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 |
|---|---|---|---|---|---|---|---|---|---|
## cacheSolve() calculates the inverse of the input matrix. If the inverse already exists
## then it is retrieved from the cached. matrix.
##makeCacheMatrix creates a vector of functions for an input matrix which
## cacheSolve() uses to retrieve an already calculated inverse
## Creates vector to store retrieve and ge... | /cachematrix.R | no_license | aneeshsathe/ProgrammingAssignment2 | R | false | false | 1,215 | r | ## cacheSolve() calculates the inverse of the input matrix. If the inverse already exists
## then it is retrieved from the cached. matrix.
##makeCacheMatrix creates a vector of functions for an input matrix which
## cacheSolve() uses to retrieve an already calculated inverse
## Creates vector to store retrieve and ge... |
## Put comments here that give an overall description of what your
## functions do
## Functions that cache the inverse of a matrix
+##
+## Usage example:
+##
+## > source('cachematrix.R')
+## > m <- makeCacheMatrix(matrix(c(2, 0, 0, 2), c(2, 2)))
+## > cacheSolve(m)
+## [,1] [,2]
+## [1,] 0.5 0.0
+## [2,] 0.0 0.5
... | /cachematrix.R | no_license | sandyjera/ProgrammingAssignment2 | R | false | false | 1,418 | r | ## Put comments here that give an overall description of what your
## functions do
## Functions that cache the inverse of a matrix
+##
+## Usage example:
+##
+## > source('cachematrix.R')
+## > m <- makeCacheMatrix(matrix(c(2, 0, 0, 2), c(2, 2)))
+## > cacheSolve(m)
+## [,1] [,2]
+## [1,] 0.5 0.0
+## [2,] 0.0 0.5
... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lexmodelsv2_operations.R
\name{lexmodelsv2_list_bot_aliases}
\alias{lexmodelsv2_list_bot_aliases}
\title{Gets a list of aliases for the specified bot}
\usage{
lexmodelsv2_list_bot_aliases(botId, maxResults = NULL, nextToken = NULL)
}
\argumen... | /cran/paws.machine.learning/man/lexmodelsv2_list_bot_aliases.Rd | permissive | paws-r/paws | R | false | true | 1,071 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lexmodelsv2_operations.R
\name{lexmodelsv2_list_bot_aliases}
\alias{lexmodelsv2_list_bot_aliases}
\title{Gets a list of aliases for the specified bot}
\usage{
lexmodelsv2_list_bot_aliases(botId, maxResults = NULL, nextToken = NULL)
}
\argumen... |
# FUNCTIONS #
filename_to_metadata <- function(file_path){
# fxn to read in Hondo file names and pull out metadata (stand, month, year)
myfiles <- as.data.frame(list.files(file_path,
full.names = FALSE,
pattern = "*.txt"))
colnames(... | /HONDO/VascularCover/scripts/01_functions.R | no_license | avhesketh/LDP_SEADYN | R | false | false | 5,910 | r | # FUNCTIONS #
filename_to_metadata <- function(file_path){
# fxn to read in Hondo file names and pull out metadata (stand, month, year)
myfiles <- as.data.frame(list.files(file_path,
full.names = FALSE,
pattern = "*.txt"))
colnames(... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inputs.R
\name{phoneInput}
\alias{myInput}
\alias{phoneInput}
\alias{zipInput}
\title{Create a telephone number input control}
\usage{
phoneInput(inputId, label, value = "", width = NULL, placeholder = NULL,
...)
zipInput(inputId, label, v... | /man/phoneInput.Rd | no_license | carlganz/formancer | R | false | true | 1,133 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/inputs.R
\name{phoneInput}
\alias{myInput}
\alias{phoneInput}
\alias{zipInput}
\title{Create a telephone number input control}
\usage{
phoneInput(inputId, label, value = "", width = NULL, placeholder = NULL,
...)
zipInput(inputId, label, v... |
source("helpers.R")
source("libraries.R")
server <- function(input, output, session) {
#Group Ride Tab
#Value Boxes
output$box1gr <- renderValueBox({
valueBox(
value = prettyNum(round(median(df5$Trip_distance),2), big.mark = ",")
,subtitle = "Median Distance"
,color = "green"
,i... | /server.R | no_license | NahoiLartem/NYT2 | R | false | false | 16,337 | r | source("helpers.R")
source("libraries.R")
server <- function(input, output, session) {
#Group Ride Tab
#Value Boxes
output$box1gr <- renderValueBox({
valueBox(
value = prettyNum(round(median(df5$Trip_distance),2), big.mark = ",")
,subtitle = "Median Distance"
,color = "green"
,i... |
#' Obtain data and feature geometry for the decennial Census
#'
#' @param geography The geography of your data.
#' @param variables Character string or vector of character strings of variable
#' IDs.
#' @param table The Census table for which you would like to request all variables. Uses
#' ... | /R/census.R | no_license | stmacdonell/tidycensus | R | false | false | 10,954 | r | #' Obtain data and feature geometry for the decennial Census
#'
#' @param geography The geography of your data.
#' @param variables Character string or vector of character strings of variable
#' IDs.
#' @param table The Census table for which you would like to request all variables. Uses
#' ... |
testlist <- list(A = structure(c(2.32784507357645e-308, 9.53818252170339e+295, 1.22810536108213e+146, 5.71368621380148e-88, 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)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_row... | /multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613104868-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 343 | r | testlist <- list(A = structure(c(2.32784507357645e-308, 9.53818252170339e+295, 1.22810536108213e+146, 5.71368621380148e-88, 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)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_row... |
library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/AvgRank/central_nervous_system.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0.55,family="gaussian",standardize=TRUE)
sink('./central_nervous_... | /Model/EN/AvgRank/central_nervous_system/central_nervous_system_063.R | no_license | esbgkannan/QSMART | R | false | false | 378 | r | library(glmnet)
mydata = read.table("../../../../TrainingSet/FullSet/AvgRank/central_nervous_system.csv",head=T,sep=",")
x = as.matrix(mydata[,4:ncol(mydata)])
y = as.matrix(mydata[,1])
set.seed(123)
glm = cv.glmnet(x,y,nfolds=10,type.measure="mae",alpha=0.55,family="gaussian",standardize=TRUE)
sink('./central_nervous_... |
library("ggplot2")
# normalizes all values given in vector 'x' to be between 0,1
normalize <- function(x){
xmin = min(x)
xmax = max(x)
y = (x-xmin)/(xmax-xmin)
return(y)
}
nlm <- function(x, y){
return(lm(normalize(y)~normalize(x)))
}
d<-read.csv("up.3.saved.txt",sep = "\t")
d$w.len <- nchar(as.charact... | /a.r | no_license | AdamKing11/Ngram_Informativity | R | false | false | 1,878 | r | library("ggplot2")
# normalizes all values given in vector 'x' to be between 0,1
normalize <- function(x){
xmin = min(x)
xmax = max(x)
y = (x-xmin)/(xmax-xmin)
return(y)
}
nlm <- function(x, y){
return(lm(normalize(y)~normalize(x)))
}
d<-read.csv("up.3.saved.txt",sep = "\t")
d$w.len <- nchar(as.charact... |
if (!file.exists(file.path(getwd(),"Project 1"))){
unzip("exdata-data-household_power_consumption.zip", exdir="./Project 1")
}
# read the data into R
data = read.table(file.path(getwd(),"Project 1","household_power_consumption.txt"),
header=TRUE,stringsAsFactors=FALSE,sep=";",na.strings="?")
# cre... | /Exploratory Data Analysis/Project 1/plot4.R | no_license | JLKW/DataScienceCoursera | R | false | false | 1,618 | r | if (!file.exists(file.path(getwd(),"Project 1"))){
unzip("exdata-data-household_power_consumption.zip", exdir="./Project 1")
}
# read the data into R
data = read.table(file.path(getwd(),"Project 1","household_power_consumption.txt"),
header=TRUE,stringsAsFactors=FALSE,sep=";",na.strings="?")
