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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9751c62ae2f0b7c0cb8d4e43a291b082ce27b46d | 8306bfb1438a2516d3f9e7bea51f9d805793798d | /tests/testthat/test-all-na.R | f914eab53bfda5afec41ee96228dbbf3a1296ac4 | [] | no_license | cran/naniar | 849ad432eea4e343ffc4302b3ae7612759f9a552 | 30710de1ca289d1dd994e203c650bebc62a61a0f | refs/heads/master | 2023-02-21T19:46:58.125455 | 2023-02-02T08:50:02 | 2023-02-02T08:50:02 | 99,764,801 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 638 | r | test-all-na.R | misses <- c(NA, NA, NA)
complete <- c(1, 2, 3)
mixture <- c(NA, 1, NA)
test_that("all_na returns TRUE when all NA",{
expect_true(all_na(misses))
})
test_that("all_complete returns FALSE when all missing",{
expect_false(all_complete(misses))
})
test_that("all_complete returns TRUE when all complete",{
expect_tr... |
cd4cdcb8311d48cf146a4147890f52397790c00b | 488854749b8d6c1e5f1db64dd6c1656aedb6dcbd | /man/xmlDOMApply.Rd | 0575931aa041b9af78bc6f11381b5b28bd8b0beb | [] | no_license | cran/XML | cd6e3c4d0a0875804f040865b96a98aca4c73dbc | 44649fca9d41fdea20fc2f573cb516f2b12c897e | refs/heads/master | 2023-04-06T18:52:11.013175 | 2023-03-19T10:04:35 | 2023-03-19T10:04:35 | 17,722,082 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,245 | rd | xmlDOMApply.Rd | \name{xmlDOMApply}
\alias{xmlDOMApply}
\title{Apply function to nodes in an XML tree/DOM.}
\description{
This recursively applies the specified function to each node in an
XML tree, creating a new tree,
parallel to the original input tree.
Each element in the new tree is the return
value obtained from invoking t... |
66e38e3072e840bd67e8be4f7f89abf32a863f4c | 9f9038d285ae8e3d3772e49a8b3115f06e0a4f89 | /man/bdImport_text_to_hdf5.Rd | 2f2b0b87a32a8888694d9a38bca6292450f1548e | [] | no_license | isglobal-brge/BigDataStatMeth | e09bfcb2ca7e1abce253083177a30167e694a2af | 27948557f53ec6fa26450272339c7788b6c6bc54 | refs/heads/master | 2023-06-24T23:56:06.548394 | 2022-10-09T20:03:25 | 2022-10-09T20:03:25 | 147,813,286 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,408 | rd | bdImport_text_to_hdf5.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{bdImport_text_to_hdf5}
\alias{bdImport_text_to_hdf5}
\title{Converts text file to hdf5 data file}
\usage{
bdImport_text_to_hdf5(
filename,
outputfile,
outGroup,
outDataset,
sep = NULL,
header = FALSE,
rowname... |
4c390db4f8c42f52add21eb6db6d69c081bc1cec | e86eef8d2532368f0d22fb3c06719b4fbb9aa708 | /curve-fitting2.R | c36b4c8caa0125655a47cf040ac026d613017b6e | [] | no_license | khoadley/Fluorometer | 61e8fbb4750c1190b07ac94274cab394463bca64 | e1dc221c4f1454e8985f1277890acaee68400f1b | refs/heads/master | 2021-01-21T21:09:59.886318 | 2017-09-28T16:18:18 | 2017-09-28T16:18:18 | 92,315,422 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,501 | r | curve-fitting2.R |
library(ggplot2)
library(car)
library(MVN)
library(MASS)
//library(doBy)
rm(list=ls())
#establish x-axis
x<-c(0)
n <-0
for (i in 1:400)
{
n <- (n + 1)
x <- append(x, n)
}
xvalue <- as.matrix(x)
#arrary for removing .5 usec intervals from data set
xer<-c(0)
s <-0
for (i in 1:401)
{
s <- (s + 2)
xer <- a... |
fef46940420e6d3328c6c60a971863f559235141 | 551653ce2ea82e0e74cbb8844c09066650a15dce | /src/pcxn_res_go03.R | 0cedc4fcbe860f863f3424ca85708ab22bc8c962 | [] | no_license | yeredh/PCxN_GOBP | cb9eafbc23f76b5b3767556b184ed2b59c9285be | 6ad58a328818801aaf8f479e1a212c3bb3e61ab9 | refs/heads/master | 2020-12-24T15:13:23.436178 | 2015-07-07T03:36:50 | 2015-07-07T03:36:50 | 37,874,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,505 | r | pcxn_res_go03.R | rm(list=ls())
# ==== Experiment-level correlation estimates ====
r_mat = cbind(readRDS("/net/hsphfs1/srv/export/hsphfs1/share_root/hide_lab/PCxN/output/GO/res/r_mat1.RDS"),
readRDS("/net/hsphfs1/srv/export/hsphfs1/share_root/hide_lab/PCxN/output/GO/res/r_mat2.RDS"),
readRDS("/net/hsphfs1/srv... |
e1ad079c922026db35358c6ef61f244cdaf80c29 | 3d36b989d2e0be9c3954f3ded454436be7074870 | /resource_tracking/prep/archive/old_code/gtm_prep/prep_fpm_summary_budget.R | b35129604fb5194cdb434d7fdbae24e84fd46517 | [] | no_license | Guitlle/gf | 32bd9e0a5e176c01240f612f305cff740965a7f9 | c96403d7d53398cb8d8d55fa5ea2d794e0094080 | refs/heads/develop | 2022-04-03T03:04:07.075040 | 2019-02-04T18:52:33 | 2019-02-04T18:52:33 | 109,864,623 | 0 | 0 | null | 2017-11-07T16:53:05 | 2017-11-07T16:53:05 | null | UTF-8 | R | false | false | 2,743 | r | prep_fpm_summary_budget.R | # ----------------------------------------------
# Irena Chen
#
# 11/8/2017
# Template for prepping GF UGA budgets where the data is in "summary" form
# Inputs:
# inFile - name of the file to be prepped
# Outputs:
# budget_dataset - prepped data.table object
# ----------------------------------------------
##download ... |
06161a67160945117c5806446b179a5d7b1d2826 | 0d1dbe187fcd1a0231cbed0b31f060770be47e9c | /man/get_artist_albums.Rd | 4e91dd2a0160b10ec22e301ff4b174cded51e90e | [] | no_license | AlicjaGrzadziel/spotifyr | 359c035c119010eedf8128f69c69535bf0119259 | b9192ebc0d932da7781cbc01203902d64dcbb112 | refs/heads/master | 2020-03-20T07:03:45.023042 | 2018-06-11T00:02:00 | 2018-06-11T00:02:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,266 | rd | get_artist_albums.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_artist_albums.R
\name{get_artist_albums}
\alias{get_artist_albums}
\title{Get Artist Albums}
\usage{
get_artist_albums(artist_name = NULL, artist_uri = NULL,
use_artist_uri = FALSE, return_closest_artist = TRUE, message = FALSE,
studi... |
4c8cd06d35bb3bf271f2f8d8a92a2b90a34949f3 | 259256f1befb13890c929f6727f7621d5587b394 | /R/RegionalGoF.R | a412f8e8049ac038cb58340f79e18a160410c1dc | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | jlthomps/NWCCompare | 739bab60679687d1ac21ac0c57315485e0c85496 | 6d1efeef3e207bd4b94353adf7763a8976951986 | refs/heads/master | 2021-01-18T05:21:12.731754 | 2014-03-27T17:06:19 | 2014-03-27T17:06:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,044 | r | RegionalGoF.R | #' Function to calculate GoF statistics for given observed and modeled statistics
#'
#' This function accepts data frames of statistics for observed and modeled daily flow time-series
#' and returns a data frame of calculated GoF statistics
#'
#' @param GagedFlowStats data frame of flow stats for observed data
#' @p... |
dbdaa7a689dffdd6c69b89444b1242fde14a35bd | 189ef03fd836f6ed9a2d443e79161e84da3fecbd | /02-Plot_habitat_effects.R | 3e229aa9c0f02675969f7d6e483480610f36ebb1 | [
"CC0-1.0"
] | permissive | qureshlatif/CFLRP-analysis-scripts | af0bdcdcf9ae9ee32811c29429bec4fd5083a346 | 12627b08490e00c0030a8340d780a7d0b43277a5 | refs/heads/master | 2021-06-26T02:06:56.119795 | 2020-12-11T15:44:11 | 2020-12-11T15:44:11 | 186,690,852 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,683 | r | 02-Plot_habitat_effects.R | library(jagsUI)
library(stringr)
library(dplyr)
library(R.utils)
library(QSLpersonal)
library(ggplot2)
library(cowplot)
setwd("C:/Users/Quresh.Latif/files/projects/FS/CFLRP")
load("Data_compiled.RData")
mod <- loadObject("mod_habitat_d0yr_reduced")
spp_trt_effects <- c("WISA", "RECR", "WEWP", "CAFI", "CONI",
... |
f1d31a50f3013a663c0fc2c6dfd5484d91b8a51e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mgcv/examples/ocat.Rd.R | d47e394d8ba81e5f4a023c0fae0e3d1200270713 | [] | 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 | 869 | r | ocat.Rd.R | library(mgcv)
### Name: ocat
### Title: GAM ordered categorical family
### Aliases: ocat ordered.categorical
### Keywords: models regression
### ** Examples
library(mgcv)
