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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e62aa7bb7fa44c439c68bc258f676b7afab7835b | 4b889fcf97ce5bd80609ae1ecfa055779bc55b57 | /20160216/server.R | 132423988fb540155b626802cfb6f502d6930d30 | [] | no_license | weitinglin/ShinyPractice2016 | dbea57c7dad740b9c25d202c3690ad5efb8d8685 | 720b51618eb314cf16ed9a55699f555147b4f876 | refs/heads/master | 2021-01-10T01:39:52.954447 | 2016-02-16T15:47:17 | 2016-02-16T15:47:17 | 51,238,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,079 | r | server.R | #20160209 shiny practice upload file server.R
#By defaut, the file size limit is 5 MB, It can be changed by setting this option.
library(shiny)
options(shiny.maxRequestSize = 9*1024^2)
#loading package
library(dplyr)
library(ggplot2)
library(reshape2)
library(XML)
#define the function
#================================... |
eb5787918cf8c2f38414b0d8df576ccb72a15202 | e7ffb8cb08334a16da20d543ba0b51cf99f1a8f3 | /run_analysis.R | e94253c77a58ce3b0d1ed5506d38f806333115ff | [] | no_license | Commander14/tidyingdata | 0b9df5d6ed66846d37bf9f333ff7af283f988eb3 | 5a864530b89110e2d9479d9d4214a1fe8d3fec61 | refs/heads/master | 2021-01-02T22:30:46.331862 | 2014-08-24T21:45:54 | 2014-08-24T21:45:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,857 | r | run_analysis.R | analyzeData <- function() {
# Load all of the applicable files
base <- "UCI HAR Dataset"
trainX <- read.table(paste(base, "/train/X_train.txt", sep=""))
testX <- read.table(paste(base, "/test/X_test.txt", sep=""))
features <- read.table(paste(base, "/features.txt", sep=""))
activities <- r... |
55a2bc8af07a839df426dc74e29a3c02165c0f1d | b47182133fa5eadbeab06735e6a78fcdeaad8094 | /shiny/server.R | ad98ff428cd5ae6963c4b7720895e8ae59345e2c | [] | no_license | zhenhuixu/PHMO | 1d274aae08b2a73c7f8cbaff672d8580c463b745 | cfaac457050fa4f3c1c81cc8a1db35b850f80500 | refs/heads/master | 2022-12-10T21:14:29.009225 | 2020-09-11T16:02:13 | 2020-09-11T16:02:13 | 294,731,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 41,986 | 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)
library(tidyverse)
library(ggplot2)
library(broom)
library(GGally)
library(s... |
1365367551ea64a082d636e2f017d4d68efa7472 | 43f4b374161525ed09d297e812b4de66fb8b06dd | /species_resolution_per_db.R | 2a5af96109698c121c876e1cff4c39d1ff0ab12a | [] | no_license | RJEGR/metagenomics | 4760f7d07e8ae38fad739cce97e803ab81c7601f | b2070fa35b2fa1af3d387734ebc1ec919b5be737 | refs/heads/master | 2021-12-14T15:31:54.338906 | 2021-12-02T06:00:50 | 2021-12-02T06:00:50 | 134,743,404 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,596 | r | species_resolution_per_db.R | #!/usr/bin/env Rscript
rm(list=ls());
# Load libraries ----
.cran_packages <- c("stringr", "reshape2", "tidyverse")
# .cran_packages <- c("stringr", "tidyverse", "reshape2", "ggplot2")
# 1. cran_package
.inst <- .cran_packages %in% installed.packages()
if(any(!.inst)) {
install.packages(.cran_packages[!.inst], ... |
265b9e56712c7b630f72db1d1575b3a5cb84df36 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/NRejections/examples/sim_data.Rd.R | a9b5f8a437eaae6fdd7484d46e6c65367fb015f7 | [] | 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 | 250 | r | sim_data.Rd.R | library(NRejections)
### Name: sim_data
### Title: Simulate MVN data
### Aliases: sim_data
### ** Examples
cor = make_corr_mat( nX = 5,
nY = 2,
rho.XX = -0.06,
rho.YY = 0.1,
rho.XY = -0.1,
prop.corr = 8/40 )
d = sim_data( n = 50, cor = cor )
|
558f1e3d1bdab98da17a21341fb9bc204ac58aa9 | e231e5d9093ea588634d710a8b24e7c0e4dbf820 | /Grenades.r | 414fe026e404b29bc5743fe68dfcde3edbf9bdd8 | [] | no_license | dannyweiland/CSGO-Analysis | 938a9e739e72c24a6d0dcc8c41c023868922df89 | 5992cd6a8f02a8a76118e84bc46a16a863cad918 | refs/heads/master | 2023-02-22T14:40:36.888008 | 2021-01-26T20:29:39 | 2021-01-26T20:29:39 | 292,067,050 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,694 | r | Grenades.r | #Mapping Grenade Spots in CSGO
#My Goal here is to see where the most common spots to throw and be hit by grenades are by visualizing them on the game map.
#Import Libraries
library(readxl)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(png)
#Import Data
grenades1 <- read.csv("mm_grenades_demos.csv")
de... |
a9038fdaf39f4029a232ad4bdb58d00776602380 | 92e597e4ffc9b52cfb6b512734fb10c255543d26 | /tests/testthat/test-function-hsSubstSpecChars.R | 98e9958c40a98c17a2433a6211e86a84b0defa3a | [
"MIT"
] | permissive | KWB-R/kwb.utils | 3b978dba2a86a01d3c11fee1fbcb965dd15a710d | 0930eaeb9303cd9359892c1403226a73060eed5b | refs/heads/master | 2023-05-12T15:26:14.529039 | 2023-04-21T04:28:29 | 2023-04-21T04:28:29 | 60,531,844 | 9 | 1 | MIT | 2023-04-21T04:28:30 | 2016-06-06T13:52:43 | R | UTF-8 | R | false | false | 132 | r | test-function-hsSubstSpecChars.R | test_that("hsSubstSpecChars() works", {
f <- kwb.utils:::hsSubstSpecChars
expect_error(expect_warning(f(), "deprecated"))
})
|
298096b9b3f206ad01724115438057f37fe634bd | 9fbd7cafab56b8cb58ca7385a726a0070d9e050d | /man/sen_nominal_votes.Rd | d29d1a9787536678eaebada87c743ba40004e737 | [] | no_license | duarteguilherme/congressbr | 6f343935a7734dfac70c6794a031db614dafd248 | e9f05428e877f56d31966b14ca00b4ec825fabf5 | refs/heads/master | 2022-11-22T05:40:27.177434 | 2020-07-14T23:20:22 | 2020-07-14T23:20:22 | 83,827,931 | 17 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,213 | rd | sen_nominal_votes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sen_nominal_votes.R
\docType{data}
\name{sen_nominal_votes}
\alias{sen_nominal_votes}
\title{Nominal votes in the Brazilian Federal Senate}
\format{
A data frame with 60691 rows and 8 variables
}
\usage{
sen_nominal_votes
}
\value{
\itemize{
... |
55b3f78be7c1825e0ac62ba5076b4f6d5a237e08 | 958894bf1eb0f125b34afccd4c760f941ceb4e99 | /clustering.R | a331d55a9fe5002385486512facedf2be633c090 | [] | no_license | alex21th/bank-telemarketing | 5fb85d76a75a3ae646344357391e2633d77f4cef | 891e172e6935b4a38991172a88123bd18874c40f | refs/heads/master | 2022-12-20T16:43:56.713145 | 2019-06-26T09:10:00 | 2019-06-26T09:10:00 | 298,642,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,928 | r | clustering.R | # Set current directory to file location
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
getwd()
# Load data
load(paste0(getwd(), '/dd_complete.RData'))
# Data dimensionality; hence p is number of predictors (without response value)
N <- nrow(dd.complete); p <- ncol(dd.complete) -1
str(dd.complete)
... |
1a9dac23654dcd11c36e99268228ffa7b3730538 | 54782788f33fa1ae0799d32c59d0d8567fa15451 | /plot4.R | ed95f410258a3f66c515d397af7594f6522f1f6a | [] | no_license | nschmidt13/ExData_Plotting1 | df9aa3e2cb1a0e33ccb3d3713745a05bbd4c06ef | 9737833d42bc10a43355dacc8d41c478f3195a84 | refs/heads/master | 2021-01-16T22:31:38.378516 | 2014-10-12T06:08:38 | 2014-10-12T06:08:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,051 | r | plot4.R | #This function loads the data from the working directory and adds a time/date column and filters to the correct dates
getData <- function () {
# Create columns types vector
colTypes <- c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")
#Read in data from... |
0588c88bc7cf39a779eb18d9b76ca598bcdb706e | 40ac18e5524b9d55e9d4f0095a518121eac92efb | /man/weiqqci.Rd | 9a4251757fc0bdc3427775bbdf0fba716ac202df | [] | no_license | vjcitn/barca | b57e32904638673951a29147322e725e3c708178 | ef531c78b7f423a739421bf63e67fa731e043655 | refs/heads/master | 2021-01-14T02:39:59.799374 | 2020-06-12T16:39:13 | 2020-06-12T16:39:13 | 81,848,206 | 0 | 1 | null | 2017-03-30T17:39:14 | 2017-02-13T16:51:33 | R | UTF-8 | R | false | true | 479 | rd | weiqqci.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qq.R
\name{weiqqci}
\alias{weiqqci}
\title{plot a qqplot relative to a weibull distribution}
\usage{
weiqqci(x, conf.int = 0.95, ...)
}
\arguments{
\item{x}{vector of numbers}
\item{conf.int}{numeric confidence coefficient for EDF CIs}
\ite... |
f8476e9c5a779061c77e1ce8a701060e210cde8b | daa39ef0a3e4d643bfdead74e0067ff6c856f304 | /Chapter-10-Conditional and Block Statements R.R | 701e10847e4e75bffe8b43a7dd7d738ffb8490cf | [] | no_license | balajidileepkumar/R_ML | ca32105e78c41f17c1397078c34c478a84e38334 | 3da18dad0d173ae28c6552a5f9a22dd308180e1b | refs/heads/master | 2021-06-30T03:32:27.506476 | 2020-12-23T15:00:16 | 2020-12-23T15:00:16 | 188,610,132 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,184 | r | Chapter-10-Conditional and Block Statements R.R | ## Conditional and Block statements
a = 10
b = 110
#if(a >b) { print(a) } else { print(b) }
if(a>b)
{
print(a)
} else if(a==b) {
print("equal")
} else {
print(b)
}
#Block Statements
###################
#Switch
###################
#a = switch(expression, case1,case2,case3,.....casen)
a = switch(3, FALSE,"ca... |
13154fecdfa2d20c93f3f016121626cee68ddc45 | 20a35379eb8907465759b651ca7c36c2e23b9a40 | /tests/testthat/test-input-validate.R | db5103c24de5a7946487accf0fd7e547b14b535e | [
"MIT"
] | permissive | jeffeaton/pkgapi | 5c175067a168f449ca2fda1d0f47b40f497084c4 | 22eef9b9bd2d0671aa03f741cdecb32d0e7166a9 | refs/heads/master | 2022-12-14T02:26:57.385116 | 2020-09-01T08:44:14 | 2020-09-01T08:44:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 485 | r | test-input-validate.R | context("input validation")
test_that("validate mime", {
expect_error(
pkgapi_input_validate_mime(NULL, "application/json"),
"Content-Type was not set (expected 'application/json')",
fixed = TRUE, class = "pkgapi_error")
expect_error(
pkgapi_input_validate_mime("application/octet-stream", "applicat... |
72476f290ce22807983bbff935f5f8a21dc57d99 | 2bed5cc5dfabd54b0d5483030500dcd0d07a0f5a | /man/docaids.Rd | 1f2b8ce52dffc3aa1a110a9b88cb0ba3ab7254bf | [] | no_license | langfob/docaids | bec4f924b18943efcaac5761cb2ac590ab354a11 | ce44c5ceb1b782dbebfb587ebff1ba0b13be290f | refs/heads/master | 2021-01-21T10:05:10.153071 | 2017-03-05T01:52:41 | 2017-03-05T01:52:41 | 83,377,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 211 | rd | docaids.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/docaids-package.r
\docType{package}
\name{docaids}
\alias{docaids}
\alias{docaids-package}
\title{docaids.}
\description{
docaids.
