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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9c1c93a016c5ad7461fd63c45276ddee22ff5720 | c741b8b9982799de6406714257cbf20c6491ade7 | /app.R | 2b8e03b11cb394b7d6261fc07bdebf4f6e2e90f0 | [] | no_license | moloscripts/KMPDC | f1d5ed4759c5049d4abb81d6db9dad7d869ceec1 | 954452a736a43da1fd3b68a8cf4ba4b954dd7414 | refs/heads/main | 2023-06-20T13:14:24.103385 | 2021-07-21T20:19:36 | 2021-07-21T20:19:36 | 388,144,754 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,102 | r | app.R |
# App developed my Data Cube Solutions
# contactdatacube@gmail.com / molo.andrew@gmail.com
# Data is reproducible
# https://stackoverflow.com/questions/54914541/global-r-dont-start/66802176#66802176
library(easypackages)
libraries("shiny","shinydashboard","tidyverse","lubridate", "plotly","Rcpp","shinyjs","rsconnect... |
6b16888e3ef2d4849a212550fb940df10461b779 | 29585dff702209dd446c0ab52ceea046c58e384e | /aroma.core/R/GLAD.EXTS.R | e65394bb7bf22e348abe58e8bb8ad03ea08097da | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,566 | r | GLAD.EXTS.R | setMethodS3("extractCopyNumberRegions", "profileCGH", function(object, ...) {
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Validate arguments
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
pv <- object$profileValues;
# - - - - - - - - - - - - - - - - - - - ... |
73f382bfbc2b6109f8325b9698b35265143d41f2 | 34228464eec9a4c0d741ff754691f39aae95b1a2 | /TCGA_adeno/LP_Test_adeno.R | 74ff9003709c9afa3a35e06a18b2a50a6812263f | [] | no_license | yingstat/MOAB | 53dd05154dd42fc14c3defb8f80713e22690cee5 | 36aba9de4e5f462973ed15782bdbc1c5482304e6 | refs/heads/master | 2023-02-21T10:01:49.539590 | 2021-01-21T18:02:28 | 2021-01-21T18:02:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,555 | r | LP_Test_adeno.R | source('MOAB_main/Benchmark_HD_LD.R')
Raw=readRDS('TCGA_adeno/data/AllX')
PointLab=readRDS('TCGA_adeno/data/nodelabs')
#########
#tSNE
########
#read in data
tEmbed=readRDS('TCGA_adeno/Embeddings/tSNE')
kVals=c(10,25,50,75,100,200,300)
kMat_tSNE=matrix(0,nrow=length(kVals),ncol=30)
for(i in 1:length(kVals)){
print('... |
db345b229e2d8744796de5d89ac5cf17a9c6c200 | 4e3ef191ae5415eb0655f6d9fe8349f1aa54e80c | /R/draw.gdp_comps.R | 5d861c48a1f524c04b1aa8e9c4437a0245bb0fcf | [] | no_license | fernote7/BETS | 2e5624a4a5210cdeb583cb664548989bfec7edb3 | 5ba58b5e1997f6f47cdba1a686d9a3a1b95624ad | refs/heads/master | 2021-01-22T17:23:21.115413 | 2017-09-05T18:09:58 | 2017-09-05T18:09:58 | 86,753,812 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,907 | r | draw.gdp_comps.R | #' @title Create a chart of the Base Interest Rate (SELIC) time series
#'
#' @description Creates a plot of series 4189
#'
#' @return An image file is saved in the 'graphs' folder, under the BETS installation directory.
#' @importFrom zoo as.Date as.yearqtr
#' @importFrom forecast ma
#' @importFrom utils read.csv
#... |
d2054922f6e8215c9f559022c5cc5663439864ef | 6e41a0025ac0cbf86cc30c06f0ea74f7bc31c418 | /man/trans.Rd | 6c571416ec0cb9077e5606d83162e5eac2ff2da7 | [] | no_license | MrLehna/HMM | 87acad48a97c9ecf114a0acc087fb7b05a3e2084 | 6cb42f8aa98c8a0c6c325c51c150638fe6cc4a04 | refs/heads/master | 2020-03-25T15:39:59.234492 | 2018-08-07T14:31:25 | 2018-08-07T14:31:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,382 | rd | trans.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Transformation.R
\name{trans}
\alias{trans}
\title{Transformation function - DM}
\usage{
trans(factor, m)
}
\arguments{
\item{factor}{see details}
\item{m}{number of Likelihoods}
}
\value{
returns a matrix with the sigma,Gamma and theta matr... |
936f4b5a92d96a11e2a0826faaf63b7aa4996175 | 402956c1f9adfd625ee01113c7eeb7ec745ff6e9 | /exemple.R | 981f278009043fb23408369876f6df6499620f44 | [] | no_license | slevu/rapport_ehpad | 83bed175e26c674bf036806cca4fc425bdcabaf2 | f9938032aa7fe207f4ead57a13c91c4a3c86d148 | refs/heads/main | 2023-02-28T08:55:33.563882 | 2021-02-10T15:46:28 | 2021-02-10T15:46:28 | 337,700,131 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,033 | r | exemple.R | ## exemple rapports par région
## Stephane - 10/2/21
##---- mock individual data ----
nregfr <- 18 ## france
ndptfr <- 101
netab <- 4
k <- 3 # nb region
region <- LETTERS[1:k]
set.seed(123)
{
ndpt_par_region <- rpois(k, ndptfr/nregfr)
reg <- rep(region, ndpt_par_region)
dep <- paste0(reg, sequence(ndpt_par_regi... |
84a4fde7a765d9ead5283ef1c16096341123f3ab | 89fd142ff8c81c3740b175aec923e3caadac859a | /man/crear_subgrafo_inducido.Rd | 2fa5d34154331f0a623b3de37b93c7b74afb5a4a | [] | no_license | guilleloro/Grafos | 941420c277cba8068678cd29cacf909559be3026 | fdf3c7d4d22b0f34eb5708b6500c6d0eeef36f20 | refs/heads/master | 2022-07-18T16:03:25.223685 | 2020-05-19T15:31:53 | 2020-05-19T15:31:53 | 265,262,325 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | true | 514 | rd | crear_subgrafo_inducido.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Seguridad.R
\name{crear_subgrafo_inducido}
\alias{crear_subgrafo_inducido}
\title{Subgrafo inducido.}
\usage{
crear_subgrafo_inducido(grafo, lonMuestra)
}
\arguments{
\item{grafo}{grafo que se quiere muestrear .}
\item{lonMuestra}... |
4a8304d05b48b7a2e89d872deff160da92556c03 | 3dfeddf016a3754b920c2bad2bf3287faad047c4 | /man/getGitCommit.Rd | 3fc06ef244ca36d74297d1116b2eadb2291817f2 | [] | no_license | acguidoum/Rpolyhedra | 459592b06fbcafff61776fce588fa1d1f1429bdc | 19a80f920fcc511e8d37811ad1827796de722ae9 | refs/heads/master | 2020-03-28T07:01:52.547368 | 2018-07-10T16:16:38 | 2018-07-10T16:16:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 296 | rd | getGitCommit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/db-lib.R
\name{getGitCommit}
\alias{getGitCommit}
\title{getGitCommit
get the last git commit sha}
\usage{
getGitCommit()
}
\value{
String with git commit sha
}
\description{
getGitCommit
get the last git commit sha
}
|
bbf7ee7aa920ef8462672f8aef9d1a87c49ea5de | 5867e13f269018253bd376566b04d33d9e1dab3c | /raster_classwork_1.R | 679ddb9d7a14b8ff7f5b17cf2c995d7a78094ec3 | [] | no_license | manidhill0n/Maninder_eagles | af0caa0d566819138cf02ce6d1079e338d954387 | b1a09cb425a8777f229f0cac1eb8f38af7d34f7f | refs/heads/master | 2020-12-24T07:05:28.757772 | 2019-06-19T09:47:10 | 2019-06-19T09:47:10 | 73,380,119 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 376 | r | raster_classwork_1.R | #dataframe to raster
install.packages("raster")
length(df$measure1)
library(raster)
r2<-raster(nrows=100,ncols=100)
r2
r2[]<-df$measure2[1:1000]
plot(r2)
r2
r1<-raster(nrows=100,ncols=100)
r1
r1[]<-df$measure2[1:1000]
plot(r1)
r1
r12<-stack(r1,r2)
r12
plot(r12[[1]])
r12[[1]]
r12$new<-r12[[1]]*r12[[2]]^2
r12
rset<... |
eacd297580b7ffd7cf72231cc30dca4ae689aecd | c0bcd0b5f2d1abd72de5e775aedebfd407a1a4a3 | /recommeder-system-getting-started/content-based-algorithm/functions.R | 1a3e529f87c508ce75bd7b9bc937d79c5cbce17c | [] | no_license | zembrzuski/machine-learning-andrewng | 707421b12aef715ae8121666b68d1c6f459f0b29 | 520fc38e83468865ec158026a10c73bdfb015dc7 | refs/heads/master | 2021-01-10T01:42:46.852296 | 2015-12-08T18:36:41 | 2015-12-08T18:36:41 | 45,475,686 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,577 | r | functions.R | # it works
addBiasColumn <- function(X) {
cbind(rep(1, nrow(X)), X)
}
addZeroColumn <- function(X) {
cbind(rep(0, nrow(X)), X)
}
# it works
costFunction <- function(X, thetas, y, rating) {
prediction <- X %*% t(thetas)
errorMatrix <- (prediction - y)^2 * rating
1/2 * sum(errorMatrix)
}
# it works
gradient ... |
83f825ccce66f249b5daae5996c9349be935a2c6 | bbc167551a93d2a6d7ea43f00fc901ff967a8c62 | /man/load.exp.GEO.Rd | 824ed0b5d6c3d31b6cedcf76fad653ee53a0b097 | [
"Apache-2.0"
] | permissive | jyyulab/NetBID | 0c4c212cddd0180b96506e741350e6b7cfcacfce | 86d62097eda88a6185b494491efdd8b49902e0c3 | refs/heads/master | 2023-04-30T04:16:40.752026 | 2023-02-28T02:57:04 | 2023-02-28T02:57:04 | 118,371,500 | 34 | 10 | Apache-2.0 | 2022-08-23T13:44:58 | 2018-01-21T20:33:57 | R | UTF-8 | R | false | true | 1,559 | rd | load.exp.GEO.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pipeline_functions.R
\name{load.exp.GEO}
\alias{load.exp.GEO}
\title{Download Gene Expression Series From GEO Database with Platform Specified}
\usage{
load.exp.GEO(
out.dir = NULL,
GSE = NULL,
GPL = NULL,
getGPL = TRUE,
update = FA... |
b044bd92d43866936200f6ebb03c612c327370c9 | 0b02833c0833f945d379408ab2c9c77afe9a0216 | /AUC Final.R | 7189154bbd83ac8e7e706144fb4375497f582c49 | [] | no_license | Karagul/Regression-with-Mortgage-Data | 393287644d8636dc8464887cc28e04b45de699c2 | 2bff880466c3813c62bf3360382d14fe14197a07 | refs/heads/master | 2020-06-14T02:58:36.477350 | 2018-11-14T22:18:33 | 2018-11-14T22:18:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 741 | r | AUC Final.R | glm.out <- c(glm11.out, glm12.out, glm13.out, glm14.out, glm15.out)
yp1 <- predict(glm11.out, propt1, type="response")
yp2 <- predict(glm12.out, propt2, type="response")
yp3 <- predict(glm13.out, scoret2, type="response")
yp4 <- predict(glm14.out, scoret3, type="response")
yp5 <- predict(glm15.out, scoret4, type="respo... |
fe6859c93fd14a8f76e962e88243965c49953870 | b5bc79574cf46cd80962d101fdb3189b054f7e50 | /plot3.R | 8f15049543b9707e7c68b52295f1b1fb919a1bdd | [] | no_license | zakia5/EDA | 7ccc25cbe44eb1055407179375f51859eaeb804d | c601bc64e78b80f7505c2ddba2b5bc7521ab3a89 | refs/heads/master | 2021-01-10T00:58:44.068044 | 2016-01-29T18:08:00 | 2016-01-29T18:08:00 | 50,677,820 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 953 | r | plot3.R | library(data.table)
## reading data
inputFile <- "household_power_consumption.txt"
data <- read.table(inputFile, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".",na.strings='?')
