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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
69f454d7126e516be1657a8b5b2c7c7021e2836e | 3aa7718eda2b2e76efde2e9d41beb4fbd6115c79 | /man/remove_accents.Rd | 5aa52868a6db29c2121417b26e57f37d7b222a72 | [] | no_license | alinemsm/rslp | 9c0e929323830615dceb0a9de1e619209ec6f549 | be0a04855d8cca473bc36ae69349510b593ac6a9 | refs/heads/master | 2021-05-01T06:22:23.372303 | 2016-10-14T12:31:54 | 2016-10-14T12:31:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 292 | rd | remove_accents.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zzz.R
\name{remove_accents}
\alias{remove_accents}
\title{Remove Acccents}
\usage{
remove_accents(s)
}
\arguments{
\item{s}{the string you want to remove accents}
}
\description{
A wrappper for stringi package.
}
|
9f841c39115e2751a56d99b3492c4e1c73343ace | 2d24c72abe89c38bc13682ca2a048ffa97dcf9c3 | /Bohn_Kleemann_Bambauer_Blatt9.R | a81bdf74762a841a9ad9bff79c6c8cfb2a9a2d31 | [] | no_license | curala70/StatistikSS2017 | e42c93fe62255774727e0e44cad6ca97328fc008 | b535c398ee1ad6ec6a0434b759ace077712108ac | refs/heads/master | 2021-01-20T02:05:54.611177 | 2017-06-27T08:45:11 | 2017-06-27T08:45:11 | 89,371,542 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,649 | r | Bohn_Kleemann_Bambauer_Blatt9.R |
# a)
KSS <- function(x) {
x = sort(x)
n = length(x)
d = c()
for (i in 1:n-1) {
Fx = (1/n)*sum(x<=x[i])
d[i] = max(abs(Fx - pnorm(x[i])), abs(Fx - pnorm(x[i+1])))
}
d[n] = abs(1-pnorm(x[n]))
return (max(d))
}
# b)
# create samples of size n, each 100 times
x10 = list()
for ... |
f5da4739cb9ac7315d12e86cd5e0a877fec75862 | 8399dd26135eb99332c0d0cb456f42ee4ec63cb5 | /congress109.R | 6bdc7124fb23bba5670bea87d441031afab79e85 | [] | no_license | tiffblahthegiraffe/STA380-class-of-2019 | fafa4ba321c5f1945b1e2ce38a5f68934b6c0ff1 | 84040d91ecdca3d42285b087ce94e22496bf1aa0 | refs/heads/master | 2020-03-25T00:38:21.157438 | 2018-08-15T22:35:44 | 2018-08-15T22:35:44 | 143,196,278 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,784 | r | congress109.R | library(ggplot2)
countdata = read.csv(url("https://raw.githubusercontent.com/jgscott/STA380/master/data/congress109.csv"), header=TRUE, row.names=1)
memberdata = read.csv(url("https://raw.githubusercontent.com/jgscott/STA380/master/data/congress109members.csv"), header=TRUE, row.names=1)
# First normalize phrase coun... |
8e9cddcbfdcc85159d855fba9322f54231d9d349 | 52c1f08ce14e5542ff36f7d5ae4b8f1da966508d | /MadingleyPlots/R/PlotMassDensity.R | 1a747930f4dbe48a7d18c82126adce62da1af960 | [] | no_license | timnewbold/MadingleyPlots | 5e61b75df743408e277456f9daba54ffe0ef1a5f | 03cca3b67eea3b4de46d14774e9814237507e841 | refs/heads/master | 2020-07-23T11:57:21.421452 | 2017-06-29T08:17:16 | 2017-06-29T08:17:16 | 73,809,091 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,832 | r | PlotMassDensity.R |
PlotMassDensity <- function(resultsDir,plotName = "MassDensity",
outDir=NULL,
label=NULL,
whichCells=NULL,endTimeStep=NULL,
numTimeSteps=12,
vars=c("herbivore abundance",
... |
ab0a3ed2c99c98f201b846b9e3573df323122fc6 | 5289bb29b4f7d11b01f327761ece631de13d8ac9 | /R/helpers.R | 71edf67f54cdcd6ffd367db35b2b07755631a5a4 | [] | no_license | mironcat/conpac | ec018115aed26a09afb520f05e4052703c15637a | b1d3f45ca2e3702cc5f89389ac5ab338117e737a | refs/heads/master | 2023-04-30T11:23:53.140232 | 2021-05-21T20:00:05 | 2021-05-21T20:00:05 | 352,006,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,830 | r | helpers.R |
getDatedMarkers <- function(formattedcpcht) {
dmarkers <- formattedcpcht %>%
filter(CLADE=='[dated' | CLADE=='AGE' )%>%
rename ( DATLEV=FAD)%>%
separate( col=EVENT,sep = '=',into=c('EVENT','AGE'))%>% #разделяем колонку EVENT на две используя в качестве разделителя '='
mutate( AGE=as.numeric(AGE))%>% ... |
de1f93579dbbb3a0c60ee537a6a8884c114d931c | f43377cd5c921dd609770789e2be686cdd012917 | /scripts/Thermal_reaction_norms.R | 1f30c3d58cf8eedaeae6bde1258236c1cbd82acb | [] | no_license | siyuChen540/PFT_thermal_response | 0e5692f2984c8697501a73f551f32a1099816e67 | 74598788f0647bac2f9ec3492f1aa7d482616bbf | refs/heads/master | 2023-08-29T02:59:51.547675 | 2021-09-14T16:10:54 | 2021-09-14T16:10:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,999 | r | Thermal_reaction_norms.R | # Code for Anderson et al. (2021)
# Marine Phytoplankton Functional Types Exhibit Diverse Responses to Thermal Change
# Stephanie I. Anderson updated 09/14/2020
# Contains:
## Phytoplankton functional type thermal reaction norms (Figure 1)
## Exponential curve fits
## Q10 calculations (Table 1)
## Comparison of therm... |
45de62a61cde9f02dc798b8db55def235f65b0d6 | 03cb2887a235ba8038a8244f6a144af06a653e60 | /R/get_peaks_chromatograms.R | f4e3750bd8c7ce65f9fc35ace93c047107a26c9a | [
"MIT"
] | permissive | Roestlab/DrawAlignR | 8724825fbf266d682183370988bc1bebcdc0f028 | 14990d47a6212e47a68327200c73714a6db15c78 | refs/heads/master | 2020-11-25T12:03:40.924982 | 2020-04-09T03:45:33 | 2020-04-09T03:45:33 | 228,648,957 | 5 | 0 | MIT | 2020-04-09T03:45:34 | 2019-12-17T15:44:30 | R | UTF-8 | R | false | false | 8,942 | r | get_peaks_chromatograms.R | #' Extract XICs of all transitions requested in chromIndices.
#'
#' Extracts XICs using mz object. Generally Savitzky–Golay filter is used, however, filter can be turned-off as well.
#' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca}
#'
#' ORCID: 0000-0003-3500-8152
#'
#' License: (c) Author (2019) + MIT
#'... |
97ef015ad4866d02ced6a87d98624b5fa9f69bec | 0dd9227755c5b154d2184e712d53f4bacc02305c | /man/wait_for_dir.Rd | 33d96d4140194359249f93936428ef9650fb7056 | [
"MIT"
] | permissive | imbs-hl/MDRDist | 811b68cad2877c83d6d4ac9c08e04a63141757b1 | 2aa6838aeeb6291b76971b34169230abcd28a909 | refs/heads/master | 2023-09-02T08:34:07.942115 | 2017-07-05T10:51:59 | 2017-07-05T10:51:59 | 85,298,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 604 | rd | wait_for_dir.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/supporting_functions.R
\name{wait_for_dir}
\alias{wait_for_dir}
\title{Waiting until recently created directory appears}
\usage{
wait_for_dir(Dir, max_wait = 30, timeout = 1)
}
\arguments{
\item{Dir}{path to the directory which we are waiting... |
4897d314e934878e15c20846bd738a1efdde53ec | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ahaz/examples/sorlie.Rd.R | 86690da2a25ac1a09079096ce7207a55f685e231 | [] | 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 | 147 | r | sorlie.Rd.R | library(ahaz)
### Name: sorlie
### Title: Sorlie gene expressions
### Aliases: sorlie
### Keywords: datasets
### ** Examples
data(sorlie)
|
e3718a21d9659ba4e70923f1dee4771c006c84d6 | 99fd08dac3a1bb59df57983ee5c737fc3fa3d721 | /main__summarize_industry_assignment.R | 060892eb917be681c2bc0705f1b5c1188d33f6d5 | [] | no_license | nareal/CRSP-Data-Summary-Statistics-by-Industry- | b01307836e1237b6460263e874e2c4687d2d816d | 31bd5652d2d64a29b27eb3cf63ba1cdeb2773ccd | refs/heads/master | 2021-01-15T22:41:40.908879 | 2011-11-22T02:06:11 | 2011-11-22T02:06:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 743 | r | main__summarize_industry_assignment.R |
rm(list=ls())
library(foreign)
library(reshape)
library(plyr)
library(matlab)
library(rjson)
library(RColorBrewer)
library(ggplot2)
library(tikzDevice)
library(classInt)
source("summarize_industry_assignment.R")
## plot_number_of_firms()
## plot_number_of_firms_per_industry(industry_classification = "mg1999"... |
a3564286c60930ab1e531d2f2475aaae0649042f | d690af5c19bb0d6b723e1b8f1687794b4e0f8830 | /tests/testthat/test-stats-nls.R | 37c2e5331e7f4461adb681eeed90be2f9e69bb15 | [
"MIT"
] | permissive | roldanalex/safepredict | 03113c5095518fef7c007c7e98342ecf15c0f9dc | 05c3b9c8770583221a73b7b68f88805402630f5f | refs/heads/master | 2021-10-09T11:32:24.866936 | 2018-12-27T06:11:45 | 2018-12-27T06:11:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 831 | r | test-stats-nls.R | context("test-stats-nls")
fit <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
test_that("function signature", {
check_safepredict_signature(safe_predict.nls)
})
test_that("input validation", {
expect_error(
safe_predict(fit),
"argument \"new_data\" is missing, with no default"
)
expect_error... |
8c449b7631a711f8eabd3c0a2035b4fb04f6a40c | f42a7b41b6acd4dac40234ff2d939c938f6f2d53 | /man/earlyReduction.Rd | 3f542af466342771deeae151c78f2bb1cfb2de65 | [
"MIT"
] | permissive | ttriche/bayesCC | 2927f9228782b9c9814f3bdf5e31c36a50e5e794 | 627a88a5af1b07b4923ecba42174d4c148df29c2 | refs/heads/master | 2023-06-26T19:57:56.436883 | 2023-05-11T18:51:53 | 2023-05-11T18:51:53 | 44,848,488 | 24 | 4 | null | null | null | null | UTF-8 | R | false | true | 2,137 | rd | earlyReduction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/earlyReduction.R
\name{earlyReduction}
\alias{earlyReduction}
\title{do dimension reduction (via NMF or SVD) before Bayesian consensus clustering}
\usage{
earlyReduction(
mat,
how = c("NMF", "SVD"),
mat2 = NULL,
joint = FALSE,
findK... |
6b206a2af2f52692bc67107fc872dd6c6afe656b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/letsR/examples/lets.subsetPAM.Rd.R | b2532f0fd3fe3066d62a3b66377ae9cbd0c69ba7 | [] | 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 | 528 | r | lets.subsetPAM.Rd.R | library(letsR)
### Name: lets.subsetPAM
### Title: Subset a PresenceAbsence object based on species names
### Aliases: lets.subsetPAM
### ** Examples
## Not run:
##D data(PAM)
##D # PAM before subset
##D plot(PAM, xlab = "Longitude", ylab = "Latitude",
##D main = "Phyllomedusa species richness")
##D
##D # Su... |
29d61a683e3c98a438e52dff949f826071235010 | 9cfbe86f685f8ef280899ca97bc425d1bfda5564 | /0323_in_class.R | a087bb791dd652a7b3dc0ee002f52b5fc139178c | [] | no_license | kisumzzz/DataAnalyticsSpring2020 | 20f3b6fa7fc112efcf5d6731d6cd024e17a61a2b | deb9496c9156991e81ab88091f5f13bfa55ed974 | refs/heads/master | 2020-12-21T11:52:53.424495 | 2020-05-05T03:06:49 | 2020-05-05T03:06:49 | 236,422,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 783 | r | 0323_in_class.R | data("USArrests")
states=row.names(USArrests)
states
apply(USArrests , 2, mean)
apply(USArrests , 2, var)
pr.out=prcomp(USArrests, scale=TRUE)
names(pr.out)
pr.out$center
pr.out$scale
pr.out$rotation
dim(pr.out$x)
biplot(pr.out, scale=0)
pr.out$sdev
# PCA with iris dataset
data("iris")
head(iris)
irisdata1 <-... |
87a9ec7985912e0486649f35fa8f96031f85d6d6 | 852d3fb58551d0c612c1c40ebf6ef5ad4d78f92b | /Visualization/BitEpiVis.R | 56d14810054c1daa74d0271debf1e4e2c9e79438 | [
"MIT",
"BSD-3-Clause"
] | permissive | aehrc/BitEpi | 4a2e34a76453d66f18863d3ff0931c7f7c837953 | 8783f9664433c8de5f03b15a47cd5d7d81bb2e09 | refs/heads/master | 2021-08-06T00:54:44.046981 | 2021-07-27T07:35:41 | 2021-07-27T07:35:41 | 211,199,347 | 8 | 4 | NOASSERTION | 2021-07-22T00:16:18 | 2019-09-26T23:43:50 | C++ | UTF-8 | R | false | false | 6,319 | r | BitEpiVis.R | library(dplyr)
library(RCy3)
library(igraph)
setwd('~/temp/cc/BitEpi')
Color=list(SNP='red',PAIR='blue',TRIPLET='orange',QUADLET='green', OTHER='gray')
# Nodes of the graph are SNPs and Interactions
# Each SNP node could be connected to multiple Interaction Node
# Each Interaction Node is conneced to the SNPs that ar... |
67b4c288cc691e1b72ce5df79a91ee5298726943 | f5e25afe6fb3abdc9ea1ebe09ad383c21c83f92f | /R/errorbar.R | 2f944886fdadac7639f37258657b78789528a0f8 | [] | no_license | cran/phonTools | 34a4527f0ab8cbe06947dab7aff6385bb56cd452 | 80e82b901140f715c0b4d2c55b214410a6498038 | refs/heads/master | 2016-09-16T04:11:26.219457 | 2015-07-30T00:00:00 | 2015-07-30T00:00:00 | 17,698,512 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 598 | r | errorbar.R | # Copyright (c) 2015 Santiago Barreda