# cre... |
make.synthetic.data <- function(
n.observation,
beta,
errorRate,
reviewFraction
) {
npredictors <- length(beta) - 1;
X <- rbinom(
n = n.observation * npredictors,
size = 1,
prob = 0.5
);
X <- matrix(data = X, nrow = n.observation, byrow = TRUE);
X <- cbind(rep(1,times=n.observation),X);
colname... | /projects/StatCan/recordLinkage/errorAdjustment/chipperfield/001-StatCan-linkAdjust/StatCan.linkAdjust/R/make-synthetic-data.R | no_license | paradisepilot/statistics | R | false | false | 1,987 | r |
make.synthetic.data <- function(
n.observation,
beta,
errorRate,
reviewFraction
) {
npredictors <- length(beta) - 1;
X <- rbinom(
n = n.observation * npredictors,
size = 1,
prob = 0.5
);
X <- matrix(data = X, nrow = n.observation, byrow = TRUE);
X <- cbind(rep(1,times=n.observation),X);
colname... |
## Nicolas Servant
## HiTC BioConductor package
##**********************************************************************************************************##
##
## HIC Normalization procedure from Lieberman-Aiden et al. 2009
##
##*****************************************************************************************... | /R/HiC_norm.R | no_license | mckf111/hiceize | R | false | false | 29,506 | r | ## Nicolas Servant
## HiTC BioConductor package
##**********************************************************************************************************##
##
## HIC Normalization procedure from Lieberman-Aiden et al. 2009
##
##*****************************************************************************************... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ezr_h2o_get_gridmodels.R
\name{ezr.h2o_get_gridmodels}
\alias{ezr.h2o_get_gridmodels}
\title{Get H2o Grid/AutoMl Model IDs}
\usage{
ezr.h2o_get_gridmodels(h2o_grid)
}
\arguments{
\item{h2o_grid}{Doesn't matter if string or model object. Can ... | /man/ezr.h2o_get_gridmodels.Rd | no_license | lenamax2355/easyr | R | false | true | 476 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ezr_h2o_get_gridmodels.R
\name{ezr.h2o_get_gridmodels}
\alias{ezr.h2o_get_gridmodels}
\title{Get H2o Grid/AutoMl Model IDs}
\usage{
ezr.h2o_get_gridmodels(h2o_grid)
}
\arguments{
\item{h2o_grid}{Doesn't matter if string or model object. Can ... |
testlist <- list(a = 0L, b = 0L, x = c(-78249985L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L))
result <- do.call(grattan:::anyOutside,testlist)
str(result) | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610131656-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 254 | r | testlist <- list(a = 0L, b = 0L, x = c(-78249985L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L))
result <- do.call(grattan:::anyOutside,testlist)
str(result) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parent_cluster.mppData.R
\name{parent_cluster.mppData}
\alias{parent_cluster.mppData}
\title{Parent clustering for \code{mppData} objects}
\usage{
parent_cluster.mppData(mppData, method = NULL, par.clu = NULL,
w1 = "kernel.exp", w2 = "kerne... | /man/parent_cluster.mppData.Rd | no_license | jancrichter/mppR | R | false | true | 5,747 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parent_cluster.mppData.R
\name{parent_cluster.mppData}
\alias{parent_cluster.mppData}
\title{Parent clustering for \code{mppData} objects}
\usage{
parent_cluster.mppData(mppData, method = NULL, par.clu = NULL,
w1 = "kernel.exp", w2 = "kerne... |
################# UniProtKB_query.R ########################
#Obtain structural annotation info for GOI from UniProtKB web API
cat("##############################################################\n")
cat("####### Running UniProtKB structural annotation query ########\n")
cat("##########################################... | /UniProtKB_query.R | no_license | tituslabumn/Pan-Cancer-Gene-Reports | R | false | false | 6,796 | r | ################# UniProtKB_query.R ########################
#Obtain structural annotation info for GOI from UniProtKB web API
cat("##############################################################\n")
cat("####### Running UniProtKB structural annotation query ########\n")
cat("##########################################... |
#' add_osm_objects
#'
#' Adds layers of spatial objects (polygons, lines, or points generated by
#' extract_osm_objects ()) to a graphics object initialised with
#' plot_osm_basemap().
#'
#' @param map A ggplot2 object to which the objects are to be added
#' @param obj A spatial ('sp') data frame of polygons, lines, or... | /R/add-osm-objects.R | no_license | jeperez/osmplotr | R | false | false | 5,272 | r | #' add_osm_objects
#'
#' Adds layers of spatial objects (polygons, lines, or points generated by
#' extract_osm_objects ()) to a graphics object initialised with
#' plot_osm_basemap().
#'
#' @param map A ggplot2 object to which the objects are to be added
#' @param obj A spatial ('sp') data frame of polygons, lines, or... |
rm(list=ls())
library(simode)
library(doRNG)
require(doParallel)
set.seed(2000)
vars <- paste0('x', 1:3)
eq1 <- 'gamma11*(x2^f121)*(x3^f131)-gamma12*(x1^f112)*(x2^f122)-gamma13*(x1^f113)*(x3^f133)'
eq2 <- 'gamma21*(x1^f211)*(x2^f221)-gamma22*(x2^f222)'
eq3 <- 'gamma31*(x1^f311)*(x3^f331)-gamma32*(x3^f332)'
equations ... | /previous_backup/GMA-System1-SNR5-newmethod/compNLStoSLS_GMA.R | no_license | haroldship/complexity-2019-code | R | false | false | 7,159 | r | rm(list=ls())
library(simode)
library(doRNG)
require(doParallel)
set.seed(2000)
vars <- paste0('x', 1:3)
eq1 <- 'gamma11*(x2^f121)*(x3^f131)-gamma12*(x1^f112)*(x2^f122)-gamma13*(x1^f113)*(x3^f133)'
eq2 <- 'gamma21*(x1^f211)*(x2^f221)-gamma22*(x2^f222)'
eq3 <- 'gamma31*(x1^f311)*(x3^f331)-gamma32*(x3^f332)'
equations ... |
## Create in-silico prep names
getPrepsDef <- function(numLatent, numPrepsPer, numMeasPer) {
lat <- rep(paste("L", sep="", 1:numLatent), each=numPrepsPer*numMeasPer)
prep <- rep(paste("P", sep="", 1:numPrepsPer), each=numMeasPer, numLatent)
meas <- rep(paste("M", sep="", 1:numMeasPer), numLatent*numPrepsPer)
p... | /code/SCM/R/preps.R | permissive | carushi/mrna-prot | R | false | false | 9,383 | r |
## Create in-silico prep names
getPrepsDef <- function(numLatent, numPrepsPer, numMeasPer) {
lat <- rep(paste("L", sep="", 1:numLatent), each=numPrepsPer*numMeasPer)
prep <- rep(paste("P", sep="", 1:numPrepsPer), each=numMeasPer, numLatent)
meas <- rep(paste("M", sep="", 1:numMeasPer), numLatent*numPrepsPer)
p... |
library(git2r)
### Name: repository
### Title: Open a repository
### Aliases: repository
### ** Examples
## Not run:
##D ## Initialize a temporary repository
##D path <- tempfile(pattern="git2r-")
##D dir.create(path)
##D repo <- init(path)
##D
##D # Configure a user
##D config(repo, user.name="Alice", user.email... | /data/genthat_extracted_code/git2r/examples/repository.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 1,546 | r | library(git2r)
### Name: repository
### Title: Open a repository
### Aliases: repository
### ** Examples
## Not run:
##D ## Initialize a temporary repository
##D path <- tempfile(pattern="git2r-")
##D dir.create(path)
##D repo <- init(path)
##D
##D # Configure a user
##D config(repo, user.name="Alice", user.email... |
# This generates a preprocessing pipeline to handle categorical features
# @param task: the task
# @param impact.encoding.boundary: See autoxgboost
# @return CPOpipeline to transform categorical features
generateCatFeatPipeline = function(task, impact.encoding.boundary) {
cat.pipeline = cpoFixFactors()
d = getTas... | /R/generateCatFeatPipeline.R | no_license | peipeiwu1119/autoxgboost | R | false | false | 1,214 | r | # This generates a preprocessing pipeline to handle categorical features
# @param task: the task
# @param impact.encoding.boundary: See autoxgboost
# @return CPOpipeline to transform categorical features
generateCatFeatPipeline = function(task, impact.encoding.boundary) {
cat.pipeline = cpoFixFactors()
d = getTas... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulator.R
\name{compare_simulated_observed}
\alias{compare_simulated_observed}
\title{Compares simulated and observed variant allele counts}
\usage{
compare_simulated_observed(simulated, observed, depths)
}
\arguments{
\item{simulated}{the ... | /man/compare_simulated_observed.Rd | permissive | alkodsi/ctDNAtools | R | false | true | 530 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulator.R
\name{compare_simulated_observed}
\alias{compare_simulated_observed}
\title{Compares simulated and observed variant allele counts}
\usage{
compare_simulated_observed(simulated, observed, depths)
}
\arguments{
\item{simulated}{the ... |
# Load the "household_power_consumption.txt" dataset and make a
# histogram of the global active power, measured on 01/02/2007
# and 02/02/2007.
# This script should be run from the same directory as the dataset.
# Load the full dataset and retain only the data from 01/02/2007 and 02/02/2007
data <- read.table("hous... | /plot1.R | no_license | AllaertF/ExData_Plotting1 | R | false | false | 927 | r | # Load the "household_power_consumption.txt" dataset and make a
