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67e4ada786a0172ed0804494ddfa6187a36c5b36 | ea10d0c311ee84dc4ecd44c49dafe873180023e4 | /SWSurfaceStations.R | 01c8fd9ab02044d48a28f3277b05401b863faae6 | [] | no_license | saadtarik/SurfaceStationSummary | f7e720182df57f08b59579110136005d1b3c3a95 | 47febbfe57dc4f1ea1f5ccb57c1f7190cc2afd0e | refs/heads/master | 2021-01-20T06:53:44.722362 | 2013-07-10T23:37:51 | 2013-07-10T23:37:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 496 | r | SWSurfaceStations.R | ## DATA TO FIND
# country code
country.code <- "SW"
# Bounding box. Latitudes and longitudes outside this range will be set to NA.
# This assumes that the country code is sufficient to identify the location of a
# station correctly.
long.range <- c(6, 11)
lat.range <- c(45.5, 48)
# years we want data for
year.range <... |
d55a28dfb5109ece9c2b5839ba07beea9eae7893 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/h2o/examples/h2o.head.Rd.R | 02f68638813f2884fa9a6e50572d71af0e3dbc16 | [] | 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 | 435 | r | h2o.head.Rd.R | library(h2o)
### Name: h2o.head
### Title: Return the Head or Tail of an H2O Dataset.
### Aliases: h2o.head head.H2OFrame h2o.tail tail.H2OFrame
### ** Examples
## No test:
library(h2o)
h2o.init(ip <- "localhost", port = 54321, startH2O = TRUE)
australia_path <- system.file("extdata", "australia.csv", package = "h... |
7319ff205d6218ee4f19e81876a6f4d0d84c02bd | 5da43dc717c58284b2c89a3aba668c1eb6ba35f2 | /R/parseCubistModel.R | 1d657b3036b70217a93cd66b87fed97cd30feac9 | [] | no_license | topepo/Cubist | 513dddbd1ad8e314fe3a62984a76eaa41f6444b5 | cb293c67673772d754cc846c8711a31b40b26175 | refs/heads/master | 2023-03-21T17:42:31.927207 | 2023-02-09T15:16:14 | 2023-02-09T15:16:14 | 23,597,893 | 37 | 16 | null | 2023-03-09T19:45:52 | 2014-09-02T22:28:15 | C | UTF-8 | R | false | false | 11,434 | r | parseCubistModel.R | ## TODO:
## 3) R function to write R prediction function
countRules <- function(x)
{
x <- strsplit(x, "\n")[[1]]
comNum <- ruleNum <- condNum <- rep(NA, length(x))
comIdx <- rIdx <- 0
for(i in seq(along = x))
{
tt <- parser(x[i])
if(names(tt)[1] == "rules")
{
... |
708f1d5e2c3d1706de319d15153368153475a58e | 498da3ea40beb28640eba6418869b020b9d9ea52 | /tests/testthat/test_pathways.R | 2b8ab8ae04b7a6f3a9beda7d864f4737edad4e28 | [] | no_license | GuangchuangYu/fgsea | 14bd5cac15a4f96a44b591917a9cab7fdb536958 | ea22e290dcf7ee55ebd086ead321ad0b6d0f49f9 | refs/heads/master | 2021-01-13T08:19:09.140795 | 2016-10-24T11:41:21 | 2016-10-24T11:41:21 | 71,782,575 | 2 | 0 | null | 2016-10-24T11:35:20 | 2016-10-24T11:35:19 | null | UTF-8 | R | false | false | 486 | r | test_pathways.R | context("Pathways")
test_that("reactomePathways works", {
if (!requireNamespace("reactome.db")) {
skip("No reactome.db")
}
data(exampleRanks)
pathways <- reactomePathways(names(exampleRanks))
expect_true("11461" %in% pathways$`Chromatin organization`)
})
test_that("gmtPathways works", {
... |
f4ca15728052206b6d3f80fe5e3877fd2058c8f4 | efa46ff6a91d57fcb8e1c14a36b46f8ac0ec76dc | /getData.R | d6579335b378a2fb569c2c7d093bc93ad76392d4 | [] | no_license | chtiprog/ExData_Plotting1 | a8cf407c83c8353b33ca3d679f4712d53c298648 | 2896d0ed2c51e489de3e9422cc6d067936915254 | refs/heads/master | 2021-01-18T03:59:50.217896 | 2015-03-08T07:07:19 | 2015-03-08T07:07:19 | 30,431,200 | 0 | 0 | null | 2015-02-06T20:18:29 | 2015-02-06T20:18:29 | null | UTF-8 | R | false | false | 875 | r | getData.R | fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
# Download and unzip the data file if it does not exist already
if (!file.exists("household_power_consumption.zip")) {
download.file(fileUrl, destfile="household_power_consumption.zip", method="curl")
unzip("household... |
cdae5722b075ad6c8e4669b584f058890fa2f1b7 | 285d35f03c59b3eca8a8639e30009cc020ee7fbf | /03-txome/20200912_tpm_analysis.R | 593d990dc7b89cdd90952585733c06722b5043d3 | [] | no_license | octopode/cteno-lipids-2021 | 5a039c0506111cc3bbd6e689bd7bf02ac585dc1c | b2c66cf59149e5c79918bdcc317303a23e59e338 | refs/heads/master | 2023-03-26T07:59:36.219753 | 2021-03-12T18:19:25 | 2021-03-12T18:19:25 | 289,368,885 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,594 | r | 20200912_tpm_analysis.R | library(tidyverse)
library(ggpubr)
env_data <- read_tsv("/Users/jwinnikoff/Documents/MBARI/Lipids/cteno-lipids-2020/metadata/20200912_Cteno_depth_temp_EST.tsv")
dir_tpms <- "/Users/jwinnikoff/Documents/MBARI/Lipids/cteno-lipids-2020/kallisto"
key_annot = c(
"OG0001664.tsv" = "ELOV6",
"OG0004874.tsv" = "ELOV2",
... |
a6a5d6f3b0d63ff9414b0ce64635fc3d38c45036 | cb6f2a406e75c379a647e0913ac407a2e067c693 | /man/compute.config.matrices.Rd | 143052d6723c0745e04692fc9922b22affbb5e8a | [] | no_license | NKI-CCB/iTOP | 9f797340aa9bf90a1bb7b1bb273c7b7f2b59a37a | e93ad3a8bbd7754153c57c44afc85970c9b682c2 | refs/heads/master | 2021-04-09T16:02:13.978456 | 2018-06-13T08:14:16 | 2018-06-13T08:14:16 | 125,842,771 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,709 | rd | compute.config.matrices.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{compute.config.matrices}
\alias{compute.config.matrices}
\title{Compute configuration matrices}
\usage{
compute.config.matrices(data, similarity_fun = inner.product, center = TRUE,
mod.rv = TRUE)
}
\arguments{
\item{data}{... |
953823b177812455e308e704a41663d0701cba51 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws/man/clouddirectory.Rd | 88fdfd944871cf9b0688d95fd1c2d63cbaa8a150 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 13,693 | rd | clouddirectory.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.R
\name{clouddirectory}
\alias{clouddirectory}
\title{Amazon CloudDirectory}
\usage{
clouddirectory(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)
}
\arguments{
\item{config}{Optional configuration of cr... |
a0767909041acdca1dabfd2b30f84f37283bec7a | 1b5c6f504c76c0cb0559ea54b1835d1dbe98a27e | /plot1.R | fc7856e25e8930b4c6e24b0b66a61a62d760b393 | [] | no_license | Fpschwartz1/ExploratoryDataAnalysis_Project1 | f4df3ce37f3e28313ea299bbdd70891493fb9026 | 49736f6e32079b38c4adaddd7d8e457739d37420 | refs/heads/master | 2016-09-06T16:56:31.513950 | 2014-07-10T23:08:23 | 2014-07-10T23:08:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 799 | r | plot1.R | # Reading the file "household_power_consumption.txt".
# There was no problem with RAM memory space in my computer
t<-read.table("household_power_consumption.txt", sep=";", na.strings = "?", header=TRUE,
colClasses=c("character","character","numeric","numeric","numeric","numeric","numeric","numeric","nume... |
8e20679c4b9df0f694f85fa2c0dc9ddeb0af154c | ae4f4f0ed037b6fa643cf51c74627f63a5c207a9 | /Demographics_T1Bifactors.R | 7e31c83a8a93584bf007ace0a9619024475fe235 | [] | no_license | PennLINC/KaczkurkinPark_BifactorStructure | bd49a0c70497e066e852ffa018cabd6806c0fa00 | 2bb62f8c398f93e22bf7c8f384649a01de4eb211 | refs/heads/master | 2021-10-23T09:24:48.906784 | 2019-03-16T20:44:21 | 2019-03-16T20:44:21 | 157,735,704 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,690 | r | Demographics_T1Bifactors.R | ###############################
#### Table 1: Demographics ####
###############################
###############################
### Load data and libraries ###
###############################
subjData <- readRDS("/data/jux/BBL/projects/pncT1AcrossDisorder/subjectData/n1394_T1_subjData.rds")
#Load libraries
library(p... |
b1b7ea49074ca6db01e41403fdbb906bc530d922 | ff61b2eececdbd441514e6e693e1a7295301ff66 | /plot1.R | 15f66d132d057192a9392757a8ec0c1c1b6c788a | [] | no_license | boukevanderpol/ExData_Plotting1 | a1f477d5210204a392c667c16d3fc57343b49ac7 | eeeb46e185a249c12cfc1f45bff0ef2660c8b110 | refs/heads/master | 2021-01-22T11:48:09.125919 | 2016-01-05T13:44:22 | 2016-01-05T13:44:22 | 48,806,642 | 0 | 0 | null | 2015-12-30T15:22:54 | 2015-12-30T15:22:54 | null | UTF-8 | R | false | false | 1,222 | r | plot1.R | # Setting the working directory where data is located.
