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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7cacc52e5e950fa90e77b3274fe74e2a8b05885a | b0a8f8d6984078682450613d8c351664341c47a8 | /Plot1.R | ed4e140738440b7eea40388ab191a374ae7bea02 | [] | no_license | silhouetted/ExData_Plotting1 | c20df765d223f815c1a4f380b8d2c26c32c2cf02 | fb6b06e8149d3a2640eb580e7cbb4169410001ab | refs/heads/master | 2020-04-05T22:11:02.827530 | 2018-11-12T18:35:17 | 2018-11-12T18:35:17 | 157,247,818 | 0 | 0 | null | 2018-11-12T17:04:38 | 2018-11-12T17:04:37 | null | UTF-8 | R | false | false | 2,026 | r | Plot1.R | #### Exploratory data analysis Week 1 Assignment script
### Reading in and subsetting the data to 1st and 2nd Feb 2007
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
# download file if it does not exist
if (!file.exists("El... |
d6b4fdaa8c469899a29b7ee5df2dbea7503f947c | 9df25083c9e3b935853cd941e1f6e9dfd4878322 | /01-PH01-SC1-FINAL.R | 4a35d431d9d33a8d49f073252f1790414656252c | [
"MIT"
] | permissive | rintukutum/precisionFDA-BCC-PH01 | 13640c440795f80281ed8835273c32c7923cd10d | e4392a4ea96b266dfdcf3f5e8b952cf73b727190 | refs/heads/master | 2020-12-29T05:26:35.916184 | 2020-02-05T15:57:30 | 2020-02-05T15:57:30 | 238,470,111 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,171 | r | 01-PH01-SC1-FINAL.R | #-----------------
# TRAIN, CV & TEST
rm(list=ls())
load('./data/sc1.data.RData')
#-------------------------------
# SPLIT DATA INTO TRAIN AND TEST
set.seed(907)
train.idx <- caret::createDataPartition(
y = sc1.data$outcome$SURVIVAL_STATUS,
times = 1,
p = 0.8
)[[1]]
tr.samp <- sc1.data$outcome$PATIENTID[train.idx... |
a96e2c29d4df17c11d0fd581aec8dbf5515a5056 | 7af0de4a6767812f392bd69a2298f45550f8abb5 | /Bagged_Loess_Lattice.R | 00d0ae511c8c7bbe9c2d7af021799907d9afb88f | [] | no_license | SudhakaranP/Statistical_Learning_Basics | 615077494c15c9ae8f28cd3e856eee7b8cd03678 | 40162b9831bdc165da5af926cc2c7ba8a9fe674f | refs/heads/master | 2021-06-14T18:56:40.890625 | 2016-12-14T02:04:34 | 2016-12-14T02:04:34 | 105,226,455 | 0 | 1 | null | 2017-09-29T03:37:24 | 2017-09-29T03:37:23 | null | UTF-8 | R | false | false | 862 | r | Bagged_Loess_Lattice.R | library(ElemStatLearn)
set.seed(105)
ll <- matrix(NA,nrow=100,ncol=155)
for(i in 1:100){
ss <- sample(1:dim(ozone)[1],replace=T)
ozone0 <- ozone[ss,]; ozone0 <- ozone0[order(ozone0$ozone),]
loess0 <- loess(temperature ~ ozone,data=ozone0,span=0.2)
ll[i,] <- predict(loess0,newdata=data.frame(ozone=1:155))
}
xyp... |
293ff38a2ed703a9fc70b6a468f439e2b5d98b3d | 171398356488ee085e053595b7e9e43d671c1586 | /HW4-Gairola-Abhijit.R | 2cdb3e9de979d1908bfcc973452e18ded7693902 | [] | no_license | dexter11235813/Math185 | 2e83986eba0af687b444bd97d7bfd02a4d0c2bd1 | ae5d60b5c38a2cba38274867657c3e33be380e97 | refs/heads/master | 2020-07-03T23:42:52.900984 | 2016-11-19T16:51:55 | 2016-11-19T16:51:55 | 74,222,485 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,222 | r | HW4-Gairola-Abhijit.R | #Q1
head(ToothGrowth)
SST = function(dat,Y...)
{
temp1 = c()
for(i in 1:2)
{
temp1[i] = mean(dat[,i,])
}
return(30*sum((temp1 - Y...)^2))
}
# for B = 1:999, we permute data within each block and calculate SST of that table, and compare it with the SST of the original data.
twowayPermTest = fu... |
05dbcc03744c5f696c565a3a23013663a05a62dc | 808f796821392a6ce4dd2a243aac01cce1513e0e | /src/plotFlightLine.R | c13602d3708600dd725d5d1abc7bdc436aeecc47 | [] | no_license | hdugan/WrightValley_AEM | f7c7b9ba72c7fe3a609cd9acade05cb705fc7de3 | 6eb83f7d761ee75d77124808409a4749bf0e4f10 | refs/heads/main | 2023-04-15T03:06:19.803795 | 2022-08-04T15:31:58 | 2022-08-04T15:31:58 | 475,973,442 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,740 | r | plotFlightLine.R | #dvdpLocation is UTMX, UTMY, depth. ex) c(416673, 1391082, 85.73)
plotFlightLine <- function(lineNo, aemDF, dvdpLocation = NULL, dvdpLocation2 = NULL) {
# Derive depths of each layer
depths = aemDF |> filter(LINE_NO == lineNo) |>
dplyr::select(DEP_TOP_1:DEP_TOP_30) |>
pivot_longer(cols = DEP_TOP_1:DEP_T... |
4a24b742e5d454d131d0a57f8f434ff38416b3f1 | 39d7f1d2d81c2a9fc37df54cccc8571f7a1d31e0 | /EM/R_codes/c_em_algorithm_generic.R | 89011d5b58571043b69db3f126b2899df14506c1 | [] | no_license | historical-record-linking/matching-codes | 4a989b65f12791a206c24e8646a1f37881bf44ec | 31aaf4a85056b3f58aa71342611ba2faead66306 | refs/heads/master | 2022-11-21T18:32:15.941226 | 2020-07-08T23:15:26 | 2020-07-08T23:15:26 | 258,260,033 | 16 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,136 | r | c_em_algorithm_generic.R |
rm(list=ls())
dropbox <- "C:/Users/acald/Desktop/test_em_data/"
# set directories
EMdistances <- paste(dropbox,"data/new_codes/em_santi_small2/EMdistances/",sep = "")
EMmatches <- paste(dropbox,"data/new_codes/em_santi_small2/EMmatches/",sep = "")
# Select threshold for Algorithm and maximum number of ite... |
0dedd99043c8ea48bf311280366ae359d6bb4841 | bdd8d4b527d36aa0c69d127aa6c071b70dc3fffb | /Admin/testando - fields of study.R | 7f90e47fe53ea76373569052f3ff3a550e94b385 | [] | no_license | antrologos/harmonizeIBGE | 2abc3fa53106c026d29e11f83bae8e642049ab86 | 8c6053b54b434eddab84f6e32f43424f22d06170 | refs/heads/master | 2022-03-13T11:16:06.906636 | 2022-02-28T17:25:21 | 2022-02-28T17:25:21 | 141,569,637 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,495 | r | testando - fields of study.R | rm(list=ls());gc();Sys.sleep(.5);gc()
options(scipen=999)
library(harmonizeIBGE)
library(Hmisc)
library(descr)
library(fst)
#======================================================================================================
setwd("E:/Dropbox-Ro/Dropbox/Rogerio/Bancos_Dados/Censos")
variaveis <- fread("E:/Google Dr... |
5ace1e957a32ec7f8ce702bcefd13bc3a46af9c3 | b4a58ba2dbffed266dc06f6dcb32f22797271f2e | /04.heatmap.R | 7ed517177dfd0895e1a617c8823366557784d54d | [] | no_license | SamYangBio/R | 063476d8cb29e1f00d63152a45d4ab78d34067a2 | 7c756d2c74071801f46c33646c7ba19104aef987 | refs/heads/master | 2020-03-24T23:26:01.455474 | 2018-10-14T10:33:13 | 2018-10-14T10:33:13 | 143,134,931 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 761 | r | 04.heatmap.R | args <- commandArgs(TRUE)
input <- args[1]
out <- args[2]
library('gplots')
a = read.table(input, sep = "\t", header = T, check.names = F)
lie = ncol(a)
hang = nrow(a)
high_pre = hang/10
high = round(high_pre, digits = 0)
high = high + 10
high = ifelse(high < 7, 7, ifelse(high>200, 200, high))
x = a[,2:lie]
y = as... |
247bec4c0d7037087dfebaca256a9699fac30649 | d2061a237532f631fde4eb2651093bed8593c403 | /man/Figure3_5b.Rd | e92d24fd6bac00379580438e859f8e2458671c58 | [] | no_license | cran/sur | 23fb12f03ea816e80e0b2fd0a1491f2ca81218dd | 612cc5e2391a019ed157f90958d31f40e9466560 | refs/heads/master | 2020-12-22T19:13:21.454433 | 2020-08-25T21:30:02 | 2020-08-25T21:30:02 | 236,903,605 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 437 | rd | Figure3_5b.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-figures.R
\docType{data}
\name{Figure3_5b}
\alias{Figure3_5b}
\title{Figure 3.5(B) Data}
\format{A data frame with 75 rows and 1 variable:
\describe{
\item{DistnB}{numeric score from a symmetric distribution}
}}
\usage{
Figure3_5b
}
\d... |
4c1eb42eb0fac1178462f410ad6b45d2d65a9bed | 67222f69dd1a5b5ced1d28df833a303924dbde35 | /2. Algorithms on Datasets/Supervised Machine Learning Techniques/Decision Tree/Company_Data/Company_Decision+Tree.R | dfd991d189ae2a2d3b632e7d4a1b72a2f44cb5f0 | [] | no_license | mandarmakhi/DataScience-R-code | 4f75906507e303fb9b438b99a5eab0a74bcc77f6 | 8c1728b306e53668b1814283da9936503e0554b9 | refs/heads/master | 2023-01-19T04:55:11.171455 | 2020-11-28T07:59:55 | 2020-11-28T07:59:55 | 263,417,867 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,263 | r | Company_Decision+Tree.R | #Decision Tree
#A cloth manufacturing company is interested to know about the segment or attributes causes high sale.
#install.packages("caret")
#install.packages("C50")
library(C50)
library(factoextra)
library(caret)
library(gmodels)
#Lets Import the Dataset
company <- read.csv("C:/Users/Mandar/Desktop/data/assig... |
2d2fac878ca51a454eb0d917e7b9081b962bd167 | c8a8ad16a9633de5bf1bd2917ae63702ae58d4ac | /R/allele_divtables-class.R | 1829b19b86b06fb84fecfae1b02ae9168f84828b | [] | no_license | douglasgscofield/dispersalDiversity | 5487b5cc8ce31e9fc64e5398afa71db9a12a6ac6 | 3c826115e6fca06b801346452e2659ec5142a715 | refs/heads/main | 2023-04-14T08:17:59.717926 | 2021-03-23T16:44:16 | 2021-03-23T16:44:16 | 4,307,834 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,852 | r | allele_divtables-class.R | #' @include divtable-class.R
NULL
#' List of divtables holding allele diversity data
#'
#' An object of class \code{allele_divtables} is a list of
#' \code{\link{divtable}} objects, each representing sites-by-allele counts
#' data for a single genetic locus. Row and column names are
#' the site names and individual a... |
fa09b7ad778847af8f23ec06c4004c67a6c9932c | 059a3965261ee3ce2b703231778b66e500ed769a | /CartoDB/man/cartodb.row.insert.Rd | 9e050665f5ec1c3868d815c894b884ee535ebb79 | [] | no_license | alexsingleton/cartodb-r | 8a43c14e5af6bf18fa8fe4177817a0a5bcb248ba | ea893c4b37edee1220fc279d50e09fb1c8c8de48 | refs/heads/master | 2020-06-09T12:47:01.919613 | 2016-12-09T13:35:34 | 2016-12-09T13:35:34 | 76,037,782 | 2 | 0 | null | 2016-12-09T13:29:41 | 2016-12-09T13:29:41 | null | UTF-8 | R | false | false | 1,129 | rd | cartodb.row.insert.Rd | \name{cartodb.row.insert}
\alias{cartodb.row.insert}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Insert a single record into CartoDB
}
\description{
Insert a single record into CartoDB
}
\usage{
cartodb.row.insert(name=NULL,columns=NULL,values=NULL, quoteChars=TRUE)
}
%- maybe also 'usage' fo... |
776c25a422350e10616e8cc7b5ee9e57605989eb | fe267079c286e35b4989240c407d69e83679f3f9 | /man/prepare.Rd | 9f2482362ac45a12268af1224e17738bba86209c | [] | no_license | cran/clustTool | f9c1bb306242520332f235aa532928049c67da44 | d238b796bcfd6926794a4e378ce0012a55eafd21 | refs/heads/master | 2016-09-05T09:32:29.184558 | 2010-08-16T00:00:00 | 2010-08-16T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,387 | rd | prepare.Rd | \name{prepare}
\alias{prepare}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Function for tranformation and standardisation }
\description{
This function can used for transformation and standardisation of the data.
