 | R | false | false | 806 | r | test_createGADS.R |
# load test data
# load(file = "tests/testthat/helper_data.rda")
load(file = "helper_data.rda")
allList <- mergeLabels(df1 = df1, df2 = df2)
### create
test_that("GADS DB creation", {
expect_message(createGADS(allList = allList, pkList = pkList, fkList = fkList, filePath = ":memory:"),
... |
672bf1eaf50ecb998847a1e751aea4ba70e213af | 299585457e6f3fd9c3e82769db4d82becc67d05a | /man/hyperg.Rd | 37595a6c0d6a354c2303582eb149aeabcb66a1e3 | [] | no_license | jcfisher/backbone | 39ca2d01f5e99cab28fefd456a69b02a5a6c9fa8 | 89fd0f4b0d786534d398559d8cf72db1abb65b16 | refs/heads/master | 2020-08-05T06:28:38.456269 | 2019-10-03T15:42:41 | 2019-10-03T15:42:41 | 212,430,280 | 0 | 0 | null | 2019-10-03T15:42:45 | 2019-10-02T20:01:09 | null | UTF-8 | R | false | true | 955 | rd | hyperg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hyperg.R
\name{hyperg}
\alias{hyperg}
\title{Compute hypergeometric backbone}
\usage{
hyperg(B)
}
\arguments{
\item{B}{Matrix: Bipartite network}
}
\value{
list(positive, negative).
positive gives matrix of probability of ties above the obser... |
dd11c840e59209f033d571ecef0ed59672fad233 | fe4cd16ffb13b2f29c12ffd520c81cee0c23f7f0 | /man/import_qiime_otu_tax.Rd | 8d1175208db3e40fd285c808363cc4f65c4a2e36 | [] | no_license | xinchoubiology/phyloseq | 64648ee089fe42bb94a934bb559ce1c307e371b0 | 6eeb569025d330c5b1b709c103075b37b2feeaff | refs/heads/master | 2020-05-20T19:24:22.015998 | 2015-06-18T17:24:38 | 2015-06-18T17:24:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,136 | rd | import_qiime_otu_tax.Rd | \name{import_qiime_otu_tax}
\alias{import_qiime_otu_tax}
\title{Import now legacy-format QIIME OTU table as a list of two matrices.}
\usage{
import_qiime_otu_tax(file, parseFunction =
parse_taxonomy_qiime, verbose = TRUE, parallel = FALSE)
}
\arguments{
\item{file}{(Required). The path to the qiime-formatted
fi... |
65cdf893c8cfd9f84088fcbbd94533178ad6a38d | e6d57bdc1566ccf94d64a19c9efb0e4f00b51190 | /app_server.R | 1edba24e10f29d9c0ee2d7afc8b979eee1eb4981 | [
"MIT"
] | permissive | tomgerber/Interactive-Midwest-Data-Report | 642abfe643c18ab149ee202191af0479c56e8760 | b7b4b7f4a921f2ccee0862a8a511b889bb1ae4ea | refs/heads/master | 2023-01-13T04:54:38.512807 | 2020-11-11T05:58:56 | 2020-11-11T05:58:56 | 311,876,451 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 405 | r | app_server.R | midwest_data <- midwest
library("dplyr")
library("plotly")
source("app_ui.R")
source("./scripts/bar_chart.R")
source("./scripts/scatter_plot.R")
server <- function(input, output) {
output$chart <- renderPlotly({
return(bar_chart(midwest_data, input$analysis_var, input$color))
})
output$scatter <- renderPlot(... |
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