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values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
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values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
83c23e5879d5584b514d4db478e9100055b08db8 | 13c3cdcac474dcf24885fec9c4bb5baa060ba6c7 | /LINESprocFromNetwork2.r | 14f673f604c57633b8b4a5421ff8bff4d77a3f9d | [] | no_license | kpierce8/hydroNetworks | 98661dd422729c8a5bfa23c57322ee7daf8244c3 | e3c7bb870243d711c169e0c7c4ade2d191591928 | refs/heads/master | 2020-06-03T15:08:27.359905 | 2011-04-20T21:23:05 | 2011-04-20T21:23:05 | 1,642,614 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,964 | r | LINESprocFromNetwork2.r | library(igraph)
library(foreign)
memory.limit(1024*3.75)
source( "E:\\CODE_local\\HydroNetworks_local\\Trunk\\NetFunctions.r")
#source( "g:\\str24project\\Trunk\\NetFunctions.r")
wria <- c("WA")
LINEID <- c("OBJECTID") #wchydro is LLID
LENGTH <- c("Shape_Length") #wchydro is LLID
library(RODBC)
cnn1 <- odbcConnectAcces... |
c6db436ff8100849a4599e19afac8e28e2f77dbd | fdb4b33f73ec0f7a04f738fa3edc1e65d60f22fa | /final_name_table.R | 237b13102783d503950d5fcf35c15541b48b1937 | [] | no_license | Mira0507/popular_baby_names | 066033a8a1cb9991e1e9c7377cfd951a1894e380 | df4d39d57c5bfbeebc4e44788f6554f407bae9a0 | refs/heads/master | 2022-04-12T06:52:41.848497 | 2020-04-11T02:45:26 | 2020-04-11T02:45:26 | 253,302,494 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,168 | r | final_name_table.R | library(tidyverse)
library(formattable)
eboy <- read_csv('e_boys.csv')
egirl <- read_csv('e_girls.csv')
uboy <- read_csv('us_boys.csv')
ugirl <- read_csv('us_girls.csv')
boy_names <- data.frame(England_Wales_Boy = eboy[1:20, 'Name'],
England_Wales_Boy = eboy[1:20, 'Count'],
... |
e2a34dd71d71f3d0f59fb160fb2647f8a404f1cc | cda850c653292f955410e904cc81af7f646e040f | /cachematrix.R | b3cb9d52efde4094670be52c17dca9b84c37768a | [] | no_license | ReProgrammatic/ProgrammingAssignment2 | 1c92edfe81c56d2d19604d8343ac365be690f9a8 | 6cf9cde5cd4d6601e20f69477868fe882a37fba4 | refs/heads/master | 2020-12-25T03:20:27.138802 | 2016-02-13T16:50:55 | 2016-02-13T16:50:55 | 51,652,153 | 0 | 0 | null | 2016-02-13T15:43:36 | 2016-02-13T15:43:35 | null | UTF-8 | R | false | false | 1,395 | r | cachematrix.R | # cachematrix.R: A set of functions that caches the inverse when working with
# matrices. Assignment for the third week of the Coursera/JHU R Programming class.
## A function that creates a list that caches the inverse of a matrix for use
## with the cacheSolve function.
makeCacheMatrix <- function(x = matrix()) {
... |
1575b1e2943897c070e8465427d6e564331dcc4e | 07bd88725a753783fe892df3bee11c511be6b7f3 | /man/bst.sel.Rd | 0da816e3996c41c30b226a1ed0499beb96e054f5 | [] | no_license | cran/bst | aaf796323637dea5bdf4efa35cf3b88c2be4dbc4 | 53dbd049b094eee6de2c02c00969900f01da88d2 | refs/heads/master | 2023-01-12T09:01:02.160478 | 2023-01-06T17:50:56 | 2023-01-06T17:50:56 | 17,694,897 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,609 | rd | bst.sel.Rd | \name{bst.sel}
\alias{bst.sel}
\title{Function to select number of predictors}
\description{Function to determine the first q predictors in the boosting path, or perform (10-fold) cross-validation and determine the optimal set of parameters}
\usage{
bst.sel(x, y, q, type=c("firstq", "cv"), ...)
}
\arguments{
\item{x... |
d9c9ee13eacd28c8f9e0f41c4e70b8674fafa18f | 7e195a7670b2fffae511a16e85cd5e430d111e92 | /Simulations/No Early Harm Scenario/low_eefun_NEH.R | fc256a49ae892ad9c96c25de72d2084480139a1d | [] | no_license | bblette1/SACE_for_PSEM | 5c07b4ea7d9f64be2b7d365ad452abcdbb6f5773 | 2e13ef5bd7362776a9d1d859a58ac2f06c3b718a | refs/heads/master | 2020-04-20T22:57:35.534561 | 2019-02-22T17:05:28 | 2019-02-22T17:05:28 | 169,156,027 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,110 | r | low_eefun_NEH.R | # Estimating equation vector for ignorance interval lower bound under NEH
# Used for M estimation variance calculation
# Full-cohort estimating equations
low_eefun <- function(data, beta0range, beta1range, beta2range, beta3range) {
function(theta) {
with(data, c( (1-Y_tau)*(1-Z)*(Y-theta[1]), ... |
b44ed5460a6616c92123f10374035470481b03db | 1d8f5112758327e42ca8507f851326133cb10055 | /src/CleanMatches.R | 851d712530ef95ad902307ca27f2c974bd3a7067 | [] | no_license | EduardoClark/Football-Homicides | 0a7234dfffabd9b229712be43bbfc770808becec | 47c036d94805346a6fe80714370e6579fed2e26b | refs/heads/master | 2021-01-01T05:48:16.845297 | 2013-12-09T19:53:41 | 2013-12-09T19:53:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,521 | r | CleanMatches.R | ###################################
### Author:Eduardo Clark
### Project: Homicides and Fútbol
### Date: September 2013
### For mediotiempo.com
###################################
#### Clean Match Data and create final dataframe with all matches
### Merge those with long month name
Partial <- rbind(F2009, F2010, F201... |
da620fe9c96b1ac4f3419a1218cb1a4115de71e6 | 138849c08c1f6fa0fc9f6fdd23d54caf2e184ce3 | /R/schedule.R | 392fec2eecb8cbca77a56461c30b036f46fecf85 | [
"MIT"
] | permissive | jameslairdsmith/gs | 01c0e8951e5ab1e3c5f54681eef9a9d029fd314c | 146ec9e72a632bc13c70c6abe1e329bfe2d14b34 | refs/heads/master | 2023-07-20T10:25:07.519470 | 2023-07-09T08:34:16 | 2023-07-09T08:34:16 | 156,515,663 | 10 | 0 | NOASSERTION | 2019-09-30T17:32:45 | 2018-11-07T08:39:52 | R | UTF-8 | R | false | false | 4,268 | r | schedule.R | #' Get the events of a schedule
#'
#' Get the event dates or datetimes from a schedule.
#'
#' @param x A schedule object.
#' @param from,to,during The limits to place on the output (see *Details*).
#' @param period_type The period type of the output. Eg. "day" (the default),
#' "hour" etc. Can be any unit accepted by ... |
599afe262f89721138c5425a9f2f29df5bb16b35 | 3caefccdc44a00da4eac6ec4ec4c39fa26bc2280 | /plot1.R | 4bf2d7c794659ec25b73f2a7f3cbbd42c5f0fe9d | [] | no_license | hawk-eye/ExData_Plotting1 | 5550ebd1b29ae9cc553e786d294c8b1f7eb84d27 | 5c14aaffe87c453aba42e8ded7c562f82b202fbe | refs/heads/master | 2021-01-16T21:15:42.993613 | 2015-09-13T23:30:04 | 2015-09-13T23:30:04 | 42,415,513 | 0 | 0 | null | 2015-09-13T22:34:00 | 2015-09-13T22:34:00 | null | UTF-8 | R | false | false | 725 | r | plot1.R | # set wd to git repo folder
setwd(".")
colnames <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
colclasses <- c("character", "character", "character", "character", "character", "character", "character", "character"... |
e8d8d175a31425968b855eebda7e586200a4c509 | 4da1b4f9a30d813fdfc68cc6a1a3387da187b432 | /function/dof/htmlpdfr/fs_funceval_groupwider.R | 58fc2ac759e29557d2f275fcd0d2d2239d5dd75b | [
"MIT"
] | permissive | FanWangEcon/R4Econ | 50b21f0f53fd73722ab49f3383102df001415fda | 7c7c4af32721817b2bc18944cfa17ecd8fb1f848 | refs/heads/master | 2023-04-03T18:41:31.038769 | 2023-03-23T05:06:53 | 2023-03-23T05:06:53 | 173,583,807 | 19 | 14 | null | null | null | null | UTF-8 | R | false | false | 2,782 | r | fs_funceval_groupwider.R | ## ----global_options, include = FALSE------------------------------------------------------------------------------------------------
try(source("../../.Rprofile"))
## -----------------------------------------------------------------------------------------------------------------------------------
# Define
it_M <- ... |
16ca6d6bb940398a821de414e8f40cc768836459 | b0eee00386b0b20ddac18152e8a6d91d08f69cdf | /margins.R | 27ad85914324bd5acb1b113c3f1d5f975e578837 | [] | no_license | btskinner/rmargins | 04f4ccec30a0b73d366e2666696185a34338b4eb | 912b4242f76e62acbdc213ebaa06f24811f395ee | refs/heads/master | 2020-08-30T08:12:19.456101 | 2019-10-29T15:40:59 | 2019-10-29T15:40:59 | 218,315,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,193 | r | margins.R |
## Gist to do margins in R -----------------------------------------------------
##
## Run your regression model and then use the predict() function with new data,
## which is just a synthetic data set that you make from the model.matrix() of
## of the regression (the X matrix in matrix notation).
##
## You create a n... |
1723d65172fadf31794d6f6fc405381b2aa5f56f | 68f3a836df8c2af90f8e9870b9ce236df4dc8b88 | /DAL1/Maylath - DAL1.R | e604e461fb59677daee81d0cda33f26ad43f2c44 | [] | no_license | mmaylath/Maylath-POSC-3410 | ec15825c2b62feea1b3093db6b241e77bf5360e4 | 1e8de09806feb6af49798b157ae740e689281493 | refs/heads/main | 2023-04-13T15:50:59.880506 | 2021-04-21T03:55:34 | 2021-04-21T03:55:34 | 329,317,453 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,248 | r | Maylath - DAL1.R | # Title: DAL 1 ####
