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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
683a0d4fbccfbe8e816eb6374cf52060952ab309 | 73eaf8da1446e91f31d2a81a2276ce79cc18a90b | /performance/performance_mod.myd.2.R | a65332d5ed80e279d751c4e2d28484c6115a376e | [] | no_license | leandrocara/Msc.Tesis-SnowWebPlatform | 8797133c7b55bbba3b950f147159fdeef5e23cad | b41124e7afc00ac4790d298cdc2307bfc432ef41 | refs/heads/master | 2021-10-27T11:42:59.352687 | 2018-11-22T13:41:10 | 2018-11-22T13:41:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,018 | r | performance_mod.myd.2.R | rm(list = ls())
library("raster")
rcl<- function(x,y){
reclassify(x, y, include.lowest=FALSE, right=NA)}
corte <- function(x){substr(x,pos,pos+6)}
# lee archivos .tif de un directorio
f.1 <- function(x,y){
x<- list.files(y,corte(x),full.names = T)
return(x[grepl(x = x,pattern = ".tif$")])}
#########
#### func... |
a01bf38b3b1f6f25268d7c1b3e7d7db2cb81fe2b | 7805360d25ad23a66027ec7bb6cb6eef0721bcca | /R/selectData.R | 7a2fad260989ab9e71fefc39020233a8953c21e5 | [] | no_license | kuanghuangying/KuangHuangyingTools | 9fc2a4e09021f868841bc318ca27280b577a84bf | d888c5701ce63d7c00d1821a4eb18653a75a684a | refs/heads/master | 2021-01-25T10:01:04.639002 | 2018-03-04T03:51:21 | 2018-03-04T03:51:21 | 123,335,175 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 191 | r | selectData.R |
#' slice my data and select 3 columns
#' @export
selectData <- function () {
selectedData <- cleanData %>%
dplyr::select(Petal.Length, Petal.Width, Species)
return(selectedData)
}
|
ff2564a9b70b4594045ae5a758b02f0a7d7e37ce | b103e27f98c7c65f62f6d11ee351ad40cc256a20 | /database_helper.R | 2738cc92a4d6cfcd1732e3deafc4f03688fee9dc | [] | no_license | martyciz/smecko | cce0f4b93109e0776597f0a1380d6b62c6298a67 | afb546077a64c384eafe8ac12426d017d4ef6d83 | refs/heads/master | 2021-01-10T07:19:04.419536 | 2015-10-23T14:23:21 | 2015-10-23T14:23:21 | 44,817,877 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 491 | r | database_helper.R | library(yaml)
library(RMySQL)
retrieve_data <- function(sql) {
db_config <- yaml.load_file("db_config.yml")
mydb <- dbConnect(MySQL(),
host=db_config$db$host,
dbname=db_config$db$name,
user=db_config$db$user,
password=db_config$db$pass,
... |
0398e82ef92affe0d2e8ae7748dd187420c45cf9 | 55f749ccdaba1b891195d81e186321432d506532 | /QTL_analyses/RQTL2_F5_mapJLR_clean_v1.R | 4caaec279f34ef7848880f645cc3c2fd227707dc | [] | no_license | itliao/IpomoeaNectarQTL | 01390f36a746276c58cc7830e31aa6794f35df40 | 4059eca1fa33b77c03dd6cfa72696d9d1300ea52 | refs/heads/main | 2023-04-13T17:51:45.477236 | 2021-12-20T18:34:43 | 2021-12-20T18:34:43 | 365,070,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,988 | r | RQTL2_F5_mapJLR_clean_v1.R | setwd("~/Dropbox (Duke Bio_Ea)/Rausher Lab/Current_lab_members/Irene_Liao_Data/Scripts_Instructions/QTL/RQTLfiles")
library(qtl2)
# Goal - QTL analyses with qtl2, specifically using the LOCO method of identifying QTLs
# using LOCO, one QTL per chromosome, genome-wide significance threshold - part of the GWS QTLs
# us... |
aa2733910459b01e0c3e99b86cd6a7f6e2cc0c92 | 9719937c30d935bd575af95c496b8c2e9e7c69a5 | /tests/warning.R | ebc5706f804663caeccec6b98a5d73dd7192aaf2 | [] | no_license | kismsu/animint | 4a804da60bef85d54e34d3273ab912e7631f805d | e25d01d8d6e3f91735fa246161e6ab9a40219206 | refs/heads/master | 2021-01-15T21:20:00.434346 | 2014-07-22T14:29:55 | 2014-07-22T14:29:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,099 | r | warning.R | library(animint)
data(WorldBank)
check <- function(p, w=""){
stopifnot(is.character(w))
stopifnot(length(w)==1)
if(is.ggplot(p)){
p <- list(plot=p)
}
stopifnot(is.list(p))
list(plot=p, warn=w)
}
wb <- ggplot()+
geom_point(aes(life.expectancy, fertility.rate, size=population),
data=WorldBa... |
83fa5b4f8aa448f62cb52679cf2c510c7367780f | 2379787de6dfb0e65b59dbf1a5b9cd74071415a4 | /ui.R | 25036615bcc69fed80d64148998f819b75a34a40 | [] | no_license | shpotes/Visualization | 2c8fd13bd32993cf26254a17537fbfbc4d3c1134 | cbc050ba8e667391eb8e0f0cc3ceb29130f8c1a4 | refs/heads/master | 2020-09-16T22:12:10.326610 | 2017-06-16T22:17:38 | 2017-06-16T22:17:38 | 94,488,504 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,225 | r | ui.R | library(leaflet)
library(ggplot2)
library(plotly)
navbarPage("EAFIT",
#img(src='logo_eafit_completo.png', width=70, height=35), id="nav",
tabPanel("Centro de Egresados",
h1("Centro de Egresados"),
tags$video(src =
"Línea del Tiempo - Centro de Egresados - Universidad EAFIT.mp4",... |
bcb0cbe1db4404c155f99c29c4825a40e7b2abf2 | ff2b55f75a9802b8de761eee6a353f9b8058b08c | /man/ADPIRA.Rd | 5fdb43487a730796b8307da0f09d7e3c74f9b651 | [] | no_license | FCACollin/rpack_pira | 426d344de5eb426240608aba725a8c328b8779e1 | 852db8a6175fe2126202b3316ba576d47ecf163c | refs/heads/master | 2023-03-08T15:58:50.987587 | 2021-02-20T18:22:15 | 2021-02-20T18:22:15 | 287,980,600 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 466 | rd | ADPIRA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-ADPIRA.R
\docType{data}
\name{ADPIRA}
\alias{ADPIRA}
\title{ADPIRA}
\format{
Information about the fornat
}
\source{
UMB.
}
\usage{
ADPIRA
}
\description{
Analysis Dataset for the study of Progression Independent of Relapse Activity
in t... |
8d081968646df24aa5abb4a26e81b164b39b5b08 | 89c706327fbac52418ccda18e44d1c98bd7759e7 | /dashboard/AppCode/shiny/server/pages/main.R | 5f6b708b37b04e405cd8a3ada9cc39a4026fffda | [] | no_license | rickdott/Montecristo | 3d313f434a2a0bec167e271a0d27b2e058125ae5 | fa66a9d6b6d0555d1e882ebec4a7d79340e2ddf6 | refs/heads/master | 2023-03-29T23:51:16.121366 | 2021-04-03T08:22:07 | 2021-04-03T08:22:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,363 | r | main.R | pages.main.getPage <- function() {
tagList(
div("Very pretty main page, a lot of time went into this! :)")
# div(class = "mainPage-wrapper",
# h1(strings[string.main.pageTitle, LANG]),
# div(class = "mainPage-frontpage mainPage-aboutApp",
# div(class = "mainPage-frontpage-aboutApp-text",
... |
403bb5d725a3c5df37ab4676764c1f9dba08cfa0 | 78a13b567411aa7b33b6717ceece8966e9254589 | /R/zzz.R | ceb6e298b9537d3e526b38fb38d0597fab1168f6 | [
"MIT"
] | permissive | jemus42/attrakttv | 8a6162a0058a21129da93b0d803c9162d519d65b | 06a3d3953a11dfafdc9824c3e715559096dbc3cf | refs/heads/master | 2021-07-19T02:34:23.231630 | 2021-07-03T19:43:02 | 2021-07-03T19:43:02 | 209,893,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 789 | r | zzz.R | #
# .onLoad <- function(libname, pkgname) {
# # op <- options()
# # op.trakt <- list(
# # trakt_db_path = "~/db"
# # )
# # toset <- !(names(op.trakt) %in% names(op))
# # if (any(toset)) options(op.trakt[toset])
#
# # local({
# # temp_path <- file.path(system.file(package = "attrakttv"), "db")
# #
... |
20bab06a6a1871d7f05fc461a035e957f0ab2b34 | 7a7c964628a66093748692a756ac045b4731bcc6 | /joe/scripts/not interesting/IT_sitedate_specific.R | 7e66aca0ec4970cb169a72f94622816f08c8d651 | [] | no_license | reverteiros/floral_traits | 85b9a71c0dd1cd34edda41ac3d0b87213ee0a96f | d2020502e9eb3c152849c8408652e87e8de3f893 | refs/heads/master | 2022-01-24T13:34:53.301712 | 2022-01-11T17:52:45 | 2022-01-11T17:52:45 | 136,968,752 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,228 | r | IT_sitedate_specific.R | # this script is going to look at the unweighted / cwm IT of all the flowers present at one sitedate...for now, you need to run the entirety of cwm_cwidth_IT before running this script for unweighted IT
require(plyr)
require(lattice)
quartz()
fspecies$sitedate<-as.factor(fspecies$sitedate)
#subset so that only sitedate... |
f9c16299682ad349d025321321b1fa5f97acb078 | 3f3b6cff551953eb133b81d95337268df1e23235 | /R_Code/6jan/sc1.r | 2fd9044e7c5bf68c41594c6a162ac0c3c669c8dc | [] | no_license | therealrahulsahu/c_sample | 31a5a79233a3b54d803418c6c1ccf72bceb67935 | 0b8026a7ee0186eeac9b53df0c22b5db9fd1c5fb | refs/heads/master | 2021-08-11T03:34:52.437837 | 2020-07-02T06:16:50 | 2020-07-02T06:16:50 | 198,704,023 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 71 | r | sc1.r | #Q.1
x=as.integer(readline("Enter No. : "))
if(x>0)
print("Positive") |
291ab7798de59698aa21cb666fe5827112376725 | 0171da74586a079e97269ba9b7a8c4146c204cd0 | /R/plotOverview.R | 364df9d8dfe1b86d2f65c895d8c98788841394aa | [] | no_license | jtleek/derfinder-1 | bfda042e772224abbc911d94e0ba2c66fe5e9d08 | a88996a426a899a5d319c628e3b9411237145caa | refs/heads/master | 2021-01-22T13:42:50.235009 | 2013-11-08T18:19:41 | 2013-11-08T18:19:41 | 14,240,696 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,589 | r | plotOverview.R | #' Plot a karyotype overview of the genome with the identified regions
#'
#' Plots an overview of the genomic locations of the identified regions (see \link{calculatePvalues}) in a karyotype view. The coloring can be done either by significant regions according to their p-values, significant by adjusted p-values, or by... |
ab3c60e868dc3af7017bcd3edffa40feaa0767ea | a462a24ff937e151e8151f3a1bdc9c3714b12c0e | /tests_st.R | ef9add355f88510fe5616248f222737f755e541c | [] | no_license | noeliarico/kemeny | b4cbcac57203237769252de2c50ce959aa4ca50e | 50819f8bf0d19fb29a0b5c6d2ee031e8a811497d | refs/heads/main | 2023-03-29T14:36:37.931286 | 2023-03-16T09:04:12 | 2023-03-16T09:04:12 | 330,797,494 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 562 | r | tests_st.R | # Los que propongo con el baseline
# me vs mebb
t.test(res_me_8$exec_time, res_mebb_8$exec_time)
t.test(res_me_9$exec_time, res_mebb_9$exec_time)
t.test(res_me_10$exec_time, res_mebb_10$exec_time)
# me vs mebbrcw
t.test(res_me_8$exec_time, res_mebbrcw_8$exec_time)
t.test(res_me_9$exec_time, res_mebbrcw_9$exec_time)
t... |
2b3fc674da0dcb312377696b2483682d5cf98939 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spatstat/examples/is.marked.ppp.Rd.R | 79d7667a6d627453bb4dd7b1307f218f03fd77e2 | [] | 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 | 260 | r | is.marked.ppp.Rd.R | library(spatstat)
### Name: is.marked.ppp
### Title: Test Whether A Point Pattern is Marked
### Aliases: is.marked.ppp
### Keywords: spatial manip
### ** Examples
data(cells)
is.marked(cells) #FALSE
data(longleaf)
is.marked(longleaf) #TRUE
|
c83aff29d5b55f20f349a19346d62ac0c12667e3 | e528ea2de3e0b68907260b1f25f520dfe0fe7945 | /part4/textmining/classfication/libsvm.R | 7e0dce3bbca390ea22aa7e6a097106dc8575e37b | [] | no_license | datasci-info/ms-partner-training-20160308 | f283b5a30abb79adb87e5ab14f8e275536100ab8 | df41c64b4bf6e04b7ff51fb204cfa523b0581898 | refs/heads/master | 2016-08-11T13:13:35.095398 | 2016-03-08T07:02:34 | 2016-03-08T07:02:34 | 53,187,363 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 184 | r | libsvm.R |
