blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
abf6d00230576a6fb48cefae615f1cb51334efb9 | d86dc6266f527a14c8705a775267e26490b32708 | /man/multipleGroupDEgenes.Rd | a0f11d05483dd4e01be7cfa3b16fac53758e8ac3 | [
"MIT"
] | permissive | Tong-Chen/YSX | a3c221ea891d9ac5136e35a231606440484e981c | 4bb1d21f3d4fa5703937207fce4301ad5ac3aa16 | refs/heads/master | 2021-06-26T03:19:10.277663 | 2021-01-12T13:14:15 | 2021-01-12T13:14:15 | 172,993,901 | 7 | 8 | null | null | null | null | UTF-8 | R | false | true | 1,365 | rd | multipleGroupDEgenes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/transcriptome.R
\name{multipleGroupDEgenes}
\alias{multipleGroupDEgenes}
\title{DE genes analysis for multiple groups.}
\usage{
multipleGroupDEgenes(
dds,
comparePairFile = NULL,
design = "conditions",
padj = 0.05,
log2FC = 1,
dro... |
dd66c3d6d294b7aac7896c676e94c36083d61a90 | 78fd77926f0f663b5da2bb5b5c167ad7c0855ab0 | /run_analysis.R | 76f2cd439a61a90dd98990c79a4b24eef9d7223a | [] | no_license | bencollins21/Getting-and-cleaning-data-course-project | 4492885609614467d4ae0524be0a09b9bfcff618 | f21082fd50252f57a3c72039f2c4566d7a5118b1 | refs/heads/master | 2020-12-04T05:44:36.388802 | 2020-01-03T18:01:56 | 2020-01-03T18:01:56 | 231,638,183 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,405 | r | run_analysis.R | ## Script: run.analysis.R
## Course: Getting and Cleaning Data
## Author: Ben Collins
## This script has been created in order to fulfill the requirements of the
## Getting and Cleaning Data course project, which are as follows:
##
## 1. Merges the training and the test sets to create one data set.
##
## 2. Extracts o... |
e986b92309d9c8f42e0a6c29081581f27aad3736 | 254e5f76e62f641ed5402c645bf9b56ccfa6b374 | /R/wbccPredictor.R | 418e320262cba360d97eeb43fc511f3c6d60100e | [] | no_license | molepi/DNAmArray | cbd5ab8d583283e2aa8b567866d408f450e6dfdf | f7ca1f7ca567bce749529d94e49e457b57287c1f | refs/heads/master | 2022-09-10T20:03:11.303962 | 2022-08-24T07:21:14 | 2022-08-24T07:21:14 | 80,819,365 | 8 | 3 | null | 2022-08-24T07:21:15 | 2017-02-03T10:29:00 | R | UTF-8 | R | false | false | 6,757 | r | wbccPredictor.R | ##' Train the cell-type composition predictor using partial least squares regression on mehtylation values
##'
##' @title Train a predictor for cell-type composition
##' @param data Matrix with DNA methylation beta-values
##' @param covar Matrix of covariates to correct for in the model
##' @param cellPer Matrix of cel... |
9e06a0a18c58fe11c45e8567738f12ceb53f28ac | a83946130e2f439f8b44e36ac6919cd94bda56f2 | /folders/anRpackage/man/scaleBar.Rd | 617837a1efda81e727e676b3c1e22c7e9b60b0f3 | [
"MIT"
] | permissive | bwtian/Rtemplate | c44088eaf542558f44b5662586856a9f7c0b476f | 275539f9d0f3699715fa03a86e6268898704b707 | refs/heads/master | 2021-01-10T22:02:09.226342 | 2014-11-26T01:26:03 | 2014-11-26T01:26:03 | 24,021,959 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,866 | rd | scaleBar.Rd | \name{scaleBar}
\alias{scaleBar}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
scaleBar(lon, lat, distanceLon, distanceLat, distanceLegend, distanceUnit, dist.unit = "km"... |
dd5028fadcdfe2fdf870936775c6b9bfe4c1b43c | 2a2646e70f5888309470ec1a2cf8f4a18f2a8780 | /R/json.R | 7422a2b242a3c456c8b49b9b96a9379559ac4622 | [
"Apache-2.0"
] | permissive | stencila/rasta | 174512f7cef34b5ff23156f73d6d2a4a39146d8d | 4fa308b6e5fd8e49b1b9053a8afcb71d5af71d6b | refs/heads/master | 2021-10-29T08:25:02.078134 | 2021-10-11T22:00:23 | 2021-10-11T22:00:23 | 169,194,351 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,237 | r | json.R | #' Functions for JSON handling
#'
#' These functions simply provide a consistent means for calling `jsonlite`
#' functions `toJSON` and `fromJSON` from `json-rpc` and `logger`.
#'
#' @name json
#' @rdname json
NULL
#' @include util.R
library(stencilaschema)
#' Declare that a node is scalar
#'
#' So that the object i... |
43027f56f0ce4d847b0874e84affb1f54f035904 | 5b649005f242829df1ea2893b956684e5d7b837c | /man/evaluate_clonealign.Rd | f047da2058aa88c40b4f318a5a4af84f60051ef7 | [
"Apache-2.0"
] | permissive | nceglia/clonealign | 11a76695d0560e719fdd669164ae4de34904777f | 773e59e5a64e5b42a94ef8703c999e5ba62e7e27 | refs/heads/master | 2020-05-03T05:15:03.063684 | 2019-03-29T18:54:49 | 2019-03-29T18:54:49 | 178,442,850 | 0 | 0 | null | 2019-03-29T16:44:32 | 2019-03-29T16:44:32 | null | UTF-8 | R | false | true | 4,021 | rd | evaluate_clonealign.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/clonealign.R
\name{evaluate_clonealign}
\alias{evaluate_clonealign}
\title{Evaluate the quality of a clonealign fit}
\usage{
evaluate_clonealign(gene_expression_data, copy_number_data, clonealign_fit,
prop_holdout = 0.2, n_samples = 2, s = ... |
e993858892e92ae19f3c29911bc792cba57015d4 | 1a08f81a8ebee2753b42333e77735f9416f4c396 | /R/network.R | 695481c82785d72b66f3d7dbb77402187255d602 | [] | no_license | gaospecial/biblioreport | 1404645207c667e71c0bd11c36af2fe9120ff124 | ab407fad6223c8ab8c5eecd80d21bb7c0471d082 | refs/heads/master | 2023-09-05T19:31:05.171230 | 2021-11-09T11:40:29 | 2021-11-09T11:40:29 | 334,099,411 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,980 | r | network.R | ## 进行（文献）网络分析的函数
## 简化网络
## 限制 node 数目和edge.weight
#' Simplified network
#'
#'
#' @param M is a bibliometrixDB object
#' @param from start year
#' @param to stop year
#' @param nNode 最多允许的节点数目
#' @param edge_weight_cutoff 边的阈值（小于此值的边会被去掉）
#' @param analysis 分析的类型
#' @param network 网络的类型
#' @param field 网络中的节点来源的列... |
9fa4805776758a30b6f8a35e642ecc52bab02dc1 | 7e8aa90cd9737ed979d1dac9350f1362bcfe4a96 | /code/test_project1.R | 0b30856e29c73a718c371a9bb8ae80707b959d24 | [] | no_license | JXU0728/STAT-628-module-1 | 1f869e14a776246dd9009f86af12003143b7ff0e | cb628e13194ab9d7973ece7317ee691fff50da66 | refs/heads/master | 2021-05-04T15:18:27.731263 | 2018-02-05T16:08:13 | 2018-02-05T16:08:13 | 120,225,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,188 | r | test_project1.R | library(ggplot2)
library(caret)
library(car)
library(rattle)
library(LEAP)
library(dplyr)
body_fit <- read.csv("BodyFat.csv",header = T)
qplot(DENSITY,BODYFAT,data = body)
scatterplotMatrix(body[,c(-1,-2,-3)])
plot(body$BODYFAT)
### DEPENDENT VARIABLE: body fat
summary(body_fit)
plot(body_fit$BODYFAT)
mo... |
7e14c206b0498c0c0415c6a97fecf93fd635dce4 | 4e6b07f669f14a8aa4de162f0251f8cf7d3bce38 | /man/susie_auto.Rd | 28c994ca9abfdf886dedfca0b94e35755c3085cc | [] | no_license | jhmarcus/susieR | 0e48816b787dfdff49ba030359b084ff2063198b | 2f1e015af4bdb1b59874f6f172aa5a9b2d8faace | refs/heads/master | 2020-03-16T23:39:00.341229 | 2018-05-10T20:59:21 | 2018-05-10T20:59:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,002 | rd | susie_auto.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/susie_auto.R
\name{susie_auto}
\alias{susie_auto}
\title{An attempt to automate reliable running of susie even on hard problems}
\usage{
susie_auto(X, Y, L_init = 1, L_max = 512, verbose = FALSE, init_tol = 1)
}
\arguments{
\item{X}{an n by p... |
9d467ff82800db24b3d2e66269c55090f9b78825 | cb56b3fb311c79a9f0fef6b7815b9ba934f16f50 | /R/util.R | 38a9978196c300047f519b721e9ad8d3f3bd175d | [] | no_license | yutannihilation/Redashr | c3d49d346eeb3b3db9c1c2f8421e65af0c5ddcc0 | 71b525872c0c7b7f7d7cf8f3eaee8ad4d869b89e | refs/heads/master | 2021-07-11T18:17:55.915287 | 2017-10-16T04:46:45 | 2017-10-16T04:46:45 | 106,435,124 | 3 | 3 | null | 2017-10-16T04:46:46 | 2017-10-10T15:23:16 | R | UTF-8 | R | false | false | 2,578 | r | util.R | # Utils
redash_request <- function(verb, url, api_key, ..., verbose = FALSE) {
res <- httr::VERB(
verb = verb,
url = url,
config = httr::add_headers(Authorization = glue::glue("Key {api_key}")),
...