# Author: Madeleine Maylath
# Date: 1/13/2021
# Lesson 1 ####
# My favorite movie is Interstellar.
# Lesson 2: R Basics ####
# Let's Try Some Basic Arithmetic ####
# Sum of 1 and 1
1+1
# Divide 365 by 12
365/12
# Your turn, multiply 10 by 12
10*12
# Your turn, add 28 + 38
28+38
# Order of Op... |
0871163516035f89b0bf4ce65b37cade9dd94b79 | c03754bd79a6f9baf6bd55e74483bed1a2dbe8a7 | /R/LAMMPSWriter.R | 981c6796fcf1cbe462c12e0727f2c2459f35831f | [] | no_license | julienide/Atoms | 675ee51c5803bef9868838b1a229dc9d037bce9b | d805bd893a152bd4edc350a2fb18de0ed2d19a3c | refs/heads/master | 2021-04-27T06:57:27.989087 | 2018-06-19T09:09:53 | 2018-06-19T09:09:53 | 122,623,126 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,007 | r | LAMMPSWriter.R | #' writeLAMMPS
#'
#'
#' @name writeLAMMPS
#' @export
writeLAMMPS <- function(x, file, type)
UseMethod("writeLAMMPS")
#' @rdname writeLAMMPS
#' @export
writeLAMMPS.Atoms <- function(x, file, type = "full"){
LAMMPSDataTypes <- c(
"angle", "atomic", "body", "bond",
"charge", "dipole", "dpd", "electron",
"... |
1ef1c512ef55b376b8a97712b7360b0aa74259f0 | abdcca10c0dbc45eeb312abe7d8de03792ebb96d | /ggplot2/ggplot2.R | 944354c53ece1d03eb07236db8c1dd043829b322 | [] | no_license | alescion/02-DataVis-5Ways | ad4b9109388e935b1f9dddfe21a3fc5fc3f97dc8 | d2b773e21d11cf745aa5fcbd09c33f56beadd090 | refs/heads/main | 2023-03-01T23:07:59.673770 | 2021-02-13T02:29:59 | 2021-02-13T02:29:59 | 338,480,827 | 0 | 0 | null | 2021-02-13T02:28:31 | 2021-02-13T02:28:31 | null | UTF-8 | R | false | false | 249 | r | ggplot2.R | library(ggplot2)
# Run in the directory of this script
cars <- read.csv(file="../cars-sample.csv",head=TRUE,sep=",")
p <- ggplot(data=cars, mapping=aes(x=Weight, y=MPG, color=Manufacturer)) +
geom_point(aes(size=Weight), alpha=0.5)
print(p) |
82c5ca552e574c5760c8bab00f8cdc832c070510 | c03d282847ddbcd8da22158e2b3fe8e85ca6f5fc | /cm_analysis/criminal_analysis.R | 5191c24b8e3b7194e44026d457892950679e0dc9 | [] | no_license | morganbooker/criminal_minds_analysis | bc7a3d6a920463bb19b121806176580d8e682d5a | 985bfea359829f0686262750331586987b9cea4f | refs/heads/master | 2020-09-14T23:26:45.755222 | 2019-12-13T22:37:14 | 2019-12-13T22:37:14 | 223,291,782 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,451 | r | criminal_analysis.R |
#### LIBRARIES ####
# Load in necessary libraries
library(fs)
library(tidyverse)
#### DATA FRAMES #####
# Load in data frames
cm_words_bau <- cm_words %>%
filter(word %in% bau) %>%
mutate(word = case_when(
word == "spencer" | word == "spence" | word == "reid" ~ "Spencer Reid",
word == "derek" | word... |
a3ecdabfa266b09e067acfe12275543257f48b6a | a845452e3eb0a62a930b472bba9784e3ab8c34f8 | /man/chancer.twitter.Rd | 62c773cba0224be9a873aa53a8fd741f348c0856 | [] | no_license | martineastwood/chancer | 79206f2c7cc7f5c32fb50aac0559c478ec67840d | 67ec091b7e9586caa95cc0adc16acf6bdcded82d | refs/heads/master | 2016-09-05T12:51:49.915751 | 2014-12-11T20:22:23 | 2014-12-11T20:22:23 | 27,889,911 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 240 | rd | chancer.twitter.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{chancer.twitter}
\alias{chancer.twitter}
\title{Get a random twitter handle}
\usage{
chancer.twitter()
}
\description{
Get a random twitter handle
}
\examples{
chancer.twitter()
}
|
8b65cbc1700f100f934b672761a97ae6f15c69f7 | 3e049d608e79fe049d24a20a00da8d09d214bae8 | /plot6.R | ea61b7451b07f9497e90174feaf53800ee82ce2c | [] | no_license | awgroeneveld/ExplDataAnalysisProject2 | ea4b7785cb177b388701cd2a6389bf4617b11074 | 4d66057d3a8a565985f6f9434a7dffb3c765b1d2 | refs/heads/master | 2021-01-20T04:37:05.546105 | 2017-04-28T13:08:43 | 2017-04-28T13:08:43 | 89,707,028 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,922 | r | plot6.R | # Assume data files are in working dir
# Since loading takes a while, check if items are already in environment
library(ggplot2)
if(!exists("NEI")){
NEI <- readRDS("summarySCC_PM25.rds")
}
if(!exists("SCC")){
SCC <- readRDS("Source_Classification_Code.rds")
}
# Create subset for Coal
SCC_vehicle <- subset(SCC, ... |
3a813757d8609a91c9753e7cd543684ee6359aa8 | 367d1e3344a5e792b3c10c29276dd3aed58e92b1 | /Labs/Lab04B/app_scatterplot.R | 86e8732eccac0a749b019df90815b7acc6998194 | [] | no_license | Bkwon23/First-Repository | 015b8ab90613d410b2e25ba3fa2d8bd7a84f8d92 | 710b801c734e10608e69587f3eb41424bfab1206 | refs/heads/master | 2023-04-19T12:25:25.173537 | 2021-05-14T04:48:29 | 2021-05-14T04:48:29 | 339,508,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,684 | r | app_scatterplot.R | library(shiny)
library(shinythemes)
library(tidyverse)
library(DT)
library(ggrepel)
library(fivethirtyeight)
###############
# import data #
###############
nflsuspensions1 <- fivethirtyeight::nfl_suspensions %>%
group_by(year) %>%
count(year)
###################################################
# define choice val... |
57064d48705eb2ce3d2a37dbfc370da49d4b306c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/OIdata/examples/sp500.Rd.R | e2b1434d87a04ee150110aea4194e83c4a8e3e79 | [] | 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 | 386 | r | sp500.Rd.R | library(OIdata)
### Name: sp500
### Title: Financial information for 50 S&P 500 companies
### Aliases: sp500
### Keywords: datasets SP 500 stocks financial money
### ** Examples
data(sp500)
plot(sp500$ent_value, sp500$earn_before)
plot(sp500$ev_over_rev, sp500$forward_pe)
plot(sp500$ent_value, sp500$earn_before, ... |
fe2581f1315a2b03f7e6db7f489cf62e58f3f580 | 1aa413301db92dd918278f7d309260b0130f8cd8 | /R/get_text.R | c85c188ba2a309fef8ec47e1f9c1ed27ce51c738 | [] | no_license | philgee1981/btcecho | 19467886cb335ddd3a6f28e67ac3edf883d979ab | 37b01871ecb72da58e11643394c89428b9c8adf9 | refs/heads/master | 2022-01-10T01:23:24.811566 | 2019-05-27T15:50:27 | 2019-05-27T15:50:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 318 | r | get_text.R | get_text <-
function(data_line,sentiment,texte)
gsub("ZZ",as.numeric(data_line$percent_change_24h),
gsub("YY",abs(as.numeric(data_line$percent_change_24h)),
gsub("XX",data_line$links,
sample(as.character(texte$description[texte$sentiment==sentiment]),1, replace = T))))
|
2b9e674d7c9dc928801717bd54fc6fdc033409da | 5143de6915d642fe4bde933fc821b6f60d401df3 | /app.r | b19a8494edf9371af7da0ac1ce50931430018def | [] | no_license | evilla-19/shiny_annotation_times | 8e762d14b828d09bc075258ff1fff8b62cd8fc88 | ebe5bc68d2131165a012d841435d8845ab3e7529 | refs/heads/master | 2020-03-22T21:59:06.534281 | 2018-11-01T07:33:56 | 2018-11-01T07:33:56 | 140,726,336 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,783 | r | app.r | ########################################
########## libraries ###################
########################################
library(shiny)
library(ggplot2)
library(dplyr)
library(DT)
library(chron)
library(plotly)
########################################
########## read in data ################
######################... |
7226cd7fade03afd3df9b1c7d0a290b369252d31 | 08bd5a7e355670ae7677e7676dc216e0a14c2a5a | /PASCAL/write_GWAS_metadata_non_neurological.R | 0a285e8b93285ad34b253570d931acbf5f4135db | [] | no_license | zmx21/GSK_NI | 1b79f1933e245a1deab902eebf83370f8c6af4ea | 3e89f15e4d906d704193346fa0ea6b914e89a881 | refs/heads/master | 2020-03-18T08:09:21.607329 | 2018-09-02T03:15:19 | 2018-09-02T03:15:19 | 134,493,420 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,588 | r | write_GWAS_metadata_non_neurological.R | library(data.table)
library(dplyr)
# source('annotate_clusters.R')
GetGWASMetdataNonNeuro <- function(sigClusters=NULL,clusterLevel=T){
GWASMetadata <- read.csv('/local/data/public/zmx21/zmx21_private/GSK/GWAS/GWASMetadata_NonNeuro.csv',stringsAsFactors = F)
if(!clusterLevel){
#Get significant genes included... |
2ee831f0d6efd5db0e89a0ab161781637f49c297 | d30d0e8bd889a4c24e81af6c1a974d046857a2e5 | /Final Project/model_code.R | a6c07fbaabd3b5edc44be39f1ae9cc51a2ac284d | [] | no_license | jmerranko/infsci_2725 | a9f75354d7bf5f7ad6cc97e11900ac9f1a2513db | 7f29c901fa4cec9b97e3c4aef5795986e1b528ba | refs/heads/master | 2021-01-10T09:54:00.006511 | 2016-04-18T19:32:55 | 2016-04-18T19:32:55 | 50,963,572 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,329 | r | model_code.R | library(foreign)
library(ROCR)
library(e1071)
library(randomForest)
library(party)
library(FSelector)
library(rpart)
library(gbm)
set.seed(1)
model=read.csv("C:/Users/John/Desktop/Project/final_predictors_no_numbers.csv")
final_test=read.csv("C:/Users/John/Desktop/Project/final_test_no_numbers.csv")
model$brand_match... |
d90ec32e2f57670cd83fd3f15e3c1036fa7f6945 | 49c918bc3e90a2eb8ec00d687d723cd0a8c9d008 | /man/fit_dynamic_model.Rd | 4dd5a1c72043c190b706997cf5b752cb7d99cc74 | [
"MIT"
] | permissive | sbfnk/measles.katanga | 06feeb9d47e6ac829ff5c2f7b90560342fd21aea | 5b1a1eee5c1217edb15f071647e90bb0a4680674 | refs/heads/master | 2020-06-25T19:44:45.559197 | 2019-11-08T15:21:39 | 2019-11-08T15:21:39 | 199,405,257 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,207 | rd | fit_dynamic_model.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fit_dynamic_model.r
\name{fit_dynamic_model}
\alias{fit_dynamic_model}
\title{Fit a dynamic model to the measles outbreak in Katanga}
\usage{
fit_dynamic_model(model, year = 2015, end = 5, start_threshold = 10,
vaccine_delay = 0, nbdata, pa... |
1f544eddd2a1760483755fe41090d0e6de42976f | ef3c1289fd1bc261481967bfc6600dc60f47d1c0 | /Code.R | fca0df513ee008628cc892b73192febfc7e69399 | [] | no_license | lpabbisetty/Thesis-Project | 1d56c3c656534789179ee8a6bd6d6ae5883d5fc8 | 7a4dffeff2e7bd597839fdf28e0460a58dfc4725 | refs/heads/master | 2021-01-25T04:22:43.694832 | 2017-06-14T01:08:03 | 2017-06-14T01:08:03 | 93,436,491 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 477 | r | Code.R | ---
title: "Thesis Project Code"
output:
html_document:
keep_md: yes
---
#trimming and mapping
#```{r}
# fastqc
#trimmomatic
#fastqc
#kallisto
#mv BrapaV2.1PacBio.cds.fa.ft.removed.updated BrapaV2.1PacBio.cds.fa
#kallisto index -i BrapaV2.1PacBio.cds.kai BrapaV2.1PacBio.cds.fa
#kallisto quant --single --plai... |
3c59ae699590e2baed9d9d1ee5b188814b114b26 | 0b13ffc2398a6c3359f071ce086e2cb893113d27 | /man/align_taxa.Rd | c44404b18253d07c8baba7a13ff0633be2e5c641 | [] | no_license | snubian/apcnames | b5beca7f24bf8409dd73bac70f622afff5d4dd4a | b169266255397530821bbece9c63d36837eb3cc6 | refs/heads/master | 2022-12-07T00:25:30.481445 | 2020-09-01T23:58:39 | 2020-09-01T23:58:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 584 | rd | align_taxa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/clean_names.R
\name{align_taxa}
\alias{align_taxa}
\title{Checks all taxa within against our list of known species
If not found, and update=TRUE, checks the unknown species against}
\usage{
align_taxa(
original_name,
output = "taxonomic_u... |
df4735fefac9bd6aa585488d326afaefb00b7b5b | f8f9f7f90086c118810020513ded3153388627c4 | /EstudoCaso1.R | 21bd7447371f32c800ef1a38aefa7759cb921f93 | [] | no_license | reginaborges/Analise_risco_cartao | e56d37787b0f94b217842c2895e15ab65ac8c8b4 | d3a9eef2221f3d6b7f7231b5fec4d2f638cdc090 | refs/heads/main | 2023-08-17T02:06:12.278549 | 2021-09-21T12:29:08 | 2021-09-21T12:29:08 | 408,810,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,479 | r | EstudoCaso1.R | # Estudo de Caso 1 - Análise de Dados em Operadoras de Cartão de Crédito
# Define pasta de trabalho
setwd("~/Dropbox/DSA/AnaliseEstatisticaI/Modulo02/EstudoCaso")
# Instala e carrega os pacotes
install.packages("mlbench")
install.packages("caret")
install.packages("e1071")
library(mlbench)
library(caret)
library(e1... |
164e638c34634f9caa42b68f4c69dee6c45367cf | 6ce02ecff727693de3b59a73fdf5b21e84d1af56 | /190911/day5.R | 8cc11cde371e9134fdb5b1e96ea74f3bc40f8ac4 | [] | no_license | jinpy7016/TIL | a487a513d1c5e4a146a1dc85bd20216696e3221b | 576b64b344e0d572dbb5cf4d0648027c8563308d | refs/heads/master | 2022-12-11T13:42:21.217212 | 2019-12-05T08:19:41 | 2019-12-05T08:19:41 | 188,196,885 | 0 | 0 | null | 2022-12-08T07:26:58 | 2019-05-23T08:52:15 | Python | UTF-8 | R | false | false | 13,376 | r | day5.R | install.packages("reshape2")
library(reshape2)
#melt
head(airquality)
names(airquality) <- tolower(names(airquality))
head(airquality)
melt_test <- melt(airquality)
head(melt_test)
tail(melt_test)
melt_test2 <- melt(airquality, id.vars = c("month","wind"),measure.vars = "ozone")
head(melt_test2)
#cast
names(airqualit... |
3ed75b15dd3e942fcbee03c1dcc303e28bec9775 | 87d46af5a5ffb1129f093e54950f01122abcdfe9 | /R/r-zlinear.R | af771aec84c0b18dc44f05f87afdf972901cc5f9 | [
"Apache-2.0"
] | permissive | rtlemos/rcrandom | 532ed70b6e92e4bc603ca65bdbfdd95c42bb0ee6 | d4c0319d5fe46791f22f26b24ab29c28865ca274 | refs/heads/master | 2021-01-21T14:07:46.584400 | 2016-07-05T03:03:57 | 2016-07-05T03:03:57 | 56,735,954 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,043 | r | r-zlinear.R | #' ZLinear
#'
#' Build piecewise linear log-probability density functions.
