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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7208201a4b9309d78b6ffe2f2c477fe59ff7c5f9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DescTools/examples/Large.Rd.R | 931cfdfc638eeb1fabd64af20bb68e7cecc97bd9 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 385 | r | Large.Rd.R | library(DescTools)
### Name: Extremes
### Title: Kth Smallest/Largest Values
### Aliases: Large Small HighLow
### Keywords: arith
### ** Examples
x <- sample(1:10, 1000, rep=TRUE)
Large(x, 3)
Large(x, k=3, unique=TRUE)
# works fine up to x ~ 1e6
x <- runif(1000000)
Small(x, 3, unique=TRUE)
Small(x, 3, unique=FALSE... |
4d7ea47ea887d718ca169aef21ff6066c75193b5 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /mDAG/R/main.R | 4b72e70b9fa0f18e50bdfdb6b348ada32a8f8388 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,362 | r | main.R | MatrixtoGraph_undirected=function(skeleton,data){
graph=list()
arcs <- which(skeleton!=0,arr.ind = T)
arcs=t(apply(arcs,1,function(z) colnames(data)[z]))
colnames(arcs)=c('from','to')
rownames(arcs)=NULL
graph$arcs=arcs
for (i in 1:ncol(data)){
nbr=arcs[which(arcs[,2]==colnames(data)[i]),1]
graph$... |
db425e355f3825bb59c0ad7a7cf531df53ef9b84 | 4e50d2345a2cfeb3c9ecb02187f88e753d1ed83c | /bin/02.taxonomy/pca.R | 0a471acfa3628136d58a38a671fe75370796de1b | [] | no_license | ms201420201029/real_metagenome_pipeline | 7c7b54e5e8a798933387f960256ebb849e9c2200 | e8f0b188f21975305565d06e4fefe4f4c9adc1f7 | refs/heads/master | 2020-04-05T06:41:34.349662 | 2018-12-04T05:48:52 | 2018-12-04T05:48:52 | 156,646,750 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,345 | r | pca.R | args <- commandArgs("T")
profile <- read.table(args[1], head = T, check.names= F)
group <- read.table(args[2], head = F, check.names = F, row.names = 1, col.names = c("", "group.name"))
library(ade4)
profile.pca = dudi.pca(t(profile), scannf = F, nf = 2)
pdf(args[3]);
layout(matrix(c(1,1,1,3,
1,1,1,3,
1,1,1,3,
2,... |
72c58fc59d33389ce7ab33ef6a57dbb27f732fb1 | bee8ce64fbb1714b18a80ed1d8deda2a006aef29 | /code/fmd-vnt-only.R | f117f902eea66197ea95571dfd5d08c17f3c7408 | [] | no_license | GustafRydevik/hindcasting-fmd | 722fed853f202045df9aa24af5b246ff70d06361 | dbad4798d72088b12285037234999b5ddedb08b7 | refs/heads/master | 2021-01-17T06:12:36.580948 | 2017-12-07T06:57:32 | 2017-12-07T06:57:32 | 53,070,026 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,403 | r | fmd-vnt-only.R |
output.path<-"./output"
code.path<-"./code"
function.path<-"./code/functions"
textdata.path<-"./textdata"
binarydata.path<-"./binarydata"
#bronsvoort_training_data
bronsvoort_training_data_clean<-bronsvoort_training_data_clean[bronsvoort_training_data_clean$FMD_cELISA!=0,]
bronsvoort_training_data_clean<-bronsv... |
45130c861fa6e3a8340ec0f79afb9ad623dc2fb9 | 154109aaf7ca07fd67145fd094ca9be21c2b1d62 | /man/boot.Rd | c7db30fb406632a7b3547bef0a3c7696d7420765 | [] | no_license | emvolz/treedater | 6e5c81919e3d6e218c6608b133da03506d3bdef6 | 7b8a72aa0ea71ded4cde6cd287529aa8ae679c68 | refs/heads/master | 2022-05-10T12:01:51.266204 | 2022-03-11T15:36:18 | 2022-03-11T15:36:18 | 75,198,675 | 20 | 10 | null | 2021-10-07T18:19:39 | 2016-11-30T15:12:18 | R | UTF-8 | R | false | true | 3,127 | rd | boot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/boot.R
\name{boot}
\alias{boot}
\title{Estimate of confidence intervals of molecular clock parameters with user-supplied set of bootstrap trees}
\usage{
boot(
td,
tres,
ncpu = 1,
searchRoot = 1,
overrideTempConstraint = TRUE,
over... |
b14b7dffeb3a9a70ae03f7284d630e9f42921028 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/phenofit/examples/R2_sign.Rd.R | ef56dc997b5e54b45bd8f858347842f1747a6d3e | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 196 | r | R2_sign.Rd.R | library(phenofit)
### Name: R2_sign
### Title: Critical value of determined correlation
### Aliases: R2_sign
### ** Examples
R2_critical <- R2_sign(30, NumberOfPredictor = 2, alpha = 0.05)
|
eb2c0f827a35af39431e47a88797b0876c8ff3ac | ce9ddae103c7b3cead05ed65c824292e82ce42b6 | /R/data.R | d850ac7ed5556af7cb6a8fe502b03f288c13c2c7 | [] | no_license | guhjy/MIMOmicsData | 48100658d00bd9217f04bb29a355715591cc1f44 | 3e9bc06aecc40f6b783ce46f2c2261dea7d40eb1 | refs/heads/master | 2020-04-13T11:48:22.226015 | 2017-10-24T09:35:30 | 2017-10-24T09:35:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 757 | r | data.R | #' Observed allele proportions in X1 dataset
#'
#' Observed allele proportions in X1 dataset coded as 0, 1 and 2
#'
#'
#' @format A matrix with SNPs as columns and genotype as rows
#'
#' @source Korcula and Vis data
"Probs"
#' Observed allele proportions in X2 dataset
#'
#' Observed allele proportions in X2 dataset co... |
d55082f14352da1f06d9e41875805324a6f8334f | d51d8b4ada0e926491ed7c5561baee2bd7c56b98 | /R/timepicker.R | c77b5ad9404dd235d85d4405fc1aaa45b9ee96ce | [] | no_license | PythonJournalist/dash-more-components | 25242305c27288bb401b46f6afec13c451dbf6d6 | 976f9e3476fec69667525adbfa3c0dec3c420ec2 | refs/heads/master | 2023-03-14T09:55:25.225700 | 2021-02-28T22:01:31 | 2021-02-28T22:01:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 810 | r | timepicker.R | # AUTO GENERATED FILE - DO NOT EDIT
timepicker <- function(id=NULL, value=NULL, format=NULL, maxDetail=NULL, maxTime=NULL, minTime=NULL, disabled=NULL, disableClock=NULL, locale=NULL) {
props <- list(id=id, value=value, format=format, maxDetail=maxDetail, maxTime=maxTime, minTime=minTime, disabled=disabled, d... |
86b725cb8ff2cc4f20de124465531d4867dc5d2d | 3053a557531d328b430b69fb7851dcb2dde22c93 | /dataone/man/listQueryEngines.Rd | 0343d63b866007003b604f9cb19476a349e14c30 | [
"Apache-2.0"
] | permissive | KillEdision/rdataone | e3bfe188ed1eba1f01d6e256f3a98a64104125ef | 3ec0efb67cc3ba951d44ce13e5750bfec8caaac4 | refs/heads/master | 2021-01-15T20:24:17.028477 | 2015-07-29T01:16:47 | 2015-07-29T01:16:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 486 | rd | listQueryEngines.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/D1Node.R
\name{listQueryEngines}
\alias{listQueryEngines}
\title{List the query engines available for a DataONE member node or coordinating node}
\usage{
listQueryEngines(node, ...)
}
\arguments{
\item{node}{The CNode or MNode to list... |
12b57fac30969031525f83c98a733564a3d749da | 155a862d1e3de6ac5993c3d47b5b97552d9d66e5 | /Projects/Project4.R | 1ed0adbdcd18379b65247a1cfd2cf314cb211b64 | [] | no_license | dsmilo/DATA643 | e11b45e6ae4d4778391b7141c8937cabb7d32722 | ff11869d3fa28603cfed2149855b6234a8c679f8 | refs/heads/master | 2020-12-25T15:08:16.558349 | 2016-10-05T16:38:14 | 2016-10-05T16:38:14 | 61,250,691 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,686 | r | Project4.R | library(recommenderlab)
# items & time ####
data(MovieLense)
ratings_movies <- MovieLense[rowCounts(MovieLense) > 20, colCounts(MovieLense) > 50]
which_set <- sample(x = 1:5,
size = nrow(ratings_movies),
replace = TRUE)
for(i_model in 1:5) {
which_train <- which_set == i_mode... |
812b7669f52d8ab1f6177440f3b758c7d370061e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tsDyn/examples/lstar.Rd.R | 9aea59c5603b350e3381c75e0375870a582000f7 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 823 | r | lstar.Rd.R | library(tsDyn)
### Name: LSTAR
### Title: Logistic Smooth Transition AutoRegressive model
### Aliases: LSTAR lstar
### Keywords: ts
### ** Examples
#fit a LSTAR model. Note 'maxit': slow convergence
mod.lstar <- lstar(log10(lynx), m=2, mTh=c(0,1), control=list(maxit=3000))
mod.lstar
#fit a LSTAR model without a co... |
9229c72e187036417ce447573a9af7890c601bc1 | 20d92a0b85ac3b0eb7713087f3fa8d324c8d3cb1 | /plot4.R | 1e4ce247d1cb5557e3a4b1abecadd2c26f422eaa | [] | no_license | caioaf/ExData_Plotting1 | 4d81d7cc58fd9033445b454565c4acd244067ef2 | 1f955de9da5224c1a12a78e95ae4071c8332c90a | refs/heads/master | 2020-03-25T00:50:34.082227 | 2018-08-01T21:08:31 | 2018-08-01T21:08:31 | 143,208,656 | 0 | 0 | null | 2018-08-01T21:05:26 | 2018-08-01T21:05:25 | null | UTF-8 | R | false | false | 1,073 | r | plot4.R | # Load data
library(dplyr)
fullData <- read.table("household_power_consumption.txt", na.strings = "?", sep = ";", header = TRUE)
fullData$Date <- as.Date(fullData$Date, "%d/%m/%Y")
data <- filter(fullData, Date >= "2007-02-01", Date <= "2007-02-02")
datetime <- paste(data$Date, data$Time)
data$datetime <- as.POSIXct(... |
42446f4065f2c739eba52944f0eaaa0e2700abc3 | 990baad326e1d000ed592fa2f0535432d3babd5c | /WeightMedian_example.R | 7ab347147b37dd641e4118871a85f31e7497d6d3 | [] | no_license | jm2ds/RStudioTest | 0797a839951e6a421ac4de65804b14a7d8de8b6f | 58bd1c91a6ebebf1f49f4d4ecaf7bebda13d9815 | refs/heads/master | 2016-09-05T16:02:35.706605 | 2014-05-23T17:25:53 | 2014-05-23T17:25:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 544 | r | WeightMedian_example.R | weightmedian <- function(directory, day) {
files_list <- dir(directory, full.names = TRUE) #creates a list of files
dat <- data.frame() #creates an empty data frame
for (i in 1:5) { #loops through the files, rbinding them together
dat <- rbind(dat, read.csv(files_list[i]))
... |
3825ca45dc5fbc19232bf8c7eb8576466cbd5680 | a23f02eb2cf26250d1ca69f4da78f764bf8f0c48 | /cathChiSquare.R | 6b36740ea39ca54007626fc0e9618d16a1f738fe | [] | no_license | mihirt41/ChiSquareCodeCVI | 82c11b427c70252aefbec7229b01e733c1568184 | 892cc78a9c93c9b9b1710a8183cc72589ccc9f37 | refs/heads/master | 2021-05-14T17:18:40.068175 | 2018-01-02T18:34:34 | 2018-01-02T18:34:34 | 116,044,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,589 | r | cathChiSquare.R | ##Script Written By Mihir Trivedi
#################################
##loading data
library(readr)
cathData <- read_csv("F:/cath/cathCasesRaceCorrected.csv")
View(cathData)
##removing weird line in data
cathData<-cathData[-1,]
##reconfirming correct dataset/numbers correspond with those in ... |
83043b76140534193e520e6946c5b08b0ae38e0f | 6eb0741293bbcaeadd68c2475596c809d6f582da | /fin.r | 38a36175e37769484d7c85558e2c0d786bd3d0cf | [
"MIT"
] | permissive | Sivatharshen/Diabetes-prediction-using-ML-and-comparing-the-accuracy-on-different-algorithms | 9718bd2239b082ec213e7657b50878c044781caa | 4ef67dd7d0b67a6f9c96fb2eee302452ee1537e0 | refs/heads/master | 2022-05-11T16:28:47.962667 | 2020-04-23T18:04:53 | 2020-04-23T18:04:53 | 258,289,269 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,814 | r | fin.r | library(corrplot)
library(caret)
pima <- read.csv("diabetes.csv", col.names=c("Pregnant","Plasma_Glucose","Dias_BP","Triceps_Skin","Serum_Insulin","BMI","DPF","Age","Diabetes"))
head(pima) # # visualize the header of Pima data
str(pima)
sapply(pima, function(x) sum(is.na(x)))
pairs(pima, panel = panel.smooth)
co... |
197c58e0aa72c14b04a792e026e1dbd3fe438a6d | 18dfced9b24ab6ab91d9e08292a4907c58110928 | /advent2018/03a.r | 44fa600b3e6d5b243058a72f7bbe39be01cad0e9 | [] | no_license | Spacejoker/problem | 84c7eeef4c8bcd1d074b67faad09cf7a7511bcfd | 37fade5619205e5e8cc2ac700dee89555721952e | refs/heads/master | 2022-12-22T13:23:35.548050 | 2022-12-19T07:02:46 | 2022-12-19T07:02:46 | 729,643 | 2 | 1 | null | 2022-12-18T05:22:23 | 2010-06-19T19:44:41 | Java | UTF-8 | R | false | false | 668 | r | 03a.r | myString <- "Hello, World!"
