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f70d845a608cb734d80dd3586732122a470ee4ac | b61d15eb9c927bf95c8f9b9be2af9ce3cd60b901 | /thurs_practice.R | 05cc329b2e69c3fcb2c63450e4721eb7b2ac5901 | [] | no_license | alturkaa/soc133 | 4e636c2a1949f283bb8302d728cfb0f0c1fb7c42 | 25b25d41078dddfe491a15b92fd3ceb0663e911c | refs/heads/master | 2022-02-09T06:29:51.419560 | 2019-08-01T18:54:18 | 2019-08-01T18:54:18 | 105,697,521 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,341 | r | thurs_practice.R | library(tidyverse) # run just this line (ctrl-Enter)
# import the data file and assign it to an object called survey. Use the read_csv function.
survey <- read_csv("class_survey.csv")
# What's the median population of the counties you all live in?
# (no code required) Why is there such a big difference betw... |
534c6fcae7d9d8b4da54931d62b85c4b660b1879 | d8fa81bdb4ff6cd0fae27dcc18be7f854595d7eb | /man/trait_match.Rd | 8986bea0d335255acb699feb66978ace85a688d4 | [] | no_license | rogini98/traitmatch | 64fe7239fa2325e260cd7b7fd377f522eb15d289 | 2d6cc34a23e1cce999b506ffafcb8c6d5455cd62 | refs/heads/master | 2022-03-13T17:07:05.028679 | 2016-02-22T10:56:16 | 2016-02-22T10:56:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 264 | rd | trait_match.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trait_match.R
\docType{package}
\name{trait_match}
\alias{trait_match}
\alias{trait_match-package}
\title{trait_match}
\description{
Run models to analyze trait matching in networks.
}
|
4c151b6d34f935fd3720bf9a1e133b8d42017760 | 01b1302af51d339f7c8827a620c4a5fb26c890f1 | /outcome_measurement/malaria/cod/prep_data.R | 81de8a40efefd0e62f4cf10be2a1d13590fc973d | [] | no_license | ihmeuw/gf | 64ab90fb5a5c49694bde1596f4b20fcf107a76e3 | 29e0c530b86867d5edd85104f4fe7dcb1ed0f1ee | refs/heads/develop | 2021-08-15T02:16:59.086173 | 2021-08-03T19:52:31 | 2021-08-03T19:52:31 | 109,062,373 | 3 | 6 | null | 2019-03-21T01:48:02 | 2017-10-31T23:17:16 | R | ISO-8859-1 | R | false | false | 34,956 | r | prep_data.R |
# ----------------------------------------------
# function to prep the DRC PNLP data
prep_data <- function(dataSheet, sheetname, index){
# column names
# ----------------------------------------------
columnNames2016 <- c("province", "dps", "health_zone", "donor", "operational_support_partner", "popu... |
6fc1e0f5d67ab29fa5aa731e0b08252e3eefad56 | dda08ebff68da583ec11f861cf1d0e75293fd2c5 | /R/seqtest.R | 0888e54b5f2354695524bd100a0014cfca80fcbb | [] | no_license | lnalborczyk/ESTER | 314f65f1a52d925f475cff2b0b54cbbf85fc5e0a | eee73e59b3e62caa936d64d563b6fa9d69e593b7 | refs/heads/master | 2021-01-11T17:03:10.560357 | 2018-05-19T08:57:35 | 2018-05-19T08:57:35 | 69,504,922 | 1 | 2 | null | 2017-01-26T14:34:38 | 2016-09-28T21:23:41 | R | UTF-8 | R | false | false | 9,307 | r | seqtest.R | #' Sequential testing with evidence ratios
#'
#' Computes sequential evidence ratios, either based on the AIC, BIC, WAIC, or LOOIC.
#' Supported models currently include \code{lm}, \code{merMod}, or \code{brmsfit} models.
#' When data involve repeated measures (and so multiple lines per subject),
#' a column indicating... |
012abc17641a9b6894df905fa1de9fc0faffa667 | ca92f6290e4575f37fdb5e50543e108aa3f64378 | /get_files.R | a3964cc5e9908bbad2064900a39bab2e6a370189 | [] | no_license | Kikzz/WorkWithDataAssignment | 607ec4ab92c217eb35d1f1e8634fa5a00f558dbb | 650f315ab1ad4ed61c9f2aa44bc0e3d02738ab43 | refs/heads/master | 2020-12-24T06:37:48.737633 | 2016-06-21T07:31:40 | 2016-06-21T07:31:40 | 60,773,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 506 | r | get_files.R | ## get_files.R
## 1. download the zip file as data.zip into working directory
## 2. unzip data.zip into working directory
## 3. Remove zip file from HDD
## download files into working directory
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip",method="curl",dest="d... |
bb126155a23c6dce64ed669152387555a2173e65 | c7183ba0271862bd10e9e857c7bba918f6d092b9 | /draft.R | a7a0b9e59bcc25176e059429161d9a1a7377836b | [] | no_license | weslwz/RepData_PeerAssessment1 | 37d2c1e903bb0e682417281e4ac891d8569f27d7 | 39550fe79959074a9f4ac99294412503f7fe0b8f | refs/heads/master | 2020-12-25T23:10:13.309393 | 2014-09-15T03:19:33 | 2014-09-15T03:19:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 845 | r | draft.R | library(knitr)
library(markdown)
knit("PA1_template.Rmd")
markdownToHTML("PA1_template.md", "PA1_template.html")
X <- read.csv("activity.csv")
library(plyr)
library(ggplot2)
A <- ddply(X, "date", summarise, total_steps=sum(steps,na.rm=TRUE), mean_steps=mean(steps,na.rm=TRUE), median_steps=median(steps,na.rm=TRUE))
A$... |
9ae0810d97f3120556077d03c7c80f3e14485149 | 5bc5eb0d1ad3a60ce230cc563b2ec9431c4249ad | /0415_pdbs/ras/sd_of_net_community.r | a54cc438a7357ffd92b989ed369858e502582f9a | [] | no_license | Hongyang449/2016 | dd1c0a124e7f0414b089a73977417053a82cc55e | 8bdd3d003a32e03555fe979055245845ed841a43 | refs/heads/master | 2021-01-13T10:06:42.247046 | 2016-12-21T21:04:08 | 2016-12-21T21:04:08 | 72,122,275 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,871 | r | sd_of_net_community.r | ## name: sd_of_net_community.r
## date: 10/31/2016
## Here I want to examine the sd (variance) of community edges within and between states (gtp vs gdp)
library(abind)
load("/Users/hyangl/project/ras/results/2016/0415_pdbs/ras/net_ras_cmap_noh.RData")
## structure of net_complete:
## list(nets_ras_gtp_vs_gdp_signif... |
291d3ede9c3dbf269331fb11f5d8220b1de50fcd | c3b03c926c7be473ce36928f96252535982f686c | /plot2.R | c3927af0266b9a851a48a3045e20535e411f424e | [] | no_license | amicarelli/ExData_Plotting1 | 39195770b173dfef6e46e68ffd8e3fc94556b39a | f9a4e945be5b442c2f09bd367834649ae42a7c41 | refs/heads/master | 2021-01-18T12:09:53.659536 | 2016-03-22T02:35:34 | 2016-03-22T02:35:34 | 54,424,734 | 0 | 0 | null | 2016-03-21T21:37:47 | 2016-03-21T21:37:46 | null | UTF-8 | R | false | false | 1,547 | r | plot2.R | ##
# This script is for the course: exploratory data analysis
# The script reads in data from household power consumption
# and creates graphs of the data
#
#
# Check to see if the data already exists in the local directory, if not, download data
#
fileDir <- paste(getwd(),"/data/HPC",sep="") #use this directory f... |
1f5db3a2eeb84b6e2c54952187d2c0db0c2298d7 | fe719dec5ad6f950c589874038ed00f15591952c | /Lalonde 3-ways.R | 702bbfd09aca0708cd2b7349ce18a6823753f9bb | [] | no_license | tuantvh/CS112---Causal-Inference | 9f3ed6731935625e97cb4ffb71e684afd2a2fe55 | 1c6362c587e60ad556d835fe95e2a538d08405e9 | refs/heads/master | 2021-10-25T19:54:45.385703 | 2019-04-06T17:23:39 | 2019-04-06T17:23:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,663 | r | Lalonde 3-ways.R | library("Matching")
library("stargazer")
library("ggplot2")
library(randomForest)
data <- data("lalonde")
#MODELS
#PART1
#high school degree and no degree
Degree <- lalonde[lalonde$nodegr ==0,]
NoDegree <- lalonde[lalonde$nodegr ==1,]
#regression model
fit.Degree <- lm(re78 ~ treat + age + black + hisp... |
95798fb313bdb659b4c970702cff78f5b8595d6f | 84a470b5c193e3854258729ad124673f6dfdbf1c | /R/makeContactMatrix.R | 1e34f688fa5a7af0844af67ddc0be4adfeee9b2d | [] | no_license | cdcepi/ACIP-SARS-CoV-2-Vaccine-Modeling | 03dbd6ef0ef2785128968264614a7613a6570999 | c0cdfbc06d49abfa7e4efed5dae64c114c34ec22 | refs/heads/master | 2023-03-07T13:06:21.763569 | 2021-02-23T20:50:17 | 2021-02-23T20:50:17 | 341,674,413 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,376 | r | makeContactMatrix.R | #' @title Make contact matrix
#' @description Rebins an arbitrary contact matrix to a new group of population ranges. The default behavior is to use the UK POLYMOD data.
#' @param ages Vector of ages. Each element represents the upper range of an age range. The lowest bound is presumed to be zero.
#' If the oldest a... |
47a17748d748fe128d6970f90fa87abd8be59167 | 2712ec1deafe0fa3a9864c786b207d9ed409539a | /R/33_stripSP.R | c80f5ef0b6d1017f874bfbc9fe18550bf52b3933 | [] | no_license | Epiconcept-Paris/GADMTools | de556df201c7c6ec4fff0b6cd1e78c02d3f6cbd3 | 9549ec2da551de579a17c1ac5c6d55122a7be660 | refs/heads/master | 2021-01-17T13:05:39.092878 | 2020-03-04T20:53:28 | 2020-03-04T20:53:28 | 42,582,951 | 9 | 2 | null | 2020-01-07T11:38:54 | 2015-09-16T11:24:35 | R | UTF-8 | R | false | false | 2,002 | r | 33_stripSP.R | ## ---------------------------------------------------------------------------
## Method : stripSP
## Return : a GADMWrapper ready to use with ggplot2
## ---------------------------------------------------------------------------
stripSP <- function(x, level=NULL) UseMethod("stripSP", x)
stripSP.gadm_sp <- function(x, ... |
c4770cc48888dc60dee7594bb552cb8644193bcc | 519db64c2a30f4e52b787db1c1d11449f2c3bff8 | /man/clust_remove.Rd | d840c4cbc1f5a2e6e501406293b16b77c3f9e013 | [] | no_license | juba/umapscan | dd71a4499ac6da989112e32eaa039df63be81eb9 | 180876adbc56c65d0a1b7fccaa7912ef823c46a3 | refs/heads/master | 2020-12-19T18:33:34.025036 | 2020-08-27T12:31:21 | 2020-08-27T12:31:21 | 235,815,547 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 883 | rd | clust_remove.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/clustering.R
\name{clust_remove}
\alias{clust_remove}
\title{Remove a cluster from an umapscan object}
\usage{
clust_remove(us, id, rm_root = FALSE)
}
\arguments{
\item{us}{umapscan object}
\item{id}{id of the cluster to remove}
\item{rm_ro... |
b6c1effbb52022b3903c7c5c91ae7fb06ab001b6 | 496548cd172301f8ae2866d02008140d1ac56fd3 | /R/f_indicator.R | d6bb237ea89475ac058d51d93615fa97edb70cbd | [
"MIT"
] | permissive | kristian-bak/kb.modelling | da04a20f743f1c4db66023d25bf9c6e8859758f4 | 93509b8cc6e1c44f005db31d9f29d61c4ad64720 | refs/heads/master | 2023-04-04T21:45:53.106511 | 2021-04-13T18:25:32 | 2021-04-13T18:25:32 | 342,622,062 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,094 | r | f_indicator.R | #' Lag function
#' @param x A vector to perform lag on
#' @param n number of elements to shift. Default is 1.