# histogram of the global active power, measured on 01/02/2007
# and 02/02/2007.
# This script should be run from the same directory as the dataset.
# Load the full dataset and retain only the data from 01/02/2007 and 02/02/2007
data <- read.table("hous... |
#******************************************Logistic Regression Case Study************************************************
setwd("E:/BA360/R/Proactive Attrition Management-Logistic Regression Case Study")
# Importing the data
mydata1<-read.csv("logistic.csv")
#****************************************Data Ana... | /Final_Code.R | no_license | vikas1296/Customer-Churn- | R | false | false | 11,709 | r | #******************************************Logistic Regression Case Study************************************************
setwd("E:/BA360/R/Proactive Attrition Management-Logistic Regression Case Study")
# Importing the data
mydata1<-read.csv("logistic.csv")
#****************************************Data Ana... |
################################################################################
## ui ##
################################################################################
library(shiny)
shinyUI(
pageWithSidebar(
# Application Titl... | /ui.R | no_license | cpatinof/mpgPredictiveModel | R | false | false | 2,166 | r | ################################################################################
## ui ##
################################################################################
library(shiny)
shinyUI(
pageWithSidebar(
# Application Titl... |
library(httr)
# 1. Find OAuth settings for github:
# http://developer.github.com/v3/oauth/
oauth_endpoints("github")
# 2. To make your own application, register at at
# https://github.com/settings/applications. Use any URL for the homepage URL
# (http://github.com is fine) and http://localhost:1410 as the c... | /cleaning_data/week2/quiz.R | no_license | tamimcsedu19/datasciencecoursera | R | false | false | 766 | r | library(httr)
# 1. Find OAuth settings for github:
# http://developer.github.com/v3/oauth/
oauth_endpoints("github")
# 2. To make your own application, register at at
# https://github.com/settings/applications. Use any URL for the homepage URL
# (http://github.com is fine) and http://localhost:1410 as the c... |
library(XML)
source("xmlFaster.R")
nps <- xmlParse("NPS_Results.xml")
system.time(data3 <- xmlToDF(nps,xpath = "/TABLE/NPS_RESULTS" ))
data3$Created <- strptime(data3$Created, "%Y-%m-%dT%H:%M:%S")
View(data3) | /Random Statistical Analysis/NPS.R | permissive | dmpe/R | R | false | false | 210 | r | library(XML)
source("xmlFaster.R")
nps <- xmlParse("NPS_Results.xml")
system.time(data3 <- xmlToDF(nps,xpath = "/TABLE/NPS_RESULTS" ))
data3$Created <- strptime(data3$Created, "%Y-%m-%dT%H:%M:%S")
View(data3) |
library(dplyr)
library(tidyverse)
library(mapdata)
library(maps)
library(RColorBrewer)
library(gganimate)
gt <- read_csv("data/database.csv")
worldmap <- map_data("world")
newworld <- worldmap %>%
filter(region != "Antarctica")
newworld$region <- recode(newworld$region
,'USA' = 'United Sta... | /terrorism.R | no_license | stepanekm/global-terrorism | R | false | false | 7,232 | r | library(dplyr)
library(tidyverse)
library(mapdata)
library(maps)
library(RColorBrewer)
library(gganimate)
gt <- read_csv("data/database.csv")
worldmap <- map_data("world")
newworld <- worldmap %>%
filter(region != "Antarctica")
newworld$region <- recode(newworld$region
,'USA' = 'United Sta... |
headsize<-source("c:\\allwork\\rsplus\\chap8headsize.dat")$value
#
headsize.std<-sweep(headsize,2,sqrt(apply(headsize,2,var)),FUN="/")
#
#
headsize1<-headsize.std[,1:2]
headsize2<-headsize.std[,3:4]
r11<-cor(headsize1)
r22<-cor(headsize2)
r12<-c(cor(headsize1[,1],headsize2[,1]),cor(headsize1[,1],headsize2[,2]),
cor(hea... | /RSPCMA/R/rsplus8.r | no_license | lbraun/applied_mathematics | R | false | false | 1,266 | r | headsize<-source("c:\\allwork\\rsplus\\chap8headsize.dat")$value
#
headsize.std<-sweep(headsize,2,sqrt(apply(headsize,2,var)),FUN="/")
#
#
headsize1<-headsize.std[,1:2]
headsize2<-headsize.std[,3:4]
r11<-cor(headsize1)
r22<-cor(headsize2)
r12<-c(cor(headsize1[,1],headsize2[,1]),cor(headsize1[,1],headsize2[,2]),
cor(hea... |
options(shiny.maxRequestSize=50*1024^2)
library(shiny)
library(broom)
library(gt)
library(tidyverse)
library(shinythemes)
ui <- fluidPage(theme = shinytheme("cerulean"),
# Application title
titlePanel("Linear Modelling"),
# Sidebar with a slider input for number of bins
sidebarLayout(
side... | /app.R | no_license | PeerChristensen/eLMo | R | false | false | 4,283 | r | options(shiny.maxRequestSize=50*1024^2)
library(shiny)
library(broom)
library(gt)
library(tidyverse)
library(shinythemes)
ui <- fluidPage(theme = shinytheme("cerulean"),
# Application title
titlePanel("Linear Modelling"),
# Sidebar with a slider input for number of bins
sidebarLayout(
side... |
rm(list=ls())
## NOTE: TO RUN THE SEARCH CODE
## YOU WILL HAVE TO USE YOUR OWN API ACCESS INFO
library("XML")
library("RCurl")
search.amazon <- function(Keywords, SearchIndex = 'All', AWSAccessKeyId, AWSsecretkey, AssociateTag, ResponseGroup, Operation = 'ItemSearch'){
library(digest)
library(RCurl)
base.html.... | /lib/api_access.r | no_license | lleiou/A-Movie-For-You | R | false | false | 10,036 | r | rm(list=ls())
## NOTE: TO RUN THE SEARCH CODE
## YOU WILL HAVE TO USE YOUR OWN API ACCESS INFO
library("XML")
library("RCurl")
search.amazon <- function(Keywords, SearchIndex = 'All', AWSAccessKeyId, AWSsecretkey, AssociateTag, ResponseGroup, Operation = 'ItemSearch'){
library(digest)
library(RCurl)
base.html.... |
chebyshev.t.recurrences <- function( n, normalized=FALSE )
{
###
### This function returns a data frame with n+1 rows and four columns
### containing the coefficients c, d, e and f of the recurrence relations
### for the order k Chebyshev polynomial of the first kind, Tk(x),
### and for orders k=0,1,...,n
###
#... | /R/chebyshev.t.recurrences.R | no_license | cran/orthopolynom | R | false | false | 1,594 | r | chebyshev.t.recurrences <- function( n, normalized=FALSE )
{
###
### This function returns a data frame with n+1 rows and four columns
### containing the coefficients c, d, e and f of the recurrence relations
### for the order k Chebyshev polynomial of the first kind, Tk(x),
### and for orders k=0,1,...,n
###
#... |
source("loader/monthly_new_editor_article_creators.R")
months = load_monthly_new_editor_article_creators(reload=T)
months$registration_month = as.Date(months$registration_month)
normalized.relative.funnel = rbind(
months[,
list(
wiki,
registration_month,
transition = "New editors / Registered users",
p... | /R/new_editors/exploration.enwiki.R | permissive | halfak/Wikipedia-article-creation-research | R | false | false | 3,110 | r | source("loader/monthly_new_editor_article_creators.R")
months = load_monthly_new_editor_article_creators(reload=T)
months$registration_month = as.Date(months$registration_month)
normalized.relative.funnel = rbind(
months[,
list(
wiki,
registration_month,
transition = "New editors / Registered users",
p... |
# rankhospital.R
# Moaeed Sajid
# V1 1/12/17
# Args (state, outcome, num = "best)
# Return hospital for a particular chosen state, outcome and position
rankhospital <- function(state, outcome, num = "best") {
#install.packages('plyr')
#library('plyr')
##Task - Read outcome da... | /rankhospital.R | no_license | moaeedsajid/HospitalAssignment | R | false | false | 2,695 | r | # rankhospital.R
# Moaeed Sajid
# V1 1/12/17
# Args (state, outcome, num = "best)
# Return hospital for a particular chosen state, outcome and position
rankhospital <- function(state, outcome, num = "best") {
#install.packages('plyr')
#library('plyr')
##Task - Read outcome da... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{DT_apple}
\alias{DT_apple}
\title{Quarterly reported EBIT from Apple as data.table object from 1995
to 2020.}
\format{
A quarterly data.table object from 1995 to 2020
}
\usage{
data(DT_apple)
}
\description{
Quarte... | /man/DT_apple.Rd | permissive | thfuchs/tsRNN | R | false | true | 410 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{DT_apple}
\alias{DT_apple}
\title{Quarterly reported EBIT from Apple as data.table object from 1995
to 2020.}
\format{
A quarterly data.table object from 1995 to 2020
}
\usage{
data(DT_apple)
}
\description{
Quarte... |
options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
#
# Test out the revised model.matrix code
#
test1 <- data.frame(time= c(9, 3,1,1,6,6,8),
status=c(1,NA,1,0,1,1,0),
x= c... | /Tools/DECoN-master/Windows/packrat/lib-R/survival/tests/model.matrix.R | permissive | robinwijngaard/TFM_code | R | false | false | 2,276 | r | options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
#
# Test out the revised model.matrix code
#
test1 <- data.frame(time= c(9, 3,1,1,6,6,8),
status=c(1,NA,1,0,1,1,0),
x= c... |
.ts <- c("id", "name", "description", "status", "app", "type",
"created_by", "created_time", "executed_by", "start_time", "end_time",
"execution_status", "price", "inputs", "outputs", "project",
"batch", "batch_input", "batch_by", "parent", "batch_group",
"errors", "warnings")
Task... | /R/class-task.R | permissive | mlrdk/sevenbridges-r | R | false | false | 15,403 | r | .ts <- c("id", "name", "description", "status", "app", "type",
"created_by", "created_time", "executed_by", "start_time", "end_time",
"execution_status", "price", "inputs", "outputs", "project",
"batch", "batch_input", "batch_by", "parent", "batch_group",
"errors", "warnings")
Task... |
#' Community detection, label propagation
#'
#' An implementation of community detection by label propagation in an undirected weighted graph based on
#' Raghavan, Albert, Kumara. Phys Rev E 76, 036106 (2007)
#'
#' @param unique_edges a data frame with columns a, b, weight representing the connections between nodes.