)
httr::stop_for_status(res)
if (verbose) {
httr::content(res)
} else {
suppressMes... |
a3c0fec25823e8b1fa34b2c3b9d5f87431557e23 | b9b158361d14bdf8a9b6965ad07a5119164cfcde | /cachematrix.R | 4cfe02ff724adcfd03dbaeb7807b9c0528b0e017 | [] | no_license | dhrubaraj/MyProgrammingAssignment2 | b2fa1ae787af26423d3c1ecfaaf795c9b2210ae0 | e94d4e609797c50af51365c5ae48805418ccb2e0 | refs/heads/master | 2021-08-28T12:04:04.101571 | 2017-12-12T06:07:17 | 2017-12-12T06:07:17 | 113,951,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,132 | r | cachematrix.R | # makeCacheMatrix creates a list containing a function to
# 1. set the value of a matrix
# 2. get the value of a matrix
# 3. set the value of an inverse matrix
# 4. get the value of an inverse matrix
makeCacheMatrix <- function(x = matrix()) {
inv_mtrx <- NULL
set <- function(y) {
x <<- y
inv_mtrx <<- NULL... |
6abd3b8d630852a916059b6a21163571f8679654 | ba7ddc3dd7e622a0341c38aa4dae5873cefb45d1 | /plot_h_up_backlog.R | 5b6c56973c54355897c71292e0776d9091d693e0 | [] | no_license | paulnikolaus/StatNC-fBm-Extension | 7003784958a77e7953e78ded433defdece02a55c | 661801e578455532e51a989fbacae6d29cf885af | refs/heads/master | 2021-06-13T01:44:51.739924 | 2021-03-20T14:36:41 | 2021-03-20T14:36:41 | 146,993,500 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,844 | r | plot_h_up_backlog.R | ##### plot_h_up_development.R #####
library("reshape2") # melt
library("ggplot2")
source("Bound.R")
# Show development of the backlog bound for different H_ups
#' @param true_hurst Value of the true hurst parameter
#' @return data frame of mean of h_up fir different sample sizes
backlog_development <- function(true_... |
db38335594b6aae77c27d7b712247f162f672d60 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ElemStatLearn/examples/orange4.test.Rd.R | 4a7d3b0f5b1d0619436b0b83d7a86976e8130fca | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 169 | r | orange4.test.Rd.R | library(ElemStatLearn)
### Name: orange4.test
### Title: Simulated Orange Data
### Aliases: orange4.test
### Keywords: datasets
### ** Examples
str(orange4.test)
|
601fdc19070751b79871367dacf5842e35eeddbb | 29e424922b3d0e510736f71314795500e8e99341 | /05_simulation_5_20180625.R | d5c170ce35c0444b53f8d91fc5b98d4cb6051a8a | [] | no_license | holgersr/Bayesian-inference-and-simulation-of-Elo-scores-in-analysis-of-social-behaviour | f9e31801820658500b0f679d16ec40c3991c1b9b | 2a0c67188c8c1be98b7ad65de83590cbd9150022 | refs/heads/master | 2021-09-19T09:20:20.299115 | 2018-06-25T06:41:06 | 2018-06-25T06:41:06 | 95,112,337 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,767 | r | 05_simulation_5_20180625.R | ## #############################################################
## Simulation for unbalanced sample where prob to be observed ##
## is proportional to the underlying strength ##
## #############################################################
set.seed(1604)
starting_scores1 <- seq(-6, 6, length = ... |
7aa97b44bae2258c01a4aa1dab98d5d6993be9ea | 400b384715f5f02ef43118f792d9eb73de314b2b | /man/min_dist.Rd | fa79752135a4296fc3d5c6c36f8587c841d2de85 | [] | no_license | yassato/rNeighborQTL | 8d250721fee767f96dd115324321a1c18ba67e1c | f3c54151f794fa0c9c213c9f0834b8e51aa4d6a4 | refs/heads/master | 2023-04-28T02:43:01.742030 | 2021-05-11T08:06:03 | 2021-05-11T08:06:03 | 253,141,548 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 899 | rd | min_dist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/min_dist.R
\name{min_dist}
\alias{min_dist}
\title{Calculating the minimum distance}
\usage{
min_dist(smap, grouping = rep(1, nrow(smap)))
}
\arguments{
\item{smap}{A matrix showing a spatial map. The first and second column include spatial p... |
5332b9ef4ccc900c07000cbf718a6085db7f39c9 | 2d9379912deaa5a698a2dea503dcd0e21b3e0086 | /First_Course.R | 5d475053ebc61d14ef2307e9bb38d5a353bdd4c9 | [] | no_license | pipinho13/R_Specialization | ad4045b35fcc41dde9a503b9985cd452524c4b67 | 8f0fb5823880dd6d8b9d838494d63f9932e33492 | refs/heads/master | 2021-01-12T05:33:57.857817 | 2017-02-12T21:21:23 | 2017-02-12T21:21:23 | 77,130,599 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,713 | r | First_Course.R | datafile <- file.path(datapath, "urban.csv.gz")
urban<-read_csv(datafile)
urban<-read_csv(datafile, col_types='cccdc')
urban<-read_csv(datafile, col_types='cccd-')
urban<-read_csv(datafile, col_types='cccd-', n_max=100)
object.size(plants)
wc_4 <- worldcup %>%
select(Time, Passes, Tackles, Saves) %>%
summarize... |
7e6b9e03c30157799e3f86175b5d01a1f23e2e2b | b64fdb45e11c5d8717727b89d1361cbfd3943f1e | /man/rel.Rd | 9cfdbd688cba1a7a96c6d9e3eb99a998e60918b8 | [] | no_license | garrettgman/ggplyr | 4c11194432d96557a02cab69bb579f791e4105d5 | 34a2861f875a9d30949a6fed737b40b742d1fba8 | refs/heads/master | 2016-09-06T02:47:39.162635 | 2012-06-21T16:33:04 | 2012-06-21T16:33:04 | 4,385,779 | 21 | 5 | null | 2012-06-21T16:39:19 | 2012-05-20T16:24:04 | R | UTF-8 | R | false | false | 674 | rd | rel.Rd | \name{rel}
\alias{rel}
\title{Make a rel class object}
\usage{
rel(x)
}
\arguments{
\item{x}{numeric the proportion of the relative width or
height to use as the final width or height}
}
\value{
an object of class rel
}
\description{
rel class objects are used to specify the width and
height of glyphs in \c... |
b508a3203865efd7a0b8a14000e258ce11dea245 | a260ca9899db02c781baaf1e6bf80d335920f417 | /server.R | 317617a0a24ca02463cdaf8c181b251f186d0b05 | [] | no_license | AlexisSem/DataProducts-Course-Project | 7176972dd51fb24909f78fa4b31a7cd3895b204e | 19d8f9c9d119cbecf6eae4412c99d357e503e203 | refs/heads/master | 2020-03-30T06:11:47.119127 | 2018-09-29T08:48:36 | 2018-09-29T08:48:36 | 150,844,467 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,334 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(reshape2)
citiesDistances <- melt(as.matrix(eurodist), varnames = c... |
b6dd75768777ace2d319c0917eb257420e7da46c | b75d2bf6815b9a7d7a12c5059d97d875c62998e7 | /bin2dec_script.R | 943d00ae58a19cfaf07c258b904bf5a3fa46c9eb | [] | no_license | AdrianBach/Uncertainty-workshop-Trondheim | 4c23e5a47ffb66c3ab8edb5f9abbe2efaa369f9e | def318743d83742535bc51412e1b16318ef847b5 | refs/heads/master | 2022-02-07T06:19:18.143732 | 2019-08-05T16:07:16 | 2019-08-05T16:07:16 | 171,331,573 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,479 | r | bin2dec_script.R | # Adrian Bach
# NINA uncertainty workshop - build a barplot of frequency of use of the different types of uncertainty representations in Conservation Policy Documents
# Script to build the binary code associated with each observation and convert it into decimal to plot
## Clear list
rm(list=ls())
## set work director... |
1cd52a975765f5364f690e7ca8cc53de6197e141 | 9e87d7b269fbd2adb83ca0b2302378b23cf47fd8 | /init.R | fe8363ab4b0560da6e630612676155e55544498f | [] | no_license | alhostat/perinataldep2019 | 6d58d98481f84cb5be1ef889c414e982fb46db42 | 39aa44158b601bd7c846ce12ff72b308698e701f | refs/heads/master | 2020-06-29T07:57:06.078272 | 2019-09-01T01:16:33 | 2019-09-01T01:16:33 | 200,479,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 482 | r | init.R |
library(tidyverse)
library(magrittr)
library(broom)
# DATA -------------------------------------------------------------------------
perinatal_data = read.csv('Data/EffectSizes.csv')
depression = filter(perinatal_data, type == 'Depression')
anxiety = filter(perinatal_data, type == 'Anxiety')
worry = filter(perinata... |
9d6d05ed1b6c98886dfda04b728fb316b5f4af06 | 2e73452c271ebbfd3e55e661fe887c3b30d75625 | /cachematrix.R | 3cef1b55403a346487472aa57a627a4747e63174 | [] | no_license | OrwahAdayleh/ProgrammingAssignment2 | 63acd6ee4627b8012ff013065e1e4a165ed2fd5b | 4ad68249e8bdbb102ff7105b882a075b8ea7f160 | refs/heads/master | 2022-12-16T04:17:36.451204 | 2020-09-09T05:38:31 | 2020-09-09T05:38:31 | 293,724,154 | 0 | 0 | null | 2020-09-08T06:42:19 | 2020-09-08T06:42:18 | null | UTF-8 | R | false | false | 784 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## function take matrix as input and return the inverse using cash variable
library(MASS)
makeCacheMatrix <- function(x = matrix()) {
var1 <-NULL
func1 <-function(y)
{
x<<-y
var1<<-NULL
}
get <- function() x
SetTheInverse <-funct... |
a80af381c99ac82e34c633efd9db06f0c0cf4788 | e4602c3639d68b9d15c0a585723ca2d597875b18 | /R/auditCommonFunctions.R | 4a62c5b5a1d415e25c2897c47ddf68fc857b5ca2 | [] | no_license | TimKDJ/jaspAudit | ba26e3b72b820eb7564445e16f04eca3d78377e3 | de3d92f9a88f082ff75b61032e1a1c60421891b0 | refs/heads/master | 2022-12-07T12:00:37.331627 | 2020-08-18T07:46:07 | 2020-08-19T11:42:52 | 288,144,681 | 0 | 0 | null | 2020-08-17T09:59:49 | 2020-08-17T09:59:48 | null | UTF-8 | R | false | false | 169,282 | r | auditCommonFunctions.R | #
# Copyright (C) 2013-2018 University of Amsterdam
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# This program i... |
1d8e57d0ed8d72d3370b876f6ee09c337b4edaa3 | 6aa2d006ebc00e835470ea883725a30e9e4c17ec | /run_analysis.R | f339d3e4206309e4745259e605157a4821406258 | [] | no_license | yashkhaitan99/Getting-and-Cleaning-Data-Assignment | b93e36984db5885b89fbca5b2f7abaf06d839340 | c6be7de9b0ba034de8a03f926ba9e6867a3b4084 | refs/heads/master | 2022-11-19T16:09:16.232704 | 2020-07-23T08:57:11 | 2020-07-23T08:57:11 | 281,896,654 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,718 | r | run_analysis.R | library(dplyr)
# test data
setwd("C:/Users/DEL/Downloads")
Test_X<- read.table("./UCI HAR Dataset/test/X_test.txt")
Test_Y<- read.table("./UCI HAR Dataset/test/Y_test.txt")
Text_sub<- read.table("./UCI HAR Dataset/test/subject_test.txt")
#Train data
Train_X<- read.table("./UCI HAR Dataset/train/X_train.txt")
