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54a3a38f72f83a6ffa7ed50797d6581c967dc00c | 000898e609c09e5f7243a56e2a24129f90749690 | /man/gradientPickerD3_example.Rd | 2d3781b6c492da2efb4eb37c498aba885fd16a68 | [] | no_license | cran/gradientPickerD3 | 233485a7e45dec09ea28dcd4dc9261ba6472eb3b | a79d9cbb1c3ac4bc45657060903c60e937a6a332 | refs/heads/master | 2021-06-27T01:44:07.758976 | 2017-09-15T10:56:09 | 2017-09-15T10:56:09 | 103,651,891 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 453 | rd | gradientPickerD3_example.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gradientPickerD3_example.R
\name{gradientPickerD3_example}
\alias{gradientPickerD3_example}
\title{gradientPickerD3_example}
\usage{
gradientPickerD3_example()
}
\description{
Creates an example shiny app which include the gradientPi... |
2a8c44bc801119f47f734dbb632af87c103ad272 | c52543d76f7082fc5bfb63d975ac6b2fed9fbe74 | /R-Prog/Exercise 4-20150608.R | a318a64d67d0bcec12cc284c9afd56b7850ce27f | [] | no_license | YoganandaMN/Coursework | d8d17298d2e3a53b7d55da432d7835b2544de9af | 30335d879d1234c8ae1aebba8542f18cb9da80fc | refs/heads/master | 2016-08-12T08:57:58.660726 | 2015-07-16T15:03:11 | 2015-07-16T15:03:11 | 36,800,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 471 | r | Exercise 4-20150608.R | #Exercise 4: Redo the previous exercise a more efficient way, i.e., without using a for loop.
#(Hint: This may be a bit more challenging than it looks; you have to think outside the box.)
RandomGeneration<-function(){
count<-1
while(count<=10){
n<-runif(10,min=-50,max=50)
print(n)
cat("\n")
s<-sort.... |
97e80462af9b2380febf53ba906527010a1851d7 | 319c8effd49600b5796cd1759063b0b8f10aeac1 | /workspace/CRISPR/mKO_FEF/species_specific/cis_trans/scatterplot_par_hyb.r.2018080917 | 15cf80cf8a4cf33382247796542d0b28024bcb9e | [] | no_license | ijayden-lung/hpc | 94ff6b8e30049b1246b1381638a39f4f46df655c | 6e8efdebc6a070f761547b0af888780bdd7a761d | refs/heads/master | 2021-06-16T14:58:51.056045 | 2021-01-27T02:51:12 | 2021-01-27T02:51:12 | 132,264,399 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,135 | 2018080917 | scatterplot_par_hyb.r.2018080917 | #!/usr/bin/env Rscript
library(ggplot2)
library(ggrepel)
args<-commandArgs(T)
pdf(args[2])
data = read.table(args[1],header=TRUE,sep="\t")
#quad1= nrow(subset(data,data$color=="yellow"))
#quad2= nrow(subset(data,data$color=="blue"))
#quad3= nrow(subset(data,data$color=="red"))
#quad4= nrow(subset(data,data$color=="gr... |
f52d39dfed73c2046fca8df526b2b11852132459 | d7dda74ede6f6987d3b60b5a95140aa961c7f78c | /Variaveis.R | b2a86830661e46d8964c712c617a2211cea8967a | [] | no_license | MaxuelAntenor/R-for-Data-Science | 19e1ba83bc78aecbee35e65661fd2cc48e54f438 | 10a3ab4c3847214ed8a7899b3d2c86bd446f0ad8 | refs/heads/master | 2022-11-18T14:50:46.396356 | 2020-07-15T15:45:30 | 2020-07-15T15:45:30 | 274,220,290 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 568 | r | Variaveis.R | # Criando variáveis
# Para executar clique sobre a linha ou selecione o conjunto de linhas
# e pressione CTRL + ENTER
# declarando o valor 10 para a variável a
a <- 10
a
# colocando b sem declarar antes, depois declarando com valor 10
# rodando a variável com letra maiúscula
b
b = 10
b
B
# irei util... |
a68ef439223943687284228352c93a18fd0c92df | d14ef67e34e3078dd351e9765bf9c67873d59641 | /R/021.R | a1026178427bb04208b18eb90ce318406decff55 | [] | no_license | mix060514/Euler-project | 2e3914f8f1bd8b229a7a26425efc6c0b955e8cfe | c2aa9ecb71df43bfe84123d93c7a34b64fbb3b4f | refs/heads/master | 2020-05-20T12:16:14.644973 | 2019-05-14T05:20:10 | 2019-05-14T05:20:10 | 185,567,481 | 0 | 0 | null | 2019-05-08T09:51:28 | 2019-05-08T08:45:46 | null | UTF-8 | R | false | false | 418 | r | 021.R | divisors <- function(n){
y <- seq_len(n) # same as 1:n
return(y[n %% y == 0])
}
# sum of proper divisors
sopd <- function(v){
if (length(v) != 1) {
v <- v[-length(v)]
}
return(sum(v))
}
sopd(divisors(220))
sopd(divisors(284))
AY <- 2:10000
a <- unlist(lapply(AY, function(x){sopd(divisors(x))}))
b <- ... |
9d07f1a01edaa2cf52bcc895aa528f211e50240d | 5d0ad197f94a53680dc4172ed3b8f1e8384a7d27 | /code/functions.R | ecdde38799fe7e807891152fe2109c352c38eed7 | [
"MIT"
] | permissive | markrobinsonuzh/os_monitor | 3356cbc8fb2a826572a8f4d64d1a454a180ffe2b | a6acd4740c657b9ebae0a09945862666bf1345f0 | refs/heads/master | 2022-02-28T20:44:27.516655 | 2022-02-17T12:43:52 | 2022-02-17T12:43:52 | 243,106,445 | 2 | 1 | MIT | 2020-10-07T05:55:18 | 2020-02-25T21:29:29 | R | UTF-8 | R | false | false | 4,261 | r | functions.R | library(dplyr)
library(rentrez)
library(RefManageR)
library(scholar)
fix_null <- function(x) {
if (is.null(x) || length(x) == 0) NA
else x
}
# grab everything from Entrez
retrieve_from_entrez <- function(pmid_search, pmid_remove=NULL, pmid_add=NULL, just_ids=NULL) {
if(is.null(just_ids)) {
x <- entrez_sear... |
044191941d2221c35bf4a1d272d4d6c607251a39 | cfb642c4568a403e7cd39b66e16dcaed0d08bd49 | /man/plotIntLay.Rd | d302e8b04e4b9ed65dcdd50d857cc1e7a173dc16 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | JVAdams/EchoNet2Fish | 5223bcdb98a43bb61cd629cb33f590cba9fd1fed | 6e397345e55a13a0b3fca70df3701f79290d30b6 | refs/heads/master | 2023-06-22T17:56:41.457893 | 2021-02-08T16:08:09 | 2021-02-08T16:08:09 | 32,336,396 | 4 | 1 | null | 2023-06-09T17:36:08 | 2015-03-16T15:59:18 | R | UTF-8 | R | false | true | 1,248 | rd | plotIntLay.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotIntLay.R
\name{plotIntLay}
\alias{plotIntLay}
\title{Plot Acoustic Survey Data using Different Colored Symbols}
\usage{
plotIntLay(
interval,
layer,
group,
grouporder = sort(unique(group)),
colorz,
main = ""
)
}
\arguments{
\i... |
75bb02b6a74265535ca51749843f381af78779ae | a0936eaffc5ff58736bfd8b1c2634f6a95294519 | /R/list_trained_models.R | f61d54ce7d8ee56e1026b07bfce5dc617e176e90 | [
"Apache-2.0"
] | permissive | ShixiangWang/sigminer.prediction | 2172ce5f3e95979de522f3b3d4b87a84f40785bd | 0572bfc65fd9eb43bf0bc73b108881953b07787f | refs/heads/master | 2022-07-10T22:22:52.245381 | 2022-06-25T09:09:40 | 2022-06-25T09:09:40 | 254,610,763 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,111 | r | list_trained_models.R | #' List Current Available Trained Keras Models
#'
#' @return A `tibble` containing summary models.
#' @export
#'
#' @examples
#' list_trained_models()
list_trained_models <- function() {
ext_path <- system.file("extdata", package = "sigminer.prediction")
if (!dir.exists(ext_path)) {
ext_path <- system.file("ins... |
9724cf7272836e3772758d3d6d2cb68a5357c8e6 | 760c7b6fb3fdf1095e45a13a026c15d81641c695 | /inst/shiny/myapp/ui.R | 4a070fadc2876de42afd773f9e33a993e5637926 | [] | no_license | reese3928/methylGSA | b3c39291c1996e430cf01b9c66ac73e32b67bce4 | b4f62eeaababc32c43be1954041ad06232891ff5 | refs/heads/master | 2021-06-07T11:26:27.911171 | 2021-05-08T18:48:43 | 2021-05-08T18:48:43 | 134,437,726 | 8 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,133 | r | ui.R | library(shinycssloaders)
ui <- navbarPage("methylGSA",
tabPanel("Main",
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
HTML("Please upload CpG IDs and thei... |
76c3d3ff701a3841ffb3aa077a1b4402a833efaa | 0b1a0e0daa124adf774e14a89dc09c1be978c5af | /man/occupancy.Rd | ad5308d8e9b4684a4f0462a8b2bcbaf2fdb4627d | [
"Apache-2.0"
] | permissive | jdyen/occupancy | d1c3e75ce1f80589e8a13330f9723aebec841808 | 72234fa32339066f14528c780cf6b32dc8e33fbd | refs/heads/master | 2020-06-14T23:03:11.455556 | 2019-07-05T06:03:26 | 2019-07-05T06:03:26 | 195,150,180 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,255 | rd | occupancy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/occupancy.R, R/package.R
\docType{package}
\name{occupancy}
\alias{occupancy}
\alias{occupancy-package}
\title{fit occupancy-detection models}
\usage{
occupancy(formula_occ, formula_detect, site_id, survey_id, data,
jags_settings = list())
... |
c97613492146a25478371068110ca736d7086c6c | 5a831da99b4134816b29b9b72945deb84bebc52c | /Scripts/summaryZOIPM.R | 40c546700b5d9a8a922fe620a18ab4ffb1794283 | [] | no_license | jucdiaz/Articulo3 | d64a5681af78534a2d5aca92e06bf3c4f6756636 | 6a1d2ad3317f3cf8b3eb48e861a51650f5973993 | refs/heads/master | 2021-01-15T12:02:49.835223 | 2017-10-03T05:01:36 | 2017-10-03T05:01:36 | 99,646,608 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,202 | r | summaryZOIPM.R | summary.ZOIPM<-function(mod){
estimate <- c(mod$Fixed_Parameters.mu,mod$Fixed_Parameters.sigma
,mod$Fixed_Parameters.p0,mod$Fixed_Parameters.p1,mod$Parameters.randoms[,1])
se <- sqrt(diag(solve(mod$HM)))
zvalue <- estimate / se
pvalue <- 2 * pnorm(abs(zvalue), lower.tail=F)
res ... |
5372c8fbdfaf39a521423a1ba9b35809f1d1cf8d | c53846243a231e977157d05c320537c3ec00a38c | /ui.R | cf9227f9054b53a1a06eecfc4d5419c823d8a779 | [] | no_license | atamaianalytics/SpatialValidation | ca7a5e310459dea4d02be932b4654e8d418cd4da | b080dc8454285f9220001cdd33b8e99f9202742c | refs/heads/master | 2021-03-19T00:37:31.019826 | 2020-03-13T16:32:08 | 2020-03-13T16:32:08 | 247,114,998 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,031 | r | ui.R |
library(shiny)
library(shinydashboard)
library(leaflet)
library(leaflet.extras)
library(sp)
library(shinycssloaders)
library(dplyr)
library(ggplot2)
library(RODBC)
library(DT)
library(lmodel2)
library(zoo)
library(DBI)
library(odbc)
library(gtools)
library(rgdal)
library(plotly)
units <- c("ppm",... |
d8e92bfc139246cbce628d3990a922666d16ff43 | f7942539a0685a3d359410a108e2ecbfe973f3db | /Assignment1_AirPolution/complete.R | cd7d28db84c1015bc0a6738c26012a0e652be7b5 | [] | no_license | MrBanhBao/Coursera-R-Programming-Assignments | 8dab6a4b7fd3b9d406e36d8463f0dba6eb2d1c26 | bb9be70b5df63a61dae21ae932a349ce4dba30de | refs/heads/master | 2021-01-16T18:57:48.290187 | 2016-06-20T19:46:56 | 2016-06-20T19:46:56 | 58,775,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 830 | r | complete.R | complete <- function(directory="specdata", id = 1:322) {
length <- length(id)
idVec <- numeric(length)
nobsVec <- numeric(length)
for(i in seq_along(id)) {
path <- paste(directory, "/",sprintf("%03d", id[i]), ".csv", sep="")
data <- read.csv(path)
... |
468ec0eaa4b3f90b2c0156b874b9993d4bca5632 | 37483f89fb907baaa6062e5ce1681cfaf1794950 | /functions_qiu.R | 8e9a3654c27112709af1fc7a18e74d87b03aee54 | [] | no_license | morndorff/GoF-Test | 8f80b7f44eddce550ca60791cebeb44ca7ec2524 | fdcb4f3a14ad0b99428926e1f8ec7a1dfb79d457 | refs/heads/master | 2020-12-24T15:50:00.743321 | 2016-03-21T17:40:00 | 2016-03-21T17:40:00 | 21,781,869 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,126 | r | functions_qiu.R | # Qiu 2009 Functions
get_y <- function(x, boundaries, sum){
int <- findInterval(x, boundaries)
tab <- tabulate(int + 1, nbins=length(boundaries) + 1) # m*f
emp_probs <- tab/length(x) #f0
return(emp_probs)
}
get_exact <- function(num_bps){
exact_probs <- rep(1/num_bps, num_bps)
return(exact_probs)
}
get... |
13ff312f2452e79b89987b69a5ddb6320835dabc | 5378b28dde3f365587dfb646d15a0eb66ac40f1e | /server.R | 646459de5a6d477510848c957ccb6f9920ec254b | [
"MIT",
"LicenseRef-scancode-other-permissive"
] | permissive | wwheeler6/CalCAT-1 | 64a69e3603c616397c9d9cbf74c7caa000734820 | ee73c7fcaade36cf3e785cc562688f2857471ffc | refs/heads/master | 2022-11-27T22:32:50.286561 | 2020-08-11T04:37:50 | 2020-08-11T04:37:50 | 286,646,310 | 0 | 0 | MIT | 2020-08-11T04:35:20 | 2020-08-11T04:35:20 | null | UTF-8 | R | false | false | 78,853 | r | server.R | # Developed by California COVID Modeling Team