#'... | /R/inferCommunitiesLP.R | permissive | sverchkov/CommunityInference | R | false | false | 2,609 | r | #' Community detection, label propagation
#'
#' An implementation of community detection by label propagation in an undirected weighted graph based on
#' Raghavan, Albert, Kumara. Phys Rev E 76, 036106 (2007)
#'
#' @param unique_edges a data frame with columns a, b, weight representing the connections between nodes.
#'... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/arrange_xdf.R
\name{arrange_.RxFileData}
\alias{arrange}
\alias{arrange_}
\alias{arrange_.RxFileData}
\title{Arrange the rows in an Xdf file}
\usage{
\method{arrange_}{RxFileData}(.data, ..., .outFile, .rxArgs, .dots)
}
\arguments{
\item{...}... | /man/arrange.Rd | no_license | yueguoguo/dplyrXdf | R | false | true | 1,425 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/arrange_xdf.R
\name{arrange_.RxFileData}
\alias{arrange}
\alias{arrange_}
\alias{arrange_.RxFileData}
\title{Arrange the rows in an Xdf file}
\usage{
\method{arrange_}{RxFileData}(.data, ..., .outFile, .rxArgs, .dots)
}
\arguments{
\item{...}... |
#### filter for values, which are Inf (infinite)
example.data <- c("1", "2", "3", "Hello", "5")
# create Nas
example.data <- as.numeric(example.data)
# find NAs
is.na(example.data)
# find non NAs
!is.na(example.data)
| /0_Code_sniplets/Filter/Find_NAs.R | no_license | DrBuschle/R-knowledge | R | false | false | 225 | r | #### filter for values, which are Inf (infinite)
example.data <- c("1", "2", "3", "Hello", "5")
# create Nas
example.data <- as.numeric(example.data)
# find NAs
is.na(example.data)
# find non NAs
!is.na(example.data)
|
####################################################################################################
### The single server simulator system
#
# - to be used with the discrete event simulator
# - analogous to how the montyHall system was used by the monte carlo simulator
#
#
### To use the single server sim... | /2460/DES/singleServerSystem.R | no_license | MrRobot245/Cis-2460 | R | false | false | 11,297 | r | ####################################################################################################
### The single server simulator system
#
# - to be used with the discrete event simulator
# - analogous to how the montyHall system was used by the monte carlo simulator
#
#
### To use the single server sim... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sesh.R
\name{read_sesh}
\alias{read_sesh}
\title{Read a saved CSV to see critical package info.}
\usage{
read_sesh(path)
}
\arguments{
\item{path}{Valid path to a sesh saved CSV.}
}
\description{
Read a saved CSV to see critical package info.... | /man/read_sesh.Rd | no_license | nathancday/sesh | R | false | true | 323 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sesh.R
\name{read_sesh}
\alias{read_sesh}
\title{Read a saved CSV to see critical package info.}
\usage{
read_sesh(path)
}
\arguments{
\item{path}{Valid path to a sesh saved CSV.}
}
\description{
Read a saved CSV to see critical package info.... |
# General two group model
p <- 2 #Number of baseline covariates
# Set true values of coefficients
b0_or <- rep(0,p+1) # E(X,Y_base)
b1_or <- rep(0,2*p+4) # log(delta) | int, X, lagY, log(A) X*log(A) lagY*log(A)
b2_or <- rep(0,2*p+4) # Y | int, X, lagY, D-6, X*(D-6), lagY*(D-6)... | /binary_covariate/SimDesign.R | no_license | qianguan/BayesianPolicySearch | R | false | false | 1,451 | r | # General two group model
p <- 2 #Number of baseline covariates
# Set true values of coefficients
b0_or <- rep(0,p+1) # E(X,Y_base)
b1_or <- rep(0,2*p+4) # log(delta) | int, X, lagY, log(A) X*log(A) lagY*log(A)
b2_or <- rep(0,2*p+4) # Y | int, X, lagY, D-6, X*(D-6), lagY*(D-6)... |
testlist <- list(hi = 0, lo = 9.83190635224081e-322, mu = 0, sig = 0)
result <- do.call(gjam:::tnormRcpp,testlist)
str(result) | /gjam/inst/testfiles/tnormRcpp/libFuzzer_tnormRcpp/tnormRcpp_valgrind_files/1610044728-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 126 | r | testlist <- list(hi = 0, lo = 9.83190635224081e-322, mu = 0, sig = 0)
result <- do.call(gjam:::tnormRcpp,testlist)
str(result) |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat_echo.R
\name{stat_echo}
\alias{stat_echo}
\title{Replicate copies of the original data for a blur/echo effect}
\usage{
stat_echo(mapping = NULL, data = NULL, geom = "point",
position = "identity", ..., na.rm = FALSE, n = 3,
alpha_fac... | /man/stat_echo.Rd | permissive | coolbutuseless/ggecho | R | false | true | 2,720 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat_echo.R
\name{stat_echo}
\alias{stat_echo}
\title{Replicate copies of the original data for a blur/echo effect}
\usage{
stat_echo(mapping = NULL, data = NULL, geom = "point",
position = "identity", ..., na.rm = FALSE, n = 3,
alpha_fac... |
## Working directory:
setwd("D:/OneDrive - Inversiones Internacionales Grupo Sura S.A/Argentina/valoración/");options(warn=-1, scipen=100)
rm(list=lsf.str());rm(list=ls(all=TRUE))
## Bloomber directory:
bb_dir <- "X:/SIM/SOLUCIONES/ARGENTINA/input/"
curr_date <- as.Date("31082018", "%d%m%Y")
# curr_date <- Sys.Dat... | /__main__.R | no_license | veldanie/ProcesoValoracionAR | R | false | false | 1,569 | r |
## Working directory:
setwd("D:/OneDrive - Inversiones Internacionales Grupo Sura S.A/Argentina/valoración/");options(warn=-1, scipen=100)
rm(list=lsf.str());rm(list=ls(all=TRUE))
## Bloomber directory:
bb_dir <- "X:/SIM/SOLUCIONES/ARGENTINA/input/"
curr_date <- as.Date("31082018", "%d%m%Y")
# curr_date <- Sys.Dat... |
frss_apikey <- readLines("resources/frss_apikey.txt") #to be specifid
maintext <- function(inputURL, host="http://frss.schloegl.net/",parsed=TRUE){
library(XML)
library(rjson)
requestURL <- paste(host,"makefulltextfeed.php?key=",frss_apikey,"&format=json&url=", inputURL,sep="")
output <- fromJSON(file... | /frss_function.R | no_license | supersambo/r_functions | R | false | false | 812 | r | frss_apikey <- readLines("resources/frss_apikey.txt") #to be specifid
maintext <- function(inputURL, host="http://frss.schloegl.net/",parsed=TRUE){
library(XML)
library(rjson)
requestURL <- paste(host,"makefulltextfeed.php?key=",frss_apikey,"&format=json&url=", inputURL,sep="")
output <- fromJSON(file... |
library(rvest)
scrape_sample_annot <- function(gse_id){
gds_search <- rentrez::entrez_search(db="gds", term=paste0(gse_id, "[ACCN] AND gsm[ETYP]"))
search_res <- rentrez::entrez_summary(db="gds", id=gds_search$ids)
res <- lapply(search_res, unlist)
res <- plyr::ldply(res)
gsm_ids <- res$accession
... | /cemitooldb/R/deprecate.R | no_license | pedrostrusso/cemitooldb | R | false | false | 881 | r | library(rvest)
scrape_sample_annot <- function(gse_id){
gds_search <- rentrez::entrez_search(db="gds", term=paste0(gse_id, "[ACCN] AND gsm[ETYP]"))
search_res <- rentrez::entrez_summary(db="gds", id=gds_search$ids)
res <- lapply(search_res, unlist)
res <- plyr::ldply(res)
gsm_ids <- res$accession
... |
# ==============================================================================
# Functions for working with FILTERS for the selection of nodes and edges in
# networks, including operations to import and export filters. In the Cytoscape
# user interface, filters are managed in the Select tab of the Control Panel.
#
#... | /R/Filters.R | permissive | kumonismo/RCy3 | R | false | false | 17,697 | r | # ==============================================================================
# Functions for working with FILTERS for the selection of nodes and edges in
# networks, including operations to import and export filters. In the Cytoscape
# user interface, filters are managed in the Select tab of the Control Panel.
#
#... |
testlist <- list(mu = -5.31401037247976e+303, var = 0)
result <- do.call(metafolio:::est_beta_params,testlist)
str(result) | /metafolio/inst/testfiles/est_beta_params/libFuzzer_est_beta_params/est_beta_params_valgrind_files/1612989111-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 122 | r | testlist <- list(mu = -5.31401037247976e+303, var = 0)
result <- do.call(metafolio:::est_beta_params,testlist)
str(result) |
#' Print function
#'
#' @param x An object of class diversityEstimates
#' @param ... other arguments to be passed to print
#' @return NULL
#'
#' @export
print.diversityEstimates <- function(x, ...) {
dv <- x
cat("An object of class `diversityEstimates` with the following elements:\n")
sapply(1:length(names(dv))... | /R/s3functions.R | no_license | paulinetrinh/DivNet | R | false | false | 16,026 | r |
#' Print function
#'
#' @param x An object of class diversityEstimates
#' @param ... other arguments to be passed to print
#' @return NULL
#'
#' @export
print.diversityEstimates <- function(x, ...) {
dv <- x
cat("An object of class `diversityEstimates` with the following elements:\n")
sapply(1:length(names(dv))... |
#' Ordered bar plot
#'
#' @description
#' p.col_ord make a ordered bar plot.