work_data <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
# assign to variables
datetime <- strptime(paste(work_data$Date, work_data... |
e3501653150517302c0231d19884ee37e14ea741 | 43666c1680c3b982c3db9b0de759bb1f380f899f | /UN/UN.R | 8b05951a577ea4b989b66c37b9f934d47cf97118 | [] | no_license | RobHarrand/datadotworld | 4490c24ab8c69d2b01c2ea4bb61e3ad25e6b0f9b | 8ced56e2fb28a64f87d1501c9f35dc7c619a1048 | refs/heads/master | 2020-03-11T08:03:13.721701 | 2018-04-17T08:42:39 | 2018-04-17T08:42:39 | 129,873,592 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,238 | r | UN.R | library(plotly)
GDI = read.csv('GDI_tidied.csv', stringsAsFactors = T, header = T)
HDI = read.csv('HDI_tidied.csv', stringsAsFactors = T, header = T)
MPI = read.csv('MPI_tidied.csv', stringsAsFactors = T, header = T)
MPI$Population_in_MDP_k = gsub(",", ".", MPI$Population_in_MDP_k)
MPI$Population_in_MDP_k = as.numer... |
a1f4660ee057a58e5d1ffa8846e70dd451e0bf50 | 5adc0dfe6cae8f90cc20cd149bf03852b0396e34 | /tests/testthat/test_calculation_nutrient.R | 761e1e0e80dbc218368bc3099471ac949b810ad9 | [
"MIT"
] | permissive | AGROFIMS/ragrofims | 43664011980affa495c949586bde192d08d4b48e | bc560a62c19c30bbc75615a19a4b9f8a235f7ddf | refs/heads/master | 2023-02-21T08:49:34.989861 | 2021-01-20T16:22:48 | 2021-01-20T16:22:48 | 277,626,238 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,935 | r | test_calculation_nutrient.R |
library(ragapi)
library(ragrofims)
context("test of calculation nutrient in products")
test_that("Test Calculation of Nutrient Pipeline with empty table API v0291", {
out <- get_agrofims_fertproducts(expsiteId= 6,
format = "data.frame",
server... |
875032b0f221c34d2a6d807115c8283ef2b2ba1a | 98b8bb57f4f1b632f99cd01237ae8b116b939e9f | /Basic and Statistics in R.R | 4db4e5672a951b14855dfe99080245cfc5796c24 | [] | no_license | UD125/Basic-and-Statistics-in-R | 404a1af2096f6469053e0889d3e2c3ddfd08a94f | 0e08f517c3b970a8d433acee7c47d53c19aca56e | refs/heads/master | 2021-07-13T22:48:03.859694 | 2017-10-14T11:49:52 | 2017-10-14T11:49:52 | 106,922,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,087 | r | Basic and Statistics in R.R | Name <- c("a","b","c","d","e","f","g","h","i","j")
Age <- c(22,43,12,17,29,5,51,56,9,44)
Sex <- c("M","F","M","M","M","F","F","M","F","F")
data1 <- data.frame(Name,Age,Sex,stringsAsFactors = FALSE)
data1
data1$Sex <- as.factor(data1$Sex)
str(data1)
which.min(data1$Age)
which.max(data1$Age)
cumsum(data1$Age)
cumprod(dat... |
274b013d1fbd0705a3be1eb94ebe2a4d4c218494 | 0fc75bfea69ffe1908941c4d1f2a9f6e7b0056c0 | /nomsplit.R | a4eeacdfa4c8a3de44f468f3a16127e64b655b34 | [] | no_license | citizen-monitoring/citizen-monitoring.github.io | 1100f3abb2c9555e5870c2e63331b731e45ac4bc | 2ae7a1c8e8af216c66ff75c82dd60efac91f7d00 | refs/heads/master | 2021-01-10T13:43:06.225014 | 2016-02-04T00:54:41 | 2016-02-04T00:54:41 | 49,733,098 | 0 | 2 | null | 2016-01-18T04:15:48 | 2016-01-15T17:10:20 | R | UTF-8 | R | false | false | 1,087 | r | nomsplit.R | # Split an input data set based on whether the respondant was nominated or not
# Function: nomsplit
nomsplit = function(x){
library(tidyr)
nomYes = filter(x, x$Nominated == "yes") # separate nominated respondents
nomNo = filter(x, x$Nominated == "no") # separate non-nominated respondents
final = list(nom... |
fbcf067c0f36727d95984b299b6cd65ef63215e9 | d3b774668f6e577cefdeea4dd2be1326ee4b5aee | /man/checkarg_file_name.Rd | 832136b4b89db4a7a0111d6aaa72b8ca75a25d09 | [
"MIT"
] | permissive | ropensci/qualtRics | 50e68a3dd3f184ee14f19126bd7783b4b9bd61d1 | c721563fa2fcb734c1ad9c4d8ccd80bbefbed15d | refs/heads/main | 2023-08-31T01:00:05.366989 | 2023-06-23T18:55:13 | 2023-06-23T18:55:13 | 70,817,337 | 188 | 64 | NOASSERTION | 2023-09-07T19:38:56 | 2016-10-13T14:51:26 | R | UTF-8 | R | false | true | 325 | rd | checkarg_file_name.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/checks.R
\name{checkarg_file_name}
\alias{checkarg_file_name}
\title{Check if survey file specified in file_name exists}
\usage{
checkarg_file_name(file_name)
}
\description{
Check if survey file specified in file_name exists
}
\keyword{inter... |
680600f0f42c6e655331ab89a3e53aadd92e6859 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Introductory_Statistics_by_Douglas_S_Shafer_And_Zhiyi_Zhang/CH2/EX2.10/Ex2_10.R | 7f0e8f16493d9951bc1f62d2da98dd52759add96 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 210 | r | Ex2_10.R | #Page 58
dataset_1<-c(40,38,42,40,39,39,43,40,39,40)
dataset_2<-c(46,37,40,33,42,36,40,47,34,45)
findrange=function(v){
range=max(v)-min(v)
print(range)
}
findrange(dataset_1)
findrange(dataset_2)
|
bc6ce74dea21f7c3f999ceb5318b4897c716e4fe | eeb4249594b67f0564e8563ab83ecf641ef3ed8f | /man/seq_scan_sim.Rd | ee5579c7d0cf69b0caf4fdd97a969714bdcf3b43 | [] | no_license | cran/smerc | 6e81aaa86f2405364f2368079a07f949317848fc | aab00112b726a9392395b1937f3f92e1bbd3cb3e | refs/heads/master | 2023-07-23T05:53:39.316070 | 2023-07-15T19:30:02 | 2023-07-15T20:30:28 | 48,088,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,016 | rd | seq_scan_sim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/seq_scan_sim.R
\name{seq_scan_sim}
\alias{seq_scan_sim}
\title{Perform scan test on simulated data sequentially}
\usage{
seq_scan_sim(
nsim = 1,
nn,
ty,
ex,
type = "poisson",
ein = NULL,
eout = NULL,
tpop = NULL,... |
5d320757e2c7398ec0593c8627583b1bea9a2eca | 66e04f24259a07363ad8da7cd47872f75abbaea0 | /Intro to SQL for Data Science/Chapter 1-Selecting columns/4.R | aa1ebe9d241bae23967c97eba30f40a54c97c1ca | [
"MIT"
] | permissive | artileda/Datacamp-Data-Scientist-with-R-2019 | 19d64729a691880228f5a18994ad7b58d3e7b40e | a8b3f8f64cc5756add7ec5cae0e332101cb00bd9 | refs/heads/master | 2022-02-24T04:18:28.860980 | 2019-08-28T04:35:32 | 2019-08-28T04:35:32 | 325,043,594 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 495 | r | 4.R | # Onboarding | Bullet Exercises
# Another new feature we're introducing is the bullet exercise, which allows you to easily practice a new concept through repetition. Check it out below!
#
# Instructions 1/3
# 35 XP
# 1
# Submit the query in the editor! Don't worry, you'll learn how it works soon.
SELECT 'SQL'
AS resul... |
f8e5419b174e5248488e7864d40d9864ad99ca0f | 7492f6d8c4b9f2504ecd35003072a27f2b39d5ff | /project3.r | adf0075bb2ed3fb7e29d18c620ce213387c7d399 | [] | no_license | linleiwen/Credit-Card-Fraud-Detection | 4d172e107acd9432bd869af07b80829e8339782d | 831ceea64ff95fda7face32a9c9d5b89372b5a66 | refs/heads/master | 2021-05-14T00:44:03.975457 | 2018-01-17T15:15:25 | 2018-01-17T15:15:25 | 116,546,088 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,699 | r | project3.r | library(randomForest)
library(caret)
library(ROCR)
library(DMwR)# SMOTE more positive cases
library(data.table)
library(zoo)
library(parallel)
library(ggplot2)
library(dplyr)
detectCores()
df <- fread("creditcard.csv")
#### Exploratory analysis
prop.table(table(df$Class))
summary(df)
sum(is.na(df)) ##... |
24127f85cca60791a831b3bf45e68b6b7143e062 | 280fae7f01002ddc95c0e7ec617740a58752403d | /R/readCPEAT.R | e784900ff2c67f0c8334916fdbb9a5da4850c74e | [
"BSD-2-Clause"
] | permissive | ktoddbrown/soilDataR | 87bb4ed675959f3fbd75024dd7b014e1966148dd | 44ab9e6ac00e49ea0106508de8ead356d9e39fa5 | refs/heads/master | 2021-04-30T07:27:34.349030 | 2018-11-09T20:07:20 | 2018-11-09T20:07:20 | 92,432,342 | 3 | 11 | null | null | null | null | UTF-8 | R | false | false | 9,398 | r | readCPEAT.R | #' CPEAT project reads
#'
#' This reads in the specified records of the CPEAT project. Currently under development, not all
#' the metadata is parsed
#'
#' @param dataDir identify the download directory
#'
#' @return a list of data frames, the first data frame with the meta data and
#' a second data frame with the r... |
5dbbff784f4c7b5b340c7aee86345b4366c759aa | 5db2138d26423f514ac44162f52dfae296697f96 | /bubble.gsadf and Copula.r | 5638be37fc474cdd95bb9b3efc848c2f9b2b5674 | [] | no_license | hpompom/financal-bubble | 94f4764a94537420745afec68e9fc3fe03353f10 | 692a8c46d3746f86167e1b32033b668e2adfd7fa | refs/heads/master | 2020-07-09T15:20:07.650556 | 2019-08-28T07:26:45 | 2019-08-28T07:26:45 | 204,008,670 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,453 | r | bubble.gsadf and Copula.r | library(readxl)
library(ggplot2)
library(exuber)
library(MultipleBubbles)
library(forecast)
library(fBasics)
library(VineCopula)
library(rugarch)
library(FinTS)
library(urca)
library(TSA)
library(xts)
library(pastecs)
library(tseries)
library(FinTS)
library(car)
library(lmtest)
library(PerformanceAnalytics)#加载包
par(fam... |
253df967031156270df9eaa381f994db6c7f0165 | 9da1a7d2f925c855ea096e8d334cb3960e168feb | /man/process_reads-function.Rd | 6a80200d32be4f0a6cda7a0845417d03b5e6c57c | [] | no_license | rpolicastro/gostripes | 6f31621da63fd9930fb55cd5be7e841a1235661a | 285c58efaf8a298dc05f36d7a7a31844258d1859 | refs/heads/master | 2020-12-21T05:01:31.697569 | 2020-12-10T19:09:13 | 2020-12-10T19:09:13 | 236,315,120 | 3 | 1 | null | 2020-12-10T19:09:15 | 2020-01-26T13:21:52 | R | UTF-8 | R | false | true | 2,151 | rd | process_reads-function.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fastq_processing.R
\name{process_reads}
\alias{process_reads}
\title{Process Reads}
\usage{
process_reads(go_obj, outdir, contamination_fasta, cores = 1)
}
\arguments{
\item{go_obj}{gostripes object}
\item{outdir}{output directory for filter... |
085e01728c333201ab89d0056e16e9df67da76c8 | ebab6c7b1d1192822a4d7a32262c8d716180e33d | /plot1.R | 8e0b12557675954d41f291cca1c88b4608d72d9e | [] | no_license | lgy-hz/Exploratory-Data-Analysis-Project-1 | 5d719e4ef29d9a45696fc403f24c8333436651df | 98eaddf07047fd37444887e1655cb87757c0dfbb | refs/heads/master | 2016-09-03T01:38:18.688467 | 2015-08-09T07:51:55 | 2015-08-09T07:51:55 | 40,429,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 482 | r | plot1.R |
file<- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(file, destfile= "data.zip",method="curl")
unzip("data.zip")
a<- read.table("household_power_consumption.txt",header=TRUE,sep=";",na.strings= "?",stringsAsFactors = FALSE)
b<-a[a$Date== "1/2/2007",]
b<-rbi... |
189fd823843d943c1e9f6de1b2e2e8b646644542 | 6f0e911ea8753d23bc8fbdfe15758768586838fa | /R/normalize_functions.R | cbaec715185aed26dd4adf0d8e527e44cf0bfc62 | [] | no_license | kdaily/MEMA | 0223092dd491eea7383b374a11dfb15ee43a7893 | 7d22734de9caf673406576d2a5e63810996011e9 | refs/heads/master | 2020-04-13T11:40:19.329909 | 2015-08-02T21:57:06 | 2015-08-02T21:57:06 | 42,480,633 | 0 | 0 | null | 2015-09-14T22:28:18 | 2015-09-14T22:28:17 | R | UTF-8 | R | false | false | 3,058 | r | normalize_functions.R | #Normalization functions for processing MEMAs
#' Normalize the proliferation ratio signal to the collagen 1 values
#' @param x a dataframe or datatable with columns names ProliferatioRatio
#' and ShortName. ShortName must include at least one entry of COL1 or COL I.
