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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9a0742e824c181ff2bfbc5c565ace599620ae95c | cdc6049c2731e6aa03c0cd93daba24b6b1e3de06 | /opensesame/splitTrialsByCond.R | 5c4a9adefb64fc4065ceb912df620ad9d1bf2ea4 | [] | no_license | disaltzman/TalkerTeam-Mapping | 4ebee00fc36cd4b9ec644ffa32c66a808247cafd | 1131d3ddf7d70fc705d75e3125390b647584802c | refs/heads/master | 2023-07-15T11:08:47.397160 | 2021-08-27T13:47:04 | 2021-08-27T13:47:04 | 257,983,179 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 691 | r | splitTrialsByCond.R | rm(list=ls()) #clear all variables
setwd("~/Documents/UConn/Research/Talker Team - Mapping/randomizations/")
files <- as.data.frame(list.files())
colnames(files) <- "files"
#
for(n in 1:length(files$files)){
randomization <- read.csv(as.character(files[n,]))
subject <- unlist(strsplit(as.character(files[n,]),spli... |
b14053cbb1f310f146cada36a1025a02925837a8 | 358bcdc100cedfa930b1b1f636b08c81dc906d8f | /Code/Fig 4.R | 87f8562e8c832b38976c48aa97eb1a35f779e1b5 | [] | no_license | btmarti25/biophysical-basis-of-thermal-tolerance-in-aquatic-eggs- | 2d5c4d5d236d0424db27115104f54219089a90b7 | 7e295600ea97d9bde76edb4eb4d065b3fd6be632 | refs/heads/master | 2022-08-31T11:22:53.028154 | 2020-05-25T09:15:07 | 2020-05-25T09:15:07 | 262,339,918 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,026 | r | Fig 4.R | library(reshape2)
library(ggplot2)
library(lme4)
library(gridExtra)
library(MASS)
library(cowplot)
library(dplyr)
library(tidyverse)
library(cowplot)
GravelSurvivalData <- read.csv(file = "~/Google Drive/Projects/ChinookEggExperiment/Final/Data/GravelSurvivalData.csv", header = T)
## fit mixed model for survial as fun... |
43651d658b53713242b0e220c82cdd9ed89e34ee | 226779647e199fbb8c8fee155c2373f8fa82857f | /Plot1.R | 5ee7f58eedc6046a533e5883ceffc6319049fb01 | [] | no_license | andrewplumb/ExData_Plotting1 | b7d816a69a508e2d51c2436c33405f864703aaef | 4fd20cfee5ceb5abaa5ba5124133b3f933c82cc6 | refs/heads/master | 2021-01-16T19:44:35.912201 | 2015-08-04T23:17:38 | 2015-08-04T23:17:38 | 40,208,840 | 0 | 0 | null | 2015-08-04T21:00:30 | 2015-08-04T21:00:30 | null | UTF-8 | R | false | false | 478 | r | Plot1.R | library(dplyr)
path2epc <- "C:\\Users\\XBBLXKV\\Documents\\household_power_consumption.txt"
epc <- read.table(path2epc, header = TRUE, sep = ";")
epcfiltered <- filter(epc, Date == "1/2/2007" | Date == "2/2/2007")
par(mfrow = c(1,1))
par(mar = c(4,4,2,2))
hist(as.numeric(levels(epcfiltered$Global_active_power))[epcfi... |
38b5cff0fd9a15ccddcc96f7f4a8501fc699929b | 0c1c0e8c68835ca908806372236af6f539490042 | /lecture/2016.06.02/notes.R | ed2857420978aff5cb57eb90df62241ca01701cc | [] | no_license | AylNels/notes | 2343f78064d1bb07dc71d23dc2b70c69b45e52c3 | e331c4ff2d736cb65f2313659ab42be469d80209 | refs/heads/master | 2021-01-15T20:47:57.257314 | 2016-06-05T20:29:02 | 2016-06-05T20:29:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,079 | r | notes.R | ## # STS 98 - Lecture 2016.06.02
##
## See [notes.R](notes.R) to follow along in RStudio.
##
## Also see the [R input](r_session.txt) from the lecture.
## Announcements
## -------------
## Extra OH: Today 5-7pm, Shields 360
##
## Final Exam: Monday (June 6), 10:30am - 12:30pm, Wellman 26
##
## Bring a blue book!
## ... |
eb2fdf40bddd94672be748839a4969eed53c2b41 | f93ba2f8fb9269b3b9b1e08060ed90bf7983ea27 | /testingdata/Code/gamma.R | ad0ff1a071552a270fddfbb1cb9dcb98d9e3d853 | [] | no_license | maniteja123/defect_prediction | b065c76e4b1ca0139d9f7e73a49c7315947f9963 | f28a1383a933e51202a01bfde660a7adb9b56c82 | refs/heads/master | 2021-01-10T07:50:34.931522 | 2016-03-27T06:39:31 | 2016-03-27T06:39:31 | 54,816,070 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,001 | r | gamma.R | library(VGAM)
library(fitdistrplus)
library(actuar)
setwd("D:\\Studies\\Project\\BugPrediction\\Softwares\\testingdata")
nextReleaseDates = data.frame(
tomcat3.3="2002-09-06",
tomcat4.1="2003-12-03",
tomcat5="2007-02-28",
tomcat6="2011-01-14",
tomcat7="2014-06-25" )
files = c("tomcat 3.3","tomcat 4.1","tomca... |
9dc1a6705848953f8ae9d9d14286931396430530 | d1f1fabdd43cb8d2ce376118d902f0b281082aef | /R/lc_gradient.R | 87aa7d1b8385d1a83cabef4e55b79d3c39738c00 | [] | no_license | cran/GB2group | 36f76d3a5997fd71e83de3ea771208d01bfb626c | 47adc1f2ef13aa307d3141398df2f6a95913d195 | refs/heads/master | 2021-06-17T19:43:21.291134 | 2021-01-26T16:00:09 | 2021-01-26T16:00:09 | 157,565,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,108 | r | lc_gradient.R |
gr.gb2 <- function(theta) {
a <- theta[1]
b <- theta[2]
p <- theta[3]
q <- theta[4]
pr <- theta[5]
t <- qgb2(pr, a, b, p, q)
lcgb2 <- pgb2(t, a, b, p + 1 / a, q - 1 / a)
return(-lcgb2)
}
gr.da <- function(theta) {
a <- theta[1]
b <- theta[2]
p <- theta[3]
q <- 1
pr <- theta[4]
t <- qgb2(pr... |
536dc760fac9f056857682a4329ec19d4c1d60cc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bayesQR/examples/prior.Rd.R | 75b8200b0f74edabdca64e8207ad6cf03d1a4728 | [] | 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 | 493 | r | prior.Rd.R | library(bayesQR)
### Name: prior
### Title: Create prior for Bayesian quantile regression
### Aliases: prior
### ** Examples
# Load the Prostate cancer dataset
data(Prostate)
# Create informative prior object
prior <- prior(lpsa~., data=Prostate, beta0=rep(5,9), V0=diag(9))
# Investigate structure of bayesQR.pri... |
416d6b26324e0fcfff837ea4961e8e92f5e552e7 | e9aea5bfb926656cf440c0df1cc7835589d68736 | /PPAC/R/cossim_sync_method1.R | 5874cfdbe7b5279cae019eb5fefff6e092d971be | [
"MIT"
] | permissive | Nian-Jingqing/emosync | 1e639968d263ffba3d86d63dcad447a1f9f54e41 | 4bada30ab5af64691c0b881cca65af55591bf67e | refs/heads/master | 2023-03-30T02:25:24.676248 | 2021-03-26T11:53:29 | 2021-03-26T11:53:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,172 | r | cossim_sync_method1.R | library(tidyr)
library(tidyverse)
library(lmerTest)
library(lme4)
library(MuMIn)
library(lattice)
library(lsa)
library(here)
source(file.path(here(), 'R', 'cossim_sync_method_functions.R'))
polku <- file.path(here(), 'data', 'ppac_all.csv')
PPAC <- read.csv(polku, header = T, sep = ",")
sync_2feats_1emo(PPAC, "anger... |
e027b1be20bd907de146b0d19113bfb8b1c8d1ca | 19a0a4d113de967aa4884b4053a7b3cb68f5c6b1 | /tests/testthat/test-airzone_metric.R | 8178078131b4e3d1d7b5002950b6e951d78d0bc3 | [
"Apache-2.0"
] | permissive | paulroberts68/rcaaqs | 5ad8feeebb9d33593446d60282241b0e7e2bb3e2 | 435f20598089506d473aa264ed5fb28861aa0536 | refs/heads/master | 2021-01-20T14:34:06.947772 | 2017-02-15T18:30:29 | 2017-02-15T18:30:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,499 | r | test-airzone_metric.R | context("airzone_metric")
test_that("parsing valid years works with some n_years being 2", {
test_s2 <- data.frame(n_years = c(3,3,2,2,3), val = rnorm(5), foo = letters[1:5])
res <- parse_incomplete(test_s2, "n_years", "val")
expect_true(all(is.na(res$val[3:4])))
})
test_that("parsing valid years works with all... |
cc1f32bf27cdce78a8f4305b223eee1ff6f2b9ef | bd8a7c215d851e6b3c44165baec15e3f13efb665 | /man/es_fileset_present.Rd | 4a1fbeef6a518811f97efe8c7b8d84b501f04ceb | [] | no_license | mYstar/easyshiny | dfe36d11f97d390cb3e7e5548f64d6939b9de36a | 9987d571a65ecdb6004cfa112ad80f027694b0fd | refs/heads/master | 2020-04-12T15:02:55.283045 | 2019-06-19T08:19:46 | 2019-06-19T08:19:46 | 162,569,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 492 | rd | es_fileset_present.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/es_filechecks.R
\name{es_fileset_present}
\alias{es_fileset_present}
\title{Check for Fileset}
\usage{
es_fileset_present(filesets, setnumber)
}
\arguments{
\item{filesets}{the fileset list}
\item{setnumber}{the number of the set to check}
}... |
c812bca4fa46ca93ec3af216b381e750e29703be | dbd79ac5dadffb31324ec696f33c948efdf9796d | /man/Gpext2terminal.Rd | d14824b5016586ba975384ac17b1d49f85e27be5 | [] | no_license | cran/Rgnuplot | a0c8659432434600da277ebd97690f6d4a1ae4f3 | c4eb64e6c9dfab0bfb0e433cc4298acce86a8ab6 | refs/heads/master | 2016-09-15T18:54:37.170197 | 2015-07-28T00:00:00 | 2015-07-28T00:00:00 | 17,693,315 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 328 | rd | Gpext2terminal.Rd | \name{Gpext2terminal}
\alias{Gpext2terminal}
\title{Determine a suitable terminal from a file extension}
\description{\code{Gpext2terminal} }
\usage{Gpext2terminal(filetype='PNG')}
\arguments{ \item{filetype}{ file extension}
}
\value{ terminal name}
\seealso{ \code{\link{GpsetTerm}}}
\author{Jose' Gama}
\keyword{progr... |
8e27d9702978c3ff68aa65bd545550b6710c61a4 | 0dc7ef54a4a4a0566fc7f1af18c06bf001c8a570 | /L1/code/util.r | 45d580c1d56c38583b5df6929f07e19c3288eb70 | [] | no_license | holypriest/MAE0217-2017 | 480f13daae41d7ab71552df79bdf5d7383380598 | 89c78f5da3ff54cc0c2ba33527a752b7eb8933d4 | refs/heads/master | 2020-12-03T02:19:24.724126 | 2017-06-30T22:30:08 | 2017-06-30T23:07:24 | 95,927,054 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | r | util.r | # Calculates basic statistical measures for the given data.