#'
#' @param data a dataframe
#' @param xaxis x axis data
#' @param yaxis y axis data
#' @param ybreaks number of y axis breaks (default=10)
#' @param percent If TRUE y axis in percent (default=F)
#' @param dec If TRUE serie come be decrescent... | /R/pcolord.R | no_license | jvg0mes/metools | R | false | false | 8,983 | r | #' Ordered bar plot
#'
#' @description
#' p.col_ord make a ordered bar plot.
#'
#' @param data a dataframe
#' @param xaxis x axis data
#' @param yaxis y axis data
#' @param ybreaks number of y axis breaks (default=10)
#' @param percent If TRUE y axis in percent (default=F)
#' @param dec If TRUE serie come be decrescent... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Gbeta1.R
\name{fitMcGBB}
\alias{fitMcGBB}
\title{Fitting the McDonald Generalized Beta Binomial distribution when binomial
random variable, frequency and shape parameters are given}
\usage{
fitMcGBB(x,obs.freq,a,b,c)
}
\arguments{
\item{x}{v... | /man/fitMcGBB.Rd | no_license | cran/fitODBOD | R | false | true | 3,615 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Gbeta1.R
\name{fitMcGBB}
\alias{fitMcGBB}
\title{Fitting the McDonald Generalized Beta Binomial distribution when binomial
random variable, frequency and shape parameters are given}
\usage{
fitMcGBB(x,obs.freq,a,b,c)
}
\arguments{
\item{x}{v... |
library(Biostrings)
library(systemPipeR)
dna_object <- readDNAStringSet(file.path(getwd(), "datasets","ch2", "arabidopsis_chloroplast.fa"))
predicted_orfs <- predORF(dna_object, n = 'all', type = 'gr', mode='ORF', strand = 'both', longest_disjoint = TRUE)
predicted_orfs
bases <- c("A", "C", "T", "G")
raw_seq_string... | /Chapter02/recipe3.R | permissive | PacktPublishing/R-Bioinformatics-Cookbook | R | false | false | 1,635 | r | library(Biostrings)
library(systemPipeR)
dna_object <- readDNAStringSet(file.path(getwd(), "datasets","ch2", "arabidopsis_chloroplast.fa"))
predicted_orfs <- predORF(dna_object, n = 'all', type = 'gr', mode='ORF', strand = 'both', longest_disjoint = TRUE)
predicted_orfs
bases <- c("A", "C", "T", "G")
raw_seq_string... |
% Generated by roxygen2 (4.0.2): do not edit by hand
\name{convert_to_unit}
\alias{convert_to_unit}
\title{Convert timings to different units.}
\usage{
convert_to_unit(x, unit = c("ns", "us", "ms", "s", "t", "hz", "khz", "mhz",
"eps", "f"))
}
\arguments{
\item{x}{An \code{microthrow_exception $ warning} object.}
\it... | /man/convert_to_unit.Rd | no_license | rgrannell1/microbenchmark | R | false | false | 1,075 | rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{convert_to_unit}
\alias{convert_to_unit}
\title{Convert timings to different units.}
\usage{
convert_to_unit(x, unit = c("ns", "us", "ms", "s", "t", "hz", "khz", "mhz",
"eps", "f"))
}
\arguments{
\item{x}{An \code{microthrow_exception $ warning} object.}
\it... |
library(brainflow)
board_id <- brainflow_python$BoardIds$SYNTHETIC_BOARD$value
sampling_rate <- brainflow_python$BoardShim$get_sampling_rate(board_id)
nfft <- brainflow_python$DataFilter$get_nearest_power_of_two(sampling_rate)
params <- brainflow_python$BrainFlowInputParams()
board_shim <- brainflow_python$BoardShim(b... | /r_package/examples/eeg_metrics.R | permissive | neuroidss/brainflow | R | false | false | 1,090 | r | library(brainflow)
board_id <- brainflow_python$BoardIds$SYNTHETIC_BOARD$value
sampling_rate <- brainflow_python$BoardShim$get_sampling_rate(board_id)
nfft <- brainflow_python$DataFilter$get_nearest_power_of_two(sampling_rate)
params <- brainflow_python$BrainFlowInputParams()
board_shim <- brainflow_python$BoardShim(b... |
rm(list=ls())
source('~/.Rprofile')
source('~/Public/DropBox/GitHub/R-Adhesion/Tracking.R')
source('~/Public/DropBox/GitHub/R-Adhesion/OOTracking.R')
setwd('/Users/jaywarrick/Documents/MMB/Projects/Adhesion/R/Testing')
load(file="20140911.Rdata")
trackList <- TrackList$new(trackList)
for(track in trackList$tracks)
{... | /OOTrackingAnalysis.R | no_license | jaywarrick/R-Adhesion | R | false | false | 8,425 | r | rm(list=ls())
source('~/.Rprofile')
source('~/Public/DropBox/GitHub/R-Adhesion/Tracking.R')
source('~/Public/DropBox/GitHub/R-Adhesion/OOTracking.R')
setwd('/Users/jaywarrick/Documents/MMB/Projects/Adhesion/R/Testing')
load(file="20140911.Rdata")
trackList <- TrackList$new(trackList)
for(track in trackList$tracks)
{... |
#' @import magrittr
utils::globalVariables(c(".","%>%"))
| /R/gVar.R | no_license | cran/CollapseLevels | R | false | false | 61 | r | #' @import magrittr
utils::globalVariables(c(".","%>%"))
|
# NORTH CAROLINA
library(tidyverse)
full_2018 <- read_csv("~/Downloads/2663437.csv")
View(full_2018)
subset_2018 <- full_2018[6517:10447,]
# TEMPERATURE
temp <- mean(as.numeric(subset_2018$HourlyDryBulbTemperature), na.rm = TRUE)
summary(temp)
# HUMIDITY
humidity <- mean(subset_2018$HourlyRelativeHumidity, na.r... | /state averages/North Carolina.R | no_license | s-kumar72/sees-mm | R | false | false | 452 | r | # NORTH CAROLINA
library(tidyverse)
full_2018 <- read_csv("~/Downloads/2663437.csv")
View(full_2018)
subset_2018 <- full_2018[6517:10447,]
# TEMPERATURE
temp <- mean(as.numeric(subset_2018$HourlyDryBulbTemperature), na.rm = TRUE)
summary(temp)
# HUMIDITY
humidity <- mean(subset_2018$HourlyRelativeHumidity, na.r... |
## summary table per gene
## - global mean, min, max
## - population mean, min, max
## - ANOVA F statistic
## - unadjusted P, adjusted P (FDR)
library(dplyr)
library(fst)
kgp.p3<-read.table("data/integrated_call_samples_v3.20130502.ALL.panel", header = T, sep = "\t", as.is = T, col.names = c("ID","sub_pop","pop","gend... | /scripts/prep.R | permissive | bch-gnome/WEScover | R | false | false | 4,663 | r | ## summary table per gene
## - global mean, min, max
## - population mean, min, max
## - ANOVA F statistic
## - unadjusted P, adjusted P (FDR)
library(dplyr)
library(fst)
kgp.p3<-read.table("data/integrated_call_samples_v3.20130502.ALL.panel", header = T, sep = "\t", as.is = T, col.names = c("ID","sub_pop","pop","gend... |
# load a time series
data(AirPassengers)
str(AirPassengers)
class(AirPassengers)
frequency(AirPassengers)
summary(AirPassengers)
plot(AirPassengers)
abline(reg=lm(AirPassengers~time(AirPassengers)))
cycle(AirPassengers)
plot(aggregate(AirPassengers,FUN=mean))
boxplot(AirPassengers~cycle(AirPasseng... | /forescast_timeseries.R | no_license | karafede/forecast_ARIMA | R | false | false | 1,159 | r |
# load a time series
data(AirPassengers)
str(AirPassengers)
class(AirPassengers)
frequency(AirPassengers)
summary(AirPassengers)
plot(AirPassengers)
abline(reg=lm(AirPassengers~time(AirPassengers)))
cycle(AirPassengers)
plot(aggregate(AirPassengers,FUN=mean))
boxplot(AirPassengers~cycle(AirPasseng... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/soccer.R
\docType{data}
\name{soccer}
\alias{soccer}
\title{Number of cards given for each referee-player pair in soccer.}
\format{
A data frame with 146,028 rows and 26 variables:
\describe{
\item{playerShort}{short player ID}
\item{play... | /man/soccer.Rd | no_license | mverseanalysis/mverse | R | false | true | 3,090 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/soccer.R
\docType{data}
\name{soccer}
\alias{soccer}
\title{Number of cards given for each referee-player pair in soccer.}
\format{
A data frame with 146,028 rows and 26 variables:
\describe{
\item{playerShort}{short player ID}
\item{play... |
% Generated by roxygen2 (4.0.0): do not edit by hand
\name{coo.plot}
\alias{coo.plot}
\alias{coo.plot.default}
\alias{coo.plot.ldk}
\title{Plots a single shape}
\usage{
coo.plot(coo, ...)
