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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
666da8177db93beec6f0182ca4384d09a10556fe | 580f4e51729818f62d340b3dec0ad080f0eba8b5 | /man/fortify.trackeRdataSummary.Rd | aa123cebd4da3f774344c199d0c5effa0ada085c | [] | no_license | DrRoad/trackeR | 77b552fab86da85f53b2c27203490686d03ed496 | 2d0a486e78c2cf073051eb667fb6c9f26fe0664f | refs/heads/master | 2020-03-19T19:12:29.544263 | 2018-04-22T23:40:33 | 2018-04-22T23:40:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 622 | rd | fortify.trackeRdataSummary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/trackeRdata_summary.R
\name{fortify.trackeRdataSummary}
\alias{fortify.trackeRdataSummary}
\title{Fortify a trackeRdataSummary object for plotting with ggplot2.}
\usage{
\method{fortify}{trackeRdataSummary}(model, data, melt = FALSE, ...)
}
\... |
b8967445d47102d26141f1aa1ad389b58d30d64c | c21e1626047c96e3f3ebd3cc347d1d64655aa8a0 | /man/fastClustering.Rd | dfb6b30ef6f7a173761e87d2ac917eaa06c5adad | [] | no_license | cran/sClust | e8917a49af7826325419127bbfcae86e0465f9fa | bbb200aacce30a9af93148fb26a8cf060a5b6b58 | refs/heads/master | 2023-07-05T06:40:26.901212 | 2021-08-23T17:50:02 | 2021-08-23T17:50:02 | 399,418,362 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,144 | rd | fastClustering.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fastClustering.R
\name{fastClustering}
\alias{fastClustering}
\title{Fast Spectral Clustering}
\usage{
fastClustering(
dataFrame,
smplPoint,
stopCriteria = 0.99,
neighbours = 7,
similarity = TRUE,
clustFunction,
...
)
}
\argumen... |
758fc46ad76ded434943e673af862ac032e9d83c | 0d4c1d4a347fbf9202d21aa1710a3b056711cedf | /vignettes/reporter-figure.R | 1e076c8bbcd6722615e926d9ae94ded003cc9bac | [] | no_license | armenic/reporter | 6a5756977da13340f7bf80cd63d13d340d97d8f9 | 00dc496ca93afef4b6e05f0f24a74dc935e91123 | refs/heads/master | 2023-02-18T06:49:22.454126 | 2021-01-19T14:55:15 | 2021-01-19T14:55:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,138 | r | reporter-figure.R | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# library(reporter)
# library(ggplot2)
#
# # Create temporary path
# tmp <- file.path(temp... |
3805e38a1eadf270d481f9ae132d5f1bd3b14545 | 12150c61edf9bb228cc80496226bba0abc3f0064 | /Plot4.R | 98c9919ed18f6620125ef9ab848d39b039830917 | [] | no_license | diazidx/Exploratory-Data-Analysis-Project | 0bfa126b49566c7f2fbf25b2340d557c68293b38 | 53a3368ced52b5c72dbc5b7a947f806d805ee429 | refs/heads/master | 2020-05-01T09:44:38.983687 | 2019-10-16T00:14:05 | 2019-10-16T00:14:05 | 177,407,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 647 | r | Plot4.R | ## lattice
library(lattice)
## read source
data01 <- readRDS("summarySCC_PM25.rds")
classification <- readRDS("Source_Classification_Code.rds")
## getting all data related with coal and sum
coalclass <- classification[grepl("Coal", classification$Short.Name), ]
coaldata01 <- data01[data01$SCC %in% coalclass$SCC, ]
em... |
aec31b8f56d0f3a2f287966df2d0744dedcb8804 | cf581ab61b20fa39bec3cb7f0e9e5c3b8c0442ef | /build/docker_install_rpkgs.R | 9defdaac18c246743ee69540f461acaf53e79f8f | [
"MIT"
] | permissive | kaybenleroll/insurance-modelling | 52e78b46cbcc87f03e434e305404c73cad0220cb | b2a134a094a511db1b58141537ba1dac28618c5b | refs/heads/master | 2023-04-10T08:09:09.801108 | 2021-03-05T16:10:21 | 2021-03-05T16:10:21 | 281,653,899 | 3 | 2 | MIT | 2022-06-07T11:41:36 | 2020-07-22T11:02:14 | Makefile | UTF-8 | R | false | false | 147 | r | docker_install_rpkgs.R | remotes::install_github(
"dutangc/CASdatasets",
ref = "cc69b33959a42f24b8aaaf732e7c3d623896eeea",
subdir = "pkg",
upgrade = "never"
)
|
af8fc0a90977bd81e75e15eec2f16bee65505580 | 96cf33c736f40c3ef3854f0834b673e63515d787 | /006_by_individual_analysis.R | 23d85be9caa13ff662a04480f108a8194e3fcd69 | [] | no_license | bodowinter/rapid_prosody_transcription_analysis | 859a2ba1e87859fb57f98e593621804b728d907f | 1ed4ec0751681ccdadd2613f587101325bfc548d | refs/heads/master | 2021-01-19T01:41:53.813893 | 2018-08-15T07:18:18 | 2018-08-15T07:18:18 | 37,758,524 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,573 | r | 006_by_individual_analysis.R | ## Bodo Winter
## By-individual analyses
## June 22, 2015
## July 27, 2016: Finishing brushes and incorporation of spectral tilt
## July 27, 2016: Replaced ggplot2 with base graphs
##------------------------------------------------------------------
## Load in data and packages + preprocessing:
##---------------------... |
5517ca78a11fda74d03f89c4a6aa768289314e94 | fe442b183f49aa2f49302e31295c1bc3ed254fb5 | /scripts/internal_use_required_packages_installer.R | c51132dca5762b59cbac8a5c7315a3c4ede767fb | [] | no_license | sergiu-burlacu/book | fcc27ded8b6997a0603aaa383184f346929cf372 | 1f5b6713944c4082fb04c35621b78fa61c9a0eba | refs/heads/master | 2023-02-12T14:55:56.223144 | 2021-01-03T21:23:18 | 2021-01-03T21:23:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 293 | r | internal_use_required_packages_installer.R | library(checkpoint)
found_packages <- scanForPackages(".", use.knitr = TRUE)$pkgs
if (length(found_packages[!found_packages %in% installed.packages()]) > 0) {
install.packages(found_packages[!found_packages %in% installed.packages()])
}
# devtools::install_github("ebenmichael/augsynth")
|
839f24a94cde0aa6ec2749e865230ce2b729b279 | 987a38b326796527b997a61af2f2be7afa80a249 | /etch.R | 4c9f77b5059248f7b25fb521d56757eec836a82d | [] | no_license | zaomy/R | 1dec13fdd674163cfe27e7c7e82040c27a1d36cd | 3bd6998b780be31fc75a6758d9e89af238a5ea1a | refs/heads/master | 2021-08-07T11:12:33.885756 | 2017-11-08T03:38:38 | 2017-11-08T03:38:38 | 109,583,386 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 346 | r | etch.R |
#################################################
# step 1: import data
dataname = "./etch1.dat"
data <- read.delim(dataname, header = FALSE, sep="", skip=0, as.is=TRUE)
#data <-read.table(dataname, header=FALSE)
colnames(data) <- c("power","etch")
boxplot(etch ~ power, data=data)
lmod = lm(etch ~ power, d... |
ad2ed80aab0aaf530d4f828f5a1e76b2ce2b13eb | 53307f9c1ea403d12f8f237423167bad974c31b9 | /plot3.R | 4c336dd4b5be97a729376b1f6f4e3c127991e514 | [] | no_license | samuelsherls/ExData_Plotting_shers | 85017d27cf890736c6846e3a67699c77d65c6fae | 6183912fa927a339146664fad96a2cb0811b9347 | refs/heads/master | 2021-01-16T20:01:36.903784 | 2015-08-06T16:00:43 | 2015-08-06T16:00:43 | 40,281,349 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 842 | r | plot3.R | dataFile <- "./household_power_consumption.txt"
data <- read.table(dataFile, header = TRUE, sep = ";", stringsAsFactors=FALSE, dec=".")
febdata <- data[data$Date %in% c("1/2/2007", "2/2/2007"),]
activepowerdata <- as.numeric(febdata$Global_active_power)
submeter1 <- as.numeric(febdata$Sub_metering_1)
submeter2 <- as.... |
81b33db27cbf2657115e8d6dd785cb2c98d10ecb | 1879fba0c2bc1acbad17656af70575beeac7c9be | /data/analysis.R | ddea3b4d2f3492c037b8df29b3cbf00ff0a27fd2 | [] | no_license | mricha41/mricha41.github.io | e3cbe46c5213df0b83abf94a0bf013bcccfd8569 | ee421d27b605dfeaf1bfdb8cd2e4fe35f18dd237 | refs/heads/master | 2021-06-17T22:28:40.826001 | 2021-03-27T04:42:27 | 2021-03-27T04:42:27 | 131,674,923 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,569 | r | analysis.R | #######################################
#Cerberus Online Survey Data Analysis
#######################################
#######################################
#Very low counts...four responses total
#######################################
#interpret results accordingly ;)
#######################################
... |
b7cddd41bef3ec04f7ad116d232d77128df8ba2a | a8710ca51f2c3bd9fa19947b0c0c36b4062aaa33 | /man/ReferenceMale.Rd | aa2f5f2c40b69096899495832d10594800c5d103 | [] | no_license | cran/ELT | 2ba6dd97a0de6620120b93fe008a834f5e8e4f6a | 938a633944b42d92572234a4d1c5c12c9463fc94 | refs/heads/master | 2021-01-17T08:48:38.579908 | 2016-04-11T09:06:26 | 2016-04-11T09:06:26 | 17,678,902 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 322 | rd | ReferenceMale.Rd | \name{ReferenceMale}
\alias{ReferenceMale}
\docType{data}
\title{ReferenceMale used for the exemple.}
\description{This data corresponds to an adjusted version of the French national demographic projections INSEE 2060 for the male population.}
\usage{data(ReferenceMale)}
\examples{data(ReferenceMale)}
\keyword{datasets... |
f4d4ee0033b75cca6c8f32f4f61d5d78ba584dfa | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BatchExperiments/examples/summarizeExperiments.Rd.R | bfac7edbbb69c233c3d4a5f83607e8f914855cfc | [] | 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 | 752 | r | summarizeExperiments.Rd.R | library(BatchExperiments)
### Name: summarizeExperiments
### Title: Summarize selected experiments.
### Aliases: summarizeExperiments
### ** Examples
reg = makeExperimentRegistry("summarizeExperiments", seed = 123, file.dir = tempfile())
p1 = addProblem(reg, "p1", static = 1)
a1 = addAlgorithm(reg, id = "a1", fun =... |
01cb591e4212460be76b35afaf1b05b85d07a67e | 69d8086cbf6b7395c62bb65a82edfc170f441777 | /oncosnpMasterfileParser.R | bdf1c95ed54f853aa3aef2d5de07706ae13efdef | [] | no_license | flywind2/pancancer_ith | 8fe9fc45082528992e0a32fa9e964ec29d67f197 | 144172fca33dc394a9b230c7774c7b8510b57fd2 | refs/heads/master | 2020-06-18T15:39:46.297774 | 2016-10-20T23:54:55 | 2016-10-20T23:54:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,731 | r | oncosnpMasterfileParser.R | masterfile = read.table("C:/Users/joseph/Documents/UCECcnvMasterFile.txt", header = TRUE, sep = "\t",
stringsAsFactors = FALSE, quote = "", comment.char = "",row.names = NULL)
samples = as.factor(masterfile[,"sample"])
for (i in 1:length(levels(samples))){
SampleID = as.character(leve... |
f0fa3f0194eb7357ba4de125a39be870d20c2205 | 5babfd17883edfc39fc662d242f62dcbe6b4c898 | /R/timestamp-package.R | 7d01de5562ad8b9cd2102c2c4c1f026077310882 | [] | no_license | Dasonk/timestamp | 6c673e5181f6aa7685f806db561ca2b45ccae874 | d81a09e5da72a41f7dee969b810fcca16fd2fe0a | refs/heads/master | 2021-01-23T02:34:30.319384 | 2017-06-02T17:16:02 | 2017-06-02T17:16:02 | 12,607,310 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 237 | r | timestamp-package.R | #' Adds a timestamp to the current prompt.
