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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e07068b09353061aa44e63df30b2f8b75dd3a791 | 633d4069f4db086c6d7111822e98a0fae9716f34 | /createdata.R | 6be43f9170a9a7937d6b2dfd065f5cdcd000957e | [] | no_license | castillosebastian/NLP_predictor_project | 5343801f0c2f49e32985ea087c4ac709812c8485 | b51e450d7a949bcc01b41d29f7ac78b1af09be46 | refs/heads/master | 2020-04-16T01:47:30.469527 | 2019-01-11T06:21:19 | 2019-01-11T06:21:19 | 165,185,652 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,488 | r | createdata.R | library(doParallel)
registerDoParallel(makeCluster(4))
library(stringr)
library(dplyr)
library(caret)
library(tau)
library(data.table)
library(downloader)
library(knitr)
library(tm)
## Create Dir
if(!file.exists("./projectData")){
dir.create("./projectData")
}
## Download the dataset and unzip folder
url <- "https... |
00e4a5030b9f5e7fa6c0d4f6f59f1575051c9177 | 58d06ff7d5c1e12e4033f2024e837b723951a7f7 | /DT_Fraudcheck.R | 9b8d1b73670d2b8f3dd3d208954b86b0ad4b0909 | [] | no_license | karthiknr2/Karthik | a7471280d31164db19afc0343cf448eebf22cb1e | ddca7b72fe4074992686f78e4781df07b7855dbd | refs/heads/master | 2023-01-07T08:54:12.702385 | 2020-10-19T19:03:29 | 2020-10-19T19:03:29 | 257,606,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,262 | r | DT_Fraudcheck.R | library(caret)
library(C50)
fc <- read.csv(file.choose())
View(fc)
Risky_Good <- ifelse(fc$Taxable.Income<=30000,"Risky","Good")
fraud <- data.frame(fc,Risky_Good)
View(fraud)
hist(fraud$Taxable.Income)
fraud_train <- fraud[1:480,]
fraud_test <- fraud[481:600,]
table(fraud$Risky_Good)
dt_model <- C5.0(Risky_... |
43d0df33f74988baea33a4b9aaaf84218279be1e | 89c9b90868f017924de545719f7f26a247cfb49c | /tests/testthat/test-splitINFO.R | 7f3e6040bde5159e839ac4443b10413d8a320590 | [] | no_license | liujohnson118/FluidigmValidation | 5009a815abe393a6ac9c1d66bd197f79a291eace | 831cc1cac97e6f1968fcea0cd7807a9d964e3b16 | refs/heads/master | 2020-11-30T01:45:02.605092 | 2016-10-21T20:51:02 | 2016-10-21T20:51:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 872 | r | test-splitINFO.R | context("splitINFO")
test_that("splitINFO works", {
data("PAT82DNA130CurettageA2S9")
dataForFilterQualityFrequency<-PAT82DNA130CurettageA2S9
filtered<-filterQualityFrequency(dataForFilterQualityFrequency,minQual=80,minFreq=0.03)
expect_error(splitINFO(subset(filtered,select=-INFO)))
#splitINFO usually should... |
3b1bfaa5a7c484964b03fb445966b585e1fdcf23 | 81c4acf23d5db8910522cdc0caab8e6a7ba5cc31 | /text_mining.R | 1d67e705d04455738f15a12f1c874219654c8afc | [] | no_license | ruhulali/R_Codes | ff2d12dc6450ae1da748c4df6ab51600dd48e7aa | e2b3b3f090e7fd8a43746ed29e750b023035b3f1 | refs/heads/master | 2021-06-08T06:44:39.003256 | 2021-04-23T16:21:16 | 2021-04-23T16:21:16 | 158,611,318 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,307 | r | text_mining.R | install.packages("tidytext", dependencies = T)
library(RTextTools)
library(e1071)
pos_tweets = rbind(
c('I love this car', 'positive'),
c('This view is amazing', 'positive'),
c('I feel great this morning', 'positive'),
c('I am so excited about the concert', 'positive'),
c('He is my best friend', ... |
940e2be1e6fd004eec103754d873475b5f390f2a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/kofdata/examples/get_time_series.Rd.R | f7adbb3c133709471607819008018d292550408c | [] | 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 | 180 | r | get_time_series.Rd.R | library(kofdata)
### Name: get_time_series
### Title: Get Time Series form KOF Datenservice API
### Aliases: get_time_series
### ** Examples
get_time_series("kofbarometer")
|
94c8b4171ac8f1510f8de1a4abe52eb571108323 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Causata/examples/BinaryPredictor.Rd.R | 0f7784d5f66cf5b1a49d26758bdb7997d4d49cc5 | [] | 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 | 1,216 | r | BinaryPredictor.Rd.R | library(Causata)
### Name: BinaryPredictor
### Title: Univariate analysis for binary classification.
### Aliases: BinaryPredictor BinaryPredictor.factor BinaryPredictor.numeric
### BinaryPredictor.data.frame BinaryPredictor.default
### plot.BinaryPredictor print.BinaryPredictorList
### ** Examples
library(ggplo... |
4aeab58aa9063870617de49472cc758710273d93 | f0a815520a23a6f7d4633374f7b4e67e463fcee3 | /cachematrix.R | 3d236717de7da23ff1fe164630d8ded175ce0bc6 | [] | no_license | elinborg/ProgrammingAssignment2 | 38386cb54b7735b3a7e938da755685225f3547c4 | d2350da9f15d573fbcbafccbd8dabfdbc1157e91 | refs/heads/master | 2021-01-20T01:11:07.375937 | 2015-02-22T22:30:14 | 2015-02-22T22:30:14 | 31,182,403 | 0 | 0 | null | 2015-02-22T21:52:47 | 2015-02-22T21:52:47 | null | UTF-8 | R | false | false | 1,006 | r | cachematrix.R | ## The following two functions cache the inverse of a invertible square matrix
## This is a function that provides functions that set and get
## the value of an invertible square matrix x.
## In addition, it provides functions setinv and getinv, that set and get the value of the inverse of x
makeCacheMatrix <- functio... |
eb31b960626d49d6eade2a3b1d3dd12e25b9eb71 | 9a12758d6038efb65e5edd8346f8a8fdc97bcdb9 | /9.shortdatabase_joint.R | 55ace70008586e4d749e0778abc832b09a6ba125 | [] | no_license | HaihuaWang-hub/RNASeq-rRNA-identification-approach2 | 2ec593d2b3c6bfb10c0f9870e5d243a53138992a | de21ac1fa9771d724e48666969bbbb2ad2f11ccb | refs/heads/main | 2023-08-06T11:55:48.669260 | 2021-10-04T19:12:03 | 2021-10-04T19:12:03 | 385,419,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 794 | r | 9.shortdatabase_joint.R | if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require('seqinr')) install.packages('seqinr')
if (!require('ape')) install.packages('ape')
setwd("F:/linux_share/approach_2_test/database")
data1 <- read.fasta(file = "short_database_1.fasta", seqtype = "DNA", as.string = T... |
f872f40bfeee4d4c33a75bfbd80da2965368eb36 | d9464f143884bee0e4db68021665a7d17cb8dcd9 | /ProgAssignment3/best.R | 579082a1a6651a0b9852473a48bce35a72c3ff8d | [] | no_license | ykarabag/datasciencecoursera | d96f92268281a80714b861c1fcba5f90925148d7 | b65c35a63952bd562943a49d4e56068a2a6d403e | refs/heads/master | 2021-01-10T01:09:37.935923 | 2016-04-10T21:04:18 | 2016-04-10T21:04:18 | 54,744,661 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,419 | r | best.R | best <- function (state = character(2), outcome = character()){
## Read outcome data and filter our unnecessary columns. Also change the outcome data to numeric
outcomes <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
outco... |
2148c6dc2da1506ce7ffc9c501e3d69f0f360a1c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/xpose4/examples/iwres.dist.hist.Rd.R | 9ab2a1a0e507b0cb712b069184f7e6b85bb4fc16 | [] | 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 | 222 | r | iwres.dist.hist.Rd.R | library(xpose4)
### Name: iwres.dist.hist
### Title: Histogram of individual weighted residuals (IWRES), for Xpose 4
### Aliases: iwres.dist.hist
### Keywords: methods
### ** Examples
iwres.dist.hist(simpraz.xpdb)
|
3917cf5552f944d48316fa1ba201f24ec2277844 | 01b1302af51d339f7c8827a620c4a5fb26c890f1 | /outcome_measurement/all/cod/dhis/meta_data/extract_meta_data.R | 10fa217ff6d73bddfad21f77bfd524e94c6bd1bb | [] | no_license | ihmeuw/gf | 64ab90fb5a5c49694bde1596f4b20fcf107a76e3 | 29e0c530b86867d5edd85104f4fe7dcb1ed0f1ee | refs/heads/develop | 2021-08-15T02:16:59.086173 | 2021-08-03T19:52:31 | 2021-08-03T19:52:31 | 109,062,373 | 3 | 6 | null | 2019-03-21T01:48:02 | 2017-10-31T23:17:16 | R | UTF-8 | R | false | false | 7,621 | r | extract_meta_data.R | # DHIS Extraction for DRC: Meta data extractopm
# Caitlin O'Brien-Carelli
# Meta data extraction code for the DHIS Extraction tool
# Extracts the names of variables, age/sex categories, data sets, facilities
# 2/20/2020
#---------------------
# This code can be run locally or on the cluster
# Calls the extracting fun... |
39583bfb4aceabe249e008b34378b34b9e60d458 | d545784864e85b543cbc837863333550fbab31f3 | /ixp/R/ixp.R | 3645b79cea4259c43a3098da696bc1fdc6aa9f32 | [] | no_license | phil8192/ixp | 64ebebc667496ead3433a6a55b2868457c65340c | 09dda001dc2240152ffcb5047f3ea7d42be44772 | refs/heads/master | 2021-11-11T05:49:35.800122 | 2021-10-27T16:11:19 | 2021-10-27T16:11:19 | 219,201,400 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 170 | r | ixp.R | #' The ixp package holds ixp data and functions for loading,
#' manipulating, analysing and visualising the data.
#'
#'
#'
#' @keywords internal
"_PACKAGE" |
c4bff690c514ae4e3cdd2b9b023383aed56d8081 | a42d50796461308e6b30e64e4777ec1a3ad9c8fb | /R/pycheck.r | b4cd2e92d27d1180e1817d093d11500281ade56f | [
"MIT"
] | permissive | mtalluto/mbm | b6de42299b1d77e4144ad0da14c2df16a8e9484f | 65127258b7799b0fffc8ffffa10b6254051e4f39 | refs/heads/master | 2021-06-01T18:27:31.622299 | 2019-08-11T20:49:41 | 2019-08-12T10:48:13 | 76,379,194 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 499 | r | pycheck.r | #' Check if necessary Python packages can be loaded
#' @examples check_python()
#' @export
check_python <- function()
{
efun <- function(module, err)
paste("Loading python module", module, "produced an error:\n", err)
modules <- c('GPy', 'numpy', 'scipy')
mTest <- sapply(modules, function(m) {
tryCatch(sp <- re... |
1dab51f066129891e9661e3be8b606aba1434f58 | 04ca21e146db0f4e27b38475661b10be7b61a77c | /code_II2020/clase_7sep.R | 2279188f25e84a6d91c9dcdd48b27f304a70d007 | [] | no_license | AdrianEnriquez4505/EST-383 | 5c2f1a67777db735ad5ecc8e584b593dc60eed6b | a6227a1afc3c50c2ed8beeeace508ec5dc24147f | refs/heads/master | 2023-04-18T18:41:25.754012 | 2021-05-04T23:39:31 | 2021-05-04T23:39:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,591 | r | clase_7sep.R | # Heterogéneas
Pueden contener objetos de diferentes clases
## dataframe (Marco de datos)
Un dataframe supone que las filas corresponden a observaciones y las columnas
a variables, donde las variables pueden ser de diferentes clases; numéricas,
texto, lógico, etc. La forma es muy parecida a una matriz.
