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aa1501ce3c3d4665644ab5564e9ebc3d33f4b24a | cb7f0408c26721655435a7d14aa0fc9a3cb753a4 | /filter_20uM_competition.R | 7b88bf40000460094705b84cfdf8156493c220c9 | [] | no_license | liuxianghui/FBDDinCell | 90b1eb2f58a532d4697df16109b636e902b4e7ec | fe7d31c0f01e8b90541435db8ea9af7b643d75a7 | refs/heads/master | 2021-04-08T10:39:35.739644 | 2016-12-09T22:40:56 | 2016-12-09T22:40:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 750 | r | filter_20uM_competition.R | data <-
read.csv("./20uMtargets_found_200uMtargets.csv", header = TRUE)
ratio_cutoff <- 5
data[data$coumarin_20uM_293T < ratio_cutoff, grep("coumarin", colnames(data), value = TRUE)[-1]] <-
"-"
data[data$pipphen_20uM_293T < ratio_cutoff, grep("pipphen", colnames(data), value = TRUE)[-1]] <-
"-"
data[data$hydroo... |
8577844cc6677a9714de53590979c3779e3c3f88 | 29585dff702209dd446c0ab52ceea046c58e384e | /comf/R/fctdTNZ.r | a0190143b6311433efa02091bdc319c254671c9d | [] | 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 | 5,575 | r | fctdTNZ.r | # Functions return:
#
# dTNZ -
# dTNZTa -
# dTNZTs -
#
# File contains 1 function:
# - calcdTNZ(182, 82, 21, 1, .5, .1, 36, 20)
# returns dTNZ, dTNZTa, dTNZTs
#
# v1.0 done by Marcel Schweiker in cooperation with B. Kingma
# Function: dTNZ ################
####################################... |
f826461369e0d8f2ce3ff9f64e282ea542734273 | aa0d6b917e3fadaec70743d46df8e85b8e3c7d55 | /src/r/SQ_Wheat_Phenology/Phylsowingdatecorrection.r | 21b57fdad1b8cb4355d460f691f78484033307b8 | [
"MIT"
] | permissive | AgriculturalModelExchangeInitiative/SQ_Wheat_Phenology | 816bc340971c2707a27db1dacc5c286e8df5d3d9 | 9f3426ada2d7913f903701b261e613ac63cbc3d3 | refs/heads/master | 2022-02-05T08:25:40.063564 | 2022-02-01T08:45:31 | 2022-02-01T08:45:31 | 159,197,361 | 5 | 5 | MIT | 2022-02-01T08:45:32 | 2018-11-26T16:12:34 | Jupyter Notebook | UTF-8 | R | false | false | 5,343 | r | Phylsowingdatecorrection.r | model_phylsowingdatecorrection <- function (sowingDay = 1,
latitude = 0.0,
sDsa_sh = 1.0,
rp = 0.0,
sDws = 1,
sDsa_nh = 1.0,
p = 120.0){
#'- Name: PhylSowingDateCorrection -Version: 1.0, -Time step: 1
#'- Description:
#' * Title: PhylSowingDat... |
0b558fe1e25e2bd8ca5bc2ec7565eb2ab14b2b36 | 18ed4435f80350e72aadb4bd2a3f21bbce67fd0e | /R/estPlots.R | 4a27410c96feeebc3fd771a0c948b138a0205359 | [] | no_license | JohannesFriedrich/api-wrapper.r | 15e76cea947bbe83802b939930541f0d93af1541 | e845eece1e3da891dc40f2aa443cf1e37311b008 | refs/heads/master | 2021-01-05T05:26:56.059461 | 2019-08-02T13:50:28 | 2019-08-02T13:50:28 | 240,896,621 | 1 | 0 | null | 2020-02-16T13:29:37 | 2020-02-16T13:29:36 | null | UTF-8 | R | false | false | 8,264 | r | estPlots.R | # // Copyright (C) 2017 Simon Müller
# // This file is part of EventStudy
# //
# // EventStudy is free software: you can redistribute it and/or modify it
# // under the terms of the GNU General Public License as published by
# // the Free Software Foundation, either version 2 of the License, or
# // (at your option) an... |
df7af518e6ab2735ab814c47f0a2235371fcb4a4 | d859174ad3cb31ab87088437cd1f0411a9d7449b | /autonomics.import/man/weights.Rd | 549f6954dc76fcdfbe91c7e4ec3e344953252a7d | [] | no_license | bhagwataditya/autonomics0 | 97c73d0a809aea5b4c9ef2bf3f886614eceb7a3c | c7ca7b69161e5181409c6b1ebcbeede4afde9974 | refs/heads/master | 2023-02-24T21:33:02.717621 | 2021-01-29T16:30:54 | 2021-01-29T16:30:54 | 133,491,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,402 | rd | weights.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getters_setters.R
\name{weights}
\alias{weights}
\alias{weights,SummarizedExperiment-method}
\alias{weights,ExpressionSet-method}
\alias{weights,EList-method}
\alias{weights<-}
\alias{weights<-,SummarizedExperiment,matrix-method}
\alias{weigh... |
eca01d653d08377837cf95466353278461892954 | e71a0417252bd1c7ddd947eca167e34cb4ddb9b4 | /R/graphics/boxplot.R | bd55967591db9b870b999b6881f8b313ffb39de6 | [] | no_license | stork119/OSigA | 902c0151cdba8c9f9c3297240d95343fc63e189f | 3a9cc4d096f5b1744931a0326c1ed18547798fcb | refs/heads/master | 2022-12-27T19:24:18.264417 | 2020-10-11T17:47:18 | 2020-10-11T17:47:18 | 78,741,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,396 | r | boxplot.R | ### ###
### boxplot
### ###
source("R/graphics/theme_jetka.R")
#### plot_boxplot_group ####
plot_boxplot_group <- function(data,
...,
output_path = NULL,
filename = NULL,
x = "time",
... |
3f3698a23df1bd312b36ac41191c5c5d1a09bdfd | f50fe7066d8d3f5551b01cde49159e136ac12510 | /R/mln.mean.sd.R | 545941fd7621e9390220e972b3c76e4950a96eda | [] | no_license | stmcg/estmeansd | dd1597769db0d60f5c36414965187e0c648eb4d2 | 68328422dfce0b5f2f5ac19e216fb5ea3851d43d | refs/heads/master | 2022-07-25T13:36:23.253555 | 2022-05-16T23:46:15 | 2022-06-17T18:34:58 | 170,224,473 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,815 | r | mln.mean.sd.R | #' Method for unknown non-normal distributions (MLN) approach for estimating the sample mean and standard deviation
#'
#' This function applies the Method for Unknown Non-Normal Distributions (MLN) approach to estimate the sample mean and standard deviation from a study that presents one of the following sets of summar... |
cb2a95984d013cdd379d2dd942e06f20f2bb92cb | 8c026eb8ce94d81cdfba0073fb2d8fc767cca4a1 | /McSwan/man/linearize.Rd | ad6ecf0aef2d72e33c4d6e9517077190994050ab | [] | no_license | sunyatin/McSwan | 832018c82b3cecd354f3eb31af63e7de227162c2 | b86869b56892bbdf4b250b012c808dbeae5becf3 | refs/heads/master | 2023-02-08T14:10:05.278919 | 2023-02-03T13:57:23 | 2023-02-03T13:57:23 | 76,260,197 | 1 | 3 | null | null | null | null | UTF-8 | R | false | true | 440 | rd | linearize.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dimension_reduction.R
\name{linearize}
\alias{linearize}
\title{Merge model-specific multiSFSs into one big matrix}
\usage{
linearize(sfs, LDA_sizePerModel = NULL)
}
\arguments{
\item{sfs}{a list of model-specific multiSFS matrices}
}
\value{... |
c88fc325ee6b3620d9461195610e02442838ff09 | 56f6db6b40c3252398c7c6fa9b1d2681a0032cdd | /code/jakobbossek.R | 586a0cd3648a86a4bdf53c2f9a7d8b64e62f69f8 | [] | no_license | MattBixley/rogaine_tsp | d1cc4321303cfd3040106f987c842d78790e10f8 | 4016adbfa95e59064191a229accf6c94e279011e | refs/heads/master | 2021-01-26T09:03:48.467085 | 2020-02-27T01:20:48 | 2020-02-27T01:20:48 | 243,396,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 693 | r | jakobbossek.R | library(salesperson)
library(ggplot2)
set.seed(1)
x = generateClusteredNetwork(n.points = 200L, n.cluster = 3L)
res = runSolver("nn", x)
print(res)
print(autoplot(x, path = res$tour, close.path = TRUE))
#n order to run the exact CONCORDE TSP solver we first need to download the executable for our operating system. S... |
7ed249a911670186aad74b5bf796f7d876acd66b | 6e92ce9aea94772c3e56e9360cc4e97b55c1f12f | /statistical_analysis_and_figures/plot_nton_status_by_length.R | a5d7c980f5dd227dbf05d3b5a66e5c4385e1e1b4 | [] | no_license | kosticlab/universe_of_genes_scripts | 510a937bec424c4df14bead4c35388605f0d8e57 | c04114b8e6fbcef41c86f44250eca19309f60718 | refs/heads/master | 2020-05-06T20:13:24.171948 | 2019-05-23T21:39:18 | 2019-05-23T21:39:18 | 180,227,956 | 11 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,618 | r | plot_nton_status_by_length.R | ###plot nton status length data
##20190221
#Tierney
library(ggplot2)
library(cowplot)
setwd('~/Dropbox (HMS)/orfletons/revisions/nton_status_by_length')
plotdata_withcoverage<-function(d,name){
#plot gene length distribution
pdf('./nton_status_contig_coverage_oral_logged.pdf')
ggplot(d,aes(x=FOLD.COVERAGE.CONT... |
b15e0cab172a0f72ec205a0edd9449bb19387669 | 7fd749dc1a52e201dfe433fa0da403414687f5c0 | /man/esize_m.Rd | bc8e27f1cd534c94064397550fb23b26704ef34b | [] | no_license | cran/r4lineups | a179b83afdc8b8c68ac812cd600e86bc0ece48e8 | 6753d4662d34ea39261878f9f0584788be7c23f7 | refs/heads/master | 2020-03-27T03:58:08.061355 | 2018-07-18T12:20:02 | 2018-07-18T12:20:02 | 145,902,377 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,077 | rd | esize_m.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/esize_m.R
\name{esize_m}
\alias{esize_m}
\title{Effective Size}
\usage{
esize_m(lineup_table, k, both = FALSE)
}
\arguments{
\item{lineup_table}{A table of lineup choices}
\item{k}{Number of members in lineup. Must be specified by... |
96922861bd25fc9215e3a062b747156cf03301f6 | 8910a2a6bfce064ce3ddaf4d76589174dc4e2a1a | /src/sports/baseball/get_current_round.R | 05dd33da49d71127fd369a351bb7cee7dfb8f9ca | [] | no_license | zmalosh/Atlantis | 552d8b3f3fdb67086e68954fd821f4429c829b86 | bddddcc9f1715b4e826b2e184d9c2db70fd1f649 | refs/heads/master | 2021-05-19T14:32:46.142479 | 2020-08-30T15:28:52 | 2020-08-30T15:28:52 | 251,758,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 628 | r | get_current_round.R | get_current_round <- function(leagueId){
source('src/sports/baseball/get_league_games.R')
games <- get_league_games(leagueId)
gameDates <- games$Round %>% stringr::str_replace_all('_', '-') %>% ymd() %>% unique()
currentDate <- date(lubridate::now())
maxDate <- max(gameDates)
isAllPast <- maxDate < currentDate... |
93b0fdfccfbe5da259af295b440fa4bf0fae1a91 | 0d0d1f5189aaa7112a725e9eb06f6b3386b52538 | /LCPfunction.R | dfc63c7f38199c346ab75dc09a8cee46767192ad | [] | no_license | adivea/LCP | e895e63fd6cde5bbd22336d20b5148e3baaa70f4 | 9a5b9650f62e10fd77fa8e74d32e7b51b5d57ecd | refs/heads/main | 2023-04-21T09:56:26.825596 | 2021-05-15T05:57:35 | 2021-05-15T05:57:35 | 367,552,884 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,163 | r | LCPfunction.R | calc.conductance <- function(raster, filename=NULL){
