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a61465e81ae3231c5a2a089152ca56338f09b1c6 | 8547aaa8ce45122e12ecf8862e52b331f6d54dcd | /math_modeling/hw/hw1/lakebedr.R | 0d6abb9a167be5739a021c0689792cd99ff2d9d4 | [] | no_license | sjyn/LahTech | fad9f3356afa18678900a742c749a829a21dac9f | adb5130e210c76e9371d34c2a4cb2a255b429d39 | refs/heads/master | 2021-01-15T15:43:50.843145 | 2016-12-07T14:55:09 | 2016-12-07T14:55:09 | 43,650,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 104 | r | lakebedr.R | #!/usr/bin/RScript
data <- read.table('lakebed.txt')
ml <- lm(data[,3] ~ data[,2] + data[,1])
print(ml)
|
e98e776b245d3b20d4f2d130ce59a1de42f1e9d0 | 707291bd32b30b00ffb1bc935913dc08aef5616f | /lecturenote/pcor-bcondo.r | 5dc657d1b3324e2fe9661a961a7d649db287d754 | [] | no_license | weininghu1012/STAT306 | b7e2e7e6e0d9362338c65a33e0fdd9619c31acea | a2a35eea51b520e1f050d9daad6eb9fc0551f593 | refs/heads/master | 2021-01-01T18:18:12.987529 | 2015-04-08T01:18:17 | 2015-04-08T01:18:17 | 29,931,606 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,033 | r | pcor-bcondo.r | # partial correlations for Burnaby condominium data set
# Assume s is a sample covariance or sample correlation matrix
# with (possible) row and column names, this function outputs
# partial correlation of first two variables given the rest.
pcor=function(s)
{ i=1; j=2
i1=c(i, j)
i2=1:nrow(s); i2=i2[c(-i, -j)]
s... |
4b25de6c8ca31e2b4fb959d55c31f3e533cf8e8b | 653aedf1b27c27d421dc36278a71058dfdead905 | /inst/extdata/GO/set5-1/treemap.R | 665d04620ee970276cc22116b29789a57d1b475b | [] | no_license | 18853857973/rnaseq.mcf10a | 44dcc4d5e99ad8ec5c0ae41b273be3041a9e528a | b14c3fb67eb9d85051f77daf9418cf5531950097 | refs/heads/master | 2020-03-14T15:38:35.022209 | 2015-10-13T15:11:26 | 2015-10-13T15:11:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,436 | r | treemap.R |
# A treemap R script produced by the REVIGO server at http://revigo.irb.hr/
# If you found REVIGO useful in your work, please cite the following reference:
# Supek F et al. "REVIGO summarizes and visualizes long lists of Gene Ontology
# terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021800
# author: Anton Krat... |
b951ab6b7a255a180d90023c489fdf2cc45c5aca | e6d16cc0cedbf0994ed6bc4dd2ad1bf8c97d4dda | /data-raw/spec/vignettes.R | 75ac72349dc0f3a1527cbe361ed92a4410279c8a | [] | no_license | Musaab-Farooqui/qss-package | b7e695f3258bdccddae8f28f8d90921eb9544f8d | 753787f2263e002c4b9d0ef39d1d01d84f737e5b | refs/heads/master | 2023-05-26T05:14:42.643740 | 2021-06-05T14:11:05 | 2021-06-05T14:11:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 154 | r | vignettes.R | cols(
self = col_integer(),
alison = col_integer(),
jane = col_integer(),
moses = col_integer(),
china = col_integer(),
age = col_integer()
)
|
721cb9c3cf5e1395af52262c0f6bed3a0a8f3649 | d7ff71e8ffb07419aad458fb2114a752c5bf562c | /tests/testthat/roxygen-examples-complete/16-dont-warn-empty-out.R | 64118f44082bb74351068bb3a98a1fb55a0603b5 | [
"MIT"
] | permissive | r-lib/styler | 50dcfe2a0039bae686518959d14fa2d8a3c2a50b | ca400ad869c6bc69aacb2f18ec0ffae8a195f811 | refs/heads/main | 2023-08-24T20:27:37.511727 | 2023-08-22T13:27:51 | 2023-08-22T13:27:51 | 81,366,413 | 634 | 79 | NOASSERTION | 2023-09-11T08:24:43 | 2017-02-08T19:16:37 | R | UTF-8 | R | false | false | 100 | r | 16-dont-warn-empty-out.R | #' Do stuff
#'
#' Some things we do
#' @examples
#' g()
#' \dontrun{
#' f(x)
#' }
#'
#' @export
g()
|
4f7e440bfe7ccf8e687fffb9bef888aca3c4a8de | 97c2cfd517cdf2a348a3fcb73e9687003f472201 | /R/src/QFPairsTrading/tests/testModifiedFuturesPair.r | 5728bba98120814e2e0a89c657bfc7fdd25646ee | [] | no_license | rsheftel/ratel | b1179fcc1ca55255d7b511a870a2b0b05b04b1a0 | e1876f976c3e26012a5f39707275d52d77f329b8 | refs/heads/master | 2016-09-05T21:34:45.510667 | 2015-05-12T03:51:05 | 2015-05-12T03:51:05 | 32,461,975 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,455 | r | testModifiedFuturesPair.r | library(QFPairsTrading)
testdataPath <- squish(system.file("testdata", package="QFPairsTrading"),'/ModifiedFuturesPair/')
tempDir <- squish(dataDirectory(),'temp_TSDB/')
readZooFile <- function(filename){
return(read.zoo(filename,format='%Y-%m-%d',sep=",",header=TRUE))
}
testConstructor <- function() {
... |
caef93e79b199e32574f0c44cc645d57231fd5b6 | 14c2f47364f72cec737aed9a6294d2e6954ecb3e | /man/isAnnotated.Rd | adde62df947d03af4a6296bd2f70f1bb23d7cb74 | [] | no_license | bedapub/ribiosNGS | ae7bac0e30eb0662c511cfe791e6d10b167969b0 | a6e1b12a91068f4774a125c539ea2d5ae04b6d7d | refs/heads/master | 2023-08-31T08:22:17.503110 | 2023-08-29T15:26:02 | 2023-08-29T15:26:02 | 253,536,346 | 2 | 3 | null | 2022-04-11T09:36:23 | 2020-04-06T15:18:41 | R | UTF-8 | R | false | true | 474 | rd | isAnnotated.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllGenerics.R, R/AllMethods.R
\name{isAnnotated}
\alias{isAnnotated}
\alias{isAnnotated,EdgeObject-method}
\title{Is the object annotated}
\usage{
isAnnotated(object)
\S4method{isAnnotated}{EdgeObject}(object)
}
\arguments{
\item{object}{An ... |
4276eabe50067b0200b842503c2631f8f45c3f1f | 8bdd8e6f050b118f661d357f626d43feeb383de4 | /man/OutputPlotForPaper.Rd | 2a5211e49432b1894f4ea656c4973084e5e88d98 | [] | no_license | StatsResearch/RobsRUtils | 34a545bfa2eada90b805ce638541840fcad2f9c1 | 8d4ff91b17b40837475302e8a04d31d3f08d04db | refs/heads/master | 2021-01-09T06:40:13.639503 | 2018-01-15T21:05:59 | 2018-01-15T21:05:59 | 81,020,969 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,128 | rd | OutputPlotForPaper.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RobsRUtils.R
\name{OutputPlotForPaper}
\alias{OutputPlotForPaper}
\title{Wrapper function for plotting ggplot2 in a more general output format}
\usage{
OutputPlotForPaper(plot.dir, plot.folder, filename, plot.obj = NULL,
plot.width = NULL, ... |
9ef86b9f0c5fb3ff6a342ebb478b3b294adb48e8 | 45aebfdd9d491ce87ed4121737f6a5d892bc7646 | /tests/testthat/test_ossvm.R | 1ca54a8651b30f939097a7ae67a4f421cae711f1 | [
"BSD-3-Clause"
] | permissive | schiffner/locClass | 3698168da43af5802e5391c3b416a3ca3eb90cbe | 9b7444bc0556e3aafae6661b534727cd8c8818df | refs/heads/master | 2021-01-19T05:21:57.704770 | 2016-08-21T19:25:12 | 2016-08-21T19:25:12 | 42,644,102 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,109 | r | test_ossvm.R | context("ossvm")
test_that("ossvm: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(ossvm(formula = Species ~ V1, data = iris, wf = "gaussian", bw = 10))
# wrong class
expect_error(ossvm(formula = iris, data = iris, wf = "gaussian", bw = 10))
expect_error(ossvm(iris, data = iris, wf = "g... |
873fad83dec87f8855fa8317c0bfc3029f20d826 | 454a2e5c87a170b9bcfe0fd2b11516b90dcc1b05 | /tests/testthat/testrun.R | baecea7e86af6c081ed4fb1565090f449ded9b5b | [
"LicenseRef-scancode-generic-cla",
"MIT"
] | permissive | test-mass-forker-org-1/CausalGrid | 1bec395e2bb68d12cf3c1e4f87d15650b1adec15 | 1aba80502457c211dbfa2099fcef91f97a4fb74f | refs/heads/main | 2023-06-03T04:20:03.314112 | 2021-06-23T18:43:50 | 2021-06-23T18:43:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,523 | r | testrun.R | # To run in the command-line with load_all: change do_load_all=T, then run the code in the first if(FALSE), subsequent runs just run that last line of the False block
# To run in the command-line with load_all: then run the code in the first if(FALSE), subsequent runs just run that last line of the False block
library... |
9513925bc9de0aa6e38b30560bf632f7dd67a263 | 6b32948c7241e204753cb88999c76cee323b4736 | /TransMetaRare/R/SKAT_2Kernel_Ortho_Optimal_Each_Q_GridRho1.R | b9964a2e8fedc5b1112a1c32091ad7cb24d4975c | [] | no_license | shijingc/TransMetaRare | e9a0e74fef68bdfb59f35741b1e777afa23b1e44 | 5206b4a880c07b2d5df3b8d26a4bf0d6f88d77a6 | refs/heads/master | 2020-03-19T10:24:07.058000 | 2018-06-06T18:05:12 | 2018-06-06T18:05:12 | 136,367,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,566 | r | SKAT_2Kernel_Ortho_Optimal_Each_Q_GridRho1.R | SKAT_2Kernel_Ortho_Optimal_Each_Q_GridRho1 <-
function( Q.all, rho2, rho1, z1.rho1is0, z2.rho1is0, Z1.rho1is0, Z2.rho1is0, z1.rho1is1, z2.rho1is1, Z1.rho1is1, Z2.rho1is1, Phi.tld, Phi.tld.hf, n.Resampling.Copula){
n.r<-length(rho2)
n.q<-dim(Q.all)[1] - n.Resampling.Copula
n.total = dim(Q.all)[1]
pval.davis <- ma... |
16c4ccc96245e9886290613430d5943e59b2811e | 40c65fff3847662ce46d2afd73acf8b68b785107 | /tests/testthat/test-check_timestep_by_date.R | 7df6e15763cd50dbd48c7f1934545da1596cd6ca | [
