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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2899c0629e0e0f84786fb88bb152b67d9b2e979b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/nutshell/examples/consumption.Rd.R | 5ff28b60b15c1a135e5031c2b18828fffe4b4e03 | [] | 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 | 309 | r | consumption.Rd.R | library(nutshell)
### Name: consumption
### Title: Per capita US Food Consumption 1980-2005
### Aliases: consumption
### Keywords: datasets
### ** Examples
data(consumption)
library(lattice)
dotplot(
Amount ~ Year | Food,
data=consumption,
aspect="xy",
scales=list(relation="sliced", cex=.4)
)
|
39540eaae3f621003e6c120df78868927f34755a | ecd850028010252cd24d7d67aa3ef3b8065bf109 | /man/Shannon.wmppp.Rd | 46f0b0532e985663342cc062dc47e2c756fc6c8d | [] | no_license | EricMarcon/SpatDiv | de98877555e9d92ec7859d4ce18bf5dbb47bf2a1 | c0346e1105130d18dc8b978a415569f653ae0cf7 | refs/heads/master | 2023-05-26T18:42:38.506723 | 2023-05-21T08:42:49 | 2023-05-21T08:42:49 | 111,210,097 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,204 | rd | Shannon.wmppp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/entropart.R
\name{Shannon.wmppp}
\alias{Shannon.wmppp}
\title{Shannon Entropy of a spatialized community}
\usage{
\method{Shannon}{wmppp}(NorP, Correction = "Best", ..., CheckArguments = TRUE)
}
\arguments{
\item{NorP}{A \link{wmppp.object}, ... |
2b51b5d9484b4e1974c93370e96473838667f21c | 7eb6dc6f827d37662b3b980649e2961540ef57c6 | /testcase.r | 0337a363421a843d4eb060a2dfb1f48ca8beb359 | [] | no_license | baile2nm/ProgrammingAssignment2 | 2bbaaa4aa895608f622396e7d36d9031cc8b5651 | 3da666041803413f8b2a27e43fef6548630bc7db | refs/heads/master | 2021-07-29T19:26:25.515814 | 2021-07-19T06:51:54 | 2021-07-21T01:36:17 | 25,451,327 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 393 | r | testcase.r | ## Simple test case for functions in cachematrix.r
source('cachematrix.r')
## Make an invertible cached matrix.
t = makeCacheMatrix()
t$set(matrix(c(2,0,2, 0,1,2, 0,0,2), 3, 3))
## Verify matrix is set up correctly.
t$get()
## For first call, the inverse must be computed.
i = cacheSolve(t)
i %*% t$get()
## On seco... |
1b1c48e732753ad794c042b293757502c90a2533 | f2c739195928f207ab848063feefd0e69defd2cc | /R Studio House Prediction - RZ.R | 2a2f171623fa652610f00f07efad28cefd855c24 | [] | no_license | OlegRyzhkov2020/house_price_prediction | fdcc2e37a26350de7897112826e01e2adbf217b6 | 491be6f9dc1a5d8116045e3a71f798e8aa91ddf8 | refs/heads/master | 2023-01-24T21:03:15.885676 | 2020-12-05T03:32:03 | 2020-12-05T03:32:03 | 310,643,052 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 399 | r | R Studio House Prediction - RZ.R | lm(data$SalePrice ~ data$MSZoning + data$Street + data$LotArea)
full.model <- lm(SalePrice ~ ., data=data)
library(MASS)
fwd.step.model <- stepAIC(full.model, direction = "forward", trace = FALSE)
summary(fwd.step.model)
AIC.base = extractAIC(fwd.step.model, k=log(n))
signif(AIC.base, digits=6)
bwd.step.model <- ste... |
7e9d49e18517e128e1b9ab9c7126db2f21d9801f | 87a0961c529b257c89b9e91ca1300068f4918dc3 | /scripts/analysis.R | 02d295876fb6c27228a0458873d9375900532aef | [] | no_license | g-tierney/polarization_migration_maps | a6a41081d457a0636ae1ddbac169bbac9d364b77 | 2c096a3ac4d9b180859d0baeef9404787c706bbe | refs/heads/master | 2021-01-21T21:19:52.528438 | 2018-01-24T18:17:14 | 2018-01-24T18:17:14 | 94,823,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,092 | r | analysis.R | # Script to load and format election data
# 1) Load data
# 2) Shape into panel of county-year level data
require(openxlsx)
require(dplyr)
require(classInt)
require(maptools) # loads sp library too
require(RColorBrewer) # creates nice color schemes
require(classInt) # finds class intervals for continuous... |
4cae698d4debdc40366336df29b0b18b4561aed0 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Amendola-Ricca-Truszczynski/selection-hard/ctrl.e#1.a#3.E#122.A#48.c#.w#9.s#52.asp/ctrl.e#1.a#3.E#122.A#48.c#.w#9.s#52.asp.R | 9ec57dcea7921f6464ebaad1d7d2eed162f1d81b | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 91 | r | ctrl.e#1.a#3.E#122.A#48.c#.w#9.s#52.asp.R | bdb0a73eb60314dfb69a0983f15e6714 ctrl.e#1.a#3.E#122.A#48.c#.w#9.s#52.asp.qdimacs 8953 26350 |
bcd2c3aedfe3f0d7780c360a18ea5ebc74871fbc | 1ab7fa0da198f73b1594162cb3e910fd5af42355 | /Fig1/Data_preprocessing/Data_formatting_fromRaw.R | e6339706afc1ae4909e161583d7c20615ffa29d1 | [] | no_license | mengysun/False_positive_ParTi | 324d7bb3e7f725fb52f3db0a25f061b9f216f05b | fe49d470de08f6c9f14d3a4257c475294733f9f2 | refs/heads/main | 2023-02-08T19:16:40.606740 | 2021-01-03T05:08:05 | 2021-01-03T05:08:05 | 301,218,710 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 702 | r | Data_formatting_fromRaw.R | library(dplyr)
dat_raw<-read.table(file="deleteome_all_mutants_controls.txt",sep="\t",header=TRUE,quote="",stringsAsFactors=F)
#get the columns contain the deletion line data
dat_raw_1<-dat_raw[,c(1:4455)]
dat_raw_2<-dat_raw_1
MA_columns<-as.character(dat_raw_1[1,])
#extract the M and A values, save into different tabl... |
269816315258b1b0ed60cdddf77b7717aa05e6f2 | 33b424627a5b4d7f7cc045043682b6e1955bbfc7 | /snp2sim_analysis/vinaAnalysis/weighted_app.R | 550860d5b5de60dd687ef8a2ef53cad669d53018 | [] | no_license | mccoymd/snp2sim | e24b198fa192d1eec5e4ed539d6fc88a66e1e236 | f5a48b46a829cfc7c8de0d7528d9875cee9c7dd5 | refs/heads/master | 2021-08-09T01:02:20.340214 | 2021-06-16T20:43:57 | 2021-06-16T20:43:57 | 137,887,506 | 2 | 4 | null | 2019-09-11T13:57:22 | 2018-06-19T12:19:08 | Python | UTF-8 | R | false | false | 8,128 | r | weighted_app.R | library(shiny)
library(ggplot2)
library(highcharter)
library(viridis)
library(plotly)
args = commandArgs(trailingOnly = TRUE)
table <- read.table(args[1], header = TRUE, sep = "")
pdbSet <- unique(table$scaffold)
ligSet <- unique(table$ligand)
varSet <- unique(table$variant)
data <- table
fulldata <- data[data$rank =... |
47568b59581d7afde944b6979c82895dbd013aa1 | 29585dff702209dd446c0ab52ceea046c58e384e | /RSDA/R/centers.interval.R | 3e2037d98e3972fbbfcd42aaf9528b1b70a8683a | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 666 | r | centers.interval.R | centers.interval <-
function(sym.data) {
idn <- all(sym.data$sym.var.types == sym.data$sym.var.types[1])
if (idn == FALSE)
stop("All variables have to be of the same type")
if ((sym.data$sym.var.types[1] != "$I"))
stop("Variables have to be continuos or Interval")
else
nn <- sym.data$N
mm <- ... |
3aa0977ae8fb8e18ceca84729be90f0c0e079f19 | 88147e2bddc2add4f51b507dbf1eed86de849495 | /bivcoptests/kldiv-test.r | 2829e675b8e386c5c885d5c716f8988d91c5bc62 | [] | no_license | hoanguc3m/CopulaModel | 39906379ed88d56f579851d45f157733c42bf926 | 1522b9a6476c5550736f652f902c61b3ac5e8fd3 | refs/heads/master | 2020-03-27T14:29:49.345584 | 2019-11-03T22:18:14 | 2019-11-03T22:18:14 | 146,665,628 | 2 | 0 | null | 2018-08-29T22:25:56 | 2018-08-29T22:25:56 | null | UTF-8 | R | false | false | 558 | r | kldiv-test.r | # KL divergence dcop1 vs dcop2
library(CopulaModel)
library(cubature)
tau=0.5
th.bvn=bvn.b2cpar(tau)
th.pla=depmeas2cpar(tau,"tau","plackett") # 11.40486
th.frk=depmeas2cpar(tau,"tau","frank") # 5.736287
cat("\nBVN vs Plackett, tau=0.5\n")
KLcopvsbvn(rh=th.bvn,dcop2=dpla,param2=th.pla,copname2="Plackett",UB=7,ipri... |
b871e634068fd9b6fd27f4bd6845eaf6488087da | 7ff4354b5ce73a35811f2d53b4e9dc1a1bae6f89 | /man/imports.most.Rd | 85929e32e1c471871a0b69cb1c9fde1b59533deb | [] | no_license | yikeshu0611/packagomatrics | e3183aace895f6ea868911b0a54b6ddf96be445b | 6057f71760fb9b62d25fd8126d09e927f121dc38 | refs/heads/master | 2022-11-22T06:19:08.517311 | 2020-07-23T02:52:14 | 2020-07-23T02:52:14 | 281,833,186 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,279 | rd | imports.most.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/imports.most.R
\name{imports.most}
\alias{imports.most}
\title{Mostly imported packages}
\usage{
imports.most(imports, n = 10, year = FALSE)
}
\arguments{
\item{imports}{results of function imports()}
\item{n}{the most frequency number}
\it... |
0cdff8e9297b4e31849ef53615528a6d6e4bd619 | b72c5fef88aad2e52c8f1221f0fa6d5fc165748c | /R/AIreport.R | 0a57fad99562284876365a7cabebacc200d7ea5d | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | JVAdams/GLFC | 62c6915efb6f5c4193ef0d580eb68ea8e8838692 | 82ea12b3d7e40465207728439d34f77068980c78 | refs/heads/master | 2023-01-05T07:51:52.885062 | 2022-12-20T21:45:29 | 2022-12-20T21:45:29 | 32,460,710 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,687 | r | AIreport.R | #' Adult Index Report
#'
#' Create a draft template-style report of the Adult Index estimates of
#' sea lamprey in the Great Lakes.
