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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
08168ee5d8a90e949a2a7ead112b3cee05973924 | aaaed74b1a9ce40ddb025d41788852f207c51c81 | /00_nulldata/02_2nd_stab/s6/00_results.R | 9c1cf3a5b78f579c67072afa571e5996b9b9c08c | [] | no_license | NeuroStat/cluster-stability-m | 3fc31d71c2d91e2cbf486afe93e8d42a367b2b7c | 15ebe541c904edb16eccb3a164c6feb8708dfe15 | refs/heads/master | 2020-12-18T18:24:30.408260 | 2020-01-22T02:20:20 | 2020-01-22T02:20:20 | 235,483,509 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,686 | r | 00_results.R | # script to output only the final result table
input <- commandArgs(TRUE)
# functions
from_cluster_to_voxels <- function(nifti.file){
require(oro.nifti)
tmp <- readNIfTI(nifti.file)
raCl <- range(tmp)
TOT <- length(which(tmp!=0))
OUT <- matrix(NA, ncol=4, nrow=TOT)
strt <-1
for(i in seq(raCl[1]+1, raCl[2])){
s... |
d79e25eb78d73d39a7b686d488f5974fcff319d7 | c8762334dcf5c843168f8b3ea65e7fa6446eb027 | /man/profile_plot.Rd | 82d3f7030ab736d9c8e706a8a7cc9d29fabb74cc | [] | no_license | spgolden/EEAR | cd92b6ee6b16eee07a77b88c66d224b12592be2e | f4cfff1d7de58d9b0f0edba587a3621d594274eb | refs/heads/master | 2020-12-30T20:58:57.139664 | 2014-05-15T22:55:34 | 2014-05-15T22:55:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,338 | rd | profile_plot.Rd | \name{profile_plot}
\alias{profile_plot}
\alias{profile_prepare}
\title{Plot Usage Profile Lines}
\usage{
profile_plot(x, line = c("weekday", "workweek", "day", "month", "season",
"year"), facets = NULL, legend = TRUE, title = "",
xlab = "Time of Day", ylab = "kWh", plot = TRUE, ...)
profile_prepare(x, line, facet... |
0d1d815462f88c38860122a56b49db33f156edd7 | 6d42def2aa82e2b20b7467e02bfade4740200d18 | /Data_Analysis_Visualization/dir.R | 0f767d5c6e69a55eb2369b17a2fcc2f5f8a44850 | [] | no_license | Hz-Lin/wur_bioinfomatics | f7d88aea294f1e75d8c49d86352810d47bd133e3 | fe970d029e11a6d32f6e266ddc8f8399eb36c4e0 | refs/heads/master | 2021-09-10T17:48:36.601979 | 2018-03-30T11:33:35 | 2018-03-30T11:33:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 64 | r | dir.R | getwd()
setwd("/home/evelina/projects/github/wur_bioinfomatics") |
c3ffedf88ac2d9cd0cd9de91516749aead545536 | 907c7acdaa598d6bc1fee3843f660bd0afd62520 | /R/MTGS.mrce.R | 59c8d5cf4f0a034d2b39448f0b193167b204e011 | [] | no_license | cran/MTGS | 25bcabaf14cb0a2d19a3c874ae392a391c771561 | c3a0dcd95f93d0ec4a315c8884b20b2aa9310aca | refs/heads/master | 2020-12-22T01:26:32.510743 | 2019-10-16T10:50:09 | 2019-10-16T10:50:09 | 236,629,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 715 | r | MTGS.mrce.R | #####################
#library("MRCE")
MTGS.mrce<-function(X, Y, r){
requireNamespace("MRCE")
n<-nrow(X)
p<-ncol(X)
q<-ncol(Y)
m<-round(n*r)
for (k in 1:25){
tst<-sample(1:n,size=m,replace=FALSE)
XTRN<-X[-tst,] ; YTRN<-Y[-tst,]
XTST<-X[tst,] ; YTST<-Y[tst,]
fit=mrce(Y=Y... |
84e1605e18649183ce60c8cba060d118454d211a | 1a23d38841cfc47b1e0178a62b129298fddd00d9 | /plot3.r | a95664aaf7691890f5882318d50125c4e125f2b8 | [] | no_license | yeokc2369/exploratorydataanalysis2 | 8b8e2b26c53935d2a3c20dc251de34364ffac6f9 | 475de8ea3d458ec42c593bba5ee10d470ed89c8f | refs/heads/master | 2021-01-10T01:12:39.173183 | 2015-10-25T22:58:25 | 2015-10-25T22:58:25 | 44,934,054 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 999 | r | plot3.r | ## Question 3:
## Of the four types of sources indicated by the type (point, nonpoint, onroad, nonroad)
## variable, which of these four sources have seen decreases in emissions from 1999–2008
## for Baltimore City? Which have seen increases in emissions from 1999–2008?
## Use the ggplot2 plotting system to make a pl... |
0fad45ee4d3003eeeb7ed46244f54ab30f316c0d | 2a2b58ecc90d0cfc5843641dce103a68ff4910cf | /R/iteration.R | 6e2932562f34f5f667e7b72250d83dcb72bbb411 | [] | no_license | cran/deSolve | feac6b8d4adab866268ed36a903c024677f9127e | 560bba8ed670b12277ba3483bdd15db91ec12dc9 | refs/heads/master | 2023-07-07T18:40:26.379280 | 2023-07-01T15:00:02 | 2023-07-01T15:00:02 | 17,695,430 | 12 | 6 | null | null | null | null | UTF-8 | R | false | false | 3,523 | r | iteration.R | ### ============================================================================
### Interface to C code for Euler's ODE solver
### with fixed step size and without interpolation, see helpfile for details.
### ============================================================================
iteration <- function(y, times, ... |
23d01feeb2f537176e48047f776e30aff333c76b | c5ef6312b01eb79e977978f7a72d87c8f386ad32 | /S0 Assemble Scripts.R | 04cb968d603262bffa559bff83bd0e696626c322 | [] | no_license | dlill/Simulations | bec6ea865616c6486dfa99f83b30f6d201ae1067 | 9a8fb0eb0717ae38d9d74a388314cd6a30211b6a | refs/heads/master | 2022-05-05T11:58:50.350345 | 2022-03-23T22:47:07 | 2022-03-23T22:47:07 | 137,068,479 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,458 | r | S0 Assemble Scripts.R | library(conveniencefunctions)
dir_final <- "~/Promotion/Writing/Papers/2017 02 MRA optimization procedure Paper/Code/"
dir_models <- "~/Promotion/Projects/MRA/Simulations/Models/"
setwd(dir_models)
file_index <- c(Cascade.Rmd = "Cascade/Cascade.Rmd",
`Cascade with cxz complex.Rmd` = "Cascade/Cascade with cxz differ... |
af03a261576340968b7d73f77a837a39451806ce | bd17e9f7aafe9273d6418a1f4710c9a0dbd69ccf | /make_table1.R | 78c88b12af3c38db110df0e7727ef9d76939a3fb | [
"MIT"
] | permissive | JohannesNE/gitt-after-braininjury | cbb74dfe5b8e820a318b3347e58c565cfd816e5b | 5df353f6c4c0e8f179901ade7ce0262e01027b68 | refs/heads/master | 2021-04-29T21:38:26.370235 | 2018-02-15T11:42:27 | 2018-02-15T11:42:27 | 121,617,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,940 | r | make_table1.R | ### Generates table 1 tex file
library("xtable")
library("Gmisc")
library("Hmisc")
getStats <- function(varname, digits=0, type = "mean", data_df=df_pt, use_html = FALSE){
if(type == "mean"){
return(describeMean(data_df[[varname]],
html = use_html,
digits = digits,
plusmin_str = ""
)... |
eef84ca7c22f94a172dd5a93623d3d0b047bad93 | c6d41e51bbfe2669c4fbdc916949bba9bb62cf30 | /R/summary.R | eb3817eb0a0c9d1effc0f8f217299b453757b331 | [] | no_license | DonjetaR/blm | 2a88a0adcc15ea90e7a3615209d435d58beccf88 | f8cb30e86fb79416fc951ecf0c87cff294e2d9ca | refs/heads/master | 2020-12-24T18:12:44.011365 | 2017-01-16T03:31:10 | 2017-01-16T03:31:10 | 76,725,862 | 0 | 0 | null | 2016-12-17T13:33:58 | 2016-12-17T13:33:58 | null | UTF-8 | R | false | false | 496 | r | summary.R | #' Bayesian linear model.
#'
#' Fits a model, given as a formula, optionally with data provided through the "..." parameter.
#'
#' @param x A formula describing the model.
#' @param ... Additional data, for example a data frame. Feel free to add other options.
#'
#' @export
summary<- function(x, ...){
cat('\nC... |
a4d75c5c11c9d680a7be6129f1bc32a6c2f9156b | bc882e24dd0a08aa93b9a5faa968e1016674d6a2 | /man/modeltime_wfs_bestmodel.Rd | 16348e1e04f84841da83d7ee282d785bacb3f93d | [
"MIT"
] | permissive | dedenistiawan/sknifedatar | 350ecb9725f8819914a1635f306a9c34bb97a167 | af29fe7e755ba926f4c6203bcb936dc225813310 | refs/heads/master | 2023-08-15T23:24:43.467326 | 2021-07-18T16:35:20 | 2021-07-18T16:35:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,152 | rd | modeltime_wfs_bestmodel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modeltime_wfs_bestmodel.R
\name{modeltime_wfs_bestmodel}
\alias{modeltime_wfs_bestmodel}
\title{Modeltime best workflow from a set of models}
\usage{
modeltime_wfs_bestmodel(
.wfs_results,
.model = NULL,
.metric = "rmse",
.minimize = ... |
ec1bade4cb292e29b738baff8e7dfb243bcfb113 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.machine.learning/man/translate_list_terminologies.Rd | 05f1e5050e5d1261a4ef86ebbf4f7d2c5f932695 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 787 | rd | translate_list_terminologies.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/translate_operations.R
\name{translate_list_terminologies}
\alias{translate_list_terminologies}
\title{Provides a list of custom terminologies associated with your account}
\usage{
translate_list_terminologies(NextToken = NULL, MaxResults = N... |
1304c6973b0ca8a7aeed606c0409af5e84ede3da | a2c19b12165936b6f38edcdc657cf64e985becf3 | /man/panorama.Rd | 69e01ee4accf303aedfef3773bcc6e77b41015a5 | [] | no_license | cran/vetools | fcfb882851ac667562bcee832cf860619ac09b73 | 945e115cc46ee4ee07684d2535d4ea60d1dccb5e | refs/heads/master | 2021-03-12T21:55:17.754628 | 2013-08-01T00:00:00 | 2013-08-01T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,153 | rd | panorama.Rd | \name{panorama}
\alias{panorama}
\alias{panomapa}
\title{
Overview of a \code{collection} of stations
}
\description{
These functions present an overview of the data quality for a
\code{collection} of meteorological stations in a temporal or
spatial perspective.