## Simulate some ordered categorical data...
set.seed(3);n<-400
dat <- gamSim(1,n=n)
dat$f <- dat$f - mean(dat$f)
alpha <- c(-Inf,-1,0,5,Inf)
R ... |
d47a15bdccdaac905a345faa649920e3bbde8930 | 322737d934a4697320224ab97bdccddf936a7729 | / Kaggle Happiness Predictor/showofhands/data_transformation.R | 81dcb7d73aa2d902ba103512c17e61f2ae9a37fc | [] | no_license | eleven-yi/Analytics-Edge | 651461b48b374f19391ed56b071989cc0f5582c3 | 8997093e44bd72b5902900038a1faa727eb844d1 | refs/heads/master | 2020-12-11T07:34:22.358890 | 2014-06-05T01:03:34 | 2014-06-05T01:05:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,862 | r | data_transformation.R | numerify <- function(vec, val1 = "Yes") {
ifelse(is.na(vec), 0,
ifelse(vec %in% val1, -1, 1))
}
completedataset <- function(dataset) {
dataset$YOB = suppressWarnings(as.numeric(as.character(dataset$YOB)))
if ("Happy" %in% colnames(dataset)) {
dataset2 = data.frame(UserID=dataset$UserID,
... |
d7280275ba9637b1a791d1b4ffc7c4bcb77fb596 | dd8c7e34e56f4439c9823676356c87f23ae81a3e | /databaseConverter.R | 51869cfc2b9fdee8dfab17ab036979a3d43fe212 | [] | no_license | Sumidu/reclab_api | 031b3029fdd66471e98154134dd43603487f306b | d1ab1af90f4773dcc5077f9bce0ba680c6cdbd16 | refs/heads/master | 2020-06-17T14:54:10.733182 | 2019-10-01T14:20:43 | 2019-10-01T14:20:43 | 195,956,035 | 0 | 0 | null | 2019-09-25T09:35:31 | 2019-07-09T07:29:14 | R | UTF-8 | R | false | false | 1,442 | r | databaseConverter.R | ##
library(tidyverse)
library(RSQLite)
library(rvest)
library(lubridate)
con <- dbConnect(RSQLite::SQLite(), "corpus.sqlite3")
dbListTables(con)
articles <- dbReadTable(con, "Articles")
strip_html <- function(x) {
html_text(read_html(x)) %>%
str_replace_all("\\n", " ") %>%
str_replace_all("\\t", "") ... |
2dc54149a5be3809b83037015a0edbbed59d5588 | cd7e5ebd49ad2bbfa73fbc684d3966bbe0213e42 | /R/run_landcover_preprocessing.R | fb09ddaafa008065aa7c78c459512ff69b976e45 | [
"Apache-2.0"
] | permissive | SebaDro/rainfall-runoff-preprocessing | 0e9250167f1e57b6d97e5a6a750f6f25a328c3c1 | 16fc6ba2c9a4a537de6a27771b4c85bdbe95c2d5 | refs/heads/main | 2023-04-12T03:35:47.253767 | 2021-05-17T14:30:46 | 2021-05-17T14:30:46 | 323,310,778 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,170 | r | run_landcover_preprocessing.R | source("./R/setup.R")
##### Path parameters #####
output_path <- "./output/" # output path to save results
subbasin_file <- "./data/wv_subbasins.geojson"
class_config_file <- "./config/clc-codelist.csv"
# TODO Download CORINE Land Cover from https://land.copernicus.eu/pan-european/corine-land-cover
# and copy the annu... |
35e55b1f495a27aa2cb3de82950e23a9c9a80f05 | 1d7e8eaef903f93803ad720c4914bff2e03e5fcc | /TTR_Interpolation/0_Utility.R | 214700c2899252dfdd68c6a117d51f327cb812cf | [] | no_license | onthejeep/TTR_Interpolation | 48950518db2d8da19361637b420358c0dc158b7c | b6ff24de57194a0bf9f7f7734c7f6630d7c1fe29 | refs/heads/master | 2021-05-07T00:52:35.656806 | 2017-12-14T22:04:19 | 2017-12-14T22:04:19 | 110,288,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,041 | r | 0_Utility.R |
Parse.Structure = function(structure = '32,32,16,8')
{
SplitStr = strsplit(structure, split = ',', fixed = TRUE);
Structure = as.numeric(unlist(SplitStr));
return(Structure);
}
Find.HotDestination = function(sqlite)
{
SelectCommand = sprintf("
select [UnLoading.ColIndex], [UnLoading.RowIndex], Number... |
a8d816d5234ecf9448ec89c32e168ec699e75460 | ed2aa34036f33d1af64648b5cbafddaa77552ec8 | /notebooks/data_trimming_util.R | e54d0e17b85bf43fcaa180c628c1b72df8e39777 | [] | no_license | shihuang047/SoH_manuscript | a86cf8fb20bc2c68b884b74ad2fb01a40e6093c4 | 3d338bad3f7dcadf26eae206e472963b9a2ae424 | refs/heads/master | 2022-12-11T17:06:07.312339 | 2020-09-09T20:07:08 | 2020-09-09T20:07:08 | 276,764,667 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,036 | r | data_trimming_util.R | #--------------------------------------------------
p <- c("biomformat","reshape2","randomForest", "optparse", "ade4", "doMC",
"ggplot2", "RColorBrewer", "vegan", "xgboost", "caret")
usePackage <- function(p) {
if (!is.element(p, installed.packages()[,1]))
install.packages(p, dep = TRUE, repos = "http://cr... |
c317bb0b36efdd0c97a627b1da9904e32bb0defa | 0d92a20f2f35dcfcd572c52f5e3b4184279bfa04 | /esm_emics.R | 3084879c8c018aa38548ddf8333583358c22702e | [] | no_license | richardcode/cmip5_anal | a578158209a67e2cae796a1d12d71d5afc8f1661 | 1d5588f177ac4d1eeb92d2d958a86b819724e1b0 | refs/heads/master | 2020-06-16T14:31:59.559643 | 2017-04-12T14:31:03 | 2017-04-12T14:31:03 | 75,091,699 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,319 | r | esm_emics.R | source("tcre_var_functs.R")
#Load in the data from the CMIP5 ensembles
cmip5_data <- read.csv('./Data/ESM_cmip5_tempems.csv')
esms <- c('Bern3D','DCESS','GENIE','IGSM','UVi')
emics_data <- cmip5_data[cmip5_data$Model %in% emics,]
esms_data <- cmip5_data[!(cmip5_data$Model %in% emics),]
emics_rcp26_data <- emics_d... |
f91da436bd0140535b7dc5d37dcb717cb79fb279 | 22f54f44eea58554d5599b0a2a99e53ca660ec54 | /src/analysis_k562ctcf/get_data.R | ab0b1f4798fbbce5ed60ad22ea872d4acaffc04a | [] | no_license | zrxing/sequence_clustering | 294fe4bdef4bed29fa96e7b0fcdb922feb852b41 | 1858157b7724eeb7d35a89ba9adb45476cd30700 | refs/heads/master | 2020-04-06T07:05:21.015737 | 2016-07-21T19:06:05 | 2016-07-21T19:06:05 | 34,276,466 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 785 | r | get_data.R | dir.name = "~/projects/sequence_clustering"
library(multiseq)
lreg = 2^10
peaks = read.table(file.path(dir.name, "data", "k562ctcf", "wgEncodeBroadHistoneK562CtcfStdAlnRep0.bed"))
samplesheet = file.path(dir.name, "src/analysis_k562ctcf", "samplesheet_k562ctcf")
peak.chr = peaks[, 1]
peak.center = ceiling((peaks[, 2] +... |
e15bee23b5cdbf9ebfd81347145733acaf2689c6 | 79ec1f24c76048d81027c5d5069a0772d4d44597 | /utilis.R | e4e487880788e8dfce3f528bc208e37034f40d10 | [] | no_license | ashakru/lymphDDX3X | 19be776f60b70cf7c4c72ecd741b83ca5c7a93ad | 983e16043d528ac655d29506653007a4e764742e | refs/heads/main | 2023-06-14T12:21:39.440518 | 2021-07-08T14:53:38 | 2021-07-08T14:53:38 | 351,418,436 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,036 | r | utilis.R | require(RColorBrewer)
# # Colour palettes
divergingPal <- c(brewer.pal(11, "Spectral"), "grey")
divergingPal_long <- c("grey",
brewer.pal(9, "YlOrRd")[c(3,5,7,9)],
brewer.pal(9, "YlGnBu")[c(2,3,4,5,6,7,8,9)],
brewer.pal(9, "YlGn")[c(8,7,5)],
... |
275dea06955ce8924096440de8d3669cc7bebeb7 | bfc80df9cdabc1987f1b752a2c56c82929f5b7c8 | /lab1_3.R | ef6517aa623b87b311d48c2e31f76779cd4e9310 | [] | no_license | leetimofey/RUDNprogs | 672571fafea4b6825d98cc3b569807a64d6e4563 | 3836298ce1847a01a049550f62c76236288bb6a4 | refs/heads/main | 2023-02-17T17:32:52.722267 | 2021-01-12T15:15:52 | 2021-01-12T15:15:52 | 313,323,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | lab1_3.R | n=-5
seq(n-1)
seq(9)
rep(c("m", "w"), 5)
rep(c(1:4), 3)
rep(c(4:1), c(3, 3, 3, 3))
rep(c(1:5), c(1:5))
rep(seq(1,11,by=2),c(2,2,2,2,2,2)) |
670ba7ee30d3e4519501cd02359a0cdf7ea30320 | 2e731f06724220b65c2357d6ce825cf8648fdd30 | /UniIsoRegression/inst/testfiles/reg_2d/libFuzzer_reg_2d/reg_2d_valgrind_files/1612737184-test.R | 4fb6453bd434fccf7fcb9c56ec5c770738f66d79 | [] | no_license | akhikolla/updatedatatype-list1 | 6bdca217d940327d3ad42144b964d0aa7b7f5d25 | 3c69a987b90f1adb52899c37b23e43ae82f9856a | refs/heads/master | 2023-03-19T11:41:13.361220 | 2021-03-20T15:40:18 | 2021-03-20T15:40:18 | 349,763,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 352 | r | 1612737184-test.R | testlist <- list(metric = 0L, vec = NULL, vec = NULL, w_vec = structure(c(2.74343508233833e-260, 8.00276746564406e-255, 8.29550922761073e-316, 7.2911220195564e-304, 0), .Dim = c(5L, 1L)), y_vec = structure(c(4.94065645841247e-324, NaN, NaN, 3.78576699573368e-270), .Dim = c(2L, 2L)))
result <- do.call(UniIsoRegressio... |
cf73ec5e2d4d487477a0ab78748c043426239a9c | 48d96957e322918327beabe20f2391628c2318ae | /R/Best_Models.R | 6aee072338e3db2833285b27f87e1b00c9868436 | [] | no_license | adw96/CatchAll | 9be39ed4e693a2dae54d96cf46fb6e835ab594f6 | 45d3d86509e0920ab92ccb997c00524a1624d62e | refs/heads/master | 2020-06-27T13:10:32.707202 | 2018-09-07T16:57:21 | 2018-09-07T16:57:21 | 97,057,555 | 3 | 1 | null | 2018-02-27T20:30:40 | 2017-07-12T22:36:42 | R | UTF-8 | R | false | false | 9,190 | r | Best_Models.R | Best_Models <- function(bestCount, maximumObservation, frequencyTau10, bestGOF0,
bestAICc, GOFTest, cvrare) {
## how to get best count...
bestModel <- rep(NA, maximumObservation + 1)
bestModelTau <- matrix(NA, nrow=10, ncol=2)
obsMax <- maximumObservation
flag <- -1
if (bestC... |
ae0b1e6b6749024e4e15d3b578165e40f51c66b6 | a667617b4a9ba5149714e6285c00f741170655ed | /R/diff_plot.R | 446ae52646e4f66ec3c473a15101f282e99f98a4 | [] | no_license | lix2k3/Rcpm | 0f387939fe56d3de218e62e21e6a2b1b40166ff5 | b09849e5487b5a2a1e02eb5920dde96cde2b0a2f | refs/heads/master | 2022-12-02T13:41:06.931097 | 2020-08-20T12:20:05 | 2020-08-20T12:20:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,594 | r | diff_plot.R | #' @title Create difference plots
#'
#' @description Create difference plots to show up- or down-regulation of certain lipids.
#'
#' @param data data frame containing all information. See details for more information on the structure.
