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8a022909d892b4903efb384d09e0772b35902e7b | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.cloudfront/R/paws.cloudfront_interfaces.R | 27dd2628da217e02171f4eb152b81d39a1817bba | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 253,427 | r | paws.cloudfront_interfaces.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common populate
NULL
create_cloud_front_origin_access_identity_input <- function (...)
{
args <- c(as.list(environment()), list(...))
shape <- structure(list(CloudFrontOriginAccessIdentityConfig = structure(list(CallerReferen... |
911745cf6e21e134d2320f049e0b7c0da9d18e01 | 5db52748f7af1e976d9c5ff550e0128410b2c2ce | /SELECTION-GO_ANALYSIS/TOpGO.R | d5a18d92c02f21599588f0d72fb00149beca469d | [] | no_license | htnani/african_rice | 3609d385826fe3e2ffeb8d469ced3b758745f03d | 930d4e5e746a475060a15c98117740b9bfcb2057 | refs/heads/master | 2020-03-30T16:25:18.496279 | 2018-05-14T09:16:06 | 2018-05-14T09:16:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,372 | r | TOpGO.R | #===============================================================================
# author: Benedicte Rhone
# date: 03/2016
#-------------------------------------------------------------------------------
# Description: GOterms enrichment analysis on a set of genomic region under
# selection using the topGO R package
... |
5d58fc96210a5b69ca254d10d43bbd95a9904463 | 7fc84b730a056db55239d42a62457ca0a334b93e | /linmech/gen-data/gen_data.R | 409d6d0d5882bf9ed6668405c366cd51574b2a66 | [] | no_license | topherconley/sim-spacemap | 90b241b7565ed25110d14eeedef523b5d82b18b5 | 590ebcd72d2da1f30d3a8c61ad418777987a971a | refs/heads/master | 2021-01-25T06:45:53.911366 | 2017-02-07T00:25:37 | 2017-02-07T00:25:37 | 80,661,174 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | r | gen_data.R | #not found
#source("~/repos/sim-spacemap/linmech/space_score_functions_10082011.txt") ##space.shd, and space.shd.adj
load("dense_p500_adj_matrix.Rdata")
true.v=vstructures(true.dir)
############################# part II: generate Guassin linear mechanism based on pancr.adj
p=nrow(true.dir)
n=100
nrep=10
source("~/re... |
c98b6793dfffa468d51d2d791df5f5e1fd89afb7 | dd462b8781178eb309a7f76c94a4c0537e6513a9 | /eda/KAO/16_KAO_merging_CD3.1_results_with_Lipidex_output.R | 70f6ab1326410ebcce11b8c67391ecff87f5a1ab | [
"MIT"
] | permissive | jsgro/COVID-19_Multi-Omics | 3198c75f5c5a17b55e829e66754c01ebb15d0689 | 1b7e6f3eb3aa78529c8a2c28a58f4c2c0cbeeafa | refs/heads/master | 2023-01-02T04:20:38.025786 | 2020-10-29T21:01:54 | 2020-10-29T21:01:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,902 | r | 16_KAO_merging_CD3.1_results_with_Lipidex_output.R | ###### 16_KAO_merging_CD3.1_results_with_Lipidex_output.R #######
## To help identifiy some of the unknown features we searched raw files
## with extra nodes in Compound Discoverer 3.1: MZ Cloud, chemspider,
## mzvault, and formula.
## These results were further filtered down in excel to include only
## features (put... |
46e4906d6f826eecb3b871b7c6b98dbd75e7b9e6 | 2605ed5c32e799ddfd7b1f739800e35093fbc24e | /R/lib/RSienaTest/sienascript | 68c264965f4dfe53d18fd47bdba0ee532c106fea | [] | no_license | BRICOMATA/Bricomata_ | fcf0e643ff43d2d5ee0eacb3c27e868dec1f0e30 | debde25a4fd9b6329ba65f1172ea9e430586929c | refs/heads/master | 2021-10-16T06:47:43.129087 | 2019-02-08T15:39:01 | 2019-02-08T15:39:01 | 154,360,424 | 1 | 5 | null | null | null | null | UTF-8 | R | false | false | 193 | sienascript | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library(RSienaTest))
suppressPackageStartupMessages(library(tcltk))
RSienaTest:::DONE(FALSE)
while(!RSienaTest:::DONE()) {Sys.sleep(0.1)}
| |
248c96453b6454a2b1b5b7b42b799c7c1eaa2f88 | 5150cf610a34c6c5be9b598277db1834d8fb16b4 | /man/brood_check.Rd | d60fc6f76f95783f598fcfc6a181335669418825 | [] | no_license | SPI-Birds/pipelines | f3ab78668e526a47bd298b0f7f4127e274a4dfd0 | cb4bd41bc26d991fa54e520bb15b54333696b4cb | refs/heads/master | 2023-08-16T18:15:29.835023 | 2023-08-09T09:51:56 | 2023-08-09T09:51:56 | 153,275,927 | 0 | 3 | null | 2022-12-04T14:48:00 | 2018-10-16T11:42:17 | R | UTF-8 | R | false | true | 3,401 | rd | brood_check.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brood_check.R
\name{brood_check}
\alias{brood_check}
\title{Perform quality checks on brood data}
\usage{
brood_check(
Brood_data,
Individual_data,
Capture_data,
Location_data,
approved_list,
output,
skip
)
}
\arguments{
\item{B... |
919d59fc5f88696753be81bfaaffcd323b620857 | 770a3350ed49746a49f7407f7b2eb906c0aeec5f | /11.5.2021/ex1.r | dfdaaa5111e43691cc8ce51750e5dbb3342ab42f | [] | no_license | ntdthanh1409/BaiTapR | 2343509abc6c312b9c1a3d8a6d4a2b69bc9f9ec0 | 5cee11f67f750cfaf4120765aa2c5828a0fd500d | refs/heads/main | 2023-05-02T18:52:53.833392 | 2021-05-27T10:28:22 | 2021-05-27T10:28:22 | 365,108,841 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 671 | r | ex1.r | setwd("C:/Users/PC/Desktop/New folder1")
Owls <- read.table('Owls.txt', header = TRUE,dec = ".")
Owls
#You should check all variables (column), it should available for analysing.
names(Owls)
str(Owls)
#Our function:
#Check how many station (tram khi tuong) in our dataset
Allnests <- unique(Owls$Nest)
N <- length(Alln... |
9b0e0aa95e97262d78d726309fdb9d706ea1c884 | ec16c798bf80bcbde5d0bb621554d3f2d906a974 | /man/predict.gp.Rd | a2196a7bb38255408fa5d0ead60fd38f698c1820 | [
"Apache-2.0"
] | permissive | mickash/SimpleGPs | 8dfec3353dd38ec259f0306657ba11d78ae4fc61 | 4c5c8adfa5b7cb20a73fd93998fdb7cbd4a07144 | refs/heads/master | 2020-09-09T11:45:45.032197 | 2019-11-13T12:33:47 | 2019-11-13T12:33:47 | 221,213,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 527 | rd | predict.gp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.R
\name{predict.gp}
\alias{predict.gp}
\title{Predict}
\usage{
\method{predict}{gp}(model, x, full = quicker(model, x))
}
\arguments{
\item{model}{The gp object}
\item{x}{The X data, as a matrix. Vectors will be interpreted as single... |
ff90d332a23c81c95cd91346860abacb319b58b0 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_PenmanMonteith/AFL_ET0_PenmanMonteith/ET0_PenmanMonteith_valgrind_files/1615842006-test.R | 3192943f80eeacbfc68f02990ba326aba8b75e59 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 863 | r | 1615842006-test.R | testlist <- list(G = numeric(0), Rn = numeric(0), atmp = numeric(0), ra = numeric(0), relh = c(-7.36599172844076e+192, 1.44942408802595e-285, -1.72131968218895e+83, -7.88781071482505e+93, 1.08231311223032e-105, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rs = numeric(0), temp = c(8.5728629954997e-312, 1.56898... |
9f8b356fed0c678168ba4cce4e648ebca3eb5fa4 | 2b32eae2b801ef212ea9817721e9c561e3aa9536 | /man/local_interactions.Rd | b10e6109d6bb974f091642467275c76eefb60457 | [] | no_license | agosiewska/iBreakDown | b8784c34ef73c274a5a792555eb3d52f57099849 | 11efabd6e5cb78b48f88034ffcb9ed9565d2f49b | refs/heads/master | 2020-04-28T06:43:16.600905 | 2019-03-18T19:16:46 | 2019-03-18T19:16:46 | 175,069,697 | 0 | 0 | null | 2019-03-18T19:16:47 | 2019-03-11T19:25:35 | Jupyter Notebook | UTF-8 | R | false | true | 3,128 | rd | local_interactions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/local_interactions.R
\name{local_interactions}
\alias{local_interactions}
\alias{local_interactions.explainer}
\alias{local_interactions.default}
\title{Model Agnostic Sequential Variable Attributions with Interactions}
\usage{
local_interact... |
1829fdf3e308849379ebbabaec4f0c76232e1961 | a52338b0bc2e42d3e51bd329144e70e67f242386 | /cachematrix.R | 46c1e0587b1d7d7a5f6fa365c3d1c785e03ca3a8 | [] | no_license | RedaAitOuahmed/ProgrammingAssignment2 | aaf65b5145de81fb5b7efb8e0d48584af0ba36fb | f6cdd2ef3555c30c806924fe12f0625df9e55003 | refs/heads/master | 2020-03-10T21:20:39.720050 | 2018-04-15T08:50:37 | 2018-04-15T08:50:37 | 129,591,836 | 0 | 0 | null | 2018-04-15T08:37:45 | 2018-04-15T08:37:45 | null | UTF-8 | R | false | false | 1,224 | r | cachematrix.R | ## makeCacheMatrix creates an object that allows to cache data about the matrix
## cacheSolve solve the inverse of a makeCacheMatrix object
## it tries to find a cached inverse, if it can't it solves the inverse and cache it
## returns a list of functions to get or set the matrix
## and to get or set the Inverse of th... |
63bbe6633e2d375e7b78b281a7791a953de4b20c | d909c0457c4648f0986efa041f6e12513f99bb6d | /handlers/data_census_utility.R | c27a8b01bd6c3d34defd60e202ab90f055248ee6 | [] | no_license | michaelgaunt404/uhsgt_dashboard | 6f0431f9b103b219f9e4870fff6067dce3b12529 | 45efcf45f1120763a7f6bd9b4405a52e9163d5fc | refs/heads/main | 2023-02-06T11:09:28.325261 | 2020-12-22T19:13:13 | 2020-12-22T19:13:13 | 311,736,962 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,403 | r | data_census_utility.R | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Utility script for all US and CA Census layers
#
# By: mike gaunt, michael.gaunt@wsp.com
#
# README: this script gets us census data
#-------- it uses tidycensus, cancensus, and tigris packages
#-------- it is not robust enough to f... |
a90e71eec51d69c586d65ab7aa68b1c11feffc0c | c5bc2307bcead541658ccd7f49db4eda9a6a3762 | /R/leslie.R | 5157d25caa45d42699fa48d2333e6e58258e04b0 | [] | no_license | shfischer/FLife | b2216da5bdf3f463cc7ea354e49115f598ecafe0 | 4979df14be234debeb468d89cf2659bb2f659836 | refs/heads/master | 2021-08-18T07:43:51.998014 | 2020-08-02T18:35:08 | 2020-08-02T18:35:08 | 238,467,826 | 0 | 0 | null | 2020-02-05T14:20:48 | 2020-02-05T14:20:46 | null | UTF-8 | R | false | false | 4,580 | r | leslie.R | #' @title Leslie matrix
#'
#' @description
#' Creates a Leslie Matrix from a \code{FLBRP} object that represents a population at equilibrium
#'
#' @param object \code{FLBRP}
#' @param fbar \code{numeric} F at whicj survival calculated
#' @param numbers \code{boolean} numbers or biomass, numbers bt default
#' @param ... |
b229c1a237a321586f63b155e94bf3fcfb9e5181 | 5678884f50ec1e751a0d5af60e4c1f406bc394dd | /R/setup.R | 35abfd38ed871d8e939acf46bd0289133714ff48 | [] | no_license | trafficonese/coinmarketcap_v2 | a4e30ae0c127b73d91aa2dc4c6dec45db210822f | 0e75e8eb23a78fbd470e9e1d55c4bdb19ce31b5e | refs/heads/master | 2020-06-18T04:53:57.073784 | 2019-08-08T08:10:27 | 2019-08-08T08:10:27 | 196,170,203 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,558 | r | setup.R | #' Setup
#'
#' Specifies API Key and the base URL for session
#'
#' @param api_key Your Coinmarketcap API key.