#' @return The input dataframe of datatable with a n... |
56d45b71505c634e5ea4d68973b7414a15356a6d | f9099901637a813dfb3d39ed8f9dc06dddb1e8f5 | /R/00_testing.R | 620fbb019937209f4bc9e512b52a7d081691d1e5 | [] | no_license | jessdiallo/testing | 8f4a1c72983103ca154269defc256cac53479e9f | e28e8aabeda236e777809504a2de86129fb3c778 | refs/heads/main | 2023-02-25T20:32:57.994338 | 2021-01-23T20:09:16 | 2021-01-23T20:09:16 | 328,758,949 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 95 | r | 00_testing.R | # this is a test script
## addition
1 + 2
## multiplication
1 * 2
## subtraction
1 - 2
|
3ae5cd551484b56d72dc14363053071759ae384b | 08d26deb861c447fa86dc7f4d58ac2b17e1905d3 | /cachematrix.R | 2adbcdbc38629c22b8f147459298c886b527c533 | [] | no_license | youngokkim/ProgrammingAssignment2 | e06e4290a7e8076fc84eafd2f28ed9582e7b5a1b | 5cf57df0c3f21edef6682d7e39779ef92c8cddee | refs/heads/master | 2021-01-01T19:08:12.705885 | 2017-07-28T03:45:34 | 2017-07-28T03:45:34 | 98,514,334 | 0 | 0 | null | 2017-07-27T08:47:58 | 2017-07-27T08:47:58 | null | UTF-8 | R | false | false | 1,699 | r | cachematrix.R | ## Assignments Part2 for R-Programming Week3
## @written by Youngok Kim, joylife052@gmail.com
## makeCacheMatrix creates a special "matrix", which is really a list containing
## a function to
## 1. set the value of the matrix
## 2. get the value of the matrix
## 3. set the value of the inverse matrix
## 4. get th... |
47826e56f8b3eb1babc15aa1e0417de58afc436e | aafb44d8881e86da345ee0438f73e200b840a778 | /R/select.sample.group.R | abc58650a1cccdfeb7e76307dd19bad586486c5e | [] | no_license | cran/RPPanalyzer | 57738f8309a4b42115bc7119eaf5936f6dd72c47 | 7c6bfda1bfd4989a8afffed015f18f64a696c489 | refs/heads/master | 2023-08-31T15:52:38.080682 | 2023-08-28T12:30:02 | 2023-08-28T13:30:37 | 17,682,834 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,584 | r | select.sample.group.R | #'
#' The function selects subgroups of samples based on different parameters which are listed in the sampledescription
#' @param params: a list of sampledescription column names and associated values
#' @combine: logical value indicating if the union (combine is TRUE) or the intersect (combine is FALSE) should be c... |
c1a27d93d500658159f10b4509dfc5033ea1e132 | 7be81350dd4f0e33d675ba5ac316cf96774a6fed | /clasesGustavo/TareasHogar/Tarea20210917/111_rpart_default.r | 424eecb89d6a78c6f60b1c63a68eba1a59af00b0 | [] | no_license | gerbeldo/labo2021 | aa3ccb20501099de6ca3bb5f05afdddc1fa63764 | 97796e15fd77ab2b19fcd731f42378980ad659d7 | refs/heads/master | 2023-08-21T15:53:31.767994 | 2021-10-20T14:46:53 | 2021-10-20T14:46:53 | 400,648,848 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 963 | r | 111_rpart_default.r | #limpio la memoria
rm(list=ls()) #remove all objects
gc() #garbage collection
#Arbol elemental con libreria rpart
require("data.table")
require("rpart")
setwd("~/buckets/b1/") #Establezco el Working Directory
#cargo los datos de 202011 que es donde voy a ENTRENAR el modelo
dtrain <- fread("./datasets... |
a554cc0b8a4549b693a7c1f1ab94cd95fd690b7e | ec5db8e0e525c5198b59a14ac0c7ac00864fde28 | /scripts/R-scripts/basic_smooth-norm.R | b3e723b70a004cdecb2b834ac5197f16e2c1bad6 | [] | no_license | ChromatinCardiff/DanielPassProcessingPipelines | bdc1a02f6ec0fb99387db85574cdeefe7c247525 | c3b02255a69f81c888f5cba14fb1b031ce99dc2e | refs/heads/master | 2021-06-02T15:13:18.958935 | 2020-01-29T01:07:27 | 2020-01-29T01:07:27 | 32,514,237 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 531 | r | basic_smooth-norm.R | library(ggplot2)
library(zoo)
require(scales)
library(plyr)
x <- read.table("~/Projects/ALD/ALD_histogram.txt", header=FALSE)
summary(x)
fn <- function(x) x/sum(x)
### Normalise column V3
x.nor <- ddply(x, "V1", transform, V3norm=fn(V3))
### SMOOTHING
x$av <- ave(x.nor$V3norm, x.nor$V1, FUN= function(x) rollmean(x,... |
d8d45b6184c293666345f9476004e74c9cf0b52b | 9c57c741b59f615f7c786da87338402eca05f8e6 | /2-Absent_bones.R | 77810f6d3137d72ae23cd27277efc68400858778 | [
"MIT"
] | permissive | AgneseLan/ontogeny-asymmetry-dolphin | 4861b193346bdd793c112b00230c5616cafcc55d | db9e69d4cef5de94aba15b422eee38dae300307f | refs/heads/main | 2023-04-06T20:11:37.967266 | 2022-07-19T16:29:28 | 2022-07-19T16:29:28 | 451,504,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,315 | r | 2-Absent_bones.R |
#===========================================================#
# #
# CURVES AND POINTS ANALYSES - ODONTOCETE FAMILIES #
# #
#===========================================================#
#CH.2 -... |
80a82b1c55b4c6addda7ac380ad450f177622cdb | ed370813a204a903d8c5f951e50c89080a30f725 | /tests/test-all.R | 7c9528a7ee70de84d5aa92448f5140a6247239e1 | [] | no_license | jayemerson/STV | f174b56529596d02c77ccaf85533a833da0d97d3 | 32940b4e746ded9e69f5f9d35233555337f76e63 | refs/heads/master | 2021-07-18T10:39:34.498545 | 2021-02-01T00:12:06 | 2021-02-01T00:12:06 | 91,092,742 | 3 | 5 | null | 2018-02-17T23:03:11 | 2017-05-12T13:09:50 | R | UTF-8 | R | false | false | 36 | r | test-all.R | library(testthat)
test_check("STV")
|
8c4362b8c3bf1d0b43f7e04794292ff3c76e3bdd | 2b7696de761986e7c295da36201f06fca701f059 | /man/hs5_hs2.Rd | 2b3b1adcc8b1646d5b1710b9cc4c8e9acd8d91f5 | [] | no_license | cran/concordance | 130b5cadccfce9cc5ef98432fc2f938c75eebd93 | b8d1e592399f05941ce24a4afd96007b8dae0ec5 | refs/heads/master | 2021-05-04T11:23:30.586684 | 2020-04-24T15:10:08 | 2020-04-24T15:10:08 | 49,413,285 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 648 | rd | hs5_hs2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{hs5_hs2}
\alias{hs5_hs2}
\title{HS5-HS2 Concordance}
\format{
A data frame with 5388 rows and 6 variables:
\describe{
\item{HS5_6d}{6-digit HS5 Code}
\item{HS5_4d}{4-digit HS5 Code}
\item{HS5_2d}{2-digit HS5 ... |
83bd07beffaecda944fb316fc2f057bd5c1b44d4 | 808e37074a3652ea10ae384f4747bd9b2e3607fd | /man/df_cea_psa.Rd | 1e92c421062e28217d4895134d055f7c17b63c96 | [
"MIT"
] | permissive | fthielen/ce16_modelling_course | 248a9eab42d32009e9b417a0fe44e339bf410717 | 62bb04618abfd5ff603b128885b68cda8dc52d9d | refs/heads/master | 2023-05-05T17:17:50.542522 | 2021-05-17T10:03:49 | 2021-05-17T10:03:49 | 368,138,798 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 801 | rd | df_cea_psa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_cea_psa.R
\docType{data}
\name{df_cea_psa}
\alias{df_cea_psa}
\title{Cost-effectiveness results from probabilistic analysis}
\format{A \code{data.frame} with with 2 rows, ane per strategy and
5 variables:
\describe{
\item{Strategy}{Strat... |
024032019b326a61867393dc582a10fac9d45dc0 | eb3d7cbbb4ded421fa211384f5ee1df251646577 | /R/geos-misc.R | 62fa966af2a6543f896bc3934f49c6a4585ebdb2 | [] | no_license | SymbolixAU/geom | f2ab70e698881079ec5c09ac7c6ee7d74420bf87 | 45a913ddb3942807635547ce471a08d0e5f3af62 | refs/heads/master | 2021-05-19T19:02:29.902255 | 2020-04-01T04:52:55 | 2020-04-01T04:52:55 | 252,074,507 | 0 | 0 | null | 2020-04-01T04:46:56 | 2020-04-01T04:46:55 | null | UTF-8 | R | false | false | 837 | r | geos-misc.R |
#' Area, length, and distance
#'
#' @inheritParams geos_intersection
#'
#' @return
#' - [geos_area()] computes areas for polygons, or returns 0 otherwise.
#' - [geos_length()] computes the length of the boundary for polygons, or the length
#' of the line for linestrings.