print (myString)
f <- file("stdin")
open(f)
xdim = 5000
ydim = xdim
pelt = seq(0,0,length.out=xdim*ydim)
while(length(line <- readLines(f,n=1)) > 0) {
row <- unlist(strsplit(line, " "))
coords <- unlist(strsplit(row[3], ","))
y <- strtoi(coords[1])+1
x <- strtoi(substr(coords[2],1... |
229099c62399dac7d5438947f6d846d6a02405db | 5f8ae737ef12df3871dcd274a9ef4b550529d592 | /GenerateData.R | a98b518eae6e29eef7a4ef9b59a0b003a1795f75 | [] | no_license | jclaramunt/LinkageData | b523a7a169cc03b52dd747b2e01403834ef3b7a1 | 98cf13b3b5eb887f34eacdfdeaccab8b5ba0e02e | refs/heads/master | 2020-12-05T10:46:57.513961 | 2020-01-07T07:48:21 | 2020-01-07T07:48:21 | 232,085,589 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,841 | r | GenerateData.R | # Example1 #
# Not unique key
dimA<-20
#Dataset A
set.seed(11)
day<-sample(x=1:28, size = dimA, replace=TRUE)
month<-sample(x=1:12, size = dimA, replace=TRUE)
year<-sample(x=1920:2020, size = dimA, replace=TRUE)
datebirth<-paste(day,month,year,sep = '/')
hair<-sample(x=c("Blond","Brown","Black","Red","Wh... |
dc9e356f137ed6fe9962ef2e1abd4c7688ff0b44 | 09fede3ddb1fe90d486ead390e557275514ebcd7 | /man-roxygen/example.R | 6f899c3a6d4858cded8203361f3e1efee1108cb2 | [] | no_license | Mrlirhan/mlr3extralearners | 05723212d650c623a8bcf5ba4eb809eb7cc6707e | 3655ccecb43f18837bc6a2078672636a399bab5d | refs/heads/main | 2023-06-29T09:31:58.113359 | 2021-08-04T17:42:26 | 2021-08-04T17:42:26 | 392,993,757 | 1 | 0 | null | 2021-08-05T10:05:51 | 2021-08-05T10:05:50 | null | UTF-8 | R | false | false | 232 | r | example.R | <%
lrn = mlr3::lrn(id)
%>
#' @examples
#' # stop example failing with warning if package not installed
#' learner = suppressWarnings(mlr3::lrn("<%= id %>"))
#' print(learner)
#'
#' # available parameters:
#' learner$param_set$ids()
|
ba6baf6833cba388a695bcc87c93fb680bd100bd | 11a2bffc556f663f912cab24ed3b07f461e6665d | /man/PCARaster.Rd | 989fff1a65af89dfa5f7176deb8d59c16f637c7a | [] | no_license | vijaybarve/ENMGadgets | dd94ab1ff1a1e7067f44178b17db057abadcdaca | 70d0ce36f84e94a523d4be01b0464e103bc25d4d | refs/heads/master | 2021-01-25T12:30:19.119139 | 2018-06-26T17:06:21 | 2018-06-26T17:06:21 | 14,593,644 | 1 | 2 | null | 2016-09-14T22:32:19 | 2013-11-21T16:52:17 | R | UTF-8 | R | false | true | 1,236 | rd | PCARaster.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PCARaster.R
\name{PCARaster}
\alias{PCARaster}
\title{PCARaster - PCA of Raster files}
\usage{
PCARaster(BioStackFiles = NA, LoadingFile = NA, CompImpFile = NA,
OPfolder = NA)
}
\arguments{
\item{BioStackFiles}{- ESRI ASCII grid files of pr... |
1f1f6bb31f336abe96f69e51439f737a0d041517 | 9b81b5fa72d6d123e65ac8ccff0c7830476d03a0 | /ui.R | f2dff10e3f1372c3a26724af6568a2c26c2aa058 | [] | no_license | pg-environmental-stats/SSD-Analysis-Internal | 73c6495310ecfda5e4153c818a822aea2a9cff14 | 2ed3276b6370b4707a712f2c17043f1ac2e715b3 | refs/heads/main | 2023-06-20T02:03:26.199371 | 2021-07-20T17:46:42 | 2021-07-20T17:46:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,725 | r | ui.R | installedPackages <- rownames(installed.packages())
SSDpackages <- c("devtools","shiny","shinyjs","shinyalert","shinyWidgets",# only if for table output
"htmlwidgets","magrittr","parallel","formattable","DT",
"RColorBrewer","multcomp","openxlsx","ADGofTest",
"... |
171424677c4fdeb24d69752e07ac580bdc4fa8b2 | 4f20b35b8bb927979f2bf50567de446fc4fceacd | /run_analysis.R | 1fab8e2239c7584ad162c25714f33b159217a3da | [] | no_license | markg1965/Coursera_Cleaning_Data | f6f4a318f8bf1b0173ac9c21aeee02ee6fab84e1 | c4aa00a71629734a982bb4370898428dfd4d1c49 | refs/heads/master | 2021-01-10T20:06:56.538140 | 2015-09-23T13:00:04 | 2015-09-23T13:00:04 | 42,933,580 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,495 | r | run_analysis.R | run_analysis <- function()
library(digest)
library(dplyr)
library(data.table)
library(tidyr)
#File Pathways
X_Test_Data_File <- "c:/Users/Mark/Documents/UCI HAR Dataset/test/X_test.txt"
Y_Test_Data_File <- "c:/Users/Mark/Documents/UCI HAR Dataset/test/Y_test.txt"
Subject_Test_File <- "c:/Users/Mark/Document... |
ad61d81f8746b2cac1dafee46485bc60da7c870e | 18e3cca024a121995bbe8068f0709593d716f378 | /man/place_spec_on_tree.Rd | 6cbcd262b3ff6d44dc70c7deb41616e98ca976d4 | [] | no_license | annakat/evolspec | 56febd447fde3cafe7a51c4d552a14afc62a66ee | ad74616e56c8a5be7272ea0b136a9f4c2d189010 | refs/heads/master | 2021-01-12T17:00:58.586504 | 2016-10-16T15:07:47 | 2016-10-16T15:07:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 610 | rd | place_spec_on_tree.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/place_spec_on_tree.R
\name{place_spec_on_tree}
\alias{place_spec_on_tree}
\title{Places an ublabeled spectrum on a tree}
\usage{
place_spec_on_tree(tree, spec, model = "BM", method = "ml")
}
\arguments{
\item{tree}{Tree of class "phylo".}
\i... |
9429fdfbfd54c27c51560b57973db4c0a8724bec | ff47639ba6d38d47b5224276022c200563e639ac | /main.R | d17434a8161342b153db27309f74588540093025 | [] | no_license | markvregel/greenest_city | 36d9bba7b048e1b46b665b67473b6229347fba29 | 27ad8767e98ab8b626e33735c3020bdbcd6f9315 | refs/heads/master | 2021-01-10T07:14:23.395572 | 2016-01-13T08:56:09 | 2016-01-13T08:56:09 | 49,502,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,363 | r | main.R | # Hakken en zagen
# Mark ten Vregelaar and Jos Goris
# 12 January 2016
# Greenest city: find the city with the highest NDVI
# Start with empty environment
rm(list=ls())
# Get required libraries
library(raster)
library(rgdal)
library(rasterVis)
# Read R Code from function in map R
source("R/NDVIextract.R")
#set inpu... |
a0a453a5ed6ea76b8e1f3e7b41fe26993fb226d6 | 00a092422e4a8ae30fe356324c2be1e26ab0e1c1 | /R/compress_space.R | beb9bfacf2f6725eb3955a1a721a0ec9e6dc2a75 | [] | no_license | cran/shattering | 103c1f844ba0c1cae5fa529325135a696f492842 | 0ba348d25ac129d4884fe4696f506b55195bbadd | refs/heads/master | 2023-07-15T15:38:16.930545 | 2021-08-21T12:50:02 | 2021-08-21T12:50:02 | 298,777,328 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,644 | r | compress_space.R | #' Function to compress the space based on the equivalence relations.
#'
#' This function compresses the input space according to the equivalence relations, i.e., it compresses whenever an example has other elements inside its open ball but having the same class label as the ball-centered instance.
