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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b117ec30e095f34cea29f5454829433cf2ff1176 | 96e0fc5d5ef1c7a9001588a48415b5d908400364 | /altergro/traf/traf.R | 60985a82366bac25f9481aab43e8f89a70509b87 | [] | no_license | Shmuma/tools | 4635af3921828e89a8599be93dd0833e819720c0 | 8a2e04913efe2516cdfe8b3f07ead5a499b07085 | refs/heads/master | 2021-01-17T08:42:22.711421 | 2017-08-09T17:54:34 | 2017-08-09T17:54:34 | 6,597,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,040 | r | traf.R | # Script downloads and calculates total traffic for the current day from 8:00 to midnight
library (methods)
library (bitops)
source("getTrafData.R")
source("parseTrafData.R")
source("getToday.R")
if (!file.exists("auth.txt")) {
print ("No auth.txt file found! You need to copy auth.txt.sample to auth.txt and edit ... |
65e7b7db608a6cc9d77517cc07f12f73e5eb1544 | fb0448f213ee6c975be8474094a0f6b2f1353146 | /R/mag.R | b51e736978333ee1e8e76c518fc0c855c55cd003 | [] | no_license | mbfumc/fici | bf9d2add6e6743c9ac2ad0259fc7c2ce49ca290f | 4ac3c87157ec776fa9dde1c6e241eedb7f35aaec | refs/heads/master | 2022-10-06T06:55:52.196521 | 2020-04-14T18:30:53 | 2020-04-14T18:30:53 | 184,465,494 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 222 | r | mag.R | #' Calculate Magnitude of Vector
#'
#' This function allows you to quickly calculate the magnitude of a vector.
#' @keywords magnitude
#' @export
#' @examples
#' x <- c(3,5,6)
#' mag(x)
mag <- function(x){sqrt(sum(x^2))}
|
52c0240dfdd0a735d0bad5fcc9aa44d93831676e | 9c9503f3f993e012b0d55b6f66259337f53957c2 | /Assingment3_test.R | d95bb7e75e8f4a2138c433ccfb0211569415e38a | [] | no_license | Pyotr001/Quiz_R | 360fc16a1dd2435c0e85cd1555b085d7032231c8 | 5ebfdb8d2c16b945d3fcbd808c4d04432cc8e18c | refs/heads/master | 2020-05-17T04:53:32.072968 | 2015-03-08T13:30:35 | 2015-03-08T13:30:35 | 31,598,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 469 | r | Assingment3_test.R | files <- list.files("rprog-data-ProgAssignment3-data", full.names = T)
outcome <- read.csv(files[3], colClasses = "character")
outcome2 <- read.csv(files[3])
dim(outcome)
names_outcome <- names(outcome)
outcome[, 11] <- as.numeric(outcome[, 11])
hist(outcome[, 11])
head(outcome[, 20:26])
testStop <- function(x){
... |
11ba87bb0225d1d417d82450325ece428664e118 | 33780efd85cb1e5e91292df5b79989df7f6ad830 | /Div_estimates.R | 1b70b5d192c59e2a82045378d764f8d0ab5aa782 | [] | no_license | lotte-korell/precipitation-change-experiments- | e485f1929966a3b7aad857e4d4261f03bc25cb8e | 558115d979988ed581ee18cdefad4403045ee9d8 | refs/heads/master | 2023-03-08T05:02:05.567325 | 2021-02-19T13:03:17 | 2021-02-19T13:03:17 | 276,665,437 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 34,284 | r | Div_estimates.R | ls()
rm(list=ls())
ls()
getwd()
setwd("C:/Users/korell/R/Meta.analysis/results")
dir()
library(lme4)
library(MASS)
library(car)
library(ggplot2)
library(MuMIn)
library(arm)
library(boot)
Div.data_local <- read.csv("Div.data_local.csv" ,dec=".", sep=";",h=T)
Div.data_gamma <- read.csv("Div.da... |
d1085343e17f97270366a1fa90f1fe5f2330b0b4 | 4d95fe5d8ba5ca6b8ced8a284905b7e455bad75e | /man/mwt.Rd | b759622d203f070d1713aaa24a49d12ed5cf7e6d | [] | no_license | Senbee/mwt | 68026a11c0f5c46b3f21f9d44860480523ce4f9d | af0134ef2df677fd246b745b3d262202c09831f2 | refs/heads/master | 2021-04-09T10:28:14.703984 | 2016-06-16T09:53:18 | 2016-06-16T09:53:18 | 60,255,860 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,190 | rd | mwt.Rd | \name{mwt}
\alias{mwt}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Function to compute global FDR based on the moderated Welch test }
\description{
MWT compares two independent groups using t-test. It is designed
to deal with small-sample microarray data where the group
variances might ... |
50df8183ee37ade4feee537ebb265d8344cfa20f | f4462380035ce0099fdfbcc8864cb5a8066344ff | /R/kdistance.R | f01e88bf9c9ab5b7182c6a94128472687ba047f6 | [
"MIT"
] | permissive | happyjane/TPCA | a58432bbcae4ed87916b7a47e6c39ade97c9d420 | f232197318a76b96a1be5979cf9d374b7441288e | refs/heads/master | 2021-01-18T08:22:52.491921 | 2015-03-04T09:12:25 | 2015-03-04T09:12:25 | 31,596,254 | 1 | 0 | null | 2015-03-03T12:02:52 | 2015-03-03T12:02:50 | null | UTF-8 | R | false | false | 342 | r | kdistance.R | dist2 <- read.table(file="./distancegraph2")
dist2 <- sort(dist2$V1, decreasing=TRUE)
dist3 <- read.table(file="./distancegraph3")
dist3 <- sort(dist3$V1, decreasing=TRUE)
dist4 <- read.table(file="./distancegraph4")
dist4 <- sort(dist4$V1, decreasing=TRUE)
plot(dist2)
points(dist3, add=TRUE, col="red")
points(dist4, ... |
f6673d2c7edf436dfaf83c0b4b6580e8693db974 | 11e9a640ad60972f0a1ff2fb8509ec998059ccb0 | /R/GetTissueTimes.R | 6b342989de263263d835a47f357bab1f50e9eade | [
"MIT"
] | permissive | jakeyeung/TissueCiradianAnalysis | f53f6a65e1e5489e6ee9c465a612c1cce108d256 | 6c29a33820c8c0ab6dabbd992cc2412b199fc7af | refs/heads/master | 2020-09-21T05:58:35.578267 | 2020-08-07T16:35:13 | 2020-08-07T16:35:13 | 224,702,276 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,596 | r | GetTissueTimes.R | # Functions to get tissue and times: new after column fixed to array.
# Jake Yeung
# Dec 4 2014
# GetTissueTimes.R
GetTissues <- function(samp.names, get_unique=TRUE){
# Samp names of form: WFAT48 (as vector)
# return WFAT (as vector, unique)
tissues <- unlist(lapply(samp.names, function(samp.name){
substr(s... |
dcd44b1f1db5bd70ff460c5b0bf5e2721e21cda0 | 8d119d59dedf7994c9b14a637fc069d3a3e0494c | /RPackageSource/man/RcometsAnalytics.Rd | 2a424bd0f30e44d19c50c4fa8fd4a596203fe0c5 | [] | no_license | CBIIT/R-cometsAnalytics | 3f77bf818050eefbcef4e5a74a5cdab0e17157dc | bc0da393319a7fc2ec53275f9545d24b982faabc | refs/heads/master | 2023-08-24T04:34:16.888409 | 2023-08-04T20:28:26 | 2023-08-04T20:28:26 | 64,139,217 | 8 | 9 | null | 2023-09-13T18:43:04 | 2016-07-25T14:02:36 | HTML | UTF-8 | R | false | true | 1,267 | rd | RcometsAnalytics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/imports.R
\docType{package}
\name{RcometsAnalytics}
\alias{RcometsAnalytics}
\title{RcometsAnalytics R package}
\description{
This R package supports all cohort-specific analyses of
the COMETS consortium \url{https://www.comets-analyti... |
2b750b5eb3f9f6fcb03881d4acfd2690bf801f4c | 6c908c2811e529f272747cbbfdca41812d825672 | /NLP/lemmatizing.R | de544d832a6f5ea7e44c63bb397a880913e3d115 | [] | no_license | basbaccarne/mict | 63109fda346537cabc56d96666712bfdc4c06274 | 4876b84ba76a71058e3aa8d5cb09164b82ada3e6 | refs/heads/master | 2021-01-12T10:31:35.378659 | 2018-03-06T13:24:57 | 2018-03-06T13:24:57 | 76,471,942 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,237 | r | lemmatizing.R | # lemmatization with korPus and treetagger
# installation > first time only
# install treetagger https://www.youtube.com/watch?v=SYMc2SllI0c
# download "http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/data/dutch-par-linux-3.2-utf8.bin.gz"
# put the file dutch-utf8.par in the treetagger/lib folder
... |
33250099ce62dfe74311eea51a7534fc6cdcaae8 | a60775370c58e09cf935769f38c9ad3903c8ec80 | /plot3.R | cc1d54120ec0277505103a74dd33b706f15d3533 | [] | no_license | connectomania/ExData_Plotting2 | 4f8a3d91ea6ba91e33f938ee6a57df484e66dab9 | fd4568917fa7a3832d367dff488e18dab06a55cd | refs/heads/master | 2020-12-03T04:02:55.762085 | 2017-06-29T18:54:10 | 2017-06-29T18:54:10 | 95,808,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,518 | r | plot3.R |
#**************************************************************************
# 3. Use ggplot2 to see which source types (point, nonpoint, onroad, ####
# nonroad) have increased vs. decreased in Baltimore from 1999-2008
#**************************************************************************
... |
bdc7c73c23ccb0cf291327b1a8214c86d536f3e7 | d9b9b90973145241af8e7f843d6b8e2f79ac1779 | /Differential_gene_expression_analyses/DE_analyses_and_plots.R | 8a66e184ca53af34490ee0c1857da51f97daf5c1 | [] | no_license | wenhaotan208/Monarch_RNA-Seq | 89e7c9587a98d525202483a667591ea9157bdfd6 | 440fd15d7efce8b9089db051af176f59cd9ce3af | refs/heads/master | 2020-05-16T00:40:12.124981 | 2019-04-22T03:07:43 | 2019-04-22T03:07:43 | 182,585,493 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 17,624 | r | DE_analyses_and_plots.R | #### Differential Gene Expression analysis ####
# DGE analyses were performed using Bioconductor package "EdgeR"
# Generate MA plots, Volcano plots, and Heatmaps
# Also test for specific gene sets of interest (immune and detoxification)
# Reference[1] https://web.stanford.edu/class/bios221/labs/rnaseq/lab_4_rnase... |
e970de1ee43e294e98351b23e48b61606c9e0b50 | 097eb75714cc53fb6c17bd89631879f2515f1714 | /man/recurse_node.Rd | d12271a770d582d85cd643e07fb2997ecb074240 | [] | no_license | hafen/osfr | 41bf1598acce3d08279ad0214c368596c49cadf1 | 01d22d8681fc2fe405b9ee6099f24ad376a2a143 | refs/heads/master | 2021-01-20T01:32:37.739614 | 2017-06-16T12:17:39 | 2017-06-16T12:17:39 | 89,292,987 | 0 | 2 | null | 2017-06-09T18:26:54 | 2017-04-24T22:31:30 | R | UTF-8 | R | false | true | 516 | rd | recurse_node.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nodes.R
\name{recurse_node}
\alias{recurse_node}
\title{Function to crawl through OSF project}
\usage{
recurse_node(id = NULL, private = FALSE, maxdepth = 5)
}
\arguments{
\item{id}{OSF parent ID (osf.io/xxxx) to crawl}
\item{private}{Boolea... |
ba724f395832a3bd8e85ba76cbff1bcf466f0f59 | 0611363f7a2fa5bf07dd6185c9f04619f234e87f | /Workshop - plot.R | b168b9d8d554d15fd69ed620eeab278cfc1cfd4e | [] | no_license | mighster/Basic_Stats_Scripts | 0b32cfae1ee892ad5af8571004e245f0cf0c25e7 | 211cb6d47b7308c23e2f84d0dcc42a4a414a4086 | refs/heads/master | 2020-04-04T18:41:43.469404 | 2018-11-05T07:15:22 | 2018-11-05T07:15:22 | 156,174,423 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 795 | r | Workshop - plot.R | ## 3Dplot
## http://www.sthda.com/english/wiki/scatterplot3d-3d-graphics-r-software-and-data-visualization
## http://www.sthda.com/english/wiki/impressive-package-for-3d-and-4d-graph-r-software-and-data-visualization#install-plot3d-package
## https://cran.r-project.org/web/packages/plot3D/plot3D.pdf
## To load i... |
e9dd95a4b97bf1118b7f42367020856363da4aee | 165f9b48b6bf83d5940d83008eb6464df61bb7c7 | /others/PopSV/automatedPipeline.R | 22d5725872d2ce011c4f61848852fa4aa15edcc7 | [] | no_license | jmonlong/reppopsv | 3ee9d903fc4dec2a356f5211e45b15ae2207c6f4 | 21682d4e630b8f8502e0664a090892421cd40efd | refs/heads/master | 2021-04-28T16:19:18.518580 | 2018-11-02T00:08:52 | 2018-11-02T00:08:52 | 122,011,014 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,713 | r | automatedPipeline.R | message(" /!\ Might need to be tweaked /!\ ")
message("Two functions :
- 'autoGCcounts' to count BC in each sample.