#
# Arguments:
# data: Array of values for which statistical measures will be applied.
# digits: Number of decimal places to be used.
basic_measures = function(data, digits=2) {
avg = mean(df$Velocidade)
stdev = sd(df$Velocidade)
# Note: Algor... |
b14f87ae1633fcc1a66e16eed9f9a3cebc723932 | 060d160980d35f8cc59a4613319444af19c6a740 | /R/studies_per_huc.R | 54ff4ba8f89c470e5951de357fe6cff598c4acbd | [] | no_license | mfoard/mountain_climate | ce4b799d34f817324af892a5b003e1bd3ab6bcc7 | 1f63a64e4c5e4f9b047e0b35c9eb55398e2f7540 | refs/heads/master | 2020-03-17T15:51:21.867803 | 2018-05-15T20:04:58 | 2018-05-15T20:04:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,471 | r | studies_per_huc.R | # Make a map to show total number of studies in each HUC.
# Subset to studies that are HUC-specific.
library(tidyverse)
library(sf)
library(ggthemes)
# Get data - should ultimately be combination of first and second round.
dat <- read_csv("../results/tabular/single_copy_results.csv")
# Get HUC6 data.
huc6_raw <- ... |
20016d59b04ee47d765c89015727919a6a538b74 | bfd8b304248b6e65cb6b6d3ede810b53187af621 | /doc/Topic Modeling Tutorial_Original.R | 91cdabd371ce6bb7813cb70cf31aee0bd6fc1f49 | [] | no_license | TZstatsADS/Fall2016-proj4-CHuang0-0 | 47c9a0511887f2c8e09c3f3b1923a85efbbf181d | 4588a56e49185cabb031ebd39918a730088b4427 | refs/heads/master | 2021-05-01T00:30:51.522807 | 2016-12-13T21:22:32 | 2016-12-13T21:22:32 | 72,900,689 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,629 | r | Topic Modeling Tutorial_Original.R | ---
title: "Topic Modelling"
date: "11/9/2016"
output: html_document
---
## References
The tutorial is replicated from the [A topic model for movie reviews](https://cpsievert.github.io/LDAvis/reviews/reviews.html) with added comment.
## Essential theories
### 1. Text problems, Unsupervises, Bag-of-Wor... |
a6668b064d0d689767c302aca3129e40d97b89f4 | 6a2ca2c8c3e362ea3eb401124673a3e31b176586 | /4_import_export.r | bc0a5f054ff8e38f781c662e5b055bc8166725be | [] | no_license | shraban020/r-analytics | 31986b333e4edf33436f55237ecd257ae2fbe787 | bc1806c8bead8d9051fa5617c2578252ab23b0ce | refs/heads/master | 2020-08-27T02:51:53.251793 | 2018-08-24T04:48:17 | 2018-08-24T04:48:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 924 | r | 4_import_export.r | # Import a CSV file
csv_file<-read.csv(file.choose())
csv_file<-read.csv(file="d://out5.csv")
# View the file
View(csv_file)
# Edit the file
csv_file<-edit(csv_file)
# Export CSV file
write.csv(csv_file,file.choose())
# Import a text file
txt_file<-read.table(file="d://out5.csv",header=TRUE,sep=" ... |
470242e691e13c559eac702ac782c0dc54251493 | ab50d0187fff17c5e3576ca4d912760ad7fe245b | /R/ate.randomForest.R | ec4916a6db0be34f1c1ee1289fc099d3930f7b65 | [
"MIT"
] | permissive | ge-li/crossEstimation | a061651439daea43d8a57ad1e22913f92007fd62 | cc0f1a3e7884475466673f5542fd795d556460d7 | refs/heads/master | 2021-12-12T21:52:09.400340 | 2017-02-20T23:03:39 | 2017-02-20T23:03:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,623 | r | ate.randomForest.R | ate.randomForest = function(X, Y, W, nodesize = 20, conf.level=.9) {
if (prod(W %in% c(0, 1)) != 1) {
stop("Treatment assignment W must be encoded as 0-1 vector.")
}
nobs = nrow(X)
pobs = ncol(X)
yhat.0 = rep(NA, nobs)
yhat.1 = ... |
d04213df90ba90856e5a224ac614ad74b7cdb50c | 44ea20642e56ff6cc836029bcda5a29390335b30 | /R/d.binormal.R | fe9abe75ec83957001536d82861d6721cce1ec37 | [] | no_license | cran/idr | e8906789b0be3ba0663d46da33f36ea46c2cfd96 | 4fa51a408935584f97292a091cf32c6e307d9cc6 | refs/heads/master | 2022-07-18T03:32:18.278592 | 2022-06-21T06:30:07 | 2022-06-21T06:30:07 | 17,696,749 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 209 | r | d.binormal.R | d.binormal <-
function(z.1, z.2, mu, sigma, rho){
loglik <- (-log(2)-log(pi)-2*log(sigma) - log(1-rho^2)/2 - (0.5/(1-rho^2)/sigma^2)*((z.1-mu)^2 -2*rho*(z.1-mu)*(z.2-mu) + (z.2-mu)^2))
return(loglik)
}
|
80af5bb0d197d160ff2e228f31f1759f613dbde1 | 94908a285737843999c5acaaad60199538a5c8d6 | /man/barplot.gety.Rd | 22c777bdaabdaff3d450a5e555baa9a53cc9348a | [] | no_license | drmjc/mjcgraphics | 800716e07757066d992a6eb1ea0470012cb8f698 | cd9e30472fea14591bc342b24cc8330307fb9b4c | refs/heads/master | 2021-01-19T01:47:00.882419 | 2016-06-07T06:10:21 | 2016-06-07T06:10:21 | 12,447,176 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 608 | rd | barplot.gety.Rd | \name{barplot.gety}
\alias{barplot.gety}
\title{Function to determine the height of each bar with extra padding.}
\usage{
barplot.gety(height, space = 0.05, ...)
}
\arguments{
\item{height}{the same vector that you passed to barplot}
\item{space}{the gap between top/bottom of bar and the
point, as a proportion... |
f163cd22aae20ceced77559c8a3cb464dbae951b | f2e2b71783b916dfff5d6ee1b7b5dc93bb55d84b | /deprecated/plot-random-walk.R | 13eec147a8d5f621360a00bcc7dddd09b43daf93 | [
"MIT"
] | permissive | bcow/GPP | 7ca6937ea1093aad4ab145fdc52a4d7bba60f5d5 | 50ca55f7f8feb94b9caa8c0108338c6807fb0f09 | refs/heads/master | 2021-01-15T10:20:50.617154 | 2016-05-04T18:49:03 | 2016-05-04T18:49:03 | 53,826,500 | 0 | 0 | null | 2016-03-14T04:19:23 | 2016-03-14T04:19:23 | null | UTF-8 | R | false | false | 1,849 | r | plot-random-walk.R | # Fit a random walk
library(ggplot2)
library(gridExtra)
load("modis-download/modis.data.RData")
sitenames <- function(name){
if(name == "MOD15A2.fn_uswiwill.txt"){
sitename <- "Willow-Creek"
}else if(name == "MOD15A2.fn_uslcreek.txt") {
sitename <- "Lost-Creek"
}else if(name == "MOD15A2.fn_ussylvan.txt")... |
993ed0250b23113a16e2ae4f0342a9ee94771844 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sfadv/examples/Farms.Rd.R | e820256aac33adeb82592dab95d2a69b467835b1 | [] | 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 | 286 | r | Farms.Rd.R | library(sfadv)
### Name: Farms
### Title: Data set of farm accountancy data
### Aliases: Farms
### Keywords: datasets
### ** Examples
head(Farms)
str(Farms)
summary(Farms)
lm.output <- lm(farm_output ~ agri_land + tot_lab + tot_asset + costs, data = Farms)
summary(lm.output)
|
8c8c72a954464760c9e7564f5848ee37d56b8cad | 401213f0cb5fb3ebac8133262b6d0e65326fffc7 | /man/conditional_np.Rd | cad113796d49ba0ba558343a96d4ded9a51c63b3 | [] | no_license | fhernanb/usefultools | 41e6464990cb436d5e2c6adf546a891bde036cbb | 6be5f5583152c7d966cdea7df503e484262e3b5c | refs/heads/master | 2020-04-23T18:42:25.417048 | 2019-02-25T14:23:25 | 2019-02-25T14:23:25 | 171,377,234 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,131 | rd | conditional_np.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/conditional_np.R
\name{conditional_np}
\alias{conditional_np}
\title{Conditional distribution of a multivariate normal distribution Np}
\usage{
conditional_np(x, mu, Sigma)
}
\arguments{
\item{x}{is a vector with the values that are known. Fo... |
f21f94f8fbab0ce0bad159cd1d17cabca561a6e6 | 234f7a4e847bd66a235ab998ada749c26fcaba8a | /analyses/ss2/check_reference/Rnotebooks/check_reference_pseudogene.R | 2fe0cac92f8767884319aff44274fe01fc637b9d | [
"BSD-3-Clause"
] | permissive | HumanCellAtlas/skylab-analysis | 2ac1c4f70894b3d634281aee519a38d9b7ff40f4 | 983e7744d416ad2492f3367fea3f7b324dc1a567 | refs/heads/master | 2022-12-12T04:56:51.220490 | 2020-02-19T17:55:05 | 2020-02-19T17:55:05 | 118,922,733 | 3 | 0 | BSD-3-Clause | 2022-06-22T01:12:45 | 2018-01-25T14:24:47 | HTML | UTF-8 | R | false | false | 13,632 | r | check_reference_pseudogene.R | ## ------------------------------------------------------------------------
library(rtracklayer)
library(ggplot2)
library(plyr)
library(reshape2)
library(plotly)
library(VennDiagram)
require(gplots)
system.time(gtf_gencode_comp <- readGFF("~/Documents/HCA/reference/refs/gencode.v27.chr_patch_hapl_scaff.annotation.gtf"... |
9cb05229a4c211d7fe176d8b522fec24c086431f | 53c8b2c6a0300d48682284fe357b2a74b2d373c3 | /demand/demand_curves.R | 46414d88027dc60c258a0bb7c65fc29d18b22a36 | [] | no_license | emlab-ucsb/future_food_from_sea | ce8140972b5ecf087d4ce9ea82db602742b88442 | 917496a5c29e2d804cc46ece3225729522d8e8e0 | refs/heads/master | 2022-11-04T21:31:01.031805 | 2020-07-27T17:28:29 | 2020-07-27T17:28:29 | 250,334,394 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,720 | r | demand_curves.R | ## Tracey Mangin
## December 4, 2019
## demand curves
## future of food from the sea
## note to user: must run files in init_cond folder first
## this creates demand curves
## attach libraries
library(tidyverse)
library(rJava)
library(tabulizer)
library(tabulizerjars)
library(rebus)
library(viridis)
library(reconP... |
3a4c7dc57640e1dce11f3fa942c3422b1c32125a | ae04c40d5560d5fd97c688909e6134b631fe4065 | /cachematrix.R | 742238e1f756d8d01871540435df049996bfc643 | [] | no_license | nodde/ProgrammingAssignment2 | b75066c05a9125fd78773489089d94884b5d2b54 | 839780c4352409d5f859c82d795fc414a9a38f3f | refs/heads/master | 2021-01-19T07:22:50.946643 | 2015-11-21T20:21:38 | 2015-11-21T20:21:38 | 44,532,474 | 0 | 0 | null | 2015-10-19T12:18:43 | 2015-10-19T12:18:42 | null | UTF-8 | R | false | false | 3,011 | r | cachematrix.R | ## Matrix inversion can be a time-consuming computation.
## Caching the inverse of a matrix after computing once
## rather than computing it in each run can conserve
## resources and enhance efficiency. These 2 functions
## present the solution to this problem on the assumption
## of a square invertible matrix.
## ... |
7b147e229337c7c801586ab853cb76a7b819922f | 5697ce07fbfd684465ca2e1864198e4a249bf526 | /R/print.metaMDS.R | 4128d74f987d2f477452e14d2cb1be8627f9482d | [] | no_license | psolymos/vegan | be0f3a42fda647f1e96f01c53ce0363c447ff7a2 | ccbd3479a29d0d4397b5726d79a6a975208c0572 | refs/heads/master | 2020-12-28T21:51:35.806024 | 2020-06-26T16:24:12 | 2020-06-26T16:24:12 | 47,991,367 | 1 | 1 | null | 2020-06-26T16:03:45 | 2015-12-14T17:46:13 | R | UTF-8 | R | false | false | 1,618 | r | print.metaMDS.R | `print.metaMDS` <-
function (x, ...)