# All rights reserved.
errorbars = function(x, y, top, bottom = top, length = .2, add = TRUE, ...){
if (add) arrows(x, y+top, x, y-bottom, angle=90, code=3, length=length, ...)
if (!add){
plot (x,y,pch=16, ylim = range(y) + c(-top, bottom))
arrows(x, y+top, x,... |
3980ae7093f7e77dea6503b893a28c4e279bb3c0 | f6f88407b149dfe2be1f46832ba4b3385ad7aada | /gdp_rates/r_scripts/assess_cumulative_impact.R | 28e8d00548c28df02076d3ac975bdc7d5cda1829 | [] | no_license | dstauffer11/colonization_effects | 3ee84872c067844a303e8dad35b72baa078d8cc3 | c8adf4472c4296a086505763e93f4a97a140edab | refs/heads/master | 2022-11-14T13:42:37.441079 | 2020-06-26T14:53:36 | 2020-06-26T14:53:36 | 275,174,879 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,360 | r | assess_cumulative_impact.R | library(CausalImpact)
library(ggplot2)
library(rjson)
library(rstan)
library(bayesplot)
library(ggplot2)
library(CausalImpact)
library(gridExtra)
library(splines)
options(mc.cores = parallel::detectCores())
country.year.neighbors.list <- fromJSON(file='data/country_year_neighbors_map.json')
gdp.data <- read.csv('dat... |
aca1ea8745d9f7de8e0684b542d4448ea1ba6a6d | a3e56dccec4c41f256583f45959ee64d6d269f57 | /man/wine27.Rd | 3a00fe10109004762099940d9b5bd12271636487 | [] | no_license | cran/MBCbook | 76b189b1b24303fc49ae748f34807c5253507229 | 71cd7f2313a55239d5b1c6c707308c783481b200 | refs/heads/master | 2020-12-22T01:03:39.836908 | 2019-07-02T06:00:03 | 2019-07-02T06:00:03 | 236,623,831 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,155 | rd | wine27.Rd | \name{wine27}
\alias{wine27}
\docType{data}
\title{
The (27-dimensional) Italian Wine data set
}
\description{
The (27-dimensional) Italian Wine data set is the result of a chemical analysis of 178 wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities... |
e0199fb03566d406dd131363af98798813d7b3dc | eac21a885ac41794e6ef37f6abdf075e85897ed1 | /day24.R | f88ab3e1c2b200e960b4fce77542afe348a75950 | [] | no_license | d-sci/Advent-of-Code-2017 | 6872104df76b9b008ef0b15238d6934f410443ca | 73c8f0d0ca27e4fad2b85215df136882f27c2afc | refs/heads/master | 2021-09-02T14:13:38.027871 | 2018-01-03T04:48:33 | 2018-01-03T04:48:33 | 116,091,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,589 | r | day24.R | #Day 24
setwd("C:/Users/David.simons/Documents/advent of code")
library(data.table)
chunks <- rbindlist(lapply(strsplit(readLines("day24.txt"), "/"), function(x) as.list(as.numeric(x))))
chunks[, id := 1:nrow(chunks)]
#part 1 ----
#recursively find strengths of all maximally long bridges
strongestBridge <-... |
279fa596811688e316c5f3eea1e04fd9dd821e41 | 282acf6c53cceeda154ea8a8f7bd87ef80d0672e | /analyze.r | fa0cf77e7371ad116e8645b43eb56c06b0e76f84 | [] | no_license | casras111/thesis | 0f7729e92cdf30e6c0fd4dbce016f0126efa7142 | 995a062a18527205af926e6d490f926d396a08b1 | refs/heads/master | 2021-03-24T13:28:17.109233 | 2018-03-01T11:16:45 | 2018-03-01T11:16:45 | 63,358,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,208 | r | analyze.r | #analyze previous runs
library(ggplot2)
library(quantmod)
library(reshape2)
library(gridExtra)
library(moments) #for skewness function
startDate <- "1996-1-1"
midDate <- "2005-12-31"
midDate_1 <- "2006-1-1"
endDate <- "2015-12-31"
if (dir.exists("C:/Users/Claudiu/Dropbox")) {
droppath <- "C:/Users/Claudiu/D... |
3597803ba44bb4c1d0a0be5d5b1885fd75226047 | 7be028e961329bd28e739e7004e1f42b68181d0d | /R/stacf.R | 4a216b4d0a96003426c6eadcb53155ee32d3e498 | [
"MIT"
] | permissive | fcheysson/starma | a6293d9ac1ced9743d1e456c444ba16db2c36263 | 2e08ba5e122dda721d387e4aead8bf3304140e8a | refs/heads/main | 2023-06-14T21:52:21.318306 | 2021-07-12T09:14:36 | 2021-07-12T09:14:36 | 385,189,347 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,116 | r | stacf.R | # The 'stacf' function is defined as, per (Pfeifer & Stuart, 1980), the
# following expression:
# acf(l,0,s) = cov(l,0,s) / sqrt( cov(l,l,0) * cov(0,0,0) )
stacf <- function(data, wlist, tlag.max=NULL, plot=TRUE, use.ggplot=TRUE) {
# If only the weights matrix of first order is specified
if (is.matrix(wlist))
... |
5610d56094120d95401a7ded30f4d49e9b01da23 | 1f2ed7e0778776371702499954ab1b11d3ad3a4c | /man/oly12.Rd | 8557a9a4c26c879b0eb34223181d43bb7882a7f4 | [] | no_license | cran/VGAMdata | 1e3b653b5a9d4921535fb7d2e6d4191aa2d9201a | fbbb0beb0bf79fff712d1b994cf51de5cb3b176b | refs/heads/master | 2023-04-07T05:39:02.437835 | 2023-01-11T19:20:02 | 2023-01-11T19:20:02 | 17,694,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,481 | rd | oly12.Rd | \name{oly12}
\alias{oly12}
\docType{data}
\title{
2012 Summer Olympics: Individuals Data
}
\description{
Individual data for the Summer
2012 Olympic Games.
}
\usage{data(oly12)}
\format{
A data frame with 10384 observations on the following 14 variables.
\describe{
\item{\code{Name}}{The individual compe... |
067d9f0f5bba8f0aeff74a5a7b3c400e903c8b8b | 88311cfdacc0ada10cfb6e05c35411d3965dc582 | /solution/2-similarity/part2a/visualise.R | 972975d94f25000a0a810d4926ff9fd2d81dc2c1 | [] | no_license | g-eorge/CCPDS-02 | 7aab360f3c77c7c4a77bc047a7d18ee8c6dc1b95 | 6e3095395723f7d679349595d6ed8f098504a1b8 | refs/heads/master | 2021-05-27T10:18:52.830892 | 2014-07-01T01:47:09 | 2014-07-01T01:47:09 | 19,005,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,579 | r | visualise.R | #! /usr/bin/env Rscript
# Dependencies
# install.packages("ggplot2")
# install.packages("reshape")
# Load packages
library(ggplot2)
library(reshape)
# The providers that are least like the others
provider_ids <- c('50195', '390180', '50441')
# Plot colours for the providers
scale_colours <- c()
scale_colours[[provi... |
a2efd9abac3869e0aa56c7d99237242abb717e80 | 93a3ca0d2105970d92aba8ae04a7638d6938101c | /man/gt_get_data.Rd | 14cff02b92e659ccca5435f0ef7426c7c5d623b3 | [
"MIT"
] | permissive | geysertimes/geysertimes-r-package | 6482a9b72299497d0ad154ec0713e404adae0b63 | 933b2465337555092f06f7cd8b12413d47bfaa89 | refs/heads/master | 2022-06-18T04:48:53.824869 | 2022-06-12T02:16:07 | 2022-06-12T02:16:07 | 169,121,861 | 2 | 4 | null | 2020-07-26T18:02:03 | 2019-02-04T17:53:23 | R | UTF-8 | R | false | false | 1,827 | rd | gt_get_data.Rd | \name{gt_get_data}
\alias{gt_get_data}
\title{
Download GeyserTimes Data
}
\description{
Downloads the data from geysertimes.org.
Reads the data and creates a tibble object in `dest_folder`.
}
\usage{
gt_get_data(dest_folder = file.path(tempdir(), "geysertimes"),
overwrite = FALSE, quiet = FALSE, version = lubridate:... |
932436766d396791fa5efa04775006eb3f7bc586 | 771706de90263db2375687df55677276af8dcb57 | /Assignment 8.R | daca64437110a38ee96f3d49b2a45f02c8515cde | [] | no_license | dduwill/Product-Review | f65fcdf62bc7ecdb5f078a8e509e0093e90e6915 | 91db629ae04414ea6ecab3ca62e82c6c77a7bb2f | refs/heads/master | 2020-04-15T04:37:35.378919 | 2016-11-15T22:02:57 | 2016-11-15T22:02:57 | 73,781,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,255 | r | Assignment 8.R | library(rjson)
library(dplyr)
require(magrittr)
library(quanteda)
library(stm)
library(tm)
library(NLP)
library(openNLP)
library(ggplot2)
library(ggdendro)
library(cluster)
library(fpc)
#read json file
setwd("C:/Users/weiyi/Desktop/R/Assignment 8")
path <- "Automotive_5.json"
data <- fromJSON(sprintf... |
2ea23c5ba92494e9669eae718b8d556be44b30aa | 3bb85139690fe4f6c4575f1ca12aac3cccc758ea | /cachematrix.R | b8e67e68e3304032fb146fe193b46b90900dbb8f | [] | no_license | IZLID-LSSO/ProgrammingAssignment2 | ed3a9fa5c5fc5cf846d869ae832be172702e108c | 428b985932e540fe4bc923a7e12f0f20ed91f53b | refs/heads/master | 2020-05-30T18:52:26.790726 | 2019-06-03T01:53:33 | 2019-06-03T01:53:33 | 189,909,119 | 0 | 0 | null | 2019-06-03T00:25:06 | 2019-06-03T00:25:06 | null | UTF-8 | R | false | false | 928 | r | cachematrix.R | ## Two functions that compute and cashe the inverse of a matrix.
