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bd81e37dfcf6b3cdb4c0bd715854b39667cedc7d | d6ff1e6257582f785915e3a0fad3d4896ebd9acb | /R_old/OVERALL_TRANSPIRATION.R | dd4c315e6edbe8f2886bcf7adad85997b5a0dd40 | [] | no_license | RemkoDuursma/Kelly2015NewPhyt | 355084d7d719c30b87200b75887f5521c270b1b5 | 447f263f726e68298ee47746b4de438fbc8fdebf | refs/heads/master | 2021-01-15T13:02:00.392770 | 2015-09-08T04:56:15 | 2015-09-08T04:56:15 | 42,089,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,247 | r | OVERALL_TRANSPIRATION.R | setwd("C:/Documents and Settings/Jeffrey Kelly/Desktop/EUC DATA/EUC OVERALL BIOMASS")
PILBIOMASS<-read.csv("PILTRANSAA.csv",sep=",", header=TRUE)
names(PILBIOMASS)
str(PILBIOMASS)
windows(width=8, height=4) #, pointsize=18)
par(xaxs="i",yaxs="i")
par(las=2)
par(mar=c(4.5,4.5,1,1))
par(xaxs="i",yaxs="i")
par(mfrow=c... |
7e8c94c982763d3b9a74d47bf81ecba200e74f3e | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/2774/plotly/coord.R | c489eb9d4c358419e3fd6f91a129c297999fc8aa | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,359 | r | coord.R | #' *** This won't be possible until plotly.js implements aspect ratios... ***
#'
#' #' Force the aspect ratio according to x and y scales
#' #'
#' #' When x and y are numeric variables measured on the same scale,
#' #' or are related in some meaningful way, forcing the aspect ratio of the
#' #' plot to be proportiona... |
a0ef2dadf331036f8762dadbd969ae0e3f89da0d | 8ac82c0214d61abd0f6224dfb3e3a6abec07cd75 | /man/load_service_status.Rd | a1705e3ee3f4072627b876f7126fd32d80e7a383 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | scottmmjackson/samsunghealthR | e4a4f0a5c7aef86251e525614adc43439551ff7b | 4c989ca44f953ebebd0944789060a5c2476cb80d | refs/heads/master | 2020-12-03T07:34:47.621463 | 2020-01-01T17:30:36 | 2020-01-01T17:30:36 | 231,243,976 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 319 | rd | load_service_status.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\name{load_service_status}
\alias{load_service_status}
\title{Load Service Status}
\usage{
load_service_status(path)
}
\arguments{
\item{path}{path to CSV file}
}
\description{
Loads Service Status CSV from Samsung Health Data.
}
|
87612036fd5fa980712ac1e05cfc398425c50685 | 86c0b4c6c1746ebf0441c62421748190d057067d | /plot/mass.R | 20769a023b830ddef7c35ab7c099b5ac260e9f87 | [
"MIT"
] | permissive | yufree/democode | 372f0684c49505965b0ba5abe0675c2b6f7fb3da | 0a332ac34a95677ce859b49033bdd2be3dfbe3c4 | refs/heads/master | 2022-09-13T11:08:55.152350 | 2022-08-28T23:09:00 | 2022-08-28T23:09:00 | 20,328,810 | 5 | 14 | null | 2017-01-06T16:07:25 | 2014-05-30T12:41:28 | HTML | UTF-8 | R | false | false | 1,185 | r | mass.R | source("http://bioconductor.org/biocLite.R")
biocLite("mzR")
library(mzR)
all <- openMSfile('./FULL200.CDF')
df <- header(all)
bb <- peaks(all)
aaaa <- sapply(bb,as.data.frame)
oddvals <- seq(1, ncol(aaaa), by=2)
aaaaa <- unlist(aaaa[oddvals])
ccc <- unique(c(aaaaa))
ccc <- ccc[order(ccc)]
# bbb <- sapply(b... |
99a524e8baa9751bbd5db7787f3567c66a6e8bee | 4450235f92ae60899df1749dc2fed83101582318 | /ThesisRpackage/R/3Article_old/GSE42861_function.R | 4e60f4de4028e99df28eb3e6e687f0b5409e866e | [
"MIT"
] | permissive | cayek/Thesis | c2f5048e793d33cc40c8576257d2c9016bc84c96 | 14d7c3fd03aac0ee940e883e37114420aa614b41 | refs/heads/master | 2021-03-27T20:35:08.500966 | 2017-11-18T10:50:58 | 2017-11-18T10:50:58 | 84,567,700 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,617 | r | GSE42861_function.R | #' main experiment
#'
#' @export
GSE42861_experiment <- function(s, save = TRUE) {
# glm
glm <- Method(name = "glm",
hypothesis.testing.method = phenotypeWayReg_glm_score(family = binomial,
factorized.X1 = TRUE),
... |
ac457a941d93eb56777aeb1bda10707ce8907e13 | c54d1c0a3d81bddb25f3f55078f305ad6c15997b | /R/get_internal_tree.R | ffd29651c660f728d71fcc716d3ca033637fb637 | [] | no_license | cran/genpathmox | dc065d3b5ea1c8632068fe3d9bfa7b063045bb2c | 517be94b39d8742cd3d39aedc152e026d865afd6 | refs/heads/master | 2023-01-12T03:39:55.183481 | 2022-12-22T10:00:12 | 2022-12-22T10:00:12 | 25,984,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,114 | r | get_internal_tree.R | #' ############################################################################################
#' @title Calculating size (numeber of individual of a node)
#' @details
#' Internal function
#' @param x matrix or dataframe with data.
#' @param size value indicating the minimun threshold of number of observations for a... |
e1cae3ee797bcc7970005fe13bca1ea459ee2833 | e8d1f9b04636822a2513458aebdf80db9a29d947 | /man/genes_mat.Rd | f50996b2dd181b192464712a3c483e2d1c8d92b7 | [] | no_license | huerqiang/prioGene | b9cb6bbade2397dc241d3c30715aece798aac2bb | 2e58c7e0b926072d2c5b1ab6cad8586b84f27d47 | refs/heads/master | 2020-06-18T04:00:40.020606 | 2020-01-08T02:58:34 | 2020-01-08T02:58:34 | 196,157,549 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 410 | rd | genes_mat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{genes_mat}
\alias{genes_mat}
\title{a one-to-many matrix of GO term and gene}
\format{A matrix with 45 rows and 3 columns}
\usage{
genes_mat
}
\description{
the first column is the gene symbol, the second column is... |
84afd0009d68337cd59225335f8ca45ec7753b3d | c2061964216f76ad0f440c76dbfe1119e0279a22 | /R/API-methods.R | 65f3d6cff7f46778421a4f00c57d3ebfa0b38824 | [] | no_license | cran/antaresRead | 046829e05e411adfb55fc652ad49ea84f2610264 | f6a182b21854e12c5c470afcd38c26f44fb2b8d5 | refs/heads/master | 2023-04-16T10:45:23.521378 | 2023-04-06T16:20:02 | 2023-04-06T16:20:02 | 87,090,660 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,663 | r | API-methods.R |
#' API methods
#'
#' @param endpoint API endpoint to interrogate, it will be added after `default_endpoint`.
#' Can be a full URL (by wrapping ìn [I()]), in that case `default_endpoint` is ignored.
#' @param ... Additional arguments passed to API method.
#' @param default_endpoint Default endpoint to use.
#' @... |
d29addc45ad1540ad95c8544e8002562baf29435 | d8affab3b21ca33c2b6397e28171c4ad69b03d98 | /regression.R | 471e4414ef889e20c3e50e5acbebf24faa2d7f99 | [] | no_license | nupurkok/analytics | 3e69e9eb88d9eb6cc4f33ae105b7993c46a69fce | b0b76dd306e443aae010cac55ffcda484c39ad42 | refs/heads/master | 2020-03-28T15:22:27.782207 | 2018-09-16T13:03:53 | 2018-09-16T13:03:53 | 148,586,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | regression.R | women
str(women)
cor(women$height, women$weight)
cov(women$height, women$weight)
plot(women)
#create linear model
fit1 = lm (formula=weight ~ height,data = women)
summary(fit1)
fitted(fit1)
cbind(women, fitted(fit1), residuals(fit1))
ndata1 = data.frame(height = c(62.5, 63.5))
predict(fit1, newdata = ndata1)
#mul... |
061fa04320c71b1db0c10b5e6894a7f9267ebd0d | a411bbff2c1718c7d1823155138ef10a0c27da89 | /R_tmca_package-master/tmca.unsupervised/man/lda_lda.Rd | fcec6f256336d70d300fe31da10c90203a5ab346 | [] | permissive | ChristianKahmann/data_science_image | 5a0e805ca2cc2d3d8d99ab652dffb4b470dc102f | eb06582d6eaa521a59193ffbfc55c0a0a3eaa886 | refs/heads/master | 2020-10-01T13:53:17.130831 | 2020-01-14T12:48:00 | 2020-01-14T12:48:00 | 227,551,494 | 0 | 0 | MIT | 2019-12-12T07:58:15 | 2019-12-12T07:58:14 | null | UTF-8 | R | false | true | 1,941 | rd | lda_lda.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/topic_model.R
\docType{data}
\name{lda_lda}
\alias{lda_lda}
\title{Implementation of LDA::lda.collapsed.gibbs.sampler}
\format{R6Class}
\usage{
lda_lda
}
\description{
Implementation of the topicmodel::LDA algorithm. Implements all of the bas... |
db63ab7bbd4d3f692bed89bc6cb9d56b96266326 | 1154ea4133e862012fb1d0680ee4dc649c87ab40 | /man/run_primersearch.Rd | f0690751ed513ebdd5c16f7d386a66c28713bec1 | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | grunwaldlab/metacoder | f02daa6191254344861c399ef517d54acd6a190f | edd7192858fffc397fb64b9dcac00ed19dbbaa12 | refs/heads/master | 2023-05-03T13:50:13.490344 | 2023-04-20T06:15:31 | 2023-04-20T06:15:31 | 23,885,494 | 128 | 27 | NOASSERTION | 2023-03-28T19:45:07 | 2014-09-10T17:57:54 | R | UTF-8 | R | false | true | 1,274 | rd | run_primersearch.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/primersearch.R
\name{run_primersearch}
\alias{run_primersearch}
\title{Execute EMBOSS Primersearch}
\usage{
run_primersearch(
seq_path,
primer_path,
mismatch = 5,
output_path = tempfile(),
program_path = "primersearch",
...
)
}
\a... |
46c4e6309d7e779524b8b1a79263f38885577650 | ebb09f52b1ee12d8ae8d4c493e6f1079ee57868c | /ExploratoryDataAnalysis/Project2/plot1.R | 344f1ab64d1fa16fc56bc45754d6205e3ffc4c86 | [] | no_license | r6brian/datasciencecoursera | a1723f812a34eee7094dfaa0bfde6c618b349d6c | 548944d3ba68d302160f05158fb90859bc4c8bae | refs/heads/master | 2021-01-19T10:29:54.605308 | 2015-08-23T20:00:04 | 2015-08-23T20:00:04 | 26,268,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 634 | r | plot1.R | # 1. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
# Read data files
NEI <- readRDS("data/exdata-data-NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("data/exdata-data-NEI_data/Source_Classification_Code.rds")
# aggregrate based upon Emissions and Years
totalEmissions <- aggregate(Em... |
1989e063feb2e6de62bd306ed726df9d77cef61f | 4ad24fafde117a7f5cfffa3733b207aa6cf4ea90 | /man/swan_reportR.Rd | 7c0eb57756c19a7684b62c004cc6bed307826131 | [
"MIT"
] | permissive | dbca-wa/rivRmon | 1fc6ede9b7317cfb1463db52d5c6c85cd4788577 | 2708ec7860b6c81e950b25251a3683d1e56cd48d | refs/heads/master | 2023-04-11T11:07:02.534309 | 2023-04-06T07:56:54 | 2023-04-06T07:56:54 | 202,643,428 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,533 | rd | swan_reportR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/swan_reports.R
\name{swan_reportR}
\alias{swan_reportR}
\title{Function to create all of the plots and tables for the annual Swan River
report.}
\usage{
swan_reportR(
inpath,
outpath,
surface = "blue",
bottom = "red",
chloro = "dark... |
d016bf7c1cea2be45570d0826610230b375be3ce | 9bc17a169325375bc993b540d2ad0f0810ca0e76 | /R/twoway.plots.R | a98edb8797477c8f6316b7dfb57853a3015db298 | [] | no_license | alanarnholt/PASWR | 335b960db32232a19d08560938d26f168e43b0d6 | f11b56cff44d32c3683e29e15988b6a37ba8bfd4 | refs/heads/master | 2022-06-16T11:34:24.098378 | 2022-05-14T22:56:11 | 2022-05-14T22:56:11 | 52,523,116 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,375 | r | twoway.plots.R | #' @title Exploratory Graphs for Two Factor Designs
#'
#' @description Function creates side-by-side boxplots for each factor, a design plot (means), and an interaction plot.