#' @param sandbox Sets the base URL for the API. If set to TRUE, the sandbox-API
#' is called. The default is FALSE.
#'
#' @examples
#' setup("xXXXXxxxXXXxx")
#' get_setup()
#'
#' @export
#' @name setup
setu... |
1de00a46b907ca4df63057af86d3f51d09afa77a | 5042c3a97c9a9fa4d0a5d6794960eec8146afa47 | /lotteryEstimator/man/srsworJointInclusionProbabilityMatrix.Rd | 3d9869a18f2ac42123b9232c8c43e46c618f7fcd | [] | no_license | Sea2Data/CatchLotteryEstimation | eef044b949aa382a0ca70a23c9c32d2ca2a1a4d5 | e364b505969e5bd861684bdb2fc55f6fe96d2b8f | refs/heads/master | 2020-12-22T06:29:23.504177 | 2020-10-25T09:12:56 | 2020-10-25T09:13:52 | 236,696,753 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 706 | rd | srsworJointInclusionProbabilityMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/srs.R
\name{srsworJointInclusionProbabilityMatrix}
\alias{srsworJointInclusionProbabilityMatrix}
\title{SRS joint inclusion probability}
\usage{
srsworJointInclusionProbabilityMatrix(n, N)
}
\arguments{
\item{n}{sample size}
\item{N}{populat... |
608a7b933c60e5b3e69b2f9d43b42a9c01721f36 | 85b3bd2f0116db14d4ea3a1882f06291b45e6db4 | /man/get.edges.Rd | 21724e6edda22eb1a37a7f128239d9148e31604f | [] | no_license | huginexpert/RHugin | 13576c8d6a0eb02886bf33884ecb9efe330cb777 | 71508b780b5362b624273010550efc1ae35b7b3a | refs/heads/master | 2023-07-06T21:00:06.086570 | 2023-06-28T11:49:59 | 2023-06-28T11:49:59 | 182,980,699 | 2 | 0 | null | 2023-06-27T06:43:19 | 2019-04-23T09:26:00 | C | UTF-8 | R | false | false | 1,192 | rd | get.edges.Rd | \name{get.edges}
\alias{get.edges}
\title{Get Edges}
\description{
List the edges in an RHugin domain.
}
\usage{
get.edges(domain)
}
\arguments{
\item{domain}{an RHugin domain.}
}
\value{
a list with one element for each node in \sQuote{domain}. Each element is in turn a list with a single element \sQuote{edges... |
ac91c4743b09279a7e067c8f7d30376e4404188e | 63f527aecf9e477e30b319661c836f41f27da2de | /man/dimnames.msexperiment.Rd | 8ef9b2a362a306ead4257d9ce6f1ada46dd3d18d | [] | no_license | wolski/imsbInfer | f0adbb4c114aa660d11c675c7d5b64d649c4b582 | 3bec123f2a5edb7285fafcf1e151e6e941d9d70f | refs/heads/master | 2021-06-11T01:48:24.771188 | 2021-03-27T09:10:06 | 2021-03-27T09:10:06 | 14,570,168 | 5 | 2 | null | null | null | null | UTF-8 | R | false | true | 475 | rd | dimnames.msexperiment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/swathDataUtils.R
\name{dimnames.msexperiment}
\alias{dimnames.msexperiment}
\title{show colnames (it does'nt let you set the columns)}
\usage{
\method{dimnames}{msexperiment}(x)
}
\arguments{
\item{x}{msexperiment}
}
\description{
show colnam... |
31c23ead81baa1b73a1d8c6e56518c44913cebee | 47672a3453dc21b3a4469878dd2801a19337f638 | /Antigenic_advance/Childhood_sens_analysis2.R | 7a2c9bebe299db7043536996733c26ed2233dadf | [
"MIT"
] | permissive | kgostic/2018_seasonal_flu_manuscript | 8d9a28e3f2be62d8452725aae03f1fa40c53ee37 | 0ddcfdb90606627ed1d38b22f1f7eecb50cc5e47 | refs/heads/master | 2020-04-10T07:33:36.920411 | 2019-09-15T21:15:21 | 2019-09-15T21:15:21 | 160,883,450 | 0 | 0 | MIT | 2019-07-06T00:13:34 | 2018-12-07T22:52:50 | HTML | UTF-8 | R | false | false | 22,182 | r | Childhood_sens_analysis2.R | ## GENERATE AN ALTERNATE VERSION OF FIG. 4A USING AGES 2-7 TO DEFINE CHILDREN
## SEE "ANTIGENIC_ADVANCE.R" FOR MAIN TEXT ANALYSES.
## Clear memory
rm(list = ls())
#setwd('2017_AZ/')
library(lubridate)
library(ggplot2)
library(ggpubr)
library(reshape2)
library(gridExtra)
setwd('~/Dropbox/R/2018_seasonal_flu/Antigenic... |
dd7745e9668d79e3df415798153764961a637986 | 2ae1860f940aef07f514afc7398f567ffb81f2b9 | /projects/WhoWillLeaveCompany.R | f036c1caa2b20d27373fe36e57b73adc64a1cc2b | [] | no_license | sebastianBIanalytics/ESEUR-code-data | fbc9d99b01b2e9cc10e7bf95064163f1356ecf56 | 9b216c79d280e77cac30a27f61f9d5475a7a864a | refs/heads/master | 2022-04-25T15:26:39.569497 | 2020-04-29T02:31:24 | 2020-04-29T02:31:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,558 | r | WhoWillLeaveCompany.R | #
# WhoWillLeaveCompany.R, 13 Feb 19
# Data from:
# Who will leave the company?: {A} large-scale industry study of developer turnover by mining monthly work report
# Lingfeng Bao and Zhenchang Xing and Xin Xia and David Lo and Shanping Li
#
# Example from:
# Evidence-based Software Engineering: based on the publicly av... |
d51e14ca050a97746755f89070218440eaaaca33 | 8a643c71bdc17b738b136f9aee845300398a56d9 | /AssignmentWeek2.R | 101718021afc74172dfec27e5fa50273486b9820 | [] | no_license | ybag/DevelopingDataProducts | 0ff1d325e8843e94699aefcc7cbd99b59b200478 | 284c3c1ce1ad5d381a1c274e63c25edcb59c2637 | refs/heads/master | 2021-01-22T11:28:33.645708 | 2017-05-29T03:13:38 | 2017-05-29T03:13:38 | 92,699,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 166 | r | AssignmentWeek2.R | library(leaflet)
map <- leaflet() %>%
addTiles()
map <- map %>%
addMarkers(lat=55.45, lng=37.37,
popup="The Kremlin and Red Square , Moscow" )
map
|
0707df95fd96e820c182c79e70aac5c9dbd29e1f | 4630a28100fbb60d6dbaf71540c0547346760bc3 | /tests/testthat/test_install.R | caab05d389d8686c6e6f4b3b832332f8c4dd1f55 | [] | no_license | Bioconductor/BiocManager | e202aa74fb2db70cbfed2295958c88d416209d3f | 125d50a723caaea36d3c27d241f78f7d96e2a3d7 | refs/heads/devel | 2023-09-01T01:22:18.656330 | 2023-08-21T20:11:04 | 2023-08-21T20:11:04 | 33,965,307 | 74 | 23 | null | 2023-09-08T13:39:13 | 2015-04-15T01:04:01 | R | UTF-8 | R | false | false | 12,099 | r | test_install.R | context("install()")
test_that("Arguments are validated", {
expect_error(
install("foo", "bar"),
"all '...' arguments to 'install\\(\\)' must be named"
)
expect_error(install(TRUE), "is.character\\(pkgs\\) is not TRUE")
expect_error(install(ask="foo"), "is.logical\\(ask\\) is not TRUE")... |
80fcf7051825536fd8a5c6a76fd5fee5db9a9d63 | 2ba6f0a982c3092e70de12fff4ccac047feecab0 | /pkg/tests/CDS.test.R | 82dd33b26cd4723ede884e2fd5bdd0b9e8c65eca | [] | no_license | kanishkamalik/CDS2 | 7b77cce8a5ae9fa367ff6818311ea8f745affbab | 18644a02c9e8031de42d548618717b09ed327053 | refs/heads/master | 2020-04-05T03:15:34.360446 | 2014-05-03T16:29:47 | 2014-05-03T16:29:47 | 19,469,784 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 625 | r | CDS.test.R | ## CDS.R test case
library(CDS)
## truth1 <- CDS(TDate = "2014-01-14",
## maturity = "5Y",
## parSpread = 32,
## couponRate = 100,
## recoveryRate = 0.4,
## isPriceClean = FALSE,
## notional = 1e7)
## save(truth1, file = "CDS.test.RD... |
82d2d8121349aac590b073e196d3aea004853694 | 2a7655dc0c233967a41b99369eed3eb4a6be3371 | /3-Get_Earth_Observations/Meteorological_variables/Process_NAM_data_step5_lower_RAM.R | b67842d0a72bb104d3a56876469e40c56262e14f | [
"MIT"
] | permissive | earthlab/Western_states_daily_PM2.5 | 0977b40d883842d7114139ef041e13a63e1f9210 | 3f5121cee6659f5f5a5c14b0d3baec7bf454d4bb | refs/heads/master | 2023-02-25T14:32:20.755570 | 2021-02-04T00:08:03 | 2021-02-04T00:08:03 | 117,896,754 | 2 | 1 | null | 2021-01-27T22:19:14 | 2018-01-17T21:48:29 | R | UTF-8 | R | false | false | 13,889 | r | Process_NAM_data_step5_lower_RAM.R | # Process_NAM_data_step5.R - take 24-hr summaries of NAM weather data
#### Clear variables and sinks; define working directory ####
rm(list = ls()) # clear all variables
options(warn = 2) # throw an error when there's a warning and stop the code from running further
if (max(dev.cur())>1) { # make sure it isn't out... |
bf1625810880a0f186dca700508a56fce82d522f | 7365ceab9a0ecb9ff25a00ac3375ec472b57a989 | /Singularity_analysis_KED.R | f045a7356758381588af5f24f620f9a54c47efb3 | [] | no_license | fcecinati/R | ffb471b8709b7c86be7cd98779575e7bb7030985 | e96466d434b970e446bd81488eb52777bd449f95 | refs/heads/master | 2021-01-20T17:19:26.543460 | 2016-07-20T13:57:40 | 2016-07-20T13:57:40 | 63,785,367 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,144 | r | Singularity_analysis_KED.R | ########################################################################################################################
########## This scripts performs KED merging with singularity analysis ########
##############################################################################... |
9ccd61832fa7f13bcb92e9505bbf1b30b190b734 | e37580222b47cd2811fd90d876dbe54e295039b7 | /man/CurrencyPair.Rd | 674ddfa701db93d7df2fc7cb7afe6430c44ff06d | [] | no_license | farzadwp/fmbasics | 0c9183244c25be3f4e77474405ad7dae15a27419 | 6fee375ceec0ac5ff199e9b3dfd146c65078a476 | refs/heads/master | 2020-06-12T18:08:01.217445 | 2019-11-28T02:31:47 | 2019-11-28T02:31:47 | 181,423,968 | 0 | 0 | null | 2019-04-15T06:17:02 | 2019-04-15T06:17:02 | null | UTF-8 | R | false | true | 748 | rd | CurrencyPair.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/currency-pair-class.R