## This function creates a "matrix" that can store (cache) its calculated inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setInverse <- function(solveMat... |
f29c2dd28757f3762f9451c10963aa33c68c9867 | 6a0a368b7509afbc729304fc0073b0b940b43e8f | /cachematrix.R | 2e08ccea84129d89fccbc8f0d461fb5a2529468b | [] | no_license | ong625/ProgrammingAssignment2 | d90bc676b9425df7181f7292b92b219219349f1f | f53cc747c49ea766affa975dc7358cb0beb0fd86 | refs/heads/master | 2022-11-27T07:58:30.358493 | 2020-08-03T04:04:48 | 2020-08-03T04:04:48 | 284,584,988 | 0 | 0 | null | 2020-08-03T02:31:45 | 2020-08-03T02:31:44 | null | UTF-8 | R | false | false | 665 | r | cachematrix.R | ## The code helps to invert matrices number input
makeCacheMatrix <- function(x = matrix()) {
r <- NULL
set <- function(y){
x <<- y
r<<- NULL
}
get <- function()x
setInverse <- function(inverse) r <<- inverse
getInverse <- function() r
list(set = set, get = get,
setInverse = setInverse... |
b9eec90cd116558e71c13bf32b0a83f7b42b91d6 | df5885ac73301c7050b373d1d3d9f89991e9dbcc | /Figure-11.R | 0f800a98b77a89475d90c65db172107c977d3287 | [] | no_license | Fabbiologia/BluePaper-10_Supplementary_informations | 2af3103ea9b6d61becbe7db03b4939d1263169ef | e52a5f985c5df74297c09e937683eab68d2c9d9f | refs/heads/master | 2020-12-15T03:08:23.841603 | 2020-04-14T16:41:36 | 2020-04-14T16:41:36 | 234,975,349 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,897 | r | Figure-11.R | library(tidyverse)
library(patchwork)
library(RCurl)
### Data loading and wrangling ----
toplot <- read.csv(text = getURL('https://raw.githubusercontent.com/Fabbiologia/BluePaper-10_Supplementary_informations/master/data/HabitatProtectedDataset.csv')) %>%
filter(Cat %in% c('Total','mpa_all', 'mpa_all_m', 'mpa_... |
5835f8915957577b0b75aaace7a25a178f36f0e5 | 2a1a58c97642e4b4e568a18ad76dc6fbf246a125 | /R/impreciseImputation.R | 25163c12c9f837cf61815b59972ca2bbc17f5204 | [] | no_license | cran/impimp | 1657934ffa51d9e8afd65d3e5f9d1c961fc863f0 | 976176f80b808f5b7c0e88830fd49c5c4fa7295f | refs/heads/master | 2020-03-30T08:04:13.559483 | 2019-02-03T17:43:16 | 2019-02-03T17:43:16 | 150,986,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,690 | r | impreciseImputation.R | # Copyright (C) 2018 Paul Fink, Eva Endres
#
# This file is part of impimp.
#
# imptree is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version... |
11144f365afc3f8a8107e52a7f64f02db70f8453 | f6a5600cd0c8cad6699710049c4edff1aa1934e4 | /code/prioritizr_frontier.r | ce0b63cf3e8fa6e7d20f4c720e48c5668f58db01 | [
"MIT"
] | permissive | pinskylab/ClimateAndMSP | 04f251d6cf2bc16f34265d6f38cc3b580e886af8 | e9a9d693e045c8318d13f37ace33036407430c8a | refs/heads/master | 2023-03-02T15:55:47.637873 | 2023-02-21T19:25:56 | 2023-02-21T19:25:56 | 30,681,601 | 0 | 1 | null | 2020-08-19T14:25:31 | 2015-02-12T02:36:09 | R | UTF-8 | R | false | false | 24,203 | r | prioritizr_frontier.r | # Set up and run Prioritizr with zones to simulate CMSP
# Fixed budget across a range of weight present vs. future to get an efficiency frontier
# set up to source from within R 3.5.3: source('code/5.1_prioritizr.r')
# May need to set R_MAX_VSIZE=60000000 or larger in .Renviron to avoid hitting memory limits (Sys.geten... |
d66901ecaea6f9a42dd71b4c806736b2f82bb08b | 48aea1547fb612b127d5b5def716d48398236159 | /man/CIMseq.testing-package.Rd | 0dc41e324b8ff6d8ece9a460967805947ff58e59 | [] | no_license | jasonserviss/CIMseq.testing | 6a1951a5d1cd53a22704df631138050bc4e057c6 | 7039f9b52fb9280bb811662aa19d4fe7f7bf8398 | refs/heads/master | 2021-03-30T17:46:24.443721 | 2020-01-27T09:55:25 | 2020-01-27T09:55:25 | 76,064,214 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 485 | rd | CIMseq.testing-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CIMseq.testing-package.R
\docType{package}
\name{CIMseq.testing-package}
\alias{CIMseq.testing-package}
\alias{CIMseq.testing}
\title{Testing and analysis of the CIMseq and method.}
\description{
Description
}
\details{
\tabular{ll}{ Package:... |
ac1ee8fbff6fb6fbf1a09f64920a125501480ea5 | 0ecd29c40cbecd945f5d8e3d2b2d27e4070ef897 | /rstan_installation_helper.R | 94b689be7d9c14d2ee8b1056bb5c5d5b568fe582 | [] | no_license | paul-buerkner/2019_DAGStat_Stan_Tutorial | 195bc4e440feff7be10f93057614c252fe2cf7f7 | 41f778f9c1188bad80d0b58542aada541d275ca3 | refs/heads/master | 2020-04-29T16:39:14.856833 | 2019-03-19T11:11:15 | 2019-03-19T11:11:15 | 176,269,018 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,144 | r | rstan_installation_helper.R | # install rstan
# Quite a few other packages will be installed as well
if (!require("rstan")) {
install.packages("rstan")
}
# The following explains how to install a C++ compiler
# which is required for Stan
# -------- FOR WINDOWS -------
# This requires using R from Rstudio 1.1 or higher!
library(rstan)
example(... |
e763167ec62779096a002d1abf989af1d5a54e5e | 7384fa7a27f0fddda69766c4d351efabb494d799 | /cachematrix.R | 08c89e9658042fa59aee19a4eab6bf0e004341fc | [] | no_license | abumeezo/ProgrammingAssignment2 | edfb04334f35c41afc956dfd5d5e1cb6c267b939 | 63e9aaeded7f04cc8bace8bf01cf13e09e643db7 | refs/heads/master | 2021-01-13T15:52:19.018228 | 2016-12-19T04:20:53 | 2016-12-19T04:20:53 | 76,826,210 | 0 | 0 | null | 2016-12-19T03:51:38 | 2016-12-19T03:51:38 | null | UTF-8 | R | false | false | 1,120 | r | cachematrix.R | ##Functions to create a special "matrix" object with a cached inverse
##and to retrieve the cached inverse if already calculated from inside the object itself
##This function creates the "matrix" object with cached inverse
##Object has internal functions to establish and return itself and its inverse
makeCacheMatrix ... |
bca932738cd7110522cc3a5917a64f1837ffd015 | 890c942249dd887b82ca07eee97f68149ffd1f49 | /R/degs.R | 8b4e4e7fc2a894ffe64827aae81c51b497c52081 | [
"MIT"
] | permissive | lefeverde/QSPpaper | 3285c4829273120508610fef2ecdef3186dd26b7 | eec8fbedd1fefd1ed88dadbc77dd385ad78f274f | refs/heads/master | 2023-01-18T21:39:56.050930 | 2022-12-24T17:35:23 | 2022-12-24T17:35:23 | 240,388,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 465 | r | degs.R | #' Wrapper to create a fit object (see \code{\link[limma]{eBayes}}) using the contrast method
#'
#' @param v a voom object
#' @param group_cont vector of contrasts
#' @param mod model matrix
#'
#' @return fit object
#' @export
#'
#' @examples
make_cont_fit <- function(v, group_cont, mod){
m <- data.frame(mod)
cont_... |
3842f59e4582f10f535df6b39f20ac6e86903009 | 93defdbd4e3c597ec4b7f95b5cdaf649e7cbb21c | /man/dot-extract_base_schedule.Rd | 237c896518dc3dc8d05d42878943ba8b815c5615 | [] | no_license | meysubb/collegeballR | 0e909cbda2ec96f386fd5385168a65790507aab3 | 1727a03dc3bf0377d65849586c0e44c9a089b591 | refs/heads/master | 2021-05-05T13:31:13.268673 | 2019-07-25T01:06:32 | 2019-07-25T01:06:32 | 105,055,203 | 17 | 5 | null | 2019-04-20T23:26:48 | 2017-09-27T18:36:46 | HTML | UTF-8 | R | false | true | 507 | rd | dot-extract_base_schedule.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extract_base_schedule.R
\name{.extract_base_schedule}
\alias{.extract_base_schedule}
\title{Extract Raw Base schedule}
\usage{
.extract_base_schedule(team_id, year, sport)
}
\arguments{
\item{team_id}{Team ID (form team_mapping)}
\item{sprt}... |
106b776f269767b3681b2d3ffc91a718ae45600c | 3b2a2137476edc5fb5dad4c3f0f29fa83252db0f | /man/notin.Rd | 1b11b4450d58a48ddd0a7cbb27d9e12455461958 | [] | no_license | woodwards/octopus | 32d8c64947d634fd8cf32b5abdf34fea478baef6 | 5be3adffe27bd0d8300ff6394a59587f86c4bd51 | refs/heads/master | 2020-09-25T09:24:18.651141 | 2020-01-06T20:48:57 | 2020-01-06T20:48:57 | 225,973,852 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 346 | rd | notin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{\%notin\%}
\alias{\%notin\%}
\title{Returns TRUE if x is not in y.}
\usage{
x \%notin\% y
}
\arguments{
\item{x}{Anything.}
\item{y}{Anything.}
}
\value{
Logical.
}
\description{
Returns TRUE if x is not in y.