}
\usage{
prepare(x, scaling = "classical", transformation = "logarith... |
443d6f2f7ee000a470d256dcb826509db41a19de | 13cdc54d90f2ba332f558eaebd799de1edfe4a17 | /scripts/visualizations.R | 48dde87aa02f04c7fb3b92fd8302e6d007970ea1 | [
"CC0-1.0"
] | permissive | UACC-renedherrera/UAZCC_deliverables | adecdaf55e811ea6ef1cfda86b4b879c19104202 | dcadbb69d121776c7538c7217ecd8876f5098bc0 | refs/heads/master | 2023-03-08T21:51:32.340103 | 2021-02-27T06:55:16 | 2021-02-27T06:55:16 | 282,557,115 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,298 | r | visualizations.R | #### set up ####
# packages
library(here)
library(tidyverse)
library(ggthemes)
# color palette
blues_8 <- c("#f7fbff",
"#deebf7",
"#c6dbef",
"#9ecae1",
"#6baed6",
"#4292c6",
"#2171b5",
"#084594")
blues_3 <- c("#deebf7",
... |
7af4f28ec4f0c862ced4c64ee8524db6ee098332 | eca503411624bec763cace42856c2c9fdf7b26d5 | /tests/testthat/test-02_flow_data_if.R | 82d4e64794ca0d2c45863dd636d432593c995c1b | [] | no_license | yuewangpanda/flow | d1eaf3da9ab32bbde89deff85ccff39f37a26e9f | e6a812392ea9d4ea91e70c5aa6fb799e14ba32fe | refs/heads/master | 2022-12-24T18:07:49.606674 | 2020-09-25T16:49:25 | 2020-09-25T16:49:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,888 | r | test-02_flow_data_if.R |
#### IF ####
# simple if call without else and empty body
test_that("flow_data works with simple if and empty body",{
fun <- function(x) {
if(x) {}
}
data <- flow_data(fun)
# flow_data(fun)
# dput2(data$nodes[1:4])
# dput2(data$edges)
expect_equal(
data$nodes[1:4],
data.frame(
id = c(0... |
a70c095b72aa6332e1e7daa48f9a51c4759d6f59 | 473cf48d1e85a74718b80a7e7aaeb86731f9ad98 | /R/ratio.plot.ade.R | c1633fba8adc1d0781246fcc169389d5f266429c | [] | no_license | cran/epade | f3584067b7b925b1420103559d9292dd7f702de1 | 779623aac20dd6cf53dcefa524d7a59fba1aecb6 | refs/heads/master | 2022-11-15T03:02:32.786022 | 2022-10-27T14:35:16 | 2022-10-27T14:35:16 | 17,695,818 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 25,545 | r | ratio.plot.ade.R | ratio.plot.ade <-
function( M, vnames=NULL, sectext=NULL, main=NULL,xlab=NULL, ylab=NULL, legenlab=NULL, rlab=NULL, col=NULL, tcol=NULL, bgcol=NULL, lcol=NULL, r=NULL, v=c(0,1), lty=c(1,2), xticks=18, hlines=TRUE, legends=TRUE, logaxe=FALSE, wall=0){
if(any(par('mfg')!=c(1,1,1,1)) & any(par('mai') < c(1.02, 0.8... |
4936e576441f4445ed2af4cf2e554aa566f4d34e | 3f4ec466b0fb4f3b585b06520f4cc2ebf25c5492 | /man/graph.ran.mean.Rd | f42dccb51afb1965a25aa2a5fb5390cfdced0da6 | [] | no_license | cran/multilevel | aa271d788894adfe7572b6519087e3ec0091d75e | d0c02975a2c319edd3f04b29e24b3b9175a78869 | refs/heads/master | 2022-05-01T03:29:43.293282 | 2022-03-07T22:20:02 | 2022-03-07T22:20:02 | 17,697,743 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,618 | rd | graph.ran.mean.Rd | \name{graph.ran.mean}
\alias{graph.ran.mean}
\title{Graph Random Group versus Actual Group distributions}
\description{Uses random group resampling (RGR) to create a distribution
of pseudo group means. Pseudo group means are then contrasted with
actual group means to provide a visualization of the group-level prop... |
c26aeb3644fc3d7d0f476c8c3a5de1357c27cca7 | 7eef8780fd24ebc5deab2b2f68bf5209c65cd056 | /plot1.R | 15fffe909a9de12a8b6dc0ee4c60d2c741c8031a | [] | no_license | agnecede/ExData_Plotting1 | fd091f10fdc8e66cb195bfe19a4fd8dcbbc09d78 | 3781872f3904fcdbb5c1373735d84202abfa51ce | refs/heads/master | 2020-04-04T00:28:07.144654 | 2014-12-06T13:48:09 | 2014-12-06T13:48:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 980 | r | plot1.R | household_power_consumption_dates <- read.csv("~/Coursera/Exploratory Data Analysis/CourseProject1/household_power_consumption.txt",
sep=";",na.strings = "?",skip=66636,nrows=2880)
names(household_power_consumption_dates) <- c("Date","Time","Global_active_power","Global_rea... |
fe19acf0eca73551cca203f1aeee677c65ea649c | 7804575cd506b4c42defb796dee66fe083061ff9 | /dynamic_ex.R | 88bbea7a9aeb1179f99846325780d35a4fae3972 | [] | no_license | tmastny/reactor | 62b9bf5bacfc46316d82792bdaa817ac2bf8ca77 | ff49938baf0d5e8d403eb39ce7257f6e3efb177e | refs/heads/master | 2020-03-25T00:29:07.550128 | 2019-11-30T16:39:24 | 2019-11-30T16:39:24 | 143,188,063 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 957 | r | dynamic_ex.R | library(shiny)
ui <- fluidPage(
#includeScript('www/enter_bind.js'),
singleton(tags$head(tags$script(src = "enter_bind.js"))),
tags$input(type='command', id='command1', class='reactnb-command',
autocomplete='off', autocorrect='off'
),
tags$br(),
# tags$input(type='command', id='command2', clas... |
90f138e8372fdee05511793ea45e331c03d3de44 | 21ac23387cf8bb8f0dba10571ed89b1300fa6d17 | /funcoes-em-R.R | 87dcc6dea01faa21227a5de305182c6f27851343 | [] | no_license | carlosafs/curso-universidade-mexico-unam | eb4e1b2e3fd38b5f0078f75dfae6166b361ed514 | 0674b4b8f91aa16ffe907e1c3563e4c2cafa8681 | refs/heads/master | 2020-05-22T18:28:47.476574 | 2019-06-07T14:52:37 | 2019-06-07T14:52:37 | 186,471,981 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 577 | r | funcoes-em-R.R |
#funcoes
#exemplo 1:
minha_funcao <- function(x, y, operacao = "soma"){
if (operacao == "soma"){
return(x + y)
}
if (operacao == "subtracao"){
return(x - y)
}
if (operacao == "multiplicacao"){
return(x * y)
}
if (operacao == "divisao"){
return(x / y)
}
}
minha_funcao(2, 2)
minha_func... |
997f08dad4c2c296f6797b046d4d5becf6912eb0 | 107535e88a0314595086a2502d00a766518bbbe3 | /ui/ui_trans.R | b8bee1b14eaec4079836d24654bc0b0518268173 | [] | no_license | aravindhebbali/exploriment | 1d8b7cc4fe518fa2ade6b22e6fe3cdb4f287c506 | 8cf4e86086751853bd7df7c3dc67699c3ab2b765 | refs/heads/master | 2021-03-16T10:13:51.198920 | 2017-07-10T12:23:43 | 2017-07-10T12:23:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 219 | r | ui_trans.R | tabPanel('Trans', value = 'tab_trans', icon = icon('database'),
navlistPanel(id = 'navlist_trans',
well = FALSE,
widths = c(2, 10),
source('ui/ui_transform2.R', local = TRUE)[[1]]
)
)
|
d78770038ebcc673391f979a632c3bcb820ad496 | 0312ccadd2937b536aaf655a0b35dd8a551b07b9 | /Plot6.R | e7008af599679a3d826543c750e739dd31fa108e | [] | no_license | daiane1989/ASSIGMENT | 78121840fb28c24d55ddd7f3ffaca46891866aaa | 17a56da5ecd0ccaff165b608449ae76e2f21a9f5 | refs/heads/master | 2020-03-27T21:16:21.531262 | 2018-09-03T00:08:25 | 2018-09-03T00:08:25 | 147,130,284 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,445 | r | Plot6.R | setwd("C:/Users/Daiane/Desktop/COURSERA/Exploratory data analysis/semana4")
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
library(dplyr)
#Compare emissions from motor vehicle sources in Baltimore City with emiss... |
e23ba59343c41efe57f93a2d22dcb0d64403c3de | 00bc0fb8ba893f6cf3a0dcb9f73f6816e267c96e | /CleanHousePrices.R | a22e6769badd615eda2bd2926e5a497a1bfd8ac7 | [] | no_license | oliengist/HousePrices | 3e72c78c3f56d0503e4dfd518bdb1dbf4053c2ec | 4369ca26203ccaa20da848ea1af19f3cda2e70a6 | refs/heads/master | 2021-01-19T21:21:22.753896 | 2017-02-20T04:47:54 | 2017-02-20T04:47:54 | 82,503,946 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,757 | r | CleanHousePrices.R |
