blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ea61855892927b1e3ea4a1c72ad8422c82f9d056 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GGEBiplots/examples/DiscRep.Rd.R | 55f38f414de554ee8f8d17bbc7b9e8369bb49fc9 | [] | 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 | 230 | r | DiscRep.Rd.R | library(GGEBiplots)
### Name: DiscRep
### Title: Discrimination vs. representativeness biplot
### Aliases: DiscRep
### Keywords: GGE
### ** Examples
library(GGEBiplotGUI)
data(Ontario)
GGE1<-GGEModel(Ontario)
DiscRep(GGE1)
|
ef40d4a06e6deb4c684d51fc81016fba5170f891 | 66d54063f1b6c995fbb2510a54b934d3fab73c21 | /Jaime_shiny_examples - copia/Usando_drive/Google_sheets/survey_example/survey_v2/app.R | 176d596fa26daea09a2e2c4b8bfd3b66c6f5b77a | [] | no_license | Jsvelezm/Shiny_usefull_examples | d6ae95711285f36e77ff33c105e45da91b15e453 | 897a5ba5416d675b9f1417b223c51ca7dcb871cd | refs/heads/master | 2022-10-13T11:13:20.915362 | 2020-06-10T18:33:32 | 2020-06-10T18:33:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,862 | r | app.R | ## app.R ##
###### Lectura de bases ######
##### librerias ####
library(shiny)
library(googlesheets)
library(shiny)
# init some useful variables
Logged = FALSE
start = TRUE
data_id = 1
# seteando la clave de la aplicación
options(googleAuthR.client_id = "807629257711-q5d10ng1egi2qfru5drik4gj9iekn... |
d95736f8c2182aacf4d3d5c75f51fca2357b5346 | e037c771ce9ad1f9d3583597937a43d29000fd3b | /bussiness_prac/비즈니스활용사례R05.R | d26b4371da7cf9cc4f9cad9cd5fe1722c717f161 | [] | no_license | Sup90/R- | ff2920046d9033ee995331dd5cb6b7196f8e539b | 7eb6e1c35615a2aed1a5374c5429d4e4d946a876 | refs/heads/master | 2021-01-01T16:41:49.953354 | 2018-11-20T12:42:22 | 2018-11-20T12:42:22 | 97,892,427 | 2 | 0 | null | null | null | null | UHC | R | false | false | 2,600 | r | 비즈니스활용사례R05.R | #5장 A/B테스트
getwd()
setwd("c:/Users/rhkdt/Desktop/R-/bussiness_prac/R")
ab_imp<-read.csv("section5-ab_test_imp.csv",header = T,sep = ",",stringsAsFactors = F)
head(ab_imp)
ab_goal<-read.csv("section5-ab_test_goal.csv",header = T,stringsAsFactors = F)
head(ab_goal)
ab_imp_goal<-merge(x = ab_imp,y = ab_goal,by = "transact... |
aff414f49fc393c4a973987384b3c862b604de67 | 7d8c2e74c2e395a9c6cf427e77b0fb5e96584012 | /Week3/Code/boilerplate.R | 21af261572e8503751fec6919aab7943e7b08621 | [] | no_license | hjosullivan/CMEECourseWork | 52ef24377923b496cd88fb8295c4d0c77e91b7dc | b0e68602d8cab0e37ebdc0f22d8a9c673a978cc6 | refs/heads/master | 2021-07-12T19:00:17.011985 | 2019-03-14T07:54:00 | 2019-03-14T07:54:00 | 151,391,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 640 | r | boilerplate.R | ##########################
## A boilerplate script ##
##########################
## Author: Hannah O'Sullivan h.osullivan18@imperial.ac.uk
## Script: boilerplate.R
## Desc: Introduction to writing R functions
## Date: October 2018
#clean environment
rm(list = ls())
MyFunction <- function(Arg1, Arg2){
#statements i... |
f821944cd5287a905d1695cd6e105ad75632459d | 3e832b24c9967221ee76aabbb0f64fce9506ac2a | /signal analysis.R | 12b536d3d22172c506fbb25b75098e6c1942e54e | [] | no_license | misophist/microstructure-analysis | 205f2938c23c93fb29d55799d5e81898a12a8760 | ed5b9dc94917100d3eb990fa97ecf58a1f14351b | refs/heads/master | 2021-01-19T19:37:42.810067 | 2014-07-01T07:44:55 | 2014-07-01T07:44:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,979 | r | signal analysis.R | dtt <- data.frame(
trl.vwap.1 = runif(100),
trl.vwap.2 = runif(100),
cog.1 = runif(100),
cog.2 = runif(100),
fwd.vwap.1 = runif(100),
fwd.vwap.2 = runif(100)
)
# for each RIC
# break dataset into 60-20-20 training-validation-test sets
# with the training set:
### run regression for each fwd.adv with all th... |
e44bccca3fed2117724aa6455a5e0e4b9ace8499 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/aspace/examples/as_radians.Rd.R | 66e6e7c7d36827c392cf2f78744ef1c6a26722ff | [] | 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 | 164 | r | as_radians.Rd.R | library(aspace)
### Name: as_radians
### Title: Converts degrees to radians
### Aliases: as_radians
### Keywords: array
### ** Examples
as_radians(theta = 90)
|
eff195558859690cebfe9edc1759da0374343303 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googleruntimeconfigv1beta1.auto/man/Waiter.Rd | cac3c215d205c30290ee3ed47c99404c7b671df1 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,649 | rd | Waiter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/runtimeconfig_objects.R
\name{Waiter}
\alias{Waiter}
\title{Waiter Object}
\usage{
Waiter(error = NULL, failure = NULL, success = NULL, done = NULL,
createTime = NULL, timeout = NULL, name = NULL)
}
\arguments{
\item{error}{[Output Only] If... |
337504139025b8c210e8b7ca479b3ff39d56c44f | cdc0504ea03ec5c439006f1e47bbc618fb983ba0 | /man/corona_lockdown.Rd | be1f0d7c557580480f0421496af3c6dcfdabebad | [] | no_license | jvanschalkwyk/corona | a0ae3df8ff81199b848747f2133d685c40b5f1d4 | 5d4621092cc8bb3772595ea5b50390cfcd564098 | refs/heads/master | 2022-12-24T20:43:37.738883 | 2020-10-01T01:34:55 | 2020-10-01T01:34:55 | 270,596,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,018 | rd | corona_lockdown.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lockdown.R
\name{corona_lockdown}
\alias{corona_lockdown}
\title{Draw multiple smoothed graphs of new daily cases, with lockdown date, if present}
\usage{
corona_lockdown(
pdf = FALSE,
minpeople = 4e+06,
mincases = 200,
cols = 7,
st... |
32a64ee9d8beee407e389429dced0a65ced9da7a | a82ebc7c1dcc3eb671542f10645ab3d457853565 | /r_modular/classifier_mpdsvm_modular.R | 76424bef76d2ee73e8364b0a02289cfc402b74e2 | [] | no_license | joobog/shogun-eval | dac24f629744521760061c7979aa579129daa666 | 12b1ba2a67d5c661c6a11580634fb1a036e61af2 | refs/heads/master | 2021-03-12T23:24:41.686252 | 2016-11-23T10:04:04 | 2016-11-23T10:04:04 | 31,391,835 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,494 | r | classifier_mpdsvm_modular.R | # In this example a two-class support vector machine classifier is trained on a
# toy data set and the trained classifier is used to predict labels of test
# examples. As training algorithm the Minimal Primal Dual SVM is used with SVM
# regularization parameter C=1 and a Gaussian kernel of width 1.2 and the
# precision... |
faf65c57baeeedd41782cf74a973c4a4a53db13b | 104e7052ad28ab830b441968543f07d36938b45a | /try_test_3.R | 5d033b2cc789a4ae910b4829d9f547c1bc4b48f2 | [
"MIT"
] | permissive | talkdatatome/kaggle | bfafd58abdd468417d6747c2b2e231c1d473ad41 | c666b19a115935ff565e4288532955ed47e07662 | refs/heads/master | 2021-01-21T13:14:14.206603 | 2016-04-30T17:12:55 | 2016-04-30T17:12:55 | 53,010,547 | 0 | 1 | null | 2016-04-06T02:16:14 | 2016-03-03T01:35:56 | R | UTF-8 | R | false | false | 532 | r | try_test_3.R | load("test_of_test.RData")
library(tm)
library(gamlr)
library(SnowballC)
dtm_STT$dimnames[[2]] <- paste("ST", dtm_STT$dimnames[[2]], sep="_")
dtmPD$dimnames[[2]] <- paste("DS", dtmPD$dimnames[[2]], sep="_")
testX <- cbind(dtm_STT, dtmPD)
### TRY - I'm still missing terms
# try adding ncol and dimnames for empty names... |
ee5aabf6b70c6fbe6ae26300b898e5aa6bec8b73 | 34445bf76bb3ec1d0e75c063c16f842a1afe5e97 | /R/myncurve.R | 8c44b26a4f73d17e9c6302f4b626f1ad1822d8f7 | [] | no_license | medgar591/MATH4753EDGAR | 4b96e7167d7bc7695f1c2991b9dae16ceeffb8f4 | a6fefb7dfce41c18de4d842e38d8d15982101a30 | refs/heads/master | 2023-04-04T20:39:53.901521 | 2021-04-20T00:22:36 | 2021-04-20T00:22:36 | 334,214,148 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 667 | r | myncurve.R | #' @title myncurve
#'
#' @param mu Mean value for a normal distribution
#' @param sigma Standard deviation for a normal distribution
#' @param a Value to calculate the probability of, P(Y>=a)
#'
#' @return Graph of the curve with shading based on a, as well as a calculation of P(Y>=a)
#' @export
#'
#' @examples
myncurv... |
1fa545e066b9e0980d508ecf21d927874dc86872 | c5f4ffb6e2525c91657a3721c29ec95e4549ec2e | /apps/prep.R | 54d6dc7ec11c94f7e0a0526898122fff584934cf | [] | no_license | g64164017/yt-subtitle-search | 52378ade2c484f4cb6b8d84fed2a8c5625f03589 | a531cfa0cf20b1eadf23f7e10596a0ec1fbd28d9 | refs/heads/master | 2021-05-05T23:00:20.761405 | 2018-03-09T23:44:51 | 2018-03-09T23:44:51 | 116,452,603 | 0 | 2 | null | 2018-01-19T17:13:57 | 2018-01-06T04:17:12 | CSS | UTF-8 | R | false | false | 1,052 | r | prep.R | ## SET WORKING DIRECTORY
# working dir = current file path
setwd(normalizePath("."))