#'
#' Adds a timestamp to the prompt. It will update anytime a top
#' level call occurs.
#'
#' @docType package
#' @name timestamp
#' @aliases timestamp timestamp-package package-timestamp
NULL |
1c2357a1119bc8135dfe5051034c6bcbd9515b5d | 5e7cfe48b3a86cf20bddff7383f0292a2c12514d | /plot2.R | 3cea92d9172cda9b9e8b22be709cbb7b665548be | [] | no_license | JongHyun-Evan-Park/Exploratory-Data-Analysis-CoursePJ2 | f0720ddcd1302c08e0803adafaf29b901e768c5c | d1665cae676f88471775939f8c014281ef890a5d | refs/heads/main | 2023-08-19T19:30:50.232168 | 2021-10-22T04:16:42 | 2021-10-22T04:16:42 | 419,962,421 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 804 | r | plot2.R | #2 Total emissions from PM2.5 in the Baltimore City, Maryland (fips == "24510") from 1999 to 2008
Url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
if(!file.exists("./data")){dir.create("./data")}
download.file(Url, destfile="./data/exdata_data_NEI_data")
FilePath <- "./data/exdata_data_NEI_d... |
9658dae79498f6fa6d6949ab0dca66afa565b2da | a8c5cff02ee11e446c465d7cd6ba2591fb90a3bd | /LoadFunctions.R | f723ad2c309ba6baa2b73a755e8d4f8b99a10cce | [] | no_license | jvduijvenbode/assignmentJonas | f06bf07bbe0f5bb3a1ee9bf9ccc83a8bebc3acbe | 5de394d03f94b4e31731f0c26efbbd148a0b1bf2 | refs/heads/master | 2016-09-05T18:32:34.939824 | 2013-12-04T14:20:56 | 2013-12-04T14:20:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,037 | r | LoadFunctions.R | #convert factor data with unnecessary comma's to numeric data
factortopopnum<-function(x){
x1<-unlist(strsplit(x,split=","))
x2<-as.numeric(paste(x1,collapse=""))
return(x2)
}
#select a year of the worldpopulation to use in this script
sel_year<-function(poptable,y){
#make year readable for dataframe
years... |
96bf3ca18e4a8eacbd2d8261354a632f4ca37081 | 01ececa7c221357eaedf85a1c2b8414fd45302a2 | /tests/testthat/test-05-sessionPath.R | 58b7b350cc19d128b937886028de8d5ba45cdb47 | [] | no_license | sonejilab/cellexalvrR | d7e30f147d3d991e6858f50b11e90a31863e759c | c533136f59fa906e1c173a46cc4f2e36608c0204 | refs/heads/master | 2023-03-31T19:05:15.669831 | 2023-03-17T07:24:40 | 2023-03-17T07:24:40 | 133,559,720 | 4 | 2 | null | 2018-05-15T18:52:17 | 2018-05-15T18:52:15 | null | UTF-8 | R | false | false | 3,997 | r | test-05-sessionPath.R | context('create sessionPath')
if ( ! expect_true( rmarkdown::pandoc_available() ,label= "pandoc is installed") ){
skip ( "Pandoc needed - but missing here")
}
prefix = './'
#prefix = 'tests/testthat'
#data = file.path(prefix, 'data/cellexalObj.RData')
#cellexalObj = loadObject( data )
cellexalObj = r... |
53c0ab6e3a3d7abb7f46ab3d83d2eae01fe3407f | 54dfd12ed56937495b2051cd0ee5f89db09477e4 | /analysis/Experiment_1/data_preprocessing.R | db7e44f16316558aad82796b150d88d2c66a76d5 | [
"MIT"
] | permissive | qed-lab/Persona | 3fab5214360975db59fa5613aca3324e14dfd78d | c2bb38633c3d1e48b1ad6d8ba53920c5ef05739a | refs/heads/master | 2021-10-19T10:31:03.965931 | 2019-02-20T00:04:59 | 2019-02-20T00:04:59 | 78,043,670 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,328 | r | data_preprocessing.R | # ================================================
# data_preprocessing.R
#
# This code loads and aggregates plan recognition configuration data.
#
# Assumptions:
# 0. All data is within the folder "~/Developer/Persona/analysis/Experiment #1"
# 1. The folder needs to contain only numbers for it to be considered.
# 2. ... |
9bf97c1d561f4a245a89c12ce0de5e47a31e5491 | d8d203b1274b616b29f4dcbb4d9d08a18535954f | /code/descriptive-summaries_scripts/table1-surgeon-cabg_function.R | bcd56149a59c48d4a75d3ec5677981a6452c6fe4 | [] | no_license | arinmadenci/volume-surgeon | 824515594dc8031208abbf37f90ab6fa6f67ee27 | c57fdbd39b47e40e2181fd56b24af387da6d850a | refs/heads/master | 2022-11-29T01:36:26.656662 | 2020-08-10T16:39:48 | 2020-08-10T16:39:48 | 254,953,882 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,388 | r | table1-surgeon-cabg_function.R | surgeon.table1.fun <- function(dat, title, file.tex){
if(!require("pacman",character.only=T)){install.packages("pacman")}
pacman::p_load(tableone, xtable)
allVars <- c("volume", "age_mean",
"comorb_ami_mean",
"comorb_dementia_mean", "comorb_afib_mean", "comorb_ckd_mean",
... |
b477ed1727a46912e657b9193a325b5b82e54dab | 94d9e1666588919eaa2ffe0dd70d999f4fc08293 | /tests/testthat/test-fields.R | bb2d5d3f124d39e67388d3749445ccfd2bfaccc3 | [] | no_license | datasketch/dsvalidate | a980428722146bc692bb5609e0789b40b39dafb5 | b6ead04b3ccf8e8d09ed3ecf46fd3ff7991770ed | refs/heads/master | 2023-07-14T20:37:16.949736 | 2021-09-02T16:53:29 | 2021-09-02T16:53:29 | 341,588,751 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,862 | r | test-fields.R | test_that("columns validated", {
df <- data.frame(id = c(1:20),
b = c(rep("A", 10), rep("B", 10)),
c = c(rep("A", 10), rep("B", 10)))
f <- homodatum::fringe(df)
dic <- create_dic(df, extended = TRUE)
specs <- list(hdType = list(is_any_of = c("Cat", "Num")),
... |
0f984bc8e3f896e0e3e739de4b137739f97bbb4c | ff2daefdd0ead3005164e2179a9b8fbb2fff6c40 | /data-mining-in-r-torgo.R | f154edec37dc13ce0e30a6f3a12d607a64e89be1 | [
"MIT"
] | permissive | prasants/r-code | 8c076832a396833f558862745a31a25740674e07 | 6c509b6e8df860f666f4869ba87ac7fcd218aaab | refs/heads/master | 2020-05-08T22:53:47.959552 | 2015-12-09T06:16:43 | 2015-12-09T06:16:43 | 34,384,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,576 | r | data-mining-in-r-torgo.R | #Data Mining with R by Luis Torgo
#Prasant Sudhakaran####
#Chapter 1####
install.packages("RMySQL")
install.packages("DMwR")
installed.packages()
library()
#To check if there are newer versions of installed packages at CRAN
old.packages()
update.packages()
#To search the r-project site
#Format : RSiteSearch('Search ... |
ed527989b7da3002ff7b31587455d3a93e7a20a9 | 2f395e94c4b57d0832efd43b4555392a1377778b | /plots_umap_types.R | e844ce68982ada81bddbaa4e55001fa13ff2a0eb | [] | no_license | jrboyd/scRNA_DK | 2e30a27cae387949ced5c2d35ff4ad34ea1fa15d | e94578f4afe91c4e970b1359662102725f023bbf | refs/heads/master | 2021-03-16T01:31:31.925322 | 2020-06-26T19:31:55 | 2020-06-26T19:31:55 | 246,893,181 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,067 | r | plots_umap_types.R | library(Seurat)
source("../SF_AutoImmune_ssv/functions_setup.R")
dksc = readRDS("datasets/DKSC.combined.Rds")
meta_dt = get_meta_dt(dksc)
dksc.integrated = readRDS("datasets/DKSC.integrated.Rds")
meta_dt.integrated = get_meta_dt(dksc.integrated)
mt_genes = rownames(dksc)[grepl("mt-", rownames(dksc))]
## combined uma... |
d031d469406eb891396bfeae8e604e819cbbc1db | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pheno2geno/tests/test_analysis.R | aa04568906abc98f8c0a7870e2b07edcb5dd3f3f | [] | 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 | 1,083 | r | test_analysis.R | require(pheno2geno)
#setwd("C:/Users/Konrad/Documents/Github/phenotypes2genotypes/tests")
children <- read.csv(file="offspring_phenotypes.csv",header=TRUE,row.names=1)
parents <- read.csv(file="parental_phenotypes.csv",header=TRUE,row.names=1)
genotypes <- read.csv(file="genotypes.csv",header=TRUE,row.names=1)
... |
c0bb5125f314c3de2919653521719e47dcba010f | 5b0bc403547001551cb6148fc111ec66b3a3b076 | /scripts/plot_PCA.R | e1ff288ebdc2105b2c0000049edc195bec0cff88 | [] | no_license | erdavenport/japaneseEel | a8fef4c91433df0a9fde421ae06d1c2bc1d83e4b | 4d8c845de4626cfda8116c931f16018c0512c552 | refs/heads/master | 2020-04-10T04:26:33.478362 | 2019-03-05T15:16:04 | 2019-03-05T15:16:04 | 160,798,270 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,003 | r | plot_PCA.R | #!/usr/bin/env Rscript
######
suppressMessages(library("docopt"))
"
Usage:
plot_PCA.R --full_plink_in=<full_plink_in> --part_plink_in=<part_plink_in> --outpath=<outpath>
Description: This script will generate a PCA plot of the eel samples
Options:
--full_plink_in=<full_plink_in> prefix on plink eigenval and e... |
2ccaa29f0e73e8c5a2ad9cc110c30a34d562bea3 | 1d930b9fad37edb5550b8109359b569ca999d53d | /R/filter-n-obs.R | 14ed4c832afded202360af1ca45219350134ad4b | [
"MIT"
] | permissive | cderv/brolgar | 1ef174dd50bdca8fb886c56114a1ad978e4c3171 | f2fee3c636d84d21a2bf2061fc1a4e1d21d5136c | refs/heads/master | 2020-06-19T18:18:09.270305 | 2019-07-14T08:09:20 | 2019-07-14T08:09:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,085 | r | filter-n-obs.R | #' Filter by the number of observations for a `key`.
#'
#' When exploring longitudinal data it can be useful to filter by the number of
#' observations in a compact way. `filter_n_obs` allows for the user to
#' filter by the number of observations for each `key`.
#'
#' @param .data data.frame
#' @param filter A d... |
5d673f5e371fbad1304aea8674233d9add8d8df5 | 7ad193bcb130588dbf788564a22574e49734c8bf | /man/MetaboSignal_NetworkCytoscape.Rd | 2e98ea249579e3f856a6e4e56acba8817809e736 | [] | no_license | Rafael-Ayala/MetaboSignal | 9f03972efbf2ee691ddd1263cfa2b3f2d9496ade | 83c25f550f92805c28e9384c844a204a2d0a3f8c | refs/heads/master | 2021-06-05T09:38:11.881994 | 2016-10-17T15:17:57 | 2016-10-17T15:17:57 | 281,378,956 | 1 | 0 | null | 2020-07-21T11:28:49 | 2020-07-21T11:28:48 | null | UTF-8 | R | false | false | 7,960 | rd | MetaboSignal_NetworkCytoscape.Rd | \name{MetaboSignal_NetworkCytoscape}
\alias{MetaboSignal_NetworkCytoscape}
\title{Build shortest-path subnetwork}
\description{
This function allows calculating the shortest paths from a set of genes to a set
of metabolites, and representing them as a network-table (i.e. two-column matrix).