install.packages("e1071")
library(e1071)
m = svm(Y~.,data=df,kernel = "linear")
m$kernel
?svm
m$SV
colnames(m$SV)
pred = predict(m,df)
table(pred,df$Y)
w = t(m$SV) %*% m$coefs
w
|
8f83ee267a92db243095810f53cf87c6318cc612 | f1922b98a7c06db029a8f412ed17f2abb10b0617 | /BarrelFIP.R | a491eabb34e42baf735bc2e49b9c58f49119b5d1 | [] | no_license | dompartipilo/barrelfip | 3bc86b002e97365fb3403b3d24752b0263325efa | 6411ade18deaec134b3814295db1f9b8c72e81aa | refs/heads/master | 2020-07-01T23:48:51.872654 | 2020-03-12T14:48:08 | 2020-03-12T14:48:08 | 201,349,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,201 | r | BarrelFIP.R | library(rvest)
library(dplyr)
library(tidyr)
library(stringr)
#read table of pitchers' statistics from baseball-reference
bref = "https://www.baseball-reference.com/leagues/MLB/2019-standard-pitching.shtml#players_standard_pitching::none"
bref = read_html(bref)
table = bref %>% html_nodes(xpath = '//comment()'... |
2c34649ed859051692c50fe9befde8990ee0c775 | 04a7c98ebecf2db764395c90455e8058711d8443 | /man/asv_best_PC_df.Rd | a4b737a08f4c7d831a6e14c7bb5314735bde044b | [] | no_license | Alice-MacQueen/switchgrassGWAS | f9be4830957952c7bba26be4f953082c6979fdf2 | 33264dc7ba0b54aff031620af171aeedb4d8a82d | refs/heads/master | 2022-02-01T01:12:40.807451 | 2022-01-17T20:56:20 | 2022-01-17T20:56:20 | 198,465,914 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 668 | rd | asv_best_PC_df.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pvdiv_gwas.R
\name{asv_best_PC_df}
\alias{asv_best_PC_df}
\title{Return best number of PCs in terms of lambda_GC following Cattrell's rule.}
\usage{
asv_best_PC_df(df)
}
\arguments{
\item{df}{Dataframe of phenotypes where the first column is ... |
27a69f126bb52cc203e2634124db387ba07b39fa | e5a9f6ab465cd0f28c26f95fc781ba59927edf8b | /R/mult-mshapes.R | 28c2b184308040851492d9244acde36190d38001 | [] | no_license | stas-malavin/Momocs | 550266555ab7724a01ca3b58777bb623de433566 | 44789d9bce9fe923a9af128f0493d16a73fe9fdd | refs/heads/master | 2020-12-26T15:49:20.330404 | 2017-03-08T20:43:13 | 2017-03-08T20:43:13 | 59,151,808 | 0 | 1 | null | 2016-05-18T21:15:59 | 2016-05-18T21:15:58 | null | UTF-8 | R | false | false | 6,482 | r | mult-mshapes.R | ##### mean shapes on coefficients todo: better handling of $slots
##### (eg r2 for Opn, etc.)
#' Mean shape calculation for Coo, Coe, etc.
#'
#' Quite a versatile function that calculates mean (or median, or whatever function)
#' on list or an array of shapes, an Ldk object. It can also be used on OutCoe and OpnCoe obj... |
64cd4871b94f242ef94a08d12e8273feb428a42b | 3124d10b460158c08f40e4490a557bfcb98510dd | /3 writing functions.R | c1e132399aeeb4ab5c8d6119bae66f8404f491e8 | [] | no_license | ChenKozulin/writing-functions | a555cc7aabe84b5c52b8f8bbf5c4235fb374aa41 | 933e68b09d4534d4c888f6ba1b87e28d92683591 | refs/heads/master | 2020-07-22T07:25:12.153407 | 2016-12-12T15:47:05 | 2016-12-12T15:47:05 | 73,831,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,161 | r | 3 writing functions.R | #' Penman Montieth Model with canopy conductance
#'
#' THis function computer evapotranspiration based on Penman Montieth Model
#' including canopy conductance
#' Canopy conductance paramters:
#'
#' @param Cleaf (mmol m⁻² s⁻¹) water vapor exiting through the canopy
#' @param Carbon % of carbon dioxide (CO2) in t... |
f66af1fa7c7d74b8efd9d58a10cc7a5da52fc296 | 6e1dd29fe70ee95bb0971e42881f8bc8275a4eb7 | /man/util_summary.Rd | 8f6793ccbd9880826f7e4e8af575ff74547275e9 | [] | no_license | githubfun/PortfolioEffectHFT | 9592efeb6d21f7cff1758e2217bd1e00c9ab7c6b | 66d8ef8a1266da762a2e4f2b805245efb23ba725 | refs/heads/master | 2018-05-02T23:20:29.881634 | 2016-09-17T07:12:35 | 2016-09-17T07:12:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 993 | rd | util_summary.Rd | \name{util_summary}
\alias{util_summary}
\title{Portfolio Summary Plot}
\usage{util_summary(portfolio, bw = FALSE)
}
\arguments{
\item{portfolio}{Portfolio object created using \link[=portfolio_create]{portfolio_create( )} function}
\item{bw}{Black and white color scheme flag.}
}
\description{
Plots a n... |
d3c5767811e441c55fea6c9b4aef445127007cab | c477a475ed696cba156f9cb99e649617fa1779f9 | /inst/tests/sourcev2.R | 4973e612f04279cf312024526c78394a391f5f4f | [] | no_license | cran/clinDR | fc07112734fbee827bab68fe9f357192f0eae1af | 94ad283337e7811d5b4ef38c588db7f086c7b27d | refs/heads/master | 2023-08-20T20:09:34.510253 | 2023-08-09T04:20:05 | 2023-08-09T05:30:30 | 113,064,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,407 | r | sourcev2.R | fitModel<-function(id,y,trt,visit,prmean0,prsd0,prmean,prsd,gparm1=3,gparm2=1.5,
mcmc=mcmc.control()){
## id must be 1,2,3... without any skipped indices (a patient with
## no observed resp must be deleted)
## trt must be 1,2
## visits must be numbered sequential 1,2,..., individua... |
8151c131b804dce4ad025aa4e1933db7a9fde5ef | bd1fe05a42481abc1e647ea909d64229ad10fe5c | /sentiment_test.R | d91a294c7b261a247a273ccd7cd3c9fe522147a7 | [] | no_license | Science-for-Nature-and-People/soc-twitter | 9f4fabc750b45bc4df169a980dd0fd2f640dc7d6 | 9f0371169984e5f0ffd9aad26b0ee57d7e49ccf6 | refs/heads/master | 2021-06-26T21:25:23.553795 | 2020-10-20T04:01:04 | 2020-10-20T04:01:04 | 130,888,103 | 1 | 4 | null | 2020-05-07T22:24:29 | 2018-04-24T17:08:13 | HTML | UTF-8 | R | false | false | 3,035 | r | sentiment_test.R | #########################################
# Testing sentiment analysis with tweets#
# This scripts analyzes twitter data #
#########################################
library(tidytext)
library(wordcloud)
library(tidyverse)
library(dplyr)
#### loading data
# not: uncomment if not yet loaded
# twitter.data.full<-stre... |
969f3ac197e5834a66e43e16cba2dcb52e8db8a7 | d2e4e8b0fde53e8e331e275f8a8777650381d5fd | /plo6.R | 0cf79aacea2f3e7b327951a2696c61f373662b29 | [] | no_license | Shivam-1117/EDA-Course-Project-2 | 3fcd82ca4f6ccf1176cc7113903e3b8cce117550 | 328acaf7262f14b19d2f3d4eeb4f843dacc6e7a1 | refs/heads/master | 2022-11-23T19:33:07.705513 | 2020-07-26T14:41:44 | 2020-07-26T14:41:44 | 282,592,435 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 943 | r | plo6.R | scc <-readRDS("Source_Classification_Code.rds")
nei <-readRDS("summarySCC_PM25.rds")
s <- grep("Motor", scc$Short.Name, value = TRUE)
id <- scc[scc$Short.Name %in% s, c(1, 3)]
nei <- nei[nei$SCC %in% id$SCC & nei$fips %in% c("24510", "06037"), c("fips", "SCC", "Emissions", "year")]
renaming <- function(x){
y <- chara... |
ff5012fb175a2a013976e09df5f4581802e020b6 | 395ad7f5c669bc493a30c6d2c220534003f184b2 | /R/functional.difftest.R | e574bb5b27cbe612222d4b4c508290201e77382c | [
"MIT"
] | permissive | elmahyai/TPDT | 5a28b5aae310f32a9a45dd4bf3337b909ae050bf | 5c8714376f235a1f96050bf36c5431f876094056 | refs/heads/master | 2021-11-30T21:08:12.787725 | 2017-11-02T13:04:39 | 2017-11-02T13:04:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,583 | r | functional.difftest.R | #' @export
#' @import fda
# Main function for TPDT
functional.difftest <- function(rawdata = NULL, funcdata = NULL, N = 10, Nsim, B = 1000,
shift = 0, sigma = 0, dependent = F, deriv = 0, ncores = ncores){
# determine which kind of data is used
if(is.null(funcdata)){
warning... |
b5abd90a8b316ebd24bc10ea07f03d29dcbaa9be | bfc6805099bffaa9d166a8fbdc803728e1b3fdf0 | /0_Data_Simulation/Simulate_Data1.R | fe2125cec7df4aa8af963af8b7027a6df0479606 | [] | no_license | etzkorn/Causal_Activity | f76d011d5501418bc495fdeca44242d3fc45e718 | c2a6c86c1cb303b01942705810b3ce9da7896a90 | refs/heads/master | 2020-07-27T07:05:47.386906 | 2018-04-01T23:56:08 | 2018-04-01T23:56:08 | 73,860,391 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,414 | r | Simulate_Data1.R | ###############################################
# This is the most simple data-generating scenario:
# step counts only depend on the treatment (amputation)
# In this script we generate data for the
# following scenario:
# We sample N people from a population of 10,000
# and each person has recorded step counts on on... |
1483e3fbac2d0515ed90cc78a6f41e1113477e6b | 5b355f0222b604f2a907001966864e3caabdefa0 | /scripts/wrapperReadYaml.R | 8ce0a6877d073f6da7f8738fc578a79f65f1b0e9 | [] | no_license | PolinaPavlovich/Storshow | d3ba6aa16f7c96173caf0c967c76e5308771871a | ac17a2b5d9d119f9c712e7119eb3a8b0840d337e | refs/heads/main | 2023-07-27T17:11:21.409294 | 2021-09-03T10:07:08 | 2021-09-03T10:07:08 | 332,421,408 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 76 | r | wrapperReadYaml.R | inputDir <- "git_repo/scripts"
source(file.path(inputDir, "help_script.R")) |
0757900cac1097b3584c9c7f8b61f84ead53928c | 002095834e32fdae1cae1ae59f4d1e03f826d8ed | /Run_SA.R | f77a5ad335055c40de754192757a9057ef68c4fe | [] | no_license | harrietlmills/UPBEAT-effects | accb3d90d0904c9503a1e9d06634f71f2ee4e79a | c7ee930aaf711591392106f1388bca9683216bca | refs/heads/master | 2020-04-17T22:09:33.164855 | 2019-01-22T14:13:05 | 2019-01-22T14:13:05 | 166,982,714 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,946 | r | Run_SA.R | ##### Run sensitivity analyses
rm(list=ls(all=TRUE))
# NAME THE RESULTS FILE
results_file <- paste0("SensAnalysis_", gsub(" ", "_", paste0(substr(date(), 5, 10), "_", substr(date(), 21, 24))))
# loading packages
library(xlsx) # working with excel
library(lmerTest) # mixed models
library(boot) # bootstrapping... |
ba00388d51a4b5e1dbda0f857397acf7b9bda84d | a8124c3361ec462e076fbe246c3571672a28a54b | /R/function.R | 5a98d183904d7e9592c904e3843e0ab5f7d05ab6 | [
"MIT"
] | permissive | ashifujjmanRafi/code-snippets | 80ea1300fb2bb5bf4bc1c2fb01222a42127c0438 | 24bd4b81564887822a0801a696001fcbeb6a7a75 | refs/heads/master | 2023-02-17T04:35:32.779975 | 2021-01-12T02:14:47 | 2021-01-12T02:14:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 796 | r | function.R | mysum = function(x, y=2) {
z <- x + y
return(z)
}
ret = mysum(3,5)
print(ret)
ret = mysum(y=10, x=10)
print(ret)
# if we not pass any value for y. y take it default values
mysum = function(x, y=2) {
z <- x + y
return(z)
}
ret = mysum(3)
print(ret)