library(tuber)
## AUTHENTICATION
## Manage API on https://console.developers.google.com/apis/credentials
app_id="find your own"
app_secret="find you own"
yt_oauth(app_id, app_secret, token="")
## COLECTING DOCS
channel_id = "UC4a-Gb... |
37f0dbe4424aa27d4a8220870171825a07a9a8ac | d2ee3f02b09c20a6c35bba1e11e7c78865569417 | /scripts/plot_umap_sf.R | 8d7f0fdc5ebca91850936fbea77db67f73c52e64 | [] | no_license | marcalva/DIEM2019 | 7bcfa2efcc4a87bf1e38ae941cee40137ca524ba | 87ca5081e095b6eac6560462b3375c65f373e7bb | refs/heads/master | 2020-08-05T17:52:32.161696 | 2020-04-22T07:38:26 | 2020-04-22T07:38:26 | 212,642,580 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,559 | r | plot_umap_sf.R |
# Splice fractions
setwd("../")
library(diem)
suppressMessages(library(DropletUtils))
library(ggplot2)
library(gplots)
library(ggpubr)
library(gridExtra)
library(ggExtra)
library(RColorBrewer)
source("scripts/common/plotting.R")
#=========================================
# Functions
#===============================... |
5227a2fb45fe1c74693d71f135fa2b9fbcf72313 | 22331b9b9a318c24ade5724acf67e9daa9ec830e | /ui.R | 9588d570b790309421fecec8f385e5dda95392fe | [] | no_license | paolo64/predwage | e14e785e3a2944d636abc450b29060cd4d8b708c | 37f2a1056b0c2e82e34370c2b618d23331f6d345 | refs/heads/master | 2021-01-18T20:31:18.687531 | 2015-07-26T20:48:56 | 2015-07-26T20:48:56 | 39,738,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,908 | r | ui.R | library(shiny)
library(quantmod)
library(ISLR);
library(ggplot2);
#require(rCharts)
options(RCHART_LIB = 'polycharts')
data(Wage)
#
# shinyUI
#
shinyUI(fluidPage(
# Application title
title = "Wage Predictor",
fluidRow(
column(4,
img(src='wages.png', align = "left")
),
column(8,... |
f2a53d605555187c6e893c4def0f59839da2bbae | 59c770cd3731ed3bbc177ea90eafda077d5cec6f | /R/degseq.R | 6c5a0e339a1aea9ec4a501446d9b2b1f03dc7849 | [] | no_license | vishalbelsare/rigraph | e52af967467ebe453bd07cfba0555354cc182a36 | b1ae1de3aca4e2b7eedb4d0f00b8a5f1df35b78d | refs/heads/dev | 2023-01-21T13:25:31.175473 | 2022-04-27T11:02:53 | 2022-04-27T11:02:53 | 129,304,592 | 0 | 0 | null | 2022-04-28T12:22:47 | 2018-04-12T19:58:07 | R | UTF-8 | R | false | false | 4,601 | r | degseq.R |
## -----------------------------------------------------------------------
##
## IGraph R package
## Copyright (C) 2015 Gabor Csardi <csardi.gabor@gmail.com>
## 334 Harvard street, Cambridge, MA 02139 USA
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of ... |
2c4e447f61be4f90d27f19a1438ef59e88735dee | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/envlpaster/examples/targetboot.Rd.R | fc0dc35ac0cb0abff590f1f85407c42715cb7f69 | [] | 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 | 594 | r | targetboot.Rd.R | library(envlpaster)
### Name: targetboot
### Title: targetboot
### Aliases: targetboot
### ** Examples
## Not run:
##D set.seed(13)
##D library(envlpaster)
##D library(aster2)
##D data(simdata30nodes)
##D data <- simdata30nodes.asterdata
##D nnode <- length(vars)
##D xnew <- as.matrix(simdata30nodes[,c(1:nnode)])
... |
951a5bf80c452cc5fc90d37b2e5d285751f72874 | 25682e28a0cc24ab1e13ebf3b124cad6e7ec06f3 | /scripts/step02-DEG-3-packages.R | 7c368629a290bb92f08e05ad2f771ff2219d3bcc | [] | no_license | ixxmu/tcga_example | 0258a2910e4213026e9b0ebd2ab2a2a4fcf517cd | 8dec1553267f76e1223cb69b2384b550fedce13c | refs/heads/master | 2020-05-09T20:24:36.408744 | 2019-05-23T01:25:08 | 2019-05-23T01:25:24 | 181,406,409 | 0 | 0 | null | 2019-04-15T03:32:44 | 2019-04-15T03:32:44 | null | UTF-8 | R | false | false | 5,165 | r | step02-DEG-3-packages.R | library("BiocParallel")
register(MulticoreParam(2)) ##我的是两个核心 ，貌似有个检测核心个数找不找代码了…………
rm(list=ls())
options(stringsAsFactors = F)
library(DESeq2)
library(stringr)
getwd='../Rdata/'
Figure_dir='../figures/'
# 加载上一步从RTCGA.miRNASeq包里面提取miRNA表达矩阵和对应的样本临床信息。
load( file =
file.path(getwd,'TCGA-KIRC-miRNA-example.Rdata... |
a6f16253e0e322733793556b7e9894b2924267a2 | a06e8825887605e10507e41a923458c03040a362 | /man/hr_train.Rd | ae2f536c2538b1810cf2983af8aeb9c592e125ad | [
"MIT"
] | permissive | rsquaredacademy/mbar | 592a7c73af88229a96a032f4c8821d94270b9640 | 4914774326ee5b96b2d605c4fa626fdd5c402975 | refs/heads/master | 2023-08-30T17:02:00.692601 | 2019-06-10T14:15:16 | 2019-06-10T14:15:16 | 181,642,656 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 386 | rd | hr_train.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tree-data.R
\docType{data}
\name{hr_train}
\alias{hr_train}
\title{Decision tree train data}
\format{An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 1029 rows and 35 columns.}
\usage{
hr_train
}
\descriptio... |
de786c1f4b4eaf6d83669832fac0fca18fb4f68f | 7946b84034a7dd7e3d5b4d69db88373d58185789 | /R/get_bio_oracle.R | f263a0d78b34e210e99680b63a9fec5b29a1afee | [] | no_license | luismurao/ntbox | 3839a7a346b390850d9d1dc77cbd50cb32a52d00 | 220112e32c53ceef4f5f1bcdb7daed6b755604bf | refs/heads/master | 2023-07-09T05:55:01.119706 | 2023-07-08T18:01:47 | 2023-07-08T18:01:47 | 79,830,037 | 7 | 9 | null | 2020-07-21T00:42:51 | 2017-01-23T17:39:41 | R | UTF-8 | R | false | false | 5,983 | r | get_bio_oracle.R | #' get_bio_oracle: Get environmental layers from Bio-Oracle.
#' @description Get bioclimatic layers from Bio-Oracle for current and future scenarios
#' @param period Time period. Posible values are: "current","2050","2100","2200".
#' @param var_type Type of variable. Posible values are: 'salinity','sea_surface_temp... |
314ffbad35d314eadc5e1c6b7ede36054e4d5a25 | bc5d84e2464651b6267b4c93fffabf1df5fa8d7f | /src/image.R | 8e936893d3486129c27efef252f007c9f8b93d08 | [
"OGL-Canada-2.0",
"MIT"
] | permissive | amygoldlist/Baby_weights_by_sex | 3e7fb0cbc33830bc0052ed61e31932f3259dda06 | c1b9b7c7c8e09005d98d3ac2e99663be2f3295d3 | refs/heads/master | 2021-08-29T19:24:23.014519 | 2017-12-14T18:51:27 | 2017-12-14T18:51:27 | 111,855,893 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,260 | r | image.R |
##image.R
##by Amy Goldlist, Decmenber 2017
## Usage: Rscript src/image.R $orig_filename $new_filename
## the origina filename is "results/baby_data.csv"
## the target filename is "results/images/baby_histogram.png"
##This script created png file with a histogram of the data, grouped by sex
# read in command line... |
e3968c25007cd066abcd15c2dfbb9fb79f4a3d9b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/matchMulti/examples/matchMulti.Rd.R | 893f369429958d34ae4df05c88e7770b94973fa0 | [] | 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 | 2,760 | r | matchMulti.Rd.R | library(matchMulti)
### Name: matchMulti
### Title: A function that performs multilevel matching.
### Aliases: matchMulti
### ** Examples
#toy example with short runtime
library(matchMulti)
#Load Catholic school data
data(catholic_schools)
# Trim data to speed up example
catholic_schools <- catholic_schools[cat... |
f6cb214fc85c5475e07bb47262dfd745f38510ae | 218f94dc54f33ea755df171448e1ca9493c446a6 | /1_Nov2018/3_Practices/Governance/2_Tables_for_Back-end_Governance.R | 37a3c920fe1bd97159a2b0d25da727ec6b593980 | [] | no_license | WWF-ConsEvidence/ConservationDashboard | b96565195092fdb7f1c7a05c010e7b28ae700cbe | 94db2429b99f2bfe2f98a902d0ed06117b01bafa | refs/heads/master | 2021-06-02T17:09:58.342296 | 2020-10-30T23:10:36 | 2020-10-30T23:10:36 | 109,167,974 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,616 | r | 2_Tables_for_Back-end_Governance.R | #
# code: Governance Practice Indicator and Initiative Tables
#
# author: Kelly Claborn, clabornkelly@gmail.com
# created: August 2018
# modified:
#
# ---- inputs ----
# 1) Governance-specific data tables (in 1_Nov2018/2_FlatDataFiles/ConsDB_Input)
#
# ---- outputs ----
# 1) Governance-specific back-end tables -... |
74e2e8dd117084ca5976c96372b3ad915cc5a49a | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/1689/DIME/huber.R | e0e118924b70520877c9665654ff27d3c21d1ca9 | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 459 | r | huber.R | `huber` <-
function(input, co = -1.345, shape = c('full','lower','upper'))
{
input <- unlist(input);
len <- length(input);
shape <- match.arg(shape)
input <- (input - mean(input))/sd(input)
change <- switch(shape,
full = which(abs(input) > abs(co)),
lower = which(input <= co),
upper = which(input >= abs(co)... |
2d7130c81b30a9b66e82074e7eb634a9356fa5e2 | 068e0cfa3f62ba1a92c95256b3bb6df35629c56c | /SiReX/server.R | 79a599e43f13415c89d1dde7f83035e244f5bef6 | [
"Unlicense",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | anhnguyendepocen/SAFJR17 | e48ec7215dbc3087137cdebe02be58780046b8e9 | 56e55ef4b6d85a34a9af92719fe8bc83218ce7b7 | refs/heads/master | 2020-04-17T11:50:10.099420 | 2017-07-31T11:31:58 | 2017-07-31T11:31:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,054 | r | server.R | ## server.R script ##
source("common.R")
function(input, output, session) {
# Serve only possible values:
observe({
# Adjust n. of individuals, simulation 1
s1_sel_n_clusters = input$s1_n_clusters
updateSelectInput(session,
"s1_n_individuals",
choices = sort... |
f1dd7353826b71c8bc97d73ab0a460159f4ac1c7 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /srm/R/SRM_PARTABLE.R | a470b80b271bd03e70e0e96f62662ec5df6f4657 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,197 | r | SRM_PARTABLE.R | ## File Name: SRM_PARTABLE.R
## File Version: 0.07
SRM_PARTABLE_ORDER <- function(LIST, ngroups = 1L) {
NEWLIST <- data.frame()
for (g in 1:ngroups) {
tmp <- subset(LIST,LIST$op=="=~" & LIST$group==g )
if (nrow(tmp) > 0L) {
tmp <- tmp[order(tmp$lhs,tmp$rhs),]
NEWL... |
0863eea31a2184b78f415c84fedb983f0ebd5ea8 | 0cc77bb4edd0aad0c9f32e61a0a7cf8ca1718aa4 | /code/sim_results_plot.R | dae067aa24b73e3c17707a722dd7404e7ea04b54 | [] | no_license | rbrown789/mixture-cure-simulation | 85ebf53c4b43bdb0a6a4b2a5163ae3e1ddea875f | 7d1b1d134f0e46034049feb24cd07672fa9552f9 | refs/heads/master | 2020-12-28T12:03:30.810304 | 2020-02-05T05:42:30 | 2020-02-05T05:42:30 | 238,325,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,296 | r | sim_results_plot.R |
