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56cd7f3445e947cd437cd7432d714dc197b16c78 | d85eb408d71b710a1eeef67042020145382d0d00 | /man/sumRaster.Rd | b3ac1cb1304ae4ea1b90b410990798762e213546 | [] | no_license | jcarlis3/umbrella | 8e74e3c7e6391e77c9466b6a731176cf9fdafd95 | 62ef91c91ad1b3952c55c373b5741fb36d14c51f | refs/heads/master | 2022-05-22T02:08:16.832137 | 2022-03-25T16:44:05 | 2022-03-25T16:44:05 | 37,620,665 | 1 | 1 | null | 2017-11-08T00:13:39 | 2015-06-17T21:01:40 | R | UTF-8 | R | false | true | 898 | rd | sumRaster.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sumRaster.R
\name{sumRaster}
\alias{sumRaster}
\title{Sum of raster within polygon}
\usage{
sumRaster(rast, poly)
}
\arguments{
\item{rast}{A raster object of class RasterLayer.}
\item{poly}{A spatial polygon object of class SpatialPolygons.... |
199e2e9b6d7664f576ce4f264f89bcce012d5a65 | acb0fffc554ae76533ba600f04e4628315b1cd95 | /R/CompilePhytos.R | 40115ae66628318b974c5f02d12e2aab19ca8eb9 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | lukeloken/USBRDelta | 83826e12a5b5a2e81adeb2119e9c2599a5f8b870 | fd6569385776d4579748b6422b5153e64606e0ba | refs/heads/master | 2021-06-09T19:08:01.976985 | 2020-05-28T21:51:10 | 2020-05-28T21:51:10 | 145,152,807 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,508 | r | CompilePhytos.R | # Consolidate phyto data
library(readxl)
library(plyr)
library(lubridate)
source('R/read_excel_allsheets.R')
source('R/g_legend.R')
# Project folder where outputs are stored
dropbox_dir<-'C:/Dropbox/USBR Delta Project'
#Where data come from
google_dir<-'C:/GoogleDrive/DeltaNutrientExperiment'
PhytoFiles<-list.file... |
c859944041971e5783db3b52e10cdccc92f708bb | 271078c2ead58ca0446d0b05194c70cb1c8dc172 | /plot4.R | 3eef07ce8e3158fccf6e395fee6127f0e8e7d43e | [] | no_license | pocketni/ExData_Plotting1 | 48bc013ab335d5a86111d938da3e300a264f1fe0 | 3ce60121db1300d94790ec8baeddfc25c08b941c | refs/heads/master | 2021-01-22T18:57:42.333688 | 2015-01-11T17:46:57 | 2015-01-11T17:46:57 | 29,093,856 | 0 | 0 | null | 2015-01-11T14:14:42 | 2015-01-11T14:14:42 | null | UTF-8 | R | false | false | 1,162 | r | plot4.R | power <- read.csv("household_power_consumption.txt", sep=";", na.strings="?", as.is=c("Date","Time"))
datea <- "1/2/2007"
dateb<-"2/2/2007"
powerseta <- subset(power, Date == datea)
powersetb <-subset(power,Date==dateb)
powerset <- rbind(powerseta,powersetb)
attach(powerset)
####
#problem 4
png(file="plot4.png", widt... |
d1ac3a8cb2d11171026d9415f12394d061eba38f | 4804e4a4166a33faf98e9ad3df60757d94a0f1d9 | /R/konfigurujKnitr.R | b84cf6d93383f7baf28890393128bd67a3079f15 | [
"MIT"
] | permissive | zozlak/MLAK | 958cb673939b684657ff88f141145f038ed2d89a | 89e88050814b2ff2594669eb38ad198163e13b87 | refs/heads/master | 2021-06-01T11:34:57.797493 | 2020-07-09T08:51:11 | 2020-07-09T08:51:11 | 23,737,268 | 2 | 5 | null | null | null | null | UTF-8 | R | false | false | 838 | r | konfigurujKnitr.R | # funkcja ustawiająca opcje konfiguracyjne knitr-a,
# tak by bardziej odpowiadały naszym potrzebom
#' @import knitr
konfigurujKnitr = function(){
opts_chunk$set(
'error' = FALSE, 'warnings' = FALSE, 'message' = FALSE,
'echo' = FALSE, 'results' = 'asis'
)
if(!is.null(opts_knit$get('rmarkdown.pandoc.to')... |
63e79588523cbf20290fc1d924c1df1572f54c7f | 034a190b9920e3cb8ee16f62007845c916eb6338 | /man/cnaPanCO.Rd | aae1a4d076846c55f9e972570b6faae80bb99434 | [] | no_license | KalariRKLab-Mayo/panoply | 04c67a09fc9e676f6231d6af45d25fe744064505 | b49c4123d5b3750016bed3c920d542d5aefe1024 | refs/heads/master | 2023-08-07T15:16:47.121134 | 2019-08-07T17:52:54 | 2019-08-07T17:52:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 794 | rd | cnaPanCO.Rd | \name{cnaPanCO}
\alias{cnaPanCO}
\docType{data}
\title{
DNA CNV (germline) and CNA (tumor) data for the TCGA Colon Cancer Subjects
}
\description{
Per-Gene DNA Copy Number Variation (germline) and Copy Number Alteration
(tumor) data for the TCGA Colon Cancer Subjects
}
\usage{data("cnaPanCO")}
\format{
A data frame w... |
d6e6b3c60cbdc0fdfcde7d250693846b763abcbb | 874be2b31a5838cb274dbf959cead0d6285714d2 | /Suppl_Figure_SAAV_chapter_OmicCircos.R | 3491816bc6c65bf901d81783417824312219a7e6 | [
"MIT"
] | permissive | bszeitz/MM_Segundo | d234a616f9df65c7898a422efdd89089addd4b71 | 7504491733d60fa61f3ab658dfea4f8910c36769 | refs/heads/main | 2023-08-21T23:09:46.039171 | 2023-08-12T12:00:35 | 2023-08-12T12:00:35 | 378,593,315 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,518 | r | Suppl_Figure_SAAV_chapter_OmicCircos.R |
#### Suppl.Figure for SAAV chapter (OmicCircos plot)
options(stringsAsFactors = F)
library(OmicCircos)
library(readxl)
setwd("C:/Users/User/PhD/MM_segundo/")
excelfile <- "SAAV_chapter_SupplTable_final.xlsx"
Verified.SAAVs <- as.data.frame(read_xlsx(excelfile, sheet=2))
Verified.SAAVs$Name <- paste(Verified.SAAVs... |
d1ad43618e527db6e80bd7e5dc4fe2249b22159c | 724167e44c800d9e8da1f85a8d412ab9d16333c5 | /man/seq_read_write.Rd | f5c3c5140d285036cfdab45b1962e6523f822549 | [] | no_license | cran/krm | 4186963ea959c2c747d852dc68ad247ba9bd94af | 96e5c5c18e6e0d200c568863888587694c71619b | refs/heads/master | 2022-11-07T16:38:22.194183 | 2022-10-18T06:40:11 | 2022-10-18T06:40:11 | 17,696,938 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,762 | rd | seq_read_write.Rd | \name{readFastaFile}
\alias{readFastaFile}
\alias{writeFastaFile}
\alias{aa2arabic}
\alias{string2arabic}
\alias{fastaFile2arabicFile}
\alias{selexFile2arabicFile}
\alias{stringList2arabicFile}
\alias{arabic2arabicFile}
\alias{readSelexFile}
\alias{readSelexAsMatrix}
\alias{arabic2fastaFile}
\alias{readAr... |
7a55b443b33d3960221f575bdbd02b5430362d1b | c86e9c80957dffc72d9facd1614376c3d0c9d322 | /LexisNexis/Review/LNDataReview-Set-2019-12-03-CourtCaseTypeOpinion-B.r | 052175d0dc426124c2479a3937f42e1ac01dc593 | [] | no_license | tbalmat/Duke-Law | ca4ae8c10235a3fa72b2c14a945dba712de4f8e2 | be82053c63070f67fb88d961842a26ea6c6de3f1 | refs/heads/master | 2022-10-06T19:28:37.270716 | 2020-06-05T16:37:38 | 2020-06-05T16:37:38 | 261,865,591 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 63,572 | r | LNDataReview-Set-2019-12-03-CourtCaseTypeOpinion-B.r | # Duke University Law Appeals Analysis
# Review of 2019-12-03 LexisNexis Data
# Followup issues to report, resulting from 2019-04-16 team meeting
options(max.print=1000) # number of elements, not rows
options(stringsAsFactors=F)
options(scipen=999999)
options(device="windows")
library(ggplot2)
#library... |
7831c0f460709ead70ac3dabdecbf8e25b83d0fb | 6519f4b85c9ac0597e1b00716adf3f2ae7641121 | /figureS4_credible_interval/BEST/BESTexamplePower.R | d0a67974a09c369aafe5cca468c4afecbe98c071 | [
"MIT"
] | permissive | flu-crew/n2-diversity | 0a9409e31c730c87561b81a6c0265f8836e573af | a0e164fc241b6c4ffab7b1c0f71e11facd8c7706 | refs/heads/master | 2023-08-02T06:32:29.027201 | 2021-09-14T16:06:25 | 2021-09-14T16:06:25 | 267,682,821 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,904 | r | BESTexamplePower.R | # Version of May 26, 2012. Re-checked on 2015 May 08.
