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10d6affa081893d4841605c8a4a2e637169afe51 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dint/examples/c.date_xx.Rd.R | 1a1273971452f0d7b6002a6d64b5ae0ac03c40bc | [] | 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 | 230 | r | c.date_xx.Rd.R | library(dint)
### Name: c.date_xx
### Title: Concatenate date_xx Objects
### Aliases: c.date_xx
### ** Examples
c(date_yq(2000, 1:2), date_yq(2000, 3:3))
# raises an error
try(c(date_yq(2000, 1:2), date_ym(2000, 1:12)))
|
e2d52aaf9e58c1af1562513062bf6f56925e875c | 657cb8d31a7edde2ba866b43417fcff13168025c | /R/ga_data.R | aecb64be94e733330caad7c969ab5dc4d3d63f2d | [] | no_license | jdeboer/googleAnalyticsR | d026832e1d48787c5c0bc58489a2932cc8ddb5b2 | 8ca31878fff7fb3b0215acad810e137b609f7018 | refs/heads/master | 2023-01-20T04:53:50.946223 | 2020-11-24T19:41:38 | 2020-11-24T19:41:38 | 48,536,792 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,664 | r | ga_data.R | version_aw <- function(){
"v1alpha"
}
#' Google Analytics Data for GA4 (App+Web)
#'
#' Fetches Google Analytics from the Data API for Google Analytics 4 (Previously App+Web)
#'
#' @seealso \href{https://developers.google.com/analytics/devguides/reporting/data/v1}{Google Documentation}
#'
#' @details
#'
#' @inheritP... |
aeb8adc9422d7da0ca90f37e6d6b496115ce2cb5 | b4df4497594607163aae708f21abda95b05e9ce6 | /R/acqlogit/acqlogit_compustat_update.R | d5d1848c207a40f6cee3fd19af21945dfb9a7cd9 | [] | no_license | sdownin/compnet | 9ce7bad397954226229da8bfc66951f266a207c6 | c154c07af36f1745b11daad377caf8bdb699016e | refs/heads/master | 2021-06-27T20:32:37.671291 | 2020-12-14T04:17:31 | 2020-12-14T04:17:31 | 62,546,122 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,124 | r | acqlogit_compustat_update.R | ###
##
## Update Compustat Fundamentals Annual Data
## to subset columns for controls and add Market-to-Book ratio
##
###
#setwd("C:/Users/T430/Google Drive/PhD/Dissertation/competition networks/compnet2")
# .libPaths('C:/Users/T430/Documents/R/win-library/3.2')
library(parallel)
library(network, quietly = T)
librar... |
a2e510c7c6bc83c4a6af69f14ba78ca74fb2678c | 784ac8673ffdf4798187b1f4a1c5532bfe73c99d | /stackoverflow/41360278.R | 3faaac316fc297351deefed50d957c7f5ed164b1 | [] | no_license | serhatcevikel/R | be4e928ffb63ad881ddd618d3e3fa85f80855dd3 | 623f0b5757140d0f695e8542b31f86be1dde9f01 | refs/heads/master | 2020-05-22T01:33:29.291220 | 2018-03-19T22:18:18 | 2018-03-19T22:18:18 | 48,517,553 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,808 | r | 41360278.R | # R: replicate a row and update the date entry by one per row
# Answer to http://stackoverflow.com/questions/41360278/r-replicate-a-row-and-update-the-date-entry-by-one-per-row
# The input and its intended output show that I want to replicate the row of the input and update the date entry. How can I do this?
# Input... |
089be1f4054096d1762d066f4f7a5840e14819d5 | 25ec9519eeb158a777ed9865dfb57aab0809c60d | /tmp/LatticeKrig/man/LatticeKrig.Rd | 51e3ca7b6ace15ee6e7a674b4ce7faaca480ab37 | [] | no_license | NCAR/LatticeKrig | cccdcaba2d16c96b722de6a2e499e09f5c36ccf2 | 5caccca61f52b53d215d9375dedb8553e6ee75b7 | refs/heads/master | 2021-09-14T10:49:13.136451 | 2021-08-23T21:58:31 | 2021-08-23T21:58:31 | 61,819,138 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 20,414 | rd | LatticeKrig.Rd | % # LatticeKrig is a package for analysis of spatial data written for
% # the R software environment .
% # Copyright (C) 2016
% # University Corporation for Atmospheric Research (UCAR)
% # Contact: Douglas Nychka, nychka@ucar.edu,
% # National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307-3000
% #
% ... |
de0855a9653b73d25f1901979b496cbad24cfd31 | 43052fc5c751616120d35ee8aafb8dbd7b6dda3f | /ui.R | 45382a4af70fe2f9e24495604922fa62094668b0 | [] | no_license | benbray111/NGramWordPrediction | faa91849c6d8b06cbc389f037fa7e8552c1ff66e | e4fdab524f6a79aceade0c4e2e6b354b374fcd80 | refs/heads/master | 2021-06-26T11:52:29.093244 | 2014-12-14T21:10:52 | 2014-12-14T21:10:52 | 58,488,082 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,262 | r | ui.R | #version 1.1
library(shiny)
# ui.R
shinyUI(fluidPage(
titlePanel(
"Word Prediction"
),
sidebarLayout(
##################
sidebarPanel(
fluidRow(
plotOutput("WordCloudOutput")
),
fluidRow("Author:", a("Ben Bray", href="http://www.linkedi... |
f607edf52c6986a5db89d9bca1363668002be77a | 48131173b92726ec3993c8d0c966899b6483c2f6 | /w4/week4.R | 4e5b512d187ea603be3a534e11d69768f0f1209f | [] | no_license | jasonqiangguo/Quant3_Lab | 21045c5efa610535ac5d67ec60e4c683f70f827d | f432a2df1374f4284a9c6caf0950ed7f2f4b139d | refs/heads/master | 2020-04-10T22:50:53.676968 | 2019-12-28T11:28:15 | 2019-12-28T11:28:15 | 69,981,465 | 7 | 5 | null | null | null | null | UTF-8 | R | false | false | 9,134 | r | week4.R | #########################################################
## Durational Model
## Instructor: Jason Guo
## Quant III Lab 4
#########################################################
library(foreign)
library(survival)
install.packages("KMsurv")
library(KMsurv)
library(Zelig)
#Independent variables:
#durat: Duration ... |
ad3f76f25654e6269a4d8b0d64b80bfaab758fc2 | b934fa93e660667ec7e5193639a02137f29e746e | /ikde.Rcheck/00_pkg_src/ikde/R/stan.multiply.R | 8f248df475f205a0681a507d03ef396b34aa84e1 | [] | no_license | tkmckenzie/ikde-scripts | b5fe5ec86de11905a7bfd7c03f3640dea37ea106 | 989c2dbc416cd489788d5a6071282d1c109d8c3e | refs/heads/master | 2020-04-10T19:35:48.600541 | 2019-01-09T17:36:21 | 2019-01-09T17:36:21 | 161,240,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 901 | r | stan.multiply.R | #' Function to replicate multiplication in Stan
#'
#' @param x First term in product
#' @param y Second term in product
#'
#' @details
#' Accepts arguments x and y. If either is a singleton, returns the value of x*y (in R notation).
#' If both arguments are matrices or vectors, returns x%*%y (in R notation).
#'
#' ... |
450feea4c4f9492d6aa00e9d61fa34796b9f076a | bf987274d72fce30a71a6ae80e7a6d3505f98664 | /R/R-intro/r-intro-ate.R | 962399d9793af50b909fa0d3c0783abb6ccc522b | [
"Apache-2.0"
] | permissive | sherrytp/be_bc_f19 | cb49ab2d8cb469d1be6328c4256c52c72ded948d | 5377ccd2f1aec45bf79b123b360a4975ba9127a7 | refs/heads/master | 2021-06-21T23:56:04.250878 | 2021-03-29T08:44:11 | 2021-03-29T08:44:11 | 207,918,928 | 0 | 1 | null | 2019-09-11T22:39:35 | 2019-09-11T22:39:34 | null | UTF-8 | R | false | false | 1,202 | r | r-intro-ate.R | # author: @lrdegeest
# simulate data -----------------------------------------------------------
n = 100
treatment <- rep(0:1, each=n)
error <- rnorm(n, mean = 0, sd = 2)
gender <- rbinom(n, 1, 0.5)
y <- 2.0 + 6.0*treatment + 0.5*gender + error
df <- data.frame(y,treatment,gender)
df$treatment_string <- ifelse(df$trea... |
aa291e742d33d3022d81eb69f338398a65235458 | 3481ac56941c2f6853c84c1fca9500529e0f74cb | /alumnos/jtmancilla/german/Ejercicio2_eda.r | d3f13ff08ab7a00b2b02612adcd262babe3dc3c1 | [] | no_license | MarcosOMtz/itam-dm | 8029cceb64dbccb9240d52da2359f905fb0c7063 | cf2efc0b5c2b66cc82d2b93b26b6c665534b6b6e | refs/heads/master | 2020-06-12T19:45:59.158190 | 2015-03-11T05:27:36 | 2015-03-11T05:27:36 | 31,153,956 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 410 | r | Ejercicio2_eda.r | source("0-load.r")
source("2-eda.r")
library(corrgram)
german.data <- load()
summary(german.data)
str(german.data)
View(german.data)
# Visualización
eda1(german.data,4)
# tomando como variable objetivo good.loan
eda2(german.data,21,4)
# visualización de NA`s
na_german <- as.data.frame(abs(is.na(german.data... |
9ef38aebf73f255c2072dde93a42ced6b518e70c | 568f6f7f1f49d15bf27530a5775e4de149ef55a0 | /man/050-summary.FMmodel.Rd | 8122c91cae44029d33f7ef2683b1d2a7fa93740c | [] | no_license | cran/FactoRizationMachines | 3679358677009b7b4ff01061d58d4f0f00f3c055 | 5ec6946326619a2247a15d07186913074139e06b | refs/heads/master | 2020-06-13T12:09:10.026331 | 2017-10-18T19:34:55 | 2017-10-18T19:34:55 | 75,382,750 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,437 | rd | 050-summary.FMmodel.Rd | \name{summary.FMmodel}
\alias{summary.FMmodel}
\alias{print.FMmodel}
\title{
Summary and Print Method for FMmodel Objects
}
\description{
Function generating the summary of a FMmodel object.
