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187622b9e47585e360ce77b71d3a870aadbf8234 | e8aab8c1784eff9bb21f7e27f669b272a76fdda2 | /run_analysis.R | ffdada4183f8cda8a0b3ba48e189dcde4e8b198e | [] | no_license | mkim7/3-getting-cleaning-data | cd98317c2467389e483d45433c9172a719fb03e8 | d4c5f9680ea74bc22b44eca609d4e02d8981c4d6 | refs/heads/master | 2020-05-02T02:26:53.199533 | 2014-06-22T04:23:17 | 2014-06-22T04:23:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,219 | r | run_analysis.R | # download from url and unzip
setwd("C:/Users/Owner/Documents/R")
zipurl<-"http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
if(!file.exists("./data")){dir.create("./data")}
download.file(zipurl, destfile="./data/project.zip")
unzip("./data/project.zip", exdir="./data")
# name file... |
cd0d6209a92d4084cff398e377c15ac77b9b8c85 | b66c3d2fa20a2d0b0cfea23e88d7b030128ce24f | /R_Code/randomforest_bagging_ensemble.R | b5f8f2a36ad724414aefc06f65d5c20d348ae45e | [] | no_license | AGARNER18/Russian-Market | 059af69320c10cc67cb994901196ed2734a9d9d4 | d70e3b85b833086fdb3f3116389e415f2183fea0 | refs/heads/master | 2020-06-18T16:09:47.091566 | 2017-12-12T23:04:07 | 2017-12-12T23:04:07 | 94,169,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,259 | r | randomforest_bagging_ensemble.R | # Column price_doc was added to test data set in excel to match columns for rbind
# NA were replaced with blank spaces in excel for both the training and test data sets
# format of time stamp was changed for easier manipulation to MM/DD/YY format
install.packages("Hmisc")
install.packages("Metrics")
install.pack... |
4bc724b924f6e73903ae18516699736a6d8c9aec | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hdnom/examples/hdnom.calibrate.Rd.R | fdda5b08eb978bb72cb8eeab30cd43afb90297e3 | [] | 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 | 986 | r | hdnom.calibrate.Rd.R | library(hdnom)
### Name: hdnom.calibrate
### Title: Calibrate High-Dimensional Cox Models
### Aliases: hdnom.calibrate
### ** Examples
library("survival")
# Load imputed SMART data
data("smart")
x = as.matrix(smart[, -c(1, 2)])
time = smart$TEVENT
event = smart$EVENT
y = Surv(time, event)
# Fit Cox model with las... |
f9e0be9d3be87011167b75ecca3004a186e26122 | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2019/nonfatal_code/nonfatal_injuries/crosswalking/mr_brt_adjustment/07_save_xwalk_version.R | 7ec7c87668cdb9acc9a68fe691ba6ae1ed469ac9 | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 471 | r | 07_save_xwalk_version.R | source('FILEPATH/save_crosswalk_version.R')
bundle <- commandArgs()[6]
version <- commandArgs()[7]
name <- commandArgs()[8]
print('starting script')
bundle_version_id <- version
data_filepath <-
paste0(
'FILEPATH',
as.character(bundle),
'_',
name,
'_adjusted.xlsx'
)
description <- 'step 4 c... |
9d896b71d918ec20db132d68c2a124fe495cce6d | 56efee66de74609ccff457427236520ac661ea5c | /man/tdmEnvTUpdate.Rd | 16f6a63f1f809720460c565e4c79038b07569054 | [] | no_license | cran/TDMR | cc3e8b256624cd53eb350ce34e32ef34c92fe1ab | 0554aaaa8c3f828a3e2e9c898deddf7cc9f4d609 | refs/heads/master | 2021-01-18T23:27:21.160115 | 2020-03-02T16:20:02 | 2020-03-02T16:20:02 | 17,693,854 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 405 | rd | tdmEnvTUpdate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tdmEnvTMakeNew.r
\name{tdmEnvTUpdate}
\alias{tdmEnvTUpdate}
\title{Update \code{envT$tdm}}
\usage{
tdmEnvTUpdate(envT, tdm)
}
\arguments{
\item{envT}{environment TDMR}
\item{tdm}{list for TDMR, see \code{\link{tdmDefaultsFill}}}
}
\value{
en... |
9af4359a94debc53c962d6344307ca314a1a30f5 | 98a1ac6724e2a3d093e7aabd3bae476c6fe9dd47 | /man/moranplotmap.Rd | 3bdf426e047f3dd94c842ff03e8659f346f4dceb | [] | no_license | Abson-dev/GeoXp | f99ed11d16ca9044b496eab9a03a0d5b73cc8638 | 742911d53f99cdd95296dcf92b259aac710eaf5d | refs/heads/master | 2020-12-31T09:49:28.270689 | 2013-08-14T00:00:00 | 2013-08-14T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,202 | rd | moranplotmap.Rd | \name{moranplotmap}
\alias{moranplotmap}
\title{Moran scatterplot and map}
\description{
The function \code{moranplotmap()} draws a moran plot, used to detect spatial
autocorrelation in the variable var. On the x-axis, is represented \eqn{x-\bar{x}}{x-bar(x)}
and on the y-axis \eqn{W(x-\bar{x})}{W(x-bar(x))}, whe... |
6b535983b5c7c286280cad55618327a09a1a8ecb | 0202519b1c23a1b49d0809bf94b29c04bf3e1a60 | /server.R | 924f7b5c7d2259fcf6408ca48defb5749f92a52b | [] | no_license | dtriepke/FallzahlPlanung | 57ecd638089f34cc9b8d84ac835f280e2da1dc93 | 9f162745e8d35e96456895ff049653ee5deb023a | refs/heads/master | 2020-03-22T03:34:22.411336 | 2018-07-02T12:32:17 | 2018-07-02T12:32:17 | 139,439,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,737 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define server logic required to draw
shinyServer(function(input, output) {
outp... |
ab4467de4ea494e445b3a4005056ecaad688584c | 5f82d1bc22e4ef72a63c58852a2d035e124f1a37 | /man/filter.Rd | 3b8e41af0510312816360680be909761ff8bc2c5 | [] | no_license | cran/bupaR | 75608804ef045f678821740aaff123991d5d36b5 | ef020af22301e7aa8c82d62e4d01dd5aebaea99e | refs/heads/master | 2023-04-20T17:49:49.645967 | 2023-04-02T21:00:06 | 2023-04-02T21:00:06 | 86,215,725 | 0 | 3 | null | null | null | null | UTF-8 | R | false | true | 523 | rd | filter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filter.R
\docType{import}
\name{filter}
\alias{filter}
\title{Filter event log}
\arguments{
\item{.data}{\code{\link{log}}: Object of class \code{\link{eventlog}} or \code{\link{activitylog}}.}
\item{...}{Conditions to filter on}
}... |
49a85296aa5a4c27b1b36073cde962cd45597fea | 29585dff702209dd446c0ab52ceea046c58e384e | /EcoGenetics/R/eco.remove.R | 75906d8b76707a4e0e85e343e70baf416ca7d49d | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,200 | r | eco.remove.R | #' Creating an updated ecogen object by removing results of the slot OUT
#'
#' @param eco Object of class "ecogen".
#' @param ... Objects to remove from eco, typed without quotations.
#'
#' @examples
#'
#' \dontrun{
#'
#' data(eco.test)
#' variog <- eco.variogram(eco[["P"]][, 1], eco[["XY"]])
#'
#' # Assignation ... |
cfe8005f9930da9916c0a2dd38a76f400662f415 | c1463667cf3ff1057859b4bbd956b7e1737bc187 | /Word Clouds in R.R | 6bd9c93098a0280bcc32a59af80c34d0ee295d2c | [] | no_license | kshirasaagar/R-U-Ready | 0c6ce8d8d0bb297a754d2229c86ff268755720d7 | 1caf81814cdd9cc779771f763f34bbfa2fc424c9 | refs/heads/master | 2021-08-03T19:23:51.091305 | 2021-01-28T23:06:36 | 2021-01-28T23:06:36 | 30,675,127 | 1 | 1 | null | 2021-01-28T23:08:06 | 2015-02-11T23:24:22 | R | UTF-8 | R | false | false | 348 | r | Word Clouds in R.R |
#Word Clouds in R
library(wordcloud)
if(require(tm)){
data(crude)
crude <- tm_map(crude, removePunctuation)
crude <- tm_map(crude, function(x)removeWords(x,stopwords()))
tdm <- TermDocumentMatrix(crude)
m <- as.matrix(tdm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
... |
a0fe089bf5540c6393220a263f126532de4e67e5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/AeRobiology/examples/trend_plot.Rd.R | 9db23878f5961937b45392c37eb2189daf4e2399 | [] | 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 | 264 | r | trend_plot.Rd.R | library(AeRobiology)