Train_... |
b501f11f9684dc4acbfdd86934d496110543269f | 1fdd33233f2416a926b903e5494d278ebb208a37 | /plot1.R | 09969b3c23cd675e8e1994b43cf8b184fd3d4fcf | [] | no_license | boizette/ExData_Plotting1 | 032aed7ea0ee22e3fe6b9c531486c67459609fad | 0906feaaea37928186a5ab8a07d0842928238ee0 | refs/heads/master | 2021-05-09T13:51:15.143171 | 2018-02-02T14:36:06 | 2018-02-02T14:36:06 | 119,046,718 | 0 | 0 | null | 2018-01-26T12:00:17 | 2018-01-26T12:00:16 | null | ISO-8859-1 | R | false | false | 1,272 | r | plot1.R | plot1<-function(){
##Initialisations diverses
##install.packages("data.table")
library(data.table)
##Récupération des données
f_url<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
temp<-tempfile()
download.file(f_url,temp)
unzip(temp,"household_power_consumption.txt")
ColNam... |
36ea754c05dac35e54bc34812e4cf22f1dd3022f | 2d45d4f3d5dbc688c36c9f6493ccca1d3ec303dc | /src/inactive/explore_phistats.R | 0825026f46e45a5a07c5ecbaf6520f15c6ae26ab | [] | no_license | TomHarrop/stacks-asw | 182aae84eb4b619e6bde285b55e30d4c91cbd232 | 8a4f8d79a64b4ce0f27385596489cc876e15bde2 | refs/heads/master | 2021-05-06T09:05:21.766140 | 2020-08-04T02:15:33 | 2020-08-04T02:15:33 | 114,046,714 | 2 | 1 | null | 2020-05-22T05:39:54 | 2017-12-12T22:40:38 | R | UTF-8 | R | false | false | 3,544 | r | explore_phistats.R | library(data.table)
library(ggplot2)
fai_file <- "output/005_ref/ref.fasta.fai"
phistats_file <- "output/070_populations/ns/populations.phistats.tsv"
phistats <- fread(phistats_file, skip = 3)
fai_names <- c("Chr", "chr_length")
fai <- fread(fai_file, select = 1:2, col.names = fai_names)
n_to_label <- 20
# chromos... |
33c1092762bb71678fdeedeb6815ec4a05e4862e | 3fbb10838b85c77c0d358fbcf011ef557c1c9d6b | /MechaCarChallenge.R | 76106b7305f00217ef3fe39d7a767e583cb686d5 | [] | no_license | PGL50/MechaCar_Statistical_Analysis | efef728111bf522823e74a2d012130c12bd87c49 | 8dd7a011e3a97fef8b042cbc4ad0ac1581fbc087 | refs/heads/main | 2023-08-14T07:57:48.065032 | 2021-10-10T12:11:24 | 2021-10-10T12:11:24 | 413,221,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,440 | r | MechaCarChallenge.R | library(dplyr)
library(ggplot2)
## Deliverable 1
#file1 <- file.choose()
#mecha_table <- read.csv(file=file1,check.names=F,stringsAsFactors = F)
mecha_table <- read.csv(file='MechaCar_mpg.csv',check.names=F,stringsAsFactors = F)
#Multiple regression
lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_cl... |
cd92f95a3a7bb08f3f82a9e3fcd6ced473a1094b | e60d4eef1860c4dc76c245668c168b2127758c0c | /cachematrix.R | 2b8266d4de9f0c1bf394cd773aa87be41c4ffbd4 | [] | no_license | supwar/ProgrammingAssignment2 | 0063432c4fd3f8edf217b140f3ae8ab804695f60 | f0213dc5157f2edcd3f34ee96e92ac223ce5271f | refs/heads/master | 2021-01-22T11:24:03.571105 | 2015-06-20T00:00:50 | 2015-06-20T00:00:50 | 37,750,561 | 0 | 0 | null | 2015-06-19T23:24:47 | 2015-06-19T23:24:46 | null | UTF-8 | R | false | false | 1,630 | r | cachematrix.R | ## The following functions can be used to create a sort of caching object
## that can be used to hold both a matrix and its inverse. Additionally,
## it provides a method that can intelligenly leverage the cache to ensure
## that the inverse is only calculated once. As of yet,
## there has been no work to ensure ... |
3d5f320806b0a71fdd15ca80a24420a41ba198e0 | c6cb7d70e0771c124475eae37b7bfed1d3462f7f | /man/ReadGeoClass.Rd | ef753adb6d78f9f140acdf8e27af785418ef7339 | [] | no_license | pslota/HYPEtools | 6430997c9b7abd7e0b27c9ea197bcc749b5fcb04 | 5bb0c6c31641aeaec6cb1a23185bc61de6cab8bc | refs/heads/master | 2021-07-24T06:33:48.648824 | 2017-11-05T17:28:11 | 2017-11-05T17:28:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,298 | rd | ReadGeoClass.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functioncollection_import.R
\name{ReadGeoClass}
\alias{ReadGeoClass}
\title{Read a 'GeoClass.txt' File}
\usage{
ReadGeoClass(filename = "GeoClass.txt", headrow)
}
\arguments{
\item{filename}{Path to and file name of the GeoClass file to impor... |
a6fb122d4bde80f041d3e087532d4f958f02916c | 1b274c4a9fc98a7f2e25b46c8ac213341a01f059 | /skrypty/modele/svm_rad_ens2.R | 101dc5b5087dfe9f24c71ca461e6e088d5be7b5b | [] | no_license | kubasmolik/magisterka | d8d3451db31a100df52f5e51fb7b099347e1d387 | 161f7976ad1c66b1d2a2ac1c2f6f981515c81646 | refs/heads/master | 2021-05-04T01:43:38.673922 | 2018-02-05T21:59:01 | 2018-02-05T21:59:01 | 120,362,429 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,018 | r | svm_rad_ens2.R | #### ENSEMBLER 1 ####
#### svm_rad_ens2 ####
df <- train_clean %>% select(., default, one_of(zmienne_ens2_woe))
sigma_est <- kernlab::sigest(default~., data = df)
sigma_est[[1]]
parametry <- expand.grid(
sigma = sigma_est[[1]],
C = c(2^(seq(-1, 4)))
# C = c(4)
)
cl <- makePSOCKcluster(detectCores() - 1, out... |
e2f3f7aa1d6dec1e77c7da5a3d23040bb5c5bc8e | ff2b55f75a9802b8de761eee6a353f9b8058b08c | /R/data-ADPIRA.R | 4bc2db64f444bfa3282fb9591f22585f32e67ec0 | [] | no_license | FCACollin/rpack_pira | 426d344de5eb426240608aba725a8c328b8779e1 | 852db8a6175fe2126202b3316ba576d47ecf163c | refs/heads/master | 2023-03-08T15:58:50.987587 | 2021-02-20T18:22:15 | 2021-02-20T18:22:15 | 287,980,600 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 308 | r | data-ADPIRA.R | #' ADPIRA
#'
#' Analysis Dataset for the study of Progression Independent of Relapse Activity
#' in the Polish RRMS patient population.
#'
#' @source UMB.
#' @format Information about the fornat
#' @examples
#' attr(ADPIRA, "metadata")
#' sapply(ADPIRA, attr, which = "label")
#' head(ADPIRA)
#'
"ADPIRA"
|
79049c5ad00cab56f718b712052a5b0000485cec | ec4d63a43632045cd00f05861c9dc01bfc03f4d5 | /EnzymeAssay/man/enzyme_assay.calc_slope.Rd | a6b2f8f2c56fafeb7262e59d30b1954d61427675 | [] | no_license | alisandra/enzyme_assay | 9cfec8e9af9d728a0551e285cec9d468169d34c1 | 4ebf526525fbb75106c45ad63bd0014caf1c05e8 | refs/heads/master | 2021-01-01T19:30:33.441556 | 2015-04-07T21:02:49 | 2015-04-07T21:02:49 | 33,488,409 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 514 | rd | enzyme_assay.calc_slope.Rd | % Generated by roxygen2 (4.1.0.9001): do not edit by hand
% Please edit documentation in R/enzyme_assayClass.R
\name{enzyme_assay.calc_slope}
\alias{enzyme_assay.calc_slope}
\title{Getting Slope from Range}
\usage{
enzyme_assay.calc_slope(object)
}
\arguments{
\item{object}{An object of the Enzyme_Assay class}
}
\descr... |
209e546fd99c16bf77ee1814e5ae035434827331 | dbfa1ca787588ef4748e98623918ec2d6cea3b4f | /Week 2/OutliersInBaseVsggplot.R | bc28f2aaaf6236e955bcc8492f97f05daa8e21c9 | [] | no_license | saurish/EDA-Course-Code | c7ccc70cbf8ed9912f2836031698467962f81cf0 | d089c62b290bf6e826be9859f52a6bc52df31991 | refs/heads/master | 2020-04-07T14:45:23.100269 | 2019-05-12T06:26:25 | 2019-05-12T06:26:25 | 158,459,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 195 | r | OutliersInBaseVsggplot.R | testdat <- data.frame(x = 1:100, y = rnorm(100))
testdat[50,2] <- 100 ## Outlier!
#plot(testdat$x, testdat$y, type = "l", ylim = c(-3,3))
g <- ggplot(testdat, aes(x = x, y = y))
g + geom_line() |
4915aae02474661cb18d60af77eb8d451c2d28c8 | e2582f3945f61a570200925be1ee43a68b834d73 | /r_scripts/gpomo_all_var_observed.R | 90a0873a6ab3bda07b06b26167ec62284f0c9add | [] | no_license | anastasiabzv/L-ODEfind | 9dfb2660796b53e471593b35c8661ddd8c364d76 | 4be0573370727228b6c6115308b6d9169cb89f6f | refs/heads/master | 2023-03-31T20:31:43.582165 | 2020-12-22T13:30:39 | 2020-12-22T13:30:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,834 | r | gpomo_all_var_observed.R | require("GPoM")
mse <- function(x, y){
means = colMeans(y)
sds = apply(y,2,sd)
xnorm = scale(x, center = means, scale = sds)
ynorm = scale(y, center = means, scale = sds)
return(mean((xnorm-ynorm)^2))
}
fit_gpomo <- function(time, ladata, max_time_derivative, poly_degree, steps){
# output: co... |
22336dc32ef60a442fb03e7d6f80004040fb6db0 | c15d71d1683916a47c1e9a6509fe26525df22753 | /R/queryCounts.R | e4e54d2746e27d53e0aa7645afde303590597031 | [
"MIT"
] | permissive | LZhang98/nsSNPHotspotViewer | 07574a48f579d2030721673b8fc2dfe6e8b11700 | 1f61b3beab5d002ac12f56ef9ef1e181c6b1adc5 | refs/heads/master | 2023-01-29T19:35:55.921753 | 2020-12-10T04:15:17 | 2020-12-10T04:15:17 | 307,894,766 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,015 | r | queryCounts.R | #' Query dataset for top genes
#'
#' Filter and sort the given dataset (must be of counts, output by \code{getSNPCounts}) based on user-
#' defined parameters.
#'
#' @param countsData A dataframe. Output of \code{getSNPCounts}.
#' @param minLength The minimum length of genes to filter by. Default is 0 (removes no... |
be33553572c9389210e7ad8af4140d6c2f45d4a5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/blackbox/examples/calcGCV.Rd.R | 17154868345b1533421e48c3e18f867fa2192d01 | [] | 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 | calcGCV.Rd.R | library(blackbox)
### Name: calcGCV
### Title: Estimate smoothing parameters by generalized cross-validation
### (GCV)
### Aliases: calcGCV
### ** Examples
# see example on main doc page (?blackbox)
|
d1a7b81d16d91865fe95b0b2a7e7599535efb574 | e1f093f20200ed2bd820d4ee0884c87c73e41d66 | /R/mantel.R | c3dea508f6ad6dcfb987c42a0fdc2e37bed0e58e | [] | no_license | cran/ecodist | 8431a5659f02211c3131e282fbd2c90765285aa0 | a34b199c4d70d5ee21e2d6abbd54d2a9729d7dd0 | refs/heads/master | 2022-05-13T06:14:42.563254 | 2022-05-05T05:50:08 | 2022-05-05T05:50:08 | 17,695,709 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,728 | r | mantel.R | mantel <- function(formula = formula(data), data, nperm = 1000, mrank = FALSE, nboot = 500, pboot = 0.9, cboot = 0.95)