#' @export
#' @examples
#' f_lag(x = 1:10, n = 1)
#'
f_lag <- function(x, n = 1) {
if (n == 0) {
return(x)
}
id <- 1:n
x <- c(x[-id], rep(NA, n))
return(x)
}
#' This function calculates the sl... |
41ba6cdbacbcf2efb0b9bcea6fc9e278934f876a | 229bf9f5443566993bfd9ba16153c1ad0aada67f | /PaulOctopus/R/paul_pipeline/execDataScrap.R | 15762c3bb3a82fce3645d7050e0571614e67d05b | [] | no_license | GiulSposito/R-x | fc096199ca2efb483d164ba42b92a7a77281f39f | 902aad081c5b7961983234f183ed1df4bf621e8b | refs/heads/master | 2021-06-27T05:41:46.789209 | 2019-05-21T17:42:01 | 2019-05-21T17:42:01 | 115,006,654 | 2 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,466 | r | execDataScrap.R | source("./PaulOctopus/R/eloratings/ern_scrap_domains.R")
source("./PaulOctopus/R/infra/bigQuery.R")
# faz o scrap das informações do site EloRating
scrapEloRatings <- function(scrap.years = 1930:2018){
# basic domains
elo.labels <- elo_scrapLabels()
elo.teams <- elo_scrapTeam()
elo.tourn <- elo_scrapT... |
5c7464e8db0fb960034db3102c21736e964c2680 | 57a1c2838d0116241b256ec8af05319c0bdbc217 | /src/common/GmicPlugin.r | 08aa511381cec71f19ef90637ae4be75383d0235 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | ganego/gmic-8bf | 7468398745a983b7b7f8055991117fc98b14f759 | ee49ae507da60d648df582772163e059faa9f4f1 | refs/heads/main | 2023-03-03T00:55:06.461444 | 2021-02-11T18:08:01 | 2021-02-11T18:08:01 | 338,084,621 | 0 | 0 | MIT | 2021-02-11T16:29:17 | 2021-02-11T16:29:17 | null | UTF-8 | R | false | false | 4,685 | r | GmicPlugin.r | #include "PIDefines.h"
#ifdef __PIMac__
#include <Carbon.r>
#include "PIGeneral.r"
#include "PIUtilities.r"
#elif defined(__PIWin__)
#define Rez
#include "PIGeneral.h"
#endif
#include "PIActions.h"
resource 'PiPL' ( 16000, "GmicPlugin", purgeable )
{
{
Kind { Filter },
Name { "G'MIC-Qt..." },
Category {... |
d2e4d77dbf5e7a42ae27dbf8935e9e21aac23eea | 8f39806714430f0dd2918f825845496c5b9c0fe7 | /man/cochrane.Rd | fab8e52baa522065b7c8d9731b623b26dca79872 | [] | no_license | cran/rmeta | 85913d9ef8b412c929d456629a59a91f7dfd689b | a521f0a0f6c6e64f4a6bef63b2fcddc0c25d11c2 | refs/heads/master | 2021-05-15T01:39:56.734524 | 2018-03-20T11:01:32 | 2018-03-20T11:01:32 | 17,699,255 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,094 | rd | cochrane.Rd | \name{cochrane}
\alias{cochrane}
\non_function{}
\title{Data for Cochrane Collaboration logo }
\usage{data(cochrane)}
\description{
Data from randomised trials before 1980 of corticosteroid therapy in premature
labour and its effect on neonatal death.
}
\format{
This data frame contains the following columns:
\describ... |
1655ec33c4f586f7a1e3ac7c6bf7ff9ccd8ef561 | b909e63baac12cc309b143539ec712fa1a0f7fd0 | /RNA-seq/scripts/full_test.R | 197f9b18756881aeb252f1cc7cfa684774bd1ab0 | [
"MIT"
] | permissive | daugherty-lab/NINL | c764bd7de81820664f6b107d2f5fd822d5fd7c3a | 7949c4879f89869abc0abe42ea5c76fa9423ec4d | refs/heads/master | 2023-08-30T15:19:49.966918 | 2022-11-09T21:29:32 | 2022-11-09T21:29:32 | 503,169,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,687 | r | full_test.R |
# R script for DESeq2 processing of 1
setwd('..')
#### installing and loading packages ####
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager", repos='http://cran.us.r-project.org')
knitr::opts_chunk$set(tidy=FALSE, cache=TRUE,
dev="png",
... |
ac6c3fbd592a4ae434cbfed3caed58c648f1e1bb | 42f2dba844a17a3b0f0bbc1007bbfe063419037b | /NNhuMG/R/phenoReduce.R | 678e8c6b571bc4b1df6cd236898a115d943aaedc | [] | no_license | steschlick/Boettcher_et_al_NN_2018 | 4569e577cc4d918ecce7d9f0e7a9a60fe3639af2 | bc502b0168294e3645d1a1975b64ab313a1a9691 | refs/heads/master | 2020-04-02T22:00:26.959428 | 2019-01-26T14:59:28 | 2019-01-26T14:59:28 | 154,818,434 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,428 | r | phenoReduce.R | #' @title Minimal Marker Cutoff Combinations
#' @description finds low-dimensional phenotypic representations of target subsets
#' @param con object returned by \code{\link{findCutoffs}}
#' @param target.num.marker desired least number of markers, Default: NA
#' @param b weighs recall and precision in F-score, Default:... |
5f589b7abab5fa8ae4702d101251d73a9196f3ad | 59070575eb00f1a92b9f7d8027312f6c783d59b0 | /code/prepare_data_elections.R | cb760a1b98d69e7d796d0eeb66f44399509c7b73 | [] | no_license | tobiasnowacki/uk-careers | e8becb49f89da0901ef0b195ddbe1afa14f7cdd5 | 277ee21208632e1b7d1d637533cdfc127b8b6fbe | refs/heads/main | 2023-08-26T01:47:35.469116 | 2021-11-05T00:22:39 | 2021-11-05T00:22:39 | 424,672,339 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,418 | r | prepare_data_elections.R | # Replication files for 'The Emergence of Party-Based Political Careers in the UK, 1801-1918'
# Cox & Nowacki (Journal of Politics, forthcoming)
# prepare_data_elections.R: Tidy data for election-level analysis
# -------------
# DEPENDENCIES
# -------------
source("code/0_functions.R")
# ---------
# LOAD DATA
# -----... |
e9ecab5c1072989c44a9c02c7658c7889418eade | b68a219ba34b6aa60497d73968517c8a31bbdc9a | /Household_power_consumption/plot3.R | 375eab5a3d7ce8f7ba4873f2d615ab00654fe2bd | [] | no_license | Ziconin/Data_Science | 935ac665f11040f9a8c21c32936bfdf4f57069cc | 418709716eb1998004c6d38533f814ac7a8638d0 | refs/heads/master | 2021-09-23T20:22:10.477323 | 2018-09-27T12:03:27 | 2018-09-27T12:03:27 | 95,986,886 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | plot3.R | readFile <- function() {
require(data.table)
dates <- fread("household_power_consumption.txt", na.strings="?")
intervals <- subset(dates, Date == "1/2/2007" | Date == "2/2/2007")
intervals
}
plot3 <- function() {
require(lubridate)
data <- readFile()
times <- dmy_hms(paste(data$Date, data$Time, sep = " "))
... |
ff959d047069ad5324832e27c27f8c129da45652 | 262680e41eb04d5b947fdb0cb3b08d43a568c41a | /rsiconfi/gastos_estaduais_SP_com_saude_2018.R | bd5f000ce8fd8926ec1a5621a0e4ec457cfb4d73 | [] | no_license | carolina-quiterio-ifood/APIs | b071279334c7a5991e9fd13d2a38552e3d09caf1 | f595f40a5952555e23422847b4f726bcec42cbc5 | refs/heads/master | 2023-03-02T00:34:58.646927 | 2021-02-10T15:20:53 | 2021-02-10T15:20:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,370 | r | gastos_estaduais_SP_com_saude_2018.R | # -*- coding: utf-8
# Abraji (https://www.abraji.org.br)
# Reinaldo Chaves (reinaldo@abraji.org.br)
# Script exemplo de uso do rsiconfi
# Baixa gastos com saúde do Estado de SP
#
#install.packages("devtools")
#devtools::install_github("tchiluanda/rsiconfi")
library(rsiconfi)
library(dplyr)
library(tidyr)
# Códigos d... |
a264ce8abefa688a1cb576b822b9cfbb8f10bd60 | 7dbbe94a839b1c94a2d30b7d2042aa5eef121854 | /zillow.R | 4ef8b8bf8f6cd85118dc1d86e2145016c17871ab | [] | no_license | mleegina/R-real-estate | 05e62567aec0a80b0c8f925ef5035fe74254d3cc | 3c8da344178aa1442e3393e3c456cef19b5f0ae1 | refs/heads/master | 2020-03-19T11:07:49.255578 | 2018-06-20T23:20:19 | 2018-06-20T23:20:19 | 136,433,230 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,940 | r | zillow.R | library(ggplot2)
library(readr)
library(igraph)
library(dplyr)
library(tidyr)
library(plyr)
library(data.table)
library(ggmap)
library(maptools)
library(ggthemes)
library(rgeos)
library(broom)
library(gridExtra)
library(reshape2)
library(scales)
library(rgdal)
# Plot themes for ggplot and ggmap to be used throughout
p... |
58e6cbb3038cc4ca98ce7f1c4f83911f1b1291c2 | a19fc7dd26a2acdda9b2442fc490cf29928eade7 | /multiSpeciesTeDistribution/Fig_1_ploter.R | 891dca318fa9ab7917e37d1c595d4876d742f763 | [] | no_license | ReubenBuck/Domain_manuscript_scripts | 96d2fed7042f5a0b6e7d79f10f80f20128b6e4e8 | 5c290e5334920e9dd70285cce242eefee3fd8be1 | refs/heads/master | 2020-04-16T19:30:03.535423 | 2016-10-23T23:08:53 | 2016-10-23T23:08:53 | 46,104,045 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,897 | r | Fig_1_ploter.R | ## lets do PCA analysis
library(ggplot2)
library(ks)
library(gplots)
library(GenomicRanges)
require(ggbio)
setwd("~/Desktop/Dist_matrix_TE_div/")
source(file="~/Desktop/Domain_manuscript/Domain_manuscript_scripts/functions.R")
spec1 <- "Human"
spec2 <- "Horse"
spec3 <- "Bovine"
spec4 <- "Dog"
spec5 <- "Mouse"
... |
17c00cfb209460292e7d98ad9bc60b442ffb83a1 | 8e341461a2e3e615dde7ca12703e9437803605ef | /analyses/scripts/FSI_sitesRisk.R | 20192f113f0b854d6b90f13a7160fd4da0daeaed | [] | no_license | cchambe12/freezingexperiment | 2aa50840fe98ee434970759437cbe5d8dfb4946b | a1cf6210979088833706763d858dafa2f8312cae | refs/heads/master | 2021-01-11T20:06:40.705078 | 2018-12-07T19:59:36 | 2018-12-07T19:59:36 | 79,467,912 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,942 | r | FSI_sitesRisk.R | ## 31 August 2017
# working on weather data to try and evaluate FSI across 4 field sites
# Clear workspace
rm(list=ls()) # remove everything currently held in the R memory
options(stringsAsFactors=FALSE)
graphics.off()
# Load libraries
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
# Set Working D... |
0836ca9866d09f79fccb431c112d07fda949aeff | 5f1f5a4a640fc3e953b1f7899c7385c1f96b300a | /man/bmedian-methods.Rd | 13c772694667c3de1e2e733532f6b9746ab8e7d6 | [] | no_license | katenambiar/pmpa | d947d2690d3146476b7aa3040958c6dbe302cc57 | 977b2b76e7446bd05be40cb1acc636dca445613f | refs/heads/master | 2023-03-16T18:14:52.546654 | 2016-11-06T18:27:02 | 2016-11-06T18:27:02 | 4,173,902 | 0 | 1 | null | 2023-03-13T04:39:47 | 2012-04-29T11:22:25 | R | UTF-8 | R | false | false | 507 | rd | bmedian-methods.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/accessor-methods.R
\docType{methods}
\name{bmedian}
\alias{bmedian}
\alias{bmedian,MultiSet-method}
\title{Median Background Intensity Accessor}
\usage{
bmedian(x)
\S4method{bmedian}{MultiSet}(x)
}
\arguments{
\item{x}{MultiSet objec... |
57b7af6cccd8185ddf515117047111b1887646ab | 5e830125d3a6d5e0b9309b990efe592c270724ca | /Production/Model/APM/man/set_param_num.Rd | 05e224e6d154be673cca7b740a17cf40c54de18d | [
"CC0-1.0"
] | permissive | ElderResearch/DAPM | 0d8a0dd921c75a328a192419a0ad86073ca10e69 | 936e0d68298667a4e62998bc6d7f8f2cda819df0 | refs/heads/master | 2021-01-19T07:46:21.187143 | 2019-01-23T17:17:32 | 2019-01-23T17:17:32 | 87,570,226 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 453 | rd | set_param_num.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{set_param_num}
\alias{set_param_num}
\title{Assign a parameter or optionally fill from NRDB}
\usage{
set_param_num(...)