#' - [geos_distance()] returns the smallest p... |
aa053186bde1c6e149a3a02c9305dae93c13dcfa | 28dcec41c7bf186f4ecee1ec5a20903c8839b65d | /NOBUILD/Sandbox/segall.R | fc845ddd59c939b1f0e91be0c7e2332c76c65502 | [] | no_license | abarbour/deform | f0cc416521fd5984636a86f23c5381962d9f2238 | 0a54d77b8d19d8efb8e944fe2e107aab0c6eb830 | refs/heads/master | 2022-03-20T06:36:41.132055 | 2022-02-07T21:57:23 | 2022-02-07T21:57:23 | 25,497,729 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,859 | r | segall.R | library(kook)
library(plyr)
library(TeachingDemos)
library(deform)
.x. <- sort(unique(c(-7:-3, seq(-3.90,3.90,by=0.15), 3:7)))
su <- surface_displacement(.x.*1e3, C.=1e13, z_src=0.7e3)
sut <- with(su, Tilt(x, z=uz))
sue <- with(su, Uniaxial_extension(x, X=ux))
F1 <- function(){
plot(c(NA,diff(ux)/diff(x),NA) ~ c(N... |
6b103889658aef26588ee3c6cb8c946b905a1476 | 89491fef8c724a2500434f220780f3300017ff38 | /inst/pruebas/demoIRIS.R | 60083c2badbbcd44d18d6d65fa3e0c8340f5b1c3 | [] | no_license | cran/FKBL | ccafa5c7acbc14abad415b641d7d3e29004a658b | ec6c9300a8c01950db07ff57a93940b98936ed48 | refs/heads/master | 2016-09-05T20:16:08.923166 | 2007-03-31T00:00:00 | 2007-03-31T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 101 | r | demoIRIS.R |
source("script.R")
IRIS=read.table("../data/IRIS.tab")
#data(IRIS)
salida<-EXPERIMENT(IRIS)
salida$e |
ebcc8cb2ac23f50564310136e7043b632f2e8da7 | 571e295f9ad4ca5762f9ca05bbc4a51b29e97d7b | /STRING/stringSERVER.R | 81f89329fb11db9e4fe4427e1f65586fcc375701 | [] | no_license | estayless/comparison-matrices-admin | 8a2aab4086692d87dae26d2e4974786cd937fcae | d5531054f7f16f98863e4c1b2caed176fea84d67 | refs/heads/master | 2023-02-15T08:58:42.416078 | 2020-12-30T10:18:15 | 2020-12-30T10:18:15 | 325,518,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,212 | r | stringSERVER.R | # linksDetailed<-reactive({
# req(input$linksDetailed)
# df<-read.delim(input$linksDetailed$datapath, header = TRUE, sep = "")
# print(df)
# })
# #SERA LA TRADUCCION CON PROTEIN INFO
# proteinInfo<-reactive({
# req(input$proteinInfo)
# df<-read.delim(input$proteinInfo$datapath, header = TRUE, sep = "\t")
# ... |
fddb2c08fb24e9c8d505e0ac4bcb8d5f7f978e27 | 2764167b5743be62adadc491ec7dfde210e0703d | /man/BASICTOPOMAP.Rd | e2f32e9e2b91429b7cf2f9d6aa9e187942a42c0d | [] | no_license | cran/GEOmap | 528a4cbe293211d324405037eb280b415e65f62e | 0149894022496cee8237868b0bb693d00ef01e41 | refs/heads/master | 2023-08-18T14:47:52.021469 | 2023-08-13T12:40:21 | 2023-08-13T13:30:31 | 17,713,753 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,013 | rd | BASICTOPOMAP.Rd | \name{BASICTOPOMAP}
\alias{BASICTOPOMAP}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Basic Topogrpahy Map}
\description{
Basic Topogrpahy Map
}
\usage{
BASICTOPOMAP(xo, yo, DOIMG, DOCONT, UZ, AZ, IZ, perim, PLAT, PLON,
PROJ = PROJ, pnts = NULL, GRIDcol = NULL)
}
%- maybe also 'usage' for oth... |
c1e817433dc4b5f53b73a3c4dfdec1e07cf8a61d | 161747aed56bfc7fbd17b87c60c25291ef12a579 | /CRAN_meta_analysis/server.R | f3c7fafec7aad03a5ffcadda6e79aaecd3ec2c47 | [] | no_license | juschu321/CRAN_meta | 62f3dc210eecd1684c2cb4d11531ff0bdcfca65f | 333375a95cd6606e16fc06b0c03b78516fc090ed | refs/heads/master | 2020-06-02T22:43:02.452187 | 2019-07-16T15:40:55 | 2019-07-16T15:40:55 | 191,332,470 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,298 | r | server.R | library(shiny)
library(shinydashboard)
library(ggplot2)
library(plotly)
server <- function(input, output, session) {
aggreggated_timeseries_data <- reactive({
selected_ctvs <- input$ctvs_select
selected_packages <- input$packages_select
date_slider <- input$year
selected_from <- input$year[... |
590ca89e0c65c4f9e7998882b5e03bf20b31e600 | 3998d54ca79a8382685426844d7c22d4fbb4429a | /setup_400m_aquifer_tube_model_domain/codes/ert_inland_bc.R | 570f58157006c78d164143cb3a339bcdbbbbc78e | [] | no_license | xuehangsong/dense_array | 5bbe30275a647536e880d7751e2f67b9c2c2c78d | 2a7584bc8e945fa8bd251a47048bb43fa96166b5 | refs/heads/master | 2021-07-16T02:33:44.584572 | 2020-09-19T17:58:02 | 2020-09-19T17:58:02 | 212,211,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,115 | r | ert_inland_bc.R | ## #This file is used for calculating transient boundary conditions
## #using universal kriging
###cov_model_sets = c('gaussian','wave','exponential','spherical')
###drift_sets = c(0,1)
rm(list=ls())
library(geoR)
library(rhdf5)
source("codes/ert_parameters.R")
H5close()
options(geoR.messages=FALSE)
input_folder... |
dab6ffbad1231225544f313bce1e417cf7bee723 | 921ef582b7c321e6cb94dd7f0a5b2404f0410c22 | /B1/Frequencies.Crosstabs.Descriptives_v2.R | 3e120c0946cebed52033957097c2d728be8d6455 | [] | no_license | thomasns2/rmodules | c7e12dc0682acfa3b6d093a5c40e30e503ea7e90 | b4ac3ca0806cdaadc3c12399b48d5f11009b563e | refs/heads/main | 2023-04-30T23:14:26.105167 | 2021-05-06T19:19:36 | 2021-05-06T19:19:36 | 360,587,142 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,099 | r | Frequencies.Crosstabs.Descriptives_v2.R | #########################################################
#########################################################
#iii. Frequencies, Crosstabs and Descriptives
#########################################################
#########################################################
getwd() # this function can be used to fi... |
5a04e0f2ad9dbbbad78646220dd240286cd7efb8 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Biere/Counter/counter_r_2/counter_r_2.R | 896d630ebae53be24e9f8581ec11f61ca07bee5f | [] | 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 | 59 | r | counter_r_2.R | 985b9ea304afe4abc21bc75835226c81 counter_r_2.qdimacs 50 121 |
8607016166762af87859ac357ced6ecc2d3e59a5 | 1de64141140b62134beffb82aa9602baeea0e35e | /presentations_reveal/muses_material_survey/rust_survey/assets/likert.R | a0fcb31677a47886a01783a07f3d66a3684238ca | [
"MIT"
] | permissive | bgaster/bgaster.github.io | ea9f5d0333714a798e902d0d4c4b4b935f69c72a | bb6376df51c431b9ee813f1df9970058231ce6e6 | refs/heads/master | 2022-12-10T02:45:54.980203 | 2021-03-03T09:48:54 | 2021-03-03T09:48:54 | 194,642,249 | 0 | 0 | null | 2022-12-07T21:53:57 | 2019-07-01T09:28:33 | JavaScript | UTF-8 | R | false | false | 3,807 | r | likert.R | # Exaxmple visualization script for Muses Material Survey Likert data
# The actual graphs we want to produce will vary, depending on what we want to show
# for a given presentation of the data.
#
# Benedict R. Gaster
library(ggplot2)
library(dplyr)
library(sqldf)
library(ggpubr)
# Load Likert CSV files, appending a... |
2aeea9b269fe447927e97d63bbf0af1c34a0532b | bc504192da5aa37ccf2c40464942c3d5c56193d7 | /HW4s2020.R | 7b86833d257d64ddada4aec3c611c9d03ca147a3 | [] | no_license | danniecuiuc/frm | 6125daab1ddb6ceb7f50b6f1a9f99437238102ce | 12d632bad3283188378ee3f4623bc3b2382165c6 | refs/heads/master | 2022-10-19T13:55:07.987734 | 2020-06-08T14:58:32 | 2020-06-08T14:58:32 | 269,361,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,323 | r | HW4s2020.R | # Solution to Fin 567 Homework 4 spring 2020 Questions 1 and 2
# Does not include solution to Question 3 because that does not require R
#
library(fOptions) #needed to compute option values
library(MASS) #needed to simulate multivariate Normal rvs
library(mvtnorm) #needed to simulate multivariate t rvs
... |
6d06454bdeeea8a52c50f80db830861da4df3134 | 249afaa1ffe3d3b27906548ee468c95718276591 | /R/BioGeoBias_bias_correction.R | 56a6da01cdb79c58baa633ba0c5fec500918189a | [
"MIT"
] | permissive | JanLauGe/BioGeoBias | 01c6409d8ff3b4d3b1d12398e3debf05856ff775 | e07b2ddd54b4c1a24766d4d82bfb8379d6503fcf | refs/heads/master | 2021-01-11T17:02:50.097840 | 2018-10-20T21:30:01 | 2018-10-20T21:30:01 | 69,507,338 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,114 | r | BioGeoBias_bias_correction.R |
#' @title Bias Correction
#' @description Choose bias correction approach and generate bias correction data
#' @param species_name (character) a valid taxon name, presumably a species name.
#' @param target_rank (character) a taxon rank for the target group (optional).