}
|
a14dae6a2825f07e4f02fbdc0e5eeef300e4ddbd | 9582bd189e5620664b2235822af87bd2426d1aa3 | /R/get_relevant_date.R | 2e8167e32acac11e05800625925e7450ea827221 | [] | no_license | gottalottasoul/RiHana | 6a30b217cf3e2eb65f622453a70e89fe87c7492a | 97e12a1df12e2b85fea2d792a2c50225131c4ecd | refs/heads/master | 2021-01-08T06:19:40.935972 | 2020-08-03T19:57:05 | 2020-08-03T19:57:05 | 241,939,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 415 | r | get_relevant_date.R | get_relevant_date <-function()
{
relevenant_dates<-tibble::tibble(
yesterdays_date = Sys.Date()-1,
ytd_date=lubridate::floor_date(Sys.Date(),unit="year"),
yoy_date = lubridate::ceiling_date(Sys.Date()-365, unit="week",week_start = 1),
past_30_days = Sys.Date()-31,
past_7_days = Sys.Date()-8,
... |
82d75fc6c5a80ba10515b600178015ce19619901 | fc9df5d512d33a2805a0d1834095afebdbc8302f | /scripts/07_script_ensemble.R | a70181e699b77138904e38b0a20f053d0fd97d62 | [
"MIT"
] | permissive | jgiovanelli/enm_r | 09ecc5f4d62db025762f4acf556b63af00ed1233 | 70cbabfa3179f6bdadc7f3754a98edc313bbd600 | refs/heads/master | 2021-01-20T13:23:36.319794 | 2017-02-20T12:32:36 | 2017-02-20T12:32:36 | null | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 22,919 | r | 07_script_ensemble.R | ### disciplina - modelagem de nicho ecológico: teoria e pratica ###
### ppg ecologia e biodiversidade - unesp 2017 ###
# Thadeu Sobral de Souza - thadeusobral@gmail.com
# Maurício Humberto Vancine - mauricio.vancine@gmail.com
###---------------------------------------------------------------------------------... |
97d203ccab2bb36b4cf23c9631ce7ec98e086775 | 9f57e0ad44b78d809c262fa0ffb659232fdb8d5e | /implementations/set_ops.R | c6793869a15014a0f26548a5531c3fe36c393fcc | [] | 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 | 1,514 | r | set_ops.R | # Implement set operations
find.unique <- function(v){
elems <- c()
for (e in v){
if (! e %in% elems){
elems <- c(elems, e)
}
}
return(elems)
}
has.elem <- function(elem, v){
status <- F
for (e in v){
if (e == elem){
status <- T
break
}
}
return(status)
}
set.unio... |
f7a007f7e2536a1a34cc4209ced3a7369b8c8fd5 | 8b1c2469dc5ed9d7d9c94da87a6c0a1d47c8604e | /R/plot_signal.R | 7f73b2d5c7811f7da81093483396c92a8d053042 | [] | no_license | dami82/CellSignalingTools | 077c4496638a090debed5f6ef8774c45747f564d | 0405a2443d3f380ce61d6d5469ffd96f92760c98 | refs/heads/master | 2016-08-11T13:49:45.042182 | 2016-03-19T22:51:53 | 2016-03-19T22:51:53 | 54,290,581 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,146 | r | plot_signal.R | plot_signal <-
function(signal_map) {
ch_1 <- signal_map$nucl
ch_2 <- signal_map$cyto
ch_ori <- signal_map$input
my_x <- ncol(ch_1)
my_y <- nrow(ch_1)
ch_0 <- matrix(0, ncol = my_x, nrow = my_y)
ch_1[ch_1 == 0] <- NA
ch_2[ch_2 == 0] <- NA
#
#faux palettes
greys <- colorpanel(n = 15, low = "black",... |
c5f37718cb2445d4f5540ebaae5e863f6e72b56a | 97836dd5cc8776c185fd75b8637c89fcc58ced73 | /R/packages.R | 08ba3d1331673afe38069ab42a9f56d06e42a308 | [] | no_license | mihagazvoda/mini-bayes-shiny | 141ccd4aaf5fea2dde51c575f4e3fc7dbedb1446 | b9eb5bb3b42c69ea1c12ddc8f0997d892cf24e06 | refs/heads/master | 2023-02-05T11:10:01.283399 | 2021-01-01T17:11:08 | 2021-01-01T17:11:08 | 314,895,002 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 71 | r | packages.R | library(shiny.semantic)
library(shiny)
library(dplyr)
library(ggplot2)
|
3bd6f86ac16232fba8d81c626f1904abac3aaaa1 | f7cb5ffe2d36c1a529a9b74ce6408dc0e0ae2413 | /man/plot_temporal_network.Rd | eb95c9f3f5ed0b9c6a44833d6038b4be33a7fdb7 | [] | no_license | alaindanet/fishcom | aa5b03f49c5e698d37003b6ffa75b88f3f18b1f4 | 76e30a7bf13cccabc1cd8fc230bb469276d7f846 | refs/heads/master | 2023-06-18T20:29:30.291089 | 2021-07-12T16:34:30 | 2021-07-12T16:34:30 | 160,175,823 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 632 | rd | plot_temporal_network.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_methods.R
\name{plot_temporal_network}
\alias{plot_temporal_network}
\title{Temporal graph for a station}
\usage{
plot_temporal_network(net, meta, dead, ...)
}
\arguments{
\item{net}{data.frame contaning year variable and network list}
... |
e56e73e58d78d5f2147eb271078b1fddad9a1992 | 7797ee6832f469768dfaffbf63d7dae6bf3426ae | /R/IRR.R | d0eb286172c302c8890482d8ebeb91bd5bdb43a8 | [] | no_license | AmateurECE/economics | 27dd67e0a04bdfd999d5e124d2114230ab518358 | 37109c8a8376627e840dc061b6fc802a64e8ff30 | refs/heads/master | 2020-05-02T23:39:26.440627 | 2020-02-04T19:02:35 | 2020-02-04T19:02:35 | 178,286,376 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 104 | r | IRR.R | IRR <-
function (cf)
{
uniroot(netPresentValue, cashFlows = cf, lower = 1e-04, upper = 100)$root
}
|
634c68216a63e019a65dee444e0794e0d48797c8 | e9f06ea90b7c9127f3b05bafa7e0d34c7074d4e5 | /Overview.R | d110f767e110627fdcb36bc5fe84fb0f498699c4 | [] | no_license | danielahertrich/masterthesis | 1dee9b1800f8dee197972aa3d009000394c8e224 | 1c2e2c6c0efc3c22a778ae106e092d8f36e5183f | refs/heads/master | 2021-01-01T19:47:57.860948 | 2017-07-29T22:42:29 | 2017-07-29T22:42:29 | 98,687,685 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,027 | r | Overview.R | library(fields)
## set parameters
art <- "NMF" #"AMr","AMp","OLr","OLp", "PCA"
q <- 100 #300, 600
it <- 50 # 150
## load dictionary
load(file=paste("dict_",art,q,"_it",it,".rda",sep = ""))
# load(file=paste("dict_",art,q,".rda",sep = "")) # for art="PCA"
dict <- spdict$H
Z <- t(dict)
score <- spdict$W
... |
54183f583effae821ab75010b7f9414d40c4a066 | de0b8ccead63362fbc4976b773cabacfa1bb9ff1 | /generic.R | 9e663835846bde9cc5937f9b3eac25422e61775e | [] | no_license | fannyrancourt/NoncentralityParam_MixtureModels | 45bdc68a8ccb5762dc875e4011a78ab64f3c8fd5 | 5bbf8fc798d32a0faf6f723f3200c146de80b5ae | refs/heads/master | 2022-12-04T14:34:24.986111 | 2020-08-18T00:44:08 | 2020-08-18T00:44:08 | 288,314,543 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 724 | r | generic.R | # generic functions
if (!isGeneric("unbiased_estimator")) {
setGeneric("unbiased_estimator", function(object) standardGeneric("unbiased_estimator"))
}
if (!isGeneric("expected_value")) {
setGeneric("expected_value", function(object) standardGeneric("expected_value"))
}
if (!isGeneric("variance")) {
setGeneric(... |
7c88d85b36dc689fde6c88d0844fdaade4f6c50e | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.internet.of.things/R/iotdataplane_operations.R | 1113c92dcaa6d109109949cd5bb4e261185e6c14 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | false | 7,155 | r | iotdataplane_operations.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common get_config new_operation new_request send_request
#' @include iotdataplane_service.R
NULL
#' Deletes the shadow for the specified thing
#'
#' @description
#' Deletes the shadow for the specified thing.