}
\usage{
panorama(collection, main, cut, ylab.push.fac... |
3acd3f6f87f5b24c92925e5d6949383e739f44c7 | e0d5629bb3d960b87b710e13b34d8f3be212d433 | /University/Brexit Investigation/Scripts/Solutionv3.R | 06d31e44e43aa1b2920520b8eb692d26dbc444da | [] | no_license | michaelfilletti/myrepository | 3af31a48a9977c6fdac8d60c33e671c74245b0f2 | 48d9cb7b8800d08b7f508f5b8663b3049d98029d | refs/heads/master | 2021-07-15T05:39:12.034572 | 2020-06-08T08:48:53 | 2020-06-08T08:48:53 | 163,402,804 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,630 | r | Solutionv3.R | #----------------------------------------------------------------------
#This script involves two main steps:
#Step 1: Extracts the data from the FOREX files & News files
#Step 2a: Cleaning and merging the ToM data and the currency data
#Step 2b: Testing whether weekends should be included
#----------------------------... |
127a409bfea645a414eef25270b65df6b0c9ccc2 | 25c63cd1d9ef691adfae8a1c6a1c752e1aef1aa2 | /R/spectrum.arma.R | bf6e6e1e0bad5f09c6c798d234b937f8f69ef201 | [] | no_license | cran/afmtools | 068da611bc7318d6160a0e251f84300867bff0b2 | 10801682a6c0419a23f28279e5fca8f288bd25f1 | refs/heads/master | 2020-12-30T10:36:50.591497 | 2011-04-15T00:00:00 | 2011-04-15T00:00:00 | 17,718,425 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 739 | r | spectrum.arma.R | spectrum.arma <-
function(ar=0,ma=0,sd.innov=1)
{
if (is.na(ar[1])) ar=0
if (is.na(ma[1])) ma=0
nar=length(ar)
nma=length(ma)
if (nar == 1 && ar ==0) nar=0
if (nma == 1 && ma ==0) nma=0
M=check.parameters.arfima(d=0, ar=ar, ma=ma)
if (!M$Total.OK) cat("WARNING: Model is not OK.")
ar.poly <- c(1... |
5fa39dcd845d173c096aa4649b3c8de9c5155e0a | 386abca575985e19b96c754d3b379b2b37840e03 | /LongestFasta.r | 64e6cd94e81d9a64547d1ba2da9d9895ac3ee0f7 | [] | no_license | ebuchbe/Elisa_Montse | 76a0ed0ce3943df8220cebe077311b05732d4657 | 61503160b81addd5192e6b2dea5eb6662ab6b970 | refs/heads/master | 2021-01-18T17:04:27.166434 | 2015-08-05T14:01:00 | 2015-08-05T14:01:00 | 39,561,969 | 0 | 0 | null | 2015-07-23T10:50:23 | 2015-07-23T10:50:22 | null | UTF-8 | R | false | false | 1,080 | r | LongestFasta.r | #install.packages("seqinr") ###this package is important for handling fasta files
#library("seqinr") ###loading the fasta file e.g. haemo.fasta
input <- args[1] ##this is the input fasta file
fasta <- read.fasta(input) ##this reads the input fasta file
sequences <- NULL
len <- NULL
both <- NULL
result <- NULL
lengths ... |
11b63ed679d3df17941a0ab1cc2a8c9c2e294ce9 | 7b17bffea97d078734c2dbd05b11be7d0428b67b | /R/cumlativeMonthlyDownloadStats/registryConnection.R | 62f77fb4108b2e72d22f5e9ba3e4f6cfbc19e9cd | [
"Apache-2.0"
] | permissive | jhnwllr/analytics | d8f9d2d417fe0706e077b4112bf4bc653dde40e5 | fad76d4b8bb3279616152cddf7594543b4598aeb | refs/heads/master | 2021-06-13T21:16:27.979552 | 2020-11-17T08:43:21 | 2020-11-17T08:43:21 | 311,628,171 | 0 | 0 | Apache-2.0 | 2020-11-10T10:49:47 | 2020-11-10T10:49:47 | null | UTF-8 | R | false | false | 300 | r | registryConnection.R |
# connect to the registry with some defaults
registryConnection <- function(
pw,
user="jwaller",
host="pg1.gbif.org",
port="5432",
dbname="prod_b_registry") {
con <- dbConnect(
RPostgres::Postgres(),
host=host,
port=port,
dbname=dbname,
user=user,
password=pw)
return(con)
}
|
67eeed612fc4fd506dbb41075cc5698d3dea599f | 6febf32c916d5734ff34df50369a0bc73b9178f2 | /R/wrapper.prepare.grid.R | d8432699239d15620ceda54924d33e9236e51ff4 | [] | no_license | asiono/rein | e190efbb0579685608bd48a171504016b2d0b7fe | ff837b696edc083a6ecf0c9435f1d8825a9f0c5d | refs/heads/master | 2021-05-12T07:21:37.391867 | 2018-03-08T13:48:05 | 2018-03-08T13:48:05 | 117,106,249 | 0 | 0 | null | 2018-01-12T09:40:12 | 2018-01-11T13:47:56 | R | UTF-8 | R | false | false | 4,073 | r | wrapper.prepare.grid.R | ################################################################################
#' @title wrapper.prepare.grid
#' @description runs all necessary functions for preparing and execute loa flow calculation from SimTOOL.
#' @param grid List containing information of grid to be optimized.
#' @param check logical, to ch... |
dee7dbc2318a30ff013eca438309a774bfb96005 | ee32e45955352d97186d9cd4e329be90f78c11c3 | /scripts/plotly_stateaves.R | ec291de691b214af97de377af980bc15f6263e1d | [
"MIT"
] | permissive | r-introtodatascience/sample_repo | a276aebc232f550929cc990777585bfad6bc7fd1 | bbb5a2a01ff4a54fa1ef0f5c2d68afba0a47aa35 | refs/heads/master | 2023-06-23T13:04:34.445954 | 2021-07-15T15:20:11 | 2021-07-15T15:20:11 | 386,334,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,232 | r | plotly_stateaves.R | #Interactive plots of state unempl averages over time.
# clean workspace and environment
cat("\014")
rm(list=ls())
### Put in your own folder path here
workingdir<-#"Path_to_your_main_folder"
# for example for me it was workingdir<-"C:/Users/Documents/GitHub/Rcourse/"
#load in folder paths
source(paste0(workingdir, ... |
63481774cd5a9b980cb1922025e6cc1ba600beb9 | 1454621e71c58f41204543640b1a6a8069c0fec9 | /code/phys_model/HO_parameters.R | 83e5ac91811216c71a49ae5d7a21b71ab0236d37 | [] | no_license | SPATIAL-Lab/CombinedDynamicHOModel | a859f810b3b0a1ca6d0f4c8e80a4e51ed2993b35 | 20792b25e5296827846e0beb4e74cb138cf8e06e | refs/heads/master | 2022-12-17T22:04:26.298227 | 2020-09-24T18:01:19 | 2020-09-24T18:01:19 | 238,896,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,706 | r | HO_parameters.R |
############################
####### HO PARAMETERS ######
############################
## used by phys/food model (HO) functions
# Functions to convert delta values into R values and vice versa # constants but needed here to convert d18Oo2 into ROo2
RstandardH = 0.00015575
RstandardO = 0.0020052
delta.to.R = func... |
0a49a0c57435414f632ca353eaf99039afedb71d | 77f862e8bc5d874799e5f775528b8d4d192fb127 | /plot4.R | 06ca926f8fbdb953b4a3bece086ac088ee66dd91 | [] | no_license | bolilla/ExData_Plotting1 | 38cf85629cee8f8620dd3ceff3a8881ee9febad4 | 24e204ad6f17b07d4c9ab3721a4026ecf1393d43 | refs/heads/master | 2020-02-26T16:47:02.847443 | 2015-11-08T22:08:24 | 2015-11-08T22:08:24 | 45,772,143 | 0 | 0 | null | 2015-11-08T08:05:05 | 2015-11-08T08:05:04 | null | UTF-8 | R | false | false | 1,723 | r | plot4.R | #Creates the FOURTH plot of the assignment
plot4 <- function(){
if(is.null(myData)){
myData <- getData4()
}
#Set 2 x2 matrix for plotting
old.par <-par(mfrow=c(2,2))
#Top Left graph
plot(myData$Global_active_power, type="l", ylab = "Global Active Power", xlab="", x=myData$theDate)
#Top right gra... |
d9fd1b8cf00f911cd3874b08db71cdda2a1ccf91 | c1034056ae08c728f8318a24eb756292108ca727 | /R/ipinfodb.R | 79b667081d8b353710fe74d11c17254a42ecef7f | [] | no_license | omegahat/Ripinfo | 9776eef10b1db7fa281e94b07871448c6a4f3306 | 9e99414618a3d67097151b9a961a8fa6cfce4edd | refs/heads/master | 2021-01-22T23:43:34.308508 | 2012-02-22T01:54:38 | 2012-02-22T01:54:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,737 | r | ipinfodb.R | getIPCountry =
#
# getIPCountry(c("www.omegahat.org", "128.32.135.25", "www.google.com"))
# getIPCountry(c("www.omegahat.org", "www.google.com"))
# getIPCountry(c("169.237.46.32", "128.32.135.26"))
#
function(ip, ..., curl = getCurlHandle(), stringsAsFactors = default.stringsAsFactors(),
byHostN... |
283414128cf5564e361616bbf925f5c2596d6130 | 15b0e0513e6206bb50c8fe6be4abbb16793eb560 | /man/surgecapacity.Rd | e06b345237ff4afecd037e47104c8beef4d2bbb3 | [
"MIT"
] | permissive | terminological/arear | 2566759ae77342a79a28b2a0d1cb389da337194b | 98cece8079c4470e029c966c6b6d103797207eba | refs/heads/main | 2023-06-07T16:02:46.919877 | 2023-05-26T14:49:17 | 2023-05-26T14:49:17 | 340,773,310 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,368 | rd | surgecapacity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{surgecapacity}
\alias{surgecapacity}
\title{Locations of UK general medical hospitals in mid march 2020 with estimates of beds available and maximal surge capacity HDU beds}
\format{
A sf geometry with:
\describe{
... |
30f34bfe726f5cf257bf38fb064e508e52bf6da7 | 5ca8793fd39a818675306047c861e8c32965022a | /website/Old_source/Function/R/ff_harmonics.R | 1a9390ed51da45392ce9c117fb84ead7a34fc063 | [] | no_license | SOCR/TCIU | a0dfac068670fa63703b8e9a48236883ec167e06 | 85076ae775a32d89676679cfa6050e683da44d1d | refs/heads/master | 2023-03-09T01:28:28.366831 | 2023-02-27T20:27:26 | 2023-02-27T20:27:26 | 188,899,192 | 8 | 5 | null | 2022-11-12T01:56:25 | 2019-05-27T19:33:38 | R | UTF-8 | R | false | false | 1,948 | r | ff_harmonics.R | #' @title ff_harmonics
#' @description Compute the spectral decomposition of an array (harmonics)
#' @details This function computes the FT of the singlal and plots the first few harmonics
#'
#' @param x Original signal (1D, 2D, or 3D array).
#' @param n Number of first harmonics to report (integer).
#' @param up... |
ab2a4550117ed0488ea6c52ca236f4c2cd56fed8 | fb0b8c413ae95c961e0351eccb22263a2d0917dd | /man/is_sorted.Rd | 62df5786abdfa51c53c0c4f30ae34effbda8931e | [] | no_license | cran/hutilscpp | f3acfdf2af949df69c8887d937050aa3daf39a02 | 22994140414c52919756eb799ddd10ed4d666f74 | refs/heads/master | 2022-10-14T17:45:09.954044 | 2022-10-07T07:00:02 | 2022-10-07T07:00:02 | 168,961,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 769 | rd | is_sorted.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/is_sorted.R
\name{is_sorted}
\alias{is_sorted}
\alias{isntSorted}
\title{Is a vector sorted?}
\usage{
is_sorted(x, asc = NA)
isntSorted(x, asc = NA)
}
\arguments{
\item{x}{An atomic vector.}
\item{asc}{Single logical. If \code... |
4255ac13e9ddd4257e53e0d74629f31c438c23b6 | 58ae28f35ed0796476c25a95b15f7464fe91c8e1 | /R/nflcombine.R | f0069a6bdc0dd686262adb546dc55209236a5a5d | [] | no_license | burrisk/midamix | 761ef51c19cefe4cf000cfac1961610b006213fb | 7f869de706edea2afbf57ff1897eefe92025d51f | refs/heads/master | 2020-05-14T20:46:52.920481 | 2019-05-24T13:34:04 | 2019-05-24T13:34:04 | 181,950,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,094 | r | nflcombine.R | #' National Football League Scouting Combine Data
#'
#' A compilation of drill results all players who attended the NFL
#' scouting combine from 1987 to 2019. Some of the measurements come from the player's
#' pro day.