- 'autoNormTest' to normalize and test all the samples.
")
autoGCcounts <- function(files.f, bins.f, redo=NULL, sleep=180, status=FALSE, file.suffix="", lib.loc=NULL, other.resources=NULL, skip=NULL, st... |
eb2a8ef938a92f6160a12337417560f257a19b42 | 47cf5c6525d3a0c4ba63fd4930b6682194a80753 | /plot1.R | c26dd7fc45798234f68e0408abb6b4ba836523d7 | [] | no_license | sameerq/ExData_Plotting1 | 6f8fd6a5d937457be776201aca4512fde0c66dca | 0509bb99d8a7cfb700725bd6994bb40a944ee676 | refs/heads/master | 2021-01-14T12:10:27.408506 | 2016-01-11T05:43:44 | 2016-01-11T05:43:44 | 49,402,190 | 0 | 0 | null | 2016-01-11T04:33:21 | 2016-01-11T04:33:21 | null | UTF-8 | R | false | false | 726 | r | plot1.R | #Data has been downloaded and unzipped into working directory as a .txt file
#This stores the data and takes the target dates we want
mydata <- read.table('household_power_consumption.txt', header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
mydata$Date <- as.Date(mydata$Date, format="%d/%m/%Y")
startDate <- as.Date... |
19394a65c7c3fcbf53aa90444b1084dc7885178f | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/3869_0/rinput.R | 4730fef94d22dbef808e822aefeb45ea32e9b735 | [] | 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("3869_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3869_0_unrooted.txt") |
6191f311039e52be987cefd2dd4e494cf9fc6d36 | a820f4282433b3e5fd12d7d2e003d4bfd5afd639 | /plot1.R | 07f8589f6eee9593f26ecd7227c8ee8d92ef429c | [] | no_license | Duangrat-praj/ExData_Plotting1 | 3438763f91a5f3a054e971ca06441917c5b90037 | 1b320414d7b51e928586c2db4bc1deff77ab9b8e | refs/heads/master | 2022-10-26T16:35:47.989037 | 2020-06-14T16:25:16 | 2020-06-14T16:25:16 | 272,239,671 | 0 | 0 | null | 2020-06-14T16:23:21 | 2020-06-14T16:23:21 | null | UTF-8 | R | false | false | 570 | r | plot1.R | data <- read.table("./household_power_consumption.txt", sep = ";", header = TRUE,
as.is = c("Date", "Time", "Global_active_power","Global_reactive_power","Voltage",
"Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3"),
na.strings="?"... |
4c041b1da1c976754b0ef08ea24796315e8a5cb0 | 62cd477be5b35bf8efac2f222dcd179354a9554d | /scripts/funding-sources.R | eee95e424f0bbb62da8a52c3cc36b5631763aa87 | [] | no_license | lisicase/info201 | ed514ebb46f7af3212926519fe48e64fa8374366 | 9671c451ce710eea8e3081356956dbeb915d413e | refs/heads/master | 2020-06-26T22:53:12.193422 | 2019-10-11T17:47:21 | 2019-10-11T17:47:21 | 199,779,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 938 | r | funding-sources.R | # Load packages
library("dplyr")
library("ggplot2")
library("stringr")
# Function to create graph of funding
funding_graph <- function(years, state) {
# Load aggregated data
funding_data <- read.csv("data/prepped/aggregate.csv",
stringsAsFactors = FALSE)
# Narrow data to specified year... |
68a3c31a6b172b31be16ad201adaf0d0cc313bc9 | 0cb61321d6cfc04ea536eec02d74d238259ec018 | /code/plot-revbayes/script-StochMapp-Plotting_with_simmap.R | 9c399d6c874ab9de744a38a55ebb061c8361843c | [
"MIT"
] | permissive | asmeseguer/aquatic_transition | 7ad5c3af7b2308b398247c816320b07317943e0d | 618a716639118b789f2a2eeab52cd41f2feac878 | refs/heads/main | 2023-08-17T21:11:59.633413 | 2021-09-24T09:27:06 | 2021-09-24T09:27:06 | 362,422,087 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,047 | r | script-StochMapp-Plotting_with_simmap.R | #####################
# This script could be used to plot the ancestral states of stochastic character mapping HiSSE. Adapted from RevBayes Manual
#####################
library(plotrix)
library(phytools)
character_file = "output/stochastic-mapping/marginal_character.tree"
sim2 = read.simmap(file=character_file, fo... |
d85a53180cd968708184f7e8a18148ccd8246a57 | 98a0bd2de4836b813642df0faf5f0b5bd31f7617 | /man/gapfillerWrap.Rd | 3f5e6b5bae8c69469619cfbbd888f4b716ecb774 | [] | no_license | inambioinfo/chimera | 7bf3834f72464e546b83f52704354acbc9c329bc | 17e0580ccd842a57f519fd968bc9df3d9ec29a0f | refs/heads/master | 2021-06-25T06:56:13.520654 | 2017-04-24T19:50:57 | 2017-04-24T19:50:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,868 | rd | gapfillerWrap.Rd | \name{gapfillerWrap}
\alias{gapfillerWrap}
\title{A function to prepare files and to run gapfiller}
\description{A function that uses GapFiller to confirm, by de novo assembly, the presence of the fusion break point. The function needs as input a list of fusion transcript generated by chimeraSeqSet function and the bam... |
78ecea3843a7d865b50bb030a40c2337c8599f12 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/qat/examples/qat_call_plot_noc_rule.Rd.R | e5bcdd5085d18ffa1fc2cb18e7d76d80d32c5349 | [] | 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 | 531 | r | qat_call_plot_noc_rule.Rd.R | library(qat)
### Name: qat_call_plot_noc_rule
### Title: Plot a result of a NOC rule check
### Aliases: qat_call_plot_noc_rule
### Keywords: utilities
### ** Examples
vec <- c(1,2,3,4,4,4,5,5,4,3,NaN,3,2,1)
workflowlist_part <- list(max_return_elements=1)
resultlist <- qat_call_noc_rule(vec, workflowlist_part,eleme... |
b209cee892972746b8a311817e9773d807782ff4 | 255e643aab0d85916e0b6474a741238f048d4081 | /Analysis/Functions/auxiliary_old.R | 545d56ade9087c19ae3269ac4e6a62219e2c9b08 | [
"MIT"
] | permissive | MarioniLab/OesophagusSingleCell2018 | e439a0b2f989d359297d2c6d91eebe1fa423c503 | 31f8e077238a05c05c89d7df80c1f2541edf8d6a | refs/heads/master | 2020-03-11T09:22:52.490276 | 2019-10-14T15:19:25 | 2019-10-14T15:19:25 | 129,908,964 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,560 | r | auxiliary_old.R | #############################################
#### Script to store auxiliary functions ####
#############################################
# load data by tissue
loadData <- function(file_vec){
# load normalized data
normdata <- sapply(file_vec, readRDS)
# add patients to column names
ps <- sapply( strspli... |
b595749748ad7ac9a64e4e90eb6efdeb2ff308f5 | 44a687ab2b10bb849d835bead9c3f4c4d2cffb6b | /R_workshop_2016/stat_progs_R/stat_progs_R/5_statistical_tests/2_non-parametric_tests/9_Kruskal-Wallis_test.r | a47a88edef3d52bd1ea4ff8b4b4b1df1441a345b | [] | no_license | lakshmibalasubramanian/codebase_laks | 031ba4c3cdabc0f4e886dec60475b7e9de40aaf2 | 664390654f2e785dc9d75107fc47643b8e3b69f3 | refs/heads/master | 2020-06-05T19:44:05.919169 | 2020-04-07T13:58:41 | 2020-04-07T13:58:41 | 192,528,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 558 | r | 9_Kruskal-Wallis_test.r | #Kruskal-Wallis Rank-sum test, non-parametric alternative to anova.
dat = read.table("insect_sprays.txt", header=TRUE, sep='\t')
mean(dat$count[dat$spray=="A"])
mean(dat$count[dat$spray=="B"])
mean(dat$count[dat$spray=="C"])
mean(dat$count[dat$spray=="D"])
mean(dat$count[dat$spray=="E"])
tapply(dat$count, dat$spray, ... |
5e06c21383fc92325d507242e80bab6386f8a528 | 12c9f9fcb40e704df8e8c79e0de681daca83b2da | /WADE_Language_Analysis.R | fc9fcd4583dcaa7451fac2fda840766ce7e76d47 | [] | no_license | laceywade/word-naming-analysis | 09f071ff238f22672badb5afaadd264c4de3dccd | ffa8d5accea60b88366fe40d06761f93be5e84e9 | refs/heads/main | 2023-07-16T15:09:30.874021 | 2021-09-05T16:32:56 | 2021-09-05T16:32:56 | 400,847,731 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,264 | r | WADE_Language_Analysis.R | library(plyr)
library(tidyverse)
library(lme4)
library(Rmisc)
library(reshape2)
library(wPerm)
'%!in%' = Negate('%in%')
################################################ LOAD & CLEAN UP RAW DATA ######################################
### Load in the formants file
formants<-read_csv("WordNamingGame_Formants.csv")
f... |
10446babb0ddca166754a8d4b21e0bfb6b7dfbd0 | e38a1ef996105c9648d23c6fe598e29f74d60874 | /R/fars_functions.R | 6668baec9a0c7eedeb53bb8cd0389ba84296ec56 | [] | no_license | adhager/farsr | cc0df152f51a736d24a1d2c99c10a0007721cac7 | bd406363ee664cd726f9298bf71d84c6eb589453 | refs/heads/master | 2020-03-23T11:03:34.347540 | 2019-06-18T13:04:57 | 2019-06-18T13:04:57 | 141,479,924 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,639 | r | fars_functions.R | #' Read FARS file
#'
#' A FARS file contains data from the US National Highway Traffic Safety
#' Administration's Fatality Analysis Reporting System, which is a nationwide
#' census providing the American public yearly data regarding fatal injuries
#' suffered in motor vehicle traffic crashes.