\name{CurrencyPair}
\alias{CurrencyPair}
\title{CurrencyPair class}
\usage{
CurrencyPair(base_ccy, quote_ccy, calendar = NULL)
}
\arguments{
\item{base_ccy}{a \link[=Currency]{Currency} object}
\item{quote_ccy}{a \link[... |
f1a031a761a44259ccb0ce4922be37c7f451358a | 961727a721c8e1a02b52c69bfb9d274d6dca2b3c | /server.R | 5ad810fed7cdd5ab01561be58315cbdc19be0fc9 | [] | no_license | olivierchabot17/coin_flips | a62524704d24b306a8f40f6ce6d1c1d7f0c40ee1 | 17947f74ccd2a36b6af902723b21d1a48dace1b0 | refs/heads/main | 2023-07-02T20:57:51.282818 | 2021-07-29T12:41:42 | 2021-07-29T12:41:42 | 390,719,342 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,989 | r | server.R | # Load Libraries
library(shiny)
library(tidyverse) #dplyr and ggplot would suffice
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
# Only flip the coins when the "Let if flip!" button is pressed
big_sample <- eventReactive(input$run, {
# ... |
9d6f5e97eb6808f8245ba62d5aa4b2ca428e169c | c25ca7b930919db4299d8ee392daa3ed5c651180 | /man/prPrepareCss.Rd | abb52c3859030955dfacbe000dc615d61319cfe3 | [] | no_license | gforge/htmlTable | ecd0e56b54da74a085c5fc545c78181eb254fcb1 | 82ffe152c9b59559686a8a7bb74c9121d9539cf3 | refs/heads/master | 2022-07-21T22:01:41.100252 | 2022-07-07T18:14:47 | 2022-07-07T18:14:47 | 28,265,082 | 74 | 32 | null | 2022-07-06T14:53:56 | 2014-12-20T11:17:53 | R | UTF-8 | R | false | true | 1,188 | rd | prPrepareCss.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/htmlTable_helpers_prepareCss.R
\name{prPrepareCss}
\alias{prPrepareCss}
\title{Prepares the cell style}
\usage{
prPrepareCss(
x,
css,
rnames,
header = NULL,
name = deparse(substitute(css)),
style_list = NULL
)
}
\arguments{
\item{... |
4514a10fdd8cc24b2ec2cae182d44b4388aa176e | 076155f2a494bccb5f68654ca84a396904b3cbe0 | /ui.R | c2e1949bf33a0d129f35b512dbd672c7ed926308 | [] | no_license | bactkinson/Plume_Detection_with_DBSCAN | db504d86004cdfcb9a758744274db7056a8df160 | 83d19f8ad8402a3737208137f1d08f79db4a4dc6 | refs/heads/main | 2023-06-09T12:24:43.694309 | 2023-05-30T03:41:47 | 2023-05-30T03:41:47 | 483,829,076 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,349 | r | ui.R | ## UI for the DBSCAN Plume Shiny App
ui <- fluidPage(
titlePanel("DBSCAN Plume Detection Tool"),
## Upload file button
sidebarLayout(
sidebarPanel(
fileInput("upload", "Upload Data File"),
## Choose epsilon value
numericInput("f_val","f value for MinPts estimate", v... |
6c33499ac98f5a122d971c4121bdb1ce6cebe4db | 1678bf365571c0cacfb1aca1096ac3b613b71d81 | /man/mlVAR0.Rd | 7070908ffbaa121e7282626752a087b01863a25f | [] | no_license | SachaEpskamp/mlVAR | 88a3d8a4112e697ad2d057006c08e908ce03c784 | 99c9c1db70665a0b115227e6408db928e13bae96 | refs/heads/master | 2023-05-25T01:28:34.646653 | 2023-05-16T11:39:37 | 2023-05-16T11:39:37 | 25,199,915 | 3 | 6 | null | 2022-03-29T15:55:05 | 2014-10-14T09:43:01 | R | UTF-8 | R | false | false | 6,751 | rd | mlVAR0.Rd | \name{mlVAR0}
\alias{mlVAR0}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Multilevel VAR Estimation for Multiple Time Series
}
\description{
The function \code{mlVAR0} computes estimates of the multivariate vector autoregression model as introduced by Bringmann et al. (2013) which can be exten... |
f91c91aea65a84682408c821900bbb20eba5ecee | 8a2b7c55acffa68c7ce43e441b136a0ea6fa6971 | /man/evolution_plot.Rd | a1d7551bc38b2393298c56881e182a35f45e4d43 | [] | no_license | DeveauP/QuantumClone | 0e0dfa6fdf64135af73d2942d31ef2fe753da782 | 45b595bd7f5387cc7a094ba67bdd2957eedf90a1 | refs/heads/master | 2021-10-28T03:52:33.780314 | 2021-10-27T13:54:14 | 2021-10-27T13:54:14 | 38,614,225 | 10 | 5 | null | null | null | null | UTF-8 | R | false | true | 502 | rd | evolution_plot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{evolution_plot}
\alias{evolution_plot}
\title{Evolution plot}
\usage{
evolution_plot(QC_out, Sample_names = NULL)
}
\arguments{
\item{QC_out}{: Output from One_step_clustering}
\item{Sample_names}{: character vector of the name... |
9957dfb2a1b631ef1da62312ed5f58f164ff8418 | 15f54cf88824f8ef2581f0a00f8d7fd208f7d0f4 | /R/wiot2006_data.R | 2fadce2a9847a6469698836c6340fc611a67f521 | [] | no_license | MatthewSmith430/GVTr | 83ee4761156096ca65a4406bd34204a36233c640 | c6cb9dd3f09a62c6df09238bdca96ad2785eabb7 | refs/heads/master | 2022-11-07T19:44:14.133448 | 2022-11-04T09:04:09 | 2022-11-04T09:04:09 | 115,260,573 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 151 | r | wiot2006_data.R | #' @title WIOD 2006 Data
#'
#' @description WIOD 2006 dataset
#' @name wiot2006
#' @docType data
#' @usage wiot2006
#' @keywords datasets
NULL
|
611edeca4385d34730aa5e04b5412f317971f331 | 262aec5f3ed3b4fb55e832c02a91e90c12b617c3 | /run_analysis.R | 453ecbf90ee624f5eab47ba867f48b8b8ea8267a | [] | no_license | r4sn4/Getting-and-Cleaning-Data-Course-Project | f574992dc31d37f1d3397c82501bc28038dea5c4 | 38e7f000496e2b98c4947f373237351a22bd0217 | refs/heads/master | 2021-01-25T05:22:47.708054 | 2015-01-26T07:27:40 | 2015-01-26T07:27:40 | 29,815,496 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,267 | r | run_analysis.R | #load library dplyr
library(dplyr)
#create working directory
setwd("E:/rasna/D drive/Datascience/GettingAndCleaningData/WorkingDir")
#Section 1 - Merge the training and the testing data sets
# read rain file data and test file data into data frame
train.dataset <- read.table("./X_train.txt")
test.dataset <- read.tabl... |
dd640526bddb22c075c6cf48d820c1a517ccdcaa | 57c8ee0f413e86f8aad75fe9cd526386fdc743c9 | /man/power5.Rd | 7d19715e874249875f7f3843c22a51c78dc163e4 | [] | no_license | beanumber/colleges | b4844a67a9d2f66f514987266f2589179d0e5f34 | e56a43b8b28515aade94b8f46425bf16bbb49f3e | refs/heads/master | 2021-09-26T19:22:26.933143 | 2018-11-01T15:56:39 | 2018-11-01T15:56:39 | 111,578,793 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 345 | rd | power5.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{power5}
\alias{power5}
\title{Subset of FBS in Power Five conferences}
\format{An object of class \code{data.frame} with 910 rows and 43 columns.}
\usage{
power5
}
\description{
Subset of FBS in Power Five conferen... |
d7712a4875f9121bcb723c7a0549fae71fcf3da6 | ff3dbad87ea3b38d3191869793a0e1ec9ed60b8f | /man/buildAnnotationStore.Rd | 16007fe96f8dcc23d5bf0799b119df3db054b2dc | [
"Artistic-2.0"
] | permissive | pmoulos/recoup | edc2b872bfc7a0ab36dc26da0d352e06e6521b75 | eb2f8497e913024ee5c902de9bb47300b1f6602b | refs/heads/master | 2023-06-08T18:44:10.636803 | 2023-04-25T14:45:35 | 2023-04-25T14:45:35 | 49,005,951 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,837 | rd | buildAnnotationStore.Rd | \name{buildAnnotationStore}
\alias{buildAnnotationStore}
\title{Build a local annotation database for recoup}
\usage{
buildAnnotationStore(organisms, sources,
home = file.path(path.expand("~"), ".recoup"),
forceDownload = TRUE, rc = NULL)
}
\arguments{
\item{organisms}{a character vector of organisms
... |
e062c294c63897e27c2c18fe5438c005f37da50d | 8c2dae1f77505691c0c14015dff9b04cd9eae24c | /bin/RHive.r | ccca2a474ee0ed8eb685659ee56745ed6e26b642 | [] | no_license | wuhujun/Rhive | ca2e3fae2874d9c2b8c05bf3701f99cb2c040a66 | 5b77a9297bdaf2ea1ac7d6cd6f6c54570cf2257b | refs/heads/master | 2021-01-22T08:29:50.637152 | 2013-06-20T08:15:59 | 2013-06-20T08:15:59 | 7,715,340 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 721 | r | RHive.r | #!/usr/local/bin/Rscript
library(rJava)
library(Rserve)
library(RHive)
rhive.init();
rhive.connect();
d <- rhive.query('select * from ia')
#rhive.query('load data inpath \'input/data.txt\' overwrite into table gener ')
dataframe <-d;
tableinformation<-rhive.query('show tables ');
tableinformation
class(dataframe);
sum... |
2e24c1c910835d139250a7ebfb05f04a45dfbbe3 | ad3780d60c680b22fc1bf8ea5cfd380de31c1151 | /R/sample_text.R | 6fcac6cf8add432e8d16850245093726e6f9db2e | [
"MIT"
] | permissive | nproellochs/textsampler | e7167f3631a9eec8b9d451112420aa70dd495cb4 | 462ded91d4704e5083f5cf8142a048da1bec3bf0 | refs/heads/master | 2020-07-23T13:06:57.146338 | 2019-09-10T17:02:17 | 2019-09-10T17:02:17 | 207,567,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,506 | r | sample_text.R | #' Sample texts from a predefined text source
#'
#' Performs text sampling. Requires input data in the form of raw texts.
#'
#' @param n Number of texts to be sampled. \code{n} is an integer greater than 0. By default, \code{n} is set to 1.