{
# Mantel test
# Written by Sarah C. Goslee
# 27 June 2000
# Updated 5 April 2001
#
# formula is y ~ x + n1 + n2 + n3 + ...
# NOT y ~ x | n1 + n2 + n3 + ...
# The | means something else in S-Plus fo... |
4a6f70eb9c8bf64ae5c8ce679c1dd4dc19e5eeda | 71fea57eff7e645225e1a4b0b19fc6c75b940e20 | /R/explore.R | ba0746f95d2c7a38290759670b83784e41ac2c84 | [] | no_license | anhqle/authoritarian_violence | 5ed8260082beabc4bd5d2a5b4fa184b736be3c11 | 639e493002d5d454d403fede99316d972ed51a67 | refs/heads/master | 2021-05-28T09:38:20.826409 | 2015-03-09T01:07:27 | 2015-03-09T01:07:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 738 | r | explore.R | # ---- Set up workspace ----
rm(list=ls())
# Load external functions
source("./R/functions.R")
# Load packages
packs <- c("lme4", "nlme", "plyr", "dplyr")
f_install_and_load(packs)
# Load data
load('./data/data_final.RData')
#---- Missingness ----
# Count
tmp <- d_merged %>%
group_by(country, year) %>%
summarize(... |
c30e1fd3da63beb72008b21ac56c759eee1bfd44 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/phytools/examples/nodeHeights.Rd.R | 1ac0091b578d844b932d533fc03b4f1981e2e97c | [] | 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 | 252 | r | nodeHeights.Rd.R | library(phytools)
### Name: nodeHeights
### Title: Compute the heights above the root of each node
### Aliases: nodeHeights nodeheight
### Keywords: phylogenetics utilities comparative method
### ** Examples
tree<-rtree(10)
X<-nodeHeights(tree)
|
4455e5e31662662f167c28d0ef335ebe59d4107a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/modTempEff/vignettes/modTempEff.R | 4dff543ad70cbc3757a32fb140744bd2ea2e5773 | [] | 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 | 4,891 | r | modTempEff.R | ### R code from vignette source 'modTempEff.Rnw'
###################################################
### code chunk number 1: preliminaries
###################################################
library("modTempEff")
options(prompt = "R> ", continue = "+ ")
###################################################... |
7a875432417d1358066d6531c270783f5c732231 | 488854749b8d6c1e5f1db64dd6c1656aedb6dcbd | /man/SAXMethods.Rd | f1df1b73e236d150496e0e0fa6a6a0891be1aec4 | [] | no_license | cran/XML | cd6e3c4d0a0875804f040865b96a98aca4c73dbc | 44649fca9d41fdea20fc2f573cb516f2b12c897e | refs/heads/master | 2023-04-06T18:52:11.013175 | 2023-03-19T10:04:35 | 2023-03-19T10:04:35 | 17,722,082 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,363 | rd | SAXMethods.Rd | \name{startElement.SAX}
\alias{startElement.SAX}
\alias{endElement.SAX}
\alias{text.SAX}
\alias{comment.SAX}
\alias{processingInstruction.SAX}
\alias{entityDeclaration.SAX}
\alias{.InitSAXMethods}
\alias{text.SAX,ANY,SAXState-method}
\alias{comment.SAX,ANY,SAXState-method}
\alias{endElement.SAX,ANY,SAXState-method}
\al... |
4f96ed2088fe58ae47fe1d285e6d2c2b010aadd1 | af395fc19b90fbfd7d382ccf4bc6dbd1b26fb209 | /0901.R | db809f4a8f38e539c09a20137dca7f93f378f499 | [] | no_license | Thughandling/R_studio | ec8f6395fe60330a022dea39ccf1e822ede68488 | d46dac5d6b71d8a7583957173904f77f991f1565 | refs/heads/main | 2023-09-04T01:28:08.305840 | 2021-09-15T06:20:52 | 2021-09-15T06:20:52 | 405,446,824 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,969 | r | 0901.R | # 1
score <- c(80,60,70,50,90)
print(score)
# 2
mean(score)
# 3
mean_score = mean(score)
print(mean_score)
# 4
fruits = data.frame('제품' = c('사과','딸기','수박'),
'가격' = c(1800,1500,3000),
'판매량' = c(24,38,13))
fruits
# 5
mean(fruits$가격)
mean(fruits$판매량)
# 6
library(ggplot2)
mpg
#cty... |
1b13e56c8080ba4e9825dfca648ff0bf44a8ddeb | fc49ca75553cdec61effbd727bac54a005dbcdaf | /R/oSCR.parfit.R | 88bad2b12b03d1603ccb79ccaa56a07e9208c349 | [] | no_license | cgguarderas/oSCR | da67fae5fb145025337c3b957fac4cfc1eee4b3d | fa009162cecb7497e793e5895e2171ca59e26dd4 | refs/heads/master | 2021-01-23T17:35:46.741331 | 2017-08-10T13:11:34 | 2017-08-10T13:11:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,027 | r | oSCR.parfit.R | oSCR.parfit <-
function(mods, data, ncores=2){
library(doParallel)
sf<- data$sf
ss<- data$ss
cs<- data$cs
wrapperX<- function(model.number, mods, sf, ss, cs=NULL){
mod <- list(mods[[1]][[model.number]], #density
mods[[2]][[model.number]], #detection
mods[[3]][[model.number]], #sigma... |
ddff4c3a6814fbd8590ace9fb311063602a7d1c7 | 1779ec29e1eea8bb271d2596ee249171180efa49 | /test.R | 9839269990ce97870276064079ab83bbd0966cc1 | [] | no_license | Olanrewajubaby/PBD_FOO | d4fe281c57f1bffc2b159f9ba73b6897c94d4c32 | 8ba2ee816f81db4c6cec0d6594b0d08fcf806ced | refs/heads/master | 2020-04-05T13:07:13.656715 | 2017-07-30T22:21:46 | 2017-07-30T22:21:46 | 95,099,769 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,540 | r | test.R |
R version 3.2.5 (2016-04-14) -- "Very, Very Secure Dishes"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence(... |
95cf6666fafee4944e4b8f5ecb6b977815f0154a | a50e07477af54821cb133805c0e8674eb7969a65 | /man/estimateSTR.Rd | 1dd6192f6152a763181ea04349046574eaae7343 | [] | no_license | bocinsky/soilDB | ac118e09082563795ff45228bcbe5b9368541c42 | 12819708abfe9ef1daf0a70a78b9aaa33aedca9b | refs/heads/master | 2020-06-06T00:39:10.068856 | 2019-06-18T18:39:14 | 2019-06-18T18:39:14 | 192,589,760 | 1 | 0 | null | 2019-06-18T18:05:19 | 2019-06-18T18:05:18 | null | UTF-8 | R | false | false | 1,449 | rd | estimateSTR.Rd | \name{estimateSTR}
\alias{estimateSTR}
\title{Estimate Soil Temperature Regime}
\description{Estimate soil temperature regime (STR) based on mean annual soil temperature (MAST), mean summer temperature (MSST), mean winter soil temperature (MWST), presence of O horizons, saturated conditions, and presence of permafrost... |
6b826dd0299b4dc6b11496ab886cafc381e464f4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/parma/vignettes/Portfolio_Optimization_in_parma.R | bc62ef5649ff160fd75b1b5eb0214020b941e0fe | [] | 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 | 91 | r | Portfolio_Optimization_in_parma.R | ### R code from vignette source 'Portfolio_Optimization_in_parma.Rnw'
### Encoding: UTF-8
|
4677e62b668d92c3efd84622705f25a6270f3062 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /immer/R/immer_matrix2.R | cede307e23c039bf9cbccfa8b98af108c18fc8cb | [] | 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 | 156 | r | immer_matrix2.R | ## File Name: immer_matrix2.R
## File Version: 0.01
immer_matrix2 <- function(x, nrow)
{
y <- TAM::tam_matrix2(x=x, nrow=nrow)
return(y)
}
|
76008fd7c9ead2fc017c02c1142aed467decec00 | 33bb983cc20cff0a5bfc8ef342addd678274b061 | /diagnostic_aderma/diagnostic_aderma.R | fbd82f9381989874f5dfb175ce8e4749d162e36b | [] | no_license | Gottavianoni/R | 00c65142fd29e62cc010147b9089eaecd85f0ea9 | 6918a4dec29faa442567f7ce271c38d001b9a2af | refs/heads/master | 2021-01-22T20:35:18.829590 | 2017-04-05T15:17:39 | 2017-04-05T15:17:39 | 85,334,011 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,150 | r | diagnostic_aderma.R | #
aderma_diag$nomproduit <- ifelse(regexpr("dermalibour",aderma_diag$nomproduit,fixed=T) != -1,"dermalibour",aderma_diag$nomproduit)
aderma_diag$nomproduit <- ifelse(regexpr("epitheliale",aderma_diag$nomproduit,fixed=T) != -1,"epitheliale",aderma_diag$nomproduit)
aderma_diag$nomproduit <- ifelse(regexpr("rheacalm"... |
3d63ec362f9e34da844d350b7741d75c3725cfb3 | 7880f40b9ce793f47e7b552c9abc379c5ca3c7fd | /R/inits.R | 752ad6815f1c8a2d5a72a43a610d5133c189c5a2 | [] | no_license | chjackson/disbayes | 1e62eb6a2f549f558c1ca1556b451eadfe929d25 | 1600b36d580fcf2c97fd0ffeb6be3cc115f1b472 | refs/heads/master | 2023-03-16T21:15:52.431687 | 2023-03-07T11:51:05 | 2023-03-07T11:51:05 | 138,712,654 | 8 | 2 | null | 2023-09-09T13:25:28 | 2018-06-26T09:05:08 | C++ | UTF-8 | R | false | false | 9,194 | r | inits.R | ## mdat <- remission, eqage, const_rem
## min <- cf_init
init_rates <- function(dat, mdata, idata,...){
inc_init <- init_rate("inc", dat)
rem_init <- init_rate("rem", dat, agg=mdata$const_rem)
optu <- fit_unsmoothed_opt(dat, inc_init=inc_init, rem_init=rem_init,
mdata=mdata,... |
a60ade0cfde1cad694f419f4c474b2f0396f511b | 63c6d111652afe309a8692aa744546a7550ffe7c | /msis510/text mining/text_mining.R | 6135ffe5d5a96ba54887af2686b2c9c1277549c4 | [] | no_license | yanmo96/UW | 65cb2aea1fa995d6b4b2c0a6ec1ee05063474038 | a3ac9c7f9d41e1c830873cb54597fa392e0854c4 | refs/heads/main | 2023-02-03T05:06:31.920247 | 2020-12-22T19:59:35 | 2020-12-22T19:59:35 | 320,718,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,371 | r | text_mining.R | # install.packages("tm")
library(tm)
# define vector of sentences ("docs")
text <- c("this is the first sentence!!",
"this is a second Sentence :)",
"the third sentence, is here",
"forth of all 4 sentences")
# convert documents into a corpus
corp <- Corpus(VectorSource(text))
in... |
d2ff97e7c1665da50f801e0e16106d9606726b01 | a1cc534128010f158bf71cd268217b479f834727 | /man/transform_scores.Rd | d19d849cc4961678d8dce37cd1acccbedba898af | [] | no_license | dcosme/qualtrics | 66555e58cc122633f4c3d1320f57a8a621d6da69 | 4d260fc79ee50b86a7f2d08240a55edd9b794528 | refs/heads/master | 2021-06-20T05:09:56.500409 | 2018-05-08T17:22:38 | 2018-05-08T17:22:38 | 134,196,406 | 2 | 1 | null | 2018-05-20T23:52:58 | 2018-05-20T23:52:58 | null | UTF-8 | R | false | true | 402 | rd | transform_scores.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/questionnaire_tools.R
\name{transform_scores}
\alias{transform_scores}
\title{transform scores}
\usage{
transform_scores(item_values, transformation, min = NA, max = NA)
}
\arguments{
\item{item_values}{item_values}
\item{transformation}{tra... |
f6b46e9e7b7c6f37f8026307e7c0e59758f62de7 | 6d8aaf52204210d9ef16085235fa971427f2e471 | /gene_category_specific_coverage.R | 992330cbae2f3d26d767046d710d7206ea8fcf27 | [] | no_license | Callum-Rakhit/panel_validation | 945acce8af983bbd408313f85e3be06c2765b773 | 59a803b269b1a5b34b9c680ae78959043620e0f3 | refs/heads/master | 2022-03-05T22:55:28.646715 | 2022-02-02T15:40:06 | 2022-02-02T15:40:06 | 204,648,131 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,551 | r | gene_category_specific_coverage.R | # TODO(Callum)
# - Take input directly from snappy
# - Automate file import and naming (no hardcoding)
##### Load/Install relevant packages #####
# Also need libssl-dev and libxml2-dev on Ubuntu 18.04
GetPackages <- function(required.packages) {
packages.not.installed <- required.packages[!(required.packages %in%... |
8895b4c6cf1cd423a737a861035a5298c27b94ae | fbcc13c2996838c30992397b145e3a3266c2f96a | /man/game_status-set.Rd | c085470f4350c8e1990b48de670aff59097aee79 | [
"MIT"
] | permissive | msaltieri/alicetwist | 768f36f0e3f7413070bf2fa0043f8561db274047 | 0550446feded5a2b8fc3a30d3a3339264d3a2388 | refs/heads/master | 2021-01-19T00:24:06.225177 | 2017-04-10T14:33:02 | 2017-04-10T14:33:02 | 87,167,107 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 466 | rd | game_status-set.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/game-attribs.R
\name{game_status<-}
\alias{game_status<-}
\title{Set the game status}
\usage{
game_status(game, player) <- value
}
\arguments{
\item{game}{An \code{alicetwist} game object}
\item{player}{The player whose hands are to
be modif... |
0384c935721dbc3aafb1bb15b4a3a1df46ba9831 | bf6cff3125023e0bede2db32a79542a7a76d2530 | /R/fitting_functions_multiple_genes.R | 3e4d06031b5728b35ccabcd9ee6dc7e1c05d6244 | [
"MIT"
] | permissive | markrobinsonuzh/regsplice | 15d655f3ee1eb496b2ce92f66a3728cced438057 | db0014e78af3fd6f16647a04d2ab91921dbd4395 | refs/heads/master | 2021-01-13T08:22:54.432366 | 2016-10-04T18:44:12 | 2016-10-04T18:44:12 | 69,993,689 | 0 | 0 | null | 2016-10-04T18:25:45 | 2016-10-04T18:25:44 | null | UTF-8 | R | false | false | 8,297 | r | fitting_functions_multiple_genes.R | #' @include class_RegspliceData.R class_RegspliceResults.R
NULL
#' Fit models.