#'
#' Requires dpyr, tidy... |
01260a00062e306c73576fbea7ea89ed05f37b20 | e0bcd3a0bfa23c1d445c2c738b8e37323a0c3b71 | /test/20190206_data_integrity_testing.R | 9f88f4679be5b2b0f5a4d2e44fea6278df51b93e | [] | no_license | active-analytics/pqam_2018 | 87a017a55c130412e4d090518f2f47a6adb67c9a | cbc0437bb9da5460939641ec39af24070b733e24 | refs/heads/master | 2021-10-20T11:22:43.513713 | 2019-02-27T14:07:28 | 2019-02-27T14:07:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 446 | r | 20190206_data_integrity_testing.R | rm(list = ls())
cat("\014")
library(tidyverse)
df_old <- read_csv("data_output/20181229_spy_weekly_opt_hist_5yr.csv")
df_new <- read_csv("data_output/spy_weekly_opt_hist_5yr.csv")
nrow(df_old)
nrow(df_new)
df_old %>%
group_by(data_date, expiration) %>%
summarize(num_row = n())
df_new %>%
group_b... |
19ddd835873c299f9c02a1f36830196701d6e1ab | e5438533f88dfcd48bcfd8fe079ef0f7de0f3438 | /man/VKSgraphic.Rd | 5e90a917c04f0a9b0f3fa75979cb47478d6b7662 | [] | no_license | cran/CVD | b4f04637fc273afb166c7dfc1f9d0a1e27326095 | 85474eb2f075943a488bb25b19a2b4c0b8ba6812 | refs/heads/master | 2020-05-18T06:51:44.040624 | 2016-11-28T12:23:17 | 2016-11-28T12:23:17 | 17,678,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,876 | rd | VKSgraphic.Rd | \name{VKSgraphic}
\alias{VKSgraphic}
\alias{VKSvariantGraphic}
\title{Graphical score for the D-15 tests}
\description{\code{VKSgraphic} computes a graphical score based on
the Vingrys and King-Smith method (VKS) for the D-15 test or similar tests.
\code{VKSvariantGraphic} shows the angles with double their value,
fo... |
8c6159686482d0963b9ce5f77a60a3207c45e4bd | 9492380587c4336fa23ad876af43af283663e136 | /6 | 17afdfb686ba0253d90da0721d443f3a48edad78 | [] | no_license | sunilpatil27/dsr_lab | c87fab7f3a6e0ddb17a41b08c36408ce7274df3e | b5bde2a8cd1144d343bb624475566189a0fb6f91 | refs/heads/master | 2020-09-09T00:31:30.346907 | 2019-11-12T18:57:33 | 2019-11-12T18:57:33 | 221,289,779 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 633 | 6 | library(visualize)
library(BSDA)
rural<-c(3.1,2.9,2.8,3.0,2.7,3.1,2.6,2.8,2.9,3.0)
urban<-c(3.5,3.0,3.1,3.2,2.9,3.4,3.0,3.4,2.8,3.4)
xrbar=mean(rural)
xrbar
xurbar=mean(urban)
xurbar
var(rural)
sd(rural)
var(urban)
sd(urban)
#Obtaining t-calculated value
t.test(x=rural,y=urban,var.equal = TRUE,conf.level = 0.95)
#t.tes... | |
ec750bf846e8c4afb8c011294671c06292cf3384 | 57a7632e12ec2b39de5131a6e614a828d00b7705 | /Plot1.R | 0693095bf71da9f2f97432d90321199ac7ed7922 | [] | no_license | bielgarcies/Project-1-Exploratory-Data-Analysis | f92ab2dc3d8e170785406e0a699b2ee14792315d | 70dfc0f31444471d53de217b77ca8ff383cc2d68 | refs/heads/main | 2023-02-08T12:58:13.432484 | 2020-12-31T17:48:57 | 2020-12-31T17:48:57 | 325,844,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 495 | r | Plot1.R | #Preparing data
household_power_consumption <- read.csv("~/Downloads/household_power_consumption.txt", sep=";")
data<- household_power_consumption
data$Date <- as.Date(data$Date,"%d/%m/%Y")
data <- subset(data, Date == "2007-02-01" | Date == "2007-02-02")
datetime <- strptime(paste(data$Date,as.character(data$Time)),"%... |
c3c9b2096c33cbb74e04be1f0a7c4a0a418ec1a7 | 05f59cd61540ce4c9961d91377cff9008b78c009 | /R/propensity_score_linear.R | 32bd9112252f598ba496e1f2b6a04eab89208125 | [
"MIT"
] | permissive | rzgross/uRbanmatching | e3abddab1946e2f84c32378820585b96abe4840d | 356b3d1ac10d72e3c5a20de08903f137c2c22965 | refs/heads/master | 2023-04-12T21:20:24.938362 | 2021-05-09T19:04:41 | 2021-05-09T19:04:41 | 355,984,522 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 937 | r | propensity_score_linear.R | #' propensity_score_linear
#'
#' Function to predict treatment using \code{glm} (binomial) or \code{lm}.
#'
#' @param use_linear_lm Whether to use lm or glm.
#'
#' @return returns a function that accepts \code{train_test_list}
#' and this returns a vector of predictions for the test data
#' @export
propensity_score_lin... |
771a5f401af9dcdd3100aeb78ec8050b25752945 | 6a9c07b8ee61bca7c38155410c24b0abb2217590 | /hashmap2.R | 3397eca929e87f61fbe5a765fb6953fae261d982 | [] | no_license | kim-carter/ASC2018 | 5004bc2d77604b71fdc43014483882b7f806c977 | a2b6f58300d9d9f87e5553d926b1c63cc8403a0e | refs/heads/master | 2020-03-27T04:29:29.744620 | 2018-08-28T12:07:58 | 2018-08-28T12:07:58 | 145,944,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 878 | r | hashmap2.R | ##
## Kim Carter 2018. Testing R hashmap package for hashtable speed
##
library(hashmap)
filename <- "testdata.tsv"
con <- file(filename,open="r")
date()
counter <- 0
t1 <- Sys.time();
# there's no nice init for this, so we have to create it with the first row of the file
line <- readLines(con, n = 1)
cols <- st... |
17bc5650f082b08610cbbb779d562eaf9358d4b8 | 8007bfcdf202c5f0246658d0ba467bba0780b7a5 | /man/sjp.likert.Rd | 3aca9e4a03a23b94ad8e2e30ccb271833a42b675 | [] | no_license | harshagn/devel | a50432d9ecd6b655cee01f1cf63092b0da5bc2f3 | cd67a4a135cf8f9bc9a618cc7c018eef3648caba | refs/heads/master | 2021-01-02T08:40:37.695668 | 2014-09-10T16:14:16 | 2014-09-10T16:14:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,216 | rd | sjp.likert.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{sjp.likert}
\alias{sjp.likert}
\title{Plot likert scales as centered stacked bars}
\usage{
sjp.likert(items, legendLabels = NULL, orderBy = NULL,
reverseOrder = FALSE, dropLevels = NULL, weightBy = NULL,
weightByTitleString = NULL, hideLegend = FALSE, title... |
1efdd2c4bd5260ef1279f68533390a1d7b249e8b | 802b6455d0aab96d7f7a5d1e48e67d97962a2859 | /R_Lab_2/SGM_TMATH_390_R_lab2.R | c7e4dce87f65ed121bde5072ff61bdb3833486fe | [] | no_license | stevexenios/Statistics | ebe66d6287b4b47ec77c847dfa26fa30cb7d67c7 | ad9f57ff66e256fe93ea52996ddb12906d371762 | refs/heads/master | 2020-09-07T18:03:26.372068 | 2020-02-19T07:59:37 | 2020-02-19T07:59:37 | 220,871,301 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,759 | r | SGM_TMATH_390_R_lab2.R | ###################################################################################################
# Steve G. Mwangi
# TMATH 390, Fall 2019
# Lab 2 R script
# October 13, 2019
###################################################################################################
#OBJECTIVES:
#1. Estimation in R
#########... |
f0bf2f01a4a2a7fe59cb7e50bb455b58f6244b5a | e3e3843080406b83178105f59b2f366643f545a6 | /man/createWebpages.Rd | 313a56f98c1feb13950d309bb7446bfacab6d76b | [] | no_license | nickmckay/lipdverseR | c1dcdb9e6752a41a00d7f9386cf684f24f3f1f46 | 56b01e8ce33dc9f71e39bdf1c5c978f3b33d1db7 | refs/heads/master | 2023-06-07T19:16:43.787187 | 2023-05-31T18:20:31 | 2023-05-31T18:20:31 | 182,172,405 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 307 | rd | createWebpages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nightlyUpdateDrake.R
\name{createWebpages}
\alias{createWebpages}
\title{Create lipdversePages old framework}
\usage{
createWebpages(params, data)
}
\arguments{
\item{data}{}
}
\description{
Create lipdversePages old framework
}
|
8f4709aed02ab7a26c5f8d9d9379dcd3f713280b | fcc552d20daeafd15451979ba1718cb7bda82634 | /testUnits/util/runitDistance_GPS.r | 988756088d68261eeab8b1c001b8a27f59972c9d | [] | no_license | cristianchilipirea/crowdMovement | b248370e5733cae964763550a588e13a470b7962 | b649c992fd07b40d84544fe47fac80c66c112767 | refs/heads/master | 2021-01-20T11:38:12.653112 | 2017-09-05T14:14:43 | 2017-09-05T14:14:43 | 101,675,043 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 309 | r | runitDistance_GPS.r | source("Scripts/crowdMovement/util/distance_GPS.r")
test.getDistance = function() {
detectionA = data.frame(latitude = c(0, 42.05), longitude = c(0, 5.02))
detectionsB = data.frame(latitude = c(0, 42.06), longitude = c(0, 5.05))
checkEquals(getDistance(detectionA, detectionsB), c(0, 2720), tolerance=1)
} |
8105a1bdab81ec6930bb37d6cda3980f92ecc81c | 1908bd86fad96d5e039ab69a2a2c722b4ab0bfbd | /R/pvaluate.R | d43641f295e896c3eee32ab5165dfac31d5a22f0 | [] | no_license | eliocamp/wave4 | 675d87878d2d0e4ea4490a0842c55e4030e50c6f | c6cb04c008ff32a17df990454ef9e84d5e72abea | refs/heads/main | 2023-02-17T20:48:40.791224 | 2021-01-20T12:25:02 | 2021-01-20T12:25:02 | 330,792,390 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 544 | r | pvaluate.R | #' Compute p.values
#'
#' Computes p.values from the result of [metR::FitLm()] (or any assumed student-t-distributed
#' statistic) with optional adjustments.
