content large_stringlengths 0 6.46M | path large_stringlengths 3 331 | license_type large_stringclasses 2
values | repo_name large_stringlengths 5 125 | language large_stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 4 6.46M | extension large_stringclasses 75
values | text stringlengths 0 6.46M |
|---|---|---|---|---|---|---|---|---|---|
#####################################################
#'
#' Estimates \eqn{\beta}s per population and a bootstrap confidence interval
#'
#' Estimate populations (Population specific FST) or individual coancestries
#' and a bootstrap confidence interval, assuming random mating
#'
#' If betaijT=TRUE, and the f... | /R/betas.R | no_license | cran/hierfstat | R | false | false | 5,920 | r | #####################################################
#'
#' Estimates \eqn{\beta}s per population and a bootstrap confidence interval
#'
#' Estimate populations (Population specific FST) or individual coancestries
#' and a bootstrap confidence interval, assuming random mating
#'
#' If betaijT=TRUE, and the f... |
library(showtext)
dest <- file.path("C:", "Windows", "Fonts", "ARIALN.ttf")
font_add("arialn", regular = dest)
showtext_auto()
setwd("E:/Documents/GitHub/EBIO-R-Code/EBIO 3080/Lab/Week 5/Data")
flyDnDs <- read.csv("FlyDnDs.csv", header = TRUE)
meanDnDs <- mean(flyDnDs$DnDs)
par(bg = "#eee9d3")
par(mar = c(4.1, 5, 4.1... | /EBIO 3080/Lab/Week 5/Pre-Lab/Week5_Pre-Lab.R | no_license | dmwo/EBIO-R-Code | R | false | false | 1,185 | r | library(showtext)
dest <- file.path("C:", "Windows", "Fonts", "ARIALN.ttf")
font_add("arialn", regular = dest)
showtext_auto()
setwd("E:/Documents/GitHub/EBIO-R-Code/EBIO 3080/Lab/Week 5/Data")
flyDnDs <- read.csv("FlyDnDs.csv", header = TRUE)
meanDnDs <- mean(flyDnDs$DnDs)
par(bg = "#eee9d3")
par(mar = c(4.1, 5, 4.1... |
\name{XTRA 1}
\alias{mixed}
\alias{mtmixed}
\alias{mtgsru}
\alias{mm}
\alias{NNS}
\alias{GSFLM}
\alias{GSRR}
\alias{GS2EIGEN}
\alias{NNSEARCH}
\alias{predict_FLMSS}
\title{
Mixed model solver
}
\description{
Function to solve univariate mixed models with or without the usage of omic information. This f... | /man/mix.Rd | no_license | alenxav/bWGR | R | false | false | 3,501 | rd | \name{XTRA 1}
\alias{mixed}
\alias{mtmixed}
\alias{mtgsru}
\alias{mm}
\alias{NNS}
\alias{GSFLM}
\alias{GSRR}
\alias{GS2EIGEN}
\alias{NNSEARCH}
\alias{predict_FLMSS}
\title{
Mixed model solver
}
\description{
Function to solve univariate mixed models with or without the usage of omic information. This f... |
#ultiple Linear/Non-linear regression to determine train delay given 46 factors
library(MASS)
library(caret)
library(xlsx)
library(neuralnet)
library(e1071)
a<-read.csv("TSC_430.csv",sep=",")
b<-read.csv("TSC_Meta_data",sheet="Sheet2",header=TRUE)
colnames(a)<-b[,2]
#Averaging factors o... | /Regression_for_predicting_traindelay.R | no_license | aishiitm/Internship-with-Tech-Mahindra | R | false | false | 5,688 | r |
#ultiple Linear/Non-linear regression to determine train delay given 46 factors
library(MASS)
library(caret)
library(xlsx)
library(neuralnet)
library(e1071)
a<-read.csv("TSC_430.csv",sep=",")
b<-read.csv("TSC_Meta_data",sheet="Sheet2",header=TRUE)
colnames(a)<-b[,2]
#Averaging factors o... |
defineModule(sim, list(
name="disturbanceDriver",
description="generate parameters for the generic percolation model",# spades::spread()",
keywords=c("fire"),
authors=c(person(c("Steve", "G"), "Cumming", email="stevec@sbf.ulaval.ca", role=c("aut", "cre"))),
childModules=character(),
version=numeric_version... | /disturbanceDriver/disturbanceDriver.R | no_license | SteveCumming/BEACONs | R | false | false | 4,872 | r |
defineModule(sim, list(
name="disturbanceDriver",
description="generate parameters for the generic percolation model",# spades::spread()",
keywords=c("fire"),
authors=c(person(c("Steve", "G"), "Cumming", email="stevec@sbf.ulaval.ca", role=c("aut", "cre"))),
childModules=character(),
version=numeric_version... |
#' pribor
#'
#' A function returning data frame of PRague InterBank OffeRed rates (PRIBOR).
#'
#' The function expects date input, and returns data frame of two or more columns - date, and relevant PRIBOR rate (as determined by `maturity` parameter).
#'
#' PRIBOR rates are reported as fractions, i.e. not as percentages... | /R/pribor.R | permissive | jla-data/czechrates | R | false | false | 4,816 | r | #' pribor
#'
#' A function returning data frame of PRague InterBank OffeRed rates (PRIBOR).
#'
#' The function expects date input, and returns data frame of two or more columns - date, and relevant PRIBOR rate (as determined by `maturity` parameter).
#'
#' PRIBOR rates are reported as fractions, i.e. not as percentages... |
#' Finding DMR
#'
#' Finding DMR by Wilcoxon, t-Student, Kolmogorov-Smirnow tests or logistic regression, logistic regression with mixed models,
#' logistic regression with mixed models with correlation matrix.
#' In Ttest, Wilcoxon and Ks are compared methylation rate between x and y prob on the same position and chro... | /R/find_DMR.R | no_license | geneticsMiNIng/metR | R | false | false | 4,878 | r | #' Finding DMR
#'
#' Finding DMR by Wilcoxon, t-Student, Kolmogorov-Smirnow tests or logistic regression, logistic regression with mixed models,
#' logistic regression with mixed models with correlation matrix.
#' In Ttest, Wilcoxon and Ks are compared methylation rate between x and y prob on the same position and chro... |
library(data.table)
library(ggplot2)
library(wpp2017)
## IHME pops/mort
source("/home/j/temp/central_comp/libraries/current/r/get_demographics.R")
source("/home/j/temp/central_comp/libraries/current/r/get_demographics_template.R")
mortality_2016_demographics <- get_demographics(gbd_team="mort", gbd_round_id=4)
pops <... | /abs_cohorts.R | no_license | ngraetz/cfr | R | false | false | 35,063 | r | library(data.table)
library(ggplot2)
library(wpp2017)
## IHME pops/mort
source("/home/j/temp/central_comp/libraries/current/r/get_demographics.R")
source("/home/j/temp/central_comp/libraries/current/r/get_demographics_template.R")
mortality_2016_demographics <- get_demographics(gbd_team="mort", gbd_round_id=4)
pops <... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add-link.R
\name{add_link}
\alias{add_link}
\title{Add other association link plot on correlation plot.}
\usage{
add_link(df, mapping = NULL, spec.key = "spec", env.key = "env",
curvature = NULL, spec.label.hspace = NULL, spec.label.vspace ... | /man/add_link.Rd | no_license | xma82/ggcor | R | false | true | 2,624 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add-link.R
\name{add_link}
\alias{add_link}
\title{Add other association link plot on correlation plot.}
\usage{
add_link(df, mapping = NULL, spec.key = "spec", env.key = "env",
curvature = NULL, spec.label.hspace = NULL, spec.label.vspace ... |
data<-read.csv2("Salaries.csv",sep=",",header=TRUE)
data$X<-NULL
salaire<-as.data.frame(data)
summary(data)
library(tree)
tree.Lin<-tree(salary~yrs.service+yrs.since.phd,data=salaire)
#1.2.c
tree.model<-tree(log(salary)~yrs.service+yrs.since.phd,data=salaire)
plot(tree.Lin)
text(tree.Lin,cex=.75)
#1.3
salar.deciles<... | /Cours 5A/Big Data/Module 3 Modélisation/TD2/Pour les pd/td2.R | no_license | hejoseph/workspace | R | false | false | 2,333 | r | data<-read.csv2("Salaries.csv",sep=",",header=TRUE)
data$X<-NULL
salaire<-as.data.frame(data)
summary(data)
library(tree)
tree.Lin<-tree(salary~yrs.service+yrs.since.phd,data=salaire)
#1.2.c
tree.model<-tree(log(salary)~yrs.service+yrs.since.phd,data=salaire)
plot(tree.Lin)
text(tree.Lin,cex=.75)
#1.3
salar.deciles<... |
#---------------
hurst_scan<-scan('mercyhurst.txt',what=character(),sep='\n')
hurst_lines<-data_frame(line=1:24066,text=hurst_scan)
hurst_lines$group<-hurst_lines$line %/% 80
hurst_words<-unnest_tokens(hurst_lines,word,text)
afinn<-get_sentiments('afinn')
hurst_words<-inner_join(hurst_words,afinn)
hurst_groups<-hurs... | /Test Example Problems/test_examples.R | no_license | justinminsk/Communication-and-Data | R | false | false | 3,914 | r | #---------------
hurst_scan<-scan('mercyhurst.txt',what=character(),sep='\n')
hurst_lines<-data_frame(line=1:24066,text=hurst_scan)
hurst_lines$group<-hurst_lines$line %/% 80
hurst_words<-unnest_tokens(hurst_lines,word,text)
afinn<-get_sentiments('afinn')
hurst_words<-inner_join(hurst_words,afinn)
hurst_groups<-hurs... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/collection_migrate.R
\name{collection_migrate}
\alias{collection_migrate}
\title{Migrate documents to another collection}
\usage{
collection_migrate(conn, name, target.collection, split.key,
forward.timeout = NULL, async = NULL, raw = FALSE... | /man/collection_migrate.Rd | permissive | melsiddieg/solrium | R | false | true | 1,955 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/collection_migrate.R