#' @param x what to plot on the x-axis, often the variable e.g. lipid
#' @param y w... |
d2a52cc27e4b48b04865fe806c6fa0c60e457022 | cc7139789c2e524d61e11c92a033666246d2da32 | /4_MultivariateAnalysis/notUsed/FA.R | 19c7f314d9b432162b4c3e30b1a64c216c4f4bd4 | [] | no_license | dmpe/bachelor | e99dc0de17d8cf54455dfec08c15e32f1916f74c | 7611bb2556cc728e64d7be50a47c32a055b6ce9d | refs/heads/master | 2021-01-10T13:00:51.218870 | 2015-10-02T15:52:25 | 2015-10-02T15:52:25 | 36,564,092 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,314 | r | FA.R | # Some small analysis using Factor analysis
library(psych)
library(GPArotation)
source("http://www.tcnj.edu/~ruscio/EFA%20Comparison%20Data.R")
# http://rtutorialseries.blogspot.de/2011/10/r-tutorial-series-exploratory-factor.html
solution <- fa(r = corelationMat, nfactors = 6, rotate = "oblimin", fm = "minres", SMC =... |
13d00be98aef62dc0a3234322678b41f2e964a10 | 80751ed622a43695c64ac86122f28c07cf5facfb | /R/plotGeneScatterPatientData.R | b4a6e2d9177c22bb1fd20de83ef6f4e3de108f5e | [] | no_license | komalsrathi/marislab-webportal | 4667c3a3d9fd5b22f75174bec47c2e9029b68b57 | 7c295a6f438a7c4ecd775c9fae0aa9266147e799 | refs/heads/master | 2020-09-11T06:34:29.479666 | 2020-02-25T19:43:21 | 2020-02-25T19:43:21 | 221,972,910 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,463 | r | plotGeneScatterPatientData.R | ####################################
# plot scatter plot of 2 genes
# Authors: Pichai Raman, Komal Rathi
# Organization: DBHi, CHOP
####################################
# plot scatter plot of 2 genes##################
plotGeneScatterPatientData <- function(datatype, gene1, gene2, myDataExp, myDataAnn, log, colorby, co... |
a041f494ba33ed92397fd3ba3e0e0100fc6ed25e | af4a387877fe45bee8cc8523f20d9c839e1c70d1 | /man/vt.uniqtl.pop.C.Rd | 79bb8895e23e0365a6999783743a19e3424f60cb | [] | no_license | cran/STARSEQ | 120868533ec0218bb1190d358e1ab248981a3abc | 60670fb5c24b6cb676e5a80d89ff1a337b973ff4 | refs/heads/master | 2021-01-23T11:09:07.420572 | 2012-05-15T00:00:00 | 2012-05-15T00:00:00 | 17,693,619 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,035 | rd | vt.uniqtl.pop.C.Rd | \name{vt.uniqtl.pop.C}
\alias{vt.uniqtl.pop.C}
\title{VT Test for Population-basd Studies of Quantitative Trait
}
\description{
This function implements the variable threshold test
}
\usage{
vt.uniqtl.pop.C(dat.ped, par.dat, maf.vec, maf.cutoff, no.perm = 1000, alternative = c("two.sided","greater","less"))
}
\argumen... |
2e368aa32e9ed4deeb0607585d4ed73783a4f8ec | 84eb5b028b41b0e224df9a9b050a707bd32b353b | /man/add_variables.Rd | 79867e1abfdda34386cac5b1c10f5df6dd0cd31d | [
"MIT"
] | permissive | minghao2016/workflows | af6fbf5e4a1c1d7e5b0e3038c52a63e43b192f75 | 25a422857f0940e90bbefdf8990464198f376cd6 | refs/heads/master | 2022-12-21T22:25:09.128867 | 2020-09-14T12:38:10 | 2020-09-14T12:38:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,768 | rd | add_variables.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pre-action-variables.R
\name{add_variables}
\alias{add_variables}
\alias{remove_variables}
\alias{update_variables}
\title{Add variables to a workflow}
\usage{
add_variables(x, outcomes, predictors, ..., blueprint = NULL)
remove_variables(x)... |
204d21dd1fc11e808e977b759a14d578dd51020a | 051880099402393c9249d41526a5ac162f822f8d | /man/tg.removeTier.Rd | a841c6d2f9b1ed34bd23cca6b18686d3f3e9081f | [
"MIT"
] | permissive | bbTomas/rPraat | cd2b309e39e0ee784be4d83a980da60946f4c822 | 4c516e1309377e370c7d05245f6a396b6d4d4b03 | refs/heads/master | 2021-12-13T19:32:38.439214 | 2021-12-09T18:42:48 | 2021-12-09T18:42:48 | 54,803,225 | 21 | 7 | null | null | null | null | UTF-8 | R | false | true | 560 | rd | tg.removeTier.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tg.R
\name{tg.removeTier}
\alias{tg.removeTier}
\title{tg.removeTier}
\usage{
tg.removeTier(tg, tierInd)
}
\arguments{
\item{tg}{TextGrid object}
\item{tierInd}{tier index or "name"}
}
\value{
TextGrid object
}
\description{
Removes tier of ... |
5aa46760dbe71c94856c18869b5a94ab41b51ab2 | 0826f40877bfa10084e1a565a3056001d5b3bafa | /man/Vdgraph.Rd | fa6a43169fbabbd062c8d476c16c2bc810bfdd7f | [] | no_license | cran/Vdgraph | 9eec47b95cf64f45b0c02ac17f993f7cee6323fc | a83a4a363f7f0d753635f15f9f29503f0e950cd4 | refs/heads/master | 2023-06-12T08:42:07.090755 | 2023-06-02T11:10:08 | 2023-06-02T11:10:08 | 17,694,064 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,196 | rd | Vdgraph.Rd | \name{Vdgraph}
\alias{Vdgraph}
\title{ this function makes a Variance Dispersion Graph of a response surface design
}
\description{
This function calls the function Vardsgr which uses Vining's (1993) fortran
code to get the coordinates of a variance dispersion graph, and then makes
the plot.
}
\usage{
Vdgra... |
65bcb3feb282e02f3a0b2830c454eacd6915e7d3 | 09eb39939021ebedb5af9b7539982ba49a3626d0 | /Plot1.R | 75d1bddc49cc138cebebe19255c06f268a600d71 | [] | no_license | laurentBesnainou/Exploratory-Data-Analysis-Project2 | bc1810e389680c0a46bc27de01f927df21c1aef1 | 995674ceef0863beb4687bfdfe16289759fb10d5 | refs/heads/master | 2021-04-29T22:37:49.527677 | 2018-02-15T14:54:57 | 2018-02-15T14:54:57 | 121,641,410 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 733 | r | Plot1.R | library(dplyr)
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
##### Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
png(file = "plot1.png",width = 800, height = 600)
##### base plo... |
d12c67ae993709985b442bcfb7ec680808af1332 | 7991d8802959a649ab92cb8b5caf3efe1ef61c26 | /circos_draft_genome_nocol.R | 9ce73a00d2a642d1164041bb43b28e988545ccdb | [] | no_license | euba/genome_example | c832a4916f980c09d27f3019cfbe86f7dbca84e3 | 697dfba2316eb280cbc19b8525ed3ac1bf6a5c4a | refs/heads/master | 2020-08-07T09:44:16.661620 | 2020-05-19T18:32:18 | 2020-05-19T18:32:18 | 213,396,701 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,277 | r | circos_draft_genome_nocol.R | #package installations
install.packages("RJSONIO")
install.packages("seqinr")
install.packages("RColorBrewer")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("OmicCircos")
BiocManager::install("KEGGREST")
#load the packages
library(RJSONIO)
library(seqin... |
00fb96e7b57e2ceff0df041670961599cbd6b287 | b2d32cb57604a26e31f0c4947ee866f59a7aa8ba | /man/modNamePaste.Rd | 8c4ad7d56251e145eac5c45ea9ee46c197870bcf | [
"LicenseRef-scancode-warranty-disclaimer",
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | atredennick/GenEst | fe1f95ca844b2bf9cf0f3358e810439b0a964410 | 38b73a290c074872c0651926bd6de283072aa8e6 | refs/heads/master | 2020-06-10T20:24:52.460911 | 2019-06-25T16:52:44 | 2019-06-25T16:52:44 | 193,736,013 | 0 | 0 | null | 2019-06-25T15:35:03 | 2019-06-25T15:35:03 | null | UTF-8 | R | false | true | 540 | rd | modNamePaste.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/app_utilities.R
\name{modNamePaste}
\alias{modNamePaste}
\title{Paste the parts of a model's name back together}
\usage{
modNamePaste(parts, type = "SE", tab = FALSE)
}
\arguments{
\item{parts}{the component parts of the model's name... |
440d5adc7fbd5a05b37bedc9c0ff0efae0d9475a | 26242ef504b67621386f0e6a9d5988e5671cf956 | /multiway_partitions.R | 5e4448bc2e5f349fa70658cac409f670785e49cb | [
"MIT"
] | permissive | mbbruch/Multiway_Partitioning | f96d3cf313343080e827f4dada28b499e1a7e6bd | 74447b556867f9c3292ad33536629f0802bf9b92 | refs/heads/main | 2023-08-10T19:11:55.770190 | 2021-09-30T03:07:24 | 2021-09-30T03:07:24 | 411,904,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,025 | r | multiway_partitions.R | library(gurobi)
library(data.table)
library(dplyr)
multiway_partitions <- function(I, K) {
sizes <- data.table(i=1:length(I),size=I)
combos <- data.table(crossing(sizes,k=1:K))
model = list()
A <- NULL; b <- NULL;
sense <- NULL;
current.row=1;
#Each item can only be in one partition
rows <- comb... |
82ab2c9f14a4570efecf8dd0688f8787eeff4d49 | 0cc22eef828da4740c8309bfb9231c92e27cb110 | /LCMM1.R | b2fd145ae2749b08583f7955b5a6e46f027cd15b | [] | no_license | gl2458/Practicum | 6893370c9050fa36b2c8de6f85dd5949757180b7 | 886fa13c873188e8173f4b2e4d536671867347a5 | refs/heads/master | 2021-05-21T09:39:16.549688 | 2020-08-14T18:35:09 | 2020-08-14T18:35:09 | 252,641,054 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,874 | r | LCMM1.R | install.packages("lcmm")
install.packages("xlsx")
library(lcmm)
library(plyr)
library(tidyverse)
library(writexl)
#data
cmpst_all2 <- read_csv(file = "/Users/rachellee/Google Drive/Practicum/Data/cmpst_all2.csv") %>%
janitor::clean_names() %>%
filter(visit == "Baseline" | visit == "Week3" | visit == "Week6" | vi... |
0975d0da6061d3a433623cb5248b0764dab34d32 | 6390c203df735c874044a8ffa0f3692bf6010a6a | /demo/demoTPP05.R | 3518e2b907984b71042abcfa382e06e7d738fb65 | [
"MIT"
] | permissive | felixlindemann/HNUORTools | c8c61ec550e2c6673c8d3e158bd7bc21208b26ab | 0cb22cc0da14550b2fb48c996e75dfdad6138904 | refs/heads/master | 2020-05-15T18:37:48.423808 | 2018-02-04T11:04:52 | 2018-02-04T11:04:52 | 16,206,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,044 | r | demoTPP05.R | geo<-new("GeoSituation")
geo<-add(geo,new("Warehouse", id="L1", x=25, y=70, supply = 350 ))
geo<-add(geo,new("Warehouse", id="L2", x=150, y=115, supply = 450 ))
geo<-add(geo,new("Warehouse", id="L3", x=80, y=140, supply = 300 ))
geo<-add(geo,new("Warehouse", id="L4", x=160, y=10, supply = 120 ))
ge... |
6fadec7d8d0dd55afb620452c8f3f8e2f72f77d6 | 439933a3fb21a29240ab4b04aebaced0569248be | /_R code for processing raw data/Make CWT recover summary plots.R | 4bbc13d95919a6481f128815fe664d821e0dafb5 | [] | no_license | nwfsc-cb/spring-chinook-distribution | e47b5e39f5ce2ab8f20413085bc13249ef3bec37 | 5bff26b6fe5102a16a9c3f2c13d659b7e831e03e | refs/heads/master | 2023-08-08T03:35:36.302066 | 2023-08-01T16:35:04 | 2023-08-01T16:35:04 | 128,123,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,258 | r | Make CWT recover summary plots.R |
A <- data.frame(apply(C,c(1,4),sum))
A <- A %>% mutate(Tot = rowSums(.))
