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c77b47f97824aff18a52fa35c02f547758d749b9 | 9f0d75ede9b67b5286d1e1ec61792d5737ba1d1f | /R/perform.scanpy.normalisation.R | d2d7fa500d5c8e50ada4cf10f6e81119a87aceef | [] | no_license | jcogan1/IBRAP | 34fcf3bbea82a22880443bd83db40929d1f8aae0 | 8202b52f38bb3cb9af48524c38cfd6c4b39e2457 | refs/heads/main | 2023-08-03T15:32:52.737201 | 2021-09-14T15:55:35 | 2021-09-14T15:55:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,349 | r | perform.scanpy.normalisation.R | #' @name perform.scanpy
#' @aliases perform.scanpy
#'
#' @title Performs Scanpy normalisation, hvg selection, scaling and variance stabilisation and regression.
#'
#' @description A new method-assay is produced. Raw counts are normalised and HVGs identified using Scanpy
#'
#' @param object IBRAP S4 class object
#' @... |
29467d4ba726271511252437e92ad5d3e9e54a40 | a6f03b97b6b594867737bf238affc382be17a786 | /Rprog4.R | 7e670fa7e24b98c1b3010a766d32c0f2ae2f9916 | [] | no_license | eldersodre/ExData_Plotting1 | db24a8cbfd402a0aa668cc701ccaf88ae412c490 | 0d0795666be6c1cbb724f4d321dcab70da6138d1 | refs/heads/master | 2021-01-14T13:47:38.727445 | 2014-10-11T22:07:19 | 2014-10-11T22:07:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,389 | r | Rprog4.R | ###### Exloratory Data Analysis ######
########### Assignment 1 #############
############### Plot 4 ###############
dataset<-read.table("household_power_consumption.txt",sep=";",header=T)
data<-dataset[66637:69516,] #Only two days
data[,3]<-as.numeric(levels(data[,3])[data[,3]])
data[,4]<-as.numeric(levels(data[,4])... |
301047779d25695b3b380dc1693e6295ea6d6387 | 8b72f83fe27a18d50540c5d3c69e67c3ac4a338d | /app.R | 1d0040975ee8c42f58939af97410d838053f8d06 | [] | no_license | datasketch/app-dummy-data | bc0213de3ae7a0c5444cb99367e9afd06431ec62 | 17491a6b57b1f642e59d7cdd3e235ae88652c74a | refs/heads/master | 2022-03-12T20:21:56.645352 | 2019-10-24T19:46:57 | 2019-10-24T19:46:57 | 217,369,579 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 833 | r | app.R | library(shiny)
library(charlatan)
source('utils.R')
variables <- c(
'Name' = 'name',
'Job Title' = 'job',
'Phone Number' = 'phone_number',
'Email' = 'email',
'City' = 'city'
)
ui <- fluidPage(
titlePanel('fakeR: Generate fake data'),
sidebarLayout(
sidebarPanel(
checkboxGroupInput(
... |
d5f8d116f98e92589f62fcdcc2759abda864e6b5 | 59780f063cd6e6b735210cd7de0590dfb06c8798 | /tech_growth_impact_real_estate/ui.R | bc9717c435fd899987a7d72550cf0bee44186d52 | [] | no_license | alexetalbott/Shiny-App-USA-Real-Estate-Tech-Jobs-Exploration | 98f98f9de8f95a051634b726900dc1334f8bedff | 70493cb11dd910857161f4e6d7d837413c1ae072 | refs/heads/master | 2020-04-14T19:16:04.600844 | 2019-01-25T02:16:27 | 2019-01-25T02:16:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,451 | r | ui.R | shinyUI(
fluidPage(
titlePanel("Home Value and Tech Worker Growth"),
theme = shinytheme("sandstone"),
tabsetPanel(
tabPanel("Map",
sidebarLayout(
sidebarPanel(
selectInput(inputId = "top_or_bottom", label = "Select:",
... |
af7a0bdf2aefbf73b198d7d32d5b9ee22e0b84d4 | f80cddcbee78d55418a5d80dac8221792c8315cc | /belajar.R | 6bb60ead565533a4d3b7e300d9b6a88606062edf | [] | no_license | kiko0217/belajar-r | df27f36a6b61a1ac2ecb4af1f7ac7655312f22bc | 7740fdb4a7e8c7335b99568194d0bfb76834f285 | refs/heads/master | 2023-02-23T15:49:27.938299 | 2021-01-28T10:25:49 | 2021-01-28T10:25:49 | 333,723,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 167 | r | belajar.R | 2+5
# demo comment
print("hello world")
x <- 2
x <- 3
x
y <-c(1,2,3,4,5)
y <-1:10
x <- y <-1:10
x+y
z <- x+y
z2 <- x*y
X <- 10
ls()
rm(X)
remove(z2)
rm(list = ls())
|
49d2063bf8efc3b7f305d5716b874e66f73d85b1 | 61f693b7a5560c87972d9008c668156f993acee0 | /man/recode_dat_intercept.Rd | 6457b17722fe6d5ed7c647b4ad0dd6d55d74a716 | [
"MIT"
] | permissive | explodecomputer/simulateGP | 7e88fb90496502f86c379d22799d4977744e0506 | 7eeba0323df54dace3146c0941c7be699dc34433 | refs/heads/master | 2023-02-08T08:08:37.564481 | 2023-01-26T13:48:22 | 2023-01-26T13:48:22 | 91,170,950 | 10 | 3 | null | null | null | null | UTF-8 | R | false | true | 439 | rd | recode_dat_intercept.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/format_mr.r
\name{recode_dat_intercept}
\alias{recode_dat_intercept}
\title{Intercept recoding to have every effect on x positive}
\usage{
recode_dat_intercept(dat)
}
\arguments{
\item{dat}{Output from get_effs}
}
\value{
Data frame
}
\descri... |
b0d558ebc209c8dec4ceb9b9f47a35423b9ead61 | 46891316c185d2a7deda1a9971e8caab0b7d6147 | /R/marfissci.get.data.R | d53ef7f559290aa8ba6f30987632595334763b3f | [
"MIT"
] | permissive | jae0/aegis.mpa | 900843423af3a9bd2490d5f1c1c95560a4a47f99 | bcd3d08007474d910042a34edcefe11105fbb3fb | refs/heads/master | 2023-06-08T21:53:46.714251 | 2023-05-27T23:29:31 | 2023-05-27T23:29:31 | 190,056,664 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,193 | r | marfissci.get.data.R | marfissci.get.data <- function(spp = NULL, gear=NULL, years = NULL, get.nonlandings=T, save.csv=T) {
channel <- ROracle::dbConnect( DBI::dbDriver("Oracle"), dbname="PTRAN", username = oracle.personal.username, password = oracle.personal.password)
#'MMM March 31, 2016
#'This is a marfissci extraction that can... |
d04ed2c5192519ea66960536c8be854e1c6d572e | 92a0b69e95169c89ec0af530ed43a05af7134d45 | /R/plan_orchard.R | afb9d3f9eb39ca54f1455e9f2e77c7bc0e072f08 | [] | no_license | gelfondjal/IT2 | 55185017b1b34849ac1010ea26afb6987471e62b | ee05e227403913e11bf16651658319c70c509481 | refs/heads/master | 2021-01-10T18:46:17.062432 | 2016-01-20T17:51:29 | 2016-01-20T17:51:29 | 21,449,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 552 | r | plan_orchard.R | #' Create project hub files in root directory
#' @return logical for succesful creation or not
#' @export
#'
plant.orchard <- function(){