#'
#' @param estimate estimate of the regression (or other fit)
#' @param std.error standard error
#' @param df degrees of freedom
#' @param adjustment method for... |
ef360b1260508871dad1c57f1fb9300049c19fcb | fc438fdcd495a4248d564a88f334a23779c2ad14 | /R/ui.R | e185ab0139d3bde6621f607fe9665a292646ee7d | [] | no_license | afukushima/PVT | 8e7d5c4fee8f8fef6a2a7cc257b601f1115a6cba | cc8b512d4cd2e5a3d6af5f991f7e371f90b5151c | refs/heads/master | 2016-09-06T01:09:49.966980 | 2015-09-17T06:02:50 | 2015-09-17T06:02:50 | 41,794,671 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,303 | r | ui.R | library(shiny)
source('ui-components/atmet-tabs.R', local=TRUE)
source('ui-components/meko-tabs.R', local=TRUE)
source('ui-components/gcms-tabs.R', local=TRUE)
source('ui-components/generic-tabs.R', local=TRUE)
shinyUI(
pageWithSidebar(
headerPanel('PRIMe Visualization Tools', 'PRIMe Visualizer'),
sidebarPane... |
41ac349124d37a17fc9e3309cb1ae63f3c7cab9e | dc5ddbb8d0a92321a988b4c9f161e09be21e88c0 | /5 - Analysing Backtesting Results.R | cd07af497f2ed17b553abd2068a3bcbac98fcbad | [] | no_license | ssh352/Trading | 857b2002367b197ac39fe5e0905372921976acd7 | b1490100148f2410741438cf5d0b22bf2d77ba6d | refs/heads/master | 2020-04-25T10:46:10.497855 | 2018-10-11T11:44:18 | 2018-10-11T11:44:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,538 | r | 5 - Analysing Backtesting Results.R | # analyze the trading strategy results
################
# Chart trades #
################
chart.Posn(portfolio.st,"NSEI")
###############
# Trade Stats #
###############
# tradeStats
# function is used to return the trade-level statistics within a portfolio.
# This function returns important statistics like numb... |
5788aa915d08772b6e903aecf395476a47274d60 | 7172c9fc447a5cc688118b4c7daa5ac4ca0fef70 | /man/OCRdataFields.Rd | eb175a04fc702da07f643b254e243913669e5618 | [] | no_license | jniedballa/camtrapR | 0b2d6dd7f3b204494f07a8230a5990916c95b5d7 | 0bcbff865569fde24a19199e3a4deaa5de3aabf9 | refs/heads/master | 2023-08-04T08:58:23.455756 | 2023-08-03T03:07:16 | 2023-08-03T03:07:16 | 147,821,210 | 38 | 3 | null | 2021-03-20T11:34:11 | 2018-09-07T12:36:39 | R | UTF-8 | R | false | true | 4,743 | rd | OCRdataFields.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/OCRdataFields.R
\name{OCRdataFields}
\alias{OCRdataFields}
\title{Optical character recognition (OCR) from data fields in digital images}
\usage{
OCRdataFields(inDir, geometries, invert = FALSE)
}
\arguments{
\item{inDir}{character. Directory... |
b73655e1a4b8909a2c67adfde9a989768dc13f63 | 97cfe591bfc17d23e1d08d3db8c652b1c9b66647 | /R/compile.R | 7330171f60a09d857a8057bef46e3030bb8c4488 | [
"Apache-2.0"
] | permissive | sibusiso16/sparklyr | dfc87fa828e7f390ad04016ab9b9bb6c81024e98 | 33fc9c96534d6dd90646430c22a5bfb3fc159316 | refs/heads/master | 2020-07-12T05:41:00.856428 | 2016-07-22T06:41:42 | 2016-07-22T06:41:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,198 | r | compile.R | #' @import rprojroot
#' @import digest
spark_compile <- function(spark_version = "1.6.1", hadoop_version = "2.6") {
version_numeric <- gsub("[-_a-zA-Z]", "", spark_version)
version_sufix <- gsub("\\.|[-_a-zA-Z]", "", spark_version)
jar_name <- paste0("sparklyr-", version_numeric, ".jar")
if (!requireNamespace(... |
5dadddabc9b5521bb16836f090f335bd73ddf3b4 | f4c80419f369de8034301b321a30a71f56909aee | /hypergeometric.R | b73d2f3bb6530b68e734a3980277b44e3d155a2f | [
"MIT"
] | permissive | embunnik/hypergeometric | a88c8cbd8c6f0d8c82a5104a30ff2dec75c05361 | d9e4bc013fbee8f3d5882d19323512494e104222 | refs/heads/master | 2021-01-20T18:53:06.713164 | 2016-06-16T00:18:40 | 2016-06-16T00:18:40 | 61,249,233 | 0 | 0 | null | 2016-06-16T00:17:42 | 2016-06-16T00:10:40 | null | UTF-8 | R | false | false | 1,632 | r | hypergeometric.R | ##Probability of getting an overlap of at least a particular size given two lists and the hypergeometric distribution
#### Data input is of the format:
## column_1 column_2 column_3 column_4
## overlap_size size_of_list_A Population_size size_of_list_B
## overlap_size size_of_list_A Population_size size_of_l... |
e24afc07f850cfcf3994859618e2521e2fbe4faa | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Mathematical_Statistics_And_Data_Analysis_by_John_A_Rice/CH1/EX1.4.2.B/Ex1_4_2_B.R | 28a9ebb41a3ad6d15aa0605fe04431484df4d671 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 77 | r | Ex1_4_2_B.R | #Page 10
total_lineup = choose(10,5) * factorial(5)
print(total_lineup) |
f9ef6d77a746ee5c65312f4a65603f9bf7d28d87 | 0e7bd9d49f15674ba5415c10e721923520e11bd2 | /R/surface.kernel.R | 94f9def17d1f18da779b9005ef2b4977f402c566 | [] | no_license | syunhong/seg | 8bbf3ac80aa31dab57652e7e8350d2449f55e622 | 6ef0afe116c16b0e55e5f47a95a9e4c162e60117 | refs/heads/master | 2022-08-20T22:33:19.085866 | 2022-08-12T21:59:15 | 2022-08-12T21:59:15 | 92,493,454 | 12 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,596 | r | surface.kernel.R | # ------------------------------------------------------------------------------
# Internal function 'surface.kernel'
#
# Author: Seong-Yun Hong <hong.seongyun@gmail.com>
# ------------------------------------------------------------------------------
surface.kernel <- function(coords, data, sigma, nrow, ncol, win... |
c7c2f01dca51d740e41f63d8bc6c9fde3a699b9f | 2491ce3e1bd5762df5d129b7f4826c66723780df | /R/fun.gld.all.vary.emp.R | e403ca0a0f0a1d57bb34fe41a780c122714dfff5 | [] | no_license | cran/GLDreg | 60e3cf2d6890604d98aad9cd09080bdc7758cb25 | 4d6ad22ceada267cf3a46ef8b4cab5b9006ae022 | refs/heads/master | 2022-06-04T22:22:08.379317 | 2022-05-13T06:30:09 | 2022-05-13T06:30:09 | 26,482,634 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 602 | r | fun.gld.all.vary.emp.R | fun.gld.all.vary.emp <-
function (q, fit, fit.simu, maxit = 20000, method = "Nelder-Mead")
{
x <- fit$x
y <- fit$y
k <- apply(fit.simu, 2, function(x, q) quantile(x, q), q)
r <- optim(k, function(k, x, y, q) {
resid <- y - x %*% c(k)
return((sum(resid <= 0)/length(resid) - q)^2)... |
69b5a56d22fa003a932f6bc3403f2394195f6879 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rdfp/examples/dfp_full_report_wrapper.Rd.R | 9944aa6e3633c7f3d162a85cf941f942623818e0 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,015 | r | dfp_full_report_wrapper.Rd.R | library(rdfp)
### Name: dfp_full_report_wrapper
### Title: Take report request and return data.frame
### Aliases: dfp_full_report_wrapper
### ** Examples
## Not run:
##D request_data <- list(reportJob =
##D list(reportQuery =
##D list(dimensions = 'MONTH_AND_YEA... |
f61daa91f40a9d7f64a917c1366dc5446177558b | 157bcf7190b4663915e23bbd3342fd99101b782e | /R/create_colormaps.R | 5967a9bfb55658fd25b26fe62681d0645a2eedfb | [] | no_license | zachcp/phyloTreeWidget | c241d1dae2bfe77f426a7446dbd7326c8ab350ba | f041640c72bd303eb42da10f8fc445fc0f5d99e4 | refs/heads/master | 2022-03-07T22:31:09.772298 | 2019-10-04T17:25:28 | 2019-10-04T17:25:28 | 116,187,708 | 2 | 0 | null | 2019-10-23T01:12:39 | 2018-01-03T22:20:12 | JavaScript | UTF-8 | R | false | false | 1,671 | r | create_colormaps.R | #' create_colormaps
#'
#' create colormaps for dataframe character/factor columns
#'
#' @param df Required. A dataframe containing information about nodes in the tree.
#' @param custommaps. Optional. Curently a stub. There needs to be a way to pass
#' in custom colors.
#' @param exclude_attr a list ... |
4924ce2c61b41fe63dec77ecb6a0906fd4b37f66 | b3357449a175852145c9099327649df50046d0c4 | /src/utils/generate.R | 6a649d1e4b4d35dad29816e74c696046c5bf030b | [] | no_license | savchukndr/temat14 | acb2e5c28beee21c67c7deca942b4edca34ca6b5 | 3fb63c9c2128de0784047be72d9daf2090bf098c | refs/heads/master | 2021-05-14T17:21:29.412546 | 2018-01-15T20:55:35 | 2018-01-15T20:55:35 | 116,044,592 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 623 | r | generate.R | # Function for genereting data frame
generate.generate_empty_df = function(unique_user_id, unique_item){
df = data.frame(user_id=c(unique_user_id))
# Filling data frame with empty rating
for (column in unique_item){
df[[toString(column)]] = 0
}
return(df)
}
# Function for filling data frame
generate.... |
fc440a779114b774c662bfde6c1f15944e855785 | de37330b37b7d0c0eaea794d634b2d44385a1585 | /R/namespace.R | c6983f3653355ccfee1a1df7a442e656391de3ce | [
"MIT"
] | permissive | poissonconsulting/slobr | 472d66b5d9dd43d689688f88d79ba80e30b814ea | 37beb9c01c473d3fd765e9a4b98db82042f1e146 | refs/heads/main | 2023-06-07T11:23:55.616470 | 2022-06-17T22:09:48 | 2022-06-17T22:09:48 | 188,949,094 | 0 | 0 | NOASSERTION | 2021-03-08T21:47:21 | 2019-05-28T03:42:44 | R | UTF-8 | R | false | false | 149 | r | namespace.R | #' @import shiny glue RSQLite bsplus
#' @importFrom shinyjs reset useShinyjs
#' @importFrom utils zip
#' @rawNamespace import(chk, except = p)
NULL
|
61c00bef8c85643cfc9452666fd094cab1596b54 | db00d3a1fde561492ac49104f272a7c98aebf0f8 | /R/mait.R | 2f499b065e7e5214e1b1507cf99e5f2a22543fc4 | [] | no_license | gigascience/cuddel-gsk-dataset | e064774dd16cb018057957f7ea1e28129395f245 | bf757f8a7fb5f34b803698c0524d7203e579f082 | refs/heads/master | 2021-01-25T04:50:11.079641 | 2018-10-12T01:22:12 | 2018-10-12T01:22:12 | 93,491,347 | 2 | 4 | null | 2018-11-16T08:01:19 | 2017-06-06T07:52:17 | R | UTF-8 | R | false | false | 15,286 | r | mait.R | # Title : TODO
# Objective : TODO
# Created by: peterli
# Created on: 17/7/2018
library(MAIT)
# The MAIT workflow involves filling slots in the MAIT object and returning the updated MAIT object as output
# First, load faahKO data into MAIT object
library(faahKO)
cdfFiles <- system.file("cdf", package="faahKO", mu... |
10e0e37b1cd072184eecca483355f570ffabbc8b | 2b9e8f79a771a5ae510956db076bd6451204dc82 | /apprentissage-automatique/TP1/Script.R | ae2bedcc257bb293d70500b44629e10a06337d9c | [] | no_license | hamza-hramchi/eilco | 24d78b7c754b4d1e2d3c7c7a0571e40013333af0 | bf9b417b91f7653bd228fee30df316ecc31588f4 | refs/heads/master | 2023-01-28T19:46:50.719722 | 2020-12-08T21:30:04 | 2020-12-08T21:30:04 | 297,595,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,774 | r | Script.R | # Title : R script
# Objective : Discover R langage
# Created by: Hamza HRAMCHI
# Created on: 28/09/2020
# -------------- Basics of R langage -----------------------------
a <- 5
a
b <- a
b
v <- c(1, 2, 5, 9)
mode(v)
length(v)