### Name: trend_plot
### Title: Calculating and Plotting Trends of Pollen Data (summary plot).
### Aliases: trend_plot
### ** Examples
data("munich")
trend_plot(munich, interpolation = FALSE, export.result = FALSE, export.plot = FALSE)
|
39837859bc38be5461807793ed22b54262561228 | 6d443800445592a4bcdc3531a850d5152942e2fd | /GUI/browse_dataset.R | a28c8231032e1c57c29926d846a8a96c68d92da3 | [] | no_license | angy89/InsideNano | 35f2004414bd1065df4db686ceefdb2096b789da | 0b5ee4502106740acc3daec100cac37f015791d3 | refs/heads/master | 2021-01-18T21:11:38.811196 | 2016-01-10T20:23:47 | 2016-01-10T20:23:47 | 45,189,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,111 | r | browse_dataset.R | plot_gene_network = function(input,output,g,g_geni2){
output$geneNetwork = renderForceNetwork({
groups_path = input$Patway_g
validate(
need(input$Patway_g != "", "Please select a Pathway")
)
if(DEBUGGING)
cat("Number of groups ",length(groups_path),"\n")
good_index = whi... |
5010761d6a6eaa3c900e865005fe07b657be249d | 731e34c16a539dec90c735fdd5d01411beb9ded0 | /man/QuartetPoints.Rd | 12cf3db5d00b78ec883a91a760508d7212bd3641 | [] | no_license | cran/Quartet | ea908984409acd1ad9fc1308fb67ba3e38f14b93 | 99856b81479273f2ce404845fd31b57941dec9ac | refs/heads/master | 2022-07-24T18:25:09.315730 | 2022-07-08T09:30:02 | 2022-07-08T09:30:02 | 166,093,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,668 | rd | QuartetPoints.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TernaryPoints.R
\name{QuartetPoints}
\alias{QuartetPoints}
\alias{SplitPoints}
\alias{BipartitionPoints}
\title{Plot tree differences on ternary plots}
\usage{
QuartetPoints(trees, cf = trees[[1]])
SplitPoints(trees, cf = trees[[1]... |
a053c6a31522a38c7a7705788cd9e0bbcf6e4b39 | 1be9a4013a6e92171aeb2dd8aec1f72eae8ad192 | /ProgrammingAssignment3/rankall.R | 904e1f607661d9083be2b5c7c7e5f979fedb49d1 | [] | no_license | tmreic/datasciencecoursera | 698069647fe3e015d8b475111a8a1d37e8daa06d | 23ac57d863f743a28f22c486662f9b833b3196f4 | refs/heads/master | 2021-01-10T02:38:44.074386 | 2015-11-27T19:44:07 | 2015-11-27T19:44:07 | 45,751,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,230 | r | rankall.R | rankall <- function( outcome, num= "best") {
options(warn = -1)
## Read outcome data
FileData <- read.csv("outcome-of-care-measures.csv",header = TRUE)
#str(FileData)
## Check that state and outcome are valid
# #Error in best("BB", "heart attack") : invalid state
# nr <- FileData[, 7]
# s <-- ... |
5d87941da99b973342fbcce9383ff0a1b707086b | 648353748435f6d0f561a85f12b68a79961e00c3 | /BayesianTools/inst/examples/plotTimeSeriesHelp.R | 076993c8408b9dbd12c6609a10025590f4ecf950 | [] | no_license | martynplummer/BayesianTools | c5ddee2c3a276e005cb62b8b76272bff0cf073b8 | 9912dffb522949535efb941c999cb76bea3c3400 | refs/heads/master | 2021-06-29T15:50:52.493685 | 2017-09-15T11:18:46 | 2017-09-15T11:18:46 | 104,320,326 | 1 | 0 | null | 2017-09-21T08:13:09 | 2017-09-21T08:13:09 | null | UTF-8 | R | false | false | 312 | r | plotTimeSeriesHelp.R | # Create time series
ts <- VSEMcreatePAR(1:100)
# create fake "predictions"
pred <- ts + rnorm(length(ts), mean = 0, sd = 2)
# plot time series
par(mfrow=c(1,2))
plotTimeSeries(observed = ts, main="Observed")
plotTimeSeries(observed = ts, predicted = pred, main = "Observed and predicted")
par(mfrow=c(1,1))
|
24b3cc73781078bd08cdb157ab18d9fefc2a0437 | 2ab596843b6790fe884db75e6b6d69d1620dd9f1 | /gabbiadini_plos_2016.R | e86e5a1d9402696a25664da4545d87d135b58fdc | [] | no_license | Joe-Hilgard/gabbiadini_plos_2016 | 9b4690a22a51a9fe1797f332d9fcceb05efb1882 | a8230c023f46fb7a7b508b610c62456838a8b80f | refs/heads/master | 2016-09-13T14:52:36.112364 | 2016-05-23T15:08:47 | 2016-05-23T15:08:47 | 56,179,345 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,475 | r | gabbiadini_plos_2016.R | # re-analysis of Gabbiadini et al. 2016
library(psych)
library(dplyr)
library(magrittr)
library(ggplot2)
library(lavaan)
library(car)
dat = read.csv("data_set_PLOS.csv")
table(dat$played_game, dat$condition, useNA = 'always')
table(dat$played_game, dat$cond, useNA = 'always')
# Experimental assignment
dat %>%
sele... |
c775fb66db136c2de797f1e3545c3fdfb1111945 | 0be3fd82803848a267ad273aa98116fe8ea35b1f | /run_analysis.R | e973dbd8d30b6ccd6c7c302676bbc7c508f8e738 | [] | no_license | Crimsoma/Clean_data | 723245f48831cad6c1355579bbe6787cce6fbe9d | 87591aee1acdc0f7e8d97b48cc4b252ae8b5d6af | refs/heads/master | 2016-09-10T21:23:33.860769 | 2015-04-26T08:30:43 | 2015-04-26T08:30:43 | 34,601,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,860 | r | run_analysis.R | download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip", "meter.zip")
unzip("meter.zip")
#assembly of proper variable names
labels <- read.table("UCI HAR Dataset/features.txt")
labels[,2] <- sub("^t","time",labels[,2])
labels[,2] <- sub("^f", "frequency", labels[,2])
la... |
0913af8bed1268fc3ce67eebb28ff3baeba96e82 | fee1d58e1fcd16fd1c627f5e0facbb93af451c77 | /server.R | 3bb111f2cbed0567575411054a438470e779388f | [] | no_license | gegp01/C3reporta | 48f53c4b89570c0b5072058aa4dba2a0819bbf9b | a461544a015e3e570a9588b9ff2223ef4e864cef | refs/heads/master | 2021-01-03T00:15:28.142974 | 2020-03-23T15:03:01 | 2020-03-23T15:03:01 | 239,830,972 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,033 | r | server.R | server <- shinyServer(function(input, output) {
X<-read.csv("https://gegp01.github.io/C3reporta/coords.csv") # Datos de Jhon Hopkins Institute y WHO
D<-X[3:104,] # reported cases
death<-X[grep("death", X$name),]
death$country<-do.call(rbind, strsplit(as.vector(death$name), ".", fixed=T))[,... |
b5c58b5a10e1eae1b2264131e9cbd8b16b870ca4 | cd8376335f7de8210143eb742ac7f18e8cc571b3 | /R/setup_demo.r | d9e7b5d6150ea3ec4a15a868b9723f7543f1bbcc | [] | no_license | srhoades10/pkgdemo | dafe86035e9f857ee6fa0de53aef731f4f77cdb0 | 6a36c881a85e2d8b9ca623b9280373c809fe2ef8 | refs/heads/main | 2023-05-14T01:54:44.215493 | 2021-06-08T13:30:40 | 2021-06-08T13:30:40 | 375,001,054 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,210 | r | setup_demo.r | # Basic function setup
#' Add 2 numbers!
#
#' @param x: Float/Int
#' @param y: Float/Int
#' @return Summed numbers
#' @export
addNums <- function(x, y, ...){
val <- x+y
return(val)
}
#' Create random table!
#
#' @param nRow: Int
#' @param nCol: Int
#' @param addSample: Bool - Add a "Sample" column, numbered ... |
8c6e4830553f123d3abd38db820161255f766083 | a689e2c6152d8bc562d73eb06976f8d6b3080367 | /messages_per_day_and_hour.R | 31373de61b2e4c95ceba486e0863bd3eca9e34cc | [] | no_license | reubenjoseph/Whatsapp_chat_analysis | 6e62dbc2b5f506a26f77ea1ec69cb1a018bbfda4 | e0a38bdd89bc194d9a21870d15571904bf2ce66e | refs/heads/master | 2022-11-29T01:13:50.201001 | 2020-07-28T18:50:09 | 2020-07-28T18:50:09 | 283,297,364 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,282 | r | messages_per_day_and_hour.R | title<-paste0("Most Messages happen at hour ",chat() %>% mutate(hour = hour(time)) %>% count(hour) %>% top_n(1) %>% pull(hour))
chat %>%
mutate(hour = hour(time)) %>%
count(hour) %>%
ggplot(aes(x = hour, y = n)) +
geom_bar(stat = "identity",fill="steelblue") +
ylab("") + xl... |
95d81e141d0fecb81ca924a717ca9c2a2c3eb045 | 38116111ccbbb1c4580d8e8c5ac3f9775e1fa384 | /man/probabilitiesFromCooccurrence.Rd | dfc3d6cc3cf1d38fbecee50cf4fd7def19af1ca8 | [
"MIT"
] | permissive | terminological/tidy-info-stats | 6c1e37684eeac8d765384b773a23f0488eb7b467 | 1b1f19a718edb44c7178943c322b45fd1e3c93b1 | refs/heads/master | 2022-11-30T08:16:46.311945 | 2022-11-18T20:37:21 | 2022-11-18T20:37:21 | 232,600,275 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,611 | rd | probabilitiesFromCooccurrence.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidyConvertToProbabilities.R
\name{probabilitiesFromCooccurrence}
\alias{probabilitiesFromCooccurrence}
\title{Helper function to calculate probability from grouped data in a tidy friendly manner}
\usage{
probabilitiesFromCooccurrence(df, gro... |
85594835fe82ce0571dcf10cefc3d72f1c7044f8 | f31ed12f347be5d88f968532e6693b7d22ba2c8b | /Final.R | 4df0505afb1696a1d7f036f0037cc110e43b58e1 | [] | no_license | norib016/Grad_School_Admission_Prediction | 46940f632a2641d91c0df35a5514d69f8dd8c92e | 41a8ebdd54b85360dc0dcb785494f55b2b733f57 | refs/heads/master | 2022-08-17T01:10:02.347117 | 2020-05-21T04:26:15 | 2020-05-21T04:26:15 | 265,753,960 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,282 | r | Final.R | # Library--------------------------------------------------
library(readr)
library(corrplot)
library(leaps)
# Data Loading and preparing ------------------------------
Admission_Predict_Ver1_1 <- read_csv("Admission_Predict_Ver1.1.csv")
data <- Admission_Predict_Ver1_1
originaldata <- data
rm(Admissio... |
745fddd767de3b7eff1e2d6da0ff10ef9b8b9164 | 8f8eac85cfbf8d3bc768318848ec964cb297b1cb | /nesi/labour_situation/0_do_all_labour_situation.R | 48719a1e0abcba45c9ba8407de51e57b31622ac4 | [] | no_license | jnaudon/datachile-etl | 5231a3762dd32f3f3def4d568fc63934d603cf8b | 8fa577378d38f8d63f6dfdb00ed515bbb439f154 | refs/heads/master | 2023-03-23T00:36:35.698292 | 2019-03-23T03:30:16 | 2019-03-23T03:30:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 638 | r | 0_do_all_labour_situation.R | source("labour_situation/1_functions/1_load_functions.R")
source("labour_situation/1_functions/5_normalization.R")
#source("labour_situation/1_functions/6_batch_load_data.R") #uncomment to recalculate from 0
#source("labour_situation/2_process_labour_situation/1_process_labour_situation.R") #uncomment to recalculate f... |
91da4635327198eb76f4d3019b8229ecc8f7675d | c231c905f627d22ff1e8423f8eac86b8b0cc3af9 | /Capstone-Movielens.R | 5d6c3eba4125854e9cae59619310569c31d8e286 | [] | no_license | Marce68/Movielens_Capstone | ff46d0695f0fad2f4ce32b06ba4eb51679bfca39 | ed9f7382ac53a1280d44d45b6727a01be4e4bf83 | refs/heads/master | 2022-09-07T02:26:33.411539 | 2020-05-31T16:27:35 | 2020-05-31T16:27:35 | 268,316,368 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,230 | r | Capstone-Movielens.R | ################################