#'
#' For more informatio... |
ffc5ff55f35c4553f2c68bd6da6d18a8beb23b70 | c8a3b33f0ec6b53c3280f6a65cfb18c115d0b615 | /cp_average/cp_compare.R | 01b766eea40361d7d60453d58ab4d94fd5d444c1 | [
"MIT"
] | permissive | marcelosalles/dissertacao | dd502c1ab78d12bfc4673fcec9816485992d358a | 692f80a0d6b28a7e929dc86f6a684cfa84b59df1 | refs/heads/master | 2021-12-11T15:42:22.545985 | 2021-12-07T17:53:47 | 2021-12-07T17:53:47 | 190,596,533 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,968 | r | cp_compare.R | library(R.matlab)
library(ggplot2)
setwd('/media/marcelo/OS/Cps/')
df_average <- read.csv('df_cp_average.csv')
tpu_data <- dir(pattern = '.mat')
first = TRUE
for (file in tpu_data){
print(file)
opened_file = readMat(file)
df_location = opened_file$Location.of.measured.points
df_cp = opened_file$Wind.pressure... |
8cf417698b9c5f71fe50ce1d3df58ae83629603f | 56b32941415e9abe063d6e52754b665bf95c8d6a | /R-Portable/App/R-Portable/library/igraph/tests/test_assortativity.R | 88b3ea8a749bfa5e82795c4133be6e41fb822ccc | [
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-newlib-historical",
"GPL-2.0-or-later",
"MIT"
] | permissive | voltek62/seo-viz-install | 37ed82a014fc36e192d9a5e5aed7bd45327c8ff3 | e7c63f4e2e4acebc1556912887ecd6a12b4458a0 | refs/heads/master | 2020-05-23T08:59:32.933837 | 2017-03-12T22:00:01 | 2017-03-12T22:00:01 | 84,758,190 | 1 | 0 | MIT | 2019-10-13T20:51:49 | 2017-03-12T21:20:14 | C++ | UTF-8 | R | false | false | 1,694 | r | test_assortativity.R |
context("assortativity")
test_that("assortativity works", {
library(igraph)
g <- read_graph(f <- gzfile("celegansneural.gml.gz"), format="gml")
assR <- function(graph) {
indeg <- degree(graph, mode="in")
outdeg <- degree(graph, mode="out")
el <- as_edgelist(graph, names=FALSE)
J <- outdeg[e... |
0e8e6d2bfd4169a5d46860959e8cac584d4664ab | 91945b9420a2447e53265865b7f96b87f2a9c515 | /server.R | a080cdb7392c5a83aca4ad3b2ab53ab2383f410a | [] | no_license | ajaypillarisetti/squid | d8de50e6164f578004e438169a4fdbc0c497faad | dd1bbe55d670f895636df45e43ead459667e2180 | refs/heads/master | 2021-05-04T10:50:13.393879 | 2017-02-28T01:31:13 | 2017-02-28T01:31:13 | 43,640,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,529 | r | server.R | ### Ajay Pillarisetti, University of California, Berkeley, 2015
### V1.0N
shinyServer(function(input, output) {
Sys.setenv(TZ="Asia/Kathmandu")
# read in data - for uploads
datasetInput <- reactive({
inFile <- input$files
if (is.null(inFile)){return(NULL)}
dta<- read.squid(inFile$datapath[1])
})
datas... |
3cabeb7f55c91cbb59ff93c0c9d944cd055fd6c4 | b08f1485d0c2c18e57134d0d3d56220fdff3cc5e | /R/FL-Airline_Data/week1.R | 3e4ccc06478e586e8801cbfba5e9ab19b67b6765 | [] | no_license | bithu30/myRepo | 250c9383d31c296a9e116032aebf4ce947d1964e | 64485bb10327ed3a84b3c15200b1dd6a90117a8e | refs/heads/master | 2022-10-21T16:26:45.997395 | 2018-05-31T02:51:31 | 2018-05-31T02:51:31 | 41,030,617 | 5 | 18 | null | 2022-10-05T07:40:41 | 2015-08-19T11:43:00 | Jupyter Notebook | UTF-8 | R | false | false | 724 | r | week1.R | #import all of the data from 2008 dataset
airline=read.csv('C://myRepo//R//FL-Airline_Data//2008.csv')
head(airline$Origin == 'IND')
sum(airline$Origin == 'IND')
sum(airline$Origin == 'ORD')
sum(airline$Dest == 'ORD')
sum( (airline$Origin == 'IND') & (airline$Dest == 'ORD') )
myIndieFlights <- subset(airline,airline$... |
1c62d943ea35003ea7109102c9a2453921a26616 | 577606de99821f75d8aa0e7bb4b9a1d5fb1746d6 | /cachematrix.R | 90a452332bcc4d68548d4ae0bf8d07ff1e9b3a8b | [] | no_license | milosgajdos/ProgrammingAssignment2 | 36221be8fa2885c10ece73225d85c5ae5e6ac477 | 325e4fcf13b9b6bbf0ccb566fcc3504afee4d070 | refs/heads/master | 2021-05-31T00:05:57.077423 | 2015-11-21T15:07:01 | 2015-11-21T15:07:01 | 46,615,974 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,790 | r | cachematrix.R | # makeCacheMatrix accepts a matrix as an argument and generates a list object
# which provides various functions to retrieve and modify the underlying matrix
# and its inverse matrix
makeCacheMatrix <- function(x = matrix()) {
# inverse matrix
inv <- NULL
# Initialize the underlying matrix objec... |
ed7d7f2f7dbc8cd8d4144852eec729964f3635d6 | 831bab411f97dde4a4d41640393bd8fe7c5dde74 | /SL_CCLE_del.R | 61f454bf32f24cdc815fb9ca8c4865987a290d03 | [
"Apache-2.0"
] | permissive | CBigOxf/synthetic_lethality | 86f4209760c4d79509fad405b81fbebf937937a2 | 705b6555882202c1ed157b1be7ef881eb73b3bb6 | refs/heads/main | 2023-03-25T03:56:43.665837 | 2021-03-18T13:53:38 | 2021-03-18T13:53:38 | 309,764,977 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,014 | r | SL_CCLE_del.R | SL_CCLE_del <- function()
{
###########################################################################
# This function implements the SL genome scan for CCLE_20Q2 dataset
# It scans through the genome looking for which genes are
# co-mutated (only deleterious) or WT based on tumour samples that
... |
807fde5f658394b63b38046f7decfeecf922ea7e | a664cda0a1bb3e7351b7c0b2c5755d224bcaf330 | /project1_plot3.R | 2debd819a9ddb432094f168c186774e3f51c45ac | [] | no_license | Sophro/ExData_Plotting1 | 2bb675578a6472cd8e746aebed1b2a80dcae58c3 | 5afdf0462d98093972fd2ebcf6c56fe82ca46158 | refs/heads/master | 2020-03-22T01:02:06.837358 | 2018-06-30T21:32:16 | 2018-06-30T21:32:16 | 139,279,005 | 0 | 0 | null | 2018-06-30T20:49:00 | 2018-06-30T20:48:59 | null | UTF-8 | R | false | false | 1,250 | r | project1_plot3.R | # Cousera Course 4, week1, Project1 29.06.2018
rm(list=ls()) # rm all variable = clear
# read input file
dat_inp <- read.table("data/household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors = FALSE, na.strings=c("NA","?"))
# select all features between two dates (as character)
tmp <- dat_inp[d... |
6ccb5aa6552118abc37f1857dd7e947f2085768e | 5f1f83ecd94dd139e4c2a09bbc36ed58b65806f5 | /example/mergeStringtieTPM.R | 41b69141f79e0da37b6c2bdb3660125e2660062c | [] | no_license | huangwb8/bioinf | 6fe50b4bf55372f9d87946c0d2591c0220a3857c | 02aba6ad6173c376d81e5cb58c973189d5787c8a | refs/heads/master | 2023-01-29T00:23:52.279428 | 2023-01-15T01:01:37 | 2023-01-15T01:01:37 | 215,574,769 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,553 | r | mergeStringtieTPM.R |
##==========================Input========================##
# path_stringtie='/home/huangwb8/Project/RNA-Seq_A2B1-KO/output/stringtie/hisat2'
##==============assistant base function=================##
source("/home/huangwb8/bin/ShellBase.R",encoding = "UTF-8")
##====================environment=====================... |
71ef9eca139630ac15908cb9cdebf0b910059c67 | d7ff71e8ffb07419aad458fb2114a752c5bf562c | /tests/testthat/test-public_api-2.R | 8606a4afdc5b5ac04fbdc1c875e6a21aa71207c2 | [
"MIT"
] | permissive | r-lib/styler | 50dcfe2a0039bae686518959d14fa2d8a3c2a50b | ca400ad869c6bc69aacb2f18ec0ffae8a195f811 | refs/heads/main | 2023-08-24T20:27:37.511727 | 2023-08-22T13:27:51 | 2023-08-22T13:27:51 | 81,366,413 | 634 | 79 | NOASSERTION | 2023-09-11T08:24:43 | 2017-02-08T19:16:37 | R | UTF-8 | R | false | false | 4,236 | r | test-public_api-2.R | test_that("styler can style R, Rmd and Rmarkdown files via style_dir()", {
msg <- capture_output(
style_dir(testthat_file("public-api", "xyz-r-and-rmd-dir"),
filetype = c("R", "Rmd", "Rmarkdown")
)
)
expect_true(any(grepl("random-script-in-sub-dir.R", msg, fixed = TRUE)))
expect_true(any(grepl("ra... |
a39c9d446891fa515459e1e35b3ca6aefaf7beff | 9cd1bf7e216a26c5cd089a8f9fa89c9f684674d0 | /R/transformation_functions.R | 3197df88d42ba9ca3201330383350d4b674f6726 | [
"Apache-2.0"
] | permissive | jukujala/R-data-pipe | 8195c81cdb74e2e2fc18146156c3d25abf390881 | d259e72175a4bf6f0cf9e002da206e979cfac425 | refs/heads/master | 2020-12-01T22:17:43.172268 | 2020-01-11T15:30:49 | 2020-01-11T15:30:49 | 230,789,713 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,076 | r | transformation_functions.R |
# this file has feature transformations
#
# Example:
# getCategTransform returns list (column, function), where column is the name
# of the output column for the transformation and the function transforms
# a data.table to feature vector.
library(data.table)
#' get function that encodes away less common values in a ... |
5447a7d0c603ffa50d3eceb8913a3dd3516356e7 | dfe6867eb701ed128c80ca08f9e4d1d447f547b0 | /man/summary.jmdem.Rd | 43a5098f0ce1d593f670aef8cf572d823f7972b0 | [] | no_license | cran/jmdem | fc8510e36ac0879621766f639af9e06e71eaa5aa | 5afc1049cec9566824a50238d8d05ee212c53977 | refs/heads/master | 2020-03-13T20:16:25.577903 | 2020-03-04T05:50:02 | 2020-03-04T05:50:02 | 131,270,464 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,550 | rd | summary.jmdem.Rd | \name{summary.jmdem}
\alias{summary.jmdem}
\alias{print.summary.jmdem}
\title{
Summarising Joint Mean and Dispersion Effects Model Fits
}
\description{
These functions are all \code{\link{methods}} for class \code{jmdem} or \code{summary.jmdem} objects.
}
\usage{
\method{summary}{jmdem}(object, correl... |
be110f33a23e2ac56d3116a03b9ddd9e4d9ac2d3 | 947316115f3c10532162697eb7636c92e04a7f46 | /Codes/Part8_10mer threshold.R | 975b091b96c784bb7712902941e49fab763a43a2 | [] | no_license | Flashiness/TFBS | 3aa96fff84c68514788a8cbdd0771b70e3115b3a | ca6186dbd9ef1e54ad51a5aa0c1f2603efc43040 | refs/heads/master | 2023-02-24T00:10:09.763563 | 2021-01-28T05:18:58 | 2021-01-28T05:18:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,427 | r | Part8_10mer threshold.R | # get the threshold for 10-mers
motifs= c("BCL11A", "CTCF", "EGR1", "GABPA", "JUN",
"JUND", "MAX", "NANOG", "POU5F1", "RAD21",
"REST", "RFX5", "SIX5", "SRF", "STAT1",
"TCF12", "USF1", "USF2", "YY1")
motif = motifs[2]
# the size of
sizes <- 100000
# ten-mer threshold
lo... |
46cb0ceedb96fb97c6bfb36fb7ac4a03419805bf | 1a0ad94b0ed048d029b61f50ead424c1adee26c1 | /scripts/parkrun_efforts.R | 3dc4fd47d0704340c88b78199a8c104ccffcfc38 | [] | no_license | padpadpadpad/parkrun_play | 2affb3bdb76f3cd0ecaaa095262cc1ce199a7bb9 | 8994914c9a219c95ed5e4aa942018f5b7b63f431 | refs/heads/master | 2021-07-07T18:18:26.437689 | 2017-10-04T17:27:30 | 2017-10-04T17:27:30 | 103,745,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,768 | r | parkrun_efforts.R | #devtools::install_github('fawda123/rStrava')
#devtools::install_github("timelyportfolio/listviewer")
mise::mise(vars = TRUE, console = TRUE)
library(rStrava)
library(tidyr)
library(dplyr)
library(ggplot2)
library(broom)
library(purrr)
library(ggjoy)
help.search('token', package = 'rStrava')
app_name <- 'stRava'
ap... |
f391338504125bd6251de4ce54eeaa9d3cd9227b | fd365694237edb699e53eef04f1c3c0ff649f3c8 | /man/opal.execute.Rd | 22d80ed2dacace371f99009b7bee644fd23b162f | [] | no_license | obiba/opalr | f73a0eb0280bc768b47711d6a1a08ce0eded7ce1 | 5ca4936deae7e3410db5ee6a02df7994ff5fa336 | refs/heads/master | 2023-08-03T06:18:07.954481 | 2023-07-21T06:58:07 | 2023-07-21T06:58:07 | 166,788,279 | 3 | 3 | null | 2021-05-13T15:50:49 | 2019-01-21T09:45:41 | R | UTF-8 | R | false | true | 898 | rd | opal.execute.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/opal.execute.R
\name{opal.execute}
\alias{opal.execute}
\title{Execute a R script}
\usage{
opal.execute(opal, script, async = FALSE)
}
\arguments{
\item{opal}{Opal object or list of opal objects.}
\item{script}{R script to execute.}
\item{a... |
f4a09aedc3e426d6aacb647c7f5949fd9cbbbeef | e4373b408d639af6635baa58a635d5ff1f9e9d95 | /man/parcoords.Rd | b6dd91c9897779bf61697e3e179a54622cf2184f | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | jkaupp/parcoords | afb4edb77be85226f6be04f660867fd234286d28 | aefd83354446a8dabc7fdb83852c5b5ece5a7760 | refs/heads/master | 2021-01-18T11:10:45.607817 | 2016-02-26T21:02:41 | 2016-02-26T21:02:41 | 59,310,893 | 0 | 0 | null | 2016-05-20T16:49:27 | 2016-05-20T16:49:26 | null | UTF-8 | R | false | true | 4,566 | rd | parcoords.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parcoords.R
\name{parcoords}
\alias{parcoords}
\title{htmlwidget for d3.js parallel-coordinates}
\usage{
parcoords(data, rownames = T, color = NULL, brushMode = NULL,
brushPredicate = "and", reorderable = F, axisDots = NULL,
margin = NULL... |
9a2d4ee5f0db4e456e5cbeb5bde3763a8194a070 | 109621995ae71dcf333e4f57ff715fb77db8b912 | /craving/scripts/createEV_scannervas.R | 1cfff61c41a542dcfdca815e2eb36c5b5d5ff3ec | [] | no_license | pederisager/Opioid_Reward_fMRI | 76ebc34eea3cead59e48216f124f61961fa7af5b | 4d8156bd17c8bff555f261bd2af1b598cc01d4ca | refs/heads/master | 2021-05-04T06:41:54.386497 | 2017-06-24T15:48:22 | 2017-06-24T15:48:22 | 70,497,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,484 | r | createEV_scannervas.R |
## Loop through all Observe and Regulate EVs and construct Food Wanting and Regulate Success EVs
attach(scannervas_long)
# For all included subjects
for (subj in unique(subject)) {
# For all sessions
for (sess in unique(session)) {
# For all the conditions (Observe/Regulate)
for (cond... |
2ce53207a500aa7e4a9cb99dac153770f9131e8a | 1a5741fb17781bce50228cab6d11842e6451df32 | /R/predict.default.R | 7905b2a71c06f2436dfa8d217d261ebc3f9071e6 | [] | no_license | paulponcet/statip | a88e78e023cb550e7ad22a5ec701d0f641050bbd | ed5cfd7d00db470f9b32a812399f9b3c0bd7b229 | refs/heads/master | 2021-01-11T15:30:39.695374 | 2019-11-17T21:15:34 | 2019-11-17T21:15:34 | 80,364,466 | 0 | 1 | null | 2019-11-17T21:15:35 | 2017-01-29T18:43:51 | R | UTF-8 | R | false | false | 950 | r | predict.default.R | #' @title
#' Default model predictions
#'
#' @description
#' Default method of the \code{\link[stats]{predict}} generic
#' function, which can be used when the model object is empty.