# John K. Kruschke
# johnkruschke@gmail.com
# http://www.indiana.edu/~kruschke/BEST/
#
# This program is believed to be free of errors, but it comes with no guarantee!
# The user bears all responsibility for interpreting the results.
# Please check the webpage a... |
3f991add69b843ec502d4b6ace01320605698f94 | 27652814ed58788adc7c07e327825aaca1ea4034 | /DMC_KT.r | 086ae5a55c9fd8368398da107a5111c1a8719027 | [] | no_license | Libardo1/CAPSTONE-1 | 33c0496f014e5ec458b592ed08f06be93d5c4622 | dc909977bd71b294a72cffdcb72f7138cac2bd09 | refs/heads/master | 2020-12-25T08:42:55.344999 | 2014-06-06T16:46:38 | 2014-06-06T16:46:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,998 | r | DMC_KT.r | # Required libraries
library(lubridate)
library(beanplot)
library(doBy)
library(modeest)
library(plyr)
library(psych)
# We can each add in our working directories here - just un# and # as you check code out and back in
# setwd("C:/Users/Jim Braun/My Documents/Predict 498 Capstone/Data Mining Cup")
#
#
#
# Read in dat... |
5a59d6386f8b97b2f5701f9ac26df9e73f1badf0 | 876ce11ab6150c9bd312a53b39ae4b4918eb3520 | /HW7.R | 82a2eb705aa3738423899f14cba8c3bfaa64936b | [] | no_license | Muu24/stat744 | 2138778dce07e6e2ed6c99e5f7e806147ac01a32 | 8e1882a4e165165b080e4f7d59b657e668f2e219 | refs/heads/master | 2021-05-13T13:57:09.738598 | 2018-05-01T19:07:09 | 2018-05-01T19:07:09 | 116,723,367 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,315 | r | HW7.R | library(leaflet)
m <- leaflet()
dat <- addTiles(m)
##Test Beijing
addMarkers(dat,lng=116.391, lat=39.912, popup="Beijing")
##Test Major cities in China
##https://zh.wikipedia.org/wiki/%E4%B8%AD%E8%8F%AF%E4%BA%BA%E6%B0%91%E5%85%B1%E5%92%8C%E5%9C%8B%E5%9F%8E%E5%B8%82%E4%BA%BA%E5%8F%A3%E6%8E%92%E5%90%8D
dat1 <- read.csv(... |
76553961d97f370e62295a65217274d71d28f131 | 9dcc1b98baf0d4df40ef9470330993660d725bca | /man/new_classification.Rd | 1d850d731a8801f28835c6d99c58b7c16ac2bd0e | [
"MIT"
] | permissive | ropensci/taxa | b1aa00a0d8256916cdccf5b6a8f39e96e6d5ea9c | ed9b38ca95b6dd78ef6e855a1bb8f4a25c14b8fd | refs/heads/master | 2022-04-30T23:28:44.735975 | 2022-04-12T05:10:10 | 2022-04-12T05:10:10 | 53,763,679 | 40 | 9 | NOASSERTION | 2021-07-08T18:11:32 | 2016-03-13T02:27:40 | HTML | UTF-8 | R | false | true | 671 | rd | new_classification.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/classification.R
\name{new_classification}
\alias{new_classification}
\title{Minimal classfication constructor}
\usage{
new_classification(taxonomy = taxonomy(), instances = integer())
}
\arguments{
\item{taxonomy}{A \code{\link[=taxonomy]{ta... |
748ea28c7c477e07e43d0477fdd8e216eb20b3d9 | d0257bb73f8ea868b66500f48abbb9463a2b3629 | /man/test_goodness_of_fit.Rd | feb9542e848fa77f749f4aa93279944a87de70d1 | [] | no_license | adamtclark/gauseR | 014b31f6f9ff89e80f80d491ee6a8c87f855918a | c3bb249c4253851b6bf5c5180dbd9d0ba1cdc3ba | refs/heads/master | 2021-11-30T22:20:21.132519 | 2021-11-28T12:08:19 | 2021-11-28T12:08:19 | 247,702,098 | 5 | 2 | null | 2020-03-16T12:56:57 | 2020-03-16T12:55:31 | R | UTF-8 | R | false | true | 1,900 | rd | test_goodness_of_fit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/test_goodness_of_fit.R
\name{test_goodness_of_fit}
\alias{test_goodness_of_fit}
\title{Test goodness of fit}
\usage{
test_goodness_of_fit(observed, predicted, bycolumn = FALSE, droptimecol = TRUE)
}
\arguments{
\item{observed}{A vector or mat... |
5b7e1c71a34441acef1a7864512c44c4632590b4 | c16e9ad8ca7ac5da16b51a8ef93cb7eb333c98db | /barPlot.r | efdd750ea45604fca44083e5827e5c4123a5bfd1 | [
"MIT"
] | permissive | Jefftopia/r_scripts | 74f6d67dbce225d46e444483afcd7d1b8255066b | 8578e1b5726a29ad8121b31ba731b7cff4286eb5 | refs/heads/master | 2016-09-06T18:31:26.658806 | 2014-05-30T16:00:13 | 2014-05-30T16:00:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,159 | r | barPlot.r | plotBarChart <- function(data,x,title,y=x,h=5,w=5,facet=FALSE,professional=FALSE,color=FALSE) {
require(ggplot2);
require(ggthemes);
# if x is numeric, cut into quantiles, then store as factor; else, factor x
if (is.numeric(data[[x]])) {
quant <- quantile(data[[x]])
data[[x]] <- factor(cut(data[[x]],quant))... |
1fc9f84314991b911817b28eef2eada91af8f092 | f317887c7d83e62235ba2cf19065dcef9244f645 | /man/textTable.ftable.Rd | 77a5ed89a3239359637b573190687da7c50faf1f | [] | no_license | rrprf/tablesgg | 3fec64842266f8a7f28e29899d31c673b5dad09c | 1a60f894869326b34eff1804c9378a1c05e78a79 | refs/heads/master | 2023-05-07T14:12:05.102317 | 2021-06-03T14:45:34 | 2021-06-03T14:45:34 | 318,291,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,485 | rd | textTable.ftable.Rd | % Auto-generated documentation for function textTable.ftable
% 2021-06-02 11:12:19
\name{textTable.ftable}
\alias{textTable.ftable}
\title{Create a \code{texttable} from an \code{ftable} }
\description{
Create a \code{textTable} object representing a flattened multiway
contingency table.
}
\usage{
\method{textTable}... |
f6b171bad267008fa62688b6030351c7122883e8 | aacce0404d82b281d2e08c475066bde0c0088c5c | /man/parse_catch_legacy.Rd | f25078dc9d72272aa5ca8fd3746cba3e1153b7dd | [] | no_license | Rindrics/gyokaikyor | ec4a152cbc8fe590b6c103bfe748d1c40436c869 | 72cec892eb921e6202464c03fff506b3d689525f | refs/heads/master | 2022-10-02T00:58:33.036355 | 2020-06-05T02:28:49 | 2020-06-05T02:28:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 385 | rd | parse_catch_legacy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parse_legacy.R
\name{parse_catch_legacy}
\alias{parse_catch_legacy}
\title{Parse legacy Excel data into data frame}
\usage{
parse_catch_legacy(legacy)
}
\arguments{
\item{legacy}{List which contains
\itemize{
\item{fname}
\item{spcs}
\item{ye... |
47e1991cc3f9991adff97d618e101c70b954fb83 | cb3371ecfa7ae3706e09355e381d1c79c9d6f859 | /tests/testthat/test_tau.R | d93dbb759bcfb00de0c1783f2cfdf9fa93b6ec68 | [] | no_license | cran/ircor | b046d2504f1a5552d1bf6fe2efb957b19a6f9b38 | 4680cdc3390308121faa0b8b6f1e8df74249bd48 | refs/heads/master | 2021-01-19T14:52:36.698551 | 2017-08-21T08:07:29 | 2017-08-21T08:07:29 | 100,934,209 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,093 | r | test_tau.R | context("tau")
test_that("tau", {
expect_equal(tau(set1$x, set1$y), .9111, tolerance = 5e-5)
expect_equal(tau(set2$x, set2$y), .2889, tolerance = 5e-5)
expect_equal(tau(set3$x, set3$y), -.6889, tolerance = 5e-5)
# check symmetry
expect_equal(tau(set1$y, set1$x), .9111, tolerance = 5e-5)
expect_equal(tau(s... |
62187c45ad54f2bd4be8bc5797fb8d3fdf8ac65c | 034b1554dfb45410ea0a41e989aab21fb845e4a4 | /R/fixDates.R | 383df2bd201d89d6799efaac535de2869e85db40 | [
"MIT"
] | permissive | tomjemmett/sqlhelpers | 6139f817681eec7f85cc9198104720f611839e31 | eea4232ca66d711134681158a716436f0555b5e7 | refs/heads/master | 2020-04-01T02:08:04.786711 | 2019-07-22T13:43:13 | 2019-07-22T13:43:13 | 152,766,420 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 263 | r | fixDates.R | #' @importFrom purrr map
fixDates <- function(...) {
dots <- list(...)
# if ... was a single list, then we need to just select the first item of dots
if (length(dots) == 1 && is.list(dots[[1]])) dots <- dots[[1]]
purrr::map(dots, convertDateToString)
}
|
9b25f0c8a22bdfc59faadd781880d441cb16430e | cb89a6a2391a80411254649e5845df77c0309f56 | /R/optVoicing.R | 658096955b3923aea8009d9a9ac4c75cfee51cdf | [] | no_license | simphon/PP2020 | 5fd8f9ca193ede57afc16ee6ea6b2c9a1a0d1705 | 0fabae638786ab175c1cc15fbfd5d173c17298bf | refs/heads/master | 2022-12-19T14:01:27.030766 | 2020-09-14T10:55:47 | 2020-09-14T10:55:47 | 293,513,254 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 42,663 | r | optVoicing.R | # optVoicing.R
# =============================================================================
# Daniel Duran
# Albert-Ludwigs-Universität Freiburg, Germany
# daniel.duran@germanistik.uni-freiburg.de
# http://simphon.net/
#
# =============================================================================
# ... |
8f35601d5613b830ea425a2eb4c8cfa408a76d05 | 196d6a47ef55f1c68584ef443dcc611d1e21835d | /data-raw/data_funnel.R | 379d3970dccb50731572c3d3fd32c1c325301e74 | [] | no_license | datastorm-open/rAmCharts | 452083b44658b99423ae790f176c856eae24f4c5 | 5f61ad704aa77fbe2af7fc6b90d96e7873615d69 | refs/heads/master | 2022-10-06T04:54:36.784313 | 2022-09-29T07:47:42 | 2022-09-29T07:47:42 | 38,245,790 | 42 | 18 | null | 2020-11-19T11:11:13 | 2015-06-29T12:10:46 | JavaScript | UTF-8 | R | false | false | 553 | r | data_funnel.R | {
data_funnel <- data.frame(description = c("Website visits", "Downloads",
"Requested price list",
"Contaced for more info",
"Purchased", "Contacted for support",
... |
b9a75dc49f972bad14917480b895cb5ffd64383c | 59f81a70f64033fd369621255d91ad623f3c2588 | /R/createLocalList.R | 20c9118fdf07197702ed0d36c931e5d28663013e | [] | no_license | cran/clampSeg | 2cc1699d18534579f359daf39e03262225328314 | 6a91363844f0090bbadc8529a7d76d1baf23826f | refs/heads/master | 2022-02-09T01:47:20.547322 | 2022-01-27T22:10:06 | 2022-01-27T22:10:06 | 93,599,323 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,932 | r | createLocalList.R | createLocalList <- function(filter, method = c("2Param", "LR"),
lengths = if (method == "LR") 1:20 else 1:65) {
if (!is(filter, "lowpassFilter")) {
stop("filter must be an object of class 'lowpassFilter'")
}
method <- match.arg(method)
if (!is.numeric(lengths) || any(!is.fi... |
908b32f15922cead4ce09348c66926f3036918e3 | cdb4a5d68b7a37b58b38145a559ec5d25250b183 | /code.R | 8cdcebe4c80ad42ea74aae7d0967f5b0bc659c63 | [] | no_license | jQSfire125/MovieLens | 3384025c52fdc95d800314b7a135d9a36c44f8d8 | a4bc8f1b495cc4618581c21b3f847ac1de34d972 | refs/heads/master | 2023-06-06T07:28:08.115866 | 2021-06-25T19:42:17 | 2021-06-25T19:42:17 | 372,559,959 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,656 | r | code.R | # 1 Overview and Introduction
# 1.1 Library imports
# Make sure the user has the required packages
if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org")
if(!require(caret)) install.packages("caret", repos = "http://cran.us.r-project.org")
if(!require(data.table)) install.packages... |
48831840f96b47cbbb48e79d9f3a7a10292d317a | 74949d1ef5c649def97ea32b64e09d228a74d67d | /plot1.R | 35bc733adb56f9afe51fce51654946520c21f7bf | [] | no_license | homeupnorth/exploredata1 | b212e808622b876818f43b230eed3e074bbd8811 | 74e083f443b50a6c34633e17eaf7a4e468689cdf | refs/heads/master | 2020-06-01T11:48:07.985762 | 2015-03-08T15:17:42 | 2015-03-08T15:17:42 | 31,853,883 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,221 | r | plot1.R | # Exploratory Data Analysis
# Project 1
# March 4, 2015
#
setwd("~/Documents/Coursera/Exploratory Data Analysis")
# download data from https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# unzip in my working directory
# read into R
powerdata <- read.csv("~/Documents/Coursera/Explorato... |
4214d7e700b69cde62c1f48c3579a69ee643aa32 | 257b39265a6b796d54e0e861825984e7e205bbd8 | /man/createTidyFromMatrix.Rd | 68198d167fe242fabe76df0a8e1d5bc83af01715 | [] | no_license | yaoguodong/zFactor-1 | 230c8576f004efb6bde669c60e249fd36134ca4f | 66d6f0732e35c8e84bcd98d28251a0badc7fe423 | refs/heads/master | 2020-04-20T04:26:18.046950 | 2017-10-23T06:22:46 | 2017-10-23T06:22:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 685 | rd | createTidyFromMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{createTidyFromMatrix}
\alias{createTidyFromMatrix}
\title{Create a tidy table from Ppr and Tpr vectors}
\usage{
createTidyFromMatrix(ppr_vector, tpr_vector, correlation)
}
\arguments{
\item{ppr_vector}{a pseudo-reduced ... |
1bc01cbb1b55a3adfa9f91ad092869fa20db07e4 | 110fed67a467812e9b7642bca98168883926d710 | /src/jd_nb_script.r | b23121bf8b17cff94bfcea8098e852b76bd71f5a | [] | no_license | tjbencomo/Titanic | 306291ece3b8bd44540c772c97ef19d0d91cd043 | b37387c95452900b4be234ba62d73aa7f7c4be75 | refs/heads/master | 2021-01-01T16:14:55.518623 | 2017-10-20T23:42:41 | 2017-10-20T23:42:41 | 97,794,465 | 1 | 1 | null | 2017-10-20T23:42:42 | 2017-07-20T05:36:31 | Jupyter Notebook | UTF-8 | R | false | false | 992 | r | jd_nb_script.r | #The data used for analysis in this notebook is from the dataframe train_edited.csv which was cleaned by Tomas Bencomo.