}
\arguments{
\item{...}{Arguments to pass to \code{set_param}.}
}
\value{
A numeric.
}
\description{
Assign... |
9a3f1abcc6e503f42d04eae61f3da3828a53142b | afade7448d058e7c099268aaa7c41b2461e691bc | /Dia 1/voice-gender-master/Web/ui.R | 95317f0f7f365171a4c9c31a6f7ba39d3df5be2a | [] | no_license | JuanCorp/TallerML | 6c7e487b6f7ec0c95a7e7dc53819dac1e52edb7b | a6277c36a8ac4f6d2247c3f1dab477b86e96f5dc | refs/heads/master | 2021-01-19T12:40:22.899983 | 2017-11-23T11:37:48 | 2017-11-23T11:37:48 | 100,796,498 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,789 | r | ui.R | ## Including the required R packages.
#packages <- c('shiny', 'shinyjs')
#if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
# install.packages(setdiff(packages, rownames(installed.packages())))
#}
library(shiny)
library(shinyjs)
shinyUI(fluidPage(
conditionalPanel(condition='!output.json',
... |
6e592580f349c4f72a264bcdd2b63d26c6e41a63 | 5629ffd9a3aed0f20ba7100b0b51525e9d5d2c7c | /waves/waveHspVCyto.R | c212e6fafcfec439a62f81bb5e033645d85e1313 | [] | no_license | Danko-Lab/polymeraseWaves | d77434fe2d59842ed54699df7cd9bc698549f02f | 7bcc9e925cb026dee7269fd1bcd785ef280f24f7 | refs/heads/master | 2021-08-17T03:17:45.486865 | 2020-04-21T13:15:07 | 2020-04-21T13:15:07 | 31,675,302 | 0 | 3 | null | 2020-04-21T04:09:56 | 2015-03-04T19:22:10 | R | UTF-8 | R | false | false | 2,922 | r | waveHspVCyto.R | ##
## callWaves.R -- identify the position of the leading edge of the wave of Pol II as it moves
## across gene bodies.
##
require(groHMM)
require(bigWig)
source("../polymeraseWave.bw.R")
## BigWig files.
pth <- "/home/cgd24/storage/home/work/polymeraseWaves/data/bigWigs/"
wtNHSpl <- paste(pth,"WT_NH... |
fa89816ad2d26516468d2d35f1bf66da7af46866 | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2019/nonfatal_code/encephalitis/nonfatal_code/02b_parallel.R | 9ca74d6b630015bf85aaf2a99a52d87e458637d5 | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,179 | r | 02b_parallel.R | #####################################################################################################################################################################################
## Purpose: This sub-step template is for parallelized jobs submitted from main step code
## Author:
## Last updated: 2019/02/27
## Des... |
4b57e2abb9fc85124e0eaf1751417611c4186a52 | b051db434b8ec8e30ec4264181ba6bf86b539ce9 | /man/plot-PeakList-missing-method.Rd | 233e1bf2b6f19ce56ff3c552a31e19fe23b46862 | [] | no_license | lorenzgerber/tofsims | 6484b58a532385bcbc906fe2921190d2221cc77f | cf0791d3324638d604dea5a111729b507f5b2192 | refs/heads/master | 2021-06-25T23:27:27.148518 | 2020-10-15T06:32:08 | 2020-10-15T06:32:08 | 73,091,887 | 1 | 1 | null | 2016-11-07T15:24:28 | 2016-11-07T15:24:28 | null | UTF-8 | R | false | true | 1,133 | rd | plot-PeakList-missing-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PeakList.R
\name{plot,PeakList,missing-method}
\alias{plot,PeakList,missing-method}
\title{Method \code{plot()} for \code{MassSpectra}}
\usage{
\S4method{plot}{PeakList,missing}(
x,
y,
...,
mzRange = c(0, 200),
plotDeriv = FALSE,
... |
feae1f9ca7b3f8ba7c896152ad454810f5a3428c | dd7768a45761e58f51551959f891333e0d791faf | /R/makefun.R | 8a497abc169b3173e8ffc8875bb815c25757d15f | [] | no_license | cran/gcl | 3d4920a52e1356401d6e860bf1fa10fa939a3d0b | fdc14f4896ba1278f82c6af1fd1fa6032ae2f86d | refs/heads/master | 2021-01-13T07:40:52.479132 | 2007-03-14T00:00:00 | 2007-03-14T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 709 | r | makefun.R | `makefun` <-
function (.thefun, .thealist, .moveddd = 1, ...)
{
frms <- formals(.thefun)
frn <- names(frms)
body <- body(.thefun)
na <- length(frms)
ddd <- names(frms)[na] == "..."
add.ddd <- F
if (ddd && .moveddd != 0) {
na <- na - 1
frms <- frms[1:na]
frn <- frn[1:... |
586035b3c8b352bbc17e5cbc371fecc105a17266 | 89569fd9c4195a5473c880445221c0f7bfe0b896 | /man/summary.binomialbcp.Rd | faa629dc9ea99ecd99c63eeb8431281e4cb8ff99 | [] | no_license | svana1/binomialbcp | 7509262864ec5c22c9ad541119fba62aa8ab1f39 | 8053e21a7111b24338d1499cca7e2579bae32b9b | refs/heads/master | 2021-05-08T03:50:46.534528 | 2016-09-24T22:53:37 | 2016-09-24T22:53:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 522 | rd | summary.binomialbcp.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/binomialbcp.R
\name{summary.binomialbcp}
\alias{summary.binomialbcp}
\title{Summarize a Binomial BCP sample}
\usage{
\method{summary}{binomialbcp}(object, ...)
}
\arguments{
\item{object}{a binomialbcp sample}
\item{...}{(unused)}
}
... |
f0fc57bce063b32a0d9cf7487722637ac0418f37 | f6ad2309ef0d422f344f3bc6f760622a3b4a0305 | /NLCD_script.R | 5c9896554995d955af510fd4d768c1ba0161368f | [] | no_license | schefferslab/connectivity_SEUSA | 49962fea96e96be27bf82bf8c3d70cc6bbe2fcc3 | 6fe634153f03a30b62f6dab787494929f4bd83ed | refs/heads/master | 2022-09-06T14:10:42.337395 | 2020-05-29T12:23:29 | 2020-05-29T12:23:29 | 268,536,885 | 0 | 2 | null | 2020-06-01T13:55:52 | 2020-06-01T13:55:51 | null | UTF-8 | R | false | false | 8,779 | r | NLCD_script.R | library(readr)
library(maptools)
library(sp)
library(raster)
library(rgdal)
library(maps)
library(ggplot2)
library(tidyverse)
library(smoothr)
library(devtools)
library(ENMTools)
library(spatstat)
library(rgeos)
library(lattice)
library(reshape2)
library(ggthemes)
library(viridis)
library(gridExtra)
library(rasterVis)
... |
23e5c6177ffdc017cf752bc382db9043bc68e5aa | a43de4a62a781d7804406334443e8eab0f77a626 | /rotinamod2.r | 1b26c1678877bda0654837d7e7670b6fae1fb97f | [] | no_license | Frank2857/Disserta-o | 106b6aa112e5e0fadf27fbc5c2d1c2db2eb9e08a | 9e0f0975e35004b0c05efae0c5d3c361770d6752 | refs/heads/master | 2020-11-27T04:26:54.095710 | 2020-02-19T23:22:32 | 2020-02-19T23:22:32 | 228,650,501 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,244 | r | rotinamod2.r | ############################################################################
###################### MODELO 2 - PRIORI NÃO INFORMATIVA ###################
############################################################################
x = as.character(dados$ANO[1:23]);x
y= dados$FEV[1:23];y
t0=2 #Ponto do possíve... |
527d6f31758a4a374a8936f20ea1825c989d69f4 | 6a645547fdcd7d8b5c4a865c0ee86c937e8b60c5 | /new_effect_size.R | e83c23a1e7fef88e1072a78f128ba8c412e407ef | [] | no_license | MegaPast2Future/megalinkers | 286a82d4cb68366c5358f8570d7c05a6243401db | 85756fa8c4ca653ad803d6c7d548033f6d720d5b | refs/heads/master | 2022-12-14T22:33:12.373730 | 2020-09-04T09:17:48 | 2020-09-04T09:17:48 | 215,502,224 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,835 | r | new_effect_size.R | new_effect_size <- function(x, y, global = T, area = 'World'){
if (str_detect(class(x), "raster|Raster")) {
cont.name <- c('Asia', 'North America', 'Europe', 'Africa', 'South America', 'Oceania')
if(global == F){
cont.value <- which(cont.name == area)
clipped.continent <- clamp(continents, lower =... |
ca662c7ea22f57f02f872e6402fa462a711261f0 | 59ea89f1162f8048d9f7f10f6e6a3a1567c56607 | /rstudio/yc_all_pow_cue_effect.R | bd119660727fa3a68678baeadc412c2e7cf61b2d | [] | no_license | elshafeh/own | a9b8199efb3511aa1b30b53755be9337d572b116 | ef3c4e1a444b1231e3357c4b25b0ba1ba85267d6 | refs/heads/master | 2023-09-03T01:23:35.888318 | 2021-11-03T09:56:33 | 2021-11-03T09:56:33 | 314,668,569 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,768 | r | yc_all_pow_cue_effect.R | library(car);library(ggplot2)
library(dae);library(nlme);library(effects);library(psych)
library(interplot);library(plyr);library(devtools)
library(ez);library(Rmisc);library(wesanderson);library(lme4);library(lsmeans)
library(plotly)
library(ggplot2)
library(ggpubr)
rm(list=ls())
cbPalette <- c( "#009E73","#E69F00",... |
380a952150d3b6c1da749944514d063112d0b249 | add1ed8394da1dfd4ba1c81bcb9a1ad9c8a63808 | /workshop_data.R | 23906a37d63a020ff4d81e9aac6a8e9e510cfb44 | [] | no_license | TomKellyGenetics/Stats_Workshop_Osumi_Group | 98a8e69f008aa86fcc4597d6fcbd3c15c9ad33d6 | 2e85947646f957004fd7fefc1e63627a09e7c226 | refs/heads/master | 2021-07-05T12:24:19.185604 | 2017-09-25T00:19:27 | 2017-09-25T00:19:27 | 104,685,871 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,347 | r | workshop_data.R | setwd("C:/Users/Tom Kelly/Documents/Downloads/20170927_Stats_Workshop_Osumi_Group")
data <- rnorm(1000000, 1, 1.25)
data1 <- rnorm(100, 1, 1.25)
data2 <- rnorm(50, 1.25, 1)
pdf("1_distribution.pdf", width=800/75, 600/75)
plot(density(data), main="distribution", ylab="frequency", xlab="value of data")
abline(v... |
7fc29666a31a21f73b6a7a41dfb644cc5e2737a2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/samon/examples/samonCombineIM.Rd.R | 908561533799ff9302492751b4f0931a3fddd249 | [] | 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 | 336 | r | samonCombineIM.Rd.R | library(samon)
### Name: samonCombineIM
### Title: Combines results from separate runs of the samonIM function
### Aliases: samonCombineIM
### ** Examples
# outputs from samonIM -- run on VAS1 with different seeds
data("V1Res001")
data("V1Res002")
# combine them
V1Results <- samonCombineIM( objlist = list(V1Res001... |
b499846e8b9157ea73ed107a2f7ca08650a952c8 | 737daba62560c93d39e0ddbec75d4d1e187b3239 | /Arvore_clasificacao_tentativa.R | 9b0ee09262806601f0ae325979834b499243cbce | [] | no_license | Cicconella/No-Show-MAE5904 | 85f268333ca07fa6bac3fd636c2e6e6aad213b34 | 00247e22fa3a58407da397ee0ad2277930a0f646 | refs/heads/master | 2020-08-14T07:53:11.436926 | 2019-11-04T02:46:05 | 2019-11-04T02:46:05 | 215,126,640 | 0 | 0 | null | 2019-10-21T21:04:19 | 2019-10-14T19:18:48 | R | UTF-8 | R | false | false | 4,237 | r | Arvore_clasificacao_tentativa.R | rm(list=ls())
library(zoo)
library(rmarkdown)
library(knitr)
library(pROC)
library(rpart)
library(rpart.plot)
options(scipen = 999)
th <- read.table("noshowappointments/KaggleV2-May-2016.csv",
header=T, sep=",")
# recode the data
df <- data.frame(
# PATIENT PROPERTIES
patid = th$PatientId,
... |
eb43340498f096cd8513f87203b15bf7a7e598e1 | 3208d5737d307dd4e625ea094028c57f48d13aff | /REE_actual_mining_choropleth_map/Leaflet_mines_top_projects_by_country.R | b0892ff39c8a50dd61e3ccd00fdddcc6a81e115c | [
"Apache-2.0"
] | permissive | DistrictDataLabs/03-mineralytics | 849b7fbbe96704c004564041edfece2a07729a1c | 1371f1608684b985a2169d2c20ea78acc814ab5c | refs/heads/master | 2021-01-13T06:38:46.863028 | 2015-11-12T03:55:02 | 2015-11-12T03:55:02 | 39,696,039 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,323 | r | Leaflet_mines_top_projects_by_country.R | library(rgdal)
library(leaflet)
library(plyr)
library(dplyr)
## code adapted from http://adolfoalvarez.cl/code/users.html
##use the mines dataframe from script "Parsing_mining_data.R"
# We download the data for the map from naturalearthdata.com
url <- "http://www.naturalearthdata.com/http//www.naturalearthdata.com/do... |
4c7fb2bbd4b56770e57a59f2458df4b535eaf75c | ec5104996880067a42771cec29dbb6f7051d6ad4 | /R/computeChemicalPower.R | e06e67a4925db1a01e565167ab4680931af13239 | [] | no_license | MarcoKlH/afpt-r | f92e23a5605168e2ed9264193c53de9d9fdf63e5 | f5850c125491c3d54fe9a2473f9f849b56956876 | refs/heads/master | 2021-03-27T18:22:37.416151 | 2020-03-18T15:32:16 | 2020-03-18T15:32:16 | 64,833,484 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 669 | r | computeChemicalPower.R | computeChemicalPower <- function (power.mech,bird,...) UseMethod('computeChemicalPower')
computeChemicalPower.power.mechanical <- function (power.mech,bird,...) {
warning('computeChemicalPower has been made redundant... power.mechanical class has also been made redundant...')