By default, the function exp... |
5c0243f6f4825f92d56e5725ac6d47dd245b1c67 | 2470551eed9989ee2c1fad7989fec6cd0948da29 | /ui.R | 23dafc0cc0181b42e4d7bd3f88763fa7a8f69400 | [] | no_license | sandeepbm/Coursera_Data_Science_Capstone | 3a7b11823dd133dace3387fb9fdcaa0f8655330e | 607040f0291d0a84b28068e57d5b3f5a660380ef | refs/heads/master | 2021-05-06T22:50:08.601710 | 2017-12-03T01:34:12 | 2017-12-03T01:34:12 | 112,865,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,866 | r | ui.R |
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Text Prediction App"),
# Input text
sidebarLayout(
si... |
3ff7a24dde4442ee38e21897e1839ae0fc49188d | ac2cb89d07dff7ec0fc0544f7583efa8b6363a5e | /code/archive 19_20/Additional/03 text/02 gutenberg.R | 9c908fa69721df8f3d3f6426d7c5f2f352fcb644 | [
"MIT"
] | permissive | JimDuggan/CT1100 | 76fac7ad12d40b4f6f255e447514667ef9fc5617 | 644ee71da3e5e97122cc3ee63323a0aac39db4be | refs/heads/master | 2021-12-23T10:43:26.968430 | 2021-10-01T13:59:18 | 2021-10-01T13:59:18 | 205,094,238 | 10 | 3 | null | null | null | null | UTF-8 | R | false | false | 369 | r | 02 gutenberg.R | library(gutenbergr)
library(tidytext)
hgwells <- gutenberg_download(c(35, 36, 5230, 159))
bronte <- gutenberg_download(c(1260, 768, 969, 9182, 767))
tidy_hgw <- unnest_tokens(hgwells,word, text) %>% anti_join(stop_words)
count(tidy_hgw, word, sort = T) %>% slice(1:20)
portrait <- gutenberg_download(c(4217),
... |
92e1a6aaca11573f93c5aee79814bef175e60614 | 533047d7c4e0738db063cc35836a8e46033e6a37 | /R/print_methods.R | a9e0b961de72b2c7fb5903b5b4b8a641d68da869 | [] | no_license | jtigani/bigQueryR | f87101981c01642b65a5018eccfefbe33a2d6514 | 9f50e963c09d3cbef3d18550c574c1bf1905cfea | refs/heads/master | 2021-01-20T12:38:01.785913 | 2017-05-05T10:05:10 | 2017-05-05T10:05:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 629 | r | print_methods.R | #' @export
print.bqr_job <- function(x, ...){
cat("==Google BigQuery Job==\n")
cat0("JobID: ", x$jobReference$jobId)
cat0("ProjectID: ", x$jobReference$projectId)
cat0("Status: ", x$status$state)
cat0("User: ", x$user_email)
cat0("Created: ", as.character(js_to_posix(x... |
50dc40f9bda4fb8b9a07b654fde855e186d3d84a | 77d130a37122031a98e8f030dff129f50a7c7068 | /r_scripts/data_wrangle/asthma_saba_msa_timeseries.R | 38ee20f703dd97576f330617ad9fe1f58fa63679 | [] | no_license | RyanGan/oregon_wildfire | 282e7f3757afb98e711192c322399105f3eb88c1 | 758ef82e2085046236f187b15fe77b924da995e0 | refs/heads/development | 2020-06-28T06:13:12.923359 | 2019-09-28T23:47:05 | 2019-09-28T23:47:05 | 74,503,206 | 0 | 3 | null | 2018-05-18T19:02:09 | 2016-11-22T18:48:29 | R | UTF-8 | R | false | false | 5,162 | r | asthma_saba_msa_timeseries.R | # ------------------------------------------------------------------------------
# Title: Creation of asthma and saba fill Oregon and MSA time series
# Author: Ryan Gan
# Date Created: 2018-09-12
# ------------------------------------------------------------------------------
# Script purpose is to create time series ... |
41c0fbf2a09f4a14965e96cc89261a18eab67cdc | 128aad713f698c8cf7d41d18289d8d140c1a46e8 | /Text Mining/Home Assignment/Code/Home Assignment Code(testing).R | 5c1bc3409718e2bccf2465fb34ec75d71fc9eb72 | [] | no_license | mbalakiran/Data-Analysis-and-Visualization-In-R | 8f3cec8708625964559ec6dc62150a16cfd0e3f4 | f6403cfc953194cdd69e7e4b0d7a6ec1d7635594 | refs/heads/master | 2021-02-19T20:14:39.273578 | 2020-03-29T09:49:23 | 2020-03-29T09:49:23 | 245,319,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,123 | r | Home Assignment Code(testing).R | install.packages("ggridges")
library(dplyr)
library(readr)
library(base)
library(ggplot2)
library(tm)
library(stringr)
library(wordcloud)
library(corpus)
library(tidytext)
library(data.table)
library(tidyverse)
library(wordcloud2)
library(reshape2)
library(radarchart)
#library(RWeka)
library(topicmodels)
library(ggridg... |
cb7733e46f925565f7e12d5299920a24702e80ca | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/matsbyname/examples/quotient_byname.Rd.R | 0d006eb501b13669fabc1a70cc2218f2572b8ebf | [] | 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 | 975 | r | quotient_byname.Rd.R | library(matsbyname)
### Name: quotient_byname
### Title: Name-wise matrix element division
### Aliases: quotient_byname
### ** Examples
library(dplyr)
quotient_byname(100, 50)
commoditynames <- c("c1", "c2")
industrynames <- c("i1", "i2")
U <- matrix(1:4, ncol = 2, dimnames = list(commoditynames, industrynames)) %>... |
7780fd0020e653b02ad8f3144fecb40e78fc81a6 | 83c72d7783be9198bd9770a96256421891d47667 | /inst/shiny/ui.R | d5714250f45499b2b6a66a6b1bdc48abd0bcd51c | [
"MIT"
] | permissive | stevecoward/ripal | 4ae1717cff36227bd5bf1e9b313f98f32cabd3ea | 9b464b321db5fcd2cb41853672eae452331e7848 | refs/heads/master | 2021-05-28T07:38:42.130572 | 2015-02-19T05:30:32 | 2015-02-19T05:30:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,642 | r | ui.R | #' ui.R
#'
#' ripal Shiny client-side renderer
#'
shinyUI(pageWithSidebar(
headerPanel("ripal - password dump analysis in R"),
sidebarPanel(
tags$head(
tags$link(rel="stylesheet",
type="text/css",
href="ripal.css"),
tags$link(rel="stylesheet",
... |
f50073b592d97199c5e751644c59f3e66ed91b41 | 97eedfe2f40d45b10a7b1e49dd6ae59bfcfe2fa1 | /runTest.PowerTest.R | 71d6a8fd143c28db0de88baaf3b5acb8ce07d6ce | [] | no_license | nihar/kruskal-wallis | 289b3a149633a3bc4edbbc9f61fda2bff285a8af | 63b36809695db0c0ae2cb0927c885fdf3e4a0076 | refs/heads/master | 2021-01-20T07:10:32.658329 | 2012-09-16T19:10:28 | 2012-09-16T19:10:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 922 | r | runTest.PowerTest.R | #---------------------------------------------------------------------------
# runTest.PowerTest.R
# Run the Monte Carlo simulation for the specified number of iterations
# @param N The number of iterations for Monte Carlo simulation
# @author Nihar Shah
#-----------------------------------------------------------... |
d8f85b51d39270fcdae445f4c3eda637eea1fd70 | 8c6a81de5c9b579cb51d19f6ec7c9371440cc61a | /Clustering/tumorClustering.R | 0d3e7e1ac827397e33d851beae2952bba7f27877 | [] | no_license | ababen/Springboard-Section7 | c414ee4acd7d28f4b9fe853508bdc7eb4a4a66eb | 44860609daac8873a834b5cf7e0f537413264576 | refs/heads/master | 2021-01-11T01:48:51.199634 | 2016-11-29T00:58:11 | 2016-11-29T00:58:11 | 70,661,718 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 979 | r | tumorClustering.R | setwd("~/R/Springboard-Section7/Clustering")
healthy = read.csv("healthy.csv", header = FALSE)
healthyMatrix = as.matrix(healthy)
str(healthyMatrix)
image(healthyMatrix, axes = FALSE, col = grey(seq(0,1,,length=256)))
healthyVector = as.vector(healthyMatrix)
distance = dist(healthyVector, method = "euclidean")
str(hea... |
03c90457a251e460fd72013fe020f8b7128ed974 | eefb8e7651673265daa41b3200bb5db4e9436d03 | /Listas/plot_functions.R | 56185533631d16983c698c8ce1c9d0aa9b5b9d54 | [] | no_license | victordalla/Trabalho_ME613 | 111146861943afac4537ed849feb07cfca0b04ad | bd324b19bf0e11c850e52badcc8df7678f39cffd | refs/heads/master | 2022-02-01T05:35:45.546630 | 2019-07-09T13:37:51 | 2019-07-09T13:37:51 | 190,424,025 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,620 | r | plot_functions.R | plot_prediction <- function(model, response, response_name = "resposta") {
# precisa de dplyr, ggplot2
p <- predict.lm(model, interval = "prediction") %>%
dplyr::as_tibble() %>% dplyr::mutate(response = response) %>%
ggplot2::ggplot() +
ggplot2::geom_line(aes(fit, fit), col = "chocolate") +
g... |
20942812b6c78ad1367aef12a175f4e653280c2d | 2f74890bca4e2405e91f30e26f6ff560653b808a | /Movie Budget-Rating Script.R | a53914b1f8bd4f2d4db5042bfaed892e8e0bcde0 | [] | no_license | andcar23/Movie-Budget-Rating-Simple-Linear-Regression | 082d90343e10e2b5703fd398914a251d6beacd4f | e212c3c2cb68069b66695bba0cb5b847edad1ab1 | refs/heads/master | 2020-07-18T08:42:31.802661 | 2019-09-04T02:47:30 | 2019-09-04T02:47:30 | 206,215,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,103 | r | Movie Budget-Rating Script.R | #Andrew Carroll
#Project 3
#April 23, 2018
library(summarytools)
library(ggplot2)
#Import Data
movie <- read.csv(file.choose())
#recode variable to drop outliers
descr(movie$budget)
movies <- subset(movie, movie$budget < 400000000 & movie$country == 'USA')
#Vieing varibale to see if it was imported corr... |
281c09c6677141560038defdca4b2c93523fe93f | 0c591324f1c2a3669bec7203556d971e73a74810 | /src/Cal_fit_Auto.R | 7193cbaaf5883c6f2cabff0cc60b5d4ee8ef2bd2 | [] | no_license | DmitryMarkovich/Thrombin_Analyzer | 58949683660091f88d8815e8f117deafcb8c60c6 | f79cc112a88a437d69f99f17681d919d8d6ea385 | refs/heads/master | 2021-01-21T04:33:22.755343 | 2018-01-30T16:23:09 | 2018-01-30T16:23:09 | 50,573,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,518 | r | Cal_fit_Auto.R | ################################################################################
Cal$set(
which = "public", name = "fit_Auto",
value = compiler::cmpfun(
f = function(silent = TRUE) {
if (!is.null(fit$Auto)) {
## print(fit$Auto);
warning(">> No fitting: Auto fi... |
b97542a2402e3f8521b513be50c3c5f510f74115 | 4175b5a7e7c6bce3b3db9694cfa13591aabd8ca2 | /better visualize.R | 605c38fd883f25b5a42ad3b687c18bddfda002c9 | [] | no_license | abhinav-sharma-6167/Kaggle-Practice-Facebook-Checkin | 0d5fe534071ce39479cfac9897eb4576fdcb020e | 90467a91e661e7a9d88c7c1ee7b8b5a21437a6ba | refs/heads/master | 2021-09-08T05:02:44.559558 | 2016-11-18T19:35:17 | 2016-11-18T19:35:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,388 | r | better visualize.R |
library(ggplot2) # Data visualization
library(readr) # CSV file I/O, e.g. the read_csv function
