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28288bca64d1a9ec83ad6ecdcf598032bf0cbff8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SensoMineR/examples/compo.cocktail.Rd.R | b5c8f0ef99ec04009e9900e12a3e3513f343ed99 | [] | 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 | 179 | r | compo.cocktail.Rd.R | library(SensoMineR)
### Name: compo.cocktail
### Title: Composition of the cocktails data
### Aliases: compo.cocktail
### Keywords: datasets
### ** Examples
data(cocktail)
|
35de9378cd129f83209e2e80b0ae09f7cfc1b872 | 865f81acbeb5e6014f1c118933e395b64053cd75 | /aniR/R/red.R | 706c1da2e94ed279f3d757ca32e6d3ce7acbb9fb | [] | no_license | ntyndall/ani | a4450c65fb2b6ff90fa71ef32078a66e334f561e | abcd5cec32a2388e05aa266040651203c98213c8 | refs/heads/master | 2020-03-23T07:38:09.880278 | 2018-10-09T14:01:38 | 2018-10-09T14:01:38 | 141,282,515 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 239 | r | red.R | #' @title Create Redis Connection
#'
#' @param db An integer value defining the database number
#' to store data.
#'
#' @return A redux redis connection
#'
#' @export
red <- function(db = 9) {
return(
redux::hiredis(db = db)
)
}
|
0ba3f27599876ee23cc3622df56469e6d43707bc | 8efdaed601b9dbcdb0ebafd975f1dcba90ce278a | /filterAlignment.R | fae35ec3b0ea7224076a1a3ec5e24d98c0aa780c | [] | no_license | ValentinaBoP/NeurosporaSpecificTE | b531bc1f1dd7a5eaca6959af0aaac2718727c7e5 | dbce1dc3bcbd5af55d4452c3410f89d42259415f | refs/heads/master | 2022-10-24T15:37:51.983046 | 2020-06-10T12:54:18 | 2020-06-10T12:54:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,656 | r | filterAlignment.R | library(data.table)
library(dplyr)
files = list.files(pattern = "window")
for (file in files){
window = fread(file)
# filter for the flanks of the same element that align close to one another
window$ID = sub(pattern = "_flank3", replacement = "_flank5", x = window$V13)
boo = window$V4 == window$ID
filter = wi... |
6817d983cb39c4fa0e73f710cc31d499ffc1e3fb | 95810d83e5600112be76ea12d344f2ecaacfa414 | /man/make_request.Rd | a6eb9a3721c4bd03f5ea1b88d7a74dc8246000f7 | [] | no_license | ExtraSpace/openfootballr | a8695abc25daf8a1c549cf4103d628aa5b295fc1 | 1d5e1fb2b43a29f9f68fced9a94af003cc53fe80 | refs/heads/master | 2020-03-21T04:05:30.463535 | 2018-06-20T22:00:28 | 2018-06-20T22:00:28 | 138,089,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 408 | rd | make_request.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/football.R
\name{make_request}
\alias{make_request}
\title{Make a url request using the header}
\usage{
make_request(url, header)
}
\arguments{
\item{url}{A character of api url to make request}
\item{header}{The header to use to request the... |
11079f021df702d82e6ce6a4ec993f03ec011d9a | b7d1239a7708bcd011f712c4119cf907816fc6df | /Midterm/generate-pictures-for-midterm-2-1-2018.R | 13939ccc6294c6b5a1409b0c3e9560168ac48cc1 | [] | no_license | wstuetzle/STAT180 | 45513e05576833ca9dd62ca732c9ffd33c6a8ee6 | 5a9ab7ab5cb63ed502181229a4fa58499c396f89 | refs/heads/master | 2021-05-13T12:47:00.509865 | 2019-02-20T20:11:20 | 2019-02-20T20:11:20 | 116,683,798 | 1 | 2 | null | 2019-05-29T22:40:01 | 2018-01-08T14:01:23 | HTML | UTF-8 | R | false | false | 1,221 | r | generate-pictures-for-midterm-2-1-2018.R | ## Generate pictures for STAT180 midterm (2-1-2018)
## ================================================
n <- 200
x <- 2 * pi * runif(n)
y <- sin(x) + 0.3 * rnorm(n)
jpeg("sin-wave.jpg")
plot(x, y, pch = 20, xlab = "X", ylab = "Y")
dev.off()
n <- 1000
count <- 0
outer <- 1
inner <- 0.7
X = matrix(0, nrow = 500, ncol =... |
83dd4042d422f35802f9c1ce46c0f23beb2ddb86 | 93847072348289fec5ec39ac260ebd70f1642cb3 | /app.R | 2bbc18e6a826cd4c89d0f713de1a7e1cf9a98004 | [] | no_license | RockfordMankini/R6_Pack_Probabilties | c4a3eaa0a57be142e72401b8f82eca3ae08d5000 | 5ad8ef13ea58e4f4ae44c59592e09dcd47ec1bc1 | refs/heads/master | 2023-04-03T09:04:56.163953 | 2021-03-29T18:02:58 | 2021-03-29T18:02:58 | 352,737,308 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,802 | r | app.R | library(shiny)
library(tidyverse)
library(ggplot2)
library(plotly)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("Rainbow Six Alpha Pack Simulator"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
... |
87f45853083ae04a1e523f76770116e29bd55843 | b946c489e67a65008de77a6a8da93f4d398e3f33 | /man/bspl.Rd | be875522500f5a96bd9c51f4042cdbd97fa09862 | [] | no_license | EPauthenet/fda.oce | 6380852e8998a0b956b46acc044c71717f5e1859 | 38c6b1d2dd6a4645235f7ec8db2dda7caad56c65 | refs/heads/master | 2022-12-10T14:37:26.095737 | 2021-02-01T13:14:00 | 2021-02-01T13:14:00 | 138,511,460 | 4 | 3 | null | 2022-12-07T02:45:42 | 2018-06-24T19:58:42 | R | UTF-8 | R | false | true | 1,560 | rd | bspl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bspl.R
\name{bspl}
\alias{bspl}
\title{B-spline fits on Multivariate Hydrographic Profiles}
\usage{
bspl(Pi, Xi, nbas = 20, fdn = list("Temperature", "Salinity"))
}
\arguments{
\item{Pi}{vector containing the levels}
\item{Xi}{array containi... |
315867565e4540e04004ccf23f4ebeded6ebd1a4 | b911d7880552de34e496b43db7672eade7821891 | /man/plinks.Rd | fbafd60e3594fd5aa969c43668511a67e99087bb | [] | no_license | cran/glmx | 5c158017436bbd6211211d7a66da02a909be7dfb | d12629f9b7211c0470e640d80ff10db0dfb60052 | refs/heads/master | 2023-04-09T15:07:18.390260 | 2023-03-27T07:10:02 | 2023-03-27T07:10:02 | 17,696,446 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,078 | rd | plinks.Rd | \name{plinks}
\alias{plinks}
\alias{talpha}
\alias{ao1}
\alias{ao2}
\alias{gj}
\alias{angular}
\alias{foldexp}
\alias{rocke}
\alias{nblogit}
\alias{gosset}
\alias{pregibon}
\alias{loglog}
\concept{parametric link}
\concept{transformation}
\title{Parametric Links for Binomial Generalized Linear Models}
\description{
... |
05f83967763fca826dab30ac9da40e1bba7f3bc7 | f4c5459aa76bc672d9b0919662f8f279df176578 | /tests/testthat/test_matrix_like_ops.R | 22839eb145950e7698849865fdcc9fa75ba76c0c | [
"MIT"
] | permissive | wvqusrai/rTorch | 204f306d91220c1972fb0ab895605470508bb32a | 0010f675b7c086a03d0ead38d883f752cf159e80 | refs/heads/main | 2023-08-31T09:13:07.842617 | 2021-10-23T09:44:19 | 2021-10-23T09:44:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,420 | r | test_matrix_like_ops.R | library(testthat)
source("helper_utils.R")
skip_if_no_torch()
# matrix like tensor operations ------------------------------------------------
context("matrix like tensor operations")
test_that("Dot product of 2 tensors", {
# Dot product of 2 tensors
# direct operation with torch
r = torch$dot(torch$Tensor(li... |
c2b81b40b37732809ad5a66bc73d0c9521875995 | f34a7d3a93744c6f2e2a4669612b0e6ad43e151b | /cachematrix.R | 2c25573e3465ec0932d2e21a97cb10000f3baa4a | [] | no_license | PhiPrime/ProgrammingAssignment2 | 55a393df80fe0c75342d29b5c18c0938aa49ff94 | 059bcbf0306f7ef8caeb94ffaaa6d49e457ab382 | refs/heads/master | 2021-01-03T07:58:45.356956 | 2020-02-16T22:01:13 | 2020-02-16T22:01:13 | 239,991,205 | 0 | 0 | null | 2020-02-12T10:59:40 | 2020-02-12T10:59:39 | null | UTF-8 | R | false | false | 1,081 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix returns 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
#4 get the value of the inverse
makeCacheMatrix <- function(x = m... |
5d5ee3c22fbec3dde804e2f09ef5c8f5dabe8e50 | 2b86cbfbbbea042945eb923b9e27773758e404ee | /scripts/3_analysis_of_results/Probalistic_HD.R | e910d6cd0ff97164bc01593c4d5bf9543e8d1e11 | [] | no_license | avouacr/mobicountR | f83b2aa0c79a5d2d92bd7865586e62d2bcf3d8ba | 61d5fd3834c407d1ba8892ca8d6b6c38b37cdb61 | refs/heads/master | 2022-04-08T19:39:43.470718 | 2020-02-21T15:16:28 | 2020-02-21T15:16:28 | 188,848,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,064 | r | Probalistic_HD.R |
# -----------------------------------------------------------------------------
# --- Voronoi_to_grid with priors from BD Topo
# -----------------------------------------------------------------------------
# Libraries declaration -------------------------------------------------------
install.packages("pryr")
inst... |
4cceb201243758b5f274eee5aa5facf820f98f28 | 30eb33b9f7963b89c4891738f74f983a1ee0ff68 | /man/cloud_summary.Rd | 83e6fc8ff578d8f2ade17b1b55d78f9d18fa04ed | [
"MIT"
] | permissive | cderv/revdepcheck | 385d3650b394720705e8105c50ebd9260255a28c | 1ba55f7d592091ea529e31be99dbde0d822d6dd1 | refs/heads/main | 2023-08-27T15:00:44.742575 | 2021-10-21T20:37:31 | 2021-10-27T13:52:55 | 422,241,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 768 | rd | cloud_summary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloud.R
\name{cloud_summary}
\alias{cloud_summary}
\title{Display revdep results}
\usage{
cloud_summary(job_name = cloud_job(pkg = pkg), pkg = ".")
}
\arguments{
\item{job_name}{The job name, as returned by \code{\link[=cloud_check]{cloud_che... |
255d3b0e529bf0809f0023a9580c074cad0c5322 | 09b7b891feae6b40c917cb7bbf506dfb5082f83f | /scripts/Figure4.R | 8b52170841113d321728a272a237070fe68e4e1f | [] | no_license | craigbrinkerhoff/2020_headwaterCO2_CT | 2228946db6fb56b710fd1a50ce8fdca1fb31734f | b4207e9c512471aac87e7c3ccadd90011cf0572c | refs/heads/main | 2023-01-01T18:10:10.589343 | 2020-10-26T20:27:37 | 2020-10-26T20:27:37 | 307,469,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,291 | r | Figure4.R | #Creator: Craig Brinkerhoff
#Date: Summer 2020
#Description: Create Figure 4
#NOTE: there are many explicit file paths throughout this script that you will need to set manually in order to run
#This script also relies a bunch on external datasets you will need to download and map to yourself. Check the manuscript for w... |
663577fcce51a04d37380cb9f80d448fd4fb753a | 252c4d47c1da5053ac911b97a635e4e3ae261186 | /man/pvssg.Rd | 2b24efff820272f922a3265fe5a78f60b8266c91 | [] | no_license | cipriuhq/pepa | dd987d68bb530658f3ab15845ae4127e86b2933e | 3faced8e00e329347db55a7e141cfd9d14617a01 | refs/heads/master | 2021-06-18T17:52:20.724587 | 2017-06-26T16:32:50 | 2017-06-26T16:32:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 371 | rd | pvssg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_pvs.R
\docType{data}
\name{pvssg}
\alias{pvssg}
\title{PVS summary global}
\format{A data frame with 163 columns and 6 rows.}
\source{
International Potato Center, potato data.