#' @param source Text source. A vector of characters, a \code{data.frame}, or a... |
03c35d1146aed2bb0078a65ee63f16ec3ba77e1e | 7afbb148ec11b3105aaead6bdd900f847e49eb18 | /man/recipes-internal.Rd | df18ea00713c5d14be7c71d4ef5e50366969b60e | [
"MIT"
] | permissive | tidymodels/recipes | 88135cc131b4ff538a670d956cf6622fa8440639 | eb12d1818397ad8780fdfd13ea14d0839fbb44bd | refs/heads/main | 2023-08-15T18:12:46.038289 | 2023-08-11T12:32:05 | 2023-08-11T12:32:05 | 76,614,863 | 383 | 123 | NOASSERTION | 2023-08-26T13:43:51 | 2016-12-16T02:40:24 | R | UTF-8 | R | false | true | 2,057 | rd | recipes-internal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/YeoJohnson.R, R/misc.R, R/printing.R
\name{yj_transform}
\alias{yj_transform}
\alias{estimate_yj}
\alias{ellipse_check}
\alias{printer}
\alias{prepare}
\alias{is_trained}
\alias{sel2char}
\alias{print_step}
\title{Internal Functions}
\usage{
... |
65c7fa95b5640b44ff593d5c927b3b12616fe757 | 94a6d258ea38c7a962c5eb87092aa6d492dafc13 | /R/process_assignment_for_courseworks.R | 5b456e363c344f466471a5f4d743ac2e38b6dbc9 | [] | no_license | P8105/p8105.helpers | bc66d685140c69cabe601b6eb3f2de375cdfba94 | 3c8b04d0c7d01a536934f9aac316c29d28a0c99a | refs/heads/master | 2022-09-26T20:40:54.289225 | 2022-09-15T19:50:40 | 2022-09-15T19:50:40 | 106,289,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 603 | r | process_assignment_for_courseworks.R | #' Process assignment for courseworks
#'
#' For a single assignment, process the google spreadsheet to submit assignment grades to courseworks
#'
#' @param path character; path to file containing all grades
#' @param assignment character; homework to process (e.g. "hw1")
#'
#' @import tidyverse
#' @importFrom janitor c... |
6f534933161a37556b78a48c1c8b73ffb6b4e12d | 49ff0bc7c07087584b907d08e68d398e7293d910 | /mbg/mbg_core_code/mbg_central/LBDCore/man/qsub_sing_envs.Rd | 864b5334206f15cf771b751ba2f5b0d22cc313bf | [] | no_license | The-Oxford-GBD-group/typhi_paratyphi_modelling_code | db7963836c9ce9cec3ca8da3a4645c4203bf1352 | 4219ee6b1fb122c9706078e03dd1831f24bdaa04 | refs/heads/master | 2023-07-30T07:05:28.802523 | 2021-09-27T12:11:17 | 2021-09-27T12:11:17 | 297,317,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,560 | rd | qsub_sing_envs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qsub_sing_envs.R
\name{qsub_sing_envs}
\alias{qsub_sing_envs}
\title{Adds environmental variables to a qsub string}
\usage{
qsub_sing_envs(qsub_stub, envs, image)
}
\arguments{
\item{qsub_stub}{A string containing the initial qsub string to w... |
f56f92510239934b12a697a75ef1eace7d5b9030 | 9d24829289bb8a48bf6ce4954e04bb7a4e2abc42 | /functions/tSNE.R | 4f857e841469536d322e39a68d9ba6c21b4ad248 | [] | no_license | michellemeier27/Semesterproject | 231a51168f104c5bce4e13ca1a4d4782a29301e8 | fe923ad7974e06a1b45271f466785e0465912c86 | refs/heads/master | 2022-12-09T13:13:02.536402 | 2020-08-31T15:57:58 | 2020-08-31T15:57:58 | 262,081,776 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 279 | r | tSNE.R | ##FUNCTION TSNE
#loading library
library(Rtsne)
#tSNE function for individual series
tSNE <- function(expression_results, perplexity_wanted=5){
transpose = t(expression_results)
tsne_results <- Rtsne(transpose, perplexity = perplexity_wanted , check_duplicates = FALSE)
}
|
cf15f1c1f4fd179a2ab80e59900db6ef82738b21 | 7e50563d67158e361915e7311f143bee47661e7a | /R/praiseme.r | e3e07a806783ac93fc1e7dcb5871e57472baed01 | [] | no_license | perikarya/praiseme | cd33e464a0948a46c2a2e02de9ad5382fb3c82b0 | 7ba4af6d06dc4fd70d98dc91aa004327b3bf38f6 | refs/heads/master | 2021-07-05T18:23:56.097390 | 2021-01-17T12:05:25 | 2021-01-17T12:05:25 | 218,241,564 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 582 | r | praiseme.r | #' Print a short message of praise to the user
#'
#' @description Takes a string as input and returns a short message of praise using the string.
#'
#' @param praisefor The verb or subject to praise the user for, entered as a string. If blank, a generic praise message will be returned.
#'
#' @return A short praise mess... |
090ae3e96bbeec5f09870beabb9d756ae0178ab9 | 5811929a423984b8e0ef5637a03c47214e620887 | /man/resid-methods.Rd | e21224b323f5a4895474685ec74967d2b1bfbe6f | [] | no_license | cran/cold | b7d6713df201004ad0ec1e8d3f6cbbdaf33b8acc | 16be37d90686c7c880ac3ed44b7dfc1a3f4a4d6b | refs/heads/master | 2021-12-31T10:22:07.475445 | 2021-08-25T09:00:02 | 2021-08-25T09:00:02 | 17,695,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 984 | rd | resid-methods.Rd | \name{resid-methods}
\docType{methods}
\alias{resid-methods}
\alias{resid,cold-method}
\title{Methods for function \code{residd}}
\description{Methods for function \code{resid} extracting residual values of a fitted model object from class \code{\link[=cold-class]{cold}}. }
\usage{
\S4method{resid}{cold}(objec... |
8602d4a2e4606d18801f9da0051d4dee915967f6 | b5bb0e21cd74d970e6ccf1e99e51e4137a9b1d3f | /R/utils.R | 19407fc439d238c1f52d2be6e5395299e5419c26 | [
"MIT"
] | permissive | reside-ic/fstorr | 559fb209bf6063e35c6e6402aaca43a18643a52d | a5fed651b59f7df0e1a846f76360218ff1f31173 | refs/heads/master | 2022-12-13T14:31:05.119498 | 2020-04-26T10:28:42 | 2020-04-26T10:28:42 | 258,980,872 | 2 | 0 | NOASSERTION | 2020-04-26T10:28:44 | 2020-04-26T08:39:27 | Makefile | UTF-8 | R | false | false | 114 | r | utils.R | `%||%` <- function(x, y) { # nolint
if (is.null(x)) y else x
}
squote <- function(x) {
sprintf("'%s'", x)
}
|
db9476c311d77ff879db49d5aa94a210f8b1e362 | 4a2f21f44561040d0b362abeb809fabb85c79d9e | /charls_education.R | 6eef8949fc806d6e2c1f94797e431f8213053f9b | [] | no_license | lt2710/parental-wealth-impact | b3936b7597d33103f633ce8ffab9827d7cf1b8d1 | 4c94ded78c654687a4b3bcd07d26d5602526204f | refs/heads/master | 2021-09-27T21:31:51.824819 | 2021-09-12T14:25:19 | 2021-09-12T14:25:19 | 207,786,312 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,529 | r | charls_education.R | ## ----setup-------------------------------------------------------------------------------------------------------------------------
#set working directory
path_to_code<-rstudioapi::getActiveDocumentContext()$path
main_directory<-strsplit(path_to_code,"/[a-zA-Z0-9_-]*.R$")[[1]]
setwd(main_directory)
#Set time variable... |
8e0ca1f569004e8dfda1a9b32d44797a6d80af45 | ef576eff03fcc17685d7b50cf70535dd4b31ceef | /cachematrix.R | ec69e7d8a57f663822b70c08057716f178b742ed | [] | no_license | hairb/ProgrammingAssignment2 | 75d4789228f2a86287b7314e09f637704476b6c1 | c6ba4fde6a106c13f4cfda9feb783f3526a345d2 | refs/heads/master | 2020-12-31T03:41:42.751209 | 2014-09-20T13:41:46 | 2014-09-20T13:41:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,517 | r | cachematrix.R | ## These pair functions provide the functionality of computing the
## inverse of a matrix while using caching to improve performance and
## reduce computations.
##
## The functions assumes that the matrix supplied is a
## square invertiable matrix.
##
## MakeCacheMatrix - This function creates an object of... |
46f6043083a2c70a9c0bb2e22f9e10f64a0d9eb5 | eaaee85c6d83360015b75fc13e4f0dfddb5b8b0d | /man/edf.all.Rd | 974153e0b73e4ad0a4e4b9f22a95d2be07564ea8 | [] | no_license | jashubbard/edfR | 4f526b52892332d1cad5cdd71213a04f25ceec33 | fafff856c9cfc9c670ef0c3c1d8e87bac9bbb94a | refs/heads/master | 2022-08-24T17:07:36.840610 | 2022-08-04T09:41:25 | 2022-08-04T09:41:25 | 42,969,569 | 24 | 5 | null | 2016-05-11T16:02:40 | 2015-09-23T00:49:36 | C++ | UTF-8 | R | false | false | 1,342 | rd | edf.all.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/edf_api.R
\name{edf.all}
\alias{edf.all}
\title{Load all data from EDF file}
\usage{
edf.all(EDFfile, samples = FALSE, eventmask = FALSE)
}
\arguments{
\item{EDFfile}{path to an EDF file}
\item{samples}{logical indicating whether to ... |
0d859dcf4af6e67721016c21f296a0b685c7a5b4 | 813eeacdbd82197708189b38945e0635c1063313 | /rscripts/scatterplot.rsx | 6bc00340e30f69471626b9cc596da2965b8566b2 | [] | no_license | sukhjitsehra/QGIS_Processing_ToolBox_Scripts | c3a582c045592ad98fe99e679062ec6d42f032b3 | 79de10c8c952f743ca82f0d15adc1c2906ca54ec | refs/heads/master | 2021-12-14T20:16:32.850285 | 2021-12-08T03:40:34 | 2021-12-08T03:40:34 | 185,743,853 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 141 | rsx | scatterplot.rsx | ##[Sehra]=group
##showplots
##Layer=vector
##X=Field Layer
library(ggplot2)
#qplot(Layer[[X]])
plot(density(log10(Layer[[X]]). adjust=0.5))
|
fb88862cbff7cfc158578fe949d96723270c81b5 | 0485c00604cf3448cedb45e6efb2f85d88790c85 | /Ch05/5_1_Barplot.R | ec81b565a3835fd1d4aeb9be38b3ce4638b3c07a | [] | no_license | zsx29/R | d89af4ec46b8068f25d1e2447f98085a6721537f | 1f349b7b3f1981010677530e05d4d467f2c6e95e | refs/heads/master | 2023-06-11T11:28:40.607617 | 2021-07-02T02:17:13 | 2021-07-02T02:17:13 | 380,932,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | 5_1_Barplot.R | # 날짜 : 2021-06-29
# 이름 : 박재형
# 내용 : barplot 막대차트 - p140
# 기본 막대차트
count <- c(1, 2, 3, 4, 5)
barplot(count)
score <- c(50, 25, 32, 21, 66)
names(score) <- c("김유신", "홍길동", "강감찬", "장보고", "김춘추")
barplot(score)
# 범주형 막대차트
season <- c("winter", "summer", "spring", "summer", "summer", "autumn", "summer", "autumn", "spring... |
0d5267046b568a808c60e3c8cb0cc557480bc58d | 9f66bb2cb478de5363af4ed856ec9409b437af4b | /bayesian_tvar_dram.R | 6ba62f2e66d8b99f720e571b1335595fc0b93f14 | [] | no_license | alexhaider/Bayes_VARs | f6a77c7dabf157e965ee090710d753648b936e3a | b1d05c5b809178d2aca9fd106349e626ff32704d | refs/heads/master | 2020-08-04T04:16:17.504291 | 2019-10-17T13:26:31 | 2019-10-17T13:26:31 | 212,000,069 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,865 | r | bayesian_tvar_dram.R | #tar_prior: list with distr and input as elements
bayesian_tvar <- function(data, P, tar_variable, max_d, tar_prior, tar_scale, tar_transform = NULL,
reps = 10000, burn = reps / 2, stability = T, max_attempts = 1e4, deltas = NULL,
lambda = 0.1, tau = 10 * lambda, e... |
8bad09cf11599abde97989d170270ad3253b2fd6 | af553244366d4049d8aae54980ac10d854eda727 | /scRNAseq_R_scripts/Fig3/Fig3_4_pseudo time monocle.R | b2444563b23363bf59082a38bc53c0a830660e3e | [] | no_license | lihong1github/Zhan-et-al-2019-scRNAseq | ecf90ebf4498b9c0ce525548a9578cafab8a13e5 | e4f7a8e9d13be80c505fcdd2eb001fb9303e3393 | refs/heads/master | 2020-06-13T21:20:24.048411 | 2019-07-03T18:15:59 | 2019-07-03T18:15:59 | 194,791,168 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,204 | r | Fig3_4_pseudo time monocle.R | # source("http://bioconductor.org/biocLite.R")
# biocLite("monocle")
library(monocle)
load(file="~/desktop/PLX_Rdata/Data_RunTSNE.Rdata")
head(experiment.aggregate@meta.data)
# add cluster_id into meta.data
cluster_id <- experiment.aggregate@ident
experiment.aggregate <- AddMetaData(
object = experiment.aggregat... |
3ed675c15481e334b82474eddf581539d87aff6f | f54d317f00a84cb4d5bb80a679301f93958cd5df | /man/read_vcf.Rd | dc435124fda1f74d95a22be90cf643c6ae5a9982 | [
"MIT"
] | permissive | dyndna/MutationalPatterns | 3adbfde2a0fab47d406fa8ec97ce8a9f1dc93c14 | b1d9d0fc69b6c8931e3b12a3b4dccc2c475d1695 | refs/heads/master | 2021-01-24T23:50:14.127615 | 2016-09-02T17:34:06 | 2016-09-02T17:34:06 | 67,236,097 | 0 | 0 | null | 2016-09-02T15:57:14 | 2016-09-02T15:57:13 | null | UTF-8 | R | false | true | 558 | rd | read_vcf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_vcf.R
\name{read_vcf}
\alias{read_vcf}
\title{Read vcf files into list of CollapsedVCF objects}
\usage{
read_vcf(vcf_files, sample_names, genome = "-")
}
\arguments{
\item{vcf_files}{Character vector of vcf file names}
\item{sample_name... |
2c69f05bb3b4f170eaf4a34e9a89f1046b9fc28c | b90227dc489c9a9a91284d4aeb861485a20577db | /aux_scripts/product_store_gen.r | a4df9d75234df8f0932be4e9c52f3160d892a105 | [] | no_license | prj9267/Retail-Database | d50de1a4e0c720b94f9ec6120d2bcde2ac46379a | 1c7545cfef70ee8c58d2272115e2c769c62860f3 | refs/heads/master | 2020-09-11T07:33:49.048693 | 2019-11-15T19:32:48 | 2019-11-15T19:32:48 | 221,990,109 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,735 | r | product_store_gen.r | # file: product_store_gen.r