A$ID <- REL$ID
A$ID_numb <- REL$ID_numb
REL.mod <- REL %>% left_join(.,A) %>%
mutate(z.ind = ifelse(Tot ==0,"zero","pos"))
REL.sum <- REL.mod %>% group_by(ocean.region) %>%
reframe(N.tot= length(z.ind),N.zero = length(z.ind[z.ind=="zer... |
b38d44f5c0d6029c8c7bae77fa0526a445c4f480 | beeee47f9dbaa4a86bce21cb971b6eef6af805cd | /R/utilities.R | e84ee05c0484aaf3964c1cd37a64cd78d0413aae | [] | no_license | assaron/GSEABase | 1787667ce3e405bff56af3facae0ec8ba8afd409 | aa8d43079c58cd627961764d3b755aae4b9f9061 | refs/heads/master | 2021-01-23T20:32:27.138842 | 2016-07-07T19:48:30 | 2016-07-07T19:48:30 | 62,832,828 | 0 | 0 | null | 2016-07-07T19:25:05 | 2016-07-07T19:25:05 | null | UTF-8 | R | false | false | 1,083 | r | utilities.R | ## Placeholder 'till something appropriate decided
.uniqueIdentifier <- function()
{
paste(Sys.info()['nodename'], Sys.getpid(), Sys.time(),
sample(.Machine$integer.max, 1), sep=":")
}
## simplified unique for vectors, preserving attributes
.unique <- function(x, y) c(x, y[!(y %in% x)])
.glue <- functio... |
1b5591d2612b9d7b1fbd59d46658991b35e7ecd6 | 125e942d1af8e002fe71051423d41490901b034e | /PracticalML/3_2_Forecasting.R | a158760af1610a23954b14a3bcb4cc0350db0eab | [] | no_license | whitish/opendatakyiv | ac30089d12aac1fde444f42b42b35897680f17b6 | aed90acbf7cb446778b4dbbd753f0c780fa3ba5e | refs/heads/master | 2021-01-10T02:15:26.518229 | 2016-03-15T11:23:27 | 2016-03-15T11:23:27 | 52,305,768 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,141 | r | 3_2_Forecasting.R | library(quantmod)
library(PerformanceAnalytics)
library(FinTS)
library(rpart)
library(rpart.plot)
library(rattle)
library(TTR)
library(forecast)
$$$$$$$$$$$$$$$$$Importing time series from finance.yahoo$$$$$$$$$$$$$$$$$$$$$$$$
getSymbols(Symbols = "CHK", src = "yahoo",from = "2010-03-03", to = "2016-03-03")
CHK<-dat... |
3509fd62618f1594b9b9c78e5cfffc77a0e37840 | 432d68b44e60d0fa1c23efb71ffa69933aa89f8a | /R/Chapter 2/exercise-8.R | c80f9de2f6fe7c3829577d26bdecdf58c2508b5b | [] | no_license | ShilpaGopal/ISLR-exercise | 5a5951118a33ef7dd8a9b3a6f7493bfd7b89b5ff | 9705905d36bd74086b133e4a5f937e7a4ab18e5e | refs/heads/master | 2020-12-04T11:12:01.024951 | 2020-03-21T10:23:12 | 2020-03-21T10:23:12 | 231,741,200 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 508 | r | exercise-8.R | college = read.csv("./data/College.csv")
fix(college)
rownames(college)=college[,1]
fix(college)
college=college[,-1]
fix(college)
summary(college)
pairs(college[,1:10])
college[1,]
attach(college)
plot(Outstate,Private)
Elite=rep("No",nrow(college))
Elite[Top10perc>50]="Yes"
Elite=as.factor(Elite)
college=data.frame(c... |
0c238c9379ae7f5422ae2b54ac72ad0641243089 | 5217d14779a01179bfd440b689a4aea067d9e043 | /MachineIntelligence/e2/multlines_sample.R | d0853b86262a8cf0958309dc374b6ea11cb2a7fc | [
"MIT"
] | permissive | CFWLoader/supreme-bassoon | f0a960a29cf052b76d5b898b4b4151776efc7536 | f20e45118a141084a0fb0d640e937e0e739cc3f6 | refs/heads/master | 2020-03-07T05:49:04.731876 | 2019-04-10T03:43:48 | 2019-04-10T03:43:48 | 127,306,468 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 422 | r | multlines_sample.R | library(ggplot2)
slope = 1
x = seq(-3, 3, 0.1)
# print(x)
y = slope * x
y2 = x**2 + -3
y3 = 0.3 * x
# print(y)
data = data.frame(x = x,y = y, y2 = y2)
# print(head(data))
p = ggplot(data, aes(x = x)) + geom_line(aes(y = y), color = "red") + geom_line(aes(y = y2), color = "blue") + geom_line(aes(y = y3), colo... |
261ab937b26f7676d1f3d2680c76d11b888c16a7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GDELTtools/examples/GetAllOfGDELT.Rd.R | a34dc481d69d79f65322a01a5057a36502a17419 | [] | 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 | 214 | r | GetAllOfGDELT.Rd.R | library(GDELTtools)
### Name: GetAllOfGDELT
### Title: Download all the GDELT files to a local folder
### Aliases: GetAllOfGDELT
### ** Examples
## Not run:
##D GetAllOfGDELT("~/gdeltdata")
## End(Not run)
|
7f7ee15ab6d2ed80f5230a7a609f30b84752988b | 1c2f5a50ab2b63a17adaac9af1dfaabaaf5d3091 | /test_run.R | c6207ded9ad3b93d4f4881bd34820003495259ee | [] | no_license | pra1981/PeakSegFPOP | 196ce4f055a950cb51110faca9e0cc4ace96d77d | 06d6e429520383ab42a7ced79b778ed5496b7df5 | refs/heads/master | 2020-04-25T14:08:59.951686 | 2018-02-07T20:48:23 | 2018-02-07T20:48:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 132 | r | test_run.R | source("test_functions.R")
file.name <- getenv.or("TEST_SUITE", "test_cases.R")
test_file(file.name, reporter=c("summary", "fail"))
|
120e442e9cf79480dbc74ab1df5a091bff9e9304 | c92e19489e4d8c40fe44ebb70ddf94b2c47fd56e | /man/print.cma.Rd | ee824ab69b658023e27bb433dc9db34040e59b76 | [] | no_license | rexmacey/AA2 | 0b1ee392c14690e36c38f50d3b5f1b968c6344bd | b9751f3ff71089a56edbecd7d6d24d02476d8dce | refs/heads/master | 2021-01-04T03:46:58.743292 | 2020-03-02T18:53:45 | 2020-03-02T18:53:45 | 240,349,624 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 254 | rd | print.cma.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cma.r
\name{print.cma}
\alias{print.cma}
\title{Print cma object}
\usage{
\method{print}{cma}(cma, ...)
}
\arguments{
\item{cma}{cma object}
}
\description{
Print cma object
}
|
aff20841a8e7518b8627edaa521c5187a809e096 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_ThornthwaiteWilmott/libFuzzer_ET0_ThornthwaiteWilmott/ET0_ThornthwaiteWilmott_valgrind_files/1612736270-test.R | 70216ba7f6f51ca5303062cf490ab034b3068b72 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,961 | r | 1612736270-test.R | testlist <- list(doy = 1.39065002449955e-309, latitude = c(7.2893454370416e-304, -8.08436732081049e-174, 3.30199188178139e-312, -2.36757568622891e-150, 5.43230890149031e-312, 0, -1.09007158655572e-175, 9.82871840871573e-306, 6.83631741178536e-304, 5.77591857965479e-275, 4.90971575050201e-315, -5.48612925997371e+303... |
4acd112b185919b961cb70f9783ebee7edc98533 | 44cf65e7ab4c487535d8ba91086b66b0b9523af6 | /data/Newspapers/1999.10.27.editorial.55258.0172.r | c78f83bd38bbd10bf08a0247256976ae513cad93 | [] | no_license | narcis96/decrypting-alpha | f14a746ca47088ec3182d610bfb68d0d4d3b504e | 5c665107017922d0f74106c13d097bfca0516e66 | refs/heads/master | 2021-08-22T07:27:31.764027 | 2017-11-29T12:00:20 | 2017-11-29T12:00:20 | 111,142,761 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,528 | r | 1999.10.27.editorial.55258.0172.r | se intimpla des ca oameni despre care ne pregatim sa scriem un articol sa sune pentru a face demersuri de pace .
ei sint obisnuiti cu ideea ca totul se poate aranja .
cei mai coltosi ameninta in speranta ca vom da inapoi .
altii fac promisiuni .
cei mai abili trec totul intr - un limbaj de miere .
ai zice ca sint ... |
c16ef59189f65246bf80d8a42da405c501f70e8b | 3c3e7e8e37a9806d8495a4a3d85488609a296870 | /data.r | 6cc760fb1b9376c634e7c388f5a2dc35a651a8fe | [] | no_license | MikeGongolidis/CSMush | f2f507760eaf3b2fe44263a09bcba205f9666e0c | d8b321db63ccc168960b25d30045dc0f9e84b107 | refs/heads/master | 2020-04-10T17:52:26.016169 | 2018-12-10T21:44:55 | 2018-12-10T21:44:55 | 161,187,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,141 | r | data.r | library(ggplot2) # Data visualization
library(readr) # CSV file I/O, e.g. the read_csv function
library(caret)
library(randomForest)
library(caTools) #<- For stratified split
library(rpart.plot)
library(gridExtra)
train01<-read.csv("mushrooms_v2.csv")
mushrooms <- train01
mushrooms$class <- NULL
names <- colnames(mu... |
cdd95dd72f26656f7791c98bf2354915a46dd7f6 | 59aa1bb6b73f544701bd462af230d08e1ffa6d1a | /smpredict/R/standardise.R | aefeba8805ecfe2d414e048ecd5a36b01fe7717a | [] | no_license | rnaimehaom/smpredict | 8cdbaad221b7ba41be38319fe4ea6da7dc2c529c | c498938bcf6dd43e6fd23eef959b0adf8d696c37 | refs/heads/master | 2023-03-16T03:43:06.804746 | 2015-04-24T21:26:10 | 2015-04-24T21:26:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,441 | r | standardise.R | StandardiseMolecules <- function(structures.file, standardised.file, is.training = FALSE, name.file = "", limit = -1) {
# handle non-existant file
if (!file.exists(structures.file)) {
print("File does not exist")
}
# handle standardised.file not sdf
split <- strsplit(standardised.file, "\\.")[[1]]
... |
7ca16d5bc4137513563af6030d84fed6d34af5a0 | 06eff7cef9e88eaad3d9f128efd509d67c7cef87 | /man/download_demo.Rd | 40e40f8ea17f92a9fcd436d509be09d7d8d97c83 | [
"MIT"
] | permissive | anastasiyaprymolenna/AlpsNMR | 384ef42297232710175b0d25382b4247697a04e5 | c870f9aa07b4a47646f742a13152ca747608f01d | refs/heads/master | 2022-12-03T10:48:41.554384 | 2020-08-26T14:43:34 | 2020-08-26T14:43:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 537 | rd | download_demo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/download_MTBLS242_demo.R
\name{download_demo}
\alias{download_demo}
\title{Download the MTBLS242 dataset}
\usage{
download_demo(to = ".")
}
\arguments{
\item{to}{A directory}
}
\value{
A folder with demo samples
}
\description{
The function d... |
94bacb6b796f567a6e7c33138aad0f1eb555dad5 | ae7f33e2c00186f3bb551e2c27618a727a9df00b | /src/plot2.R | e2c5c48090d1bc2a0b8e7b19c3d4a18b9bbf2166 | [] | no_license | jsko0112/ExData_Plotting1 | 4c07dcb1a56e3d3868333c4e6328e340b059b3be | a1fe3173ca55c013da86d05d0788b14b2330472f | refs/heads/master | 2020-11-30T12:31:52.155405 | 2014-06-08T04:44:21 | 2014-06-08T04:44:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 466 | r | plot2.R | # 1. Get the data from URL
# 2. Make tidy data with subset
# 3. Cache the tidy data
source("./getting and cleaning.R")
#Plot Name : make the full plot name by common function(getPlotName())
plot.name <- "plot2"
#readData
data <- readData()
#save the plot as PNG
png(getPlotName(), width=480, height=480)
message("Maki... |
1324f275f98d87612ff46185eb082b6952194ceb | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PerformanceAnalytics/examples/SmoothingIndex.Rd.R | f794653e4c94473dadfbc309c549650c7f4550f0 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 286 | r | SmoothingIndex.Rd.R | library(PerformanceAnalytics)
### Name: SmoothingIndex
### Title: calculate Normalized Getmansky Smoothing Index
### Aliases: SmoothingIndex
### ** Examples
data(managers)
data(edhec)
SmoothingIndex(managers[,1,drop=FALSE])
SmoothingIndex(managers[,1:8])
SmoothingIndex(edhec)
|
37bee5fbb78aace85cd752f139d274aad2632709 | 54a74eed54ab34a9ea60287c7e8e233696d42c0e | /R/data_highschool.R | ffde27fe2a7caa24b35cc5dd637480761eb06446 | [
"BSD-3-Clause"
] | permissive | schochastics/ggraph | 9cf86d7a040e3f4d1325a40670feef366ed74144 | 85b6df1a06df70746096e26244e9b28931d670f4 | refs/heads/master | 2020-07-01T18:58:36.741201 | 2019-08-15T12:39:19 | 2019-08-15T12:39:19 | 201,263,971 | 1 | 0 | NOASSERTION | 2019-08-14T17:33:03 | 2019-08-08T13:25:55 | R | UTF-8 | R | false | false | 727 | r | data_highschool.R | #' Friendship among high school boys
#'
#' This dataset shows the friendship among high school boys as assessed by the
#' question: "What fellows here in school do you go around with most often?".