setwd("~/R/EDA/project1")
# loading packages
library(data.table)
library(dplyr)
library(tidyr)
library(readr)
#library(lattice)
#library(ggplot2)
library(lubridate)
# Loading the data into R
x <- read_csv(file = "household_power_consumption.txt",
... |
192985ee87f5e25fedcb8b3152d2d4b8e9a5a5d8 | 47d717b2d089c1a02c518c6121a8c50e9ea79c93 | /man/exampleProteomicsData.Rd | a4e34ae4fa1e8b93b23ab1eeaf1326138986cdf2 | [] | no_license | elolab/PowerExplorer | 9bd0fbee338de3137910ee42a28c99ef3b77f7c6 | f5cbe6e70bb2800ccfbaf3be8c72cadc0b3c9129 | refs/heads/master | 2023-03-31T12:40:23.303622 | 2018-11-07T11:50:19 | 2018-11-07T11:50:19 | 357,198,197 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 710 | rd | exampleProteomicsData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exampleProteomicsData.R
\docType{data}
\name{exampleProteomicsData}
\alias{exampleProteomicsData}
\title{Randomly Generated Proteomics Dataset}
\format{An list contains \code{"dataMatrix"} and \code{"groupVec"}}
\usage{
data(exampleProteomics... |
40874601d95cf871b01ee76003560f42f77badd3 | 5a9beb9f519afb900b0329ace2d0f132c2848cc8 | /Text Mining with R/Sentiment Analysis with Tidy Data.R | fdf76e80e71e7ed9618c1abfada673824d8a1f92 | [] | no_license | ZehongZ/R-Studio | d6d8525d29c4fc005f07a6db252f427f844ad3b1 | 1c06ea907552e8958f476e1ad3e9a9efe31e8549 | refs/heads/master | 2021-07-09T10:58:00.965761 | 2020-08-28T07:54:16 | 2020-08-28T07:54:16 | 173,672,330 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,207 | r | Sentiment Analysis with Tidy Data.R | #The sentimens Dataset
library(tidytext)
data("sentiments")
#get_sentiments() to get specific sentiment lexicons without the columns that are not used in that lexicon
get_sentiments("afinn")
get_sentiments("bing")
get_sentiments("nrc")
#Sentiment Analysis with Inner Join
library(janeaustenr)
library(dplyr)
library(st... |
3eff5efcdf9b2810851f7234020da1327cd060b3 | 2db3a064b96b1427bddadc747805d31356f908a7 | /R/Met.Save.Data.R | 62499bd01ceaa7060dd8cd1af74f2e8c224ef770 | [] | no_license | cran/Metabonomic | d6981fc2ac985d5675a0d2b5d3d2697fd90a9385 | 89950548805f047697b68dae60163f145937c159 | refs/heads/master | 2021-01-20T02:29:05.117518 | 2010-09-13T00:00:00 | 2010-09-13T00:00:00 | 17,717,831 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 281 | r | Met.Save.Data.R | Met.Save.Data <-
function()
{
fileName<-tclvalue(tkgetSaveFile())
write.table(datos$datos, file = fileName, append = FALSE, quote = FALSE, sep = "\t",
eol = "\n", na = "NA", dec = ".", row.names = FALSE,
col.names = FALSE, qmethod = c("escape", "double"))
}
|
cb4e973abaeb913c90a35914c0ca4aaa33795561 | 66a4f6d2f5293a94b97e88325d6c0c6048771f7c | /liuq_srp.R | 6110317076b929322f0ee09830da1017623581f9 | [] | no_license | QMmmmLiu/Multiplicative-PHQ-9 | 140967a514f9ec0e7705fc3205f81c4ff900dcf0 | 51d4b038ce8ca2df036f28ba0363dcbda29ee03f | refs/heads/master | 2020-07-19T05:09:28.877341 | 2019-09-19T18:56:41 | 2019-09-19T18:56:41 | 206,379,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,405 | r | liuq_srp.R | phq=x[,c(2,4,354:362)]
sapply(3:11,function(i) levels(phq[,i])<<-c(1,2,3,4))
sapply(3:11,function(i) phq[,i]<<-as.numeric(phq[,i]))
phq.r=data.frame(cbind(phq[,3:11],phq$login,phq$Asmnt,
rowSums(phq[,3:11]),
exp(rowMeans(log(phq[,3:11])))))
colnames(phq.r)[-c(1:9)]=c("... |
a664851e77e1c2f2a52fd15796d683362e0662b2 | 02e865334769049a7a92ffe4b3d37cb66c97ae04 | /Unit 5 /twitter.R | 82377bafcc09d42d8abc88d40012afe7fc099810 | [] | no_license | egorgrachev/Analytics_Edge | 5d17c046273fe1461842d4b2e0d81eaec4c3f1be | 9d4a5e611f0ac5dc39ce566b7f68739fecac66dd | refs/heads/master | 2021-01-16T01:02:05.038312 | 2015-04-15T09:31:37 | 2015-04-15T09:31:37 | 33,805,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,066 | r | twitter.R | install.packages("tm")
tweets = read.csv("AnalyticsEdge/Unit 5 /tweets.csv", stringsAsFactors=FALSE)
str(tweets)
tweets$Negative = as.factor(tweets$Avg <= -1)
str(tweets)
table(tweets$Negative)
library(tm)
install.packages("SnowballC")
library(SnowballC)
corpus = Corpus(VectorSource(tweets$Tweet))
corpus = tm_map(corp... |
b446f472549525ce5f48f9b7f3693edb3b84c735 | 29585dff702209dd446c0ab52ceea046c58e384e | /GERGM/R/gergm.R | c8c69104f6dce16f7c7a2b8e88c9b0877612c91b | [] | 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 | 25,379 | r | gergm.R | #' @title A Function to estimate a GERGM.
#' @description The main function provided by the package.
#'
#' @param formula A formula object that specifies the relationship between
#' statistics and the observed network. Currently, the user may specify a model
#' using any combination of the following statistics: `out2s... |
dc3f82ffcadc78767b72d147eddfc112ba87f6f4 | cd901f78760d0856a58e2791d94751b3e3e5c3e8 | /R/callECNV.R | 9b420101cf8ad31054837fe666163e70f36940cc | [] | no_license | sanadamakomi/exonCNV | 4d6056596d2a17df5e56075400441207bf6eb77f | 92aaeb8ea242aa6965e3910ae5825c68ec30c65b | refs/heads/master | 2022-08-10T09:24:41.165518 | 2022-08-04T07:59:10 | 2022-08-04T07:59:10 | 175,590,331 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 13,045 | r | callECNV.R | #' @title Fit data
#' @param mergedCovFile Path of merged coverage file.
#' @param parameterFile Path of metrics file.
#' @param path Path to write to.
#' @param lowdepth A numeric value, regions that avaerage depth less than this
#' value will replaced by NA.
#' @export
#' @author Zhan-Ni Chen
performFitPoisson <- f... |
8f9bb8e889384c1789bfaf60469f8c5e1236c95b | b8a0090cea7e4b950d067e7cb051d7996b981988 | /manuscript-figure-plots/make-cell-type-count-table.R | 4eb183175deadc3832d718da3ea9a72f615a8a8e | [] | no_license | dnarna909/2020-sn-muscle | 7f828c168a663528fdf7025113d1265ba039868f | 74c0879afbe8fdf149fa60c8552d0eea0ed98f18 | refs/heads/master | 2022-11-13T03:16:22.519454 | 2020-07-01T21:38:45 | 2020-07-01T21:38:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,096 | r | make-cell-type-count-table.R | #!/usr/bin/env Rscript
library(dplyr)
library(tidyr)
args <- commandArgs(T)
CLUSTER_NAMES <- args[1]
CLUSTER_ASSIGNMENTS <- args[2]
LIBRARY_LABELS <- args[3]
library_to_modality_and_species <- read.table(LIBRARY_LABELS, head=T, as.is=T, sep='\t') %>%
dplyr::select(library, species, modality)
clusters <- read.tab... |
594dae575e19344bd0e65e4fe1decdccddf68969 | cf15fbeea99db004b475b65fdce55219a94c8182 | /x_y_arcmaps.r | 7c4935eb7f96867274ac96d0497193d6069ea488 | [] | no_license | tessam30/RProgramming | d09383a630e05c74452ae07c9856f0d634f790c4 | 87476adeb7c4a532de9f34febcc98d722e8d9c7f | refs/heads/master | 2021-01-02T22:31:02.584799 | 2017-10-24T02:09:12 | 2017-10-24T02:09:12 | 34,971,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,572 | r | x_y_arcmaps.r | # Creating x/y great-arc maps
# https://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/
# required libs
library(maps)
library(geosphere)
library(mapproj)
map("state")
map("world", proj='bonne', param = 10)
xlim <- c(-171.738281, -56.601563)
ylim <- c(12.039321, 71.856229)
map("wo... |
7be58e46a0edeb86b0c2066e38c2be7dbd50b907 | 86066ea78219cab7f897c1e5e02850da2f66e82f | /StatisticalInferenceTrial1.R | a658660f4992213df6167e4551479c72a56d0a77 | [] | no_license | vishmaram/StatisticalInferenceTrials | 471f00d97ff33277271abd13fe3e928d0bb1a2e7 | 3e68e0b08f0c0d3aac1ff9f5bcb24f2622cb7281 | refs/heads/master | 2021-01-01T03:55:58.468196 | 2016-05-26T23:35:10 | 2016-05-26T23:35:10 | 59,789,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30 | r | StatisticalInferenceTrial1.R | # This is used for learning -1 |
b9af6d44bb6fab62853bcff389e261f962d9dea4 | 33193bde7e91aecd9f72392957640ce8790717a3 | /app.R | a1a5491d2961f050932524263dcdeb23245ae902 | [] | no_license | keshavbans/CryptoCurrency | 4c69759cbb04383cc82ede6a3190ec5626c1baeb | 37c541a7bbcfaf4fc919080fa4dd4b9cde1f0314 | refs/heads/master | 2021-08-29T08:00:07.135688 | 2017-12-13T13:39:57 | 2017-12-13T13:39:57 | 114,125,255 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,909 | r | app.R | # Upload neccessary libraries (shiny, ggplot2)
library(shiny)
library(ggplot2)
# Upload Datasets
bitcoin <- read.csv("bitcoin_price.csv")
dash <- read.csv("dash_price.csv")
ethereum <- read.csv("ethereum_price.csv")
iota <- read.csv("iota_price.csv")
litecoin <- read.csv("litecoin_price.csv")
monero <- read.csv("moner... |
79255a33157adbd79f87428a8c6cbf8c413ee6c5 | 468075902da967e77578f8445a542faf2ee51227 | /R/LEstep.R | 11f7927fa655bfc4f4fffabc550e0875c6e58b98 | [] | no_license | fchamroukhi/HDME | 202dd27585ff2a50fe0c59b62bb8e5836cf74d3f | 09d44e933cc4cd60e85cf920621708da44d12016 | refs/heads/master | 2020-06-19T18:22:12.092085 | 2019-10-23T13:24:54 | 2019-10-23T13:24:54 | 196,819,715 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 530 | r | LEstep.R | #E-step
Le.step = function(eta, wk, Y, X, K, R)
{
# source("Pik.R")
# source("LPi.R")
n = dim(X)[1]
tau = matrix(rep(0,n*K), ncol=K)
pik = Pik(n, K, X, wk)
for (i in 1:n)
{ Sum = 0
for(k in 1:K)
{ #be careful for case R > 2
ETAk = as.matrix(eta[k,,])
if(R==2) ETAk = t(ETAk)
P_eta... |
8070eab984b0b67dc982f4c22b1a2a4027def742 | dd62c2f20d16320a51860352282be509867bb72b | /munge/homicide.R | 95b6a68bf384910317bca606d9b2fde6407d28cf | [] | no_license | WaverlyWei/Violence-Project | 73b94a957fee2ff273223c49e145b18e63f0d098 | d1dd9ab2345d2e71c5f8d7ebb039ce23f33d94c4 | refs/heads/master | 2020-03-26T04:45:55.756501 | 2019-08-21T17:43:47 | 2019-08-21T17:43:47 | 144,521,679 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,463 | r | homicide.R | library(readstata13)
library(vimp)
library(SuperLearner)
library(ctmle)
library(ggplot2)
library(Amelia)
library(superheat)
setwd("/Users/waverlywei/Desktop/New Trauma Project ")
dat.1 <- readxl::read_excel("Master by Neighborhood for Alan.xlsx",sheet = 1)
dat.2 <- read.dta13("chronicdz3.dta")
# variable selection wit... |
f498bcb0a98b668a1cae0ee619810e79cdec80c8 | 6b7eac94cab95036dfcb8f49f992524947aa40ca | /man/sum_betas.Rd | 81039b32f2842b015ddbbfcc6e11d075a0919461 | [
"MIT"
] | permissive | Urban-Analytics/rampuaR | d9e4a7b4acfbf06cccc0b25a68dfafebc1836256 | 4a73131228b872a517916e964ac732ff3b25d519 | refs/heads/master | 2023-01-14T11:27:10.922266 | 2020-11-24T15:20:49 | 2020-11-24T15:20:49 | 280,127,722 | 2 | 0 | MIT | 2020-11-05T10:31:09 | 2020-07-16T10:44:09 | R | UTF-8 | R | false | true | 657 | rd | sum_betas.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/covid_status_functions.R
\name{sum_betas}
\alias{sum_betas}
\title{Summing betas for use in COVID probability calculation}
\usage{
sum_betas(df, betas, risk_cap_val = NA)
}
\arguments{
\item{df}{The input list - the output from the create_inp... |
11809b36e4adc6a86274b78363d9dbbf53bf48b5 | 7daf72d1abe4b13d1e26dc46abddfebcfc42d9e8 | /man/min_n.Rd | e8608dc45ee126f08a8ecbf6cc02541d81a41a50 | [
"MIT"
] | permissive | farcego/rbl | 6c39a7f2e63564c75860aa6a7887b2b49ffb73fb | b1cfa946b978dae09bf4d4b79267c4269e067627 | refs/heads/master | 2020-03-21T15:25:49.368438 | 2017-06-15T09:22:11 | 2017-06-15T09:22:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 711 | rd | min_n.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_functionals.r