}
\details{
The summary contains for instance:
- the number of training examples the model was b... |
0b04da6bd56f01c86f25cbe557dd31bfa6c77329 | 89bee9af99ec25372b20bca5325e3f06399a95a7 | /Documents/DiffusionAnalysisFunctions_Annotated.R | e777b50a9b814b0a15bbdad27fe5ea3cd32ea0c3 | [] | no_license | SteveLund/FP-Algebra | bace9593abe79cdb28b85598aff114e5a4dc80e3 | bbad74eddb797f6c27c21a69b0800be10eb62b36 | refs/heads/master | 2020-06-01T06:20:20.981968 | 2014-04-28T17:09:43 | 2014-04-28T17:09:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,706 | r | DiffusionAnalysisFunctions_Annotated.R | require(foreach)
require(doMC)
require(aroma.light)
require(nlme)
require(mgcv)
require(mnormt)
### Wrapper function for estimating the bandwidth for the kernel density estimate at each observation time
bw.est<-function(dat,bw="SJ",record) {
try(registerDoMC(cores = 6))
BW<- foreach(i = 1:length(dat), .combine ... |
8aa24f5e73e3921ec12c199773016569a9904656 | 78ed9e5357b26cceaf8329c404685de78dbe21b0 | /R/gurobi_MIPsearch.R | 73fc6fb5659ed5591ca14e107c0b980f45de5330 | [] | no_license | cran/DoE.MIParray | efad96f87a0099d075c57a894d5d75f020b9207d | 15d5cfa8739d71da99e9707571664adedbff8567 | refs/heads/master | 2023-08-14T19:25:32.853829 | 2021-09-28T15:20:02 | 2021-09-28T15:20:02 | 105,168,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,951 | r | gurobi_MIPsearch.R | gurobi_MIPsearch <- function(nruns, nlevels, resolution=3, maxtime=60,
stopearly=TRUE, listout=FALSE, orders=NULL,
distinct=TRUE, detailed=0,
forced=NULL, find.only=TRUE, nthread=2,
heurist=0.5, MIQCPMethod=0, MIPFocus=1,
gurobi.params = list(Be... |
69e44b7425734165511a015e466b5549527238bb | 73c273fdf85a99b3d6156986537cf82b0876fc5f | /man/accessions_by_spp.Rd | 6a9657f5a1f7155034f6fcd21e2c7e9435d9183e | [
"MIT"
] | permissive | NCBI-Hackathons/GeneHummus | e55ce7d1fd231db5516ffac039a329c255a68316 | 1fb36181760e0c1b91e65dd3cbd05af27010d8c4 | refs/heads/master | 2021-06-03T15:49:38.606418 | 2020-09-02T21:10:25 | 2020-09-02T21:10:25 | 131,613,965 | 8 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,203 | rd | accessions_by_spp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/accessions_by_spp.R
\name{accessions_by_spp}
\alias{accessions_by_spp}
\title{Compute the total number of accession proteins per species}
\usage{
accessions_by_spp(my_accessions)
}
\arguments{
\item{my_accessions}{A data frame with accession ... |
f2dbe528f2d455bf77b67771ba8308204099adb5 | 1b5dd93ca968d80b0fbdac792708b7aa5efe0512 | /ibd/run_hmmIBD_per_category.R | 51687fdab6430de07072332cd05cfb1a4461ac70 | [] | no_license | amyibrahim/malaria-hub | d509c8a37093874bf7781864891022a35341ba91 | 60b4008e260f958de83ac64da9234dbf0721db31 | refs/heads/master | 2023-04-02T20:02:26.079437 | 2021-02-09T15:50:50 | 2021-02-09T15:50:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,578 | r | run_hmmIBD_per_category.R | options(scipen = 999)
require(optparse)
require(scales)
require(data.table)
require(dplyr)
require(crayon)
source("~/software/malaria-hub/utils/helpers.R")
option_list = list(
make_option(c("-d", "--workdir"), type = "character", default = NULL,
help = "Specify main directory",
metavar =... |
0103e81d1a7565fb7fefab82aeb6d533a7529699 | 6492055107a6d56d4b5eeb9c778f20dcf614cc3c | /Plots/NMDS2.R | 65a78e664f7df8baf318b69476b6c68ceddfd954 | [] | no_license | qmbautista/Bautista-de-los-Santos_EnvSci_2015 | e03956f20d2d79f6e403b5aa81d6f08513be4dca | 318d46d22a8b67f699cbc82604c6457b1064e9a8 | refs/heads/master | 2021-01-22T10:02:05.221421 | 2015-10-18T18:23:55 | 2015-10-18T18:23:55 | 42,651,704 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,467 | r | NMDS2.R | #NMDS of metabolic potential (output from Tax4Fun)
#Input Tax4Fun phylotype table was subsampled to 11587 reads
#Using Bray Curtis and Jaccard distances
#From: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/ecological.html
library(ggplot2)
library(vegan)
shared_subsampled<-read.table("nmds_input2.txt", header... |
eaea63a246e07459c8e74154e7283100d0340cc8 | 6d5efbc79c352e2c4adc525d9ce72ff4401ff1de | /R/common.R | 7c8ce52004dca38c723377febc83109b22365e48 | [] | no_license | nicholasjhorton/textclassificationexamples | d86e96c2cd0071220efb30338cc54f44b4c7d72c | cb138e24665c5a321c9bc93147d0305912b2ad7a | refs/heads/master | 2022-11-19T09:59:30.028888 | 2020-07-21T17:38:00 | 2020-07-21T17:38:00 | 274,704,594 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 170 | r | common.R | #' Common Clickbait Phrases
#'
#' XX
#'
#' @format A data frame with 184 rows and 1 variable:
#' \describe{
#' \item{phrases}{String}
#' }
#' @source \url{XX}
"common"
|
c2cfd22602b95c4071791b6dd57ca3b7d370f979 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Miller-Marin/trafficlight-controller/tlc02-uniform-depth-175/tlc02-uniform-depth-175.R | ccc93b486a6ef602f1edea2a233954154bdee9d2 | [] | 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 | 77 | r | tlc02-uniform-depth-175.R | 922c20b5845064fc81206888ce01773e tlc02-uniform-depth-175.qdimacs 40305 106296 |
9e10e8cbf9a430dfab194d90a6e6ab431bc43fe5 | 69c7e900c9894c61f433dc9c445bd636342a6af8 | /sac1.R | 65a291ef74e976e8c1f6910b9c087c72edcb7fa6 | [] | no_license | ZugenLiu/community-detection | c8eb85b7cb7711fddb1d6b99ed6de9bbcd665e58 | 6872fc0adb7944997d0140d32455ec6938c7cd94 | refs/heads/master | 2020-12-30T11:52:39.304384 | 2016-04-05T01:01:23 | 2016-04-05T01:01:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,438 | r | sac1.R | library(igraph)
library(lsa)
graph=read_graph("C:/Users/viswa/Desktop/project/data/1.txt",format= c("edgelist"))
attribute_data <- read.csv("C:/Users/viswa/Desktop/project/data/1.csv",header = TRUE)
#SC1 Algorithm
#Cosine update of the function
cosine_update <- function(k,memebership,values,x)
{
... |
f2d67f4167534d13a20f23bd006c30b4030b185f | 4cd339b04f1fe93cd79bdbba27a7c0d9a304a3b9 | /1 ActualizarR.R | 5208113d9f37f9dff5857a3979336579f720dfff | [] | no_license | rmunoz98/Metodos_estadisticos_biogeo | 2f522f78529c4da3746abe85ab1fbc13ecc7e873 | ee042189a090cd638ce7198c788b60a299484c32 | refs/heads/master | 2021-01-18T18:49:57.209005 | 2016-07-11T16:03:14 | 2016-07-11T16:04:11 | 63,081,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 603 | r | 1 ActualizarR.R | #Instalar el paquetes
install.packages("stringr")
install.packages("installr")
#Cargar el paquete
require(installr)
#Ayuda del paquete
#help("installr")
#Revisar si hay versiones nuevas de R
check.for.updates.R()
#Instalar y correr la ?ltima versi?n de R
#OJO Despues de la actualizaci?n deberas volver a instalar l... |
7287bc6fa875d16d181ec1c5bfb755b8c073af7c | fb917906af7ed0f22b3508e62670be115d90d4c6 | /plot4.R | a91cb6e56030511f949c4a75920cebb592e07fcf | [] | no_license | mbelletato/ExData_Plotting1 | 6daf35fa0604c2e6a6df59c2f640cda958e3a26a | 14b4f67148aa70fcff10f43eca622df541656c66 | refs/heads/master | 2021-01-18T11:19:12.215547 | 2014-05-12T00:09:57 | 2014-05-12T00:09:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,407 | r | plot4.R | ## Coursera : Exploratory Data Analysis
## @Author: mbelletato
## Course Project 1
## plot4.r
Data <- read.table(file = "household_power_consumption.txt",
header=T, sep=";", na.strings="?", skip=66636,
nrows=2880,
col.names=c("Date", "Time", "Global_active_po... |
c001abd8e360da0db4af3285897b0d0dbdb24d0b | 7cfac30af2947ff10691175c6eba4c9c449af14b | /R/reg_RSE.R | 973e3e7610485d73bd17f5cb645100309f1c02f1 | [
"MIT"
] | permissive | adriancorrendo/metrica | 69257453e4f8be3c5357951e546012203f786b26 | a5ca847f5b6dc85e89fb23988258e864fc26a033 | refs/heads/master | 2023-08-22T11:23:12.808032 | 2023-04-14T04:30:35 | 2023-04-14T04:30:35 | 414,721,777 | 69 | 9 | NOASSERTION | 2023-03-06T20:32:37 | 2021-10-07T18:52:35 | R | UTF-8 | R | false | false | 1,806 | r | reg_RSE.R | #' @title Relative Squared Error (RSE)
#' @name RSE
#' @description It estimates the RSE for a continuous predicted-observer dataset.
#' @param data (Optional) argument to call an existing data frame containing the data.
#' @param obs Vector with observed values (numeric).
#' @param pred Vector with predicted values (... |
d1efb62b139a1a5e3ef524f47cc23a5ed6538a4b | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /output/sources/authors/2392/pln/nrbcpln.R | 7a3ec002b447cff5abe203ff7f2b10a5030577e1 | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,465 | r | nrbcpln.R | nrbcpln<- function (x, ncat, nitem=NULL, alphas=NULL, betas=NULL, abound=c(-10,10),
bbound=c(-1,10), nq=48, mxiter=200, se=TRUE, iprint=FALSE) {
myInput<-check.input(x, ncat, nitem, nq, mxiter, iprint)
## get starting values if not present already
if(!check.alphas(alphas, myInput$nitem, myInput$ncat)){
... |
c18e34addadd8d2dfce33b92a1e9a551bd14a8d7 | 8721a65ead5cbfe12ba224aedda27e532a5be289 | /R/map_qc.R | 1bb987b906e4b14955da0ba45946d021b43a8e2b | [] | no_license | cran/mapfuser | d482e861ed76062303bf99151b67be85752cee29 | 89fb804542f627ae4a05b27f100c66199e3d8b5a | refs/heads/master | 2021-07-09T22:10:44.228292 | 2017-10-10T09:13:54 | 2017-10-10T09:13:54 | 106,401,446 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,561 | r | map_qc.R | # map_qc
#' Wrapper function of genetic map cleaning per linkage group.
#'
#' The raw data is first splitted to separate linkage groups if sublinkage groups exist (e.g LG 1.1 and 1.2).
#' Subsequently a graph is created from the adjacency matrix that counts the number of overlapping markers between the set of genet... |
53cf63afbf85acd82a95e5da84df657d97780149 | 12d6bba3fb62d3569bf606e9ddd7a817be141577 | /plot4.R | 45924bdac41519f1a7f590dfd08b3526d4f8d64c | [] | no_license | saurabh27/ExData_Plotting1 | 93e0187a175b44538da614a70afb9f359026478a | 31aeaf401ae57300f4932f0d9fc490ce415f6015 | refs/heads/master | 2021-01-21T07:30:25.723818 | 2014-05-09T23:28:12 | 2014-05-09T23:28:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,465 | r | plot4.R | # read in the complete dataset, would be better to filter first on
# the date field (e.g. using the grep program) but this will not
# work in Windows
# read the file assuming it is in the current working directory
e = read.table("household_power_consumption.txt", header = T, sep=";", na.strings="?")