# Further description about how the file was cleaned can be found in his notebook.
# Import necessary libraries
library(ggplot2)
# import the data
df <- read.csv('datasets/train_edited.csv')
head(d... |
3e95bcca63cb969c40909a4976cb6d85bee6c713 | 19a29f667faf1a30b4aaadc84c6007c6d5df150f | /processed_real_data_models/cr_card_models.R | fb37e626ba874e8b3c58bf662abb1e1a28470595 | [] | no_license | yordanivanov92/MT-Fin_Fraud_Detection | 71a96e0c2966a799a04f572543036c5c70913c00 | 137f828447c254e7fd767c8baa6f2a0a13d2db34 | refs/heads/master | 2021-05-15T18:11:36.209834 | 2018-05-06T15:51:26 | 2018-05-06T15:51:26 | 106,425,028 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,089 | r | cr_card_models.R | # Using real credit card data from Dal Pozzlo
library(dplyr)
library(caret)
library(DMwR) #SMOTE
library(purrr)
library(pROC)
library(gbm)
library(PRROC)
library(caTools)
set.seed(2142)
###########################################################################
############################### BankSim data ############... |
eea2ea6f76069f0a63218d4d68fa5991f45ec109 | 766555a2ce29b4c79602bc10b404782ddeaf1eb5 | /man/tsal-boot.Rd | d1c398b4220f813198b5dfbaeb889ef781b92e0e | [] | no_license | cran/tsallisqexp | 2b5c5356d70b23684b096c94ec066eeb375cc876 | 70af02c7d313e10da43ba9a03a402d6ee5aeec5d | refs/heads/master | 2021-07-16T17:51:53.501020 | 2021-02-10T04:50:02 | 2021-02-10T04:50:02 | 32,381,379 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,886 | rd | tsal-boot.Rd | \name{tsal.boot}
\alias{tsal.boot}
\alias{tsal.bootstrap.errors}
\alias{tsal.total.magnitude}
\title{Bootstraps methods for Tsallis Distributions}
\description{
Bootstrap functions.
}
\usage{
tsal.bootstrap.errors(dist=NULL, reps=500, confidence=0.95,
n=if(is.null(dist)) 1 else dist$n,
shape=if(is.null(dist)... |
782116abf4acb3df89569f4b7851f5f5a3c44422 | 495408837d7bc870a07c9f59a66900959ae3bed5 | /test-triang.R | b1314ef2975fdef1b34a7ee48253c2e899f5a77c | [] | no_license | bogdanoancea/pop_test | 5099204970c5f7082570370115f26eca94513d0f | 05a5ffe9ecad01d495273dd457c5efe3f557c6c7 | refs/heads/master | 2021-09-13T22:33:03.608104 | 2018-05-05T09:47:25 | 2018-05-05T09:47:25 | 119,249,914 | 0 | 0 | null | 2018-01-28T19:42:39 | 2018-01-28T10:52:43 | R | UTF-8 | R | false | false | 2,772 | r | test-triang.R | library(rbenchmark)
library(pestim)
dtriang2 <- function(x, xMin, xMax, xMode){
if (any(xMin > xMax)) stop('xMax must be greater than xMin.')
if (!(all(xMode >= xMin) & all(xMode <= xMax))) stop('xMode must be between xMin and xMax.')
n <- length(x)
if (length(xMin) == 1) xMin <- rep(xMin, n)
if (length(... |
bc8b80e5bd18d04ec7765796963f7a3e8bf07349 | 67be68eee5fa348fbff1cf5c435d7347a255a677 | /cachematrix.R | fdc774c1a207d9d415b1fca31eacd7bc58cd6c1e | [] | no_license | gvtorres/ProgrammingAssignment2 | de2aba6533855af07878f28e32396c4972ac6594 | 2794df7d04387366972ac5af31d072a26ccb9e5a | refs/heads/master | 2021-01-22T09:27:42.936575 | 2015-04-23T11:33:25 | 2015-04-23T11:33:25 | 34,422,763 | 0 | 0 | null | 2015-04-22T23:55:52 | 2015-04-22T23:55:51 | null | UTF-8 | R | false | false | 965 | r | cachematrix.R | # This R file contains the functions corresponding to Program Assingment
# 02 from the R Programming course
# The maing goal is to learn about the <<- operator
# This function creates a special kind of matrix that can
# cache its inverse and call it in order to avoid unecessary
# computations
makeCacheMatrix <- func... |
87390786826cd3d3c4c641b7a662c07a7ff1839c | 6f41667e796a7a27e6d73707ae781caf28c581ff | /cachematrix.R | 4e0090b6f556fafb8261504876bd369b23e1929c | [] | no_license | Laffite-Buffon/ProgrammingAssignment2 | ade5ae9da65a85d3a1c85ef3a89ce84626d963ca | 67fdd4ce84225a5216707f8dab7ca5785f2edeeb | refs/heads/master | 2021-01-18T14:53:55.193698 | 2015-10-25T20:24:35 | 2015-10-25T20:24:35 | 44,774,557 | 0 | 0 | null | 2015-10-22T21:33:09 | 2015-10-22T21:33:09 | null | UTF-8 | R | false | false | 2,634 | r | cachematrix.R | ## makeCacheMatrix is a function that creates a special function with a
## characteristics to be able to return a solved matrix obtaining it the
## from the cache. To do that, after must apply the cachesolve function.
## the first function has several parts:
## Firstly, It assign a value NULL for "slv".
## Secondly,... |
43bc00dea681ecf1b941a736aa8295403ecc4220 | 4dbfcd501cf1acd63a8f956a40c90993aef00ce3 | /R/All_new_functions.r | 7beaa552bab46a5471820d0611da2f9453f46c22 | [
"MIT"
] | permissive | changwn/ICTD | 11a21902439f534d364bf518aef510d8814133cf | acb0d5c2c859b4c756e1ff50e6624046a2f68d36 | refs/heads/master | 2021-06-24T14:06:40.249973 | 2021-03-30T22:44:39 | 2021-03-30T22:44:39 | 224,000,547 | 3 | 0 | null | 2019-11-25T17:06:23 | 2019-11-25T17:06:22 | null | UTF-8 | R | false | false | 14,435 | r | All_new_functions.r |
MRHCA_IM_compute_full_pub_new<-function(data_CORS_cancer,list_c,IM_id_list,immune_cell_uni_table=marker_stats20_uni,step_size0=20)
{
MR_M<-list_c[[1]]
cor_c<-list_c[[2]]
down_lim<-list_c[[3]]
print("Compute MR IM genes")
MR_IM_result_c<-list()
print(nrow(MR_M))
s... |
658a5c4e1d7ddc86f1772334c598253e27a821d8 | 13110ac3fe1f3de135975f586e3b995ecb4588d2 | /R/upm.R | cba0699a836e4491b8e88121fcc2e32ef4fce15e | [] | no_license | biostata/tpidesigns | e933b32cd99cc522e9afdbdbf09210e1cc5e439b | 215a886f48d0dc7dd3ebd838e3f32fa1e1c73fa1 | refs/heads/master | 2022-03-15T23:15:50.532759 | 2019-12-04T04:07:13 | 2019-12-04T04:07:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,775 | r | upm.R | #' Calculation of Unit Probability Mass
#'
#' \code{UPM} calculates Unit Probability Mass for an interval (a, b) when the Underlying distribution is beta or mixture of two beta distributions.
#' @import stats
#' @param a,b Range Parameters between which UPM is needed to be calculated.
#' @inheritParams weights_formulat... |
dc42810662989a31a39f0af384e1632d39fb4ec5 | 96ddc8c398f162250ebaef2ab76436ab5a60c3bb | /pre_process/optimize.R | c4d1c360d7ea90860ed72f791473986f9d473407 | [] | no_license | kylemonper/EDF-forest-mgmt | 670ebb91547420d5d1379ea8daabdeb92709cb89 | 216d23f78648eac2c97a716b01d1e7c1514435cb | refs/heads/master | 2022-12-25T10:05:10.522874 | 2020-09-30T16:05:18 | 2020-09-30T16:05:18 | 272,543,764 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,353 | r | optimize.R | #####################
##### Optimize #####
###################
library(tidyverse)
### final step:
## we now have final discounted values for each package for this plot, now select the package with the lowest CPU
relative_carb <- read_csv("output_data/relative_carb_og_05.csv")
price <- 200
## new method for selectin... |
68d6a75ccc888c69955546a70763f009352547a3 | 0b4a31d79b6a5258c7e681a74a6b13c71576f853 | /notebooks/CoxRegression.R | b8cfe95515a01fab870f1b8a022846d84300304d | [] | no_license | liacov/OPTproj | 10c7c6f3ad324f1ad3a39066d733c6578376b433 | a5c06347a454e0c206125d146ab43d17bffa003a | refs/heads/master | 2023-03-30T13:09:43.668082 | 2021-04-08T09:22:54 | 2021-04-08T09:22:54 | 265,876,095 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,441 | r | CoxRegression.R | # set directory to where the RNA and Clinical folders are
setwd("/Users/federicomatteo/Downloads/")
library(survival)
# read RNA file
rna <- read.table('RNA/KIRC.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data.txt',nrows=20533, header=T,row.names=1,sep='\t')
# and take off first r... |
9fe3300425a4feac08231e038912051a63c1ad63 | b0b4891b6df683a8b754e4469eb40d2694cb18e0 | /Examens/Examen2017.R | a6f777534736971c6cadb5e8c7d0ba32c2035483 | [] | no_license | JulienNique/M2DM-PC4DS | 79c5c816e414fbd1798ca36bbdef57152e9428ed | ceda6f989b30000827fef6cb3c9271f69c50f31d | refs/heads/main | 2023-01-03T09:12:03.596313 | 2020-10-22T16:50:41 | 2020-10-22T16:50:41 | 302,307,999 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,335 | r | Examen2017.R | ## Fichier: 2017pc4ds-trump.r
## Etudian : D. Trump
## Description : Rendu de l'examen du PC4DS 2017
## Date : 26 janvier 2017
rm(list = ls())
################################################################################
#### ####
####... |
bf4fd9b52deb2ce807809d718fcbc1ba3506239b | 973c2d68485cfc1c6fde427effb95fed8442ca5d | /analysis/switchde/3_parse_switchde_results.R | fa41a6759d54c0f92c054ffe2862ef25ee539ee6 | [] | no_license | kieranrcampbell/ouija-paper | 89bb0965a645901ded92c02515143a40bafbc256 | 02f50ffa002af1cb12dfea6a8196adb803c60845 | refs/heads/master | 2020-11-30T00:34:11.735315 | 2018-02-03T19:33:27 | 2018-02-03T19:33:27 | 95,869,625 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,390 | r | 3_parse_switchde_results.R |
suppressPackageStartupMessages(library(scater))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(readr))
suppressPackageStartupMessages(library(aargh))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(cowplot))
plot_switchde_results <- function... |
7da8d79183b177bb23546b2f3b6c29035e9d2d95 | d9d872e10dfd029fc3fd7f4badf5c384576a0b3a | /Figures/NewVersion.R | e6465e155b6fc908307ee9f2fb9258490d0ee1d5 | [] | no_license | DustinRoten/STACK-project | 7cfebfa3a9bf99d86a1fe11fde4e003584d4e00b | 2d011106deaae5fa2f3161df90ca300824cd86ea | refs/heads/master | 2021-09-14T17:21:14.679431 | 2018-05-16T15:24:40 | 2018-05-16T15:24:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,249 | r | NewVersion.R | library(ggplot2)
library(reshape)
source("TEST-DEMOFunctions.R")
PlantLAT <- 39.28682
PlantLON <- -96.1172
Dispersion <- read.delim("JEC-10000m2.txt", header = TRUE, sep = "")
Origin_Dispersion <- ShiftToOrigin("S", Dispersion, PlantLAT, PlantLON)
Functions <- c("ShiftDispersion", "RotateDispersion", "RadialDilation... |
c398776df99c90312c0af0e832953cbf036f4d2d | fb6f5e9fa22093856c7628acd0e937b6090767de | /code/figure5.R | 228c56cfb6e696341afaacfde59c6c6e5f1117df | [] | no_license | eafyounian/hichip | 1987b59ebe36239680f1318cdb710bac7715d066 | d9fc3dc663c40b9b8aee233d86cfd5194b525acc | refs/heads/main | 2023-09-03T18:58:37.475840 | 2021-11-17T21:45:30 | 2021-11-17T21:45:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,019 | r | figure5.R | # Figure 5 and analysis
# meta_analysis_functional_data.R
#######################################
# functions and libraries
library(metafor)
library(dplyr)
library(ggplot2) # plot
library(ggpubr) # plot
# https://stats.stackexchange.com/questions/30394/how-to-perform-two-sample-t-tests-in-r-by-inputting-sample-statist... |
35ed0eac96f7c7c4f3df56d708d6ac7176fb90eb | f2a0a8fda06fc7c1a7602472aab8569df5101d48 | /man/resettestFrontier.Rd | 6ca8e93247ea95bed2fdf6fcde32b199e8cdb63e | [] | no_license | cran/frontier | 833b64b32ae93e7f5c8333ccbbd3670f0fa12182 | 91725b1e6bb2df9b47c3d9eda2d545996a0f0c54 | refs/heads/master | 2021-01-01T19:07:23.783127 | 2020-04-17T15:10:03 | 2020-04-17T15:10:03 | 17,696,150 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,363 | rd | resettestFrontier.Rd | \name{resettestFrontier}
\alias{resettestFrontier}
\title{RESET test for Stochastic Frontier Models}
\description{
Generalized Ramsey's RESET test (REgression Specification Error Test)
for misspecification of the functional form
based on a Likelihood Ratio test.
