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e209f55a8af9fa5adfea58ff4ffd90056864da73 | 73763542bf5879333697d2b0e3831db9509ba2ee | /Self_Assigment_R/nomor2.R | ad5f766a7c80697cbd3d541c9850949be13a87d2 | [] | no_license | Aminurachma/my-first-repository | 4ababe7fcea99062de099d8c041deb4c9e2fb7e1 | 3e0e037b72f923abc24766a6911168f54bc77ea8 | refs/heads/master | 2021-05-16T18:42:22.238113 | 2020-06-21T06:23:22 | 2020-06-21T06:23:22 | 250,424,058 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 195 | r | nomor2.R | library(dplyr)
dataset <- read.csv('D:/TugasDM/AssessmentR_Dataset_superstore_simple.csv')
dataset_2 <- dataset %>% select(category, sub_category,profit) %>% filter(category == 'Office Supplies') |
49414a0bda7aee20d1f867d981216d7bf84db558 | 42276c737c5874699b685bd882aa0ddc4073275f | /plot3.R | a290870123b8a8fff6c8234427a3d152d0105cc7 | [] | no_license | lszperling/ExData_Plotting1 | da82093d110fef43f169ddd8b92200ae13240748 | ebeb7194eb951d7ee30401faaa007e39b392b7af | refs/heads/master | 2020-12-03T07:57:53.446660 | 2014-12-07T19:49:07 | 2014-12-07T19:49:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 715 | r | plot3.R | source("load_file.R")
sub <- load_data()
#opens the png file were the result will be writen
png(filename = "plot3.png",width = 480, height = 480)
#Create the base plot with one line and the add the other 2 in the same scale
plot(sub$coso, sub$Sub_metering_1, type = "l", main="", ylab=... |
7f5f2d473cd457496595b9adfb5412417dab98a7 | 142dbad11a73fd0a8c62ce3cd0ea1901b46d7b95 | /script/Fekete_Gergo/understanding-of-FDR-2.R | a270e12848db935d17bcffb8fc3cea382d9ff2e1 | [] | no_license | feketegergo/R-class-2020 | fee771b589ba1757ee87e885e97eb08a1b456fb5 | f0e72d1b852d34c1432ae21a74d06c5213930e02 | refs/heads/master | 2022-12-07T04:46:15.407622 | 2020-09-02T10:26:20 | 2020-09-02T10:26:20 | 284,748,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,450 | r | understanding-of-FDR-2.R |
library("tidyverse")
rm(list=ls())
mean_NT<-1.2
sigma_NT<-0.15
mean_AMP<-0.25
sigma_AMP<-0.8
N_noEffect<-4000
N_effect<-400
od_NT<-rnorm(15,mean = mean_NT, sd = sigma_NT)
tbl1<-tibble( experiment_id=character(),p=numeric(), type=character())
for( i in 1:N_noEffect)
{
#od_NT<-rnorm(4,mean = mean_NT, sd = sigma_... |
4903e36ab45982c52d43c9cf6051ab835debd94a | 89491fef8c724a2500434f220780f3300017ff38 | /demo/demoIRIS.R | 7aad1d9839ea811b4baab87ad18299b29962301b | [] | no_license | cran/FKBL | ccafa5c7acbc14abad415b641d7d3e29004a658b | ec6c9300a8c01950db07ff57a93940b98936ed48 | refs/heads/master | 2016-09-05T20:16:08.923166 | 2007-03-31T00:00:00 | 2007-03-31T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 519 | r | demoIRIS.R | data(Pl)
data(iris.train)
data(iris.test)
trainl=iris.train
testl=iris.test
kB<-list()
classTST<-list()
classTRN<-list()
e<-1
errorTST<-data.frame(confS0=e,confS1=e,confS2=e,confS3=e,confS4=e,anali=e,rew=e)
errorTRN<-data.frame(confS0=e,confS1=e,confS2=e,confS3=e,confS4=e,anali=e,rew=e)
kB<-expGetKb(train=tra... |
fcf1e69307935c8be0825060bbedc38bcc6bf522 | 7ef0e1b1f6d798075a83873b96dd441db3d7ec0e | /man/helloworld.Rd | 6c3780dccaa1f174d192e4a9f5000fc13db56720 | [
"MIT"
] | permissive | nischalshrestha/hellopkgdown | 2f820f56455304f327cf3b1d3e77724bcdba1239 | b2b03c7fa47e1f04e6c9e5548396732a3307927b | refs/heads/main | 2023-03-07T11:41:52.892115 | 2021-02-23T19:21:36 | 2021-02-23T19:21:36 | 341,633,782 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 303 | rd | helloworld.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helloworld.R
\name{helloworld}
\alias{helloworld}
\title{Prints Hello, World! to the console.}
\usage{
helloworld()
}
\value{
No value returned
}
\description{
Prints Hello, World! to the console.
}
\examples{
helloworld()
}
|
0e3c1173260c2ab3120838347eb946d091ae30d9 | d6cce6fbbb675f21d6010bcc710bd551497e21cc | /man/generator_PLN.Rd | 2a32ae87724d39ee7c33709fb14dc81608a4d5a6 | [] | no_license | aulenbac/EMtree | b6915965aa71572f09bf3067978f6dc90c35e8d2 | cfd17f0a808132e89a3729519241bc8a41a03fd3 | refs/heads/master | 2022-04-09T20:40:24.803364 | 2020-04-01T08:33:16 | 2020-04-01T08:33:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 723 | rd | generator_PLN.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gener_data.R
\name{generator_PLN}
\alias{generator_PLN}
\title{Simulate count data under the Poisson log-Normal model}
\usage{
generator_PLN(Sigma, covariates = NULL, n = 50)
}
\arguments{
\item{Sigma}{Covariance matrix of the normal hidden l... |
d7c3b47ede84aedaaedd84a30b73ef1ed31158b4 | f00d232f784faf22f8cb321d9f84b2e4344aa855 | /R/fitness.r | 3fc5315c25b2cdf9de85dc909e4e022c26a03b74 | [] | no_license | vsbuffalo/tempautocov | 44474dcf3917e9942d6786efab57f9269c82cb7e | 090a8d89d8727b290440ba54f7715cf8dc707782 | refs/heads/master | 2022-03-04T08:58:53.905486 | 2019-09-26T01:07:38 | 2019-09-26T01:07:38 | 170,267,653 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,381 | r | fitness.r | ## fitness.r -- check math of exponential and other fitness models
trait2offspring <- function(N=1000, sigma2=1) {
# sigma2 is trait variation, for the trait that goes
# into fitness function
z <- rnorm(N, 0, sqrt(sigma2))
wz <- exp(z)
wbar <- mean(wz)
p <- wz/(N*wbar)
stopifnot(abs(sum(p) - 1) < 1e-5)
... |
af53df27faa23c4f8229518a5340407b6972b77f | 6fb233afe964a99ff4397b4be1fe1ed73949fac1 | /man/TU_SVM.Rd | cb4d10fad3adc105e0d409e1c2b5422f4ec7087e | [
"MIT"
] | permissive | OSU-BMBL/rSeqTU | b67f5327687ddc154982c7891c82885a2e19cf81 | 676d6eadca858ff4c705dfa21be0c59b7cc75e4a | refs/heads/master | 2021-07-04T21:41:43.617523 | 2020-10-14T00:46:47 | 2020-10-14T00:46:47 | 188,394,107 | 0 | 0 | MIT | 2019-05-24T09:31:50 | 2019-05-24T09:31:50 | null | UTF-8 | R | false | true | 1,007 | rd | TU_SVM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SVM.R
\name{TU_SVM}
\alias{TU_SVM}
\title{Using Support Vector Machine to train and generate TU prediction results}
\usage{
TU_SVM(positive_training, negative_training, positive_strand_testing,
negative_strand_testing, file_RNAseqSignals, f... |
8c66c6b2f9a9edf3505594009a7bbdd66d9b31da | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/treeducken/man/is_extinct.Rd | 38f7638ce1523fde1461adff22feafa71d3a0107 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,247 | rd | is_extinct.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.R
\name{is_extinct}
\alias{is_extinct}
\title{Identify extinct tips from tree}
\usage{
is_extinct(phy, tol = NULL)
}
\arguments{
\item{phy}{a 'phylo' class object}
\item{tol}{tolerance in decimal values for branch length... |
87333654fccfedb5982947e12f613baf9d3630ec | 5c42184a79d320791411c0adcb22de7ba9ef2262 | /plot1.R | 45b122ce5642ccbff16c6add14ee4a855b8abebb | [] | no_license | earthspatial/ExData_Plotting1 | 0dc83726eba4308f89a2d96c9d8ce4ed0e117736 | 171b462dbcb175bb6346225fd3d618c78375275c | refs/heads/master | 2021-01-18T08:10:28.626406 | 2014-05-09T18:36:09 | 2014-05-09T18:36:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 683 | r | plot1.R | #load onlyt the days in question
data <- read.table("c:/temp/household_power_consumption.txt",skip = 66637, nrow = 2880, sep = ";")
#load the names of the columns
names <- readLines("c:/temp/household_power_consumption.txt",1)
#split the column names vector apart
theColumnNames <- strsplit(names, ';')[1]
#add the n... |
e2cd765b9c68d2e4a79b3f80c7f87b61d58ec7e3 | 0fbc58702c39addfa7949391d92533922dcf9d49 | /man/music.Rd | 3f382de9ad3de1b6596a20c035e9c6baa4e22641 | [] | no_license | yihui/MSG | d3d353514464f962a0d987efd8cf32ed50ac901a | 8693859ef41139a43e32aeec33ab2af700037f82 | refs/heads/master | 2021-11-29T08:12:02.820072 | 2021-08-15T17:14:36 | 2021-08-15T17:14:36 | 1,333,662 | 30 | 12 | null | 2021-08-15T17:14:37 | 2011-02-06T05:42:53 | R | UTF-8 | R | false | true | 391 | rd | music.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MSG-package.R
\docType{data}
\name{music}
\alias{music}
\title{Attributes of some music clips}
\description{
Attributes of some music clips
}
\examples{
data(music)
}
\references{
Cook D, Swayne DF (2007). Interactive and Dynamic Graphics for... |
3656d88cb291a1cdac48ef5cb3ca5a3a09895c0d | 100ef7748001814334951538fa23869ee00f58f7 | /geog418-518-a4-master/Thiessen Polygons.R | 23dac4bc0b95783177c07d21042190d79290f581 | [] | no_license | dahewett/geostatistics | c51ca284288cc0a086886879629a852f7e083cab | 3dd1c66391503f88c60160f1f8dfa270fdc2addd | refs/heads/master | 2022-10-19T14:32:47.146003 | 2020-06-03T03:37:34 | 2020-06-03T03:37:34 | 152,347,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,136 | r | Thiessen Polygons.R | #################################################
##Spatial Interpolation with Thiessen Polygons
# Create a tessellated surface
th <- as(dirichlet(as.ppp(ozone.mean.spdf)), "SpatialPolygons")
# The dirichlet function does not carry over projection information
# requiring that this information be added manually
proj... |
c4075a5c8f6cfd3236f4726683cd21b04a072109 | 473ef88aafd5cc4dd8eee11e3e6881402824e8e3 | /analyze.R | 00f276d09c524cb1257ad7e27dec555dcc74e2ae | [] | no_license | rsangole/CVG_Parking | 1ddac0f409ce628a3a9fbf06deb41546024eb13d | 614d49f79a6cbd36e6b4cbecad95dba6ef3836d6 | refs/heads/master | 2021-01-10T04:26:01.721484 | 2016-03-14T16:26:06 | 2016-03-14T16:26:06 | 53,560,645 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,333 | r | analyze.R | setwd("~/Documents/Data Science/CVG_Parking")
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)
data.cvg <- tbl_df(read.csv('o.txt',sep = ',',header = F))
names(data.cvg) <- c('Date','TerminalGarageUtilization','ValuParkUtilization')
data.cvg$Date <- as.POSIXct(data.cvg$Date)
data.cvg <- data.cvg %>%
... |
3a519bd6968688989469c935943dfa404397c6fc | 34a1872c598758ad8b6c0a82132f52b2f124489e | /TestScripts/proportionCheck.R | 7b5c992020514d0fb8a3ce5516283c9b09a0fc41 | [] | no_license | DataAnalyticsinStudentHands/SyntheticDataSet | 2f73599723d53c5ca0e04535de55bf05c44aaaac | 82877e75367dbd9ff68976c61b9e8f237224cf2d | refs/heads/master | 2023-08-22T20:00:35.665190 | 2023-08-16T17:14:39 | 2023-08-16T17:14:39 | 77,935,514 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 21,280 | r | proportionCheck.R | # This function compares the proportions in the model to the proportions in the Census Data. Ideally they should be within 10% of each other
# The inputs are the model (sam), the Census Data (census), and the tract number (x)
prop_Check <- function(sam, census, x){
# These are temporary data frames. part is for the... |
86f360eb9de38c0d39f2c63a4dbd12fb42dfbe0e | 8b65e595ae3c4331c81b2e0dc42546d006ffbace | /R/cv.R | 946550b8622523715dcb65eb8d7121622e630d1f | [] | no_license | rpruim/statisticalModeling | 91b16556b92d5f12a6da7c7abbc9fac640f3cd2d | 1d57112ca169d7960dd41c055ceb5c84c106b961 | refs/heads/master | 2021-01-20T14:25:21.586119 | 2017-02-24T22:45:45 | 2017-02-24T22:45:45 | 82,751,296 | 2 | 1 | null | 2017-02-22T02:29:08 | 2017-02-22T02:29:08 | null | UTF-8 | R | false | false | 5,003 | r | cv.R | #' Compare models with k-fold cross-validation
#'
#' @param ... one or more models on which to perform the cross-validation
#' @param k the k in k-fold. cross-validation will use k-1/k of the data for training.