# get library
library(gdistance)
# calculate difference in elevation
heightDiff <- function(x){x[2] - x[1]}
# the difference is non-symetrical (going up is harder than going down)
hd <- transition(raster,heightDiff,8,symm=FALSE)
hd
# calculate s... |
7c72c340b2f6a818ade4b104c3c192f7420eac23 | eb15ab937869ba62e2fd828fe65218efc2390840 | /R/data_faker.R | a94f630af01b1a308c076ca9235a2d98f0db4e1c | [] | no_license | madrury/r-data-faker | 3a2c7b7c1c2976a2c12a566c49914601ed799bb7 | 8e2d7ca68bca6c6f5a7a54406a4cd435d3dbf33a | refs/heads/master | 2021-01-10T07:45:12.767013 | 2015-12-21T17:55:39 | 2015-12-21T17:55:39 | 48,385,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,589 | r | data_faker.R | .make_binary_matrix <- function(n_rows, n_columns) {
matrix(
sample(c(0, 1), n_rows*n_columns, replace=TRUE, prob=c(.5, .5)),
nrow=n_rows, ncol=n_columns
)
}
make_linmod_data <- function(n_rows, n_columns,
n_columns_with_large_effects=0,
n_nuisa... |
63d994f7ba7944000c54ea1017756950f1840fee | f43bf697b66f51d3807e39843911cf7ebd5c6f4d | /R/3.raster-based_analysis.R | 9a76da8eccc1a6fac652b512e40e2512e5665f54 | [
"CC0-1.0"
] | permissive | dongmeic/suppression | 06a9479861ded64e87b6764b96bc0bd89a7b9fcf | db97e818f62ef8bf42533882101fea6c309dc0e0 | refs/heads/master | 2021-01-23T05:20:13.957321 | 2020-12-20T05:11:13 | 2020-12-20T05:11:13 | 92,959,614 | 0 | 0 | null | 2020-04-03T16:54:55 | 2017-05-31T15:16:45 | Jupyter Notebook | UTF-8 | R | false | false | 4,122 | r | 3.raster-based_analysis.R | ## Dongmei CHEN
# objectives: raster-based analysis
# reference: 1. /Users/dongmeichen/Documents/scripts/beetle/r/fire_suppression/raster_scaling.R;
# 2. /Users/dongmeichen/GitHub/suppression/figures_maps_regression.R;
# 3. /Users/dongmeichen/GitHub/suppression/regression.R
# input: /Volumes/dongm... |
4880764c055fdb48f29083dabc598d0f09f06d80 | 6e1144258a10d87fde18712f8817e39dd2b3a604 | /mycarto.R | 60a089b41ccae9acb7e3e3ff01719bed7b6b2d0b | [] | no_license | jameswoodcock/main-sort-analysis | d07ad00ad4d993d36733ad30767c74fc1f8048dc | 532c3faff06a4fcbda3fb6cf731f5f0eafa5976f | refs/heads/master | 2021-01-01T05:59:54.937120 | 2015-07-08T20:23:27 | 2015-07-08T20:23:27 | 28,271,327 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,213 | r | mycarto.R | mycarto <- function (Mat, MatH, level = 0, regmod = 1, coord = c(1, 2),
asp = 1, cex = 1.3, col = "steelblue4", font = 2, clabel = 0.8,
label.j = FALSE, resolution = 200, nb.clusters = 0, graph.tree = TRUE,
graph.corr = TRUE, graph.carto = TRUE, main = NULL, col.min = 7.5,
col.max = 0)
{
cm.col... |
e62c62946e37f36c11c921194fa5fa2cf2186d09 | 3b417786e77e03bef575db9eb4558fd52f026e1b | /plot2.R | 03f625b1c5400229f9edab6650809292ee8f2d02 | [] | no_license | r-datascience/exploratory_data_analysis | 4984106ac9613468611a816b14923e4c01de3951 | a291b839f7e892f00a2e06dbe9bee4ba3011f088 | refs/heads/master | 2021-05-28T07:51:41.343480 | 2015-02-08T23:40:09 | 2015-02-08T23:40:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 450 | r | plot2.R | data<-read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?")
data$as.Date<-strptime(paste(data$Date, data$Time), "%d/%m/%Y %H:%M:%S")
data$Date <- as.Date(data$Date, "%d/%m/%Y")
data<- data[data$Date >= "2007-02-01" & data$Date <= "2007-02-02",]
plot(data$as.Date, data$Global_active_p... |
5bab5a18395c11c9122a05b63b0dfa079f42e122 | 24edba72b6483d25a0c4167fc70c25e27c76a86f | /R/GenMatching.R | 536f8dd881e420610083d71ed4974d09d5c18315 | [] | no_license | JasjeetSekhon/Matching | aaea3c27bc863f07bfc93ee10bfd4ac066b59d02 | 55226a53ea324b428b3d6090d865c025b0e7bb35 | refs/heads/master | 2022-11-15T17:32:38.337362 | 2022-11-08T00:58:04 | 2022-11-08T00:58:04 | 132,082,360 | 19 | 5 | null | 2020-02-05T23:00:03 | 2018-05-04T03:39:44 | C | UTF-8 | R | false | false | 38,031 | r | GenMatching.R | FastMatchC <- function(N, xvars, All, M, cdd, ww, Tr, Xmod, weights)
{
ret <- .Call("FastMatchC", as.integer(N), as.integer(xvars), as.integer(All), as.integer(M),
as.double(cdd), as.double(ww), as.double(Tr),
as.double(Xmod), as.double(weights),
PACKAGE="Matchin... |
348143b2b44b3db225adc5cbba16e5588d54e85e | 861d85c0a3d8dc4be9caee89df4cacea16b461df | /UniversityCleanCode.R | a490b8ee4779d8fbce456bfe9d8e19d83dd08800 | [] | no_license | s-mcknight/Sports-Education-Ranking | 70f5bf7ee5736ba412f51ca5693f597f0f87f205 | f54a5483fd578ffbf07cd891bf50010c4f2aecf8 | refs/heads/master | 2020-03-13T22:16:49.267524 | 2018-04-27T15:35:40 | 2018-04-27T15:35:40 | 131,313,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,674 | r | UniversityCleanCode.R | #read in data
colleges <- read.csv("Colleges.csv", header=TRUE)[,-1]
rownames(colleges) <- colleges$SchoolName
#smaller version of dataset with only important variables
colleges_small <- colleges[, c(2, 4:6, 11, 13, 15, 17, 21:22, 24:34)]
#manipulating variables
colleges_small$Div <- as.factor(colleges_small$... |
40c1bbb48bc3a23bf635c74ac6d66147c33cea21 | 9d0500397db28edeba4ab6d3b801cb5edcdc9371 | /man/decathlon_s2p.Rd | adb5bf154233a60a17d6c8cc133bb335e82c331b | [] | no_license | VictorNautica/multievents | 8e7dff642127baa34c0882a3bcfd3c865b00f3db | 9e0bc4665446e0a66e9f06915fc8c262b047a13c | refs/heads/master | 2021-07-14T12:20:43.589043 | 2020-06-07T16:14:55 | 2020-06-07T16:14:55 | 162,026,737 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 980 | rd | decathlon_s2p.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/decathlon_s2p.R
\name{decathlon_s2p}
\alias{decathlon_s2p}
\title{Convert decathlon scores in to points}
\usage{
decathlon_s2p(
X100m = 9999,
LJ = 0,
SP = 0,
HJ = 0,
X400m = 9999,
X110mh = 9999,
DT = 0,
PV = 0,
JT = 0,
X15... |
5a0ae955046e63ee9a4d5839ed47cc834f60cd48 | 23d1c6c910bb2cf19164c934242efbce7653f9ef | /R/newtons_method.R | cff832b05e44a17b9a2fda62e1dc489ff385ffac | [] | no_license | ooelrich/OBBP | c12c9d96cf400c54e6e86081e1650f5ef4f18ad2 | 6c0679ae11438f3635400610617fed0de03b1b68 | refs/heads/master | 2022-04-28T20:42:01.651349 | 2020-04-30T12:28:27 | 2020-04-30T12:28:27 | 257,657,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 762 | r | newtons_method.R | #' Newton's method for finding optima
#'
#' A simple implementation of Newton's method to find posterior mode.
#' For use with the Laplace approximation in exercise 2, lab1.
#'
#' @param y Vector of outcome variables.
#' @param k_xx Covariance matrix of the GP.
#' @param precision Measured by the squared distance betwe... |
0f923d1e037d1e38e0a2c76a44a12a911dfbaa07 | 387afc2394b4ea9857019236b991ba01428a5dd5 | /geom_density_ridges/geom_density_ridges - limited axis - TidyTuesday 30-7-2019.R | a3a849de7135a47cb3aed0b43a44e347ec2ae09c | [] | no_license | JuanmaMN/tidyverse-ggplot2 | 0a757bdafa0520fa5b68d72aaf3260693632a482 | 1a103a1cc879bfa389922f4ac33fb2c951cd3234 | refs/heads/master | 2023-06-08T08:29:06.833615 | 2023-06-07T17:44:41 | 2023-06-07T17:44:41 | 202,595,817 | 14 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,560 | r | geom_density_ridges - limited axis - TidyTuesday 30-7-2019.R |
# Upload the data ---------------------------------------------------------
video_games <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-30/video_games.csv")
View(video_games)
str(video_games)
colnames(video_games)
# unique(video_games$owners)
# Upload the ... |
25e79c478825e5a8c7f985ef21683f87f9d20ff6 | f50655f401237b4a30623f2f0e95ee47cbc204bf | /provided/simulator-synthesis.R | b85d9445378295c81a29c68b9b50b3c0aac9a1c6 | [] | no_license | ascheppach/Consulting_MBO | 1d0a60de959ba5dfc4941507c95664ebf2bd3be3 | a08e758f96ea199cebb9107c002a6b09c28926c3 | refs/heads/master | 2020-12-19T06:02:18.387552 | 2020-04-10T19:37:31 | 2020-04-10T19:37:31 | 235,640,695 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,598 | r | simulator-synthesis.R | #!/usr/bin/env Rscript
library(mlr)
library(mlrMBO)
library(smoof)
# data
data_synthesis <- read.csv("provided/synthesis.csv")
data_synthesis$X <- NULL
# model from all data points
model = train(makeLearner("regr.randomForest"), makeRegrTask(data = data_synthesis, target = "target"))
# Bayesian Optimization
fun = ... |
d76d99a3a6b959f71ebc1438458a103c8ed48ddd | 109b2a458d516e49be1ec092f29a97a60cb272ad | /plot1.R | 79d4e644aa02c21188d8c8f31128ebb4a0b99ed6 | [] | no_license | cheedep/ExData_Plotting1 | d9a5036cb394b7a7384c6d415983ecb5125a9a2a | e69e5c1bfab61457b1a3a6480a8bc3399e542714 | refs/heads/master | 2021-01-17T22:55:14.602287 | 2015-02-08T19:47:15 | 2015-02-08T19:47:15 | 30,469,493 | 0 | 0 | null | 2015-02-07T20:41:10 | 2015-02-07T20:41:09 | null | UTF-8 | R | false | false | 696 | r | plot1.R | makePlot1 <- function(){
mydf <- read.csv("household_power_consumption.txt", header = TRUE,sep = ";", na.strings="?", stringsAsFactors=FALSE)
mydf$Date <- as.Date(mydf$Date, format="%d/%m/%Y")
mydf$Time <- strptime(mydf$Time, "%H:%M:%S")
febData <- subset(mydf, Date == "2007-02-01" | Date =="2007-02-02"... |
4a700d9cb43cea1528c7e142fc62231b486183c4 | 615d2dd18afed5427c70649afd3e5085f17ba289 | /cachematrix.R | f78030033e6c7cf0e866e0739824a8c33ce5def2 | [] | no_license | sheltonmath/ProgrammingAssignment2 | 8fef9d01856c102227023038c15682983f743e43 | 857353cf01bd2e3290bc3e22394069e8d8bf0ecf | refs/heads/master | 2021-01-18T15:47:48.079104 | 2015-12-27T22:27:13 | 2015-12-27T22:27:13 | 48,660,536 | 0 | 0 | null | 2015-12-27T21:09:35 | 2015-12-27T21:09:34 | null | UTF-8 | R | false | false | 870 | r | cachematrix.R | ## The functions below allow for the cashing of matrix inversion.