########################################################################
# This script generates all the results plots from Simulation 1. Apologies
# to anyone trying to figure out what's going on, cause I didn't anything.
#
########################################################################
library(survival)
... |
aa721338654971d9bdfb6d356abbd749d438883c | aba5794905d20a12d0207026b7d843a5d81c31ad | /man/load_filtered.Rd | f6f33c2c5980e633929571c2687f1c6fc9b1489b | [] | no_license | kgori/svfiltr | 9097a4eba8c0792766d3b81f9e515d71b0e79980 | 106a22110cf10310fba7da38e95a389ca28acb54 | refs/heads/master | 2021-01-12T14:55:10.535079 | 2016-11-04T21:15:51 | 2016-11-04T21:15:51 | 68,912,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 294 | rd | load_filtered.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/svIO.R
\name{load_filtered}
\alias{load_filtered}
\title{Load a filtered data frame}
\usage{
load_filtered(filename)
load_filtered(filename)
}
\description{
Load a filtered data frame
Load a filtered data frame
}
|
7e0aa7769f74b8c47cb6bfd46dd8b0d569e78a79 | 80bace7c01fc4fb4e0a0a1742da436b240c36349 | /man/merge_rgSets.Rd | 7287bfa09cd04325230813c42a4cbfb939de07ee | [] | no_license | ttriche/miser | 58b5995459b7586c4dcdde2d0cdb9d2224803c36 | 71953a0c463b59260a2ea14e3b17d8a698479f95 | refs/heads/master | 2023-07-20T00:53:55.058931 | 2023-07-10T14:43:39 | 2023-07-10T14:43:39 | 152,498,256 | 4 | 3 | null | null | null | null | UTF-8 | R | false | true | 648 | rd | merge_rgSets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/merge_rgSets.R
\name{merge_rgSets}
\alias{merge_rgSets}
\title{convenience function for merging large RGChannelSets}
\usage{
merge_rgSets(rgSet1, rgSet2)
}
\arguments{
\item{rgSet1}{the first RGChannelSet}
\item{rgSet2}{the second RGChannelS... |
157651fac4f3b133b663e496e7ed818ca2da4cbf | c0db54d7ec766ee9c087bcf967612a3c25169d7f | /R/calc.MMS.R | 522e0d9baae9cfc6a9a089cc0c3a9b89abb0a810 | [] | no_license | dataspekt/crodi | 5c7b78be89a446748e92f3f5d456a3ff33aa7e36 | b423da4e18facba5c26dec1166379ab6208232e1 | refs/heads/master | 2020-03-20T19:47:26.787067 | 2018-06-17T12:42:04 | 2018-06-17T12:42:04 | 135,913,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 342 | r | calc.MMS.R | #' Calculate min-max scaling
#'
#' @param x Indicator values.
#' @param refval Refrence value.
#' @param reverse Reverse direction of indicator values.
calc.MMS <- function(x, refval, reverse = FALSE)
{
if(reverse){
x <- 1-x
refval <- 1-refval
}
((x-min(x))/(max(x)-min(x))) / ((refval-min(x... |
926ccfa8835af89260cb4f06e4d227e23c89e9c1 | d26b1b446e18850cae39119828067d21a5079acd | /man/CBS_PBMC_array.Rd | 6f9e42fcc1e84776a57669084d1a4b6b1df9f542 | [] | no_license | ziyili20/TOAST | c623dcb2c64a89f00f84fddfab759da62d6434a5 | 102b6b1fa801561976b070adf1e15378bde43f76 | refs/heads/master | 2022-08-31T11:28:40.598741 | 2022-08-24T19:35:31 | 2022-08-24T19:35:31 | 145,612,563 | 12 | 4 | null | 2021-07-20T06:07:43 | 2018-08-21T19:53:08 | R | UTF-8 | R | false | false | 1,111 | rd | CBS_PBMC_array.Rd | \name{CBS_PBMC_array}
\alias{CBS_PBMC_array}
\docType{data}
\title{
An example dataset for partial reference-free
cell composition estimation from tissue gene expression
}
\description{
The dataset contains 511 microarray gene expressions
for 20 PBMC samples (mixed_all) and PBMC microarray
reference for the matched 511... |
5b6c203be0b0613d1d0a415c36d1fb0ca55d65ba | bdd4a0cf241425e857757b95afbf072ee017cf25 | /reproduce/doubleeffect-est-heuristic.R | 6a0de512f9b92ab4cbb93c2d6c9980cd87058ff7 | [] | no_license | CMLennon/WERM | aac17916b957792b14b6a1eb233b8182adad1586 | 9fe27c49a6db62b2e29f775e641aa11f6bbc0834 | refs/heads/master | 2022-12-30T13:36:58.092893 | 2020-10-22T05:56:22 | 2020-10-22T05:56:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,845 | r | doubleeffect-est-heuristic.R | library(xgboost)
library(boot)
source('WERM_Heuristic.R')
multiHeuristic = function(OBS,D,numCate){
################################
# Data Setup
################################
W = OBS[,1:D]
X = OBS[,(D+1)]; X0 = rep(0,length(X)); X1 = rep(1,length(X));
R = OBS[,(D+2)]
Z = OBS[,(D+3)]
Y = OBS[,... |
cbd45d7b83b64e718de502c14acfb00e4e178236 | f20346056c31fbf071e862897ccdef3dcb9a129e | /R/dist_matrix.R | cc8e6441e2de05062fbb93f61d996c03c9ca9bd1 | [
"CC0-1.0"
] | permissive | kbroman/Talk_CTC2019 | 38758a75bdae92bd412b44632b54c194d0032ed4 | 77e23f7ea71e130d15a6aac7944cae229aa341a5 | refs/heads/master | 2020-05-29T23:32:55.456873 | 2019-06-10T20:39:52 | 2019-06-10T20:39:52 | 189,437,953 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 865 | r | dist_matrix.R | # image of distance matrix
file <- "../Data/dist_matrix.rds"
if(file.exists(file)) {
d <- readRDS(file)
} else {
z <- readRDS("../Data/sample_results_allchr.rds")
d <- matrix(nrow=length(z), ncol=nrow(z[[1]]))
rownames(d) <- names(z)
colnames(d) <- rownames(z[[1]])
for(i in seq_along(z)) {
... |
54bb5bb250ac5032c96306f3da527bc9cd93f774 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/JumpTest/man/jumptestperiod.Rd | cbc83ae48304a4736394faa9d2d8eaa887e56ecb | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,283 | rd | jumptestperiod.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/testsim.R
\name{jumptestperiod}
\alias{jumptestperiod}
\title{Nonparametric jump test for a long period}
\usage{
jumptestperiod(retmat, method = "BNS")
}
\arguments{
\item{retmat}{log return matrix, with intervals saved in columns}
... |
53471262996608c3db899d53e9f2febc0aed1fc2 | e77503b75af8918e1ad6529afe4781805b32fd8e | /R/ps.match.pscore.R | 9d82d77c522bec2839e666e29bcad583ce765407 | [] | no_license | cran/nonrandom | ba91609f41e53b89415c1450582912c3b0f431d5 | 9341226967cd90d052dc2e32a7400a602bc404ae | refs/heads/master | 2021-01-15T22:00:49.870115 | 2014-04-04T00:00:00 | 2014-04-04T00:00:00 | 17,719,145 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,635 | r | ps.match.pscore.R | ## ###################################################
## Function to match data if object is of class pscore
## ###################################################
ps.match.pscore <- function(object,
object.control = NULL,
matched.by = NULL,
... |
3bf39cb34abc34f977d7a864d6a5931c800b61f1 | 9b78537a128feef02ff8249e1226b3eeb11156bd | /rScripts/oplsWithDiffAna.R | e8096bc553bc96f6d7efd810c067a1d4a7be277b | [] | no_license | yz8169/p3bacter | 0c3a0624bdad9bf6e02b1a18de442a3e8577c83c | 8f94634f7e286c523dd3c677287884c324d6f1e3 | refs/heads/master | 2021-01-26T01:25:33.254726 | 2020-02-26T12:28:31 | 2020-02-26T12:28:31 | 243,256,732 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,685 | r | oplsWithDiffAna.R | # Title : TODO
# Objective : TODO
# Created by: yz
# Created on: 2018/3/20
library(optparse)
option_list <- list(
make_option("--v", type = 'character', action = "store", default = "1", help = "vip Cutoff"),
make_option("--l", type = 'logical', action = "store", default = "F", help = "log 10 transform"),
make_opti... |
f11cea199f6aebccb87587099109e332adbc49e3 | fca29f054d4328f70a035e8cccecc9d6e18e966c | /Plot1.R | c57fefca54ab50e88c95eebe0732859e748acc57 | [] | no_license | rutlandneil/ExData_Plotting1 | cb5314c084695a65feeb679fd819dd0f7b7d6d56 | 997a4cc78b639c23a2dd02c0036c8477dbab360c | refs/heads/master | 2020-12-25T23:46:44.653644 | 2015-10-11T21:11:58 | 2015-10-11T21:11:58 | 44,062,500 | 0 | 0 | null | 2015-10-11T17:39:35 | 2015-10-11T17:39:34 | null | UTF-8 | R | false | false | 1,168 | r | Plot1.R | library(data.table)
library(dplyr)
library(lubridate)
#sets the location of the zip file to a location in your current working directory
zipLoc<-'./exdata_data_household_power_consumption.zip'
#unzips the file into a folder called exdata_data_household_power_consumption
unzip(zipLoc)
raw<-tbl_df(read.table('househol... |
31c45c79222de8a8c4af5b61e7f933ef047e3a43 | 6fa2802ec42c4e52baeeb0b5d61ad0cadbcd372b | /R/rmoutlier1d.R | c9be9e964aa9deb0ea4cf822e73222e9b0524a53 | [] | no_license | Taigi/l1kdeconv | 6c8529b6b54ecbe07e08a270b457aef2200850cd | 463499db63e7bd0839be883c7ccdc97cfa672200 | refs/heads/master | 2021-04-18T21:31:14.281323 | 2017-07-08T03:41:45 | 2017-07-08T03:41:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 940 | r | rmoutlier1d.R | # vim: set noexpandtab tabstop=2:
#' Remove the Outliers in a Vector of 1D Coordinates
#'
#' Remove the Outliers in a Vector of 1D Coordinates
#'
#' @param x a numeric vector
#' @param dy_thr the threshold for dy
#' @param clustersize_thr the threshold for cluster size
#' @param gapsize the threshold of points in recog... |
c9f81a7888c5b298a8672c692edda3ecc3754306 | e33ec1ba05866fd6655a9525b2f5632ed49e3d13 | /R/am.smath.r | bf97fa7c0e831d7ae857ff175bdfa0700de2fb70 | [] | no_license | ameshkoff/amdata | d38070a6162ab736de20ff6c651272a7b5f7ac5e | 1cb7e5c214c8fdf2e34ca71b74722dce3f9247a9 | refs/heads/master | 2020-12-24T11:52:40.715039 | 2017-08-22T18:23:20 | 2017-08-22T18:23:20 | 73,110,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 333 | r | am.smath.r | #' Test if is whole number
#'
#' Test if the number is the whole number
#'
#' @param x numeric
#' @param threshold numeric: tolerance, threshold
#' @return Logical
#' @seealso ...
#' @export
amm.is.wholenumber <- function(x
, threshold = .Machine$double.eps ^ 0.5) {
abs(x - round(x)) <... |
d03f80bf1557d07f4cea12a0e4a2c6aaf17ed1a0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ggTimeSeries/examples/stat_marimekko.Rd.R | 9e41f7e9cd4d0120ee4f6038701f27559d625fd1 | [] | 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 | 519 | r | stat_marimekko.Rd.R | library(ggTimeSeries)
### Name: stat_marimekko
### Title: Plot two categorical variables as marimekko
### Aliases: stat_marimekko
### ** Examples
{
library(ggplot2)
ggplot(
data.frame(
x1 = round(3 * runif(10000), 0),
y1 = pmax(pmin(round(3 * rnorm(10000), 0), 3), -3),
weight = 1:10000
)
) +... |
8d870b6db21f7694bb8cf84dd29e798634a96ec1 | 64e38921903014f892033a6c802cee381956c37c | /man/get_res_value.Rd | 7f1100872346a3fc48cf57b08c7cb446902a424f | [
"MIT"
] | permissive | scienceverse/scienceverse | 53092891f145f456b02c351d848516271dffb013 | 2519e7af87eae9439828cd36d4468160cf6e09a5 | refs/heads/master | 2021-06-28T12:32:23.106786 | 2020-11-09T17:22:09 | 2020-11-09T17:22:09 | 182,833,527 | 31 | 2 | NOASSERTION | 2020-06-26T12:30:50 | 2019-04-22T17:17:36 | HTML | UTF-8 | R | false | true | 408 | rd | get_res_value.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{get_res_value}
\alias{get_res_value}
\title{Get value from results list}
\usage{
get_res_value(txt, results)
}
\arguments{
\item{txt}{text of result to check against names}
\item{results}{named list of results}
}
\value{
value ... |
dab4d94c16049795393d5ede301e0bd014033c48 | dee361052b87ddd442608360f0dfecf765731859 | /R/wallet-deposits.r | 2d2b404e2addcd6a018cad333c14bfb101fc4654 | [
"MIT"
] | permissive | zamorarr/rcoinbase | b828d8779bd963883bcf5bb8f5449914bdd9c5d5 | e3c97694c8cdefafab23e52b7f32e6558fd92957 | refs/heads/master | 2021-08-29T19:33:08.909098 | 2017-12-14T19:23:29 | 2017-12-14T19:23:29 | 112,635,105 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 726 | r | wallet-deposits.r | #' List deposits
#'
#' Lists deposits for an account.