#
orchard.site <- file.path(path.expand.2("~"),"ProjectPaths","projectid_2_directory.csv")
if(!file.exists(orchard.site)){
dir.create(file.path(path.expand... |
1d4caab112fcad0ea0d0377e4e4965a3fdfedbed | bbd1cf229fc2dee5faf4111312486676faabd956 | /ETE_2019/cours_3/script_3.R | ab97960754c4fc25d8f2ea26aadae63d09e587b4 | [] | no_license | nmeraihi/ACT3035 | c28cbc4ef9daba9daea604eec793a11cb505538e | b0eaa56825c72c5c65185243d30b500ec4dcc02d | refs/heads/master | 2022-12-21T21:11:34.641905 | 2022-12-07T22:53:13 | 2022-12-07T22:53:13 | 133,547,375 | 1 | 4 | null | null | null | null | ISO-8859-1 | R | false | false | 3,341 | r | script_3.R | vect <- c(T, F, T, F, F, F, T)
as.numeric(vect)
vec <- 1:10
vec[5]<1
# if(respect ou pas de la condition){
# action a predre
# }
for(i in 1:10){
print("Bonjour")
}
rep("Bonjour", 10)
for(i in 1:5){
if(i==2){
next
}
print(paste("Bonjour le chiffre ", i ))
}
for(i in 1... |
28a67bce2244e623a841e3cb8e377c4bdc6d7dfc | 9326d857c238ff56f993437fb44a5c90961d0753 | /man/banner.Rd | 0d7e5f6244802be1b02bb8b540cf0dec2ab4dd92 | [] | no_license | moj-analytical-services/shinyGovstyle | e1e9b4062710b229f269f9b0bb58c1398383f7e1 | a033342e971b9f090c06b6e17b82b20d27dce50c | refs/heads/master | 2023-07-11T05:45:21.430131 | 2022-02-22T10:36:38 | 2022-02-22T10:36:38 | 192,864,104 | 34 | 4 | null | 2022-02-07T12:41:32 | 2019-06-20T06:41:58 | CSS | UTF-8 | R | false | true | 901 | rd | banner.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/banner.R
\name{banner}
\alias{banner}
\title{Banner Function}
\usage{
banner(inputId, type, label)
}
\arguments{
\item{inputId}{The input slot that will be used to access the value.}
\item{type}{Main type of label e.g. alpha or beta. Can be... |
6d20fa7ae319a4180df29ae3d21daee158e573a8 | be9d5be42158084f14b81c4ef4a32f89948a34e0 | /runAnalysis.R | 560cbd1a150221bb6effd24cfd5d9ba5fd7812dc | [] | no_license | xzw0005/GettingAndCleaningData | b2885fc281604dcb8a05d1e5d6f6b62e348a5fe8 | 000cba4d77ef59e827ab7323aa3d93ef902559e9 | refs/heads/master | 2021-01-09T21:52:19.478761 | 2015-05-24T22:31:12 | 2015-05-24T22:31:12 | 36,117,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,549 | r | runAnalysis.R | setwd("F:\\Coursera\\GettingAndCleaningData\\getdata-project")
train = read.table("./UCI HAR Dataset/train/X_train.txt")
dim(train)
print(object.size(train), unit = "MB")
test = read.table("./UCI HAR Dataset/test/X_test.txt")
dim(test)
print(object.size(test), unit = "MB")
head(test)
features = read.table("./UCI HAR D... |
8aea8dca4dbbc8a2c1d1c4217050b9217066dce8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RgoogleMaps/examples/GetMap.Rd.R | de49f1b92eb0863f14e9c943e29b327d099131fe | [] | 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 | 3,766 | r | GetMap.Rd.R | library(RgoogleMaps)
### Name: GetMap
### Title: download a static map from the Google server
### Aliases: GetMap
### ** Examples
lat = c(40.702147,40.718217,40.711614);
lon = c(-74.012318,-74.015794,-73.998284);
center = c(mean(lat), mean(lon));
zoom <- min(MaxZoom(range(lat), range(lon)));
#this ov... |
5ebaaa682d31c8f95ad4e82aafca081980629484 | 91134c9c434ee7ce2529efa478faa820dce61e0a | /Programs/shinypopfit/ui.R | 1ce8332cb1337a73c43ece63bf15d891f387ffd0 | [] | no_license | Adam-Brand/Pooled_Testing_HIV | 9855603f21eac6f479b035cadf446907d2a550e8 | c82c184da1a028fdc4170ba4b105e6e212d09e6f | refs/heads/master | 2023-06-19T00:59:02.218768 | 2021-07-19T11:48:31 | 2021-07-19T11:48:31 | 266,970,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,802 | r | ui.R | #==============================================================================
# FILENAME: ui.R
# PROJECT: Pooled testing in HIV
# PURPOSE: ui.R file for the shiny program shinypopfit
#
#
# AUTHOR: Adam Brand
# INPUT datasets: none
# OUTPUT: none, this is a user interface for the a shiny app
... |
085e9f19cdf1374a07bb01958b32026effa47e93 | eb98c8ee3611c8cff81b1c73e1db6b5971faa16d | /man/endowport.Rd | 67088f442735446a3912151894e359929e8f8eb2 | [] | no_license | nathanesau/stocins | 565fbd2250d390af22e1cbfc8c4df16c63277f42 | 047d7b88b96c888fbbdcd87bb9389d4ef55fae8a | refs/heads/master | 2021-01-23T05:24:12.831326 | 2017-04-07T17:29:32 | 2017-04-07T17:29:32 | 86,301,821 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 600 | rd | endowport.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/insuranceModels.R
\name{endowport}
\alias{endowport}
\alias{z.moment.iport.endowport}
\title{Endowment insurance portfolio (identical policies)}
\usage{
\method{z.moment}{iport.endowport}(moment, ins, mort, irm)
}
\description{
A po... |
1df07601d85b11f7e1103745c26c8e85ee014145 | f51b84af824432b03c5d2379483171a6fec4b9e3 | /R_code/data_extraction.R | 790ddd815196e927b06af8c5bb850134a964dac4 | [] | no_license | ZiyingFeng/Exploratory-Data-Analysis---Household-Power-Consumption | 587a2a8e671c8d6935f6cdd54d92665c21f9b64d | 803e20d8ab3165eb43855fa4765c74bfc4f2e679 | refs/heads/master | 2021-06-17T15:15:28.410524 | 2017-06-06T17:00:57 | 2017-06-06T17:00:57 | 93,230,599 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | data_extraction.R | # Read the raw data
data <- read.table("./household_power_consumption.txt", header = TRUE, stringsAsFactors = FALSE)
# Convert the first column to date
data[,1] <- as.Date(data[,1], format = "%d/%m/%Y")
# Extract data from the dates 2007-02-01 and 2007-02-02
data1 <- subset(data, data[,1]>="2007-02-01" & data[,1]<="2... |
f024e47571a1a9923cd28b47e07379e5ae650fac | 864ee1d2fc91865f46666b7e810bcfbc9da6a007 | /R/initial_centroids.R | 6f1d10948396ac975eea86f9354f2557836daccd | [
"MIT"
] | permissive | gabiborges1/kfactr | f4f5d927777057d367da47eefaf9ab70717fb1fc | 6150e2ad8df65e751ec1228d0afd1719228435b1 | refs/heads/master | 2020-04-28T01:57:59.871497 | 2019-03-10T22:26:15 | 2019-03-10T22:26:15 | 174,880,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,030 | r | initial_centroids.R | # Hello, world!