# Author: Oliver Engist
# Corresponding dataset: https://www.kaggle.com/c/house-prices-advanced-regression-techniques
# Description: Run this script to produce a cleaned up testing and training dataset of the original
# House Price datasets.
#---------------------------------------------------------------------------... |
3bfcdfc654f9d9c42054ebc6d6adc766a90e0249 | c9e02a75abbd1d5048446a65aa23b10f79492b2f | /scripts/cheaters.R | a97b4dc2876923ad576fc00fbe2bc7fa24960645 | [] | no_license | somasushma/R-code | f8290d3ecd8ea87ef778b1deb0b7222e84b811be | 2e1f0e05ae56ebe87354caeb374aebb19bf00080 | refs/heads/master | 2021-10-27T14:42:26.847193 | 2021-10-25T22:02:51 | 2021-10-25T22:02:51 | 137,162,116 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 809 | r | cheaters.R | te=c(27, 20, 7, 4, 1,1)
ye=c("European", "Indian", "Chinese", "West Asian", "Korean", "Japanese" )
df=data.frame(cbind(ye,te))
colnames(df)=c("origin", "count")
df$count=as.numeric(as.character(df$count))
df$percentage= round(df$count/sum(te)*100,1)
df$pop=c(197.3, 3.18, 3.79, 10.5, 1.7, 1.3 )
totalpop=sum(df$po... |
b34ff97e28c7d0c97cc2d3671c272a3952fd50e2 | 8033c0798ee7f8764b51f55372384a42bfb93c1d | /man/EarningsPerformance.Rd | c6caa09e5ab33b13506a3bf3d9cb8ca7f6828aca | [] | no_license | Texas-UCF/quantkit | 5a2b9d945957b7de02c6c99be6d976a0b167edc9 | cba3c06ddf8d5f73be7813f32d875f79fdf623e1 | refs/heads/master | 2021-01-24T01:14:15.731465 | 2015-11-09T23:51:23 | 2015-11-09T23:51:23 | 42,608,607 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 711 | rd | EarningsPerformance.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/EarningsPerformance.R
\name{EarningsPerformance}
\alias{EarningsPerformance}
\title{Earnings Performance.}
\usage{
EarningsPerformance(ticker, searchStart = Sys.Date() - 365,
searchEnd = Sys.Date(), timePeriod = 7)
}
\arguments{
\it... |
c41a1f398e58e5b2c367b7ea3115818ef265cd8f | d82a996f50f6b553f645af24a6dd1600b19084cf | /lascar_data_analysis/Lascar Duplicate Identification.R | 5d59254c5213b22978d271c522b8ad721061c964 | [] | no_license | ashlinn/GRAPHS_exposure_data | 99f3035d2746b318f42113b3759a33543e83d91a | 9f5923734d00f5a63f66fbc30d318537bd235da9 | refs/heads/master | 2021-01-21T11:18:34.561236 | 2018-04-06T20:03:51 | 2018-04-06T20:03:51 | 91,735,083 | 0 | 0 | null | 2017-05-18T20:30:11 | 2017-05-18T20:30:11 | null | UTF-8 | R | false | false | 3,530 | r | Lascar Duplicate Identification.R | # Lascar file duplicate/problem identification by session
# set path
path <- "~/Dropbox/Ghana_exposure_data_SHARED (1)/Main_study_exposure_assessment"
################# Run script from here to the end
## get Lascar files
files<-list.files(path,recursive=T,pattern="^(CU_CO|CU_C0|CO_USB|COL_USB|CU-CO|CU-C0|CO-USB|COL-... |
e837e01db2f501783c6604d0ae61ec50edf29835 | 6b451cbe2d5ce230262a6fcbdc22c33561bd3fb6 | /R/R_Functions.r | 897843be81ac5d6c266cb6dbf691fe79001d2e50 | [] | no_license | phattdoan/Code-Base | 9423d794e391439ecde7a500027dda6b3523bffa | f0a988c5cc765d25e5a9c208c02ed82a8b9464bf | refs/heads/master | 2021-03-27T18:23:21.970097 | 2017-09-25T14:45:13 | 2017-09-25T14:45:13 | 102,536,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,294 | r | R_Functions.r | -----------------------------------------------------------------------------------------------
## read CSV
####################################
raws_score = read.csv(file = "Data/RAF2016_v2.csv", header=TRUE, sep=",")
head(raws_score)
----------------------------------------------------------------------------------... |
2693581b1eb0c25941c54841e16c2e8d55f33a8d | e3cc14879310ef6b79a31e5e391867339e49d27e | /Shiny/server.R | cbc8259552044511e0fb9752622a92ee55a5a237 | [] | no_license | Angel-RC/Twitter | 481b679346b790d14e501f280a59da8cc4d02ff4 | a8ae7be20b16057696eab2b4b76b6973b01b9e14 | refs/heads/master | 2020-03-22T07:28:17.239204 | 2018-07-04T08:42:35 | 2018-07-04T08:42:35 | 130,845,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,595 | r | server.R |
shinyServer(function(input, output, session) {
# Datos ----
# ·······························································································
startTime <- as.numeric(Sys.time())
weatherData <- reactive({
invalidateLater(5000, session)
N <- min((as.numer... |
80f8fbe38fec49112f9122081f0888843874f012 | 2dc29399693050696b7dec30b90bcfb00b17cae3 | /r-scripts/Chapter-11.R | 4b1e6c17c1f0b2e55aa6d6cb943cdebb1fefdb76 | [] | no_license | luis8185/comparing-groups | 1b74f64ebbc1363583635b2b4e55c4e67f5b8041 | 57731d07f3307c0161d4077d99b0b124a4a33c37 | refs/heads/master | 2021-12-22T08:59:39.707618 | 2017-09-13T23:00:51 | 2017-09-13T23:00:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,199 | r | Chapter-11.R | ###################################################
### Comparing Groups: Randomization and Bootstrap Methods Using R
### Andrew S. Zieffler, Jeffrey Harring, and Jeffrey D. Long
### December 05, 2010
### Chapter 11: Planned Contrasts
###################################################
###############################... |
67783af7d9551699952dedbc2f17451eef649964 | c4f324c98487791c39f550645743e2b5dad3b52a | /man/make_bed.Rd | 541af206012711959e601ad2c34ecf4713dcfede | [] | no_license | russelnelson/GeneArchEst | 06219a1d222087b13f662739f60ebe513aa2bc1f | 0d126aae50f9b68ee2c36ea6c6d9ba16e7c41a9c | refs/heads/master | 2023-03-13T00:48:25.816747 | 2021-03-04T23:08:15 | 2021-03-04T23:08:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,646 | rd | make_bed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{make_bed}
\alias{make_bed}
\title{Make bed files from genotype/phenotype data}
\usage{
make_bed(
x,
meta,
phenos,
plink_path = "/usr/bin/plink.exe",
return_objects = F,
missing_genotypes = -9
)
}
\arguments{
\item{... |
887c7689199589b41bd032258e902e5a30f6862c | 0d1ef2cb3818bcadd602a0f46cbfec7ed55a9aef | /h2o_3.10.4.4/h2o/man/h2o.tanh.Rd | 928a5e0b9b5b63c56d6e3f820c142548be2c2207 | [] | no_license | JoeyChiese/gitKraken_test | dd802c5927053f85afba2dac8cea91f25640deb7 | 3d4de0c93a281de648ae51923f29caa71f7a3342 | refs/heads/master | 2021-01-19T22:26:29.553132 | 2017-04-20T03:31:39 | 2017-04-20T03:31:39 | 88,815,763 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 317 | rd | h2o.tanh.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{h2o.tanh}
\alias{h2o.tanh}
\title{Compute the hyperbolic tangent of x}
\usage{
h2o.tanh(x)
}
\arguments{
\item{x}{An H2OFrame object.}
}
\description{
Compute the hyperbolic tangent of x
}
\seealso{
\code{\link[base]{tanh}} for the base R implementation.
}
|
aa6e8b6bc95c3f03d34d237c0725bc21c443fde0 | 4c3ff90922b2fa72e82e7ab9a3ba8e7c8ad51113 | /code/rnaseq_code/relative_coverage_plots.R | 5d9526c01755d4a8740f0abd17a95b1a03b8ea81 | [
"MIT"
] | permissive | felixgrunberger/pyrococcus_reannotation | cdb6390aa160c879599ddcc92986ed8491ae3af2 | d3fb45473bb0c92a2edf99ab770ac8f32e69e55c | refs/heads/master | 2020-04-29T18:04:05.504125 | 2019-10-22T08:18:24 | 2019-10-22T08:18:24 | 176,313,200 | 0 | 0 | MIT | 2019-10-22T08:18:02 | 2019-03-18T15:16:47 | R | UTF-8 | R | false | false | 11,590 | r | relative_coverage_plots.R | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #
# Average read coverage of 3 data sets in relative position to 4 TSS
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> #
# load libraries
# >>>>>>>>>>>>>>>>>>>>>>>>>... |
773b8a9e28edcee6102a17ae68541b611e4af33b | 400a462eff41e452bce0a7c387b996de717b523a | /olderCode/tests/bestFileAndNumComponentsForEachAnalyte.R | c8f1db25d35f812da54472bcd3da210be2021233 | [] | no_license | BirgandLab/Brittany | 6740de2ad254874860d4f1eaa8cd09cf241f2e7d | 25c2658b1742f9460087d99ae3008f6f679f6564 | refs/heads/master | 2020-05-18T00:27:54.544829 | 2015-04-03T17:54:00 | 2015-04-03T17:54:00 | 20,567,831 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,508 | r | bestFileAndNumComponentsForEachAnalyte.R | library(oce) #used for despiking
library(hydroGOF) #forgot what I used this for
library(pls) #Load the pls package
#******Specify file paths and names
inPath<-"C:/Users/FBLab/Documents/GitHub/Brittany/inputFiles/" #Specify folder where data is located
outPath<-"C:/Users/FBLab/Documents/GitHub/Brittany/output/"
fitPat... |
066f29f667c33ba6248eeacdad279a2f982ee12a | 5bc791cca37dfb30d845fdf2beaaff775e1739e5 | /OReilly learning R/Chapter 5 Lists and Data Frames.R | fe0f2202c1ce41d86ff5791994c8fee7fdcce357 | [] | no_license | AScheuss/Rlearning | 061e46d269f9f8bf8a350f70ed07207326640918 | 403b36c36ef2dd1f11f4b9496f5a9ad9389ca8f7 | refs/heads/master | 2021-01-01T17:28:03.748341 | 2017-10-09T09:49:53 | 2017-10-09T09:49:53 | 22,141,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,240 | r | Chapter 5 Lists and Data Frames.R | #### Chapter 5 Lists and Data Frames
## Lists and data frames let us combine different types
### Lists (Vectors with different types)
# Creating lists
(a_list <- list(9.4,'total', c('cool','wicked','nice'),c(3,45,34),asin,month.abb))
# Naming works as with vectors
names(a_list) <- c('a number', 'a string', 'nice stri... |
e2c78fac72cfca94c990fc0e44d28ecdbc97fe49 | 4419dcaad86d41cca6ad026a6a6c72e408fa62eb | /R/data.R | 10a84bcca40dfc709d8345d43660260ce52e0fa3 | [
"MIT"
] | permissive | poissonconsulting/mcmcr | c122a92676e7b1228eedb7edaebe43df823fdeb8 | ca88071369472483e7d73914493b99fd9bda9bd5 | refs/heads/main | 2023-06-24T09:43:04.640793 | 2023-06-13T00:19:29 | 2023-06-13T00:19:29 | 70,411,531 | 15 | 3 | NOASSERTION | 2022-06-21T03:07:27 | 2016-10-09T15:18:09 | HTML | UTF-8 | R | false | false | 142 | r | data.R | #' An example mcmcr object
#'
#' An example [mcmcr-object()]
#' derived from [coda::line()].
#'
#' @examples
#' mcmcr_example
"mcmcr_example"
|
3ab166299f0149642a88d9d8b0fda646a53289a7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/knitr/examples/knit2html.Rd.R | 430e043884436461815cdeb2426b3e67b6993bee | [] | 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 | 339 | r | knit2html.Rd.R | library(knitr)
### Name: knit2html
### Title: Convert markdown to HTML using knit() and markdownToHTML()
### Aliases: knit2html
### ** Examples
# a minimal example
writeLines(c("# hello markdown", "```{r hello-random, echo=TRUE}", "rnorm(5)", "```"),
"test.Rmd")
knit2html("test.Rmd")
if (interactive()) browseU... |
2be47663e190061163a0e0322347842a9669d5a5 | 3a412887ad6ee81f6da922bcf84f9bba946eeb9f | /snake_collision.R | 48f94b8cf662b7293cdaf95e78a48e770ba2ec75 | [
"MIT"
] | permissive | katerobsau/SketchSnake | 9953f7d9c79d5e8c93d0882f95b43bc8a618c58b | 14adaaad901657900dea8125a71d4a3c6ea60d6f | refs/heads/main | 2023-04-14T22:52:29.444931 | 2021-04-08T02:44:50 | 2021-04-08T02:44:50 | 349,600,864 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,989 | r | snake_collision.R | #! load_script(src = "https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/p5.js")
bkgrd_x = 400
bkgrd_y = 300
setup <- function() {
frameRate(10)
createCanvas(bkgrd_x, bkgrd_y)
}
draw <- function(){
#Draw the background colour
background(0, 0, 33)
# Snake details
snake_col = color('rgb(0,255,0)'... |
4f336d8f6e7a32dbfb60812f7560a558d3f517e0 | fc9ad71c519d3851ddbf97a071e1ba5c98590b77 | /Regresi-n/ejercicios.R | ddefa0002ffe703c76733fea81459cd3e8c82862 | [] | no_license | rgarciarui/Regresi-n | e7c2cb8c50089ec133074e5a4525d3c8fb15e5fe | d523a06988087b4173930198d3957764e77737c5 | refs/heads/master | 2020-09-16T08:51:16.928682 | 2018-06-12T00:20:08 | 2018-06-12T00:20:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,803 | r | ejercicios.R |
######SEGUNDO PUNTO ARTICULO DE ROCAS
datos <-read.table(file="https://raw.githubusercontent.com/fhernanb/datos/master/rocas", header=T)
require(rgl)
attach(datos)
plot3d(x=hume, y=esfu, z=poro, lwd=2, col='pink',
xlab='Humedad', ylab='RCU', type='s',
zlab='Porosidad')
mod1 <- lm(esfu ~ hume + p... |
a7437d7bcdf135c57a01ac0f4247fb6dd1855616 | daaa779292260ca6373168bf5d30e3920001a63a | /R/query_user_features.R | 8f045dd6dc91e973a1ba2b608a5c1a26c4d39ae8 | [
"MIT"
] | permissive | meteomatics/R-connector-api | f075d2b84acffac052c4e74735d73ac24cef186c | f372db8bb736cea3b73b57f5369eb0cf39c2872a | refs/heads/master | 2023-03-10T16:15:05.825948 | 2023-01-26T16:07:08 | 2023-01-26T16:07:08 | 114,877,153 | 6 | 11 | null | 2023-01-26T16:07:10 | 2017-12-20T11:00:08 | R | UTF-8 | R | false | false | 2,031 | r | query_user_features.R | #' @title Query User Features
#'
#' @description
#' Provides information about your Meteomatics licensing options
#'
#' @param username A character vector containing the MM API username.