## This speeds up computation by preventing the need to repeatedly perfom inversion.
## makeCacheMatrix creates a matrix that is able to store its own inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y... |
2d5e42483007254b39ae6c90090a86ff042e8c8e | b2ba89e08e7535a77e6e9d94d6e8d4720a338a4c | /processing_scripts.R | f5a491b7a663da7b7336714e153f06f6e575630e | [] | no_license | kinxiel/state_of_js_GalacticEdition | a5a91a8849f1397ad539899ca76a050a36d88fb5 | 9318baca4f31ef09cb4997379b4c6c4300eb6740 | refs/heads/master | 2020-04-11T22:43:56.130134 | 2018-12-17T15:12:13 | 2018-12-17T15:12:13 | 162,146,091 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,154 | r | processing_scripts.R | # Packages
library(tidyverse)
# Some random scripts for data extraction
# Data Import
raw <- read.csv("data/sojs18.csv")
##################################################################
## JavaScript Flavors ##
##############################################################... |
8ba5060ed35fa5c8e6775ae9c02ce46417a1a2ba | c2b6d7b0f0ce47fac4a0bc52cbfc575921a54599 | /man/getNBGaussianLikelihood.Rd | 6189d1d5cd00fa319b75441c2cff88e604d1fc16 | [
"MIT"
] | permissive | de-Boer-Lab/MAUDE | ba32c080a3175522269fa807955ffbfabd085ef8 | 7aa20cc9b28c06d2772fec23c20ef2ec56a7c026 | refs/heads/master | 2023-04-17T12:29:00.794114 | 2022-02-26T22:53:31 | 2022-02-26T22:53:31 | 135,627,989 | 4 | 2 | MIT | 2022-02-26T22:39:47 | 2018-05-31T19:43:46 | HTML | UTF-8 | R | false | true | 1,553 | rd | getNBGaussianLikelihood.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MAUDE.R
\name{getNBGaussianLikelihood}
\alias{getNBGaussianLikelihood}
\title{Calculate the log likelihood of observed read counts}
\usage{
getNBGaussianLikelihood(x, mu, k, sigma = 1, nullModel, libFract)
}
\arguments{
\item{x}{a vector of g... |
8edc6c379095583552d5288311a9b0cfab200074 | 42886f7b175ea5f5f7c40c5c9cf1ee8d91625598 | /Lab1/Question4.R | c3943d8ba927ba0d962138fbba1476e551c49a8a | [] | no_license | janish-parikh/CS-605-Data-Analytics-in-R | 7d8655ea4a08b2f696c89a832c63b659010ff91a | c17be6edf9a1da9dae80fefeb07c85e4a1021942 | refs/heads/master | 2022-12-19T11:45:54.412299 | 2020-09-05T03:25:42 | 2020-09-05T03:25:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 916 | r | Question4.R | #Question4
dataset<-c(43, 37, 50, 51, 58, 105, 52, 45, 45, 10)
#Function to find mean,median,sd,all quartiles and return a vector
explore<-function(x){
data<-c("Mean"=mean(x),"Median"=median(x),
"Standard Deviation" = sd(x),"Quartile-" =quantile(x))
return(data)
}
explore(dataset)
##Below Q1-1.5*IQ... |
830ca30a45b39511d17f1c664ca8f063f24f2215 | d3a4449541e6778cd0f55f2322fe74de3fd78110 | /R/tableGUI_main_layout.R | 6fb58516b3b090e0aba6a4093f80e26f82a0da8f | [] | no_license | cran/tabplotGTK | b828e93c3b568296b1fe9b96d9453d4ca6381968 | efcea81fdb1f9ef1f310907636d2eac447d08e3c | refs/heads/master | 2021-01-02T22:31:49.166851 | 2012-07-11T00:00:00 | 2012-07-11T00:00:00 | 17,719,480 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,988 | r | tableGUI_main_layout.R | tableGUI_main_layout <- function(e) {
# browser()
with(e, {
######################################################
## create GUI
######################################################
## create window
wdw <- gwindow("Tableplot",visible=FALSE)
sbr <- gstatusbar("Preparing...", cont=wdw)
g <-... |
ac842b1038aee66e521467c697d0f1df0ce5b197 | 76e47464f4313b79f95fecf01067aa3a6b713d8b | /R/rISIMIP-package.R | 3b4f8ed915ebf684777ecd6a97cf6696c2cecfa3 | [
"MIT"
] | permissive | zejiang-unsw/rISIMIP | 460116d1f6e23826bb9de57e8ee731d29f379730 | 9f9af06dd51d936932795c4cf2a99216fbfcea23 | refs/heads/master | 2021-01-04T09:46:43.105805 | 2019-12-20T11:36:35 | 2019-12-20T11:36:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,582 | r | rISIMIP-package.R | #' R Package for handling ISIMIP data
#'
#' Reading and processing ISIMIP NetCDF files
#'
#' @name rISIMIP-package
#' @aliases rISIMIPpackage
#' @docType package
#' @title R Package for handling ISIMIP data
#' @author RS-eco
#' @import raster ncdf4
#' @keywords package
#'
NULL
#'
#' @docType data
#' @name landuse-total... |
30728d60155f82bad6e49799be353e542cd4fcda | 36f4c8d36cac5b5816b9d431aa235a070751c509 | /Calsedel 05 de Marzo del 2018 Dump y Source.R | 359c4ae42fbe39a240636a73657db08e7d18c0a9 | [] | no_license | Laugcba/Software_Actuarial_III | d93a82dabe452e2c713f2e981aca8f93ec88672e | 1c95cddc8beda42b767c7637c1cdf623fcbc8cec | refs/heads/master | 2021-05-05T00:03:18.967895 | 2018-05-22T02:44:48 | 2018-05-22T02:44:48 | 119,462,774 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 4,006 | r | Calsedel 05 de Marzo del 2018 Dump y Source.R | #Dump y source
setwd("~/GitHub/Software_Actuarial_III")
x <-"Software Actuarial III"
y <- data.frame(a=1,b="a")
dump(c("x","y"),file="data.R")
rm(x,y)
source("data.R")
#Dump y source trabajan con las instrucciones de codigo que permitan volver a construir un objeto en lugar de obtenerlo desde alguna ubicación guardad... |
041e88e335e8da97eddc1cbb9ee393af87e7ccd2 | 3997adde33a37af6136686bd6acb9b4e2d9964b4 | /install_glasp.R | 46a329abe2accefbc45bf0b423e427fbb37961a5 | [] | no_license | Spain-AI/glasp-code | f2aa4d56ccd825e22d7305241910df0165ad613f | 1936f2910d2eb0112fff2a97432489c178f1b2fc | refs/heads/master | 2022-09-08T14:38:41.641771 | 2020-05-26T09:10:18 | 2020-05-26T09:10:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 62 | r | install_glasp.R | devtools::install_github("jlaria/glasp", dependencies = TRUE)
|
1f7fdc9c8c54824febf36d754444e7637aae9706 | 07b5f7c7e8e990d5d4742ed0cac51d4f1bd020cf | /man/hc_polygon-dispatch.rd | 7463145b075503ce4b71319fb932956513a5d2ab | [
"MIT"
] | permissive | jokergoo/HilbertCurve | 2e94b30d883407b44e60ace6e2655decfca261cd | f8143a54354e732f2353fb6e098bb3b189478270 | refs/heads/master | 2023-04-13T10:27:27.361062 | 2023-03-22T13:38:45 | 2023-03-22T13:38:45 | 38,319,620 | 39 | 8 | null | null | null | null | UTF-8 | R | false | false | 488 | rd | hc_polygon-dispatch.rd | \name{hc_polygon-dispatch}
\alias{hc_polygon}
\title{
Method dispatch page for hc_polygon
}
\description{
Method dispatch page for \code{hc_polygon}.
}
\section{Dispatch}{
\code{hc_polygon} can be dispatched on following classes:
\itemize{
\item \code{\link{hc_polygon,GenomicHilbertCurve-method}}, \code{\link{GenomicH... |
7b2212b0d77f32d35d3318f0723a9e2de1a8666b | 8bd5557b6d1662c31f96aafe729d2137c90d4edf | /explore/mtcarsPlotEx02.r | dcf497874f75feabea6d155ea1573f24387fb510 | [] | no_license | Wenbo87/reg_models01 | ca85339c1aa3dffc57ee7dc1316c96735476b16e | 513a0b75c824e6fc3dbc8e936c09fd46e4449fe0 | refs/heads/master | 2020-12-24T17:08:00.362909 | 2014-11-23T21:47:27 | 2014-11-23T21:47:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 979 | r | mtcarsPlotEx02.r | ## Exploratory scatter plot with linear model for 'mtcars' data
## Model mpg versus transmission type
mtcarsPlotEx02 <- function() {
# Load libraries
library(datasets)
# Create vectors for 'mpg' and 'am'
mpg <- mtcars$mpg
am <- mtcars$am
# Fit linear model for mpg ~ am
fit <- lm(mp... |
ec4e3a001ddc6f3f8af433c35569c88ed1d9262f | a0830531052bd2330932c3a2c9750326cf8304fc | /vmstools/man/old/clipPolygons.Rd | 67fa74992a485599d5d3c3706e6c3e7bb1bdbab6 | [] | no_license | mcruf/vmstools | 17d9c8f0c875c2a107cfd21ada94977d532c882d | 093bf8666cdab26d74da229f1412e93716173970 | refs/heads/master | 2021-05-29T20:57:18.053843 | 2015-06-11T09:49:20 | 2015-06-11T09:49:20 | 139,850,057 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,429 | rd | clipPolygons.Rd | \name{clipPolygons}
\alias{clipPolygons}
\title{
Function to clip Polygons together
}
\description{
This function removes from a set of polygons A the areas overlapping with another set of polygons B.