#
# This is the script containing the functions to
# initialize the centroids
#
# Author: Gabriela Borges
# Data Inicial: 10-03-2019
# Data da Última Modificação: 10-03-2019
#' Initializes the clusters using the traditional approach.
#'
#' @param data A dataframe.
#' @param k A integer number.
#' @r... |
2db7fbb1c8e4a6b8931cac8c733547c62a6f76ed | 3abf5d69da2fb9b7ffdecc54285ac563801b3479 | /glmnet_cv_penalty/summary_glmnet.R | c64b233c8b8c165474bbc5e0e003b08c020bcba3 | [
"Apache-2.0"
] | permissive | eitail/machine-learning-summary | eb209a1beb4808886d3a588aba12dba7318c9e38 | f3de55cfc30433b16f005e24264f9869ce0f0cd0 | refs/heads/master | 2020-04-08T22:15:11.062336 | 2017-06-11T11:04:38 | 2017-06-11T11:04:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,674 | r | summary_glmnet.R | rm(list=ls())
#about glmnet
#glmnet(x, y, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),
# weights, offset=NULL, alpha = 1, nlambda = 100,
# lambda.min.ratio = ifelse(nobs<nvars,0.01,0.0001), lambda=NULL,
# standardize = TRUE, intercept=TRUE, thresh = 1e-07, dfmax = nvars... |
d1284b5e76cff85cd580958a513a00c67b8598d7 | 1877590ba1981d9e117bf04359d6799f763fbcec | /Untitled.R | 751a60cc85feb77d3ccf75481dc470685b64a4dc | [] | no_license | mastreips/USPTO-Datamining-Scripts | 61b0ba774046dfa0774ac07ec96cf1ee2f46a480 | 20de1a9d69b0e5c300c95c2ad24959b9fbdaa2ec | refs/heads/master | 2021-01-10T07:15:49.497932 | 2015-09-23T14:30:23 | 2015-09-23T14:30:23 | 43,006,262 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 954 | r | Untitled.R | library(XML)
library(RCurl)
library(xlsx)
URL <- getURL("http://patents.reedtech.com/parbft.php")
rt <- readHTMLTable(URL, header = TRUE)
rt
url <- "http://patents.reedtech.com/parbft.php"
doc <- htmlParse(url)
links <- xpathSApply(doc, "//a/@href")
free(doc)
links
write(links, file="upto_links.txt")
# wget --no-pro... |
467a44e21f0a28f9ca173e007a22ed82ac4e7f90 | e9a2b3624a6117ebc23d5d4131a9c95a0b26c78f | /hw3/mimiciv_shiny/global.R | 9ab7b66832a943833b1e72c1210b9decb4b3d044 | [] | no_license | Larryzza/biostat-203b-2021-winter | cff0c8c81d558eb6d4dc70439bab5dc5dc5fd62c | 4d09738b28106818c9b1626507a597b59e69b9ed | refs/heads/main | 2023-03-29T11:54:27.248471 | 2021-03-27T04:46:45 | 2021-03-27T04:46:45 | 329,160,615 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,913 | r | global.R | library(DT)
library(shiny)
library(dplyr)
library(plotly)
library(tableone)
library(data.table)
library(tidyverse)
library(shinydashboard)
library(shinyWidgets)
library(shinycssloaders)
library(wesanderson)
cores <- wes_palette("BottleRocket1", 5)[3:5]
icu_cohort <- readRDS("icu_cohort.rds")
icu_cohort <- icu_cohort... |
8646acfdbb4084916bb4b158a95ebe8b6aeb2fb0 | e3705c3a76fb0f4bacdae56518807dd81bf73e66 | /Lab 4.R | 05ea952b2e83a4fe2bc4fdb015a400b182257a5e | [] | no_license | AnuC01/DataAnalytics2020_ANU_CHANDRASHEKAR | 6acf6fbf6edbbdc46330c127d301ff418ecb66d3 | 3452c182ca6db2701ccc0a16de15eb6af5a5d486 | refs/heads/master | 2023-02-01T22:22:15.344015 | 2020-12-16T06:20:04 | 2020-12-16T06:20:04 | 294,528,367 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,410 | r | Lab 4.R | #LAB 4
#Heatmap(), image(), and hierarchical clustering example
set.seed(12345)
help(par)
par(mar = rep(0.2,4))
data_Matrix <- matrix(rnorm(400), nrow = 40)
image(1:10, 1:40, t(data_Matrix)[,nrow(data_Matrix):1])
par(mar=rep(0.2,4))
heatmap(data_Matrix)
set.seed(678910)
for (i in 1:40) {
coin_Flip <- rbinom(1, ... |
ac4fdeb4cf0bbd5dde002c900053143c63eaa553 | 7756e8d3711b5cfed11011b2089ca17563734ee9 | /exercise-2/exercise.R | 6ad253c5004a6a85857e135abfbfd9168ec990ce | [
"MIT"
] | permissive | chiuyt19/m7-functions | 5827d586cdfb8df66727743caa5baa384766d1f3 | 97587b289ea2f55329daef6bd66608d9422b9ded | refs/heads/master | 2021-01-19T01:18:32.327373 | 2017-04-06T01:07:48 | 2017-04-06T01:07:48 | 87,237,872 | 0 | 0 | null | 2017-04-04T21:43:05 | 2017-04-04T21:43:04 | null | UTF-8 | R | false | false | 1,376 | r | exercise.R | # Exercise 2: writing and executing functions (II)
# Write a function `CompareLength` that takes in 2 vectors, and returns the sentence:
# "The difference in lengths is N"
CompareLength<-function(a,b){
dif<-abs(length(a)-length(b))
all<-paste("The difference in lengths is ", dif)
return(all)
}
# Pass two vector... |
fdf4e20804573392f681498e56e9b07291d1bcf7 | 2eaac83849c8f97d02c613a84aba44d7f73ab5fb | /02-07-19 media.R | 59cd2bb1ef46fad31f746ecd68983fb9a57fb7d8 | [] | no_license | whipson/tidytuesday | ef47b117c7d77cff809d2f091a94884a2a2440fd | 73d1aa89c73258687133f0c18ed9c03ad35476e5 | refs/heads/master | 2021-07-11T16:29:42.368183 | 2020-07-15T13:15:02 | 2020-07-15T13:15:02 | 179,088,594 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,628 | r | 02-07-19 media.R | library(tidyverse)
library(ggalluvial)
library(extrafont)
media <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-02/media_franchises.csv")
media_clean <- media %>%
mutate(original_media_lumped = fct_lump(original_media, 8),
revenue_category = case_when(rev... |
2b77ee04cd90090b53af718d34ff87e231109c99 | 65b5014564a796bc2e438367f4456607ecc33fa9 | /man/as_url.Rd | 2c82819c512fc6834eff3624b44d04866dacdf55 | [] | no_license | liao961120/pttR | 73a0cfc904cd7aeec66016e54a4dbb14b45f9e2d | c439aa0c1334a1f23a0e59dd59539a3f478a6ef4 | refs/heads/master | 2021-06-05T09:57:29.527674 | 2019-12-12T08:57:29 | 2019-12-12T08:57:29 | 144,726,983 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 629 | rd | as_url.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ptt-handy.R