#' @param ntrials how many random partitions to make. Each partition will be one case in the
#' output of th... |
6ee1dd2a44006a909cbfd0febc871b3622aaedb4 | ea4a5b8e4d9be6141a055ed0f489062283767fc1 | /Week7_Data Transformation/Code/DataTransformation.R | 33d44c8bcf64be94f22698010e01acd259d5a060 | [] | no_license | anjalirawat82/EDAAssignment_CodePortfolio_AnjaliRawat | a88572f31f3eb7e7c79c016f5cc2edc08837b58d | 7b01e99790ba028410cf556f8fef5eca055de0e9 | refs/heads/master | 2020-04-23T09:31:38.249844 | 2019-02-20T04:30:55 | 2019-02-20T04:30:55 | 171,071,324 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,760 | r | DataTransformation.R |
## Data Manipulation
## filter() - Extract/Filter rows that meets logical criteria.
## Logical and Boolean Operators to use with filter() - < , > , <=, >=, !=, ==, isna(), !isna(), !, |, & , % in %
## Exercises - 5.2.4
library(nycflights13)
library(tidyverse)
library(dplyr)
## Find all Flights that had an a... |
bf62da0c4ed5d66c15d43b77462556e781b3440f | 4d975be8f019859b1673955d59c285b9208f51f2 | /scripts/evaluacion.R | 1bb657b8d34418ac2c9bb6d67c72d529457b8cc3 | [] | no_license | FranciscoJLopez/aguacate_af | efd53e0e5419dc3b421a4211737ede558fa722fa | 36c78c25640eb6dc2642594e0846fb7c3f03b396 | refs/heads/master | 2020-06-27T04:00:21.956721 | 2019-07-31T12:07:53 | 2019-07-31T12:07:53 | 199,838,954 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 59,248 | r | evaluacion.R | library(tidyverse)
library(openxlsx)
library(extrafont)
# font_import() # use this function first if R was updated
loadfonts(device = "win")
prmtrs <- edit(prmtrs)
#### general prices and yields updater ####
yld_nal_est <- agt_nal %>%
filter(anio %in% c(2014:2018), idmodalidad != 2) %>%
summarise(yld_nal = sum(v... |
18fc641c18df03f921db1ef7478efab52ef047e9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RLumModel/vignettes/RLumModel_-_Getting_started_with_RLumModel.R | dbb6b438d8a76bf0c14863aded6d9a61c7b3b93b | [] | 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 | 10,326 | r | RLumModel_-_Getting_started_with_RLumModel.R | ## ----global_options, include=FALSE---------------------------------------
knitr::opts_chunk$set(fig.pos = 'H', fig.align = 'center')
## ---- echo=FALSE, message = FALSE----------------------------------------
library(RLumModel)
library(knitr)
library(kableExtra)
## ---- eval = FALSE---------------------------------... |
d80ec93d6f518f5a1afd47e7042337070ff12025 | 6c3ab47960ff279b77c1af10f36f5297bfdd10ef | /plot4.R | 76f849d0fc2c3a282c43f461bd7b0e8d37201873 | [] | no_license | MaxShang/ExData_Plotting1 | f865997f4c2c7358b932cce2d0298aa03195945d | dbe1eed78f340f5bcab951c838cc4bbab1082aa5 | refs/heads/master | 2020-04-07T06:23:40.482598 | 2016-06-07T00:46:04 | 2016-06-07T00:46:04 | 60,554,028 | 0 | 0 | null | 2016-06-06T19:13:19 | 2016-06-06T19:13:19 | null | UTF-8 | R | false | false | 1,097 | r | plot4.R | library(data.table)
dt2<-fread(input ="household_power_consumption.txt",sep = ";",header = TRUE,na.strings = c("?"))
dt<-dt2[dt2$Date=="1/2/2007" | dt2$Date=="2/2/2007",]
t<-paste(dt$Date,dt$Time)
t<-strptime(t,"%d/%m/%Y %H:%M:%S")
par(mfrow=c(2,2))
with(dt,
plot(t,Global_active_power,
type="l",
... |
6ff84e8bd122e6f8dca76777ce512dd44f7f131e | b8e668c36ff178a28244ff752913ba0af209dd19 | /dropNonLinearMarkers.R | 785de1f48a0659e008ddc9f54a720ded221ebf2d | [] | no_license | jtlovell/eQTL_functions | ab0a4cc0de2dcc134316c0032676888cfcb4d6a4 | 0d583f66534215f8307b191cde42fa38117d5d7a | refs/heads/master | 2016-09-05T18:26:29.857769 | 2015-08-05T21:46:41 | 2015-08-05T21:46:41 | 37,740,661 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | r | dropNonLinearMarkers.R | dropNonLinearMarkers<-function(markers, bp, cm, plotit=T,...){
dat<-data.frame(markers, bp, cm, stringsAsFactors=F)
dat<-dat[order(dat$bp),]
cm=dat$cm
bp=dat$bp
if(plotit){
plot(bp,cm, type="n",...)
points(bp[diff(bp)/diff(cm)>=0],cm[diff(bp)/diff(cm)>=0])
points(bp[diff(bp)/diff(cm)<0],cm[diff(bp... |
b462356ec26b310faa6aaf7839c48409f314f651 | 43a225d1d8f202bbaec4d9fab24f48228fdbf717 | /web_scraping.R | 1ddb71bcde277f3060c49be23b5b23fd393ac10e | [] | no_license | karafede/Text_mining | d6a066891b0337bd94ef1298a57f55ed9ec62766 | dbb91921284940cbc55268b9467c3bd76589fb13 | refs/heads/master | 2021-01-18T19:51:10.527506 | 2016-09-26T21:19:48 | 2016-09-26T21:19:48 | 69,293,713 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 601 | r | web_scraping.R |
library(rvest)
# install selectorGadget
# http://selectorgadget.com/
lego_movie <- read_html("http://www.imdb.com/title/tt1490017/")
# parsing the file with html():
# use html_node() to find the first node that matches that selector,
# extract its contents with html_text(), and convert it to numeric with a... |
b503645bc38f34af1106a74714704dcab353ac99 | 388219bd0cd85580cba19b7e4d47105c65587685 | /old/old20161024/modelLabel.R | e0ae95790a040a553541781b265b7502b3e6874f | [] | no_license | yiyusheng/SMART | 758a32c32688ac369214d7d8696e9dfa80c03a8b | af59b2bfa50333a6f7b4dfe6f447da023890ff29 | refs/heads/master | 2020-04-12T07:34:21.124629 | 2018-05-18T09:06:53 | 2018-05-18T09:06:53 | 57,004,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 385 | r | modelLabel.R | # Label model for lead time prediction
# Only process 4 type of disk model
# Date: 2016-06-21
# Author: Pezy
modelLabel <- function(dm){
r <- rep('0',length(dm))
r[dm == 'ST3750330NS' | dm == 'GB0750EAFJK'] <- '750G2'
r[dm == 'ST31000524NS' | dm == 'MB1000EAMZE'] <- '1000G2'
r[dm == 'ST1000NM0011' | dm == 'MB10... |
1356873af6291e1c909dfd1c9961ca2dc6b36201 | def57d9f3803901ef424e7842fd169e2656f0a13 | /trees for Titanic.R | 7b30c48ae16419be1b3b7ea5b646b83e16dd8857 | [] | no_license | janiceli123098/DataAnalytics2020_Jiarun_Li | 7c949bbc70ff9ef8f2b234ef13a32112d9d98b4b | 69725614a10626ec70f19526197aadb9e7531a48 | refs/heads/master | 2023-01-15T17:46:31.324285 | 2020-12-02T02:12:40 | 2020-12-02T02:12:40 | 292,886,337 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 399 | r | trees for Titanic.R | library(ggplot2)
library(dplyr)
library(GGally)
library(rpart)
library(rpart.plot)
library(randomForest)
library(party)
data("Titanic")
dt <- rpart(Survived~.,
data=Titanic,
method="class")
plot(dt)
rpart.plot(dt, type=0, extra=2, cex=1.5)
rf <- randomForest(Survived~., data=Titanic, import... |
994cd09dabfe41fa69261f376f20fde630921338 | ea8bbb6225bc5309f77839872356168dfeb1fb87 | /examples/oper.R | 61f865cb163c123884b9953f412e38098996628d | [] | no_license | Lasica/mowpartboost | 8f45674dbdbb05a28680ea5b16fdd3f3e793cea8 | 188cd6edb8ded42182febe96aa84011cd0cf7fc2 | refs/heads/master | 2020-05-07T13:07:32.013894 | 2019-06-11T01:13:10 | 2019-06-11T01:13:10 | 180,535,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 716 | r | oper.R | 2*2
sapply(1:10, function(a) sapply(1:10, function(b) a*b))
`*` <- function(a, b) .Primitive("*")(a+1, (b %/% 2))+2
2*2
sapply(1:10, function(a) sapply(1:10, function(b) a*b))
####
cpx <- function(re, im) `class<-`(list(re=re, im=im), "cpx")
c1 <- cpx(2, -1)
c2 <- cpx(3, 2)
re <- function(cp) UseMethod("re")
im... |
0942fcbea59943563bcb43a581935461d99f76ab | f5bf1754fcafb536ddd97fe457551700b3961ecf | /R/swiRcharts.R | 45eaea3df8d28741546a623f457e1dfe0d7ca562 | [] | no_license | d-qn/swiRcharts | 4989411e6f132639bd9947fb5b549614a85480c3 | 940c1b7d2b379956dc1287f73affa0d1f47ef587 | refs/heads/master | 2020-05-22T06:44:48.340676 | 2018-02-28T07:24:37 | 2018-02-28T07:24:37 | 45,199,662 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,565 | r | swiRcharts.R | ##' Helper functions for rCharts
##'
##' Misc helper functions to create interactive charts with rCharts
##'
##' @rdname swi_rcharts
##' @param x,y,z a numeric of same length
##' @param color,name,series a character of same length
##' @import rCharts
##' @export
##' @examples
##'
##' \dontrun{
##' #Example for hSeries ... |
81548cc97f3e5b7074d355611aaf674d5d5008c7 | 1a79c363b1d4b5f92eee4048b24a879ecfa0aa05 | /R/trio.R | 0cf9597b22313f5a9ee84b80184ff624d89f55ca | [] | no_license | cran/MetamapsDB | 21f4f10768f94c3b052102e348f5de18e8840715 | 9b7f5d461b6bcbdddee7f7633125d4822192699d | refs/heads/master | 2022-11-21T05:39:28.935110 | 2017-12-06T08:51:00 | 2017-12-06T08:51:00 | 113,299,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,940 | r | trio.R | #' Find all trios surrounding the KO of interest
#'
#' Searches graphDB for all cases of trios surrounding the KO of interest
#'
#' @param KOI the ko id (for remote)
#' @param koi the ko id (for local)
#' @param toUnique if to return only unique (only applicable for remote)
#' @param withDetails with details (only app... |
b487d80a277e3d1548552ce2172c75da442a20d5 | f6401021b5655bf14acba25664a389e0a2d024b5 | /scripts/DataMunging/02_CalculatingProvAnnualClimaticVariables.R | 7357f9ca63080c0ff1ca830505227031adc82365 | [] | no_license | JulietteArchambeau/HeightPinpinClonapin | 56726076b40f274d1bfc4b4534382907cc41027b | 8395f9b320665c8610a80ae0a4e396bbcb1e1616 | refs/heads/master | 2023-04-19T00:41:01.330853 | 2023-02-03T15:22:55 | 2023-02-03T15:22:55 | 254,827,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,564 | r | 02_CalculatingProvAnnualClimaticVariables.R | #################################################################################################"
#################### February 2019 ##########################"
#################### Computing the mean of climatic variables ##########################"