#'
#' @format A data frame with 10502 rows and 13 variables:
#' \describe{
#' \item{year}{year}
#'... |
69577894d3698b74389d5f9626f0a814740cd8ef | 709c16710d7cae612de6c779cafb7199813e0f24 | /Study 1 - Treatment Pathways/R Version/MainAnalysis.R | 6643ae1527485ae9454fdb5b03e8102e034517a7 | [] | no_license | OHDSI/StudyProtocols | 87a17fc3c00488b350f9416c584a1d0334d8dfcb | 8de0454c6be4c120ba97d7376907d651327573a4 | refs/heads/master | 2023-04-27T18:59:35.785026 | 2020-02-16T00:32:52 | 2020-02-16T00:32:52 | 27,415,586 | 37 | 41 | null | 2023-04-25T19:55:45 | 2014-12-02T04:49:53 | R | UTF-8 | R | false | false | 3,584 | r | MainAnalysis.R | ###########################################################
# R script for creating SQL files (and sending the SQL #
# commands to the server) for the treatment pattern #
# studies for these diseases: #
# - Hypertension (HTN) #
# - Type 2 Diabetes... |
329a4073fad8e2b2eedcb840da467facadcbd407 | 9a1b28b43abec0215c24261d1d4412ea201e24fc | /man/kurtz2013e2bld.Rd | e306a927e82bbc77a03180d0a02ffb7128c23802 | [] | no_license | ajwills72/sixproblems | 5c824666387f39b5a733f679a30db06f969baf4c | 3b850a8d7989ef6805dcfaf62e15449a1dd4a3ea | refs/heads/master | 2021-07-07T22:49:49.251549 | 2020-07-01T15:08:31 | 2020-07-01T15:08:31 | 131,574,504 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,321 | rd | kurtz2013e2bld.Rd | \name{kurtz2013e2bld}
\alias{kurtz2013e2bld}
\docType{data}
\title{
Kurtz et al. (2013, Exp. 2) Type II versus Type IV data set
}
\description{
Block-level indidivdual-participant data for the Kurtz et al. (2013) comparison of Type
II and Type IV problems.
}
\usage{data("kurtz2013e2bld")}
\format{
A data frame wi... |
83d142faf4aca6c58cda90fc43fa38758083d977 | 51a57d4a937f3e6d7621d61c1caa7dbd741fb8f7 | /Regression/code/feature_engineering_black.R | bb8be5bca863654a6ec85adba3cbc936e2bd7672 | [] | no_license | SiriusIE/2019_Advanced | 163e1ad47bbb457fb43c291538d245703251979c | 629e013b60768670400115c9c94a2d75a6a81159 | refs/heads/master | 2020-04-25T23:38:18.736181 | 2019-06-06T16:09:03 | 2019-06-06T16:09:03 | 173,152,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,836 | r | feature_engineering_black.R | source('Regression/code/load_libraries.R')
raw_data<-fread('Datasets/Regression/BlackFriday.csv', stringsAsFactors = T)
str(raw_data)
raw_data[, length(User_ID)]
raw_data[, length(unique(User_ID))]
raw_data[, which(sapply(names(raw_data),function(x) grep(pattern='ID',x))>0):= NULL]
str(raw_data)
raw_data[, Occupat... |
9b430febc2ca9ab595ba45642fad54442c943bcf | 98c40fe72bfe9caafc3db5ca0a2c0944cad33988 | /man/after.envelope.Rd | e6b9c9d6f55cce75eac69e224636bb571255bde0 | [] | no_license | datawookie/emayili | c91d38dc5bf0fc38cff45260dc0ba99bce7e754f | cb0f2f7e6c8738a30ddd88834abd88b63585244c | refs/heads/master | 2023-09-01T00:02:36.105432 | 2023-08-30T10:49:01 | 2023-08-30T10:49:01 | 187,310,940 | 158 | 41 | null | 2023-08-03T06:52:34 | 2019-05-18T03:45:55 | R | UTF-8 | R | false | true | 334 | rd | after.envelope.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/envelope.R
\name{after.envelope}
\alias{after.envelope}
\title{Append children to message}
\usage{
\method{after}{envelope}(x, child)
}
\arguments{
\item{x}{Message object}
\item{child}{A child to be appended}
}
\description{
Append children... |
1f545de8ac0f4165375b775cea3f9d534c8b1ee3 | 395daa1ec2a5f403dda8d65cfa88d5280afe2665 | /R/varkernelslicerange.R | 0a6273c320f2a6402d6e366521e0be782645c8d5 | [] | no_license | CWWhitney/uncertainty | 4f0cdc86b453e5fbe48767d34d8db435c6979b63 | 0e6a25ba719a59f3104fd8c716d2aff3923f79be | refs/heads/main | 2023-04-07T22:04:54.398851 | 2022-06-13T20:52:49 | 2022-06-13T20:52:49 | 374,667,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,767 | r | varkernelslicerange.R | #' Estimated output variable values given the expected values of the influencing variable,
#' based on a slice of 'z' from the Kernel density plot of the influencing variable and output
#' variable data.
#'
#' Plot representing probabilities (shown along the y-axis) for the expected outcome variable (shown along the ... |
34f21e06a4ca78a56da8b15d0cf425c65c4882e7 | 3e4b9bafef96b533a6def6c963a4e4bb2897ced9 | /ukbiobank_prs/phenotype/organize_ukbb/af.R | bddc8a9c904de038c1f83de2dc2baccfc851ea72 | [] | no_license | EmadHassanin/combining_prs_gps | 28e36b692449eae5ef3da01435f34c6fc157c14e | ff9041e829ffd01da38f26042975f3daaf72f457 | refs/heads/main | 2023-02-01T05:17:03.945028 | 2020-12-15T08:03:47 | 2020-12-15T08:03:47 | 321,477,204 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,440 | r | af.R | af_all <- self_reported %>% filter(code_selfreported == 1471 |
code_selfreported == 1483) %>%
mutate(age_selfreported = if_else(age_selfreported < 0 , mean(age_selfreported), age_selfreported )) %>%
full_join(icd9 %>% filter(str_detect(code_icd9 , "^(4273)")),
... |
0eae03597be86c66f35b1704caf007afa314785d | 68058bd8c023c42962a86584c939f9c32b7a69a4 | /simulation.R | 5556e64aedcd7e068b4c2b933f7d6ef61684eaf4 | [] | no_license | vinayakpathak/gp | ea1f64490cfb5c4295876dd970bc49a428106411 | da568d2d0806d97d467794c8d803d762132564e3 | refs/heads/master | 2020-03-10T03:20:09.292527 | 2018-04-22T03:19:26 | 2018-04-22T03:19:26 | 129,162,023 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 722 | r | simulation.R | w_values <- c(1,1,1,1,1,1,1,1,1,
1,1,1,1,1,-1,-1,-1,-1,
1,1,1,-1,-1,-1,1,1,1)
D <- 1
M <- 9
mu <- rep(0, M)
N <- 308
sigma <- 0.1
#W <- matrix(w_values, M, D)
W <- matrix(rnorm(M * D), M, D)
data_list <- list(D = D, M = M, N = N, sigma = sigma, W = W, mu = mu)
fit <- stan(file = "simulatio... |
08fe39f0321aa37d7df63ccf812d584715bc17a1 | c839af452b6ec822ef520e743a1a4bee6ed99e60 | /R/get_nt_urban_aboriginal_pop.R | 4ba2a2afd9d6a7a1dab5ef099a4593750e1c5f27 | [
"MIT"
] | permissive | goldingn/contact_matrices_australia | 48c6b0945f5dc59b720325d706f91d6bf6ebe442 | 237bda4a083721801b91d63a197e86f71b98feb2 | refs/heads/main | 2023-09-05T01:39:48.329749 | 2021-11-05T08:14:43 | 2021-11-05T08:14:43 | 408,035,456 | 0 | 0 | MIT | 2021-11-08T09:02:14 | 2021-09-19T04:59:20 | R | UTF-8 | R | false | false | 459 | r | get_nt_urban_aboriginal_pop.R | #' .. content for \description{} (no empty lines) ..
#'
#' .. content for \details{} ..
#'
#' @title
#' @return
#' @author Nick Golding
#' @export
get_nt_urban_aboriginal_pop <- function() {
# aboriginal population in urban alice springs
population <- get_nt_lhd_aboriginal_pop() %>%
filter(
district == ... |
3812729da10139c5ff154168cd6d18db4f5e8569 | 61daa4c3bb5bc77e89cc9ab3a5424647e732d538 | /inst/virtual_patient_simulator/bs4/calc_change.R | 77c711a6ecd2347c39d152972d16699fe0bc9214 | [] | no_license | DivadNojnarg/CaPO4Sim | 7a3afafd188b9db1e9929f514ef6a7c46808d5d1 | 8bd3d12702e159c95817c7a724af6824a0cd1517 | refs/heads/master | 2022-11-10T10:52:03.637983 | 2022-11-02T13:01:22 | 2022-11-02T13:01:22 | 99,161,393 | 43 | 31 | null | 2020-07-16T05:30:18 | 2017-08-02T21:13:53 | R | UTF-8 | R | false | false | 5,461 | r | calc_change.R | #-------------------------------------------------------------------------
# This code contains the function that calculates the percentage of
# change of each flux, which is then needed to change the color of arrows
# depending on the resulting variation. (see global.R for color change)
#
# David Granjon, the Int... |
7ed17dfbb69db13b5cd4d14dfa208d6889abe6c8 | d514d43ef0958a662bb685d89a6c9ac541b44390 | /man/importLRR_BAF.Rd | 9e229a9d85e52d8a6c7a9ef3ee7d1fbabb48840a | [] | no_license | inambioinfo/CNVRanger | e133a885213f7ecea57ad6e0437c016df1ff3f25 | dbb80477bf2d681d604d6e7cfffe4389d55ddd5b | refs/heads/master | 2020-04-16T07:36:13.024957 | 2019-01-11T01:46:33 | 2019-01-11T01:46:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,216 | rd | importLRR_BAF.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pheno_assoc.R
\name{importLRR_BAF}
\alias{importLRR_BAF}
\title{Import LRR and BAF from text files used in the CNV analysis}
\usage{
importLRR_BAF(all.paths, path.files, list.of.files, verbose = TRUE)
}
\arguments{
\item{all.paths}{Object ret... |
a2e24e8ca9e11b1e88564429d43d0d4716d2d36d | 7835fe6cd53ce1781f9f146e1b6ba65dfdbf7f3c | /cachematrix.R | cd2f818aeef8a7cf93eb6bc95ccb0e2aba8aad82 | [] | no_license | JBrown0303/ProgrammingAssignment2 | 7cf2cba09e7f59b0619087dc13a58185a3ee1bb7 | 0ae363d4b328126551159603a5c6fdd363a58053 | refs/heads/master | 2020-04-01T15:49:43.075529 | 2018-10-16T21:54:10 | 2018-10-16T21:54:10 | 153,353,437 | 0 | 0 | null | 2018-10-16T20:59:24 | 2018-10-16T20:59:24 | null | UTF-8 | R | false | false | 1,364 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Function that sets and returns a chaced matrix and its inverse
makeCacheMatrix <- function(x = matrix()) {
#initialize the inverse as Null
i <- NULL
#create function for caching the input matrix
set <- function(y) {
x <<- y ... |
b6b53b0572acbb04111b2538b4aa6df179844420 | 1b622a48cf7c8853d67605cb6501aea6ef4df678 | /IntroductionToR/Day 5/ExercisesDay5DataAnalysis2019.r | 01015d2edd9e429e425b3af4187ef9db247f6d11 | [] | no_license | ThibaultSchowing/unistuff | 0ab6e1c3ec27dda631b0399a1cfca351e959b7fc | 380506b30a69df6c82f639064b3d5700fe90de14 | refs/heads/master | 2020-09-08T19:43:24.176721 | 2020-05-14T21:59:24 | 2020-05-14T21:59:24 | 221,225,865 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,799 | r | ExercisesDay5DataAnalysis2019.r | # -------------------------------------------------------------------
# R course 2019
# Exercizes day 5
# -------------------------------------------------------------------
# Exercize 5.1 -------------------------------------------------------------------
#
# 1) Calculate summary statistics for the data set "Studen... |
3ac86e0a7014305ac6dac63c2e40fe69284bdc50 | 50284f0424b73a2fabe8705809c3fa018867ccd6 | /R/is.evenodd.R | f5f9d9e6121ae76207e3700e2f7ce0347bbd7c6e | [] | no_license | InfProbSciX/PeRLib | 2756830a8b897f3fad94dc417b2eb8eeafb64b8c | 9f68dc8c903950de2b2025fbaa79de6ae3a21b90 | refs/heads/master | 2020-03-22T08:21:55.349877 | 2018-07-04T20:11:32 | 2018-07-04T20:11:32 | 139,762,021 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 853 | r | is.evenodd.R | #' Check if a vector of numbers is Even
#'
#' \code{is.even} Returns a vector of TRUE/FALSEs by applying x%2 to a vector x.
#'
#' @param x A numeric vector.
#' @param n An integer specifying if a number of even numbers should be returned.