\method{coo.plot}{default}(coo, xlim, ylim, border = "#333333", col = NA,
lwd = 1, lty = 1, points = FALSE, first.point = TRUE,
... | /man/coo.plot.Rd | no_license | raz1/Momocs | R | false | false | 2,519 | rd | % Generated by roxygen2 (4.0.0): do not edit by hand
\name{coo.plot}
\alias{coo.plot}
\alias{coo.plot.default}
\alias{coo.plot.ldk}
\title{Plots a single shape}
\usage{
coo.plot(coo, ...)
\method{coo.plot}{default}(coo, xlim, ylim, border = "#333333", col = NA,
lwd = 1, lty = 1, points = FALSE, first.point = TRUE,
... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-movie.R
\docType{data}
\name{movie_87}
\alias{movie_87}
\title{Basquiat}
\format{igraph object}
\source{
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
https://www.imdb.com/title/tt0115632
}
\usage{
movi... | /man/movie_87.Rd | permissive | kjhealy/networkdata | R | false | true | 994 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-movie.R
\docType{data}
\name{movie_87}
\alias{movie_87}
\title{Basquiat}
\format{igraph object}
\source{
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3
https://www.imdb.com/title/tt0115632
}
\usage{
movi... |
source("incl/start.R")
message("*** Futures - lazy ...")
strategies <- c("batchtools_local")
## CRAN processing times:
## On Windows 32-bit, don't run these tests
if (!fullTest && isWin32) strategies <- character(0L)
for (strategy in strategies) {
mprintf("- plan('%s') ...\n", strategy)
plan(strategy)
a <- 4... | /tests/future,lazy.R | no_license | pythseq/future.batchtools | R | false | false | 1,227 | r | source("incl/start.R")
message("*** Futures - lazy ...")
strategies <- c("batchtools_local")
## CRAN processing times:
## On Windows 32-bit, don't run these tests
if (!fullTest && isWin32) strategies <- character(0L)
for (strategy in strategies) {
mprintf("- plan('%s') ...\n", strategy)
plan(strategy)
a <- 4... |
library(dplyr)
ss.bounds <- readRDS("ss.bounds.rds")
alpha <- 0.025
method <- 'wald'
scenario <- 5
param <- 1
anal_type <- "mice"
ss <- ss.bounds%>%
dplyr::filter(method == "wald", scenario.id == scenario)
do_val <- 0.15
x1 <- parallel::mclapply(X = 1:10000,
mc.cores = parallel::detect... | /sim_pgms/wald/do15/2xcontH0_sc5_do15_mice.R | no_license | yuliasidi/nibinom_apply | R | false | false | 3,330 | r | library(dplyr)
ss.bounds <- readRDS("ss.bounds.rds")
alpha <- 0.025
method <- 'wald'
scenario <- 5
param <- 1
anal_type <- "mice"
ss <- ss.bounds%>%
dplyr::filter(method == "wald", scenario.id == scenario)
do_val <- 0.15
x1 <- parallel::mclapply(X = 1:10000,
mc.cores = parallel::detect... |
\docType{package}
\name{WMTools-package}
\alias{WMTools}
\alias{WMTools-package}
\title{Tools for simulating activations in Warmachine(R)}
\description{
Simulate ranged and melee attacks in the game of
Warmachine(R)
}
\details{
\tabular{ll}{ Package: \tab WMTools \cr Type: \tab Package
\cr Version: \tab 0.1 \cr Date: \... | /man/WMTools-package.Rd | permissive | CSJCampbell/WMTools | R | false | false | 1,854 | rd | \docType{package}
\name{WMTools-package}
\alias{WMTools}
\alias{WMTools-package}
\title{Tools for simulating activations in Warmachine(R)}
\description{
Simulate ranged and melee attacks in the game of
Warmachine(R)
}
\details{
\tabular{ll}{ Package: \tab WMTools \cr Type: \tab Package
\cr Version: \tab 0.1 \cr Date: \... |
# tSNE javascript------------------------------------------------------
div(id = "tsne_js",
#useShinyjs(),
#extendShinyjs(script = source(file.path("js", "tsne.js"), local = TRUE)$value),
fluidRow(
numericInput('inNumIter', label = "Number of Iterations:", value = 100,
min = 10,... | /ui_files/tSNE_JS.R | no_license | asRodelgo/shinyTCMN | R | false | false | 561 | r | # tSNE javascript------------------------------------------------------
div(id = "tsne_js",
#useShinyjs(),
#extendShinyjs(script = source(file.path("js", "tsne.js"), local = TRUE)$value),
fluidRow(
numericInput('inNumIter', label = "Number of Iterations:", value = 100,
min = 10,... |
library(caret)
library(RSNNS)
set.seed(1)
data(iris)
#将数据顺序打乱
iris = iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
#定义网络输入
irisValues= iris[,1:4]
#定义网络输出，并将数据进行格式转换,将类别变量处理成向量形式
irisTargets = decodeClassLabels(iris[,5])
#从中划分出训练样本和检验样本
#splitForTrainingAndTest将输入值和目标值拆分为训练集和测试集。 得到的是列表
#测试集从数据的结尾获取。如果要对数... | /project/neural_net.R | no_license | zhengfuli/Statistical-Learning-in-R | R | false | false | 1,892 | r | library(caret)
library(RSNNS)
set.seed(1)
data(iris)
#将数据顺序打乱
iris = iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
#定义网络输入
irisValues= iris[,1:4]
#定义网络输出，并将数据进行格式转换,将类别变量处理成向量形式
irisTargets = decodeClassLabels(iris[,5])
#从中划分出训练样本和检验样本
#splitForTrainingAndTest将输入值和目标值拆分为训练集和测试集。 得到的是列表
#测试集从数据的结尾获取。如果要对数... |
# write summary to file
writeSummary <- function(fittedModel, parEstFile = NULL) {
if (!(missing(parEstFile) || is.null(parEstFile))) {
sink(file = parEstFile, type = "o")
try({
print(summary(fittedModel))
# cat("\n\n#################################\n#### Group Parameter Estimates\n")
... | /R/writeSummaryToFile.R | no_license | mariusbarth/TreeBUGS | R | false | false | 1,112 | r |
# write summary to file
writeSummary <- function(fittedModel, parEstFile = NULL) {
if (!(missing(parEstFile) || is.null(parEstFile))) {
sink(file = parEstFile, type = "o")
try({
print(summary(fittedModel))
# cat("\n\n#################################\n#### Group Parameter Estimates\n")
... |
#######################
#Exercice 3
y0 = seq(0, 10, by=2)
y0
y1 = seq(2, 18, 2)
y1
y2 = rep(4, 20)
y2
y3 = seq(0, 9.5, 0.5)
y3
#Extraction y3
y3[3]
y3[-3]
#Comparaison
matrix(y3, nrow=2)
matrix(y3, byrow=TRUE)
#Matrices
A = matrix(seq(1:12), nrow=4, byrow=TRUE)
B = matrix(seq(1:12), nrow=4)
#... | /TP1/TP1.R | no_license | LaurenRolan/AnaDon | R | false | false | 860 | r | #######################
#Exercice 3
y0 = seq(0, 10, by=2)
y0
y1 = seq(2, 18, 2)
y1
y2 = rep(4, 20)
y2
y3 = seq(0, 9.5, 0.5)
y3
#Extraction y3
y3[3]
y3[-3]
#Comparaison
matrix(y3, nrow=2)
matrix(y3, byrow=TRUE)
#Matrices
A = matrix(seq(1:12), nrow=4, byrow=TRUE)
B = matrix(seq(1:12), nrow=4)
#... |
library(readr)
library(ggplot2)
library(ggthemes)
library(RColorBrewer)
ckd_beta <- read_csv("ckd_beta.txt")
#not a bad plot
label_colors <- brewer.pal(n = 4,name = "Paired")[c(2,4)]
ggplot(ckd_beta) +
geom_point(aes(MDS1,MDS2,color=DiseaseState),size=3) +
theme_bw() +
annotate("text",label="Stress=0.13",x=Inf... | /rat-scatter.R | no_license | englandwe/dataviz-examples | R | false | false | 1,058 | r | library(readr)
library(ggplot2)
library(ggthemes)
library(RColorBrewer)
ckd_beta <- read_csv("ckd_beta.txt")
#not a bad plot
label_colors <- brewer.pal(n = 4,name = "Paired")[c(2,4)]
ggplot(ckd_beta) +
geom_point(aes(MDS1,MDS2,color=DiseaseState),size=3) +
theme_bw() +
annotate("text",label="Stress=0.13",x=Inf... |
# Process data.
library(ichseg)
library(here)
library(dplyr)
library(tidyr)
library(neurobase)
library(readr)
library(extrantsr)
# root_dir <- "~/CLEAR_PITCH"
root_dir = here::here()
# Process data.
# Change batch_type if want to use original data
# Original data was used to train model
batches = c("batch", "test... | /programs/process.R | no_license | muschellij2/clear_pitch | R | false | false | 2,558 | r | # Process data.