# ret = mysum() # ERROR!!! at least pass an one val... |
59a0fb6fded267e2ed925ff08930b222e992a8ec | 44d4f8c212732ede685b93da43bad99cbeb7f3b1 | /R/validation/dist/runkl.R | 5e24c3d4de5eb9b65a6ca3f62e62f25ae387e001 | [] | no_license | sharlec/HTTP-video-QoS-to-QoE | 31c3fd7bb4708c72a982dbc732d38e7be9aa0a28 | 50b91796a35db46c5f211ecade70db8d56fbf9fd | refs/heads/master | 2020-09-09T04:00:54.851590 | 2017-05-06T06:38:34 | 2017-05-06T06:38:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 423 | r | runkl.R | library('entropy')
dokl <- function(df){
k <- KL.plugin(df$original, df$predicted)
return(k)
}
dirname <- commandArgs(trailingOnly = TRUE)[1]
filenames <- list.files(dirname, pattern="*.txt", full.names=TRUE)
data <- lapply(filenames, read.csv)
KLdivergence <- lapply(data, dokl)
out <- cb... |
e48e711892953a14bed28605cf5b1ca154b9b95b | 08b4eaf203fbbe87b09fdb2dc96b5d11fff2c171 | /R/utils_preprocessing.R | e24a24f5ea9bb074d51c15014bf0447944baa22e | [] | no_license | cran/scDiffCom | a8f28d7f92acfba6b84e123707c437300a9adfd9 | 26fbcb29d53a04e49208cb38f3e515f4a59827aa | refs/heads/master | 2023-07-09T07:30:59.085372 | 2021-08-17T06:20:05 | 2021-08-17T06:20:05 | 397,309,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,761 | r | utils_preprocessing.R | extract_analysis_inputs <- function(
seurat_object,
celltype_column_id,
sample_column_id,
condition_column_id,
cond1_name,
cond2_name,
assay,
slot,
log_scale,
threshold_min_cells,
LRI_table,
LRI_species,
verbose
) {
seurat_inputs <- extract_seurat_inputs(
seurat_object = ... |
45a1c900752ecd93b0135ac340e1adde184c80b4 | 232c8b0213342e9e973ec8ffb695743759ee89b3 | /R/bayou-mcmc-utilities.R | 167d60c313880b4479c9c53bf9c2edab8373581d | [] | no_license | uyedaj/bayou | 304c98ba9516fb91688b345fb33c9a41765d06cd | b623758bf7b08900e2cd60c9247c2650b564d06b | refs/heads/master | 2021-07-05T03:02:21.376172 | 2021-05-10T14:51:11 | 2021-05-10T14:51:11 | 21,963,529 | 19 | 10 | null | 2019-11-06T18:58:40 | 2014-07-18T01:29:03 | HTML | UTF-8 | R | false | false | 22,637 | r | bayou-mcmc-utilities.R | #' Loads a bayou object
#'
#' \code{load.bayou} loads a bayouFit object that was created using \code{bayou.mcmc()}
#'
#' @param bayouFit An object of class \code{bayouFit} produced by the function \code{bayou.mcmc()}
#' @param saveRDS A logical indicating whether the resulting chains should be saved as an *.rds file
... |
1f9174fa49360968b0fc32be20c424dc5f65e9e7 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googledataflowv1b3.auto/man/ShellTask.Rd | 6c3ed4ca28385e25ae4dfb1883ca80dc8a813f57 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 514 | rd | ShellTask.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataflow_objects.R
\name{ShellTask}
\alias{ShellTask}
\title{ShellTask Object}
\usage{
ShellTask(exitCode = NULL, command = NULL)
}
\arguments{
\item{exitCode}{Exit code for the task}
\item{command}{The shell command to run}
}
\value{
ShellT... |
18494f549ab1a648d2ad541d36ab0ad04dae6267 | f3093af2209a4f58150f61650811808479b06b65 | /Fns/fnCalcMEASO_TimeSeries_Effort.R | 61b80349dafc7edef5cddde4a2cc40e8b488084a | [] | no_license | AndrewJConstable/Southern-Ocean-Catch | 56636217ad105b0a279cde6a8f7f97a56184a9c9 | 11c1b5370557c5fe2ca345c197e3ff4c6d60af47 | refs/heads/master | 2023-08-03T14:45:08.558446 | 2023-07-21T01:12:44 | 2023-07-21T01:12:44 | 291,855,588 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,869 | r | fnCalcMEASO_TimeSeries_Effort.R | fnCalcMEASO_TimeSeries_Effort<-function(m # MEASO threee-letter area code eg. "AOA"
,mData # dataset to be summarised
,DepVar # the name of the dependent variable aggregated in the variable, m
... |
cf1f7390fa51c6fbf781ea952df010aba9c8bfba | 5b7a0942ce5cbeaed035098223207b446704fb66 | /man/lsExportResponses.Rd | 1ec7570a00740826d6d97252c827d75870581a4d | [
"MIT"
] | permissive | k127/LimeRick | 4f3bcc8c2204c5c67968d0822b558c29bb5392aa | a4d634981f5de5afa5b5e3bee72cf6acd284c92a | refs/heads/master | 2023-04-11T21:56:54.854494 | 2020-06-19T18:36:05 | 2020-06-19T18:36:05 | 271,702,292 | 0 | 1 | null | 2020-06-12T03:45:14 | 2020-06-12T03:45:14 | null | UTF-8 | R | false | true | 1,463 | rd | lsExportResponses.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lsExportResponses.R
\name{lsExportResponses}
\alias{lsExportResponses}
\title{Export responses}
\usage{
lsExportResponses(
surveyID,
lang = NULL,
completionStatus = "all",
headingType = "code",
responseType = "short",
lsAPIurl = g... |
c10a2296727d78de5dc25c80e657970a92072737 | 5be5233c70855f78773e177f9a2ff5795aafb8c5 | /cbsots/tests/testthat/test_get_ts_82596NED.R | af11180ff58ff91ae465e6cf16f7abac04efa7c2 | [] | no_license | timemod/cbsots | 5057c3d38754aae175776d857f9c4916a9e5af73 | 3523b0eaa87eeee6425d80cbceb9668ca76c3ce1 | refs/heads/master | 2023-06-22T22:35:10.602907 | 2023-06-12T08:06:00 | 2023-06-12T08:06:00 | 121,116,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 746 | r | test_get_ts_82596NED.R | library(cbsots)
library(testthat)
rm(list = ls())
# Use UTF-8 endocing, because the Titles contains diacritical characters
# and the data files have been created with UTF-8 encoding
options(encoding = "UTF-8")
ts_code_file <- "tscode/tscode_82596NED.rds"
#edit_ts_code(ts_code_file)
ts_code <- readRDS(ts_code_fi... |
3d8351cf1e38a66505c0f9a860dbde1405d8becf | ee99fdde8656c0324d2e540a0ba94fabed28a95c | /scripts/s04_mask_clip.r | de8a819b19228891459ce621e1e246126789ef8e | [] | no_license | Chris35Wills/synthetic_channel_mesh | b68b3eb57e857b583e473972f6768c05d89a53ae | cab157e42ea684a34d4a75aa247d5fcb8ad7fbc7 | refs/heads/master | 2020-12-03T02:00:26.537645 | 2017-07-17T10:07:44 | 2017-07-17T10:07:44 | 95,893,204 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,040 | r | s04_mask_clip.r | # Program: *mask_clip.r
# Functions to clip coverage of the synthetic mesh keeping points within the channel according to a mask
# Also limits overflow into other channels at confluences
#
# @author Chris Williams
# @date: 08/03/16
if (!require("raster")) install.packages("raster")
if (!require("sp")) install.packages... |
93e5a047df134303fbe35190c0286830cff64f66 | c23b034a6600759c25c948265502cde27f3d2080 | /exposure/exposure/v10/v10.graph.combined.r | 6f2fc76f77cdba265d652f9d1905397188fed67e | [] | no_license | YWAN446/Exposure-Assessment-Model | f6c5553ba9d561d62b6e05dd62238b55ff870f10 | 35b0d8e7d3fe70d36ab824329252b41a75c9d3ce | refs/heads/master | 2020-03-12T16:53:44.149873 | 2018-04-23T20:31:43 | 2018-04-23T20:31:43 | 130,725,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,002 | r | v10.graph.combined.r | pdf(paste("./output/","exposure","-hh-combined-by-age",".pdf",sep=""));
for (k.age in 1:3){
if (k.age==1){
int.dirt1<-HH.1[[14]]
int.flo1<-HH.1[[16]]
int.offgr1<-HH.1[[15]]
int.drain1<-HH.1[[13]]
int.septage1<-HH.1[[17]]
int.produce1<-HH.1[[18]]
dw1<-HH.1[[19]]
int.total1<-HH... |
f2349063f092d27ae13269c39e2fb280a4aee331 | ca06fec45eaaa886c34910541aa1b8012989f947 | /movielens Project- Jersson Placido.R | 72518e1739d702d245935423f16fd8146c0a105d | [] | no_license | jepeteso/movielens | 3c41bc5075152900562cb595cc854d8cdb9ba61e | c034ddd491088093f17e274b61422bce63725a22 | refs/heads/master | 2023-04-02T02:59:06.345315 | 2021-04-02T21:09:24 | 2021-04-02T21:09:24 | 354,122,053 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,419 | r | movielens Project- Jersson Placido.R | #####################################################################################
# Create the train set (edx) and test set (validation set| final hold-out test set)
####################################################################################
if(!require(tidyverse)) install.packages("tidyverse", repos = "... |
3b4d7e1b91f36b663f26bb428d6e984cb6789d61 | 88be221ad6071f4742bbfc7aae11c89efc8d58c8 | /lib/performance measure.R | 15f7d02dd4fa62042d397d30c87e14b54e253dc0 | [] | no_license | claudialeee/Optical-Character-Recognition | c25c108b41ba49d61eda36eaa5d6414bdf03484a | 7505e94b3bc2f714681585d229f4a39b7d2f12b3 | refs/heads/master | 2020-04-11T00:46:05.709872 | 2018-12-03T19:33:25 | 2018-12-03T19:33:25 | 161,395,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,208 | r | performance measure.R | ###################################
## input: dataframe with corrected words(first column) and corresponding truth words(second column)
# the order of columns only affect line 19. It should be corrected words
## output: performance measure table
###################################
measure <- function(df) {
... |
fe5980508873438f280f7ab1dc07e4ed4767b07a | 0d454f32f0c1d538e9907435f361c51bfa988f67 | /R/rearray.R | 6bd4890eab5b5a65164f56048ffd107728ee1418 | [] | no_license | jscamac/jagstools | 3218b00b03b176d0257588ec184e9bfe954e18b1 | 462c3f690f7a3489f75cc236823197994591c20b | refs/heads/master | 2021-01-18T08:29:35.207840 | 2015-03-10T23:21:00 | 2015-03-10T23:21:00 | 31,931,926 | 1 | 0 | null | 2015-03-10T01:28:16 | 2015-03-10T01:28:16 | null | UTF-8 | R | false | false | 1,883 | r | rearray.R | # x = the jags object
# param = the exact name of the parameter to be converted back to an array
# (can be a vector of param names, in which case a list of arrays will be returned,
# can also be 'all', in which case all parameters with dimensions will be returned)
# fields = the names of jags summary columns to includ... |
0661820a3193df122bdcef47c120f1ca618d4fa3 | e06d96c26b6faa3f85e39c3e0f79be9678effa7d | /Archive_July/code/code_paper/plot_R_r_akd_FULL.R | 663b9b9dcc9e7734f61b3d5ec495e06139fd32aa | [] | no_license | juliachenc/covid19 | 9d2a3653032fd311582a7a7408f0a2cd76960f04 | 0d329b968876fd96ba390baee30e015778ea02f8 | refs/heads/main | 2023-06-04T15:56:16.962225 | 2021-06-23T18:34:32 | 2021-06-23T18:34:32 | 343,538,883 | 1 | 1 | null | 2021-04-05T19:19:08 | 2021-03-01T19:53:08 | HTML | UTF-8 | R | false | false | 17,617 | r | plot_R_r_akd_FULL.R |
#############################################
## PLOT CODE
## Creates plots for time varying parameters:
## R(t)
## r(t)
## Alpha(t), Kappa(t), Delta(t)
plot.together <- function(traj.CI=traj.CI, data.in=data.in, init.date.data=NULL, date.offset.4plot=NULL, time.steps.4plot, vars.to.plot, y.lab.in, y.max.in, chart.ti... |
6355f637d3207f658d52a634548bda96f67bef6c | ed7240eaeb54b899882755bce3f68480edac2def | /helper_functions/similarityFunctions.R | 004039053f292f9bb7bafe81d12e51ed394036a1 | [] | no_license | asRodelgo/NBA | 09eb2866d90583a22edc75b08c448843cbdac5c7 | 763496e7d913361556945004ca8a040e65771877 | refs/heads/master | 2020-05-22T01:31:50.932824 | 2017-12-10T20:58:25 | 2017-12-10T20:58:25 | 60,845,763 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,833 | r | similarityFunctions.R | # Find similar players ------------------------------