#' @param streamPEs
#' A data frame of "complete" stream mark-recapture estimates
#' (meaning all contributions to the Adult Indices have
#' already been calculated). The data frame... |
8325412c0493a774fe7ec2349be044d7747058f9 | c17df37f05205cbd428b37fa5b942020e6752e8c | /_render.r | 88134fdbeee2b60b41d0a4c5248ed408c56a6b31 | [] | no_license | Kan-Pang/2020EconometricsTA | 624644225021467486cddb4d9d0d1ee506e42ebc | e96fc031ac781d3fec1331367ef86304f9160fe6 | refs/heads/main | 2023-02-22T14:37:03.278603 | 2021-01-29T08:52:51 | 2021-01-29T08:52:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | _render.r | #Render
#'
#+ library and wd
library(rmarkdown)
library(bookdown)
Sys.setenv(RSTUDIO_PANDOC = "C:/Users/katoo/AppData/Local/Pandoc")
options(repo = "https://cran.rstudio.com/")
## ---- render
# pdf (rmarkdown)
rmarkdown::render(
input = "II/TAsession_13/handout.rmd",
output_file = "handout.pdf",
output_dir = "... |
40e1f828f3dae098774270c333b50cf5372a4b8e | 44803ba65f6fb45e837e40e9a422f0653d36883d | /SSMs_Exploration/Mutations_ChisqTEST.R | b51af0da38dca917600c3b6399b6247567189985 | [] | no_license | MMaqueda/MutSig_WGSvsWES | b5a265cc20dd1d9c4b4fbd57894c385c4a679f5a | bdf92a8eb696a9f6680c887261f6f88e964179fd | refs/heads/master | 2022-09-07T04:03:03.630288 | 2022-07-26T12:02:04 | 2022-07-26T12:02:04 | 141,121,595 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,782 | r | Mutations_ChisqTEST.R |
#' This script computes a Chi-square test in order to test if the number of mutations in a specific region
#' of the genome is different from the rest (WGS-that specific region). We will compute the test with median
#' number of SSMs per tumor type and then per sample (and determine % samples with significant p-value)... |
7d6a257c62c71448ec624109e87cd78df1eda16e | 077fd87077da8a399d51196ba7c81e970e752639 | /man/works.Rd | 82f3a9eb80edfb444c3688191f4f8d95ddaf3e99 | [
"MIT"
] | permissive | bibliometrics/rorcid | 6c5cc58ae977204cfcabc4831020c02c3b891a1e | a4c2c24c292ac4812115b025ce469e39f7572cf4 | refs/heads/master | 2021-06-01T19:23:37.397645 | 2016-09-22T02:56:21 | 2016-09-22T02:56:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,113 | rd | works.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/works.R
\name{works}
\alias{works}
\title{Get works data}
\usage{
works(x)
}
\arguments{
\item{x}{Input from a call to \code{\link{orcid_id}} or
\code{\link{as.orcid}}}
\item{...}{Ignored.}
}
\value{
An S3 object of class \code{works}
}
\de... |
6cf9da14d774f6cf3ecf3751e2a3470e038100d4 | 1b637d140ccad852fc1431ce48ae4da50629d0e6 | /R/sumstat_omega.R | 776b1f318904b599fa09b10be0e3c5398485333d | [] | no_license | ijwilson/coala | aaa671daf9763057ea5a34efc90c98ba8dfbacf5 | 909e6f237d42e3972e3e54384473efde9d8841e7 | refs/heads/master | 2021-03-16T06:04:40.515782 | 2017-04-01T09:49:28 | 2017-04-01T09:49:28 | 91,571,290 | 0 | 0 | null | 2017-05-17T11:57:55 | 2017-05-17T11:57:55 | null | UTF-8 | R | false | false | 5,870 | r | sumstat_omega.R | #' @importFrom assertthat assert_that is.number
stat_omega_class <- R6Class("stat_omega", inherit = sumstat_class,
private = list(
req_segsites = TRUE,
binary = NULL,
min_win = NULL,
max_win = NULL,
grid = NULL,
create_empty_result = function(locus, locus_length) {
data.frame(locus = loc... |
e23e1bcc46e22751f3d8b22649ba47fe831ea6f9 | 6929d12941949f290d98379913048ee93143e491 | /R/fct_get_filtered_stocks.R | 3702ba9f0fde988ffd43a6125e5a48b3d07498c4 | [
"MIT"
] | permissive | srmatth/value.investing | 9aeaaa476e7731a6cf5a6012f4c0ae38f016dc42 | 720dbd7ba4f38c2ca896515d72cb2bfa4009582d | refs/heads/master | 2023-07-19T04:06:13.679639 | 2021-09-18T16:46:33 | 2021-09-18T16:46:33 | 275,306,180 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,443 | r | fct_get_filtered_stocks.R | #' Get Filtered Stocks
#'
#' Use FinViz.com to filter stocks and get a list of stocks that
#' meet some certain set of specifications. Can return a dataframe
#' with lots of data, or just the stocks and their names
#'
#' @param exchange
#' @param cap
#' @param fa_div
#' @param fa_pe
#' @param fa_peg
#' @param fa... |
16972366234e2c5e485d7b44c6a8de6536ac5841 | cd0418e481f24acd9ef0ddd76fc454bf8c7509ec | /man/expectedDist.Rd | 992f64360ba4ba6de373aca8be1a1290bf585a9d | [] | no_license | MarioniLab/sarlacc | 7dbacb52a2d7fe311262eb1344a8ee75138dea8a | 712fdbd11e47205f0714df446cc60bd56354b64c | refs/heads/master | 2021-07-14T22:39:52.159291 | 2019-02-27T15:22:35 | 2019-02-27T15:22:35 | 108,133,692 | 12 | 6 | null | 2018-07-06T12:18:25 | 2017-10-24T13:51:07 | R | UTF-8 | R | false | false | 1,842 | rd | expectedDist.Rd | \name{expectedDist}
\alias{expectedDist}
\title{Calculate expected distances}
\description{Calculate expected distances between subsequences of the adaptor that should be identical across reads.}
\usage{
expectedDist(sequences, max.err=NA)
}
\arguments{
\item{sequences}{A \linkS4class{QualityScaledDNAStringSet} of r... |
a104fda89d089fbf923dea12de88c40e860f6974 | 152c16bd35fe3536441db22f5c1634cf7660fe36 | /scripts/evaluate_all_methods.R | f1cf3cf38c33b731c306d91b45ed36f0686c4632 | [] | no_license | computbiolgeek/kcnq1_predictive_modeling | 970e58747fffd862ae1c6056708b8c0b36f15a42 | 9d3d78c6be164b9c0a083e77720dcd66f48d03d7 | refs/heads/master | 2020-06-25T08:28:18.840223 | 2017-10-26T23:00:38 | 2017-10-26T23:00:38 | 94,237,357 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,145 | r | evaluate_all_methods.R | #!/usr/bin/env Rscript
# cross-validate optimal cutoff of SIFT
library(boot)
library(ROCR)
# load the input file
data <- read.csv(file = "dataset_all_method_evaluation.csv", header = TRUE, sep = ",")
# split the dataset into negatives and positives, drop levels that do not occur
subsets <- split(x = data, f = as.fac... |
d5391c934164e0b5c29a8e7ddaf0cff9aad7e98c | 93145f2c98ab2a3226cf9eec8ce81b22bf76ec6a | /NEP-PEP.pattern.R | afc360edf8be6eef8bd7ee69d4919f4b8d201c62 | [] | no_license | yanjunzan/expression_R | a4a334804043f2a07d7cafca2be4a82608b6de2e | c60672b30a25fe61fabd6d4300cf1aa77e48368d | refs/heads/master | 2018-01-12T08:00:20.461355 | 2018-01-11T13:42:24 | 2018-01-11T13:42:24 | 43,816,564 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,041 | r | NEP-PEP.pattern.R | c <- read.table("~/Desktop/C_matrix.txt",header=T,sep = "\t",stringsAsFactors = F,colClasses = c("character",rep("numeric",24)))
out <- array(NA,c(88,8))
c[,1] -> rownames(out)
colnames(out)<- c(0,1,2,4,5,7,10,14)
for( i in 1:nrow(c)){
for (j in 0:7){
out[i,j+1]<- mean(as.numeric(c[i,c(2,10,18)+j]),na.rm=T)
}
}... |
c64146054b7cfe42c7fc2f5f0787ad8bf039d987 | 90b053ff36d4abb9df860c667efdeed69391e597 | /usefulCodeSnippets.R | 8c69acf078d60ddc0f54b2527cc608eae9d823ea | [] | no_license | hf-thompson-lab/LANDIS_Klamath_REU2018 | 27cd020b60587276309641f09afac8d2140fc565 | 3ff7efb14222e8d3ae7776cc2da63bca6eca3125 | refs/heads/master | 2023-08-16T12:45:25.757349 | 2018-08-01T15:29:38 | 2018-08-01T15:29:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,387 | r | usefulCodeSnippets.R | #Evan Waldmann
#6/28/18
# Useful code with descriptions
## Section - ploting with pauses ----
sim <- function(path, n=100, pause=0.25) {
for (i in 1:n) {
x<- raster(paste0(path,i,".img"))
plot(x, legend=T, asp=NA, main=i)
dev.flush()
Sys.sleep(pause)
}
invisible(x)
}
setwd("C:/Users/hfinter... |
2284f68f302063203097a38f1e173e004e920ded | 7ccb8a44b40362b231ed81306fd4783374efdd6a | /cachematrix.R | 77b792db336d80efa8e6e6331b12c0011de643b6 | [] | no_license | valerielim/JohnHopkins-RProgramming | bdf68dcfa5f3dd78499f5d9e03742d040889087e | 416f8309b9e1ac87278ecaae65094d20cef2f7a1 | refs/heads/master | 2021-08-07T12:42:48.493537 | 2017-11-08T05:55:14 | 2017-11-08T05:55:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | cachematrix.R | # --------------------------------------------------------------------- #
# Title: Programming Assignment 2
# Date: 4 Nov 2017
# By: Valerie Lim (for Coursera)
# --------------------------------------------------------------------- #
## This script allows user to store a cached Matrix, and it's find its
## Inverse... |
366361713ac4c9998f279d85ec55b6f2b013f8f0 | 497e3dbc7b40c28246576b8f474451e3e44e69a4 | /Project/utilities_optim.R | 8130fe135d34f6929871fd09251eec3807690cb1 | [] | no_license | ibrahim00017/Optimisation_AI_Cotonou2021 | a822d387cc302d629a31eb377eed159a77eb91d4 | b2b46a3bdc0368aa4bec47ce784b2de800615824 | refs/heads/main | 2023-06-26T11:27:46.066406 | 2021-07-28T20:42:58 | 2021-07-28T20:42:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,453 | r | utilities_optim.R | # set of utilities for simple optimization program
#
# Rodolphe Le Riche, CNRS LIMOS, july 2021
#
# calculate f and gradient of f by forward finite difference
f.gradf <- function(x,f,h=1.e-8){
d<-length(x)
res <- list()
res$gradf <- rep(NA,d)
res$fofx <- f(x)
for (i in 1:d){
xp <- x
xp[i] <- x[i]+h... |
8db412ca1ac0bdad2660ed9a774eaf05e0c99124 | b0853612d9c52495cd86ff867295cb3c98320081 | /K-NN알고리즘.R | 878e1c08bb40fd03eee8ed702cd886cc9a775dc5 | [] | no_license | OHSEHEYON/machine-learning | 0519bcc442054885a2ea44e24e75d987278afac2 | ff4f3fcb19c0a8a22860be58180615edd2898f75 | refs/heads/master | 2022-11-12T19:41:00.418452 | 2020-07-07T06:39:16 | 2020-07-07T06:39:16 | 276,387,566 | 0 | 0 | null | null | null | null | UHC | R | false | false | 1,587 | r | K-NN알고리즘.R | wbcd <- read.csv("C:\\R2\\dataset\\wisc_bc_data.csv")
str(wbcd)
# 첫번째변수는 id. 무의미한 정보를 제공하므로 제외.
wbcd <- wbcd[-1]
str(wbcd)