}
\usage{
resettestFrontier( object, power... |
427449fee0eb286d187866d4e5eaf6047344de93 | 0fda90787687660b41d4fbf320e4b56372a46b8b | /README.RD | 31ecc2126268f08bb1b4983e121e5b559d4ed756 | [] | no_license | zxcchen/blog | 23ce220e667763738d3001a33ea875e2c6d20952 | 430049c8e9fe6fe098d25c1e015c69a0e67248ab | refs/heads/master | 2021-01-23T03:59:34.939564 | 2018-05-16T08:27:30 | 2018-05-16T08:27:30 | 86,142,401 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,099 | rd | README.RD | 在线预览
http://qiqi.bling.ink
博客系统
为了熟悉Node.js，熟悉现代前端开发使用的各种工作流工具，我开发了这个博客系统。
工程目录结构:
├── README.RD
├── client
│ ├── blogpost.js
│ ├── main.js
│ └── utils.js
├── common
│ ├── common.js
│ └── config.js
├── error.html
├── gulpfile.js
├── jsconfig.json
├── package.json
├── resources
│ ├── css
│ ├── img
│ └... |
a1151572ced0575f53ae9a5c8212c80db0eb1a69 | b4521cfd5b2f3cc2373a8c2087a4a11037f54ef6 | /ui.R | 57c78ba112148c30b1729e6057f2b420df3c2cb2 | [] | no_license | Dark-angel2019/digital-product-coursework4 | 8021eb72f5b198f782fc5c045e0b379edd6f991e | 58e5e505faeb96b5516e16441a2123b47ca7837c | refs/heads/main | 2023-01-30T20:21:11.947132 | 2020-12-14T02:25:14 | 2020-12-14T02:25:14 | 321,208,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,244 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
## An app to calculate the length of hypotenuse of a isosceles right triangle
## ... |
d29d8d008f6f3f9154b2feb471976e64237af984 | 2bb56a3bc4869feca3561351ca8a26e61ed16fb4 | /session3/session3.r | 22ec6cbabee519243983ff6559bcaa8ae979a41e | [] | no_license | mvogel78/2015kurs2 | e42aed33d5cbd587c51c249e874d5aa7505e800c | 078350cf6fb80d34d00d43dd2f5fcdbe9a5206d8 | refs/heads/master | 2021-01-22T04:41:39.737535 | 2015-08-30T18:16:18 | 2015-08-30T18:16:18 | 38,581,661 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,892 | r | session3.r | require(Hmisc)
x <- spss.get("session3/ZA5240_v2-0-0.sav")
## y <- stata.get("data/ZA5240_v2-0-0.dta")
## the column V417 contains the net income,
## calculate the mean using the mean() function!
## What is the problem?
nrow(x)
ncol(x)
mean(x$V417,na.rm = T)
mean(x$V417,trim=0.05,na.rm = T)
## summarize the net... |
d13b4ddec23fa92864ad1aec5f1062bbe580a3c3 | b136b4bfef2449633275481a5aa62c60e32f07bd | /man/cdf.Z.Rd | b0d8b5b406a1a6d4beacef47cfece4377e7cd218 | [] | no_license | cran/MHTcop | cba5339e5c2875ee8d9dfc318aeb132c8e89dcae | 496ee271b9e68adff69523e19dee05c469678ee4 | refs/heads/master | 2020-03-08T17:36:15.334807 | 2019-01-21T15:10:03 | 2019-01-21T15:10:03 | 128,273,287 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 569 | rd | cdf.Z.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cdf.Z.R
\name{cdf.Z}
\alias{cdf.Z}
\title{Evaluate the inverse Laplace-Stieltjes transform of a copula's generator}
\usage{
cdf.Z(cop, z)
}
\arguments{
\item{cop}{The copula}
\item{z}{Argument to the inverse Laplace-Stieltjes tran... |
b5db7bb72331eb35803ef7facbfb99ed49020429 | ed6d7dbac0c32cce7e784b712466878d817f97f0 | /plot4.R | cd86fb04d7ad2e131634e671533628a702b6f314 | [] | no_license | nsdfxela/ExData_Plotting1 | a1eabdccd3f7ac9e9b122c3c222e63fcfa67abdb | 45420d6a4fc041f7af5ef337dabd4764df545cfe | refs/heads/master | 2021-01-21T06:14:14.980633 | 2014-05-11T16:51:52 | 2014-05-11T16:51:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,684 | r | plot4.R |
getData <- function(){
dataPlace <- read.csv("./data/household_power_consumption.txt", sep=";", header=TRUE)
dataPlace["Date"] <- as.Date(strptime(dataPlace[,"Date"], format="%d/%m/%Y"))
dataPlace <- dataPlace[dataPlace[,"Date"]<="2007-02-02" & dataPlace[,"Date"]>="2007-02-01",]
dataPlace$Time <-as.POSIXct( st... |
0295a4ae564d807b3db3f82bb3a7cc2574104514 | a3c78700a65f10714471a0d307ab984e8a71644d | /modules/assim.sequential/R/met_filtering_helpers.R | 523d75bb685ddf92cf025fd69a89e486e3fc8b2b | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | PecanProject/pecan | e42a8a6a0fc9c0bb624e0743ab891f6cf131ed3f | ce327b92bf14498fa32fcf4ef500a7a5db5c9c6c | refs/heads/develop | 2023-08-31T23:30:32.388665 | 2023-08-28T13:53:32 | 2023-08-28T13:53:32 | 6,857,384 | 187 | 217 | NOASSERTION | 2023-09-14T01:40:24 | 2012-11-25T23:48:26 | R | UTF-8 | R | false | false | 2,306 | r | met_filtering_helpers.R | ##' Sample meteorological ensembles
##'
##' @param settings PEcAn settings list
##' @param nens number of ensemble members to be sampled
##'
##' @export
sample_met <- function(settings, nens=1){
# path where ensemble met folders are
if(length(settings$run$inputs$met[["path"]]) == 1){
path <- settings$run$inp... |
9fcb9b79752eb44177b74e36e49e5a2c3d82be32 | 58a1c70f5695d29f54232a261734aae2a675838e | /R/df_점수형.R | 7578069ffc35b009e0dc3f0dfb48201ca5188b51 | [] | no_license | wpsl94/DataMining_Mid-2021 | 090b7e8f5e8d51708dba8067c92976e98e8595c6 | 6718fa74a4da91bdb318ad4f8ed245404bc6d650 | refs/heads/main | 2023-04-15T14:37:19.081979 | 2021-04-28T11:12:07 | 2021-04-28T11:12:07 | 362,017,970 | 0 | 0 | null | null | null | null | UHC | R | false | false | 7,161 | r | df_점수형.R | library(foreign)
library(MASS)
library(dplyr)
library(ggplot2)
library(readxl)
###데이터 재가공(점수형으로 변환)
##2015년
df.wr.2015 <- raw_welfare.2015
df.wr.2015 <- rename(df.wr.2015,
sex=h10_g3,#성별
area=h10_reg7, #지역코드
birth=h10_g4, #태어난 년도
edu... |
9cbf9647222bc736df38bc8477cb80c410980892 | d354983f75228b3aa82eea518f42de95e3fa32f7 | /functions/colorRampPalette/red-to-blue.R | eb5c226f013198f41b64225767bf5b939e91a564 | [] | no_license | ReneNyffenegger/about-r | f1f1d1f6c52f0446207978e78436ccbd91b89d20 | ae511ae632e1f8827cab91d4e36c1a9349fda5ab | refs/heads/master | 2022-01-14T10:19:45.230836 | 2021-12-27T19:50:37 | 2021-12-27T19:50:37 | 22,269,629 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 110 | r | red-to-blue.R | paletteFunc <- colorRampPalette(c('red', 'blue'));
palette <- paletteFunc(8);
barplot(1:8, col=palette);
|
536066e33304eeabb5770000832d8712b62203cb | db8eeb68541dba916fa0ab9567fe9199d95bdb6a | /man/chargeCalculationGlobal.Rd | 3043d4277bc76c561fea51d54b9ec72120313a94 | [] | no_license | alptaciroglu/idpr | f26544ffe869854a0fd636fcf7a2fa85a41efeed | e5f7838d27fb9ada1b10d6a3f0261a5fa8588908 | refs/heads/master | 2023-01-27T20:40:33.186127 | 2020-12-05T21:57:55 | 2020-12-05T21:57:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 6,140 | rd | chargeCalculationGlobal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chargeCalculations.R
\name{chargeCalculationGlobal}
\alias{chargeCalculationGlobal}
\title{Protein Charge Calculation, Globally}
\usage{
chargeCalculationGlobal(
sequence,
pKaSet = "IPC_protein",
pH = 7,
plotResults = FALSE,
include... |
fd5673e7faf17b89b9b2b5c0b9de046b8262a493 | 875c89121e065a01ffe24d865f549d98463532f8 | /tests/testthat/test_collectStrays.R | a3e63cb38dd6025ee63526c3783b2910f3eb0a0c | [] | no_license | hugomflavio/actel | ba414a4b16a9c5b4ab61e85d040ec790983fda63 | 2398a01d71c37e615e04607cc538a7c154b79855 | refs/heads/master | 2023-05-12T00:09:57.106062 | 2023-05-07T01:30:19 | 2023-05-07T01:30:19 | 190,181,871 | 25 | 6 | null | 2021-03-31T01:47:24 | 2019-06-04T10:42:27 | R | UTF-8 | R | false | false | 694 | r | test_collectStrays.R | skip_on_cran()
tests.home <- getwd()
setwd(tempdir())
test_that("collectStrays work as expected", {
xdet <- list(Test = example.detections[1:5, ])
colnames(xdet[[1]])[1] <- "Timestamp"
collectStrays(input = xdet)
expect_true(file.exists("temp_strays.csv"))
output <- read.csv("temp_strays.csv")
expect_equal(nrow... |
0169a227470e89a6f2b6312490a5e25e25b61830 | 83ce22426dd1f7e2cd620b9ba33f13f636b1baec | /ODETest.R | e06ddc780238aa4b4ad0b5ca12ceacaf022d1a84 | [] | no_license | rslasater82/weightloss | cf17133e29e3a1d0dd9b1d88c06d14bb4d389180 | 02176204c58be8cdc0cb52d0ec9aef223c42348f | refs/heads/main | 2023-01-12T23:01:17.590530 | 2020-11-20T20:15:15 | 2020-11-20T20:15:15 | 300,404,776 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 838 | r | ODETest.R | library(deSolve)
parameters <- c(a = -8/3,
b = -10,
c = 28)
state <- c(X = 1,
Y = 1,
Z = 1)
Lorenz <- function(t, state, parameters){
with(as.list(c(state, parameters)), {
#rate of change
dX <- a*X + Y*Z
dY <- b * (Y-Z)
dZ <- -X*Y + c*Y - Z
... |
62e9a6781beaaf66e3c9a10fafaae00f7e7795a8 | c7638d2d2cb0266caa7d13f137018cc9428c6536 | /R/files.R | 809148c2c1750ce1c27c7bda182b1feb9365b03f | [
"MIT"
] | permissive | cran/batchr | 74d6ff7646c9a72ea71d5544f3f8fab4b517339c | 3c9e7fb1098b2f744959432e941e0f042a406d67 | refs/heads/master | 2023-08-12T17:21:26.377485 | 2021-10-03T03:10:02 | 2021-10-03T03:10:02 | 340,015,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,180 | r | files.R | #' Batch Files
#'
#' Gets the names of the files that are remaining to be processed by
#' [batch_run()].