\name{min_n}
\alias{min_n}
\title{Create a function that return a result if number of valid obs >= n}
\usage{
min_n(f, n)
}
\arguments{
\item{f}{a function having a vector of data as first argument}
\item{n}{the minimum n... |
4dc621bb49649cf025fcf66e8da1ee6d18153fe8 | b66a11af854338b50f57b714c34336b030d00c97 | /code/theme.R | 40476591f0e5f5d479228167a3095765b38007ca | [] | no_license | aritrakn/code_pitfalls_iml | 60f9ae0e2011e6b923db54f64800e595b320f143 | 40296afedb6461469c7a32ae563e8d22e27ed625 | refs/heads/main | 2023-07-04T22:20:33.076448 | 2021-08-17T07:06:56 | 2021-08-17T07:06:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 93 | r | theme.R | library("ggplot2")
th = theme_bw() +
theme(text = element_text(size = 16))
theme_set(th)
|
486d6b2a5962e05d022dc3d77acef00222888882 | 238f01972914d67ddbc3a8dfb43ad01a49f7de90 | /man/RFortLangComp-package.Rd | 4b191eeba588e1d0b2ea53f2b810b0b3ee707cc8 | [] | no_license | aadler/RFortLangComp | 115680aef39996c30bcab8212fa8f938f5b66756 | 42a0ecbc926dd6a26f9447cb91205769c3729ad4 | refs/heads/master | 2020-04-13T03:43:52.322220 | 2019-05-26T20:15:33 | 2019-05-26T20:15:33 | 162,940,340 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,063 | rd | RFortLangComp-package.Rd | \name{RFortLangComp-package}
\alias{RFortLangComp-package}
\alias{RFortLangComp}
\title{
R, Fortran, and C versions of layer loss functions for speed comparison
}
\description{
This package contains various flavors of R, Fortran, and C versions of a simple layer loss cost function. These flavors represent levels o... |
98ee26c3eae0c71f3dd39cf4e9f4566a3ed572b9 | 7441a5909020383eb5b328439c2025367c9375ae | /man/trendfilter.Rd | 444853d6ad1e4d6cf7e074f4a870fe985b0ddb52 | [] | no_license | cran/genlasso | 1c306ff866222fd38561173aa936de8fe64e4d47 | c2367f08977cfcc615f3e0e33ad885ab3d72a94e | refs/heads/master | 2022-08-29T09:01:57.881638 | 2022-08-22T07:10:10 | 2022-08-22T07:10:10 | 17,696,330 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,854 | rd | trendfilter.Rd | \name{trendfilter}
\alias{trendfilter}
\title{
Compute the trend filtering solution path for any polynomial order
}
\description{
This function computes the solution path for the trend filtering
problem of an arbitrary polynomial order. When the order is set to
zero, trend filtering is equivalent to the 1d fuse... |
f9df8fff27614445aaecc05c4a5b0f90402efda8 | 69b49ce61413bc8190227621b0aa8dfaf951a048 | /src/Concerto/TestBundle/Resources/R/concerto5/R/concerto.file.getUrl.R | c5b2a779bebfff148567e90a6aed46138de449ba | [
"Apache-2.0"
] | permissive | campsych/concerto-platform | de926ae820f2a3cf6985598f3824dee8f4615232 | 988b67e8d52acbf25fdc9078e7592cc07d2dd9a3 | refs/heads/master | 2023-08-31T08:09:05.570628 | 2023-08-23T16:43:03 | 2023-08-23T16:43:03 | 55,242,761 | 164 | 109 | Apache-2.0 | 2023-07-26T15:10:48 | 2016-04-01T15:34:25 | PHP | UTF-8 | R | false | false | 203 | r | concerto.file.getUrl.R | concerto.file.getUrl = function(filename, noCache=F){
url = paste0(concerto$mediaUrl, "/", filename)
if(noCache) {
url = paste0(url, "?ts=",as.numeric(Sys.time()))
}
return(url)
} |
d7a4c09dfd2d2982401f3b9f0f23545ea5e2dbda | 2161e2c9b1463f3f0b8d27a9447c136e5e08d2b9 | /man/SumRelAbund.Rd | d2735c66a33cca47fd6ffb92ccb85a90f4ca0c47 | [] | no_license | NCRN/NCRNbirds | 14a258e8182849bb0434eb4368fa291105d56a7c | 5a512b736d674d9308c27667e7a99b142aebfcef | refs/heads/master | 2023-08-16T13:00:26.367713 | 2023-07-11T15:54:50 | 2023-07-11T15:54:50 | 32,335,489 | 5 | 12 | null | 2023-08-17T15:09:47 | 2015-03-16T15:44:44 | R | UTF-8 | R | false | true | 3,144 | rd | SumRelAbund.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SumRelAbund.R
\name{SumRelAbund}
\alias{SumRelAbund}
\title{SumRelAbund}
\usage{
SumRelAbund(
object,
parks = NA,
points = NA,
AOU = NA,
years = NA,
times = NA,
band = 1,
visits = NA,
CalcByYear = FALSE,
max = TRUE,
sort... |
b55cd5dd7d7e1ee72afd2243c91460d6124c089f | c0b85e47a0c19abb799841a0cadbcf9c0fb75052 | /R/contrastLimma.R | 221efd1a51551bc4aebe3c684fc44a52e4a90326 | [] | no_license | jtlovell/physGenomicsPVFinal | e4c4c3eb737a67d3aef390a3d89f12d4829d875f | 9dbf6632a7e783e365e1604602a76436505f79b4 | refs/heads/master | 2021-01-10T09:27:05.839396 | 2016-02-02T21:16:17 | 2016-02-02T21:16:17 | 50,861,717 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,281 | r | contrastLimma.R | contrastLimma<-function(counts, info, formula, use.qualityWeights=TRUE, block, tests="all",geneIDs=NA, useBlock=TRUE, getTopTable=TRUE, getEbayes=T...){
if(is.na(geneIDs)){
geneIDs<-rownames(counts)
}
design<-model.matrix(as.formula(formula), data = info)
y <- DGEList(counts = counts)
y <- calcNormFactor... |
ccd9b5fd626574828c6aa5135b9c225076f32e3f | f86e886e41d8f3b8de507189566bec977c3a5d52 | /tools/mapping_quality_stats/mapping_quality_stats.r | cdc360f799d996a1ced4a33b1203dc1f7234051c | [
"MIT"
] | permissive | bgruening/tools-artbio | e8398bbab54af97eafc32d6f69b746b9c9a8bf67 | e1e871049975dff030bf1e6fe2df8b8fa8997141 | refs/heads/master | 2023-08-05T07:19:03.096000 | 2023-07-20T00:14:25 | 2023-07-20T00:14:25 | 53,533,173 | 0 | 0 | MIT | 2023-07-20T11:47:22 | 2016-03-09T21:28:48 | Python | UTF-8 | R | false | false | 809 | r | mapping_quality_stats.r | ## Setup R error handling to go to stderr
options(show.error.messages = FALSE,
error = function() {
cat(geterrmessage(), file = stderr())
q("no", 1, FALSE)
}
)
warnings()
library(optparse)
library(ggplot2)
option_list <- list(
make_option(c("-i", "--input"), type = "charac... |
93e6dbed243ca9f9ceb50ef5473ab614c2c94c94 | d73c90a41950a261f89e9559942a682a855a2e52 | /GWASanalyses_CVLT_nooutlier_nlme.R | a8c6b1bd8c7f58944eda53647a9a12f4843bb649 | [] | no_license | amandazheutlin/RNA_GWAS | ab5cb8e68a46941d5bf2f3b78d059d86bdb7e2db | 3d6d90c88becbf16fb9b242d61ddb82c67baa5c2 | refs/heads/master | 2021-01-15T11:29:18.520083 | 2017-08-07T20:43:20 | 2017-08-07T20:43:20 | 99,617,978 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,772 | r | GWASanalyses_CVLT_nooutlier_nlme.R | # RNA GWAS
# swedish data
# exclude outlier (ID = 2008601953)
# load data and add neurocog variable
setwd("/mnt/nfs/swe_gwas/ABZ/RNA_GWAS")
load("swedenclean.rdata")
CVLT = read.table("GWAS-CVLT.txt",header=T)
swedenclean$CVLT = CVLT[match(swedenclean$StudyID,CVLT$IID),3]
swedenclean <- swedenclean[order(swedenclean$C... |
a2bbd00929a3242048affa00e53a1c8e5d31aca7 | 34f05b36f66e0e4a35fcdc711fc111af8948dea5 | /GS3008/Source/buttonsneedle.R | ec13bc77740168f4c025e43fdf221198f8e2816c | [] | no_license | mikigom/mikigom_course | a81f39cbd54188257978d03bb5899ab72d716c24 | 95cac5a43eab97c275d92e9b82cca1ef1f133a1a | refs/heads/master | 2020-07-05T10:06:11.529685 | 2017-04-21T21:46:17 | 2017-04-21T21:46:17 | 66,783,387 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 597 | r | buttonsneedle.R | buffon.needle <- function(d, l, n, m){
touch = 0
probability = 0
random_postion=0
random_theta=0
theta_vector = rep(1,n)
probability_vector = rep(1,n)
for (i in 1:n){
touch = 0
random_theta <- runif(1, min=0, max=pi)
for (k in 1:m){
random_position <- runif(1, min=0, max=d/2)
if(rando... |
4d798b49d701539e9b263b927961594003001365 | aaac559889d1968ee128d67460bcf4a4272e39fb | /figure/Plot 1.R | 13fa24c9be5bf9f9bbb9eaf39af5fba0403f8cfb | [] | no_license | Omar-Ma/ExData_Plotting1 | 7a6f9cd928afe2f42ac50f6d0e9edc5e680b99a7 | 4bfad1eb25ea314250548c63f399a7424c03ef17 | refs/heads/master | 2021-01-09T07:02:54.416243 | 2014-10-12T23:04:21 | 2014-10-12T23:04:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 264 | r | Plot 1.R | da<-read.table("household_power_consumption.txt",sep=";",header=T)
da$Date<-as.Date(da$Date,"%d/%m/%Y")
da1<-subset(da,Date=="2007-02-01"|Date=="2007-02-02")
hist(da1$Global_active_power,xlab="Global Active Power (kilowatts)",col="red",main="Global Active Power")
|
860a0770b47e2126caa7906f3b4766a873016a3c | 765a4a79c4ca4fc7c91b97e53118ab804b4044ba | /gtex_analysis/pre-process/combineAllTissueTpm.R | 460c9ac08e17908e207b53186cc6d642f3949de9 | [] | no_license | pughlab/net-seq | bbf2e658ef5602bcae6de24d9e6dc8f5149cea18 | 6fc6717a293d049a660532159be165363010a6cb | refs/heads/master | 2021-05-12T08:32:20.578205 | 2019-07-16T14:29:00 | 2019-07-16T14:29:00 | 117,287,433 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 632 | r | combineAllTissueTpm.R | args <- commandArgs(TRUE)
tpm.dir <- '/mnt/work1/users/pughlab/external_data/GTEx/FPKMmatrix/tpm/rdata'
all.tissue.tpm <- list.files(tpm.dir, pattern="tpm.Rdata")
all.tpm.mat <- data.frame()
for(each.tissue.tpm in all.tissue.tpm){
load(file.path(tpm.dir, each.tissue.tpm))
if(each.tissue.tpm %in% all.tissue.tpm[1])... |
2b4446714ec512f99195b6518111c1a20e434e25 | b1d10d40427e33e895be6ed3c76aeb45447810b5 | /R/document.R | f59a3ff01969f998097f928b5a047a93ce062043 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | brazil-data-cube/rlccs | 0594484126727cb0cf9ad47f4204abda902f426c | 218921499978f04f5b33df5be46a4ff95dd07030 | refs/heads/master | 2023-03-11T15:01:16.441672 | 2021-02-23T13:28:31 | 2021-02-23T13:28:31 | 337,800,683 | 6 | 3 | MIT | 2021-02-23T13:28:32 | 2021-02-10T17:31:33 | R | UTF-8 | R | false | false | 1,720 | r | document.R | #' @title Document development functions
#'
#' @describeIn extensions
#' The \code{RLCCSDocument()} function is a constructor of
#' LCCS documents. Currently, this class is used to represent the return of all
#' LCCS-WS endpoints. The general use of this document is possible since the
#' service return follows the same... |
4703320613e8814f0ec872d0afbc8a0a542db6e0 | 6a6277580cba9e63e3a1f15b515bbeb7635aa032 | /Plot4.R | ee655d451f7fc36a5f8edbf7a501553dd153d845 | [] | no_license | NMDC70/Plotting2 | e6acbce5745b17c3cc70c3f5ebbb1eeeca0e705f | 2b7c0f9a846fb9dc6befdc4856a9b50e5d65302d | refs/heads/master | 2021-01-10T16:24:56.938308 | 2015-10-18T06:43:22 | 2015-10-18T06:43:22 | 44,427,625 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,000 | r | Plot4.R | NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
coal <- c("Coal|coal")
SCCSubsetcoal <- SCC[grep(coal, SCC$Short.Name), ]
## Compared with EI sector(total 99 observations) & other columns of SCC searching for coal
## in Short.Name gives the largest subset (230 observations)
coal1... |
3d29fdde7f6bc19f51d625a6018038a9a70e98a0 | 8194aec24987b5a235f5d83785444f115f55cdc3 | /man/triangle.design.Rd | f9c144e77358e7b0bdf5b5579ee384b54a53bac4 | [] | no_license | cran/SensoMineR | f4bf5739257fb64d8742a8eaaa4d8f87357d41d4 | 1b0d41884381acd0033408afcd8a024754d0810d | refs/heads/master | 2020-08-08T23:39:58.342203 | 2020-05-19T13:50:03 | 2020-05-19T13:50:03 | 18,806,219 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,731 | rd | triangle.design.Rd | \name{triangle.design}
\alias{triangle.design}
\title{Construct a design for triangle tests}
\description{
Construct a design to make triangle tests.