Un data fr... |
584c0bdfb62f373f35fd2178c510413702aa450c | fd570307c637f9101ab25a223356ec32dacbff0a | /src-local/specpr/src.radtran/SRC/cnvg1g.r | 4b12ac468071a7cdda477c2df3fd582700242700 | [] | no_license | ns-bak/tetracorder-tutorial | 3ab4dd14950eff0d63429291c648820fb14bb4cb | fd07c008100f6021c293ce3c1f69584cc35de98a | refs/heads/master | 2022-07-30T06:04:07.138507 | 2021-01-03T22:19:09 | 2021-01-03T22:49:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,110 | r | cnvg1g.r | subroutine cnvg1g (sum1a, sum2a)
implicit integer*4 (i-n)
#
# iterate to converge on best grain size for one mineral
#
include "defs.h"
include "lmrefl.h"
include "convh.h"
include "../../src.specpr/common/lblg"
real*4 sum1, sum2
dmove = 1.0
iter = 0
dlast = ddflt(1)*1000.
10 dsave = ddflt(1)
call eval1g... |
8e7ed1afd4d1e03ea5a1139fb6c622da5ebfb12e | 7c5caeca7735d7909c29ee3ed6074ad008320cf0 | /R/duplicate.R | 4bbbce8a4efa7a8b190c6a98f9b864ac85292da1 | [] | no_license | ncss-tech/aqp | 8063e800ed55458cfa7e74bc7e2ef60ac3b1e6f5 | c80591ee6fe6f4f08b9ea1a5cd011fc6d02b5c4a | refs/heads/master | 2023-09-02T07:45:34.769566 | 2023-08-31T00:14:22 | 2023-08-31T00:27:14 | 54,595,349 | 47 | 12 | null | 2023-08-17T15:33:59 | 2016-03-23T21:48:50 | R | UTF-8 | R | false | false | 1,292 | r | duplicate.R |
#' @title Duplicate Profiles of a SoilProfileCollection
#' @description A simple function to duplicate the contents of a \code{SoilProfileCollection} object. Old profile IDs are saved as a site-level attribute (\code{oldID}) and new IDs are generated using a numeric serial number.
#'
#' @author D.E. Beaudette
#'
#'... |
1265e8aa406d1fd51b7613b3b16b8adcda0b473d | a9a6e636c6dd4b5c472f7c999166305d69b48632 | /man/make_df_index.Rd | 820fe30ea403340d04a92372991534c1660dd20a | [] | no_license | jiqiaingwu/ActigraphyUtah | 2fa17e62bd4758ea220ea4e0dbbe15a47792c023 | 195df9fd927ae3d9c19db00297860e175e8c310b | refs/heads/main | 2023-04-13T10:19:05.751824 | 2021-04-22T21:16:07 | 2021-04-22T21:16:07 | 336,078,135 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 537 | rd | make_df_index.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_df_index.R
\name{make_df_index}
\alias{make_df_index}
\title{Utility function to map table with their columns/rows in a bigger table (Map Over Multiple Inputs Simultaneously)}
\usage{
make_df_index(v)
}
\arguments{
\item{v}{import Actigr... |
0bb29bf5520e5245a8266ca30113eed92f5fa98e | ef4a08ecc44c8c30a1ded2df9bf18abc72de5687 | /man/plot.diverse_tt.Rd | c827af09d321542b045bcc68cacacc9482ad0ddb | [] | no_license | pmartinezarbizu/dada2pp | de7008f8f1a68a55f7399b8a60ee805e8ab952d9 | a4c70de6028cf6429cdd387ea2c6abf0776404ff | refs/heads/master | 2023-07-27T21:54:29.676912 | 2023-07-18T12:11:47 | 2023-07-18T12:11:47 | 160,071,012 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 409 | rd | plot.diverse_tt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.diverse_tt.r
\name{plot.diverse_tt}
\alias{plot.diverse_tt}
\title{Plot Method for divers.tt}
\usage{
\method{plot}{diverse_tt}(x, ty = "ASVs")
}
\arguments{
\item{x}{}
}
\value{
A graph with 4 boxplots
}
\description{
}
\details{
}
\e... |
e714cdd4c9a5a1201dd4fa48416e3a4e0aa3a6a6 | c90dac176024e17fc2f984c2e5bf3390ee08235b | /Lab/Chapter 5/Chap 5.r | e46c81b923dc1c6dc377e0456c27473fdfc701df | [] | no_license | HariharasudhanAS/ISLR-Exercises | 4317851a5c6fafe0f63f4f0be3b6363ea5bb9593 | 0b6066ce81c19cefeb582703f3b3a7f3148d5af3 | refs/heads/master | 2020-03-31T00:26:17.718212 | 2018-12-11T11:05:53 | 2018-12-11T11:05:53 | 151,739,548 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,166 | r | Chap 5.r | library(ISLR)
set.seed(1)
#random selection
train = sample(392,196)
# model fitting
lm.fit = lm(mpg~horsepower, data=Auto, subset=train)
summary(lm.fit)
attach(Auto)
# MSE in test set
mean((mpg-predict(lm.fit, Auto))[-train]^2)
# testing for quadratic and cubic
lm.fit2 = lm(mpg~poly(horsepower,2),data=Auto,subset=t... |
cb755067a487a22f7a107e71750720be01d3e3dd | c9e0c41b6e838d5d91c81cd1800e513ec53cd5ab | /man/gtkWindowHasToplevelFocus.Rd | bfa01543153027fa7d9bbd88dd288ce7b3681419 | [] | no_license | cran/RGtk2.10 | 3eb71086e637163c34e372c7c742922b079209e3 | 75aacd92d4b2db7d0942a3a6bc62105163b35c5e | refs/heads/master | 2021-01-22T23:26:26.975959 | 2007-05-05T00:00:00 | 2007-05-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 631 | rd | gtkWindowHasToplevelFocus.Rd | \alias{gtkWindowHasToplevelFocus}
\name{gtkWindowHasToplevelFocus}
\title{gtkWindowHasToplevelFocus}
\description{Returns whether the input focus is within this GtkWindow.
For real toplevel windows, this is identical to \code{\link{gtkWindowIsActive}},
but for embedded windows, like \code{\link{GtkPlug}}, the results w... |
aec61f7ced2b00e401517b7330672f3523bc97de | 8a4ded6b300e28278a8b1f952209fd3b2ae4af5e | /evaluation/stats_final.r | e9beca633d23d40bcd8c859885cf090812fd837e | [] | no_license | mario206/jsNaughty | 8bb76c0f37f6d1d1e1b675d4a360d53f4c405cbb | a5d46d2bddedf7dbc708166b1777ac96b51056bb | refs/heads/master | 2020-04-02T06:25:06.811612 | 2017-05-08T20:42:15 | 2017-05-08T20:42:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,645 | r | stats_final.r | #From the stats.r file we want to produce 3 plots:
#1) a comparison boxplot between the three different consistency measures with and without
#hashing. The no hash and hash boxplots (three each) should be in different colors.
#2) a comparison boxplot between our best measure, jsnice, and their mixed equivalent. (also w... |
c55a474de0e811f386850b26a65efcb07d93e5f0 | d160549f5fcee328daaf69d93fdd59123905168b | /man/check_phred.Rd | cd1192d3a4566d273885ee04381945194093cc13 | [] | no_license | ddiez/ngstools | 636b228128e2ceee7d4cf22fad657e2e706def16 | 9cd94b458390d44c091ff43b54784a9d10370e00 | refs/heads/master | 2021-01-18T23:59:43.630413 | 2017-05-02T09:36:10 | 2017-05-02T09:36:10 | 72,809,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 388 | rd | check_phred.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phred.R
\name{check_phred}
\alias{check_phred}
\title{check_phred}
\usage{
check_phred(filenames, nlines = 1000)
}
\arguments{
\item{filenames}{name of fastq file(s) to check.}
\item{nlines}{number of quality lines to check.}
}
\description{... |
9a7f82c3a87e5d53270c9a079213865bfa0537e7 | 0b72304cf8e6d26211bd702dc40423bf1a2b5296 | /man/conv.check.Rd | f50930e30e63a0cb91f1138d5a692c6a583ef80c | [] | no_license | cran/GJRM | 0ce5f2fc18661b2e4f6a9de4ab66dd07bed35b80 | 382349bb8af640694b472807a8c61a55e64760a9 | refs/heads/master | 2023-06-22T20:55:23.493200 | 2023-06-21T17:50:02 | 2023-06-21T17:50:02 | 97,197,872 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 541 | rd | conv.check.Rd | \name{conv.check}
\alias{conv.check}
\title{Some convergence diagnostics}
\description{It takes a fitted model object and produces some
diagnostic information about the fitting procedure.
}
\usage{
conv.check(x)
}
\arguments{
\item{x}{\code{gjrm} object.}
}
\author{
Ma... |
94b81ee4d062371a8283c45695bca69ba78d5665 | 434c6c56502ed849f65cecf2877d5eb21a30b44e | /man/short.form.Rd | 475d38b5b4eb802014ecb2b1fec53c7d854742db | [] | no_license | lansexiaobang/marima | afa077e000bceaf33594a5bdb97fd4d96144ee21 | 9985537297dd4874ef09a61e546d19ff7a019604 | refs/heads/master | 2021-04-06T09:05:29.895309 | 2017-01-13T23:59:20 | 2017-01-13T23:59:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,615 | rd | short.form.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/defining.programs.R
\name{short.form}
\alias{short.form}
\title{short.form}
\usage{
short.form(poly = NULL, name = "Lag=", leading = TRUE, tail = FALSE,
digits = 6)
}
\arguments{
\item{poly}{matrix polynomium (0-1 array as construced by def... |
deeb042bd7cf5500185a96409d810d8e01b05627 | c8f8ac023f0021201d25568a705bf5085e664853 | /man/melt.mefa.Rd | 9aea826c6e1963393a36455d54680f2fd517fba2 | [] | no_license | psolymos/mefa | ca7d5716229c82453a49d76334282b4c4a6f07c1 | db3922bc72429d9f61499143f8c8d6f8f31166d3 | refs/heads/master | 2021-10-13T04:40:46.657736 | 2021-10-07T01:48:21 | 2021-10-07T01:48:21 | 25,432,520 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,530 | rd | melt.mefa.Rd | \encoding{UTF-8}
\name{melt}
\alias{melt}
\alias{melt.mefa}
\title{Melt (Convert) an Object of Class 'mefa' Into 'stcs'}
\description{
The function converts a wide formatted 'mefa' object into a long formatted 'stcs' object. The resulting segment column can take various formats.