#'
#' @param object
#' A model object, possibly empty.
#'
#' @param newdata
#' An optional data frame in which to look for variab... |
3c407cd50a4ba56258c79d931356f15a0d59f49a | c3eed653d66fbca3e32f925d1ea4575fcef3af01 | /Cap14 - Análise de Regressão com R e Azure Machine Learning/01-Solution-Exercicios-Cap13-Parte1.R | effa3e380409a6e3a823ec0d9e6399619454aa05 | [
"MIT"
] | permissive | FelipeChristanelli/BigDataAnalytics_R_AzureMachineLearning | a54fda2f753f08ae8a69cc6466441a78414b1e5a | 568202484c3edd5146a501ab5a5b419055d6c01d | refs/heads/main | 2023-02-21T05:07:16.662032 | 2021-01-16T15:07:35 | 2021-01-16T15:07:35 | 313,734,151 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,036 | r | 01-Solution-Exercicios-Cap13-Parte1.R | # Lista de Exercícios - Capítulo 13
# Para este script, vamos usar o mlbench (Machine Learning Benchmark Problems)
# https://cran.r-project.org/web/packages/mlbench/mlbench.pdf
# Este pacote contém diversos datasets e usaremos um com os dados
# de votação do congresso americano
# Seu trabalho é prever os votos em ... |
43816f15ee91be590928f16127158585099b4e23 | ed7a6d8459fe0d0b4eceb27708499fb628075d78 | /TxDbLite/R/indexIfNoneFound.R | f2c7a8e6dcac5a961136dcecfc49b676b18bea98 | [
"MIT"
] | permissive | RamsinghLab/Arkas-RNASeq | a80b5d24fb4137191ffc7b4aeabbb7c3f9c01fa0 | 75ae2baa6a85b6d040be2b3a29ddac9d9cf106a2 | refs/heads/master | 2023-04-12T07:02:00.071576 | 2017-04-11T22:31:31 | 2017-04-11T22:31:31 | 87,348,768 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 338 | r | indexIfNoneFound.R | #' helper function for findDupes
#'
#' @param fastaFile the name of the FASTA file
#'
#' return the index, invisibly
#'
#' @export
indexIfNoneFound <- function(fastaFile) {
if (!file.exists(paste0(fastaFile, ".fai"))) {
message("Indexing ", fastaFile, " to extract sequence names...")
invisible(indexFa(... |
1ff9f3b73afb498f94eb856d5d7e62eeb752e89d | 587a4ce6eefc0da2eb5a3516b19180ff06710538 | /man/download.Rd | cb9388c34b6a6c94c75f2093ec0bdb71ac29be27 | [] | no_license | RM1900/RCache | cc81847e760ec89697e9eb52c1d31452c7376d24 | 0a10561f1ab71b5587d34fa2aba7342098f13d28 | refs/heads/master | 2020-12-25T23:56:38.634463 | 2013-04-06T22:50:04 | 2013-04-06T22:50:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 120 | rd | download.Rd | \name{download}
\alias{download}
\title{Download a file.}
\usage{
download(url)
}
\description{
Download a file.
}
|
4a5e4333f7b5bdfe3a7c327ee171e154c43c9c79 | 68664bd25f27d41ae5c5b7618363e5c373ba5d4f | /scripts/classifierdemo.R | 7752552ad9d8c396a5687568d289b63f37884f02 | [] | no_license | shriv/datalandz_biosecurity | 5e5bb2b58f1f7bb5bbb2c8ff4168acf6e2fe4d23 | e27f6f0aebf33c43046b96a3ea79b6b6117a2ee6 | refs/heads/master | 2020-03-27T17:04:02.461164 | 2018-09-03T06:15:41 | 2018-09-03T06:15:41 | 146,826,890 | 0 | 1 | null | 2018-08-31T20:49:43 | 2018-08-31T01:44:08 | R | UTF-8 | R | false | false | 995 | r | classifierdemo.R | require(dplyr)
require(shiny)
require(keras)
require(rjson)
install_keras(tensorflow = "gpu")
setwd("~/datalandz_biosecurity")
# instantiate the model
model <- application_resnet50(weights = 'imagenet')
# load the image
img_path <- "./data/)"
img <- image_load(img_path, target_size = c(224,224))
x <- image_to_array(... |
4ad7d9cc0df5aed0e63172eb9a3839c1b79b6c4f | eb5645e68df318af0bcf2e42d6da495dd5adaaf8 | /plot2.R | f32e1f75654f2c5a9711e27958ebc45b06e04ef4 | [] | no_license | vnaidu/ExData_Plotting1 | 599e1885b6797d79d52b83a9c77bf6d6ba77e6bb | 60c9c9371ee35e2971dda99a30b0505fa096f504 | refs/heads/master | 2021-01-09T07:05:16.062046 | 2016-07-16T11:01:04 | 2016-07-16T11:01:04 | 63,457,802 | 0 | 0 | null | 2016-07-16T00:20:37 | 2016-07-16T00:20:37 | null | UTF-8 | R | false | false | 378 | r | plot2.R | source("dataHelper.R")
source("plotHelper.R", local = TRUE)
pwrc <- GetData()
# Start plotting device using default options
startDev("plot2")
# Calculate date start/end for graph
plot(pwrc$DateTime,
pwrc$Global_active_power,
type = "l",
xlab = "Day",
ylab = "Global Active Power (kW)",
cex.la... |
b6e9208b406755863a09b2a21e34c021733902c5 | fcc13976b8952fedec00b0c5d4520edc6d5103b9 | /man/dataUploadUI.Rd | cebb834800f3bb43c3087edef36a25f833debee4 | [] | no_license | anngvu/DIVE | 851173b4515ab4fd8c26e171158aa17f079785db | e80d254fc4be2c4a3c12f4a1b4507beff3fe3663 | refs/heads/master | 2023-07-26T00:30:07.924714 | 2021-09-08T15:04:34 | 2021-09-08T15:04:34 | 173,828,093 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 825 | rd | dataUploadUI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataUpload.R
\name{dataUploadUI}
\alias{dataUploadUI}
\title{Shiny module UI for user-uploaded data}
\usage{
dataUploadUI(id, ...)
}
\arguments{
\item{id}{Character ID for specifying namespace, see \code{shiny::\link[shiny]{NS}}.}
\item{...}... |
db65a28064f4a7bd77b234ff112f48564ce3f882 | 0dba5c813820966df254b96d2ff4e8590243fab1 | /2.cfa.R | 15862afdb2e303200688c21b062433513fda39c5 | [] | no_license | PamelaInostroza/Data_analysis-ICCS | 1045dc8a5c06cdf6c255a4fb217999dce0a4c0fc | 294a61b75a9783d1c2d071e579b3ea14b2c17a40 | refs/heads/main | 2023-02-08T15:59:26.489558 | 2020-12-31T00:09:17 | 2020-12-31T00:09:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 44,560 | r | 2.cfa.R | library(lavaan)
library(lavaan.survey)
library(semPlot)
library(sjlabelled)
memory.limit(15000)
options(survey.lonely.psu="adjust")
InverseCod <- c("BS4G6","BS4G9","BS4G13","IS2P24C","IS2P24D","IS2P24F","IS3G24C","IS3G24D","IS3G24F")
#Cycles 2-3 vars reverse whole scale
C1vars <- VarsToUse %>% filter(Domain == "Scale... |
51f9b6a867c182c9a1c509304222464f460c5457 | 2df6b8f6f367e980b7f5cbd438a2b06b24b302d2 | /datai1/R script (1).R | 233e0b14824835c8921a444c7e12ee1f8a2f4a3d | [] | no_license | dcamposliz/dtsc1 | af4f6c6c0b0f340b5baa4fb1fc1d5cdee7db0cf4 | 09cfa6c5ff32046b0c91bd6bd1206eb280be47ce | refs/heads/master | 2021-01-10T11:53:00.278925 | 2015-12-28T08:40:39 | 2015-12-28T08:40:39 | 48,018,237 | 0 | 1 | null | 2015-12-28T08:40:39 | 2015-12-15T03:50:16 | R | UTF-8 | R | false | false | 26,557 | r | R script (1).R | ## Note separate functions should be used in this file
# However for this project perhaps it can all be on one document
## Set Working Directory to the datai1 folder
setwd("C:/Users/Peter/myProjects/dtsc1/datai1")
## These must be installed the first time
install.packages("xlsx")
install.packages("dplyr")
install.pac... |
85bb10135032273719a8bcaba2e906f2ba1bb1c2 | 012cc18008110914ddaa63e00cffcf6ba99c3c4e | /plot4.R | 36b238e1017fc2f47960c051186d3dc0e50a1091 | [] | no_license | zackgouttel/ExData_Plotting1 | 03bfe07fbbc901ab80891e283cf7d164a4e9b9f9 | 49ad10daaa8b2e12904994051485d713f4a72492 | refs/heads/master | 2022-09-22T09:39:56.077110 | 2020-06-07T00:37:18 | 2020-06-07T00:37:18 | 269,798,054 | 0 | 0 | null | 2020-06-05T22:42:59 | 2020-06-05T22:42:58 | null | UTF-8 | R | false | false | 1,061 | r | plot4.R | sub_data<-function(df=household_power_consumption)
{
d1<- dmy("1-2-2007")
d2<- dmy("2-2-2007")
p1_sub<-subset(x = df, Date==d1 | Date==d2)
}
draw_plot4<-function(df=household_power_consumption)
{ plotdata<-sub_data(df=household_power_consumption)
plotdata$datetime<-as.POSIXct(paste(plotdata$Date,plotdata$Time)... |
6348251170741ce24688d63910bb2b3062604571 | c7c9fbc40a719c1d2198697ebcbd9f48b2496572 | /global.R | 650fc94d5865d847a55885ef02e38b123241d8d9 | [] | no_license | sarikayamehmet/ShinyLdap | cd6066282b35a85cfdc313c664b46ac85bde7010 | 9f4ffa463a078d26ae2b90b54eaa0c020ad12c4b | refs/heads/master | 2020-06-07T03:53:12.254606 | 2019-05-15T21:38:12 | 2019-05-15T21:38:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,049 | r | global.R | # GLOBAL.R