#' @param M sparse m... |
cb07f958d32904a13c14a75496af0d2a1f7f685b | 27127fa3729cca6769185a3468a1311de123d260 | /Git.code_dHIT.upload.R | bc6970c377dba4bfc84addcef61b5768a0a845c1 | [] | no_license | alexachivu/dHITpaper_2021 | 4f71b681e1442d3aaa323962d79e43be2b993446 | 1539452c509a5f6ccc0197cf9308d308e11ee43c | refs/heads/main | 2023-04-17T19:15:01.033368 | 2021-11-21T03:58:59 | 2021-11-21T03:58:59 | 429,227,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,220 | r | Git.code_dHIT.upload.R | #Code to upload on git: --> dHIT paper
### Packages used: rtracklayer, GenomicRanges, BRGenomics, DEseq2, ggplot.
#1 .Function: Imports BigWigs into R as GRanges object
#Parameters:
# BW_plus.path = path to positive/plus strand PROseq
# BW_minus.path = path to negative/minus strand PROseq
# ... |
10af9480f4a212ac9c045aadf0cb683db8b69de1 | 7f3f07533ba8d565b076a24a7fff3d4d05e3d417 | /rankhospital.R | 6da4a014f427716122e4c1d4b50f8d59538a1483 | [] | no_license | brazeiro63/ProgrammingAssignment3 | bf2d07e91e656d7f1a609a6e3f5a639a34afe930 | dd87558af90c75f0a3fb04b9a210876acf255183 | refs/heads/master | 2021-01-02T09:15:00.830019 | 2015-07-25T17:04:01 | 2015-07-25T17:04:01 | 39,693,732 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,735 | r | rankhospital.R | rankhospital <- function(state, outcome, num = "best"){
## Read outcome data
outcomeData <- read.csv("outcome-of-care-measures.csv",
colClasses = "character")
## Clean parameter
outcomeVec <- strsplit(outcome, split = " ")[[1]]
outcomeCap... |
c3ee75600a12138104c79f013a9d9170a1658dc3 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query55_query04_1344n/query55_query04_1344n.R | 486ee3706eb37216d22fb93b75c3919c1fe8172c | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | query55_query04_1344n.R | 7c0d7227c72298ccca274f5958638700 query55_query04_1344n.qdimacs 331 555 |
5140d4171f164008230dd2a99becf2a37741bc69 | b5904ad0771f8ab6f49ad4af85f64241b51f8396 | /code (2).r | e5a83580bb5760bc6c7de2e95aa4e5501f151a5c | [] | no_license | jmbuecken/macroeconometrics | 8b4ec76fbc7dd27000ae943dc971642e5a7cfcc0 | 3590c9bf3a21722ee8211aa33f95b819543cdfc3 | refs/heads/main | 2023-06-02T14:11:29.904925 | 2021-06-25T14:43:40 | 2021-06-25T14:43:40 | 373,765,742 | 0 | 2 | null | 2021-06-15T15:39:40 | 2021-06-04T07:59:16 | R | UTF-8 | R | false | false | 1,226 | r | code (2).r | ### Macroeconomics Project ###
require(tidyverse)
rm(list = ls())
# read data
# Data: https://fred.stlouisfed.org/series/DCOILBRENTEU
# choose greatest timeframe!
oil.df <- read_csv("DCOILBRENTEU.csv")
colnames(oil.df) = c("Date", "Price")
oil.df$Price = as.numeric(oil.df$Price)
all(!is.na(oil.df)) # Table has NA... |
4fbde4bd939322515f31b1877ec5914d2ea417f9 | ae3919b76ab9025a661fc1e2f323ba1950095375 | /man/repYpost.Rd | 9b6d91564ea3e6d7db782fa73016a24ed94361f1 | [] | no_license | cran/geoCount | 2d9a86fe7ef2d29dcee2cf94a5a30f052d0972c5 | 86880c19678c46bb245eae3215126cf1f4199ab5 | refs/heads/master | 2021-01-20T05:52:39.771026 | 2015-01-20T00:00:00 | 2015-01-20T00:00:00 | 17,696,337 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 947 | rd | repYpost.Rd | \name{repYpost}
\alias{repYpost}
\title{Generate Replicated Data with Posterior Samples of Latent Variables}
\usage{
repYpost(res.m, L, Y.family="Poisson")
}
\arguments{
\item{res.m}{a list with elements containing the posterior samples of latent variables and parameters for observed locations}
\item{L}{a vector... |
e04327756cffdedb01c682d59bf613b7d2277f35 | 8e45eafc3ab0d1f65c0b811fd4f313598a9618c9 | /R/data.R | 064f816bb61962fe5a4c6678a11b4016b88bc874 | [] | no_license | SydneyBioX/CiteFuse | 9e5c53a238ddb6265c6dd30038239392cd54c551 | b6075864d8930967946022fdfbbf57a6a120061f | refs/heads/master | 2023-02-16T07:41:24.133860 | 2023-02-07T04:19:48 | 2023-02-07T04:19:48 | 223,888,651 | 28 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,627 | r | data.R | #' A subset of ECCITE-seq data (control)
#'
#' Data from Mimitou et al. ECCITE-seq PBMC control sample data, which is a list
#' of three matrices of RNA, ADT and HTO
#'
#' @usage data(CITEseq_example, package = 'CiteFuse')
#'
#' @references Mimitou, E. P., Cheng, A., Montalbano, A., et al. (2019).
#' Multiplexed detect... |
e099e27289e08009904a0a05ae9ca90afc5309cc | b8edae4a4880310b701eddade612e88d13df9cee | /cachematrix.R | 08378d78724b495931e6b72fa4268a6cec107298 | [] | no_license | Fluctuzz/ProgrammingAssignment2 | f34c282e83ab7063f1963c498fd4d83b8a02ee6d | 9be26460fc8b4e194167fa70da3357e73bb2923a | refs/heads/master | 2020-05-01T19:52:23.481707 | 2019-03-25T20:50:52 | 2019-03-25T20:50:52 | 177,658,852 | 0 | 0 | null | 2019-03-25T20:24:47 | 2019-03-25T20:24:46 | null | UTF-8 | R | false | false | 1,065 | r | cachematrix.R | ## The following functions can create a "special" matrix and to calculate the inverse
## of that "special" matrix. The inverse of the "special" matrix is cached.
## Creates a "special" matrix, which can cache its inverse. Returns a list of functions
makeCacheMatrix <- function(x = matrix()) {
inverse <- NULL
... |
c09c09c1d22af21aa0a64490a6d015ec6160ec6a | 8e20060c5475f00e9a513f76725bcf6e54f2068a | /man/delete_vertex_attr.Rd | b3ccb7652743d72eb5c84e7fb82b1cad915c1079 | [] | no_license | DavisVaughan/rigraph | 8cc1b6c694ec03c1716d8b471d8f910e08c80751 | a28ac7fe7b45323a38ffe1f13843bb83bdb4278f | refs/heads/master | 2023-07-18T20:34:16.631540 | 2021-09-20T22:55:53 | 2021-09-20T22:55:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,191 | rd | delete_vertex_attr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/attributes.R
\name{delete_vertex_attr}
\alias{delete_vertex_attr}
\alias{remove.vertex.attribute}
\title{Delete a vertex attribute}
\usage{
delete_vertex_attr(graph, name)
}
\arguments{
\item{graph}{The graph}
\item{name}{The name of the ver... |
4f3aabdfa475f3b5556bc19c70e2d7e5e633eb60 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.analytics/man/kinesisanalyticsv2_add_application_input_processing_configuration.Rd | 64c5a58a49b6f7d10cd8de14220790c309ca53cb | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 2,357 | rd | kinesisanalyticsv2_add_application_input_processing_configuration.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kinesisanalyticsv2_operations.R
\name{kinesisanalyticsv2_add_application_input_processing_configuration}
\alias{kinesisanalyticsv2_add_application_input_processing_configuration}
\title{Adds an InputProcessingConfiguration to a SQL-based Kine... |
df90ad2fac7f25b56c1cb8376b6e4076b1a34287 | d90b947d2703d4b3b9e6f83d11be703ea02fef60 | /man/missing_s3.Rd | f6c81ac006134cea3eda7f22aacfdb6c9a1130dd | [] | no_license | andrie/devtools | b0276d804cb1f8f552743ba72ad46f6015610073 | f297de4debe1b02cec281f6387be4a2c2f430d1c | refs/heads/master | 2021-01-15T19:22:30.648803 | 2012-07-23T09:17:48 | 2012-07-23T09:17:48 | 2,048,076 | 10 | 0 | null | null | null | null | UTF-8 | R | false | false | 329 | rd | missing_s3.Rd | \name{missing_s3}
\alias{missing_s3}
\title{Find missing s3 exports.}
\usage{
missing_s3(pkg = NULL)
}
\arguments{
\item{pkg}{package description, can be path or package
name. See \code{\link{as.package}} for more information}
}
\description{
The method is heuristic - looking for objs with a period
in their ... |
8453a9fd9ab2285c2c86c66942ed27c5ef1db633 | 62cfdb440c9f81b63514c9e545add414dc4d5f63 | /R/qat_plot_block_distribution_1d.R | 80d2592da9cec21e67cf0b201eae2e6f036e080b | [] | no_license | cran/qat | 7155052a40947f6e45ba216e8fd64a9da2926be4 | 92975a7e642997eac7b514210423eba2e099680c | refs/heads/master | 2020-04-15T16:53:45.041112 | 2016-07-24T01:26:59 | 2016-07-24T01:26:59 | 17,698,828 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,239 | r | qat_plot_block_distribution_1d.R | qat_plot_block_distribution_1d <-
function(resultlist, filename, blocksize=-1, measurement_name="", directoryname="", plotstyle=NULL) {
## functionality: plot statistical parameters of a blockwise scan of a measurement-vector
## author: André Düsterhus
## date: 23.02.2010
## version: A0.1
## input: resultlist from qat_... |
44de70740c6785655c620c8e740e07b8e84ab304 | b4c1bc98a83dc8f4ad492911faca4c709c146288 | /R/population_attributable_fraction.R | 39720cdbca2fbb35fb165aa5d633f498e094aff0 | [] | no_license | danielgils/ITHIM-R | facd3e74e3b4cdea279245372e3f2ec3bcb35cc0 | 7e306f0aea3e6ea21521104206a0281e5882af13 | refs/heads/master | 2023-09-05T05:33:00.779268 | 2021-11-10T10:07:42 | 2021-11-10T10:07:42 | 277,875,917 | 0 | 0 | null | 2020-07-07T17:04:21 | 2020-07-07T17:04:20 | null | UTF-8 | R | false | false | 389 | r | population_attributable_fraction.R | #' Calculate population attributable fraction
#'
#'
#'
#' @param pop
#' @param cn
#' @param mat
#'
#' @return population attributable fractions by demographic group
#'
#' @export
population_attributable_fraction <- function(pop, cn, mat){
##!! hard coding of indices: 1=sex, 2=age or age_cat
paf <- apply(mat... |
6f07277b44bb3845015b4b94b1a7b317bfc0f0b7 | 93c8032caa615b4d59d9a6d5f48e415cfde02874 | /Interface/cmdstan-2.16.0/data/data.data.R | 4e826ad0733e6dd5d76aa2839acbdfa717908ea2 | [
"BSD-3-Clause"
] | permissive | paolariva2/PART_BayesPACS | 1e13c49c22929bc08e05d523a24040617250bd79 | c773a4cec41b4b798ebc3a7ff3ea87f5b23457da | refs/heads/master | 2021-01-15T23:59:28.951925 | 2017-08-11T10:52:28 | 2017-08-11T10:52:28 | 99,946,790 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,395 | r | data.data.R | N <- 66
Q <- 8
Y <-
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0)
X <-
structure(c(0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,... |
4762a858aba0806d0e7a93cf8e8f94ab414380cf | 13c395755f0ba62cd4d6abdc981c964d6fe559d2 | /Python/Py_projects/project/R/전력량_지도데이터.R | a1f83f238352bb27d31f9629d98151ef875a6eef | [] | no_license | db3124/bigdata_maestro | c18d081f6c5d4900747466185bcb1ffce1387bb3 | 6edcf7316c157056b2ec6d4ce0f20239e8d8f524 | refs/heads/master | 2022-11-28T11:05:13.483421 | 2020-08-13T09:30:59 | 2020-08-13T09:30:59 | 263,229,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,547 | r | 전력량_지도데이터.R | # 라이브러리 설치
install.packages('stringi')
install.packages('devtools')
devtools::install_github('cardiomoon/kormaps2014')
install.packages('mapproj')
install.packages('ggiraphExtra')
install.packages('ggthemes')
# 라이브러리 로드
library(kormaps2014)
library(dplyr)
library(ggplot2)
library(readxl)
library(mapproj)
library('ggir... |
54cbd218053cc3d74f50812ce16ad65816e22472 | 2c71a5672f9dcdcd584cf467a9b5ca4e249ab72a | /man/condpowcure.Rd | f18b59b2a739ccbde71132c336ddcc16ed04723d | [] | no_license | raredd/desmon | 2eb3b8d2b2254dc689aba53b20a211f677f2fc93 | 1d92579b7f946e50cf9184b056ce9c4742a60f24 | refs/heads/master | 2022-06-21T10:16:28.224741 | 2022-05-26T00:09:41 | 2022-05-26T00:09:41 | 210,757,161 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 4,452 | rd | condpowcure.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/condpowcure.R
\name{condpowcure}
\alias{condpowcure}
\title{Perform Simulation to Calculate Conditional Power of a Logrank Test}
\usage{
condpowcure(
time,
status,
rx,
nsamp = 500,
crit.val = 1.96,
control = 0,
control.cure,
c... |
7603109215378abdfbc74c01c7d4a9a45e9adff4 | 3eeb2c22f4c378914b6594823d976fddf7653965 | /man/min_bic_2020_11_26_death.Rd | 0c3bcaf40a1497c0147b296183b7fd420bc24205 | [
"MIT"
] | permissive | vjcitn/sars2death | 943a262e71c9c5896d627fe54eaa3e052ee132bb | 6ae6e9f04c8cf5f4515ab0b03fdf3908e17d54fe | refs/heads/main | 2023-01-15T18:19:34.986340 | 2020-11-27T12:21:20 | 2020-11-27T12:21:20 | 316,250,492 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 404 | rd | min_bic_2020_11_26_death.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{min_bic_2020_11_26_death}
\alias{min_bic_2020_11_26_death}
\title{a statewide collection of best AR/MA model orders, 11/26, death only}
\format{
S3 instance
}
\usage{
min_bic_2020_11_26_death
}
\description{
a stat... |
06ca28eed7324cb44ab9d5a84da66c8e53242da6 | dc284fe45eea59ade9e1a75095af6285be51af3c | /C/make_figure_simulation_sub.R | ed9cbb0a10a5cabc1e98def22ce4d60f7f8ea2f2 | [] | no_license | gui11aume/analytic_combinatorics_seeding | c6d47ecd4a7df4428e0d0a24a578bc276bb01004 | f6dabd7cf074f7069bcf12bca996fe35530b19f0 | refs/heads/master | 2020-05-26T00:29:23.183443 | 2017-10-18T16:09:52 | 2017-10-18T16:09:52 | 84,981,428 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,343 | r | make_figure_simulation_sub.R | f = function(z, p, d) 1 - ((1-p)*z)^(d+1)
g = function(z, p, d) 1 - p*z*(1-((1-p)*z)^d)/(1-(1-p)*z)
dg = function(z, p, d) (((d + 1)*p + (d*p^2 - d*p)*z)*(-(p - 1)*z)^d - p) / ((p^2 - 2*p + 1)*z^2 + 2*(p - 1)*z + 1)