\name{as_url}
\alias{as_url}
\title{Turn PTT board name to URL}
\usage{
as_url(x, pre = "https://www.ptt.cc/bbs/")
}
\arguments{
\item{x}{Character. A board name or a partial URL (ending in
\code{.html}) with base URL removed.}
\... |
aaaaeb4388baf6bdab2719814e76e547fbbd3e29 | 84e7c052fae39843d3f67be78049e175ea8c441c | /R/ALAdistributions.R | ecc7ab741c5bbc2b59d4f57574c37e62ea9f0fba | [] | no_license | AngeVar/GLAHD | 935b7d346dc9fb4cf8a8b552dda1400fe7100fda | 80ff898cd15b2e670ea0ed7c31db83a69b657faf | refs/heads/master | 2020-05-22T07:57:11.913885 | 2017-07-04T06:01:13 | 2017-07-04T06:01:13 | 36,908,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,141 | r | ALAdistributions.R | setwd("W:/WorkingData/GHS39/GLAHD/Varhammar_A/")
#load some libraries
library(raster)
library(rgdal)
library(stringr)
library(scales)
#load file
seed_atsc<- read.csv("GLAHDseed.csv")
#load known distribution
d <- read.csv("distributions_bioclimv2.csv")
dist <- subset(d,d$Coordinate.Uncert... |
87eb1e60324202ae373b26db8384eb0687e894d5 | 8d876c616b3021e9359fefb4f4e7d100156ba144 | /tests/testthat/test-history.R | 099ef36ebed3656536beaa15e6c25f4b0034b801 | [] | no_license | pkq/covrpage | 4feb664baffe6a2706e3b38f27ef812ee25ff577 | 1265cd29681b1d00a341011d063430aca5344c29 | refs/heads/master | 2021-06-23T22:58:49.851707 | 2021-06-22T01:39:06 | 2021-06-22T01:39:06 | 133,855,867 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,446 | r | test-history.R | testthat::context("coverage history")
testthat::describe("fetch md files", {
testthat::skip_on_ci()
file.copy("../assets/covrpage_benchmark/covrpage", tempdir(), recursive = TRUE)
td <- file.path(tempdir(), "covrpage")
wd <- getwd()
setwd(td)
repo <- git2r::init()
git2r::add(repo, path = ".")
git2r::c... |
fda2e276e0c5fc37e33d52fce0a4ee1353c7bd9f | 9541504f1b8ce81b7627e4a1068baf9f49745cbd | /workspace/r-basic/03/data-exploration-2.R | 9463c5ebd720574089abb8497830a66df2893c19 | [] | no_license | hsson428/ssac-academy | 781954f0a0c9ba9b98e797cb49937e23cc258974 | d332cad5e29732a8e079d29498df5a9e53bf452c | refs/heads/master | 2023-03-10T04:18:07.318823 | 2021-02-22T06:53:19 | 2021-02-22T06:53:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,341 | r | data-exploration-2.R | # package import
library(dplyr)
library(ggplot2)
# 1. 컬럼 이름 변경
df_raw <- data.frame(var1 = c(1, 2, 1),
var2 = c(2, 3, 2))
df_raw
df_new <- df_raw
df_new2 <- rename(df_new, v1 = var1, v2 = var2)
df_new2
colnames(df_raw)
colnames(df_raw) <- c("vx", 'vy')
df_raw
#####
copied_mpg <- mpg
copie... |
e71be5fe94b28f6c7ae2f3dd76caaa04581e0744 | 572718492ee0e6f58cdaecd298b463ce9410545d | /man/TCGA.PAM50_genefu_hg18.Rd | 2b063e09eddf0e9518567c5a4990450df8905b78 | [] | no_license | cgpu/bioconductor-Omic-Circos | 6c8c0e770052b9ca66db3d61bd5ed84f70432613 | 6ba6184d9c641bca2d258309acbe80387b0e53e3 | refs/heads/main | 2023-01-06T11:22:03.230979 | 2020-11-03T10:07:27 | 2020-11-03T10:07:27 | 309,644,147 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 297 | rd | TCGA.PAM50_genefu_hg18.Rd | %%
\name{TCGA.PAM50_genefu_hg18}
\alias{TCGA.PAM50_genefu_hg18}
\docType{data}
\title{
BRCA PAM50 gene list (hg18)
}
\description{
Breast cancer PAM 50 gene list (hg18).
}
\author{
%% ~~ possibly secondary sources and usages ~~
Ying Hu <yhu@mail.nih.gov>
Chunhua Yan <yanch@mail.nih.gov>
}
|
59aa80d452c603a16d94c1d036d9712dc8716210 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/wavethresh/examples/InvBasis.wp.rd.R | 5c9724d1a08fd9e477ee61c510f62ef391ae4580 | [] | 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 | 261 | r | InvBasis.wp.rd.R | library(wavethresh)
### Name: InvBasis.wp
### Title: Invert a wp library representation with a particular basis spec
### Aliases: InvBasis.wp
### Keywords: smooth
### ** Examples
#
# The example in InvBasis.wst can be used here, but replaced wst by wp
#
|
c8e25bfa420ec887fa02afa06933a51c57e7958e | 87985172c0206ec527473d1aa4db81c812de013f | /tests/testthat/test-presents.R | 017615e6367a6a77360553d652f46919e3000a53 | [] | no_license | adamsma/helloRworld | c9840513c4967f77864b8f7ef72784f66e8df7f6 | e4850aa140997e57e4ee0f2670dbcedf2e536e65 | refs/heads/master | 2020-03-28T10:23:01.706623 | 2018-09-15T16:53:27 | 2018-09-15T16:53:27 | 148,103,992 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 395 | r | test-presents.R | context("Testing Giving Presents")
test_that("Presents gives presents",{
expect_message(Presents(), "^Hello,")
expect_message(Presents(), "Here's some car data for you:")
expect_output(Presents(), "mpg|cyl|disp|hp|drat|wt|qsec|vs|am|gear|carb")
})
test_that("Data generated is subset of mtcars", {
e... |
b0a05789d4b7222f25c1600584de79423b17caa1 | 20b4c4ad2f546739e7b3c3b6107094aecf4720ca | /jsserver/Ranalysis/DyanmoAnalysis.R | 5b7f7290d9d672bf7c6d9171e5c0b96cde2dca02 | [] | no_license | ngopal/VisualEncodingEngine | 111437109dd8ed00f01bbdf91b3c2697f4cc69a4 | 77b57529e3880047ba8af74225eb0b2b68301a3b | refs/heads/master | 2020-04-06T06:51:31.883311 | 2016-09-06T21:04:15 | 2016-09-06T21:04:15 | 60,572,650 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 32,576 | r | DyanmoAnalysis.R | library(randomForest)
library(RJSONIO)
library(ROCR)