#' The question was posed twice, with one year in between (1957 and 1958) and
#' shows the evolution in friendship between ... |
f3c8f2cb063998766b69c584a7bae0c19b430ec8 | d5f0e278606a16785a66ab2db91220cfdfd19d62 | /R-Mini-Project Script.r | 58b4cffa837732bb68ae87c5aa9d053f936c89a0 | [] | no_license | carolm5/MINI-PROJECT | 7c896198c6facdfa1aa830c4c49d3c5e9b75aa12 | c55f39a16d203ebc7067d3b031eefb548859f4c5 | refs/heads/main | 2023-08-15T06:17:24.494298 | 2021-09-17T12:45:27 | 2021-09-17T12:45:27 | 406,406,961 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 7,451 | r | R-Mini-Project Script.r | ## MSB7102 Mini-project, Semester I, 2021
**1.Import the data described above into R, provide descriptive summaries of the subject data (using appropriate graphics and statistical summary measures) given in the diabimmune_16s_t1d_metadata.csv file. In addition, use appropriate test(s) to check for association/inde... |
3855d9246f5aaaae49948a2f6ba2d9c9e7997163 | 3376043d518eda22caabf0df3db554a9cc43eedd | /ggtext.R | 7f6efe1e62a690665db08b6dcaee1f0039d977c5 | [] | no_license | MaxCodeXTC/youtube-r-snippets | 23641522e3cbb2a15fe27386e1df03b296753a5d | 2a367fa4bd6323ac2e8d8fb3f0084164ba659247 | refs/heads/master | 2022-11-09T19:43:20.757946 | 2020-06-23T20:54:26 | 2020-06-23T20:54:26 | 275,516,907 | 1 | 0 | null | 2020-06-28T05:52:16 | 2020-06-28T05:52:16 | null | UTF-8 | R | false | false | 2,107 | r | ggtext.R | library(ggtext)
library(ggplot2)
ggplot(iris) +
geom_point(aes(x = Sepal.Length,
y = Petal.Length)) +
facet_wrap(~ Species) +
theme(
strip.text = element_textbox(
size = 12,
color = "white", fill = "red", box.color = "black",
halign = 0.5, linetype = 1, r = unit(5, "pt"), w... |
3e3eef5f44d1f5e247b753e7934107c7be431060 | cc3f9eeaec6c6b1b47525f9317c71bb5dc4e7ed3 | /analysis/Fig1F S3C_TCGA_SignaturesBySubtype.R | 9827426dbb5ec9fed612a747f74a0f2020d9bf16 | [] | no_license | HongyuanWu/LUAD.subtypes | 9061d3b35cf6dd19b7f135afd48ed818983a12c5 | fd82d06eb19c4d527c47eb233204200ebfff9330 | refs/heads/master | 2023-01-02T04:30:17.322022 | 2020-10-18T20:32:26 | 2020-10-18T20:32:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,288 | r | Fig1F S3C_TCGA_SignaturesBySubtype.R | study <- "TCGA"
data1 <- cbind(read.table("../data/TCGA_KRASmut_subtypes.tsv", sep="\t", header=TRUE, check.names=FALSE), KRAS="MUT")
data2 <- cbind(read.table("../data/TCGA_KRASwt_subtypes.tsv", sep="\t", header=TRUE, check.names=FALSE), KRAS="WT")
data1 <- data1[,-which(colnames(data1) %in% "NMF")]
data <- rbind(data... |
0e48d72aa2c906a705b4dabdb1ac776a1e79ff89 | 873394d6cd11b544d602a7038c6e4890073727c7 | /Lab_4.R | 3f762548abf6c412d0e6274089086be7a4b898d4 | [] | no_license | michaelchoie/Introduction-to-Statistical-Learning | bb44c1c48155034da0ed99af776bd0f7cb17a2c6 | fb52ee1124bfe7c0267727790a9aec4cbe26c9a8 | refs/heads/master | 2021-08-19T23:43:46.172749 | 2017-11-27T17:55:50 | 2017-11-27T17:55:50 | 106,619,417 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,141 | r | Lab_4.R | #####################################################
# LAB 4.6.1: Stock Market Data
#####################################################
# Load library & data
library(ISLR)
Smarket <- data.frame(Smarket)
# View structure
str(Smarket)
summary(Smarket)
# Create correlation matrix (appears to be little collinearity)
... |
d28fb9e943082dcd0e082eb871005b14c0f0cc43 | b0f77cca265f871fa01914deb0e7c6c8582ed6c3 | /R_Scripts/eda__df_cut.r | 5a8901956ff8305b4fcd2b28db17b7d60bef83e6 | [
"MIT"
] | permissive | joshuakevinjones/Code_Files | 2e94b8d0a79b73591e98828e89b5d80b8ed824d4 | eefd7337ae10c743c80d79aaeacf4d5d54229b56 | refs/heads/master | 2021-01-22T13:26:37.565713 | 2020-06-11T18:02:44 | 2020-06-11T18:02:44 | 100,659,992 | 0 | 0 | null | 2017-08-25T21:37:34 | 2017-08-18T01:27:31 | null | UTF-8 | R | false | false | 635 | r | eda__df_cut.r | # df cuts
# create some simulated data
ID <- 1:10
Age <- c(26, 65, 15, 7, 88, 43, 28, 66 ,45, 12)
Sex <- c(1, 0, 1, 1, 0 ,1, 1, 1, 0, 1)
Weight <- c(132, 122, 184, 145, 118, NA, 128, 154, 166, 164)
Height <- c(60, 63, 57, 59, 64, NA, 67, 65, NA, 60)
Married <- c(0, 0, 0, 0, 0, 0, 1, 1, 0, 1)
# create a dataframe of t... |
4a68497bc1cfa61f6b8a95d55799c06d7204eaec | 6cd87c1f8d0fb438d85a4bb0e25736ac2aaefd42 | /February_strange_attractors/hopalong.R | ba095268f37de1cafbe90b7f8ba6f2acfd82ee7b | [] | no_license | 2008haas/aRt | c47ad1392f4393aaa86c21f9a65ddcae4ecf3e60 | 9585e845539acfc36792c084caed178ab4b0dd98 | refs/heads/master | 2022-12-13T13:40:54.677181 | 2020-09-19T02:36:45 | 2020-09-19T02:36:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,333 | r | hopalong.R | library(ggplot2)
library(dplyr)
library(purrr)
#cream #FAF4E7
#charcoal #1E1E1E
opt = theme(legend.position = "none",
panel.background = element_rect(fill="white"),
axis.ticks = element_blank(),
panel.grid = element_blank(),
axis.title = element_blank(... |
0d2edb0917aa9e45fe6a2f314b1aa042883feeb4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/R.oo/examples/print.Exception.Rd.R | e9effbe5be7949d0c0c80c01e61424263073509a | [] | 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 | 274 | r | print.Exception.Rd.R | library(R.oo)
### Name: print.Exception
### Title: Prints the Exception
### Aliases: print.Exception Exception.print print,Exception-method
### Keywords: programming methods error internal methods
### ** Examples
## Not run: For a complete example see help(Exception).
|
4cbc4147c579ed2390852c6f0a69372db9232afd | d08fd1c0a9969e8fa2847585adc23501da901c4f | /Pre-Processing/PreProcess.R | a29d1f22d4dc095374c4fe06dbda27e9eeb64c6f | [] | no_license | ArunkumarRamanan/bosch-production-line-performance | 124ebae622370041978fed5eff27378a19f4230f | d4ba078bc6449d40d1d7fb4e7129850f276f81a0 | refs/heads/master | 2020-05-09T23:01:27.686275 | 2016-11-21T16:52:52 | 2016-11-21T16:52:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,918 | r | PreProcess.R | #Bosch Production Line Performance
#Preprocess Data
#Many columns are duplicated. Use digest to detect and filter duplicate columns
#Reduce RAM requirement by reading columns in batches
#Authors: Tyrone Cragg and Liam Culligan
#Date: October 2016
#Load required packages
library(data.table)
library(diges... |
7a8368c427ec990a12e0a4298d899dea23f8c370 | 9f6a670f53570efe5774dcd02a2f3b4aec303ffb | /server.R | 4526970c8c8ef937bb7087a81eb642e2368bc451 | [] | no_license | ravikeron/Capstone_Project | d75af85626cd917159ac77cea77757fd0ab75d03 | c04756357dbd093928e3dc03d3fa59918e29fed0 | refs/heads/master | 2021-01-17T18:03:32.547793 | 2016-07-21T17:40:05 | 2016-07-21T17:40:05 | 63,888,380 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,382 | r | server.R | #
# This is the server logic of a Shiny web application for predicting the next word in a sentence
#
library(tm)
library(stringr)
library(shiny)
# load the 2-gram, 3-gram and 4-gram models created separately from the given input files
# These are built into term document matrix after taking a sample and cleaning
# Th... |
f9336e9a6db7688c655b1a71587ef805e177e0bf | 102d4103ca1e3a2ab268cd9cd37d6b439c4fc3cc | /data and code for paper/code_for_prediction.R | 15fbd5f2199c7c1337097a4375f03677cc113cc2 | [
"CC0-1.0"
] | permissive | Vicky-Zh/Tracking_and_forecasting_milepost_moments_of_COVID-19 | 8f4b81d78d56fc064e1c8b10cf10e5afc1f74f02 | 4bf61343ea892bfaeb84ef034e99c948afff4e08 | refs/heads/master | 2021-05-21T09:53:34.384692 | 2020-05-05T03:47:42 | 2020-05-05T03:47:42 | 252,644,815 | 1 | 0 | CC0-1.0 | 2020-05-05T03:47:43 | 2020-04-03T05:56:49 | R | UTF-8 | R | false | false | 11,155 | r | code_for_prediction.R | #=======================================
# Prediction of turning points
# Author: Yanwen Zhang
# Date: Apr 2, 2020
# Description: In the paper <Tracking_and_forecasting_milepost_moments_of_the_epidemic_in_the_early_outbreak__framework_and_applications_to_the_COVID_19>, we proposed a method to predict "turning points", ... |
243ce38baf30070d839e99e96ea21419ebb5e17b | 6f6d83b98d6fa5964ea0b684d4d829022ba4867e | /man/expand_word.Rd | 1a0b55b979b49bc293e5d47b109e3cf8f387e131 | [
"MIT"
] | permissive | rmsharp/wordPuzzle | 186d3ab49d8836f8849a1fdd3b469f07db4929c9 | 733449bbc3b91a8dd82d5d4e4034dab11bec9fd3 | refs/heads/master | 2020-05-09T20:35:24.804214 | 2019-04-21T23:26:54 | 2019-04-21T23:26:54 | 181,413,004 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 423 | rd | expand_word.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expand_word.R
\name{expand_word}
\alias{expand_word}
\title{Returns a list with each character from the word in seccessive list elements}
\usage{
expand_word(word)
}
\arguments{
\item{word}{a 1 element character vector contain a word to be ex... |
13c182788063b3523a78a5f231d5b057cebca099 | f281f08b82846459b3bfd53546e1abda60082a67 | /rsrc/generate_pathway.R | 5c44af1b2b4b172c42e172f0758b3330f91157de | [
"Apache-2.0"
] | permissive | MadFace/MadFace | 07de49a31cb5e0a5b5c9a6c6c3a8fe3410545dee | dad6df197bc1ad330863f0f84da4d97dfb7a3b7d | refs/heads/master | 2021-08-14T18:02:46.020078 | 2017-11-16T11:22:18 | 2017-11-16T11:22:18 | 108,241,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,232 | r | generate_pathway.R | library(igraph)
library(rjson)
WIDTH <- 2048
HEIGHT <- 2048
plot.params.1 <- as.list(NULL)
plot.params.1[["vertex.label.cex"]] <- 1
plot.params.1[["vertex.size"]] <- 3
SUB.WIDTH <- 480
SUB.HEIGHT <- 480
plot.params.2 <- as.list(NULL)
plot.params.2[["vertex.label.cex"]] <- 1
plot.params.2[["vertex.size"]] <- 30
##... |
9644d8c71ee87209934884961acc4992963448a8 | 907af44f17d7246e7fb2b967adddb937aa021efb | /man/fslmean.Rd | 6870e10115de92a714b272fc7ba716590852fe23 | [] | no_license | muschellij2/fslr | 7a011ee50cfda346f44ef0167a0cb52420f67e59 | 53276dfb7920de666b4846d9d8fb05f05aad4704 | refs/heads/master | 2022-09-21T07:20:18.002654 | 2022-08-25T14:45:12 | 2022-08-25T14:45:12 | 18,305,477 | 38 | 23 | null | 2019-01-10T20:57:47 | 2014-03-31T19:35:03 | R | UTF-8 | R | false | true | 659 | rd | fslmean.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fslmean.R
\name{fslmean}
\alias{fslmean}
\title{Image Mean}
\usage{
fslmean(img, nonzero = FALSE, verbose = TRUE, ts = FALSE)
}
\arguments{
\item{img}{Object of class nifti, or path of file}
\item{nonzero}{(logical) Should the statistic be t... |
0fae7e6dfad3aab7201e875d84ed54810734f9cc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/koRpus/examples/strain.Rd.R | 985d2ce5252669295a9fb6fd7384ab66839ae798 | [] | 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 | 192 | r | strain.Rd.R | library(koRpus)
### Name: strain
### Title: Readability: Strain Index
### Aliases: strain
### Keywords: readability
### ** Examples
## Not run:
##D strain(tagged.text)
## End(Not run)
|
4c972272e219996d50298ae8df46d0c1a7d31be0 | d198cb229e2dff928df9a23b682f7297d0770491 | /mGFLMi.R | ec689f7070acde919a8b9e54916b51d583c42eed | [] | no_license | aaron-scheffler/CARR-GFLM | 42ade1cd7e99e30b28390fec13e99a660b9f3d21 | f9f863d6244c5a98c8623fc23092c12f231b80a2 | refs/heads/main | 2023-08-24T14:40:49.544678 | 2021-09-24T23:44:36 | 2021-09-24T23:44:36 | 410,129,161 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,393 | r | mGFLMi.R | mGFLMi <- function(X, # data.frame in long format with five labeled columns (described below)
# and row length equal to the length of the vectorized region-referenced
# functional predictors across all subjects
# ... |
b3ee61111d1be9cb63be40d8fd9b783852fc8c90 | cd206a99a134a388c7ab086b2135f4a0f487f300 | /runme.R | 17715676cdf841f5f2328a59d61b7599c03507b0 | [] | no_license | jai-somai-lulla/diabetesPrediction | 67163cd35a3fd02258cd042650873e5bee657afc | f75d8b578df18266c30fa5bc15093e97b6c639e0 | refs/heads/master | 2020-04-02T14:22:49.340145 | 2018-11-01T15:53:51 | 2018-11-01T15:53:51 | 154,522,028 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,498 | r | runme.R | library("e1071")
args <- commandArgs(TRUE)
g1 <- as.double(args[1])
g2 <- as.double(args[2])
#print((g1+g2)[1])
if(!file.exists("naive.rda")) {
library(caret)
#NAIVE BAYES BLINDX 0.6510417
raw=read.csv("diabetes.csv")
#without cata 0.7292
raw$Outcome=sapply(raw$Outcome,as.factor)
for(i in 2:8){
(raw[which(raw[... |
99fd31ba35af5d59819b164d85d27a01b0355c6f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Boom/examples/timeseries.boxplot.Rd.R | 73fc8261ccb65e2958688d486b7bfbf529f3e956 | [] | 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 | 375 | r | timeseries.boxplot.Rd.R | library(Boom)
### Name: TimeSeriesBoxplot
### Title: Time Series Boxplots
### Aliases: TimeSeriesBoxplot
### Keywords: hplot
### ** Examples
x <- t(matrix(rnorm(1000 * 100, 1:100, 1:100), nrow=100))