#'
#' @param Y response variable
#' @param fac1 factor one
#' @param fac2 factor two
#' @param COL a vector with two colors
#'
#' @author Ala... |
b4e93e3bcccb0eb0d1014bd355bcfff5a5be6187 | 280019f481fe09da00296f45e5fa530051780756 | /ui.R | 10b50c14611abb664f6dbfc7ea4c164e2ac58b15 | [] | no_license | linareja/2017_Buenos_Aires_Elections | 1effb2b1d39bf660e9fa678a6a78ac3000f2122c | d500aaedb233fe541fe00dc63f0d488043467111 | refs/heads/master | 2021-09-12T16:33:33.715908 | 2018-04-18T18:19:56 | 2018-04-18T18:19:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,802 | r | ui.R |
library(shiny)
dashboardPage(
dashboardHeader(title = "2017 Elections in Buenos Aires Province"),
dashboardSidebar( sidebarMenu(
menuItem("Overview", tabName = "overview", icon = icon("globe")),
menuItem("Analysis", tabName = "analysis", icon = icon("bar-chart"))
)),
dashboardBody(
tabItems(
... |
564a95d83be7184c25e4953fc74f13401f3970ba | b6ed5857732c3261abab33a6665e7193d6862aef | /tests/testthat/test-read-oneshot-eav.R | d2b16cc13808d4cf760c57f846c793651568b48e | [
"MIT"
] | permissive | cran/REDCapR | 5ac1ebdb03fbf7dfa1aab23a2c23f711adcd4847 | a1aa09eb27fb627207255018fa41e30fa5d4b0fc | refs/heads/master | 2022-08-27T14:49:33.798497 | 2022-08-10T15:10:18 | 2022-08-10T15:10:18 | 24,255,971 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,860 | r | test-read-oneshot-eav.R | library(testthat)
credential <- retrieve_credential_testing()
update_expectation <- FALSE
test_that("smoke test", {
testthat::skip_on_cran()
expect_message(
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token)
)
})
test_that("default", {
testtha... |
ae049e4f7dded0c1877205b17e89aab67356d759 | cf4263e82b2c118bc3ecea5dc62d561e7487cbd3 | /tests/testthat/test_flatten_data.R | 327e274c4b13ccbaaa4edf5a2d6be774fcc94394 | [
"MIT"
] | permissive | EDIorg/ecocomDP | 151a2d519ff740d466fafab74df5171a6ef196bf | 0554d64ce81f35ed59985d9d991203d88fe1621f | refs/heads/main | 2023-08-14T02:07:19.274860 | 2023-06-19T22:27:30 | 2023-06-19T22:27:30 | 94,339,321 | 26 | 10 | NOASSERTION | 2023-07-26T22:21:00 | 2017-06-14T14:22:43 | R | UTF-8 | R | false | false | 7,103 | r | test_flatten_data.R | context("flatten_data()")
# Compare L0 flat and L1 flat - The column names and values of the L0 flat and L1 flattened tables should match, with an exception:
# 1.) Primary keys, row identifiers, of the ancillary tables are now present.
# Column presence -------------------------------------------------------------
t... |
b86c261c092bf8de25ef965c9d593fe8bc2c1c47 | 8d28b939007e0887f3a1af5b54a24c68dd3d4204 | /man/rayleighQlink.Rd | d99efc6fd1fdf6169706cd149907ed4706f3e5a4 | [] | no_license | cran/VGAMextra | 897c59ab2b532b0aa1d4011130db79f5c95eb443 | ac7e3df54136fd4c9e49b754f6747a11d7c3b122 | refs/heads/master | 2021-06-06T03:52:23.167971 | 2021-05-24T03:10:07 | 2021-05-24T03:10:07 | 138,900,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,858 | rd | rayleighQlink.Rd | \name{rayleighQlink}
\alias{rayleighQlink}
\title{
Link functions for the quantiles of several 1--parameter continuous
distributions
}
\description{
Computes the \code{rayleighQlink} transformation, its inverse and the
first two derivatives.
%
}
\usage{
rayleighQlink(theta, p = stop("Argument 'p' must be ... |
8ce7a9d3e16bf2b520b938c008850a5ca1577fb8 | 92456ce1d280dd99f0df1cc2a2567c5021286f03 | /R/prepare_data.R | 5c8fabf25b3ad3505598af1c3c14f7a6948f57d1 | [] | no_license | nzfarhad/AFG_MSNA_19_Analysis | 41643620a065ff3eaba40779624101b55562efe4 | 66b4cfe032b7665475606dcab5eae4fcacba0e9c | refs/heads/master | 2020-07-28T17:27:34.829098 | 2020-01-28T10:01:02 | 2020-01-28T10:01:02 | 209,478,936 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 100,120 | r | prepare_data.R | # Title: Preparation of data for woa survey
# Authors: Sayed Nabizada, Jarod Lapp, Christopher Jarvis,
# Date created: 20/09/2019
# Date last changed: 25/09/2019
# Purpose: This script is for recoding variables in the whole of
# of Afghanistan survey data
# indicators and composite scores are create... |
17d0b4508a89eda9690757bbd1a506dc8eba11fb | de83a2d0fef79a480bde5d607937f0d002aa879e | /P2C2M.SNAPP/R/draw.samples2.R | 4afd6ee40fe1adb1f7db29b2654b926047494a2b | [] | no_license | P2C2M/P2C2M_SNAPP | 0565abc0ea93195c9622dc5d4e693ccde17bebc7 | 94cd62285419a79f5d03666ec2ea3e818803d0db | refs/heads/master | 2020-05-07T18:54:40.440682 | 2020-01-10T15:59:45 | 2020-01-10T15:59:45 | 180,788,408 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | draw.samples2.R | ##### Randomly sample from posterior #####
draw.samples <- function(num_sims, gens_run, sample_unif){ # num.sims = user input # of simulations to perform; gens_run = # of markov steps saved; sample_unif = if true, sample posterior uniformly. Otherwise sample randomly
burnin <- ceiling(gens_run * 0.10)
non_burnin ... |
5e6123c9c6678ffff155f6d6bb0973954d846370 | 925c515b771a8ea7ca31cc530308d594c30fba07 | /code/TableS3.R | 3591bcb04a019b009fd1c4d141478c8c465a6176 | [] | no_license | melofton/freshwater-forecasting-review | 41ba42f0aee6180d7a731fcf838dccc8f7590588 | c06097cbab6d88c1dc30d0f2c3cf8a3baddaeacc | refs/heads/main | 2023-07-06T21:54:48.183725 | 2023-06-27T20:18:46 | 2023-06-27T20:18:46 | 478,673,588 | 0 | 1 | null | 2022-07-08T19:45:20 | 2022-04-06T18:05:25 | R | UTF-8 | R | false | false | 541 | r | TableS3.R | #Matrix analysis
#Author: Mary Lofton
#Date: 06JUL22
#clear environment
rm(list = ls())
#set-up
pacman::p_load(tidyverse, lubridate, cowplot,ggbeeswarm, viridis)
#read in data
dat5 <- read_csv("./data/cleaned_matrix.csv")
##Table 3 ####
dat10 <- dat5 %>%
mutate(ecosystem_type = ifelse(ecosystem == "river" | grep... |
1bc891cc48422875088ad36e2f4ff1053e811f2d | 218aae83a9d0994561991ba8affe528f1e381457 | /R/edgepoints.R | e7cee35ceeef11b83b9b9d0b6abfaa1dc7d09ef5 | [] | no_license | cran/edci | 4efcf830e8cec5d1522397140afd5650655b66b3 | d24ed3f7d6bd543f5b1fa07b8db821d42c8fe795 | refs/heads/master | 2020-12-25T16:56:26.204461 | 2018-05-16T20:49:37 | 2018-05-16T20:49:37 | 17,718,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,698 | r | edgepoints.R | edgepoints = function(data, h1n, h2n, asteps = 4, estimator = "kernel", kernel = "mean",
score = "gauss", sigma = 1, kernelfunc = NULL, margin = FALSE) {
epDelta = function(x) {
if (x < 0)
-1
else
1
}
epAt = function(x, y) {
if (x == 0) {
if (y >= 0)
pi/2
else
... |
4e4ff604aaf7b5ff470c8227b043cf073c00c388 | d3fdbf9442b8e0ffbc208ad50087f0ece05f405e | /Modulo 3- Resampling-Bayesianos-Markov/Ejercicio 3.3/Ejercicio3_3_MarianaSilvera.R | c9857fb242ead0825c194c8628a35108f8e2f36e | [] | no_license | msilvera/R-DataAnalysis2021-OTGA | b176f5f48076ce57ed1c7935fbe37ada31f21bda | 1bc03219b4d36c73d2196534c111878476d4373d | refs/heads/main | 2023-06-14T21:55:42.036064 | 2021-07-04T18:38:51 | 2021-07-04T18:38:51 | 380,082,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 947 | r | Ejercicio3_3_MarianaSilvera.R | library(FSAdata)
library(MASS)
library(dplyr)
#library(help="FSAdata")
#cargo los datos
#data <- WalleyeErie2
summary(WalleyeErie2)
data <-subset(x=WalleyeErie2, subset = !is.na(w)) #elimino los datos incompletos
summary(data)
set.seed(1) # semilla para el random
data <- data %>% mutate_at(vars("age"), ... |
eb3d9c97b02f6f8d4ca16e857d987432473f6d4c | 89d2d6b83bb0fcad3db66b139a617b0cc40bf34a | /R3-Aliona.R | dfc622532aca8311dc0e2430e94dcfdf29a65c9b | [] | no_license | alionahst/R3 | 5e6760cab681ab10149267ed31884ccb16cc6eb5 | d980eddc32efd762b3178bc3933b8ba486929944 | refs/heads/master | 2023-01-03T05:25:17.275377 | 2020-10-20T22:13:42 | 2020-10-20T22:13:42 | 305,684,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,799 | r | R3-Aliona.R | #Chapter 3: Basic graphics and data - Aliona Hoste
demo(graphics)
plot(iris)
#1. Plot a cheat-sheet with values of color and point type (col = , and pch = ) from 1 to 25, and export it as a jpeg of 15 cm wide, 6 cm high and resolution 100 points per cm.
plot(0, 0, xlim = c(0, 26), ylim = c(0.5, 1.5)
, yla... |
e592da7c303ff271742d942dd9f20f44ce746226 | 66a2afd9c0dab1d55e6d236f3d85bc1b61a11a66 | /man/sf_execute_report.Rd | 1aaa6ef65435cef0fc7a78005cd3df56a250d1cc | [
"MIT"
] | permissive | StevenMMortimer/salesforcer | 833b09465925fb3f1be8da3179e648d4009c69a9 | a1e1e9cd0aa4e4fe99c7acd3fcde566076dac732 | refs/heads/main | 2023-07-23T16:39:15.632082 | 2022-03-02T15:52:59 | 2022-03-02T15:52:59 | 94,126,513 | 91 | 19 | NOASSERTION | 2023-07-14T05:19:53 | 2017-06-12T18:14:00 | R | UTF-8 | R | false | true | 8,973 | rd | sf_execute_report.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analytics-report.R
\name{sf_execute_report}
\alias{sf_execute_report}
\title{Execute a report}
\usage{
sf_execute_report(
report_id,
async = FALSE,
include_details = TRUE,
labels = TRUE,
guess_types = TRUE,
bind_using_character_co... |
7015d5870ad5056141a600ab0b532cfd67a48a59 | e56da52eb0eaccad038b8027c0a753d9eb2ff19e | /man-roxygen/tipsForTreeGeneration.R | b3874469bd8aa861c1cbae942f72fce3a7ff9898 | [] | no_license | ms609/TreeTools | fb1b656968aba57ab975ba1b88a3ddf465155235 | 3a2dfdef2e01d98bf1b58c8ee057350238a02b06 | refs/heads/master | 2023-08-31T10:02:01.031912 | 2023-08-18T12:21:10 | 2023-08-18T12:21:10 | 215,972,277 | 16 | 5 | null | 2023-08-16T16:04:19 | 2019-10-18T08:02:40 | R | UTF-8 | R | false | false | 174 | r | tipsForTreeGeneration.R | #' @param tips An integer specifying the number of tips, or a character vector
#' naming the tips, or any other object from which [`TipLabels()`] can
#' extract leaf labels.
