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 |
|---|---|---|---|---|---|---|---|---|---|
#Chicago crime- Motor Vehicle Theft dataset
rm(list = ls())
setwd('/home/harish/R/Data_Analytics_using_R')
mvt = read.csv("mvt.csv")
str(mvt)
mvt$Arrest = as.numeric(mvt$Arrest)
mvt$Domestic = as.numeric(mvt$Domestic)
str(mvt)
summary(mvt)
DateConvert = as.Date(strptime(mvt$Date, "%m/%d/%y %H:%M"))
mvt$Date = DateCon... | /Data_Analytics_using_R/chicago_crime.R | no_license | harishaaram/R-tutorial | R | false | false | 1,433 | r | #Chicago crime- Motor Vehicle Theft dataset
rm(list = ls())
setwd('/home/harish/R/Data_Analytics_using_R')
mvt = read.csv("mvt.csv")
str(mvt)
mvt$Arrest = as.numeric(mvt$Arrest)
mvt$Domestic = as.numeric(mvt$Domestic)
str(mvt)
summary(mvt)
DateConvert = as.Date(strptime(mvt$Date, "%m/%d/%y %H:%M"))
mvt$Date = DateCon... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/package.R
\docType{package}
\name{knitr-package}
\alias{knitr-package}
\alias{knitr}
\title{A general-purpose tool for dynamic report generation in R}
\description{
This is an alternative tool to Sweave with a more flexible design and new
fea... | /man/knitr-package.Rd | no_license | mwouts/knitr | R | false | true | 1,130 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/package.R
\docType{package}
\name{knitr-package}
\alias{knitr-package}
\alias{knitr}
\title{A general-purpose tool for dynamic report generation in R}
\description{
This is an alternative tool to Sweave with a more flexible design and new
fea... |
library('data.table')
library('dplyr')
library('tidyverse')
library('lubridate')
library('recommenderlab')
library('stringr')
#-----------------------------------------------
# 1. Read the Synthetic tag data file for assessment data
#-----------------------------------------------
# ==> Commenting for Upskill Evaluati... | /Code/divya/PulseScoreCalculator.R | no_license | theivanai05/wls26092020 | R | false | false | 10,968 | r | library('data.table')
library('dplyr')
library('tidyverse')
library('lubridate')
library('recommenderlab')
library('stringr')
#-----------------------------------------------
# 1. Read the Synthetic tag data file for assessment data
#-----------------------------------------------
# ==> Commenting for Upskill Evaluati... |
## script for the behavioral analyses as reported in
# "The neural computation of goal-directed behavior in complex motivational states"
# Saulin, Horn, Lotze, Kaiser, & Hein
### load packages
library(dplyr)
library(car)
library(lme4)
library(tidyr)
library(quickpsy)
#####################################... | /behavioral_analyses.R | permissive | AnneSaulin/complex_motivations | R | false | false | 8,753 | r | ## script for the behavioral analyses as reported in
# "The neural computation of goal-directed behavior in complex motivational states"
# Saulin, Horn, Lotze, Kaiser, & Hein
### load packages
library(dplyr)
library(car)
library(lme4)
library(tidyr)
library(quickpsy)
#####################################... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diagnose.R
\name{appraise}
\alias{appraise}
\title{Model diagnostic plots}
\usage{
appraise(
model,
method = c("direct", "simulate", "normal"),
n_uniform = 10,
n_simulate = 50,
type = c("deviance", "pearson", "response"),
n_bins =... | /man/appraise.Rd | permissive | robinsonjj/gratia | R | false | true | 2,335 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diagnose.R
\name{appraise}
\alias{appraise}
\title{Model diagnostic plots}
\usage{
appraise(
model,
method = c("direct", "simulate", "normal"),
n_uniform = 10,
n_simulate = 50,
type = c("deviance", "pearson", "response"),
n_bins =... |
test_that("`convert_input()` able to call the respective download function for a data item with the correct arguments", {
mocked_res <- mockery::mock(list(c("A", "B")))
mockery::stub(convert_input, 'dbfile.input.check', data.frame())
mockery::stub(convert_input, 'db.query', data.frame(id = 1))
mockery::stub(co... | /base/db/tests/testthat/test.convert_input.R | permissive | PecanProject/pecan | R | false | false | 1,594 | r | test_that("`convert_input()` able to call the respective download function for a data item with the correct arguments", {
mocked_res <- mockery::mock(list(c("A", "B")))
mockery::stub(convert_input, 'dbfile.input.check', data.frame())
mockery::stub(convert_input, 'db.query', data.frame(id = 1))
mockery::stub(co... |
TsallisBeta <-
function(NorP, NorPexp = NULL, q = 1, Correction = "Best", CheckArguments = TRUE, Ps = NULL, Ns = NULL, Pexp = NULL, Nexp = NULL)
{
UseMethod("TsallisBeta")
}
TsallisBeta.ProbaVector <-
function(NorP, NorPexp = NULL, q = 1, Correction = "Best", CheckArguments = TRUE, Ps = NULL, Ns = NULL, Pe... | /R/TsallisBeta.R | no_license | thalesshannonwatson/entropart | R | false | false | 3,734 | r | TsallisBeta <-
function(NorP, NorPexp = NULL, q = 1, Correction = "Best", CheckArguments = TRUE, Ps = NULL, Ns = NULL, Pexp = NULL, Nexp = NULL)
{
UseMethod("TsallisBeta")
}
TsallisBeta.ProbaVector <-
function(NorP, NorPexp = NULL, q = 1, Correction = "Best", CheckArguments = TRUE, Ps = NULL, Ns = NULL, Pe... |
#' inpdfr: A package to analyse PDF Files Using Ecological Tools.
#'
#' The inpdfr package allows analysing and comparing PDF/TXT documents using both
#' classical text mining tools and those from theoretical ecolgy. In the later,
#' words are considered as species and documents as communities, therefore
#' allow... | /R/inpdfr.R | no_license | frareb/inpdfr | R | false | false | 771 | r | #' inpdfr: A package to analyse PDF Files Using Ecological Tools.
#'
#' The inpdfr package allows analysing and comparing PDF/TXT documents using both
#' classical text mining tools and those from theoretical ecolgy. In the later,
#' words are considered as species and documents as communities, therefore
#' allow... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/C4.R
\docType{data}
\name{Cups}
\alias{Cups}
\title{Chapter 4 Cups}
\format{
A data frame with 32 rows and seven columns
}
\usage{
Cups
}
\description{
A manager of a manufacturing company, shift managers, cup machine operators,
and a lone st... | /man/Cups.Rd | no_license | minceb/Stats2Labs | R | false | true | 1,643 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/C4.R
\docType{data}
\name{Cups}
\alias{Cups}
\title{Chapter 4 Cups}
\format{
A data frame with 32 rows and seven columns
}
\usage{
Cups
}
\description{
A manager of a manufacturing company, shift managers, cup machine operators,
and a lone st... |
if(FALSE){
require("RHJDBC")
# initition
cp = dir(system.file("java", package="RHJDBC"),full.names = TRUE)
.jinit(classpath=cp)
options( java.parameters = "-Xmx8g" ) # set jvm
drv <- JDBC("org.apache.hive.jdbc.HiveDriver")
con <- dbConnect(drv,...)
class(con) = "JHDBCConnection" # 设置类
dbRemoveTable(c... | /R/example.R | no_license | lengyuyeke/RHJDBC | R | false | false | 534 | r |
if(FALSE){
require("RHJDBC")
# initition
cp = dir(system.file("java", package="RHJDBC"),full.names = TRUE)
.jinit(classpath=cp)
options( java.parameters = "-Xmx8g" ) # set jvm
drv <- JDBC("org.apache.hive.jdbc.HiveDriver")
con <- dbConnect(drv,...)
class(con) = "JHDBCConnection" # 设置类
dbRemoveTable(c... |
# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
c_D_cross_2d_box <- function(x, bbox, types, intensities, r) {
.Call('_Kcross_c_D_cross_2d_box', PACKAGE = 'Kcross', x, bbox, types, intensities, r)
}
c_K_cross_2d_box <- function(x, bbox,... | /R/RcppExports.R | no_license | antiphon/Kcross | R | false | false | 3,045 | r | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
c_D_cross_2d_box <- function(x, bbox, types, intensities, r) {
.Call('_Kcross_c_D_cross_2d_box', PACKAGE = 'Kcross', x, bbox, types, intensities, r)
}
c_K_cross_2d_box <- function(x, bbox,... |
library(keras)
library(dplyr)
library(jsonlite)
library(readr)
library(magrittr)
library(caTools)
# samples <- c("The cat sat on the mat.", "The dog ate my homework.")
expert_data <- read_csv('Data/Train/Expert/data.csv')
expert_data %<>% filter(sentiment != "л")
expert_data %<>% filter(sentiment != "l")
set.seed(1993... | /03_3_1_expert_deep_learn_binary.R | permissive | dongwook412/NorthKoreaReactionInRussia | R | false | false | 2,337 | r | library(keras)
library(dplyr)
library(jsonlite)
library(readr)
library(magrittr)
library(caTools)
# samples <- c("The cat sat on the mat.", "The dog ate my homework.")
expert_data <- read_csv('Data/Train/Expert/data.csv')
expert_data %<>% filter(sentiment != "л")
expert_data %<>% filter(sentiment != "l")
set.seed(1993... |
library(splatter)
library(scater)
params <-newSplatParams()
params<-setParams(params,update=list(nGenes=10000,batchCells=2000, de.prob=0.08,seed=100))
sim <- splatSimulate(params,group.prob=c(0.3,0.3,0.3,0.05,0.05),method="groups",verbose = FALSE)
#count zeros function
count_zeros <- function(x){length(which(x==0... | /data_generator/SIM_III.R | no_license | cyxss/DenseFly4scRNAseq | R | false | false | 2,078 | r | library(splatter)
library(scater)
params <-newSplatParams()
params<-setParams(params,update=list(nGenes=10000,batchCells=2000, de.prob=0.08,seed=100))
sim <- splatSimulate(params,group.prob=c(0.3,0.3,0.3,0.05,0.05),method="groups",verbose = FALSE)
#count zeros function
count_zeros <- function(x){length(which(x==0... |
# Allow convenient use of functions from other packages
#' @include Pipes.R
#' @include Error_Handling.R
NULL
#' Find the percent of missing values
#'
#' Count the percent of missing values in a vector.