{
cat("\nCall:\n")
cat(deparse(x$call), "\n\n")
if (x$engine == "monoMDS")
cat(x$model, "Multidimensional Scaling using monoMDS\n\n")
else if (x$engine == "isoMDS")
cat("non-metric Multidimensional Scaling using isoMDS (MASS package)\n\n")
cat... |
a4d4bf5fce045b818465baa4472e7ecb2670747b | f511c0143691c35244a6a02c353446e813e15cdd | /OldCode/TCHallBadgersTIDY.R | a8270c85e40fcd86e4a00c4aea3dec2374936535 | [] | no_license | davehudson67/Inbreeding | e581eccfc25e723cc0816dd88bfc049ffad6e43f | 1b3212f96aa8026a6a2bd1f34df7e9cf84e18f87 | refs/heads/master | 2023-06-30T03:02:44.474462 | 2021-07-11T11:04:42 | 2021-07-11T11:04:42 | 264,190,703 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,222 | r | TCHallBadgersTIDY.R | library(tidyverse)
library(lubridate)
rm(list=ls())
## read in data
CH <- read_csv("all.diag.results.csv")
## keep only required variables
CH <- select(CH, tattoo, date, pm, captureyear, sex, age, age_years)#, statpak, IFNgamma, cult_SUM, brock)
## set date variable correctly
CH$date <- dmy(CH$date)
## adjust colu... |
e2f663f7c5a15bfae77dea11e8dce14cde8c5e63 | 9820b3e3da2974cb12f9aac79c25481825cbf3a5 | /shiny/dvdstore/database_functions.R | 7d0f90789084a508bfd6effe0f7f5e03e99c2f92 | [] | no_license | OliBravo/my_apps | 152d96bc7e4e26bccf980c0c9b616e4940216952 | 1e21694bd0c1fce34c425f88a327a73a32be474f | refs/heads/master | 2023-02-02T09:11:02.279128 | 2020-12-22T13:27:04 | 2020-12-22T13:27:04 | 111,607,649 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,487 | r | database_functions.R | # database functions
db_connect <- function(login, password){
# opens a connection to dvdrental database
if (login == "" | password == ""){
return(NULL)
}
# valid logins-passwords are: web-web, mike-mike123, jon-jon123
DB_NAME <- "dvdrental"
DB_HOST <- "localhost"
DB_PORT <- 5432
... |
da871c8cd1f0dd397ac594548126dfe510abec8c | 24975c66d61805ffd50147890b9fc34769f18324 | /Notes_scripts_bank/ex1_twitter_compare_STAN.R | 2a9270a6c5e83e2eb0b4fc191db622e10f44f229 | [] | no_license | npetraco/MATFOS705 | 53081de4e38a1aae8e0d67bf093a1bcd6b9f0258 | dc54407b7b13ebf8315282cbaf0c9b742212884d | refs/heads/master | 2023-05-28T03:08:46.839815 | 2023-05-16T14:47:15 | 2023-05-16T14:47:15 | 121,066,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,352 | r | ex1_twitter_compare_STAN.R | library(bayesutils)
library(loo)
# Extra options to set for Stan:
options(mc.cores = 1)
rstan_options(auto_write = TRUE)
# Load a Stan model:
stan.code <- paste(readLines(system.file("stan/poisson-gamma_multiple_wloglik.stan", package = "bayesutils")),collapse='\n')
# Translate Stan code into C++
model.c <- stanc(mo... |
deea11b974bb8c336799c5a89dc939d8eb46ee38 | bdd00c8db273e7b8557598dd021931265c6b8aa9 | /run_analysis.r | 1c585ea4a4a741ec6d1068acdc05d4bcb50ca0e2 | [] | no_license | gyurisc/coursera-getting-and-cleaning-data | 734bc3ae980f80ac95f782ba031283ef5b573c9d | 3fd6c0fbce4f64c47e3f9897e725849201b77fb7 | refs/heads/master | 2020-06-12T17:47:02.528753 | 2014-06-21T14:43:51 | 2014-06-21T14:43:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,896 | r | run_analysis.r | # Step 1. Merge the training and the test sets to create one data set.
# Setting working directory to point to the location of the git repository root
# e.g setwd("/Users/krisztiangyuris/Desktop/r_workingdir/coursera-getting-and-cleaning-data")
# Loading files
print("Loading data...")
trainData <- read.table("data/tra... |
6ffad53b15f6c208f9e2dbdacb3c52ddf7e64f67 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Matching/examples/Match.Rd.R | 27fecbfc0ab79c63535cc7c849a734baac56fa88 | [] | 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 | 1,388 | r | Match.Rd.R | library(Matching)
### Name: Match
### Title: Multivariate and Propensity Score Matching Estimator for Causal
### Inference
### Aliases: Match
### Keywords: nonparametric
### ** Examples
# Replication of Dehejia and Wahba psid3 model
#
# Dehejia, Rajeev and Sadek Wahba. 1999.``Causal Effects in
# Non-Experimental... |
9092b978220addb57abb25c5682436a283093e58 | 0363e9059653e5ce2a8fd4dfa1bcfe981072ea82 | /man/circle.Rd | e359d76173b4b08c27b6c331a76f8523d42ec710 | [] | no_license | mwrowe/microRutils | 7725bd4d5e2ac60337932f384562ed39abcf86a1 | 654cd867bafe126593089441f63c88906ecf60ed | refs/heads/master | 2021-07-07T19:59:43.732449 | 2021-06-10T16:59:33 | 2021-06-10T16:59:33 | 245,310,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,702 | rd | circle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/circle.R
\name{circle}
\alias{circle}
\title{Plot a Circle or Regular Polygon}
\usage{
circle(x, y = 0, add = T, segs = 100, how = "radius", ...)
}
\arguments{
\item{x}{A numeric vector of length 1 or 2:
\itemize{
\item If length(x)==1: or... |
ba3e36baa67ed21ba11d5219784a581d3adf17ac | 207b4bb6f3aaeef7eaaf043874578fe051ff63b8 | /man/get_batch_details.Rd | a9ccc9eca7ab7d371b2568a1b97ae73990850f30 | [
"MIT"
] | permissive | cran/captr | 451359bf791c0aef9e3f86a92e0c3df5c6b12274 | 0aa5de3e9d1e35f00797cda47438668e0f66546f | refs/heads/master | 2021-01-10T13:17:44.930177 | 2017-04-15T19:29:34 | 2017-04-15T19:29:34 | 48,077,495 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 749 | rd | get_batch_details.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_batch_details.R
\name{get_batch_details}
\alias{get_batch_details}
\title{Get Details of a particular batch}
\usage{
get_batch_details(batch_id = "", ...)
}
\arguments{
\item{batch_id}{ID for the batch}
\item{\dots}{Additional... |
fd25f5a98700a632d8838894613165b66e59e663 | ee6d73b5c686b08448d2ce4a6e86e8355717acec | /R/tidytext_topic_models.R | cbe7cbf7cfd0a39c8c6c0c278faaf47e659097f3 | [] | no_license | codymg/lab_work | c5a232d255a9d08f98a96570014ab06a3d574a87 | 92d8fabc230b40eb868a31fe8e7b808093a5cab7 | refs/heads/master | 2021-01-06T21:44:52.591920 | 2020-02-27T21:23:51 | 2020-02-27T21:23:51 | 241,488,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,781 | r | tidytext_topic_models.R | library(rvest)
library(purrr)
library(tidyverse)
library(SnowballC)
library(tidytext)
library(stopwords)
library(corpus)
library(textstem)
library(ggwordcloud)
library(ggthemes)
library(caret)
library(igraph)
library(ggraph)
library(topicmodels)
library(tictoc)
library(tm)
library(drlib)
trump_dat <- readRDS(url("htt... |
4bebb532add44258ec57419aeb74828a59951b7b | 57ee4c4f40ad9e168f8ab27b80f1e81ef20bf76a | /pkg/sdam/man/rpmp.Rd | c1f679ded930694823a0e9bb07094075a84a476b | [] | no_license | mplex/cedhar | 72d93228e9436ebcccce4c183f966c0ba488fd24 | c951f25a1edaa5e5cda315051c264a97ebe69db1 | refs/heads/master | 2023-08-31T01:07:10.424651 | 2023-08-29T17:33:07 | 2023-08-29T17:33:07 | 215,776,073 | 1 | 2 | null | 2019-11-27T08:27:52 | 2019-10-17T11:29:53 | TeX | UTF-8 | R | false | false | 976 | rd | rpmp.Rd | \name{rpmp}
\docType{data}
\alias{rpmp}
\title{
Maps of ancient Roman provinces and Italian regions
}
\description{
This is a list with specifications to plot cartographical maps of ancient Roman provinces and Italian regions.
}
\usage{
data("rpmp")
}
\format{
A list of lists object of 59 Roman provinces ... |
0a7ee561d07c0c8efe9a24a0b95aec2f0a776ccc | 12ae74bd0ba9d5494d7301b521b45d1bfa5ff84a | /R/grapes_equals_grapes.R | e8e09367ed628419e2d0a52f24c0a68d6cb04e9f | [] | no_license | cran/do | 62b609a0f0cc0f0c0cc879adb821b1d9d95b6632 | fa0d7c8f9799326ffa6f0763f490c2873597131b | refs/heads/master | 2021-08-15T11:59:00.793187 | 2021-08-03T10:40:02 | 2021-08-03T10:40:02 | 206,034,685 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 658 | r | grapes_equals_grapes.R | #' Locate Accurately
#'
#' @param a vector for matching
#' @param b vector for searching
#'
#' @return If length of a is one, a vector will be return. If length of a is more
#' than one, a list for each element will be return.
#' @export
#'
#' @examples
#' a=c(1,2,3,4)
#' b=c(1,2,3,1,4,1,5,6,1,4,1)
#' a... |
866e3cd9b4d0f64fc094413e1a39a9d993f8a184 | de5acbf5d3d770f5a8a6805d4ded21265a9465a7 | /tree_generator.R | 547772cf07de1ff743df8465200a7331b72a0748 | [] | no_license | arodgers11/R | 901dc35737281ce00b51be04c36014ccbb84ccec | 53388609f76d38857bf34070fcb79553d3e897c1 | refs/heads/master | 2022-07-22T00:03:57.378771 | 2020-05-14T05:37:42 | 2020-05-14T05:37:42 | 192,859,206 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 531 | r | tree_generator.R | #Generates random binary trees with n nodes
library("phytools", lib.loc="~/R/win-library/3.4")
for(n in 6:20) {
d<-c()
t=unroot(rtree(n))
s=paste('./Test Trees/','d',n,'.m',sep='')
cat(paste('n=',n,';\n\n',"d = [ ",sep=''),file=s,append=FALSE)
for(i in 1:(n-1)) {
for(j in (i+1):n) {
d<-c(d,length(n... |
26883be0fc9403846a5a800b5aaa0bb6052b1e63 | 334c555684570d5499b70b2ff35476a84d6f392a | /ui.R | ce7ec4b2242de68a495a2557075c110ed486aa10 | [] | no_license | mndrake/leaflet-demo | 57342a582935c8c634fc30b4e3fba7ff34eff454 | c9c9c2ad96e6504171b0e61c5353cd8693fb781f | refs/heads/master | 2021-01-21T05:23:11.073297 | 2017-02-27T01:43:11 | 2017-02-27T01:43:11 | 83,180,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,325 | r | ui.R | library(shiny)
library(DT)
library(leaflet)
navbarPage('Map Demo', id = 'main',
tabPanel('map', value = 'map',
div(class = 'outer',
tags$head(includeCSS('assets/style.css'),
includeScript("assets/gomap.js... |
ed410d106a8f46373293440c1568d21160d2f3dd | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/forestSAS/examples/shrinkedge.Rd.R | 7e825485b31a8ed92362ab74a52987a85d787d7b | [] | 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 | 613 | r | shrinkedge.Rd.R | library(forestSAS)
### Name: shrinkedge
### Title: Shrink the edges for the point pattern
### Aliases: shrinkedge
### ** Examples
library(spatstat)
data(finpines)
finpines$window
# window: rectangle = [-5, 5] x [-8, 2] metres
#Shrink the rectangle [-5,5]x[-8,2] to [-3,3]x[-5,-1]
shrink.trees<- shrinkedge(finpines,... |
fb0d381313e78880fe557c70288592109dfcefd6 | a3d179a426b333958fbe885ba39334225d5559f4 | /Machine-learning-model/Model_building.R | 70ead5bb70a3b6252ab9297ef50a5664f5e11313 | [] | no_license | hwu84/Small-Molecule-Screen-Facility | e0d1e43fb102e0b5126eccd874ba30a1f29ef177 | 33c47df9b59d7cc584d4cc87674967484c1ffd33 | refs/heads/master | 2021-01-10T10:14:13.052893 | 2016-03-17T20:52:50 | 2016-03-17T20:52:50 | 52,896,901 | 0 | 0 | null | 2016-03-17T20:52:51 | 2016-03-01T17:44:08 | R | UTF-8 | R | false | false | 28,030 | r | Model_building.R | # Code include preprocess data, generate training and test set, 5 fold cross validation, model training based on optimal parameters,