|
15f17c33f851b0ab97d37c7507f338f9cc08551e | d30fa10aa7b3837145a1d1f0bcff6a55372ea4eb | /plot_kmer_dist.R | a39c14daba7632aabe23b7da8c1d0a54f095915a | [] | no_license | mborche2/Matts_Satellite_Size_Code | 541bfdada9a61238ecb6c59594dbfd5e60766e97 | 824fcf6e8f4ab555df774baa9cd8caf6dd8200ae | refs/heads/master | 2023-03-28T07:29:31.677930 | 2021-03-23T18:57:52 | 2021-03-23T18:57:52 | 348,837,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 689 | r | plot_kmer_dist.R | library(ggplot2)
setwd("/n/core/bigDataAI/Genomics/Gerton/jennifer_gerton/jeg10/")
for (i in 1:21){
filenam <- paste("plots/kmer_frequency_asp/kmer_frequency_",toString(i),"_array.tsv",sep = "")
array_specifics <- read.table(filenam,header = FALSE)
freq_table <- table(array_specifics[,2])
freq_df <- as.data.fr... |
393e68c42ae3b36432c1265386c913a44b8e6d7e | c97fa9aadc45c44fad6433ae10c772060bde355c | /MyNotes/03 - Geting and Cleaning Data/01 Class_Data.Table_Package.R | 41cd46ae55c1ae3679c91b186b783fad89090d5a | [] | no_license | vitorefigenio/datasciencecoursera | 9866816242d39fa9fc9520bc4d543efc815afeb5 | 03722d0c7c6d219ec84f48e02065493f6657cc0a | refs/heads/master | 2021-01-17T11:17:58.099767 | 2016-02-28T03:06:37 | 2016-02-28T03:06:37 | 29,034,385 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 943 | r | 01 Class_Data.Table_Package.R | #data.table package
# Create Data.Table
install.packages("data.table")
library(data.table)
DF = data.frame(x=rnorm(9), y=rep(c("a","b","c"), each=3), z=rnorm(9))
head(DF,3)
DT = data.table(x=rnorm(9), y=rep(c("a","b","c"), each=3), z=rnorm(9))
head(DT,3)
# comando ara ver tdas as abelas criadas na memória
tables()
... |
12d5a52eb7e5fb10a0b5d87bdc8740c29b7c2a5a | 39315660a0226ae527ec8e0c7e6ae866df675b5f | /exercise1/computeCost.R | 5057e5c02d42dd31073fb2393dff4c7ded690bc3 | [] | no_license | Lemmawool/R-Practice | 28a7ce208f7d012eb4bc886fdb27b72754a171e9 | 0c3bed53e27953e9f19f92fd6e7b595a7e379262 | refs/heads/master | 2021-05-14T13:44:03.051151 | 2018-01-22T02:02:51 | 2018-01-22T02:02:51 | 115,955,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 105 | r | computeCost.R | computeCost <- function(X, y, theta){
m = length(y)
return ((1/(2*m)) * sum((X %*% theta - y) ^ 2))
} |
e662f9c90536aa7a7802ef2046cda55ac460d02e | 63227ea5a4085bb789824448502c95a98d8f375f | /cachematrix.R | 4e87ebeb3d25a82fe63b07127301dae99ce920d6 | [] | no_license | lfdelama/ProgrammingAssignment2 | f81f6ae4cf9246cc21a2fce019bc59a04949303d | 417909969f9fdd8c4d23700e1fcf535237a2c2ec | refs/heads/master | 2020-12-24T14:18:50.589091 | 2014-05-22T21:32:07 | 2014-05-22T21:32:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,314 | r | cachematrix.R | ## These two functions below are used to cache the inverse of a square matrix,
## so every time the same inverse is required, it doesn't need to be recomputed.
## This function creates a special matrix which
## contains a list of the following functions:
## - set, to set the value of the matrix
## - get, to get the ... |
48f4c3afd8bf9957f151bbbad760e9b7f9c317fe | 64e7ac1d0437b1d874b4ed070e6bda152decddee | /plot2.R | e2892d66195304ed5b560890e85b152215d7920e | [] | no_license | mooctus/ExData_Plotting1 | 072db8facebd27a8a8aab985be057b9b2c2b8122 | 005cba7dd9d88e94113a57eb6f8d77b9a3618811 | refs/heads/master | 2021-01-12T20:07:17.994147 | 2014-05-09T15:54:17 | 2014-05-09T15:54:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | r | plot2.R | Sys.setlocale(category = "LC_ALL", locale = "C")
df <- read.table(file="household_power_consumption.txt", sep=";", na.strings="?", header=TRUE)
df$Time <- strptime(
paste0(df$Date, " ", df$Time),
format=paste0("%d/%m/%Y %H:%M:%S")
)
df$Date <- as.Date(df$Date,format="%d/%m/%Y")
df1 <- df[df$Date %in% as.Date(c('200... |
ec9f4ad17398e6d6778438d88eaed81be1b890ff | e5a584e854ce025a135511f692dfc8e7ec178d49 | /grid.R | 07406eefe438ffe694bf2a7367e2ff8229779ff3 | [] | no_license | statspheny/sta242hw2 | 0fdb99c18f21c91dc990fd345c63daaf51067daa | 20aab766d40e91c16f3742f80bc3a953dc0c8846 | refs/heads/master | 2021-01-01T18:11:27.640548 | 2013-02-12T06:40:39 | 2013-02-12T06:40:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,593 | r | grid.R | newTrafficGrid = function(nrow,ncol) {
## This is a function that will
x = matrix(0,nrow=nrow,ncol=ncol)
class(x) = "trafficGrid"
return(x)
}
generateBMLgrid = function(nrow,ncol,nred,nblue) {
## the dimension of the grid
bmldim = c(nrow,ncol)
## randomly sample from nrow*ncol to get the... |
7c95eaaba2e639e23869e5b4d852212db33c02c7 | 1ea27108545233075e57b2cc5c3b0ceeeb0c76d9 | /R_model_garch.R | e47c8a221f9921a9953a323155a4a2c5882370dc | [] | no_license | zhen-yang8/Stats451_group | a2af5cd72a2bede65e98d14df8eb41d5b07ac2fb | d8f3a2b44352498433e99fc72e2af53b3b322fed | refs/heads/main | 2023-01-22T13:16:31.244087 | 2020-12-05T01:05:49 | 2020-12-05T01:05:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 918 | r | R_model_garch.R | library(loo)
library(rstan)
garch11_setup <- rstan::stan_model(file = './Stan/model_garch.stan')
dat = read.csv("./data/bitcoin_train.csv")
test = read.csv("./data/bitcoin_test.csv")
y = dat$log_return
N = length(y)
y_test = test$log_return
J = length(y_test)
stan_data <- list(y = y,N = N,sigma1 = 0.01, J = J, y_test =... |
e9b0112a388da3956102d6a142e3159e47c7b12f | 4c9b4fc35664cf660189b490ffb11ff562d06439 | /man/computeOverallImbalance.Rd | 63d73f98eecfbf4bb1770928a3396fa98a914531 | [] | no_license | atrihub/SRS | 41494f7cef824d0424828a1e0d835b432e7695ac | a0cf093d1565a937d0bc0821d6aaa781e649a951 | refs/heads/master | 2023-05-08T07:28:31.477902 | 2021-05-24T19:30:27 | 2021-05-24T19:30:27 | 104,920,017 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,861 | rd | computeOverallImbalance.Rd | \name{computeOverallImbalance}
\alias{computeOverallImbalance}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ A function to compute overall imbalances for each treatment }
\description{
This function computes the overall treatement imbalances for each treatment
}
\usage{
computeOverallImbalan... |
cce30da1726ce79e12a8071b14d1336ed9eafb45 | 7c32bd1a1ea4b9a9bab53dcd206e9154206e7bab | /samples/R_Models/LogisticReg_Rmodel/training.R | e09007a64eaa9d9b28bb13e47e141645067d10df | [
"Apache-2.0"
] | permissive | paataugrekhelidze/model-management-resources | 55d92159fb5ffd97460c44f0d495cdb8308d96da | e3cc8719f349f9755690a4cf87f7e75574966e9c | refs/heads/main | 2023-08-21T11:56:30.560413 | 2021-09-23T18:02:59 | 2021-09-23T18:02:59 | 424,690,327 | 0 | 0 | Apache-2.0 | 2021-11-04T17:57:08 | 2021-11-04T17:57:08 | null | UTF-8 | R | false | false | 622 | r | training.R | # Copyright (c) 2020, SAS Institute Inc., Cary, NC, USA. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
inputdata <- read.csv(file="hmeq_train.csv", header=TRUE, sep=",")
attach(inputdata)
# -----------------------------------------------
# FIT THE LOGISTIC MODEL
# ---------------------------------------... |
473c2e4ddd8a5eda52aa13b7c5dd97e6401b60c7 | 4487f71ef15b6712e60cc28a6e6e4918abf612fa | /Popgen_HW1.R | 2994020562d6e41de964c2af4352d54ccc0e6aaa | [] | no_license | maccwinter/Genanalyse | 8cf3d5e4c370da0489b33f39d8920f7016a5e014 | 757073491f7e062ecda1c3b009dc2924ef4e163a | refs/heads/master | 2020-07-31T03:54:31.650358 | 2019-09-25T18:03:13 | 2019-09-25T18:03:13 | 210,476,455 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,498 | r | Popgen_HW1.R | #Popgen HW 1
#1B
#The genotype frequences can be represented as by the following, where the allele frequencies of S, I and G are represented by fs, fi, and fg, repectively.