#'
#' @param account_id Account Id
#' @export
#' @family wallet-deposits
#' @references \url{https://developers.coinbase.com/api/v2#list-deposits}
get_deposits <- function(account_id) {
endpoint <- paste("accounts", account_id, "deposits", sep = "/")
coinbase_ge... |
ac77e4840e20e338bdef4aa4ef311afb0a328955 | 60491b8d44eaa4ee02c7ae9d90d9d6991febbcd6 | /code/24_7_study/food/food_data_preparation.R | 4cf778dab2fa2fcb0975e15dd09b1190be0da23f | [
"MIT"
] | permissive | jaspershen/microsampling_multiomics | ae2be38fe06679f43b980b76ea152109bbdd8fce | dea02f1148e5aad3243c057a98f565f889be302f | refs/heads/main | 2023-04-14T17:44:20.010840 | 2022-09-05T23:23:26 | 2022-09-05T23:23:26 | 469,214,924 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,095 | r | food_data_preparation.R | ##
no_function()
masstools::setwd_project()
setwd("data/24_7_study/food_log/data_preparation/")
library(tidyverse)
rm(list = ls())
load("../../all_omes_wear_food")
dim(all_omes_wear_food)
data =
all_omes_wear_food %>%
dplyr::filter(MolClass == "Food")
data %>%
dplyr::filter(is.na(Intensity))
data$SampleID ... |
a01d97c333f7065575545dd509dffbece03ba16e | 4798cb29678fb3e54a317ef28ff1ddaec260cb89 | /HD_RGB_Flight_Height_Tool/old_scripts/Flight_Height.R | a728fc5cc8234776e51eb88c0207f707d3119f0f | [] | no_license | HiDef-Aerial-Surveying/RBG_Flight_Height_Analysis | 5dd481b3542edb662d75b67a020e24b06f1b97e8 | 167076025cc73526ae586e794bfcc4b7516fff78 | refs/heads/master | 2023-06-22T09:36:37.840795 | 2021-07-23T15:11:06 | 2021-07-23T15:11:06 | 320,638,015 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,145 | r | Flight_Height.R | ############################################################
### Flight height calculation module
### v 0.0.1
### Grant Humphries, Ruth Peters-Grundy
### April, 2020
### R version 3.6.3 "Holding the Windsock"
###########################################################
# Load libraries -------------------------------... |
7fb82593ac54efe18e8cb5a9f131a58b0a5f4885 | 257fe6f1e3416c381e8eb8bcd2d7d3471a182213 | /Week3/hw2.R | 2896ab391cda48c39d996e3d106199206971a566 | [] | no_license | RobertCPhillips/EdxAnalyticsEdge | ede27095cc6600083c4b13139b82c87e434f2cb1 | f03d4add0a4c016683c4fbf1892d9abf3a5a1ba4 | refs/heads/master | 2020-06-04T01:23:33.791474 | 2015-08-30T15:10:57 | 2015-08-30T15:10:57 | 40,155,589 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,617 | r | hw2.R | par <- read.csv("parole.csv")
str(par)
summary(par)
# male: 1 if the parolee is male, 0 if female
# race: 1 if the parolee is white, 2 otherwise
# age: the parolee's age (in years) when he or she was released from prison
# state: a code for the parolee's state. 2 is Kentucky, 3 is Louisiana, 4 is Virginia, and 1 is an... |
dabea44efbe2213f3ecb9b3c2bf883f22ecb5058 | b8dbee4b91b48121bff4329ce2f37c89d8836290 | /seqUtils/man/importFastQTLTable.Rd | 5471b0ce3a2980c5aa2aed3003f0636cfe0fe0ea | [
"Apache-2.0"
] | permissive | kauralasoo/macrophage-tuQTLs | 18cc359c9052bd0eab45bd27f1c333566fb181d8 | 3ca0b9159f3e5d7d1e0a07cdeadbeb492e361dcb | refs/heads/master | 2021-03-27T19:29:12.456109 | 2019-02-19T13:05:26 | 2019-02-19T13:05:26 | 93,025,290 | 1 | 3 | null | null | null | null | UTF-8 | R | false | true | 484 | rd | importFastQTLTable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qtl_fastqtl.R
\name{importFastQTLTable}
\alias{importFastQTLTable}
\title{Import fastQTL output table into R.}
\usage{
importFastQTLTable(file_path)
}
\arguments{
\item{file_path}{Path to the fastQTL output file}
}
\value{
data_frame containi... |
5b1df12f07406750459e3d34876c135be84b2bad | 29585dff702209dd446c0ab52ceea046c58e384e | /MXM/R/pc.skel.R | ec91a9411ee5750593584f9761a075c82f1a2154 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,320 | r | pc.skel.R |
pc.skel <- function(dataset, method = "pearson", alpha = 0.05, rob = FALSE, R = 1, graph = FALSE) {
## dataset contains the data, it must be a matrix
## type can be either "pearson" or "spearman" for continuous variables OR
## "cat" for categorical variables
## alpha is the level of significance, set to ... |
8ca09997d890ab6758c9ac94fcf42495605f519f | aa66922233141af22e5aca895e5b1f05fea78702 | /Testing2.R | 00eb95670ac3a235ef9dffca1393b731321a1886 | [] | no_license | Saza-02/Test2proj | cf248ac557e2acfea7bca685966a133ebadec49a | 757023387ba3b1cf44eb1d64864530cb10fe0669 | refs/heads/main | 2023-06-23T22:52:55.645205 | 2021-07-28T00:15:57 | 2021-07-28T00:15:57 | 390,160,786 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 133 | r | Testing2.R | "Hello Testing"
var1 <- c(2,4,6,8,10)
var2 <- c("Aby","Mary","Danny","James","Lewis")
##adding sex
Var3 <- c("F","F","M","M","M")
|
5501b3f711f93d53e2d8fc73847e98495bd053eb | 854e26b5063c7844bc99c771a591757f5148ac71 | /index.R | 794c6cee933d77cb5c7fd821392d2d8cae051583 | [] | no_license | walbertusd/solar-prediction | 38b42111ffda26fb50784bfdd692096efbe56cf3 | 2991bd47725a8f11e6e1c9799abd3d7a9d3be2e1 | refs/heads/master | 2020-12-03T00:35:00.872538 | 2017-07-18T14:50:59 | 2017-07-18T14:50:59 | 96,042,820 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,188 | r | index.R | # R script
# NOTE: uncomment str({var}) to inspect var, use print.nc instead for NetCDF data
# Each different section separated by 3 newline
# Sys.setenv('MC_CORES' = 3L)
# required library
library('RNetCDF')
# library('parallel')
# library('dplyr')
# Read train data
train <- read.csv('./input/train.csv')
# str(tra... |
db57e565558baac8a94f4f762b86d5170c50dde3 | 0078429c9abba55467bfb46cdecbcda79c31dac4 | /inst/article/annotation.sets.R | 32c07e2f9804c577aa3ec8b0ad76894e77948838 | [] | no_license | Bhanditz/bams | fb2e4fa0f1ddc9665febbb0bc6263faa39652709 | 1d61aa458c42522cc35e6e6e819e19ffab80d18e | refs/heads/master | 2020-04-19T08:42:46.562481 | 2013-03-13T00:00:00 | 2013-03-13T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | annotation.sets.R | data(neuroblastoma,package="neuroblastoma")
data(neuroblastomaDetailed,package="bams")
annotation.sets <- list(original=neuroblastoma$annotations,
detailed=neuroblastomaDetailed)
## standardize annotation levels.
standard <- c(breakpoint=">0breakpoints",
normal="0breakpoints")
fo... |
6f749aeab5f58639f0507c1d2c02fdf0ff1f58ef | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/minval/examples/downloadChEBI.Rd.R | efe0fd676ad207bdac12431f8ac20557983b6069 | [] | 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 | 379 | r | downloadChEBI.Rd.R | library(minval)
### Name: downloadChEBI
### Title: Download the ChEBI database
### Aliases: downloadChEBI
### ** Examples
## Not run:
##D # Download ChEBI database with associations
##D ChEBI <- downloadChEBI(release = '142')
##D
##D # Download ChEBI database without associations
##D ChEBI <- downloadChEBI(releas... |
0ab119c650d4ff1891e03b46c380af29c7b49e16 | cf91203113637dc04746da3b6a5305baafe1067b | /datasets and codes for cleaning up the data/Tests/test.R | 229e730cb70c0015d60ca5d1cf96bc4b5ff443fc | [] | no_license | lysiabean/Data-Science-Group-Project | 4e510d6b0bb47430ce5fb498b0e496c2843df4cf | bf503160b709fd779ddb16857b05bd6c9caa18f3 | refs/heads/master | 2021-01-10T07:42:45.592075 | 2015-10-20T16:42:54 | 2015-10-20T16:42:54 | 44,619,933 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 363 | r | test.R | mylist = c(1354, 2028, 2690, 3502, 2882, 3182, 3434, 4064, 4158, 4009,
4698, 4523, 2318, 2024, 1639, 1366, 1228, 894, 835, 952, 447, 385, 442, 782)
temp = c(0.24, 0.46, 0.95, 1.4, 1.14, 0.73, 0.58, 0.62, 0.49, 0.29, 0.24, 0.18, 0.08, 0.07,
0.06, 0.04, 0.05, 0.04, 0.04, 0.07, 0.04, 0.05, 0.07, 0.... |
d3de338dc3580031d5b72d21fe5adfd6366c1422 | 8d32387ef0d9bf05e9cf7aec8f48dfbaa8d4e31d | /vignettes/StatComp18052.R | efbeeb81708b7f66b6ad66007a1b042404add59d | [] | no_license | Miwa1996/StatComp18052 | 9ee18561e65a82db0e285c8ee22839b4ff449b47 | 70b34f1a535c55ed6855879ea23eb1165a8d4587 | refs/heads/master | 2020-04-16T03:51:32.774750 | 2019-01-18T10:08:54 | 2019-01-18T10:08:54 | 165,247,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,751 | r | StatComp18052.R | ## ------------------------------------------------------------------------
a=rnorm(10)
a
## ------------------------------------------------------------------------
matrix(1:6, 2, 3)
## ------------------------------------------------------------------------
x=rnorm(10)
y=rnorm(10)
plot(x,y)
## ----Code chunk1, ech... |
4a2d9ef96013ab44ca340d6e45f6ae9399d5d1b5 | 00a6e8378c523b048399b3a7438f0fe22a6f5d4e | /R/Pre_Analysis.R | 4ec80f9f5d8af7b720e6a43f71188dcb00103f70 | [] | no_license | sxinger/DKD_PM_temporal | 46c117401ff7757ab440b216e4074efd5cf0bcb4 | dbbb35a2e18411422665958e27ecb1be7f675a62 | refs/heads/master | 2020-04-10T12:12:30.682542 | 2019-04-23T01:03:17 | 2019-04-23T01:03:17 | 161,014,841 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,405 | r | Pre_Analysis.R | #### Pre-Analysis ####
rm(list=ls())
gc()
source("./R/util.R")
require_libraries(c( "Matrix"
,"pROC"
,"xgboost"
,"dplyr"
,"tidyr"
,"magrittr"
))
pat_tbl<-readRDS("./data2/pat_episode2.rda")
fact... |
698da318fbba217e728f09db679999f242d59971 | a020b9ef9587b5bc883f6283b1fa6ecd46f02676 | /PCR.R | 47dea103ab9b2b195a72a2470bb9c60bab67b4d6 | [] | no_license | mariondechallens/First-Internship | b279528581e0421f7488f934ee6f790d98514985 | e15b6eb60893329cade8276ad8b3d9872fb375b1 | refs/heads/master | 2021-05-11T11:12:19.807367 | 2018-03-29T12:53:51 | 2018-03-29T12:53:51 | 118,123,150 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,939 | r | PCR.R | .libPaths(c("C:/Marion/Rstudio/packages_install",.libPaths()))
library(pls)
source(file = "C:/Marion/T2S_LabStatistics/MOTI_NTS_analysis/MOTI_regressions/moti_reg_facto.R") ##for functions
##trying principal components regression
data<-read.table("C:/Marion/T2S_LabStatistics/SOA/total_cleaned_data.csv",header=TR... |
8c52d288408852cf960128db0a7d3c95b79a5c04 | 89d5a7062a6991a49efcd21313c9f2daeb26261c | /R/tidy_cashflows.R | 2f018765b311613601e2f814fc739e398d2b3274 | [] | no_license | anttsou/qmj | 3786eb2bdff69831ae6e4bdda9d37d9c03af27a6 | ffc56ea6d7a00e8f2f958df9c44a6008211882d3 | refs/heads/master | 2021-01-19T00:47:23.680802 | 2016-07-10T21:48:59 | 2016-07-10T21:48:59 | 29,163,706 | 10 | 7 | null | 2016-01-10T23:36:58 | 2015-01-13T00:08:25 | R | UTF-8 | R | false | false | 1,442 | r | tidy_cashflows.R | #' Makes raw cash flow data usable and readable.