#
# Using t-sne algorithm, find players that have similar characteristics to a given player.
# The objective is to predict his performance in a given year based on the historical performance
# of similar players (see: Nate Silver's CARMELO or PECOTA systems)
#
# Ex:... |
5014238cad5095a01844c840fdd682b949a55e71 | 277dbb992966a549176e2b7f526715574b421440 | /R_training/실습제출/전나영/191104/dplyr_lab2.R | 4953bb41c0314fcca610c42ee3299d00ca478aca | [] | no_license | BaeYS-marketing/R | 58bc7f448d7486510218035a3e09d1dd562bca4b | 03b500cb428eded36d7c65bd8b2ee3437a7f5ef1 | refs/heads/master | 2020-12-11T04:30:28.034460 | 2020-01-17T08:47:38 | 2020-01-17T08:47:38 | 227,819,378 | 0 | 0 | null | 2019-12-13T12:06:33 | 2019-12-13T10:56:18 | C++ | UTF-8 | R | false | false | 1,744 | r | dplyr_lab2.R | # 문제1
install.packages("ggplot2")
library(ggplot2)
str(mpg)
mpg <- as.data.frame(mpg)
# 1-1
mpg %>% nrow()
mpg %>% ncol()
# 1-2
install.packages("dplyr")
library(dplyr)
mpg %>% head(10)
# 1-3
mpg %>% tail(10)
# 1-4
mpg %>% View()
# 1-5
summary(mpg)
# 1-6
str(mpg)
# 문제2
# 2-1
mpg <- mpg %>% rename(city = cty,
... |
9b3ca29f4757323df2219b91d8a9ed893325c20e | ff9eb712be2af2fa24b28ecc75341b741d5e0b01 | /man/stat_n_text.Rd | 35d8ec5050f26a387a777b5b559f099263bffc2b | [] | no_license | alexkowa/EnvStats | 715c35c196832480ee304af1034ce286e40e46c2 | 166e5445d252aa77e50b2b0316f79dee6d070d14 | refs/heads/master | 2023-06-26T19:27:24.446592 | 2023-06-14T05:48:07 | 2023-06-14T05:48:07 | 140,378,542 | 21 | 6 | null | 2023-05-10T10:27:08 | 2018-07-10T04:49:22 | R | UTF-8 | R | false | false | 6,787 | rd | stat_n_text.Rd | \name{stat_n_text}
\alias{stat_n_text}
\title{
Add Text Indicating the Sample Size to a ggplot2 Plot
}
\description{
For a strip plot or scatterplot produced using the package \link[ggplot2]{ggplot2}
(e.g., with \code{\link[ggplot2]{geom_point}}),
for each value on the \eqn{x}-axis, add text indicatin... |
93fa4a5c1505577bc85d7a2067f011526771ff51 | 39e6b4b0a85bab8f160f5b0d06f07a67ef0c3ae4 | /cachematrix.R | e576fdeaeebd0acf03121e45a972e1cacdc62e1f | [] | no_license | monty111191/ProgrammingAssignment2 | d1eb1c317f6c1a48c3f78daaa6b28c1c2220f896 | 3aa828bb56841e11b34e14a7685e99785a5cf9f0 | refs/heads/master | 2021-01-24T02:52:34.692860 | 2016-01-24T03:55:15 | 2016-01-24T03:55:15 | 50,161,881 | 0 | 0 | null | 2016-01-22T06:15:42 | 2016-01-22T06:15:42 | null | UTF-8 | R | false | false | 1,671 | r | cachematrix.R | ## Taking the inverse of a matrix is usually quick for a singule matrix.
## However, if you need to repeatedly inverse the matrix can be time
## consuming over a large data set. Therefore, it might be of use to cache
## the inverse of a non-changing matrix and bring it up later on rather
## than continually computing ... |
cdc0c8a0284ef9709f4f969262d21682697a04e3 | d0f777a502a7d8483096e371d14223b414f6720d | /ECFGenomeRcpp_v3.30/R/annotate_group_v6.255.R | 173437633365f733ef6a4d0fd255b306bcc65d1c | [] | no_license | horiatodor/ECFGenome-Rcpp | 86fb14ca2948ba9ba2568a285e6f106b6c806ee5 | 7feee7f81a91f58c40f731987312a741514fa48e | refs/heads/master | 2022-11-15T11:08:53.089067 | 2020-07-09T02:15:58 | 2020-07-09T02:15:58 | 278,220,782 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,951 | r | annotate_group_v6.255.R | #function that takes in a list of pwm_scans, annotates them, and then does the cog thing...
#cuttoff is how far upstream of the start site we want to look. -1000 is there to keep back compatbiliity
#operon tells us how to handle (if at all) operon structures
#none means no operon structures are taken into consider... |
712b1b2018f492493b2ef18ab697f1b058ef8de2 | 4e8f1eb4fbd4a65cd8c3930f3ce3dbdd6ccd45d7 | /R/utils.R | 41594eacd542a4189c21894fe2194e4f8e667754 | [] | no_license | dreamRs/shinylogs | cbcb2d7582f4a2a3950d34c30c370df080ffa38f | 0195ac0a1f85d213c82143cfee712c9baddd1963 | refs/heads/master | 2023-03-19T01:12:52.425499 | 2022-04-18T16:02:24 | 2022-04-18T16:02:24 | 161,770,136 | 94 | 14 | null | 2023-03-14T17:58:50 | 2018-12-14T10:36:28 | R | UTF-8 | R | false | false | 792 | r | utils.R |
dropNulls <- function(x) {
x[!vapply(x, is.null, FUN.VALUE = logical(1))]
}
get_timestamp <- function(time = NULL) {
if (is.null(time))
time <- Sys.time()
format(time, format = "%Y-%m-%d %H:%M:%OS3%z")
}
is_sqlite <- function(path) {
is.character(path) && grepl(pattern = "\\.sqlite$", x = path)
}
get_us... |
d696ef2d3a51d3633eb42b2e41add2be1a3d4fc5 | ae8a72dd35911a3a9d6b472b152e22a382d67d3b | /varTest/results/varMod/varModFit.R | 8f6e2513bfa097344dcfa9f65798eae56630fe7a | [] | no_license | inspktrgadget/atlantis | 883a1555c3c930007ebc475dc3dd5fca14e2d717 | 3a324ea7194f2a93ad54f6f7ce0f4e55dc2419e6 | refs/heads/master | 2021-09-10T23:03:37.983689 | 2018-04-03T20:45:54 | 2018-04-03T20:45:54 | 116,151,163 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 771 | r | varModFit.R | library(plyr)
library(tidyverse)
library(parallel)
library(Rgadget)
homeDir <- "~/gadget/models/atlantis/varTest/varModels"
setwd(homeDir)
file.create("gadgetFitOutput")
#mod_dir <- dir("varModels")
mod_dir <- sprintf("varModel_%s", c(0.269, 0.276, 0.282, 0.288, 0.294, 0.3))
null_list <-
mclapply(mod_dir, functio... |
84355bbacec87317a4acd2ed2c4c4c5a00ab6345 | 62fd8b80332420d977bc7da5330c8df821936aef | /week 23.R | 45d594e0e8c186e239b1207e55dd2acba99709e6 | [] | no_license | NdiranguMartin/TidyTuesday | a7dfea863d2add9b544bceae9594508923ef21f2 | 7100d27daf6ee566e9c73c7f384e112aa052eff7 | refs/heads/master | 2020-07-08T03:51:05.040442 | 2020-05-31T16:42:52 | 2020-05-31T16:42:52 | 203,556,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34 | r | week 23.R | # I will have my week 23 code here |
f3672c91490ecb9b669946ad9f66c6ff13f45c5a | a828726d268e86fd86aec54247fc8bb8211eec43 | /man/save_brush_history.Rd | 7f3c3782092c3dc856f6f95278c6c977ae6bedfd | [] | no_license | XuChongBo/cranvas | b6536c8f43ccaac2ac9f18c1953fe13e9fc94155 | f5a37363044bdbf946011f2c8ab019cefbefbe39 | refs/heads/master | 2020-05-29T11:49:04.840631 | 2013-11-22T16:49:13 | 2013-11-22T16:49:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,906 | rd | save_brush_history.Rd | \name{save_brush_history}
\alias{save_brush_history}
\title{Create the brush history}
\usage{
save_brush_history(data, index = selected(data))
}
\arguments{
\item{data}{the mutaframe created by \code{\link{qdata}}}
\item{index}{the indices of rows to be stored in history;
an integer vector or a logical vector (w... |
74c7b326e7d6d96637567c3afcda579094f4feb0 | eea741791ea776e38cde479ef0f201defaae9a4f | /cholla_climate_IPM_SOURCE.R | 7bdbe9c236586043d8505198c4982c9ced6921ae | [] | no_license | texmiller/cholla_climate_IPM | 28f541d9266ad0a3a182833fb713f0852a7bfb85 | 5c16d93001b9941fb00115df6d008e08e140c236 | refs/heads/master | 2021-06-05T10:01:51.402460 | 2021-05-06T18:28:11 | 2021-05-06T18:28:11 | 142,463,901 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 13,037 | r | cholla_climate_IPM_SOURCE.R | ### Purpose: build IPM using the climate-dependent vital rates that were fit elsewhere
# misc functions -------------------------------------------------------------------
volume <- function(h, w, p){
(1/3)*pi*h*(((w + p)/2)/2)^2
}
invlogit<-function(x){exp(x)/(1+exp(x))}
getmode <- function(v) {
uniqv <- unique(v... |