# Importing data
################################
# Note: this process could take a couple of minutes
if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org")
if(!require(caret)) install.packages("caret", repos = "http://cran.us.r-project.or... |
47e16c4461484766b28970d1f1eb3f5bbd7ea06b | 5de5c723f8a2269c1682d3df9ea22bb5dda1d4ee | /R/is.RStudio.R | acba1ba2159e60d5d31fa67974e54f77848cfac9 | [] | no_license | paulponcet/observer | 0048f28d5b7c16a3d31d82a85c0273f02a49d540 | 5db216f81d570ea71d4590afb6bf65e221b3a365 | refs/heads/master | 2021-01-12T05:08:17.191667 | 2017-01-29T18:47:48 | 2017-01-29T18:47:48 | 77,863,016 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 96 | r | is.RStudio.R |
# used by the 'View_obs' function
is.RStudio <-
function()
{
Sys.getenv("RSTUDIO") == "1"
}
|
988004b2dac07e8dc6647b820f1e3f606dd230b7 | f5488e63bbedd3340a86b2a7073ca9de3ab53532 | /Week 2 Russian Twitter.R | 7f3026c8879928a52e023472d09c684bc238c9c3 | [] | no_license | ClioLI/Computational_Comm | f4e8d8b2b54a62694f47e878dec497aaa5c4dc7f | 1f2b5ccac92330fe05f184dacdca042f0ce5e7a8 | refs/heads/main | 2023-03-22T23:10:10.406941 | 2021-03-19T07:01:20 | 2021-03-19T07:01:20 | 349,328,867 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,438 | r | Week 2 Russian Twitter.R | ###############################################################################################
## Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015-2017: ##
## Supplementary code ##
## ... |
307131f173f5cfc9545417ea1fc0955b7f5fd8c5 | 0c6b7e0a02150655d3391a91a8aae3b806d4a6c7 | /Scripts/Analisis Nacional/scrap_ALL_sinca.R | 7ea247f3ca1b4c6c53fc198368ec2413bfe9ae39 | [] | no_license | pmbusch/Reportes-SINCA | f3a23699334821c20457e109a5a1d714ee9b9691 | 4607f692aa5c109eaa4a5dfead9b0ace72c4c55a | refs/heads/master | 2023-02-22T04:06:44.125280 | 2023-02-05T00:10:43 | 2023-02-05T00:10:43 | 279,363,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,864 | r | scrap_ALL_sinca.R | ### Proyecto SINCA
## Descarga Masiva Datos SINCA. Descarga unicamente los CSV, sin modificarlos
## PBH Jul 2020
## Ultima atualizacion: PBH Jul 2020
## Carga Datos de las estaciones -------------
# Scripts necesarios
source('Scripts/00-Funciones.R')
source('Scripts/03_f_scrap_sinca.R')
library(tidyverse)
library(lubr... |
aa0598bbdf7cbc422f71cb9ffd3bcfc777419f4a | f2643256c6611d7de0db96d162f594388c2c2c50 | /analyses/Trial 2/uniquemobilenumbers.R | 15819b22ea95e8e90f8e341a3219c5a93e875d3e | [] | no_license | raubreywhite/trial_dofiles | e06a5b3b39e9195eda79dd33856d67c918ec4053 | eface3b83b107cf7e621b3c654e65b5cbd45b711 | refs/heads/master | 2022-06-14T03:26:17.492945 | 2022-06-02T07:27:04 | 2022-06-02T07:27:04 | 114,857,557 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,065 | r | uniquemobilenumbers.R | nrow(d)
nrow(d[(ident_dhis2_control==F &
ident_dhis2_booking==T)])
as.numeric(d$mobile)
uniquemoblienumbers <-unique(d[ident_dhis2_control==F &
ident_dhis2_booking==T,
c("bookyearmonth",
"bookorgdistrict",
... |
58a67329b215c28d68415397f0d2775428a6a081 | 6d98dfe9d7ed3319ccda5cc7978885114c68c09d | /man/fseq_env.Rd | e7c9166172324311caea7856aa5a786d4194d5d6 | [
"MIT"
] | permissive | MyKo101/mpipe | 44c552abc758f316ba6aed2b03f933a4afa43383 | d1e16c77525288123b0491b836bcac05f0c0b790 | refs/heads/main | 2023-02-22T04:27:48.580286 | 2021-01-31T01:32:54 | 2021-01-31T01:32:54 | 256,473,147 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,973 | rd | fseq_env.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fseq_env.R
\name{fseq_env}
\alias{fseq_env}
\alias{fseq_get_env}
\alias{fseq_print_env}
\alias{fseq_set_env}
\alias{fseq_check_env}
\alias{fseq_copy_fun}
\alias{fun_var_env}
\title{Alter the environments of fseq functions}
\usage{
fseq_get_en... |
539c0ee8880a839dafda9115927287a15c7be708 | d834d99c197aab4256952dcb9b6575f3f6cd1282 | /volumes_interpolation.R | e33332ec634310f8e072ff3f91372b3a9381b269 | [
"MIT"
] | permissive | rmsandu/R-data-analysis | 07a7760de2ae5d21477e0f29d818651f7a6f2cb1 | 15c522e56df068354d6140bee6c7189412488275 | refs/heads/master | 2022-09-17T23:56:51.489101 | 2020-06-04T17:56:30 | 2020-06-04T17:56:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 313 | r | volumes_interpolation.R | library(tableone)
library(dplyr)
library(ggpubr)
library(xlsx)
setwd("C:/develop/data-analysis")
data_ellispoid <- read.csv("C:/develop/data-analysis/Ellipsoid_Brochure_Info.csv")
print(data_ellispoid)
data_radiomics <- read.csv("C:/develop/data-analysis/Radiomics_MAVERRIC_111119.csv")
print(data_radiomics)
|
35ef9303dd42b95527ab01a78ee21501a77d3254 | e1c092c2a59f0998366612a309f9147cbcee423a | /R/d.e.mcmc.R | 404e699a1077491b7ac6406bd1821e1aa5da081a | [] | no_license | SandaD/MCMCEnsembleSampler | 58d88cd0314628229908764ac3f5d65450af868f | 7b5cdddc139c66a0c5d139b5fa74252979e4a43c | refs/heads/master | 2020-07-05T01:30:45.217542 | 2018-04-25T14:52:36 | 2018-04-25T14:52:36 | 74,132,038 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,765 | r | d.e.mcmc.R | ## ................................
## MCMC Ensemble Sampler
## ..........................
## Sanda Dejanic - R implementation of Ter Braak's differential evolution move
## ...........................
##' MCMC Ensemble sampler with the differential evolution jump move
##'
##' Markov Chain Monte Carlo sampler: using ... |
7d98b93f2add7f724cfda022d85b14b2a5e2470a | 50066dae4216d17bd6f0dcb9a11d872e73246eb6 | /man/choose_interp_extrap_method.Rd | a9af70617c2905fd724c25a84162e5163579d02a | [] | no_license | cran/PKNCA | 11de9db2cb98279c79d06022415b8772e7c1f5ea | 8f580da3e3c594e4e1be747cb2d8e35216784ed2 | refs/heads/master | 2023-05-10T16:54:19.131987 | 2023-04-29T18:30:02 | 2023-04-29T18:30:02 | 48,085,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,544 | rd | choose_interp_extrap_method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interp_extrap_helpers.R
\name{choose_interp_extrap_method}
\alias{choose_interp_extrap_method}
\title{Choose a method for calculation in the interval between concentrations}
\usage{
choose_interp_extrap_method(conc, time, interp_method,... |
af9eb464b5a13d8ec884984a38e279b2d98a1fb2 | e189d2945876e7b372d3081f4c3b4195cf443982 | /man/HF_TASKS_AUTO.Rd | 3e3bafe30ebf10c1abc8e387d9c62b3843cbe869 | [
"Apache-2.0"
] | permissive | Cdk29/fastai | 1f7a50662ed6204846975395927fce750ff65198 | 974677ad9d63fd4fa642a62583a5ae8b1610947b | refs/heads/master | 2023-04-14T09:00:08.682659 | 2021-04-30T12:18:58 | 2021-04-30T12:18:58 | 324,944,638 | 0 | 1 | Apache-2.0 | 2021-04-21T08:59:47 | 2020-12-28T07:38:23 | null | UTF-8 | R | false | true | 237 | rd | HF_TASKS_AUTO.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blurr_hugging_face.R
\name{HF_TASKS_AUTO}
\alias{HF_TASKS_AUTO}
\title{HF_TASKS_AUTO}
\usage{
HF_TASKS_AUTO()
}
\value{
None
}
\description{
An enumeration.
}
|
7bb9bb23a658d71d5db68f4d9424ce8916ab679b | 26a0244b2ce388e20de3ec4683a8a9e51e4e85a3 | /man/redmm.Rd | 6b879747d9990d9763006baad5d0f301e4452f17 | [] | no_license | covaruber/sommer | aeb064451c11c4de3e91e3d353909a3c35770040 | 00b16ebe469472cfa81a07fcb7235ed5b82c28d0 | refs/heads/master | 2023-08-17T09:24:22.399784 | 2023-08-09T00:01:26 | 2023-08-09T00:01:26 | 161,532,081 | 26 | 20 | null | 2021-09-23T18:32:51 | 2018-12-12T18:59:08 | R | UTF-8 | R | false | false | 2,050 | rd | redmm.Rd | \name{redmm}
\alias{redmm}
\title{Reduced Model Matrix}
\description{
`redmm` uses a feature matrix M from a random variable x and performs a singular value decomposition or Cholesky on M and creates a model matrix for x.
}
\usage{
redmm(x, M = NULL, Lam=NULL, nPC=50, cholD=FALSE, returnLam=FALSE)
}
\arguments{
... |
daa3bb787505b5a77aea94205a929196ab5713d2 | abcc7db0e92d17b720af9bc98f4cdac6739e1cf6 | /ASA_CSSA_SSSA/R/cbPalette.R | 367243571c90f1569aea59ff30e9aa6e69dfe378 | [] | no_license | dakotajudo/ASA_CSSA_SSSA | b4a93a1b7b9172c941abffca95ba278559279948 | 0a66202f93bdebfd8d411e288cd8d2c9fbb33f42 | refs/heads/master | 2021-09-27T11:56:47.419032 | 2021-09-10T14:08:11 | 2021-09-10T14:08:11 | 14,312,149 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 187 | r | cbPalette.R | cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#0072B2", "#D55E00", "#F0E442","#CC79A7","#000000","#734f80", "#2b5a74", "#004f39", "#787221", "#003959", "#6aaf00", "#663cd3") |
d40afc6755c652cbf1759c41ea53a10b8a8aa3d5 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/cusum/R/cusum.R | d8e3afafe5656e6877a7df8fba26846000f95e0c | [] | 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 | 4,147 | r | cusum.R | #' Non-risk-adjusted CUSUM Charts
#'
#' Calculate non-risk-adjusted CUSUM charts for performance data
#' @export
#' @import checkmate
#' @import stats
#' @import graphics
#' @param failure_probability Double. Baseline failure probability
#' @param patient_outcomes Integer. Vector of binary patient outcomes (0,1... |
94f7e2208abe3c8462d484e5486b9e4b5cec7255 | f060d34e74ce23f1449d4bb78067bab3dcc49347 | /man/G.Rd | 599067d8de040d089e5ffc5da9a198873375ccca | [] | no_license | tamartsi/MetaCor | 8bd34816049ff9573dadc0910f39961edc6ec819 | 9e64e0d5359411fb17f18bf87db7db4010c53cd9 | refs/heads/master | 2021-01-10T02:51:06.464548 | 2018-11-21T20:30:50 | 2018-11-21T20:30:50 | 49,463,770 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 593 | rd | G.Rd | \name{G}
\alias{G}
\docType{data}
\title{
A simulated genotype matrix
}
\description{
A matrix of simulated genotypes to help demonstrate the package usage.
}
\usage{data("G")}
\format{
A data frame of simulated genotype data for 6000 individuals. IDs and 10 allele counts are provided for each individual.
