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fd5d5f2d24d08721a96c0580f87e70166dfb03cd | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ggfortify/examples/autoplot.glmnet.Rd.R | 2f8a89a1bbffdcb6bc19bac10aaf2c1916eb8b70 | [] | 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 | 208 | r | autoplot.glmnet.Rd.R | library(ggfortify)
### Name: autoplot.glmnet
### Title: Autoplot 'glmnet::glmnet'
### Aliases: autoplot.glmnet
### ** Examples
autoplot(glmnet::glmnet(data.matrix(Orange[-3]), data.matrix(Orange[3])))
|
df8445dadb197fdf55dc19a52da905974aeb4542 | 05435fbdd6a6f58d2ebd4092129f4219132b707f | /code/Tross_Sebens_ER_model5_time.R | 8d8859d94a05aa3f37a419ba74b0fd057739af4a | [] | no_license | earobert/BE_2019_01_25 | 760587a5ec2ad6ba367c232bb1b311442213ea59 | e15f1e74c14328ba708cbe9ceb98e82c892cce28 | refs/heads/master | 2020-03-21T08:39:29.381960 | 2019-02-16T00:36:42 | 2019-02-16T00:36:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,383 | r | Tross_Sebens_ER_model5_time.R | #_____________________________________________________________________----
# This is the main working model right now. Now I'm adding time as an aspect in...
#===============================================#
# Model 5 - there is no gamma, and no cost of threads
# MLE estimation of cost of byssus given growth and threa... |
fa2ff59477effa8323d8a6a1fc3146dc30f02ef9 | 9638273b355612ca5b366eb79927129ac51fa6d9 | /scripts/DBI_connection.R | d59cf11727dff2212141c4370ab0419284ed5a08 | [] | no_license | RyanFarquharson/ACPMD | f8984344205a7faff890df8b75186dee929dfdcf | c7dc779096754ac330c505d8dcd7571321699647 | refs/heads/master | 2020-04-07T22:49:57.484592 | 2019-02-01T05:45:37 | 2019-02-01T05:45:37 | 158,787,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,670 | r | DBI_connection.R | # connected to Access database using Connections tab in Rstudio
# help available at https://db.rstudio.com/odbc/
library(DBI)
con <- dbConnect(odbc::odbc(), "ABS198296")
# Look at what is in the database
dbListTables(con)
dbListFields(con, "Ag1983")
dbListFields(con, "Item_list")
dbListFields(con, "ASGC96")
# Crea... |
5ced8e7a4cd043df9ee6c270a65b84d8dd56041e | 8c1daa6967fd693652dd1eac38a9f666fc65c8ee | /man/get.varitas.options.Rd | 18f9928ed177db607d5afa9f2f761493dd22ce10 | [] | no_license | cran/varitas | ae90d05e61f5004a07d09ec5861724218215fdcd | 603e4ec1d6d90678eb54486f7a0faf6a76a14114 | refs/heads/master | 2021-01-13T21:42:36.802682 | 2020-11-13T23:30:03 | 2020-11-13T23:30:03 | 242,504,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 731 | rd | get.varitas.options.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get.varitas.options.R
\name{get.varitas.options}
\alias{get.varitas.options}
\title{Return VariTAS settings}
\usage{
get.varitas.options(option.name = NULL, nesting.character = "\\\\.")
}
\arguments{
\item{option.name}{Optional name of option... |
d04fd40113bfe51e877ae4ed1812e479dc40d88c | 645ff6a53c2093037c7154cdd87714942385ffd4 | /R/collection_rebalanceleaders.R | a6cdc72c0f0929770187e35750b6be6ec63b578a | [
"MIT"
] | permissive | 1havran/solrium | 04c6754d14509e0e46e50d39f074d17b190eb050 | a30015c1d1a28fc7293d67854c12d8f3fc99fad0 | refs/heads/master | 2021-01-01T06:09:13.350637 | 2017-02-01T19:44:27 | 2017-02-01T19:44:27 | 97,371,390 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,075 | r | collection_rebalanceleaders.R | #' @title Rebalance leaders
#'
#' @description Reassign leaders in a collection according to the preferredLeader
#' property across active nodes
#'
#' @export
#' @param name (character) Required. The name of the collection rebalance preferredLeaders on.
#' @param maxAtOnce (integer) The maximum number of reassignments ... |
9af3d143f5b1634500e606df0d89c5a3cac2c874 | 8748271d8301a95a15c9b2effb121e8bae0a418a | /Rcodes/Eq1ProdFun1/explanatory.R | 15688660b938e2e5223b8e6a4ed88b2c2ffe5200 | [] | no_license | MonikaNovackova/FoodSystemGitH | 2a8c9606451cd5b68e387b0f460a50140059da80 | 849480432be11062d16812830d894e6f3f7fd8c5 | refs/heads/master | 2021-10-10T21:55:14.555493 | 2019-01-17T16:05:13 | 2019-01-17T16:05:13 | 157,595,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | explanatory.R | rm(list=ls())
WDuni<-c("/home/m/mn/mn301/foodSystems/dataFS") # uni
WDhome<-c("/home/trennion/foodSystems/dataFS") # doma
setwd(WDuni)
setwd(WDhome)
#wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww... |
9182d5fd934bd0a1726664a81c36a50006d94416 | 1181197b6995d81a5982597db18e9a2bf4d3346e | /normalising/TPM_normalised_look.R | 68f9539e1c053e92a28db0b30b79cdc95e96e3ff | [] | no_license | oknox/Research-project | 011a6054a43b3cb11a1688227d05d1439934487d | c9e19cd5952f64f5e41336dd039c2254605860c0 | refs/heads/master | 2020-03-26T11:41:12.994576 | 2018-08-15T13:21:16 | 2018-08-15T13:21:16 | 144,854,214 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 476 | r | TPM_normalised_look.R | #Looking at TPM normalised counts
setwd("Library/Mobile Documents/com~apple~CloudDocs/CAMBRIDGE/Research project/")
library(data.table)
norm_counts <- fread("Transcripts/Nonlogged_tpm_normalised_counts.csv", data.table = F)
jcounts <- norm_counts[,2:ncol(norm_counts)]
jcounts <- as.matrix(jcounts)
#Make histogram of ... |
5b102647cd68cab5c83a9aad65c4d5a68ee2fcdf | d86913a4c99d7666bf457f81be2581ad0241d86e | /ui.R | bb9d891c8100efa5a3b30133fec6a36b61a05f22 | [] | no_license | cgtyoder/DevDataProdWk4 | 5de37ad6aa3874194b71fb4acd0eaf5e7c92e760 | 84108a3718623e980ec6fdfbbdd5b52bf2da4639 | refs/heads/master | 2021-09-08T09:14:46.761295 | 2018-03-09T02:46:54 | 2018-03-09T02:46:54 | 124,477,513 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,011 | r | ui.R | library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("mtcars Data"),
# Sidebar with 3 selectors to narrow data
sidebarLayout(
sidebarPanel(
h3("Instructions:"),
h5("Change the values of the selectors below to plot the observations from the mtcars data set which... |
381e6f51618909cb67e1f89ab481291411ec779d | 0b46530108be813ad07dddc3bf3cd10b01f8c10c | /man/temperature_graph.Rd | b372752b8441c878deb14f0427064dc5a64cde9c | [] | no_license | jcorain/NewBoRn | 86a76d0a596eefeb4096e87baa3411df016fc1f1 | 62f7764fe5514c416f941e0180ef97f38012cdb4 | refs/heads/main | 2023-02-27T07:33:16.558981 | 2021-01-28T17:05:53 | 2021-01-28T17:05:53 | 329,053,524 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 723 | rd | temperature_graph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/graph.R
\name{temperature_graph}
\alias{temperature_graph}
\title{temperature_graph}
\usage{
temperature_graph(dataframe = NULL, birthdate = NULL)
}
\arguments{
\item{dataframe}{The dataframe you want to analyze}
\item{birthdate}{The birthda... |
142a41e2f4cca2e2f5ad056fdea3998cb5063e53 | f1dd4979186d90cc479c48d7673f4ce4c633cf35 | /psf/astro/zone099.r | e63d92b44589045e112d34d6cbeafc5b9c3f6dc2 | [] | no_license | flaviasobreira/DESWL | 9d93abd3849f28217ae19c41d9b270b9b1bd5909 | 6ba2a7f33196041aa7f34d956535f7a0076ae1f2 | refs/heads/master | 2020-03-26T05:50:05.292689 | 2018-08-12T21:43:34 | 2018-08-12T21:43:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 441 | r | zone099.r | 350947
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48643... |
7001ce8b84707ae51617654bd508a64963e11e84 | fac8df50cfc58cc485cfba09115866d7650afc39 | /testfiles/r/input/functions/functions0.r | 38158127ecd3c7c029ac8d0d8bd2da793406e5fe | [
"MIT"
] | permissive | jorconnor/senior-design-rpl | 5be9b69fc646976334413de881e0b5751f2d15d2 | 10afb254e3591c6fc367a95f42df43fbee51fd26 | refs/heads/master | 2021-01-09T05:51:17.992817 | 2017-05-09T18:50:46 | 2017-05-09T18:50:46 | 80,846,535 | 0 | 1 | null | 2017-04-19T14:57:40 | 2017-02-03T16:24:40 | Java | UTF-8 | R | false | false | 519 | r | functions0.r | # Read file
statesInfo <- read.csv('stateData.csv')
# subset data by region if region is 1
subset(statesInfo, state.region == 1)
stateSubset <- statesInfo[statesInfo$illiteracy == 0.5, ]
library(ggplot2)
library(plyr)
library(twitteR)
# Attributes and dimensions of data
dim(stateSubset)
str(stateSubset)
source(file... |
4ab4091dfadf985dcc908888b2a94726f35adf10 | 229be3eec8eda763405e9147de5279d0c783b4ac | /xmlR/mergedCatalog.R | da580f47973f53459f577239f9f9027391f08ca2 | [] | no_license | gvravi/healapp | 98ed06127651361048d7f5e43add08f24c9d01bb | 5ca7d0774f313f137bf4308706dc4d8fbf8d7976 | refs/heads/master | 2020-12-24T06:08:17.933512 | 2016-11-08T11:35:20 | 2016-11-08T11:35:20 | 49,939,162 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,593 | r | mergedCatalog.R | # Chapter 3 section 3.2
setwd("~/R/xmlR")
library(XML)
#Read XML document into R with xmlParse
doc1 = xmlParse("merged_catalog.xml")
#Identify root element of xml class
root = xmlRoot(doc1)
#Name of root
xmlName(root)
#No of children in Root
xmlSize(root)
#Accessing Nodes in the DOM(Document Objec... |
62c1f2b58277ec39562f1a7718a3b5b272bfaea4 | 2db27775c676d46d2f28f239b148463aa9288371 | /man/eptplot.Rd | fba15d76362a47964329a176a3e25d019d697cde | [] | no_license | cran/EPT | f2af2930cc2791fa75a91a42a78c0d69f59b1408 | a86526fab7d443983e0fa72ab21523f8d9c1ed77 | refs/heads/master | 2022-01-21T09:38:38.437692 | 2022-01-05T00:10:02 | 2022-01-05T00:10:02 | 236,593,183 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,811 | rd | eptplot.Rd | \name{eptplot}
\alias{eptplot}
\title{Plot of Components by Ensemble Patch Transform of a Signal}
\description{
This function plots ensemble patch transform of a signal for a sequence of size parameters tau's.