# generate holdout predictions from 5 fold cv, evaluate enrichment from test results.
# Using rank percentile ( rank/row length).
# Author: Haozhen Wu
library(AUC)
library(data.table)
... |
fe5b58a7c05f7ac2b4095cea2456d21b526a8f6d | 67f566943ef74373bef603f2a6b0f3ebe914be2b | /man/Mqrcm-package.Rd | eabb56ac0eba8473b7f15533ced0a6fb1323609e | [] | no_license | cran/Mqrcm | 78aac457f7fa191e73c7af81cac49c1ca1cd1e89 | 7776ed3d279c94cf2cc40dd1a72c540ad6184d6c | refs/heads/master | 2021-06-16T23:40:08.883883 | 2021-02-02T02:10:06 | 2021-02-02T02:10:06 | 145,909,724 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,849 | rd | Mqrcm-package.Rd | \name{Mqrcm-package}
\alias{Mqrcm-package}
\docType{package}
\title{
M-Quantile Regression Coefficients Modeling
}
\description{
This package implements Frumento and Salvati (2020) method for M-quantile regression
coefficients modeling (Mqrcm), in which M-quantile regression coefficients are described
by (flex... |
78ad948d2c0d5d8b9973bcd38b1b498d28af0ab3 | 4df13b51cd129b9471cc4fdf53cdc45fe2e4a8c3 | /stats/real-time/server.R | ea96e62d86cd4984572905ec8c9aa73bbafd1d25 | [] | no_license | jacoboqc/CountingPeople | 3e1b56c6680059eba25f98fe5f8030b7ebc7c188 | f5875d3215cc00f05d3e90ee059764d403a444c8 | refs/heads/master | 2021-01-19T12:30:54.054027 | 2017-04-27T22:52:55 | 2017-04-27T22:52:55 | 82,319,529 | 1 | 0 | null | 2017-04-25T11:25:23 | 2017-02-17T17:06:53 | JavaScript | UTF-8 | R | false | false | 3,960 | r | server.R |
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
library(dplyr)
library(tidyr)
library(httr)
library(ggplot2)
library(scales)
library(grid)
library(RColorBrewer)
library(rmarkdown)
source("./serve... |
67494dec08e469c2b8d70b7f5c24987974f5ca98 | f5fdbe59345699e686537eb1140c33b3b017eb9b | /man/map2.Rd | e854ac670ed30d1117f38fc09dce7f11562c75be | [] | no_license | jonasbhend/geoutils | 19f792ad42789bb3d1dea0f21341f0abe412610a | 1fdafe1404e6320176ca072afe50cb0035853761 | refs/heads/master | 2021-01-21T04:25:06.651578 | 2016-07-28T07:10:29 | 2016-07-28T07:10:29 | 18,552,441 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 493 | rd | map2.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{map2}
\alias{map2}
\title{Addition to plot proper map without borders}
\usage{
map2(interior = F, add = T, ...)
}
\arguments{
\item{interior}{logical, should country borders be plotted? defaults to FALSE}
\item{add}{logical, should map be added to existing plo... |
f8ef19ffaf8a9200330333646e9d85f422749e32 | 46839194e5859098f71638b9332317313e34d888 | /tests/testthat.R | 12b13a930317eb93d36e16a0e5c44cec6052e65d | [] | no_license | kaneplusplus/envi | f07c7bdad9e4f4692f82b8926800a3c431c1d224 | be440c152036c8f5098d6c57cd3037c3f3154925 | refs/heads/master | 2020-11-26T11:40:02.235934 | 2020-01-05T21:56:48 | 2020-01-05T21:56:48 | 229,060,785 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(envi)
test_dir("testthat")
|
090b2381b2b7d5ae402a6d6c2d55e01c41bc6a0c | e189d2945876e7b372d3081f4c3b4195cf443982 | /man/icevision_CoarseDropout.Rd | e90010466259df8c7b1e5cceeb292203a28a36fa | [
"Apache-2.0"
] | permissive | Cdk29/fastai | 1f7a50662ed6204846975395927fce750ff65198 | 974677ad9d63fd4fa642a62583a5ae8b1610947b | refs/heads/master | 2023-04-14T09:00:08.682659 | 2021-04-30T12:18:58 | 2021-04-30T12:18:58 | 324,944,638 | 0 | 1 | Apache-2.0 | 2021-04-21T08:59:47 | 2020-12-28T07:38:23 | null | UTF-8 | R | false | true | 1,099 | rd | icevision_CoarseDropout.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/icevision_albumentations.R
\name{icevision_CoarseDropout}
\alias{icevision_CoarseDropout}
\title{CoarseDropout}
\usage{
icevision_CoarseDropout(
max_holes = 8,
max_height = 8,
max_width = 8,
min_holes = NULL,
min_height = NULL,
mi... |
bcf6d67e49481481824fb880aaa604cd0c071b55 | 4cecc8cc52436a08674442d4df18b25234e0cbfa | /man/nonparam.Hankel.rd | bcecfddf2e3f91dc87734e3c7049cbcced8cdb8e | [] | no_license | anjaweigel/mixComp_package | 3be8e19eff9a943dadb3e2bb755954f21219d3c4 | eb27f7ec39fc1e5bdaf5fe4a6e4b2a8f29a16254 | refs/heads/master | 2022-12-07T17:35:08.432093 | 2020-08-26T09:07:31 | 2020-08-26T09:07:31 | 279,328,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,309 | rd | nonparam.Hankel.rd | \name{nonparamHankel}
\alias{nonparamHankel}
\alias{print.hankDet}
\alias{plot.hankDet}
\title{
Estimate Mixture Complexity Based on Hankel Matrix
}
\description{
Estimation of a mixture's complexity based on estimating the determinant of the Hankel matrix of the moments of the mixing distribution. The esti... |
e0a8c3ad91745dd7dac7554c7248d2df107cf52f | 5ca9ef906adb4fea0e442717ea71cd582d065d79 | /plot3.R | 5f4df5bf9d918012a4ca031b509368e522fc4a7b | [] | no_license | radasian/ExData_Plotting1 | c555b2bab492b83105ca7ace20163e8ae80c5164 | 76e759ea21a5081b00d6ac9d34ba3aa18e88ab16 | refs/heads/master | 2021-01-17T11:24:48.800500 | 2015-02-05T08:41:05 | 2015-02-05T08:41:05 | 30,292,802 | 0 | 0 | null | 2015-02-04T10:08:10 | 2015-02-04T10:08:10 | null | UTF-8 | R | false | false | 1,411 | r | plot3.R | # You should first set your working directory using a command similar to the below,
# adjusted for your setup:
#setwd('d:/coursera/Exploratory Data Analysis')
# Load in the data, using a script which holds the loading elements common to all plot
# scripts, and can therefore be included in each plot's script. This impr... |
75a503eb8d99e1e46dc9bd8ce014cfd20ea497dc | 5279da0a6b4687c4f87b4ef60dff7eaef6d7af99 | /Plot1.R | e70626d2305c643d7c363981c77488b7028443a7 | [] | no_license | pennychase/ExData_Plotting2 | f3e9462547025163de1d254f124c3f5110b475b6 | 542d253e025d10c39fa4881fc83e1958b5ffc4d9 | refs/heads/master | 2016-09-06T05:38:41.153818 | 2014-08-21T23:18:15 | 2014-08-21T23:18:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,849 | r | Plot1.R | ## R Code to construct a plot to answer Question 1:
## Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
##
## This script reads in the the National Emissions Inventory (NEI) data for 1999, 2002, 2005, and 2008.
## It computes the total emissions by year, and uses the base plotting sys... |
f5ce6dbd12368f8019d97bd6cc828c7e9cb35298 | 7f0d7049dcb95857fe407abd2d4c72e7588092cf | /predictsFunctions/R/CorrectSamplingEffort.R | 01e616163d64a386a1f988f8c5547db9b2fe6d50 | [] | no_license | timnewbold/predicts-demo | 4f2f3794243c90a64031409fb17b1a99068531a0 | 1a4f1b9c4d08d63c1bcfd6dbd1b501f5652417f7 | refs/heads/master | 2023-05-28T22:20:18.735109 | 2023-05-22T19:03:28 | 2023-05-22T19:03:28 | 154,857,571 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 970 | r | CorrectSamplingEffort.R | CorrectSamplingEffort <-