}
\detail... |
7597ccac92683ed1bdb0fe987d96bafada60e3cc | 6febf32c916d5734ff34df50369a0bc73b9178f2 | /R/reactive_power_control.R | 214fd5bb223fb416a788ac1981ae6552ba6977be | [] | no_license | asiono/rein | e190efbb0579685608bd48a171504016b2d0b7fe | ff837b696edc083a6ecf0c9435f1d8825a9f0c5d | refs/heads/master | 2021-05-12T07:21:37.391867 | 2018-03-08T13:48:05 | 2018-03-08T13:48:05 | 117,106,249 | 0 | 0 | null | 2018-01-12T09:40:12 | 2018-01-11T13:47:56 | R | UTF-8 | R | false | false | 3,074 | r | reactive_power_control.R | ################################################################################
#' @title reactive_power_control
#' @description change feed-in power based on specific power factor
#' @param lines lines data of the grid
#' @param S_cal omplex vector giving all powers in internal notation for cal_nodes
#' @param verb... |
b900e2fc801e05a67c8abdba551d815665076135 | 230fabadbc7881e514a9e3288be18743026f7cc3 | /man/ApplyFactorRange.Rd | 7c5dbae9472c1024e8cd613276860fed75510438 | [] | no_license | cran/rrepast | d15e5f00a973c569957c26671c3e9002a1c51ccf | 46ca64781419e5c475521e0ade9e9786b6427cd1 | refs/heads/master | 2021-05-04T11:21:56.188742 | 2020-02-19T05:00:05 | 2020-02-19T05:00:05 | 48,087,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 514 | rd | ApplyFactorRange.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RRepast.R
\name{ApplyFactorRange}
\alias{ApplyFactorRange}
\title{Corrects the LHS design matrix}
\usage{
ApplyFactorRange(design, factors)
}
\arguments{
\item{design}{The LHS design matrix}
\item{factors}{THe collection of factor... |
852ae2bb8e33afe37cc04a233da29193eb16c6df | e668745c508439e49bdde3ce44650d61a050ea2c | /tests/ggbio_plotGrandLinear.R | 82c2b79fc5dd45326dbac5b3cf555579f27392b7 | [
"MIT"
] | permissive | kevinrue/NGS | 6d89145d95b3fab374403a3e93cf2f27d5b0d6f4 | 626ba681b382341364c32df2dbb3f79e2e29430c | refs/heads/master | 2021-05-16T03:10:04.160214 | 2018-04-30T16:52:47 | 2018-04-30T16:52:47 | 19,390,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 436 | r | ggbio_plotGrandLinear.R | library(ggbio)
library(GenomicRanges)
df <- data.frame(
seqnames = c(rep("chr1", 1200), rep("chr2", 1000), rep("chr3", 800)),
start = c(1:1200, 1:1000, 1:800),
distance = abs(rnorm(3000)) * 1E3
)
gr <- GRanges(
seqnames = c(rep("chr1", 1200), rep("chr2", 1000), rep("chr3", 800)),
ranges = IRanges... |
5673ce2ea0a98de4dd90320dcf89fc5efe862d9d | 68ba96b2062f8a50fd5768ec1b9617012f22ee27 | /R/AIC_lik.R | b37aa268e5cf229dc81a4e4a71548bd314b0a6f2 | [
"MIT"
] | permissive | adsteen/Lloyd.et.al.Cell.abundance.metaanalysis | 16076ca3d5cba6a6bb68a9ff8f4d2234d83fb426 | 65803ccebbd1c2ff6c6fc95b337b1b198f6f82ed | refs/heads/master | 2016-09-06T18:10:07.337523 | 2013-11-07T03:12:49 | 2013-11-07T03:12:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 414 | r | AIC_lik.R | ##' Returns liklihood of models relative to the best (based on AIC)
##'
##' @description Calculates relative liklihood of models based on Akaike Information Criterion
##' @param AIC_df data frame of AIC results, as provided by the AIC function
##' @return The input data frame, with a `liklihood` column
AIC_lik <- fun... |
377b8977e9f99e70f6eed05f4d3db81cc321c70b | 0a98b5f5a492a19f13a91617370f61dcba9437f6 | /ch-9-scraping/httpdate.r | 2399692fa1e10f7a75693c3887a44ccb723b686b | [] | no_license | petermeissner/Wiley-ADCR | 3fd75d32773497bfb3805aa3c32b31be81dcbb58 | 97f1e4ec88cad81b039e7ab6bb5e58091aba18d5 | refs/heads/master | 2021-01-21T08:21:00.977759 | 2016-07-22T09:48:13 | 2016-07-22T09:48:13 | 63,941,264 | 1 | 0 | null | 2019-11-21T23:42:55 | 2016-07-22T09:25:56 | HTML | UTF-8 | R | false | false | 1,208 | r | httpdate.r | HTTPdate <- function(time="now", type=c("rfc1123","rfc850","ascitime")[1]){
if(time=="now") {
tmp <- as.POSIXlt(Sys.time(),tz="GMT")
}else{
tmp <- as.POSIXlt(as.POSIXct(time),tz="GMT")
}
nday <- c("Sun", "Mon" , "Tue" , "Wed", "Thu" , "Fri" , "Sat")[tmp$wday+1]
month <- tmp$mo... |
8bac11aa1a3f960ed8d79c935147a81e620460c0 | 8dd269c185df1d1400f597d12a8e0224a34f93ee | /Sentiment Analysis(Eng).R | dc6acedfa00235117352551981ea75b8f1772037 | [] | no_license | ErikWallentin/Sentiment-analysis-of-the-company-Nintendo | 804e187409e5de59f6402d94ceca31e81f3d7559 | 1c6d2eba3beb613fc5e7f35761f1708a6757516d | refs/heads/master | 2020-04-24T19:10:03.809481 | 2019-02-23T10:56:09 | 2019-02-23T10:56:09 | 172,140,402 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,385 | r | Sentiment Analysis(Eng).R | #####################################################################################################
# Many companies and organizations benefits greatly from finding out what the public thinks about them.
# One way to do that is to investigate what people are writing about them on social media.
# A large social p... |
11d3c3002fb7f00342f7e8868f5cccf2ec93b4e1 | 6515b40520740f675f1e9060b813f40a68b137bd | /R/AICc.R | 133bc39ee46eb7733f15945ef6cc25239594412e | [] | no_license | TaddyLab/gamlr | 2e760bae5f5dd15582a0549757183f1853128a65 | b441d514da9068c34fe1300a62c8cbc46e7ea0a5 | refs/heads/master | 2023-05-01T13:56:28.369330 | 2023-04-16T17:28:22 | 2023-04-16T17:28:22 | 11,212,706 | 21 | 8 | null | 2018-02-10T01:39:06 | 2013-07-06T03:54:15 | C | UTF-8 | R | false | false | 242 | r | AICc.R |
## corrected AIC calculation
AICc <- function(object, k=2){
ll <- logLik(object)
d <- attributes(ll)$df
n <- attributes(ll)$nobs
ic <- -2*ll+ k*d*n/(n-d-1)
ic[d+1 > n] <- Inf
attributes(ic)[c('df','nobs','class')] <- NULL
ic
}
|
305c3c4b304dc9f54848f3099ee79d9ae7b47071 | 91e0ea814dd7f6cb60fd2fa343320acb99de9294 | /2011/puntos_ideales_2011.R | 00fb28b00fcce35a976e756826d613a3ea78eacc | [] | no_license | fedecarles/votaciones | 61f4560d82cba5b5eec9d20d1964f7e18f75bd6a | c4e3fc499d1a771810d80e52c960bc634c82f7a7 | refs/heads/master | 2021-01-20T21:29:50.638642 | 2012-01-09T00:07:28 | 2012-01-09T00:07:28 | 3,132,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,807 | r | puntos_ideales_2011.R | # File-Name: puntos_ideales_2011.R
# Date: 08.01.12
# Author: Federico Carlés
# Email: fedecarles@gmail.com
# Data: 129.csv, Honorable Cámara de Diputados de la Nación
# Packages Used: pscl (Simon Jackman), ggplot2 (Hadley Wickham)
... |
5f86f0f5c29b3a9cd936a28bd4e93f622e53ebab | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmomco/examples/quacau.Rd.R | 4172b183431448c6b8fe949c19954ea2e74be2f5 | [] | 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 | 255 | r | quacau.Rd.R | library(lmomco)
### Name: quacau
### Title: Quantile Function of the Cauchy Distribution
### Aliases: quacau
### Keywords: distribution quantile function Distribution: Cauchy
### ** Examples
para <- c(12,12)
quacau(.5,vec2par(para,type='cau'))
|
934025518829b7c1772da74dad8dbc28702bcbe6 | 51ef9fa1b2212c659152fac242bda47e7bf15d6a | /man/get_last_n_exchangerate_tables.Rd | 29926b2e69863ab963a52221f6cf8de70601152a | [] | no_license | cran/rnbp | dc5771835c012e6872cc1a7128ad017234db0dad | 7ccc244007541379fc729d5ab869bd329ef06280 | refs/heads/master | 2021-06-25T03:25:57.463674 | 2021-06-07T06:30:02 | 2021-06-07T06:30:02 | 199,285,520 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,062 | rd | get_last_n_exchangerate_tables.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/endpoint_tables.R
\name{get_last_n_exchangerate_tables}
\alias{get_last_n_exchangerate_tables}
\title{Retrieves the last n exchange rate tables.}
\usage{
get_last_n_exchangerate_tables(table, n)
}
\arguments{
\item{table}{specifies which tabl... |
53b4bfe7e6ad7e7a31a6d6cd1a2749c52ef2354b | c77069c2dc6dbf3f9449a44e06d70b540a1912b5 | /R/remove_site.R | 4e09eaa5fe3b527392c1aaebb5f9b35f692ed216 | [] | no_license | cran/phenology | 62b323a9231c3701568de58c57a804e043abe6a2 | 991d2c35dcbcf1fcff23cbcc0c2f82b74a868dfb | refs/heads/master | 2023-04-15T03:37:51.464388 | 2023-04-01T09:10:02 | 2023-04-01T09:10:02 | 17,698,504 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,512 | r | remove_site.R | #' remove_site removes beach information from a set of parameters.
#' @title Removes site information from a set of parameters.
#' @author Marc Girondot
#' @return Return a set of modified parameters
#' @param parameters Set of parameters
#' @param help If TRUE, an help is displayed
#' @description This function is use... |
df849911c8c7fcc7fa63627c665ff3c2bb5874bb | 8bfd2b99cff65dff6a1b08723622fc7b6cc2d2ff | /man/read_empatica_events.Rd | 335b8c8097763857dec8bc5e8db747153d222f4a | [] | no_license | bwrc/empatica-r | 59eef1e037a743bf89e0bea713b39366e6ae93e7 | e2b0d42468830009c6972453bafcc0a1d7496845 | refs/heads/master | 2020-04-15T15:23:47.741959 | 2017-07-12T19:08:04 | 2017-07-12T19:08:04 | 43,059,899 | 5 | 4 | null | 2017-07-12T19:08:05 | 2015-09-24T10:17:51 | R | UTF-8 | R | false | false | 434 | rd | read_empatica_events.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/utilities.R
\name{read_empatica_events}
\alias{read_empatica_events}
\title{Read Empatica events (button presses)}
\usage{
read_empatica_events(f)
}
\arguments{
\item{f}{The name of the file containing the events (\code{tags.csv}).}
}... |
9de00d01c57647f7356ca799f371926f9529010a | b35f701f42ab3ad56ecbebe9e08a454efd276c7d | /R/proportional_hazards_data.R | 004ce0ee570b22f781feffb6bd954c51d75fa308 | [
"MIT"
] | permissive | ClinicoPath/censored | 460e760d97763f3a0cab4246a4e1208d85791821 | 848be00c4ce826c22f29eb9aa444649671cdb69a | refs/heads/master | 2023-04-29T05:55:21.976333 | 2021-05-17T13:24:31 | 2021-05-17T13:24:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,057 | r | proportional_hazards_data.R | # These functions define the proportional hazards models.
# They are executed when this package is loaded via `.onLoad()` and modify the
# parsnip package's model environment.
# These functions are tested indirectly when the models are used. Since this
# function is executed on package startup, you can't execute them ... |
324b73eca3ef8ca7103e94ba9e794c0828244f82 | 12bd3d5a0377fb2c709b99b71f0f43bfedace4c2 | /R/ReadInterleavedNexus.R | c8c22027b56cdb742fe185d5591548dbe59e229d | [] | no_license | bbanbury/phrynomics | 58666270c415c46cdf1e2b7faab34f865fbf3a35 | 42c393473d0627d5c2b95989f0f6036dc5038c70 | refs/heads/master | 2023-05-25T05:13:17.346748 | 2023-05-16T00:15:38 | 2023-05-16T00:15:38 | 14,935,999 | 25 | 7 | null | 2023-05-16T00:15:39 | 2013-12-04T21:13:46 | R | UTF-8 | R | false | false | 1,481 | r | ReadInterleavedNexus.R | #' Read Interleaved Nexus File
#'
#' Read Interleaved Nexus File
#'
#' This function reads in SNP datasets that are interleaved and converts them to data frame. These can then be written as standard nexus or phylip formatted files.