Newton = function(p, d) {
# Set initial value close to solution.
oldz = 1
while (TRUE) {
... |
0b687702bddd98719a828ece716a0d0551a260e6 | 201a8a213e2993159f3ee1e2923af2d54b0546d2 | /R/job_create.R | 6350bcea5269442e2f9e557e771699c3b84d15f1 | [
"MIT"
] | permissive | djnavarro/workbch | b66bd43b39e3c42bd0eef0d089fa4ec9d3698cb7 | 6cc7a28e92a24acee1f61ad60c124f701108a96a | refs/heads/master | 2020-06-12T23:38:07.160985 | 2020-04-23T02:24:58 | 2020-04-23T02:24:58 | 194,461,800 | 41 | 3 | NOASSERTION | 2019-08-09T04:47:23 | 2019-06-30T01:05:15 | R | UTF-8 | R | false | false | 4,740 | r | job_create.R | #' Create a new job
#'
#' @param jobname name of the job to create
#' @param description brief description of the job
#' @param status should be "active", "inactive", "complete", "abandoned", "masked"
#' @param owner should be a name or a nickname
#' @param priority numeric
#' @param tags a string containing comma sepa... |
045ec8a6e06e73bde73078c67ce57cd855560e43 | 99fd4277aa21e4a702c73b26a95a201489d9b415 | /scripts/21_extractStations/hbm_extract_stations/hbm_extract_stationsI.R | 29f03b696be62489ced929981568b6e6b7070708 | [] | no_license | neumannd/HBM_tools | 774bc67afdaaa25281ed1e64b4a478ca9746d3c7 | e287f2876fc3ae549c99263fb6f2e7aba70d5a8a | refs/heads/master | 2020-03-10T14:41:52.339594 | 2018-06-19T18:00:18 | 2018-06-19T18:00:18 | 129,432,728 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,065 | r | hbm_extract_stationsI.R | library('ncdf4')
source('support/hbm_constants.R')
source('support/read_varlist.R')
source('support/read_varmapping_L0L1.R')
source('support/generate_varmapping_L2L2.R')
source('support/createL1file.R')
source('support/createL2file.R')
source('support/get_basic_grid_data.R')
source('support/latlon2cell.R')
source('... |
c0a26ccb0e69c4030c63e29a2281beb8c400b02d | a59b0019cd455e5c8c59263d5248b388eb235257 | /tests/testthat/test-family-utils.R | f1c9d75ea2ba475f867da0454d3005c81f48d522 | [
"MIT"
] | permissive | dill/gratia | 4df529f5e636a0139f5c355b52a2924bebf7aca4 | 26c3ece0e6a6298ab002b02019b0ea482d21dace | refs/heads/master | 2023-04-08T18:35:18.730888 | 2023-03-20T12:52:33 | 2023-03-20T12:52:33 | 160,169,115 | 0 | 0 | NOASSERTION | 2018-12-03T09:54:30 | 2018-12-03T09:54:30 | null | UTF-8 | R | false | false | 46,276 | r | test-family-utils.R | ## Test family and link utilities
## load packages
library("testthat")
library("gratia")
library("mgcv")
library("gamm4")
val <- 1
l <- list(mer = 1:3, gam = 1:3)
test_that("link() works with a glm() model", {
f <- link(m_glm)
expect_type(f, "closure")
expect_identical(f, gaussian()$linkfun)
})
test_tha... |
34d17c894c9b1e2996959ad892f4dd73e40f861a | 112b7f027320cf8ccffefc19b552f7427886eb50 | /run_analysis.R | f83a33faa7b1e46d2f37a5700dab156d7afb0f2c | [] | no_license | hiicharles/getdata-008 | f70769860e9e49bf2fb79c0d67a0c38e07aa522d | 9f3a9f617b575fe07d1c98d8dcecaee6692f78a7 | refs/heads/master | 2021-01-15T13:01:44.771228 | 2014-10-26T18:03:30 | 2014-10-26T18:03:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,009 | r | run_analysis.R | ## Getting and Cleaning Data Course Project
## Course ID: getdata-008
## Submission login (email): hiicharles@gmail.com
## Date: 2014-10-26i
## Instructions
## You should create one R script called run_analysis.R that does the following.
## 1. Merges the training and the test sets to create one data set.
## 2. Extra... |
fb71a840be2dac05f1acfc03309f26084224bd1b | e470b224ede4a63bec073def3511184d904f9702 | /scripts/parse_picard_complexity.R | 23d9182a695ad26c2013b484d86507d0a9bf88e9 | [] | no_license | mardzix/cutandtag-standard | 091a136a850f777d0d93ec2db7d2426caa9df225 | 48b1d9b63b826798e9858ef949bf17461cfeff61 | refs/heads/master | 2023-05-27T19:54:14.113583 | 2021-06-17T08:36:49 | 2021-06-17T08:36:49 | 377,759,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 759 | r | parse_picard_complexity.R |
args <- commandArgs(trailingOnly=TRUE)
out.file <- args[length(args)]
reports <- args[-length(args)]
parse_picard <- function(report){
cat(paste0("*** Reading file ",basename(report),"\n"))
d <- read.table(file = report,
nrows = 2,
stringsAsFactors=FALSE,
s... |
bed5b6697f6675a93936febba4e25df3cb8f2bfe | 876801a26a4c31dd95e7bbab466729ea4619feae | /cachematrix.R | becbf3c12f953652cb916310062af2a52da4cfa7 | [] | no_license | spbachu/ProgrammingAssignment2 | 79ba236e7b29c8b464ea16eb5a7447330b96eacd | bc414f6cb85c16287aad7c0b203997f55a31490d | refs/heads/master | 2020-12-01T09:28:46.284336 | 2014-05-17T21:52:55 | 2014-05-17T21:52:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 940 | r | cachematrix.R | ## Caches the inverse of a matrix computation
##makeChacheMatrix creates a special "matrix", which is really a list containing a
##set,get,setmatrix,getmatrix functions
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
g... |
8e43b5e623bea306e8300ca15f694e575b5d7585 | 8ff11b361633b2c805b8fc2ea415c51cca22929f | /R/formatting.R | fb90615d4bcd340c3b0e3e430c20920d360264f6 | [] | no_license | Pascal-Schmidt/tblGoat | 689da114ba6b147b30bf696921a0301dd7e4a686 | ad5964a8eaaec322889a48093031909a88eda587 | refs/heads/master | 2022-04-29T02:09:40.178624 | 2020-04-27T20:03:57 | 2020-04-27T20:03:57 | 258,673,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,101 | r | formatting.R | #' Title
#'
#' @param df
#' @param df_mode
#' @param grouping_var
#' @param header
#' @param mode_tbl
#'
#' @return
#'
#' @examples
formatting <- function(df, df_mode, grouping_var, header = TRUE, mode_tbl) {
df_mode %>%
# make variabel name bold
dplyr::mutate(name = ifelse(name %in% colnames(df),
past... |
99f3f51243f6fbd77db456913b71f8c6061d7be1 | 6345226fb321ac2719b99c2c6e2d5693bc74ecec | /inst/doc/polychrome.R | 7c127369c53fe2e167ce42e44870e405b0f08e3e | [] | no_license | cran/Polychrome | 0d8b9a387573e82d4b96212409fb3d1051a4708c | 51e403e6e32306e3b0d272e2818e2e1b1c7aa099 | refs/heads/master | 2022-05-31T04:34:19.681591 | 2022-05-03T06:20:12 | 2022-05-03T06:20:12 | 85,283,338 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,409 | r | polychrome.R | ## -----------------------------------------------------------------------------
library(Polychrome)
## -----------------------------------------------------------------------------
mypal <- kelly.colors(22)
## ----fig.width=7, fig.height=5------------------------------------------------
swatch(mypal)
## --... |
e9352936154761c40ac8adbdc9f9c2411de3c4d3 | d75a1e1e95ae70ce048a0c26fb0f9c283fd5dd70 | /man/NOAH_2B.Rd | 926e08ddc2a44780e55d42b843dea3742ca81179 | [] | no_license | Owain-S/kmdata | 49d65b279e7e84e170550f7d1fbdc8573f28784c | 22569373a88f64ef480ea895c8ef7b7b5ced260e | refs/heads/master | 2023-05-25T22:58:06.758825 | 2021-06-01T19:36:49 | 2021-06-01T19:36:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 970 | rd | NOAH_2B.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{NOAH_2B}
\alias{NOAH_2B}
\title{NOAH, figure 2B}
\format{
A data frame of 235 observations and 3 variables:
\tabular{lll}{
\tab \code{time} \tab event time (in months) \cr
\tab \code{event} \tab OS event indicator ... |
73c023d5df7967d3a2428bf7f5784acea6e449b4 | af9e48f7a5f4a2ff9547122d866ba7f5dc63a89b | /tidy-data.R | d53aaebf7d3c1f6c3170d7b6ad574dbb06d7bd16 | [
"MIT"
] | permissive | joethorley/bioRxiv-028274 | 3844d9461755f6d2afd9d2647b7efcf52b8007c2 | 37002f17a9ec7732b25cf91ecf4560caa3d5adeb | refs/heads/master | 2021-01-02T08:36:01.044493 | 2018-07-04T00:57:46 | 2018-07-04T00:57:46 | 99,027,969 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,422 | r | tidy-data.R | source("header.R")
pdo <- readRDS("output/clean/pdo.rds")
leks <- readRDS("output/clean/leks.rds")
counts <- readRDS("output/clean/counts.rds")
wells <- readRDS("output/clean/wells.rds")
# buffer each well by 60 m
wells %<>% st_buffer(set_units(60, "m"))
# produce list of sf objects by year
wells_years <- list()
yea... |
b4774d5cc90125c87d270d0410eb54f9c838fcf8 | 18fe92d34e448d7f20043384100454c4a96fbfd6 | /R/plot.R | 7691de4b8dd02f01faed7eb06792866b1597c963 | [] | no_license | cran/EpiModel | 7564726177fa86a6a7f60c07c6e2c86147919180 | bee708b011b790a5518c7cbf9ea52a3fe0752ee7 | refs/heads/master | 2023-06-22T02:25:35.668350 | 2023-06-20T17:20:05 | 2023-06-20T17:20:05 | 17,679,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 82,209 | r | plot.R |
# Main Exported Methods ---------------------------------------------------
#' @title Plot Data from a Deterministic Compartmental Epidemic Model
#'
#' @description Plots epidemiological data from a deterministic compartment
#' epidemic model solved with \code{\link{dcm}}.