# instala os pacotes faltando.
list.of.packages <- c("shiny", "readr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library('shiny')
library('readr')
ldap_url <- 'ldap://jacarta.intranet....:389'
ldap... |
85fb6603227d7edcf09e6cfd28db6ceb0a4b9e81 | 7d1415b3ad71b079d39d0de3311e294c7083968f | /plot2.R | d12189e61182d27ee23962282d46bf54c58eb053 | [] | no_license | svsubbarao/ExData_Plotting1 | a9df7979d7aa5a0a0049ee86f791c05c753fac9e | 43741eb66d824dcfd132c0c0a27c4f6cdab77996 | refs/heads/master | 2021-01-18T10:00:23.191081 | 2015-09-11T04:48:22 | 2015-09-11T04:48:22 | 42,287,924 | 0 | 0 | null | 2015-09-11T04:39:10 | 2015-09-11T04:39:10 | null | UTF-8 | R | false | false | 652 | r | plot2.R |
## read the data from the file
dataR <- read.csv("household_power_consumption.txt" ,header=T,sep=";",stringsAsFactors=F, na.strings="?",colClasses=c("character", "character", "numeric", "numeric", "numeric","numeric","numeric", "numeric", "numeric"))
## subset the data from large data set
dataR <- dataR[dataR$Date %i... |
a873cc881055fa210d667d38e56306b801c3e975 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query57_query49_1344/query57_query49_1344.R | 033c382faa0a82f56a44eaf69945cdbae3019c57 | [] | 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 | 717 | r | query57_query49_1344.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 10617
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 10617
c
c Input Parameter (command line, file):
c input filename QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4... |
4ee9d87f0cedd1e381147fec1c09c691bf836795 | f7c8e2cdfa83f8188ced6e561934f65bfe96d640 | /man/Class_Spells.Rd | 861d26420af65387d4a68e04414fe51a6147ea72 | [] | no_license | vinny-paris/DnD | e4eb946c2dbc5b338d9318b685286b953fbeb034 | 22ce06b34b1f37709cbd2698b0c6f9f3d61e708e | refs/heads/master | 2021-01-19T01:08:21.405350 | 2017-09-12T18:42:13 | 2017-09-12T18:42:13 | 87,227,701 | 10 | 1 | null | null | null | null | UTF-8 | R | false | true | 489 | rd | Class_Spells.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class_Spells.R
\docType{data}
\name{Class_Spells}
\alias{Class_Spells}
\title{Spells Known by Class fully labeld}
\format{An object of class \code{list} of length 8.}
\source{
{Dungeon and Dragons Fifth Edition Player's Hand Book}
}
\usage{
C... |
4c95e4435a92c5865efe84893cfa59b4a838c21a | ba6d284c860e9aee3054c1d384b6250402ebd81e | /man/read_recoveries.Rd | b57d33a9280cdc7633f81f899c05cae5b5b2f587 | [
"MIT"
] | permissive | jnshsrs/corona | 13921b6bdad742316b46a1f849e94a2010d3d891 | 61dc1739f2ad49c8c486b19839ebcd7c9f409d61 | refs/heads/master | 2021-05-21T04:13:01.666292 | 2020-04-06T19:16:26 | 2020-04-06T19:16:26 | 252,537,378 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 508 | rd | read_recoveries.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read-data.R
\name{read_recoveries}
\alias{read_recoveries}
\title{Read Corona Recovered Numbers}
\usage{
read_recoveries(file = NULL)
}
\arguments{
\item{file}{The file path to load from. Do not specify it, to load from
JHU github repository ... |
9cbcf398ab8b57e3e68b6d2aa3260f3323352b2d | 605169a6c3f45c2c103a0bac7ecc53c720bc355f | /A4-w1.R | ea250092bbe0c7dc38bbc4b858564191eb4c1c76 | [] | no_license | layaSharifi/ExData_Plotting1 | e41b11c0bea1947fce9f0c30e46a4a9d94582f6a | ce66a7394b7bbd6a53bdc9e87f3cd8c9425428ad | refs/heads/master | 2020-12-31T05:57:34.953417 | 2017-02-01T21:07:01 | 2017-02-01T21:07:01 | 80,633,779 | 0 | 0 | null | 2017-02-01T15:32:24 | 2017-02-01T15:32:24 | null | UTF-8 | R | false | false | 2,751 | r | A4-w1.R |
##Data Loading and Processing
install.packages("data.table")
library(dplyr)
setwd("C:/Users/lsharifi/Desktop/Rot2/coursera/A4-w1")
power<- read.table ('household_power_consumption.txt', sep=";" , header=TRUE, na.strings="?",
stringsAsFactors=FALSE
)
View(power)
#convert date and time variables to Date/Ti... |
a249c95196148fc95c5cd03d567ba8b715f1d887 | 6379ae1e1875cc0e82d47163722f314fe9797ff6 | /week2.R | 32bcc354ea4c57cf565d4915a94865c1c312788b | [] | no_license | aarjan/strategic-business-modeling | 7e1c7b168ea34524d7c289c53596729049af8688 | 9fa0f86001d6441e34ecf7804ee8ef6d35ce52a5 | refs/heads/master | 2020-03-18T23:56:01.883265 | 2018-06-20T11:56:49 | 2018-06-20T11:56:49 | 135,440,224 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,259 | r | week2.R | rm(list=ls(all=TRUE))
# Let's load our dataset and call it data
data=read.table('data/DATA_3.01_CREDIT.csv',sep=',',header=TRUE) # The function read.table enables us to read flat files such as .csv files
# Now let's have a look at our variables and see some summary statistics
str(data) # The str() function show... |
777a0d034d1f2374cf84d5119c8b50bcae87a910 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rem/examples/reciprocityStat.Rd.R | 4c15c6b618e23cea1732363c888e3dae6ac315cc | [] | 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 | 4,549 | r | reciprocityStat.Rd.R | library(rem)
### Name: reciprocityStat
### Title: Calculate reciprocity statistics
### Aliases: reciprocityStat reciprocity
### ** Examples
# create some data with 'sender', 'target' and a 'time'-variable
# (Note: Data used here are random events from the Correlates of War Project)
sender <- c('TUN', 'NIR', 'NIR', ... |
b559376ac94aeb5bcfb4e9f6ed5b5d18c119b071 | 0c8251dda435aa2a13f3bf1631641f4b595ba183 | /R/tam.jml.R | 09ba97a36925ac0ce9fe68ef7377d864e361196f | [] | no_license | yaozeyang90/TAM | e7212ff14c364126ae32738ef590dc5a6df6d0f1 | 2f9cedef0a282460415eadeb62de220774066860 | refs/heads/master | 2020-03-11T22:43:27.522233 | 2018-04-17T09:26:36 | 2018-04-17T09:26:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,224 | r | tam.jml.R | ## File Name: tam.jml.R
## File Version: 9.27
tam.jml <- function( resp , group = NULL , adj=.3 , disattenuate = FALSE ,
bias = TRUE, xsi.fixed=NULL , xsi.inits = NULL , theta.fixed = NULL ,
A=NULL , B=NULL , Q=NULL , ndim=1 ,
pweights = NULL , verbose = TRUE , control = list() , version = 2 )
{
CALL <- ... |
56007d1b6ea70d5d3d0ad75d3e2a8c120fa71f5b | 2f227338295aaaa3caff4e211516820aee08b2ad | /man/gd_df_by.Rd | 9c0f1721f8b8eba80eee8d1569c3e71389afe75e | [] | no_license | acobos/gocDescriptives | b0e310f2eef2eca66e5743ab811609b3f6b11ad7 | fb4b3ea7a7b1c3d4471126f3702d167cd5159df3 | refs/heads/master | 2021-09-12T07:03:24.822767 | 2018-04-15T08:55:46 | 2018-04-15T08:55:46 | 128,122,489 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,167 | rd | gd_df_by.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gd_df_by.R
\name{gd_df_by}
\alias{gd_df_by}
\title{Describes a dataframe by groups}
\usage{
gd_df_by(df, by, ...)
}
\arguments{
\item{df}{\code{dataframe} to be described.}
\item{by}{Name of the variable defining groups. Should be \code{char... |
ab138f40c98d03a44e40782a523a8d52f89e4664 | f9d49957d442694c7b8ad14c7a27b91db1032a35 | /term-project/sample/code/03_02_OneSamplet.R | cf46167f1a0be0b36aaca4e40630580ffe47a2f6 | [] | no_license | tmussa1/r-programming | f92b1d095c14cd9709514c32dcddc3d2d19e3006 | 8ca41facb7e8d7abdc2425673b4543d0b35d4052 | refs/heads/master | 2023-01-23T21:47:29.837096 | 2020-12-16T18:23:07 | 2020-12-16T18:23:07 | 297,534,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,889 | r | 03_02_OneSamplet.R | # Title: Comparing one mean to a population:
# One-sample t-test
# File: 03_02_OneSamplet.R
# Project: R_EssT_2; R Essential Training, Part 2:
# Modeling Data
# INSTALL AND LOAD PACKAGES ################################
# Install pacman ("package manager") if needed
if (!require("pacman")... |
dbfee92cb1006bc0ea1aa549d64eb0b44e82ea0f | 007b929dbdb6ec3040d635ccbf7906f2d84a87b5 | /htree/man/hrf.Rd | 5279324620ca592ba8b94fc7d1ed598dc1bdd110 | [] | no_license | joxton/htree | 7d8b85a0619121453e11c704eecafdc45ee6d30a | 60b4f37ff41d6ddf303a432a3423b16c310ed712 | refs/heads/main | 2023-08-28T03:37:53.592237 | 2021-10-17T14:39:12 | 2021-10-17T14:39:12 | 418,086,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,993 | rd | hrf.Rd | \name{hrf}
\alias{hrf}
\title{Random forest for longitudinal data}
\description{Fits a random forest of historical regression trees to longitudinal data.}
\usage{
hrf(x,
time=NULL,
id=NULL,
yindx,
ntrees = 100,
method="freq",
mtry=NULL,
se=FALSE,
B=100,
R=10,
nsamp=5,
hist... |
ea47394f6429da64ff5d11f5518ca2387607ec24 | c8f83302772325930ecab8f61d4f39c06e9e2e98 | /Make_R0_Incursions.R | bad01f3c1d3ee97602cac5b2d08f72efed572565 | [
"MIT"
] | permissive | boydorr/TransmissionScale | 5fda1bb6b2ae62f522cea0b2a9af1901f57456d4 | 566e1830254d57d262dfc5771ef564218981fb44 | refs/heads/main | 2023-04-11T21:47:34.835197 | 2022-04-21T14:55:56 | 2022-04-21T14:55:56 | 450,220,751 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,941 | r | Make_R0_Incursions.R | # Make incursions to simulate multiple runs to compute R0, Rebecca Mancy, started 2021-08-10