}
\usage{
melt(x, \dots)
\method{melt}{m... |
3f8118f1ba4b46f94dd5153cb26a7959e42483d0 | dced7914ebb2d205ba856e6bf7638390b75cc43c | /d3donut/sin.R | d14aae08d2dfdcdf24f36e2f1d7929a2bbb8cf04 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | clarkfitzg/junkyard | b292218cef35445363691264c48639c521471264 | a88c72d9dea984985c3677fd44137f9665064662 | refs/heads/master | 2023-02-16T02:48:04.448950 | 2023-01-30T15:21:57 | 2023-01-30T15:21:57 | 21,373,484 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 269 | r | sin.R | sin_row = function(phase = 0){
x = seq(0, 2*pi, length.out = 50)
(1 + sin(x - phase)) / 2
}
out = sapply(seq(0, 50, length.out = 500), sin_row)
out = data.frame(t(out))
write.table(out, "sin.csv", sep = ","
, col.names = FALSE, row.names = FALSE)
|
f8597ed0d174cdff7e30b80d7191c51c7a9b92a6 | 0318386625386e0b7488ca27b2a3025e98c641c5 | /cachematrix.R | aeef40c4f5fc2ee54cbd374c1cf5d1a81e194039 | [] | no_license | PavithraBJ/ProgrammingAssignment2 | a853b5eb70a4a516daca4bde2071a959f84b9292 | e1d4ae3bb86286666982d84062fd9c135f4f2ade | refs/heads/master | 2021-01-25T11:03:20.404778 | 2017-06-10T13:55:10 | 2017-06-10T13:55:10 | 93,909,814 | 0 | 0 | null | 2017-06-10T02:03:38 | 2017-06-10T02:03:38 | null | UTF-8 | R | false | false | 1,045 | r | cachematrix.R | ##makeCacheMatrix creates a list of functions to set the matrix and cache the inverse of the matrix
##cacheSolve computes the inverse of matrix if it is not cached yet and matrix is not changed and caches the inverse
##inv stores inverse of the matrix, set <- sets the matrix, setinv <- sets the inverse of matrix
##ge... |
6a0c80a6ae7f0fc80d4489f17742c963d7ccc5c2 | d74d80e33b2802b6e0f27a9ffc6020e3c65c5779 | /_common.R | e6b21de404f81b1f9eb13e19f7232e9c89678a0f | [] | no_license | espinielli/flights_in_R | 9b3da990f4cebc859ff3d0fa81979e0754bcdf06 | a2b0fb70b632a27a7044be61e7345458fed31244 | refs/heads/main | 2023-05-05T06:40:54.616341 | 2021-06-03T13:04:03 | 2021-06-03T13:04:03 | 372,894,391 | 0 | 0 | null | 2021-06-01T16:24:44 | 2021-06-01T16:24:44 | null | UTF-8 | R | false | false | 289 | r | _common.R | set.seed(8765)
options(digits = 3)
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
cache = FALSE,
fig.align = 'center',
fig.width = 6,
fig.asp = 0.618, # 1 / phi
fig.show = "hold"
)
options(dplyr.print_min = 6, dplyr.print_max = 6)
|
a620c0f57fbae8020b45c7e077ce7eadbe1b65d0 | 2bb0ddfce85c2cec938adebc837e66d12b340203 | /R/gmt.r | 379db8155658894760d14a9871336a5c5fcce584 | [] | no_license | NaNa-diamond/ActivePathways | 0b00d00b1e078b995bc577cef27c09d23d4eb3b3 | 395c1b1f6f00e6544125f1588179a3970b69f87c | refs/heads/master | 2022-06-05T01:35:56.132429 | 2020-05-02T20:52:02 | 2020-05-02T20:52:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,080 | r | gmt.r | #' Read and Write GMT files
#'
#' Functions to read and Write Gene Matrix Transposed (GMT) files and to test if
#' an object inherits from GMT
#'
#' A GMT file describes gene sets, such as pathways. GMT files are tab delimeted
#' and each row contains a term id, a term name, and all genes annotated to the
#' term.
#'
#... |
47532b0477add3c65ac24f8667af588b3f2e34b5 | 0dcbc7478a27c36d97286072d197216303c28f2c | /ChinaContamination/ChinaContamination.R | f5cfa00a8198ab2495c6b80ffd0539bb97a2dd12 | [] | no_license | hacalf/edxDataScience | 3160f4987c373de49b018fb2d4fab75bdb9d8256 | 8ff5e3465b2714e6c1c17f07d4a8c99731df6f75 | refs/heads/master | 2022-10-26T12:45:35.338732 | 2020-06-21T04:29:00 | 2020-06-21T04:29:00 | 259,980,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,048 | r | ChinaContamination.R | #### Environment Settings
# Packages required for this proyect
pkgs <-c("tidyverse", "caret","kernlab", "caTools","ranger", "randomForest","knitr")
# If a package is missing it will added in the missing packages list
missing_pkgs <-pkgs[!(pkgs %in% installed.packages())]
# The packages in the list will be installe... |
e97d4bcdb1d2e85a2de49f3b056019bbfdb3b66b | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query01_query26_1344n/query01_query26_1344n.R | 901239f1e0cbeaeefcbe0d99a7de6fb133310872 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | query01_query26_1344n.R | bf9d60ab8aa6c40c9b6e7173f4c2e0d2 query01_query26_1344n.qdimacs 349 581 |
d9bdf8ac51f72e4211b65f365e1c329379756c76 | c3f6f6a26105eba04d502f745febe8556a8a59d6 | /man/createIGraph-GraphElements-method.Rd | 5683bc1244be4637a71cd5af5802efe5ad76669b | [] | no_license | sandraTL/PathQuantPadigm | f6eb7a018e1b749058d95bc3366aeebf6ff57289 | aa45b64935a51c425c78d64fceed66f040af7e65 | refs/heads/master | 2020-04-13T15:23:47.110870 | 2016-03-05T21:58:53 | 2016-03-05T21:58:53 | 40,988,816 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 415 | rd | createIGraph-GraphElements-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Graph.R
\docType{methods}
\name{createIGraph,GraphElements-method}
\alias{createIGraph,GraphElements-method}
\title{Creation of object igraph with data from XML file of pathway
of interest}
\usage{
\S4method{createIGraph}{GraphElements}(objec... |
e1be088293579badb95437741c3df6c9ec7f081a | 127e43a5a0271f562a286784443b9fcccb54dc4b | /app-2/ui.R | 4d9fce9ae9cbaa0fb3682d91f1e313844a39ddb5 | [] | no_license | DawnEve/shiny_GO | 15747bb9ac2bd84b24f4b08e0bb4c857c6d064a4 | a741bcaa27c66d8a18a7ef2812c83b87f4cbf175 | refs/heads/master | 2022-11-23T07:00:52.236350 | 2020-07-25T14:30:22 | 2020-07-25T14:30:22 | 279,522,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 380 | r | ui.R | library(shiny)
library(dplyr)
library(broom)
shinyUI(
pageWithSidebar(
# Application title
headerPanel("kmeans VS hclust"),
sidebarPanel(
numericInput('n', 'Number of obs', 500 ,min=200 ,max=1000),
selectInput("type", "Select a clust approach:",c("kmeans","hclust"),"kmeans")
... |
22b7feeb36ebb35082bd4e94bda8be61129539f9 | 80f6ebe26244d309d5145b8ffeda94c0826186e8 | /sim_difmedias.r | 45a706f36f7bbf79220ffa757662c625cd666687 | [] | no_license | maibru/TestHipo | a9d81f29ea66431fd7a8ca396e138b25b203950d | f53e8241d4b24aea9533956324d3ffcfe7dc9ab8 | refs/heads/master | 2020-04-22T11:08:19.378554 | 2019-02-15T11:03:55 | 2019-02-15T11:03:55 | 170,329,047 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 1,162 | r | sim_difmedias.r | dif.medias<-function(mu1=4700,mu2=4900,sig=100,n1=4,n2=4){
## Obtenemos una muestra de tamańo n1 de la normal con media mu1
## y desviación típica sig
y1<-rnorm(n=n1,mean=mu1,sd=sig)
## calculamos la media muestral de y1
m1<-mean(y1)
## calculamos la varinza muestral de y1
v1<-var(y1)
## Obt... |
f1959eb56b565e33dbf4187453d198e4c1da949c | 06a9935261e4e50e568a6ba1f389b07f78447a2f | /experiment-1/analysis/06_plot_examples.R | f58f383f96542809099984659244b7bc6242fa4b | [] | no_license | VanRijnLab/cold-start-afl | 0a26364e589764c2717efaff4a3dc6e59a9468d9 | ad0511ef556923925c1e72f780099f8bd0838271 | refs/heads/master | 2021-06-28T00:30:17.277456 | 2020-12-18T23:27:42 | 2020-12-18T23:27:42 | 196,563,642 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,141 | r | 06_plot_examples.R | ## ----------------------------------------------------------------------------
##
## Visualise rate of forgetting development for a single fact in session 1,
## plot examples of model predictions, activation
##
## Author: Maarten van der Velde
##
## Last updated: 2020-05-13
##
## --------------------------------------... |
d15697099f52e887f6c966e8b47bdc52d39d2744 | af8b1cfa36e31284367560dac2800456de9bb284 | /R/distance.R | c11562e9fb994de0226a1f13fd029280b55fed78 | [
"MIT"
] | permissive | LudvigOlsen/rearrr | f07cdf8fe92647335fb5a26ffc1416162543c59a | 40d150b440ae06507873fad20a28345c08d48cf3 | refs/heads/master | 2023-04-19T04:24:49.834419 | 2023-03-01T10:48:10 | 2023-03-01T10:48:10 | 259,158,437 | 24 | 3 | null | null | null | null | UTF-8 | R | false | false | 5,429 | r | distance.R |
# __________________ #< 60cfc78f594e5611a6eaaf34a2b212ae ># __________________
# Calculate distances ####
#' @title Calculate the distance to an origin
#' @description
#' \Sexpr[results=rd, stage=render]{lifecycle::badge("experimental")}
#'
#' Calculates the... |
7c67b14af4d62ea65875f87fe8c68fe076ac3f63 | 913c7d05a4d6582d8dd539fbeadf9774dbba6296 | /东西印度洋功能比较-箱线图.R | 98e0753c7bcab3d8225776993c2d24e6dc95c296 | [] | no_license | KeqiangYan/Meta | 6fe8c35030637f3c9c3a0765ad861cafc5b4882e | 0546543f78a3df647e92f7cdb71ad5c2e98ff590 | refs/heads/master | 2021-01-21T21:38:01.749837 | 2017-07-27T09:06:46 | 2017-07-27T09:06:46 | 98,519,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 697 | r | 东西印度洋功能比较-箱线图.R | c<-read.table("all_COG_iBAQ.txt",header = T,sep = "\t")
Sample<-c("S08","S10","S12","S14","S16","S18","S20","S22","S24","S26","S28")
c1<-data.frame(c,row.names = 1)
c2<-t(c1)
c1<-as.data.frame(c2,row.names = F)
c2<-cbind(Sample,c1)
cc2<-c2[,c(1,3,5,7,10,12,15,17)]
library(reshape2)
cc3<-melt(cc2,id="Sample")
group<-c(r... |
2128b1b34c5ec24b44f4ff16269d9618afb59c92 | a69974c3e38a83fcb05e1c37b1f1becbbd682c88 | /Optogenetics SuHGFP.R | c78ff4c41547e4e74e948181a431a1d823c93d82 | [
"CC0-1.0"
] | permissive | jmt86cam/CSL-sequestration | 9317f724d01c6a676c421585f6c423030408ba05 | f2d57c547b5ffb7e4c84c411e05337f638f7dd77 | refs/heads/main | 2023-04-10T02:15:16.350586 | 2023-02-05T18:13:36 | 2023-02-05T18:13:36 | 597,759,738 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,744 | r | Optogenetics SuHGFP.R | # clear the environment:
rm(list = ls())
# clear the console: ctrl+L
# clear all the plots:
dev.off()
#### Packages for this script ####
install.packages("ggplot2")
install.packages("cowplot")
install.packages("dplyr")
install.packages("stringr")
install.packages("ggsignif")
install.packages("ggpubr")
install.packag... |
097266d6cada64a1d3651b6bc1e4331c4949a4fe | 14a1e75d1ac6e4a92c8677e4421b80a7314cf986 | /03 Visualizations/storing_io_qualitative.R | 6f570e52c42699d10de8c2a0c55fdbe9dc46661f | [] | no_license | syairahusna/DV_RProject3 | 52037da4420b113cafa8dda882f730311af2d01d | 1647439cf3bff30931b1cf9d94399f66719e9309 | refs/heads/master | 2021-01-23T08:11:03.280291 | 2015-03-05T01:32:51 | 2015-03-05T01:32:51 | 31,343,466 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 363 | r | storing_io_qualitative.R | #png("/Users/Syairah/DataVisualization/DV_RProject3/00 Doc/categoricals_io_1.png", width = 25, height = 10, units = "in", res = 72)
#grid.newpage()
pushViewport(viewport(layout = grid.layout(6, 12)))
print(l[[1]], vp = viewport(layout.pos.row = 1:3, layout.pos.col = 1:12))
print(l[[2]], vp = viewport(layout.pos.row... |
ba5e5c93c6e164312fcd05161261cc825ead908f | 0f75c0b71eb84cb36db6f826c4b0a42cab522f4f | /170926 R8000 barcode-1/171006 R8000-1 and -2 hiseq.R | 9eb7fb1cb313ab7434381dc453f8eb57fe694eac | [] | no_license | cliff-b/Raw-R-files | 6360713213ae87bf227e4b4661ca71ed0546644f | 5296f8e6d6b5b97c3175223418da9aafb43e6ae8 | refs/heads/master | 2021-05-02T07:39:30.760921 | 2018-02-08T23:43:34 | 2018-02-08T23:43:34 | 120,834,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,298 | r | 171006 R8000-1 and -2 hiseq.R | ################################################# Standard libraries + viridis to make things pretty + extrafont for comic sans #################################################
library(dplyr)
library(tidyr)
library(extrafont)
library(viridis)
library(cowplot)
################################################# Read in ... |
18ab2705485dd9924bf70ec488746b7676b9eee3 | 21fd652b847972a38051ef89e25854cb0623d8d4 | /libs/is_p_star.r | e7e1b3c200741e9d701dcd5f4b56350e4d66d5b9 | [] | no_license | douglask3/VCF_vs_sites | be73e6b3cd99ed06357b5214f7f23bb64a570fde | 71b358a8f21fd6f338df71b97a1a04efb5463709 | refs/heads/master | 2021-11-26T03:30:03.582583 | 2021-11-12T16:44:05 | 2021-11-12T16:44:05 | 219,031,403 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 192 | r | is_p_star.r | is_p_star <- function(P) {
if (P > 0.1) out = ' '
else if (P > 0.05) out = '.'