#' @param kingdom (character) the kingdom of the ... |
e18dd8452366516366e10d5f0d6af4115f731dc8 | a0f93433c57753c4dab79805c57b3cb0031b8304 | /man/plot_rand_KLD.Rd | d36bb388818c05bbbf6602e3aa4e2168c3bee970 | [
"MIT"
] | permissive | alexisvdb/singleCellHaystack | 9dcc59288d3ba2c34bdfc906d45317d6d885f25d | c705b95cd2bf01575df938574864c54ba78714d3 | refs/heads/master | 2023-08-16T02:58:48.850113 | 2023-08-05T14:05:46 | 2023-08-05T14:05:46 | 170,470,927 | 71 | 8 | NOASSERTION | 2022-10-21T07:09:12 | 2019-02-13T08:35:23 | R | UTF-8 | R | false | true | 742 | rd | plot_rand_KLD.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/debug.R
\name{plot_rand_KLD}
\alias{plot_rand_KLD}
\title{plot_rand_KLD}
\usage{
plot_rand_KLD(x, n = 12, log = TRUE, tail = FALSE)
}
\arguments{
\item{x}{haystack result.}
\item{n}{number of genes from randomization set to plot.}
\item{log... |
3a783bf040292bf4517f97c09ac54a69ac0bfc1e | f02a605c4f0aa4f79723639a60cdae6556d2aa80 | /lecciones/fundamentos.R | 09cca2f022ff97a1e9bdea690189c83a2eae6eb8 | [] | no_license | manununhez/datascience-course | 4c7c086d1190a3c4265b4ff731621ff49216ae98 | b45d0ad6a3ee4b1593a697686dfcc2a53ccdc579 | refs/heads/master | 2021-05-08T02:45:24.374062 | 2018-02-01T02:35:46 | 2018-02-01T02:35:46 | 108,132,058 | 0 | 0 | null | 2017-10-24T13:39:09 | 2017-10-24T13:39:09 | null | UTF-8 | R | false | false | 12,260 | r | fundamentos.R | v = c() #creamos vector
nu = c(0.5, 0.6) #vector
l1 = c(FALSE, FALSE, TRUE)
l2 = c(T, F)
ch = c('a')
it = 9:29
co = c(1+0i,2+4i)
v = vector('numeric', length = 10) #otra forma para crear vectores. Vector de 10 columnas llenas de cero (1x10)
v[1] = 5
v #auto-impresion
print (v) #impresion explicita
y = c(1.7, 'a')... |
8390fcb5303920ace34e31f54011266499c3c150 | 5f4e127bf2a52486df01088384d7c5926cfa277e | /man/resumo_cba_por_iniciativa.Rd | f21243d0e26e7353bf28a32b34b475b49559949a | [] | no_license | pedroliman/oshcba | 0b4ac93c2135e85b71dde834b6eb313980b98db9 | 01ef42e96a7089fc6f4f35912e825a484b017208 | refs/heads/master | 2020-02-26T15:04:57.043923 | 2018-08-01T00:48:22 | 2018-08-01T00:48:22 | 94,784,015 | 0 | 1 | null | 2017-07-17T20:52:06 | 2017-06-19T14:12:00 | R | UTF-8 | R | false | true | 433 | rd | resumo_cba_por_iniciativa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analise.R
\name{resumo_cba_por_iniciativa}
\alias{resumo_cba_por_iniciativa}
\title{resumo_cbr_por_iniciativa}
\usage{
resumo_cba_por_iniciativa(resultados_cbr)
}
\arguments{
\item{resultados_cbr}{data.frame com resultados por iniciativa (na ... |
017517016731fd9b79c673a76f87bc5ac481b0ff | 7f459c973e6ea48343f7e7a935429ffd646ef4da | /plot1.R | cecf1625ab53665e4e507dcedeed7d01db8becbf | [] | no_license | cvscastejon/Plotting-Graphs | eedf9c6daa58578283092a411611fd4900e96c52 | ef5c5f02d6c7eed41675d737a649cc9c31b12c46 | refs/heads/master | 2021-05-26T16:25:54.661921 | 2020-04-08T16:21:00 | 2020-04-08T16:21:00 | 254,136,507 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 989 | r | plot1.R | #date manipulaton done using lubridate package
library(lubridate)
#dataset reads entire table
household <- read.table("household_power_consumption.txt", sep = ";",
header = TRUE, colClasses = c("character", "character", "numeric",
"numeri... |
2d09ee54adc1a8c74ae0fef39c0a6f77f625b8b0 | 9deb3a3350deecbff27dde00f3831116d2861d04 | /Scripts/analyser_resultat.R | 784746058188e8097437a171c275cfb036f52ae7 | [] | no_license | LucasNoga/Workspace-R | 8e55674712af47e7af78dc4208cd6d7be56bfcae | dce2f79dd4ecf075d4dafea3844b7a1371f1c804 | refs/heads/master | 2020-12-29T23:39:36.673353 | 2020-02-06T21:18:42 | 2020-02-06T21:18:42 | 238,780,108 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 377 | r | analyser_resultat.R | # Mise à jour de votre espace de travail comme d'habitude
setwd("D:/Dev/R")
# On charge la variable que l'on avait précédemment enregistrée
load("Data/resultat.RData")
# Et on y applique un nouveau traitement
nouveau_resultat <- (resultat + 3)^4
nouveau_resultat <- sqrt(nouveau_resultat)
print(paste("Le n... |
1106fb9d5e698a01a4265f85b62021d5e7442520 | 6520309a6cd2aed30f642fd61a88227e442f835d | /klay_longer_shot_prior.R | 160303e07b1e7d7663895419cd82a034a5a07299 | [] | no_license | bhc3/hothand | efaebb8f35ec2727c50467e61304eff0d5df217e | a0df13f1ccffbb1bf2e67f2dca29065bd07a8316 | refs/heads/master | 2016-08-11T21:14:05.651531 | 2015-10-29T20:12:36 | 2015-10-29T20:12:36 | 45,071,005 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 795 | r | klay_longer_shot_prior.R | ## Create vector of Klay's prior shot, for use in analyzing whether the result
## of one shot influences the next shot. This function will be applied to longer
## shots (i.e. eliminating 'short' ones). It considers two factors in
## determining the prior shot. (1) Is it the start of a new game? If so, the
## prior sho... |
66a11eaa1d0bcba28ca4595814f777cbc38f6314 | c6c0881ca260a793a70f5814ab6993c61dc2401c | /unweighted_prs/run_PRS.R | 12f06b988bf98ce2b1f6c3c6e86a9ca5cbb7828d | [] | no_license | luyin-z/PRS_Height_Admixed_Populations | 5fe1c1bef372b3c64bfd143397709c7529a2705a | bf04ba884fd16e5e8c0685ccfbc86ed72d02c7f2 | refs/heads/master | 2023-03-16T17:05:56.658896 | 2020-09-18T16:58:04 | 2020-09-18T16:58:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,584 | r | run_PRS.R | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
#**************************************
#* CALCULATE POLYGENIC SCORES **
#**************************************
source('~/height_prediction/scripts/PolygenicScore_v2.R')
library("optparse")
library(data.table)
library(dplyr)
library(biomaRt)
library(p... |
c2eab11ba0acd1b8efda74a4435bba06fddb3013 | 1ec7a5d283eab88a9cceffecf61ad06569cc92c2 | /publication_data/CompareListeria.R | 64cbdd24f530412f100afa00117b7fc531e03f2c | [] | no_license | alexsweeten/snacc | 1facba386900323d15de9f1532b863dd8d8b5c7a | 8207466655535bcb0aa72b4983508b98f429356f | refs/heads/master | 2022-03-05T01:54:10.318998 | 2019-11-12T03:02:24 | 2019-11-12T03:02:24 | 149,658,052 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,351 | r | CompareListeria.R | require(ape)
require(phangorn)
require(treespace)
publication_trees<- readRDS("publication_trees.rds")
# reference tree obtained from https://github.com/johnlees/which_tree/blob/master/tree_compare.R
listeria_realtr <- midpoint(read.tree(paste(sep="/","benchmark_trees/RealTree_Listeria.nwk")))
listeria_realtr... |
253d240ca34ecdf70de35208ccb7536841137138 | 584167605daffbc5d5046f43a34030f12185d815 | /man/get_info.Rd | b679b5a6bad998f3d137bbb569ae3aebe95fdb94 | [] | no_license | ibarraespinosa/openmpf | 1be34733f095b4313588c94ed293af1548af9c27 | c55939295f7642ee0063d141111a197b2997c803 | refs/heads/master | 2021-02-19T10:40:59.087134 | 2020-03-06T21:31:09 | 2020-03-06T21:31:09 | 245,305,220 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 229 | rd | get_info.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_env_info.R
\name{get_info}
\alias{get_info}
\title{get info}
\usage{
get_info()
}
\description{
\code{\link{get_info}} displays environment info
}
|
2f944d5bfe7b18bfa13a64cc2b92be8b35127487 | c59ded81315e5651b49437d0be06ea66e4d10f5f | /R/retrieve_aop_data.R | 35ee4d886987d547288f1deab52549407740cedb | [
"MIT"
] | permissive | vscholl/neonVegWrangleR | 7870d4e5b6ff56b8ae51763d2fe5c3f492032cb3 | e2bf156d07fbc3cbe8d2685adbc484d78540b949 | refs/heads/master | 2021-09-08T23:27:55.657138 | 2021-09-03T15:28:35 | 2021-09-03T15:28:35 | 215,654,953 | 1 | 2 | null | 2021-09-03T15:28:36 | 2019-10-16T22:22:27 | R | UTF-8 | R | false | false | 2,781 | r | retrieve_aop_data.R | #' download AOP data where vst data exists for specified year and site
#'
#'
#' @inheritParams str_detect
#' @return A list of dataframe
#' @export
#' @examples from_inventory_to_shp()
#' @importFrom magrittr "%>%"
#' @import neonUtilities, tidyverse, readr
#'
retrieve_aop_data <- function(data, year = 2019,
... |
7c9ff1dc1f763ce6daa8bb72cb525c77c2cf8866 | 912b0a1ed246b67ecb114401cdebe9ae3f359e31 | /Homework1_2/Homework1_2.R | 034f5f77148db92a3e48ad3a6f1a251915516576 | [] | no_license | josemprb/IntelligentDataAnalysis | 5da84b7a10c38dda26d3296119103cac2aa50000 | f168475e3ea85c511f9aaa4b9bef81a2f598063d | refs/heads/main | 2023-07-19T13:29:04.936479 | 2021-08-30T12:42:30 | 2021-08-30T12:42:30 | 401,339,141 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,986 | r | Homework1_2.R | setwd("C:/DATOS/MasterEIT/EntryYear/1Semester/IntelligentDataAnalysis/Labs/HomeDir")
# Importing data set
cars=read.table("cars-PCA.txt")
colnames(cars)=c("mpg","cylinders","engine_displacement","horsepower",
"weight","acceleration","model_year","origin","car_name")
#####
# 1.2.1
### a) C... |
ef4a12cdd37519d9f96cb2a68893ef097e64468a | e9a5a9e952a9ccac535efe64b96cc730b844677b | /man/setRowHeight-methods.Rd | bb4f3eedaaf007da7b8885e13ba55d20699097e9 | [] | no_license | miraisolutions/xlconnect | 323c22258439616a4d4e0d66ddc62204094196c9 | ae73bfd5a368484abc36638e302b167bce79049e | refs/heads/master | 2023-09-04T05:27:42.744196 | 2023-08-30T07:10:44 | 2023-08-30T07:10:44 | 8,108,907 | 114 | 35 | null | 2023-08-30T07:10:46 | 2013-02-09T11:17:42 | R | UTF-8 | R | false | false | 1,566 | rd | setRowHeight-methods.Rd | \name{setRowHeight-methods}
\docType{methods}
\alias{setRowHeight}
\alias{setRowHeight-methods}
\alias{setRowHeight,workbook,character-method}
\alias{setRowHeight,workbook,numeric-method}
\title{Setting the height of a row in a worksheet}
\description{
Sets the height of a row in a worksheet.