# Copyright 2020, State of California, Department of Public Health
#
# John Pugliese, PhD.
# California Department of Public Health
#
# Jason Vargo, PhD.
# California Department of Public Health
#
# Nice!!
#
# Alpha Version : Released 6/8/2020
#
##########################... |
7013ed16548a62f4d999c7a14d45431fd50cb6fe | 2d6d15e8bd267bd142a45b89d159973f7aa56e81 | /Activity 5/activity 5 script.R | 69b0c86d13a29e47c81bdcf759410b9cd68d3d57 | [] | no_license | kbitsber/ENVST206 | 03e069b95c9f40c7879f0a7463c12b97b4b9d224 | c27cb3b775c95c4e312b10d60b8b6559540e275f | refs/heads/master | 2023-01-22T22:46:25.193423 | 2020-11-24T23:23:38 | 2020-11-24T23:23:38 | 291,063,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,889 | r | activity 5 script.R | #read in weather station file from the data folder
datW <- read.csv("/Users/kristenbitsberger/Documents/GitHub/ENVST206/Activity 2/Data/a02/noaa2011124.csv")
#specify that the name column should be a factor
datW$NAME<- as.factor(datW$NAME)
#set up a vector of all names for each level
nameS <- levels(datW$NAME)
nameS
#... |
fee9104001c792dc11407cc01c2d849fa4e7b87d | da51412f8bbc686f070cf9c468aa51bb302e36dc | /Read_dataset/TCGA_PanCancerAtlas_Publications/Genomic_and_Functional_Approaches_to_Understanding_Cancer_Aneuploidy/1.Generation_TCGA_Cancer_Aneuploidy_dataset.R | 66eee52b42744dc985c887e0e654e47ccb05bb30 | [] | no_license | haojiang9999/HCA_script | f7c7451e951abb2da83ada71688dd7833276dc4c | 46eaaecb016559982818f9e2c7c7f5c689b27622 | refs/heads/master | 2020-12-21T13:14:58.616545 | 2020-04-01T01:58:28 | 2020-04-01T01:58:28 | 236,439,415 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,596 | r | 1.Generation_TCGA_Cancer_Aneuploidy_dataset.R | #### 1.Generation_TCGA_Cancer_Aneuploidy_dataset.R
# Paper:Genomic and Functional Approaches to Understanding Cancer Aneuploidy
### 1.Read table
TCGA_Aneuploidy <- read.csv("Table_S2_Chromosome_Arm_Calls_and_Aneuploidy_Scores_Figure1.csv",
header = TRUE)
sampleID <- as.character(TCGA_... |
2a7641fe5c48e94b70e54f59b944eea6963cf42c | a485f853eca5e3d1783405c56f33d62ea2ed5972 | /pipelineMERGED.R | 8a365122c99be63040841fc16ed0e72895166888 | [] | no_license | findcomrade/DSEA | 488ba28fad958a57171f9b1025aba16eefd97e76 | d2213a94997f840771f688bc335f23c653b0faa2 | refs/heads/master | 2021-01-02T22:31:59.786365 | 2014-07-20T13:20:31 | 2014-07-20T13:20:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,958 | r | pipelineMERGED.R | #
# DSEA: the Second Step - ENRICHMENT
#
# Author: Dmitrii Bychkov, FIMM 2013
# (dmitrii.bychkov@helsinki.fi)
#######################################################
setwd("/home/comrade/Ubuntu One/DSEA/r-code")
source('pipeline_sup.R')
library(grid)
library(gplots)
library(ggplot2)
library(RJSONIO)
library(reshape2)... |
5240d4bcd50441d1eb15c27a3c8538c4e051a502 | 7747a3fdf0fdc57b767d8ed199b323afb4d491a2 | /R/sim_detect.r | 574efc81a32ab93995b1e59a82d3e2d0a78a20dc | [] | no_license | ianjonsen/simsmolt | dcafaad041d6caa29cd573cd543dbeab7e14868a | 09c9a8b8132bedaa499dd71c5c2fc6e2439256eb | refs/heads/master | 2022-07-28T10:03:06.683400 | 2022-07-07T14:13:08 | 2022-07-07T14:13:08 | 155,731,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,973 | r | sim_detect.r | #' @title simulate acoustic transmissions & detection, using \code{simulate} & \code{sim_setup} output
#'
#' @description simulates transmissions & detections along simulated track segments within a defined range of acoustic array(s)
#'
#' @author Ian Jonsen \email{ian.jonsen@mq.edu.au}
#'
#' @param s - a simsmolt c... |
7b4fa82fb8bf987cd6938ea70da4f5fecfeb4928 | 3675404b45f273e879b36ea7a6af282502bab2e6 | /Project/Analytics/Other_materials/R/phase_plane_newborn_model_1.R | 6e1c94089ea075658b6d8a04310bd9eac5d7047d | [] | no_license | rgrzhang/RogerZhang | 7914c335a8b22f11b45145c7914d740b1effc3ea | 9a51308fe4f559d3c931ecaf2e8bf10979e2dddc | refs/heads/master | 2021-05-10T13:58:20.420233 | 2020-07-13T03:19:03 | 2020-07-13T03:19:03 | 118,496,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,087 | r | phase_plane_newborn_model_1.R | library(phaseR)
FHN <- function(t, y, parameters) {
p <- parameters
dy <- numeric(2)
dy[1] <- 11/9136*(1-p)-4.5*y[1]*y[2]-11/9136*y[1]
dy[2] <- 4.5*y[1]*y[2]+11/9136*p-y[2]
return(list(dy))
}
phasePlot <- function(FHN, p=0.2){
FHN.flowField <- flowField(FHN, x.lim = c(0, 0.4),
... |
38b571c834ee09f2e79568da9d8c50bbe2377a9c | 060c6a303098ef689c43ea0feff68c68272477e9 | /RNASeq_analysis/UCFFigs.R | a7ccc5537cccef6878f6331af2e5b5cdeec1d1ea | [] | no_license | Sage-Bionetworks/Synodos_NF2 | e1c004191de438d6efa2d565f7d1c1e36a90efaa | 1506b57c74469439e81fe8afbc6de9add681c57c | refs/heads/master | 2022-12-20T16:20:09.620615 | 2022-12-14T22:53:52 | 2022-12-14T22:53:52 | 20,036,276 | 2 | 0 | null | 2014-05-28T18:09:16 | 2014-05-21T20:29:18 | R | UTF-8 | R | false | false | 1,658 | r | UCFFigs.R | library(synapseClient)
library(VennDiagram)
library(ggplot2)
library(ggrepel)
synapseLogin()
sch.new <- read.table(synGet("syn9884855")@filePath, sep = "\t", header = T) %>% filter(BH<0.05)
sch.old <- read.table(synGet("syn9884855", version = 11)@filePath, sep = "\t", header = T) %>% filter(BH<0.05)
for(x in unique... |
bb1708984dcb38170c8b12ffe5c65294ffcf80ea | e25af04a06ef87eb9fc0c3c8a580b8ca4e663c9b | /R/unif-alts.R | bb4e4e9c4d3e37347856d21f2a9aeb64b555f01a | [] | no_license | cran/sphunif | c049569cf09115bb9d4a47333b85c5b7522e7fd8 | 4dafb9d08e3ac8843e8e961defcf11abe2efa534 | refs/heads/master | 2023-07-16T01:12:47.852866 | 2021-09-02T06:40:02 | 2021-09-02T06:40:02 | 402,474,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,639 | r | unif-alts.R |
#' @title Local projected alternatives to uniformity
#'
#' @description Density and random generation for local projected alternatives
#' to uniformity with densities
#' \deqn{f_{\kappa, \boldsymbol{\mu}}({\bf x}): =
#' \frac{1 - \kappa}{\omega_p} + \kappa f({\bf x}'\boldsymbol{\mu})}{
#' f_{\kappa, \mu}(x) = (1 - \k... |
58b84bb5a5d26644c823b15457d4367c3a14f31c | ebad9ef7a3678fcf491ee0574377714648e4c0e8 | /man/officer_prep.Rd | 18d765dd26430ea825233e3e6f06ea66b2d8e450 | [
"MIT"
] | permissive | dpowerstp/arrprocess | 3b7339cd8feb1eda32878715a0ad79683ea57828 | b1eb48431c6d8588ea80639d349f22c274854c0f | refs/heads/main | 2023-07-07T02:26:43.997908 | 2021-08-09T18:45:19 | 2021-08-09T18:45:19 | 394,312,589 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 540 | rd | officer_prep.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/officer_prep.R
\name{officer_prep}
\alias{officer_prep}
\title{function to re-factor anonymous officer values by values present in given dataset}
\usage{
officer_prep(df, officer_anon_df)
}
\arguments{
\item{df}{df with officer_anon and offic... |
dd8397bd2a9bee629c08cd50f110f031721aa7cd | 0b73cc18412dfad492ab5e8b9f8c2ec0735d8e0c | /tests/testthat/test-13-upgrade_old_files.R | 1708eb472db81b7ab7466da7d29cb2e90a179c08 | [
"MIT"
] | permissive | UZH-PEG/dmdScheme | 30f2b38b1d1e292443408d589f3ffdcf4dc51375 | 7441e6fbc8807aa6343a982c22b80dfae7d079bd | refs/heads/master | 2023-04-16T17:08:19.543231 | 2022-08-30T07:29:04 | 2022-08-30T07:29:04 | 188,025,722 | 0 | 0 | NOASSERTION | 2022-03-18T14:32:08 | 2019-05-22T11:33:47 | R | UTF-8 | R | false | false | 709 | r | test-13-upgrade_old_files.R | context("13-upgrade_old_files()")
fn <- tempfile(fileext = ".xxx")
file.create(fn)
test_that(
"upgrade_old_files() raises error if file is of wrong extension",
{
expect_error(
object = upgrade_old_files(file = fn),
regexp = "x has to have the extension 'xls' 'xlsx' or 'xml'"
)
}
)
unlink(fn)... |
978c675e4125fa0d6f04c3a994a0ab79fccb2739 | 50c1aa6ee2cde2e58b2e55d014ef33e37383ac66 | /R/WSPG-DT model R code.R | e8def68ac60cc84dfc98ea58a88dbcbeb7903c64 | [] | no_license | terryferg/Analytic-Notes-examples | 934533a37391d9150a393b365c76960588abe3c8 | 6ceb2cf10bc04524e303fc99fd7463438a6cf826 | refs/heads/master | 2021-01-12T01:15:10.585952 | 2017-01-09T17:16:37 | 2017-01-09T17:16:37 | 78,361,464 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,529 | r | WSPG-DT model R code.R | #data <- Red.Decision.Tree...Associations
#data <- Red.Decision.Tree...Associations[Red.Decision.Tree...Associations$User==2,]
#data <- Red.Decision.Tree...Associations[Red.Decision.Tree...Associations$PricePoint==1,]
#data <- Red.Decision.Tree...Associations[Red.Decision.Tree...Associations$PricePoint==2,]
#data <... |
4e9a8c8f796dafe8b9db5ee1c3bb2387de998438 | 016d4c8380b71bd9641a4f27bf4825ca66300980 | /man/virtual_temperature.Rd | 97564a5bacc81ff0150d8bd25bc98a211d87fcd5 | [] | no_license | cran/aiRthermo | a55926857cea020fd8bd975726f4c284f18b7281 | 3e8289b8068ed38835fc728b99418566cc30de41 | refs/heads/master | 2021-01-01T06:03:00.580148 | 2018-09-16T21:40:03 | 2018-09-16T21:40:03 | 97,342,340 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 921 | rd | virtual_temperature.Rd | \name{virtual_temperature}
\alias{virtual_temperature}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Virtual Temperature
}
\description{
This function calculates the virtual temperature from given pressure and mixing ratio.