#'
#' This Reference Class is analogous to ZQuadratic.
#' Objects of this class provide approximations to the input
#' log-p.d.f. that are slightly more numerically stable,
#' but probably worse, than those of ZQuadratic.
#' To mitigate the wors... |
880a832ced2ad634930f96355034f5e209778fe4 | ca85aca58c75b824f5f7668673a4e9958e3be049 | /man/digform.Rd | f67b7d63957acc48138f3439363d495ac31d239d | [] | no_license | jmjablons/icager | 1a2fd0586f73682b7f4dd3578e5c2a50af46638e | 7dddccef96b892ccf3750a8af348f2f547d46937 | refs/heads/master | 2022-12-03T14:54:28.876604 | 2020-08-17T13:13:52 | 2020-08-17T13:13:52 | 228,870,393 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 631 | rd | digform.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main.R
\name{digform}
\alias{digform}
\title{Provide Formatting Standards On Call}
\usage{
digform(version = NULL)
}
\arguments{
\item{version}{Character or integer. Version of software:
\describe{
\item{#1}{"new", "star", "newer", 1}
\item{#... |
4a215cf0aa1e001b3d15ce178a47e6178052ef84 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/photobiology/examples/getTfrType.Rd.R | c7ca1afb3930e3899ce53bd02aa999ff8ac46fc6 | [] | 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 | 158 | r | getTfrType.Rd.R | library(photobiology)
### Name: getTfrType
### Title: Get the "Tfr.type" attribute
### Aliases: getTfrType
### ** Examples
getTfrType(polyester.spct)
|
1512f1083d0545753a3ef7281e111b2f69158d2a | 3b661bdf871a79038cbd2eda3842a19aada9d266 | /homework/Lab3.R | 07aa10bd52716e365ea0441d9bba0d22994efe9d | [] | no_license | HaoliangZheng/Statistical-Computing | 3fd3460c74311ac2828a9aec10fab4ac7dc0b76a | 1d8634d5b02f1e69a2a0b89d098ca19f86d9b49d | refs/heads/master | 2020-04-27T10:58:27.570861 | 2019-03-13T12:08:46 | 2019-03-13T12:08:46 | 174,277,942 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,701 | r | Lab3.R | ##apply(X, MARGIN, FUN, ...)
ary=array(1:27,c(3,3,3));ary
apply(ary,1,sum)
apply(ary,c(1,2),sum)
dat=data.frame(a=1:10,b=1:10);dat
apply(dat,1,sum)
#对数组、数据框，按行或列进行循环计算
##tapply(X, INDEX, FUN = NULL, ..., simplify = TRUE)
##(table,分组计算)
x=1:10;x
set.seed(2)
t=round(runif(10,0,2));t
tapply(x,t,sum)
#以向量t为索引,把数据集X进行分组,再... |
c769360da2efa02cab5976ebdeb56c54d042fef5 | 8ef27de17d0110828d77ca91b4f4e71af73fc12f | /R/plotDistributionAssay.R | dfaa6bcba52fac9e831c38d5ead4aa4dea8eb8df | [] | no_license | marcpaga/pulsedSilac | 95537ce75dc65a9573186708b2917ac700c7cbe6 | 23e5e48083b5edfc99c5dbc42bef487610bec5af | refs/heads/master | 2020-05-17T09:29:16.236700 | 2020-03-07T12:58:50 | 2020-03-07T12:58:50 | 183,634,007 | 2 | 0 | null | 2019-12-05T09:54:02 | 2019-04-26T13:31:31 | R | UTF-8 | R | false | false | 5,275 | r | plotDistributionAssay.R | #' @rdname plotDistributionAssay
#' @name plotDistributionAssay
#' @title Distribution of assay data per condition and timepoint.
#'
#' @description Plot the distribution of the data stored in an assay using
#' boxplots or density distributions.
#'
#' @param x A \code{SilacProteinExperiment}, \code{SilacPeptideExperime... |
226a815ee3027bc9bc88628e0f0866927706b549 | 9021558019864ccb907fc7ce25432563f932bbd3 | /R/bnnSurvivalBaseLearner.R | a39e673effd7e92acac20beae63207286c0b52b8 | [] | no_license | mnwright/bnnSurvival | 5cd58925995ce3e2d55c249f392575bc93828c94 | 694072bc55d210dde9e3cccf03e1fe7a29dfd0e3 | refs/heads/master | 2021-01-10T06:06:34.396035 | 2017-07-26T20:02:25 | 2017-07-26T20:02:25 | 52,605,495 | 1 | 1 | null | 2016-02-29T12:59:40 | 2016-02-26T13:15:46 | R | UTF-8 | R | false | false | 3,014 | r | bnnSurvivalBaseLearner.R |
setClass("bnnSurvivalBaseLearner",
representation(
bootstrap_sample = "integer",
feature_space = "integer")
)
## Constructor: Randomly generate bootstrap sample and feature space
bnnSurvivalBaseLearner <- function(num_samples, num_features, num_features_per_base_learner,
... |
bb6000e316f03b230bbcd887f8d257d3f1f1100e | d048d1c7b18ee45baac7af24d1d63fb6f4aa94f4 | /R/checkbehaviour.R | da401e78b7cce3580a4c5558745ef1533309e3b8 | [] | no_license | gobbios/socialindices | 723eacb83b0d631e6e5c188d905edaf3e9c05511 | 511f282ef35bdbb253273f9fc0fbaed3ff66f909 | refs/heads/master | 2023-02-18T02:53:23.729892 | 2023-02-04T10:37:43 | 2023-02-04T10:37:43 | 96,256,278 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 966 | r | checkbehaviour.R | #' data checks
#'
#' check interaction data for inconsistencies
#'
#' @param b.source data frame with behavioural data
#'
#' @return NULL
#' @export
#'
#' @examples
#' # nothing yet
checkbehaviour <- function(b.source) {
x <- b.source
temp <- varname("IDs", x)
if(length(temp)==2) {
res <- which(as.character(... |
ba3b53ebaa1374c762430bf2a89fb74b225beacc | 358c57a5a40607272997d8428390fb1a4b11b815 | /R/fastvarImp.R | 15764b7a23b6bdca9e6d43a68e404578fe73aa98 | [] | no_license | nicolas-robette/moreparty | 6ae430db14c807b78077807b2fe6d8cc419d52b3 | ee447af5def57b964e523e108020b40e048b0df7 | refs/heads/master | 2023-08-10T18:41:29.628127 | 2023-07-20T22:28:49 | 2023-07-20T22:28:49 | 214,511,417 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,795 | r | fastvarImp.R | #' @import party measures varImp
#' @importFrom stats complete.cases
#' @export
fastvarImp = function(object, mincriterion = 0, conditional = FALSE, threshold = 0.2,
nperm = 1, OOB = TRUE, pre1.0_0 = conditional, measure = "multiclass.Brier",
parallel = TRUE, ...) {
# Some test... |
685e19b8d96bf0b9633367a604d5ef84bcbae26d | 8b0dee9d51374e8bced0f0fd8efa8b6f0c14c9d7 | /man/pullstrength.Rd | eeeb1f691f6d041f5734cbd7d7a328d602ad6b79 | [] | no_license | rwoldford/qqtest | 4b9c595ea4c8f7e9ee6f1947e5f94e20c72be0a0 | f3737db73bfd00e36067d394d749a7232c3f3bb9 | refs/heads/master | 2021-02-11T16:26:56.146877 | 2020-03-16T15:49:28 | 2020-03-16T15:49:28 | 244,509,892 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,589 | rd | pullstrength.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pullstrength.R
\docType{data}
\name{pullstrength}
\alias{pullstrength}
\title{Strength of pull for 519 males aged 23-26.}
\format{A data frame with 7 rows and 4 variates:
\describe{
\item{strength}{Pull strength lower bound in pounds.}
\i... |
e61fa485f4ee3733506839560cc2b8ccd2158062 | cb20852e1607f6145194199bcca82b040090edd4 | /code/var_gamma/join_files.R | 03b8898ed30da1fb0f5f2d83d0fe72a523e372ed | [
"MIT"
] | permissive | yangchuhua/gtex-gwas-analysis | 7f2f60950ae362ffd1dc8734804dfe36d230cef6 | 300aa123dd769ab175fbf7bb9b26c9e3e4439fa0 | refs/heads/master | 2023-07-04T16:50:41.747961 | 2021-08-06T17:35:09 | 2021-08-06T17:35:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 389 | r | join_files.R | library(dplyr)
library(stringr)
rows <- vector(mode="list", length=0)
dir <- "var_gammas"
for (file in list.files(dir)) {
df <- read.table(file.path(dir, file), header=TRUE)
tissue <- str_match(pattern="(.*)_var_gamma.txt", string=file)[2]
df$tissue <- tissue
rows <- c(rows, list(df))
}
df <- bind_rows(rows... |
345332ec2b7bb1258ddfb9b51cae2369e2140bc5 | fdab0c18eab28477d0980723c5ac5b4ba10c506f | /veryoldpictures/shocks/low/span2/1yr/RUNME.r | 3d319d3f97d8bbbd717c9223a953e8bf2ae1fc32 | [
"MIT"
] | permissive | MIT-Informatics/PreservationSimulation | 58b53595841c39e1fe00a05241be43ed0bcf6430 | 38c6641a25108022ce8f225a352f566ad007b0f3 | refs/heads/master | 2021-08-25T10:35:46.066554 | 2021-08-24T20:17:13 | 2021-08-24T20:17:13 | 17,369,426 | 9 | 0 | NOASSERTION | 2021-03-20T02:55:37 | 2014-03-03T15:03:30 | R | UTF-8 | R | false | false | 143 | r | RUNME.r | # where to run?