#'
#' @aliases ReadInterleavedNexus
#' @param file A file name specified by either a... |
fde7955c91afd1cd0e158a7711bf08b1231c5c5a | 5c09b66c0bd8fb0f7b3bb93d9a039810a0702e47 | /R/MachineLearning/XI - FrequenceItens.r | 3f6412f7dbd38b9f9fa653f6467787b6127b9dfe | [] | no_license | leandrocotrim/curso_R_PY | a4ccb1020c7aa33dd4a38a0b1d3fe41e9f44028e | 5a9844e9dbd4f765837ea25dee489866ad51bbd1 | refs/heads/master | 2020-06-15T00:15:40.035284 | 2019-08-16T11:08:24 | 2019-08-16T11:08:24 | 195,161,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 425 | r | XI - FrequenceItens.r | library(arules)
transacoes = read.transactions(
'..\\projects\\curso_R_PY\\R\\MachineLearning\\transacoes2.txt',
format='basket', sep = ',')
dim(transacoes)
summary(transacoes)
# melhor visualização
inspect(transacoes)
image(transacoes)
# rules
regras = eclat(transacoes, parameter = list(supp = 0.1, maxlen = 1... |
d237a7a2ef4ac059d319b76e72bac848e904b0d9 | 44cb5ed0fd4f72e67bc467bda7ab5ae918838595 | /R/immune/run_base.R | b6c5ca0f9df85c7fcbf809ffe8386ebb36ca1342 | [
"MIT"
] | permissive | Kcjohnson/SCGP | 2d5e020a5444edc0e4dceb1df147d81562d93769 | e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285 | refs/heads/master | 2020-09-06T00:33:17.964512 | 2019-12-11T15:18:11 | 2019-12-11T15:18:11 | 220,259,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 934 | r | run_base.R | #!/usr/bin/env Rscript
library(DBI)
library(odbc)
library(reshape2)
con <- DBI::dbConnect(odbc::odbc(), "GLASSv3")
q <- "SELECT * FROM analysis.gene_tpm"
tpm <- dbGetQuery(con,q)
args = commandArgs(trailingOnly=TRUE)
# test if there is at least one argument: if not, return an error
if (length(args) < 2) {
stop(... |
119f86547864997d28f4651a5c9487dc7e15176b | a17cf22be2304c96d267fc1b68db7b7279c4a293 | /R/mergeTables.R | a9010b172741bedff8ec24fafc3d734dae691069 | [] | no_license | robertdouglasmorrison/DuffyTools | 25fea20c17b4025e204f6adf56c29b5c0bcdf58f | 35a16dfc3894f6bc69525f60647594c3028eaf93 | refs/heads/master | 2023-06-23T10:09:25.713117 | 2023-06-15T18:09:21 | 2023-06-15T18:09:21 | 156,292,164 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,296 | r | mergeTables.R | # mergeTables.R - combine two 1-D tables that may or may not have common categories
`mergeTables` <- function( t1, t2) {
nam1 <- names(t1); nam2 <- names(t2);
#allNames <- .Internal( unique( c( nam1, nam2), FALSE, FALSE))
allNames <- unique.default( c( nam1, nam2))
ord <- .Internal( order( TRUE, FALSE, allNa... |
c9dbf9f7f74b8515df341bc1ebd350e99355a859 | e590b198ae4387935d51f452835a4e629eca9b3f | /R/brownieREML.R | 6f4e357e8d5ceb5c314a3da7ff913c03c9ba5d4c | [] | no_license | mrhelmus/phytools | 526c035cf18c83c62875d119c7d36a497d7af04b | a8ed2a28248996ac949f31800f3398428370e5aa | refs/heads/master | 2021-01-15T20:43:37.310385 | 2014-06-28T00:00:00 | 2014-06-28T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,489 | r | brownieREML.R | # This function is a simplified REML version of brownie.lite()
# written by Liam J. Revell 2011, 2013
brownieREML<-function(tree,x,maxit=2000){
# bookkeeping
if(!is.binary.tree(tree)) tree<-multi2di(tree)
x<-x[tree$tip.label] # order in tip.label order
n<-length(x) # number of species
p<-ncol(tree$mapped.... |
4df711cb9ea7995065a6c70f83ce682fa66addaf | 27814ee35a8d43a54f9c1105a0c0d38e0f6ec16b | /R/grenander.R | 62c4d38def04b7f0ab960e844b4e7a9ef89d7b3d | [] | no_license | cran/fdrtool | 31243ed0476e56daf7cc1484fedc89c0341bccf3 | 9c0c761e6a73e3fdc166d7ea8cc70894bd3fa8bd | refs/heads/master | 2021-11-25T01:33:09.804545 | 2021-11-13T19:30:11 | 2021-11-13T19:30:11 | 17,696,021 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,932 | r | grenander.R | ### grenander.R (2007-06-13)
###
### Grenander Density Estimator
###
### Copyright 2006-2007 Korbinian Strimmer
###
###
### This file is part of the `fdrtool' library for R and related languages.
### It is made available under the terms of the GNU General Public
### License, version 3, or at your option, any late... |
3ae1ebe0607d1fa8a058ac1a6c27c5f57a2b22ee | 669667464586efb1d2ce211ba5d9e4a5eb875e48 | /analysis/DSTL07analysis.R | 5951982f076fad0a703fc4b202f2f38c66a8e403 | [] | no_license | ceredmunds/DSTL07 | 7e1fd3312370774b6c9e23e039ac0d610e4f3135 | 4746227658de27be2100c6374e2b8504606a6e71 | refs/heads/main | 2023-05-08T05:32:29.834669 | 2021-06-03T09:43:37 | 2021-06-03T09:43:37 | 302,604,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,778 | r | DSTL07analysis.R | # Preprocessing of DSTL07 experiment data
# C. E. R. Edmunds, Queen Mary, University of London.
# 18-3-2021
# Setup --------------------------------------------------------------------------------------------
rm(list=ls())
require(data.table); require(ggplot2); require(viridis); require(emmeans); require(BayesFactor... |
ef76cd6d1078d0d398a52acb0f820a4b48795917 | 42b248125cb6d6ce5211bd9232fc9c99be7a4159 | /scripts/load.R | f2aaca85724c20a011772eabe4fb08751c14f026 | [] | no_license | STAT547-UBC-2019-20/group05 | 633275a4579cecfd3d500167e6e10739b33e92ff | aa76741a6bf15f6956cc15f2ba039f8dc653c38e | refs/heads/master | 2021-01-16T12:28:02.682524 | 2020-04-07T20:02:06 | 2020-04-07T20:02:06 | 243,120,535 | 0 | 5 | null | 2020-04-05T21:56:22 | 2020-02-25T22:55:46 | R | UTF-8 | R | false | false | 921 | r | load.R |
"This script downloads an online data file (via URL) and exports this data file into csv in the data folder.
Usage: scripts/load.R <url_to_read>" -> doc
suppressMessages(library(tidyverse))
suppressMessages(library(here))
suppressMessages(library(docopt))
suppressMessages(library(RCurl))
suppressMessages(library(re... |
63bdea11cf0c7d565252781487864275d3adf5ac | af8b1cfa36e31284367560dac2800456de9bb284 | /R/positioning.R | 994945a30a29d016879829f6196b886a2f227dc0 | [
"MIT"
] | permissive | LudvigOlsen/rearrr | f07cdf8fe92647335fb5a26ffc1416162543c59a | 40d150b440ae06507873fad20a28345c08d48cf3 | refs/heads/master | 2023-04-19T04:24:49.834419 | 2023-03-01T10:48:10 | 2023-03-01T10:48:10 | 259,158,437 | 24 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,845 | r | positioning.R |
# __________________ #< 9839bd1092dbb2df7eddcf5af55ff3f2 ># __________________
# Positioning ####
## .................. #< 517be4e0a4cdf05713d79ec1bc5e9e50 ># ..................
## Position max wrapper ... |
929dcfd550a59efc4487e817f46382a722a18fb1 | bdf6c66cb5577c9eb3f3e6cc25e85743210dc6a6 | /Plot4.R | f9360f887d2573adb3420abce09c218bc075b200 | [] | no_license | rohanpatil88/ExData_Plotting1 | 9820bee42eef3a15780a2f20e689456d8930ba9f | 4472cffc191381fb23ac64b526f92016924f594d | refs/heads/master | 2021-01-18T05:20:43.169184 | 2014-08-10T01:37:13 | 2014-08-10T01:37:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,627 | r | Plot4.R | household_power_consumption <- read.csv("D:/household_power_consumption.txt", sep=";") #Loading data from txt file
household_power_consumption[household_power_consumption == "?"] <- NA #Replacing the ? with NA
household_power_consumption <- na.omit(household_power_consumption) #Removing the NA values
household_power_co... |
915c69b767af1e7c8b1caf1a09dfc1e049c36d41 | 259fe6446e0f059be228f95745db1aa54ad5ce31 | /man/layer_to_dense_DeepTRIAGE.Rd | a9cb26e106da97b71f8c7963620b08941759ca4d | [] | no_license | tpq/caress | 9fd1c306e8f6bb23f88203f6e6329a72d4689aaa | 04386b3ab61ef9036e91ab1bbd6e42a1265b5ea9 | refs/heads/master | 2021-06-24T08:16:31.155396 | 2021-03-03T03:34:27 | 2021-03-03T03:34:27 | 202,971,472 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,375 | rd | layer_to_dense_DeepTRIAGE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/2-layers-DeepTRIAGE.R
\name{layer_to_dense_DeepTRIAGE}
\alias{layer_to_dense_DeepTRIAGE}
\title{Apply a DeepTRIAGE Layer}
\usage{
layer_to_dense_DeepTRIAGE(
object,
result_dim,
embed_dim = result_dim * 4,
random_embedding = FALSE,
h... |
431746cb188b242d72b999b61ab2451f5fa5ceae | 7c39da976f28af016e5b1f847e68473c659ea05d | /R/uniprot.R | f62aebcde49cb7938b6dfeb6ad2073763c9fc5c6 | [] | no_license | cancer-genomics/trellis | b389d5e03959f8c6a4ee7f187f7749048e586e03 | 5d90b1c903c09386e239c01c10c0613bbd89bc5f | refs/heads/master | 2023-02-24T05:59:44.877181 | 2023-01-09T20:38:36 | 2023-01-09T20:38:36 | 59,804,763 | 3 | 1 | null | 2023-01-11T05:22:52 | 2016-05-27T04:45:14 | R | UTF-8 | R | false | false | 24,573 | r | uniprot.R | reduceProtein <- function(gup, hugo, strwrap.width){
gup <- gup[gup$hugo==hugo]
short.desc <- as.character(gup$shrt_desc)
gup$short.desc <- short.desc
red <- reduce(gup, with.revmap=TRUE)
revmap <- red$revmap
gup2 <- relist(gup[unlist(revmap)], revmap)
## if short description is null, remove
gup3 <- end... |
3a045c1e386f7aee0e47921c10cbad7bb95976a5 | e9eefc7f75f79219f12952a5a0753db7125e54c0 | /man/print.femeta.Rd | 90177cf3c600abaee9a4729f961b5e64f07bb685 | [] | no_license | KenLi93/FEMetaBin | 0eac83e4f5866a7490e68c61f4928e1bfb69e22f | f79ab41f9c0e8853cbb42894d7b56110051b69f8 | refs/heads/master | 2020-08-14T06:57:23.038430 | 2019-10-16T17:14:37 | 2019-10-16T17:14:37 | 215,117,738 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 600 | rd | print.femeta.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/femeta.R
\name{print.femeta}
\alias{print.femeta}
\title{Printing results of fixed-effects meta-analysis}
\usage{
\method{print}{femeta}(x, digits = getOption("digits"), prefix = "\\t",
...)