}
\usage{
triangle.design (nbprod , nbpanelist, bypanelist = nbprod*(nbprod-1)/2,
labprod=1:nbprod, labpanelist=1:nbpanelist)
}
\arguments{
\ite... |
8c336c8c1b60836712ebdc7be401cbe9102c5af8 | 00e7438f79f95ffab664390a0cbacaf407f4433b | /Gender vs Age MH/MH - Gender vs Age Statistics.R | e362f807fc3c8ad394bc1f15f807ab11f0ba7b4c | [] | no_license | Key2-Success/HeartBD2K | 95b410f2b7233419650e6972058112532a7223d8 | 21ad025c40a396707e97dede993ac8c8b393bf13 | refs/heads/master | 2018-12-14T19:51:21.941506 | 2018-09-13T22:38:29 | 2018-09-13T22:38:29 | 108,905,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,744 | r | MH - Gender vs Age Statistics.R | library(tm)
library(qdap)
library(qdapTools)
library(stringi)
library(stringr)
library(purrr)
# load in distinct dataframes as well as distinct MH (chosen from random sample of 1000)
load(file = "~/Kitu/College/Junior Year/Extracurriculars/Data Science Research Internship/MH - Gender vs Age/adult vs gender/d... |
aeaa51ff8047fdd6b7311e93d3b7e68d47a1106d | b9c36e4ab2b701917065c94c923cf9452384ebe3 | /brakingsystem-dagstat16.R | 9148ee434b2d2411513ad719c40bb242a0ae2adc | [] | no_license | geeeero/dagstat16 | eef139c7fbd5fa1db1f4fc95286065704ce4278c | 4b695d0ace18f055385cea9a4339fc4cc4c650ea | refs/heads/master | 2021-01-10T11:18:58.401725 | 2016-09-07T16:55:25 | 2016-09-07T16:55:25 | 52,964,355 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,931 | r | brakingsystem-dagstat16.R | #install.packages("devtools")
#library("devtools")
#install_github("louisaslett/ReliabilityTheory")
library("ReliabilityTheory")
library(ggplot2)
library(reshape2)
bottomlegend <- theme(legend.position = 'bottom', legend.direction = 'horizontal', legend.title = element_blank())
rightlegend <- theme(legend.title = elem... |
d59b1300f2b53f3aaf3c173905c36476df1d4d9f | e06965698053952f7f97c60349a590e42d08b633 | /man/make_processor.Rd | d405803f18c3371b3c83ce96f1a023df97d8c242 | [
"Apache-2.0"
] | permissive | kcf-jackson/sketch | a9940c89ed8183627914861a11893856b1c47429 | b597f01e540f35aab1f5ee2d3744f6f64c70c94d | refs/heads/master | 2022-11-01T03:28:32.088340 | 2022-10-23T14:22:05 | 2022-10-23T14:22:05 | 222,058,097 | 106 | 5 | NOASSERTION | 2022-10-23T14:22:07 | 2019-11-16T06:36:59 | HTML | UTF-8 | R | false | true | 466 | rd | make_processor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assets-to_shiny_tag.R
\name{make_processor}
\alias{make_processor}
\title{Make a handle to process header}
\usage{
make_processor(pred, fun)
}
\arguments{
\item{pred}{A function, taking a string and returning a logical.}
\item{fun}{A functio... |
9c9a16850edf7f28efe78803ec4b0ed253558b37 | b7421dc801628ffc279cc6a89c4f915bc1a21e79 | /man/clear_job_processing.Rd | 6ffcb43ec08ec53992a3e69ad5504052cd5cadef | [] | no_license | wush978/RzmqJobQueue | 58033aeec908e1ff2264e011d0658be379c13987 | 9204035457cecbaa6c45ebaa5a4682b88abfe359 | refs/heads/master | 2021-01-22T14:39:48.978653 | 2013-06-25T08:10:51 | 2013-06-25T08:10:51 | 8,583,104 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 275 | rd | clear_job_processing.Rd | \name{clear_job_processing}
\alias{clear_job_processing}
\title{clear_job_processing
Clear the hash values in redis of jobs under execution}
\usage{
clear_job_processing()
}
\description{
clear_job_processing
Clear the hash values in redis of jobs under execution
}
|
1fb9baca8e0bbe62d3fe250a3cba492abcfc5ead | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/stm/examples/plot.estimateEffect.Rd.R | acf3164070489218e131405268f45bd901180a82 | [] | 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 | 1,550 | r | plot.estimateEffect.Rd.R | library(stm)
### Name: plot.estimateEffect
### Title: Plot effect of covariates on topics
### Aliases: plot.estimateEffect
### ** Examples
## Not run:
##D
##D prep <- estimateEffect(1:3 ~ treatment, gadarianFit, gadarian)
##D plot(prep, "treatment", model=gadarianFit,
##D method="pointestimate")
##D plot(prep, "... |
07142c4e7231ea3fdf0c201b536ae1a4aa5b2cf3 | a9fb5a228b2316e5b43f58e4b8d6c858cb7784f7 | /R/DsATACsc-class.R | 82c7e6abd261deb4c42fc2ef417b9c0d0333e559 | [] | no_license | GreenleafLab/ChrAccR | f94232d5ac15caff2c5b2c364090bfb30b63e61a | 43d010896dc95cedac3a8ea69aae3f67b2ced910 | refs/heads/master | 2023-06-24T05:29:29.804920 | 2023-03-17T13:01:49 | 2023-03-17T13:01:49 | 239,655,070 | 17 | 7 | null | 2023-05-05T09:51:23 | 2020-02-11T02:01:37 | R | UTF-8 | R | false | false | 45,139 | r | DsATACsc-class.R | #' DsATACsc
#'
#' A class for storing single-cell ATAC-seq accessibility data
#' inherits from \code{\linkS4class{DsATAC}}. Provides a few additional methods
#' but is otherwise identical to \code{\linkS4class{DsATAC}}.
#'
#' @name DsATACsc-class
#' @rdname DsATACsc-class
#' @author Fabian Mueller
#' @exportClass DsAT... |
40f089fb76f9a7717c03b4ad6f6faf39b1f258d9 | 3c0609b158edf4b860c0da58a0b864825c29c41f | /example.R | e838ee20c97ed0b2827a06e9aa87cdd7b241104b | [] | no_license | clsong/JAE-Song_et_al-2017 | aed2003b663c344b93c2d71637a03db159354b7c | 48b20693e0e778a89ade759bf8221a5ce65bb243 | refs/heads/master | 2021-05-08T16:13:13.311513 | 2019-03-15T15:03:27 | 2019-03-15T15:03:27 | 120,145,206 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 553 | r | example.R | #R-code of "Why are some plant-pollinator networks more nested than others?" by:
#Chuliang Song, Rudolf P. Rohr, and Serguei Saavedra
#Journal of Animal Ecology
rm(list=ls())
source('toolbox.R') #load the toolbox
web <- load_data() #load network.csv
print(NODF <- nestedness_NODF(web)) # this calculates the raw value o... |
d203a8a24acacf8ab5450d72c97a396e33ea3d8a | 147e55d3e91a7bd1865de3f6ea62077ce6591241 | /GenGraph/ui.R | b5c8695180b1a4cd897da137e059e57c0e9e616c | [] | no_license | jolivero2001/shiny-dashboard | a995736252fa3df38004be94e57f634f70e8e0eb | 524f6c50cd6259dea5a103bcf7f7c39d5958256d | refs/heads/master | 2021-06-03T19:57:24.808410 | 2021-04-11T14:43:24 | 2021-04-11T14:43:24 | 102,426,793 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,919 | r | ui.R | library(shinydashboard)
library(ggvis)
library(shiny)
library(dplyr)
library(ggplot2)
library(DBI)
library(shinyjs)
library(lazyeval)
library(shinyAce)
library(knitr)
library(tidyr)
library(corrplot)
library(ggraph)
#dm <- dropdownMenu(type="messages")
mm <- dropdownMenu(type="notifications")
tm ... |
e15ff56d6b149eeacaaa9eadcf076da9e51935af | 175178c455fbe90cfc4a6bde924a350f218a673c | /phenotype.R | 6845b18b75efa2262b96234eb909b7186800c1f9 | [] | no_license | yanweicai/Replica_CC_TOVIDLTAK | 210a8cdb149d85c71a22601d61d053fa72039924 | 69a26a2595c0bbd8270d3cec70bf9b178732041b | refs/heads/master | 2023-02-15T15:18:11.302746 | 2021-01-09T03:45:10 | 2021-01-09T03:45:10 | 324,623,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,920 | r | phenotype.R | library(lme4)
library(lmerTest)
library(qvalue)
library(varComp)
library(miqtl)
library(ggplot2)
library(tidyverse)
setwd("~/Dropbox/ValdarLab/IDSScross_git/src/")
# read in data table
info <- read.table(file="../data/info_3study.txt",header=TRUE,sep = "\t")
info$CCbyStudy <- paste0(info$CC,'_',info$Study)
options(con... |
82b91cc3d089b5201f58deb93209c7386d56742e | 7d9cbb939c81cf32bce02b7b4d43dcefad0b65dc | /R/allezTable.R | 36257d3086e54940e0ab3c626be9702dce0ab2bc | [] | no_license | atbroman/allez | e677a6309b0b2a5465788b155a8bf338eb77d4ea | 2a7a57b3f5b20b25c971a524745b4cadb8341b49 | refs/heads/master | 2020-05-03T21:42:42.393259 | 2017-03-03T16:01:32 | 2017-03-03T16:01:32 | 9,983,732 | 1 | 4 | null | 2015-08-19T19:15:16 | 2013-05-10T14:59:06 | R | UTF-8 | R | false | false | 2,619 | r | allezTable.R | ## Outputs top GO/KEGG categories ##
## score >= 0
## z.score is one-sided: z.score < 0 indicate enrichment
## for genes outside of gene set
allezTable <- function(allez.out,
n.low=5,
n.upp=500,
n.cell=0,
zthr=5,
... |
e141c88ba9ba94df2ea23d39909e0594e95f2685 | 40e98bcc4d58a29c44594ee70132af4f90216a65 | /hw4/min_span_tree.R | 1a03d64535f731339e9cf549c1e7a9ebfea0b7ce | [] | no_license | Sta523-Fa14/hw_examples | b0365ec7379a85a7f9d0ad2e333c8ff000f6c011 | a862a6dfc252c42696a80cea95c620f6a76fdcd0 | refs/heads/master | 2016-09-05T23:33:41.418471 | 2014-11-24T18:00:07 | 2014-11-24T18:00:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 86 | r | min_span_tree.R | min_span_tree = function(g)
{
return(list(list(edges=c(1L), weights=c(1))))
}
|
9211c6168ba8de699f22dc2d55bcb42fa3e4226f | 6b9a398030a320ca38a3ff8c11adbb235deddcdf | /manuscripts_etc/Manuscript Figures/Mixtures.R | 237c5fc7cc032580fe4b5f90bc42a57ccaa5c449 | [
"MIT",
"CC-BY-4.0"
] | permissive | alexholcombe/nStream | ffb9dc89eaec1222b957d44c634aad68282b8f51 | fddf0ad89a5a2353f0f76fef70923500d6cad628 | refs/heads/master | 2020-05-23T08:09:21.402053 | 2019-12-12T04:35:40 | 2019-12-12T04:35:40 | 80,474,968 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,223 | r | Mixtures.R | rm(list=ls())
library(ggplot2)
devtools::load_all('~/gitCode/mixRSVP/')
###Guessing Distribution bounds###
minSP <- 7
maxSP <- 11
targetSP <- rep(minSP:maxSP, each = 20)
minSPE <- 1 - maxSP
maxSPE <- 24 - minSP
SPE <- seq(minSPE, maxSPE, .1)
###Guessing Probs###
guessingDist <- createGuessingDistribution(minSPE, m... |