# B와 M이 직관적이지 않으니 표기법을 mapvalues()함수를 이용하여 팩터의 라벨명을 변경.
# 각각의 비율을 prop.table을 통해 확인
library(plyr)
wbcd$diagnosis <- mapvalues(wbcd$diagnosis, from=c('M','B'), to=c('악성','양성'))
prop.t... |
670474453df6fde7d87e413c53c0c86b9f7d3658 | c686ceafe8628946f1fdb60a7e9513a273747b3f | /R/PCA.R | 4ac12ab9df59e6e926042f0b7ef071635787582b | [] | no_license | cran/MVar.pt | a9edfb289984c39b144fad4f17629d586ce7b5c0 | 663e5e1cd34e3f0a00a31ca2597807c5b49c3f80 | refs/heads/master | 2023-08-31T09:10:37.861275 | 2023-08-19T15:02:31 | 2023-08-19T15:30:37 | 28,622,302 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,881 | r | PCA.R | PCA <- function(data, type = 1) {
# Funcao Executa a Analise dos Componentes Principais - PCA
# Desenvolvida por Paulo Cesar Ossani em 07/2013
# Entrada:
# data - Dados a serem a analizados
# type - 1 para analise utilizando a matriz de covariancia (default)
# 2 para analise utilizando a m... |
f3e8033a63920a12555a9b2d1c9ae39958033746 | 60a346e86bc9c39ef069d13f78f0f06400d0045f | /server.R | 32ac7e1ecbeb301abdf9203dcd5220bfd31bb90d | [] | no_license | ZainebYahya/EV_Shiny | 490cf8a753e30587a1151639896525b4435dcd4d | 5ffee2f8eb1ca99a37f9885fedfb8029c58f4a2e | refs/heads/main | 2023-02-24T06:36:36.451631 | 2021-02-01T04:41:50 | 2021-02-01T04:41:50 | 334,832,853 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,055 | r | server.R | server <- shinyServer(function(input, output, session) {
reactable(df,
defaultPageSize = 5,
bordered = TRUE,
defaultColDef = colDef(footer = function(values) {
if (!is.numeric(values)) return()
sparkline(values, type = "box", width = 100, height = 30)
... |
234ce66c3340b4369a125ce1557180ead1a8afc7 | 7ad110193d4538f285b50051ebafe492cdc284bb | /tests/testthat/test-instant-metrics-format.R | 798a40df92086286cddf8c045df08987e9e4218d | [
"MIT"
] | permissive | glenn-m/promR | 51e05d332ab7b6708a3fab5ef9fdf55e8d682ed7 | 111cf41791418fd7e4f208bc17d6d21f816d71c6 | refs/heads/master | 2023-04-04T18:20:20.373807 | 2023-03-16T00:44:42 | 2023-03-16T00:44:42 | 155,071,735 | 3 | 3 | MIT | 2020-09-19T04:16:04 | 2018-10-28T13:02:42 | R | UTF-8 | R | false | false | 476 | r | test-instant-metrics-format.R | context("Instant metrics data is formatted correctly")
test_that(desc = "Instant metrics results are data frame",
code = expect_is(object = metrics_current,
class = "data.frame"))
test_that(desc = "Instant metrics are named",
code = expect_named(object = metrics_current)... |
a21b0a3d2aca1d74f8379e7b76383fdf456cdcdc | f932835f7a32fed4801c3593399ccb64846412a5 | /code/day2/challenges_and_answers.R | 0e65a960d84226d8c6d4760915936cfa21501c3b | [] | no_license | ocean-tracking-network/2021-03-30-glatos-workshop | 411b72ccbe5410e50660f430a5eec14d17d3182c | eb3b03357b8077e84435f9b5dd4ccc16d9ef894c | refs/heads/master | 2023-05-08T11:19:24.625685 | 2021-04-06T21:13:41 | 2021-04-06T21:13:41 | 347,158,565 | 0 | 0 | null | 2021-04-06T21:13:41 | 2021-03-12T18:21:15 | R | UTF-8 | R | false | false | 1,188 | r | challenges_and_answers.R | # Basic Visualization and Plotting: CHALLENGES ---------------------------------
# GLATOS workshop 2021-03-31
# Instructor: Ryan Gosse
# Challenge 1 ----
# Create a bubble plot of the station in Lake Erie only. Set the bounding box using the provided nw + se cordinates and
# resize the points. As a bonus, add points... |
a11a2e591a755463fb96e3ff8df9222cdbff3ada | e9f08e6597d447cd57df5647f109795d14c7c152 | /Bioinformatics/PCAcovariates.r | dbd73b014de5578f5d8fcf667324bbc2021e4e6b | [] | no_license | Liuy12/SomeUsefulScripts | 81634b7a7f524d06bd41d1874109544d99505cc6 | 8b638e0ea8e267e18588021cf65499425b884f3c | refs/heads/master | 2023-01-28T12:23:33.863612 | 2023-01-04T22:19:45 | 2023-01-04T22:19:45 | 27,837,879 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,350 | r | PCAcovariates.r | PCAcovariate <- function(normdata, sampleinfo, output, npcs){
library(ggplot2)
library(heplots)
library(gridExtra)
pcares <- Principalstats(normdata, method = 'pca', cvCutoff = 0.1, npcs=npcs)
gps <- vector(mode = "list", length = npcs*ncol(sampleinfo))
for(i in 1:npcs){
for(j in 1:ncol(sampleinfo)){
... |
3963918c6ec6291fa3bdc4272847e6bf1dd6c3f1 | 1f972b88bab3c8f930949d3db86747e1f0a4d517 | /R/01_r_ps_run_msa_coverage_progress.R | 15e57cfc52be05ed01abbb647390a013b039e739 | [] | no_license | achalak/kobohrtoolbox | 0530ac5c694fa750d4f46aead7b9585fa20f6822 | 0e985beaa2e7d737fc5d7f804dd7312c08c5a868 | refs/heads/master | 2021-01-20T02:02:42.436576 | 2017-08-24T14:50:30 | 2017-08-24T14:50:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 607 | r | 01_r_ps_run_msa_coverage_progress.R | #load libraries
library(httr)
library(jsonlite)
library(lubridate)
library(tidyverse)
library(stringr)
library(readxl) #read excel file
library(dplyr)
library(ggplot2)
library(rgdal)
library(openxlsx) #'write xlsx'
#load file r_kobo_utils.R file first
options(stringsAsFactors = FALSE)
#language setting
Sys.setlocale(c... |
95fb01e5f17a101c624e6ddc99467ba6a78fc70e | cf606e7a3f06c0666e0ca38e32247fef9f090778 | /test/integration/example-models/misc/gaussian-process/gp-predict.R | 329b22a95671817715bf0e44a976d3d4237f89f0 | [
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown"
] | permissive | nhuurre/stanc3 | 32599a71d5f82c759fd6768b8b699fb5f2b2d072 | 5612b357c1cd5a08cf2a57db97ce0e789bb87018 | refs/heads/master | 2023-07-05T02:27:08.083259 | 2020-11-12T15:37:42 | 2020-11-12T15:37:42 | 222,684,189 | 0 | 0 | BSD-3-Clause | 2019-11-19T11:50:39 | 2019-11-19T11:50:38 | null | UTF-8 | R | false | false | 235 | r | gp-predict.R | library(rstan)
stan_dat <- read_rdump('gp-predict.data.R')
fit_predict <- stan(file="gp-predict.stan",
data=stan_dat,
iter=200, chains=3);
print(fit_predict, pars = c('rho','alpha','sigma')) |
38a4f71945fd48908b02d8ab6b1e64a5e3f17dcf | 3f1d1377ab5bcc2777db0e8b68209374f92bf6f8 | /R/curlAuthConstants.R | b7502e51d2ee357aa40c7b563016b2b1c51c6fe3 | [
"curl"
] | permissive | omegahat/RCurl | 5dc3299c75dce0d0ab2481902125aebaa9cb7c9e | e07c076963fc6436e0b05db04bb0b3a20ba378e9 | refs/heads/master | 2022-06-25T08:55:39.848816 | 2022-06-07T02:34:35 | 2022-06-07T02:34:35 | 4,004,831 | 20 | 18 | NOASSERTION | 2022-06-07T02:34:35 | 2012-04-12T13:17:38 | Turing | UTF-8 | R | false | false | 1,218 | r | curlAuthConstants.R | # RCurl:::AUTH_BASIC | RCurl:::AUTH_DIGEST | RCurl:::AUTH_NTLM
CURLAUTHValues = structure(c(-.Machine$integer.max, -17, 8, -18, 32, 16, 1, 2, 4, 0), .Names = c("CURLAUTH_ONLY",
"CURLAUTH_ANY", "CURLAUTH_NTLM", "CURLAUTH_ANYSAFE", "CURLAUTH_NTLM_WB",
"CURLAUTH_DIGEST_IE", "CURLAUTH_BASIC", "CURLAUTH_DIGEST", "CURLA... |
917b11184ca54f6f3029ab9ff132bf4abebea705 | f995139d5c4ff0c6dc2785edfebb84df43a6bb3f | /R/0282-extract-bcfishpass2-crossing-corrections.R | e8f301e39f914240143462ea85b0e2af91740e18 | [
"Apache-2.0"
] | permissive | Mateo9569/fish_passage_bulkley_2020_reporting | 4633713f012edc331552f2ef367b2f3456f14bae | 49e25c50cef023c3f93654816618f714431da94a | refs/heads/master | 2023-04-12T20:39:05.124435 | 2021-05-04T21:43:43 | 2021-05-04T21:43:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,193 | r | 0282-extract-bcfishpass2-crossing-corrections.R | ##all the data is in bcfishpass.crossings now so we will redo this and use it for analysis going forward
##in here we also have workflows to match our crossings to modelled crossings in case they are not already.
source('R/packages.R')
source('R/functions.R')
source('R/private_info.R')
source('R/0255-load-pscis.R')
... |
02dd086b24002e47464f82e62c35bfaae9e9ea32 | 9bee3fb579661c4387db895569fc2e36be118b5e | /R/calmet_define_geophys.R | a073788956ba328fa1ba113f533f3884b1c1d4b2 | [
"MIT"
] | permissive | timelyportfolio/PuffR | 717257618606b5b40c7247061d9a7018a2f47a07 | 04c6f301958b9eca4fe60d68ce524b357091d5f7 | refs/heads/master | 2021-01-20T16:27:48.290257 | 2014-09-08T07:03:24 | 2014-09-08T07:03:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,768 | r | calmet_define_geophys.R | #' Define the CALMET domain and generate a geophysical input file
#' @description Define the CALMET domain and determine the best gridded values for land use, terrain heights, and micrometeorological parameters for creation of a geophysical input file.