}
\usage{
virtual_temperature(P, Temp, w, consts = export_constants())
... |
171095fcc8676c3e2d02a70d3674aaded284ff82 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /MGMM/R/05_Mix_MVN.R | 2441ca19debccb958a7f233eba8239e7fef4b028 | [] | 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 | 14,894 | r | 05_Mix_MVN.R | # Purpose: Fits a multivariate normal mixture in the presence of missingness.
# Updated: 19/07/18
#------------------------------------------------------------------------------
#' Parameter Initialization for Mixture of Multivariate Normals.
#'
#' @param split_data Data partitioned by missingness.
#' @param k Numb... |
e94fb9c7ef915fe8a0579fc9f74cc9630ff92c27 | 61f21afe4f78dd93079dd4108ac6e5020eb6e021 | /modular_classes.R | 69c3bf2c2f628176e30c448d4995d38de3e87e0d | [
"MIT"
] | permissive | diogro/EL-snp_selection | 35e81c78bc04981f0d2b97e7ad8ed802f1d4b5c6 | 3a05ec689250578874de9eb3b816faf9ccbb84bc | refs/heads/master | 2023-04-04T14:18:19.438842 | 2023-03-29T18:45:18 | 2023-03-29T18:45:18 | 56,320,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,336 | r | modular_classes.R | modular_matrix = matrix(
c(1, 1, 0, 0,
-1, -1, 0, 0,
0, 0, 1, 1,
0, 0, -1, -1,
1, 0.5, 0, 0,
0.5, 1, 0, 0,
-0.5, -1, 0, 0,
-1, -0.5, 0, 0,
0, 0, 0.5, 1,
0, 0, 1, 0.5,
0, 0, -0.5, -1,
0, 0, -1, -0.5), ncol = 4, byrow = TRUE)
intra_antagonistic_matrix = matrix(
... |
7754e81d41a3ab3e5c17202019199a3de64ddb41 | 7cc51784a7a3b1ba46441d1f68d43e82f7d1688b | /man/focalExtract.Rd | 7b94d71d5eb81fa46a0d1a7920aab2cb846c55f9 | [] | no_license | rvalavi/myspatial | 39d4e8754c9fdf771fc0e70dbce21acff4ae2c7e | d44713ca8d6574b8ffd088ecf8e2db963b4da3e3 | refs/heads/master | 2021-06-17T13:50:20.255470 | 2021-05-10T02:29:39 | 2021-05-10T02:29:39 | 204,388,450 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 923 | rd | focalExtract.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extraction.R
\name{focalExtract}
\alias{focalExtract}
\title{Focal extraction from a raster object
A function to extract raster values from neighbourhood of points.}
\usage{
focalExtract(
r,
p,
neighbourhood = c(3, 3),
fun = mean,
n... |
2309686398bc109575019d35b5e312f1a0fe629a | 49e55ac34a33c1fda61bb722657c4531858b4a49 | /R/order_by.R | 6c8bdfa2667a63ee1d8cc1f84cdfb736e78baa12 | [] | no_license | Denis-pereira/rquery | c83a8c30a04e8ef53269a6e8638c7c47bb3d4ab2 | 09f76333e0eff4637b8ec229ee9a0e4ee3b5f929 | refs/heads/master | 2020-04-24T05:09:08.435940 | 2019-02-19T17:32:55 | 2019-02-19T17:32:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,347 | r | order_by.R |
#' Make an orderby node (not a relational operation).
#'
#' Order a table by a set of columns (not general expressions) and
#' limit number of rows in that order.
#'
#' Note: this is a relational operator in that it takes a table that
#' is a relation (has unique rows) to a table that is still a relation.
#' However, ... |
f7fda034a527647c2e0e35b370e9d6697f76ee32 | c6d22e9bea028bfee229c323d0b501c28fad95dd | /man/plot.magree.Rd | a079dea479b5554ff7682f24f29ed2b6ae909a07 | [] | no_license | cran/magree | f6b46e954c13999e5fdd4269a01bcef15dd88f70 | ac3acc89451207f393c06e5586aa35830cc2a8a4 | refs/heads/master | 2021-07-09T23:24:13.614475 | 2020-09-03T03:10:02 | 2020-09-03T03:10:02 | 76,046,518 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,258 | rd | plot.magree.Rd | \name{plot.magree}
\alias{plot.magree}
\alias{plot.oconnell}
\alias{plot.schouten}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
plot methods for magree, oconnell and schouten objects
}
\description{
plot methods for magree, oconnell and schouten objects
}
\usage{
\method{plot}{magree}(x, type ... |
bce765d3577b183f131e5f8ea5c3ef72f8619f92 | 6b32948c7241e204753cb88999c76cee323b4736 | /TransMetaRare/man/x.list.Rd | 229ed4176677d0d88cab767ff2528d83f6061523 | [] | no_license | shijingc/TransMetaRare | e9a0e74fef68bdfb59f35741b1e777afa23b1e44 | 5206b4a880c07b2d5df3b8d26a4bf0d6f88d77a6 | refs/heads/master | 2020-03-19T10:24:07.058000 | 2018-06-06T18:05:12 | 2018-06-06T18:05:12 | 136,367,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 471 | rd | x.list.Rd | \name{x.list}
\alias{x.list}
\docType{data}
\title{Example dataset, a list of non-genetic adjusting covariate matrices}
\description{
a list object of covariates. It has 4 elements for 4 study cohorts. Each element is a matrix of covariates. The first, third and last elements have two covariates (two columns), and the ... |
a91db51b02c6fb26be262fe1318ff1c1508e8c1a | f1d78ff9d5603149ffedf9428342fadcccf2cb69 | /plot4.R | d35f48c2cf72e6e2e1e3be67dc33e8cfdb2d12ef | [] | no_license | BuiQuocChinh/ExData_Plotting1 | 6eb19e632e8cbdc25d32f3448619da0ef749cf01 | da4d431ebf5995cf0d79dcdee8f508aac5e5901b | refs/heads/master | 2020-12-28T21:05:58.473776 | 2014-05-11T11:54:15 | 2014-05-11T11:54:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,726 | r | plot4.R | #This R script is used to produce the plot4 for the Assignment 1
#Load data from current working directory
Data <- read.table("household_power_consumption.txt",header=TRUE, sep=";",
stringsAsFactors=FALSE,
colClasses=c("character","character","numeric", "numeric","numeric","numer... |
f1a20986ee4cfd1a75b304cd37b8b3adc88b40be | 35d547a03919cf01465b49bdb273e28a9984d88d | /Section 6/Section6_Basic_RNN.R | bfda7f068ce7b3c5d17ca05688b591305e267e92 | [
"MIT"
] | permissive | PacktPublishing/R-Deep-Learning-Solutions | be51a80c95cb609d9a5b03f7fbbce685c481ae5d | 55b82518d8c5111f96286200d76dea5a70b07397 | refs/heads/master | 2021-06-25T00:51:23.747975 | 2021-01-19T12:57:13 | 2021-01-19T12:57:13 | 187,345,999 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,342 | r | Section6_Basic_RNN.R | library(tensorflow)
require(imager)
require(caret)
datasets <- tf$contrib$learn$datasets
mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
plot_mnist<-function(imageD, pixel.y=16){
require(imager)
actImage<-matrix(imageD, ncol=pixel.y, byrow=FALSE)
img.col.mat <- imappend(list(as.cimg(actImage)), "c"... |
9deaa7cd77c8f9b4d6a19599fc8f4023d90efce1 | 6f91ab8f666ed398aed6d5f9a31cd29fe3ce29d8 | /Cardwell_Wind2DailySummary.R | 2f210fd96309b7a5efb3c31f31daf4fcf39df614 | [] | no_license | CassieJames/NESP-processing-scipts | 366bb10084eef53f48c12289434147c1d0af8315 | 31c735d4dfe32c878ce0b076a143ebe848dae852 | refs/heads/master | 2023-01-24T14:55:45.670542 | 2020-12-09T01:01:23 | 2020-12-09T01:01:23 | 277,948,260 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,883 | r | Cardwell_Wind2DailySummary.R | #---
# title: "Wind Data Treatment"
# author: "Eduardo Teixeira da Silva", modified by C James (4/02/2020)
# date: "3 March 2016"
# output: html_document
#---
# This code gets wind data from the database as hourly readings with directon and quality coentrol, and output it into the data base as decomposed wind i... |
a71474f5c9fda3612c9368be051f738d3eda6c18 | 2c61bde0bf72a60019eee9add698c7b58c756d46 | /src/hic_genome_interaction_matrix.R | cb221f784e9c669379b58e6e2a0c2fe0ec621a96 | [] | no_license | sarahinwood/mh-hic-viral | 09b175eeb17d8f277050e36c88065726a64802c1 | 7ec0e5dbe16c258e5ae1995b7518db8cd4ff847a | refs/heads/master | 2021-07-02T17:59:22.492211 | 2021-06-17T20:39:30 | 2021-06-17T20:39:30 | 241,481,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,376 | r | hic_genome_interaction_matrix.R | #!/usr/bin/env Rscript
#######
# LOG #
#######
log <- file(snakemake@log[[1]], open = "wt")
sink(log, type = "message")
sink(log, append = TRUE, type = "output")
#############
# LIBRARIES #
#############
library(data.table)
library(dplyr)
###########
# GLOBALS #
###########
matlock_bam <- snakemake@input[["bam"]]... |
e354a67dd48768dd0a305fc79b28e1e49499a42c | 4ecc3c39c7c5b4d7d72c881381b3b2af6da50edb | /man/BayesPois.Rd | 9f030099ee22ea75e171b35edefb66d9a8cefff4 | [] | no_license | cran/Bolstad2 | e1a089ef39275817172e106e80efb2dd913d6b1f | 34d0854c3b8af01aac374d6911975ebecdb293b1 | refs/heads/master | 2022-04-30T06:26:54.804699 | 2022-04-11T08:22:32 | 2022-04-11T08:22:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,749 | rd | BayesPois.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BayesPois.R
\name{BayesPois}
\alias{BayesPois}
\title{Bayesian Pois Regression}
\usage{
BayesPois(
y,
x,
steps = 1000,
priorMean = NULL,
priorVar = NULL,
mleMean = NULL,
mleVar,
startValue = NULL,
randomSeed = NULL,
plots ... |
18a903a73294293170a549d1c370b8ee896dab54 | e039685fc9bdac3a7ffbeedb5aa22e4275f5c6a0 | /model-evaluation/Finding High Correlation [caret].R | cce3ea8b4ab18b4ae4030db12233c4745bf2ce18 | [] | no_license | cajogos/r-machine-learning | fb227124d2a393a612b22c065421a96b16c0cbe8 | 261ebe2c5def39a6db4f31395a9d92fe26a81eda | refs/heads/master | 2020-08-21T05:27:38.660252 | 2019-12-25T19:02:35 | 2019-12-25T19:02:35 | 216,102,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,202 | r | Finding High Correlation [caret].R | # Finding highly correlated features with the caret package
# NOTE: In regression or classification models perform better if highly correlated attributes are removed
rm(list = ls(all = TRUE)) # Clean-up environment
dev.off() # Clean-up any plots
# --- The prepared churn dataset --- #
library(C50)
data(churn... |