power.chem <- power.mech
power.chem... |
83e58d2dac5d34e097cf982167d23e02fd052f62 | 64ff5f128bcc6486a17c64c9757fb4fa0d9b15f7 | /agriculture_yarsa_gaasc_2075-76/yarsa_regression.R | f4e2ffc46a5709d441bb8cd5135226f29c688892 | [] | no_license | DeependraD/expdean_data | 433c329695fe7d3f8329ed7771a50b11c3ce24d2 | ae5a8d73fa46af4f324d2b5083b059073171834f | refs/heads/master | 2020-09-17T14:50:55.870433 | 2019-11-26T16:51:02 | 2019-11-26T16:51:02 | 224,097,658 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,496 | r | yarsa_regression.R | regressors_economic <- c("family_size"
,"harvester_members_household"
,"expenses"
,"yarsha_income_2074"
,"highest_price"
,"average_price"
,"lowest_price"
,"method_curing"
,"time_curing"
... |
59ebf8927348d627e5dab0bc5266499a882aaacf | 82b58caf997774e921c42e9df9ae301a0d403c0a | /man/ipm_analyse_l.Rd | afa161d9a93145ecaa92d1a1c8ae0c4b80fea06a | [] | no_license | zywhy9/IPM | a290bbda1c517e4bd2d2492832bfdbad710eab21 | 76881ed3b8091ee7217b66bb0e74fd39ee7c229f | refs/heads/master | 2022-04-12T21:14:12.813942 | 2020-04-10T14:35:23 | 2020-04-10T14:35:23 | 252,565,934 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,069 | rd | ipm_analyse_l.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ipm_analyse_l.R
\name{ipm_analyse_l}
\alias{ipm_analyse_l}
\title{Analyse data using composite likelihood for a simulation study}
\usage{
ipm_analyse_l(
data,
Plot = FALSE,
priors = NULL,
maxtime = 5,
unit = "mins",
save = 20000L,... |
f23dfc7410ed065f8e904163f9d24f40dd113d41 | 7d67f9e92dbafa6e479ec12e4d78df44fba2adf8 | /R/create_text.R | 7091c43be3f5e84e6edb6a772e8ba92649ec4e57 | [] | no_license | ntyndall/translink.bot | 854d2a0a709f6a74c10b4a153dd89553d082bcda | b543f1cf9d62f278135c66cfc2404572f9f7ae3a | refs/heads/master | 2020-04-03T12:21:33.897027 | 2018-11-13T15:16:30 | 2018-11-13T15:16:30 | 155,249,704 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,008 | r | create_text.R | #' @title Create Text
#'
#' @export
create_text <- function(allresults, dbr, startStation, stopStation) {
# Get ETA's
etas <- allresults$callingpoints %>%
lapply(
FUN = function(x) {
x %>%
`[[`("etarr") %>%
`[`(x %>% `[[`("Name") %>% as.character %>% `==`(stopStation) %... |
694a92350be6d967f4be2177afcb408a6d73076e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RcppMLPACK/vignettes/RcppMLPACK-intro.R | d84c71df9b69c4b57480cf743a1d32b9ae1347a1 | [] | 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 | 76 | r | RcppMLPACK-intro.R | ### R code from vignette source 'RcppMLPACK-intro.Rnw'
### Encoding: UTF-8
|
10c64d79ea801759779dcb366d53864e45fb5505 | a8412d5d6fa04a7176e6331096b2197cb18b9ebf | /R/build_network_window.R | 596501f603a6c455ca1d3579e277e1763761b878 | [
"BSD-3-Clause"
] | permissive | melinagallopin/appinetwork | fe66f7a752c05800a0ce3e5f334873a0a41f12d9 | 3790f8d3265d5ec5fc2ab924506c3eb49a924cbb | refs/heads/master | 2021-06-04T05:28:38.648596 | 2020-11-05T17:20:29 | 2020-11-05T17:20:29 | 146,662,875 | 0 | 2 | BSD-3-Clause | 2020-11-05T17:20:31 | 2018-08-29T21:51:28 | R | UTF-8 | R | false | false | 22,204 | r | build_network_window.R |
build_network = function(organism, db, ipl, th, method, degree, remove.sdi, remove.ul, remove.si, update, mainpath, f_pos) {
cat("\n\n>BUILD NETWORK")
# Parametres de la fonction
Os <- organism
os <- tolower(organism)
ex.type <- tolower(method)
inter.type <- tolower(degree)
r.i <- tolower(remo... |
760a1a4de1bff6e766342b2345a1ea5111a64327 | 27e801915229fd513cbd12392ad4644138a30f87 | /man/ninvwish.Rd | 06c50767d4b64ea0e664cfa78960dcd36434dc6a | [] | no_license | cran/norm | 66426c95e0916c379b838f933421e922afe31473 | 4c778c26e12db884174ec427f9ca544a28a6adda | refs/heads/master | 2023-06-26T05:11:59.632171 | 2023-06-18T22:20:02 | 2023-06-18T22:20:02 | 17,697,940 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,004 | rd | ninvwish.Rd | \name{ninvwish}
\alias{ninvwish}
\title{
Random normal-inverted Wishart variate
}
\description{
Simulates a value from a normal-inverted Wishart distribution. This
function may be useful for obtaining starting values of the
parameters of a multivariate normal distribution for multiple
chains of data augmentation.
}
\u... |
d00e7f8b3dfcd9c395e3da20fe5735e4ef38a3b1 | 3eaebe3906513c683843732bc72b9abeb66065b0 | /man/ctrmean.Rd | 426805a9f3e52033b4dd22134a7ed658ddd968cb | [] | no_license | cran/depth | 2049230a64a976ab80ed08c4393646302ba3112a | a4a789404eda3cf1c73b0e7124fc0a11c7b5a8e0 | refs/heads/master | 2021-01-10T21:36:30.107439 | 2019-11-21T10:22:54 | 2019-11-21T10:22:54 | 17,695,467 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,637 | rd | ctrmean.Rd | \name{ctrmean}
\alias{ctrmean}
\title{Centroid trimmed mean}
\description{
Computes the centroid of a Tukey depth-based trimmed region.