#' @param lat_dec_deg the latitude of the CALMET domain in decimal ... |
6367d45d22fef13b11a57a47d0ba6d597c060444 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query60_nreachq_1344/query60_nreachq_1344.R | 64819f33d5ff4199756c4095d47869bfb2c87cef | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 717 | r | query60_nreachq_1344.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 10479
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 10479
c
c Input Parameter (command line, file):
c input filename QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4... |
fb2c29e190cf1b12f53373e8af2ed415b8d1717b | df6cd74dfc3474a4c93a3bf1492b23bcb0cded0e | /server.R | f0e2ab791ac3f7fb209ef335cb6877a3877a535b | [] | no_license | drbinzhao/Energy-Analytics-with-ShinyR | 62ec8bd9ff674b2f2fc768afbf9fd63c9d3ac15b | 16653861ad117e1c416b3315ac38d1846ed9ed8c | refs/heads/master | 2021-01-23T17:42:12.499825 | 2016-12-29T21:38:41 | 2016-12-29T21:38:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,255 | r | server.R |
shinyServer(function(input,output){
# Date formatting
fulldata$date<-paste(substr(fulldata$date, 1, 4), "-", substr(fulldata$date,5, nchar(fulldata$date)), sep = "")
fulldata$date<-as.Date(as.yearmon(fulldata$date))
###########################################
# Overview Tab
#################... |
b74724a4f61dd9f3a0e5ffee9d864a4c32bbad58 | 50cd6fe561f74ff2afc85a222c11df4ba27ceda9 | /Fig 1(geotimescale).R | 1e29c6c2abbfdcdbc10429fb1ab77f428868fa5a | [] | no_license | larijer/Masters_insect_work | 42daf07305e59ac4fda8073612bcd7e1640de114 | 5a1d609861d2f98afe931a8e80106078c791957a | refs/heads/master | 2021-01-25T04:08:32.721069 | 2015-02-20T01:34:22 | 2015-02-20T01:34:22 | 31,046,206 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,512 | r | Fig 1(geotimescale).R | ###JERED KARR
####CODE FOR BUBBLE PLOTS
####AUGUST 29 2013
###NEEDS to install gts and MLR and ratio function as well as line and text functions
gts <-read.csv("Geologic timescale.2013.csv") #geologic scale
gts <-read.csv("~/Desktop/untitled folder/Geologic timescale.2013.csv")
gts$Stage <- as.character(gts$Stage)
... |
f6996b9354767f600d0b27d9f212f33e91c1493e | 42ac78fed8e8494cc54a533e6cb9b4c18ca51369 | /branches/trunk-lme4/R/AllClass.R | 0873350974c1675f09512f9be2797506dad5d0a5 | [] | no_license | LTLA/Matrix | 8a79cac905cdb820f95190e99352cd9d8f267558 | 2b80087cfebc9f673e345000aeaf2170fc15b506 | refs/heads/master | 2020-08-07T20:22:12.075155 | 2019-09-28T21:21:10 | 2019-09-28T21:21:10 | 213,576,484 | 0 | 1 | null | 2019-10-13T00:56:38 | 2019-10-08T07:30:49 | C | UTF-8 | R | false | false | 7,543 | r | AllClass.R | ## Class definitions for the package
setClass("lmList",
representation(call = "call",
pool = "logical"),
contains = "list")
setClass("lmList.confint", contains = "array")
## -------------------- lmer-related Classes --------------------------------
setOldClass("data.frame")... |
50486609acd9dbe13eb32b8dbeb29f25f352afe2 | 9c9822731b8d63c9508d5e22f6623babb9f1b405 | /run_analysis.R | 948f1603c39ea02d691551e99bf9c2fc8af67448 | [] | no_license | maheshwickramarachchi/DataScienceCourse03 | 42c680eb67007d7f87a9a73845bdd4239883e780 | 7a9f908ffa9fbdca8bf9ce4976499cf94164409e | refs/heads/master | 2021-01-18T22:13:52.441245 | 2016-06-11T16:14:39 | 2016-06-11T16:14:39 | 60,907,374 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,074 | r | run_analysis.R | #Downloarding Data
folderName<-"UCI_HAR_Dataset.zip"
fileURL<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileURL,folderName)
unzip(folderName)
#Loading Data
features<-read.table("./UCI HAR Dataset/features.txt",stringsAsFactors = T)
features[,2]<-as.character... |
3837a8a9d1727974c4d02f326ab703532035d455 | f15b3be9ba52ba4b378590c73c23df6b590ad776 | /R/hullMuirMaxDistance.R | 98101989fdd2755d2fc1f579166a99f05b05dbf6 | [] | no_license | cran/windAC | 76fc8747c2465b50d8d926e364147cf82d3d337b | ec65551fe0edf90d71291c188c7bceeb2f3291f7 | refs/heads/master | 2023-04-06T03:21:29.992300 | 2023-03-22T18:20:04 | 2023-03-22T18:20:04 | 218,116,250 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,924 | r | hullMuirMaxDistance.R |
#' @name hullMuirMaxDistance
#'
#' @title Calculate the Hull and Muir (2010) maximum distance
#'
#' @description Calculate the maximum fall distance from a turbine using the
#' regression model from Hull and Muir (2010).
#'
#' @param hubHeight Numeric, turbine hub height.
#' @param bladeRadius Numeric, turb... |
0bf98833f9ed1c352223be2644b93d6a8a93e61e | f557e6f075e60798cff69f66f22dfaad93b22f8e | /noaa.R | 9d6b3bcd085ac5868fa953b129e6b8c605d09118 | [] | no_license | eugenechoGH/mercuryGH | 6b9d4bd176cb83747a989e0731827c3cd1505a84 | 580e366581fbf0bb94d917741896a2458981dd2f | refs/heads/master | 2020-04-07T05:55:49.003540 | 2018-11-18T18:56:45 | 2018-11-18T18:56:45 | 158,115,819 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,417 | r | noaa.R | library(rnoaa)
library(data.table)
library(lubridate)
library(devtools)
library(stringr)
# token: WMfLumOaZKcCSkuyMiPzdssqTTSnnkaR
ncdc(datasetid = "PRECIP_HLY", locationid = "ZIP:28801", datatypeid = "HPCP",
limit = 5, token = "WMfLumOaZKcCSkuyMiPzdssqTTSnnkaR")
ncdc_stations(token="WMfLumOaZKcCSkuyMiPz... |
b2776dbd304c38d2253305b2e4fe54d3d2fea931 | 13e8bd394fc0b606ceb0cf73d0be8ef720910758 | /man/BadLM.Rd | 5d142425d60fde55eb699e927352929d4ab972b3 | [] | no_license | cran/lmviz | 7e6634a084a5469cf56505dae9c4577f0aba62b5 | ed9121c3f19c7112815107bcab1189c41f46db00 | refs/heads/master | 2021-07-04T08:41:16.480716 | 2020-08-24T19:40:02 | 2020-08-24T19:40:02 | 158,114,582 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,040 | rd | BadLM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BadLM.r
\name{BadLM}
\alias{BadLM}
\title{BadLM shiny app}
\usage{
BadLM(dist.custom = NULL, dist.custom.veravar = NULL, dist.custom.param = NULL)
}
\arguments{
\item{dist.custom}{custom generator for Y, see examples below}
\item{... |
ba24aad063dcf38d190b85756870d5f1942e1d51 | 733a62f25f5a690d6099aa31b5f31393d57c09f4 | /R/load_packages.R | 3122a15647cffb804ab81c9187435560d76efcce | [
"MIT"
] | permissive | guhjy/ck37r | 3788d5e1bc85a71d8fb1718af6372f6d0ff35cb1 | 2e93bcf56fe53a885b7cb176e6d1a0aec4a88262 | refs/heads/master | 2020-12-31T07:34:31.647291 | 2017-03-13T20:25:58 | 2017-03-13T20:25:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,085 | r | load_packages.R | #' Load a list of packages.
#'
#' Load packages and install them from CRAN if they aren't already available.
#'
#' @param pkgs Character vector of packages to load.
#' @param auto_install Install any packages that could not be loaded.
#' @param update Update packages where possible.
#' @param verbose If T display more ... |
24f5d54c657cb0d5d78bf5d7ccdba2c7da91b684 | 00cfb5369afb87eb8d471886e6078b9f67b8d25a | /dynamics.R | 9b287c221ff788e10fd0a57de2851dd238105133 | [] | no_license | munrohannah/PhD_Scripts | b9c8a55c6657881c0b31affbdc1fd70fd7b193eb | 65eedc7381f7aae563ed53f35026604a64163234 | refs/heads/master | 2022-11-11T06:12:00.322187 | 2020-06-30T16:49:10 | 2020-06-30T16:49:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,122 | r | dynamics.R | setwd("D:/Rfiles/TickPrev") #how to set my working directory
data<-read.csv("TickDynamics.csv",header=TRUE) #how to import a file, csv file, and has a header
head(data) # gives the first few lines of data
str(data) #check the data type and varible
#setting up each factor so not treated as a continous variable
da... |
579b09842908f99470fac911b46d2d42d99d14c2 | 4a5e95779efd63836de9a3af8c19d4dba1f0e748 | /rcdk/man/getsmiles.Rd | 8f9951b9e1e7ccb88ac13a5987e29f84d13cf6c1 | [] | no_license | yccai/cdkr | 2ac693148d878c2a7d8536d8366c6952187bdb27 | 562d39639e0f1c1b2d669c86e45260290a3ef044 | refs/heads/master | 2021-01-01T16:20:19.728781 | 2016-12-27T11:45:49 | 2016-12-27T11:45:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,590 | rd | getsmiles.Rd | \name{get.smiles}
\alias{get.smiles}
\title{
Get the SMILES for a Molecule
}
\description{
The function will generate a SMILES representation of an
IAtomContainer object. The default parameters of the CDK SMILES
generator are used. This can mean that for large ring systems the
method may fail. See CDK Javadoc... |
66b9e221e5c47139604b33790ec813a541793656 | cf26b183b4a36144637938283813abd4a23cb303 | /man/R2Matlab.Rd | c53dbeb060e9448e0790ab7fb38b442034395827 | [] | no_license | cran/popdemo | 5312fbff68852f6cbb602ac1b1abae05e8879719 | 2f6b882b1b0851d1942c5892071c956a5457a6a0 | refs/heads/master | 2023-03-16T02:09:54.158218 | 2021-11-16T13:20:02 | 2021-11-16T13:20:02 | 17,719,255 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,559 | rd | R2Matlab.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/R2Matlab.R
\name{R2Matlab}
\alias{R2Matlab}
\title{Convert matrices into Matlab style strings}
\usage{
R2Matlab(A, noquote = FALSE)
}
\arguments{
\item{A}{a numeric matrix of any dimension}
\item{noquote}{(optional) if \code{noquo... |
319f380ca96c7051df36ff76ca0da8f3874b5325 | 62f84d7157e0e3bfc57cc6d6942ea9205adc4463 | /man/titer.toLog.Rd | e8637e0c29b6d68bb402cd1aadb439b40b1f26d2 | [
"MIT"
] | permissive | SamT123/acutilsLite | 251da4cf955c05a4e52a6b10e59fa2876759ea4a | fb36cd0f0786b9a9822ebda76fe4a44538569c4b | refs/heads/master | 2023-03-02T20:52:23.145170 | 2021-02-15T10:03:21 | 2021-02-15T10:03:21 | 315,282,286 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 228 | rd | titer.toLog.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/acdatabase_merging.R
\name{titer.toLog}
\alias{titer.toLog}
\title{titer to logtiter}
\usage{
titer.toLog(titers)
}
\description{
titer to logtiter
}
|
eac3cfe3696c9b2fcdab4df903ddbb98b30f3740 | af8aeaa3b352ee10ffadc073c2cb9e6940465dac | /Meta_QC/3. Correlation plot of betas.r | 30dce8822a1737c6f05547386993110aec3c0833 | [] | no_license | ammegandchips/PACE_Paternal_BMI | 357895485b6c17eba3901c4ce5267e45cbe688dc | 724d1fde1b04a0a98083ba6fae1c1b161134f600 | refs/heads/master | 2020-06-26T00:49:41.386778 | 2020-02-10T19:33:16 | 2020-02-10T19:33:16 | 96,999,961 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 642 | r | 3. Correlation plot of betas.r | # Cohort QC: Correlation plot of betas in all 12 results files
time_point <-"birth"#or childhood
require(corrplot)
require(plyr)
extract.coefficients <- function(ewas.dataframe){
ewas.dataframe[,which(colnames(ewas.dataframe)=="Effect")]
}
correlation.plot<-function(cohort,cohort_name){
x <- data.frame(do.call(cbi... |
232acd2405e7f401e38a68728c20bf8a9cef2648 | c6ae9c0e47903ee3d672c013292caf4b182c6a7c | /R_basics.r | c6ff2d9cb91913d29bec97539ebb80dcbe2dec75 | [] | no_license | Fahofah/R_Exercises | 2332750623a78c90fd74d776f074b8db617fea71 | 7b83a4d5e3724de5a4d0f4cb1776d14bd3ca757d | refs/heads/master | 2021-06-25T20:53:54.294313 | 2017-08-22T16:10:41 | 2017-08-22T16:10:41 | 100,287,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,062 | r | R_basics.r | ## CTRL+L --> CLEARS CONSOLE