################### ... |
79971eea3d216d5bd3f772ff3fd321a5e95fa394 | 1f2b1298f5731525bbe48f614adf3b5e8edfa814 | /R/get2by2table.R | d3ccfcd56002ff6ab2a42358c4f9c7cf20c0a4a4 | [
"MIT"
] | permissive | mortenarendt/MBtransfeR | cc30ab0ab3c492c07917755487e7271c64869ee4 | 52b4f2d84abc76fd1efe6bce7624e65925a9c294 | refs/heads/master | 2020-12-23T22:42:51.588719 | 2020-08-18T15:37:20 | 2020-08-18T15:37:20 | 237,222,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 544 | r | get2by2table.R | #' Calculates all 2x2 presens absense tables between two similar sized, and matched matrices
#'@param o1 matrix of counts (n x p) - n is samples and p is variables
#'@param o2 matrix of counts (n x p)
#'@import dplyr
#'@return a data.frame (p x 4) with the 2x2 table Ns (stats)
#'@export
get2by2table <- function(o1,o2){... |
51da1e27e99948310c82dd94c576e8b643ce27f9 | 73744a740941b13641c0175c8e583b20cfd023a1 | /analysis/books/01b_tidy_LCL.R | 31fd27e712db032d2922d57050286fbacbd3b879 | [] | no_license | mllewis/WCBC_GENDER | 8afe092a60852283fd2aa7aea52b613f7b909203 | ed2d96361f7ad09ba70b564281a733da187573ca | refs/heads/master | 2021-12-25T22:41:21.914309 | 2021-12-22T19:08:36 | 2021-12-22T19:08:36 | 248,584,454 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,139 | r | 01b_tidy_LCL.R | # read in raw corpus and save tidied version
library(tidyverse)
library(tidytext)
library(textclean) # for contraction cleaning
library(here)
LCNL_INFILE <- here("data/raw/corpora/cdtc_lcnl_corpus_by_book/")
BOOKID_KEY <- here("data/raw/corpora/key_book_id.csv")
CONTRACTION_LIST <- here("data/processed/words/contracti... |
a50345a6354392aa4f27ede26121593bf8bc1aa0 | 77794ca29c315b0cdb36d7e12fd9c297d93221a4 | /Model_when_optimizing.R | 7519ec68e906ff2fec7d1b6e878a428238c2aa9c | [] | no_license | carolksun/NIH18 | 8b9debe9802ac97fdfb0faeb0d1aa61ea183d509 | 9e4360caa7c6f708b802688d4a8c300c90e0f2f8 | refs/heads/master | 2020-04-04T17:49:40.242545 | 2018-11-05T00:08:53 | 2018-11-05T00:08:53 | 156,137,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,709 | r | Model_when_optimizing.R | library("ranger")
library("dplyr")
Scores <- read.table("Scores.txt", header = TRUE)
Master <- read.table("Master.txt", header = TRUE)
ID <- read.csv("List of Patients.csv", header = TRUE)
ID <- as.character(ID$x)
Master2 <- merge(Master, Scores, by="patient")
rfdata <- Master2 %>% select(msdss_last, contains("SL"))... |
104d0583bef56562823f7d7d49506c7fe421538c | 658f9fc2e63cad23e6262fc7caa1abf01ecd70cb | /Mobily/Redemption-Weekly-Report/Analyses/detailed_Mobily_Redemptions.R | e69c125c95b0654b3ce1e133c5398d5cd7b6dc6e | [] | no_license | aliarsalankazmi/Aimia-Projects | 1353758f829a14db97a40097f94ed283fe21dff9 | e54345c12c6057ecef222b87885ec6c932ca8923 | refs/heads/master | 2016-08-12T06:18:07.109940 | 2016-01-03T06:31:24 | 2016-01-03T06:31:24 | 46,541,306 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,550 | r | detailed_Mobily_Redemptions.R | df5 <- df1 %>%
filter(rdm_type == 'Mobily') %>%
select(redemption_we, msisdn, item_sub_category, pay_type_code, points_denomination) %>%
group_by(redemption_we, pay_type_code, item_sub_category, msisdn) %>%
summarise(total_redemptions_perCustomer = n(),
points_redeemed_perCustomer = sum(points_denom... |
8046e141c872e058226403e80447d828d7d3d90a | f0ef630f83b1e312d4b9ef82a09e813c1b8ed7bd | /cachematrix.R | e9907a01169edb316803c0f9065f1a4ee95dabe7 | [] | no_license | adsmaniotto/ProgrammingAssignment2 | 843d02963e790a596864549337ac6ff9ce699821 | 535b5ab6f20e5f323305c09b27e54b90367a56cd | refs/heads/master | 2020-12-25T11:15:18.501044 | 2014-05-21T03:18:10 | 2014-05-21T03:18:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,481 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Creates a special "vector" of functions using a matrix as an input
makeCacheMatrix <- function(x = matrix()) {
## Reads in a matrix
inv <- NULL
## Set matrix and inverse variables in a different env... |
63980b0b663d5523af95c8f81a2406d9b7452ae6 | f5435fd1b9f39bec9b199a573aaf7a5a2de2889f | /R/subsample.R | c2dc4d3b11941b1e5cca1082ee56c3cb00ad4874 | [] | no_license | brycefrank/spsys | 7977680a1482e294e8316e8c6f3f30124bfa15ab | d88d56661dcf1d6b6b77786a816a27ed1638e099 | refs/heads/master | 2022-12-27T11:41:34.098804 | 2020-07-31T18:02:44 | 2020-07-31T18:02:44 | 257,992,951 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,946 | r | subsample.R | # Various functions for creating subsets of sampling frames used for assessment
# and to create neighborhood centers and anchors.
#' Retrieve the set of all possible starting positions
#'
#' In two-dimensional systematic samples, all possible samples can be obtained
#' merely by moving the starting position within a ... |
3f850b8a0a4c70ba5c37f2ff0f0a0cbab29e538b | e3a1e3f9e092a20e849042dad1044e622965689c | /man/convertCorpus.Rd | 6c71cc4221255cbed26ff8d91226183f35ef8a26 | [] | no_license | bryant1410/stm | b9be5fc117b0cbf4448589844ef3b5d9a7ad7fba | 760aa2cade89ce8f9cae02fc6535622908d07502 | refs/heads/master | 2021-01-19T20:05:49.289923 | 2017-04-17T08:07:39 | 2017-04-17T08:07:39 | 88,486,274 | 0 | 0 | null | 2017-04-17T08:07:40 | 2017-04-17T08:07:40 | null | UTF-8 | R | false | true | 2,269 | rd | convertCorpus.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convertCorpus.R
\name{convertCorpus}
\alias{convertCorpus}
\title{Convert \pkg{stm} formatted documents to another format}
\usage{
convertCorpus(documents, vocab, type = c("slam", "lda", "Matrix"))
}
\arguments{
\item{documents}{the documents... |
8a4a94dc0d130c6ebe60da34c1a06677cdeea082 | 50ed17f78083221eb95cdc0638157aa58f819eb0 | /man/InitPop.Rd | 82394c59256a5373c5588a6c0833e25cc06d972c | [
"Apache-2.0"
] | permissive | k-silva/amss | 9db034d2dd3c2d5ee80298cf6d86eb2266c7c851 | cbf5e7f6c668de493077ce1d08a2ab963891f0cc | refs/heads/master | 2022-02-27T11:23:30.357174 | 2018-06-07T21:17:26 | 2018-06-07T21:17:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,609 | rd | InitPop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/module_natural_migration.R
\name{InitPop}
\alias{InitPop}
\title{Initialize population segmentation.}
\usage{
InitPop(data.dt, pop.total, market.rate = 1,
prop.activity = rep(1/length(kActivityStates), length(kActivityStates)),
prop.favor... |
8b67a12f63b8bdb1848af8ff32d3c873f766e91c | 4339f893a3b34fb03f584205e787383b0a44d302 | /2_Function_Check_RUT.R | 84fc30b1b4e738a92e1258240c0390de30e0e601 | [
"MIT"
] | permissive | benyimr/Padron_Electoral_R | 6f80abe21b54d1ff3af57a3047651e38a78bc5a6 | eede31c2b03caec2f5bd93148f664ec939b9d5cb | refs/heads/master | 2020-04-06T21:11:47.124434 | 2018-11-16T11:00:29 | 2018-11-16T11:00:29 | 157,795,777 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,580 | r | 2_Function_Check_RUT.R | #FUNCTION FOR CHECKING THE RUT (VERIFICATION DIGIT)
check_rut <- function(string1){
datos_temp <- data.frame(rut1 = rep(NA,length(string1)))
datos_temp$rut1 <- string1
datos_temp$rut2 <- stringr::str_remove_all(string = stringr::str_sub(string1, start = 1, end = (str_locate(string1, pattern = "[-]"))[,1]-1)... |
87374666c6b0c86abfedc4432446d3e0a1146d81 | 972fa9f59ad0d4e07b44bda60f11aba400ea423f | /1_basic_data_manipulation/450x_examples_1.R | 142e0b9158881ab99461bca1bd86e4facd4c1946 | [] | no_license | tobiasnowacki/ps450x | fadf30e1c5ebbca3a55b208aa2a5210bc6cc8664 | 68e3a5604925f61201f66f13d863803452eee235 | refs/heads/master | 2020-08-05T03:43:55.606669 | 2020-02-07T19:13:58 | 2020-02-07T19:13:58 | 212,381,583 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,393 | r | 450x_examples_1.R | # head package:utils R Documentation
# Return the First or Last Part of an Object
# Description:
# Returns the first or last parts of a vector, mat... |
d0eac3d87e988c4281f7290e4b1b780592aad19b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pedquant/examples/pq_perf.Rd.R | d23f87e8c9c7a2b9474dcd397066758dd5dea404 | [] | 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 | 304 | r | pq_perf.Rd.R | library(pedquant)
### Name: pq_perf
### Title: creating performance trends
### Aliases: pq_perf
### ** Examples
## No test:
# load data
dat = md_stock(c('000001', '^000001'), date_range = 'max', source = '163')
# create performance trends
perf = pq_perf(dat)
# pq_plot(perf)
## End(No test)
|
215e02af97a159811e0f3ee60acab218c59c8f3c | 91f533c44a10ff2d45aa17f60cdc2520862fdbe8 | /kernel/aggregate_result_as_table.R | b06dd04b8cc99f17ef38a6e00f4b5b5a45c2326d | [
"MIT"
] | permissive | liuguoyou/CTOQ | 6494756ca1444e7d51a23e82f504dd104ee13008 | c3c50c0e6883b18d950eeae8326af5febf2d40a5 | refs/heads/master | 2021-06-06T21:23:03.069405 | 2016-09-02T01:32:44 | 2016-09-02T01:32:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,915 | r | aggregate_result_as_table.R | #library(ggplot2)
library(plyr)
library(reshape2)
library(xtable)
#args <- commandArgs(trailingOnly = TRUE)
args <- c('result/sum.all.txt', 'error')
input <- args[1]
disp.type <- args[2]
df <- read.table(input, header=F)
names(df) <- c('n', 'k', 's', 'method', 'data', 'ans', 'time')
df$method <- factor(df$method)
df... |