#' @examples
#' is.even(1:5)
#'
#' ## FALSE TRUE FALSE TRUE FALSE
#' @export
is... |
2145b8cf7f0dd99884b599f502fafecc8e7d9b84 | 1e36964d5de4f8e472be681bad39fa0475d91491 | /man/SDMXDimension.Rd | 9087a8882d4dab0425e39d9cad346b8a4f6d54c0 | [] | no_license | cran/rsdmx | ea299980a1e9e72c547b2cca9496b613dcf0d37f | d6ee966a0a94c5cfa242a58137676a512dce8762 | refs/heads/master | 2023-09-01T03:53:25.208357 | 2023-08-28T13:00:02 | 2023-08-28T13:30:55 | 23,386,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,884 | rd | SDMXDimension.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class-SDMXDimension.R,
% R/SDMXDimension-methods.R
\docType{class}
\name{SDMXDimension}
\alias{SDMXDimension}
\alias{SDMXDimension-class}
\alias{SDMXDimension,SDMXDimension-method}
\title{Class "SDMXDimension"}
\usage{
SDMXDimensi... |
ec0990a01dde572cbbd0781ae8d1858aa2537086 | a069edc2eeb426ca950c15d1a8d2e0afa8b98239 | /inst/examples/interactive/src/count/script.R | 32ead43f34dfd3b8f4465b00cdd8e8d3193c623e | [] | no_license | kmaheshkulkarni/orderly | 62913a83e7f0a1e46072fe69e62ea97b499334af | cd8c6bfffb9e9851e80ae57fdf738a3ca0c8f247 | refs/heads/master | 2020-07-08T08:20:29.418822 | 2019-08-13T16:19:38 | 2019-08-13T16:19:38 | 203,616,293 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 271 | r | script.R | p <- jsonlite::read_json("parameters.json")
end <- Sys.time() + p$time
while (Sys.time() < end) {
cat(sprintf("%s waiting...\n", runif(1)))
Sys.sleep(p$poll)
}
png("mygraph.png")
par(mar = c(15, 4, .5, .5))
barplot(setNames(dat$number, dat$name), las = 2)
dev.off()
|
0af130219998b2c6d532da2e75a205d6ef9f238e | 8f3c2fe27572f66ee3387f00e8c12bf774055f96 | /8-9-jiance-duochong-gongxianxing.r | 6820d791f09aa4cd96e1bc676c23a8f5aa3c33c4 | [] | no_license | papadalin/R-book-code | a26f18b667c26b6a0355720751457e773bcc9e42 | 6c81959762a14916bb056ca3a8db953e54ed7246 | refs/heads/master | 2020-04-13T13:25:39.584386 | 2015-09-01T14:00:10 | 2015-09-01T14:00:10 | 41,002,710 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 453 | r | 8-9-jiance-duochong-gongxianxing.r | #liyong tongjiliang VIF(Variance Inflation Factor, fangcha pengzhang yinzi) jinxing
#jiance.yiban qingkuang xia, sqart(vif) > 2 biaoming cunzai duochong gongxianxing
#wenti ,hui daozhi zhixinqujian pengzhang guoda ,yingxiang jiashe jianyan jieguo
library(car)
fit <- lm(formula = Murder ~ Population + Illiteracy + Incom... |
36f6d78cc51afd1f32279df58b059edcaff6c169 | bc6980ed8352efdce2cdd1b7965e7c1fd697fdf4 | /man/data.frame.cgOpsum.list.Rd | a931fde9bb6bafe2b8dd78874b62922e305a8093 | [] | no_license | jrh05/opsumToolbox | 12d4d2a086939a2fa64d0b1c087a605f5e4cfb6a | 60c9a61a5a076a12fe5fe95df6cd94cf0ddd561e | refs/heads/master | 2021-09-05T14:36:29.747919 | 2018-01-07T06:44:28 | 2018-01-07T06:44:28 | 115,367,468 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 357 | rd | data.frame.cgOpsum.list.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cgOpsumExtractorFunctions.R
\name{data.frame.cgOpsum.list}
\alias{data.frame.cgOpsum.list}
\title{data frame Method for cgOpsum list}
\usage{
data.frame.cgOpsum.list(object)
}
\arguments{
\item{object}{A cgOpsum.list object.}
}
\description{
... |
7b6e7629907d2701f90c71b279ba7b3f7b3fd819 | 48c61dee0351116997721984723165617e1d3111 | /tests/testthat/test-SHOWF_UFun.R | c265d43ec2cbb46fb4d1d687043976b01540df82 | [] | no_license | mlysy/realPSD | 4747062fc78dec25750f4f4143e610980b9a0522 | f10afcc83b08f2fe7e0823c6a6334ede38ae8fa8 | refs/heads/master | 2023-02-28T13:12:09.200872 | 2021-01-13T00:00:14 | 2021-01-13T00:00:14 | 189,429,001 | 0 | 1 | null | 2021-02-02T10:39:11 | 2019-05-30T14:32:24 | R | UTF-8 | R | false | false | 2,325 | r | test-SHOWF_UFun.R | source("realPSD-testfunctions.R")
context("SHOWF UFun Tests")
test_that("The SHOWF UFun returned is the same in R and TMB", {
ntest <- 50
nphi <- sample(2:5,1)
for(ii in 1:ntest) {
# pick model
model <- sample(c("SHOWF_log", "SHOWF_nat"), size = 1)
ufun_r <- get_ufun(model)
# simulate data
N... |
a63f5befb5024738eddc05082c9c189643396a36 | 15341939deb8974a78a98ec48c6840d69b148580 | /R/tree-utilities.R | 23cb7ae7a41d7eccc2d43420287346adacebe121 | [] | no_license | mwpennell/arbutus | 44d2544d4d5d669fffd5c726bdd4b05da5272164 | 781fa94901a356fd22770647d28c4223ffc4ad66 | refs/heads/master | 2022-10-26T08:46:34.963778 | 2022-10-05T16:31:54 | 2022-10-05T16:31:54 | 17,573,460 | 9 | 2 | null | 2022-10-05T16:31:55 | 2014-03-09T20:51:53 | R | UTF-8 | R | false | false | 3,021 | r | tree-utilities.R | ## these are utility fxns for extracting information from trees
## get node heights (this is equivalent to the internal geiger fxn heights.phylo() )
edge.height <- function(phy){
phy <- reorder(phy, "postorder")
n <- length(phy$tip.label)
n.node <- phy$Nnode
xx <- numeric(n + n.node)
for (i in nrow(phy$edge):1) ... |
a5ad351a1bca3cd401cf4bd32495ada38f766a0d | d69e83586753456a6bb387a0122479ceb2bdc6e0 | /R/catSpatInterp.R | 2e5ac9ef6fb75f41082d8c8cd4e1c14cc8c67773 | [] | no_license | cran/swfscMisc | 6f1e85dba988db1e235f3e2f58ef0b831567736d | 3644ab0ba65c3e24f11627865d00910e4d97c82f | refs/heads/master | 2022-05-13T09:41:35.823940 | 2022-04-21T12:30:02 | 2022-04-21T12:30:02 | 22,867,576 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,786 | r | catSpatInterp.R | #' @title Categorical Spatial Interpolation
#' @description Create a raster of probability of categorical values
#' interpolated across a 2-dimensional space given a set of points where
#' each is assigned to one of several classes.
#'
#' @param data matrix or data.frame containing points and grouping designatio... |
ee5fb82626a8a8c021ac23f4dff80908f2053b1b | a8c39687b8088d171f376d4d9b23561e08a56ce3 | /R code for analysis_Yuhueng/scatter3d.R | 9c365b43ba71cadb2877484676201b4f6d280b40 | [] | no_license | Zongmin-Liu/MSI2_HyperTRIBE_codes | ce5bbfd4e71b299fcad500a3e3e6e28d7091fd0c | 735986cef8e6b32eb8f02efb3e46029aee36273b | refs/heads/master | 2022-04-10T01:11:58.776631 | 2020-03-11T03:27:35 | 2020-03-11T03:27:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,011 | r | scatter3d.R | library(openxlsx)
library(scatterplot3d)
df <- read.xlsx( "mouse_lsk_snp_counts_dedupped.xlsx" )
rowmask <- ( df$`Sample_Hyper-dADAR_12617_IGO_08269_2.ref.count` + df$`Sample_Hyper-dADAR_12617_IGO_08269_2.alt.count` >= 25 ) &
( df$`Sample_Hyper-dADAR_121417_IGO_08269_5.ref.count` + df$`Sample_Hyper-dADAR_121417_IGO_0... |
3da7b90fa6d7ed03ad5a271471aac2880e274bfc | 72afe86e08b02ee3726ac303b4b81801f1ea5538 | /App/global.r | 7b2104c896df43a2aaee6ad1cf0e9d27a5a01a25 | [] | no_license | trevorbix/AutoDinwiddie | 9a33168b8d6ff3ec21d40ceda8f290ff043159a0 | feabea9195c80e1271081d95228ca335d1bf8e7e | refs/heads/master | 2020-03-28T13:23:44.712410 | 2018-09-11T23:03:08 | 2018-09-11T23:03:08 | 148,391,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 588 | r | global.r | suppressWarnings(library(stringr))
suppressWarnings(library(DMwR))
suppressWarnings(library(shiny))
suppressWarnings(library(shinythemes))
suppressWarnings(library(knitr))
suppressWarnings(library(kableExtra))
suppressWarnings(library(Matrix))
suppressWarnings(library(matrixStats))
suppressWarnings(library(DT))
suppres... |
4d2ae97abef1dc9b575732445c510e4c525f93d0 | eca03552d6cacef5ebdf689ffb76549bdcfe24e2 | /euler22.R | ab981074d45e39be15b8bc8943999eaa83eed01a | [] | no_license | liuminzhao/eulerproject | 792cd4a4df255b774c40a7b6b8a4e4f3b4335d0e | 844e5bc1909c35d5ad398fbbe8df16d7366caf47 | refs/heads/master | 2021-01-01T06:26:50.206519 | 2019-03-21T04:40:16 | 2019-03-21T04:40:16 | 5,541,720 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 386 | r | euler22.R | dat <- read.table('names.txt', header=FALSE, sep=',')
y <- as.vector(dat)
r <- rank(y)
x <- c('COLIN')
subscore <- function(x){
which(LETTERS==x)
}
score <- function(x){
splitx <- as.matrix(unlist(strsplit(x,"")))
sum(apply(splitx, 1, subscore))
}
e22 <- function(y){
y.score <- apply(as.matrix(y), 2, sc... |
52300f9f77ed3dc42ab98b58a54d8dbeb6305b22 | 5f71d135dff92755cdb1fed882f7a5450b834abc | /loaddata.R | dfb4c321e40564b9514aaf6f7f51a68efcde511d | [] | no_license | schweitzerb/RepData_PeerAssessment1 | 0b80d333219d0967c3e7afa4c23ffd6ed560e8c4 | c3094edf9d22029e128304520d2401684895236f | refs/heads/master | 2021-01-21T18:38:49.125798 | 2014-11-10T22:29:24 | 2014-11-10T22:29:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,488 | r | loaddata.R | ### Libraries
library(plyr)
library(ggplot2)
### Read Data
stepsData <- read.csv("activity.csv")
### format variables
stepsData$date <- as.POSIXct(as.character(stepsData$date),format = "%Y-%m-%d")
stepsData$interval <- sprintf("%04d",stepsData$interval)
##stepsData$interval <- format(strptime(stepsData$interval, form... |
e02a1a4f2a67b311393cff12fda0d1f60c7a22a8 | 89b872fd80ca1953ff3239f60234fbb95d5e2ca6 | /server.R | 7092aa8b875db2f245fb68959d5814dda2170ad9 | [] | no_license | eefermat/Zoek_Data_Analysis | 45ef00f9ba2cd2917270d0b93c1d0d7e63cb5969 | a2ec0ef879b314b6d3a1d333b35f14a76455ccf5 | refs/heads/master | 2020-12-29T02:19:10.823276 | 2017-04-23T15:39:13 | 2017-04-23T15:39:13 | 53,297,042 | 0 | 0 | null | 2017-04-27T02:30:19 | 2016-03-07T04:59:48 | R | UTF-8 | R | false | false | 72,147 | r | server.R | library(shiny)
library(ggplot2)
library(dplyr)
library(scales)
require(ggmap)
require(XLConnect)
require(magrittr)
require(jsonlite)
setwd("~/Dropbox/App")
member<-readRDS("member_data")
#member_birth<-readRDS("member_birth")
login<-readRDS("login")
orders_member<-readRDS("orders_member")
orders<-readRDS("orders")
br... |
9a6f2c000fcd8469152ee64aa250501bb36245dd | 6591be39877bf07b7f901864a87bd80d00cdbc23 | /plot3.R | b2d2f044341f04cb169b6701cd2b1d98f1a8fcc7 | [] | no_license | winnchow/ExData_Plotting2 | acea75dc59ad7f720031e21f5735ecc608c80d39 | d8a114d8be1938b4b10ca297a5aa365189504215 | refs/heads/master | 2020-04-22T06:34:32.710717 | 2014-07-21T12:26:26 | 2014-07-21T12:26:26 | null | 0 | 0 | null | null | null | null | IBM852 | R | false | false | 949 | r | plot3.R | # Plot 3
# 3. Of the four types of sources indicated by the type (point, nonpoint, onroad,
# nonroad) variable, which of these four sources have seen decreases in emissions
# from 1999íV2008 for Baltimore City? Which have seen increases in emissions from
# 1999íV2008? Use the ggplot2 plotting system to make a plot ... |
077df18b9a11628cef6242da7e2a2246bdc433a6 | 2d47450c41c23f6d008bfca5bf08d3161bb13491 | /man/ba_locations_details.Rd | 77222e910ebad65d8f1404d1beefc9e695ceaf89 | [] | no_license | khaled-alshamaa/brapi | 2c14727d65fc82a77d243bdc40c10b67955a04d5 | 5f2a5caa48d72e2412ead128b9143cc1882a060c | refs/heads/master | 2022-03-21T20:19:07.470329 | 2019-10-16T15:51:00 | 2019-10-16T15:51:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,407 | rd | ba_locations_details.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ba_locations_details.R
\name{ba_locations_details}
\alias{ba_locations_details}
\title{ba_locations_details}
\usage{
ba_locations_details(con = NULL, locationDbId = "",
rclass = c("tibble", "data.frame", "list", "json"))
}
\arguments{
\item... |
c52192cf12bb6ef3e018c753dde290db827d9ed4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/crqanlp/examples/clean_text.Rd.R | 4914fc7e3ee8181e25eb38c98b1097b70c3e14e0 | [] | 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 | 507 | r | clean_text.Rd.R | library(crqanlp)
### Name: clean_text
### Title: Clean text
### Aliases: clean_text
### Keywords: misc
### ** Examples
library(gutenbergr)
## let's get Alice's Adventures in Wonderland by Carroll
# gutenberg_works(author == "Carroll, Lewis")
rawText = gutenberg_download(11) ## take the text
rawText = as.vector(ra... |
f217909fce2884dd46ceecbe205be86265423c3c | e2fa06c4f96f07a872b6f5c8952bb42d00e4f8f0 | /man/plot.ml_g_fit.Rd | 37d4b658d58ba8bf8d7f57629bf7515a5dd9bf98 | [] | no_license | cran/msme | 8db2ef035cb84e72c56ad668e833ead148af0887 | e9f7649fc57ece8fcc178289196dc3e4707fbd35 | refs/heads/master | 2020-07-01T01:54:53.055045 | 2018-03-18T21:31:41 | 2018-03-18T21:31:41 | 17,697,696 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 895 | rd | plot.ml_g_fit.Rd | \name{plot.ml_g_fit}
\alias{plot.ml_g_fit}
\title{A plot method for objects of class ml_g_fit.}
\description{
This function provides a four-way plot for fitted models.