function(diversity) {
# Assume that any missing values of sample effort mean equal sampling
# effort was applied.
missing <- is.na(diversity$Sampling_effort)
cat('Correcting', sum(missing), 'missing sampling effort values\n')
diversity$Sampling_effort[missing] <- 1
# TODO Check ... |
f7ed2a6bce307081ade59dc61f82a991f1c0b558 | c8a22a50238433f5db26db1e78c852aad1343c90 | /week_05/day_5/homework.R | e33ff875b5b4e1a75e8deabe18d66b94f40d4248 | [] | no_license | abbygailju/codeclan_homework_AbbyU | a54f29a5e6a24f8d1e45ea83c830e93382965f59 | fe57eafe868cc32a8274b45ebfd814470263ff51 | refs/heads/main | 2023-02-23T00:52:06.897527 | 2021-01-20T22:07:03 | 2021-01-20T22:07:03 | 305,484,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,129 | r | homework.R | library(tidyverse)
library(shiny)
library(shinythemes)
library(DT)
ui <-
fluidPage(themes = shinytheme("superhero"),
titlePanel("Differences in cuisine between Indonesia and the British Isles"),
sidebarLayout(
sidebarPanel(
h5("CulinaryDB is a database of recipes collected from multiple websites. Here I... |
ab496868502c3b4178ba6a46887fc26fd8986f76 | a11470a5ca9a46b6d723bfd4aa1c5f40838649d8 | /dataset_structures.R | 204805d2766165bef54560b2dc10eeee95ff4892 | [] | no_license | julianhatwell/interpret_basics_auxr | 3c2f9393c291f2e3228e048de3e7d9810217b905 | 7564bf89c02374507ef37edce828311beece1347 | refs/heads/master | 2021-05-10T12:40:59.193357 | 2020-10-24T06:20:40 | 2020-10-24T06:20:40 | 118,448,401 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,066 | r | dataset_structures.R | dataset_structure <- function(dname, class_col
, what = c("shp", "bal", "dim"
, "lnr", "ovr"
, "nei", "nwk")) {
library(ECoL)
library(nFactors)
library(caret)
library(vcd)
results <- numeric(0... |
9d34e899fa904765dc94ae09463b6e92a07fc97b | dd3f117ff7bb9d51d22b6bf7f5b9e14a2c5f3640 | /SLDSR/scripts/plot_LDSC.R | e58c1e0380f2b9e74f6d66dda7a0fd3b019c0b88 | [] | no_license | hansenlab/egtex_brain_wgbs | a87db299b5a01fbb7fef6d7e142cf48b2fabd3f9 | b9d96af6d0b172d2da9979fc8993ec282c01e71d | refs/heads/master | 2023-03-24T00:41:15.658170 | 2021-03-24T22:18:55 | 2021-03-24T22:18:55 | 276,696,245 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,005 | r | plot_LDSC.R | # Plot LDSC results for 'adjusting for baseline' analyses
# Peter Hickey
# 2020-05-22
# NOTE: Run with module load conda_R/3.6.x
# Setup ------------------------------------------------------------------------
library(GenomicRanges)
library(readr)
library(dplyr)
library(ggplot2)
library(scales)
library(gplots)
libra... |
71ddb2eb0f16d6e80d9c0f47a17ec44ac944ce9a | 062c2f0f3f55b9c9d8aa31120a3520c74573107b | /Figure 2/Figure 2B.R | ff3f7f1e3e1b279188ca5c979dcaf6f4e80e2efa | [] | no_license | ThieryM95/Drug_resistance_data_and_visualisation | 67a46b9451b28d544692b884e23e75c30e52bec7 | 7acd259f23b3120be1fe1286b63bff75331fe658 | refs/heads/main | 2023-04-17T10:28:03.152000 | 2022-06-08T08:10:49 | 2022-06-08T08:10:49 | 458,226,427 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,059 | r | Figure 2B.R | ##################################################################################
# Code to visualize the effect of each factor during the global #
# sensitivity analyses of the spread of parasites resistant to drug A+ B #
# ... |
81034c24634e2938ce97655fb82f3834e89781a4 | b5822b9c2a756f4e540c426e7e84af35dae8caec | /rockchalk/R/mcGraph.R | 445a6f322afd276339fa387e9ac63c704ba12e61 | [] | no_license | pauljohn32/rockchalk | 0c75b7a7bc142669efcfabbc70d511f60c3f47e0 | fc2d3d04396bf89ef020e824f50db3c348e3e226 | refs/heads/master | 2022-08-20T02:49:56.898990 | 2022-07-26T01:20:12 | 2022-07-26T01:20:12 | 8,965,635 | 8 | 5 | null | 2022-07-18T00:36:58 | 2013-03-23T04:07:35 | R | UTF-8 | R | false | false | 8,751 | r | mcGraph.R | ##' Illustrate multicollinearity in regression, part 1.
##'
##' @description
##' This is a set of functions that faciliates the examination
##' of multicollinearity. Suppose the "true" relationship is
##' y[i] = 0.2 * x1[i] + 0.2 * x2[i] + e
##' where e is Normal(0, stde^2).
##'
##' mcGraph1 draws the 3D regression sp... |
bfc1257730526399458d6fe8c5fe75b9130dbf5d | 5d690f159266b2c0f163e26fcfb9f9e17a0dc541 | /rLiDAR/man/LASmetrics.Rd | 350fcfb2511122eefce1c4abd879060ce402c240 | [] | no_license | albrizre/spatstat.revdep | 3a83ab87085895712d7109c813dcc8acb55493e9 | b6fc1e73985b0b7ed57d21cbebb9ca4627183108 | refs/heads/main | 2023-03-05T14:47:16.628700 | 2021-02-20T01:05:54 | 2021-02-20T01:05:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 5,734 | rd | LASmetrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LASmetrics.r
\name{LASmetrics}
\alias{LASmetrics}
\title{LiDAR-derived metrics}
\usage{
LASmetrics(LASfile, minht, above)
}
\arguments{
\item{LASfile}{A LAS standard LiDAR data file}
\item{minht}{Use only returns above specified height break... |
8eeb131a3019e6b89fc4cbfb5370510a1269a4eb | 98ef4dbe50ff5df8de97f58152d7fc1b5065d795 | /R/cTotal.R | 201f3c08669303ec3ebf8659d283b90bf021ed10 | [] | no_license | dmarcondes/rugbypackage | 85ab7a27127d2fb1a2cf29a283f2cb00392ef519 | ab0d3542ea1cea33eafa95ece2c5cd645f601d4a | refs/heads/master | 2021-01-20T07:51:08.458615 | 2017-08-22T10:29:17 | 2017-08-22T10:29:17 | 18,717,669 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | cTotal.R | total <- function(){
total <- sum(Game[,14])
print(total)
} |
9f013b162e5bee2729790747caa5aea85e57c950 | e6bfd2c5d9db1f6ccde6f8116398c0bf28cca16e | /R/Poly_Gibbs_GammaTrace.R | 6dc262a6499710a446bc8b21dbe68feff3b28ede | [] | no_license | zovialpapai/PolyGibbs | 5fccafef33d5577d05d19f1fd68f5e5c2d256c9b | ce86cbe1b9eee93c999a6f4f342b6280fd72c8ee | refs/heads/master | 2020-09-02T14:30:14.936587 | 2019-12-06T07:46:50 | 2019-12-06T07:46:50 | 219,241,547 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,186 | r | Poly_Gibbs_GammaTrace.R | #' Plots for Diagnosis of Convergence Distribution .
#'
#' \code{Multinom_traceplot_gamma} Plots for diagnosis of Parameters estimates by Ordered Multinomial Regression via data augmentation and Gibbs sampling.
#' @import MASS
#' @import truncnorm
#' @import graphics
#' @import stats
#' @import mvtnorm
#'
#' @param gam... |
1df0ea60504d3a6827db87b2ceefd06152940fff | 8865d5b376c757f009935b581638efb905154687 | /Linear Regressions on Twitter Data.R | 978e05696734d52ed30b6fce694df5a7834b02ba | [] | no_license | meganbaird/My-Projects | e2913c5b81530e33c4d5156a4ff0efff5c504751 | a8c3ceb8b2507eaca1ac26a7e9032bff0a026e66 | refs/heads/master | 2020-06-30T21:01:14.222264 | 2019-08-07T01:45:19 | 2019-08-07T01:45:19 | 200,951,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,865 | r | Linear Regressions on Twitter Data.R | setwd("C:/Users/bairdm2/Downloads")
getwd()
list.files()
tweets <- read.csv("xg2.csv")
#################################FAVORITE COUNT#############################
tweets_fav<-lm(favorite_count~source+verified+xxa+xxe+xxf+xxh+xxi+xxl+xxfe+
xxma+xxn+xxp+xxq+xxu+xxw+xdtw,
data=twee... |
3c26da14dc1139fdd14f8f00860e1ef072bd4b32 | fd99be475e4227add55ead15cf1e1829316517ce | /scripts/blast_and_align.R | 2e77db29635db715a0e20ce59cd5989beb95415b | [
"MIT"
] | permissive | AndersenLab/20191001_Hawaii | 38f2d1d4780722008c7d5938b29d1f9641daf82f | 2655506efc4518da252faa98f7578c22d51b9e4f | refs/heads/master | 2022-06-27T23:35:56.221121 | 2020-01-08T17:15:17 | 2020-01-08T17:15:17 | 221,272,355 | 0 | 0 | MIT | 2022-06-27T16:56:09 | 2019-11-12T17:19:35 | HTML | UTF-8 | R | false | false | 2,113 | r | blast_and_align.R | # install.packages("BiocManager")
library(BiocManager)
# install(c("sangerseqR","annotate","genbankr"))
# BiocManager::install(c("DECIPHER", "Biostrings", "sangerseqR"))
library(devtools)
# install_github("roblanf/sangeranalyseR")
library(sangerseqR)
library(sangeranalyseR)
library(tidyverse)
# install.packages("micro... |
6fe9cbc5244f0c2947089277221c9f06dcff084a | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/81_1/rinput.R | 91dc412a06dc01db929954f9c1c7aa4f20705428 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 131 | r | rinput.R | library(ape)
testtree <- read.tree("81_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="81_1_unrooted.txt") |
a97710865480cf102f464f50b56bb0adb8323faf | 922aa270fa30066044e7ae475f31e4426b59cfac | /man/similarity_metrics.Rd | 086121fdf854c011c2cb8d5658388c10e7dc7fdf | [] | permissive | jakobbossek/mcMST | 361a3708a3413126fbfe61f6ae930e3ee326356b | 4d5a18dfb79a9949c99fadf3a93c6f0f44b0cba3 | refs/heads/master | 2023-03-16T12:54:59.937066 | 2023-03-13T18:49:28 | 2023-03-13T18:49:28 | 96,212,733 | 2 | 3 | BSD-2-Clause | 2019-10-16T11:48:01 | 2017-07-04T11:51:54 | R | UTF-8 | R | false | true | 1,623 | rd | similarity_metrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metrics.R
\name{similarity_metrics}
\alias{similarity_metrics}
\alias{getNumberOfCommonEdges}
\alias{getSizeOfLargestCommonSubtree}
\title{Metrics for spanning tree comparisson.}
\usage{
getNumberOfCommonEdges(x, y, n = NULL, normalize = TRUE... |
48d4d634c583e83b7a941c1f947f1f9d5b860e1c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/COUNT/examples/affairs.Rd.R | df40f3207b11fd42c8b69469d0d62102316cdd9d | [] | 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 | 460 | r | affairs.Rd.R | library(COUNT)
### Name: affairs
### Title: affairs
### Aliases: affairs
### Keywords: datasets
### ** Examples
data(affairs)
glmaffp <- glm(naffairs ~ kids + yrsmarr2 + yrsmarr3 + yrsmarr4 + yrsmarr5,
family = poisson, data = affairs)
summary(glmaffp)
exp(coef(glmaffp))
require(MASS)
glmaffnb <- gl... |
1a4ab43cf5a84621ccd38fbd73bb9896b50a530e | b739104b55e758ab8d0655b7f02ee46604498ece | /sir_sim_corr.R | 75af931aa2a60b47447cfc8ffc623988d11678c3 | [] | no_license | parksw3/serial | a7b5601fb444714bfcc9e392e5877a80a93c8a85 | 1c22d26e912cc207f9375eebbacc33e74e89e567 | refs/heads/master | 2023-01-28T03:59:11.252960 | 2020-12-07T06:49:52 | 2020-12-07T06:49:52 | 254,206,298 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 503 | r | sir_sim_corr.R | source("sir_corr.R")
nsim <- 10
corr <- c(0, 0.25, 0.5, 0.75)
simlist_corr <- vector('list', length(corr))
for (j in 1:length(corr)) {
print(j)
simlist <- vector('list', nsim)
i <- 1
while (i <= nsim) {
print(i)
sir_sim <- sir.full2(size=40000, I0=10, seed=i, rho=corr[j], keep.intrinsic = FALS... |
5848ea77d4e829f76c418db362d9d711a5f08076 | f7b07bb3556b9cc730d01bf96b304c668bfaeea3 | /plot3.R | 3bb1382e1e9f9d329a04823f6bf0f83526a69065 | [] | no_license | 007bishesh/ExData_Plotting1 | 43277e2a4d04f8ad722ed3aacfa5fbbe6fe9f1b6 | 6abc398dba4f4707aa1a848304c2eef3d6e557bc | refs/heads/master | 2021-01-21T20:07:52.107417 | 2014-11-09T05:50:22 | 2014-11-09T05:50:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,617 | r | plot3.R |
##Downloading File
temp <- tempfile()
fileUrl<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileUrl,temp)
##Reading the data
power_consump<-read.table(unz(temp, "household_power_consumption.txt"), sep = ";", header= TRUE,colClasses = "character"
... |
ca02b49a238ed409194076bc16ecb9ace0086e92 | d9db5f542c88863788839ae522cccc6c832fa759 | /tests/testthat/test-wflow_publish.R | 02966c9ddf348f6e75fac04c33a04f6707849f7a | [
"MIT"
] | permissive | jdblischak/workflowrBeta | fc15f45d8d65f25a7562cc962aae0e20f2f2ad21 | 2a79ade2971e939cc785502c5a0fba54f209890d | refs/heads/master | 2020-03-07T11:30:55.104317 | 2018-04-02T17:48:32 | 2018-04-02T17:48:32 | 127,457,737 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,352 | r | test-wflow_publish.R | context("wflow_publish")
# Setup ------------------------------------------------------------------------
library("git2r")
# Setup workflowr project for testing
site_dir <- tempfile("test-wflow_publish-")
suppressMessages(wflow_start(site_dir, change_wd = FALSE))
# Delete workflowr project on exit
on.exit(unlink(sit... |
cb54513abcfa067ab3233327a36205e16a791d1a | d4599d2a5faeaa5e40994b8486e6becc59141fe1 | /man/focus.Rd | 9573d2c5666f4784280a18330975166352d69ca3 | [] | no_license | Allisterh/forecast-pimfc | 6a61c568768a792babc49ba1f27cc89997c63cfa | 928986ec4932e7247fff857da58e53733ee00cd4 | refs/heads/master | 2023-03-16T23:02:45.366133 | 2017-03-13T23:35:45 | 2017-03-13T23:35:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 455 | rd | focus.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{focus}
\alias{focus}
\title{Expectativas de inflacao da Focus}
\format{Um data frame com 168 observacoes de 2002.1 a 2015.12 para a
expectativas do mercado para o IPCA mensal e para IPCA em 12 meses,
ambas para u... |
29fbcdb697cd0e8886fb44a465a4bae97804a010 | 9816d5a8c5c6099a8fd4cccc27895f20a8989a61 | /class_1/Rintro.R | 4791c36a26dc5dc71be937c6f4b5c0d6feca6d33 | [] | no_license | qingqiao-hz/Git_R | 79681eb802113b52818ebf6c09aedbb5c3127eaa | ebc4ae3864ad06cc0bedcda76f86fb634a436e46 | refs/heads/master | 2020-07-13T17:37:57.245491 | 2019-08-29T09:43:59 | 2019-08-29T09:43:59 | 205,124,369 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,879 | r | Rintro.R |
x <- 5
print(x)
y <- 'hello there'
y
y <- sqrt(10)
y
z <- x+y
z
#R下标从1开始,py下标从0开始
x <- 1:5
x[1] <- 17
x
x[3:5] <- 0
x
#R中 -3代表不要第三个数，py中-3代表取出倒数第三个值
x[-3]
y <- c(1,5,2,4,7)
y
y[2]
y[-3]
y[c(1,4,5)]
i<- (1:3)
i
z <- c(9,10,11)
y[i] <- z
y
y <- y^2
y
#log默认以E为底
y <- 1:10
y <- log(y)
y
y <- exp(y)
y
x <- c(5,... |
380ba84ff21997affe31b413b7fbb06ed3e833ee | 01ab1a31cd719e71ca21cf02eb34d601f2b16e96 | /R/plot_gr_microplate.R | 3989f9456c0ed31495fb3dc48dbc7eb52d193b57 | [] | no_license | MartinBanchero/mpxtractor | cd52afa115db1b3aa9d6d70a2ab639c61ca8646a | 78e16314006e440ba51d4f819e809b13e7aa26a9 | refs/heads/master | 2022-03-18T06:16:28.183064 | 2022-02-27T20:08:03 | 2022-02-27T20:08:03 | 246,330,861 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,394 | r | plot_gr_microplate.R | #' Function to plot growth rates over microplate frame.
