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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ea997373e1c22dc1f7c93acc4c7e3e532b5d46bf | c3a10ba8fb8bcd3769728612ff737babd683670a | /R/Code/bbg_datamgr.r | 2a541d1b77348a3da9923e1f1311202fcabb08c6 | [] | no_license | apexdevelop/working_dir | 3f707bf81bda1e16d58ea358a0dcd71ce3993722 | e514c41e99699aa52d3b4a553c99b31b72d45202 | refs/heads/master | 2020-03-11T16:48:47.009053 | 2019-05-31T19:01:20 | 2019-05-31T19:01:20 | 130,128,853 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,541 | r | bbg_datamgr.r |
# You can specify that a row be returned for all dates in the requested period, even when markets or
# closed or otherwise no data is available, by specifying include.non.trading.days=TRUE. If you request
# multiple securities, this is automatically set to TRUE. You can use na.omit or na.exclude to remove
# these rows... |
3bee3b03f6ebe254911e97071cd6b57ffdedec4e | b28ca303e610d6ce0a158c44181a103beea50ac8 | /man/EllipAxes.Rd | d15824a81b99e0019fb16cd5db913ac72d7a20bf | [] | no_license | cran/RockFab | ba61ed4ea01c84f7efa7ff12e354326fbe7e362c | 87537ff68f2cbc4e680d11a995e0ac61d8db7db8 | refs/heads/master | 2022-07-09T16:31:16.194443 | 2022-06-23T06:18:46 | 2022-06-23T06:18:46 | 17,693,392 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 941 | rd | EllipAxes.Rd | \name{EllipAxes}
\alias{EllipAxes}
\title{
Calculate ellipsoid axial lengths based on octahedral shear strain and Lode parameter.
}
\description{
Function uses the octahedral shear strain and Lode parameter of a desired strain ellipsoid and returns the normalized axial lengths X Y and Z.
}
\usage{
EllipAxes(... |
d2df3a5ce57dee3ea3ad51860de0d8a1d7f0d8b5 | 6e5f307e8ad2619605b6a43d8b4965d9e3c7abaf | /plot1.R | 54e07cc4781936c31b84b517b1382fb0bf49413d | [] | no_license | brettryder/ExData_Plotting1 | 13bc668ebd4e6e91531e180355a191d74dad0737 | b9cc8ad1402c2f9fbf0a7ea82ffb4b012b7afdfc | refs/heads/master | 2020-12-03T02:26:07.895387 | 2014-09-07T14:40:28 | 2014-09-07T14:40:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | r | plot1.R | cnames<-c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
y<-read.table("household_power_consumption.txt",skip=66637,nrows=2880,sep=";",stringsAsFactors=FALSE,col.names=cnames)
d<-paste(y$Date,y$Time)
datetime<-strptime(d,"%d/%... |
f19e3e79b40279c9da7dbaf23474172b685c9fbe | ab78c055a5e0a60cb91733bb55442cbc98891255 | /R/GEOquery_patch.R | 1be33f1d2c85492150a619423a3d327eb8792cfd | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rmartin84/rbbt-marq | efd5cc0646d5eca199798c4ee2201fb97ab57f37 | b7127afef5fc91256b5af33f06ae1e73a1761ae1 | refs/heads/master | 2021-01-21T18:33:47.745915 | 2010-04-11T16:07:07 | 2010-04-11T16:07:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,817 | r | GEOquery_patch.R | library(GEOquery);
# The original version of the function failed if the dataset had extra probe ids not in the platform.
# This version fixes that
"GDS2eSet" <-
function(GDS,do.log2=FALSE,GPL=NULL,AnnotGPL=TRUE) {
require(Biobase)
# exclude non-numeric columns
if(is.nu... |
877d29b9eade00ab5ef166227f9976f4c4dea051 | 2152276a40e8004ea7c9494fd0094629681ad43b | /man/pb_add_tag_column.Rd | d45d03a0b6bc9b4b1c5877f531ed762e97384c74 | [
"MIT"
] | permissive | RMHogervorst/pinboardr | 10f5b084e0dbead500b569d5589d761063eb74ba | d4d145f97c054a09e8923c972acb924a084d4b70 | refs/heads/master | 2022-11-30T06:19:09.718937 | 2020-08-08T10:03:10 | 2020-08-08T10:07:16 | 262,958,200 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 623 | rd | pb_add_tag_column.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{pb_add_tag_column}
\alias{pb_add_tag_column}
\title{Extract one tag from tag columns into separate column.}
\usage{
pb_add_tag_column(dataframe, tag)
}
\arguments{
\item{dataframe}{the dataframe of bookmarks}
\item{tag}{tag you ... |
eb0fd843a964d3c5cddf94067538eccfc6781479 | 9047685851ec9bebdd8ea43f51eae52a071d7b6a | /R/plot.pointestimate.R | 52d2a2711499af9995d42c9837136b1d4cb2e2db | [] | no_license | sarawade/mcclust.ext | 4f87bb7372efde1d26f3a851ffa4052413acda96 | 4cd79fa68a5807f188285073bf61dd3ae7748c94 | refs/heads/master | 2020-06-04T06:30:16.288712 | 2019-06-14T09:43:41 | 2019-06-14T09:43:41 | 191,905,481 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,588 | r | plot.pointestimate.R | plot.c.estimate=function(x,data=NULL,dx=NULL,xgrid=NULL,dxgrid=NULL,...){
if(is.null(data)){
stop("data must be supplied")}
if(!is.data.frame(data)){
stop("data must be a data.frame")}
p=ncol(data)
n=nrow(data)
if(!is.matrix(x$cl)) x$cl=matrix(x$cl,nrow=1)
k=apply(x$cl,1,max)
n.c=nrow(x$cl)
par(a... |
eab4b01f8aa71539df553e8be71c487bd15f6b4e | 4a9bf7ca2c4973b53940c4cc26d58e6f18522d0f | /R/updateObject.R | 07a2d5d6f95d4e6e19dc0393e95dcf76cf5a8938 | [] | no_license | AswinSSoman/clusterExperiment | d3aa604a060ad21fa7b5ab62eeb8bafedd7e105c | 2328bd62f1b7f2c1c84fd1b25e1944946a63d7f9 | refs/heads/master | 2020-04-01T21:00:42.473582 | 2018-09-17T15:16:44 | 2018-09-17T15:16:44 | 153,632,551 | 1 | 1 | null | 2018-10-18T13:57:01 | 2018-10-18T13:57:01 | null | UTF-8 | R | false | false | 5,651 | r | updateObject.R | #' @title Update old ClusterExperiment object to current class definition
#' @name updateObject
#' @description This function updates ClusterExperiment objects from previous
#' versions of package into the current definition
#' @param object a \code{ClusterExperiment} (or \code{clusterExperiment} from
#' older ver... |
5863f3806950c22c44124d1a578b071c057bbfed | 91470d02494415246cae0f0754c175d11d21e6de | /Analisa_banco_unico.R | f265ab2cee75613629425ff2bfbe4e41c127183e | [] | no_license | nemesis-lab/pesquisa-suicidio | 30f28afaf9a7b33800c7ae4d7c593417fe941dd1 | 64b5efe285aaf9f7b73ef40656dbb0dcd6d48d77 | refs/heads/main | 2023-05-08T01:57:11.947588 | 2021-05-25T23:08:12 | 2021-05-25T23:08:12 | 346,498,442 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,739 | r | Analisa_banco_unico.R | library(dplyr)
library(tidyverse)
library(readr)
library(pastecs)
library(fmsb)
library(writexl)
# CARREGA O BANCO
banco <- read_csv("C:/Users/Carlos Garcia Filho/Desktop/Comparativo - Brasil, Argentina e Colômbia/data_final.csv")
# INSPECIONA AS VARIÁVEIS
df <- as.data.frame(table(banco$SEXO))
df... |
422134a64bf10e0e5475677871f7011a778c3600 | b3b1d17bccd5e33d92acf5ffba72ee2a36c9abda | /R/temp_registry.R | dfd7cd2f576734ddb60878302f8c90fd1b29398a | [] | no_license | HenrikBengtsson/future.batchtools | 59a0ebac832e890da5a88b029af8b4a6bf9031cd | 10b8d482625eaf4e802b93088feddda3b775651b | refs/heads/develop | 2023-08-05T06:45:02.100130 | 2023-02-24T10:42:02 | 2023-02-24T10:42:02 | 56,456,829 | 87 | 11 | null | 2023-04-30T10:11:52 | 2016-04-17T20:27:22 | R | UTF-8 | R | false | false | 4,359 | r | temp_registry.R | #' @importFrom batchtools makeRegistry saveRegistry
temp_registry <- local({
## All known batchtools registries
regs <- new.env()
make_registry <- function(cluster.functions = NULL, config = list(), ...) {
## Temporarily disable batchtools output?
## (i.e. messages and progress bars)
debug <- getOpti... |
4d99b9bff78cea5905f57e7c2e4351ccb1296c15 | f353cccd6aa37ff29641ffd9543fa88dbc03f3d3 | /stetige/Exponential.R | d8b78691db216129012247bec594f4d22656000d | [] | no_license | oduerr/stat | e41afb6a094237c312c06171d26de1c5d9e99077 | f35276735941859ba18fcfe513b47673ff4c4582 | refs/heads/master | 2023-06-27T10:31:55.971635 | 2023-06-12T07:05:48 | 2023-06-12T07:05:48 | 152,250,292 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 47 | r | Exponential.R | pexp(2500, rate=1/5000)
qexp(0.5, rate=1/5000)
|
783e86411a55e0db7ff242aca88b787cac2e0879 | 4dc3bead007549c03ad14e589a5ebe7157ac4da0 | /man/logLik.regional_mix.Rd | fa5db7c9fff06b1a4754847d6c48f6f03d14c576 | [] | no_license | skiptoniam/ecomix | 428615e5a7fc4af8f834f129b79b3279bb9d132d | 73f167135380f73b886ab8ce1ead2b0a4fe29e74 | refs/heads/master | 2023-04-08T18:52:49.673475 | 2022-05-27T01:49:47 | 2022-05-27T01:49:47 | 80,573,432 | 5 | 2 | null | 2021-01-09T05:29:32 | 2017-01-31T23:36:08 | R | UTF-8 | R | false | true | 415 | rd | logLik.regional_mix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/regional_mix_s3-class.R
\name{logLik.regional_mix}
\alias{logLik.regional_mix}
\title{Extract log-likelihood from a regional_mix model.}
\usage{
\method{logLik}{regional_mix}(object, ...)