# Research Questions
# 1. What are the ranked importances of node encodings?
# 2. What are the ranked importances of edge encodings?
# 3. How important is network structure to noticeability?
# 3a. Where do participants tend to click?
#Converting HEX color to INT
r... |
857110be0eece3da79614529e14f40de2bf079e4 | 6c9d3d4a6b6d4a5447f4c015f2079e65aaa36c55 | /R/transform_functions.R | 7a08cece6ab770422f0a0fc8ce9c477df9312073 | [] | no_license | cran/omu | 31f6f8a72b070e9db8a0283b4694f6b6f0dc3102 | 43eee47e4bd12da86e86f4ad1f221c8653625f70 | refs/heads/master | 2023-04-16T05:24:11.938933 | 2023-04-06T21:00:03 | 2023-04-06T21:00:03 | 145,907,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,974 | r | transform_functions.R | #' transform_samples
#' @description A functional to transform metabolomics data across samples.
#' @param count_data Metabolomics data
#' @param func a function to transform samples by. can be an anonymous function
#' @examples
#' data_ln <- transform_samples(count_data = c57_nos2KO_mouse_countDF, log)
#' @export
tra... |
1794ea1f47390e433eb53294fea036122a21c888 | eeffd0498b95546f503ecda2b6ee9c95bc931190 | /R/alignment.R | ea9e02fdab58e6317b967530ac7c6ccbd51c7532 | [] | no_license | nijibabulu/clustably | 8021da139c4a1956d9b2b92915057c96035858ca | fbb450e07b63c7d6477372e5a7c08debee7b39e1 | refs/heads/master | 2020-06-18T06:39:53.594507 | 2019-07-23T17:21:34 | 2019-07-23T17:21:34 | 196,199,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,799 | r | alignment.R |
#' Perform a global alignment of labelings on a set of objects
#'
#' Greedily group cell labelings and recode them into a single tibble
#'
#' @param labels a list of factors with labelings
#' @param cells a character vector of all cells ids in the experiment
#'
#' @importFrom tibble as_tibble enframe
#' @importFrom dp... |
a1d1bcc714ce9bdd2b0505e34efd2721a4cc2d5e | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query09_query50_1344/query09_query50_1344.R | a542842ddfa8571bf8dab831574b69753b8a9f72 | [] | 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 | 72 | r | query09_query50_1344.R | 522baf8daaf5c12f22c8618452f473ec query09_query50_1344.qdimacs 4807 39775 |
f7eb5d1350f80aa9e0e94e3a5972551883699b77 | d5967b81f2c0ae9e63f86e3396fbe07ece2ecec6 | /cachematrix.R | db4faf68030a815154df20ef470e022120c7df4d | [] | no_license | Akema1/ProgrammingAssignment2 | 3000a3a76d0c7294f39c11089c925a43411ed2ee | cf1d062f67364c436e6b10872f2c49d8b03531df | refs/heads/master | 2021-01-17T06:45:11.191020 | 2015-11-22T02:10:30 | 2015-11-22T02:10:30 | 46,634,258 | 0 | 0 | null | 2015-11-21T21:17:34 | 2015-11-21T21:17:33 | null | UTF-8 | R | false | false | 869 | r | cachematrix.R |
#makeCacheMatrix creates a list of 4 functions to store a matrix and a cached value of it's inverse
#set the value of the vector
#get the value of the vector
#set the value of the mean
#get the value of the mean
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NU... |
e5ab096483cef84fcbabac63477776eba2d2030c | 8b20d5cd8e94b57d28b2216ea620b152dcfc375a | /ui/ui_visualize.R | 1f87c9f8eed157f8fe9380c0720e75d984fc4f8a | [] | no_license | aravindhebbali/explorer | 6248f690ff0294ff965f65f0277ae1b19a2ed3f7 | c1f5c63ce8206a04d64eda3572f4e139df1e09d7 | refs/heads/master | 2021-03-27T16:21:45.253318 | 2017-06-11T07:21:52 | 2017-06-11T07:21:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 603 | r | ui_visualize.R | tabPanel('Visualize', value = 'tab_viz', icon = icon('line-chart'),
navlistPanel(id = 'navlist_viz',
well = FALSE,
widths = c(2, 10),
source('ui/ui_bar.R', local = TRUE)[[1]],
source('ui/ui_bar2.R', local = TRUE)[[1]],
source('ui/ui_box.R', local = TRUE)[[1]],
source('ui... |
bffbf2908c247a0f1ec7ca6cb5e5d73e0697ec36 | f81687e1f90efa76dad7b5dcdb6ae503be6a704d | /R/date_utils.R | ca415e4029ea5cae766fbc285b7ce9058b6cfe93 | [] | no_license | fmichonneau/sok-marine-biodiversity | f5c4025e39cd0926b996f853d31d0a686fe4091b | fbe4338739d883cbf54378d8e8ea85d2c30160bf | refs/heads/master | 2022-03-11T04:02:53.629611 | 2022-02-25T15:02:10 | 2022-02-25T15:02:10 | 68,727,281 | 2 | 2 | null | 2022-02-25T15:02:11 | 2016-09-20T15:41:53 | R | UTF-8 | R | false | false | 504 | r | date_utils.R | parse_year <- function(recs) {
recs %>%
dplyr::mutate(parsed_date = parse_date_time(datecollected, c("Y", "ymd", "ym", "%Y-%m-%d%H:%M:%S%z"))) %>%
dplyr::mutate(
year = year(parsed_date),
year = replace(year, year > 2017 | year < 1850, NA),
year = as.integer(year)
) %>%
dplyr::select... |
6993524dad4719cff8151a86d276f2c5e8ee033b | cac943a39da206c154f41bc7e4a5c6645b9fe062 | /runMain.R | 27445a185fa79d970d2ce7603087effc40353cf1 | [] | no_license | CIAT-DAPA/usaid_procesos_interfaz | 57dc6ea42f97ec57c3496eec58fae44463be6d04 | e769be5c897ecc6073111eed32516e922e0b2cf6 | refs/heads/main | 2023-09-03T10:44:13.768665 | 2017-03-08T20:18:48 | 2017-03-08T20:18:48 | 83,475,773 | 2 | 3 | null | 2023-01-26T16:04:13 | 2017-02-28T20:24:50 | Python | UTF-8 | R | false | false | 6,411 | r | runMain.R | # Librerias y prerequisitos:
# . gunzip
# . R librarys
library(funr)
library(lubridate)
library(reshape)
library(stringr)
library(trend)
library(data.table)
library(tidyverse)
library(magrittr)
library(lazyeval)
library(foreach)
## DIRECTORIO PRINCIPAL
# dirCurrent <- paste0(get_script_path(), "/", sep = "", coll... |
f17b780a28821f703429b0139fe5899d8e275ec1 | 6c2029a4a11b86ff9b2d016fdeda82689840633e | /man/summary.expl_reg.Rd | 77ff4038ee2ba400fe67c8bb37f8238d1cb1e7f6 | [
"CC-BY-4.0",
"MIT"