#' @param password A character vector containing the MM API password.
#'
#' @return A named character vector containing the licensing... |
430cbdc0ed15a09ef56767a244bdc9fa03b99ddf | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/4483_0/rinput.R | 212794632189f7fcb735c6c8a385d08e8151a6c4 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("4483_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="4483_0_unrooted.txt") |
865a533d8ff7b091356107e33d56a91609c841ad | 3fdae0215a0bb7c66eddb9e4fe6f2093378c1e61 | /R/setExposureFromInput.r | a224ce42315a3c5c357ef36f08d787b63bbb526b | [] | no_license | Mapowney/resistance | 00c6276db839e2e4b7617fd38c442274198c7356 | e3da4c92b4d878b03344754c1112a640bbbaf85c | refs/heads/master | 2020-04-05T22:50:00.653943 | 2016-11-25T14:17:51 | 2016-11-25T14:17:51 | 61,367,744 | 0 | 0 | null | 2016-06-17T11:01:52 | 2016-06-17T11:01:52 | null | UTF-8 | R | false | false | 1,897 | r | setExposureFromInput.r | #' set exposure to insecticide from input vector
#'
#' fills an array of exposure values from input vector (i.e. where calculations have already been done)
#' see setExposure() to do the calculations
#'
#' @param input input vector
#' @param scen_num scenario number
#'
#' @examples
#' input <- setInputOneScenario()
... |
49d9c93e005db41257a31d3818659882c45077b6 | 24388c5aceeca5827720230f20deaa8c27be5606 | /man/x.Rd | 5d3faa9938d5406fa5133fe2ef0f8b985aa43e0a | [
"MIT"
] | permissive | zhangcal/CalvinZhangGIS3Lab7R | 995a2d88c124bbce056cd32e538a903d7491ec31 | 4c09f4f63763cb95cc86bc0c5b647b06d7768b22 | refs/heads/main | 2023-04-30T19:43:44.895441 | 2021-05-12T09:04:26 | 2021-05-12T09:04:26 | 366,654,242 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 389 | rd | x.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mtcars.R
\docType{data}
\name{x}
\alias{x}
\title{A list of 1000 randomly generated numbes}
\format{
Mtcars dataset from R
\describe{
\item{mpg}{numeric, Miles/(US) gallon}
\item{cyl}{numeric, Number of cylinders}
...
}
}
\usage{
x
}
\descri... |
1ba4e2d8a484d34aedae0d92c45c791c6c76f696 | 80e691dfc84372960d52ce874c6d922b0a144ff5 | /cachematrix.R | d92fdc5a8f51e1f59c956f72b5c2c851c0db4aad | [] | no_license | dhyvc/ProgrammingAssignment2 | 881b0d21e27c7616267ab2d12f472004a0aeca2d | e2e258b45f78e34686e8edafb02eccbced7b2914 | refs/heads/master | 2021-01-18T02:42:41.091394 | 2014-12-11T15:24:07 | 2014-12-11T15:24:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,270 | r | cachematrix.R | ## This file includes a pair of functions for calculating
## the inverse of a matrix, either by returning a previous
## cached value, or calculating it and caching it for
## future use.
## The first function creates a set of helper functions for
## supporting caching the calculation of the inverse of
## a matrix. The... |
73be600dbb9064f7f28b53cb3426d959817e836d | ff6192122cbc1c6e5ada69f7f9e4a97f4b891d7e | /man/resolve_rest_format.Rd | 1c03cac40ff056781aa580d084e42c4b8ed1c088 | [
"MIT"
] | permissive | MoseleyBioinformaticsLab/jpredapir | a3b2d3b35058c0ff489390cc0cb4b12a72e4ec9f | 50fb1dcfab4729636df37512f387f7efaef56b7c | refs/heads/master | 2020-03-22T19:22:37.959403 | 2018-09-28T04:03:40 | 2018-09-28T04:03:40 | 140,523,772 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 572 | rd | resolve_rest_format.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api.R
\name{resolve_rest_format}
\alias{resolve_rest_format}
\title{Determine JPred REST interface submission format.}
\usage{
resolve_rest_format(mode, user_format)
}
\arguments{
\item{mode}{Submission mode, possible values: "single", "batch... |
29394dd0dffe065e52f4b78ab6b88a2a4623d7b6 | 580684d0451310bfbefcd793fd2cc77ae87251fd | /Scraping_Bild.R | 9cc1d7583207bce313fa8d7cef5f9ee7ca5a958b | [] | no_license | Topf/webscraping_news | 0d7f92fc386f51545a1037541fea67abf109e274 | 5d23712679f6fa6a8c9b69d1f88042110c5def75 | refs/heads/master | 2022-10-11T17:59:17.044045 | 2020-06-09T19:14:01 | 2020-06-09T19:14:01 | 271,087,393 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,800 | r | Scraping_Bild.R | #install.packages("rvest")
#install.packages("tidyverse")
#install.packages("data.table")
#install.packages("plyr")
#install.packages("xlsx")
library(rvest)
library(tidyverse)
library(data.table)
library(plyr)
library(xlsx)
testit <- function(x)
{
p1 <- proc.time()
Sys.sleep(x)
proc.time() - p... |
77dd17a365a396fd29fe4027614586eec05c1a76 | b56eaa12d1d095388767fbe45fe1710a2426cbc4 | /class_2/class_2.R | 03aa72ae9f54d04bd11eafa4fab9ed214af8601b | [] | no_license | misrori/web_scraping | fdb91af923fe66e09d24b9adf95ee73758982282 | 0554c53f28002a82cf0ee9ff1cd6ed8b6e2ea08e | refs/heads/master | 2020-08-27T04:45:58.309951 | 2019-12-04T14:12:37 | 2019-12-04T14:12:37 | 217,248,015 | 6 | 16 | null | 2019-11-29T14:43:43 | 2019-10-24T08:19:55 | Jupyter Notebook | UTF-8 | R | false | false | 4,238 | r | class_2.R | # warm up task
#get the news, the summary, and the title into a dataframe
#https://www.economist.com/leaders/
library(rvest)
my_url <- 'https://www.economist.com/leaders/'
get_one_page <- function(my_url) {
t <- read_html(my_url)
my_titles <-
t %>%
html_nodes('.flytitle-and-title__title')%>%
html_text()
my_r... |
4719eccabdab8783b374519996ca763e90f6dae1 | 749c84ae1571427a5f8c86787ec767c9c9751326 | /llike.r | f27de1d914c579a0cab00329a08032b4997eb63b | [] | no_license | eki1381/GWMLR | 3ef626e0d80db37341624edd75a00a474e517c23 | 5d7d8f4c826023f0b973afa666cef37c3d611367 | refs/heads/master | 2021-01-19T06:41:06.088314 | 2016-07-14T15:51:20 | 2016-07-14T15:51:20 | 61,521,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 411 | r | llike.r | llike <- function(y.design.2,x.design.1,beta.1.temp,N,J,K){
res <- 0
for(i in 1:N){
eq.1.2 <- 0
eq.2.1 <- 0
for(j in 1:(J-1)){
eq.1.1 <- 0
for(k in 1:(K+1)){
eq.1.1 <- eq.1.1 + (x.design.1[i,k]*beta.1.temp[k,j])
}
eq.1.2 <- eq.1.2 + (y.design.2[i,j]*eq.1.1)
eq.2.1 <... |
b121debb0c1ed6c06ad823db622ae45618c65842 | 3244df900eb5aafe74a49c02f5f332824f220554 | /derive_model_from_url.R | cb4dfa85ded3b6e2da62fa245fbcb3466796fa94 | [] | no_license | Studentenfutter/cars-inequality | d1d94aa0922006cf7c29276600c94ae19dfd3b3e | 2a5c62a507c5863a7ab21c5c7218587c35b27ac7 | refs/heads/master | 2022-12-07T12:51:21.547515 | 2020-08-20T12:23:07 | 2020-08-20T12:23:07 | 200,675,462 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,321 | r | derive_model_from_url.R | name_list <- list( list('VW-Polo', 'kleinwagen'), list('Opel-Corsa','kleinwagen'),
list('Ford-Fiesta', 'kleinwagen'), list('Mercedes-Benz-E-Klasse','Sportwagen'),
list('BMW-Z4','Sportwagen'), list('Porsche-911','Sportwagen'),
list('Opel-Astra', 'kompaktklasse'), ... |
c4d14acc1aacf308c494e4d8c7ac55698dd874c7 | c52b4ecffe7752fe5b999814ad8d735e81473269 | /tests/testthat/test-point_error.R | 5968c70c641d3216312f8517437a129d0ae3f42a | [
"MIT"
] | permissive | UAB-BST-680/tblStrings | bd96275243f9e30ce4c8f4183ed7979f84f4e427 | e753b19131985438eb9d87253647951fee1f1f2a | refs/heads/master | 2021-04-18T03:19:53.766910 | 2020-03-23T17:34:13 | 2020-03-23T17:34:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,682 | r | test-point_error.R |
test_that(
"vector behavior is correct",
{
points <- c(1:10)
errors <- rep(20, 10)
points[2] <- NA
errors[2] <- NA
points[3] <- NA
errors[4] <- NA
# Construction ----
p <- pointErr(points, errors, style = 'brac',
brac_left = '[[', brac_right = ']]')
q <- pointErr(poi... |
5089caa86ecbdecc581f28cd24be9eeddf158227 | 84ec00770c4947c0af1980c971df718eac2740a1 | /plot4.R | 4fa506343b787914506a248c541cfb38625f4def | [] | no_license | rbroderson/ExData_Plotting1 | 6b6d392c948cedfcc800703644d4da69ae4f2764 | ca40c9db0a407814069b6e629e4a34acddab99e2 | refs/heads/master | 2021-01-18T00:14:30.981192 | 2015-04-10T16:51:52 | 2015-04-10T16:51:52 | 33,684,780 | 0 | 0 | null | 2015-04-09T18:15:20 | 2015-04-09T18:15:20 | null | UTF-8 | R | false | false | 1,507 | r | plot4.R | #IMPORTANT: The household_power_consumption.txt file must be in your working directory.