# now we select ... |
484c288fe87c8313df571d50850ad0f91fc24266 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/AeRobiology/examples/ma.Rd.R | 72bbe5a78ca03847645f38735cf9ebf6c3c5eeaa | [] | 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 | 178 | r | ma.Rd.R | library(AeRobiology)
### Name: ma
### Title: Moving Average Calculator
### Aliases: ma
### ** Examples
data("munich")
ma(data = munich$Betula, man = 10, warnings = FALSE)
|
27cf5b928ba54646f342b4da31774cf345949777 | 6d320eec67f4fe8b608a379044cffc98ce027b09 | /06_scripts/data_analysis/4_GLMM_Metabar.R | ceacfdce58175881254726f7572ed868ccd9fa3a | [] | no_license | WynneMoss/Moss_amphibian_eDNA | 46945cf7a8dbfbef8e1cb41a72f84af84e5d70d6 | 59b88e0d13a8a85542843031e240cebc198330d0 | refs/heads/main | 2023-04-18T04:25:26.261476 | 2021-11-10T16:21:11 | 2021-11-10T16:21:11 | 349,119,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,122 | r | 4_GLMM_Metabar.R | #######################################
###### eDNA Amphibian Detection #######
#######################################
# Script 4: GLMMs for metabarcoding sample level data
##### Analyze probability of detection at the sample level
# using GLMMs
# metabarcoding detection data
# evaluate the probability of detection a... |
ec3e8950bb846a39055d5e44f28a4c3e5f6c3f17 | ef8dffd6d63d4338f6d7871c69c3cab21f585444 | /LUAD_CNV_draft2.R | e18cf5b612f2c8d0a70bce6274ff7126eddf067a | [] | no_license | natalie-stephenson/GRB2-SH2-Screen_WIP | 34bd6ead60b104018bac681e5ee99603efa91626 | 6713fe7d3a6dad2cfe79050a42296b7c437c010c | refs/heads/master | 2022-11-13T19:30:30.808847 | 2020-07-05T10:53:58 | 2020-07-05T10:53:58 | 277,148,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,843 | r | LUAD_CNV_draft2.R | ###################################################################################################################
# #
# Script to pull CNV data from the TCGA LUAD data files and annotate the genes altered in GRB2 depleted samples. #
# ... |
1d61f403362a67a39e4dc08343dff5e99b3929e6 | 57854e2a3731cb1216b2df25a0804a91f68cacf3 | /man/moveToGroup.Rd | 13104ff8177d44ec56ca8ef014e652edb40971b3 | [] | no_license | persephonet/rcrunch | 9f826d6217de343ba47cdfcfecbd76ee4b1ad696 | 1de10f8161767da1cf510eb8c866c2006fe36339 | refs/heads/master | 2020-04-05T08:17:00.968846 | 2017-03-21T23:25:06 | 2017-03-21T23:25:06 | 50,125,918 | 1 | 0 | null | 2017-02-10T23:23:34 | 2016-01-21T17:56:57 | R | UTF-8 | R | false | true | 832 | rd | moveToGroup.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shoji-order.R
\name{moveToGroup}
\alias{moveToGroup}
\alias{moveToGroup<-}
\title{Move entities to a group}
\usage{
moveToGroup(x, value)
moveToGroup(x) <- value
}
\arguments{
\item{x}{VariableGroup}
\item{value}{Variable, VariableCatalog s... |
429926a66452c6805c19b21efd454a53a19c944a | 814605d65bc126d0a5332ae5a41606b64de20ac1 | /notebooks/estudo_bandwidth.R | 87b51e88618c9d69affca10349b92f5a8b855605 | [] | no_license | netoluizbezerra/dissertacao | a559233bde42214722a1bec1bb27b9b17d6e311f | cebb922e3f9f29e4cc7ac34affbd25a41fb0921d | refs/heads/master | 2021-05-04T20:41:11.590678 | 2018-02-05T10:34:13 | 2018-02-05T10:34:13 | 119,832,013 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,844 | r | estudo_bandwidth.R | ####################
# Bandwidth issues #
####################
rm(list = ls())
library(kedd)
ndraws=100
horizon.simul=2000
alpha1=0.10
beta1 =0.85
mu=0.00
omega= 2
burnin = 100
model = garchSpec(model = list(mu = mu ,omega = omega, alpha = alpha1, beta = beta1, shape = 5), cond.dist = "std")
R <- ndraws #Numero de repe... |
d5bd04698aa3ec1e81d5b4cf450d92609ed1c68a | bb9d1aa63b3d9b0a9cfe233bb7e41f59ee35afc6 | /man/gf_add_font.Rd | 9bb5989bca59f3465a75d83eda59d2a25f8f6474 | [] | no_license | timelyportfolio/googlefontR | b438108b8462c7c19e2518ad16eacd969bb48863 | 39b568785112bb06fd41fa4eeb541a8c0204f3e7 | refs/heads/master | 2020-04-19T15:50:08.607959 | 2016-09-01T18:58:02 | 2016-09-01T18:58:02 | 67,147,173 | 17 | 0 | null | null | null | null | UTF-8 | R | false | true | 759 | rd | gf_add_font.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dependencies.R
\name{gf_add_font}
\alias{gf_add_font}
\title{Add Google Fonts to 'tags' or 'tagList'}
\usage{
gf_add_font(tag_list = htmltools::tagList(), fontname = NULL,
customstyle = character(), addstyle = TRUE)
}
\arguments{
\item{tag_... |
6b5b1c229dbe58e97942959d920472b203db3042 | 0c96e7150dbf6fe7aab21e1bbe85d72b3df9201b | /man/substrRight.Rd | c2764d801819dcaaea8bfcf16dec2a8561f7d6d3 | [] | no_license | KangarooCourt/ascendedge | fceaf94767fa5de822e31bc94c9f5ad433c2dc53 | a055751520b1e281044130968c85f3520f2d014f | refs/heads/master | 2020-08-31T02:30:41.415323 | 2019-10-30T15:27:27 | 2019-10-30T15:27:27 | 218,553,360 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 484 | rd | substrRight.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/substrRight.R
\name{substrRight}
\alias{substrRight}
\title{Last Letters}
\usage{
substrRight(x, n)
}
\arguments{
\item{x}{A character string.}
\item{n}{A number. This is the number of letters from the end of a string to be displayed.}
}
\va... |
dce71b4c5feee789b895d9eff9c03e712a94a596 | 0cc8920c857ada69f8d15d734663062c1337f109 | /R/spaceopt/scoring/space_allocation_model.R | c69042ef65049367fb01c87588ae7185e8446f5b | [] | no_license | kpushkar/scalene | 783f08a13ade0da14454f3b19b71b1c1cc92cdae | d38412597dcb8b3081b6e0b9567fd2c4e93a91df | refs/heads/master | 2021-01-12T02:27:28.296528 | 2017-01-26T10:36:02 | 2017-01-26T10:36:02 | 77,958,383 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,848 | r | space_allocation_model.R | # now lets run the space_allocation_loop for all possible space
# and then use it for our graphical interface
source("R/utils/scalene_utilities.R")
source("R/spaceopt/scoring/space_allocation_loop_FN.R")
# let's read the store space info
STORE_DB <- read.csv("data/spaceopt/input/Store_DB.csv",
st... |
d7faf646ea3534bb92d013c46f3a410f3f264c42 | 899420d8106be354a2010f5964fc5802f533294c | /man/ncdf_stack.Rd | 584d03f29bb89922c4612e2cf4077be701ee1a13 | [] | no_license | annakrystalli/sedMaps | 4cea5a3e51feb27427d01188b607efe7c40b160c | a93da7c5ba1125f5716cbb60674b80cfb74ad36b | refs/heads/master | 2021-06-24T17:26:34.668563 | 2021-06-19T12:25:27 | 2021-06-19T12:25:27 | 149,792,890 | 1 | 1 | null | 2018-09-22T10:16:53 | 2018-09-21T16:59:44 | R | UTF-8 | R | false | true | 360 | rd | ncdf_stack.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ncdf-read.R
\name{ncdf_stack}
\alias{ncdf_stack}
\title{Load a ncdf file into a rasterBrick}
\usage{
ncdf_stack(path)
}
\arguments{
\item{path}{path to ncdf file}
}
\value{
a rasterBrick, one layer for each varname in the ncdf.
}
\description... |
df1727a6c06765a81b28aec922664d8cf1ec9793 | 98bed9cd8ba75660bc63fc359f767dd7925c8441 | /R/test_brackets_balanced.R | 787eeba51ff2dc3de7b5d176d77058bc790d1e44 | [
"MIT"
] | permissive | CoEDL/yinarlingi | 91e0418528ad8da2ba9d3301d2f091b465ab3e72 | 5d720213c51776408aa94493a66a25776f8fbfd3 | refs/heads/master | 2020-03-21T21:49:48.123051 | 2020-02-13T23:18:09 | 2020-02-13T23:18:09 | 139,087,134 | 0 | 0 | MIT | 2019-02-28T23:41:51 | 2018-06-29T01:43:23 | R | UTF-8 | R | false | false | 765 | r | test_brackets_balanced.R | #' Test that all brackets and parentheses are well-balanced
#'
#' @param wlp_lexicon a Warlpiri lexicon data frame, or path to a Warlpiri dictionary file
#'
#' @importFrom stringr str_extract_all
#'
#' @export
#'
test_brackets_balanced <- function(wlp_lexicon) {
wlp_lexicon %>%
skeletonise_df() %>%
... |
6f7296b0a2996c9d7bc2ace400078eabae80aa20 | 86a4fee27f34bc1c641bbd336ee8ad458bd8e135 | /ReadIn_TRENDEFV2.r | 18a4a4ba578b0da5f9cf17ccef27384851eee974 | [] | no_license | ETHPOP/BSPS2015Model | 8b8d6bf11122133434e501f3e34b99bcb9cd4991 | ee661a0834269a277d437b2722446a45abdf558a | refs/heads/master | 2016-09-05T12:07:36.380607 | 2015-08-20T12:58:13 | 2015-08-20T12:58:13 | 40,672,143 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,586 | r | ReadIn_TRENDEFV2.r | #setwd("C:\\Workspace\\Projections\\Projections\\Rprojection\\Januar2010Projections\\Trend")
#setwd("C:/WorkSpace/CSAP/EthnicProjections/Projections/Rprojection/August2011Version2Projections/4TRENDEFV2")
#DIR<-"E:/ProjectionTestRuns/4TRENDEFV2"
#DIR<-"N:/ProjectionTestRuns/4TRENDEFV2"
MainDIR<-"N:\\Earth&Environment\\... |
61fde7ff86f3d3b65ee030b175e7a8b3952059b8 | f78f96c58629c2296e2ad3abb758f275f59afdd2 | /tests/testthat/test-ts_resolve_names.R | ac6c4de1add5a53cb1732b5fb335a676cb9d953f | [
"MIT"
] | permissive | joelnitta/taxastand | e1ff81a343abdd710e8a15882333b77c9a6ef2ff | 3894436b120367976fb6331ced1c19d001c251f7 | refs/heads/main | 2022-10-06T19:45:08.438418 | 2022-09-20T07:20:58 | 2022-09-20T07:20:58 | 192,684,959 | 18 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,248 | r | test-ts_resolve_names.R | data(filmy_taxonomy)
test_that("Input checks work", {
expect_error(
ts_resolve_names(10, data.frame(genus = "Foogenus")),
"query must be of class"
)
expect_error(
ts_resolve_names(data.frame(genus = "Foogenus"), 10),
"ref_taxonomy must be of class"
)
})
test_that("Produces expected output with... |
a0e1a7a7ea9224ddf41a3b874b96cc39a20a102f | d8ad1a37ee95792d1ac896526b60d2ca4d05192e | /Classificacao e pesquisa de dados/Lab1CPD/GraficoLab01.R | fb548c3e5987ce00d2cd157f2a6db38a875f94f4 | [] | no_license | Ghilga/trabalhos-de-programacao-UFRGS | bdd438f8d14d8deb5aa6d469b9e0e6ebf047a894 | 15aa33c8e0d621437e5bdd586db8a0662302c15e | refs/heads/master | 2020-03-17T02:36:02.847591 | 2018-06-10T01:35:10 | 2018-06-10T01:35:10 | 133,196,754 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | r | GraficoLab01.R | data <- read.csv("Table.csv")
plot(x, y, type = 'l')
binaryInsertion = c(2.013, 136.126, 1303.799)
plot(x, binaryInsertion, type = 'l')
xdata <- c(1000,10000, 100000)
Bubble <- data$tempo[1:3]
Insertion <-
insertion <- c(2.158, 158.35, 16053.568)
BinInsertion <- c(2.013, 136.126, 1303.799)
y3 <- c(1, 6, 14, 28, 47, ... |
e3fbfea7087304ed76c6c33596ba0c7407d61e76 | fa68ef96ae1da0b56d535855ec1035cc35d0a490 | /R/zzz.R | 729760bde098c3b904518a6d3cc68cbfb1da2cb7 | [] | no_license | TanguyBarthelemy/rjdoutliers | a03e16964ea919c40c03b0dcb095b3677b49bd73 | 2691a8388ec3a7aa35b8f32b3b4743a04d43356c | refs/heads/main | 2023-07-21T04:56:36.607754 | 2021-06-02T19:00:54 | 2021-06-02T19:00:54 | 546,089,599 | 0 | 0 | null | 2022-10-05T14:00:52 | 2022-10-05T14:00:51 | null | UTF-8 | R | false | false | 813 | r | zzz.R | #' @import rJava RProtoBuf
NULL
.onLoad <- function(libname, pkgname) {
# For debugging: to see if Jars are effectively loaded
# options(java.parameters = "-verbose:class")
# TODO : devtools will look only in RJDemetra3\java for JAR files so copied them there too
result <- .jpackage(pkgname, lib.loc=libname... |
f7281ed48a126996e3367d9c4f80a692335988ac | 28583a31129a7b363413653717b55d2f2460cc15 | /LBSPR/Raw Files/Adrian Assessment Code/All_Functions/GridSearchFunction.r | 32ed375c885922621f91e229aee0a7fcca3518d1 | [] | no_license | DanOvando/Galapagos-Code | 209409033684c0e75e36acc8d23b7ff2e806cb21 | 7dd7bb23ce3af766f9d81451652f05c03b4237c9 | refs/heads/master | 2021-01-01T15:18:24.158617 | 2014-07-10T23:25:50 | 2014-07-10T23:25:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,702 | r | GridSearchFunction.r |
RunGridSearchFunction <- function(LowerSelL50, UpperSelL50, LowerSelL95, UpperSelL95, InitialSelPrec, FinalSelPrec, LowerF_M, UpperF_M, InitialF_MPrec, FinalF_MPrec,
ObservedFreqVector, ObservedPropVector, genM, genLinf, genLinfCV, genLengthSD, genK, gent0, genAges, genLengths, genminLen, genmaxLen, gennumClass, G... |
3c6842f010f42c6caee7bcd34561e08e2a8bf938 | f0352034f8467e2c82a31443ae6e3125039879ac | /man/assignUnassigned.Rd | de5ca1de06b7476b84cb811994be2e3b6340e944 | [] | no_license | epurdom/clusterExperiment | 8d5d43a250a1a3c28d4745aae4b72285458ba1a2 | ae86ee09697c13ccd5d32f964e28ab7d82b455d6 | refs/heads/master | 2022-11-04T01:54:19.806886 | 2022-10-11T22:00:27 | 2022-10-11T22:00:27 | 47,139,877 | 39 | 15 | null | 2021-01-27T21:26:28 | 2015-11-30T19:06:53 | R | UTF-8 | R | false | true | 3,235 | rd | assignUnassigned.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assignUnassigned.R
\name{assignUnassigned}
\alias{assignUnassigned}
\alias{assignUnassigned,ClusterExperiment-method}
\alias{removeUnassigned,ClusterExperiment-method}
\alias{removeUnassigned}
\title{Assign unassigned samples to nearest clust... |
93f00aa6fa8494a437800b0867597032a6bfed53 | 55bfb6f0c613d1beb67b40aa99e531eb644d4351 | /R/get-sequence.R | 7080d34474298848224ed84bf5e3219c8a887553 | [] | no_license | EricBryantPhD/mutagenesis | 3bc391acb86b4796eff0c2ae826d6c65af507d6f | 0fe642a2addf0734f31df29aa3be1c069cf420d2 | refs/heads/master | 2020-12-09T11:03:54.657411 | 2018-01-27T23:10:32 | 2018-01-27T23:10:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,852 | r | get-sequence.R | #' Get genomic sequences given ranges
#'
#' @param chr `[character]`
#'
#' Chromosome names. Must match names returned by `names(genome)`.