#' Counts the percent of NA values for non-character vectors,
#' and the number of NA or "" values for character... | /R/Missing_Values.R | permissive | KO112/KO | R | false | false | 4,760 | r | # Allow convenient use of functions from other packages
#' @include Pipes.R
#' @include Error_Handling.R
NULL
#' Find the percent of missing values
#'
#' Count the percent of missing values in a vector.
#' Counts the percent of NA values for non-character vectors,
#' and the number of NA or "" values for character... |
##=========================================================
## File: predict_OCRI.R
## History: initially coded as: build_olk_index.R
## Author: Jianying Li
## Comment: compare different data processing parameter
## settings and compare seven models (ROC - metrics)
## produce model comparison box pl... | /mixturemodel/pipeLineScripts/predict_OCRI.R | no_license | ImageRecognitionMaster/myOCRI-iii | R | false | false | 6,242 | r | ##=========================================================
## File: predict_OCRI.R
## History: initially coded as: build_olk_index.R
## Author: Jianying Li
## Comment: compare different data processing parameter
## settings and compare seven models (ROC - metrics)
## produce model comparison box pl... |
## this sctipt takes DNA database and for each fragment, make separate FASTA file
## all "our" sequences used
## only those external source sequences used which (a) belong to species / fragments absent in "our" and (b) longest
library(shipunov)
set <- read.table("_kubricks_dna.txt", sep="\t", h=TRUE, as.is=TRUE)
set ... | /20_make_sets.r | no_license | ashipunov/Ripeline | R | false | false | 1,548 | r | ## this sctipt takes DNA database and for each fragment, make separate FASTA file
## all "our" sequences used
## only those external source sequences used which (a) belong to species / fragments absent in "our" and (b) longest
library(shipunov)
set <- read.table("_kubricks_dna.txt", sep="\t", h=TRUE, as.is=TRUE)
set ... |
# Лабораторная №11_1 ------------------------------------------------------
# выборки -----------------------------------------------------------------
n = 40
eps.sigma = 2
eps.a = 0
eps = rnorm(n, eps.a, eps.sigma)
a1 = 1
a2 = 2
a3 = -1
b1.1 = 1.5
b2.1 = 2
b1.2 = 3
b2.2 = 0.75
b1.3 = -2.1
b2.3 = -1
t = c(1:n)
y1 = a1... | /11_1/11_1.R | no_license | Morgolt/statistic | R | false | false | 4,098 | r | # Лабораторная №11_1 ------------------------------------------------------
# выборки -----------------------------------------------------------------
n = 40
eps.sigma = 2
eps.a = 0
eps = rnorm(n, eps.a, eps.sigma)
a1 = 1
a2 = 2
a3 = -1
b1.1 = 1.5
b2.1 = 2
b1.2 = 3
b2.2 = 0.75
b1.3 = -2.1
b2.3 = -1
t = c(1:n)
y1 = a1... |
setwd("C:/Users/ctmun/OneDrive/Desktop/DS Explor Anal")
elecpwr <- read.table("C:/Users/ctmun/OneDrive/Desktop/DS Explor Anal/Proj I Pwr/household_power_consumption.txt",
header = TRUE, sep = ";", stringsAsFactors = FALSE)
head(elecpwr, n=3)
class(elecpwr$Date)
## [1] "character"
library(dpl... | /Plot3.R | no_license | TommyJM/ExData_Plotting1 | R | false | false | 1,092 | r | setwd("C:/Users/ctmun/OneDrive/Desktop/DS Explor Anal")
elecpwr <- read.table("C:/Users/ctmun/OneDrive/Desktop/DS Explor Anal/Proj I Pwr/household_power_consumption.txt",
header = TRUE, sep = ";", stringsAsFactors = FALSE)
head(elecpwr, n=3)
class(elecpwr$Date)
## [1] "character"
library(dpl... |
#input the table name here
table_name <- "DS_20141028_RST_0ZV157LCHOGDG"
#input db or data link info here(either olive or analystdb)
database_name <- "analystdb"
#build strings
tbl <- paste(table_name, database_name , sep ="@")
sqlc <- paste("select * from ", tbl) #sep = "")
con <-odbcConnect("Olive11", uid="olive"... | /MN/sqlsave.R | no_license | ckeune/RWork | R | false | false | 968 | r |
#input the table name here
table_name <- "DS_20141028_RST_0ZV157LCHOGDG"
#input db or data link info here(either olive or analystdb)
database_name <- "analystdb"
#build strings
tbl <- paste(table_name, database_name , sep ="@")
sqlc <- paste("select * from ", tbl) #sep = "")
con <-odbcConnect("Olive11", uid="olive"... |
### Usage notes for these scripts:
# Almost every function or object depends on the flowCore (and often flowViz) package(s) from biocondcutor.
# To install these, run these commands in R:
# source("http://bioconductor.org/biocLite.R")
# biocLite("flowCore")
# biocLite("flowViz")
#
# The most useful script for an R ... | /NemCytometer_2015.R | permissive | NemLab/Rfunctions | R | false | false | 44,619 | r |
### Usage notes for these scripts:
# Almost every function or object depends on the flowCore (and often flowViz) package(s) from biocondcutor.
# To install these, run these commands in R:
# source("http://bioconductor.org/biocLite.R")
# biocLite("flowCore")
# biocLite("flowViz")
#
# The most useful script for an R ... |
#import data
data=read.csv("Mall_Customers.csv")
X=data[,4:5]
#Finding no of clusters using dendrograms
dendro=hclust(dist(X,method="euclidean"),method = 'ward.D')
plot(dendro,
main = paste("DendroGrams"),
xlab = "Customers",
ylab = "Euclidean Distance")
#Build the model
hc=hclust(dist(X,method="euclid... | /Part 4 - Clustering/Hierarchical Clustering/Hierarchical_Clustering.R | no_license | AchyuthReddy001/Machine-Learning | R | false | false | 662 | r | #import data
data=read.csv("Mall_Customers.csv")
X=data[,4:5]
#Finding no of clusters using dendrograms
dendro=hclust(dist(X,method="euclidean"),method = 'ward.D')
plot(dendro,
main = paste("DendroGrams"),
xlab = "Customers",
ylab = "Euclidean Distance")
#Build the model
hc=hclust(dist(X,method="euclid... |
#' @title Event Constructor
#'
#' @description Event constructor
#' @param id A character identifier
#' @param type A character indicating the event type
#' @param time A character string indicating the event timestamp
#' @param ... A list with the event attributes.
#'
#' @return The function returns an object ... | /R/event.R | no_license | cran/eventr | R | false | false | 1,897 | r | #' @title Event Constructor
#'
#' @description Event constructor
#' @param id A character identifier
#' @param type A character indicating the event type
#' @param time A character string indicating the event timestamp
#' @param ... A list with the event attributes.
#'
#' @return The function returns an object ... |
# Jake Yeung
# Date of Creation: 2022-05-04
# File: ~/projects/scchic/scripts/revision_scripts/revisions_from_istbea/28-pseudotime_cubic_spline_each_gene_get_derivatives_command_args.R
#
rm(list=ls())
library(scchicFuncs)
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(topi... | /scripts/revision_scripts/revisions_from_istbea/28-pseudotime_cubic_spline_each_gene_get_derivatives_command_args.R | no_license | jakeyeung/sortchicAllScripts | R | false | false | 8,021 | r | # Jake Yeung
# Date of Creation: 2022-05-04
# File: ~/projects/scchic/scripts/revision_scripts/revisions_from_istbea/28-pseudotime_cubic_spline_each_gene_get_derivatives_command_args.R
#
rm(list=ls())
library(scchicFuncs)
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(topi... |
# datacleaning.R - created by jl on 10 october 2016. | /processing-and-analysis/command-files/datacleaning.R | no_license | slievefoy/l-c-test | R | false | false | 52 | r | # datacleaning.R - created by jl on 10 october 2016. |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/all-generics.R
\name{stats}
\alias{stats}
\title{Access the stats of an object.}
\usage{
stats(x, name = NULL, ...)