}
\usage{
pvssg
}
\description{
This data set has summaries ... |
2ba1a1a32b136783e19c925b6471163fe09ab89d | d6b5625d77f30dcca110516748f7f076ee15e5c4 | /R/package-setup.R | 874be2cddc97f838a48b09c19163c3f23f5c3d6a | [
"MIT"
] | permissive | JosiahParry/proj-template-example | 2829adc95f2cadb558d41ec20d7501fb3cd539b0 | 17e24cd60f0acc7ad55aa9cf6c55af1f25fe8f8f | refs/heads/master | 2020-12-27T22:33:22.950920 | 2020-02-03T23:59:02 | 2020-02-03T23:59:02 | 238,086,952 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 162 | r | package-setup.R | library(usethis)
# after creating package get started!
use_mit_license("Pkg Creator")
use_readme_rmd()
use_news_md()
# - R functions `R/`
use_r("gs4_skeleton")
|
02463471400513ff0ec2b53cf4eb458de35a3ca2 | 627a50c426830203637f5ae8215a320c0a0aa4eb | /R/plot.GGInetwork.R | b30dd8fab6c001d603b66837ead70f729f362039 | [] | no_license | MathieuEmily/GeneGeneInteR | 5f46a432d6760856ad79fc6c12cd612f31a4a7ac | 60b96b60d686a0c290edad4646484027706ee81d | refs/heads/master | 2021-05-24T01:49:18.126390 | 2020-11-27T21:22:07 | 2020-11-27T21:22:07 | 183,239,562 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,748 | r | plot.GGInetwork.R | plot.GGInetwork <- function(x,method=c("heatmap","network"), threshold=NULL, col=c("#D6604D", "#104E8B"), colbar.width=0.15, title=NULL, hclust.order=FALSE, use.log=FALSE, NA.col="#D3D3D3", draw.pvals=NULL, draw.names=NULL, interact=FALSE, method.adjust=c("none","holm","hochberg","hommel","Bonferroni","BH","BY","fdr")... |
f56f9e9020bc07bc6c442b0c20fe9c15a40f30ba | aa18ff5ac84f719c8719da0de4ae0d8f25791eb5 | /project/R/MatchingKnowledge.R | a7a773477207d3660c236ccf12c4bf6982cac5c3 | [] | no_license | jleslie17/2017_VR | e68ce6517957b54d24e66ae60c3934637c9a66fb | 4485e4c01eab757c63fe73b5fd70c3271fa37dd5 | refs/heads/master | 2021-06-14T06:58:36.570822 | 2017-05-04T19:45:37 | 2017-05-04T19:45:37 | 80,775,532 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,300 | r | MatchingKnowledge.R | # Use this script to match attendees based on knowledge, '3.1'
source('AddUnderscores.R')
source('SpreadResponses.R')
library(lsa)
source('GetMatches.R')
source('CompanyMatchesOutput.R')
path <- getwd()
datadir <- paste(path, '/data/', sep = '')
#Load and clean data
FileMS2 <- 'Milestone2utf8.csv'
File130916 <- 'SSL... |
91ff2bd716fb372593dde0000104eb2c3eccb260 | ffcf770ed00b5456cbe0fb23d13e943e8053c594 | /TPP_FDP.R | 492fc0e236609f9d8bc3fb55b4c8825454be9140 | [] | no_license | panshiliuzhi/FDR_Lasso | 497e58592abac21db17dc9ee8be99f97947e6ea7 | 3d7df8e8f56e4caad2925f7a3e5dccdc293f7c19 | refs/heads/master | 2022-04-16T07:45:19.889256 | 2018-02-12T11:30:38 | 2018-02-12T11:30:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,995 | r | TPP_FDP.R | library(glmnet)
TPP <- function(model, beta) {
# True positive proportion
true_coefs <- which(beta > 0)
k <- length(true_coefs)
coefs <- coef.glmnet(model)@i
common_coefs <- intersect(true_coefs, coefs)
length(common_coefs)/max(k, 1)
}
FDP <- function(model, beta) {
# False discoveries proportion
tru... |
22a30e805951090bd4b971710bade475ae8e086f | 3dea25f109bc6fb4fdbe45cdc576e8dfb98c3cfb | /scripts/04_deAnalysis.R | d3af61958b3b89490faa0f4f849cfce8ebc5f655 | [] | no_license | alanlorenzetti/ccrescentus_rnaseq_rhle | 13a406d3aa6517afabbcb272958b72c748359f27 | e5c103757b4e05dfbb5fee86c971bc991821131b | refs/heads/main | 2023-06-03T05:27:30.631542 | 2021-06-29T18:07:25 | 2021-06-29T18:07:25 | 381,445,565 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,099 | r | 04_deAnalysis.R | # alorenzetti 202106
# description ####
# this script will prepare data
# generate the counts, and
# perform differential expression
# analysis
# preparing data ####
# providing info about data
samples = c("NA1_10C", "NA2_10C", "NA3_10C",
"NA1_30C", "NA2_30C", "NA3_30C",
"rhIE_1_10oC_S4", "rhI... |
80e3c26210c833d9d9b7f861612c4b54648bc270 | 2931c0b42f80075e212b4a28cd0b9b1586eac6af | /Tareas/Método_Bisección.R | b7c6fc1d16ddc9c6043658c68b6b32592a40eb8a | [] | no_license | mgalvis60/Analisis_Numerico_1910 | f4edfe9091643c362d8366acb224c4e869024ca7 | d3a8def7b35956c3cf54efe4e9660a09439ef7b1 | refs/heads/master | 2020-04-20T04:32:38.040374 | 2019-05-15T12:09:06 | 2019-05-15T12:09:06 | 168,630,617 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 620 | r | Método_Bisección.R | #Mateo Galvis López
#Bisección en R Codigo
#Biseccion
Fx <- function(x) exp(x)-pi*x
# Halla la raiz de Fx
biseccion <- function(a,b) {
x<-seq(-3,3,0.001)
plot(x,Fx(x),type="l",col="blue")
abline(h=0,col="blue")
x<-b
d<-(a+b)/2
i<-0
error<-abs(a-b)/2
while (error > 1.e-4) {
i<-i+... |
62bbfe8d18dd8ca63163714f5c8453f60d5748a9 | b961e5df200a979211606950cf0ccaa36bb5cf7d | /myProject/monte_carlo.R | 40ffdbd02f47aa51b0c78d1c8dd9fee73261df8b | [] | no_license | navbharti/Rworkspace | 27f45a262ade1cccdd742f1e83a24f91a6304df7 | 0415761023a61887e97cdbff55ccfa976651c3b7 | refs/heads/master | 2021-01-17T17:54:40.844402 | 2016-08-14T12:44:17 | 2016-08-14T12:44:17 | 62,465,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 922 | r | monte_carlo.R | library(rafalib)
library(downloader)
url <- "https://raw.githubusercontent.com/genomicsclass/dagdata/master/inst/extdata/mice_pheno.csv"
filename <- "mice_pheno.csv"
if (!file.exists(filename)) download(url,destfile=filename)
library(dplyr)
dat <- read.csv("mice_pheno.csv")
controlPopulation <- filter(dat,Sex == "F" &... |
7a080ebc48b059d988cc41c95c2e6184d9595793 | 41d8aa2ea38571c2aae84e8581a4820ba6b985bf | /test.R | ed6291817be1f7d6b6683ab410db003e8dcd8d26 | [] | no_license | arunmarria/housePricePrediction | f0fe9e72cd8534c492aca1230d690eb6eac570d7 | 78b83827191b0859b9f9eee572f8ed59c7773e48 | refs/heads/master | 2020-04-10T17:59:46.341974 | 2019-01-02T14:59:35 | 2019-01-02T14:59:35 | 161,190,693 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 34,448 | r | test.R |
## the script is for data preparation of actual test data and it involves performing same operations on
## test data as performed on train data
## loading data
house_test <- read.csv("test.csv")
## Getting glimpse of data
head(house_test)
str(house_test)
## understanding data
## changing MSSubclass to factor
f... |
3276053a53223f06b6c07e39767a377293c8a1ee | 8aa69c336da13f338e944e586171e8bdf0c3f87a | /glmmTMB/man/sigma.glmmTMB.Rd | 19cf886fa1902174ba512bfc165247b86100e3f4 | [] | no_license | glmmTMB/glmmTMB | 4b5612a1cf6ce7567117b3318086fd7b3840e3da | 0e9d26a02e5e36b74120d8c8a35eae0e0960a73b | refs/heads/master | 2023-08-29T14:39:15.855753 | 2023-08-25T22:28:15 | 2023-08-25T22:28:15 | 40,176,799 | 230 | 63 | null | 2023-09-04T11:10:37 | 2015-08-04T09:53:51 | HTML | UTF-8 | R | false | true | 3,923 | rd | sigma.glmmTMB.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/VarCorr.R
\name{sigma.glmmTMB}
\alias{sigma.glmmTMB}
\alias{sigma}
\title{Extract residual standard deviation or dispersion parameter}
\usage{
\method{sigma}{glmmTMB}(object, ...)
}
\arguments{
\item{object}{a \dQuote{glmmTMB} fitted object}
... |
35f33b5263b9d0b94df6302ba13c80ab07de5e3d | 7658adb5dacf17ffdb3badb94218d81315eca3c6 | /4. Multivariate/4.3 (Matlab-R) Location estimator/4.3 R/R_Multiv_meanmedian.R | 070b43f87a0a414f81da78757218e512251d1509 | [] | no_license | saullopezc/curso-atipicos | e186fb07192ca61f340e1befaf0576b969c20e87 | 0c4c5cd01c38b7f344909438396b26123afdefb7 | refs/heads/master | 2022-11-20T03:48:06.147674 | 2020-07-25T00:47:59 | 2020-07-25T00:47:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 374 | r | R_Multiv_meanmedian.R | set.seed(3)
library(MASS)
install.packages("depth")
library(depth)
mu1 <- c(0,0); mu2 <- c(10,0); sigma <- matrix(c(1,0,0,1), nc = 2)
X <- rbind(mvrnorm(80, mu1, sigma), mvrnorm(20, mu2, sigma)) #dimension 100x2
plot(X)
# Mean vector
m=c(mean(X[,1]),mean(X[,2]))
# Component-wise median
med1=c(median(X[,1]),median(X[... |
0093c0c9aaafd3f1113d270ca73cd341eda1b4c9 | d265fd0e263981fd3918711b35514e2eb3c095b5 | /Exam R 20171020.R | dfb3e275b49cd0180c96fb2dc60de9fa06bc250d | [] | no_license | hodfa840/Rexam | b279ae2b9cfbc8797a61cb4defecf806d515e325 | a2a15ed2a1a5e1a7aa6db99ff3a7bd0e6a240977 | refs/heads/master | 2022-02-13T04:12:06.912418 | 2018-11-23T15:14:17 | 2018-11-23T15:14:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,992 | r | Exam R 20171020.R | #1.1
rm(list = ls())
#1.2
f <- function(x){
return(x*x)
}
#1.3
# file "Read-and-delete_me" have some information about package. We should delete it after reading
# file "NAMESPACE"
# file DESCRPTION contains introduction information about the package like name, author, license..
# folder R has R script... |
855a56dda4600aa53f939475f3e5f867a9c42911 | 4c2f5c46b33ab8cb94df18bceb40365c3a1f9c05 | /man/grpFactor.Rd | 8469d3c53a63a15f0e97164f10d5847b92fc091c | [] | no_license | htc502/ewrefxn | bbd11c85d4a04ac8a0e5289859f57234b918ae18 | 0303fb85b22db08c26028e518992fc9f7547d49a | refs/heads/master | 2020-04-15T23:47:01.806327 | 2020-01-29T17:42:20 | 2020-01-29T17:42:20 | 40,641,809 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 541 | rd | grpFactor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/microarray.R
\name{grpFactor}
\alias{grpFactor}
\title{'combine' multiple factors}
\usage{
grpFactor(...)