#'
#' @param strand `[character]`
#'
#' Sequence strands (+|-).
#'
#' @param start `[integer]`
#'
#' Start coordinates of ranges.
#'
#' @param end `[integer]`
#'
#' End coor... |
ff4b353aa6af09fa5d2b59cab872e0413612d5ce | aec820a0c7109fe2184b7d956742915023fd30c1 | /R_Operation_Sequencing/gantt_johnson.R | 17d71df1182d65ec671d6540099f823d746e4ed8 | [] | no_license | hendry062105/Practice_2020 | 350dc2996652c7f658157d08946a1c78da1fa240 | 98f26b1b599c9d09f2c8d08cdc362c7823e887be | refs/heads/main | 2023-08-27T18:47:12.021806 | 2021-10-14T22:30:20 | 2021-10-14T22:30:20 | 417,289,588 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,183 | r | gantt_johnson.R | gantt_johnson=function(t0,starts,ends){
limits_interval_v2()
df_limits = data.frame(starts,ends)
library(plotrix)
gantt.info<-list(labels=c("M1","M2"),
starts=starts,
ends=ends,
priorities=c(1,4))
months <- seq(t0, by=1, length.out=ends[length(ends)])
month... |
4fe30af39d85675b3638458bea33b85425ec4164 | dd5e447826e762b22762f8f522641b3172d71d9e | /intermediate-r/Functions in R-495.r | 8d2eaf0ea3b74cdb16c5fc8a6ec73dd3a538f9be | [] | no_license | Guarinho/Data_Analyst-R | 32494e4fb31d4d7bf6c68c3e450d3d2be97e64aa | ff701fcdf4f8cc107364f7086cfaf32e40cfca3c | refs/heads/master | 2022-11-25T04:54:37.117485 | 2020-08-02T14:51:54 | 2020-08-02T14:51:54 | 282,522,307 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,098 | r | Functions in R-495.r | ## 2. Components Of A Function ##
first_vec <- c(1, 5, 4, 2, 3, 7, 6)
second_vec <- c(9, 2, 1, 8, 3, 4, 5, 6, 10, 7, 12, 11)
third_vec <- c(8, 3, 5, 1, 7, 1, 10)
find_longer_vector <- function(vec_one, vec_two) {
if (length(vec_one) > length(vec_two)) {
return("First")
} else if (length(vec_one) < length(ve... |
47d902919c85eaeb79467587cb88f9caeaf64fc7 | 25bae842912a19b7295da3b5e8dbdda94081b28f | /Exploratory_Analysis/Week2_Assignment/Code/Plot2.R | 7bd2863c20411894ab049072e902e7ff29831320 | [] | no_license | prateeksarangi/DataScienceCoursera | 97823363f49bc00c55d67e22fe58f2fb3e999691 | 4665a8ff1c922d669187b120bc859eaec2f359c1 | refs/heads/master | 2022-11-23T07:14:27.300289 | 2020-04-17T19:41:43 | 2020-04-17T19:41:43 | 228,541,800 | 1 | 0 | null | 2022-11-22T04:40:38 | 2019-12-17T05:43:36 | HTML | UTF-8 | R | false | false | 710 | r | Plot2.R | data <- read.csv("~/ExData_Plotting1/household_power_consumption.txt", sep=";"
, na.strings = "?"
,colClasses = c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")
)
data$Date <- as.Date(data$Date, "%d/%m/%Y")
dataSelect <- data[(da... |
f4f9f020a8c0404a2cc07d35fdb2af56026d7b14 | ca427633f94cf6e220bafbd6287791af0579183e | /bael_growth.R | 841e8f60896a9cc5c88294ee40f58ac6932420de | [
"MIT"
] | permissive | elahi/cupCorals | 78f204b9a3e3c9b68029da231113e1d32af7bdf8 | 4150f7875ba283e9159a032c620a5b093e55ba97 | refs/heads/master | 2020-05-21T15:10:03.216493 | 2020-05-19T02:31:07 | 2020-05-19T02:31:07 | 41,657,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,682 | r | bael_growth.R | #################################################
# Author: Robin Elahi
# Date: 151208
# Coral growth
# Figure 4
#################################################
# rm(list=ls(all=TRUE))
##### LOAD PACKAGES, DATA #####
library(lme4)
library(ggplot2)
theme_set(theme_classic(base_size = 12))
library(AICcmodavg)
librar... |
28dda657ab198850512d07ddb6401fe2759ba905 | 1ed42775543aab64d1376bb521b2f7360862cd73 | /webSite/images/demo/View Data/analyze/analyze.R | 69d6738130c2b27e9d3a4b0b26bdef42bfe25138 | [] | no_license | yja2397/wayne-s-crop | 6aa4bac7f7418c6d3dab7f6fd148c159b68dab44 | ede7d95a4d205e079e2f787ac84146dfaefe31e5 | refs/heads/master | 2020-04-14T06:54:28.419328 | 2019-02-18T19:11:46 | 2019-02-18T19:11:46 | 163,698,625 | 1 | 0 | null | 2018-12-31T21:34:43 | 2018-12-31T21:34:42 | null | UTF-8 | R | false | false | 4,191 | r | analyze.R | # library, setup
library(ggplot2)
library(UsingR)
library(car)
library(gridExtra)
par("mar")
par(mar=c(1,1,1,1))
test = read.csv("data.csv",header = TRUE)
acc = read.csv("tem_hum_com.csv", header = TRUE)
test <- na.omit(test)
test[,4] <- test[,4] * 0.01
data = test[,3:4]
time = test[,1]
data$time = time
colnames(data... |
3cd59b60018dde57abf464d35aa74c5fb1d4e8d7 | f4ee4f52b8e2e685c0518ebe94eb14a8028b845b | /QualityControlForGEO.R | a5215819347f00416edf0af5c1fd214192907571 | [] | no_license | TaoyuMei/Masters_Thesis_public | 37f0dc46610ff9d7f3d57732aa4dd73b239b18c8 | 093bc175a90806fc90f4b1b6f152322c41440f3b | refs/heads/main | 2023-01-23T08:56:00.108469 | 2020-11-16T05:20:16 | 2020-11-16T05:20:16 | 313,198,315 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,410 | r | QualityControlForGEO.R | # QualityConotrolForGEO.R
library(fastqcr)
library(ngsReports)
library(stringr)
# dealing with GSE raw data ------------------------------------------
## generating reports for each fastq files
setwd("/binf-isilon/alab/students/vrw936/scratch/rna_seq_for_mrna")
GSEs<- grep(pattern = "GSE.*", x = list.fil... |
25d39afc3ea0bff3fba49deba81a7cc376464acf | d6fefc7986e9e912bc20a216381952c7c2dd56d4 | /functions/summarizedGenes_genelevel.r | 232b4a707bb9baccb293a6e31a968fea8bcde9f8 | [] | no_license | YalanBi/AA | 74674ebfc778eedfd9f9221f9177f5f7d8b6b4fc | b9be902c90e4d86b5f0152d67479145051741db8 | refs/heads/master | 2021-01-10T20:44:20.030509 | 2013-10-03T14:25:22 | 2013-10-03T14:25:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,741 | r | summarizedGenes_genelevel.r | #
# Functions for analysing A. Thaliana Tiling Arrays
# last modified: 01-07-2013
# first written: 15-04-2013
# (c) 2013 GBIC Yalan Bi, Danny Arends, R.C. Jansen
#
#************************************************* this is the final version for summarizing probes into gene level! ^_^ *********************************... |
0c16aa51b91bc0cc75a3b9f2eddeaf7083c4f924 | 3c258c7fe3244f4a41dea7d264098ac614eef19a | /man/extremes.Rd | d33f0ccf58cd53f796e22cd846c52179bcc3328c | [
"LicenseRef-scancode-warranty-disclaimer",
"CC0-1.0",
"LicenseRef-scancode-public-domain-disclaimer"
] | permissive | USGS-R/repgen | 379be8577f3effbe7067e2f3dc5b5481ca69999e | 219615189fb054e3b421b6ffba4fdd9777494cfc | refs/heads/main | 2023-04-19T05:51:15.008674 | 2021-04-06T20:29:38 | 2021-04-06T20:29:38 | 31,678,130 | 10 | 25 | CC0-1.0 | 2023-04-07T23:10:19 | 2015-03-04T20:24:02 | R | UTF-8 | R | false | true | 593 | rd | extremes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sig-extremes.R
\docType{methods}
\name{extremes}
\alias{extremes}
\alias{extremes,list-method}
\title{Extremes report.}
\usage{
extremes(data, ...)
\S4method{extremes}{list}(data, ...)