#GSS = fs^2
#GFF =ff^2
#GII = fi^2
#GSF = 2fs*ff
#GSI = 2fs*fi
#GFI = 2ff*fi
#1B
#tot represents the total population number
tot <- 141 + 111 + 1... |
9093cbd3ee1f51d6141b1c6c53647dbc68a901a3 | db65898c2edba5bca72e85b7d0382ae03e19d244 | /man/CASCrefmicrodata.Rd | 532129ad14d7c79f53e0c70c76cc549fc29a851e | [] | no_license | sdcTools/sdcMicro | 9193dd10c9cec6a16d90b32d4f8a78c84283bbd3 | 74ba57c4b579d2953d6e7bfa36a6cd648e7fff10 | refs/heads/master | 2023-09-05T05:20:31.170506 | 2023-08-30T10:54:09 | 2023-08-30T10:54:09 | 12,051,341 | 61 | 32 | null | 2023-08-30T09:52:47 | 2013-08-12T08:30:12 | R | UTF-8 | R | false | true | 1,442 | rd | CASCrefmicrodata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataSets.R
\docType{data}
\name{CASCrefmicrodata}
\alias{CASCrefmicrodata}
\title{Census data set}
\format{
A data frame sampled from year 1995 with 1080 observations on the
following 13 variables. \describe{
\item{AFNLWGT}{Final weight (2 i... |
029876e4c43604c12ff1493065a5d9dac214c441 | 38e6bf92a54267ad564bcfc2550f49d807b11686 | /src/QC/2_proteinGroups_QC.R | 4e5743b081b061f95b613b6fab46638f538e920a | [] | no_license | JoWatson2011/APEX2_Analysis_Watson_Ferguson_2022 | 997972a98a1ff4b4d35f7bc0a6cd4bad1566bc89 | 1ffb39f59a7f57a14ed08164742fc2bccb5a3399 | refs/heads/master | 2023-04-10T19:58:05.692462 | 2022-10-14T09:52:03 | 2022-10-14T09:52:03 | 490,328,742 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,314 | r | 2_proteinGroups_QC.R | library(dplyr)
library(tidyr)
library(gplots)
library(data.table)
library(patchwork)
# library(readr)
library(RColorBrewer)
library(ggplot2)
# Read proteinGroups
proteinGroups <- readRDS("data/proteinGroups.RDS")
experiment_cols <- grep("LFQ intensity .*_P_",
colnames(proteinGroups),
... |
2becc35d251a4f5a52e9bafcff89b1210c8ecd27 | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googleaiplatformv1.auto/man/GoogleCloudAiplatformV1ListSpecialistPoolsResponse.Rd | fc915960a3fddb469c1f7beb4f1ece82fc7d08c2 | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 922 | rd | GoogleCloudAiplatformV1ListSpecialistPoolsResponse.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aiplatform_objects.R
\name{GoogleCloudAiplatformV1ListSpecialistPoolsResponse}
\alias{GoogleCloudAiplatformV1ListSpecialistPoolsResponse}
\title{GoogleCloudAiplatformV1ListSpecialistPoolsResponse Object}
\usage{
GoogleCloudAiplatformV1ListSpe... |
b7e1117a806ad701ebde8e552a73573769a5ea2b | 8c2253bd47fd3d76f28950d1ef24450b24c4a0d7 | /R/extract_timeseries_annual_landings.R | 3cf07b590b7a7c0115215fcba4003ec7175a9a33 | [] | no_license | cran/StrathE2E2 | bc63d4f0dffdde94da1c7ea41133c09033c0cd4e | 629dc5e7f2e323752349352bb2d651a56c6f4447 | refs/heads/master | 2023-02-25T13:18:59.217896 | 2021-01-22T21:40:05 | 2021-01-22T21:40:05 | 278,343,976 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,487 | r | extract_timeseries_annual_landings.R | #
# extract_timeseries_annual_landings.R
#
#' read designated model
#'
#' returns a model object with run and data slots
#'
#' @param model current model
#' @param build model build object
#' @param out model output
#'
#' @return inshore/offshore annual landings
#'
#' @noRd
#
# --------------------------... |
c484eb8d4c0cfa39aa4c9cd14eed0f904cb5df74 | 4ce2d115fc47d9ae734d2bbb54382cdcc820a658 | /BuildComponentLambMortRateCovs/BuildComponentLambMortRateCovs.R | 0f9cc3c85dcad391ab1de5aaba048c232519c962 | [] | no_license | kmanlove/ClusterAssocDataPrep | 051765049c1975b51ea3c7ed2021249f0bcb3a60 | 236d0c7512a8b924e8986a20e5e690af72c0fd0c | refs/heads/master | 2016-09-06T16:17:42.915787 | 2013-10-08T14:05:33 | 2013-10-08T14:05:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,050 | r | BuildComponentLambMortRateCovs.R | #-- this script reads in the same data as the variance decomposition --#
#-- coxme models --#
filepath <-
"~/work/Kezia/Research/EcologyPapers/ClustersAssociations/Data/RevisedData_11Sept2013/"
relocdata <- read.csv(paste(filepath,
"RelocsWithNetworkMeasures/FullEweDataAllSummerRelocs_MinEdge.1_18Sept201... |
dda371063b542d0077a11342b18cb2ba5bfdbe82 | bfaa0d42780ea870d3f0a5e6e529ba0507dd328f | /man/export_header.Rd | 4d859e4958f1b8f66c5ceedc5ed2a7a9ab2d8da6 | [
"MIT"
] | permissive | jpshanno/ingestr | 20d9b709aa40737adaf99f94d779b89d308345de | ef2d692f552fb1ff6b91f6f2053887eae3db5e20 | refs/heads/master | 2021-06-26T16:34:02.405816 | 2020-09-23T14:33:25 | 2020-09-23T14:33:25 | 137,138,501 | 20 | 4 | MIT | 2018-12-10T17:13:10 | 2018-06-12T23:32:09 | R | UTF-8 | R | false | true | 511 | rd | export_header.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ingest_header.R
\name{export_header}
\alias{export_header}
\title{Export header dataframe to a temporary file}
\usage{
export_header(header.info, input.source)
}
\arguments{
\item{header.info}{A dataframe containing file header information}
... |
309d012b61e85077c644b851e354d2fca0852908 | be00bde77c9d86a3da0d38c0eaf2c424ec9b7369 | /man/Gmatrices.Rd | 31e4632d5ccac8d7633d8e955adbb145f5f5a178 | [] | no_license | martinbaumgaertner/varexternal | e719e6b9d68daa66eed7e86d9e40fef0dd4ad08c | 8547e2dca370963da6fd808eea62a1557154f5ac | refs/heads/master | 2022-05-29T17:35:42.614015 | 2022-04-22T20:09:18 | 2022-04-22T20:09:18 | 222,429,967 | 3 | 2 | null | null | null | null | UTF-8 | R | false | true | 634 | rd | Gmatrices.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Gmatrices.R
\name{Gmatrices}
\alias{Gmatrices}
\title{Gmatrices}
\usage{
Gmatrices(AL, C, p, hori, n)
}
\arguments{
\item{AL}{VAR model coefficients}
\item{C}{MA representation coefficients}
\item{p}{lag order}
\item{hori}{forecast horizon... |
66cc4ee237a4799b69dfff1561e8a7b171621da0 | b033ba5c86bbccca8f33a17a91d7d8ba1fc41976 | /man/perm_kCCA.Rd | 11f9486a6e7f1662cbfc5306fc860e0c15f06107 | [] | no_license | neuroconductor/brainKCCA | 889419ba83967592cc5f70cddaf8a23d4abbe27f | e8e08788b4ec395cfe5ba670d13332e03a35814f | refs/heads/master | 2021-07-19T05:44:31.800018 | 2021-05-17T13:38:42 | 2021-05-17T13:38:44 | 126,418,981 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,833 | rd | perm_kCCA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/perm_kCCA.R
\name{perm_kCCA}
\alias{perm_kCCA}
\alias{perm_kCCA_par}
\title{Calculation of Strength of the Connectivity among Multiple Brain Regions}
\usage{
perm_kCCA(x, y, sig = 0.1, gama = 0.1, ncomps = 1, permNum = 50,
kernel = ... |
92c314d901aa115f62b732ba0a5207c4460f1d33 | acf25199f5311f05b2d3a5119fe2a2e06fb82901 | /analysis.R | 6dca4128a89724d6a24d772d9f48c69aec95f017 | [] | no_license | AndrMenezes/mm2017 | 5dc6e9a6ac1c983b1e570a11fdb4144a446b8beb | 2756e0a2171ca943cc78ab771c63832727e2892c | refs/heads/master | 2021-04-15T09:28:56.306028 | 2019-06-25T22:11:27 | 2019-06-25T22:11:27 | 126,765,309 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 6,409 | r | analysis.R | # Definições gerais -------------------------------------------------------
setwd('C:/Users/User/Dropbox/4° Série/Modelos Mistos/Trabalho')
# setwd('C:/Users/André Felipe/Dropbox/4° Série/Modelos Mistos/Trabalho')
rm(list = ls(all.names = TRUE))
bib <- c('lme4', 'lmerTest', 'lsmeans', 'hnp', 'dplyr', 'ggplot2... |
ad8c47cf5866a4e8ae0b9980486cf83f6692621f | 04a98a7e184fd449985628ac7b8a92f19c1785a4 | /R/clsd.R | 26dbaf3caa1bc277896344527c47f9a940da1738 | [] | no_license | JeffreyRacine/R-Package-crs | 3548a0002f136e9e7c1d5c808f6a3867b20b417e | 6112a3914e65f60a45c8bcfc4076e9b7ea1f8e7a | refs/heads/master | 2023-01-09T18:23:59.615927 | 2023-01-03T16:20:22 | 2023-01-03T16:20:22 | 1,941,853 | 12 | 6 | null | 2023-01-03T16:20:23 | 2011-06-23T14:11:06 | C++ | UTF-8 | R | false | false | 38,963 | r | clsd.R | ## These functions are for (currently univariate) logspline density
## estimation written by racinej@mcmaster.ca (Jeffrey S. Racine). They
## make use of spline routines in the crs package (available on
## CRAN). The approach involves joint selection of the degree and
## knots in contrast to the typical approach (e.g. ... |
34045744083b6a451af5c1e46238c58314740b16 | 928176f46b5551d2e0af8ca160f06caae49b2303 | /get_mle_pure_r_code.R | 6f985c3fbd127ab96d38b2c97f06e9bee72527f9 | [] | no_license | tianqinglong/bootstrap_prediction | 8632c54c76f9635614084157a80cc4d487bdb0ee | 4a2ba7db7b57a6694cdda8d0912d8109aae40fdd | refs/heads/master | 2020-07-03T17:59:00.034956 | 2019-08-23T01:20:28 | 2019-08-23T01:20:28 | 201,996,897 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 563 | r | get_mle_pure_r_code.R | get_Weibull_mle_R <- function(censor_data) {
n_minus_r <- censor_data[[4]] - censor_data[[1]]
sobj <- Surv(time = c( censor_data[[3]],
rep(censor_data[[2]], n_minus_r)
),
event = c( rep(1, censor_data[[1]] ),
... |
f982dd38b90c24f19f65c15eb8b122712fb30623 | db4d0b8b4fe2601054f07bffe7f86c252e4a0e99 | /explorar_SpatialPolygonsDataFrame.r | bb1d4d9aabe26e3e5c3a6848e054b9fa708e57ea | [] | no_license | manuelcampagnolo/vector_datasets_R | f5aaf1fb58d87e8d85143135147f141e10538110 | ca22950ba05ac00fab78140c5408ea58ae9107ae | refs/heads/master | 2016-09-03T07:40:26.430196 | 2015-06-22T01:36:26 | 2015-06-22T01:36:26 | 37,262,455 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,849 | r | explorar_SpatialPolygonsDataFrame.r | # ler shapefile de areas protegidas (ICNF)
icnf<-readOGR(dsn=getwd(),layer="AP_JUL_2014",encoding="ISO8859-1")
plot(icnf)
# qual é o CRS do cdg?
icnf@proj4string
# quantos multi-poligonos há?
length(icnf@polygons)
# como são as primeiras linhas da tabela de atributos?
head(icnf@data)
# procurar a linha da tabela de ... |
ddc2d036394d28351b0b08666555189595167a36 | 8b6da8afa2945d53aea5380957b5714b74e732b6 | /plot3.R | 561dcab8559a392e4533ea6755a363abb7890889 | [] | no_license | neoeahit/ExData_Plotting1 | a9cf4c7b95e56bd5695f5fdd0a9524c89a167d5e | 42063482f7e35778c6cc6ddcabf7940d4c449e78 | refs/heads/master | 2021-01-22T05:43:25.381771 | 2017-02-13T06:36:59 | 2017-02-13T06:36:59 | 81,690,572 | 0 | 0 | null | 2017-02-11T23:45:30 | 2017-02-11T23:45:29 | null | UTF-8 | R | false | false | 392 | r | plot3.R | data=read_data()
png("plot3.png", height=480, width=480)
with(data, plot(Time, Sub_metering_1, type="l",ylab="Energy sub metering", xlab=" ", col="black"))
with(data, lines(Time, Sub_metering_2, col="red"))
with(data, lines(Time, Sub_metering_3, col="blue"))
legend(x="topright", lwd=1, legend=c("Sub_metering_1", "Sub_m... |
be3be50699979a29d74330b627a0d96fa4c94f08 | 6e5d78bb8fe6d0026e110a6c29c60a012f16e1ff | /Data Mining Course/9. support vector machines.R | ff88d3f146493b9636db76720843dfad3c7e08f5 | [] | no_license | richarddeng88/Advanced_Data_Mining | b2d2b91e9a6f100fc4db8be0c6a53140ee64e6fe | ef386c9fa5293ad0ea69b779b36251b15f8b59f0 | refs/heads/master | 2021-01-15T15:45:23.576894 | 2016-10-22T22:02:42 | 2016-10-22T22:02:42 | 47,933,660 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 569 | r | 9. support vector machines.R | #================================= support vector classifier========================================
set.seed(1)
x <- matrix(rnorm(20*2), ncol=2)
y <- c(rep(-1,10), rep(1,10))