}
\arguments{
\item{...}{factors to be passed in}
\item{sep}{the separater used for combine the factor levels, with '_' as the default}... |
1ae5a9478d7f7c8f4eb7c7c4cd40d2e9706f4995 | 06c91835ef96c077e8549e7948c36dc6775fdd52 | /man/get_bounds.Rd | d3a372d82a87903348d8a1bc9c9a6867912fe5d3 | [] | no_license | sscogges/controlhet | 3293a7f0ad949108f90bea6278d6d61e231013dc | 0150cbb9fd2247baecf7513c7ea00db232442dae | refs/heads/master | 2021-01-20T04:53:51.475552 | 2017-08-25T10:32:35 | 2017-08-25T10:32:35 | 101,393,161 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 660 | rd | get_bounds.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multipleversions_code_110316.R
\name{get_bounds}
\alias{get_bounds}
\title{Find upper and lower bounds for P(Y(1) | D(0) = 0, D(1) = 1) and
P(Y(1) | D(0) = 2, D(1) = 1)}
\usage{
get_bounds(Y0, Y1, D0, D1)
}
\arguments{
\item{Y0}{observed outc... |
1d3120871a41fb91d3323f26a7f20e48ffd37144 | 8982143e6eff27f3cd338ced46ef545425af1176 | /iteration/map-multi-args.R | 27a77c2185271af9976c427dbaf110ae2eb40d91 | [] | no_license | uroszivanovic/R-for-Data-Science | 825f493769e3646de15ebced983a4ffa2aee1d45 | 46ccf93a1a3afd958941461b32eea8ae78319267 | refs/heads/master | 2022-06-20T18:42:24.850635 | 2020-05-06T16:46:49 | 2020-05-06T16:46:49 | 257,034,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 965 | r | map-multi-args.R | library(tidyverse)
mu <- list(5, 10, -3)
mu %>%
map(rnorm, n = 5) %>%
str()
sigma <- list(1, 5, 10)
seq_along(mu) %>%
map(~rnorm(5, mu[[.]], sigma[[.]])) %>%
str()
#map2() - iterates over two vectors in parallel:
map2(mu, sigma, rnorm, n = 5) %>% str()
###like map(), map2() is just a wrapper around a fo... |
937dbb28c12716328c1187a2632b8da752266750 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ubci/examples/ubci_index.Rd.R | 05e6b245f83b2bab935648e30d2c3c733cf8d5d1 | [] | 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 | 271 | r | ubci_index.Rd.R | library(ubci)
### Name: ubci_index
### Title: get ubci index
### Aliases: ubci_index
### ** Examples
## No test:
index <- ubci_index(index = "UBMI")
index
ubci_index(index = "UBMI", from="2018-05-15")
ubci_index(index = "UBMI", to="2018-05-15")
## End(No test)
|
68949c73c8b68f3d65f8ddedffac6c57d03140ea | 02011265ec12c28d36953a558e613b23950969a4 | /cachematrix.R | d724d9d5da669aa1e7fd356f693e9c5f53070d92 | [] | no_license | sankarshanbaliga/ProgrammingAssignment2 | 3201c5f490f1beeb228bd1d1f6defd198c3a7af0 | c16fe9312dc8649814bd7236ba4204fe4c7e4647 | refs/heads/master | 2021-01-14T10:27:19.157159 | 2014-04-26T15:34:11 | 2014-04-26T15:34:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,040 | r | cachematrix.R | ## These functons will cache of the inverse of a matrix in order to reduce
## time spent on time consuming operations
## This function:
## Sets the value of the matrix
## Gets the value of the matrix
## Sets the value of the inverse
## Gets the value of the inverse
makeCacheMatrix <- function(x = matrix()) {
inv ... |
63d8ae1df8ea86fe39b1a30d04cf870162d36f66 | d711652887bcaebd96da64b7a1163dd8f0102c21 | /parseByBreeding.R | e35acaa8c5c905e4a7e859c0b21890fa4586022b | [] | no_license | kaiyaprovost/GISProject | 51d3e14e7d019fb08a0dc9d2bb8e2db6c6f4b277 | 660d1007f3c1de5c3da08acd3317ba29b1927fad | refs/heads/master | 2020-06-14T12:09:11.430868 | 2016-11-29T00:28:28 | 2016-11-29T00:28:28 | 75,026,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,064 | r | parseByBreeding.R | library(maptools)
library(rgeos)
path = "/Users/kprovost/Documents/PhylogeographyReview/AllRangeMaps/All/"
outDir = "/Users/kprovost/Documents/PhylogeographyReview/AllRangeMaps/onlyBreeding/"
#setwd(path)
file.names = dir(path, pattern =".shp")
print(file.names[1])
## SKIPPED BECAUSE WEIRD OR NO BREEDING
## Acrosep... |
5efecabb9930242e2b46e2f9c7ccb0021eb75b94 | 9bfa780023ac5f601556dbabdcf6adffd104e5fd | /Kocom/R_Pkgs/installpkgs2.R | 0bc47f6b28f5d9a0a25c792ad99fb06eca76f762 | [] | no_license | ljinsup/Kocom | 31b048a9d0add4b70653c4fc9807561c07b16ad2 | 7449a868f742e493e9546ba8f8fff454c426d9c5 | refs/heads/master | 2021-01-21T13:48:07.466157 | 2016-05-30T11:26:49 | 2016-05-30T11:26:49 | 51,070,316 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 536 | r | installpkgs2.R | cat("==========================================================================")
install.packages("RSclient_0.7-2.tar.gz", repos=NULL, type="source")
cat("==========================================================================")
install.packages("CEMS.tar.gz", repos=NULL, type="source")
cat("=======================... |
c0667446deadb3e3cac1978d97d50f03c12e6041 | de427995e5a749a1c0d6c05cce92e4b771902ad9 | /R/enumfactor.R | eef33851f06ee145a0b91e84137b4baa2b2b1a26 | [] | no_license | sophof/enumfactor | 4ee2840ebdcc5a3f9a045d606afbe148bf02762c | b984cfa9085c81924fc413b0e6ae0aba9692c010 | refs/heads/master | 2020-05-07T15:03:25.743007 | 2019-04-10T17:28:27 | 2019-04-10T17:28:27 | 180,620,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,306 | r | enumfactor.R | #' @export
enumfactor <- function(x, ...) UseMethod("enumfactor")
#' @export
enumfactor.default <- function (x = character(),
levels,
labels = levels,
exclude = NA,
nmax = NA,
indices) {
out <- enu... |
c84f576cc6e406a1f49fd4143e035ecca0304f0e | 16e142bfbcc71936637e3a08b8e791d4e6b7b007 | /man/setttings.Rd | bdc12421cd3ca38f05e7dc1985f848f4759c1767 | [] | no_license | behrica/quanteda | c030ad280ac4b2797de3056e799ef1babdd37a82 | b2daa7485cabe0bdd28ffb69a88283964e74fc26 | refs/heads/master | 2020-12-27T15:05:01.930496 | 2014-11-14T23:37:49 | 2014-11-14T23:37:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 338 | rd | setttings.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{settingsInitialize}
\alias{settingsInitialize}
\title{\code{settingsInitialize} returns a list of legal settings, set to their default values}
\usage{
settingsInitialize()
}
\description{
\code{settingsInitialize} returns a list of legal settings, set to their ... |
841721d69c3ac1e24354739efb024ab5c328e530 | 1fbac5899858da3a434aacbefd0cbd7b399a58e7 | /plotting/plot_kobe_timeseries.R | 5e7153cb9058da8adb0d91cba1e8dbde4461558c | [] | no_license | Ovec8hkin/pws-herring-basa | f4c3e30871e5ea0f9afb46104c0c930018ea257f | caef7da3a6ae71aaf9b23e5e6b54ea1aebbfacaa | refs/heads/main | 2023-04-06T21:51:04.323235 | 2023-01-18T20:37:00 | 2023-01-18T20:37:00 | 488,278,803 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,386 | r | plot_kobe_timeseries.R | library(ggplot2)
library(ggdist)
library(tidyverse)
source(paste0(here::here("functions/"), "fun_read_dat.R"))
b.star <- 40000
f.star <- 0.20
total.sims <- 1
set.seed(1998)
sims <- sample(1:1e4, size=total.sims)
nyr <- 10
control.rules <- c("base")
data <- data.frame(year=NA, biomass=NA, exploit=NA, sim=NA, cr=NA... |
8d5acae4a1bf44d125e05fe6dc0375b6c4181ffd | 894823803baee99ae414f5d74262c8069ff81014 | /Scripts/redes.R | 6d553a2742f243cbd03dded980074232974486cb | [] | no_license | itsriodejaneiro/capacitacao-jornalistas | 8df7b5685e9695520e06d4cd98b87fb767dbc262 | 973817fd135f97211b0f3079233c82d41753ade1 | refs/heads/main | 2023-06-10T14:12:36.777405 | 2021-06-28T21:31:44 | 2021-06-28T21:31:44 | 380,345,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,630 | r | redes.R | rm(list = ls()) # Limpando o "Enviroment"
options(scipen = 999)
paleta_its <- c("#A3248B","#54318C","#255C8E","#00A6B7","#E1F1FD", "#8a8a8a")
# 0. Pacotes -----------------------------------------------------------------
install.packages('igraph')
install.packages('ggraph')
install.packages('readr')
install.packages('... |
f19ebf1826c8b19dccaf295e18da2d16ade3b069 | f166b8646ba0fe35cf8e4243ce3157620c2ddfe8 | /annotation_liftOver_hg19tohg38.R | f70a7b6f240725ddad5a551221b0533d85e40d30 | [] | no_license | ding-lab/cptac_methylation | 0214a5a3921743fd5aa7707782cca22b263cefb8 | 6b3933cac40f8738dbd5bbd760cf714227fe8e95 | refs/heads/master | 2022-10-13T06:41:28.318746 | 2021-01-21T21:06:29 | 2021-01-21T21:06:29 | 135,501,473 | 8 | 5 | null | 2022-09-01T16:47:42 | 2018-05-30T21:55:42 | R | UTF-8 | R | false | false | 3,731 | r | annotation_liftOver_hg19tohg38.R | ##########################################################################################
### author: Sunantha Sethuraman <s.sethuraman@wustl.edu>
### usage: Rscript annotation_liftOver_hg19tohg38.R
### created: 08/07/2018
### description: Lifts over the annotation file for methylation data from hg19 to hg38
#########... |
a4d9bf4a13def3d635a1ee5ef1f16bd732cb6684 | fe803e1beb20030c97b7e8ccde2ba58af2e58173 | /bayesian-data-analysis/stan/chapter3/timeseries.r | 6b03f28ec08d3951ff14c7a4f38a8b02b0aed8c3 | [] | no_license | yc3356/courses | 0c9c1d9fa063a0419fee2c8b664bf768492f9d4e | a1902818b5d4b2f78a9880774295144374f84eac | refs/heads/master | 2022-08-01T10:18:29.076698 | 2020-05-13T06:59:46 | 2020-05-13T06:59:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 729 | r | timeseries.r | series <- matrix(scan("Series1000.txt"), nrow=1000, ncol=135, byrow=TRUE)
T <- 135
N <- 1000
par(mar=c(3,3,2,0), tck=-.01, mgp=c(1.5,.5,0))
plot(c(1,T), range(series), bty="l", type="n", xlab="Time", ylab="series")
for (n in 1:N){
lines(1:T, series[n,], lwd=.5)
}
slope <- rep(NA, N)
se <- rep(NA, N)
for (n in 1:N){
... |