}
\usage{
eptplot(eptransf, taus = eptransf$parameters$tau)
}
\arguments{
\item{eptransf}{R object of ensemb... |
36fa6520452a9ad646304b1bc9c41f4c05fffde2 | 63f247fa699153303a6481f86d98764a7d88529d | /2/2.2/3.R | ae457f167939d3dda903c701d720388e40cdb550 | [] | no_license | Alex1472/Statistic-on-R.-Part-1 | 86ec8c2e1024de0aec3c3922b863f156cb57b200 | ce071d7c93cedf395c452f61a48fbafd4eaf99d1 | refs/heads/master | 2020-03-27T21:10:15.314847 | 2018-09-09T01:05:29 | 2018-09-09T01:05:29 | 147,120,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 86 | r | 3.R | df = read.table("dataset_11504_15.txt")
bartlett.test(V1 ~ V2, df)
t.test(V1 ~ V2, df) |
084ac597a2037909c759ec1ee47db45f8818c7c0 | 508fa9bfaae7fab2b5662b93f5dd858cae7767ff | /R/geneModel.R | 8391b8cda83916b5c3eb4b99766252a2a2060d75 | [] | no_license | cran/refGenome | 2ec5e1be73d89ec48a1506d0323bf2ebb0c305f2 | 5f92690b4099770bedf3ae1d8cef5a28ac6250a9 | refs/heads/master | 2021-01-17T10:10:20.769482 | 2019-05-22T16:10:09 | 2019-05-22T16:10:09 | 17,699,105 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,619 | r | geneModel.R |
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
# Declaration of generics for geneModel.r
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
setGeneric("getTranscript",
function(object, i) standardGeneric("getTranscript"))
setGeneric("getExonDa... |
ec9391c3d35ef2e5491a5e6e425716ec7f643096 | 5639bad159509b15a8cc6951e22fed8cf8439b1b | /scripts/endo_tss/endo_tss_format.R | cf0764cb70779105aac864252e95142652f292f6 | [] | no_license | KosuriLab/ecoli_promoter_mpra | 3f9fe47e40a81f9bafd27f672d0e7ece5924789f | 484abdb6ba2d398501009ee6eb25ca7f190d3b4c | refs/heads/master | 2021-07-09T00:38:46.186513 | 2020-08-10T17:28:54 | 2020-08-10T17:28:54 | 179,758,077 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,910 | r | endo_tss_format.R | library(dplyr)
library(tidyr)
options(stringsAsFactors = F)
# args = commandArgs(trailingOnly=TRUE)
#
# infile <- args[1]
# outfile <- args[2]
infile <- '../../processed_data/endo_tss/lb/rLP5_Endo2_lb_expression.txt'
outfile <- '../../processed_data/endo_tss/lb/rLP5_Endo2_lb_expression_formatted.txt'
# infile <- '.... |
322da95fb3bb94cc03ba560bfdd7d0a1acd3dd8b | 4e4bc3bb5e2186fde35f3b2fcd2d2ea92195d1c5 | /Plot3.R | 72f06c239f92b9d06016927de0132e451b3b0d3b | [] | no_license | thuangpham/ExData_Plotting1 | 36a4315c00fd8a2b9455f081cc1504adea226de3 | d74834248ff48e68c1b3c37e16a796e3e19023a3 | refs/heads/master | 2021-01-17T09:59:07.337152 | 2016-07-16T11:10:22 | 2016-07-16T11:10:22 | 63,211,217 | 0 | 0 | null | 2016-07-13T03:28:19 | 2016-07-13T03:28:17 | null | UTF-8 | R | false | false | 1,027 | r | Plot3.R | #set the working directory
setwd("C:/DataScience/Exploratory Data/ExData_Plotting1/ExData_Plotting1")
library(dplyr)
file<-"Data/household_power_consumption.txt"
df <- read.csv(file, sep=";", header=TRUE,stringsAsFactors=FALSE,skip = 66637, nrow = 2880,
na.strings="?")
name <- sapply(read.table... |
3f48d7612c431bfb7b09ff726f9b86e664848d5c | 26ba3082f3586332d3a81c7cd5f71a0bd7eb6889 | /R/omim.R | acb89e1735f619fe29f62235fe9a29cee919e278 | [] | no_license | ExoLab-UPLCMS/MetaboliteHub | 0ec60bcf2b50169b9288077786c206049be99d4d | b5491b63ccec3589a4a3c1d84a1ec544cb303e30 | refs/heads/master | 2022-04-11T15:45:06.191108 | 2020-03-31T15:34:51 | 2020-03-31T15:34:51 | 203,401,595 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 663 | r | omim.R | #' Retrieves diseases and metabolic pathways alttered present in OMIM database.
#'
#' For a gene, the functions searches on OMIM database and retrieves what is publicated there, the diseases and metabolic alterations reported.
#'
#' @param x is the gene of interest.
#' @return returns a list of the different alteration... |
8f3ee363eeff6c02fe24966486b8af6453f03de5 | 643a4f814e3696da39814bec6ff21e90c16995f3 | /man/getLevels-LagOperator.Rd | 58616fcbeccee1570d2c4c037c199d34a9811d32 | [] | no_license | LiangCZhang/quantspec | 186a66f508155f093fe37dcd9067bfe12db54265 | 6e7e929893a68bd1af76ac259180584aa28b3813 | refs/heads/develop | 2021-01-12T08:15:11.620545 | 2016-03-28T16:17:11 | 2016-03-28T16:17:11 | 76,523,170 | 2 | 1 | null | 2016-12-15T04:13:16 | 2016-12-15T04:13:15 | null | UTF-8 | R | false | true | 730 | rd | getLevels-LagOperator.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class-LagOperator.R
\docType{methods}
\name{getLevels-LagOperator}
\alias{getLevels,LagOperator-method}
\alias{getLevels-LagOperator}
\title{Get attribute \code{levels} from a \code{LagOperator}.}
\usage{
\S4method{getLevels}{LagOperator}(obj... |
5176825fc5a82d5a5efbe88cc96499e99a116c14 | 3ee04b4129e86c9218a34f402349649727baa646 | /man/jtrace_is_installed.Rd | 718f9149e3969b0b7fd927eda87eba12f5e2cd9f | [
"MIT"
] | permissive | gongcastro/jtracer | c34233cfcebba4dce8e7c5be72f09b626c3573ec | ed4126d5a6b92034182eb9e77d6c357453af34c5 | refs/heads/master | 2023-09-04T11:31:43.980588 | 2021-10-15T15:37:16 | 2021-10-15T15:37:16 | 365,167,721 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 556 | rd | jtrace_is_installed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/install.R
\name{jtrace_is_installed}
\alias{jtrace_is_installed}
\title{Check if jTRACE is installed}
\usage{
jtrace_is_installed()
}
\value{
A logical values indicating whether jTRACE has been already installed
}
\description{
Check if jTRAC... |
22a07916ff0f0748a8456e393ac5c860d520e09e | 4a033f9a65e4dcf36533b6a5ec92620fe229ab75 | /cachematrix.R | 4ee892721e29593f049e77da669456871c6f1e6e | [] | no_license | JayaCh/ProgrammingAssignment2 | bd8ec125361e3ace7d94af9d8d20c05a8f2bd96b | 4fac1e45ff804e2ab9289cb9b819d28854abdc1a | refs/heads/master | 2020-12-01T09:32:10.031816 | 2014-11-20T15:36:31 | 2014-11-20T15:36:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,938 | r | cachematrix.R | ## makeCacheMatrix: Creates and returns the list of getMatrix, setMatrix, getInverse and setInverse functions.
## The superassignment operator is used in the setMatrix and setInverse methods to cache the
## values of input matrix and the inverse matrix.
## cacheSolve: Calculates the inverse matrix of the matrix, that ... |
03ec4a8bd4fd198b69ae2bf3c3bdbb202a063f69 | d916b13e8151b66bae458dba011ad02a3a699f1d | /src/ggplot2 separate mean segment.R | 6dc074eb7396c51901373d897ccf8233adb57166 | [
"MIT"
] | permissive | korkridake/cssejhucovid19 | 8fdc3906332ef047bab75e42d0f01f49d30574a1 | fc13516eeeca87b5345a169587be5776b061f83d | refs/heads/main | 2023-05-26T17:36:51.182358 | 2021-06-16T08:31:31 | 2021-06-16T08:31:31 | 375,930,293 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,627 | r | ggplot2 separate mean segment.R | ### separate mean segment
# install.packages("ggplot2", "dplyr")
library(ggplot2)
library(dplyr)
set.seed(22)
d <- data.frame(t = seq(as.Date("2011-1-1"), by = "month", length.out = 48),
v = as.integer(rnorm(48, mean=50, sd=10)))
### plain line chart
ggplot(d, aes(x=t, y=v)) +
geom_line()
### ad... |
f751cc15b40e1bd6afbbe480b8997989d520a895 | e66f8e5ef689a7b6dae10ca5958a4b510dbde13f | /man/write_shinyrates_data.Rd | ddb91b28e24213b89e4182587f610e6a3b0115c7 | [] | no_license | delabj/pogoshinyrates | 11a5914a7ec017917f378085cf9edf0e5f146c79 | 98d04f1a704e0bac74cc1c9ecec7ccd193913d35 | refs/heads/master | 2022-11-12T19:50:17.295599 | 2020-07-09T21:26:10 | 2020-07-09T21:26:10 | 264,212,546 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 565 | rd | write_shinyrates_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_shinyrates_data.R
\name{write_shinyrates_data}
\alias{write_shinyrates_data}
\title{Write the shinyrates data.}
\usage{
write_shinyrates_data(df, name = "shinyrates.csv")
}
\arguments{
\item{df:}{a data frame to write}
\item{name:}{the n... |
9f4e557abd5141db60232133b3353c53bac968f7 | d64c6c986730b2e989673679796cf4c968474de8 | /data_wrangling.R | afa48e082a411443149593ca6961ec576f50aa2c | [] | no_license | han-tun/vizrisk | 4e5e1e341706075d7f8458cfb693d192fcceaa09 | 5219f3cde6b6089024e932c1b5acf45a562c7def | refs/heads/master | 2020-12-08T11:40:37.503228 | 2020-01-10T06:29:47 | 2020-01-10T06:29:47 | 232,973,137 | 0 | 0 | null | 2020-01-10T05:34:12 | 2020-01-10T05:34:11 | null | UTF-8 | R | false | false | 3,645 | r | data_wrangling.R | library(tidyverse)
library(geosphere)
library(geojsonio)
##landers
landers <- read_csv("landers.csv")
landers_mainshock <- landers %>% filter(mag == 7.3)
landers_rupture_length <- 80 * 2 * 1000
landers_mainshock_time <- landers_mainshock$time
landers_dist <-
landers %>%
mutate(mainshock_lon = landers_mainshock$... |
81537d634660c74a072f68a35427f0f431c56852 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ProFit/examples/profitOpenCLEnv.Rd.R | ccb77717548f7f9c5f26af419c1d15333082c72d | [] | 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 | 728 | r | profitOpenCLEnv.Rd.R | library(ProFit)
### Name: profitOpenCLEnv
### Title: Create OpenCL Pointer Object
### Aliases: profitOpenCLEnv
### Keywords: GPU OpenCL
### ** Examples
modellist = list(
sersic = list(
xcen = c(180, 60),
ycen = c(90, 10),
mag = c(15, 13),
re = c(14, 5),
nser = c(3, 10),
ang = c(46, ... |
aee2a95454f0bc35e6460ea0fdcb2359123e57ca | ce94e221e5fd686cfb1218b0a9625decb77ac0c7 | /man/mmplot.Rd | a171650f07b6e5f553ab0dd39128931d7248b144 | [] | no_license | daniel-gerhard/medrc | bb95f91a63e150dd4a114fbfa409dcddc89195ea | 232b2f3887510add1851e6eae21f6ac529b6bf33 | refs/heads/master | 2020-12-24T08:24:02.948797 | 2017-12-27T03:39:07 | 2017-12-27T03:39:07 | 10,939,171 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 511 | rd | mmplot.Rd | \name{mmplot}
\alias{mmplot}
\title{Plot multiple medrc objects}
\description{Plot multiple predicted dose-response curves based on fixed effect estimates from multiple medrc objects}
\usage{
mmplot(x, ..., ndose=25, logx = FALSE)
}
\arguments{
\item{x}{An object of class medrc}
\item{...}{further objects of cl... |
5ed4082e7f679a2ac91b72b38b4b23cb8e729967 | 3b54cf65d257611c74f23fab637a6c86c05d84c6 | /R/sequences.R | 758ceb34f8a85153c6619d6072fce0a45072bd98 | [] | no_license | epigen/RnBeadsAnnotationCreator | 624a02f2ae039a6ee4c3ddb0a497296bddbe9058 | 62ee92cd76c601966656c3bf134e3e7a9ba794a7 | refs/heads/master | 2022-07-07T20:30:18.419055 | 2022-06-22T07:55:34 | 2022-06-22T07:55:34 | 64,145,268 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,458 | r | sequences.R | ########################################################################################################################
## sequences.R
## created: 2015-12-10
## creator: Yassen Assenov
## ---------------------------------------------------------------------------------------------------------------------
## Utility fu... |
c38574489df6112f22a40f81cb60e32b59092048 | bc304dc82564cf44b7fe73d8722f1ac6e680dacc | /Elections/BosniaHoR-drive .R | 5f72efe209d935b3cb2c392ee8dc2fe5d5062a7b | [] | no_license | rs2903/BiH | 7f993a06724e6a72a73810d2f59823c67dba8882 | 6c2f9b7413634ea0e0bd347cd74756cb989986ae | refs/heads/master | 2020-06-19T04:09:30.983391 | 2019-07-12T09:34:33 | 2019-07-12T09:34:33 | 196,551,852 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,670 | r | BosniaHoR-drive .R | # load packages -----------------------------------------------------------
require(ggplot2)
#require(plotly)
require(stringi)
require(tidyr)
require(purrr)
require(dplyr)
#require(rio)
require(lubridate)
require(readxl)
require(stringdist)
library(xlsx)
library(reldist)
library(ggthemes)
library(highcharter)
#setwd(... |
c52460d6ba5253d1840d661a215f9aa9ee8e7ea6 | 1201b4b111882eef0960608d2e9262349e1bf88c | /R/swing.r | c844d86997b7c4211b9a72df9ac4427d073c4402 | [] | no_license | awaardenberg/KinSwingR | c96610e04f340a06072f036bd77ea37e980a859a | 6d8e6b4c8f49e50346727c6d7948c06636856ff7 | refs/heads/master | 2020-03-29T08:05:28.567017 | 2019-04-30T05:32:15 | 2019-04-30T05:32:15 | 149,693,793 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,537 | r | swing.r | #' Swing statistic
#'
#' @description This function integrates the kinase-substrate predictions,
#' directionality of phosphopeptide fold change and signficance to assess
#' local connectivity (swing) of kinase-substrate networks. The final score
#' is a normalised and weighted score of predicted kinase activity. If
#... |
75c910857c226328ddf684d55f0354897617714e | 6b3215ae22fb53df23457105f4249e2a7e56bd2e | /inst/doc/Intro.R | 1a61f4303ad9686cdc1ae48f31953d8bf70b4177 | [] | no_license | cran/lmvar | 26f45be9ebeb93467ae389ec00f034bb8054c239 | a40d38d4d227aa3aade534bae73fb4625d4ae96d | refs/heads/master | 2021-01-22T14:15:23.807165 | 2019-05-16T09:10:10 | 2019-05-16T09:10:10 | 82,301,074 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,130 | r | Intro.R | ## ---- include=FALSE------------------------------------------------------
knitr::opts_chunk$set(fig.width=6, fig.height=6)