49bdfda9a87f4e203cdba0e802a795b91d76585c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Ecfun/examples/missing0.Rd.R | dd83adebc22461f5e9f1e867d9cfdad02ba7cc7c | [] | 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 | 535 | r | missing0.Rd.R | library(Ecfun)
### Name: missing0
### Title: Missing or length 0
### Aliases: missing0
### Keywords: manip
### ** Examples
tstFn <- function(x)missing0(x)
# missing
## Don't show:
stopifnot(
## End(Don't show)
all.equal(tstFn(), TRUE)
## Don't show:
)
## End(Don't show)
# length 0
## Don't show:
stopifnot(
##... |
db23262e9676ef4e7861ad141bf2f26fd9d4fcb5 | 5d447149434c27efd1c05eeb2dfa05e033ee1f20 | /03_aus_testing.R | 10965f0f6d942275bf9ed7cbb9a7947df209ec72 | [] | no_license | rkodwyer/covid-19 | 84dd5cee68de540f54c0d8239615043d93b5329b | d46c586cd56925e0948200cc26a341883d88d979 | refs/heads/master | 2022-10-23T11:06:30.397882 | 2020-06-06T04:06:54 | 2020-06-06T04:06:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,878 | r | 03_aus_testing.R | ### IMPORT DATA ---------------------------------------------------------------
# From Guardian Australia JSON feed
# Guardian Australia https://www.theguardian.com/au
aus_guardian_html <- "https://interactive.guim.co.uk/docsdata/1q5gdePANXci8enuiS4oHUJxcxC13d6bjMRSicakychE.json"
aus_org <- fromJSON(aus_guardian_html, ... |
8e7788c9a144999ddac9a31ff52c64dc78b40dd9 | 73640cd1b41aac73971607aa9921a22ca9f1f4a9 | /man/hive_to_hdfs_txt.Rd | 9b4ed4eab926b2f0dc3b5230382d849c5aca0b7e | [] | no_license | mndrake/honeycomb | 9f2d8be43c5a96634a5f98b12bce8207af2cc8ef | 6fe6e84cf770f4501ed4883ff4e60717cb27dd9c | refs/heads/master | 2022-12-17T08:22:38.588249 | 2018-02-07T19:37:29 | 2018-02-07T19:37:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 537 | rd | hive_to_hdfs_txt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tbl_Hive.R
\name{hive_to_hdfs_txt}
\alias{hive_to_hdfs_txt}
\title{Export a Hive table to a txt file in HDFS}
\usage{
hive_to_hdfs_txt(hive_tbl, hdfs_path, delim = "\\t")
}
\arguments{
\item{hive_tbl}{A \code{tbl_Hive} object}
\item{hdfs_pat... |
8e0650fad351b6aa3fda9bc1eda8ecb881df483a | 3e1f6dfde5c940f7acde208d098e56a54550945f | /dash_docs/chapters/sharing_data/examples/scoping_wrong.R | 2c189d1b1200fe455c5606e89794b610ffea1841 | [
"MIT"
] | permissive | plotly/dash-docs | a4d1b9e450aa19e811f8ae043fd56de330cce63a | f494e987701be1085ba9fb7b29bd875ee2146d5b | refs/heads/master | 2023-08-03T02:18:16.257115 | 2021-12-14T18:51:52 | 2021-12-14T18:51:52 | 84,095,619 | 396 | 210 | MIT | 2023-01-18T20:29:56 | 2017-03-06T16:30:08 | Python | UTF-8 | R | false | false | 1,050 | r | scoping_wrong.R | library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)
app <- Dash$new()
df <- data.frame(
a = c(1,2,3),
b = c(4,1,4),
c = c('x', 'y', 'z'),
stringsAsFactors=FALSE
)
app$layout(
htmlDiv(
list(
dccDropdown(
id = 'dropdown',
options = list(
list(label = 'x',... |
fbc930341a4b66b83326d83c05f63059e7b86428 | 40f4cb44ab742a168ca3f82d36a3e38dcaa6f844 | /man/loadIsAssociatedTo.Rd | 2a9a834516671e5894924e6f5d69b143d23e9a21 | [] | no_license | sankleta/BED | 34e3f91fceffbb1164e65ab8a4cb24e6431b898b | 85c5c5ba4bbc927155d454dc6612512c7b197805 | refs/heads/master | 2021-04-30T05:55:28.535605 | 2018-02-06T11:18:59 | 2018-02-06T11:18:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 665 | rd | loadIsAssociatedTo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loadIsAssociatedTo.R
\name{loadIsAssociatedTo}
\alias{loadIsAssociatedTo}
\title{Feeding BED: Load BE ID associations}
\usage{
loadIsAssociatedTo(d, db1, db2, be = "Gene")
}
\arguments{
\item{d}{a data.frame with information about the associa... |
38a600b0aca0b7b10208145dc903b44a7704e30e | 651dc73b7660d3ace5aa640ac9c6f173374756c6 | /Report Creation.R | 1233c2648d53ca7ca4b698d4c312ebee54d46481 | [] | no_license | abhijithasok/Report-management-automation-using-VBA-R-Python | 28788c47c2325f6868c03f25aee2757babb3b603 | bd6df1762aba12bffc44038db4da1fd3bb652c22 | refs/heads/master | 2021-01-13T04:21:26.278318 | 2016-12-28T05:24:48 | 2016-12-28T05:24:48 | 77,451,982 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,636 | r | Report Creation.R | # Many of the packages below meant for enabling R to handle xls and xlsx serve the same-
# -purpose. All are loaded just for options
library(colorspace)
library(ggplot2)
library(ggrepel)
library(devtools)
library(readxl)
library(XLConnect)
library(installr)
library(rJava)
library(xlsx)
library(gdata)
libr... |
112b0777204e8d444d6ab264c2884be23229c0e5 | 1fbd3028d66ff1bc14aab8cf415afbe6841b4679 | /scripts/catalog_mapping_check.R | c29ee9f49a0060ddedbb4308b8e3cb9e73ac6208 | [] | no_license | QingxiaCindyChen/survival_gwas | 21386cb2ab3cb93ac0bb80d081b0dc68a3c9e705 | db8cf6ce3a53a88e2af51478d9bf86706630b05e | refs/heads/master | 2023-04-11T10:26:19.435541 | 2021-04-19T18:56:55 | 2021-04-19T18:56:55 | 359,566,061 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,122 | r | catalog_mapping_check.R | library('data.table')
library('readr')
procDir = 'processed'
studyFilename = 'gwas_catalog_v1.0.2-studies_r2018-08-28.tsv'
studyData = read_tsv(file.path(procDir, 'gwas_catalog', studyFilename))
setDT(studyData)
colnames(studyData) = gsub('/|\\[|\\]|\\s', '_', tolower(colnames(studyData)))
phecodeData = read_csv(fil... |
c294a0780b29f78e9dc2caf971ece377fb913764 | 55c414b82fa630447793f152a0fa8d0803c06fc3 | /pet_demo/demo.R | 6b6b255239b357fdb88ed3f6c9d73188bcf4f739 | [] | no_license | LabNeuroCogDevel/data_parsing_scripts | f102215a51f4020a923c593e7f501a6559513fa3 | bbfd0c899621f24dd818a64ef9b4708cb0d9927a | refs/heads/master | 2021-01-21T10:37:35.827295 | 2017-03-08T20:59:46 | 2017-03-08T20:59:46 | 83,460,779 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,408 | r | demo.R | library(dplyr)
library(tidyr)
library(xlsx)
library(curl)
gsurl<- 'https://docs.google.com/spreadsheets/d/1Sk5I09hybI-4VJKd_6ST1d_J76fVd1D8zVjZ9vBLYd0/pub?output=xlsx'
curl_download(gsurl,'pet.xslx')
sex <- read.xlsx('pet.xlsx',sheetName="PreTest") # sheet 3
demo <- read.xlsx('pet.xlsx',sheetName="Demographic")# ... |
ca4f15bf41f98da53a3554fe9f62b4c8b9bf75ed | 2d8409a80bdf7b6bd03d14cbade1659427198d51 | /code/covid/make_covid_figures_and_tables.R | 52af16c77c20dd8f8be510b96f7b6e6ed9b30fe6 | [] | no_license | joeflack4/ending_hiv | 1ba6d2d8d4494d10528b97d1a850a44b2cd41fe2 | 7b337e6f978ea0cf6eb17d1a5c55267106952008 | refs/heads/master | 2023-07-25T03:07:46.399438 | 2021-08-24T17:20:38 | 2021-08-24T17:20:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,013 | r | make_covid_figures_and_tables.R |
if (1==2)
{
tab = make.covid.summary.table(df)
write.csv(tab, 'code/covid/summaries/jheem_covid_summary_4_cities.csv')
}
library(ggplot2)
library(scales)
OUTCOME.LABELS = c(incidence="Incident Cases (n)",
new='Reported Cases (n)',
prevalence="Prevalent Cases (n)",
... |
618ec1de180387d0266ad6724fb55793cbc0c736 | 0f062674ed0146a2dc8e9a3c8b965679bef8d9dd | /plot2.R | 94098b9c88674b92bb52b77f02d2468eb371611d | [] | no_license | maybetoffee/explorator_data_analysis | 5a93a94aef7406b9e3982da346634ddcd2e57e65 | 5909541b9bb50e0477a62295dc4d01ef004a3eb8 | refs/heads/master | 2021-01-01T17:14:51.754501 | 2017-07-22T13:41:38 | 2017-07-22T13:41:38 | 98,033,365 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 739 | r | plot2.R | getwd()
setwd("D:/coursera_R")
#loading the data
data<-read.table("./household_power_consumption.txt",header=T,sep=";",na.string="?")