}
\examples{
"a... |
d038121a9e733c43eb036e15362a4ee823293615 | 76dbc1754d4fac81e75fc054858ba91f99b55b2d | /R/mortalityhazard-consthaz.R | 78c3b9f0e7b177a3e4e2d9f308644f8668e20ab6 | [] | no_license | dfeehan/mortfit | e51ac12507385bd9024e8109aa1a3eaea2895fb5 | 8dfd82e93fde1bf408dbe59eb004cc8694603f88 | refs/heads/master | 2021-01-18T00:00:39.351697 | 2020-11-08T16:23:12 | 2020-11-08T16:23:12 | 18,040,328 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 753 | r | mortalityhazard-consthaz.R | ########################
# constant hazard object
consthaz.haz.fn <- function(theta, z) {
alpha <- exp(theta[1])
return(rep(alpha,length(z)))
}
## these starting values have been updated based on preliminary analysis
consthaz.haz <- new("mortalityHazard",
name="Constant Hazard",
... |
b7f48bb3fa02594c8937ebfd82abd33ccec55b9d | 2171709c5b23d8e5f7c2194d4c77b8d1d3c232f3 | /man/Content.Rd | 2acf178c31593e5c7d08ef59848501167ba451c6 | [] | no_license | colearendt/connectapi | 9472351abc6f24c5d3bb9acc41721754d13f52af | 00a01aa74aee8df5fcc67cee14b80c5239168b52 | refs/heads/master | 2021-07-19T07:33:16.802365 | 2020-05-19T12:50:52 | 2020-05-19T12:50:52 | 168,455,783 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,214 | rd | Content.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/content.R
\name{Content}
\alias{Content}
\title{Content}
\description{
An R6 class that represents content
}
\seealso{
Other R6 classes:
\code{\link{Bundle}},
\code{\link{RStudioConnect}},
\code{\link{Task}},
\code{\link{Vanity}}
}
\concept{... |
303967312cdd875937c88bf2ee59ce2095c505da | c7ecb5298854ca192e5613f81e74265bd53f9e96 | /Project 2/Drop Out Loop-Spec.R | aaed1efb20844600dc54aa7d6bb26a6c12a52206 | [] | no_license | tommsmit/R_Projects | 14e89784956ed333c6e8c03c33738bd9c01aad82 | bafc0c5a60b295b5d75b6668734a422ff7440ba3 | refs/heads/master | 2023-07-16T01:39:01.576903 | 2021-08-24T21:07:53 | 2021-08-24T21:07:53 | 360,184,072 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 59,900 | r | Drop Out Loop-Spec.R | ### Drop out Loop 2: Special Programs ###
library(rvest)
library(tm)
library(pdftools)
library(stringr)
library(dplyr)
library(plyr)
library(data.table)
library(Hmisc)
library(tictoc)
library(tidyverse)
require(XML)
library(ggplot2)
library(shiny)
school_year<-(c("1998-99","1999-00","2000-01","2001-02","2002-03","200... |
9e62fa555b9ac5c6277e638e12d85dd8bad0a61a | 40962c524801fb9738e3b450dbb8129bb54924e1 | /DAY - 5/Class/LineChartColourful.R | da0eb2436fa8ea8450359f2aa1bd10f3c028ff39 | [] | no_license | klmsathish/R_Programming | 628febe334d5d388c3dc51560d53f223585a0843 | 93450028134d4a9834740922ff55737276f62961 | refs/heads/master | 2023-01-14T12:08:59.068741 | 2020-11-15T13:23:31 | 2020-11-15T13:23:31 | 309,288,498 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 286 | r | LineChartColourful.R | #Line plot
marks <- c(7,12,28,3,41)
age <- c(14,7,6,19,3)
#Line chart only accepts numbers(no string)
plot(marks,type = "o",col = "red", xlab = "marks", ylab = "Age",
main = "Marks Vs Age")
#Mutiple lines in a single chart used for comparison
lines(age, type = "o", col = "blue")
|
598cfe56ca964a7de5cc71cd7ecf017c4b7f1dd1 | abea0b5d000d7c01d390eeb615427bc0322aa30f | /src/modify_asos/R_asos_pred.R | fc954d48612c38621c769051b30885b6075037a0 | [] | no_license | janmandel/firewx-evaluation | 5e176d8762f34b4e88a9446f1d898b3698abc5e5 | 51ca3c4a1c63d8c6ba00e910a87f4c87c2c0ac53 | refs/heads/master | 2020-05-05T01:10:49.662013 | 2017-08-24T17:40:06 | 2017-08-24T17:40:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,517 | r | R_asos_pred.R | ############# ASOS FORECAST DATA - EXTRACT LANDFIRE / FIX FORMATTING ############
### Set needed packages
library(geosphere)
library(raster)
library(rgdal)
library(sp)
library(data.table)
library(plyr)
### Set Working Directory
setwd("/home/wpage/Documents/ASOS")
### Read-in Observed lat/long and La... |
f19c197e31d2b6d9a9f323c8d5dd85aa3142d8e8 | 8bebde68b834700de79052db26f459dd8636fec7 | /R/hvalir.R | 48b98d0aecf25d68280e49eccd5a939f39ec8ac0 | [] | no_license | vonStadarhraun/mar | da025e84d86bba2db0a46c1f6f1917d98878535f | 8d56708739faf9cd6eed98309c8df9f5f769416d | refs/heads/master | 2022-11-30T19:05:54.908761 | 2020-08-12T12:51:32 | 2020-08-12T12:51:32 | 286,978,909 | 0 | 0 | null | 2020-08-12T10:00:47 | 2020-08-12T10:00:46 | null | UTF-8 | R | false | false | 585 | r | hvalir.R | #' Hvalir
#'
#' @param con Tenging við Oracle
#'
#' @name hvalir_hvalir
#'
#' @return SQL fyrirspurn
#'
#' @export
#'
hvalir_hvalir <- function(con) {
tbl_mar(con, 'hvalir.hvalir_v') %>%
dplyr::mutate(veiddur_breidd = to_number(replace(nvl(veiddur_breidd,0),',','.')),
veiddur_lengd = to_number(r... |
ef672cf98a55274e2a56efafc28a0ef2f6ab2a93 | 3b107075ed5cf4c005d62c6fd13d6c42bd3e96ef | /R/zTDGSpill.R | 25511e00b1b3a84c2f756ce77ea689b80c68290c | [] | no_license | ryankinzer/pitph2 | 14f9a5a6683e2598b16639e98335ae6ca8d8e50c | b1edbe76a3866e07ead26e3c1a2233f1cadbf8a0 | refs/heads/master | 2020-03-27T09:26:38.893562 | 2018-10-09T20:11:11 | 2018-10-09T20:11:11 | 146,341,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,473 | r | zTDGSpill.R | #------------------------------------------------------------------------------
# The function estimates the amount of TDG generated from spill. The output
# doesn't represent the amount of TGD being reported at monitoring sites.
# Monitoring site TDG is calculated with the companion function zTDGMON().
# Both TDG fun... |
2d57f739aad3688e089189c6d8987c0d15c85dca | f7e93d31f57542cf25fa0894b4a69355f40469a0 | /man/theme_timeline.Rd | 60f994c67b9d6b160a2bd378edda2f8b6f8e2096 | [
"MIT"
] | permissive | kamenbliznashki/noaaeq | 92a239b7c4e6aa06c3d78fab1ff2b4ed88e92c45 | ecdca2eb4810196e1e6076c5a5df901e2d41ab1e | refs/heads/master | 2020-12-06T10:21:08.917068 | 2020-01-08T02:28:32 | 2020-01-08T02:28:32 | 232,437,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 609 | rd | theme_timeline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_timeline.R
\name{theme_timeline}
\alias{theme_timeline}
\title{Custom theme for use with the earthquake timeline plots}
\usage{
theme_timeline()
}
\description{
The theme properly formats the axes, background and gridlines.
}
\examples{
... |
b198d7658f48c81c7ffeac61b925bc5f4d294e76 | 78b6410be67a167fde91abb6a039847a45ce46cc | /man/n.Rd | 936a4db3d8837da4756b750a42198aaec5ac5bd8 | [] | no_license | reyesem/IntroAnalysis | fea3283abc4bd995339acfc7e74f2193812317e2 | 54cf3930879303fb128faf81bd1710b385300d6c | refs/heads/master | 2023-07-12T08:45:27.546965 | 2023-06-29T22:07:02 | 2023-06-29T22:07:02 | 123,822,392 | 0 | 0 | null | 2022-08-15T14:34:13 | 2018-03-04T19:42:24 | HTML | UTF-8 | R | false | true | 317 | rd | n.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/variable_summaries.R
\name{n}
\alias{n}
\title{Compute sample size.}
\usage{
n(x)
}
\arguments{
\item{x}{any vector.}
}
\description{
This is just an alias for \code{length(x)}.
}
\examples{
summarize_variable(am ~ 1, data = mtcars, n)
}
|
026481c9e465b343fb6f067a5c184a04f58f3b24 | c46a6ff80331d7f47bc3c379b7b6f51644a3925b | /Chapter_07/customTests.R | 5db1d26f0be81fbd374a2eb220993307b8955d55 | [] | no_license | elmstedt/stats20_swirl | 6bb215dc600decaf03ecf441cf0e28bdbd525536 | 6de97f3613f941c5c39a85b9df4f26fa3b62e766 | refs/heads/master | 2021-05-22T02:29:59.080370 | 2020-10-06T07:42:50 | 2020-10-06T07:42:50 | 252,929,124 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,076 | r | customTests.R | # Put custom tests in this file.
# Uncommenting the following line of code will disable
# auto-detection of new variables and thus prevent swirl from
# executing every command twice, which can slow things down.
# AUTO_DETECT_NEWVAR <- FALSE
# However, this mean... |
6ec41fb7d5a6a83ed3a7aae73ecfc3f1dae4526f | 96a7892b0ba2eb4e26979911642d725ce0225fae | /HW2/HW2.R | 8e30dc7bd1d47253b1e8439e3be44e00d2f77bcb | [] | no_license | sachinshindegit/R-Programming | 868e2052bfed62a51e1155d71e2ec25228723ec0 | bd55286e79be0e675bd72fdcfb765b88f3989ba5 | refs/heads/master | 2021-01-10T04:35:27.053033 | 2016-01-10T21:10:14 | 2016-01-10T21:10:14 | 43,481,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 161 | r | HW2.R | library(boot)
set.seed(1)
Y=rnorm(100)
X=rnorm(100)
Y=X-2*X^2+rnorm(100)
plot(X,Y)
set.seed(1)
Data <- data.frame(X, Y)
z <- glm(Y ~ X)
cv.glm(Data, z)$delta[1] |
96b76019a45035db539b7cfd23cde55311116efa | bebba2b371a41e0fae55e2b5853a2870f9e6814a | /archive/isotria_lifehistoryfigs.R | 333a5c29873cf15f8592a5d148e86803bfb0f534 | [] | no_license | AileneKane/isotria | fa8015a69e1e80c095d598625d762ddcb2700d2a | 230ee3e8f63cc450ced49a0f6932c4558f3c0c02 | refs/heads/master | 2021-08-28T01:38:51.555826 | 2021-08-16T23:24:41 | 2021-08-16T23:24:41 | 66,016,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,866 | r | isotria_lifehistoryfigs.R | #Figures and individual life history traits estimated from posterior samples of multistate model for Isotria medioloides Alton NH population
#Data provided by Bill Brumback
#Coding by Ailene Ettinger with help frmo Andy Roly and Elizabeth Crone
#this file has code for all figures in the manuscript, and for estimating l... |
d661b4879f506a74cc74b88c3aa9a78080aa1a36 | 19706720652dd327c738e5b4ac30859fa87130e9 | /cleaner final.R | aee8c455f09dd780e4d7939c9360a6c4b5841c26 | [
"Apache-2.0"
] | permissive | souravbose1991/toxic_element | bea9cbc03b714e5dffde20ed1331a305698a6ae1 | abca8b6d1d88a925e411d0cc182cdd49966d08bf | refs/heads/master | 2021-04-29T21:24:40.310464 | 2018-08-19T18:27:50 | 2018-08-19T18:27:50 | 121,615,503 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,761 | r | cleaner final.R |
train <- fread("train.csv", key=c("id"))
test <- fread("C:\\Users\\HP LAP\\Desktop\\Kaggle\\Data\\test\\test.csv", key=c("id"))
stopwords.en <- fread("stopwords-en.txt")
profane <- c("damn", "dyke", "fuck", "shit", "ahole", "amcik", "andskota", "anus",
"arschloch", "arse", "ash0le", "ash0les", "as... |
a5820d8a9bf966a5f97255e07733d4ccfc6b0d6d | 7cd1f7f9555954476d9538c070e5a43ef93ce3d2 | /man/parse_keyvals.Rd | d24551531900347e221d2ff7830714ef4b71fe67 | [] | no_license | vsbuffalo/msr | f015447cc8815ea6ae9a24f5a451537edfa0b087 | 18fb0020ceb8c6e45b82dfd036dda7e03f64a163 | refs/heads/master | 2021-01-11T14:35:19.611932 | 2018-05-25T19:41:19 | 2018-05-25T19:41:19 | 80,166,891 | 20 | 2 | null | null | null | null | UTF-8 | R | false | true | 430 | rd | parse_keyvals.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parse_ms.r
\name{parse_keyvals}
\alias{parse_keyvals}
\title{Parse MS's key/value pairs, e.g. segsites and positions
returning a list of key/vals (where vals can be list too)}
\usage{
parse_keyvals(x)
}
\description{
Parse MS's key/value pair... |
59621a895e08d583a6c05de5f2b8c7b3b64f45b4 | f44335c0bb9597c994c06611ef34b4c4fe9637c1 | /R/listas.R | 20397d379c01d21a25d0028fc57a6a93b87136af | [] | no_license | cran/INQC | 7467d5c33cf59d1602fc6c82549f49613743d297 | ddce985594be74cdf93b135c0febd4ef7cfb3c1e | refs/heads/master | 2023-03-17T14:39:12.484132 | 2021-05-24T13:00:02 | 2021-05-24T13:00:02 | 334,129,195 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,407 | r | listas.R | listas<-function(country='all',name='allstations.txt'){ #NECESITO parametrizar listas. Usar esa parametrizacion par subset de downloads too.