#'
#' [batch_completed()] can be used to test if there are any
#' files remaining.
#'
#' @inheritParams batch_config
#' @inheritParams batch_run
#' @return A character vector of the names of the remaining files.
#' ... |
5d0d88c7b0afc731b6b342d5c858c3d630715b64 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/breathtestcore/examples/read_breathid.Rd.R | 1f2d228d65d401c6d925b6698da343a4d9ea6ea9 | [] | 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 | 277 | r | read_breathid.Rd.R | library(breathtestcore)
### Name: read_breathid
### Title: Read BreathID file
### Aliases: read_breathid
### ** Examples
filename = btcore_file("350_20043_0_GER.txt")
# Show first lines
cat(readLines(filename, n = 10), sep="\n")
#
bid = read_breathid(filename)
str(bid)
|
9ca984b295c772b49d92f1deff2bf46998054d7c | e55ffb2edab5f9658f23c46a23b84c78348b99eb | /rstudio-ws/Visualizing-of-StationGrid-2014/visualizing-of-ClusterCenters.R | 53e7510f7e177cab89662cab04ee98331bd39e02 | [] | no_license | un-knower/hadoop-ws | 6689dd20fd8818f18cfef7c7aae329017a01b8a9 | 913bbe328a6b2c9c79588f278ed906138d0341eb | refs/heads/master | 2020-03-17T23:15:21.854515 | 2015-04-25T08:09:03 | 2015-04-25T08:09:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,438 | r | visualizing-of-ClusterCenters.R | # -----------------------------------------------------------------------------
# 基本图形化展现
# ClusterCenters
# -----------------------------------------------------------------------------
# 运行方法: 在R环境中,使用下面语句
# 修改 中的这两个语句
# dataSetID <- "s01" # s98
# 创建图形输出目录 s01_ClusterCenters
# 执行 - linux版本
# sourc... |
f636dba9a891b0f50a9edb4e83e4b217b6efc280 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GpGp/tests/test_loglik.R | a5de871d47e45d1494d0108bf93ae257f7f71d89 | [] | 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 | 1,372 | r | test_loglik.R |
context("Likelihood Functions")
test_that("likelihood approximations are exact when m = n-1", {
n1 <- 12
n2 <- 12
n <- n1*n2
locs <- as.matrix( expand.grid( 1:n1, 1:n2 ) )
ord <- order_maxmin(locs)
locsord <- locs[ord,]
m <- n-1
NNarray <- find_ordered_nn(locsord,m=m)
NNlist <- gr... |
89817f35b183944f07604d0088044f4c5be327a0 | 2e4675b463ed76047b6875e41e1abd34b48d3692 | /lib/gdh.db.in/man/getSites.Rd | 95bda63a7f177b7cf2899453c87b7fefaf3fc589 | [] | no_license | cerobpm/gdh | a8787d479f88c8125a7af233979b2f4d1db10ead | 57dc8e89771cfe1ce9c151a563aa107068474fc5 | refs/heads/master | 2020-03-21T01:42:31.461283 | 2018-06-21T00:35:20 | 2018-06-21T00:35:20 | 137,957,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,543 | rd | getSites.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gdhFunctions.R
\name{getSites}
\alias{getSites}
\title{getSites (gdh.db.in package)}
\usage{
getSites(con, siteCode = NULL, featureType = NULL, north = NULL,
south = NULL, east = NULL, west = NULL)
}
\arguments{
\item{con}{Conexión a base d... |
8ee3b7056e7450916aba4ce0dbe58ea94935ec33 | 5ce68155b082c4298bf68c77bbbb668460f16a93 | /run_analysis.R | 84b57ebc844ce3ce3028b5247e86287ef7a83736 | [] | no_license | philiprad/GettingAndCleaningDataCourseProject | 3d73d484c8bcb6da27cdb41823fd07ea5eea45ed | ee58cadc14f9677295b05f754187295508b7ce07 | refs/heads/master | 2016-09-05T11:01:44.199744 | 2014-05-25T17:46:17 | 2014-05-25T17:46:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,622 | r | run_analysis.R | #Loading test data
X_test<-read.table("X_test.txt",sep="")
y_test<-read.table("y_test.txt",sep="")
subject_test<-read.table("subject_test.txt",sep="")
test.data.set<-cbind(subject_test,X_test,y_test)
#Loading training data
X_train<-read.table("X_train.txt",sep="")
y_train<-read.ta0ble("y_train.txt",sep="")
subject_tra... |
7a530d59281d36d4367414cae7501c3f8f877bd8 | fa54716d6e66e4c1b7559a8d3164406bdde5eb0f | /kaggle.R | 50fb7e4490e1aaad9b05c1fa7427423a3abf770b | [] | no_license | shrilekha17/Titanic_data_set | 8100c1085e637ba83a7512be567ff57c7456bac8 | df2c2d75b93848af4c1705dd2cb45bd50056a61a | refs/heads/master | 2020-03-20T22:38:29.781446 | 2018-06-18T21:23:21 | 2018-06-18T21:23:21 | 137,807,983 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,121 | r | kaggle.R | read.csv("/Users/shrilekha/Desktop/Kaggle/Titanic/train.csv", header = TRUE)
train = read.table("/Users/shrilekha/Desktop/Kaggle/Titanic/train.csv", header = TRUE, sep = ",")
train
test = read.table("/Users/shrilekha/Desktop/Kaggle/Titanic/test.csv", header = TRUE, sep = ",")
test
head(train)
str(train)
install.packag... |
5a305ee4064bef1b758f15a39b412528ea110683 | 6cc2ba52d7fc77cb9c105397d85b32b8ca90e00a | /Tecan/helpers/plates_helpers.R | 57d428574984de4c8f12a33f587829b0b3db6946 | [] | no_license | Ploulack/HB | dd8abea825a1fc653d14062ab8481d3ed9eaca1e | 9f8fb6fcbdad2b341bcd39bd9256d5ed5e2ab4b2 | refs/heads/master | 2021-09-15T09:04:23.999913 | 2018-04-06T16:15:44 | 2018-04-06T16:15:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,745 | r | plates_helpers.R | repeat_parts <- function(part_keys, n) {
map2(part_keys, n, ~ rep(.x,.y)) %>%
as_vector()
}
parts_to_vector <- function(parts) {
repeat_parts(parts$part, parts$n_pcr)
}
primers_to_vector_repeated <- function(parts) {
c(repeat_parts(parts$l_primer, parts$n_pcr),
repeat... |
6c2495135fab06f260cd3c980d58566708630290 | aa2a544ee1dbdc89b96ea937b3370884e604f7bd | /man/lookup.enm.Rd | d441479e2b22414f1481d124c25fe7d38c33ff37 | [] | no_license | jamiemkass/ENMeval | dae21510cf7978ff7a6c446b98db310a86afa2a8 | 199bf0181716b25ea5033be16ed8c6efadcfbd95 | refs/heads/master | 2023-08-15T03:42:15.250740 | 2023-01-09T10:47:05 | 2023-01-09T10:47:05 | 29,864,043 | 16 | 13 | null | 2023-06-21T14:31:07 | 2015-01-26T14:18:11 | R | UTF-8 | R | false | true | 359 | rd | lookup.enm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{lookup.enm}
\alias{lookup.enm}
\title{Look up ENMdetails abject}
\usage{
lookup.enm(algorithm)
}
\arguments{
\item{algorithm}{character: algorithm name (must be implemented as ENMdetails object)}
}
\description{
Internal fun... |
3c60b8866727e7f5512da8b983f765362f40dcaf | eeb5ad87d1cdd86dfedbb969a67b58d46dd05098 | /raw_please_ignore_this_folder/59.R | 8eae2c9606a1a99fed39cc8b93f7f7a70fe74b26 | [
"MIT"
] | permissive | KathrinBusch/16S-AmpliconCorePipeline | a222942e542639fa961face68da1f4687d700392 | 6cda7a2dafb4ad5de03894c8a7cbd0dcf0c8d11a | refs/heads/master | 2023-04-07T05:13:52.250618 | 2022-09-03T19:39:43 | 2022-09-03T19:39:43 | 292,565,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 255 | r | 59.R | R
phy = read.table(file ="phylum_relabund.txt", header = F, row.names=1)
names(phy) = phy[1,] [phy[1,] !="phylum"]
for(i in 1:ncol(phy)){
write.table(phy[,i],row.names = row.names(phy), col.names =F,file=paste0(names(phy)[i],".txt"))
}
quit("no") |
990d28b0a68de61b78dd007c4d996e70ae2a681e | b35ad89464853d9b34fe6837aee196a70c943fd8 | /Revision V01V02 Combined Redo.R | c90dba6abedc0ada1504a00234eebc8549c00b58 | [] | no_license | dijunrui/ScratchSleepMethodPaperQC | 6f36bcf36ecbd2cb2173b37083d0e7c2d9446bf1 | 46429a73c9812a7c91c1ce9c0cd836e54de2f008 | refs/heads/master | 2023-01-08T16:31:12.939479 | 2020-11-04T00:39:07 | 2020-11-04T00:39:07 | 288,811,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,841 | r | Revision V01V02 Combined Redo.R | #######################################################
## Amendment to the analyses for understanding ##
## correlation between prediction and video ##
## for both V01 and V02 ##
## Di, Junrui 10/23/2020 ##
########################################... |
ab567cfe6784426b3db9e80657c3e9da7c3c9258 | b4cf1178b97f1f747f1c6cd8469bb24e1fd8c6e5 | /man/app_vistributions.Rd | 07adb3c4f2d7f225e3e35b98e4defedb306e22ec | [
"MIT"
] | permissive | benitezrcamilo/xplorerr | c0ddc8bd984354db6b6bd3c2f4074a25cb9640d4 | 37c2a74b52760cc5d383d9b7b64f9175a04c566f | refs/heads/master | 2023-04-29T06:18:03.612353 | 2021-05-21T08:36:50 | 2021-05-21T08:36:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 315 | rd | app_vistributions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/launch.R
\name{app_vistributions}
\alias{app_vistributions}
\title{Visualize distributions}
\usage{
app_vistributions()
}
\description{
Launches app for visualizing probability distributions.