}
\usage{
clipPolygons(shapeAll, europa)
}
\arguments{
\item{shapeAll}{These are the polygons A (as shape... |
72c6e7588e3569855a8baf597b668db104062999 | b99a692b325e6d2e6419172fc37fd3107ffb79c2 | /tests/testthat/test_hodge.R | 5dd698a7621bd6c02208359afd940f1d6c4715e7 | [] | no_license | RobinHankin/stokes | 64a464d291cc1d53aa6478fe9986dd69cf65ad1e | 0a90b91b6492911328bad63f88084d7e865f70a9 | refs/heads/master | 2023-08-17T07:46:50.818411 | 2023-08-15T01:56:56 | 2023-08-15T01:56:56 | 177,894,352 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 877 | r | test_hodge.R | ## Some tests of hodge() ... see also test_misc.R which looks at issue #61
options(warn=999)
test_that("Function hodge() behaves itself", {
expect_true(TRUE)
foo1 <- function(x){ # checks that ***x == x, also positivity
n <- max(index(x))
discrepancy <- x |> hodge(n) |> hodge(n) |> hodge(n) ... |
00456d394e5ec4b9c4aec6ba5d18c653331e3e09 | 54634bec205f0d321851b978b32bcf2accb74dd7 | /확률을 높이는 방법을 구색.R | 47e25a2463ffa49dd16acf7f3cc964e804503118 | [] | no_license | kso8868/workR | 40df0100c951d3d1e46a8f17114f31c1925711a8 | 6f07e87c6fc0cb815a7da50718dcbd3a5f06d910 | refs/heads/master | 2020-09-21T16:14:54.878232 | 2019-12-19T10:37:38 | 2019-12-19T10:37:38 | 224,845,101 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 926 | r | 확률을 높이는 방법을 구색.R | # 문3)
# UCLA 대학원의 입학 데이터를 불러와서 mydata에 저장한 후 다음 물음에 답하시오.
mydata <- read.csv( "https://stats.idre.ucla.edu/stat/data/binary.csv" )
head(mydata)
str(mydata)
# (1) gre, gpa, rank를 이용해 합격 여부(admit)를 예측하는 로지스틱 모델을 만드시오(0: 불합격, 1:합격).
mydata_model <- glm(admit~., data = mydata)
mydata_model
#admit = (-0.1824127) + (0.000... |
0d5b93a3075a28f3adafea8cb3d46da7cd305d6b | 01c977c292984050d40baf29b1095a6efaeda8c7 | /plot1.R | 3ac9dffbdda7e1ffded4420b32a8b2eaf2e6a227 | [] | no_license | cwmiller21/ExData_Plotting1 | 536e61f35aa917c7c6015ab5681f8d50bfe0f160 | 8d3c232321306e0c5294bbbab179898c0a823d42 | refs/heads/master | 2021-01-17T22:43:22.884930 | 2014-07-13T14:01:17 | 2014-07-13T14:01:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 868 | r | plot1.R | # This R script is designed to load the household power consumption data
# and then create plot 1 as a png file
# options
options(stringsAsFactors=FALSE, show.signif.stars = FALSE)
# load data
hpc <- read.table("household_power_consumption.txt", header=TRUE, sep=";",
na.string="?")
# subset data to inclu... |
11e0833daebe2fcf4d0d3a91bd98543389dd91b5 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.compute/man/serverlessapplicationrepository_put_application_policy.Rd | 8dd605987ad7bb7690acb2a623c20d64d00c115b | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 1,509 | rd | serverlessapplicationrepository_put_application_policy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/serverlessapplicationrepository_operations.R
\name{serverlessapplicationrepository_put_application_policy}
\alias{serverlessapplicationrepository_put_application_policy}
\title{Sets the permission policy for an application}
\usage{
serverless... |
4a0f6002c47bdc59ef4b455e68e892e9c75fd5e0 | 70b237f6c2f62b26d07250470653ec5cb913631c | /R/direct.R | 11f51758318cd83406c0acf75524ba50e2a6fd09 | [] | no_license | cran/sae | 078469aaf95e06e59eae054fc366137fc72583ba | c5904ed07bd6bfb5219e7f209d63fa0937443cbb | refs/heads/master | 2021-06-05T21:02:14.261262 | 2020-03-01T10:40:02 | 2020-03-01T10:40:02 | 17,699,442 | 5 | 3 | null | null | null | null | UTF-8 | R | false | false | 5,076 | r | direct.R | direct <-
function(y,dom,sweight,domsize, data, replace=FALSE) {
result <- data.frame(Domain=0,SampSize=0,Direct=0,SD=0,CV=0)
missingsweight <- missing(sweight)
missingdomsize <- missing(domsize)
# direct estimator case
# type 1: sampling without replacement
# type 2: sampling without re... |
a758f39a89c3c7fe17e26d9d28c3288288e2b38a | ca47052858a683345a6dac4da5c8df0e676fd3c3 | /man/whichFishAdd.Rd | 99d6422d80ba5241d78d33d7eeff0daa9e809a34 | [
"MIT"
] | permissive | GabrielNakamura/FishPhyloMaker | b9a54cf366cc73c7fb485474ff07211facaad5dc | fb3441de070c2099803c03eb923628e1847ff64e | refs/heads/main | 2023-04-12T13:42:02.136319 | 2023-02-17T23:29:59 | 2023-02-17T23:29:59 | 336,899,540 | 6 | 6 | NOASSERTION | 2021-09-25T16:55:46 | 2021-02-07T22:01:58 | R | UTF-8 | R | false | true | 952 | rd | whichFishAdd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/whichFishAdd.R
\name{whichFishAdd}
\alias{whichFishAdd}
\title{Function to inform which species must be added to the mega-tree phylogeny in the insertion process.}
\usage{
whichFishAdd(data)
}
\arguments{
\item{data}{A data frame with three c... |
6abd9d5a9f3f1d763ca8ed130caf0946eed10df2 | e395badb85f0194d29053a5e6e810b2ab5f9b9b4 | /server.R | f0b36edc2847b15b0d219f204fc4439cf5a1cb17 | [] | no_license | CurlySheep/558shiny | c020133af5ca8a05fa4235141de1b4aefa3856c2 | 2e69091a5b101af6b49e6a9497714affaf84bf55 | refs/heads/main | 2023-07-08T18:19:28.988133 | 2021-08-03T03:31:05 | 2021-08-03T03:31:05 | 391,017,769 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,803 | r | server.R | library(shiny)
library(shinydashboard)
library(DT)
library(ggplot2)
library(tidyverse)
library(highcharter)
library(gbm)
library(caret)
shinyServer(function(input, output, session) {
#####################Tag 1####################
# Create URL links
output$link1 <- renderUI({
tagList("Health Commission ... |
7395b23a9c268ebfb7d3ffa8b98ef3366e7dc5bb | 5d249985365879bbaf10c47ac1b1fff6c8347724 | /plot1.R | ac19ca0573dc9a5c4520eee32657abeff0369450 | [] | no_license | vanitu/ExData_Plotting1 | e4a4a6f68eef2b20a2d689aeca99c2a767cf9fe1 | 52367a3067dabc7f5c3f981c499c9029f61cc8bc | refs/heads/master | 2021-01-17T22:33:53.831093 | 2016-05-31T06:35:46 | 2016-05-31T06:35:46 | 60,061,153 | 0 | 0 | null | 2016-05-31T05:41:36 | 2016-05-31T05:41:35 | null | UTF-8 | R | false | false | 362 | r | plot1.R | library(dplyr)
library(ggplot2)
library(data.table)
consumpt<-fread('consumpt_1-2Feb2007.csv',na.strings=c("?",",,"))
#Create a PLOT1
hist(consumpt$Global_active_power,
xlab="Global Active Power,(kilowatts)",
col='green',
main='Global Active Power'
)
#Copy Plot to PNG device
dev.copy(png, file = "... |
c4a6a74cb1314abcfaf0fb99cbca8f5cfd8a564e | 6b1f60e568efe261b11e9bd0cb076f29551442fa | /Prediction_of_air_pollution_index_based_on_history/Arimamodel.R | fcb2d66441ea3db2a668df73b6614b8037b73ced | [] | no_license | Rishabh1998/Prediction_of_air_pollution_index_based_on_history | beada495f2f55e9294c052e028bbf35d57b50365 | 43297ae894fc1fb308f07acdaf2adf81c53f76f7 | refs/heads/master | 2020-03-30T11:56:22.722950 | 2018-07-25T16:11:12 | 2018-07-25T16:11:12 | 151,200,725 | 0 | 0 | null | 2018-10-02T04:38:01 | 2018-10-02T04:36:30 | R | UTF-8 | R | false | false | 13,495 | r | Arimamodel.R | packages <- c("dplyr", "lubridate", "ggplot2", "hydroGOF", "e1071", "forecast", "tseries", "padr")
if(length(setdiff(packages, rownames(installed.packages()))) > 0){
install.packages(setdiff(packages, rownames(installed.packages())))
}
lapply(packages, require, character.only = TRUE)
setwd("C:/Users/Jhingala... |
f5fcca3f2422df5e86f3a261438f6fe130d7f31d | a0b77be4c1b958f282aa4882fdd38f06c837cae6 | /SImulations fcn.R | a9ae5c25e9519248055257409b4c1b2185c3c582 | [] | no_license | katherineliuu/rf-research | dfbb515600259d904ac39021287fb6ef3ae75be7 | af614811d76f75139c77b66d1bce03ec99d047f3 | refs/heads/master | 2020-06-06T17:31:27.006344 | 2019-08-05T16:37:43 | 2019-08-05T16:37:43 | 192,806,782 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,091 | r | SImulations fcn.R | # Simulation studies to investigate RF and LM prediction intervals
library(randomForestSRC)
avgResults <- array(NA, dim=c(8,3)) # average MSE, meanWIdth, coverage rate for 4 simulations
RFOOBInterval <- function(x,
y,
x0,
ntree = 1000,
... |
b4954b770e730b9ddd5f17a5aee7e394fc9db11f | f81ac43a1d02013a9cb9eebc2a7d92da4cae9169 | /tests/testthat/test_category.R | 8015dfd756ebb6635000abd5313d7041af2090c8 | [] | 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 | 3,443 | r | test_category.R | context("category")
suppressWarnings(RNGversion("3.5.0"))
set.seed(123)
dichotomy_matrix = matrix(sample(0:1,40,replace = TRUE,prob=c(.6,.4)),nrow=10)
colnames(dichotomy_matrix) = c("Milk","Sugar","Tea","Coffee")
dichotomy_matrix[] = 0
expect_equal_to_reference(as.category(dichotomy_matrix, prefix = "zero", compre... |
af95faedaad217f1c4598ebfb0a0ed02646d2192 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Matrix/examples/abIseq.Rd.R | a3353ab04882015e2b12f7e2d7ab17c245160a73 | [] | 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 | 335 | r | abIseq.Rd.R | library(Matrix)
### Name: abIseq
### Title: Sequence Generation of "abIndex", Abstract Index Vectors
### Aliases: abIseq abIseq1 c.abIndex
### Keywords: manip classes
### ** Examples
stopifnot(identical(-3:20,
as(abIseq1(-3,20), "vector")))
try( ## (arithmetic) not yet implemented
abIseq(1, 50,... |
f4acca731c3309a1fbffe2669621eee7ca6e0475 | e6052cfff5c65990ce6e2c901526b5accd574751 | /tune_parameters.R | 40de200c4ab35d19a06b3354e9f13cfeacd23a89 | [] | no_license | jstn2/UT-utilities | 0a5eeb9698ff2310fd60e747a35b813afbab833d | 19671dcce73623fbb38bc2dbc108af3561201f8d | refs/heads/master | 2022-12-07T09:42:06.625386 | 2020-09-02T20:41:29 | 2020-09-02T20:41:29 | 272,289,620 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 22,066 | r | tune_parameters.R | # ..........................................................................