#'
#' Processes raw cash flow data from quantmod to return
#' a tidied data frame. Raw cash flow data must be formatted
#' in a list such that every element is a data frame or
#' matrix containing quantmod data.
#'
#' \code{tidy_cashflows} produces a data frame that ... |
e1514d6084b61bfa8bc4337cd1958739448d8fe3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Evapotranspiration/examples/ET.GrangerGray.Rd.R | b3c96257e5ee107887073b2066009ab3dbef3aa3 | [] | 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 | 558 | r | ET.GrangerGray.Rd.R | library(Evapotranspiration)
### Name: ET.GrangerGray
### Title: Granger-Gray Formulation
### Aliases: ET.GrangerGray
### Keywords: Granger-Gray evapotranspiration open-water evaporation
### potential evapotranspiration
### ** Examples
# Use processed existing data set and constants from kent Town, Adelaide
data("... |
c74637ecaaf4610dcd9aa4e79a3df6ebe7493f45 | cb4b8d511a14f1655120bb8737266296c5e46059 | /R/birds/GLLVMs/gllvm_treatments_again_nomid.R | 26a5f6ff7ef5df936c6b139e7438287bd607f644 | [] | no_license | Josh-Lee1/JL_honours | 40361e2f8b78fac9676ff32a8e0ce7a0603f6152 | db6792a039d824fdb518f9e06c3cc27ecca6da8a | refs/heads/master | 2023-03-29T22:28:19.500012 | 2021-04-15T04:40:20 | 2021-04-15T04:40:20 | 295,877,409 | 0 | 0 | null | 2021-03-16T06:17:06 | 2020-09-16T00:02:18 | HTML | UTF-8 | R | false | false | 10,989 | r | gllvm_treatments_again_nomid.R | library(gllvm)
library(tidyverse)
library(lattice)
library(janitor)
library(ggpubr)
library(ggplotify)
#read in data created in Traits.R
df <- read.csv("Data/Processed/ALLdata.csv") %>%
select(-c(X)) %>%
mutate(Burnt = Fire =="Burnt") %>%
mutate(Rainforest = Formation =="Rainforest")
#make some string change... |
3fa3e004f6976aa1253ad1e82f4c21167ecf23d3 | a3eda6ec1641566de1546df9113320ed68e8a33b | /1205 Crime Shiny R - Jue.R | 5161ad47190941d6fe9cff8bf48689784702dc1f | [
"Apache-2.0"
] | permissive | leafree/LA_City_USCGroup16-master | 8e3ac80def8151a7a68aed4b683011fc13fbd652 | 099473346b5c1f184f365e29f843f29e39412a1e | refs/heads/master | 2021-08-23T22:17:13.685084 | 2017-12-06T20:44:25 | 2017-12-06T20:44:25 | 110,632,279 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,232 | r | 1205 Crime Shiny R - Jue.R | library(ggplot2)
library(dplyr)
library(ggmap)
library(stringr)
library(tidyr)
library(shiny)
crime=read.csv("crime.csv")
la_map = get_map(location = "Los Angeles", zoom = 10)
crime$VICTIM.DESCENT = factor(ifelse(crime$VICTIM.DESCENT == "B", "Black",
ifelse(crime$VICTIM.DESCENT == ... |
5522b1d9d51e09cbbd03016a7067d000513ca7bc | 257b5303c5276cf90bc5110c1785cc144076031f | /code/11b_ldsc_cell_type_enrichment_makeRawPDF.R | 03719042735455be34a6cca900b3c99caba4de6b | [] | no_license | xiaotianliao/mpn-gwas | 65bb7cc1f37b9c4af98a776128b7d91d06e4e5db | fb271abe98a43e140c2cdf8c200d556a477e00e0 | refs/heads/master | 2023-08-22T16:06:14.066422 | 2020-10-14T15:50:09 | 2020-10-14T15:50:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,502 | r | 11b_ldsc_cell_type_enrichment_makeRawPDF.R | library(tidyverse)
library(BuenColors)
# Choose Color Palette
THE_PALETTE <- jdb_palette("solar_rojos")
# Import data
enrichments <- fread("../data/ldsc/MPN_meta_finngen_r4_ukid_heme_1000GP3_UK10K.cell_type_results.txt") %>%
dplyr::rename(pvalue = "Coefficient_P_value")
# Set up coordinates
cellCoordsDF <- data.fr... |
6cdf427a2aef6dcea3b47187aae16e8a3639ed9b | 442b7c5546eafe421e6930a1e57f76d8bfa5b97f | /test_spike.R | c381e8910ab75d7b08972267d30bdfe10199a0ee | [] | no_license | catsch/TEST_RT_QC_CHLA | 72b08152222fe1832bd96747b0ea249f0be6913b | 443385041bcbe5d07696ab5b226133f991e96e9a | refs/heads/main | 2022-12-24T20:45:15.596265 | 2020-10-09T15:28:59 | 2020-10-09T15:28:59 | 302,668,086 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,631 | r | test_spike.R | ##############################################################################
# Test of the Spike test
# Catherine Schmechtig
# September 2020
##############################################################################
library(ncdf4)
library(stringr)
source("./read_VSS.R")
source("./RunningFilter.R")
source("./... |
e074df1f4aa6573a4d15f074bd110a44b3b0a6c0 | 5cc908812d4f6918cec28acc1f715357e9b8f7ce | /Midterm/COVID19KNN.R | 8a329ad82cacd538804f9d21900650a573010904 | [] | no_license | jingyi199858/CS513Stevens | 8e97b52fbe71a30639df073c005a313737505797 | 98bb39f5dc4133217b3f8f4f607d24d3971d7217 | refs/heads/main | 2023-03-16T16:12:13.051237 | 2021-03-13T19:30:48 | 2021-03-13T19:30:48 | 347,460,141 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 523 | r | COVID19KNN.R | remove(list=ls())
dev.off()
file<-file.choose()
bc<- read.csv(file, na.strings = "?", colClasses=c("Infected"="factor" ))
is.factor(bc$Class)
bc_clean<-na.omit(bc)
index<-sort(sample(nrow( bc_clean),round(.30*nrow(bc_clean ))))
training<- bc_clean[-index,]
test<- bc_clean[index,]
library(kknn)
predict_k1 <- kknn(for... |
6ca214f67aea08f62352cffcc953c01262efd7e2 | cb0e47764f06380921b8a2d1ed0d03ccbd27abbf | /6 media e mediana na pratica.R | 8d3e9db188023d37934604a1d23dd4713cbcd73c | [] | no_license | arielgustavoletti/R_Enbio | dc939ec216cea5f7b7ae15ff5e6ceb06c0ed6bf2 | f038a5b7bb964bbc5750a9e0e19e7c2b4724a3b3 | refs/heads/master | 2020-07-17T22:57:36.740772 | 2019-09-04T10:43:51 | 2019-09-04T10:43:51 | 206,118,498 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 989 | r | 6 media e mediana na pratica.R | ###################################
#Média e mediana na prática #
#Prof. Ariel Gustavo Letti #
#Minicurso R - IV ENBIO #
###################################
#Carregando os dados:
setwd("C:/R_Enbio")
dir()
dados<-read.table("inseto.txt", h=T)
summary(dados)
########################... |
dc42bff2a1aca927c5ee4571134f8026b17c4fbe | 68651c45b76e30217cad7a87db9e7d716b1e37a2 | /area_alita.R | f0af8fa350825870aa830c5703e4abcad2f4d13b | [] | no_license | jfloresvaliente/useful_scripts | bfbda81421dde06b673e7060ebeacf9a06e2abff | 391ceb34d330c931112c5dd12d7447622f4ea454 | refs/heads/master | 2021-06-13T00:33:24.689975 | 2021-03-12T12:06:24 | 2021-03-12T12:06:24 | 111,957,568 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,210 | r | area_alita.R | # library(pracma)
dat <- read.table('C:/Users/jflores/Documents/JORGE/ECOGRANJA_WALLPAKUNA/terreno/Alita.csv', header = F, sep = ';')
plot(dat[,2], dat[,1], type = 'l')
# for(i in 1:dim(dat)[1]){
# points(dat[i,2], dat[i,1], col = 'red')
# print(i)
# Sys.sleep(time = 1)
# }
# library(geosphere)
# lon <- c(-81, ... |
0d391de16287419d044e69e34ba53a4639fcb6f1 | b44b3a8fda90d9ea7ed56db16af5b366d10239f4 | /Experimental_design_figure.r | 6023572bf20ff96ef0ea73ccb7cdfa657da52475 | [] | no_license | ss3sim/Empirical | d6980f347d81d0b29122d04377c8092966c1276f | 3e5136a6cd318741d64ab7ce2fc7c6c9148d633f | refs/heads/master | 2020-04-06T04:59:21.122727 | 2015-07-31T17:41:44 | 2015-07-31T17:41:44 | 21,745,602 | 0 | 0 | null | 2014-07-24T00:47:42 | 2014-07-11T17:44:20 | Scheme | UTF-8 | R | false | false | 9,864 | r | Experimental_design_figure.r | print.letter <- function(label="(a)",xy=c(0.1,0.925),...) {
tmp <- par("usr")
text.x <- tmp[1]+xy[1]*diff(tmp[1:2]) #x position, diff=difference
text.y <- tmp[3]+xy[2]*diff(tmp[3:4]) #y position
text(x=text.x, y=text.y, labels=label, ...)