#'
#' Model fitting functions for \code{regsplice} package.
#'
#' There are three model fitting functions:
#'
#' \code{fit_reg_multiple} fits regularized (lasso) models containing an optimal subset
#' of exon:condition interaction ter... |
8d21fcd38663d3c05b33cbee6d2d492ff9982f60 | 6cd38c6b6d5490fa44ef25ac8604b16c12c0d8e7 | /radar_chart_PBDE_scripts.r | 59c4804b2f5f6f215edfea28c4aec7910130c50e | [] | no_license | maopeng2018/scripts | 26d71e19cf6c83207a98c2795a3269c5ea796737 | 87dce6e0963a9a6aa8c3ba243c4dd9243716adbd | refs/heads/master | 2020-03-31T09:17:28.763605 | 2018-10-08T14:25:37 | 2018-10-08T14:25:37 | 152,089,734 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,312 | r | radar_chart_PBDE_scripts.r | setwd("D:\\Projects\\creA_project\\analysis\\visualize\\CAZyme")
FPKMstatis <- read.table("RPKMs_significant_genes_fold2p001.txt",sep="\t",head=T, na.strings="NA", dec=".", strip.white=TRUE)
cazyme=read.table("A_niger_CAZymes_substrate_Paul.txt",head=T,sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
combine <- ... |
17a0bf9454673ce8f64964095d0434ee7b628369 | 2363bb535bd8d73fbeaed0195dfe9abf3c0e7b77 | /script_observation.R | 444c3c8a38cf25138262d74112497e8323f7f01a | [
"Apache-2.0"
] | permissive | OHDSI/ETL---Korean-NEDIS | 7bd5713f7ee35a445d813c3ef0ffa6511fee4efd | b3bbfb04e17ce9e3eed8f3dc1b11869944362336 | refs/heads/master | 2020-03-20T10:19:50.090905 | 2019-06-10T08:12:29 | 2019-06-10T08:12:29 | 137,366,496 | 1 | 0 | Apache-2.0 | 2019-02-16T04:36:25 | 2018-06-14T14:07:20 | null | UTF-8 | R | false | false | 1,967 | r | script_observation.R | # Observation severity
observation_ETL <- function() {
tz = Sys.timezone()
KTAS_observation <- EMIHPTMI %>% select(EMIHPTMI_seq, ptmiidno, ptmiktdt, ptmikttm, ptmikts1, ptmikidn) %>% filter(!is.na(as.numeric(ptmikts1)))
observation <- data.frame()
KTAS_observation <- data.frame (# observation... |
b2a2a7671af522cafee9b6ea94388e4059364e60 | ff342d1c9481e23f1790c3cea299cd20248faba5 | /man/tune_accessor.Rd | 8f3bf7239b78bc1cbf0d1c4d38b58bf27762fe55 | [
"MIT"
] | permissive | rorynolan/tune | 95ff4347d3a392ffdfacaa486628877a2305cb3b | fc40e3033ff926be09ffbbec2192fbd0c69af650 | refs/heads/master | 2023-01-18T18:35:32.964619 | 2020-11-17T15:36:46 | 2020-11-17T15:36:46 | 262,448,449 | 0 | 0 | NOASSERTION | 2020-05-08T23:24:17 | 2020-05-08T23:24:16 | null | UTF-8 | R | false | true | 1,152 | rd | tune_accessor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{.get_tune_parameters}
\alias{.get_tune_parameters}
\alias{.get_tune_parameter_names}
\alias{.get_tune_metrics}
\alias{.get_tune_metric_names}
\alias{.get_tune_outcome_names}
\alias{.get_tune_workflow}
\title{Various accessor fun... |
7f3200c0ecf5707651ea06d0c16c77c660038caa | e7cc8f75121b87e9d1617d507a2a38caf25015a0 | /package/clinDataReview/tests/testthat/test_transformData.R | eabef271a4f0b82af844ea559d8b133d38b351a2 | [] | no_license | ClinicoPath/clinDataReview | 1fa16e25a47341c117b4f98fd73f2c94788cf9d4 | dda13974128136992e2953866ac7f370a021b85f | refs/heads/master | 2023-06-23T06:05:20.955803 | 2021-07-14T07:44:19 | 2021-07-14T07:44:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,063 | r | test_transformData.R | context("Test 'transformData' function")
test_that("Test errors of 'transformData' function", {
expect_error(transformData(data))
transformation <- list(type = NULL)
expect_error(transformData(data, transformations = transformation))
transformation <- list(type = "transfor... |
447c85bc28a381f33a647e053cfeb709d003c5bb | 717cff43cbac7aa6b6b49829ebd07a958dae48a9 | /curso_ML/arvore_decisao_credit_data.R | 7e564a47bfd2d7ec872ccbe4dd2e6bdcb8ba14a7 | [] | no_license | henrikots/dados-credito-curso | 0ad6dae6cdcadb50340e150ee3c048be431ffe8f | 843eeff3ad785db546732c185316940f2b294db9 | refs/heads/master | 2020-06-03T17:24:08.995375 | 2019-07-03T00:16:52 | 2019-07-03T00:16:52 | 191,666,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,331 | r | arvore_decisao_credit_data.R | library(rpart)
library(caTools)
base <- read.csv("credit-data.csv")
base$clientid <- NULL
mean(base$age[base$age > 0], na.rm = TRUE)
#ajustar base inconsistente com média do valor
base$age <- ifelse(base$age < 0, 40.92, base$age)
#ajusta valores faltantes com a média
base$age <- ifelse(is.na(base$age), mean(base$a... |
49404f425c358c86f5d693aaf82e19999ded4a78 | 755def7e1f9ec6c9b2f3082bc9b81e924370ca37 | /getting_started.R | 47aa62b36182fb5d2763de98dc54ede6854af985 | [] | no_license | agupta54/data-analysis-for-life-sciences | 6fbf9f2618c460a5d7b062479864be218a3c062d | d7269ae4a91025189a88dc8b86d2aa87a7f0a81f | refs/heads/master | 2020-03-25T21:58:44.763138 | 2018-08-27T13:08:28 | 2018-08-27T13:08:28 | 144,198,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,114 | r | getting_started.R | dat <- read.csv("femaleMiceWeights.csv")
### Exercises ###
"1."
dat$Bodyweight
"2."
dat[12,2]
"3."
dat$Bodyweight[11]
"4."
length(dat$Bodyweight)
"5."
View(dat)
"6."
?sample
set.seed(1)
dat$Bodyweight[sample(13:24,1)]
### ### ###
dat <- read.csv("femaleMiceWeights.csv")
head(dat)
library(dplyr)
chow <-... |
914ab147f51dfecb0e12d96402b8a2a52e261716 | 7fc3d62d0dff3542a33cae700d0cc7e790302026 | /R/GetSeasonDate.R | f8031032106ca14ecb4c850656eb0b21680f3535 | [] | no_license | d-farnham/ORB_Paper | c21e1fb2010f280af918917be9854aa09a51d117 | bd7871fb2c43afce4d939310a1bcbffd19beef1d | refs/heads/master | 2021-03-27T14:58:57.576475 | 2018-03-19T04:20:45 | 2018-03-19T04:20:45 | 94,268,439 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 440 | r | GetSeasonDate.R | GetSeasonDate <- function(date, method = 'DJF'){
require(lubridate)
month <- lubridate::month(date)
GetSeasonMonth(month, method)
}
GetSeasonMonth <- function(month, method = 'DJF'){
require(lubridate)
if(method == 'DJF'){
seasons <- c(rep('DJF', 2), rep('MAM', 3), rep('JJA', 3), rep('SON', 3), 'DJF')
} else ... |
f0e974e1394fd2f6a7c9bf92a7f5887e58077a1c | 473a7fa8482db09d6d0b49c2f3c4730fdb64bbf7 | /SecondPlot.R | e008a60ac5d8f3390802237912b8feb7fa8c8945 | [] | no_license | shabnamh/DataIncubatorChallenge | 8d3c8425fe3f8a7056cdb1be5156014754e4ba05 | c49aec0e5d6d72ab856dc977305d912b1ced2ae4 | refs/heads/master | 2020-03-14T13:15:17.576065 | 2018-04-30T19:34:00 | 2018-04-30T19:34:00 | 131,629,143 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,756 | r | SecondPlot.R | #Question 3 - Second Plot
#Second plot is going to show the percentage delay per month of the busiest airports in USA with over 100,000
#domestic flights in 2017 with the most arrival delay.