library(dplyr)
#Import train dataset
train <- read_csv("../input/train.csv")
train <- train %>% group_by(place_id) %>%
mutate(check_ins = n()) %>%
ungroup() %>%
arrange(desc(check_ins))
most_popular_place <... |
3ee9ee7152701f7e2e0438a67044ccf084af1166 | b3a0ed1700c1313c0453320feb84859567848e86 | /explanatory-data-analysis/webcrawling-naverblog-warmtone.R | 28659331dc7504f12a8191bd561dfc2b9d21d2a1 | [] | no_license | cs13syy/snu-fira-bigdata-analytics | f7e45acc3a5d0d0288c0e13ff314c366e00a3736 | d8ce6273b4ff4e136d923e1522f60b2576c02c3e | refs/heads/master | 2020-03-21T02:03:15.123119 | 2018-10-28T13:45:51 | 2018-10-28T13:45:51 | 137,976,936 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,128 | r | webcrawling-naverblog-warmtone.R | # 라이브러리
library(rvest)
library(dplyr)
library(KoNLP)
# header 설정 : api 승인을 위한 과정
client_id = 'XXXXXXXXXXXXXXXXXXXXXX';
client_secret = 'XXXXXXXXXXX';
header = httr::add_headers(
'X-Naver-Client-Id' = client_id,
'X-Naver-Client-Secret' = client_secret)
# 키워드 쿼리 변경
query = '웜톤'
# iconv(query, to = "UTF-8", toRaw =... |
701c15f592e6797ca733054a5cb04a80bfafe81f | 7d14874f217714b4c6eeaff7e40cf6b12668ac47 | /tick_dataset_results_analysis/manuscript_figures/old figs/manuscript_figures_06272022.R | b1f6efcb43d7f546ff5afa19e25bde4697f221e8 | [] | no_license | rowan-christie21/ixodes_scapularis_research | 9368bd4f01f3934e6fb585717de2e3b47eaab688 | 78129b0bcde96aa66d18a10b35f66f291adcb901 | refs/heads/main | 2023-04-07T22:19:49.141465 | 2022-08-02T01:58:00 | 2022-08-02T01:58:00 | 512,540,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 35,723 | r | manuscript_figures_06272022.R |
#----------------------------------------------------------------------------------------
# 6/26/2022 Code for figures in manuscript
# title: Longer study length, standardized sampling techniques, and broader geographic scope leads to higher likelihood of detecting stable abundance patterns in long term deer tick s... |
bc2353df318fc51df0c74ed2c76f65ab7529d0cb | ae418ff00f688c16ebca5db69bfaf3cb6f05b1c0 | /R/melt-internal.R | e058142d0acc4cb1dd8e9ac63a0daa25fe656605 | [
"MIT"
] | permissive | enginbozaba/bcbioRNASeq | 8461b7dcbe0b0e92589016402e790184b3c364f2 | 0216e92a166d28392ecf7ec6057cc510b3f4c0c9 | refs/heads/master | 2020-05-02T20:02:04.141120 | 2019-01-28T14:03:24 | 2019-01-28T14:03:41 | 178,177,306 | 1 | 0 | MIT | 2019-03-28T10:07:37 | 2019-03-28T10:07:35 | null | UTF-8 | R | false | false | 1,261 | r | melt-internal.R | #' Melt Counts Matrix to Long Format
#'
#' @author Michael Steinbaugh
#' @keywords internal
#' @noRd
#'
#' @seealso [reshape2::melt()].
#'
#' @return `grouped_df`, grouped by `sampleID` and `geneID`.
#'
#' @examples
#' counts <- counts(bcb_small)
#' sampleData <- sampleData(bcb_small)
#' x <- .meltCounts(counts, sample... |
9d2f4dd315b1f4b543fc833bf02e16fab4554093 | e47a4995c1f02d90521f4cdb5a3becdba2520d49 | /man/WLmult_dauer_res.Rd | b475b60463e69024187283dc6d16147274b2e6e7 | [] | no_license | SenguptaLab/MF.matR | 9af3fbf77628250748af1a2a4a4515c09093e936 | c64b0097131c6a2103e9489aa76692ae78085533 | refs/heads/master | 2023-01-24T22:06:28.450669 | 2023-01-12T22:19:19 | 2023-01-12T22:19:19 | 167,600,474 | 0 | 4 | null | 2023-01-12T22:19:20 | 2019-01-25T19:17:28 | R | UTF-8 | R | false | true | 803 | rd | WLmult_dauer_res.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/WLmult_dauer_res.R
\name{WLmult_dauer_res}
\alias{WLmult_dauer_res}
\title{Wrapper for 'Import.WL.data' and 'plot_Residency' to allow multiple datasets to be simultaneously
analyzed. Uses recursive search for a *position.csv file, then makes ... |
31cc5c46eb3063f42a8bd33959b52c127007159c | 0b551347a29f4e01e9273615ce0c5242f9bdb63a | /pkg/tests/testthat/misc/test_simple2.R | 3198fa3c33d0d1a903046860f278ad3f5182bf5a | [] | no_license | timemod/dynmdl | 8088fecc6c2b84d50ecb7d7b762bddb2b1fcf629 | 8dc49923e2dcc60b15af2ae1611cb3a86f87b887 | refs/heads/master | 2023-04-07T21:30:53.271703 | 2023-03-03T13:02:30 | 2023-03-03T13:02:30 | 148,925,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,685 | r | test_simple2.R | library(dynmdl)
source("simple_model_utils.R")
mod_file <- "mod/simple2.mod"
# compile the model
report <- capture_output(mdl <- dyn_mdl(mod_file))
mdl$set_period("2015/2017")
data_per <- mdl$get_data_period()
nper <- nperiod(data_per)
lead_per <- mdl$get_lead_period()
lag_per <- mdl$get_lag_period()
eigvals <- ... |
d2c2a03e859572f0aae5a2901ae55ba130a76228 | 0a4d3bed2892a640ad8d2a6fb77f95212b0ce618 | /code/transmission/deseq2_transmission_mcav_sym.R | 10e202d9cef3b6c3ce45c7021f5a5e770496e095 | [] | no_license | mstudiva/SCTLD-intervention-transcriptomics | ec2b24ad9259628157ad4f5352a4aae6d1f9f4d9 | 1f537dc0d2a4d7317b2d68b910caf670167fdb9a | refs/heads/main | 2023-06-14T01:34:23.522098 | 2023-06-02T15:21:41 | 2023-06-02T15:21:41 | 418,661,181 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,202 | r | deseq2_transmission_mcav_sym.R | #### PACKAGES ####
# run these once, then comment out
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install(version = "3.10")
# BiocManager::install("DESeq2",dependencies=T)
# BiocManager::install("arrayQualityMetrics",dependencies=T) # requires Xquartz, xq... |
e422ea4aeb196e35a3d2cf4bc29b5bd6b24843e6 | 6a4593ac8bb196d85d58b2042aa3cca0fbde0eb7 | /R/anl_2.R | 02feece0d95ba77fa9a744c2b8e8e9cacfc8b8d1 | [] | no_license | whiteaegis/Imputation | 59883addc98378a736d96fea1f2ea6c3d3c9eb77 | f85be4a1f8f94db1e6c6bb847caeb1246e0998f8 | refs/heads/master | 2023-02-25T01:00:06.548131 | 2021-02-02T07:56:28 | 2021-02-02T07:56:28 | 335,208,709 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,043 | r | anl_2.R | setwd("C:/Users/user1/Documents/imputation/anl2.test1")
final<-wwknn_final
sum(final[data.p]==kk[data.p])/sum(zz=="N")
result1<-array(0,ncol(kk),1)
for(i in 1:ncol(kk)){
result1[i]<-sum(final[,i]==kk[,i])/length(zz[,i])
}
result2<-array(0,ncol(kk),1)
for(i in 1:ncol(kk)){
result2[i]<-sum(final[(data.p[data.p[,2]==i... |
ca074633abd346f91b681f422fea89cf9f6566a9 | 5a4fe535d64e16351a3f72e3a2fd934157de95b8 | /R/TBploter.R | 09ac0bf09259f6de668719889d4c7acd64a37436 | [
"MIT"
] | permissive | likelet/PlotAppForTBtools | a94f6cce39ac6a385f487d96de2518ce304d82d4 | 2307ff3c333768cf806059b48d24e3ff4c14e2d0 | refs/heads/master | 2021-01-20T20:52:32.738356 | 2016-08-15T11:47:09 | 2016-08-15T11:47:09 | 65,454,944 | 37 | 2 | null | null | null | null | UTF-8 | R | false | false | 206 | r | TBploter.R | #' Run the default TBploter app for analysis locally
#'
#' \code{TBploter} run TBploter locally
#' @author Qi Zhao
TBploter <- function() {
shiny::runApp(system.file("TBploter", package = "TBploter"))
}
|
4fbbce6e3b54104a10bc0be0e8fd61c1393c862a | 1d193689ccfe0c2fabd8c24b322f7b8613744fea | /Total Price.R | a902fcb247a8a88c87259a2e652177e5d8bce1b0 | [] | no_license | jrjaskol/ITOxygenDenso | 7e4e368395194493cb8c6eaaf8bf6926d62787a2 | 4b7908d57899348c903283450badd6b457b1b184 | refs/heads/master | 2020-08-22T14:42:33.594040 | 2019-12-11T07:59:08 | 2019-12-11T07:59:08 | 216,418,190 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,041 | r | Total Price.R | setwd('~/Desktop/IT_Oxygen')
install.packages('xlsx')
library('readxl')
data <- read_excel('~/Desktop/IT_Oxygen/Product_Data/Product Data.xlsx')
dateArray=data$Date
productArray=data$product
avgPriceArray=data$Avg
price=data$price
price.function <- function(date, price, perContract, perSpot, product.amount, produc... |
373ecdea7c8ad03020f06f4c6c29af1b0f3f2660 | 8de53d7d7b2af23ac04fc82b379e5a9b2a18512f | /R/projection.R | 2bb9f3509c2f97d0503e5f2761e26db0fa5c968e | [] | no_license | KNMI/DutchClimate | 16f05f2ac8452710775d1613d3a755ed4b97511a | 1eb957d85fa7835ad7cebbb0df47bf6bc91957cb | refs/heads/master | 2021-01-21T03:39:13.109562 | 2017-06-25T20:54:13 | 2017-06-25T20:54:13 | 64,229,649 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,438 | r | projection.R | #' Compute daily climatology
#'
#' @param data data.table daily data
#' @param startYear int starting year
#' @param endYear int end year
#' @export
ComputeDailyClimatology <- function(data, startYear, endYear) {
tmp <- data[year(date) %in% seq.int(startYear, endYear), .(tg = mean(tg)),
by = .(month(dat... |
70ccb3cf3978eb8f432816da98ecd6fe3071ab15 | b5bf6c06807aab62869706c4104bb62d50587cbf | /R/get-vcssanova-basis.R | 0dd9ace492290142ebdfe5d63b5a1bfee9448e5e | [] | no_license | weirichd/cautious-guacamole | 39b07c4c0aea014ee9b47293e9ac6046bfba3319 | 0c3e496a9de9b15b9a96c4bfd84300b47d6c9629 | refs/heads/master | 2021-07-12T11:30:38.258113 | 2017-10-18T23:04:18 | 2017-10-18T23:04:18 | 107,332,709 | 0 | 0 | null | 2017-10-17T22:54:26 | 2017-10-17T22:54:25 | null | UTF-8 | R | false | false | 4,791 | r | get-vcssanova-basis.R | get_vcssanova_basis <- function (formula=as.formula("y~x"), data,
type = NULL, wt, subset=NULL,
offset=NULL, na.action = na.omit,
partial = NULL, method = "v",
alpha = 1.4, varht = 1,
... |
59cb1bdcd204bfb06a51ced887d1008c95f712b7 | 866f5a41c375d2c5be9d60d9a5f9a6ad13f96081 | /R/plotVisregList.R | f856b99176eb63811754966ed149567f565d2390 | [] | no_license | pbreheny/visreg | c375126d98b4859423b4d8004c1721b62bf529aa | bc06525c2bc2f612717c9f8caa27434b9ee11a7c | refs/heads/master | 2023-08-03T05:16:58.849754 | 2023-08-01T22:03:15 | 2023-08-01T22:03:15 | 5,400,642 | 62 | 20 | null | 2017-06-23T21:08:24 | 2012-08-13T15:20:12 | R | UTF-8 | R | false | false | 523 | r | plotVisregList.R | plot.visregList <- function(x, ask=TRUE, ...) {
n <- length(x)
prompt.user <- FALSE
if (ask & (prod(par("mfcol")) < n) && dev.interactive()) {
oask <- devAskNewPage()
prompt.user <- TRUE
on.exit(devAskNewPage(oask))
}
for (i in 1:length(x)) {
p <- plot(x[[i]], ...)