}
\arguments{
\item{x}{object of class "femeta"}
... |
25f52454dd2cea1f8fe2b26317f720991335c864 | 703bef959e3644411c7e7c9acb9953cd57652d5c | /ex_1_5.R | 710855e8f15d9d69269d438188acfadb249cb9b8 | [
"MIT"
] | permissive | crazymidnight/learn-r | 8dd9342bbf1ae4bb3621c1ee62c932f96316f044 | f4ecd2493fa7f80e070eb3840aa7cf99b8594f6e | refs/heads/master | 2020-04-23T13:48:22.208063 | 2019-06-05T19:22:43 | 2019-06-05T19:22:43 | 171,210,026 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 354 | r | ex_1_5.R | # A
resA <- matrix(c(4.3, 3.1, 8.2, 8.2, 3.2, 0.9, 1.6, 6.5), nrow=4, ncol=2, byrow=TRUE)
print(resA)
# B
resB <- resA[-3,]
print(dim(resB))
# C
resC <- resA
resC[,2] <- sort(resC[,2])
print(resC)
# D
resD <- resC[-4, -1]
print(resD)
print(matrix(resD))
# E
resE <- resC[3:4,]
print(resE)
# F
resC[c(4, 1), c(2, 1)]... |
80c1f420a840bad7da0d8a72c2ed30f0d2e5191d | 627119064049fb9cf070d73e3766cceac02ab514 | /R/ltza.R | 0bb09779b4b178f3f41b31c13236b5600ecf894d | [] | no_license | cran/HKprocess | 7061c227c0593010f2abde4db3fea1fd008aed31 | b7aea6c4c497d4b822c11a460390029ab09537e7 | refs/heads/master | 2022-11-14T08:24:10.955085 | 2022-10-26T21:17:59 | 2022-10-26T21:17:59 | 49,682,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 434 | r | ltza.R | ltza <- function(r, x) {
nx <- as.integer(length(x))
EPS <- .Machine$double.eps
# Call the ltza.c from the C library
out <- .C("ltza",as.double(r),nx,as.double(x),nx,EPS,
tr = array(0,dim = c(1,4)),fault = as.integer(1),PACKAGE = "HKprocess")
return(setNames(as.vector(out$tr),c("t(x) *... |
e5bfcadfa8d83d0e29a7fd158257804c96114d0f | d7dd84b329e0489dd72cc6da8c760e61a5bb1953 | /fmac-predict/.Rprofile | c713add8d84b8e6767fd8c08c637901044831ac0 | [
"Apache-2.0"
] | permissive | seanahmad/fin-pyth-examples | 3a4752f8d0e3ea63748d7f9c25c62c442934b8be | 414f7661ca1f6d8022872f38179ce3aff08534f3 | refs/heads/master | 2023-07-18T04:02:38.387745 | 2019-06-22T14:04:14 | 2019-06-22T14:04:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | rprofile | .Rprofile | Sys.setenv(RETICULATE_PYTHON = "~/python/bin/python")
|
23c6f1a786f000ca733b0d491e030901b10c9ca8 | b6bd266b6b10290665231f1cc9bc892b51cf6716 | /man/pm25_2019.Rd | da1f6f78f2f9e4cea55313dc228605fc2d90e272 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | tereom/estcomp | 9a95e9a0be674d1f029801d3818a8aee8cf3f718 | 817f7e20ab82bffd064db4ccd68f5303a72844e5 | refs/heads/master | 2020-06-30T15:26:14.627799 | 2019-11-05T16:17:34 | 2019-11-05T16:17:34 | 200,871,105 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 692 | rd | pm25_2019.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pm25_2019.R
\docType{data}
\name{pm25_2019}
\alias{pm25_2019}
\title{PM2.5 pollutant measurements in Mexico City}
\format{A data frame with 5088 rows and 26 columns:
\describe{
\item{date}{Date when the measurment was taken}
\item{hour}{H... |
a6405c56e88fb3209a1cd296c78c52579fa7f299 | e68e99f52f3869c60d6488f0492905af4165aa64 | /R/nn.R | 1084704388d825071d9bb0e192ae91ba025a59f7 | [
"MIT"
] | permissive | mlverse/torch | a6a47e1defe44b9c041bc66504125ad6ee9c6db3 | f957d601c0295d31df96f8be7732b95917371acd | refs/heads/main | 2023-09-01T00:06:13.550381 | 2023-08-30T17:44:46 | 2023-08-30T17:44:46 | 232,347,878 | 448 | 86 | NOASSERTION | 2023-09-11T15:22:22 | 2020-01-07T14:56:32 | C++ | UTF-8 | R | false | false | 25,817 | r | nn.R | #' @include utils-data.R
NULL
get_inherited_classes <- function(inherit) {
inherit_class <- inherit$public_fields$.classes
# Filter out classes that we eventually add in our normal flow.
inherit_class <- inherit_class[inherit_class != "nn_Module"]
inherit_class <- inherit_class[inherit_class != "R6ClassGenerat... |
5b240e2a52d11f3c0f998392644451ff1a25a652 | a82df8590bdcf2bb4e01e0b610a0765706dc0dbe | /R/HelloWorld.R | 202a71e48068ec523fb2de2741a9664bf6eccdf8 | [] | no_license | bryankuo/lab | db1923ff1479a0dfe3d4fe6b41fb47f97fb19b37 | 521865b25f632887d05a951365e2497c4c22631b | refs/heads/master | 2023-09-04T00:44:33.832607 | 2023-09-03T11:08:28 | 2023-09-03T11:08:28 | 64,229,571 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 45 | r | HelloWorld.R | myString <- "Hello, Wolrd!"
print (myString)
|
b4db6d2f4156c2aa4cfb6119a761794e2250141f | 80e4f2090549ef36daf4ea0d976f42fa596bf9ba | /Project/new_copula_garch.R | 6fb1bc3f63da4426677a7499cc633b45b39a847c | [] | no_license | alexgarland/Applied_Quant_Finance | 4a5a411bd0d0fd10172b0961f7f3ad84b15d756e | 80e4d25395e1e78f81da676840fbab1d9aab8f25 | refs/heads/master | 2020-05-20T05:59:55.018518 | 2016-07-03T19:40:07 | 2016-07-03T19:40:07 | 51,231,778 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,340 | r | new_copula_garch.R | library(copula)
library(rugarch)
library(readr)
library(ggplot2)
setwd("~/Applied_Quant_Finance/Project")
rm(list=ls())
spec = ugarchspec(mean.model=list(armaOrder=c(5,5)),
variance.model=list(model = "eGARCH", garchOrder=c(5,5)),
distribution.model = "sstd")
most_data <- read_cs... |
7f8cbc129b2eee0d2f9e41d6c86ff68a5fe4c582 | 45aebfdd9d491ce87ed4121737f6a5d892bc7646 | /man/dalr.Rd | a76f4987cdfe01060880e33af88683167e4fe573 | [
"BSD-3-Clause"
] | permissive | schiffner/locClass | 3698168da43af5802e5391c3b416a3ca3eb90cbe | 9b7444bc0556e3aafae6661b534727cd8c8818df | refs/heads/master | 2021-01-19T05:21:57.704770 | 2016-08-21T19:25:12 | 2016-08-21T19:25:12 | 42,644,102 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 12,011 | rd | dalr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dalr.R
\name{dalr}
\alias{dalr}
\alias{dalr.data.frame}
\alias{dalr.default}
\alias{dalr.formula}
\alias{dalr.matrix}
\title{Discriminant Adaptive Logistic Regression}
\usage{
dalr(X, ...)
\method{dalr}{formula}(formula, data, weights, ..., ... |
490edda9a5d5b80a12b78aa73add9c0e6f58d7f7 | 65c2638762ac591ae8251e3790b4e69b273d3cd0 | /lab/lab3.R | bb07b4383518dec502bda74377b4d62f1b3458ee | [] | no_license | QidongS/ph245 | 889252d77a79b30b131e1630615040eab5e82ecf | b40f2c74dbdfa34e4d6108771622d32f93f146b0 | refs/heads/master | 2020-09-03T09:02:26.550078 | 2019-12-05T03:01:03 | 2019-12-05T03:01:03 | 219,431,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 457 | r | lab3.R |
matrix.100 <- matrix(data = 1:100, nrow = 10, ncol = 10, byrow = FALSE,
dimnames = NULL)
numbers <- c(4,5,9,10,12,15,16,17)
mat1 <- matrix(numbers, nrow = 4, ncol = 2, byrow= FALSE)
mat2 <- matrix(numbers, nrow = 4, ncol = 2, byrow = TRUE)
a <- matrix.100[c(1,3,5),]
mat3 <- mat1[,c(1:2)]
data <- read.csv(... |
b6d361561f0532ee5e2073f57cdafbc2bbabc203 | cf4be0c293910912aa0ac773c9301cdb1a74dbe5 | /R/transactions.R | c897b238e9aac28c0bfa813124176b5ae15acbb2 | [
"MIT"
] | permissive | Karagul/investmentsim | ee946f76aafc2e567255c413a211106104918e5f | 4b39d756e13a5629f6691a5dbe4c6eecaa72fd77 | refs/heads/master | 2021-01-08T16:16:41.510663 | 2019-09-11T09:43:34 | 2019-09-11T09:43:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 746 | r | transactions.R | ## Transactions
## ------------
##
## A transaction has type:
## Time -> Amount
#' A non-transaction
#'
#' A transaction that does nothing.
#'
#' @param date a date
#' @export
no_transactions <- function(date) 0
#' Create a transaction path
#'
#' Creates a transaction path the applies each transaction on a given da... |
9c8a14b3f8554c2a9ca588afec04e0f648716fe2 | 1aa278020398e19d726c226c44f6406e0952fbf1 | /global.R | e357359b2c1cd079e4b3c08c44991a0f902edf60 | [] | no_license | angoodkind/TypeShift | 11d82eae82e2ac7d31f6f529a2d2a491fabf7d7d | 900af378ad24d9784f211eeddc1fea133c7d144d | refs/heads/master | 2021-01-17T14:26:32.373460 | 2016-07-06T13:50:58 | 2016-07-06T13:50:58 | 53,366,533 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,167 | r | global.R | library(tidyr)
library(dplyr)
library(reshape)
# read in CSV and format
df <- read.csv('tokens.csv',sep='|',na.strings = "")
# df <- read.csv('token_viz_debug.csv',sep=',',na.strings = "")
df <- within(df, user.answer <- paste(UserID, QuestionID, sep='-'))
df$Token <- as.character(df$Token)
df <- df[ , !names(df) %in%... |
49fce69ccad070950f1e5be6f497a43f6a263530 | acdb497aa8a47599d3b7bd9438be2101b6ef415a | /man/CH11TA01.Rd | aabea7171f1ddd1ffdcfb7af91683cdb5f5f4804 | [] | no_license | bryangoodrich/ALSM | 106ce1ab43806ec7c74fc72f9a26a094bf1f61d1 | 6fe1a413f996d26755638e9b2c81ae0aafd1a509 | refs/heads/main | 2022-07-15T15:55:23.708741 | 2022-07-03T19:55:04 | 2022-07-03T19:55:04 | 39,878,127 | 16 | 9 | null | null | null | null | UTF-8 | R | false | false | 378 | rd | CH11TA01.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{CH11TA01}
\alias{CH11TA01}
\title{CH11TA01}
\format{\preformatted{'data.frame': 54 obs. of 2 variables:
$ V1: int 27 21 22 24 25 23 20 20 29 24 ...
$ V2: int 73 66 63 75 71 70 65 70 79 72 ...