library(dplyr)
library(ggplot2)
#First, read all of the files and rbind them at the end to make one data frame with the whole... |
7a1195f0ba47ba2a51373758f0c6c5c1f0e5ff54 | a7e65db3007641a3aa1565f8272463339ef5e84a | /Model_Prod_4_01.R | 3b9abc7d01a299c0828faeac7b8f4004635087ba | [] | no_license | marcelomedre/B2WLabs | 514281cd8f9853e632993128b32c32563d2878d6 | a5e28f102433ae7538a1f8e06b4aa8174fa7b98d | refs/heads/master | 2020-05-21T06:09:28.893973 | 2017-03-16T17:53:04 | 2017-03-16T17:53:04 | 84,585,851 | 0 | 0 | null | null | null | null | IBM852 | R | false | false | 5,674 | r | Model_Prod_4_01.R | setwd("C:/Users/Marcelo/Desktop/Data Science/B2W Labs/B2WLabs/")
rm(list = ls())
# B2W LABs | Pricing Challenge
# Deliverables:
# 1) Models for Demand Forecasting: The main objective is to create a model to predict the quantity sold for
# each product given a prescribed price. Along with the statistical model, we n... |
86e9ab9f98e695abb898458bedff89edf14013d3 | 8bb792d0bd1d40e29da8383769a212d5bce054bb | /tests/testthat.R | 86f18c5c51b4a1c3179d5ab388ea0229f9cda478 | [
"MIT"
] | permissive | Covid19R/covid19Rdata | e49dc68d6cdcc1ef9a054682770dfecd2ab1f40d | 96cf7bbee8a01ab8ceed1426af11b7e7b0fa8da2 | refs/heads/master | 2023-02-20T19:37:26.089220 | 2021-01-24T17:02:50 | 2021-01-24T17:02:50 | 251,089,377 | 2 | 1 | NOASSERTION | 2020-05-03T22:47:27 | 2020-03-29T17:21:20 | R | UTF-8 | R | false | false | 68 | r | testthat.R | library(testthat)
library(covid19Rdata)
test_check("covid19Rdata")
|
29ef62fe1a9fa6ef4444cffcd169b9084a634974 | 6b35d0783e6d5dd59e89216218e8d1feb25d281b | /Lecture8/Lect8c.R | 3674dc6c6509279880543dee47cf57cc215e97a8 | [] | no_license | commfish/uw-fish559 | c11a56e6085e0263b541b96620e5f169a4420e03 | 3fccd3547f97a59d5241a234aa1bc2869f8cd5eb | refs/heads/master | 2020-04-02T07:56:43.880613 | 2018-11-13T22:53:43 | 2018-11-13T22:53:43 | 154,221,956 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,177 | r | Lect8c.R | Sign<-function(a,b)
{
if (b > 0)
return(abs(a))
else
return(-1*abs(a))
}
# =====================================================================================================================================================================
Fit<-function(Xinit,Ndim,FUNK,doGraph)
{
# Tolerances, etc,
Ftol = 0.... |
421cab97b815e1c51005a50f2fa4f9883e11a301 | ca96aa69a485886e69efa306a21d06d8769cc6d1 | /man/get_prism_annual.Rd | 4219ecc983ccb6525386b4c90a7fc9ecff7a5a95 | [] | no_license | yangxhcaf/prism | 46b14662b57d1651e41e85b4d9843bfb21c0480f | 55895dcf2360a777e204c8d6ccb0dd1a196d3f5a | refs/heads/master | 2020-06-04T04:15:40.348812 | 2018-12-10T23:03:03 | 2018-12-10T23:03:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,560 | rd | get_prism_annual.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_prism_annual.R
\name{get_prism_annual}
\alias{get_prism_annual}
\title{Download annual daily averages}
\usage{
get_prism_annual(type, years = NULL, keepZip = TRUE)
}
\arguments{
\item{type}{The type of data to download, must be "ppt", "tm... |
8443ab1732d77564244e4756937773df4a4a2082 | 865001d77275f63f3a75ea1dde05fe2387de1b88 | /R/corr.R | a7dab07b5112001f02b86b065709647924149a9c | [] | no_license | thiyangt/tsdataleaks | 1141658da0594386df8b62a0b41ee17bfb1efd6c | ecd0c5893639d2e1ac00750d1b72b2ad784f5972 | refs/heads/master | 2023-04-04T00:05:37.738186 | 2021-04-13T00:52:48 | 2021-04-13T00:52:48 | 282,431,352 | 3 | 1 | null | 2021-03-16T10:13:37 | 2020-07-25T11:36:09 | R | UTF-8 | R | false | false | 1,217 | r | corr.R | #' Correlation calculation based on rolling window with overlapping observations.
#'
#' @param x time series
#' @param y subsection of the time series to map
#' @param cutoff benchmark value for corr, default 1
#' @param boost Logical value indicating whether to boost performance by using RCpp. For small datasets setti... |
23cbe6e68bb5e55dfec800fcbf69a08a62e99451 | 4755ffa39c7be31111e91301c336b86142f19dbb | /cachematrix.R | 1f3a1db8ade69499c44e4631c0b7c2c623bc2da6 | [] | no_license | MarkVHoward/ProgrammingAssignment2-1 | b406e011f92af6d48c7c5dbc06018c416ebf6a51 | 296f191cd9472889c9e9c97c4e04c601c809060d | refs/heads/master | 2021-01-18T11:30:10.677799 | 2015-08-19T08:12:14 | 2015-08-19T08:12:14 | 40,948,120 | 0 | 0 | null | 2015-08-18T03:14:47 | 2015-08-18T03:14:47 | null | UTF-8 | R | false | false | 1,229 | r | cachematrix.R | ## These two functions together calculate and display the inverse of a matrix
## The first function, makeCacheMatrix, creates a matrix object that can cache
## the inverse of matrix
## The second function first checks to see if the inverse of the matrix has been
## calculated, if it has then it displays the inverse fro... |
bc148ed3ce42f21e2b02d0d290b9a2ab88c07c23 | 29585dff702209dd446c0ab52ceea046c58e384e | /softclassval/R/performance.R | 8d39f4605862c5f575e5895c9c861e6c8787e368 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,578 | r | performance.R | ##' Input checks and reference preparation for performance calculation
##'
##' Checks whether \code{r} and \code{p} are valid reference and predictions. If \code{p} is a
##' multiple of \code{r}, recycles \code{r} to the size and shape of \code{p}. If \code{r} has
##' additional length 1 dimensions (usually because dim... |
147dabc23ebeb6aee28745149e66b1037d3da8d1 | 9729b52a87cf9387e7f1b97905a470e94a8c41a7 | /R/StaticApi.R | ef417d7ae9ecf8fef38ebf5a322c5f655d2d0c84 | [
"MIT"
] | permissive | terminological/roogledocs | b3e210953fe0fbdb1c9e2d2b18652eb51cd126aa | e3f333f425b198e37dec60d98f7562849fc47ccd | refs/heads/master | 2023-06-10T13:47:15.619743 | 2023-05-26T14:44:50 | 2023-05-26T14:44:50 | 475,030,092 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,747 | r | StaticApi.R | # Generated by r6-generator-maven-plugin: do not edit by hand
# This is a collection of the static methods described in the Java API
# and serves as an alternative R centric entry point of the roogledocs generated R library.
# Version: 0.2.0
# Generated: 2022-11-21T13:35:06.572
# Contact: rob.challen@bristol.ac.uk
# ... |
1f36acf8ba5b9b9531526c1bc4dec33b850a592b | 793429873754b0f7b44ec4f04e7f877976cb87cf | /manipulation/map/retired/1-encode-multistate-mmse.R | 546df09aa6b3f0e67d85ba30c6904a79e795e616 | [] | no_license | IALSA/ialsa-2018-amsterdam | c11d16451a1fd4b6d7f703371a73aba5febd76e1 | 7af21117757da67337ab54a95a04a092a249a51c | refs/heads/master | 2021-09-04T22:10:53.310639 | 2018-01-22T15:41:29 | 2018-01-22T15:41:29 | 108,875,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,045 | r | 1-encode-multistate-mmse.R | # knitr::stitch_rmd(script="./manipulation/rename-classify.R", output="./manipulation/rename-classify.md")
#These first few lines run only when the file is run in RStudio, !!NOT when an Rmd/Rnw file calls it!!
rm(list=ls(all=TRUE)) #Clear the variables from previous runs.
# ---- load-sources ------------------------... |
1f066c612396cc67d67d7b978fff723455a342d6 | 557ca5e102157cf9c0817aa6d8a6c1595b544047 | /tests/testthat/test-transformations.R | a6ad4689417aa404e0e27ab27b1685bd7a42d128 | [] | no_license | cran/lotri | 1360e7f3fa981b896cd5402df61edbb283c74fe9 | 8330bd5e94d74f8398104692a941b377d13daf42 | refs/heads/master | 2023-04-06T00:52:24.154740 | 2023-03-20T17:20:12 | 2023-03-20T17:20:12 | 186,655,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,090 | r | test-transformations.R | test_that("transformations", {
expect_equal(lotri(s1 + s2 + s3 ~ cor(sd(1,
0.25, 4,
0.90, 0.50, 9))),
lotri(s1 + s2 + s3 ~ sd(cor(1,
0.25, 4,
... |
711937c9d6f0861386102afd6e6a69d4eee9621d | 7cd8b7c0cd60c18c366d8d6c027df8077c81789e | /Schedules/Original/Regional/_EDP/extract.R | 592a6e4dbff0b1ca00ac4cc7c6de9165cba8e144 | [] | no_license | tyler-richardett/virginia_competitive_youth_soccer | 5c6c99378c888b4f03a2794b9c49a59924d32a9f | 022ad240bbe29ff7677e09efdb5438677d291911 | refs/heads/master | 2021-04-12T11:59:01.294354 | 2018-07-11T01:45:28 | 2018-07-11T01:45:28 | 126,563,080 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,589 | r | extract.R | library(dplyr)
library(rvest)
filenames <- list.files()
filenames <- filenames[-grep("R$", filenames)]
filenames <- filenames[-grep("csv$", filenames)]
## Used this to add class names to the table rows.
## for (i in 1:length(filenames)) {
## tmp <- readLines(filenames[i])
## tmp <- gsub('tr bgcolor="#... |
4168f0f9c810f3caa177448e1dec8d7d3cc65d2c | 6cfdbcb855132784afafe626f3735b62de76d3db | /R/delKwv.R | e00a5a8141146e7560f8c534c261109f88e02be0 | [] | no_license | cran/FITSio | 5c6f18fef0bb127ceedcbb2177ad0da8a0f61b27 | 70ec50e8b4cac12b280725d628493b6d867261b1 | refs/heads/master | 2021-07-11T17:58:25.587739 | 2021-04-03T18:10:02 | 2021-04-03T18:10:02 | 17,679,149 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 584 | r | delKwv.R | delKwv <-
function(keyw, headerName) {
# Function deletes 'keyword = value /note' card image from FITS header
#
# For multi-card select, use grep, e.g. idx <- grep('^TEST[7-9] ', header)
#
# A. Harris 2012.10.13
# find keyword; '=' in col. 9 defines as keyword
keyw <- sprintf('%-8s=', substr(keyw, 1, ... |
a937c927eef4dc1929d969f4638b4889a67d3e4a | 60e613aa8e6ac746abc0540c657d92c52006e085 | /server.R | 5b1111d094d7513d1846bd154b030ebd8cf3ec3d | [] | no_license | elombardi-cleve/intRo | 730ce57f827b959f0b4165532d51e07249cf45ff | 991c3267c625e6d92ab8d2ec2c6563f6e775271a | refs/heads/master | 2021-01-24T02:52:52.520195 | 2015-03-21T15:00:12 | 2015-03-21T15:00:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,282 | r | server.R | ###
### Shiny Server definition
###
shinyServer(function(input, output, session) {
## Module info
module_info <- read.table("modules/modules.txt", header = TRUE, sep=",")
## Get directory ready for code printing
userdir <- file.path(tempdir(), tempfile())
dir.create(userdir, recursive = TR... |
65abb91589590e39c2f28b63368e9557d71e28d8 | 602a082d7001f49a40c75f809211b80f9ba7e989 | /code/vax_plots.R | d47bd8157fc8b714b0d5acb7d697e9e9f31a4570 | [
"MIT"
] | permissive | ideogen/COVID-19-Danmark | f7fe8bac1f78f019db564f98ddebb5f29076d834 | d17e907e50f4ab8904f1d9f33b13632e8075adf6 | refs/heads/master | 2023-02-19T19:17:38.886897 | 2021-01-22T13:30:08 | 2021-01-22T13:30:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,699 | r | vax_plots.R | vax <- pdf_text(paste0("../data/Vax_data/Vaxdata_", today_string, ".pdf")) %>%
read_lines()
tabel_4 <- which(str_detect(vax, "Tabel 4"))[2]
age_vax <- vax[(tabel_4 + 4):(tabel_4 + 13)]
#age_vax_colnames <- vax[(tabel_4 + 1)]
age_vax %<>%
str_squish() %>%
strsplit(split = " ")
# age_vax_colnames %<>%
# str_s... |
491b11ff407cadfcda008dbbb8fb860429421eb8 | 5aa62329c37dd3623203a1f177c3b047043dfe3b | /R/plot.loc_score.weight_mesh_val.R | 50dbf64afc5d1a1bf65cc64cd583ea64b4ace96c | [
"MIT"
] | permissive | mrmtshmp/geospat | d6d4c42702e53c1be5b5180c3c8aecf9949c0aeb | e53a7b4b89c27c9c129afa057f8f92a543369e16 | refs/heads/master | 2021-07-17T20:34:24.783044 | 2020-10-05T08:31:49 | 2020-10-05T08:31:49 | 218,003,862 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,091 | r | plot.loc_score.weight_mesh_val.R | #' Make histogram of diatance to pharmacy and each pharmacy's scores.