}
\arguments{
\item{x}{An object containing stats}
\item{name}{(Optional); slotname.}
\item{...}{Additional arguments.}
}
\v... | /man/stats.Rd | no_license | DKMS-LSL/dr2s | R | false | true | 406 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/all-generics.R
\name{stats}
\alias{stats}
\title{Access the stats of an object.}
\usage{
stats(x, name = NULL, ...)
}
\arguments{
\item{x}{An object containing stats}
\item{name}{(Optional); slotname.}
\item{...}{Additional arguments.}
}
\v... |
testlist <- list(type = -1650614883L, z = -5.29946982737853e-169)
result <- do.call(esreg::G1_fun,testlist)
str(result) | /esreg/inst/testfiles/G1_fun/libFuzzer_G1_fun/G1_fun_valgrind_files/1609893456-test.R | no_license | akhikolla/updated-only-Issues | R | false | false | 119 | r | testlist <- list(type = -1650614883L, z = -5.29946982737853e-169)
result <- do.call(esreg::G1_fun,testlist)
str(result) |
### creating area averaged line plot
rasterdir="/Users/heatherwelch/Dropbox/JPSS/global/eastern_pacific_rasters/"
csvdir="/Users/heatherwelch/Dropbox/JPSS/global/eastern_pacific_csvs/"#;dir.create(csvdir)
rasterlist=list.files(rasterdir,pattern = ".grd")
setwd(rasterdir)
df=data.frame(number=1:length(rasterlist)) %>%... | /code_03.05.19/eastern_pacific_chla_v2_log.R | no_license | HeatherWelch/JPSS_VIIRS | R | false | false | 2,542 | r | ### creating area averaged line plot
rasterdir="/Users/heatherwelch/Dropbox/JPSS/global/eastern_pacific_rasters/"
csvdir="/Users/heatherwelch/Dropbox/JPSS/global/eastern_pacific_csvs/"#;dir.create(csvdir)
rasterlist=list.files(rasterdir,pattern = ".grd")
setwd(rasterdir)
df=data.frame(number=1:length(rasterlist)) %>%... |
#Bhumit Shah 1001765834
#Kaustubh Rajpathak 1001770219
#Project 1
setwd("C:/Users/kkr0219/Documents/Data Mining Datasets/bank-additional-full")
bankData <- read.csv(file = 'bank-additional-full.csv',header=TRUE, sep=";")
#Cleaning and pre-processing
#Removing rows with unknown values
nrow(bankData[bankDa... | /Decision Tree & Naive Bayes Classifiers/Project_It_1.R | no_license | kaustubh41096/Data-Mining | R | false | false | 7,422 | r | #Bhumit Shah 1001765834
#Kaustubh Rajpathak 1001770219
#Project 1
setwd("C:/Users/kkr0219/Documents/Data Mining Datasets/bank-additional-full")
bankData <- read.csv(file = 'bank-additional-full.csv',header=TRUE, sep=";")
#Cleaning and pre-processing
#Removing rows with unknown values
nrow(bankData[bankDa... |
pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
files <- list.files(directory, full.name =TRUE)
## 'pollutant' is a character vector of length 1 indicating
## the name of the pollutant for whic... | /r-programming/pollutantmean.R | no_license | tttonytian/datasciencecoursera | R | false | false | 748 | r |
pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
files <- list.files(directory, full.name =TRUE)
## 'pollutant' is a character vector of length 1 indicating
## the name of the pollutant for whic... |
library(h2o)
h2o.init()
data <- h2o.importFile("http://h2o-public-test-data.s3.amazonaws.com/smalldata/airlines/allyears2k_headers.zip")
parts <- h2o.splitFrame(data, c(0.8,0.1),seed = 69)
train <- parts[[1]]
valid <- parts[[2]]
test <- parts[[3]]
y <- "IsArrDelayed"
xWithDep <-setdiff(colnames(data),c(
"ArrDela... | /Week4/R/visualization_model_exprimentation.R | no_license | mrjaypandya/Practical-Machine-Learning-on-H2O | R | false | false | 748 | r | library(h2o)
h2o.init()
data <- h2o.importFile("http://h2o-public-test-data.s3.amazonaws.com/smalldata/airlines/allyears2k_headers.zip")
parts <- h2o.splitFrame(data, c(0.8,0.1),seed = 69)
train <- parts[[1]]
valid <- parts[[2]]
test <- parts[[3]]
y <- "IsArrDelayed"
xWithDep <-setdiff(colnames(data),c(
"ArrDela... |
library(stringr)
library(adegenet)
library(ggplot2)
library(ggpubr)
library(MCMCglmm)
library(dplyr)
library(tidyr)
library(DESeq2)
library(reshape)
########################################################################################################
################################################################... | /scripts/11_dapc.R | no_license | rsbrennan/tonsa_reciprocal | R | false | false | 34,374 | r |
library(stringr)
library(adegenet)
library(ggplot2)
library(ggpubr)
library(MCMCglmm)
library(dplyr)
library(tidyr)
library(DESeq2)
library(reshape)
########################################################################################################
################################################################... |
#### Libraries I need ####
library(tidyverse)
library(mclust)
#### Read in Data ####
dat <- read_csv("big5clean_construct.csv")
sc_dat <- read_csv("normalized.csv")
#load("./Connor's files/SubClusters.RData")
#### Run Mixed Model with 4 clusters ####
k <- 4
mc <- Mclust(sc_dat[,2:6],k)
# Get the probs ... | /Connor's files/SubclusterMixedModel.R | no_license | CRGreenhalgh/Big5Clustering | R | false | false | 3,423 | r | #### Libraries I need ####
library(tidyverse)
library(mclust)
#### Read in Data ####
dat <- read_csv("big5clean_construct.csv")
sc_dat <- read_csv("normalized.csv")
#load("./Connor's files/SubClusters.RData")
#### Run Mixed Model with 4 clusters ####
k <- 4
mc <- Mclust(sc_dat[,2:6],k)
# Get the probs ... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/colorblind.R
\name{colorblind_pal}
\alias{colorblind_pal}
\alias{scale_colour_colorblind}
\alias{scale_color_colorblind}
\alias{scale_fill_colorblind}
\title{Colorblind Color Palette (Discrete) and Scales}
\usage{
colorblind_pal()
scale_colo... | /man/colorblind.Rd | no_license | quartin/ggthemes | R | false | true | 3,560 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/colorblind.R
\name{colorblind_pal}
\alias{colorblind_pal}
\alias{scale_colour_colorblind}
\alias{scale_color_colorblind}
\alias{scale_fill_colorblind}
\title{Colorblind Color Palette (Discrete) and Scales}
\usage{
colorblind_pal()
scale_colo... |
host.dir <- "."
all.counts <- read.table("ESpresso/counttable_es.csv", header=TRUE, row.names=1, colClasses=c("character", rep("integer", 704)))
serum <- sub("ola_mES_([^_]+)_.*", "\\1", colnames(all.counts))
batch <- sub("ola_mES_[^_]+_([^_]+)_.*", "\\1", colnames(all.counts))
targets <- data.frame(Serum=serum, Batch... | /reference/ESpresso.R | no_license | jaymgrayson/PlateEffects2016 | R | false | false | 973 | r |
host.dir <- "."
all.counts <- read.table("ESpresso/counttable_es.csv", header=TRUE, row.names=1, colClasses=c("character", rep("integer", 704)))
serum <- sub("ola_mES_([^_]+)_.*", "\\1", colnames(all.counts))
batch <- sub("ola_mES_[^_]+_([^_]+)_.*", "\\1", colnames(all.counts))
targets <- data.frame(Serum=serum, Batch... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lightsail_operations.R
\name{lightsail_detach_instances_from_load_balancer}
\alias{lightsail_detach_instances_from_load_balancer}
\title{Detaches the specified instances from a Lightsail load balancer}
\usage{
lightsail_detach_instances_from_... | /paws/man/lightsail_detach_instances_from_load_balancer.Rd | permissive | sanchezvivi/paws | R | false | true | 1,195 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lightsail_operations.R
\name{lightsail_detach_instances_from_load_balancer}
\alias{lightsail_detach_instances_from_load_balancer}
\title{Detaches the specified instances from a Lightsail load balancer}
\usage{
lightsail_detach_instances_from_... |
## 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
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- funct... | /cachematrix.R | no_license | alfredleung/ProgrammingAssignment2 | R | false | false | 959 | 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
## This function creates a special "matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- funct... |
# Convert the wealth rating to a factor
donors$wealth_rating <- factor(donors$wealth_rating, levels = c(0,1,2,3), labels = c('Unknown','Low','Medium','High'))
# Use relevel() to change reference category (i.e.)
donors$wealth_rating <- relevel(donors$wealth_rating, ref = 'Medium')
# See how our factor coding impacts t... | /ml/scripts/transform_examples/factorRecode.R | no_license | pickle-donut/RScripts | R | false | false | 402 | r | # Convert the wealth rating to a factor
donors$wealth_rating <- factor(donors$wealth_rating, levels = c(0,1,2,3), labels = c('Unknown','Low','Medium','High'))