e5925556b5583ccbd2ebc1da89581e0f56b053c2 | eca7e6e4e027cfb1fb4b3de0a05a30dabab285ba | /man/modCompare.Rd | 8c66297709a7b847d921dff9cc6a5cde5dfbf15e | [] | no_license | BagchiLab-Uconn/RSPPlme4 | 4076c68a0de603dd429a42c30d10d7a596d01536 | 9b92e37924cfb4f4749efbf4f335a86632dc5466 | refs/heads/master | 2023-08-17T17:12:40.959835 | 2023-08-09T12:25:48 | 2023-08-09T12:25:48 | 94,588,272 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 486 | rd | modCompare.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modCompare.R
\name{modCompare}
\alias{modCompare}
\title{Compares the Deviances of Two Models.}
\usage{
modCompare(modsH1, modsH0)
}
\arguments{
\item{modsH1}{A more complex model of class \code{\link{klmer}}}
\item{modsH0}{A simpler (null) ... |
2b9f5e523642c4398103f6d2ccf4114cf8b9bbd5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DAMisc/examples/panel.2cat.Rd.R | 2ca18edfbb235f60d60cc5a7d048c03e00b5f8f9 | [] | 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 | 392 | r | panel.2cat.Rd.R | library(DAMisc)
### Name: panel.2cat
### Title: Lattice panel function for confidence intervals with capped bars
### Aliases: panel.2cat
### ** Examples
library(car)
library(lattice)
library(effects)
data(Duncan)
Duncan$inc.cat <- cut(Duncan$income, 3)
mod <- lm(prestige~ inc.cat * type + education,
data=Duncan)
... |
376f97d221cd79d9f60b2dd0d1ce0b7da22c49e8 | 87bdf3725cc8bb122b670a53b0cdb366678d5d7c | /jsm_2020_app/server.R | 37af3ff57d6d9b42f6704fc02d4df8e7736cfb05 | [] | no_license | brandonkopp/ASA-Presidents-Shiny | 4b6e25c9e6cc07abae44c900251a65921c9e2c56 | 340d952a101ef9284727f7d7b03549bf7a7aa518 | refs/heads/master | 2023-03-27T08:22:26.111496 | 2020-07-24T16:53:34 | 2020-07-24T16:53:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,896 | r | server.R | require(shiny)
server <- function(input, output, session) {
source("global.R", local=TRUE)
###### WELCOME ####################
observeEvent(input$glossary, {
showModal(modalDialog(
title = "Glossary",
includeHTML("./html/glossary.html"),
easyClose = TRUE,
footer = NULL
))
})... |
75d7933fcce04ccc9179a2620ffa69cfdd48f7d0 | 0921a01e8b564edb12d217c0dfdba580f5f58964 | /run_analysis.R | fc35d0dd7f9e21080b129cad222bb48e704285eb | [] | no_license | hlopezo/Getting-and-Cleaning-Data-Course-Project | 125f4c4ab5ad6cb3ed17425980402fe75720f986 | d0620fb3009d29407b4ca9b581d0f1f359b22c6c | refs/heads/master | 2022-11-26T06:29:40.529562 | 2020-07-13T20:24:53 | 2020-07-13T20:24:53 | 279,200,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,251 | r | run_analysis.R | rm(list=ls())
#reading features and activity data
features <- read.table("C:/Users/Henry/Desktop/Henry/UCI HAR Dataset/Getting-and-Cleaning-Data-Course-Project/features.txt")
#View(features)
activities <- read.table("C:/Users/Henry/Desktop/Henry/UCI HAR Dataset/Getting-and-Cleaning-Data-Course-Project/activity_labels... |
091f91add47fbaddaef1d783def8bfdbfafcd206 | 2468dbf1813bee70de399dd77cd3d78d27ec5694 | /Phase enrichment.R | cd2c4e206cde874d2ddc1f05da74ca4d2e99ae21 | [] | no_license | Nagel-lab/Heat_stress_translatome | 43f7940349902eaea81ce3ac8852ed3e42e6ce65 | 51c3ef1ac0be15c133f6cb0e3c1f14abbc497750 | refs/heads/main | 2023-03-08T05:49:16.703830 | 2021-02-24T22:45:52 | 2021-02-24T22:45:52 | 342,018,308 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,316 | r | Phase enrichment.R |
# This script shows how to analyze the phase enrichment from a list of DRGs, as compared to circadian total mRNAs
results_TOT <- read.table("total_cycling.txt", header = T)
phases_TOT <- results_TOT[,c(1,5)]
names(phases_TOT) <- c("AGI","LAG")
# replace the phase 25.5 by 1.5 and 24 by 0 to avoid repeating t... |
49c67b325cb2e42f3694eb1ed2794af366d4056b | 40c01ced1dd4fefa82825819258d07fc4f21e7f6 | /man/ci2crit.Rd | b81857f1f83a273f942104b8fb3c894c99f7cb59 | [
"MIT"
] | permissive | jeksterslabds/jeksterslabRboot | dcfef61e25da41b124be53e9492d714af3932372 | 06b3dd3c2ac5ddb0c9791c13b1dfc719522dc6b0 | refs/heads/master | 2022-11-21T17:41:11.098857 | 2020-07-16T07:24:59 | 2020-07-16T07:24:59 | 276,638,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,205 | rd | ci2crit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/alpha2crit.R
\name{ci2crit}
\alias{ci2crit}
\title{Confidence Intervals to Critical Values}
\usage{
ci2crit(
ci = c(0.999, 0.99, 0.95),
dist = "z",
two.tailed = TRUE,
right.tail = TRUE,
...
)
}
\arguments{
\item{ci}{Numeric vector.
... |
da228b948b6546ba6d44a229dd8c7315a52fd8c6 | 4cb5426e8432d4af8f6997c420520ffb29cefd3e | /P74.R | 750d243b3bd914c49549308cb3b8af61f9d9edaf | [
"CC0-1.0"
] | permissive | boyland-pf/MorpheusData | 8e00e43573fc6a05ef37f4bfe82eee03bef8bc6f | 10dfe4cd91ace1b26e93235bf9644b931233c497 | refs/heads/master | 2021-10-23T03:47:35.315995 | 2019-03-14T21:30:03 | 2019-03-14T21:30:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,608 | r | P74.R | # making table data sets
library(dplyr)
library(tidyr)
library(MorpheusData)
#############benchmark 1
#How to solve this could be our future work.
dat <- read.table(text=
"ID MGW.one MGW.two HEL.one HEL.two
A 10.00 19 12 13.00
B -13.29 13 12 -0.12
C -6.95 10 15 4.00
", header=T)
#da... |
0578cb9b17f5d661d0fe42c3225f311c4a8b27bc | 8824061cab2431fb2421dd20675ecd02f1900313 | /AIPS-PCA.R | b50b41067b7f2b2cf06e09a682d6e7b2568fdf44 | [] | no_license | biomedicaldatascience/AIPS | 20e1f732eeb56941a3188196fec7c5b5c49c81b8 | 080b59e16d66e835688606e786113268a75fd15e | refs/heads/master | 2021-01-03T11:54:21.618166 | 2020-02-12T18:46:48 | 2020-02-12T18:46:48 | 240,072,812 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,898 | r | AIPS-PCA.R | #AIPS.pc computes PC scores using function either "eigen" or "svd".
AIPS.pc <- function(infile, K=NULL, method="eigen",outplot) {
#read merged data coded with Additive Components
#When merging data in Plink, the data with Ancestry Informtive Markers(AIMs) should be in the first part of the merged data.
#If the d... |
9daff7661301911300e3c79318f60a8d24fc42db | b4007c30747e4213f7540294c59c77b8f72e3ab8 | /E3_script_two_conf.R | 6e1ebdf7af1496f1f706b443ef39ab75e6a1c739 | [] | no_license | noraracht/kraken_scripts | 412245cee06e434ba3667b22a638a8b1aff2c46d | b43ef75eb2d0c7dfae06930ab64a838e5a0764f2 | refs/heads/master | 2020-07-06T17:01:57.971381 | 2020-04-13T15:27:48 | 2020-04-13T15:27:48 | 203,085,698 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,033 | r | E3_script_two_conf.R | require(ggplot2); require(scales); require(reshape2)
getwd()
setwd("/Users/admin/Documents/Skimming/tree_of_life/dros_contam_test")
d= read.csv('Drosophila_contam_both_species3.csv')
print (d)
dm = (melt(d[,c(1,2,grep(pattern = "*Dros*", names(d)))],id.vars = 1:2))
dm$Pair=""
dm[grep("sim_WXD1",dm$variable),"Pair"] = ... |
c42985b88f3024cc80db1ba97403ad458734eede | f853610d699f1a4e815cd3ea52aedd188f695d30 | /plot3.R | 49a34f1747995152a38fcd3429b8173041ed3ef9 | [] | no_license | jysmith/ExData_Plotting1 | 4cda8048e225ec5ec506b76eec346d574d88a9ba | c1b0d2c4e5acdbf54cb7385647ab5a078704a1ed | refs/heads/master | 2021-01-18T11:19:38.270733 | 2015-02-08T17:03:35 | 2015-02-08T17:03:35 | 30,498,146 | 0 | 0 | null | 2015-02-08T17:00:32 | 2015-02-08T17:00:32 | null | UTF-8 | R | false | false | 895 | r | plot3.R | # Read data file and extract the part for 2007-02-01 and 2007-02-02
data_raw <- read.table("household_power_consumption.txt", header=TRUE,
sep=";", na.strings="?", nrows=2080000,
colClasses=c(rep("character",2), rep("numeric",7)) )
data <- data_raw[data_raw$Date == "1/2/20... |
e08a7627b9f47ca7eb40099e4af5a832be0c7ebe | 62d26b0055a5d5ec25adc12c4f628e97a5fc50c5 | /R/py_int/covid_run.R | 33d238734af48b1c2745337c7cf7e2b1fa33d8aa | [
"MIT"
] | permissive | Urban-Analytics/RAMP-UA | 8eae83ed06145ab9d6695045130d20e01100b0ca | ae5f26d6c5c9e03bb0902078f8ada316c766290e | refs/heads/master | 2023-07-25T00:06:57.050746 | 2022-11-08T09:55:55 | 2022-11-08T09:55:55 | 259,974,353 | 12 | 11 | MIT | 2023-07-06T22:15:36 | 2020-04-29T16:05:13 | Jupyter Notebook | UTF-8 | R | false | false | 14,285 | r | covid_run.R | ####################################################################################################################
####################################################################################################################
######################################################################################... |
ecceafc3217cc259f9e17d8ab9e461f75c708bb5 | 90b33d58d125d9a561411e1256c60daf45563ca0 | /man/optimalSD.Rd | c711f85ac1317d739e73110e49b3ebb76070a2da | [] | no_license | edzer/sensors4plumes | e78d336ae390c8bcc993561b5e1eb2a4eb7cd40e | a834b2194b0c75be0d74bb27c94f3459d5f3dbb1 | refs/heads/master | 2020-05-24T02:10:33.850671 | 2017-03-28T14:33:48 | 2017-03-28T14:33:48 | 84,813,077 | 3 | 0 | null | 2017-03-13T10:20:28 | 2017-03-13T10:20:28 | null | UTF-8 | R | false | false | 712 | rd | optimalSD.Rd | \name{optimalSD}
\alias{SDgenetic}
\alias{SDglobal}
\alias{SDgreedy}
\alias{SDmanual}
\alias{SDssa}
\docType{data}
\title{
Optimised sampling designs
}
\description{
For each of the optimisation algorithms a resulting sampling design is provided. These are taken from the examples of the respective cost functions.