## ---- message = FALSE----------------------------------------------------
# As example we use the dataset 'cats' from the library 'MASS'.
require(lmvar); require(MASS)
# A plot of th... |
82f381c3feb4ef90080b5e5b84d5fec877818c37 | d9274644eeb7a0414c7f80a93878681baddc8812 | /final_code_check/script.R | d71136596715d77ee75723d92115fc2447841897 | [] | no_license | lynnzoo/hfd-research | 99979cfc7f4068b455d5ba0ae9e9e134782d7d45 | 220588b3c09bb34e5777766816d859a121d7a945 | refs/heads/master | 2022-02-17T07:11:23.303625 | 2019-08-23T01:41:26 | 2019-08-23T01:41:26 | 196,340,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,605 | r | script.R | #!/usr/bin/Rscript
#######################
## title: "Running Pipeline for HFD Choropleth Maps"
## author: "Shannon Chen"
#######################
## Automatically detect the file path of this file and set the working directory the to folder where this file is located
this_dir <- function(directory)
setwd( file.path(ge... |
0c056b5b601014e2c0a55f09f51bb6b757dc5947 | 6cb339f74f32213420a1ad2b535a7d819231581d | /cbind.fill.R | cf16bb8ded380ab547fe6f3971c4118713389915 | [] | no_license | MHS-R/miscHelper | 879518d6d3e8229cc84c37f5c34dc38c4c4400da | 16cffcd7aa96e5e97db5b2a81b1468a74fe4f65e | refs/heads/master | 2021-01-25T06:18:03.605379 | 2017-06-15T15:31:04 | 2017-06-15T15:31:04 | 93,549,719 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 574 | r | cbind.fill.R | #' Function to draw observations from stratified norm sample
#'
#' This function creates a stratified random sample, given an arbitrary number of
#' factors and levels within those factors.
#'
#' @param ... x, y, z can be matrices or data.frames
#' @references http://stackoverflow.com/questions/7962267/cbind-a-df... |
1b9efc1280bc2a3557f1a26363f416417694fa06 | 117ee80f5a04d6dd69d665eaa5d5b50c997c7faf | /analysis/dropout/simulate_dropout.R | 3a781d1a72b425f538d11cf2cf3ba2c91fb79ea8 | [] | no_license | kieranrcampbell/phenopath_revisions | 1a60f4e3a8d330dea73af435492cdf4f604af996 | 2c52ed25cedb0abb49465e6bf71b1b25640fd068 | refs/heads/master | 2021-03-16T08:28:27.378349 | 2018-10-11T18:11:06 | 2018-10-11T18:11:06 | 103,834,518 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,145 | r | simulate_dropout.R | library(SummarizedExperiment)
library(scater)
library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
library(polyester)
library(Biostrings)
library(readr)
source("analysis/simulations/simulate_funcs.R")
set.seed(12345L)
get_dropout_relationship <- function() {
sce_rds <- "data/dropout/tec_sceset_qc.rds"
... |
3b8204a5b4baed74df194a9c0c15e8ed476fdea4 | 6542f5b12f43a4021e9defd0f230e1e3ce719432 | /regresionLogistica.R | fe2676f47d5fdf155006b68d119376392165dcdd | [] | no_license | andreamcm/HDT6RegresionLogistica | f9434d49fc7589588c9b7d034aa52462e6c04b01 | a720b486bc7f9f77f6d850fd0aaee34e1fdbd061 | refs/heads/master | 2020-05-02T16:19:35.061664 | 2019-04-01T07:09:13 | 2019-04-01T07:09:13 | 178,065,228 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,622 | r | regresionLogistica.R | #-----------------------------------------------------------------------------------------------------------------------------------------------
# Universidad del Valle de Guatemala
# Autores: Andrea Maria Cordon Mayen, 16076
# Cristopher Sebastian Recinos RamÃ?rez, 16005
# Fecha: 18/03/2019
# arboles.R
#-----... |
94cbc57c23a179fc47e805e46d192eeea0146c9a | 74453745dd2a15c8e310e8f4446ccada9702435e | /R/index.R | db6236fd865d07d196fc29fd850b2ecabb534e6d | [
"MIT"
] | permissive | rstudio/renv | ffba012525e8b1e42094899c3df9952b54ecb945 | 8c10553e700cad703ddf4dd086104f9d80178f3a | refs/heads/main | 2023-08-29T08:45:28.288471 | 2023-08-28T22:01:19 | 2023-08-28T22:01:19 | 159,560,389 | 958 | 169 | MIT | 2023-09-14T00:55:28 | 2018-11-28T20:25:39 | R | UTF-8 | R | false | false | 4,622 | r | index.R |
the$index <- new.env(parent = emptyenv())
index <- function(scope, key = NULL, value = NULL, limit = 3600L) {
enabled <- renv_index_enabled(scope, key)
if (!enabled)
return(value)
# resolve the root directory
root <- renv_paths_index(scope)
# make sure the directory we're indexing exists
memoize(
... |
629966b86f6e6692d3ee7f56c6f4ba59cae033f0 | 173e8e734ee2d8e3eeeac0a86ce06ab48db15a08 | /man/Corner_text.Rd | 747af63a09bd4c77d96a1d31d134d78974e696f6 | [
"MIT"
] | permissive | aemon-j/gotmtools | b0ba213f83d0f3ccbee9f34b7efc69fc0e0dc86a | 4eb90e9e6ad960c36a78cc51e9c77b4d826a6197 | refs/heads/main | 2023-04-28T07:43:19.483815 | 2021-01-28T19:17:37 | 2021-01-28T19:17:37 | 220,067,540 | 5 | 5 | MIT | 2022-02-04T15:27:59 | 2019-11-06T18:52:40 | R | UTF-8 | R | false | true | 437 | rd | Corner_text.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Corner_text.R
\name{Corner_text}
\alias{Corner_text}
\title{Add text to the corner of a plot}
\usage{
Corner_text(text, location = "topleft")
}
\arguments{
\item{text}{text to be added to the plot as a language object}
\item{location}{locati... |
86b58266f14fd3c549d28c535ede6d4715134572 | 813e8ed246baf7ff8d039df4fb92b0b97e6ddad8 | /e-commerce tweet.R | d75c3c854eb3bbd90ad0a342d5de155ed4846d21 | [] | no_license | iqbalhanif/Twitter-Crawling | 94c40d342d6465b63591111aa54ce409d95249ff | deaba7ed0c84035f6f3653d55e60b4db999d2ee9 | refs/heads/master | 2020-06-02T20:47:17.239429 | 2020-02-04T02:56:25 | 2020-02-04T02:56:25 | 191,304,925 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,566 | r | e-commerce tweet.R | install.packages("rtweet")
install.packages("httpuv")
install.packages("openssl")
install.packages("httpuv")
install.packages("TweetR")
install.packages("devtools")
library(rtweet)
library(jsonlite)
library(magrittr)
library(dplyr)
library(stringr)
library("httr")
library(httpuv)
library(openssl)
librar... |
b72da82118651aef3e311690c27fee9169c226c5 | d8763dda7b7d7010df41aed1727346c765332de8 | /rl/ssteps6/findWayFinLearn2.R | 087b761f75e310e0856660e83fb85200f6943f4e | [] | no_license | shadogray/MA-ICA | 9a5aa9e9a5e9810cfd10b0c9a1ffad572b3d6329 | 6b48b3fd418ed3e187dfd2603e2f062df2996860 | refs/heads/master | 2020-12-31T11:24:38.442199 | 2020-02-07T20:49:03 | 2020-02-07T20:49:03 | 239,016,502 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,879 | r | findWayFinLearn2.R | library(tidyverse)
#library(timeSeries)
#library(corrr)
library(lubridate)
source('finlearn.R')
sig.v <- NULL
orig.s <- NULL
test <- F
evalPlot <- T
evalPrint <- T
local <- F
args <- commandArgs(trailingOnly = T)
if (length(args) > 0) sig.v <- unlist(strsplit(args[1],','))
if (length(args) > 1) orig.s <- unlist(strsp... |
382c9f106bb435a4ae0f11d2884b852c07ec79c3 | d47f618c48d9be0c052402e82b4787c3397a7f84 | /script_packages.R | 442c3504921c73f56a62a660aea31806c1d5d2c0 | [] | no_license | agenis/citynames-clustering | 9b927095991b02b8b8c31166623111c51132ab84 | 52d8db81b5369dc60a2d77200abf20d9e1425cb4 | refs/heads/master | 2020-04-15T08:07:28.330775 | 2019-01-08T00:02:40 | 2019-01-08T00:02:40 | 164,518,596 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,849 | r | script_packages.R | # packages
library(tidyverse)
library(tm)
library(stringdist)
library(maptools)
library(cluster)
library(dbscan)
library(fpc)
deps_names= structure(list(dpmt = c("01", "02", "03", "04", "05", "06", "07", "08",
"09", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19",
... |
07cfad4d92c2242c0f9ee7ed28715707f21eba7e | 94f2d32736ee169853a4969bae5436d4764f1f5f | /R/keyword_relevance.R | 073c1da7e14882d27873a8a877d2bee7544b75dd | [
"MIT"
] | permissive | johannesharmse/watsonNLU | 55eaf62fba16d0a37494fe34d392682b9cac5ab4 | bfd042d9bda0df78002bb49fa6c9a15dbb515e09 | refs/heads/master | 2020-03-08T06:32:43.344968 | 2018-11-13T00:33:02 | 2018-11-13T00:33:02 | 127,974,332 | 4 | 3 | MIT | 2018-04-15T22:39:38 | 2018-04-03T22:10:37 | R | UTF-8 | R | false | false | 6,635 | r | keyword_relevance.R | #' Watson Natural Language Understanding: Relevance of Keywords
#'
#' See the \href{https://github.com/johannesharmse/watsonNLU/blob/master/README.md}{sign-up} documentation for step by step instructions to secure your own username and password to enable you to use the Watson NLU API. The \strong{keyword_relevance} fun... |
e0787f7c587aa18948811b10276c55d2c3bf89a4 | 6cb4fbdd76a338d95f8348ef1351ec124aebc39f | /R/utils.R | 91442cfea043e9fc1890ff1e93cb0d9e9617d0ec | [] | no_license | kevinykuo/vctrs | f0224bd1b011535935a8bc8d198da181d090b6ef | 2992d4737d35ff4dbd6c15b895fc4c2dc6c71066 | refs/heads/master | 2020-03-25T23:16:53.434722 | 2018-08-10T00:25:01 | 2018-08-10T00:25:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 909 | r | utils.R | indent <- function(x, n) {
if (length(x) == 0)
return(character())
pad <- strrep(" ", n)
out <- Map(gsub, "\n", paste0("\n", pad), x)
unlist(out, use.names = FALSE)
}
ones <- function(...) {
array(1, dim = c(...))