p1 <- read.table("household_power_consumption.txt", sep=";", header=TRUE)
library(datasets)
p1s <- p1[as.Date(p1$Date, "%d/%m/%Y") %in% as.Date(c('2007-02-01', '2007-02-02')),]
p1s$newtime <- as.POSIXct(paste(p1s$Date, p1s$Tim... |
141fd5f5ac2bc66f25feb9d59179e65d076b7d64 | 18509ba0e3ccd7570eb75915689856993e9cce20 | /create_tidy_data.R | bf121b6cbec40420a9a16888d67626abec194069 | [] | no_license | DolphinWorld/coursera_cleardata | 11744e88b70efa850cee12589b7253278555eaa3 | 16fb1b26217179e961376b5a781be85ef903ff0d | refs/heads/master | 2020-05-17T02:51:28.189582 | 2015-02-24T03:10:04 | 2015-02-24T03:10:04 | 31,164,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 334 | r | create_tidy_data.R | #gather data, so that the column becomes: subject, activity, feature, and measure
data_gathered <- gather(data, feature, measure, tBodyAccmeanX:fBodyBodyGyroJerkMagstd)
#create data_tidy with summarized means
data_tidy <- ddply(data_gathered, c("subject", "activity", "feature"),
summarize, mean=mea... |
f8cea05bf5bc5506ebadd909f20da006b4d9a17a | 744bcd91563f50597a96f02571726d769df984b4 | /BeyleGovJar.R | c34109c3d201ee013ceb274ec6f9fa7756ae4642 | [] | no_license | zoeang/StateGovernors1950-2008 | a9e2c240c02e6aac4a6663caaf3bd46962207d20 | a74f509cd00fae1028f2eb239af80ebe9855d966 | refs/heads/master | 2020-03-17T07:30:02.958459 | 2018-08-16T19:42:11 | 2018-08-16T19:42:11 | 133,400,859 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,596 | r | BeyleGovJar.R | #==============================================================================
#Beyle Gov Job Approval Rating
#==============================================================================
jar<-read.csv('C:/Users/zoeja/OneDrive/Documents/Summer 2018/GovernorData/StateGovernors1950-2008/BeyleJAR/BeyleGovJAR.csv')
sta... |
fd1e50d4fc9971b414138b7d2c4c44a163dac844 | 5a389396139299bbbcd0a7725d704bbad0b89f2a | /man/h3_string_to_int.Rd | 45a262c2de4c102551f6809a037bde3af2afed39 | [] | no_license | NickCH-K/placekey | 0296e50e61c90f1bfe52d0a5f60c2df5556427be | 3ede82969e3fbbf2decaebeff45734a1b13b436f | refs/heads/master | 2023-01-08T11:34:09.452507 | 2020-11-02T07:36:53 | 2020-11-02T07:36:53 | 309,290,589 | 1 | 0 | null | 2020-11-02T07:35:32 | 2020-11-02T07:29:37 | R | UTF-8 | R | false | true | 463 | rd | h3_string_to_int.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{h3_string_to_int}
\alias{h3_string_to_int}
\title{Convert an H3 index string to 64 bit integer}
\usage{
h3_string_to_int(index)
}
\arguments{
\item{index}{h3 index as a hex representation character vector. See \code{\link{... |
4fc37035ac4bddf17001ce1eb8c34e76dc122dbb | 584025b690582ab9ac588c77ebc5e746e95816f8 | /man/print.html.Rd | 187f86f639a24df57fceda20a507f3cce01dd39d | [] | no_license | rstudio/htmltools | 27b459793e6c7dfb12bcd196e72378072d85e92a | 251526c9886ca9ab5460c7bd4d73c0d61e2b40b8 | refs/heads/main | 2023-08-17T23:16:13.900249 | 2023-08-14T19:49:04 | 2023-08-14T19:49:04 | 18,398,793 | 210 | 82 | null | 2023-09-08T14:34:59 | 2014-04-03T10:11:03 | R | UTF-8 | R | false | true | 760 | rd | print.html.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tags.R
\name{print.shiny.tag}
\alias{print.shiny.tag}
\alias{print.html}
\title{Print method for HTML/tags}
\usage{
\method{print}{shiny.tag}(x, browse = is.browsable(x), ...)
\method{print}{html}(x, ..., browse = is.browsable(x))
}
\argumen... |
ff4b945230227ab2de40a813a96d610313205f15 | 144e1b215a8546d820f929055138b06eb67eda74 | /set_buy_sell_rules.R | 9080c98de9321ab3f6893ba8ffee726bcda0015d | [] | no_license | Mentalaborer/TradeNow | 9acdb1bd5a9e0822fded0ec89aab9f80771845cb | 7b82093f0324ab2a314216fa950f7a8e42c7a5e2 | refs/heads/master | 2020-11-26T01:04:15.277242 | 2020-07-05T20:52:12 | 2020-07-05T20:52:12 | 228,915,006 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,351 | r | set_buy_sell_rules.R |
######## DEPRECATE - THIS IS NOW FUNCTIONALIZED ######
# Purpose: create trading signal based on simple filter rule. Recall that simple
#filter rule suggests buying when the price increases a lot compared to the yesterday price
# source('global_filters.R')
###### Generate Day Trading Signals ######
# ### Si... |
e689c5510b87cac69f94e9f786fedb61b1f593d5 | b8fc0f17b7eade56da95f5a6fc882173c4c6e3c9 | /GARCH_func_code.R | 8e38525e85db0eddf057c9f32fa75caf85825434 | [] | no_license | alvinchow8/Quant-Stock-Price-Analysis | 3c61084610cd3a4a84dc323e872bcc03ff839c43 | 4910f898d5e0e752113d3f33fd2aefd0f4a6f092 | refs/heads/master | 2020-03-21T23:01:54.312968 | 2018-06-29T14:57:01 | 2018-06-29T14:57:01 | 139,162,136 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,356 | r | GARCH_func_code.R | require(roxygen2)
# ===========================================================
# create_GARCH_obj (asset) creates GARCH objects
#' @param asset - individual asset t x 1 vector
#' @param sigma1 - initial sigma1 to initialize GARCH objects
#' @param sigma2 - initial sigma2 to initialize GARCH objects
#' @param T... |
29da9f1e5f7df18ec4cda169802ebd4aba7ad338 | 3420dacc496da4fb837abc78d6c7a63ef21d23c1 | /Q3_Rcode.R | ec2525cb4718f48e157649aa80060263fb6a45c9 | [] | no_license | hartwemm/4MB3Assignment2 | 6813852b6815ae3070bd8ddb8ed8fe40c4fa8c3d | b09caea78e7bd3dc52a1b69c997a53dda5f514b3 | refs/heads/master | 2021-05-04T23:58:52.019540 | 2018-02-04T17:55:02 | 2018-02-04T17:55:02 | 119,454,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,883 | r | Q3_Rcode.R | ### Part a
library(deSolve)
### Part b
SIR.vector.field <- function(t, vars, parms=c(beta=2,gamma=1)) {
# Writes vector field of these differential equations
with(as.list(c(parms, vars)), {
dS <- -1*beta*S*I # dS/dt
dI <- beta*S*I-gamma*I # dI/dt
dR <- gamma*I # dR/dt
vec.fld <- c(dS=dS, dI=dI, ... |
5ba989393916def04fb544b4b28b35ef3ef05599 | 6361479b3220f09837498043250003429460e421 | /initial.R | 6578b038380df41d1dd00e5a924f86c78006e622 | [] | no_license | momokeith1123/AFDBPRDVAR | 00ddff4126e9d9adb778e17090f4e06fe92cdfe2 | d79da5767f37ce98cba5db1a729b2901885c73fb | refs/heads/master | 2020-08-12T18:21:41.907237 | 2019-10-30T15:34:46 | 2019-10-30T15:34:46 | 214,818,637 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,640 | r | initial.R | # 3-
#
library('data.table')
library('dplyr')
library('openxlsx')
library(stringr)
library(rebus) # for the regular expressions String Manipulation in R with stringr
setwd(DIR[["function"]])
source("loadHvarResultFile.R")
# source("getSQL.r")
source("functionSQL.r")
# Load Security Fiter given a perfva... |
8eb7cd03ecb125ee3eaee10a58e22b30af5d35cd | 67c2a90c7edfac3cfd891cb332c45e71cf4a6ad1 | /R/osink.R | 2ca7b9157c6fe1acc75ee7f051d8004671010bb5 | [] | no_license | alexanderrobitzsch/CDM | 48316397029327f213967dd6370a709dd1bd2e0a | 7fde48c9fe331b020ad9c7d8b0ec776acbff6a52 | refs/heads/master | 2022-09-28T18:09:22.491208 | 2022-08-26T11:36:31 | 2022-08-26T11:36:31 | 95,295,826 | 21 | 11 | null | 2019-06-19T09:40:01 | 2017-06-24T12:19:45 | R | UTF-8 | R | false | false | 200 | r | osink.R | ## File Name: osink.R
## File Version: 1.09
osink <- function( file, suffix, append=FALSE)
{
if ( ! is.null( file ) ){
sink( paste0( file, suffix), split=TRUE, append=append )
}
}
|
c9676bfc6427ae00995c2f299f47ac09e6efa136 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/textreg/man/testCorpora.Rd | b9fca1cd2c060c4edf2159d8de6d91f610bddfd4 | [] | 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 | 404 | rd | testCorpora.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/package_and_data_documentation.R
\docType{data}
\name{testCorpora}
\alias{testCorpora}
\title{Some small, fake test corpora.}
\format{A list of dataframes}
\description{
A list of several fake documents along with some labeling schemes primar... |
82eeed7096fe121f6d0195e0bef422bac734e062 | 1e9c4294652b0f4699d85516afd54fb5697b4800 | /r_exam/제대로_알고_쓰는_R_통계_분석/sourcebook/source/Chapter03/source/10.for_ex.R | cdd0a7f10da109602005c7f3bef5cc6f32d60cb6 | [] | no_license | mgh3326/GyeonggiBigDataSpecialist | 89c9fbf01036b35efca509ed3f74b9784e44ed19 | 29192a66df0913c6d9b525436772c8fd51a013ac | refs/heads/master | 2023-04-06T07:09:09.057634 | 2019-06-20T23:35:33 | 2019-06-20T23:35:33 | 138,550,772 | 3 | 2 | null | 2023-03-24T22:43:06 | 2018-06-25T06:10:59 | Jupyter Notebook | UTF-8 | R | false | false | 357 | r | 10.for_ex.R | v <- c(1, 4, 5)
for( i in v ) {
print( i )
}
r.n <- rnorm(10)
sum <- 0
for(i in 1:10) {
sum <- sum + r.n[i]
}
print(sum)
sum(r.n)
dan <- 2
for( i in 2:9 ) {
times <- dan * i
print(paste(dan, "곱하기", i, "=", times))
}
(m <- matrix(1:12, ncol=3))
for(i in 1:nrow(m)) {
for(j in 1:ncol(m)) {
cat( i, "행", j,... |
95ffcc872a473989e8ba7873c6a8354ff92ab4b6 | 2572e98ace5eb5f10a0ad2e11ab24f11b79a0aa9 | /testing/DX_GC.R | 8464316b01af00b9bb1ec7785af44015c2c4921a | [] | no_license | ncoutrakon/fin | fca1f02495c6b19d888e2184db32d9775c15d52b | 590ff249b80cce29459cc095c705cac22804e366 | refs/heads/master | 2021-06-08T06:17:19.466922 | 2016-10-26T08:05:39 | 2016-10-26T08:05:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 807 | r | DX_GC.R | #find.instrument("dollar")
#getInstrument("DX")
#GetAndClean("DXU5")
rm(list=ls())
.useDV()
dxlist <- future_id("DX", c(3, 6, 9, 12), year=2014:2015, format = "CY", sep="")
gclist <- future_id("GC", c(3, 6, 9, 12), year=2014:2015, format = "CY", sep="")
listofList = ls()
source("fmonth.R")
for (lst in listofList)... |
614fbcaa55b3b1868947a3c58f41b908e556d907 | a747cd77ba47e0c62907a64ea4f3a235f280f124 | /man/tiger.Rd | 74f74912802ff37b836ced0f4950bf83ddeffd1c | [] | no_license | cran/rsvd | b4d24e0d50ab1855686be7c1a37ffbfbba3aac03 | 6357bde286052945966ca51b91590ee7f90affed | refs/heads/master | 2021-07-10T18:45:13.049205 | 2021-04-16T04:40:03 | 2021-04-16T04:40:03 | 48,087,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 582 | rd | tiger.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tiger.R
\docType{data}
\name{tiger}
\alias{tiger}
\title{Tiger}
\format{
An object of class \code{\link[rsvd]{rsvd}}.