## rm(list = ls())
#BASIC
#...4
function(theString){
theString
}
#5,6,7
addThese <- function(num1,num2,isSum){
if(num1==0){
ans <- num2
}else if(num2==0){
ans <- num1
}else{
if(isSum){
ans <- num1+num2
}else{
ans <- num1*num2
}
}
print(ans... |
7a013abcb1d64ef7db2bfa45efaa4767bfdf9c58 | 968dec6ad5fa38ebf41d69cfde6eeacc924f187d | /ML Rasmus/LES_sandomega_nvarer_8var.R | ca6018011b22ef14d89c7a08163cc13e8abec0f3 | [] | permissive | Anaconda95/SpecialeJR | 0ac32f93274e3c3b1a29b58632ca6e241ead1824 | 88349f3cdcd27984ffd53b52ba7ad828db02a8fe | refs/heads/main | 2023-07-13T09:03:19.887729 | 2021-08-10T08:52:24 | 2021-08-10T08:52:24 | 337,058,938 | 1 | 0 | Apache-2.0 | 2021-02-08T14:08:20 | 2021-02-08T11:49:17 | null | UTF-8 | R | false | false | 15,440 | r | LES_sandomega_nvarer_8var.R | #clear workspace
rm(list=ls())
library(mvtnorm)
setwd("~/Documents/GitHub/SpecialeJR /ML Rasmus")
#no scientific numbers
options(scipen=999)
# Indlæs data
df<-read.csv("simpeldata8grup.csv",sep=';')
df<-read.csv("C:/specialeJR/ML Rasmus/simpeldata8grup.csv",sep=';')
#make prices and shares
df <- transform( df,
... |
4d916fbdd2a6defc13b1a00754558da3a9a1318f | 68562f910349b41cdf4432c0921940f0513ab516 | /tests/testthat/helper-session.R | 92589c75d8ca95a55194c14d445b0f127e7bdbf6 | [
"MIT"
] | permissive | gadenbuie/xaringanthemer | 2990406aff24a458695c6e4793c891dff5feb506 | 85091cd16af5a938b6d927ff5f6b0fe990ee0e63 | refs/heads/main | 2022-09-15T18:32:49.954381 | 2022-08-20T18:03:58 | 2022-08-20T22:47:52 | 129,549,154 | 446 | 28 | NOASSERTION | 2022-08-20T16:58:02 | 2018-04-14T19:44:17 | R | UTF-8 | R | false | false | 453 | r | helper-session.R | with_clean_session <- function(.f, args = list()) {
empty_wd <- tempfile()
dir.create(empty_wd)
owd <- setwd(empty_wd)
on.exit({setwd(owd); unlink(empty_wd, TRUE)})
args$.f <- .f
res <- callr::r_safe(function(.f, ...) {
tryCatch(
list(result = .f(...), error = NULL),
error = function(e) list... |
d12b9e6ebd0c64db36466588bf65b32472f93e4a | 3689d75f984128f39e0e609aad51619aeafb0905 | /R/unify_rent_and_value.R | 3ebf07c85e4828bd55fd09fadff31b1bf1be89e0 | [] | no_license | buchmayne/housing-policy-tools | 29c2ff841e6093ab1dd9442dbbe10002c6f3327c | 2748e86181d0ae4b02400d9c4274904db38af278 | refs/heads/main | 2023-03-11T03:04:55.464754 | 2021-02-10T20:01:07 | 2021-02-10T20:01:07 | 337,231,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,414 | r | unify_rent_and_value.R | #' Internal function to unify rent and ownership CHAS bins
#'
#' @param renters_data renters data from CHAS already formatted
#' @param owners_data owners data from CHAS already formatted
#' @export
unify_rent_and_value <- function(renters_data, owners_data) {
unified_renters <- renters_data %>%
dplyr::mutate(... |
7e6140f3bc644b4519cf8519f9d3199d70cb5447 | 6a7947971ca15e8d3c4aa5b38b668562439a784c | /network_informed_clustering_function.R | 10e944bc726da0b8ea15881bed49ca30be94ea2a | [] | no_license | SutherlandRuss/TCGA | 8679de5d1dbd210e847bab317355361cef1df326 | b89cfcb4e4ecdb2ab66aa5a17a774606abf1b40c | refs/heads/master | 2021-01-25T10:29:00.454489 | 2013-09-16T16:41:16 | 2013-09-16T16:41:16 | 11,202,576 | 1 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,954 | r | network_informed_clustering_function.R | #' @param mutM A logical matrix genes are rows and samples are columns.
#' TRUE if the gene is mutated in the sample and FALSE otherwise.
#' @param geneAdj A list of genes that are adjacent to.....
#' @param geneID An lexicographically ordered list of genes.
#'
#' @return mutNet The logical matrix ... |
62d4bfa6980549cb7c22cdab1149f4b3ed887aa9 | 883fe42938c7d6b27ea646374df0aee4145f7774 | /lab-5-SnarkyPuppy23/model.R | 6068be884efd1e9e9d9a7bb0e5dfcdc780439bef | [] | no_license | AaronNadell/COGS-319 | 36f64b89e1e5b7955ae42491551c6689a7fd8ddf | e6f7cb4f7d9924eadbbec5fcb492b081d5ddad03 | refs/heads/master | 2023-03-19T12:05:31.467953 | 2021-03-13T22:30:31 | 2021-03-13T22:30:31 | 207,401,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,854 | r | model.R | library(dplyr)
library(ggplot2)
# this model will simulate a group of subjects completing the experiment described in the README.
# your job is to implement the reinforcement learning model
# experiment parameters
n.subjects <- 60 # how many simulated subjects to include
n.trials <- 30 # how many trials will each sub... |
8f4b50dfe775085dd2a8414afd22a418eb918671 | 36596de50312413cb2d42223c05e009b4c2da50a | /recuperador_de_datos.R | 8435826c6a5bbdee3aba5746611400dc1e34ee0f | [] | no_license | RayanroBryan/rastreador_covid_19_costa_rica | dbf3692939ba956b7dc1c6f201cb57764591e71e | bf473160209afa9e35f2447ecdbc194146902634 | refs/heads/master | 2021-05-23T13:29:01.363605 | 2020-06-23T01:11:34 | 2020-06-23T01:11:34 | 253,311,030 | 1 | 0 | null | 2020-04-05T19:04:58 | 2020-04-05T19:04:57 | null | UTF-8 | R | false | false | 18,227 | r | recuperador_de_datos.R | # Dependencias
if(!require("pacman")) install.packages("pacman")
library(tidyverse)
library(lubridate)
library(echarts4r)
library(echarts4r.maps)
library(easynls)
library(reticulate)
####### Cargar/tratar datos -----------
#### llamado a data por cantones
tem_cr_caso_pc <- read.csv("datos/covid19_cantones_cr.csv")... |
34c06600148ab075acd841405e60f115ba80050e | fc7e1f1d1efc653df716dbad0e9047abc1c0039f | /CHEMDisplay.R | fbb41834d765193a0c1b666864c0eb4ffa8b0cf3 | [] | no_license | AspirinCode/ChemInformatics-1 | 07d926a6bb7176ee022c5b5ada26f36618a5256a | ff434ea508d393dcb4d389d250054dff9db184cb | refs/heads/master | 2020-07-06T05:44:22.455752 | 2019-06-20T06:44:57 | 2019-06-20T06:44:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,844 | r | CHEMDisplay.R | displayClusterDend <- function(simMatrix, Namevector) {
colnames(simMatrix) <- Namevector
row.names(simMatrix) <- Namevector
hCluster <- hclust(as.dist(1-simMatrix), method="complete")
dend <- as.dendrogram(hCluster)
dend %>% dendextend::set('labels_cex',0.5) %>% highlight_branches_col(viridis(100))
retur... |
49822620ce4ba6b4f37e0032ced58f41ea26338f | b4b5a5998f2fecc590a5df4026c0d12d83d46cf7 | /man/add_grid_layer.Rd | 55efde3f5980861269fb5a201fbd07f52eef07eb | [
"MIT",
"BSD-3-Clause"
] | permissive | crazycapivara/deckgl | 50335a155c99307e40b21b2f82561f3cf15afb17 | 1741c8bc84d69e26694d670879911a0d2bb2c5c2 | refs/heads/master | 2023-04-03T06:19:35.377655 | 2023-03-26T10:46:36 | 2023-03-26T10:46:36 | 145,043,708 | 83 | 10 | NOASSERTION | 2023-03-26T10:46:41 | 2018-08-16T22:07:48 | R | UTF-8 | R | false | true | 1,899 | rd | add_grid_layer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layers_grid-layer.R
\name{add_grid_layer}
\alias{add_grid_layer}
\title{Add a grid layer to the deckgl widget}
\usage{
add_grid_layer(
deckgl,
data = NULL,
properties = list(),
...,
id = "grid-layer"
)
}
\arguments{
\item{deckgl}{A ... |
ecfcf370ca574dc3d2ce383968a70de4ea0909d8 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.management/man/configservice_put_external_evaluation.Rd | c677b8570dd2c1b15688311abdc3af8a2e8f6403 | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 874 | rd | configservice_put_external_evaluation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/configservice_operations.R
\name{configservice_put_external_evaluation}
\alias{configservice_put_external_evaluation}
\title{Put external evaluation}
\usage{
configservice_put_external_evaluation(ConfigRuleName,
ExternalEvaluation)
}
\argum... |
2ff7def8feffb7ae3abaa29d5d76a93dafab8a5a | 0e96f69fad9a8cd571eacecec2774e85c9d47454 | /thirteen.R | 4bfd745c0369f02bcb3b17fabf21fe953a97b3f9 | [] | no_license | Tarun-Sharma9168/R_programming | 748e55b00682a07ff17b2e06aa7952e345141a7f | 9c31a494d1047fab0ef1224ed6db07574931b6af | refs/heads/master | 2020-09-26T14:36:18.442916 | 2019-12-06T07:51:38 | 2019-12-06T07:51:38 | 226,274,649 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | r | thirteen.R | marks=c(70,95,80,40,30)
barplot(marks,main = "Comparing marks of the five subject",xlab="Subject",ylab="Marks",names.arg=c("A","B","C","D","E"),col="darkred",horiz=FALSE)
|
46ae850b7f31e73c82d3ffc523ebb94a13330474 | 29585dff702209dd446c0ab52ceea046c58e384e | /glrt/R/Chisqstat3.R | 019f8249a5a35b63dcb88442636016e2e6fc48d3 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 599 | r | Chisqstat3.R | Chisqstat3 <-
function(U, V, counts)
{
k = length(U)
n = sum(counts)
N = apply(counts, 2, sum)
if(N[1] == 0)
{
chisq = t(U[1:(k-1)]) %*% solve(V[1:(k-1), 1:(k-1)]) %*% U[1:(k-1)] / n
df = k - 1
}
else
{
equal = 1
ratios = t(apply(counts, 1, "/", N))
r = 2
while(equal == 1 && r <= k)
... |
47ae03281966baff213e27c997081c2c9fc18bce | 66a863e177ecef6830da370498981f0bbb935446 | /run_analysis.R | 295c297884cf6d4f2f11aff9b7cc7f034c9ac12c | [] | no_license | shahanuj855/Getting-Cleaning-Data-Project | e04c908653dfbc324eaaefdc7e2f828ae89c5954 | 3e6e98f074ddff1be92ae7c609c5a18d0e477fa0 | refs/heads/master | 2020-03-29T00:43:16.613816 | 2018-09-18T21:47:31 | 2018-09-18T21:47:31 | 149,348,212 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,201 | r | run_analysis.R | fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileURL,"./UCI HAR Dataset.zip")
unzip("UCI HAR Dataset.zip",exdir = getwd())
#Creating data frames for testing and training
library(data.table)
features <- read.csv('./UCI HAR Dataset/features.txt', heade... |
82f2c5bbcc6a93ad5dc779e49e7dbb117ca411f1 | af84f4fb4bd7c41432482cce6170da3e6af3a130 | /PE/9.R | a82f3f9d2aa77901ed03a08c225dc94f5d713e97 | [] | no_license | tkmckenzie/pan | a65fc375eea8171c9b64f8360c5cf7b152830b7d | 5337e4c1d09f06f2043551e1dd1ec734aab75b49 | refs/heads/master | 2023-01-09T11:54:29.636007 | 2022-12-21T22:26:58 | 2022-12-21T22:26:58 | 156,240,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 233 | r | 9.R | rm(list = ls())
max.N = 1000
a.b.range = 1:max.N
a.b = combn(a.b.range, 2)
a.b.sums = apply(a.b, 2, function(v) sum(v^2))
a.b.c = rbind(a.b, sqrt(sums))
a.b.c.sums = apply(a.b.c, 2, sum)
prod(a.b.c[,which(a.b.c.sums == 1000)])
|
82c874f44d8b7a9f089a974e36279fcfb9df1630 | a717f53ded765a2e7cf52e829ae4a31d1b8cdd34 | /R/msaeDB-Package.R | 0b9646d84728d61c4505e196770b733593d4c9f4 | [] | no_license | zazaperwira/msaeDB | 79f43dca177cd233fbc0c0c947be74ba245be660 | 070089d107c48caebc6024ae6bd615535f40033b | refs/heads/master | 2023-03-31T01:45:38.180574 | 2021-04-01T03:08:05 | 2021-04-01T03:08:05 | 327,575,056 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,253 | r | msaeDB-Package.R | #' msaeDB : Multivariate Small Area Estimation with Difference Benchmarking
#'
#' Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among respons... |