#' Creates listings for stations ('non-blended' case) linking STAID and SOUID
#' @description This function takes all the elements and rbinds them into a single list to pr... |
dfb109a560663c8d93fbb9c33b8952b1378225af | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /sppmix/man/GetBDCompfit.Rd | 152097f9e40cfb5b44abc45f41fba8d431dc3149 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,275 | rd | GetBDCompfit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/postgen_ops.R
\name{GetBDCompfit}
\alias{GetBDCompfit}
\title{Retrieve parts of a BDMCMC fit}
\usage{
GetBDCompfit(BDfit, num_comp, burnin = floor(BDfit$L/10))
}
\arguments{
\item{BDfit}{Object of class \code{damcmc_res}.}
\item{n... |
075a2ad8ae7a55d5a7ee9969a0d77f07609120b9 | 06d9afe4e9666407ff607b142649d4c6e944d674 | /man/ezDesign.Rd | bb0a61f165e066500fbb5c5dc7723429155bbefd | [] | no_license | cran/ez | fe4ae993c2ed1042d6f84c64e368970c502a5bff | 1d7a35d30f31b1671e7f6548b15864ddfe61c5ef | refs/heads/master | 2021-07-10T23:03:03.489960 | 2016-11-02T18:17:31 | 2016-11-02T18:17:31 | 17,695,925 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,248 | rd | ezDesign.Rd | \name{ezDesign}
\alias{ezDesign}
\title{Plot the balance of data in an experimental design}
\description{
This function provides easy visualization of the balance of data in a data set given a specified experimental design. This function is useful for identifying missing data and other issues (see examples).
}
\usage{
... |
257cddc3647acb198e381e68d9529afca110fa07 | e2f3fee3cb8f1abdee08724f0fe8a89b5756cfbe | /COSTdata/man/FRS_ob_1999.Rd | b8723bdcffadb91d85ed0e31506de4222be3d5d9 | [] | no_license | BackupTheBerlios/cost-project | 1a88c928f4d99db583a95324b31d6a02d9bd20c9 | 4ab39d16c48f031ca46512545895cb17e5586139 | refs/heads/master | 2021-01-21T12:39:53.387734 | 2012-03-26T14:58:36 | 2012-03-26T14:58:36 | 40,071,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,125 | rd | FRS_ob_1999.Rd | \name{FRS_ob_1999}
%\alias{FRS_ob_trips}
\alias{FRS_ob_1999}
\docType{data}
\title{FRS observer data}
\description{
FRS observer data in the COST data exchange format.
\cr Consists of 53 demersal sampling trips from 1999. Discards length distributions are sampled by haul, the landed length distribution is sampled by tr... |
b43b2f4dcf308e25c52ed4a407ee709ad3aed10d | 0e6d3ed19aa2ef50bf4e4bd164cb3383c106a84f | /GWAS/JIA/individual_level/jia_analysis.R | f149c4fd13a82aa4724fa2e0239e4807753193cf | [
"MIT"
] | permissive | ollyburren/basis_paper | 4cdefd86a8811efb0bbeeae6975b804f6f7639a6 | 7393390c1b1f5b673049202d293994704cebeafb | refs/heads/master | 2020-03-29T22:06:03.889635 | 2019-10-23T17:06:15 | 2019-10-23T17:06:15 | 150,402,845 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,249 | r | jia_analysis.R | library(cowplot)
## analyse jia projections
DATA.DIR <- '/home/ob219/share/as_basis/GWAS/individual_data/individual_proj'
all.files <- list.files(path=DATA.DIR,pattern="*.RDS",full.names=TRUE)
BASIS_FILE <- '/home/ob219/share/as_basis/GWAS/support/ss_basis_gwas.RDS'
VARIANCE_FILE <- '/home/ob219/share/as_basis/GWAS/s... |
9b6d331ec04ffa4c2f18ec4999de1c23bd1c5120 | bdbb30b1fa9d20d16b37dfe43c8796e43d919934 | /Application/ui.R | 644b4c3e57605864ff63835f64f7ac45299fec31 | [] | no_license | pogh/Course-2015.09-Developing-Data-Products | 8f4abc75c4b55ef1523bd0e14e484deb1171c86c | 27cc269c0286896e1abe8a3955f3d30973ef8524 | refs/heads/master | 2021-05-30T04:25:58.459676 | 2015-09-14T09:33:54 | 2015-09-14T09:33:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 838 | r | ui.R | require(markdown)
shinyUI(fluidPage(
navbarPage("Exploratory Linear Modelling on the ‘mtcars’ Dataset"),
fluidRow(
column(4,
includeMarkdown("documentation1.md"),
hr(),
uiOutput("colNamesDropdown1"),
uiOutput("colNamesDropdown2"),
... |
1da759a17a1602f2b5465ce3b2c0a9df7d20e93d | 3f63ed18371a3237d501badeef43e2fe6a41cd45 | /vectorFieldsInR.R | 66db7bd2bbcdb7966fdad915ec313c999c539a3a | [] | no_license | johnwithrowjr/R_Libraries | 9afff950b412dc14aace92d39cf9819bf96e52e4 | 69cdd55ef8f2d2cb1f5e5a1e806d4379fac98a8d | refs/heads/master | 2021-01-18T23:50:35.750753 | 2016-06-10T21:32:31 | 2016-06-10T21:32:31 | 55,651,823 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 978 | r | vectorFieldsInR.R | plotVectorDivergence <- function(z)
{
vectorField(atanc(z),mag(z),rep(1:dim(z)[2],dim(z)[1]),rep(1:dim(z)[1],each=dim(x)[2]))
}
createVectorDivergence <- function(rstX,rstY,betamax=-1)
{
matX <- as.matrix(rstX@data)
dim(matX) <- rstX@grid@cells.dim
n <- dim(matX)
matY <- as.matrix(rstY@data)
dim(matY) <- rstY@gr... |
656ed8b42aa10270230095d95b02dc45794fcc29 | bdc863461d5b665914b5cc369c4d445283917f29 | /Tests/Candidates/10^3/Data 10^3 Test/PLOT FPOP 10^3 MultiTest Candidates.r | 71ee89d907cb41e93d689eff29f44535bdd04a74 | [] | no_license | lpishchagina/FPOPdim2 | 706b5942f7a07a4de834c0f7964444c18f600fa1 | 353d96dc1450e4bac90c1b270a3f0312d01ee626 | refs/heads/main | 2023-04-01T17:52:52.647384 | 2021-04-13T21:16:16 | 2021-04-13T21:16:16 | 346,318,274 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,465 | r | PLOT FPOP 10^3 MultiTest Candidates.r |
library(ggplot2)
library(ggpubr)
################################################################################
fname <- "PLOT FPOP 10^3 MultiTest Candidates.png"
f1name <- "PLOT FPOP1 10^3 MultiTest Candidates.png"
f2name <- "PLOT FPOP2 10^3 MultiTest Candidates.png"
f3name <- "PLOT FPOP3 10^3 MultiTest Candidate... |
977c327793b62f70fec96111fe29503937b35a27 | 08747ab934d09afeb31876584d2b8ab96a524648 | /Projet_final/ui.R | 3cf91599b2d4cccb359647c00b47900f0cce70e7 | [] | no_license | vneyret/Rapportfin | 3900a3c99bbb628abe546bd10a9743c28b2eae9b | 9b35cfbaca42528d63e923e27df05d0ae4148d79 | refs/heads/master | 2022-07-24T09:53:22.124311 | 2020-05-15T08:21:29 | 2020-05-15T08:21:29 | 263,560,439 | 0 | 0 | null | 2020-05-13T14:59:35 | 2020-05-13T07:44:48 | HTML | UTF-8 | R | false | false | 9,030 | r | ui.R | library(shiny)
library(shinythemes)
ui <- tagList(
fluidPage(theme = shinytheme("flatly")),
navbarPage(
# title=div(img(src="logo.png"), "ISARA Projet Shiny G1"),
"ISARA Projet Shiny G1",
#Onglet Informations
tabPanel("Informations",
fluidRow(column(width=2),
... |
90f271db65fe132d760e43161b2a9afc6503719c | 3a5f227074d2d903633cd893b57af9125b536aaf | /man/ei_ind.Rd | 32f570b62116b363974db9109ee062e94937a883 | [] | no_license | cran/ITNr | eefd3a398e7bca302b05782913f300dae1fb7f26 | 35a833cc39458b6cf5f6b491a4327cca2effa63a | refs/heads/master | 2023-06-24T15:43:02.117877 | 2023-03-31T13:10:11 | 2023-03-31T13:10:11 | 120,759,051 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 750 | rd | ei_ind.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/individual_EI_function.R
\name{ei_ind}
\alias{ei_ind}
\title{Individual/Node level E-I Index}
\usage{
ei_ind(gs, attrname)
}
\arguments{
\item{gs}{igraph object}
\item{attrname}{Attribute name}
}
\value{
Group level results dat... |
848d9a10277a31349aaf0a97ce408f5ccc03bd2c | aae143af482690863b42f76af555f2617fabfc39 | /R/build_nhtsa_url.R | f4c86cb80fea4c99baf7c5a602ab2097fa666a82 | [
"MIT"
] | permissive | burch-cm/vindecodr | ded9ef48f6d36a2a98d021684211a64a0185117b | 68b4debc479a7709dbefe6159c73a22b7b7a231f | refs/heads/main | 2023-01-13T08:03:59.566698 | 2020-11-23T20:45:05 | 2020-11-23T20:45:05 | 314,322,534 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,246 | r | build_nhtsa_url.R | #' Build a NHTSA URL
#'
#' @description
#'
#' A family of functions to build URLs for the National Highway Transportation
#' Safety Administration (NHTSA) vehicle identification number (VIN) decoder API.