}
\arguments{
\item{object}{A fitted regional_mix mode... |
c9c4116c13db90cceca29929b29de4978c018ad4 | 958b5f9aa004a0c7f70330d4cf82fd3d503bc8d4 | /plot1.R | 39c782c534e45bd2d26d8c0760df8fde23e00611 | [] | no_license | AthenaMarine/CreatingPlotsWithR | 7e7dbf6b8d2df6b1a2911130ee91aec667f09e10 | 5032777c2f5db3733c21d90fc2d17072e4fd1104 | refs/heads/master | 2021-01-17T05:38:32.311155 | 2015-09-13T01:20:19 | 2015-09-13T01:20:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,112 | r | plot1.R | ## plot1.R - This script creates a histogram using test data
## from the UCI Machine Learning Repository on household power consumption data
## for the days February 1-2, 2007
## raw data should be downloaded and unzipped into your working direcotry from here:
## https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fh... |
a59db5d9599a9ab5780561063f0d60ff7915a015 | 2cd2c95d90ef8eef58f415112a80899bd2d7dca9 | /tests/TWQ-savedvalues.R | f225bfc958effa86970e4d2a3e0c0194b41546c3 | [] | no_license | cran/robustloggamma | c945aed6f108395fd347e805fc6137170652ceb7 | 888332487b843ca3dc3c73b82c6e748ec7ac490d | refs/heads/master | 2021-05-16T02:27:17.930624 | 2019-04-22T05:59:44 | 2019-04-22T05:59:44 | 17,699,306 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 253 | r | TWQ-savedvalues.R | structure(list(TQtau = structure(c(2.08212123663978, 1.76545564459274,
1.19), .Names = c("mu", "sigma", "lambda")), TWQtau = structure(c(2.05574667510975,
1.82264903228846, 1.12), .Names = c("mu", "sigma", "lambda"))), .Names = c("TQtau",
"TWQtau"))
|
b15bd49cdce712e4ce4578b0dc98a3fc901132f3 | 618679439003f1173700c9cabf62988090f04eb2 | /man/detection_rate.Rd | 787ab0355171079976e719a3706a8622be357d8f | [] | no_license | Stefanos-Apostle/SeSAMeStr | 7ecd44ec12215c8d2247183ac1d2e675ff641f48 | e14b581e05fe9adec2da44df1372f23e97de7245 | refs/heads/main | 2023-04-19T05:14:35.239874 | 2023-03-24T17:25:51 | 2023-03-24T17:25:51 | 529,333,654 | 1 | 1 | null | 2023-01-19T03:52:48 | 2022-08-26T16:39:15 | R | UTF-8 | R | false | true | 443 | rd | detection_rate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SeSAMeStr_utils.R
\name{detection_rate}
\alias{detection_rate}
\title{Plot Detection Rate}
\usage{
detection_rate(sdfs, out_dir)
}
\arguments{
\item{sdfs}{list of data frames of intensity values for each replicate; output of get_sdfs}
\item{... |
0269876b3a231d080b36c22fb555a685bfca90e4 | 4e5934e0c2703813c85befb735b3d91ebd33730d | /R/1_GetHMDData_Italy.R | 897d631e0fac064bdbf0e82c4d6b92908f1b7131 | [] | no_license | jmaburto/N-IUSSP-Lifespan-Inequality | 0c51248307192f497b006ba9590ce43ba2a13d86 | 819313e22690d7f1d77de43c9551dd628234ea9f | refs/heads/master | 2021-05-22T13:35:05.620727 | 2020-07-02T12:47:47 | 2020-07-02T12:47:47 | 252,948,432 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,602 | r | 1_GetHMDData_Italy.R | ####### Program for getting most recent data from HMD
############# Written by JMA, thanks to Tim Riffe
############# 20/07/2019
library(HMDHFDplus)
library(data.table)
# Set working directory
# get all countries in HMD
XYZ <- getHMDcountries()
# set your username for HMD
us <- "jmaburto@colmex.mx"
# set your passwor... |
bd1d8a26c7d8a717dcc4e87b58c06295d6807a66 | 57222f96e553dd2802316928f2f2c7825ef05197 | /115-bookmarking-updatequerystring/app.R | e86fef0a6ec20af4906099db3bf1a36ef4fea0f4 | [
"MIT"
] | permissive | rstudio/shiny-examples | 6815bb4d8198e4b90765926a4865fdef1d1dc935 | c7bf00db4a8a68e579e39ed07d516a33661a853e | refs/heads/main | 2023-08-17T16:35:03.411795 | 2023-08-03T19:51:30 | 2023-08-03T19:51:30 | 13,722,949 | 2,046 | 4,576 | NOASSERTION | 2023-08-03T19:51:31 | 2013-10-20T17:05:23 | JavaScript | UTF-8 | R | false | false | 403 | r | app.R | ui <- function(req) {
fluidPage(
textInput("txt", "Text"),
checkboxInput("chk", "Checkbox")
)
}
server <- function(input, output, session) {
observe({
# Trigger this observer every time an input changes
reactiveValuesToList(input)
session$doBookmark()
})
onBookmarked(function(url) {
up... |
45683a54d101f14f9fe4683928c6d0e456a124d6 | 50a705f2063721b64eeac8b1906e16bfe5deb26a | /clustering/GiniClust2/Rfunction/PreProcess_and_filter_for_10X_full.R | 6e4630d0cfb41b6ddd41c786197c3b30e9ef3d85 | [
"MIT"
] | permissive | xianingz/scRNAseqBenchmark_Clustering | 5a7ee9d66bd18d87087c3d6df8ca7d85660191b5 | 845451743dc76ab0cc9106ad98551b80a0c70012 | refs/heads/master | 2020-04-29T18:09:46.732009 | 2019-08-09T16:08:24 | 2019-08-09T16:08:24 | 176,316,263 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 759 | r | PreProcess_and_filter_for_10X_full.R | #Preprocess and filter 10X data using 10X code: https://github.com/10XGenomics/single-cell-3prime-paper
wd<-"single-cell-3prime-paper-master/pbmc68k_analysis/"
source(paste(wd,file.path('util.R'),sep=""))
pbmc_68k <- readRDS(paste("data/",file.path('pbmc68k_data.rds'),sep=""))
all_data <- pbmc_68k$all_data
m<-all_data... |
f76f8a9c10a654f67180fdf01802401491e544fd | ba2845eadc8880147e906ab727d322d875226efa | /Analyses/source/stanprep_gddmods.R | 65c867f9c2cad1962f76e36666f069ec2104e53f | [] | no_license | AileneKane/radcliffe | 80e52e7260195a237646e499bf4e3dad4af55330 | 182cd194814e46785d38230027610ea9a499b7e8 | refs/heads/master | 2023-04-27T19:55:13.285880 | 2023-04-19T15:15:02 | 2023-04-19T15:15:02 | 49,010,639 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,409 | r | stanprep_gddmods.R |
expgdd_bb<-expgdd_gdd[expgdd_gdd$event=="bbd",]
expgdd_lo<-expgdd_gdd[expgdd_gdd$event=="lod",]
expgdd_fl<-expgdd_gdd[expgdd_gdd$event=="ffd",]
datalist.gddbb <- with(expgdd_bb,
list(y = cumgdd_air,
mois = soilmois_janmar, #soil moisture
sp = ... |
c4814a905c999b97c575ebfe4d4196e3eb58b800 | 334145f4753d39c1024d6e4f256d30ee50fe657e | /man/LPDistance.Rd | ad8c21a701936fe53da814d682f0b0b3cfb692f2 | [] | no_license | cran/TSdist | 6aaefaefd78c37fbfb07efe164cdb44c19fc2f53 | d28f6f0c3aa4c5004caf33724b5c7fc064846553 | refs/heads/master | 2022-09-15T21:13:30.275246 | 2022-08-31T08:40:02 | 2022-08-31T08:40:02 | 19,747,325 | 5 | 6 | null | null | null | null | UTF-8 | R | false | false | 2,665 | rd | LPDistance.Rd | \name{LPDistance}
\alias{LPDistance}
\title{
Lp distances.
}
\description{
Computes the distance based on the chosen Lp norm between a pair of numeric vectors.
}
\usage{
LPDistance(x, y, method="euclidean", ...)
}
\arguments{
\item{x}{
Numeric vector containing the first time series.
}
\it... |
e5ac278c4ab4482240a3b5cc9071bd2282dd74ed | 994b7b7d412e48848ebf4494de810745595caab2 | /R/incrementor.R | 002d4ecf1fe725ab6acecb3f0adf5022e68ac4f3 | [
"MIT"
] | permissive | cderv/sortable | b24f1e8b798f74322cc6fb194411982923178efb | 621ac4151c7e8fd122439ac2c076049a71c7af5b | refs/heads/master | 2020-09-20T09:09:58.313527 | 2019-11-20T23:44:19 | 2019-11-20T23:44:19 | 224,432,931 | 0 | 0 | NOASSERTION | 2019-11-27T13:05:02 | 2019-11-27T13:05:01 | null | UTF-8 | R | false | false | 347 | r | incrementor.R | incrementor <- function(prefix = "increment_"){
i <- 0
function(){
i <<- i + 1
paste0(prefix, i)
}
}
increment_rank_list <- incrementor("rank_list_id_")
increment_bucket_list <- incrementor("bucket_list_id_")
increment_bucket_group <- incrementor("bucket_group_")
increment_rank_list_input_id <- incre... |
ba09fa6e5610787c367c6a7aad6014558fa309d7 | fe8830fa8d36b7484da141ed224160b843f5026f | /man/ggbootMV.Rd | d4d2042a966d6963666350f15c5c2f45014fc4e8 | [
"CC-BY-4.0",
"LicenseRef-scancode-public-domain",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | MiguelRodo/ggboot | 6d70ddf743337ac19ab890e0579b59011ccafc1b | fa721e57e18e9da9c95219782fb9e96ca84e0d14 | refs/heads/master | 2020-03-29T19:57:55.298443 | 2019-02-22T07:07:21 | 2019-02-22T14:05:25 | 131,585,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,556 | rd | ggbootMV.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggbootMV.R
\name{ggbootMV}
\alias{ggbootMV}
\title{mv med plot info func}
\usage{
ggbootMV(data, group, B, seed = NULL, comp = 1:2, locType = "mean",
scale = FALSE, checkPlot = FALSE, dispPlot = FALSE, labelVec = NULL,
legendTitle = "Grou... |
bd0a92e1d63f3fa444700d165223d123a77aaadd | 8ff9d6461858f60280e4cc9814cb4febf6fdd608 | /Plot1.R | b9089c8c0b03959bf7ffb686e716001a6b2bb1a8 | [] | no_license | aditi2345/ExData_Plotting1 | a38061fcb617de8500363966dcaa281ca33d2735 | 923f8bcb509413ce6f746e72074adf9371504219 | refs/heads/master | 2022-07-18T21:01:48.092293 | 2020-05-17T15:49:36 | 2020-05-17T15:49:36 | 264,693,536 | 0 | 0 | null | 2020-05-17T15:07:19 | 2020-05-17T15:07:19 | null | UTF-8 | R | false | false | 102 | r | Plot1.R | hist(globalActivePower, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)") |
800fc8d8c0c92fcbc41f8046bc837f7647f60ba2 | 549d00ef71da6563a9493dbc9dfb5c1b0332948d | /man/anysearchTwitter.Rd | 6ace0140a205de0f61330b614d438072f5a60c0d | [] | no_license | dusty-turner/puertoRicoR | 9e419577976fb3f1e4163056f17015225f1fd9f7 | ee082b777e878059eac0a53e16e5301c67a16e9f | refs/heads/master | 2021-09-15T09:28:22.241889 | 2018-05-29T21:30:25 | 2018-05-29T21:30:25 | 108,535,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 858 | rd | anysearchTwitter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/anyseaRchtwitter.R
\name{anysearchTwitter}
\alias{anysearchTwitter}
\title{Any Search Function}
\usage{
anysearchTwitter(searchterm = "Anywhere", n = 1e+05, since = "2017-10-25",
until = "2017-10-26",
olddataname = "Puerto Rico 23 Sept - ... |
abc11410a8b326c1f2e71c6060fe333f3ff8125e | 96b809c3a2961bb76e0c9291b7b18e7b3a5f42c3 | /hw4/read_graph.R | 4b792d0132d939664100f953aa1d7686f7bc0675 | [] | no_license | ra125/STA-523 | 140c4cc0fb3a3785d8402ff051d3ecca9c5b76a7 | 15b1a2fb963797999f9a7fc839bff86f5cdc8be4 | refs/heads/master | 2021-01-10T04:54:26.213037 | 2014-11-24T22:15:22 | 2014-11-24T22:15:22 | 44,842,551 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,498 | r | read_graph.R | #1. Read a DOT language of graph data from a txt file
#2. Once the format is not ritht, report error
#3. If not weight for every line, asigned default weight to every edge
#4. Return an object of the graph
library(stringr)
read_graph=function(filename)
{
gdata<-read.table(filename, sep="\n", quote="")
gdata<-unlis... |
546ca7cd22d01335bd6811891c462a77bba606e0 | 6f0c07f057490adf1cd74c7048f9709996f3cf76 | /man/plotStats.Rd | 9947425ce8cba72ebd3d317969a5e70c85e14eea | [] | no_license | Al-Stu/seaStackR | 2da48afa65e25193c23abae56fcdfc9238f3a1be | 69899c41d048b50bf08869fd9c88078c8e7920c0 | refs/heads/master | 2023-01-13T14:27:27.988011 | 2020-11-20T14:17:48 | 2020-11-20T14:17:48 | 302,414,162 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,070 | rd | plotStats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/seaStackR_stats_functions.R
\name{plotStats}
\alias{plotStats}
\title{Add summary statistics to a plot}
\usage{
plotStats(
plot,
SD_fill = "grey30",
SD_colour = NA,
SD_size = NULL,
CI_colour = "red",
CI_size = 2,
CI_width = 1,
... |
48dbecc603861e7861fd3dc9ad92914de73c94c0 | bffd95e4ee6d169caa6687e18b22611550c8df93 | /man/cbind.tis.Rd | d8fec36cb93ae5c96b68fee0f119565c42f283e0 | [] | no_license | cran/tis | 286eb68a7aeb0be636a07babcb16a362d62aa5f2 | f254c391711e5cbdc4deba8ea68c2a234317d4bd | refs/heads/master | 2021-10-13T14:19:03.631652 | 2021-09-28T18:50:02 | 2021-09-28T18:50:02 | 17,700,509 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,881 | rd | cbind.tis.Rd | \name{cbind.tis}
\alias{cbind.tis}
\title{ Combine Series Into a Multivariate (Matrix) Time Indexed Series }
\description{
This is \code{cbind} for \code{tis} objects. It binds several
\code{ts} and \code{tis} objects together into a single matrix time
indexed series.