"MIT"
] | permissive | epinowcast/epinowcast | b4d4562603938e9a184d3450d9387f92908cd6bc | 98ec6dbe3c84ecbe3d55ce988e30f8e7cc6b776d | refs/heads/main | 2023-09-05T18:19:10.985900 | 2023-09-05T12:13:49 | 2023-09-05T12:13:49 | 422,611,952 | 23 | 5 | NOASSERTION | 2023-09-14T09:57:09 | 2021-10-29T14:47:06 | R | UTF-8 | R | false | false | 2,714 | r | test-check_timestep_by_date.R | test_that("check_timestep_by_date() handles dates and groups correctly", {
# Create a dataset with two date columns, two groups, and multiple reference
# dates for each report date
obs <- data.table::data.table(
.group = c(rep("A", 50), rep("B", 50)),
report_date = as.Date(rep(
rep(seq(as.Date("2020... |
17dc5ea6d5260c0c694c53ea6b658750684ad0d0 | c459dd32d88158cb064c3af2bc2ea8c7ab77c667 | /tumor_subcluster/calculate_scores/calculate_Epithelial_scores_EMTmoduledown_wPT.R | b0e73beabd47246feb991e3918e0a1f2d1f0c606 | [] | no_license | ding-lab/ccRCC_snRNA_analysis | d06b8af60717779671debe3632cad744467a9668 | ac852b3209d2479a199aa96eed3096db0b5c66f4 | refs/heads/master | 2023-06-21T15:57:54.088257 | 2023-06-09T20:41:56 | 2023-06-09T20:41:56 | 203,657,413 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 3,259 | r | calculate_Epithelial_scores_EMTmoduledown_wPT.R | # set up libraries and output directory -----------------------------------
## set working directory
dir_base = "~/Box/Ding_Lab/Projects_Current/RCC/ccRCC_snRNA/"
setwd(dir_base)
source("./ccRCC_snRNA_analysis/load_pkgs.R")
source("./ccRCC_snRNA_analysis/functions.R")
source("./ccRCC_snRNA_analysis/variables.R")
## set... |
732082a0201dd741211c0e229e1f1b16776e7159 | 7e323ebc12c514729ff4df23ff7fe6d8d2c3e395 | /R/na.mean.R | 7084c3b54a358c23e142fba369166eba034aca17 | [] | no_license | einarhjorleifsson/fjolst2 | 05fc78df588d4d749983dde53123e28effaad9f6 | a7385f789086e1e8c8e00452aa001e3dbc0259a2 | refs/heads/master | 2021-01-19T09:44:53.857406 | 2015-07-15T11:46:04 | 2015-07-15T11:48:54 | 39,079,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 341 | r | na.mean.R | #' Internal function
#'
#' @param v1 xxx
#' @param v2 xxx
na.mean <-
function(v1, v2)
{
ind <- c(1:length(v1))
ind1 <- ind[is.na(v1) & !is.na(v2)]
ind2 <- ind[is.na(v2) & !is.na(v1)]
v <- (v1 + v2)/2
if(length(ind1) > 0)
v[ind1] <- v2[ind1]
if(length(ind2) > 0)
v[ind2] <- v1[ind... |
a1ef3bedfebb982edc6bfc18d4968e9e07eb2266 | d28025f79c4ec3bbf4e73c350b71ae99441dfbe7 | /R/showData.R | c19e934ab0e9a47f855e9b0a6484ba00a47d6d75 | [] | no_license | BioversityCostaRica/ClimMob | 1dcdbe99623290eb55a13129e34531577eae5787 | dcf4a4405ca2eaaadd183481fa9899e86c29f734 | refs/heads/master | 2020-04-06T03:34:27.177664 | 2015-08-13T17:18:31 | 2015-08-13T17:18:31 | 40,671,361 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 886 | r | showData.R | .showData <- function(la,datalocal)
{
# Read the texts messages from the file MultilanguageShowData.txt
tt <- as.matrix(read.delim(system.file("external/MultilanguageShowData.txt", package="ClimMob"), header=FALSE, encoding="UTF-8"))
colnames(tt) <- NULL
# Check if myData matrix of data exists
if(!exists("... |
e299551ae25d9036e89e8cf4b4bd06bdd97a96d4 | 684d0b2e106b2284eaebd194ee3a692f4e379b0a | /lab1_special_plot.R | d3838f7699eb4aeebee39a9d88007b01e8db7f3b | [] | no_license | snakepowerpoint/Multivariate_Statistical_Analysis | 39dd3f87a88187983ba1759f280081488bcc13fa | 816c855c6fad5dd934e6a702ff90ef9a25109df6 | refs/heads/master | 2021-04-26T22:25:06.221341 | 2018-03-06T14:21:39 | 2018-03-06T14:21:39 | 124,088,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | lab1_special_plot.R | setwd('D:/MyGitHub/Multivariate_Statistical_Analysis/data')
t16<-read.table("T1-6.dat")
library(lattice)
xyplot(V2 ~ V4, data = t16,
groups = V6,
type = c("p", "smooth"), span=.75,
auto.key =list(title = "Iris Data",
x = .15, y=.85, corner = c(0,1),
border = TRUE, lines = TRUE))
xyplot(V3 ~ V5, data = t16,
groups = ... |
b81967a137566169c29c9e52adf420fddfa927d2 | 04236ab1161ec860ab3b1d0c3225fcbdc54923a3 | /2020.d/2.racine-carre.d/racine-carre.R | 50a4c30365c01822774cf968f105f04bfdb46e62 | [
"MIT"
] | permissive | homeostasie/petits-pedestres | 957695cdb8a7823ed2e3fe79f7b441410928cba9 | 557c810e26412bc34ebe063dcd904affe5a27855 | refs/heads/master | 2023-05-31T03:35:13.365160 | 2023-04-26T21:47:56 | 2023-04-26T21:47:56 | 163,504,589 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 584 | r | racine-carre.R | NombreMax = 10000000
nombreTest = c(0:NombreMax)
racineCarre = sqrt(nombreTest)
partieEntiere = floor(racineCarre)
partieEntiereCarre = partieEntiere^2
resteCarre = nombreTest - partieEntiereCarre
Zettaleaf = function(n,a){n + a*(2*n+1)/(4*n^2+2*n+a)}
testRacineCarre = Zettaleaf(partieEntiere, resteCarre)
ecart = racin... |
139a08e310b2e58fadf5542dea740dc169c5f564 | cafb54d209345a987d5f090dcc88c5a0bbd02757 | /man/ECLDedUp-class.Rd | ae79eaa4096bfd729c14be8bfc388b684c5e3b53 | [] | no_license | cran/rHpcc | 70290f02bfbd0e079cb7e7ebefb88bc08c6a9f9d | 7c0e3fb4fa2e87de5b1a3676e4e9bd8949602304 | refs/heads/master | 2016-09-05T17:37:48.534715 | 2012-08-13T00:00:00 | 2012-08-13T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,424 | rd | ECLDedUp-class.Rd | \name{ECLDedUp-class}
\Rdversion{1.1}
\docType{class}
\alias{ECLDedUp-class}
\title{Class \code{"ECLDedUp"}}
\description{
Creates an ECL "DEDUP" definition.
The DEDUP function evaluates the recordset for duplicate records, as defined by the condition parameter, and returns
a unique return set. This is ... |
892db2952631392ad8bd01a569a393de0d55579a | 1ca988fbe3bc59eb676996102941b94207ca0885 | /R/SBML.R | ee4eee0125e4ea6c81e6c0128e185b538c093fe4 | [] | no_license | cran/rsbml | d281dd9ff82ac212d0e4ef1e16461ae2563e580b | acc1b1366d3d54aa68db5d591deb249278b15202 | refs/heads/master | 2021-01-16T18:42:11.273477 | 2007-04-11T00:00:00 | 2007-04-11T00:00:00 | 17,719,367 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,119 | r | SBML.R | setClass("SBML", representation(level = "integer", ver = "integer", model = "Model"),
contains = "SBase", prototype = list(level = as.integer(2), ver = as.integer(1)),
validity = function(object) rsbml_check(object))
setGeneric("level", function(object) standardGeneric("level"))
setMethod("level", "SBML", functio... |
b0c9cfb330d0f4100f37e338eda1f5b10d03a28b | c592a22262174d6c671fb48a82c0d19be5ef7e20 | /man/ajv.errorsText.Rd | 356a296451a1ef53566467f78d24b30ad18bb081 | [] | no_license | cran/ajv | 4596a33934c1fc28a52eefb4140632e6c6613e2f | cf385ce8af33f2e0470856f8050850076de27edc | refs/heads/master | 2021-01-20T01:22:26.654576 | 2017-04-24T15:23:22 | 2017-04-24T15:23:22 | 89,262,865 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 892 | rd | ajv.errorsText.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ajv_methods.R
\name{ajv.errorsText}
\alias{ajv.errorsText}
\title{A wrapper for the Ajv.errorsText method}
\usage{
ajv.errorsText(this)
}
\arguments{
\item{this}{An AJV instance, provided implicitly when called via \code{my_instance$... |
beb904249fcc50d713c472e148ea195dd7ee01de | e55a20ae844186b0ae773da1f94fe4425133caf9 | /gmaps/gmaps.R | d2ecacb16c2b3f9eac7d5d1a8a7ee8571bc29627 | [] | no_license | Teebusch/datathon2018 | 1c2ef90500344b3aa1ebed6ea43bc16fd0ef3ffd | d09a4e3c22664fab3f4a0f33027e7a725e194a5a | refs/heads/master | 2021-03-24T09:13:07.571475 | 2018-02-24T22:05:19 | 2018-02-24T22:05:19 | 121,769,729 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 400 | r | gmaps.R | #Adding Google Maps
center = paste(min(df$latitude)+(max(df$latitude)-min(df$latitude))/2,
min(df$longitude)+(max(df$longitude)-min(df$longitude))/2, sep=" ")
map <- get_map(location = center, zoom = 10, maptype = "terrain", source = "google")
ggmap(map) +
geom_path(data = df, aes(x = longitude, y =... |
713a9ab64531b4184d00990ea6fcbd92abd3cebf | afcda04b51a9dc6c91442cde4095d88aa6736f1d | /man/mat_list_dir.Rd | 4fc53121a2021c8f55e56230508a5fd3815b4a8d | [
"MIT"
] | permissive | MatthieuStigler/matPkg | 0f3cca09842d9d8bca40470ac7ca6fdd976c8d0d | 545df24b8a730d63d674945aef321e60a102b016 | refs/heads/master | 2023-07-07T03:31:46.306634 | 2023-06-26T14:05:03 | 2023-06-26T14:05:03 | 168,240,066 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 546 | rd | mat_list_dir.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/list_directories.R
\name{mat_list_dir}
\alias{mat_list_dir}
\title{List files in a directory}
\usage{
mat_list_dir(path, pattern = ".R", recursive = TRUE, add_ext = FALSE, ...)