721f9e052f03b33825fea4fa9d909bb3e064dbea | 0819482cde73b1436a2721b1e61679f34b8e9ebf | /section04/exercise4_6.R | 50fd01dabfc523ade8e148eb55fe33e154e10ccb | [] | no_license | YeseniaDS/UT-Austin-SDS383-Statistical-Modeling-II | 575da6e0498a1c78be902ece62e680b9465fb540 | d78e200060dad6c7c10801daadee82366e91bb65 | refs/heads/master | 2023-06-08T05:12:37.399033 | 2018-04-29T05:49:48 | 2018-04-29T05:49:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,435 | r | exercise4_6.R | rm(list = ls())
setwd("D:/2018 UT Austin/R Code/Statistical Modeling II/section04")
library(MASS)
#---------------------------------------------------------------------
faithful = read.csv("faithful.csv")
x <- faithful$waiting; y <- scale(faithful$eruptions)
n <- length(x)
#----------------------------------... |
b9e5ad3eedb439997a3a0297929105166c9e3746 | f0a9139920d61bdb5b814dfe6e315de0e045292f | /Monthly counts.R | 1d8e8008d7b66c0537c0a638b08a635ef920ca82 | [] | no_license | mzkaramat/BillPrecitor | bae5dd0a3a7e0361d8f22bba69e6741ab47d0574 | 83e19c5c55628fc6fac0315446c954b95c26ef29 | refs/heads/master | 2021-01-20T05:25:19.436769 | 2017-04-30T13:05:42 | 2017-04-30T13:05:42 | 89,777,452 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,480 | r | Monthly counts.R | library(plotly)
require("RPostgreSQL")
library(plotly)
pw <- {
"G7iFeMJnkukW"
}
# loads the PostgreSQL driver
drv <- dbDriver("PostgreSQL")
# creates a connection to the postgres database
# note that "con" will be used later in each connection to the database
con <- dbConnect(drv, dbname = "congress",
... |
6f8e8b75b61f63c5da92aae4cac63e7c99543f1b | e3d3bfacc366e44e695db121189b81d1807df552 | /man/XLgeneric.Rd | 5e70cfcabc0de2f97a555ecf76c41ce68b8b650d | [] | no_license | cran/table1xls | 0118cd99362b34714ac7e70f5aab8208f67049f3 | 304833adabec176fc3d8b10c8c33143f2c836809 | refs/heads/master | 2021-01-17T07:40:08.188080 | 2017-07-26T21:04:36 | 2017-07-26T21:04:36 | 17,700,353 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,647 | rd | XLgeneric.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/XLgeneric.r
\name{XLgeneric}
\alias{XLgeneric}
\title{Write generic rectangular data to a spreadsheet}
\usage{
XLgeneric(wb, sheet, dataset, title = NULL, addRownames = FALSE,
rowNames = rownames(dataset), rowTitle = "Name", colNames... |
ab4c36772bbf1654d1f16af29c9f36039516ec26 | d2f14956ce85d338da1fa41cd16a1f5f472f1c88 | /ui.R | 2b643331e6609efb639adee293fbdfadefd7fecb | [] | no_license | BLKhoo/developing-data-products | bf18ff8a94134f598cb153fc589660523af98a33 | c714672f6aae18812a36c273858ae30cbc78a9b5 | refs/heads/master | 2016-09-01T07:46:20.120816 | 2015-09-27T17:18:06 | 2015-09-27T17:18:06 | 43,255,599 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,314 | r | ui.R | # Application to determine the low enough price based on stock CAGR and lowest point in the growth trend line based on linear
# regression and also the log price assume stock price over time would have 2^t growth where t is the time.
library(shiny)
shinyUI(pageWithSidebar(
headerPanel("What should be a low enoug... |
a101ff8676d735f391753916c814f56e20ef1b50 | 42b641dd7b2c00d6448c7d7cb0ffedd5ee37124a | /scripts/R-scripts/scale_analysis_CT.R | 1d18ed913c4f4cd6ff3f52d6a81791ecf4a8f894 | [] | no_license | hurlbertlab/core-transient | 2f1c8366912f6f4aec80f821585b3e196265af29 | 56b8cbb1f9049f4dd4a46f16b4d3944f6600b71a | refs/heads/master | 2021-01-23T14:46:51.972712 | 2020-06-09T14:35:50 | 2020-06-09T14:35:50 | 17,714,944 | 6 | 1 | null | 2016-01-25T18:32:24 | 2014-03-13T15:28:18 | R | UTF-8 | R | false | false | 9,014 | r | scale_analysis_CT.R | # scale analysis function
# Load libraries:
library(stringr)
library(ggplot2)
library(grid)
library(gridExtra)
library(MASS)
library(dplyr)
library(tidyr)
library(lme4)
library(plyr)
# Source the functions file:
source('scripts/R-scripts/core-transient_functions.R')
getwd()
# Set your working directory to be in th... |
fe87e4a9ade9062263058a64308f11f6a351f92f | c31f112a1fb43dd4fd3c808e905600cfec50bec3 | /app_server.R | bed4c7217f40bdfc6b04a3b4c0ecc5e6cf25048f | [] | no_license | bmetsker/finalproject_info201 | 20d32691a4714bce0ea7d349b1efc63865e51874 | a33f2a8c4a02327bddffdec26adb7b6428f73d9e | refs/heads/master | 2020-08-29T19:59:39.197574 | 2019-12-04T21:16:50 | 2019-12-04T21:16:50 | 218,157,122 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 953 | r | app_server.R | library("ggplot2")
library("plotly")
library("dplyr")
source("Metric1Analysis.R")
source("Metric2Analysis.R")
source("scripts/Metric3Analysis(Kunpei).R")
server <- function(input, output) {
output$Metric1Graph <- renderPlotly(
FedCompareGraph(input$Metric1Date[1], input$Metric1Date[2], paste(input$Metric1Stock,... |
b99aa2bafc8c426166f502c13cf6000612760a15 | a36df1ec0c5bfb81e09479011d03d9c18e595400 | /man/getInfoTabela.Rd | 1644b10b6837fa5088d4483659521fe15381783e | [] | no_license | vilaralmeida/qualificajampa | 63eb893c87749e0d87bc7ea214077648148b5fb6 | 746b82c493aecdb80f5368a86f6109126fcde3ef | refs/heads/master | 2021-04-28T16:04:53.441409 | 2018-02-19T23:23:42 | 2018-02-19T23:23:42 | 122,005,851 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 641 | rd | getInfoTabela.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getInfoTabela.R
\name{getInfoTabela}
\alias{getInfoTabela}
\title{get Info Tabela}
\usage{
getInfoTabela(tabela = "pessoal")
}
\arguments{
\item{tabela}{Qual Tabela terá a Informação Recuperada.
Opções: pessoal, publicidade, receitas, despesa... |
ef8e9fcf6e722139e0c0904a96e7145f262090ec | 2ff0e105ced702c9ec86e8cdad170e9b4e4e22b6 | /XGB.r | 848f8e34897dccfa78b46c97e632ee04f363640f | [] | no_license | MaheshParamati/MachineLearning-Projects | e5f3b20118a3681e1541c7b7acbc13b405fde04c | fa8e09d9f57f3b1bf1663473b02e0631ec52d481 | refs/heads/master | 2021-01-17T15:51:25.800660 | 2017-03-06T19:55:26 | 2017-03-06T19:55:26 | 84,114,852 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,694 | r | XGB.r | setwd("C:/Workspace/")
traindata <- read.csv("traindata.csv", header = T) #Read Train Data
labeldata <- read.csv("label.csv", header = T) #Read Train Data Labels
testdata <- read.csv("testdata.csv", header = T) #Read Test Data
traindata$status_group <- labeldata$status_group #Add Train Data Labels to Actual Data
#Crea... |
37dbd7a4912507c900f0d382881a571699711b63 | 5900c665320c9d3d2075820471bf48b0d5998cb5 | /Chap6_ClusterAnalysis/cluster_analysis.R | e5869515abcccc5227acb4e4dd907b5715d995ea | [] | no_license | LaguaFluc/Data-Mining-and-Modeling | 756e3e3f1c60f55d56395b396c4636e6c7ce90f2 | 0bf0ad1ff4eae70fca92e446612d35ca99ff96fa | refs/heads/main | 2023-04-03T05:57:03.759486 | 2021-04-14T00:43:14 | 2021-04-14T00:43:14 | 353,004,791 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,160 | r | cluster_analysis.R | # -----------1、获取数据并标准化
iris.4 = iris[, 1:4]
iris.4 = scale(iris.4, center=T, scale=T)
# -----------2、对前四列数据做聚类
##k均值聚类
K <- 4
cluster.iris <- kmeans(iris.4,centers = K,iter.max = 99,nstart=25)
cluster.iris$size
# -----------3、画图
plot(iris.4, col=cluster.iris$cluster)
plot_data = function(data, k=4){
# 找到前四列的数据
# 我... |
9a39952d4a70f353f226afaf4801cbdc1189df0c | 6a811d1fbe579e346b9813857f9059be010a67a7 | /scripts/tables/loop.R | 20c25d8a93fd4c63b7c8ca1620dfb67ffc509d28 | [] | no_license | HongyuanWu/rkipcreg | 317634c4d3b500ad23cf39c6f50843e889237692 | c50b5e3d0d6576c268fe160ac37f0db6e3cc3f11 | refs/heads/master | 2021-10-10T23:28:30.353773 | 2019-01-19T04:04:05 | 2019-01-19T04:04:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 842 | r | loop.R | # loading required libraries
library(tidyverse)
library(xtable)
# load data
load('data/tf.rda')
# generate table
loop %>%
mutate(study = str_split(study, '_', simplify = TRUE)[, 1]) %>%
group_by(tf, feature) %>%
summarise(n = n(),
ave = mean(cor), sd = sd(cor),
study = paste(study, coll... |
4a7cfbdce5dd512dd2b1e64c9c421e6291ea3b82 | 880f4265c481c2e9364341003023ea3f6ebf9ec2 | /R/farforecast.R | cef22a80a52178f0349975ecd275fb9499c02b77 | [] | no_license | cran/ftsa | a2205b07d06d14ea71b354123544fe66e6ef66ea | 65415c7011cbb1bdd6d5b9de5895d2061d3fb471 | refs/heads/master | 2023-06-09T10:33:28.167641 | 2023-06-01T16:00:02 | 2023-06-01T16:00:02 | 17,696,159 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,716 | r | farforecast.R | farforecast <- function(object, h = 10, var_type = "const", Dmax_value,
Pmax_value, level = 80, PI = FALSE)
{
order_select = method.FPE(object = object, D = Dmax_value, var_type = var_type, Pmax = Pmax_value)
order = order_select[2]
ftsm_object = ftsm(y = object, order = order)
... |
5be1d5bd6962c542684b95e2972d139e0dd2397b | cbcd97b5a92511dd94390e0fed7aa6de4e14f45b | /cachematrix.R | 8d3de0b4558ceab4276d3320196c73e046aa2838 | [] | no_license | mcm987/ProgrammingAssignment2 | 6ea331609830c5078048442f15c5628eccccbc34 | a1ecff17d013eae7560015f234dec90e27fe0aab | refs/heads/master | 2020-12-24T14:01:10.132820 | 2016-02-17T12:04:14 | 2016-02-17T12:04:14 | 51,905,042 | 0 | 0 | null | 2016-02-17T07:55:38 | 2016-02-17T07:55:37 | null | UTF-8 | R | false | false | 2,202 | r | cachematrix.R | ## These two functions are build analog to the example "Caching the Mean of a
## Vector" in the assignment description. The function "makeCacheMatrix" creates
## a special "matrix" object and provides a list with functions to 1) set the
## value of the matrix, 2) get the value of the matrix, 3) set the value of
## th... |
3de39b2cbc3fb0b6e86e159f4f59798fa4702228 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/samplingbook/examples/Sprop.Rd.R | 9d1bc05ae5f7f46f2de6aec57ebf94dfb564a707 | [] | 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 | 783 | r | Sprop.Rd.R | library(samplingbook)