# -------------- Input/Output --------------------------------------
print("Enter numer... |
2592471a9b4f2b9cdb93b029d0dde8cbdce786b4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/apTreeshape/examples/shift.test.Rd.R | 561bb63a3daa402086759eb7d31f9f4c568c34ed | [] | 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 | 944 | r | shift.test.Rd.R | library(apTreeshape)
### Name: shift.test
### Title: Testing diversification rate variation in phylogenetic trees
### Aliases: shift.test
### Keywords: htest
### ** Examples
## Detecting diversification rate variation in bird families (135 tips)
data(bird.families)
tree.birds <- as.treeshape(bird.families, model... |
360650d111faaa728e140f4aabf6193f5695656c | d517fbecd093d7c6ba2e69d8d97ddb3b9281b622 | /S_meliloti_GO_analysis_rsubread-data.R | 3330559682f32d28cbe1d49fb4e9f9af4a93d431 | [
"MIT"
] | permissive | ahalfpen727/Gene-Expression-SecondaRy-Analysis | 1505c22092830c784bf858997b3b987fa0378d72 | 7c6eb0cf178b3a1a35ebf3d6990f75a48dc80ac6 | refs/heads/main | 2023-02-06T04:34:24.822876 | 2020-12-31T09:06:08 | 2020-12-31T09:06:08 | 305,691,492 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 41,085 | r | S_meliloti_GO_analysis_rsubread-data.R | #####################################################################
# library(STRINGdb); KEGG_+_GO_analysis post-featurecounts.R
#####################################################################
source("https://bioconductor.org/biocLite.R")
# biocLite("limma");biocLite("Rsubread"); biocLite("STRINGdb")
browseVign... |
9bd3b2a15f0d93f5fec25f6756717ddb995c4546 | 3dd11d7fbaca863b0ac383fc2e6bf53efba6dac8 | /R/workspace.R | bf893694b149fc490b5c97a98f2a20995453abe3 | [] | no_license | agroimpacts/EnergyAccess | a755ce2dcb1d9bad9c9a115e354862a7aec45633 | 744e6522ab9f16f8a50b0bc57d15093d7e3bffc1 | refs/heads/master | 2021-08-30T11:22:57.732571 | 2017-12-17T18:03:15 | 2017-12-17T18:03:15 | 111,222,242 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,500 | r | workspace.R | library(sp)
library(rgdal)
library(rgeos)
library(raster)
library(devtools)
library(roxygen2)
library(gstat)
library(dismo)
library(RColorBrewer)
library(viridis)
library(mapview)
library(ggplot2)
library(reshape2)
library(gridExtra) #arranging ggplots in grid
library(grid) #arranging ggplots in grid
#=================... |
b78d8778e6f599aedf8109f7e1d963a15b24dcfb | c3e7d1ff8719f4c24b87b01eba1ccbbe5250e463 | /man/convertToWellLocation.Rd | 19c2f530dbf4ea978c684e5f6d66876ffffdec0a | [] | no_license | nbenn/singleCellFeatures | 4016508a11b2e9ed5103e45af7347bc63175e014 | 9b3b72a2cd0aaabc78ccde770f068b6d2c16b82c | refs/heads/master | 2020-04-03T03:02:30.944690 | 2016-06-15T09:13:15 | 2016-06-15T09:13:15 | 32,792,562 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 531 | rd | convertToWellLocation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convertToWellLocation.R
\name{convertToWellLocation}
\alias{convertToWellLocation}
\title{Covert compatible objects to WellLocation}
\usage{
convertToWellLocation(x)
}
\arguments{
\item{The}{object to be used as basis for new WellLocation obj... |
2c5957146729ab93a122564b9a14dfd5903a1f69 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/dfcomb/R/dfcomb.R | 565c55fa8809fbdcfcef703d6d45461ab57ec596 | [] | 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 | 18,407 | r | dfcomb.R | CombIncrease_sim = function(ndose_a1, ndose_a2, p_tox, target, target_min, target_max, prior_tox_a1, prior_tox_a2, n_cohort,
cohort, tite=FALSE, time_full=0, poisson_rate=0, nsim, c_e=0.85, c_d=0.45, c_stop=0.95, c_t=0.5,
c_over=0.25, cmin_overunder=2, cmin_mtd=3, cmin_re... |
9cd3b4e28fbeb67e2d38933ef2f5de2c36252f67 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EngrExpt/examples/uvoven.Rd.R | 0fde771085c3f68b0e39a3fd6b6cb2b1dd40def6 | [] | 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 | 590 | r | uvoven.Rd.R | library(EngrExpt)
### Name: uvoven
### Title: UV absorbance for lens cured in different ovens
### Aliases: uvoven
### Keywords: datasets
### ** Examples
str(uvoven)
summary(uvoven)
densityplot(~ uv, uvoven, groups = oven, auto.key = list(columns = 2),
xlab = "UV absorbance")
qqmath(~ uv, uvoven, groups ... |
2e58e26e15e31aaf7e4ed7696e018ad707a74f29 | dead36e975d42bcc18f7922012c7ddf8d3327c21 | /R/holland.R | 21e109d2f981c3cc931ea533f0eb4442221bf3d7 | [] | no_license | carlislerainey/unnecessary | a741d705dc59540dcabc22bb8e9f2b9daf79a24e | 1f72f238055e4c562162a89b6b270847987e44f8 | refs/heads/master | 2023-01-10T15:53:27.308825 | 2022-12-25T10:54:28 | 2022-12-25T10:54:28 | 116,427,263 | 3 | 1 | null | 2018-08-17T10:41:13 | 2018-01-05T21:12:04 | R | UTF-8 | R | false | false | 7,001 | r | holland.R |
# load packages
library(tidyverse)
library(ggrepel)
library(kableExtra)
# sandwich package also required
# MASS package also required
# set seed
set.seed(8904)
# load data
holland <- haven::read_dta("data/Enforcement.dta") %>%
# keep only the variables we use
select(city, district, operations, lower, vendors, b... |
ed98bfc0a0c94e85a15f417913e95896e23951c5 | f9d2eca47d9604aba2373e1793f5b00fb3722ccc | /dev/callee_scripts/produce_maps.R | 6bc25aebf71470dd54ede16ec1548e2c28872b0c | [
"MIT"
] | permissive | linneasoli/Sus | c2a73d642e18df95dbe1bdf4516e7e987415a55b | 0c82720d110469ad2a36b00ca7640f617d2a069e | refs/heads/main | 2023-06-14T05:48:44.223296 | 2021-07-09T18:14:02 | 2021-07-09T18:14:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,761 | r | produce_maps.R | # Produce left and right maps ---------------------------------------------
# Dependent script: needs 'borough' object
make_circle <- function(x) {
borough %>%
st_transform(32618) %>%
st_union() %>%
st_centroid() %>%
{. + c(0, -3500)} %>%
st_set_crs(32618) %>%
st_buffer(26000) %>%
... |
17fcf3c84e059eeff4147aa1f993f74f7c9c82f6 | f05b62a978e2abea690fb5be403389f0d083df5b | /DM1_boxplots.R | 7e0f1f52930dbe268e625e7be60b2c10ca1411f0 | [] | no_license | vedereka/PMIP4_Benchm_proj | 87ae2480b112499c552f65c546ce807474ae323c | 80d26927790c2384fca14f6c6b75fb62be4b5596 | refs/heads/master | 2023-05-13T02:53:05.784000 | 2021-03-01T14:32:48 | 2021-03-01T14:32:48 | 337,995,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,368 | r | DM1_boxplots.R | ### produce data only and data-model latitudinal boxplots
# Boxplot #1: Bartlein (B), Cleator at the Bartlein sites (CL) and all Cleator data (CL_all)
# Boxplot #2: Bartlein (B), all Cleator data (CL_all) and model data
# Statistical summaries of all variables are saved in output/
# These are the things that will requi... |
260d2ee1572f7d4890919464c0fd909388965fa9 | aa3ec60c734dc3c8bd7f959fa7604ef3dc82c2df | /R/epc_interp_grid.R | 517079c5d50719ac7375b93551c27eb7c2521a5d | [] | no_license | fawda123/tb_chl | d6ba36e1fc5a6bfd40a838af472c6fe47deae713 | 47b5715cc6976e6aa0bc50e98fe79f9b65b61d9d | refs/heads/master | 2020-07-26T00:11:13.450383 | 2017-08-29T18:46:59 | 2017-08-29T18:46:59 | 21,586,396 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,971 | r | epc_interp_grid.R | ######
#created Sep 2013, M. Beck
#creates interpolation grids for TB hirsch model
#used to create heat maps and to get normalization data
######
#get interpolation grids
######
#salinity grid
#includes beta estimates for weighted regressions for each obs
#includes back-transformation from Moyer et al. 2012
rm(list=... |
26c7234e8515936c9ca32925f77b0ab4f205407f | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /GeneralizedUmatrix/man/upscaleUmatrix.Rd | 5a25a4313663c6b395bab74e91f7cef152a0286d | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,250 | rd | upscaleUmatrix.Rd | \name{upscaleUmatrix}
\alias{upscaleUmatrix}
\title{
Upscale a Umatrix grid
}
\description{
Use linear interpolation to increase the size of a umatrix. This can be used to produce nicer ggplot plots in \code{\link{plotTopographicMap}} and is going to be used for further normalization of the umatrix.
}
\usage{
... |
8503eab461da432c244f134c4474e04c64108081 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/c3/examples/pipe.Rd.R | 175c70e97f750727e2e615a8faf007c269a86200 | [] | 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 | 140 | r | pipe.Rd.R | library(c3)
### Name: %>%
### Title: Pipe operator
### Aliases: %>%
### ** Examples
data.frame(a=c(1,2,3,2),b=c(2,3,1,5)) %>%
c3()
|
b23615113befa22cbfddbff906d65664072b9fc3 | 2f5cdf1e21345e7d3271aef35027f11c7df7bd1a | /episcanpy/preprocessing/notes_benchmarking_multiomic.R | dd585e00c05d202dfaa1e742d79bf85baec03c2b | [
"BSD-3-Clause"
] | permissive | colomemaria/epiScanpy | 8d6992d5e9797d574ccdfff92f028beede5143ed | 070a9467dce121345da19f1b3955e7fa1eabf19a | refs/heads/master | 2023-08-08T08:38:27.289808 | 2023-05-09T12:46:19 | 2023-05-09T12:46:19 | 170,289,801 | 127 | 37 | BSD-3-Clause | 2023-07-21T13:30:51 | 2019-02-12T09:28:15 | Python | UTF-8 | R | false | false | 1,661 | r | notes_benchmarking_multiomic.R | .libPaths( "/home/icb/anna.danese/miniconda3/envs/scmoib-seuratv4/lib/R/library")
library(Seurat)
library(SeuratDisk)
library(Signac)
# Load the peak ATAC
file = "/home/icb/anna.danese/project_anna/scmoib/brain_peaks_filtered_not_normalised.h5ad"
file_seurat = "/home/icb/anna.danese/project_anna/scmoib/brain_peaks_fil... |
52d260ab2ed65539769a9af226f642d6915b9601 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MAGNAMWAR/examples/FormatMCLFastas.Rd.R | ef251361f0ac26cc6fb585f5478daf25b68d0446 | [] | 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 | 358 | r | FormatMCLFastas.Rd.R | library(MAGNAMWAR)
### Name: FormatMCLFastas
### Title: Format all raw GenBank fastas to single OrthoMCL compatible
### fasta file
### Aliases: FormatMCLFastas
### ** Examples
## Not run:
##D dir <- system.file('extdata', 'fasta_dir', package='MAGNAMWAR')
##D dir <- paste(dir,'/',sep='')
##D formatted_file <- Fo... |
73b53943212a73a31bff56644905c3124fac7c64 | ed00cdd70acf530004fb6f56d2e2c090b10813ff | /R/pairangle.R | 85bad707608473f9599466c307d328eef0e04c31 | [] | no_license | adchSTATS/genfun | a7400075fd296eeba1a3e83bc4d64397a0e15aad | e6189fe729797397516a619c5c1e88e7adb3a717 | refs/heads/master | 2021-08-08T17:32:10.692919 | 2017-11-10T19:21:43 | 2017-11-10T19:21:43 | 108,581,350 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 711 | r | pairangle.R | #' Pairwise angles
#'
#' Computes the matrix of angles between all pairs of vectors.