\name{collection_migrate}
\alias{collection_migrate}
\title{Migrate documents to another collection}
\usage{
collection_migrate(conn, name, target.collection, split.key,
forward.timeout = NULL, async = NULL, raw = FALSE... |
library(RWeka)
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make ... | /scripts/weka.R | no_license | nddsg/forward-backward-ppr | R | false | false | 8,964 | r | library(RWeka)
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make ... |
# This script creates a SQL table of CFPB comment metadata
# subset to Dodd-Frank dockets or RINs as identified by Davis Polk
library(DBI)
library(RSQLite)
library(tidyverse)
# API version
v4 = FALSE
## now pulling from new search of API v4
if(v4){
load(here::here("data", "CFPBcomments.Rdata"))
comments_all ... | /functions/sql_comment_metadata_CFPB.R | no_license | zoeang/rulemaking | R | false | false | 5,635 | r | # This script creates a SQL table of CFPB comment metadata
# subset to Dodd-Frank dockets or RINs as identified by Davis Polk
library(DBI)
library(RSQLite)
library(tidyverse)
# API version
v4 = FALSE
## now pulling from new search of API v4
if(v4){
load(here::here("data", "CFPBcomments.Rdata"))
comments_all ... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility_define_param.R
\name{define.param}
\alias{define.param}
\title{Define climateR configuration}
\usage{
define.param(param, service = NULL)
}
\arguments{
\item{param}{the parameter(s) of interest}
\item{service}{the dataset for which a... | /man/define.param.Rd | permissive | mbjoseph/climateR | R | false | true | 616 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility_define_param.R
\name{define.param}
\alias{define.param}
\title{Define climateR configuration}
\usage{
define.param(param, service = NULL)
}
\arguments{
\item{param}{the parameter(s) of interest}
\item{service}{the dataset for which a... |
context("logging x and y values")
# Helper to check if a logging result has the correct structure
expect_logging_result_structure = function(x) {
expect_true(is.list(x))
expect_true(is.data.frame(x$pars))
expect_true(is.numeric(x$obj.vals))
}
test_that("logging for functions with matrix input works well", {
f... | /tests/testthat/test_logging.R | permissive | DrRoad/smoof | R | false | false | 4,426 | r | context("logging x and y values")
# Helper to check if a logging result has the correct structure
expect_logging_result_structure = function(x) {
expect_true(is.list(x))
expect_true(is.data.frame(x$pars))
expect_true(is.numeric(x$obj.vals))
}
test_that("logging for functions with matrix input works well", {
f... |
############################################################################################################
# ANÁLISE EMPÍRICA - ESTIMAÇÃO OLS E EFEITOS FIXOS (LAST YEAR)
############################################################################################################
... | /4.5 - Estimação RDD Paramétrico (last year).R | no_license | joseeduardo-gs/do-political-parties-matter | R | false | false | 23,941 | r | ############################################################################################################
# ANÁLISE EMPÍRICA - ESTIMAÇÃO OLS E EFEITOS FIXOS (LAST YEAR)
############################################################################################################
... |
# Andy Philips
# andrew.philips@colorado.edu
# 06/08/17
# --------------------------------#
shinyUI(fluidPage(
includeCSS("style.css"),
titlePanel("Central Limit Theorem Simulator"),
sidebarLayout(position = "right",
sidebarPanel(
radioButtons("dist", "Choose Distribution",
c("Normal" =... | /CLT-Simulator/ui.R | no_license | anhnguyendepocen/Shiny-2 | R | false | false | 1,656 | r | # Andy Philips
# andrew.philips@colorado.edu
# 06/08/17
# --------------------------------#
shinyUI(fluidPage(
includeCSS("style.css"),
titlePanel("Central Limit Theorem Simulator"),
sidebarLayout(position = "right",
sidebarPanel(
radioButtons("dist", "Choose Distribution",
c("Normal" =... |
setwd("C://Project1")
getwd()
dataset <- read.csv("./household_power_consumption.txt", header=T, sep=';', na.strings="?", nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"')
dataset$Date <- as.Date(dataset$Date, format="%d/%m/%Y")
data <- subset(dataset, subset=(Date >= "2007-02-01" &... | /plot4.R | no_license | williewilkins/ExData_Plotting1 | R | false | false | 1,150 | r | setwd("C://Project1")
getwd()
dataset <- read.csv("./household_power_consumption.txt", header=T, sep=';', na.strings="?", nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"')
dataset$Date <- as.Date(dataset$Date, format="%d/%m/%Y")
data <- subset(dataset, subset=(Date >= "2007-02-01" &... |
#basic class that points to the database and allows easier manipulations
setClass("BuxcoDB", representation(db.name="character", annotation.table="character"), prototype=prototype(db.name=character(0), annotation.table="Additional_labels"))
.run.update.statement <- function(db.con, query){
state <- dbSendStatement(d... | /R/BuxcoDB.R | no_license | dbottomly/plethy | R | false | false | 26,316 | r | #basic class that points to the database and allows easier manipulations
setClass("BuxcoDB", representation(db.name="character", annotation.table="character"), prototype=prototype(db.name=character(0), annotation.table="Additional_labels"))
.run.update.statement <- function(db.con, query){
state <- dbSendStatement(d... |
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/dse22e.R
\docType{data}
\name{dse22e}
\alias{dse22e}
\title{Dataset for Exercise E, Chapter 22}
\format{A \code{data.frame} with 9 rows and 7 variables:
\describe{
\item{y}{}
\item{x1}{}
\item{x2}{}
\item{x3}{}
\item{x4}{}
\item{x5}{}... | /man/dse22e.Rd | no_license | danielgil1/aprean3 | R | false | false | 540 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/dse22e.R
\docType{data}
\name{dse22e}
\alias{dse22e}
\title{Dataset for Exercise E, Chapter 22}
\format{A \code{data.frame} with 9 rows and 7 variables:
\describe{
\item{y}{}
\item{x1}{}
\item{x2}{}
\item{x3}{}
\item{x4}{}
\item{x5}{}... |
#' FindBestModel
#'
#' @description Find the best model for a given forecast time
#'
#' @param dfPerf The performance dataframe
#' @param forecast The forecast time
#' @param vecOutcome The vector of outcome considered
#'
#' @return The label of the best model
#' @export
FindBestModel <- function(dfPerf, forecast = 7, ... | /R/FindBestModel.R | no_license | thomasferte/PredictCovidOpen | R | false | false | 615 | r | #' FindBestModel
#'
#' @description Find the best model for a given forecast time
#'
#' @param dfPerf The performance dataframe
#' @param forecast The forecast time
#' @param vecOutcome The vector of outcome considered
#'
#' @return The label of the best model
#' @export
FindBestModel <- function(dfPerf, forecast = 7, ... |
#This is an applicaiton that predicts the next word based on ngram user input
library(shiny)
# Define UI for the application
shinyUI(fluidPage(
titlePanel("Next Word Prediction"),
sidebarLayout(
sidebarPanel(
# Create the text input box
textInput("inpNgram", label = h5("Please provide th... | /ui.R | no_license | cmba50/Final | R | false | false | 691 | r | #This is an applicaiton that predicts the next word based on ngram user input
library(shiny)
# Define UI for the application
shinyUI(fluidPage(
titlePanel("Next Word Prediction"),
sidebarLayout(
sidebarPanel(
# Create the text input box
textInput("inpNgram", label = h5("Please provide th... |
# These functions are depricated
# Only relsurv uses them, and I'm working on that
ratetable <- function(...) {
datecheck <- function(x)
inherits(x, c("Date", "POSIXt", "date", "chron"))
args <- list(...)
nargs <- length(args)
ll <- sapply(args, length)
n <- max(ll) # We assume this is t... | /Recommended/survival/R/ratetableold.R | no_license | lukaszdaniel/ivory | R | false | false | 1,791 | r | # These functions are depricated
# Only relsurv uses them, and I'm working on that
ratetable <- function(...) {
datecheck <- function(x)
inherits(x, c("Date", "POSIXt", "date", "chron"))
args <- list(...)
nargs <- length(args)
ll <- sapply(args, length)
n <- max(ll) # We assume this is t... |
\name{toBiblatex}
\alias{toBiblatex}
\alias{toBibtex}
\alias{toBibtex.BibEntry}
\title{Convert BibEntry objects to BibTeX or BibLaTeX}
\usage{
toBiblatex(object, ...)
\method{toBibtex}{BibEntry}(object, note.replace.field = c("urldate",
"pubsate", "addendum"), extra.fields = NULL, ...)
}
\arguments{
\i... | /man/toBiblatex.Rd | no_license | aurora-mareviv/RefManageR | R | false | false | 3,918 | rd | \name{toBiblatex}
\alias{toBiblatex}
\alias{toBibtex}
\alias{toBibtex.BibEntry}
\title{Convert BibEntry objects to BibTeX or BibLaTeX}
\usage{
toBiblatex(object, ...)