}
\usage{
ctrmean(x ,alpha, eps = 1e-8, mustdith = FALSE, maxdith = 50,
dithfactor = 10 ,factor = .8)
}
\arguments{
\item{x}{Bivariate data as a matrix, data frame or list. If it... |
15fee7d8421c8dc208fd759f266e62d05f5e5bff | 527fab53ceb3bb687e0628a56344a9e269dd2b8e | /pso.R | 707737f5b84a5005f95975c3cfed6f6ab7710589 | [] | no_license | sylwesterf/scripts | c09aec1bd500be0f8e856b845c0f7523040825a9 | 9d1fd41e79e827c962cb2b5c79ba71652ae2c311 | refs/heads/master | 2021-05-03T12:37:08.108358 | 2019-02-10T18:07:51 | 2019-02-10T18:07:51 | 72,124,524 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,623 | r | pso.R | particleSwarm = function(f, popSize=10, d=2, l.bound=0, u.bound=1, w, c1, c2, maxIter=100, criterion=FALSE)
{
# psoAlgorithm
# INPUT
# - f objective function
# - popSize number of particles
# - d number of variables
# - l.bound lower boundary (for initial particles)
# - u.bound u... |
c8e86e9d36c640beb2ea24362d3e84d797a535ca | 4f3ba3b0cef1d704fb806deab97feec6fe794524 | /tabs/server/project_specific/data_form/plant_tissue_culture_module.R | f96efdbe0b727b196f15e5c937b759a4f12e28bd | [] | no_license | mkaranja/Banana-Tracker | fe72155f8593813c4a7c608898ae60ceadeb2d7a | 3fbb615ac61ba48fa37f72c788217ae1d9e95e3c | refs/heads/master | 2021-02-17T23:21:47.655521 | 2020-09-29T13:40:37 | 2020-09-29T13:40:37 | 245,135,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,922 | r | plant_tissue_culture_module.R |
tab_files <- list.files(path = "tabs/server/project_specific/data_form/plant_tissue_culture_module", full.names = T, recursive = T)
suppressMessages(lapply(tab_files, source))
observeEvent(input$plant_tissue_culture_module, {
showModal(modalDialog(tags$h2(style="color:#800000;text-align:center;",paste("Plant... |
0492d7e465856f2a8c7dbf4f1e6c0772b48e1804 | 206ce8f05936e26267af547c39978ecc19bef0f8 | /Code/S4_analysis_code.R | efb2ce092e5e7f8bcda3d434f1cc578bdac9edcc | [] | no_license | philipclare/missing_data_simulation | 28df0089cb388ceb767ff1b04309762298fa34c4 | 54bba7aae4702f1d1ee1c7e1e573211b50ad948f | refs/heads/master | 2020-04-29T03:18:55.805749 | 2019-07-22T03:21:08 | 2019-07-22T03:21:08 | 175,804,444 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 64,883 | r | S4_analysis_code.R |
args <- commandArgs(trailingOnly = TRUE)
# args <- c(1,10,"C:/Users/z3312911/Cloudstor/PhD/Katana/missingsim/")
start <- as.numeric(args[1])
stop <- as.numeric(args[2])
n <- stop-start+1
nimp <- 40
packages <- paste0("/home/z3312911/RPackages/")
.libPaths(packages)
load(paste0(args[3],"seeds.RData"))
set.seed(eval(s... |
fac4764d73f6026b1a7311d5deabbe43a54e2119 | fe2b7a0831171064b569860b2a605e19300bbba5 | /00_learn/r/assignment1/corr.R | 4340dc2cace3fd611db6241ec8db6aeb699ecc16 | [] | no_license | calimaborges-drafts/the-archive | 2a47bda99a1abc48c837f15ee2dcb071f838a5b4 | 37195d243e30a0cd5236445d2bf440e44bd448b2 | refs/heads/master | 2021-09-15T11:29:51.479772 | 2018-05-31T13:40:31 | 2018-05-31T13:40:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,218 | r | corr.R | source("readfiles.R")
source("complete.R")
corr <- function(directory, threshold = 0) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
working_data <- complete(directory)
working_data <- subset(working_data, nobs > threshold)
... |
62b232c4c29cc45fa072d67ef91736ae13ced215 | 6de2eabe819ca2ba9596a22fa94a90be3e8dccd6 | /tests/testthat/test-msn.R | 371b413c8b44e23c0700519c4d203007adae0385 | [] | no_license | knausb/poppr | ba6f2a2f199e5ba26654bf9712737a0acd058b7d | e1c4cff46083a6735ee43b9b4f713db0e0fb058b | refs/heads/master | 2020-12-28T23:16:42.816458 | 2016-10-07T18:04:23 | 2016-10-07T18:04:30 | 70,727,479 | 1 | 0 | null | 2016-10-12T18:08:57 | 2016-10-12T18:08:57 | null | UTF-8 | R | false | false | 13,899 | r | test-msn.R | options(warn = -1)
ucl <- function(x){
unclass(x$graph)[-10]
}
set.seed(9005)
gend <- new("genind"
, tab = structure(c(1L, 1L, 0L, 0L,
1L, 1L, 2L, 2L,
0L, 1L, 1L, 0L,
2L, 1L, 1L, 2L),
.Dim = c(4L... |
fb4dc5d94025a9df1e23279e07c6fdc4cfeed90f | 5947e79a3b1fff45aae0e14ea0f599b3c75198f2 | /Model 1 Analysis_SzPt20_Final.R | ab3938901c4edef91fb09b4dd77f73c8d3882f6a | [] | no_license | Fedzzy/Diameter-Class-Growth-and-Yield---Thesis | 0d13123c3ead8f79e4c9a49635e824b374445e9c | c36890bf0f4723b454243099d031c5c63333ac4b | refs/heads/master | 2020-04-06T16:16:03.048697 | 2018-11-14T22:10:56 | 2018-11-14T22:10:56 | 157,612,864 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,935 | r | Model 1 Analysis_SzPt20_Final.R | #======================Regression Analysis of Different Varibles - Model 1 - SzPt20=======================
#Loading in Sourced Functions
source(file="Compiled Analysis Functions.R")
#Load in Plot Level Parameter Data.Rdata
load(file="Plot Level Parameter Data.RData")
#========================================... |
3430c71528a1b9b0e7ceea18e32a01ce3a9ef998 | ff83ee8611ebd3d0280ce1718869d5dca42d1403 | /main.r | e7246c505aff0f7030832c1b5d39f2f172e89cfa | [
"MIT"
] | permissive | Clpr/HealthInequality2018Dec | 661b9b679a68138cfce273634768fbe0d3a11d08 | d88d80c97e46f3e0b10c2c15e83eb0932957e69d | refs/heads/master | 2020-04-13T08:45:59.170010 | 2019-04-11T14:07:13 | 2019-04-11T14:07:13 | 163,091,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,919 | r | main.r | # <-- encoding UTF-8 -->
# Portal for the code of: Socioeconomic Status Inequality and Morbidity Rate Differences in China: Based on NHSS and CHARLS Data
# Author: Y. Jiang, H. Zheng, T. Zhao
# -------------------------------------------
## DOC STRING
#
# The script is the portal of all programs of our submtted paper;... |
98670902aa4b6d8e89d6157d35d4c6c049769548 | af1f3fcbc176817ba9d452652d40e24967540833 | /rd_regress.R | 25e66c14b299f7955a866204332af65c11a8dc21 | [] | no_license | cjs-quant/rd-simulation | 96e982fd844e261339b0cfdb67ce3f71787c7de9 | 300d89781fc67565f0ad53f79f0929a9b29571d7 | refs/heads/main | 2023-03-10T02:49:18.585471 | 2021-03-01T20:59:16 | 2021-03-01T20:59:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,304 | r | rd_regress.R |
rd_regress = function(data, order, bw_l, bw_r, kernel, discontinuity) {
# DESCRIPTION:
# takes RD data and computes an RD regression with
# provided order and bandwidths. returns betas, variances,
# t-statistics, p-values, and the data used in the RD
# ARGUMENTS:
# data: the input data in the RD - RV in... |
7cd0341393916f4dfb083a3cb8f95cb339c51d5b | fd3a072634790d3b0f9ebe1f5466e7ab4d717690 | /man/loglk.Rd | f669e2f730a5b39437b671c8d6305c487095db89 | [] | no_license | WeiAkaneDeng/SPAC2 | e23de6f0b015550c0917b95741fcdb727bdf5734 | 9dd25a397e9a3647205419389db48364909b04d1 | refs/heads/master | 2022-01-27T09:01:01.571072 | 2022-01-14T23:17:06 | 2022-01-14T23:17:06 | 125,806,629 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 797 | rd | loglk.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PPCA_profile_log_likelihood.R
\name{loglk}
\alias{loglk}
\title{Profile log-likelihood of the PPCA model with sample eigenvalues.}
\usage{
loglk(lam, n, tau = 1e-05)
}
\arguments{
\item{lam}{a numerical vector of positive sample eigenvalues}
... |
e7c8793a553d68d5019559dd931cb074bc8f5c22 | a7f0b0a405fc7f1d4c9e4ebb754529f3877a20dd | /RcodeData/linpower.r | 0afc512d78cbd9e161c2e8ef144b71e60b5f7f30 | [] | no_license | tmuntianu/advancedstatistics | 4298c0d0f3a52675d85d67f3aac3b7b11ab5cdc1 | 3885b67da560702df4b6d05b240ada6fde2f727c | refs/heads/master | 2020-12-22T19:03:58.858657 | 2020-01-29T04:08:34 | 2020-01-29T04:08:34 | 236,890,079 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,079 | r | linpower.r | linpower <-
function(n=10,m=3,k=2,NL=30,beta=c(1,.5,-.5),sigma=2,alpha=.05,nSim=10000,st=3)
{
set.seed(st)
C=matrix(c(0,1,1,0,-1,0),nrow=k);a=C%*%beta
X=cbind(rep(1,n),1:n,(1:n)^2)
XX=t(X)%*%X;iXX=solve(XX);ix=iXX%*%t(X)
cx=C%*%iXX%*%t(C)
icx=solve(cx)
d=runif(m,min=-1,max=1)
Cd=C%*%d
qff=qf(1-alpha,df1=... |
b066dbddfaea319f920f236aa97872efdaab2404 | 4050c25b8aa1bd07808af59300bf8058c7890949 | /Scripts/iii.LineageAnalyses/Use/OrderBiomeDiversity.R | 302a15669a16edf198dcccc3f770a139a18b4c2c | [] | no_license | KerkhoffLab/Bryophytes | 6faf03b9f73be24eeff7017f092451e824ac15ca | 9bb7a8e4c0fa5a9f16e4dbfc937b643da0a69ab4 | refs/heads/master | 2021-07-23T17:12:46.284440 | 2021-07-22T16:26:02 | 2021-07-22T16:26:02 | 193,754,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,860 | r | OrderBiomeDiversity.R | #Plotting alpha diversity of BRYOPHYTE orders by biome and continent
#Adapted from BiomeDiversity.R
#Kathryn Dawdy and Hailey Napier, July 2020
# 0.0 FIRST ----------------------------------------------------------------
#Run DataProcessing.R, Continents.R, BiomeContinents.R, BiomeDiversity.R,
#ORange.R, OrdBiom... |
0f590f98766cd991cf3a4b4b41cb6ce5db8f3cb5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RSDA/examples/lynne2.Rd.R | 90f08f78f295ffc03d6bf9a36ce6df0961ba1049 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 179 | r | lynne2.Rd.R | library(RSDA)