# Use relevel() to change reference category (i.e.)
donors$wealth_rating <- relevel(donors$wealth_rating, ref = 'Medium')
# See how our factor coding impacts t... |
source("load_data.R")
plot1 <- paste(getwd(),"/plots/plot1.png", sep="")
if(!file.exists(plot1)){
png(plot1, width = 480, height = 480)
hist(as.numeric(as.character(ranged_power$Global_active_power)), main = "Global Active Power", xlab = "Global Active Power (kilowatts)", ylab = "Frequency", col = "red")
... | /plot1.R | no_license | mountain-lion/exdata-030 | R | false | false | 518 | r | source("load_data.R")
plot1 <- paste(getwd(),"/plots/plot1.png", sep="")
if(!file.exists(plot1)){
png(plot1, width = 480, height = 480)
hist(as.numeric(as.character(ranged_power$Global_active_power)), main = "Global Active Power", xlab = "Global Active Power (kilowatts)", ylab = "Frequency", col = "red")
... |
#!/usr/bin/env Rscript
# detect script being run by snakemake
# if so, make a mock commandArgs function
if ('snakemake' %in% ls()) {
logfile <- snakemake@log[[1]]
con <- file(logfile, 'w')
sink(con, type='output')
sink(con, type='message')
commandArgs <- function(...) unlist(c(
snakemake@i... | /snakemake/scripts/make_cosmic_indel_table.R | no_license | parklab/luquette-glia-analysis | R | false | false | 1,732 | r | #!/usr/bin/env Rscript
# detect script being run by snakemake
# if so, make a mock commandArgs function
if ('snakemake' %in% ls()) {
logfile <- snakemake@log[[1]]
con <- file(logfile, 'w')
sink(con, type='output')
sink(con, type='message')
commandArgs <- function(...) unlist(c(
snakemake@i... |
library(plotly)
library(ggplot2)
score <- read.csv("../data/학생별과목별성적_국영수_new.csv", header=T)
score
p <- ggplot(data = score,
aes(x = 이름, y = 점수, col = 과목)) +
geom_point()
ggplotly(p)
###########################################################################
| /R/source/20191224_quiz_interactive_graph.R | no_license | Jade2290/bigdata_class | R | false | false | 329 | r | library(plotly)
library(ggplot2)
score <- read.csv("../data/학생별과목별성적_국영수_new.csv", header=T)
score
p <- ggplot(data = score,
aes(x = 이름, y = 점수, col = 과목)) +
geom_point()
ggplotly(p)
###########################################################################
|
# function for lasso feature selection
# Author: Jason Zhao
#Usage:
# 1. Load the function by: source("path/to/this/script")
# 2. call the function by: featureSelectionLasso(yourDataframeWithoutLabel, LabelVector)
#Note:
# dataFrame is a m*n dataframe
# IF m represents features and n represents samples, keep defaul... | /featureSelectionLasso.R | no_license | jasonzhao0307/R_lib_jason | R | false | false | 1,168 | r | # function for lasso feature selection
# Author: Jason Zhao
#Usage:
# 1. Load the function by: source("path/to/this/script")
# 2. call the function by: featureSelectionLasso(yourDataframeWithoutLabel, LabelVector)
#Note:
# dataFrame is a m*n dataframe
# IF m represents features and n represents samples, keep defaul... |
% Auto-generated documentation for function plot.pltdTable
% 2021-06-02 11:12:19
\name{plot.pltdTable}
\alias{plot.pltdTable}
\title{Display a \code{pltdtable} Object on a Graphics Device }
\description{
Display a \code{pltdTable} object on a graphics device. This is an alias
for \code{print.pltdTable}. This is an... | /man/plot.pltdTable.Rd | no_license | rrprf/tablesgg | R | false | false | 671 | rd | % Auto-generated documentation for function plot.pltdTable
% 2021-06-02 11:12:19
\name{plot.pltdTable}
\alias{plot.pltdTable}
\title{Display a \code{pltdtable} Object on a Graphics Device }
\description{
Display a \code{pltdTable} object on a graphics device. This is an alias
for \code{print.pltdTable}. This is an... |
#######################
#gating_LTEE_GAP1_Variants.R
#
#started: 01/07/2016
#modified: 1/25/2019
#
#author1: G Avecilla, S Lauer, D Gresham
#author2: N Brandt
######################
######################
#This script is specific for analyzing the data obtained in LTEE_GAP1_Variants in Gln,
#For any other purpo... | /gating_LTEE_GAP1_VAR.R | no_license | brandtn/Flow_Cyto_Analysis | R | false | false | 9,894 | r | #######################
#gating_LTEE_GAP1_Variants.R
#
#started: 01/07/2016
#modified: 1/25/2019
#
#author1: G Avecilla, S Lauer, D Gresham
#author2: N Brandt
######################
######################
#This script is specific for analyzing the data obtained in LTEE_GAP1_Variants in Gln,
#For any other purpo... |
#' read_Haarlem
#'
#' read station pressure at Haarlem station
#'
#' @param infile input file name
#'
#' @keywords util
#' @export
read_Haarlem <- function(infile){
# read in data
rawdata <- read.table(infile, sep=',', skip=54, header=TRUE, stringsAsFactors=FALSE)
## convert to output format
out <- dat... | /R/read_Haarlem.R | no_license | jonasbhend/pressurehelper | R | false | false | 1,103 | r | #' read_Haarlem
#'
#' read station pressure at Haarlem station
#'
#' @param infile input file name
#'
#' @keywords util
#' @export
read_Haarlem <- function(infile){
# read in data
rawdata <- read.table(infile, sep=',', skip=54, header=TRUE, stringsAsFactors=FALSE)
## convert to output format
out <- dat... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rPsuedoWishart.R
\name{rPsuedoWishart}
\alias{rPsuedoWishart}
\title{Random Psuedo Wishart Matrix}
\usage{
rPsuedoWishart(n, df, Sigma, covariance = FALSE, simplify = "array")
}
\arguments{
\item{n}{integer: the number of replications.}
\ite... | /man/rPsuedoWishart.Rd | no_license | BenBarnard/rWishart | R | false | true | 1,860 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rPsuedoWishart.R
\name{rPsuedoWishart}
\alias{rPsuedoWishart}
\title{Random Psuedo Wishart Matrix}
\usage{
rPsuedoWishart(n, df, Sigma, covariance = FALSE, simplify = "array")
}
\arguments{
\item{n}{integer: the number of replications.}
\ite... |
# copy_webapps_templates.r
# INSTRUCTIONS
# Run this script one time only. This will download the WebApps'
# template files to your local repository. You will then edit the files using
# RStudio. When editing, save, commit, and push all files normally and they will
# automatically be updated on the WebApp every hour ... | /copy_webapps_templates.r | no_license | eblondel/ohibc | R | false | false | 2,019 | r | # copy_webapps_templates.r
# INSTRUCTIONS
# Run this script one time only. This will download the WebApps'
# template files to your local repository. You will then edit the files using
# RStudio. When editing, save, commit, and push all files normally and they will
# automatically be updated on the WebApp every hour ... |
## Plot 3
# Read Data
hhPc<-read.table("hhpc.txt",header=TRUE)
# Construct Plot 3
plot(hhPc$Sub_metering_1,type="l",col="black",ann=FALSE,xaxt="n")
lines(hhPc$Sub_metering_2,type="l",col="red")
lines(hhPc$Sub_metering_3,type="l",col="blue")
axis(side=1,at=c(0,1440,2880),labels=c("Thu","Fri","Sat"))
title(ylab=... | /plot3.R | no_license | JacGu/Exploratory-Data-Analysis | R | false | false | 681 | r | ## Plot 3
# Read Data
hhPc<-read.table("hhpc.txt",header=TRUE)
# Construct Plot 3
plot(hhPc$Sub_metering_1,type="l",col="black",ann=FALSE,xaxt="n")
lines(hhPc$Sub_metering_2,type="l",col="red")
lines(hhPc$Sub_metering_3,type="l",col="blue")
axis(side=1,at=c(0,1440,2880),labels=c("Thu","Fri","Sat"))
title(ylab=... |
#Multispecies dependent double-observer model (MDAM)
#Authors: Jessie Golding and Danielle Fagre
#Code for analyzing Danielle Fagre's pilot season (summer 2015)
#songbird data from the National Bison Range
###Master code###
#Set working directory
#CHANGE when changing users
setwd("C:/Users/jessie.golding/Documents/G... | /master.R | no_license | jgoldfinch/MDAM | R | false | false | 716 | r | #Multispecies dependent double-observer model (MDAM)
#Authors: Jessie Golding and Danielle Fagre
#Code for analyzing Danielle Fagre's pilot season (summer 2015)
#songbird data from the National Bison Range
###Master code###
#Set working directory
#CHANGE when changing users
setwd("C:/Users/jessie.golding/Documents/G... |
#=============================================================================#
# Author: Guido Espana & Yutong Yao & Alex Perkins
#=============================================================================#
# user input ---------------
#=============================================================================#
... | /analysis/scripts/supplement_figure_S18_absolute_cost_brazil_30y.R | no_license | confunguido/public_health_assessment_dengue_vaccine | R | false | false | 12,186 | r | #=============================================================================#
# Author: Guido Espana & Yutong Yao & Alex Perkins
#=============================================================================#
# user input ---------------
#=============================================================================#
... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nnmf.R
\name{nnmf}
\alias{nnmf}
\title{Fast non-negative matrix factorization by ANLS with MSE loss}
\usage{
nnmf(
A,
k = NULL,
max.iter = 1000,
rel.tol = 0.001,
n.threads = 0,
verbose = TRUE,
trace = 5
)
}
\arguments{
\item{A}{... | /man/nnmf.Rd | no_license | ttriche/scNMF | R | false | true | 1,298 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nnmf.R
\name{nnmf}
\alias{nnmf}
\title{Fast non-negative matrix factorization by ANLS with MSE loss}
\usage{
nnmf(
A,
k = NULL,
max.iter = 1000,
rel.tol = 0.001,
n.threads = 0,
verbose = TRUE,
trace = 5
)