\method{toBibtex}{BibEntry}(object, note.replace.field = c("urldate",
"pubsate", "addendum"), extra.fields = NULL, ...)
}
\arguments{
\i... |
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31584307429747e+77, 9.53818252170339e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)))
result <- do.call(CNull:::communities_individual_... | /CNull/inst/testfiles/communities_individual_based_sampling_alpha/AFL_communities_individual_based_sampling_alpha/communities_individual_based_sampling_alpha_valgrind_files/1615769617-test.R | no_license | akhikolla/updatedatatype-list2 | R | false | false | 362 | r | testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31584307429747e+77, 9.53818252170339e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)))
result <- do.call(CNull:::communities_individual_... |
library(testthat)
library(cprint)
test_check("cprint")
| /tests/testthat.R | no_license | bgreenwell/cprint | R | false | false | 56 | r | library(testthat)
library(cprint)
test_check("cprint")
|
setwd("~/Documents/PushMe/Data")
require(ggplot2)
require(reshape2)
require(splines)
require(signal)
require(entropy)
require(RSEIS)
rm(results)
results = data.frame("time"=0, "meanX"=0, "meanY"=0, "meanZ"=0, "sdX"=0, "sdY"=0, "sdZ"=0, "sdT"=0, "energyX"=0, "energyY"=0, "energyZ"=0, "energyT"=0, "lat"=mean(sample$Latit... | /continuousMethod.R | permissive | jhrrsn/push_me | R | false | false | 2,928 | r | setwd("~/Documents/PushMe/Data")
require(ggplot2)
require(reshape2)
require(splines)
require(signal)
require(entropy)
require(RSEIS)
rm(results)
results = data.frame("time"=0, "meanX"=0, "meanY"=0, "meanZ"=0, "sdX"=0, "sdY"=0, "sdZ"=0, "sdT"=0, "energyX"=0, "energyY"=0, "energyZ"=0, "energyT"=0, "lat"=mean(sample$Latit... |
######## load packages
library(tidyverse)
library(corrplot)
library(fastDummies)
library(rpart)
library(rpart.plot)
library(caret)
library(glmnet)
library(randomForest)
library(ROCR)
library(pROC)
library(naivebayes)
library(xgboost)
library(e1071)
library(vegan)
library(factoextra)
library(kableExtra)... | /Bank_Marketing_Campaign_Analysis.R | no_license | davidzhang647/Machine_Learning_Projects | R | false | false | 13,767 | r | ######## load packages
library(tidyverse)
library(corrplot)
library(fastDummies)
library(rpart)
library(rpart.plot)
library(caret)
library(glmnet)
library(randomForest)
library(ROCR)
library(pROC)
library(naivebayes)
library(xgboost)
library(e1071)
library(vegan)
library(factoextra)
library(kableExtra)... |
# Hierarchical Clustering
dataset<-read.csv("Mall_Customers.csv")
X=dataset[4:5]
#Using the dendogram to find the optimal number of clusters
dendogram=hclust(dist(X,method="euclidean"),method="ward.D")
plot(dendogram,
main=paste("Dendogram"),
xlab="Customers",
ylab="Euclidean Distances")
#Fitting hie... | /03_Clustering/Hierarchical_Clustering/Hierarchical_Clustering_R_AM.R | no_license | AMDonati/ML_Algorithms_inR | R | false | false | 736 | r | # Hierarchical Clustering
dataset<-read.csv("Mall_Customers.csv")
X=dataset[4:5]
#Using the dendogram to find the optimal number of clusters
dendogram=hclust(dist(X,method="euclidean"),method="ward.D")
plot(dendogram,
main=paste("Dendogram"),
xlab="Customers",
ylab="Euclidean Distances")
#Fitting hie... |
#' @title Creatinine Normalisation
#' @description Creatinine Normalisation (CN) is a useful method much like region of interest normalisation that can normalise spectra based on the total area of the creatinine signal at the chemical shift 3.05ppm.
#' @details `creNorm()` works by dividing each element in a row with t... | /R/creNorm.R | permissive | kbario/concentr8r | R | false | false | 5,031 | r | #' @title Creatinine Normalisation
#' @description Creatinine Normalisation (CN) is a useful method much like region of interest normalisation that can normalise spectra based on the total area of the creatinine signal at the chemical shift 3.05ppm.
#' @details `creNorm()` works by dividing each element in a row with t... |
screePlotAPA <- function(data, rep=1000, cent=.05) {
library(nFactors)
library(ggplot2)
ev <- eigen(cor(data)) # get eigenvalues
eig <- ev$values # eigenvalues
ap <- parallel(subject = nrow(data), var = ncol(data), rep = rep, cent = cent)
eig_pa <- ap$eigen$qevpea # The 95 centile
nS <- nScree(x=ev$values... | /Factor Analysis/screePlotAPA.R | no_license | storopoli/R_Scripts | R | false | false | 2,205 | r | screePlotAPA <- function(data, rep=1000, cent=.05) {
library(nFactors)
library(ggplot2)
ev <- eigen(cor(data)) # get eigenvalues
eig <- ev$values # eigenvalues
ap <- parallel(subject = nrow(data), var = ncol(data), rep = rep, cent = cent)
eig_pa <- ap$eigen$qevpea # The 95 centile
nS <- nScree(x=ev$values... |
library(nimble)
### Name: Wishart
### Title: The Wishart Distribution
### Aliases: Wishart dwish_chol rwish_chol wishart
### ** Examples
df <- 40
ch <- chol(matrix(c(1, .7, .7, 1), 2))
x <- rwish_chol(1, ch, df = df)
dwish_chol(x, ch, df = df)
| /data/genthat_extracted_code/nimble/examples/Wishart.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 252 | r | library(nimble)
### Name: Wishart
### Title: The Wishart Distribution
### Aliases: Wishart dwish_chol rwish_chol wishart
### ** Examples
df <- 40
ch <- chol(matrix(c(1, .7, .7, 1), 2))
x <- rwish_chol(1, ch, df = df)
dwish_chol(x, ch, df = df)
|
library(dplyr)
library(ggplot2)
library(lubridate)
library(stringr)
# Input -------------------------------------------------------------------
data = read.csv('gun-violence-data_01-2013_03-2018.csv')
# Understand data ---------------------------------------------------------
glimpse(data)
count_unique ... | /Gun Violence in US/EDA.R | no_license | kennhan/DataVis | R | false | false | 3,389 | r | library(dplyr)
library(ggplot2)
library(lubridate)
library(stringr)
# Input -------------------------------------------------------------------
data = read.csv('gun-violence-data_01-2013_03-2018.csv')
# Understand data ---------------------------------------------------------
glimpse(data)
count_unique ... |
##Plot 2
#Geting the data and transforming the first two collumns into a single variable for time
tabela1 <- read.table(file = "household_power_consumption.txt",sep = ";",header = T,colClasses = c(rep("character",2),rep("numeric",7)),na.strings = "?")
tabela1[,1]<- as.Date(tabela1[,1],"%d/%m/%Y")
tabela2 <- subset.dat... | /Plot 2.R | no_license | Alibanio/ExData_Plotting1 | R | false | false | 744 | r |
##Plot 2
#Geting the data and transforming the first two collumns into a single variable for time
tabela1 <- read.table(file = "household_power_consumption.txt",sep = ";",header = T,colClasses = c(rep("character",2),rep("numeric",7)),na.strings = "?")
tabela1[,1]<- as.Date(tabela1[,1],"%d/%m/%Y")
tabela2 <- subset.dat... |
#install.packages("smoothmest")
#install.packages("extraDistr")
#install.packages("truncdist")
library(MASS)
#library(smoothmest)
library(stats)
library(extraDistr)
set.seed(106)
eps=runif(15)
dexp_err = -log(log(eps^-1))
f = function( x, alpha , beta, gamma){
alpha*exp(-exp(beta-gamma*x))
}
x = seq(from=1,to=15)
y ... | /proj1.R | no_license | sinasanei/Genetic-Algorithms-and-their-applications-in-statistics | R | false | false | 5,033 | r | #install.packages("smoothmest")
#install.packages("extraDistr")
#install.packages("truncdist")
library(MASS)
#library(smoothmest)
library(stats)
library(extraDistr)
set.seed(106)
eps=runif(15)
dexp_err = -log(log(eps^-1))
f = function( x, alpha , beta, gamma){
alpha*exp(-exp(beta-gamma*x))
}
x = seq(from=1,to=15)
y ... |
library(tidyverse)
library(imager)
#############################
#
# Color transformation
#
###########################
bi <- load.image("images/black_iris.jpg")
bi_df <- bi %>%
as.data.frame() %>%
mutate(cc = factor(cc,labels=c('R','G','B'))) %>%
group_by(cc) %>%
mutate(cd = ecdf(value)(value) * 3.5) %>%
u... | /okeeffe.R | no_license | doritge/img_processing | R | false | false | 1,987 | r | library(tidyverse)
library(imager)
#############################
#
# Color transformation
#
###########################
bi <- load.image("images/black_iris.jpg")
bi_df <- bi %>%
as.data.frame() %>%
mutate(cc = factor(cc,labels=c('R','G','B'))) %>%
group_by(cc) %>%
mutate(cd = ecdf(value)(value) * 3.5) %>%
u... |
##
## Plot closeness
##
closeness <- function(file){
# Cumulative proportion of intronic sQTLs/non-sQTLs distances to the closest exon
table<-read.table(file)
distances<-as.factor(table[,2])
df<-as.data.frame(table(factor(distances)))
df<-df[-c(1),]
df<-rbind(c(0,0),df)
total= sum(df$Freq)
df$h = df... | /4.Blueprint/Enrichments/closeness.R | no_license | dgarmar/MT | R | false | false | 975 | r | ##
## Plot closeness
##
closeness <- function(file){
# Cumulative proportion of intronic sQTLs/non-sQTLs distances to the closest exon
table<-read.table(file)
distances<-as.factor(table[,2])
df<-as.data.frame(table(factor(distances)))
df<-df[-c(1),]
df<-rbind(c(0,0),df)
total= sum(df$Freq)
df$h = df... |
setwd("C:/Users/smull2/R/exploratory")
pfile<-file("household_power_consumption.txt","r")
power.raw<-read.table(text = grep("^[1,2]/2/2007",readLines(pfile),value=TRUE),sep=";",dec=".",na.strings="?",header=FALSE,stringsAsFactors=FALSE)
str(power.raw)
##Use colClasses to help when readLines
##colClass<-read.table("hou... | /Plot1.R | no_license | smullen17/ExData_Plotting1 | R | false | false | 1,501 | r | setwd("C:/Users/smull2/R/exploratory")
pfile<-file("household_power_consumption.txt","r")
power.raw<-read.table(text = grep("^[1,2]/2/2007",readLines(pfile),value=TRUE),sep=";",dec=".",na.strings="?",header=FALSE,stringsAsFactors=FALSE)
str(power.raw)
##Use colClasses to help when readLines
##colClass<-read.table("hou... |
normalize_pipe_rhs <- function(rhs, binding) {
if (!rlang::is_quosure(rhs)) {
stop(paste("'rhs' parameter must be a quosure. Try calling",
"'rhs <- rlang::enquo(rhs)' first"))