else if (P > 0.01) out = '*'
else if (P > 0.001) out = '**'
else out = '***'
out
} |
97615a9b2583c6b28723a0a914c73b487a5673ec | 36813b0527adde7f6e483df15d52b448a66a530b | /neural_tune.R | 56d1cbd4608ccc41be351eaa7ceba6fcfe27b0fb | [] | no_license | shay-lebovitz/301-3_Regression | 18fa68a133e9cba72c87cdbd228c16894740c1c2 | 5617798be94b7f6cdec8f233cb26b2020416e706 | refs/heads/main | 2023-05-13T10:21:59.974448 | 2021-05-28T18:37:40 | 2021-05-28T18:37:40 | 362,631,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 721 | r | neural_tune.R | # Load package(s) ----
library(tidyverse)
library(tidymodels)
# load required objects ----
load('data/setup.rda')
# Define model ----
slnn_model <-
mlp(mode = 'regression', hidden_units = tune('hidden_units'),
penalty = tune('penalty')) %>%
set_engine('nnet', MaxNWts = 1500)
# Parameters
slnn_params <- p... |
fa02cbd1b8d8fcc4d94ce6b47717d7f6c302b9b5 | 3a0f710ff92138256bf7fbc16849462b1244542a | /plot3.R | d7c9461a29c2089d257a4fa86a981e7048bfe991 | [] | no_license | gabrielaolivou/ExData_Plotting1 | e19cf6f7a732beca2175aa00933f379f8f829de4 | 196d8cf6a72cb17ad054c74b8935e4da6a6abfa5 | refs/heads/master | 2020-04-02T05:05:53.494235 | 2018-10-22T02:59:08 | 2018-10-22T02:59:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,186 | r | plot3.R | #Set Working Directory
setwd("C:/Users/Gabriela Olivo/Desktop/Coursera/Exploratory Data Analysis/Week 1/Project Course")
#Read Data
HPC_data <- read.table("./data/household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
# Filter Dates
HPC_data2 <- HPC_data[HPC_data$Date %in% c("1/... |
824c255f1428ea5c6d60935a5c625a1c23567e8a | 6a192ede793c1aa1c63c5dc388d87f704d4c8f02 | /Unit6 Clustering/stocks.R | 3443cc45773bc3de79157fd8d79a0b5664b5e793 | [] | no_license | jpalbino/-AnalyticsEdgeMITx | 454341f8e69cc615ab7b8286ba9bbee9df66026d | 9d6b152f5ac2ca9728e5363face0ba0e50da879c | refs/heads/master | 2021-01-18T08:12:17.814468 | 2019-11-05T21:33:18 | 2019-11-05T21:33:18 | 57,457,758 | 0 | 0 | null | 2016-04-30T19:14:06 | 2016-04-30T19:14:05 | null | UTF-8 | R | false | false | 15,551 | r | stocks.R | #
# The Analytics Edge
# Unit6 Clustering
# PREDICTING STOCK RETURNS WITH CLUSTER-THEN-PREDICT
# Calin Uioreanu
#
#In the second lecture sequence this week, we heard about cluster-then-predict, a methodology in which you first cluster observations and then build cluster-specific prediction models. In the lecture se... |
842c23bfa41725f7e3e762206ca54ca5e01b8136 | 9f57e0ad44b78d809c262fa0ffb659232fdb8d5e | /implementations/histogram.R | 7a62600695efd3170262edad691c704a478bd9b9 | [] | no_license | abhi8893/Intensive-R | c3439c177776f63705546c6666960fbc020c47e8 | e340ad775bf25d5a17435f8ea18300013195e2c7 | refs/heads/master | 2020-09-22T00:57:36.118504 | 2020-08-31T09:23:57 | 2020-08-31T09:23:57 | 224,994,015 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 181 | r | histogram.R | # Implement a histogram function
# Option to provide variable bin width by specifying a list of ranges
# and the corresponding number of bins
# - as a wrapper over original hist
|
26a8bf609cd25a9241e7bccb7e05f74cc44307b1 | 91f3da7a0175133a73489f16784faac1f7b37251 | /R-scripts/08_primary_producer_analysis.R | 57a086739d29b50e6dd5084767d97a8e218379e3 | [
"MIT"
] | permissive | JoeyBernhardt/fatty-acids | 7114457388dce867a636567b85ce36f73a1628f8 | d81cc65fb9bb952f3f953f6a00ff83d1ef4b18a4 | refs/heads/master | 2021-07-25T08:05:03.635526 | 2020-10-11T15:28:43 | 2020-10-11T15:28:43 | 224,154,297 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,344 | r | 08_primary_producer_analysis.R |
#### This script contains code used to answer the question: Do DHA, EPA and ALA concentrations in primary producers vary by ecosystem type?
#### last updated by JB October 8 2020, adapted from code written by Ryan Shipley (any mistakes are JB's fault!)
library(betareg)
library(tidyverse)
library(plotrix)
library(co... |
72412e5656460719ee069b6d402e3151a82ed6b6 | f18c938eca55595667cb039ebdb577de72e1fe3f | /plot4.R | bfa6904a439667115be13d23b9bf6c868600dee1 | [] | no_license | srajiv007/ExData_Plotting1 | 0406d4214451b2048b9f51e7c0ba41f84f6ac0b8 | 435899216f54d082d6612792b6c633fb1dc9ba01 | refs/heads/master | 2021-01-17T22:23:14.833309 | 2015-11-08T13:44:44 | 2015-11-08T13:44:44 | 45,744,005 | 0 | 0 | null | 2015-11-07T16:22:43 | 2015-11-07T16:22:43 | null | UTF-8 | R | false | false | 2,683 | r | plot4.R | # Step 1 : Read the data from file into a data.frame
power_data<-read.csv("data/household_power_consumption.txt", header=TRUE, sep=";")
# Step 2: Define the filter dates
start_dt<-as.Date("2007-02-01", format = "%Y-%m-%d")
end_dt<-as.Date("2007-02-02", format = "%Y-%m-%d")
library(datasets)
# Step 3: Transform power ... |
e99d448c7ce2a3a68ceafa50bcf2311645dc266e | aaaa3f260cb68632dd104c5333478c9617101946 | /R/rpart.r | 89bb10656b955fd66458baa5da7f63456562d011 | [] | no_license | Molmed/emil | 957dd213f1ea1f56f9c346c265db0b1be04373a1 | 570e26f7e5d47aba292f884ff2fc810c4a26fbe7 | refs/heads/master | 2021-01-17T13:42:30.778308 | 2017-10-25T06:07:41 | 2017-10-25T06:07:41 | 19,534,115 | 3 | 0 | null | 2019-12-20T15:17:19 | 2014-05-07T12:45:14 | R | UTF-8 | R | false | false | 1,141 | r | rpart.r | #' Fit a decision tree
#'
#' @param x Data set (features).
#' @param y Response.
#' @param ... Sent to \code{\link{rpart}}.
#' @return A fitted decision tree.
#' @author Christofer \enc{Bäcklin}{Backlin}
#' @export
fit_rpart <- function(x, y, ...){
nice_require("rpart", "is needed to fit decision trees")
model... |
b39f7f62ccbaa4d997620d4d831ee534c7012bb7 | 712c71892a6edd61227e2c0c58bbc1e9b43893e4 | /man/copyProject.Rd | 4b5bdcadf0bd569d2aea512641b3e76069c5be3c | [] | no_license | gelfondjal/adapr | 130a6f665d85cdfae7730196ee57ba0a3aab9c22 | b85114afea2ba5b70201eef955e33ca9ac2f9258 | refs/heads/master | 2021-01-24T10:20:14.982698 | 2020-01-28T22:56:18 | 2020-01-28T22:56:18 | 50,005,270 | 33 | 3 | null | 2018-10-18T16:09:57 | 2016-01-20T04:48:49 | R | UTF-8 | R | false | true | 878 | rd | copyProject.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/copyProject.R
\name{copyProject}
\alias{copyProject}
\title{Copies project to a different directory}
\usage{
copyProject(
project.id = getProject(),
targetDirectory = getProjectPublishPath(project.id),
dataCopy = TRUE,
programsOnly = ... |
039e91c47954e386a5036d9007c4ba8fb5059cc7 | 90c8e94716f2456a8621b62cc0e798add3daf9da | /Data_Analysis_Project_Panel.R | 6834e15bda989a8517a5519fbed25e812e6c8e88 | [] | no_license | alexandernt/Data-Analysis-Final-Project- | a7912174ca67b4426f51db58e48920640b2b88d8 | fcc58019f343662979a0a349fcb39aaf88d6a568 | refs/heads/master | 2023-05-14T00:53:45.474123 | 2021-06-08T20:47:44 | 2021-06-08T20:47:44 | 372,255,498 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,536 | r | Data_Analysis_Project_Panel.R | ##Starting project.