}
\usage{
\method{cbind}{tis}(\dots, union... |
ba65dddd3ce0d9c173cc3615c81cf1265da60999 | c1a2b48b6e9b7ed98750646b9de2d28ff68651c0 | /man/ApiCall.Rd | 54466fdc6681896547bd2caadfff7c0a18502091 | [] | no_license | shekaralle/SampleAPIPackageR | 446ca1092b6e15794e4d26bccb5a03c047e163db | 635d81e1f31a59bbd6fb9c8de3d5074495f9c717 | refs/heads/master | 2022-12-13T10:40:21.627531 | 2020-09-11T07:13:52 | 2020-09-11T07:13:52 | 294,166,444 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 345 | rd | ApiCall.Rd | \name{ApiCall}
\alias{ApiCall}
\title{Api Call}
\usage{
ApiCall
}
\description{
This function will call an API using GET and will display data if the request was successful with status code, and if the request was not successful it will show the error status code 404
}
\examples{
ApiCall("http://api.open-noti... |
68babfc1b77222159494c5313df19d5a870ec6c9 | 607b31d18cd361c331135771e4ce2d796dfc16c4 | /man/processVEP.Rd | 49b0368be241f4a9df852e6980022bd02daee289 | [] | no_license | mmm84766/slimR | 66f44045ab4ac1ebeaa13a8644ca379e6ca172f9 | 6c9d268576c11af27c5b98a5cad5a08ed6cd7253 | refs/heads/master | 2020-03-22T15:44:52.494556 | 2017-08-29T13:56:12 | 2017-08-30T09:17:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 642 | rd | processVEP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getVariationData.R
\name{processVEP}
\alias{processVEP}
\title{processVEP}
\usage{
processVEP(vepFilePath, overwriteRDS = FALSE, nodeN = 4)
}
\arguments{
\item{vepFilePath}{path to the VEP results obtained from running
variant_effect_predicto... |
63919128f45a7e3ff15aa959d03151c085f578df | a25dd3919b75d287c0c7573c3f1ad734fba137ae | /man/markdown_notes.Rd | bb169c1769c87868f727f89c409a4ff5985a8e0a | [
"MIT"
] | permissive | danielkovtun/shinyNotes | f9beda359987fd2582103b01bcddc695861d92e7 | 18007c347029563408f6f01b675de12f4edea580 | refs/heads/master | 2023-02-20T07:56:29.108599 | 2023-02-19T17:35:43 | 2023-02-19T17:35:43 | 229,818,392 | 6 | 0 | NOASSERTION | 2023-02-19T17:35:44 | 2019-12-23T20:27:32 | R | UTF-8 | R | false | true | 718 | rd | markdown_notes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{markdown_notes}
\alias{markdown_notes}
\title{Demo notes formatted with markdown for testing \code{shinynote} module.}
\format{
A \code{tibble} with 3 rows and 3 variables:
\describe{
\item{formatting}{text forma... |
122f1b8722871e0b68d2cdcfb9cc6b8677fd031e | da572721f12154f19c7adc42da4ef768da411a8e | /tarea2.R | 43a76afc11cc0e449d1e138e2f1147445039305b | [] | no_license | MichaelMobius/tarea_R | fd9a5f5006ea66cd62f769a09d4211f23287f30a | 81d42d58ac02c275779eb75f747346848781e54b | refs/heads/master | 2020-06-25T20:55:47.962534 | 2017-07-13T00:00:00 | 2017-07-13T00:00:00 | 96,988,712 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,286 | r | tarea2.R | tsunami_data <- read.csv("tsevent.csv",stringsAsFactors=FALSE)
eruption_data <- read.csv("volerup.csv", stringsAsFactors=FALSE)
str(tsunami_data)
str(eruption_data)
tsunami_filter<-tsunami_data[!(is.na(tsunami_filter$LATITUDE)|tsunami_filter$LATITUDE==""), ]
tsunami_filter<-tsunami_filter[!(is.na(tsunami_filter$LONGI... |
c0259d3935113a03f949b02000b6b7fa5a92cad6 | aac79aca9c8d7d5cfc09986673f3212bea1a0721 | /cachematrix.R | edfee1db16bb54ebe186c1e9ff3e838f770962c4 | [] | no_license | JonathanYde/ProgrammingAssignment2 | b37fbc9e035549dad68ff47daec2977906860fb9 | 58af35577b1cb91cc84838789058d9262f79fb54 | refs/heads/master | 2021-05-18T20:40:02.350924 | 2020-03-30T19:57:20 | 2020-03-30T19:57:20 | 251,409,973 | 0 | 0 | null | 2020-03-30T19:38:04 | 2020-03-30T19:38:03 | null | UTF-8 | R | false | false | 2,546 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## I am to write two functions.
#The first function stores a matrix and calculates its inverse matrix.
#The second function either
# 1) calls the solve function from the first function and then stores the inverse matrix in the cache.
#... |
0df712c74aaba5e722a32dd7101deed227588fce | e19229006dcd95a02e060877f049f5b30a742781 | /alc4.R | 54f6f23784d0ed21ecd1fb3757f9c589a2abf6da | [] | no_license | Greg131/RBasics | 7be20c9ba8a076f2189808674fa32a927424ac49 | c3e515fbc58824cc895d995fe3a8d76e0a0feb4c | refs/heads/master | 2020-06-04T14:40:30.697545 | 2015-08-11T16:45:08 | 2015-08-11T16:45:08 | 40,327,985 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,921 | r | alc4.R | structure(list(t = c(121, 121, 40, 39, 66, 64, 5, 30, 34, 5,
15, 5, 27, 91, 71, 107, 98, 120, 165, 155, 98, 160, 95, 15, 170,
97, 64, 125, 91, 39, 15, 21, 135, 22, 78, 113, 8, 5, 127, 95,
104, 94, 207, 38, 34, 44, 98, 79, 35, 45, 93, 38, 29, 33, 84,
136, 35, 36, 12, 84, 30, 15, 23, 22, 99, 135, 14, 259, 131, 123,
... |
4e6ec7a9d816c5fc0b929e8b0fed07b0fd0918fa | aad6041bed87bc02c69519e64c3def3fef4177a3 | /man/loadLm-class.Rd | ac24e8a1e6598f7d5d5a930f12ba1d530f6c770a | [] | no_license | jordansread/loadflex | ac0b5c55ddce683801fce7d2070c205e9b3e30e1 | 9d57dd491449cda5477f36fe7b94b009616e035c | refs/heads/master | 2022-08-07T07:28:49.916697 | 2016-12-12T21:36:08 | 2016-12-12T21:36:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 871 | rd | loadLm-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loadLm.R
\docType{class}
\name{loadLm-class}
\alias{loadLm-class}
\title{A load model class specific to simple linear models (\code{\link{lm}}s) for
flux estimation.}
\description{
loadLms can take any lm formula.
}
\section{Slots}{
\describ... |
3ccba09cf6a546c6aac361fc6b28aaee6514e651 | f84403fdad56b286e3dc5752e0128e7574315dba | /W3.R | c884402fb32d5a3dd1d28ccec6daa8bac7a11c57 | [] | no_license | therealowen/ECON-390A | 4f6ee47c354f312abf5d040eb522033f33266879 | 5b68b5400cc7869622bdbd8b4016c2ba54654da5 | refs/heads/master | 2022-02-11T01:25:35.220464 | 2019-04-22T01:50:47 | 2019-04-22T01:50:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 649 | r | W3.R | ##ECON 390A W3-NOTE
#Jan 28, 2019.
#Instructor:Masud
getwd()
setwd("~/Desktop/ECON390A")
x <- 2 ;y <- 6;z <- x+y
x_1 <- 6; x_2 <- 18
x^4; x/y
y1 <- "Drak"; y2 <- "M"; y3 <- "S"; y4 <- "B" ##string var
class(x); class(y1)
##data type: integer, Charater(string), Factor, Bollean, logic(T/F)
rm(y1); rm(x); rm(y)
##Four typ... |
de4c3c50cff27fb3a8b8eac7143f3a1deef0cb8b | d47833e60e3b9760619cf9c348d97b188f342db3 | /MobileNetworkDataSimulationTemplate/code/src/deduplication/man/buildCentroids.Rd | 17c2f8040ae586065ba0a3097f7dcd69150ce120 | [] | no_license | Lorencrack3/TFG-Lorenzo | 1a8ef9dedee45edda19ec93146e9f7701d261fbc | e781b139e59a338d78bdaf4d5b73605de222cd1c | refs/heads/main | 2023-06-04T00:23:16.141485 | 2021-06-29T21:31:25 | 2021-06-29T21:31:25 | 364,060,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 644 | rd | buildCentroids.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/buildCentroids.R
\name{buildCentroids}
\alias{buildCentroids}
\title{Builds the centroid of the rectangular tiles.}
\usage{
buildCentroids(ntiles_x, ntiles_y, tile_sizeX, tile_sizeY)
}
\arguments{
\item{ntiles_x}{The number of tiles on OX.}
... |
4b13255927ae2d9a5fb370bd3d099fae358a7247 | d2cae0fc228057f023001a90daf6983792dc1797 | /robustness_script.R | d065961f608a1d9bca541538a77006a34ad6aaae | [] | no_license | losapio/Trait-based-Network-Robustness | 8ab8f7e57077575d63ec1085caaaeaac4aac822c | 895dd65232101762838a41647a9a46ad8e6d5485 | refs/heads/master | 2020-08-28T00:06:11.982204 | 2019-10-25T12:39:29 | 2019-10-25T12:39:29 | 217,529,044 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,819 | r | robustness_script.R | # Resistance of plant–plant networks to biodiversity loss and secondary extinctions following simulated environmental changes
# Gianalberto Losapio* & Christian Schöb
# Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
# Corresponde... |
229e21bd3adf7353946802a7366a51eee9455393 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /B_analysts_sources_github/jmzeng1314/bioweb/ui.R | 28e6ac1b64648e485573ea54043e3477bf729bb2 | [] | 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 | 2,366 | r | ui.R | library(shiny)
library(shinydashboard)
library(ape)
library(RCurl)
library(RMySQL)
options(stringsAsFactors = F)
#http://www.r-bloggers.com/mysql-and-r/
#Connect to the MySQL server using the command:
#mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A
#The -A flag is optional but is recommended for speed
#Don... |
d95cf82cd487d040e873488c9787ce0ce922611c | 35d2664803bb6878acce80589c7cebc0ce44ee33 | /code/decommissioned/helper/general/rm_colnms_by_regex_mtch.R | fdb4dc582e7a12b57608aadc0802ce4baff77a49 | [] | no_license | Basketball4Nerds/nba_basketball_research | dd6e4e22f6f38438b3cb88346484e32320d9f7df | 4771940c1f57a5d054cc833eab86ef789a6e5d7e | refs/heads/master | 2020-03-17T01:30:40.239997 | 2018-05-26T18:49:33 | 2018-05-26T18:49:33 | 133,156,005 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 176 | r | rm_colnms_by_regex_mtch.R | ## this function removes any columns matched by given regex expression
rm_colnms_by_regex_mtch <- function(df, regex_expr) {
return(df[ , !grepl(regex_expr, names(df))])
}
|
11303c03985ce325a8ab8164ed4afa0beb39542d | 2660d7ae26e5570d7cff646c3830b2ae0f10d9b0 | /cachematrix.R | 0cf05cbbf92f76d4a8f150d5f1ecbae937503f5e | [] | no_license | khemkaiitr/ProgrammingAssignment2 | dc420d8eab83a80fbab9767256b15ace8a609ba9 | ef9c9073ea103d52defdb50aa68a6fe543d443b9 | refs/heads/master | 2021-01-21T18:25:07.828138 | 2014-04-26T12:10:57 | 2014-04-26T12:10:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,586 | r | cachematrix.R | ## makeCacheMatrix is a function that returns a list of different functions,