x[y==1,] <- x[y==1,]+1
# we check weather the classes are linearly seperable.
plot(x, col=c(4,2))
## we encode the response as a factor and con... |
668ff8306e879494bacdc420772840578efbe1d2 | 7c084e50e556bc0468b4dde2852d65f44df13e41 | /in_progress/models/costBenefitAnalysis/scriptsForPaper/interventionGroupsPresenter.R | 781c717a62ab17ec7de8479fd129a2b96ba8abc5 | [] | no_license | mmcdermott/disease-modeling | 0d2379bb2d2a41ecf120fd5476b8768c76a10fd0 | 2d0eb0caba95216718d60ab9a2b6706021121f3d | refs/heads/master | 2016-09-06T09:18:23.086497 | 2014-08-03T23:23:22 | 2014-08-03T23:23:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,860 | r | interventionGroupsPresenter.R | library(ggplot2)
source('interventionGroups.R')
source('deSolConstants.R')
#Title Generation:
plotTitle <- function(base,interventionName,final="") {
if (final != "") {
return(ggtitle(paste(c(base,interventionName,final),collapse=" ")))
} else {
return(ggtitle(paste(c(base,interventionName),collap... |
9e6bb20d8f5f8e3c6c993d70755bde46c6950f81 | fed4b7e86cea3d0bd3f25449135f397f6e1bd9c9 | /PairwiseTTests.R | 292268ec0234f0038ae2e31f924445acb392bf18 | [] | no_license | pkiekel/CentraliaCollegeMath246 | a55589f3ed3ebdc855f00e2ff12a23cfebb5bc1e | 7183404a11621ae24dc9aa4d579cf167be025ba9 | refs/heads/master | 2021-01-21T13:33:44.419092 | 2018-08-07T17:17:46 | 2018-08-07T17:17:46 | 55,106,389 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 365 | r | PairwiseTTests.R | hsb2<-read.table("http://www.ats.ucla.edu/stat/data/hsb2.csv", sep=",", header=T)
attach(hsb2)
tapply(write, ses, mean)
tapply(write, ses, sd)
a1 <- aov(write ~ ses)
summary(a1)
pairwise.t.test(write, ses, p.adj = "none")
pairwise.t.test(write, ses, p.adj = "bonf")
TukeyHSD(a1)
a2 <- aov(write ~ ses + ... |
27c82966efdf77ed3395561674ffab9ad03992a0 | 7ebe092c7171d9c370b7c89995bc00f6ffa305cd | /lib/model.fitting.functions.R | e8ee93b743d7ce90c167869d0de8b1269d2ead2c | [
"MIT"
] | permissive | stoufferlab/annual-plant-dynamics | df25eb9b4d3c4cfe70858ffb7cbd2b3f8d813aa8 | 2849ef4cffae362dcf156c97a6600a90847f0d2c | refs/heads/master | 2023-04-16T18:11:45.500162 | 2022-08-01T01:30:40 | 2022-08-01T01:30:40 | 516,149,479 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,617 | r | model.fitting.functions.R |
##################################
# model fitting functions
##################################
# produce two-species model prediction given:
# 1. model parameters
# 2. a set of initial conditions for all state variables
fecundity.model.predict = function(params, plants.i, plants.j, seeds.i, seeds.j, time, focal, ver... |
fd2af82179b5d33372f94e17b08b4e623a57821d | f885f99d0090261317b8528128a1a72958760610 | /R/case_id.r | 274fa4184b0086795ca57376400478a236da5c85 | [] | no_license | BijsT/bupaR | 7a78d0d15655866264bab2bb7882602804303272 | 19f5e63c7393be690addf3c3977f1d00d0cdbfaf | refs/heads/master | 2021-08-26T06:40:32.388974 | 2017-11-21T23:12:47 | 2017-11-21T23:12:47 | 111,611,796 | 0 | 0 | null | 2017-11-21T23:11:10 | 2017-11-21T23:11:09 | null | UTF-8 | R | false | false | 524 | r | case_id.r | #' @title Case classifier
#'
#' @description Get the case classifier of an object of class \code{eventlog}
#'
#' @param eventlog An object of class \code{eventlog}.
#'
#' @seealso \code{\link{eventlog}}, \code{\link{activity_id}},
#' \code{\link{lifecycle_id}}, \code{\link{activity_instance_id}}
#'
#'
#'
#' ... |
5efcf92d21ba448b7a1f574dd4c0b968d1ffb23a | b63ad7afa41c810687e5d312056a4443cfa42aac | /R/R6UMLR2Base.R | 96506864078e8b63f86f1973d0f9c9d835e14b32 | [] | no_license | Grandez/umlr2 | 0f42ba1499e64785ea95feedef94e086a120140a | 0cbde78918086afcded5fc7cfa8e83e00d53ce11 | refs/heads/master | 2022-12-05T01:31:27.667844 | 2020-08-27T22:06:36 | 2020-08-27T22:06:36 | 283,737,694 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,991 | r | R6UMLR2Base.R | #' @name UMLR2Base
#' @title UMLR2Base
#' @rdname R6UMLR2BASE
#' @docType class
#' @description Clase base del paquete.
UMLR2Base = R6::R6Class("R6UMLR2BASE"
,portable = FALSE
,lock_objects = TRUE
,lock_class = TRUE
,public = list(
#' @description Inicializador
#' @param ... datos para ... |
94915657aefeb358d8de4ae8a3546cf4a404150d | 632acd6591c71ab7f638092b152794c230f26967 | /siliconvalley/dplyr.R | 57037402292fe0415a81305eae77a395dd737b51 | [] | no_license | kimjh2807/rstudio | 2fdc3dba4dcccf659b2d3a9a3e1cf0331815ef4e | dfdc1831fa65cf82c0cd926967494d573ff45fc5 | refs/heads/master | 2021-06-03T05:50:57.981205 | 2020-04-03T06:54:11 | 2020-04-03T06:54:11 | 111,923,598 | 0 | 6 | null | null | null | null | UTF-8 | R | false | false | 2,580 | r | dplyr.R | # dplyr (p.51~)
library(dplyr)
# tbl_df
iris
i2 <- tbl_df(iris) # tbl_df()
class(i2)
i2
# glimpse
glimpse(i2) # all variable can see with transpose
# %>%
iris %>% head
iris %>% head(10)
# install "gapminder"
install.packages("gapminder")
library(gapminder)
gapminder <- tbl_df(gapminder)
gapminder
glimpse(gapmind... |
7b7f9a85728ae8f8fcd5cbba408868c243dc395a | 2c1805e79d915c88faa0f6c258fc41e95937dba5 | /R/Unity/quest_step_position.R | 7e998d751f34c2b35955d200d636e3a8af9203ea | [] | no_license | hejtmy/VR_City_Analysis | b85c14ddc7aad5db8aeeb353ae02462986b20e59 | b149d3f52d76fc8fb0104fa42ec7b38ae7470ba0 | refs/heads/master | 2021-01-18T16:16:53.962471 | 2017-05-21T22:01:26 | 2017-05-21T22:01:34 | 49,779,651 | 0 | 0 | null | 2017-02-18T17:35:16 | 2016-01-16T15:48:50 | R | UTF-8 | R | false | false | 1,073 | r | quest_step_position.R | quest_step_position = function(quest = NULL, step_id){
#parameter validation
if(is.null(quest)){
SmartPrint(c("ERROR:quest_step_position:MissingParameter", "TYPE:quest", "DESCRIPTION:", "parameter not provided"))
return(NULL)
}
if(!is.numeric(step_id)){
SmartPrint(c("ERROR:quest_step_position:WrongP... |
11dbdcadcac15db777b2c15029723b7e54d7acc8 | f5f887250c22676073946936c27306e1d61c48e8 | /test_shiny_app.R | b8340a1fede633f5713ea2e7a08c74e480c8ce47 | [] | no_license | conorotompkins/model_allegheny_house_sales | 8b6d015056c4fc3b55d6e23a37edb2c7ab559f7a | a844e549ab1ab28574847a1ba7215ee9668a223c | refs/heads/main | 2023-03-23T23:35:22.513918 | 2021-03-17T21:17:36 | 2021-03-17T21:17:36 | 320,881,494 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,072 | r | test_shiny_app.R | #set up
# Load R packages
library(shiny)
library(shinythemes)
library(shinyWidgets)
library(tidyverse)
library(tidymodels)
library(usemodels)
library(hrbrthemes)
library(scales)
library(leaflet)
#https://towardsdatascience.com/build-your-first-shiny-web-app-in-r-72f9538f9868
#https://shiny.rstudio.com/tutorial/
sourc... |
d4efb8d877548f6ad6c44367caa59af48aae1aa1 | 2c707faace6d70238496097c5bbe8923d847b6fd | /man/print.Btest.Rd | 88c2e080fa8210ec97af1982bd2865cf6cc5209a | [] | no_license | comodin19/BayesVarSel | 573d5a872e08556e4eb8ceefae9d18a63dcbf281 | c534bf878f5b863d4f5f7bc283c957ce47532fe4 | refs/heads/master | 2023-03-17T02:59:13.682232 | 2023-03-08T11:34:23 | 2023-03-08T11:34:23 | 82,951,316 | 9 | 10 | null | 2023-03-08T11:34:25 | 2017-02-23T17:11:23 | C | UTF-8 | R | false | true | 941 | rd | print.Btest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Btest.R
\name{print.Btest}
\alias{print.Btest}
\title{Print an object of class \code{Btest}}
\usage{
\method{print}{Btest}(x, ...)
}
\arguments{
\item{x}{Object of class Btest}
\item{...}{Additional parameters to be passed}
}
\description{
P... |
5cc7c6d913ff866a08f142818926d595f57634f5 | e0219998a64a696a974e41fc341115704d4a9787 | /source/ini_pr_i.R | eae90295da687b245ac5827cc360cb2472be5348 | [] | no_license | micheledemeo/datacontrol | 81022d65b11d219d11753e7d842c46e102207c8b | c9189a5174976c52d478f682670291785f8b3d49 | refs/heads/master | 2020-06-02T09:16:36.883941 | 2016-08-27T11:23:22 | 2016-08-27T11:23:22 | 27,638,132 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,567 | r | ini_pr_i.R |
# aggiorna i sent in flotta
setkey(flotta, id_battello)
flotta[.( all[sent==1,unique(id_battello)] ) , sent:=1 ]
flotta[is.na(sent) , sent:=0]
# strati con almeno un sent=0 e almeno 2 unità con sent=1
flotta[,remove_to_hv:=0]
setkey(flotta, id_strato,sent)
#str_sent: strati con almeno un campionario inviato. Mettendo... |
8866763c368c5648482638a048015b4b2a052fbc | ca807743c5b9f9c4e17ee8e5526486a8288e4193 | /RUN_FIRST-create_data_matrix.R | 295d2f7d7f82b99fcf3b3d85bf7671db34649630 | [] | no_license | wsdaniels/COmodeling | bb144f09435d4b193e2e1c66f0cc37a8c626f4d4 | 20785951935226beae72f5a7ced746761e24fbc8 | refs/heads/main | 2023-05-27T17:24:19.414034 | 2023-05-10T20:40:39 | 2023-05-10T20:40:39 | 404,949,625 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,242 | r | RUN_FIRST-create_data_matrix.R | rm(list = ls())
# Install and load required packages
if ("lubridate" %in% rownames(installed.packages()) == F){
install.packages("lubridate")
}
if ("RAMP" %in% rownames(installed.packages()) == F){
install.packages("RAMP")
}
library(lubridate)
library(RAMP)
# Set base directory
base.dir <- 'https://raw.github.com... |
60d0040694a46d68d8b547b133051395f8e802be | e8bb53f264224f2b72b9b6e2f715080f98914fdf | /04_ExploratoryDataAnalysis/code/Lesson1_LatticePlottingSystem_w2.R | 43a92cc5e4374d55738643f9c19ae9d9007e0e2c | [] | no_license | pritraj90/DataScienceR | 0ef2550590f101bd0886ba7db22c6aa6df755ce0 | 54a67ad080756699138d083bd495da1dfa100d09 | refs/heads/master | 2020-03-21T16:31:22.158157 | 2018-03-07T16:52:26 | 2018-03-07T16:52:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,065 | r | Lesson1_LatticePlottingSystem_w2.R | # Week2 : Lesson 1: Lattice Plotting System
library(lattice)
library(datasets)
# Simple scatterplot
xyplot(Ozone~Wind, data = airquality)
# Convert Month to a factor variable
airquality <- transform(airquality, Month = factor(Month))
xyplot(Ozone~Wind | Month, data = airquality, layout = c(5, 1)) # by month
# Latt... |
43445899dd3c124dd5a260b25f36a345e7580ddf | 87a10b6ceddd21d6d0195f79648fa2fab473638d | /Food Services by County.R | 33f2cd065815fd47109bc916140e42e7a6b7dc7b | [] | no_license | vineetdcunha/Data_visualization | 8ef1b63a47e6f2082567b3f45367a096ed28ab8b | 83682c55c0d2a6ff25dbc97311296a2ca353071a | refs/heads/main | 2023-01-23T10:05:43.196622 | 2020-11-24T18:43:08 | 2020-11-24T18:43:08 | 313,099,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,237 | r | Food Services by County.R | library(tidyverse)
library(geojsonio)
library(RColorBrewer)
library(rgdal)
library(sf)
library(broom)
# Download the Hexagones boundaries at geojson format here: https://team.carto.com/u/andrew/tables/andrew.us_states_hexgrid/public/map.