# setwd("C:/cygwin64/home/landau/working/PreservationSimulation/shocks/low/span2/1yr")
source("./GetShockLowData_1yr_span2.r")
|
c855dad24708277529b6554aaffaaa91b008a6f8 | 722b1b22e1c2cf99d79d23dbe1181c103236b3b2 | /Scripts/plotmaker.R | b2968127e058f899042ae1e909d21c7d3a01ba71 | [] | no_license | GFJudd33/UNHRCprocedures | 4bd15c1a2410f64cf0a08386499260965b885859 | 8af19f845c4460535476cf579fa3b3eba71825ae | refs/heads/master | 2020-12-24T16:07:33.194168 | 2016-03-10T13:49:28 | 2016-03-10T13:49:28 | 40,205,597 | 0 | 0 | null | 2015-08-04T19:57:55 | 2015-08-04T19:45:42 | null | UTF-8 | R | false | false | 1,508 | r | plotmaker.R | ################################################################################
###
### Plot generator for UNHRC engagement.
### Created: 9/23/2015
### Last Modified: 9/23/2015
### Author: B C Smith
###
### Script for user-friendly plot generation.
###
### Inputs:
### COMPARE = A vector o... |
8ac3a72b228e5916194a39aa6d2094d7ad6ce859 | 1764b413b0650aa8c9c7a9382bf1f93e26df9eda | /benchmark/benchmark_real_kddcup_final.R | a27a335d89fc11f7a9f7f202fc968467aeb72e03 | [
"MIT"
] | permissive | pombredanne/bloomstream | f0eb3d265d2c5c742a4726edf5977dc3d07dd131 | 9195e3dea4bc7321a9dbf7a968d9c66285e38f7a | refs/heads/master | 2020-12-06T10:48:36.656806 | 2019-01-08T11:50:32 | 2019-01-08T11:50:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,187 | r | benchmark_real_kddcup_final.R | library("stream")
library("streamMOA")
library(hcstream)
source("DSC_HCStream.R")
source("benchmark_extract_utils.R")
source("benchmark_comparison_utils_final.R")
source("benchmark_algs_utils_final.R")
source("benchmark_plot_utils.R")
# KDDCUP99 NETWORK INTRUSION DETECTION DATASET
# 24 attack types + non attack
# con... |
0ac0c8f717066a11dc0b3259c8ed436fe4a526b9 | 0fb039e61656457c70a7466e73f1324ce898013c | /pdf_text.R | c157c67c75f0fdbf68d69b10de1504a04c8a7a8d | [] | no_license | vijayrajmeena/pdf-text-via-tiff | 3636c89f290a43fe65e35390bd9f468565cf23d2 | 12d4ce326a805a7a63613b741e07899f48a45a50 | refs/heads/master | 2021-01-01T18:51:45.564339 | 2017-07-26T18:20:44 | 2017-07-26T18:20:44 | 98,452,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 791 | r | pdf_text.R | library(pdftools)
library(tiff)
library(tesseract)
dest <- "C:/Users/XXXX/YYY/ZZZ"
# make a vector of PDF file names
myfiles <- list.files(path = dest, pattern = ".pdf", full.names = TRUE)
sapply(myfiles, FUN = function(i){
file.rename(from = i, to = paste0(dirname(i), "\\", gsub(" ", "", basename(i))))
})
lapp... |
9720a108b0f30b2d89108f7fa5b6b9b0f893461f | c2f1df344efb95a137b6449a9ec7221d654afcd8 | /iris.r | d164d244c3b571b48411732660ad007b29b600ec | [] | no_license | gtg7784/R_svm | 091b629fe5a9644c9e6e5e2abe516bfa1a0a3037 | 964a75d5ce1cf6d43152374e2a3c6708457c3156 | refs/heads/master | 2022-06-25T12:51:49.944035 | 2020-05-02T17:18:43 | 2020-05-02T17:18:43 | 253,235,690 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,539 | r | iris.r | # SVM : Iris Prediction
install.packages("e1071")
library(e1071)
set.seed(123)
x <- matrix(rnorm(20*2),ncol=2) # Normal distribution
y <- c(rep(-1,10),rep(1,10)) # Scatter
x[y==1, ] <-x[y==1, ] +1
plot(x, col=(3-y)) #
dataset <- data.frame(x=x,y=as.factor(y)) # Binary Classification as.factor(y)
str(dataset)
svm_fit... |
0e814c624b3b4271842a32c1256e05773f441593 | 717ba891c2624890e482e48abcde055ed87a830c | /variantComparisons.R | aeea9c5d673f3e6fff1592909865bc75015bd86e | [] | no_license | yob1997/testing_repo | 14713c0c562c1ee803d5581a60e0d796e3b68d96 | 1f47f0d7dc51b12581466380ae382cce09b01b2d | refs/heads/master | 2020-11-25T20:03:16.150406 | 2019-12-18T13:31:25 | 2019-12-18T13:31:25 | 228,823,648 | 0 | 0 | null | 2019-12-18T13:31:27 | 2019-12-18T11:14:28 | Shell | UTF-8 | R | false | false | 9,301 | r | variantComparisons.R | # - Comparison of all samples against the T=0h sample;
# o make a table which lists the number of differential variants found for each of those samples,
# and also include the corresponding experimental metadata (experiment type like DCTK, SCTK; time point of sample; replicate ID, strain ID)
# o an R list object fo... |
1782cdf5342a33435de2a57852c430619973b1f1 | 590bcfd7d763bcde72a2dd7b114c72d830138af1 | /finalvisualizations/shinyanimation.R | 85ce473e697295934639ff68623d38ee70267a24 | [] | no_license | stelmacm/budwormvisualizationStats744 | d76a8edea81e8956902e8c32fc9449faed077463 | a19d72fc4a30d670c592bb3c9d7e47832306796b | refs/heads/master | 2020-09-10T17:18:17.409876 | 2020-01-02T03:36:53 | 2020-01-02T03:36:53 | 221,774,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,538 | r | shinyanimation.R | library(plotly)
library(ggplot2)
library(tidyverse)
library(gapminder)
library(gganimate)
library(shiny)
library(grid)
library(colorspace)
#Set up palette and import and data and factor
cbPalette <- rev(sequential_hcl(7, 'BurgYl'))
newdata <- readr::read_csv("https://raw.githubusercontent.com/stelmacm/budwormvisualiz... |
878f154c1914ed03e20c675a6767ca64dde54c6e | 6c584706e6eab645e11357bde8f393013c69e4c9 | /Linguagem de Programação Estatística/Aula 1/exercicio1.r | bed4eb834b7b792660cbba02657dd97b77d154c2 | [] | no_license | charlesartbr/fiap-mba-big-data-data-science | cce1b64c301187a049cd9929d5fafd7e6985503e | de4d8372a7ce26ac8e4556925416e5c9e1932020 | refs/heads/master | 2022-09-05T00:33:21.367281 | 2022-08-09T14:01:28 | 2022-08-09T14:01:28 | 185,289,505 | 0 | 1 | null | 2021-01-15T16:47:00 | 2019-05-07T00:10:35 | Jupyter Notebook | UTF-8 | R | false | false | 467 | r | exercicio1.r | # Execício 1
aa <- ((sqrt(16) / 2) * 3 ^ 2) / 2 * (9 - 2 ^ 3)
aa
# Execício 2
bb <- -(-2 ^ 3) + ((-1) ^ 0) - sqrt(25 - 3 ^ 2) - (5 ^ 3) / 25
bb
# Execício 3
aa > bb
# Execício 4
a = c(1, 2, 3)
# Execício 5
a + 1
# Execício 6
a / 2
# Execício 7
a * 2
# Execício 8
b <- c(4, 5, 6)
# Execí... |
60d114a7747d5362f07cbd94a1d0c01b845cb549 | b2180e2c3ceb047c1103f042fff2af2d54146815 | /man/SAENET.predict.Rd | dfc1cc906dc271e8b5c6c8b7d023bd3edcc2d1f2 | [] | no_license | cran/SAENET | 323c57888760b6f91731fab0d0ea69cfab2634e5 | f37c03c673dceea8052dcea5e961fa768974a966 | refs/heads/master | 2020-05-07T12:39:06.840609 | 2015-06-04T00:00:00 | 2015-06-04T00:00:00 | 30,255,903 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,813 | rd | SAENET.predict.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/SAENET.R
\name{SAENET.predict}
\alias{SAENET.predict}
\title{Obtain the compressed representation of new data for specified layers from a stacked autoencoder.}
\usage{
SAENET.predict(h, new.data, layers = c(1), all.layers = FALS... |
07bf80bd59a269fea90faa81d55b09424156b3b5 | f81ac43a1d02013a9cb9eebc2a7d92da4cae9169 | /R/values2labels.R | a5cc2d316e03ee94109b6cf2d0f0926f65c8c13a | [] | no_license | gdemin/expss | 67d7df59bd4dad2287f49403741840598e01f4a6 | 668d7bace676b555cb34d5e0d633fad516c0f19b | refs/heads/master | 2023-08-31T03:27:40.220828 | 2023-07-16T21:41:53 | 2023-07-16T21:41:53 | 31,271,628 | 83 | 15 | null | 2022-11-02T18:53:17 | 2015-02-24T17:16:42 | R | UTF-8 | R | false | false | 1,576 | r | values2labels.R | #' Replace vector/matrix/data.frame/list values with corresponding value labels.