}
\arguments{
\item{path}{dir path}
\item{pattern, recursive}{pa... |
ee9cabd5adf7158424e109fa39910db2dfd848ce | 1e659affda4579642682050ba86d440e4724ac15 | /R/mypackage.R | 9c8a3d75df5bc5db90925fbf2b52ab593ac85cea | [] | no_license | Chengwei94/llrRcpp | b19762de68e0d4039dfc13baaf6cadc441c0b22c | 58e970184298e62014152663ae2e24da73d162ca | refs/heads/main | 2023-04-30T03:05:57.232335 | 2021-05-11T10:50:37 | 2021-05-11T10:50:37 | 307,415,631 | 0 | 0 | null | 2021-05-05T06:48:25 | 2020-10-26T15:20:40 | C++ | UTF-8 | R | false | false | 124 | r | mypackage.R | #' @useDynLib llrRcpp, .registration=TRUE
#' @import Rcpp
#' @importFrom graphics lines
#' @import metaheuristicOpt
NULL
|
47b44cabce6d69ec79ca615db344850d3d0b1b7d | 01d151bf3c209dcb7aa83e2ed1222fbf47de6e3b | /Tau/exploratory.R | 3f9a785afb5b74e2acdb07afa875a351acd5f816 | [] | no_license | alexsanjoseph/Kaggle | d303a545cae45cf9cd49d003e7dcad213a051452 | bb2c7a92ad44dc9ff6db8d1ef19d4e73dd24c350 | refs/heads/master | 2021-01-21T17:03:00.114934 | 2017-12-19T04:01:11 | 2017-12-19T04:01:11 | 38,832,773 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,783 | r | exploratory.R |
source("bootstrap.R")
library(xgboost)
source("Tau/evaluation.R")
dir_name = "../Kaggle-Data/tau/"
train_file = paste0(dir_name, "training.csv")
test_file = paste0(dir_name, "test.csv")
check_agreement_file = paste0(dir_name, "check_agreement.csv")
check_cor_file = paste0(dir_name, "check_correlation.csv")
sample_su... |
d172080ee8981e6cb45d4c60730bc8d15709a863 | 818081fbffe4388a449d3510895294a220212eca | /ciudad-real/histograms.R | 8fb17f07995aedc92541edf8c751b96d10cd519c | [] | no_license | RubenCantareroNavarro/covid19-routes-survey | 86a709342f5762a8b3f8aa5b6df207221a4e7bf1 | bc110bf062c1b829ed8f8ad94fece201a70f0ea8 | refs/heads/main | 2023-04-20T10:25:05.462218 | 2021-05-05T15:07:09 | 2021-05-05T15:07:09 | 305,677,583 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 819 | r | histograms.R | # https://bookdown.org/jboscomendoza/r-principiantes4/histogramas.html
# https://estadistica-dma.ulpgc.es/cursoR4ULPGC/9c-grafHistograma.html
# http://matepedia-estadistica.blogspot.com/2016/05/histograma-con-frecuencia-relativa.html
library(readxl)
case_1_summary <- read_excel("/home/ruben/Escritorio/caso_1.xlsx")
#V... |
7e1c0c3a779cb16ba62c53fce7a8540631160550 | 53b7280e5902c81e8e23b2eb7c842e269c776e9d | /plot3.R | aaa84de888f82fb208594d9869b113cc94945ddf | [] | no_license | mikeburba/ExData_Plotting1 | b1a3e402d817e62cd612275f0c2de23cf0ca0053 | 3b311d1a9f0639c113bbb932d6adddae5a0e664e | refs/heads/master | 2021-01-15T11:02:08.151732 | 2015-07-12T21:53:22 | 2015-07-12T21:53:22 | 38,962,541 | 0 | 0 | null | 2015-07-12T13:16:21 | 2015-07-12T13:16:21 | null | UTF-8 | R | false | false | 398 | r | plot3.R | plot3 <- function(data) {
with(data, {
plot(DateTime, Sub_metering_1, type="n", xlab="", ylab="Energy sub metering")
lines(DateTime, Sub_metering_1, col="black")
lines(DateTime, Sub_metering_2, col="red")
lines(DateTime, Sub_metering_3, col="blue")
legend("topright", lty=1, col=c("black", "red", "... |
0b42829878f2208abd7665025569edf0ed03a9aa | 7a766f8e81afb68f686a1c37e9bfcc7bea6a4948 | /application/test3x3.rd | 9c361e1d083621cd8b646954ce79bb7cffc56184 | [] | no_license | guidocalvano/ThoughtWeave | 8dc82828d849c634ecb61e6149249acb69dca746 | d426a920fbddec39604da4126b04313406ce562e | refs/heads/master | 2021-01-01T18:08:02.752481 | 2010-11-16T00:14:06 | 2010-11-16T00:14:06 | 1,083,584 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 174 | rd | test3x3.rd | 0.15862346878971767 0.5519442537113229 0.10169057318091146 0.21594283109872103 0.982064880271643 0.2351744985160451 0.5999709168135455 0.1746795603860024 0.03724900146367904 |
0f8569a9af65384ff68db9bae8e835e04615ad25 | ad345ec40bc18b2a7685b0c4d127aab6e8963ff8 | /3.r | 8b77fa21d036555ccd8202631bd914d4d06aa4b2 | [] | no_license | izeh/i | d3b01469e763e15e39d4e08de12d8c4dd911aaba | c5f3c96a268843c7fc92f331b587361bba327170 | refs/heads/master | 2021-06-19T04:47:41.461105 | 2019-07-14T04:30:52 | 2019-07-14T04:30:52 | 102,161,352 | 2 | 6 | null | null | null | null | UTF-8 | R | false | false | 679 | r | 3.r | source("https://raw.githubusercontent.com/rnorouzian/i/master/i.r")
C = beta.id(.6, .8, .6) ; options(warn = -1)
curve(dbeta(x, C$a, C$b), n = 1e4, axes = FALSE, lwd = 2, yaxs = "i", xpd = TRUE,
xlab = "Proportion of preference for (B)", ylab = NA, font.lab = 2)
axis(1, at = axTicks(1), labels = paste0(axT... |
0439d7d7bbc6a1349a53bead27b213f3c6c07e4b | 75928a1e86e09c05d0baf89cf13cca389a67aa1e | /man/metR.Rd | 31979754551ee996a49e119a3c58ee365b382ab8 | [] | no_license | eliocamp/metR | da2e478190a27e08d6f534a508558ccd0dc0975e | b26ff96a4b0d3c94e880ae13f68f87f7a06a78fd | refs/heads/master | 2023-08-31T18:03:26.430340 | 2023-03-25T14:14:33 | 2023-03-25T14:14:33 | 96,357,263 | 146 | 27 | null | 2023-07-02T11:36:32 | 2017-07-05T20:09:40 | R | UTF-8 | R | false | true | 1,560 | rd | metR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metR-package.R
\docType{package}
\name{metR}
\alias{metR}
\alias{metR-package}
\title{metR: Tools for Easier Analysis of Meteorological Fields}
\description{
\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
... |
30262ae0b505c98f20a4c0461ded7185cb029b82 | bcfb6ac4dcc3feb9f4761a272c39bb22ef58afec | /BrexitFrame.R | 9f01a07828accb281794a68b91a9c63d5f20b9dc | [] | no_license | PolPsychCam/Twitter-Brexit-MoralFoundations | dc70b825e95f864b92f843ad486072c8ee04a21c | fc472ffb83413a90b89f040892a97b42800d5b7c | refs/heads/master | 2022-12-31T01:38:19.164791 | 2020-10-20T12:50:52 | 2020-10-20T12:50:52 | 279,547,513 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,576 | r | BrexitFrame.R | #### Denotes code segment
## Explains process below
# Provides note
#### Directory, Files, Libraries####
##Clears the environment, sets the working directory, calls the libraries
rm(list = ls())
setwd("C:/Users/jaack/OneDrive - University Of Cambridge/Summer Political Psychology/Brexit Twitter/Data")
library... |
0e9c5e6137aa25259a2d76d57d8d7fe9c1c4de67 | 4a558dc177db28e3e2b1dd9f1ccf3426e6eb79c9 | /sunday 2014_08_03/stats.R | 4ebf2e8211d757ede362fbde7fa14392d777f215 | [] | no_license | cmcoffman/rlgl | 3dc46562c027f69bfd89d6b9be6aa517e780529e | d7649d1703e8cd9f1cdc38b20afceecb0418c5d3 | refs/heads/master | 2016-09-05T15:22:29.613025 | 2014-08-05T15:57:59 | 2014-08-05T15:57:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 104 | r | stats.R | #stats
lm0=lm(GFP~green.intensity*OD.600+plate+factor(row)*factor(column), data=cshl.all )
summary(lm0)
|
0815b6492d152ff96577f247371353c5a7f20ddd | cf75f57c49e44070bfb93e04b28e9386a2d2783a | /man/itree.Rd | 3e4d23ba63ce3e1db44672f429333bce0febe36c | [] | no_license | ficol/ZUM | f7975b259f2991a56fe4fd14cff5278051ed9953 | 7e9d3ce484ff47e6246f2c5e6719668c66385114 | refs/heads/main | 2023-08-17T08:12:40.055435 | 2021-08-08T03:48:17 | 2021-08-08T03:48:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 675 | rd | itree.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iforest.R
\name{itree}
\alias{itree}
\title{Builds isolation tree}
\usage{
itree(X, max_depth, curr_depth = 0)
}
\arguments{
\item{X}{data to create tree}
\item{max_depth}{maximal depth of tree}
\item{curr_depth}{current depth of tree}
}
\v... |
e115eae608dc774cf1734ef4430c415ea56fc92b | e766292e77e01e5fb3fcb1ae8bdbab7562a1f113 | /src/resources/datacleaner.r | 39d3f6d462acfb0fc954ab3d9b8d5541b824f16c | [] | no_license | kiskacsa08/DiplomaServerSide | af8f7580a165110166265655c7899299599e2a27 | e3a16b9ee5bbec7fbcc39b5fd5d4342b5ebd3504 | refs/heads/master | 2021-01-10T08:25:17.374465 | 2016-04-27T15:57:44 | 2016-04-27T15:57:44 | 49,012,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,607 | r | datacleaner.r | library(RJDBC)
TEAMIDS <- 0:22
# Convert result string to result class (1=home win, 0=draw, -1=away win)
result2class <- function(goals){
if (is.na(goals)) {
return(NA)
}
# Convert to string
goals <- as.character(goals)
# Split the string
g <- strsplit(goals, "–")
# Convert to number
g <- as.numer... |
efa6bf3620fd3c837201ee491c6c30c58860d60b | e5ef3cde1e45a34dfb6388d9b934231bb4ea929a | /Assignment 2 code.R | 5a0dc672c0e323587987bc6867a8fa6c5e01ddf8 | [
"MIT"
] | permissive | gopala-goyal/music-records-project | 25935a9ae9b1c4500ffee95fd016996d41742f16 | 990dc8c3539d09c37f2c4a3b48793bdc66ff6724 | refs/heads/main | 2023-07-14T23:34:59.773819 | 2021-09-09T19:45:13 | 2021-09-09T19:45:13 | 402,879,513 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,743 | r | Assignment 2 code.R | # Source of data and code: Dimitris Bertsimas @ MIT
MusicRecord<-read.csv("Documents/Study/MMA 2022S/867 - Predictive Modelling/Music-records-project/MusicData.csv") #load data