] | permissive | multinormal/fhi.informed-health-choices-norway.2019 | c0c3f3e2ae7745f3564789084e848ad50dc28509 | e14a1ee1ebf6120a7522416bbdfe98e605964da3 | refs/heads/master | 2021-01-05T15:42:33.037842 | 2020-08-19T07:40:03 | 2020-08-19T07:40:03 | 241,065,111 | 0 | 0 | NOASSERTION | 2020-08-19T07:40:04 | 2020-02-17T09:17:32 | R | UTF-8 | R | false | true | 436 | rd | summary.expl_reg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods_expl_reg.R
\name{summary.expl_reg}
\alias{summary.expl_reg}
\title{Summarize an instance of \code{expl_reg}}
\usage{
\method{summary}{expl_reg}(object)
}
\arguments{
\item{object}{an instance of \code{expl_reg}.}
}
\value{
a 1-row \co... |
c0a9e43d60068f73daff50326c93cdf53cd4c437 | 959b8d01689825ce765ef1f783c579c43831d9a9 | /R학습파일/200804.R | 012525ca3a1cf600728d57adc72bcac4afe2cd4d | [] | no_license | leeyouhee/R2 | 9f7117e2b99f37ad1ef9bf2e4242c21468196629 | a7f448247d81ecaea148703b4ffa2be2aaa54ea7 | refs/heads/master | 2022-12-10T20:41:48.616158 | 2020-09-01T03:37:10 | 2020-09-01T03:37:10 | 283,909,285 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,339 | r | 200804.R | #명사 추출
#여행지 조사(사전에 builddictionary 후 다시 추출)
install.packages('multilinguer')
library(multilinguer)
install.packages(c('stringer', 'hash', 'tau',
'Sejong', 'RSQLite','devtools'), type = 'binary')
install.packages('remotes')
remotes::install_github('haven-jeon/KoNLP', upgrade='never',
... |
972c67ce67ec02ee24aea0bd08bdee5fbd14e5e2 | de9d448132f90f073d29add688de2fcf72527a89 | /man/group2list.Rd | bdbd024f4118ae6c18236afc2bcf3acdc8cdb5bb | [
"MIT"
] | permissive | NMikolajewicz/scMiko | 33f137e9e3a6318fb0386506ac4666a3822463f0 | bd00724889db265817fc54d0d50b14647d32438d | refs/heads/master | 2023-06-09T05:51:30.199131 | 2023-06-04T20:23:51 | 2023-06-04T20:23:51 | 249,496,034 | 20 | 4 | null | null | null | null | UTF-8 | R | false | true | 765 | rd | group2list.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/network_functions.R
\name{group2list}
\alias{group2list}
\title{Named list of cells grouped by meta feature from Seurat object}
\usage{
group2list(object, group = "seurat_clusters", is.num = F, prefix = "")
}
\arguments{
\item{object}{Seurat ... |
476be3fb3194223ec4268f028b97e63dc620571d | 1fc5725383d5a594a97824c2a2c1eb3224dda916 | /man/ifelse_pipe.Rd | 6f54c9c332b449fe2e9f0bb72f145a0544e7aea6 | [] | no_license | stemangiola/ARMET | 61fa21aec29a21a3450e778ace702acb68719741 | dd5ea830c14634b82cde26e101b375bdb94580ab | refs/heads/master | 2022-07-16T16:55:55.381918 | 2022-07-06T08:06:24 | 2022-07-06T08:06:24 | 120,414,626 | 3 | 1 | null | 2022-05-24T01:44:56 | 2018-02-06T06:56:54 | R | UTF-8 | R | false | true | 685 | rd | ifelse_pipe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{ifelse_pipe}
\alias{ifelse_pipe}
\title{This is a generalisation of ifelse that acceots an object and return an objects}
\usage{
ifelse_pipe(.x, .p, .f1, .f2 = NULL)
ifelse_pipe(.x, .p, .f1, .f2 = NULL)
}
\arguments{
\item{.x... |
2de0f62950d55e76a79c4f2bba6fd44e4417bdcd | 08f3b72fabbab22bfbd90eb6b3984dc85cb971d8 | /R/offset_latlon_by_meter.R | 0a11b50d23a439b8fa66cebb0d6443c235076143 | [
"MIT"
] | permissive | benmack/lucas | 8408801bbfcdcb7efd95d3fb8fb706b043218641 | a4c3376455653bf0a307456b48a91831107b7883 | refs/heads/master | 2021-01-20T10:32:18.399915 | 2019-04-07T10:01:12 | 2019-04-07T10:01:12 | 66,085,066 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 780 | r | offset_latlon_by_meter.R | #' Offset a latitude/longitude by some amount of meters
#'
#' @param lat latitude of the starting point(s) in decimal degree
#' @param lon longitude of the starting points(s) in decimal degree
#' @param de offset towards east (in meter)
#' @param dn offset towards north (in meter)
#'
#' @return latitud and longitude o... |
a19152d8ecace07487cd0c66725a23db2bde660e | e5a65dbebf3eb475e289040bfd70552785339e34 | /plot4.R | bbb4741ca2a6d8b093ac9c8e52f956f30d76dab1 | [] | no_license | jallred/ExData_Plotting1 | df6073455a234035fd0a380bc4ec551b76764724 | d714e4f6d667ceedb4d1700ba9ca05c04753983c | refs/heads/master | 2021-01-16T19:16:21.782903 | 2015-02-09T02:18:18 | 2015-02-09T02:18:18 | 30,211,197 | 0 | 0 | null | 2015-02-02T22:05:19 | 2015-02-02T22:05:19 | null | UTF-8 | R | false | false | 1,127 | r | plot4.R | library(datasets)
# read in the data, and name the columns since we're skipping the first row (and other rows)
t<-read.table("household_power_consumption.txt", skip=66637 ,nrows=2880, sep=";")
colnames(t) = c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub... |
ceb435962916e9c943c71c8814ee5457992d907a | 8d274be5f5624f442cff46a76ea160fb87e7d8b2 | /src/draw_strain_pair_hgt_network.R | dd4017bcb176f9f206a5f0640ea1f825d5fc397a | [
"MIT"
] | permissive | tauqeer9/RecentHGT | aa2a372a291b044936f4d8585f4782af7475fc9a | 87ef638050a15d6df399f8987cea132be33c7591 | refs/heads/master | 2021-04-18T04:25:39.111881 | 2018-11-13T13:16:25 | 2018-11-13T13:16:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 893 | r | draw_strain_pair_hgt_network.R | library(igraph)
links <- read.csv("trimmed_strain_pair_hgts.txt", header=T, as.is=T)
nodes <- read.csv("strains_network_nodes.txt", header = T, as.is=T)
links <- aggregate(links[,3], links[,-3], sum)
links <- links[order(links$from, links$to),]
colnames(links)[4] <- "weight"
rownames(links) <- NULL
net <- graph_from_da... |
26e726e7b2b86d01dbff044914a1b91ca042f8a2 | 01dc5196de85da11065f1bac96cbf798e436221d | /R/read_nifti_batch.R | 6a42242c6e261ba200f3acac7ebc0dad94d15311 | [] | no_license | neuroimaginador/utils4ni | 841f6c95657c71a059223e6f526cb9f3856e0eae | 7aab7022345e9d33d8570b591251a6a788d40d73 | refs/heads/master | 2020-04-04T19:16:08.263669 | 2018-12-03T19:16:46 | 2018-12-03T19:16:46 | 156,198,988 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 315 | r | read_nifti_batch.R | #' @title Read Multiple Files
#'
#' @description This function reads several NIfTI files.