}
\arguments{
\item{A}{... |
# Factors
## Run the following lines and explore the outputs.
## The data set we want to convert into factor
data <- rep(c(1, 2, 3), 4)
data
## Convert data into factors
fdata <- factor(data)
fdata
## We want to display values as Roman numerals
roman_fdata <- factor(data, labels=c("I", "II", "III"))
roman_fdata
## To ... | /public/problems/factor_lab.R | no_license | statisticsmart/Rprogramming | R | false | false | 2,779 | r | # Factors
## Run the following lines and explore the outputs.
## The data set we want to convert into factor
data <- rep(c(1, 2, 3), 4)
data
## Convert data into factors
fdata <- factor(data)
fdata
## We want to display values as Roman numerals
roman_fdata <- factor(data, labels=c("I", "II", "III"))
roman_fdata
## To ... |
\name{structure.diagram}
\alias{structure.diagram}
\alias{structure.graph}
\alias{structure.sem}
\alias{lavaan.diagram}
\alias{sem.diagram}
\alias{sem.graph}
\title{Draw a structural equation model specified by two measurement models and a structural model}
\description{Graphic presentations of structural equation m... | /man/structure.diagram.Rd | no_license | cran/psych | R | false | false | 9,740 | rd | \name{structure.diagram}
\alias{structure.diagram}
\alias{structure.graph}
\alias{structure.sem}
\alias{lavaan.diagram}
\alias{sem.diagram}
\alias{sem.graph}
\title{Draw a structural equation model specified by two measurement models and a structural model}
\description{Graphic presentations of structural equation m... |
#written June 6, 2012
#modified March 14, 2023
#note that lower.tri and upper.tri return the matrix in a different order
"lowerUpper" <-
function(lower,upper=NULL,diff=FALSE) {
if(is.null(upper)) {upper <- lower #return two from one
upper[lower.tri(upper)] <- t(upper)[lower.tri(t(upper))]
lower <- t(lower)
... | /R/lowerUpper.R | no_license | cran/psych | R | false | false | 980 | r | #written June 6, 2012
#modified March 14, 2023
#note that lower.tri and upper.tri return the matrix in a different order
"lowerUpper" <-
function(lower,upper=NULL,diff=FALSE) {
if(is.null(upper)) {upper <- lower #return two from one
upper[lower.tri(upper)] <- t(upper)[lower.tri(t(upper))]
lower <- t(lower)
... |
test_that("get_par_list() returns the expected list", {
unknown_pars <- list(list(par_name = "par_beta", min = 0),
list(par_name = "inv_phi", min = 0, type = "meas_par",
par_trans = "inv"))
actual <- get_par_list(unknown_pars)
expected <- list(list(par_name = ... | /tests/testthat/test-loglik_fun.R | permissive | jandraor/readsdr | R | false | false | 12,110 | r | test_that("get_par_list() returns the expected list", {
unknown_pars <- list(list(par_name = "par_beta", min = 0),
list(par_name = "inv_phi", min = 0, type = "meas_par",
par_trans = "inv"))
actual <- get_par_list(unknown_pars)
expected <- list(list(par_name = ... |
library(biomaRt)
human.mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL",
dataset = "hsapiens_gene_ensembl",
host = "useast.ensembl.org")
listAttributes(human.mart)[grep("^mim", listAttributes(human.mart)$name), ]
dat <- getBM(attributes = c("hgnc_symbol", "chromosome_name"... | /R/Genome Analysis Class/part3_2.R | no_license | BJWiley233/Practical-Computer-Concepts-Files | R | false | false | 1,763 | r | library(biomaRt)
human.mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL",
dataset = "hsapiens_gene_ensembl",
host = "useast.ensembl.org")
listAttributes(human.mart)[grep("^mim", listAttributes(human.mart)$name), ]
dat <- getBM(attributes = c("hgnc_symbol", "chromosome_name"... |
#' Add a GRN to a dynwrap object
#'
#' @inheritParams common_param
#' @param regulatory_network A data frame consisting of three columns: `"regulator"`, `"target"`, `"strength"`.
#' @param regulatory_network_sc A data frame consisting of four columns: `"cell_id"`, `"regulator"`, `"target"`, `"strength"`.
#' @param reg... | /R/add_regulatory_network.R | permissive | dynverse/dynwrap | R | false | false | 2,898 | r |
#' Add a GRN to a dynwrap object
#'
#' @inheritParams common_param
#' @param regulatory_network A data frame consisting of three columns: `"regulator"`, `"target"`, `"strength"`.
#' @param regulatory_network_sc A data frame consisting of four columns: `"cell_id"`, `"regulator"`, `"target"`, `"strength"`.
#' @param reg... |
setwd("~/Desktop/ESCP/R Class")
Fueleff <-read.csv("1-FuelEfficiency.csv")
head(Fueleff)
plot(GPM~WT, Fueleff)
model_1= lm(GPM~., data=Fueleff)
summary(model_1)
Fueleff= Fueleff[-1]
model_1= lm(GPM~., data=Fueleff)
summary(model_1)
cor(Fueleff)
library(leaps)
X=Fueleff[ ,2:7]
Y=Fueleff[ ,1]
#new function. I use Ma... | /Multiple Linear Regression Class.R | no_license | Shivanandrai/R-Code- | R | false | false | 1,471 | r | setwd("~/Desktop/ESCP/R Class")
Fueleff <-read.csv("1-FuelEfficiency.csv")
head(Fueleff)
plot(GPM~WT, Fueleff)
model_1= lm(GPM~., data=Fueleff)
summary(model_1)
Fueleff= Fueleff[-1]
model_1= lm(GPM~., data=Fueleff)
summary(model_1)
cor(Fueleff)
library(leaps)
X=Fueleff[ ,2:7]
Y=Fueleff[ ,1]
#new function. I use Ma... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blbglm.R
\name{split_data_glm}
\alias{split_data_glm}
\title{split data into m parts of approximated equal sizes}
\usage{
split_data_glm(data, m)
}
\arguments{
\item{data}{data frame}
\item{m}{numeric, split data into m parts of approximated... | /man/split_data_glm.Rd | permissive | XiaoqiNa/blblm | R | false | true | 404 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blbglm.R
\name{split_data_glm}
\alias{split_data_glm}
\title{split data into m parts of approximated equal sizes}
\usage{
split_data_glm(data, m)
}
\arguments{
\item{data}{data frame}
\item{m}{numeric, split data into m parts of approximated... |
# Load packages and get the data
packages <- c("data.table", "reshape2")
sapply(packages, require, character.only=TRUE, quietly=TRUE)
path <- getwd()
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url, file.path(path, "dataFiles.zip"))
unzip(zipfile =... | /run_analysis.R | no_license | nashr00/Coursera-Project-Getting-and-Cleaning-Data | R | false | false | 2,495 | r | # Load packages and get the data
packages <- c("data.table", "reshape2")
sapply(packages, require, character.only=TRUE, quietly=TRUE)
path <- getwd()
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url, file.path(path, "dataFiles.zip"))
unzip(zipfile =... |
# Exercise 2: working with data frames
# Create a vector of 100 employees ("Employee 1", "Employee 2", ... "Employee 100")
# Hint: use the `paste()` function and vector recycling to add a number to the word
# "Employee"
employees <- paste("Employee", 1:100)
# Create a vector of 100 random salaries for the year 2017
#... | /chapter-10-exercises/exercise-2/exercise.R | permissive | shi-lin-li/book-exercises | R | false | false | 2,625 | r | # Exercise 2: working with data frames
# Create a vector of 100 employees ("Employee 1", "Employee 2", ... "Employee 100")
# Hint: use the `paste()` function and vector recycling to add a number to the word
# "Employee"
employees <- paste("Employee", 1:100)
# Create a vector of 100 random salaries for the year 2017
#... |
filterRegroup <- function(DF.input = NULL) {
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
DF.output <- DF.input;
DF.output <- DF.output[DF.output[['Amount']]>0,];
is.4000s <- (3999.9999 < DF.output[,'AccountCode'] & DF.output[,'AccountCode'] < 5000);
DF.output <- DF.output[is.4000s,];
### ~~~~~~~~~~~~~~~~... | /projects/DataForGood/2016-Youville/code/filterRegroup.R | no_license | paradisepilot/statistics | R | false | false | 1,245 | r |
filterRegroup <- function(DF.input = NULL) {
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
DF.output <- DF.input;
DF.output <- DF.output[DF.output[['Amount']]>0,];
is.4000s <- (3999.9999 < DF.output[,'AccountCode'] & DF.output[,'AccountCode'] < 5000);
DF.output <- DF.output[is.4000s,];
### ~~~~~~~~~~~~~~~~... |
\name{msBP.Gibbs}
\alias{msBP.Gibbs}
\title{
Gibbs sampling for density estimation for msBP model
}
\description{
Gibbs sampling for Markov Chain Motecarlo sampling from the posterior distribution of an msBP model.