#convert the data variable to data format
data$Date<-as.Date(data$Date,format="%d/%m/%Y")
data<-subset(data,subset = (Date>="2007-02-01" & Date<="2007-02-02"))
#convert date and t... |
e80347710c762e4e4bbc3c928e0de58563f15e44 | 5adb020e37747a3fbb15fc8d33c94eca682c003d | /scripts/07.anova.R | ac282cff6e88e2195d613e1504a906a5107bed73 | [] | no_license | andrew-hipp/oak-morph-2020 | a28c73a32d3a8dce3b381b3c51cb69ef30b30132 | f7acc4bbac1536ab422a5ec8ec80c783a58c16ea | refs/heads/master | 2023-04-12T09:37:17.335773 | 2022-08-05T19:37:32 | 2022-08-05T19:37:32 | 140,023,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 401 | r | 07.anova.R | #ANOVA FOR BLADE LENGTH AND SLA
anova.1 <- lm(bladeL ~ site + tree, data = oak.dat)
anova(anova.1)
anova.2 <- lm(Area.Mass ~ site + tree, data = oak.dat)
anova(anova.2)
#ANOVA USING PCA SCORES
PC <- predict(temp, newdata = oak.dat)
oak.dat <- cbind(oak.dat, PC)
anova.3 <- lm(PC1 ~ site + tree, data = oak.dat)
anova(... |
c9bd665cca93dd9087d68b073ed6f380c19d161e | 3ee83c8a4c66054ce26684a4c032ca5e6f98bd9b | /plot2.R | 85643b3869e3a3d5dad7a08e1b3501f9ca31b0bb | [] | no_license | tuomiel1/ExData_Plotting1 | 430208d5a45a295c77c9a07f06f5b0202db52ab9 | c96dee46130ba0e92931dbb64f67b4fa0fefd86d | refs/heads/master | 2021-01-17T22:30:24.847060 | 2015-12-13T23:51:29 | 2015-12-13T23:59:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,244 | r | plot2.R | #The following script implements submission 1/part 2 of Exploratory Data Analysis course in Coursera
#This script depends on:
##A working outside connection to the web: for downloading the data
##Access to data.table and ggplot2 libraries for easier plotting and data handling
##LOAD PACKAGES
require(data.table)
requir... |
d4187cb2fa9e36bffaff8cdf5e70e8bf2e939324 | 2da2406aff1f6318cba7453db555c7ed4d2ea0d3 | /inst/snippet/as-matrix.R | 623b365d041301d611f215539398b168960103c3 | [] | no_license | rpruim/fastR2 | 4efe9742f56fe7fcee0ede1c1ec1203abb312f34 | d0fe0464ea6a6258b2414e4fcd59166eaf3103f8 | refs/heads/main | 2022-05-05T23:24:55.024994 | 2022-03-15T23:06:08 | 2022-03-15T23:06:08 | 3,821,177 | 11 | 8 | null | null | null | null | UTF-8 | R | false | false | 117 | r | as-matrix.R | x <- 1:3
A %*% x # vector x treated as a column matrix
as.matrix(x) # explicit conversion to a column matrix
|
6e66f7b847792133d985cf557132983bdecfa05f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rgeos/examples/topo-unary-gSimplify.Rd.R | 2ef15ea1d53be9a6dd23686e4281c9cae3b0c6ab | [] | 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 | 679 | r | topo-unary-gSimplify.Rd.R | library(rgeos)
### Name: gSimplify
### Title: Simplify Geometry
### Aliases: gSimplify
### Keywords: spatial
### ** Examples
p = readWKT(paste("POLYGON((0 40,10 50,0 60,40 60,40 100,50 90,60 100,60",
"60,100 60,90 50,100 40,60 40,60 0,50 10,40 0,40 40,0 40))"))
l = readWKT("LINESTRING(0 7,1 6,2 1,3 4,4 1,5 7,6 6,7... |
91bd9a22729cb5d6d4c94040945e7f08c37858aa | dae6befcea92b6171d6e592d58ecb7c499a2ae9a | /tests/testthat/test-set-pars.R | d9403a696ff5ac5db2111b9c68389b1a7230a91d | [
"MIT"
] | permissive | krlmlr/term | 7b85ba675bbdff76e28e89d3c20c6726bd253303 | f46b5b47455330ce3130ad858ac36055663dfa3d | refs/heads/master | 2020-12-27T08:22:42.952071 | 2020-02-01T23:46:58 | 2020-02-01T23:46:58 | 237,830,909 | 0 | 0 | NOASSERTION | 2020-02-02T20:20:03 | 2020-02-02T20:20:02 | null | UTF-8 | R | false | false | 1,982 | r | test-set-pars.R | context("set-pars")
test_that("set_pars", {
expect_identical(set_pars(as.term("a"), "b"), as.term("b"))
expect_error(
set_pars(as.term("a"), c("b", "a")),
"^`value` must be length 1, not 2[.]$", class = "chk_error"
)
expect_error(
set_pars(as.term(c("a", "a")), c("b", "a", "c")),
"^`value` must... |
ea28d57c7343809e2b8d7fe803c4671d809e8a2f | 9244358cbe08a51cb2472625b0518671a35e43dd | /R/get_overview_options.R | 801593430f261d92959d5231edc5fb002f8cb3bf | [
"MIT"
] | permissive | KWB-R/wasserportal | b65461be84b05e62aa32fa75cefc4ac9cc4725f0 | 7ef43fe6ff55a4dfcd8e7ac92cc9a675e65c5c99 | refs/heads/master | 2023-09-03T15:39:15.593616 | 2023-02-19T21:37:04 | 2023-02-19T21:37:04 | 344,412,257 | 0 | 0 | MIT | 2023-02-19T21:37:06 | 2021-03-04T09:03:26 | R | UTF-8 | R | false | false | 597 | r | get_overview_options.R | #' Wasserportal Berlin: get overview options for stations
#'
#' @return list with shortcuts to station overview tables
#' (`wasserportal.berlin.de/messwerte.php?anzeige=tabelle&thema=<shortcut>`)
#' @export
#'
#' @examples
#' get_overview_options()
#'
get_overview_options <- function()
{
list(
surface_water = lis... |
482bcb1274ff7ae64524082e0066960f9877a117 | c0eecbccaaa2663b670fc5298f793e62017821b5 | /tests/testthat/test-user.level.functions.R | cfcf2c24adecefcd32a73111b1357164f5c21cc4 | [] | no_license | datapplab/SBGNview | 6ce7de127da865dfa2306a155b928bbdc9801213 | bbaeea8a37a23faca63377ee7094dfc4b920a387 | refs/heads/master | 2023-04-07T13:55:02.661178 | 2022-06-11T22:25:05 | 2022-06-11T22:25:05 | 189,049,462 | 20 | 7 | null | 2023-03-16T10:32:40 | 2019-05-28T14:55:44 | R | UTF-8 | R | false | false | 1,908 | r | test-user.level.functions.R | library(testthat)
###################################################
test_that("changeDataId for compound", {
cpd.sim.data <- sim.mol.data(mol.type = "cpd",
id.type = "KEGG COMPOUND accession",
nmol = 50000,
nexp = 2)
... |
b1e0b0f0330dacf88e6a00ec55472536de4d45e1 | 3ced7fc9cbc72d3d3bc7492c03053581a2edc901 | /4.4.duration_deregistered.R | e72054f695086e582843d6c756afea9a5c5f3dfa | [] | no_license | edugonzaloalmorox/survival-quality | 4553bde947f825cf7e38b768fbea8136792212c1 | 239ef37995fec47d35c348e13ee2da5b83d71c38 | refs/heads/master | 2021-04-30T15:27:58.251502 | 2018-02-12T12:04:01 | 2018-02-12T12:04:01 | 121,241,206 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,882 | r | 4.4.duration_deregistered.R | #################################################
# Fill information regarding those de-registered and those registered
# 1 - September 2017
# @ Edu Gonzalo Almorox
###################################################
library(rio)
library(dplyr)
library(forcats)
library(tibble)
library(ggplot2)
library(lubridate)
libr... |
04df8ce21af29541f71fdf27e57450826acd126f | cf5998744c0c76ef67647473da3c1b79d07fbff7 | /man/read.starbeast.Rd | 7356b6ec73a49fe95b8788bb01c87eb1cad49dc2 | [] | no_license | fmichonneau/starbeastPPS | 00a3797e72ce882475669ce556628d08ec61db5f | 3c07296a35d327ce840bc25d81a7a183259d9c19 | refs/heads/master | 2020-12-24T15:23:36.039533 | 2014-10-31T21:07:18 | 2014-10-31T21:07:18 | 18,690,628 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,013 | rd | read.starbeast.Rd | \name{read.starbeast}
\alias{read.starbeast}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
this function reads in the results of a *BEAST phylogenetic analysis}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
read.starbeast(beast.xml, combinedfiled... |
9a80d53f36f82e223ff0ad34915cd1c09351aa05 | bf7fcd367258cb8f02540ae11c7cabca55c08250 | /R/ThorntonHIVRep.R | cbd1393cd1f354468c3a16bc2c96ca6b384d2679 | [] | no_license | zachary-chance1/CI-Assignment-3 | 9102b14739d39b47933b1f8dba798bebc31c9440 | 50ce9aeca929f46b96f527eaa0fc99eb26c82939 | refs/heads/master | 2022-07-20T03:14:45.317310 | 2020-05-20T17:12:51 | 2020-05-20T17:12:51 | 265,462,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,424 | r | ThorntonHIVRep.R | library(tidyverse)
library(haven)
read_data <- function(df)
{
full_path <- paste("https://raw.github.com/scunning1975/mixtape/master/",
df, sep = "")
df <- read_dta(full_path)
return(df)
}
hiv <- read_data("thornton_hiv.dta")
# creating the permutations
tb <- NULL
permuteHIV <- functio... |
9de20b21feccd302d25de4352faf64f7ad2fd633 | fe268b6c71d1026785606bebe233e2fad88b492c | /R/dat_proc.R | 4b123e7863f70dfad1fc92d3a2b50fc19b01a664 | [] | no_license | SCCWRP/ClearLakeRisk | b8ff1bdc1a0c84d84eb8087df6b233600dddaa6a | 25e5a677d4b72e1bd10cd9d77520d189dfb04fcb | refs/heads/master | 2020-07-27T04:05:15.512429 | 2019-09-20T21:02:22 | 2019-09-20T21:02:22 | 208,861,587 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,020 | r | dat_proc.R | # setup -------------------------------------------------------------------
library(tidyverse)
library(readxl)
library(lubridate)
library(here)
# wrangle bio -------------------------------------------------------------
biodatraw <- read_excel(here('data/raw', 'Refined Biological Results.xlsx'))
# selecting lat, lo... |
971dcd6748ce2f31f98eb0c692c7f8ca968700cb | 06590105205560d7b2d32ef25ed01c41de511e0b | /Code/Base/menuTable/module/exploreTable1TabModuleUI.R | c01add7a059dcdc94387c7c08436dfef61ecab2f | [] | no_license | ai4ir/SmartSolutionShiny | c10009dd911f943432ab06dc305e469adb894aa3 | 1a8be48a0cb747ebc8d05cbd502b5d5b3c106962 | refs/heads/master | 2023-06-07T22:40:28.446335 | 2021-06-21T04:47:38 | 2021-06-21T04:47:38 | 305,106,080 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,158 | r | exploreTable1TabModuleUI.R |
exploreTable1TabModuleUI <- function(Id) {
ns <- NS(Id)
fluidPage(
fluidRow(
column(1, actionButton(ns("rowVar"), label="행 변수 선정")
),
column(1 #, actionButton(ns("colVar"), label="열 변수 선정")
),
column(1
),
column(1
... |
6b5adbded7ca7c3b555cd38b0a10394f5943ed8a | 6cd917b5e4e86779b7eed51c56a4e0918de95f58 | /R Programming/chaid 2.r | 9a15e23a7b992f2b4ac18c7eaf4128bba97212f3 | [] | no_license | prabanch/Analytics | 007c57c0c4e140fded113700d8506766910c04f9 | 829fd616b510893769df3b9308c07ee4c862dfa0 | refs/heads/master | 2020-05-27T01:37:18.984246 | 2017-11-19T19:10:31 | 2017-11-19T19:10:31 | 82,520,969 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,351 | r | chaid 2.r | #Read the data