e3c378c80b282828a9f4c7808516c1ad63d84de0 | 2f50d7a4d4aa8acc9060b8fed4c06364f3170a33 | /plot1.R | 3410350d3547440b1aba6e16b16a8ab9de1de288 | [] | no_license | chandrashivak/ExData_Plotting1 | 306750d6287290b997c5fe34488409c0eae6faef | 90e12e1f740387c324eae13f94c93ed3ee1f82b1 | refs/heads/master | 2021-01-18T02:42:13.606356 | 2014-06-06T16:38:32 | 2014-06-06T16:38:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 578 | r | plot1.R | epcdata <- read.table("household_power_consumption.txt", header = TRUE, na.strings = "?", nrow = 2075259, sep = ";", colClasses = c("character", "character", "numeric", "numeric", "numeric", "numeric","numeric","numeric", "numeric"))
epcdata <- transform(epcdata, Date =as.Date(Date, "%d/%m/%Y"))
subepcdata <- subset... |
6121607687ce6c803009a2ed38dae1e4aad06a7f | a0843cd25f406060933e970d3d3d1d96eca286ad | /Community Paper/PLSR.R | c40f30c5c3ba69554c054f8b532a6e7430c89234 | [] | no_license | klapierre/C2E | 6d5239a72707850978a4ffa1af01532ad0f6357e | c2b3acdbfc69a8aa6750cf8742bd4095166d22dc | refs/heads/master | 2022-07-30T05:56:57.732219 | 2022-06-22T15:10:10 | 2022-06-22T15:10:10 | 75,030,900 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,050 | r | PLSR.R | #emily's working directory
setwd("/Users/egrman/Dropbox/C2E/Products/CommunityChange/March2018 WG")
#kevin's working directory
#setwd("C:\\Users\\wilco\\Dropbox\\C2E\\Products\\CommunityChange\\March2018 WG\\")
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(grid)
library(vegan)
library(pls)
theme_set(... |
ce4918685fd8a3dff4160ba1ea21a5fad2036c11 | d4fb7b0bf2830af9f44f95f1ffdbc906c11379ab | /tests/testthat/test-graph_evol.R | 2fd29339d4c5f64ddd35500ed76a068b3779cc14 | [
"CC-BY-4.0",
"etalab-2.0",
"CC-BY-3.0",
"LicenseRef-scancode-etalab-2.0-en"
] | permissive | jengelaere/enr.reseaux | b973be63f793652717c340d0fbf0785f960d1aff | 72c4b44aa13c5fbb68ad18a2281d9a19478a8917 | refs/heads/main | 2023-04-10T19:43:47.856755 | 2021-04-06T18:58:27 | 2021-04-06T18:58:27 | 355,301,058 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 261 | r | test-graph_evol.R | test_that("graph_evol works", {
liste_ter <- dplyr::filter(liste_zone_complete, CodeZone %in% c("200060010", "49", "52"))
graph <- graph_evol(fil = "Eol", indic = "Puissance", liste_ter)
testthat::expect_equal(attr(graph, "class"), c("gg", "ggplot" ))
})
|
6b160115b15a5f5cdb206ebe0688ab23bf95a111 | 0e7763471f0c1b4883fff8853106368db7502849 | /tests/testthat.R | 04fc451dffb11ed1a2bc081f00b489d7ae999ffd | [
"MIT"
] | permissive | virgesmith/humanleague | d7b000c492aeb76fce2dff0fda4b5fa4650b5cd6 | dceb216f9f40eb07bec7ecb5eaa1fed6224a23fd | refs/heads/main | 2023-06-24T04:06:21.268692 | 2023-05-19T14:49:47 | 2023-05-19T14:49:47 | 95,961,787 | 16 | 4 | NOASSERTION | 2023-08-27T11:25:44 | 2017-07-01T12:03:50 | C++ | UTF-8 | R | false | false | 65 | r | testthat.R | library(testthat)
library(humanleague)
test_check("humanleague")
|
645a15d25548e0db569c5fbbe873a36c9d83a679 | 4c699cae4a32824d90d3363302838c5e4db101c9 | /06_Regressao_com_R/tools/Tools.R | 6ed2a709c4a226fb2c59d154c112b21293d98751 | [
"MIT"
] | permissive | janes/BigData_Analytics_com_R | 470fa6d758351a5fc6006933eb5f4e3f05c0a187 | 431c76b326e155715c60ae6bd8ffe7f248cd558a | refs/heads/master | 2020-04-27T19:39:10.436271 | 2019-02-06T11:29:36 | 2019-02-06T11:29:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,100 | r | Tools.R | # Este scrit contem diversas funcoes utilitarias usadas em diversos scripts R.
# Para usar as funcoes deste script, utilizamos a funcao source() para carregar este script.
set.asPOSIXct <- function(inFrame) {
dteday <- as.POSIXct(
as.integer(inFrame$dteday),
origin = "1970-01-01")
as.POSIXct(strptime... |
48367a5aa20d7261d3d6b0b18ce97e7526a4b0d1 | fe3ecb9b1ddd8de17b8cc93209134f86cd9c4a6f | /2_R/chap04_1_Function.R | 40250d6c6b89b9fbc82ae35172f160ee22480e6f | [] | no_license | nsh92/Bigdata-and-Machine-Learning-Education-at-ITWILL | d1a7292ee4865a3d0c664dd6ecf3afc0d6325847 | 3cb5661001597499178a2c85f4ccf70dcf0855d6 | refs/heads/master | 2022-11-21T23:10:51.421708 | 2020-07-23T12:49:11 | 2020-07-23T12:49:11 | 275,540,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,943 | r | chap04_1_Function.R | #chap04_2_Function
# 1. 사용자 정의함수
# 형식)
# 함수명 <- function([인수]){
# 실행문
# 실행문
# [return 값]
# }
# 1) 매개변수없는 함수
f1 <- function(){
cat('f1 함수')
}
# 함수 정의만 했지 호출하지 않으면 쓰일 일이 없음
f1() # 이렇게 함수를 호출함
# 2) 매개변수 있는 함수
f2 <- function(x){
x2 <- x^2
cat('x2=', x2)
}
f2(10) # 실인수를 넣음
# 3) 리턴이 있는 함수
f3 <- function(x,... |
cbe9a9c330c04566a6b74ac0ef8f6eff8cdf850d | dc3d19a142a1be6b15977c73d3962dc26f871c9c | /plot3.R | 7a2a2d568c47e84b3918b3c38d0a4ae7b6f3bc94 | [] | no_license | msinclairstevens/ExData_Plotting1 | 0bb069f11e583b9a6851f52f1e12b7c4f0bd059b | 52fb5cc3cb8f7fbc2761227aabc0f61698b228f8 | refs/heads/master | 2021-01-21T08:33:26.148481 | 2015-07-08T21:48:07 | 2015-07-08T21:48:07 | 38,706,847 | 0 | 0 | null | 2015-07-07T18:28:28 | 2015-07-07T18:28:27 | null | UTF-8 | R | false | false | 2,036 | r | plot3.R | #The data file is large, so read only a subset into R.
data <- read.table("./data/household_power_consumption.txt",
header=FALSE,
sep=";",
na.strings="?",
nrows=10000,
skip=62000,
stringsAsFactors=FALSE,
... |
356732b6f426f2391b813af07d7fed7a9cfa029a | ba1edf30bca6e023562e4aed21c0ca009d22f431 | /db/man/append.covariate.Rd | f2511579bdd528e8a6689c9a43e8f1c8981f3a43 | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | rgknox/pecan | 79f080e77637dfb974ebb29313b5c63d9a53228e | 5b608849dccb4f9c3a3fb8804e8f95d7bf1e4d4e | refs/heads/master | 2020-12-27T20:38:35.429777 | 2014-05-06T13:42:52 | 2014-05-06T13:42:52 | 19,548,870 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,141 | rd | append.covariate.Rd | \name{append.covariate}
\alias{append.covariate}
\title{Append covariate data as a column within a table
\code{append.covariate} appends one or more tables of covariate data
as a single column in a given table of trait data.
In the event a trait has several covariates across several given tables,
the first table given ... |
33fa19ea5ec4ca737d5c067650aea12967ae1b3d | 9969b02c26fa5388ac971b8212c761c6abf98efb | /R/est_passage.r | 02a0cdccc7d24faa892e207f13a5d32868ede800 | [] | no_license | tmcd82070/CAMP_RST | 0cccd7d20c8c72d45fca31833c78cd2829afc169 | eca3e894c19936edb26575aca125e795ab21d99f | refs/heads/master | 2022-05-10T13:33:20.464702 | 2022-04-05T21:05:35 | 2022-04-05T21:05:35 | 10,950,738 | 0 | 0 | null | 2017-05-19T20:42:56 | 2013-06-25T21:24:52 | R | UTF-8 | R | false | false | 31,471 | r | est_passage.r | #' @export
#'
#' @title F.est.passage
#'
#' @description Compute passage estimates, given catch and efficiency trial
#' data.
#'
#' @param catch.df A data frame with one row per \code{trapvisitID} for a
#' particular \code{FinalRun} and \code{lifeStage}.
#'
#' @param release.df A data frame resulting fro... |
518f6d309c1d84ab9a492d676513df20f27e0e33 | 061994e8bc10cee792da0792cc0ed81809323097 | /R/listChildSites.R | df7f126e1a62465be62ac5df76f7cc1a93ac7821 | [] | no_license | KevinSee/PITcleanr_old | 69c1ca77078b79fa35198e805753dc417f066905 | 3da9b0c370720b87ec981ed56200bfccce43d126 | refs/heads/master | 2023-04-06T11:19:39.967020 | 2020-06-17T21:00:17 | 2020-06-17T21:00:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 901 | r | listChildSites.R | #' @title Child Sites
#'
#' @description Recursive function for finding all child sites of parent site
#'
#' @author Kevin See
#'
#' @param parent_site Site code from parent
#'
#'
#' @import dplyr purrr
#' @export
#' @return NULL
#' @examples listChildSites()
listChildSites = function(parent_site, parent_child_df) {
... |
0738331475fd8d6cdb8232c86be16ba731c35332 | b27d0a4f5dc800f928e5ef31210879899955becd | /model for mapping/temp.R | b8125a4af99589b0a2a4e31ac8cd4fd1ca91ad2a | [] | no_license | gaohong5752/Geoderma_paper_code | a8fa8a76891d34428bad83d80cc7aa29ac2eea32 | 19c22eb605b533fa6dd0932bba01d6419c270c7d | refs/heads/main | 2023-06-27T11:57:05.560818 | 2021-07-22T09:31:44 | 2021-07-22T09:31:44 | 388,402,440 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,715 | r | temp.R | #RSP -ok
##ns3
SNA RSP ns3 15
```{r}
mn="All_pSNA_RSP_NS3_pen15"
shpPoint<-readOGR(paste(point_shp_folder,mn,".shp",sep = "")) ###
sampData <- extract(covStack, shpPoint, sp = TRUE,method = "simple")
sampData<-sampData@data
sampData<-sampData[sampData$soilID %in% soil_type,]
sampData<-na.omit(sampDa... |
910557abb25de6740ddb94c1c355c9cccf3a0c24 | 1ff0f0217347e7ec30167a5524ffb8260e49e823 | /man/findLQR.Rd | ca0ae0596701540849caa688209fa546db54714c | [] | no_license | vaofford/amplican | 0ee096b58585ceb24c6e451872af2a2fd87b2de6 | 7774dda136bdd3dd78c6c8c1f596195b847f77f3 | refs/heads/master | 2020-09-15T08:21:02.149838 | 2019-06-06T18:33:47 | 2019-06-06T18:33:47 | 223,392,406 | 0 | 0 | null | 2019-11-22T11:48:36 | 2019-11-22T11:48:35 | null | UTF-8 | R | false | true | 1,251 | rd | findLQR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers_filters.R
\name{findLQR}
\alias{findLQR}
\title{Find Off-targets and Fragmented alignments from reads.}
\usage{
findLQR(aln)
}
\arguments{
\item{aln}{(data.frame) Should contain events from alignments in GRanges
style with columns eg.... |
7fa39fb3a4f5b8daaa34656d88a084d903677cf1 | 8d93ceb14c4c9504cecef60d7b9f878dc8ff355d | /CredibilityModels/R/bs.R | 8b40b7a0ea2f84ec9a0b0bfed12d404659b26a41 | [] | no_license | Andreas329/MyPackages | 4105c1830fae9240aa37fe4bea25d87055fe0f16 | 2b6f6c73bb8032418104540184bbd7bcada5faa4 | refs/heads/master | 2021-09-05T20:09:50.714375 | 2018-01-30T19:56:03 | 2018-01-30T19:56:03 | 108,578,621 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,070 | r | bs.R | #' Fits Buehlmann-Straub-Model.