#'
#' \code{values2labels} replaces vector/matrix/data.frame/list values with
#' corresponding value labels. If there are no labels for some values they are
#' converted to characters in most cases. If there are no labels at all for
#' va... |
54bbbcc0eec5a7864f915d673b89810f03fdfde9 | 29585dff702209dd446c0ab52ceea046c58e384e | /gutenbergr/tests/testthat/test-metadata.R | 946909b186565754919d6de86d0f967bcb3e077b | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 706 | r | test-metadata.R | context("Gutenberg metadata")
test_that("gutenberg_works does appropriate filtering by default", {
w <- gutenberg_works()
expect_true(all(w$language == "en"))
expect_false(any(grepl("Copyright", w$rights)))
expect_gt(nrow(w), 40000)
})
test_that("gutenberg_works takes filtering conditions", {
w2 <- gutenb... |
168c3272de3e2d2dfe1393d2b032bf1b8bf29eed | e953c138d3808d92fcc9848824985be5bc42f034 | /r/ifnotnull/2.r | bcb42da5ccf4c5ed557f13e04534f5cdf941fc4f | [] | no_license | hotoku/samples | 1cf3f7006ae8ba9bae3a52113cdce6d1e1d32c5a | ce0d95d87e08386d9eb83d7983bd2eaff0682793 | refs/heads/main | 2023-08-09T09:05:15.185012 | 2023-08-04T09:29:06 | 2023-08-04T09:29:06 | 222,609,036 | 0 | 0 | null | 2022-03-30T01:44:03 | 2019-11-19T04:35:27 | Jupyter Notebook | UTF-8 | R | false | false | 810 | r | 2.r | if_not_null <- function(val, default=NULL){
if(!is.null(val)){
val
} else {
default
}
}
is.rstudio <- function(){
commandArgs()[1] == "RStudio"
}
mystop <- function(msg){
stop(msg)
}
if_not_null(1) %>%
if_not_null(l1$val) # => 1
if_not_null(1) %>%
if_not_null(stop("val is not set")) # => 1
i... |
ea57475281e5c6826685ab10f909be268d3b64e5 | 20c8677824f667184fb4c3c1eb83ed97fe205121 | /Plot1.R | 1c56752f6f74a83ee66bbc64afff2ba5b6b87211 | [] | no_license | acsabarreto/ExData_Plotting1 | 22cba7a0a6d4fd3add8ced73eecff09f2908882b | 92f4ac451c00a6dee64f58b1f4437626257d2786 | refs/heads/master | 2020-12-30T14:01:15.221203 | 2017-05-15T00:30:05 | 2017-05-15T00:30:05 | 91,279,853 | 0 | 0 | null | 2017-05-15T00:22:35 | 2017-05-15T00:22:35 | null | UTF-8 | R | false | false | 470 | r | Plot1.R | holding <- read.table("household_power_consumption.txt", header = TRUE, skip = 66636, nrows = 2881, sep = ";")
colnames(holding) <- c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
holding$Date <- strptime(holding$Date, "%d/%m... |
8eb03768706c6e2d674de455637a5d50181c3994 | 1d7b8d97be6d3b3aed26bc19ea6855bbdb2d21bc | /man/estimate-Diffusion-method.Rd | e050bcda9f4e97eb8466c34bb4e79acbad280284 | [] | no_license | cran/BaPreStoPro | 18633df8e18b518225e7c01147473684d9369a46 | f3e8f06b07ec4b4ca0be9de3d481734d5e154c31 | refs/heads/master | 2021-01-19T01:11:31.691784 | 2016-06-07T14:28:11 | 2016-06-07T14:28:11 | 60,611,461 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,464 | rd | estimate-Diffusion-method.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/estimate.R
\docType{methods}
\name{estimate,Diffusion-method}
\alias{estimate,Diffusion-method}
\title{Estimation for diffusion process}
\usage{
\S4method{estimate}{Diffusion}(model.class, t, data, nMCMC, propSd,
adapt = TRUE, propo... |
ea8446bc7670a4a23d4f738dd2c4652397600535 | c3d47a7fd6b256fa25d7a801d6403ed098436e27 | /cachematrix.R | e3919db8e571444a25ec74e233f9933fa9fda909 | [] | no_license | jiyanez/ProgrammingAssignment2 | 5946c92c93bf3597f089e6a2e457f391e9db65fd | d93411a74a61f01a4b5f9a59d27b147d7df4686e | refs/heads/master | 2022-11-15T08:51:39.200122 | 2020-07-06T11:05:34 | 2020-07-06T11:05:34 | 277,514,307 | 0 | 0 | null | 2020-07-06T10:41:11 | 2020-07-06T10:41:10 | null | UTF-8 | R | false | false | 1,294 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## makeCacheMatrix sets a list of 4 functions. The first one sets the matrix,
## the second one recovers the input matrix, the third one caches the inverse,
## and the last one recovers the cached inverse
## Note that if you give a mat... |
585592576f56b64d47bd112fdf9dfff40558397e | e754c3be8ed820b03964fcd0d9717315c3451061 | /man/performance_distribution.Rd | d124c3ed67b198d26c8507fc53c86c47034936e8 | [] | no_license | lejarx/Rnumerai | 73b6791a1cc0ab0655c2fd9c97f3683d4e12f291 | 9bb22342c4ceb1c3b47a311b91967e6062f58bc1 | refs/heads/master | 2022-04-20T12:35:04.516834 | 2020-04-17T19:12:49 | 2020-04-17T19:12:49 | 258,499,341 | 1 | 0 | null | 2020-04-24T11:56:29 | 2020-04-24T11:56:28 | null | UTF-8 | R | false | true | 635 | rd | performance_distribution.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/source.R
\name{performance_distribution}
\alias{performance_distribution}
\title{Get the performance of the user as a distribution}
\usage{
performance_distribution(
username,
metric,
merge = FALSE,
round_aggregate = TRUE
)
}
\argumen... |
74e9fa70b75f989e3ee9f65465a556481c29db0f | 16db8326cd3a6ef625f82e21719c5a3b01b4f0bd | /project one week 2/project one/Population, Samples, and Estimates Exercises.R | c72f48b0c0db4134b8aa24685986adff3b48cc3d | [] | no_license | diegomali/Statistics-and-R | 8d27091b52bf0b0743d3c4b5daa2938bdf392d2b | 86c8b00baf6434a277a8a9daf481cceba315bd72 | refs/heads/master | 2020-06-22T13:06:58.481172 | 2016-11-30T20:06:07 | 2016-11-30T20:06:07 | 74,591,707 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,981 | r | Population, Samples, and Estimates Exercises.R | library(downloader)
url <- "https://raw.githubusercontent.com/genomicsclass/dagdata/master/inst/extdata/mice_pheno.csv"
filename <- basename(url)
download(url, destfile=filename)
dat <- read.csv(filename)
##We will remove the lines that contain missing values:
dat <- na.omit( dat )
library(dplyr)
controlMales <- fi... |
3a1b3767e05c0bbf7f596bbf0b0cd38c35ff93fb | d73d1fffc2c69ed18638262380186db28ef129c8 | /R/pdfinalsignal.R | f0af336e1ad16d9eb035ec5b79bb00b9f8e99442 | [] | no_license | estalenberg/ptrm | 7f48a545e679fcefcddaf7009c8f7304e21883bf | 262755ead3ee8b6e0900775134ac401e799ddc4c | refs/heads/master | 2020-04-30T12:26:35.587724 | 2019-07-31T09:22:56 | 2019-07-31T09:22:56 | 176,826,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,957 | r | pdfinalsignal.R | #' Final dynamic price signal
#'
#' Formula for calculating the final dynamic price signal over hours and years
#'
#' @param yearslabel label of projection years from 2020
#' @param pd_ev peak demand adjusted for offgrid, ee, solar, batteries and ev
#' @param signal.in dynamic variable of dynamic price signal
#'
#' @ex... |
4fb0e1d28f8d415d100859c8f7b7de63d6a22258 | b74674a867526ef01ad189adda2da14b8be4fa59 | /man/filter_mentions.Rd | 109521f62c20768a242941179fb4b848e13d093d | [
"MIT"
] | permissive | brandseye/brandseyer2 | d33a84c7194b397068793da6a305154addf73aa2 | 59fab98f11b9b05cbe5cac6409b2453374b7267e | refs/heads/master | 2021-09-15T17:17:38.686089 | 2021-09-01T19:04:48 | 2021-09-01T19:04:48 | 132,138,395 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,482 | rd | filter_mentions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filter_language.R
\name{filter_mentions}
\alias{filter_mentions}
\title{Filter for only specific mentions}
\usage{
filter_mentions(.account, filter)
}
\arguments{
\item{.account}{An account or \code{\link[=query]{query()}} object.}
\item{fil... |
83cf941a3af6834c4d7d3e905bf3d6cfb8623a7b | d308f49b3ad7e7b9a63c2e88386b3d6c64bd0959 | /homework/lab02.R | 3609f9746de28fd0a64a62517c00fdbcd0a750b9 | [] | no_license | jihunsuk/DS_lecture | f982aacb4b5e3ce7b41311aef66b90d973488812 | d50c829224443aef6fca75073dc4b75932c18a6f | refs/heads/master | 2020-03-25T19:45:49.732218 | 2018-08-09T04:10:32 | 2018-08-09T04:10:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 794 | r | lab02.R | data(iris)
newiris <- iris
newiris$Species <- NULL
kc <- kmeans(newiris, 3)
str(kc)
table(iris$Species , kc$cluster)
plot(newiris[,c("Sepal.Length", "Sepal.Width")], col = kc$cluster)
points(kc$centers[,c("Sepal.Length", "Sepal.Width")], col = 1:3, pch = 8, cex = 2)
library(cluster)
dissE <- daisy(newiris)
sk <-... |
9fdb32821e669e2a91028b76ac44787f57fe7078 | 4440ab544b02ff806e7386494bb959f633c05cfb | /tests/testthat.R | 2076dd575845e5fe32e3678ad6ac5adf020e45f8 | [] | no_license | AndBooth/deconcat | da2208f809de910852db2e1dc1676508ae4c9010 | 32b8d30ce73193f593c0508ee3dc4cf1f2d8c0e9 | refs/heads/master | 2020-03-10T14:10:44.947833 | 2018-04-13T15:20:18 | 2018-04-13T15:20:18 | 129,419,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(deconcat)
test_check("deconcat")
|
244cd05549947227cb7b132075f951ca5e1b6d54 | 91f90bb3b4df7dafe932f9d1ed1466938957d90e | /R/slsPkgs.R | d712949cf832060f1f93095687c35ade01249490 | [] | no_license | nemochina2008/paper_kilimanjaro_scintillometer | 1c7447d5135923bf2088c9b4b89404edbfb26f8b | 9d90e42345c95a4129c9976fb27aa0e7eafad49c | refs/heads/master | 2021-05-16T17:52:53.016234 | 2017-08-22T06:48:01 | 2017-08-22T06:48:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 393 | r | slsPkgs.R | lib <- c("doParallel", "caret", "lubridate", "plotrix", "plyr", "dplyr",
"reshape2", "scales", "ggplot2", "latticeExtra", "gridExtra", "grid",
"TSdist", "Rsenal", "Orcs", "rgeos", "rasterVis",
"rgdal", "MODIS", "zoo", "raster", "kza", "stargazer")
jnk <- sapply(lib, function(i) {
suppres... |
b6b954c689c6cbcb3835248b26beac6fe7b4aff9 | 045c744b365954f244104044e6c539b3a4b31584 | /getDATRAS.R | 13c64e93e332663e6abc06380443ff2c50194fe4 | [] | no_license | CrystalTu/DATRASscript | cfaf7029e0d50b26195362cf94bbdd6f229c6f37 | a4a9aa0cdf61f3fc37932c1c1ff2aa04acac7ac7 | refs/heads/master | 2021-01-10T17:38:45.711559 | 2015-10-08T11:04:35 | 2015-10-08T11:04:35 | 43,882,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,013 | r | getDATRAS.R | #' getDATRASfun.R
#' Extracts age data files from DATRAS
#' @param record, survey, startyear, endyear, startquarter, endquarter, parallel = FALSE, cores = NULL
#' @return none
#' @seealso none
#' @details the update is slow, avoiding straining the server or client.
#' please allow this call to run overnight for a co... |
e677c2c4251ff8e868f44b975721da64dcb45c83 | 2f3c391660eb3c5da973eabb83bd1a645efb330d | /Efficiency_LY_20200306.r | c2dbe2344b633ae2c547935b527e1db418881ad4 | [] | no_license | ly129/MiCM2020 | 0a8d82b64042d95ebee38f770d3739ad15cfa76b | 1e3c721fb31156cdf5d3c1b1f97062d3734fcf1d | refs/heads/master | 2021-01-06T02:08:25.833551 | 2020-03-07T19:56:33 | 2020-03-07T19:56:33 | 241,197,307 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 11,825 | r | Efficiency_LY_20200306.r | # double
class(5); is.double(5)
# integer
class(5L); is.double(5L)