}
\usage{
\method{plot}{ml_g_fit}(x, ...)
}
\arguments{
\item{x}{
the fitted model.
}
\item{\dots}{
other arguments, retained for comp... |
f729067e724ebbb481622814ccc02ab07dc30c00 | aee0ef401ae2be8b3bf3edfe6c71b4bbb4abd018 | /man/Jac.Rd | a3bf6ee9b17874b450a5cfc46a63f8e674d96e3c | [
"MIT"
] | permissive | yl2883/yixiaopackage | 625aa43ec3031a8c18d4a03d715acd670fff2ba9 | f71143bb8a873b59b5c2c1682561e62047359a7d | refs/heads/master | 2023-05-05T06:06:59.105255 | 2021-05-23T19:39:25 | 2021-05-23T19:39:25 | 369,407,970 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 304 | rd | Jac.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/solve_ols.R
\name{Jac}
\alias{Jac}
\title{Jacobi Method}
\usage{
Jac(A, b, x)
}
\arguments{
\item{A}{Linear System of interest}
\item{b}{Target Vector}
\item{x}{Initial guess of the solution}
}
\description{
Jacobi Method
}
|
787b8226b17204eed0c42de99e8b889d58466a56 | 8f7fc1147fe90b857d6d7b17c7cfaaa4986f613f | /man/asypow.noncent.Rd | 55fd76377bf8ee5646a0a84070ba6aa524634cff | [
"LicenseRef-scancode-public-domain"
] | permissive | cran/asypow | 29ff0945ff524649f26c86bd3ada827488b47de5 | c8840aad7c1bc9af790051a7c45c330f3a56a967 | refs/heads/master | 2021-01-01T15:50:58.487649 | 2015-06-25T00:00:00 | 2015-06-25T00:00:00 | 17,694,518 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,851 | rd | asypow.noncent.Rd | \name{asypow.noncent}
\alias{asypow.noncent}
\title{
Asymptotic Noncentrality Parameter
}
\description{
Given an information matrix, alternative hypothesis parameter values, and
constraints that create the null hypothesis from the alternative,
calculates noncentrality parameter, degrees of freedom and p... |
9f4d446b1541d895e505689ac6f25dff27dec5db | 516b43cbce39caf6640c3fef8a47ff7d46d1dd6e | /man/Rcpp_Lcm.Rd | c8eb2cede1dd144596e8352df3f86e946cde3c38 | [] | no_license | QuanliWang/NPBayesImpute | d302b6e75dca461682dcd8d4d50d9e687cd221ad | e151964ee26bb21184e0867e0668b0f159bffa75 | refs/heads/master | 2021-06-04T05:24:45.981912 | 2018-09-13T17:40:40 | 2018-09-13T17:40:40 | 20,529,100 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 123 | rd | Rcpp_Lcm.Rd | \name{Rcpp_Lcm}
\alias{Rcpp_Lcm}
\title{
RCPP implemenation of the library
}
\description{
\link{Rcpp_Lcm-class}
} |
b1610e96847940b7401e5ef596611aeb1c3a37f4 | d4409010fa25433c80c2dd98cda5ad547f36f8de | /Simulation/dhmm_model_selection.R | 540fa50b2fba78e92645c18c1d87582dfd9cfba4 | [] | no_license | lruijin/pHMM | 014bc8326b9bcff127f6e90bf1534e46975865e0 | 093c9e703ff213ac6fd67ebca4d50944c44c5ca5 | refs/heads/master | 2023-09-01T17:23:20.492224 | 2021-10-27T17:49:48 | 2021-10-27T17:49:48 | 267,655,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,888 | r | dhmm_model_selection.R | suppressMessages({
library(doMPI)
library(foreach)
})
#library(doMC)
nrun=1000
sim_each <- function(no.run, seed = 2256,burnin = 3000, nsim. = 5000){
source("DHMM_v1.R",local=T)
#source('utils.R',local=T)
set.seed(seed + no.run);
daf <- genSim(N = 390, rx. = rep(c(0,1),each = 390/2),fitRx = c(FALSE,TRU... |
3feac763587107d801fac1fed09aed1391b79344 | b2bd46d3bea50ada3aff0ce47a98fb7e8bfd969c | /FrequencyApp/app.R | 27b5ff4830629ec8a3a148945ef12d201a740191 | [] | no_license | Ighina/FrequencyApp | 105c1e6dfa870996668f4c868b6ff7847a5c5820 | 3dce9d55ff986ced1ea46223d863de61ef10e09e | refs/heads/master | 2020-04-09T15:13:17.983384 | 2019-01-19T12:53:36 | 2019-01-19T12:53:36 | 160,419,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 40,632 | r | app.R | list.of.packages <- c("shinythemes", "shiny", "igraph", "tm", "tokenizers","stringr","readr","DT")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(shiny)
library(shinythemes)
ui_prova<-navbarPage('Sectio... |
dcf0117b6309a205aff17605bcb7361637e94863 | c115602521d415d90914c31c3a7b160964e7d0fd | /man/makeCustomResampledMeasure.Rd | f3f74bdb8592e3e5e6aa63b0fc7a005c2299ccf2 | [] | no_license | praneesh12/mlr | 295b5aefa72b8c56b070ac768de828d044ce0087 | 6069b98e8edb79148c005af08037ef89ea0d7cd0 | refs/heads/master | 2021-04-29T23:04:30.865508 | 2018-02-13T22:05:00 | 2018-02-13T22:05:00 | 121,545,775 | 1 | 0 | null | 2018-02-14T18:28:00 | 2018-02-14T18:28:00 | null | UTF-8 | R | false | true | 2,716 | rd | makeCustomResampledMeasure.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Measure_custom_resampled.R
\name{makeCustomResampledMeasure}
\alias{makeCustomResampledMeasure}
\title{Construct your own resampled performance measure.}
\usage{
makeCustomResampledMeasure(measure.id, aggregation.id, minimize = TRUE,
proper... |
c86cdad0d0cb428f3606a381a692a3742002e092 | 9fe15d7b960415851bba647bd4b9ad31dfd76dd1 | /Code/CDP_marques.R | 96339804864c43e2efa3133b36b856a617648d18 | [
"MIT"
] | permissive | micnngo/CDP | 3a432fb077d6061245c3d08a973eed1d5bc9e457 | 6a7a1372cb601880c5d2f00e062a60ac54a68b6c | refs/heads/master | 2020-12-29T20:00:49.463794 | 2020-03-10T16:31:05 | 2020-03-10T16:31:05 | 238,714,918 | 1 | 1 | MIT | 2020-03-10T16:31:06 | 2020-02-06T15:03:39 | R | UTF-8 | R | false | false | 6,865 | r | CDP_marques.R | ### CDP: Marques data set
### Load libraries and functions
source('postDPMManalysis_functions.R')
Rcpp::sourceCpp('reassign_obs_parallel.cpp')
library(SingleCellExperiment)
### Load data
marques <- readRDS('../Data/marques.rds')
# condense genes
g_idx = grep("loc", rownames(marques))
g_names = rownames(marques)[... |
e6e17b1e9f8ead16d91e6d7acd413ec7268db430 | beda4301d4b6b6dcd1db053e3f20cf24bd223ff6 | /R/pub_hlth_status_by_cnty_shp.R | 94e49147e2ec8f7576e09103a761bfa984e26725 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | Fredo-XVII/R.COVID.19 | b587b582c8fa6a30dcb04fa46e35643bdbfe528b | b43412625a2469055085db0055f3519f281ef681 | refs/heads/master | 2022-08-23T15:34:31.518645 | 2022-08-04T04:37:47 | 2022-08-04T04:37:47 | 250,618,422 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,582 | r | pub_hlth_status_by_cnty_shp.R | # Functions Roxygen format
#' @title COVID-19 Public Health Status by County shapefiles from ESRI - DEFUNCT
#'
#' @description
#' This functions pulls the public health status by county in the US as of the
#' current date with the shapefiles, which are saved to the current working
#' directory. The data comes fr... |
59c945ebd5db96b581e77fac180a63b96f2bec32 | e9a5a9e952a9ccac535efe64b96cc730b844677b | /man/setHyperlink-methods.Rd | e3fc4785fb2859080616c8649321f1d1ef6a80f6 | [] | no_license | miraisolutions/xlconnect | 323c22258439616a4d4e0d66ddc62204094196c9 | ae73bfd5a368484abc36638e302b167bce79049e | refs/heads/master | 2023-09-04T05:27:42.744196 | 2023-08-30T07:10:44 | 2023-08-30T07:10:44 | 8,108,907 | 114 | 35 | null | 2023-08-30T07:10:46 | 2013-02-09T11:17:42 | R | UTF-8 | R | false | false | 2,782 | rd | setHyperlink-methods.Rd | \name{setHyperlink-methods}
\docType{methods}
\alias{setHyperlink}
\alias{setHyperlink-methods}
\alias{setHyperlink,workbook,missing,character-method}
\alias{setHyperlink,workbook,missing,numeric-method}
\alias{setHyperlink,workbook,character,missing-method}
\title{Setting hyperlinks}
\description{
Sets hyperlinks for ... |
198a60dd7398a98f8e5a53b26b0bcf649bf264c6 | 59f01adeda5026f870ae374abfafa3edbffa5c6e | /tests/testthat/test-predictions.R | 37c634f00dd6ddb36b08956336f44a5436b26de7 | [
"MIT"
] | permissive | dcossyleon/tune | 00a90451f150c85f61a5b6743367c424888426ec | ad58dff5a27a82b3e9aa47a966a65a81355b2eaf | refs/heads/master | 2022-10-11T04:14:12.340091 | 2020-06-05T23:17:17 | 2020-06-05T23:17:17 | 271,087,691 | 1 | 0 | null | 2020-06-09T19:07:13 | 2020-06-09T19:07:12 | null | UTF-8 | R | false | false | 3,223 | r | test-predictions.R | context("extracting predictions")
source(test_path("../helper-objects.R"))
check_predictions <- function(split, pred, tune_df) {
assess <- rsample::assessment(split)
n_te <- nrow(assess)
n_pm <- nrow(tune_df)
ind_te <- as.integer(split, data = "assessment")
expect_true(tibble::is_tibble(pred))
expect_equ... |
366662b1e66a81e435252f94be48f569440f6722 | 637cb9135b7e092e4cb20c5096064330b1902a16 | /scripts/src/test_0_install.R | cb0dd52e35e532da6dd5d45e9a6dd571b7f33305 | [
"MIT"
] | permissive | erictleung/dada2HPCPipe | a59897ea6568eb1b95dedfd44c90acd1bae2591c | c6b26273e6b8e8098d47b4f01acc1a37f0e4deed | refs/heads/master | 2021-07-04T04:20:42.520253 | 2018-06-29T23:09:58 | 2018-06-29T23:09:58 | 96,589,393 | 3 | 1 | null | 2017-09-25T08:37:23 | 2017-07-08T01:56:44 | R | UTF-8 | R | false | false | 306 | r | test_0_install.R | # Title:
# Test Package Installation
# Description:
# This script simply tests the loading of the package into R to make sure it
# does so without any errors
# Load library
cat("Loading dada2HPCPipe package...\n")