#'
#' @param formula a formula
#' @param data a data.frame in which to interpret the variables named in formula.
#' @param weights expression indicating the column of data containing the weights.
#' @return bs returns an object of class cm.
#' @export
#' @examples
#' bs(KS ~ Region, test... |
a9a8ee5669669bfcbaa5be6bf8a32b127f7c3627 | d8978ecd115f95d9e4f6d987d54c2cb6541a6bf4 | /code/4_analyzeData/wrds/tpPriceDecomp.R | 9673276ec8451da2f2f4626959bc7166b0410890 | [] | no_license | emallickhossain/WarehouseClubs | f0eaab1b645e13654de655c2f13e47aa72b02a42 | 7867171cdb3ca3fe32ec778dd8043d538ab1f6ef | refs/heads/master | 2021-06-28T05:37:21.813087 | 2020-09-16T21:49:48 | 2020-09-16T21:49:48 | 149,994,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,164 | r | tpPriceDecomp.R | # Calculate price decomposition
library(data.table)
library(ggplot2)
library(ggthemes)
library(stringr)
library(lfe)
threads <- 8
yrs <- 2004:2017
# Getting CPI to deflate values to Jan-2010 base
cpi <- fread("/scratch/upenn/hossaine/cpi.csv")
cpi[, "base" := cpi[date == "2010-01-01"]$value]
cpi[, "newIndex" := value ... |
1bd5bbacf3f22cb759ad8dd5f25f4a9dd52d4732 | 4344aa4529953e5261e834af33fdf17d229cc844 | /input/gcamdata/man/module_aglu_L161.ag_R_C_Y_GLU_irr.Rd | 2e2b5f1f3454464ec10025cba97dbd22a99d58d3 | [
"ECL-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | JGCRI/gcam-core | a20c01106fd40847ed0a803969633861795c00b7 | 912f1b00086be6c18224e2777f1b4bf1c8a1dc5d | refs/heads/master | 2023-08-07T18:28:19.251044 | 2023-06-05T20:22:04 | 2023-06-05T20:22:04 | 50,672,978 | 238 | 145 | NOASSERTION | 2023-07-31T16:39:21 | 2016-01-29T15:57:28 | R | UTF-8 | R | false | true | 1,426 | rd | module_aglu_L161.ag_R_C_Y_GLU_irr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zaglu_L161.ag_R_C_Y_GLU_irr.R
\name{module_aglu_L161.ag_R_C_Y_GLU_irr}
\alias{module_aglu_L161.ag_R_C_Y_GLU_irr}
\title{module_aglu_L161.ag_R_C_Y_GLU_irr}
\usage{
module_aglu_L161.ag_R_C_Y_GLU_irr(command, ...)
}
\arguments{
\item{command}{AP... |
2c682fccabf1da026a748964d29010fe464c8720 | e3e10eb45e55cb84ad28e2ab2f3a2dff46bf9328 | /man/network.glmnet-MultiAssayExperiment-method.Rd | 0fbb8582cbbf39fe422f9893d49e7807e8d4bb51 | [] | no_license | averissimo/network.cox | cbd8954c23139d59293f811f29b1313443d07610 | 88411403e99d4963a1e99fde9499755ebc5828bf | refs/heads/master | 2020-03-22T11:23:35.165518 | 2018-07-06T10:16:17 | 2018-07-06T10:16:17 | 139,968,129 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 588 | rd | network.glmnet-MultiAssayExperiment-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/network.glmnet.R
\docType{methods}
\name{network.glmnet,MultiAssayExperiment-method}
\alias{network.glmnet,MultiAssayExperiment-method}
\title{Calculate GLM model with network-based regularization}
\usage{
\S4method{network.glmnet}{MultiAssay... |
f06dc377ec72ac01ca9d61fcfba6e4a9ac841a8b | 4298020386c5271f265713e663a2fb6d78772be5 | /imma.R | 12ca12950e8f2790d770bb9433643b2b53f30899 | [] | no_license | armdhn23/Peta-Leaflet-R | 5221a039d1b3dd1010dad4b4cefaf15ddd922df8 | 7e301d3418942ecaa29c439e33cf9168e47a559a | refs/heads/master | 2022-12-08T12:50:09.673987 | 2020-08-26T11:55:04 | 2020-08-26T11:55:04 | 290,482,508 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 727 | r | imma.R | library(leaflet)
data <- data.frame(long=c(119.449521, 119.450739, 119.449419,119.447934),
lat=c(-5.098641, -5.100104, -5.101071, -5.098983),
nama=c("Titik 1", "Titik 2", "Titik 3", "Titik 4"))
data
# Show first 20 rows from the `quakes` dataset
leaflet(data = data) %>% addTile... |
e794a471b2bda124a2ac67f71376db06ec407630 | a53bdbe22bbee6c25fdd8085201327a59f61f6a4 | /04_Factor_Analysis.R | 0314f5e6fc58bad252b7d762fb4bd78f5237f227 | [] | no_license | mmendezs/IM | c8ff699feed55b55e7588b21e8739b1a2263b523 | 6b23792fc3672c6c82da17c402d4fe83e2edb3e5 | refs/heads/master | 2020-12-31T00:29:42.679837 | 2017-05-23T10:29:59 | 2017-05-23T10:29:59 | 85,386,754 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,266 | r | 04_Factor_Analysis.R |
library(dplyr)
library(ggplot2)
library(psych)
#library(gridExtra)
survey <- read.delim("survey.csv",sep = ';')
# Separamos datos por supermercados
dia <- filter(survey, Establecimiento=='dia')
carrefour <- filter(survey, Establecimiento=='carrefour')
mercadona <- filter(survey, Establecimiento=='mercadona')
## psy... |
f16e4393c6b4ae8f3ae68a942192d0a4098275c0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/staTools/examples/RMSE.Rd.R | 3c12bde569073dc87710a1dd6ec4d66cec142adb | [] | 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 | 185 | r | RMSE.Rd.R | library(staTools)
### Name: RMSE
### Title: Root Mean Squared Error
### Aliases: RMSE
### Keywords: error mean root squared
### ** Examples
x = runif(10)
y = runif(10)
RMSE(x,y)
|
ec2b2ed706ced4f22a8670c527a82cbd2ab3a73c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmSupport/examples/varScore.Rd.R | 87b10f407cf7b06fd731de63dfa150b41e0c7be3 | [] | 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 | 283 | r | varScore.Rd.R | library(lmSupport)
### Name: varScore
### Title: Creates a total score from a sum of items
### Aliases: varScore
### Keywords: manip
### ** Examples
##not run
##varScore(d, c('I1', 'I3', 'I4'), Reverse= c('I2', 'I5'),
## Range = c(1,5), Prorate=TRUE, MaxMiss = .25)
|
9147d92b3a02b6bdb6329a8634171a8031c855f9 | b8d522f0bca0b379e982c5b094243f8a75979472 | /R/featureCoverage.R | 6e3a7b8a49776ea1aadc82e58b99f78c23c39259 | [] | no_license | aryeelab/scmeth | 45d2dc4ce77b2e42c67737a1eeffa8a4570ceab3 | b7b86da69dcfb13a14b8ab5099649c2d41469d55 | refs/heads/master | 2021-03-24T09:43:58.358028 | 2019-06-10T21:12:11 | 2019-06-10T21:12:11 | 75,974,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,401 | r | featureCoverage.R | #' Coverage based on the genomic feature
#'
#'Provides Coverage metrics for the sample by each genomic features provided
#'by the user
#'@param bs bsseq object
#'@param features list of genomic features, e.g. genes_exons, genes_introns,
#'cpg_islands, cpg_shelves
#'Names are based on the annotatr packages, so all the f... |
067f734b1fef9c67abb98581b28962eb79df747e | b69ea4c85c60f4a3c59d302eea64c620270cfaae | /data-processing-code-salvage/05_preliminary_anal_MAP_misc_2017-05-11.R | 3a033e55a4baf5d6b46ae2caae1f409c8b97cba7 | [] | no_license | rvanmazijk/Hons-thesis-code-salvage | 52c1dbef9c136afabe385550d0258262c59d2773 | b3492c5f5c212d75631557c8f280c898f91718a7 | refs/heads/master | 2020-03-20T01:30:39.817374 | 2018-08-30T14:30:26 | 2018-08-30T14:30:26 | 137,078,280 | 0 | 0 | null | 2018-08-30T14:30:27 | 2018-06-12T13:47:58 | HTML | UTF-8 | R | false | false | 6,563 | r | 05_preliminary_anal_MAP_misc_2017-05-11.R | # Formal "preliminary" analyses on MAP --- misc. & "non-core" analyses
# Hons thesis
# Ruan van Mazijk
# created: 2017-05-11
# last edited: 2017-05-11
# Setup ---------------------------------------------------------------------------------
# Here, I load all the setup info and my own defined functions (in `... |
ad75e3dce00c150d04b3c90feb9bf1efa65aea26 | 613071c35cf546977440326fac50843a8be32cff | /src/ui.R | 4ed67591484badfaf868dc6003bbc6ca003c1293 | [
"MIT"
] | permissive | peter0083/crime_data_shiny_app | f4bc381a061b7b9d58377e9f30dc6fd68894fb47 | 87f9a1b2f2942e8491a573b5b22610d459dba030 | refs/heads/master | 2021-01-19T13:15:24.596203 | 2017-04-24T03:28:32 | 2017-04-24T03:28:32 | 88,078,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,798 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(colourpicker)
# Define UI for application that draws a histogram
sh... |
b4ef92838ff1c17a3b42fdb8c3ba425179f4a4c2 | 378a17586220fc92add9c226045f2cf7de327955 | /Rscripts/diffExpMds.R | b31e27a791609d08be18926f11b0bec32a0b3e5b | [] | no_license | CrickWu/code | 30535fbc14e473ba024c636ea1dec4d3b29aa855 | 9bc8d94aa9c891ae49782e8ab384ca3e89919d97 | refs/heads/master | 2020-04-06T04:32:43.518685 | 2013-11-13T20:49:51 | 2013-11-13T20:49:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,953 | r | diffExpMds.R | ## .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-.