# How precise is double precision?
options(digits = 22) # show more digits in output
print(1/3)
options(digits = 7) # back to the default
object.size(rep(5, 10))
object.size(rep(5L, 10))
# logical
class(TRUE); class(F)
# character
class("TRUE")
# ... |
bce0c4c16c2cb27351b118e2e77476043fb030aa | 1757fb5db6b90cfb4a09e73cf1178fdbe851bf3d | /train_bs_par.R | a69ed97a504401d8568862380078299e7f9a0e8b | [] | no_license | gvschweinitz/BLS_19_Does-Machine-Learning-Help-Us-Predict-Banking-Crises | b03a2ea494926033fed547d1b0e5a11f80696e8f | 578a204b6f4fade8bf05793535607b68a463ea3a | refs/heads/master | 2023-01-21T10:03:37.193918 | 2020-12-01T09:28:38 | 2020-12-01T09:28:38 | 313,629,287 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,785 | r | train_bs_par.R | train_bs_par <- function(data.db.train,data.db.test,parameters,methodz,do.bootstrap=TRUE,R.BS=1000,blocklength=12,sample.countries = FALSE,min_cr = 5){
# ------------------------------------
# CALL
# train_bs(data.db.train,data.db.test,parameters,methodz,R.BS,blocklength)
# train_bs(data.db.train,data.db.te... |
277bce6f74fcfd2d772e4b19b12efbe09ea9afa7 | e1289344fb044f39fa637744fe32e1f1cd12771e | /User_Info | ebb407f47084a49e9e0ca1c6cb1e80a32de48bc9 | [] | no_license | sarachronister/BioSense_RODBC | 6575618c1e1c2755dce7bb9b12e81ea262d8160d | c31c72680395c6e410a0ea7d658ab797760aac86 | refs/heads/master | 2020-05-07T19:37:28.344391 | 2019-04-12T23:12:08 | 2019-04-12T23:12:08 | 180,821,258 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 312 | User_Info | # Replace the *********** with your BioSense username and password in the appropriate lines
# Update when you need to change your password
# Save to your home directory "~/"
password <- dput(charToRaw("***********"))
UserID <- dput(charToRaw("***********"))
username <- dput(charToRaw("BIOSENSE\\***********"))
| |
7c60ddf388ee21b70958904ea7a7d726a5633780 | 291681834ef34a96b587ecc3c5ba46dd9661019c | /1-simple-deep-net.R | 39088b1d604120f9a7550bb39f92e14a7fb358f9 | [] | no_license | Vassar-COGS-319/lab-8-f20-jodeleeuw | 5a84303feb2d6e478e58d03f2f507373b0e269e6 | 478174e4e97c8872ae8993a29018767489540d2c | refs/heads/main | 2023-01-08T20:37:37.667736 | 2020-11-02T21:33:00 | 2020-11-02T21:33:00 | 309,488,011 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,233 | r | 1-simple-deep-net.R | Sys.setenv('CUDA_VISIBLE_DEVICES'="0")
# The aim of this lab exercise is to show you how to construct and train a deep
# neural network using modern machine learning software packages.
# We are going to use tensorflow and keras.
# tensorflow is a machine learning library developed by Google. It is open source,
# wid... |
fec30acc5404526ff6a4190841bfb726f58f7812 | 9fae6e827ec9da28342f4fbc495f817706c7042a | /.Rprofile | 96eb73b697e2c56dda0e8f45ad85114cb56aff2b | [] | no_license | marionhr/Spatial-Extreme-Value-Modelling-of-Sea-Level-Data-in-the-Oslo-Fjord | 5d8feb64079c8708f6b9680679fc336257a3c549 | 85d019616689a07f71438481d751cd50f37e54b2 | refs/heads/master | 2023-06-17T05:45:27.344478 | 2021-07-17T14:55:30 | 2021-07-17T14:55:30 | 382,840,051 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 113 | rprofile | .Rprofile | .First <- function(){
myPaths <- .libPaths()
myPaths <- c("C:/Users/mario/Git/R",myPaths)
.libPaths(myPaths)
} |
aaf92aeca93e0efedcb6512b74127a9341e4b3c0 | 7062d1f3dea3e1481e00062e3aa11f9d9db3ced7 | /run_analysis.R | 4b264f47db020d88fdf4b92346c956d503e5b785 | [] | no_license | MarcoVH/Getting-Project | f09f479e46c4f2a332d142ae71910f7df4685ba1 | dfd8a653b5a2e68b6edff802e930145989a1b168 | refs/heads/master | 2021-01-10T16:19:08.795995 | 2015-10-25T14:52:35 | 2015-10-25T14:52:35 | 44,799,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,447 | r | run_analysis.R | #Set your working directory to where the "UCI HAR Dataset" folder is, as in:
#setwd("C:/Users/Marco/Documents/Data Science Specialization/Getting and Cleaning Data/Project")
# Package check -----------------------------------------------------------
if (!require("data.table")) {
install.packages("data.table")
}
req... |
7fc087521580ed2f5f7b832d087bb45aa348f34a | eee62499791ec04e22614ccc2a46f4e2210da9f6 | /man/smoothfda.Rd | 06c9fe1164b9c50f0a1260cd4b9f3e552a992e54 | [] | no_license | dapr12/fda.classification | df0f9936744d84a945c6be13ebb467986bfbdd54 | 2c129c59f71bf645c88fc715f62699cf7458bebf | refs/heads/master | 2020-04-01T09:32:53.097672 | 2014-08-21T19:19:55 | 2014-08-21T19:19:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 847 | rd | smoothfda.Rd | % Generated by roxygen2 (4.0.1.99): do not edit by hand
\name{smoothfda}
\alias{smoothfda}
\title{Smoothfda - Computes the functional spatial median of the functional data}
\usage{
smoothfda(fdaobj, bandwidth, degree = 1)
}
\arguments{
\item{fdaobj}{An object of the FDA Class.}
\item{bandwitdh}{Bandwidth}
\item{degre... |
187631a66ebd2a9ca214a68be8a5c4c5fb04acbe | 8cc7a398b6616ee30692bef651d67fea406d546e | /Market Response.R | 8a49abf3c46c595f806053a2e437990a0cd385cc | [] | no_license | sinafakhar/Market-Response | 435ca5218c24c183782916bfeb5072b80c891078 | c0bf7290309d6e59dd375b57e6cba280b55deff0 | refs/heads/main | 2023-02-02T11:55:57.202269 | 2020-11-25T21:23:40 | 2020-11-25T21:23:40 | 313,301,914 | 0 | 1 | null | 2020-12-19T11:49:59 | 2020-11-16T12:54:30 | HTML | UTF-8 | R | false | false | 18,211 | r | Market Response.R | setwd("./Market Response/")
# getwd()
library(readxl)
library(ggplot2)
library(skimr)
library(DataExplorer)
library(dplyr)
library(Hmisc)
library(corrplot)
library(PerformanceAnalytics)
library(coefplot)
library(gpairs)
library(lmtest)
library(caret)
library(MLmetrics)
data = read_excel("Data.xls")
str(data)
sum(is.na(... |
8e99ae77bf14cee39642cd33e4a7bba598597609 | 8672c4a4e0b99cd479e93ead76b34b290ed6cbdd | /Markdown-Notebooks/predimred.R | 870e21d1d70d278ff1133117151b54384dd6f56a | [] | no_license | knudson1/dimred | 7f308f9a1bdb58687d3904dba959832b1f527493 | 93d3954450e391862a1ecd99518284e17fc882b1 | refs/heads/master | 2022-11-26T02:43:18.363686 | 2020-08-06T18:28:19 | 2020-08-06T18:28:19 | 281,415,534 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 30 | r | predimred.R | library(glmm)
data(salamander) |
a48fd1fbd906b4d9ea38b7f1b57ef58cddf4184a | 9fd5e51e5dd1b4541c5c1a6052530d801073a026 | /man/print.deseasonalize.Rd | fb371ff4031dcf7c5861538aeb5d397faa1b0efe | [] | no_license | cran/deseasonalize | 8d056778bd4909b61a1687ee7219e6fd537984e8 | fde22060bb555ec3a8ec8cfa3b5ffd0c85bd5022 | refs/heads/master | 2016-09-06T04:25:01.167970 | 2013-04-10T00:00:00 | 2013-04-10T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 511 | rd | print.deseasonalize.Rd | \name{print.deseasonalize}
\alias{print.deseasonalize}
\title{ Print Method for "deseasonalize" Object }
\description{
A terse summary is given.
}
\usage{
\method{print}{deseasonalize}(x, ...)
}
\arguments{
\item{x}{ object of class "deseasonalize" }
\item{...}{ optional arguments }
}
\value{
A terse ... |
92bd876ce66893ed5e42390569286d9a1e036924 | 56b6b269936453d4730e2f14759d42b1082849af | /R/column_or_string.R | a30f004ca9ce632bc1a07cf941fcece912ffd535 | [
"MIT"
] | permissive | AJFOWLER/adjham | c9b4e3aa902756ea8d3705e9bbd2f4244035404a | 12133bdf21db283a8fdfda465212cb6d0a7e3e41 | refs/heads/master | 2023-03-09T00:17:06.984643 | 2021-02-24T11:24:09 | 2021-02-24T11:24:09 | 340,010,746 | 0 | 0 | null | 2021-02-24T11:24:10 | 2021-02-18T10:16:18 | R | UTF-8 | R | false | false | 175 | r | column_or_string.R | #' Column or strings
#' @description This function will detemrine columns or pasted string; not necessary currently
#'
#'
#'
column_or_string <- function(){
return(TRUE)
}
|
9ea68ff286ee53ebda459d07fa7058ab938ff4d0 | 011ef657c4c1fd79cb4ac68d86a9d04e70056e6e | /R/import_json.R | 384d90a7325e48dcd130f88ee12beaa6f1fc4e23 | [] | no_license | cdcepi/predx | 8cf3d52c6a2c75a277f5de08b8f8793810c74ee3 | 82917511064bdaebaf9982eec98be2169b8d1288 | refs/heads/master | 2021-06-24T16:26:32.315548 | 2019-12-27T16:35:16 | 2019-12-27T16:35:16 | 174,559,039 | 6 | 3 | null | 2019-10-28T16:40:07 | 2019-03-08T15:13:15 | R | UTF-8 | R | false | false | 1,288 | r | import_json.R | #' Import a \code{predx}-formatted JSON file as a \code{predx} data frame
#'
#' @param json_file
#'
#' @return A \code{predx} data frame.
#' @export
#'
#' @examples
#' predx_demo <- as.predx_df(list(
#' location = c('Mercury', 'Venus', 'Earth'),
#' target = 'habitability',
#' predx = list(Binary(1e-4), Binary(1e-4),... |
41dd51f9d5fd417ba21f5d750c3fe005bf7c6276 | 138c2843cd7f8aff3bd326f669961260a2c77b8f | /R/clevr_dataset.R | 64106acc38817c74e79458d2ce3b0a67102e0588 | [] | no_license | leslie-arch/R-vqa | 30a27c39c0b562ddbcaf31cca85446bffcc6469b | 6bec4b883c40583326cbff64afc02932524834b3 | refs/heads/main | 2023-08-08T04:48:08.471098 | 2021-09-23T03:10:07 | 2021-09-23T03:10:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,875 | r | clevr_dataset.R |
source("mask_utils.R")
new_clevr_dataset <- function(name, cfg)
{
message('Creating dataset: ', name)
value <- list()
image_dir <- paste(cfg$DATASETS$DIR, cfg$DATASETS[[toupper(name)]][['IMAGE']], sep = '/')
anns_file <- paste(cfg$DATASETS$DIR, cfg$DATASETS[[toupper(name)]][['ANNS']], sep = '/')
class(va... |
e749985afc40aa16dfeb0ccdf0dd861f7d07ecb5 | 13e11079ba6fcd554a2f6015cd4e40204d1cb292 | /man/get_all_datasets.Rd | af17085a3cccd063101873edcf48165869cb9ad3 | [
"MIT"
] | permissive | hut34/hutr-package | cdae26631d0f4e640b7c22f1eff703692a47b8ad | 3519c7f5acc5fe2bdb12a96f42539542ecd11b32 | refs/heads/master | 2020-12-28T18:33:10.945324 | 2020-04-24T03:25:38 | 2020-04-24T03:25:38 | 238,440,955 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 582 | rd | get_all_datasets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_all_datasets.R
\name{get_all_datasets}
\alias{get_all_datasets}
\title{get_all_datasets}
\usage{
get_all_datasets()
}
\value{
The metadata of all uploaded or purchased data is returned as a dataframe.