# Make precision-recall curves for tuning the event detection parameters
#
# Justin DuRant
# ..........................................................................
library(plyr)
library(leaps)
library(stringr)
library(randomFores... |
8482fe72e75d4462d8ae5cd1b9d691c05bd48ce5 | c88b0cbeda0edf9e745e324ef942a504e27d4f87 | /Budongo cognition/BFactoring.R | 1a8ffd7f85fa7f3060d0b98696ff31ef0b0d7423 | [] | no_license | Diapadion/R | 5535b2373bcb5dd9a8bbc0b517f0f9fcda498f27 | 1485c43c0e565a947fdc058a1019a74bdd97f265 | refs/heads/master | 2023-05-12T04:21:15.761115 | 2023-04-27T16:26:35 | 2023-04-27T16:26:35 | 28,046,921 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,057 | r | BFactoring.R | library(BayesFactor)
data(puzzles)
## neverExclude argument makes sure that participant factor ID
## is in all models
result = generalTestBF(RT ~ shape*color + ID, data = puzzles, whichRandom = "ID",
neverExclude="ID", progress=FALSE)
result
BF.p = lm(as.numeric(participat) ~ Dominance + Co... |
70aefb6f7c935712a37b2ba9a4a5c244d9a5a2b1 | 3bd22cace07e560a11159b69ef0679f4d71bac38 | /Database Management/Mine a Database/LoadDataWarehouse.LiuY.XuM.R | 3eaff5ff9e95d5ee3183c2a113ae942de8d9465f | [] | no_license | xiajingdongning/liuyangli | 9bad094ce729338cef5c59ad0d7ce3bb5c05e497 | 9eb7be94e886fd75bb6a355ae4b82eeed04df1f3 | refs/heads/master | 2023-09-01T02:49:35.135879 | 2023-08-19T00:56:13 | 2023-08-19T00:56:13 | 234,586,231 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,268 | r | LoadDataWarehouse.LiuY.XuM.R | # Name: Yangli Liu
# Name: Mingyi Xu
# Course: CS5200 Spring 2023
# Date: 4/19/2023
# Install Dependency
if("RSQLite" %in% rownames(installed.packages()) == FALSE) {
install.packages("RMySQL")
}
if("RSQLite" %in% rownames(installed.packages()) == FALSE) {
install.packages("RSQLite")
}
# Load Dependency
library(RM... |
d5a455bc888bd4a9a14f41e557434325ed78b699 | cc8d779bc656c99b24e4eb19a5423b8effbd8d16 | /man/fhStart.Rd | 994d5e33f0206eb90398e62894cfe1d3f9fb72a0 | [] | no_license | plantarum/flowPloidy | 621c63af28d0723e8c8c255491abca43242a6d35 | e9f5a800f01de31853978f8566c4a63a271dfefc | refs/heads/master | 2023-04-06T20:28:25.763379 | 2023-03-17T14:50:06 | 2023-03-17T14:50:06 | 113,072,730 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,103 | rd | fhStart.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FlowHist.R
\name{fhStart}
\alias{fhStart}
\title{Calculate the where to start analysis for a \code{\link{FlowHist}}
histogram}
\usage{
fhStart(intensity)
}
\arguments{
\item{intensity}{numeric, the fluorescence intensity channel bins}
}
\valu... |
0557e32631df9887c0de89da98d6620bb62e93e1 | 144fc787ba3309d7abc8f8c7bd92f51b3e93bb9b | /Data_Cleansing.R | c487ad017191c3383407d0d55be3cda53e5970c7 | [] | no_license | nguyendoanbb/GermanCredit | 426ccd9ebf60da8adbf8934a7867bc81b7ddcb70 | 63e3ce900a915fd89d1869384fe68a42bb25d504 | refs/heads/master | 2020-04-20T04:47:56.085448 | 2019-02-06T16:59:53 | 2019-02-06T16:59:53 | 168,638,366 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 817 | r | Data_Cleansing.R | ########################################################################
#Data transformation
head(data) #20 features and 1000 observations, some variables need editing in order to be useful for analysis
str(data)
#removing single quote from all values in the table
for (i in c(1,3:4,6:7,9:10,12,14:15,17,19:20)){
dat... |
90065386b8d2108267b6865a4c5098931b43c3b6 | 9f3f65c30ccaea7b7054590e8159c27b7d16e242 | /pitcher_deception.R | a19565e3021e949d7b6770c9752e6e309419a02d | [] | no_license | jeaninem8/mlb_deception | a4030fcd3e964c31e1dc936a7b2b184c851bb124 | 1b870b53b8cd35828c9d20b68d5627477024457c | refs/heads/main | 2023-02-04T20:47:41.120178 | 2020-12-19T03:30:13 | 2020-12-19T03:30:13 | 305,537,164 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,285 | r | pitcher_deception.R | # Jeanine Minnick
# creating a leaderboard of MLB's most deceptive pitchers
# use distance from average release point, spin rate, strike rates both swinging and looking (except on 3-0), and hit rate on strikes thrown
library(dplyr)
library(data.table)
library(grDevices)
library(geometry)
# using data from 2017-Octob... |
8ed9e3e2914346e16fcba3bac01e4c8e31927d21 | 285541e8ae77482ac7eeb5b51ce06edeb96ef246 | /man/adult_trees.Rd | 573f88b671e161176facf8f83caf627da9f1d357 | [] | no_license | myllym/GET | 2033c4f590da7cce114b588e7e39b243b543dcdf | 72988291d9c56b468c5dddfb5bc2c23f519b6dca | refs/heads/master | 2023-08-24T23:23:14.364346 | 2023-08-15T21:33:51 | 2023-08-15T21:33:51 | 68,914,145 | 12 | 5 | null | 2022-11-16T07:55:16 | 2016-09-22T11:20:34 | R | UTF-8 | R | false | true | 1,305 | rd | adult_trees.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/adult_trees.r
\docType{data}
\name{adult_trees}
\alias{adult_trees}
\title{Adult trees data set}
\format{
A \code{data.frame} containing the locations (x- and y-coordinates) of 67 trees
in an area of 75 m x 75 m.
}
\usage{
data("adult_trees")... |
a634a17893304a55a69a3a2f38409ce2e1c1925c | eee5f4bfeba72f55c603eb8fbaa04d50af0165a8 | /R/xmastreewire.R | 89966f899d38eddab7f492c4f59561f87cd14199 | [] | no_license | cran/christmas | a5a1b761fd27fb25e9dd813ac8b18d2e73b04787 | 9e9d13c9e15639ec71a657f52f36844a14692ce5 | refs/heads/master | 2022-12-27T17:02:41.158735 | 2022-12-18T16:50:02 | 2022-12-18T16:50:02 | 236,570,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,355 | r | xmastreewire.R | #' @title Wire Christmas tree.
#'
#' @description A random wire Christmas tree (2021 card).
#'
#' @param year Year to be printed. Default is \code{2022}.
#' @param language Language to be used in the card. One of \code{c("english",
#' "spanish", "catalan")}. Default is \code{"english"}.
#' @param seed Seed for reprod... |
bba1310a1f6ff76dd86b8958d3df502a586f8e6e | 285fdf4489063a0a025e01aed5dea62a4de50c1c | /HWFiles/Sync7.R | 98aa7902dcbae12d31bca676e2ef27b7e74c053b | [] | no_license | jlwoznic/IST687 | 7050b35be92b38b630f16d61547e716dda0ac1d5 | b7474d68eea0cb862fc40ac3bd978a3cdd4d65b3 | refs/heads/master | 2022-05-10T16:17:21.227459 | 2020-04-10T16:19:13 | 2020-04-10T16:19:13 | 254,680,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,124 | r | Sync7.R | #
#
#
# pre-cursor functions,,,readCensus, Numberize
#
# read in the census data set
#
readCensus <- function() {
urlToRead <-"http://www2.census.gov/programs-surveys/popest/tables/2010-2011/state/totals/nst-est2011-01.csv"
#read the data from the web
testFrame <- read.csv(url(urlToRead))
#remo... |
30f3fda04c3d61cfae5b49c00bfefb9f39b40cdd | f119a12f993427f39e51b19d182ac18cb784984f | /MOD13Q1/003_MOD-ExtentFix.R | 8e5ee1bb2f189615a3ff67d0bfecab8f9d9d063f | [] | no_license | JepsonNomad/KangerSis_green-up | f05eca3c8849fceb45b52a49df91d0cd72cf929d | 92457a808d072a7caab166cc0e5f6f2276f94b26 | refs/heads/master | 2023-03-30T01:21:23.749643 | 2021-04-05T21:33:25 | 2021-04-05T21:33:25 | 278,746,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 992 | r | 003_MOD-ExtentFix.R | # Due to extent issue in Earth Engine export
# This script resets spatial metadata on raster objects to reflect correct projection information.
library(raster)
library(rgdal)
library(stringr)
setwd("PATH/TO/DIR/MOD13Q1/")
#### Projection information ----
MOD1 = raster("MOD13Q1_comp_DOY.tif", 11) # choose a random ra... |
4a9798273b16439ba542a837c72634fe9a768337 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /ETAS/man/catalog.Rd | 33627788653b5f7177a24e7e86a2b6028ce9bbe6 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,098 | rd | catalog.Rd | \name{catalog}
\alias{catalog}
\title{Create an Earthquake Catalog}
\description{
Creates an object of class \code{"catalog"} representing
an earthquake catalog dataset. An earthquake catalog is a
chronologically ordered list of time, epicenter and magnitude
of all recorded earthquakes in geographical region ... |
587aa9e2f933acc0d9b7274884725d108b796a65 | 9adc8c6da1ed43422fe584a522c94a4433464a4c | /man/prepareTransactions.Rd | 93a4e1cf548676e4593c8d5957f26420a91e7023 | [] | no_license | klainfo/arulesCBA | 314206c434d8d0986baaf7f3ef27ed86ea9add1e | 627a318caa984177b7faf02db37b498bcccbc036 | refs/heads/master | 2022-04-19T08:26:11.582798 | 2020-04-20T12:50:08 | 2020-04-20T12:50:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,061 | rd | prepareTransactions.Rd | \name{prepareTransactions}
\alias{prepareTransactions}
\title{Helper to Convert Data into Transactions}
\description{
Converts a data.frame into transactions by applying class-based discretization.