library(ichseg)
library(here)
library(dplyr)
library(tidyr)
library(neurobase)
library(readr)
library(extrantsr)
# root_dir <- "~/CLEAR_PITCH"
root_dir = here::here()
# Process data.
# Change batch_type if want to use original data
# Original data was used to train model
batches = c("batch", "test... |
# Source file to plot the percent loss of NPV from applying harvest rate trajectory from
# each assumed (columns) into each true state of nature (rows).
# Presently used for main manuscript figure
# Last Updated, 2/27/2017
rm(list = ls())
wd <- '/Users/essing/Dropbox/Desktop/Rcode/EggPredationModel'
setwd(wd)
req... | /R/src/Plot_Color_Map_NPV_herring.R | no_license | tessington/PNAS-EBFM | R | false | false | 5,905 | r | # Source file to plot the percent loss of NPV from applying harvest rate trajectory from
# each assumed (columns) into each true state of nature (rows).
# Presently used for main manuscript figure
# Last Updated, 2/27/2017
rm(list = ls())
wd <- '/Users/essing/Dropbox/Desktop/Rcode/EggPredationModel'
setwd(wd)
req... |
is.installed <- function(package) {
is.element(package, installed.packages()[, 1])
}
utils_starts_with <- function(lhs, rhs) {
if (nchar(lhs) < nchar(rhs)) {
return(FALSE)
}
identical(substring(lhs, 1, nchar(rhs)), rhs)
}
aliased_path <- function(path) {
home <- path.expand("~/")
if (utils_starts_with... | /R/utils.R | permissive | yitao-li/sparklyr | R | false | false | 15,199 | r | is.installed <- function(package) {
is.element(package, installed.packages()[, 1])
}
utils_starts_with <- function(lhs, rhs) {
if (nchar(lhs) < nchar(rhs)) {
return(FALSE)
}
identical(substring(lhs, 1, nchar(rhs)), rhs)
}
aliased_path <- function(path) {
home <- path.expand("~/")
if (utils_starts_with... |
Sys.time()
# Load packages
library(gdata)
library(pheatmap)
library(RColorBrewer)
##############################################################################
# Test arguments
##############################################################################
prefix='23_01_pca1_mergingNEW2_'
outdir='../carsten_cyto... | /Nowicka2017/02_heatmaps.R | no_license | yhoang/drfz | R | false | false | 22,187 | r |
Sys.time()
# Load packages
library(gdata)
library(pheatmap)
library(RColorBrewer)
##############################################################################
# Test arguments
##############################################################################
prefix='23_01_pca1_mergingNEW2_'
outdir='../carsten_cyto... |
# hospitalization data for St. Louis Metro
# =============================================================================
# load data
stl_hosp <- read_csv("data/MO_HEALTH_Covid_Tracking/data/metro/stl_hospital.csv")
# =============================================================================
# define colors
pa... | /source/workflow/20_stl_hospital_plots.R | permissive | slu-openGIS/covid_daily_viz | R | false | false | 19,158 | r | # hospitalization data for St. Louis Metro
# =============================================================================
# load data
stl_hosp <- read_csv("data/MO_HEALTH_Covid_Tracking/data/metro/stl_hospital.csv")
# =============================================================================
# define colors
pa... |
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(mlbstatsR)
## ----echo=FALSE-----------------------------------------------------------... | /inst/doc/mlbstatsR.R | no_license | cran/mlbstatsR | R | false | false | 1,906 | r | ## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(mlbstatsR)
## ----echo=FALSE-----------------------------------------------------------... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chart_amCandlestick.R
\name{amCandlestick}
\alias{amCandlestick}
\title{Plotting candlestick chart using rAmCharts}
\usage{
amCandlestick(data, xlab = "", ylab = "", horiz = FALSE,
positiveColor = "#7f8da9", negativeColor = "#db4c3c"... | /man/amCandlestick.Rd | no_license | aghozlane/rAmCharts | R | false | true | 3,847 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chart_amCandlestick.R
\name{amCandlestick}
\alias{amCandlestick}
\title{Plotting candlestick chart using rAmCharts}
\usage{
amCandlestick(data, xlab = "", ylab = "", horiz = FALSE,
positiveColor = "#7f8da9", negativeColor = "#db4c3c"... |
library(dplyr)
library(lubridate)
mydata <- read.csv("household_power_consumption.txt", sep=";")
startDate <- ymd("2007-02-01")
endDate <- ymd("2007-02-03")
mydata <- mutate(mydata, DateTime = dmy_hms(paste(as.character(Date), " ", as.character(Time))))
mydata <- filter(mydata, DateTime >= startDate)
mydata <- filte... | /plot3.R | no_license | lcheeme1/ExData_Plotting1 | R | false | false | 914 | r | library(dplyr)
library(lubridate)
mydata <- read.csv("household_power_consumption.txt", sep=";")
startDate <- ymd("2007-02-01")
endDate <- ymd("2007-02-03")
mydata <- mutate(mydata, DateTime = dmy_hms(paste(as.character(Date), " ", as.character(Time))))
mydata <- filter(mydata, DateTime >= startDate)
mydata <- filte... |
####Week 2 Statistical Linear Regression MOdels
##Least squares is an estimation tool
##Consider developing a probabilistic model for linear regression
## Yi=b0+b1*Xi+Ei
## E are assumed iid N(0,sigmal^2)
## Error term -- maybe considered as missing variables in model
##Note
##E[Yi|Xi=xi]=mui=b0+b1*xi... | /Week 2/1_StatisticalLinearRegressionModel.R | no_license | hd1812/Regression_Models | R | false | false | 344 | r | ####Week 2 Statistical Linear Regression MOdels
##Least squares is an estimation tool
##Consider developing a probabilistic model for linear regression
## Yi=b0+b1*Xi+Ei
## E are assumed iid N(0,sigmal^2)
## Error term -- maybe considered as missing variables in model
##Note
##E[Yi|Xi=xi]=mui=b0+b1*xi... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/biopaxToCytoscape.R
\name{getPublicationRefs}
\alias{getPublicationRefs}
\title{getPublicationRefs()}
\usage{
getPublicationRefs(df, tdf)
}
\arguments{
\item{df}{the main biopax data frame}
\item{tdf}{a subsegment of the biopax data frame fr... | /man/getPublicationRefs.Rd | no_license | biodev/packageDir | R | false | true | 535 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/biopaxToCytoscape.R
\name{getPublicationRefs}
\alias{getPublicationRefs}
\title{getPublicationRefs()}
\usage{
getPublicationRefs(df, tdf)
}
\arguments{
\item{df}{the main biopax data frame}
\item{tdf}{a subsegment of the biopax data frame fr... |
# Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of finalWoo
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | /finalWoo/R/Main.R | no_license | OHDSI/StudyProtocols | R | false | false | 10,539 | r | # Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of finalWoo
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
#load in lubridate
library(lubridate)
#read in streamflow data
datH <- read.csv("activity5/stream_flow_data.csv",
na.strings = c("Eqp"))
head(datH)
#read in precipitation data
#hourly precipitation is in mm
datP <- read.csv("activity5/2049867.csv")
head(datP)
#only use mo... | /activity5/activity5_script.R | no_license | CaioBrighenti/GEOG331 | R | false | false | 8,289 | r | #load in lubridate
library(lubridate)
#read in streamflow data
datH <- read.csv("activity5/stream_flow_data.csv",
na.strings = c("Eqp"))
head(datH)
#read in precipitation data
#hourly precipitation is in mm
datP <- read.csv("activity5/2049867.csv")
head(datP)
#only use mo... |
makeVector <- function(x = numeric()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setmean <- function(mean) m <<- mean
getmean <- function() m
list(
set = set,
get = get,
setmean = setmean,
getmean = getmean
)
}
cachemean <- funct... | /example.R | no_license | ittegrat/RProgramming_Assignment2 | R | false | false | 499 | r |
makeVector <- function(x = numeric()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setmean <- function(mean) m <<- mean
getmean <- function() m
list(
set = set,
get = get,
setmean = setmean,
getmean = getmean
)
}
cachemean <- funct... |
prev_dir = setwd(system.file("tests/test_data/", package = "cancereffectsizeR"))
luad = CESAnalysis(genome = "hg19", progression_order = 1:4)
luad = load_maf(luad, maf = "luad.hg19.maf.txt", sample_col = "sample_id",
tumor_allele_col = "Tumor_Seq_Allele2", progression_col = "fake_stage")
luad = calc_ba... | /tests/generate_test_data/generate_luad_cesa_multi.R | no_license | chriscross11/cancereffectsizeR | R | false | false | 2,404 | r | prev_dir = setwd(system.file("tests/test_data/", package = "cancereffectsizeR"))
luad = CESAnalysis(genome = "hg19", progression_order = 1:4)
luad = load_maf(luad, maf = "luad.hg19.maf.txt", sample_col = "sample_id",
tumor_allele_col = "Tumor_Seq_Allele2", progression_col = "fake_stage")
luad = calc_ba... |
scoreF <- function(Z,R) {
sum((Z-R)^2/Z)
}
| /pkg/R/scoreF.R | no_license | r-forge/polrep | R | false | false | 47 | r | scoreF <- function(Z,R) {
sum((Z-R)^2/Z)
}
|
data(gapminder, package = "gapminder")
dataset <- gapminder %>%
select(-continent, -lifeExp, -pop) %>%
mutate(country = as.character(country)) %>%
tidyr::pivot_wider(
names_from = year,
values_from = gdpPercap
) %>%
mutate_if(is.numeric, scales::rescale, to = c(.06, .1)) %>%
mutate(
countr... | /data/preprocess.R | no_license | JohnCoene/gdp-app | R | false | false | 881 | r | data(gapminder, package = "gapminder")