}
\description{
\code{get_all_dataset... |
8800bec897b1f19890419893dbbff036726feefb | 2873487ba14d0f0658baa0aca0c804cbd53bd055 | /tests/testthat/helper-file.R | d42786caf0e8a5e3ebc496838e9f36a259de5458 | [] | no_license | colearendt/xlsx | ca930d09339f795d41427333369f95ef6e73538e | 4ed1d0127fdec1581808656b9c636b0fbd0ac4fd | refs/heads/main | 2022-07-11T14:21:46.066697 | 2022-01-30T09:30:59 | 2022-01-30T09:30:59 | 39,347,430 | 53 | 26 | null | 2022-06-29T18:42:07 | 2015-07-19T20:13:53 | R | UTF-8 | R | false | false | 447 | r | helper-file.R |
test_ref <- function(file) {
rprojroot::is_testthat$find_file(paste0('ref/',file))
}
test_tmp <- function(file) {
tmp_folder <- rprojroot::is_testthat$find_file('tmp')
if (!file.exists(tmp_folder))
dir.create(tmp_folder)
rprojroot::is_testthat$find_file(paste0('tmp/',file))
}
remove_tmp <- function() {
... |
6f3cd37a0bb2b62eac7667e2e41bf220e15f2b38 | 62095a14e8bb00d56d0ab904d67e092f40792cfd | /r-base-dev-ext/requirements.R | cd8b856f1f93bce58abc4a1507d98b5705dc8dca | [] | no_license | mikebirdgeneau/dockerfiles | 3b5ff40b7f947e5734bdf1755340b48d58d83ee4 | 4dc0c8167df6508463f00a4920a5502c42648746 | refs/heads/master | 2021-01-20T20:37:04.883579 | 2017-04-26T04:49:35 | 2017-04-26T04:49:35 | 65,513,473 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 313 | r | requirements.R | pkg_list <- c(
"roxygen2",
"data.table",
"ggplot2",
"gridExtra",
"lubridate",
"stringr",
"foreach",
"sp",
"gstat",
"rgdal",
"GA",
"R6",
"lintr",
"testthat",
"caret",
"forecast",
"shiny",
"bookdown",
"FinCal",
"documair",
"rmarkdown",
"mvtnorm",
"miniUI",
"pander")
|
a9c366cb4cf6b88df0ff5486d17fd678405cf50a | c18729fa6d0bf8b882588c4566e1125f0e3a0c4b | /R/simulate_ar1.R | 6eeaa284ad927b6f505015a685a32f5df6e02204 | [] | no_license | yiqunchen/SpikeInference | 6cf1b05777fb27bd673b73c80cb4e4923dc7c74d | f92ebb3686e1c831bec27a08df3083fb7dd0ea55 | refs/heads/main | 2023-07-08T11:17:03.983740 | 2021-07-29T01:38:25 | 2021-07-29T01:38:25 | 332,932,190 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,628 | r | simulate_ar1.R | #' Simulate fluorescence trace based on a simple AR-1 generative model
#'
#' @details
#' Simulate fluorescence trace based on a simple AR-1 generative model:
#' \deqn{y_t = c_t + \epsilon_t, \epsilon_t \sim N(0, \sigma^2),}
#' \deqn{c_t = gam * c_{t-1} + s_t,}
#' \deqn{s_t \sim Poisson(poisMean).}
#'
#' @param n Numeri... |
2202ca20e9d365eda774fc8a6a743e1d41abea76 | 0ea4a50d0feb866b1ed722ce7a29c867f1f44184 | /rprog-009/Projs/3/best.R | 6c342c5603746531aad79c69b5be78d058953a8e | [] | no_license | agarxiv/datasciencecoursera | 13d594ff5ff05451dff3d6259fd6781112a9e002 | 21a76db991512f8753729ee79d0281d2e38c71a8 | refs/heads/master | 2021-01-19T14:56:30.513623 | 2015-02-03T22:09:34 | 2015-02-03T22:09:34 | 26,572,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 882 | r | best.R | best <- function(state, outcome) {
## Read outcome data
## Check that state and outcome are valid
## Return hospital name in that state with lowest 30-day death
## rate
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
if(state %in% data$State){
data.state <- data[data$State==state,]... |
753d527bbe73e73cbed0df8966b586c27cda9484 | 1da61f5738750db2e5bbec4ac0562d5a09b6e7c7 | /R/admin-panel.R | d6c8d54add9dd1e2386aca5a2e60e41d3e560fe0 | [
"MIT"
] | permissive | fmhoeger/psychTestR | 03607ed434d8cec5164224b691ae58d2e2c1999b | 01d06ea2b3b37d5283c02bb81450118374385127 | refs/heads/master | 2020-12-14T03:34:36.205333 | 2020-10-04T21:50:54 | 2020-10-04T21:50:54 | 274,735,900 | 2 | 0 | NOASSERTION | 2020-10-04T21:50:55 | 2020-06-24T18:01:22 | R | UTF-8 | R | false | false | 19,221 | r | admin-panel.R | admin_panel.statistics.ui <- shinyBS::bsModal(
"admin_panel.statistics.ui",
title = "Statistics",
trigger = "admin_panel.statistics.open",
shiny::uiOutput("admin_panel.statistics.num_participants"),
shiny::uiOutput("admin_panel.statistics.average_time"),
shiny::uiOutput("admin_panel.statistics.latest_result... |
9a7633c7c6de3e363ae620a755ec7d66e047b1ac | a693cd98dd57799c39a48c3b2433d91dbc7d1217 | /R/invoicer_email.R | 67e7994710e92aed09322fa1c23d5bac6481bf9b | [] | no_license | anthonypileggi/invoicer | c9bf75904fa30f9f66ff098b794e6391e01c40ed | 5bfbf876a7bc03fb6e82055f00427e3d46f4dffb | refs/heads/master | 2020-03-29T06:34:17.966329 | 2018-11-12T14:22:06 | 2018-11-12T14:22:06 | 149,630,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,534 | r | invoicer_email.R | #' Send an invoice via email
#' @param x invoice object
#' @param id invoice id
#' @export
invoicer_email <- function(x, id) {
# get details for the invoice we are sending
if (is.null(x)) {
if (is.null(id))
return("You must provide either an invoice object (x) or an invoice id (id).")
# identify file... |
cd05720161704f9e1ae0356e39981a3863cb4779 | b90990e94b63b4a18eb6f3b0332bdae16f38591a | /code_to_run_AD_Github.R | d748043dc60f2838f0fc95c4a1bce595a653fcd8 | [] | no_license | Yuanzhi-H/Real-Time-Detection-Methods | 228c7a85c3140816a6ffd0cc996955eef020caa4 | 5cd5bf5910dc282e41a82efe67e328af346a11b1 | refs/heads/master | 2020-04-11T13:02:47.722425 | 2018-12-14T15:22:42 | 2018-12-14T15:22:42 | 161,801,860 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,230 | r | code_to_run_AD_Github.R | # Use the code below for the real-time simulation of the turning process
# Last edited on 20/11/2018
# Yuanzhi Huang
# Clear workspace and load in packages ------------------------------------
rm(list=ls())
require(ggplot2)
# Load data ---------------------------------------------------------------
DATBASE=readRDS(fi... |
be1481e001cda45d17c1d9489bfc2d50795bd387 | d064a4ee7b4f3d11afb5c089e55b77f2e6882692 | /man/Etna_boundary.Rd | 4f7ad8e17f4eb3f907f8b2d48e442ef605f01add | [] | no_license | cran/movecost | 9a35613d033990acf2104d98fff4a242dd7178ee | 2d1b2e6d39f569bbc4d20e0434f7ee1800344128 | refs/heads/master | 2023-04-13T16:03:42.612617 | 2023-04-12T08:20:07 | 2023-04-12T08:20:07 | 145,903,913 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 459 | rd | Etna_boundary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Etna_boundary.r
\docType{data}
\name{Etna_boundary}
\alias{Etna_boundary}
\title{Dataset: bounding polygon representing a study area on Mount Etna (Sicily, Italy)}
\format{
SpatialPolygonDataFrame
}
\usage{
data(Etna_boundary)
}
\description{... |
b9d45d6f5b0995b89eb6e6168279a6ac0ad815e4 | 452a067dc37b86e4370045f3b799cbaf17b7d7d5 | /man/print.mjca.rd | 4b0f5c1528cb488b8b1240def3f71ddc1f9c948b | [] | no_license | cran/ca | 3752d68a550b2531561f8cfac07e3190ee13ab49 | 00cc046a4c3158f017c0ddfc03fd9acb6d38d2b8 | refs/heads/master | 2021-01-22T07:13:55.357336 | 2020-01-24T05:59:53 | 2020-01-24T05:59:53 | 17,694,909 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,215 | rd | print.mjca.rd | \name{print.mjca}
\alias{print.mjca}
\title{Printing mjca objects}
\description{Printing method for multiple and joint correspondence analysis objects}
\usage{\method{print}{mjca}(x, ...) }
\arguments{
\item{x}{Multiple or joint correspondence analysis object returned by \code{\link{mjca}}}
\item{...}{Furthe... |
e13fd1edeec59a5a24c42a6e72c15cc0e55fa0f7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/macleish/examples/mass_gis.Rd.R | 49e022333b88aaea120ff577b1d4ffe6a3793111 | [] | 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 | 437 | r | mass_gis.Rd.R | library(macleish)
### Name: mass_gis
### Title: Retrieve elevation layers from MassGIS
### Aliases: mass_gis macleish_intersect
### ** Examples
## Not run:
##D # have to download the shapefiles...cound take a while...