bea9254b3d6741d8fc5c0234ec8f3b4cae381e3e | 9aa4cc14706efc160068bb205291326b30e9c16d | /tests/testthat/test_options.R | 20925ce2437607ef1e6ad696d5ff18a2494c9850 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | arturochian/parcoords | 15c01e252b06d40576b2c67fa279605d6834d724 | a16eec94cd86b92909aa2b02e035bf3970d01961 | refs/heads/master | 2021-01-12T19:53:15.650837 | 2015-01-28T23:13:43 | 2015-01-28T23:13:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 961 | r | test_options.R | test_that("basic creation",{
expect_is( parcoords(data.frame()), c("parcoords","htmlwidget") )
expect_error( parcoords() )
})
test_that("options",{
# use mtcars dataset
data(mtcars)
# check rownames T
expect_identical( parcoords(mtcars)$x$data, data.frame(names = rownames(mtcars),mtcars,stringsAsFactors=... |
e572ce54f1b0968d41d0d41b629db61ed709585a | 34d07ad26869ffbb81c534c2fd49209708ffbffa | /UserInput2JASON.R | ecdc7380eb648589cea7b4d9942c490bfe544903 | [] | no_license | ericaenjoy3/GRFLoop | 22ec10b86e5eee527031c49f7d5c6102f3fbd0da | 3d41f8f4200f569da3ae7c8c5493ad751f0caf67 | refs/heads/master | 2021-05-09T19:20:44.279147 | 2018-08-15T23:36:13 | 2018-08-15T23:36:13 | 118,637,045 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,135 | r | UserInput2JASON.R | #!/usr/bin/env Rscript
###
# Input data file into JSON for read into H3K27AC_LoopType.R
# input:
# (1) H3K27ac HiChiP file (minimum 4 columns: locus1, locus2, gene1, gene2): hichip
# (2) ChIP-seq file(s) to overlap with at least one of hi-chip anchors for loop validation: vchip
# (3) ChIP-seq file(s) to overlap at no... |
a195f30178188747d154ef1199c31fad5cd0fd4d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/umx/examples/umx_set_optimization_options.Rd.R | e4c98f60cef9d01d84c42f8512e35c971cd68eaf | [] | 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 | 374 | r | umx_set_optimization_options.Rd.R | library(umx)
### Name: umx_set_optimization_options
### Title: umx_set_optimization_options
### Aliases: umx_set_optimization_options
### ** Examples
umx_set_optimization_options() # print the existing state(s)
umx_set_optimization_options("mvnRelEps") # show this one
## Not run:
##D umx_set_optimization_options("... |
30d0d6b2c46892043170f049888aa8b9ccb6b08f | eb6641b3761be376a4a404456b7694ae58ba19c3 | /plot2.R | b6eeb76789eb32d07b9b0509ccbc2d112f8629c3 | [] | no_license | Indy275/ExData_Plotting1 | d9840ca8d1ea3a46ef5eeb21c33e3777ee98bf81 | a385dc2b45919169a90bf4b1ed45897e18bc0047 | refs/heads/master | 2022-07-03T02:27:30.421708 | 2020-05-14T20:11:04 | 2020-05-14T20:11:04 | 264,014,000 | 0 | 0 | null | 2020-05-14T20:06:18 | 2020-05-14T20:06:17 | null | UTF-8 | R | false | false | 719 | r | plot2.R | power_consumption <- read.table("rprj/ExplorDataAnalysis/household_power_consumption.txt", sep = ';', header=TRUE, na.strings='?', nrows=2075259, check.names=F, stringsAsFactors=F, comment.char="", quote='\"')
power_consumption$Date <- as.Date( as.character(power_consumption$Date), "%d/%m/%Y")
power_subset <- subse... |
9419ea4d97afae91e832030140c4095c97f7fef2 | 0121d69e4551d8d1fa755e3b306a345c5a809bb4 | /7ARIMA.R | dc7b7ad4e3da49c0608ceca9dbac5174ff904ef5 | [] | no_license | omarterp/forecast-learn | 856bed4b8916a406711b7b049dd284a98691be5c | 38174d73d21877857b33987e5888bcbc19a07d20 | refs/heads/master | 2021-01-10T05:54:59.821986 | 2015-10-23T02:50:12 | 2015-10-23T02:50:12 | 43,187,835 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,449 | r | 7ARIMA.R | Amtrak.data <- read.csv("/Users/jasonmerrick2/Documents/Teaching/Exec Ed/Decision Analytics/Forecasting/Amtrak data.csv")
ridership.ts <- ts(Amtrak.data$Ridership, start = c(1991,1), end = c(2004, 3), freq = 12)
library("forecast")
plot(ridership.ts)
nValid <- 36
nTrain <- length(ridership.ts) - nValid
train.ts <- w... |
ed1f07d4d8316fa143572e122ff906edd8318dce | 599d6c8aff53bac7170f6d207f0851eb99cc85ae | /man/genotypes_pca.Rd | 42724d9396a8c39468561bc405cc246c59567443 | [] | no_license | jinhyunju/icreport | 6f76f993c7f7ba83676ac4e5cfc18b2c792fb128 | 9f0adca08badf4e47a52a227ee47614946b1f844 | refs/heads/master | 2020-04-16T00:22:48.646301 | 2016-09-15T22:08:09 | 2016-09-15T22:08:09 | 27,740,842 | 4 | 2 | null | 2015-10-14T15:42:28 | 2014-12-08T23:45:39 | R | UTF-8 | R | false | false | 806 | rd | genotypes_pca.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{genotypes_pca}
\alias{genotypes_pca}
\title{Custom PCA function for analyzing genotype SNP data.}
\usage{
genotypes_pca(genotype.mx = NULL, info.df = NULL, check.covars = NULL,
cor.threshold = 0.05)
}
\arguments{
\item{genotype.mx}{Genotype matrix with diemns... |
55aaece3dcbd5aaa31bebd82e60faa9455fd799a | df301198556ac71ffbe3b56127811b202c605422 | /9.20.2021_RST_hex_occupancy_and_div_accum_code.R | a24264924396b004b286507c5fb2579d0e79cee2 | [] | no_license | mavolio/BES_StreetTrees_Redlining | 6fd98dedf2fd56ce35557439cfa6100984b025fb | 0c26b0c8aff5e2d5637a464053b8cc183fc34fec | refs/heads/master | 2023-04-13T00:27:15.236621 | 2022-09-26T17:41:33 | 2022-09-26T17:41:33 | 349,562,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,072 | r | 9.20.2021_RST_hex_occupancy_and_div_accum_code.R | #####--------------------------------------------------------------------------------------------
### Baltimore_street_tree_redline ###
#####--------------------------------------------------------------------------------------------
# Last updated: 20 Sept 2021
# Author: Karin Burghardt
# Contact: kburghar@umd.edu
... |
c7ac77f4b54b825f509d1412cda5b0999c3f267e | ecd10e14781c11f0a934ba6b4b1b758ad2de07a3 | /resource_code/Untitled.R | 8e5039edea1f483899c2889b03095cd210aa04b0 | [] | no_license | scottkelleher/course_project_debate | 6e445543b2f5c805ff366926d5b63d521ef907f5 | 6239432b98920d8f482c5fade3dda0e773c2cab2 | refs/heads/master | 2021-01-18T23:14:23.879428 | 2016-12-12T20:28:33 | 2016-12-12T20:28:33 | 72,665,039 | 0 | 3 | null | 2016-12-05T18:50:39 | 2016-11-02T17:36:03 | HTML | UTF-8 | R | false | false | 315 | r | Untitled.R | getwd()
library(readr)
setwd("/Users/Tulsigompo/Desktop/Brucellosis_data_final")
Bruce<-read.csv("/Users/Tulsigompo/Desktop/Brucellosis_data_final/Brucelosis_R_data_New.csv",header = TRUE, sep=",",skip = 0)
list.files()
colnames(Bruce)
table(Bruce$Place_of_Origin)
table(Bruce$Brucelosis_status)
table(Bruce$Breed)
|
5fe66b95bf354a53eadb13488acde8042a2ecaba | 211b6cc8ab75ae26ea2a5900e1664546796b6c47 | /featureanalysis.R | 9222682f6d2a4fd3ec30f94b658df738bbfcb962 | [] | no_license | justinwang1/rulebreaksaesthetics | 88230fdd1db19744613f19bd4fc427b78c1f2c14 | 6e3cf8b2d0c21227dd0a164cade9d52e44247b3d | refs/heads/master | 2020-03-31T04:10:52.868347 | 2018-10-07T02:23:11 | 2018-10-07T02:23:11 | 151,894,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,427 | r | featureanalysis.R | <<<<<<< HEAD
setwd("C:/Users/jstwa/Desktop/ML/")
source("AesthSetup.R")
#Percentile some features - OPTIONAL
highData$Blur <- to.percentile(highData$Blur)
highData$Size <- to.percentile(highData$Size)
highData$Avg..S <- to.percentile(highData$Avg..S)
highData$Avg..V <- to.percentile(highData$Avg..V)
#t tests table f... |
12a19073c1c3f4c6ee0598b1716465551716c146 | ce236ff46444554634a802672bdcfcf33070bef2 | /man/classify.frsvm.Rd | 97281930fbd61c0690a4d49927736e1607a2b0fd | [] | no_license | ktargows/netClass | ed102f18545d9b58a01676971cd635295041bac1 | 1e3afbb9b798407e9032c5ab881e2fde801a9627 | refs/heads/master | 2021-01-20T21:46:05.907127 | 2013-12-03T00:00:00 | 2013-12-03T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,198 | rd | classify.frsvm.Rd | \name{classify.frsvm}
\alias{classify.frsvm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Training and predicting using FrSVM
}
\description{
Training and predicting using FrSVM
}
\usage{
classify.frsvm(fold, cuts, x, y, cv.repeat, DEBUG = DEBUG, Gsub = Gsub,
d = d, op = op, aa = aa, Cs =... |
40725b9543aa7f3285ead4dbef20e356e64097ce | 543f91156d72a12796a2fe34ebb2f2ed03058326 | /r/src/calc_trajectory.r | 39535f286dcafbe28cc9ba925af1cc88e3262a32 | [] | no_license | ddlddl58/stilt | 1bc8a8308a158189d07f06b28700e074bc3aace1 | bb16f2f3a438fccdb0332d5321e24e3afbcdc00a | refs/heads/master | 2020-09-25T17:12:00.912210 | 2019-10-28T22:37:35 | 2019-10-28T22:38:23 | 226,051,456 | 1 | 0 | null | 2019-12-05T08:21:12 | 2019-12-05T08:21:11 | null | UTF-8 | R | false | false | 6,045 | r | calc_trajectory.r | #' calc_footprint generates upstream influence footprint
#' @author Ben Fasoli
#'
#' Aggregates the upstream particle trajectories into a time integrated
#' footprint, expanding particle influence using variable 2d gaussian kernels
#' with bandwidths proportional to the mean pairwise distance between all
#' particles a... |
e0ece2bb5ac9f453cf4bb5661d6de1a41869be72 | e907785f763bcfabec58dd01278d6996cd2525ab | /man/inputInterestGene.Rd | ef123b3977067b8198c8192e7c0aef4aa528463a | [] | no_license | cran/SPMS | 2a8a9796677f908cae849e3a61c183394ef0c980 | 7f337e242f6f743ec4d6b88bfb1a264b63bd1ea8 | refs/heads/master | 2016-09-05T16:19:08.045425 | 2013-05-08T00:00:00 | 2013-05-08T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,198 | rd | inputInterestGene.Rd | \name{inputInterestGene}
\alias{inputInterestGene}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Input interestGene.