#'
#' @param x An \code{EpiMode... |
2010c4e2cd7de476b47b990255aded82e22472d3 | e68e99f52f3869c60d6488f0492905af4165aa64 | /man/nnf_conv_tbc.Rd | 0315c055b7de93f41a23c74db87f61c49d586778 | [
"MIT"
] | permissive | mlverse/torch | a6a47e1defe44b9c041bc66504125ad6ee9c6db3 | f957d601c0295d31df96f8be7732b95917371acd | refs/heads/main | 2023-09-01T00:06:13.550381 | 2023-08-30T17:44:46 | 2023-08-30T17:44:46 | 232,347,878 | 448 | 86 | NOASSERTION | 2023-09-11T15:22:22 | 2020-01-07T14:56:32 | C++ | UTF-8 | R | false | true | 699 | rd | nnf_conv_tbc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nnf-conv.R
\name{nnf_conv_tbc}
\alias{nnf_conv_tbc}
\title{Conv_tbc}
\usage{
nnf_conv_tbc(input, weight, bias, pad = 0)
}
\arguments{
\item{input}{input tensor of shape \eqn{(\mbox{sequence length} \times
batch \times \mbox{in\_channels})}}
... |
93db0fa39ed6db26a3eea920fdaf8cec2df5b985 | 852db404bb02ebb5a9508d46e17da13f61eee7bb | /plot3.R | e219208cbcf7f0a44b4547d9abcffc72adcc9a24 | [] | no_license | manjunathyelipeta/ExData_Plotting1 | 48b6a20246ed21d055e35cc02f8782c46ef5b8a8 | e50fcba509575b710aef39fd0a697ac607ec92a0 | refs/heads/master | 2021-01-15T18:41:46.509700 | 2015-09-13T13:39:55 | 2015-09-13T13:39:55 | 42,116,736 | 0 | 0 | null | 2015-09-08T14:17:12 | 2015-09-08T14:17:12 | null | UTF-8 | R | false | false | 1,023 | r | plot3.R | ass_eda1 = read.table("household_power_consumption.txt",header = TRUE,sep = ';',stringsAsFactors = FALSE,colClasses = c(rep("character",2),rep("numeric",7)),na.strings = "?")
ass_eda1$Date = as.Date(ass_eda1$Date,"%d/%m/%Y")
filtered_df = ass_eda1[ass_eda1$Date >= as.Date("2007-02-01") & ass_eda1$Date <= as.Date("20... |
f51d0ef891edcb6c3bb79a0bb38bf7032bf6b9ce | e55183e9cb2effdb3ee1613237c2964b22460510 | /app.R | cc92f7386ced30cf60f5b278f0d28d87d094e71e | [] | no_license | peiqingzhang/movie_predictor | 9df2e553a87ba4fc1b4b20db4db60044a11c2985 | c3960b369627ea9d2da3ecf1a9924c4c2e551e8d | refs/heads/main | 2023-03-04T05:14:49.234816 | 2021-02-10T20:42:36 | 2021-02-10T20:42:36 | 333,903,251 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,490 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(httr)
library(tidyverse)
library(shiny)
values <- reactiveValues()
values$review_text = ""... |
b11fad52981119b510334d774b60eb02f14d89f2 | eefdf8ef5b585f11fa348fef725c59a6c10d5d53 | /R/mes.R | b64b3dfe87accdda605e6b341c6b4d0714b066c7 | [] | no_license | bupianlizhugui/mes | a1fb89375141945cc415ece98d4fddd928014b4f | 12ed2eb75d639aa83867462997f3e8b3507f7992 | refs/heads/master | 2021-01-22T21:37:12.027728 | 2016-03-30T15:48:08 | 2016-03-30T15:48:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,540 | r | mes.R |
H.se <- function(X) {
if(any(X==0)) X <- X[X>0]
p <- X/sum(X)
-sum(p * log2(p))
}
mes <- function(dat, N, seed=1, ix=FALSE, hx=FALSE) {
dat.table <- is.table(dat)
counts <- dat
if(!dat.table) counts <- table(counts)
if(any(counts<=0)) counts <- counts[counts>0]
if(ix & dat.table) {stop('Cannot re... |
44eda255273059f235546e857020474c3b0ac536 | d4bb4f725018c3c727976bd87d69e21582f0894e | /loadExcel.R | 3731c559b9601b6e96847a5b72be527d7a507d18 | [] | no_license | prayaggordy/HiMCM | 3bd30b53db99db909508c3c0f286cab9ab9eecad | 17b90ed17426cdbfa2e4592e8465ca72314b2d47 | refs/heads/master | 2020-04-05T16:28:34.961860 | 2018-11-19T23:37:18 | 2018-11-19T23:37:18 | 157,014,339 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,149 | r | loadExcel.R | require(tidyverse)
#given data
excel <- suppressWarnings(suppressMessages(read_csv("COMAP_RollerCoasterData_2018.csv"))) %>%
rename_all(tolower) %>%
set_colnames(gsub(gsub(names(.), pattern = "[()]", replacement = ""), pattern = "([[:punct:]])|\\s+", replacement = "_")) #remove parentheses, replace punctuation (spac... |
2f11d9b9bb3a329755ade176ef2d1622de6ef24a | d3d2fbf3eaf7075f4679997c422fefaa5d524f67 | /Dimensionality_Reduction.R | 1954e21b82096709696a359149d76ce12e6f06f8 | [] | no_license | harshitsaini/Business-Analytics-Data-Mining | eaca89f755d15ecbb580be57e2ff95a725086a33 | 9a0613906c2a7f945cbd2a2855d07dc9b2e98778 | refs/heads/master | 2021-04-03T09:12:41.303022 | 2018-04-23T12:22:37 | 2018-04-23T12:22:37 | 125,231,756 | 15 | 5 | null | null | null | null | UTF-8 | R | false | false | 3,164 | r | Dimensionality_Reduction.R | library(xlsx)
df1= read.xlsx(file.choose(),1, header= T)
df1= df1[,!apply(is.na(df1), 2,all)]
Age= 2017- df1$Mfg_Year
df1= cbind(df1,Age)
dfb= df1
df1= df1[,-c(1,2,3)]
head(df1)
str(df1)
#Summary Statistics
countblank= function(x) sum(x=="")
dfsum= data.frame(Average= sapply(df1[,-1],mean),Median= sapply(df1[,-1],... |
4c09367b3e390883297b29db37cdb4003ccc52cd | 8a1d46bd149a192ff81493a50a48c4bf544784ba | /single cell plassのコピー.R | f7d051b494918ab15e85e3c90d8294778e148333 | [] | no_license | kaede1021/datascience_traning | 7155b502c5ef0f9041b01caf1e62e0f5ff77b9c3 | 2d1ae6d82dfc9025a31181f82a098d9517a76026 | refs/heads/master | 2022-12-04T13:04:40.374255 | 2020-08-18T13:59:29 | 2020-08-18T13:59:29 | 288,460,943 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,858 | r | single cell plassのコピー.R | getwd()
setwd("./Desktop/single")
library(Seurat)
library(dplyr)
library(patchwork)
#ノーマライズされたデータセットでは、NormalizeDataはスキップする。
#今回のデータセットではノーマライズされている。
Data = read.table(gzfile("dge.txt.gz"),sep="\t") #gz fileの読み込み
Data[1:5,1:5]
Plass <- CreateSeuratObject(counts = Data, project = "Plass", min.cells = 3, min.features = 2... |
818f384176b9eb50aee9b44e9b2b4565da9cdf32 | aac3d25b50bf5fcd9e78c8ad1d412c580503d665 | /R/EC_gui.R | a9f7ce3fb36f7c7a0fba312b3563e0bd31eae77a | [] | no_license | yusriy/fluxMPOB | e232eba9b30627d570636630288931a889e41948 | 98d3a7755f819d113527b965e75c91d022a4cd2c | refs/heads/master | 2021-01-09T21:48:09.798017 | 2016-10-16T03:16:02 | 2016-10-16T03:16:02 | 48,431,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,847 | r | EC_gui.R | ## GUI to import eddy covariance data
#### Preliminaries ####
library(gWidgets)
library(gWidgetsRGtk2)
# Create a window
win <- gwindow("Tab delimited file upload")
# Create the group to add widgets
grp_name <- ggroup(container = win)
# Add a label
lbl_data_frame_name <- glabel("Data frame to save data to: ", cont... |
2f516be312fa240e8c05fb63831ff01deb56c9cb | df1d2b978c77d0934b02fbaf88c1730a8a82ed3e | /R/mysd2_250918singleChi.R | 4a3fdb83aba4b8d43e018522d82c0d0b9120effe | [] | no_license | portokalh/adforesight | 3dc0f5f692014cbb6cc373634fb6caca9ab191fa | d43c1e4c35459122e2ae55169a7a77a4943f8e4d | refs/heads/master | 2020-04-05T13:03:34.866181 | 2018-12-20T16:14:47 | 2018-12-20T16:14:47 | 95,047,388 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,066 | r | mysd2_250918singleChi.R | #load libs
library( knitr )
library(ANTsR)
library(visreg)
library(robustbase)
library(groupdata2)
library(ggplot2)
library(caret)
require(broom)
#Manganese Enhanced MRI predicts cognitive performnace Alex Badea and Natalie Delpratt
#reuses results of sparsedecom2 for best performing fold Alex Badea 7 dept 2018
#u... |
4a338bb00d46381519a5622e17b5b99ff62858ad | 40e8b14246b5cc4b587f4d88121474720f9f1c39 | /tests/testthat.R | 48c57c7d8a6e48e91bd54d3d2b2bbf3e10ed2814 | [
"MIT"
] | permissive | coolbutuseless/snowcrash | 644bbfaf74f101df6f919572653b3150e1ded9a7 | 9b1472784c03d907b552e9c4172a2b7137df3402 | refs/heads/master | 2022-12-19T16:03:44.755180 | 2020-09-27T03:35:32 | 2020-09-27T03:35:32 | 294,376,185 | 10 | 0 | null | null | null | null | UTF-8 | R | false | false | 62 | r | testthat.R | library(testthat)
library(snowcrash)
test_check("snowcrash")
|
5b463a0efc5a74d4683cba3a9e591f024cbc7ef3 | 7e3ce11bc22c009a399a36490ed962836d6aaff6 | /signals/hk_stock/hk_stock_signal_20day_Function_multil.R | 8810675b5fa0733212cea200d01f1ad145a52099 | [] | no_license | tedddy/Learn_R | 2340a1f73e0ab4a7e07b5aa97181bc42e7acd22f | a04cd823fb382b5457e3b043ec346b1ee5ab1724 | refs/heads/master | 2021-01-17T13:33:36.685310 | 2016-10-17T23:26:46 | 2016-10-17T23:26:46 | 25,558,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,972 | r | hk_stock_signal_20day_Function_multil.R | # 载入需要的Package
library(quantmod)
signal_BuySell <- function (code) {
tckr = paste(code,"HK",sep=".")