library(dada2HPCPipe); packageVersion("dada2HPCPipe"); cat("\n")
cat("No errors!\n")
|
53c01765bf3035b6d1c1ad063775b941261f69c1 | 48bec1ab1d9b96416ac4ce2462c8718ad3271b2e | /R/tests/testy.R | 7962b4da147608e7cad174b13b79384ea76c4a2b | [] | no_license | kaletap/spectral-clustering | 8a0c9550363f03ae5b036247a9166a20500e0ead | e013ad0822f9b6558fc0fbcc7aae66a5aeca2de1 | refs/heads/master | 2020-04-18T18:06:51.553670 | 2019-02-13T18:49:21 | 2019-02-13T18:49:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,487 | r | testy.R | library(mclust)
library(dendextend)
library(genie)
source("spectral.R")
read_data <- function(benchmark, dataset){
matrix_file_name <- paste(dataset, ".data.gz", sep="")
labels_file_name <- paste(dataset, ".labels0.gz", sep="")
matrix_path <- file.path("..", "benchmarks", benchmark, matrix_file_name)
labels_... |
220db8d1aaed96677d132c63eb28061de825a2a1 | 4a65f8536366b0dea196bb6a2f1384089fe425f7 | /man/moto.Rd | bc759880bab876fe72c0e7bf56c7515df5749d41 | [] | no_license | RobinHankin/hyper2 | f577043872f55150619a7f04e2c945ce038c85e2 | f19fcc4eee6882f6ec5f6eef9c553be42a50bee1 | refs/heads/master | 2023-08-31T02:53:40.288486 | 2023-08-23T01:22:35 | 2023-08-23T01:22:35 | 105,470,077 | 5 | 3 | null | 2023-06-01T21:57:44 | 2017-10-01T19:55:58 | TeX | UTF-8 | R | false | false | 1,206 | rd | moto.Rd | \name{moto}
\alias{moto}
\alias{motoGP}
\alias{motoGP_2019}
\alias{moto_table}
\alias{moto}
\alias{moto_maxp}
\docType{data}
\title{MotoGP dataset}
\description{Race results from the 2019 Grand Prix motorcycling season}
\usage{data(moto)}
\details{
Object \code{moto_table} is a dataframe of results showing ranks of 2... |
528571a7006b929f9d50c93ecdb7c6fb25377a56 | 6ac4b51b3bbf24b1f1265a5c58193dda48f21b5d | /R Programming for Statistics and Data Science/1. Sections 1-6 - The Basics/1. Vectors,Matrices,Lists.R | 07d6e09e29e4e18d00ce51cdf4e5a4b33449af4d | [] | no_license | DanInacio/R | 114b08660e2d8d5dc38fc679e6e04c4dc4f6a57e | ebcb99d3b704ce71b976c013ddfe9001191339bb | refs/heads/main | 2023-03-29T09:08:05.047602 | 2021-04-07T23:22:50 | 2021-04-07T23:22:50 | 355,334,712 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,791 | r | 1. Vectors,Matrices,Lists.R | # ----------------------------------------------------------------------------
# Exercise 1: Creating Objects in R
x <- 10:20
y <- x +2
z <- y*3
answer <- (z-6)/3
# One line of code
x <- (((10:20) + 2)*3 - 6)/3
# ----------------------------------------------------------------------------
# Exercise 2: Data Types in ... |
9e14b72bb504d697f925f9eb9b1f317d5f90c983 | 3ffa653e649a203832994147cb4f73aa9709b16a | /bin/ibd-plot-genomefile.r | d19f0291dcb14439036d8f39240d077f89046f61 | [
"MIT"
] | permissive | jkaessens/gwas-assoc | fa0ed3e36b2607263a66810b5a83fd266e22c9d4 | 1053c94222701f108362e33c99155cfc148f4ca2 | refs/heads/master | 2023-02-07T13:07:05.624146 | 2020-12-20T15:15:28 | 2020-12-20T15:15:28 | 323,265,733 | 0 | 0 | MIT | 2020-12-21T07:47:59 | 2020-12-21T07:44:58 | null | UTF-8 | R | false | false | 821 | r | ibd-plot-genomefile.r | #######################################
### 2010, David Ellinghaus ###
### 2019, Florian Uellendahl-Werth ###
#######################################
rm(list=ls())
file.genome = commandArgs()[4]
inc<-10000
library("stream")
genome<-DSD_ReadCSV(file.genome,sep="",skip=1,take=c(7,8))
wng<-T
lines_n<-0
png(fil... |
17bbecd3a104265c5754ae8d7144ef732cc30cc9 | 9113e3269573abcd007a25d3413a3b0ee0ed8e4e | /1-layout/example-html.R | 5f6ada8c67487001eb89765e2a422688e13577de | [] | no_license | datasketch/rladies-stl-personalized-shiny-apps | ddab860b53cce453783add025d5cb44a14a356f3 | aa72ed4f0ea3e610a53b86a2453e9a230b41cf59 | refs/heads/main | 2023-02-20T04:05:19.813308 | 2021-01-20T01:51:39 | 2021-01-20T01:51:39 | 330,156,512 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 435 | r | example-html.R |
library(shiny)
ui <- fluidPage(
h1("Hello"),
tags$blockquote("Lorem teat tesatas tast at at eat"),
div(style = "background-color: red;",
p("Hello R Ladies"),
h2("Subtitle")
),
fluidRow(
column(3,
img(src="https://via.placeholder.com/150"),
),
column(9,
img(src="ht... |
a21e1f30816e9e1b7989267a20bdc03bdf731d54 | dfed572e96372e1a46c367516f24e931145325f0 | /faza1/kody/analiza_zdarzenia.R | 5f92c84342312a17ca08901a256bbcd51e65ca8e | [] | no_license | flaminka/RandBigData-PROJEKT2 | db49bab57d5e6e4bc138b6553923fe8891a7c768 | b0e247a531a922250878820229d73b23d4ac5fea | refs/heads/master | 2021-01-21T14:48:43.211396 | 2016-06-13T21:27:01 | 2016-06-13T21:27:01 | 58,480,439 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 996 | r | analiza_zdarzenia.R | #'
#'
#'
#'
#'
#'
analiza_zdarzenia<- function(zdarzenie, dane, mostf, k){
require(dplyr)
date=as.Date(dane$created_a)
dane$created_at=date
date=date%>%unique()%>%sort()
dats=filter(mostf, rzecz==zdarzenie)%>%select(date)%>%arrange(date)%>%t()%>%as.Date
d=as.Date(dats[1]:(dats[1]+k-1), origin ... |
51101f0c0b9db00143c770cf1c387bf34e749cdf | b68ab237085809c789b14cbba81e95d6ac03e2ae | /chap2_ex2.R | 40e95257151a948e6e18d370576edd6207e4a259 | [] | no_license | kgaythorpe/Rtest | 37f31f9070de48fe6d394c530c407f4fd21cd1ec | f17e3411498bd22f6b22733395caeadf4cd21562 | refs/heads/master | 2021-06-29T05:15:55.424547 | 2017-09-27T14:29:52 | 2017-09-27T14:29:52 | 104,993,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,983 | r | chap2_ex2.R | vignette("chapter2",package="jrShiny")
install.packages("d3heatmap")
library(d3heatmap)
d3heatmap(mtcars)
d3heatmap(x, k_row = 4, k_col = 2, scale = "column")
install.packages("DT")
library(DT)
data(iris, package="datasets")
datatable(iris)
install.packages("edgebundleR")
library(edgebundleR)
cor_mt = cor(mtcars... |
9ef353e95fd9dc6156c1c250821e0231641e03a1 | 7d31f360f1ece69b09a4b51e3986ac44025efc7c | /package/clinUtils/man/getPaletteCDISC.Rd | 6f9b5b1239090a740de72ccdd9f6a18ed22d8afa | [] | no_license | Lion666/clinUtils | a0500a773225ffafc29b7d7f7bcc722dd416743c | dc6118f32d311657d410bdeba02f3720f01d62df | refs/heads/master | 2023-08-12T08:48:42.923950 | 2021-09-21T14:56:18 | 2021-09-21T16:14:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,031 | rd | getPaletteCDISC.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots-palettesCDISC.R
\name{getPaletteCDISC}
\alias{getPaletteCDISC}
\title{Get standard palette for typical CDISC variables.}
\usage{
getPaletteCDISC(x, var, type, palette = NULL)
}
\arguments{
\item{x}{Character vector of factor with
variab... |
ca5dc01b36a933144869e9f6a2e13e43d2df21f9 | 49bd976847a38b926b0ecab8da3f191cd60c786d | /bin/findInterfaces.R | aa120b284b24608a555a4affd2ff87dc2660c2ba | [] | no_license | jweile/dmsPipeline | 50f68c59efb85f57661dbd167c625a522d8d0a06 | 512c310fe78822d006ab55f61d9b759e0bf71edc | refs/heads/master | 2023-08-12T08:23:33.701095 | 2017-06-30T17:58:24 | 2017-06-30T17:58:24 | 417,578,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,471 | r | findInterfaces.R | #####################################
# Compare Y2H to compl data to find #
# interface positions #
#####################################
source("lib/resultfile.R")
source("lib/liblogging.R")
source("lib/libyogitools.R")
source("lib/cliargs.R")
source("lib/topoScatter.R")
source("lib/pymolCol.R")
librar... |
cf7477fac59e77516eb089dd2ff22b12d20f5ffe | f86d3a504220a17a39a7250ad8c623c03e78fcf1 | /man/amnfis.Rd | ce437d991ab09531e64f4d191255ed2b7806048c | [] | no_license | deybvagm/amnfis | 0879c19635d9171fd0aba5496ac834a7779fefdf | 3ae2c825061834e12399490d921031aef6c09316 | refs/heads/master | 2020-04-07T06:25:33.469324 | 2016-03-06T16:21:21 | 2016-03-06T16:21:21 | 47,901,230 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 267 | rd | amnfis.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/hello.R
\name{amnfis}
\alias{amnfis}
\title{Hello world package}
\usage{
amnfis(X, d, k)
}
\arguments{
\item{k}{number of clusters}
}
\value{
data
}
\description{
AMNFIS program
}
|
30a8b7ba20d09a5413e7c1e6604d4f042944796f | aa5057cc56659f28ecbdd2522fcf6c509d7d671b | /man/assignSeason.Rd | 46033b567669060385ddc5ede9f4f87e738c6523 | [] | no_license | ksauby/dataproc | 8fd0640707d4f6ba80e9868bc3f3c98f5d3c74b8 | 24f69d9160b0de4be79487bb11bd7982dd4d3ad6 | refs/heads/master | 2021-04-24T18:00:13.990345 | 2019-01-30T00:42:36 | 2019-01-30T00:42:36 | 42,401,878 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 551 | rd | assignSeason.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assignSeason.R
\name{assignSeason}
\alias{assignSeason}
\title{Assign Season based on Date}
\usage{
assignSeason(dat, SeasonStarts)