}
\usage{
msBP.Gibbs(x, a, b, g0 = "normal", g0par=c(0,1), mcmc,
grid = list(n.points=40, low=0.001, u... | /man/msBP.Gibbs.Rd | no_license | david-dunson/msBP | R | false | false | 8,742 | rd | \name{msBP.Gibbs}
\alias{msBP.Gibbs}
\title{
Gibbs sampling for density estimation for msBP model
}
\description{
Gibbs sampling for Markov Chain Motecarlo sampling from the posterior distribution of an msBP model.
}
\usage{
msBP.Gibbs(x, a, b, g0 = "normal", g0par=c(0,1), mcmc,
grid = list(n.points=40, low=0.001, u... |
# Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of SkeletonCompartiveEffectStudy
#
# 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.a... | /SkeletonCompartiveEffectStudy/R/SynthesizePositiveControls.R | no_license | NEONKID/StudyProtocolSandbox | R | false | false | 9,233 | r | # Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of SkeletonCompartiveEffectStudy
#
# 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.a... |
shinyUI(
dashboardPage(
dashboardHeader(title = "Interactive plotting"),
dashboardSidebar(
fileInput(inputId = "Ifile",
label = "Insert File",
accept = ".csv"),
checkboxInput("Iheader", "Header", TRUE),
... | /shiny/drill_down/app2/ui.R | no_license | askhari139/R_Learning | R | false | false | 789 | r |
shinyUI(
dashboardPage(
dashboardHeader(title = "Interactive plotting"),
dashboardSidebar(
fileInput(inputId = "Ifile",
label = "Insert File",
accept = ".csv"),
checkboxInput("Iheader", "Header", TRUE),
... |
library(data.table)
library(dplyr)
library(plyr)
###MERGE the training and the test sets to create one data set.
# imports list of all features that were collected per subject
features<-fread("./Dataset/features.txt",
sep = " ",
col.names = c("featID","feat")
)
#import... | /run_analysis.R | no_license | Scott-Purvis/R_Cleaning_Project | R | false | false | 3,000 | r | library(data.table)
library(dplyr)
library(plyr)
###MERGE the training and the test sets to create one data set.
# imports list of all features that were collected per subject
features<-fread("./Dataset/features.txt",
sep = " ",
col.names = c("featID","feat")
)
#import... |
\docType{package}
\name{dxR-package}
\alias{dxR}
\alias{dxR-package}
\title{DNAnexus R Client Library}
\description{
dxR is an R extension containing API wrapper functions
for interacting with the new DNAnexus platform.
}
\details{
\tabular{ll}{ Package: \tab dxR\cr Type: \tab Package\cr
Version: \tab 0.167.0\c... | /src/R/dxR/man/dxR-package.Rd | permissive | vhuarui/dx-toolkit | R | false | false | 446 | rd | \docType{package}
\name{dxR-package}
\alias{dxR}
\alias{dxR-package}
\title{DNAnexus R Client Library}
\description{
dxR is an R extension containing API wrapper functions
for interacting with the new DNAnexus platform.
}
\details{
\tabular{ll}{ Package: \tab dxR\cr Type: \tab Package\cr
Version: \tab 0.167.0\c... |
# Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test Pet")
model.instance <- Pet$new()
test_that("id", {
# tests for the property `id` (integer)
# uncomment below to test the property
#expect_equal(model.instance$`id`, "EXPECTED_R... | /tests/testthat/test_pet.R | no_license | wing328/petstore-r-client | R | false | false | 1,250 | r | # Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test Pet")
model.instance <- Pet$new()
test_that("id", {
# tests for the property `id` (integer)
# uncomment below to test the property
#expect_equal(model.instance$`id`, "EXPECTED_R... |
# This file contains all the code needed to parse and print various sections of your CV
# from data. Feel free to tweak it as you desire!
#' Create a CV_Printer object.
#'
#' @param data_location Path of the spreadsheets holding all your data. This can
#' be either a URL to a google sheet with multiple sheets conta... | /CV_printing_functions.R | no_license | nuhorchak/resume | R | false | false | 8,725 | r | # This file contains all the code needed to parse and print various sections of your CV
# from data. Feel free to tweak it as you desire!
#' Create a CV_Printer object.
#'
#' @param data_location Path of the spreadsheets holding all your data. This can
#' be either a URL to a google sheet with multiple sheets conta... |
library(leaflet)
library(htmltools)
library(shiny)
shinyServer(function(input,output){
output$mymap<-renderLeaflet({
birds<-read.csv("BirdTestData.csv", header = TRUE)
birds$COMMON.NAME<-as.character(birds$COMMON.NAME)
birds$LOCALITY<-as.character(birds$LOCALITY)
birds$OBSERVATION.DATE<-as.Dat... | /server.R | no_license | cincysam6/eBird-Data-Explorer | R | false | false | 737 | r | library(leaflet)
library(htmltools)
library(shiny)
shinyServer(function(input,output){
output$mymap<-renderLeaflet({
birds<-read.csv("BirdTestData.csv", header = TRUE)
birds$COMMON.NAME<-as.character(birds$COMMON.NAME)
birds$LOCALITY<-as.character(birds$LOCALITY)
birds$OBSERVATION.DATE<-as.Dat... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PsFunctions.R
\name{stratifyByPs}
\alias{stratifyByPs}
\title{Stratify persons by propensity score}
\usage{
stratifyByPs(data, numberOfStrata = 5, stratificationColumns = c())
}
\arguments{
\item{data}{A data frame with the three columns desc... | /man/stratifyByPs.Rd | permissive | Mbaroudi/CohortMethod | R | false | true | 1,573 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PsFunctions.R
\name{stratifyByPs}
\alias{stratifyByPs}
\title{Stratify persons by propensity score}
\usage{
stratifyByPs(data, numberOfStrata = 5, stratificationColumns = c())
}
\arguments{
\item{data}{A data frame with the three columns desc... |
testlist <- list(Beta = 0, CVLinf = -1.37672045511449e-268, FM = 3.81959242373749e-313, L50 = 0, L95 = 0, LenBins = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), LenMids = numeric(0), Linf = 0, MK = 0, Ml = numeric(0), Prob = structure(0, .Dim = c(1L, 1L)), SL50 = -1.685... | /DLMtool/inst/testfiles/LBSPRgen/AFL_LBSPRgen/LBSPRgen_valgrind_files/1615828464-test.R | no_license | akhikolla/updatedatatype-list2 | R | false | false | 487 | r | testlist <- list(Beta = 0, CVLinf = -1.37672045511449e-268, FM = 3.81959242373749e-313, L50 = 0, L95 = 0, LenBins = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), LenMids = numeric(0), Linf = 0, MK = 0, Ml = numeric(0), Prob = structure(0, .Dim = c(1L, 1L)), SL50 = -1.685... |
## -- Pre Processing --
m_no_transform <- function(m) m %>% m_set(pre_processors = c(m$pre_processors, "none"))
m_center_transform <- function(m) m %>% m_set(pre_processors = c(m$pre_processors, "center"))
m_scale_transform <- function(m) m %>% m_set(pre_processors = c(m$pre_processors, "scale"))
m_standardize_tran... | /Lib/m_pre_processing.R | no_license | matthew-coad/Climate | R | false | false | 3,339 | r | ## -- Pre Processing --
m_no_transform <- function(m) m %>% m_set(pre_processors = c(m$pre_processors, "none"))
m_center_transform <- function(m) m %>% m_set(pre_processors = c(m$pre_processors, "center"))
m_scale_transform <- function(m) m %>% m_set(pre_processors = c(m$pre_processors, "scale"))
m_standardize_tran... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DE.R
\name{format_de_results}
\alias{format_de_results}
\title{Minimal formatting of de results}
\usage{
format_de_results(dds, txi, contrast, keep_stats = TRUE, add_mean_dds = FALSE)
}
\arguments{
\item{dds}{The DESeqDataSet object returned ... | /man/format_de_results.Rd | no_license | CharlesJB/rnaseq | R | false | true | 1,396 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DE.R
\name{format_de_results}
\alias{format_de_results}
\title{Minimal formatting of de results}
\usage{
format_de_results(dds, txi, contrast, keep_stats = TRUE, add_mean_dds = FALSE)
}
\arguments{
\item{dds}{The DESeqDataSet object returned ... |
\name{frNN}
\alias{frNN}
\alias{frnn}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Find the Fixed Radius Nearest Neighbors}
\description{
This function uses a kd-tree to find the fixed radius nearest neighbors
(including distances) fast.