spdf <- geojson_read("us_states_hexgrid.geojson", what = "sp")
# Bi... |
f2ebf5f917934428031c40c49ba1cdc6bc46b6b2 | 2e4afcf0f120a9d36ae9eee3c0d10df688c4cb37 | /js_RcircosPlotting.R | c5ae82f9f7fc29241b1673e5cccc5da4c3f5b937 | [] | no_license | CellFateNucOrg/afterMC-HiCplots | e7b83de0319371baa949696d1f34f1a1f19f3f2f | 8b3af55da3be3f60e3632a2052e46187d6e39a3b | refs/heads/master | 2022-02-11T07:54:11.044287 | 2022-01-31T17:53:41 | 2022-01-31T17:53:41 | 244,604,363 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,044 | r | js_RcircosPlotting.R | #' Prepare reads for plotting with Rcircos
#'
#' Input data frame should have column names ReadID, Chr, RefStart and RefEnd
#' @param readData - data frame of fragments detected in MC-HiC
#' @param readsToDraw - vector of read IDs
#' @return data frame with 6 columns for pairs of interacting loci
#' @export
prepareLink... |
71c39d0b805961cdded955fab3ef74f21a8eff6c | fc89e754459db5c69cf7f22a3cedd16fbcfc60c0 | /man/print.PCAbiplot.Rd | 5e991caef6e8527b42ae786e642e901c1dd1c2d3 | [] | no_license | Displayr/flipDimensionReduction | 4a450aba63621ee63a9a2bd8551e94be791d8e52 | bd844f99b5666981c9e15e9dc457ec6698814119 | refs/heads/master | 2023-06-25T07:32:55.843774 | 2023-06-13T09:05:42 | 2023-06-13T09:05:42 | 59,715,778 | 12 | 6 | null | 2023-05-11T04:51:51 | 2016-05-26T03:11:08 | R | UTF-8 | R | false | true | 441 | rd | print.PCAbiplot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/principalcomponentsbiplot.R
\name{print.PCAbiplot}
\alias{print.PCAbiplot}
\title{\code{print.PCAbiplot}}
\usage{
\method{print}{PCAbiplot}(x, ...)
}
\arguments{
\item{x}{An object created using \code{PrincipalComponentsBiplot}.}
\item{...}{... |
9c8ab84ec32ae6275a2bae294019ab3f35ad196d | b1f28e14d2b8079fa66d39d67c20fb53d2ee78e2 | /man/spearE.Rd | 1cbeeea4cb0e99237954c5a25529fe76e3bfd5e3 | [] | no_license | lucyov26/RankMetric | b506369a41bfa524ab723e2dd11fc60a505315f4 | d3c0bb0fd8b0910affaa0df0657918add9ccaf64 | refs/heads/master | 2020-03-23T18:00:29.363701 | 2018-11-01T21:08:19 | 2018-11-01T21:08:19 | 141,885,244 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 581 | rd | spearE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rpack2.R
\name{spearE}
\alias{spearE}
\title{Spearman's Rho for rankings with Ties}
\usage{
spearE(x, y)
}
\arguments{
\item{x, y}{integer vectors}
}
\value{
Returns Spearman's rho between the two rankings.
}
\description{
Computes Spearman's... |
9944e9f4e6458a58f53d345ced1e3888dbfd8ec8 | 5e6f223565e881eded9629a722dcc9d887479f83 | /man/remove_identation.Rd | d269973361592d02dc2ac10898059dae2b0fcf2d | [] | no_license | systats/semantic.doc | 058fdd708ddd218928c6187870366d16f0442115 | 33a0a1cb2ca4b91829faa70c18290b897ebe032f | refs/heads/master | 2020-04-03T23:53:58.350166 | 2018-12-02T19:23:59 | 2018-12-02T19:23:59 | 155,633,696 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 270 | rd | remove_identation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{remove_identation}
\alias{remove_identation}
\title{Removes HTML identation before cat}
\usage{
remove_identation(x)
}
\description{
Loads additional style and template file
}
|
4504238bc35c861acdf4c5858cacd0d7f7fb23e5 | e48a5a75c97b0e8b4d3c3b3f7f8484173baa7a3d | /ui.r | f86dac8e8e4f5b2dc3def2fa10b7a15ca612d861 | [] | no_license | patilv/bb50citiesrank | 08f3e08509c33ee6d409a2c8b81139dee51e1a2f | 1a407ba3cbff264ae676cdb3fdcf51c4fae59fe6 | refs/heads/master | 2021-01-01T19:24:31.875817 | 2013-06-24T10:35:23 | 2013-06-24T10:35:23 | 10,894,910 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,738 | r | ui.r | library(shiny)
shinyUI(pageWithSidebar(
headerPanel("50 Best US Cities of 2012 - Ranked by Bloomberg Businessweek - Location and Characteristics"), #Application title
# User input for determine characteristic to use for sizing dots on map
sidebarPanel(
selectInput("var1", "Characteristic 1:",
... |
54e5a7076b9ff98e8bbb8c87a838f75b5ddcc12c | e141aebdf1eee3f692848a88e6a4ef1db6b854a9 | /plot2.R | e18d95007a883045442199bd99e8dde1d245aeeb | [] | no_license | bobb72/ExData_Plotting1 | fd9fe3dea393c40cd6928919e9c35b3181d0ab3c | 94eacf8c495e3f6ac7337c047e0ee5da9a391ad2 | refs/heads/master | 2021-01-23T21:03:23.022982 | 2016-06-08T10:26:05 | 2016-06-08T10:26:05 | 60,681,145 | 0 | 0 | null | 2016-06-08T08:20:22 | 2016-06-08T08:20:22 | null | UTF-8 | R | false | false | 1,192 | r | plot2.R | # Here is the data for the project:
# https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
# Create an R script called plot2.R that reproduces the plot as per course instructions
#setwd("C:/Users/Bob/Desktop/DS Specialization/4_Exploratory_Data_Analysis/w1_assignment/exdata_data... |
2da797878ff65d5df11d5bb9a8c0f1a06bd422b4 | 9cc0308c75c50b5869c783fdd83fb00d36703e98 | /R/Time_Series.R | 8daabdead67b75dfb44f50408048f5711e61f9c6 | [] | no_license | SantiagoGallon/TimeSeries | 2cbf0157cb195bf8b213357f915739cf91d04592 | c40bdd4733796d5ba6f22e0a799afdadd56937da | refs/heads/master | 2020-09-01T01:47:32.635616 | 2019-11-01T22:59:15 | 2019-11-01T22:59:15 | 218,847,481 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,672 | r | Time_Series.R | rm(list = ls())
ls()
library(astsa)
library(forecast)
library(fma)
# Import data
# Índice Accionario de Capitalización Bursatil, Bolsa de Valores de Colombia (daily 15/01/2008 - 22/06/2016)
data <- read.table("/Users/Santiago/Dropbox/Teaching/Time Series/data/colcap.txt", sep="\t", header=TRUE)
x <- ts(data[,2], sta... |
1075e3cff2e78edf30dea16a30c5360b51512a3a | dc3665fa074c42cd25d3eca313b90f4ae4482520 | /vendor_behavior.R | 29c382822735c9e2c2d05a9f9dbadf9b15b404e9 | [] | no_license | andfdiazrod/darkweb_functions | 5f6a350e6902bfbb9a9ce8886425ed62c48dbf3e | b8f20f47c916494103a9f7f2f418ed2a39f80b6d | refs/heads/master | 2022-05-16T02:01:45.786947 | 2019-11-29T16:53:37 | 2019-11-29T16:53:37 | 216,660,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,605 | r | vendor_behavior.R | vendor_behavior <- function(df){
columns = c("day_format", 'vendor_name',"in_sample","appears", "consistency",
"relative_consistency")
vendor_consistency <- data.frame(matrix(ncol=length(columns)))
colnames(vendor_consistency) <- columns
vendor_consistency$day_format <- as.Date(vendor_consistency... |
69170b9ed1285a26df786dda9db67a76a136ec4b | f8c92559534dba1aaec173f86b22bfd2bff913bc | /Lecture 1/hw1_factor8.R | fbff896114e429129a6ddeff0a094d9cabb093c0 | [] | no_license | Zijie-Xia/GR5206-Introduction-to-Data-Science | 499f8b2999a194431891fb6c019e82542ca111bd | f017e55171a79f1833e8ad4fe0b9697a8634678e | refs/heads/master | 2020-12-07T07:06:56.675558 | 2020-01-08T22:01:39 | 2020-01-08T22:01:39 | 232,179,811 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,002 | r | hw1_factor8.R | # HW1: factor8