### Name: Sprop
### Title: Sampling Proportion Estimation
### Aliases: Sprop
### ** Examples
# 1) Survey in company to upgrade office climate
Sprop(m=45, n=100, N=300)
Sprop(m=2, n=100, N=300)
# 2) German opinion poll for 03/07/09 with
# (http://www.wahlrecht.de/umfragen/politbarometer.htm)
... |
04c41c3bd58284eb0d1a9210a9dd53d73c342062 | 3dfcad5e4ca29823a6e7899dcd22aaf7f5df971c | /man/getOutputDataSet.AromaTransform.Rd | 31473dfd3b14ba691f69ab1d40e6d5bdd48b14de | [] | no_license | HenrikBengtsson/aroma.core | f22c931029acf55f3ad2fdb6eb3bc2f0d2ba04e4 | 1bf20e2b09f4b8c0ca945dfb26fdf1902c187109 | refs/heads/master | 2022-12-01T09:01:49.295554 | 2022-11-15T18:29:17 | 2022-11-15T18:29:52 | 20,845,682 | 2 | 3 | null | 2018-04-21T02:06:48 | 2014-06-15T01:32:43 | R | UTF-8 | R | false | false | 1,847 | rd | getOutputDataSet.AromaTransform.Rd | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% AromaTransform.R
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\name{getOutputDataSet.Ar... |
a6b0cc7578a07abdd1b93695b38d1c898398ba9f | ded24dcc44f53ec1adcf336ed211e4b8c4076c70 | /dataset_labelselect.R | 5c72cf5142262e7970a5e7546a81ce1d18b4ce0b | [] | no_license | hying99/capstry | 76ecd155d3a3a11a1e9e512249a34b3835235140 | e7288ab030e72cdee53bc882f9727d798aa5d2a7 | refs/heads/master | 2023-07-04T07:46:47.850022 | 2021-08-08T08:42:09 | 2021-08-08T08:42:09 | 393,864,850 | 1 | 0 | null | 2021-08-08T08:42:10 | 2021-08-08T05:15:42 | Java | UTF-8 | R | false | false | 474 | r | dataset_labelselect.R | ###select.labels
Labelselect <- function(select.table)
{
samplenames <- row.names(select.table)
labelsnames <- colnames(select.table)
select.labels <- vector("list",length = nrow(select.table))
names(select.labels) <- samplenames
for (i in 1:nrow(select.table)) {
for (j in 1:ncol(select.table)) {
if... |
404434dd1e118beebaf4336824cfb8b7ae45b15d | 3c9734e0a0506e25dbb0d34802c3ef3eb27caf47 | /tests/testthat/test_population.R | 0cedd9637fe540adcb6c265a2dc2cfda0e32ada3 | [] | no_license | Jorisvdoorn/lab5group8 | 3a1f7368315af62e18442f35bbcd62d44c4f9a61 | 366d5a9b5dad18ab55cdd8e84cc449a75a601004 | refs/heads/master | 2020-08-03T17:47:15.162998 | 2019-10-07T00:19:11 | 2019-10-07T00:19:11 | 211,832,865 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 274 | r | test_population.R | context("population")
test_that("population rejects errounous input", {
expect_error(population_mod <- population$new("Municipality"))
})
test_that("class is correct", {
population_mod <- population$new()
expect_true(class(population_mod)[1] == "population")
})
|
4f071261fd70f24ea51a584fbe55a4e6891fb7c0 | 4adbd5b00d14eba6b814f0d52bddbec57d623fed | /harvest-cloud/scripts/effect_origin/linear_hypotheses.R | a2c456db3a247d650749e865eacf5f6d6279bb00 | [
"MIT"
] | permissive | PerinatalLab/metaGWAS | 809f6ebaec7a6321f3fc6b4ed11a8f732c71a34d | 494ef021b0d17566389f6f3716d1e09f15e50383 | refs/heads/master | 2023-04-08T08:50:31.042826 | 2022-11-10T21:14:33 | 2022-11-10T21:14:33 | 267,803,075 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,671 | r | linear_hypotheses.R | library(data.table)
library(dplyr)
library(tidyr)
library(car)
format_haps= function(hap){
variants= paste(hap$chr, hap$pos, hap$ref, hap$eff, sep =':')
ids= names(hap)[5:ncol(hap)]
hap= as.data.frame(t(hap[, 5:ncol(hap)]))
names(hap)= variants
hap$PREG_ID= ids
return(hap)
}
h1= fread(snakemake@input[[1]])
h2= fread(... |
6c41e8a488c002340396812276d448ee0628cd95 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googleadmindirectoryv1.auto/man/CalendarResource.Rd | 71324ce4ce9e2354fe279bca9f3b2f97b2c4bc22 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,119 | rd | CalendarResource.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/admin_objects.R
\name{CalendarResource}
\alias{CalendarResource}
\title{CalendarResource Object}
\usage{
CalendarResource(etags = NULL, resourceDescription = NULL,
resourceEmail = NULL, resourceId = NULL, resourceName = NULL,
resourceType... |
2086c4bc68540fd781ad9352118bf391a1217955 | 251ccd25d1fa210198b8c68f465b086d3cf76797 | /plot6.R | e3d3fc282dfbe131bdc9e46fc9f0de6a85bd33c8 | [] | no_license | Joe-Lindenmayer/EDA-Week-4-Project | 0abfbf79f6b72ebf76a61f6cc56a432f68cbc56f | 27ba350e61e42e203b7f37fa06f85b919f0bbe05 | refs/heads/main | 2023-01-28T04:19:01.372896 | 2020-12-11T15:06:41 | 2020-12-11T15:06:41 | 320,350,101 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,266 | r | plot6.R | library(ggplot2)
archiveFile <- "exdata_data_NEI_data.zip"
if(!file.exists(archiveFile)) {
archiveURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(url=archiveURL,destfile=archiveFile)
}
if(!(file.exists("summarySCC_PM25.rds") && file.exists("Source_Classification_Cod... |
d6f1b8d4271691f68e40686fbf9014b914cdf5cd | 28bef7897d90393b7c0c0f50cab0ecfa717b2627 | /schedule_and_results.R | fc2a01af535f41a19f0d3add4342e80d0bdd0ee2 | [
"CC0-1.0"
] | permissive | 200256845/psy6009 | 9838a939a2df1774b4d4a66fe92b6bcfeb362b6c | c2d4722e03bc7bdabd9afc57e5c6c8d6a203482d | refs/heads/master | 2023-07-27T04:38:17.563872 | 2021-09-13T14:49:59 | 2021-09-13T14:49:59 | 403,243,211 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,159 | r | schedule_and_results.R | # The NBA schedule and results, of basketball-reference.com, are scrapped below
# for the 2016-17, 2017-18, 2018-19, 2019-20 and 2020-21 seasons
# Packages
library(rvest)
library(xml2)
library(data.table)
library(here)
library(dplyr)
library(lubridate)
library(XML)
library(stringr)
library(tidyverse)
# Note:
# The NB... |
94351caa7664a7130e2b1b3f5c358995827209a0 | 3215b0e4972171cd74c178d9492b8e8c3977c0fc | /MCMC_Diabetes_Drug_Effect.R | cc81cee7c1cfb0a68ccd491a7f12234736a1a71c | [] | no_license | YidiJiang/Applied-Bayesian-Methods | 585bfe4553ff118260f32d8d152ce3d1addb2ed4 | 99c178917cd555b1fcce8f540143df3da64dbd32 | refs/heads/master | 2020-06-26T03:59:57.626517 | 2019-07-29T20:35:31 | 2019-07-29T20:35:31 | 199,522,326 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,873 | r | MCMC_Diabetes_Drug_Effect.R | # Fit models to the data with a Bayesian analysis use Openbugs
# Fit model 1: Yi is the difference between two analysis (meta analysis with i=12), delta is a normal
# distribution with mean= theta (true mean: difference between treatment group and control group), there is
# only one true mean for 12 studies.
li... |
cef26b2469c4f8545cf2a44f02557d0847aa9bfc | 777ab372f7f9a7741256a6e562cf9e450a8a798f | /R_Scripts/piclapply_run_sim.R | fde3936e13c75a8123ce8fcac72a57af655b984c | [] | no_license | dan410/SimStudy_weighted_cov_est | cb2d166e9f60c5c63e795ef582799e9f9fb60caf | 8d37e48531adc9f1fe75c19548c32c42f7066113 | refs/heads/master | 2021-01-18T21:21:00.868125 | 2015-08-30T19:49:09 | 2015-08-30T19:49:09 | 22,741,502 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,352 | r | piclapply_run_sim.R | setwd('~/Dissertation_projects/SimStudy_weighted_cov_est')
##### MLE using piclapply #####
require(picRutils)
obj.fun <- function(dat, weight){
# assign weights to each point based on the intensity values
for( j in 1:length(unique(dat$ID))){
dat$wt[dat$ID == j] <- (1/intensity[j])^(weight)
}
#######... |
5de9ea7c2a06b0d2f87f9e8a0723789234b3aeca | 89e5a4b76e9899aa767da1f1df372db7d4aa7ff2 | /analiza/analiza.r | 58f3d750a5c65ecb522d70ec30cffbd0bec2a20c | [
"MIT"
] | permissive | ZilavecM14/APPR-2015-16 | 940f7ef1547aa13e9398d5372f3fe13473a465ef | 292e9daecdb74eaa88dfae808928b02198ceb0d7 | refs/heads/master | 2020-12-01T13:06:16.233841 | 2016-03-31T09:35:11 | 2016-03-31T09:35:11 | 45,672,237 | 0 | 1 | null | 2015-11-06T09:25:21 | 2015-11-06T09:25:21 | null | UTF-8 | R | false | false | 3,231 | r | analiza.r | # 4. faza: Analiza podatkov
novo <- skupaj %>% filter(vrsta_kratka == "Skupaj")
#Izriše graf na katerem so napovedi do leta 2022 za vse štipendije skupaj
analiza1<-ggplot(data=novo, aes(x=leto, y=stevilo))+ xlim(2008, 2022) +
geom_line(size=0.5)+
geom_point(size=3, fill="black")+
ggtitle("Napoved števila štipen... |
b0f5d4d8588ece6f9712f528276b1794c4b0ee18 | 7578a071a321f9c7d30842bc6d142dae824d3b9c | /cachematrix.R | 01ce3a241c07859122a9a305a42899380f0cf9dd | [] | no_license | teconomix/ProgrammingAssignment2 | 50b6dfa28627a29d8f02eebb832f243aebde2c94 | a0fc8fd70045078230d7fcd36514fb5e3ff831ff | refs/heads/master | 2021-01-17T06:31:45.523773 | 2014-10-24T15:08:21 | 2014-10-24T15:08:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,172 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix is a function that stores the inverse of a matrix and
## returns a list of elements that contain the original matrix,
## the inverse matrix and functions to get and set those
makeCacheMatrix <- function(x = matrix()... |
43aa5baeaf8fc66e5ad2100a0a2802826de7f24f | d97e2169ce9cd893920a54cffa3e754d1e309e6f | /man/getap.Rd | 9244a52b942606b866e43c458b6ea202781ac6e1 | [] | no_license | bpollner/aquap2 | 5ccef0ba4423413e56df77a1d2d83967bffd1d04 | 7f7e2cf84056aad4c8a66f55d099b7bdaa42c0be | refs/heads/master | 2021-07-22T15:07:22.912086 | 2021-05-27T12:50:22 | 2021-05-27T12:50:22 | 30,932,899 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,780 | rd | getap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prep_metadata.r
\name{getap}
\alias{getap}
\title{Get Analysis Procedure}
\usage{
getap(fn = "def", ...)