# author: Dylan R. Wagner
# Desc:
# Generates product store relation data for use in
# the database.
#
library(data.table)
MAX_PRICE <- 50
MAX_INV <- 1000
# Read in the source store data
stores_data <- fread("../data/stores.csv")[, "Store_ID"]
items_source <- c("../data/items... |
a489badbf8df91b7f9a40491a6a9d4e4f01c4742 | 8dd6b95b372d598de24bc87e16e6ff972e7219e9 | /man/offTargetAnalysis.Rd | b05250de3ff325d28f89773bb6970d21e529cce0 | [] | no_license | LihuaJulieZhu/CRISPRseek | 83efe310342e3b5fbc8989d5771287760264adb8 | 6970b7d1bdc967ab7176642e19dcb8d10a1b1fa4 | refs/heads/master | 2022-07-11T23:29:46.834606 | 2022-06-21T15:45:29 | 2022-06-21T15:45:29 | 126,867,552 | 4 | 2 | null | 2022-01-13T19:00:55 | 2018-03-26T17:51:03 | R | UTF-8 | R | false | true | 26,659 | rd | offTargetAnalysis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/offTargetAnalysis.R
\name{offTargetAnalysis}
\alias{offTargetAnalysis}
\title{Design target-specific guide RNAs for CRISPR-Cas9 system in one function}
\usage{
offTargetAnalysis(
inputFilePath,
format = "fasta",
header = FALSE,
gRNAou... |
91ce05ca80c2c2b0b02b50f938e21daa1ddd83ef | 9e8936a8cc7beae524251c8660fa755609de9ce5 | /man/details_rand_forest_partykit.Rd | 25184df2ea24471a757a26a988087087610534fc | [
"MIT"
] | permissive | tidymodels/parsnip | bfca10e2b58485e5b21db64517dadd4d3c924648 | 907d2164a093f10cbbc1921e4b73264ca4053f6b | refs/heads/main | 2023-09-05T18:33:59.301116 | 2023-08-17T23:45:42 | 2023-08-17T23:45:42 | 113,789,613 | 451 | 93 | NOASSERTION | 2023-08-17T23:43:21 | 2017-12-10T22:48:42 | R | UTF-8 | R | false | true | 3,866 | rd | details_rand_forest_partykit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rand_forest_partykit.R
\name{details_rand_forest_partykit}
\alias{details_rand_forest_partykit}
\title{Random forests via partykit}
\description{
\code{\link[partykit:cforest]{partykit::cforest()}} fits a model that creates a large number of ... |
89127199baf7aec8f023ae8ab8bb728ce3c7ed81 | 3244df900eb5aafe74a49c02f5f332824f220554 | /carthefts.R | e063b94b53b9b027e4707f7d6ff454fcd1577886 | [] | no_license | Studentenfutter/cars-inequality | d1d94aa0922006cf7c29276600c94ae19dfd3b3e | 2a5c62a507c5863a7ab21c5c7218587c35b27ac7 | refs/heads/master | 2022-12-07T12:51:21.547515 | 2020-08-20T12:23:07 | 2020-08-20T12:23:07 | 200,675,462 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,251 | r | carthefts.R | # Scrape car thefts per Landkreis
theft_url <- "https://www.bka.de/SharedDocs/Downloads/DE/Publikationen/PolizeilicheKriminalstatistik/2018/BKATabellen/FaelleLaenderKreiseStaedte/BKA-LKS-F-04-T01-Kreise-Fallentwicklung_csv.csv"
curl::curl_download(theft_url, "data/other_data/theft.csv")
# Files have been manually clean... |
a814be6f80cfad643ae77bd923e0bbd6b224a689 | 1f0c447b18085ba2452b7288c81852de360d37ba | /08-2020_nba_playoffs_excitement_index/01-main-v2.R | 010402b7882c85102d35ad4f6ff6bb4fe0ddf30e | [] | no_license | tRackRR/sports_viz | dcc968768c44bcf3f2e07d3f609847b5995bf62e | 692031f49bb628a821aaff6ed7de57eef8d7810e | refs/heads/master | 2023-05-29T01:15:26.297604 | 2021-06-18T11:53:23 | 2021-06-18T11:53:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,674 | r | 01-main-v2.R |
library(tidyverse)
dir_proj <- fs::path('08-2020_nba_playoffs_excitement_index')
dir_data <- fs::path(dir_proj, 'data')
fs::dir_create(dir_data)
path_export <- fs::path(dir_data, 'nba_playoffs_excitement_index_2.rds')
path_gif <- fs::path(dir_proj, '2020_nba_playoffs_excitement_index_20201010.gif')
n_sec <- 20
fps <... |
1506cde7c1ab01580aa79106ddfe6d49b2e5b507 | af65f9ce96bd5f04015afeb34cdc14bc2dcecdca | /R/make_analysis_data.R | a3b8830f75f057bcb6e72c3facf01c9b3d484394 | [] | no_license | bcjaeger/LDL-imputation | 2af09ae49ffaf3ad999886910e4ae3e335509996 | 75578de048b1f0cdda2b75d5de513d97c91cbbfb | refs/heads/master | 2022-12-14T15:06:15.312440 | 2020-09-07T16:20:51 | 2020-09-07T16:20:51 | 293,577,304 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,080 | r | make_analysis_data.R | ##' .. content for \description{} (no empty lines) ..
##'
##' .. content for \details{} ..
##'
##' @title
##' @param nhanes
##' @param miss_perc
make_analysis_data <- function(nhanes) {
data_analysis <- nhanes[c("pop_one", "pop_two")] %>%
map(
~ mutate(.x, ldl_to_impute = chol_ldl_mgdl_sampson) %>%
... |
ce4c684b5a3f430e7672c9e7282b9ac0cb68b545 | 2e96f176654ecefecdbc5be48f800d68c16878d4 | /sfg-aqua-scripts/aqua_functions.R | a4ee5c623fbab3b3c1f675f96822de2bcd662749 | [] | no_license | tclavelle/sfg-aqua | c74491a515e4c3a554d8d3c734f861cbfae9c0ad | ae8edef5f80731f27d72ddaa6c74834e8085fae0 | refs/heads/master | 2020-04-18T06:41:33.577533 | 2019-03-01T22:27:50 | 2019-03-01T22:27:50 | 65,924,148 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,043 | r | aqua_functions.R | ##############################################################
##
## Functions script for aquaculture-fisheries model
##
##############################################################
# r = 1.5
# p = 850
# c = 20
# q = 0.0001
# Kmax = 1000
# phi = 0.5
# A = 300
# delta = 0.05
biomassGrowth <- function(r, B, K) {
b_... |
040d3d54c1a043804c723d07b8e16b7361e74e10 | f210b3be7b705c76280ba3c5e543a73f0bd47b5c | /scripts/potential_gdp_calculations.R | 07ecbb26e61d55381caa0e3758d2546bf46567ba | [] | no_license | ricardomayerb/new_normal | 0356fab0412b7bda8527b196689035727ddf3379 | 011128b071c80ed6e3c5ed242fe45cf9e8ff485a | refs/heads/master | 2021-01-19T20:46:29.203141 | 2017-06-13T22:00:38 | 2017-06-13T22:00:38 | 88,550,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,510 | r | potential_gdp_calculations.R | library(dplyr) # use dplyr::first and dplyr::last
library(ggplot2)
library(xts) # use xts::first and xts::last
library(tidyr)
library(lubridate)
library(tibble)
library(tidyquant)
source("./functions/funcs_for_new_normal.R")
load("./produced_data/WEOApr2017_cepal_and_others")
subject_dict_co <- WEOApr2017cepal18_othe... |
3976fa700a5e1786b360979f92b55d3739e4a7ee | 633aaea8f18b9baea73a50addb1e20cfe4623f1e | /journal_dashboard/altmetrics.R | 5f9322334da837c9cd0dc10b708c4e4667578eb8 | [] | no_license | BohdanTkachuk/journal_dashboard | d2158b8a917c0b2fbed86e48e8907ceff84653c1 | 6f73ce75bd0436888a41d155235273fb0e99a568 | refs/heads/master | 2022-11-13T14:29:38.801012 | 2020-06-28T16:28:43 | 2020-06-28T16:28:43 | 271,330,530 | 0 | 0 | null | 2020-06-26T20:16:44 | 2020-06-10T16:34:57 | null | UTF-8 | R | false | false | 3,311 | r | altmetrics.R | library(shinyWidgets)
library(shinydashboard)
library(data.table)
library(plotly)
library(dplyr)
source("load_data.R")
# https://stackoverflow.com/questions/34093169/horizontal-vertical-line-in-plotly
vline <- function(x = 0, color = "red") {
list(
type = "line",
y0 = 0,
y1 = 1,
yref = "paper",
... |
f02187b77bbe7b61a35b3d2b86068806ecfbeb37 | b3a5c21adf890f0b66790f23332f0082e7f1b40a | /man/cli_li.Rd | cbb1761640018dde02f95bcfd9b1fe7ae5b7cfeb | [
"MIT",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | permissive | r-lib/cli | 96886f849fe69f8435f2d22fccf5d00dee7a5ce4 | c36066ca6a208edbeb37ab13467a4dc6f5b5bbe2 | refs/heads/main | 2023-08-29T14:19:41.629395 | 2023-08-18T13:18:33 | 2023-08-18T13:18:33 | 89,723,016 | 560 | 69 | NOASSERTION | 2023-09-13T11:46:10 | 2017-04-28T16:10:28 | R | UTF-8 | R | false | true | 2,828 | rd | cli_li.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cli.R
\name{cli_li}
\alias{cli_li}
\title{CLI list item(s)}
\usage{
cli_li(
items = NULL,
labels = names(items),
id = NULL,
class = NULL,
.auto_close = TRUE,
.envir = parent.frame()
)
}
\arguments{
\item{items}{Character vector of... |
9b6465657d81cff0a5bd52c8a12c9480fbf0263e | 3053a557531d328b430b69fb7851dcb2dde22c93 | /dataone/man/MNode-class.Rd | 129be90ba417584f8a657b29bda701b294041810 | [
"Apache-2.0"
] | permissive | KillEdision/rdataone | e3bfe188ed1eba1f01d6e256f3a98a64104125ef | 3ec0efb67cc3ba951d44ce13e5750bfec8caaac4 | refs/heads/master | 2021-01-15T20:24:17.028477 | 2015-07-29T01:16:47 | 2015-07-29T01:16:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,701 | rd | MNode-class.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/D1Node.R, R/MNode.R
\docType{methods}
\name{listObjects,D1Node-method}
\alias{MNode,D1Node-method}
\alias{MNode,character-method}
\alias{MNode-class}
\alias{archive,MNode,character-method}
\alias{create,MNode,character-method}
\alias{... |
5dbcab79a8d59420a6c999e3d06a745a02d3b45f | 2a5e4fea8a2661320eaab0ea9069affb7e72fd44 | /twitter/code/plotting.R | 507c36797422577f45cd38c2402b8d7289ad1c7b | [] | no_license | luiscape/hdx_management_dashboard | 23a3556dad06553be1511acf03f43b81727c2c7f | 2a701eccaed2d9b1a133f675ec37abd4d671e5c8 | refs/heads/master | 2016-09-15T17:24:47.448858 | 2015-04-06T14:39:48 | 2015-04-06T14:39:48 | 22,891,628 | 0 | 0 | null | 2014-08-15T22:09:05 | 2014-08-12T20:34:42 | JavaScript | UTF-8 | R | false | false | 691 | r | plotting.R | ## Plotting ##
# timeline with the number of followers
ggplot(twitter_friends) + theme_bw() +
geom_line(aes(date, followers), stat = 'identity', color = "#F2645A", size = 1.3) +
geom_area(aes(date, followers), stat = 'identity', fill = "#F2645A", alpha = .3) +
geom_bar(aes(date, new_followers), stat = 'identity',... |
1444d861f79dd747b3931cc4ce28f4af24175676 | a230cd371d7f8e27c1324b0d38dda14f0e97aaf3 | /R/cleanup.R | 650f3a6f56107bfd0502a587396e35292c625f01 | [] | no_license | Eforberger/preview | 51233a4219ba70483c376bbc641e6406fe86baf6 | b7e1bab3e6393a5a312b3c0b723b8999dd1d777c | refs/heads/master | 2022-11-23T12:59:13.298908 | 2020-07-30T15:33:18 | 2020-07-30T15:33:18 | 283,580,480 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 356 | r | cleanup.R | #' Cleanup
#'
#' Removes the first two rows of the data frame df. Qualtrics data has two extra rows at the top that can mess up data manipulation.