}
\usage{
\S4me... |
fa373cb51dbc58bef621228731ef59ba6d05014d | 7e6fb336d601caafe06cc417c988e5674ac50fb0 | /IsoriX/inst/NEWS.Rd | bcbea37180e2f21e3cae05081c1f4a09a1ddcf60 | [] | no_license | courtiol/IsoriX | a5da2b3d4aa87408a3a4e3c2f4ea832d6ed5a777 | f85641cd7741b6bd004fa0443af67d50d8dbd199 | refs/heads/master | 2023-02-10T09:55:32.368822 | 2023-01-13T01:44:35 | 2023-01-13T01:44:35 | 68,238,793 | 12 | 6 | null | 2023-01-13T01:44:36 | 2016-09-14T20:00:46 | R | UTF-8 | R | false | false | 15,332 | rd | NEWS.Rd | \name{NEWS}
\title{IsoriX News}
\encoding{UTF-8}
\section{version 1.0}{
\subsection{Upcoming features planned for future releases}{
\itemize{
\item (version 1.0 does not exist yet)
\item feature requests can be defined and watched here: \url{https://github.com/courtiol/IsoriX/issues}
}
}
}
... |
2b2f91401c24ce03bdd83039a98678b3754516f3 | d8f7bfbe482d98ead30ac58b9c7ae4c254e93579 | /Modern Methods of Data Analysis/script9.R | a88ce0a9b28d50473616819d63832a510473e37d | [] | no_license | Leoberium/Rmisc | 1aa29ab9883f319bdf6b684f78f738079869efee | 7f48c97dd81ae3fcdcbeb364e69c588754b07946 | refs/heads/master | 2020-09-25T05:49:40.574778 | 2019-12-04T19:50:27 | 2019-12-04T19:50:27 | 225,931,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,411 | r | script9.R | library(caret)
library(doMC)
library(glmnet)
library(MASS)
library(pROC)
registerDoMC(8)
load('training.RData')
load('testing.RData')
str(training)
str(testing)
load('pre2008Data.RData')
load('year2008Data.RData')
str(pre2008Data)
str(year2008Data)
fullSet <- names(training)[names(training) != "Class"]
predCorr <- co... |
7cc84c72311362d961a5fc9c980fb685aafad1ab | a5f3268b700913ea94ae6ba21caa5144d234703a | /R/methods-sensNumber.R | 4b6cea2950c964fc3da357d9d5057a2bbf142cfb | [] | no_license | saisaitian/PharmacoGx | d2e7d20e5fc8ae40c169b57c71fbe380d0841424 | eeee68a021549bdfab23e85cdf57c15ae54a52cc | refs/heads/master | 2023-01-24T14:31:16.963359 | 2020-11-23T18:16:15 | 2020-11-23T18:16:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,533 | r | methods-sensNumber.R | #' sensNumber Getter
#'
#' Get the sensitivity numbers for a `PharmacoSet` object
#'
#' @describeIn PharmacoSet Return the summary of available sensitivity
#' experiments
#'
#' @examples
#' data(CCLEsmall)
#' sensNumber(CCLEsmall)
#'
#' @param object A \code{PharmacoSet}
#' @return A \code{data.frame} with the number... |
0ae420e65994c01230d0d12f3b0b97999dcdd31f | 1ca0218682294cf8d638022589fdac63456fe540 | /Documents/BigDataCourse/Projects/Project 3-1/UCI HAR Dataset/Submitted files/run_analysis.R | 7c662ef8ea4cbcc7dd73bc0bc2f6ef85507d5d6b | [] | no_license | Sausan/Tidy-Data | c32109cb50408c4422ecb6eb3f38461373e14b15 | 2774bfb21b3003ebb6c9a77de199e1f864fd853e | refs/heads/master | 2021-01-15T17:07:07.946302 | 2015-03-22T16:17:43 | 2015-03-22T16:17:43 | 31,187,230 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,739 | r | run_analysis.R | run_analysis <- function(){
library(dplyr)
# 1- read the test data frames from the three files in the test folder
test_subject <- read.table("test/subject_test.txt")
X_test <- read.table("test/X_test.txt")
Y_test <- read.table("test/Y_test.txt")
# 2- read the features from the feature file in th... |
5fcfb359e04761668abfdcf1f6df4fbe792aa5b9 | 5ec06dab1409d790496ce082dacb321392b32fe9 | /clients/r/generated/R/ComAdobeGraniteQueriesImplHcQueryLimitsHealthCheckInfo.r | 7569edec2f0cff3e49653d1cde57af19c20da66a | [
"Apache-2.0"
] | permissive | shinesolutions/swagger-aem-osgi | e9d2385f44bee70e5bbdc0d577e99a9f2525266f | c2f6e076971d2592c1cbd3f70695c679e807396b | refs/heads/master | 2022-10-29T13:07:40.422092 | 2021-04-09T07:46:03 | 2021-04-09T07:46:03 | 190,217,155 | 3 | 3 | Apache-2.0 | 2022-10-05T03:26:20 | 2019-06-04T14:23:28 | null | UTF-8 | R | false | false | 4,558 | r | ComAdobeGraniteQueriesImplHcQueryLimitsHealthCheckInfo.r | # Adobe Experience Manager OSGI config (AEM) API
#
# Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API
#
# OpenAPI spec version: 1.0.0-pre.0
# Contact: opensource@shinesolutions.com
# Generated by: https://openapi-generator.tech
#' ComAdobeGraniteQueriesImplHcQuer... |
dc1ce7d5364794b6ad14c062e30e2260c50ba645 | 94c16636d7d4c98c918fde5096cf3a4118c02415 | /R/lincomb.R | 76eecaaf6eb8d520d04ff4bb7208a5e016ca2d21 | [] | no_license | RandiLGarcia/dyadr | 66c87d6be3b3eb4e7bf37568dc43f6e037d34961 | 5d317dceb2e278887b9684e172bd79a0c12974af | refs/heads/master | 2021-07-14T20:50:59.289227 | 2021-03-17T13:27:55 | 2021-03-17T13:27:55 | 61,908,363 | 17 | 14 | null | 2020-07-29T15:24:03 | 2016-06-24T19:43:21 | R | UTF-8 | R | false | false | 1,330 | r | lincomb.R | #' @name lincomb
#' @title Tests of contrasts
#'
#' Test the sum (S), the average (A), or the difference (D) of two effects from the same model.
#'
#'
#' @param outp is the model object. For exmaple, summary(mod). It can be a gls or lme object.
#' @param v1 is the number of the first effect.
#' @param v2 is the number... |
7942b0244f0fb6f2bbb672ea126594a9adbeb448 | 7cccd60294728a159c0063cdda0798905ab03ecf | /SVM.R | 0e86ba50b6281e743018fd02150b46cb1f52c244 | [] | no_license | kalitiptur/mycode-R | fb70756b70f2ab02ac0a11bed0d6eec2729614d9 | fb1a0d9a631806bbb822e8fd2ce33691b59ddbdd | refs/heads/master | 2021-09-04T16:58:39.569352 | 2020-11-30T06:56:46 | 2020-11-30T06:56:46 | 191,102,554 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 301 | r | SVM.R | library(e1071)
set.seed(1)
x = matrix(rnorm(20*2), ncol = 2)
x
y = c(rep(-1,10), rep(1, 10))
y
x[y==1,]=x[y==1,] + 1
plot(x, col = (3-y))
dat = data.frame(x=x, y = as.factor(y))
head(dat)
svmfit = svm(y~.,data = dat, kernel = "linear" , cost = 10,scale = FALSE )
plot(svmfit, dat)
|
fc51a2d4ce6ac46b5077c0d34abe07984272c565 | feaf72289a4f75ddf283fd4319a69ba603931432 | /Session8_TimeSeries_II/02_VSN.r | ec51df7773dc977ba5e5924ab2a67756c768a31b | [] | no_license | macomino/series-temporales | 41523a74a1cfdcab9b96159f124a33dfa564d94d | aca4899ef3c006f44bec8def0fa897077d11afa0 | refs/heads/master | 2020-04-27T20:46:23.689077 | 2019-03-15T20:52:56 | 2019-03-15T20:52:56 | 174,670,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,576 | r | 02_VSN.r | autoplot(elec)
BoxCox.lambda(elec)
autoplot(BoxCox(elec, lambda = 0.26))
############################################################
# DATA TRANSFORMATION
###########################################################
# Ajustamos datos de series temporales porque en gral, datos mas limpios y claros
# nos llevan a una ... |
aa8ee58fe6d62dbc6e3ffb9598c96562748a6f76 | 8f536537be5bf214525ea11bb84c568c9fb82fe7 | /R/gibbs_bin.R | 8bed3b4ae34a5ce9f16b87c532cbaf56d442d751 | [
"MIT"
] | permissive | yuliasidi/bin2mi | 5fa742f72d21034c7def62bb30d078e63c18d2ff | 51ec9b77d0afb0498ca59fbb91fd71e80479dede | refs/heads/master | 2021-06-22T15:01:14.716873 | 2021-02-20T18:53:31 | 2021-02-20T18:53:31 | 197,215,389 | 0 | 0 | NOASSERTION | 2021-02-20T18:53:32 | 2019-07-16T15:00:09 | R | UTF-8 | R | false | false | 1,007 | r | gibbs_bin.R | #' @title Gibbs sampling for beta-binomial distribution
#' @description performs Gibbs sampling for beta-binomial distribution
#' @param dt tibble
#' @param B numeric, Default: 1000, number of iterations
#' @param y.m string, Default: 'y.m', column with incomplete binary data
#' @return complete dataset after B iterati... |
9c720c64b62595a83aa5c697bed1b2400303985b | c555c086b271eaca27472410f3aa5c97709958d9 | /tests/testthat/dummy.R | 7cbc62fdd8ac1009f548cddc3fdd00b897133d4b | [
"MIT"
] | permissive | filipezabala/embedr | c3e9e299bc1ed9a8e7f811a96c7e53b60fe53b73 | 64eee3d975392c20f2242c9663f607e3790a322e | refs/heads/master | 2023-03-20T14:37:29.654223 | 2020-07-09T07:54:50 | 2020-07-09T07:54:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 72 | r | dummy.R | # this file just exists for the sake of testing the is.local() function
|
51d15895ac285d3cc0e9a8992bb028f1f9e446ff | 0e595bb86c1a6751c169a32383281ff233d27f40 | /man/qtlSignTest.Rd | 1c679c9d1cfa6db157b1214ea7507f673a96a313 | [] | no_license | pinbo/qtlTools | bd4b5e684c7353eedacc5cf48aec3344a7caa5d2 | 96f6b61e255314f6a5a32e38105c160dc52c037a | refs/heads/master | 2022-01-04T23:47:17.044866 | 2018-10-02T20:52:47 | 2018-10-02T20:52:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 942 | rd | qtlSignTest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qtlSignTest.R
\name{qtlSignTest}
\alias{qtlSignTest}
\title{Conduct the sign test for expression bias}
\usage{
qtlSignTest(effect = NULL, trans.effect = NULL, verbose = T, ...)
}
\arguments{
\item{effect}{Signed cis-eQTL or allele-specific ex... |
b81be94afecb4e84bc6af2db46dfe99b9f6e678f | 4f094c97c55155204a7b56a6a316646d4868570c | /TSP.R | e585ff7fb484491bf7e37653d52d3e29f84e28d0 | [] | no_license | Super-rookie-Py/DataRst04-TSF | 9f050800bae38c022c1264bd41dca6a93f4bd504 | 12a5bb4e1760563142c10421056dbe0fe2d9b289 | refs/heads/master | 2022-08-12T20:00:40.753067 | 2020-05-20T14:26:56 | 2020-05-20T14:26:56 | 265,590,754 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,781 | r | TSP.R | ### 2020/05/20 keonwoo Park
## Data Structure 2
## 최단거리구하기
####### Distance Table
data_distance<-matrix(,nrow=5,ncol=5)
data_distance[,]=Inf
data_distance[1,2]=1
data_distance[1,3]=3
data_distance[1,5]=2
data_distance[2,1]=1
data_distance[2,3]=1
data_distance[3,2]=1
data_distance[3,1]=3
data_distance... |
04dc3809fe443c6adacc2c4e2e26f02a3329fb7e | 9a2e827e3c2a1e739a3222fbb2a30705d18a7a17 | /Graphs Function.R | 90d580f19fbb5760ca35c674b80c8a50cdde4ac9 | [] | no_license | darshan2696/My-Codes | 53192ce2873f01ca88f0b07e19109b9a7aaea7d6 | d7548cd98c84bd7ac6e1d54a0551112188948559 | refs/heads/master | 2022-12-18T11:22:37.584467 | 2020-09-24T07:13:29 | 2020-09-24T07:13:29 | 281,865,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,500 | r | Graphs Function.R | # A FUNCTION THAT EXPORTS GRAPHS
#----------------------------------
View(cars)
Graphs <- function(data,var= 1:ncol(data), direct= "",tresh=10) #feed your working directry path into direct
{
setwd(direct)
for (i in var) ... |
2faa169116d06052c162e0bbf7aecf7a65da8a2d | 340f0cdacd7bd1994627cb34203915bd17d56186 | /R/normalizer.R | 7b1f23682080eb77faedc53c7b74b82a88c31be6 | [] | no_license | PCRuniversum/chipPCR | af593b813f8c59d5027d8a118955666f0fff283e | b7c751a8716c814c63825d50007699dbfb7a22f4 | refs/heads/master | 2023-03-11T02:18:02.994570 | 2021-02-27T20:04:47 | 2021-02-27T20:04:47 | 19,281,268 | 2 | 3 | null | 2020-07-27T13:48:14 | 2014-04-29T15:22:30 | R | UTF-8 | R | false | false | 903 | r | normalizer.R | normalizer <- function(y, method.norm = "none", qnL = 0.03) {
if (qnL <= 0.001 || qnL >= 0.999)
stop("qnL must be within 0.001 and 0.999.")