}
\usage{
frNN(x, eps, sort = TRUE, search = "kdtree", bu... | /man/frNN.Rd | no_license | augustoqm/dbscan | R | false | false | 2,292 | rd | \name{frNN}
\alias{frNN}
\alias{frnn}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Find the Fixed Radius Nearest Neighbors}
\description{
This function uses a kd-tree to find the fixed radius nearest neighbors
(including distances) fast.
}
\usage{
frNN(x, eps, sort = TRUE, search = "kdtree", bu... |
\name{apclusterDemo}
\alias{apclusterDemo}
\title{Affinity Propagation Demo}
\description{
Runs affinity propagation demo for randomly generated data set
according to Frey and Dueck
}
\usage{
apclusterDemo(l=100, d=2, seed=NA, ...)
}
\arguments{
\item{l}{number of data points to be generated}
\item{d}{dimension... | /fuzzedpackages/apcluster/man/apclusterDemo.Rd | no_license | akhikolla/testpackages | R | false | false | 2,649 | rd | \name{apclusterDemo}
\alias{apclusterDemo}
\title{Affinity Propagation Demo}
\description{
Runs affinity propagation demo for randomly generated data set
according to Frey and Dueck
}
\usage{
apclusterDemo(l=100, d=2, seed=NA, ...)
}
\arguments{
\item{l}{number of data points to be generated}
\item{d}{dimension... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{dm3_HindIII_10000}
\alias{dm3_HindIII_10000}
\title{Genomic features for dm3 genome and HindIII restriction enzyme at 10 Kbp}
\format{A data frame with 13047 rows and 5 variables:
\describe{
\item{chr:}{chromos... | /man/dm3_HindIII_10000.Rd | no_license | 4DGenome/hicfeatures | R | false | true | 857 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{dm3_HindIII_10000}
\alias{dm3_HindIII_10000}
\title{Genomic features for dm3 genome and HindIII restriction enzyme at 10 Kbp}
\format{A data frame with 13047 rows and 5 variables:
\describe{
\item{chr:}{chromos... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plumber.R
\name{cr_plumber_pubsub}
\alias{cr_plumber_pubsub}
\title{Plumber - Pub/Sub parser}
\usage{
cr_plumber_pubsub(message = NULL, pass_f = function(x) x)
}
\arguments{
\item{message}{The pubsub message}
\item{pass_f}{An R function that... | /man/cr_plumber_pubsub.Rd | no_license | heoa/googleCloudRunner | R | false | true | 1,630 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plumber.R
\name{cr_plumber_pubsub}
\alias{cr_plumber_pubsub}
\title{Plumber - Pub/Sub parser}
\usage{
cr_plumber_pubsub(message = NULL, pass_f = function(x) x)
}
\arguments{
\item{message}{The pubsub message}
\item{pass_f}{An R function that... |
titanic <- read.csv("C:/Users/Sareh/Documents/GitHub/sds383d/data/titanic.csv")
titanic <- titanic[!is.na(titanic$Age), ] # remove missing age rows
x <- matrix(titanic$Age, nrow(titanic), 1)
y <- as.numeric(titanic$Survived == "Yes")
log.posterior.function <- function(beta, X, y) {
- 0.5 * t(beta) %*% beta... | /Section 3/exercise 3.2&3.3.R | no_license | Sarehkch/Statistical-Modeling-II | R | false | false | 610 | r |
titanic <- read.csv("C:/Users/Sareh/Documents/GitHub/sds383d/data/titanic.csv")
titanic <- titanic[!is.na(titanic$Age), ] # remove missing age rows
x <- matrix(titanic$Age, nrow(titanic), 1)
y <- as.numeric(titanic$Survived == "Yes")
log.posterior.function <- function(beta, X, y) {
- 0.5 * t(beta) %*% beta... |
# Functions for smoothing or dis-aggregating data over map regions.
# m is a map object
# z is a named vector
# res is resolution of sampling grid
# span is kernel parameter (larger = smoother)
# span = Inf is a special case which invokes cubic spline kernel.
# span is scaled by the map size, and is independent of... | /R/smooth.r | no_license | meensrinivasan/maps | R | false | false | 4,935 | r | # Functions for smoothing or dis-aggregating data over map regions.
# m is a map object
# z is a named vector
# res is resolution of sampling grid
# span is kernel parameter (larger = smoother)
# span = Inf is a special case which invokes cubic spline kernel.
# span is scaled by the map size, and is independent of... |
# 1. Eliminar datos sin valor
# El parámetro na.strings rellena los valores NA
data <- read.csv("/cloud/project/missing-data.csv", na.strings = "")
# na.omit elimina las filas que contengan un string NA
data.limpia <- na.omit(data)
# Consultando valores específicos con is.na
is.na(data[4,2])
is.na(data[4,1])
is.na(... | /01-missing-data.R | no_license | arturocuya-upc/upc-cc50-lab-2021-09-22 | R | false | false | 1,256 | r |
# 1. Eliminar datos sin valor
# El parámetro na.strings rellena los valores NA
data <- read.csv("/cloud/project/missing-data.csv", na.strings = "")
# na.omit elimina las filas que contengan un string NA
data.limpia <- na.omit(data)
# Consultando valores específicos con is.na
is.na(data[4,2])
is.na(data[4,1])
is.na(... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/openfair.R
\name{openfair_tef_tc_diff_lm}
\alias{openfair_tef_tc_diff_lm}
\title{Run an OpenFAIR simulation at the TEF/TC/DIFF/LM levels}
\usage{
openfair_tef_tc_diff_lm(tef, tc, diff, lm, n = 10^4, verbose = FALSE)
}
\arguments{
\item{tef}{P... | /man/openfair_tef_tc_diff_lm.Rd | permissive | davidski/evaluator | R | false | true | 1,489 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/openfair.R
\name{openfair_tef_tc_diff_lm}
\alias{openfair_tef_tc_diff_lm}
\title{Run an OpenFAIR simulation at the TEF/TC/DIFF/LM levels}
\usage{
openfair_tef_tc_diff_lm(tef, tc, diff, lm, n = 10^4, verbose = FALSE)
}
\arguments{
\item{tef}{P... |
#' Cleans separate Fama-French data frames and adds them to global environment after
#' splitting the set into thirds, quintiles and deciles.
#'
#' @param data A data set.
#' @param spr Calculates spreads for relevant data by default.
cleanSubFF <- function(data, spr=TRUE) {
# Checks for cleanData folder
if (!fil... | /R/cleanSubFF.R | no_license | jrs9/FFAQR | R | false | false | 2,049 | r | #' Cleans separate Fama-French data frames and adds them to global environment after
#' splitting the set into thirds, quintiles and deciles.
#'
#' @param data A data set.
#' @param spr Calculates spreads for relevant data by default.
cleanSubFF <- function(data, spr=TRUE) {
# Checks for cleanData folder
if (!fil... |
library(car)
library(lmtest)
library(nortest)
library(tseries)
library(moments)
library(gvlma)
library(perturb)
library(leaps)
library(MASS)
load("hbat.RData")
# remember, X19 is overall customer satisfaction with the company and indpendent variables measure different perceptions on the service
# provided
Full<-lm(X19... | /RegressionModels.R | no_license | Linika30/DataAnalysis | R | false | false | 7,540 | r | library(car)
library(lmtest)
library(nortest)
library(tseries)
library(moments)
library(gvlma)
library(perturb)
library(leaps)
library(MASS)
load("hbat.RData")
# remember, X19 is overall customer satisfaction with the company and indpendent variables measure different perceptions on the service
# provided
Full<-lm(X19... |
#' Add graph object to a graph series object
#' @description Add a graph object to an extant graph series object for
#' storage of multiple graphs across a sequential or temporal one-dimensional
#' array.
#' @param graph a graph object to add to the graph series object.
#' @param graph_series a graph series object to w... | /R/add_to_series.R | no_license | UweBlock/DiagrammeR | R | false | false | 3,495 | r | #' Add graph object to a graph series object
#' @description Add a graph object to an extant graph series object for
#' storage of multiple graphs across a sequential or temporal one-dimensional
#' array.
#' @param graph a graph object to add to the graph series object.
#' @param graph_series a graph series object to w... |
testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.32784507357642e-308, 7.00600001791186e-310, 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_beta/AFL_communities_individual_based_sampling_beta/communities_individual_based_sampling_beta_valgrind_files/1615832663-test.R | no_license | akhikolla/updatedatatype-list2 | R | false | false | 362 | r | testlist <- list(m = NULL, repetitions = 0L, in_m = structure(c(2.32784507357642e-308, 7.00600001791186e-310, 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... |
#' Plot score tracks
#'
#'
#' @param dame GRanges object containing a region of interest, or detected with
#' find_dames
#' @param window Number of CpG sites outside (up or down-stream) of the DAME
#' should be plotted. Default = 0.
#' @param positions Number of bp sites outside (up or down-stream) of the DAME
#' ... | /R/score_tracks.R | permissive | JianpingQuan/DAMEfinder | R | false | false | 8,640 | r | #' Plot score tracks
#'
#'
#' @param dame GRanges object containing a region of interest, or detected with
#' find_dames
#' @param window Number of CpG sites outside (up or down-stream) of the DAME
#' should be plotted. Default = 0.