#'
#'@param df A data frame from qualtrics.
#'
#'@return A data frame
#'@export
cleanup <- function(df)
{
# this just gets rids of the extra rows qualtrics adds
df <... |
5d90f8e649e2d20680f9c6f0ffc482266e679087 | 65b5253f00d430d3f7013309b4dc33de0dd9220c | /R/getWeight.R | 9063c6482df2b5bf8f2405e2f5e9771ad4a5e142 | [
"BSD-2-Clause"
] | permissive | gvegayon/mcMST | dcc5180e0e9a7fa9cbf547e43544ba48b6d39fcf | 617269003bff20412795603a67fd5a0d41ffd07f | refs/heads/master | 2021-01-23T07:33:54.846484 | 2017-09-05T17:43:52 | 2017-09-05T17:43:52 | 102,508,528 | 0 | 0 | null | 2017-09-05T17:05:49 | 2017-09-05T17:05:47 | R | UTF-8 | R | false | false | 800 | r | getWeight.R | #' Get the overall costs/weight of a subgraph given its edgelist.
#'
#' @template arg_mcGP
#' @template arg_edgelist
#' @return [\code{numeric(2)}] Weight vector.
#' @examples
#' # generate a random bi-objective graph
#' g = genRandomMCGP(5)
#'
#' # generate a random Pruefer code, i.e., a random spanning tree of g
#' p... |
b8d2df0b2473f31c233e209f4346a452f984d528 | 29585dff702209dd446c0ab52ceea046c58e384e | /FitAR/R/Get1G.R | 79b285e35eb78cfabaafb1f90717bf8c68c626cf | [] | 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 | 144 | r | Get1G.R | `Get1G` <-
function(phi,n){
p<-length(phi)
x0<-sum(c(1,-phi))^2
x<-x0-rowSums(GetB(phi))-GetKappa(phi)
c(x,rep(x0,n-2*p),rev(x))
}
|
83754389287689c331a33fdc3e3598a656942dbe | 818cb255f3f00080a7aa68282e65f4c1d0310c77 | /Programming_Projects/R Projects/glmnet/man/cv.glmnet.Rd | a56faa8b0f9693b606b32ca762ba3a04c52c38cd | [] | no_license | pmnyc/Data_Engineering_Collections | fdca0f9a3de71f5c9855e5bbb45c574d1062077d | b7d29cd4c134cb1252e5c45dd500d969fe0f6029 | refs/heads/master | 2021-06-24T22:15:32.913229 | 2020-11-08T10:12:04 | 2020-11-08T10:12:04 | 153,053,634 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 7,978 | rd | cv.glmnet.Rd | \name{cv.glmnet}
\alias{cv.glmnet}
\title{Cross-validation for glmnet}
\description{Does k-fold cross-validation for glmnet, produces a plot,
and returns a value for \code{lambda}}
\usage{
cv.glmnet(x, y, weights, offset, lambda, type.measure, nfolds, foldid, grouped, keep,
parallel, ...)
}
\arguments{
\item{... |
adf9899f50f515d95c429ba29f1c20311c0e2627 | 3700053c7b6331b485bb0a2cec93640f564197da | /pm25_plot3.R | 06c504a0c34a97ba3061f8e376c3748a534cb0d3 | [] | no_license | jxs3221/pm25 | 8ccf9c91cfd69685fa3297559a0dd7854ac03ac4 | 31bbe259926d81b98420ff3e4069345ae5a12bb4 | refs/heads/master | 2020-06-05T17:40:25.412396 | 2014-09-21T19:25:17 | 2014-09-21T19:25:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 737 | r | pm25_plot3.R | # pm25_plot3.R
# This will create a plot that shows what type of source of emissions of PM2.5 decreased in
# Baltimore from 1999 to 2008
# Read in the source datasets
library(ggplot2)
library(plyr)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#Subset the data for Baltimore ... |
515b36566fe2b51380a367ebba7bc484a8b68044 | 69f4d5a9d333d6cfcc7169a31b6f70fff475d4ca | /scripts/sim.R | a1d0765e74c354601c38733c5777824927899d66 | [] | no_license | michalim/ua-time-series | 39d639aa62f04b2c332990789eae6e396c795389 | b83b11c83e720c4af2fe570f022c04b468fcc9b4 | refs/heads/master | 2020-05-18T01:07:00.065785 | 2011-10-20T19:35:29 | 2011-10-20T19:35:29 | 37,431,596 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,962 | r | sim.R |
# this library provides the means to generate random p-dimensional Gaussian
library(MASS)
# this is a function to create an episode
# arguments are
#
# ep.length - length of episode
# mn.vec - men vector of Gaussian
# cov.mat - covariance matrix of Gaussian
# cut.ponts - boundaries to partition variables
create.epi... |
889c182afc4396ea63cb5246d304aa6586d56273 | 0c61299c0bfab751bfb5b5eac3f58ee2eae2e4b0 | /metadata_lit.R | 9ddff0f40c3f458d9a3d5b4cdeab332aa555d6c6 | [] | no_license | jwerba14/Species-Traits | aa2b383ce0494bc6081dff0be879fc68ed24e9c2 | 242673c2ec6166d4537e8994d00a09477fea3f79 | refs/heads/master | 2022-10-13T10:57:54.711688 | 2020-06-12T01:57:21 | 2020-06-12T01:57:21 | 105,941,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 374 | r | metadata_lit.R | ## literature metadata
library(tidyverse)
lit <- read.csv("meta_lit.csv")
## downloaded per search
down <- lit %>%
group_by(Search) %>%
summarize (num_cite = n_distinct(Title), download = n())
## extracted data per search
ext <- lit %>%
filter(Data.Extracted == "yes") %>%
group_by(Search) %>%
summar... |
406243258cb90477293420c4208133c6b96b5681 | 721236736dbc7fdd5e67fe650f12edcb145f27f1 | /code/analysis/grf_examples.R | f32daad3ea724b03b4d71517ebe46b293d2c33d0 | [] | no_license | NikiJane/name_matching | e05d2e4f1c6c4e3351573429d52c828798f700ea | 57784b7d36a7840322c823ceaccce916e3c396d8 | refs/heads/master | 2020-08-30T15:28:52.512893 | 2019-05-16T14:12:22 | 2019-05-16T14:12:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,581 | r | grf_examples.R | # Thom Covert, April 2019
# example use of grf to possibly classify our name pairs better
#===========
# standard setup
#===========
root <- getwd()
while(basename(root) != "name_matching") {
root <- dirname(root)
}
source(file.path(root, "data.R"))
# TC's random forest utilities
source(file.path(root, "code", "fun... |
0286f9524802d3ce87f7db91ca9671796ed08cf6 | d233138052e7037e924f4e79fa683af8163bd9bb | /cloud_script.R | 88eeb6c0fc9dad8c932396c0f05abc44df61b039 | [] | no_license | sergioquadros/radar | 67691f870bbff240ccf8aee082c3f06ce0a46e87 | e257ebc676cac3ddd713ff870967d52c721682a6 | refs/heads/master | 2020-03-22T16:14:49.387571 | 2018-07-09T20:13:57 | 2018-07-09T20:13:57 | 140,313,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,536 | r | cloud_script.R | # diretories: RMSP(with 38 746 files), Saida_Pluv(with 108 files) at workdir one
# Files at workdir diretory:
# cloud_script.R; navegacao_rmsp.dat; coordenadas_pluv_saisp2.dat
library(knitr); library(rmarkdown);library(tidyverse)
library(gridExtra); library(corrplot); library(magrittr)
library(lubridate); library(para... |
2992fb6580857ba7af585fecb2a22258fcb03c3a | f7eb46fb3b16b16d66cf5f8c95e0893fce7aa6db | /code_files/simulation/extended_main_sim.R | a8ee214a662a193c013e0c8b67b3ce52e5d19549 | [
"CC-BY-4.0"
] | permissive | bjoelle/Poorly_dated_fossils_SI | eca8df7f40e03175026fe3199bf9d76181626f49 | 6613ba27385a64454ad7f4bf568db67a635fac25 | refs/heads/main | 2023-04-15T11:33:42.043820 | 2022-10-11T12:54:25 | 2022-10-11T12:54:25 | 352,789,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,983 | r | extended_main_sim.R | # simulate datasets for additional conditions
# (no deposit, burst deposit, low morphological clock rate & relaxed morphological clock)
run_simulation_extended = function(save.folder) {
library(FossilSim)
library(phyclust)
#### Fixed args for all simulations
args = list(
# Output parameters
ntrees =... |
1908dc9f05f6fd980d73c5971ac2f51bc8115063 | 5906b6e56fd54b7a038961372318632a8f4009d1 | /man/unifrac.Rd | 169dcd6c4a354dc6a4b19d34dfaad07ec1a74c11 | [] | no_license | skembel/picante | dc8c8b38c45f6d2088563d4e9119a0aa21e8f115 | b891440afaa83185442f98d45db90a515cf6ab8a | refs/heads/master | 2023-09-04T02:58:33.047287 | 2023-07-10T15:17:01 | 2023-07-10T15:17:01 | 13,666,942 | 25 | 14 | null | 2023-07-10T15:12:30 | 2013-10-18T02:14:54 | R | UTF-8 | R | false | false | 1,572 | rd | unifrac.Rd | \name{unifrac}
\alias{unifrac}
\title{ Unweighted UniFrac distance between communities }
\description{ Calculates unweighted UniFrac, a phylogenetic beta diversity metric of the the unique (non-shared) fraction of total phylogenetic diversity (branch-length) between two communities. }
\usage{
unifrac(comm, tree)
}... |
e94d93be866735e5c3bc4bb1b8428b2416413bc6 | ba5ee64247395ad6f288b4ccb2e13f14f98e5fd0 | /process_file.R | d5384d2b0d956cb1f6cde642449885fe78e28405 | [] | no_license | iceiony/word_count | 2a395449796b68656345d4a6dc3f063d3044a654 | 0305df0d41b672e11fabd80e73968c0ced97819d | refs/heads/master | 2021-01-22T05:24:03.924371 | 2017-02-12T01:01:46 | 2017-02-12T01:01:46 | 81,657,576 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 898 | r | process_file.R | extract_sentences <- function(doc){
sent_ann <- Maxent_Sent_Token_Annotator()
doc_ann <- annotate(doc, sent_ann)
doc[doc_ann]
}
process_file <- function(doc){
sentences <- readChar(doc, file.info(doc)$size) %>%
as.String() %>%
extract_sentences() %>%
... |
06806433f0319f20e2caeb01193558d20fddf065 | 82a835dcfcf9388ad76e728bf5071975b05c22b4 | /ML-Toolbox/Code recipes/R/caret/rfe/rfe.r | 4fbc97c1b75895788cd3b2f96f5c0d0dd097911a | [] | no_license | nmanwaring/ML-Resources | 36788c2279b7a82ce5cabc7bd5518c06d1244f46 | 43a9811bb436a43b7ac9be257690da8095878239 | refs/heads/master | 2021-09-28T08:00:54.309901 | 2018-11-15T16:56:22 | 2018-11-15T16:56:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 878 | r | rfe.r | library(readr)
votes <- read_csv("~/ML Toolbox/R Scripts/caret/rfe/house-votes-84.csv")
View(votes)
house_votes_84[rev_house_votes_84 == '?'] <- NA
is.na(votes)
cleaned_votes <- na.omit(house_votes_84)
View(cleaned_votes)
#load library and set seed
library(caret)
set.seed(998)
# define an 75%/25% train/test split of ... |
4e14d8d03782108f3497a412aa13084bd36ea9cf | be9b0aacf8f18680d58a25f2a47430169e566ecb | /4.survival/AdvancedSurvivalAnalysis.R | 6b87f0e22e3ea0d05fc2150122569d0283507b1a | [] | no_license | zhangyupisa/Arrrgh | db0d0f3ce96586fb7be42c4d7e24ddb0cc30971c | 96538054651b06f6697932dd761246e982e548a1 | refs/heads/master | 2020-09-22T02:36:25.627853 | 2016-11-20T08:07:49 | 2016-11-20T08:07:49 | null | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 5,200 | r | AdvancedSurvivalAnalysis.R | ## Univariate Cox Template
## This program generates univariate Cox models for multiple variables and outputs the results for further analyses
## It is designed as a compromise between genericity and flexibility
# 0. Preparations
# Loading necessary packages
library(survival)
library(MASS)
library(ggplot2)
... |
395512addb2455430ab738b82676dc065df6a1ea | e54e7a8f0140a33da41e420f4149c5c737175a89 | /R/extract_weather_data.R | e4666428cb79343320d78824f0da8f10131d59fd | [] | no_license | one-acre-fund/arc2weather | 0d80547adca12a0bcdd1a299d9886ce192dc16cd | 25b6ab102f8cf74e6e60b48dac760ba07d198e86 | refs/heads/master | 2020-03-28T04:33:39.962589 | 2019-02-04T16:49:59 | 2019-02-04T16:49:59 | 147,722,497 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,139 | r | extract_weather_data.R | #' Combines the full functionality to take the raw inputs and produce data.frame of extracted weather data and dates.