}
\description{
Users can input to interest genes according to the path. The genes input by user can be perpared in one file and each line represents one gene. The gene ID... |
7786bd8b8e88c90dfa7567784dd59c15ad276153 | d825e8fa597121edb8ad38329e45b675f60a3c33 | /inst/plumber.R | 01f0ca25f23cf0a82b8ccd2a9edbbdc51f5d7d36 | [] | no_license | AndreGuerra123/BSSEmsembleR | 559b05e75ee1e926f63774d0b1836532dc8efc4e | 3d5a825f010ffd21f1b1787385129617d5a14a9d | refs/heads/master | 2020-03-27T14:32:25.365818 | 2018-09-06T03:46:52 | 2018-09-06T03:46:52 | 146,668,332 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,451 | r | plumber.R | #* @apiTitle BSSEnsembleR
#* @apiDescription a plumber back-end for real-time ensemble modelling
# ---- GENERICS ------ #
isValidString<-function(x){
!all(is.null(x) || is.na(x) || !is.atomic(x) || identical(x,"") || !is.character(x))
} #Done
OBID <- function(){ #Done
ei <- as.hexmode(as.integer(Sys.time())) # 4-... |
3f32b9c5f1d0ed9892bf0f12f76f7e8cb606b280 | 988a0594b826dc860629d8a9138acab52c29acb5 | /Asst9.2.r | dc76974a082f942e8abad126a259fd45bada3eca | [] | no_license | vasanthi72/DARET_Assignment9.2 | c48b110947be357b8fb06e0778cf5b81eff57f32 | 93b3e62b672218d197387d641639ba5f8ecf883a | refs/heads/master | 2020-03-24T23:55:11.515102 | 2018-08-01T13:59:26 | 2018-08-01T13:59:26 | 143,160,395 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 734 | r | Asst9.2.r | data(mtcars)
table(mtcars$am)
trans <- as.data.frame(table(mtcars$am))
trans
SamProp <- trans[1,2]/sum(trans$Freq)
PopProp <- 0.4
n <- sum(trans$Freq)
z <- (SamProp - PopProp) / (sqrt(PopProp*(1-PopProp))/n)
z
((19/32)-0.4)/sqrt((0.4*(1-0.4))/32)
SamProp
19/32
z <- (SamProp - PopProp) / sqrt((PopProp*(1-PopProp))/n)
z
... |
b678cfe12e44763cb4f291e52e682de11faa452e | 0cd9371fd96cb4ec5aaf816520d4ca64b2a82e49 | /run_gsea.R | f4443905b6962eff32959b5075fb17ae478df720 | [] | no_license | JManc2003/gsea | c018a682e1a66db5d1c5168f1b94ec7cf19d1bd9 | 39e189d63a66951599381873adfca96b91ca7097 | refs/heads/master | 2020-06-14T06:12:02.682673 | 2014-07-13T17:52:02 | 2014-07-13T17:52:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,143 | r | run_gsea.R | # run_gsea.R will read in parameters from an input file "inputParams.txt," create
# submission scripts to run the java version of GSEA on sherlock, and submit them.
# USAGE RSCRIPT gsea.R gseadir inputprefix inputdata inputchip inputcls inputdb outdir
disorder = c("BRA")
gseadir = "/share/PI/dpwall/SOFTWARE/GSEA-P-R/g... |
161b8f50005f2a57cce394b265e09a6f9b40874c | 757c2db95bc362678b15b4f23968f7e3f6ebabac | /데이터정제_지자체관련자료.R | 3f0db5ed4d71222558797b50d8b3191cdc488c7e | [] | no_license | key9187/Dart-crawling | 86071df6cc63a867151d2b0ba977677c277d53b6 | ce7ea964adc0561d7617de06e38b5dc845ea0099 | refs/heads/main | 2023-07-08T06:32:43.477513 | 2021-08-05T06:38:09 | 2021-08-05T06:38:09 | 390,225,208 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,536 | r | 데이터정제_지자체관련자료.R |
######################################## 0. 환경설정 ########################################
# if(!require(readxl)){install.packages('readxl')}; library(readxl) # 버전 바뀜
# if(!require(XLConnect)){install.packages('XLConnect')}; library(XLConnect) # xls읽을 수 있지만, 자바설치 필요
if(!require(openxlsx)){install.packages('openxlsx... |
e97ba938ab52ed326b8fca6abd3b01627bd6a2f3 | 2764143779bda1d3a777b1311a27506498704cad | /R/partialDependence.R | 963dd81ef3b58aaac6270ab7a14c3f46c9560680 | [
"MIT"
] | permissive | hansvomkreuz/autoML | 7ed716170c8d3e3ea33f2f813455c28be0ceb04d | 36af7614f63a4ecb21da735591c7ff509193d33b | refs/heads/master | 2021-01-26T00:17:22.648913 | 2020-02-20T15:46:42 | 2020-02-20T15:46:42 | 243,238,443 | 1 | 0 | MIT | 2020-02-26T10:44:50 | 2020-02-26T10:44:49 | null | UTF-8 | R | false | false | 1,845 | r | partialDependence.R |
#' Generate partial dependence plots
#'
#' Creates a list of partial dependence plots for each feature used by the model. Partial dependence is simply the average prediction path a model takes whilst iterating through unique values of a feature and keeping the rest of the features static
#'
#' @param train [dat... |
6e48472a8027c10b9e37feefb744689048f07080 | 32811ffa5097c963274e6eb74ca2fd28fa7320ee | /tests/test-shared_residues.R | 4a8143a3f370a4b6a5bd44d68e3a64476ea0973c | [
"MIT"
] | permissive | Arcadia-Science/2022-actin-prediction | bf978ad5d54c4a4a53aae169f7dabc91ce428778 | 03425fca69fe45288f5f6e90be84c91564634459 | refs/heads/main | 2023-06-22T08:35:54.115985 | 2023-06-20T18:31:08 | 2023-06-20T18:31:08 | 545,442,889 | 4 | 0 | MIT | 2023-06-20T18:31:09 | 2022-10-04T11:33:25 | R | UTF-8 | R | false | false | 768 | r | test-shared_residues.R | test_that("check that giardia residue calculations match expectations", {
source("../R/shared_residues.R")
df <- readr::read_tsv("P51775_ACT_GIAIN-longitudinal_actin_contact_full.tsv")
df_summary <- calculate_shared_residues(df)
test_summary <- readr::read_tsv("P51775_ACT_GIAIN-longitudinal_actin_contact_summar... |
e7e907dabfa7cdf7569c777b8027090fb0f1bd65 | 86888c1d5a4e086590cc51d6826bc845ddb56b9a | /Actividad-1 y 2-Trabajo-Final.R | 378e069086b39d6cecc762f4fc8ff73de206ef80 | [] | no_license | BrandonNarvaez/ClaseAlgoritmosyLenguajes | ad2c48248f2ac8883a6360a0637bcf5f0d5ba610 | c9a20b5039fcbeadb432f41cb94c7c5c692f3e24 | refs/heads/master | 2020-03-26T22:47:52.334931 | 2018-12-07T16:53:17 | 2018-12-07T16:53:17 | 145,487,839 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 213 | r | Actividad-1 y 2-Trabajo-Final.R | santiago<-function(nombre,a,b,c){
print(paste(nombre,a+b*c))
}
santiago("santiago",4,5,6)
x=1
y=1
z=1
for (h in 1:5) {
print(h)
santiago("santiago",x,y,z)
x=x+1
y=y+1
z=z+1
}
|
8823d07468d177090795d7ac720d6d5303cdce1e | 53f27fb6f7699cfcadeb1b01eb8965a6c7187c16 | /Paddy_Lab.R | 22021160bd03635cff1975825a65fff3273dc287 | [] | no_license | mllimesha/DPA-Assessment | c27c5bb4bf7250e9b7bbb47be2e84ee0e38d0ce6 | c4031f4e4dc2de9c2359dff6cad12fc46b7be0c3 | refs/heads/master | 2022-10-21T15:32:45.598512 | 2020-06-13T15:09:50 | 2020-06-13T15:09:50 | 272,031,768 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 364 | r | Paddy_Lab.R | paddyData = read.csv("PaddyNew.csv", header = TRUE)
view(paddyData)
plot (paddyData [2:3])
par(mfrow=c(1, 2))
hist(paddyData$Sown_Acres, cex.main = 0.75)
p = ecdf(paddyData$Sown_Acres)
plot(p, cex.main = 0.75)
# Stacked histogram #
ggplot(paddyData, aes(fill=Season, y=Production_Bushels, x=Year)) +
geo... |
eb662c51410c73b22f7ba3351e116d2abdeace8e | 0733ee5d0081e6bda5a86214e85dd9e9582c4225 | /R/5_get_compound_data.R | 2c6cc4a70d9c517e8751813eff98ffb4e8893194 | [
"MIT"
] | permissive | gjgetzinger/monaR | 26c83a9adc06e47a245de1d5736ee03e23d76cd0 | ccca6dd98318c638cf66fbaa85c90b7fb20f68a7 | refs/heads/master | 2022-11-06T06:29:51.896774 | 2020-06-21T23:18:39 | 2020-06-21T23:18:39 | 258,864,327 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,534 | r | 5_get_compound_data.R | #' Extract chemical data from MONA queries
#'
#' @param df A tibble returned by a MoNA query
#' @param var The variable to return from the meta data
#'
#' @return a tibble containing extracted chemical data
#' @export
#'
mona_getChem <- function(df, var) {
UseMethod("mona_getChem")
}
#' @describeIn mona_getChem Get ... |
bd41230aa5887682f7a4befe56e5adceba35af85 | f2a982ef2ad5d0a1086830a59f2700bc7e0c668a | /man/read_csv_sampled.Rd | 94780b36fd41403c125af348227f39d409c471b3 | [] | no_license | jimsforks/cleanser | 6f87363fefd5c0223c17d349ffa19f8d5ff1956c | 1597f2bfcf58a0084c2810fea236e38a51385e43 | refs/heads/master | 2022-03-16T23:49:13.342589 | 2019-09-27T07:43:25 | 2019-09-27T07:43:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,465 | rd | read_csv_sampled.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chunked_sampled.R
\name{read_csv_sampled}
\alias{read_csv_sampled}
\title{Read in a file with randomly sampled lines.}
\usage{
read_csv_sampled(file, size = 100, header = TRUE, sep = ",",
dec = ".", nlines = NULL, skip = 0, fill = TR... |
f70758ea428a7f3f113c0e218e08de76cda674eb | 0900287305c2a1c8e3ea89ce22dd86b42d3146e1 | /man/seabirds.Rd | f834216a420093c49bfe6b249f4676b66c1b28b5 | [] | no_license | cran/GLMsData | 29ff0d385b86d8b69b647a4a3cdde59c3fa995ef | adf13787011fc2603433c97edc40940a7408b48e | refs/heads/master | 2022-09-11T10:58:41.904753 | 2022-08-22T05:20:08 | 2022-08-22T05:20:08 | 128,936,898 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,324 | rd | seabirds.Rd | \name{seabirds}
\alias{seabirds}
\docType{data}
\title{Counts of seabirds}
\description{
The number of four species of seabirds
}
\usage{data(seabirds)}
\format{
A data frame with 40 observations on the following 3 variables.