}
\source{
\href{https://en.wikipedia.org/wiki/File:Siberischer_tiger_de_edit02.jpg}{Wikimedia}
}
\usage{
data('tiger')
}
\d... |
5dc4b941bab0181423d7bf88acf4f65afd737ea4 | a8e8000b370d54c2f6a097ee59876827f4daafbe | /9.8/2p.R | 70548598f9da9dd4584bd466a591538895c1e1de | [] | no_license | weidaoming/R | 142be073ebdf097740ae5a02a7e75308a06e30d1 | 5048ca1d46025ba41d03b00049a17b309e8dfedc | refs/heads/master | 2021-07-12T10:47:27.552074 | 2017-10-18T07:09:09 | 2017-10-18T07:09:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 128 | r | 2p.R | #比例检验
#pwr.2p.test(h=,n=,sig.level=,power=)
pwr.2p.test(h=ES.h(.65,.6),sig.level = .05,power=.9,alternative = "greater") |
5e0736a025ae59c19e141a67e05ae9092d3a999c | 3f4d651c3d7431db4da76e7b89031911dc6eb913 | /R/dfe_acad_year.R | 5623230b6d26bb3a7b90c7104fe3e7bb57ed1a59 | [] | no_license | TomFranklin/dferap | 394ada7b7b28da2a761b8016d97c2a0e8fd30c6d | a43e34e4d7f1ee1808028c32f4f369eef716bfc1 | refs/heads/master | 2020-04-03T10:40:30.294751 | 2018-10-29T16:05:58 | 2018-10-29T16:05:58 | 155,199,335 | 0 | 0 | null | 2018-10-29T16:05:59 | 2018-10-29T11:19:09 | R | UTF-8 | R | false | false | 827 | r | dfe_acad_year.R | #' @title Change the style of academic year
#'
#' @description The \code{dfe_acad_year} function converts academic year numbers e.g. 201213 into strings with a forwward slash "2012/13"
#'
#'
#'
#' @details The registers are sorted by publication date and name by
#' alphabetical order. The top five registers are output... |
db7bcf32b363237c7ab0b365af5d15e643ca2a8c | 8663fe97b8f27cd63e8957f5c74413a208429ea3 | /snp_reads.R | 121ce23d5cff59d1bc90cba95a95494dc8686502 | [] | no_license | Alexander-Palmer/hide-and-RNA-seq | 9dd20824de3292e6ef691f40dd95a939df1b4e6b | b24225feaa48b9f31e210e29c94996ac2fe46735 | refs/heads/master | 2021-06-13T00:57:08.858995 | 2021-04-27T04:50:48 | 2021-04-27T04:50:48 | 185,336,932 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 51,567 | r | snp_reads.R | ###Find sum of columns containing original SNP type
fileNames <- Sys.glob("*.txt")
#Create average columns
for (fileName in fileNames) {
sample <- read.delim(fileName)
sample[paste('av_284')] <- (sample[["A284_1"]] + sample[["A284_2"]])/2
sample[paste('av_285')] <- (sample[["A285_1"]] + sample[["A285_2"]])/2
... |
3c9953846d718c2e6e63c84bee4363755586ec8c | fd25b96601cf7ac4e7762183c0faeec87963aede | /supervised/image_process3.R | 17921c3ff0dbac7a136e997d369b1256daec8759 | [] | no_license | edz504/plankton | 08b9cf88db36a97a89b33d3e06da3bb169e15e5e | 3d45f585bd8644feaa9f991402c80d0fc3e0d548 | refs/heads/master | 2021-01-13T02:05:55.243329 | 2015-02-09T03:56:02 | 2015-02-09T03:56:02 | 28,106,911 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,252 | r | image_process3.R | # using some features from EBImage package
# INSTEAD of pixel hues
library(EBImage)
# count the number of total images
wd.top <- "C:/Users/edz504/Documents/Data Science Projects/Kaggle/plankton"
wd.train <- paste(wd.top, "/train", sep="")
setwd(wd.train)
num.img <- 0
for (class in list.files()) {
setwd(paste(wd.t... |
98e61cb31ad8768496d7d69d743b0ad9566742bc | bab6c49cc2eb5fe74dcbfbd77d352d5df3aa375f | /RPackage/tests/testthat/test-horvitzThompson.R | c4e2cdfe18c7ba41b9ad0d3c15d87ab12b1afdfd | [] | no_license | rohan-shah/chordalGraph | a32671fc1c8cb2004058622e7c23d1c67c677232 | 49a7a7649e24379c1f632d25a109ecac2ad770bd | refs/heads/master | 2021-01-18T21:57:30.341495 | 2017-06-26T01:56:45 | 2017-06-26T01:56:45 | 42,837,660 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,000 | r | test-horvitzThompson.R | context("Test horvitzThompson function")
test_that("Horvitz Thompson algorithm gives unbiased results for 5 x 5 graph",
{
data(exact5, envir = environment())
nReps <- 600
results <- matrix(data=NA, nrow = nReps, ncol = 11)
for(reduceChains in c(TRUE, FALSE))
{
for(i in 1:nReps)
{
capture.output(results[i,] ... |
736dacf98c33155e3335bf41e64d1d68e22d8035 | bbc3943cfd57260dfc3c0603a48a80eadfd19220 | /man/plot_rw.Rd | d8841a07ad399706e617953ddd2230d8727f2e63 | [] | no_license | umatter/netensembleR | cef47df9c67c139bba8a5238aa2deecb605249b1 | 6f5f62a62d3d25f7ec330a1df0086809af8a208c | refs/heads/master | 2021-01-19T08:41:20.719220 | 2016-07-19T07:25:19 | 2016-07-19T07:25:19 | 87,662,883 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,235 | rd | plot_rw.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_rw.R
\name{plot_rw}
\alias{plot_rw}
\title{Plot reciprocated graph component}
\usage{
plot_rw(g, extract=TRUE , rcolor="red", othercolor="grey", ...)
}
\arguments{
\item{g}{a directed weighted graph (object of class igraph)}
\item{extra... |
afe2917a2daee2bb54e3adf01ddfe7684fe00396 | f313cd982f70daffa643a2f2b1147d284607d613 | /others/add_cordinates.R | 9398159db8556d5d2070469e46d36f779778115c | [
"Apache-2.0"
] | permissive | xuzhenwu/PML-shiny | a0cc59f55c51edc8fb6f278029db038d11faf2b4 | 6d22b310cc264690cff5894b7fc763593540f20f | refs/heads/master | 2023-02-17T17:46:52.206485 | 2021-01-20T09:55:30 | 2021-01-20T09:55:30 | 284,678,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 571 | r | add_cordinates.R | library(ncdf4)
dir <- "F:/pml_data/"
fn_proto <- "F:/pml_dataset/output_of_PMLV2.0_GLDAS_NOAH_15D_A201301a_BJ_10mx10m.nc"
fl <- dir(dir, "*.nc", full.names = TRUE)
nc_proto <- nc_open(fn_proto, write = T)
nc$dim$lat$vals <- nc_proto$dim$lat$vals
nc$dim$lon <- nc_proto$dim$lon
for(i in seq_along(fl)){
fn <- fl[... |
af015d0788a1cb9cfa5d3b4e1dd79ebfdb323d6b | 812e9c9df8f276ba93c4f9c79e97fb5430f5ed60 | /RCode/SimulateWeightedTTestClusters.R | c959914f91251b8d5ee27a1e063f15b7b1e7c55f | [
"MIT"
] | permissive | janhove/janhove.github.io | d090a22fa90776926301b9cb5b318475c2010cb5 | 44bb9fe9c8070ecda5cec049e9805da71063326f | refs/heads/master | 2023-08-21T18:52:38.584816 | 2023-08-06T09:11:17 | 2023-08-06T09:11:17 | 22,544,198 | 7 | 5 | null | 2020-05-27T09:56:52 | 2014-08-02T10:41:02 | HTML | UTF-8 | R | false | false | 3,698 | r | SimulateWeightedTTestClusters.R | # Function to generate 1 dataset with clustering
createData.fnc <- function(ICC = 0.1, # intraclass correlation coefficient
clusterSizes = c(10, 50, 13, 80, 14, 86, 62, 45, 41, 8), # cluster sizes
treatment = 0) { # treatment effect (= 0 for simulating null hypothes... |
c6ec59368ae99d2a50554b2897a6bbbb28802d6a | fe7788c1e4eba9b2668835da8535b05fcc94e32b | /Bin/Rscript/TZ2.r | 2bcf915b64e559a1f80984e05f471f95b1e1237d | [] | no_license | yiliao1022/Pepper3Dgenome | 77045dd1480abdfe217d47f7c20ff360c080108b | d4a8bc6e181eba45db1dff405f3a179fe4e9b99c | refs/heads/main | 2023-04-14T20:07:09.192776 | 2022-05-30T04:34:10 | 2022-05-30T04:34:10 | 338,714,255 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 268 | r | TZ2.r | library(CALDER)
contact_mat_file_LJTZ2 = "/home/yiliao/OLDISK/genome_assembly/hic_explorer/13_bnbc/100k/Chr12/matrixTZ2.Chr12.csv.txt"
CALDER_main(contact_mat_file_LJTZ2, chr=12, bin_size=10E4, out_dir='./Chr12_LJTZ2', sub_domains=TRUE, save_intermediate_data=FALSE)
|
46946b684e8480d1e2e39baaf0ce40b08861e5e4 | f439a076bc3fcac2c8d7eb72e69dc8d24a00b263 | /Unit 3 Logistic Regression/Assignment3_Loans.R | 6eabf728ff70b2052479c9c37c667810c1b9ecd0 | [] | no_license | jakehawk34/MIT-Analytics | 73f9afb0cbfbbd8202e415f0c50c8e638aa76db1 | daa2ca2eca44ba6c74ba5773d992f68e8c775b90 | refs/heads/main | 2023-05-07T13:54:40.796512 | 2021-05-21T00:31:11 | 2021-05-21T00:31:11 | 344,290,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,390 | r | Assignment3_Loans.R | # Assignment 3
# Predicting Loan Repayment
loans = read.csv("loans.csv")
str(loans)
summary(loans)
table(loans$not.fully.paid)
mean(loans$not.fully.paid)
missing = subset(loans, is.na(log.annual.inc) | is.na(days.with.cr.line) | is.na(revol.util) | is.na(inq.last.6mths) | is.na(delinq.2yrs) | is.na(pub.rec))
str(mis... |
66b0be209695ff6d873419933b41c7413a34a115 | 33e47772221e64495af2f76fbe946106cd9dd5b5 | /src/co-localizations/fetal_brain_sQTL/find_overlaps.R | 3a42635c66354050930d050b72ffdd63395d84fd | [] | no_license | mikelaff/mirna-eqtl-manuscript | 8ddaed87bd3eee1923afa6f5ef0ebd80de7c205c | a87deb4dc063e03e5371ff7c4d19db782e295e12 | refs/heads/main | 2023-08-31T20:01:11.324071 | 2023-08-24T23:44:31 | 2023-08-24T23:44:31 | 590,512,745 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,891 | r | find_overlaps.R | # find co-localization miRNA-eQTL to fetal brain sQTLs
# for each miRNA-eQTL:
# look for overlap with sQTL at r2 >= 0.8
library(here)
library(dplyr)
library(readr)
library(magrittr)
library(mikelaffr)
# OUTPUT FILES ###############################################################################################... |
566ad851ed0d7a7ded4c3d0aef1c2119fb0b8fc4 | 556d752f9666653cd8cfb6b9658e78fb0db26e14 | /R/pinktoe.R | 15a99d3e9d64db36c80176afcbc2ca2693e92a6e | [] | no_license | cran/pinktoe | 8f49f92a4782f8b7df89746dc7ef5f8b8788936d | 4655b78545f204281d4fec1b28b3e1d28f165475 | refs/heads/master | 2016-09-03T06:46:53.343607 | 2004-09-08T00:00:00 | 2004-09-08T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 877 | r | pinktoe.R | "pinktoe" <-
function (treeobj, textfn, tittext, treeid = "", cgibindir = paste("/~magpn/cgi-bin/",
treeid, "/", sep = ""), htmldir = paste("/home/magpn/public_html/Research/Politics/TREE/",
treeid, "/", sep = ""), localdir = "Tree/", stateprintfn = partyprint,
requirelib = "../party.lib", commonhtml)
{... |
e6733999632f367a0decbc19e36c3034e548c881 | 07283623f9530c8c1ac7408eb099059d6deb7919 | /R/salvage_prediction.R | 81823343c91957e2a1cea93274d23d7f2883a294 | [] | no_license | hinkelman/DSM2Analysis | 75314f00a8a0a0723d0f43813558148b29c80035 | ebbe09bb57f504b6e1acb8f2939d5491b1abae4a | refs/heads/master | 2023-04-30T16:50:14.140023 | 2021-05-12T20:12:59 | 2021-05-12T20:12:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 501 | r | salvage_prediction.R | #' Salvage prediction
#'
#' Predicted salvage based on salvage_newdata and salvage_model.