### Name: lynne2
### Title: Symbolic interval data example.
### Aliases: lynne2
### Keywords: datasets
### ** Examples
data(lynne2)
display.sym.table(lynne2)
|
ac4a4904ac523f45f604ef13dbc5bb55852ad555 | 28240bc82c7ff1e5e2cf0dce945fd30b293d9f9c | /videogames.R | b16ae093538a5aaef560ee2933408784a7a55ec2 | [] | no_license | Siddhesh19991/video-game-analysis | fca57043e572ba89e63a73a15b40f70075e4ef86 | 7bf456520ca96ad24f9b18944b60bdb633d05c9f | refs/heads/master | 2022-07-22T06:43:03.338064 | 2020-05-24T13:59:25 | 2020-05-24T13:59:25 | 266,552,021 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,863 | r | videogames.R | vg<- read.csv("~/Desktop/vgsales.csv")
vg$Platform<-as.factor(vg$Platform)
vg<-vg[complete.cases(vg),]
vg$Genre<-as.factor(vg$Genre)
vg$Year<-as.numeric(vg$Year)
vg$Publisher<-as.factor(vg$Publisher)
#to check for repeated observations
library(dplyr)
dim(distinct(vg))
dim(vg)
#EDA
library(ggplot2)
... |
ac6aa7afce6d2daf6baa6e8baf4a36002201790e | 3aa093dba669d1f52f43f86d4a23b861ec36d327 | /graphs/plots.r | e787bfdd6bf0ed28f0ff6d5f1cc6bd3a06a0212d | [] | no_license | ramhiser/paper-rlda-comparison | 905cccbd8e3bd9d985e672c0ce39d5db5569dcae | 2840a6dde85caf5136c07e4227d622e4f34265b3 | refs/heads/master | 2016-09-10T16:44:31.387420 | 2011-07-26T16:03:19 | 2011-07-26T16:03:19 | 967,339 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,357 | r | plots.r | library('ggplot2')
load("duin.RData")
load("guo.RData")
duin_summary_q30 <- subset(duin_results$summary, q == 30)
duin_summary_q50 <- subset(duin_results$summary, q == 50)
duin_summary_q100 <- subset(duin_results$summary, q == 100)
guo_summary_q30 <- subset(guo_results$summary, q == 30)
guo_summary_q50 <- subset(guo... |
2a76b95f5b2d87483c81221e8a2fee7b8a313343 | fa0e8940e02fe2d60e7417ae45fbd061baec7703 | /SCRIPTS/Cluster_DTW_embolse_events_filtrados.R | a02c4b5e0485b67fbe3c7ece09b42eaf9044bbed | [] | no_license | CIAT-DAPA/aeps_Grupo_Santa_Maria_DM | dc903583db403567340a416894c6420d09d15288 | c1adfcf7a87d98006274822daea74e7276abb73a | refs/heads/master | 2020-04-12T12:26:10.310273 | 2018-12-19T21:18:49 | 2018-12-19T21:18:49 | 162,491,842 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,821 | r | Cluster_DTW_embolse_events_filtrados.R |
# Analisis cluster de patrones de embolse
# Hugo Andres Dorado B
# 11-10-2018
library(ggplot2)
library(dtw)
library(dtwclust)
library(plyr)
library(zoo)
rm(list=ls())
source('SCRIPTS/funciones_cluster_temporal.R')
source('SCRIPTS/Cluster_DTW_embolse_FUN.R')
# Leer y alistar datos
embolse <- read.csv('DATOS/con... |
585a1544f75418ab8ffa91b16181fa8469123862 | bb1010954d2d068c4c01a756c9d38f6c2cd1523a | /plot3.R | b49fadb126aaa57e7f678ea4eca4277a57709ad4 | [] | no_license | dse-arvind/Peer-graded-Assignment-Course-Project-2 | 68548509a5c82b67424085c3597815b5be9e90ea | 74e1e32c3a25649138bd3f56d16d22feb0ee1217 | refs/heads/master | 2022-11-05T23:45:57.064556 | 2020-06-27T06:42:07 | 2020-06-27T06:42:07 | 275,313,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 307 | r | plot3.R | g<-ggplot(aes(x = year, y = Emissions, fill=type), data=NEIdataBaltimore)
g+geom_bar(stat="identity")+
facet_grid(.~type)+
labs(x="year", y=expression("Total PM"[2.5]*" Emission (Tons)")) +
labs(title=expression("PM"[2.5]*" Emissions, Baltimore City 1999-2008 by Source Type"))+
guides(fill=FALSE)
|
0ed6884305fa42f6e3db3106a99a7de2e466f817 | 3008be2f3ad8c2dceaa18e997d1243772b1b1301 | /Effect of Coupons grocery Retail/marketing_project.R | 3514373970ecc49e60ad23121253ba6d464b3e2f | [] | no_license | as75999/Projects | a1feb70b8e78ca9add47ecfcf5b758d6966ee7f1 | f5b8e5891e0ceda2b8c6364d1e3ef2be516acaf3 | refs/heads/master | 2016-08-12T03:31:50.743275 | 2016-01-19T20:34:39 | 2016-01-19T20:34:39 | 49,965,388 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,910 | r | marketing_project.R | library(sqldf)
#loading files
data=read.csv('D:/Fall/MA/Project/grocery_data.csv')
# getting the column names
colnames(data)
#penetration by commodity
sqldf('SELECT commodity,count(distinct household) household from data group by 1')
#Penetration by brand
#pasta
sqldf('SELECT brand,count(distinct household) household... |
97138bb780a17510733b93d9cda1fdcaae4b1245 | ef4eb23543224c14f4cae67190d1f82bd881a4a4 | /IDESSA/BushEncroachment/AerialImages_MODIS/ProcessAerialImages/unsupervised.R | 07c7486b8ab43d3300b189ca9b33ff7e8748c037 | [] | no_license | environmentalinformatics-marburg/magic | 33ed410de55a1ba6ff943090207b99b1a852a3ef | b45cf66f0f9aa94c7f11e84d2c559040be0a1cfb | refs/heads/master | 2022-05-27T06:40:23.443801 | 2022-05-05T12:55:28 | 2022-05-05T12:55:28 | 9,035,494 | 6 | 7 | null | null | null | null | UTF-8 | R | false | false | 5,084 | r | unsupervised.R | ################################################################################
### UNSUPERVIDED CLASSIFICATION FOR SOUTH AFRICA AERIAL IMAGES
# script calculates predictor variables from aerial images and applys a k-means
# clustering algorithm
########################################################################... |
a2bbbf6b0282e218e6cd65b4a6989be26675b1c5 | e306d18bae6892b8439ab880913b269f2d94d9b3 | /Heri Folder/rscript_plots.R | f93c704c3f7fe768d463ef2963affccd4962fac3 | [] | no_license | LizzieChung/Covid_19_Project | 8aed83f9e89d13d5445a5e23075d3becc0bbd554 | 1e8af1068ef197f6acd170624fff0816520966f2 | refs/heads/main | 2023-05-29T22:09:59.121164 | 2021-06-17T13:08:04 | 2021-06-17T13:08:04 | 377,888,843 | 0 | 0 | null | 2021-06-17T16:05:14 | 2021-06-17T16:05:14 | null | UTF-8 | R | false | false | 4,280 | r | rscript_plots.R | library(tidycensus)
library(tidyverse)
library(tigris)
library(lubridate)
options(tigris_use_cache=TRUE)
census_api_key("8ce6395e8ec025d0c06764b840e7e5fe70221625", overwrite = TRUE, install=TRUE)
data("county_laea") #Dataset with county geometry for use when shifting Alaska and Hawaii
class(county_laea)
#fips_co... |
48beb8e5a692020d874c188d61672089b2e0b7d7 | aee33d9c208e5ee3be85d574ad1217cdde06ce68 | /2. Модуль 2/2.3. Работа с Яндекс Директ API/attributions_report.R | fdfcec770907a36ed6497b62163ef0744f884e11 | [] | no_license | selesnow/r_for_marketing | 7f02cf55a2357df3d5de9bb87943c3dbf8377142 | 8117f879b769f0b66031b86ed4e2b5fc2427449d | refs/heads/master | 2022-06-21T12:20:51.236644 | 2022-05-28T09:31:22 | 2022-05-28T09:31:22 | 142,575,304 | 9 | 4 | null | null | null | null | WINDOWS-1251 | R | false | false | 730 | r | attributions_report.R | # отчёт по конверсиям с моделью аттрибуции за статичный период
attribution_report <- yadirGetReport(DateFrom = "2018-11-15",
DateTo = "2018-11-20",
FieldNames = c("Date",
... |
a772d280ce5048a93b735be333c841cd844972ca | dc612e7334832bf81fe1d9b17e8f779fe40f9ee2 | /plot3.R | b55efa42b22e79c98ecfeb267bedea9f90f29aab | [] | no_license | hestiles/ExData_Project2 | 7819a1ce8d3c6b325c573e2d6e678ed3984b5f18 | 1b647dd23b7683fee55a4f2eab6fcb8822b0a884 | refs/heads/master | 2020-05-07T10:37:48.116799 | 2019-04-10T17:57:55 | 2019-04-10T17:57:55 | 180,425,453 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 509 | r | plot3.R | download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", destfile="emissions.zip")
unzip("emissions.zip")
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.r")
library(dplyr)
##Filter to Baltimore
Bal<- filter(NEI, fips=="24510")
##sum by year & type
Bal3<-aggreg... |
4291268cc3d86699bbfb02774d2c73ae0e75d57d | 5fe7b5cbb596152e3dc05bb570d885c3fb17236b | /Main Simulation/2DataGeneration.R | 3347bf82001b95a0cf1e876970af8c7b0a074b9c | [] | no_license | AndresFPA/DependSim | 8151fec9415a8cc2ac9bd260f503163a646b3622 | a1cd6f7445fbbf34a2c2a2621e45a7f721f28119 | refs/heads/main | 2023-06-01T22:54:50.019409 | 2021-06-07T11:41:20 | 2021-06-07T11:41:20 | 350,783,528 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 865 | r | 2DataGeneration.R | #Comparison of dependence tests
# Variable x is randomly generated
# Variable y is generated based on one type of relationship with y (linear, quedratic, etc.)
# noise is the error introduced while creating the relationship (high, medium, low)
# samp is the sample size (10, 20, 35, 50, 75, 100, 150, 500)
DataGeneration... |
ae8d297c0ee35a9713fa039202d682ca5c1a483e | 76e7b85a99b127e8f71b52c00dc2c9d3baa38676 | /Advanced Programming in R /Lab Bonus/awesomebonus/R/airport_delays.R | 88816e28a2fa0cbf59b64cc756585540f266a0d1 | [
"MIT"
] | permissive | aydinardalan/MSc_Statistics-and-Machine-Learning | 44b65e4dd39d992de0bdb4cd1dcb88186d5d0371 | 0b171fecb481a713b7d61263129275c0aecdb2d7 | refs/heads/master | 2023-03-17T04:01:48.076902 | 2020-04-22T12:31:01 | 2020-04-22T12:31:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,812 | r | airport_delays.R | #' Function for generating plot of mean arrival delays at different airports.
#'
#' \code{visualize_airport_delays} generates a map plot with color scale for mean arrival delay at different airports.Dataset used is the nycflights13.
#'
#'
#' @return \code{visualize_airport_delays} returns a plot.
#'
#' @examples
#' ... |
a67eb4416727bf1d728d89b7d0b0540381876486 | e5413540e82627c1ac6cabcc37ffa0e7a192b2f8 | /plot3.R | f7e577b12a7a0b6de848600a7519275400b761e2 | [] | no_license | tlaytongoogle/ExData_Plotting1 | c47441d6ed0bd7b6fe9dfa85a9989d018849540c | bb3d3de8796315cfa34f73fe8a61a50e6589f7c5 | refs/heads/master | 2021-01-21T09:47:06.457037 | 2015-06-05T17:57:33 | 2015-06-05T17:57:33 | 36,808,748 | 0 | 0 | null | 2015-06-03T14:23:50 | 2015-06-03T14:23:49 | null | UTF-8 | R | false | false | 1,129 | r | plot3.R | # This script must be run in a working directory which contains the electric power consumption dataset
# from the UC Irvine Machine Learning Repository, named "exdata-data-household_power_consumption.zip"
unzip("exdata-data-household_power_consumption.zip", "household_power_consumption.txt")
data <- read.csv(
"house... |
3d7a2600396455944a1e60a7c9bae44880fbe0a1 | 7de2bbabb8dcc0e203a9d24a92379d2e6dfb4c15 | /R/pb.bay.R | 8905991e57363e730a0635d35f95d4d9ebb4267e | [] | no_license | cran/altmeta | 5e87ebf847a968e8af4022775384b87d3229eb39 | 7d39be3ddeb63215a90cf6e42c903e94fb40f4bd | refs/heads/master | 2022-09-18T11:48:51.398907 | 2022-08-29T06:30:09 | 2022-08-29T06:30:09 | 52,831,806 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,712 | r | pb.bay.R | pb.bay <- function(y, s2 = NA, sig.level = 0.1, n00, n01, n10, n11, het = "mul",
sd.prior = "unif", n.adapt = 1000, n.chains = 3,
n.burnin = 5000, n.iter = 10000, thin = 2, upp.het = 2,
phi = 0.5, coda = FALSE, traceplot = FALSE){
out <- NULL
if(is.element... |
fb41e2a70b976c839e6e4c0161651d4bdaca63d4 | e45c6f36a065b6a44e873a773428105de4d3758e | /bases/br_sp_seduc_fluxo_escolar/code/fluxo_municipio.R | 651e86a020896957df2fde97a348d26750039086 | [
"MIT"
] | permissive | basedosdados/mais | 080cef1de14376699ef65ba71297e40784410f12 | 2836c8cfad11c27191f7a8aca5ca26b94808c1da | refs/heads/master | 2023-09-05T20:55:27.351309 | 2023-09-02T03:21:02 | 2023-09-02T03:21:02 | 294,702,369 | 376 | 98 | MIT | 2023-08-30T21:17:28 | 2020-09-11T13:26:45 | SQL | UTF-8 | R | false | false | 4,790 | r | fluxo_municipio.R | # bibliotecas
library(tidyverse)
library(rio)
library(DBI)
library(bigrquery)
library(reshape2)
# diretorio
setwd("~/Documentos/bdmais")
# descobrindo onde as bases são iguais
nomedasvar <- function(ano){
colnames(import(paste0("bases_cruas/seduc/fluxo-escolar-municipio/Fluxo_Escolar_por_Municipio_",ano,".csv"),
... |
56c4b8f1889f278cfee9f5267dee0d0892088613 | 3c148d5fac76e8e32d284787873885e3b64fd38a | /R/carbonise.R | 9009e6bce884f211134ea3de9af61ffb8bdc1056 | [
"MIT"
] | permissive | drdcarpenter/carbonhabitats | b9599e97817433b24fd91d3bf5a64629b2e88d51 | a67f0989503d069fe90c6e6b82ae635ad5443d60 | refs/heads/master | 2022-12-13T06:59:45.663086 | 2020-09-11T14:06:04 | 2020-09-11T14:06:04 | 277,628,133 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,198 | r | carbonise.R |
#' @title carbonise
#' @description Calculate the carbon storage (above ground biomass and soil carbon) and sequestration for UK priority habitats.