##D elevation <- mass_gis()
##D macleish_elevation <- macleish_intersect(elevation)
##D if (requ... |
3e93442cdbbf8a4135534514207c1b5ceec672f4 | da5acef6a354bf77a53c0c538afc671f91c77abe | /cachematrix.R | 2d47e29e75587848dbdef9a0cc0812d1b33ab8a3 | [] | no_license | Matsolof/ProgrammingAssignment2 | 6a4403b4af250160b9bec476e02c5805550a689c | 43d95cd1185953f6fc5f93facc6c1b5f1230fd68 | refs/heads/master | 2021-01-21T07:53:18.217199 | 2014-12-17T12:48:08 | 2014-12-17T12:48:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,030 | r | cachematrix.R | ## With these two functions any invertible matrix may be
## inverted and the inverted result cashed
## This function (object?) creates an instance of a certain
## matrix and its inverse (by calling casheSolve)
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- ... |
4629da5a0ba9362d8d8bd6904038fef14d4f304f | 07e2aa9f2a3a3f1fb365254045705208906aa913 | /Week 6 R Challenge.R | 8b3fba6f4960aace33d0948393edd9a88008434e | [] | no_license | LSESUCODING/Bootcamps | 8e43f70fd9c984e56af215494375357895e147e8 | 19476ef724eb58be033fd313017ec08deb5c86df | refs/heads/master | 2020-08-02T09:03:27.279066 | 2020-03-01T10:02:08 | 2020-03-01T10:02:08 | 211,296,460 | 3 | 7 | null | null | null | null | UTF-8 | R | false | false | 162 | r | Week 6 R Challenge.R | install.packages("rgl")
library(rgl)
open3d() # create a new plot
spheres3d(x = 0, y = 0, z = 0, radius = 1) # unit sphere centered at origin
axes3d() # add axes
|
2a8da1345fc67517dbcfcc698938bfd29b6469b7 | f18f32ed6fc1a8d132fdc697210b3e78d9381893 | /src/ConvertModelPerfDataFrame.R | 363f69094e694dac90d0666fe9bfcba723c4b9e8 | [] | no_license | jimthompson5802/kaggle-OttoGroup | 43b98fd800101e80cf3bf601d86263a079ade68c | 1e14c6187f69af1511d4c0677a7274116bc6d74b | refs/heads/master | 2021-01-10T09:11:15.892890 | 2015-07-15T23:32:34 | 2015-07-15T23:32:34 | 36,255,130 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,042 | r | ConvertModelPerfDataFrame.R | ##
# code to convert to new model Performance data frame
#
WORK.DIR <- "./src/gbm2_model"
# convert current bestTune data to string
bestTune <- apply(modPerf.df[,9:35],1,function(x){as.list(x)})
bestTune <- sapply(bestTune,function(x){paste(names(x),as.character(x),sep="=",collapse=",")},
simplify=... |
e35f58b67203c4195996b12df60d5d13370e8d29 | 68d55860e526f82501a8db3b1afc5a869142feef | /PredApp/findWords.R | b41137ccbfa5d68ef89b9364362ad25acd03a1b7 | [] | no_license | diegommora/Capstone_Project | 3b4d9392e8b0cf8766ec0d36d6d0ca79d825a981 | 640222d174baadcf64496c8f16ee0a183f630e22 | refs/heads/master | 2020-07-02T14:11:15.540351 | 2019-08-09T23:21:57 | 2019-08-09T23:21:57 | 201,552,675 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 879 | r | findWords.R | findwordsQuad <- function(sentkn,quadgramFinal,trigramFinal,bigramFinal){
l <- length(sentkn)
predictquad <- quadgramFinal %>% filter(X1==sentkn[l-2] &
X2==sentkn[l-1] &
X3==sentkn[l])
predi... |
04a310df9bbcfdb78bfc0654ea1c51e55c018550 | 656dc61dba9cba909935d2e50cd6c23ee4f49977 | /RforML/Code/1. Data Types.R | 7115cbd610263085def4641569d9380b41cc6d08 | [] | no_license | akshafmulla/RProgrammingforML | e391696c70d9c3e11e0a03b8ae00f976e8c6b680 | 57666331b7a2bccf5ddb1d7e464ce25a6bc620a2 | refs/heads/master | 2020-04-25T18:50:30.152156 | 2019-02-27T22:46:49 | 2019-02-27T22:46:49 | 172,998,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,448 | r | 1. Data Types.R |
#shortcut to run Ctrl+enter
2+3
a = 2+3
a
## Assignment ##
x = 3
x
a3 = 3
x <- 3 # alt + - "<-"
x = 3
x = x+1
x
############################################################
## Data Types in R ##
############################################################
# Numeric - real and decimals
... |
0da0dba9792ae9effa4ed5a04d91cb430e9e9000 | 2cf8d3bfa83c0c36f29731c2a58f4f86ca88b1ff | /QuGene/Data-processing/var-files.r | a33b31624676c9acb15104c67c67c4b82fcee6fa | [] | no_license | Cork18/peas-andlove | 169b42b6015da424411e51d6dddbc218f772f9fd | f9a3ab1a49333e49dedbd715ae5c79baf10b1e21 | refs/heads/master | 2023-08-23T10:31:52.221291 | 2021-11-04T17:33:30 | 2021-11-04T17:33:30 | 400,336,812 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,384 | r | var-files.r | #########
VAR FILES
#########
###################
READING IN THE DATA
setwd("/Users/jennylin/files/McGill/Thesis/QUGENE/3run_output")
library("readr")
SWvar <- read_table("z_out2.var", col_names=TRUE, locale=default_locale(), skip=1,)
library("dplyr")
varsort <- SWvar %>% filter(Trait == "SW") %>% dplyr::select(2... |
cc9388991c75130ba3e6d04011bf096c4f90d870 | 1289c7c8bc395d751cc3fdc89041967e331785fd | /9.29/beta estimate.r | 6dfb620cbc961944e2dfa5e8bacb799e289c7ae0 | [] | no_license | Quincy15/Statistical-Software | fb67e35b46f89bc3cdb54a4eb2989ac3e0f1e98a | 5171371b797dd46694261adc78938cda9890b8ea | refs/heads/master | 2022-12-22T15:06:25.975713 | 2020-09-29T01:57:44 | 2020-09-29T01:57:44 | 296,006,467 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,409 | r | beta estimate.r | rm(list = ls())
#install.packages('Quandl')
#install.packages('quantmod')
library(zoo)
library(xts)
library(Quandl)
library(TTR)
library(quantmod)
#---------------------1.下载和清洗数据-------------------------------
Quandl.api_key("QGc4jxwf72tdN3h9BzSs")
#单只股票的数据/Microsoft Corporation (MSFT) Stock Prices
G1 <- Quandl('EOD/... |
1b354fd59fe196ed00f72bb5f1612a83129fede8 | 8d4b2f0ec29916d0e9c0b72aa6d3d37d6afa1dcd | /inst/slurm2/sim.R | ef9effb370212d266edf7e8f2d93c5bf8072a5a8 | [] | no_license | EpiModel/EpiABC | fbffee553b2d384990dba43c18fac790bb56f5bf | ad80ccb31734e24d42d282232796716afbd00383 | refs/heads/master | 2021-06-24T00:03:20.664401 | 2020-12-05T14:09:47 | 2020-12-05T14:09:47 | 164,778,789 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 418 | r | sim.R |
library("methods")
suppressMessages(library("EpiABC"))
suppressMessages(library("EpiModel"))
prep <- readRDS("data/abc.prep.rda")
batch <- as.numeric(Sys.getenv("SLURM_ARRAY_TASK_ID"))
wave <- as.numeric(Sys.getenv("wave"))
# Wave 0
if (wave == 0) {
abc_smc_wave(input = prep, wave = wave, batch = batch)
} else {
... |
d7e01a35699a90d477d730dec90893b705bf1659 | 5f323f30bd7641a4990d9e7f4890f474c785c2f0 | /R/resource.R | 42fed2c3a15f1ba4691e8df2d8b5337370793aff | [] | no_license | cadnza/pkgTestTools | 810023fedee454fc47e1bb3318c6fa2c06ecb6c3 | 34a75894d764bce34671846f6b5daaa9b9988a71 | refs/heads/master | 2023-06-26T07:45:47.854291 | 2021-08-02T00:31:09 | 2021-08-02T00:31:09 | 391,668,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 324 | r | resource.R | # Get function to load resource from inst ----
resource <- function(path){
root <- rprojroot::find_package_root_file()
instDir <- "inst"
pathInstalled <- file.path(root,path)
if(file.exists(pathInstalled)){
return(pathInstalled)
}else{
pathUninstalled <- file.path(root,instDir,path)
return(pathUninstalled)
... |
1e3286d58cb1203a560670de7c1dd31ecf31463b | dc82fe420c0433e256bc5245bba1793ade7e530c | /ui.R | 86ac6321bfb08b2a9ad660ccb797a07c19e040ec | [] | no_license | lytze/QPRD | 8582a61ef3fea5cede316a12a0d1d4c7836b187e | 3417173f8079ce2db88e0f8a5f886e1d84b57734 | refs/heads/master | 2020-07-24T17:12:18.702142 | 2019-09-12T10:16:19 | 2019-09-12T10:16:19 | 207,992,435 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,242 | r | ui.R | require(shiny)
require(shinythemes)
shinyUI(fluidPage(theme = shinytheme("united"),
tags$head(
tags$title('QPRD by Lytze'),
tags$link(rel = "stylesheet", href = "lib/font-awesome/css/font-awesome.min.css")
),
titlePanel(
h2("Quantile / Probability / Random Distributoin")
),
sideba... |
62dd1f1cc6745c1ff409a8085f1a8e8843169816 | 0b28447d54df04e8261b2149851ed0bcc6505566 | /man/plotMakefile.Rd | d4d7a2f13cd81125140edcb38018cb454ff68dd9 | [] | no_license | tdhock/plotMakefile | fc86557596eb56f26a0ed07601972631f9b80fcc | 5d1776847168490d9ac9d149c9baa33cb8e42b13 | refs/heads/master | 2021-01-19T00:44:35.194949 | 2014-02-21T14:21:05 | 2014-02-21T14:21:05 | 16,812,564 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,833 | rd | plotMakefile.Rd | \name{plotMakefile}
\alias{plotMakefile}
\title{plotMakefile}
\description{plot a Makefile using library(diagram).}
\usage{plotMakefile(makefile, sort.fun = sortDefault, colors = NULL,
categories = NULL, curve = 0, segment.from = 0.1, segment.to = 0.9,
box.type = "none", legend.x = "bottom", ...)}
\arg... |
400ee004c71e3fc1d1c227a7c1e7755c48777493 | 5916f0096fb77d11bfd9a69b607095d205a6f333 | /OTUtable-package/OTUtable/man/metadata.Rd | af5aa8f74e2602a3d0f3a760419b5c3b5dcf186a | [] | no_license | McMahonLab/North_Temperate_Lakes-Microbial_Observatory | 35123ec4ec8f591a7d9f864bc1f30a67a71e006e | 63543ee4cf7424125ac3064a5429b66855ccff73 | refs/heads/master | 2021-01-18T01:57:02.207024 | 2017-06-29T16:16:40 | 2017-06-29T16:16:40 | 47,776,636 | 2 | 2 | null | 2017-06-29T16:16:40 | 2015-12-10T17:21:50 | R | UTF-8 | R | false | false | 653 | rd | metadata.Rd | \name{metadata}
\alias{metadata}
\title{
Lake metadata for OTU table
}
\description{
A dataset containing temperature and oxygen profiles from the lakes in this study
}
\usage{
data(taxonomy)
}
\format{
A dataframe with 6 columns (measured variables) and 13,607 rows (depth profiles)
}
\details{
Missing data indicat... |
4f68392f9bdb99b7d712723a478d1c15877c61d1 | 92e240738a4ccf673b9f3610386eaa08eef26d6f | /volatility/low-vol/returns.R | cce3889a7641677003aef6679b87b90b14c9db0d | [] | no_license | stockviz/blog | 564a4671202b92a2d63f13f0207fd8a35810c0b6 | e00c055742a1229c612669ee29d846a6e2475a43 | refs/heads/master | 2023-09-01T15:59:07.746886 | 2023-08-31T04:01:37 | 2023-08-31T04:01:37 | 138,372,618 | 12 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,028 | r | returns.R | library('RODBC')
library('quantmod')
library('PerformanceAnalytics')
library('lubridate')
library('ggplot2')
library('ggthemes')
library('reshape2')
library('dplyr')
source("d:/stockviz/r/config.r")
source("D:/StockViz/public/blog/common/plot.common.R")
options(stringsAsFactors = FALSE)
options("scipen"=100)
reportP... |
ca9f9cd36146e3f423c501c80c0bc7d4bdd4db54 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/8229_1/rinput.R | a44be156df5b356bb9a4891470b3c37dc72e94c0 | [] | 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 | 135 | r | rinput.R | library(ape)
testtree <- read.tree("8229_1.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="8229_1_unrooted.txt") |
de0e8603f17d945c2655167a6b09617ca9ef73d2 | 469e730e10ba8e75ed7dcced6797a881400265ed | /bn_learn_prac_discrete.R | aa69aac9ff9f8f0435598a0d8ecb52ea9978910f | [] | no_license | msank00/bayesianNetworkLearning | d9d38b71bcf986cb999e6a466ef757952652790a | 9e9432af4bbf2ab6447c97f6dcd361ada863a38c | refs/heads/master | 2020-05-16T21:05:09.421188 | 2019-04-24T20:21:10 | 2019-04-24T20:21:10 | 183,296,737 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,190 | r | bn_learn_prac_discrete.R | # URL: http://www.bnlearn.com/examples/fit/
dir_name = "/home/sankarshan/Documents/code/bayesianLearningInR"
setwd(dir_name)
library(bnlearn)
data(learning.test)
# data generation.
LV3 = c("a", "b", "c")
a = sample(LV3, 5000, prob = rep(1/3, 3), replace = TRUE)
c = sample(LV3, 5000, prob = c(0.75, 0.2, 0.05), repla... |
6d9cba76f0e5490c9c5427c59c92cded24df144e | 3c37e20c65d3918fede609648fccc7e997527c47 | /R/noise.R | 2380d276b8ae32b5f62a49034b264b787ff13b6c | [] | no_license | cran/Rwave | 67cb1e7b8e234c1f49b1ac4cbadef024a04afc21 | 72a5fc18fdb0c4ae4cf6aa9985617268585ffae5 | refs/heads/master | 2022-11-04T20:48:09.750339 | 2022-10-21T22:17:49 | 2022-10-21T22:17:49 | 17,713,902 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,984 | r | noise.R | #########################################################################
# $Log: Noise.S,v $
# Revision 1.2 1995/04/05 18:56:55 bruno
# *** empty log message ***
#
# Revision 1.1 1995/04/02 01:04:16 bruno
# Initial revision
#
# (c) Copyright 1997
# by ... |
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