# How many songs does the dataset include for which the artist name is "Michael Jackson"?
table(MusicRecord$artistname == "Michael Jackson")
... |
edc7aaead48779da348375a357e1ebd0dcf2848a | 0045a18d56af8eb1d47d935da6c4dbede9ac9d72 | /Lyon-Cours_2017-11-09 Methodes allocation.R | b93ed6d0dc8bf95bf8a85f67208c9e6e15ce4972 | [] | no_license | emarceau/TheoRisque2018 | 040a82c3f5179a7fde97dae751804251ae1c6400 | 89867759f9edb309317415d4d239a24b254fb7c3 | refs/heads/master | 2020-04-11T15:56:47.194147 | 2019-11-11T12:18:30 | 2019-11-11T12:18:30 | 161,908,259 | 0 | 0 | null | null | null | null | ISO-8859-2 | R | false | false | 938 | r | Lyon-Cours_2017-11-09 Methodes allocation.R | # Lyon A2017
# jeudi 9 novembre 2017
#
# methode allocation Euler
# simulation
# TVaR et VaR
# X1,...,Xn = indépendantes
set.seed(2017)
nsim<-100000
nrisk<-3
matU<-matrix(runif(nrisk*nsim),nsim,nrisk)
X1<-qgamma(matU[,1],2,1/10)
X2<-qlnorm(matU[,2],log(20)-0.5,1)
X3<-qgamma(matU[,3],0.5,1/40)
S<-X1+X... |
b8b213f0f62b9638c424d068f63989e9dc7bf1a8 | f1556a59213e9dafb25db0d01760a1443c55b6b2 | /models_old/LGBM_01/functions.R | b1ae77738c2c721df934c4d1af690d946f94d380 | [] | 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 | 7,287 | r | functions.R | source("functions.R", encoding = "utf-8")
# レシピの作成
create_recipe <- function(data) {
recipe <- create_feature_engineerging_recipe(data)
recipe %>%
# 不要項目の削除
recipes::step_rm(
id,
video_id,
title,
publishedAt,
channelId,
channelTitle,
collection_date,
# tags... |
4952cb5c8a86075aefe0234d33d6ca5838e6d71b | e59a11834b12ffc260d068b8478416beac8adb5d | /R/sa_functions.R | f98d79fcc71f171d8f6a9151be9eea54966e38ce | [] | no_license | slevu/garel | c9f3020a35f4653695cfd9af1b421aca9a21f758 | 50ef9d6234cc1d0627c2ff1cea55d40af0270094 | refs/heads/master | 2020-03-20T02:02:40.739987 | 2019-02-28T11:58:11 | 2019-02-28T11:58:11 | 137,097,441 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,242 | r | sa_functions.R | #' Compute infector probabilities on clades
#' @param tr phylo tree
#' @param parms_SA Parameters from \code{\link{get_sa_parms_range}}
#' @param p proportion of subtype represented (useless)
#' @return dataframe(donor, recip, ip)
#' @details \code{phydynR::phylo.source.attribution.hiv.msm} wraps \code{phydynR::phylo.s... |
f96aeec4613104adf3d03e9cf118bf13261cb9d7 | 9564d47ab4bd212cc73a35432f4d780e2c227873 | /Ngram Builder.R | 429400b4c7ce7ae0c88cfa11978949517563fee0 | [] | no_license | ronaldyeo/Data-Science-Capstone-Project | c2f831838a6cc6cbbcb4a22c47567d0706dc4346 | 58287879da66ea745bfe8b24c544f99398b72c81 | refs/heads/master | 2023-03-17T18:01:49.983659 | 2021-03-07T11:36:03 | 2021-03-07T11:36:03 | 345,294,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,171 | r | Ngram Builder.R | library(tidyverse)
library(quanteda)
library(lexicon)
library(tidytext)
library(data.table)
#Set seed for reproducibility
set.seed(2)
#Read files
en_US.news.txt <- file("./en_US.news.txt.gz", open = "r")
en_US.news <- readLines(en_US.news.txt, encoding="UTF-8", skipNul=T)
en_US.blogs.txt <- file("./en_US.blogs.txt.... |
c5c8245c94c782f0833a2b9f13519f3177ca5559 | 0eb25213e0641b6502707aaa5a0006be777aaea5 | /man/colorschemer.Rd | 02bcc7de7251569dca620b0f1feba73daac5525e | [] | no_license | tlcaputi/gtrendR | 5a720dbd82a4d2c045300ef7316a3101308ec413 | 01b3db0fb35b9add4cebdc8eb7170d9494d5d0ef | refs/heads/master | 2022-11-10T00:54:56.121774 | 2022-10-30T21:33:06 | 2022-10-30T21:33:06 | 249,570,129 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 310 | rd | colorschemer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{colorschemer}
\alias{colorschemer}
\title{Colorscheme}
\usage{
colorschemer(col)
}
\arguments{
\item{col}{A data frame that includes a column `timestamp`}
}
\description{
Colorscheme
}
\examples{
colorschemer("red")
}
|
24799d7f3a1974cf0b7a58e7ee5758470dada7bf | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/HIV.LifeTables/examples/mortmod.45q15.Rd.R | d4c32b86e16f79afd48255e0e8130ef91a273d70 | [] | 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 | 317 | r | mortmod.45q15.Rd.R | library(HIV.LifeTables)
### Name: mortmod.45q15
### Title: Age-specific mortality rate model as a function of HIV
### prevalence, child mortality (5q0), and adult mortality (45q15)
### Aliases: mortmod.45q15
### Keywords: models misc
### ** Examples
mortmod.45q15(child.mort=0.06, adult.mort=0.20, prev=2.5)
|
b03206d5ad5a27de8c82b6f0f92ba4e4e792c163 | ba1edf30bca6e023562e4aed21c0ca009d22f431 | /models/biocro/R/get.model.output.BIOCRO.R | 851c69224a2ac638f227de51878d698125c99438 | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rgknox/pecan | 79f080e77637dfb974ebb29313b5c63d9a53228e | 5b608849dccb4f9c3a3fb8804e8f95d7bf1e4d4e | refs/heads/master | 2020-12-27T20:38:35.429777 | 2014-05-06T13:42:52 | 2014-05-06T13:42:52 | 19,548,870 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,756 | r | get.model.output.BIOCRO.R | #-------------------------------------------------------------------------------
# Copyright (c) 2012 University of Illinois, NCSA.
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the
# University of Illinois/NCSA Open Source License
# which accompanies this d... |
63480c4f5e1453e814f7c04ac75e9a6ddb0fe1b0 | c6ad1a79050cc22a8bae99b5517dcf78b03a08cf | /R/02-perturbations/00_wrangle-envision.R | fd9dacd75f5e3444f003cab360a65b5d8414bb4d | [] | no_license | evertbosdriesz/cnr-selective-combos | 0235c1e74ed23f56c115e1f92c98944046cee0d7 | ebbc365bf17f9304ec98c394327d4ba806bff2f9 | refs/heads/master | 2023-04-18T03:36:04.072694 | 2022-05-30T14:23:34 | 2022-05-30T14:23:34 | 321,024,454 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,791 | r | 00_wrangle-envision.R | # Tidy and Normalize the Envision data
#
# Normalization: Treatment - POA/(DMSO - POA)
library(tidyverse)
library(readxl)
library(here)
library(stringr)
# Get the well annotations
annot <- read_tsv(here("results", "perturbations", "well-annotations.tsv"))
mapping <- read_tsv(here("results", "perturbations", "treatmen... |
7e75e2b03f01abb2df200771d49d907d75676d7b | 97ad56b218663daeff7cfe2518c4b60139d3050a | /preprocessing_code/retrosplits_preprocess.R | a8805401ebcb668e4a89557833983d0ac89b0c78 | [] | no_license | katieshan/baseball | 37b900f425fef9e69997c11141c1cb17d8f5f614 | 1584872ceb6a68be13d391e059f557f142b93c89 | refs/heads/master | 2020-04-26T07:04:45.657187 | 2020-04-21T20:18:42 | 2020-04-21T20:18:42 | 173,383,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,563 | r | retrosplits_preprocess.R | setwd("C:/Users/580377/Documents/Personal/GitHub/baseball")
setwd("C:/Users/Katie/Documents/GitHub/baseball")
source("preprocessing_code/calcpoints.R")
library(tidyverse)
library(lubridate)
#this function calculates modes
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
#T... |
97a7c1722080537adab4d51d3a2a5117bed7b5f3 | 7152e85ab884aff7001b56c6dd927e14a2811ff1 | /data-science/data-science-with-r/Dataframe_operations/Data_Frames_Operations.R | 352e40bc22af3347f54586bc4a4476dbfc640f81 | [] | no_license | gautam-kumar-22/Data-Science-With-Python-R | 29685878abc3501803637216dd225f5d6f1f33ff | 3c0e4e26ec0886623f904dcbb1d3e7883393a497 | refs/heads/master | 2023-04-16T11:50:39.686462 | 2021-04-21T09:37:40 | 2021-04-21T09:37:40 | 360,201,702 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,574 | r | Data_Frames_Operations.R | ############################
# Data frame operations #
############################
## Extracting data from a table data
## Working directory .
getwd()
setwd("/home/labsuser/2021/Jan_16/Dataframe_operations")
getwd()
## Extracting data from an excel
library(readxl)
#install.packages("readxl")
my_df <- re... |
da1872822e16dcef88fb113971214e0e07fd0d0a | b01b94db7226001798b33fe627eac033b2be0773 | /group3project/man/loglik.pom.Rd | 3d4c23b0dd85a1bca2ba17ee418d6cda67f526ec | [] | no_license | kennyitang/BIOS735-Group3 | f793f22a827f3efcefa0c26f4a90c38bd12e15ad | 10e298447f4b938c7d3c9e1f5cbc2ffb8bcee8e2 | refs/heads/master | 2021-01-02T11:27:49.567359 | 2020-04-27T14:55:02 | 2020-04-27T14:55:02 | 239,602,553 | 1 | 0 | null | 2020-04-23T21:34:36 | 2020-02-10T20:07:47 | HTML | UTF-8 | R | false | true | 942 | rd | loglik.pom.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pom_loglik.R
\name{loglik.pom}
\alias{loglik.pom}
\title{Log likelihood of proportional odds model}
\usage{
loglik.pom(y, X, param)
}
\arguments{
\item{y}{a vector of ordered factor responses with J levels.}
\item{param}{current values of th... |
21f7ce609d07140c1f47a1ebd1aba543c0b74b8c | 3aac8d9944540359b72123e8dd7ad6bf9b05b707 | /plot2.R | b538aa4148ffafc0e0d23f3c04216dbb204f1aeb | [] | no_license | jose-barrera/ExData_Plotting1 | 7a1e1393dcdf0245ef7fa6d698cc059289f6a4a0 | 4b4517c7e85674baef7b34bdbf376e6dd4094e18 | refs/heads/master | 2021-01-15T17:28:21.035838 | 2015-02-08T22:19:08 | 2015-02-08T22:19:08 | 30,501,177 | 0 | 0 | null | 2015-02-08T18:40:51 | 2015-02-08T18:40:51 | null | UTF-8 | R | false | false | 623 | r | plot2.R | ## Reading data
data <- read.table("household_power_consumption.txt", sep=";", header=TRUE,
na.strings = c("?"))
data$Time <- strptime(paste(data$Date,data$Time),"%d/%m/%Y %H:%M:%S")
data$Date <- as.Date(data$Date,"%d/%m/%Y")
## Subsetting plot data
plotdata <- data[data$Date >= "2007-02-01" & da... |
6fc073ffd19442c3107f109b74fe0ed1b9290191 | 0f1920a21f21514e3cf993b3e244d5b4011aaf8e | /cachematrix.R | e0db7afd0f001ce0e23b23a090a18941ba9e34a0 | [] | no_license | KaiBerlin/ProgrammingAssignment2 | 2a4f7f8f945d5b7b4fd66eac45f399823619f1f1 | b600086ae86c99632cb546ed77d48b5701121aac | refs/heads/master | 2020-03-12T02:07:11.936325 | 2018-04-21T09:19:02 | 2018-04-21T09:19:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,058 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix - Prepares a matrix for caching of the inverse operation
## Usage: diag(x)
## - x is a matrix
## return the matrix with cache
makeCacheMatrix <- function(x = matrix()) {
iv <- NULL
set <- function(y) {
x <<- ... |
02d5e2848b7b9338260acc95bff447cd4854e50b | 7e72d93a908bfc5781f0d95daa2a213f4c38dc58 | /collection/tests/testthat/test.general.r | ca34b5067ecc2f9c6639e846734c5a07c1ea1ef7 | [] | no_license | lbartnik/varia | 349ea81e92198daf680a2d022f8b9157913a78f6 | 3ca8179880a55b3c1104a972aa482c190c6adfed | refs/heads/master | 2021-01-25T04:08:05.628355 | 2015-09-16T21:06:27 | 2015-09-16T21:06:27 | 24,222,581 | 0 | 0 | null | 2014-11-26T10:36:41 | 2014-09-19T08:39:09 | R | UTF-8 | R | false | false | 248 | r | test.general.r | context("general tests")
test_that("full flow", {
skip('turned off for now')
if (!require(dplyr, quietly = T)) skip('dplyr not found')
collection("col") %>%
select(flag == 1) %>%
ccply(summary) %>%
save_to('col_result')
})
|
172bde28f33fc4d71d7849464326316071006c68 | b01f3ca15d81dc03e0f1657b0fa7c1a77747e63f | /MAPS2_2_filter_ASV_table.R | 1b0f0dcc85b2921c4c23b793bacd53ce599c7f9d | [] | no_license | FlorianProdinger/MAPS2 | f8abc1e4f26a573bf4784eecaf3a9ceb62e5bc3a | 9cb4121945457ff03c8c5cc3f029a5d504349e4e | refs/heads/main | 2022-12-31T14:52:15.767399 | 2020-10-22T05:23:42 | 2020-10-22T05:23:42 | 301,575,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,858 | r | MAPS2_2_filter_ASV_table.R | #!/bin/R/3.6.1
#check user input
#read out user input
print("[R] script for reading jplace and the blastx output")
if (length(commandArgs(trailingOnly = T)) == 0){
print("please enter a vaild directory after the R script name")
quit()
} else if ( dir.exists( commandArgs(trailingOnly = T)[1] )){
input_u <- comm... |
8fddf234073c71a28f9d3dc007c32451560e3277 | 4951e7c534f334c22d498bbc7035c5e93c5b928d | /introduction/citations/ESEUR-cites.R | 0408dec2319d5f96915bd01592da92a8c8b2a133 | [] | no_license | Derek-Jones/ESEUR-code-data | 140f9cf41b2bcc512bbb2e04bcd81b5f82eef3e1 | 2f42f3fb6e46d273a3803db21e7e70eed2c8c09c | refs/heads/master | 2023-04-04T21:32:13.160607 | 2023-03-20T19:19:51 | 2023-03-20T19:19:51 | 49,327,508 | 420 | 50 | null | null | null | null | UTF-8 | R | false | false | 1,333 | r | ESEUR-cites.R | #
# ESEUR-cites.R, 26 Jun 20
# Data from:
# This books BibTex file
#
# Example from:
# Evidence-based Software Engineering: based on the publicly available data
# Derek M. Jones
#
# TAG citations
source("ESEUR_config.r")
library("plyr")
pal_col=rainbow(2)
# NA entries, there is no data
# 'available' entries are c... |
f7f5f2f419faafa38841f4c11434872a0e19e0c8 | 179356a2a9b6a3159610238f6452e7a143badeaa | /Analysis-Pipeline-Core Signaling Pathway-Radiogenomics.R | 0f25b67320e6dd03a8691f0fd72ea68f3bcbe92d | [] | no_license | jieunp/radiogenomics | ae03f3b02bf73e5f384beb93e5e124a6e419df09 | b1b0274311b0290e34ddbe006a014ff5d715fe56 | refs/heads/master | 2020-07-02T03:00:56.041156 | 2019-08-20T01:04:21 | 2019-08-20T01:04:21 | 201,394,997 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,085 | r | Analysis-Pipeline-Core Signaling Pathway-Radiogenomics.R | # The R code is written in collaborative work of Dr. Ji Eun Park and Prof. Seo Young Park