}
\arguments{
\item{data}{Local data (as list), or URL.}... |
5bb9250e9a27d2a9e016cd93c84a44d5f20d4552 | a22e8e1b9ff3f4ed8a589bcbbbd280fccb4475d0 | /Diagnóstico/01_Mercado Laboral/viz-ml.R | cf1c277ba1d472e2359d24a0b4bfbc2abcfa5414 | [] | no_license | paulapereda/estilo_cess | 15257c4b761f838c63d88973edc50338432946b6 | 43cf5baeb8a1b158c1551cf636c9f7730784949f | refs/heads/main | 2023-04-26T21:02:53.036329 | 2021-05-04T13:50:08 | 2021-05-04T13:50:08 | 312,658,107 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,013 | r | viz-ml.R | library(lubridate)
library(tidyverse)
library(scales)
library(readxl)
library(here)
source(here::here("estilo_cess.R"))
# Gráfico - Informalidad Sectorial
g <- read_excel(here("Diagnóstico", "01_Mercado Laboral", "Informalidad Sectorial.xlsx")) %>%
pivot_longer(- formal, names_to = "sector", values_to = "valor") ... |
09ee8f120d1fb1ce9282e06b4810460d28b21900 | 1a83ac47bb1ffe39b416dfce1964051fa77d5b7c | /man/perimeter.Rd | 9981bd74c056f26e1ad06644763769f422945402 | [] | no_license | cran/sampSurf | 9052ab60378e3295ecade04e573e6770c145cf74 | 9388a099e8cef6109c544bcc95770bc9a60670e6 | refs/heads/master | 2021-06-05T21:01:53.864724 | 2021-03-05T14:50:02 | 2021-03-05T14:50:02 | 17,699,448 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,028 | rd | perimeter.Rd | \name{perimeter}
\alias{perimeter}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Function to Return the Graphical Perimeter of an Object in
Package \sQuote{sampSurf} }
\description{ Most classes in the \pkg{sampSurf} package have some kind
of spatial representation that conforms to a clas... |
7c6bfac237b2e4cc9b5e7f69d592c13fe2270d6d | b3d6bc3df6ab9e65c05c625edbae736bcf7cd56f | /man/BDMethod-setter.Rd | 38a78be04ed3511b229da5fd269cbc78ebd8cd75 | [] | no_license | areyesq89/SummarizedBenchmark | fbd1976902330ed4630b29981eef055fd855ac35 | 6ac1a724155a316d9773afdffc9243d6434d9389 | refs/heads/master | 2021-08-29T07:31:28.673604 | 2021-08-24T05:48:26 | 2021-08-24T05:48:26 | 102,158,369 | 14 | 6 | null | 2018-02-13T18:11:04 | 2017-09-01T22:46:56 | R | UTF-8 | R | false | true | 1,544 | rd | BDMethod-setter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllGenerics.R, R/BDMethodList-accessors.R,
% R/BenchDesign-accessors.R
\docType{methods}
\name{BDMethod<-}
\alias{BDMethod<-}
\alias{BDMethod<-,BDMethodList,character,BDMethod-method}
\alias{BDMethod<-,BDMethodList,character,NULL-method}
\a... |
fe522553ade0096ef5f9bd54191ce3ae8027f7d1 | 24851be32893bfb1027b2a33164ef515fc4fb76b | /stan/old/bayesr2prod.r | 22a8a80eecb287645949caa7ff8e37b8f47c6179 | [] | no_license | qdread/forestlight | acce22a6add7ab4b84957d3e17d739158e79e9ab | 540b7f0a93e2b7f5cd21d79b8c8874935d3adff0 | refs/heads/master | 2022-12-14T03:27:57.914726 | 2022-12-01T23:43:10 | 2022-12-01T23:43:10 | 73,484,133 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,351 | r | bayesr2prod.r | # Calculate Bayesian R2 of the production models, manually.
# Workflow
# -------------------------------------------------------------------------
# 1. Load the model fit
# 2. Load the data by sourcing the stan rdump for that model
# 3. Extract the parameter estimates for each draw from the model fit
# 4. Plug the db... |
d669653ea6ecdf210b065c31f99ab2434d2aad4d | ec345334e4334be7cfbdc760d0027301418082b1 | /R Code/3 Indicator Selection+Portfolio Management/Contract_numbers7_9.R | 0a62655c89a2a3f469ca18ddc33746de996710e2 | [] | no_license | MaryShao/Back-Testing-for-Futures-Trading-Strategies | 92085c83020d193dac4207b9c5ca481423b534a9 | a7247e3bf3f6a2314ecc9a964232cef44ef5ee22 | refs/heads/master | 2021-02-06T01:41:35.229932 | 2020-02-29T02:36:51 | 2020-02-29T02:36:51 | 243,861,450 | 1 | 0 | null | null | null | null | GB18030 | R | false | false | 4,262 | r | Contract_numbers7_9.R |
# Data import
# 每手
contract<- read.csv("C:/Users/m8sha/Desktop/contract_number.csv", stringsAsFactors=FALSE)
rownames(contract)=contract[,1]
contract=contract[,2:3]
#coef=contract[,-1]
# 合约价格
setwd("C:/Users/m8sha/Desktop/DATA/Sig_Price")
f = list.files()
tmp=read.table(f[1],header = TRUE)
tmp = tmp... |
94a0fcd20a3f3cea261f3c6ecc35abf9465d1422 | b58131c3658c3c0e7e975eeb92904d299b472c96 | /R/chrom2chrom.R | dd0f9f50263760762c8ec529714211a770918766 | [] | no_license | ibn-salem/hyperbolic_nucleus | 28f7f338a1cd6ab7ca81ec602272831b500a79d1 | 8242826da801cd2fca8a625cf71638d7b18de882 | refs/heads/master | 2021-01-19T10:17:45.301835 | 2017-10-20T14:26:34 | 2017-10-20T14:26:34 | 82,173,313 | 1 | 0 | null | 2017-10-20T13:46:34 | 2017-02-16T11:24:34 | R | UTF-8 | R | false | false | 442 | r | chrom2chrom.R |
library(dplyr)
library(circlize)
load("results/edge_lists.RData")
load("results/noteDF.RData")
df <- tibble(from = left_join(inData, noteDF, by = c("g1" = "id"))$chr,
to = left_join(inData, noteDF, by = c("g2" = "id"))$chr)
df <- df %>%
group_by(from, to) %>%
summarise(value = n()) %>%
filter(!i... |
5b6acb7cbb176a6d7c4bbcc9a44b30b119295c88 | 7672493ea65b7c97dfebb1fef2d4da4132f2bbf3 | /codes/generate_lncrna_dataframe.R | 722c295bb23361f961c58a72007a75412dcaebac | [
"Artistic-2.0"
] | permissive | andronekomimi/SNPVizualTools | a49c9869e651970aa9e288c1e1cb7b54eb78f13d | 504b70be6d2d42eef33be499eb984690f630e7f9 | refs/heads/master | 2016-09-11T02:55:29.744674 | 2015-04-09T13:06:27 | 2015-04-09T13:06:27 | 33,318,730 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 888 | r | generate_lncrna_dataframe.R | library(parallel)
chr_list = c("chr1","chr2","chr3","chr4","chr5","chr6","chr7","chr8","chr9",
"chr10","chr11","chr12","chr13","chr14","chr15","chr16","chr17",
"chr18","chr19","chr20","chr21","chr22")
# chr_list = c("chr1","chr2","chr3")
files_path <- "/home/nekomimi/Workspace/COLLAB/mitr... |
eb1c67fd6ce0e2aa8c433b4779c66bfcdafa92b2 | 51306eddda32ae60a1782ed52c4c4b5aeb8845b1 | /man/mpe.Rd | 3c37f70d9257efe9ab4c48b6849a849e0cd92be1 | [
"MIT"
] | permissive | max-graham/metrics | 0d1fac194b5b2a248e59291fadd5be6819bbcdbe | 7254729e547d567c8f5539c25607335390112d9f | refs/heads/master | 2021-01-20T05:10:43.284914 | 2019-03-20T00:33:23 | 2019-03-20T00:33:23 | 89,755,685 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 704 | rd | mpe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/error.R
\name{mpe}
\alias{mpe}
\title{MPE}
\usage{
mpe(x, target, na.rm = FALSE)
}
\arguments{
\item{x}{numeric vector}
\item{target}{numeric vector}
\item{na.rm}{logical; should NAs be removed before calculation?}
}
\value{
An atomic numer... |
a3864eaa10436bde97f4587c17c8cad30064a6b8 | ee7bfc39868fce34a55dbf3ecc075d262e6f8e36 | /Red-deer-browsing.R | 896c3cef0d11b8a07b54196b7e48bb6e141900b0 | [] | no_license | marielaj/Red-deer-browsing- | 245ac95241c53d03bee6469590d3a5358cac7744 | 1cafe7c873a25ac111c493a4c5f1a2dae3d504a2 | refs/heads/master | 2020-04-14T16:31:20.468411 | 2019-01-15T10:16:18 | 2019-01-15T10:16:18 | 163,954,099 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 229 | r | Red-deer-browsing.R | #Master prosjekt - Red deer browsing
#Importerer Species_2009-2018 dataset
library(readxl)
Species_2009_2018_2_ <- read_excel("~/Master/Data/Species 2009_2018 (2).xlsx")
View(Species_2009_2018_2_)
View(Species_2009_2018_2_)
|
2bdcd9cfd1482db4def0a173d061b3631063a13f | c8b4efc2d2ad4424322ecb64a2e04e5f97fdd048 | /binomial/tests/testthat/test_summary.R | 50edfe9ef41065702fa8c40576c46b9f0e377620 | [] | no_license | stat133-sp19/hw-stat133-gwynethjocelyn | e038bccb55ff084624094dc532b06f23b1c9db5d | 641c1c9210598ed63cfc473b66bb4923d459747a | refs/heads/master | 2020-04-28T07:14:16.583311 | 2019-05-02T08:49:18 | 2019-05-02T08:49:18 | 175,084,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,037 | r | test_summary.R | library(testthat)
context("tests for summary functions")
#Test aux_mean
test_that("aux_mean works as expected", {
expect_equal(aux_mean(10, 0.2), 2)
expect_length(aux_mean(10, 0.2), 1)
expect_type(aux_mean(10, 0.2), 'double')
})
#Test aux_variance
test_that("aux_variance works as expected", {
expect_equal(au... |
b1f4e78e629be8ab619d7bec1a228780e834bc6e | 04d0a997364ad1bab775fb920edfe5b60cf6d740 | /man/ToWrd.Rd | 826e7496bace7c9912c70a9458fe902e73f90671 | [] | no_license | mainwaringb/DescTools | a2dd23ca1f727e8bbfc0e069ba46f44567e4be24 | 004f80118d463c3cb8fc2c6b3e934534049e8619 | refs/heads/master | 2020-12-22T15:12:41.335523 | 2020-03-21T17:30:52 | 2020-03-21T17:30:52 | 236,836,652 | 0 | 0 | null | 2020-01-28T20:40:03 | 2020-01-28T20:40:02 | null | UTF-8 | R | false | false | 7,234 | rd | ToWrd.Rd | \name{ToWrd}
\alias{ToWrd}
\alias{ToWrd.table}
\alias{ToWrd.ftable}
\alias{ToWrd.character}
\alias{ToWrd.lm}
\alias{ToWrd.TOne}
\alias{ToWrd.Freq}
\alias{ToWrd.default}
\alias{ToWrd.data.frame}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Send Objects to Word
%% ~~function to do ... ~~
}
\des... |
682e9e94b63c8873a3ab9150b30c096c38fde3e2 | 03775e3d1331e2ffe8c6595872a8128273baf67e | /man/season_rosters.Rd | a979aa414abbabd165c27b41e0d0fd071b98aeb1 | [] | no_license | bensoltoff/nflscrapR | a11686f92221fd510d10c0b2dc692dcc06bc2f8f | 7f647893eadca6c6b5253faeae9c991394a285f9 | refs/heads/master | 2020-12-25T01:06:41.025582 | 2016-06-14T17:41:43 | 2016-06-14T17:41:43 | 61,142,565 | 1 | 0 | null | 2016-06-14T17:32:11 | 2016-06-14T17:32:10 | null | UTF-8 | R | false | true | 958 | rd | season_rosters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GameandRosterFunctions.R