#' @param mat A numeric matrix with each row representing a vector.
#' @return A square matrix whose \code{[i,j]} entry is the angle between the two vectors
#' represented in row \code{i} and \code{j} of \code{mat}.
#' @details The (i,... |
4884c78ec7a210d8eba2910cfea588aa567b40e2 | 081576efbe245e5c437d4c45a30c10453cd2cac8 | /man/keep_cat_cols.Rd | 4a8fc1c0518316c7a526e713d3ebe1577d3343e7 | [
"curl",
"MIT"
] | permissive | fei0810/UCSCXenaShiny | 93ecad178e560cbfe6668978bb77dac31e5b169a | 398605a5da859886313649036da739feee0edec3 | refs/heads/master | 2023-04-18T00:58:07.866434 | 2021-04-28T12:39:02 | 2021-04-28T12:39:02 | 274,140,433 | 1 | 0 | NOASSERTION | 2021-04-28T12:39:02 | 2020-06-22T13:11:24 | R | UTF-8 | R | false | true | 627 | rd | keep_cat_cols.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{keep_cat_cols}
\alias{keep_cat_cols}
\title{Keep Only Columns Used for Sample Selection}
\usage{
keep_cat_cols(x, keep_sam_cols = TRUE, return_idx = TRUE)
}
\arguments{
\item{x}{a \code{data.frame} with many columns.}
\item{kee... |
e4057fe69a362bd12a32f4812cf8d7d205aeab7f | 913127177bc7fe98cddbe2bd1592a274590f626c | /cachematrix.R | add74c35b7970611ae38a177c32dcc13f85c8c10 | [] | no_license | shsab/ProgrammingAssignment2 | fcdf798749de9821a8ce85abe96f2c5f6b419e2b | f8472b7359e51b50b4eb261fafc2bea721fae626 | refs/heads/master | 2021-01-14T12:30:40.709538 | 2015-04-26T07:01:18 | 2015-04-26T07:01:18 | 32,295,407 | 0 | 0 | null | 2015-03-16T01:58:25 | 2015-03-16T01:58:22 | null | UTF-8 | R | false | false | 1,239 | r | cachematrix.R | ## Cretae a special matrix that contains the matrix and
## Caches its invert.
## Assume matrix provided is invertable
makeCacheMatrix <- function(x = matrix()) {
xInv <- NULL
set <- function(Y){
x <<- y
xInv <<- NULL
}
get <- function() x
setInvert <- function(xInvert) xInv <- xInvert
getInvert <-... |
ea7673c763a4b77ab54a6712b1cf7f637d246f10 | 34b1ab46a70fe81143874a40d6493c0254f1e5c9 | /time_series/tsoutliers.test.02.R | 355d083e5d82bf846826cf9c383fcc9db68d3cb4 | [] | no_license | yama1968/Spikes | 5f974a20812dbd88f789cabf7720826d358f8e76 | 498b0cacfc23627ecee743f012a6fda6451cda7f | refs/heads/master | 2021-06-06T00:33:33.637745 | 2020-11-14T18:49:25 | 2020-11-14T18:49:25 | 29,531,065 | 2 | 0 | null | 2020-11-12T21:13:21 | 2015-01-20T13:29:35 | Jupyter Notebook | UTF-8 | R | false | false | 593 | r | tsoutliers.test.02.R |
library(tsoutliers)
library(ggplot2)
data(Nile)
resNile1 <- tso(y=Nile, types=c("AO", "LS", "TC"), cval=3,
tsmethod="auto.arima", args.tsmodel=list(model="local-level"))
print(resNile1)
r <- resNile1
points <-data.frame(x = r$outliers$time-1,
y = Nile[r$outliers$time-start(Nile)[[... |
b4ed9fa4b40b7402c708e5a5461cbbdb8557cfd7 | 31df5e6e37bee75ba4b22a972cfa57a632ba2d23 | /scripts/getUniqExon/human/step5_ensembl_nonoverlap_exons_bed.R | 0515716505d3d9d8a420b6c3053915aa8c083f97 | [] | no_license | Sun-lab/asSeq | 47fa0b451530114ce2e0cdcd40e64afbf21335d7 | 3dcfb91d603213057c570a98d532c6cfc5618929 | refs/heads/master | 2022-09-28T13:23:00.427278 | 2022-09-11T16:33:06 | 2022-09-11T16:33:06 | 134,029,698 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,601 | r | step5_ensembl_nonoverlap_exons_bed.R |
## when writing data into text file, it may use scientific format
## when you read it into c, and using atoi. it will make mistakes
## say 97000000 is written as 9.7e+07, and c think it is 9
## options("scipen") can control write out behavior
options(scipen=20)
# -----------------------------------------------------... |
1b2a744c6835e0fb82457e7a81daf4c187b86764 | f92a7585f054a393674716f4e178a78f099f4917 | /bin/csv_scrape.R | 06c6b3ccfe2329d88cea3e11bbbe8132f4fe8bc6 | [] | no_license | parvezrana/forvol | 7a29c9c2c77777ddb0ed94c47f398b04056f2e84 | 767d1739061fce7fc207a500ebff0216173d6512 | refs/heads/master | 2020-03-14T13:42:17.524275 | 2018-01-03T18:09:12 | 2018-01-03T18:09:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 630 | r | csv_scrape.R | ### Scrapes an input xlsx workbook of all subsheets and converts to csv
options(java.parameters = "-Xmx8000m")
library('xlsx')
CSVScrape <- function(workbook.path, dest.path) {
## Get number of sheets
sheets <- getSheets(loadWorkbook(workbook.path))
num_sheets <- length(sheets)
## Read the sheet and output a... |
dcbeeab47545fe36cf009b6090bb8417116c38ba | b8de739b0994bff1f15c8b888f7165ef58bc7d67 | /Program/deps/silencing.r | 0e061f59a72caf55ce4043511db360a928569b35 | [
"MIT"
] | permissive | arendma/GRN_code | dc00fb81ebb9c9f144e68fd25c451b18bb44d4fe | 7630cd82dfef3c9e8dac061db4838d3fd4ae1476 | refs/heads/main | 2023-04-16T20:15:10.035250 | 2022-12-05T07:34:36 | 2022-12-05T07:34:36 | 450,481,870 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 431 | r | silencing.r | Silencing <- function(G)
{
diag(G)=1
I <- diag(ncol(G))
D <- diag((G - I) %*% G)
c_inv <- try(solve(G),silent = F)
if (class(c_inv)[1] == "try-error")
c_inv <- mpinv(G)
S <- (G - I + diag(D)) %*% c_inv#solve(G)
diag(S) <- 0
#S = abs(S)/max(abs(S))
return(S)
}
mpinv <- function... |
8b02a455d0ef3e46c90d6715f659b228feacf191 | 92cd10bc06fb5961bc272fcdc50eb8c99e7a70ea | /man/tree_var.Rd | 2a0f25f12a49d5a3d913a512cf99f109705c75bf | [] | no_license | alexandereric995/lares | b9af1622432d059be8f0e804fef8afe174c601c0 | db81f7bf3c959efffbab3a38d5e0373fd56ba714 | refs/heads/master | 2023-02-06T13:01:41.441067 | 2020-12-29T16:45:32 | 2020-12-29T16:45:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,763 | rd | tree_var.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trees.R
\name{tree_var}
\alias{tree_var}
\title{Recursive Partitioning and Regression Trees}
\usage{
tree_var(
df,
target,
max = 3,
min = 20,
cp = 0,
size = 0.7,
ohse = TRUE,
plot = TRUE,
...
)
}
\arguments{
\item{df}{Data f... |
ae821ad2e2b9e1daeacacf2c604401363b361924 | baeee810a3d266a7d2aa08b725cb3df28dab1a06 | /pnn_diabetes.R | 1fbc0247bd3b33c98a20c60d9867cf5623904873 | [] | no_license | alfredt/5206-CA1 | d2e09befbc8db8b03ce06cfae71085ea048b380c | 5c0e7786f37b7f2afd0851d7740861596214e86f | refs/heads/master | 2020-12-30T18:02:59.158409 | 2017-05-19T11:12:44 | 2017-05-19T11:12:44 | 90,946,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,067 | r | pnn_diabetes.R | pkgs <- c('caret', 'doParallel', 'foreach', 'pnn')
lapply(pkgs, require, character.only=T)
registerDoParallel(cores=8)
data_all <- read.csv(file="Diabetes.csv", head=FALSE, sep=",")
# PRE-PROCESSING DATA
column = ncol(data_all)
data_all <- data.frame(scale(data_all[-ncol(data_all)]), data_all[ncol(data_all)])
requir... |
4561b53ce473cbabf317b8b7c5d49b3b689e8b40 | 5378b28dde3f365587dfb646d15a0eb66ac40f1e | /R/model_output_lists.R | a69844e976e1a7331ae746fe9f30b4540a3b976a | [
"MIT",
"LicenseRef-scancode-other-permissive"
] | permissive | wwheeler6/CalCAT-1 | 64a69e3603c616397c9d9cbf74c7caa000734820 | ee73c7fcaade36cf3e785cc562688f2857471ffc | refs/heads/master | 2022-11-27T22:32:50.286561 | 2020-08-11T04:37:50 | 2020-08-11T04:37:50 | 286,646,310 | 0 | 0 | MIT | 2020-08-11T04:35:20 | 2020-08-11T04:35:20 | null | UTF-8 | R | false | false | 7,572 | r | model_output_lists.R |
JHUoutputs <- c("Peak* Hospitalizations" = "hosp_occup",
"Peak* ICU Beds" = "icu_occup" ,
#"Peak* COVID Hospital Occupancy" = "hosp_occup",
#"Peak* New COVID Daily Hospital Admissions" = "hosp_admit" ,
#"Peak* COVID ICU Bed Occupancy" = "icu_occup" ,
... |
88a15e19eee49539b298179026f23c7faafff133 | e2f37b60e1cd4fdf9c002cd267a79f2881b248dd | /demo/unit_test2.R | b72d2b4f3655491aedbab16c9d7091e099bf5e0b | [
"CC0-1.0"
] | permissive | cboettig/pdg_control | 8b5ac745a23da2fa7112c74b93765c72974ea9b9 | d29c5735b155d1eb48b5f8b9d030479c7ec75754 | refs/heads/master | 2020-04-06T03:40:50.205377 | 2017-10-17T02:47:24 | 2017-10-17T02:47:24 | 2,390,280 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,277 | r | unit_test2.R | require(PDGcontrol)
# Define all parameters
delta <- 0.1 # economic discounting rate
OptTime <- 50 # stopping time
sigma_g <- 0.2 # Noise in population growth
gridsize <- 10 # gridsize (discretized population)
sigma_m <- .2 #
sigma_i <- .2 #
interval <- 1
# Chose the state equation / population dy... |
84b7b5a405ec12e87646de3b382ecd03c9207e56 | dcefc68f0e065d9cd6f50e0061bab61c4eee995c | /badges.R | e4ad710bce1fc81f3d3f84a9f4c0284ac9b3f9a4 | [] | no_license | halpo/halpo.github.io | 9765eee7e3be31316ae77f26f9d16207b13af541 | 0b98f0d4a1b921d4f436dc07b8c36db732628d5c | refs/heads/master | 2022-11-26T06:44:57.122627 | 2020-08-10T01:48:36 | 2020-08-10T01:48:36 | 197,820,361 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,938 | r | badges.R |
# Utilities -------------------------------------------------------
make_md_badge <- function(name, href, src){
as.character(glue::glue("[]({href})"))
}
make_badges <-
function( pkg
, user = gh_whoami()$login
, repo = pkg
, branch='master'
, stage = NULL
)... |
8b17fadb9921a7f6d2bbd3bfa4706100e812e6e9 | e6024449a5e52e00251d476ef3fdb3544cb4b43e | /R/knit2hugo.r | f2c55e0a2ccb16e4cdeac215a18ca48b349e86b4 | [] | no_license | kjhealy/kjhutils | 99b05520de91c150efb21626dba0d8502b36e8c5 | 054b9a6002b96053f9392edcbe9eb1cf2ca4eb30 | refs/heads/master | 2021-03-12T23:41:10.370372 | 2019-03-16T20:56:46 | 2019-03-16T20:56:46 | 9,861,040 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,108 | r | knit2hugo.r | ##' knitr output hook for hugo highlight shortcode.