}
\examples{
\dontrun{
app_descriptive()
}
}
|
99e9de16bd81e4c6454d6efcc30f339c28b2f860 | 29585dff702209dd446c0ab52ceea046c58e384e | /PolyPatEx/R/potentialFatherCounts.R | 0cd519d7fe00523b18b13ac8872b5c5de4f0fd98 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,062 | r | potentialFatherCounts.R |
## Function potentialFatherCounts
##
##' Count the number of potential fathers detected for each progeny.
##'
##' Given the output from \code{\link{genotPPE}} or
##' \code{\link{phenotPPE}}, \code{potentialFatherCounts} returns,
##' for each progeny, the number of candidates that are identified as
##' potent... |
ccedafe76fb6b5642c9a1465355935a191ebe417 | fdc70ccba8006e91d9d39b30eaa028c0778055af | /coronaVis.R | e4a8cab6c37a5b09600e8156885688deea4d7776 | [] | no_license | muammara/corona | 56cbfc42253c303fcd53ebe44b5918b070f70300 | d3baac52930c6f3171d8d236019c2bf8f73e835e | refs/heads/master | 2021-04-22T01:15:41.568104 | 2020-03-24T23:52:43 | 2020-03-24T23:52:43 | 249,838,074 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 740 | r | coronaVis.R | install.packages("googleVis")
library(googleVis)
wd='C:/Users/muamma/Documents/python/Coronavirus'
setwd(wd)
flagfilename<-'coronafilename' #Storing the name of the latest file
filename<- readChar(flagfilename, file.info(flagfilename)$size)
print(filename)
fieldsformat=c("numeric", "character","factor","factor","fac... |
13278fc2ffffee8c7722a91a1e1727b6df82085c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/radiant.model/examples/predict.crtree.Rd.R | 79bf7224b940856d137728748cc79250492fa03f | [] | 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 | 384 | r | predict.crtree.Rd.R | library(radiant.model)
### Name: predict.crtree
### Title: Predict method for the crtree function
### Aliases: predict.crtree
### ** Examples
result <- crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes")
predict(result, pred_cmd = "pclass = levels(pclass)")
result <- crtree(titanic, "survived", "pclass", l... |
fa789fb1fbfb70fa19cffc0994bbab57ce54b6d5 | 91294be1f45be0ebe4e588866decab350e7e59a7 | /RemoteSensingScripts/CrappyTerainaScav.R | 217ea31bcc8627c57cdc3986d3854c0f4da12eb8 | [] | no_license | Zheng261/CrabitatResearch | 6530f5bbc9df8b6406addcbbf48ed7b798c025fd | 769c00061088638a9b8d581311eb4e0db7b79ff6 | refs/heads/master | 2021-06-24T02:27:57.075776 | 2019-05-25T10:52:57 | 2019-05-25T10:52:57 | 140,462,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,189 | r | CrappyTerainaScav.R | setwd("/volumes/Seagate 4tb/Pacific-islands-planet-imagery")
library(glcm)
library(imager)
library(tmap)
testimg <- brick("TerainaClipped.tif")
plot(subset(testimg,4))
names(testimg) <- c("Blue","Green","Red","IR")
testimg <- subset(testimg, order(c(3,2,1,4)))
plotRGB(testimg,stretch="lin")
#palmyraimg
####### GLCM O... |
7683a1194750eb9bb7985b455db308c7e0e60a19 | eda93cdf31b1342dc43dd19d663994cb0103459f | /DoFiles/KV_balanced.R | 95a558a332df20286d5e633e3d67370868da10ed | [] | no_license | racheljoyforshaw/hetCyclicalMPCS | 509ac5d5ca923b1489f8814e46161558993f22b5 | c43a325ac3827e6b091649c29264343c1b8c47c2 | refs/heads/master | 2023-05-06T05:39:44.279845 | 2021-05-28T10:39:16 | 2021-05-28T10:39:16 | 371,655,814 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,781 | r | KV_balanced.R | # Kaplan Violante - consumption - IV, weighted
# need min of 3 periods for identification, so pre-recession sample is 2003,5,7; post-recession is 2009,11,13
f0913_balanced <- f0913
f0913_balanced <- f0913_balanced[, -grep("primarySamplingUnit", colnames(f0913_balanced))]
f0913_balanced <- f0913_balanced[, -grep("stra... |
361a713083c9b4ecf923000dceda3b634b8a62ac | 4fc3c300ebc5318c49268c52aa7842795cda91fc | /man/CoupledPF-package.Rd | 7953b045917b063a7a006836ff507cf24d9aac59 | [] | no_license | pierrejacob/CoupledPF | a10699de0ee195620bf6f24be7b02ebceb7fed80 | 6a349d0b6d51bd54b656f35ba65877dc34cbc503 | refs/heads/master | 2021-01-20T20:32:56.090492 | 2016-06-04T21:56:18 | 2016-06-04T21:56:18 | 60,315,237 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 315 | rd | CoupledPF-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CoupledPF-package.R
\docType{package}
\name{CoupledPF-package}
\alias{CoupledPF}
\alias{CoupledPF-package}
\title{CoupledPF}
\description{
...
}
\details{
...
}
\author{
Pierre E. Jacob <pierre.jacob.work@gmail.com>
}
\keyword{package}
|
d6ef908b0c763f081d293a5e072e6cd34ff70e52 | a8adeffe3d9f17976e02ef3ec82914e29b865c2c | /hw1.R | 2fd5856c185122371d82003f393c334edb53805b | [] | no_license | mjschaub/naive-bayes | 483917ccd17d6b389b1ee25fc9b0a0d787b893ba | b58b31621f1a940336bdcddf66217ab7789e0836 | refs/heads/master | 2020-03-26T20:50:50.754671 | 2018-08-20T01:19:46 | 2018-08-20T01:19:46 | 145,349,748 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,164 | r | hw1.R | library(caret)
library(klaR)
#load diabetes data
setwd('D:/CS498/HW1 - naive bayes/')
raw_data<-read.csv('pima-indians-diabetes.txt', header=FALSE)
x_data <- raw_data[-c(9)]
labels <- raw_data[,9]
#1a
training_score<-array(dim=10)
testing_score<-array(dim=10)
for (wi in 1:10){
data_partition <- cre... |
42e6c018081695798913007bb4ad4466794cf481 | 8a208c7405ba0ec615145958c34c73dcb30822c3 | /Titanic-1st-Tutorial.r | 5479a857bb3cfb6b60d78a79fb981886da8196b3 | [] | no_license | giffen-n/titanic-test | 368c668a84207984980f9a413a793994e023511d | e58eadb215d4278f9a7a7b13a7795f2f24a7c951 | refs/heads/master | 2020-12-24T16:43:05.711513 | 2014-12-24T05:13:10 | 2014-12-24T05:13:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 434 | r | Titanic-1st-Tutorial.r | # Nicholas Giffen - 23 Dec 2014
# Titanic Tutorial 1 by Trevor Stephens
# Full guide available at: http://trevorstephens.com/
# Set Working Directory and load data sets
setwd("~/titanic")
train <- read.csv("~/titanic/train.csv")
test <- read.csv("~/titanic/test.csv")
# Observe structure of training dataframe
str(t... |
7eb9b75d0215041216b47a4cc210cb74342bc5dd | fb5d577ce7e37678f7612590b1e78df887b42d7f | /treat_blocks_lines_output.R | b3dc1af18e89962dc0c731b4345f104be5d7ab04 | [] | no_license | S-Homayounpour/wildfire_risk_shiny | 86d1a52927fb98c791169a2654a490a4a5548275 | cf701d1ba355c1e6c35f7a421d03cd55cd3d256b | refs/heads/master | 2021-09-15T19:29:20.673973 | 2021-09-06T02:53:28 | 2021-09-06T02:53:28 | 174,888,654 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,542 | r | treat_blocks_lines_output.R |
library(WildfireRisk)
library(RSQLite)
do_treat_blocks_line <- function(block.risk, line.risk) {
# load point data from database
con <- dbConnect(SQLite(), dbname = attr(line.risk, "dbname"))
pointdata <- dbReadTable(con, "pointdata")
dbDisconnect(con)
# get indices of intersecting scan l... |
c82edb78bc525766fa144c65dbb29859962a9fcf | fc77f67a23c359160a60ce135182b94ba697cd2c | /lab3/lab3.R | ea1a32393c15f61a5bd6a2e339e5b396cd8569df | [] | no_license | edlinguerra/LCA-ME | e4ebd9869dc678405c3aae2196fa4a90946916e1 | b84108c42bf4a828649449f5d8f575aee338c29c | refs/heads/master | 2023-02-23T23:28:49.471949 | 2023-02-14T22:16:06 | 2023-02-14T22:16:06 | 238,227,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,629 | r | lab3.R | #Script del laboratorio 3
#Nombre y Apellido:
#Preguntas:
# 1. Importa los datos en R, y verifica sus características y estructura. ¿Cuántas dimensiones tiene la
#tabla que importaste? ¿En qué difiere esta de aquellas usada en las pruebas de *t* para dos muestras?