}
##########################################################... |
9c746916d20e93616a77f23681664af8246dde92 | c849f263fb96f4e85c36a0e3eeeacf4a3cf93b9f | /make_data_tables_for_analysis.R | 00468f682fc771ac945ce7a49404f6b31a830866 | [] | no_license | jescoyle/FIA-Lichens | 4e1ebc5e9cccca3e381e8d2968a79b041a4c13c6 | 79f849bebb7d5a1f60889a7c21f039c6f212f12d | refs/heads/master | 2020-05-18T01:10:36.809038 | 2015-10-29T17:37:38 | 2015-10-29T17:37:38 | 13,964,511 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,693 | r | make_data_tables_for_analysis.R | ## This script compiles data tables for FIA lichen plots into:
## master : a data table with all variables from 2228 plots
## model_data : data from plots used in models from all plots without any data missing, includes PCA variables
## trans_data : environmental data log or sqrt transformed to reduce skew
## working_d... |
92027302b7d7738dc17797ee1fb9ab31a632a153 | 6e1b4af227d6321f1175930f4e3f21ed800c1a78 | /man/classify.Rd | 4034db932761ad6cf863b4980f9d41268915b41c | [] | no_license | bastianmanz/GPM_rain | 521079aedb5afad3b7de482d2bff33afc18e7426 | a990df2579e43301b0ea2fdaf28c548cfdb240ae | refs/heads/master | 2021-01-10T13:22:22.508211 | 2016-03-11T13:30:15 | 2016-03-11T13:30:15 | 53,668,785 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,271 | rd | classify.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/classify.R
\name{classify}
\alias{classify}
\title{Function to classify gauge time-series (zoo object) based on some prescribed aggregation level.}
\usage{
classify(gauge.ts, aggregation_level = "season")
}
\arguments{
\item{gauge.ts}{A zoo o... |
606fee4963ad959071b8451b562d8d4f6b634289 | 61fb4bc8d3edb365bc5985b7723241ae76d6a727 | /Risk Parity/Risk Parity- Nakul T.R | d41b1df5c0eeefc9b56d3016c7024fb4c7f2c48a | [] | no_license | nakulthakare/Asset-Management | 81b8f47f3332774ec7c234c7758112c1bb4da1a4 | ee3ff4df5c2958f138ebf02069b4981c64d45590 | refs/heads/master | 2022-02-10T22:20:45.711083 | 2019-01-05T01:37:54 | 2019-01-05T01:37:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,350 | r | Risk Parity- Nakul T.R | suppressMessages(library(data.table))
suppressMessages(library(zoo))
suppressMessages(library(moments))
suppressMessages(library(lubridate))
#Question 1
CRSP_Bonds<-fread("C:/Nakul/UCLA Coursework/Spring 2018/QAM/PS_2/4f31512f6a62c031.csv") #Loading downloaded data without edits
#head(CRSP_Bonds)
PS2_Q1 <- f... |
e6243cf7f166ed53ef274fae2c78851f97573a9a | b4eaabbe0b3b1eea7589ceff6e0f0f37d3927da0 | /man/peloton_api.Rd | 936063f54ddbdde9c6b54748123102bdcecb3322 | [
"MIT"
] | permissive | bweiher/pelotonR | 80ba084e0eed53b84b6cfaaea90128e262abe3d8 | 39d5355702bd50d42a9768cf729aca4ff697b304 | refs/heads/master | 2022-03-12T19:29:48.177555 | 2021-01-09T01:07:19 | 2021-01-09T01:07:19 | 214,738,068 | 13 | 4 | NOASSERTION | 2021-01-09T01:07:21 | 2019-10-13T00:50:09 | R | UTF-8 | R | false | true | 796 | rd | peloton_api.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/peloton_api.R
\name{peloton_api}
\alias{peloton_api}
\title{Makes a \code{GET} request against one of Peloton's API endpoints}
\usage{
peloton_api(path, print_path = FALSE, ...)
}
\arguments{
\item{path}{API endpoint to query}
\item{print_pa... |
e102f24768cc386ee838e348570545c23842374c | 0ca1dbdb92004d400981e72666c59d5ff890834d | /load_data.R | 43cd54c6f460bb7e66fd81694545751c84d0e75b | [] | no_license | carleenxu/ExData_Plotting1 | e521c1cfc0aa0a9012a1d4c6bc005852124de6c8 | fc7a9c93bcb44c84b8d35ec9044b6888adf3319b | refs/heads/master | 2021-01-13T06:35:34.400162 | 2017-02-08T12:08:54 | 2017-02-08T12:13:01 | 81,177,505 | 0 | 0 | null | 2017-02-07T07:04:08 | 2017-02-07T07:04:08 | null | UTF-8 | R | false | false | 1,215 | r | load_data.R | ## load dataset in R
## load data from the dates 2007-02-01 and 2007-02-02
## convert the Date and Time variables to Date/Time classes in R using
## the strptime() and as.Date() functions
## in this dataset missing values are coded as ?
load_data <- function() {
## Change working directory
setwd("D:/Study/DS... |
08e3da8f287deb69039f6ce9e894af992bb5d47d | e7a58742771bed318f764e7f1d8fc205ba892558 | /shiny_students/server.R | bc2ea74e4133db35132907ede7d9e2d0e3385e9c | [] | no_license | GiveMeMoreData/Stack_analysis | 02afef531c26b17cc6a5b87313222248e0166315 | 90f5433bb1916539ff8128390df09098f276cfb6 | refs/heads/master | 2020-06-04T03:13:22.098350 | 2019-06-20T06:56:49 | 2019-06-20T06:56:49 | 191,850,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,096 | r | server.R | library(shiny)
options(stringsAsFactors = FALSE)
library('dplyr')
library('data.table')
library("stringi")
library("ggplot2")
# tu dane nie zależące od imputu
wd <-"C:\\Users\\Bartek\\Desktop\\pd3\\"
### STUDENTS
## Loading data
#Gaming
... |
1d118c1d2380326d807a6536587943b5df657c6f | 66fc2b0b0d1e24e676d60f6a2b85dae006fd1136 | /Section 6 Advanced Visualization with GGPlot2/Histograms and density charts.R | 35d416f9d2a775554f8d95fbd48b08449459acb8 | [] | no_license | OmkarGurav6/Udemy-R-For-Data-Science-With-Real-Excercises | f8d1365707cde5e6fffd04b1126d9ff3c721f4e1 | 676dc71e576065e79e338fa4765248b821d22c26 | refs/heads/main | 2023-02-25T11:20:39.172558 | 2021-01-30T10:53:22 | 2021-01-30T10:53:22 | 334,387,824 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 547 | r | Histograms and density charts.R |
s <- ggplot(data = movies, aes(x = BudgetMillions))
s + geom_histogram(binwidth = 10, fill="Red")# setting red color
s + geom_histogram(binwidth = 10, aes(fill=Genre), colour= "Black")#color is used to set color of border.
s + geom_density(aes(fill=Genre), position = "stack")
t <- ggplot(data = movies... |
085995f1393ab714ec5f444d7ac27d2af1565ecd | e67f3901197c81d982db034d42ddde4f3d3c703f | /src/util/libraries.R | e27d5a2b6e80fbb11e5ac9e70595ec9bebe45bd7 | [] | no_license | sneakers-the-rat/openpolicing | 29d3ab03a3fa9e323660521318da48b15ac515af | 97f58158eca31c56fd3b35083d7cbf1ac6949b66 | refs/heads/master | 2020-05-01T04:42:59.217759 | 2019-03-23T11:46:51 | 2019-03-23T11:46:51 | 177,282,043 | 0 | 0 | null | 2019-03-23T11:44:32 | 2019-03-23T11:44:32 | null | UTF-8 | R | false | false | 180 | r | libraries.R | library(dplyr)
library(tidyr)
library(readr)
library(parallel)
library(sandwich)
library(rgdal)
library(xtable)
library(lmtest)
library(stringr)
library(ggplot2)
library(lubridate) |
95984b1440161b66f528c7ee442fe1fb916b275b | 9cf0fb3cbdfbca9aab68cc6aa0bb571b705d4e03 | /PraceDomowe/PD_06/pd06_komosinskid/pd06_komosinskid.R | d4cf9f2a7594775cbe0a86b92197f278a8057644 | [] | no_license | vaidasmo/TechnikiWizualizacjiDanych2017 | 65905dc831727870774d90dfdd32212160f9b487 | d251cf5e5d66f837704c753a092cec7594810dce | refs/heads/master | 2021-05-14T17:58:10.216912 | 2017-12-22T22:07:51 | 2017-12-22T22:07:51 | 116,060,512 | 1 | 0 | null | 2018-01-02T21:48:48 | 2018-01-02T21:48:48 | null | UTF-8 | R | false | false | 308 | r | pd06_komosinskid.R | # pd06
#mapa kolorow
library(ggplot2)
library(plotly)
library(shiny)
v <- seq(from=0, to=255, by=51)
db <- expand.grid(v,v,v)
names(db) <- c("r", "g", "b")
db$kolor <- rgb(db$r,db$g,db$b, maxColorValue = 255)
p <- plot_ly(data=db, x=~r, y=~g, z=~b, text=~kolor, marker=list(color=~kolor))
p
|
3b6b50346d21fdeceb90122a6fc28e6684b60d07 | f3c0608636363a56550044a7c346daa919c8fb52 | /R/build.R | 6b39bd03479b179616b62a5a85e0ae29fcdd9717 | [] | no_license | ShirunShen/tri2basis | 9f442a8fef47cfe920f06e7c1b1cefb2fd5ba4d4 | 2d93bf51217b4c56da4537fa2d3dece1981d51f1 | refs/heads/master | 2020-12-28T13:34:37.320216 | 2020-02-10T20:58:31 | 2020-02-10T20:58:31 | 238,351,745 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 488 | r | build.R | ######################## build
######################## evaluate the matrix for inner product
build=function(d.bu){
result=indices(d.bu)
I.bu=result[,1];J.bu=result[,2];K.bu=result[,3]
m.bu=(d.bu+1)*(d.bu+2)/2
Mat.bu=matrix(0,m.bu,m.bu)
for (j in 1:m.bu){
for(k in 1:m.bu){
Mat.bu[k,j]=choose(I.bu[j]... |
ba1a6b5e943231dd99c2ed1c66098e66430a5a2c | 5d873a96e5024a1b7f89f676ec7e190607c34337 | /R/create_chart1.R | 3b54b71a9f67163242ccbff7df2850c6bb40e2f4 | [
"MIT"
] | permissive | prcleary/dhis2bulletin | 5ae47ae15441c793c28aafde50e19540894d2bec | f1b418b19da57202ca896fd449d6443b9ccfc2fd | refs/heads/master | 2022-01-24T01:07:42.553336 | 2022-01-05T16:01:04 | 2022-01-05T16:01:04 | 230,428,628 | 0 | 0 | null | 2019-12-27T16:34:31 | 2019-12-27T11:02:44 | R | UTF-8 | R | false | false | 2,520 | r | create_chart1.R | #' Create One Type Of Chart for Bulletin
#'
#' @param datatable placeholder
#' @param plotfilename placeholder
#' @param plotwidth placeholder
#' @param plotnrow placeholder
#' @param plotheight placeholder
#' @param plotxlabel placeholder
#' @param plotylabel placeholder
#' @param plottitle placeholder
#' @param plots... |
b9d86e9184bb000e3aebdf80fb256f73e8ef79ca | bd23162e4b8c3c779557160a774bffb765adce86 | /prepare.R | cefc9ca52991f8831caa382939664870be07709b | [
"MIT"
] | permissive | ktmud/github-life | a8ab2ee91c85c2a62a348f6764742dcf1b00c338 | 421e46f9832879bb8c81d8731d3524ef20fc3065 | refs/heads/master | 2021-01-19T22:24:48.671526 | 2017-11-11T18:50:26 | 2017-11-11T18:50:26 | 88,812,727 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 130 | r | prepare.R | #