#'
#' @import sp
#' @import sf
#' @import ggplot2
#' @importFrom raster shapefile
#' @importFrom plyr ldply
#' @importFrom dplyr select
#' @importFrom dplyr left_join
#' @importFrom plyr .
#' @importFrom plyr ddply
#' @importFrom tibble rownames_to_c... |
be8ba8c76116794673fbfc0a4d3acab4949d11f9 | bd23162e4b8c3c779557160a774bffb765adce86 | /shiny/ui.R | ce59873063dc3f2d8e6b6f59c217fa4dd58aed9c | [
"MIT"
] | permissive | ktmud/github-life | a8ab2ee91c85c2a62a348f6764742dcf1b00c338 | 421e46f9832879bb8c81d8731d3524ef20fc3065 | refs/heads/master | 2021-01-19T22:24:48.671526 | 2017-11-11T18:50:26 | 2017-11-11T18:50:26 | 88,812,727 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,523 | r | ui.R | # == Sidebar ----------------------
sidebar <- dashboardSidebar(
sidebarMenu(
menuItem(
"Overview",
icon = icon("globe"),
tabName = "overview"
),
menuItem(
selected = TRUE,
"Explore a repository", icon = icon("book"), tabName = "single-repo"
),
menuItem(
"Reposi... |
569d78769d03e477e165e6ed9fb4955c9b911454 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/fuzzr/vignettes/fuzzr.R | 6db63d671fd2011c5b95c6f31c78850497307fd7 | [] | 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 | 1,855 | r | fuzzr.R | ## ------------------------------------------------------------------------
my_function <- function(x, n, delim = " - ") {
paste(rep(x, n), collapse = delim)
}
my_function("fuzz", 7)
## ---- results = "asis"---------------------------------------------------
library(fuzzr)
# Note that, while we are specifically fuz... |
bddda7f64b92049fd6c990acea360ddcf90e2f2b | a7aff7f5192b861e715482bfed80bd91711f69c9 | /tests/testthat/test_3_AOItransitions.R | 121e3db501a3164659d63614f204a98c6dea6223 | [] | no_license | cran/eyeRead | 47699e1338fe956c4d7bb265291d0e8b69e7b166 | 834b380b715dcea97746f88b14903d4d4dbcc2b3 | refs/heads/master | 2022-12-03T19:18:26.352237 | 2020-08-07T12:00:02 | 2020-08-07T12:00:02 | 286,204,707 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 560 | r | test_3_AOItransitions.R | context( "AOItransitions" )
test_that( "specFile is found", {
expect_true( T )
} )
test_that( "Function returns the correct error when AOI is contains less than two elements", {
expect_error(
AOItransitions( AOI = c(1)),
regexp = some_errors$AOI_short
)
expect_error(
AOItransitions( AO... |
6166046a728a0a4eaa0aaf95eae2c28bd96d0bb8 | 6f4abe6d0fc4a57a70ca7ea4bf1c494c8c1cff1f | /cachematrix.R | 7e690ddf1d45f79a27ca9eb2872e08969af7298d | [] | no_license | nik3011/ProgrammingAssignment2 | 8c3f04ace7cd17f8137a04c95a9e8a49eb01d92d | d8b2ba26fcc0f5fbd96e924a4da1d7a6272fba0a | refs/heads/master | 2021-01-11T07:10:20.217645 | 2016-10-31T12:30:19 | 2016-10-31T12:30:19 | 72,410,996 | 0 | 0 | null | 2016-10-31T06:58:07 | 2016-10-31T06:58:04 | null | UTF-8 | R | false | false | 637 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
makeCacheMatrix <- function(x = matrix()) {
z <<- NULL
set <- function(y){
x <<- y
z <<- NULL
}
get <- function() x
setzerse <- function(zerse) z <<- zerse
getzerse ... |
29080ee48d6ea548359c4a448517f9add1d399ca | cab57cb34db27f86f8b3615c4d79da588f8b8ce7 | /tmp-tests/add-columns.R | a67dd090a6c5b10b9ab57b25b713770f3281bb49 | [] | no_license | privefl/bigstatsr | ed943f3e444732d551a2dea23379876b6c4185dd | 778ffd44b2bda0715bd345511c3379f4c0b78b26 | refs/heads/master | 2022-11-06T10:10:09.883359 | 2022-10-14T07:28:28 | 2022-10-14T07:28:28 | 68,272,648 | 160 | 36 | null | 2022-04-12T10:55:03 | 2016-09-15T06:50:50 | R | UTF-8 | R | false | false | 311 | r | add-columns.R | N <- M <- 5000
system.time({
X <- FBM(N, M)
})
file.size(X$backingfile)
system.time({
# file.create(tmp <- tempfile())
X2 <- FBM(N, 1)
addColumns(X2$backingfile, N, M - 1, 8)
X2 <- FBM(N, M, backingfile = sub("\\.bk$", "", X2$backingfile),
create_bk = FALSE)
})
file.size(X2$backingfile)
|
c8ece7df287dfbc3fac9595cdc5db7a1c863c107 | b0df31777c389b953bf9bb3e9371ec48893b039f | /Computer Adaptive Testing.R | c2563f01fe942ef413be892aee4c94ca0a104f36 | [] | no_license | meyerjoe-R/Spoketh | bd1f15f61b418c64c369b71556540dde730b1396 | 5940476c29ac2625bf7a9e47c1635417df089da2 | refs/heads/main | 2023-06-28T06:56:15.137647 | 2021-07-26T17:45:32 | 2021-07-26T17:45:32 | 323,737,355 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,391 | r | Computer Adaptive Testing.R | install.packages("catR")
require(catR)
#load data
data(tcals)
?tcals
#create a bank, take out content balancing info
bank <- as.matrix(tcals[,1:4])
#how should it start?
start <- list(nrItems = 1, theta = 0)
#create item select parameters, maximum likelihood and maximum fisher
test <- list(method = "ML", it... |
c278264e9aaaba24bd556628e5dc23dae945e2ce | af312ff271bd46826cecf652ea5a48610c1781d5 | /posterior_output.R | 0930a183f6668fe92572962cfce4dc4573e835d4 | [] | no_license | daniel-trejobanos/AsynchronousGibbs | 49bcc85eeb04996ba18a99179079661915ef326b | 558d64df24ee8702328032eca45f405678ae2493 | refs/heads/master | 2020-05-21T07:30:09.474315 | 2019-08-08T08:37:50 | 2019-08-08T08:37:50 | 185,963,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,721 | r | posterior_output.R | ## TODO : Add the level of parallelism as a grouping variable and add this facet to the plots
##' Posterior analysis code
##' First we select the data path, lets start with a single data set size
data.path <- "/scratch/temporary/dtrejoba/AsynchronousGibbs/data/2k5k"
##' We read the short runs only
files.path <- list.f... |
7d7f6a238cca3f377775ae5f3c74e91f03acb765 | 5ab2f745bef6ed1c6bb7f8bd08a94296cb611833 | /man/FindHighVar.Rd | d5fdaa0005a2d55b340491cfa8d033e1d867ca92 | [
"MIT"
] | permissive | XQBai/CCNMF | f447426c6269921d110c7bd247a170ff4a9e5d88 | ac503d7dac205d4a275a4b809ddc22fb2aef258b | refs/heads/master | 2023-05-26T01:19:26.462686 | 2023-05-24T19:17:44 | 2023-05-24T19:17:44 | 237,326,870 | 10 | 2 | null | null | null | null | UTF-8 | R | false | true | 293 | rd | FindHighVar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analysis.R
\name{FindHighVar}
\alias{FindHighVar}
\title{Find high variable genes for each cluster by vst}
\usage{
FindHighVar(Data, label, number)
}
\description{
Find high variable genes for each cluster by vst
}
|
9893af4233fa09a172cb0d5290a4075e406af7ad | c418d9cf880f0d72aff9f8e6379841a6075a7350 | /man/print.qic.select.Rd | 45fdafebffbe5e072fb9a533840a5403c7424af3 | [] | no_license | cran/rqPen | a214037b20cdbedd6b4ecd806b9865b7adea56be | af9b426431d8cdc8d4661fc94d3316547383e83f | refs/heads/master | 2023-07-22T11:29:00.428914 | 2023-07-17T15:10:02 | 2023-07-17T17:31:56 | 25,911,914 | 2 | 3 | null | null | null | null | UTF-8 | R | false | true | 472 | rd | print.qic.select.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mainFunctions.R
\name{print.qic.select}
\alias{print.qic.select}
\title{Print a qic.select object}
\usage{
\method{print}{qic.select}(x, ...)
}
\arguments{
\item{x}{qic.select object}
\item{...}{optional arguments}
}
\value{
Pr... |
eaaecddae3025b8ba691cd313f820f0d8fafe11d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tmcn/examples/toPinyin.Rd.R | 88c748e485f0698858b8924fdabc55fae4b1a4a0 | [] | 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 | 213 | r | toPinyin.Rd.R | library(tmcn)
### Name: toPinyin
### Title: Convert a chinese text to pinyin format.
### Aliases: toPinyin
### Keywords: string
### ** Examples
toPinyin("the quick red fox jumps over the lazy brown dog")
|
54e2d3a8607b789afe4d13133a3e075f8f592387 | 8a5453a31e02c4c519025b1800bf56fcb3cfa981 | /PromoterPrimerDesign/scripts/ampliconInfo4PCRcheck.R | 8adc1ee808b8815a95925c2d9192835ec4d91eb2 | [] | no_license | jsemple19/dSMF_AmpliconPrimerDesign | 635ea8d8f55347bc80101e32a894bac0d918f58b | c9d3dd10c520b4d71a5c9098bda4560e697c3e82 | refs/heads/master | 2023-01-02T15:40:36.364548 | 2020-10-21T10:20:22 | 2020-10-21T10:20:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,516 | r | ampliconInfo4PCRcheck.R | #script to get list of amplicons, their length and provide an output in the order they appear on a gel to help
# with checking amplicon PCR results
chosenX<-read.csv("./XdcPrimers/DM_DE_promoter_test_primers_WS250_subset.csv",header=TRUE,stringsAsFactors=FALSE)
chosenX<-chosenX[chosenX$finalSelected=="y",]
#names(cho... |
b060e606793d7ceb7de2718e498d284e9a2ce8d2 | 8c9c011750934e22946b97efdb25368358768677 | /Google-read-csv/Google-read-csv.R | 2a54d47d09c83f9152898387afcad2f049b38e8c | [] | no_license | ccowens/OddsAndEnds | b4e3fdaab3e688163303284587e5c6c1dd44d215 | 79f3c0b0a340c1d96e84d6262b34af6d47e60eeb | refs/heads/master | 2021-01-22T03:06:17.024635 | 2015-07-12T04:26:32 | 2015-07-12T04:26:32 | 38,942,747 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,493 | r | Google-read-csv.R | GoogleReadCsv <- function(sheet.ID, ...) {
# version of read.csv for reading Google spreadsheets into data frames
# using the trick of appending export?format=csv to the URL
# Based on discussion in:
# http://stackoverflow.com/questions/22873602/importing-data-into-r-from-google-spreadsheet
# The sheetID is best cop... |
6779d96b3c57125a22057f244ca15350fe646164 | 1115ed2b4ab8073a9fb7937387ff2758258ed4dd | /UnivariateMethods/Ankeny_validate.R | ffbabf9ddc0779414d0abdbeb960fb7885fe5978 | [] | no_license | chxyself25/Change-pointDetection | e343bc8cf47e141f579ab6f6b136257087f72a72 | 4a1b1b60074efb887d1f71c2b83ca3630fc9e409 | refs/heads/master | 2022-12-03T10:29:51.145653 | 2020-08-08T21:07:10 | 2020-08-08T21:07:10 | 285,974,409 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,463 | r | Ankeny_validate.R | sourceDir <- function(path, trace = TRUE, ...) {
for (nm in list.files(path, pattern = "[.][RrSsQq]$")) {
if(trace) cat(nm,":")
source(file.path(path, nm), ...)