hr <- read.csv('C:/Users/prabanch/Desktop/PGBA/dm/HR.csv')
#Find the structure
str(hr)
attach(hr)
View(hr)
#Summarise the data
summary(hr)
head(hr)
f=sapply(hr, is.factor)
which(f)
chisq.test(Age, Attrition)
chisq.test(hr)
hr$Age
View(hr)
i =0
while(i <= length(names(hr)))
{
ifelse((sapply(... |
517344a11f48efeb1f5aac290051c96cae46b396 | fa853333757506c8415434340c0547347086e02c | /man/func8.Rd | d44f9ed51afc5c61953e7a84378c352b9b70de30 | [] | no_license | Xinyu-Jiang/XinyuJiangTools | efec2a5ea270a22f48a6adb1b7a80e15708a539c | ccd57b2b229c2f17c6c0f0d94b290d0842c542cc | refs/heads/master | 2021-01-25T10:00:59.108334 | 2018-03-10T01:34:14 | 2018-03-10T01:34:14 | 123,335,145 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 282 | rd | func8.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/XinyuJiangToolsRfunctions.R
\name{func8}
\alias{func8}
\title{Quiz 2 - 1}
\usage{
func8(a, x)
}
\arguments{
\item{a}{matrix}
\item{x}{vector}
}
\value{
object
}
\description{
calculates $x^T A^{-1} x$
}
|
486b8b9e690f70cb39418244e14231cca3bd4810 | 539f352d0959cd134a2846728281d43c5266e121 | /src/example_analysis_config.R | 5bfec8184de7b004803878df8c68e9b71a6ef550 | [
"MIT"
] | permissive | chendaniely/multidisciplinary-diffusion-model-experiments | 56420f065de42f4fe080bc77988fcfe9592182c1 | 04edf28bb1bfadaff7baf82b8e3af02a3f34bf6d | refs/heads/master | 2016-09-06T10:03:27.798846 | 2016-02-28T19:01:26 | 2016-02-28T19:01:26 | 24,729,632 | 2 | 0 | null | 2015-11-24T17:04:32 | 2014-10-02T17:46:19 | R | UTF-8 | R | false | false | 1,798 | r | example_analysis_config.R | library(testthat)
###############################################################################
# USER CONFIGURATIONS
###############################################################################
config_name_batch_simulation_output_folder <-
'02-lens_batch_2014-12-23_03:41:22_sm_partial'
# 'bkup_02-lens_ba... |
4d409641aeca54915bf0504924fbc0bc57e0aece | d84a3f8b27940f2ac851d633b6ed2a47edb029b5 | /code_analysis/RNAseq20_twoaxes_effects_allgenes.R | ec72796eb83eabd05f5f426bc9ed79ed5bc6de82 | [] | no_license | jalapic/mouse_socialhierarchy_immune | 4621fc7ab34460f373033c0bf35fc8faee7fd9b7 | 38579026acf5262acb327dc3ff3bd08c1d7dc4bf | refs/heads/main | 2023-08-15T10:30:23.494090 | 2021-08-23T03:36:54 | 2021-08-23T03:36:54 | 398,898,170 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,634 | r | RNAseq20_twoaxes_effects_allgenes.R | my_tissue = "Liver"
my_tissue = "Spleen"
if(my_tissue == "Liver"){limma_list <- readRDS(glue("results_RNAseqRDS/limma_{my_tissue}_second.RDS"))
} else {limma_list <- readRDS(glue("results_RNAseqRDS/limma_{my_tissue}_second.RDS"))}
limma_list$status %>%
select(symbol, logFC, P.Value) %>%
rename(logFC_status = l... |
f27c1940690b0aa644892d6553aa54e70a19b6eb | 506865b72bc04160b1a965e7d880490800085f17 | /bauhaus/scripts/R/ccsMappingPlots.R | 06bf97af6d5b87d722abd40a58c58c2286934a82 | [] | no_license | nlhepler/bauhaus | aec1e306f92fe824ea60cd8f81195c4a8024400a | 7d2e8453a9028a84baf5c9b1f52fd5d63337f1d7 | refs/heads/master | 2020-04-03T13:02:39.138674 | 2016-08-20T18:45:47 | 2016-08-20T18:45:47 | 66,022,485 | 0 | 0 | null | 2016-08-20T18:45:48 | 2016-08-18T18:48:49 | Python | UTF-8 | R | false | false | 5,696 | r | ccsMappingPlots.R | library(pbbamr)
library(dplyr)
library(ggplot2)
library(xml2)
library(stringr)
library(feather)
toPhred <- function(acc, maximum=60) {
err = pmax(1-acc, 10^(-maximum/10))
-10*log10(err)
}
getConditionTable <- function(wfOutputRoot)
{
read.csv(file.path(wfOutputRoot, "condition-table.csv"))
}
## This is... |
85b60ff2e83482aada15493564b9fc381d4168a6 | 2c570af4ad5f6015c5d144a1e58dbc76492abea2 | /R/extract.R | 42d81690d68e3c7d34ddcfb056147a455deaeb93 | [] | no_license | jhuovari/statfitools | 1fdcf8a1e1b287302f060702903c4a97d7df6a6d | 4fd48239419c95a81cbe6368cd36425328ca7d7b | refs/heads/master | 2020-12-24T11:46:34.144165 | 2016-11-07T06:54:28 | 2016-11-07T06:54:28 | 73,012,116 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,444 | r | extract.R | #' Extract a code component
#'
#' This uses a regular expression to get a code from beging of a character
#' string. Useful to extract names from code-name variables.
#'
#'
#'
#' @param x A character vector (or a factor).
#' @param numbers_as_numeric A locigal. Whether to try to convert a code to
#' a numeric.
#' @re... |
27f2f54f4d6aaefbe6dbe8e9021ab3b0a64f1416 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /acc/R/readRaw.R | 93ca046a81b01e22a0c3a3e7d398710683f8f17b | [] | 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 | false | 5,681 | r | readRaw.R | #' @export
#' @importFrom utils head tail
#' @importFrom R.utils countLines
#' @importFrom iterators ireadLines nextElem
#' @importFrom Rcpp evalCpp
#' @useDynLib acc
readRaw <- function(filepath,type,resting=NULL){
filelength <- countLines(filepath)
fname <- filepath
it <- ireadLines(con=fna... |
2ad96dfd017a010605e933fe73f65f2aa5330636 | fd238af8ac37e4080e533e27c3702c8d99dd3d1b | /R/opt_paramsNLS.R | 9920218d4de84a828427978f06b8783bcc41d212 | [
"MIT"
] | permissive | SharpRT/NFRR_Philippines | 4f0fd1a48816c2dbe6622ca19d8c025842c00fe8 | 258958f19be3df9de9135ba4f0e464c2b21c5733 | refs/heads/main | 2023-04-09T11:58:10.359390 | 2021-12-16T15:19:36 | 2021-12-16T15:19:36 | 410,866,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,149 | r | opt_paramsNLS.R | #' @importFrom stats nls
NULL
#' Performs non-linear least squares parameter optimisation.
#'
#' Performs non-linear least squares parameter optimisation.
#' @param initParams initial guess parameter values for optimiser to improve upon
#' @param plotSum target experimental data
#' @param tColStr name of matrix's colu... |
8096333b23bfd8883bae701dae06c6b51751220c | 3ebbd6220109f68462519f33460cae1c1c5de352 | /man/etm_model.Rd | 7013c7cb3dcb637198ab3dadac77cedaeba5340f | [
"MIT"
] | permissive | adjidieng/ETM-R | 8a69fc79a32c825cf77b73a25e6f13dd6e644e72 | 65d089be64a882a80238174ebe21784f7bf1acd8 | refs/heads/main | 2023-05-14T14:16:56.862479 | 2021-06-07T17:27:59 | 2021-06-07T17:27:59 | 349,517,189 | 5 | 1 | MIT | 2021-06-07T17:28:00 | 2021-03-19T18:18:03 | Python | UTF-8 | R | false | true | 2,397 | rd | etm_model.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/etm_model.R
\name{etm_model}
\alias{etm_model}
\title{Create Word Embeddings and Run the Embedded Topic Model on a Corpus}
\usage{
etm_model(dataset_name, data_path, num_topics = 50, epochs = 1000,
save_path, use_embed, embed_path)
}
\argum... |
54bed228720f4c743e9af1a5629ce9be53003027 | 3f312cabe37e69f3a2a8c2c96b53e4c5b7700f82 | /ver_devel/bio3d/man/pca.Rd | 66103be71bc3f16b1c0b3c296ff25bd51c99598c | [] | no_license | Grantlab/bio3d | 41aa8252dd1c86d1ee0aec2b4a93929ba9fbc3bf | 9686c49cf36d6639b51708d18c378c8ed2ca3c3e | refs/heads/master | 2023-05-29T10:56:22.958679 | 2023-04-30T23:17:59 | 2023-04-30T23:17:59 | 31,440,847 | 16 | 8 | null | null | null | null | UTF-8 | R | false | false | 1,835 | rd | pca.Rd | \name{pca}
\alias{pca}
\title{ Principal Component Analysis }
\description{
Performs principal components analysis (PCA) on biomolecular structure data.
}
\usage{
pca(...)
}
\arguments{
\item{\dots}{ arguments passed to the methods \code{pca.xyz},
\code{pca.pdbs}, etc. Typically this includes either a numeri... |
f887920de26c306333b084b969d19cb899df4529 | 544509f8706dcea9e791d5828b89a3282cdcd673 | /DSSA-5101-DATA-EXPLORATION/Project5/walshCode.r | 76e47bbbe818071b361b6b13c6bd9125235708cf | [] | no_license | walshg3/DataScience | 0474b88195199b7275d82e589d8cd7018a1a2050 | 7198212ea7b7a19832062b370d25650201262626 | refs/heads/master | 2021-08-07T06:25:54.340050 | 2020-08-11T06:23:30 | 2020-08-11T06:23:30 | 208,895,573 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,282 | r | walshCode.r | library(tidyverse)
library(ggplot2)
### Function Calls ###
acprint_merge <- function(source_csv, demographic_csv) {
# Source
source_df <- read_csv(
source_csv,
col_types = cols(
Date = col_date("%m/%d/%Y") #convert col to date
)
)
# Demographic data sent back
demographic_df <- read_cs... |
df9e66a2ccc3a47c39d497ff790b73d8666be24e | a2718fd2bab9eb1b86b77b8c9b0d776973d11315 | /man/createSeasonalityCovariateSettings.Rd | cb2c33ace59f71114480e626015beec4f95ebd02 | [
"Apache-2.0"
] | permissive | OHDSI/SelfControlledCaseSeries | df61f7622fa19bb7b24a909a8da4f18c626cf33c | 28cd3a6cb6f67be5c6dfba0682b142ff3853e94c | refs/heads/main | 2023-08-31T02:00:48.909626 | 2023-04-13T11:42:06 | 2023-04-13T11:42:06 | 20,701,289 | 13 | 12 | null | 2023-09-07T04:50:44 | 2014-06-10T20:53:47 | R | UTF-8 | R | false | true | 1,498 | rd | createSeasonalityCovariateSettings.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CovariateSettings.R
\name{createSeasonalityCovariateSettings}
\alias{createSeasonalityCovariateSettings}
\title{Create seasonality settings}
\usage{
createSeasonalityCovariateSettings(
seasonKnots = 5,
allowRegularization = FALSE,
compu... |
31008648d3ae3628ed16b96b29d094484d46e8f2 | a1738539620913a8cf50d51517fcd9df5c79fbd6 | /R/DLMextra.R | 40da8cc652c2f353dee1c35a6f7b25f434b833fc | [] | no_license | Lijiuqi/DLMtool | aa5ec72fd83436ebd95ebb6e80f61053603c901c | bfafc37c100680b5757f03ae22c5d4062829258b | refs/heads/master | 2021-05-05T22:07:48.346019 | 2018-01-02T22:22:45 | 2018-01-02T22:22:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 959 | r | DLMextra.R |
#' Load more data from DLMextra package
#'
#' Downloads the DLMextra package from GitHub
#' @param silent Logical. Should messages to printed?