}
vec_coerce_bare <- function(x, type) {
# Unexported wrapper around Rf_coerceVector()... |
e6fd8a594a530be7a7b218537fece680cd895fe4 | 4bd41b3ea5014a386820af21c3f3039800cdc688 | /scropt.R | d0afdb303f325ffe597289c1eeae5c93b0ed50ca | [] | no_license | RichChu1/ma350-demo | 650d5e08cb9ba222fc30e6d1066d18ddd886b4c3 | 8a274e02b9bb512a48553805f474ca0603b9fe11 | refs/heads/master | 2021-04-18T04:36:24.637506 | 2020-03-23T18:19:28 | 2020-03-23T18:19:28 | 249,505,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 41 | r | scropt.R | plot(rnorm(1000))
# New line of comments |
f36ba77fc0a3a94d906906cb08467fea414276c0 | 5a71730ca0a0088621fc8a2f41a9f42e39acc7f8 | /cachematrix.R | d4042ed885550f2e9b7d5cdb623de4308dc7e3fa | [] | no_license | ajtrask/ProgrammingAssignment2 | 9bd2907d9cc6b321d46d4c5d33889315588f47ec | 9684a605e63a4b9b7dc0b16ba3842e9b9a4db3a6 | refs/heads/master | 2021-01-18T18:43:54.224953 | 2016-02-06T00:48:24 | 2016-02-06T00:48:24 | 50,941,865 | 0 | 0 | null | 2016-02-02T18:25:53 | 2016-02-02T18:25:53 | null | UTF-8 | R | false | false | 1,419 | r | cachematrix.R | ## These functions (makeCacheMatrix and cacheSolve) implement
## a matrix "object" with the ability to cache its inverse
## makeCacheMatrix creates a matrix "object" that has funtions to:
## set and get the matrix
## set and get the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
minv <- NULL
... |
a7e26b792cd63b9920a9fb624ffde57ae0a789dc | c9a9019ebc1e1ab5921dfa63ad07e50f1db523d4 | /CourseProjectEx.R | 60572fdf1274b259bde38f3c86377afd13e9747a | [] | no_license | burtks/ADEC7430-KBFinal | a715e6d94d9595ca19c5bb24b89506b3fb1be289 | ed621cdd2f2063e0f63084ff4c7df7095af72fa8 | refs/heads/main | 2023-01-04T04:31:55.863843 | 2020-10-19T00:28:18 | 2020-10-19T00:28:18 | 304,186,574 | 0 | 2 | null | 2020-10-19T00:28:19 | 2020-10-15T02:20:14 | HTML | UTF-8 | R | false | false | 8,210 | r | CourseProjectEx.R | # ADEC 7430 Big Data Econometrics
# Course Project - Example R Script File
# OBJECTIVE: A charitable organization wishes to develop a machine learning
# model to improve the cost-effectiveness of their direct marketing campaigns
# to previous donors.
# 1) Develop a classification model using data from the mo... |
ca9c49e8c535851f3957375de37b274a400962d9 | 3f41dcde4498fcf47a5f8314de6086ffec3dd082 | /man/makeplot.asdsf.Rd | 529ac1bf862d260c102a04e40c72b847844846b7 | [] | no_license | arborworkflows/RWTY | 6f961f76b69776d9c752be0483416dec7448661b | fbf5b695a1c8d16f7532123fb86715152f430b06 | refs/heads/master | 2020-12-31T02:01:50.609590 | 2016-08-17T22:16:18 | 2016-08-17T22:16:18 | 65,751,392 | 0 | 1 | null | 2016-08-15T17:31:51 | 2016-08-15T17:31:51 | null | UTF-8 | R | false | true | 1,229 | rd | makeplot.asdsf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/makeplot.asdsf.R
\name{makeplot.asdsf}
\alias{makeplot.asdsf}
\title{Plot the Standard Deviation of Split Frequencies over the course of an MCMC.}
\usage{
makeplot.asdsf(chains, burnin = 0, window.size = 20, min.freq = 0)
}
\arguments{
\item{... |
a81b14d9bd610adb9f8346678fdc284a7fb98aa5 | 318c102d1f9055cac2a790363c8c3f6af024702c | /man/TempTraject.Rd | feb6ada2487e17313f9745cf3f7448e86ca8e22a | [] | no_license | cran/OceanView | 342ae464107823ff3324d26d9e28290779e768b3 | 1272fbf444372dfa4475c255da5be3d764651d3f | refs/heads/master | 2021-07-21T02:21:50.718897 | 2021-07-12T07:00:13 | 2021-07-12T07:00:13 | 18,805,213 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,940 | rd | TempTraject.Rd | \name{Profile data set}
\docType{data}
\alias{TrackProf}
\title{
Temperature profiles made along a ship track.
}
\description{
Profiles of temperature made along a ship track, originally made available by US NOAA NODC.
The data were merged from 29 input files named \code{gtspp_103799_xb_111.nc}
till \code{g... |
288916873461bfcda4a21c6a7f8eb32040ec355d | 7329459bb72ddd723bc58358e80b5f0db3db730c | /man/Normal_ct.Rd | 773405664d95d210173887d7414ca70a1adfb1d7 | [] | no_license | knygren/glmbayes | 6f2411da073f3d6bfcb727e8d02d4888cacb8fef | 3c25c08c1f4ac71a0e67d47341fb0cf39497d5f8 | refs/heads/master | 2021-01-17T10:49:54.755257 | 2020-08-29T21:24:08 | 2020-08-29T21:24:08 | 18,466,002 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,454 | rd | Normal_ct.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EnvNorm_ct.R
\name{Normal_ct}
\alias{Normal_ct}
\alias{pnorm_ct}
\alias{rnorm_ct}
\title{The Central Normal Distribution}
\usage{
pnorm_ct(a = -Inf, b = Inf, mu = 0, sigma = 1, log.p = TRUE, Diff = FALSE)
rnorm_ct(n, lgrt, lglt, mu = 0, sigm... |
75d754748ca6e1b3c918aed44989829dd24c9690 | 1c531619b82ab3ab8d50f554f9e1707bcd8f3dfc | /class scores.R | 7762c90f334134c9291b626998f26c058b5f0aa6 | [] | no_license | acehjy97/20180721 | 0ab5278f8cdd727e1df2d49120970aadecfba8d0 | 2c4371d08543ff23f622a5a5351591932afabf12 | refs/heads/master | 2020-03-23T16:15:58.924940 | 2018-08-04T09:19:09 | 2018-08-04T09:19:09 | 141,801,193 | 0 | 0 | null | null | null | null | UHC | R | false | false | 3,179 | r | class scores.R | library(rJava)
library(DBI)
library(RJDBC)
library(XML)
library(memoise)
library(KoNLP)
library(wordcloud)
library(dplyr)
library(ggplot2)
library(ggmap)
library(rvest)
library(RColorBrewer)
library(data.table)
library(reshape)
read.csv("class_scores.csv")
###################################... |
270d1b12289d4b708ce4160e1dd7f07343db3046 | af72407b36c1ee3182f3a86c3e73071b31456702 | /data-raw/ames.R | 4fc5ade4acd17e9fd1b2ac680d3048f0ad46174d | [
"MIT"
] | permissive | bcjaeger/ipa | f89746d499500e0c632b8ca2a03904054dc12065 | 2e4b80f28931b8ae6334d925ee8bf626b45afe89 | refs/heads/master | 2021-07-12T20:52:23.778632 | 2020-04-26T16:44:01 | 2020-04-26T16:44:01 | 207,016,384 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | ames.R |
## code to prepare `amesHousing` dataset goes here
set.seed(329)
# library(tidyverse)
# library(AmesHousing)
ames_complete <- ames_missing <- drop_na(make_ames())
rows <- sample(nrow(ames_complete), 1200)
for(i in rows){
cols <- sample(ncol(ames_complete)-1, size = ncol(ames_complete) - 5)
ames_missing[i, co... |
1193a1c2b1ce70c45e74207be77dab84b98d6f00 | 718f5c39e5749f259cfa3a64ac9fd7c314dd3408 | /Log log.R | 4056d246622321d202cfddf162322455830f6b94 | [] | no_license | Reinaldodos/Covid19 | 3be30953d482c18b7c53d320a26cc134728e8d7a | e791b7dd69559fe60070b5480ea7494815029b17 | refs/heads/master | 2023-03-02T22:33:37.553423 | 2021-02-18T11:51:01 | 2021-02-18T11:51:01 | 255,569,854 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 766 | r | Log log.R | TEST =
TEST %>%
group_by(country) %>%
mutate(Daily_cases = Number - lag(Number)) %>% ungroup %>%
drop_na(Daily_cases) %>%
filter(Daily_cases>0)
TEST %>%
filter(Daily_cases> 200) %>%
count(country) %>%
top_n(n = 12, wt = n) %>%
semi_join(x = TEST, by = "country") %>%
# filter(str_detect(string = c... |
eb2dd23442e249a96bcbc62e2dffeaa7fbcfdec1 | b3c5f225ac5fc4e3280591206bac374565c65a86 | /R/check_bw.R | bb4753183ef663670b3cb62684020831c6e31049 | [] | no_license | ClaudiaRHD/chipAnalyseR | 4d5c404b51130ab650383022801bb2c50eba3111 | 2e41b66048e3945f90d3ac30be22700d4157ba04 | refs/heads/master | 2021-04-15T10:09:31.490707 | 2020-06-15T09:41:37 | 2020-06-15T09:41:37 | 126,216,813 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,108 | r | check_bw.R | #' checks for bwtool binary
#' @description Takes inserted path to bwtool and checks if bwtool is already installed.
#' @param bw_path The path to directory where bwtool is installed on the computer. Default value is NULL.