\name{season_rosters}
\alias{season_rosters}
\title{Season Rosters for Teams}
\usage{
season_rosters(Season, TeamInt)
}
\arguments{
\item{Season:}{A 4-digit year associated with a given NFL season}
\item{TeamInt:}{A ... |
1b06c39652a050ba6b22159c14420aad36e2eb1a | 99df423066e647677dc2cabb8f77a936a56e1998 | /[3].Check_ES.R | 1664b3c61762f95d3523cdea95a6142b13d244ed | [] | no_license | martin-vasilev/reading_sounds | 661e080c35240d0c64bae4bed9e8780d3150c3c7 | 484e3cc1cf054e26930c85cae4a26ba6467bf1de | refs/heads/master | 2023-04-10T05:41:19.162364 | 2023-03-24T11:28:21 | 2023-03-24T11:28:21 | 65,729,588 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,725 | r | [3].Check_ES.R |
# A script for visualisation and checking of effect sizes
### Violin Plots
rm(list=ls())
load("Data/data_old.Rda")
data<- data_old
source("functions/settings.R")
library(vioplot)
# code adapted from:
# https://www.r-bloggers.com/exploratory-data-analysis-combining-box-plots-and-kernel-density-plots-into-violin-plot... |
896029e763a29feec3ef19a15aacbcd1ee4bf6b3 | abea0b5d000d7c01d390eeb615427bc0322aa30f | /src/ndfd_extract/R_extract_mypass.R | 801128325dbee91a143a2d4a8fd7361ad385dde2 | [] | no_license | janmandel/firewx-evaluation | 5e176d8762f34b4e88a9446f1d898b3698abc5e5 | 51ca3c4a1c63d8c6ba00e910a87f4c87c2c0ac53 | refs/heads/master | 2020-05-05T01:10:49.662013 | 2017-08-24T17:40:06 | 2017-08-24T17:40:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,813 | r | R_extract_mypass.R | ###### This will extract the forecast for six variables for each day
###### in months Jan-June. This corresponds to the months on the 'My Passport' drive
## Define variable Year/Month/Day and base path name
Month = c("01","02","03","04","05","06")
blabel = "/media/wpage/My Passport/NDFD/nomads.ncdc.noaa.gov/NDFD/"
Yea... |
3065c0dd19db6d926238ebd20a43b23b8d397728 | eb127bbb4e75966296b4a2234250ba6819e513b1 | /__old_code_analysis/analyze_transmissions_FCT.R | d08f7df2df247a223419a51a1b51869ac342ce58 | [] | no_license | davidchampredon/stiagent | 29cc33cc8e1a54763ccd5f12f05949ac80354575 | dc6cd187b7649ee4517fc27ea66aff377c8ff892 | refs/heads/master | 2021-01-10T12:50:45.273558 | 2016-03-21T03:45:58 | 2016-03-21T03:45:58 | 43,753,973 | 0 | 0 | null | 2015-11-18T01:53:12 | 2015-10-06T13:56:06 | C++ | UTF-8 | R | false | false | 1,504 | r | analyze_transmissions_FCT.R | plot.infector.gender <- function(transm){
t.gender <- ddply(transm,c("stiname","gender_from"),summarize,n=length(gender_from))
t.gender
g = ggplot(t.gender)+geom_bar(aes(x=factor(gender_from),y=n,fill=factor(gender_from)),stat="identity")+facet_wrap(~stiname,scales="free_y")
g = g + ggtitle("Transmission by inf... |
210f552a9b71994599719182b8e3a9991f0c4995 | 332de09406153981b30b41206afe4b5618b7f248 | /cachematrix.R | f196d14e297cc158849c88e4f05026c1ed907f25 | [] | no_license | samadari/ProgrammingAssignment2 | d916d9f56a666d1409605b4b189bf1ddb9b41d42 | 01e5cd834fd7b1b49cc0d6669daf7b951be908ba | refs/heads/master | 2021-01-16T21:47:03.152944 | 2015-03-15T18:42:52 | 2015-03-15T18:42:52 | 32,274,879 | 0 | 0 | null | 2015-03-15T17:48:51 | 2015-03-15T17:48:49 | null | UTF-8 | R | false | false | 1,678 | r | cachematrix.R |
## Functions to compute the inverse of a matrix and save it in the cache
## so next time the inverse of the matrix is needed it's faster to get
## Function to create an object associated to a matrix; it contains a list with
## a function to set the value of the matrix (and its inverse to NULL) in cache
## a functio... |
b04f492581ee2c151ed0511115d9529a4c70be0d | d133e983aeddf91d6f933c8ce2f7ac60b2b192d5 | /shiny/edit_data.R | a2c823d4707c0e2da59e9f385fee7d2296f6e240 | [] | no_license | amd112/thesis-sp18-driscoll-envjustice | b6ae3651e4a4989b9f259fbaf494ec6054b9208b | 4d6b8dd2539a862c95cb0a9b521c31376b29db67 | refs/heads/master | 2021-09-11T23:25:05.209075 | 2018-04-12T22:00:58 | 2018-04-12T22:00:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,757 | r | edit_data.R | library(shinydashboard)
library(leaflet)
library(dplyr)
library(curl)
library(ggmap)
library(rgdal)
library(readr)
library(data.table)
library(stringr)
library(rmapshaper)
data_county = read_feather("shapes/data_county.feather")
data_tract = as.data.table(read_feather("shapes/data_tract.feather"))
race_tract = as.da... |
72aa91751e764ce57e739c96be5de70b95e6afff | 11be48c2bb50eb77193ee1830a0d569d09079756 | /R_microhaplotypes_diversity.R | 20c8382c9b15cb560e471ba45b91366cbd997562 | [] | no_license | wangdang511/APG_salamanders_R_code | bd53026f5a4b1e32af8bafd8a4991077d834907f | 3cd530bf27045c12afb7d03bb6eb924bb3d6a745 | refs/heads/main | 2023-06-12T17:08:59.862073 | 2021-07-01T10:30:29 | 2021-07-01T10:30:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,941 | r | R_microhaplotypes_diversity.R | library(seqinr)
library(ape)
library(hillR)
library(tidyverse)
# The main function is get_div
# - gets rds produced by R_microhaplotypes.R
# - extracts binary genotypes, sequences of alleles, and calculates diversities for each segment
#
# The body makes filtering and calculated weighted means per gene
# The effect i... |
560b532083acb03f6632a6b32e3d070cc5de670a | 2b16b69d6ed4e31a6796bf90d5b1dbe70269d506 | /R/MTWhoPrimaryOutcomes.R | 8094113fa97282fef36571d19e10d700cf664535 | [
"MIT"
] | permissive | andrewbrownphd/MetaTurkR | 4e35da8fcdae742a4f85e3ba6223f46b062763e4 | e1d6d81029ff08a5b4a0313f3498515f285882bb | refs/heads/master | 2021-01-17T06:48:21.649056 | 2019-11-22T22:43:29 | 2019-11-22T22:43:29 | 53,510,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,339 | r | MTWhoPrimaryOutcomes.R | #' Extract Primary Outcomes from WHO trial database
#'
#' This method extracts the HTML elements associated with primary outcomes in the
#' WHO trial database, which also pulls in records from additional registries.
#' Primary outcomes are denoted by \code{DataList12} in the HTML table. Multiple
#' entries are tab d... |
8cb46366e6089fb868642911c312aee4b821c3cc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/oro.nifti/examples/afni-class.Rd.R | 5f8b2956655ab9f96a5621107631f796cabdc672 | [] | 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 | 169 | r | afni-class.Rd.R | library(oro.nifti)
### Name: afni-class
### Title: Class "afni"
### Aliases: afni-class show,afni-method
### Keywords: classes
### ** Examples
showClass("afni")
|
bbf3f301d8700422005b7a65532a3beae2214296 | 84b5ea895d1b31c59130ac0ce07d09a34f1abb24 | /plot4.R | 96c5a19bcd3534f7223e45f89365692c4218dd05 | [] | no_license | chrisfmontes/ExData_Plotting1 | ad68d69e96843d8e7911f06de0eb9b40fc929105 | 4973d5acac1e110da0276b135c50c8faf13d9321 | refs/heads/master | 2021-05-28T22:28:42.777472 | 2015-06-05T03:59:38 | 2015-06-05T03:59:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,235 | r | plot4.R | power <- read.table("household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?",)
power$Date <- as.Date.factor(power$Date, "%d/%m/%Y")
power <- subset(power, Date >= as.Date("2007-02-01") & Date <= as.Date("2007-02-02"))
power$Time <- paste(power$Date, power$Time, sep = " ")
power$Time <- strptime(pow... |
8c3e392dcab15f64400598b055df66f19291afee | 647e39424fe0ec8b784c6285be1cbc9e929c8e17 | /AI/R/MachineLearning/Unsupervised/Clustering/PAM.R | d6cefc9063ba6346b90b19cefd1fdc09a7c265d5 | [
"Unlicense"
] | permissive | FedeScience/myrepo | 765b92add2ea245bc626d5b81fedc726005adde4 | cd2834fa37cf94d3b21208387adc1bb61d7ec243 | refs/heads/main | 2023-02-06T15:42:31.496965 | 2020-12-24T14:42:59 | 2020-12-24T14:42:59 | 322,318,564 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 274 | r | PAM.R | #@ https://www.datanovia.com/en/blog/types-of-clustering-methods-overview-and-quick-start-r-code/
# Compute PAM
library(cluster)
library(factoextra)
library(magrittr)
# Load and prepare the data
data("USArrests")
pam.res <- pam(my_data, 3)
# Visualize
fviz_cluster(pam.res) |
cbdaeaaa1ecd0fe3c4b8a6228b32f21ec949d4c7 | 72908a67604889444952b56ba9cb570bdc29c426 | /easy_r/Scripts/sc05-2.R | 692ee6f2a110941cf1b59e69eb93600f2c155a59 | [] | no_license | ckiekim/BigDataWithR-Lecture | 01c37469725892373cb288ec22cbd230774e6c2d | 85ad5ed2043e4db2ea3784fc74a6e3635c051cde | refs/heads/master | 2020-04-30T13:11:49.089291 | 2019-03-21T07:47:56 | 2019-03-21T07:47:56 | 176,849,315 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,562 | r | sc05-2.R | # Iris data
head(iris)
tail(iris)
View(iris)
dim(iris)
str(iris)
summary(iris)
iris3
data(mtcars)
head(mtcars)
?mtcars
dim(mtcars)
View(mtcars)
str(mtcars)
summary(mtcars)
data(AirPassengers)
ap <- AirPassengers
head(ap)
View(ap)
head(AirPassengers)
View(AirPassengers)
?AirPassengers
summary(ap)
data(airquality)
he... |
cf30392b1ea83220229e5f06bde1bb4eed9be333 | 5740ab7010175765df3d4a5aac84cddad11e5c2d | /src/R/get_cpu_cores.R | b309e9bb39283032cdc643209b41f445b1c1ad86 | [] | no_license | howl-anderson/sdmengine.common | 4cb53e3391ff1fe9c3182fabc85bd7354e9915d7 | 727c2eab56771297d2d6fe2475482a52aca3de4d | refs/heads/master | 2020-06-13T23:04:25.026311 | 2015-09-04T07:00:55 | 2015-09-04T07:00:55 | 41,902,305 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 647 | r | get_cpu_cores.R | #!/usr/bin/env Rscript
#' get_cpu_cores
#' @param workshop_dir Directory of workshop
#' @return CPU cores that can be used