}}
\usage... |
72a08666828eb62e00d99a3c33743df854040131 | e3a1a949ac383c9abe8f72fb04fa85c5107d3131 | /R/area_between.R | 05e08c7aaf1c112fa5797d24904dc8955599c924 | [] | no_license | dleguyader/riverbed | 4d881e9931e18ac1e859c6a542aee7a02f916908 | 4e1697632b4d47409a5230e619f311b80b071052 | refs/heads/master | 2022-06-13T21:30:05.240121 | 2020-05-05T11:03:33 | 2020-05-05T11:03:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,948 | r | area_between.R | #' A function to calculate area between two profiles
#'
#' This function takes two profiles defined as (l1,z1) and (l2,z2) as inputs and calculates area between them
#' @param s1 tibble with columns l and z describing first profile
#' @param s2 tibble with columns l and z describing second profile
#' @param h if provid... |
7ea5e097c2e3b7a53c425c9a0dfaf4bb70c1a005 | ae5429663cd1e071e62fbef6d8081bd7f955a90b | /man/sscs_units.Rd | 3092fd47f560d8cba0e1c842bd0ba02c32110f30 | [
"MIT"
] | permissive | btupper/softshell | 160c41f60dda677e5cebeab359d176607ab661cd | a12dce6de32be94ee221ef52d9b47234dccbaca1 | refs/heads/master | 2021-01-06T18:48:24.494347 | 2020-02-20T17:38:20 | 2020-02-20T17:38:20 | 241,447,237 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 394 | rd | sscs_units.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sscs.R
\name{sscs_units}
\alias{sscs_units}
\title{Retrieve the units for a given variable (if known)}
\usage{
sscs_units(x = "crop_legal")
}
\arguments{
\item{x}{character, the name of the variable}
}
\value{
the units for the specified vari... |
46a6e484b9c2026db3d8e5eaf5bad7f0a66b6f61 | fa37c0b201d6a837e16d9f8165d958b58c7064b8 | /app/bs4Dash_version/navbar.R | 8d1b000431fb0fd52fc1e84c9e13b4b5a23af9f4 | [] | no_license | DivadNojnarg/Lorenz_Apps | 043ee113e22b42a22e01cebf946f98e178390606 | 4be1a1a81df9bb77868462a90d7405eb58c1262b | refs/heads/master | 2021-03-22T04:44:15.475044 | 2019-05-26T21:43:43 | 2019-05-26T21:43:43 | 102,622,015 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 425 | r | navbar.R | navbar <- bs4DashNavbar(
status = "white",
skin = "light",
rightUi = fluidRow(
utilsUi(id = "utils"),
# reset is not included in the module
# shinyjs does not work in that specific case
actionBttn(
icon = icon("trash"),
inputId = "resetAll",
label = " Reset",
color = "da... |
982d7245f312eded10a8dad9d039c9fa96c473eb | d172c7a2c218c9016fc8a0ba234d48f7c4b19979 | /assessment/censusPostAnalysis.R | dddcb9564ab60aebdc9240f3690593ce38d25095 | [] | no_license | anbeier/MasterThesis | 90ecde060f2eca6d4e26ce79cd1f088325c8a61f | f6e24043a93461c77cf0ac064ee59778636d63ae | refs/heads/master | 2021-01-23T09:04:24.080237 | 2015-01-28T01:16:31 | 2015-01-28T01:16:31 | 63,418,308 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,438 | r | censusPostAnalysis.R |
fqsFileList <- function() {
ls <- NULL
f <- 'census_fqs_delta0.7_alpha0.5.txt'
ls <- append(ls, f)
f <- 'census_fqs_delta0.8_alpha0.5.txt'
ls <- append(ls, f)
f <- 'census_fqs_delta0.9_alpha0.5.txt'
ls <- append(ls, f)
return(ls)
}
folderList <- function() {
ls <- NULL
f <- 'census_delta0.7_alpha0... |
8b92d8d226df9054d604f9932ad81222fb2af0c1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PopGenome/examples/get.status-methods.Rd.R | 60707cd8e5200bf678a173251fa3931408abe519 | [] | 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 | 210 | r | get.status-methods.Rd.R | library(PopGenome)
### Name: get.status-methods
### Title: State of calculations
### Aliases: get.status-methods get.status,GENOME-method
### Keywords: methods
### ** Examples
# get.status(GENOME.class)
|
c546e5fc0f82fc5f1c027ec2ef8866313708e8e6 | cd628e1c910d766d54b4fa8417a82890e16cdca3 | /man/tournaments.Rd | f404ced0c4827ee9a82e9cf57cb3e0cdbe8192ad | [] | no_license | dashee87/deuce | 33f34ddaef4a8943adf5d704225eaaf06ec3a1ba | de7015ec55e5470ca497472171e50b6c822487dd | refs/heads/master | 2021-01-13T10:16:44.665211 | 2016-06-28T01:03:35 | 2016-06-28T01:03:35 | 69,028,710 | 0 | 0 | null | 2016-09-23T13:44:27 | 2016-09-23T13:44:26 | null | UTF-8 | R | false | false | 484 | rd | tournaments.Rd | \name{tournaments}
\alias{tournaments}
\docType{data}
\title{Names and codes for ATP tournaments}
\description{
Data frame of ATP tournaments at the 250 level and above.
}
\usage{data(tournaments)}
\format{
Data frame of \code{code}, \code{location}, \code{tournament}, \code{tier}, \code{rounds} and \code{surface}.
}... |
ca9a35b7f748ba3620b862502ee8421de4c7b54e | 85f1b159e885d0e6a8aa972e3a234f9cac33ffa3 | /HW_10.R | 0d0e3695dc3069e840a8ab23a2cb8034492fc2eb | [] | no_license | sis0004/HW_10 | 103e821b094f4ea6b65461573a472332f4b0db65 | 4f9b80cc97863874b0bc9bc4efa4fd376fd3900b | refs/heads/master | 2020-09-01T16:29:19.683010 | 2019-11-08T14:08:14 | 2019-11-08T14:08:14 | 219,004,766 | 0 | 0 | null | 2019-11-07T19:15:13 | 2019-11-01T14:49:51 | R | UTF-8 | R | false | false | 1,418 | r | HW_10.R | per_capita_co2 <- function(country,year) {
data <- read.csv("data/co2_emissions_tonnes_per_person_gapminder.csv")
year <- paste("X",year,sep = "") #edit the year to match the dormat in the data
#check if the data has the expected year
if (!(year %in% colnames(data))) {
stop(sprintf("No data for %s in ... |
be4490778e157f3718321d604ad43c4042e6f5c1 | 1e6695a7107fbe76b472ded8ea28c398b09a08e5 | /PBSdata/man/spongeCPZ.Rd | 729b16205508df47868f5df540ddbc00b2b47f6f | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain"
] | permissive | pbs-software/pbs-data | ed55421071c3b2460023c166004aa672991bf749 | fa50fc4cb28e7c5cc3736b9a363b0703a200cf67 | refs/heads/master | 2023-07-27T12:22:36.734426 | 2023-07-06T15:57:14 | 2023-07-06T15:57:14 | 37,491,693 | 2 | 2 | null | 2022-07-14T18:34:19 | 2015-06-15T21:14:11 | R | UTF-8 | R | false | false | 3,479 | rd | spongeCPZ.Rd | \name{spongeCPZ}
\alias{spongeCPZ}
\alias{spongeAMZ}
\docType{data}
\title{
Topo: Sponge Reef Zones
}
\description{
Sponge reef core protected zones and adaptive management zones within the
proposed MPA \emph{Hecate Strait and Queen Charlotte Sound Glass Sponge Reefs}.
}
\usage{
data(spongeCPZ)
data... |
17e10ed5653404c7b45e82f2743bb86e707bb652 | 7786980abbb9f9f92d0ba45a6b526066bc4f77b8 | /R/plot_grouped.R | f18c535b960e1030eb34f9ad534d8fba42d5b647 | [] | no_license | alastairrushworth/inspectdf | d2fc64d31da1e903b43eea7c9aec893bb27c6759 | 5c516e3ee28c63a56622948ab612bc8f3d48ba47 | refs/heads/master | 2022-08-29T15:37:21.670913 | 2022-08-09T06:14:32 | 2022-08-09T06:14:32 | 157,981,172 | 251 | 23 | null | 2022-08-09T06:27:38 | 2018-11-17T12:12:30 | R | UTF-8 | R | false | false | 2,028 | r | plot_grouped.R | #' @importFrom tidyr unite
plot_grouped <- function(df, value, series, group, plot_type,
col_palette, text_labels, ylab){
# get group names
grp_attr <- attr(df, 'groups')
ngroups <- ncol(grp_attr) - 1
grp_cols <- df %>%
ungroup %>%
select(1:ngroups) %>%
mutate_all(as... |
4d9bd0bcca6652ca3bb85fe06373aebe1149c83b | 2b864fa89488650a9840c49f8312ebccc3fefffc | /ggplot2 - hadley wickham/chapter10.R | b0724c08743d6f70a02626076b71d8e61789d9dc | [] | no_license | harryyang1982/r-codes | 8147d3f70fd7cf435ecb34d1bc1acd921b75f7bd | 89fb033f11f26c285837c0e04b74d6453c16be50 | refs/heads/master | 2020-12-30T11:52:12.212063 | 2018-04-12T06:25:58 | 2018-04-12T06:25:58 | 91,541,975 | 0 | 5 | null | null | null | null | UTF-8 | R | false | false | 5,917 | r | chapter10.R | library(ggplot2)
library(dplyr)
ggplot(diamonds, aes(x, y)) +
geom_bin2d()
filter(diamonds, x==0 | y== 0)
diamonds_ok <- filter(diamonds, x >0, y >0, y < 20)
ggplot(diamonds_ok, aes(x, y)) +
geom_bin2d() +
geom_abline(slope=1, color="white", size=1, alpha=0.5)
#10.2.2 Missing Values
x <- c(1, NA, 2)
x ==1
x ... |
86d9fa7484d2949c6e2e9e8ba9af530639763dd0 | eb2cd7490e2e4fc1c18fed6019c3888892b6bb0d | /Assignment_2/p9/DocFreq.R | 568d658d21ba9135e62dbab60082958b90aaf162 | [] | no_license | ksingla025/Machine-Learning-Assignments | 8f75e1a36de1cfdffcba7fe2826c559cb0171bb6 | 0ea8518af52b080ac6635782f1952437e2d3674d | refs/heads/master | 2020-05-18T08:29:39.321179 | 2014-11-22T12:38:04 | 2014-11-22T12:38:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 156 | r | DocFreq.R | DocFreq<-function(word) {
word_id = which(vocabulary == word)
documents_count = length(which(word_per_doc_count[, 2] == word_id))
documents_count
} |
bb1b6c9adbbe8979a820c52b9841eecfb8a68ff9 | 339532c1047f1c4654692339478ada6c90f0420e | /R/unused/covplot.R | 3d76191cc03778a22ee881bcb429535767d94be9 | [] | no_license | marcottelab/pepliner | 1647a5541830b4f23f82295c5471b5e54c0ae4d1 | 2e21bf81d56bacdcc9c6ee75bb3cdce3a8213de4 | refs/heads/master | 2021-01-01T18:52:08.640836 | 2018-07-27T20:37:57 | 2018-07-27T20:37:57 | 98,455,138 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,787 | r | covplot.R |
# suppressPackageStartupMessages(library(tidyverse))
# suppressPackageStartupMessages(library(cowplot))
# suppressPackageStartupMessages(library(lazyeval))
covplot_row <- function(row,elementid){
# Dummy variable height of each rectangle. Not very important since plots are resized afterwards according to number... |
b2fdfe53f029decf9a72e4305314c30feb5f98c8 | 52f9424c4009606f818d90d3b28022711979f5d8 | /plot5.R | c53e35584eb1719337999b0793f40a0916eaf97f | [] | no_license | sun33170161/ExData_Plotting2 | c32b42bb473710eb9944d802ce3c9d91c7c0fa1a | 145dd4963ca61eafc35e369616f113d792d6339e | refs/heads/master | 2021-01-10T03:07:07.179362 | 2015-05-24T17:16:42 | 2015-05-24T17:16:42 | 36,183,371 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | plot5.R | # Load Data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Plot
png("plot5.png")
# Select Coal Combustion Related data
SCC.Motor <- SCC[grep("motor vehicle", SCC$Short.Name, ignore.case = T), c("SCC", "Short.Name")]
data <- subset(NEI, subset = NEI$SCC %in% SCC.Motor$SCC)
dat... |
103a86e1bf8e4e76ae6535128638db6694efd904 | 1f294ea77e05ecb7ce682c84eebe7e8e2a398585 | /R/prepare_response_variables.R | 28cc59ad3d9be1aa6df6a32472aca5e3d660cd06 | [] | no_license | coreytcallaghan/JBI-20-0736 | 68c915b3724df54fe7d7ad21f4db3232fcabf49f | c597d885fc7fd370161b7e8aa7a05075c7049ebf | refs/heads/main | 2023-02-18T17:02:47.576843 | 2021-01-19T08:10:17 | 2021-01-19T08:10:17 | 330,902,828 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,831 | r | prepare_response_variables.R | ## This scrip reads in the summaries
## exported from the 'make_ggridges_and_get_urbanness.R' script
## and then saves out a couple RDSs of 'response variables'
## I also do some manual filtering of the species included
## ensuring tha they largely occur in the United States
# packages
library(dplyr)
library(purrr)
li... |
698d61107214314ecc95830833ece0c7119fdf30 | b2ef0c59f3bb43b33db35f7fdd221c803ef33bb2 | /Intro/autocorrelation.R | 12f2b0d8290cb7789a99bf5c36cae5eca8cf924c | [] | no_license | jocoder22/R_DataScience | 086a4fc9ba27a3a3435d8479ae983c41e45834c9 | 44d5c24b1674e3a05a66eed122eed819dad4dabf | refs/heads/master | 2020-07-23T20:37:47.927767 | 2019-11-06T04:17:38 | 2019-11-06T04:17:38 | 207,699,600 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 772 | r | autocorrelation.R | library(timeSeries)
library(astsa)
library(quantmod)
# Download stocks
symbols <- c("AMZN", "AAPL", "TSLA", "MSFT")
getSymbols(symbols)
amazon <- getSymbols("AMZN", auto.assign = F)$`AMZN.Adjusted`
getSymbols(symbols, from="2016-10-29")
stocks <- Ad(merge(AMZN, AAPL, TSLA, MSFT))
returns <- returns0(EuStockMarket... |
2f64aea07e80aa7c022a38592e2f2b05bd0b24da | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/factorstochvol/man/covmat.fsvdraws.Rd | a707e0841cb6b7fdc6b3958fbeeac01957ecafeb | [] | 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 | true | 1,998 | rd | covmat.fsvdraws.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities_fsvdraws.R
\name{covmat.fsvdraws}
\alias{covmat.fsvdraws}
\title{Extract posterior draws of the model-implied covariance matrix}
\usage{
\method{covmat}{fsvdraws}(x, timepoints = "all", ...)