#' @import ggplot2
#' @import data.table
#' @import cowplot
#check if bwtool is available on com... |
6cf119625a89160c73d08a8738d4297496fe8c10 | 4aae56c278bde19385a0e52ce75edc8bdd241740 | /man/multinomial_metrics.Rd | bc482f5f3762e96041b540b4236f8458708588e8 | [
"MIT"
] | permissive | LudvigOlsen/cvms | 627d216939203b7e6e1da6b68802d2208138b622 | 38dc4d5117d67d00c81fb05677b771d3b39a6c28 | refs/heads/master | 2023-07-05T08:33:44.190664 | 2023-06-30T04:06:31 | 2023-06-30T04:06:31 | 71,063,931 | 38 | 7 | NOASSERTION | 2022-09-23T10:57:12 | 2016-10-16T16:54:21 | R | UTF-8 | R | false | true | 4,697 | rd | multinomial_metrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/choosing_metrics_functions.R
\name{multinomial_metrics}
\alias{multinomial_metrics}
\title{Select metrics for multinomial evaluation}
\usage{
multinomial_metrics(
all = NULL,
overall_accuracy = NULL,
balanced_accuracy = NULL,
w_balanc... |
d71649acf3303ef445c0556985631de660bbfa55 | 5d820c8e7f4b458f6783a35d1022d4f6a8f8dc02 | /man/crop.Rd | c4a0a27e470a67fdb93881556b451b3003008c2a | [
"MIT"
] | permissive | UBC-MDS/image-compression-toolkit--R | 307a5968f1adda8638b6cfe0552d1b1ac60b9353 | d0879412ab8d24e621e8da4754392d4893660ba4 | refs/heads/master | 2020-04-21T19:25:07.974713 | 2019-03-06T05:09:25 | 2019-03-06T05:09:25 | 169,805,189 | 0 | 2 | NOASSERTION | 2019-03-06T05:09:26 | 2019-02-08T22:06:29 | R | UTF-8 | R | false | true | 616 | rd | crop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/func_crop.R
\name{crop}
\alias{crop}
\title{Crop images}
\usage{
crop(img_path, H, W, out_path)
}
\arguments{
\item{img_path}{---- String , file path of the image .}
\item{H}{---- Integer, the desired height of the cropped image}
\item{W}{-... |
d641a6c0ebbbce9892399b355fc3bc6f82dc09c3 | 1c9c5d98c6a1d4132762ed058f6b007bb5648afb | /Coursera Statistics Princeton/Stats1.13.HW.02.LAB.R | 1538610bcc7ebf78e3c450286b03dcdd50e979a9 | [
"MIT"
] | permissive | dmpe/R | 4475efefd5921551dde96ef19e971773b33d9451 | ba8cb71f54ec969073d2b64a6cfdbc9a6042d178 | refs/heads/master | 2022-08-14T07:26:06.512865 | 2021-04-17T11:03:40 | 2021-04-17T11:03:40 | 8,522,174 | 685 | 440 | MIT | 2021-04-17T11:03:41 | 2013-03-02T15:04:10 | R | UTF-8 | R | false | false | 2,449 | r | Stats1.13.HW.02.LAB.R |
library(psych)
setwd("C:/Users/Dima/Documents/R/coursera/")
file <- read.table("Stats1.13.HW.02.txt", header=T)
names(file)
dim(file) # number of rows
mean(file$SR)
var(file$SR)
subfile1 <- subset(file, file$time=="pre")
mean(subfile1$SR)
subfile2 <- subset(file, file$time=="post")
sd(subfile2$SR)... |
93be9995fe7290e933e39ef95010df69ac8cc78c | 155cfb7d883ad64a68c77185d41c9501ed8b91d8 | /Fig3_Figure_smoothed_LA_data_per_species.r | 41aa18613c9947a3cb3fb7472119d76212a0bad2 | [
"MIT"
] | permissive | nielsjdewinter/Sr_spiking | 7db1ad2602491bbd8603037bcb5a1dc0e3beaffc | 9a43c00646bf2e517cb80fe9d24bccf47e0900d1 | refs/heads/main | 2023-04-14T19:47:50.192420 | 2023-03-21T08:36:50 | 2023-03-21T08:36:50 | 559,837,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,796 | r | Fig3_Figure_smoothed_LA_data_per_species.r | # Plot smothed Sr profiles for Figure 1
# Project "LAICPMS_Sr_spiking"
require(tidyverse)
require(RColorBrewer)
require(readxl)
require(ggpubr)
# Load data - Batch 1
Cedule_G003_1 <- read.csv("Batch1/HR/Cedule_003_1.csv", header = TRUE)[-c(1, 2), ]
Cedule_G003_2 <- read.csv("Batch1/HR/Cedule_003_2.csv", hea... |
269afecf3cac76201afd2afd239ec12503e13f37 | b32192ef5ee4869289ead0cac00702906bb4b488 | /src/utsg2021/getSocio.R | 2a3831d037c331c26ab62c6b28df5fdab7e48054 | [
"MIT"
] | permissive | 3rfm-its-davis/covid-19-ldt | 10fe23996512a38de01813abdd82f6a77070ded9 | 11367f772054798b182de49ccdca558b7ca18264 | refs/heads/master | 2022-12-20T09:40:23.217388 | 2020-09-20T10:52:26 | 2020-09-20T10:52:26 | 275,208,352 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,344 | r | getSocio.R | getSocio <- function(raw, y) {
socio <- raw %>%
inner_join(y, by = "ResponseId") %>%
select(c(
"ResponseId", "City", "B02_5", "D01",
"D09_2", "C07", "C12_3", "C12_13", "C13_3",
"I01_1", "I04_2", "I04_3", "I04_1", "I04_5",
"I04_4", "I05", "I09_USD", "I07", "I12"
)) %>%
... |
cef7ad4f0acd315163d6db2ca47f03d907b01c0f | 2e27d0ee5455c14be50ac40871cbb9538b29b8d1 | /R/sphere.smooth.R | 58b63dff88b387a9dfca58816e7cdc3aed64f483 | [] | no_license | antiphon/sphere | 66975fa754559dc61f4bfa0aa51e4757f22353b2 | e6e3b7e5e31741503718da18d102695f277258a3 | refs/heads/master | 2022-05-06T13:33:35.720737 | 2022-03-25T05:37:34 | 2022-03-25T05:37:34 | 29,916,065 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,220 | r | sphere.smooth.R | #' Smoothed unit sphere values
#'
#' Krige on a sphere, return a plottable orb.
#'
#' @param latlon latitude-longitude of data
#' @param v values on data points
#' @param N refinenement of the icosahedron triangulation
#' @param s smoothing sd for gaussian smoothing
#'
#' @import rgl
#' @export
sphere.smooth <- fu... |
c3d09b90f1dd8913a1d417b2b76f2222de6d0d3a | fb0cbf6db81ee5ff6dfe73b618e2d649251b8b77 | /Model1.R | 1a19db5090f166db08a2f519145625b356c292e2 | [] | no_license | PGC-PTSD-EWAS/PGC-PTSD-Longitudinal-Analysis | 6a5841a9a2a33388959851e555a608315611b677 | 8ff7f5832bccac3343f5fefc3ee1d408885edeee | refs/heads/main | 2023-08-12T00:50:22.291579 | 2021-10-11T14:49:57 | 2021-10-11T14:49:57 | 416,046,475 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,674 | r | Model1.R | ################################################################################
# Model 1: RE model to evaluate CpGs associated with PTSS in each cohort
################################################################################
library(lme4)
library(lmerTest)
library(data.table)
# Load methylation data
beta.no... |
c265d727f2d15a84fc01b8c9811e4ef91bd6b76d | f697336c25ca6fadd13c8746309f3d1b47d13864 | /man/validateIcon.Rd | da76da12c5f002c963b470e63df5c4340ceae6b2 | [] | no_license | cran/shiny.pwa | c1116d1086192c064789e3cda570206be92e1268 | d15ae361ec8be31278ec92d6b7282a6d06d17e7c | refs/heads/master | 2023-05-31T08:19:17.120970 | 2021-06-19T15:50:02 | 2021-06-19T15:50:02 | 298,777,337 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 637 | rd | validateIcon.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/validators.R
\name{validateIcon}
\alias{validateIcon}
\title{Validates the provided icon. If the icon does not exist returns a default one.}
\usage{
validateIcon(icon)
}
\arguments{
\item{icon}{Path location for an icon relative to t... |
0f0586aab28f60679599806ec5e3d55c0bbf8cea | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/iccbeta/examples/Hofmann.Rd.R | 403c16c7fc7aa655a2787c484e365afdb2e12e45 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,482 | r | Hofmann.Rd.R | library(iccbeta)
### Name: Hofmann
### Title: A multilevel dataset from Hofmann, Griffin, and Gavin (2000).
### Aliases: Hofmann
### Keywords: datasets
### ** Examples
## Not run:
##D
##D if(requireNamespace("lme4") && requireNamespace("RLRsim")){
##D data(Hofmann)
##D library("lme4")
##D
##D # Random-Intercept... |
242d38a87b2456fca802d0bffb11124b8a01c425 | db2b9f7e9ae8019b3ad7f9c154ac38389c129bd8 | /aargh/dataprocessing.R | f50d7ee20def4d08f26428c20acb54c31bfb8ee5 | [] | no_license | borealbirds/foam | bc70fb46efb744d202eddc6804f086e82766b1de | 9b30065e500156372767bc66ea5d7c59d86bc5ca | refs/heads/master | 2021-09-04T10:10:20.055552 | 2018-01-17T21:24:27 | 2018-01-17T21:24:27 | 114,273,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,624 | r | dataprocessing.R | ##---
##title: "Data processing for nationam BAM analyses"
##author: "Peter Solymos"
##date: "Apr 18, 2016"
##output:
## pdf_document:
## toc: true
## toc_depth: 2
##---
### Preliminaries start here
## Define root folder where data are stored
ROOT <- "c:/bam/May2015"
ROOT2 <- "e:/peter/bam/Apr2016"
## Load re... |
7912041bec3dd5b4fc230887ea4aaae6f414b69a | 011e1a0d512282aca1397335d1be5db1ae75de5b | /somPlot/plotUMatrix.R | 22a7580707e88b9f108292f829330144d1c2911e | [] | no_license | spmunc/TrumpNN | 6df78a3bda1e08e5d542b2c236f803d7777caf5c | 33b3c3f7ea66d0e478ea10b0509ca834c0c94665 | refs/heads/master | 2021-01-21T13:14:20.872935 | 2016-04-23T20:53:18 | 2016-04-23T20:53:18 | 54,686,068 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,670 | r | plotUMatrix.R | ##########################################
##PLOT HEXAGONAL U-MATRIX
##from "kohnonen" library output
##
##
##BY SETH E. SPIELMAN, UNIVERSITY OF COLORADO
##
##NEEDS SOME LOVE.
##RUDIMENTRARY BUT FUNCTIONAL
##
##BORROWS CODE FROM
##http://nbremer.blogspot.nl/2013/11/how-to-create-hexagonal-heatmap-in-r.html
#########... |
2838e0a3b514ff1140270e92d73aba62c5ad793b | 78255ea5630895b338fd588752b66a2e8a157702 | /R/getters.R | 069ec70029886abddc749d8a1276a9e8a7b90cd8 | [] | permissive | Illumina/happyR | d2164760d2bb5b30b01c3464f0ecea203baeef3c | 97d093a8b00d6f631e76cfac3411c29db0cd7044 | refs/heads/master | 2021-01-18T09:55:50.592615 | 2019-07-12T12:02:04 | 2019-07-12T12:02:04 | 100,359,406 | 16 | 2 | BSD-3-Clause | 2019-07-12T12:02:05 | 2017-08-15T09:01:57 | R | UTF-8 | R | false | false | 7,052 | r | getters.R | #' Extract hap.py Precision-Recall data
#'
#' Simpler interface to retrieve a data.frame
#' of PR metrics from a happy_result object.