# Select a method for the normalization
method.norm <- check.method(c("none", "luqn", "minm", "max", "zscore"), method.norm)
# TODO Test meaningfulness of qnL
... |
ae817912397b72fee42464a50f254c79852f04a7 | 1bc0fe0762e4ee96144c521211f7ddf4fec54be1 | /code/2_2AllHabitatsNMDS.R | 21396c29fb9c19f3d07f5374356695fda035985e | [] | no_license | melaniekamm/IthacaBees_BySeasonScale | 6e66573e2895af02c45778a9a299003b8e425dc9 | 99080523594b658fa0f2e7e20aff0434f9c317c4 | refs/heads/main | 2023-06-23T21:47:15.470526 | 2023-06-15T21:08:12 | 2023-06-15T21:08:12 | 363,954,203 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,076 | r | 2_2AllHabitatsNMDS.R | library(dplyr)
rm(list=ls())
if (!dir.exists('./figures')) {
dir.create('./figures')
dir.create('./figures/supplementary')
dir.create('./figures/supplementary/NMDS_plants')
}
plant_rich <- read.csv('./data/Iverson_plant/allplants/richness_by_site.csv')%>%
dplyr::mutate(Site= gsub(Site, pattern='Bla... |
5973c2630b60f0df54e48554d2e358810b034ef8 | 96895e3d650501bb3ebf4fa4bcbd4ccc8aed3999 | /Part1Exercises/ICA/STAT3019ICA1/step 2 K-means.R | 170d6b905c872465dd5d137f57738db1f4ad2cba | [] | no_license | linyina/Clustering-in-R | 5570fad12c872296250d84b66d91df2785aa1888 | c33a466fe421a2bb8808ac11dcbf94d365cb3a92 | refs/heads/master | 2020-03-22T11:46:06.990177 | 2018-08-14T08:50:34 | 2018-08-14T08:50:34 | 139,994,814 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,479 | r | step 2 K-means.R | ############################################
##### Step 2: Kmeans clustering #####
############################################
## random try
sdortmundk5<- kmeans(sdortmund, centers = 5, nstart = 100)
pairs(sdortmund, col=sdortmundk5$cluster, pch=clusym[sdortmundk5$cluster])
sdortmundk5$centers
## the means (sca... |
b9b90e6410d6e78e307b99f1f979becdfe31e42a | 1b983bf2ce7086842e0aaa737bc553cf57dcab6a | /1_dataprocessing/patch1_2_1.R | ca3a8ae194db85c19bc680c3da72fc8b697fd495 | [] | no_license | floatSDSDS/Yelp_Dataset_regionPredictor | 22cc6a66480ee2fbea329c630777b7ccfa93fd82 | 7a1d8901a0fe603b0c0e6ff6af752fcf81958edf | refs/heads/master | 2021-01-12T12:44:30.369839 | 2016-10-03T08:14:21 | 2016-10-03T08:14:21 | 69,851,476 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,829 | r | patch1_2_1.R | i<-1;
set_test<-list();
for (i in 1:25){
temp<-1;
len<-length(test[[i]][2]$date);
##################################################test
########################################attribute
##################################wifi,noise
wifi<-test[[i]][[3]]$Wi.Fi;
temp[wifi=="paid"]<-0;
temp[wifi... |
e1401b70bcd63d75782f69e435e4aa984d6260d1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tsDyn/tests/lstar.R | 8dc0832df91fcd76ed9f389043828949952d889e | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,552 | r | lstar.R | library(tsDyn)
mod.lstar <- lstar(log10(lynx), m=2, mTh=c(0,1), control=list(maxit=3000))
mod.lstar
deviance(mod.lstar)
c(AIC(mod.lstar),BIC(mod.lstar))
mod.lstar2 <- lstar(log10(lynx), m=1, control=list(maxit=3000))
mod.lstar2
deviance(mod.lstar2)
c(AIC(mod.lstar2),BIC(mod.lstar2))
## include: none
mod.lstar_noCons... |
7c259256a5857d97ced9c0d51e12468e3bd0bb53 | cc3af520071dae6080c9abd70d08013eb769ec0a | /man/bootmatch.Rd | 01719001540570c1816c7725fea7df86db103e24 | [] | no_license | Libardo1/PSAgraphics2 | 432c8a6c68c42d703570fd8b710b1b4bb9c139a6 | be41bd2f72b98fa976bab8e0446db8a86d27d10d | refs/heads/master | 2021-01-18T07:33:35.032074 | 2013-10-25T18:50:56 | 2013-10-25T18:50:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 865 | rd | bootmatch.Rd | \name{bootmatch}
\alias{bootmatch}
\title{Bootstrap treatment units for propensity score analysis}
\usage{
bootmatch(Tr, Y, X, M = 100, ratio = 3, nstrata = 5,
sample.size = (ratio * min(table(Tr))), ...)
}
\arguments{
\item{Tr}{numeric (0 or 1) or logical vector of treatment
indicators.}
\item{Y}{vector o... |
269e4d1bf3bd3efec9e93ccbba09182a6ef216fd | 673d3cb2c9608d08f1ef65e7e72133783c441a96 | /Tong_Anal/3 chfls.R | 65498382236b31826b9105b453f4a3988320715e | [] | no_license | SilverwestKim/Univ | 6db157d36317ab72d7b10ed4d015851d36c62273 | 6f9cc90c3500e60548ef70334ecbdd794fb95337 | refs/heads/master | 2021-05-02T02:29:18.693866 | 2018-11-07T14:39:54 | 2018-11-07T14:39:54 | 120,883,806 | 0 | 0 | null | null | null | null | UHC | R | false | false | 1,247 | r | 3 chfls.R | # 데이터 불러 오기
chfls<-read.csv("CHFLS.csv",header=T)
str(chfls)
table(chfls$R_happy)
xtabs(~R_happy,data=chfls)
barplot(table(chfls$R_happy))
# somewhat happy가 가장 많고 very happy가
# 그 다음으로 많다. 전체적으로 not too happy와
# very unhappy의 비율이 높지 않다.
# 건강상태 vs. 행복정도
table(chfls$R_health)
barplot(table(chfls$R_health))
# not go... |
f63b1747fb2da238b3073956a23a8cb51b7891c4 | ba9921f0c04b74a1136c3fea0430cca3961e28d9 | /plot4.R | 8e363f501af67b3cb5d6ced6bdf9a01355a56c45 | [] | no_license | cmconklin/ExData_Plotting1 | 88d20115aad3cc7f7e42473fb53fc3e8f17e4957 | d975eb615df6d96bb062f6d97c9b7403d123ab7a | refs/heads/master | 2021-01-24T21:19:47.493095 | 2015-11-08T22:26:39 | 2015-11-08T22:26:39 | 45,774,653 | 0 | 0 | null | 2015-11-08T09:41:25 | 2015-11-08T09:41:24 | null | UTF-8 | R | false | false | 4,496 | r | plot4.R | ## This R script will create Plot3 of he Assignment 1,
## Global Active Power usage between 2007-02-01 and 2007-02-02
## The print statements provide a clue as to what steps are
## to be executed.
##
## The datafile must already exist in a relative directory "../data"
##
## To execute: > source("plot3.R")
##
## This i... |
1b66c967a195c71e47d65929cef141408cd1cbdf | 3077edf6801a2a9b16b5627ef73f1187d6e8124f | /all_functions.R | 891909711924494bb9f4bd9e86394de5ce91f7b1 | [] | no_license | raypallavi/BNP-Computations | d301ca05ec6d9585c7cf364d2e8f643247c79017 | 5dfbd841514f8fe703d72f6a3e7fd17ad4369ded | refs/heads/master | 2020-04-20T10:05:05.251677 | 2019-04-05T23:36:06 | 2019-04-05T23:36:06 | 168,781,139 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 12,766 | r | all_functions.R | ### All required functions for using ESS
### Functions related to Wood and Chan algorithm of drawing samples
### MH for sampling from \nu and \ell
### Covariance matrix and design matrix (using basis function) are also defined
### And all related and dependant functions are here
### Required libraries:
library(... |
3c1186d376b13fba6002c351796637939997e2cc | 602c144363277f2efc062d78bce139dd7fb75480 | /man/Inflacja.Rd | 2cf9682ced1bf48af011306489300eb9c8f7d629 | [] | no_license | mbojan/mbtools | 637c6cfba11a63d7a052867d0afa211af00060ad | ed7680f0f9ae68ea12f6bca403f1b686f20e9687 | refs/heads/master | 2022-07-14T03:25:05.525650 | 2022-06-25T18:43:09 | 2022-06-25T18:43:09 | 29,505,211 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,442 | rd | Inflacja.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Inflacja.R
\docType{data}
\name{Inflacja}
\alias{Inflacja}
\title{Monthly inflation rates in Poland}
\format{
A data frame with 195 observations on the following 6 variables.
\describe{
\item{year}{Year}
\item{month}{Month}
\item{infl1}{a tim... |
915a061c8b7170c1b94511f0a74a55a885364487 | bc377a0484066010d16658f143ee40699c44a3f1 | /run_analysis.R | 9e474be3a41afe4aa44bc907e5159f9b80763c35 | [] | no_license | wannabeDataScientist/clean_data_project | df45ba48dfd8936898e1b2a730fdf7103940609b | 349c3092d8bc2c241872d3e7fa8fd32edf0285f1 | refs/heads/master | 2021-03-12T20:14:25.293931 | 2014-04-27T23:28:38 | 2014-04-27T23:28:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,952 | r | run_analysis.R | # read sensor data
sensor_data<-function(category)
{
# read the column names
qfile_name <- file.path(".", paste("features", ".txt",sep=""))
col_names <- read.table(qfile_name, header=FALSE, as.is=T, col.names=c("MeasureID", "MeasureName"))
#read sensor data
qfile_name <- file.path(category, paste(... |
4568aeb75de73a37c452d997643e2e33277088b5 | 97c80ac45d43631596077983dd970a54490a0d57 | /ui.R | c88f683232f0dd2dc85f24df88f8089a0bb6ad5c | [] | no_license | nurfitryah/suicide-rate | 40838f88f6e0fb5a07deec58ec45c2b5e9a6629c | a204564577a595bd72ee6c307c998864bf010b0e | refs/heads/master | 2022-12-12T19:13:43.989385 | 2020-09-15T11:15:11 | 2020-09-15T11:15:11 | 295,701,046 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,455 | r | ui.R | options(shiny.maxRequestSize=200*1024^2)
dashboardPage(title = "Suicide Rate",
dashboardHeader(title = "Suicide Rate"),
dashboardSidebar(
sidebarMenu(
menuItem(
text = "Introduction",
... |
07c1ebc8324fa11d02178a15718d6e68169ebefe | fb6bce8e6c5b983277274fcc41a08465f42ef6c2 | /fragment_type_selection.R | 2967482ba96e9082f0c00c44fd5924bfd04597dd | [] | no_license | Cantalapiedra/digestR | 1a14eca70cd12b6a71e588f6f81dc2a42cf89ca1 | f1e94ac6c18dfc445c664ba817fabc4eca64ed6d | refs/heads/master | 2021-01-19T22:05:53.531751 | 2017-02-27T08:34:57 | 2017-02-27T08:34:57 | 82,571,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,212 | r | fragment_type_selection.R | #!/usr/bin/env Rscript
## fragment_type_selection
## 2017 CPCantalapiedra
library(data.table)
# Read command line arguments
args = commandArgs(trailingOnly=TRUE)
if (length(args)==2) {
frag_file = args[1] # bed file
frag_types_file = args[2]
} else {
stop("At least one argument must be supplied (input file).n... |
c23ba6eac660097befed27c7a8112cfab390e14a | 9e62400e609e288f753254161299727f6b8c134e | /program/server_local.R | fa1bcf0ef9735bb9b049d6d924e611e9f9ef5f94 | [] | no_license | MaximilianLombardo/kandinsky | 16d11d1f9ea115557d74f9d18c286919cedfb497 | acf6a76dc3ffdeed12f85107b48b056c060cbd3a | refs/heads/master | 2023-07-16T00:43:58.302008 | 2021-08-22T22:23:53 | 2021-08-22T22:23:53 | 293,324,763 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 35,858 | r | server_local.R | # ----------------------------------------
# -- PROGRAM server_local.R --
# ----------------------------------------
# USE: Session-specific variables and
# functions for the main reactive
# shiny server functionality. All
# code in this file will be put into
# the framework inside the ... |
fe4f60a286894f3fa6697ab56e924380ab554931 | d2e16353b0f1f431907f8794dde9024147c1c9de | /test script.R | d1bf71f1bc9922e74acbb65ab9b5d11c470f4dd6 | [] | no_license | SteBrinke/Exp-R-Sschijf | d3e2c8d036865528be894cefe7a15b182f7506f5 | a1b598a15af8658781a3cf3392e7e0b1f0d0cbb5 | refs/heads/master | 2020-04-23T20:37:17.894924 | 2019-02-19T10:24:14 | 2019-02-19T10:24:14 | 171,446,777 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 48 | r | test script.R | #test script
x <- 1:10
y <- mean(x)
z <- var(x)
|
02a9622cd2b1bc11ef8b79d56aed1b8896531410 | 226e31ed001c66cd60b70ee3a452d47d11ec9f69 | /Fraser_et_al_FigureS10.R | 82f816ba618a30125c95560b64f51605e49ca6ba | [] | no_license | mfraser3/ZNRF3_2021 | bb2fdee09b68df052271b9271c23bc23060b1b2c | 1eacd557f6b032c73137f556ad9f67dac6d9958b | refs/heads/main | 2023-04-18T14:10:48.115586 | 2021-08-20T21:08:12 | 2021-08-20T21:08:12 | 398,320,558 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,025 | r | Fraser_et_al_FigureS10.R | # FRASER ET AL - FIGURE S10 #
## LOAD LIBRARIES ####
library(tidyverse)
library(survival)
library(survminer)
## LOAD AND TIDY DATA - CPCG ####
CPCG_ZNRF3_OUTCOME_ADJUSTED_PGA <- readRDS("/Users/michaelfraser/OneDrive/Work/Manuscripts/2020/ZNRF3/FINAL/Nature Cancer/Data and Code/CPCGENE.OUTCOME.ADJUSTED.PGA.rds")
x ... |
52888fcc0c29b222b1519112a542f2a478739c16 | 8518aa91916c77ad8b3757fe824c1873e7609d54 | /R/asymprob2.r | d24d2234007429706165fe5d00471a4d30dd9238 | [] | no_license | cran/BinGSD | 190799b72085e6bd74458e05f12f407c8f8ec812 | 94b7b5de408aa79152c26a23335fedb10096f374 | refs/heads/master | 2020-12-21T21:09:19.043736 | 2019-10-30T16:00:18 | 2019-10-30T16:00:18 | 236,562,129 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 791 | r | asymprob2.r | #compute the lower boundary crossing probabilities given the design, under H0.