}
\arguments{
\item{fn}{Character length one. The filename of the analysis procedure file
to load. If left at 'def', the default filenam... |
d5663103afb36f0834b34c66680b23a29c57bd7f | a40c81bdda0a632276c6a157fb6f2b83b051c300 | /R/resp.patterns.R | ca7d79e822f54cbb0bd5d9cb8a6b67c8780c0809 | [] | no_license | cran/mRm | e6d5b2cdc18095d71356cdbe4b8de6ee3dd3bc5f | ccfce449042ef17167b2e626f16121ac3a4d52f8 | refs/heads/master | 2020-05-30T07:35:20.249582 | 2016-12-27T13:15:45 | 2016-12-27T13:15:45 | 17,697,237 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,305 | r | resp.patterns.R | # Function: Determines different response patterns in a sample.
# Copyright (C) 2011 David Preinerstorfer
# david.preinerstorfer@univie.ac.at
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Soft... |
6bb50a10aef5d00c39076fc3c1c6090ad0b0e98e | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /cran/paws.migration/man/sms_list_apps.Rd | 4d1f39602ac76516dbe8833f6ab097df521d3732 | [
"Apache-2.0"
] | permissive | johnnytommy/paws | 019b410ad8d4218199eb7349eb1844864bd45119 | a371a5f2207b534cf60735e693c809bd33ce3ccf | refs/heads/master | 2020-09-14T23:09:23.848860 | 2020-04-06T21:49:17 | 2020-04-06T21:49:17 | 223,286,996 | 1 | 0 | NOASSERTION | 2019-11-22T00:29:10 | 2019-11-21T23:56:19 | null | UTF-8 | R | false | true | 761 | rd | sms_list_apps.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sms_operations.R
\name{sms_list_apps}
\alias{sms_list_apps}
\title{Returns a list of summaries for all applications}
\usage{
sms_list_apps(appIds, nextToken, maxResults)
}
\arguments{
\item{appIds}{}
\item{nextToken}{The token for the next s... |
e110000ebd3200723c428c3f25568029a8717efa | 5ad1da3166c1239e68ab5497fd1cda807e0847c1 | /20_process_data/src/downsizeForNAs.R | f08e21549e48e8f1c6c13127a50a5f4aecbdb717 | [] | no_license | srcorsi-USGS/BeachBMPs | 20f46f5416b68caaee14497b939230012c66ef8c | 1e9fc132f1c7526c044939393f6df5138c2412cc | refs/heads/master | 2021-06-08T22:04:01.996276 | 2021-01-22T22:39:56 | 2021-01-22T22:39:56 | 112,625,301 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 811 | r | downsizeForNAs.R | df63rd <- make("df63rd")
response <- "Ecoli"
df <- df63rd[which(!is.na(df63rd[,response])),]
dfMaxRows <- df[,colSums(is.na(df)) <= nrow(df)*0.3] #Remove columns with more than 20% NAs
dfMaxRows <- na.omit(dfMaxRows)
plot(dfMaxRows$pdate,dfMaxRows$Ecoli)
plot(df$pdate,df$Ecoli)
IVcount <- apply(df,MARGIN = 1,funct... |
31b4bfa05764a10c80b539c1005c2a83ab99c0b5 | 9477c6d638c2918ca4b03eefe0cbe067b790ceaa | /R/find_linear_peptides.R | a2a002a6cb31ac3e42e831fdca6fcd09d03b9fa0 | [] | no_license | cran/RHybridFinder | bbbfddab71db4890cd3d6c5ebf463731e6803542 | 3a89236619fc5f249a816ae80fd4ea35daf251df | refs/heads/master | 2023-07-07T14:29:51.392978 | 2021-08-17T15:30:24 | 2021-08-17T15:30:24 | 379,599,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 966 | r | find_linear_peptides.R | #' @title find_linear_peptides
#' @description this is a non-exportable function, the goal of this function is
#' to check for linear peptides among the high-ALC unassigned spectra from the
#' denovo results.
#' @param input_for_HF the prepare_input_for_HF output
#' @param proteome_db proteome database
#' @return list... |
08c72e900e420b38b06a27d211eb75cb10208918 | ed48b04e4ecf2f68aa301987c029e7c11c65540c | /R/mymlnorm.R | 79293c5a20286b9e6c952731d4a0b1f427ee7f9c | [
"MIT"
] | permissive | RILEYBYNUM/MATH4753BYNUM | 2296e1f9fe6a96ec75e9fd51c7528f7e1b8d1b70 | e7c969c5741279b1bb6b659674f0083e734a2b9d | refs/heads/master | 2023-04-10T03:11:06.922694 | 2021-04-22T21:33:55 | 2021-04-22T21:33:55 | 334,220,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,498 | r | mymlnorm.R | #' @title mymlnorm
#'
#' @param x data
#' @param mu mean
#' @param sig standard deviation
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
mymlnorm=function(x,mu,sig,...){ #x sample vector
nmu=length(mu) # number of values in mu
nsig=length(sig)
n=length(x) # sample size
zz=c() ## initialize a new vec... |
67ee342ee00d452ebfa65dde659d40314ce32979 | a226f4b4cf54dd0e8164a727d24dca99e79e1354 | /R/stringsAsNumeric.R | 5b17869f0c76dca800ebd9657303fa7888618ccc | [] | no_license | beckerbenj/eatGADS | 5ef0bdc3ce52b1895aaaf40349cbac4adcaa293a | e16b423bd085f703f5a548c5252da61703bfc9bb | refs/heads/master | 2023-09-04T07:06:12.720324 | 2023-08-25T11:08:48 | 2023-08-25T11:08:48 | 150,725,511 | 0 | 1 | null | 2023-09-12T06:44:54 | 2018-09-28T10:41:21 | R | UTF-8 | R | false | false | 1,498 | r | stringsAsNumeric.R |
#### As numeric
#############################################################################
#' Transform string to numeric.
#'
#' Transform a string variable within a \code{GADSdat} or \code{all_GADSdat} object to a numeric variable.
#'
#' Applied to a \code{GADSdat} or \code{all_GADSdat} object, this functio... |
f12ab4ea690dc990b3693fb7542ee46943084c36 | 6923f79f1eaaba0ab28b25337ba6cb56be97d32d | /Introduction_to_Probability_Statistics_Using_R/code/17-appendix.R | 9a7726ca8914f92106a55cb684e9ddbaa7401194 | [] | no_license | burakbayramli/books | 9fe7ba0cabf06e113eb125d62fe16d4946f4a4f0 | 5e9a0e03aa7ddf5e5ddf89943ccc68d94b539e95 | refs/heads/master | 2023-08-17T05:31:08.885134 | 2023-08-14T10:05:37 | 2023-08-14T10:05:37 | 72,460,321 | 223 | 174 | null | 2022-10-24T12:15:06 | 2016-10-31T17:24:00 | Jupyter Notebook | UTF-8 | R | false | false | 1,462 | r | 17-appendix.R |
sessionInfo()
x <- c(3, 5, 9)
y <- c(3, "5", TRUE)
matrix(letters[1:6], nrow = 2, ncol = 3)
matrix(letters[1:6], nrow = 2, ncol = 3, byrow = TRUE)
matrix(c(1,"2",NA, FALSE), nrow = 2, ncol = 3)
A <- matrix(1:6, 2, 3)
B <- matrix(2:7, 2, 3)
A + B
A * B
try(A * B) # an error
A %*% t(B) # this is alright
... |
c90396a3067167ac58f64a282d1ea52809c29fc8 | e833729350807bebe936487cbe037f1d29d7fea0 | /19_SQL vs tidyverse/chinook_12_tidyverse_recursivitate.R | c6dc965d3c23eb4342671595101547583ff4018a | [] | no_license | catalinaelenaleonte/Baze-de-date-I | 1993ff47429f0c9434647e373cd2ed0ca039bbfd | 707b82c1358b27daf1321ea36e676d76f6aa8da5 | refs/heads/master | 2020-08-30T22:02:54.768911 | 2019-10-14T12:53:22 | 2019-10-14T12:53:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,808 | r | chinook_12_tidyverse_recursivitate.R | # # -- Interogari tidyverse vs SQL - BD Chinook - IE si SPE:
# # --
# # -- 12: (Pseudo) Recursivitate
# # --
# # -- ultima actualizare: 2019-05-10
# #
library(tidyverse)
library(lubridate)
setwd('/Users/marinfotache/Google Drive/Baze de date 2019/Studii de caz/chinook')
load("chinook.RData")
# #
# #
# # -- ... |
caf93070544067de30d9c18e7dc26d8d8d761648 | 92d0ec74ce59f8d98e858d02598cced7b0d30c54 | /data analysis/simulation_code/Figure5/plot_Figure5.R | 3a59753433d96422f0780c2336ef6f6737c7fca1 | [] | no_license | Miaoyanwang/ordinal_tensor | 1a8bd71f227294c16e12c164af12edaa134f3067 | ba90e6df7ac5865c755373c84370042d4f1d528d | refs/heads/master | 2023-04-22T07:24:17.635342 | 2021-05-10T02:35:09 | 2021-05-10T02:35:09 | 209,680,209 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 911 | r | plot_Figure5.R | ### Figure 5: MSE vs. number of ordinal levels; half observations ##
library(ggplot2)
d=rep(20,3)
rlist=c(3,5,8)
Klist=2:7
################ plot ############
load("Figure5.RData")
Output$rank=as.factor(Output$rank)
levels(Output$rank)=c("r=3","r=5","r=8")
p=ggplot(data=Output,aes(x=K,y=MSE))+geom_line(aes(color=rank),... |
d3b0c048872d98f226821dc530c5f6b71b8197ff | 9d3350a99175dd0a11846549330297ccef72aeae | /R/create_binned_data.R | 76e553886fe878c3103576b057e195a60a1c0870 | [] | no_license | xingzhis/NeuroDecodeR | 9cb11ff0f7c7eab76239379e4cb18372a402de9d | 7fb38d4e28a1a1f2cd0f1ea23e72f3258979152c | refs/heads/master | 2022-12-23T04:01:11.780678 | 2020-10-06T02:30:01 | 2020-10-06T02:30:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,135 | r | create_binned_data.R | #' Convert data from raster format to binned format
#'
#' This function takes the name of a directory that contains files in raster
#' format and averages the data within a specified bin width at specified
#' sampling interval increments to create data in binned format used for
#' decoding.