# please contact jieunp@gmail.com if you have further question.
# These are codes for radiogenomics analysis "prediction of core signaling pathway in IDH-wildtype glioblastoma"
## Line 5-129: Feature selection via Student's t-te... |
28822dfb35f859d61052a5a30416f4c0389c2142 | d82a996f50f6b553f645af24a6dd1600b19084cf | /MicroPEM_Data_Analysis/MicroPEM_data_cleaning.r | e32ef9ff7414133ccccc6727a9bf66caa42333b7 | [] | no_license | ashlinn/GRAPHS_exposure_data | 99f3035d2746b318f42113b3759a33543e83d91a | 9f5923734d00f5a63f66fbc30d318537bd235da9 | refs/heads/master | 2021-01-21T11:18:34.561236 | 2018-04-06T20:03:51 | 2018-04-06T20:03:51 | 91,735,083 | 0 | 0 | null | 2017-05-18T20:30:11 | 2017-05-18T20:30:11 | null | UTF-8 | R | false | false | 55,601 | r | MicroPEM_data_cleaning.r | ################################# FIND THE DATA FILES ############################################
# define a file directory
filedirectory <- "/Volumes/My Passport for Mac/WD passport/Columbia-Ghana Project/MicroPem Raw Data/Nephelometer_processed_correct"
Datafiles = list.files(filedirectory,full.names = TRUE) ... |
ed4eb096534ef6ea16775b55742f819f575bc8d5 | 2c324eeb4da26a3aa54e781f4f78cb5083cb009b | /test.r | 8038bb65af5f7caa32c4190a42b86b3d4d9d60c5 | [] | no_license | ravipurama/ggplot-on-4th-day | eaf51eb5c6b0a95990196ec8430e3f8113ab76fd | ddad92f600c408fb55aa0b414d5dd27b1204c3a9 | refs/heads/master | 2020-04-10T03:14:59.512394 | 2018-12-07T03:52:12 | 2018-12-07T03:52:12 | 160,765,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 45 | r | test.r | #this is my second git
data(iris)
head(iris) |
19cea5fd3a7454e303577b1f171a7c2b6b5a35b3 | 71cc5cf6f154f6195da4423f847faf650d140d6d | /man/jockes.Rd | 7fc47afddb62060d2e897a9ee085733082258094 | [] | no_license | c0reyes/TextMiningGUI | 0ece005b8b7d9360d38b23664dfedc1b138856a2 | 3c7acfbd2288d5a3c671b5a0c6ab67c60f68955b | refs/heads/master | 2023-04-01T20:27:07.006420 | 2021-04-18T17:18:21 | 2021-04-18T17:18:21 | 273,744,535 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 110 | rd | jockes.Rd | \name{jockes}
\alias{jockes}
\title{jockes}
\description{
Data from: https://github.com/taivop/joke-dataset
}
|
48aec9064858a148f522b6f2f0ff395b4fc69e4a | 501f7fe8d182b3c2c9c1290088a1d0765af1f728 | /6/6-1.R | 7275f1a520230b50d271e44cdc4a3eb4a2348a31 | [] | no_license | N-Hirahara/R_kadai | c6418765af81cc6f6b68a5b9d752c066f76c1f05 | 91c13679dc5910b9c71b3f685587de070f06db0f | refs/heads/master | 2020-08-16T02:28:19.083525 | 2019-12-13T04:38:40 | 2019-12-13T04:38:40 | 215,442,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 600 | r | 6-1.R | wd <- read.csv("6/weather_all.csv", header=TRUE, sep=",")
# 晴れの割合，平均最高気温，平均最低気温，合計降水量からなるデータを作成
fr <- rep(0, 47)
hm <- rep(0, 47)
lm <- rep(0, 47)
sm <- rep(0, 47)
for(i in 1:47)
{
wi <- wd[wd$prefid==i,]
fr[i] <- nrow(wi[wi$weather=="Fine",])/nrow(wi)
hm[i] <- mean(wi$temphigh)
lm[i] <- mean(wi$templow)
sm[i] <- ... |
2a30ccb52ecb13f86e7d778bb17c412a7fb51d27 | b35d8d930b0fd5255bf6ade8e05070badddaadf0 | /man/eurusd.Rd | cf0f22f62ff72353098b12281b81d25121afe571 | [] | no_license | ilda-kacerja/foRex | 2e8af71faf33c4708ab86fb527bb04676c3d7b1a | 1b642a1096e5f051c12c54f1c41070e71aa6e9cb | refs/heads/master | 2020-05-29T16:56:26.164542 | 2019-05-29T16:45:04 | 2019-05-29T16:45:04 | 189,263,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 266 | rd | eurusd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eurusd.R
\name{eurusd}
\alias{eurusd}
\title{Exchange rate for the current day}
\usage{
eurusd()
}
\value{
number
}
\description{
Exchange rate for the current day
}
\examples{
eurusd()
}
|
ccef719e990b8f58fc91735303bdcab310d32b22 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /paws/R/cloudwatchrum_operations.R | 1f9973a98afb57931c914d1299f033e2d977a769 | [
"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 | false | 47,651 | r | cloudwatchrum_operations.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common get_config new_operation new_request send_request
#' @include cloudwatchrum_service.R
NULL
#' Specifies the extended metrics and custom metrics that you want a
#' CloudWatch RUM app monitor to send to a destination
#'
#' @descri... |
342ce94c87ed009c225da23fdec0377a2be298bc | 7cb9ac0c1f2f6f9c916ab6c67dcc55d328385c92 | /R/derived_variables.R | 6e382c594d41fc08842d173440c7587099a2c04f | [] | no_license | Westat-Transportation/surveysummarize | 3c4d5b63fcd4eef891b4ff48ea2e88f421552213 | aebb3032507fda319fdcd5484658a2825aa4c7d5 | refs/heads/master | 2023-08-17T05:09:38.018122 | 2023-08-08T21:36:31 | 2023-08-08T21:36:31 | 186,884,470 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,294 | r | derived_variables.R | #' @title Add derived Variables from a configuration worksheet (csv).
#'
#' @description Add custom variable to dataset and codebook.
#'
#' @param data Object returned by \link[summarizeNHTS]{read_data}.
#' @param config_csv File path to a csv with fields "NAME", "TABLE", "TYPE", "DOMAIN", "VALUE", "LABEL".
#'
#' @expo... |
5292ef35750dbc1d05359685ea039d00a7d3335c | 9b407ebd913589c2f7d383d7a21bb29ede5c632f | /Plot3.R | 7d3da95294323139163e51daf664d280ea4ef942 | [] | no_license | sfavors3/ExData_Plotting1 | a720c1cd542efc9fd858de6f4175c7f0fd93e82c | a722fa259926047735bc58f0d01d78789f222953 | refs/heads/master | 2021-01-22T14:40:16.443844 | 2014-10-10T08:45:16 | 2014-10-10T08:45:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,134 | r | Plot3.R | # This code creates a line graph named plot3.png taken from household_power_consumption data
# from 2/1/2007 through 2/2/2007
##create pdf file to save graph
png("plot3.png", width=480, height=480)
##set the working directory and declare variables
title<- "Global Active Power"
##imports data table and extract appro... |
c86d46089b0d249ec65810e561dd77a97ab07a31 | f96c243bd74c91e32037c17fc1fdd522de6b1dd7 | /man/formFilters.Rd | 41c9e1660778253f56f845554c3f306774ce44dd | [] | no_license | cran/insiderTrades | eed4ad8d8be58fcae1dc463c84da394d139ff544 | de306e71e705e44d327d2d618377ac18069ad0c3 | refs/heads/master | 2023-07-30T17:47:50.472428 | 2021-10-04T19:20:05 | 2021-10-04T19:20:05 | 413,661,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,034 | rd | formFilters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formFilters.R
\name{formFilters}
\alias{formFilters}
\alias{formFilterNonderivativeTransactions}
\alias{formFilterDerivativeTransactions}
\alias{formFilterNonderivativeHoldings}
\alias{formFilterDerivativeHoldings}
\title{formFilters}
\usage{... |
5967258845e38d341c3e3f9497b1b304a5a0e876 | c016dda10b08538e7a9d894c1b19e7d05defe172 | /R/mod_data_import.R | f85b02c0386ec5e25d56aba829360e87efe12e13 | [
"Artistic-2.0"
] | permissive | HelBor/wpm | 37c3fb1fa358fe8ed817781fd310ffbb34a7c35b | 62002a8d64da032454bf3686158c37585843b332 | refs/heads/master | 2021-06-25T13:19:01.187012 | 2021-06-15T11:53:24 | 2021-06-15T11:53:24 | 226,340,423 | 3 | 0 | null | 2020-11-30T16:48:39 | 2019-12-06T13:52:50 | R | UTF-8 | R | false | false | 8,153 | r | mod_data_import.R | mod_data_import_ui <- function(id){
ns <- shiny::NS(id)
shiny::fluidRow(
# inputs part
shiny::column(width = 6,
shinydashboard::box(
status = "warning",
width = 12,
collapsible = TRUE,
solidHeader = FALSE,
... |
cbcab7090deec0e08768c69bb96e44017deb27b4 | 8ce08913391970b7f6b3e4183310e0e384b026ad | /code/figures/diet_variance.R | 707de14182c236009361b02705c7ce0ba9bfbef8 | [] | no_license | cfree14/forage_fish | dbf636b2429db458477229510b3549120beb63dd | 3779bd43b4a8a8ec50a6e322bbe4de6bab83be1b | refs/heads/master | 2021-11-11T06:50:18.878151 | 2021-11-01T15:08:12 | 2021-11-01T15:08:12 | 136,339,563 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 938 | r | diet_variance.R |
# Clear workspace
rm(list = ls())
# Setup
################################################################################
# Packages
library(reshape2)
library(tidyverse)
# Directories
datadir <- "data/hilborn_etal_2017/"
plotdir <- "figures"
# Read data
diets <- read.csv(paste(datadir, "hilborn_etal_2017_diet_inf... |
eda562abf5c6fd1dd7e2afff4214cc6ef9ef78bd | 3c234755377b4637654507f44b0d6e4bfa63a367 | /ui.R | 979c080d572fb4316264c3e1959b2866df58571d | [] | no_license | vfulco/china_pollution | ca3eca11a24486b98f6142f6a5d3d61e0a565681 | ce3ce991e7e70ede4e5a5e0765df54cac97a965e | refs/heads/master | 2020-03-22T11:56:09.571443 | 2015-12-26T14:43:24 | 2015-12-26T14:43:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,696 | 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://www.rstudio.com/shiny/
#
library(shiny)
shinyUI(pageWithSidebar(
# Application title
headerPanel("Air pollution in China"),