#'
#' The `build_nhtsa_url()` function returns a closure containing the appropriate
#' endpoint and file format re... |
ccfbb670d39ba8a8666244c163a4ffa80e79f4af | 547e448dd1b38c8b8fd4e4edc9cdff799670d498 | /Archived/05-01-16/X2 - Make Gephi Files.R | f9f3e2ad0d607a6fbe9b3a42a06be4c18473c99e | [] | no_license | BrianAronson/ADHD-Peer-Influence | c20bd69fd918a92091f79622f9180504148f1b16 | d726d8fff6ef967972ed41c73e602e2ede394956 | refs/heads/master | 2020-12-05T00:03:56.800043 | 2020-01-05T23:43:19 | 2020-01-05T23:43:19 | 231,944,033 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,339 | r | X2 - Make Gephi Files.R |
#Attempt igraph
# plot(g2,layout=layout.fruchterman.reingold(g2),vertex.size=2,
# vertex.label=NA, edge.arrow.size=.025,vertex.color=DifficultyAttention)
#Edgelists
#Rename edgelists
Geph1.0<-get.edgelist(g0)
Geph1.1<-get.edgelist(g1)
Geph1.2<-get.edgelist(g2)
Geph2.0<-get.edgelist(... |
3763359fbf3daa406bdd3441e9c0ae34689cf8e1 | 0affcfeafed053dab542218ea09a6a11716b27da | /LaLigaEconomics.R | bba9859f8340b6fcabf088df1b1fa104f52a6184 | [] | no_license | trickytaco/laliga-economic-analysis | 26d3bdcfd6db6f9db9bbb502653ea613bc7b786a | bf59198e6eea4c51b983eb721acc53cee16c465f | refs/heads/master | 2021-01-10T01:59:43.791331 | 2016-04-04T01:54:01 | 2016-04-04T01:54:01 | 55,268,664 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,938 | r | LaLigaEconomics.R | #Library to read Excel files
library(xlsx)
#library(ggplot2)
#Set the working directory
setwd("D:\\Statistics\\LaLiga\\laliga-economic-analysis")
#Read in the standings for each season from 2005-06 to 2014-15
standingsList <- list()
sheetNames <- c("2005-06", "2006-07", "2007-08", "2008-09", "2009-10",
... |
7e4c63d17f456748e5f7ca0fabd873c8f51571ec | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/715_0/rinput.R | 73ce2a48d58103a6d781e11f94fae244b103ca16 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 133 | r | rinput.R | library(ape)
testtree <- read.tree("715_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="715_0_unrooted.txt") |
66c3e36d314b270a6e79a6e1a1026ac91eeb8b5a | 2f475f7067440bc1f4561aec79ac0216bf27d36f | /iowa_case_ts/server.R | 42ea4631d7a0b9be917f7df21c1ec7b7acb5c063 | [] | no_license | mkim0903/RshinyApp | c1979d0e1c611167db3d504c5de3e3f592b65ed3 | f959718525631b0812564baf752e8b8d754965a7 | refs/heads/main | 2023-07-12T18:20:21.835733 | 2021-08-25T21:39:07 | 2021-08-25T21:39:07 | 381,122,241 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,193 | r | server.R |
cols <- c("#045a8d", "#cc4c02")
iowa.case.ts = function(date.update, plot.type) {
dfplot = slid::dfplot
if (plot.type == 'counts'){
ts <-
ggplot(dfplot, aes(Date, DailyCases, colour = Group) ) +
## Plot observed
geom_line(colour = 'darkgray') +
geom_point() +
scale_color_manual(values... |
d2351b965f6ad3d032926ee5b2de98aba59cafea | 4d0716e85ee73c0ba88a83b45f141652bbf7625d | /other/2.1/Assignment_2.1_HillZach.R | c272afb03e02355763378ed3cd69a2bdb67e3814 | [] | no_license | midumass/DSC-520 | dc83acec5077b80fd15f7f0e2b879c8d17cb27bd | fcd08ca23a583dc633a6556484b77c76b9b96b4e | refs/heads/master | 2022-08-14T08:25:56.821159 | 2020-05-28T04:08:20 | 2020-05-28T04:08:20 | 255,189,729 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,683 | r | Assignment_2.1_HillZach.R | # Assignment: Assignment 2.1 DSC 520
# Name: Hill, Zach
# Date: 24MAR2019
# 1. What are the observational units in this study?
#
# Units of Observation in this study are the grades of students in a proferssor's course.
#
# 2. Identify the variables mentioned in the narrative paragraph and determine
# which are categor... |
8a9d0d6af6ec667d53b51d68fc093e8cec327a16 | c49aa09f1f83ee8f8c9d1e716ae38381ed3fafca | /feature_selection/ex_9/roc9_1_4.R | 1498421675e62e021a128c7f50f27de5d366dd45 | [] | no_license | whtbowers/multiomics | de879d61f15aa718a18dc866b1e5ef3848e27c42 | 81dcedf2c491107005d184f93cb6318865d00e65 | refs/heads/master | 2020-04-11T03:25:40.635266 | 2018-09-24T08:51:06 | 2018-09-24T08:51:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,084 | r | roc9_1_4.R | setwd("/home/whb17/Documents/project3/project_files/feature_selection/ex_9/")
#setwd("/project/home17/whb17/Documents/project3/project_files/preprocessing/ex_9/")
library(pROC)
library(ggplot2)
set.seed(12)
# To direct to the correct folder
date <- "2018-08-07/"
ex_dir <- "ex_9/"
# Features selected in Kaforou 201... |
a4f5e28b82a274b7869ac62a84df804d542baa20 | 050b136eb6bb7c7d57c18ea894104acf890e3bb7 | /src/prep-inputs-static.R | da5f205f0e388e4e70a3769739da500676274ee2 | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause",
"BSD-3-Clause"
] | permissive | LBNL-UCB-STI/gem | fc5bf991a4e2c95368f68bd7478f1cde40891a01 | 3ce8dcac69fe504bfec62b9cec7d9af0c1f1178e | refs/heads/master | 2023-04-24T19:04:33.083931 | 2021-02-04T22:29:06 | 2021-02-04T22:29:06 | 157,785,909 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,765 | r | prep-inputs-static.R | #############################################################################################
# Grid-Integrated Electric Mobility Model (GEM)
#
# This has functions needed to prepare static inputs (those that will never vary in an
# experiment).
#
# Argument: none
# Returns: list containing data tables used to run th... |
e3e022e147158a2de9e733700afb2289f0276fa6 | 27d0436a8c9725ca98962d239571478de7727a2a | /man/extract_ffd.Rd | f4d6786fb0083d7adca38772a5744188c7cc857a | [
"MIT"
] | permissive | lee269/iapdashboardadmin | 7d3d762c1956c88512c324d3aea15b3f6958e89c | 43312e4012f871f62f3ada29f085ee65670293a9 | refs/heads/master | 2020-12-02T08:50:05.691888 | 2020-02-22T12:09:54 | 2020-02-22T12:09:54 | 230,950,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 563 | rd | extract_ffd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extract_ffd.R
\name{extract_ffd}
\alias{extract_ffd}
\title{Extracts FFD level data at 4 digit from a comtrade bulk download zipfile}
\usage{
extract_ffd(file)
}
\arguments{
\item{file}{zipfile to process}
}
\value{
A tibble containing trade ... |
35baf40e4e470c3b5aed6424e0f20996ec5807bf | ee1af63213eaf268bf38a51e52883e43ca811937 | /hands-on-with-r/project01.R | de0909a328de5f6767dcfbf941ca516ee0d0bee0 | [] | no_license | geocarvalho/r-bioinfo-ds | 06ce4ae515981989274ade8f582988ea6fef6ffa | 596daf835f2d8c64055e96906e6f3bda7fa3d42b | refs/heads/master | 2023-05-11T14:45:41.841356 | 2023-04-28T08:02:47 | 2023-04-28T08:02:47 | 92,194,815 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 693 | r | project01.R | # Chapter 1: Objects and functions
roll <- function(list=1:6, size=2) {
sum(sample(x=list, size=size, replace=TRUE))
}
# Chapter 2: Packages and help pages
library(ggplot2)
x <- c(-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1)
y <- x^3
qplot(x, y)
#histogram
x <- c(1, 2, 2, 2, 3, 3)
qplot(x, binwidth=1... |
77d10504bcdf3ab695860ce83d8d4a9fbf7e8dfa | e37c3e8e0b32162ca7ed72fc78d3815a87ecbb2b | /pairwise_approach/Carrie/Code/RunRatings.R | cdecac2e9c77f34a47cbb91fdd516533f4160e7d | [
"MIT"
] | permissive | cfowle/elo_sailor | 7169e15f1eebaf5c9287f4a960ba8202a393f290 | b3d436e749c20ffcadc73c030f1d28508c73fa90 | refs/heads/master | 2020-06-04T10:41:46.569880 | 2019-08-01T12:04:29 | 2019-08-01T12:04:29 | 191,988,433 | 1 | 2 | MIT | 2019-07-29T11:44:32 | 2019-06-14T18:23:28 | Python | UTF-8 | R | false | false | 2,081 | r | RunRatings.R | ###############################################################################
### PROJECT: ELO SAILOR
### CREATED: 2019-06-24
### MODIFIED: 2019-06-24
### REVIEWED: NO
### SUMMARY: RUNS ratingsS
###############################################################################
##Import college test dataset
college ... |
8892e8eb0099dc16393fb1f55709beefed00f7e6 | fef6ba95f4a6a98e26f7f9f81bc457c562e62364 | /tests/testthat/test-checkItemExists.R | e3dbee9e8645468c0f2793a132f20146891e3b97 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | USGS-R/hazardItems | 48b6701b082cde4a51edd417a86aa963ed2f9383 | dcf69e2df7d4b0db5054c8193bcc4aca4d41e859 | refs/heads/main | 2023-04-13T20:30:37.493049 | 2020-08-13T20:42:26 | 2020-08-13T20:42:26 | 10,981,467 | 5 | 10 | NOASSERTION | 2023-04-07T23:06:59 | 2013-06-26T22:59:23 | R | UTF-8 | R | false | false | 211 | r | test-checkItemExists.R | context("testing checkItemExists")
test_that ("check if item exists", {
setBaseURL("prod")
expect_false(checkItemExists("CHEX123")) # bad itemID
expect_true(checkItemExists("CCGftiy")) # good itemID
}) |
9812e60704c3e71b4cca65493ad23c41abaf1b0c | 1e7d70ac2935728335327b6b8e7755f48c6cbbb3 | /ui.R | 798764f9a9b034a6ea14bd9aa46e6e1dcd4e93f7 | [] | no_license | cpulec/chitest | 09b4a2782d9a54d07c19e6db74d6423fc5af3212 | 2da95246506cec543b721121f3edd0dbd6c54d54 | refs/heads/master | 2021-01-18T17:10:45.917941 | 2014-02-28T04:01:16 | 2014-02-28T04:01:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,716 | r | ui.R | library(shiny)
# library(shinyIncubator)
# library(ggplot2)
# library(ggmap)
library(rCharts)
library(doSNOW)
library(foreach)
# Define UI for miles per gallon application
shinyUI(pageWithSidebar(
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Application title
## ~~~~~~~~~~... |
150be4e469061e64ffdb1fc96e05101c366297cd | a94b8d7428112111fb0ec7a2db31dcca2a929f7e | /Figures/FigureS3/FigS3F.R | ce2185c421eaa19d51df22c33246f8dc42f5813b | [] | no_license | edcurry/esc-se-regions | 7152064aa3d34bdcc27c1a18fcbdc2ea199ad153 | 63c41f671a0e6b54003b0fdc48146dad082017e5 | refs/heads/master | 2020-07-24T01:34:44.984848 | 2020-02-28T12:10:36 | 2020-02-28T12:10:36 | 207,763,046 | 0 | 0 | null | 2020-01-20T12:57:52 | 2019-09-11T08:33:25 | R | UTF-8 | R | false | false | 1,991 | r | FigS3F.R | ####################################################################
#
# Wout Megchelenbrink
# Jan. 17, 2020
# SE engaged in closest or more distal promoter interactions
###################################################################