#'
#' This function takes a dataframe with the raw data and the information from the
#' layout file. Calculate growth rate and plot this growth rates over a microplate
#' frame.
#'
#' @param df_data Is a dataframe that combines data files with layout files
#' @par... |
c64dc15d89f478984bbc1a7aefe3ac65fdc6b72d | 8eb0e554e6eae7aa81cfd18e9438cea9dbfc751f | /Spinalcord_MappR.R | b02c08f569ccc9e2344ee5f00acc7eb167231ea9 | [] | no_license | nstifani/Spinalcord_MappR | f2eb90f3694cbfc12960e7301a3a33558b2ed7d5 | c8fec07de95084575e00a3f883ab8cb9b46078e7 | refs/heads/master | 2021-01-12T07:31:45.867107 | 2017-03-14T00:51:48 | 2017-03-14T00:51:48 | 76,971,777 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 73,629 | r | Spinalcord_MappR.R | ## Density Plot data from Spinalcord Mapper
## Script written by Nicolas Stifani contact nstifani@gmail.com
## Note: This Plugin Assume that Y coordinates are Inverted
## Manual CellCounter does not report "inverted Y". So the X=0 Y=0 is the top left corner.
## To Invert Non-Inverted Y Coordinates one must take the abs... |
0014a1ef2bd70e4361b6ac9557b13008be36fa5a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EcoGenetics/examples/eco.bearing.Rd.R | 3a47dacec1af6cbf3c47e6e6e18aab8dff6dc272 | [] | 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 | 418 | r | eco.bearing.Rd.R | library(EcoGenetics)
### Name: eco.bearing
### Title: Angular Spatial Weights
### Aliases: eco.bearing
### ** Examples
## Not run:
##D
##D data(eco3)
##D
##D "circle" method
##D
##D con <- eco.weight(eco3[["XY"]], method = "circle", d1 = 0, d2 = 500)
##D bearing_con <- eco.bearing(con, 90)
##D
##D W_list <- e... |
387971ee04e851ca151a960301a50bf034b58eca | aa38f279e1592851d8f1ce66bc0f9a6fa6484dba | /R/function.R | 702c48412bf3797e067b48b1a2e1e35c7526e8d8 | [] | no_license | asancpt/edison-pk1c | 17833b7baace6331f4204aa63fcb27acbbc89168 | 864f09908954fcfb361d0b048860ad30fdd766fb | refs/heads/master | 2021-07-07T04:49:12.571278 | 2018-01-18T03:30:58 | 2018-01-18T03:30:58 | 96,659,387 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,441 | r | function.R | Get_os <- function(){
sysinf <- Sys.info()
if (!is.null(sysinf)){
os <- sysinf['sysname']
if (os == 'Darwin')
os <- "osx"
} else { ## mystery machine
os <- .Platform$OS.type
if (grepl("^darwin", R.version$os))
os <- "osx"
if (grepl("linux-gnu", R.version$os))
os <- "linux"
... |
46d8789ebaa2bdeae9e81df049c42d8476efdd60 | c17beff6c0cb1303cf4608a81951e0a5456411c1 | /run_analysis.R | 2a779293f455a92a440a6b3d0fd3986effa61320 | [] | no_license | JohnNjenga/Project | 8d22325b30dd1ef9779c6bd70ea4df35c298f021 | 682dbfc373524874ae2c7d3c4c73ddc15f56a862 | refs/heads/master | 2016-09-06T12:43:56.988298 | 2014-05-25T22:52:39 | 2014-05-25T22:52:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,183 | r | run_analysis.R | #get help on unzip to get an idea of what is expected
help(unzip)
#get the url of the zipped file
myzippedurl<-"http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
#pass the the url to the download.file function
download.file(myzippedurl,destfile="Dataset.zip")
#list the files i... |
4fd09ac98f979e207c93feb614b4f8d3e2b72b2f | f6531909d94d2e1ded759ccd3dfd3db215708b09 | /cachematrix.R | dde071586116a03b4549344b1daeddb79b938b01 | [] | no_license | charlescoulton/ProgrammingAssignment2 | 73f25f480f8be9c4c2cb59c3b9450f6c704bc93e | 93b9dedc382ed2c4d37b6c122e2135af0f8b7c1c | refs/heads/master | 2020-12-25T11:16:06.889948 | 2015-08-23T11:38:15 | 2015-08-23T11:38:15 | 41,210,316 | 0 | 0 | null | 2015-08-22T14:18:54 | 2015-08-22T14:18:53 | null | UTF-8 | R | false | false | 1,868 | r | cachematrix.R |
# makeCacheMatrix creates a list containing a function to
# 1. set the value of the matrix
# 2. get the value of the matrix
# 3. set the value of inverse of the matrix
# 4. get the value of inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
inverse_x <- NULL #sets inverse_x to Null, providing ... |
964b20bda054d8972b7e8989cc979c0ffcc30dd3 | 360df3c6d013b7a9423b65d1fac0172bbbcf73ca | /FDA_Pesticide_Glossary/acifluorfen.R | 82eead67e289e327e41ee65e7b0edc6a397bf8f0 | [
"MIT"
] | permissive | andrewdefries/andrewdefries.github.io | 026aad7bd35d29d60d9746039dd7a516ad6c215f | d84f2c21f06c40b7ec49512a4fb13b4246f92209 | refs/heads/master | 2016-09-06T01:44:48.290950 | 2015-05-01T17:19:42 | 2015-05-01T17:19:42 | 17,783,203 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 222 | r | acifluorfen.R | library("knitr")
library("rgl")
#knit("acifluorfen.Rmd")
#markdownToHTML('acifluorfen.md', 'acifluorfen.html', options=c("use_xhml"))
#system("pandoc -s acifluorfen.html -o acifluorfen.pdf")
knit2html('acifluorfen.Rmd')
|
1227b01b20e44d10379301c7148b6bed97c1d90d | 2e62efd4f8a176c1bee1ee5240e8a925bbaeee93 | /R/basic.R | 78fe8545b3d9c7228d29d0d0181caed6d9110a3c | [] | no_license | Gongzi-Zhang/Code | 8ff1b30a373817eb1aa440ec2f2348b0aa792fd6 | 20c11d7acbaf017b286a10a232f1eb29b62d1ac4 | refs/heads/master | 2021-12-28T12:05:56.754735 | 2020-04-10T14:07:04 | 2020-04-10T14:07:04 | 120,005,998 | 0 | 0 | null | 2018-02-02T16:53:29 | 2018-02-02T16:41:49 | null | UTF-8 | R | false | false | 20 | r | basic.R | NA # not available
|
bd7ac24e09c9a82b0bf981a438a02666bed38908 | 5fcd2765fe189b62a3a4e1d884b5add7445ea101 | /praca licencjacka/praca_lic_skrypt_bpz.R | 762368b27ce00db658fa63bd20643052f6ecdf6b | [] | no_license | michalcisek/magisterka | ede62bbd8cb857fa4cc3cbcdeda0812830c05b97 | a35ebb373ab8f64a05ac281179509d66f560164a | refs/heads/master | 2021-03-30T16:35:15.329993 | 2017-07-06T06:27:52 | 2017-07-06T06:27:52 | 83,968,183 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,738 | r | praca_lic_skrypt_bpz.R | # 1. WCZYTYWANIE DANYCH ---------------------------------------------------
rm(list=ls())
wig20<-read.csv("wig20.csv",header=T)
attach(wig20)
install.packages("Rcpp")
library(Rcpp)
install.packages("changepoint")
install.packages("quantmod")
install.packages("signal")
install.packages("mFilter")
install.packages("aru... |
a0edc87ed6388f4f165739ee42d6afbe87f27a42 | 8cbc4419065621a01ba5d1d4d06c6acaf6a5361d | /R Language-AK.R | 37b84bf0e9970fd35f3727a03576a7c197b8fff2 | [] | no_license | ayakulo/AK-R-Language | 1e3eba5b455c006f81474fbac421c9e5ace5dc01 | e11fdbd82e526c6a674d6671344e54dd9a04eb6d | refs/heads/main | 2023-07-01T10:11:31.284218 | 2021-08-14T04:06:46 | 2021-08-14T04:06:46 | 395,883,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,524 | r | R Language-AK.R | #Ayan Kulov
#TP058560
#installed packages
install.packages("tidyverse")
install.packages("forcats")
install.packages("ggplot2")
install.packages("dplyr")
install.packages("hrbrthemes")
install.packages("ggpubr")
install.packages("scales")
#loading packages
library(tidyverse)
library(forcats)
library(gg... |
cee3f8f9084f5a54ca111aebe5cf21651e811371 | 061403fe5db0657f3d1853e8745046495d0c2786 | /data-science/9-developing-data-products/course-project/shiny-application/ui.R | a21b140f0be08e0d6a959ff73f1c644741f8af74 | [] | no_license | zoyanhui/Coursera-Learning | 48989f178f1a2eb3090b12b835a771c6669ac543 | ad12759aaef5d9a690286f2d81171470f9d16ff3 | refs/heads/master | 2021-01-18T15:08:22.993423 | 2016-02-28T14:56:50 | 2016-02-28T14:56:50 | 45,096,523 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,293 | r | ui.R | library(shiny)
library(datasets)
data("mtcars")
cyls <- levels(as.factor(mtcars$cyl))
shinyUI(
pageWithSidebar(
headerPanel("Predict Miles/(US) Gallon of Car"),
sidebarPanel(
selectInput(inputId="cyl", label="Number of cylinders", choices=sort(cyls),
... |
49cfbb9b35fefbe001d3f5932ea1694a58b5ef5c | e54b90b8a6edfec70bebd47a7f35a1f782560780 | /plot1.R | ac98681e61934ef2777751c29dbe620ee8c0732f | [] | no_license | okedem/ExData_Plotting1 | 5d59191aec993be3448cfabf8442be3987a1f8da | faabfb019adc88c5ddf2fbcb992e4d1759ac8c09 | refs/heads/master | 2021-01-16T17:43:56.220497 | 2016-01-10T19:02:06 | 2016-01-10T19:02:06 | 49,380,462 | 0 | 0 | null | 2016-01-10T18:53:27 | 2016-01-10T18:53:27 | null | UTF-8 | R | false | false | 491 | r | plot1.R | # Exploratory Data Analysis, course project 1, plot 1