#instlar ambos paquetes en caso no los tengan
libra... |
9f437078ae108eaf2ea8332aa04dc2511175f78c | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /metafolio/inst/testfiles/est_beta_params/libFuzzer_est_beta_params/est_beta_params_valgrind_files/1612988963-test.R | 361a77311b910b1b678458c29e2e16dc615436e8 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 140 | r | 1612988963-test.R | testlist <- list(mu = -2.7226523566839e-40, var = -2.72265235668397e-40)
result <- do.call(metafolio:::est_beta_params,testlist)
str(result) |
6f7253d0c9de97d1f164866ccc91dd1eb0feb630 | 02f363c8bfc69406f63a0441726c12cce970d093 | /code/R/09_mod_start.R | d8ab56bf78284b3df7dcf2cde7890fb169251051 | [] | no_license | wgar84/Primaset | 4d07e290cae9a3f3b502a1b7a08db7b4c89e97be | 2367871dd798e1144b64045e7854836c91cdb91d | refs/heads/master | 2020-06-27T08:25:09.859983 | 2019-01-18T14:39:44 | 2019-01-18T14:39:44 | 94,248,303 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,873 | r | 09_mod_start.R | require(geomorph)
require(shapes)
require(evolqg)
require(plotrix)
load('../Raw Data/Aux.RData')
load('Primates/Sym.RData')
load('Primates/Info.RData')
dim(prima.sym$coord)
sag.sym <- prima.sym $ coord [, , prima.info $ GSP == 'Saguinus_geoffroyi']
sag.sizeshape.gpa <- procGPA(sag.sym, scale = FALSE)
sag.ss.tan ... |
5b26acdd2370aa7e6256209090b8790903dcbe9b | f3328142651592d3ddf06f333153c50158ac87df | /ChicagoCrime.R | 656bf0e0db8f813d2cea5eb0a1b6c47ea1f9db96 | [] | no_license | rachaelbardell/Chicago_Crime_Map | 41faaccd7fd1477e5119eeafbe603800763fdd36 | bb4adfd5d3c9869828bec37815fa65360a25af8c | refs/heads/master | 2016-09-06T21:47:34.189589 | 2013-08-22T16:10:48 | 2013-08-22T16:10:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,991 | r | ChicagoCrime.R | library(ggplot2)
data <- read.csv("http://data.cityofchicago.org/views/x2n5-8w5q/rows.csv", stringsAsFactors = F)
names(data) <- tolower(names(data))
data <- subset(data, !is.na(longitude) & !is.na(latitude))
ggplot(data)+geom_point(aes(x=longitude,y=latitude), alpha = .2, size =.4)
#highest crimes
crimes <- tabl... |
4772be0912db6f88428639344f0929cb9907c1d4 | 741ee389d11bd329b79075c6a2e6b4eda11b6b23 | /E4/droslong.r | dd8451169f9bd16ec0ffacb7401a8e4b9b8f4ca7 | [] | no_license | thanhan/SM2 | f30b1c04104e01d1261122708b6b31e7662002e1 | 50a6e37683d2aa9677745682c905398bee1128bc | refs/heads/master | 2021-01-11T17:22:48.515232 | 2017-04-26T05:03:00 | 2017-04-26T05:03:00 | 79,769,579 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,463 | r | droslong.r | library(lattice)
library(mvtnorm)
library(MCMCpack)
d = read.csv('droslong.csv')
xyplot(log2exp~time | gene, data=d)
xyplot(log2exp~time | group, data=d)
# model: log2exp = a[group] + b1[gene] + b2[gene] * time + b3[gene] * time2 + e
# e ~ N(0, pre = p)
# a ~ N(ma, pre = pa)
# (b1, b2, b3) ~ N(mb, var = sb)
# (mb, ... |
90ce4d28a6335b161722240c7164eb268831ef9f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tensorflow/examples/sub-.tensorflow.tensor.Rd.R | fb77d59e2c53b85f0b71937e472bed9d74a35c9b | [] | 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 | 3,080 | r | sub-.tensorflow.tensor.Rd.R | library(tensorflow)
### Name: [.tensorflow.tensor
### Title: Subset tensors with '['
### Aliases: [.tensorflow.tensor
### ** Examples
## Not run:
##D sess <- tf$Session()
##D
##D x <- tf$constant(1:15, shape = c(3, 5))
##D sess$run(x)
##D # by default, numerics supplied to `...` are interperted R style
##D sess$r... |
df683d5e7d989c057c21c959ca15f8fbd3942153 | 12b677e6782d44285a78a5dec7cc65b869ed7ea0 | /src/Optimus/OptimusBundle/Servicios/Util/EnergySource/OLD/simulateData.R | ea3f11eb1d6ce2e93a70c6ea615830e46288b2cc | [
"MIT",
"BSD-3-Clause"
] | permissive | epu-ntua/optimusdss.symfony2app | 128aad7820a503534e16c0ee74c2379fbba30585 | 0e2b2d8d445d4e6b8f5c1cb8183c7c4e1b9c28b6 | refs/heads/master | 2021-05-01T03:41:26.979434 | 2017-02-08T16:46:55 | 2017-02-08T16:46:55 | 58,220,691 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,149 | r | simulateData.R | simulateData <- function(data){
startingcapacity<-0 #Apo pou ksekinhsa (as ksekinhsoume me 10)
Pcapacity=50 #estw 50max kai 4 min
Ncapacity=4
data2 <- data
#NA afta pou thelw na ftiaksw egw
data2$Storage <- 0; data2$Grid <- 0
data2$Capacity <-0 ; data2$Capacity[1]=startingcapacity
data2$Grid[... |
f35aba4c53b2fd667e658f4ab106280c64db658a | b05ff0cb36e1be4f7808b956a0743acc9e0a5d93 | /R/predDensities2000_2010.R | 5c8cb8f5be896b605b7dbbecc63b79b618da2818 | [
"CC0-1.0"
] | permissive | dongmeic/climate-space | b649a7a8e6b8d76048418c6d37f0b1dd50512be7 | 7e800974e92533d3818967b6281bc7f0e10c3264 | refs/heads/master | 2021-01-20T02:13:12.143683 | 2020-04-03T16:47:56 | 2020-04-03T16:47:56 | 89,385,878 | 0 | 0 | null | 2020-04-03T16:47:57 | 2017-04-25T17:01:45 | Jupyter Notebook | UTF-8 | R | false | false | 1,281 | r | predDensities2000_2010.R | DATA_DIR <- '~/dongmei/sdm/data/cluster/year/'
dat.early <- read.csv(paste(DATA_DIR, 'X_test.csv', sep=''))
y.early <- read.csv(paste(DATA_DIR, 'y_test.csv', sep=''))
dat.late <- read.csv(paste(DATA_DIR, 'X_train.csv', sep=''))
y.late <- read.csv(paste(DATA_DIR, 'y_train.csv', sep=''))
early <- cbind(y.early... |
572d97134f0e20b383ca8b009b97d0581b9cc0a5 | 8b07efbe40855dea61fe4018519fb658bad5ab05 | /Consumption_Trend_Analysis/code/code_20201015_시각화추출.R | 92d5095051e6c7e45347c115273e704e9fb0915b | [] | no_license | ne-choi/project | 02d605d59c753d331174317c09e6d2f5057bc7cd | ca6fcbb36b236065ec3568ee88281863740a91b7 | refs/heads/main | 2023-08-23T01:11:53.001104 | 2021-10-21T14:21:43 | 2021-10-21T14:21:43 | 302,356,308 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,539 | r | code_20201015_시각화추출.R |
# 20201015 수정 내용
#
# 필요 작업:
---
library(readxl)
library(dplyr)
library(tidyr)
library(reshape2)
library(ggplot2)
# 1. 데이터 전처리
# 1) Mcorporation 64개 데이터 합치기
# 파일 합치기
files <- list.files(path = "sample/Mcorporation/상품 카테고리 데이터_KDX 시각화 경진대회 Only/use", pattern = "*.xlsx", full.names = T)
products <- sapply(files, ... |
a39a7c7f82dea38b78e9d5ece13ae41eb5928b9c | 59aae554beba38bf265dd19fb4071784fdddce27 | /compile_gitbook.R | 820d1805ff953a35e77a6ef29925ba55ce340d22 | [] | no_license | RemkoDuursma/prcr | 0a8276b9afa3d6c88c95ffe2f10503d175dce9de | 392b3e17fc4bb8128a3c68fa64b8d85080509f63 | refs/heads/master | 2021-06-08T18:18:44.051004 | 2021-05-19T08:37:00 | 2021-05-19T08:37:00 | 165,263,332 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 118 | r | compile_gitbook.R |
bookdown::render_book(input="index.Rmd",
'bookdown::gitbook', new_session = TRUE, clean=TRUE)
|
6769fcc5b2f4ff65a3855beb686dc4c8fd998daf | 4dfcc827ed8501d3a2274ff922c0bc1112cf293e | /man/reach_style_color_reds.Rd | 12f259450e7d555854ce5bc5f88aa50ca17f6806 | [] | no_license | mabafaba/reachR2 | 5324e7ff1e413fe2e8e7bf5d39cd6736cc53d783 | 0ecbb831a637369718c759286720bbbf08966725 | refs/heads/master | 2020-03-19T01:34:57.913274 | 2018-06-13T13:17:24 | 2018-06-13T13:17:24 | 135,556,190 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 329 | rd | reach_style_color_reds.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/colours.R
\name{reach_style_color_reds}
\alias{reach_style_color_reds}
\title{Reach brand reds triples}
\usage{
reach_style_color_reds(transparent = F)
}
\description{
Reach brand reds triples
}
\examples{
}
\seealso{
\code{\link{function_... |
8d071f5c5595e120ae5ca676388f394049208f75 | 10e518cfefb3e44d245fa2ca35b809d4f0da9b38 | /man/load_pqt.Rd | 0e61ea52d96bdc34fc00c46ebc6622b0556f9fd1 | [
"MIT"
] | permissive | bryanwhiting/bonds | e07048449ada74e40139c713209abd5f9f16913a | 0805a87b89b0554c811dd9cb8d7aed73ffc99254 | refs/heads/main | 2023-08-22T07:39:12.987012 | 2021-10-29T07:02:45 | 2021-10-29T07:02:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 401 | rd | load_pqt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{load_pqt}
\alias{load_pqt}
\title{Read in a multipart parquet file using a given file range}
\usage{
load_pqt(dir_root, tab, range = NULL)
}
\arguments{
\item{range}{}
}
\value{
}
\description{
Read in a multipart parquet file ... |
8b66a24ff40ebeb13f90565017a54609ef18d243 | cc870392539610a12db89082d4fd5855c5b3c718 | /workshop3glm_slides.R | 337117c058450b4b71a62d149c6e777b2bf29d3f | [
"MIT"
] | permissive | rosemm/workshops | 438b88ac15459009952134d4a5af624828a12ea0 | 8a96a380fa3119e965ff74fa4a86c35b0f58097d | refs/heads/master | 2020-12-03T04:03:08.418841 | 2017-06-29T18:51:45 | 2017-06-29T18:51:45 | 95,809,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,201 | r | workshop3glm_slides.R | ### R code from vignette source 'workshop3glm_slides.Rnw'
###################################################
### code chunk number 2: workshop3glm_slides.Rnw:115-120
###################################################
install.packages("ggplot2")
install.packages("dplyr")
library("ggplot2")
library("dplyr")
#######... |
45b62e8045616f6f3dff005585c743115b853adc | d7b1f6f13781ebf0daa817ac6e328513813db7e6 | /scripts/db_complete_missing_task_logs.R | 9806fa0ca6cfaecffbcb306ac126d135506a9c13 | [] | no_license | petermeissner/wikipediadumbs | 2d381d09d1925c921f753b371b21236177b051f5 | f8565d9796ee0273efede8f662809df251bafbf7 | refs/heads/master | 2020-08-06T11:10:49.340760 | 2018-11-20T09:07:50 | 2018-11-20T09:07:50 | 212,954,772 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,168 | r | db_complete_missing_task_logs.R | library(wpd)
library(ggplot2)
library(data.table)
library(dplyr)
# get data
tasks <-
wpd_get_query_master("select * from upload_tasks")$return
# plot progress
tasks %>%
group_by(task_date) %>%
summarise(
task_status = sum(task_status == "done")
) %>%
ggplot(aes(x = task_date, y = task_status)) +
geo... |
944182bbe8359f196f6624ff10d2f24dd38d651a | 5a5bc9e1b0d59859b4e213b092e19afe232819e1 | /R/coast/calc_ldif_block.R | 7077b8a90c9dd099e4eb8a1565c687b64445e6cd | [] | no_license | jrmosedale/microclimates | bf469e07b688e9342c0a8d767db84ee428e778f3 | ae2e61969631506c523bd618c9106a61b00355dd | refs/heads/master | 2021-04-30T15:18:19.091728 | 2018-02-12T11:31:16 | 2018-02-12T11:31:16 | 121,236,443 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 456 | r | calc_ldif_block.R | # Calculates single raster ldif.block
# Input: ldif.stack of ldif for each wind direction
# wind direction for block
# direction interval for which ldif calculated
calc.ldif.block<-function(ldif.stack,wdir.block,interval=10){
ldif.block<-raster
wdir.layer<-round(wdir.block/interval)+1 # creates rast... |
f1d6cc6674868360b4cb8fbe50c5f05e85b3dd42 | 60e8d991a5c569c80c50c31fda9722589cd867ed | /DrugAbuse_and_Psych.R | 8ee41f3845e9feead55e9755b5f02e38da1c485b | [
"MIT"
] | permissive | asulovar/DeepPhenoVIZ | 7b4932bac7c5ababad46152d1e9e49dfa4b3e1ed | 0427e877afa255b3a994c6db43e435ed5f5d9902 | refs/heads/master | 2020-03-09T14:51:53.212791 | 2018-04-09T23:27:02 | 2018-04-09T23:27:02 | 128,845,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 41,599 | r | DrugAbuse_and_Psych.R | #Author: Arvis Sulovari, PhD
##1) What psychiatric disorders are developed as consequences of substance abuse?