# Prepare the repository list for scraping
#
source("scrape.R")
source("include/db.R")
ConsolidateRepoList <- function() {
} |
3ad9a228ffc0e9409fd5a5b8db25ad968177521e | a68fcf7bad70e91af4b398df8bee04b9b0bda82e | /S34_S38_phylogenetic_comparative_methods/scripts/resources/slouch/R/model.fit.R | 0561894e0257879394091e2d9fdc79479138f5ae | [] | no_license | hj1994412/teleost_genomes_immune | 44aac06190125b4dea9533823b33e28fc34d6b67 | 50f1552ebb5f19703b388ba7d5517a3ba800c872 | refs/heads/master | 2021-03-06T18:24:10.316076 | 2016-08-27T10:58:39 | 2016-08-27T10:58:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 96,274 | r | model.fit.R | `model.fit` <-
function(topology, times, half_life_values, vy_values, response, me.response=NULL, fixed.fact=NULL,fixed.cov=NULL, me.fixed.cov=NULL, mecov.fixed.cov=NULL, random.cov=NULL, me.random.cov=NULL, mecov.random.cov=NULL, intercept="root", ultrametric=TRUE, support=NULL, convergence=NULL)
{
# SET DEFAULTS ... |
61326fc9ca2a6ecb66ea6e77fbb97e0bf21ffe98 | b4846c2330b9a5528af4c2df65f0c3fdeae789ce | /Higher Terms/higher_terms.R | 3bd716cfb0dd0a3294bb31b8fe95a5b7e72426d4 | [] | no_license | devitrylouis/degree_project | a0f96e275a340411a42dfa3dc62487510d4faecc | 72585664377016a986d6f41d9900f67c3be67085 | refs/heads/master | 2021-06-15T12:40:54.229035 | 2017-04-20T13:48:15 | 2017-04-20T13:48:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,728 | r | higher_terms.R | higher_terms <- function(df,k_max)
{
names <- character()
predictors = c("MSSubClass","LotFrontage","LotArea","OverallQual","OverallCond"
,"YearBuilt","YearRemodAdd","BsmtFinSF1","BsmtFinSF2","BsmtUnfSF","1stFlrSF","2ndFlrSF"
,"LowQualFinSF","BsmtFullBath","BsmtHalfBath","Full... |
0d53ec392ce3eb8b078e60f901b738397d2e8048 | 1496d1fca7f4711766376602239a9608c7efe669 | /r-programming/corr.R | 8a4d33065ecc307d74921af4b368ca4e7d15c1fd | [] | no_license | mattyb678/coursera-courses | 98da8d30c8122f7a6435b644e17ec988724b2682 | a06629500ac9623105380ac1eb792eaa20ba74ea | refs/heads/master | 2021-01-10T07:25:11.685381 | 2016-02-16T17:18:06 | 2016-02-16T17:18:06 | 51,853,875 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 355 | r | corr.R | corr <- function(directory, threshold = 0) {
files <- list.files(directory)
cors <- numeric()
for(i in 1:length(files)) {
data <- read.csv(paste(directory,files[i],sep="/"))
data <- data[!is.na(data$sulfate) & !is.na(data$nitrate), ]
if (nrow(data) > threshold) {
cors <- c(cors, cor(data$nitrate... |
291f39dbf7fb9736f1fafbe373d0c1f29c4470eb | 1e4d6814b572dcb6ae984261210c74859997bcc4 | /R/stan_adapted.R | 62ae97687feaba4715cf99bd28172dbb6575947e | [] | no_license | retodomax/cowfit | bba01468ea699bcd617f4f6fcec88d8495ec76af | e1eacd9d3622c63301bef7da4af4eca88d06747d | refs/heads/master | 2022-11-25T00:18:53.381555 | 2020-07-26T13:01:59 | 2020-07-26T13:01:59 | 277,785,758 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,656 | r | stan_adapted.R | #' stan_glmer() which transformes Z matrix with Lt
stan_adapt <- function (formula, data = NULL, family = gaussian, subset, weights,
na.action = getOption("na.action", "na.omit"), offset, contrasts = NULL,
..., prior = normal(), prior_intercept = normal(), prior_aux = exp... |
68506a94243a3def08ae20c6c8013e61f127f6cc | 930c0c45143b14875d30c4b382fa82611df218ce | /scripts/6-LASSO.R | 26a01636120ec846c15dd485d2cfbd6f7db70a5f | [
"BSD-3-Clause"
] | permissive | Christensen-Lab-Dartmouth/VAE_methylation | dab9f1bb5a8df4096d24f660dc426aefcc7c88ce | 3d56e3aa7c489a38dc85f56755ac3ba487a7d838 | refs/heads/master | 2021-05-26T10:55:01.799593 | 2019-05-31T21:48:06 | 2019-05-31T21:48:06 | 128,078,667 | 8 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,479 | r | 6-LASSO.R | ######################
# Comparing significant CpGs from EWAS to LaWAS results
#
# Author: Alexander Titus
# Created: 08/20/2018
# Updated: 08/20/2018
######################
#####################
# Set up the environment
#####################
require(data.table)
library(glmnet)
#############... |
2e010efb6d7e8acefaf7c31ed7852a5778e67b09 | 068100cfbf0a84379536169bf70bf72ad54ca4f4 | /scripts/mapped_truth_with_sj.R | a9b1de8e3d1685212590351bfe7865a7ccc2ac13 | [] | no_license | imallona/discerns_manuscript | 17c6bf1559b2b51859398c491407f8a58b511927 | c438121896ef0fe4c9884032c43e80a10906526a | refs/heads/master | 2023-09-04T04:39:05.881681 | 2020-10-19T14:37:59 | 2020-10-19T14:37:59 | 402,038,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,851 | r | mapped_truth_with_sj.R | ## Compare the mapped and the true location of a read. The mapped coordinates
## including splice junctions are considered.
library(rtracklayer)
library(GenomicAlignments)
library(stringr)
library(dplyr)
library(GenomicFeatures)
library(data.table)
BAM <- snakemake@input[["bam"]]
GTF <- snakemake@input[["gtf"]]
SIM_I... |
6d9db9d5076b87e42f2c09a60eda638573695869 | c443e68905ea44d277deafa11ce2bb3463e5ab61 | /man/slopesolvers-package.Rd | 52eaf8766b7ec1e63bce0bfe70c135cfb948f09b | [] | no_license | jolars/slopesolvers | 8a69116ed29eea156f71f107a408c44310e0c7c4 | 3ef2ea6ff174d6ef35f663e24eada5d900c51010 | refs/heads/master | 2021-01-05T17:01:19.335470 | 2020-02-17T13:09:36 | 2020-02-17T13:09:36 | 241,083,259 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 708 | rd | slopesolvers-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/slopesolvers-package.R
\docType{package}
\name{slopesolvers-package}
\alias{slopesolvers}
\alias{slopesolvers-package}
\title{slopesolvers: A Suite of Solvers with Associated Functions for SLOPE}
\description{
A set of solvers for SLOPE to co... |
fb7da413607cc3206b66cfde617cdeb2989ca916 | 3c887e5568e6815edb9e6adde83e2b5fa36800cb | /plots/plot.R | 1577a902372a815ed90f3e55c8217d1123156847 | [] | no_license | Mytherin/MonetDBLiteBenchmarks | 04db316f24f64b61c8d81611e8effa4389e36f7a | 5fdacde734a36f82f52c6e55f2bb8ec3a5208cb9 | refs/heads/master | 2020-03-14T17:32:43.717433 | 2018-06-21T10:27:04 | 2018-06-21T10:27:04 | 131,722,589 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 931 | r | plot.R | library(dplyr)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(stringr)
library(grid)
library(reshape2)
theme <- theme_few(base_size = 24) +
theme(axis.title.y=element_text(vjust=0.9),
axis.title.x=element_text(vjust=-0.1),
axis.ticks.x=element_blank(),
text=element_text(family="serif"),
legend.... |
c9f7df1f570fdcb8a8e8b8bf3c8d653605901f91 | fee0fc1f748a72a845c1b81bb99f159e08fd6fb9 | /man/MTi.Rd | bed9a8f8f7dc9c210135e053bf15e083bd82121e | [] | no_license | francisco-fjvm/MatrixCollection | 718245ed44eb61be9a4203259fc11c5629b12bc3 | 313643d7b0fd78a7330c8ff4793ad7f25f46c82e | refs/heads/master | 2020-04-24T12:58:02.817971 | 2019-02-22T01:28:43 | 2019-02-22T01:28:43 | 171,585,274 | 2 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 340 | rd | MTi.Rd | \name{MTi}
\alias{MTi}
\title{ Matriz triangular superior con entradas aleatoria enteras}
\description{ Función para crear una matriz superior aleatoria}
\usage{ MTi(n, a, b) }
\arguments{
\item{n}{ Tamaño de matriz}
\item{a}{ Límite inferior del intervalo}
\item{b}{ Límite superior del intervalo}
}
\examples{M... |
c4ba7ce75e73c31c8463240193bd17cfe463ac41 | cb9dcfc00cc07dbef7d49a320af6b581a58fbc65 | /Regression Template.R | 58757257d9011b124ac2b121a65f160abfb814b5 | [] | no_license | ZyanWC/R-Machine-Learning | 8c0c62582a0216ffbc4814600a34eb385122d9b8 | 30eae32a43b5d83379d58a63e4f2ab8e2829fa3e | refs/heads/master | 2021-05-09T16:32:28.416097 | 2018-01-27T00:18:16 | 2018-01-27T00:18:16 | 119,117,199 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,487 | r | Regression Template.R | #Regression Template
#Importing the dataset
dataset = read.csv("Position_Salaries.csv")
dataset = dataset[2:3]
#Splitting the data into Training and Test set
#install.packages("caTools")
#library(caTools)
#set.seed(123)
#split = sample.split(dataset$Purchased, SplitRatio = .8)
#training_set = subset(datase... |
c6f6eac074adc11ad4658c6a5f1b25ba6905eed5 | 3db9b63f9eadda8129c5057a246476dc47b41dea | /App1/ui.R | 3c425bb8a6f1421eaa3c9d10fcfabc882e9811d5 | [] | no_license | vikramjeet312/dataProduct-shiny | f4686bbf239915ec363d8fbef624d6b87aa2826d | 8dfece76e52dd2c7e253b50268d3cb373fcf4ebe | refs/heads/master | 2021-01-10T01:27:45.893373 | 2015-11-22T08:02:20 | 2015-11-22T08:02:20 | 46,651,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 563 | r | ui.R | library(shiny)
shinyUI(fluidPage(
titlePanel(title = "EPL Stats Season: 2014"),
sidebarLayout(
sidebarPanel(
sliderInput("teams", "Top n-teams", 2,20,1,pre = "Top ", post=" teams"),
selectInput("column", "Choose a Statistic", choices = c("Shots Per Game"=1,"Possession %"=2, "Pass Success"="", "A... |
505a9d8562120d223f21b85b583583aba9e48c1f | daab105ecede477a1e89a851b93e337525e26b20 | /cachematrix.R | 367091f942033c3e162c5f47f64371fc748eaede | [] | no_license | Zeeshanasif/ProgrammingAssignment2 | aa449ec3aff5297438764db1115969744d661c85 | 76f3df9a92ef31a9aadf2b63c14a9114c88af572 | refs/heads/master | 2020-12-03T09:31:57.627642 | 2014-11-23T20:23:21 | 2014-11-23T20:23:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,138 | r | cachematrix.R | ## The pair of below function uses lexical scoping rules to to cach the inverse