#'
#' @md
#' @param facility Water export facility: CVP, SWP, both
#' @param newdata Data frame with input data needed for model predictions
#'
#' @export
#'
salvage_prediction <- function(facility, newdata){
n... |
55e0dd38903fb5720c7f6844af04fe2992eada2d | b1c0f88b081a05d267e9570f264fda772546d879 | /lib/Optimization/Algorithms/MetaHeuristics/GeneticAlgorithm/JSSOperators/PartialPriorityList/PPLCrossoverMachines.R | 20ac6ed63f62fa46b4c1691b6179046196409d1e | [] | no_license | pedabreu/OptimizationFramework | 71adf7f46025069b08b8187ca144038a2c464d6b | d04fc81eebdfd9e2cceb6d4df98d522161ba7ebb | refs/heads/master | 2020-12-31T07:19:00.219609 | 2017-03-29T00:07:20 | 2017-03-29T00:07:20 | 86,566,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 836 | r | PPLCrossoverMachines.R | setConstructorS3("PPLCrossoverMachines",function()
{
cross <- Crossover()
cross$chromossomeClass <- "PPLChromosome"
extend(cross,"PPLCrossoverMachines")
})
setMethodS3("run","PPLCrossoverMachines", function(this,
male = NULL,
... |
2c63ca9b7b33e50a2f9d202bd8e071fd5fc336ec | c32e53644e09f1ed99b37395bbc05bda216818fa | /R/cmdscale_lanczos.R | 6d5466faf6fc9566c333e9bef406590ed5c6d169 | [] | no_license | dill/poridge | 241f4c490f5ce4df6172dd81f37cc22fbdbdb731 | d7aa5c88ca7e049592ae6c4bab51849e40bdc508 | refs/heads/master | 2021-01-12T19:52:11.501232 | 2016-06-23T18:50:48 | 2016-06-23T18:50:48 | 35,626,343 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,667 | r | cmdscale_lanczos.R | #' Faster multi-dimensional scaling
#'
#' This is a modified version of \code{\link{cmdscale}} that uses the Lanczos procedure (\code{\link[mgcv]{slanczos}}) instead of \code{eigen}. Called by \code{\link{smooth.construct.pco.smooth.spec}}.
#'
#' @param d a distance structure as returned by \code{\link{dist}}, or a fu... |
65ef37bde9e5ee2f4972a7d4af074f8b64f35a2c | 7959c075b8d8fd90c423863d6cc51cb29ea517c5 | /day1.R | 42be8046402fda15ed3076c7b28669e6c0e0e947 | [] | no_license | salientsoph/Rexam | d8373e7bbfc85fc38cc203add6572574b88c7926 | 0d0b9cb7fc378654c886ca70ba56498770e8b4a8 | refs/heads/master | 2022-12-24T06:02:13.720153 | 2020-09-25T14:18:31 | 2020-09-25T14:18:31 | 293,553,326 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,234 | r | day1.R | # R 공부 첫 시작!
# ':'은 벡터 만들 때 사용
v1 <- 1:10
v1 = 1:10
1:10 -> v1
# : 사이엔 띄어도, 안 띄어도 됨
# 한 줄에 여러개 넣고 싶으면 ';' 사용
# print 안 쓰는 건 단독일 때만 가능.(console창에만 나타남)
print(v1)
v1
1:100
100:1
(v2 <- v1 + 100); v2 # ()로 묶으면 식으로 간주해서 console 창에 나옴
v3 <- v1 * 10; v3
ls() # list의 약어.
# c(): 벡터 함수 만들기
v4 <- c(10, 5, 7, 4, 15, 1) # ... |
489cc13fb198a9bd67739ea2f5bb711b219a60ea | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Amendola-Ricca-Truszczynski/selection-hard/ctrl.e#1.a#3.E#114.A#48.c#.w#3.s#10.asp/ctrl.e#1.a#3.E#114.A#48.c#.w#3.s#10.asp.R | 6c697f2922f10821768e1092af510d0c00bc27fa | [] | 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 | 90 | r | ctrl.e#1.a#3.E#114.A#48.c#.w#3.s#10.asp.R | 4c8fc571d0cbd29ef611044c6cdc5017 ctrl.e#1.a#3.E#114.A#48.c#.w#3.s#10.asp.qdimacs 2897 8206 |
6401ba9adaee85d217bae45e844d4e9213096d92 | cde9f43c78142fd5fbb15ce42da626c3b18881a4 | /man/scrape_lse_sectors.Rd | c408fd15aea747de9cdf693c772aa4ffac29101b | [] | no_license | lina2497/webscrapeR | b282f03bfd5f212de1641a69cec6d8d798a6a82b | a33691b00bf81de045cdb695b78b2ab33731ee26 | refs/heads/master | 2020-09-08T03:52:48.060848 | 2019-11-12T16:11:11 | 2019-11-12T16:11:11 | 221,007,168 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 743 | rd | scrape_lse_sectors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scrape_lse_sectors.r
\name{scrape_lse_sectors}
\alias{scrape_lse_sectors}
\title{Scrape share prices from a particular sector from the London Stock Exchange website.}
\usage{
scrape_lse_sectors(url, pause = 0)
}
\arguments{
\item{url}{A url f... |
1865521686d70951f1eb3c911ca922a461a3e2be | 58a116de33b64fbbdc4a7d6d7c9d90289092ecf9 | /scripts/network1.r | 6b0c8452a9b601802131a41d50bc66331016a8e1 | [] | no_license | rian39/imaginaries | 1eba2b1b351159854fa0e83bf80d8d60ae83ca0b | b91e60c19dda3e50c9c266a28ddfdfb4c72503fa | refs/heads/master | 2021-01-18T21:14:50.202293 | 2016-08-17T11:56:36 | 2016-08-17T11:56:36 | 11,882,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,384 | r | network1.r | library(network)
g3 = network(imag[,c('term', 'author')],loops=TRUE,bipartite=TRUE)
plot(g3, displaylabels=TRUE)
g3 = network(imag[,c('term', 'author')],loops=FALSE,bipartite=TRUE)
plot(g3, displaylabels=TRUE)
g3 = network(imag[,c('term', 'author')],loops=FALSE,bipartite=FALSE)
plot(g3, displaylabels=TRUE)
g3 = network... |
8fc6d3e5ea258ecd5d67d6f80afe9a80eab9b800 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /irt/man/plot_empirical_icc.Rd | 8bd3cefa92d1d777c5776a1045cd8765b2798345 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 3,222 | rd | plot_empirical_icc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_IRT.R
\name{plot_empirical_icc}
\alias{plot_empirical_icc}
\title{Plot Empirical Item or Test characteristic curve}
\usage{
plot_empirical_icc(
resp,
item,
type = "eicc",
bins = 10,
ip = NULL,
theta = NULL,
title = "",
su... |
351369686daf60f7547b3bbc28f00cddd6045d3d | 4855e806d6a5b65643c49ed3b602db276fe76d30 | /library/rsconnect/man/showUsers.Rd | 9bb249f285949adde1b7e1485735543d8fe1e6ce | [] | no_license | Cococatty/InteractiveMap | 5701a607a7605a4958c037b6b5559841c67126eb | 698b173ab0393cc38fdfd69f09b169dd87fd9f3d | refs/heads/master | 2021-01-10T18:14:56.274796 | 2016-02-17T09:02:23 | 2016-02-17T09:02:45 | 47,664,845 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 805 | rd | showUsers.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/auth.R
\name{showUsers}
\alias{showUsers}
\title{List authorized users for an application}
\usage{
showUsers(appDir = getwd(), appName = NULL, account = NULL,
server = NULL)
}
\arguments{
\item{appDir}{Directory containing applicati... |
c7934fe456068f30c26de8acdf9f376df5ee28ef | 7040a7db0d8da630d5223cd5ebdd7b88d71e10bd | /Code/fuzzy_matching (1).R | fec43e46ef15629b89b42dcb6e294e9e1bd34be0 | [] | no_license | josephsguido/coi | ea8713767fbdd74fea8cb0ee27787902cd2c94cf | c5c2b82106dfa28f9e3ae36efb5196469944b2d7 | refs/heads/master | 2023-02-22T23:06:16.418004 | 2020-06-14T01:55:58 | 2020-06-14T01:55:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,800 | r | fuzzy_matching (1).R | library(dplyr)
applicants <- read.csv(url("https://www.dropbox.com/s/o4j1h7tqbmb5746/pull_All_Years_rename_56.csv?raw=1"))%>% pull(x)
residents <- read.csv(url("https://www.dropbox.com/s/7v67tpo8v52q58k/pull_residents_distinct_23.csv?raw=1")) %>% pull(x)
set1 <- applicants
set2 <- residents
library(stringdist)
fuzzym... |
cc0138b07a059b5c460e17e018b34d0036ca5029 | f2bb9dc756f74ccfd1aa7bb4e1aa9d682d93e628 | /R/FilterOutputStream.R | 3a258845822bc6920229f54e4ec7611281bbb357 | [] | no_license | HenrikBengtsson/R.io | 660437c62f692db4fecfbae08648eb56237e9ca2 | 7ff13117d31299027e9029c2ec9d92ec5079273b | refs/heads/master | 2021-01-01T19:43:25.136246 | 2014-06-19T04:11:28 | 2014-06-19T04:11:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,503 | r | FilterOutputStream.R | setConstructorS3("FilterOutputStream", function(out=NULL) {
if (!is.null(out)) {
if (!inherits(out, "OutputStream"))
throw("Argument 'out' is not an OutputStream: ", data.class(out));
}
extend(OutputStream(), "FilterOutputStream",
out = out
);
})
setMethodS3("close", "FilterOutputStream", func... |
6d42927d5cd7645f0e5512a0849f4245a1d9f463 | 7bb3f64824627ef179d5f341266a664fd0b69011 | /Basic_Engineering_Mathematics_by_John_Bird/CH28/EX28.9/Ex28_9.R | 2c089aab45ae6f07a53b707750ccf904d6c266f4 | [
"MIT"
] | permissive | prashantsinalkar/R_TBC_Uploads | 8bd0f71834814b1d03df07ce90b2eae3b7d357f8 | b3f3a8ecd454359a2e992161844f2fb599f8238a | refs/heads/master | 2020-08-05T23:06:09.749051 | 2019-10-04T06:54:07 | 2019-10-04T06:54:07 | 212,746,586 | 0 | 0 | MIT | 2019-10-04T06:03:49 | 2019-10-04T06:03:48 | null | UTF-8 | R | false | false | 209 | r | Ex28_9.R | #page no. 294
# formula used: volume of pyramid = area of base*perpendicular height/3
#given: square pyramid
l = 6 # side of square base
h = 16 # perpendicular height
volume = ((l*l)*h)/3
print(volume)
|
6b524471cea7873cb98c56faa444d5ffd2687d00 | ec139ad96578d965d6c66a340ffc2a6859f92f0e | /R/plotRangeObj_final.R | f8e9a88ee9a979ba624ff5f26814d69967237c68 | [] | no_license | cammiller/imagingPC | 5769541c387d029978ec489128e727120a6fa539 | 8022b40d9125adc2a0c195e21c194b171e5019c8 | refs/heads/master | 2022-01-18T05:31:55.227296 | 2019-06-27T20:09:44 | 2019-06-27T20:09:44 | 194,138,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,595 | r | plotRangeObj_final.R | #' Plot a range object.
#'
#' \code{plotIDs} the semivariance estimates, covariance model, and number of
#' pairs of observations used to estimate semivariance.