# This code uses the sqldf package, use the commented-out command below if needed.
#install.packages("sqldf")
library(sqldf)
data <- read.csv.sql("household_power_consumption.txt",sep=";",
sql = "select * from file where Date='1/2/2007' or Dat... |
e2737fc58f559b598bac875401463232c3709fa0 | 0a691e5ede55e5373ae82fa69d6378d3e586c3b9 | /extra-exercises/histograms.R | a554ef1be6da112e0d35e610654b890c4abc9df0 | [] | no_license | sarah127/udacity-data-analysis-with-r | e54d7d2fd5cb7f6e3d7d9de6c42f356ebb18eebe | e59db45a9f029ae68fddf365a40cad893ccc17d1 | refs/heads/master | 2021-07-11T23:06:40.167791 | 2020-06-05T22:22:12 | 2020-06-05T22:22:12 | 138,321,821 | 0 | 0 | null | 2018-06-22T15:57:41 | 2018-06-22T15:57:41 | null | UTF-8 | R | false | false | 2,521 | r | histograms.R | # Histograms excercise based on webpage:
# http://flowingdata.com/2014/02/27/how-to-read-histograms-and-use-them-in-r/
setwd('~/Repos/players-analysis-with-r/extra-exercises')
# Load and Tidy the dataset
players <- read.csv('NBA-Census-10.14.2013.csv',stringsAsFactors=FALSE)
names(players) <- gsub("\\.\\.",".",names(... |
5ba8b9ba9faa0a68bb588a366dc9110fe78da9bd | d97ac05c04ac282164943b6f2ad1202a96f4f835 | /mpm/ps6/pset6_q1.R | d69f71334537647531bf42f71b7a08c65f85a2db | [] | no_license | TomBearpark/ECO518_code | d81bbba0bd674b7662ec544e27a96b9d2b1bc6f4 | 6a998f58d63160dace441cca159a406edbf264fd | refs/heads/main | 2023-04-14T16:33:03.008259 | 2021-04-29T19:32:32 | 2021-04-29T19:32:32 | 335,358,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,324 | r | pset6_q1.R | # Code for ECO518 MPM PSet 1
###########################################################
# 0 Set up, packages
###########################################################
rm(list = ls())
library(tidyverse)
library(readxl)
library(sandwich)
library(stargazer)
theme_set(theme_bw())
dir <- paste0("/Users/tombearpark/D... |
7a2969b4d98e8b1835991fa9fbb26990220b8863 | 351830915f1c61a935e60c8f048a7adbdcc6ec5d | /amd_templates/metadata/prevotella/analysis_metadata.R | d421fc1a113b230fc27f617608ee97ec0e147ad9 | [] | no_license | ohsu-microbiome/utilities | 60ab203da11966abd862f6b18975c246f09f2f72 | dc1980f89dc6feb5161172745130a11fbee9dba6 | refs/heads/master | 2021-07-01T10:56:09.523510 | 2020-06-19T22:04:44 | 2020-06-19T22:04:44 | 152,392,715 | 2 | 3 | null | 2020-09-29T02:00:51 | 2018-10-10T08:53:42 | HTML | UTF-8 | R | false | false | 932 | r | analysis_metadata.R | #!/usr/bin/env Rscript
localdir = getwd()
clustering_level = 'Genus'
analysis_type = 'prevotella'
knitr_options="
knitr::opts_chunk$set(
echo=TRUE,
dpi=300,
fig.width=12
)"
relative_abundance_cutoff = 0.002
prevalence_cutoff = 0.1
min_count_cutoff = 0
raw_exp_vars='c()'
calculated_exp_vars = 'c("FractionPre... |
cef5f05f42dcc67fb6ff014f5ddfae254a520d43 | a0830531052bd2330932c3a2c9750326cf8304fc | /vmstools/man/getMetierClusters.rd | 3fd2dab2656f69804381b758881d55f4a2dfb16f | [] | no_license | mcruf/vmstools | 17d9c8f0c875c2a107cfd21ada94977d532c882d | 093bf8666cdab26d74da229f1412e93716173970 | refs/heads/master | 2021-05-29T20:57:18.053843 | 2015-06-11T09:49:20 | 2015-06-11T09:49:20 | 139,850,057 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,505 | rd | getMetierClusters.rd | \name{getMetierClusters}
\alias{getMetierClusters}
\title{
Finding metiers from a reduced EFLALO dataset, step 3: clustering logevents using various multivariate methods
}
\description{
This function represents the third step in the workflow processing logbooks data for identifying metiers.
This step allows ... |
206f01bf28f77b01ad715e4d955ce234d5e02096 | 44718933513647e2fa74fc6cfaee6547631c9be9 | /app.R | 3605d82239addc778f4d9243da0cc9c604218c84 | [] | no_license | paleolimbot/shinyex_enfr | a0ffefa1f9ee6be0a4e416b5efb78633f785c582 | 2a9541d224d5251ef87c328627b1012db4f3b525 | refs/heads/master | 2023-03-27T02:16:06.482552 | 2021-03-19T13:23:14 | 2021-03-19T13:23:14 | 349,430,970 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,834 | r | app.R |
library(shiny)
library(shiny.i18n)
library(shinyjs)
# Translations are defined in translation.json. I'm using "key" as the
# key language, but you could omit this an use "en" or "fr" as the key
# language as well. I like the ability to abbreviate the key because there
# are some longer bits (like the text of an "abou... |
dfb755cb2a319df79882c2ccc34e4936780f4f19 | 74df9ce87872f43ff6836563cd8019eb9b95f5b0 | /2_observations/src/munge_flow_dat.R | 80f3d13a10f83a3a92b5c6bd36bd82227af84614 | [] | no_license | USGS-R/delaware-model-prep | 017f0d9f727d5d5b4449cd69758c4b32f12860ed | 45e1ffeee7d6ea4a95e374e16cbc1196bf703f41 | refs/heads/main | 2023-06-08T19:38:46.764070 | 2023-06-01T23:56:42 | 2023-06-01T23:56:42 | 202,405,091 | 2 | 14 | null | 2023-04-07T23:28:32 | 2019-08-14T18:31:12 | R | UTF-8 | R | false | false | 1,524 | r | munge_flow_dat.R | # munge flow dat
get_flow_sites <- function(flow_ind, temp_sites_ind, out_ind) {
flow_dat <- readRDS(sc_retrieve(flow_ind, 'getters.yml')) %>%
distinct(site_id) %>% pull(site_id)
flow_sites <- paste0('USGS-', flow_dat)
# find sites not in temperature data
temp_sites <- readRDS(sc_retrieve(temp_sites_ind, 'g... |
0ff0ccc7e7f2c33252d60d0dba5140348c5147f5 | 3eeb5ee6e7b43bb4a1ed950c883b3c7af4af2a17 | /forestplot.R | 4894e27b2eae9ddfde3dc82f83009b09bb49a16a | [] | no_license | raghunandanalugubelli/CASAS | d59b655174a0b211a943e110db4e464c1f4133c7 | ed3000e878816e89482feba48fd76f2e2943e912 | refs/heads/master | 2021-09-03T07:14:34.453033 | 2018-01-06T20:38:56 | 2018-01-06T20:38:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,212 | r | forestplot.R | forestplot <- function(CI_Data)
{
addgap <- ifelse ((max(CI_Data[, 3:4])- min(CI_Data[, 3:4])) >10 , 2.5, 0.15)
p <- ggplot(CI_Data,aes(factor(ID)))+ labs(x=NULL, y=NULL)
p <- p + geom_text(data=CI_Data,aes(x= factor(ID),y = min(CI_Data[1:10, 3:4]-addgap), label=CI_Data[,2],vjust=0.5, hjust=0.25, si... |
74f1843a3c56232c1acbddfce0c581e7364c35de | 5b5d378306b858d380511aa0dae4cec09a3ff823 | /cachematrix.R | 2cbc5c65f134fb21bc4a5a09b566c2fdd08e69a9 | [] | no_license | rangastyle/ProgrammingAssignment2 | ad9eb258058b5cdb89a431567a413450cba73940 | 6e320843188e65ae0a65d100dd0dc46922fe8c0e | refs/heads/master | 2021-01-24T15:22:55.754228 | 2015-05-24T05:29:33 | 2015-05-24T05:29:33 | 36,102,829 | 0 | 0 | null | 2015-05-23T01:41:09 | 2015-05-23T01:41:08 | null | UTF-8 | R | false | false | 1,228 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
makeCacheMatrix <- function(x = matrix()) {
# x is an invertible square matrix
# The function makeCacheMatrix creates a list containing a function to:
# 1.set the value of the matri... |
8ea4dadfb4fbdb87468def00de29793ec5d934b9 | 175f5203aa1b0bc905702d0741a882eb455f8e10 | /man/cross_dat_analy.Rd | d4dce4d2ea706f23c0de12e038dc922072e21f34 | [] | no_license | cran/twl | 97982fdae0a8d62a3df23721aaccd7250c4b38ef | bc1e7a04b9037889e4989fa0e9c17ef7b8db1481 | refs/heads/master | 2020-03-27T04:57:09.158214 | 2018-08-24T10:00:03 | 2018-08-24T10:00:03 | 145,981,527 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,358 | rd | cross_dat_analy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/post_mcmc.R
\name{cross_dat_analy}
\alias{cross_dat_analy}
\title{Compares clustering across datasets using metrics described in associated TWL manuscript}
\usage{
cross_dat_analy(clus_save, BURNIN)
}
\arguments{
\item{clus_save}{list of samp... |
dd86ffda397112f9cee6f25ba2042778e51dcea2 | 86151a6ecec532ac065621a1ffdfd827504176a3 | /man/download_gpm_imerg.Rd | 39eb937fe6664e5c076af0622b6b9e59375961ce | [] | no_license | imarkonis/pRecipe | 3454f5ce32e6915a6caef1dbc041d12c411c9ae5 | 07c6b1da653221a0baeeb2aa81b8744393ff587e | refs/heads/master | 2022-11-02T20:27:40.979144 | 2022-10-28T10:52:04 | 2022-10-28T10:52:04 | 237,580,540 | 0 | 0 | null | 2020-02-01T07:44:23 | 2020-02-01T07:44:23 | null | UTF-8 | R | false | true | 472 | rd | download_gpm_imerg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/download_gpm_imerg.R
\name{download_gpm_imerg}
\alias{download_gpm_imerg}
\title{GPM_IMERG data downloader}
\usage{
download_gpm_imerg(folder_path = ".")