## x has 1000 rows, and 100 columns. Column i is N(i, i^2) noise.
time <- as.Date("2010-01-01", format = "%Y-%... |
03c0c6cf1474d09a80f72fb1e946954db36df36b | 295b502d7e367edfa0ee4017f1a7b6a4135211d3 | /R/ELMBJ.R | c6d4637249337b7db7336e41cbfc68394d46c524 | [] | no_license | whcsu/SurvELM | e5b09b504af20ad5e322c687504090cf19689cb9 | c9297f6bd29ff3448e84d1420aeed1215e611ba9 | refs/heads/master | 2021-05-11T02:52:36.034952 | 2020-01-28T08:57:32 | 2020-01-28T08:57:32 | 117,897,717 | 10 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,725 | r | ELMBJ.R | ##' A Kernel Extreme Learning Machine Using the Buckley-James estimator
##' @title SurvELM ELMBJ
##' @param x The covariates(predictor variables) of training data.
##' @param y Survival time and censored status of training data. Must be a Surv \code{survival} object
##' @param Regularization_coefficient Ridge or Ti... |
91bb16620a07e67b847111508c5e85f63f5c38c3 | 815debc2788802e65b9eaebc4f43471e69e3794d | /sdal19148496-report-dmml1 2/Bank/knn/bank_knn_sample_train.R | cb4d2e0ff031802d7a311fd2be99082c7b37de73 | [] | no_license | sobil-dalal/DMML1 | 9429f5f9221146f3eb8145e930f220dd1b749eca | 4d2900f4c983f1530638a12fa3cbea255d8b1ad3 | refs/heads/master | 2023-03-14T06:03:40.688885 | 2021-02-26T02:42:45 | 2021-02-26T02:42:45 | 294,698,664 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,508 | r | bank_knn_sample_train.R | library(caret)
# creating training and testing dataset from exisitng sample
indx <- createDataPartition(bank$y, p = 0.8, list = FALSE)
bank_train <- bank_n[indx,]
bank_test <- bank_n[- indx,]
# creating lables for test and training data sets
bank_train_labels <- bank[indx,21]
bank_test_labels <- bank[- indx,21]
... |
0ae46438dbd43b330036d44294e51730270d3af4 | 2ec442671c9de078bb2ea441b7905caa875883b6 | /man/chk4aiii_missing_pct.Rd | 4eb1c97d0c13ceaabd87feb09212f665355758a6 | [
"MIT"
] | permissive | axmedmaxamuud/HighFrequencyChecks | ae39427ace2880bae98001b5f955a95c10a9e3f4 | d84d7e80cb0b6805330a3d21fa6ff360b62530c7 | refs/heads/master | 2021-05-17T23:35:38.027268 | 2019-05-14T11:36:48 | 2019-05-14T11:36:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 876 | rd | chk4aiii_missing_pct.Rd | \name{chk4aiii_missing_pct}
\alias{chk4aiii_missing_pct}
\title{
Report the percentage of missing values (NA) per fields
}
\description{
This function provide a report showing the percentage of missing values (NA) for each fields.
This report can be global (all the surveys) or displayed for each enumerator ID
}
\usage{... |
615f5b58f033504df2a5f88e9cef80427cb4ca01 | 18beba89bd528840d3aab7a171fa671c5ac0cf3a | /man/Download_DNAmethylation.Rd | 2f7f7ebf1bb93ba323bd4e3707e330276d1b09a8 | [] | no_license | mpru/BIMEGA | 8d748401ad29f252c9c87b6ec04bca2d185d9a62 | 6b445dc7581a2b78aae559b34c382a2f74d1391f | refs/heads/master | 2021-01-22T17:33:56.718268 | 2016-06-19T04:21:29 | 2016-06-19T04:21:29 | 61,449,411 | 0 | 0 | null | 2016-06-18T20:55:01 | 2016-06-18T19:34:43 | R | UTF-8 | R | false | true | 1,689 | rd | Download_DNAmethylation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Download_Preprocess.R
\name{Download_DNAmethylation}
\alias{Download_DNAmethylation}
\title{The Download_DNAmethylation function}
\usage{
Download_DNAmethylation(CancerSite, TargetDirectory, downloadData = TRUE)
}
\arguments{
\item{CancerSite... |
86147312fc62a69efd743a0eb463a267798bab54 | 9b2aa890ed7f5d87af800c61d7ec5ab8a07ffb66 | /numeros/perfecto.R | 7b0ad062158a72cda1dcf0c068ca00dd2aae1e9a | [] | no_license | jjdeharo/General | 65b1456a5ef849d50a86318d825ac81bdf497a8e | 99a7b4616f029c1b1d14427b1dd0ba5fcf785457 | refs/heads/master | 2021-01-23T08:39:44.975976 | 2015-04-11T11:07:16 | 2015-04-11T11:07:16 | 32,942,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 739 | r | perfecto.R | # Halla n números perfectos
# Un número perfecto es un número natural que es igual a la suma de sus divisores propios positivos, sin incluirse él mismo
perfecto <- function(n=4) {
i <- 0
a <- 5
amigos <- vector()
while (i < n) {
a <- a + 1
if(a == tieneamigo(a)) {
i <- i + 1
amigos[i] <- a
... |
c40f60e285966a19adc8805a9ce26861ffcb964e | 6cc2ba52d7fc77cb9c105397d85b32b8ca90e00a | /Tecan/auth/google_auth_functions.R | 9a55909a8aacfcb452cda45190361f0b549b8375 | [] | no_license | Ploulack/HB | dd8abea825a1fc653d14062ab8481d3ed9eaca1e | 9f8fb6fcbdad2b341bcd39bd9256d5ed5e2ab4b2 | refs/heads/master | 2021-09-15T09:04:23.999913 | 2018-04-06T16:15:44 | 2018-04-06T16:15:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,320 | r | google_auth_functions.R | get_token <- function (auth_code, client_id,
client_secret,
redirect_uri,
scope_list)
{
my_drive_app <- httr::oauth_app("google",
key = client_id,
secret = client_secret)
req <- httr::POST("https://accounts.google.com/o/oauth2/token",
... |
78e2ca80e9cc284ec379aaed560dfcad7c57ec20 | 094905a6ed952725fdb49115e33d529811f0be74 | /inst/scripts/SampleAnnotationExample55Data.R | c609024990beed2b1ef5005534201fb0596c4cae | [
"MIT"
] | permissive | isglobal-brge/methylclock | 48122d60cd68e16b13f2606f291cf80d3524ef6e | 6a1a333889db9e3d2c10adbea362fa2bb3b82f41 | refs/heads/master | 2023-06-25T08:19:29.515722 | 2022-07-11T14:11:23 | 2022-07-11T14:11:23 | 175,777,273 | 29 | 18 | MIT | 2021-03-23T09:15:45 | 2019-03-15T08:15:22 | C++ | UTF-8 | R | false | false | 704 | r | SampleAnnotationExample55Data.R | # Data was obtained from