## when an input matrix is provided. Inside these list the four functions are:
# 1. set() sets the value of the matrix using input matrix.
# 2. get() gets the matrix
# 3. setInverse() Sets the value of inverse if calculate... |
6d805dc87b6e9a92206c25e1a71a74b574714e7d | ab1730e13eb49478aa0cc443d40c68921cbe1662 | /man/GolfRound.Rd | 9bf47fc5ba8612df2e4f5f8eea8f0e93555f713e | [] | no_license | cran/Lock5Data | 688fa73667eacea4f4a325763907bba30f55c030 | 770f5fd2b8956dbb0c822133223f9c811561f130 | refs/heads/master | 2021-08-02T06:23:39.102769 | 2021-07-22T21:40:10 | 2021-07-22T21:40:10 | 17,680,339 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 645 | rd | GolfRound.Rd | \name{GolfRound}
\alias{GolfRound}
\docType{data}
\title{Golf Round}
\description{
Scorecard for 18 holes of golf
}
\format{
A data frame with 18 observations on the following 4 variables.
\describe{
\item{\code{Hole}}{Hole number (1 to 18)}
\item{\code{Distance}}{Length of the hole (in yards)}
\item{\c... |
e1a94104488901af86721988dcc60651947c390b | 25bdc974db618dbbff5dfdc2e7335e46499d1e7f | /r.core/src/test/resources/com/blogspot/miguelinlas3/r/eclipse/core/compiler/bison/sort.R | 7fba39bcd12156a0df92701bcc65061e46246333 | [] | no_license | migue/r-ide | a67b1953dbac98d5b8845255ac53bf6bd4fd07e9 | d51e27dd732d25012228943975e41a9b9d85d7de | refs/heads/master | 2016-09-05T09:05:45.818767 | 2011-10-29T21:50:41 | 2011-10-29T21:50:41 | 2,564,260 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,632 | r | sort.R | ### Name: sort
### Title: Sorting or Ordering Vectors
### Aliases: sort sort.default sort.POSIXlt sort.int
### Keywords: univar manip arith
### ** Examples
require(stats)
x <- swiss$Education[1:25]
x; sort(x); sort(x, partial = c(10, 15))
median.default # shows you another example for 'partial'
## illustrate 'stabl... |
a043e906d94a85df332687b75fb69b737c1e97fa | 84c937f0c53026854bc9f7133d63abdbaa5b461f | /tests/test_packs.R | d8804a6ba7fa477782c9cb643621e516778c7906 | [] | no_license | MilesMcBain/deplearning | 9a11efe728604f841c6c293fd4ff7145bc431b91 | 1cec42a230c8c4198cedf9433f1044a9d66619da | refs/heads/master | 2021-01-21T14:47:58.736411 | 2018-06-20T21:47:32 | 2018-06-20T21:47:32 | 95,333,085 | 57 | 3 | null | 2018-06-20T21:47:33 | 2017-06-25T02:09:16 | R | UTF-8 | R | false | false | 463 | r | test_packs.R | library(datapasta)
require("mgcv.helper")
library("tidyr")
library(rstudioapi)
library(A3)
library("abc")
library(naniar)
library(visdat)
library(dplyr)
library(switchr)
library("rtimicropem")
library(notARealPackage)
require("packagemetrics")
#library("dejaVu")
p_load(xml2, jsonlite)
p_load("devtools")
#library(Rboris... |
7fba290bffce36018f2dd1106fcfa66fcc7a8308 | aa2afda5c0cc54c9a80635840cfe6f6e999a4905 | /carros/app.R | 290c9bbdf3b815899ae7339e2b7c1cce1e662485 | [] | no_license | rodrigorocha1/app_rshiny_prever_qualidade_carro | 70e10835d40908f19475bcccb79f93e31d6ef832 | 23931bb888d39de77b54c4880feed43f362413ac | refs/heads/master | 2022-04-10T12:17:33.217211 | 2020-03-28T21:25:48 | 2020-03-28T21:25:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,772 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(e1071)
carros = read.csv("../car.data", sep = ",")
modelo = naiveBayes(class ~., data = c... |
9a7572aa04af49a35fe2f561624d626e471fbbeb | 9e788d01b18c923961d1fe958c7477c04abbc5cc | /plot1.R | 6686458ac42604dfe3d94f06868d21dd81f74162 | [] | no_license | amturner5/ExData_Plotting1 | 53a8c12340c1bdfe676551c21df11e860b23592a | 2266e3f2fe0c7d0e81af07a04c6fee8727f49d78 | refs/heads/master | 2020-03-20T21:32:19.587625 | 2018-06-20T04:57:57 | 2018-06-20T04:57:57 | 137,746,094 | 0 | 0 | null | 2018-06-18T11:56:58 | 2018-06-18T11:56:58 | null | UTF-8 | R | false | false | 1,182 | r | plot1.R | ##download from url and unzip to local repository
url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/00235/household_power_consumption.zip"
download.file(url, "C:/Users/Dell/test-repo/EDAProgrammingAssignment1/ExData_Plotting1/household_power_consumption.zip")
unzip("C:/Users/Dell/test-repo/EDAProgram... |
749cf1bc056bee13ace5fde897a24ef2e5acc0dd | ef49d1238c49c0b8429c5cf00ac86eba407abbe7 | /man/chapter_11_exercise_24.Rd | e54a08935e5639474dc3376d00fb622bde22ea95 | [] | no_license | yelleKneK/AMCP | be46c4969bf4e4bb7849a904664d9b3c17e494ef | 72e0e0ff5053d42da9a1c0e2e1ec063586634e8a | refs/heads/master | 2022-11-23T06:39:24.885288 | 2020-07-24T19:57:16 | 2020-07-24T19:57:16 | 282,302,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,367 | rd | chapter_11_exercise_24.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/documentation.R
\docType{data}
\name{chapter_11_exercise_24}
\alias{chapter_11_exercise_24}
\alias{Chapter_11_Exercise_24}
\alias{C11E24}
\alias{c11e24}
\title{The data used in Chapter 11, Exercise 24}
\format{
An object of class \code{data.f... |
0dca40a0298128f3c20d2102f4ba8138b43242f8 | 88eb8150423c78d613ff9c0f54b01fb19dc235f2 | /Practica10/pr10sim.R | 7faecf0aab4b1b079c35ba60ee37a8fc01d321a7 | [] | no_license | claratepa/Simulacion | f3975321a8df4c0c9988390a066fc11e475f46de | 4c37582eaed0030949ca9177ec4068200dedb865 | refs/heads/master | 2022-11-05T13:39:49.226957 | 2020-06-17T22:48:32 | 2020-06-17T22:48:32 | 235,903,438 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,014 | r | pr10sim.R | library(testit)
suppressMessages(library(doParallel))
clust <- makeCluster(detectCores() - 1)
registerDoParallel(clust)
knapsack <- function(cap, peso, valor) {
n <- length(peso)
pt <- sum(peso)
assert(n == length(valor))
vt <- sum(valor)
if (pt < cap) {
return(vt)
} else {
filas <- cap + 1
... |
95dc808eaae818c2d43c4d416b95f35225ab769b | c08542d73325c80137da6dd857bcef07cae664ed | /cachematrix.R | c971c18fab2691682fc49b391a5895e18984a314 | [] | no_license | moolins12/ProgrammingAssignment2 | 6f28fc71aab0a92ca147b361a1d4fa4b36f306f5 | 0c1290fbd2c33fcaba4a223efdcaccaf8c088288 | refs/heads/master | 2020-03-18T07:41:53.832119 | 2018-05-23T18:35:42 | 2018-05-23T18:35:42 | 134,468,209 | 1 | 0 | null | 2018-05-23T18:35:43 | 2018-05-22T19:51:20 | R | UTF-8 | R | false | false | 1,333 | r | cachematrix.R | ## I have created two functions below that execute the matrix inverse operation, but stores the data within the cache
## to improve computing performance should the operation need to be executed many times.
## The first function creates cached variables for executing the inverse within the makeCacheMatrix function. In... |
662a9f01149f614b1d7c8aa773311172def69523 | 70f1a9a178a4204f675893bf7809131b3a8a9f3b | /project5.R | dd30db59eef48f931625a586938dda4ba29b453e | [] | no_license | yashCS101/modeoftransport | 4a1488e6e01facbe06c386864e9116f21c18f98e | eaffd740d4c9419cb0c7aa209cd1906c670886ed | refs/heads/master | 2022-08-23T17:58:59.768544 | 2020-05-22T07:13:55 | 2020-05-22T07:13:55 | 260,863,584 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,204 | r | project5.R | setwd('C:/Users/USER/Desktop/R Data Sets')
### Importing data
data = read.csv("Cars_edited.csv")
### Libraries
### Data understanding
dim(data)
str(data)
summary(data)
# Checking for NA
sapply(data,function(x) sum(is.na(x)))
#Removing NA
data = na.omit(data)
# Checking # of unique value... |
aedd25f258be85c76123ca28276a05e8f26377ed | 270090ceb851cf0c89f16ec2ce9a76df64b11818 | /R_code_snow.r | f99d40155e103cb489f50c18b50c48b10ba12f60 | [] | no_license | Mattiacroari/ecologia-del-paesaggio | c6009e9f9320e148bcf314c6698529df03f23d70 | f427e08356c18d23c6958b19681efaec60c8f273 | refs/heads/master | 2021-04-23T17:23:19.753710 | 2020-07-21T10:07:35 | 2020-07-21T10:07:35 | 249,945,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,477 | r | R_code_snow.r | # prima cosa fare sewd
setwd("C:/lab/")
install.packages("ncdf4") # per caricare immagini da Copernicus
library(ncdf4)
library(raster)