# 设定开始和结束日期
Sys.Date()-30 -> start
Sys.Date()-1 -> end
# 从Yahoo下载数据
hk <- getSymbols(tckr, from = start, to = end, auto.assign = FALSE)
# 提取Close列
hk.C <- hk[,4]
# 生成最... |
21d7f1c9158911d8ea7b5ed2aa0762c2167136b8 | 9c15de6799c592361701427ee10d128ebc499e3d | /C03/W04/quiz.R | 6ed783f239e3a933cc50a3b1f7abeb6bb20839a5 | [] | no_license | mahmoudjahanshahi/datasciencecoursera | 2136d5eea723c2f09b82069131ccd23dbc2f0687 | 350c40de3616aa25775bb27582ae04d0d8a630c6 | refs/heads/master | 2023-03-02T04:41:58.646229 | 2021-02-11T11:57:52 | 2021-02-11T11:57:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,806 | r | quiz.R | #Q01
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
filePath <- "./C03/W04/src/communities.csv"
download.file(fileURL, destfile = filePath ,method = "curl")
df <- read.csv(filePath)
strsplit(names(df), "wgtp")[123]
#Q02
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%... |
6b2922179e2017ce586549648468e214d3b12f0e | 6b629e8bc4bb0b1c93bb217cb218af5ae5e587c8 | /MR/mibiogenOct2020/mibiogen-ukbiobank.R | 795584edb627b5ff5a366469ca87a051ce490a8c | [] | no_license | DashaZhernakova/umcg_scripts | 91b9cbffea06b179c72683145236c39f5ab7f8c2 | 1846b5fc4ae613bec67b2a4dd914733094efdb23 | refs/heads/master | 2023-08-31T10:45:17.057703 | 2023-08-23T14:47:43 | 2023-08-23T14:47:43 | 237,212,133 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,807 | r | mibiogen-ukbiobank.R | args <- commandArgs(trailingOnly = TRUE)
library(TwoSampleMR)
library(MRInstruments)
library(MRPRESSO)
source("/groups/umcg-lld/tmp03/umcg-dzhernakova/umcg_scripts/MR/run_MR.R")
mibiogen_path <- args[1]
bact <- basename(mibiogen_path)
pval_thres = 5e-08
#pval_thres = 1e-05
out_filebase <- paste0... |
e8d11d1fc93e2bb584537477e97bd9a8e5c91a5f | 1e1939479e8014f48e7362a27be9dfc68719c6e8 | /R_packages/quantify/pkg/R/qDSMAStatus.R | 6536b722766389b39682156444157368e881bf80 | [
"MIT"
] | permissive | wotuzu17/tronador | bffec07586340bc5320d3baf092ba6388a6ee98c | 8d55d26ab1accd0499e6264674408304a70d5e1b | refs/heads/master | 2021-01-10T00:59:45.599781 | 2015-04-25T07:50:22 | 2015-04-25T07:50:22 | 32,209,141 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 576 | r | qDSMAStatus.R | # function calculates quantile status of first derivative of SMA of
# a rollingwindow
# currently, only 3 levels are supported.
qDSMAStatus <- function (TS, n, rollingwindow) {
SMA <- SMA(Cl(TS), n=n)
FD_SMA <- diff(SMA)
FD_SMA_Q <- cbind(
rollapply(FD_SMA, rollingwindow, quantile, probs=.25, na.rm=TRUE),
... |
f1114fcd4a9a841751d09a91721cae9ec30d88f3 | 9277e549802eb213f90c7d61624aace84003e820 | /man/update_recessions.Rd | d649cbfccd4af760d9e1333533f0f197050b4f4e | [
"MIT"
] | permissive | CMAP-REPOS/cmapplot | 5f479a7e217666e03c86132054915b23475ceb5a | 13563f06e2fdb226500ee3f28dc0ab2c61d76360 | refs/heads/master | 2023-03-16T03:25:59.202177 | 2023-03-08T04:28:04 | 2023-03-08T04:28:04 | 227,153,492 | 9 | 1 | NOASSERTION | 2023-03-08T04:28:06 | 2019-12-10T15:25:18 | R | UTF-8 | R | false | true | 1,741 | rd | update_recessions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_recessions.R
\name{update_recessions}
\alias{update_recessions}
\title{Update recessions table}
\source{
\url{https://www.nber.org/data/cycles/cycle dates pasted.csv}
}
\usage{
update_recessions(url = NULL, quietly = FALSE)
}
\arguments{... |
a1e39ef2bb17de15fee5d1447cab784876508412 | 1478cc4003f4e402c612c54da5f4e0f3f5edd52f | /R/species.R | 8bfb89c7ae2f400751cc16bf5808f18f18665b44 | [
"MIT"
] | permissive | msleckman/ESM262ClimatePackage | 10eb6fc18af8e7d41e770710218e2d1dcc2a18a0 | 8e087a3925f884ebc3002ecc525ed138055dfeb4 | refs/heads/master | 2020-06-01T05:52:53.696683 | 2019-06-13T03:48:08 | 2019-06-13T03:48:08 | 190,666,248 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,469 | r | species.R | #' Sample Plant Characteristic Data
#'
#' @description
#' Sample dataset for various Santa Barbara plant species providing climate and growth-based plant characteristics.
#'
#' @details
#' Species information given by species identifier, scientific name, and common name.
#' Dataset provides drought tolerance capability... |
578bd3d8041e783639dd49bac020311401e7caf7 | 37ce38ba0eff95451aebea810a1e2ab119f89a85 | /R/TN_Combine.R | 8854f4700438c2af1f2149cf7efbf826a6e1458c | [
"MIT"
] | permissive | SwampThingPaul/AnalystHelper | 39fdd58dc4c7300b6e72ff2713316809793236ce | eb570b69d7ea798facaf146d80bc40269a3d5028 | refs/heads/master | 2023-07-21T00:19:21.162374 | 2023-07-11T17:24:36 | 2023-07-11T17:24:36 | 179,672,539 | 1 | 0 | MIT | 2020-03-21T20:05:31 | 2019-04-05T11:53:19 | R | UTF-8 | R | false | false | 870 | r | TN_Combine.R | #' Nitrogen concentration data handling
#'
#' @param NOx Nitrate-Nitrite (NOx)concentration (numeric)
#' @param TKN Total Kjeldahl Nitrogen (TKN) concentration (numeric)
#' @param TN Direct measure Total Nitrogen (TN) concentration(numeric)
#' @keywords "water quality" nitrogen
#' @export
#' @return This function handl... |
84595a4b93d50f33f0224b7c932b428f08915d1c | ba86155005777258d2b08ddb5c1b407beb86fc41 | /R/print.contrast.R | f639761262e893b7135fd6835e5d6556b6f68d83 | [] | no_license | cran/contrast | 6640d7f7dc932585249f4c9ea4b6d3d650a8da97 | d8d17b9d09ca977c05b605be131fabfef6c89c86 | refs/heads/master | 2022-10-28T19:10:13.808567 | 2022-10-05T16:20:09 | 2022-10-05T16:20:09 | 17,695,238 | 0 | 1 | null | 2017-02-22T19:06:34 | 2014-03-13T04:19:34 | R | UTF-8 | R | false | false | 2,113 | r | print.contrast.R | # This method is used for printing the objects returned by the contrast methods.
# It was copied from the rms package, written by Frank Harrell.
#' Print a Contrast Object
#' @param x Result of `contrast()`.
#' @param X A logical: set `TRUE` to print design matrix used in computing the
#' contrasts (or the average con... |
5dd428315c54f359d59e929f699f88674558673d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/etasFLP/examples/italycatalog.Rd.R | 566d32302b921de29471c2882671cac427728ee5 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 215 | r | italycatalog.Rd.R | library(etasFLP)
### Name: italycatalog
### Title: Small sample catalog of italian earthquakes
### Aliases: italycatalog
### Keywords: datasets earthquake
### ** Examples
data(italycatalog)
str(italycatalog)
|
ac89bb17337fc1641d41a67db233e996f6e124f4 | 3b9a9525adbcdaad1e1ce56936500ec073ac77cc | /ui.R | 8ae3bdad3a5ea1569578572ec0b15b94ee085b5b | [] | no_license | wx-chen/IrisShinyApp | 61522ffe54feaf502e7c3600a1bbfb228a049934 | aefff296a37e9ac873e363a455751a5e028c3d43 | refs/heads/master | 2016-09-06T06:33:35.901726 | 2014-07-18T22:05:08 | 2014-07-18T22:05:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,537 | r | ui.R |
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Interesting Facts about Iris"),
# Sidebar with selection of iris species, a... |
cc19f636ffa3f40fb4d0f3e11f75acbf092b6e9d | 5444935089dd69c6a53118902f52dda84634f884 | /capital one/evaluate.R | f6303fe5bb6f942a7fb8a1ead9d3ea58aa00227e | [] | no_license | kusakewang/Machine-Learning | 96c5acbc9597785728169ca3729634dae83b891f | 203d87e9651a6fb3c575451bf26dd6f4a7b4591c | refs/heads/master | 2021-01-19T20:15:16.378520 | 2016-05-23T15:10:43 | 2016-05-23T15:10:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,113 | r | evaluate.R | EvaluateAUC <- function(dfEvaluate) {
require(xgboost)
require(Metrics)
require(pROC)
CVs <- 5
cvDivider <- floor(nrow(dfEvaluate) / (CVs+1))
indexCount <- 1
outcomeName <- c('cluster')
predictors <- names(dfEvaluate)[!names(dfEvaluate) %in% outcomeName]
lsErr <- c()
lsAUC <- c()
for (cv in seq(1:... |
6d75e2900c399425601aa672d9ae39fe81187840 | e2de741b6608cab0db43a71e486be123049f2dfb | /R_Analysis/_Statistical_Analysis/explore_diff_in_EPA_risk.R | d7e7b9af7736e14ab21bd73b1580d2a94e702d52 | [] | no_license | jpf1282/tribal_lands | 1b07d410c734faf4848b2ffe3602e124a2f61836 | ecd397f1c8e8b7fd8f9306907ebde66785ac35c6 | refs/heads/master | 2021-06-23T21:39:58.754161 | 2020-12-22T15:59:19 | 2020-12-22T15:59:19 | 161,838,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,032 | r | explore_diff_in_EPA_risk.R | library(readr)
library(tidyr)
library(knitr)
library(DT)
library(ggbeeswarm)
library(dplyr)
library(stringr)
library(scales)
library(broom)
library(ggplot2)
# Import variables to be joined to tribes ---------------------------------
# Import variables to be joined (climate and precipitation)
setwd("/Users/kathrynmcc... |
4a9fa9fff69b3c1b0a22b1386ea0e09a0a0cd484 | 5553da94973b7022872ea6215bdb122710f51a15 | /matrixInversionTests.R | 5272907480a8cf8e5b8398eb0d58dc1e0d53bc58 | [] | no_license | SamBuckberry/ProgrammingAssignment2 | 6493045ad96cc7bc2f3b43d10d8ba72f6583ed9a | d0bf1e1fa83efd514af73e341c02a4846371138d | refs/heads/master | 2021-01-17T20:39:12.745463 | 2014-04-23T15:10:32 | 2014-04-23T15:10:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 317 | r | matrixInversionTests.R | # Create test matrix
myMatrix <- matrix(rnorm(n=10000, mean=100, sd=25), nrow=100, ncol=100)
# Get the inverse matrix
system.time(myInverse <- solve(myMatrix))
# Setup the matrix
system.time(a <- makeCacheMatrix(myMatrix))