#asymprob2(n.I,lowerbounds,K)
asymprob2<-function(n.I,lowerbounds,K){
sigma=matrix(0,K,K) #the covariance matrix of multivariate normal distribution.
for(i in 1:K){
for(j in 1:K){
sigma[i,j]=sqrt(n.I[min(i,j)]/n.I[max... |
92fa0b1c975f5b464df478924779edeed6d665e7 | 33f6b19d9bdcd986121e7772c33a8d246af1a964 | /R/friends.R | 7ccd99c6210592fd220d4db0ee12edc061a267b1 | [] | no_license | cranndarach/rtweet | 6f538c2e4394c42bce618909702b65bb8194b467 | 17d69a0723be3bce97b1b44c7bcd896cac4896cb | refs/heads/master | 2021-01-17T05:04:37.389407 | 2016-09-10T03:29:44 | 2016-09-10T03:29:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,792 | r | friends.R | #' get_friends
#'
#' @description Requests information from Twitter's REST API
#' regarding a user's friend network (i.e., accounts followed
#' by a user). To request information on followers of accounts
#'
#' @param user Screen name or user id of target user.
#' @param page Default \code{page = -1} specifies first... |
ef7e251a51eaba8aad98eb7a4fa65f31bae42e51 | c319000e5d98025fb8dfd4617d74bd44b32bb606 | /geog0323/final/r/distdir.R | 6d56885e361b991f137030e49a82924c1cdcc1c6 | [] | no_license | kufreu/kufreu.github.io | 7011047203686fbf9f0fec334fdc3c8341e4d1df | 8a55f269e44bfba670f2462c09da4e260b8dc79b | refs/heads/master | 2021-08-02T23:21:33.186303 | 2021-07-24T22:40:42 | 2021-07-24T22:40:42 | 207,661,358 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,523 | r | distdir.R | #### distdir ####
# this function calculates distance in meters and direction in degrees from an origin (origin) to a destination (input)
# this function is dependent on geosphere, dplyr, and sf
# written by kufre u.
distdir <- function (input, origin, prefix = "") {
library(geosphere)
library(dplyr)
library... |
220dc82cc3e2c63ff3eac6100a8765c632fe74e3 | 0905cabde5e431aea433b339fd76b4aa5c8d6ee9 | /Plot2.R | 5994215bbfafc7748414e49311eebd2c030f5e2d | [] | no_license | Juan0001/ExData_Plotting1 | c97143b13dffedd06e3c30c5a3c470dda4bceec4 | c92f5238984ed65f90a81ad84767993b5ea0ad30 | refs/heads/master | 2020-12-25T10:42:18.169652 | 2015-03-04T01:14:25 | 2015-03-04T01:14:25 | 30,330,526 | 0 | 0 | null | 2015-02-05T01:10:31 | 2015-02-05T01:10:30 | null | UTF-8 | R | false | false | 846 | r | Plot2.R | ## Read the data and subset data from the dates 2007-02-01 and 2007-02-02
data <- read.table("./Data/household_power_consumption.txt",
header = TRUE,
sep = ";",
stringsAsFactors = FALSE)
library(dplyr)
wData <- filter(elecData, Date == "1/2/2007" | Date == "2/... |
e71e15ce77c607dee234c2167c5e1ae67a2f70ec | 7fcc5697a1eda15658a8dba645ce94bed1501c2f | /afl_data/R/helpers.R | e84a67d8e1408c3e352ef061d5b03ad4661b04e4 | [
"MIT"
] | permissive | tipresias/bird-signs | c69cd7238a31513d3f1b47d1b28b5113178ba94b | 464ebd3fe5a2fc144f47978ef1c215819df89316 | refs/heads/main | 2021-06-17T15:19:37.320376 | 2021-04-29T12:46:01 | 2021-04-29T12:46:01 | 196,322,870 | 1 | 3 | MIT | 2021-03-21T02:54:48 | 2019-07-11T05:02:02 | R | UTF-8 | R | false | false | 186 | r | helpers.R | convert_to_snake_case <- function(string) {
string %>% stringr::str_to_lower() %>% stringr::str_replace_all("\\.", "_")
}
is_empty <- function(data_frame) {
nrow(data_frame) == 0
}
|
c39ab54c2be81fc446d47ef0900f5aec5aec18ab | 6803e12cc707b1e467e46d1d5ae64d34af943e42 | /cachematrix.R | b2db79ae1a925669f276b7f2e0cf307b7b451648 | [] | no_license | JLeung46/ProgrammingAssignment2 | 5b66acfea874c8de8f83a7ec8837766e7c8a8174 | e71c3a368ee15541d67d4f34e846239d2a113d06 | refs/heads/master | 2021-01-17T18:01:06.999219 | 2015-01-25T20:08:12 | 2015-01-25T20:08:12 | 29,195,296 | 0 | 0 | null | 2015-01-13T15:06:51 | 2015-01-13T15:06:51 | null | UTF-8 | R | false | false | 1,418 | r | cachematrix.R | ## makeCacheMatrix creates an object that contains functions to
## set the value of the matrix, get the value of the matrix,
## set the inverse of the matrix, and get the inverse of the matrix.
## cacheSolve checks if the inverse of the matrix has been calculated.
## If so, fetch it from the cache, otherwise calcul... |
5934a1c59d78c43d6510a6f73aafa5c4be7e56bc | e6d29f8a2fea50e45e37285d3d47985763fd8c03 | /example.R | 03de573aef24df19d83ed8c79b261fe761918155 | [
"MIT"
] | permissive | Mariana-plr/IPAQlong | de0b7b8d532e2d3dccd20204c42911b8c0f897c2 | 6f57ae46fa8779e08fa8efac56ce9b394b8f267e | refs/heads/main | 2023-08-04T09:11:44.930621 | 2023-07-29T12:03:08 | 2023-07-29T12:03:08 | 471,093,506 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,027 | r | example.R | # install IPAQlong
install.packages("devtools")
devtools::install_github("Mariana-plr/IPAQlong")
library("IPAQlong")
# generate example data (IPAQ long form): participants in rows, questions 1:25 (parts 1-4) in columns
data <- matrix(NA,nrow = 3, ncol = 25)
data[,1] <- c(1,1,1) # response to question 1 is either ye... |
572a65709eeef3d09e15d69e9cdde45645b8a609 | 7f950e1930f11ff7219c0fbf997a6efa0166cd9c | /ui.R | 0514e33eef520504f2496a230cc7b2f9ffd095ae | [] | no_license | phytoclast/BioClimR | ed16b3fae59cac41092de67d4da57f879ac0138c | 431cdd6d440dc29300e004093c015a1d13f413f5 | refs/heads/master | 2020-04-19T13:08:54.262863 | 2020-03-13T20:17:05 | 2020-03-13T20:17:05 | 168,210,580 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,487 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
#calculate percentiles
library(plyr)
fluidPage(
header... |
7689d7f7520c1ac48ace1a55ac4470e3484bfabc | 9e618621cf49c5730984612d7234fa7ff827b7b8 | /man/computeSIR.Rd | e072cd064fd7f6a85d224b8dc55d38fd4aa2d9c0 | [] | no_license | MARCTELLY/SimuLearning | ac5ae0326affa66112f757416c0d31faa8be9de7 | 2b8b5f277358e265c8bcb03230c3e153b5b7b890 | refs/heads/main | 2023-03-23T20:15:17.288350 | 2021-03-14T23:08:05 | 2021-03-14T23:08:05 | 346,826,336 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 568 | rd | computeSIR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/computeSIR.R
\name{computeSIR}
\alias{computeSIR}
\title{computeSIR
Compute susceptible, Infected and Removote at a given time}
\usage{
computeSIR(alpha, beta, initSusc, timeOfSpread)
}
\arguments{
\item{alpha}{infection rate}
\item{beta}{re... |
2550005358e08b9ea1d8e97d8d68ecc4fcb16e0e | 75e77231822c4ca6cd737663f59686daabd66a9a | /job_assignment.R | de90254e017f52c36ce0ad93ce0db9be00315675 | [] | no_license | aman11111/Duck-Worth-Lewis-Method-Improvement | 7d780fa0279b1d16b037dd72b8fa138fb33414e2 | b5b091b21acfd227a1191694d23a260f8b6e1b01 | refs/heads/master | 2021-01-25T06:56:12.332255 | 2017-06-07T11:47:57 | 2017-06-07T11:47:57 | 93,628,614 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,732 | r | job_assignment.R | setwd("F:\\")
df <- read.table("ggevent.log", sep = ",", header = FALSE)
#(giving names to the columns)
colnames(df)<-c("ai5","x","y","sdkv","event","ts","z","timestamp","game_id")
library(dplyr)
#(removing columns which are not required further)
df1<-select(df, -x, -y, -z, -sdkv, -ts, -game_id)
library(string... |
0e3b2717e1a9acb70e7d49474a33b1def52990a9 | 2b1c1c7b88d8a55532931528b1e8f76cb9a67cc6 | /man/filter_state.Rd | 67940a74d13f5efa5869f5ee8e06c0cda920c3a5 | [] | no_license | ktargows/tigris | 61931e7c57ecbd8ac75b8a4a46cfdf322eab318b | 50c5da523b4ccc5816b85f91c4d39dbdd99761f2 | refs/heads/master | 2021-03-13T04:08:23.931958 | 2017-05-16T18:36:25 | 2017-05-16T18:36:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 659 | rd | filter_state.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/states.R
\name{filter_state}
\alias{filter_state}
\title{Filter a \code{states} Spatial object for only those states matching the
contents of the \code{state} vector.}
\usage{
filter_state(states, state)
}
\arguments{
\item{states}{object ret... |
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