}
\arguments{
\item{dat}{dataframe including "Date" in \code{POSIXct} format.}
\item{SeasonStarts}{SeasonStar... |
4cb260589f91c1470edc762f5e0abbce26b5099f | 02edc103803e9cdceb22a2c0b8c1c70c5bb7f1c0 | /data/data_wrangling_human.R | c37ae0494bd0837389a7990915df38904d5c75bc | [] | no_license | suprajohde/IODS-project | 5fa15e93cd84e5287c1ae9e5199f98d05ad90c45 | 36396ea351c7f8dd76b0eace8ef46c6a1b713d19 | refs/heads/master | 2021-05-01T13:45:38.470093 | 2017-02-24T23:46:34 | 2017-02-24T23:46:34 | 79,580,550 | 0 | 0 | null | 2017-01-20T17:24:23 | 2017-01-20T17:24:23 | null | UTF-8 | R | false | false | 1,095 | r | data_wrangling_human.R | ## Tiina Autio
## W5 Data Wrangling
## Data source: http://hdr.undp.org/en/content/human-development-index-hdi
human <- read.table("http://s3.amazonaws.com/assets.datacamp.com/production/course_2218/datasets/human1.txt", header = TRUE, sep = ",")
names(human)
str(human)
summary(human)
library(stringr)
library(dplyr)... |
f6492633defad3cec2f583c995c0d2926c96a972 | 88767dec5d03291c4fe6acb377d21ea46608fb6a | /03d_Pres_uncty.R | 5fd0bde5001aab8900bd0ba30be104f938c8ff11 | [] | no_license | themisbo/Deconvolution-in-well-test-analysis | 14ba7653214d579a13d071e5f1c2406b09591b1a | 4590659d9fadbfa597d720b5d341fc046d4b1f1b | refs/heads/master | 2023-08-14T20:50:11.753559 | 2021-09-26T12:47:39 | 2021-09-26T12:47:39 | 226,081,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,564 | r | 03d_Pres_uncty.R |
obsPress <- real_TLS$swPress
obsRate <- real_TLS$swRate
trueResp <- real_TLS$TlsResp
truePress <- real_TLS$TlsPress
which_dd <- which(obsRate$Rate != 0)
N_Nodes = 30
t0 = 1e-3
tN = ceiling( obsPress[nrow(obsPress)]$Time )
tau = seq(log(t0), log(tN), length.out = N_Nodes)
t = exp(tau)
source("func... |
48ca1501b0e6f026c3fe0da0afc8b4fa21f8da8f | 60b03bddbdbb611a850842d5a3fc983311804b7a | /00 - Recording NYC Cases.R | 27914b29771d058e59dd34a2b5b023b8641e3499 | [] | no_license | timkiely/covid-19-model | 8facdc4479215e0007426cf5a6e52f1e4e125efd | b31301ba05dd43d1744a7a6212ad4e6802265f6a | refs/heads/master | 2021-02-16T11:29:26.956184 | 2020-11-05T20:15:44 | 2020-11-05T20:15:44 | 245,000,665 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,226 | r | 00 - Recording NYC Cases.R |
# COLLECTIONG NYC COVID DATA ----
# Tim Kiely, March 2020
# Daily stat source:
# NOTE: as of 4/6 this page no longer displays data
# https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-daily-data-summary.pdf
# browseURL("https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-daily-data-summary.pdf")
... |
2ef285684ee2bdd2e8397d8b68179236613981a9 | 73fbcce9fc9b2eaa15098609a012dd40fc594198 | /notes/arules.R | 134a164610bfae7b6c441200b3b643626f2e26a4 | [] | no_license | jacolind/ship-sales | 536385012b4c5833caa127950541100a45cc2f5c | 91abc84a3cf18d65c7e11e72c8ea30ad4669e36f | refs/heads/master | 2021-04-15T11:51:47.956925 | 2018-04-06T11:57:06 | 2018-04-06T11:57:06 | 126,536,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 298 | r | arules.R | install.packages(arules)
library('arules')
data('Epub')
Epub
summary(Epub)
year <- strftime(as.POSIXlt(transactionInfo(Epub)[["TimeStamp"]]), "%Y")
table(year)
Epub2003 <- Epub[year == "2003"]
length(Epub2003)
image(Epub2003)
transactionInfo(Epub2003[size(Epub2003) > 20])
arinspect(Epub2003[1:5])
|
e178073b8b6de40d8a50ec22c4be65a7924c0e69 | 26aba2dfa619c6c6a10b77e7d38e1c313c5bf2f7 | /man/Output_df-class.Rd | b15aad3804cbf68625f837e000643318d457cda4 | [] | no_license | Pablo-Lopez-Sfi/dependenciesMap | 4655ea1a732412500dcccda7b7e433d4d666ec68 | 38370adb260bddfa724bc9d8ebf2e87fdc4f9cce | refs/heads/master | 2022-11-16T22:19:49.544103 | 2020-06-26T12:18:22 | 2020-06-26T12:18:22 | 259,566,743 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,772 | rd | Output_df-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dependencies_code.R
\docType{class}
\name{Output_df-class}
\alias{Output_df-class}
\alias{Output_df}
\title{Outputs in the data preparation program from data frames previously generated (can be used to modified a global variable)}
\descriptio... |
c4b38a3950101b58580f886f620f6e952b179489 | 7acfddc8f6e83c086aff89440719ec4f21ebdfa4 | /man/query_acs.Rd | 1837c0cc87cddd83760cd5ac449103ff7c4685d1 | [] | no_license | crazybilly/fundRaising | f16cafc02d23fd1fd140ba586cb6940516d19292 | 42f4f4ae725f62e31a7e16c10f172fff801737ea | refs/heads/master | 2021-07-29T20:03:49.287552 | 2021-07-26T14:08:32 | 2021-07-26T14:08:32 | 145,170,311 | 4 | 3 | null | 2019-07-11T13:57:23 | 2018-08-17T22:03:05 | R | UTF-8 | R | false | true | 1,971 | rd | query_acs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/acs_screening.R
\name{query_acs}
\alias{query_acs}
\title{Obtain 5-Year American Community Survey Estimates}
\usage{
query_acs(
var = c("B19013_001E", "B25077_001E"),
year = NULL,
state,
county = NULL,
tract = NULL,
blkgrp = NULL,... |
927dbadfdb6318875b9f05dea1189663a69c73c6 | 3b9563287bd7129a0d97d55a94d979799c959499 | /man/setup_dap.Rd | c218ead2be0ef49e5d0b2e28e010844f56b06359 | [] | no_license | JoFAM/daprojects | 008241b104eb1267992e24e73917b56490ae75d7 | 21c6972202bf7b83e14383767fd5a290b54cfcce | refs/heads/master | 2021-05-09T01:46:00.807147 | 2018-01-29T10:57:28 | 2018-01-29T10:57:28 | 119,185,747 | 0 | 1 | null | 2018-01-28T15:36:17 | 2018-01-27T17:24:56 | R | UTF-8 | R | false | true | 1,102 | rd | setup_dap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/setup_dap.R
\name{setup_dap}
\alias{setup_dap}
\title{Set up a project for data analysis.}
\usage{
setup_dap(path, desc = character(0), readme = TRUE, addexamples = FALSE,
...)
}
\arguments{
\item{path}{a character vector with the path wher... |
c02230d841f6b4b9fc5a8c70e8d178c58ab84e9e | f020db9cb67886a11c1b8721b04af1ba22136ed5 | /tests/testthat/test-purge.R | 5c9a54d69b2766b51c4016a6af65de84a8fa7c13 | [] | no_license | slopp/renv | 2b5ac42ae89682f6cdb022221b53530679484982 | fd88fbe2d95a0f417a7fae4f188acdeb99691798 | refs/heads/master | 2020-04-20T22:14:33.890121 | 2019-07-04T06:53:31 | 2019-07-04T06:53:31 | 169,133,285 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 275 | r | test-purge.R |
context("Purge")
test_that("we can purge packages from the cache", {
renv_tests_scope("breakfast")
renv::init()
expect_true("breakfast" %in% basename(renv_cache_list()))
renv::purge("breakfast")
expect_false("breakfast" %in% basename(renv_cache_list()))
})
|
356e7c7c508b7dec5cbdb71f7dea5f08504d4ed9 | a62777fae97b178f476b16941cb91af41c0ca44f | /R/get_network.R | af82a34b903067b2866ff926b748ef464ecfbed4 | [] | no_license | dmzonana/asnipe | 6bbabb3017dea170d62fc06baad876665b54b293 | 76965eca05d4128c9c557ddea069495e96e83141 | refs/heads/master | 2020-05-25T14:56:25.650472 | 2016-09-13T14:45:37 | 2016-09-13T14:45:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,448 | r | get_network.R | get_network <- function(association_data, data_format = "GBI", association_index = "SRI", identities = NULL, which_identities = NULL, times = NULL, locations = NULL, which_locations = NULL, start_time = NULL, end_time = NULL, classes = NULL, which_classes = NULL) {
#### CHECK INPUTS
if (is.null(association_data)) { ... |
2e3a23945d8ad20a88cd68ce9b1ed4a54167f8e6 | cb93cf0799e3eedca6f9e720e09bb60e0f77ff10 | /R/GenericTabularFileSet.R | f0e53d0fe3904c6fc658ffc2f4e214ed2f68d38b | [] | no_license | HenrikBengtsson/R.filesets | 254c37b4546e8280b9972d06840b918e12e0b4e9 | 17181ae1c84dbf7bad1214d37e6f133ed2deeba4 | refs/heads/master | 2023-01-08T23:58:09.708417 | 2022-07-21T09:52:18 | 2022-07-21T09:52:18 | 20,844,863 | 3 | 1 | null | 2018-04-03T22:12:45 | 2014-06-15T00:25:31 | R | UTF-8 | R | false | false | 1,319 | r | GenericTabularFileSet.R | ###########################################################################/**
# @RdocClass GenericTabularFileSet
#
# @title "The GenericTabularFileSet class"
#
# \description{
# @classhierarchy
#
# An GenericTabularFileSet object represents a set of
# @see "GenericTabularFile"s.
# }
#
# @synopsis
#
# \arguments{
# ... |
d470cf2c009e033a553fbdc90e8f48c6adefdd9b | 6bb82b6ec315060653cc675155321b8b53e85ed9 | /03 R Syntax 1 (Data Typs and Strings)/03_4_Strings.R | 7ffa7fce3391809b6363a6c0b5358c6ad28af4c9 | [] | no_license | inkyscope/R-for-Data-Analytics | d9d66a58fd67a5c4731c186010de281657bd82d6 | fd9e04e4184e7edee6ee08ca238ac4c24a83a5c7 | refs/heads/master | 2023-01-31T12:43:00.186966 | 2020-12-11T07:11:54 | 2020-12-11T07:11:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,267 | r | 03_4_Strings.R | # 03-4 Handling Strings ----------------------------------------------
S <- "Welcome to Data Science!"