}
\arguments{
\item{x}{Object of class \c... |
6fa73a89988b7357e2477c45e74a1668728c56d4 | 76f4709e9a63caf474181ce3711af32528ce83d3 | /man/append_values.Rd | 28230e56d6e84a35d4bcb0363803b70e9bace63a | [
"Apache-2.0"
] | permissive | vats-div/tidyjson | aee5fb91624dcaba951e1c8e606e9aad148e7767 | 4129f886e78bd26fb34be1b286b68d94c7a21fab | refs/heads/master | 2021-01-17T05:04:54.592904 | 2015-02-24T17:32:04 | 2015-02-24T17:32:04 | 30,717,563 | 0 | 0 | null | 2015-02-12T18:41:24 | 2015-02-12T18:41:24 | null | UTF-8 | R | false | false | 773 | rd | append_values.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/append_values.r
\name{append_values}
\alias{append_values}
\alias{append_values_logical}
\alias{append_values_number}
\alias{append_values_string}
\title{Append keys to a new column}
\usage{
append_values_string(x, column.name = type,... |
95079248c38c05fe089a682720e9aea27402413a | 47dc57d3a38ee0c3d43b71ea464a6b0b925d6649 | /00 - Memos/Memo_K-Means_Clustering.R | 444e4c9f751fbe3b6bbc1b9c3bf8a6d2c6f943df | [] | no_license | arobert1976/Data-Science-Foundations-using-R | 2e8619282a342f750e9997c04735238071159c2e | 550c225c05a03a4019fa70dcf9fbe0e3fcead9e0 | refs/heads/main | 2023-01-19T07:57:03.679812 | 2020-11-20T13:36:03 | 2020-11-20T13:36:03 | 314,508,595 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,056 | r | Memo_K-Means_Clustering.R | # Creating 3 clusters
set.seed(1234)
par(mar=c(0,0,0,0)) #sets the margins to 0
x = rnorm(12, mean=rep(1:3, each=4), sd=0.2) #12 valeurs : 4 autour de 1, 4 autour de 2, 4 autour de 3.
y = rnorm(12, mean=rep(c(1,2,1), each=4), sd=0.2) #12 valeurs : 4 autour de 1, 4 autour de 2, 4 autour de 1.
plot(x, y, col="blue",... |
0c7cbec0968f13f7c0078edd9bf539c5eb34f883 | 2e8213b1ea0567d6c8a2af7b20fcee29abc13a55 | /man/spatial_query.Rd | a48e391cec991bb0755a718df4a9ac6a17b3bc36 | [
"MIT"
] | permissive | monicalosu/getarc | 0b17d7754ba1e8344e32b505a5be765bc19e76c2 | b8f783a839523bcc14b11da3b97f3cf963eda1fa | refs/heads/master | 2023-04-06T14:03:33.366167 | 2021-03-27T07:36:50 | 2021-03-27T07:36:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 850 | rd | spatial_query.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spatial_query.R
\name{spatial_query}
\alias{spatial_query}
\title{Spatial Query}
\usage{
spatial_query(x, spatial_filter = "esriSpatialRelIntersects")
}
\arguments{
\item{x}{an sf or sfc object}
\item{spatial_filter}{the spatial relationship... |
2cce6d1d020589da7c50d18e8a96ef6df2c11bee | 274c11c96f4976067674e3c76b7f490aba020123 | /man/branch.Rd | 849023298ed57f2746e54e0e3d63131d5c626bac | [] | no_license | FvD/gert | 2b169acfd21a1179a946bedc042e43f7d7d904f9 | 5c2de88c1b07a140b774d1240c07b06a94e6dae1 | refs/heads/master | 2020-06-06T16:04:26.407104 | 2019-06-18T14:05:28 | 2019-06-18T14:05:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,153 | rd | branch.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/branch.R
\name{branch}
\alias{branch}
\alias{git_branch_list}
\alias{git_branch_checkout}
\alias{git_branch_create}
\alias{git_branch_delete}
\alias{git_branch_fast_forward}
\title{Git Branch}
\usage{
git_branch_list(repo = ".")
git_branch_c... |
4f3bad608953057ebcdd8ae46969afeff5569c77 | c9cf13661b5635be5f2e1791bb5a5bb8efcd3beb | /R/search_for.R | 3faf1cb85a943feed22e6eb672072d2d9f8ae49b | [] | no_license | annamariakl/antiplugr | 7c7c0bfbd7a65a431a26a88f3e155d5232eecb74 | 74a4a217bf770f6d017e1759af3a390a72b00124 | refs/heads/master | 2020-03-24T20:16:44.889970 | 2018-09-02T20:05:01 | 2018-09-02T20:05:01 | 142,969,280 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,853 | r | search_for.R | #' Search for similar sentences
#'
#' \code{search_for} is used to search for similar or exact sentences in a PDF.
#'
#' @param x File name/path of the PDF.
#' @param sen Sentence to be used to search in the text.
#' @param exact If you search for the exact sentence, the default is FALSE and the
#' cosine distance is u... |
80f95d6726ebb1f8468e380bd45fb16578477431 | 655988e12085dcc1b11cd59d13d6372cff7c49ad | /R/Imports.R | 37e0b000f7993241bb7d4a5d877f0580824ee816 | [] | no_license | griu/RemixAutoML | 0952ba173e27267d87f3eb7b51d4530196c8219e | 9d60292869ad36b86e7c0aa49328727ed85be366 | refs/heads/master | 2020-07-11T10:27:04.039578 | 2019-08-26T06:46:11 | 2019-08-26T06:46:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 356 | r | Imports.R | #' @import data.table
#' @import foreach
#' @importFrom data.table data.table %chin% .I .N .SD := as.data.table fwrite is.data.table rbindlist set setcolorder setnames setorderv as.IDate as.ITime
#' @importFrom lubridate %m+%
#' @importFrom foreach %dopar%
#' @importFrom stats optimize pchisq
#' @import doParalle... |
319967ac9d7a86f0afef0e4d8a1f0a69fe352889 | 416550c21c0e3f49ae34ef843b4c352910c3c2f9 | /man/detectPeaks.Rd | 8966271cc8ee566808d459840694654afb7eb51d | [] | no_license | thomasp85/MSsary | 34dc8e93fd13a33ba6f78598626bb134d6cb151c | bf182b67b072256c4ff16b8c72678109f899ecc5 | refs/heads/master | 2021-01-22T12:12:39.641522 | 2015-01-26T11:44:40 | 2015-01-26T11:44:40 | 25,297,627 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 276 | rd | detectPeaks.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/generics.R
\name{detectPeaks}
\alias{detectPeaks}
\title{Detect peaks in chromatographic MS data}
\usage{
detectPeaks(object, ...)
}
\description{
Detect peaks in chromatographic MS data
}
|
e455200fa528d4eb5f726c688f7bb3fb9d7a5437 | 0d7d795e5015f890e4b518bbebb0c199d4aa8e4f | /PBSddiff/R/pcod_iscam.r | 4b724357261dbbcd5be53bcc7c7124e450611aa0 | [] | no_license | pbs-software/pbs-ddiff | e46645e91dc4dbfd1b30b1da234f8651ae6bb377 | b46f1266f3eac21a29fae3e9738fdd9ee74c7e4f | refs/heads/master | 2023-07-08T20:54:51.247865 | 2023-06-22T22:42:11 | 2023-06-22T22:42:11 | 100,047,840 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 53,872 | r | pcod_iscam.r | #**********************************************************************************
# pcod_iscam.r
# This file contains the code for a front end GUI controller for pcod_iscam using
# Tcl/Tk windows implemented using the R package 'PBSModelling'. The data
# structure used is an opList, which is a list of lists, see loa... |
89fd630657513f68eae74f75a675317cd4916f86 | 0012be753cc009d8e162d2a2a3aa058ab88d59b9 | /static/sample-scripts/test-periods.R | 00577ed868bb49bc205b89535276b6d18c298730 | [
"MIT"
] | permissive | weecology/updating-data | 5f640fe8d2bae30da7b82b91394217d027e163da | 0c69e8bc43dc1e062b578c1a7d511e5cf6261727 | refs/heads/master | 2021-06-06T00:22:23.387450 | 2021-05-06T19:42:23 | 2021-05-06T19:42:23 | 160,896,919 | 0 | 3 | MIT | 2021-05-06T19:42:24 | 2018-12-08T02:38:03 | R | UTF-8 | R | false | false | 257 | r | test-periods.R | library(testthat)
library(dplyr)
context("checks that period values are valid")
base_data <- read.csv('../data/data.csv',
stringsAsFactors = F)
test_that("period numbers are valid", {
expect_true(all(base_data$period < 1000))
})
|
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