#' @param positions Number of bp sites outside (up or down-stream) of the DAME
#' ... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/output.R
\name{write_flags}
\alias{write_flags}
\title{Add quality flags to a data file in Station Exchange Format version 0.2.0}
\usage{
write_flags(infile, qcfile, outpath, note = "")
}
\arguments{
\item{infile}{Character string giving the ... | /man/write_flags.Rd | permissive | csaybar/dataresqc | R | false | true | 1,171 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/output.R
\name{write_flags}
\alias{write_flags}
\title{Add quality flags to a data file in Station Exchange Format version 0.2.0}
\usage{
write_flags(infile, qcfile, outpath, note = "")
}
\arguments{
\item{infile}{Character string giving the ... |
#---------------------------------------------------------------------------------------------------------
# labeledHeatmap.R
#---------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------
#
# .rever... | /R/labeledHeatmap.R | no_license | cran/WGCNA | R | false | false | 20,188 | r | #---------------------------------------------------------------------------------------------------------
# labeledHeatmap.R
#---------------------------------------------------------------------------------------------------------
#--------------------------------------------------------------------------
#
# .rever... |
setwd("C://Users//varun//Downloads")
library(dplyr)
best <- function(state, outcome) {
## Read outcome data
outcome_data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
## Check that state and outcome are valid
unique_state<- unique(outcome_data$State)
if(!(state %in% unique_state)){
... | /best.R | no_license | varun15chaturvedi/ProgrammingAssignment2 | R | false | false | 1,649 | r | setwd("C://Users//varun//Downloads")
library(dplyr)
best <- function(state, outcome) {
## Read outcome data
outcome_data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
## Check that state and outcome are valid
unique_state<- unique(outcome_data$State)
if(!(state %in% unique_state)){
... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggurvplot_arguments.R
\name{ggsurvplot_arguments}
\alias{ggsurvplot_arguments}
\title{ggsurvplot Argument Descriptions}
\arguments{
\item{fit}{an object of class survfit.}
\item{data}{a dataset used to fit survival curves. If not supplied th... | /man/ggsurvplot_arguments.Rd | no_license | woodhaha/survminer | R | false | true | 9,969 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggurvplot_arguments.R
\name{ggsurvplot_arguments}
\alias{ggsurvplot_arguments}
\title{ggsurvplot Argument Descriptions}
\arguments{
\item{fit}{an object of class survfit.}
\item{data}{a dataset used to fit survival curves. If not supplied th... |
library(ggplot2)
library(dplyr)
options(na.action = "na.exclude")
# Helper functions ----------------------------------------------------------
deseas <- function(var, month) {
resid(lm(var ~ factor(month))) + mean(var, na.rm = TRUE)
}
# Explore multiple cities ----------------------------------------------------
t... | /R/tx-explore-all-dplyr.r | permissive | pssguy/plyrToDplyr | R | false | false | 5,383 | r | library(ggplot2)
library(dplyr)
options(na.action = "na.exclude")
# Helper functions ----------------------------------------------------------
deseas <- function(var, month) {
resid(lm(var ~ factor(month))) + mean(var, na.rm = TRUE)
}
# Explore multiple cities ----------------------------------------------------
t... |
library(dplyr)
library(Seurat)
library(reticulate)
library(sctransform)
library(SeuratData)
library(cowplot)
library(ggplot2)
use_virtualenv(".virtualenvs/UMAP")
#Path to output from cellranger
cell_ranger_path_drug
cell_ranger_path_veh
drug.data <- Read10X(data.dir = paste0(cell_ranger_path_drug, "/outs/filtered_fe... | /Seurat_pipeline_sctransform.R | no_license | JohnstoneKats/SingleCellAnalysis | R | false | false | 8,512 | r | library(dplyr)
library(Seurat)
library(reticulate)
library(sctransform)
library(SeuratData)
library(cowplot)
library(ggplot2)
use_virtualenv(".virtualenvs/UMAP")
#Path to output from cellranger
cell_ranger_path_drug
cell_ranger_path_veh
drug.data <- Read10X(data.dir = paste0(cell_ranger_path_drug, "/outs/filtered_fe... |
#' @title Decompose portfolio variance risk into factor/residual risk
#'
#' @description Decompose portfolio variance risk into factor/residual risk
#'
#'
#' @param object fit object of class \code{tsfm}, or \code{ffm}.
#' @param weights a vector of weights of the assets in the portfolio. Default is NULL,
#' in whi... | /R/portVolDecomp.R | no_license | FoeinLove/FactorAnalytics | R | false | false | 6,624 | r | #' @title Decompose portfolio variance risk into factor/residual risk
#'
#' @description Decompose portfolio variance risk into factor/residual risk
#'
#'
#' @param object fit object of class \code{tsfm}, or \code{ffm}.
#' @param weights a vector of weights of the assets in the portfolio. Default is NULL,
#' in whi... |
workers <- 11
prefix <- "DIY 3.4 GHz Intel Core i7 6core"
if (workers > 1) {
library(doParallel)
cl <- makeForkCluster(nnodes = workers)
registerDoParallel(cl)
}
library(caret)
library(xgboost)
library(lubridate)
library(sessioninfo)
rand_int <- sample.int(10000, 1)
set.seed(598)
dat <- twoClassSim(2000, noiseV... | /Ubuntu/2016 DIY 3.4 GHz Intel Core i7 6core/xgb_fork_11.R | no_license | minghao2016/par-caret-bench | R | false | false | 1,349 | r | workers <- 11
prefix <- "DIY 3.4 GHz Intel Core i7 6core"
if (workers > 1) {
library(doParallel)
cl <- makeForkCluster(nnodes = workers)
registerDoParallel(cl)
}
library(caret)
library(xgboost)
library(lubridate)
library(sessioninfo)
rand_int <- sample.int(10000, 1)
set.seed(598)
dat <- twoClassSim(2000, noiseV... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ppeldata.R
\docType{data}
\name{manifesto}
\alias{manifesto}
\title{Manifesto Project Main Dataset of Party Preferences}
\format{An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 4456 rows and 175 columns.}
\... | /man/manifesto.Rd | no_license | vladtarko/ppeldata | R | false | true | 1,573 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ppeldata.R
\docType{data}
\name{manifesto}
\alias{manifesto}
\title{Manifesto Project Main Dataset of Party Preferences}
\format{An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 4456 rows and 175 columns.}
\... |
###############################################################################
#
#
#
#
#
#
###############################################################################
lxfit <- function(x,y, anal_type = 'normal', isIncrease = TRUE,mEXTREMA = 2,
nsamps = 50000,
nburn ... | /R/samplerfunctions.R | no_license | david-dunson/lxsplines | R | false | false | 11,199 | r | ###############################################################################
#
#
#
#
#
#
###############################################################################
lxfit <- function(x,y, anal_type = 'normal', isIncrease = TRUE,mEXTREMA = 2,
nsamps = 50000,
nburn ... |
#dataset
dataFile <- "exdata_data_NEI_data.zip"
#file path
if (!file.exists(dataFile)) {
download.file(dataUrl, dataFile, mode = "wb")
}
library(dplyr)
library(ggplot2)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#Question #5:
#How have emissions fr... | /plot5.R | no_license | Walfare123/EDA-Week-4-Course-Project | R | false | false | 1,057 | r | #dataset
dataFile <- "exdata_data_NEI_data.zip"
#file path
if (!file.exists(dataFile)) {
download.file(dataUrl, dataFile, mode = "wb")
}
library(dplyr)
library(ggplot2)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#Question #5:
#How have emissions fr... |
testlist <- list(A = structure(c(1.38997190089722e-309, 3.81575932257023e-236, 3.81571422914747e-236), .Dim = c(1L, 3L)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result) | /multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613125915-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 226 | r | testlist <- list(A = structure(c(1.38997190089722e-309, 3.81575932257023e-236, 3.81571422914747e-236), .Dim = c(1L, 3L)), B = structure(0, .Dim = c(1L, 1L)))
result <- do.call(multivariance:::match_rows,testlist)
str(result) |
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/get_sites.R
\name{get_sites}
\alias{get_sites}
\title{Return Site Information.}
\usage{
get_sites(sitename, altmin, altmax, loc, gpid)
}
\arguments{
\item{sitename}{A character string representing the full or partial site name.}
\ite... | /man/get_sites.Rd | no_license | Nitin-Joshi-perk/neotoma | R | false | false | 1,168 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/get_sites.R
\name{get_sites}
\alias{get_sites}
\title{Return Site Information.}
\usage{
get_sites(sitename, altmin, altmax, loc, gpid)
}
\arguments{
\item{sitename}{A character string representing the full or partial site name.}
\ite... |
## TODO: when it's get stable, define billing object
## PrivilegesEnum <- setSingleEnum("Privileges", c(""))
## Billing
## to make it simple to update, return a list, not an object, because no action defined an this object
Billing <- setRefClass("Billing", contains = "Item",
fields = list(id = "c... | /R/Billing-class.R | permissive | teamcgc/sevenbridges | R | false | false | 3,346 | r | ## TODO: when it's get stable, define billing object
## PrivilegesEnum <- setSingleEnum("Privileges", c(""))
## Billing
## to make it simple to update, return a list, not an object, because no action defined an this object
Billing <- setRefClass("Billing", contains = "Item",
fields = list(id = "c... |
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