}
\arguments{
\item{folder_path}{a character string with the path where the data will be... |
749030220a5dc97f97bb1ebcfcb09f9a7208d2ee | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.management/R/synthetics_service.R | 62d45d5f99196b8a9c763f4f1f5475534a0c7521 | [
"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 | false | 8,303 | r | synthetics_service.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common new_handlers new_service set_config merge_config
NULL
#' Synthetics
#'
#' @description
#' Amazon CloudWatch Synthetics
#'
#' You can use Amazon CloudWatch Synthetics to continually monitor your
#' services. You can create and m... |
9346aed167afbdf4fc21defe245837de3b3f8bea | f3b996edc7dc15421abdf298f5b44c32d493e3ce | /scripts/LOCA_FTP_loop.R | a53b48e1d5e11b585e1b48fbe81c054d91bca3b6 | [] | no_license | mapdonnelly/CDPH_heat_project | 4ba40ac0596fa0e8e5cbb06a305cc6cd489e236e | b0e4e01c37aa761c2573d49d481c6ecd0f5f0ef3 | refs/heads/master | 2021-07-07T15:13:44.419156 | 2019-03-27T17:48:53 | 2019-03-27T17:48:53 | 138,775,988 | 0 | 1 | null | 2018-07-02T19:58:50 | 2018-06-26T18:12:34 | null | UTF-8 | R | false | false | 3,731 | r | LOCA_FTP_loop.R | rm(list = ls())
library(ncdf4)
library(tidyverse)
library(parallel)
setwd('~/Desktop/GitHub/CDPH_heat_project/')
#rcpMat <- c("45","85")
#names(rcpMat) <- c("RCP4.5 (emissions peak 2040, stabiliazation by 2100)","RCP8.5 (emissions continue to rise throughout the 21st century)")
modelMat <- c("ACCESS1-0","CanESM2","C... |
80556a080ea0ce4654355d42e0245fd40f063348 | 1aa92f850ce632811aaa74d769527a8037d8c484 | /tests/check_transf_sigmas.R | efb1aab143710dba82973880cdebcd608c4540c9 | [] | no_license | cran/mvord | 253c6e7deaf07bf5ac111571b6db307219f1597c | 6699126154748d7510647afc7bda27066aad3549 | refs/heads/master | 2021-06-02T15:11:40.519370 | 2021-03-17T12:20:12 | 2021-03-17T12:20:12 | 102,715,261 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,568 | r | check_transf_sigmas.R | library(mvord)
#check z2r
mvord:::check(identical(mvord:::z2r(355),1))
mvord:::check(identical(mvord:::z2r(0),(exp(0)-1)/(1+exp(0))))
mvord:::check(identical(mvord:::z2r(2),(exp(4)-1)/(1+exp(4))))
error.structure <- cor_ar1(~ 1)
ndim <- 5
covar_error <- matrix(rep(1,10), ncol = 1)
attr(error.structure, "n... |
8f3571ac56ed9c1b371103c6822ccc373ae82675 | ec94dddf45e332663da3e37db2feeb709221d763 | /man/Decision-makers-single-quote-attributes-class.Rd | 77b6c4898d605ff3a4a7365dfcc361d28bf8d80a | [
"Apache-2.0"
] | permissive | AntoineDubois/sdcv2 | 44687ab28a1c7aa3c82702ee2506257a20475994 | 53041ecc32698089a66a0df7911dd7c0f461cc34 | refs/heads/master | 2023-07-16T20:07:11.525114 | 2021-09-06T15:27:46 | 2021-09-06T15:27:46 | 386,579,310 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 603 | rd | Decision-makers-single-quote-attributes-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/decision makers.R
\docType{class}
\name{Decision makers' attributes-class}
\alias{Decision makers' attributes-class}
\alias{ob_decision_makers_att}
\title{ob_decision_makers_att}
\arguments{
\item{N}{The number of decision makers}
\item{p}{T... |
8cbdf539594734e22b50be5ed49ae62ea7f70ac6 | 834c63050072298b639c55c4585726bf20e20a00 | /scratch_ranking.R | e7356526a799632cd0090ebb7a1ffc1ee8465b54 | [] | no_license | benilak/Senior_Project | 409793cb198aa77bff51092ba28981d1d4ac92cc | e180b945693f4cf36df1314471f5a05faf31fb3f | refs/heads/master | 2020-06-30T19:03:39.800501 | 2019-09-23T20:33:41 | 2019-09-23T20:33:41 | 200,920,841 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,446 | r | scratch_ranking.R |
source("get_industry.R", encoding = "utf-8") # to read trademark symbol (R) in webpage
source("get_stockrow.R")
source("get_keystats.R")
library(ggrepel)
ind_compare <- get_industry(c("WDC", "MSFT", "AAPL"), get = "compare")
ind_names <- get_industry(c("WDC", "MSFT", "AAPL"), get = "names")
### scratch junk, keep f... |
3388da36c6306cca64b0b1d75db8b2ff8ad8f841 | c69bb8c12eb205627783c8ae7a10280235873724 | /R/convert.tz.R | 3ee3ae1a9a60e91479b0479d3de18a75bb3300f8 | [] | no_license | cran/HelpersMG | 384b45838d5fa110fe31c3eaca5b545774136797 | c3bd166e7d24bf4d51414819539262db9e30495a | refs/heads/master | 2023-06-21T20:05:01.243339 | 2023-06-14T19:02:05 | 2023-06-14T19:02:05 | 33,549,168 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 868 | r | convert.tz.R | #' convert.tz Convert one Date-Time from one timezone to another
#' @title Convert one Date-Time from one timezone to another
#' @author Marc Girondot \email{marc.girondot@@gmail.com}
#' @return A POSIXlt or POSIXct date converted
#' @param x The date-time in POSIXlt or POSIXct format
#' @param tz The timezone
#' @desc... |
9d20805b1005e5b6dde275e4fb127694c31b0244 | 5831cc1a1b4406d1cf7e8faa219b6293c51b9099 | /ML_5_3_problem.R | 775b13cc36704ee5da9a463d83752cfe3dede726 | [] | no_license | sujiths93/Machine-Learning-Assignment-5 | 3e73229b2ea96f95ba66a975ceade1f3beea3523 | 51b11daf2a08db552ad4722a83ef697820f6b7eb | refs/heads/master | 2021-01-01T04:01:12.468269 | 2016-04-22T05:11:13 | 2016-04-22T05:11:13 | 56,827,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 877 | r | ML_5_3_problem.R | #QUESTION 3
vec=seq(0,10,0.5)
dis=function(w,x)
{
Q=c(1,x,x^2)
z=sum(w*Q)
y=1/(1+(exp(-z)))
return(y)
}
#3a
w0=3;w1=-.05;w2=-.08
w=c(w0,w1,w2)
y=NULL
for(i in vec)
{
y[i]=dis(w,i)
}
plot(y,type='l',main="Probability of person joining queue",xlab = "Length of line",ylab="Probability")
X=chipotle
dis1=f... |
3408b44f146fb4e7f72d5257ac426eb33867a9c6 | 197590555db25e2b43692e4a89c3c8388c03fdf1 | /tests/testthat/test-declare-design-from-template.R | 3f04df3d034d2fb7c8b39879d8129ae162accbc1 | [] | no_license | yadevi/DeclareDesign-1 | f843ef77d937d1dd0975b99f8ab5914c20461c1c | badcd6e6edbb2e0bb3cf51b3da1047664acea789 | refs/heads/master | 2021-06-14T03:53:23.472797 | 2016-12-05T06:16:05 | 2016-12-05T06:16:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,349 | r | test-declare-design-from-template.R | rm(list=ls())
library(testthat)
library(DeclareDesign)
context("Declare Design from Template")
test_that("declare_design_from_template works", {
simple_template <- function(N, n){
population <- declare_population(noise = "rnorm(n_)", size = N)
sampling <- declare_sampling(n = n)
potential_outcomes... |
3e1c81bb0696314131597ee63602e08336f69d71 | 0f3a072c237893f1b2f2e49a935c4df14a05d497 | /04.results/plots.R | 7b002fa4bd7da5ae5d6e4e58af737335b051b6b3 | [] | no_license | noeliarico/clustering | 22a25b282ac6fe00c9ad773051afa431614bdb51 | fcc56ae8c93ca64c8f35dc9b31a397daa114a531 | refs/heads/master | 2020-08-30T09:12:37.570380 | 2020-03-10T10:25:15 | 2020-03-10T10:25:15 | 218,328,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 438 | r | plots.R | plot_real_clusters(s1c, "real_cluster", "V1", "V2")
plot_real_clusters(s2c, "real_cluster", "V1", "V2")
plot_real_clusters(s3c, "real_cluster", "V1", "V2")
plot_real_clusters(s4c, "real_cluster", "V1", "V2")
plot_real_clusters(a1c, "real_cluster", "V1", "V2")
plot_real_clusters(a2c, "real_cluster", "V1", "V2")
plot_re... |
0fb0aa1a2ca164759faf1ed3bb5fa04eb1690a29 | 80b4f0e0bbbf09b68a517ff02f1e409e9b6508e9 | /Mapa_IBGE_RJ.r | 4d2e03ba2c7c5dce5c9f67b718b8e96ed74b712b | [] | no_license | arthurwlima/BacterialIndicators_CoastalRJ | 55ee011f9c4ea07cb0617e3b9c3f9d342f2f3f82 | 492dcf78a2284d0aad1d0c4507d4724ceb8b5371 | refs/heads/master | 2020-05-04T18:33:29.568181 | 2019-04-12T18:56:48 | 2019-04-12T18:56:48 | 179,358,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,841 | r | Mapa_IBGE_RJ.r | ## 20190320: Ajustar tamanho dos circulos e a escala das legendas
# http://environmentalcomputing.net/making-maps-of-your-study-sites/
# wget(ftp://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc250/versao2017/shapefile/Limites_v2017.zip)
# gunzip ./Limites_v2017.zip
library(sp)
library(rgeos)
libra... |
c327c9e7266d39c51c9dd7d1f6cdc097d9871ec9 | 30f79e55a7c527c019467e256f0713b04dc9903e | /script/variation_partitioning_revise3yr.R | 54e1a3797c2d2f1f9a0d6733fc9e7ad6dccb984b | [] | no_license | tengguangliang/fishsize_varpart | 4e09739b65e755f800e9d953b2a6d64e917cdccc | 01831f722f622f5ae4eee6ace4ee8d0d012b05e6 | refs/heads/master | 2021-09-10T21:28:37.286045 | 2018-04-02T14:16:03 | 2018-04-02T14:16:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,202 | r | variation_partitioning_revise3yr.R | # 3yr (time-interval) lag for temperature (except AI and GOA)
# 20180225
meanA3yr <- matrix(NA,28,2) # West US: 12 species, Alaska: 6 species, North Sea: 9 species
cvA3yr <- matrix(NA,28,2)
lag=3
# Arrowtooth flounder
B <- arrowtoothFnum
idxF <- match(arrowtoothF$YEAR,exploitation$YEAR)
idxSST <- match(arrowtoothF$Y... |
0127d6615b12bc3480270114ff6cc1ec5234c0b0 | 50916bc5d8cb3a788e13b3bb109230460d53b263 | /IFCAM_DoE.R | eaea2e2c6fade54f88f5c2ca95d96eaa88639131 | [] | no_license | Subhasishbasak/Applied-Machine-Learning | b3ca7f7019639b4c8132a6af62bc6a85f6260c3b | bce48a2ce6f2875d1c71315aa4be50ca2a552ec7 | refs/heads/master | 2021-07-20T11:18:57.047462 | 2020-05-19T10:32:26 | 2020-05-19T10:32:26 | 166,579,219 | 0 | 0 | null | null | null | null | IBM852 | R | false | false | 4,146 | r | IFCAM_DoE.R | #IFCAM practical session Day 2
#TD3 : Design of Numerical Experiments
#Required R packages: DiceDesign, randtoolbox
library(DiceDesign)
?mindist
A=matrix(runif(18),ncol=2)
mindist(A)
cr=0
for (i in 1:1000){
A=matrix(runif(18),ncol=2)
c1=mindist(A)
if(c1>cr){
A1=A
cr=c1
}
}
A1 ... |
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