if (inherits(p, 'gg')) ... |
63aa3973b645bd1a27926798d0a74ffcba91e92d | a8f4f4647a2718059cf36ee92e773cb3810a1807 | /ui.R | af56221b36783b52b99bdd77431a2c45b06ec139 | [] | no_license | chechuco/data_product_proj | 8ab7949b571ea9bc6f2c1edb4cae724476828b62 | 8517e0641a7259219290e8f108dd3912c16a0323 | refs/heads/master | 2020-12-24T10:16:24.391659 | 2016-02-15T05:59:01 | 2016-02-15T05:59:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 887 | r | ui.R | library(shiny)
shinyUI(fluidPage(
titlePanel("Stock Price Graph and Return"),
sidebarLayout(
sidebarPanel(
helpText("Input a valid stock symbol and date range to examine the price (adjusted for splits and dividends) trend and monthly return."),
helpText("Information source is from yahoo finance.... |
bee6a7d28723007921f7a357df8b2af1a680a686 | c8e71af48d925c34d1cb9f4dad262c970e8968d5 | /man/bac.Rd | e61b3efacdd8754bf41a0c9807de1b9c54a845fc | [
"MIT"
] | permissive | tessington/qsci381 | 43c7cd323ab64cf28ba738be35779157c93e62cf | b981f0bd345b250d42ff5f1c0609e5e61f5911f7 | refs/heads/master | 2022-12-24T20:56:56.045374 | 2020-09-24T20:50:29 | 2020-09-24T20:50:29 | 284,817,926 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 822 | rd | bac.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-bac.R
\docType{data}
\name{bac}
\alias{bac}
\title{Beer and blood alcohol content}
\format{
A data frame with 16 observations on the following 3 variables.
\describe{
\item{student}{a numeric vector}
\item{beers}{a numeric vector}
... |
201ecfe824c3712283af54f56e6e8ffec8870b78 | 4b0cff5e09efd41994db11d589ef3069266ccce4 | /man/generateSignificance.Rd | 3d0de41e80a5c4f7497ab4ff6f76c516a47fc3bc | [] | no_license | cran/Jmisc | 7d43070011ebd9b56327ca8704dacbeeb5e84c2c | 0b141061bedc22bc9c7e7b6fa97dde67066f06a9 | refs/heads/master | 2022-07-13T05:12:17.641540 | 2022-06-22T04:53:25 | 2022-06-22T04:53:25 | 17,680,105 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 711 | rd | generateSignificance.Rd | \name{generateSignificance}
\alias{generateSignificance}
\title{Generate t-statistics, p-value and significance}
\usage{
generateSignificance(x, row_names)
}
\arguments{
\item{x}{A matrix or data.frame}
\item{row_names}{names of row}
}
\value{
a data.frame
}
\description{
Generate t-statistics, p-value and signifi... |
feb52e467de39cef002140db090492fa80ab0e37 | 6483fea671e8edd3bd34446d1cfc787d0f9127bb | /TeamReyLew.R | d01bb18154fa428f6029c630e484da44fb541200 | [] | no_license | sharnsl/ReyLew | f622453122544b12eeb68733e15d566d4fa4b981 | 00cb889938607186c99bd60a0a2c8f1cb234a70a | refs/heads/main | 2023-01-24T09:45:01.995037 | 2020-11-12T04:12:20 | 2020-11-12T04:12:20 | 304,148,941 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 565 | r | TeamReyLew.R | keeps <- c("year", "month", "intent","sex","age","race","education")
guns2 <- guns[keeps]
keeps <- c("Date","Age","Sex","ResidenceCity","ResidenceState","DeathCity","Location","DescriptionofInjury","COD","OtherSignifican")
data2 <- data[keeps]
drugdata <- na.omit(data2)
gunsdata <- na.omit(guns2)
keeps <- c("... |
f5a82b13311c928874b744f432da68133c49f0f1 | 8284e88e32d095f7582001ce62013d7773ff1cc9 | /Desktop/Doctorado/Variabilidad Estructural/FUNCTIONS/ReadHeme.R | 64758912d6d9fce179e82b33d74a59d1293f8b9a | [] | no_license | marialauramarcos/PruebaGit | 4396a5cd95447e227393b92675890d78e637a312 | c849361d5ea37af8fb75364ff515c39bc83461f7 | refs/heads/master | 2021-01-10T07:12:32.456064 | 2015-10-04T18:50:08 | 2015-10-04T18:50:08 | 43,513,416 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 648 | r | ReadHeme.R | #Function that reads a pdb file and returns coordinates of NA, NB, NC, ND and Fe of the Heme group
#A pdb file name and the chain must be specified
readHeme <- function(pdb.fname,chain){
pdb <- read.pdb(file=pdb.fname,het2atom = TRUE)
selNA <- atom.select(pdb,chain=chain,elety="NA")
selNB <- atom.select(pdb,c... |
4b35a51bccd9076b9305e55e972b79269c4c7e87 | e2366c7366dcaa4d86d88d8eb8493aae405f1086 | /plot2.R | 241546ab323bd23d788f217bea5f0b0fa7b27d0b | [] | no_license | gverma14/ExData_Plotting1 | 91e4de1793276169efb8260598dfaca7bd4bc98e | 59d8a5f4c669a639b8fb98fe09d39a10d5c4ea53 | refs/heads/master | 2021-01-17T23:17:38.904569 | 2014-10-11T22:00:53 | 2014-10-11T22:00:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 954 | r | plot2.R | plot2 <- function(filename = "household_power_consumption.txt")
{
#checks if database has already been imported
if (!exists(as.character(substitute(consData)))) {
#if not already imported, imports dataframe into global environment for future use
consData <<- read.table(filename,header = T,sep = ... |
21a149f0e47be6e3440548999c8d62ad34ff9fb1 | f1b9b81a57dad419c7216445b9a75df120a47791 | /R/Class-SDMXAgencyScheme.R | 4353fdea7a511d6ee669ad49d6e152d9702d8536 | [] | no_license | opensdmx/rsdmx | d71dc83799d76da3233ddfc0d4fa75ce5ff097b9 | 3c0c2316ff4fa237cdc62731d379a17369e05ae3 | refs/heads/master | 2023-08-31T01:56:25.934458 | 2023-08-28T09:57:08 | 2023-08-28T09:57:08 | 10,642,895 | 96 | 38 | null | 2021-04-21T20:41:10 | 2013-06-12T13:01:55 | R | UTF-8 | R | false | false | 2,197 | r | Class-SDMXAgencyScheme.R | #' @name SDMXAgencyScheme
#' @docType class
#' @aliases SDMXAgencyScheme-class
#'
#' @title Class "SDMXAgencyScheme"
#' @description A basic abstract class to handle a SDMXAgencyScheme
#'
#' @slot id Object of class "character" giving the ID of the concept scheme (required)
#' @slot agencyID Object of class "charact... |
40ab4f41bfd72518a53e7ab31325ce32ec5cfc8f | 75ac4422811de46609cdcb009dabdaa73203f3cf | /Analyzing_twitter_ sentiments_project/R-Codecs/BagofWords.R | 4b2904f01aed79581c1151815641977dab0a2e94 | [] | no_license | hemanthkannan003/MyProjects | ec170af29b7712dd47f380a1dfdb290af1482ee4 | e43cdb2cadd0c635682cae0264523ab2fa08c2d4 | refs/heads/master | 2021-01-25T06:30:28.869872 | 2018-04-16T21:59:59 | 2018-04-16T21:59:59 | 93,587,382 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,053 | r | BagofWords.R | library(ff)
library(bigmemory)
library(NLP)
library(wordcloud)
library(tm)
library(SnowballC)
library(plyr)
library(stringr)
library(quanteda)
library(FSelector)
input <- read.csv(file="manual_machine.csv",head=TRUE,sep=",")
CleanTweets<-function(input)
Text<-input$text
senti<-input$sentiment
text<-gsub("\r?\n|\r|... |
e3b44abaddcd35c8bd5963d0d84030f79238ddd8 | 037546e5139aedd8522d4b15ed1bde29f5629774 | /Square Lakes Together Armley.R | 3f8d235182f7bc206641d725952e836456288b8e | [] | no_license | Rivers-Project-2018/Jack-Willis | 280761e9783e1525f9a65f9c13a716b35b2e7957 | adca82dd3b278303cc1a23ba1e99494264e3fd94 | refs/heads/master | 2020-04-08T11:59:26.412175 | 2019-03-18T16:58:36 | 2019-03-18T16:58:36 | 159,328,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,090 | r | Square Lakes Together Armley.R | par(mfcol = c(2,2))
plot(1, type="n", xlab="x [m]", ylab="y [m]", xlim=c(0,2150), ylim=c(0,2150))
rect(xleft=0, ybottom=0, xright=172, ytop=2150, lwd=2, col = "blue")
rect(xleft=172, ybottom=0, xright=2150, ytop=2150, col="lightblue", lwd=2)
arrows(x0=0, y0=1075, x1=2150, y1=1075, length = 0.05, code=3)
arrows(x0=172, ... |
114a58d595f2153f40197f9d190b3ece91d1b0b4 | 50a02ea701f5b7b2e1c1dc549c386646e896baa1 | /man/matrix2syt.Rd | 8ca862102868f4efbd10dd77d96950a658c4c55a | [] | no_license | stla/syt | f39a02e1b55e07918120344ab21ae9c8436c3890 | 8976f87401b34a52b1ca2d7f33bf54530db550c0 | refs/heads/master | 2021-06-18T12:29:52.899574 | 2021-01-16T09:47:48 | 2021-01-16T09:47:48 | 140,968,144 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 434 | rd | matrix2syt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/syt2matrix.R
\name{matrix2syt}
\alias{matrix2syt}
\title{Standard Young tableau from a matrix}
\usage{
matrix2syt(M)
}
\arguments{
\item{M}{a matrix}
}
\value{
A standard Young tableau.