##Installing packages & libraries
install.packages("tidyverse")
install.packages("here")
install.packages("ggplot2")
install.packages("skimr")
install.packages("janitor")
install.packages("readxl")
install.packages("rmarkdown")
library(tidyverse)
library(here)
library(ggplot2)
library(skimr)
librar... |
4d134ad69aa76d44be88d723e24183977e069560 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/wordspace/examples/RG65.Rd.R | bc462ba047a9e179da18a724509af3310816d6a3 | [] | 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 | 233 | r | RG65.Rd.R | library(wordspace)
### Name: RG65
### Title: Similarity Ratings for 65 Noun Pairs (wordspace)
### Aliases: RG65
### Keywords: datasets
### ** Examples
head(RG65, 10) # least similar pairs
tail(RG65, 10) # most similar pairs
|
f55d47241bccc4ff12bf896b2e5ab91f33c7e16f | 8603d007f1b000f2acf61ada7e98a7aa8d9c8080 | /R/Model Effects Plot.R | 7bccb40110b6155573da7ea3e648fa63da794075 | [] | no_license | vsluydts/ggregplot | 942c3b3c5eba8eb31e65870ab4e248e5cd5af228 | f89ddb3ea5fb1a8fbf66aba923d74dddd6f61572 | refs/heads/master | 2020-11-25T21:06:28.980261 | 2019-12-10T03:29:06 | 2019-12-10T03:29:06 | 228,847,877 | 0 | 0 | null | 2019-12-18T13:38:48 | 2019-12-18T13:38:48 | null | UTF-8 | R | false | false | 2,252 | r | Model Effects Plot.R | # EfxPlotComp
Efxplot <- function(ModelList, Sig = TRUE,
ModelNames = NULL,
VarNames = NULL, VarOrder = NULL,
Intercept = TRUE,
tips = 0.2){
require(dplyr); require(ggplot2); require(INLA); require(MCMCglmm)
graphlist<-list()
if(!... |
18957949f46ee095f3c4f68e9510f31c98c25540 | 0e6fe4d2f5a8c84aedc0705581acded74e0c67f8 | /man/solveWt.Rd | 54cc9e0810808aa966689c574224860037b697b7 | [] | no_license | cran/iopsych | 5b90206fe927287549e5b9ddffc60d988a7b8054 | c23bd432035c025f77c5cc8a81d3eeacbe284404 | refs/heads/master | 2016-08-11T15:21:11.371180 | 2016-04-04T13:38:31 | 2016-04-04T13:38:31 | 55,406,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,125 | rd | solveWt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/regression.R
\name{solveWt}
\alias{solveWt}
\title{Find r given arbitrary predictor weights}
\usage{
solveWt(r_mat, y_col, x_col, wt)
}
\arguments{
\item{r_mat}{A correlation matrix.}
\item{y_col}{A vector of columns representing ... |
20002b8909c793faea887d95b3e2187c4d5e85a7 | 0f7405b90fade51b2ce5218869513065f8c6ad34 | /R/ExerciseData.R | 49795770f9225da763271348220c123ae6754567 | [] | no_license | anhnguyendepocen/AdvancedRegression | 4dcaf01554af22c27b51f39094149f6bbdada810 | 5bbd35c8196759dd80bea953a13faa80c8764b99 | refs/heads/master | 2022-03-09T23:29:17.615787 | 2019-10-05T14:08:37 | 2019-10-05T14:08:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 502 | r | ExerciseData.R | #' Exercise Data
#'
#' Data used for the different exercises
#' @rdname ExerciseData
"BetaExerciseData"
#' @rdname ExerciseData
"GammaExerciseData"
#' @rdname ExerciseData
"HierarchicalExerciseData"
#' @rdname ExerciseData
"LogisticExerciseData"
#' @rdname ExerciseData
"LongitudinalNormalExerciseData"
#' @rdname E... |
401274226118fd7f8e8ee65c76a7a072ff01d0db | 673d4c7e8f3510b650ed21014c9235ba790d97dd | /man/likchisq.Rd | 4946f0b925787cca312bedb499342d209b302700 | [] | no_license | BenBarnard/likelihoodExplore | 1ce6f64e314f68834255bc4f13cab6b04b81755d | e16bdc37a8758c232cfc249b217f8b6e14f95768 | refs/heads/master | 2021-04-09T15:42:59.071771 | 2019-11-30T21:27:19 | 2019-11-30T21:27:19 | 60,886,619 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 882 | rd | likchisq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Chi-Squared.R
\name{likchisq}
\alias{likchisq}
\title{Chi-Squared Log Likelihood Function}
\usage{
likchisq(df, ncp = 0, x, log = FALSE)
}
\arguments{
\item{df}{degrees of freedom (non-negative, but can be non-integer).}
\item{ncp}{non-centr... |
655a02038df1e32a5e717a9d71c673766de062b9 | cf6e3baeac2f1a51776589f720b854193d180bdd | /man/perm_distribution.Rd | fecc693eb0e5f60f1f911159bae28f669e99e2d9 | [] | no_license | b-brune/robTests | fd29e83cde06e1ca74b9fa1777635844b805d466 | e5da4923a66a1f291a9a601182564670b66c65eb | refs/heads/master | 2021-11-25T17:10:02.707361 | 2018-12-20T11:06:12 | 2018-12-20T11:06:12 | 162,573,392 | 0 | 0 | null | 2021-04-20T15:23:53 | 2018-12-20T11:56:21 | R | UTF-8 | R | false | true | 1,131 | rd | perm_distribution.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/permDistribution.R
\name{perm_distribution}
\alias{perm_distribution}
\title{Permutation Distribution}
\usage{
perm_distribution(x, y, type, sampled = FALSE, n.rep = NULL)
}
\arguments{
\item{x}{numeric vector of observations.}
\item{y}{nume... |
0e18f80d80ed69041e566514fa19b2c33cf2648b | 8b8cea28523d21ccc1b5227c604f85a621b13dce | /Intuitive-RNA-Seq-Web/about.R | d560879cd3f666763892551a1484155e0590c269 | [] | no_license | ThomasKAtkins/intuitive-RNA-seq | ca5d4f73f3c793e0e4cdf146964b63561fe681ef | 424663df2a46013a36cf2eb78b42662fee6bbc5c | refs/heads/main | 2023-06-11T08:40:55.814057 | 2021-06-26T01:46:50 | 2021-06-26T01:46:50 | 380,347,381 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 209 | r | about.R | about <- tabPanel("About",
h1("About"),
p("Intuitive RNA-Seq is a project developed for the 2021 IQBIO REU hackathon by Ashmika Behere, Stanton Li, Miguel Hernandez, and Thomas Atkins.")
)
|
a5999536d64bbe92a3e3b444ab9f0824df01f2ba | c4115cb5af53941e43684ccc5964f7102013e257 | /R/get_gho.R | 872610abd351227aa6c37875466e33451cfd0766 | [] | no_license | cran/rgho | f816ec332701770edbfbb225c119f75b9864d03b | ccb04061bc0ce8a336acfe1dcf68315d72678d9c | refs/heads/master | 2023-08-29T11:58:23.535882 | 2023-08-09T12:10:02 | 2023-08-09T13:30:34 | 54,256,363 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,791 | r | get_gho.R | #' GET a GHO URL
#'
#' @param url the url to retrieve, given as a character
#' string.
#'
#' @return A `ODataQuery` object.
#' @keywords internal
get_gho <- function(url = getOption("rgho.baseurl")) {
ODataQuery$new(url)
}
#' @rdname man-gho
#' @export
get_gho_dimensions <- function() {
resp <- get_gho()$path("D... |
f74e39e83118929a7b5857166743eee92868f2eb | 77d4c68d919779238599353c2bea4b1056b59f27 | /ExampleRunThrough/3_RunCompass_Example.R | bb7db5907e5c20f4b34821b8fc5521b7cee56206 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | jqliuh/flowHelpers | 8dfaedfbf94c600292aded335edf817d4736700c | c8f3f715fd1bebc1d539ee63ff6e4fb744e840ec | refs/heads/master | 2020-04-06T13:09:36.052480 | 2018-10-02T18:45:20 | 2018-10-02T18:45:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,655 | r | 3_RunCompass_Example.R | #!/usr/bin/env Rscript
library(flowHelpers)
library(flowWorkspace)
library(here)
gsPath <- "/home/malisa/uw/COMPASS/20170331_TB-ICS_ACS/GatingSetList4COMPASS"
seed <- 1
mapMarkers <- list("CD154", "IFNg", "IL4", "TNFa", "IL22", "IL17a", "IL2")
individuals <- "PATIENT ID"
uniqueruns <- "Peptide"
# Run COMPASS for CD8+... |
1173fcd8717136af1ba73b3e6b4d17ac664bc071 | dcdce563dc89b47cea2bcfee8ee608c291fdc2e9 | /R/plot.msc.R | 23cf7306263cfaa5386791a61e38a197230c4ece | [] | no_license | cran/CombMSC | cb4412f580013286fb28a69303cc8077be926476 | b17252d5f1924a5a7122fb20c6b9e307b41c80cc | refs/heads/master | 2021-01-22T05:15:50.935459 | 2019-01-04T17:23:10 | 2019-01-04T17:23:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,405 | r | plot.msc.R | `plot.msc` <-
function(x, sumfun.Index = "def", fancy = FALSE, grayscale = FALSE, draw.Grid = TRUE, ...){
# 3-D plotting function. Used in plot.msc only when the "fancy" option is TRUE and
# the number of MSCs is 3, which allows this particular type of display
showResults <- function(fram, resp, maintitle = ... |
cad672d98cae33c5ec82c1764885f243b7e6e238 | 89d219d3dfa744543c6ba1c1b3a99e4dcabb1442 | /man/monthNumber.Rd | 19da299fb02dfe9aa15565e0113e780baf618afc | [] | no_license | pteetor/tutils | e2eb5d2fba238cbfe37bf3c16b90df9fa76004bb | fe9b936d8981f5cb9b275850908ef08adeffef4e | refs/heads/master | 2022-06-17T11:34:30.590173 | 2022-06-14T02:02:15 | 2022-06-14T02:02:15 | 77,761,145 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 892 | rd | monthNumber.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/monthFunctions.R
\name{monthNumber}
\alias{monthNumber}
\alias{monthNumber.default}
\alias{monthNumber.integer}
\alias{monthNumber.numeric}
\alias{monthNumber.character}
\alias{monthNumber.Date}
\alias{monthNumber.yearmon}
\title{Convert mont... |
9e26cb43fd3bd3aebd991f2870ccdbebe09a2bea | 0edd7703d14f0e27dad2fdc10d4d6f67ffe9f8e5 | /man/infmat_bes.Rd | ac2e385cb0edaa300ce1f71be8b3092c19a9ffc4 | [] | no_license | cran/bbreg | e9fb514f07831a0f86c061e8d15c3c0b1b9e0ff6 | cb22d2d4a7c66545152fc6019035ee563b95a280 | refs/heads/master | 2022-02-19T21:23:03.434884 | 2022-02-14T07:00:05 | 2022-02-14T07:00:05 | 249,200,976 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,335 | rd | infmat_bes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EM_Bes.R
\name{infmat_bes}
\alias{infmat_bes}
\title{infmat_bes}
\usage{
infmat_bes(theta, z, x, v, link.mean, link.precision, information = FALSE)
}
\arguments{
\item{theta}{vector of parameters (all coefficients: kappa and lambda).}
\item{... |
fb1e022ef6c41856de545548a5778c1622a9212b | 7d277b1c06f5d15d191b52736a4ad5f47473a07e | /appSourceEmail.R | 4219d022b190880e438be1ad249ca9def932f080 | [
"MIT"
] | permissive | OwnYourData/app-relationship | a9092374014f39ef28e779aab0e2d17095550007 | 5c65cb622281dc4ea42aec5512d1fb73439072c4 | refs/heads/master | 2021-06-22T00:51:52.918742 | 2017-06-04T20:56:18 | 2017-06-04T20:56:18 | 58,932,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,287 | r | appSourceEmail.R | # ui for setting up emails
# last update: 2017-03-06
appSourceEmail <- function(){
tabPanel('Email', br(),
fluidRow(
column(2,
img(src='email.png',width='100px', style='margin: 35px;')),
column(10,
... |
d6dd0f2d6110812ac752ce4c4670dbf77f38bb7b | 8156558b6b34ea0966959f59ed4df3cefbe66775 | /S2_AnomalySaving.R | 8bb764f26b7f915c4b8cb9ade326d6e6a6ca57a7 | [] | no_license | earthlybimo/SpatialDampedAnomalyPersistence-SDAP | 400b5ad888fedcf7b96bec1152f56a7af5fe2783 | 8d65986560453b815daab7cdb311f8e68728f9e2 | refs/heads/main | 2023-04-11T01:47:24.821108 | 2021-10-07T13:59:16 | 2021-10-07T13:59:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,902 | r | S2_AnomalySaving.R | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
### This script uses the Climatological SIP and Initial condition for each date to find the SIP anomalies. It is designed to be run as part of a batch process, with system args giving 1. Hemisphere, 2. Month no. and 3. Year. If only 2 syst args are given, all... |
82a811a9232d1cf3b9f375b34bb9424e19f485df | ceec3182ad1b8cf4007c90109f2948f0704bc3a8 | /HW2/BLB/BLB_lin_reg_job.R | b6938e7c967814ea21b2ed14ecc2b1d9d338a48e | [
"MIT"
] | permissive | jmwerner/STA-250 | a42550ec5e8a5a1a36394525791ad75f3d019e9a | 203b442f809117d4ed0dfaeb50ed0c2ad2d08a33 | refs/heads/master | 2020-12-24T11:25:46.878804 | 2013-12-12T11:18:55 | 2013-12-12T11:18:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,646 | r | BLB_lin_reg_job.R | ####################################
# Author: Jeremy Werner #
# Assignment: STA 205 HW2 #
# Due date: November 13, 2013 #
####################################
rm(list = ls()) #Starting with a clean workspace (for safety)
mini = FALSE
# Load packages:
library(BH)
library(bigmemory.sri)
libra... |
9c732c9c6a853d6a404b471d39d386286edc48ec | 9706133df19d93395a748068a07ea071a03eeeaa | /projects/roland_garros_project/modelling/player_varying_intercept.R | 129a32bb286d6355540f1b095019766d0efd86e4 | [] | no_license | petertea96/tennis_analytics | 7fa2d924f6a6db8515c095dd9e34d01748c599db | e267a4a463d4e0b3522117b370289a6ae9b3cb27 | refs/heads/master | 2023-07-31T21:54:59.511368 | 2021-09-22T03:01:03 | 2021-09-22T03:01:03 | 291,520,867 | 3 | 2 | null | 2020-10-22T06:04:20 | 2020-08-30T17:42:36 | HTML | UTF-8 | R | false | false | 29,009 | r | player_varying_intercept.R | ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
### == PLOT MODEL 2 RESULTS (JUST PLAYER-VARYING INTERCEPTS) =====
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
# -- Plot Player Intercepts (Wide vs T) for Model 2 (Not Model 4)
# -- Plot tranformed intercept from lo... |
6783cf113944c84ab478eaf6cdef776df31beea3 | 2883f55324a139a3398635e1ee70e47b440040f6 | /R/turbidity.R | 22038c436f457d5b2da4d7e97f12c97338ee611e | [
"CC0-1.0",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer"
] | permissive | jsta/lakeattributes | 52f57ae2344ce25fc4eed412ae2c4cc09baa55f5 | 8b33c881401ec6e369f2b919dbca694875f641b4 | refs/heads/master | 2021-07-09T05:49:01.942800 | 2019-02-03T14:53:19 | 2019-02-03T14:53:19 | 97,721,674 | 0 | 0 | null | 2017-07-19T13:50:22 | 2017-07-19T13:50:22 | null | UTF-8 | R | false | false | 603 | r | turbidity.R | #'@title Turbidity data table
#'
#'
#'@name turbidity
#'
#'@return turbidity data.frame with all available observed turbidity data.