89355b3aa57b5a628e11f3f4f500e93747585ab6 | 6159b0aa17a0b3e6eb4edaf8021d70963a9bca96 | /TCR_analyses/TCR_clonality_umaps.R | f6acd59c70947353b9b35163213e1d0bde9e7761 | [
"MIT"
] | permissive | IzarLab/fresh_vs_frozen_comparison | c9854a7cbeb1da5e627e664c4677a46502447338 | 02ed44b0851dd5391967339b4900aa3384b9932f | refs/heads/main | 2023-04-15T04:12:50.648881 | 2023-02-24T19:57:53 | 2023-02-24T19:57:53 | 446,448,844 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,463 | r | TCR_clonality_umaps.R | library(rlist)
library(ggplot2)
library(Seurat)
library(infercnv)
library(stringr)
### title: Create umaps of T cells with TCR clonality plotted in different colors according to frequency, for cutaneous melanoma, uveal melanoma primary, uveal melanoma metastasis, and sequential cutaneous melanoma samples under pembrol... |
d298fc845acd70f4eb0d203211f14b465072a9f3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spatstat/examples/nndist.lpp.Rd.R | 46434bbb7a3a70174f37f982216458bc9f2a0164 | [] | 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 | 230 | r | nndist.lpp.Rd.R | library(spatstat)
### Name: nndist.lpp
### Title: Nearest neighbour distances on a linear network
### Aliases: nndist.lpp
### Keywords: spatial
### ** Examples
X <- runiflpp(12, simplenet)
nndist(X)
nndist(X, k=2)
|
f192d6bac1d90db06cef850a812cbc47372569d1 | 78dbd8fd96aed35aee83352e6c57e1dee727600c | /R_Scripts/total_trials_enrollment_count.R | f7fc972492ed88cf6e6221fb62336e0a00069a32 | [
"MIT"
] | permissive | kylienorwood/ClinicalTrialsViz | 9b8cb92df05fc35f0428da7a6ed3ae944f783982 | 9e6083b6a3297fdb5b3b52e42d2d7f4ec54210f6 | refs/heads/master | 2020-06-06T01:04:11.580591 | 2019-07-29T18:26:48 | 2019-07-29T18:26:48 | 192,596,260 | 1 | 0 | null | 2019-06-18T18:54:05 | 2019-06-18T18:54:05 | null | UTF-8 | R | false | false | 1,384 | r | total_trials_enrollment_count.R | # Install or load libraries
tryCatch({
library(RPostgreSQL)
}, error = function(e) {
install.packages("RPPostgreSQL")
library(RPPostgreSQL)
})
tryCatch({
library(tidyverse)
}, error = function(e) {
install.packages("tidyverse")
library(tidyverse)
})
# Connect to database
drv <- dbDriver('PostgreSQL')
con <... |
8ec9f9bcd2b4e613abb6aa63ad72d80c698bad5c | bc9b42cd4cb22cd3eccd5d27838f48fec54f9d1e | /Rcode/temp.R | f00b089a7ff6eb95d758df3c81672b0881703fe6 | [] | no_license | mbh038/Waves | 61dd0a0a3334dae6314899fa1d6c08b58ca0411c | ec829db267f17bf2477748be3cd7cad2fef54287 | refs/heads/master | 2021-01-19T03:59:46.710943 | 2017-05-24T15:25:08 | 2017-05-24T15:25:08 | 84,421,038 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,215 | r | temp.R | # EnergySim
windMW<-20#seq(0,50,5)
solarMWp<-0#seq(0,50,5)
windPower<-read.table("../data/specs/windPowerCurve.csv",header=FALSE,sep=",")
# read in demand files
houses=18000
ddata<-read.csv("../data/profiles/EESP/domDem10.csv")
demand<-numeric()
demand<-houses*ddata$W/1e6
rm(ddata)
sipfilepathstem<-"../data/... |
84a3f4922c32dbe5379e2a98e10982e40737f05c | 83c489d0e7fca84beb4ecb5a8a3d7f666b076fcf | /man/TGGLMix.Rd | 61b66689d8b7aa5f7c1606e64c8a2b8887200323 | [] | no_license | tohein/linearMTL | 9f3a02640b5b6e51425f62005744ef3047683a06 | bd27f8e90ea297ddca7c6f5e110e6b62ba0cff55 | refs/heads/master | 2021-03-27T14:40:25.364761 | 2018-11-09T15:37:36 | 2018-11-09T15:37:36 | 106,738,759 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,955 | rd | TGGLMix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tggl_mixture.R
\name{TGGLMix}
\alias{TGGLMix}
\title{Fit a tree-guided group lasso mixture model model (TGGLMix).}
\usage{
TGGLMix(X = NULL, task.specific.features = list(), Y, M, groups, weights,
lambda, gam = 1, homoscedastic = FALSE, EM.... |
31b0845941e08e15335b0f5427c56e4f7ee5440a | 69e39dbd8fc9d4c1584d53ad1b1195f63783f68d | /cachematrix.R | 91b70bc9d550a1c050659538442ff054a9be0c11 | [] | no_license | poplock10/Coursera_Course_Work | e12a890d8bcdfc4747f6521e8f1b7c2a1aacc064 | 9991f28e83539b97e473153370b41aa359bc9957 | refs/heads/master | 2021-01-17T13:40:08.712776 | 2016-05-27T15:49:18 | 2016-05-27T15:49:18 | 34,121,984 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 713 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## This matrix object can cahe its inverse.
makeCacheMatrix <- function(x = matrix()) {
v<-NULL
set<-function(y){
x<<-y
m<<-NULL
}
get<-function() x
setmatrix<-function(solve) v<<- solve
getmatrix<-func... |
9624b8d0fddb2687e73ede1e8abd986abc899650 | a6d290acb31b1ec2df14f8713c11f61f254dbe33 | /Ideas/geog4ga3.Rcheck/00_pkg_src/geog4ga3/man/Paez_Mart.Rd | c524a6263b6ced558f7b6eea2c814eba4c8930a5 | [] | no_license | snowdj/Spatial-Statistics-Course | c1e9f66af3515dfd5d9673702d08d2c2068dd97d | fbf4e815b5eecdb804a474444a8417ac1a39085a | refs/heads/master | 2021-05-23T17:39:26.129887 | 2020-03-29T21:34:54 | 2020-03-29T21:34:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 672 | rd | Paez_Mart.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geog4ga3.R
\docType{data}
\name{Paez_Mart}
\alias{Paez_Mart}
\title{Paez_Mart}
\format{A data frame with 395 rows and 3 variables}
\source{
Data frame developed by A Paez in 2008
}
\usage{
data(Paez_Mart)
}
\description{
A dataset containing ... |
7f6cb61e3b53e017d5f65dcc35550d6325404b4d | 4bcf7ad50dd79c619ed271b32f31029e9495a128 | /data/Lake/process_Lake_dataset.R | 633c680fccfe80342b9aba93a148305865183f84 | [] | no_license | kevinc13/single-cell-deep-learning | 11db2497654193433835522dbaae00a2cab29f18 | 3600353d798f03909655150ec6113f7f449b210b | refs/heads/master | 2020-08-04T04:51:41.141189 | 2019-10-01T04:56:53 | 2019-10-01T04:56:53 | 212,012,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 201 | r | process_Lake_dataset.R | library(Biobase)
library(data.table)
library(scater)
setwd(paste("~/Documents/Research/XinghuaLuLab/single-cell-deep-learning/",
"data/Lake", sep=""))
lake <- readRDS("original/lake.rds") |
8d875015d8b416c7045bd860fa93e3882d8b1986 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.application.integration/man/locationservice_batch_put_geofence.Rd | c8e4fe2fad0057a1fc664d4b96a30d0926acb5be | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 942 | rd | locationservice_batch_put_geofence.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/locationservice_operations.R
\name{locationservice_batch_put_geofence}
\alias{locationservice_batch_put_geofence}
\title{A batch request for storing geofence geometries into a given geofence
collection, or updates the geometry of an existing ... |
0c63ee16fc790c7cec0fd59b0000fca3749e68dd | 9246290d41983050547f8c2547a4f7ac99b916b2 | /man/createEnvDataFrame.Rd | 35bfb740b75311070769396e8e551cd0a912cb9e | [] | no_license | ABoVE-AotM/above | 88f6ebbae5f31668ae14af0924fa5ae4c63f8548 | 38beba4117809c46b6c10deb1c4c005949ac1a6c | refs/heads/master | 2021-05-22T11:54:21.681864 | 2020-05-28T05:04:29 | 2020-05-28T05:04:29 | 83,578,459 | 1 | 4 | null | null | null | null | UTF-8 | R | false | true | 1,098 | rd | createEnvDataFrame.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createEnvDataDataFrame.R
\name{createEnvDataFrame}
\alias{createEnvDataFrame}
\title{Convert data frame to Movebank Env-Data request file}
\usage{
createEnvDataFrame(lats, lons, times, savefile = TRUE, fileout = NULL)
}
\arguments{
\item{save... |
6143fda2d2c0f5ea45643e0fc1e00c2d581c0242 | d3be5a411632b06e90aa7f96ae77e2d47be68e9b | /Potionmaster/app.R | 63d63f0a7c93a57e826629b08d8623a82cbecffc | [] | no_license | mpawliwllevac/R-Scripts-Reference | 737a9d6dbf53f7a68f0b88e581cfb7fcba501e15 | eb7e452e4ab57424af0ea6d5b50eafc0b7a407b9 | refs/heads/master | 2022-12-27T15:07:23.322794 | 2020-10-04T15:57:58 | 2020-10-04T15:57:58 | 287,603,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,063 | r | app.R | library(shiny)
library(tidyverse)
library(DT)
library(shinydashboard)
library(kableExtra)
library(dplyr)
# To upload:
# library(rsconnect)
# deployApp("C:/Users/Matthew/Dropbox/R Working Directory/RShiny/folder")
theme_update(
plot.margin= unit(c(0.25,0.25,0.25,0.25), "cm"),
plot.title = element_text (colour="bla... |
13eedfd1d1598a33f4873998c40d308509578218 | 0fd22566d3a72d3b0fc321e2a5d6803e4e88a9d9 | /lib/Comparison/DiffBind.r | dfdf596acc9c6e3897831098972acc0785f0e956 | [
"Apache-2.0"
] | permissive | marisolrs/ngsperl | fdf0a8c0ad659782017dae309c4443d0f2cea313 | 6a26d2fa282a816a74bc7af1513466aca34709ea | refs/heads/master | 2020-03-28T06:28:10.110835 | 2018-09-06T14:55:06 | 2018-09-06T14:55:06 | 147,836,773 | 0 | 0 | null | 2018-09-07T14:47:04 | 2018-09-07T14:47:04 | null | UTF-8 | R | false | false | 1,994 | r | DiffBind.r | options(bitmapType='cairo')
args = commandArgs(trailingOnly = TRUE)
pvalue<-0.05
foldChange<-1.5
library(DiffBind)
DEBUG=0
if(!DEBUG){
configFile=args[1]
comparisonFile=args[2]
outputPrefix=args[3]
}else{
configFile="/scratch/shavertm/20170512_atac-seq/bwa_macs2callpeak_narrow_CallAsSingleEnd_d... |
f9e012fe0f3544100bf31228ba2605613683f0f5 | 3cc6265e82e373d377dae488831cfdb1caad1dfe | /codedepends/simple2.R | e6407f89ce1b19d7990a11a224bb17d0be4e2e41 | [] | no_license | clarkfitzg/phd_research | 439ecc0d650da23bfad1e1a212e490c2746a6656 | dfe46c49f6beba54389b0074e19f3c9b1ea04645 | refs/heads/master | 2020-04-15T14:02:03.890862 | 2019-09-20T02:33:07 | 2019-09-20T02:33:07 | 59,333,323 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 207 | r | simple2.R | # Fri Jan 27 10:28:50 PST 2017
# Simple script as test case
n = 10
y = rnorm(n)
x = 1:n
# Never used
z = runif(n)
df_xy = data.frame(x = x , y = y)
fit = lm(y ~ x, data = df_xy)
plot(x, residuals(fit))
|
311b1c120cac70b293afe5c24d1c0f64ba5e1db3 | 442cd73dafb1a10df4d7546f20c4a912e56403cb | /test_repo.R | 9843f8261af494bc5a3d8ebd360624903775cf71 | [] | no_license | devine8845/test_repo | d6a3fc115a98acf0ef4aea01b3af389998e73c55 | ef9b052646494fdd8cfb8ace68ef698eac64b961 | refs/heads/master | 2020-04-18T08:08:35.853898 | 2019-01-24T15:55:48 | 2019-01-24T15:55:48 | 167,386,013 | 0 | 1 | null | 2019-01-24T15:55:49 | 2019-01-24T15:02:38 | R | UTF-8 | R | false | false | 119 | r | test_repo.R | # test
# devine8845/test_repo
library(tidyverse)
starwars %>% glimpse()
starwars %>% select(mass, species)
|
184804668b92b418a4c98356d1691f3994c39377 | 8fd3836f4292a8a3d917e9737f037afabb5db502 | /R/consec.R | 2c25de36578bef8ce83b25a345b65e11a43cf6cd | [] | no_license | everydayduffy/climvars | beb8202b60db00012273a6fac50c8548274fcc1e | 886a5d2642cc132642563ab3fffcc6b9994706cf | refs/heads/master | 2023-06-12T15:42:48.936770 | 2021-07-09T08:59:09 | 2021-07-09T08:59:09 | 256,446,786 | 0 | 0 | null | 2020-04-17T08:32:12 | 2020-04-17T08:32:11 | null | UTF-8 | R | false | false | 1,124 | r | consec.R | #' consec: Calculate consecutive measurements below or above a specified
#' threshold.