#'
#' @export
get_cpu_cores <- function(workshop_dir) {
configure <- load_configure_file(workshop_dir)
cpu_setting <- configure$cpu
if (is.null(cpu_setting)) {
# default reser... |
724237d526805b29d1b16944eace00dc69be39f2 | 38373485330e50b09d27ea265ee0535b368f0579 | /code/soccer-preprocessor.R | 6988bd53cbd87dcff3079b4afb55881d166cc7cc | [] | no_license | s81320/vis | 5300e346349acd568cd7ff4ad06751960aeb42b8 | b96755388ebdbd50c42d145e9e6fc26b2c1c45c4 | refs/heads/master | 2022-11-18T03:34:05.794807 | 2020-07-21T17:25:05 | 2020-07-21T17:25:05 | 270,222,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,178 | r | soccer-preprocessor.R | preprocess <- function(io_dir, input_csv_name) {
# directory and file exists check
if (!dir.exists(io_dir)) {
stop(
paste0(
"The provided input-output directory does not exist! (", io_dir, ")"
)
)
}
input_csv <- file.path(io_dir, input_csv_name, fsep=.Platform$file.sep)
if (!fil... |
1448e6876cec530e03ca09172d26b1d4e94c2729 | 815906cb89ebcf9683dd355a27025638c8a0850a | /man/Barplots.Rd | 3d5ae84fd6fcce04613edd55b7f0b306422f1d19 | [] | no_license | mathiaskalxdorf/IceR | 7510690cc7e8784e36d2adb30f04e68d8f8566a4 | ebf1a670e9f64007a85352f9ba0c925bd5dfb949 | refs/heads/master | 2023-04-08T04:27:42.515154 | 2022-07-30T04:39:34 | 2022-07-30T04:39:34 | 271,002,267 | 16 | 5 | null | null | null | null | UTF-8 | R | false | true | 1,319 | rd | Barplots.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/General.R
\name{Barplots}
\alias{Barplots}
\title{Generate barplots}
\usage{
Barplots(
Data,
ErrbarData = NA,
Name = "",
xlab = "X-axis",
ylab = "Y-axis",
main = "Titel",
col = "lightblue",
AvgLine = T,
digits_average = 0,
... |
fe2254e279d807bf045cd4ec3710ecd1c51667ac | 0e92c0b362b230341f9cc31207df8139dbc3ac18 | /man/boundaries.Rd | e2ce1914328dff0d8028f5472864bcbf4a5e01b3 | [] | no_license | cran/raster | b08740e15a19ad3af5e0ec128d656853e3f4d3c6 | dec20262815cf92b3124e8973aeb9ccf1a1a2fda | refs/heads/master | 2023-07-09T20:03:45.126382 | 2023-07-04T10:40:02 | 2023-07-04T10:40:02 | 17,699,044 | 29 | 35 | null | 2015-12-05T19:06:17 | 2014-03-13T06:02:19 | R | UTF-8 | R | false | false | 1,521 | rd | boundaries.Rd | \name{boundaries}
\alias{boundaries}
\alias{boundaries,RasterLayer-method}
\title{boundaries (edges) detection}
\description{
Detect boundaries (edges). boundaries are cells that have more than one class in the 4 or 8 cells surrounding it, or, if \code{classes=FALSE}, cells with values and cells with \code... |
0959664ad8cc6ee40b50ad49d5ca006bb20d7569 | 6b629e8bc4bb0b1c93bb217cb218af5ae5e587c8 | /gender_differences/old/associate_ext_factors_to_phenotypes.R | 86d23fb63f652b298c05ab0cf6ccd5728e5a8cc1 | [] | no_license | DashaZhernakova/umcg_scripts | 91b9cbffea06b179c72683145236c39f5ab7f8c2 | 1846b5fc4ae613bec67b2a4dd914733094efdb23 | refs/heads/master | 2023-08-31T10:45:17.057703 | 2023-08-23T14:47:43 | 2023-08-23T14:47:43 | 237,212,133 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,737 | r | associate_ext_factors_to_phenotypes.R |
source("/groups/umcg-lifelines/tmp01/users/umcg-dzhernakova/scripts/umcg_scripts/gender_differences/preprocessing_gam_fitting_functions.R")
setwd("/groups/umcg-lifelines/tmp01/users/umcg-dzhernakova/gender_difs/factors")
rm_outliers <- function(merged_tab){
merged_tab <- na.omit(merged_tab)
w <- merged_tab[... |
7df25efe5be0105d853a83fc749fb7e040602f46 | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2019/nonfatal_code/resp_asthma/crosswalk/run_MR_BRT_asthma_ild_clean.R | d0af6863ab56ed394ec100806bb3c4975290a2c3 | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,515 | r | run_MR_BRT_asthma_ild_clean.R | ##########################################################################################################################################
## Purpose: Prep and run MR-BRT
## Created by: USERNAME, column re-arrangement based on script by USERNAME
## Date: March 2019
##
## Step 1: Create master file of all crosswalks f... |
225af4d81e0ebe3c83a2829d9dd2d754002dabf0 | c2d9b62b1fff20d16c3f425c981b05a4398aef55 | /2_R_Programming/r_programming_week2.R | 8dedb53f687339e7f2cdabca2fb26713a92c29a4 | [] | no_license | Ailuropoda1864/coursera-jhu-ds | 214808800cd4cbb5f587821c990f3b32af47bb1a | a210076085f6953a7a149596339fb7f4b2061756 | refs/heads/master | 2018-12-09T18:49:22.695056 | 2018-09-12T01:56:08 | 2018-09-12T01:56:08 | 120,560,640 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,295 | r | r_programming_week2.R | DIR <- '/home/fay/code/r_coursera/2_R_Programming/data/week2/specdata'
# Part 1
#' Calculate the mean of a pollutant (sulfate or nitrate) across a specified
#' list of monitors.
#'
#' @param directory A character vector of length 1 indicating the location of
#' the CSV files.
#' @param pollutant A character vector... |
42d5197627503de9df388bd92a0efd89aed3d465 | 09f4710323cae92f8af5d96f54b070bbf93bc4df | /Biomass/R_scripts/archives/overlay_cropped.R | acb1378a31d68c4cac5398ae98c9b72967d4c369 | [
"MIT"
] | permissive | WoodResourcesGroup/EPIC_AllPowerLabs | 0a7885345d2228aedb66ec9355f80ae38fef5545 | bf3240672f02fa93243cb2241e9c49249ce710aa | refs/heads/master | 2021-01-11T19:48:03.613416 | 2018-04-11T23:53:07 | 2018-04-11T23:53:07 | 79,397,605 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,469 | r | overlay_cropped.R | ### ATTEMPTS TO ACTUALLY CALCULATE BY PARK
### SOME OF THIS CODE IS PASTED FROM units_overlay.R on Carmen's PC
## Open LEMMA
if( Sys.info()['sysname'] == "Windows" ) {
setwd("C:/Users/Battles Lab/Box Sync/EPIC-Biomass/GIS Data/LEMMA_gnn_sppsz_2014_08_28/")
} else {
setwd("~/Documents/Box Sync/EPIC-Biomass/GIS Dat... |
b26c70e6aa3831ae7bc37e46dd2435d190df53a8 | 33e13418d80d2a094071bf41b94aff10c0e3204a | /metrics/Compartments/rpgms/genset.r | 710f953ed1b4a55a71689cf2e6bfc4ff88c011b6 | [] | no_license | jpbida/FKS | 97f5102ef311924c215d4dac11485da0ec507366 | 2587c06adc8ed52a02a4be68d84afbdfd8672040 | refs/heads/master | 2020-04-24T14:36:38.318250 | 2012-07-25T19:43:34 | 2012-07-25T19:43:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 852 | r | genset.r | ### For each of the datasets ###
datasets<-c(
"allr4_75.px",
"allr4_50.px",
"allr4_25.px",
"allr3_75.px",
"allr3_50.px",
"allr3_25.px",
"allr2_75.px",
"allr2_50.px",
"allr2_25.px",
"allr1_75.px",
"allr1_50.px",
"allr1_25.px"
)
#### Break the space into cross sections squares ####
for(dataset in datasets[1])
{
print(da... |
bf3d45d30a5263255858c265923ba9335a3366fa | ead04fbe576b37496435e375b904100381b6fa08 | /comparison_lda_qda_mda.R | b5d5a0d6a498da11fef8737be8c0841aa635a9f1 | [] | no_license | akshayvkale/LDA | e313b0b3a9ba4b1ff40cf9db9577ae0f47afeb28 | 35238c72d220cb8c9810768252742f78c338afa0 | refs/heads/master | 2022-11-27T09:47:41.669489 | 2020-08-04T10:30:54 | 2020-08-04T10:30:54 | 284,947,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,849 | r | comparison_lda_qda_mda.R | library(MASS)
library(mvtnorm)
library(mda)
library(ggplot2)
?rmvnorm
n<-500
x11<-rmvnorm(n,mean = c(-4,-4))
x12<-rmvnorm(n,mean = c(0,4))
x13<-rmvnorm(n,mean = c(4,-4))
x21<-rmvnorm(n,mean=c(-4,4))
x22<-rmvnorm(n,mean = c(4,4))
x23<-rmvnorm(n,mean = c(0,0))
x31<-rmvnorm(n,mean = c(-... |
a04708c161590a3ed57094a6f0bfbb3d67c4179b | 71e7a3518e75dba5226c7c5224068910c60bfb7e | /R/aoi_map.R | 1c747db8b6e735a20aef73fbbfff9a6fe8898fc6 | [
"MIT"
] | permissive | mikejohnson51/AOI | f430078cdae4aeb0720e78bbf6a6987af8b5f677 | a7d54a0f6951a8e61f8f7a5f09056d8069fbe1f6 | refs/heads/master | 2023-08-04T22:39:28.531795 | 2023-07-26T18:21:38 | 2023-07-26T18:21:38 | 139,353,238 | 31 | 2 | MIT | 2021-04-02T04:28:39 | 2018-07-01T18:47:25 | R | UTF-8 | R | false | false | 3,348 | r | aoi_map.R | #' @title Generate Leafet map and tool set for AOI
#' @description
#' Provides a precanned leaflet layout for checking, and refining AOI queries.
#' Useful \code{leaflet} tools allow for the marking of points, measuring of
#' distances, and panning and zooming.
#' @param AOI any spatial object (\code{raster}, \code{sf... |
ce0501ed09eda06bf4578ce503ec10342fafde32 | b03b4b9bb4ff8a48f39d3bad19e24509a463369c | /DEseq.R | 6aae3a95ad651fdae9cff32c21993796c0019905 | [] | no_license | rkweku/Additional-Scripts | 9d7a5082e800b11558b410a900520a32749037c4 | bf2ad15db661bd3ad3b687b27286ca5c259ae44f | refs/heads/master | 2020-04-18T01:09:18.660966 | 2018-11-27T05:18:22 | 2018-11-27T05:18:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,469 | r | DEseq.R | ## RNA-seq analysis with DESeq2
# Import & pre-process ----------------------------------------------------
library("DESeq2")
# Import data from featureCounts
## Previously ran at command line something like this:
## featureCounts -a genes.gtf -o counts.txt -T 12 -t exon -g gene_id GSM*.sam
# countdata <- read.table("... |
9173975e2a4044d5538ac7d5f15fa5bb3274fece | c77938ab77375bd8a524daba269e83a201c22cdf | /modules/predictBirds/R/fitModel.R | ac1866966490ff87caf1fb81776ecc5e54f6191f | [] | no_license | tati-micheletti/borealBirdsAndForestry | dbe385d5129770caec12e986371fc76f2317801e | 27d11df65759ed720d2a6646132597a117c3fc39 | refs/heads/master | 2021-03-19T17:13:20.851222 | 2020-09-17T19:07:29 | 2020-09-17T19:07:29 | 121,669,535 | 0 | 4 | null | 2018-08-02T21:52:30 | 2018-02-15T19:03:31 | HTML | UTF-8 | R | false | false | 1,521 | r | fitModel.R | fitModel <- function(inRas,