# per visualizzare il file "NC", va prima importato
# raster importa una singola immagine es. neve il 18/5
# brick riporta vari livelli di immagine es. neve dal 18/5 al ...
raster("c_g... |
8781748a35b584c92e85c42cb46c9e2aa6445259 | 6cb50a95d62f1318f867d6b695bf3c31bb894d97 | /inst/doc/dataset_generation.R | 0da2fc28988bda099872818c1732d5140f8691e2 | [] | no_license | TankMermaid/seqtime | c15364705f265538e826004f8100189a6885ea4b | 960903d37e2ba95763ef58a1eaf78d94375fe829 | refs/heads/master | 2020-03-29T21:34:12.267387 | 2018-06-13T06:46:21 | 2018-06-13T06:46:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,517 | r | dataset_generation.R | ## ----setup, include=FALSE------------------------------------------------
library(knitr)
## ----message=FALSE, warning=FALSE----------------------------------------
library(seqtime)
library(ggplot2)
library(reshape2)
## ------------------------------------------------------------------------
N = 50
S = 40
A = gener... |
f54487563f13cd9e7a3bce5e79be4e19a866d7d9 | dac60727c63329ea417e4a943f52f040b1010ac1 | /RCheckCode/checkFields.R | fc8244cad1cabe1db1502f61c4496e1e5bf21260 | [] | no_license | duncantl/SOXMLDB | fd8ca872f062e0d047b4e0154a6cd4504d53e8b6 | 913c04514fb9bf53ffb9f024a04b698735803f29 | refs/heads/master | 2023-05-14T06:39:56.130966 | 2023-05-08T14:16:12 | 2023-05-08T14:16:12 | 172,417,441 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 528 | r | checkFields.R | library(DBI)
library(RSQLite)
checkDBFields =
function(db, tables = dbListTables(db))
{
structure(lapply(tables, checkTableFields, db), names = tables)
}
checkTableFields =
function(tblName, db)
{
fvars = paste0(tblName, ".vars")
if(!file.exists(fvars))
return(NA)
vars = readLines(fvars)
db... |
141540da31edc8219628901ec8710f09f948271a | 4e759f241817ae276d80124e3cf8a7984c587c6d | /stockdata_timer.R | 65d401bd2ce12f63e601ba9198318192981d1e33 | [] | no_license | jimmyhenriksson/Public-Sentiment-and-Stock-Performance | f5da4b98110c62274adcd8023d52fd538281e3f0 | 9982cb87a8fd4639e34ced49286d1ff7b70edb5b | refs/heads/master | 2023-04-11T10:11:30.011446 | 2021-04-28T20:56:47 | 2021-04-28T20:56:47 | 189,740,811 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,736 | r | stockdata_timer.R | # A script that collects and stores tweets for all
# Dow Jones stocks (and more) by using Rtweet.
# Takes the time and amount constraints into account.
install.packages("rtweet")
install.packages("tidytext")
install.packages("data.table")
install.packages("ggplot2")
install.packages("ROAuth")
install.packa... |
8e9eba4c5a8d203ad215e2bc60e2e459fa6f4c80 | 361954cc1036c8e77f6410e5c63955260375f071 | /man/auto_layout.Rd | 082ddb4fb58e5f604624b095ffdd2d204259bd1a | [] | no_license | HemingNM/ENMwizard | a4d8f883560e0a5d34c12507489d51e057640f30 | b8f30a1e7c255ce43c2f45541e418f06879dbc74 | refs/heads/master | 2023-06-21T20:25:35.622227 | 2023-06-12T12:36:24 | 2023-06-12T12:36:24 | 104,896,526 | 17 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,430 | rd | auto_layout.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/7.f.plot.mdl.sel.crit.diff.R
\name{auto_layout}
\alias{auto_layout}
\title{Automatically select the layout.}
\usage{
auto_layout(n, layout = T)
}
\arguments{
\item{n}{the number of plots}
\item{layout}{should the function return a preallocat... |
4a9eeb0caf5be5f8b19aaeff68e10d78e0fccd9d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bmrm/examples/hclust.fca.Rd.R | 3a23da0f0b6f4848f0c3c3aa31e6679e284430f1 | [] | 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 | 371 | r | hclust.fca.Rd.R | library(bmrm)
### Name: hclust.fca
### Title: Find first common ancestor of 2 nodes in an hclust object
### Aliases: hclust.fca
### ** Examples
hc <- hclust(dist(USArrests), "complete")
plot(hc)
A <- outer(seq_along(hc$order),seq_along(hc$order),hclust.fca,hc=hc)
H <- array(hc$height[A],dim(A))
image(H[hc... |
784750e637740b68172512d6137f54227edecbb3 | defc646bb56990743469ae0bffa9cc99252c4b8f | /man/chooseColors.Rd | 962a5fd949a8a8d91927c0f9e2555bca767b02da | [] | no_license | feiyoung/PRECAST | b1abe112a9d9df173f1e7e9259e92c19a33ea05b | b866f4b940e5815b2f85e8a8f8b2d0824809db39 | refs/heads/main | 2023-06-08T01:01:41.235579 | 2023-06-05T02:10:15 | 2023-06-05T02:10:15 | 500,674,213 | 7 | 3 | null | null | null | null | UTF-8 | R | false | true | 769 | rd | chooseColors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/visualization.R
\name{chooseColors}
\alias{chooseColors}
\title{Choose color schema from a palette}
\usage{
chooseColors(
palettes_name = c("Nature 10", "Light 13", "Classic 20", "Blink 23", "Hue n"),
n_colors = 7,
alpha = 1,
plot_col... |
228a493b50b668729b051e1ab88c735d11bac042 | 37750963164b3f1cc706e14dda03a6e28e2d7cf3 | /eggAnalysis.R | 8954a8b2d46e081c0f85c7ac23395de95ba8e9f6 | [] | no_license | zhuzhi/r_idea | b4ac53517c6f6f1a835def89b0a6ba9b491fc2e9 | 9aed42f5277101af34c754648823a609b5280c06 | refs/heads/master | 2020-04-15T12:47:50.024633 | 2016-12-18T15:09:52 | 2016-12-18T15:09:52 | 64,978,940 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 106 | r | eggAnalysis.R | install.packages("eegAnalysis")
library("eegAnalysis")
sim$data
sim$classes.Id
sim$rec.Id
sim$n.signals
|
1c3351b5a393fa4abe709d4802e6bd43e9d391d3 | 5db2dac679963587ac50ad850ea3a2ccb508465a | /data-raw/lotr_study.R | aa094544e799013ca8798b095d1d6d1a70e36a8b | [
"MIT"
] | permissive | softloud/simeta | be88fe336eeee9610086823adce839493781c0ef | 2a7e979077c57812a7d29c3e23e8c00080e1cb03 | refs/heads/master | 2023-04-16T23:27:16.936986 | 2023-03-25T11:49:23 | 2023-03-25T11:49:23 | 200,359,586 | 2 | 2 | NOASSERTION | 2020-01-28T09:55:16 | 2019-08-03T09:56:12 | HTML | UTF-8 | R | false | false | 461 | r | lotr_study.R | # https://www.kaggle.com/paultimothymooney/lord-of-the-rings-data/data
library(tidyverse)
lotr_names <-
read_csv("data-raw/lotr_characters.csv") %>%
pluck("name")
lotr_study <-
tibble(
study = lotr_names,
year = seq(1950, 2020) %>% sample(size = length(lotr_names), replace = TRUE)
) %>%
dplyr::fil... |
89876322f32d7adb6ba7eabe31547dab1920901a | 92a3de2eaa4a99aa389221059afd037d98a36f7e | /covid_interactome/covid_interactome/server.R | dde0ba92952315d8988c0a41e76c63594010769d | [] | no_license | ictr/shiny_docker | 8f0f3e5b352c822f196968d1c002478811d35ec2 | 15cdf325472bbda77836c3f3544d5791af0684fb | refs/heads/master | 2022-04-17T13:29:33.648458 | 2020-04-16T07:45:24 | 2020-04-16T07:45:24 | 256,038,486 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 590 | r | server.R | library(shiny)
library(igraph)
load('/srv/shiny-server/covid_interactome/data/data.RData')
# We tweak the "am" field to have nicer factor labels. Since this doesn't
# rely on any user inputs we can do this once at startup and then use the
# value throughout the lifetime of the application
# Define server logic require... |
46f7861fb072ce4c71a34326602e343e32f0087b | ed633d145dfa8b32511b3cb13ba95b822e2559c8 | /man/comp.cyl.Rd | fc9b36f5237c0ffba9a0d1a52a0f0f205d991793 | [] | no_license | wendellopes/rvswf | 51a09f034e330fbb7fd58816c3de2b7f7fdba9dc | ee243c3e57c711c3259a76051a88cc670dfe9c4b | refs/heads/master | 2020-05-19T19:38:18.987560 | 2016-09-11T22:57:37 | 2016-09-11T22:57:37 | 19,242,694 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 611 | rd | comp.cyl.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/comp.cyl.r
\name{comp.cyl}
\alias{comp.cyl}
\title{Compare results for Cylindrical Bessel Functions.}
\usage{
comp.cyl(nmax, x)
}
\arguments{
\item{x}{The argument of \eqn{J_n(x)}.}
\item{n}{The order of the Cylindrical Bessel functi... |
b1abd7fa144abb3bb7618c3a495b53b98d0c2776 | 40c8a75763ed26621ad8c61436c07bc1f4f93da8 | /man/minBBoxSpatialPolygons.Rd | 3dec86049228277fcad442d463b85dab5a59971a | [] | no_license | RobinKohrs/runout.opt | af7fe30b48359bcdb67a7d4d5dee0b04ac74315b | 4758ea2a23cf085847b3e55ee6ae4a83c0d7171a | refs/heads/main | 2022-12-30T16:33:44.583191 | 2020-10-22T16:47:08 | 2020-10-22T16:47:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 469 | rd | minBBoxSpatialPolygons.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/runout_geometry.R
\name{minBBoxSpatialPolygons}
\alias{minBBoxSpatialPolygons}
\title{Minimum area bounding box for spatial polgyons}
\usage{
minBBoxSpatialPolygons(spPolygons)
}
\arguments{
\item{x}{A SpatialPolygonsDataFrame}
}
\value{
A Sp... |
9f9918a18ae982e24e1a92f975474a6788a2a8a4 | c39217c8139268442ce0711a2e538ab4fc724cb9 | /man/outliers_mahalanobis.Rd | e5b2ea0361893a1d096b3d0b0ca0db1c09b71a71 | [
"MIT"
] | permissive | mdelacre/Routliers | 008638b50de30732fd2ae6d9fa639e09d658665b | e4dac8814ea53db87083ef59d468c05dc6cedeea | refs/heads/master | 2021-06-05T07:18:01.907962 | 2020-10-09T10:12:18 | 2020-10-09T10:12:18 | 145,856,180 | 11 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,162 | rd | outliers_mahalanobis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/outliers_mahalanobis.R
\name{outliers_mahalanobis}
\alias{outliers_mahalanobis}
\title{mahalanobis function to detect outliers}
\usage{
outliers_mahalanobis(x, alpha, na.rm)
}
\arguments{
\item{x}{matrix of bivariate values from which we want... |
c9c9c8f1ef1a0c327f71030533af69d208153462 | 0add2348eeb978bbe1b910d391627ec37b39b4f8 | /R/utilities.R | 5f5af2e8324e951a104fb96b730328e05830f47a | [] | no_license | mayerantoine/Rdhis2tracker | 7df0ada90e2c481e26595cfd05a4de7538726819 | 7c788a8070a3fa403967611892217d0da7c47394 | refs/heads/master | 2020-03-19T02:38:06.377666 | 2018-10-25T00:12:01 | 2018-10-25T00:12:01 | 135,646,146 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,407 | r | utilities.R |
#' @title Execute a Call to the DHIS2 Web Api
query <- function(url_params) {
if (grepl(x = url_params, pattern = "http"))
{
url <- url_params
}
else {
url <- paste0(getOption("baseurl"), url_params)
}
#should we use try , timeout other http error
result <- GET(url)
stop_for_status(result)
... |
326dcca8c2aaabae67d00ba73f1eefbad7c4a7a5 | 78ca13976f1211a92fc9239eb5d060e8362520f9 | /CAP.r | 9b5b56a46127f50840c3a8fb6f23cce070f9b411 | [] | no_license | seandrummond/ml-football-betting | 4b8cfc66dcf940d1fed77cc62471f3b2764566e6 | b9e93f1b799d8fe0649a99a361095baa28f344c9 | refs/heads/master | 2020-03-31T00:40:19.141903 | 2018-10-08T20:50:57 | 2018-10-08T20:50:57 | 151,737,952 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,838 | r | CAP.r | ####################