#'
#' @description `consec` is used to find consecutive values above or below
#' a user-defined threshold.
#'
#' @param var a vector of values for which a threshold is to be applied.
#' @param threshold a value with which to threshol... |
a72de0ad5fcac56d4c7409fa70add294bcfe64b8 | a21e9a2003bbee8b38607874cbe15db96bd12dd2 | /clean.R | 9cb65f5555c43c6922bb806345a8a8c1470f5534 | [] | no_license | aronlindberg/VOSS-Sequencing-Toolkit | 53f6af8d46c5b026fecb1c0b32fdef3133da3d60 | 2fa54ed3c3f2d421eb35a5ced2b1e990b9e49f41 | refs/heads/master | 2016-09-08T01:21:43.117669 | 2013-09-24T14:23:46 | 2013-09-24T14:23:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 188 | r | clean.R | # Insert data cleaning/transformation commands here. Possibly the parser (JSON->CSV->Sequence format)?
# Needs to result in a file named "cleaned_data.rdata", which is then loaded in do.R
|
48b62d18d45f6ae7363013be308b2aa0a20ad49d | bad538073a6ed5bd9f8f0653db02fdd433137380 | /packages/RSuite/R/13_load_prj_parameters.R | 4e0ae494448a317c920cf62f36b332c36006b7b8 | [
"Apache-2.0"
] | permissive | WLOGSolutions/RSuite | 5372cfc031b518e38c765b574a99944324a46275 | da56a5d1f4a835c9b84b6cebc1df6046044edebe | refs/heads/master | 2021-06-08T14:07:27.322986 | 2021-05-03T14:09:11 | 2021-05-03T14:09:11 | 104,214,650 | 157 | 11 | Apache-2.0 | 2021-05-03T08:27:54 | 2017-09-20T12:48:44 | R | UTF-8 | R | false | false | 4,684 | r | 13_load_prj_parameters.R | #----------------------------------------------------------------------------
# RSuite
# Copyright (c) 2017, WLOG Solutions
#
# Utilities for management of project parameters.
#----------------------------------------------------------------------------
#'
#' Loads project parameters from specified path.
#'
#' @param ... |
ff85d0a056a9d7e91f063094b27a2a97c40e3bbf | 272defd587476d83f956c25bdfe2227d940dfe03 | /R/ipcf-cross-all-st.R | 38c6b9dab2f597a642e728c076af501606c426f1 | [] | no_license | antiphon/Kcross | cb867df8165b68835f464060b0a9c18685f3d491 | 4314df5a8f544f1f835a784582c1042e32af1431 | refs/heads/master | 2021-01-11T08:42:39.039491 | 2019-05-09T11:26:19 | 2019-05-09T11:26:19 | 76,651,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,125 | r | ipcf-cross-all-st.R | #' Space-Time SOIRS Kcross-PCF
#'
#'
#' @param x point pattern as an n x (d+2) -matrix of coordinates
#' @param int intensity at points
#' @param r vector of spatial lags
#' @param t vector of temporal lags
#' @param bbox bounding box for translation correction
#' @param sigmas vector c(ss, st) with space and time ban... |
33fd389a0c26c19562a3a7fd06165c9a4c12465a | 29585dff702209dd446c0ab52ceea046c58e384e | /rstanarm/R/predict.R | 3f75cd24016b8865e95f27ae9223dfd5a459511c | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,908 | r | predict.R | # Part of the rstanarm package for estimating model parameters
# Copyright (C) 2015, 2016 Trustees of Columbia University
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of... |
3b8f8a5a6203d2a9b55c63a094bd385aa443ea2b | 08481da2b6d3690aa157a161f9df284c802a5177 | /man/create_nmrdata.Rd | 3d2e4478ad26ae4766b6a5cb65a248f975f1f4cc | [
"MIT"
] | permissive | brgordon17/coralclass | 605dfedaaaf48dfd4ad589b6aaf3c7d0bfc44603 | 18de22b48a3bf0cff99c2c82bb206d92d5a53058 | refs/heads/master | 2020-06-24T11:01:51.980043 | 2020-06-15T11:02:49 | 2020-06-15T11:02:49 | 198,944,888 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,442 | rd | create_nmrdata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_nmrdata.R
\name{create_nmrdata}
\alias{create_nmrdata}
\title{create nmrdata.}
\usage{
create_nmrdata(savecsv = FALSE, saverda = TRUE, csvname = "nmrdata",
remove.vars = TRUE, vars = c("3.31"), ...)
}
\arguments{
\item{savecsv}{Logic... |
1c6277f15315efd47b3a515ebccc0b84160ee144 | 011ef657c4c1fd79cb4ac68d86a9d04e70056e6e | /tests/testthat/test-Binary.R | e60e980c8b91090ec84cee4dd4cefe9d99ca64f2 | [] | no_license | cdcepi/predx | 8cf3d52c6a2c75a277f5de08b8f8793810c74ee3 | 82917511064bdaebaf9982eec98be2169b8d1288 | refs/heads/master | 2021-06-24T16:26:32.315548 | 2019-12-27T16:35:16 | 2019-12-27T16:35:16 | 174,559,039 | 6 | 3 | null | 2019-10-28T16:40:07 | 2019-03-08T15:13:15 | R | UTF-8 | R | false | false | 1,353 | r | test-Binary.R | context("Binary")
test_that("Binary accepts probabilities", {
expect_is(Binary(0.9), "Binary")
expect_is(Binary(0), "Binary")
expect_is(Binary(1), "Binary")
})
test_that("Binary rejects non probabilities", {
expect_silent(Binary(0.9))
expect_error(Binary("0.9"))
expect_error(Binary(NA))
expect_error(Bin... |
620980f425ee79053b1106ee6bbca4a44e78fc33 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bayesplot/examples/MCMC-traces.Rd.R | cfc788e252aa919a23a3f5ac7004392cad3eacbd | [] | 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 | 2,587 | r | MCMC-traces.Rd.R | library(bayesplot)
### Name: MCMC-traces
### Title: Trace plot (time series plot) of MCMC draws
### Aliases: MCMC-traces mcmc_trace mcmc_trace_highlight trace_style_np
### ** Examples
# some parameter draws to use for demonstration
x <- example_mcmc_draws(chains = 4, params = 6)
dim(x)
dimnames(x)
# trace plots of... |
61a0051efad2cd9b65041309cdcd45da8070f4f6 | 7d38918f07c1ea0a00c3a1e0af7edec7aa17dde0 | /man/rWBclimate.Rd | 8ed4b0fb39d63212e193f0c22d84073233cdc3e0 | [] | no_license | cran/rWBclimate | 8da14296a6330d0387a7c03c483dbd1b1282c907 | 90b18d09b5bbcb6cb09bf914f52ff90e66633aff | refs/heads/master | 2021-01-19T03:18:37.288168 | 2014-04-18T00:00:00 | 2014-04-18T00:00:00 | 18,929,953 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 129 | rd | rWBclimate.Rd | \docType{package}
\name{rWBclimate}
\alias{rWBclimate}
\alias{rWBclimate-package}
\title{rWBclimate}
\description{
rWBclimate
}
|
2c00a6f42c69761e9f540a1d124e2288fa51e7df | 18d59963400b3e6b116c5ba12fb111cca0e6ff0c | /r-files/run-monte-carlo.R | f072f18eab51f5754f1d1611128d8abdf9f9347f | [
"Apache-2.0"
] | permissive | kbrannan/Big-Elk-Cadmus-HydCal-Updated-WDM | 10000608e9455e0c9b53a505a8dfff7788b8935e | 7fc4fe34667fda0d0e5bbabcd7126423f726bf54 | refs/heads/master | 2020-12-18T12:34:52.880254 | 2016-08-03T22:40:15 | 2016-08-03T22:40:15 | 55,079,166 | 0 | 0 | null | 2016-05-09T22:35:50 | 2016-03-30T16:30:58 | TeX | UTF-8 | R | false | false | 1,226 | r | run-monte-carlo.R | ## script run monte carlo anaysis for calib.pst
## follows the preocedure recommended by John Doherty (2016-04-04)
## 1. Calibrate
## 2. Get the posterior covariance matrix using PREDUNC7
## 3. Sample that
## 4. Put each sample into a PEST control file using PARREP
## 5. Run ADDREG1 to add regularisation which tells t... |
16702f6cf7a7e21f2ea554471d853e1b68fa263f | f1c70a922f34dfb38bcd9b949db5f6da1a3ab57a | /man/format_numbers.Rd | 6a7e7b9c990917fa97b716d495529b0ef13fe0a7 | [
"MIT"
] | permissive | neyhartj/neyhart | 0d8705edd9888119ba58f1f5716f0b84858a87da | a33d6aae63446c1c6046cba35ea0fe0846ccf9af | refs/heads/master | 2022-10-24T06:08:39.844403 | 2022-10-07T17:56:44 | 2022-10-07T17:56:44 | 60,227,524 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 447 | rd | format_numbers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{format_numbers}
\alias{format_numbers}
\title{Format numbers for printing}
\usage{
format_numbers(x, signif.digits = 3L)
}
\arguments{
\item{x}{A numeric vector.}
\item{signif.digits}{An integer specifying the number ... |
5dd7391ab2f72fb1687fa85ae922b5f01b95d6f2 | 91622d34905a1ac8c585cc6205418e9b4e499167 | /R/thetaUncon2thetaList.R | 338bc686667460bb58ee3f7cd1f095794efd88c2 | [] | no_license | minghao2016/fHMM | 46766503e2ee7c177b288bdd4b0221e67c559150 | d5fe6baeb41f2f003ddf19c5619d51042d44ffd7 | refs/heads/master | 2023-03-12T22:58:13.100210 | 2021-02-23T14:55:05 | 2021-02-23T14:55:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 345 | r | thetaUncon2thetaList.R | #' Bring uncontrained model parameters in list form
#' @param thetaUncon Unconstrained model parameters in vector form
#' @param controls A list of controls
#' @return Constrained model parameters in list form
thetaUncon2thetaList = function(thetaUncon,controls){
return(thetaCon2thetaList(thetaUncon2thetaCon(thetaUnc... |
90cadcaa2777889b69c054adefa73dfacbe0dd36 | 2a0d1fc07d673b8c7cf07c6596dac2630ae3fd7c | /scripts/geocache-weight.R | e5df348ed20f49fb25eb1dd5dac00cbf433a6f48 | [] | no_license | stelmacm/WNS | aef0320ac63d789590cc229e7f24e2fe248c036d | a455ce91c547c8174444d8fb811b4236c2ae38f3 | refs/heads/master | 2022-04-29T11:23:05.577810 | 2022-03-04T14:31:02 | 2022-03-04T14:31:02 | 241,282,675 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,471 | r | geocache-weight.R |