}
\description{
Converts a matrix to a standard Young ta... |
e780ea0d492eb6d4f9898b465ca37c227474aae3 | fb0b8c413ae95c961e0351eccb22263a2d0917dd | /man/divisible.Rd | 3f043b9f081a23fbdddd551bc53bf22d095c38f3 | [] | no_license | cran/hutilscpp | f3acfdf2af949df69c8887d937050aa3daf39a02 | 22994140414c52919756eb799ddd10ed4d666f74 | refs/heads/master | 2022-10-14T17:45:09.954044 | 2022-10-07T07:00:02 | 2022-10-07T07:00:02 | 168,961,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 772 | rd | divisible.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/divisible.R
\name{divisible}
\alias{divisible}
\alias{divisible2}
\alias{divisible16}
\title{Divisibility}
\usage{
divisible(x, d, nThread = getOption("hutilscpp.nThread", 1L))
divisible2(x, nThread = getOption("hutilscpp.nThread",... |
2fce555080710e43aaa30436a1dc04ee51ac77e1 | 0fbec50ff92d15c52df3597330356a5f789bf887 | /SigTaxa/ThreeModel_HeatMap.R | 9fccc90ea8b5b2ad62fd52a61e49f3e79b39f498 | [] | no_license | lynchlab-ucsf/lab-code | b54e63f650e68a78d6c28b0b28b3e28835b775bb | 324d273c0afcbfb9edea2f42f9b58e599e0f62fd | refs/heads/master | 2022-05-30T22:27:57.912333 | 2022-05-13T22:37:40 | 2022-05-13T22:37:40 | 182,319,934 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,556 | r | ThreeModel_HeatMap.R | # Three-Model Heat-Map
```{r}
library(extrafont)
# Function to bring in and format all Diff-Abundance Data
make_plots <- function(csvfile,outcome_val) {
read.data <- read.csv(csvfile)
sig.data <- read.data[read.data$qval.best < 0.1,]
sig.data$outcome <- outcome_val
sig.data
}
tip.order <- read.table("/data/Users/kmcc... |
7f9abb172a8ed301a0b75c0bb034b2b7c287a75c | d0ea7cb41a07daafa3a8ecc46525701adfe0b26e | /man/infqnt.Rd | a70d4bfc21359110d87fbc671e5f6a2b1997df58 | [] | no_license | cran/timeslab | 72e988debce4a9805f7cf6987bdf009b685e1b22 | ba1972872f3db49ff069195197c30762767f3835 | refs/heads/master | 2023-04-09T15:02:14.680416 | 1999-01-01T01:20:17 | 1999-01-01T01:20:17 | 17,719,496 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 343 | rd | infqnt.Rd |
\name{infqnt}
\alias{infqnt}
\description{Plot Informative Quantile Function of a Data Set}
\title{Plot Informative Quantile Function of a Data Set}
\usage{infqnt(x)}
\arguments{
\item{x}{Array of length $n$ containing the data.}
}
\value{
\item{infqnt}{returns a plot of the informative quantile
function for the d... |
a4084b1ff47346d93a4aa8dfae547534ba09c06f | d75b7bc015b47d94254bcc9334ba15972d3ec9a1 | /4. FOURTH YEAR/Medical Statistics/2. Special trial Designs/Crosover (Problemas Masculinos).R | 8c2327b816d725b9d5fe0ff4d02d01dee529e50b | [] | no_license | laurajuliamelis/BachelorDegree_Statistics | a0dcfec518ef70d4510936685672933c54dcee80 | 2294e3f417833a4f3cdc60141b549b50098d2cb1 | refs/heads/master | 2022-04-22T23:55:29.102206 | 2020-04-22T14:14:23 | 2020-04-22T14:14:23 | 257,890,534 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 3,295 | r | Crosover (Problemas Masculinos).R | # Para determinar un posible efecto cardiovascular de Sildenafil durante el ejercicio en hombres
# con problemas coronarios, se ha dispuesto un ensayo clínico con intercambio, en la que la variable
# respuesta es un índice de fatiga medido tras una prueba de esfuerzo. El tratamiento (o el
# correspondiente placebo) ... |
8b99bd7234cee755ef915f3171f1d7e8eb87cf34 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610555140-test.R | 19f3aa5e7533c77d6b9d36ece729f75bd2dc3ae9 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 240 | r | 1610555140-test.R | testlist <- list(data = structure(c(2.78671099579809e-309, 2.34365931087967e-308, 1.9285913724733e-168, 2.84809453888922e-306, 0, 0, 0, 0, 0), .Dim = c(3L, 3L)), q = 0)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) |
63a571ba2dcdc1991c6fea371db06e787ce458bb | 9fe6b499985b1573050a3309165b217acaf50034 | /man/p_oneFhatone.Rd | 906e09b84fa1d0fde007cf72035b110dc77843ea | [] | no_license | chvrngit/wmpvaer | efa2f381ee006bf852c23b9869ba63ed391b1799 | d1f006b00e7b6fd1a9943f382f0d55dfe640cfd7 | refs/heads/master | 2022-11-10T21:16:43.369196 | 2019-11-30T21:30:30 | 2019-11-30T21:30:30 | 107,996,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 683 | rd | p_oneFhatone.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/objects_and_functions.r
\name{p_oneFhatone}
\alias{p_oneFhatone}
\title{p_oneFhatone, computation of an approximate 1F1 value}
\usage{
p_oneFhatone(s, n, m, omega)
}
\arguments{
\item{s}{The value needed for the first derivative to e... |
61252276fdedc8623c4efd2cbbfdf378c10c12db | 84ef24f89f4bf70a019783cfb34d553b2199c460 | /MakeDict.r | c7f20e903d377c08437898be9004dcdec826835c | [] | no_license | xxxjvila/rutines | cdfe0d02452e783bdd8fb71929e37a6e283e8543 | 71449c53973bf126bd028432242cbd96d40c8a96 | refs/heads/master | 2020-12-02T08:18:18.843230 | 2017-07-10T17:50:05 | 2017-07-10T17:50:05 | 96,804,033 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 626 | r | MakeDict.r | MakeDict <- function(data){
xdat <- data
value.labels<-NULL
for (i in 1:ncol(xdat)){
temp<-attr(xdat[,i],"value.labels")
if (!is.null(temp)){
temp<-sort(temp)
temp<-paste(paste(names(temp),"=",temp,sep=""),collapse=";")
} else {
temp<-""
}
value.labels<-c(... |
d66bacd29fb8bec422443ffbd29e47d3ab1b91cc | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/5345_7/rinput.R | eed13a767b57f0935573bcdd64a991c2d1f37e7e | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("5345_7.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="5345_7_unrooted.txt") |
c006cc87bb017a52c17b25452a3c89c9009e20ab | 092e6cb5e99b3dfbb089696b748c819f98fc861c | /src/smoothLDS_SS_withOffsetsAndInputs.R | 64aa632de170d8b6b82dbf534c8ec93608e77c57 | [] | no_license | joacorapela/kalmanFilter | 522c1fbd85301871cc88101a9591dea5a2e9bc49 | c0fb1a454ab9d9f9a238fa65b28c5f6150e1c1cd | refs/heads/master | 2023-04-16T09:03:35.683914 | 2023-04-10T16:36:32 | 2023-04-10T16:36:32 | 242,138,106 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,808 | r | smoothLDS_SS_withOffsetsAndInputs.R | smoothLDS_SS_withOffsetsAndInputs <- function(B, u, C, c, Q, xnn, Vnn, xnn1, Vnn1, initStateAt=0, m0=NA, V0=NA) {
if(initStateAt==1 && (!is.na(m0) || !is.na(V0)))
warning("m0 and V0 are not used when initStateAt==1")
if(initStateAt==0 && (is.na(m0) || is.na(V0)))
stop("m0 and V0 are needed when ... |
2202a61ebb3612328afcaca6dd83ac5818c8e9e8 | dc7f48710f8761ee3a7bbb7e43e39b45ee181631 | /PIUMET/Non_Obese_NAFL/05.selecting_diff_features.r | 386aea24403d51efa85f7c4ca43c66307860c695 | [] | no_license | ddasic123/NAFLD | 0d6fe42274a6fe03ddc7812a6d4db3e94aad14e3 | 60997bf4100aed32e3880ea37226c1ea786da33c | refs/heads/main | 2023-09-05T21:09:23.902002 | 2021-11-10T02:44:38 | 2021-11-10T02:44:38 | 419,341,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 727 | r | 05.selecting_diff_features.r | rm(list = ls())
library(data.table);library(plyr);library(igraph)
setwd("d:/data/NAFLD/")
#
metabolite = "lipid_bile"
disease = "non_obese_nafl"
#checked!!!
dat1 = read.table("SNU_DEG_nafld.txt")
gene_up = rownames(dat1)[dat1$logFC > 0 & dat1$adj.P.Val < 0.05]
gene_do = rownames(dat1)[dat1$logFC < 0 & dat1... |
f9dfc8a306f2ab465d65bafec9b5a9c235939fe2 | 68c6d5dee2884a7e361c0633fd9bf158d18df60c | /Exploratory Data Analysis/ex2/plot5.R | 3c84758773047d72a4361c7b24c998568d84f627 | [] | no_license | OlegRozenshtain/datasciencecoursera | 961c56bea1191ba2743646dead70cec932998673 | ef2f464196d48712b5b65cc4ec490cc17b4cbeba | refs/heads/master | 2021-01-10T22:11:06.105728 | 2015-08-02T16:16:53 | 2015-08-02T16:16:53 | 27,956,877 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 2,001 | r | plot5.R | # How have emissions from motor vehicle sources changed from 1999–2008 in
# Baltimore City?
plot5<-function()
{
# read Environmental Protection Agency database on emissions of PM2.5
# for 1999, 2002, 2005, and 2008
emissionsData<-readRDS("summarySCC_PM25.rds")
#read mapping from the source classifi... |
fda55cf644ca24e242015f21461b0c0cbddaeb7e | cb54fbf79c8ddb2c1d2a4fa2404d2e95faa61db3 | /Solution_4.R | 8a5c8a84223029b6bd8549e975b0e765db2cec26 | [] | no_license | abhay30495/CASE-STUDY-Healthcare-Org | 139cdc4714fc752cd779c5ee986e811ecbabe4a9 | 1838d9e1ad3d11000e357ad3fe1b8108097270c4 | refs/heads/master | 2020-05-19T03:36:55.563598 | 2019-05-03T20:03:14 | 2019-05-03T20:03:14 | 184,806,122 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,885 | r | Solution_4.R | ##Question 4
> View(diabetes)
> View(diabetes_1)
> diabetes_1=diabetes[c(1:133),c(3,8,9)]
> View(diabetes_1)
> library(caTools)
> data=sample.split(diabetes_1,SplitRatio = 0.8)
> train=subset(diabetes_1,data=="TRUE")
##Warning message:
##Length of logical index must be 1 or 133, not 3
> test=subset(diabete... |
e7a0485d55b9e7815ab0fc9aad97962f5c7b8b9e | e11e91e1e46d0577f429a20c7af304e3b35f7d40 | /TrafficDataCleansing.R | a53d65d43fec36492f9901174c4f993cff104d87 | [] | no_license | viveks13/Traffic-Analysis | 6820af3ce6e6acf1cca84aa1d1920890e4f3426b | 649990d4aae987bf9e03f778a1b885994bcddaa2 | refs/heads/master | 2020-03-19T00:28:33.704757 | 2018-06-28T12:53:16 | 2018-06-28T12:53:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,099 | r | TrafficDataCleansing.R | library(ggplot2)
library(tibble)
library(dplyr)
library(reshape2)
library(scales)
library(leaflet)
library(lubridate)
path = "D:\\NUIG Project Data Set\\Census Data Set\\Traffic data after 2012"
setwd(path)
file.names <- dir(path,pattern = ".csv")
newDf <- data.frame(Route = "X",Yeartaken = 2011,PeakAm = ... |
805766c16dd99f0d60a10ee88efcc46a0a4fcdd7 | 0b3e8045987a9565a20231ea1fdd4dddf9eafb15 | /code_figures/figure_image.R | 29099597ff0f844132f50da458f1bc9bbe973762 | [] | no_license | wangxsiyu/Lu_Drought_Identification | 75972680c607f3d35261413f6a51a221c40c9fe5 | caed8465c7b31f245534d772df60457333bb6d36 | refs/heads/main | 2023-04-02T17:21:34.193557 | 2021-04-19T22:28:50 | 2021-04-19T22:28:50 | 349,785,695 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,152 | r | figure_image.R | library(rstudioapi)
funcdir = dirname(rstudioapi::getActiveDocumentContext()$path)
setwd(funcdir)
data = read.csv("../../data/runoff/runoff.csv")
{
library(fields)
### image
flname = "f001"
starty = 1957
endy = 2020
#### obs
obs = data
idxx = which(obs$year >= starty & obs$year <= endy)
obs = obs[idxx,]
obs_q = obs[,f... |
f9f549b124415e4ca55118161a69f7efc65222b9 | e0b87eb63633d601f3e9e23e35b62b72835f091c | /R/nl_corrts.R | eec9d2fec4ad8df6cda5b0e3d9a201f5f6a4c28c | [] | no_license | Allisterh/nlr | 7737c85aa80485f5173469d2479d89be72c427ba | 7f45b89f1748d356af588920b6f7ae2a5269f80d | refs/heads/master | 2022-04-02T20:53:11.162698 | 2019-07-31T11:40:02 | 2019-07-31T11:40:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,609 | r | nl_corrts.R | #******************************************************************************************************
nl.corrts <- function(formula, data, start=getInitial(formula,data),
control=nlr.control(tolerance=0.0010, minlanda=1 / 2 ^ 10, maxiter=25 * length(start)),
correlation=NULL,...)