#' @param x habitats sf dataframe
#' @param habitats name of the column containing priority habitats types
#' @return sf features with stored above ground carbon and sequ... |
b6234b0c37690f63178e760d1630f231f1ab0f63 | 56d70f2de8ff6e5edd00fb1e9ecd2ab5058e04c4 | /NorDistSim.R | 64861d700dc27b2c4b54152f22d5c920983d3a2b | [] | no_license | mikeabd/Advanced-Stats-R-Source-Code | 5d69cb8f670d17df59c26b098b7057741618d5cd | 47f942ae8c59b91c3e74cef97bc25c58bb7bf968 | refs/heads/master | 2021-01-02T08:30:29.056664 | 2018-03-26T17:48:10 | 2018-03-26T17:48:10 | 99,014,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,219 | r | NorDistSim.R | #Installing required packages
install.packages(c("dplyr", "ggplot2", "forecast", "lubridate", "ROCR"
, "rjson", "timeSeries", "xts", "highfrequency"
, "manipulate", "tseries", "bsts", "boot", "mixtools"))
# Loading Required Packages
library(dplyr)
library(ggplot2)
library(forecast... |
ed6dc3c43d9d1aa150e4a2cc3119fa72f82107d1 | f372c4ac0ee9d301b753dd9db774a3cf405cb15c | /plot4.R | 136f93c7a6bc5d0ad7f27e84829f18c76176a3a5 | [] | no_license | perati/ExData_Plotting1 | d8a0a84691a5848f8452d85975b7269c59455c40 | ff5d8f1fb22ece463d86dcc80b96fea4c2315d3d | refs/heads/master | 2021-01-22T16:10:34.859716 | 2015-10-09T15:26:49 | 2015-10-09T15:26:49 | 43,891,500 | 0 | 0 | null | 2015-10-08T13:48:24 | 2015-10-08T13:48:24 | null | UTF-8 | R | false | false | 2,386 | r | plot4.R |
# Plot 4. Combination of 4 plots - Variables / Time
#read data stored in working directory
data<-read.table('household_power_consumption.txt',
header = TRUE, sep=";",
na.strings="?",
col.names = c("Date", "Time", "Global_active_power",
... |
903c1d7a09dc8c04cd8f0defc137ef1613e830c9 | c9aea1cc5af64391ad2287bb2647f2d24e66bc19 | /R/serve.R | 91a10c896d8fed1fb44a306956a44ccce841ce67 | [] | no_license | rstudio/blogdown | 3b4ab7f040bbcb3e54b12616b522d6297774be65 | 6ab3a2be7a763473430bf750e07a5a15c8c7eeef | refs/heads/main | 2023-07-07T07:39:16.369785 | 2023-06-25T01:50:03 | 2023-06-25T01:50:03 | 72,671,522 | 1,805 | 468 | null | 2023-01-03T14:42:32 | 2016-11-02T18:57:35 | R | UTF-8 | R | false | false | 12,690 | r | serve.R | #' Live preview a site
#'
#' The function \code{serve_site()} executes the server command of a static site
#' generator (e.g., \command{hugo server} or \command{jekyll server}) to start a
#' local web server, which watches for changes in the site, rebuilds the site if
#' necessary, and refreshes the web page automatica... |
01b65a98a6cd0f4d744485853da948b46d190da1 | 83d35a0c687e56de320bbe025fe876df41ea3bf6 | /R/findBAFvariance.R | ef6b3be5cc213627e4bbf7c34f8622d06e14a0b2 | [] | no_license | smgogarten/GWASTools | 797f4cc0d90299195fea29ee1fc24c492267541a | 720bfc6bede713dfcfbff1dd506f4c9f338caa9d | refs/heads/devel | 2023-06-26T13:37:21.371466 | 2023-06-22T12:37:41 | 2023-06-22T12:37:41 | 100,623,140 | 11 | 8 | null | 2023-06-22T12:34:02 | 2017-08-17T16:18:11 | R | UTF-8 | R | false | false | 3,143 | r | findBAFvariance.R | # Description: This function determines which scans/chromosomes
# are a given number of SDs from the mean, using new BAF values
# produced by meanBAFSDbyChromWindow. The function returns a matrix
# (with columns "scanID", "chromosome", "bin", and "sex")
# of scan/chromosome combinations more than X SDs from the mea... |
584e41b78ca630181f7f946bdaace0c97068d1ce | ca00a4f40a7066ff97bcab825d42accf79c2c541 | /man/myWaitForJobs.Rd | 8f61aa6e483339ceb904fa470becddab5cac0b14 | [] | no_license | heiniglab/eQTLpipeline | 63b6f62f70efd749024e080829a76afede5a9745 | 46aa63e5b76b2e420e843b6c85cdd3def11d5bf6 | refs/heads/master | 2023-08-20T19:06:33.463554 | 2021-11-02T16:11:23 | 2021-11-02T16:11:23 | 422,500,174 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 768 | rd | myWaitForJobs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eqtl_lib.R
\name{myWaitForJobs}
\alias{myWaitForJobs}
\title{basic eQTL interface}
\usage{
myWaitForJobs(reg, waittime = 3, nretry = 100)
}
\arguments{
\item{reg}{BatchJobs registry}
\item{waittime}{time to wait before updating job status}
... |
f739a97c903e09085eb6c1a01c645f7c34dae2d4 | 589bf3773cd27c8d5f022747a7344596e7bd8067 | /man/logR.Rd | ddf11e4782199b306f7215e782629f9114d5fa2f | [] | no_license | MikkoVihtakari/MarineDatabase | 974b67991ee2636028f5060e93eae875bd79e800 | 73879eecbedb92e126ce469504b8b18f635ff952 | refs/heads/master | 2022-11-06T18:17:31.395171 | 2020-06-29T14:23:09 | 2020-06-29T14:23:09 | 112,598,897 | 1 | 1 | null | 2017-12-08T09:22:11 | 2017-11-30T10:36:22 | R | UTF-8 | R | false | true | 5,235 | rd | logR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/logR.R
\name{logR}
\alias{logR}
\title{Calculate logarithmic response ratio from a long table}
\usage{
logR(
X,
response,
levels,
groups,
control,
ci.type = NULL,
ci.conf = 0.95,
base = "e",
paired_tests = FALSE,
unlog = F... |
26a71bf75a2a98da8c4565aa459aaa0be26b67bd | a97ad6188fc5b0cb601158976f3799d640f3dc83 | /03_plotting.R | 710249ad8c4a32c2a025d56b6316cd282d9738f7 | [] | no_license | guidocor/R-for-the-lazy-psychologist | fe48c58fec8c9609fb28be23eb8444a96cecca67 | dbece1778c26111301baa87f64d3a7a14fdf8e8e | refs/heads/master | 2020-03-23T16:56:58.461197 | 2019-11-04T22:44:31 | 2019-11-04T22:44:31 | 141,834,918 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | 03_plotting.R |
# https://www.datacamp.com/courses/data-visualization-with-ggplot2-1 |
1f89da983614bab7c33c798d919e012ae846c5cc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mapproj/examples/map.grid.Rd.R | 4cc4d135f202988f3a17d6367e8b9574a8444ed8 | [] | 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 | 998 | r | map.grid.Rd.R | library(mapproj)
### Name: map.grid
### Title: Draw a latitude/longitude grid on a projected map
### Aliases: map.grid
### Keywords: aplot
### ** Examples
library(maps)
m <- map("usa", plot=FALSE)
map("usa", project="albers", par=c(39, 45))
map.grid(m)
# get unprojected world limits
m <- map('world', plot=FALSE)
... |
3122c3c44818ebf954b6dc78c8ece36bc254c681 | e5999ba1e3ee43d0ae195f8ff4518ff54f8ce96e | /C-score, dist.meu.R | 253dc1834e8b90cd27b1fb1efbd2a4685547e8ef | [] | no_license | csdambros/R-functions | 55fea4358621c1eafe64f40390139027cfc2ba6e | f41ad4fd82056c7d0f8140b6cdb70683a8ec050c | refs/heads/master | 2022-03-11T13:52:43.523166 | 2022-03-01T18:53:45 | 2022-03-01T18:53:45 | 9,036,691 | 2 | 1 | null | null | null | null | ISO-8859-1 | R | false | false | 795 | r | C-score, dist.meu.R | ###função criada por Cristian de Sales Dambros em 20/11/2009
###Retorna o índice C-score (Roberts & Stone 1990) para a dada matriz
cscore<-
function (x)
{
Nsites <- nrow(x)
S <- colSums(x)
R <- ncol(x)
P<-((R * (R - 1))/2)
ab <- 0
for (i in 1:R) {
... |
c58428e6d16f7c64758067b2d755a7d9a831d18f | 6514cc40135346964e217ac6c67fae3bd139d9a0 | /func.R | f48285976d9ddb3f64a8463a4b1ef1749b785847 | [] | no_license | asura349/publish | ca2a8e63da04f0a7156f7a0135b1fb82f05d95b2 | 201347cc34cb49d5fee4661b2cc0e7c33b65a456 | refs/heads/master | 2020-12-03T10:55:08.360907 | 2020-02-11T01:21:14 | 2020-02-11T01:21:14 | 231,289,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 886 | r | func.R | # 1 - Model fit summary.
fit.summary<- function(fit)
{
return(fitMeasures(fit, c("chisq", "df", "pvalue", "rmsea", "srmr", "CFI", "tli", "aic", "bic")))