}
\usage{
prepareTransactions(formula, data, disc.method = "mdlp", match = NULL)
}
\arguments{
\item{formula}{ the formu... |
8b301b3dab523335f1c062ab73d8d9499107fa0a | 98e3d6171bbde7bcfea9158d6ef1e72f57e86ba6 | /R/hide-variables.R | 0a2069c9615d5ae17dd6d5edd76e27a940827adf | [] | no_license | mainwaringb/rcrunch | b818ce8a542a8f0dadd811f448df20f0857b16fe | a162d8e314773a9479a1ae92818b321a94b4a2ee | refs/heads/master | 2022-11-28T15:54:13.310104 | 2020-06-14T18:07:08 | 2020-06-14T18:07:08 | 262,774,601 | 0 | 0 | null | 2020-06-14T14:32:21 | 2020-05-10T11:43:46 | R | UTF-8 | R | false | false | 2,438 | r | hide-variables.R | setMethod("hidden", "CrunchDataset", function(x) hidden(folders(x)))
setMethod("hidden", "VariableCatalog", function(x) hidden(folders(x)))
setMethod("hidden", "VariableFolder", function(x) {
return(VariableFolder(crGET(shojiURL(rootFolder(x), "catalogs", "hidden"))))
})
#' Hide and Unhide Variables
#' @param x ... |
e4e88d03ab3eaac60dcc2ff6944a951210af63e0 | 91fde329639324b8b5ca4684b8e93b66ea2e93cb | /03_Visualizations/Categorical.R | a1500d9a40d915ff30f957ace589a5db930bdb59 | [] | no_license | Alice16/DV_RProject3 | a1e5ce24fba3ea306beeb1f71e81318e23be0707 | 2ebad385b538393d2f9fff64139d523dd2793b05 | refs/heads/master | 2021-01-22T08:52:49.709502 | 2015-03-06T16:46:41 | 2015-03-06T16:46:41 | 31,392,492 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,196 | r | Categorical.R | myplot <- function(df, x) {
names(df) <- c("x", "n")
ggplot(df, aes(x=x, y=n)) + geom_point()
}
categoricals <- eval(parse(text=substring(getURL(URLencode('http://129.152.144.84:5001/rest/native/?query="select * from LEGISLATOR_ROLE"'), httpheader=c(DB='jdbc:oracle:thin:@129.152.144.84:1521:ORCL', USER='C##cs329e_y... |
90e17087d0531f16ed1f8df055a0f0af33b7a5d2 | e374a5e7aaf75fb4e8314ab6fe90377898823d8a | /tests/testthat.R | 6ecaacd7145bf81baad90068b48dadb87976c8ae | [] | no_license | abhinav-piplani/earthquakeR | 35ed1006b1eb799ab88b57ffb381c14283aaac7f | fc2501ef86807de5a9e3aeaf8def124425e525b3 | refs/heads/master | 2021-05-12T04:05:26.335500 | 2018-01-14T17:13:22 | 2018-01-14T17:13:22 | 117,152,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,673 | r | testthat.R | library(testthat)
library(earthquakeR)
library(lubridate)
# Check eq_data
test_that("eq_data", {
data <- eq_data()
expect_true(is.data.frame(data))
})
# Check eq_clean_data
test_that("eq_clean_data", {
raw_data <- data.frame(251,7,9,"25.500","35.500","GREECE: CRETE")
colnames(raw_data) <- c("YEAR", "MONTH",... |
d80ead0c7e1a8c358e55c388a27b6258a2d45645 | 232fc9b238a636bf4b068dc6eb75dd65df6cf410 | /CLEANUP/02_variables.R | cc0dcae3e87a1fe902db6fc11c21d753fd6d768e | [] | no_license | the-data-center/Who-Lives | bca55b4a94b7c79660b642f222e13de8992294df | 7c98a4c2d74d2080b9ae81d76fa1a162fab07302 | refs/heads/master | 2023-08-03T11:08:44.824444 | 2023-07-31T19:02:29 | 2023-07-31T19:02:29 | 168,587,086 | 0 | 0 | null | 2023-07-31T19:02:30 | 2019-01-31T19:55:06 | HTML | UTF-8 | R | false | false | 407 | r | 02_variables.R | mycensuskey = "b6844db29933c9dce9e13fa37f1d015281001b95"
#GET CURRENT YEAR FROM BLS CPI CALCULATOR
cpi04 <- 1.41 #$1 in 2004 = $X in current year
cpi79 <- 3.83
cpi89 <- 2.16
cpi99 <- 1.59
cpi10 <- 1.21
# the current/most recent year - the year the data you're updating represents
year <- 2021
yearPEP <- 2021
year.ch... |
00a22612b28db4dee0e38adbc479c7f8f44a426d | 80f66a992fc733aab681dfa1e103db8105609725 | /ZZ_Archived/NYCTRS_Results_SensitivityTests2.R | 03e9960a679105766e786fb37d29671b4e66ee2f | [] | no_license | yimengyin16/RSF_NYCTRS | cd5cfcad260f1d03fe06f472a263f2e96325acb1 | 82326660d9bc525089772a84837851dfad3713f1 | refs/heads/master | 2021-06-05T16:12:11.367594 | 2019-03-17T14:30:25 | 2019-03-17T14:30:25 | 152,126,629 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,372 | r | NYCTRS_Results_SensitivityTests2.R | # Risk measures for NYCTRS
library(knitr)
library(data.table)
library(gdata) # read.xls
library(plyr)
library(dplyr)
options(dplyr.print_min = 100) # default is 10
options(dplyr.print_max = 100) # default is 20
library(ggplot2)
library(magrittr)
library(tidyr) # gather, spread
library(foreach)
library(doParallel)
libr... |
1d2e82b738f64723f5a4fac071cf58a6d3c43ce0 | e56262bee9693f61021fea5fc000ebcf46ac34bb | /man/C_node_depth.Rd | 5eb2f0d08d1d4d0e9b6b2ebb1cca8047b003a025 | [] | no_license | nanoquanta/TreeTools | d1ed57deb83122366b422117642eb986df1457bf | a858cf1c96de19b786b8243ef3d4ddfd6d0d8dd1 | refs/heads/master | 2020-08-26T09:35:04.083356 | 2019-10-19T10:59:15 | 2019-10-19T10:59:15 | 216,997,642 | 0 | 1 | null | 2019-10-23T07:41:41 | 2019-10-23T07:41:40 | null | UTF-8 | R | false | true | 308 | rd | C_node_depth.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phylo.R
\name{C_node_depth}
\alias{C_node_depth}
\title{Node depth
Wrapper for the ape function}
\usage{
C_node_depth(nTip, nNode, parent, child, nEdge)
}
\description{
Node depth
Wrapper for the ape function
}
\keyword{internal}
|
b9fb41a6c9c8468de4958a9f066916996e85445a | b7fe71b49afb5978a6628b1833dda3760a4bde1a | /man/daphnia.Rd | cd27c31c64fbfef95d8f1d784133fb4a3c3995e5 | [] | no_license | cran/vitality | 65944312461109593f18fb52371d15f13c0b91b8 | e4ab8e6ce8bbee13404c98e3906afb04bd6ecd76 | refs/heads/master | 2021-01-21T21:55:04.170484 | 2018-05-13T20:26:30 | 2018-05-13T20:26:30 | 17,700,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 471 | rd | daphnia.Rd | \docType{data}
\name{daphnia}
\alias{daphnia}
\title{Sample Daphnia Data}
\format{data frame}
\source{
http://cbr.washington.edu/analysis/vitality
Anderson, J.J. (2000). "A vitality-based model relating stressors and environmental properties to organism survival." Ecological Monographs 70(3):445-470 (Figure 5)
... |
f236b3ad5e461df4304cbdc1296a972487e99afd | c9d7e4f0fcc61eb7c5215fdffced4b9db3c34d7e | /man/print.cirq.devices.line_qubit.LineQubit.Rd | f5c6925c3aa38661eb20dd14b5e9f4fee601de68 | [
"Apache-2.0"
] | permissive | turgut090/Cirq | 091424a209295d0478459dcaa80a6d74384f9690 | cfa48055034a83655e56fb9a6c9f0499dd48d710 | refs/heads/master | 2022-10-06T04:37:32.333261 | 2020-06-07T06:06:11 | 2020-06-07T06:06:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 427 | rd | print.cirq.devices.line_qubit.LineQubit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generic_print_op.R
\name{print.cirq.devices.line_qubit.LineQubit}
\alias{print.cirq.devices.line_qubit.LineQubit}
\title{LineQubit}
\usage{
\method{print}{cirq.devices.line_qubit.LineQubit}(x, ...)
}
\arguments{
\item{x}{an object used to sel... |
e8862f8f492ac0bd630bfe124c2a863c93ee7ccc | 910a9f85f4712cfb05be5b6a8e0c9c36096aeea6 | /diuretic_control_gout_GWAS_setup_all_controls.R | 9905ef4aef8ff7d044f1deb90a0d099641b73ba5 | [] | no_license | lizhihao1990/Cadzow2017_Ukbiobank_Gout | 8345f7edbd2f0142f4eb08ebb2d3233b1eb85d4c | 15eefd10ba5b58cbeb7d4f785971e4c96d6616f3 | refs/heads/master | 2020-05-09T15:29:25.186983 | 2017-08-15T22:24:42 | 2017-08-15T22:24:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,599 | r | diuretic_control_gout_GWAS_setup_all_controls.R | # Murray Cadzow
# University of Otago
# 21 April 2016
# script for creating normal gout GWAS using all cases for each gout criteria and all controls
#control criteria: diuretics
#gout criteria: all, self report, self report + ULT, winnard, hospital
# run ukbiobank_gout.R first to create required affection columns
# ... |
975d7e9e44d87b19dedef5a4aeb573cf3f53e62e | 0c139ccd885ec95ba4da22f890270ba46fdaebbb | /code/fame_analysis.R | 1552432a2e16dcb0f5ebfeec28a0ab80e1b0b360 | [] | no_license | demichelislab/FaME | 0bf0cb0d00c07ab595bbb0b44fd505293772e69f | 7ddb3bc1748a958b3f03cdb6f73eebb29f523853 | refs/heads/main | 2023-04-07T03:49:15.210084 | 2021-09-21T15:00:46 | 2021-09-21T15:00:46 | 346,786,494 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,828 | r | fame_analysis.R | librarian::shelf(
parallel
)
# Finds the path where to load the source file
orig_path <- getwd()
source_file <- head(grep('^-?-f(?:ile)=', commandArgs(trailingOnly = FALSE), value = TRUE, perl = TRUE), 1)
if (length(source_file)) {
new_wd <- sub('^-[^=]+=(.*?)/?[^/]+$', '\\1', source_file)
print(paste('Changing ... |
6fc5e67bfef1bbe76a2bab4614139c995932dce5 | d899a92e376c20f1426889565917d90c0dca22a3 | /man/no_whitespace.Rd | 0e419de027ad95ca1efa29f67ba2d301ce721d1e | [] | no_license | gmoyerbrailean/clickme | e6779cdabf78c4ed206cf0b9314f6549a51fccc1 | 8d9eaff51d7c38a13129d2aa76b69e288d87e5f2 | refs/heads/master | 2021-01-12T19:32:30.183248 | 2013-09-14T00:48:32 | 2013-09-14T00:48:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 165 | rd | no_whitespace.Rd | \name{no_whitespace}
\alias{no_whitespace}
\title{Remove whitespace from a string}
\usage{
no_whitespace(str)
}
\description{
Remove whitespace from a string
}
|
5f546d118bb952f0ca29f684832f736913e44edc | 11577d5ab6897ad8ca94cc7a82f1259b9b9b6994 | /datasets.R | e6fbde7f4acbe41f859e8b41fa53d1bcdb1cedf8 | [] | no_license | nbest937/thesis | 33a39d104c23561d2c2a736ff4efcfd8df0581a0 | 3490178bef857253de34e0c0b40c69ea3137cfa4 | refs/heads/master | 2018-12-31T21:16:54.384049 | 2012-03-22T22:15:06 | 2012-03-22T22:15:06 | 717,267 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 22,921 | r | datasets.R | ###################################################
### chunk number 1: initialize
###################################################
#line 17 "/home/nbest/thesis/datasets.Rnw"
# load helper functions
# code will appear in appendix
source... |
4d79f906921ccb94e25b7cb05f80fbcffade31ac | 34e2217b2255e5bb192c2c724dbe78ca4c1b3c64 | /man/document_link_params.Rd | bca4929e3df502d3f7edb5134f7d2c51cacdf542 | [] | no_license | kongdd/languageserver | df335d28f97868793b6a56b64b9671a24afa57ce | d3ae514ad9b708178217522029e97f087e98b343 | refs/heads/master | 2020-08-01T14:40:13.188378 | 2019-09-26T07:09:25 | 2019-09-26T07:09:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 409 | rd | document_link_params.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interfaces.R
\name{document_link_params}
\alias{document_link_params}
\title{parameters for document link requests}
\usage{
document_link_params(uri)
}
\arguments{
\item{uri}{a character, the path to a file as defined by \href{https://tools.i... |