#
# 1. Create an ordered factor `f1` consist of letters 'a' to 'z' ordered alphabetically.
# 2. Create an ordered factor `f2` consist of letters 'a' to 'z' in descending alphabetical order.
# 3. Create a 30 elements, ordered factor `f3` consist of letters 'a' to 'z' followed by 4 NA. The order of `f3` is... |
0b133bf89d70c4ffdc4a35b6638ac5e6ac069a18 | 9d680e799f36291ef0406729e61315b8b3d9d0a1 | /man/UNIMANtransient.Rd | b0cd06bfdd1d649fb46422754ab18e49b5cd2592 | [] | no_license | cran/chemosensors | ffe070d193178a9274c6273fbdea6e256d028550 | b8bf614e42a6b0bea7c4eb5eec14c06f679d17b1 | refs/heads/master | 2021-01-01T16:59:55.106040 | 2014-08-31T00:00:00 | 2014-08-31T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | rd | UNIMANtransient.Rd | \docType{data}
\name{UNIMANtransient}
\alias{UNIMANtransient}
\title{Dataset UNIMANtransient}
\description{
Dataset UNIMANtransient
}
\keyword{data}
\keyword{datasets}
|
13b38bd70df1c10c75b0a356f69cc07a7161787a | facce126b08e76ad542ff63258afe1e327e2d563 | /cpp_adv_r_questions.R | 66e1e24427b58632a7d6798a712d4f4c9594f245 | [] | no_license | bweiher/r_cpp_learning | 7f7c1d3f989850e5e3f5deabfd7144269fd83def | f395529814df37e724702df6f14effb59226799f | refs/heads/master | 2020-04-01T04:10:29.847091 | 2018-12-17T02:20:44 | 2018-12-17T02:20:44 | 152,852,572 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,271 | r | cpp_adv_r_questions.R | # rcpp questions
library(Rcpp)
cppFunction("double f1(NumericVector x) {
int n = x.size();
double y = 0;
for(int i = 0; i < n; ++i) {
y += x[i] / n;
}
return y;
}")
x <- 1:10
for(g in seq_along(x)){
y = x[g] / length(x)
print(y)
}
f1(x)
median(x)
f1(c(1,2,3))
# Vector input, vector output
cp... |
d9cce87a0ab01cf0916523ea4b8c368636748c74 | a9c8e9612975e42f68e5a08b0d65fcbb5edb7616 | /plot3.R | 1dcc2f9f1da5febed5dfd9582e13349d76d37dce | [] | no_license | RaghavVacher/ExData_Plotting1 | e41b562dd975e12882f3b55b6eb46ae5543d2c4a | b665bc853f08ca05462953765239928f788caede | refs/heads/master | 2023-02-07T05:25:51.740550 | 2020-12-28T08:46:02 | 2020-12-28T08:46:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 846 | r | plot3.R | x <- read.table("C:\\Users\\Hp\\Downloads\\exdata_data_household_power_consumption\\household_power_consumption.txt", skip = 1, sep = ";")
colnames(x) <- c("Date", "Time", "Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
sub <- subset(x, ... |
89308d7bff06ed1304981eed274130bbda2cbe6c | 9478cff072f07ea24c94b233a96a3cdb30f27e95 | /basic_script.R | f4d600dd0387463be5f7450481f35a8bc04917d0 | [] | no_license | gozdebudak/r-programming-basics | fce5d091af385c25e4369e6bc1d82e2c42c5beba | 22cfeaf3f3302013d2c872dc694d53de1e1b92a6 | refs/heads/master | 2023-04-22T21:26:28.445300 | 2021-05-09T17:58:49 | 2021-05-09T17:58:49 | 365,807,244 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,158 | r | basic_script.R | data <- read.csv("sample_data.csv") # Reading CSV file and creating dataframe object
print(data) # Printing the data
print(is.data.frame(data)) # Checking if the data object is a dataframe
print(ncol(data)) # The column count of the data dataframe
print(nrow(data)) # The row count of the data dataframe
names(data) #... |
630dd959cb6f7b3ccc8af78c22f6d416623f5e0c | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/FlexReg/man/Consumption.Rd | 9b21829dbea8440a4ebc8dcb2b2bdcc6d6cd0772 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,743 | rd | Consumption.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DATA.R
\name{Consumption}
\alias{Consumption}
\title{Italian Households Consumption data}
\format{
A data frame containing 568 observations on the following 8 variables.
\describe{
\item{\code{NComp}}{the number of household members.}
\item{\... |
b17506d87cf048ebc6452ccbbe09f5b538194b99 | caf361bdbc2459187fb58fae876bad5497e532a1 | /man/plot_result_rank.Rd | 8b5006dcde855ff93247eb1a5d0624afbce32ce4 | [
"MIT"
] | permissive | ddiez/scmisc | 35efffabe859ddc6ac9c2c20f00d283a376def44 | f19819e7e736cfd167fd4b0c29d7290d66ab961a | refs/heads/master | 2023-08-17T04:08:03.971880 | 2023-08-06T13:35:17 | 2023-08-06T13:35:17 | 180,719,852 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 693 | rd | plot_result_rank.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/haystack.R
\name{plot_result_rank}
\alias{plot_result_rank}
\alias{plot_result_rank.haystack}
\alias{plot_result_rank.data.frame}
\title{plot_result_rank}
\usage{
plot_result_rank(x, highlight = NULL, sort.by = "log.p.adj")
\method{plot_resu... |
6cf05033fe8aaab69014012d593bb736d2a3070b | 9ef445e42d40f7bedfb6091877a1c1ca8e2cb8d1 | /server.R | 016d1238d59e2f89c70d20134a994290ea56c311 | [] | no_license | marco-vene/datitalia | 03a1d0285ab4f3b6a646639078494acbc29b3060 | e02c4c6aacefee4d2a1b8b62a3f2f66af73d2454 | refs/heads/master | 2020-09-07T15:37:22.771643 | 2019-11-10T18:29:12 | 2019-11-10T18:29:12 | 220,829,641 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,161 | r | server.R | # Define the server
shinyServer( function(input, output) {
# output$trendPlot <- renderGirafe({
# ggiraph(code = print(trendLine(dati, input$gruppo, input$metrica)))
# })
output$trendPlot <- renderPlotly({
trendLine(dati, input$gruppo, input$metrica, input$periodo)
})
output$de... |
b08f8c6ad9736f9a76e630d88ff43d8d938cfcc3 | ddb120b0aaa38527d4eded97552e63d1cad7fb9a | /Project3/server.R | eab74849a4929780e3558b5542a7a86097df77c7 | [] | no_license | dwatie/project3 | df858e17c541ca9039c154a47665734c8189c589 | 593d6da14ebf6c80ef2ab97a5b6d81fbe265954c | refs/heads/main | 2023-07-01T05:25:48.125878 | 2021-08-03T03:32:39 | 2021-08-03T03:32:39 | 390,863,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,039 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinydashboard)
library(tidyverse)
library(DT)
library(caret)
library(plotly)
libr... |
02a84ccdc624056781ec1b970cb8184d48945ba0 | 68562f910349b41cdf4432c0921940f0513ab516 | /tests/testthat/test-style_xaringan.R | 1fc0ebd071fe53304582101422ddc11b8cf70c81 | [
"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 | 483 | r | test-style_xaringan.R |
test_that("style_xaringan() writes to specified outfile", {
tmpfile <- tempfile(fileext = ".css")
expect_equal(style_xaringan(outfile = tmpfile), tmpfile)
expect_true(file.exists(tmpfile))
expect_true(grepl("xaringanthemer", readLines(tmpfile)[3]))
})
test_that("style_xaringan() warns if base_font_size is not... |
c831e18ce1ad8d2e4000e4d8f190a872bfffcdaf | 4b402d90385a6a291c4761d08adac6d5ce547d18 | /antweb.R | d199f2e25e93344bc0428b6dad21ae05c241ed55 | [] | no_license | karthik/antweb_paper | 273cf0425f3154f0afb742690c067c4c73e22b65 | 07337be82311cde0f16e69aa603649f48b5629e2 | refs/heads/master | 2020-05-27T12:35:37.434962 | 2014-10-23T15:05:48 | 2014-10-23T15:05:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,025 | r | antweb.R |
## @knitr counts
library(AntWeb)
genera <- aw_distinct("genus")$count
species <- aw_distinct("species")
species <- species$count
## @knitr how_many_species
madagascar <- aw_data(country = "Madagascar")
total_results <- madagascar$count
offset <- seq(0,ceiling(total_results), by = 1000)
madagascar_all <- lapply(offs... |
63e89e861bb9084efd36049f01cb602b882b7065 | 6d96dbaeb9e3985a278e81cacb92eabed0908e1e | /R/create_dsproject.R | 74783a7d4729075d0eb0944f9539d8278edca045 | [
"MIT"
] | permissive | cimentadaj/dsproj | f23368f11ab5dec53b4999ac1159ce31a2669361 | aa99fa025921cf82524064935185b63d5d71a5a8 | refs/heads/master | 2020-04-13T18:42:51.477873 | 2019-02-10T20:27:17 | 2019-02-10T20:27:17 | 163,382,997 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,680 | r | create_dsproject.R | #' Creates a template of folders and files for the 'ideal' data science project
#'
#' @param path A path where to create the project template. Can be relative, absolute and non existent.
#' @param open whether to open the RStudio project or not. Set to
#' FALSE by default
#'
#' @details
#' The function accepts a v... |
4df02e56965e31ec8a15aeeee2542ac2245c3df0 | d1a87fe12e6f3eba49346d4d89c8f1a931e8715a | /Face_update/face_update.R | 4cfcabecb6da360c0c19cf64d2af04875cb0445d | [] | no_license | danmrc/azul | 9f557876557c046112a9374fcd7fabf612271090 | 87752b17778368b63ff43054a56b83048cc973c4 | refs/heads/master | 2023-08-22T01:13:39.342663 | 2023-08-04T22:10:26 | 2023-08-04T22:10:26 | 141,443,005 | 3 | 1 | null | 2023-07-11T17:31:52 | 2018-07-18T14:04:22 | HTML | UTF-8 | R | false | false | 632 | r | face_update.R | parse_website <- function(url){
require(xml2)
pag <- read_html(url)
fs <- xml_find_all(pag,xpath = "//h3[@class= 'item-title']/a")
ss <- xml_attr(fs,"href")
return(ss)
}
checkBlog <- function(newList,oldList,token){
require(Rfacebook)
teste <- prod(newList == oldList)
if(teste==1){
return("No updat... |
1dac1bac6c182ffed80613f44ec5cdc6fd63d6d9 | 58bf560d8a6dd1b6cca4a86bce02dff95eec244d | /man/it_hospbed.Rd | 75df3454cdbc1a6c83c429983e903165912606fb | [
"MIT"
] | permissive | c1au6i0/covid19census | 5576b1d8fd11395e34975e8df146676e7cb5e4e4 | 4a66bd27da2387e4c2ee25a0deb59f829abc53a6 | refs/heads/master | 2023-05-03T00:05:57.186159 | 2021-05-18T13:54:37 | 2021-05-18T13:54:37 | 271,893,792 | 4 | 0 | NOASSERTION | 2021-05-18T13:54:38 | 2020-06-12T21:32:31 | R | UTF-8 | R | false | true | 779 | rd | it_hospbed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets_it.R
\docType{data}
\name{it_hospbed}
\alias{it_hospbed}
\title{hospital beds}
\format{
An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 21 rows and 5 columns.
}
\source{
\href{http://www.dati.salut... |
1a5a569560105ee5717001f2ef97cc695ecf55e8 | 16cbcd4b55e9df1e91f2d69702790023c9cf6780 | /799435798.r | d3095c5443b480fb5b9b95b7ba5ed7f9e14ddc78 | [] | no_license | erex/MT3607-peer-review | 3f65c9a168f34e947fe0e531e773029384c19314 | bc0750e9a7fb5f2d0a7c7e35b34b3a80213d9fde | refs/heads/master | 2020-06-03T06:12:34.093705 | 2014-10-10T09:49:51 | 2014-10-10T09:49:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,095 | r | 799435798.r | #I confirm that the attached is my own work, except where clearly indicated in the text.
my.rnorm<-function(n,mean=0,sd=1){
#Purpose:
#Returns n pseudo-random variables from a normal distribution
#Inputs:
#n- number of observations: a numeric scalar,
#mean - mean: a numeric scalar with default 0,
#sd- standard ... |
84aa0d7c65b9eaa63ebdeb7fd77a5c6e2dfb4a44 | 39f7d071437cb3489029f0f751600b71ac798962 | /1a 使用R語言進行資料分析/助教課 Week_5/finalExamSol.R | 06e5a392dad0af947345ec51379a186549992ab6 | [] | no_license | evan950608/Evan-R-Programming | 070044950d51e2370d827c41c300160a169f3773 | 586a0f706b71b7e53ea78ad7e1b76be35de0ea81 | refs/heads/master | 2020-04-07T11:49:01.354918 | 2019-01-26T12:05:19 | 2019-01-26T12:05:19 | 158,342,221 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,702 | r | finalExamSol.R | #### 注意事項 ####