#'
#'
#' @param raster_dir_n... |
461e9fa36b6a57be5a76f0a795f848cf79961f64 | 01c34cd1967cc13fc4e24d9dc030673977d0473e | /Untitled.R | 9701d8c5404c9540d030c5bb0c6e5d68378e0b5a | [] | no_license | wupi/course-explanatory-analysis | e1bf6b242f1e15a0901ce2061e8471785893a359 | 2a834b5a97a6e61a83e29028e8c8c3f24f332def | refs/heads/master | 2021-01-17T15:59:20.765238 | 2016-06-13T04:54:57 | 2016-06-13T04:54:57 | 59,265,300 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 398 | r | Untitled.R | #exploratory analysis - final project
#plot 1
scc <- readRDS("./course-explanatory-analysis/final_project/Source_Classification_Code.rds")
nei <- readRDS("./course-explanatory-analysis/final_project/summarySCC_PM25.rds")
plot1 <- aggregate(Emissions ~ year, nei, sum)
with(plot1, plot(year, Emissions, type = 'l'))
de... |
f305144fbc4e58425710f2d5a9f4f5c512fa8926 | 32f2c862c462c6dcb41fa83e0eb7a9ab8a054e44 | /scripts/00b_Hwavi_Visuals.R | 670ecabb861a5f88f2354afe25cad2c44eab0605 | [] | no_license | JosueVega/Hwaviness | 5e36bfb26ee524d2eb4bc6bf9f9c95d0a3b07a58 | 5074a768e90e414791c6f2a1df7861aa9866cb2b | refs/heads/master | 2021-09-07T19:47:10.122962 | 2018-02-28T03:49:38 | 2018-02-28T03:49:38 | 112,789,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 57,979 | r | 00b_Hwavi_Visuals.R | #Josue Vega
# Separate visual projects for HWaviness
#################################
# Table 1: ANOVA table for hyphal waviness
rm(list=ls())
library(lme4)
library(readr)
setwd("~/../Desktop/B. cinera/Hwaviness")
mydata <- read.csv("../BR_JV_ManualHyphalDat_032817(edit).csv")
# Mixed Model 2-Way ANOVA -> Parametr... |
727494f463adfa777241d84aa7563df9f04bfb18 | 2099a2b0f63f250e09f7cd7350ca45d212e2d364 | /ACL-Dataset/Summary_rnd/P14-2114.xhtml.A.R | 08d95a8c3001c6b1c8f9d28c50c38a97d773c98e | [] | no_license | Angela7126/SLNSumEval | 3548301645264f9656b67dc807aec93b636778ef | b9e7157a735555861d2baf6c182e807e732a9dd6 | refs/heads/master | 2023-04-20T06:41:01.728968 | 2021-05-12T03:40:11 | 2021-05-12T03:40:11 | 366,429,744 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | r | P14-2114.xhtml.A.R | <html>
<head>
<meta name="TextLength" content="SENT_NUM:4, WORD_NUM:115">
</head>
<body bgcolor="white">
<a href="#0" id="0">Still, we observe that compared to the baseline approach, the performance of our proposed framework evaluated on the 4-tuple achieves nearly 17% improvement on precision.</a>
<a href="#1" id="1">... |
82d4fd852c6200636ca18e9efeb0e984e66c9417 | 094a4be67a12253c075fabff45e0148bed39246b | /6sense.r | a18aea6e12814eda60c5082c5268d9e6fb048f54 | [] | no_license | bamhand/gems | 6abafbc0eb9ee01cbd5122530a44a232cda4a70b | 016cc18b9ca71d4d59187de1e3543534d319e443 | refs/heads/master | 2021-01-20T19:39:56.566314 | 2017-07-28T15:17:52 | 2017-07-28T15:17:52 | 62,260,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,230 | r | 6sense.r | rm(list=ls())
library(dplyr)
library(magrittr)
library(tidyr)
library(doParallel)
train<-read.csv(file="c:/Users/f367573/Desktop/R/6sense/training.tsv", sep="")
train$id<-train[,1]
train$date<-train[,2]
train$action<-train[,3]
train<-train[,4:6]
train<-group_by(train, id, action) %>% summarize(count = n()... |
a98bc7367f4d02b46bc753cec6c839ae5f9204bd | 62b4a877ce27a7a7a3b00ec565d5a5b86a1f8c0c | /asymmetry.R | 1c1d72ba147c343374c73fbcabc913410711d548 | [
"MIT"
] | permissive | sdwfrost/exploratory-phylodynamics | 4d18bdcc3dbadbcc53d4ca4c2ff7966d60e708c9 | 2720e48759406d0a427ac7e0fec9a2443b51fbe0 | refs/heads/master | 2021-01-24T16:09:30.167642 | 2018-08-15T14:17:51 | 2018-08-15T14:17:51 | 40,710,041 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,234 | r | asymmetry.R | library(ape)
library(magrittr)
library(treeImbalance)
lasv.chronos <- read.tree("LASV_chronos.nwk")
lasv.tipdates <- strsplit(lasv.chronos$tip.label,"-",fixed=TRUE) %>% lapply(.,tail,1) %>% unlist %>% as.double
lasv.cherries.obs <- ct(lasv.chronos)
lasv.sackins.obs <- snt(lasv.chronos)
treelist <- list()
nsims <- 10... |
2e2de578f904d2f59f65c26b9b010b384eea412c | 8eb4b0e89610dc7fdcc68463bdc57e5adbf10849 | /R/xclip.R | a236994ad82d29cd6149b1c2d5dc37da0b74e524 | [] | no_license | cmcouto-silva/snpsel | f0b1fa1675fb10e15329cae874441f241f3a5e15 | e3898308f5b0b2ae071cefdc111f5334a0319cf7 | refs/heads/master | 2023-05-07T22:19:08.444440 | 2021-06-06T15:55:12 | 2021-06-06T15:55:12 | 127,948,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 457 | r | xclip.R | #' @title Remove additional spaces/paragraphs from clipboard text
#' @description This function removes any additional whitespace or paragraphs from copied text (clipboard)
#' in a single block of text, adding it to the clipboard OS
#'
#' @return Single block text in the clipboard.
#' @export
#'
#' @author Cainã Max Co... |
3655efc855f403f16274adc75e3842d69c40b31d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/memery/examples/meme.Rd.R | 3808c1b267b1b51e8baaea686c4fdf51ec2f8f23 | [] | 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 | 2,370 | r | meme.Rd.R | library(memery)
### Name: meme
### Title: Generate a meme
### Aliases: meme meme meme_gif
### ** Examples
# Prepare data and make a graph
library(ggplot2)
x <- seq(0, 2*pi, length.out = 50)
panels <- rep(c("Plot A", "Plot B"), each = 50)
d <- data.frame(x = x, y = sin(x), grp = panels)
txt <- c("Philosoraptor's plo... |
f26f795f561d515cc233d124b865bde20586fe64 | ea651d3961b61d5692e2e87b085ec86905ccf21d | /plot3.R | 810472b955d6f3fd5964791d5c4a34c72dbf6748 | [] | no_license | SMinkes/ExDataPA2 | bd6b1cd346d8d55e4fa902d431d707fdc6f6fe40 | 1c92f3a898e6b2cd9fbd281f89e4d6b6bbabfb7c | refs/heads/master | 2021-01-21T07:45:44.136918 | 2015-02-22T15:07:04 | 2015-02-22T15:07:04 | 31,166,368 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 834 | r | plot3.R | ## install required packages
install.packages("plyr")
library(plyr)
install.packages("dplyr")
library(dplyr)
install.packages("ggplot2")
library(ggplot2)
## Loading the data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
## Create the subset for Baltimore
Baltimore <- subset(NE... |
6c8383e8513d7461cffdccbb3272c63035b0871c | 98b08ae1f7356558e8699ca8ee1d08fb1bec8dc5 | /man-roxygen/all_form_types.R | 363f59f13063d850e6a76ca931275c2d91e1a91b | [
"MIT"
] | permissive | MrJoan/hubspot | edf4695a98f1d0b30fcf29a6abb3ddc1b00f502e | c816d3f7a709efdddcafce830c9b1ca0a4414900 | refs/heads/master | 2022-04-05T16:59:01.240250 | 2020-02-25T09:58:58 | 2020-02-25T09:58:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 156 | r | all_form_types.R | #' @param all_form_types By default non-marketing forms are filtered out of this endpoint. To request all forms, use this paramter with the value of TRUE.
|
661342acf0486463be0149e445f462262731228e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/beautier/examples/get_site_model_names.Rd.R | a811e2f5d96877f354dcbb5aeb646f4297f4cd85 | [] | 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 | 340 | r | get_site_model_names.Rd.R | library(beautier)
### Name: get_site_model_names
### Title: Get the site models' names
### Aliases: get_site_model_names
### ** Examples
# Check all names
names <- get_site_model_names()
testit::assert("JC69" %in% names)
testit::assert("HKY" %in% names)
testit::assert("TN93" %in% names)
testit::assert("... |
697aecbdb14ae10727b380af19a6fe4a5427d54b | 29585dff702209dd446c0ab52ceea046c58e384e | /tsDyn/R/TVECM.sim.R | 2af2102367da7d42610e421f9c92d0ded35163a5 | [] | 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 | 18,217 | r | TVECM.sim.R |
#' @export
#' @rdname TVECM.sim
VECM.sim <- function(data,B,VECMobject, beta, n=200, lag=1,
type=c("simul","boot", "check"),
include = c("const", "trend","none", "both"),
starting=NULL, innov=rmnorm(n, varcov=varcov), varcov=diag(1,k),
... |
d4e36824e2f13bd113878598f064b8c06fb732ee | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/anominate/examples/densplot.anominate.Rd.R | ff41cec3abef21b0fdf839a2a92d6f72f1b1ccfa | [] | 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 | 303 | r | densplot.anominate.Rd.R | library(anominate)
### Name: densplot.anominate
### Title: alpha-NOMINATE Density Plot Function
### Aliases: densplot.anominate
### Keywords: ideal point estimation, NOMINATE, Bayesian latent variable
### models
### ** Examples
data(sen111)
data(sen111_anom)
densplot.anominate(sen111_anom)
|
0f61e930614fae525e5398bdf2ec1150e0a5b25d | c282d03b8dfdf943cf80c03b5ad2d50fb5a06509 | /overview.R | c55f249282ee2eea37f2e64f5ea4b30e6f8c0477 | [] | no_license | sprocketsullivan/attention_confidence | 14131ec10bae6e1a7a5753ada1435d829a27c543 | c93174b6a5efa63a835062c9116587bd91fdce51 | refs/heads/master | 2021-05-02T01:03:58.993851 | 2017-04-04T14:24:08 | 2017-04-04T14:24:08 | 78,653,698 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 124 | r | overview.R | #description of analysis files and execution
#read_in_data.R
#reads in data, cleans data, an produces the following plots:
# |
3c89cad1cd404d8e1d99e337976195a4aa35b909 | 9964189e463815a446c2659f57b9c726408f8313 | /script/week8_assignment.R | 90b172cdf74843b77c3042808b3f4e76b8de8ff0 | [] | no_license | fish497-2018/Cohen-reef_fish | 3845ee51dcb77ba875e02c5dd4a184d4a7329fd1 | 0aed17913596b149d419ec1552d4897469832d16 | refs/heads/master | 2020-03-17T11:53:41.401990 | 2018-05-31T17:24:17 | 2018-05-31T17:24:17 | 133,567,777 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,705 | r | week8_assignment.R | #load the packages needed
library(dplyr)
library(ggplot2)
library(tidyr)
#read in the data
reef_fish <- read.csv('data/RLSreeffishdataset.csv')
View(reef_fish)
#start looking for a trend in data
ggplot(reef_fish, aes(Depth)) +
geom_histogram()
ggplot(reef_fish, aes(Diver, Family)) +
geom_point()
ggplot(reef_fish,... |
2bb13da0214fa1d7c1afcc4c8eaee8c1df4d7143 | 81b443dbe06b02c7ce9c36878a34d79c5863db7b | /chapitre-5.R | 925dd2ae0c859e0214ee98b71edfeb1b9d7520d4 | [] | no_license | aejb22122/Analyse-de-donnees-these-paris-saclay | 7367be49a56938a9fa5a2f3f3113744aa3a45d01 | 53e278ff71e5de0050cce269606162e65ac760a2 | refs/heads/master | 2020-12-02T16:28:50.690770 | 2020-01-14T22:25:23 | 2020-01-14T22:25:23 | 96,557,982 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,532 | r | chapitre-5.R | # ---- Thèse de doctorat Annick Eudes JEAN-BAPTISTE ----
# Codes de réplication des calculs du Chapitre # 5 - Contexte empirique
# ---- Préliminaires ----
# Adding the packages used in this analysis
install.packages("xlsx") # Lire les fichers excel
install.packages("ggplot2") # Installer le... |
307ceb376a51917c0c5ffe8cf408602c80ffef7a | b59cc783d2da2f32737432c1b13cf72c5802f067 | /man/inla.mesh.2d.Rd | 376b8036995478294c354cf3c93f5281e04b2281 | [] | no_license | jdsimkin04/shinyinla | 9a16007b375975a3f96b6ca29a1284aa6cafb180 | e58da27a2a090557058b2a5ee63717b116216bf7 | refs/heads/master | 2023-06-05T08:34:34.423593 | 2021-06-24T00:27:04 | 2021-06-24T00:27:04 | 330,322,338 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 3,414 | rd | inla.mesh.2d.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mesh.R
\name{inla.mesh.2d}
\alias{inla.mesh.2d}
\title{High-quality triangulations}
\usage{
inla.mesh.2d(
loc = NULL,
loc.domain = NULL,
offset = NULL,
n = NULL,
boundary = NULL,
interior = NULL,
max.edge = NULL,
min.angle = N... |
eecb660fb5e3741af481c9b68d2f9963bb384b6f | fff9ee52053ff5acd4d358add0793bf4ed6b2aba | /Exract_Names_From_Text_String_v2.R | 8f7d7f0c01780f4e9704c82e02bc4c26e5c2de91 | [] | no_license | jfedgerton/Cleaning_CHAMP_Speakers | a6baabb378521c0cdd0d6e930b977cb2a36ff480 | b57c3e376a0c49d2fff361027b5f20adaa90ba7b | refs/heads/master | 2020-04-21T13:09:12.997216 | 2019-02-07T14:59:05 | 2019-02-07T14:59:05 | 169,588,961 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 22,133 | r | Exract_Names_From_Text_String_v2.R | ## Clear working directory
rm(list = ls())
## Load R libraries
library('tidyverse')
library('foreach')
library('doParallel')
## Load R data
load("CHAMP-Net/Data/Show-Year CSVs/formatted_data/2000 to 2010/Updated/Text_Search.Rda")
str(names)
names <- names %>%
mutate(.,
Na... |
2ba8d5934e3747b61eac2725e089ddacbf67e2f0 | 0e279497c0cd75ff64aede481a49b3389104a403 | /R/stoch_ord2.R | 7acd7fc172208da871c6ee202df57b0e61a38ff9 | [
"BSD-2-Clause"
] | permissive | spertus/permuter | b1391c45cc33f7f1bf93cec0373ec631f53c6bc0 | f2f03c65ff553bcda45efe07ba269615d2fa0249 | refs/heads/master | 2021-06-22T00:44:03.284030 | 2017-08-28T20:40:59 | 2017-08-28T20:40:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,707 | r | stoch_ord2.R | #' Description about the function
#'
#' @param x A number.