dataset <- gapminder %>%
select(-continent, -lifeExp, -pop) %>%
mutate(country = as.character(country)) %>%
tidyr::pivot_wider(
names_from = year,
values_from = gdpPercap
) %>%
mutate_if(is.numeric, scales::rescale, to = c(.06, .1)) %>%
mutate(
countr... |
# Matrix inversion is usually a costly computation and there may be some benefit
# to caching the inverse of a matrix rather than compute it repeatedly. The
# following two functions are used to cache the inverse of a matrix.
# makeCacheMatrix creates a list containing a function to
# 1. set the value of the matrix
# ... | /cachematrix.R | no_license | neelamsingh/ProgrammingAssignment2 | R | false | false | 2,046 | r | # Matrix inversion is usually a costly computation and there may be some benefit
# to caching the inverse of a matrix rather than compute it repeatedly. The
# following two functions are used to cache the inverse of a matrix.
# makeCacheMatrix creates a list containing a function to
# 1. set the value of the matrix
# ... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chart.Drawdown.R
\name{chart.Drawdown}
\alias{chart.Drawdown}
\title{Time series chart of drawdowns through time}
\usage{
chart.Drawdown(
R,
geometric = TRUE,
legend.loc = NULL,
colorset = (1:12),
plot.engine = "default",
...
)
}
... | /man/chart.Drawdown.Rd | no_license | braverock/PerformanceAnalytics | R | false | true | 1,787 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chart.Drawdown.R
\name{chart.Drawdown}
\alias{chart.Drawdown}
\title{Time series chart of drawdowns through time}
\usage{
chart.Drawdown(
R,
geometric = TRUE,
legend.loc = NULL,
colorset = (1:12),
plot.engine = "default",
...
)
}
... |
# TASK 4 ------------------------------------------------------------------
a <- (5:14)
a
# TASK 5 ------------------------------------------------------------------
a[1]
a[7]
a[1]
a[7]
b <- c(a[1],a[7])
b
# TASK 6 ------------------------------------------------------------------
a<b
b>a
a>=b
# TASK 7 ------... | /R test 1.R | no_license | MinaCarnero/R-Test-1 | R | false | false | 567 | r |
# TASK 4 ------------------------------------------------------------------
a <- (5:14)
a
# TASK 5 ------------------------------------------------------------------
a[1]
a[7]
a[1]
a[7]
b <- c(a[1],a[7])
b
# TASK 6 ------------------------------------------------------------------
a<b
b>a
a>=b
# TASK 7 ------... |
forecast <- function(obj, ...) UseMethod("forecast")
| /R/generics.r | no_license | lnsongxf/bvar | R | false | false | 53 | r | forecast <- function(obj, ...) UseMethod("forecast")
|
# -*- tab-width:2;indent-tabs-mode:t;show-trailing-whitespace:t;rm-trailing-spaces:t -*-
# vi: set ts=2 noet:
#
# (c) Copyright Rosetta Commons Member Institutions.
# (c) This file is part of the Rosetta software suite and is made available under license.
# (c) The Rosetta software is developed by the contributing memb... | /inst/scripts/analysis/statistics/hbonds/BAH_chem_type_comparison.R | no_license | momeara/RosettaFeatures | R | false | false | 4,081 | r | # -*- tab-width:2;indent-tabs-mode:t;show-trailing-whitespace:t;rm-trailing-spaces:t -*-
# vi: set ts=2 noet:
#
# (c) Copyright Rosetta Commons Member Institutions.
# (c) This file is part of the Rosetta software suite and is made available under license.
# (c) The Rosetta software is developed by the contributing memb... |
#' AMARETTO_Download
#'
#' Downloading TCGA dataset for AMARETTO analysis
#' @param CancerSite TCGA cancer code for data download
#' @param TargetDirectory Directory path to download data
#' @param downloadData TRUE
#' @return result
#' @importFrom curatedTCGAData curatedTCGAData
#' @importFrom httr GET stop_for_status... | /R/amaretto_download.R | permissive | vjcitn/AMARETTO | R | false | false | 13,759 | r | #' AMARETTO_Download
#'
#' Downloading TCGA dataset for AMARETTO analysis
#' @param CancerSite TCGA cancer code for data download
#' @param TargetDirectory Directory path to download data
#' @param downloadData TRUE
#' @return result
#' @importFrom curatedTCGAData curatedTCGAData
#' @importFrom httr GET stop_for_status... |
library(rlist)
M <- m <- 20
N <- 19
phi <- function(t1.index,t2.index,m){
if(t1.index-t2.index==1){
garp <- (2*((t1.index)/m)^2)-0.5
}
if(t1.index-t2.index!=1){
garp <- 0
}
garp
}
grid <- expand.grid(t=1:M,s=1:M) %>% subset(.,t>s) %>%
transform(.,... | /code/simulations/smoothing_spline_cholesky.R | no_license | taylerablake/Dissertation | R | false | false | 9,859 | r |
library(rlist)
M <- m <- 20
N <- 19
phi <- function(t1.index,t2.index,m){
if(t1.index-t2.index==1){
garp <- (2*((t1.index)/m)^2)-0.5
}
if(t1.index-t2.index!=1){
garp <- 0
}
garp
}
grid <- expand.grid(t=1:M,s=1:M) %>% subset(.,t>s) %>%
transform(.,... |
## Trace of a matrix
matrix.trace = function(A){
r = dim(A)[1]
trace = 0
for(i in 1:r)
{
trace <- trace + A[i,i]
}
return(trace)
}
#' Batched FSM for sequential experiments
#'
#' @description
#' Extension of the FSM to cases where units arrive sequentially in batches.
#' @param data_f... | /R/fsm_batch_without_strata.R | no_license | cran/FSM | R | false | false | 16,725 | r | ## Trace of a matrix
matrix.trace = function(A){
r = dim(A)[1]
trace = 0
for(i in 1:r)
{
trace <- trace + A[i,i]
}
return(trace)
}
#' Batched FSM for sequential experiments
#'
#' @description
#' Extension of the FSM to cases where units arrive sequentially in batches.
#' @param data_f... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClassDefinition.R, R/MNF.R
\name{MNF}
\alias{MNF}
\title{Class MNF}
\usage{
MNF(dataObject)
MNF(dataObject)
}
\arguments{
\item{dataObject}{object of type massImage}
}
\value{
object of class MNF
}
\description{
Class \code{MNF} contains ... | /man/MNF.Rd | no_license | lorenzgerber/tofsims | R | false | true | 1,347 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClassDefinition.R, R/MNF.R
\name{MNF}
\alias{MNF}
\title{Class MNF}
\usage{
MNF(dataObject)
MNF(dataObject)
}
\arguments{
\item{dataObject}{object of type massImage}
}
\value{
object of class MNF
}
\description{
Class \code{MNF} contains ... |
# load libraries
library(stringr)
library(tidyverse)
library(rvest)
# scrape data
get_tornadoes <- function(year) {
base_url <- "http://www.tornadohistoryproject.com/tornado/Oklahoma/"
url <- str_c(base_url, year, "/table")
tor_html <- read_html(url)
tor <- tor_html %>%
html_nodes("#results") %>%... | /R/get_tornadoes.R | no_license | aashareddy14/523-lab07 | R | false | false | 620 | r | # load libraries
library(stringr)
library(tidyverse)
library(rvest)
# scrape data
get_tornadoes <- function(year) {
base_url <- "http://www.tornadohistoryproject.com/tornado/Oklahoma/"
url <- str_c(base_url, year, "/table")
tor_html <- read_html(url)
tor <- tor_html %>%
html_nodes("#results") %>%... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AccessorsChIA.R
\name{average_component_size}
\alias{average_component_size}
\title{Return the mean component size of a CHIA object.}
\usage{
average_component_size(chia.obj)
}
\arguments{
\item{chia.obj}{A list containing the ChIA-PET data, ... | /man/average_component_size.Rd | no_license | ehenrion/ChIAnalysis | R | false | true | 483 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AccessorsChIA.R
\name{average_component_size}
\alias{average_component_size}
\title{Return the mean component size of a CHIA object.}
\usage{
average_component_size(chia.obj)
}
\arguments{
\item{chia.obj}{A list containing the ChIA-PET data, ... |
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