#'
#' @param rangeObj An object of class \code{rangeList}. This object is a list
#' that contains the estimated range parameter(s). This is obtained by... |
833c14e632e0409eb42a1ff77000f5ae1939f086 | bfafdbde9bf19502bbc3c7b7b637a702b7b06b99 | /CEO_Departures/departures.r | 5bf35802e5ea1697d266fd0244978e0ab7711995 | [] | no_license | aichunC/tidytuesday | 2073102ff1a75d6ec7931c6b31d9c2398c3cf01e | a5a645b5293962e32fc6332b37497d1cb6a33018 | refs/heads/main | 2023-05-23T06:27:21.466192 | 2021-06-10T03:26:42 | 2021-06-10T03:26:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,718 | r | departures.r | library(tidytuesdayR)
library(tidyverse)
library(forcats)
# get the data file, read description about the variables.
tuesdata <- tidytuesdayR::tt_load(2021, week = 18)
departures <- tuesdata$departures
departures2 <- departures %>% select(coname,fyear,exec_fullname,departure_code,
... |
9426426a1ed4526886880ade48b7bd9588afd93a | 3857f5effb1c16505f2747e54406955130162af3 | /PPSD code for all syntax.R | b42c5e05497ec5094b2d738ff84904ad52e9783f | [] | no_license | hamzakashif902/PPSD-repositary | 737084b03d9f7fd5365d2cb7a81ae9c322b4800a | 4dbc64e80ee965dae3e381e5af44025333dca77c | refs/heads/master | 2020-12-02T09:33:47.898775 | 2019-12-30T19:25:56 | 2019-12-30T19:25:56 | 230,964,882 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,267 | r | PPSD code for all syntax.R | #assigning value and printing value
a = 1 #using =
print(a) #printing a/ printinh variable
b <- a+2 #using <-
print(b)
4 -> a #using ->
print(a)
#printing multiple data types
cat("value of a: ", a, "value of b: ", b)
#check data type of variable a
print(class(a))
#list all availab... |
52dedca4469623457123efa18e3248c3fc88cb1b | 6e88d4c9b6ecde3701a339b6c6a7b2fda27739a2 | /man/add_budget_line.Rd | 49651a08107ed059b6e039d2bd37fd18feca649f | [
"MIT"
] | permissive | MatthewHeun/ReboundTools | a5b753dedb35eab5cab8d343dd266fb24a4529d5 | ed1040616279229eb662f7ac2b9d2c3ea39673e4 | refs/heads/main | 2023-07-26T14:21:47.921003 | 2023-07-05T14:36:20 | 2023-07-05T14:36:20 | 324,182,246 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,219 | rd | add_budget_line.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iso_lines.R
\name{add_budget_line}
\alias{add_budget_line}
\title{Add a budget line to a data frame of budget lines}
\usage{
add_budget_line(
.DF = NULL,
meta,
graph_type = ReboundTools::graph_types$consumption,
line_name,
colour = ... |
e9b35d40fe9959cfebe51b3922ed11c8c2db8417 | 7fc3d62d0dff3542a33cae700d0cc7e790302026 | /R/load_reanalysis.R | fcd6ef9f416f1a5f355cc3d50c5829b523fd7e84 | [] | no_license | d-farnham/ORB_Paper | c21e1fb2010f280af918917be9854aa09a51d117 | bd7871fb2c43afce4d939310a1bcbffd19beef1d | refs/heads/master | 2021-03-27T14:58:57.576475 | 2018-03-19T04:20:45 | 2018-03-19T04:20:45 | 94,268,439 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,579 | r | load_reanalysis.R | source("R/GetSeasonDate.R")
# load the reanalysis data -- start with the Z
list_Z_files = list.files(path = '/Users/davidfarnham/Google Drive/ORB_Paper/Raw_Data/REANALYSIS_data/six_hourly/Z/')
Z_700 = data.frame()
for(file_num in 1:length(list_Z_files)){
Z_ncdf = nc_open(paste0('/Users/davidfarnham/Google Drive/... |
b98935a01e556f4fae960d80540d1d2ce2b617db | 84dcc770d7766a3171efe7aa46e50dbcb496c9b5 | /Spatial_CUSUM/dependence.R | 421e982bb526cea849ec077468a32e4d32e3a775 | [] | no_license | xinzhang-nac/Spatial_CUSUM | 0b36e1a04526ea26578d86c41a2eaf1ffa980023 | 677eedf773af01525d788f2d2ee9932a1d8f4541 | refs/heads/master | 2020-04-09T03:58:59.994822 | 2019-05-15T14:31:29 | 2019-05-15T14:31:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,774 | r | dependence.R | multi_iter<-function(N.grid=100,shift=0,scale=0,times=10,k=5,alpha=0.05){
#generating data
Status<-rep(0,(N.grid)^2)
grid.point = expand.grid(x=seq(1,N.grid,by=1),y=seq(1,N.grid,by=1))
if(scale==0){
#nondependence
grid.point$Observed<-rnorm((N.grid)^2)
}else{
#dependence
model <- RMexp(var=1,... |
488af0c79cf17980210e27a25c0f6cfa18a4330c | b437adefdb097c34f01f2470790ef8c6fe3648df | /scripts/Niche_Models/old/PaleoNicheModels.R | 9c03e6b940c8fb9e91cdee9067d4895eb2c66d8e | [] | no_license | kaiyaprovost/GDM_pipeline | 607887afed2f6faddb2584eebb9eb7ff0120fea1 | 05e8f5d0a46781d727b60fe913c94137b9b35824 | refs/heads/master | 2022-09-20T22:55:39.707325 | 2022-08-31T18:24:19 | 2022-08-31T18:24:19 | 237,048,262 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,768 | r | PaleoNicheModels.R | require(dismo)
require(ENMeval)
require(phyloclim)
require(sp)
require(rgdal)
require(rgeos)
require(ENMTools)
require(spocc)
taxon = 'Ambystoma_tigrinum'
#Build a best model
#get occ
occ = occ(taxon, from = 'gbif', limit =900)
occdf = occ2df(occ)
summary(occ)
#get climate
# Env = stack("/data/spbio/climgrids/bio.gri... |
b356bccf7e4150dbe06e5ffd96033e1ae4b3f185 | 03f9b872f9e89453d1faf9b545d23fbad83bb303 | /R/run_cstacks.R | fce61a23878e1389bc4a8d961d719ff9aae70802 | [] | no_license | kawu001/stackr | 99fa54f4b4e1c8194550752bb238864597442c08 | 684b29b9895c773f48d0e58cba3af22fc2c98a56 | refs/heads/master | 2023-01-06T06:47:55.234575 | 2020-11-05T13:51:20 | 2020-11-05T13:51:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,236 | r | run_cstacks.R | #' @name run_cstacks
#' @title Run STACKS cstacks module
#' @description Run \href{http://catchenlab.life.illinois.edu/stacks/}{STACKS}
#' \href{http://catchenlab.life.illinois.edu/stacks/comp/cstacks.php}{cstacks}
#' module inside R! The function runs a summary of the log file automatically
#' at the end (\code{\link{... |
ad51dfdc6ffe52cdeee4f8e74a042b5b6d3d1976 | a0d43f26abeafbd8c159b9afbfca2e7582636092 | /Mestre dos Derivativos/Volatilidade.R | af67b3c10167da634b35045fbccbb6f8a04838ce | [
"MIT"
] | permissive | tarsoqueiroz/Rlang | f22038a0ada392d641cafecee1a0a91ba8574110 | b2d4fdd967ec376fbf9ddb4a7250c11d3abab52e | refs/heads/master | 2021-06-02T11:47:56.188320 | 2021-04-09T20:37:38 | 2021-04-09T20:37:38 | 132,541,417 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,770 | r | Volatilidade.R | #
# Ensaio para Volatilidade
#
# Source in "Mestre dos Derivativos/Ensaios"
# Vol BMF&Bovespa - 2018.08.13 0.0203835155197 0.0466064087617 0.0372254792514 0.0299117135965 <=
# Base MT5 - 2018.08.08 0.02071104 0.04883672 0.03799101 0.03005564
# 2018.08.09 0.02042073 0.04778... |
4903c3f6e5539cbc83ce1a5dc0328e2054866b32 | d033124f40b197e390209da730fb87e8639426c6 | /inst/app/simple_app.R | 6edcd6688200de48f2df57ebe6df4cea745033c1 | [
"MIT"
] | permissive | hamilton-institute/hamiltonThemes | 677ffa263cd8225386093208c1c4a8219a938672 | eacbf6b0917fd3fd7592dc28388855fb378a3654 | refs/heads/master | 2023-01-25T04:40:53.968597 | 2020-12-08T19:36:59 | 2020-12-08T19:36:59 | 311,664,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,025 | r | simple_app.R | ui <- bs4Dash::bs4DashPage(
sidebar_collapsed = TRUE,
sidebar_mini = FALSE,
body = bs4Dash::bs4DashBody(
hamiltonThemes::use_bs4Dash_distill_theme(),
hamiltonThemes::use_bs4Dash_distill_css(),
shiny::fluidRow(
bs4Dash::column(
width = 4,
shiny::br(),
shiny::selectInput(
... |
7fbce5a94178b7228b11a3608f91b9ba9a3d80af | 82ff8a6cf9c4c6a871f57fc9418ca656bde9ec0a | /R/configuration.R | e32456029580948d15c2b20f03ca464e9031e8bb | [
"MIT"
] | permissive | mhkhan27/rhdx | 08800dc13e6f0a1ba9725845ddc2e96e8abf509e | c443336b2d9e2d57dc6c0f6a81ab7bc6db3a631c | refs/heads/master | 2023-08-14T09:47:20.267482 | 2021-10-13T09:09:40 | 2021-10-13T09:09:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,165 | r | configuration.R | #' HDX Configuration
#'
#' HDX Configuration allow to connect to an HDX server
#' and setup project where you can interact with the HDX platform
#'
#' @format NULL
#' @usage NULL
#'
#' @importFrom tools file_ext
#' @importFrom yaml read_yaml
#' @importFrom jsonlite fromJSON
#' @importFrom crul HttpClient
#'
#' @example... |
ed04ac0d4106893e07e54c7aae66a070c91bb18f | f6839b533bdf2aaed9ca6dcd18b33a797d2c976c | /621/Assignment3/Professors Code/Clewlow3_13.R | 0b70e82f7ab4a5040a72e9bfe4a6c8d6c059b7a3 | [] | no_license | saeed349/R-Projects | 68fe11b57145daaf166381188844c968c424bcab | 504ee4741effd8ba3a0e1b706c238e08acbdca60 | refs/heads/master | 2021-01-09T05:28:25.377394 | 2017-05-16T22:11:54 | 2017-05-16T22:11:54 | 80,773,896 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,055 | r | Clewlow3_13.R | Clewlow3_13 = function(isCall, K=100, Tm=1,
S0=100, r=0.06, sig=0.2, N=3, div=0.03, dx=0.2)
{
# Implicit Finite Difference Method: i times, 2*i+1 final nodes
# Precompute constants ----
dt = Tm/N
nu = r - div - 0.5 * sig^2
edx = exp(dx)
# got the constants formulas from clewlow 3.33,3... |
459df3d67008765d444b9e45b7f17ebbe5566b62 | bb58aec3d341aab8b91fd210de4ee41389ea156c | /R/Basic_EDA.R | 102c4670a80a22ed91305f8236400744b5d81752 | [] | no_license | John-Snyder/COVID-19-Modeling | a5821b9e276d3cd3da604c067371c708f737a440 | de47faaf1d6483ba9bf8cb91e1c8a532db75afae | refs/heads/master | 2021-04-10T00:51:46.197055 | 2020-03-30T02:04:37 | 2020-03-30T02:04:37 | 248,898,575 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,247 | r | Basic_EDA.R | library(dplyr)
library(tidyr)
library(ggplot2)
source("./R/Import_TS_data.R")
covid19_long <- read.csv("./Data/COVID19_TS_long.csv")
covid19_long <-
covid19_long %>%
filter(Confirmed>1) %>%
group_by(Province.State,Country.Region) %>%
arrange(Date) %>%
mutate(Days_Since_First = 1:n(),
Country_Provi... |
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