#'
#'\tabular{lll}{
#'Name \tab Type \tab Description\cr
#'site_id \tab character \tab Unique identifier of site\cr
#'year \tab numeric \tab Year of observation\cr
#'date \tab character \t... |
feab20c1d391cfbc2e8752c2d21aa4fb7176f570 | f02303dd25516e4a35460cf8cff5e221e036f308 | /lta.R | 221838f9bdc7cfc756c31af6546deb79d2bd628f | [
"MIT"
] | permissive | IMS-Bio2Core-Facility/BIC016a-Samuel-Furse | 4848e45334a50a05c8a1f8460e154649aef8c3ba | 059575b039623a0cf62326b3cf4b9af4b07e5cd4 | refs/heads/main | 2023-04-11T18:46:52.074863 | 2021-09-10T09:04:32 | 2021-09-10T09:04:32 | 405,012,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,474 | r | lta.R | rm(list = ls())
options(warn=-1)
#### PRELIMINARIES #############################################################################################
#*Uploads the needed libraries --------------------------------------------------------------------------------
require(ggplot2)
require(data.table)
require(... |
1abcbd5acbccbcf403e1afc7ba68d7fa8f00aa0a | aee8226b9bc42b68a547b7748e3e59acc698ce9a | /heatmap plot_world.R | ef0f5b65404c081e1c6f6071a352978aa88e2b21 | [
"CC0-1.0"
] | permissive | LaiShengjie/Holiday | 3f1d4c218a33ae501deea31216be3c5f21a82508 | b5b5b2595c9159736abdf155d4e6b9cc7bd73ae0 | refs/heads/main | 2023-08-14T01:06:43.868363 | 2021-09-19T13:53:33 | 2021-09-19T13:53:33 | 316,921,040 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,045 | r | heatmap plot_world.R | ### Heatmap ####
# Image scale
image.scale <- function(z, zlim, col = col,
breaks, horiz=TRUE, ylim=NULL, xlim=NULL, ...){
if(!missing(breaks)){
if(length(breaks) != (length(col)+1)){stop("must have one more break than colour")}
}
if(missing(breaks) & !missing(zlim)){
break... |
4cc403a5472d857d29033e327c1d575612c76d03 | 9c20dfe75c41283db15d80bef6ff60fbf4cb5255 | /Kaplan meier plots in R.R | ef2c096bacd79fa021b68bbff680d121ca3114dc | [] | no_license | kristyrobledo/CodingConundrums | c9eadaa313afa5e04e47ecf5e016936b9bee328b | 7713b19a28774c75b9ae0e48b8ff7d648a40d278 | refs/heads/master | 2023-08-04T11:30:56.604221 | 2023-07-27T03:02:00 | 2023-07-27T03:02:00 | 237,126,082 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,142 | r | Kaplan meier plots in R.R |
#read in packages
if(!require(tidyverse)){
install.packages("tidyverse")
library(tidyverse)
}
if(!require(survival)){
install.packages("survival")
library(survival)
}
if(!require(survminer)){
install.packages("survminer")
library(survminer)
}
if(!require(glue)){
install.packages("glue")
library(glue... |
4b7964ef4b19bdc083330e9a8969133563ed1af2 | 69ebb8938df16e190c6758f49ee1575cee79ec76 | /pratical machine leanring/quiz4.R | 75bb02fb19e8807136cdc01708b7e8d0760d5dec | [] | no_license | bikash/Coursera-Practical-Machine-Learning | 360ba206e8785ab10a41300f116225ca003b18f8 | cc2bbe3a980ad706ad2fb097c6f4662a7efc62bd | refs/heads/master | 2020-05-18T20:55:38.613912 | 2016-01-05T00:13:37 | 2016-01-05T00:13:37 | 33,836,906 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,307 | r | quiz4.R | ## Quiz 4
# Question 1
# Load the vowel.train and vowel.test data sets:
# library(ElemStatLearn)
# data(vowel.train)
# data(vowel.test)
# Set the variable y to be a factor variable in both the training and test set. Then set the seed to 33833.
# Fit (1) a random forest predictor relating the factor variable y to t... |
42adf673b738662bf389adb266d345fbb8a68482 | 2551bc0184e234dcc4f5b47723cc3f9039add0d2 | /plot3.R | c310751c415d5fb7f1c24838bbb0f897da2b9a43 | [] | no_license | ncd10/ExData_Plotting1 | 0504de2f844367239609a6c3c06abe6986fb43dd | dddced0bcc3065bdaafab2f03110b9f44764e08b | refs/heads/master | 2020-12-27T09:32:34.933615 | 2015-04-07T19:24:39 | 2015-04-07T19:24:39 | 33,508,995 | 0 | 0 | null | 2015-04-06T22:23:16 | 2015-04-06T22:23:16 | null | UTF-8 | R | false | false | 1,146 | r | plot3.R | ##plot 3
#reading in data
household_power_consumption <- read.csv(
"C:/Users/user/Desktop/exdata-data-household_power_consumption/household_power_consumption.txt",
sep=";", na.strings="?")
#keeping only the specified dates
data<-household_power_consumption[household_power_consumption$Date=="1/2/2007"|
... |
0f2fc15cc7f13f7b1ca7ac2f5c507eba50e493b6 | 38023bae01994887a7c9280943dbce5eaa993230 | /man/citippy_path.Rd | 20d33bccc197c2cbe34398a1284f9d8cbf962267 | [
"MIT"
] | permissive | awconway/citippy | 9730c3fc15fcb4a9b8183b36c15f2a53d5710897 | 64824ac6ae9e1c7b8b2b47f5aa79f3933ae17c63 | refs/heads/master | 2020-12-11T14:50:55.190052 | 2020-02-19T17:44:17 | 2020-02-19T17:44:17 | 233,876,620 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 405 | rd | citippy_path.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/citippy_path.R
\name{citippy_path}
\alias{citippy_path}
\title{add citation to rmarkdown document that will show as tooltip on hover}
\usage{
citippy_path(ref_path)
}
\arguments{
\item{ref_path}{Path to your .bib file with bibtex entries}
}
\... |
5639a8461ac17824bebacebbb60b44fe9b54806d | 12d311d9865b976a471d45d795e4ccac94013659 | /Scripts/Load_Data/covidPCR_load.R | b3ae542b22daca39ab848b8709e1e9700dc813e2 | [] | no_license | pmbusch/Analisis-COVID-MP2.5 | cfe4bec4bc22f5aed2ba412dd38585c7e474c1e6 | edc3d3a7c3a2e3751d38cc984fd96a3b5e661df4 | refs/heads/master | 2023-01-07T21:21:16.080455 | 2020-11-11T13:21:47 | 2020-11-11T13:21:47 | 281,760,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 883 | r | covidPCR_load.R | ### Analisis-COVID-MP2.5
## Datos Covid. Fuente: https://github.com/MinCiencia/Datos-COVID19
## Producto 7: Exámenes PCR por región
## PBH Julio 2020
## Descarga de datos ---------
# Url para descarga directa de datos desde el Github del Ministerio de Ciencia
url <- "https://raw.githubusercontent.com/MinCiencia/Datos-... |
89b84958bf92e8be411a9e37f9c58fca49d74c1f | ff8272e4d55dcada2597638fcae9d56ff80b4fc7 | /Rcode_SeqTrack_09Feb.R | f306aec9acded38cfa81543a7b9ccc32c5ddf7da | [] | no_license | AdrianAllen1977/R-code | 537c08dd29d29514661c7e3b35679bef46e96c54 | 1a1cd56007bfca2cae36a05537402b13b0df94b0 | refs/heads/master | 2023-07-20T08:25:56.300787 | 2023-07-17T18:20:42 | 2023-07-17T18:20:42 | 172,774,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,802 | r | Rcode_SeqTrack_09Feb.R | ## Reconstruction of transmission trees in an endemic phase of
## Mycobacterium bovis infection in cattle and badgers in NI
## 1.) PREPARATIONS
## 1.1) load packages
library(ape)
library(adegenet)
library(igraph)
library(tcltk)
## 1.2) Set your working directory on your computer
## Import note: Check t... |
0e70c2af60b2cadac100ef00e13f003d1ca09c5c | 0ac77bbd8f8a8b1dd37f0369a540234aaddbb5e9 | /08_ShinyLab/RestrictionOfRange/server.R | 54e4bd1a2f8c538ed08a87d3cefe831865bd41ee | [] | no_license | civilstat/36721-F15 | dd8199dd643d41cffd313402ca3264a802e9190d | cef0e1dfba17d82a6a1e4dd7143f2c1f2a629894 | refs/heads/master | 2021-01-10T13:38:43.183702 | 2015-10-05T22:10:12 | 2015-10-05T22:10:12 | 43,716,109 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,317 | r | server.R | # Restriction of Range exercise
# server.R
#
# 08 Shiny Lab Session
# Jerzy Wieczorek
# 9/24/15
# 36-721 Statistical Graphics and Visualization
library(shiny)
library(ggplot2)
# Define server logic
shinyServer(function(input, output) {
# Create a random dataset for plotting (once per Shiny session),
# where Y... |
14ec9100395aa35cc6a098d0a5d47584c35dac66 | dc248480f6cfe92b936babf693386e91261de933 | /man/sass_cache_options.Rd | 54765a0ee09d057f4ccb77bc288abe277ce37d44 | [
"MIT"
] | permissive | rstudio/sass | 943c07acee0696b19d63ec562bd901a722ed818e | 4f42680340264e1655745a99cbd7e0c0481b6a01 | refs/heads/main | 2023-08-12T12:56:39.941402 | 2023-07-26T21:23:52 | 2023-07-26T21:23:52 | 144,749,268 | 81 | 22 | NOASSERTION | 2023-07-26T21:22:12 | 2018-08-14T17:02:58 | C++ | UTF-8 | R | false | true | 441 | rd | sass_cache_options.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sass_cache.R
\name{sass_cache_options}
\alias{sass_cache_options}
\title{Caching Options for Sass (defunct)}
\usage{
sass_cache_options(cache, cache_dir)
}
\arguments{
\item{cache}{No longer used.}
\item{cache_dir}{No longer used.}