\describe{
\item{\code{Quadrat}}{the quadrat;
a numeric ... |
bab45081d56bf71ddced57fcd55448296a1c7c44 | 61aba3afa4f16ce312aa5b0b8d5c939e54afc6d2 | /Influencer _analysis.R | b8bc2ccb54959ca534c66ac4a9f511cbaf362567 | [] | no_license | Vivek-Sakthivel/SentimentAnalysis | 4e06a94ffa3725599e70b18fe4c947db9b34e5aa | d3e18083bfcd3b99d144aae30077a88676eb638c | refs/heads/master | 2020-04-21T01:51:37.221584 | 2019-08-17T19:49:27 | 2019-08-17T19:49:27 | 169,236,614 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 922 | r | Influencer _analysis.R | #To extract the top 20 tweeters of the extracted hashtag in the collected corpus
toptweeters-function(tweetDataset)
{
sampleTweets - twListToDF(tweetDataset)
sampleTweets - unique(sampleTweets)
# Make a table of the number of Tweets per user
tweeterData - as.data.frame(table(sampleTweets... |
9f4092eaa760b659cc969d725a8dfcea22f5a89e | 8c5c5530bdcabed4ca47875f5bb2cb355cf93100 | /run_analysis.r | ea875d25d70a6de47d4ff7119f7f1fdf2e747e7d | [] | no_license | JadfGitHub/GettingAndCleaningDataCourse | b69aeb2512b2006de19919992627caa6ca5cd2c8 | 5c0c4e43279bc49ce62e22c7afa30e99752d1a0c | refs/heads/master | 2021-01-23T19:36:07.914844 | 2014-05-25T20:17:08 | 2014-05-25T20:17:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,566 | r | run_analysis.r | #load the various datasets
activity_labels <- read.table("./UCI HAR Dataset/activity_labels.txt", quote="\"")
subject_test <- read.table("./UCI HAR Dataset/test/subject_test.txt", quote="\"")
X_test <- read.table("./UCI HAR Dataset/test/X_test.txt", quote="\"")
y_test <- read.table("./UCI HAR Dataset/test/y_test.txt... |
af6374022b26dea7d9c930d41df30a4d02863603 | a9c565654a27a7013dd7d5323bd9d018111211e5 | /R/yml_to_pkg.R | 279d6ec713cbe5636c286fa94749a08cf9195065 | [] | no_license | c5sire/yml2shiny | ee956ae6788571820a2386c030c2e7c91c50c782 | e4723746ef8bdcf615719c1a21c47ca2192a84b3 | refs/heads/master | 2016-09-05T12:14:15.407786 | 2015-05-04T20:47:29 | 2015-05-04T20:47:29 | 35,058,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,188 | r | yml_to_pkg.R | cols_to_sl3 <- function(columns){
cols <- unlist(columns)
types <-as.vector(cols)
ff <- function(type) {switch(type,
integer = "INTEGER",
real = "REAL",
string = "TEXT",
logical = "TEXT",
date = "TEXT",
time = "TEXT",
blob = "BLOB"
)}
tt <-... |
050571b1d5b407ba046a3c1123cb1fa26f8e028b | ebbe08d58a57ae2e9d308a12df500e1e0ef8d098 | /microbiome/figures/fig2_colorbar.R | 2f2ccded9c21f4dbb99d794b947700b345e8f176 | [] | no_license | Drizzle-Zhang/bioinformatics | a20b8b01e3c6807a9b6b605394b400daf1a848a3 | 9a24fc1107d42ac4e2bc37b1c866324b766c4a86 | refs/heads/master | 2022-02-19T15:57:43.723344 | 2022-02-14T02:32:47 | 2022-02-14T02:32:47 | 171,384,799 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,174 | r | fig2_colorbar.R | library(ggplot2)
# color bar
df_plot <- data.frame(pval = c(0, 1, 2, 3, 4), TF = as.character(c(0, 1, 2, 3, 4)))
df_plot$NUL <- rep('1', nrow(df_plot))
plot_bar_up <-
ggplot(data = df_plot, aes(x = TF, y = NUL, fill = pval)) +
geom_tile() +
scale_fill_gradient(low = 'transparent', high = '#8B0000', brea... |
8113eddeb804d9ff3f7c1179d68bb940a25383a0 | 2da2406aff1f6318cba7453db555c7ed4d2ea0d3 | /inst/snippet/pheno-weight01.R | ec40bf1ec515db16385c032ac3e97befb9448b61 | [] | 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 | 92 | r | pheno-weight01.R | pheno.lm <-
lm(log(weight) ~ log(waist) + log(height), data = Pheno)
msummary(pheno.lm)
|
892aaad6b21217a2c479aabf36adb6de9e4c0bab | c44188038f65d2e665ad8f7ee138a1cb48256efa | /man/kmeans.Rd | 056898c37afb8e11760353935c60dbe7e5aef5f5 | [] | no_license | astamm/game | 91972389d0e7dca54f1705d5f6fc7222c44a9071 | 353bc1f9ed8333f47933928feca0119f81a57f75 | refs/heads/master | 2020-05-04T23:00:19.845264 | 2019-06-04T08:55:48 | 2019-06-04T08:55:48 | 179,530,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,771 | rd | kmeans.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kmeans.R
\name{kmeans}
\alias{kmeans}
\alias{kmeans.default}
\alias{kmeans.sgd}
\alias{kmeans.gmd}
\title{K-Means Clustering}
\usage{
kmeans(x, ...)
\method{kmeans}{default}(x, centers, iter.max = 10L, nstart = 1L,
algorithm = c("Hartigan-... |
821bc2b0efcd26f6e7f8e78aa5b7ec526fbc2e20 | f5d2dd91994929a25bd36dc78b246bee85202adf | /man/getWeights.Rd | d783975ac07ee60a9c5078104e67e44f9b536bb2 | [] | no_license | environmentalinformatics-marburg/Reot | 1350feb80c342aa6c94172d68c58d5e55ae8ad1c | 1a3e09b08e960b80b236d571d3c637b8e29272fd | refs/heads/master | 2020-04-24T22:32:13.185940 | 2014-08-25T09:29:07 | 2014-08-25T09:29:07 | 11,943,730 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 597 | rd | getWeights.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{getWeights}
\alias{getWeights}
\title{Calculate weights from latitude}
\usage{
getWeights(x)
}
\arguments{
\item{x}{a Raster* object}
}
\value{
a numeric vector of weights
}
\description{
Calculate weights using the cosine of latitude to compensate for area
dis... |
69c35fc26bbd4c30c5464e2f22de203460004a57 | 6c9474206ce5cdd33adca1308ec9686ae6ca8b77 | /phase1/daySummary.r | 93f88746e23ea12c74ffd0e52a5b3143ce56febf | [
"MIT"
] | permissive | rohbockn/tickerFeed | 69a35cf824fa0720b3e41327a4f984d58a76768c | de85034e9423916c80445725e6b16cb22be9d957 | refs/heads/master | 2021-09-05T04:22:40.434570 | 2018-01-24T06:31:33 | 2018-01-24T06:31:33 | 116,171,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,963 | r | daySummary.r | # Title: daySummary.r
# Objective: Gather data on positions and report at the end of day
# Created by: NR
# Additional editors:
########################################################################
# Preamble
########################################################################
# Set options
options(stringsAsFa... |
d250f9f442329def7e25d4755d65d5e3231fbad2 | 51863f0eec5d4536ae91f63e4366f6f361eff1b9 | /Assignment for homework- Lecture 2.R | 9c97bc4a779726dfac9b144252528f94e709bd65 | [] | no_license | kaoriito/BKN-599--Introduction-to-statistical-learning-for-Biological-Science-in-R | 1745e77e3e147cc257aad855bbf2e4b910910b9d | 264649f0c18620a761594c6aede9f7a7155f0c51 | refs/heads/master | 2020-04-30T19:51:02.148254 | 2019-03-21T21:04:01 | 2019-03-21T21:04:01 | 177,050,093 | 1 | 0 | null | 2019-03-22T01:24:24 | 2019-03-22T01:24:23 | null | UTF-8 | R | false | false | 816 | r | Assignment for homework- Lecture 2.R | # BKN 599- Lecture 2- Jan 17th 2019
# clear workspace variables
rm(list = ls());
# it means ctrl+L. clear window
cat("\014")
# close all plots
graphics.off()
library(ISLR)
# Use the lm() function to perform a simple linear regression with
# mpg as the response and horsepower as the predictor. Use the
# summary(... |
991ba3c7da93aedd16ca181045932b6b14f250ef | 414dcb572c9f3c417c505b0a2a0bb05596bab5c5 | /R/stepwiseReversible.R | 8bc0e99c1f4e46cdca275d7fa038baeea81804a9 | [] | no_license | magnusdv/pedmut | e053c69c4296ec60c5a33ba5eb810451cb1f1d0d | f02631674e4848ddde92504f1d3c1f1bce202d58 | refs/heads/master | 2023-06-02T16:04:14.385448 | 2023-05-25T12:49:53 | 2023-05-25T12:49:53 | 147,926,814 | 2 | 0 | null | 2023-05-22T20:32:50 | 2018-09-08T11:17:08 | R | UTF-8 | R | false | false | 3,975 | r | stepwiseReversible.R | #' Reversible stepwise mutation model
#'
#' A reversible stepwise mutation model is created following the approach of
#' Dawid et al. (2002).
#'
#' For the stepwise reversible model, the mutation rate \eqn{r_{i,j},\, i\neq
#' j} is proportional to the overall mutation rate \eqn{\lambda} for given
#' values of the rang... |
f0b19e99bb49fd2b4f0046b4ef7b107185e6544b | 01c7b98d9d798c0346701fee1d221c51b324080e | /R/device_query.R | 93d2968483f3824c14507b98892f40f7cb03c801 | [
"MIT"
] | permissive | strategist922/RCUDA-1 | a59f153fc3bbcfd184d1320a3bbed97d3ce419af | 7f9cd49dc075bcfbc4422c6eebd150c291fd6397 | refs/heads/master | 2020-09-20T05:42:11.415829 | 2016-11-21T17:21:27 | 2016-11-21T17:21:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 287 | r | device_query.R | #' gpuquery
#'
#' This function returns the information of available GPU device in system
#' @seealso \code{\link{creategpu}}
#' @export
#' @examples
#' gpuquery()
gpuquery <- function()
{
ext <- .Call(
"devicequery",
PACKAGE = "supplement"
)
}
|
feacd6369e7beff5b2e2fcff99d76d0fb25eb025 | cfbb6b9abcfc107153a5f24e58823384e37ccc6d | /scripts/UKB_gxdrug/cv_overfit.R | ef45d8be2bafc47a0bdce20a5e94596c45360cfc | [] | no_license | drewmard/druggene | 63f6cca52d4bd0237b02363a14666d7397e4035e | b0339ec688645bacae7d34dd5be7fdbac99216e6 | refs/heads/master | 2023-05-22T16:57:53.280916 | 2021-06-14T19:29:03 | 2021-06-14T19:29:03 | 331,698,407 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,871 | r | cv_overfit.R | pgs_name <- type.lst[i]
pgs_name <- 'PGS000007'
dataf.mg$combined <- scale(apply(data.frame(scale(dataf.mg[,'PGS000007']),scale(dataf.mg[,'P_0.00001.R2_0.1.KB_250'])),1,mean))
ind <- sample(1:10,nrow(dataf.mg),replace=TRUE)
res <- list()
for (ind.sub in 1:10) {
print(ind.sub)
df.train <- dataf.mg[ind!=ind.sub,]
... |
a812007e83d8fe29e49ce8b36da8b47497d7f60d | 7aa114908d996c3e873c78bce8b17afe08cb7816 | /man/uniqueTargets.Rd | 04b400922135be7afe80afb4bef1fe91ae0aba30 | [] | no_license | peiwen18/miRNAmRNA | 9c5f861d97c77cacc5089c50cb08df2313965a11 | 0756073a2ebb57e75543fe745f54e75b44747b31 | refs/heads/master | 2021-06-21T12:20:10.396855 | 2017-01-24T11:55:03 | 2017-01-24T11:55:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 607 | rd | uniqueTargets.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/db_functions.R
\name{uniqueTargets}
\alias{uniqueTargets}
\title{Unique mirs}
\usage{
uniqueTargets(path, dbName, mir, tables = NULL)
}
\arguments{
\item{path}{path to database}
\item{dbName}{database name}
\item{mir}{microRNA for w... |
c8c4ff2647651761b98a94dff38fb542db1bb824 | a46fe604555ba1016139daec6710758512ec2c89 | /man/getClinicalByGene.Rd | e8da9b7f13fcc87938e96cc4c604533ef5761249 | [] | no_license | melsiddieg/cellbaseR | 201898d272734b0a97218d305c0d32987929036f | 8b724bb65402ed8b445829f57fe58fbf09d4a7c1 | refs/heads/master | 2021-07-30T12:52:15.888698 | 2021-07-28T06:07:50 | 2021-07-28T06:07:50 | 56,924,006 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 275 | rd | getClinicalByGene.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/user.R
\name{getClinicalByGene}
\alias{getClinicalByGene}
\title{getClinicalByGene}
\usage{
getClinicalByGene()
}
\description{
A convienice method to fetch clinical variants for specific gene/s
}
|
da0f2a083e29f0ad2fac77b1c18f499f17f6afff | 91c07d83227fe6d47c4937baa24a7d57b6a8b4e4 | /CCreplicationsV1.R | 6db6a9764aa93f6b31334efc7c70b51bc4498030 | [] | no_license | BGFarrar/P-value-simulations | ca14462e2bdd913560f73dcdb9642c8fdcdf4a0f | ffec0eab51d0db9e732b74a32a472192195214c2 | refs/heads/master | 2021-07-16T22:24:34.395882 | 2020-08-23T09:29:42 | 2020-08-23T09:29:42 | 203,995,022 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,223 | r | CCreplicationsV1.R | ## Simulation code accompanying "What can comparative cognition expect from replication studies?"