}
# Turn bare symbols into functions.
if (rlang::is_symbol(rlang::f_rhs(rhs))) {
rlang::f_rhs(rhs) <- rlang::lan... | /R/normalize.R | no_license | atheriel/rrails | R | false | false | 3,588 | r | normalize_pipe_rhs <- function(rhs, binding) {
if (!rlang::is_quosure(rhs)) {
stop(paste("'rhs' parameter must be a quosure. Try calling",
"'rhs <- rlang::enquo(rhs)' first"))
}
# Turn bare symbols into functions.
if (rlang::is_symbol(rlang::f_rhs(rhs))) {
rlang::f_rhs(rhs) <- rlang::lan... |
library(pollagg)
# Fit a model
y <- matrix(c(10, 900, 50, 50), ncol = 2, byrow = TRUE)
n <- rowSums(y)
fit <- yapa(y = y, n = n, dates = NULL, iter = 1000, chains = 3)
# Test model fit
if(!all(dim(fit$params$theta) == c(1500, 2, 2))) {
stop("yapa is not returning results as expected")
}
# Test plot
p <- plot(fit)
... | /tests/test-yapa.R | permissive | alexpavlakis/pollagg | R | false | false | 621 | r | library(pollagg)
# Fit a model
y <- matrix(c(10, 900, 50, 50), ncol = 2, byrow = TRUE)
n <- rowSums(y)
fit <- yapa(y = y, n = n, dates = NULL, iter = 1000, chains = 3)
# Test model fit
if(!all(dim(fit$params$theta) == c(1500, 2, 2))) {
stop("yapa is not returning results as expected")
}
# Test plot
p <- plot(fit)
... |
library(TrenaProjectBrainCell)
library(RUnit)
library(trenaSGM)
library(org.Hs.eg.db)
#------------------------------------------------------------------------------------------------------------------------
if(!exists("tp")) {
message(sprintf("--- creating instance of TrenaProjectBrainCell"))
tp <- TrenaProjectB... | /inst/unitTests/test_TrenaProjectBrainCell.R | permissive | PriceLab/TrenaProjectBrainCell | R | false | false | 15,715 | r | library(TrenaProjectBrainCell)
library(RUnit)
library(trenaSGM)
library(org.Hs.eg.db)
#------------------------------------------------------------------------------------------------------------------------
if(!exists("tp")) {
message(sprintf("--- creating instance of TrenaProjectBrainCell"))
tp <- TrenaProjectB... |
testlist <- list(id = NULL, id = NULL, booklet_id = c(8168473L, 2127314835L, 171177770L, -1942759639L, -1815221204L, 601253144L, -804651186L, 2094281728L, 860713787L, -971707632L, -1475044502L, 870040598L, -1182814578L, -1415711445L, 1901326755L, -1882837573L, 1340545259L, 1156041943L, 823641812L, -1106109928L, -10... | /dexterMST/inst/testfiles/is_person_booklet_sorted/AFL_is_person_booklet_sorted/is_person_booklet_sorted_valgrind_files/1615940028-test.R | no_license | akhikolla/updatedatatype-list1 | R | false | false | 826 | r | testlist <- list(id = NULL, id = NULL, booklet_id = c(8168473L, 2127314835L, 171177770L, -1942759639L, -1815221204L, 601253144L, -804651186L, 2094281728L, 860713787L, -971707632L, -1475044502L, 870040598L, -1182814578L, -1415711445L, 1901326755L, -1882837573L, 1340545259L, 1156041943L, 823641812L, -1106109928L, -10... |
library(ape)
testtree <- read.tree("2548_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2548_1_unrooted.txt") | /codeml_files/newick_trees_processed/2548_1/rinput.R | no_license | DaniBoo/cyanobacteria_project | R | false | false | 135 | r | library(ape)
testtree <- read.tree("2548_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="2548_1_unrooted.txt") |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stats_met.R
\name{stats_met}
\alias{stats_met}
\title{Statistical Methods}
\usage{
stats_met()
}
\value{
}
\description{
Statistical Methods
}
\examples{
}
| /man/stats_met.Rd | permissive | sbalci/histopathRaddins | R | false | true | 236 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stats_met.R
\name{stats_met}
\alias{stats_met}
\title{Statistical Methods}
\usage{
stats_met()
}
\value{
}
\description{
Statistical Methods
}
\examples{
}
|
###
comp_plot_sdhunter_year <- function(group = spgp,
var = "sdhunter",
prov = "",
zone = "",
M = out2,
castes = jdat$castes ){
dsum = as.data.frame(M$summary... | /functions/comparison_plotting_function_sdhunter_year.R | no_license | AdamCSmithCWS/CWS_National_Harvest_Survey | R | false | false | 1,314 | r | ###
comp_plot_sdhunter_year <- function(group = spgp,
var = "sdhunter",
prov = "",
zone = "",
M = out2,
castes = jdat$castes ){
dsum = as.data.frame(M$summary... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_gg.R
\name{plot_gg}
\alias{plot_gg}
\title{Transform ggplot2 objects into 3D}
\usage{
plot_gg(
ggobj,
width = 3,
height = 3,
height_aes = NULL,
invert = FALSE,
shadow_intensity = 0.5,
units = c("in", "cm", "mm"),
scale = ... | /man/plot_gg.Rd | no_license | hiter-joe/rayshader | R | false | true | 9,105 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_gg.R
\name{plot_gg}
\alias{plot_gg}
\title{Transform ggplot2 objects into 3D}
\usage{
plot_gg(
ggobj,
width = 3,
height = 3,
height_aes = NULL,
invert = FALSE,
shadow_intensity = 0.5,
units = c("in", "cm", "mm"),
scale = ... |
isat_my=function(y, mc = TRUE, ar = NULL, ewma = NULL, mxreg = NULL,
iis = TRUE, sis = TRUE, tis = FALSE, uis = FALSE, blocks = NULL,
ratio.threshold = 0.8, max.block.size = 30, t.pval = 0.001,
wald.pval = t.pval, vcov.type = c("ordinary", "white", "newey-west"),
... | /ISA_indicator_saturation_v2.R | no_license | zuzanale/Master-thesis | R | false | false | 18,439 | r | isat_my=function(y, mc = TRUE, ar = NULL, ewma = NULL, mxreg = NULL,
iis = TRUE, sis = TRUE, tis = FALSE, uis = FALSE, blocks = NULL,
ratio.threshold = 0.8, max.block.size = 30, t.pval = 0.001,
wald.pval = t.pval, vcov.type = c("ordinary", "white", "newey-west"),
... |
## These functions can compute the inverse matrix of 'x'
## to save time, if the matrix has been calculated, the inverse
## matrix should be retrieved from the cache
## This function creates a special "matrix" object that can cache its inverse
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(... | /cachematrix.R | no_license | wangjun341/ProgrammingAssignment2 | R | false | false | 954 | r | ## These functions can compute the inverse matrix of 'x'
## to save time, if the matrix has been calculated, the inverse
## matrix should be retrieved from the cache
## This function creates a special "matrix" object that can cache its inverse
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(... |
\name{dtWV}
\alias{dtWV}
\title{Noncentral t Distribution Density by W.V.}
\description{
Compute the density function \eqn{f(x)} of the t distribution with
\code{df} degrees of freedom and non-centrality parameter \code{ncp},
according to Wolfgang Viechtbauer's proposal in 2002.