{
tols1 <- nlsqr(... |
382a504dcba29823254cd4f41c8a3832e63e211d | d406b9068f635eed507ed21f000aaa55e3ec034c | /man/okun.Rd | fb67ea8b25cb62c98ccb1119fb5f0c2e285ab6a0 | [] | no_license | Worathan/PoEdata | 273eab591e56b08252dff3f681c1c9e0b34f7d79 | d415eb1b776b04c29ee38d58bbbd1bf37ef92eb1 | refs/heads/master | 2020-04-20T11:10:02.167677 | 2019-02-02T08:35:48 | 2019-02-02T08:35:48 | 168,808,539 | 0 | 0 | null | 2019-02-02T07:53:41 | 2019-02-02T07:53:41 | null | UTF-8 | R | false | false | 657 | rd | okun.Rd | \name{okun}
\alias{okun}
\docType{data}
\title{
Okun Data
}
\description{
Obs: 98, quarterly (1985Q2 - 2009Q3)
}
\usage{data("okun")}
\format{
A data frame with 98 observations on the following 2 variables.
\describe{
\item{\code{g}}{percentage change in U.S. Gross Domestic Product, seasonally adjusted.}
\i... |
eeba378f9b3a90e923269198c5b1c9a2af90333b | 738af78c24b08b63b4fdbd947ea2b2a5dc34d0ce | /geo2r-volcano-rot.R | 8f2b97829febdcde41e9ea1f78a18a027c1d7b2c | [] | no_license | pklemmer/rotenone-geo2r-volcano | b09fd04ebc1037408fd39564af6482bf3b40c850 | 1f156b893fe4d70df02be0a2b808ffbca8d75111 | refs/heads/main | 2023-04-08T06:12:08.345859 | 2022-06-22T13:25:49 | 2022-06-22T13:25:49 | 495,457,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,912 | r | geo2r-volcano-rot.R | sessionInfo()
install.packages("readr")
install.packages("ggplot2")
install.packages("ggrepel")
install.packages("svglite")
library(readr)
#Installing required packages
setwd("~/GitHub/rotenone-geo2r-volcano")
xp1 <- read_delim("Expression/8_dmso_vs_8_rot50_12.tsv",
delim = "\t", ... |
4423dd8a084a381fe47d6e1149cd60c43bbbe31a | 45701b348dcbc54758a61aa381ea9f7821c2cb49 | /inst/app/ui/data_tab.R | ff30b39fdc2549eeb1f2d5d1a494c1f11ccc0996 | [
"CC-BY-4.0"
] | permissive | Rstat-project/markr | 11c58642185d922de3fe948fb0f7e7de5f3cdfdd | 72299eb48613a898ca1c97f50fa27fe3f07c3c77 | refs/heads/master | 2023-06-02T18:43:04.504269 | 2021-06-16T11:36:10 | 2021-06-16T11:36:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 848 | r | data_tab.R | data_tab <- tabItem(
tabName = "data_tab",
h2("Data"),
fileInput("tbl_file", NULL, width = "100%", placeholder = "Load Table"),
fluidRow(
column(2, downloadButton("dl_tbl", "Download")),
column(10, radioButtons("tbl_yml_switch", NULL, c("table", "yaml"), "table", inline = TRUE))
),
fluidRow(
... |
8071c3d00d92b74914edc40572063bc701775d52 | 72d364a45b3ffbe7f1fcfdc33e9bf125b5d122e9 | /dh/R/strings.R | 08370a6de16c5fdff0eef6b6b4ac48bbd15c8442 | [
"MIT"
] | permissive | dhaase-de/dh-r-dh | 53a5281080ea196e349a90e92b0eb2bfda0cc4ae | a4f067f1bfb1ac1337c024d2b82361b5370068b0 | refs/heads/master | 2021-01-11T17:20:51.563117 | 2017-01-23T00:59:48 | 2017-01-23T01:00:31 | 79,754,214 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,236 | r | strings.R | # deparse and substitute an expression ("turn symbol into string")
"desub" <- function(expr) {
deparse(substitute(expr))
}
# fills strings to a given length (vectorized)
"fill" <- function(v, to.length = 4, with.character = "0", from.left = TRUE) {
v.fill <- character(length(v))
remaining <- to.length - s... |
b6c7e083d1c81a6911698b39c893adfadb3f1cbe | f50a745235da540a8a28299da8007f5a4c417fb8 | /old.R | 4a13fe3aa722409b330ce1c46140f3e63fa99052 | [] | no_license | lara-maleen/Project_Fur_Seals | b26d039f73282665cee9fdf07b7d90e8e1084dd3 | e4ed571c794bd94123c65476ca443ee11cbb4ae4 | refs/heads/master | 2021-08-29T17:59:56.758010 | 2021-08-23T14:42:33 | 2021-08-23T14:42:33 | 178,859,144 | 0 | 0 | null | 2019-06-18T10:32:13 | 2019-04-01T12:33:22 | R | UTF-8 | R | false | false | 39,901 | r | old.R | #Minimal Model IBM - Fur Seal Project
#Hypothesis 1: Marginal-Male Theory
#Code for running in cluster (without replicates and storing information in matrix every t)
rm(list=ls())
##### START SIMULATION.RUN-FUNCTION #####
simulation.fun <- function(time=100, #number of generations
age=15, #a... |
590232d924089a5d9c9528378cdb39251b152dc5 | 7f93d02173ea31f52112f8ced858c9b9aa839094 | /LIME/script_LIME.R | 87a0833cbb6801889e8f0b134310b1ab9ab1745e | [] | no_license | KunWang-Fishery/DataPoorMethods | c9274449ffb0d15066dbb7b0a0da9587e5438d0c | 2ee26e5dc02afb8511a164c9eb47023a675cf49a | refs/heads/master | 2023-04-21T20:43:49.174102 | 2021-05-05T15:38:42 | 2021-05-05T15:38:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,538 | r | script_LIME.R | ##############################################################
########### Run length based models LBSPR and LIME ###########
########## mpons@uw.edu - September 2018 #####################
##############################################################
rm(list=ls())
########
R.version$os # to check how lucky you a... |
63c3f6ace5bec05eab27d0ded4c458fb10034fa2 | 6e2334f8dca54e7a54e71b8790475cbb7bd354d2 | /plot2.R | 27e857cf981f107a80f9b4d8635ebf8082ea766d | [] | no_license | sashaxp/ExData_Plotting1 | df040be4120364fdb8d2341f81f131febc2efaa9 | e2e95b2feb91e4e7f2aa50a29778d17c1f748e44 | refs/heads/master | 2021-01-18T11:57:57.470538 | 2015-01-18T14:10:10 | 2015-01-18T14:10:10 | 29,423,270 | 0 | 0 | null | 2015-01-18T11:09:50 | 2015-01-18T11:09:48 | null | UTF-8 | R | false | false | 493 | r | plot2.R | dat <- read.table("household_power_consumption.txt", header=T, sep=";", na.strings="?")
dat$Date<-as.Date(dat$Date, format="%d/%m/%Y")
dat2 <- dat[ which(dat$Date>='2007-02-01' & dat$Date<='2007-02-02'), ]
## Converting dates
datetime <- paste(as.Date(dat2$Date), dat2$Time)
dat2$Datetime <- as.POSIXct(datetime)
plot... |
b6a4987eb9ca70e4c6d2238a0345f4c2c494ec8f | cb3b2c16ff49b1e87bfaddab62a3ecb2fbf67e1e | /man/hbic_med.Rd | 4afb5f569b5740154dbe4ecb136aab9bccac00e0 | [] | no_license | seonjoo/smm | 466668bbe89c8ed7d48f27d2827c5803d790e9a3 | cf0eff3d803550be0a58a21cfba36ec1da84eb11 | refs/heads/master | 2021-06-05T09:42:34.788365 | 2021-02-09T14:50:17 | 2021-02-09T14:50:17 | 134,604,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 341 | rd | hbic_med.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hbic_med.R
\name{hbic_med}
\alias{hbic_med}
\title{Function to do HBIC computation for multiple mediations model}
\usage{
hbic_med(fit, fit.n)
}
\arguments{
\item{fit.n}{}
}
\value{
}
\description{
Function to do HBIC computation for multipl... |
95f3a10524df3235bcaf4d16115000476541fc2a | a9c610bd87d4270f1ce8fa5a16c1d374b02cb419 | /app/install.r | 57a3793c40cafd69f38d56ccb269839429569df8 | [] | no_license | johnjung/clustering_workshop_tool | 7eee343d1e042ad68c474ab87a0c66536e319175 | da0a55cac20ffc6ab43a21a7a3503027b3c0e403 | refs/heads/master | 2020-12-22T13:01:58.788996 | 2020-03-04T22:20:33 | 2020-03-04T22:20:33 | 236,791,607 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 533 | r | install.r | install.packages('GGally', repos='https://ftp.ussg.iu.edu/CRAN/')
install.packages('ggdendro', repos='https://ftp.ussg.iu.edu/CRAN/')
install.packages('ggplot2', repos='https://ftp.ussg.iu.edu/CRAN/')
install.packages('igraph', repos='https://ftp.ussg.iu.edu/CRAN/')
install.packages('svglite', repos='https://ftp.ussg.i... |
e96a0d771986f8f54c86ed9b925d67e8012ceaee | cfc6a45c07d8f73165930dbaf10ceb2578aa9f8e | /Other tools/Weyerhauser_huc12_temp_data_pull.R | 240f5c8d18cd53637f8a3755d7297b43ce688f0c | [] | no_license | TravisPritchardODEQ/IR2018 | b9deae2c794ecb7b53f3fc64e5293ab1fe750043 | 94baee642789cce9f8e49771d28ff45e61ca310f | refs/heads/master | 2021-06-21T22:21:06.892038 | 2020-12-10T16:07:05 | 2020-12-10T16:07:05 | 137,256,651 | 2 | 0 | null | 2020-03-24T16:13:43 | 2018-06-13T18:41:05 | R | UTF-8 | R | false | false | 1,899 | r | Weyerhauser_huc12_temp_data_pull.R | # This code generates temperature assessment results and temperature data
# in response to a request from Weyerhaeuser
# The provided a list of HUC12 and wanted watershed units from those HUCs
# Saving this script due to the function to extract HUC12 from WS AUs
# and combining the assessed data with station informatio... |
12385e0477ebaa8042bf3014d057bc4f5807ae12 | 5f2da1bef657a78b702a1a963ecf293388b6bb2a | /man/eval.cart.Rd | 67e858163bafc1df15b136db93fe84c0caf4eaf5 | [] | no_license | cran/delt | d311a4453dbe8e232cc31d9c824fdc97bd669a11 | 351c15327bf12fa06add21b4a2ec6b57d39fada6 | refs/heads/master | 2021-01-20T12:06:20.790793 | 2015-06-02T00:00:00 | 2015-06-02T00:00:00 | 17,718,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,361 | rd | eval.cart.Rd | \name{eval.cart}
\alias{eval.cart}
\title{ Calculates a CART histogram }
\description{
Calculates a CART histogram.