rm(list=ls())
source("include/style.R")
chic <- unique(fread("DATA/CHIC_promoter_... |
8743fd44f3d7479bb4990233972a5977904c7105 | 71821a5612e50fc8120afc8c5dc18019dadb9e84 | /1BM17CS024_DSR Lab/Lab2/cbind.R | f78ae5e812f0c136cc145a94797762e3d217e457 | [] | no_license | dikshajain228/Semester-7 | 825229cd63c4a047ac5dd5c3896a43b9835a791d | 996def1ada173ac60d9fd0e4c9da4f954d2de4f0 | refs/heads/master | 2023-02-04T22:19:25.984283 | 2020-12-20T07:48:06 | 2020-12-20T07:48:06 | 297,544,965 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 254 | r | cbind.R | list.files()
getwd()
setwd("C:/Users/Dell/Documents/R")
getwd()
delim <- read.delim("perfume.csv", sep = ',')
delim
head(delim)
colnames(delim)
new <- delim
temp<-data.frame(num=c(1:100))
temp
new<-cbind(new,new_col=temp)
new
head(new)
|
d44a51ac738a2869acee95fb1d8940bfd1810f43 | 3aef5a679c390d1f2c7ecba35eca09864164c5a5 | /data-raw/onc3.R | 0f087bb2c1f2a326da1f93a057bae16f89da2c36 | [] | no_license | jeff-m-sullivan/hesim | 576edfd8c943c62315890528039366fe20cf7844 | fa14d0257f0d6d4fc7d344594b2c4bf73417aaf3 | refs/heads/master | 2022-11-14T07:35:15.780960 | 2022-09-02T03:13:49 | 2022-09-02T03:13:49 | 140,300,858 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,063 | r | onc3.R | # Data for a 3-state (Stable, Progression, Death) oncology model
rm(list = ls())
library("flexsurv")
library("hesim")
library("data.table")
# Simulate multi-state dataset -------------------------------------------------
sim_onc3_data <- function(n = 2500, seed = NULL){
if (!is.null(seed)) set.seed(seed)
# D... |
1cbc2384137ca30e8790dca42aca2d05566cdca0 | 9d28e9c8305feb5f585761e629ae7ac862a23265 | /exercises/solution_07_09.R | 3d0851e6993bc424c333d324b4de72c58d2213c7 | [
"MIT",
"CC-BY-4.0"
] | permissive | awconway/NUR1027-FALL-2019 | 1e94759346933311c8b56da079f132dfc3b0abcb | 5dd4fea0a17ebea7fd4c1e58b79626e94418d7a5 | refs/heads/master | 2023-01-21T09:53:53.446575 | 2022-08-15T18:27:45 | 2022-08-15T18:27:45 | 190,033,550 | 0 | 1 | MIT | 2023-01-11T20:46:07 | 2019-06-03T15:34:45 | HTML | UTF-8 | R | false | false | 68 | r | solution_07_09.R | SEM <- 0.258
measurements <- 3
round(SEM*1.96*sqrt(measurements), 2) |
eae92fbc23053f30d0a5b1d1fb3ab44e87d05629 | fe36c4fdae6bdc7f426631675ebd4b4eedc6be87 | /man/load_table_lineage.Rd | bc053c98f21dea9efe3465c9476b8bb62b82d37f | [
"MIT"
] | permissive | nyuglobalties/blueprintr | 13f5c40ff263fdf069b3a3785312fad3513e493c | 56d1da3f03b86ba3533107fab1926315505f8f57 | refs/heads/main | 2023-08-11T10:15:08.603385 | 2023-07-28T20:40:29 | 2023-07-28T20:40:29 | 230,140,058 | 1 | 2 | NOASSERTION | 2023-07-28T20:40:31 | 2019-12-25T18:33:30 | R | UTF-8 | R | false | true | 691 | rd | load_table_lineage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lineage-tables.R
\name{load_table_lineage}
\alias{load_table_lineage}
\title{Read blueprints from folder and get lineage}
\usage{
load_table_lineage(
directory = here::here("blueprints"),
recurse = FALSE,
script = here::here("_targets.R... |
539b0a1819efe5889eb847664a11002f3ac3e650 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/clttools/examples/normal.simu.plot.Rd.R | 0c75052b36d8ec4ec1f3d141d4c28bbd7345ab62 | [] | 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 | 220 | r | normal.simu.plot.Rd.R | library(clttools)
### Name: normal.simu.plot
### Title: Histogram and Q-Q plot of simulated Normal distribution
### Aliases: normal.simu.plot
### ** Examples
normal.simu.plot(n = 5, mean = 3, sd =2, times = 100)
|
81e22b3228fa1e5e093300fbd64cb0b112dc03ba | 6cbb51fe996e65a51a8d9f2f35e3159721933f25 | /inst/shiny/ui_09_2_seuratWorkflow.R | a21987a2716ffcd99297f0ef6e9b4ee3793d4de3 | [
"MIT"
] | permissive | compbiomed/singleCellTK | 927fb97e257ba89cddee9a90f9cb7cb375a5c6fb | 990e89e7ccfbf663f23c793454f72fb8c6878a32 | refs/heads/master | 2023-08-11T09:17:41.232437 | 2023-07-26T20:43:47 | 2023-07-26T20:43:47 | 68,756,293 | 144 | 89 | NOASSERTION | 2023-09-06T18:22:08 | 2016-09-20T21:50:24 | R | UTF-8 | R | false | false | 26,282 | r | ui_09_2_seuratWorkflow.R | # User Interface for Seurat Workflow ---
shinyPanelSeurat <- fluidPage(
tags$script("Shiny.addCustomMessageHandler('close_dropDownSeuratHM', function(x){
$('html').click();
});"),
h1("Seurat"),
h5(tags$a(href = paste0(docs.artPath, "cnsl_seurat_curated_workflow.html"),
... |
80c5dda1e99a993694f8b45ab60f4a84ed2e49d0 | d2f0c07eeba563b88021010e450ac7a29779972b | /dataJoin.R | 131c4d8f4a6894edb0c20b00b7c8b5b3a8b770e5 | [] | no_license | lefpoem/R_log | dc60495063fe26a8fe8291708a98594a9ff27df2 | 74c9f927daa4c185655d365186b01df71061dc1e | refs/heads/main | 2023-06-07T05:56:55.643987 | 2021-07-04T09:17:39 | 2021-07-04T09:17:39 | 381,149,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 591 | r | dataJoin.R | # data frame1
df1 = data.frame(SiteId=c(1:6),Site=c("Goolge","Baidu","Numpy","Zhihu","CSDN","Pokect"))
# data frame2
df2 = data.frame(SiteId=c(2,4,6,7,8),Country=c('CN','USA','CN','USA','IN'))
print(df1)
print(df2)
# inner Join取交集
df3= merge(x=df1,y=df2,by="SiteId")
print("----NATURAL JOIN----")
print(df3)
# full Join取... |
e2560aeff0c3a26c563912f604f867531936378c | 8c9ce99672ce84da4400238e6f8278c130210100 | /Gene Info sorter_V4.R | 05fb39369b2cfa65064481eccb7bea1f8bad63b4 | [] | no_license | debabratadutta6/Sesame-transcriptome | 183cb8120b6611fe01889907c72676a7fa299d70 | 36b7199a52fb21e3b2ee735d484267a37cfc2a39 | refs/heads/main | 2022-12-31T02:03:31.042727 | 2020-10-24T03:58:45 | 2020-10-24T03:58:45 | 306,801,341 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,055 | r | Gene Info sorter_V4.R | winDialog("ok", "Please select Stringtie Assembled Transcripts file")
options(stringsAsFactors=FALSE)
data1 <- choose.files(default = "", caption = "Select input datafile",
multi = TRUE, filters = Filters,
index = nrow(Filters))
data11 <- read.csv(data1, header=F, sep=" ")
data11 <- da... |
5e84bcd5ad7e2aa57c3b32656ce54a7553c8f625 | 556d3d35f85264e5c5c27b5de5c158dc7d2500dc | /rankhospital.R | 627ff676413d1243ba4df197315e0f86d7252f38 | [] | no_license | Dcroix/Programming-Assign-Three | 68ec8c66f34546c1dfc69fc8642f49ed9e93d930 | c39034d0555fba57ee5a1f68646fe6bfccd08708 | refs/heads/master | 2020-05-15T22:03:22.886827 | 2019-04-21T09:59:39 | 2019-04-21T09:59:39 | 182,516,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,042 | r | rankhospital.R | #This function returns the best or worst performing hospital for the identified state and outcome
rankhospital <- function(state, outcome, num = "best"){
data <- read.csv("outcome-of-care-measures.csv")
states<- levels(data[,7])[data[,7]]
state_flag <- FALSE
for (i in 1:length(states)){
if (state == states[... |
408cba7ba434ecd613de3acbf9317d3829dea857 | 4bf26f1905d2d51a85591a24b29740bef8472abe | /src/install.dependencies.R | 2cc60d62ee70f3f37384aaaa54c862169506155d | [] | no_license | NEONScience/swift.aqua | c8fc9d4f508602b29f14d3c0ff57c4ac08a8560d | b7c19f09dbab6a28797bcee4de04ca0e124c4060 | refs/heads/master | 2022-10-25T10:19:51.846812 | 2020-06-15T20:59:20 | 2020-06-15T20:59:20 | 272,516,953 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 375 | r | install.dependencies.R | # Install Packages for intialization
install.packages("fst")
install.packages("shiny")
install.packages("dplyr")
install.packages("plotly")
install.packages("ggplot2")
install.packages("DT")
install.packages("tidyr")
install.packages("data.table")
install.packages("shinycssloaders")
install.packages("shinydashboard")
i... |
a4f69aefc17cb0244749c0520c58a2bcfbef1b1b | 4017621a72dcf76a3a9b66905ea0374e7acd0517 | /data_analysis_scripts/InteractiveViz.R | 76fd2adfb85cb192eeddfec162b25df58841ee5d | [] | no_license | JackLich10/data_plus_basketball | c9fed4ace98a74504ecf7c320c655ac5eaaea9a8 | 40ad762ec54b6f761ee81c54417cbd6e2f098518 | refs/heads/master | 2023-03-18T11:00:57.002177 | 2021-03-12T01:46:57 | 2021-03-12T01:46:57 | 196,605,447 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,745 | r | InteractiveViz.R | # load packages
library(tidyverse)
library(broom)
library(ggiraph)
library(rvest)
library(modelr)
# load data
Duke201415teamstats <- read_csv("data/Duke201415teamstats.csv")
ShotChart <- read_csv("data/shot_chart_NN_SVM.csv")
# change from wide to long
long <- subset %>%
dplyr::select(game_number, opponent, PPS, eP... |
68591ec4c7d0250bc1583b41fed00290291b665d | d812db15a12cfce3666d69812fbbb0da4b070c14 | /code/package-list.R | 56d0bec79761df9c608d21ef9501abf32f7da3b9 | [
"MIT"
] | permissive | jvpoulos/patt-c | 97fba2cec409113747f246cec1cc36ee6cf21f5d | d471872f710210516c540f313437f8fa69a91e21 | refs/heads/master | 2021-07-07T15:02:19.941944 | 2020-07-31T03:27:53 | 2020-07-31T03:27:53 | 156,440,652 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 411 | r | package-list.R | packages <- c("ggplot2","ggpubr","gridExtra","reshape2","dplyr","MASS","gbm","rpart","foreach","doParallel","downloader","SAScii","RCurl",
"foreign","plyr","downloader","digest","SuperLearner","class", "randomForest","glmnet","gam","e1071","gbm","xgboost","ROCR","reporttools")
weights <- c("cluster","HMi... |
b2c158d78aeadd38533f16f792cc15f78df908f0 | 0feedfcb9f76e63e15727486747d9693d4863e5a | /主代码/portfolio_characteristics_rkt (in one box).R | 191eb09f1efec89177fd594e0d8f3fd34fd40375 | [] | no_license | jaynewton/paper_6 | be06bd623707d87e0446f25eed0851d86738f561 | 331ce16dd031e9e3506fc91ac00bb7769cc2095f | refs/heads/master | 2020-04-02T02:33:06.048234 | 2018-11-11T11:24:59 | 2018-11-11T11:24:59 | 153,915,405 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,731 | r | portfolio_characteristics_rkt (in one box).R | #################################
load("F:/我的论文/第五篇/RData/da_all_m.RData")
load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_individual_m.RData")
load("F:/我的论文/第五篇/RData/da_inst_m.RData")