}
\usage{%--> ../R/t-nonc-fn.R + ... | /man/dtWV.Rd | no_license | cran/DPQ | R | false | false | 3,305 | rd | \name{dtWV}
\alias{dtWV}
\title{Noncentral t Distribution Density by W.V.}
\description{
Compute the density function \eqn{f(x)} of the t distribution with
\code{df} degrees of freedom and non-centrality parameter \code{ncp},
according to Wolfgang Viechtbauer's proposal in 2002.
}
\usage{%--> ../R/t-nonc-fn.R + ... |
### Load packages
require( geomorph )
require( ape )
#Clear workspace
rm( list = ls() )
#Set working directory to where turtle landmark data (from supplemental folder) is stored
setwd( "INSERT DIRECTORY PATH" )
#Load landmark data and transform to correct format for GPA commands
temp.file <- list.fi... | /Dataset 11. Labyrinth shape model comparisons.R | no_license | SerjoschaEvers/Turtle-Labyrinth-Ecomorphology-and-Evolution-Data | R | false | false | 15,719 | r | ### Load packages
require( geomorph )
require( ape )
#Clear workspace
rm( list = ls() )
#Set working directory to where turtle landmark data (from supplemental folder) is stored
setwd( "INSERT DIRECTORY PATH" )
#Load landmark data and transform to correct format for GPA commands
temp.file <- list.fi... |
library(igraph)
load_graph <- function(filename) {
edgelist <- read.table(filename, sep = "", header = F)
return(edgelist)
}
convert_graph <- function(graph_df) {
e <- c()
for(i in 1:nrow(graph_df)) {
row <- graph_df[i,]
e <- c(e, row[[1]] + 1)
e <- c(e, row[[2]] + 1)
}
return(graph(edges = e, n = max(gra... | /hypermap/hyper_embed.R | no_license | mananshah99/hyperbolic | R | false | false | 476 | r | library(igraph)
load_graph <- function(filename) {
edgelist <- read.table(filename, sep = "", header = F)
return(edgelist)
}
convert_graph <- function(graph_df) {
e <- c()
for(i in 1:nrow(graph_df)) {
row <- graph_df[i,]
e <- c(e, row[[1]] + 1)
e <- c(e, row[[2]] + 1)
}
return(graph(edges = e, n = max(gra... |
# Solution to question 2
# V, the given matrix:
V <- matrix(c(3, -1, 1, -1, 5, -1, 1, -1, 3), 3)
# Q (orthogonal here) contains eigenvectors in its columns:
Q <- eigen(V)$vectors
# D contains the inverse square roots of of V's eigenvalues
# on the diagonal, zeros elsewhere:
D <- diag(1/sqrt(eigen(V)... | /week2/question2.R | no_license | david-dobor/8004 | R | false | false | 450 | r | # Solution to question 2
# V, the given matrix:
V <- matrix(c(3, -1, 1, -1, 5, -1, 1, -1, 3), 3)
# Q (orthogonal here) contains eigenvectors in its columns:
Q <- eigen(V)$vectors
# D contains the inverse square roots of of V's eigenvalues
# on the diagonal, zeros elsewhere:
D <- diag(1/sqrt(eigen(V)... |
\name{LaplacesDemonCpp-package}
\alias{LaplacesDemonCpp-package}
\alias{LaplacesDemonCpp}
\alias{.colVars}
\alias{.iqagh}
\alias{.iqaghsg}
\alias{.iqcagh}
\alias{.laaga}
\alias{.labfgs}
\alias{.labhhh}
\alias{.lacg}
\alias{.ladfp}
\alias{.lahar}
\alias{.lahj}
\alias{.lalbfgs}
\alias{.lalm}
\alias{.lanm}
\alias{.lanr}
\... | /man/LaplacesDemonCpp-package.Rd | permissive | sakex/LaplacesDemonCpp | R | false | false | 2,319 | rd | \name{LaplacesDemonCpp-package}
\alias{LaplacesDemonCpp-package}
\alias{LaplacesDemonCpp}
\alias{.colVars}
\alias{.iqagh}
\alias{.iqaghsg}
\alias{.iqcagh}
\alias{.laaga}
\alias{.labfgs}
\alias{.labhhh}
\alias{.lacg}
\alias{.ladfp}
\alias{.lahar}
\alias{.lahj}
\alias{.lalbfgs}
\alias{.lalm}
\alias{.lanm}
\alias{.lanr}
\... |
#Use fold change change data to look for trends in gene expression changes and make line plots
install.packages("ggplot2")
install.packages("reshape2")
install.packages("pheatmap")
install.packages("tidyr")
install.packages("reshape")
library(ggplot2)
library(reshape2)
library(pheatmap)
library(tidyr)
library(resh... | /sleuth.maplot.rerun.R | no_license | Jawara22/fragillis | R | false | false | 18,289 | r |
#Use fold change change data to look for trends in gene expression changes and make line plots
install.packages("ggplot2")
install.packages("reshape2")
install.packages("pheatmap")
install.packages("tidyr")
install.packages("reshape")
library(ggplot2)
library(reshape2)
library(pheatmap)
library(tidyr)
library(resh... |
\name{tesseract}
\alias{tesseract}
\alias{TesseractBaseAPI-class}
\alias{SetImage}
\alias{Recognize}
\alias{SetRectangle}
\alias{SetSourceResolution}
\alias{GetInputName}
\alias{SetInputName}
\alias{GetDatapath}
\alias{GetInitLanguages}
\alias{ReadConfigFile}
\alias{GetSourceYResolution}
\alias{IsValidWord}
\alias{Init... | /man/tesseract.Rd | no_license | aorimi/Rtesseract | R | false | false | 3,256 | rd | \name{tesseract}
\alias{tesseract}
\alias{TesseractBaseAPI-class}
\alias{SetImage}
\alias{Recognize}
\alias{SetRectangle}
\alias{SetSourceResolution}
\alias{GetInputName}
\alias{SetInputName}
\alias{GetDatapath}
\alias{GetInitLanguages}
\alias{ReadConfigFile}
\alias{GetSourceYResolution}
\alias{IsValidWord}
\alias{Init... |
#' Tests of radiative transfer models
library(PEcAnRTM)
context("SAIL models")
data(model.list)
setkey(model.list, modname)
p <- defparam("pro4sail")
pout <- pro4sail(p)
test.dim <- c(2101,4)
test_that("Returns matrix", {
expect_is(pout, "matrix")
})
test_that("Correct dimensions", {
expe... | /modules/rtm/tests/testthat/test.sail.R | permissive | davidjpmoore/pecan | R | false | false | 437 | r | #' Tests of radiative transfer models
library(PEcAnRTM)
context("SAIL models")
data(model.list)
setkey(model.list, modname)
p <- defparam("pro4sail")
pout <- pro4sail(p)
test.dim <- c(2101,4)
test_that("Returns matrix", {
expect_is(pout, "matrix")
})
test_that("Correct dimensions", {
expe... |
### XXX is the stuff in this file correct or should we be exporting *formatted* values to
### meet the needs of consumers of this? Do we ened to support both?
#' Create Enriched flat value table with paths
#'
#'
#' This function creates a flat tabular file of cell values and
#' corresponding paths.
#'
#' List columns... | /R/tt_export.R | permissive | jcheng5/rtables | R | false | false | 6,986 | r |
### XXX is the stuff in this file correct or should we be exporting *formatted* values to
### meet the needs of consumers of this? Do we ened to support both?
#' Create Enriched flat value table with paths
#'
#'
#' This function creates a flat tabular file of cell values and
#' corresponding paths.