# CAP LUT Generation
#
# Description:
# This file takes in the prepared data and built models and calculates the accuracy of each
# model when they model is above a certain level of confidence.
#
####################
###### Import Libraries #####
library(randomForest)
library(caret)
li... |
4ab047e5c6fbef557bb298f58fc121f54a37736c | af84dbfbdca0ee1a354924881b6578c37a66efcf | /R-Program/Boxplot.R | 2a8e25c0485d69680638546ae40e67465047fbb4 | [
"Apache-2.0"
] | permissive | Johnwei386/Warehouse | 96db3b3b7c258b41688395942f766c2f4299aa56 | 77da078a176930c0107431b7a0ff7b01d6634ba7 | refs/heads/master | 2023-07-19T22:12:33.331111 | 2023-04-13T10:15:41 | 2023-04-13T10:15:41 | 95,307,122 | 3 | 0 | Apache-2.0 | 2023-07-06T21:16:41 | 2017-06-24T15:48:20 | Jupyter Notebook | UTF-8 | R | false | false | 839 | r | Boxplot.R | # R创建箱型图
# boxplot(x, data, notch, varwidth, names, main)
# x是向量或公式，它用来筛选数据帧到不同的值
# data是数据帧。
# notch是逻辑值。 设置为TRUE以绘制凹口。
# varwidth是一个逻辑值。 设置为true以绘制与样本大小成比例的框的宽度。
# names是将打印在每个箱线图下的组标签。
# main用于给图表标题。
#input <- mtcars[,c('mpg', 'cyl')] # 指定列提取内置数据集mtcars
#print(head(input))
# 创建箱型图
png(file = "boxplot.png")
# ~左边的... |
5a578c93a92085cc9b91d16508841114587dba4c | f4105cb1aad7f9110478aa4253a748ee6b585c38 | /R/PlotActivityByDemogGrp.R | 5315e872ef59d4bb37891b4daedd00350aabc4e2 | [] | no_license | kmanlove/SheepBehavior | da8611fa81e2a5abfffca7bcf9ec5db696a7bcf2 | bc54d918212393a9e5d6b0e27364381eef8d3d2e | refs/heads/master | 2021-01-01T20:40:49.984218 | 2015-05-13T16:16:45 | 2015-05-13T16:16:45 | 31,034,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,157 | r | PlotActivityByDemogGrp.R | PlotActivityByDemogGrp <- function(data.in) {
# function builds boxplots of activity patterns by demographic group
#
# Args:
#
# data.in = cleaned scans data
#
# Return:
#
# boxplots
lambs.bb <- subset(data.in, Pop == "BB" & IndividID == "l")
lambs.aso <- subset(data.in, Pop == "Aso" & IndividI... |
c5590193a7b66357e1a8e94f8f34aa2d936bc39e | 2e731f06724220b65c2357d6ce825cf8648fdd30 | /dexterMST/inst/testfiles/mutate_booklet_score/libFuzzer_mutate_booklet_score/mutate_booklet_score_valgrind_files/1612727160-test.R | 58f5223ddeee08317a96eff40c72bba4cc0453a3 | [] | no_license | akhikolla/updatedatatype-list1 | 6bdca217d940327d3ad42144b964d0aa7b7f5d25 | 3c69a987b90f1adb52899c37b23e43ae82f9856a | refs/heads/master | 2023-03-19T11:41:13.361220 | 2021-03-20T15:40:18 | 2021-03-20T15:40:18 | 349,763,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,680 | r | 1612727160-test.R | testlist <- list(id = NULL, score = NULL, id = NULL, booklet_id = c(-1L, NA, -1L, -1L, -1L, -1L, -1L, 1258233921L, 16744494L, 0L, 255L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -1L, -246L, 11157162L, -2147287296L, 15461355L, -336860181L, -350214640L, -1L, -14595414L, 906002649L, -640034343L,... |
a1137f61f71dd0c8429c07337647261c46124c2c | 847a367fb971e99f05334bc556d447b5d3634a68 | /wikipedia.R | a8d0c81a8338981291735976258f378421dccedc | [] | no_license | rodrigoestrellac/Web-Scraping-With-R | bc93f5364d2cc695dc9eb29b3a93aa57daed6521 | dd48e315cd896841fc6357c75dff9a97c44f8c2d | refs/heads/master | 2022-11-21T22:46:22.114006 | 2020-07-20T13:52:24 | 2020-07-20T13:52:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 579 | r | wikipedia.R | library(tidyverse)
library(rvest)
# Get the data
url <- "https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory"
html_data <- read_html(url)
df_raw <- html_data %>%
html_node("#thetable") %>%
html_table() %>%
as_tibble(.name_repair = "unique")
# Clean the data
df_clean <- df_raw %>%
select(... |
639d2472b55bbc4964ba0553cae7125bc9d9e5a5 | 44039e6fa92f04c587839174ce79797cf4ca575d | /R/gage_labels.R | 5f58809e3fec166cf6b660df28aa19589365820f | [
"CC0-1.0"
] | permissive | mpdougherty/razviz | 58ee97162d9c0360a9b50060f83131a7508cce3d | a4edd0b9fe89707ba29c1bb4d767d8011bc5c846 | refs/heads/master | 2023-04-04T20:11:30.106910 | 2021-03-26T19:22:08 | 2021-03-26T19:22:08 | 273,563,278 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,857 | r | gage_labels.R | #' @title Create Gage Labels
#'
#' @description Create a data frame of gage labels used by the longitudinal
#' profile plot to symbolize stage elevations.
#'
#' @export
#' @param gage_locations data frame; A data frame of gage locations.
#' @param stage_interval_label numeric; The number of feet between gage stag... |
d872d749d3c17a2ca9874fc53f152bc80957c87d | 925ac2cdee50f0a8858c1a818e0d6a983d7e63f8 | /cachematrix.R | d52fd2c3e7cf79865e2ed2acba2445d9af80d982 | [] | no_license | DataScientist2016/ProgrammingAssignment2 | b91ce35076d77bacf71b815f2a4d7bdb534bb228 | b0325210e729623021eafc0c2f325f443da66294 | refs/heads/master | 2021-01-18T01:52:16.201506 | 2016-04-14T14:09:47 | 2016-04-14T14:09:47 | 56,234,573 | 0 | 0 | null | 2016-04-14T12:13:23 | 2016-04-14T12:13:22 | null | UTF-8 | R | false | false | 1,225 | r | cachematrix.R | ##makeCacheMatrix: This function creates a special
##"matrix" object that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
##set the value of the matrix
set <- function(y) {
x <<- y
m <<- NULL
}
##get the value of the matrix
get <- function() x
##set the inv... |
e859b733be82b374689e7cee5f8359db2af9351b | fae88e7ffde9e6915b8dc3693761d077d7a84545 | /R/bmi3.R | 610d06071bd9e71883a1b31fc795108444ce60be | [] | no_license | William2025/MATH4753lu0010 | 436c085ecd758e695e28c582c414286e1b518534 | 32078ada724cb1dac8a2bdbd5630d2e849e248d4 | refs/heads/master | 2023-02-28T20:55:46.058145 | 2021-01-29T17:22:18 | 2021-01-29T17:22:18 | 334,214,221 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 322 | r | bmi3.R | #' Create BMI Groups
#'
#' More detailed description.
#'
#' @param x Numeric vector.
#'
#' @return Factor variable
#'
#' @examples
#' bmi.vals <- rnorm(n = 20, mean = 25, sd = 3)
#' bmi3(bmi.vals)
#'
#' @export
bmi3 <- function(x) {
bmi.groups <- cut(x, breaks = c(0, 25, 30, Inf), right = FALSE)
return(bmi.groups)
... |
75ad8e345f08fbda613b5dc810351d1572f74d59 | 3162c50b248d9dbb3c210db68d209a916cdc5a56 | /scripts/processing/gyrb/merge_amplicon_metagenomic_datasets.R | dd1b632ccba53e48eb16fea752e077a84a9ff67a | [
"MIT"
] | permissive | ahnishida/captive_ape_microbiome | b68f818c20df396180e960b1a23e6b0c3daef0ab | afa2310dfd53aa94526aedf3d3bc00f9852e7921 | refs/heads/master | 2023-04-17T17:20:47.683032 | 2021-06-08T21:21:50 | 2021-06-08T21:21:50 | 287,132,087 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,976 | r | merge_amplicon_metagenomic_datasets.R | library(dada2); packageVersion("dada2")
library(phyloseq)
library(DECIPHER)
library(tidyverse)
library(phytools)
library(seqinr)
library(stringr)
library("genefilter")
library(zoo)
setwd('/Volumes/AHN/captive_ape_microbiome')
# this merges amplicon datasets and filters out reads that aren;t assign.
# filtered ASVs a... |
978a6aecc0f8d2ccb8173758635f156c20357a34 | 85b589129d60b08f61a591d8d336d0b8700b61bc | /plot2.R | d168dc47776064eebc472e2e321dada1d6e24d83 | [] | no_license | adrianthong/ExData_Plotting1 | b74b8a5001b867286200e5e1998ca998eb357785 | 6f04a1254c2807d1804cb0c5f0e07dbaf2bca950 | refs/heads/master | 2021-01-18T12:33:54.290274 | 2015-08-04T15:31:46 | 2015-08-04T15:31:46 | 40,188,573 | 0 | 0 | null | 2015-08-04T14:09:41 | 2015-08-04T14:09:41 | null | UTF-8 | R | false | false | 1,397 | r | plot2.R | ### Coursera Course 4: Exploratory Data Analysis
### Date: 4-Aug-2015
### Author: Adrian Thong
### Assignment 1: Plot 2
### Input: household_power_consumption.txt
### Output: plot2.png
### Description:
### Read only lines for the dates 1/2/2007 and 2/2/2007
### Convert to PNG file as required by Assignment 1 plot
#... |
baa1dbb2d3efb9f1515c14ef0e9089d0cbd448f2 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /GUILDS/R/evaluateLogLik2.R | 764e03158caf2c45a1f657d30cab42f7f642b049 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 776 | r | evaluateLogLik2.R | evaluateLogLik <- function(v, theta_x, theta_y, alpha_x, alpha_y,
J, Sx, Sy, Nx, Ny, KDA_X, KDA_Y) {
nx <- v
ny <- 1 - v
I_X <- alpha_x * nx * (J - 1) / (1 - alpha_x * nx - alpha_y * ny)
I_Y <- alpha_y * ny * (J - 1) / (1 - alpha_x * nx - alpha_y * ny)
a <- lgamma(J + 1)
b <- rep... |
451959195e3dc2e73fb02a66fbcbde771b40d5ba | d31a37cdd54374410825ceeed36b9370564dcb43 | /run_analysis.R | 1ef175dbd9b3d2f068da2eb437906cab5159221b | [] | no_license | aiedward/Getting-and-Cleaning-Data-Project | 308eb6640adfde24403d8f312fe10ac697c17ed5 | 8e706d3bd30540ec693ad752ad8fcf8488b80f8f | refs/heads/master | 2020-07-24T00:44:05.929598 | 2016-11-15T13:09:30 | 2016-11-15T13:09:30 | 73,798,353 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,191 | r | run_analysis.R |
## The data is in the working directory under a sub-directory named "UCI HAR Dataset"
## setwd('C:/Users/Edward')
## indices and tidy names for "mean" or "std" features only
## read names of all featuresdd <- read.table("features.txt")
feature_names_original<-read.table("UCI HAR Dataset/features.txt")
# dim(feature_... |
474505ad62d04569c34485b0d159c2e307d6b9e7 | ef567cee0d5cdce6b6e6a0b4d517acb97c1974af | /man/circle.Rd | f09caef27ae41ff9719af1df17b0065fd49b4967 | [] | no_license | znerp/labBook | 4086b5779374d2d9726466f1665e87d96f0a89e6 | 455c89d025bb51e59b5efddf222bec83ae5a5b29 | refs/heads/master | 2021-05-11T06:37:59.619526 | 2018-01-18T14:56:07 | 2018-01-18T14:56:07 | 117,993,672 | 0 | 0 | null | 2018-01-18T14:30:40 | 2018-01-18T14:30:40 | null | UTF-8 | R | false | true | 227 | rd | circle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotting_functions.R
\name{circle}
\alias{circle}
\title{Draw a circle}
\usage{
circle(x, y, r, num_segs = 100, ...)