# Sidebar with a slider input fo... |
a47b013cac29bca5383c27da9f57fbae7337b8ec | d42229401a0acbf19b28dcef2c521b6236b638b6 | /atividades/day_5/explorando.R | 42897cb05b4c069317e8ecbb92c45743c208dc29 | [] | no_license | LABHDUFBA/2021-SICSS | 4fbab9bdf1ad5ea9cae7e3dafbff1c836f318251 | 1fc011d29ba60a46f0a48b06a9c16bc4627aac73 | refs/heads/master | 2023-05-30T21:11:53.163451 | 2021-06-21T18:25:07 | 2021-06-21T18:25:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,337 | r | explorando.R | # instalar o pacote do fragile families
# devtools::install_github("fragilefamilieschallenge/ffmetadata")
# ffmetadata::search_metadata(background_raw)
background_raw <- haven::read_dta("atividades/day_5/ffchallenge_SICSS_2021/FFChallenge_v5/background.dta")
train <- readr::read_csv("atividades/day_5/ffchallenge_SICS... |
48202d4a9a7a19da9e66bf3d356ed40894e43bc5 | 1dc0ab4e2b05001a5c9b81efde2487f161f800b0 | /experiments/train_noisy/an_nn_20.R | b8259634c9da79b36162be4a20ce853819e0a04d | [] | no_license | noeliarico/knnrr | efd09c779a53e72fc87dc8c0f222c0679b028964 | 9f6592d1bbc1626b2ea152fbd539acfe9f9a5ab3 | refs/heads/master | 2020-06-01T02:44:34.201881 | 2020-03-13T13:30:52 | 2020-03-13T13:30:52 | 190,601,477 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,323 | r | an_nn_20.R | # ecoli_20an_nn -----------------------------------------------------------
out <- fitNum(ecoli_20an_nn)
fit_ecoli_20an_nn_d <- out$fitd
fit_ecoli_20an_nn_r <- out$fitr
sink("executed", append = TRUE)
cat(paste0("- ecoli_20an_nn ---> ", now(), "\n"))
sink()
# glass_20an_nn -------------------------------------------... |
e49b6d590747aea66fb3f20192de95c86bc60b07 | aff4230bba7510f6fccdccd32646641e50573244 | /basic_r/code/01basic/script02(데이터타입).R | cff8d3592cab0e58c54052cc75d0c367bb81f535 | [] | no_license | jhr1494/R | b6e7bcf71081673cf0ac4bbd2fed11baa1125bf7 | 3fcb607fa582c2f874ed404e8c2b27c21ae1f30f | refs/heads/master | 2023-02-14T18:45:42.929548 | 2021-01-11T14:29:13 | 2021-01-11T14:29:13 | 326,626,594 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,010 | r | script02(데이터타입).R | # 자료형
# 숫자형 변수
a <- 1
class(a) # numeric
a <- 3.14
class(a) # numeric
# 문자형 변수
b <- "1"
class(b) # character
c <- c(1:10)
class(c) # integer(=numeric)
d <- TRUE
class(d) # logical
# 범주형변수 factor()로 생성
# 등급, 분류와 같이 명목이 뚜렷한 표현에 적합
e <- factor( c(1,2,3,2,2,2,1,3,4) ) # 1, 2, 3, 4의 범주
class(e) # factor
levels(e) # 1,... |
5b1e64ef11459af121750d557e1bd768ffabb00a | 6ed3b6bea49ac58324852cf2679f2497284ad1ed | /run_analysis.R | a8eb88a60f734ca8a894cf4ee1d8243a56fb8cfb | [] | no_license | fndfernando/Project-Getting-and-Cleaning-Data | 81c461c0e37bdc0a38c153c0e579e9ca678386ee | 1721b631f00898c8929077561133bea2fedae088 | refs/heads/master | 2021-01-21T14:01:22.417369 | 2016-05-24T14:17:40 | 2016-05-24T14:17:40 | 50,536,585 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,636 | r | run_analysis.R | library(dplyr)
## get the colunns names
colname<- read.table("Dataset\\features.txt")
##get the activity label - question 3
activitylavel <- read.table("Dataset\\activity_labels.txt")
names(activitylavel) <- c("id","activity.label")
## get the test data and define the colum names
x_test <- read.table("Dataset\\test\\... |
bef67985fa75fee8c35e626115c3c7a62a954dad | 9f5ab4936f557c3903cfa0582004e9fd60fda797 | /R/plot_spaghetti.R | 355d9534f3ad6271b9ac80b2d6c9892dd49347d1 | [] | no_license | mstei4176/ct_real_assess | 3b423ad2b437a38e5354b7fa141c0ad725766e4b | a354036b08d3379178bc71ccc8bbf9be1b388c82 | refs/heads/master | 2022-11-21T13:14:10.892600 | 2020-07-22T14:38:23 | 2020-07-22T14:38:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,515 | r | plot_spaghetti.R | # spaghetti of every town of average sales price used in Shiny app
# town_name and property specified from Shiny ui
plot_spaghetti <- function(dt, town_name, type){
dt <-
dt[,
mean(as.numeric(sale_price), na.rm = TRUE),
.(town, year, property_type)][, .(
`Average Price` = V1,
... |
29459b8c7bf7e87942f901a7e88363f838a33ad3 | f43868b912d70d3ed9c7fe484fc3ea9ce1ec6d97 | /main.R | 051dfc5ecb0173f6f0b0bfc3a1594f2be753f705 | [] | no_license | IgorTkaczyk/labs-R-advanced | 142f550d59ad65422d774f950672e351a2560eaa | 7b03121ed52996a687070b7f5e2e23e52854831d | refs/heads/master | 2020-09-12T18:58:58.398807 | 2019-11-18T18:53:07 | 2019-11-18T18:53:07 | 222,518,728 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 86 | r | main.R | ### zajecia 1
print('Hello')
dum_der <- function(f, x, h) {
(f(x+h) - f(x))/h
} |
5d7d6d7e0c4f409e293c4dfcfd9729ad4096dc89 | 503d06adc134f2a41547a782efe1b08ccd20cce5 | /R/imports.R | 79a88fcbcc8cf1e0edefb540805032cb625c36b5 | [] | no_license | StuartWheater/dsMiceClient | 1f03293a5475fdaf92b28c76760081c63b5e9d63 | 929006a6d71ab02eb9075eeff76eb87063c127a6 | refs/heads/master | 2022-01-26T05:26:15.897569 | 2019-06-25T18:07:06 | 2019-06-25T18:07:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 161 | r | imports.R | #'@import mice
#'@importFrom stats lm.fit rchisq rgamma rnorm
#'@importFrom opal datashield.login
#'@importFrom dsBaseClient ds.mean
NULL
|
5cfd4e36a1869515ca3b317ac60463797cb91b34 | b58ef6361161adfad9bdc7cc1b23c4988030fbe3 | /stuff/MeetingDataMerge.R | 10f8ca15dd95067a9765f9af671fa9d40309cc33 | [
"MIT"
] | permissive | DSPG-ISU/DSPG | 01b9ec9a3dd02cd2ee7e52a28ba22f6d312ad2f8 | c20b50c1dd28eedd879a9226b5f6511a0471c870 | refs/heads/master | 2023-02-26T12:54:52.616558 | 2021-02-05T05:44:04 | 2021-02-05T05:44:04 | 277,006,430 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,404 | r | MeetingDataMerge.R | # Iowa Recovery Meetings Data Cleaning and Merging
#
# Data Sources:
# https://www.aa-iowa.org/meetings/
# https://iowa-na.org/na-meetings/
# https://adultchildren.org/mtsearch
# https://al-anon.org/al-anon-meetings/find-an-alateen-meeting/
# http://draonline.qwknetllc.com/meetings_dra/usa/iowa.html
# https://w... |
0e438c28aaa5692cec97883e74436ca178db7f80 | fae3b5d5d08abef91629ab8d0b087ac36e46ea06 | /NBAtools/R/grandpa.R | e9f2e51bbab6fdab896fcaffea105bfced12f512 | [] | no_license | gustavonovoa/NBAtools | 130d8c5e72f01553a962752969f38df57a2c5f79 | 1a139fb7922d233c3718968138b113fdf3980089 | refs/heads/master | 2022-02-02T03:23:02.227165 | 2019-06-24T02:13:11 | 2019-06-24T02:13:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 528 | r | grandpa.R | #` A function to find the oldest player
#` This function returns the oldest player (first alphabetically if tied) for a given year
#` @param Pass a year, return points
#` @keywords old age nba
#` @export
#` @examples
#` grandpa()
NBAdata<-readr::read_csv("Seasons_Stats.csv")
grandpa<-function(year){
... |
1956784ae3c2e48ab1bd97c94f7c5f1b86a19b0e | ce139910de57ee90a2f14a5f9dc6707d6558a71e | /mogp_emulator/demos/gp_demo.R | 3d2d5b1fa3ec4ba18aed47dcb887f3c6b2bbc5c5 | [] | no_license | homerdurand/HistoryMatching_ExeterUQ | 0d30a35ca3099c39466b875c21f12f700a6c08bd | 7944785eaa3b789669a1c3f779755cdf6c65c5e5 | refs/heads/main | 2023-07-23T13:58:09.844857 | 2021-09-03T12:36:24 | 2021-09-03T12:36:24 | 390,157,314 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,109 | r | gp_demo.R | # Short demo of how to fit and use the GP class to predict unseen values based on a
# mean function and prior distributions.
# Before loading reticulate, you will need to configure your Python Path to
# use the correct Python version where mogp_emulator is installed.
# mogp_emulator requires Python 3, but some OSs sti... |
ca5cc6ec4e42480561daeda23143eb072657c8e4 | bc0424df42c5083b060c9e403c5319f49bbaca51 | /cachematrix.R | 6dc1cb0a7e906e981ee1f0b992728ede92af4abc | [] | no_license | realkenlee/ProgrammingAssignment2 | c69c30f656c2611f6851626c62bfce38f50d6aa1 | a558ecf91cf2b09c58488579666f873798f724af | refs/heads/master | 2021-01-20T23:02:41.398825 | 2015-09-15T22:41:23 | 2015-09-15T22:41:23 | 42,483,238 | 0 | 0 | null | 2015-09-14T23:38:23 | 2015-09-14T23:38:22 | null | UTF-8 | R | false | false | 1,534 | r | cachematrix.R | ## The following is submission to Coursera's R Programming Assignment #2
## offered by Johns Hopkins University
## Below are implementation of two functions
## makeCacheMatrix: This function creates a special "matrix" object that can cache
## its inverse.
## cacheSolve: This function computes the inverse of the sp... |
bf3809889bd9af6874c6004732d7574eefc9f2a5 | c91b227c26552d207765aee509d896d1daa424a3 | /R/plots.R | 16287121c3ffe58b02e52898c3648509a8a40096 | [] | no_license | cran/amei | c3827194ce5c6fdd2f3ef4b13be5cc7a2ee9964e | 6ec4dc1abc827534b1920db6d4911e38cd16224c | refs/heads/master | 2016-09-06T07:55:28.224132 | 2013-12-13T00:00:00 | 2013-12-13T00:00:00 | 17,694,356 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,606 | r | plots.R | `plot.epiman` <-
function (x, type = c("epi", "costs", "params", "fracs", "stops"),
showd = FALSE, showv=FALSE, prior=FALSE, main = NULL, ylim=NULL, tp = NULL, ...)