## Ben Farrar
if (!require("effsize")) install.packages("effsize")
library(effsize)
if (!require("ggplot2")) install.packages("ggplot2")
library(ggplot2)
#### Secion 1 Stimulation Study ####
## Compute req... |
cc35489e7905385b9282e05e841fffb1a29c72b0 | 412d7ac8d78ee6eac43787b8a8f1883ebcffd6da | /R/S1_eval_embedding.R | 0c5fdcc745138af6bf91298a7a32833eba2152de | [] | no_license | fhaertner/GeometricAnalysisDMs | 76669248c750ce7a919f545d046854c75d559140 | d0c23d650a3bfa7f695df94e04689c07b59e8a27 | refs/heads/master | 2020-03-10T02:02:20.435952 | 2019-03-12T21:05:32 | 2019-03-12T21:05:32 | 129,126,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,709 | r | S1_eval_embedding.R |
# Given an igraph network, its temperature and coordinates, evaluates the
# embedding to H2
library(NetHypGeom)
library(cowplot)
library(dplyr)
# Load network and coordinates --------------------------------------------
outname <- "emb_eval_hpin"
#net <- readRDS("data/hint.rds")
#load("results/coords_hPIN_150k.RData... |
523db8105675db8d47be179f06b90918195b5ae4 | a816bcd3416d393ef0b933c968ed100124dee404 | /BG_model/herb_resist_proccess_functions.R | d74fd2ebafc9b7861efa0ca0d9bf956e821855d3 | [] | no_license | ShaunCoutts/BG_herb_res | c97ec21f07d864faaaed2a1243a99d8b74a39113 | 8056b1320d262efe8208753b9c15922494a4e99b | refs/heads/master | 2021-01-17T04:02:14.378459 | 2018-07-31T15:43:15 | 2018-07-31T15:43:15 | 42,309,872 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,202 | r | herb_resist_proccess_functions.R | #set of functions for herbicide resitance model covering various processes like genotype production and seed bank dynamics
#working_loc = '/home/shauncoutts/Dropbox/projects/MHR_blackgrass/BG_model'
#setwd(working_loc)
#test_obj_name <- load('nonspatial_model_test_answer_key.Rdata') #load the test key
## EVAL_POINTS_... |
9c0cfdaae553c14a37d24f0780af6cb24c501c8b | dbc0c9c17314a709824f167225cdd45839e4f56c | /chap3/tf_idf_phy.R | 3290f1a0cdf812dfbff783d52fe69e9b465bb561 | [] | no_license | trisgelar/tidytextmining | 3c608b5aaf741ccbab3309f4e65d4a9e7bc86f86 | 46ef954e7a74987ffd0d4d0e68ac2bba9a43a85d | refs/heads/master | 2020-03-25T06:26:56.170153 | 2018-08-06T09:34:01 | 2018-08-06T09:34:01 | 143,502,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,059 | r | tf_idf_phy.R | library(gutenbergr)
physics <- gutenberg_download(
c(37729, 14725, 13476, 5001),
meta_fields = "author"
)
physics_words <- physics %>%
unnest_tokens(word, text) %>%
count(author, word, sort = TRUE) %>%
ungroup
physics_words
plot_physics <- physics_words %>%
bind_tf_idf(word, au... |
439122625063ffb40ae84ebaa1a1d61b9f22119a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/powdR/examples/powdRlib.Rd.R | 38d5678e053a391790022a412b89896b259f5088 | [] | 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 | 354 | r | powdRlib.Rd.R | library(powdR)
### Name: powdRlib
### Title: Create an XRD reference library
### Aliases: powdRlib
### ** Examples
#load an example xrd_table
data(minerals_xrd)
#load an example phases_table
data(minerals_phases)
#Create a reference library object
xrd_lib <- powdRlib(xrd_table = minerals_xrd,
p... |
2a720e39aa91816ef1d5d0f936fe718718d7b73d | b82635a880c39fed94e77ad850968a32db1f603a | /Constrained_Optimization_Lagrange_Multiplier.R | ae4a536e22db69bba9eb04b0b454acd29d492110 | [] | no_license | btemovska/Machine_Learning_OSU_class | b70b84602d64fa6e4d43dd02ba423ce2d961a5a7 | 836205e27771447e7980898b3323dae51514d3b7 | refs/heads/master | 2023-01-08T02:13:51.247815 | 2020-11-13T03:11:52 | 2020-11-13T03:11:52 | 306,176,695 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 958 | r | Constrained_Optimization_Lagrange_Multiplier.R | install.packages("Rsolnp")
library(Rsolnp)
# fn1 return the value of value of the function by taking x as input vector
fn1=function(x){ x[1]^2+2*x[2]^2+5*x[3]^2}
#eqn1 is the function that returns the constraint
eqn1=function(x){
z1 = x[1]+x[2]+x[3]
return(c(z1))
}
#initial values
x0 = c(0,0,0)
x_optimal = solnp(x0... |
fedd9e065269d408fa3ad370339859375083381c | 8940282cdb736e66f8a48b3533ba180630172f17 | /c/hello/src/Object.r | ebf8f76913ad0631509a775864fbe673fb6ceca1 | [] | no_license | bsdelf/research | 306e2fccb122bd2956aaa5219ac779cd07a6dc8c | a9f6739603e9e804f9079f29183ec777be4bd714 | refs/heads/master | 2021-01-02T23:12:16.842153 | 2012-05-02T05:21:26 | 2012-05-02T05:21:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 248 | r | Object.r | #ifndef Object_r
#define Object_r
struct Class
{
size_t size;
void* (* ctor)(void* self, va_list* app);
void* (* dtor)(void* self);
void* (* clone)(const void* self);
int (* differ)(const void* self, const void* b);
};
#endif
|
00b8d1c99576a7dc867fd2efbca7fe361fd8532f | c92d5cb354087582a6ee725e789d891c2d6e5cc7 | /man/remove_bottom.Rd | f7732daace719f17d2056679355ffeba5aa11151 | [
"MIT"
] | permissive | josesamos/flattabler | bc6063389362843f8181e1724c1f18304529d9d0 | 6ac67dc39d52653a286bd0a0cb15f545de75cdc4 | refs/heads/master | 2023-09-04T09:01:00.265314 | 2023-08-13T09:05:08 | 2023-08-13T09:05:08 | 275,351,769 | 3 | 0 | NOASSERTION | 2023-08-09T19:17:23 | 2020-06-27T10:47:12 | R | UTF-8 | R | false | true | 1,412 | rd | remove_bottom.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pivot_table.R
\name{remove_bottom}
\alias{remove_bottom}
\alias{remove_bottom.pivot_table}
\title{Remove bottom rows from a pivot table}
\usage{
remove_bottom(pt, n)
\method{remove_bottom}{pivot_table}(pt, n)
}
\arguments{
\item{pt}{A \code{... |
5118d574e4dfa922fd138a58b30dd4069d49b2ac | 56d196a66c8bbbd195478446d48b299eaa0cbb64 | /Chapter8/Problem62.R | cb284258939567a24f9914dec196f9b529cc165d | [] | no_license | aulchen/rice_stats | bbe8370faae2ead11b68fad3fc489abb571bac53 | 13705602642b5c470d80f2edd23384b30092786d | refs/heads/master | 2023-07-16T04:30:34.610651 | 2021-08-25T20:31:53 | 2021-08-25T20:31:53 | 271,953,575 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,129 | r | Problem62.R | distSeq <- seq(0, 0.6, by = .01)
partA <- list()
partA$prior <- function(x) dgamma(x, shape = .25, rate = .5)
partA$posterior <- function(x) dgamma(x, shape = 20.25, rate = 102.5)
partB <- list()
partB$prior <- function(x) dgamma(x, shape = .25, rate = .025)
partB$posterior <- function(x) dgamma(x, shape = 20.25, rate ... |
e45c922196153c7bfb6511d098751ba02785efb8 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /MultivariateRandomForest/man/Imputation.Rd | a939bde280c6fcf8313a7a20033e1fab3d8097a8 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 578 | rd | Imputation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Imputation.R
\name{Imputation}
\alias{Imputation}
\title{Imputation of a numerical vector}
\usage{
Imputation(XX)
}
\arguments{
\item{XX}{a vector of size N x 1}
}
\value{
Imputed vector of size N x 1
}
\description{
Imputes th... |
acaf61879d62f2c7672c6950d96ceceb0bff3a72 | 00eb5271deba1c20f9445643085bf7dc89566661 | /Activity5.R | d5b096c9d842d422cbbafa5979fa7aaccf40ca34 | [] | no_license | mmcgrawcolgate/GEOG331 | c6ab13a8380af562ce1fadb071e23073494c670d | 25e833cbecc858f3d03c51d153774d4e430eb9f6 | refs/heads/master | 2023-02-06T03:51:33.214733 | 2020-12-18T18:26:04 | 2020-12-18T18:26:04 | 292,890,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,691 | r | Activity5.R | #ACTIVITY 5 CODE
#by: Matt McGraw
##READING IN DATA AND MAKING DATAFRAMES##
library(lubridate)
#read in streamflow data file
datH <- read.csv("stream_flow_data.csv",
na.strings = c("Eqp"))
#read in precip data file
datP <- read.csv("2049867.csv")
#create new dataframe u... |
a0954921d81899e8a8a06b724fb8ac2bb01cfd4b | e1e27fea18045b209988f932d8bcf47f1135a546 | /Hurricane-Ike.R | cc6c1048202384443547cb010168da5fba5e3fec | [] | no_license | GabrielAlejandra/Build-a-New-Geom | a909236083dc0430650b89efd7fde1dc25e31789 | 87284977788095bd2c2efdf4665f9ea14405c32d | refs/heads/master | 2021-09-01T17:14:45.962273 | 2017-12-28T02:47:44 | 2017-12-28T02:47:44 | 115,577,741 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,420 | r | Hurricane-Ike.R | install.packages("tidyverse")
install.packages("geosphere")
install.packages("ggmap")
install.packages("ggplot2")
library("readr")
#library("tidyverse")
#library("geosphere")
library("ggplot2")
library("ggmap")
library("magrittr")
#' Read the data base
ext_tracks_widths <- c(7, 10, 2, 2, 3, 5, 5, 6, 4,... |
7c4875c364a56798673eb03855a68769561f98d9 | 280302213d20c55dae9b555a6ec2890767ffd5c8 | /Project 1/plot3.R | 3b6be623da089cff5b75c36ab235d17cfde72ff3 | [] | no_license | amiles2233/ExData | 7b34ff81da75b91f8e29f91f5ae0449778f4aea9 | 39113bec0c5eca7d169c8df08aa61b4b8ef5ef0a | refs/heads/master | 2021-01-20T21:29:22.861741 | 2015-09-27T19:53:46 | 2015-09-27T19:53:46 | 41,609,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 665 | r | plot3.R | # Set Working Directory
wkdir <- "C:/Users/amile_000/Documents/Coursera Data Science/Exploratory Data Analysis/Project 1"
setwd(wkdir)
# Read in Data
source(read_data.R)
# Plot 3
png(filename = "plot3.png",
width = 480, height = 480, units = "px",
bg = "white")
plot(x=dat$DateTime, y=dat$Sub_metering_1, col... |
3cea7969281f8dc2ade2a163cd565c52dbf2475a | 48d3c07dbcfd56902fd73f2b436f180703b565bf | /wk2/corr-with-apply.R | 4f81934d6bd32dfef38bf322a399e724496216c2 | [] | no_license | fosdick/datasciencecoursera | 445798c60ff91967ce125c417d1568dc79bb3bb4 | 8091a52040224287d4705c5155ca153f98423915 | refs/heads/master | 2021-09-15T01:26:09.209577 | 2018-03-04T20:14:49 | 2018-03-04T20:14:49 | 118,483,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,032 | r | corr-with-apply.R |
complete <- function(directory,id = 1:332) {
csvfiles <- sprintf("%s/%03d.csv", directory, id)
nrows <- sapply( csvfiles, function(f) sum(complete.cases(read.csv(f)), na.rm=TRUE))
rowlabels <- nrow(nrows)
data.frame(id=sprintf('%3d', id),
nobs=sapply( csvfiles, function(f) sum(complete.cases(read... |
48f9ac8faec26f23b9b14553ca88c68f2919118f | dae6befcea92b6171d6e592d58ecb7c499a2ae9a | /R/is-inconsistent-terms.R | 0f6576df3b13c6eb63a9275f0af78865053ca2d1 | [
"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 | 542 | r | is-inconsistent-terms.R | #' Is Inconsistent Terms
#'
#' Tests whether a term vector has inconsistent elements.