# Solve the matrix
system.time(b <- cacheSolve(a))
# Check the results
all(myInverse==b)
|
52b29299baaf3d7ef186fd4d70347a7b15ab81c6 | 0d7f4c22f0fcb7a448de4b5a5f199b87bef4ef47 | /Figures/example1.9.R | d7126363a9f8d9d5c1997c468a5bb7ddce7713cd | [] | no_license | gjhunt/drew_thesis | c86d473d6fa5a022f707b442acddcb136a75cda5 | 1b83987fcd9f19ece1e83e9df18d78504ed4a859 | refs/heads/master | 2021-01-17T17:26:12.332048 | 2017-02-23T15:14:45 | 2017-02-23T15:14:45 | 82,938,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 319 | r | example1.9.R | plot.new()
par(pty="s")
par(mfrow = c(1,1))
plot(c(4,0,1),c(0,2,1), col="midnightblue",main="",xlab="",ylab="",asp=1,xlim=c(0,5),ylim=c(0,5) , pch=4,lwd=4)
grid(col="black")
abline(h=0,col="black")
abline(v=0,col="black")
X<-matrix(c(1,4,1,0,1,1),byrow=T,nrow=3)
b<-ginv(X)%*%c(0,2,1)
abline(coef=b,col="Red",lwd=2) |
75d6219dcbbadbf1b44fc7eacfa7135f62fe4e98 | 8987dcc442aeb76d5663fb6385d7a9196d3bbba4 | /cachematrix.R | e9b7ba28d19f42014c6ac7104b5f8eb5e581667d | [] | no_license | megs161195/ProgrammingAssignment2 | 3662bb218258d1473fbbbfc55bc7f621f6b0ad60 | a30a07f99becb673366366805676dc2f29def71e | refs/heads/master | 2021-01-22T22:20:45.449492 | 2017-05-31T15:25:14 | 2017-05-31T15:25:14 | 92,769,886 | 0 | 0 | null | 2017-05-29T19:37:47 | 2017-05-29T19:37:46 | null | UTF-8 | R | false | false | 694 | r | cachematrix.R |
## This function sets the value of matrix, gets the value of matrix,
## sets the value of inverse, gets the value of inverse
makeCacheMatrix <- function(x = matrix()) {
i<-NULL
set<-function(y){
x<<-y
i<<-NULL
}
get<-function() x
setinv<-function(solve) i<<-solve
getinv<-function() i
... |
39a32a24d10ec682c8aa009f39db427e1c688299 | c9e02a75abbd1d5048446a65aa23b10f79492b2f | /scripts/attractor_xpower4.2.R | f20414d21b34738cdb1ae9859826b815212b3693 | [] | no_license | somasushma/R-code | f8290d3ecd8ea87ef778b1deb0b7222e84b811be | 2e1f0e05ae56ebe87354caeb374aebb19bf00080 | refs/heads/master | 2021-10-27T14:42:26.847193 | 2021-10-25T22:02:51 | 2021-10-25T22:02:51 | 137,162,116 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,000 | r | attractor_xpower4.2.R | windowsFonts(f1 = windowsFont("Constantia"),
f2 = windowsFont("Book Antiqua"),
f3 = windowsFont("Cambria Math"))
#e
e=exp(1)
#function: midpoint------------
midpoint= function(x,y) c(sum(range(x))/2, sum(range(y))/2)
#function: distance------------
distance= function(x,y) sqrt((mid... |
2e382bed7975133a204ebd28593d31e8d7f31cfe | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/billboarder/examples/bb_piechart.Rd.R | 48f0e508dc4d2b406a37cfbf980d68012984c81c | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 531 | r | bb_piechart.Rd.R | library(billboarder)
### Name: bb_piechart
### Title: Helper for creating a pie chart
### Aliases: bb_piechart
### ** Examples
stars <- data.frame(
package = c("billboarder", "ggiraph", "officer", "shinyWidgets", "visNetwork"),
stars = c(9, 177, 43, 44, 169)
)
# Default
billboarder() %>%
bb_piechart(data =... |
bf3ff2995c563b6d7abb776b8af2c49d7dd67c6f | 9e2296d74051d725efcc28cab16ca7703c8a6c1b | /man/add_ui_sidebar_basic.Rd | 2062143a05bf370916902bd1c927ae66b55d9031 | [] | no_license | neuhausi/periscope | 59f5d74cc7e399a9a9e03e19199409a6438a4a91 | e0364b0db9b9bbcbc4b6c295bbbb6fa1d1d65fd4 | refs/heads/master | 2023-07-06T05:44:50.295396 | 2023-07-03T21:39:01 | 2023-07-03T21:39:01 | 171,934,957 | 27 | 1 | null | 2023-07-03T21:39:02 | 2019-02-21T19:49:03 | R | UTF-8 | R | false | true | 1,337 | rd | add_ui_sidebar_basic.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ui_helpers.R
\name{add_ui_sidebar_basic}
\alias{add_ui_sidebar_basic}
\title{Add UI Elements to the Sidebar (Basic Tab)}
\usage{
add_ui_sidebar_basic(elementlist = NULL, append = FALSE, tabname = "Basic")
}
\arguments{
\item{elementlist}{list... |
74aa232d397785dc681742aacfa21f827b2630c8 | 5bf589d943c0dcf7e9f6a331d25cc3dfef8d8d48 | /src/clase_1/ej1.R | 23eeaf63b9f8123547e3775498c6a709ef46dbfa | [] | no_license | joagonzalez/ditella-data-mining | 8b161df85c56f95b85007997f09302a84b969596 | d124319b1d63ef8a54cf8c29d6f08f740badf788 | refs/heads/master | 2023-04-17T01:18:53.616889 | 2021-05-09T00:36:10 | 2021-05-09T00:36:10 | 352,432,877 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,997 | r | ej1.R | # install.packages('e1071')
# install.packages('mlbench')
library(e1071)
library(mlbench)
# Read dataset
setwd('C:/Users/a310005/Desktop/DiTella/Data Mining/Clase_1')
getwd()
data <- read.csv('bankruptcy_data_red.csv', sep=';')
data <- na.omit(data)
head(data)
summary(data)
nrow(data)
ncol(data)
# Spli... |
17aaa3dba6447073f1cd04ac8ffde0ad3a704904 | 54ffa208f4de8d19504ee4194e30eb9f4d091a34 | /Final_Project/Buecherl_FinalProject.R | 7c7796cfa5b3a19f3e4bc91c4bcbf1a6b1d6028e | [] | no_license | LukasBuecherl/CompBioLabsAndHW | a3250bbf23033ceb27d50862da9d0a344396d410 | ad2e4052517fa287b7c87b04e3bcb11d031d2049 | refs/heads/main | 2023-05-01T18:20:52.945081 | 2021-04-29T17:10:10 | 2021-04-29T17:10:10 | 334,219,541 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,471 | r | Buecherl_FinalProject.R | # EBIO 5420: Computational Biology
# Professor: Samuel Flaxman
# Student: Lukas Buecherl
# Final Project
###############################################################################
#
# CODE WRITTEN BY LUKAS BUECHERL FOR THE FINAL PROJECT FOR COURSE EBIO 5420.
# CONTACT: lukas.buecherl@colorado.edu
# GitHub of proje... |
973d6821151c9fb3c931ccef824147782f77c07d | b11a9a886f0809ab2e342134dc41da7b95e8b422 | /R/font.R | d3188ff3346a1e8bab0fdf09ca933b8e9927b360 | [] | no_license | kassambara/ggpubr | dbf17d6a921efe5e39b87ab566f3c9fd4f4ef047 | 6aeb4f701399929b130917e797658819c71a2304 | refs/heads/master | 2023-09-01T19:43:28.585371 | 2023-02-13T18:28:59 | 2023-02-13T18:28:59 | 63,722,465 | 1,041 | 195 | null | 2023-08-06T16:55:18 | 2016-07-19T19:35:48 | R | UTF-8 | R | false | false | 3,861 | r | font.R | #'Change the Appearance of Titles and Axis Labels
#'
#'@description Change the appearance of the main title, subtitle, caption, axis
#' labels and text, as well as the legend title and texts. Wrapper around
#' \code{\link[ggplot2:element]{element_text}()}.
#'
#'@param object character string specifying the plot compo... |
18a311a3bba43462d20bb3f5c1915fb514489922 | 35385dd99e197efdb1c0b7ccc89dd44a03384af6 | /NATreatment.r | 8a58ebbee13f7c929382c709e52630c439c3a550 | [] | no_license | rphccf/Final-Project-Quantitative_Methods-Msc-Economics-Course- | 872359240476c9ca185211eef35c63ba05f740bc | dae2883401cf536fffd2d0264ae1b906f4f84dce | refs/heads/master | 2020-09-17T09:57:58.317476 | 2019-11-26T01:33:14 | 2019-11-26T01:33:14 | 224,070,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,569 | r | NATreatment.r | require(xts)
## No Loop abaixo e feito o tratamento para a existencia de um NaN na primeira linha, o que prejudicaria o desenvolvimento
## do modelo. A melhor solucao encontrada, que nos fez perder o menor numero de informacoes possiveis, foi excluir a primeira
## linha da base de cotacoes recebidas do YAHOO ate q... |
bf5a67d88105b555da8ee68e163328d7e7061459 | 2a675299288d5bf42795f125c76d90b5ec149158 | /man/get_reported_financials.Rd | 080c79c7f62b58c2b29eb98896ac069102d229d4 | [] | no_license | atamalu/finntools | 6cd25a84eb696b09724f16942e5589b1b1d7fda4 | 6b8f911db2f34fd57506be6c6c43dbef9c591ffc | refs/heads/master | 2022-11-16T14:29:34.381913 | 2020-07-15T21:59:42 | 2020-07-15T21:59:42 | 277,924,208 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 917 | rd | get_reported_financials.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_reported_financials.R
\name{get_reported_financials}
\alias{get_reported_financials}
\title{retrieve reported financial data}
\usage{
get_reported_financials(
symbol,
api.key,
frequency = "annual",
write.file = FALSE
)
}
\argument... |
25984537ee2a63cd83ef1a8d7bafb526565b0030 | 364ddfeadc15a6861f4372bdaf67c8b3df19ec81 | /Graphing-Data-with-Quadrants.R | 095132a7ea866b09a57b532bb8d7ef76172462ed | [] | no_license | ttitamu/ctrma-txtag-dash | c0fdfcdd5f5b7489603c595f0348f9d1ef1e2cc8 | 50d5deddf23d90e34e09120fcec8dded48377182 | refs/heads/master | 2021-05-14T08:40:39.345146 | 2018-03-02T16:08:45 | 2018-03-02T16:08:45 | 116,305,751 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,463 | r | Graphing-Data-with-Quadrants.R | # Graphing Data with Quadrants & Labels
# Michelle Plunkett
# January 4, 2018
# Load required packages
if (!require("pacman")) install.packages("pacman")
pacman::p_load(ggplot2, installr, data.table, DT, ggrepel)
# Get the user's home directory path (Defining this value allows other users to easily run this script)
h... |
64bdc37b1301e9ba3e62660fe7668971de2a7e04 | 167c33afe106c8e8e1c2e7c9b3859e096f95add1 | /R/plot_metrics.R | 6e6dd9c9365020bd575fd0adad1c30d7f8d15ca6 | [] | no_license | paulhendricks/Rperform | a705d91f05059494c8e92a1b89c6fd3b4f3000be | aaf97e7907c5c6fe5a8e072cb7f6b78d959a961e | refs/heads/master | 2021-01-16T21:57:30.189292 | 2016-05-19T10:45:04 | 2016-05-19T12:50:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,436 | r | plot_metrics.R | #' Plot test-file metrics across versions.