# Note that the files required to run this script are large and are not provided.
rm(list=ls())
library(ggplot2)
library(dplyr)
library(tidyr) # library(sp)
library(sp)
ff <- "java_output/"
end.in.days <- 5112
f_LOWER_VAX... |
ca108dbb315f871c0bd405cbff9d3d0a4526856b | 9c17405743a5f066a2ff01ddff1e696704c8b9aa | /materials/labs/5_Friday/proteomics/proteomics.R | 196ea6349812efb6eed53cfe8fc3103d3e6bd616 | [] | no_license | BioXiao/CSAMA2015 | 2a5510463012da4ce79cfb91c4fae7316cf3a5d2 | 1e3648514f2b6dd72b6c694b3183b64caf210206 | refs/heads/master | 2021-01-21T19:19:22.454977 | 2015-10-29T12:08:02 | 2015-10-29T12:08:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,456 | r | proteomics.R | ## ----env0, message=FALSE, echo=FALSE, warning=FALSE----------------------
library("knitr")
opts_knit$set(error = FALSE)
library("BiocInstaller")
library("RforProteomics")
library("nloptr")
## ---- env, message=FALSE, echo=TRUE, warning=FALSE-----------------------
library("mzR")
library("mzID")
library("MSnID")
libr... |
807f9ef210976b6ddc29887be2ad012c042b126b | d998fb34120769092d9e273e625fec4dea381704 | /man/read_taxa.Rd | a3fb8009dcab5504e6fcd3da4e48816e373cec60 | [] | no_license | cran/benthos | 56c3be3b8ece470823486c636243baad07d290b1 | 6615e8093e4d5bd925e7a33e47319952a044bc4f | refs/heads/master | 2022-09-20T20:31:47.639967 | 2022-08-22T09:10:06 | 2022-08-22T09:10:06 | 111,669,759 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 858 | rd | read_taxa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/io.R
\name{read_taxa}
\alias{read_taxa}
\alias{get_taxa}
\alias{validate_taxa}
\title{Read and Validate Taxa Data}
\usage{
read_taxa(filename)
get_taxa()
validate_taxa(.data)
}
\arguments{
\item{filename}{name of taxa file}
\item{.data}{ta... |
9b7d73955f281d6791a941edc4312e759c8f10de | 80393834bd9ac5b564fc040fd5112af949891f54 | /TodosAlgoritmosSemestre/euler.R | 9f6ebcdb6f695220b748ba239a771c11343b4265 | [] | no_license | SantiagoRomeroPineda/AnalisisNumerico | 59cc9a0fbefea1cbdc29fa4413b1337ecb2242bc | 4250af68396dade545f0ee27d5155e6a63dc1e0a | refs/heads/master | 2023-01-20T15:47:32.796976 | 2020-11-27T14:18:31 | 2020-11-27T14:18:31 | 285,291,665 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,115 | r | euler.R | library(PolynomF)
require(deSolve)
rm(list=ls())
euler1 = function(f, t0, y0, h, n) {
#Datos igualmente espaciados iniciando en x0 = a, paso h. "n" datos
#f es la derivada es decie dy/dt =f(t,y)
t = seq(t0, t0 + (n-1)*h, by = h) # n datos
y = rep(NA, times=n) # n datos
y[1]=y0
for(i in 2:n ) y[i]= y[i-1]+h... |
d34588d1c3edc99e54ae6e1a483a1a52528d10a2 | 91b827e48e510661d90d81d0155130b2703ae51d | /analysis/4-binary-effectmod/4g-PR-prev-adj-scoop.R | af318db6bfe1d4d20e2d72033c3110c5b0a031b3 | [] | no_license | LaKwong/WBB-STH-Kato-Katz | 2f0cf1a906ced8badb35b18df72adbcf41013534 | d4d97093fb4e04f6cb7bcf919e41c42ea25cff83 | refs/heads/master | 2020-09-02T05:37:17.925364 | 2019-04-24T19:43:26 | 2019-04-24T19:43:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,126 | r | 4g-PR-prev-adj-scoop.R | ##############################################
# WASH Benefits Bangladesh STH Kato-Katz Study
# Primary outcome analysis
# STH adjusted analysis
# Binary STH outcomes
# Effect modification by whether household
# has a dedicated scoop for removing feces
# by Jade Benjamin-Chung
# jadebc@berkeley.edu
###############... |
12445347df345d96289d49b351e3c2efc3c4c7ce | bff9a887ea03299db2217c5b7b2d249eab946a10 | /Siganus_sutor.R | 3630fb302883994babe773d455f4deeb3c8173d0 | [] | no_license | zanbi/https-github.com-merrillrudd-LIME_application | 82a0efde5a0b74110d8e33715e2fcaa7bcf79ec8 | 0608e8d466ab63676f5cca278203afd04c286b3d | refs/heads/master | 2022-04-16T16:43:25.864524 | 2018-03-29T19:25:08 | 2018-03-29T19:25:08 | 256,052,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 50,435 | r | Siganus_sutor.R | rm(list=ls())
## load LIME package
devtools::install_github("kaskr/TMB_contrib_R/TMBhelper")
devtools::install_github("merrillrudd/LIME", build.vignettes=TRUE, dependencies=TRUE)
library(LIME)
## load LBSPR package
devtools::install_github("adrianhordyk/LBSPR", build.vignettes=TRUE, dependencies=TRUE)
library(LBSPR)
... |
ad3f0c198c9c6a6ca52dd7cd62423cab8726b93d | 7ca0ee6639ecad09598c5c58d058ccda00884f6b | /modules/about.R | dbcd95fb74839555eb7e8720f2c4da8165de1de5 | [] | no_license | FlowWest/Explore-SIT-Model | edcf404fa66be7c2d7284b992dded66a9bfdda9f | 7f47fc2a5f9e3a0c5bbb03e11f2f5331354105aa | refs/heads/master | 2021-06-20T21:59:30.842006 | 2017-07-25T21:29:37 | 2017-07-25T21:29:37 | 94,383,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,172 | r | about.R | aboutUI <- function(id) {
ns <- NS(id)
tagList(
fluidRow(
column(width = 12, style = 'padding-left: 30px;',
tags$h1('Fall Chinook Science Integration Team Model'),
tags$br(),
tags$img(src = 'model.png', width = '80%'),
tags$br(),
tags$br(... |
528c0723604855891c762929224043b4ad842923 | 2ed4494745086bc2fb1f018de1c0cc2a5db71f25 | /R/app/server/helpers.R | ac5a4da23b9655b1714ef10c36cfc0990d73f72a | [] | no_license | shi-xin/density_estimation | 8e7868cb0bdca1a544f68efabe21178c8ab48ab5 | 86c4adee22df9b918e7b2514a3b84716185a72f6 | refs/heads/master | 2022-09-17T05:55:15.007284 | 2020-05-27T02:24:26 | 2020-05-27T02:24:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,380 | r | helpers.R | source("server/helpers_mixture_modal.R")
# Initialize python ------------------------------------------------------------
use_python_custom = function(path) {
tryCatch({
reticulate::use_python(
python = path
)
suppressWarnings(invisible(reticulate::py_config()))
path_found = dirname(reticulate:... |
91379bfd78ffbfd42917200eee75042ee2a4b670 | ba49eb475d4fcd6d61655270ec34fe829657494e | /man/dynamic_structure.Rd | f7a26ec5dd74a5cb07aaba9ed8e99f9a59c02a9b | [] | no_license | cran/Bios2cor | f3b630684862d4f48e7137361e39094358336f63 | 50b2948bfdd3888e6593015247419ce1f5b58d8a | refs/heads/master | 2022-07-31T18:21:28.244917 | 2022-07-08T08:25:23 | 2022-07-08T08:25:23 | 101,306,648 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,370 | rd | dynamic_structure.Rd | \name{dynamic_structure}
\Rdversion{1.1}
\alias{dynamic_structure}
\title{
Creates the data structure for the analysis of side chain dihedral angles
}
\description{
Given a structure pdb file, a trajectory dcd file and frame indices, gathers information on side chain dihedral angles in a unique structure. Th... |
a51f0f5d8a4ba06329bd12007283d3724a3d4b85 | 5b722b3e230f078b59c232a0554c416150232bbf | /man/video-images.Rd | 8c29ee31e5dce2c30aa3727c0669df09b9f9b5bc | [] | no_license | jsta/glatos | 3abdd25c67455cacf04cbb7a9ca53718745ab917 | f1e27cf63da53b2ae4c46e4b47d338c1f558d004 | refs/heads/master | 2022-07-29T00:04:04.783120 | 2022-07-05T18:56:00 | 2022-07-05T18:56:00 | 220,477,927 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 541 | rd | video-images.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extdata.r
\name{video images}
\alias{video images}
\title{Video frames of walleye movements in Lake Huron}
\format{Folder contains 30 sequentially labeled .png image files}
\source{
\url{http://glatos.glos.us/home/project/HECWL}
}
\usage{
sys... |
ce21640568a8e6ed86464e5545b60d781f089b00 | 18e0c41ddb514c981090eb4ab2e3f358d88dbd70 | /plot6.R | 712963a9fd2b51ecfbaa3c2ef4d38ddfe05936d4 | [] | no_license | andhdo/coursera_ds_exdata_project2 | dd37d018b71ea773e58ecf74166878a98cec7644 | 13499a09a90d5e012944d1c769d184eb8e95ecfe | refs/heads/master | 2021-01-23T20:12:56.663660 | 2014-08-21T11:29:20 | 2014-08-21T11:29:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,364 | r | plot6.R | # see complete source code at: https://github.com/andhdo/coursera_ds_exdata_project2
#
plot6 <- function() {
# be sure to load the files and precomputations
source("load_data.R")
load_prerequisites()
# load libraries
library(ggplot2)
# get the specific data to plot
NEI <- load_data_nei()
SCC ... |
23443270a9274d2e1c5bbee2900f3463b7f1a26a | 7c210bf1d85690915e99065a269336126914971e | /data.table/arun_ex4.R | 769e50fe6f1d96404ade2b4245f2b0bd5c2047fe | [] | no_license | ariasmiguel/tidyverse | 4c5e1411fa87dc3cd221c999ef4fd20d9411fb4d | ea1b7b40e0e0e18797fc087ab1f14e86eab18163 | refs/heads/master | 2021-01-21T13:35:03.597505 | 2017-10-04T15:30:40 | 2017-10-04T15:30:40 | 102,133,802 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,455 | r | arun_ex4.R | # -----------------------------------------------------------------
set.seed(20170703L)
DF1 = data.frame(id = sample(1:2, 9L, TRUE),
code = sample(letters[1:3], 9, TRUE),
valA = 1:9, valB = 10:18,
stringsAsFactors = FALSE)
DF2 = data.frame(id = c(3L, 1L, 1L, 2L, 3L)... |
30356faa57d0f6d409f39cae86aa140a5e5966e3 | c9a347ea394f398128c19688085292f80b35993b | /Plot_prop_change.r | f71063ac74861ccbbc3f7bfe6726f4302e86874b | [] | no_license | liuzh811/ForestDegradationWestAfrica | f1c780a09b3670588a8ab87e06bba1365d6b7b82 | 0e6e9a69281d7fb99d7218084c41cafe531a5917 | refs/heads/master | 2021-01-17T08:12:30.719691 | 2016-10-02T03:43:42 | 2016-10-02T03:43:42 | 40,625,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,054 | r | Plot_prop_change.r |
clasnames <- c("Water",
"RainForest",
"Woody Savannas",
"Savannas",
"Grasslands",
"Cropland/Natural Mosaic",
"Others")
#after run calculating all indices and plot trend
lc_rc1 = lc_rc*100 + TCW.trd2.grd
lc_rc1.df = data.frame(... |
890e1d9dda93dc3830d0e4eb9c039fe51bc2f61b | 86b335a36147f0895e82ba13dc122695d1fa155a | /clusterProfiler.R | 4613cc65f22b422f5dfa8d91082612c0ec3a606e | [] | no_license | Gig77/ccri-iamp21 | 65eaf17d2ce59683d944458ff62806232cf1a3c5 | 395bb6517c8cbf7c83d68d65869bf19021e6df93 | refs/heads/master | 2021-01-17T18:23:29.641089 | 2016-07-27T13:02:22 | 2016-07-27T13:02:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,986 | r | clusterProfiler.R | # install
source("http://bioconductor.org/biocLite.R")
biocLite("clusterProfiler")
biocLite("org.Hs.eg.db")
library(clusterProfiler)
# example data
library(DOSE)
data(geneList)
# get genes IDs and their log2 fold changes
deseq <- read.delim("/mnt/projects/iamp/results/deseq/iAMP-vs-DS.tsv")
# merge Entrez gene ID
l... |
c74bf959e0bbcb4c5af577c8f8ad5366ef6c3c7b | ddd4c23a99df404114ca93254b9925edd941e5cc | /R/rm.R | 19fda9c8a909a33504e51fcd267be4a688ecf91e | [] | no_license | cran/subselect | 80a3ecdc41983ce0cdb7a2fe3e9b843541872642 | 803cc11ee30c42ca4bc40a999ce4ebba5493e50c | refs/heads/master | 2023-07-21T07:10:13.194980 | 2023-07-10T22:30:12 | 2023-07-11T07:34:43 | 17,700,176 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,418 | r | rm.R | rm.coef<-function(mat, indices)