}
\descri... |
32ddb0b64ad2dd1663c523c683da6905e1066e5d | 4ba57c79ec94010ade2836a669a7fd7864d5a85c | /02.R | 7b7e570020ef7f96dc7b86eccda90bad5a9148ed | [] | no_license | yansonz/R-Practice-1 | b55211317399a04433881a4eb49b3a284ea3cd74 | 832ebd1d07a73f522b795f57048ba49c32bf23a0 | refs/heads/master | 2021-01-17T07:41:43.131387 | 2016-09-28T02:31:23 | 2016-09-28T02:31:23 | 67,960,773 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 866 | r | 02.R | n = 5
x = seq(-1, 1, by = 0.2)
total = 0
for(k in 0:floor(n/2)) {
total = total + (-1)^k * factorial(2*n - 2*k) / ( factorial(k) * factorial(n-k) * factorial(n-2*k) ) * x^(n-2*k)
}
1/2^n * total
pn1 = function(n) {
x = seq(-1, 1, by = 0.2)
total = 0
result = numeric(length(x))
for(i in 1:length(x)) {
fo... |
3b475fbfdde65cba3afee6048416f94ce7a67d56 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/quantreg/examples/ranks.Rd.R | 9abe9ecbfe92890aecb35e1a6af9112a0f6f5040 | [] | 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 | 194 | r | ranks.Rd.R | library(quantreg)
### Name: ranks
### Title: Quantile Regression Ranks
### Aliases: ranks
### Keywords: regression
### ** Examples
data(stackloss)
ranks(rq(stack.loss ~ stack.x, tau=-1))
|
6bf681616458bd6c10bc672876e1db54ef4fc681 | 075e88689548f684bcb1d1fb38955e91f9ea587a | /tmp.R | f7cd20133d2234c1f1d6312f90756696437d3b5f | [] | no_license | jdhoffa/ds-incubator | e9a09332162cc477f63fd98d0230dafa288776ea | 064da003097ecb64c6f0a0bd2801104257f0e93e | refs/heads/master | 2021-07-11T22:09:16.826301 | 2020-10-17T23:27:22 | 2020-10-17T23:27:22 | 215,499,492 | 0 | 0 | null | 2019-10-16T08:41:55 | 2019-10-16T08:41:55 | null | UTF-8 | R | false | false | 33 | r | tmp.R | library(random)
library(RandVar)
|
8c9f4fdd706234409a6aab0fed681f8682510097 | a09b5d25ec0ef9c2db757b7c4333c485da222d7e | /R/CNVScopeserver.R | 2555091c486fde9935d050031e658fcb4e680e0e | [] | no_license | cran/CNVScope | 438a4cc4facbc1b892dd228844733603a929085d | fe9eed310d36d96f6cd1ebcc14d9bec82b5a58ca | refs/heads/master | 2022-05-07T22:30:03.560897 | 2022-03-30T22:40:08 | 2022-03-30T22:40:08 | 153,879,861 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 87,622 | r | CNVScopeserver.R | #' Server component of the CNVScope plotly shiny application.
#'
#' Server function of the CNVScope shiny application. run with runCNVScopeShiny
#' @name CNVScopeserver
#' @keywords CNV heatmap shiny plotly
#' @import ggplot2 magrittr
#' @rawNamespace import(shiny, except = c(runExample,renderDataTable))
#' @r... |
dd48ba8df0ec1f0219fbfffb076323a4134bded6 | 9d3a4709b30707fd58a6e179c2719a9c0d117ccd | /machine-learning-ex5/R Version/ex5.R | 59a010ab45adddcc07002c5129530169715da88e | [] | no_license | lucariel/ML_coursera | 0d02b150de5b09af3e4ccc4be2e44637e4850562 | 8b4c57814fcc6b0939b1eea25bd0ea29c7ca4cb1 | refs/heads/master | 2020-04-21T14:40:08.104809 | 2019-03-22T13:26:44 | 2019-03-22T13:26:44 | 169,642,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,471 | r | ex5.R | #Ex 5 ML Coursera
library(tidyverse)
library(R.matlab)
source('linearReg.R')
source("lbfgsb3_.R")
#=========== Part 1: Loading and Visualizing Data =============
data<-readMat("ex5data1.mat")
##Training data
X<-data$X
y<-data$y
##Cross Validation data
Xval<-data$Xval
yval<-data$yval
##Test Data
Xtest<-data$Xtest
ytes... |
22ecb47edb968e3b678bb4a0e7a6fc20b56cb70f | 67bfba6e24fe9156a352fb4b5b8d184269001783 | /man/exampleGTV.Rd | 2fd7c8c65152779b141f6cfd3a9946c84bda94e0 | [] | no_license | hsong1/GTV | 0bd8dcc4965f6466ae0ebe0f6185d3194b59ab71 | e215097ab1a77af2a0429b85e2a0274dcb866e06 | refs/heads/master | 2020-04-21T14:22:57.598506 | 2019-11-03T21:34:35 | 2019-11-03T21:34:35 | 169,633,065 | 2 | 2 | null | null | null | null | UTF-8 | R | false | true | 406 | rd | exampleGTV.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exampleGTV.R
\docType{data}
\name{exampleGTV}
\alias{exampleGTV}
\title{Create Example Data Set}
\format{An object of class \code{list} of length 3.}
\usage{
exampleGTV
}
\description{
a block complete graph G that has K connected components ... |
8da6f05c4e6cb52f52f4af7c4afa255270b1a77a | 9d814a56ab7711c90907625f37c0597466795d15 | /global.R | 5c603508a6f930eb81de018c265de2b0a2838924 | [] | no_license | jrbautista5/Codebook | 30d86f25dcb777fdef0cc67d0045198f209ea6e4 | a72c8ed22f463d3459ebfe3c835d21e0e24e637f | refs/heads/master | 2020-04-22T18:04:32.367724 | 2019-02-13T19:19:59 | 2019-02-13T19:19:59 | 170,564,289 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 35 | r | global.R | LA <- readRDS("clean_payroll.rds")
|
ce5b8513a923b9ebbba426a07157f6028aecb94c | 41c984f669791f0819ef544b58036a3ec70c95d7 | /mtcars/server.R | 6b3713bbd818e216eceaf29c5eae8a4fbded821a | [] | no_license | prasad81/shiny-application-and-reproducible-pitch | d9eb3a721231c7ee4f5973e1ad9011a9f1d9dc25 | 6a492aac36a4b5fc9dda4b2380de17f2cb0df06f | refs/heads/master | 2021-01-23T04:59:29.663229 | 2017-03-27T05:20:35 | 2017-03-27T05:20:35 | 86,266,253 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,123 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
data <- mtcars
#names(data) <- c("mpg", "cyl", "disp", "hp", "drat", "wt", "qsec", "vs... |
1ad955fecb35136cf8c687e486332b1cc7006073 | 5f0cfcec5194f11137db76056ef2b3836ab80ff8 | /man/solnfreq.Rd | 2560c1d191d1f5eeea031e741aeb79e30ef868f0 | [] | no_license | JakeJing/treevo | 54d341655f1e6ddac5ab73df38c890be557e7d17 | 3429ba37e8dc7c79cf441361d07c000f07423b6e | refs/heads/master | 2021-01-12T01:20:10.296046 | 2016-10-03T01:09:15 | 2016-10-03T01:09:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 494 | rd | solnfreq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summaryStatsLong.R
\name{solnfreq}
\alias{solnfreq}
\title{Calculate frequency of finding best solution in Geiger}
\usage{
solnfreq(x, tol = .Machine$double.eps^0.5)
}
\arguments{
\item{x}{A returned object from fitContinuous()}
\item{tol}{T... |
06dc2557debcfe035b885ac430d9de58a0741fe4 | 3f0829cfe5994fac1aaa2f7542d9ccae71a6e733 | /whisky.R | dab3c853a904118c6cd84434495ec128332a452f | [] | no_license | Megha2535/Whiskey_Analysis | 48127a4dfd9fb5beca8360bd0cc44dcf471f774a | d29c410505d91c4569268f79ed47873536199085 | refs/heads/master | 2022-12-16T13:43:04.926433 | 2020-09-10T19:10:23 | 2020-09-10T19:10:23 | 294,498,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 352 | r | whisky.R | install.packages("data.table")
library(data.table)
install.packages("tidyverse")
library(tidyverse)
install.packages(plotly)
library(plotly)
library(tm)
library(wordcloud)
library(wordcloud2)
library(ggExtra)
library(DT)
library(cluster)
library(gridExtra)
library(grid)
#------------------------------------------------... |
71960acb63dc9b4549d500f63c1d50d91923eadd | 47eb9c0ee62df42354db9a74510c1c948cac441c | /plot1.R | 6c53d98bee1fa3500089181fccd863084645051b | [] | no_license | LMoller/ExData_Plotting1 | e5949bfcc195b3e8f6e8b337c7f0550d9be012df | 9f76075366c4501d98016d3f11da7bbac169adc1 | refs/heads/master | 2021-01-23T04:29:22.173189 | 2017-03-27T03:48:20 | 2017-03-27T03:48:20 | 86,201,957 | 0 | 0 | null | 2017-03-26T02:25:14 | 2017-03-26T02:25:13 | null | UTF-8 | R | false | false | 645 | r | plot1.R | ##Download file from link and unzip the file
temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",temp)
powerfile <- unzip(temp)
unlink(temp)
##Read the file
powerconsumption <- read.table(powerfile, header=T, sep=";")
subsetpowerconsump <- po... |
d994fe131d8a1d857627a3b12f34e06e81088f68 | e3ebbba983200dfd89209e107ee19ec201abc5ee | /cachematrix.R | 06ace03153b977da2573dcdda31841566b041e99 | [] | no_license | sappy17/ProgrammingAssignment2 | 850519feb871dddc94a1d83d73246e11c2e7a198 | 5ce31ea2a34a2d91703ba8d83fbf32ad2aa8752c | refs/heads/master | 2021-01-18T20:49:50.760008 | 2017-04-06T15:24:06 | 2017-04-06T15:24:06 | 86,992,557 | 0 | 0 | null | 2017-04-02T14:43:26 | 2017-04-02T14:43:26 | null | UTF-8 | R | false | false | 899 | r | cachematrix.R | ## For calculation of the inverse of a matrix.
## The function is suppossed to calculate the inverse of a matrix. We input the matrix into the function to get back the inverse of it.