#'
#' @param rawRasterData list of raw raster data from Arc2 weather site
#' @param gpsFile GPS file, probably from data warehouse, of GPS points for which we want to extract weather values
#' @param la... |
a1303a490ee605bc26e279ed065bd95d106f14f3 | bbff57b6e8029c2626077269790eea6d9932aff8 | /man/filter_bmdk.Rd | 2d10fde0830da9fc73e819051c38562541b83f5a | [
"MIT"
] | permissive | abcsFrederick/BMDK | 303869902486228c5d91f335875a74dc1400b70c | cd774919ea7440837beab48281c6277db1263d5d | refs/heads/master | 2023-07-03T04:33:47.243351 | 2021-08-06T14:38:34 | 2021-08-06T14:38:34 | 269,632,777 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 817 | rd | filter_bmdk.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filter_bmdk.R
\name{filter_bmdk}
\alias{filter_bmdk}
\title{Runs the BMDK features data through a series of filtering methods}
\usage{
filter_bmdk(dat)
}
\arguments{
\item{dat}{a list containing 3 elements: case, a list of case/control status... |
735adc0259749a283b41a85494fa6b664789563b | 4d216630e99eda5974b2655baf8928ca7da754bd | /man/load_observations.Rd | 8b6b4464019f9b8fef8e9714a89f00f85e6728dc | [] | no_license | ashiklom/edr-da | 467861ec61cd8953eb272e2844414a522db7268f | b092600954b73fa064300c6e7b21d0413d115b94 | refs/heads/master | 2021-07-12T18:59:20.190169 | 2021-04-12T14:00:17 | 2021-04-12T14:00:17 | 71,824,349 | 2 | 5 | null | 2018-02-01T13:29:03 | 2016-10-24T19:26:27 | R | UTF-8 | R | false | true | 609 | rd | load_observations.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/load_observations.R
\name{load_observations}
\alias{load_observations}
\title{Load AVIRIS observations into a list}
\usage{
load_observations(
sites,
aviris_specfile = here::here("aviris/aviris.rds"),
use_waves = seq(400, 1300, by = 10)... |
43df3d142c98a2c5bc37aabdcc49e499148838f7 | 3197c8c3a176cff9e2c81cbaf4ed44338eec3b72 | /R/post_model_modifications.R | 4dbc74a1d05c48ed72cf1fc01f5c189a5cd985d3 | [] | no_license | glep/pricing_game_submit | c1918349dd14026496fd5b629d6458f36d008309 | cedd0080ca524316193830daab8329c4a9e65bdb | refs/heads/main | 2023-04-04T22:07:44.725540 | 2021-03-29T00:51:04 | 2021-03-29T00:51:04 | 332,944,272 | 7 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,104 | r | post_model_modifications.R | train_model_correction <- function(model, df, pred, type) {
if ("makemodel" %in% type) {
model <-
train_make_model_correction(
model = model,
df = df,
pred = pred
)
}
if ("city" %in% type) {
model <-
train_city_correction(
model = model,
df... |
fda88bb8e929121f69048ae4772936bb2617144a | fbc5705f3a94f34e6ca7b9c2b9d724bf2d292a26 | /DCamp/Importing data in R_1/readr data.table/read_csv().R | 3e17ae7a1b44670b7f8aeb1698302abae58e4cba | [] | no_license | shinichimatsuda/R_Training | 1b766d9f5dfbd73490997ae70a9c25e9affdf2f2 | df9b30f2ff0886d1b6fa0ad6f3db71e018b7c24d | refs/heads/master | 2020-12-24T20:52:10.679977 | 2018-12-14T15:20:15 | 2018-12-14T15:20:15 | 58,867,484 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 126 | r | read_csv().R | # Load the readr package
library(readr)
# Import potatoes.csv with read_csv(): potatoes
potatoes <- read_csv("potatoes.csv")
|
91eec877bb03e30dc488841ec5c1bcc6876567d5 | 5c1ec4aeaf4a90466984737f81b639e9f96b950b | /R/cofaTest_helpers.R | f3b48c0183166c1b782217a298a9b79cd251ab82 | [] | no_license | halleewong/cofa | b45f41a53a915d1ec2ecb1f1cd6729570571a48e | e4c9e4412edd4dac9c608bcb8fb90e5d5bd85fbb | refs/heads/master | 2020-03-20T10:45:43.684862 | 2019-10-27T03:25:00 | 2019-10-27T03:25:00 | 137,383,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,466 | r | cofaTest_helpers.R |
# takes the list of frequency matrices <results> and flattens them for
# histogram plotting
gatherFMValues <- function(results,k=10){
values = c()
for (i in 1:length(results)){
fm <- results[[i]]
values <- c(values,as.numeric(fm[lower.tri(fm, diag=FALSE)]))
}
tbl <- data.frame(values=values)
tbl$lab... |
9efab0f829c7d49fcff71110ca107aa3544cfb73 | 47481d6045728644b18c4e5e5ebcfbeb88701dc9 | /cachematrix.R | 7d07f9ec9e07da0519674fb68458ec8fc56eb084 | [] | no_license | shimshock/ProgrammingAssignment2 | 83bbf85efa028fc3d62be32937042857d2862796 | 83b4ae4420f3b5eb93f3c35023e29abf8340c179 | refs/heads/master | 2020-05-29T08:52:53.342614 | 2016-09-30T03:03:24 | 2016-09-30T03:03:24 | 69,582,183 | 0 | 0 | null | 2016-09-29T15:41:33 | 2016-09-29T15:41:32 | null | UTF-8 | R | false | false | 883 | r | cachematrix.R | ## This function will cache the inverse of a matrix
## When trying to invert a matrix it will first check to see if thee is a cached verison
## if there is it will use it
## Creates the matrix in cache
makeCacheMatrix <- function(x = matrix()) {##defines the function
inv<- NULL
set<- function(y){
x<<-... |
94aa24a6e04ae82412903b55480a8315e37ff86f | a629eab419035f8eb96cf4b9c2cfd56f353df3c8 | /submission-project1/plot1.R | 5d37cba64aecab76b2fc443c50d265de1e760f17 | [] | no_license | eegithub/ExData_Plotting1 | 14f8e0017eb3f9449b30a63a8c219b61e380d377 | c18bd89edb49650169a309a251778c105ec45af1 | refs/heads/master | 2021-01-22T12:02:28.164226 | 2016-03-03T00:45:34 | 2016-03-03T00:45:34 | 52,935,669 | 0 | 0 | null | 2016-03-02T05:08:35 | 2016-03-02T05:08:35 | null | UTF-8 | R | false | false | 481 | r | plot1.R | #Pre-requisite : create_data.R has been executed => project-data.csv exists in RWork directory
#Getting & parsing data
read.csv("project-data.csv",header=TRUE, stringsAsFactors = FALSE)->pdata
as.Date(pdata$Date)->pdata$Date
strptime(pdata$Time, format="%Y-%m-%d %H:%M:%S")->pdata$Time
#Creating 1st plot
hist(pdata$Gl... |
437592d3b0fd0574bf64fc94d092bda035518dbf | 7e7bb7bfdf62c24b7fecf78f5247d28839728710 | /Student Recruiting/munge/01-A.R | cceb3f8d2f6faf950753d0c46128e204ad9c9218 | [] | no_license | kippchicago/Data_Analysis | 1ad042d24c7a1e11e364f39c694692f5829363a4 | 8854db83e5c60bc7941654d22cbe6b9c63613a7f | refs/heads/master | 2022-04-09T10:10:43.355762 | 2020-02-20T18:03:40 | 2020-02-20T18:03:40 | 5,903,341 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,463 | r | 01-A.R | # Munge student and mailer data
student_addr<-stus %>%
select(first=first_name,
last=last_name,
grade=grade_level,
address=street,
lat=lat.x,
long=lon.x
) %>%
mutate(type="KIPPster",
cohort=NA)
mailer_addr_selected<-mailer_addr %>%
select(first,
... |
b8ae49941232dee1de87dc256d6a8d8097c6da3c | 9a8ccb09b9cf666761760992f0238e9af3e9873f | /R/categories_risk.R | 56ec2425dde0f9fed75ad20f4c0a0de68492a3b0 | [
"MIT"
] | permissive | emraher/tbat | 68bee38086a1a7abd71fb792d4015bd3d6cfd82e | 947e8cdd292249e2998b6e8b85a370ac5da507da | refs/heads/master | 2023-04-28T12:42:31.851122 | 2023-04-05T20:38:00 | 2023-04-05T20:38:00 | 315,597,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 998 | r | categories_risk.R | #' Get parameters and codes for risk data from TBB
#'
#' @return A tibble
#'
#' @examples
#'
#' \dontrun{
#' dt <- categories_risk()
#' }
#'
#' @export
#'
categories_risk <- function() {
categories <- httr::POST("https://verisistemi.tbb.org.tr/api/router",
body = '{"filters":[],"route":"kkbKategorilerAll"}',
... |
c4341c5571418c0d33ecd2e785c9f7ece25c541e | dee458bc9dc3660f216b27f8d69d41d280639215 | /cachematrix.R | 79a3997b06de55d115e8a8878c9bedc64ae63db1 | [] | no_license | tvanelferen/ProgrammingAssignment2 | 909c58927ad0a5449ca246fc1df46f56da160d52 | dc12b0d83143d7abbdab1ade8031d957a69d34ab | refs/heads/master | 2020-12-24T22:59:22.035739 | 2015-09-20T10:16:21 | 2015-09-20T10:16:21 | 42,780,257 | 0 | 0 | null | 2015-09-19T16:24:05 | 2015-09-19T16:24:04 | null | UTF-8 | R | false | false | 1,934 | r | cachematrix.R | ## This R.programm contains two main-functions and does two things:
## makeCacheMatrix: This function creates a special "matrix" object that can cache its inverse
## and cacheSolve: This function computes the inverse of the special "matrix"
## returned by makeCacheMatrix above. If the inverse has already been calcula... |
15f54f55854b0e8f113833084c9a9985537518e0 | 81c4acf23d5db8910522cdc0caab8e6a7ba5cc31 | /xlsx_to_spss.R | c65a94ec2d2a10c316b9a33c62806bba021b231f | [] | no_license | ruhulali/R_Codes | ff2d12dc6450ae1da748c4df6ab51600dd48e7aa | e2b3b3f090e7fd8a43746ed29e750b023035b3f1 | refs/heads/master | 2021-06-08T06:44:39.003256 | 2021-04-23T16:21:16 | 2021-04-23T16:21:16 | 158,611,318 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 391 | r | xlsx_to_spss.R | setwd("Z:/eBay/Item Delivery tpNPS/US/Coder/Allocation/12_Dec'17")
library(xlsx)
mydata <- read.xlsx("duplicate_removal_file.xlsx",sheetName="Sheet1")
library(foreign)
write.foreign(mydata, "Z:/eBay/Item Delivery tpNPS/US/Coder/Allocation/12_Dec'17/us_dec17.txt",
"Z:/eBay/Item Delivery... |