#' @param y A number.
#' @param z
#' @param alt
#' @param B
#' @param cat
#' @param rep
#' @param seed
#' @return something
stoch.ord2 <- function(y, x, z = NULL, alt = c(-1, 1), B = 1000, cat = 0, rep = FALSE,
seed = 101) {
K <- length(unique(x)... |
8d035b81c90c7c431691c86cb9fe92143632180c | d5597a9ffb8565d723a2068c069228d28d39181c | /Plot3.R | ac8df610ce0e8202ef2949802ac59ca4bf7c67f2 | [] | no_license | darthvelez/ExData_Plotting1 | 11a485c0cf1c6645425cb8d8fb4d52c9ed472ca9 | 607ba31c37ccc44fe7b1c7e6e2a8436c9bdace9b | refs/heads/master | 2020-12-24T14:18:09.984156 | 2016-08-28T12:50:46 | 2016-08-28T12:50:46 | 40,596,442 | 0 | 0 | null | 2015-08-12T11:12:12 | 2015-08-12T11:12:12 | null | UTF-8 | R | false | false | 1,174 | r | Plot3.R | #Download File#
if(!file.exists("exdata-data-household_power_consumption.zip")) {
download.file("http://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", "household_power_consumption.zip")
unzip("household_power_consumption.zip")
}
#Subsetting the Data#
hpc <- read.table("household_p... |
050c521dcb702be0708cb603af9d2b248adae156 | d3d58e46ed61ee6d4bc61fa4c004100b7b1fd708 | /R/query-db.R | 4dc1d12069b166168b29f44fdf7f354aacc59572 | [
"MIT"
] | permissive | poissonconsulting/subfoldr2 | b6abd83463710d9ce6ad02e3b77668a71db5c1a8 | 6766176b1ce575a87e59da849d51515c96818777 | refs/heads/main | 2023-07-19T19:08:52.225956 | 2023-07-07T01:01:00 | 2023-07-07T01:01:00 | 162,794,273 | 1 | 0 | NOASSERTION | 2023-07-07T01:01:02 | 2018-12-22T08:20:25 | R | UTF-8 | R | false | false | 645 | r | query-db.R | #' Query Existing Database
#'
#' Really just a wrapper on DBI::dbGetQuery().
#'
#' @inheritParams sbf_save_object
#' @inheritParams sbf_open_db
#' @param sql A string of the SQL statement to execute.
#' @return A scalar numeric of the number of rows affected by the statement.
#' @family database functions
#' @export
sb... |
d9f70b7039d89ae614e207ebb37c7e416e8b1566 | d283ddaddffd4938149732aee730db4d32ea4009 | /TMB-survival/Survival&Maxstat.R | 99fd2c6054aebb3050ec3e4268ba7fde7abc703c | [] | no_license | kkang97/TMB-melanoma | f0bef605c3462b596354def1ad70fa025cd29444 | 62c6a0d5d96f09a403fd89c8c1acfca1b7dd1bc6 | refs/heads/master | 2022-11-30T18:40:21.863026 | 2020-08-06T12:15:26 | 2020-08-06T12:15:26 | 281,552,858 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,475 | r | Survival&Maxstat.R | #install.packages('survival')
#source("https://bioconductor.org/biocLite.R")
#biocLite("qvalue")
setwd("C:\\Users")
options(stringsAsFactors=FALSE)
library(survival)
library(survminer)
outTab=data.frame()
picDir="picture"
dir.create(picDir)
library(survival)
library(qvalue)
rt=read.table("ExpTimeImm... |
38b9397b1a4a3364767db68f4d01b0f275b577fb | 5b1fda0d0baf10a436c0f63ceec471417c5fe417 | /R/Model.R | 925b1df5586e5c31d5a0c92f15d38006367b97cb | [] | no_license | DMKM1517/WebOfScience1 | 43fb592fe7ed355f164822fa401c555ef817fadc | def6a141b78acf0173bd8f2707a0f6b91ddb3277 | refs/heads/master | 2021-01-21T04:35:16.835218 | 2016-08-01T21:57:11 | 2016-08-01T21:57:11 | 53,050,005 | 1 | 0 | null | 2016-03-31T21:33:18 | 2016-03-03T12:52:36 | Mathematica | UTF-8 | R | false | false | 5,740 | r | Model.R | library(RMySQL)
library(dplyr)
library(tm)
library(plyr)
library(stringdist)
library(stringi)
library(stringr)
library(phonics)
################## FUNCTIONS ##################
phonetics<- function(author){
#Step1 - Strip accents from Authors
authors_noaccent <- stri_trans_general(author,"Latin-ASCII")
f... |
5a37073309de3ac3cc7fda1dc48697a38f1d1dd9 | 6708c52f2ce9f941e92d6d741dd9b4c9c06a3834 | /session2/02_vectors_and_lists.R | f34202feb3c96a9a1612cfba2a5182834366c90a | [] | no_license | zhiconghu/am10.mam2022 | a30cf78f6d263c25fe8128fd2ce06e9fca4806ed | 25c19ec4d2bed417cf782bc468d4bfb01e72f8b9 | refs/heads/main | 2023-08-27T12:47:08.929208 | 2021-11-10T13:36:50 | 2021-11-10T13:36:50 | 427,504,394 | 0 | 0 | null | 2021-11-12T21:47:04 | 2021-11-12T21:47:03 | null | UTF-8 | R | false | false | 2,381 | r | 02_vectors_and_lists.R | library(tidyverse)
library(gapminder)
# Vectors - one dimensional, one type
my_colours <- c("grey80", "tomato")
# subset and get the first element from this character vector
my_colours[1]
# [1] "grey80"
gapminder %>%
mutate(rwanda = ifelse(country == "Rwanda", TRUE, FALSE)) %>%
ggplot(aes(x = year, y = lifeExp, ... |
81093a963735701c2a5994a0578a78c5613a9535 | 73ef006799b04993516d7852a5bb7d177526d4d0 | /4-results-discussion/data-n-analyses/results-analysis/R/scripts/4-ordination-classification-fitted-vars.R | a23254e9096758080d2b9b24eecf99e14aad9568 | [] | no_license | sklayn/phd | 8c1bb1fb375b00c27b8cbe94da919ddc12bfd323 | 46438fd0dd33255828433dd5c2501563fc406d2b | refs/heads/master | 2021-08-16T07:32:52.391780 | 2021-05-08T10:54:06 | 2021-05-08T10:54:06 | 61,437,700 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,614 | r | 4-ordination-classification-fitted-vars.R | #### Ordintaion (nMDS) on the abundance data ####
# set seed in case we have to rerun the computation
set.seed(1)
mds.sand <- metaMDS(num.zoo.abnd.sand)
# basic summary of the MDS
mds.sand
# diagnostic plots for the MDS
pdf(file = file.path(figs.dir, "explor_diagnostic-plots-mds_sand.pdf"),
paper = "a4r",
w... |
436591647b0ad168f68d580e1fc42219c2d34234 | 80c6644d745190dd3702b5668ef5eae423a27859 | /funcion_hipsometria.R | 46acf3574f91f2c49ed5df1554d39072638358e9 | [] | no_license | fmanquehual/proyecto_agua_en_R | 356bfdf9c4156587a2ccfd3addaadfd634aed537 | e4a51c5192f174708a982b62a42626fff51078a1 | refs/heads/master | 2023-01-14T13:22:57.448036 | 2020-11-13T05:15:59 | 2020-11-13T05:15:59 | 300,746,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,063 | r | funcion_hipsometria.R | hipsometria <- function (x, absolute = TRUE, main = "", col = "blue", AddDen =T, colDen="red", ...)
{
require(raster)
require(rgdal)
z <- getValues(x)
zmin <- minValue(x)
zmax <- maxValue(x)
areaCN <- length(which(!is.na(z))) * xres(x) * yres(x) #Area of catchment
sfun <- plot.stepfun(ecdf(z))
... |
4f5d26569fdb99d7d088644f16a445990641f140 | f0ae1581d7b319f995c1caef0adfc2d14d1071e5 | /CpGisl.level.info.R | 2e5f87295b83cb1557a8e28f79e55f62f4a9e34a | [] | no_license | gluebeck/Scope-of-methylomic-drift-in-BE | 2391d7e43352f716164a08b5e8a565efa7bf9b7d | 07ac9e6ed587250575a2a40fe5c1b3423a33bcd4 | refs/heads/master | 2021-01-17T18:06:07.222666 | 2017-03-10T01:08:57 | 2017-03-10T01:08:57 | 70,959,079 | 1 | 3 | null | 2017-03-09T22:22:33 | 2016-10-15T01:39:20 | R | UTF-8 | R | false | false | 2,673 | r | CpGisl.level.info.R | CpGisl.level.info = function(set = ILS.hypo.drift5, isls = Islands.hypo.drift5,
cpgs = CpGs.hypo.drift5.isl, dat, full = T, prom = T) {
# input set: island names of interest
# input isls and cpgs: vectors of the same length with CpG names and associated island names
# input data: met... |
617907e4c639ea457c83661d84129794ff9ed666 | 948209c5a164e300d001bf5e5fb4d94e158c5759 | /SSI Modeling.R | 970cf5466b96f7150fae88cff2c28652dffc4fee | [
"MIT"
] | permissive | samirsafwan/Predicting-Balloon-Altitude-Using-ML | 64ee9a277713588e5d3191dfe23919ec2185a578 | ef0089c8e6ce6f6186470119b85c95b78945c83d | refs/heads/master | 2021-05-08T03:05:22.283364 | 2017-11-02T01:31:06 | 2017-11-02T01:31:06 | 108,226,273 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,220 | r | SSI Modeling.R | setwd("~/Downloads")
data=read.csv("ssi54-3.csv")
# GBM
library(gbm)
set.seed(1)
boost.mse=rep(0,19)
predicted.trajectory=vector()
for (i in 1:19) {
sample=data[1:(1396*i),]
test=data[(1396*i+1):(1396*(i+1)),]
boost.fit=gbm(altitude~., data=sample, distribution='gaussian', n.trees=2000, shrinkage=0.1, cv.folds=... |
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