}
\description{
Draw a circle
}
|
eb2a4f757063b822b3830990f6f64bff53ce90d9 | b9192fd071beac2e0b21f3f534b81f7ad9df0231 | /R/batch_strata_ads.R | 6de8e05413400af228888b68ba0775ebb5daa9dd | [] | no_license | danrthomas/fbsample | 895424fb8f1a86dd8c929fb8e9b14c89d47bd5aa | 4a44c5fd2e13e90ba3c072f19703fd16c5fc9d79 | refs/heads/master | 2023-03-21T02:26:13.462698 | 2021-03-08T20:19:05 | 2021-03-08T20:19:05 | 345,750,907 | 0 | 0 | null | 2021-03-08T18:44:39 | 2021-03-08T18:11:43 | R | UTF-8 | R | false | false | 6,622 | r | batch_strata_ads.R | #' Create Facebook ads for a batch of strata
#' @param fbacc \code{FB_Ad_account} object that identifies your Facebook Ad account. Note this is not your Ad account number.
#' @param strata_targeting list. Information about each strata that you plan to target.
#' @param optimization_goal character. Optimization goal.
#'... |
0d104c9361b8e6beeb2f1543eac2c3db82355ae9 | ca53a2af3684e94d613c72915d8283b86fd424c7 | /plot3.R | b49b2db2b0faeb5a874e58d539b293e59fe12413 | [] | no_license | SLaw7/ExData_Plotting1 | 96951aa09fee92be9323009b55b009cbca25390f | 063b1a42323fad50e901baad0f8fe7d7a200da5f | refs/heads/master | 2020-04-08T10:18:55.327302 | 2015-02-06T17:10:02 | 2015-02-06T17:10:02 | 30,375,585 | 0 | 0 | null | 2015-02-05T20:04:24 | 2015-02-05T20:04:23 | null | UTF-8 | R | false | false | 882 | r | plot3.R | table <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?", nrows = 2880, skip = 66636, stringsAsFactors = FALSE)
table2 <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?", nrows = 5, stringsAsFactors = FALSE)
names(table) <- names(table2)
... |
943069740ba17a5f5e227db873dc2b161dba84ca | 3678bd577662e320c9bba8bdb6ce8ae554872559 | /R/fdr_gam.R | bf7d96116c6627484edd45ec9b610d8c280b1c26 | [] | no_license | cran/mSTEM | 94efece0611b855303c4264f24763430430de4bc | a4d566139cce621b9fbdf8719250c830eb408179 | refs/heads/master | 2020-08-05T00:00:19.490406 | 2019-10-02T09:00:05 | 2019-10-02T09:00:05 | 212,323,373 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,552 | r | fdr_gam.R | #' Parallel computing fdr and power of change points estimation for different \code{gamma} and \code{nu}
#'
#' @param c number of cpu cores used for parallel computing
#' @inheritParams GenDY
#' @param Nu a vector of different \code{nu}s
#' @param Gamma a vector of different \code{gamma}s
#' @inheritParams Fdr
#' @inhe... |
cf2901d17093c77a5338c68ca1746229986b6c6d | 09ee223baa1c6338fd695b415fc7f88a6dcb1720 | /Sarek/manta-PF.R | 4f09dd41013c6e221abfb9da78769444d599a982 | [] | no_license | szilvajuhos/pathfindr | 47efea3848435c83e881d57eec19271fa07452be | b74c8be56fb50cef4a098152079667e2bab48c17 | refs/heads/master | 2020-03-29T19:30:15.733699 | 2020-01-30T13:41:15 | 2020-01-30T13:41:15 | 150,266,650 | 0 | 0 | null | 2018-09-25T13:06:36 | 2018-09-25T13:06:35 | null | UTF-8 | R | false | false | 19,858 | r | manta-PF.R | #!/usr/bin/env Rscript
# call like:
# Rscript ~/dev/pathfindr/Sarek/manta-PF.R -r ~/dev/pathfindr/Sarek/reference_data/ -o bugger -s /data0/btb/P2233/test/
options(warn=0)
suppressWarnings(suppressMessages(library("optparse")))
write("\n *** Pathfindr: filtering and prioritizing somatic mutations \n",stdout())
# Comm... |
71a224fe0fd9f315755176700e9b8f2ea8b886ed | 0058c9840491f1f07e7288254529bc4112c6c311 | /R/interactive_hourly.R | 54b43a92dbfad4af1aadeaceb5fadc4e7ad3edcf | [] | no_license | adamdsmith/MERL | d219579bcc750e1f86e62cdb8588e54bf857a776 | 2a2ddd260b189a352c0c00deaa7033a4929bb4ae | refs/heads/master | 2021-07-01T18:36:53.764891 | 2017-09-22T19:58:10 | 2017-09-22T19:58:10 | 104,397,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,668 | r | interactive_hourly.R | pacman::p_load(dplyr, dbplyr, leaflet, leaflet.extras,
htmlwidgets, htmltools, viridis, lubridate)
### Extract relevant detection data and join with active receivers
db <- list.files("./Data/", pattern = "*.motus$", full.names = TRUE)
merl <- src_sqlite(db) %>% tbl("alltags") %>%
select(tagProj, tag... |
f91675c1b43fe1694ce9125b0f165a007563933a | 32b0b6fbfcd12b206d1433effa8c1db75f4e2235 | /main_code/delay_report.R | ab6e6b91d4107f064c7818ee7e6713ef100103f7 | [
"Apache-2.0"
] | permissive | wxl1379457192/NPIs_code | 5854db921d29e336e6eef50ebab26b388c9bed94 | 175efdd814298809b0d37b002d257f677f0a8fc5 | refs/heads/main | 2023-06-27T17:59:08.224720 | 2021-08-02T14:28:34 | 2021-08-02T14:28:34 | 353,044,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,593 | r | delay_report.R | Reported_delay <-function(COVID_D1,Policy_P1)
{
############# read epidemic and policy data ################
D1 <- read.csv(COVID_D1, stringsAsFactors = FALSE)
P1 <- read.csv(Policy_P1, stringsAsFactors = FALSE)
#D1<-read.csv(url(COVID_D1))
Con_list <-intersect(D1$iso_code, P1$CountryCode)
D1[is.na... |
62ee74d1f40d15bd556cf05a4f4efc9bae5b98b3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dataone/examples/listObjects.Rd.R | 9287e44140a56d11f89042b556295bcfa86f5ffc | [] | 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 | 557 | r | listObjects.Rd.R | library(dataone)
### Name: listObjects
### Title: Retrieve the list of objects that match the search parameters
### Aliases: listObjects listObjects,D1Node-method
### ** Examples
library(dataone)
cn <- CNode("STAGING")
fromDate <- "2013-01-01T01:01:01.000+00:00"
toDate <- "2015-12-31T01:01:01.000+00:00"
formatId <-... |
225e4e667367c4e82cc24f76e91fbf88760c23e0 | e05524a39391ee68ac529178e1111e4092e5dcd0 | /ITWILL_Semiproject/multi_lm.R | f9fb62d8ce5af1b02f36f540705fe0002d1682c1 | [] | no_license | DominKim/R_Data_Anaylsis | 28d9b7c38777bcb06826d3e34b1e0bff2bc45954 | 6fd207015fe6e3c814e48ca857ac721fad8f9ab3 | refs/heads/master | 2022-04-25T02:30:20.818057 | 2020-04-28T11:34:09 | 2020-04-28T11:34:09 | 245,743,926 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,032 | r | multi_lm.R | ######################################################################
if(!require("cvTools")) install.packages("cvTools"); library(cvTools)
if(!require("car")) install.packages("car"); library(car)
if(!require("corrplot")) install.packages("corrplot"); library(corrplot)
if(!require("dplyr")) install.packages("dply... |
49850f12f69431bafc76d7f8f0d070592947d23d | 29585dff702209dd446c0ab52ceea046c58e384e | /prcbench/tests/testthat/test_tool_pm.R | ff1372994fcc4f3649e179e47f7821a86eeba246 | [] | 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 | 1,448 | r | test_tool_pm.R | context("Tool: PerfMeas")
# Test ToolPerfMeas
# create_toolset
#
test_that("ToolPerfMeas - R6ClassGenerator", {
expect_true(is(ToolPerfMeas, "R6ClassGenerator"))
expect_equal(attr(ToolPerfMeas, "name"), "ToolPerfMeas_generator")
expect_equal(grep("PerfMeas",
body(ToolPerfMeas$private_me... |
e8852acaf4f6942a9f40713a47612fd87e93ed21 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googleanalyticsv3.auto/man/McfData.query.Rd | 3589eff951f631fe7c65518c0f88e7af53feebb3 | [
"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 | 1,271 | rd | McfData.query.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analytics_objects.R
\name{McfData.query}
\alias{McfData.query}
\title{McfData.query Object}
\usage{
McfData.query(dimensions = NULL, end.date = NULL, filters = NULL,
ids = NULL, max.results = NULL, metrics = NULL, samplingLevel = NULL,
se... |
176697a3a089d64ba9e71f3c38dbf1b652ef91ab | 97c60175123108718c5cc1a767b7eb0d9df714db | /05-Gap-Filling/03_plot_model_Rsq_with_diff_3_day_ave_weights.R | 1621deb77a89707afd465116e98d0da6c02940dd | [
"Apache-2.0"
] | permissive | gioscript/Missing-Drainage-Data | 12b170311b957a9280890ce8041274f5edbbfb3d | 25330b00161aaf0c7b1ac195bc4e92f9e4c13307 | refs/heads/master | 2020-04-04T12:30:16.493702 | 2020-02-28T22:49:26 | 2020-02-28T22:49:26 | 155,928,632 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,799 | r | 03_plot_model_Rsq_with_diff_3_day_ave_weights.R | # this peice of code is used to find the best weights for calculating 3-day moving average precipitation
# based on visual results it looks that:
# a. non-weighted (i.e. equally weighted) average performs better than 10-30-60
# b. improvments over non-weighted average was achieved by giving more weight to the ... |
7e05633cc57376e98d3d03650d738e1f64aca328 | 0ea96e7b4d16c6595415d657103f7a2391421706 | /R/plot_functions.R | 0e480a6e71d9019b228427b678e704634d985521 | [
"MIT"
] | permissive | dozmorovlab/multiHiCcompare | c913334612e9b6fc8807341d370f11715ed51da0 | dcfe4aaa8eaef45e203f3d7f806232bb613d2c9b | refs/heads/master | 2022-04-29T02:56:04.486050 | 2022-04-22T01:43:22 | 2022-04-22T01:43:22 | 144,622,761 | 4 | 7 | NOASSERTION | 2020-03-30T13:53:16 | 2018-08-13T19:10:22 | R | UTF-8 | R | false | false | 9,059 | r | plot_functions.R | #' Make MD plots for all combinations of a condition
#'
#' @importFrom graphics abline legend lines par persp
#' points smoothScatter axis
#' @param hicexp A hicexp object.
#' @param prow The number of rows to use for the
#' grid of MD plots. Defaults to 3.