#'
#' @param happy_result a happy result loaded
#' via \code{\link[happyR]{read_happy}}
#' @param var_type subset for either insertions
#' and deletions \code{"indel"}, SNVs \code{"s... |
17e8e22764312dd6ceafae11394e98a7f738a651 | d06f4860f0815281085689b706a923740476b386 | /_site/landing/code/newsletter/src/datacamp.R | 42a137d349f8d9576f48e59a9c1c308a29ce3eb6 | [
"Apache-2.0"
] | permissive | jacobgreen4477/withmakers.github.io | b94d3a9e6247e161328224761047ad3478f4e436 | 28d3b68572b195f9bc84a44f32d31228dd31a16b | refs/heads/master | 2022-01-08T21:31:08.236198 | 2019-05-15T11:55:35 | 2019-05-15T11:55:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,323 | r | datacamp.R | # title : newsletter
# author : Hyunseo Kim
# depends : rvest, dplyr, stringr, data.table, R2HTML, NLP, openNLP
# datacamp blog ----
# Get the time
baseHTML <- get_baseHTML("https://www.datacamp.com/community/blog")
Sys.sleep(3)
# Get the title
timeHTML <- get_time(baseHTML, '.jsx-566588255 a .date')
# 일자 변수 변환: f... |
b5957db01a9f93fa1bbfce5341c0d1b395e3d765 | 8c26f6153cbaec6957389cd7e659def3c468e10e | /week37-friends.R | a06e735dc5f823fff116de46b815209263efaac9 | [] | no_license | TrevorKMDay/TidyTuesday | 59be20bf67772617b73b470caed0bdacd7761310 | 93ff776ba797f1073f11fde26ed8169939a68121 | refs/heads/master | 2022-12-21T21:05:23.980437 | 2020-09-22T14:31:44 | 2020-09-22T14:31:44 | 297,407,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,356 | r | week37-friends.R | library(tidyverse)
library(corrplot)
library(viridis)
# Load data and separate into semantically named dfs
friends <- tidytuesdayR::tt_load('2020-09-08')
friends_info <- friends[[1]]
friends_dirs <- friends[[2]]
friends_emot <- friends[[3]]
# Join emotion info with who said it
lines_emot <- right_join(friends_info, f... |
c721b1bbd304b3289b143693000fdb8e957bf071 | 1a03b5b0240dae14b110848d33f7da34245903db | /man/ml_predict.Rd | 9796128b1f6e2536820a7680d989f30b4308e500 | [] | no_license | skranz/mlogitExtras | 1c3dfacb04b870243c78d0d5796b3919fdb256bf | 62bb14fed7a1302888d699be9ea489ed8ab32746 | refs/heads/master | 2023-07-08T14:40:47.504079 | 2023-06-26T06:47:31 | 2023-06-26T06:47:31 | 269,279,209 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,179 | rd | ml_predict.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.R
\name{ml_predict}
\alias{ml_predict}
\title{Alternative predict function for mlogit objects}
\usage{
ml_predict(mod, newdata, num.draws = 1000, use.halton = TRUE)
}
\arguments{
\item{mod}{An estimated mlogit model}
\item{newdata}{A... |
f76418c682f03ea46888f1b2c8b15366708a6cae | 06888de22ecff4d48a621c778aa35b18a28c32f1 | /R/bedtools.R | b5b61112d854b1b8bc7134f3603e308cd92c25c9 | [
"MIT"
] | permissive | joelnitta/baitfindR | fcb9db8bc978a6004e988003a5b1fb4675f84162 | de73d9451115ad143da249e5ef2146bb929cd17b | refs/heads/master | 2021-07-20T14:16:15.081289 | 2020-04-29T20:04:47 | 2020-04-29T20:04:47 | 140,532,881 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,128 | r | bedtools.R | # bedtools functions ------------------------------------------------------
#' Extract regions from a fasta file
#'
#' Wrapper for bedtools getfasta.
#'
#' @param bed_file Path to bed file with locations of regions to extract.
#' bed file is a tab-separated file with columns for chromosome (e.g., chr1),
#' start posit... |
82c59c5fe2484138e581f45a8368c63f8103bd8d | e508870d7b82ca065aff9b7bf33bc34d5a6c0c1c | /pkg/man/listProduct.Rd | 3ebc402520b1b0eae3006db6765831e9d5fdab1f | [] | no_license | Dong-po/SoilR-exp | 596be0e6c5d291f00c6e08c348952ee23803e15e | c10d34e035deac8af4912c55382012dfc247eedc | refs/heads/master | 2021-09-03T11:12:49.199268 | 2018-01-08T15:52:17 | 2018-01-08T15:52:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 566 | rd | listProduct.Rd | \name{listProduct}
\alias{listProduct}
\usage{listProduct(...)}
\examples{listProduct(list('a','b'),list(1,2))}
\arguments{
\item{...}{lists}
}
\description{Creates a list of all combinations of the elements of the inputlists
(like a "tensor product list " The list elements can be of any class.
The function is used in ... |
33956c49db6741f7bd71bb226f7c7a8970af0f45 | cef4d8774f2eb276a8212ce460fb546de6122a91 | /Rplot5.R | 8d54ebd70fec3619cc34a2def413b63aa45911b0 | [] | no_license | universaljames/PM2.5-in-US | e085406a3bc5a05d0e0e1b9f02cdcfdc5c947d70 | ac08cea08cebefde38f1c62a6949b7b21d75cdfc | refs/heads/master | 2022-10-26T09:32:48.127894 | 2020-06-15T05:04:28 | 2020-06-15T05:04:28 | 272,344,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,138 | r | Rplot5.R | #Data Preparation
DataFile <- "NEI_data.zip"
DataFileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(url=DataFileURL,destfile=DataFileFile,method="curl")
unzip(DataFile)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
colToFactor <- c("yea... |
77c9ef64627876f3b9a1edfe3f6b1dd150a0a324 | 1203cc14b7416390beb8149c3a75c28c9177a681 | /scripts/monthly report text 5_18.R | 3347b99a56c2f5a34de613fec4257e9478ee6e33 | [] | no_license | AZASRS/DB_R_Testing_Environment | 7801fc9bcf078be259ed8ef80335729623bdb1dd | bf65706a2244dc67e63cd25382488d2219756b11 | refs/heads/master | 2020-03-20T06:49:08.414225 | 2018-07-27T00:31:20 | 2018-07-27T00:31:20 | 137,261,803 | 0 | 1 | null | 2018-07-16T22:23:26 | 2018-06-13T19:33:14 | R | UTF-8 | R | false | false | 11,744 | r | monthly report text 5_18.R | #monthly report
## @knitr setup
setwd("P:/IMD/Karl/R projects/private investment performance")
load('pmedata.rdata')
source('../basic financial.r')
require(xtable)
require(zoo)
require(lubridate)
# get values for valdate and lastcfdate
valdf=read.csv('tfundvals.csv')
valdf$dates=as.Date(valdf$dates, format='%... |
e6a529edee5ad54e133f0b522650b43f7eddc826 | 44cf65e7ab4c487535d8ba91086b66b0b9523af6 | /data/Newspapers/2001.11.22.editorial.68247.0774.r | 58577a32b40697cacc8c4d959abfc6e8ae8d8d33 | [] | no_license | narcis96/decrypting-alpha | f14a746ca47088ec3182d610bfb68d0d4d3b504e | 5c665107017922d0f74106c13d097bfca0516e66 | refs/heads/master | 2021-08-22T07:27:31.764027 | 2017-11-29T12:00:20 | 2017-11-29T12:00:20 | 111,142,761 | 0 | 1 | null | null | null | null | MacCentralEurope | R | false | false | 3,466 | r | 2001.11.22.editorial.68247.0774.r | uluitor progres !
americanii si tarile membre ale Uniunii Europene ne vor bombarda cu telegrame si scrisori de felicitare .
Salutul lor cordial va deveni el insusi un eveniment politico - mediatic .
celebrul Gioni Popescu , zis eminenta cenusie a Serviciului Roman de Informatii , zis Gioni Descurcaretuí , a fost pro... |
ab7f0153fca08c3381ee41336d37a5cd0220e8e6 | ced1997e24a3100493ed8b4bdd116a19f101ffd7 | /analyse-simulation-15-08.R | 85bd762e63e6a28e3ff232fd08fccf1cdc8b6ee2 | [] | no_license | tobiriebe/analyse | bb2db1c43d589d1da25fcb93032d0136454fcd6b | 41ea6ab06f8f6f2fc3b258a6b772274ed5f0159f | refs/heads/master | 2020-04-06T06:56:12.088598 | 2016-09-05T13:32:58 | 2016-09-05T13:32:58 | 65,828,973 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,465 | r | analyse-simulation-15-08.R |
analyseSimulation <- function(dataFile) {
library(pROC)
library(Rmisc)
data <- dataFile #save data file for later functions appearing
Y <- dataFile$Y
Ynoise <- dataFile$Ynoise
yBin <- dataFile$yBin
yBinNoise <- dataFile$yBinNoise
originalX <- dataFile... |
69e77210610c1a76aeef766f5d70cea258352054 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /pcIRT/R/LRT.MPRM.R | 3bc3eafd93db1fbc77e384e3730ae63a0a42764e | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,119 | r | LRT.MPRM.R | #'@rdname lrt
#'@method LRT MPRM
#'@export
LRT.MPRM <-
function(object, splitcrit="score", ...){
if(is.character(splitcrit) && splitcrit == "score"){
sc <- rowSums(object$data)
scm <- ifelse(sc > median(sc), 1,0)
}
else{
if(!is.vector(splitcrit)){stop("Error: split criterium has to be a vector!",... |
a2f470ed1e23a9fe5801bf834bf350a537963957 | a53b238211a2229b99941d5c9ced8b5dd42aa098 | /man/getNOAAGuages.Rd | ef5127ca7476ea49609823f6b0eeca7e7d0bfa04 | [
"CC0-1.0"
] | permissive | JerryHMartin/waterDataSupport | ca2695806cebc91f674ca37937d47ccc3bd3af76 | 5260b296294252f01c711a538eaff53eb869eb0e | refs/heads/master | 2023-01-06T21:32:31.788682 | 2023-01-05T15:47:11 | 2023-01-05T15:47:11 | 142,612,276 | 0 | 0 | null | 2020-07-14T18:27:11 | 2018-07-27T18:41:02 | R | UTF-8 | R | false | true | 1,124 | rd | getNOAAGuages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getNOAAGuages.R
\name{getNOAAGuages}
\alias{getNOAAGuages}
\title{getNOAAGuages}
\usage{
getNOAAGuages(
siteID,
plotmap = TRUE,
zoomFactor = 10,
limitToWatershed = TRUE,
getElevations = TRUE,
leafletmap = NULL
)
}
\... |
dd13d140d5b064301632eebafa3c0c9a325476a0 | 9b9af459e4837a5d15c693c4ad0935be645a5667 | /cachematrix.R | 695acab0cca753d0014b3cf0fc6c3162d904804f | [] | no_license | billcary/ProgrammingAssignment2 | 0a92f8edd021eb07b51b0c0ea1e5654076d6a51d | 5b732476eb3ccd374589a536a3fc6f6cf0efdc79 | refs/heads/master | 2020-12-25T12:17:25.306531 | 2014-07-23T02:48:29 | 2014-07-23T02:48:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,966 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## This function creates a special "matrix" object