# summary of GC finds - GC
# polygons are included here in order to grab the centroid later
# pull out a unique list of county polys
# not from Cali or Wash for now!
uniq.df<-presence.df %>% filter(.,STATEPROV != c("California","Washington"))
uniq.df<-uniq.df[!duplicated(uniq.df$county),]
# convert coords from co... |
256a77502ffe7aeee6f69433d3438622f0d5b921 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query04_query42_1344/query04_query42_1344.R | 52fd4134e9e73bafad8d364820d0dabc424e86cd | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | query04_query42_1344.R | 2c849bc775f9c3b3738411302b579f9f query04_query42_1344.qdimacs 732 2172 |
6911a7c9b27561d20a3a275be80b7fcc3b286522 | aa52164361900c4ce7c708c7a4cb28b03aa68970 | /Models/Simple+Interactions+more-modified.R | 161ebad333f6dced67d65c19574088be8412c56d | [
"MIT"
] | permissive | kvdesai/wikipedia-challenge | 3297561a48e7aa0eddf02e51e00e2864d300fc41 | 6d558b0b50c67cc37410ccefeba9471c1374ff94 | refs/heads/master | 2020-03-17T01:16:19.281604 | 2018-05-17T13:26:14 | 2018-05-17T13:26:14 | 133,146,074 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,766 | r | Simple+Interactions+more-modified.R | #source('D:/ICDM11/mygit/Kalpit/not_so_simple9a.r')
rm(list = ls(all.names=T))
#setwd('D:/ICDM11/wikichallenge_data_all/Honourable Mention')
source('Models/helper_functions.r')
data_train = read.csv("AllFeatureSets/training_features.csv", header = TRUE);
data_lead = read.csv("AllFeatureSets/leaderboard_features.... |
a04652277eeae9930f3faa2b86749f5ca3b24ec3 | 411837f5378044985c31f1014b276857f358511e | /Dataset_videogamesales/vgsales_DecisionTree with Convert sales.r | 302bcdc55cbcc5173e0da8e0ec9a1470b19e23e6 | [
"Apache-2.0"
] | permissive | siliconninja/CSSE286-VideoGameSaleTeam | 6580555874f959967361e7901d959eec065efa6f | aced01e75f52b91858aab8e7103a12d8005c276b | refs/heads/main | 2023-08-15T23:47:35.393873 | 2021-10-14T14:04:08 | 2021-10-14T14:04:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,385 | r | vgsales_DecisionTree with Convert sales.r | vgsales <- read.csv("vgsales.csv")
table(vgsales$Genre)
set.seed(123)
str(vgsales)
vgsales <- vgsales[-1]
vgsales <- vgsales[-1]
vgsales <- vgsales[-c(4)]
convert_sales <- function(x){
x <- ifelse(x > 1 ,"High" ,ifelse(x<0.1, "Low", "Medium"))
}
vgsales$NA_Sales <- sapply(vgsales$NA_Sales, convert_sal... |
7d8868fb26dbdbbe5d0f2e63585273aefa268ac2 | ea6a12f3aa403ff73b4146104e52ab06acb3c190 | /run_analysis.R | ecea0d1215d42f47ecffe9809fa7ab6f0acb36eb | [] | no_license | louislouisloui/project_week4 | dd040c799612f4e015163fdadc8322d098a02c32 | 5f614f0f89bfb1f70d1877fe7cd602ccef6665f5 | refs/heads/master | 2021-01-10T08:17:43.419836 | 2016-03-04T00:36:09 | 2016-03-04T00:36:09 | 52,871,363 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,546 | r | run_analysis.R | extract_test <- function() {
require (dplyr)
#Create the data frames
tbl_y_test <- tbl_df(read.table(file = "./data/UCI HAR Dataset/test/y_test.txt"))
tbl_subject_test <- tbl_df(read.table(file = "./data/UCI HAR Dataset/test/subject_test.txt"))
tbl_x_test <- tbl_df(read.table(file = "./data/UCI HAR Data... |
d7f5ba68aa071b542b0802550312e483af67d045 | c53ed8d506bd5b805ab284b854841af32ad51c42 | /man/atomic.Rd | 02c6821e08296f50fdafefe490a2ba33b176e2f0 | [] | no_license | hdhe/animaker | 478b5b3da0e2e272301bad00d50e104cf380d105 | b7f4481312d90609ea1807f32e3eb29860d1253c | refs/heads/master | 2020-05-29T11:39:47.299017 | 2013-06-10T05:55:30 | 2013-06-10T05:55:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 816 | rd | atomic.Rd | \name{atomic}
\alias{atomic}
\title{
Create an atomic animation
}
\description{
An atomic animation describes an animation by providing two key
parameters, the starting time and the duration. A label can be given
to describe what an animation is supposed to be doing.
}
\usage{
atomic(start = 0, durn = 0, label ... |
234f972715bfff71d898de79d7723d7f4252d2e2 | 16bacff32811d40d33b9c9a5a8a9cce76e2478a2 | /wilcox_tests/wilcox_test_RLCC.R | 3d898bbde6d1e18d40177bc8a9529b0d79811464 | [
"MIT"
] | permissive | wangpanqiao/BioScripts | c6f792035a782dfbdac01da3e0069fef6a49227a | 8285ae1078f2be42f2c92f85850d58ebcfd69ef6 | refs/heads/master | 2022-04-12T16:28:34.944651 | 2020-02-28T18:47:37 | 2020-02-28T18:47:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,485 | r | wilcox_test_RLCC.R | install.packages("coin")
require(coin)
library(ggplot2)
getwd()
setwd("d:\\Hernan\\Proyectos\\Por-Usuario\\Gaby\\")
grupo_control_RLCC <- scan("joined_2019-12-20_20-15-51_nsp.txt")
grupo_caso_RLCC <- c(3650, 730, 2555, 365, 2190, 1460, 1825, 730, 2920, 4380, 2920, 1095, 1095, 1095, 730, 2920, 365, 1825, 3285,... |
c90c51b520979bd5531d9021b1f276c04f4c45e8 | 34304457fbc594cd3e20847a6c03c0feee0de6ab | /analyses/decsensSimsFstarAuerbach.R | e363ae996d8cc02627ad80f88c5a98fb98db2e9f | [] | no_license | lizzieinvancouver/decsens | 0d5acea1c671e1c6cf6b93a96e9019520e6e4230 | 41fc5fa662bac88ca390d4804073f14c6b8087d9 | refs/heads/master | 2023-07-21T22:55:34.257871 | 2023-07-13T10:21:12 | 2023-07-13T10:21:12 | 233,109,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,303 | r | decsensSimsFstarAuerbach.R | ## Wrote 15 Oct 2020 ##
## From J Auerbach's 12 Oct 2020 email ##
#Plot 1 (model from section 1.1)
# Example plot for Section 2.1
thresholds <- seq(100, 400, length.out = 10)
cov_experiment <- var_experiment <- numeric(length(thresholds))
for(experiment in seq_along(cov_experiment)) {
mean_temp <- leaf_out <- nume... |
49171d67c6737b817bee95f5f26c6c9473217c50 | ed8739273f94dffb05f22913bd36ccae4e520196 | /3. Getting and Clearing Data/summarizing_data.R | 5584559a380950052a80b184093ee8a95f41dc37 | [] | no_license | martafd/datasciencecoursera | 5b5a24e62f5e5d01968d33cb8a46379682569446 | 5104e6b0439558683ea9fd03a8e562ae473ffff4 | refs/heads/master | 2020-05-25T12:47:07.023734 | 2016-10-08T19:01:53 | 2016-10-08T19:01:53 | 65,119,593 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,916 | r | summarizing_data.R | setwd('/home/marta/Documents/Data_Science/3. Getting and Clearing Data')
fileUrl <- 'https://data.baltimorecity.gov/api/views/k5ry-ef3g/rows.csv?accessType=DOWNLOAD'
download.file(fileUrl, destfile = './restaurants.csv', method = 'curl')
data <- read.csv('restaurants.csv')
head(data, 3)
tail(data, 3)
summary(data)
str... |
a979766e1a5e77a0d6b4ba6a54e09ca0f5f9cef5 | 1c13cd855fefb41c98fa2e8c9cfda5e2bd1a503e | /man/getDevianceResiduals.Rd | bed30726100e8fac45b3c706fffa8206706b6350 | [] | no_license | daniellemccool/REMThesis | 08867411e58ff5680e69d3aa1350e1537dfa586b | 82a2578a096fd8c1c86b49ebb50d6b7b0eaefe22 | refs/heads/master | 2021-01-15T13:18:10.158596 | 2014-09-15T10:31:15 | 2014-09-15T10:31:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 351 | rd | getDevianceResiduals.Rd | % Generated by roxygen2 (4.0.2.9000): do not edit by hand
\name{getDevianceResiduals}
\alias{getDevianceResiduals}
\title{Calculate Deviance Residuals}
\usage{
getDevianceResiduals(fits)
}
\arguments{
\item{fits}{}
}
\description{
Calculates the Deviance of the residuals
}
\examples{
getDevianceResiduals()
}
\keyword{d... |
9c8bdd51d098a4ad00edf23570a09c9821bd7631 | fd365694237edb699e53eef04f1c3c0ff649f3c8 | /man/opal.file_mv.Rd | f6e34dbaf6006e85d568671c5284d827799bbe2d | [] | no_license | obiba/opalr | f73a0eb0280bc768b47711d6a1a08ce0eded7ce1 | 5ca4936deae7e3410db5ee6a02df7994ff5fa336 | refs/heads/master | 2023-08-03T06:18:07.954481 | 2023-07-21T06:58:07 | 2023-07-21T06:58:07 | 166,788,279 | 3 | 3 | null | 2021-05-13T15:50:49 | 2019-01-21T09:45:41 | R | UTF-8 | R | false | true | 1,411 | rd | opal.file_mv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/opal.file.R
\name{opal.file_mv}
\alias{opal.file_mv}
\title{Move and/or rename a file}
\usage{
opal.file_mv(opal, source, destination)
}
\arguments{
\item{opal}{Opal object.}
\item{source}{Path to the file in the Opal file system.}
\item{de... |
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