## /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ / / \ \ / / \ \
##`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' ' '
## Aug 2011 nanomed project (Sonia Human Th17 CARs)
## Bonneau lab - "Aviv Madar" <am2654@nyu.edu>,
## NYU - Center fo... |
388ca1a667c6ac15ffe0a74be30ec50764a58efd | 7a8fc2ffd84f18804046abc9bc41439ad9857cdf | /ActigraphAuswertung/RScripts/Auswertung_Kreuzkorrelation.R | 3413cc0b3ba98b59123d1477470b5503ad306fbd | [] | no_license | JJasonWang40/motion-sensor-analysis-package | b769827d8254343f48268adc1c31ed5047c71018 | d547316f1fac375bf03866ff89cd238ef4156ee4 | refs/heads/master | 2016-08-12T20:57:47.540913 | 2012-04-03T17:30:30 | 2012-04-03T17:30:30 | 46,698,895 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 603 | r | Auswertung_Kreuzkorrelation.R |
if (length(dev.list()))
{
dev.off()
}
print(paste("length(datensatz0Daten) = ", length(datensatz0Daten), " length(datensatz1Daten) = ", length(datensatz1Daten)))
jpeg(filename = paste(outputFolder, outputPrefix, datensatz0Name, "-", datensatz1Name, "_Kreuzkorrelation.jpg"), width = outputWidth, height = ... |
ba06f0bb0588d1851fa34144a33616e7d0019777 | 5a6509d299864f09c63e78a383e7b4077b766c53 | /R/api_check_batch.R | decbe164c3096aec76da1ac5d81018eda341a2c0 | [] | no_license | UCSF-MSLAB/msbwaiter | 4395cc69e32c52007c630618a0efba807c25c315 | c7527927b1bee0b2ae82fe9733847ea9708be167 | refs/heads/master | 2021-01-10T15:21:26.088000 | 2016-05-13T18:07:57 | 2016-05-13T18:07:57 | 44,769,673 | 0 | 1 | null | 2016-05-12T13:47:27 | 2015-10-22T19:54:12 | R | UTF-8 | R | false | false | 6,703 | r | api_check_batch.R | #' Create or update MS Bioscreen data entries?
#'
#' \code{api_check_batch} fetches all the bioscreen data for the specified endpoint. \code{api_check_batch} then compares each entry
#' in the inputed sufl data set with the corresponding entry in the bioscreen data set. For each entry in the inputed sufl data set,
#' \... |
8730450aaa78195142ee0ea39fabe50ecc30253e | 6cc8e2e57cc6f906bc64f8394c6683dc0d614918 | /R/plotSlice.R | fe20970801dba9a2dc4cb623b5ce63444a1e1669 | [] | no_license | cran/mgcViz | cf3f445892ad2954e8ca06dec808cc551bd012d4 | de825975b97a2f6a020b84f8f6d30b06bca65c60 | refs/heads/master | 2021-11-01T08:37:43.917586 | 2021-10-05T06:10:12 | 2021-10-05T06:10:12 | 145,894,342 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,593 | r | plotSlice.R | #'
#' Plotting sequence of slices of 2D smooth effect
#'
#' @description This function allows to slice a multi-dimensional (D > 2) smooth effect,
#' and to plot the resulting sequence of 2D slices in an array of plots.
#'
#' @param x a smooth effect object, extracted using [mgcViz::sm].
#' ... |
a7e20aa90cc40505389959b729fa562118a3d8ca | 05696931b4dee4845baeaf2220f0c960069b3cfd | /tests/testthat.R | f25165a9fd3a741a69e5bc880e8a0b4da1adc256 | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | kvantas/hyetor | a3c056e2f7f01c7273b567ff89fabd8c77edbe7f | 4e24367dcd173300b7c0cef964ac7ea82e44fe4c | refs/heads/master | 2020-03-27T04:42:32.757172 | 2019-08-31T20:27:59 | 2019-08-31T20:27:59 | 145,962,234 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(hyetor)
test_check("hyetor")
|
553508eadbd73d2c803a4a5a3fc575276edda4a9 | 6d0fe73f4a177861980154fb5551303dc1a5b5aa | /R/convert.old.datasheets.R | e44244244ff15ce2ef5792f148735caa834c6e33 | [] | no_license | npetraco/dustproj | ce8d9937f358035867499fbac5939eb39cc8d951 | 18fd1d1d928b9ffd2780b2439892b399f79fd093 | refs/heads/master | 2022-07-09T00:43:08.260175 | 2022-06-23T12:31:38 | 2022-06-23T12:31:38 | 159,550,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,486 | r | convert.old.datasheets.R | #' Convert a row of a read in (flattened) study to the reference class,subclass and attribute
#'
#' The function will XXXX
#'
#' @param XX The XX
#' @return The function will XX
#'
#'
#' @export
convert.study.row<-function(study.row, conversion.info){
#print(study.row)
study.class <- study.row[1]
study.subcla... |
71df5d91885e21db958bd008cb904c7d564c5057 | cc63b7d10e50f7a96b28c77f354270f848cf6546 | /man/download_BRENDA_regulators.Rd | 2e02c4f4f701b450dcbc55f6f5cc92ea6666a15d | [
"MIT"
] | permissive | shackett/fluxr | 83e2ff3916b97a96f8814833d1a5c2459553bde0 | e37133060c822312a9ec056a70a566736c4d8931 | refs/heads/master | 2020-12-26T03:12:01.457667 | 2016-09-20T21:29:20 | 2016-09-20T21:29:20 | 68,309,569 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 324 | rd | download_BRENDA_regulators.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/access_databases.R
\name{download_BRENDA_regulators}
\alias{download_BRENDA_regulators}
\title{Download BRENDA Regulators}
\usage{
download_BRENDA_regulators()
}
\description{
Calls python to summarize all regulators in BRENDA by E.C. number.... |
b4092bfe6c89ae9d3aa0a958071902cb8a866b5f | 14afb56ffe97046a3c596ab8cd35f57b9a257edc | /run_analysis.R | 88e4ecef82f6a70bad8f563ddd1e31337bc6412a | [] | no_license | craquiest/GetCleanData | 54f92ace9f307f73931ee77c830851d9ca82e028 | 74b5fca08daa0bcb98c27456cb0739f3163e3468 | refs/heads/master | 2020-06-04T22:06:08.751544 | 2019-06-17T00:38:54 | 2019-06-17T00:38:54 | 192,209,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,488 | r | run_analysis.R | library(tidyverse) # load packages for dplyr, readr, tidyr and others
library(stringr) # load for regular expressions
# Set a veariable for the name of "root" folder
# that is the result of file unzip
data_folder <- "UCI HAR Dataset"
# For each piece of raw data:
# 1. set the file path in platform independent... |
76cdc5973025135d6a61733163c759943ee1739e | 415302fcd498e1aaf4f35d84d3f13fa59577c264 | /helper.R | 68bb43ec5d95b3671b0202228a91ddd0e80066cf | [
"MIT"
] | permissive | pseegaha/Shiny-SoSV | 926536c02a530f69e8d7c920bbccd849eaa0514e | 76305d0354e3480736e5e8f24c638904998269bb | refs/heads/master | 2020-07-17T02:03:02.078922 | 2019-06-27T02:23:07 | 2019-06-27T02:23:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,973 | r | helper.R | library(ggplot2)
library(ggsci)
load("./data/gam.RData")
#color_SVCaller <- c(pal_npg("nrc")(10)[1],pal_npg("nrc")(10)[3:10],pal_jco()(6))
color_SVCaller <- c(pal_npg("nrc")(10)[1],pal_npg("nrc")(10)[3:4],
pal_jco()(6)[1],pal_npg("nrc")(10)[6:7], pal_jco()(6)[2],
pal_npg("nrc")(... |
c92c649b3d52f4c98ce8e2de8edc21366264880a | b643cde1b3abb2913390d05e18213215a8e1618d | /R/nSphereVolume.R | 8b0ca336f309fde08248ee460f98107ccbee738a | [] | no_license | cran/mvst | c8385afe55775c59f0264a11c0d868640446706e | c0f1828ed74ed5bb0dfc8a2d73141bdcaf3e8fbd | refs/heads/master | 2021-01-17T20:55:36.515387 | 2018-07-24T13:30:03 | 2018-07-24T13:30:03 | 63,795,821 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 219 | r | nSphereVolume.R | nSphereVolume = function(n, r=1, LOG=TRUE){
# Function that returns the volume of a n-sphere with radius r
logV = (n/2) * log(pi) + n * log(r) - lgamma(n/2+1)
if(LOG == TRUE) f = logV else f = exp(logV)
return(f)
}
|
86f360c334494dfa996d965a82f513ad9cf986fc | 7554373d5d5cee9adb7d7074e6479359c5ce5c45 | /man/getParams.Rd | 987161f1ccc1b37e81b3d86719f62d974fa6e257 | [] | no_license | philipmgoddard/nnePtR | ad6d1ed0e44cad1dcbdc2ad2be0ee0e4e50f9c73 | 29d5ff1b0c5eb3c90d3d8d75a3bb571a0bf4e359 | refs/heads/master | 2021-01-10T05:35:56.775078 | 2016-02-17T13:18:20 | 2016-02-17T13:18:20 | 51,033,274 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 515 | rd | getParams.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/accessors.R
\docType{methods}
\name{getParams}
\alias{getParams}
\alias{getParams,nnePtR-method}
\title{Set the generic for accessor (getter) for fitted coefficients}
\usage{
getParams(object)
\S4method{getParams}{nnePtR}(object)
}
\... |
686e59d79af644d4bd464a0aa89e1fdbeb4bb435 | e26420970229a0c55ec092168797ed6e42f5708f | /R/data.R | 55abd04117a0799908deb63a30ee5dfc02a81503 | [
"MIT"
] | permissive | kcha/psiplot | cd26b3d39301d20d82e453e4882b60a214afb600 | 7c840f69f7b321b8690e9f3f03191a0cd699a371 | refs/heads/master | 2022-06-03T02:54:17.403767 | 2022-03-18T15:30:05 | 2022-03-18T15:30:05 | 27,505,866 | 2 | 1 | MIT | 2022-03-18T15:30:06 | 2014-12-03T20:20:07 | R | UTF-8 | R | false | false | 1,076 | r | data.R | #' Sample events with PSI data
#'
#' Contains sample PSI and quality score data as produced by \code{vast-tools
#' combine}.
#'
#' @docType data
#' @name psi
#' @usage psi
#' @format A 5 x 22 data frame
#' @keywords datasets
NULL
#' Sample psiplot configuration settings for datasets \code{psi} and \code{crpkm}
#'
#' E... |
bd545815750a924e01d973435fb24d86466b66ca | a9282dbec9a9698b88230a089bbaa458933a6945 | /qtl/go_homo_readcross.R | 2f6d387920bf110603825eaee3772a7fcbd1c8e6 | [] | no_license | dvalenzano/R-sessions | 23cdd993be2ac741770bbc4aa35db0bbba484fb5 | af9d6d2b98afe53cdab7d0d87598eae240af940f | refs/heads/master | 2021-07-04T22:02:50.900279 | 2021-05-15T09:10:31 | 2021-05-15T09:10:31 | 24,762,137 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | go_homo_readcross.R | library(qtl)
Go_homo<-read.cross("csv", "~/", "Go_homoF2_converted.csv", genotypes=c("1", "2", "3"))
save(Go_homo, file="Go_homo.Rdata")
|
af3570dab6186ab6e1e31a9eb431136afd92ca7a | a9b45ca4a280b106d73e168c126599cc809fd9ff | /man/example.Rd | 18a3a3839887601e976d40b5fa32daba5e6a34f6 | [] | no_license | cran/coefficientalpha | e8fd566f85f06226298a21c9504cbb99d005e527 | 0341d1c7121d0aacbb0e951374c38010d72d02c7 | refs/heads/master | 2023-08-31T14:45:12.497515 | 2023-08-27T06:50:07 | 2023-08-27T07:30:54 | 17,695,163 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 153 | rd | example.Rd | \name{example}
\alias{example}
\docType{data}
\title{An example data set}
\usage{
data(example)
}
\description{
An example data set with 10 variables.