## that can cache its inverse. It provides methods
## to set and get the original matrix, as well as
## methods to set and get the inverse of the matrix.
makeCacheMatrix <- function(mt... |
712e001b85c89e27462e388a88a901c3095efc8f | 728315d8c5d09e13c67641030b92d59c5e7e2222 | /easy/split_the_number.r | 7e146b753d1a8e2d4fbb2064783c2042a0709049 | [
"MIT"
] | permissive | shortthirdman/code-eval-challenges | 88ea93c0e9385b2a0db95a05b1f3f753c900a62d | cf2197927830326539399fdd3e16c9b8a4468f7d | refs/heads/master | 2023-03-06T19:34:44.607154 | 2023-02-26T13:30:56 | 2023-02-26T13:30:56 | 92,970,178 | 4 | 0 | MIT | 2023-02-26T13:30:57 | 2017-05-31T17:14:59 | Go | UTF-8 | R | false | false | 443 | r | split_the_number.r | splitnum <- function(s) {
r <- 0
v <- 0
o <- 1
d <- 1
for (i in 1:nchar(s[2])) {
c <- substr(s[2], i, i)
if (c == '+') {
r <- r + v*o
o <- 1
v <- 0
} else if (c == '-') {
r <- r + v*o
o <- -1
v <- 0
} else {
v <- v*10 + as.integer(substr(s[1], d, d))
... |
23f6b1accf2bd39f23e761114905feb69973c6da | 2c6bdee82bc3df0a9ddf65e55e2dc4019bd22521 | /SimpleRandom/server.R | ee1895c814979b1a4d5993c6d3528b242d3892ec | [] | no_license | homerhanumat/shinyGC | 0eb1b55bcc8373385ea1091c36ccc1d577dc72fb | dc580961877af2459db8a69f09d539f37fd6e2ee | refs/heads/master | 2021-07-11T18:20:53.862578 | 2021-06-21T19:18:32 | 2021-06-21T19:18:32 | 37,830,825 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,490 | r | server.R | library(shiny, quietly = TRUE)
library(scales, quietly = TRUE)
library(dplyr, quietly = TRUE)
library(ggplot2, quietly = TRUE)
library(tigerstats, quietly=TRUE)
function(input, output) {
rv <- reactiveValues(
newVar = TRUE,
variable = "income",
factor = FALSE,
psData = NULL,
yMax = NULL
)
... |
d06411bcbdb200ceafe733963a6262a32b3f397e | b84d89b3f67fbd57e2d41f42c23c1f82fe7ba9fd | /R/fitted.TEfitAll.R | bc09bd16ba24957b992f9d769deea9fd31dbb1f2 | [
"MIT"
] | permissive | akcochrane/TEfits | 04305849bd8393c9e816312085a228ccdbd621e3 | e11b07b2d9fed9eb6e8221c8cfdd86b9e287180e | refs/heads/master | 2023-06-08T00:04:21.025346 | 2023-06-07T22:20:11 | 2023-06-07T22:20:11 | 225,967,950 | 1 | 0 | MIT | 2023-06-07T22:20:13 | 2019-12-04T22:22:41 | HTML | UTF-8 | R | false | false | 1,163 | r | fitted.TEfitAll.R |
#' Get fitted values and summary statistics from a set of TEfit models
#'
#' @param TEs3s A set of models fit by TEfitAll()
#'
#' @method fitted TEfitAll
#' @export
#'
#' @examples
#' \dontrun{
#' m <- TEfitAll(anstrain[,c('acc','trialNum')],groupingVar = anstrain$subID)
#' fitted_data <- fitted(m)
#' plot(fitted_dat... |
3b17042127610e4c1a4b8711b1428ff3edd46a49 | 63e5fc70d2e6233457fc9ad407d7e4984bfc8997 | /man/plot_hisafe_voxels.Rd | 9719889d54e242ab28a4c3070521efc8d412f520 | [] | no_license | Boffiro/hisafer | 32f648f6aca222d01006d25da1b237846b13113e | 8773fe3d5d2aa6d307af0088a6f6e79cc9a087d0 | refs/heads/master | 2023-05-06T17:53:22.060974 | 2020-10-16T09:48:48 | 2020-10-16T09:48:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,829 | rd | plot_hisafe_voxels.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot_hisafe_voxels}
\alias{plot_hisafe_voxels}
\title{Tile plot of Hi-sAFe voxels output variable}
\usage{
plot_hisafe_voxels(
hop,
variable,
date.min = NA,
date.max = NA,
simu.names = "all",
X = NA,
Y = NA,
Z = N... |
bb6107d4f95c04c9a2d6dc2b99b5c04e323f52c1 | dd23f7848ee431d060d56af188a88d0dedaa0522 | /3-exploratory-data-analysis/week4/plot1.R | bc82a7969d1c4a442ef4f0033e0cd8be775a1b76 | [] | no_license | ryancey1/datascience-coursera | 3d0b33d57dbb738399cfe09aba41230ea065318e | d71ae50686a3e00185bcf1debcb3e415692c58ff | refs/heads/main | 2023-03-11T07:51:27.275516 | 2021-02-20T23:14:54 | 2021-02-20T23:14:54 | 323,975,435 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,346 | r | plot1.R | # plot1.R -----------------------------------------------------------------
# 1. Have total emissions from PM2.5 decreased in the United States from 1999 to
# 2008? Using the `base` plotting system, make a plot showing the total PM2.5
# emission from all sources for each of the years 1999, 2002, 2005, and 2008.
# HOUS... |
367d37293be18766da9d706edbfa86df875adcf6 | 5500882cda0a3b4af35d647acc4c36e3d8e5ba18 | /hw3/hw3_code.R | 7682c6ced0397cfc0ee3512c22b063331074fbec | [] | no_license | pinesol/texas | 85b1fe1f5ffed386eb1fe3053d9649ca1b9ef442 | cee029dd32bc21689f8d40063bffc7f1f3690efa | refs/heads/master | 2021-01-21T13:33:39.735392 | 2016-05-11T02:07:06 | 2016-05-11T02:07:06 | 52,619,333 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,004 | r | hw3_code.R | #install.packages("lda")
#install.packages("topicmodels")
#install.packages("stm")
#install.packages("LDAvis")
# TODO save worksave so you can load it in the markdown file
require(quanteda, warn.conflicts = FALSE, quietly = TRUE)
library(topicmodels)
data(immigNewsCorpus, package = "quantedaData")
# Get 4 most comm... |
4ebcac1b25a286f51c94e1b1bf884617eedb3d4e | 3682cbe55c4d8f6f8ec1cbef4f40d7f9d20734cb | /shinyAppProject1/server.R | 0d1260a7b52716220ad44638b1fd8c21175691f9 | [] | no_license | jeremiahkramer/WWTSLEDWestInternProject | f30c0d1d957733401a30c5856360e28e9d4fba16 | 0937f834196e81f17e9b671114ec5556e0dc70ec | refs/heads/master | 2020-06-22T06:15:11.064774 | 2019-08-29T20:52:01 | 2019-08-29T20:52:01 | 197,654,888 | 0 | 1 | null | 2019-08-05T19:21:28 | 2019-07-18T20:49:31 | Python | UTF-8 | R | false | false | 2,076 | r | server.R | library(shiny)
library(ggplot2)
library(DT)
function(input, output) {
#create pie chart output
output$chart1 <- renderPlotly({
#handle if all P&L selected
if(input$`P&L1` == "All" && input$AM == "none"){
#prepare dataframe for plot_ly
chart_data <- data.frame(
"Category" = unique(dat... |
1295c5a4acbcef98e05464ebaf7f96ff35d597a1 | e077f629946716c73ff9a53d82e3c8006c6dec2b | /man/neuromorpho_field_entries.Rd | f51879a9974f2bb0207e06b2867f2fb09e351931 | [] | no_license | natverse/neuromorphr | 8c8a006b51b560ed686ff6d4513dda267cefe55e | 7f23e7861bcdea1206d3d43d43bd2f08667f69b9 | refs/heads/master | 2023-05-03T19:42:50.918806 | 2023-04-19T00:23:54 | 2023-04-19T00:23:54 | 184,127,352 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,116 | rd | neuromorpho_field_entries.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/search.R
\name{neuromorpho_field_entries}
\alias{neuromorpho_field_entries}
\title{Return the available meta data entries for neuromorpho neuron fields}
\usage{
neuromorpho_field_entries(
field = "species",
neuromorpho_url = "http://neuro... |
3f6e5442fbbbd09caf0fe93e2c86b38b58fbaca3 | 0abc4ff64360255655b9f304716ad8122e18685a | /R/models/LinearRegression.R | 4c6b727cfa9e30d6124467d9b66344bdf0aaa940 | [] | no_license | rodrigoqaz/tcc-mba-fatec | 01ceadded6c8e794fcc600779d3ecec087d1402e | df37f28526b8218e05b04d2ff55244a319059e92 | refs/heads/master | 2023-01-14T14:20:17.984056 | 2020-11-19T13:25:00 | 2020-11-19T13:25:00 | 297,507,152 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 764 | r | LinearRegression.R | # Linear Regression:
library(tidyverse)
library(tidymodels)
LinearRegression <- function(train, test) {
set.seed(4242)
# Cria a receita:
recipe <- train %>%
recipe(Y~.) %>%
update_role(day, new_role = "id variable") %>%
step_corr(all_predictors()) %>%
step_center(all_predictors(), -all_outc... |
937e9b0f856bec1cfd644d6cdde19a737fd5c3da | 577cecbf31ea2ea0e82851d6f1555853e3f4f9f1 | /Fig4B_plot_insulation_Sox9.R | 3c7c8081f6c358cf496a344d86058606128d1a97 | [] | no_license | bianlab-hub/Chen_Sci_Adv_2022 | a5972fc6e5b0e6711d739b74e51ded7b9c0125de | 2072fc1e81a6d576de42de4e2d047d7b7e391b72 | refs/heads/main | 2023-04-13T00:27:23.818781 | 2023-02-28T10:18:36 | 2023-02-28T10:18:36 | 547,825,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,074 | r | Fig4B_plot_insulation_Sox9.R | library(dplyr)
library(ggplot2)
MC_insulation <- read.csv('MC_HiC_combined_50kb_insulation', sep='\t', header=T)
MC_insulation_chr11<-dplyr::filter(MC_insulation,chrom == 'chr11')
FL_insulation <- read.csv('FL_HiC_combined_50kb_insulation', sep='\t', header=T)
FL_insulation_chr11<-dplyr::filter(FL_insulation,chrom... |
966bde1c73208ff6ad702d4f0255d048a7669c57 | c445257a86a6ce0104edd866bd6a1cd1a776a695 | /testing.R | ccd8cbd6016f39d218da8f5ac8f0789ab06a1221 | [] | no_license | bweiher/recallR | 969b67c5a060f5ea341723f2a759afaceba9b77b | d0f767a7ed3b15d3207c7f94f94f208249674266 | refs/heads/master | 2020-03-16T13:31:28.374522 | 2018-10-11T02:57:02 | 2018-10-11T02:57:02 | 132,692,782 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,022 | r | testing.R | library(glue)
installed_packages <- as.character(as.data.frame(installed.packages())$Package)
installed_packages_p <- paste(installed_packages, collapse = ",")
file <- ".Rprofile"
pat <- "#libs:"
# TODO decide about methodology ,,, do we wanna
# TODO do a check if its already been written to Rprofile. ~ DELTE In th... |
1ffa9821eadc9db422f2ddd5c2a95e7452c2cf64 | c12ddf66b4ce31aec34d8fdd99c99fea12f36a81 | /tests/testthat/helper-versioning.R | 8223826c78adcb4b90a18c80a73261ddf68c04a2 | [] | no_license | r-lib/oldie | ac49efbbde20e19d5a81961e74f27e1a4a304c81 | 36d18ec67997f748ee88391cf8b13da6a7a67a5e | refs/heads/master | 2021-01-20T05:19:30.070679 | 2019-07-25T12:23:56 | 2019-07-25T12:24:12 | 101,428,392 | 18 | 4 | null | 2018-06-18T11:14:32 | 2017-08-25T17:48:09 | R | UTF-8 | R | false | false | 385 | r | helper-versioning.R |
future_rlang_ver <- function() {
version <- pkg_ver("rlang")
version <- ver_trim(version, 3)
version <- ver_bump(version, "minor")
version[[3]] <- 0
version
}
past_rlang_ver <- function() {
version <- pkg_ver("rlang")
version <- ver_trim(version, 3)
if (version[[3]] == 0) {
version <- unbump(ver... |
e0e9b22723fac1cb5ec9883d36dba4f85d8aeec0 | ebbfc719a132c3716fedfa84942c600563f78392 | /man/writeGhcn.Rd | 4e819260178c2aedcb6f4bec5b4e487b414b8281 | [] | no_license | cran/CHCN | 48f859ae33d6d6edb8d72841762cf8451fcff21f | 270b2dc2ebd78033ed0eccbc807d417e64671879 | refs/heads/master | 2021-01-23T02:30:16.880022 | 2012-06-07T00:00:00 | 2012-06-07T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 689 | rd | writeGhcn.Rd | \name{writeGhcn}
\alias{writeGhcn}
\title{A simple wrapper to \code{write.table}
}
\description{Simply writes a file to the data directory using
\code{write.table}
}
\usage{
writeGhcn(data, directory = DATA.DIRECTORY, filename = "TaveCHCN.dat")
}
\arguments{
\item{data}{The data you want to w... |
6f46476065fa4301455563118aff56af9dc40817 | ddc2b096e681398f576a95e40c7fd366b65f50a2 | /SDPSimulations/PrevalenceRegressions.R | 588be5327cd89d7272b5882c34680ea4de401e25 | [] | no_license | sbellan61/SDPSimulations | f334d96743c90d657045a673fbff309106e45fce | cfc80b116beafabe3e3aed99429fb03d58dc85db | refs/heads/master | 2021-03-27T20:48:25.857117 | 2017-09-19T20:13:37 | 2017-09-19T20:13:37 | 21,144,447 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,258 | r | PrevalenceRegressions.R | ####################################################################################################