#' @param pcol The number of columns to use for
#' ... |
8446003031e58ecec1650e1a807e363126fa05d5 | 17bce903f04119fd1e888216675eea2834191315 | /man/dplookup.Rd | f021a81b0a241631ac9a878417f36aabf77e45f1 | [] | no_license | cran/UScancer | a81d91adbf571edd9d575c6887efb3886d3f4346 | c8b0bde7b24f5ec32957e5b32b072a27737d32b0 | refs/heads/master | 2020-12-24T16:49:53.609812 | 2014-08-08T00:00:00 | 2014-08-08T00:00:00 | 17,693,982 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 949 | rd | dplookup.Rd | \name{dplookup}
\alias{dplookup}
\docType{data}
\title{
%% ~~ data name/kind ... ~~
Census demographics profile lookup table
}
\description{
A lookup table for US census demographic profile data element to its full name. Only contains the codes for sex and age groups.
}
\usage{data(dplookup)}
\format{
A \code{\link... |
83a77a9027ee5f7684ba47eeeaabd5e58ac19aad | d690af5c19bb0d6b723e1b8f1687794b4e0f8830 | /tests/testthat/test-keras-mlp-classification.R | 235862b18103773c55faddfec7f8dd68cf4dd0c5 | [
"MIT"
] | permissive | roldanalex/safepredict | 03113c5095518fef7c007c7e98342ecf15c0f9dc | 05c3b9c8770583221a73b7b68f88805402630f5f | refs/heads/master | 2021-10-09T11:32:24.866936 | 2018-12-27T06:11:45 | 2018-12-27T06:11:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,300 | r | test-keras-mlp-classification.R | # context("test-keras-mlp-classification")
#
# library(testthat)
# context("simple neural network execution with keras")
# library(parsnip)
# library(tibble)
#
# ###################################################################
#
# num_pred <- names(iris)[1:4]
#
# iris_keras <- mlp(mode = "classification", hidden_uni... |
03bd564d1bb78675aa7dfa71fe987d461cf2f9c9 | 92348974c2b669227c168e5264affe0922c153dc | /integrateIt/R/integrateIt-internal.R | ae2d395817af78f5982ba354e7f4c47bc243fc51 | [] | no_license | jmetz2131/Metz-Midterm | a33a363345f75cfeef1aecc9eff96e1ea5f06260 | 247e7be34c283a249ed4c30ad6a4725397ff74fa | refs/heads/master | 2021-01-10T09:53:53.619416 | 2016-03-24T06:14:42 | 2016-03-24T06:14:42 | 54,599,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,963 | r | integrateIt-internal.R | .__C__Candidate <-
<S4 object of class structure("classRepresentation", package = "methods")>
.__C__Simp <-
<S4 object of class structure("classRepresentation", package = "methods")>
.__C__Squares <-
<S4 object of class structure("classRepresentation", package = "methods")>
.__C__Trap <-
<S4 object of class structure("... |
dd4705d3066d4e43a8ee85cc5ba694332af6df08 | df7c8f0ffc8ea6314f36f0cff60522ecabeb50a7 | /R/iron_condour.R | 65b36e950af015a14cb8196ae084c53aa5c288af | [] | no_license | cran/roptions | 60ac5e47b9da1668fee96061ad0a678a3503fef1 | eae716417df9453ea387a2bd289dffdd26562726 | refs/heads/master | 2022-06-24T16:41:53.934062 | 2020-05-11T10:10:06 | 2020-05-11T10:10:06 | 263,169,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,842 | r | iron_condour.R | #' @title Iron Condour Strategy Function
#' @description This function can be used to develop a Iron Condour Strategy.
#' @param k_long_call Excercise Price of Long call Option
#' @param k_short_call Excercise Price of Short call Option
#' @param k_long_put Excercise Price of Long Put Option
#' @param k_short_put ... |
e379475cf0f0e868da551a822fd08f152e9e29a3 | 8f2f7844354a8b68ad1afe5e5fd322b6bd1b1b8e | /lab4/5.R | 894a642354e12546ad8bdd76d2bc7f0f46454186 | [] | no_license | KUGDev/EMMvSA | 0f6f97df07ce5e4c0e50272863b5b8481a730853 | 08bfdeab9f84c333774aefb0ed341a444dce2656 | refs/heads/master | 2023-04-13T22:53:15.367460 | 2021-05-02T23:10:44 | 2021-05-02T23:10:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,409 | r | 5.R | library(e1071)
area.pallete = function (n = 3)
{
cols = rainbow (n)
cols [1 : 3] = c("PaleGreen", "PaleTurquoise", "Pink")
return(cols)
}
symbols.pallete = c("SeaGreen", "Blue", "Red")
set.seed(0)
C = 1
kernel = "polynomial"
degree = 1
data = iris[c("Petal.Width", "Petal.Length", "Species")]
trainIdx... |
4436ca7ee2896ce1f9a73ae1d13a831a920ed4bc | 8f599a3252717ce917ea6a408db24fbc17de8428 | /hope mci sandbox/Judgements.R | 3907003437eeb338e8eb974759cd32fc567a7d8a | [] | no_license | cmishra/dsicapstone-predicting_extremism | 423ef9f4c55192f8d79bdef328e3cee5e0725296 | 35fe30e0254e83f5f2ec0f5800106f85549e2bac | refs/heads/master | 2021-01-21T04:27:45.746344 | 2016-04-07T18:28:47 | 2016-04-07T18:28:47 | 42,951,048 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 10,303 | r | Judgements.R | remove(list=ls())
library(tm)
library(topicmodels)
library(openNLP)
library(NLP)
library(stringr)
setwd('/Users/hopeemac/Documents/Education/Classes/UVA MSDS/15F/Capstone/westboro_sermons.pdf')
# Import all WBC Sermons
corpus <- VCorpus(DirSource('.'))
doc1 <- as.String(corpus[[1]])
##### Sentence segmentation -- a... |
ecbc1269d75e6b41982a1a7e7f53f4f0be8bf2fd | 1547182269fd52c15f1c554227264d730f57ebb2 | /R/mgMEM.R | 3a88c0d54b5fc6de2b6123b3a25f18cec05df77e | [] | no_license | cran/memgene | 084fbf5a3bf28229a198f265bca94a436f592ea4 | 6672a2f264d0a75120527ea31d1b82c28b5870a8 | refs/heads/master | 2022-03-16T22:52:16.117662 | 2022-02-21T18:00:02 | 2022-02-21T18:00:02 | 20,113,465 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,905 | r | mgMEM.R | mgMEM <-
function(locD, truncation = NULL, transformation = NULL) {
locD <- as.matrix(locD)
## Internal function to perform truncation and transformation
.distTransform <- function(locD, transformation, truncProp) {
locDTr <- locD
if (is.null(truncProp) || !(truncProp)) {
truncProp <-... |
0ac7b885386e6cb372faa7f0a53803e6dd859ce7 | 06f61362cc94229cc4438ee8770f2bc6007efeae | /R/Temp2018/Optimize.ibd.R | 6f408747dd4e9698023d896974e2e5162c522698 | [] | no_license | bellerbrock/OptimalDesignMM | 39ce6fa32212d4756d8866a329c79bf6cb6c6a27 | 8217e9475913511dede791b282df50e1c528f1bc | refs/heads/master | 2021-09-11T18:15:44.140016 | 2018-04-10T21:05:52 | 2018-04-10T21:05:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,492 | r | Optimize.ibd.R | #' Optimize a RCB Design using a pairwise swap algorithm
#'
#' \code{Optimize.ibd} optimizes an experimental design using a simple pairwise swap algorithm.
#'
#' @import Matrix
#'
#' @param matdf an experimental layout (from function rcbd)
#' @param n the number of iterations desired
#' @param traceI a trace or determ... |
32935514b8ad044adfaad5bff1a50914b7ace0fd | ae4102a9effc90e20c79098109a3b1f6cbfa3b9c | /man/sofa-package.Rd | 9349ca49c963417e0697a3f6ac152908907c5de2 | [
"MIT"
] | permissive | lmilev/sofa | c539764de516ff7c1338d93ef80d62287cb3c6d2 | 549d333e41a333a59aea25db12bc568b47be22c4 | refs/heads/master | 2021-01-24T09:57:10.138729 | 2018-01-28T17:56:52 | 2018-01-28T17:56:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,290 | rd | sofa-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sofa-package.r
\docType{package}
\name{sofa-package}
\alias{sofa-package}
\alias{sofa}
\title{R client for CouchDB.}
\description{
Relax.
}
\section{About sofa}{
\pkg{sofa} provides an interface to the NoSQL database CouchDB
(\url{http://cou... |
2482560a2b9f5dd83fe74044c1e5e75d90c65f82 | 67b12601515fbfa1c555d14310a111b62250d5a1 | /bugs/fallarmyworm_nonlinear.R | 57da3e582f354c604b4aa0bccfb18350b8faa738 | [] | no_license | cesaraustralia/darabug2 | 11e2faeb38783e40ba22b7165976fec5b6cc530d | 2e5b0466dac19c69980c74b7eb55d4ddac14aed8 | refs/heads/master | 2023-07-17T14:19:50.898006 | 2021-08-25T06:44:36 | 2021-08-25T06:44:36 | 85,540,254 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,505 | r | fallarmyworm_nonlinear.R | fallarmyworm_nonlinear<-function(){
# returns a structure containing species name, developmental function for each stage, and life-history category for each stage (egg, immature, pupa, adult)
name <- 'Fall armyworm (non-linear)'
sciname<-'Spodoptera frugiperda'
source <- 'Ali, A., R. G. Luttrell, and J. C. Schn... |
03508544414917040abb883f590545b4eb143f91 | 3fb5533d68b4b8b7949e99442f6ba7ff6ef61735 | /man/agTrend.gam-package.Rd | 6ae265f656dd1a139b16f87bcc7591ee8545d049 | [] | no_license | dsjohnson/agTrend.gam | 7555015c8c81eb3748b7c877a50766a727987393 | b3deac6e1937711c84751febe6c87387afb58439 | refs/heads/master | 2020-04-05T01:26:42.017711 | 2018-11-22T00:49:56 | 2018-11-22T00:49:56 | 156,436,565 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,381 | rd | agTrend.gam-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/agTrend.gam-package.R
\docType{package}
\name{agTrend.gam-package}
\alias{agTrend.gam-package}
\alias{agTrend.gam}
\title{R package for fitting temporal trends to abundence data aggregated over large regions when subregions have missing data}... |
5830a16ae89564126305e9567f0cd471cbcb1333 | 8652da947f69556909d32037b5a05801571231c5 | /tests/testthat.R | d108d32033dd073435f746788e3177eb805d974c | [
"MIT"
] | permissive | AndreSjuve/texaid | 54b5dcb412fe6aba567eb0d71df7447a92b9e53d | 3d175d9e03c8714d01b9191d07f59bd83ab09021 | refs/heads/master | 2023-08-18T09:14:18.482368 | 2021-10-01T15:29:01 | 2021-10-01T15:29:01 | 412,502,974 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(texaid)
test_check("texaid")
|
18590ff94e4ba4939bf574578dfbb290cebae833 | 687a85612a09c61a77853b70acffbc383de6e5aa | /man/brca.Rd | 632716aa4afd0398a4255124b564f7346fd37c53 | [] | no_license | dinukap/AdaSampling | 5b319993ee93707fb0f27b1c34bb1ecc4743248f | 81978628d1f5dec40599de3a07b052c24d9569e0 | refs/heads/master | 2021-05-10T07:57:37.390294 | 2018-03-02T03:56:12 | 2018-03-02T03:56:12 | 118,868,301 | 4 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,256 | rd | brca.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{brca}
\alias{brca}
\title{Wisconsin Breast Cancer Database (1991)}
\format{A data frame with 683 rows and 10 variables:
\describe{
\item{clt}{Clump thickness, 1 - 10}
\item{ucs}{Uniformity of cell size, 1 - 10}... |
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