load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_price_m.RData")
load("F:/我的论文/第五篇/主代码/individual investor ... |
cbf6621a8586452b820d509a3b1c81b69c4007c0 | 99fb6ea41554f6ebe7fbd21f368c68e0980770d1 | /executable/microbiome_statistics_and_functions.R | 75115a89dd59c9b4e3a08b7beb7f418f0f5a4cf0 | [] | no_license | GreathouseLab/Preg_Diet_microbiome | 6b504335492f4edec4dd268271a5ca5ed6a66ffd | 6e05d84a5952452db90443014dd19db798e58004 | refs/heads/master | 2020-06-26T15:21:45.116303 | 2020-01-07T02:15:55 | 2020-01-07T02:15:55 | 199,672,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,724 | r | microbiome_statistics_and_functions.R | # =================================================== #
# =================================================== #
#
# Microbiome Analysis Functions
# Jun Chen, PhD
#
# ==========================... |
ac157c65e4ffd20028c82d2554858ea9b09b726b | 233711a9c97ed63ac7fccbdbc896890b01784d03 | /PrevisaoMacro/ipca-sarima.R | 18eb55ea9bc09e3efd1b0d6aa2e7664b50130c4a | [] | no_license | econoquant/EconoQuantCode | d092f3efa226c0a7b5bfd6d8620f2cfb303c4d37 | 35d0fba8fd2d8afae65815da4283be0a8c88502b | refs/heads/master | 2020-12-02T18:01:13.243372 | 2017-07-12T13:26:32 | 2017-07-12T13:26:32 | 96,462,158 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,783 | r | ipca-sarima.R |
################### Carregar dados #######################################
ipca <- read.csv('ipca.csv',header = T, sep = ';', dec = ',')
ipca <- ts(ipca[,2], start = c(1980,1), freq = 12)
##################### Selecionar subamostra ###############################
library(changepoint)
library(ggfortify)
auto... |
715324680f72139831abc888b1aa7f1a66577d1a | d6c9f897714cea47c9b74547dd268462efd971b6 | /Classifier/src/Classifier.R | 8cf4592e0a246aa71e5bf929d106feb69fa8dd9d | [] | no_license | SilambarasanM/Data-Preparation-and-Analysis | dc883ba9d556f81150dd1dda6758b9fef3063991 | dba14b6e07cd31319d95bc70226283d1f6dd3b7a | refs/heads/master | 2021-01-18T21:25:16.695758 | 2016-05-16T08:42:11 | 2016-05-16T08:42:11 | 52,257,666 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,531 | r | Classifier.R | #Make sure system is connected to internet while running this code to install libraries
#Install and load the required R libraries
if (!require("plyr")) {
install.packages("plyr", dependencies = TRUE)
library(plyr)
}
if (!require("ggmap")) {
install.packages("ggmap", dependencies = TRUE)
library(ggmap)
}
if (!req... |
83d5723d47e4820dbb5d33bd01b7242c30a37b72 | ca3fbf9bcf0349b35471e75344e95b5c92cdc2be | /plot1.R | 56ae2d49388688c0349c65883868d023be8a9d1e | [] | no_license | AntoninPrunet/ExData_Plotting1 | 198642bdf789a278dd10129bb9f8cccf63c9fbe3 | 3f41d5a229f6a4a766f344017809fe8df22869b7 | refs/heads/master | 2022-05-22T00:28:47.652991 | 2020-04-28T16:17:01 | 2020-04-28T16:17:01 | 259,387,287 | 0 | 0 | null | 2020-04-27T16:21:23 | 2020-04-27T16:21:22 | null | UTF-8 | R | false | false | 657 | r | plot1.R | if (!file.exists("household_power_consumption.txt")) {
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", destfile = "Household Power Consumption")
unzip("Household Power Consumption")
}
library(data.table)
library(lubridate)
library(dplyr)
x<-fread("house... |
0367e75f3c900e317a3aab1cc017b7f84281c9c2 | 23a0e63a84671fd7304cfae5cb8002e34d310a14 | /references_cleane.R | 0d10bc9112b8a1bc15c2efed835cff18b843cc25 | [] | no_license | rxdavim/bibtex-cleaneR | 35d1a91c7028ad4fc514e227bd89dd38d52dece2 | 1f3ecd91e433f2b70168f06574500c77cd3b387f | refs/heads/main | 2023-08-10T00:54:35.740059 | 2021-09-06T09:40:24 | 2021-09-06T09:40:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,749 | r | references_cleane.R | library(fulltext)
library(bib2df)
library(bibtex)
library(RefManageR)
# library(readtext)
library(doi2bib)
library(dplyr)
library(curl)
library(stringr)
library(RecordLinkage)
library(rcrossref)
library(aRxiv)
library(foreach)
library(doParallel)
library(doSNOW)
getAbstractFromDOI <- function(doi){
cat("\nGetting ab... |
567d4b5ef97a5c606892441a1e5de3ac20375808 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tm/examples/writeCorpus.Rd.R | c54b336c27648d6585feae6e0bb24591d5a38b0b | [] | 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 | 265 | r | writeCorpus.Rd.R | library(tm)
### Name: writeCorpus
### Title: Write a Corpus to Disk
### Aliases: writeCorpus
### ** Examples
data("crude")
## Not run:
##D writeCorpus(crude, path = ".",
##D filenames = paste(seq_along(crude), ".txt", sep = ""))
## End(Not run)
|
07cdcb9c9abf05a7ad8f8010d18724bdfd077ad2 | c46a6ff80331d7f47bc3c379b7b6f51644a3925b | /Chapter_04/scripts/f4.R | d9da9fd4c29f524c47df0532a8073ea26f51981d | [] | no_license | elmstedt/stats20_swirl | 6bb215dc600decaf03ecf441cf0e28bdbd525536 | 6de97f3613f941c5c39a85b9df4f26fa3b62e766 | refs/heads/master | 2021-05-22T02:29:59.080370 | 2020-10-06T07:42:50 | 2020-10-06T07:42:50 | 252,929,124 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 143 | r | f4.R | f4 <- function(x) {
# put the code below into an appropriate while loop.
if (x %% 2 == 0) {
x <- x / 2
} else {
x <- 3 * x + 1
}
x
}
|
c74b103e292211a342f40f191b32404db43df2a6 | 663cb73a0c47ab0fc81bc0efab7d1bbce8b369b1 | /ParseHTML.R | bb3c6c280661b8e7ad440e78229522d340391658 | [] | no_license | wangguansong/nlxj-profiles | f48920eeee6b3fb5bf637207857cf67162ad5cdd | 15661e472b259668c19ffbd4097ef3b3996e3669 | refs/heads/master | 2021-01-10T01:35:22.337574 | 2015-11-24T23:06:47 | 2015-11-24T23:06:47 | 46,497,747 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,665 | r | ParseHTML.R | ########################################################################
########################################################################
ParseHTML <- function(filename) {
# Parse the HTML file, find the line contain the informations.
# Return a list of strings??? corresponding to the <p> tags, which
# c... |
24c88bcc998524b842a51396d7f075e9e4b745d5 | 071cd8492b051065de257750d5b16cd47409c996 | /locus_discovery_config_files/BFP_AD_config.R | d771af6c5d8defa501a6a0a4127132f27c9dbd9b | [] | no_license | wpbone06/AD_and_Cardiometabolic_Trait_Bivariate_Scans | 28734236a73b0c069241ccfebfe46648fbf6d93f | 3dd4e32d8ddd23e8d55b64299ed31e59de915c90 | refs/heads/master | 2021-04-08T19:03:33.886867 | 2020-03-24T19:40:01 | 2020-03-24T19:40:01 | 248,802,625 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,007 | r | BFP_AD_config.R | trait1 = "AD"
trait2 = "BFP"
trait1GWASStr = c("Alzheimer")
trait2GWASStr = c("body fat percentage","body fat %", "Body Fat Percentage","Body Fat")
expPath="/project/voight_datasets/GWAS/01_alzD/AD_sumstats_Jansenetal.txt"
outPath="/project/voight_GWAS/wbone/bivariate_scan_project/BodyFatPer_CHD_bivarscan/BFP_CHD_input... |
73c75396ffdcd5cda4920e453749f47e44cbe7fc | 7967712d2e16907605f7d0acde096950eae5c3d4 | /components/functions.R | b98f8550617006082ffbec3c08319e69da3094e2 | [
"MIT"
] | permissive | pablo-vivas/ProbabilityDistributionsViewer | 8efdff6f7347ee940f8efa202beec8b5ba298153 | ba24761a890e7d4f06f180316ca088d8210c4940 | refs/heads/master | 2020-08-07T02:17:51.092184 | 2018-07-24T12:31:36 | 2018-07-24T12:31:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,797 | r | functions.R | library(shiny)
library(shinydashboard)
library(htmltools)
# Variables ----
boxcolor <- "blue"
mean.icon <- icon("star", lib = "glyphicon")
variance.icon <- icon("resize-horizontal", lib = "glyphicon")
# Functions ----
## ui ----
### Custom selectInput
selectLanguageInput <- function(inputId, choices, selected = NULL... |
6a367ae7c8e2e213fbbec7b85acd42ce9d25e96a | 5d9470e54c69e914800f770ff3ca95b72e0d02b0 | /R/ziaq.R | b981d1d0b3aefd66e171db06132ee3f08391474d | [] | no_license | gefeizhang/ZIAQ | fffbd9da58490e455c58d65c2bf0bf49fadf6703 | 017da9ab92fac73faf4ae4e50934270893d296c1 | refs/heads/master | 2020-07-04T07:35:38.288467 | 2020-02-20T18:38:50 | 2020-02-20T18:38:50 | 202,207,402 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,573 | r | ziaq.R | #' Zero-inflation adjusted quantile regression for single cell RNA sequencing data
#'
#' This function fits the zero-inflation adjusted quantile regression model for the
#' differential expression analysis in single cell RNA sequencing data
#' @param Y_matrix a matrix for expression values with row representing ind... |
b0a38d25523a09e2481c70e89072d7e2ef84b452 | 8de7c88fd3ce03591c538694b3361f6b6c7fbf61 | /R/transformPhylo.sim.R | 21322e57e625cbe770b000a1a52c691cdae0c592 | [] | no_license | ghthomas/motmot | b093742a4ed264076ca41bbc4fddf29d3cc00a93 | c24372f5d5efbfbee6196c5459d0def31d547e54 | refs/heads/master | 2021-01-01T17:57:38.949773 | 2018-07-30T10:12:35 | 2018-07-30T10:12:35 | 10,839,257 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,521 | r | transformPhylo.sim.R | transformPhylo.sim <- function(phy, n=1, x=NULL, model=NULL, kappa=NULL, lambda=NULL, delta=NULL, alpha=NULL, psi=NULL, nodeIDs=NULL, rateType=NULL, cladeRates=NULL, branchRates=NULL, rate=NULL, group.means=NULL) {
switch(model,
"bm" = {
transformPhy <- phy
phyMat <- VCV.array(transformPhy)
... |
8a0036224b05274ea9e07024c73ec1308b2abb25 | ef10085faba12cbca8e6ef55e3575031dd82da71 | /app.R | c9930c6aa221793647d6816a6b9d2d138417cd76 | [] | no_license | RforOperations2018/project2-clarissp | eb3e75bb4c0c88ac90da665b46888f8f590ecb5b | 7ee468dd088ae6f802ff3671432df0e1a09d7a14 | refs/heads/master | 2020-04-01T05:39:59.319277 | 2018-10-21T18:36:15 | 2018-10-21T18:36:15 | 152,913,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,174 | r | app.R | #Project 2
require(shiny)
require(rgdal)
require(leaflet)
require(leaflet.extras)
require(dplyr)
require(readxl)
require(stringr)
require(shinydashboard)
require(reshape2)
require(dplyr)
require(ggplot2)
require(plotly)
require(shinythemes)
require(RSocrata)
require(httr)
require(jsonlite)
#Shapefile for County Boun... |
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