The estimate is represented as an evaluation tree.
An CART histogram is a multivariate adaptive histogram
which is obtained by pruning an evaluation tree of an overfitting
histogram.
}
\usage{
eval.cart(... |
d566219d6d47b5506557dda8b01ddee882253f4f | 6053b45fed30ced7465aa0589c33749781571e9e | /bugs/nimble-normal-linear.R | 58a655afacaf33863240482415ddd350ef37d132 | [] | no_license | Freshwater-Fish-Ecology-Laboratory/model-templates | d8627a84f62c8a3ce6198c2d5f8f7f8a6021a16e | 563eb5b45b898455a262d8074d1f67bcb0e63d50 | refs/heads/main | 2023-08-27T19:34:16.876560 | 2021-11-06T00:16:25 | 2021-11-06T00:16:25 | 406,216,920 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,819 | r | nimble-normal-linear.R | ### Simulate river temperature in relation to discharge
source("header.R")
discharge <- runif(100, 0, 50)
bDischarge <- -0.2
bIntercept <- 25
bSigma <- 2
x <- bIntercept + bDischarge*discharge
temperature <- rnorm(100, mean = x, sd = bSigma)
data <- data.frame(Discharge = discharge,
Temperature = ... |
d97aadefde0e0ed48181e4cedbb9e272e4d75b2c | 2567e5f2f0400ed30e8a3aa2f777968e080a3ee6 | /Kaggle-SantanderCustomerSatisfaction/kaggle_randn_sample.R | 5062e620ce5faa05a9a7a0cbba6837b923d4c0a3 | [] | no_license | xlhtc007/Practice | 90b9261ce59451e6a3f823ed6bd6645ccd6fb2c7 | 32b73c3c8b219d58317187178626c6176ebee026 | refs/heads/master | 2020-04-07T11:50:03.873855 | 2018-08-14T14:43:33 | 2018-08-14T14:43:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,150 | r | kaggle_randn_sample.R | train_feature <- createDataPartition(y = df_train_x$TARGET, p = 0.7,
list = FALSE,
times = 1)
df.train <- df_train_x[train_feature,-1]
df.test <- df_train_x[-train_feature,-1]
df.test%>%count(TARGET)%>%mutate(pct =n/sum(n)) # 0.03876173... |
b3ff06552e12cf2095bbb913f05f06b90f126dd6 | 7c39da976f28af016e5b1f847e68473c659ea05d | /man/fuseCDS_Rlist.Rd | 5b08623dc73c8ff124316d76286dfcb54b513a54 | [] | no_license | cancer-genomics/trellis | b389d5e03959f8c6a4ee7f187f7749048e586e03 | 5d90b1c903c09386e239c01c10c0613bbd89bc5f | refs/heads/master | 2023-02-24T05:59:44.877181 | 2023-01-09T20:38:36 | 2023-01-09T20:38:36 | 59,804,763 | 3 | 1 | null | 2023-01-11T05:22:52 | 2016-05-27T04:45:14 | R | UTF-8 | R | false | true | 775 | rd | fuseCDS_Rlist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fusion-utils.R
\name{fuseCDS_Rlist}
\alias{fuseCDS_Rlist}
\title{Extract the CDS involved in each rearrangement of a RearrangementList object}
\usage{
fuseCDS_Rlist(rlist, jxns)
}
\arguments{
\item{rlist}{a \code{RearrangementList}}
\item{jx... |
ea097e1005fc16f7bed2eb4ca3664bc75facbad0 | 455e0abb21d3fc00025a6c082c6555815ad137bf | /lue-dependencies_cross-validation.R | 582864e83261abd0ab69f7285f744bc925381b66 | [] | no_license | kjbloom/lue-controls-publication | c8dd343030b7af23223e7a5d5f60c398fbcb5ef0 | 4b15d1758cd000a6368c37f083a7a8b868647f41 | refs/heads/master | 2023-04-11T21:57:59.292740 | 2022-11-01T09:53:26 | 2022-11-01T09:53:26 | 560,121,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,317 | r | lue-dependencies_cross-validation.R | ###########################################################
### Cross-validation of the final empirical model ###
### Supplementary figure 6, Bloomfield et al. 2020, GCB ###
###########################################################
## load the necessary packages:
library(lme4)
library(reshape2)
# Read in t... |
13cc14141e1238b982804929ef98d63ba3866c99 | 6e33bd44e1245ba2a6e57077047865d04fabf6bf | /man/estimate_bias.Rd | 48afb458e732b1f88db1c741f98cf5471ffacb20 | [
"MIT"
] | permissive | mikemc/metacal | 6914b0c599af4e45a19637381dfd06cfb9e49fb2 | f56792d02bd722ab16c1ed215b07c1187829a226 | refs/heads/main | 2022-02-16T05:06:09.983748 | 2022-02-15T00:59:59 | 2022-02-15T00:59:59 | 192,036,279 | 16 | 3 | null | null | null | null | UTF-8 | R | false | true | 2,739 | rd | estimate_bias.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estimate.R
\name{estimate_bias}
\alias{estimate_bias}
\alias{estimate_bias.matrix}
\alias{estimate_bias.otu_table}
\alias{estimate_bias.phyloseq}
\title{Estimate bias from control measurements}
\usage{
estimate_bias(observed, actual, ...)
\m... |
fa299f9e35a26642e55848126e244608a12b4756 | 649b5fcbe310f5a31e5deeae99d97c9782afe10b | /tests/testthat.R | b7a97db57f40faf46523fe177933a11f13bd1e7e | [] | no_license | sepkamal/powers | faa8809de0aa10b3379a52c5704a96b1c0cc8be4 | f5700e4f2af05b6ec84df07b9de1f0f1980c8f04 | refs/heads/master | 2021-08-22T04:23:29.056650 | 2017-11-29T07:42:25 | 2017-11-29T07:42:25 | 111,840,771 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 71 | r | testthat.R | library(testthat)
library(powers)
library(dplyr)
test_check("powers")
|
14173fa522678412ca8343d9928040b09949741a | 18ed091bdc4c2dac3728249710985d6b6da9c48f | /ui.R | d002228361aafa4cc4713276c7c7950615e5db34 | [] | no_license | smosqueda/shiny-pagespeed-graphing | de52c11cea8b32379ef39298dd9928efec02c228 | 6c96893ea56db935cb28d168f3c60966a1281efa | refs/heads/master | 2020-06-24T17:06:34.893807 | 2016-11-24T02:55:54 | 2016-11-24T02:55:54 | 74,631,600 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 703 | r | ui.R | library(shiny)
#source("helper.R")
#library(survival)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
# Application title
titlePanel("PageSpeed Stats"),
fluidRow(
column(2,
wellPanel(
sliderInput("days", "Number of Days:",
min = 1, max ... |
a501a7b087d01d4b4c0938582b648edf76368317 | 87bda86c8f157f8eb02bb6eac56621d20009625f | /R/kiss-rule.R | ef3d054a75fb202d13a4f4dc8ce31a4c3f33c966 | [] | no_license | jack-palmer/kissr | 3da4935b3b4260703b2132ea85d01668b4e4936d | f57b22665e9fd597e45454122c50eb32556d1c36 | refs/heads/master | 2020-12-28T23:51:00.347963 | 2016-07-18T17:32:27 | 2016-07-18T17:32:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,473 | r | kiss-rule.R | #' \code{KissRule.Event} objects are used to define target segments to run a report on.
#' @param eventId Which event are you targeting on? We need the index or id.
#' @param frequencyValue How many times the event needs to have happened for the
#' rule
#' @param frequencyOccurance How we are comparing against the
#'... |
9c19d7edb31aeffdbbb255143fc422cd3cdfae2e | c4bd48cc8156e85212ca2d6ef522a8ea7b318aeb | /R/samp_tab.R | 3acc7b897c98862a8644e791b9e83f57520a9e5b | [] | no_license | ErlendNilsen/HFP | 9af4007e8baf2bb5587ddf3c96fdfa93ea822592 | 8ceb7415fcb2d264edea0be17fb6037dba887042 | refs/heads/master | 2020-07-09T02:57:43.875307 | 2019-08-23T07:14:23 | 2019-08-23T07:14:23 | 203,856,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,593 | r | samp_tab.R | #' Data table for sampling effort
#'
#' Generates a data table with an overview of the LineID's, stratification
#' level ('År' or 'Områdenavn') and the effort (distance taxated) for each LineID.
#' @param strat Level of stratification - 'No', 'OmradeNavn' or 'Year'
#' @keywords table sampling effort
#' @export
#' @exa... |
3734c6571fb395dfce61eb9ea394a99ece556935 | f0feee021d3b59eaf0b223ea2bc57d4173bb25da | /man/eq_map.Rd | ef1961733c969db861055f0750af4824a43229bc | [] | no_license | Liddlle/capstone | e8446896dc845d19c2c36936c9238b5a1e095596 | a6be26260f4895a0741db5ff6eb2ab223f69b54e | refs/heads/master | 2020-05-18T17:43:17.665636 | 2019-05-30T15:11:01 | 2019-05-30T15:11:01 | 184,564,063 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,258 | rd | eq_map.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/leaflet.R
\name{eq_map}
\alias{eq_map}
\title{Mapping the earthquake epicenters
The function maps the epicenters (LATITUDE/LONGITUDE) and annotates each point
with in pop up window containing annotation data stored in a column of the data fra... |
60acefdd2bfa3e4c8a8e930798bda6984a35e7e0 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Sauer-Reimer/ISCAS89/s05378_PR_2_5/s05378_PR_2_5.R | 1d5ec04cfc6b650d15ede7ecb3739fb2cc158c3f | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 65 | r | s05378_PR_2_5.R | c300bde402bcb07b3a1592139e17e864 s05378_PR_2_5.qdimacs 4996 14064 |
dc92352cefaac3b96f03a3259b875a7b317cadab | 7c65bcebea70b5769503833ef4a8472e18caabdd | /plot1.R | 9b38bdc929758ee0c4731579b11ca20474e3de73 | [] | no_license | brodo80/Exploratory-Data-Analysis-Project-1 | edb76802501c9db11374e0e328b66c059127f4f8 | 0c0490486472532278559be367d6fec77ea919d0 | refs/heads/master | 2020-12-06T19:56:43.476978 | 2016-09-10T22:39:43 | 2016-09-10T22:39:43 | 67,897,549 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 864 | r | plot1.R | url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url,"//Txdf2fpw01cbtp/txcbt-redirected/smb001/Downloads/data.zip")
unzip(zipfile="//Txdf2fpw01cbtp/txcbt-redirected/smb001/Downloads/data.zip", exdir = "//Txdf2fpw01cbtp/txcbt-redirected/smb001/Downloads")
data ... |
f6bcd52152044a6217b76b262215884ddaa64ffb | 709d477a2fe8c61ebe2b0d5fe63084e0a59e4322 | /R/style.R | f1dd53321d9b0926ad9c3cd6d17316c02abcdcde | [
"MIT"
] | permissive | kshtzgupta1/ramlegacy | 37caa01dfc8bd0eb9476a22aa6fef3d1f11beb34 | 4d56b90cf838e3915a590404059ca8f08d2e4ce1 | refs/heads/master | 2020-05-07T16:44:48.277304 | 2019-04-11T01:19:13 | 2019-04-11T01:19:13 | 147,247,622 | 1 | 2 | NOASSERTION | 2019-03-23T01:54:47 | 2018-09-03T19:55:57 | R | UTF-8 | R | false | false | 302 | r | style.R | completed <- function(msg) {
packageStartupMessage(crayon::green(cli::symbol$tick), " ", msg)
}
not_completed <- function(msg) {
packageStartupMessage(crayon::red(cli::symbol$circle_cross), " ", msg)
}
notify <- function(msg) {
packageStartupMessage(crayon::blue(cli::symbol$star), " ", msg)
}
|
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