}
... |
c210c66e9cdece4c85251001c813d50d323fce4c | 00741d47c446fbe1f0163732b59be757d64d2298 | /Script/evaluation/ARI/ARI_utils/conclude_ARISampled_dat6.R | 3903226c184c7a460446831fa48f93c540101e1b | [] | no_license | JinmiaoChenLab/Batch-effect-removal-benchmarking | aebd54fda05eb9e8ba21afcd11c5d10158dfaec5 | 60d52c29e29b7849b1505167da572165cc5d5b82 | refs/heads/master | 2022-03-11T17:47:01.250098 | 2022-01-24T02:50:57 | 2022-01-24T02:50:57 | 206,039,306 | 64 | 51 | null | null | null | null | UTF-8 | R | false | false | 2,060 | r | conclude_ARISampled_dat6.R | # Author : Nicole Lee
# Date : 29/08/2019
# Purpose: Third function to be called in ARI pipeline
# Following calculation of ARI scores for all batch-
# correction methods, this function is used to
# produce F1 score based on normalised ARI scores
# Returns a CSV file containing med... |
36c97ce47e42b600246c45e9e6f95f180b00612a | eaf6d592d069f12a673f0cb63c97f2e271ad4b03 | /man/split_replace_raster.Rd | 736c29f1280b4ad0ca2a76f7df8c1dcbe5e20fb9 | [] | no_license | inder-tg/geoTS | bcaecf12928bd535eb4066e92febf7d7b7140aa7 | 0cd98b78556e876692247722a16133d15e1ab06b | refs/heads/master | 2022-07-26T09:58:59.697717 | 2022-07-20T14:23:10 | 2022-07-20T14:23:10 | 185,881,619 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,390 | rd | split_replace_raster.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/split_replace_raster.R
\name{split_replace_raster}
\alias{split_replace_raster}
\title{Split a Raster* object and replace cell values (optional)}
\usage{
split_replace_raster(raster, partPerSide, save = T, replace = F,
valToReplace, replace... |
c30ff88d70e370ec5ad5b15a67ea6669bd9b640a | 6a2f6ab46c35441db0288fbde4be1a5188f2ec30 | /man/ti_monocle_ddrtree.Rd | eaa65e72a55e2a41469c2156efb5c80022e591b8 | [] | no_license | herrinca/dynmethods | f7595c8ce4f06cb2cb4b809c49ceebd705330940 | 0a5768cf4452b2b745ee675bbd013140d54029da | refs/heads/master | 2020-03-26T22:19:11.513964 | 2018-08-21T18:03:51 | 2018-08-21T18:03:51 | 145,448,352 | 0 | 0 | null | 2018-08-20T17:17:18 | 2018-08-20T17:17:18 | null | UTF-8 | R | false | true | 1,891 | rd | ti_monocle_ddrtree.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ti_monocle_ddrtree.R
\name{ti_monocle_ddrtree}
\alias{ti_monocle_ddrtree}
\title{Inferring a trajectory inference using Monocle DDRTree}
\usage{
ti_monocle_ddrtree(reduction_method = "DDRTree", max_components = 2L,
norm_method = "vstExprs",... |
32cc3d82c118148987c3188cf521ebf51a56eabf | 59b832f22a3f29d3eed81e9123178f756ce82555 | /Recombination_Functions/plot_recombination_functions.Rscript | 7c9b89e4d9a342f8260ae48ceb838fcd2d2fccd9 | [] | no_license | cory-weller/HS-reconstruction-gwas | a5e727347a07cd475b93520181047e220ca479c7 | 7663d6c68e1d63bdec1de364b5876fd589cebefb | refs/heads/master | 2023-02-20T17:02:45.982270 | 2021-01-18T14:28:56 | 2021-01-18T14:28:56 | 271,129,562 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,085 | rscript | plot_recombination_functions.Rscript | #!/usr/bin/env Rscript
bed <- fread('recombination_map.bed')
setnames(bed, c("chr", "start", "stop", "c"))
x.chromosome <- "X"
dmel <- TRUE
bed[chr==x.chromosome, chr := "X"]
# Correction for Drosophila
if(dmel==TRUE) {
# stoare & add maximum value of 2L onto every start, stop for 2R
# store & add maximum val... |
a9ca505fd191c589e9bd619fb6940cbcb87f800f | 8cea90e27b19a97ce2445f60824b55da001b6e85 | /plot_sample_means.R | c329adb7b8e0b6e03e84c434894ea5e42cda51e2 | [] | no_license | glaubius/Rscripts | 090aea7eb54b8e3cf0b0835e5fa69477c19d91d2 | f950d95add5d052e44a6a71fcfa344a271556d8c | refs/heads/master | 2020-12-31T07:10:11.148474 | 2017-03-07T21:22:42 | 2017-03-07T21:22:42 | 80,557,632 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,678 | r | plot_sample_means.R | y## Plot sample means with confidence intervals
## adapted from https://www.youtube.com/watch?v=x4ekQ1nanQ4
## Inputs: list of rmse_values, list to save sample_means, matrix of cis
num_means <- length(rmse_values) - 1
sample_mean <- matrix(nrow=num_means, ncol=2)
cis <- matrix(nrow=num_means, ncol=2)
for (i in 1:num... |
1709a944e8efdde1fb6493760b02a86a9e9e1c1f | 1853a82480662e1f24356b14c4774e5078fc0a1c | /edge_thread_compare_female.R | e7d701f51a5af6d46704c66bb58b0e1e01814468 | [] | no_license | lots-of-things/edge_forum | a6954e0411bbf407eb1496cb7193ed271193d742 | 7a4dbbde7308288b93ff42c8a8b59bb90b986a36 | refs/heads/master | 2020-07-23T03:09:19.357241 | 2017-07-10T04:22:23 | 2017-07-10T04:22:23 | 94,350,039 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,178 | r | edge_thread_compare_female.R | # group by individual and measure fraction of each gender not counting the individual
# Getting a uniquified view of thread metrics for plotting
thread_info = data_thread %>%
filter(UniqueContributors>5) %>%
select(Year,
Title,
Link,
Type,
ThreadId,
DebateSize,
... |
827beb4919ac7d292e9611497c72b352e9acb81a | 39a61aba62505091e3d7033bb62113976473d912 | /expression/candidate_genes_allen_expression_byregions_PT.R | 845c680004fef0d281a4a811e7873faca468add2 | [] | no_license | amaiacc/GeneticsPlanumTemporaleAsymmetry | 83dcd1d3abe81ad2aca33dcebe854e69689b7a4a | 8224b0ffa531226784b1bdb7710bdf118ea3e9f2 | refs/heads/master | 2020-08-31T07:36:59.201396 | 2020-01-02T13:41:53 | 2020-01-02T13:41:53 | 218,637,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,460 | r | candidate_genes_allen_expression_byregions_PT.R | #----------------------------------------------------------------------
# Load libraries for plotting
#----------------------------------------------------------------------
library(ggplot2)
library(grid)
library(ggbeeswarm)
library(grid.Extra); library(grid)
library(lme4);library(fmsb)
# allen brain packages... |
57167c9a7aeb6a9fd9482f39d2f8b1f13837a886 | fe4aadb9b9d7f2f2e05aa17d9f438364d52fa7fe | /tests/testthat/test-source_web_tool_scripts.R | 09f1336bc50973a19551a78443567a88395490c8 | [
"MIT"
] | permissive | fiona511/PACTA_analysis | 32d2ba6e871f648ee7f5f31b6e9d0a53d0c37c5b | 6b4684868afc9c90f9625177b5e18d919cefab4e | refs/heads/master | 2023-03-07T18:45:56.103190 | 2021-02-18T12:23:38 | 2021-02-18T12:23:38 | 340,041,597 | 0 | 0 | NOASSERTION | 2021-02-18T12:21:48 | 2021-02-18T12:21:47 | null | UTF-8 | R | false | false | 255 | r | test-source_web_tool_scripts.R | test_that("stop_on_error stops on error", {
expect_error(stop_on_error(exit_code = 0), NA)
expect_error(stop_on_error(exit_code = -1), NA)
expect_error(stop_on_error(exit_code = 1), "error")
expect_error(stop_on_error(exit_code = 99), "error")
})
|
03d74df81d798c96fbb35428bb2a39c0194dd070 | bf61596f18dc48b2e6bd6ae6ccd5e9aaa1e575b1 | /RFILES/tab2_png.R | 79e9bd4e8d87eb0b9a93e9ccc0828b5fb8fe6a85 | [] | no_license | rfaridi/bd_remittance | 0dee712fbdb7f3fb7d96f7aee6990a3f51fce52d | bffd63becb8679de7cd13e7b2e795b836dc48b98 | refs/heads/master | 2021-01-13T14:52:48.242693 | 2016-12-14T16:18:20 | 2016-12-14T16:18:20 | 76,474,568 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 352 | r | tab2_png.R | load(file="./RData/remitFin.RData")
source('functions.R',echo=F)
library(dplyr)
tab2 <- remit.fin %>%
select(FY2013:FY2015)
dvipng.dvi(dvi.latex(
latex(tab2,col.just = strsplit("ccc", "")[[1]],
rowlabel='Countries',
rowlabel.just="c",
rgroup=cc.rg,
n.rgroup=cc.g,
booktabs = ... |
e77eec80313ab8c3c2245be02ee45da2cafc673d | 4575fac146c9e774b29c8f1e34fc8bfa83a0d747 | /script/Union.manhattan.r | d228ef52bf12b0dda1c2322f8d80b93d2b788692 | [] | no_license | yywan0913/Tibetan_Han | db591b41ded4a997a494ca5587657808d19c5f44 | 18bd32db2ccda4c7e34ca32819392998201de3a2 | refs/heads/master | 2023-05-04T01:52:06.119935 | 2021-05-19T06:00:19 | 2021-05-19T06:00:19 | 368,444,043 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,521 | r | Union.manhattan.r | library(data.table)
library(reshape2)
args = commandArgs(TRUE)
outpdf = args[1]
region.num = as.numeric(args[2])
if(is.null(region.num)) region.num = 3
chromfile = "input/chrom.len"
svfile = "input/sv.fst.fordraw.xls"
snpfile = "input/snp.fst.fordraw.filter.xls"
indelfile = "input/indel.fst.fordraw.xls"
#outpdf = "uni... |
a3cc65cea4b64566415b77bb248eb09be1315101 | 89bd53b22672cbe74e727e8e45defc891af1052d | /oldRcodes/usingdeltas/results.gopher/allsol.R | 92c6e2c66ffcec767f2fab86d0e49458e948589e | [] | no_license | hbhat4000/sdeinference | a62e8f5ddc6bbc913dbc8dc4c210ff30cf16143f | 14db858c43a1b50001818399ef16e74ae926f51b | refs/heads/master | 2020-04-04T05:30:05.922893 | 2018-07-11T22:59:38 | 2018-07-11T22:59:38 | 54,491,406 | 8 | 5 | null | null | null | null | UTF-8 | R | false | false | 680 | r | allsol.R | allsol = matrix(0,nrow=8,ncol=6)
allsol[1,] = c( 1.020837, 0.000000, 1.404953 , 31, 0.05, 300 )
allsol[2,] = c( 1.041597, 0.000000, 1.430114 , 30, 0.02, 300 )
allsol[3,] = c( 1.048930, 0.000000, 1.438882 , 34, 0.01, 300 )
allsol[4,] = c( 0.671489, 0.000000, 1.143841 , 31, 0.01, 100 )
allsol[5,] = c( 1.052622, 0.000000... |
a5ea234e2421626e2f4fa1f8110e6c01506ea11b | 6cbc43051fa0df8e06c91391966443bd640b1fcb | /tests/testthat.R | 371733308754e8aab1e48f885da7541b0d32f593 | [] | no_license | sboysel/Rgitbook | 30da6a2dc408fc9f49e882a53ca182711830fcb0 | 2192bb6cecbec7a72638b0f8a4062b81fd6e68fe | refs/heads/master | 2020-12-28T09:29:58.319512 | 2016-01-18T06:23:46 | 2016-01-18T06:23:46 | 49,751,718 | 0 | 0 | null | 2016-01-16T00:06:37 | 2016-01-16T00:06:37 | null | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(Rgitbook)