}
|
7532d697dbc898be33606bd83028ad4e79379e32 | a7c789203a78f7c060ea27a56dfbd9b978e6b556 | /man/npn_get_data_by_year.Rd | 90cf772bb48948fa7e6df02ec9439ca329a02d51 | [
"MIT"
] | permissive | usa-npn/rnpn | ab7f7a3e9f7e6e24b61f46c207c6de58161b6a0d | cedaf06fd1194911d0452584c22d44b38f7f245e | refs/heads/master | 2023-08-18T10:36:43.221260 | 2023-08-07T21:06:45 | 2023-08-07T21:06:45 | 2,175,238 | 5 | 2 | NOASSERTION | 2023-06-30T15:59:30 | 2011-08-08T18:57:41 | R | UTF-8 | R | false | true | 1,251 | rd | npn_get_data_by_year.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/npn_data_download.R
\name{npn_get_data_by_year}
\alias{npn_get_data_by_year}
\title{Get Data By Year}
\usage{
npn_get_data_by_year(
endpoint,
query,
years,
download_path = NULL,
six_leaf_layer = FALSE,
six_bloom_layer = FALSE,
a... |
97f08bd4ec20861c5524156a0dbb47543f27bba4 | 1459da78bda762c3cb31e8cdebf900f41be7d694 | /Analysis/Summary_Error/extremes_predictionIntervals.R | eb75a07869d205d9b3e2b889210e580668932162 | [] | no_license | benee55/SharmaEtAl22 | 27147df2b639a8e43340d34a93452b957e4acfa6 | 43eca0ae1afa1eb2a6b5d5640aafe989a79aa14b | refs/heads/main | 2023-04-14T13:49:20.965673 | 2022-11-30T06:40:22 | 2022-11-30T06:40:22 | 572,384,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,259 | r | extremes_predictionIntervals.R | rm(list=ls())
setwd("~/Dropbox/FamosHydroModel/")
load("Official_Fast/input/fullObservations.RData") # Load Full observation
load("manuscript/revisionCode/resultsStremflow_calibration_full.RData")
load("manuscript/revisionCode/calibrationParameters.RData")
obsErrVar<-mean(famosParMat[,1])
obsErr<-rnorm(n=nrow(famosPar... |
e3737f822d9711d2889e38fd240ff040473ab212 | 294728c360c797cd9b8a2a7d28a2737eacb6b0c7 | /man/find.H.Rd | 8f2fd2580d409fe53c774d6befa85d707cdbcfa3 | [] | no_license | cran/CPHshape | c6c6c68c35b14a737408a27a55cd83de460d0730 | c7adde3057f665cf290d06f1ca5f115c962c039d | refs/heads/master | 2016-09-10T10:46:19.276881 | 2012-02-27T00:00:00 | 2012-02-27T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | rd | find.H.Rd | \name{find.H}
\alias{find.H}
\title{Calculate the cumulative hazard function}
\description{
This function calculates the cumulative hazard function at location \code{t} based on input from either \link[=find.shapeCPH]{find.shapeCPH} or \link[=find.shapeMLE]{find.shapeMLE}.}
\usage{find.H(t, h.val, h.ranges)}
\arguments... |
af917bd0f0de21e5fd5afaa7a2d283c98a90d85a | 470fc335d1b2a52bdc8b740c0f8b6c6ea55ee538 | /man/gguka_line.Rd | 9d12f69dd68a637576ce37d6ebab7e99fc43638d | [
"MIT"
] | permissive | fanner018/Kansler | e95625e751363c72abbd2e61b68e48079ca8cde0 | cda344a0f43e1341141cae673d6290d04bd55a78 | refs/heads/main | 2023-05-14T10:43:40.866539 | 2021-03-12T15:52:46 | 2021-03-12T15:52:46 | 373,612,006 | 0 | 0 | NOASSERTION | 2021-06-03T18:57:35 | 2021-06-03T18:57:34 | null | UTF-8 | R | false | true | 506 | rd | gguka_line.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Kansler grafik.R
\name{gguka_line}
\alias{gguka_line}
\title{UKA linjediagram 1}
\usage{
gguka_line(df, x = x, y = y, g = g, pal = "uka_1", format = Svensk_antal)
}
\arguments{
\item{df}{dataframe}
\item{x}{x-variabel}
\item{y}{y-variabel (... |
491872e92cbdaa2cb530ce6e18d03581e16f2f3c | 8ad35c4567d61024c65737019651e55edd791045 | /code/TESTE.R | 3c0698d468543cb2f219e9a32895dfd6e270e72b | [
"Apache-2.0"
] | permissive | rbteix/master-s-degree | 6c7761e7b20d868f2a0fa9626c0f596c53b41126 | 5439782e021786c15579615db0d199baf6e6506f | refs/heads/main | 2022-12-29T12:22:52.637466 | 2020-10-14T02:22:22 | 2020-10-14T02:22:22 | 303,877,794 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 5,124 | r | TESTE.R |
#CÓDIGO CRIAR CPD COMO EXEMPLO DISSERTAÇÃO
y_ts_CP <- ts(c(rnorm(250,mean=1,sd=.5), rnorm(250,mean=3,sd=1), rnorm(250,mean=2 ,sd=1))) # rand signal w\ changepoint
options(warn=-1)
library(changepoint)
cptfn <- function(data, pen) {
ans <- cpt.mean(data, test.stat="Normal", method = "PELT", penalty = "M... |
4d65953506495716adac027513f90aa514111328 | f7d8c513e5e4b81e710828281b9d198367c6313a | /Assign2Notes.R | b18b0be172c9c43be2488c555a58ee80c08bb3a2 | [] | no_license | alanfuller1219/ProgrammingAssignment2 | a1beca77a8f3bc64ba94c282b6cb92f1fa045e38 | e0d6fa012a572fa0515e06600e41674ba9c61977 | refs/heads/master | 2021-01-21T09:43:22.231258 | 2015-06-20T22:54:29 | 2015-06-20T22:54:29 | 37,777,287 | 0 | 0 | null | 2015-06-20T16:44:34 | 2015-06-20T16:44:34 | null | UTF-8 | R | false | false | 1,092 | r | Assign2Notes.R |
#do a bit of testing with an invertible matrix
v2 <- c(3,3.2)
v3 <- c(3.5,3.6)
mat7 <- cbind(v2,v3)
dim(mat7)
solve(mat7)
#returns the following
> v2 <- c(3,3.2)
> v3 <- c(3.5,3.6)
> mat7 <- cbind(v2,v3)
> dim(mat7)
[1] 2 2
> solve(mat7)
[,1] [,2]
v2 -9 8.75
v3 8 -7.50
>
## Test using the functions created... |
5d6f9fe1099523def3885fe67deab6a0870beb8c | e4e07501cd6da34beb76abc3e82b5784db1fe011 | /R/jzs_partcorSD.R | 499183a1523675bb5a11ca331138188ee45534e4 | [] | no_license | MicheleNuijten/BayesMed | ff239c8b598e95546fd5bed5d79963c8489192e1 | 2a156ed80e81bb0065b6cee017d86e15d87ff9d1 | refs/heads/master | 2021-06-02T20:09:07.919564 | 2020-01-29T13:13:28 | 2020-01-29T13:13:28 | 7,775,559 | 10 | 4 | null | null | null | null | UTF-8 | R | false | false | 7,396 | r | jzs_partcorSD.R | jzs_partcorSD <-
function(V1,V2,control,
SDmethod=c("dnorm","splinefun","logspline","fit.st"),
alternative=c("two.sided","less","greater"),
n.iter=10000,n.burnin=500,
standardize=TRUE){
runif(1) # defines .Random.seed
if(standardize==TRUE){
M <- (V1... |
7374233ce404a1f790520ebbf99077c5954d6630 | f6a1375e6453107cba75567ec0c3ba23a5ac7958 | /TopmedPipeline/tests/testthat/test_filterVariants.R | 1c259ee66d3d09bc7a52b8a65e3e2b865f5011c7 | [] | no_license | UW-GAC/analysis_pipeline | 7c04b61c9cafa2bcf9ed1b25c47c089f4aec0646 | df9f8ca64ddc9995f7aef118987553b3c31301a1 | refs/heads/master | 2023-04-07T03:13:52.185334 | 2022-03-23T21:15:46 | 2022-03-23T21:15:46 | 57,252,920 | 42 | 30 | null | 2023-03-23T20:13:40 | 2016-04-27T22:25:56 | R | UTF-8 | R | false | false | 5,810 | r | test_filterVariants.R | context("filterVariants tests")
library(dplyr)
library(gdsfmt)
library(GenomicRanges)
.testData <- function() {
showfile.gds(closeall=TRUE, verbose=FALSE)
gdsfile <- seqExampleFileName("gds")
seqOpen(gdsfile)
}
.testBinaryData <- function() {
gds <- .testData()
samp <- seqGetData(gds, "sample.id")... |
09f2085370ba7790736591ed1328e6cb38f6a5de | 67dbc2b767ef8f3e57423a89edb27b5b59b0a9ef | /CT050/exploring_CRAN.R | c1ee8b2f6bf9558532843fdcced707d3dc4bdabb | [] | no_license | marcusfreire0504/R_SocialNetworkAnalysis | 8fe44af0eebecb6064e818a09fd76bf957016340 | 75a64e0e4daac64cb95c7ce18a7619950ed53a06 | refs/heads/master | 2021-09-04T19:21:43.281470 | 2018-01-21T17:06:20 | 2018-01-21T17:06:20 | null | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 4,639 | r | exploring_CRAN.R | library(needs)
needs(stringr)
needs(tidyverse)
needs(igraph) # manipulacao de grafos
needs(tidygraph) # visualizacoes de redes
needs(ggraph) # visualizacoes de redes
pdb <- tools::CRAN_package_db()
saveRDS(pdb,"./CT050/pdb.rds")
pdb <- readRDS("./CT050/pdb.rds")
pbaut <- pdb$Author
aut <- pbaut %>%
str_repl... |
b78ecb3d71217ef32220413f41609c276e4be195 | b06013d50cf47363e68006960d9146d1e58621d2 | /Practica1/Ejercicio1/Ejercicio1.R | bb9678608550fb98cc1e806ccfccb4ecbbe05158 | [] | no_license | jlorenzor/Proyecto-de-investigacion-CM274 | a5117abd89c6ff907298c9fd42ba9e5fa07dc82f | 2504c7aebe4548d2a8be4db23bbb84831d308c43 | refs/heads/master | 2021-09-16T21:05:45.963728 | 2018-06-25T05:54:48 | 2018-06-25T05:54:48 | 286,544,210 | 1 | 0 | null | 2020-08-10T17:56:49 | 2020-08-10T17:56:48 | null | UTF-8 | R | false | false | 1,415 | r | Ejercicio1.R | # Nombre : Miguel Angel Oviedo Rodriguez 20131463I
# Respuesta(a)
# La función seq() genera una secuencia de números con una progresión de 0.3
seq(5, -11, by = -0.3)
# el último número no es -11, ya que éste no es un término de la progresión, sin embargo
# se puede hacer el siguiente artificio:
c(-15:33)*(-1/3)
# Res... |
2f0864d82a6a75d6b6f2e78abd4548af0197ca25 | 5189800985b464babdcc822978bc80954d3c70c4 | /tests/timeseries.R | 3fed8a08d5f0ad165c63fcd161c2f2d0678644b2 | [] | no_license | iNZightVIT/dev | 7bcfdd091bd58f87b7417250669f73892f0575cb | 36ce74ef40f9c8f220d3a8ae6c2d763bbe025b71 | refs/heads/master | 2021-08-01T07:48:32.069245 | 2021-07-21T01:21:06 | 2021-07-21T01:21:06 | 16,119,296 | 0 | 2 | null | 2019-09-23T21:57:30 | 2014-01-21T21:19:41 | R | UTF-8 | R | false | false | 1,880 | r | timeseries.R | library(devtools)
#library(tidyverse)
##install_github("iNZightVIT/iNZightTS@dev")
##library(iNZightTS)
load_all("~/iNZight/iNZightTS")
document("~/iNZight/iNZightTS")
data("visitorsQ")
data("visitorsA2")
load_all("~/iNZight/iNZightTS")
ta <- iNZightTS(visitorsA2, var = "Australia")
P1 <- plot(ta, t = 20, ylab = "Vis... |
de2e83ec6f92df4ad259372e6d9c7182c322594c | 5e832862b2e36be6ba27e874e98499bc399de699 | /man/parameter.update.Rd | 4a4fc7502a3a721bcdf314aade272a9e133cfe32 | [] | no_license | dmgatti/DOQTL | c5c22306053ddbd03295207702827cf2a715bb70 | a1a4d170bf5923ca45689a83822febdb46ede215 | refs/heads/master | 2021-01-17T02:08:27.831277 | 2019-05-24T19:22:35 | 2019-05-24T19:22:35 | 13,506,518 | 15 | 12 | null | 2019-02-27T13:46:31 | 2013-10-11T18:33:24 | R | UTF-8 | R | false | false | 1,840 | rd | parameter.update.Rd | \name{parameter.update}
\alias{parameter.update.alleles}
\alias{parameter.update.intensity}
\title{
Parameter updating in HMM
}
\description{
Not intended for external use.