if(trace) cat("\n")
}
}
library(doParallel)
library(signal, lib.loc = "/home/xchang/R/x86_64-pc-linux-gnu-library/3.6/")
library(dplyr, lib.loc... |
19994fb35ca7b906f8b7a35ff9af296fe73b732d | 7b2cacf99fe488c001d09b6a51eac439bdfa5272 | /analysis/gene_scoring/ensemble_classifier.R | 564e91d0c62af864b14719077b48bb67aa469ba0 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | talkowski-lab/rCNV2 | d4fc066478db96322b7aa062f4ece268098b9de3 | 7e97d4c1562372a6edd7f67cdf36d4167da216f8 | refs/heads/master | 2023-04-11T08:48:40.884027 | 2023-01-25T15:59:13 | 2023-01-25T15:59:13 | 178,399,375 | 14 | 4 | MIT | 2022-03-16T16:42:46 | 2019-03-29T12:13:54 | R | UTF-8 | R | false | false | 5,453 | r | ensemble_classifier.R | #!/usr/bin/env Rscript
######################
# rCNV Project #
######################
# Copyright (c) 2020 Ryan L. Collins and the Talkowski Laboratory
# Distributed under terms of the MIT License (see LICENSE)
# Contact: Ryan L. Collins <rlcollins@g.harvard.edu>
# Ensemble classification of gene scores
opti... |
36c81017f06aefe255c25283347ff68a20dad0cb | 9b3bf1dd0178c131b80d944cdaff5c33d1c62bd5 | /ModelSelection.R | b877c33401caf377e353ff5f0bffd713f08a2797 | [] | no_license | himankjn/Regression-Models | 7f7265f980f457a0a4df659d2e43bfdbd6049b26 | 74f09bdc960e4f6c015dbf412c5a9120e1d52580 | refs/heads/master | 2022-02-23T10:17:39.463472 | 2019-10-09T14:03:45 | 2019-10-09T14:03:45 | 196,245,456 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,247 | r | ModelSelection.R | #model selection
##In Multiple linear regression model selection can be a huge question
## a model with two categories same slope but different intercepts.i.e. multiple regresiion
##a model with two categories different slopes and different intercepts i.e with interaction
##which variables to include?
## including unn... |
05f95b1a3b10c26f90fc4f2b0705bda9575c07a5 | 6e3c81e90730c199a0536854374010f3527bc292 | /analysis_scripts/variable_maps.R | 139047fda92e3ce7643ae7628598c77b633be9a4 | [] | no_license | camposfa/plhdbR | 0d01acc33a5d4878c9ddf3d2857396d837dc8fca | 4472c22d45835dde05debf4c0659c21a19c3bdcd | refs/heads/master | 2020-05-17T23:51:35.521989 | 2017-09-12T13:26:02 | 2017-09-12T13:26:02 | 32,407,178 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,225 | r | variable_maps.R | var_map <- c(
rainfall = "Rainfall",
mean_temp = "Temperature",
index_nino3.4 = "ENSO Index",
spei_03 = "Drought Index",
dmi = "IOD Index",
dmi_spc = "IOD Index (spc)",
enso_spc = "ENSO Index (spc)"
)
var_map2 <- c("rain_monthly_mm" = "Rainfall",
"tavg_anomaly" = "Temperature",
... |
3a5cb82d4424318080d2738e20c3623080686de3 | e8305a8f19a31ea5def1ebcbddeb3a600769399a | /scripts/0_occurrences.R | 03abca58755a8c90e812526d9cb013187dd3bbbf | [] | no_license | AndreaSanchezTapia/sanchez-tapia_ffgc_2020 | db6f5541ab236ba4e61fae021db36aa98261626a | f9752177bdd60576ff88f9c1c8f630d9389ddcc4 | refs/heads/master | 2022-12-23T23:04:04.682201 | 2020-10-06T01:56:47 | 2020-10-06T01:56:47 | 254,691,753 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,861 | r | 0_occurrences.R | # reads community matrix
com.ord <- read.table("./data/comord.txt", header = T, row.names = 1)
# list of names and taxonomy
nomes <- read.table("./data/nomes.txt", header = T, sep = "\t")
# not all names collected are in the plots
nomes.filt <- nomes[nomes$cod %in% names(com.ord), ]
# creates genera
genera <- strsplit... |
b4f65ea942e3e361cc07e9365390e2965dcdca81 | 5e1e1088cd7876fa4c49448e8df4ea6dac14940e | /build_helper.R | 042c008c9d69bc55093e2bcb95e7b8ccd01dd50b | [] | no_license | gkoumasd/shinySatRDay | 982a59562e7d73e6a4e9076bf5d9870f641000ff | 8d9bbb4b840f0d7137c7a464f6a56ab44ff1bfbc | refs/heads/master | 2021-01-18T06:32:12.910273 | 2016-09-03T05:42:15 | 2016-09-03T05:42:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 225 | r | build_helper.R | # package build helper
mypath <- # path where your files are stored
#mypath <- "C:\\Users\\kross-smith\\Documents\\02_projects\\Training\\satRday\\shinyGadget"
devtools::build(mypath)
devtools::install(mypath)
|
c3cd35c5e914dd5a218aa823b24087e1dd6d664a | a4b5e0805f0dfd69654fa3a74668d575c7e9aab8 | /LoadMethylation.R | f7073edcd0f64533b9330b7fd993b925044395a7 | [] | no_license | peng-gang/Methylation | 10fc18a65f7e86d726a0373dba3f6814e5526dd4 | 51036d2c26f6a7b29a2e1a9232f58cb97e44ef54 | refs/heads/master | 2021-05-17T16:02:32.584684 | 2020-04-14T19:52:33 | 2020-04-14T19:52:33 | 250,860,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,176 | r | LoadMethylation.R | # read raw methylation data
library(minfi)
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
load("data/target.RData")
#read Raw data
loadData <- function(target, out_dir){
# read raw data
rgSet <- read.metharray.exp(targets = target)
# change sample name from sentrix information to real sample name (... |
86b18b9999278706f76c491b6e03b4d46667abac | d65d6b0d817882f198f08ebb87308bfe85e4d850 | /tests/testthat/test-basic.R | 2c59baafd7f87394225ac52856a295beb4eef315 | [] | no_license | rdpeng/filehashsqlite | 28d239efd6cf2a4a33adb119c3c20a1b3c02d361 | 98284b31f919239c6a2cedf08c6d4a132afd7e57 | refs/heads/master | 2022-05-15T06:06:48.871023 | 2022-05-11T16:44:46 | 2022-05-11T16:44:46 | 509,720 | 8 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,284 | r | test-basic.R | context("Basic Tests")
test_that("DB Creation", {
dbCreate("test1", "SQLite")
db <- dbInit("test1", "SQLite")
expect_s4_class(db, "filehashSQLite")
dbUnlink(db)
})
test_that("Insert object", {
dbCreate("test2", "SQLite")
db <- dbInit("test2", "SQLite")
set.seed(... |
9def092f1ddddc76eb57b17c5a0432beb5ca6fbb | c2b9863434f1ab0893126f463248a91028eea763 | /R-plots/PlotGenderRatios.R | bfba59d88ff8c65cbe444cfdcaade5e75e18bc16 | [] | no_license | emmenru/women-in-smc | b38afc49e25db1d8a78d387b76423802c1dbee28 | f75d93ca19497a4cc49d43386fe246747076327d | refs/heads/master | 2022-12-21T23:06:49.571700 | 2021-09-16T15:00:45 | 2021-09-16T15:00:45 | 84,766,100 | 1 | 0 | null | 2022-12-09T05:23:20 | 2017-03-12T23:51:27 | TeX | UTF-8 | R | false | false | 3,079 | r | PlotGenderRatios.R | # plotting library
library(ggplot2)
library(plotly)
library(dplyr)
library(tidyr)
library(lattice)
data_stats_corrected=0
data_stats_corrected <- read.csv("~/kurser/creative_coding/women-in-smc/output_data/ICMC_Stats_Corrected.csv")
# select only percentage columns
myvars <- c("Year", "Female", "Male", "Unknown")... |
ed9a1ff9b9287f1695b32e735eb896c96be92100 | 5e15ffd3f55a5139ad5c91ae597de8782b791845 | /old files/climate_community_relationship_MAP.R | 3d72b215b5e2a45eda6313e81f6508d1942f64f2 | [] | no_license | SallyKoerner/DomDiv | a0015c253634a20fa96de4d3094e8cd6babba57c | 5361d07690e3e88ea5562d48cecdbad2b19ca4b3 | refs/heads/master | 2022-01-28T12:15:26.641008 | 2022-01-21T20:44:33 | 2022-01-21T20:44:33 | 161,543,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,797 | r | climate_community_relationship_MAP.R | setwd('C:\\Users\\lapie\\Dropbox (Smithsonian)\\working groups\\DomDiv_Workshop\\Dominance_Diversity')
setwd('C:\\Users\\mavolio2\\Dropbox\\DomDiv_Workshop\\Dominance_Diversity')
library(grid)
library(knitr)
library(kableExtra)
library(lme4)
library(tidyverse)
###ggplot theme set
theme_set(theme_bw())
theme_update(... |
eb4c19343c72bf47fcb7d362ed9bca33919cb07a | e9eb22e63999ff29edcecac8b4f5521266e73f4b | /r-hta-2020/speed-tests-fun.R | a3fb050fb9c7a63ce906a3f1b2fddbc6a697e06b | [] | no_license | hesim-dev/hesim-presentations | 315342a76b995ebe709ada506e4ca47c587a22f9 | 4ecd20d214194e58d31681e0336604aa7dd9d83f | refs/heads/master | 2021-07-03T12:17:11.773073 | 2020-10-13T16:08:01 | 2020-10-13T16:08:01 | 186,916,093 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 17,956 | r | speed-tests-fun.R | # Run heemod -------------------------------------------------------------------
run_heemod <- function(n_samples) {
ptm <- proc.time()
# Define parameters
param <- define_parameters(
age_init = 60,
sex = 0,
## age increases with cycles
age = age_init + markov_cycle,
## operative ... |
a777effcafd4ac523771b83873238fd050840414 | a6fac5b0295e76c2ae12002a7847097174244f7d | /Functions_NCOV.R | 05534b2eceb4da63a061fef55da8d276f93aee0d | [] | no_license | kaiyuanmifen/COVID19_prediction_USA | 377894228f0a70f6b79b77f922a7b2167e2dfee3 | 83a39bb9f13bdef303bc08786a626904e08b6436 | refs/heads/main | 2023-01-03T23:15:16.349142 | 2020-11-03T21:58:00 | 2020-11-03T21:58:00 | 309,776,734 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 42,366 | r | Functions_NCOV.R |
packages <- c("ggplot2", "dplyr", "reshape","ppcor","glmnet","MLmetrics")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
install.packages(setdiff(packages, rownames(installed.packages())))
}
# Hierachical clusting choosing the best number of K
HierachiClustering=function(AllP... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.