#' @export
#'
#' @importFrom devtools install_github
DLMextra <- function(silent=FALSE) {
if (!silent) message("\nDownloading 'DLMextra' from GitHub")
tt <- devtools::inst... |
2c98b1ca7fb376e605c00747b0d5c9ff5155d193 | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.iotanalytics/man/cancel_pipeline_reprocessing.Rd | bbdcb84b3ccf41f21dfedf517af120ffeb818e89 | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 739 | rd | cancel_pipeline_reprocessing.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.iotanalytics_operations.R
\name{cancel_pipeline_reprocessing}
\alias{cancel_pipeline_reprocessing}
\title{Cancels the reprocessing of data through the pipeline}
\usage{
cancel_pipeline_reprocessing(pipelineName, reprocessingId)
}
\argume... |
1fec1ad3b645f38739b979c13bac9a7d36af6986 | bbfc445d3b3c4ebe5ac9f2e3ad6a27bf0f049319 | /moeny.R | 27e68c7cfca3d338a1896c1ff1636d4b602fbda5 | [] | no_license | sckingsley/Money | 2887a082b1dcfd0ce6b6dea7c6aca8b71eb7417b | b7d038c6672596a3e65571f28cea48e95bdd47c3 | refs/heads/master | 2016-09-06T01:34:40.264081 | 2014-03-22T00:53:21 | 2014-03-22T00:53:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,339 | r | moeny.R | View(Money)
setwd("~/Money")
View(Money)
View(Money)
Money <- read.delim("~/Dropbox/Metrics Data Files/Money.txt")
View(Money)
View(Money)
FILE <- "~/Dropbox/Metrics Data Files/Money.txt"
setwd("~/Money")
FILE <- "~/Dropbox/Metrics Data Files/Money.txt"
setwd("~/Money")
Money <- read.delim("~/Money/Money.txt")
View(Mon... |
42fefce274ebd03a8356dd7df8ae5415ea3e0e55 | bf5c82a303681312929b6488dd9d3cf65886a831 | /R_script/README_r_script.R | 87cd039c68e714fccdf61a8730981414f1523e6c | [] | no_license | mrecos/Put_a_prior_on_it-Blog_post | 247670c75295274c92ac1aba67580daca088f5f6 | 99efa2429fb15ba288a90761781eef74c9a8896e | refs/heads/master | 2021-01-10T22:53:46.999967 | 2016-10-18T01:42:38 | 2016-10-18T01:42:38 | 70,365,270 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,498 | r | README_r_script.R | ## ----libraries, echo=TRUE, message=FALSE, warning=FALSE------------------
library("ggplot2")
library("grid")
library("dplyr")
library("tools")
library("tidyverse")
library("acs")
library("reshape2")
library("readr")
library("tidytext")
library("scales")
library("ggplot2")
library("ggrepel")
library("broom")
library("... |
6e41b07d46e4bc4369cbc1e65a81bcd9436d03ee | 6c03c37bacf9b3ffdda05d6a62f05a66fdfad1ec | /drake/functions/documents/my_generate.R | 52e3518678df7663266658b8fde95fcc5fd925a7 | [] | no_license | GiuseppeTT/me812 | dfe3c4d36265626bfad63840eb6b43e2c61d0223 | b850a6c8a740a1de30d1877d7e87963953195321 | refs/heads/main | 2023-02-14T20:20:00.691766 | 2021-01-06T19:46:31 | 2021-01-06T19:46:31 | 307,148,408 | 0 | 0 | null | 2021-01-06T19:46:32 | 2020-10-25T17:02:02 | R | UTF-8 | R | false | false | 347 | r | my_generate.R | my_generate_report <- function(
source_path,
output_path,
parameters,
...
) {
my_render_sweave(source_path, output_path, parameters, ...)
}
my_generate_presentation <- function(
source_path,
output_path,
parameters,
...
) {
my_render_sweave(source_path, output_path, parameters, ... |
c01c452fd198eba22d14568c1029da7454f59a9e | d9d00a88f700653b2c4af2b1845f82dd970d0915 | /Dx bias results.R | 361be4ec75552eaf737111af23c969e40b37a247 | [] | no_license | Mayeda-Research-Group/CancerAD-diagnosissims | f2c631255261634aa25cec11e09b9cde0e5ad475 | cd6c5e5b14d096f96cadd1f3d88c87b47af79dd8 | refs/heads/main | 2023-03-17T04:04:05.758211 | 2023-03-09T21:14:41 | 2023-03-09T21:14:41 | 326,770,344 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,109 | r | Dx bias results.R | #Examining simulation results
#---- Package Loading ----
if (!require("pacman")){
install.packages("pacman", repos = 'http://cran.us.r-project.org')
}
p_load("here", "tidyverse", "ggplot2", "magrittr", "foreign", "deSolve",
"numDeriv", "pracma", "dplyr", "RColorBrewer")
# Load sim results
load... |
6aae2616ea250366130a7f35f4399fbd08251675 | 375b5316501a3557b3984c83ca31efa9a3d34d96 | /ksm/day04/한국복지패널.R | 901187e9eacf0d2c9a831f88ee8a18735e6042ac | [] | no_license | ssemto/ksm0487 | 58a00bde1d592cfaf34c1cf99e62cc87cd96a019 | 9f51a33e4560680ba8a0eda20c53b3edbc0eebe5 | refs/heads/master | 2020-04-24T21:57:05.235222 | 2019-03-03T08:59:29 | 2019-03-03T08:59:29 | 172,295,250 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,855 | r | 한국복지패널.R | install.packages("foreign")
library(foreign)
library(dplyr)
library(ggplot2)
install.packages("readxl")
library(readxl)
raw_welfare <- read.spss(file = "C:/ksm/day04/Koweps_hpc10_2015_beta1.sav", to.data.frame = T)
welfare <- raw_welfare
View(welfare)
head(welfare)
summary(welfare)
welfare <- r... |
fd28e7ac717c42eff34927cf25da5b63a5789b71 | 29585dff702209dd446c0ab52ceea046c58e384e | /fitdistrplus/tests/ppcomp.R | d45e7be2fdb3b9463d509df084d1927991ca0176 | [] | 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 | 4,557 | r | ppcomp.R | library(fitdistrplus)
# ?cdfcomp
# (1) Plot various distributions fitted to serving size data
#
data(groundbeef)
serving <- groundbeef$serving
fitW <- fitdist(serving,"weibull")
fitln <- fitdist(serving,"lnorm")
fitg <- fitdist(serving,"gamma")
#sanity checks
try(ppcomp("list(fitW, fitln, fitg)"), silent=TRUE)
tr... |
986dccd29679c0b925341f5b3636fc7cde5fcd3f | bcd0e4df4a224b149d561f2470c2d463a93c1bc5 | /code_original/extraScript_lookAtOtherModels.R | 0494c6383324b19f4aba6a56ccb217a123589bb7 | [] | no_license | marissalee/E8-NichePlots | de570190a9da6adca53a8822c0c06dda01452de4 | 020128d7b085a8f0cbc4236c43db2fb3d84f193a | refs/heads/master | 2021-06-12T22:46:56.205264 | 2021-03-19T16:38:25 | 2021-03-19T16:38:25 | 22,583,913 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,597 | r | extraScript_lookAtOtherModels.R | #extraScript_lookAtOtherModels.R
#reshape data.choice
data.T<-subset(data.choice, depth == 'T')
data.T1<-data.choice[data.choice$variable %in% c(allVars,'mv_g.m2'),
c('plotid','plothalfid1','inv','year','variable','value')]
data.T1$variable<-factor(data.T1$variable, levels=c(allVars,'mv_g.m2'))
d... |
a787615eeb1d1b64c8a6eef9a88ab1a65a8dfcb3 | 44415fd86412a96b039d60a6ba83b8065bde6f1d | /man/lizards.Rd | 6bf5e2e1d67fd4feb65414990074e9ef0b184e9a | [] | no_license | cran/AICcmodavg | f9451566b4415350ff91d4e1fffc323ca6f6082e | 69bf7930f2228ed6fb06683cd766a16b0bf5cdce | refs/heads/master | 2023-04-08T21:23:38.333939 | 2023-03-20T15:20:02 | 2023-03-20T15:20:02 | 17,677,598 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,006 | rd | lizards.Rd | \name{lizards}
\Rdversion{1.1}
\alias{lizards}
\docType{data}
\title{
Habitat Preference of Lizards
}
\description{
This data set describes the habitat preference of two species of
lizards, \emph{Anolis grahami} and \emph{A}. \emph{opalinus}, on the
island of Jamaica and is originally from Schoener (1970). McCullagh an... |
e9c8219caa12891ef299befcd6ded09e53cf6e36 | 5bde8725af216500ff7aa08c45fda093f6ab1706 | /man/calc_ice_season.Rd | 9ae97dbd4812f77bfd13f9049639af805b0a04f4 | [] | no_license | AustralianAntarcticDivision/aceecostats | 37086bd2178a5abd68a9b71f9fe0b04dd68b6ecb | b789f6453a676a19aeb9d9f57b210a663378f2cb | refs/heads/master | 2021-05-01T01:19:30.966700 | 2019-03-05T23:01:21 | 2019-03-05T23:01:21 | 72,392,976 | 2 | 2 | null | 2016-12-19T01:20:17 | 2016-10-31T02:52:30 | HTML | UTF-8 | R | false | true | 284 | rd | calc_ice_season.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calc-ice-season.R
\name{calc_ice_season}
\alias{calc_ice_season}
\title{actually calculate the ice season}
\usage{
calc_ice_season(yfile, threshval = 15)
}
\description{
actually calculate the ice season
}
|
27e22de5c367a9a10abc2ca1c08cb6667fbdd006 | 3e74b2d423d7b4d472ffce4ead1605621fb2d401 | /variancePartition/R/plotStratifyBy.R | 13666655f53f019e732d075f282e77b28025c711 | [] | no_license | jamesjcai/My_Code_Collection | 954988ee24c7bd34139d35c880a2093b01cef8d1 | 99905cc5d063918cbe6c4126b5d7708a4ddffc90 | refs/heads/master | 2023-07-06T07:43:00.956813 | 2023-07-03T22:17:32 | 2023-07-03T22:17:32 | 79,670,576 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 10,113 | r | plotStratifyBy.R |
#' plotStratify
#'
#' Plot gene expression stratified by another variable
#'
#' @param formula specify variables shown in the x- and y-axes. Y-axis should be continuous variable, x-axis should be discrete.
#' @param data data.frame storing continuous and discrete variables specified in formula
#' @param xlab label... |
fd5776c2ed2d353fa70d9239fe93b69400c696cd | feabcc19c0457cdd946433dd0869d0b4b9885384 | /R/subgraph.R | f327c23537d49148162bed8f383c267feed194ba | [] | no_license | cran/lava | e9dd8f8dcdceb987b8a27e62a2b1663b3b060891 | b731197dbd9edb76987ccacf94dd95c6a54e4504 | refs/heads/master | 2023-03-05T00:01:23.232939 | 2023-02-27T07:12:30 | 2023-02-27T07:12:30 | 17,697,007 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 613 | r | subgraph.R | subgraph <- function(g,from,to,Tree=new("graphNEL",node=c(to,from),edgemode="directed"),...) {
adjnodes <- graph::adj(g,from)[[1]]
if (length(adjnodes)==0)
return(Tree)
for (v in adjnodes) {
if (v==to) {
Tree <- graph::addEdge(from, v, Tree)
}
re1 <- graph::acc(g,... |
1b6cfb1c850ce8c18427c163fa757918b9272782 | f07436dc70374dc828d94bcbf1fe29dd7fc2926c | /R/tadpole.R | 2b2928a1e2a9334558058e802ccd8d5562a340b1 | [] | no_license | franzbischoff/lab_hida_alzheimer | e8cd1af84804293110a2e8be638b693d4a90ddab | e08ee35f58d80ebdcf3b6d2defadf02ad86768de | refs/heads/master | 2022-12-17T02:16:23.058424 | 2020-09-25T01:04:39 | 2020-09-25T01:04:39 | 260,707,432 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,100 | r | tadpole.R | options(digits = 3)
library(knitr)
library(ADNIMERGE)
library(ggplot2)
library(dplyr)
library(caret)
library(Hmisc)
library(gridExtra)
library(RColorBrewer)
source("https://adni.bitbucket.io/myfunctions.R")
theme_set(theme_bw())
### TADPOLE ----
###
### Each row represents data for one particular visit of a subject, a... |
07152f2b5595834550e833ac8024831c48074dae | 6ac55e9eb21c4a8df6f7b1b30d0aec6bc8bfcfdb | /33_GenevsGenome.R | 09adcafb5ba24d72c1ba8236838f0ade6c4110fe | [] | no_license | raramayo/R_vikas0633 | c7b9efecaa21699510e9422cd8049d6e3caa21bc | 44b04ab0bbdfb4fd3e660d1a0c01b618ed32f100 | refs/heads/master | 2022-05-03T07:38:29.384021 | 2014-12-09T13:12:33 | 2014-12-09T13:12:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 646 | r | 33_GenevsGenome.R |
setwd('~/Desktop/100_Thesis/100_datafile/')
infile='eukaryotes.gene.txt'
d <- read.table(infile, header=TRUE, sep='\t', fill=TRUE)
head(d)
pdf(paste0(infile,'.pdf'),height=5,width=7.5)
library('ggplot2')
d <- d[complete.cases(d),]
d$Group <- factor(d$Group, levels = c("Animals","Plants","Fungi","Protists","Other... |
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