{
type <- match.arg(type)
if (type == "epi") {
if (is.null(main))
main <- "Evolution of Epidemic"
PlotEpi(x$soln... |
e6fcb75e10350d10f77972dc57e862df5b2b3c28 | d65b6061f2470b42adcf56eb76da4b561e076e6d | /postprocessing.R | aab606e880adc5c6353d761fda26840b4b919616 | [] | no_license | Rosemary94/COVID_vax_CD8 | 60feb732da09580a44394bad030e19d87f60fbf0 | 6b64879d3df5320fffd2043579b477c7d1fee675 | refs/heads/master | 2023-06-15T04:26:47.109367 | 2021-07-09T21:55:58 | 2021-07-09T21:55:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,295 | r | postprocessing.R | source("functions.R")
##part 1: seurat analysis
#Load aggregated 10X data and create Seurat object
cov1.data <- Read10X(data.dir = "libs_aggregate/filtered_feature_bc_matrix/")
cov1 <- CreateSeuratObject(counts = cov1.data$`Gene Expression`)
#Remove low quality cells
cov1[["percent.mt"]] <- PercentageFeatureSet(cov1,... |
f5f6ce2bc4931a08025387a19255696f6a160bd0 | 04de6fa4413d180e31040d2915eff9b9049c06f0 | /modules/Table_Data_module.R | 986bf81e389b899aea98936b19b4be68b50ecc5d | [] | no_license | bastianilso/ILO-Project | 7b49b9b31a1108df5fb930a46d4f9273e7a8dffb | e4f9c37e1405fec6e57fc76a852b6a3da3b11fc8 | refs/heads/main | 2023-06-12T00:31:19.642002 | 2021-06-24T07:26:21 | 2021-06-24T07:26:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 295 | r | Table_Data_module.R | #Table_Data
Table_Data_UI <- function(id) {
ns = NS(id)
list(
fluidRow(
tableOutput(ns("Table_With_Data")),
)
)
}
Table_Data <- function(input, output, session, df) {
ns <- session$ns
output$Table_With_Data <- renderTable({
table <- df()
return(table)
})
} |
7100524824a2a4552677d1745476ea3ef3b2b8b1 | b83cfb6a045040319338cf5e16c0392887993ee0 | /Previous/spell correction.R | f64a6aee8a4e171af9846d290a17d922956c895c | [] | no_license | xiaojiezhou/OftenUsed | 75360c9318158e24045a646e97d61fbf9f02b44e | d016df176fa24b9763da0ce4aa408184f533273e | refs/heads/master | 2022-06-22T03:52:56.112580 | 2020-05-06T14:42:37 | 2020-05-06T14:42:37 | 260,552,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,570 | r | spell correction.R | library(XLConnect)
library(tm)
wb = loadWorkbook("B:\\XiaojieZhou\\2014\\GCR Survery\\From Others\\Global English CX Results to Jun 1.xlsx")
cmm = readWorksheet(wb, sheet = "report1433191502074", header = TRUE,startCol=13,endCol=13)
# cmm = readWorksheet(wb, sheet = "report1433191502074", header = TRUE,startCol=13,en... |
5fe768ac48332ecd70a2c1c9bffd97ac58db7306 | 3f12973f2e3b3f96543181df0d0699aeb3c2a382 | /Actividad 2.R | 6ed81b9ebaa89ba27847cc0e3755d079561e8b88 | [] | no_license | Maria031/ALGORITMOS | 60d6457a499ff466ac6b37c6d07b73a90f8e58d8 | 4f96206e85634f9c68a370c92cf9e3a17901bf11 | refs/heads/master | 2020-03-28T07:28:22.379279 | 2018-12-07T03:01:00 | 2018-12-07T03:01:00 | 147,903,662 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 73 | r | Actividad 2.R | write.csv("HolaMundo//C://Users//madey//Documents//Algoritmos//Hola.csv") |
3092725b2fb218efec8eb58db1f4da5a3636df4a | 221072e790a97e05eea0debadbe87955b81ae2e1 | /R/Fast5Files-methods.R | 05eabb57dca88c2dad4b602f2c400646a658af67 | [
"Apache-2.0"
] | permissive | Shians/PorexploreR | 2ca3d93956ba4592564c8019f1a76252a0696283 | 5086f3e704c6c6036a80d103752d9bce984a0e15 | refs/heads/master | 2020-06-06T05:51:25.714267 | 2019-06-20T07:44:16 | 2019-06-20T07:44:16 | 192,655,626 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 414 | r | Fast5Files-methods.R | #' @include Fast5Files.R
#' @importFrom dplyr sample_n
NULL
setMethod("show", signature = "Fast5Files",
function(object) {
print(object)
})
setGeneric("sample_files", function(object, n) {
standardGeneric("sample_files")
})
setMethod("sample_files", signature = c("Fast5Files", "numeric"),
function(object, n)... |
e9c684a947a6b0396b259f140cba3490aed202c5 | a16224a32558f9ec254688fb7c83de3c4c35fc25 | /R/list.all.R | b53fca258576c1e5d45507689e432dd665418cfa | [
"MIT"
] | permissive | renkun-ken/rlist | fb807f6f0162f52b08aa141104566f7c6e8c2dd6 | bfaa2c50f79c9e8cdb3bce481262829549ba8e7e | refs/heads/master | 2023-03-21T19:47:49.571870 | 2023-03-11T12:54:35 | 2023-03-11T12:54:35 | 20,375,257 | 187 | 31 | NOASSERTION | 2022-06-12T14:48:11 | 2014-06-01T10:33:11 | R | UTF-8 | R | false | false | 2,127 | r | list.all.R | #' Examine if a condition is true for all elements of a list
#'
#' @param .data A \code{list} or \code{vector}
#' @param cond A logical lambda expression
#' @param na.rm logical. If true \code{NA} values are ignored in
#' the evaluation.
#' @seealso \code{\link{list.any}}
#' @return \code{TRUE} if \code{cond} is evalua... |
963f72b7afb9c4c1bc4d1bb265e54eeca2284d90 | 961e8f85b7f0e2b6b82b648333449cfc26c6f2c5 | /project/data/test.R | 372efabfb6490aa0bf5b16d845bc8f40dc720ccc | [] | no_license | kyclark/ecol596 | b3002e6e2dc1919edf58eab45f45f3763e8274ab | c28cb09c2a70fe49e4b4b3129cda474fcf66bd11 | refs/heads/master | 2021-01-21T13:49:03.267593 | 2016-04-29T16:31:43 | 2016-04-29T16:31:43 | 51,519,545 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,189 | r | test.R | require(zoo)
require(R.utils)
argmax = function(x, y, w=1, ...) {
n = length(y)
y.smooth = loess(y ~ x, ...)$fitted
y.max = rollapply(zoo(y.smooth), 2*w+1, max, align="center")
y.min = rollapply(zoo(y.smooth), 2*w+1, min, align="center")
delta.max = y.max - y.smooth[-c(1:w, n+1-1:w)]
delt... |
07e8bd9afad1d2c3fe2ee4d913e9ebe6ea44e3d5 | 5f48f90d335918eb85af87ab829bf6f50d07832b | /server.R | e44fc09d0b985f341d9f9451e2cf35a717e076e2 | [] | no_license | manilwagle/Developing_Data_Products | f690c64e8a950f2838ee36a0bc40cb13ba9b1944 | 593cc27fcb8a03d6edf3e15f368a8bb6944994b1 | refs/heads/master | 2021-01-10T07:50:11.057542 | 2015-10-13T23:15:55 | 2015-10-13T23:15:55 | 43,963,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 767 | r | server.R | library(shiny)
data(mtcars)
# Make cyl (number of cylinders) and am (0 = auto, 1 = manual) into factors
mtcars$cyl <- factor(mtcars$cyl)
mtcars$am <- factor(mtcars$am)
# Train a regression model to predict MPG from
# - wt (weight in 1000 lbs)
# - hp (horsepower)
# - cyl (number of cylinders)
# - am (0 = auto, 1 = ma... |
e5cb18dc77115a659701651defd82e5e2047da83 | d2fdf04be8786c603176b9c779a6dac2611cba81 | /tests/testthat/test-monolix.R | 5074c80cd0327e931b6f1433ea0e8693a5ce9888 | [] | no_license | nlmixrdevelopment/babelmixr | 4038f202f62fc9fafc586070661d73058620a4a6 | 040fc8eabad7fab0cf53841ec0fbba616bd742b5 | refs/heads/master | 2023-03-05T22:20:08.056448 | 2021-02-22T21:31:18 | 2021-02-22T21:31:18 | 308,112,742 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,661 | r | test-monolix.R | test_that("model to input information", {
pk.turnover.emax3 <- function() {
ini({
tktr <- log(1)
tka <- log(1)
tcl <- log(0.1)
tv <- log(10)
##
eta.ktr ~ 1
eta.ka ~ 1
eta.cl ~ 2
eta.v ~ 1
prop.err <- 0.1
pkadd.err <- 0.1
##
temax <- lo... |
317f3ab88ae49e7dbfe365407dfb8b5274c52090 | 105e158b10a08b907b95c9347e37fae682f70cdc | /GTRENDS FINAL PRACTICE.R | da4f341e9c8a7209ef51747fda4e68c06914ffcf | [] | no_license | btindol178/R--Data-Science-Journey- | 301919f2cc9eed87ba912b09e383be3b1d9c1c8d | 23ddcaa0a72552d780faedf18d88b2c081f3cf52 | refs/heads/master | 2023-03-20T13:59:34.871495 | 2021-03-08T00:12:56 | 2021-03-08T00:12:56 | 172,145,392 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,626 | r | GTRENDS FINAL PRACTICE.R | devtools::install_github("PMassicotte/gtrendsR") # only run once
install.packages("gtrendsR")
## load library
library(gtrendsR)
library(dplyr)
# searching for covid-19 Trends
res0 <- gtrends(c("covid-19"), geo=c("US-AL","US-AK","US-AZ","US-AR","US-CA"),time = "2019-11-01 2020-03-24")
res1 <- gtrends(c("cov... |
3570e9f562c275357fa13b0359fd813bbfdcbc71 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.machine.learning/man/sagemaker_list_user_profiles.Rd | d738ceea733aadebae92c557fb2ac8474410e3bb | [
"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 | 1,322 | rd | sagemaker_list_user_profiles.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sagemaker_operations.R
\name{sagemaker_list_user_profiles}
\alias{sagemaker_list_user_profiles}
\title{Lists user profiles}
\usage{
sagemaker_list_user_profiles(
NextToken = NULL,
MaxResults = NULL,
SortOrder = NULL,
SortBy = NULL,
... |
d99c854478c1b9745c958904c20acd23b7f68330 | 422de0cda897c0340eb4e01bdc0a44a5230a4c0d | /man/authoriseGitHub.Rd | d32d14b4401654ebd816aa3498273d4e9b14fbe6 | [] | no_license | gitter-badger/archivist.github | 794e91762e37197cef0ec9c6eb7c37493d3e4ec4 | 35c8288158f8db978b6b41842d67255876958e70 | refs/heads/master | 2020-12-29T00:12:37.405951 | 2016-04-03T20:25:21 | 2016-04-03T20:25:21 | 59,998,255 | 0 | 0 | null | 2016-05-30T09:35:47 | 2016-05-30T09:35:47 | null | UTF-8 | R | false | true | 1,112 | rd | authoriseGitHub.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/authoriseGitHub.R
\name{authoriseGitHub}
\alias{authoriseGitHub}
\title{Authorise with GitHub API}
\usage{
authoriseGitHub(ClientID, ClientSecret)
}
\arguments{
\item{ClientID}{A 20 characters long string with Client ID. See https://github.co... |
3fcf24558b31cf309412e9afe3576ef0e926bf10 | cafe1a30c92ec40d0bd1ae7af8fb3c11a118d0d5 | /plot2.R | 3d88a4aa76ff76368a5c09e81d01ce4611076940 | [] | no_license | Matt-Coursera/Explore_DA | a75f208d86b7c7d295006eee6292dbfd045f0ee8 | 198e47a429ee69a8e1052f09ab2e50f0fcd9b4b4 | refs/heads/master | 2021-01-23T12:26:13.797150 | 2015-08-10T01:05:22 | 2015-08-10T01:05:22 | 40,451,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 674 | r | plot2.R | #Reading in the data
dataFile <- "./data/household_power_consumption.txt"
data <- read.table(dataFile, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
#Subsetting to more manageable set of data
subSetData <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
#Setting up the date & time
datetime <- strptime(paste(... |
3227c054832b55a54965394808ffea7a8e470748 | b29a6b3ac4cb5ffc293e4102f74ef0168e6f7f18 | /R/trimesh_construct.R | 61b4a47e31075ebf1acf5cf56978ea31f881306c | [
"MIT"
] | permissive | thomasp85/unmeshy | 3d5022fcd820b6eac19c5b8aaf2fae5ecbc67859 | c7b8dd2734dac3fd5d45abca941969f4951987d5 | refs/heads/master | 2022-12-15T13:21:23.031934 | 2020-09-11T12:20:43 | 2020-09-11T12:20:43 | 294,034,087 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,601 | r | trimesh_construct.R | #' Create a trimesh object
#'
#' These functions helps in creating trimesh objects. A trimesh is a subclass of
#' mesh3d as defined in the rgl package. It is exclusively using triangles
#' (unsurprisingly) and adds two additional elements: `it_info` which is a
#' data.frame with information about the triangles in the m... |
d3cc945b55cc0adbe0342430445a503aba658bf2 | 80ff29b3ea83408f561a7c776f524530e8f3e5ee | /Packages/Stats202A/R/Lasso.R | e85ab327143f9a70a553a439c72b87e60a4e3790 | [] | no_license | greek-geek/Statistics-Programming | b3dd537a4db97b2646c851e6d331d2a4263010f3 | c4d8c9a0eb6c9bb13f024efced317e0cde1b76b0 | refs/heads/master | 2021-09-12T07:05:33.370104 | 2018-04-15T09:15:44 | 2018-04-15T09:15:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,781 | r | Lasso.R | #' Lasso Function
#'
#' This function performs Lasso regression on X and Y.