#'
#' List columns... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mod_table_pieces.R
\name{mod_table_piecesui}
\alias{mod_table_piecesui}
\alias{mod_table_pieces}
\title{mod_table_piecesui and mod_table_pieces}
\usage{
mod_table_piecesui(id)
mod_table_pieces(
input,
output,
session,
scale_obj,
st... | /man/mod_table_piecesui.Rd | permissive | federman/shinylego | R | false | true | 523 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mod_table_pieces.R
\name{mod_table_piecesui}
\alias{mod_table_piecesui}
\alias{mod_table_pieces}
\title{mod_table_piecesui and mod_table_pieces}
\usage{
mod_table_piecesui(id)
mod_table_pieces(
input,
output,
session,
scale_obj,
st... |
#
# This file is for plot 1 for Expolitory Data Analysis Project 1
#
###############################################################################
# Step 1 Set the file locations and read the file
# Note: This file assumes the data file exists in the "explore_p_1" subdirectory
# under the working directory
###... | /plot1.R | no_license | MGoodman10/ExData_Plotting1 | R | false | false | 1,310 | r | #
# This file is for plot 1 for Expolitory Data Analysis Project 1
#
###############################################################################
# Step 1 Set the file locations and read the file
# Note: This file assumes the data file exists in the "explore_p_1" subdirectory
# under the working directory
###... |
context("Checking findVariance")
test_that("One cat column, one measure col, and no date columns give correct df", {
df <- data.frame(Gender = c('F','M','M','M','M','F','F','F'),
LOS = c(3.2,NA,5,1.3,2.4,4,9,5))
dfRes <- findVariation(df = df,
categoricalCols = "Gender"... | /tests/testthat/test-find-variance.R | permissive | Quantitative72/healthcareai-r | R | false | false | 17,564 | r | context("Checking findVariance")
test_that("One cat column, one measure col, and no date columns give correct df", {
df <- data.frame(Gender = c('F','M','M','M','M','F','F','F'),
LOS = c(3.2,NA,5,1.3,2.4,4,9,5))
dfRes <- findVariation(df = df,
categoricalCols = "Gender"... |
# Apriori
# Data Preprocessing
# install.packages('arules')
library(arules)
dataset = read.csv('~/Dropbox/github/machine_learning_udemy/machine-learning-udemy/part-5-association-rule-learning/Apriori/Market_Basket_Optimisation.csv', header = FALSE)
dataset = read.transactions('~/Dropbox/github/machine_learning_udemy/m... | /part-5-association-rule-learning/Apriori/apriori.R | no_license | adamzolotarev/machine-learning-udemy | R | false | false | 691 | r | # Apriori
# Data Preprocessing
# install.packages('arules')
library(arules)
dataset = read.csv('~/Dropbox/github/machine_learning_udemy/machine-learning-udemy/part-5-association-rule-learning/Apriori/Market_Basket_Optimisation.csv', header = FALSE)
dataset = read.transactions('~/Dropbox/github/machine_learning_udemy/m... |
#capturing old digitized data
require(digitize) ##there is no angle correction on this if the file is rotated you get wrong values
ca = ReadAndCal('~/Documents/Adam/Lobster/LFA38/Jan2020/CampbellandDugganTotalEffortTotalTraps.png') #click xlow, xhigh, ylow, yhigh and calibratin is now ca
dp = DigitData(col = 'red') ... | /inst/IP/DigitizingOldFigs.r | no_license | jfontestad/bio.lobster | R | false | false | 1,281 | r | #capturing old digitized data
require(digitize) ##there is no angle correction on this if the file is rotated you get wrong values
ca = ReadAndCal('~/Documents/Adam/Lobster/LFA38/Jan2020/CampbellandDugganTotalEffortTotalTraps.png') #click xlow, xhigh, ylow, yhigh and calibratin is now ca
dp = DigitData(col = 'red') ... |
hero <- function(bullets, dragons) {
ifelse(bullets / dragons >= 2, TRUE, FALSE)
} | /R/8_kyu/Is_he_gonna_survive.R | no_license | y0wel/Codewars-Kata | R | false | false | 84 | r | hero <- function(bullets, dragons) {
ifelse(bullets / dragons >= 2, TRUE, FALSE)
} |
# plotmo.R: plot the model response when varying one or two predictors
#
# Stephen Milborrow Sep 2006 Cape Town
plotmo <- function(object = stop("no 'object' argument"),
type = NULL,
nresponse = NA,
pt.col = 0,
jitter = .5,
smooth.col = 0,
level = 0,
func ... | /plotmo/R/plotmo.R | no_license | ingted/R-Examples | R | false | false | 67,472 | r | # plotmo.R: plot the model response when varying one or two predictors
#
# Stephen Milborrow Sep 2006 Cape Town
plotmo <- function(object = stop("no 'object' argument"),
type = NULL,
nresponse = NA,
pt.col = 0,
jitter = .5,
smooth.col = 0,
level = 0,
func ... |
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31638858795802e+77, 9.53818252170339e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0), .Dim = c(5L, 1L)))
result <- do.call(CNull:::communities_individual_based_sampling_beta,testlist)
str(result) | /CNull/inst/testfiles/communities_individual_based_sampling_beta/AFL_communities_individual_based_sampling_beta/communities_individual_based_sampling_beta_valgrind_files/1615833592-test.R | no_license | akhikolla/updatedatatype-list2 | R | false | false | 270 | r | testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.31638858795802e+77, 9.53818252170339e+295, 1.22810536108214e+146, 4.12396251261199e-221, 0), .Dim = c(5L, 1L)))
result <- do.call(CNull:::communities_individual_based_sampling_beta,testlist)
str(result) |
data <- read.table("./data/household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
data1 <- subset(data,as.Date(data$Date,"%d/%m/%Y")=="2007-02-01")
data2 <- subset(data,as.Date(data$Date,"%d/%m/%Y")=="2007-02-02")
data3 <- rbind(data1,data2)
data4 <- strptime(paste(data3$Date,data3$Tim... | /plot3.R | no_license | weizuo/ExData_Plotting1 | R | false | false | 805 | r | data <- read.table("./data/household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
data1 <- subset(data,as.Date(data$Date,"%d/%m/%Y")=="2007-02-01")
data2 <- subset(data,as.Date(data$Date,"%d/%m/%Y")=="2007-02-02")
data3 <- rbind(data1,data2)
data4 <- strptime(paste(data3$Date,data3$Tim... |
# set up parallelized portion from qsub
jobnum <- as.numeric(commandArgs()[3])
root_path <- commandArgs()[4]
outpath <- commandArgs()[5]
if (Sys.info()[1] == "Linux"){
j <- "FILEPATH"
h <- paste0("FILEPATH",Sys.info()[6])
package_lib <- paste0(j,'FILEPATH')
} else {
j <- "FILEPATH"
h <- "FILEPATH"
packag... | /gbd_2017/nonfatal_code/ntd_schisto/extract_par_species_parallel.r | no_license | Nermin-Ghith/ihme-modeling | R | false | false | 8,805 | r | # set up parallelized portion from qsub
jobnum <- as.numeric(commandArgs()[3])
root_path <- commandArgs()[4]
outpath <- commandArgs()[5]
if (Sys.info()[1] == "Linux"){
j <- "FILEPATH"
h <- paste0("FILEPATH",Sys.info()[6])
package_lib <- paste0(j,'FILEPATH')
} else {
j <- "FILEPATH"
h <- "FILEPATH"
packag... |
## ----setup,echo=FALSE,include=FALSE--------------------------------------
library(knitr)
library(bodenmiller)
library(ggplot2)
library(dplyr)
library(reshape2)
library(RColorBrewer)
knitr::opts_chunk$set(warning=FALSE,
fig.keep='high',
fig.align='center')
do.f... | /data/genthat_extracted_code/bodenmiller/vignettes/bodenmiller.R | no_license | surayaaramli/typeRrh | R | false | false | 4,239 | r | ## ----setup,echo=FALSE,include=FALSE--------------------------------------
library(knitr)
library(bodenmiller)
library(ggplot2)
library(dplyr)
library(reshape2)
library(RColorBrewer)
knitr::opts_chunk$set(warning=FALSE,
fig.keep='high',
fig.align='center')
do.f... |
# load the required packages
install.packages("caret")
install.packages("rattle")
install.packages("rpart")
install.packages("rpart.plot")
install.packages("randomForest")
install.packages("repmis")
install.packages("e1071")
library(caret)
library(rattle)
library(rpart)
library(rpart.plot)
library(randomForest)
librar... | /predict.R | no_license | CherryPon/predictionAssignment | R | false | false | 1,664 | r | # load the required packages
install.packages("caret")
install.packages("rattle")
install.packages("rpart")
install.packages("rpart.plot")
install.packages("randomForest")
install.packages("repmis")
install.packages("e1071")
library(caret)
library(rattle)
library(rpart)
library(rpart.plot)
library(randomForest)
librar... |
library(raster)
library(rgdal)
library(ggplot2)
library(reshape2)
library(plyr)
# --------------------------------------------------------------------------------
# Note: This first part I found in a tutorial online:
# https://downwithtime.wordpress.com/2013/12/04/naturalearthdata-and-r-in-ggplot2/
# --------... | /natearthtest.R | permissive | coreyabshire/ivmooc-gtap | R | false | false | 7,272 | r | library(raster)
library(rgdal)
library(ggplot2)
library(reshape2)
library(plyr)
# --------------------------------------------------------------------------------
# Note: This first part I found in a tutorial online:
# https://downwithtime.wordpress.com/2013/12/04/naturalearthdata-and-r-in-ggplot2/
# --------... |
library(Signac)
library(Seurat)
library(ggplot2)
library(harmony)
library(dplyr)
options(future.globals.maxSize = 50000 * 1024^2)
load('../data_processed/obj_all.rda')
######## Quality control to filter cells ########
## Filter cells
# RNA-seq: nCount_RNA, percent.mt
# ATAC-seq: nCount_ATAC, nucleosome_signal, TSS... | /preprocessing.R | permissive | xbendl/singlecell-multiomics-developmental-human-brain | R | false | false | 9,098 | r | library(Signac)
library(Seurat)