#'
#' @param file_list (list) Path of files to import
#'
#' @return A list with one entry for each file read.
#'
#' @export
#'
read_nifti_batch <- function(file_list) {
return(lapply(file_list, read_nifti_to_array))
}
|
f5959b52f099f0db11025af2b920e890ca5c071e | 8b8b10d0d42d1200664b3a96e999e3d92d249434 | /man/MaxentVariableSelection-package.Rd | 9ae3097b520cf76a9d6b13862ba913374b53b870 | [] | no_license | tim-salabim/MaxentVariableSelection | fd69776dc46d1e4ac3d0e00c2713349fe55e4c10 | cb2fc28332d4e36c301b590b2b84c6bcf93a72b4 | refs/heads/master | 2021-01-16T20:40:18.095055 | 2016-06-20T19:00:43 | 2016-06-20T19:00:43 | 61,561,450 | 0 | 0 | null | 2016-06-20T16:07:01 | 2016-06-20T16:07:00 | R | UTF-8 | R | false | false | 3,055 | rd | MaxentVariableSelection-package.Rd | \name{MaxentVariableSelection-package}
\alias{MaxentVariableSelection}
\docType{package}
\title{Selecting the Best Set of Relevant Environmental Variables along with the
Optimal Regularization Multiplier for Maxent Niche Modeling}
\description{Complex niche models show low performance in identifying
the most important... |
159b91ef1592e109a58aacc405d21575705dfc72 | 4fa1c9b43411b719d9051732af48d9c910dcd3ac | /R/utils.R | 3f8e009b7ea6742b740d75b9c9ec781028b432fe | [] | no_license | lian0090/BGData | df317c64830231b0e7843644642436c9303a8350 | 1e6ea1f73d46bf718a50bff5397d6b24a628f35c | refs/heads/master | 2020-12-11T08:00:34.991075 | 2015-05-14T17:20:02 | 2015-05-14T17:20:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,121 | r | utils.R | #' Computes a genomic relationship matrix G=xx'.
#'
#' Offers options for centering and scaling the columns of x before computing
#' xx'. If \code{centerCol=FALSE}, \code{scaleCol=FALSE} and
#' \code{scaleG=FALSE}, \code{getG} produces the same outcome than
#' \code{tcrossprod}.
#'
#' @param x matrix, ff_matrix, rmmM... |
437ce835010c57a551e31a5e94f08a747063c774 | 9dfc302b8e5dd1b1298dbc89873e80348ac7f954 | /data/create_alc.R | 37407d4231fe9ba0c2f1c600e37043800df2bace | [] | no_license | Jonharju/IODS-project | 14728455a741a6e7442336421e375fb1dfc37f19 | c97ff8be442114e6af4138e2871b21f028ed25ec | refs/heads/master | 2021-01-13T04:37:14.384031 | 2017-02-24T21:30:53 | 2017-02-24T21:30:53 | 79,477,561 | 0 | 0 | null | 2017-01-19T17:32:40 | 2017-01-19T17:32:39 | null | UTF-8 | R | false | false | 1,426 | r | create_alc.R | #Jonas Harjunpää 04.02.2017
#This file contains the wrangled data of a study about student alcohol consumpiton
#from here https://archive.ics.uci.edu/ml/datasets/STUDENT+ALCOHOL+CONSUMPTION
setwd("C:/Users/Jonas/Documents/GitHub/IODS-project/data")
getwd()
# read the math class questionaire data into memory
math <- rea... |
adf0b9868c15be513cf4a7e9960423306086cfb3 | 86d388a76b1debbdfbec5de7cd7c61d1f248cc04 | /ShinyApp/ui.R | afcd0b3e183d808deb6483c9b017c670338e3bb4 | [] | no_license | info370/project-teamname-v2 | f72ce6d2015d116801cc9136e5b3212488468e8b | b649da1978894d6f7841832d6757b28e41b04ab8 | refs/heads/master | 2021-08-29T16:02:05.840312 | 2017-12-14T07:56:27 | 2017-12-14T07:56:27 | 107,476,645 | 0 | 1 | null | 2017-12-14T05:44:19 | 2017-10-19T00:04:25 | HTML | UTF-8 | R | false | false | 17,730 | r | ui.R | library("shiny")
library(plotly)
library(shinythemes)
library(tidyverse)
require("maps")
library(geosphere)
library(stringr)
library(rgdal)
library(caret)
library(lubridate)
# library(maptools)
if (!require(ggmap)) { install.packages('ggmap'); require(ggmap) }
library(ggmap)
install.packages
here_long <- -122.3095
her... |
7e43c147dc6e208bebb52cda446e2ec4326ff0e7 | 5e1b775edcb7683f1039eb20f2e6f9594a59f8eb | /Chapter 9/ch9E.R | fae5827ff8fcd03e1412c91f23310bf67df49b44 | [] | no_license | loufin/Business-Analytics | 1973a649fca58da8aa4092a212eeb15e1c240098 | e15f69154df19e7a017ba47ce9dd30f2c39d9b7d | refs/heads/main | 2023-04-28T22:56:29.092261 | 2021-05-12T14:05:35 | 2021-05-12T14:05:35 | 335,809,886 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,367 | r | ch9E.R | # 9.1
auctions.df <- read.csv("eBayAuctions.csv", header=T)
train.index <- sample(rownames(auctions.df), 0.6*dim(auctions.df)[1])
valid.index <- setdiff(rownames(auctions.df), train.index)
train.df <- auctions.df[train.index,]
valid.df <- auctions.df[valid.index,]
library(rpart)
library(rpart.plot)
class.tree <- ... |
df19f463faa39b86260640d16743845cfdf18c05 | 21f4c8b6fa59bd8970fa4fc5aeef254bf228c603 | /3-assemble.r | 973b8c487226a09f8fcea6359bab4a6f08d50fbd | [
"MIT"
] | permissive | brentonk/doe-h2o | e35dd6693ec9ee8b2372cc22319695d10a9358df | 0f01c2d6bc58b674196a0b63165200abd7866c3d | refs/heads/master | 2020-06-22T04:04:38.676379 | 2019-07-25T16:17:05 | 2019-07-25T16:17:05 | 197,627,941 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,423 | r | 3-assemble.r | ### Assemble directed dyad predictions into a single data frame, then calculate
### the undirected scores
library("tidyverse")
library("assertr")
library("foreach")
sessionInfo()
## Load up the results from each individual year scoring, validate, and extract
doe_dir_dyad <- foreach (yr = 1816:2012, .combine = "rbin... |
49d0e9069c0159216d9e816b84caa9674eb66eef | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dbparser/examples/parse_drug_enzymes_polypeptides_go_classifiers.Rd.R | b20dfd3865ff5d57289a2c2456b404e35726d06a | [] | 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 | 431 | r | parse_drug_enzymes_polypeptides_go_classifiers.Rd.R | library(dbparser)