test_check("Rgitbook")
|
eec61d91b440a83564e263feee94d2f59ea915d4 | 2db9f112c91b32b183f96c52781cb3d72fc56ed5 | /wordcloud.R | dd3e180e7f5cbd81815e80f06c0ed069ee456608 | [] | no_license | ravikrcs/WordCloud | 4e63a3ed5a81ae35e9101966b10576a62f6f845a | e0ef93e036726ce02d6f48285b825dbe92d55ca5 | refs/heads/master | 2020-05-06T13:58:47.625907 | 2019-04-08T14:27:24 | 2019-04-08T14:27:24 | 180,166,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 880 | r | wordcloud.R | install.packages("tm")
install.packages("wordcloud")
install.packages("RColorBrewer")
library(tm)
library(wordcloud)
library(RColorBrewer)
text_file ="C:\\Users\\raj\\Downloads\\v74i07.txt"
textfile=readLines(text_file)
file1<-Corpus(VectorSource(textfile))
file2<-tm_map(file1,stripWhitespace)
fil... |
da8413887eabd196be1bce614830411659c839dd | 5140c5ba4359cd71640c71db0361630d03c95b82 | /CRISPR/gQTL_viewer/global.R | 39eadf6144a874242707d7ec6108805d87507465 | [] | no_license | scalefreegan/steinmetz-lab | ec672b5ae254f4203368a9b9f062c8c111e79edb | f88200afff4adf81cc18c65e42d177f87104c1d7 | refs/heads/master | 2020-04-04T07:38:52.502810 | 2018-08-07T11:51:44 | 2018-08-07T11:51:44 | 34,375,573 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,968 | r | global.R | .local = FALSE
if (system("hostname",intern=T) == "mac-steinmetz55.embl.de" || system("hostname",intern=T) == "interzone.local") {
print("yes")
.local = TRUE
} else {
print(system("hostname"))
}
# Import packages ---------------------------------------------------
library(shiny)
library(dplyr)
library(reshape2)... |
2869153596665a48e20afd8b8a4fff4bf8731de9 | 8a3f1e19b810de50cba01cf5359d84f09b85a04e | /Macroeconometrics/VAR-Oct 10.R | 091aa1237d7c7e43e86e6c56655947688864590a | [] | no_license | naafeysardar/sardar | cd7343e25aea1331568df4874d950473a80ad60b | 8d17b285ee90df3e490e1b8f2461d70f9bd11310 | refs/heads/master | 2021-07-21T03:28:07.120860 | 2020-08-21T16:24:08 | 2020-08-21T16:24:08 | 207,188,568 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,022 | r | VAR-Oct 10.R | library(vars)
data.raw <- read.csv("u-inf.csv", header = TRUE)
dataset <- ts(data.raw, start = c(1948,1), frequency = 12)
u <- dataset[,"u"]
inf <- dataset[,"inf"]
varfit <- VAR(dataset, p=1)
varfit
# Make Forecasts
varpred <- predict(varfit, n.ahead = 12)
varpred
# Inflation Forecast
varpred$fcst$"inf"
varp... |
65dff45e03d6e8f258193e8dadb7bed5ab51acb2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gdpc/examples/gdpc.Rd.R | 1add01c4834ee1d51ce5b3e5425da35f4d96d6c5 | [] | 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 | 626 | r | gdpc.Rd.R | library(gdpc)
### Name: gdpc
### Title: Generalized Dynamic Principal Components
### Aliases: gdpc
### Keywords: ts
### ** Examples
T <- 200 #length of series
m <- 500 #number of series
set.seed(1234)
f <- rnorm(T + 1)
x <- matrix(0, T, m)
u <- matrix(rnorm(T * m), T, m)
for (i in 1:m) {
x[, i] <- 10 * sin(2 * ... |
aa8ceb871df9a24595f09f7f4cbe1290a8d8116c | 1b846992a7d75f424df987d7d77a844fa588d389 | /run_Analysis.R | a05441647e1dd253d21121f191b155686846304f | [] | no_license | coryjpiette/CleanData_FinalAssignment | 850c624b3d052572e196c7b3edf6310024213ada | 466528093630cacd1ea307670967b05829a587d5 | refs/heads/master | 2021-01-08T05:30:04.290824 | 2020-02-23T14:33:02 | 2020-02-23T14:33:02 | 241,926,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,132 | r | run_Analysis.R | # reading all the files into R
training_data <- read.table("C:/Users/shubhayush/Documents/coursera/data/getdata_projectfiles_UCI HAR Dataset/UCI HAR Dataset/train/X_train.txt")
test_data <- read.table("C:/Users/shubhayush/Documents/coursera/data/getdata_projectfiles_UCI HAR Dataset/UCI HAR Dataset/test/X_test.txt")... |
30552a01c4d2c9d24412f67a23b529c20cacda13 | b9cfd96d6a96d0b8721b455bc22cf11503f83d2f | /man/getSaddlePointsOfGame.Rd | 7542cc0ff4756707145ec7f26f791ddd43afcbb2 | [] | no_license | ChristophJW/solveTPZSG | e5fb41a1ab5b4fa4296f6cd93b9bc5a0cd2a7714 | 34926b7472bcd53455ec67557ee68d5e5acb9265 | refs/heads/master | 2021-04-09T14:02:48.473623 | 2018-04-19T07:14:06 | 2018-04-19T07:14:06 | 125,489,481 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 392 | rd | getSaddlePointsOfGame.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/function.R
\name{getSaddlePointsOfGame}
\alias{getSaddlePointsOfGame}
\title{Find the saddlepoints of the game.}
\usage{
getSaddlePointsOfGame(matrix, maxCol)
}
\arguments{
\item{matrix}{A matrix}
\item{maxCol}{A numeric}
}
\value{
The matri... |
768b802ce6051f874d172bfed96af1930e57f0e3 | 6b3805d48275edd2b4431e5127206720fcc24008 | /R/corregp.r | 7e395acb655d6dc47c1230d64981bcbe350659f9 | [] | no_license | cran/corregp | d188c093d43270f0d943ccef2148ed8397f86d29 | 994df0352e125c2b1763e3c116ba24e33f7f5fd3 | refs/heads/master | 2021-05-04T11:23:05.651296 | 2018-03-14T09:20:10 | 2018-03-14T09:20:10 | 48,078,492 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 122,031 | r | corregp.r | #' Functions and Methods for Correspondence Regression
#'
#' This package provides functions and methods for performing correspondence regression, i.e. the correspondence analysis of the
#' crosstabulation of a categorical variable Y in function of another one X, where X can in turn be made up of the combination o... |
3a1a1e9f22adb252cb41467727570723ebe276c5 | 36795a7fa830cc052d5dd565322bf4e98968a246 | /data-raw/sir-scratch.R | e773322801a15f6709e3b00e128b1f69fac7f377 | [] | no_license | mlaviolet/tidyepi | 1e64fdc1bf21ca054a627d07f3879cacfe5168d2 | 8806bd13c3162dfaeb897de8f73393b62d986333 | refs/heads/master | 2022-05-17T09:04:07.263946 | 2022-05-10T02:07:29 | 2022-05-10T02:07:29 | 183,680,215 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,523 | r | sir-scratch.R | library(dplyr)
library(tidyr)
# library(purrr)
data(cancer)
data(seer_weight)
level <- 95
sir <- cancer %>%
filter(Year == 2015) %>%
mutate(std_group = if_else(Sex == "Female", 1, 0)) %>%
gather(key, value, n, pop) %>%
# Female is referent
unite("std_group", std_group, key) %>%
select(agegroup, std_group, ... |
ff713f987494d21ec24184a7e7b3df2f1d47d337 | d30e6c440f48ebdeca74505d946db88b8ccea14c | /myapp/ui.R | a2663b124db221220c8abe3085f76071cd82b1b8 | [] | no_license | JessvdK/RLadies_ShinyWorkshop | 71aa595419e7c5d73128ddc4252ef32861f17f60 | 8c64cd790adcd7f4b9021954eba40df637effdeb | refs/heads/master | 2020-03-24T08:38:17.476343 | 2018-07-27T16:46:19 | 2018-07-27T16:46:19 | 142,602,269 | 3 | 4 | null | null | null | null | UTF-8 | R | false | false | 312 | r | ui.R | library(shiny)
# Define UI for app
ui <- fluidPage(
# App title ----
titlePanel("Hello Shiny!"),
# Sidebar layout with a input and output definitions ----
sidebarLayout(
# Sidebar panel ----
sidebarPanel("Sidebar Panel"),
# Main panel ----
mainPanel("Main Panel" )
)
) |
e61dd853e97fb065e736c936f73abec9cb446e82 | 57925a8ca3b068b6689d13f0c0ad5ac084f50d9e | /PraceDomowe/PD5/gr2/NowikowskiAndrzej/pilkakopana/app.R | 401ab9f68aa628b0b7f79a9df3b0f362a65931d1 | [] | no_license | ramusz1/WizualizacjaDanych2018 | 41b4172761b5aaedd6ed2150af1e847b8862f0a3 | 52153880f70aa028963d40dcfeb1d3cb94272c6b | refs/heads/master | 2020-04-24T15:19:19.028703 | 2019-05-30T11:33:51 | 2019-05-30T11:33:51 | 172,061,362 | 3 | 0 | null | 2019-02-22T12:17:59 | 2019-02-22T12:17:59 | null | UTF-8 | R | false | false | 19,391 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinydashboard)
library(dplyr)
library(ggplot2)
library(ggthemes)
# utils
get_points <- f... |
3cce4bb3dcf40483c521455b181625b3f13e2681 | fd13440b88de8d110a6b1850732bd09db959b6c0 | /output/run_all_plots.r | c5810fecd814a0f47b4c2ab6df628f8db5a7c32b | [] | no_license | gmcewan/SalmonFarmTreatmentStrategy | 866c379401d0afda29397b68c2b9be786608b1bc | 9ba1330d49fc8e0a6c054792dbadbef316aadb07 | refs/heads/master | 2020-05-25T15:44:28.614631 | 2016-10-11T17:47:46 | 2016-10-11T17:47:46 | 70,181,841 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,501 | r | run_all_plots.r | source("plot-resistances.r")
source("plot-licecounts.r")
source("plot-treatments.r")
# get all the output directories
output.dirs = list.dirs(path=".", recursive=FALSE)
multi.dir.list = c("./responsive",
"./mosaic-30",
"./rotation",
"./periodic-longer",
"./periodic-shorter",
"./combination")
do.... |
2beccf48dffe0edbde18713fd4e9a126e9b334d4 | f8eec53636689e647d3d828059506d3bcac2406b | /public/slides/admitidos-graphs.R | 752b498f83f0b8760a8e19ac144cb45bb3d357be | [] | no_license | mamaciasq/martin | e16ab27cd9b1cd10ae98eac8bc12754e6095980f | 7841a5ad043323df85d70d7682bd3907acecc542 | refs/heads/master | 2021-01-24T10:15:18.477631 | 2018-04-27T04:45:03 | 2018-04-27T04:45:03 | 123,045,807 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,238 | r | admitidos-graphs.R | library(tidyverse) # version 1.2.1
library(readxl) # version 1.0.0
library(DT) # version 0.4
library(highcharter) # version 0.5.0.9999
library(treemap) # version 2.4-2
source("admitidos-pregrado.R", encoding = 'UTF-8')
source("funciones.R", encoding = 'UTF-8')