}
\usage{
parameter.update.alleles(geno, b, pseudocounts, prsmth)
parameter.update.intensity(data, params, prsmth, founder.means)
}
\argume... |
e64546be85709f2697d96b8b3e09c4c18c6db6aa | 1dfac533dc072be28012b6919b479449986c811a | /Plot3.R | d464dbad00adc18b3097a73d67c1fda01821bc93 | [] | no_license | thimmaru/Exploratory-data-analysis_Course-project-2 | db04ddc3cacd97766692710b914c49ecf26d5f0c | 22c02b9e16cbadf640cdb5e216fae49c532b4cde | refs/heads/master | 2020-05-21T18:55:54.467861 | 2019-05-11T14:45:56 | 2019-05-11T14:45:56 | 186,143,313 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,346 | r | Plot3.R | #Set working directory
setwd("C:\\Users\\TRUDR\\OneDrive - Monsanto\\Migrated from My PC\\Desktop\\Data\\Ex._data\\Course project 2")
#Step 0. Create directory, downloading the dataset from the source and unzip
if(!file.exists("./data")){dir.create("./data")}
#Load ggplot2
library(ggplot2)
#url for the dataset so... |
e5377359028de55c76f0a7779530b69573d4e1e7 | c3ed0eea77de3338cc4820ca27dc04384676b29b | /man/bin_values.Rd | f7ebbc97c64e250c98bd82518e482896586aa4b5 | [] | no_license | jrboyd/seqtsne | f3bd94ee23140dc71373af4f1e7ce9ffe60d702f | 5a67cbe5af281ec42906689d1a9961d8fe9de68d | refs/heads/master | 2022-10-30T18:33:13.005047 | 2022-10-28T20:22:58 | 2022-10-28T20:22:58 | 177,857,441 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 504 | rd | bin_values.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_util.R
\name{bin_values}
\alias{bin_values}
\title{bin_values}
\usage{
bin_values(x, n_bins, xrng = range(x))
}
\arguments{
\item{x}{Values to assign to bins}
\item{n_bins}{Number of bins to assign values to}
\item{xrng}{Optional ... |
55d9a731ef9a071a111b358364771f89149350f7 | be76339484a140ae82ab62c5176bdd5822563061 | /performance_measures.R | 288fbf9d758057691bdac9f38e534b7f22e873de | [] | no_license | majormajor2/Couponing-Challenge-HU | 4007d3075356712f4b46cb50a8e69b16200e41fb | 51bfc73efcb1d7206d8dc700929f932b1cece388 | refs/heads/master | 2020-03-28T11:36:46.454852 | 2017-02-15T20:11:32 | 2017-02-15T20:11:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,211 | r | performance_measures.R | #### Model Performance Measures ####
# Helper function to compute measures of predictive accuracy
predictive_performance = function(y=NULL, prediction=NULL, cutoff=.5, returnH = TRUE, shallPlot = TRUE)
{
# Assumptions:
# y is a vector of factors
# prediction is a vector of probability predictions
if (leng... |
14b629a487fc3f7a7e000944ca6ac183b4bcc4f4 | 6293d033c25ea4402d43ec857132bb86a50af048 | /3-prepare-2011.R | 0fe0f57dda9173acfbf523460fa72b53f83a12cd | [
"MIT"
] | permissive | hafez-ahmad/in_household | bacbc1e5faa51f9351915b4017b88cef7c7f231b | 58e27466147a9eca1191e33cf094d4c0dec144df | refs/heads/master | 2022-02-11T14:13:35.318415 | 2019-07-02T14:42:12 | 2019-07-02T14:42:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,192 | r | 3-prepare-2011.R | ##### Script returns tidy dataset for given year
library(tidyverse)
# create directory for tidy data output
if (!dir.exists("6-tidy-data")) {
dir.create("6-tidy-data")
}
joined11 <- readRDS("5-joined-data/2011.rds")
tidy11 <- joined11 %>%
# change raw numbers to integer type
mutate_at(10:14, as.intege... |
1f32d19cb9825830ff76d1ae739b8f7830914a2f | 561c2118e306ce45b249e2cbbd4755230e28766a | /preprocessing/vital_except_5min.R | ee60dbf4b5aaff63ca1244e541f486414a7d8e35 | [] | no_license | Doyun-lab/MACE_classification | f89c2c5c0090647f76bb7328f057b056b0fe8a82 | 555e9543888a9db9dd563fdb1facd28d466f4e89 | refs/heads/main | 2023-08-21T15:31:48.093643 | 2021-10-21T06:34:14 | 2021-10-21T06:34:14 | 338,050,366 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,255 | r | vital_except_5min.R | library(stringr)
library(dplyr)
setwd('E:\\')
vital_TIVA = readRDS('preprocessing\\vital_TIVA_NA_END_0225.rds')
vital_volatile = readRDS('preprocessing\\vital_volatile_NA_END_0225.rds')
# 수술시간이 5분 미만이라 제외한 케이스
vital_volatile = subset(vital_volatile, vital_volatile$Case_ID != "08_181012_1120")
nrow(vital_... |
4000ace1a85230ff2cd545de768eec5f428a1db2 | 8a3c62d2f1f81f66f00ba29905988a48841604c3 | /TP_02.R | 842156fe4a03703cee1e387c97bb5f95953873f9 | [] | no_license | Mxberd/Tps_individuales_entrega | c97073ba9e381f1ac64964e5b25dc8e201c6a3eb | 9455eae7307ff323b2f25587cd1053f8f117d4ad | refs/heads/master | 2023-07-29T05:20:42.353969 | 2021-09-06T13:35:49 | 2021-09-06T13:35:49 | 403,631,533 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,364 | r | TP_02.R | #TRABAJO PRACTICO 02
#Para empezar, llamamos a nuestras librerias.
library(tidyverse)
library(datos)
library(janitor)
library(sf)
library(zip)
library(skimr)
library(sf)
#Tambien acomodamos la visualizacion de los valores numericos elimando notacion cientifica.
options(scipen = 999)
#Estaremos trabajando con el ... |
2de8ea5d0f67f2977212a68cafbd91e0e58ae817 | c7fdfaa84c3b38160506ee9a4f544e39a1ddb300 | /figure_data_generation/annotated_gene_fusions.R | 2cdc1668763d6a858add9a08a6a0b1ac2e9489a7 | [] | no_license | iguana128/Gene-fusion_NB | 459cada1af191b8678daa8d65df9668cc52bc0d3 | d3e31cfe835f8d8daf24ee8a3e6dfaca88a3a8ff | refs/heads/master | 2020-03-29T19:48:50.484503 | 2018-09-25T19:31:14 | 2018-09-25T19:31:14 | 150,282,120 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,374 | r | annotated_gene_fusions.R | rm(list=ls())
## this script is wittern to display the gene fusion statistical and annotation results
library(ggplot2)
## load the fusion results
setwd("C:/Users/ZLiu/Desktop/genefusion/Updated_fusion_results_2018.06.05")
load("Chimerascan_results.RData")
load("SOAPfuse_results.RData")
load("TopHat_results.RData... |
9a7e4fca173306684afc4cc713942f0e0fd41a77 | 3b7fd86eebc402c757e656d7f0c6183041f5583e | /App/server.R | 411a9e4b2124e7c21eef34b396b1403758fb1b05 | [] | no_license | antoniogmzstat/EDA_Shiny | 3769d758efafab77facb0068253dabba2295cc1c | ec1f536c9a2526be8854556f53b00acc49459e54 | refs/heads/main | 2023-02-26T13:03:11.097590 | 2021-02-01T20:10:03 | 2021-02-01T20:10:03 | 331,009,560 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 505 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
setwd("~/Proyectos_Shiny/EDA_Shiny")
server_functions <- list.files("./App/server_functio... |
67b6a8c56252e62281b84cbd69645b3f93042c7d | c5d3d69241ab09957e856ddc5204b2216c8a8b86 | /man/write_10x.Rd | 9ef66c8ab6cdc9a00b5f6605744d27aece83f45b | [] | no_license | scottleh/ccfindR | 2fea30d35c68b14e1e7ec4a4c38d90f0f38ddf69 | 4f3bf6b745a860dab2c5de67d267b942cd4a2a9e | refs/heads/master | 2022-02-23T15:52:44.939509 | 2019-10-02T15:28:36 | 2019-10-02T15:28:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 903 | rd | write_10x.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{write_10x}
\alias{write_10x}
\title{Write 10x data files}
\usage{
write_10x(object, dir, count = "matrix.mtx", genes = "genes.tsv",
barcodes = "barcodes.tsv", quote = FALSE)
}
\arguments{
\item{object}{Object of class \code{sc... |
17282ce524c036f9ad8d6f1b3f51fb609c94ede6 | 0ef60d5dd2963770fe9226996fbd27ee47ca1480 | /ShinyApps/sirModel/server.R | 33c362042f2f5754d1e59f2a7b7198e738242a0b | [] | no_license | fernandosm/VPS-dinamica | f0c131d059c5c738a5dbab3d9615e15e03d126a6 | 756f9feaf78ad3b0772155b9ac95828e3b04f956 | refs/heads/master | 2021-01-20T06:59:50.657137 | 2014-04-08T03:23:02 | 2014-04-08T03:23:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,555 | r | server.R | # file name: server.R
#
# Purpose:
# Executa o modelo SIR.
#
# inputs:
# b: taxa de contatos potencialmente infectantes
# g: taxa de recuperação = inverso do período infeccioso
# m: taxa de natalidade = taxa de mortalidade
# vo: taxa de vacinação constante
# (... |
30412d8c2d65fcb883775be4b9cacd3caa5b5c83 | e66aa41a10f0346fa36bfdcdf400a71ed403ed58 | /plot1.R | 2a34b55aa394b0cd2014606ff7b7215811a1306a | [] | no_license | ashishjha89/ExData_Plotting1 | 89adb694ff2c35acdf383822b5c297b7d38d58ff | c0b84c440aed63c20093e8e25db7e30cede0e3fe | refs/heads/master | 2021-01-23T03:43:37.033402 | 2016-08-26T21:31:48 | 2016-08-26T21:31:48 | 66,495,439 | 0 | 0 | null | 2016-08-24T19:59:13 | 2016-08-24T19:59:13 | null | UTF-8 | R | false | false | 1,101 | r | plot1.R | library(lubridate)
# Read the full table
power.consumption <- read.table("household_power_consumption.txt", sep = ";", header = TRUE)
# Change the type to date
power.consumption[, "Date"] <- dmy(power.consumption[, "Date"])
# Extract only the required data
power.consumption.data <- power.consumption[power.consumption[,... |
ca939fe8b0bb48c89319d0df451361e4d30e34ff | 5779ad8fc88297c6c6f15a69e655a3e644a491fa | /threshold_search.R | dc04a7c55530d0d680341032b626265afd26cabb | [] | no_license | johnwoodill/cbb-dynamic-model | 3ab580dcc1bbf24e4c8eb1cee961072bfc32b13b | db4901849d7b93295e7769dd21639b8f62ce0b27 | refs/heads/master | 2021-03-19T14:29:09.949462 | 2018-06-18T17:51:58 | 2018-06-18T17:51:58 | 72,034,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,834 | r | threshold_search.R | rm(list=ls())
library(tidyverse)
library(ggthemes)
library(markovchain)
# Decision function
source("R/decision.R")
# Cherry growth
source("R/cherrygrowth.R")
# Dynamic cherry pricing function
source("R/cherrypricing.R")
# Initiate parameters
source("1-parameters.R")
# Get calibrated markov chains
calibration_typ... |
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