#
# https://horvath.genetics.ucla.edu/html/dnamage/
#
# and refers to :
#
# Horvath S (2013) DNA methylation age of human tissues and cell types.
# Genome Biology.2013, 14:R115.
# DOI: 10.1186/10.1186/gb-2013-14-10-r115 PMID: 24138928
#
# Gibbs, W.... |
a433a787c61b650b91d057fafa4a86b5c19060f1 | e40e988267966490d147c73824cc5f44e4878c41 | /R/cor_tests.R | 7b222983d2739489635d1dfd2da3792f1d03003e | [] | no_license | MiRoVaGo/P_plus_E | 5061f378d831c0a3d9a7b80fca98e866a4629126 | 5341281e1b5b43ce623384085a8f7c6e04bc3727 | refs/heads/main | 2023-04-07T17:58:49.937304 | 2022-11-07T12:40:13 | 2022-11-07T12:40:13 | 410,815,948 | 1 | 0 | null | 2022-03-15T09:09:34 | 2021-09-27T09:10:31 | R | UTF-8 | R | false | false | 2,993 | r | cor_tests.R | #Required Libraries
library(dplyr)
library(data.table)
library(readr)
#Load Data
global_20cr <- read_csv('./../data/20cr.csv') %>% as.data.table() %>%
.[, PpE := P + E]
global_era20 <- read_csv('./../data/era20.csv') %>% as.data.table() %>%
.[, PpE := P + E]
global_era5 <- read_csv('./../data/era5.csv') %>% as.data... |
7eea755fa991b9b155e67a95110b53a9c7fb108e | 57fd5e509ed5d9204d76789e9b6cfee18dcb52ab | /simulate_tournament.R | 21f977ebc9b31f542ad8f20e24c48c55456ed850 | [] | no_license | drewlanenga/jackboot-firebase | 8495d81db0d529bfe96ef639f73aa3cfbb21a1fc | 28992680e18c92a2e6978b093fc31672766c75df | refs/heads/master | 2021-01-02T23:07:18.759552 | 2014-02-04T17:52:50 | 2014-02-04T17:52:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,196 | r | simulate_tournament.R |
source('load.R')
#
# functions to help with the 'simulation'
#
simulate.from.lm <- function(n.sim, x.bar, std.error, df)
{
(rt(n.sim, df) * std.error) + x.bar
}
predict.game <- function(team.coefficients, opp_or, opp_dr, loc, n.sim = 10000)
{
sim <- list()
sim[['intercept']] <- simulate.from.lm(n.sim, team.c... |
e9ab2cdd8bf8b003e485aecfcd0ce7f3a48636ca | 2275a7fa3595c07ad52f6a92e535ec4491c95ec4 | /rcodes/3ldgn_compara_med.r | 87cbb65097b60e4076921270f3d27f8cf1fa6505 | [] | no_license | AlfonsBC/Statistical-methods | 777b021ecf0af50999b8d614eaf3c801abec71d2 | c0c5676cb26587c0210527c945db98bac2bbf224 | refs/heads/main | 2023-03-22T02:50:14.758038 | 2021-03-17T03:47:38 | 2021-03-17T03:47:38 | 345,008,708 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,293 | r | 3ldgn_compara_med.r | # Definimos un directorio de trabajo
#setwd("/Volumes/GoogleDrive/Mi unidad/MET_ESTAD/7DATOS")
# librerias necesarias
library("data.table")
library(ggplot2)
# Los datos se descargan de
# https://datos.cdmx.gob.mx/dataset/base-covid-sinave
# aquí también se descarga el diccionario de variables
####################
# L... |
bfd3b1314b324788e4246af3a512eaf0cee5b138 | 5bb106daabc909357fb7fdf3940a755d635a81ee | /data-raw/fluff.R | 1dec0b058284f19040cb9ee633a04c26b8423d67 | [] | no_license | alketh/codeword | b7abcead4c9c3f67d89b244603b9d4f3b7485659 | bb42607c17c5d80e757f49b2b5a5da5cacd89e6b | refs/heads/master | 2021-05-09T11:02:35.902491 | 2018-01-26T01:33:32 | 2018-01-26T01:33:32 | 118,981,599 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 113 | r | fluff.R | xxx <- readLines("data-raw/agg-data.R")
dummy_script <- xxx
save(dummy_script, file = "data/dummy_script.rda")
|
19724e030bae5a828319acd5cf07631556739a45 | 8eaf931982f7e38b1a5fd934f2da31383edb459f | /global.R | eda23ed2fdceb9adbacb176d84ea92e3f56f660a | [
"MIT"
] | permissive | alexdum/roclib | a1b0356ab43b287ffcbba3c9a518f9f4f229c3c9 | e82696ce5e6437eebebc5e885ecdb82fa2c79edd | refs/heads/main | 2023-08-29T17:13:57.795073 | 2021-11-15T20:55:28 | 2021-11-15T20:55:28 | 369,631,626 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,339 | r | global.R | suppressPackageStartupMessages({
library(leaflet)
library(shinythemes)
library(shinyjs)
library(shiny)
library(shinydashboard)
library(dplyr)
library(ggplot2)
library(ggridges)
library(viridis)
library(plotly)
library(rgdal, quietly = T)
library(sf)
library(RColorBrewer)
library(raster)
li... |
7db9d021432539ae3b0352228cbcdfddde707607 | bfce76dad46a2b28a1eb2ae3622c62becd3e5d52 | /NapaPest toxEval Data Prep Code.R | 94c1c354bb311f25b26e1bab721f3a53e64cb337 | [] | no_license | jadeealy14/FRI_R_Work | 467bee3bdbe97783852139f485591ccbf5f620db | 27a31da4f92bca4a5922a1bf738cd5abb29e3e61 | refs/heads/main | 2023-03-29T00:29:24.423120 | 2021-04-02T04:20:30 | 2021-04-02T04:20:30 | 342,083,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,169 | r | NapaPest toxEval Data Prep Code.R | NapaResultsCAS <- merge(NapaResultsCAS, NapaPestSites, by= "SITE_NO", no.dups = TRUE)
##------ toxEval data file -------
data <- NapaResultsCAS[, colnames(NapaResultsCAS) == "SITE_NO" | colnames(NapaResultsCAS) == "SAMPLE_START_DT" | colnames(NapaResultsCAS) == "CAS.Number" | colnames(NapaResultsCAS) == "RESULT_VA" ... |
21f443562cf00274b950fee91b8cf5f4ea01ef89 | 1ab3fe36ec133cb90fcfc4071c15b37edc1d1c79 | /Seccion 11 - Conexiones por doquier - Análisis de Redes Sociales/155 - Las matrices de adyacencia y listas de aristas de un grafo.R | ef0b83900e3a947ec17ae9ed2b6acacde7a6b14f | [] | no_license | achiola/r | 419d182bd6ec546af4ef0dc10b7b59678ada561b | 08a5c2b78b58193d7fdbbf0fa612c52ec21df925 | refs/heads/master | 2020-07-06T14:24:46.427229 | 2020-01-07T21:43:38 | 2020-01-07T21:43:38 | 203,048,278 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 785 | r | 155 - Las matrices de adyacencia y listas de aristas de un grafo.R | install.packages("Matrix")
library(Matrix)
load("Seccion 11 - Conexiones por doquier - Análisis de Redes Sociales/meetup-hiking.Rdata")
unique(users$user_id)
unique(users$group_id)
group_membership <- sparseMatrix(users$group_id, users$user_id, x=T)
#matriz de adyacencia
#si usuario A y usuario B comparten 3 grupo... |
71bb2a04a964607398472d3d0fa94e77f7a629fb | 31f3d6031b5ac2310317b72a20ef8f2c29d55049 | /r/src/background/plot.bg.r | 4a64035815ce2900c615b455df27e65c29815c8f | [] | no_license | uataq/X-STILT | 638c3c76e6e396c0939c85656a53eb20a1eaba74 | eaa7cfabfc13569a9e598c90593f6418bc9113d5 | refs/heads/master | 2023-07-22T03:14:50.992219 | 2023-07-14T16:40:55 | 2023-07-14T16:40:55 | 128,477,511 | 12 | 5 | null | 2023-06-19T22:57:03 | 2018-04-06T22:46:36 | R | UTF-8 | R | false | false | 1,550 | r | plot.bg.r | #
plot.bg = function(site, site_lon, site_lat, sensor, sensor_gas, recp_box,
recp_info, sel_traj, densf, obs_df, plm_df, intersectTF,
bg_df, bg_side = NA, bg_deg, bin_deg, map, td, picname,
font.size, pp_fn = NULL) {
# plot map first
uni_sides =... |
2940d3bb3127c04ab54941968cadfdbaa11177c8 | 6ad337e2b26380a4ebf1ac301bb3e8aff19b846b | /R/aCTR.R | d569dabf790ad9a91fe06dc0790990232d3b7eb9 | [] | no_license | kaseyriver11/k3d3 | 2824f2c078c2f0ba0659333b0bd68909442c4270 | 85c21f7725f6afe06a95d773716ddadff4386622 | refs/heads/master | 2020-12-29T02:44:20.607587 | 2017-06-04T22:56:11 | 2017-06-04T22:56:11 | 38,123,059 | 5 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,963 | r | aCTR.R | #' D3 Visualizations: Collapsible Tree
#'
#' Creates a collapsible tree given an appropraite json file.
#'
#' @param data the json file being used for the visualizations.
#' @param width width for the graph's frame area (in pixels) - default is null.
#' @param height height for the graph's frame area (in pixels) - defa... |
b1211a5e2d56162e92a2259e31af17fcc5576d21 | 6f7403d41fe5f3cf5bddfd3cdd78f38131098ac2 | /pca_wine-v2.R | 07134be6b990162e51c12f2dbe15d76ab519eb97 | [] | no_license | nirajpjaiswal/R_With_ML | 9490777f57ec8e11e2482bfee43e8c95ecd4396f | 1aa5e8450212b29d28b649b9745d3528b8211073 | refs/heads/main | 2023-05-06T15:38:44.515907 | 2021-05-27T07:13:12 | 2021-05-27T07:13:12 | 356,593,420 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,303 | r | pca_wine-v2.R | # PCA
# dataset: wine
# method: SVD (singular value decomposition)
# Use this for class demo
library(caTools)
library(e1071)
library(caret)
path="F:/aegis/4 ml/dataset/unsupervised/pca/wine.csv"
wine=read.csv(path)
View(wine)
# y-variable (customer_segment) categorises customers based on various pa... |
4321060a7f249e15443b1f3b820e9a5bd10b7d9f | 706aa50f561d7f8ebd0cb266e53e30d316f546bc | /code/Make_Figure_2.R | afdfaa4baffebb52d2ad8d84f21c6033cecb6b0a | [] | no_license | willbrugger/vaccine_reevaluation | 623b18c32edf54a6771eab3a398e7e3c90320bfc | 15523c63c1e157796c83c10e2bb6cb68e960b4f0 | refs/heads/main | 2023-07-11T10:32:29.881584 | 2021-08-20T22:16:24 | 2021-08-20T22:16:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,069 | r | Make_Figure_2.R | library(tidyverse)
library(readxl)
# IMR Data
imr_estimates = read_xlsx("https://github.com/tkmika/BIO465_Vaccine/blob/main/data/UNIGME-2020-Country-Sex-specific_U5MR-CMR-and-IMR.xlsx", sheet=2)
imr = tibble(imr_estimates) %>% rename('Country_code' = 'Child Mortality Estimates', 'Country' = 2, 'Uncertainty_bounds' = 3... |
4cf0686bdc812235e33f09b633a0c7498721d709 | 3be35f6e9bf55ed92efb3d0cdcf2ebd36b931b4d | /man/eigen.test.Rd | 6adf4d6b639575c5b625413ce20e3b641d2244a9 | [] | no_license | cran/vcvComp | 9097272287ff319bded4b49819afd05de3fbc511 | d965eb36cde4192dd4ff540c9850a7bfa7eac1ef | refs/heads/master | 2020-12-22T23:04:28.707405 | 2020-12-17T08:00:02 | 2020-12-17T08:00:02 | 236,956,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,825 | rd | eigen.test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eigen.test.R
\name{eigen.test}
\alias{eigen.test}
\title{Difference test for successive relative eigenvalues}
\usage{
eigen.test(n, relValues)
}
\arguments{
\item{n}{the sample size(s), given as a number or a vector of length 2}
\... |
e4ca17e1cd544b6cff9cee9a1e28cf3227c46899 | 493583c405b9e6267b25b7db400ee32f18ae092f | /inst/doc/do/ALB.BULK.R | 3ec30eb171b066cbe06ec4ef0bca6ffb6888fe3d | [
"MIT"
] | permissive | dbescond/iloData | 69a3e2b78b3d868799384c1dd085b1e1e87c44cd | c4060433fd0b7025e82ca3b0a213bf00c62b2325 | refs/heads/master | 2021-01-21T19:54:33.877674 | 2018-07-05T11:30:47 | 2018-07-05T11:30:47 | 92,175,594 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,137 | r | ALB.BULK.R | #############################################################################