col <- c( "#8cc63f", # verde
"#f15a24", # nar... |
ffd039747124c7533301a1a80753b66bf647e445 | bbbb9a5e75c7f0e51f153f20d4d990a1f33b60a5 | /R/frm_fb_mh_refresh_imputed_values.R | 219741041409939f79dde32fa3126144ce8ee8ca | [] | no_license | strategist922/mdmb | 01417c2fb8de64586d518234f6b4f1269c71b1fa | 82bc222769d2f170e016a6ba223327bd4eaee723 | refs/heads/master | 2021-01-21T12:30:35.438258 | 2017-08-20T12:43:21 | 2017-08-20T12:43:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,004 | r | frm_fb_mh_refresh_imputed_values.R |
frm_fb_mh_refresh_imputed_values <- function( imputations_mcmc , acc_bounds, ind0 )
{
impute_vars <- imputations_mcmc$impute_vars
NV <- imputations_mcmc$NV
mh_imputations_values <- imputations_mcmc$mh_imputations_values
if (NV > 0){
for (vv in 1:NV){
# cat("-------" , vv , "------\n")
# vv <- 2
va... |
f57465968d2cba8ea210dd5a1391ff87f32a896e | 28f660487cc9a1047c942ca31bb03e2b5ce66ac3 | /inst/doc/PlotsAndStats.R | 48f91abc84bb69e5f6c14eb893eda1f973aa4002 | [] | no_license | cran/cheddar | 8f8f412e0acb74e86980f63e2c7e254489039064 | f01d603cb35255f41c74511e93e4f395d0155bcc | refs/heads/master | 2022-09-14T18:17:49.023624 | 2022-09-01T13:40:05 | 2022-09-01T13:40:05 | 17,695,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,536 | r | PlotsAndStats.R | ### R code from vignette source 'PlotsAndStats.Rnw'
###################################################
### code chunk number 1: PlotsAndStats.Rnw:33-44
###################################################
library(cheddar)
# Makes copy-paste much less painful
options(continue=' ')
options(width=90)
options(prompt='> '... |
b8a2976e748b9e9f3852a43cba3f9d72aff7626a | 0e5605d06591219417601c639c40d4d45c665774 | /HomeDepot/R/Long_xgb_v2.R | c09042bfed0f5afb4daaf5a3011f208420d51feb | [] | no_license | nguyenhailong/Kaggle | 2e7898d01144d76e43857328d398ca167863c5a4 | 6e36f95ae5455f003fe1c452d98a49ef28d0de35 | refs/heads/master | 2020-05-26T05:10:50.090619 | 2017-02-19T14:41:36 | 2017-02-19T14:41:36 | 82,465,390 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,690 | r | Long_xgb_v2.R | # Based on Ben Hamner script from Springleaf
# https://www.kaggle.com/benhamner/springleaf-marketing-response/random-forest-example
library(readr)
library(xgboost)
library(dplyr)
library(tidyr)
setwd('~/GitHub/Kaggle-Telstra/R')
#my favorite seed^^
set.seed(2401)
cat("reading the train and test data\n")
train <- read... |
03f39406e7fb2a4e1bb0e74076174698618f5604 | 665c32727f3920aaaa8e2535f66d8df09d55944a | /man/write_survival.Rd | 0bedc448fa7449d481f42bf0214435dd243baaa1 | [] | no_license | cran/survivalAnalysis | 5dd6ad5938641467b41655c955416422ca078d7a | ea726a3265120f159a15cede3191b892bc79bf73 | refs/heads/master | 2022-02-23T03:34:21.388556 | 2022-02-11T13:00:02 | 2022-02-11T13:00:02 | 147,193,710 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,593 | rd | write_survival.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/output.R
\name{write_survival}
\alias{write_survival}
\title{Print the essentials of a SurvivalAnalysisUnivariateResult.}
\usage{
write_survival(
...,
file,
label = NULL,
p_precision = 3,
hr_precision = 2,
time_precision = 1,
in... |
fab367fd4d81549afc44b9ad2011673a949c4b78 | e8c685f68dc752f5f332b44e5b4d76c048a3e436 | /R/st.err.R | 9006d9ba54c9105322096398624d12a4473df5f8 | [] | no_license | cran/IsoCorr | a8d2b82206ae741f2014d6516e1f08da00b54df0 | e9b1a16aac0cc741ee34e0baa6bfb607242127d5 | refs/heads/master | 2022-12-25T20:23:38.517959 | 2020-10-01T07:30:12 | 2020-10-01T07:30:12 | 301,808,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 116 | r | st.err.R | st.err <- function(x, na.rm = FALSE) {
if(na.rm == TRUE){
x <- na.omit(x)
}
sd(x)/sqrt(length(x))
}
|
5a9db761bda740a871ed9bdba765c800310848db | 6786bc8704dd3adfb85aad5fa881f82ac9f82032 | /R/utils.r | 29e2e4990d1fb391f1d189970f5becdd24f36869 | [] | no_license | hadley/mutatr | 1d5247e8bb320ae29cfb7156f09a268873695f10 | 05d1a9bfe7dc2db970aa2d4ad588caa427f1718f | refs/heads/master | 2021-01-23T13:18:21.256231 | 2010-06-28T17:11:17 | 2010-06-28T17:11:17 | 277,007 | 8 | 0 | null | 2013-03-30T13:35:17 | 2009-08-13T14:01:02 | R | UTF-8 | R | false | false | 350 | r | utils.r | #' Environment name.
#' Extract the name of an environment from its printed output.
#'
#' @param env environment
#' @keywords internal
envname <- function(env) {
gsub("<environment: |>", "", utils::capture.output(print(env))[1])
}
#' Is this a mutatr object?
#'
#' @param x object to test
#' @export
is.mutatr <- func... |
c8d2348e27f067d55a3100bc54af984d0a13ebfb | 584e0856fc8c9b514e9abe4b5405572f3a1f4463 | /R/p53sf.R | 54028f27f49b746e232ee865b6146b26be3c682c | [
"MIT"
] | permissive | shaoyoucheng/p53retriever | 9af93dae438956af9f10e5887071b615f02720ad | 108be4b09ec040d0e299dd6c067d5049caf54612 | refs/heads/master | 2021-12-10T15:37:30.667361 | 2016-08-24T08:28:18 | 2016-08-24T08:28:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 68,451 | r | p53sf.R | #' Locate candidate p53 responsive elements (full and half) on a DNA sequence
#'
#' @param seq.ini A character string containing the sequence. The sequence must be composed exclusively of DNA bases (a,c,t,g)
#' @return A dataframe containing the responsive elements located on the input sequence.
#' @export
# Halves ... |
bd79727e646a2fd1cf96e2b25666e85adf3b9732 | 1c7545bb3e9c165b8a630422480d086000047ab2 | /historic/template/ui.R | 2ee8b6b792fe8ce97c9069ec787487ed8016db0f | [] | no_license | MCTTAN/virtulis | b4c20a3266f0065158ce6fcdd140d971207af7e5 | c88ad5aa1921dc9598635e77add26baa6de2130a | refs/heads/master | 2020-06-29T14:51:20.104027 | 2020-04-23T00:29:00 | 2020-04-23T00:29:00 | 200,564,063 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 943 | r | ui.R | if(!require(leaflet)){
install.packages("leaflet")
library(leaflet)
}
library(leaflet)
months <- seq(1,12)
years <- seq(2000,2008)
HTML('<div data-iframe-height></div>')
navbarPage(
title="IBM Data Science Experience", id="nav",
tabPanel(
div(class="outer",
tags$head(
# Include our custom... |
e67e5ac58b987d7eb36707f37fb2e72801c8ecff | f1556a59213e9dafb25db0d01760a1443c55b6b2 | /models_old/LGBM_01/スコア.R | 00964dfc267128d01a6ebe92c9b7b8a210240b30 | [] | no_license | you1025/probspace_youtube_view_count | 0e53b0e6931a97b39f04d50a989a1c59522d56a7 | f53d3acd6c4e5e6537f8236ad545d251278decaa | refs/heads/master | 2022-11-13T13:22:51.736741 | 2020-07-12T04:14:35 | 2020-07-12T04:14:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,464 | r | スコア.R |
# train_rmse: xxxxxxxx, test_rmse: xxxxxxxx - xxx
# train_rmse: 1.024154, test_rmse: 1.076321 - categoryId + likes + dislikes + comment_count(ベースライン)
# train_rmse: 1.039356, test_rmse: 1.081406 - categoryId(自前 LabelEncoding)
# train_rmse: 1.024154, test_rmse: 1.076321 - comments_disabled(カテゴリ指定)
# train_rmse: 0.921... |
7590cec06ddc16649932338c41344aa5ae041737 | f101dadb65e8613751a8db176f23516d17b1bc09 | /src/server/manhattan.R | d3e3cba5125af9df123f317aa5fcea0ae6cc3809 | [] | no_license | Raistrawby/Rshiny_project | 017b1c3b7319c9ac309b074b60953d9e0a6afc0b | 42fbb9c822bc960dc4af32d3d39a4ad7760ba174 | refs/heads/main | 2023-05-23T01:59:47.217100 | 2021-06-09T09:31:48 | 2021-06-09T09:31:48 | 339,028,705 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,105 | r | manhattan.R | library(tidyverse)
# load_data <- function() {
# geneExpression = readFile("", T, "SYMBOL", org.Hs.eg.db)
# geneList <- get_geneList(geneExpression, 0.75)
#
# go_gse <- gse_analysis(geneList, "SYMBOL")
# go_sea <- sea_analysis(geneList, "SYMBOL")
#
# KEGG_GSEA <- get_KEGG_GSEA(geneList$GSEA, "hsa")
# ... |
57231407f55ec7f5a76cd24bf0e259acfa5c6bc8 | c0856a1759cd37e8537bc67762ff962d90b47ee9 | /server.R | 36a1b967f8fcf28b316acf14a6e7d2b155786f49 | [] | no_license | OmidAghababaei/Developing_Data_Products_Project | 9cdcdfebe7db2782557eda946ed20e57a3eaae52 | 180ef6c46e6f739025f0538ebf0fcb5060f5435b | refs/heads/main | 2023-03-05T01:21:03.914473 | 2021-02-14T05:21:37 | 2021-02-14T05:21:37 | 338,712,847 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,339 | r | server.R | library(shiny)
library(ggplot2)
library(grid)
library(gridExtra)
library(plotly)
shinyServer(
function(input, output) {
Solar<- airquality$Solar.R
Ozone<-airquality$Ozone
Temperature<-airquality$Temp
model1 <- lm(Ozone ~ Solar.R, data = airquality)
model2 <- lm(Temp ~ Solar.R, data = airqu... |
34b58babcedac6540d7a07bcc924b7d2e7a6160d | 1544494905a24c6f85b42438668e9bfb5a5bf5bd | /data/my trait data.R | b5a7a073d5c28091a23a8657f3afd53aed37ddc3 | [] | no_license | maudbv/Abundance-richness-correlation-BP | 42b96e0a094ce7eb5f6c6bb6b53c8ddf3e52c862 | 6ef20818fc08866e25a574324cf5ec00f39c3089 | refs/heads/master | 2021-06-22T16:34:33.407196 | 2020-12-18T12:43:43 | 2020-12-18T12:43:43 | 30,317,336 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,519 | r | my trait data.R | # Import trait data from Banks Peninsula (from summer 2014/2015)
library(doBy)
# import Seed mass data (in g)
mySM <- read.csv(file="data/traits/SM measurements.csv",na.str=c("","NA"), as.is=T, stringsAsFactor=F)
mySM.mean <- summaryBy(SM.mg. ~ Sp.code, data= mySM, na.rm= T)
# import height data (in cm)
myH<- read.cs... |
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