# 0. 請勿改動 angry_fruit.R，否則將導致此檔案中讀取資料出錯
# 1. 變數名稱請勿改動，若造成判斷錯誤一蓋不負責。
# 2. 請不要用 rm(list=ls()) 之類的東西，我們的 judge 會壞掉。
# 3. ggplot2 的 ggplot() 會回傳東西，第二大題的所有答案都請存到變數中
# ex: gg_exam <- ggplot(data=..., aes(...)) + ...
# 4. 提交答案之前請再次檢查變數存的東西是否符合題目要求。
# 5. 滿分不是一百分
#### 0 ####
# 0.0 (5%)
# 請自行查詢 require() 回傳值
# 請... |
bd7799bf6f31a58a482690980539bd8ca50838b9 | 7bdee0060e806b64dede482401398149cac7271e | /cointegration/pairs_plot.R | 2768fee098d1c7f5f66f0c8a1ac1e9b063f11473 | [] | no_license | maxim5/stat-arbitrage-r | b775133cdb0aa2e906fde3bcf92dfdf542ab5393 | 5e21c623015f2e8df7bd9ae07435da61bb188669 | refs/heads/master | 2022-11-29T07:58:23.162422 | 2015-08-26T09:10:55 | 2015-08-26T09:10:55 | 287,816,691 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,412 | r | pairs_plot.R | #!/usr/bin/Rscript
suppressMessages(library(ggplot2))
suppressMessages(require(reshape2))
invisible(Sys.setlocale("LC_TIME", "en_US.UTF-8"))
load("pairs.RData")
Plot.Price = function(symbol1, symbol2) {
series1 = all.logs[[symbol1]]
series2 = all.logs[[symbol2]]
dates = as.Date(rownames(all.logs))
data.to.... |
afd471986821ab83c70ad6bd8e3990c4423f09e9 | f73e7dbcc24064028c81f9f778f9892bd55d9066 | /shiny/ui.R | e8f4a2e0534c9642cc3952490b60ed30fff55bf2 | [] | no_license | kchaaa/INFO-498F-Final-Project | 030d235760ad20a62e63cf9afcf9a07174ea61a1 | 63aad059a020e3ef784de29a7cd0c2af1e6a277a | refs/heads/master | 2021-01-10T08:49:05.885601 | 2016-03-11T21:21:33 | 2016-03-11T21:21:33 | 52,401,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 232 | r | ui.R | library(shiny)
library(plotly)
library(ggplot2)
shinyUI(fluidPage(
titlePanel("Flint Water Contamination"),
sidebarLayout(
sidebarPanel(
h4("Test Plot")),
mainPanel(
plotOutput("plot1")
)
)
)
) |
30092f46b9fa632a94afab8325a7969db674f14c | ae5a7c06fc184ff1c4029c1479a0a31f2cdd481a | /man/ScoreRushing.Rd | af0741b8fdd0bb41b160c102be8636de8dbe6efa | [] | no_license | kuhnrl30/Touchdown | 3726c7a2bb2cfba6ad67b7eec4d49ee8b91b13c4 | 36a620f134fb4669d709e60daf19e395b2db64ec | refs/heads/master | 2021-01-19T02:20:25.826833 | 2016-07-25T03:49:59 | 2016-07-25T03:49:59 | 41,216,698 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 962 | rd | ScoreRushing.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/ScoreRushing.R
\name{ScoreRushing}
\alias{ScoreRushing}
\title{Score the rushing stats}
\usage{
ScoreRushing(x, RushingYds = c(10, 1), RushingTD = 6, FumbleLost = -2)
}
\arguments{
\item{x}{dataframe of player statistics data. Should ... |
3ad426a297e95eeb7c1e5fcbca87562d796a73c9 | 92befee27f82e6637c7ed377890162c9c2070ca9 | /R/summary.lsem.R | f1a4e61558f390df2c6ec42f1e672834d3b21603 | [] | no_license | alexanderrobitzsch/sirt | 38e72ec47c1d93fe60af0587db582e5c4932dafb | deaa69695c8425450fff48f0914224392c15850f | refs/heads/master | 2023-08-31T14:50:52.255747 | 2023-08-29T09:30:54 | 2023-08-29T09:30:54 | 95,306,116 | 23 | 11 | null | 2021-04-22T10:23:19 | 2017-06-24T15:29:20 | R | UTF-8 | R | false | false | 3,312 | r | summary.lsem.R | ## File Name: summary.lsem.R
## File Version: 0.412
#-- summary lsem
summary.lsem <- function( object, file=NULL, digits=3, ... )
{
# open sink for a file
sirt_osink( file=file )
cat('-----------------------------------------------------------------\n')
cat('Local Structural Equation Model \n\n')
... |
fd26877562d244d617b56f04f2ec149e1201f122 | 2a0e90441bb5edc22344aff9019dea4d825183bf | /auto_learner_manish_1.r | 1e7be0463b39cc70d1d21f31585d6a32fc6b0a24 | [] | no_license | srijan55/1ml | bb06ac62601beb6a87d4823564bd6fe487fd930f | 802f839e719a6a351c7d1b9b0695e995c4acfaf0 | refs/heads/master | 2021-01-22T06:58:50.134172 | 2015-07-02T02:07:45 | 2015-07-02T02:07:45 | 38,128,894 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,677 | r | auto_learner_manish_1.r | library(e1071)
library(Matrix)
library(SparseM)
##########################
## Load raw training data
##########################
rawdata<- read.csv("train_category.dat", sep="\t", nrows = 100 )
################################
## Feature Engineering
#################################
rawdata$UserID <-as.factor(rawdata... |
74ccce38fd397f404e954cf868cc6c776fb26e74 | 2d88e86736d81b32e957b62bd8b0041e2a9778ad | /R/scores.tables.tweak.R | 1e4cae57350c86dd2118b7c9151872ffa2c8a421 | [] | no_license | cran/amber | c1659595049f230f54db3893704fc67ddb2429ed | e6ef59a25270413a1875c84feac786551bf69315 | refs/heads/master | 2021-07-23T06:25:02.408885 | 2020-08-28T10:20:02 | 2020-08-28T10:20:02 | 212,134,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,522 | r | scores.tables.tweak.R | ################################################################################
#' Tweak summary table
#' @description This function allows the user to tweak the summary table computed
#' by \link{scores.tables}. Contrary to \link{scores.tables}, this function can be used
#' to create a single summary table that inclu... |
977dca5df9467a27cb8d8503ed42572c8bf96028 | a49107cd976c16910f405e92891f81089d46b235 | /16 Dates and Times.R | 8d8f841c0e2b6e5d052bfaca3d7af932a3a8ea5a | [] | no_license | nmoorenz/R4DS | 8fcf397516d95054e24287249267fe58ce6b7d4b | 1227401db2459120b2890ff5f2a391c1daf8b13b | refs/heads/master | 2021-09-18T10:22:10.851213 | 2018-07-12T21:16:20 | 2018-07-12T21:16:20 | 114,934,177 | 0 | 0 | null | 2018-07-12T21:16:21 | 2017-12-20T21:47:41 | R | UTF-8 | R | false | false | 6,694 | r | 16 Dates and Times.R |
############################################
# 16 Dates and Times
library(tidyverse)
library(lubridate)
library(nycflights13)
# 16.2 Creating date/times
today()
now()
# 16.2.1 From strings
# ymd() mdy() dmy()
ymd("2017-01-31")
ymd(20170630)
ymd_hm("2017-12-25 10:00", tz = "NZ")
# 16.2.2 from co... |
63a7afb8be5cb5181294e32011df820beebdd09f | 09a34862ad70328988389e8a304dcfcfddd2146e | /old_versions/toyData.R | c48825ee51a24a2af3137693b216722c1382bcc1 | [] | no_license | kathiesun/TReC_matnut | 63c33db7ebaee80ffc262975b27e41a3d32ef997 | e8a2f463960a3c59e54d39690a065b0563a957a8 | refs/heads/master | 2023-04-19T11:42:38.918064 | 2021-05-09T19:29:52 | 2021-05-09T19:29:52 | 321,777,614 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,684 | r | toyData.R | setwd("~/matnut/src")
library(rstan)
library(tidyverse)
# ---------------------------------
# Generate toy data
# ---------------------------------
set.seed(1)
nmice=2
ngenes=3
nkmer=4
total_counts_per_kmer = pk_matrix = data = list()
pg = rbeta(ngenes, 1, 1)
total_counts_gene =... |
9d360197f39944a7f69d4bef7fe2e7995d9c7f3b | da1ae08c144c508573a8482a71bd2a2ebe5c21e9 | /R/s3.R | 8beb6fa2b043d7c7eef1d17cf9ca6c065c00367e | [
"MIT"
] | permissive | AmrR101/singleCellHaystack | 48c068eb35319edf7842db17ae6542a715f5420c | 68f41d8cc9cb44b7eff95f318592f33306dae0a4 | refs/heads/master | 2023-02-10T01:12:04.548101 | 2021-01-07T08:44:41 | 2021-01-07T08:44:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,167 | r | s3.R | #' The main Haystack function
#'
#' @param x a matrix or other object from which coordinates of cells can be extracted.
#' @param dim1 column index or name of matrix for x-axis coordinates.
#' @param dim2 column index or name of matrix for y-axis coordinates.
#' @param assay name of assay data for Seurat method.
#' @pa... |
a7be91c8531bcdb9ab6f7c76e049369856581dde | 3e374bdfbc0d3bb2933fc248285263dd3e45ec48 | /R/xgboost.R | 52d1017ea94a7a01a9d572afe400d4290cc9970e | [
"Apache-2.0"
] | permissive | RBigData/pbdXGB | c309fc6ce317f5db6c3ac9a838ef71724657f062 | c76b807cfd60aaa61e8ab233873c6e3ea41b3e6c | refs/heads/master | 2020-09-07T05:46:55.322685 | 2020-02-16T23:44:09 | 2020-02-16T23:44:09 | 220,674,364 | 0 | 0 | Apache-2.0 | 2020-02-16T23:44:10 | 2019-11-09T16:59:56 | C++ | UTF-8 | R | false | false | 1,213 | r | xgboost.R | # Simple interface for training an xgboost model that wraps \code{xgb.train}.
# Its documentation is combined with xgb.train.
#
#' @rdname xgb.train
#' @export
xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
params = list(), nrounds,
verbose = 1, print... |
466692faf81626a468779c27caa9e486cf6312b9 | 01588666e7f7f7c5fbe2e7fea1c1c732851f3f7e | /cachematrix.R | 91c1b97dbcaaf9ca3a288ddc941b7f62bb08d287 | [] | no_license | sanpau/ProgrammingAssignment2 | 38e4537ee82a9ed30a93caa109bdaddec466ac1f | 88626791f6adb10705301618a70ed9ded6e664a7 | refs/heads/master | 2020-07-06T08:47:40.899163 | 2016-11-18T17:29:42 | 2016-11-18T17:29:42 | 74,050,666 | 0 | 0 | null | 2016-11-17T17:25:55 | 2016-11-17T17:25:55 | null | UTF-8 | R | false | false | 1,772 | r | cachematrix.R |
## pair of functions that
## cache the inverse of a matrix.
## `makeCacheMatrix`: This function creates a special "matrix" object
## that can cache its inverse.
makeCacheMatrix <- function(x = matrix()) {
invt <- NULL
# function that sets the value of the matrix IN set
set <- function(y) {
x <<- y
... |
e8db9e2229b109ccb9f01002b79904edfe10f166 | 601d899094f8f73a5356e30fd8d721801b6de757 | /R/transformations.R | 707f34bfaea19ea9f8fc62be97b67974c386a40e | [
"MIT"
] | permissive | vmikk/vmik | e8b5b7e90342fad800ac8a139b1590b1becc454e | 4d11200e3928f248050303203e3c7829139817dd | refs/heads/master | 2020-04-07T08:12:12.754247 | 2018-11-19T07:16:02 | 2018-11-19T07:16:02 | 158,204,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,111 | r | transformations.R | ## TO DO:
# - IHS - add 'back' flag to perform reverse transformation
# - gelman_scale - see also arm::rescale(, binary.inputs = "full")
# Standardizing by сentering and вividing by 2 standard deviations
gelman_scale <- function(x){
x.obs <- x[!is.na(x)]
mm <- mean(x.obs)
ss <- sd(x.obs)
res <- (x - mm)/(2 * s... |
12be28f962e06a5b47e7664009f16b6f5eb80dd8 | dec3db3c118c3aea6f73288b43c5e87c90f60091 | /FigS2_ExtendedData2/FigS2.R | ce60865db5963d9b9f0c0e8468f067480629d387 | [] | no_license | livkosterlitz/Figures-Jordt-et-al-2020 | c3b48d702107a3697d3fecd42410d031ff4a328c | 70e06b4affeddcff15b5c78a8922ee1b8dcd08bf | refs/heads/master | 2021-01-08T10:28:43.307167 | 2020-08-06T16:54:15 | 2020-08-06T16:54:15 | 242,003,143 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,229 | r | FigS2.R | library(tidyverse)
library(cowplot)
library(lattice)
library(gridExtra)
library(grid)
library(egg)
#########################
#FigureS2########
#####################
###Ancestor###
dat <- read.csv("FigS2_low.csv")
dat <- dat %>%
#select(-Mixture, -CFUs) %>%
group_by(Host, Antibiotic, Day) %>%
summarise(N = n()... |
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