# Program to prepare Short-term indicators.
# Short term indicators
# Auteur: David Bescond ILO / Department of statistics
# Date: April 2016. last update May 2017
############################################################################... |
0f72f5b49ca718af0054350029bd3309cb1de05f | 9620db4a06584153b0176b8e2022bef8a7b6ed05 | /analysis/exploratory/exploratory-analysis_v3.R | 8de6f106e7a259a2a22496a7aedde97427ecc4c0 | [] | no_license | UCRCSI/blog_housing | de57be8d95b50c110870bf4ccd589dcdf970befc | 66bcd87dd18c2f3745f2bbb8cc8f55e598008d2a | refs/heads/master | 2020-06-10T23:19:50.061668 | 2020-03-19T20:59:01 | 2020-03-19T20:59:01 | 193,786,315 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,718 | r | exploratory-analysis_v3.R | # Load relevant libraries
library(tidyverse)
library(survey)
library(srvyr)
library(forcats)
library(reshape2)
library(openxlsx)
library(rlang)
library(tidycensus)
# Loading and cleaning data -----------------------------------------------
## Load data
housing <- read.csv("raw data/ahs2017_flat_r.csv")
## Clean data
... |
5f63f64b6618330e6c629c22664a215f7baaa420 | ef6622052965084d42588ee7b9c75d029e54392f | /man/soccerPitchBG.Rd | a019d72caece9685be25dbe4cacee596b6db0102 | [] | no_license | cRistiancec/soccermatics | a1f10a8602b003feef44f62eba9d423e26b99b12 | 184e2072c45359c374d9aac9580263e7237aa4b9 | refs/heads/master | 2020-04-04T22:18:31.005372 | 2018-11-04T18:14:06 | 2018-11-04T18:14:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,338 | rd | soccerPitchBG.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/soccerPitchBG.R
\name{soccerPitchBG}
\alias{soccerPitchBG}
\title{Plot a soccer pitch ggplot object}
\usage{
soccerPitchBG(lengthPitch = 105, widthPitch = 68, fillPitch = "white",
colPitch = "grey60", grass = FALSE, arrow = c("none", "r", "... |
88be5e054424989755756c8d1874f46303f66482 | 08481da2b6d3690aa157a161f9df284c802a5177 | /R/create_pamdata.R | f2ad33cbf629b4bd6e206f442f6c7d598eb39721 | [
"MIT"
] | permissive | brgordon17/coralclass | 605dfedaaaf48dfd4ad589b6aaf3c7d0bfc44603 | 18de22b48a3bf0cff99c2c82bb206d92d5a53058 | refs/heads/master | 2020-06-24T11:01:51.980043 | 2020-06-15T11:02:49 | 2020-06-15T11:02:49 | 198,944,888 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,318 | r | create_pamdata.R | #' Create pamdata.
#'
#' \code{create_pamdata()} reproduces the mean FvFm data.
#'
#' This function reproduces the PAM data that was collected in this experiment.
#'
#' @param path The path where the .csv is located
#' @param saverda Logical indicating if a .rda file should be saved to /data
#'
#' @return Returns a dat... |
638f5598b70568894e9e55ee7e536a3e4ab0a2f9 | 1ed87c596958af5205fe6efe481d97f456e1fae6 | /rExamples/customization/charis.R | 863e8dd909016cc7ee0b41e28bf95390c90b6d0c | [] | no_license | aaronxhill/dataviz14f | 1530a3d16803c3e49d0f940dde687da6ebe3b6f5 | 290187d53b1e88bcf255c23dd2ba8e3af7294ea2 | refs/heads/master | 2020-03-30T19:02:34.451718 | 2014-11-15T00:33:58 | 2014-11-15T00:33:58 | 23,426,895 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,674 | r | charis.R | library(ggplot2)
library(grid)
fpath <- "/Users/aaron/classes/dataviz14f/rExamples/customization"
##### Charis #####
fname <- "charis.png"
# original code:
ggplot(iris) +
geom_point(aes(x=Sepal.Length, y=Sepal.Width, shape=Species), color = "DarkGoldenrod1", alpha=.4) +
geom_point(aes(x=Petal.Length, y=Petal.... |
99f6dc680c73e7042b37f61dbbe481e7db7323b8 | 67a6f1af8a7e28e3e64f123ce48fff3017364094 | /man/writeEnrichment.Rd | 6ca72a127cda5047eaef1e679d019715f248917c | [] | no_license | tastanlab/NoRCE | 3257f0af8da9cbff2152313edc9df9e3b2b4dd1c | e8779cec9bdece0e71a8f85389259a8d1714465f | refs/heads/master | 2020-05-28T14:17:11.643250 | 2019-05-26T12:11:24 | 2019-05-26T12:11:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,196 | rd | writeEnrichment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{writeEnrichment}
\alias{writeEnrichment}
\title{Write the tabular form of the pathway or GO term enrichment results}
\usage{
writeEnrichment(mrnaObject, fileName, sept = "\\t", type = "pAdjust",
n)
}
\arguments{
\item{mrnaObjec... |
f56cf4db666b4555f1d89397df8eed1ffa6f766f | 12ab6551a0f4088005d556799ae5dea6e1b9c596 | /simulations/simulation_vre.R | dbae066eb4fdcb07ba4efedd4d0f4a215b9d1b2c | [] | no_license | jl3859/causal_mlm | f0a25c1a7663bd15022876de7aa5bc2b5226df70 | 64b7da1df9efcf1de56e4816c0c900bfac7fceb2 | refs/heads/master | 2020-09-19T13:30:22.046712 | 2020-01-06T22:15:18 | 2020-01-06T22:15:18 | 224,231,011 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,006 | r | simulation_vre.R | library(lfe)
library(lme4)
library(lmerTest)
# Source data generation process functions
source("dgp_script.R")
# Set iteration number for simulations
iter <- 1000
# Set seed for reproducibility
set.seed(2123)
# RANDOM EFFECTS VIOLATION SIMULATION ####################################################################... |
f8cb3fb1448cfafda15fba8719025791fe95ab15 | 91f62e042ef580971bf2d17f8817ac12bda51df5 | /feb 3 lab2-first part.R | 76f95f6747a3642dcb46064a37fa747986abfcc2 | [] | no_license | Yuewangluisa/DataAnalyticsSpring2020 | ab5a08e3ce194203df292477bfc842a73fe61bd4 | 06e817d3497074e86088ef4d401f515f64ea1e3b | refs/heads/master | 2020-12-20T01:54:12.194770 | 2020-04-21T20:41:10 | 2020-04-21T20:41:10 | 235,923,843 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,689 | r | feb 3 lab2-first part.R | multivariate <-read.csv(file.choose(),header=TRUE)
attach(multivariate)
names(multivariate)
multivariate
plot(Income,Immigrant,main='Scatterplot')
plot(Immigrant,Homeowners)
help(lm)
mm<-lm(Homeowners~Immigrant)
mm
plot(Immigrant,Homeowners)
abline(mm)
abline(mm,col=2,lwd=3)
summary(mm)
attributes(mm)
mm$coefficients
H... |
26658946f1b91d4c58716d656934aedf627966f7 | 95a2abbc422cf8d569c2e4d8098df052e053ebd6 | /R/occurrence_images.R | 4e9b827d4a69421488272e329ae26ca375818cc7 | [] | no_license | AtlasOfLivingAustralia/ALA4R | 215b3cfd7fefc5af360f15ad8d8573b7c993da27 | d048d7ecb22a16932d9474278957ba45a8763e0d | refs/heads/master | 2021-09-29T18:19:06.842268 | 2021-09-13T05:35:10 | 2021-09-13T05:35:10 | 24,315,261 | 39 | 13 | null | 2021-03-30T01:58:19 | 2014-09-22T05:20:49 | R | UTF-8 | R | false | false | 3,676 | r | occurrence_images.R | #' Find images using occurrence ids
#'
#' @references \itemize{
#' \item Associated ALA web service for image search counts:
#' \url{https://images.ala.org.au/ws#/Search/search}
#' }
#' @param occ_id character: IDs of occurrences as single sring or vector of
#' strings
#' @param fq string: (optional) character string ... |
9d1611eae5a18408a0ae0880bad48b635fa5de0c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PanelCount/examples/CRE_SS.Rd.R | c96030546d905c1bf912ebe1696bd99bc324ce88 | [] | 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 | 399 | r | CRE_SS.Rd.R | library(PanelCount)
### Name: CRE_SS
### Title: A Sample Selection Model with Correlated Random Effects
### Aliases: CRE_SS
### ** Examples
## No test:
data(rt)
# Note: estimation may take up 10~15 minutes
est = CRE_SS(isRetweet~fans+tweets+as.factor(tweet.id),
num.words~fans+tweets+as.facto... |
b52db7b89556ec99b0996158ca673ce4a9c2ce9d | e070a2dfc51711762288679a10750ac1df139341 | /source/paper/MGFigures/utilities.R | 2552466d44caea82e8720ee82a30fe102623a2bc | [
"MIT"
] | permissive | hakyimlab/MetaXcan-Postprocess | abfd03cc9dc4a577032589a94d6902b4cb4201d7 | a65e36f29c6af412471cab36e31dabb325a93999 | refs/heads/master | 2020-05-21T13:30:17.256593 | 2017-05-11T19:02:08 | 2017-05-11T19:02:08 | 53,421,284 | 1 | 2 | null | 2017-05-11T19:02:09 | 2016-03-08T15:05:00 | Python | UTF-8 | R | false | false | 1,052 | r | utilities.R | library(stringi)
simpleCap <- function(x) {
s <- strsplit(x, " ")[[1]]
paste(toupper(substring(s, 1,1)), substring(s, 2),
sep="", collapse=" ")
}
build_allele_key <- function(data.frame) {
allele_key <- sprintf("%s%s",data.frame$ref_allele, data.frame$eff_allele)
striHelper <- function(x) stri_c(x[stri... |
7560834e03412d1fb56b48b42107d18fd7690abd | 00b153ac44ca3fd122d6b478a40ad3b9a9e7cd27 | /FISTULA/R-SCRIPTS/Patient311143_Hemodynamics.R | dac306317746dc77a2956982ea5b116d2e9d5418 | [] | no_license | RosamariaTricarico/PROJECTS | 27e53e14e802fde6cdab2d36ea565ad0dc95666f | b77c086b57ca13dc092e389d8b89f5625e09c634 | refs/heads/master | 2020-04-21T10:17:55.394177 | 2019-03-16T13:40:23 | 2019-03-16T13:40:23 | 169,481,853 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,107 | r | Patient311143_Hemodynamics.R | Hemodynamics <- read.csv(file="Hemodynamics_Fistula-Vein.csv", head=TRUE, sep=",")
View(Hemodynamics)
Patient1 <- filter(Hemodynamics, Patient == "031143 HJ")
#head(Patient1)
View(Patient1)
Baseline <- filter(Patient1, Scan == 1)
Week6 <- filter(Patient1, Scan == 2)
Month6 <- filter(Patient1, Scan == 3)
n <- dim(Base... |
a0d106f4bf8871020d0fc23249fb9b42bacb9414 | c2a6015d964e0a004fa4ac9c59df8aed039cc4fc | /man/is.nr.Rd | 220e2791d192dd7bace5255cf7f9ebd0194e2575 | [] | no_license | cran/ufs | 27083e54b6e4c89f802c4de9218dbbd7c7d4260d | 74bcfb60160bced552d79d301b739bb965d1a156 | refs/heads/master | 2023-06-23T09:48:11.331297 | 2023-06-09T15:30:03 | 2023-06-09T15:30:03 | 145,907,951 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 533 | rd | is.nr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/is.nr.R
\name{is.nr}
\alias{is.nr}
\title{\code{NULL} and \code{NA} 'proof' checking of whether something is a number}
\usage{
is.nr(x)
}
\arguments{
\item{x}{The value or vector to check.}
}
\value{
TRUE or FALSE.
}
\descriptio... |
428ce186d2da223abe87f69ebb4aaf45e0c94f77 | 676663a4c9cad2ceba8561a8b03a60a7d382f721 | /script/ref-youtube-library.R | bc1805db37c32f3a03d9de539fd91c4488334cbe | [
"MIT"
] | permissive | minhyukyang/text-mining-using-r | d099ade222f6ed5e71d18f61b919b6a440af65b3 | a72d1e4a6b7975cb072f7783f450bea847520860 | refs/heads/main | 2023-01-06T16:42:24.273862 | 2020-11-08T11:36:20 | 2020-11-08T11:36:20 | 305,714,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,299 | r | ref-youtube-library.R | # iscrape 1.0.1
# FUNCTIONS
#' @title Get user page
#' @description Gets the user page
#' @param username A character denoting a valid instagram username.
#' @return Returns an httr webpage object text or NA.
#' @details
#' If the username is not valid, it does not return a webpage,
#' NA is returned with a warning.
#... |
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