{
# Computes the matrix correlation between data matrices and their
# regression on a subset of their variables. Expected input is a
# variance-covariance (or correlation) matrix.
# error checking
if (sum(!(as.integer(indices) == indices)) > 0) stop("\n The variable indic... |
65a1d3af71b415e853618de9f778a6b1b977f5e3 | aec6b08ba76e9edf6ae2ae410face966f624fd5e | /Module4.R | 923954baa2912ce1edb14e25ba80f3dd7764534e | [] | no_license | mahesh-ml/R-data-analysis | 46d339d1dbad5fa5ca8ec2e89f0e6456dda89948 | 3bdd637f32a97e65f5c7b928c03dccf920b3873a | refs/heads/main | 2023-01-25T02:04:53.374270 | 2020-12-06T17:42:38 | 2020-12-06T17:42:38 | 318,367,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,911 | r | Module4.R | #Module 4 Problem 1 Divide 743 by 2 and obtain the result such that the output gives us a value without the decimal point.
a<- 743%/%2
a
#Module 4 Problem 2 Print a 3*4 array of three dimensions, which has the input of vectors sequencing from 13554:13590 and index the second row and third column element of third dim... |
bf4f161ce46fb83892082b534e4c455f384b5d07 | cedbb22841ea152a733ccb9aff3ffa66560e5428 | /2-1-2-reshaping-data.R | e31cdd5e96f3e96996d0a8ff54f012ad328f2d3d | [] | no_license | andre-lie/course-6-data-wrangling | 98a50aab3458ae1ef53cadd3f81c616308220f5e | 825aa9b47e2ae08e560c9d5a9f23d66e8f6c2598 | refs/heads/master | 2023-01-21T09:11:49.995948 | 2020-11-28T06:14:28 | 2020-11-28T06:14:28 | 307,327,160 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,108 | r | 2-1-2-reshaping-data.R | # original wide data
library(tidyverse)
path <- system.file("extdata",package="dslabs")
filename <- file.path(path, "fertility-two-countries-example.csv")
wide_data <-read_csv(filename)
# tidy data from dslabs
library(dslabs)
data("gapminder")
tidy_data <- gapminder %>%
filter(country %in% c("South Korea","Germany")... |
d4f34e47dcca2a8a8b2146b086d608c7c2cf13eb | 442f9770e53101c4461c9031dfd69d3dfa69a757 | /man/plotMultiHist.Rd | 57d1d3f968bd262aeaa4c8cecb806c3e91a465ef | [] | no_license | cran/WGCNA | edaf87638c6cf0c9105dbb67637ebe059f598cb1 | 31f538c2f9d7d48f35f7098b4effe17331357d0d | refs/heads/master | 2023-01-25T02:34:33.041279 | 2023-01-18T11:10:05 | 2023-01-18T11:10:05 | 17,694,095 | 48 | 54 | null | 2019-08-17T13:25:00 | 2014-03-13T03:47:40 | R | UTF-8 | R | false | false | 1,649 | rd | plotMultiHist.Rd | \name{plotMultiHist}
\alias{plotMultiHist}
\title{
Plot multiple histograms in a single plot
}
\description{
This function plots density or cumulative distribution function of multiple histograms in a single plot, using
lines.
}
\usage{
plotMultiHist(
data,
nBreaks = 100,
col = 1:length(data),
scaleBy =... |
16e63a850a1cced72e9c30676e6e269d02abbf6c | 7416b60a1a57cf2ae49d9d75ab773e03394b9256 | /man/predsu_pred.Rd | c5f68c7ef913fe7c9bc864bf4792ae065f44281d | [] | no_license | olssol/predSurv | b50532ec7ab8d0732892e5fb9f6226a8ffc9f504 | ebb8b8303f5c1ac8f9f719b6d252ebfe4ed39ea1 | refs/heads/master | 2023-04-03T14:01:11.719289 | 2021-04-25T20:21:56 | 2021-04-25T20:21:56 | 361,344,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 345 | rd | predsu_pred.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predSurv_tools.R
\name{predsu_pred}
\alias{predsu_pred}
\title{Predict survival time}
\usage{
predsu_pred(prob_ints, condition_t = 0, option = c("upper", "lower"))
}
\description{
Predict survival time conditioning on censoring time based on ... |
89c2f05acdba21f0a82a43aff3e92d5864547853 | b17f27d710cab5ce27f51c41307af2df6c349fec | /R/iProFun.eFDR.R | af440bb13aa0d36a3cfd440c9709f530aefaeb23 | [] | no_license | JiayiJi/iProFun-1 | 12a45cc2462408a0d046f0f8835bc4137b4345fb | e3d0d63cf4e024429abb8527232ddb8c6f7abda7 | refs/heads/master | 2023-04-24T03:51:34.990871 | 2021-05-02T20:24:58 | 2021-05-02T20:24:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,372 | r | iProFun.eFDR.R | #' Summarize the association probabilities for an outcome.
#'
#' Summarize the association probabilities for an outcome across different association patterns.
#' The summation includes variables with missingness in some data types.
#' @export iProFun.sum.prob.1y
#' @param prob The posterior probabilieis of association ... |
93d12c8ae1e72fbc99961b3f5d1c7fd7b75569c7 | 0d46d44af30f9ef892746f61ef24c75e38aa2c9e | /RInvest/stocks.R | df0b11e07d109815ad726b64635dcece86cd32e5 | [] | no_license | ntwo1980/Invest | e79acead286749d20ff8ebed2f24cce02c9cadee | 56f8e70615277826ad48d4cfbdd6f28b12d7f3c4 | refs/heads/master | 2020-04-06T07:00:09.272009 | 2016-09-06T05:14:43 | 2016-09-06T05:14:43 | 65,250,554 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,109 | r | stocks.R | source("utils.R")
load.stocks.data <- function(file) {
data <- read.table(file, header = T, sep = ",", as.is = T, fileEncoding = "utf-8",
colClasses = c("factor", "character", "Date", "numeric", "numeric", "numeric"),
col.names = c("Code", "Name", "Date", "PE", "DPE", "PB"))
data <- sqldf("s... |
74c398309fa117d1c069fc44dc67c74087db7f49 | 1df04d65fa9ea2eea223e4a7f10049cffac4f94f | /backup/CCode/SHM/SHM_CM.R | 70265eeecc7c9bdefd636912e488c60ead181e4f | [] | no_license | jhncl/Model-in-C | aa3b51071b609afdb2c129840fdade7ece80de95 | 0b42e3cd587acb651c40fd965ac01a04eaa992d4 | refs/heads/master | 2021-01-18T20:30:09.524459 | 2014-04-28T09:22:46 | 2014-04-28T09:22:46 | 2,867,397 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 9,662 | r | SHM_CM.R | filename="M_SHM_A_FULL"
library(rjags)
library(qfa)
library(qfaBayes)
Control<-c("Adam_cdc13-1_SDLV2_REP1.txt","Adam_cdc13-1_SDLV2_REP2.txt","Adam_cdc13-1_SDLV2_REP3.txt","Adam_cdc13-1_SDLV2_REP4.txt")
DescripControl<-"ExptDescriptionCDC13.txt"
a<-rod.read(files=Control,inoctimes=DescripControl)
qfa.variables(a)... |
d76440123c796da2763ecab73945d878c8793615 | 12ccdd95c02ca34822d0a3862a28bb17170955f5 | /man/residuals.ccrm.Rd | 78af565b0e09246f8e686c19ec0f149e5ab883b4 | [] | no_license | cran/iRegression | d156b210e062f4548d5cd1fa8fc64cb00832b4f1 | 4d2c16e000ed423412f980b5fe7ff27519216fe3 | refs/heads/master | 2021-01-19T01:41:43.944423 | 2016-07-18T20:09:24 | 2016-07-18T20:09:24 | 17,696,732 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 460 | rd | residuals.ccrm.Rd | \name{residuals.ccrm}
\alias{residuals.ccrm}
\title{Extract Constrained Centre and Range Method Residuals}
\description{Returns the residuals from an object class \code{ccrm}.}
\usage{
\method{residuals}{ccrm}(object, ...)
}
\arguments{
\item{object}{ an object class \code{ccrm}. }
\item{\dots}{ other argu... |
80b40eab6e3743e017743147cc8a06724fea0ce5 | 2c70811ab30b8450945af59f16253efc4e453a5b | /ProgrammingAssignment1/complete.R | d00e31af821cf0a938061031d6ddae489c1102da | [] | no_license | manuelmunoz/datasciencecoursera | 9dd6615e7980b2a983de46e7f1cc30b0cd3b910e | 1caa0e9530c2c351accf31baa99ac022d5a7d8d7 | refs/heads/master | 2021-01-23T12:18:15.446667 | 2016-08-05T19:42:30 | 2016-08-05T19:42:30 | 64,493,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 587 | r | complete.R | complete <- function(directory, id = 1:332) {
# Initializing counters
h <- 0
suma <- 0
contador <- 0
final <- data.frame()
# Retrieving list of data files
ficheros <- dir("specdata")
# Going through data files
for(h in id) {
# Dumping a single file in a data frame
datos <- read.csv(pa... |
1c5ec38c2bd86ba2434d1c5ac02dc46923a4fe34 | 3fc582b5ab0a1d2f778fd2582ce7c0bb670aa11d | /man/ComputeCropCalendar.Rd | 32a89f402490edc24a5e90d0aa29df731e3c5105 | [] | no_license | bluegulcy/aquacrop | 43b56a8becb9244fe3028af853f3c19c99bdd5dd | 50a1d815d04259e8792aba763fc53e0f896bf1a0 | refs/heads/master | 2020-09-02T10:55:40.510230 | 2019-10-08T10:03:12 | 2019-10-08T10:03:12 | 219,206,208 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 663 | rd | ComputeCropCalendar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ComputeCropCalendar.R
\name{ComputeCropCalendar}
\alias{ComputeCropCalendar}
\title{Compute additional parameters needed to define crop}
\usage{
ComputeCropCalendar(Crop, CropName, CropChoices, Weather, ClockStruct)
}
\arguments{
\item{Crop}{... |
f606c7f9ad76ff64034b324e7cea901e0df3def3 | e3204b0f913527581139df64c8189a91fdeb342c | /.Rproj.user/E300CC8A/sources/per/t/5DF6BD11-contents | bc4808766cd89d0e0a3857a311dead903863dbab | [] | no_license | DaoudaTandiangDjiba/ExamEdacySalingPricePredictions | c23bc1c675bee0832198361f09ef5cde6865c9ea | 078153e4ffc87dc422456d6bcce1edfb83c82390 | refs/heads/master | 2020-04-06T15:06:11.115065 | 2018-11-14T16:14:06 | 2018-11-14T16:14:06 | 157,565,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,088 | 5DF6BD11-contents | #
# Import packages
library(plyr)
library(tidyverse)
library(MASS)
library(car)
library(e1071)
library(caret)
library(cowplot)
library(caTools)
library(pROC)
library(ggcorrplot)
library(reshape)
library(scales)
library(xgboost)
library(gbm)
library(randomForest)
# Load the train and test dataset
train <- read.csv("t... | |
5932949187afe985118e9165879c12ae6ffc4386 | 287408d83dd37285f619ac76eb7607bc9a7f3928 | /R/pcalc.R | f3e9ddee64a76304e3991f6eab7b3d635df2fb8e | [] | no_license | victoraLab/pcReasy | fad747c80ba97d44fdbacd7775da19c74820a4d1 | 5a21263fa92ee9f92d24cf402d213b5fa61803f2 | refs/heads/main | 2023-07-08T10:14:22.227070 | 2021-08-17T01:24:45 | 2021-08-17T01:24:45 | 397,015,165 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,993 | r | pcalc.R | #' Calculate PC reaction
#'
#' Returns a efficient protocol for pc reaction setup.