#'
#' Given a test-file path, plot the metrics of entire file and individual
#' testthat blocks against the commit message summaries of the specified number
#' of commits in the current git repository. If the parameter save_data is set
#' to true, it also stores the corres... |
edb4ecd11369c7acc3fae25c18bb7ab9f89ca247 | 50f60bc47e66819835a6d4f927074d7e144be5e5 | /man/stf_mono_metadata.Rd | f5dd46989f5438456135d4384ed7eb91cdf1d1fa | [
"MIT"
] | permissive | jjesusfilho/stfstj | b4df8d5ca7ae9d4fa95b39bab1921c8506f32995 | 441088fc9015cf5c7a2847d64ad9aa9744646bae | refs/heads/master | 2018-10-30T10:45:16.494106 | 2018-08-23T18:33:19 | 2018-08-23T18:33:19 | 111,905,940 | 0 | 1 | null | 2017-12-08T21:32:22 | 2017-11-24T10:25:57 | R | UTF-8 | R | false | true | 612 | rd | stf_mono_metadata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stf_mono_metadata.R
\name{stf_mono_metadata}
\alias{stf_mono_metadata}
\title{Returns metadada from Brazilian Supreme Court monocratic decisions}
\usage{
stf_mono_metadata(open_search, parties_names = TRUE)
}
\arguments{
\item{open_search}{Wo... |
b319e33a01bdc646e21811b68796b10413ff224e | 7acfddc8f6e83c086aff89440719ec4f21ebdfa4 | /R/acs_screening.R | 68ed23567a7cc5de229f00f6a391f8a5dc50790a | [] | no_license | crazybilly/fundRaising | f16cafc02d23fd1fd140ba586cb6940516d19292 | 42f4f4ae725f62e31a7e16c10f172fff801737ea | refs/heads/master | 2021-07-29T20:03:49.287552 | 2021-07-26T14:08:32 | 2021-07-26T14:08:32 | 145,170,311 | 4 | 3 | null | 2019-07-11T13:57:23 | 2018-08-17T22:03:05 | R | UTF-8 | R | false | false | 7,605 | r | acs_screening.R | #' Create a full name column using the first and last names
#'
#' @description creates a full name column - a convenience helper function for acs screening
#'
#' @param x a data frame
#' @param first_name_col a column from the data frame containing the first name
#' @param last_name_col a column from the data frame con... |
36956a00980f2c785e4b84f4f2d1a0afbba3f125 | 649ae76f788227b6f1ce8e92b006fd09563c0b27 | /tempDates.R | 52231559f2bf0547232f6ee1421845a5a8b665bf | [
"MIT"
] | permissive | andyblueyo/city-weather | 92e3d92debdc15c9cfc3074cb43a70165c3a86a4 | 0fbf883ec82522a64875a6414d36698ed3f22e64 | refs/heads/master | 2021-03-30T17:33:04.597922 | 2017-11-21T08:53:37 | 2017-11-21T08:53:37 | 90,248,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 721 | r | tempDates.R | library(dplyr)
tempDates <- function(cal.date){
# read in the file names, can pull this out
location <- read.csv("data/location.csv", stringsAsFactors = FALSE)
files <- location$file_name
# convert csv files with appropriate dates
# also pull this out
charDate <- function(csv){
csv <- read.csv(paste... |
8a74748e04ab22f2da0efea92c91d0b6ff498fa4 | 8fff7a6210471ab26465e10e647121335e1314d9 | /Project_01_KNN/R/NN1toKmaxPredict.R | 1e7954a057b2c5a9943035a01ad8613797ae8eed | [] | no_license | ShoeRider/CS499_MachineLearning_TeamProjects | a981df2f8ebd8e162ace8b0e50b553bdc9e8247e | 2fc32ff1533fc9b9146c09f2e87006c4a23891bb | refs/heads/master | 2020-04-20T02:46:06.502369 | 2019-05-04T07:31:06 | 2019-05-04T07:31:06 | 168,580,135 | 0 | 2 | null | 2019-02-19T18:43:45 | 2019-01-31T19:05:07 | R | UTF-8 | R | false | false | 2,004 | r | NN1toKmaxPredict.R | print(getwd())
#source("R/General.R")
#' NN1toKmaxPredict
#'
#' Wraps around c++ knn_interface code to call it using r
#'
#'@param TrainingData numeric imput feature matrix [n x p]
#'@param TrainingLabels numberic input label vector [n],
#'either all 0/1 for binary classification or other real numbers for re... |
136e8f8a0087ddfb7d9276b4a7a69681be55d01e | ee0689132c92cf0ea3e82c65b20f85a2d6127bb8 | /93-wksp3/environ.R | 31efa841d1180706d481278782de1d2e0d565a74 | [] | no_license | DUanalytics/rAnalytics | f98d34d324e1611c8c0924fbd499a5fdac0e0911 | 07242250a702631c0d6a31d3ad8568daf9256099 | refs/heads/master | 2023-08-08T14:48:13.210501 | 2023-07-30T12:27:26 | 2023-07-30T12:27:26 | 201,704,509 | 203 | 29 | null | null | null | null | UTF-8 | R | false | false | 204 | r | environ.R | # Environment
#objects in memory
ls()
#create an object
x = 1:5
ls() #check
y=100:200
ls() #check
#remove one
rm(x) #remove x from environ
ls() #check
#remove all
rm(list = ls())
ls() #check
|
8092a109e9ab9d5a59eb45d46fbd45d482f0c65f | 91e06a1f477bc52792c65d98d8155fa212072837 | /man/tidy_bib_file.Rd | 490ca70cfcddaaa639c1b361a788f4125bc5f6dd | [] | no_license | mbojan/citr | 905ff08458d02cd6760e43ebf0a6226e84cfaf54 | 543554bfc00cc0f853fd930ff953c9a09e78acce | refs/heads/master | 2021-01-23T13:29:51.069381 | 2017-03-21T08:32:44 | 2017-03-21T08:41:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 801 | rd | tidy_bib_file.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidy_bib_file.R
\name{tidy_bib_file}
\alias{tidy_bib_file}
\title{Tidy bibliography file}
\usage{
tidy_bib_file(rmd_file, messy_bibliography, file = NULL,
encoding = getOption("encoding"))
}
\arguments{
\item{rmd_file}{Character. One or mor... |
cc700db2c2984760187f915040f0dd256006b7d8 | 164e7663830d35fb46c824ce46ee4376370d1272 | /R/convertFx.R | fc443788a0443a885ef121223c2fbac64c49fe40 | [
"MIT"
] | permissive | mpascariu/MortalityLaws | 2e839f96aaaf559f68a7a9552339e47245957ae2 | 0ae2da33bcb3326be2733bd2c036ca9637a4f74a | refs/heads/master | 2023-08-10T21:58:13.258992 | 2023-07-21T12:25:03 | 2023-07-21T12:25:03 | 74,010,070 | 24 | 8 | NOASSERTION | 2021-11-24T09:58:20 | 2016-11-17T09:12:51 | R | UTF-8 | R | false | false | 2,950 | r | convertFx.R | # -------------------------------------------------------------- #
# Author: Marius D. PASCARIU
# Last Update: Thu Jul 20 21:11:11 2023
# -------------------------------------------------------------- #
#' Convert Life Table Indicators
#'
#' Easy conversion between the life table indicators. This function is based
#' ... |
17b1ea7a8f2af7262d4cc48b85cc485979804eb4 | e99a7f80f244408f4532b2e5ae682fb7ab641fa4 | /Massachusetts/MA_Moving_Averages.R | f47182ca7bcfa20fceae1bb299ff30801d8f6c25 | [] | no_license | AndrewDisher/New-England-Weather | 98a75bd171dd49a8c35c6200f00d0f2fdf0c897f | 882031c40d7578b37217399890abb2c915c23650 | refs/heads/master | 2022-11-17T08:39:52.000970 | 2020-07-08T16:17:40 | 2020-07-08T16:17:40 | 275,846,324 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,162 | r | MA_Moving_Averages.R |
#
# Andrew Disher
# 3/27/2020
# Climate Change in Massachusetts
# Purpose: To Analyze Weather Data
#
library(zoo) # Includes the rollmean() function for finding the simple moving average of a time series.
library(ggplot2)
library(dplyr) # Includes cummean() function for finding the cumulative moving average... |
cb1319ab249172f3552b1d036738d5e0bb9e1c64 | 06f1ce91c6141d4b41081bea4284403a30c76c69 | /R/hit_map.R | 6a361ffa31e5e2f4eada0b93f46712fa92939843 | [] | no_license | VizWizard/phenoScreen | 5dea9caff26542a9fa67ef21c287f408685c1d0a | 583ff96bf35b92d73f9d745b9539d22a08ea07fd | refs/heads/master | 2021-01-20T02:54:17.701837 | 2016-06-14T17:41:58 | 2016-06-14T17:41:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,678 | r | hit_map.R | #' Platemap to identify 'hits' in a screen
#'
#' Produces a plot in the form of a micro-titre layout,
#' with colours indicating wells above or below a nominated threshold.
#'
#' @param data Vector of numerical values to score
#' @param well Vector of well identifiers e.g "A01"
#' @param plate Number of wells in comple... |
6a114ca7f40fc9973a1193dc4d800b31b2b69f8a | bcde4003dfb3725293245f407a2398310f1e8151 | /man/getPrevalenceSingleSample.Rd | ba44c305a4f28aee2ba7b374e14407ce4076fd42 | [] | no_license | cwcyau/OncoPhase-1 | d9f88501fac95f7e1c5f7692bbb0d6c0120dea75 | d8ffb3ac080de12a25228cf358d121eddcbaffa4 | refs/heads/master | 2021-01-22T16:26:29.942830 | 2016-08-09T14:04:34 | 2016-08-09T14:04:34 | 65,290,817 | 0 | 0 | null | 2016-08-09T11:48:39 | 2016-08-09T11:48:39 | null | UTF-8 | R | false | true | 4,702 | rd | getPrevalenceSingleSample.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/OncoPhase_methods.R
\name{getPrevalenceSingleSample}
\alias{getPrevalenceSingleSample}
\title{Somatic mutations cellular prevalence on a single sample.}
\usage{
getPrevalenceSingleSample(input_df, mode = "PhasedSNP", nbFirstColumns = 0,
reg... |
dc0f4d749b4bcbb2dfc03b1b232ff9f11d6c747f | 40f8efa31fe5dbf1dbbe54d689ca0bcda716a8a9 | /CIBERSORT_data/margeCIBERSORTTables.R | ce8416fabd1856722b9994e7caa18786c896cdf3 | [] | no_license | Shicheng-Guo/CIBERSORT | 02deba2c03e68d1cc829a4bce70dde309c53c7ef | c321c766b50a0a1c738614bdc037dda0124a787e | refs/heads/master | 2021-01-18T10:21:32.907921 | 2016-06-16T18:14:10 | 2016-06-16T18:14:10 | 61,145,865 | 1 | 0 | null | 2016-06-14T18:20:35 | 2016-06-14T18:20:34 | R | UTF-8 | R | false | false | 296 | r | margeCIBERSORTTables.R | sigMat <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/LM22.csv")
mixData <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/ExampleMixtures-GEPs.csv")
head(sigMat$GeneSymbol) #no idea why it needs a dot between Gene and symbol, Rstudio found it out
head(mixData$GeneSymbol) |
8557fd17091e2d071f153b7b333278b8e80e07d5 | 475507c1fb088a4ac31dc66b17c3718d09cf66ba | /run_analysis.R | 3cdc5b25cfd511656fc84b2ae7fe251be0b9cf4a | [] | no_license | neerajasharma/Getting-and-cleaning-data_week4_project | bd5f93c9db7dab806c7206e86815b270020cc76a | b785e08a1b1e19e5ca79c50fb3164b70f01fc1aa | refs/heads/main | 2023-09-04T11:24:41.483073 | 2021-10-26T11:28:01 | 2021-10-26T11:28:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,949 | r | run_analysis.R | # Load packages
install.packages("data.table")
library(data.table)
install.packages("dplyr")
library(dplyr)
# set working directory
setwd("C:/Users/bspadmin/Documents/gettingandcleaningdata")
# download ZIP file from the web
URL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zi... |
7279f91c392554954af0652a65cef0b87c5c6bad | 8bd02cf887641c987e73caa5bb1b8a82eddedc37 | /web_scraping.R | e96200e6e997c5f2e49af8f74da4ff0fac21612b | [] | no_license | turgeonmaxime/bfi-2012poll | cec8ac8a7814834bbc674d6fcf7965831a4b2300 | e3d75a317880c53adefc4520195b8634a2e2ac8e | refs/heads/master | 2021-05-13T23:27:23.631489 | 2018-01-06T21:04:47 | 2018-01-06T21:04:47 | 116,515,471 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,322 | r | web_scraping.R | # BFI 2012 poll web scraping
library(rvest)
library(magrittr)
library(dplyr)
# There are 1205 voters
n_max <- 1205
# Schemas
schema_details <- tibble::tibble(
ID = numeric(0),
Name = character(0),
Details = character(0)
)
schema_votes <- tibble::tibble(
ID = numeric(0),
Title = character(0),
... |
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