#'
#' @inheritParams params
#' @return A logical scalar indicating whether the object's terms are inconsistent.
#' @seealso [term-vector()] and [consistent_term()]
#' @export
#'
#' @examples
#' is_inconsistent_terms(as.term("b[2]"))
#... |
87bea30d94a7329e9826657c4036e0b354264192 | 909d7b3eee81c7c55f1a6e40a8e466ebb29e59cb | /PCA_IRIS.R | 0dd24a16534e72b7148e46f4b623afe0ac494e71 | [
"MIT"
] | permissive | malunjkarusa/DataAnalytics-R | 2046248c4a38cf34533c923cd134a5a5a675e43f | c5b3eff41d1fd9e96d72609007fdbd656d5da32c | refs/heads/master | 2020-07-29T17:31:47.786249 | 2020-02-01T21:06:31 | 2020-02-01T21:06:31 | 209,902,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,827 | r | PCA_IRIS.R | # pacman is a package manager package - You just need to install pacman once,
# and load it in every new session. Then, the function p_load, which is from pacman package,
# allows you to install (if not already) and load all the required packages by passing their
# names as arguments in the p_load function.
... |
fc05096ec1bb0495e2c5c6f936f044e1c73aae2c | 1482c0c2e994197d04c2149eb19ce2f313cd7a45 | /man/selectControlsHier.Rd | 3d502a9efb4409fc439bd1e68c37e9c11d6c35bb | [
"MIT"
] | permissive | alexloboda/SVDFunctions | 4adffe4b7e101a68b5cf756d8fefee45610303c5 | 666dbc820f81a3ab03e706fea380deaeb1d6f4f5 | refs/heads/master | 2023-05-11T13:44:28.623205 | 2023-03-28T15:12:38 | 2023-03-28T15:12:38 | 153,036,232 | 6 | 1 | null | 2019-05-14T17:17:20 | 2018-10-15T01:28:35 | C++ | UTF-8 | R | false | true | 1,179 | rd | selectControlsHier.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hier.R
\name{selectControlsHier}
\alias{selectControlsHier}
\title{Select a set of controls that populationally matches a set of cases.}
\usage{
selectControlsHier(
controlGMatrix,
originalControlGMatrix,
cases,
SVDReference,
contro... |
7687737a4e7522422380e3a041833df73aad1b5f | feb501c5f88cc204b6617ddddbbe4aa1b914ef63 | /man/as.triangle.Rd | 34f813fe6836152d2cfc01352847b85c694f8454 | [] | no_license | stjordanis/trinalysis | 50ddc9c0251abc524470b6bc4d2bc40e178a3ba1 | a8dfbe66cae49854832611aa63e84e9a4a1dbb43 | refs/heads/master | 2020-06-28T02:06:34.002320 | 2019-05-06T14:34:13 | 2019-05-06T14:34:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 898 | rd | as.triangle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/as.triangle.R
\name{as.triangle}
\alias{as.triangle}
\title{as.triangle}
\usage{
as.triangle(triangleDT, valueCol = "Transactions",
descriptiveHeaders = TRUE, idCols = "Cohort")
}
\arguments{
\item{triangleDT}{A triangle in tall, data.table... |
92b5a4113ee2288b70fc3c80bb74a77b3d6db02f | 90cb71d7d5e4b0169d5b3fe8272c11d59471dff7 | /R/utils/OsmDataApiDownload.R | 7f6b4a582330d8b6fc5e1a457ebb3ea6c215b4eb | [] | no_license | davibicudo/matsim-toy | 3958201c3330782b4e202ca525054a4152a179ee | 69866f3f23859bc42390e7a2a23ba99f40e8c650 | refs/heads/master | 2022-12-21T05:55:55.677946 | 2022-12-16T23:51:57 | 2022-12-16T23:51:57 | 189,230,803 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,334 | r | OsmDataApiDownload.R |
get_osm_data <- function(city_name, country_name) {
# print variables used in function
print(mget(setdiff(ls(), c("opt", "option_list", match.call()[[1]]))))
# load needed libraries
pacman::p_load("osmdata")
# get bounding box for city
bbox <- getbb(place_name=paste0(city_name, ", ", country_name... |
dd3bf97fa60c56bd2523a547621b4f15615a193f | 5c68fc959e3be7f6b77151774d1d05b9a5e359be | /R/3_customize.R | 674b17ec32b1388cca50718cf0708ffd3aadaae8 | [] | no_license | mmontesinosanmartin/itoiz_article | 26717eda5e58b35d2e1bdb9b10877978255de6e5 | 26821b2d6a05f58d8150f577e83076b59c926960 | refs/heads/master | 2021-05-26T02:43:30.794613 | 2020-06-12T14:33:48 | 2020-06-12T14:33:48 | 254,020,006 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | 3_customize.R | ###############################################################################
# R Code: Using RGISTools to estimate the water levels in reservoirs and lakes
###############################################################################
# Militino, A.F., Montesino-SanMartin, Pérez-Goya, U.,M., Ugarte, M.D.
# Public U... |
fd8a49875d5978ed6164d54f32857430bc7b5e99 | 21f8a43099e22dbb00a670fcbede1697d17c34ce | /# 03 - Getting and Cleaning Data/Project 1/run_analysis.R | 6e918c0a1ec2e138bf006f8383913ddb5138c949 | [] | no_license | thefier/datasciencecoursera | 75bd4ab50a3f7869dacd931704604b1a925402f5 | 473b16cbbf6ba87e66022aa24522647205c7ab05 | refs/heads/master | 2021-01-19T10:57:45.879059 | 2014-07-20T22:00:18 | 2014-07-20T22:00:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,638 | r | run_analysis.R | run_analysis<-function(){
## 1. Read Files
## 1.1 Variables to read the Folders
ir.ma<-"./UCI Har Dataset"
ir.te<-"/test"
ir.tr<-"/train"
## 1.2 Read names
names.x<-read.table(paste(ir.ma,"/features.txt",sep=""),
... |
d4df6a401351d16ff527605f0e8e28315c226602 | 1dc1a1a4e717c20112517501c43f9a966ab0c0e5 | /R/od-funs.R | d830e75ca7d0d347d7b4515ff47092fcd4102851 | [
"MIT"
] | permissive | Robinlovelace/stplanr | 5d11640b9b644e40d81b97ee1a2debb77ffb4e26 | d1f10fe2335c2494ba153fd09675756e2c1572b3 | refs/heads/master | 2021-01-23T09:02:00.128500 | 2018-09-13T08:19:36 | 2018-09-13T08:19:36 | 30,063,520 | 14 | 4 | null | null | null | null | UTF-8 | R | false | false | 21,392 | r | od-funs.R | #' Extract coordinates from OD data
#'
#' @section Details:
#' Origin-destination ('OD') flow data is often provided
#' in the form of 1 line per flow with zone codes of origin and destination
#' centroids. This can be tricky to plot and link-up with geographical data.
#' This function makes the task easier.
#'
#' @par... |
bfa48e5f648ce4a01354629e7984f91fe1f48565 | 71c95568a68e1d956b2ae5e4ef01e5cd94a039d2 | /daily.R | 137adf26ca025902f671b54d00dbdf6512859da3 | [] | no_license | EmilHvitfeldt/paletteerbot | 2c7dd70e2caed9514d77c2aba0768b61dff5c85d | 7a1c69bf3af153de02d74f4eabdfb5bb5ade4b9d | refs/heads/master | 2023-02-06T06:02:23.384313 | 2021-01-03T21:09:32 | 2021-01-03T21:09:32 | 286,087,969 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,434 | r | daily.R | library(paletteer)
library(tidyverse)
library(lubridate)
library(patchwork)
library(tidygraph)
library(ggraph)
library(rtweet)
library(glue)
library(emo)
lapply(list.files("./R", full.names = TRUE), source)
continuous_colors <- readRDS("data/continuous_colors.rds")
discrete_colors <- readRDS("data/discrete_colors.rds... |
b1f681fac9e7fd2dfa93992d431e0cac3e8a2d84 | a53c1be476315400ceba9a42651019b09d7a7fee | /assets/scripts/4_MMA/_functions/bootstrap.condor.r | 33e67dc7c76c2083230354d1befee28fdc126c90 | [
"CC0-1.0"
] | permissive | BPRC-EcoInformatics/BPRC-EcoInformatics.github.io | d8535043c8d4ae9cfba207efc99b43d8377a56b8 | 9f3b2b2e44271d547dd1af33323fd033eaf41b0e | refs/heads/master | 2021-01-13T00:56:32.704123 | 2017-12-14T22:50:19 | 2017-12-14T22:50:19 | 54,073,297 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 633 | r | bootstrap.condor.r | # Make bootstrap files for Condor directory "boot"
# Load functions
bootstrap.condor <- function(species.name,nreps) {
data <- read.table(paste(species.name,"_data_pres_abs_best.txt",sep=""), header=TRUE)
data <- data[data$pres==1,]
setwd(paste("C:\\MMA\\",species.name,"\\_condor\\boot",sep=""))
boot.mat <-... |
7cc49edbfa068229f032ba09af69b4d2e38bb011 | dd15d6f7bc83ac75695b64866e02f7ed81b75b7a | /man/plot_genedrop_lm_slopes.Rd | da799d9bcc1a157687c8719f3fd652dcd7c15533 | [] | no_license | susjoh/genedroppeR | fe2a912e065ca9fbd45bbd10107e832389c1af2a | 3d9aae940707ba0316b9f2069c4f0794956501c9 | refs/heads/master | 2022-12-11T20:18:14.059881 | 2022-11-25T20:32:20 | 2022-11-25T20:32:20 | 178,249,087 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,154 | rd | plot_genedrop_lm_slopes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/genedrop_plot_functions.R
\name{plot_genedrop_lm_slopes}
\alias{plot_genedrop_lm_slopes}
\title{Plot a histogram of Gene-Drop Simulation linear regression slopes}
\usage{
plot_genedrop_lm_slopes(genedrop_object_summary,
n_founder_cohorts = ... |
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