length(S)
nchar(S)
S1 <- "My name is"
S2 <- "Pilsung Kang"
paste(S1, S2)
paste(S1, S2, sep="-")
paste(S1, S2, sep="")
paste("The value of log10 is", log(10))
S1 <- c("My name is", "Your name is")
S2 <- c("Pilsung"... |
39c0e8287a256af2300a4b14f1cda5a4659892ff | 6af844903b9d066581408450a1e743c1283468e5 | /R/windowed_scalogram.R | 1db8bdc4a183c3f73ac5b55ebc09bd3d0a672bd8 | [] | no_license | rbensua/wavScalogram | 8ab5766639ec0154383b0ecc43858cedfe52019c | b811b3382de8190c0ed8249bcee04394a007ceda | refs/heads/master | 2021-06-24T13:45:41.879344 | 2019-06-12T09:53:42 | 2019-06-12T09:53:42 | 136,003,978 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,507 | r | windowed_scalogram.R | #' @title Windowed scalograms of a signal
#'
#' @description This function computes the normalized windowed scalograms of a signal for
#' the scales given. It is computed using time windows with radius \code{windowrad}
#' centered at a vector of central times with increment of time \code{delta_t}. It is
#' importa... |
11469b63781763b45a00f2cb1f1201d6d87a70dc | e53f8d45dac571308c04cbd2f06e04c6ce332696 | /code/animateOptimization.R | 0c2eaeafffa97479e2b2144e0527df59aa29a0bd | [] | no_license | jeanlucj/BO_Budgets | beb6963a409be387c4d11fc469ee2d6daf992dce | 7396b22b55c4ccae5df4abffd8acd1eceda7978c | refs/heads/main | 2023-08-06T06:40:15.752144 | 2021-10-07T18:44:40 | 2021-10-07T18:44:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,409 | r | animateOptimization.R | ##################################################################################################
# A script with animation examples using R
# by: Isaac J. Faber, examples derived from https://yihui.name/animation/examples/
# WARNING!!!! without dependcies installed you can only render to HTML
# Make sure you install ... |
1d0d31b4ef766eda514e790f17523d2abf6893c7 | 7eb128f9b7899c33d4854009edbd38dd566cba72 | /R Tutorials/data mining/arules.R | 8127182aa92cd017e07ae20bc25a6d3a815c76c2 | [] | no_license | chengjun/Research | 1149add090ec563f544c4b5a886c01b1392a25d4 | c01e3d2eac2bca74671abb9cd63e1b06e5566fc8 | refs/heads/master | 2021-06-15T16:35:15.005107 | 2019-08-06T09:03:29 | 2019-08-06T09:03:29 | 11,498,113 | 4 | 6 | null | 2021-04-15T09:27:11 | 2013-07-18T08:36:12 | Mathematica | GB18030 | R | false | false | 3,354 | r | arules.R |
library(arules)
data("AdultUCI"); names(AdultUCI); dim(AdultUCI)
AdultUCI[["fnlwgt"]] <- NULL
AdultUCI[["education-num"]] <- NULL
AdultUCI[[ "age"]] <- ordered(cut(AdultUCI[[ "age"]], c(15,25,45,65,100)),
labels = c("Young", "Middle-aged", "Senior", "Old"))
AdultUCI[[ "hours-per-week"... |
4fe4c4e00ee651d3be54fb08407aa1afbd1f661e | d008d74a9c473ca61b96923409811ccb47b96406 | /R/utils.R | 2371e74fed432b5ba895a9a19028031e0acee4fa | [] | no_license | AustralianAntarcticDivision/raadtools | 0246d25a1480888780aa23ea27c1f9985bdaafa0 | d8cc9c553f52e4b5a14c7f2fd738d5bc1318c304 | refs/heads/main | 2023-07-24T23:03:01.099186 | 2023-07-07T01:28:52 | 2023-07-07T01:28:52 | 34,773,324 | 19 | 5 | null | 2023-03-14T10:06:42 | 2015-04-29T05:00:19 | HTML | UTF-8 | R | false | false | 5,098 | r | utils.R |
nc_rawdata <- function(x, var) {
nc <- ncdf4::nc_open(x)
on.exit(ncdf4::nc_close(nc))
ncdf4::ncvar_get(nc, var)
}
xrange <- function(x) c(xmin(x), xmax(x))
yrange <- function(x) c(ymin(x), ymax(x))
update <- function() {
cat('\ndevtools::install_github("AustralianAntarcticDivision/raadtools")\n\n')
}
set_ut... |
9278e60377f6fd8091f39d2000a8919b52190d54 | 9326d857c238ff56f993437fb44a5c90961d0753 | /R/radio_button_input.R | 7103864e9cd266e3765158f8505f39511ae41e7f | [] | no_license | moj-analytical-services/shinyGovstyle | e1e9b4062710b229f269f9b0bb58c1398383f7e1 | a033342e971b9f090c06b6e17b82b20d27dce50c | refs/heads/master | 2023-07-11T05:45:21.430131 | 2022-02-22T10:36:38 | 2022-02-22T10:36:38 | 192,864,104 | 34 | 4 | null | 2022-02-07T12:41:32 | 2019-06-20T06:41:58 | CSS | UTF-8 | R | false | false | 8,786 | r | radio_button_input.R | #' Radio Button Function
#'
#' This function create radio buttons
#' @param inputId The \code{input} slot that will be used to access the value.
#' @param label Input label.
#' @param choices List of values to select from (if elements of the list are
#' named then that name rather than the value is displayed to the use... |
1be20bb31febaeae61fa9e024a2786366bcaba0d | f6c7dd6052f342a919c4f26731ebaace434efb03 | /man/h5DF.Rd | 52d2e0e4ff5d4f96d324ae2189db04dad8c28862 | [
"MIT"
] | permissive | ActiveAnalytics/activeH5 | 9ec5aac0f05a87b8302f013289c4d7ae056bc38c | d43bfbb6110a9be5698f3e63eae55023f4ba3fd5 | refs/heads/master | 2021-01-10T20:29:00.289895 | 2014-11-30T23:07:49 | 2014-11-30T23:07:49 | 23,437,558 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,192 | rd | h5DF.Rd | \name{h5DF}
\alias{h5DF}
\title{Specification of h5DF reference class}
\usage{
h5DF(...)
}
\description{
The h5DF object is an interface to a special type of HDF5
file that holds the data for large data frames in chunks.
Data frames are converted to numeric matrices, characters
are converted to factors and fa... |
0808d4fe35a446f9c8e63166f735ce8a76d75cb3 | b797f4e2b9e0cfe2d9e1a913842f202bc2bfc7fd | /mallet/man/MalletLDA.Rd | 82c19cf9d27f3dffa496f9ee51d5995c7d1ec135 | [] | no_license | mimno/RMallet | 680596989de8c4896102cd17f8a6714ab8989bb9 | cff720fe1a50e2ca6016aed89a18296bdd11cec2 | refs/heads/master | 2022-07-31T13:45:38.152176 | 2022-07-21T05:41:28 | 2022-07-21T05:41:28 | 19,386,227 | 36 | 10 | null | 2022-07-21T05:13:00 | 2014-05-02T19:30:23 | R | UTF-8 | R | false | true | 2,363 | rd | MalletLDA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mallet.R
\name{MalletLDA}
\alias{MalletLDA}
\title{Create a Mallet topic model trainer}
\usage{
MalletLDA(num.topics = 10, alpha.sum = 5, beta = 0.01)
}
\arguments{
\item{num.topics}{The number of topics to use. If not specified, this default... |
51bd6d051366850a28b4f6da93cb1c44195590d1 | 3cc4ca2dd309b93fd261a9502830d0955315ef86 | /R/element_utils.R | b979413207cf28fe1430459ffb7d458490a30f8c | [
"MIT"
] | permissive | rstudio/remarker | 198820ee027018c4a524df0e080d7c7484072edc | 8f46d2463a32e097c2c9c90a9644590cb25cb707 | refs/heads/main | 2023-08-15T01:48:19.757759 | 2021-10-05T15:04:13 | 2021-10-05T15:04:13 | 354,125,173 | 11 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,325 | r | element_utils.R | #' Extract information from the Attr of an Element
#'
#' @param x A Block or Inline Element object which has an Attr child.
#'
#' @examples
#' h <- Header(
#' attr = Attr(
#' classes = Texts("x", "y"),
#' attributes = TextText_s(list("a", "a_value"), list("b", "b_value"))
#' )
#' )
#'
#' el_get_class(h)
#'
... |
7e4b8d1f94385a2860a0753806578c9c79db3910 | 8d43decd9ab0411d01e45a539315beef646a9c73 | /tests/testthat/test-cache.R | 0ae65c2b8b67e1a6448f9b8827f38cdd9b06dd1d | [
"MIT"
] | permissive | robertzk/cachemeifyoucan | 2137fbaba0c5f21c8ba671815d444af7682983aa | 7ce4915224d10526c2efdae41c692116304caba8 | refs/heads/master | 2021-01-17T09:29:27.531909 | 2016-05-11T18:07:08 | 2016-05-11T18:07:08 | 23,096,482 | 8 | 9 | null | 2017-09-12T20:01:06 | 2014-08-19T04:11:40 | R | UTF-8 | R | false | false | 4,820 | r | test-cache.R | context('cache function')
# Set up test fixture
# Set up local database for now
# https://github.com/hadley/dplyr/blob/master/vignettes/notes/postgres-setup.Rmd
describe("cache function", {
db_test_that("blacklisting NAs retains caching when values are NA", {
expect_cached(blacklist = list(NA), {
df_re... |
14ded03659fce5bfb105664d2a515ee1b83bf8d9 | e1373ea202ba95aeb29cc31d42e23bf4463c147f | /01_geocoode_addresses.R | 9b50d68a438e240b767ca487c9ccf5c739f29d57 | [
"MIT"
] | permissive | michaelqmaguire/geocoding-with-r | a7a70c7004e125a570a0a4550f141f9836d0847f | e639e1ea4807ba91f46e0bc1da645d38ca19d2c2 | refs/heads/main | 2023-05-05T14:26:31.239383 | 2021-05-26T14:58:43 | 2021-05-26T14:58:43 | 369,616,341 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,368 | r | 01_geocoode_addresses.R | library(tidyverse)
library(tidygeocoder)
pharmacies <-
tribble(
~ Name, ~ Address, ~ CityStateZip, ~ Phone,
"Malcom Randall VA Medical Ctr", "1601 SW Archer Rd", "Gainesville,FL 32608", "(352)376-1611",
"Shands Hospital-University FL", "1600 SW Archer Rd", "Gainesville,FL 32610", "(352)265... |
6b291b47090860e5e45616e7cf2139407d3d64b7 | 867b80d5b06016d0ac49856f99cc07318f41dc02 | /label_mesa_data_with_analysis.R | dfd2b022ce05985820e08eed0cef88e535d5d284 | [] | no_license | sechilds/cirpa2017 | 51e39a70ecd6a05945e931cf1e3aa1a646477ac6 | fb0d41c236f78b4a23dfdea789158d356fec878a | refs/heads/master | 2021-07-16T19:02:36.219852 | 2017-10-22T22:46:08 | 2017-10-22T22:46:08 | 106,300,275 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,420 | r | label_mesa_data_with_analysis.R | library(tidyverse)
library(labelled)
# work through and label the data from the mesa project
# list of useful variables for the workshop
workshop_vars = c('x_id',
'x_idregion_y1',
'proi1_y1',
'proi2_y1',
'proi3_y1',
'proi4_y1',
... |
615ef82b93b54ec62bb1603662e042b11056f218 | 97932fb906650536ff644f4b57e1b05a74695e1d | /R/fees.R | 7f6ba3529105ffaee886cf424828f9ae90a69e1e | [] | no_license | muschellij2/squareupr | 350ed186d711182abfb6ad5dde0c068e5325ee29 | 37bf28750127235c09f7f57278faf484b04aac0d | refs/heads/master | 2021-05-26T23:23:03.404648 | 2019-07-11T20:50:36 | 2019-07-11T20:50:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 435 | r | fees.R | #' List Fees
#'
#' Lists all of a location's fees.
#'
#' @template location
#' @template verbose
#' @return \code{tbl_df} of fees
#' @details Required permissions: \code{ITEMS_READ}
#' @examples
#' \dontrun{
#' my_fees <- sq_list_fees(location)
#' }
#' @export
sq_list_fees <- function(location, verbose=FALSE){
sq_l... |
cd2eb4c7222f606ebdde1c4b80cd3c26f83cca97 | 95abece5eaa8ee7b85a680dc255cd7c657e693d4 | /pileup/mpileup_wrapper.R | 9aa0a9ebd145635f3ae28902ed51b0ff57c42cba | [] | no_license | inambioinfo/MSK_LeukGen | 49525d343a68715614da5e1c6537ab23fbbc0395 | 6d0f03a4b19d4257c9a7128dccbb09b17616838d | refs/heads/master | 2020-03-14T13:30:32.020170 | 2016-04-12T19:56:50 | 2016-04-12T19:56:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,512 | r | mpileup_wrapper.R | # R wrapper script for mpileup -> nucleotide frequencies
# mpileup_wrapper.R
# Author: Komal S Rathi
# Institute: Memorial Sloan Kettering Cancer Center
# Created: ##------ Fri Nov 6 14:27:25 2015 ------ #
# Last Modified: ##------ Wed Nov 11 16:48:00 2015 ------##
# Function: This script takes in a number of bam file... |
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