##2) What genetic mechanisms lead to drug abuse, followed by comorbid psychiatric disorders
#Discovery datasets
ssadda <- read.delim("C:/Users/arvis/[...].txt")
ssadda_AA <- ssadda[which(ssadda$A8a_RACE==4... |
c110b1484012b5260bd0d390588da36d1fcd6e39 | 68f8aa9d06a5b60bb12a206c75ca903cdaa000ac | /file1.R | 755fe8b6b4926e6739dac1f6a3d185ddd64e6d15 | [] | no_license | binayak91/analytics | f382fc3a83d9698def51ca476eec5e4249db2e82 | b3fcf41e01b45ed5238001c38dbe6cce4286847c | refs/heads/master | 2020-03-30T06:38:16.190691 | 2018-10-02T13:31:18 | 2018-10-02T13:31:18 | 150,876,632 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 104 | r | file1.R | women
git config --global user.email "um18280@stu.ximb.ac.in"
git config --global user.name "binayak91"
|
d645aa12ccbccd2fb32aef522ac747b9d5e1b73d | b23983ee89e5b116c99f766d6f09c1d9cfe9c55a | /workout03/BInomial/man/bin_variance.Rd | 28ac1333adff58a92b1fb210238b2a867cedd16d | [] | no_license | stat133-sp19/hw-stat133-dbian17 | 503ee167b1345b3baae87a5cf13b7ecb69fb8584 | b8d058673b37c10fafb655cd734f39e417bd8a15 | refs/heads/master | 2020-04-28T08:59:04.197050 | 2019-05-04T06:05:17 | 2019-05-04T06:05:17 | 175,149,731 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 441 | rd | bin_variance.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Binomial.R
\name{bin_variance}
\alias{bin_variance}
\title{bin_variance}
\usage{
bin_variance(trials, prob)
}
\arguments{
\item{n}{number of trials}
}
\value{
the variance of the given binomial distribution with "trials "trials and probabilit... |
eb65a9afa41a90b77b7bb2aff9cbbaa28fb58f1e | f8050f1ad4950555990d4dd4552240ee86575b50 | /R/autojags.R | 2444a4c8173b5c80d6b3c98b9d2dd20956b5c093 | [] | no_license | SMandujanoR/jagsUI | aac45c6ae26f51ae46d701278aed8591b9f58843 | f4838f60c1f6f4a358d4adacde51f8f080c32cf9 | refs/heads/master | 2021-03-06T01:27:21.806892 | 2020-03-09T20:35:15 | 2020-03-09T20:35:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,146 | r | autojags.R |
autojags <- function(data, inits=NULL, parameters.to.save, model.file,
n.chains, n.adapt=1000, iter.increment=1000, n.burnin=0,
n.thin=1, save.all.iter=FALSE, modules=c('glm'),
factories=NULL, parallel=FALSE, n.cores=NULL, DIC=TRUE,
no... |
544738e9502ba135cf6f66a45298fb84b116a90d | b43fd480c7bc8d424a07db8faf5f4c164a48f8d6 | /5th_Titanic_campaign/hw5_106356013.R | a6e29733f0aa4296c6a7b9a366146032bda7b00a | [] | no_license | YuTaNCCU/2017_DataSciencePractice | ecf66749a4706056b6789b8519a4b6bf8167d784 | 082dc44a4de09256f0fdf228ee8da6f238323d62 | refs/heads/master | 2021-09-27T02:06:09.961489 | 2018-11-05T14:53:51 | 2018-11-05T14:53:51 | 104,413,903 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,038 | r | hw5_106356013.R | #setwd("~/Desktop/hw5-YuTaNCCU")
#system("Rscript hw5_106356013.R -fold 5 –out performance.csv")
################
### 讀取指令 ###
print('(1/7)讀取指令')
################
args = commandArgs(trailingOnly=TRUE)
if (length(args)==0) {
stop("USAGE: Rscript hw4_106356013.R -fold 5 –out performance.csv", call.=FALSE)
}
i<-1
whi... |
d770ef426508142cc441da6946f5fda879d2fc7b | b6fe639016db185ea6dc74c65e7aee63d62699c8 | /load_data.r | 7333396c1f649cd14d04a5109b78b8b0242c646b | [] | no_license | aluuu/ExData_CourseProject2 | 580ff072d54a39834ee965e2bffe3218e08997d4 | c5c984ac79b51072ee651b4a944e870df49c8fa0 | refs/heads/master | 2020-12-25T14:23:48.576273 | 2016-09-03T09:34:54 | 2016-09-03T09:34:54 | 67,282,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 387 | r | load_data.r | library(dplyr, warn.conflicts = F)
if(!dir.exists("./data")){
dir.create("./data")
}
dataset_archive <- "./data/dataset.zip"
if(!file.exists(dataset_archive)){
download.file(url="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", destfile = dataset_archive)
unzip(zipfile = dataset_archive,... |
7ba78260b268a886504d60a4a798fe57a6ab70ac | f5285634e415e8156a6a358aff499378cc25460c | /rnaseqVis/server.R | c547fa155a706adf293d560c6577a8aba6102e36 | [
"MIT"
] | permissive | woodhaha/shinyApps | 4176d1113af37997de50014f88b0fc0618b31ca9 | 743d4ad13728bccab58a15e6a636071c9a1fab98 | refs/heads/master | 2020-12-30T15:08:11.512283 | 2016-08-23T14:35:14 | 2016-08-23T14:35:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 894 | r | server.R | # server.R
# load libraries
library(ggplot2)
library(dplyr)
# load data (only one time when the app is launched)
data <- read.delim("data/20accessions/normalized_expression.txt")
# define server logic to build the boxplot
server <- function(input, output) {
output$text1 <- renderText({
paste("You have selec... |
d7f79bac38f3ea0f4550d5f4bc4ea2ca3ce0412d | 397e6d164d5d37ba2fa12a6d0ea6faa4f8b1cb62 | /AirlineAnalysis.R | edc21a8cb64feeeeca9a27b2202543c98ab4756b | [] | no_license | acummings2020/Airline-Insights | 8a3ea8c9888446e8a66cc627cfcc9f4cff9de41a | 22e8df51e86ac394bc9a3c2e6f06c0431d538854 | refs/heads/main | 2023-02-01T11:52:38.082143 | 2020-12-15T17:39:42 | 2020-12-15T17:39:42 | 321,742,827 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,091 | r | AirlineAnalysis.R | #The following URL is the location of the survey datafile
#NPS = %promoters - %detractors
library(arulesViz)
library(car)
library(carData)
library(caret)
library(ggmap)
library(readr)
library(tidyverse)
library(rjson)
library(jsonlite)
library(ggplot2)
library(ggmap)
library(maps)
library(mapproj)
librar... |
2f9b0114a1d9574c1f185d2c5dfc4c72e1a1d872 | e1b8c775c04daac6026fde92c258af558fbefcdf | /shiny_data_update.R | 1ad15021f01d7a9156dc272ba0ffc6b3621ff5c9 | [] | no_license | jansodoge/shiny_bbl_lineups | 8c3cd7661e9cbd121de2129fc6bf94e37c6a58e4 | 01df18ec3843180f6b5fc3a2ccfc145041782023 | refs/heads/master | 2021-02-22T17:51:42.162706 | 2020-03-06T09:48:27 | 2020-03-06T09:48:27 | 245,381,865 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,910 | r | shiny_data_update.R | source('basketball_metrics.R')
source('pbp_log_editing.R')
source('lineups_level_5_men.R')
source('player_level_analysis.R')
# Run this script to update the data the shiny app uses for lineups
### merge the files downloaded within the respective file (the file needs to be updated
### with downloading game files prior ... |
17a0bc6b5244a8d58fa3e393c72752e5f3f23962 | c526975e8e3be6a12b329d8edb170dee18e8068a | /ChIP-SEQ_SOP/含帮助文档的代码示例/2_差异比较diffBind.r | 89c2f51f1595bfc77e585e85cee398771693e116 | [] | no_license | wangqing207/SOP | 13536407dc89a3cc223f8abf85075a1aab0efb00 | 8c383d8de61105f599eb8c348012b35214199dc7 | refs/heads/master | 2021-05-15T08:12:10.948946 | 2017-10-23T05:09:56 | 2017-10-23T05:09:56 | 107,934,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,423 | r | 2_差异比较diffBind.r | args <- commandArgs(T)
f_name<- args[1] ####csvFile of the information
if(!length(args==1)){
q()
}
library(DiffBind)
#group1 <- 1
#group2 <- 2
######################################################################
tamoxifen = dba(sampleSheet=f_name,peakCaller="macs")
# tamoxifen = dba.count(tamoxifen,peaks="group_... |
4a3a0e82d638528d52b7680a44643146b2d86afa | 3f02cb4dfd2e35fb7346830341e29df511f0137e | /R/derive_var_atirel.R | e6e00845ba08edd3189b134d6e57e91abdc59ed1 | [] | no_license | rajkboddu/admiral | ce08cb2698b62ca45ba6c0e8ed2ac5095f41b932 | ffbf10d7ffdda1c997f431d4f019c072217188b1 | refs/heads/master | 2023-08-11T11:14:44.016519 | 2021-09-08T10:24:45 | 2021-09-08T10:24:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,921 | r | derive_var_atirel.R | #' Derive Time Relative to Reference
#'
#' Derives the variable `ATIREL` to CONCOMITANT, PRIOR, PRIOR_CONCOMITANT or NULL
#' based on the relationship of cm Analysis start/end date/times to treatment start date/time
#'
#' @param dataset Input dataset
#' The variables `TRTSDTM`, `ASTDTM`, `AENDTM` are expected
#' @par... |
c5287060ac140b773fe73eae347db2bbd0cf437f | 9327a27882b8ff73337462e9ecb8897006efab42 | /GenerateToyDataSet.R | 77f01c75ac12b3876a4b562f85ed6151b5a2ce2c | [] | no_license | RJHKnight/ACAlpha | 92cee9c2616c61e81af54a1ac461e8d8263c69a7 | 30e0942710a9340b08bfec87b081163457e07479 | refs/heads/master | 2021-04-28T16:49:37.179391 | 2020-03-15T06:35:31 | 2020-03-15T06:35:31 | 120,616,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,876 | r | GenerateToyDataSet.R | library(dplyr)
# n hidden core factors, all following a random path of t_max steps
# m stocks, each following a path determined by
# beta_n * n + epsilon
# Initially make beta_n time invariant, then we can look a time varying betas.
# For simplicity, we will not introduce any scale to the n_t
# beta_n will be randoml... |
8a8377a751d9819891751b0ca0c49cf5ac108c07 | d1de1a007fd386b28c6ea2fbf82785265d3ce292 | /Week-4/Programming Assignment 3/ProgrammingAssignment-3-Quiz.R | 9ec69b1231fa6203c34c1da0bb4e67968e918947 | [
"MIT"
] | permissive | JohamSMC/R-Programming-Course | db7e415b6cd970aac990a2fd90bb97919b81d41b | 79eacac46c69b2b3398ebcdb5665203820b40599 | refs/heads/main | 2023-03-07T15:09:43.555333 | 2021-02-19T18:01:58 | 2021-02-19T18:01:58 | 340,447,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,451 | r | ProgrammingAssignment-3-Quiz.R | source('../best.R')
source('../rankall.R')
source('../rankhospital.R')
best("SC", "heart attack")
# > best("SC", "heart attack")
# [1] "MUSC MEDICAL CENTER"
best("NY", "pneumonia")
# > best("NY", "pneumonia")
# [1] "MAIMONIDES MEDICAL CENTER"
best("AK", "pneumonia")
# > best("AK", "pneumonia")
# [1] "YUKON KUSKOKWIM... |
1414744bf8a7f17750c09d74df408cb99b67931f | 29585dff702209dd446c0ab52ceea046c58e384e | /mets/R/pmvn.R | 70dd30f6f2a0e999fcb2eee6d8b442ba5d0f5bc9 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,515 | r | pmvn.R | ##' @export
pbvn <- function(upper,rho,sigma) {
if (!missing(sigma)) {
rho <- cov2cor(sigma)[1,2]
upper <- upper/diag(sigma)^0.5
}
arglist <- list("bvncdf",
a=upper[1],
b=upper[2],
r=rho,
PACKAGE="mets")
res ... |
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