##'
##' Output hook suitable for use with hugo's syntax highlighter shortcode
##'
##' Put render_hugo() in the first chunk of your document to make
##' knitr output hugo-friendly {{< highlight >}} shortcode code fences
##' instead of markdown's default triple-backtick... |
9e38de5e7fe8e0bbfa5081cc2564f8c2cef7cec7 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Biostatistics:_Basic_Concepts_And_Methodology_For_The_Health_Sciences_by_Daniel_W._Wayne,_Chad_L._Cross/CH10/EX10.3.1/Ex10_3_1.R | a1afddbdd7e0d592d283710c4cc544e63b5d089d | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 1,121 | r | Ex10_3_1.R |
##Example 10.3.1 Pg.493
##Multiple regression equation
age<- c(72,68,65,85,84,90,79,74,69,87,84,79,71,76,73,86,69,66,79,87,71,81,66,81,80,82,65,73,85,83,83,76,77,83,79,69,65,71,80,81,66,76,70,76,67,72,68,102,67,66,75,91,74,90,66,75,77,78,83,85,76,75,70,79,75,94,76,84,79,78,79)
edlevel <- c(20,12,13,14,13,15,12,1... |
4e728ebcee106dbc40ad13b5b2af8115b88ed191 | e6b8d6b34577d601bee023a979a3dda88036df09 | /MultivariateStatistics/guassian mixture clustering base script with example.R | 4bbbc11c38d7edbf2cc6d0273caabf416e65df0b | [] | no_license | mikaells/CeMistWorkshop2020 | 307fbcaacc811d2d7da0eefc2398c35f1c7d0373 | 6d4547d2b9ee81f9a199cfeb6d8e67e9e9d5e31f | refs/heads/master | 2023-01-30T22:24:57.365726 | 2020-11-24T10:56:28 | 2020-11-24T10:56:28 | 300,240,862 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,007 | r | guassian mixture clustering base script with example.R | #### clustering base script 2020 workshop ####
library("mclust")#mixture clustering package
# load data
Tab=iris #iris data is alredy in R base
X=iris[,-5] # Take out species
#from 1 to 10 cluster #compare different models with different covariance structure
GMM.model <- Mclust(X, G=1:10)
mclustAIC<-functi... |
1045c83fbd8aded5c547bdbb23e31772e9d593e8 | 5b09cfc911f713193308f6fc53906ceeeceeaa32 | /R/stackoverflow.R | 573b3946a62ac053afbc3711e0af33cb2b556656 | [] | no_license | stevecondylios/rawr | c2e91550f8c153913504f004a0928bead77d16c7 | 1fb218cde7710c8f280bd9148d63693c4a50fdf2 | refs/heads/master | 2021-10-22T14:08:54.947648 | 2021-10-18T16:38:40 | 2021-10-18T16:38:40 | 205,609,858 | 16 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,433 | r | stackoverflow.R | #' Retrieve raw R code from Stack Overflow website
#'
#'
#' @name stackoverflow
#'
#' @usage stackoverflow(url, method, padding)
#'
#' @param url Link to a page on Stack Overflow website (or any Stack Exchange)
#' @param method Not all websites are formatted consistently. To overcome this, try a different
#' method... |
ffb32375dbf2ec4d23e55a54ddd094dd08e9989e | e37ef7efb1f111c1fa531ece65be38af11556ae6 | /codes/DifferentialMethylation2.R | 7c9ec0b743f525e260ed0bcc0ed23fcb3444e926 | [] | no_license | hjkim88/CRC | 17ea3a6d0c51bae5b1c6debaeb16cd65a8d5c1d8 | e92d7723e6e562dde5bc4158844bc41db503d8e8 | refs/heads/master | 2021-07-07T09:19:54.401524 | 2020-09-03T19:50:00 | 2020-09-03T19:50:00 | 180,240,623 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 88,176 | r | DifferentialMethylation2.R | ###
# File name : DifferentialMethylation2.R
# Author : Hyunjin Kim
# Date : July 6, 2019
# Email : hk2990@cumc.columbia.edu
# Purpose : Find differentially methylated probes and regions,
# perform pathway analysis on differentially methylated genes.
#
# Workflow
# ... |
6c782790652401418b069ed3adaf59a171f857d4 | f13ad56d41c9daa694959aded6fe1a207dc6660e | /plot1.R | 091b089cb3a309c70ec475d6adc871295a3a3561 | [] | no_license | Cuffnela/ExData_Plotting1 | 256d40adb1d4b873111d337bb9422da4fbbe90a4 | d12958e7498f7cdd9fad7a23b8d70225c6841fe9 | refs/heads/master | 2020-10-02T06:04:13.926184 | 2019-12-13T02:08:46 | 2019-12-13T02:08:46 | 227,717,864 | 0 | 0 | null | 2019-12-12T23:52:17 | 2019-12-12T23:52:16 | null | UTF-8 | R | false | false | 1,387 | r | plot1.R | ################################################################################
# #
# Coursera Project #
# Exploratory Data Analysis Week 1 ... |
021f8573c41964a73f834bfe25eb954dc619b837 | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /cran/paws.security.identity/man/wafregional_delete_permission_policy.Rd | f92a115d435cb0ee2b6358e497a0989be0de6b9a | [
"Apache-2.0"
] | permissive | johnnytommy/paws | 019b410ad8d4218199eb7349eb1844864bd45119 | a371a5f2207b534cf60735e693c809bd33ce3ccf | refs/heads/master | 2020-09-14T23:09:23.848860 | 2020-04-06T21:49:17 | 2020-04-06T21:49:17 | 223,286,996 | 1 | 0 | NOASSERTION | 2019-11-22T00:29:10 | 2019-11-21T23:56:19 | null | UTF-8 | R | false | true | 800 | rd | wafregional_delete_permission_policy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wafregional_operations.R
\name{wafregional_delete_permission_policy}
\alias{wafregional_delete_permission_policy}
\title{Permanently deletes an IAM policy from the specified RuleGroup}
\usage{
wafregional_delete_permission_policy(ResourceArn)... |
05dcdfff999819401e1b2f953537aa82e75e420c | b7b104e4f7b87ebca206c0156e3c06c5e1689b11 | /gamlss - heathcote.R | e50bf540f0d0bf36812e4ec498fe1ea2f51e9c9c | [] | no_license | Bramtimm/Thesis-project | 3712457ac42186b2f31b066dda26c59b43b18495 | cc97e57e17dcd5ffc561a94baaa40f44e1a5f985 | refs/heads/master | 2020-04-05T08:42:54.922057 | 2019-01-24T15:38:25 | 2019-01-24T15:38:25 | 156,726,266 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,933 | r | gamlss - heathcote.R | # GAMLSS object for WALD
dSwald<-function(x, mu=mu, sigma=sigma,nu=nu, log=FALSE){
t=x
x=t
fy <-seqmodels::dinvgauss(t=t,kappa=mu,xi=sigma,tau=nu,sigma=1,ln=log)
if(is.vector(x)==TRUE){ as.vector(fy)
}else if(is.matrix(x)==TRUE){
fy <- matrix(fy,nrow=nrow(x),ncol=ncol(x))
}else if(is.array(x)==TR... |
8a2b56d69b95fd96e5b32c24e1f2fe8353a38469 | 7e1fff52409b817e17f8ce7a7d61298df54c78db | /diffMetAnal_functions.R | 9258e6f39a595ac7f17974ad784f11441c070100 | [] | no_license | guisantagui/MSK_repo_new | fad0b0bce6a7c33358be22eefb4ec5e78879b40f | ccc27afe72fb48c7d12f8fd3873a647b601696d3 | refs/heads/master | 2021-07-03T23:22:31.328607 | 2021-02-09T23:27:58 | 2021-02-09T23:27:58 | 220,031,177 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,831 | r | diffMetAnal_functions.R |
# Quantile normalizes metabolomic matrix
quantNorm <- function(m){
meanrank <- function(x){rank(x)/length(x)}
apply(m, 2, meanrank)
}
# Removes the metabolites with "_?" (the tag we used to label ambiguous metabolites)
rmAmbig <- function(topDiffMets){
removed <- topDiffMets[-grep("_?", names(... |
d04a83887bdb96e2bd2a6b3e8d60d42e2c0cff69 | c166a28030821b7eb0523a7f2515701abc8e9eaa | /Rnsgaii/TestScript.R | 35001b19ff830291c83b9a688a416c18b31c8865 | [
"MIT"
] | permissive | UCD-GW-Nitrate/nsgaii | c82e96e397698dfe2e14af366b25ccf3d8908912 | f2d985dada0850c3bd648fd38c13778ec24c3597 | refs/heads/master | 2023-04-07T00:35:40.054396 | 2021-04-16T21:26:06 | 2021-04-16T21:26:06 | 216,633,904 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 275 | r | TestScript.R | library(plotly)
plot_ly( x = pH[[401]][,2], y = -pH[[401]][,1],
marker = list(size = 5,
color = 'rgba(255, 182, 193, .9)',
line = list(color = 'rgba(152, 0, 0, .8)',
width = 0)))
|
4c650bb5cdac55efe7f19ec2cf8130bd8a8631c8 | 6cf28ab84a3653da0f3b0f838de1d1522fabb677 | /bindings/R/rlibkriging/tests/testthat/test-NoiseKrigingFit.R | 11533804ecdd02de218d195227c39b6e2139aff5 | [
"Apache-2.0"
] | permissive | libKriging/libKriging | e23ed9488159db9e66d8a2e23aa8b5bdc647bd62 | 2a75a7e0be4245dece2672652c8c2311e3454595 | refs/heads/master | 2023-08-21T23:18:49.311663 | 2023-07-11T10:49:17 | 2023-07-11T10:49:17 | 192,920,489 | 29 | 8 | Apache-2.0 | 2023-08-04T16:06:21 | 2019-06-20T12:58:55 | C++ | UTF-8 | R | false | false | 7,474 | r | test-NoiseKrigingFit.R | context("Fit: 1D")
f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
n <- 5
set.seed(123)
X <- as.matrix(runif(n))
y = f(X) + 0.1*rnorm(nrow(X))
k = NULL
r = NULL
k = DiceKriging::km(design=X,response=y,noise.var=rep(0.1^2,nrow(X)),covtype = "gauss",control = list(trace=F),nugget.estim=F,optim.method='BFGS',m... |
11b47377338841096bbaa3d13f0a0c306f944f69 | c73553a8936ec72cbbfbcc5e88cabbc946ce32a6 | /R/model-evaluation.R | 2c197322b94b8b61aedebb191827fef8e52703f1 | [
"MIT"
] | permissive | stephaneghozzi/trendbreaker | b0f9a1475f56bd7f14fb10573c5fe2b807c508e5 | 5002f7f12dc90c079f4391eb88be4166916403c5 | refs/heads/master | 2022-10-22T02:47:22.895042 | 2020-06-17T09:17:19 | 2020-06-17T09:17:19 | 275,192,241 | 1 | 0 | NOASSERTION | 2020-06-26T15:47:17 | 2020-06-26T15:47:16 | null | UTF-8 | R | false | false | 5,301 | r | model-evaluation.R | #' Tools for model evaluation
#'
#' These functions provide tools for evaluating models, based on the goodness of
#' fit or on predictive power. `evaluate_aic` evaluates the goodness of fit of a
#' single model using Akaike's information criterion, measuring the deviance of
#' the model while penalising its complexity.... |
10329058e62b462b79612463b2cd7c49c44a5c90 | 408def85f938dd10412aa89c144c380e09d1879d | /man/sdf_schema_json.Rd | 61f92d1a2de3e91959b0edffffbb17c5d000ea8a | [
"Apache-2.0"
] | permissive | mitre/sparklyr.nested | 3faa7ace6b59b15bf6078cf71e98d9f68e000fca | e7abb7067b008d7eb4f8b76816e3cf54c13b8088 | refs/heads/master | 2023-09-04T12:50:04.186509 | 2023-02-20T21:38:02 | 2023-02-20T21:38:02 | 101,699,537 | 30 | 5 | Apache-2.0 | 2022-05-19T10:22:22 | 2017-08-29T00:14:48 | R | UTF-8 | R | false | true | 3,012 | rd | sdf_schema_json.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/schema.R
\name{sdf_schema_json}
\alias{sdf_schema_json}
\alias{sdf_schema_viewer}
\title{Work with the schema}
\usage{
sdf_schema_json(
x,
parse_json = TRUE,
simplify = FALSE,
append_complex_type = TRUE
)
sdf_schema_viewer(
x,
si... |
6b24d597c7bc571dbd1e3108feaec81d729fe72f | bcbe85dd43636833ef0f07fbdafdcc355993a619 | /plot3.R | 0ed0cd311d9f50f346bac9371f0990eb5a9e7f93 | [] | no_license | yaseminyf/ExData_Plotting1 | a93dc2066b2dcbbae82834531411869acb440a54 | 8be3c7a2925849321c2227df5eae44157030891f | refs/heads/master | 2020-04-01T22:25:26.906800 | 2018-10-20T21:52:44 | 2018-10-20T21:52:44 | 153,707,619 | 0 | 0 | null | 2018-10-19T01:09:51 | 2018-10-19T01:09:50 | null | UTF-8 | R | false | false | 1,248 | r | plot3.R | # read in the file
hpc <- read.table("./household_power_consumption.txt", header=TRUE, sep = ";", na.strings="?")