c08e034ce381896b190b6ee3dc832590811d84f4 | 26e26aca4102f40bc848120c4ebc99bb40d4a3c1 | /R/Archive/August 2020/FPLine decile.R | bdf459aa8c55380bd6e64116c01db39d2fce1120 | [] | no_license | IPRCIRI/IRHEIS | ee6c00dd44e1e4c2090c5ef4cf1286bcc37c84a1 | 1be8fa815d6a4b2aa5ad10d0a815c80a104c9d12 | refs/heads/master | 2023-07-13T01:27:19.954174 | 2023-07-04T09:14:58 | 2023-07-04T09:14:58 | 90,146,792 | 13 | 6 | null | 2021-12-09T12:08:58 | 2017-05-03T12:31:57 | R | UTF-8 | R | false | false | 16,622 | r | FPLine decile.R | #166-Step 6- FoodBasicNeeds.R
#
# Copyright © 2018:Majid Einian & Arin Shahbazian
# Licence: GPL-3
rm(list=ls())
starttime <- proc.time()
cat("\n\n================ Prepare Data =====================================\n")
library(yaml)
Settings <- yaml.load_file("Settings.yaml")
library(readxl)
library(data.table)
lib... |
f46162645e9d1d6b02750689f6c8ff8532565949 | 3d8a30386c98b68d36330212ccfde745ced8cce7 | /data-raw/process_internal_data.R | a5d656fbfc15174b3ab5707178dcca93cc397be2 | [
"MIT"
] | permissive | robbriers/stationaRy | f0c8d915a65f656aab9b189b1fa6d8c9389635d5 | 517d0316057198bcfdb3dce7489f25e428b1c073 | refs/heads/master | 2022-11-15T02:43:35.665986 | 2022-10-25T09:12:43 | 2022-10-25T09:12:43 | 194,041,119 | 0 | 0 | null | 2019-06-27T07:04:24 | 2019-06-27T07:04:24 | null | UTF-8 | R | false | false | 193 | r | process_internal_data.R | library(stationaRy)
library(sf)
library(usethis)
history_tbl <- stationaRy:::get_history_tbl(perform_tz_lookup = TRUE)
usethis::use_data(
history_tbl,
internal = TRUE, overwrite = TRUE
)
|
ed82372c0bf51f2e5b8dc171bb7e622100d83c50 | eacbb8f1937441c570c61679b50b37bbd480fe90 | /vignettes/MRCIEUGTEx.R | 6c05dc7bc450fae81ae1c107ff59063afa0e64ab | [] | no_license | mbyvcm/MRCIEUGTEx | 6ae821064d09adc4fb385bd9c1a9e17a4da09736 | 25af5ed685b92711d9612e7480c2f6eabdcfd080 | refs/heads/master | 2021-06-17T11:28:29.808399 | 2017-04-21T13:47:00 | 2017-04-21T13:47:00 | 86,478,628 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 715 | r | MRCIEUGTEx.R | ## ---- eval=FALSE---------------------------------------------------------
# # requires devtools
# install.packages('devtools')
# library(devtools)
#
# # install package from github
# install_github("mbyvcm/MRCIEUGTEx", quiet = T)
# library(MRCIEUGTEx)
## ---- eval=FALSE---------------------------------------... |
522cb54143c010b0a7f246702439dda76d0265b2 | a611bd21c8fbbeae34f1a90013d34655ea817a18 | /lession7/text_mining_basic.R | e7a92af57c116d3c83b8f94d1f886e48c594125f | [] | no_license | chunam76/RStudy | cec36c3b795588afe087cd470e30c9c81a8df92f | 7bfca66e4141ad4e7bbae5789d351f130c4db754 | refs/heads/master | 2020-04-26T22:12:17.708891 | 2019-03-15T04:50:37 | 2019-03-15T04:50:37 | 173,864,984 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 613 | r | text_mining_basic.R | ## 텍스트 데이터 분석
# 1) text mining 패키지 불러오기
library(tm)
# 2) Crude 데이터 불러오기
data("crude")
tdm <- TermDocumentMatrix(crude)
tdm
# 3) 단어 탐색
inspect(tdm)
# 4) 10회 이상 존재하는 단어만 출력
findFreqTerms(tdm,lowfreq=10)
# 5) oil 단어와 관련 높은 단어 출력
findAssocs(tdm,"oil",0.7)
# 6) 단어빈도 막대 그래프
freq <- sort(rowSums(as.matrix(tdm)), decre... |
17c3ed87cc6037c9ba192ab8fb7a97c80ed81760 | c79fa021f5bb195a4abfcf81d88a49b5ae86ce73 | /tests/testthat.r | 8d69e45f5e38f72a8bda00702db677ee7911eb75 | [
"MIT"
] | permissive | topepo/sparsediscrim | 7c99e48f9552455c494e6a04ab2baabd4044a813 | 60198a54e0ced0afa3909121eea55321dd04c56f | refs/heads/main | 2021-08-08T17:04:45.633377 | 2021-06-28T00:27:34 | 2021-06-28T00:27:34 | 313,120,774 | 4 | 0 | NOASSERTION | 2021-06-28T00:27:34 | 2020-11-15T20:51:32 | R | UTF-8 | R | false | false | 70 | r | testthat.r | library(testthat)
library(sparsediscrim)
test_check("sparsediscrim")
|
0b53f13f7ea90a44108eff25c3937c4618abdcb1 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610055947-test.R | 6d91a9c6947992ab3e6c2c8ccd1561288ee4a448 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 704 | r | 1610055947-test.R | testlist <- list(a = -1073741825L, b = 704643071L, x = c(1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, -1195919433L, -1212696654L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212696648L, 1212172360... |
a4afefd6a88de60529826a31ac51b97f34d4423d | 79afffae6d108b1a93aea7c72a55cf1fc7247498 | /man/Polynomial2.rd | 0b1fe47bff3e335613ac0c3daed7a54513350ae2 | [] | no_license | cran/assist | efbbad8da52741412f5dc933457774672de90b12 | 866a22f739a0e84d8631044225e3676651c987f2 | refs/heads/master | 2023-09-01T13:13:28.031385 | 2023-08-22T07:00:02 | 2023-08-22T07:30:44 | 17,718,448 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,268 | rd | Polynomial2.rd | \name{Polynomial2}
\alias{linear2}
\alias{cubic2}
\alias{quintic2}
\alias{septic2}
\title{
Calculate Reproducing Kernels for Polynomial Splines on [0, T]
}
\description{
Return a matrix evaluating reproducing kernels for polynomial splines at observed points.
}
\usage{
linear2(s, t=s)
cubic2(s, t=s)
quintic2(s, t=s)
se... |
15b33a47cf0c82075b580348a3e9d9e0b60b5e71 | b9897a73fa885f30ef468afa3b9ed0aba18bc9ec | /plot4.R | e0834f8fede6a5e62f3b19e6629dccc1c60eb9db | [] | no_license | anilmuthineni/ExData_Plotting1 | 096b509daf2ffd2eebefb3f58daaecb101fb7431 | 452c6a3b2aadedb675de34fe0f7e60c253dc0536 | refs/heads/master | 2021-01-20T11:19:50.517429 | 2016-07-15T19:04:53 | 2016-07-15T19:04:53 | 63,436,760 | 0 | 0 | null | 2016-07-15T16:42:39 | 2016-07-15T16:42:38 | null | UTF-8 | R | false | false | 1,509 | r | plot4.R | # Load power consumption data
power_consumption_data <- read.table("household_power_consumption.txt", sep = ";", header = TRUE)
# Create a column which contains both date and time
power_consumption_data$Datetime <- strptime(paste(power_consumption_data$Date, power_consumption_data$Time, " "), format = "%d/%m/%Y %H:%M:... |
a5be61181ba5a852738fa73be486cb50c219f11a | 779215d6b0ac83368f9c71f7a4aff494d64a0835 | /myIBP/uniqueMatrix.R | 3d48a36995e2cc13d552e42c1c91c634f960e4e8 | [] | no_license | luiarthur/byuMsProject | a257eccd12addca37b5a289ab47bb80f3fd1aecf | 0a3c101de8b311639dd60355ff62f562e8399bbe | refs/heads/master | 2021-01-19T08:32:20.541339 | 2015-03-31T16:14:12 | 2015-03-31T16:14:12 | 31,347,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,620 | r | uniqueMatrix.R | toMat <- function(s) {
dims <- regexpr(": \\d* \\d*",s)
begin <- as.integer(dims)+2
end <- begin+attr(dims,"match.length")
dims <- substr(s,begin,end)
pos <- as.integer(regexpr(" ",dims))
dims <- c(substr(dims,1,pos-1),substr(dims,pos+1,nchar(dims)))
dims <- as.integer(dims)
mat <- substr(s,1,begin-3)
... |
9496b9b253313750ca12e03ab296805156dfacdf | a790ee7a53ce16fb0a9340cb848f4ec1fd69cbb7 | /assist/banditAlgo.R | c9f49b2621fb7bc9aab8277c9e6e961bec5ec05f | [] | no_license | NetZissou/Bandit | 27c0045f77b1d81d1c4204cbf3c51461419e38a8 | 9ec387a22f0b51de378dfcac0d12baadcfc1cb89 | refs/heads/main | 2023-06-26T18:34:10.546210 | 2021-07-30T18:51:34 | 2021-07-30T18:51:34 | 340,757,733 | 1 | 0 | null | 2021-05-10T16:09:55 | 2021-02-20T21:32:02 | R | UTF-8 | R | false | false | 5,572 | r | banditAlgo.R | # Upper Confidence Bound
# Importing the dataset
library(tidyverse)
get_most_frequent_bandit <- function(bandit_selected) {
return(
as.numeric(
names(
sort(
table(bandit_selected), decreasing = T)
)[1]
)
)
}
dataset <-
tibble(
Ad.1 = rbernoulli(10000, p = 0.3),
... |
ae956f2f23389729b66af70cbb891f73871b4d99 | 5e85df6e3edead3eca4a2a4730f1705d1228c23d | /unsorted_code/confint regressioni.R | ead7c6f8211624c483b95e7abb48f793fc3609d2 | [
"MIT"
] | permissive | giorgioarcara/R-code-Misc | 125ff2a20531b2fbbc9536554042003b4e121766 | decb68d1120e43df8fed29859062b6a8bc752d1d | refs/heads/master | 2022-11-06T14:44:02.618731 | 2022-10-26T07:34:09 | 2022-10-26T07:34:09 | 100,048,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 650 | r | confint regressioni.R |
x=rnorm(100)
y=x+rnorm(length(x),sd=1)
confidence.level=.99
mod.lm=lm(y~x)
#### CALCOLO VARIANZA RESIDUA MODELLI
residual.error=sqrt(deviance(mod.lm)/df.residual(mod.lm))
residual.error=sqrt(sum(resid(mod.lm)^2)/df.residual(mod.lm))
dat=data.frame(x=x,y=y)
xref=seq(-4,4,0.5)
conf=qt(confidence.level, mod.lm$df... |
922959e522c5e4cdfffa76d7b86c4462b1949dfc | 7f09a3ac9f6e8b4d36f56e779fc0da3602288b85 | /Rcode/dyncpsimu.r | 2f330e528d0b102cb605da79f10a94a417c28d6f | [] | no_license | singlesp/TVDN | 707d0467205369d4c398ee5f41bd63d95ec6fa9a | 3f33488084df84de6207450c2312e6061b94229c | refs/heads/master | 2023-08-28T17:29:34.046832 | 2021-10-14T02:14:01 | 2021-10-14T02:14:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,194 | r | dyncpsimu.r | rm(list = ls())
library(fda)
library(MASS)
library(glmnet)
library(mvtnorm)
library(R.matlab)
fMRI = readMat('../data/fMRI_sample.mat')
fMRI = fMRI$time.series
time = seq(0, 2, length.out = 180)
set.seed(2021) ##6chg6rank 2021
step = diff(time)[1]
dfMRI = fMRI
basis = create.bspline.basis(range(0, 3), nbasis = 15, nord... |
47bdff4c92b1a56ca85278e560be35f1d9441225 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/12597_0/rinput.R | e75ffab880b04e98af7f3f420a07f94169ba4e4a | [] | 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 | 137 | r | rinput.R | library(ape)
testtree <- read.tree("12597_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="12597_0_unrooted.txt") |
ba344c354026732566111c1a6c3c90eb6f8bf18b | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diffrprojects/inst/testfiles/dist_mat_absolute/libFuzzer_dist_mat_absolute/dist_mat_absolute_valgrind_files/1609961646-test.R | 79f19c964b45b0878b2b7a6ed8f1e28411d8bc71 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 437 | r | 1609961646-test.R | testlist <- list(x = c(-1L, -1L, -256L, 0L, 16777215L, -1537L, -687865865L, -2097153L, -1895825409L, -42L, 439346687L, -2049L, -536870913L, -134225962L, 439353164L, 520093695L, -2745809L, -1L, -2686977L, -134225921L, -1L, -1L, -704643072L, -268435457L, 5046271L, -449314817L, -54964L, -701287629L, 872374298L, 805306... |
d7f0621d598d749cc6aac55b5720bbdcf419d151 | 9aaa5cbb46e412971a8d70fbab1894f86177564c | /R/nearth.R | 3b14d5a91641e980878ff9122367334a7732efbd | [] | no_license | BigelowLab/nearth | 06c73292a27024fbbf810b0372481d74e462becb | e4956a12cb82926602c809eccdca6ad607bef011 | refs/heads/master | 2021-01-24T02:59:02.912855 | 2019-01-23T14:42:26 | 2019-01-23T14:42:26 | 49,924,500 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,258 | r | nearth.R | #' Convert a 4-element bbox vector to a matrix of two columns (x and y)