inputModel,
x,
tileYear){
# Not a raster predict anymore. This is now a data.frame predict.
if ("glmerMod" %in% class(inputModel)){
prediction <- predict(object = inputModel,
newdata = inRas,
... |
41dc9363ff32c8737af3dc6b1ba54267fd8446a5 | 5d7c787375367158323d48bea255f1422b522ef9 | /SF360/server.R | 649c5566705f1c70b00ce88acf055fc29ae7c4d9 | [] | no_license | nightowl21/Extra-Projects | 21087d0a6aae4486b9dfa85d893d25a9f3f064f9 | 82be6e6b47f60ff169e1985aec78f61fae4c8df2 | refs/heads/master | 2021-06-19T09:05:03.201565 | 2017-06-14T05:46:21 | 2017-06-14T05:46:21 | 50,462,583 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,721 | r | server.R | source("helper_file.R")
icons <- iconList(
active = makeIcon(makepath("active.png"), iconWidth=18, iconHeight=18),
arts = makeIcon(makepath("art-and-culture.png"), iconWidth=24, iconHeight=24),
restaurants = makeIcon(makepath("dinner-eat-restaurant-icon.png"),
iconWidth=24, iconHeight... |
11fb68e71d490608003d34df90be9544a876558e | 5177b6787faf6aa0975a14e716502d395c42d5f1 | /tidymodels.R | 1476c8659fde0e957309f64113aa859c46310fc8 | [] | no_license | rpodcast/renv_learning | d50465c9c40e561f1975220cb054744e7eb7434b | b6ac53fb8d7beb69f5b67f73871f27ea7984c87c | refs/heads/master | 2023-08-22T19:19:31.579084 | 2021-10-08T03:27:55 | 2021-10-08T03:27:55 | 414,778,030 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 32 | r | tidymodels.R | library(pak)
library(tidymodels) |
3336f9f8919fa702e298971733e01303275e53f0 | 0116fa27069272135b3cb53efa60c1d5e8fc5bfc | /man/make_qsubfile.Rd | e63d091d1cbbe3d89f5e41961dbb452fb4e4dfb9 | [] | no_license | sinnhazime/jobwatcher | 540496726f828d50e3e24ec83b69b19f541b39e6 | 2e4c3e7be4985484c25270f9fdaf85fab41b1d55 | refs/heads/master | 2020-04-13T14:07:00.888210 | 2019-08-29T04:20:00 | 2019-08-29T04:20:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,094 | rd | make_qsubfile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qsub.R
\name{make_qsubfile}
\alias{make_qsubfile}
\title{make a file suitable for \emph{qsub}}
\usage{
make_qsubfile(..., name = NA_character_, first_line = binbash(),
parallel = parallel_option(), arrayjob = arrayjob_option(),
directory ... |
6376af6b14e4b91cf68288adf9c3c20aa081b4cf | f06e99784403917490dc25e848c26d9f49410027 | /lecture 8.R | a868af47c2d0726e9952a4bbf4dea3be9baffaed | [] | no_license | prl907/st503 | 4220122927ba3b29d620027b38978fa7845d826c | 53a83740206e4ad813d9930f1766c430ed7f2df8 | refs/heads/master | 2020-03-27T12:06:46.089698 | 2018-12-20T18:43:31 | 2018-12-20T18:43:31 | 146,526,853 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,286 | r | lecture 8.R | library(faraway)
data(teengamb)
o <- lm(gamble ~ sex + status + income + verbal, data=teengamb)
summary(o)
e <- residuals(o)
r <- rstandard(o)
id <- row.names(teengamb)
# partial regression/residual plots
#method 1
d <- residuals(lm(gamble ~ sex + status + verbal, data=teengamb))
g <- residuals(lm(income ~ sex + st... |
c360916501a9e765bc89b0eb074efe9ad3733b38 | 14870a84eaf1f692d7a7f6212ae1e4a31f2458e2 | /completeApp/app/global.R | 86ae5acd2dea75d6c39e4ae8cf8fe5c5d178b172 | [] | no_license | MathieuMarauri/shinyApps | 2476bd0199deb7eaa5e315fbb90d08f577f9c1d4 | f7c787a4f974db481b98048d44813d3d730b4c39 | refs/heads/master | 2021-07-09T18:15:28.796178 | 2017-10-12T08:44:40 | 2017-10-12T08:44:40 | 104,908,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,744 | r | global.R |
# Packages ----------------------------------------------------------------
library('shiny')
library('shinydashboard')
library('shinyjs')
library('shinyBS')
library('shinyWidgets') # the dev version 0.3.4.930
library('highcharter')
library('DT')
library('data.table')
# Function -------------------------------------... |
c1cdf738d94bce0c5a37b3b5d52628915b1b9f61 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.database/man/redshiftserverless_list_snapshots.Rd | ebb413d51d7e4dd1667546b671fcc4abc3f1c471 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,404 | rd | redshiftserverless_list_snapshots.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/redshiftserverless_operations.R
\name{redshiftserverless_list_snapshots}
\alias{redshiftserverless_list_snapshots}
\title{Returns a list of snapshots}
\usage{
redshiftserverless_list_snapshots(
endTime = NULL,
maxResults = NULL,
namespa... |
97bf8953ba012fcd4f1a7638de128d8401563a7a | c743e20bebbaf6f59056e697c23b29e291877915 | /man/node_preprocess.Rd | 7729b35f62725639f0185668c447e15c9c910665 | [] | no_license | cran/netregR | f22457d928c27c4b54280f97e1d0bdd2ff86c815 | c831e31a8a6bed1093ecc1e43669ea73c982a435 | refs/heads/master | 2020-03-08T20:40:55.031462 | 2018-08-01T22:10:02 | 2018-08-01T22:10:02 | 128,388,283 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 318 | rd | node_preprocess.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{node_preprocess}
\alias{node_preprocess}
\title{Pre-processes data for ordering etc.}
\usage{
node_preprocess(Y, X, directed, nodes, subtract = NULL)
}
\description{
Pre-processes data for ordering etc.
}
\keyword{internal}
|
e0508fc8800d01f447274708bb3aabc1eacd0924 | ce828d49e40d96aa975b792e23d0ed4172828ef7 | /Pass-through Cambio Inflacao/R/scripts/BACEN-SelectMerge.r | 4080e9224b9ac11373ce196e5053f78b87bc568b | [] | no_license | btebaldi/MetodosQuantitativos | bd5ee54a088707f0408636c3955f3087ee3f7ae2 | c38ae095064e0b021b3ebdd0427bd3d383742b80 | refs/heads/master | 2021-01-20T06:26:20.750079 | 2020-07-12T16:54:38 | 2020-07-12T16:54:38 | 89,879,517 | 0 | 0 | null | null | null | null | MacCentralEurope | R | false | false | 969 | r | BACEN-SelectMerge.r | ## --- Bibliotecas R
library(RODBC);
## --- Programa Principal
databasefile = "../database/MQA-PassThrough-Database.accdb"
## Abre a conex„o com o banco de dados
conn = odbcConnectAccess2007(databasefile);
query = "select CODE, DT_REFERENCIA, VALOR from SERIES_DEFINICAO, SERIES_DADOS_ECONOMIA where SERIES... |
ad5d6cf7b0205b8f881c3c3f94018c33aba21bdf | ec71dc1a7ade785d3cab60c9878b939969636760 | /R/data.R | 98da6fc9fb6a709184cff8a87c75d9c0ea60180d | [] | no_license | Elsa-Yang98/ConformalSmallest | a800255f55446f173575a6367ca193bc5acb89b0 | adcc98543289600952d80eccbe278d387ff55295 | refs/heads/main | 2023-07-07T14:44:30.746429 | 2021-08-10T08:10:52 | 2021-08-10T08:10:52 | 373,796,629 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 711 | r | data.R | #' Blog data
#'
#'@format A dataset of dimension 280+1:
#' \describe{
#' \item{subject}{Anonymized Mechanical Turk Worker ID}
#' \item{trial}{Trial number, from 1..NNN}
#' ...
#' }
#'
#'@source blogData_train.csv
"blog"
#' Concrete data
#'
#'@format A dataset of dimension 8+1
#'
#'@source concrete.csv
"concrete"... |
ff4826cc5c850a131bcbaaa8fd0442ec24692cf2 | a34c74086329dfd2aa7f8ad588e07f5bc7c05870 | /functions and packages/gmes_calc_tobacco.R | 0e90a4b1a601e869ea88c455204f2186c161ed2f | [] | no_license | CourtneyCampany/WTC3_tree | cbc1a0813edf337eba367d428974588f85bc440a | 500c06a5c134fb6b419901b8708b26f22232c733 | refs/heads/master | 2020-04-09T18:37:49.332701 | 2018-09-12T14:49:24 | 2018-09-12T14:49:24 | 30,519,484 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,397 | r | gmes_calc_tobacco.R | ####gmescalculation----------------------------------------------------------------------------------------------
gmcalc_tob_func <- function(x, a=4.4, ab= 2.9, e=30, b=29, f=16.2,del_growth = -8 , delR=-38,
k25r=0.718, k25g=38.89, Ea_r = 46.39, Ea_g = 24.46,Rgc=8.314472, c_r=18.72, c_g=13.49){... |
835e0b4f19ce3bcff8cf8ad57afb46bf49c5006d | 2c5ad7606ebf6c29ef24d15aa7947177a207225d | /helper.r | b118fee8a59251716a256ba0a8cd66687df9e577 | [] | no_license | WillemVervoort/RainfallCS | 3013070a50a502674348c201534b74105f0c5c95 | c20541d8aa8d353876404bb15993b626575ed98c | refs/heads/master | 2021-01-15T18:01:06.889053 | 2014-12-11T11:14:10 | 2014-12-11T11:14:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,625 | r | helper.r | ## Helper functions for Rainfall CS project
# 1. multiplot function from http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_%28ggplot2%29/
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matri... |
3fa097e0c6bbcb7a3fcb670fc68c6925f1f5d514 | bbbf014b7675036df4ca5d7039cf338375ac89a7 | /Part3/workflow/scripts/plot_integrated_data_by_species.R | b13468e39a1c1677ea87b69d75889c0a7a11e6b6 | [
"MIT"
] | permissive | Woodformation1136/SingleCell | 17c0a1bf9bf134178a2675f0ae06819417ea744c | cb4a463a7278b37c90c357ac8d4aaa0d57a8e774 | refs/heads/main | 2023-04-18T08:19:10.542089 | 2022-12-23T17:40:44 | 2022-12-23T17:40:44 | 385,837,434 | 0 | 1 | null | 2022-12-23T17:40:45 | 2021-07-14T06:29:16 | R | UTF-8 | R | false | false | 4,428 | r | plot_integrated_data_by_species.R | library(Seurat)
library(scales)
library(magrittr)
ori_par <- par(no.readonly = TRUE)
# Define functions =============================================================
# Output figure
output_png_figure <- function(
plotting_function,
output_figure = FALSE,
output_path = "temp.png",
output_without_margin... |
ce76b33f40348d102f0d59067d36e7c3a5dc3c81 | efc373c2ecb0fc0b00db2f05808f2b50de3af5f1 | /BasePower.r | d1cb4aee6b309753b869457c3f6d937788a00eb6 | [] | no_license | obaidpervaizgill/Fun-Shorts | ff27bccac7ac215bf2f32e11d7f89ff3a77040a7 | dd074fba2c79d07bf320359ef94d7531b02208ac | refs/heads/master | 2020-12-25T14:49:24.671557 | 2017-06-24T15:10:25 | 2017-06-24T15:10:25 | 62,437,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 423 | r | BasePower.r | #base power - function
#num is number you are trying to find the x for where x is some power to the base 10
#e.g. 100 == 10^x the function should return 2, here 100 is num and x is what we are trying to find
findNum<- function(num){
x <- seq(1,100000,0.01)
for(i in x){
y <- as.integer(10^i)
... |
c6e03d1bb567c398c9f1189743b8093ec8347760 | 1ff3a51b463c951aa02ef40a89c5a884c94f9516 | /man/overlaidSimpleRegressionPlot.Rd | 87ecfa1e1f4115f1a9a8b16a8798e7a5f6900426 | [] | no_license | cran/fit.models | 3a250a89603637cfd2296b4cf25f6bcc8e38eda6 | 2548545703702dbc11c8a2b9ceda8da77777386e | refs/heads/master | 2021-01-10T01:00:23.547075 | 2020-08-02T13:30:02 | 2020-08-02T13:30:02 | 17,696,066 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 817 | rd | overlaidSimpleRegressionPlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/overlaidSimpleRegressionPlot.R
\name{overlaidSimpleRegressionPlot}
\alias{overlaidSimpleRegressionPlot}
\title{Scatter Plot with Overlaid Fits}
\usage{
overlaidSimpleRegressionPlot(x, lwd.reg, col.reg, ...)
}
\arguments{
\item{x}{a \code{fit.... |
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