1ed8dadab08f5058d5fdc5235f6d813149339c1a | df68b9ef313b9a22e4fec5be4ee90752815d3db2 | /man/GenomicDistributions.Rd | 2482bcd30b8ac018a93db5105dd51a6761ea3446 | [] | no_license | joseverdezoto/GenomicDistributions | 05e69f98b7a7cde473f06d8ff6e0b5424e711c35 | 8a1a1067932cd52686b94f355e1a972f71d54591 | refs/heads/master | 2020-08-23T15:04:45.503463 | 2019-09-20T14:12:50 | 2019-09-20T14:12:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 831 | rd | GenomicDistributions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/package.R
\docType{package}
\name{GenomicDistributions}
\alias{GenomicDistributions}
\alias{GenomicDistributions-package}
\title{Produces summaries and plots of features distributed across genomes}
\description{
If you have a set of genomic r... |
be8e2d4ec0c5ba74bf23e65a21608b1da9adf01a | 8e4f92643e35a4f3c828de5fcf2d9cb3b96c0e2b | /CE_Traits_Correlate_Scripts/ceMT_Trait_COR.R | 9a68dc421184363367f8771f84a8546717dd1d0c | [] | no_license | RWilcox90/MOST | b82122e9498e1541d48cbf7ec66090402296baca | 33e3de88cec5cd722dd006471796b7fbca614602 | refs/heads/master | 2021-01-23T01:18:02.755051 | 2017-06-06T16:15:59 | 2017-06-06T16:15:59 | 92,867,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,359 | r | ceMT_Trait_COR.R | ce <- read.csv("lmeCE_Trait_Files/ceMT_Traits.csv")
Int <- as.data.frame(list(Var="Int", CSQ1=cor(ce$Intercept, ce$CSQ_1, use="complete.obs"),
CSQ2=cor(ce$Intercept, ce$CSQ_2, use="complete.obs"),
PHQAnx=cor(ce$Intercept, ce$PHQ12_Anx, use="complete.obs"),
... |
8aedf4a638230c4984d7456d80a0b5e6fc81ecd0 | 0dcf732360bdcd82a50d534e6b5cdd1587f66163 | /plot1.R | 568ba87ddefd4650e6f0c457331e4d94d8e9e4db | [] | no_license | rmnmrgrd/ExData_Plotting1 | dbe4b8e7978152baa291e38752c78d71fb6150d7 | f8ae2a89bc1b1bb04145557e7c8e8d0727a0b8cb | refs/heads/master | 2020-03-27T21:13:06.333802 | 2018-09-02T22:12:25 | 2018-09-02T22:12:25 | 147,125,423 | 0 | 0 | null | 2018-09-02T22:09:38 | 2018-09-02T22:09:37 | null | UTF-8 | R | false | false | 388 | r | plot1.R | library(dplyr)
d <- read.csv("household_power_consumption.txt", sep=";", stringsAsFactors=FALSE)
d <- mutate(tbl_df(d[d$Date == "1/2/2007" | d$Date == "2/2/2007", ]), Global_active_power_num = as.numeric(Global_active_power))
png("Plot1.png")
# Plot 1
hist(d$Global_active_power_num, main="Global Active Power... |
9da4f4786b981d89926a1841d5924b4ba6582359 | b4c641bacf51d5aa00983f75de026f15216d6864 | /man/si_style_nolines.Rd | 1e2a956e504e11d2bd6be78207660f2d7f4c17ac | [
"MIT"
] | permissive | USAID-OHA-SI/glitr | c932a66475afe3b8506aa83478e074b93d211131 | 0ed30aa9c39e863fc3ef52111057686f30878914 | refs/heads/main | 2023-02-16T22:33:04.983558 | 2023-02-15T14:42:37 | 2023-02-15T14:42:37 | 250,594,305 | 7 | 1 | MIT | 2023-09-07T19:10:26 | 2020-03-27T17:05:36 | R | UTF-8 | R | false | true | 672 | rd | si_style_nolines.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/si_themes.R
\name{si_style_nolines}
\alias{si_style_nolines}
\title{SI style plot with blank canvass}
\usage{
si_style_nolines(...)
}
\arguments{
\item{...}{inherits parameters from \code{\link[=si_style]{si_style()}}}
}
\description{
Incorpo... |
138bf38000642881a443955183352cb3e0d79050 | ef94b469c66c612e7709e9df86c79e57892d7b7d | /A4SriramanKrishnamurthy.r | 6cd8ed179f8eea18ccaf8ddbb5470332a2746785 | [] | no_license | rksriram91/RAssignmentsUconn | 0f421f90419591bd1e954fd99d0fc36d71457015 | eb2002a9295087e22bdb022de101afd962f295f6 | refs/heads/master | 2021-01-09T05:59:12.373337 | 2017-04-03T04:10:22 | 2017-04-03T04:10:22 | 80,881,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,655 | r | A4SriramanKrishnamurthy.r | #Create a vector called x and place values 10 to 1000.
x<-10:1000 # creates x vector as specified
# Create a y vector that takes the square root of the log of numbers in x.
y<-sqrt(log(x)) # creates y vector as specified
#y<-log(x)
#NOTE : The question asks to take sqrt of log values of x. However the Graph shown i... |
0d024d57a37527b8d3c8b9af705e39001535838e | 0bf2f4a9c118a423b1eb3dbe2ab3296c89a64aa7 | /server.R | d84e0142f573edc1fc8e5ca4ca2f4137223a76c3 | [] | no_license | josephine-doerre/module_9_data_products_assignment | 2d5b1f6d8cab9fbf71957c69647c9d835027ccd9 | ad9e29eddd8c2816f11cf3d7f3675116381c8929 | refs/heads/main | 2023-04-11T20:31:49.711033 | 2021-04-27T13:15:03 | 2021-04-27T13:15:03 | 360,106,164 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 256 | r | server.R | server <- function(input, output, session) {
output$samplesize <- renderText({
size <- sample.size.mean(input$e, input$S, N = Inf, level = 0.95)
paste0('Assuming N=Inf the Sample Size needed is: ', size$n)
})
}
shinyApp(ui, server) |
e2b9a220a31a3f774d30830a60cfdd6102e6a820 | a3540659efafb4664485bcac2b01b54558ea8b87 | /R/urls.r | b4476533e1f5c79b28839d11a49df82fe1488f4f | [] | no_license | jefferis/gscraper | d9f254717f15a5df7296bc3386eeb21eb6c399ef | 6f00ddcc6697887c6c8e177ea611f09c54a843d2 | refs/heads/master | 2021-05-25T11:21:24.631511 | 2020-10-22T09:23:35 | 2020-10-22T09:23:35 | 10,742,048 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 532 | r | urls.r | #' Find paths on server of resources defined by urls
#'
#' Does not attempt to parse queries etc
#' @param url Character vector of one or more urls
#' @return character vector of paths
#' @author jefferis
#' @export
#' @seealso \code{\link{parseURI}}
#' @examples
#' remotepath("http://cran.r-project.org/web/packages/s... |
0c5a920177badc757ffc7ccb0ce04e5989f9bd60 | 0db61575fb70f8a8212550245d33fbab48dc69b4 | /MachineLearningRepos/Assignment 3/ExploratoryDataAnalysis/Expl_Data.R | 3676a94fe03c1645f7695f79826ce3be3e3b153f | [] | no_license | SathyaSrini/SoloProjects | e364fee143f171751bb0e9b6c95a225611ea71ba | 6eaab6cc0f29054dd7988f2e5c1db913a3ad49e4 | refs/heads/master | 2020-05-21T19:12:42.628164 | 2016-10-23T06:06:31 | 2016-10-23T06:06:31 | 64,565,249 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,455 | r | Expl_Data.R | #install.packages('caTools',dependencies = TRUE,repos="http://cran.rstudio.com/")
#install.packages('moments',dependencies = TRUE,repos="http://cran.rstudio.com/")
#install.packages('nortest',dependencies = TRUE,repos="http://cran.rstudio.com/")
#install.packages('fitdistrplus',dependencies = TRUE,repos="http://cran.rs... |
2daebfb1fbfc599e45c3ecf6a7b4c6003ef9ffd8 | b373edb8d6860bda63ebf1b974e9241bed4c17f2 | /analysis/nh fig global metanetwork.R | 7b5b10630efe9211a06b72de5ba6d2ad0ce47bf0 | [] | no_license | evancf/network-homogenization | 96df8b5f30dedff9eea6f36f0654a85bde6a6849 | 0ced631fb8e01f52d4c54e6417514fb405498a6d | refs/heads/main | 2023-01-24T13:33:14.893819 | 2020-12-05T21:04:17 | 2020-12-05T21:04:17 | 318,887,612 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,744 | r | nh fig global metanetwork.R | # Need some other packages for this
ipak(c("gridExtra", "base2grob", "gplots"))
# First metanetworks with and without introduced interactions ------------------
node.size <- 0.25
# Native metanetwork
p1.labs <- as.data.frame(c("Americas", "Europe", "Africa", "Asia",
"New Zealand", "Hawaii... |
c24b5293f55383c57859d2affc3f1143bdd5658d | de71c62e745b048c95c08f7e516d4aaa215a0194 | /man/betamle.Rd | 17e5a91022bdef8bdafad35b2a23db03ca94fab5 | [
"MIT"
] | permissive | jjbrehm/BadApple | 55db2fa7208a5231f06b4aded8b9838dd12fd174 | 0fe9a9742c53fdafa8788c90f905f8d2d3c7d913 | refs/heads/master | 2023-02-05T21:42:05.624824 | 2020-12-22T18:45:47 | 2020-12-22T18:45:47 | 277,906,381 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 328 | rd | betamle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/betamle.r
\name{betamle}
\alias{betamle}
\title{Title}
\usage{
betamle(fmu, method = "BFGS", data, brange)
}
\arguments{
\item{fmu}{formula object}
\item{method}{character string}
\item{data}{dataset}
\item{brange}{vector}
}
\description{
... |
8bca42c5e62d85a701acb5cab2aeb7810d228893 | ded1169a3fdc34017f372c04a4b5f49e6441a9e9 | /rakesh/fileconv.R | 108af696a4157441d9ff0201ef8488b73a592308 | [] | no_license | shantanudas/fmri | da7ead2d0a56d900e645cd48e2eb792cce848fc7 | 6a78d65944d14a4063d74eb9d6a70c3212584521 | refs/heads/master | 2021-01-12T00:22:06.745964 | 2017-01-11T23:40:10 | 2017-01-11T23:40:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,139 | r | fileconv.R | p2n <- function(x) as.numeric(sub("%","", x))/100
err1 <- read.table("rakesh/error_rates/err_rates.delim", sep = " ", header = TRUE)
View(err1)
err1$TrainErr <- err1$TrainErr/100
err1$TestErr <- err1$TestErr/100
err_knn400 <- read.table("rakesh/error_rates/knn400.txt", header = FALSE)
colnames(err_knn400) <- c("k", "... |
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