#'
#' @export
#' @param x a 4-element numeric vector of left, right, bottom, top coordinates
#' @param close logical, if TRUE then close the polygon such that the first
#' and last verices are the same
#' @return a matrix of 2 columns and eithe... |
80b3468f16964db6bd310f0af628649ee998f6f3 | 3a6fa2e7370f06fefc35b327a157e11cb40fb7a7 | /man/bisonR-package.Rd | 4a6c462de0a995da00b232e362a9b11c305042b9 | [
"MIT"
] | permissive | JHart96/bisonR | 70e5294ea3cc08d80e8815d9a9ee64100cda53db | f1d1b0731fe63c4c6e01f877e6040f313cdfabb5 | refs/heads/main | 2023-08-18T03:18:45.911681 | 2023-07-28T18:06:39 | 2023-07-28T18:06:39 | 471,447,630 | 4 | 1 | NOASSERTION | 2023-07-28T18:06:41 | 2022-03-18T16:52:44 | R | UTF-8 | R | false | true | 274 | rd | bisonR-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bisonR-package.R
\docType{package}
\name{bisonR-package}
\alias{bisonR-package}
\alias{bisonR}
\title{The 'bisonR' package.}
\description{
An R package for Bayesian Inference of Social Networks
}
|
51f071ff475d74fa7b9c6b31f18a3cdd64e54b5a | cbdba435e722691c8dc1b4fdaf7bad8eac798d12 | /R/custom.period.summary.R | b0f9191a17fd1030048a722a5fbb4ddd6d4687d6 | [] | no_license | PALkitchen/activPAL | 3d02eb612d3b8e4f09f1d329e94133c8e2a321ed | 353266e8822db94ae097b675ba8420f42c73e972 | refs/heads/master | 2023-07-13T08:34:16.664201 | 2023-07-04T19:12:44 | 2023-07-04T19:12:44 | 195,839,458 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 8,592 | r | custom.period.summary.R | custom.period.summary <-
function(input_folder,file_name,id,events_file_data,full_events_file,custom_periods){
walk_test_30_s <- activpal.stepping.process.file.by.period(full_events_file,30,86400,custom_periods)
walk_test_2_min <- activpal.stepping.process.file.by.period(full_events_file,120,86400,custom_per... |
820505e0e1b8616d7feebaa3521e623917d21c1a | c2abe804dca918b233df380b23a1acc3e2fa9315 | /GG_plot.R | 47f8d602ec7b4e489dc3a9fc6bd7b638612d8059 | [] | no_license | anumaryjacob/git_demo_ipalnt | ab44effacf58763a1abea9c80cec66d587daac3a | 478df4c77f60187fd7e16a7fdecea61e5cbf7222 | refs/heads/master | 2021-01-25T03:18:49.056313 | 2015-02-23T00:07:02 | 2015-02-23T00:07:02 | 31,173,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,309 | r | GG_plot.R | # learning GG plot
# February 22, 2015
#making changes to check
install.packages("ggplot2", dependencies = TRUE)
install.packages("plyr")
install.packages("ggthemes")
install.packages("reshape2")
head(iris)
library("ggplot2")
library("reshape2")
library("plyr")
library("ggthemes")
myplot <- ggplot(data = iris, aes... |
a9d5d41a6d8881ca5f9e46467d2a7f57bb90d5b7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/svWidgets/examples/Img.Rd.R | d8534c51236881f20c19ba1e0425a7272c2193cd | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,047 | r | Img.Rd.R | library(svWidgets)
### Name: Img
### Title: Manipulate image resources for the GUIs
### Aliases: imgAdd imgDel imgGet imgNames imgType imgRead imgReadPackage
### print.guiImg
### Keywords: utilities
### ** Examples
## Not run:
##D ## These cannot be run by examples() but should be OK when pasted
##D ## into an i... |
71f844a817c1efd3d0d7f44be77c1b3fd9c871f7 | cd5e312b4260bf3a40ed0df893617bdbde7ec47c | /man/chitest.plot2.Rd | dd4299f8b4fcd9ff75c9e8e7619045ebae0e3fc3 | [] | no_license | tjssu/ssutat | bbc4ae9146153c0d7605ef57998fac6eb68bfb0f | c7cc4ba7e3d62e146f80c50e33800bac9c9cf79d | refs/heads/master | 2022-11-28T21:53:29.239850 | 2020-08-12T01:58:32 | 2020-08-12T01:58:32 | 285,228,403 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,075 | rd | chitest.plot2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ch12-fn.R
\name{chitest.plot2}
\alias{chitest.plot2}
\title{Plot the Chi-square Test}
\usage{
chitest.plot2(stat, df, alp = 0.05, side = "two", pup = 0.999, dig = 4,
ppt = 50)
}
\arguments{
\item{stat}{Chi-square test statistic}
\item{df}{... |
694b8974072739d07d93e2d93a12d2a5d9fc714d | 28a2590fb4e6f6bb331d8537e6d4a94eff362d09 | /r/coviz-ponge/app/app.R | 355e594aeefc6623a71b591942a8b51c4c864080 | [] | no_license | timueh/sars-cov2-modelling-initiative | b611c75b4f14510c87c58cde8263742def72e1a2 | c47f6c8c8b8f1975bed43608f6e753f499d244db | refs/heads/master | 2022-12-06T16:23:46.112057 | 2020-08-27T10:34:15 | 2020-08-27T10:34:15 | 255,846,969 | 8 | 6 | null | 2020-05-04T11:17:11 | 2020-04-15T08:03:15 | HTML | UTF-8 | R | false | false | 11,822 | r | app.R | ## COVID-19 German forecasting tool
## Johannes Ponge, Till sahlmüller European Research Center for Information Systems (ERCIS) at Muenster University (johannes.ponge@uni-muenster.de), March 2020
## includes code adapted from the following sources:
#https://github.com/eparker12/nCoV_tracker/
# load required packages
... |
7c0c870383131b2d0af8492e0a05fef5744f1753 | 29585dff702209dd446c0ab52ceea046c58e384e | /GenForImp/R/missing.gen.R | 089f0d0b46dbe152a27b61f046ca29183570782d | [] | 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 | 271 | r | missing.gen.R | missing.gen <-
function(mat, nummiss){
p <- ncol(mat)
repeat{
mmiss <- missing.gen0(mat, nummiss)
ind.na <- is.na(mmiss)
max.na <- max(as.numeric(names(table(apply(ind.na, 1, function(x) table(x)["TRUE"])) )))
if(max.na < p) {break}
}
mmiss
}
|
abb7ed1f78f178e0ea8474f163b7737ddbafd32b | 7f83f684b76b225e21f00ef721f846d371025521 | /Atividade_2_5.R | 171cc7e0c4cea4f9b6c6eb1570f5d0ef69b8be1c | [] | no_license | vladmonteiro/eletiva_analise_de_dados | 2225771ba7c145742f478d1835fc3852605f6c7d | 650d283a68148ece6fda730f2fda237ea6c67d04 | refs/heads/master | 2023-06-10T13:56:19.802780 | 2021-07-05T02:33:37 | 2021-07-05T02:33:37 | 356,731,299 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 797 | r | Atividade_2_5.R | library(poliscidata)
# carregamento do banco world
banco2 <- world
# comando para listar todos os países incluídos na base dados banco2
banco2$country
# comando que indica países que registram IDH abaixo de 0,500
banco2$hdi<=0.5
# comando que indica o tipo de regime atribuído a cada país da base
banco2[ , "d... |
84214d0aaad4a66861f30e90caa61b408c2fe0c4 | ff69d89ee00a965096ca3aba779a527f748e74de | /2017.09 DAR analysis TEMPLATE.R | 712c57eeed18d5222492b0a341227e0257d695aa | [] | no_license | jchap14/ATACseq-Analysis | 5cb1fe63caa0ca2eaaa3969b5d32f494b88a03a1 | 86a5d2896eb2242cd41382a76d7d05d20860bf1c | refs/heads/master | 2021-04-03T06:44:20.542323 | 2018-03-16T06:06:33 | 2018-03-16T06:06:33 | 124,723,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,182 | r | 2017.09 DAR analysis TEMPLATE.R | ##########################################################################################
########### DETERMINE DIFFERENTIALLY ACCESSIBLE (DA) REGIONS and GENERATE FIGS ###########
##########################################################################################
############################# Step 1: Always d... |
22daa6818a90b95f967130b27b96d0b39a929acf | 5b730d892bbfb255a0de73255f244895109b5c6f | /inst/unitTests/test_ssgsea.R | 595364ba84a4144cf85f797c18e420aae51400d1 | [] | no_license | rcastelo/GSVA | 8a8079d44b5cbb96951cca0a38844acad6412830 | 0870c685c354a8c83af2563854bb8bfe37cf86e9 | refs/heads/devel | 2023-08-08T23:20:50.711327 | 2023-07-28T17:30:29 | 2023-07-28T17:30:29 | 102,104,624 | 136 | 46 | null | 2023-09-13T14:40:51 | 2017-09-01T11:02:06 | R | UTF-8 | R | false | false | 1,790 | r | test_ssgsea.R | test_ssgsea <- function() {
p <- 10 ## number of genes
n <- 30 ## number of samples
nGrp1 <- 15 ## number of samples in group 1
nGrp2 <- n - nGrp1 ## number of samples in group 2
## consider three disjoint gene sets
geneSets <- list(set1=paste("g", 1:3, sep=""),
set2=paste("g", 4:6, sep... |
bfc4b871870e813dc61130ef8939dc594d890ad5 | ee0689132c92cf0ea3e82c65b20f85a2d6127bb8 | /Unsorted/dec17.R | b65d1aee88d42a2605fb37674563a7589eea80cc | [] | no_license | DUanalytics/rAnalytics | f98d34d324e1611c8c0924fbd499a5fdac0e0911 | 07242250a702631c0d6a31d3ad8568daf9256099 | refs/heads/master | 2023-08-08T14:48:13.210501 | 2023-07-30T12:27:26 | 2023-07-30T12:27:26 | 201,704,509 | 203 | 29 | null | null | null | null | UTF-8 | R | false | false | 3,296 | r | dec17.R | paste("a", "b", se = ":")
x= 1:5
y= NULL
is.null(x)
f <- function(a, b) a^2
f(2)
paste("a", "b", sep = ":")
paste("a", "b", se = ":")
Sys.Date()
class(as.Date('1970-01-02'))
x = 1:5
apply(x, FUN=sqrt)
?apply
data(package = .packages(all.available = TRUE))
paste('Data', 'Science', 'from', 'MUIT', sep='-')
month.abb[1:... |
f0e5138632d11bd78cee2fb89df1da0b4b62eddf | 35f844f6f5145265ffdd48c14522756030263dba | /man/TableCells.Rd | 8c63eb80025797432a39d06d896df503b942a2af | [] | no_license | cbailiss/basictabler | 708aa9e2da21b65ef65f529be868ebcbfee4373c | 63486f6acd7b163c28839dd607a98f4bd0e7920d | refs/heads/master | 2021-07-16T05:12:03.971848 | 2021-07-01T20:48:46 | 2021-07-01T20:48:46 | 104,359,939 | 33 | 2 | null | null | null | null | UTF-8 | R | false | true | 28,952 | rd | TableCells.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TableCells.R
\docType{class}
\name{TableCells}
\alias{TableCells}
\title{R6 class that manages cells in a table.}
\format{
\code{\link{R6Class}} object.
}
\description{
The `TableCells` manages the `TableCell` objects that comprise a
`BasicTa... |
75426aae05fa797a9af401ac1c3d6750c3e15b8d | dab20b21827a84261e457e87fd9082a1b6488a1c | /script_raw/BaseModel_prediction.R | a61a274b41293a272b7695874b92bc31bdbf9bb0 | [] | no_license | MikyPiky/Project2Script | 583da1740541f755a8fbe0195366e7fa7882ab44 | 93424306f2baeda5c769a09eff6c99d873bda41c | refs/heads/master | 2021-01-02T22:37:01.995232 | 2018-01-26T20:41:08 | 2018-01-26T20:41:08 | 99,354,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,494 | r | BaseModel_prediction.R | #### Description of Script ####
'
- Use models estimated in BaseModel.R to predict siloMaize yield Anomalies
- Loop through those models to make prediction for each year (maps) and comId (time series)
'
#### Output ####
## Files
'
- Maize_meteo including the predicted values from the models in BaseModel.R "./d... |
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