}
# 2 - Modification indices sum.
mi.sum<- function(mi, mod.list)
{
# l1: number of factor
l1<- length(mod.list)
for (i in 1:l1)
{
... |
c8a680416b176bd4d12bded1b592df2b27f5af92 | 5082121b040e484a304d3b11e2844bfb3f6c8409 | /R/DubObs.R | 449b1e642535bd335bd91649177c5b11f5f565ce | [] | no_license | eosnas/AKaerial | 65033cce6add058720edd7646bf12d771c9d2e22 | 91be73f4ed2f04e9c2b058aa372547e355ecde76 | refs/heads/master | 2020-03-23T00:37:36.200318 | 2018-07-13T17:38:36 | 2018-07-13T17:38:36 | 140,874,904 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,336 | r | DubObs.R | DubMatch = function(front.data=NA, rear.data=NA, combined.data=NA, front.obs, rear.obs, time=6, open=5){
#check if combined data was given, if not, read in each file
if(class(front.data)=="data.frame"){
f=front.data[front.data$obs==front.obs, ]
}
if(class(rear.data)=="data.frame... |
79f232c969be18a4e61e643d283f0aef320d4d45 | 533bf408ab134cb0ae829ebc0b65b0f28cfb9b4c | /man/GEVASummary-class.Rd | 4e8982bec481c19ae0ef3284fe239a28a06f6271 | [] | no_license | sbcblab/geva | ff24b15c102efb6df73d7a6509aa5020d0c40756 | e0ed0aa243e1ddde676b1b9284fbb67916a84e49 | refs/heads/master | 2023-03-19T08:45:59.394924 | 2021-03-10T00:18:59 | 2021-03-10T00:18:59 | 241,670,846 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,331 | rd | GEVASummary-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/c_GEVASummary.R
\docType{class}
\name{GEVASummary-class}
\alias{GEVASummary-class}
\alias{show,GEVASummary-method}
\alias{plot,GEVASummary,missing-method}
\alias{inputdata,GEVASummary-method}
\alias{inputvalues,GEVASummary-method}
\alias{inpu... |
2b79a4a0d88e68a203482b6edd963dfb120062f6 | ee0394a3a407f2176bd30b6a6361519cc773584f | /TimeSeries_Models.R | 634c2deed4ed8704332b91e2c520b6e905c504c2 | [] | no_license | send2ankur/MS_Research | 4e9c184861de3532cba4f098700eaaaefdc3dd13 | 3047cb168c8f69d99c860b52aba3324b425ed948 | refs/heads/master | 2021-01-05T06:08:35.524210 | 2020-02-17T14:46:14 | 2020-02-17T14:46:14 | 240,909,498 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,915 | r | TimeSeries_Models.R | library(dplyr)
library(forecast)
library(tseries)
library(dataPreparation)
# Import rds objets for delhi and ghaziabad hourly files
delpb <- readRDS("df_delpb_hourly.rds")
gha <- readRDS("df_gha_hourly.rds")
###########################################
# Delhi
###########################################
... |
131919a5c21ae4e1d505bc616c236ef470c716e7 | 836a779c4e3405bb6ffe79117964b6b516cb3bc2 | /man/errAAll.Rd | b47825942fc595a24f5dd11b08a1f442b7aa29b5 | [] | no_license | RajeswaranV/proportion | 657fe69c4ad62c00ee95077d40683d4452d42c66 | c0e0a60d43113004c0366c71a2b2dac36bf1fc86 | refs/heads/master | 2022-06-21T13:51:47.915249 | 2022-06-11T06:31:18 | 2022-06-11T06:31:18 | 46,557,957 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,582 | rd | errAAll.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/511.Error-Failure_LimitBased_ADJ_All.R
\name{errAAll}
\alias{errAAll}
\title{Calculates error, long term power and pass/fail criteria using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)}
\usage{
errAAll(n, alp, h, ... |
3e34aed9576e87f174d0cb49fa95d816276409d3 | 81b37b77651e5d858a6748b1c5d2e093a434efc3 | /R/rip.R | 955a6743297e10034183b63e0ceb7c479e8ba3c8 | [
"MIT"
] | permissive | gregrs-uk/polite | e8b5df084301ef0141945b1e9d12cebbd703fad4 | 0e48571f176d961a685673d722b6fed3d581ee56 | refs/heads/master | 2020-04-12T03:39:27.961876 | 2018-10-21T15:06:13 | 2018-10-21T15:06:13 | 162,273,203 | 0 | 0 | null | 2018-12-18T10:47:36 | 2018-12-18T10:47:36 | null | UTF-8 | R | false | false | 1,635 | r | rip.R | #' Polite file download
#'
#' @param bow host introduction object of class polite, session created by bow() or nod
#' @param new_filename optional new file name to use when saving the file
#' @param suffix optional characters added to file name
#' @param sep separator between file name and suffix. Default "__"
#' @para... |
b209b1bb7a0fd8c82b2fa59b99d7d9fe5f18f2da | ac2c8ae91a297d8e0f6527b771658645c92c4cbd | /day_4/corona/global.R | 7bb8d8755efc55f53ab6da4a7adc77ce6c7f71b6 | [] | no_license | mohammad-alfaifi/shiny_workshop | 9bb5de0c22cf1dfffb51d3b7b33ec4ab47c2d3b8 | 57457391e94693e958c0100af0b4504bd233f513 | refs/heads/master | 2021-05-19T03:59:05.037885 | 2020-04-29T10:24:03 | 2020-04-29T10:24:03 | 251,519,025 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,505 | r | global.R |
library(tidyverse)
library(tidylog)
library(janitor)
library(shiny)
library(leaflet)
library(scales)
library(plotly)
library(knitr)
library(shinydashboard)
d <- read_csv("data/corona.csv")
d_last <-
d %>%
filter(date == max(date)) %>%
mutate(color = case_when(
confirmed_num <= quantile(confirmed_num, .2... |
eaa698c45a08e92062cdaa626c08f75a2e11a00e | f387d8bac0ca340364a8f2f3767233c20797e2e4 | /extraindo_dados_TWEETER.R | 526d4d630c3d8243021d600e8d655297824696c2 | [] | no_license | WOLFurriell/twitterR | 24ba737808bac151de8f24340a86ba006eb0a40b | f2317e282e62f9391b5c4e731381961a15886928 | refs/heads/master | 2020-04-15T14:10:29.591089 | 2019-01-08T22:40:32 | 2019-01-08T22:40:32 | 164,744,504 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,916 | r | extraindo_dados_TWEETER.R | library(twitteR)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(tm)
library(dplyr)
library(data.table)
library(WriteXLS)
rm(list = ls())
setwd("D:/TWITTER/Bancos_tweets")
#-----------------------------
#colocando as chaves
api_key <- "xxxxxxxxxxxxxxxxxxxxxxxxxxx"
api_secret ... |
40b3588cc575cd6348f22ad60f1d72001ba5723e | 580eb05c82c46724defaa72508b2fcf44340a5de | /man/eel.test2.Rd | 26480e878851a44d0d3413c36d3cc57e15aeb730 | [] | no_license | cran/Compositional | 5bf22542a6242cc9c4ff66d5e081332e65f4fc76 | 608fa713f4c50933b580d1b3097bc81051777f37 | refs/heads/master | 2023-07-06T09:05:20.638372 | 2023-06-29T18:10:02 | 2023-06-29T18:10:02 | 51,369,033 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,436 | rd | eel.test2.Rd | \name{Exponential empirical likelihood hypothesis testing for two mean vectors}
\alias{eel.test2}
\title{
Exponential empirical likelihood hypothesis testing for two mean vectors
}
\description{
Exponential empirical likelihood hypothesis testing for two mean vectors.
}
\usage{
eel.test2(y1, y2, tol = 1e-0... |
a04c33d4146d7683212e649976b92558c5040118 | 461ea396babcd50cf55f83ba7e9b4e50e2588807 | /myFunctions/R/visualization.R | c49bb36020c1afd8148202e2b1c4331d4ab3f257 | [] | no_license | vikram-g/R-Functions | e9f7dca39b41fa3b2c5552dd0013b6f72fa480e9 | aabb1ac6616b75ac23d4985af99036c8fb3c9a27 | refs/heads/master | 2020-03-27T00:33:00.735484 | 2018-08-22T03:27:38 | 2018-08-22T03:27:38 | 145,633,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,114 | r | visualization.R | #'
#'Plot logit curve
#'
#'Plot the logit curve along with dot histogram of the actual target values.
#'
#' @param df Dataframe containing the target as well as variable to plot
#' @param varName Name of the variable that has to be plotted
#' @param target Name of the target variable
#' @return Logit plot as output
#... |
3a0fcac3e71c590e4402ac7b02172d2e7e560964 | d10a821c609e9afea8ef8c022a64e57a6c440184 | /R/test_new_gam.R | 43f96a604e369fadcba5db977d9bd7e52c1fd3a7 | [] | no_license | ToonVanDaele/trias-test | d8aef7d6c5ca2c1b6a6d19a8ac6be551768b08f1 | 04fe9e1d81bdb6b69cdad56b70d3f972ab5d5917 | refs/heads/master | 2020-05-28T02:06:58.706784 | 2019-12-06T16:57:20 | 2019-12-06T16:57:20 | 188,849,550 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,544 | r | test_new_gam.R | ### Test gam methods
library(tidyverse)
library(mgcv)
library(gratia)
# Load data
df_s <- readRDS(file = "./data/df_s.RDS")
df_pp <- readRDS(file = "./data/df_pp.RDS")
df_xy <- readRDS(file = "./data/df_xy.RDS")
spec_namE1s <- readRDS(file = "./data/spec_namE1s.RDS")
# Select species
spn <- "1718308"
spn <- "1690429... |
410f3b8df8f1a87369bdf413b24a6e5961b4983b | 7a2e2f6e661540a4a918ed5275b68dd1a8188d8e | /data_prep/db_init.R | 8c206078fae52ef6f6b3ca25b654fa61351363ae | [] | no_license | RichardHHill/course_planner | a92881fafab24f5e95c4d7d5ce21083fe94c99d6 | 5d2fd51fe7ec581c0781b99bb94878b95b62e08d | refs/heads/master | 2021-07-13T12:29:30.357835 | 2020-08-08T01:59:40 | 2020-08-08T01:59:40 | 189,785,402 | 0 | 0 | null | 2020-08-04T21:41:33 | 2019-06-01T23:11:32 | R | UTF-8 | R | false | false | 1,575 | r | db_init.R | library(dplyr)
library(RPostgres)
Sys.setenv("R_CONFIG_ACTIVE" = "default")
app_config <- config::get(file = "shiny_app/config.yml")
conn <- tychobratools::db_connect(app_config$db)
# DBI::dbExecute(conn, "DROP TABLE IF EXISTS semester_courses")
# DBI::dbExecute(conn, "DROP TABLE IF EXISTS majors")
# DBI::dbExecute(c... |
24cd3377a5103584bbfebfa6bff0d1fdd03df7a9 | 0a03b56e5c7f9aa8c50ba1bc8448adc0f3c5d5a9 | /man/rename_region_code_column.Rd | 7058435e876fd45f662d5bcbd637bb3e95e7acd7 | [
"MIT"
] | permissive | FelipeJColon/covidregionaldata | ae94fa51215d28c62e99fae607fd2ae6230e6ebb | 5dd0c648d531263ae8677d3a36dd9f659d13a2f8 | refs/heads/master | 2023-02-13T18:24:14.755101 | 2021-01-08T17:17:37 | 2021-01-08T17:17:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 880 | rd | rename_region_code_column.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_region_codes.R
\name{rename_region_code_column}
\alias{rename_region_code_column}
\title{Helper to rename the region code column in each dataset to the correct code type for each country (e.g. ISO-3166-2).}
\usage{
rename_region_code_colu... |
ff6ddf2aa9fa26409345b9438ff974c9084a5da3 | d865df19aa3903d609005b695fcc0cda41de3773 | /ch3_operctovol_20190102.R | c6336d8a499479fb5e4c61f0db301e8aa439edcd | [] | no_license | willking2/barnacle-IPM | 93fa9799cbae3a94f57a6d5c9cb562d940f45213 | 907f36044e245a8d7bd5a3dbaf4643dd8b78b9b3 | refs/heads/master | 2020-04-24T00:12:51.143192 | 2019-05-10T20:54:31 | 2019-05-10T20:54:31 | 171,559,521 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,329 | r | ch3_operctovol_20190102.R | ### ch 3 using expt 2.4.1 ch 2 data
### Will King
### comparing operculum length to volume for banracles used in ch 2 mesocosm and field experiments. All barnacles pooled together and chosen randomly for measuring (all temps, meso/field, crowding, size pooled together)
### use this to do regression to predict vol from... |
41b62c5b1546122e596714a9d548047d5a27f2bc | 3e3cbe2c54db5ac4267d17764877d0e824e08be5 | /man/trp.valueMatrix.Rd | f74119dd57eeac58851e1a783ad55d2a97f8c328 | [] | no_license | avilesd/productConfig | 7444e5d04ce526654b8554a1d6af3adb71d7e8d5 | 516dd365c7f2c1733de2644a180a9098013a4125 | refs/heads/master | 2020-04-06T07:02:03.512297 | 2016-08-18T23:14:38 | 2016-08-18T23:14:38 | 37,199,135 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 5,094 | rd | trp.valueMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TRPFunctions.R
\name{trp.valueMatrix}
\alias{trp.valueMatrix}
\title{Returns a Value Matrix using three reference points}
\usage{
trp.valueMatrix(dataset, userid = NULL, attr = NULL, rounds = NULL,
cost_ids = NULL, tri.refps = NULL, beta_f ... |
6b2f93e7b4ebd7203c5cb23794389b7b7d116226 | 4c79590a7f0ca235979a1e663f865240fc7f0f2a | /Test1.R | 7c0c6e369ac2536f4aa4259c3252a5bb52909f1c | [] | no_license | ToniBarta/Clustering-data-machine-learning-in-R | 4379cc5f5f79c09b24f1be8eb31c07c3128f692a | e8bc8dbd1cc38a365a1df769d69829a225230f78 | refs/heads/master | 2021-01-13T14:19:52.138770 | 2014-03-09T19:34:12 | 2014-03-09T19:34:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,972 | r | Test1.R | library(RPostgreSQL)
library(hash)
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname="momentus_development")
dbListConnections(drv)
# library(RPostgreSQL)
dbGetInfo(drv)
summary(con)
# rs <- dbSendQuery(con,"select songs_skipped from intra_weather_categories")
# fetch(rs,n=-1) ## return all elements
d... |
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