## of a Matrix. Please not that the nomenclature has been kept the same as the
## example for this assignment. To get inverse of a matrix say "MAT"
## type MAT$getinv() and to set inverse MAT$setinv().
## example for creating a matrix... |
c4fab4d5e5d1e755535fc5f8ccca89a7896bd0c6 | a728f406ceed9e6480880856242f9e52748c3e0f | /C_Code/main.R | 537a6b3a03ca50764a1f0d1e2cdf8bc9aeb1389b | [] | no_license | qc-an/Renewables_EM_participation | 6c0ef3990099ff0cdf59f635fd031a31ec6325e4 | dc4d35c74913f75e174d7c65560e4aec0703ad10 | refs/heads/master | 2021-09-10T09:08:18.224490 | 2018-03-23T09:12:34 | 2018-03-23T09:12:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,252 | r | main.R | ## Assignment 2 ##
## Renewables in electricity market ##
## Author : Florian Guillebeaud ##
###################################
###################################
setwd("~/Documents/DTU/B_Semester-2/31761_Renew_ElectricityMarkets/Assignments/Assignment2")
###################################
####################... |
187905c53ce23e9c318b9976ad6faedb247f864a | a0d8cc13c2552f6abeff5d39c45bc578a7d67eb5 | /intial R code.r | 4541bf71717c9c1ec8ffaba0a0e10606cac8ae1f | [] | no_license | ajsarver87/service_parts_forcast | 72b6dfe9f11243ba9d78402ea00f3a72950c6fab | 73db1705baafeb56665dff036a51916dfc0e43e5 | refs/heads/master | 2020-03-22T22:22:11.678256 | 2018-07-12T18:34:32 | 2018-07-12T18:34:32 | 140,748,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,439 | r | intial R code.r | #LIBRARIES
library(TSA)
library(forecast)
library(foreach)
library(doSNOW)
library(doParallel)
library(plyr)
#Custom Function for fitting automatically chossing the best model between ARIMA and ETS
fitting.function <- function(x, h){
temp.mod.arima <- auto.arima(x, seasonal = TRUE)
temp.mod.ets <- ets(x)
if((tem... |
bab8e5e7a8eb247cda099e49a8417b8af203e960 | 9f6226caf5268ce2ae0d8e9b5abcfe6b7f5c8c0d | /R/token.R | 3977ac77669bed7cb24c29573149d09bd7a1d3a7 | [] | no_license | dgkf/reflow | 910efc856ec64ce1fa170057aef2dc472ec33634 | a8bcda6b24b738e3c829674c75ed834b1a484ae3 | refs/heads/master | 2023-09-04T16:06:12.070079 | 2021-11-09T02:32:31 | 2021-11-09T02:32:31 | 405,763,101 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 178 | r | token.R | token <- function(x) {
UseMethod("token")
}
token.xml_node <- function(x) {
token(xml2::xml_name(x))
}
token.character <- function(x) {
gsub("[^a-z]", "_", tolower(x))
}
|
7497dfcdc55cb173d15f50ef457d4534dbab195d | d2591ae7dbf33133b7576d90593e546f8bc92e40 | /r-scripts/RandomForest.R | 325b1aafd7fdecb15352ecd30371b502c5e2e792 | [] | no_license | hreiten/mnist-digit-recognizer | 3e1d26597df74853accb3a41c7e61ce30216493a | 715c9a889eb8086240a500884976220261301011 | refs/heads/master | 2021-09-15T07:00:35.353669 | 2018-05-28T06:23:17 | 2018-05-28T06:23:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,207 | r | RandomForest.R | rm(list=ls(all = T))
set.seed(1000)
Sys.setenv(TZ="Africa/Johannesburg")
library(ggplot2)
library(randomForest)
library(tikzDevice)
library(caret)
library(xtable)
source("HelpFunctions.R")
exportspath <- "../exports/tree_based_methods/randomforest/"
# read in data
data <- read.csv("../data/Train_Digits_20171108.csv"... |
2f579865c1609468b0132e4e0a8cbebf53c8b923 | 7a0fd3bfeebef43dd86047941fd56d6d5c3cdcb1 | /CI1107219/Atividade-Perceptron/src/perceptron.R | f7b64ea543e39323a139fb11449b72ea112ae53c | [] | no_license | alvesmarcos/deep-learning | 207e2f612bd362b1efb63fe4546a66b33fe55fc8 | 10ce4c7535142acb23a07c486257ccf8f5d1dc7d | refs/heads/master | 2020-04-10T08:49:57.017278 | 2018-05-08T19:22:59 | 2018-05-08T19:22:59 | 124,267,816 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | perceptron.R | library(ggplot2)
threshold <- function(y) {
ifelse(y>0, 1, 0)
}
activation_func <- function(z, func) {
switch(func,
'degrau' = ifelse(z>0, 1, 0),
'sigmoid' = 1/(1+exp(-z)),
'tanh' = (2/(1+exp(-2*z)))-1,
'relu' = ifelse(z>0, z, 0))
}
forward <- function(w,b,x_i) {
z = (x_i%*%t(w)) + b
activat... |
b5e4328ac70d9d46741a45c88169854f8814301e | 72449ca51b9c019f8268e929bbe5c3e850235158 | /fall2016_prematch_code/Generate_clean_golden_set.R | 896498c3918d6608f8ee240e1b9debfabdccf011 | [] | no_license | joshuaschwab/social-networks | 15f4660e0a9f491c2471bf1497b17a1d215a70e5 | c1026bea660e6e0a94e4fb9c9f7f0fd8c371fc5e | refs/heads/master | 2020-12-03T04:01:32.282732 | 2017-07-04T04:39:13 | 2017-07-04T04:39:13 | 95,803,938 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 897 | r | Generate_clean_golden_set.R | # load the online golden set
#####
#parish = 'muyembe'
#parish = 'nsiika'
#parish = 'nyatoto'
#parish = 'magunga'
# parish = 'ogongo'
#parish = 'mitooma'
#parish = 'rugazi'
#parish = 'nsiinze'
#parish = 'nankoma'
#parish = 'kisegi'
#parish = 'kitwe'
#parish = 'rubaare'
#parish = 'nyamrisra'
parish = 'kitare'
golden_s... |
629c7331b1dd7252517d0697a5b4894c13501f51 | c9f5de2870f782a56c98c88497c24bb7b521bdb3 | /plot2.R | cb885c3c9f1c7dc317bf490cbd8fea17ca0e434e | [] | no_license | lcheeme1/ExData_Plotting1 | 501573f6e2ac063fc3a8df4438f0bafabd10a83b | df39b4bf9697990242dba5a715167e9f07a8cb9c | refs/heads/master | 2021-01-18T15:18:55.245242 | 2014-11-09T14:20:57 | 2014-11-09T14:20:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 581 | r | plot2.R | library(dplyr)
library(lubridate)
mydata <- read.csv("household_power_consumption.txt", sep=";")
startDate <- ymd("2007-02-01")
endDate <- ymd("2007-02-03")
mydata <- mutate(mydata, DateTime = dmy_hms(paste(as.character(Date), " ", as.character(Time))))
mydata <- filter(mydata, DateTime >= startDate)
mydata <- filte... |
c6879cd4499826a9d34e5e66d64f6f5f06db97a4 | 7074008683ce97e0e40682bf680d404ba3e02aee | /01-removing-careless-motivation.R | 18ef38f5c452ede231b8fe944034fb93cbfb2d41 | [] | no_license | geiser/rachel-imi-evaluation | 82a08b6a2d60b6437269f962c4e0d3e54e54c287 | 5e360c1441090846123c9d6db93def203f0ba86f | refs/heads/master | 2020-03-18T04:35:01.893755 | 2018-05-21T16:23:51 | 2018-05-21T16:23:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,178 | r | 01-removing-careless-motivation.R | wants <- c('readr', 'dplyr', 'devtools','readxl')
has <- wants %in% rownames(installed.packages())
if (any(!has)) install.packages(wants[!has])
if (!any(rownames(installed.packages()) %in% c('careless'))) {
devtools::install_github('ryentes/careless')
}
library(daff)
library(readr)
library(dplyr)
library(careless)
l... |
11f76060321d2452797bef4dba10895130b137d0 | edc9289ab789afe6c5c720be9338508a01976305 | /Dataset_operators.R | 14d84b1129bb023813537a5de0cfe79d3c58774c | [] | no_license | SuruthiVinothKannan/RBasics | f4ded39e27d5e41a385ee6b043ef04f0e249e613 | 7b137b926610128dcd4a36b2c1bb87e2493b8a37 | refs/heads/master | 2022-11-17T18:06:44.134231 | 2020-07-17T20:21:21 | 2020-07-17T20:21:21 | 280,517,587 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 598 | r | Dataset_operators.R | # Creating Subset of data
getwd()
install.packages("openxlsx")
library(openxlsx)
test=read.xlsx("Revenue_dataset.xlsx")
dim(test)
head(test,10) #default of head()& tail() 6 records
tail(test,15)
subset=test[100:105,]
dim(subset)
names(subset)
str(subset)
rm(may_subset) # To remove a dataset
... |
e170dd655cbf88a1c5a34d7f88e6843440574fc7 | 530474c7537d174c797f8be66da1087bf7cf1c59 | /R/samplePlotCompilation.R | 3dc037f21ce9358a23501863539c5d1fceadc508 | [
"Apache-2.0"
] | permissive | bcgov/FAIBCompiler | 409d88e9444ca26847b62e43668b41eb945f84e0 | 3baf38a21c5493b7d7cf0f4695e1cc6322eeabe3 | refs/heads/master | 2023-08-05T08:47:43.934344 | 2023-08-02T21:35:23 | 2023-08-02T21:35:23 | 195,121,227 | 0 | 0 | Apache-2.0 | 2020-02-12T18:00:30 | 2019-07-03T20:19:19 | R | UTF-8 | R | false | false | 18,024 | r | samplePlotCompilation.R | #' Compile sample and plot level information
#'
#'
#' @description This function is to compile sample and plot information.
#'
#' @param compilationType character, either \code{PSP} or \code{nonPSP}. If it is \code{PSP}, it
#' is consistent with original PSP compiler, otherwise, it
#' ... |
c0af21f199ccdd1d91c781fc9543f5985cfb1866 | 22f3f32e253acdcb407f5e2bf934bf0ff4a3d280 | /scripts/sc_cities_sc_city_boundaries.R | ae62f0a9729baf967b8bfa57ce2451db11f3c7a4 | [] | no_license | ottoman91/_sc-evictions_ | c8e4b059ba8db73fd86df38fc8c9d8efacf6809e | bebbaaf463f510103bf8d291fc0d9d07c02ee93b | refs/heads/master | 2020-12-23T08:46:35.470630 | 2020-01-31T01:10:24 | 2020-01-31T01:10:24 | 237,101,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 673 | r | sc_cities_sc_city_boundaries.R | # Script that joins the Eviction Data for South Carolina cities with the City Boundaries
# Author: Usman Khaliq
# Version: 2020-01-30
# Libraries
library(tidyverse)
# Parameters
path_sc_cities <- "data/sc_cities.rds"
path_sc_city_boundaries <- "data/sc_city_boundaries.rds"
rds_file_path <- "data/sc_cities_sc_city_bo... |
e53080fcadd215b1d663820e07aa3e19513b9b68 | 087c25946bb6d396cff7f6d21c25c704939a6b21 | /dictionary.R | 0e4e8cdc07f967e67a6461b65115e9e14db71dc9 | [] | no_license | alightner/acculturationMarketInt_trust2020 | 31b52c3d83eff7dc81fbb7a829148d6c587a22c9 | ab8d27d00d555b013ab6dd496046edb3e7e6f834 | refs/heads/master | 2023-01-02T04:29:55.803127 | 2020-10-27T13:58:36 | 2020-10-27T13:58:36 | 307,703,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,200 | r | dictionary.R | # dictionary --------------------------------------------------------------
var_dict <- c(
"id"="id",
"age"="age",
"sons"="sons",
"daughters"="daughters",
"farms"="farming",
"donkeys"="donkeys",
"chickens"="chickens",
"cattle"="cattle",
"goats"="goats",
"sheep"="sheep",
"TLU"="TLU",
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.