# combine the Date and Time variables in a string
dt <- paste(as.character(hpc$Date), as.character(hpc$Time))
# convert the type of the newly formed string to class Date
datetime <- strptime(dt,"%d/%m/%Y ... |
4314b4750b02dada7357a347fd697c4629bca671 | ec2c6eb45f2155d66f4daa62cb9cf60211b769d7 | /add_concat.R | dd78a55203d217f1fe1c95bd0afa170b526b96f0 | [] | no_license | mindcrime/LearningR | dedf431d39d4622e6d601b395bbae9147cab1e41 | 04de699585927edc73797bf78a4b5bf18ce45a7e | refs/heads/master | 2020-05-09T18:10:34.277064 | 2015-12-25T12:14:59 | 2015-12-25T12:14:59 | 40,801,362 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 329 | r | add_concat.R | # Author: prhodes
###############################################################################
foo = 1:4 + 6:9
# paste adds the string to each element of the vector!
cat( paste( "vector added: ", foo ) )
cat( "\n" )
# the "c" function concatenates values to make a vector
cat( c( 1, 3, 6, 10, 15 ) + c( 0, 1, 3, 6,... |
6730b9918f616bea300f12b48d35d1bcdb801f48 | a69b74b9f6ebc26eae26c2d79748fb11588999ff | /button_commandsReviewed.R | 2bab6734062b74369a93bfe3491533185f119b2b | [] | no_license | BryanSWeber/TheButtonAnalysis | e89f1395cbc3032d6097bfd207d240e9b9996287 | 56408eb74dacf465b35d56e83942bda1d3874d7e | refs/heads/main | 2023-05-04T08:02:21.109902 | 2021-05-25T00:31:35 | 2021-05-25T00:31:35 | 370,519,283 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,515 | r | button_commandsReviewed.R | #install.packages("corrgram")
#install.packages("zoo")
#install.packages("forecast")
#install.packages("lubridate")
library("lubridate", lib.loc="~/R/win-library/3.1")
#Source of data
#http://tcial.org/the-button/button.csv
button <- read.csv("~/Button Data/button5_20.csv")
button$time<-as.POSIXct(button$now_timestamp,... |
96399f9985bff2187ee77836b40e8b5341bb7d7f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ltsa/examples/TrenchMean.Rd.R | 0b5349da95ded11c3ca7fa85afb9511506bc7e26 | [] | 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 | 499 | r | TrenchMean.Rd.R | library(ltsa)
### Name: TrenchMean
### Title: Exact MLE for mean given the autocorrelation function
### Aliases: TrenchMean
### Keywords: ts
### ** Examples
#compare BLUE and sample mean
phi<- -0.9
a<-rnorm(100)
z<-numeric(length(a))
phi<- -0.9
n<-100
a<-rnorm(n)
z<-numeric(n)
mu<-100
sig<-10
z[1]<-a[1]*sig/sqrt(1-... |
86fc52127e874c7b6c24094c831148ed9d96825e | dce03b2fc14d766358e899a86a560b967555b6b4 | /plot2.R | 089d9dd5695e3f5faf458909a5328319168e7c14 | [] | no_license | gitcub/ExData_Plotting1 | ec3153eba14038cd4e2de3c8ff2678e6557ceb7c | cf8845079809422950ea30e18a0c589c2f809ab3 | refs/heads/master | 2020-12-02T16:35:49.144858 | 2015-11-08T18:13:44 | 2015-11-08T18:13:44 | 45,787,781 | 0 | 0 | null | 2015-11-08T16:09:53 | 2015-11-08T16:09:53 | null | UTF-8 | R | false | false | 898 | r | plot2.R | # source data
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileUrl, destfile = "dataset.zip", method = "curl", mode = "wb")
unzip("dataset.zip")
unlink("dataset.zip")
# load data
rawData <- read.csv("household_power_consumption.txt", header = TRUE, s... |
ec1bf3b1e559d941c5de799211ee9e64737ac57c | 083f86ded1e5acc975958810d77a86b817b1cddf | /ui.R | 26971bc907b6453e3e8167df4d526a68164f741f | [] | no_license | arpignotti/DevDataProduct | 259de4e0b722aee2fe2c8e1d24e967479d8b822b | 45cfa3a2c255c0292db3717671bfb254e12e93a3 | refs/heads/master | 2021-01-10T13:56:51.353941 | 2015-12-22T01:53:19 | 2015-12-22T01:53:19 | 48,344,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | ui.R | library(shiny)
library(RCurl)
library(maps)
library(mapproj)
source("helper/helpers.R")
x <- getURL("https://raw.githubusercontent.com/arpignotti/DevDataProduct/master/Data/dset.csv")
dset <- read.csv(text = x)
shinyUI(pageWithSidebar(
headerPanel("Medicare Advantage Parent Organizations' MA Market Share"),
side... |
ea24446586f7a43719b4bf71fa71b960fcf916fc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/semTools/examples/standardizeMx-deprecated.Rd.R | fbca0a4ea0997140aa79db7709047c0fb2d124a7 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,960 | r | standardizeMx-deprecated.Rd.R | library(semTools)
### Name: standardizeMx-deprecated
### Title: Find standardized estimates for OpenMx output
### Aliases: standardizeMx-deprecated
### Keywords: internal
### ** Examples
## Not run:
##D library(OpenMx)
##D data(myFADataRaw)
##D myFADataRaw <- myFADataRaw[,c("x1","x2","x3","x4","x5","x6")]
##D one... |
519162315e1019f6d6edea8e2b744d987712621a | 1e6b7b9682e5a5d51df135a8a644273d7cb7108b | /Amadeus_Summary_Regional.R | d3ce92810b659f27d14bafe4e790550da1ecf6db | [] | no_license | Orbis-Amadeus-Oxford/Amadeus-Datawork | 96b1eaa00464ad2676b14927d9468c0136ef501d | 094d1c48384dbd514437ad08979aa7ce1add5b08 | refs/heads/master | 2020-04-01T21:03:32.278584 | 2019-09-25T14:13:50 | 2019-09-25T14:13:50 | 153,635,496 | 0 | 3 | null | 2020-02-24T15:29:57 | 2018-10-18T14:15:40 | R | UTF-8 | R | false | false | 11,293 | r | Amadeus_Summary_Regional.R | #trace(stop, quote(print(sys.calls())))
require(entropy) # for entropy
require(acid) # for weighted entropy
require(dplyr)
require(matrixStats) # for weighted sd and median
# Note that instead of using radiant.data.weighted.sd we might want to correct the sd for the sample size
# after consideri... |
07884ffae0db6142a2ab9c66d5ebba5856afa539 | 5038954117333b59dda21d2d12a05fc6fc82061b | /man/topN_mat.Rd | 5a7c958bc05cb1c7d5063c6ef3d6e538f9256e2e | [] | no_license | fossbert/binilib | b4887c996d2fbbcdfb7c4f988822460a9e37e37e | 173e5fe7e4fb16dec10e1c57fe30cd155a5b895a | refs/heads/master | 2021-06-05T14:55:00.691039 | 2021-04-19T09:20:05 | 2021-04-19T09:20:05 | 157,078,242 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,080 | rd | topN_mat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helper_functions.R
\name{topN_mat}
\alias{topN_mat}
\title{For a numeric matrix containing samples or conditions in its columns and typically genes
in its rows, this function will find the top N genes for a given condition. You can choose to ... |
d07daf150549245b8223dfe1eb29342ef3ea81b6 | 60820a424e8088dd9c588561b874f9f9b493afad | /stats/ToLog_function.R | b72aadcb8b3fc44b243364855b4ce436506ebae9 | [] | no_license | PWaryszak/Jena_Exp | 20a3ab6f29c692e387ac5918123d2927800c3331 | 95f0a10c363ed95a8b4ef9ed3871df3631d3b178 | refs/heads/master | 2021-05-30T16:59:45.393286 | 2015-07-22T13:38:53 | 2015-07-22T13:38:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,583 | r | ToLog_function.R | #########################
#Function files investiguating log-transformation vs GLM
#author Lionel Hertzog, date 21.07.2015
#code heavily based on the supplementary material of
#Ives, A. R. (2015), For testing the significance of regression coefficients, go ahead and log-transform count data. Methods in Ecology and Evol... |
559e6a62ef1af26b3f560c262155d376fda4ec09 | 3a9b76a8f5b8b7cb931471830dbbe202b388695a | /man/runRECA.Rd | 930b939b66312a9617212378a067bafbf88dae3e | [] | no_license | edvinf/prepRECA | 3411ff4fdcd85608f91ee687cd7d7f8c4226f8f9 | 85dd2bb39c4c408a98678d01cfd280627f00e2be | refs/heads/master | 2020-09-01T03:10:04.259873 | 2019-11-06T15:38:29 | 2019-11-06T15:38:29 | 218,865,912 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,052 | rd | runRECA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prepRECA.R
\name{runRECA}
\alias{runRECA}
\title{Run R-ECA}
\usage{
runRECA(RecaObj, nSamples, burnin, lgamodel = "log-linear",
fitfile = "fit", predictfile = "pred", resultdir = NULL,
thin = 10, delta.age = 0.001, seed = NULL, caa.burnin... |
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