## Plot HIV prevalence & SDP vs fit transmission coefficients, and fit contact mixing coefficients.
rm(list=ls()) # clear workspace
library(metatest);library(coda);library(faraway); library(hie... |
c7dbf01a5c3ed94ef37d14ecb2ac967420135c28 | 765e74a96d7a0a7d56c2cf056350ccf1265ee9f5 | /R/cub_dist.R | 4fa533b98f72163dc97396078e504b0866fa7fec | [] | no_license | fhernanb/cubm | a2a480af9a5516f6d6fc0dfe13fb553c705907bd | 1c88e80f68e8ab21a681ecfd0d551e065de30f30 | refs/heads/master | 2021-07-02T06:53:45.658112 | 2020-12-07T21:58:35 | 2020-12-07T21:58:35 | 74,044,405 | 4 | 7 | null | 2016-12-02T15:56:17 | 2016-11-17T16:14:06 | R | UTF-8 | R | false | false | 4,253 | r | cub_dist.R | #' cub distribution
#'
#' Density, distribution function, quantile function and random generation for the cub distribution given parameters pi and xi.
#'
#' @param x,q vector of quantiles.
#' @param p vector of probabilities.
#' @param pi uncertainty parameter belongs to \code{(0, 1]}.
#' @param xi feeling parameter ... |
40cb279c336514a19f1d9a393720178662cd724a | 3d299999dd13ccfc9353379d5a34111364cf5a81 | /R/main.R | b5337d861bf7e9931bf45a885d0e55d3e725ee8b | [] | no_license | shizelong1985/networkpanel | 08777aa5b0549e873c1aa770bf0ea2fc30679ade | 7aa7f68e0669369ee792f69392172abe459442b3 | refs/heads/master | 2023-03-17T06:54:14.324282 | 2020-06-19T08:09:45 | 2020-06-19T08:09:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,597 | r | main.R | #' Distance matrix normalization
#'
#' This function normalize the distance matrix
#' @param dist The original distance matrix
#' @keywords distance matrix
#' @export
#' @examples
dist_normalize <- function(dist) {
distNorm = matrix(0, nrow(dist), ncol(dist))
d = dist[upper.tri(dist)]
d = rescale(... |
036f2b5370a351fff28145bb2dc2ee390537f5cd | 501e69463cc39cbb331a251b16c25ac4f4855caa | /man/vcov.Rd | 62f1fec494d71b8d5130492699e379fe8c935817 | [] | no_license | cran/mexhaz | 22b3040da11a6cf5bcc16b957d3e3cd95e87d476 | 55e138836e58bbb04ec05eb5848afe60d43bab90 | refs/heads/master | 2022-11-10T11:59:07.330374 | 2022-10-31T13:47:48 | 2022-10-31T13:47:48 | 58,455,607 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 694 | rd | vcov.Rd | \name{vcov}
\alias{vcov}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Method for extracting the covariance matrix}
\description{
This is a generic function.}
\usage{
vcov(object, ...)}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{a fitted o... |
3bd1fac85bfd220876b43e558ce5797c8ad908d6 | dc75a9160840901a4f6252c05e307bee4178c78b | /R/Chunk.R | a425179dff25393afa853093060c201ea80a4ee1 | [] | no_license | lewinfox/balance-tracker | 5c1b05324e87f8fd82503fb9fd7041e9ea76565d | 6beec7a3476773a3ecf4563fae8d28e3b478d510 | refs/heads/master | 2020-07-02T22:14:26.306996 | 2019-08-10T21:46:49 | 2019-08-10T21:46:49 | 201,684,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,416 | r | Chunk.R | #' A single "lump" of cash
#'
#' A Chunk is the result of a single payment into an account. Money can be
#' debited from a Chunk but not credited to it. The value of a Chunk cannot fall
#' below zero - if this happens the Chunk is destroyed.
Chunk <- R6::R6Class(
classname = "Chunk",
public = list(
value = 0,
... |
8793851aa9383e225af3b2fdb2de19416610d246 | a1502451963856d226e8523097e89407b6ec85bc | /run_analysis.R | 7875dae4082d8fe5aa43bbbe52f9a6d9db00154f | [] | no_license | Rowena752/Getting-and-Cleaning-Data-Course-Project | d0e224115923a346909a997b10c30aac9a51988f | b87a6649b97a9547e4f2d8df935aac083c4ca518 | refs/heads/master | 2020-05-25T02:00:03.160311 | 2017-03-14T22:30:52 | 2017-03-14T22:30:52 | 84,900,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,761 | r | run_analysis.R | packages <- c("data.table", "reshape2")
sapply(packages, require, character.only=TRUE, quietly=TRUE)
path <- getwd()
path
#Download the file and put in folder
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
f <- "Dataset.zip"
if (!file.exists(path)) {dir.create(path)}
d... |
6df3956c417830d0034a6889cb495a47ab16eee9 | 5f5bdc7d277212c3f3b0d1d7721ae7813c7339f7 | /shiny-retirement-planning-with-stocks/global.R | 67804544d8438c9ace00178fb35968bc59951fd9 | [] | no_license | michaelwfouts/shiny-retirement-planning-with-stocks | 5cb4e44cb19c36b0b21fd62d95a6bdb61e8490b3 | 24e725a3a3279a3b859e7fb99d2ad4b3bd90d580 | refs/heads/main | 2023-08-11T06:06:31.622600 | 2021-09-17T12:29:26 | 2021-09-17T12:29:26 | 407,531,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 192 | r | global.R | # Load in modules
library(shiny)
library(plotly)
library(dplyr)
library(shinyWidgets)
library(bslib)
# Source module scripts
source("R/infoModule.R")
source("R/calculatorModule.R") |
c0ba647a69741e11943c7330c2f826ad0d7b714f | f044402735a52fa040c5cbc76737c7950406f8b2 | /BrCa_Age_Associated_TMA/Packages/biostatUtil/man/sem.Rd | 9d214289bd6592396aece704c41e2251de268309 | [] | no_license | BCCRCMO/BrCa_AgeAssociations | 5cf34f3b2370c0d5381c34f8e0d2463354c4af5d | 48a11c828a38a871f751c996b76b77bc33d5a3c3 | refs/heads/master | 2023-03-17T14:49:56.817589 | 2020-03-19T02:18:21 | 2020-03-19T02:18:21 | 247,175,174 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 552 | rd | sem.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{sem}
\alias{sem}
\title{Standard error of the mean}
\usage{
sem(x, missing.value = NA, return.missing.value = NA)
}
\arguments{
\item{x}{input vector}
\item{missing.value}{values that are missing}
\item{return.missing.value}{t... |
5f2548a2ad7abf957268affe68d354e7a29fa4fa | 169a6494a475f42d0452d3ade4622bde1eb939cc | /R/scrapenames.r | 48c17f68152a85535653388a6ff8cfff72f40310 | [
"MIT"
] | permissive | ropensci/taxize | d205379bc0369d9dcdb48a8e42f3f34e7c546b9b | 269095008f4d07bfdb76c51b0601be55d4941597 | refs/heads/master | 2023-05-25T04:00:46.760165 | 2023-05-02T20:02:50 | 2023-05-02T20:02:50 | 1,771,790 | 224 | 75 | NOASSERTION | 2023-05-02T20:02:51 | 2011-05-19T15:05:33 | R | UTF-8 | R | false | false | 5,141 | r | scrapenames.r | #' @title Resolve names using Global Names Recognition and Discovery.
#'
#' @description Uses the Global Names Recognition and Discovery service, see
#' http://gnrd.globalnames.org/
#'
#' Note: this function sometimes gives data back and sometimes not. The API
#' that this function is extremely buggy.
#'
#' @export
#' ... |
ddc63567bb8b0ce6718b9b4c1aabf46123e1056a | 648ceb127101da98e0371f90e83c2613b20ee5d1 | /R/get_all_funs.R | ba605d7babb31521fef48aaeac9b96c79d772188 | [] | no_license | paulponcet/bazar | b561b9914300d4eb72d998028b4c2db061f9b07e | cacccfed36ed5650dbef2e78f584e0c07c321581 | refs/heads/master | 2021-01-11T21:59:55.950238 | 2019-07-13T23:51:42 | 2019-07-13T23:51:42 | 78,890,817 | 0 | 0 | null | 2019-04-05T09:14:21 | 2017-01-13T22:12:23 | R | UTF-8 | R | false | false | 871 | r | get_all_funs.R | #' @title
#' Functions exported by a package
#'
#' @description
#' \code{get_all_funs} provides all the functions exported by a given
#' installed package.
#'
#' @param pkg
#' character. The package of interest. (Must be installed already.)
#'
#' @return
#' A character vector, the functions exported.
#'
#' @e... |
281dd1e188e0bdd1edff5ab887d91b94aa90fb68 | fb0ac2162a0aece893c57fa06c2a4acced5f7cd4 | /run_analysis.R | e982a3cf422a3bd531e96d224b739409d7344855 | [] | no_license | JoeAllyn/Getting-and-Cleaning-Data-Project | b32a4b80d0af3f693132772aafc0f8fe5a866ffa | 748513e7547d7da21fa9b2ff53192534ae9a3fbb | refs/heads/master | 2020-06-22T16:30:31.173787 | 2016-11-23T15:23:10 | 2016-11-23T15:23:10 | 74,586,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,558 | r | run_analysis.R | # Getting and Cleaning Data - Project
# Merges the training and the test sets to create one data set.
# Download Data Files and Unzip
filename <- "UCIHARDataset.zip"
if(!file.exists(filename)) {
fileurl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fi... |
3e7fe210bb1ecfc04c9837c84d00b87320eef94d | f92a7585f054a393674716f4e178a78f099f4917 | /man/common_lookup.Rd | 32f94ddc7ea92f00f9b09b8d3a7cefd1aea512cb | [] | no_license | parvezrana/forvol | 7a29c9c2c77777ddb0ed94c47f398b04056f2e84 | 767d1739061fce7fc207a500ebff0216173d6512 | refs/heads/master | 2020-03-14T13:42:17.524275 | 2018-01-03T18:09:12 | 2018-01-03T18:09:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 435 | rd | common_lookup.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lookup.R
\name{common_lookup}
\alias{common_lookup}
\title{Returns the common name of a species
given some input FIA species code}
\usage{
common_lookup(spcd, latin = FALSE)
}
\arguments{
\item{spcd}{The FIA species code}
}
\value{
A string o... |
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