## Here I am using solve function to calculate the inverse. Then for testing my code I have selected a matrix and tried to find the i... |
db8de8d66dcdc9326055ccf0ba05fe9963ac6f75 | fe1887fbc9bc4ed192c24109ad05f1575935dc1c | /Chapter_4/tables_4.4_B1_cumulative_gas_emissions_field.R | 0c3e9b7aee688d7243412cc11e9897bd4e42cf12 | [] | no_license | marenwestermann/PhD-thesis | 6efb3cb9b68b56d380920dc27daa0de77ddadefc | 3942cc2099eb5edaccbef08f8866dc33da28bf9a | refs/heads/master | 2020-03-20T20:43:06.078371 | 2018-06-18T07:52:08 | 2018-06-18T07:52:08 | 137,702,409 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,127 | r | tables_4.4_B1_cumulative_gas_emissions_field.R | total_gases <- read.csv("C:/Users/Maren/Dropbox/UQ PhD/PhD work/experiments/(3) field experiment/stats/gases total/field_exp_gases_total.csv")
##N2O
anova_N2O <- aov(total_gases$N2O_ln ~ total_gases$treatment)
summary(anova_N2O)
pairwise.t.test(total_gases$N2O_ln, total_gases$treatment, p.adjust = "none")
describeBy(... |
c3876f1fd0bf16de47b16f312f41717e77b3f71e | 80bde6ac4965b70b26ce0f3204b70f33f3bb5890 | /man/plotScores.ldecomp.Rd | fc3303669cca1d7a9bdef5295b3df0f3f889a4d2 | [] | no_license | KasperSkytte/mdatools | 5c8abccbb2b735a5b058327714d9d354183eca9c | 4147ee5ca5fc537ae91eee5cb2c42aed4e6b3e54 | refs/heads/master | 2020-07-04T13:06:41.067552 | 2019-06-19T08:44:56 | 2019-06-19T08:44:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,024 | rd | plotScores.ldecomp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ldecomp.R
\name{plotScores.ldecomp}
\alias{plotScores.ldecomp}
\title{Scores plot for linear decomposition}
\usage{
\method{plotScores}{ldecomp}(obj, comp = c(1, 2), main = "Scores",
type = "p", xlab = NULL, ylab = NULL, show.labels = FALSE... |
cebc6f605dabdc55426f2d56e848383ec8349bb5 | 71da24fe85112d599e2af8214fdff1017c6348fc | /lab2/lab2_template/R/demo.R | 3f857aacfefe4c00d12e4dab62f797f52fd43baf | [] | no_license | jpdunc23/stat-215a-fall-2020 | df6343c379f9744c6130da5fcc9ed2be1d4befac | 135b38d610e60e60240c195122fc0d135f9967f7 | refs/heads/master | 2023-01-29T18:54:41.918578 | 2020-12-12T02:25:37 | 2020-12-12T02:25:37 | 290,987,481 | 13 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,022 | r | demo.R | library(maps)
library(ggplot2)
library(dplyr)
# load the data
ling_data <- read.table('data/lingData.txt', header = T)
ling_location <- read.table('data/lingLocation.txt', header = T)
# question_data contains three objects: quest.mat, quest.use, all.ans
load("data/question_data.RData")
# ling_data has a column for ea... |
93aa330541845e1e608a6e0f12d9f068f9142786 | e189d2945876e7b372d3081f4c3b4195cf443982 | /man/PrecisionMulti.Rd | 0285b4f84e60753d01132fdd94e420e1b5c7973d | [
"Apache-2.0"
] | permissive | Cdk29/fastai | 1f7a50662ed6204846975395927fce750ff65198 | 974677ad9d63fd4fa642a62583a5ae8b1610947b | refs/heads/master | 2023-04-14T09:00:08.682659 | 2021-04-30T12:18:58 | 2021-04-30T12:18:58 | 324,944,638 | 0 | 1 | Apache-2.0 | 2021-04-21T08:59:47 | 2020-12-28T07:38:23 | null | UTF-8 | R | false | true | 551 | rd | PrecisionMulti.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metric.R
\name{PrecisionMulti}
\alias{PrecisionMulti}
\title{PrecisionMulti}
\usage{
PrecisionMulti(
thresh = 0.5,
sigmoid = TRUE,
labels = NULL,
pos_label = 1,
average = "macro",
sample_weight = NULL
)
}
\arguments{
\item{thresh}... |
762c32673fca262de4cfb9e6e4a47ee16e1d528c | 34699f008ef631f3f87ca7e810d1c9e8b791078d | /NFL_old/projectionsCreation/mixtureModels/mixtureProjections.R | e4030e54c5005e8907305e1ca5032da074a35a6d | [] | no_license | OliverCGreenwald/dfs | ee0943d16ed60a7e661bedfc889776dab7c04c69 | ded1a05dcf9febcd30cb82aea19a1241f28c7fef | refs/heads/master | 2021-07-07T02:14:52.966084 | 2017-10-05T03:10:09 | 2017-10-05T03:10:09 | 107,742,009 | 1 | 0 | null | 2017-10-21T01:31:09 | 2017-10-21T01:31:08 | null | UTF-8 | R | false | false | 5,945 | r | mixtureProjections.R | #setwd("~/Projects/DFS/")
#setwd("~/Documents/PrincetonFall16/fantasyfootball/DFS/")
####### DESCRIPTION #######
# In this file we cluster n player projections into k tiers. Offense only. Sunday only.
# The larger the TierRank, the better the player. Set kCutoffs higher if want more players
# to be ranked. Unranked p... |
5abe9f831b708057380480b60a791165534b4d4c | 29df0ca13daf4bf82bd806fed640f6775e48326c | /man/nuggetvalue.Rd | c900f140a6dd272e0699ad6bae75d69c822e1a0f | [] | no_license | DiceKrigingClub/DiceKriging | 7da1cba69f192516d3e6a98967268e372c9a3028 | b7fa4542c36ed7c9f2da760f62ee49c6dbb92e18 | refs/heads/master | 2021-11-23T01:49:25.693561 | 2021-10-29T13:48:55 | 2021-10-29T13:48:55 | 130,677,010 | 7 | 2 | null | 2018-09-20T14:02:39 | 2018-04-23T10:02:05 | R | UTF-8 | R | false | false | 379 | rd | nuggetvalue.Rd | \name{nuggetvalue}
\alias{nuggetvalue}
\alias{nuggetvalue<-}
\title{Get or set the nugget value}
\description{Get or set the nugget value.}
\usage{
nuggetvalue(x)
nuggetvalue(x) <- value
}
\arguments{
\item{x}{ an object containing the covariance structure.}
\item{value}{ an optional variance value standin... |
d0f10ea88ae146b5a2f69cab11e89e02b2908087 | f57bdf4e211ad7984fb5005883ae4d116c8f2e92 | /R/pedParts.R | 48dfad47f04dbaad8d8d1592b4116d7515051fdb | [] | no_license | cran/paramlink | 70d2e3776063411628bd5b5663a408737886d040 | 8af1f6544e37d20717829b5fbf7286fd303c68cc | refs/heads/master | 2022-05-22T10:39:49.492616 | 2022-04-15T07:10:02 | 2022-04-15T07:10:02 | 17,698,214 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,972 | r | pedParts.R | #' Pedigree subsets
#'
#' Utility functions for 'linkdat' objects, mainly for extracting various
#' pedigree information.
#'
#' @param x a \code{\link{linkdat}} object. In \code{related.pairs} possibly a
#' list of \code{linkdat} objects.
#' @param id a numerical ID label.
#' @param original.id a logical indi... |
0d11e35d4abc2808a72e2508a210b2d0f906d208 | 1b690676d146556cbdc986b72cc6f1ff22c0d781 | /Fixed.Mod.R | 410619819ae49f18226391c374fff251c90c183f | [
"MIT"
] | permissive | benniu720/Proteomics-Toolkit | d39ab86064cfb6f94dea4038040074ee9caa5e61 | 6a2802b243b76e3b0e3a5170d084f88f16cfd09a | refs/heads/master | 2021-01-23T12:52:55.820128 | 2017-01-27T04:40:07 | 2017-01-27T04:40:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,865 | r | Fixed.Mod.R | Fixed.Mod<-function(peptides, ptm1, res1, ptm2, res2, ptm3, res3, ptmfml, resfml){ ## the ptm1, res1, ..., ptm3, res3, correspond to those on the sidebar. And the ptmfml and resfml correspond to the user-input PTM in the sidebar.
source("Res.to.Num.R")
source("form.R")
PTM<-data.frame("C"=c(0,0,0,0,0,2,0,0,0,0,0,... |
389a3c051431ed8c546c30d3d8da58e3561014c4 | bb19e95b3c3d21bfd8e9db69fd8ea19979c4df0b | /shinyStarost/server.R | 6f3a6adb25b72792c8227ea5e6214b5c01eb7058 | [
"MIT"
] | permissive | janrudof/APPR-2017-18 | 8e03db72b15c32d9624c88da79fc5e8a48e6afe8 | baeb7b25da12f4aa9b448f844fe918ec108579af | refs/heads/master | 2021-09-07T02:16:52.408436 | 2018-02-15T17:57:32 | 2018-02-15T17:57:32 | 110,083,621 | 0 | 0 | null | 2017-11-09T07:45:07 | 2017-11-09T07:45:07 | null | UTF-8 | R | false | false | 1,114 | r | server.R | library(shiny)
shinyServer(function(input, output) {
output$napovedstarost <- renderPlot({
tabela1.preciscena <- tabela1.zdruzena %>% filter(VRSTA_POTOVANJA == "Sli na zasebno potovanje", STAROST != "Starost - SKUPAJ", STAROST == input$starost)
tabela1.preciscena$VRSTA_POTOVANJA <- NULL
tabela1.preciscen... |
0a420c81075d15cc35569f057eb1b130d8af2f1c | 2968dbc3a339019de7f72489196e4c7ea68858ca | /man/gg_arma.Rd | f46182c114b7de0b15dfe811a4a41a2eefbe44a1 | [] | no_license | tidyverts/feasts | 1266009079b445424aa2768d02e2e236358564ba | 1ad4cfd6df3868566d79932dffc3cd337029f766 | refs/heads/master | 2023-06-08T14:27:31.603297 | 2023-03-22T14:21:40 | 2023-03-22T14:21:40 | 135,676,255 | 275 | 25 | null | 2023-05-29T14:37:17 | 2018-06-01T06:23:07 | R | UTF-8 | R | false | true | 969 | rd | gg_arma.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/graphics.R
\name{gg_arma}
\alias{gg_arma}
\title{Plot characteristic ARMA roots}
\usage{
gg_arma(data)
}
\arguments{
\item{data}{A mable containing models with AR and/or MA roots.}
}
\value{
A ggplot object the characteristic roots from ARMA ... |
2014fb13be0a3bc8d4902c8b135c16bbf9016586 | 15a6602e0e0a18decf216f78c8c99775036b3590 | /R/barplot_by_groups.R | 9b1e9107fb262e238dd3b43607329e3cf841d16d | [
"MIT"
] | permissive | zackarno/butteR | 51832a78b1c3b186e2f7caf93f7da1ab3f8612ce | 54cc596b411ddd7528b4e2d74078278dc276c575 | refs/heads/master | 2021-06-20T22:42:04.595204 | 2021-05-04T16:53:33 | 2021-05-04T16:53:33 | 207,075,047 | 6 | 11 | NOASSERTION | 2020-09-18T10:56:05 | 2019-09-08T07:05:53 | R | UTF-8 | R | false | false | 2,407 | r | barplot_by_groups.R | #' Barplot grouped binary columns
#' @param design Design object from survey or srvyr package.
#' @param list_of_variables Vector containing column names to analyze.
#' @param aggregation_level Column name to aggregate or dissagregate to OR vector of column names to dissagregate to.
#' @param binary Logical (default=TR... |
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