#'
#' @param v The final volume desired for each PC reaction. Numeric.
#' @param rxn The number of total reactions. Numeric.
#' @param initialConc Data Frame containing the initial concentrations of each component.
#' @param finalConc Da... |
ea670d04e2c0d27d6b2d400f431ddf1e53b07781 | b9af64c8c92854e98da10e3ab316ba153e408dfc | /NGS/script_all.R | be9477ff4a8523e7464aaf87c26506f885ac4108 | [] | no_license | igemsoftware2021/iGEM_Simulation | ba27f07c0e17a55a5b51eb3c1b7d4ab2bf3d6326 | 431584687ddab68d85f73658dcfb19419ae534f1 | refs/heads/main | 2023-08-24T14:25:13.752231 | 2021-10-22T01:41:11 | 2021-10-22T01:41:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,075 | r | script_all.R | library(ggplot2)
name = c()
pos = c()
ref = c()
var = c()
read_mut = c()
read_tot = c()
file=list.files(pattern = "A122_10")
for (i in file){
tmp = read.csv2(i, sep = "\t")
name = c(name, rep(substring(i, 1,11), nrow(tmp)))
pos = c(pos, tmp$Position)
ref = c(ref, tmp$Ref)
var = c(var, tmp$VarAllele)
read_... |
ded517cf1e61bd5aefe5fe388585813d434922df | c766cdf25b2c437cf66f3f833821ba5e061cb3e1 | /man/sqlGetQuery.Rd | bfe82ccb2857cf7f098274e724e7d9ac343ca26d | [
"MIT"
] | permissive | danielfm123/dftools | 91692018cb67b5251c4fd8d5af6bc6548e3a4fc1 | 51baaac3219a7e94239bfa1c16c76aea4ed6f468 | refs/heads/master | 2021-08-06T01:41:39.523063 | 2021-07-14T18:19:25 | 2021-07-14T18:19:25 | 204,495,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,881 | rd | sqlGetQuery.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sqlGetQuery.R
\name{sqlGetQuery}
\alias{sqlGetQuery}
\title{Gets result from sql query}
\usage{
sqlGetQuery(server_name, query, param=c(), dt=FALSE, key=c(), ...)
}
\arguments{
\item{server_name}{character, name of the DB server from sqlServe... |
c15adde00b3e2867573d214e1bb8f0d2635b34bd | 5032968fceb435ad9d47df6885782cbf71439564 | /PCA hierchial +k means scm.R | 7b9363ac1dff80483c1886687e1e2bfde1a07e7e | [] | no_license | surjithcm7/EXCEL-R-ASSIGNMENTS | 5063f3f8434246de939a288163724067a95c3856 | c948bba5f35c31f39bfdbcf229d963654fc4afa8 | refs/heads/master | 2021-03-19T00:47:02.103035 | 2020-03-13T16:46:50 | 2020-03-13T16:46:50 | 247,115,828 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,001 | r | PCA hierchial +k means scm.R | library(readr)
data<-read.csv(file.choose())
View(data)
#since type column is irrelevant we can remove it
mydata<-(data[-1])
View(mydata)
attach(mydata)
cor(mydata)
#the correlation values are somewhere negative somewhere positive somewhere high
#which implies the columns are correlated each other
##... |
034d969a6469c90daf114ab5422b85eae47eac11 | 99dd753646dc29f1f34b9faf655e119c0cc5655d | /ui.R | 2324d30114493e16e07763b8942becd98d800a35 | [] | no_license | hanyangunivleejoohyun/r-test | 4914273d422195da8e50d83a96bf08eb66bccfed | 76a91a0082afad22313b916601877c8b11e8003d | refs/heads/master | 2020-05-23T21:50:52.097164 | 2017-03-20T06:22:03 | 2017-03-20T06:22:03 | 84,793,403 | 0 | 0 | null | 2017-03-14T00:43:56 | 2017-03-13T06:49:49 | R | UTF-8 | R | false | false | 1,532 | r | ui.R | library(shiny)
shinyUI(fluidPage(
titlePanel("Wordcloud in Moby Dick example"),
# 사이드바 UI
sidebarLayout(
sidebarPanel(
textInput("word", label = h3("분석할 단어를 입력해주세요(example : dog)"), value=""),
textInput("num", label = h3("문맥 분석할 앞,뒤 단어수를 입력해주세요."), value = "3"),
sliderInput("scale", labe... |
e0eb16a8646271ce60fc4d69d81d0ceca6084ee7 | 24194d2bd6986aeb089e5557158470194923d128 | /man/winsd.Rd | 25b0f790426e96746e587815f7d23bfe5494e0a4 | [] | no_license | shizidushu/fsrs | 35e83a11a77c6110225748b90baf147f030b8ba7 | 32e9ff6e132c3eeee04f2ee9472735f9bdd2fd00 | refs/heads/master | 2021-01-25T14:16:43.047809 | 2018-08-03T03:51:24 | 2018-08-03T03:51:24 | 123,678,350 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 899 | rd | winsd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summation.R
\name{winsd}
\alias{winsd}
\title{Winsorized sample standard deviation}
\usage{
winsd(x, trim = 0.2, na.rm = FALSE)
}
\arguments{
\item{x}{An \R object. Currently there are methods for
numeric/logical vectors and \link[=Dates... |
2dc7c099eb3e52aabebc0e7c6aea21f4ea7c0b8c | dc5b0c038d56db8a2f777e76971df77eb9cb2955 | /stl-async.R | 3f76b0ec4e674a5ccd51c4340c91bcc66c9f2379 | [] | no_license | gianordoli/health-issues-server | ffe0c4d9b41c029240f61e2f8d9772b57464ccef | a279094da05131c9adcdc042c1ed889b4ef2c78d | refs/heads/master | 2021-07-11T17:05:43.287268 | 2017-10-05T13:59:14 | 2017-10-05T13:59:14 | 105,484,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,167 | r | stl-async.R | needs(stlplus)
vectorToTS <- function(data) {
print("vectorToTs")
ul <- unlist(strsplit(data,","))
dataMatrix <- matrix(ul, length(data), 2, T)
# Retrieving first and last months and weeks
firstDateRow <- head(dataMatrix[,c(1)], n=1)
firstDate <- strsplit(toString(firstDateRow), "-")
firstYear <- as.int... |
70ecc5bfa76a86b53a8d5d88d1924dcdf7a8f3c1 | 567a0e51b6b09ccb245fd18d429491950df6801e | /R/Sankey/Sankey diagrams.R | 01188944883f752888016c6548b2e1c1294e93c2 | [] | no_license | samofashhurst/data-recipes | a6f4ab58ca64cf62d34d83562bce1cb21b6d9bc7 | 9f1eb30220736c7f6acc563f0a50ff230e0c14df | refs/heads/master | 2021-10-23T12:04:12.196235 | 2021-10-13T04:30:23 | 2021-10-13T04:30:23 | 226,813,792 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,554 | r | Sankey diagrams.R | # This script creates a Sankey diagram, which can be used e.g. for seeing common paths between pages during a browser session
# install.packages("networkD3")
library(networkD3)
# Set working dir to dir of script
initial_wd = getwd()
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# Add names for the page... |
1ccc91bf40badd26afc78ccb0b0dbf95e683f8dd | 069b5c0f325197dfbcc6584288a70ae7c51c8b81 | /RegressionModel/Quiz1.R | 97f94887b65fbf2700558de4a4ae8578feaaf08b | [] | no_license | JohanLi1990/DataScienceCoursera | 3db341c5a2074f9bc25121a699a0374026fdf233 | 99aa5574e530b6f9b9dd9bf208008e2c845625ae | refs/heads/master | 2021-01-23T13:44:02.512726 | 2017-12-25T00:40:32 | 2017-12-25T00:40:32 | 38,518,965 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 839 | r | Quiz1.R | #Quiz 1-1
x <- c(0.18, -1.54, 0.42, 0.95)
w <- c(2, 1, 3, 1)
for(i in c(0.3, 1.077, 0.0025, 0.1471))
{
ans <- sum(w*(x-i)^2)
print(ans)
}
# Quiz 1-2
x <- c(0.8, 0.47, 0.51, 0.73, 0.36, 0.58, 0.57, 0.85, 0.44, 0.42)
y <- c(1.39, 0.72, 1.55, 0.48, 1.19, -1.59, 1.23, -0.65, 1.49, 0.05)
fits <- lm(y ~ x-1)
summar... |
00bf26efd8e220b21245cbba95fbaf4ca76f4b3f | bc248968b4e8e6da338695bf782ca6711dc8410a | /man/CosineDFV.Rd | ce6ecde222ff57c748b3b4fa2391f7bd223e87b6 | [] | no_license | PengNi/dSimer | 75a1f87f2d3494c51b3aa76e75d09d52a01f3b99 | e5c408e583c3759685fe0e41133916dbce49d265 | refs/heads/master | 2020-03-28T03:01:27.040649 | 2017-01-19T11:59:16 | 2017-01-19T11:59:16 | 49,204,477 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,432 | rd | CosineDFV.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CosineDFV.R
\name{CosineDFV}
\alias{CosineDFV}
\title{calculate disease similarity by using feature vectors}
\usage{
CosineDFV(D1, D2, d2f, dcol = 2, fcol = 1, ccol = 3)
}
\arguments{
\item{D1}{a vector consists of disease ids/names}
\item{D... |
5ff39c898110f9e569c8773f2dbf0f1567515d5a | 6504f689aa3c557f7d11634ce0b09363a9e79bb4 | /plot6.R | de24ad330ad0d752a68f17d84d3f592890fc2171 | [] | no_license | ryanbthomas/EDAProj2 | d7c3f6c6462a08f7aae6b6231574d5e2e6aaf700 | 3cbf61edd8e131cc467e0d52a0f1ecc78d770db9 | refs/heads/master | 2022-02-02T02:03:05.393310 | 2015-06-19T00:08:14 | 2015-06-19T00:08:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 254 | r | plot6.R | #plot6
g <- ggplot(plot6_data) +
aes(x=year,y=Emissions/1000, fill=fips) +
geom_bar(stat="identity",position="dodge") +
scale_fill_discrete(name="City",breaks=c("06037","24510"), labels=c("Los Angeles","Baltimore City"))
g <- g + geom_p |
86e773fa25a96cada511772a88312690a8cf817a | 2297b13b601747e5009339028dc7d6614208ec1d | /Model Ensembling/Boosting/iris_XGBoost.R | a8269184acb19591093e89b05a091ef8df218aad | [] | no_license | prathmesh31/machine_learning_classwork | c3c649aed6519a3872ac10d806e84f3f1f6b7eb7 | 9e612a9bb57653aa6a7c17e23b4cb5277c0253d8 | refs/heads/master | 2020-03-24T03:37:34.664141 | 2018-07-26T10:55:59 | 2018-07-26T10:55:59 | 142,426,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 753 | r | iris_XGBoost.R | data("iris")
library(caret)
set.seed(1992)
intrain<-createDataPartition(y=iris$Species,p=0.7,list=FALSE)
training <- iris[ intrain , ]
validation <- iris[-intrain , ]
library(xgboost)
mat_train <- as.matrix(training[,-5])
mat_validation <- as.matrix(validation[,-5])
lbl_spec <- ifelse(training$Species=="setosa",0,... |
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