#' @param X
#' @param Y
#' @keywords Lasso
#' @export
Lasso <- function(X, Y, lambda_all){
# Find the lasso solution path for various values of
# the regularization parameter lambda.
#
# X: n x p matrix of explanatory var... |
7e749080256b5d99f9c2a2ec09202122aa476fe6 | 86d134d36d286fe5307561d25e89c165b5eee91e | /app.R | 3db24d7765485df0bc23b8eece39ae141e1d676f | [] | no_license | kshitionweb/Open-Data-and-Visualization-using-Shiny-R | 5e787f1b634be126e470241b2fd80482c2c6a416 | b4e19b5bf0f74ab9d1ff74ae397b057e24b80c12 | refs/heads/main | 2023-04-11T01:11:56.254644 | 2021-04-20T21:56:56 | 2021-04-20T21:56:56 | 359,960,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,448 | r | app.R | # Load packages
pkgs <- c("shiny", "shinythemes", "leaflet", "leaflet.extras",
"tidyverse", "plotly", "scales")
library(conflicted)
conflict_prefer("select", "dplyr")
conflict_prefer("cols", "vroom")
conflict_prefer("filter", "dplyr")
conflict_prefer("lag", "dplyr")
conflict_prefer("layout", "plo... |
87e086aadcbfd7e754490aac7fcc4b604e9252e7 | 8baed20bf6814e71bea9cd94b4111d67ec00f85e | /FastGO.R | 73d4c0fc2603717db1835057e797935e75240c14 | [] | no_license | laramangravite/StatinDifferentialNetworks | 61af0b2cba026effe90f3dd012f313e514877fa5 | 74477e41c2fa21e46e92fc4cf1fed558f8e1935d | refs/heads/master | 2020-04-28T15:17:47.020632 | 2012-11-29T18:25:03 | 2012-11-29T18:25:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,719 | r | FastGO.R | ##############################################################################################
## Fast Gene Ontology enrichment analysis for modules using Fisher's exact test
##
## Author : Zhi Wang
## Contact : Sage Bionetworks
## Email : zhi.wang@sagebase.org... |
a4ab0dc36d2134af9707084505afde171da70799 | 89ef0527cfab8a5890eb80fea15d2263ad3ba133 | /tasks/sorting-algorithms-quick-sort/r/sorting-algorithms-quicksort.r | 464bd504f924f8844b166eee665e22d3e13afc09 | [
"CC0-1.0"
] | permissive | stefanos1316/Rosetta_Code_Data_Set | aed9585dd67a8606b28cebc7027512ce54ff32e0 | 8120b14cce6cb76ba26353a7dd4012bc99bd65cb | refs/heads/master | 2021-07-08T22:23:35.920344 | 2020-07-09T14:00:06 | 2020-07-09T14:00:06 | 132,714,703 | 0 | 0 | NOASSERTION | 2020-06-22T16:41:47 | 2018-05-09T06:55:15 | Java | UTF-8 | R | false | false | 727 | r | sorting-algorithms-quicksort.r | qsort <- function(v) {
if ( length(v) > 1 )
{
pivot <- (min(v) + max(v))/2.0 # Could also use pivot <- median(v)
c(qsort(v[v < pivot]), v[v == pivot], qsort(v[v > pivot]))
} else v
}
r=0
executeTask <- function(i) {
qsort(c(16+i,93,-99,95,-96,-24,-53,-71,96,-66,-21,72,-12,-32... |
8f25bb6fb4df14ef60c26c7f1137bd78c0a96a4e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/moezipfR/examples/moezipfR.mean.Rd.R | 0203a5c66fd0b82951c474808a0bf5bf92d8fe8b | [] | 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 | 176 | r | moezipfR.mean.Rd.R | library(moezipfR)
### Name: moezipfR.mean
### Title: Expected value.
### Aliases: moezipfR.mean
### ** Examples
moezipfR.mean(2.5, 1.3)
moezipfR.mean(2.5, 1.3, 10^(-3))
|
d1bbd46c1dff33f9f7a6aa1f0e5061cd2898de38 | a9e85ae71357f3cfd99c98b3723b4103a55fbe93 | /plot2.R | 9c0aa801428de80bd61cf4ad7415aea6d441a7af | [] | no_license | sean-sebry/ExData_Plotting1 | dc352b3a9e5a1c05d0da4c4b5903ff8edefba041 | 0298529914dd1f3f78781c2bbaae4ce7207b0075 | refs/heads/master | 2020-09-20T15:34:52.711003 | 2019-11-28T20:38:09 | 2019-11-28T20:38:09 | 224,524,065 | 0 | 0 | null | 2019-11-27T22:01:52 | 2019-11-27T22:01:51 | null | UTF-8 | R | false | false | 408 | r | plot2.R | global_active_power_data <- data_clean %>%
filter(date > ("2007-01-31")) %>%
filter(date < ("2007-02-03")) %>%
mutate(date_time = paste(date, time)) %>%
mutate(date_time = ymd_hms(date_time))
glimpse(global_active_power_data)
png("plot2.png")
with(global_active_power_data, plot(x = date_time, y = global_ac... |
481ea0306681b92ef3636c1d08a835ad45dbb14e | e2120bc56e9e019613f2726777e47ad18d5106de | /R-scripts/03_tag_plot.R | 000d7c33611ce94b1d3a5d99b359c11356cd9b11 | [
"MIT"
] | permissive | JoeyBernhardt/colour-of-noise | cf9c3b03354a2dd37df279c5c8ce45ff8b0fa265 | aef274825a33de1705f91734b229393cf87b5f99 | refs/heads/master | 2021-07-21T04:15:55.688138 | 2020-09-29T19:55:23 | 2020-09-29T19:55:23 | 216,596,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,546 | r | 03_tag_plot.R | ### mod tag plot
library(tidyverse)
library(cowplot)
theme_set(theme_cowplot())
events <- read_csv("data-raw/xy-tag.csv") %>%
gather(key = variable, value = time) %>%
group_by(variable) %>%
mutate(lag = time - lag(time)) %>%
mutate(event = 0)
events_wide <- read_csv("data-raw/xy-tag.csv") %>%
mutate(lag ... |
71842b43d61ba30a7401f05f22dfcace3618b7c8 | b3f48abdb6e0f8871bed3bbd243ac97fcb757615 | /tests/testthat/test-data_prep.R | 0ebf1497a60378db847e74320417e708f2e3cc2a | [
"MIT"
] | permissive | davisadamw/stocked | 010153b6f5aa9607245681e44af35c1642a97359 | eaf00c3ab65c8fc35ae5bbb8dcdbe5b4c4850b97 | refs/heads/master | 2023-02-13T16:50:53.749695 | 2021-01-14T08:44:27 | 2021-01-14T08:44:27 | 218,654,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,194 | r | test-data_prep.R | test_that("basic center and scale works", {
# check on very basic data
expect_identical(center_and_scale(1:3), c(-1, 0, 1))
# check on random data
ck_data <- stats::rnorm(100, mean = 10, sd = 4)
ck_data_cs <- center_and_scale(ck_data)
ck_data_manual <- (ck_data - mean(ck_data)) / stats::sd(ck_data)
# fi... |
afdfddbbb4f918863de68bb531537edaf86d66c1 | fdea7c2cb18296bcb6117b6bc72dd4f9bb91b66f | /rscripts/readNewPhyto.R | e22c7de5ea19494616f6e5eda8660caa84aad8c4 | [] | no_license | USEPA/Phytoplankton-Data-Analysis | 71413b111cff0bdb7be831e06b054388a877fc63 | b16557b84e41cc929dd3f3c85ba9fd114a492d3a | refs/heads/master | 2021-03-27T12:46:45.669570 | 2016-09-30T17:31:49 | 2016-09-30T17:31:49 | 15,807,175 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,250 | r | readNewPhyto.R | ################
#### Will Barnett, August 2016
################
################
#### This script is called from masterScript.R, and reads in
#### the 1988 / 2012 / 2014 files provided by Nathan Smucker in April 2016
################
## Change working directory
datDir <- "originalData/algae/EFR Phytoplankton Data... |
0c64d0690f33aeebcd4c4f40b399f840dc436adf | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/pangoCairoShowLayoutLine.Rd | 1bd3d15d39a257672e9058baf86468b6fa56585c | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 585 | rd | pangoCairoShowLayoutLine.Rd | \alias{pangoCairoShowLayoutLine}
\name{pangoCairoShowLayoutLine}
\title{pangoCairoShowLayoutLine}
\description{Draws a \code{\link{PangoLayoutLine}} in the specified cairo context.
The origin of the glyphs (the left edge of the line) will
be drawn at the current point of the cairo context.}
\usage{pangoCairoShowLayoutL... |
ee4bdee91a9c5c57fba89c1c54acc8aaf700dfd9 | 33a08ca3c15a5fc678d78777b53d8868a7bc7897 | /R/Segmentation.R | 51216442335f54bc68afa144f5f772b7e34c7419 | [] | no_license | hjanime/DCS | 09f10d37d549eae3837b1c15d65d319f8c45e8ea | 33570353b7812fc0d1fb2744cc34d6e92375b90f | refs/heads/master | 2020-03-23T02:44:05.383267 | 2018-07-15T01:18:57 | 2018-07-15T01:18:57 | 140,989,262 | 0 | 0 | null | 2018-07-15T01:17:32 | 2018-07-15T01:17:32 | null | UTF-8 | R | false | false | 3,561 | r | Segmentation.R | # Purpose : Using the border score, identify candidate boundaries in each group.
#' Call Peaks
#'
#' Scans across a chromosome. Calculates the mean and variance of the border
#' score within the window [focus-h,focus+h]. Identifies foci with a
#' standardized border score above the threshold.
#' @param foci Focus coo... |
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