library(ggplot2)
library(harmony)
library(dplyr)
options(future.globals.maxSize = 50000 * 1024^2)
load('../data_processed/obj_all.rda')
######## Quality control to filter cells ########
## Filter cells
# RNA-seq: nCount_RNA, percent.mt
# ATAC-seq: nCount_ATAC, nucleosome_signal, TSS... |
library(data.table)
library(ggplot2)
library(fts)
library(Rmisc)
library(lubridate)
library(gtools)
library(dplyr)
library(zoo)
setwd("D://codes//Rfile//investment theory and practice//")
files <- list.files(".//factor_daily//return//")
factor.data <- NULL
for (file in files){
path <- paste0(".//fac... | /Volatility-Managed Portfolios Sometimes it Works/codes/vol-timing.R | no_license | coolgan/Quantitative-Finance | R | false | false | 24,009 | r | library(data.table)
library(ggplot2)
library(fts)
library(Rmisc)
library(lubridate)
library(gtools)
library(dplyr)
library(zoo)
setwd("D://codes//Rfile//investment theory and practice//")
files <- list.files(".//factor_daily//return//")
factor.data <- NULL
for (file in files){
path <- paste0(".//fac... |
# Data Types
# Vector
# Lists
# Matrices
# Array
# DataFrames & Factors
my.var <- 432
is.vector(my.var)
# Characters
# Numeric and Integer
# Logical
# Complex
| /R/dataTypes.R | no_license | thedatatot/rahul_kumar | R | false | false | 166 | r | # Data Types
# Vector
# Lists
# Matrices
# Array
# DataFrames & Factors
my.var <- 432
is.vector(my.var)
# Characters
# Numeric and Integer
# Logical
# Complex
|
source("main.R")
# most 5 star reviewed companies
top5StarCompanies = reviews %>%
filter(stars == 5) %>%
group_by(business_id) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
ungroup() %>%
mutate(BusinessID = reorder(business_id,Count)) %>%
head(10)
top5StarCompanies = inner_join(top5StarCompani... | /src/5Star-Reviewed-Restaurants.R | no_license | HENRY-JERRY/mit-805-2020-yelp-project | R | false | false | 806 | r | source("main.R")
# most 5 star reviewed companies
top5StarCompanies = reviews %>%
filter(stars == 5) %>%
group_by(business_id) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
ungroup() %>%
mutate(BusinessID = reorder(business_id,Count)) %>%
head(10)
top5StarCompanies = inner_join(top5StarCompani... |
#' Budget manager
#'
#' This package is a personnal project to manage a budget
#'
#' @name budgetmanager
#'
#' @import magrittr
#'
NULL
| /R/budgetmanager.R | no_license | denrou/budgetmanager | R | false | false | 136 | r | #' Budget manager
#'
#' This package is a personnal project to manage a budget
#'
#' @name budgetmanager
#'
#' @import magrittr
#'
NULL
|
traf <- read.csv("flow_occ.txt")
nint <- nrow(traf) # number of 5-min intervals, 1740
## Suppose starts on March 14th, 2003, Friday at midnight (we don't know the time)
## then ends at 1am on Thur, March 20th
day <- rep(c("Friday", "Saturday", "Sunday", "Monday", "Tuesday",
"Wednesday", "Thursday"),
... | /trafficJams/data-raw/flow_occ.R | no_license | debnolan/DynDocs | R | false | false | 860 | r | traf <- read.csv("flow_occ.txt")
nint <- nrow(traf) # number of 5-min intervals, 1740
## Suppose starts on March 14th, 2003, Friday at midnight (we don't know the time)
## then ends at 1am on Thur, March 20th
day <- rep(c("Friday", "Saturday", "Sunday", "Monday", "Tuesday",
"Wednesday", "Thursday"),
... |
context("junit")
test_that("multiplication works", {
expect_equal(1 * 2, 2)
expect_equal(2 * 2, 3)
expect_equal(3 * 2, 6)
expect_equal(4 * 2, 6)
})
test_that("summation works", {
expect_equal(1 + 1, 2)
expect_equal(2 + 2, 5)
expect_equal(3 + 3, 6)
expect_equal(4 + 4, 8)
})
| /tests/testthat/test-junit.R | no_license | yutannihilation/testthatJunitRporterTest | R | false | false | 292 | r | context("junit")
test_that("multiplication works", {
expect_equal(1 * 2, 2)
expect_equal(2 * 2, 3)
expect_equal(3 * 2, 6)
expect_equal(4 * 2, 6)
})
test_that("summation works", {
expect_equal(1 + 1, 2)
expect_equal(2 + 2, 5)
expect_equal(3 + 3, 6)
expect_equal(4 + 4, 8)
})
|
context("ifan vs dfan")
test_that("Can create FFTrees object with dfan", {
object <- FFTrees(diagnosis ~., data = heartdisease, algorithm = "dfan")
expect_is(object = object, class = "FFTrees")
})
test_that("Different results with ifan and dfan", {
trees_ifan <- FFTrees(diagnosis ~.,
... | /tests/testthat/test-ifan_v_dfan.R | no_license | Barardo/FFTrees | R | false | false | 631 | r | context("ifan vs dfan")
test_that("Can create FFTrees object with dfan", {
object <- FFTrees(diagnosis ~., data = heartdisease, algorithm = "dfan")
expect_is(object = object, class = "FFTrees")
})
test_that("Different results with ifan and dfan", {
trees_ifan <- FFTrees(diagnosis ~.,
... |
## ----eval=FALSE,include=FALSE-------------------------------------------------
## source("../../rnw2pdf.R")
## rnw2pdf("lecture-survival")
## rnw2pdf("lecture-survival",tangle=TRUE)
| /lectures/survival/lecture-survival.R | no_license | rbchan/applied-popdy | R | false | false | 185 | r | ## ----eval=FALSE,include=FALSE-------------------------------------------------
## source("../../rnw2pdf.R")
## rnw2pdf("lecture-survival")
## rnw2pdf("lecture-survival",tangle=TRUE)
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth.R
\name{orcid_auth}
\alias{orcid_auth}
\alias{rorcid-auth}
\title{ORCID authorization}
\usage{
orcid_auth(scope = "/authenticate", reauth = FALSE,
redirect_uri = getOption("rorcid.redirect_uri"))
}
\arguments{
\item{scope}{(character) ... | /man/orcid_auth.Rd | permissive | pkraker/rorcid | R | false | true | 2,216 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth.R
\name{orcid_auth}
\alias{orcid_auth}
\alias{rorcid-auth}
\title{ORCID authorization}
\usage{
orcid_auth(scope = "/authenticate", reauth = FALSE,
redirect_uri = getOption("rorcid.redirect_uri"))
}
\arguments{
\item{scope}{(character) ... |
library(TOAST)
library(peakRAM)
# config = "n_100_DE_pattern_2_1_1_replicate_1"
test_TOAST_TPM = function(config) {
RDatafile = sprintf(file.path("../simulation", config, "simulation.RData"))
load(RDatafile)
# Will use: observed_TPM, rho_from_TPM, clinical_variables, adjusted_signature_gene_TPM (only for cel... | /R/simulation_test_TOAST_TPM.R | permissive | Sun-lab/CARseq_pipelines | R | false | false | 3,283 | r | library(TOAST)
library(peakRAM)
# config = "n_100_DE_pattern_2_1_1_replicate_1"
test_TOAST_TPM = function(config) {
RDatafile = sprintf(file.path("../simulation", config, "simulation.RData"))
load(RDatafile)
# Will use: observed_TPM, rho_from_TPM, clinical_variables, adjusted_signature_gene_TPM (only for cel... |
# --------------------------------------------------------
# ARGUMENTS:
# data - a dataframe
# respvar - a string; variable name of the response variable
# env - a string; variable name of the environment variable
# is.random - logical; indicates whether genotype/treatment is random or not; default value is FALSE (FIXE... | /R3.0.2 Package Creation/PBTools/R/graph_mea1s_diagplots.R | no_license | djnpisano/RScriptLibrary | R | false | false | 2,559 | r | # --------------------------------------------------------
# ARGUMENTS:
# data - a dataframe
# respvar - a string; variable name of the response variable
# env - a string; variable name of the environment variable
# is.random - logical; indicates whether genotype/treatment is random or not; default value is FALSE (FIXE... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wfm.R
\name{docs}
\alias{docs}
\alias{docs<-}
\title{Extract Document Names}
\usage{
docs(wfm)
docs(wfm) <- value
}
\arguments{
\item{wfm}{an object of type wfm}
\item{value}{replacement if assignment}
}
\value{
A list of document names.
}
... | /man/docs.Rd | no_license | markwestcott34/austin | R | false | true | 435 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wfm.R
\name{docs}
\alias{docs}
\alias{docs<-}
\title{Extract Document Names}
\usage{
docs(wfm)
docs(wfm) <- value
}
\arguments{
\item{wfm}{an object of type wfm}
\item{value}{replacement if assignment}
}
\value{
A list of document names.
}
... |
#' Creates a dataframe with predictions from each model
#' and real choices
#' params tree dataframe
compare_link_prediction <- function(df.tree, params.Gomez2013, params.Aragon2017){
# We can compare with barabasi becaus the choice is independent of the alpha
parents <- df.tree$parent
parents.users <- df.tree$p... | /R/link_prediction.R | permissive | elaragon/generative-discussion-threads | R | false | false | 3,312 | r | #' Creates a dataframe with predictions from each model
#' and real choices
#' params tree dataframe
compare_link_prediction <- function(df.tree, params.Gomez2013, params.Aragon2017){
# We can compare with barabasi becaus the choice is independent of the alpha
parents <- df.tree$parent
parents.users <- df.tree$p... |
#' Extract multiple subclades from a phylogeny based on node numbers
#'
#' Given a tree and a vector or list of nodes, this function extracts subclades and
#' returns a list of smaller trees.
#'
#' @param tree An ape-style phylogenetic tree.
#' @param nodes A named list or vector of node numbers that subtend the clade ... | /R/extractClade.R | no_license | jesusNPL/addTaxa | R | false | false | 3,302 | r | #' Extract multiple subclades from a phylogeny based on node numbers
#'
#' Given a tree and a vector or list of nodes, this function extracts subclades and
#' returns a list of smaller trees.
#'
#' @param tree An ape-style phylogenetic tree.
#' @param nodes A named list or vector of node numbers that subtend the clade ... |
library(shiny)
library(shinydashboard)
library(data.table)
library(ggplot2)
data = data.table(group = rep(c(1, 3, 6), each = 10), x = rep(1:10, times = 3), value = rnorm(30))
sidebar <- dashboardSidebar(
uiOutput("Sidebar")
)
body <- dashboardBody(
uiOutput("TABUI")
)
# Put them together into a dashboardPage
ui ... | /R/shiny-examples/08_dynamic-tabs/app5.R | no_license | michellymenezes/ladybird-umbrella | R | false | false | 1,926 | r | library(shiny)
library(shinydashboard)
library(data.table)
library(ggplot2)
data = data.table(group = rep(c(1, 3, 6), each = 10), x = rep(1:10, times = 3), value = rnorm(30))
sidebar <- dashboardSidebar(
uiOutput("Sidebar")
)
body <- dashboardBody(
uiOutput("TABUI")
)
# Put them together into a dashboardPage
ui ... |
# testSource
# Load the library
require(RCurl)
# Provide the web address of the file:
fileURL <- getURL('https://raw.githubusercontent.com/SCBI-MigBirds/MigBirds/master/data/exampleBirdData.csv')
# Read in the data:
birdCounts <- read.csv(text = fileURL)
birdCounts
| /testSource.R | no_license | SCBI-MigBirds/scbi-migbirds.github.io | R | false | false | 274 | r | # testSource
# Load the library
require(RCurl)
# Provide the web address of the file:
fileURL <- getURL('https://raw.githubusercontent.com/SCBI-MigBirds/MigBirds/master/data/exampleBirdData.csv')
# Read in the data:
birdCounts <- read.csv(text = fileURL)
birdCounts
|
# @title .onAttach
# @description Load required data into gloval enviroment
# @keywords internal
.onAttach<- function (libname, pkgname){
packageStartupMessage(paste0(
" ==============================================================\n",
" \n",
" CAMPARI analysis tools ... | /R/misc.R | no_license | clangi/CampaRi | R | false | false | 8,170 | r | # @title .onAttach
# @description Load required data into gloval enviroment
# @keywords internal
.onAttach<- function (libname, pkgname){
packageStartupMessage(paste0(
" ==============================================================\n",
" \n",
" CAMPARI analysis tools ... |
###Script for task 04
###Author: April Armes
###Group: IMAAS
library(ggplot2)#loads ggplot 2
diamonds # calls diamonds dataset
nrow(diamonds)#tells you how many rows diamonds dataset has -> a lot.
set.seed(1410) #makes the results the same for everyone
dsmall <- diamonds[sample(nrow(diamonds), 100), ] #creates a vect... | /R/Task_4.R | no_license | Nuapril/GEOL_590 | R | false | false | 4,234 | r | ###Script for task 04
###Author: April Armes
###Group: IMAAS
library(ggplot2)#loads ggplot 2
diamonds # calls diamonds dataset
nrow(diamonds)#tells you how many rows diamonds dataset has -> a lot.
set.seed(1410) #makes the results the same for everyone
dsmall <- diamonds[sample(nrow(diamonds), 100), ] #creates a vect... |
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/Transition_functions.r
\name{cjs_gamma}
\alias{cjs_gamma}
\alias{ms2_gamma}
\alias{ms_gamma}
\title{HMM Transition matrix functions}
\usage{
cjs_gamma(pars, m, F, T)
}
\arguments{
\item{pars}{list of real parameter values f... | /marked/man/cjs_gamma.Rd | no_license | bmcclintock/marked | R | false | false | 900 | rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/Transition_functions.r
\name{cjs_gamma}
\alias{cjs_gamma}
\alias{ms2_gamma}
\alias{ms_gamma}
\title{HMM Transition matrix functions}
\usage{
cjs_gamma(pars, m, F, T)
}
\arguments{
\item{pars}{list of real parameter values f... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gutenberg_richter_densities.R
\name{qGR}
\alias{qGR}
\title{Inverse cumulative distribution function for Gutenberg Richter distribution}
\usage{
qGR(p, b, mw_min)
}
\arguments{
\item{p}{vector of probabilities}
\item{b}{Gutenberg-Richter b v... | /R/rptha/man/qGR.Rd | permissive | GeoscienceAustralia/ptha | R | false | true | 628 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gutenberg_richter_densities.R
\name{qGR}
\alias{qGR}
\title{Inverse cumulative distribution function for Gutenberg Richter distribution}
\usage{
qGR(p, b, mw_min)
}
\arguments{
\item{p}{vector of probabilities}
\item{b}{Gutenberg-Richter b v... |
## The functions are used to calculate and cache the inverse of a matrix
## The following function creates a list containing functions to set and
## get the values of a matrix and its inverse
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <- ... | /cachematrix.R | no_license | luciawrq/ProgrammingAssignment2 | R | false | false | 963 | r | ## The functions are used to calculate and cache the inverse of a matrix
## The following function creates a list containing functions to set and
## get the values of a matrix and its inverse
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <- ... |
source("Project1Lib.R")
fn_Plot4 <- function(x)
{
fname <- "plot4.png"
cat("Creating",fname, "\n")
#
attach(hpc)
par(mfrow=c(2,2))
plot(hpc$DateTime, hpc$Global_active_power, type="l", ylab="Global Active Power", xlab="")
plot(DateTime, Voltage, type="l", ylab="Voltage",xlab="datetime")
#
yrange<-ran... | /plot4.R | no_license | nate43026/ExData_Plotting1 | R | false | false | 962 | r | source("Project1Lib.R")
fn_Plot4 <- function(x)
{
fname <- "plot4.png"
cat("Creating",fname, "\n")
#
attach(hpc)
par(mfrow=c(2,2))
plot(hpc$DateTime, hpc$Global_active_power, type="l", ylab="Global Active Power", xlab="")
plot(DateTime, Voltage, type="l", ylab="Voltage",xlab="datetime")
#
yrange<-ran... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DataOrganization.R
\name{NumberPermsAnySize}
\alias{NumberPermsAnySize}
\title{Total Number of Permutations of all sizes}
\usage{
NumberPermsAnySize(n, min = 1)
}
\arguments{
\item{n}{The total number of choices}
\item{min}{The total number ... | /man/NumberPermsAnySize.Rd | no_license | alexbrodersen/teis | R | false | true | 796 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DataOrganization.R
\name{NumberPermsAnySize}
\alias{NumberPermsAnySize}
\title{Total Number of Permutations of all sizes}
\usage{
NumberPermsAnySize(n, min = 1)
}
\arguments{
\item{n}{The total number of choices}
\item{min}{The total number ... |
library(datafsm)
context("Main evolve_model function")
test_that("evolve_model() returns correct type of object", {
cdata <- data.frame(period = 1:5, outcome = c(1,2,1,1,1),
my.decision1 = c(1,0,1,1,1), other.decision1 = c(0,0,0,1,1))
result <- evolve_model(cdata, cv=FALSE)
... | /tests/testthat/test_mainfunc.R | permissive | jdblischak/datafsm | R | false | false | 1,556 | r | library(datafsm)
context("Main evolve_model function")
test_that("evolve_model() returns correct type of object", {
cdata <- data.frame(period = 1:5, outcome = c(1,2,1,1,1),
my.decision1 = c(1,0,1,1,1), other.decision1 = c(0,0,0,1,1))
result <- evolve_model(cdata, cv=FALSE)
... |
gcv_function <-
function(alpha,gamma2,beta)
{
f = (alpha^2)/(gamma2 + alpha^2);
### length(f);
### length(beta);
if(length(f)>length(beta))
{
f=f[1:length(beta)];
}
else
{
if(length(beta)>length(f))
{
iend = length(beta)
beta=beta[... | /R/gcv_function.R | no_license | cran/PEIP | R | false | false | 430 | r | gcv_function <-
function(alpha,gamma2,beta)
{
f = (alpha^2)/(gamma2 + alpha^2);
### length(f);
### length(beta);
if(length(f)>length(beta))
{
f=f[1:length(beta)];
}
else
{
if(length(beta)>length(f))
{
iend = length(beta)
beta=beta[... |
##########################################################################################################################
## Loop BKMR model through 100 seeds
## 11/18/2019
##########################################################################################################################
#install.packages("bkm... | /BKMR/HPC/bkmr_loop_model_25.R | no_license | yanellinunez/Commentary-to-mixture-methods-paper | R | false | false | 7,118 | r | ##########################################################################################################################
## Loop BKMR model through 100 seeds
## 11/18/2019
##########################################################################################################################
#install.packages("bkm... |
#' @name windfarmGA
#' @description The initiating function of an optimization run which will
#' interactively check user-inputs. If all inputs are correct, an optimization
#' will be started.
#'
#' @export
#'
#' @param dns The data source name (interpretation varies by driver — for some
#' drivers, dsn is a file... | /R/windfarmGA.R | no_license | allenfieldin/windfarmGA | R | false | false | 8,756 | r | #' @name windfarmGA
#' @description The initiating function of an optimization run which will
#' interactively check user-inputs. If all inputs are correct, an optimization
#' will be started.
#'
#' @export
#'
#' @param dns The data source name (interpretation varies by driver — for some
#' drivers, dsn is a file... |
source('global.R')
fluidPage(
#tab title
title="time2pub",
#titlePanel(h2("Journal-Specific Fast-Track time2pub Visualization",align='center')),
mainPanel(width=12,
# tags$style(type="text/css",
# ".shiny-output-error { visibility: hidden; }",
# ... | /ui.R | permissive | joshuamwang/time2pub | R | false | false | 2,291 | r | source('global.R')
fluidPage(
#tab title
title="time2pub",
#titlePanel(h2("Journal-Specific Fast-Track time2pub Visualization",align='center')),
mainPanel(width=12,
# tags$style(type="text/css",
# ".shiny-output-error { visibility: hidden; }",
# ... |
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