### Name: parse_drug_enzymes_polypeptides_go_classifiers
### Title: Extracts the drug groups element and return data as data frame.
### Aliases: parse_drug_enzymes_polypeptides_go_classifiers
### ** Examples
## No test:
parse_drug_enzymes_polypeptides_go_classifiers()
parse_drug_enzymes_polypepti... |
63e1998f86c955f4e199e2e871ebdfadad6bc809 | 7b602a40bfebdedc2f9f3c1b8dc31c3207564c40 | /data_analysis/markdown/json_to_data.R | c7ff085c91dde7c4eea1927a308809c8b2f5c929 | [] | no_license | ejyoon/Polimp | f4d4696b0ebb807b70f3f03125ee0f1a4f55a82f | d601a2b9019ec27c4799599a0a7472e2830faf17 | refs/heads/master | 2021-01-18T21:03:57.898918 | 2017-05-08T04:12:28 | 2017-05-08T04:12:28 | 32,059,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,702 | r | json_to_data.R | rm(list = ls())
library(jsonlite)
library(ggplot2)
source("/Users/ericang/Documents/Courses/Psych254/zt12rp/data_analysis/helper/useful.R")
raw.data.path <- "/Users/ericang/Documents/Courses/Psych254/zt12rp/production-results/"
processed.data.path <- "/Users/ericang/Documents/Courses/Psych254/zt12rp/data_analysis/proc... |
415b0c92e97bfc6529083b9b03743f1ef7b2d717 | 8c3a9fc8db02ccfecb510402f3d4962f982ab79a | /dashboard/global.R | 4d2a5c706e5e94de85879ee61d5c8cfe464fdecb | [
"MIT"
] | permissive | rithwik/datakind-egovernments | c402a84983d06384f56a6131b7a52de67ac7e6f6 | 12807f7582450c76eb7cb62ac1b7f6b9a7df6925 | refs/heads/master | 2021-01-17T18:44:50.864333 | 2016-06-23T06:01:46 | 2016-06-23T06:01:46 | 59,551,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 786 | r | global.R | library(xts)
library(hash)
library(data.table)
library(dplyr)
library(dygraphs)
library(plotly)
df = fread("../data/coc.csv")
df$Complaint.Date <- as.Date(df$Complaint.Date, format = "%m/%d/%Y")
df$Resolution.Date <- as.Date(df$Resolution.Date, format = "%m/%d/%Y")
df$NumComplaints <- 1
choicesForTime <- c("Daily", "... |
cecd5ce88fa00bde0d2cdae4171e4b31ce0ea8d8 | 11529bd6430cdf97087b3148a20f38b5acab2fcd | /man/createSpellConfig.Rd | b720732b8fadb8356bb2d2a8aad9e8eb9272faeb | [] | no_license | omegahat/Aspell | 288c4fbf626d3c34550134cb330814ffe6661082 | 41cba211ebe7820f6c8e1a201112fa5bee3b8aad | refs/heads/master | 2020-06-05T01:43:38.082431 | 2018-12-30T00:58:45 | 2018-12-30T00:58:45 | 3,999,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,677 | rd | createSpellConfig.Rd | \name{createSpellConfig}
\alias{createSpellConfig}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Create a new aspell configuration object.}
\description{
This function creates a new apsell configuration object
and allows the caller to set options within that.
Options can also be set later... |
b2d5817ea54d14185f532147f3634b38130deda3 | 190197a40d6779a986bacbf4ef8d4f293502b853 | /toyCarLine.r | dc1c547f016bb9a876e68f83512de173ae3273ad | [] | no_license | fgarzadeleon/SoftwareCarpentry | 3b008778801318d885003e3c938a3b042b08b44d | b3458e767587e090e494d60c81df051b0df3cb18 | refs/heads/master | 2021-01-09T20:57:37.535968 | 2016-07-13T11:12:50 | 2016-07-13T11:12:50 | 63,233,647 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 198 | r | toyCarLine.r | ## toyCarLine.r
## Federico Garza de Leon fgarzadeleon@gmail.com
## plot the cars data as an example
plot(cars)
z <- line(cars)
abline(coef(z), col = "purple")
dev.print(pdf, "toyLinePlot.pdf")
|
95e6cf8e7e984d3ab86ae080d6774d0b73cf4015 | 65317ea9159976b3fda084b2321c9afe959f6794 | /R/varNamesToChar.r | 247bc8f434ae4c48c039139e157d4d179ac2e3e6 | [] | no_license | cran/reporttools | c5a306a433bad475952b4515c30c557438050c5c | fc18cc11152b5ae783ff34376120bc08196a12a9 | refs/heads/master | 2021-10-28T08:56:09.300219 | 2021-10-12T15:10:02 | 2021-10-12T15:10:02 | 17,699,149 | 1 | 0 | null | 2014-09-04T01:06:29 | 2014-03-13T06:05:19 | TeX | UTF-8 | R | false | false | 222 | r | varNamesToChar.r | `varNamesToChar` <-
function (varnam)
{
tmp2 <- ""
tmp1 <- strsplit(varnam, ", ")[[1]]
for (i in 1:length(tmp1)) {
tmp2 <- paste(tmp2, tmp1[[i]], "\", \"", sep = "")
}
return(tmp2)
}
|
99a144b350f7e23d60a3ec04e5d2939922d409c7 | ab70aaa2fd087d4e935a228aeed6f7a341044f4f | /tests/testthat/test_bro.R | 8579da6207e08a5a395c0643e4a85b521d4a43aa | [
"MIT"
] | permissive | Ironholds/webreadr | f1d04bc6c7e7dbddb3e10a2089e2225ce8b71e9a | 545932629e3e7082911c91ee41a9ac0474832e1d | refs/heads/master | 2021-07-19T15:53:37.050370 | 2020-10-28T19:52:18 | 2020-10-28T19:52:18 | 32,247,262 | 43 | 15 | NOASSERTION | 2021-07-15T14:42:23 | 2015-03-15T05:54:02 | R | UTF-8 | R | false | false | 1,883 | r | test_bro.R |
context("Test reading Bro file formats")
test_that("Bro app logs can be read", {
file <- system.file("extdata/app_stats.log", package = "webreadr")
data <- read_bro(file)
expect_equal(nrow(data), 1)
expect_equal(ncol(data), 6)
expect_equal(class(data$timestamp), c("POSIXct","POSIXt"))
})
test_that("B... |
760b3fbeb73d199bff9acd3d37e94a35201ab573 | 15895f0c1e41f82796d75cbcb9756f7a27ebaae7 | /run_analysis.R | 27515f348f9c5c9eb8e6496db17eaca1628ed161 | [] | no_license | BobWeerts/ds03Project | 97ace8fb1d24d3e3d6576546602c80a774aa6cce | b91b06911f096563fb20c5667722982ad80c7227 | refs/heads/master | 2020-06-03T20:22:10.821283 | 2015-08-23T23:00:21 | 2015-08-23T23:00:21 | 41,269,744 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,056 | r | run_analysis.R |
run_analysis <- function() {
# 1. Merge the training and the test sets.
s_test <- read.table('./UCI HAR Dataset/test/subject_test.txt')
s_train <- read.table('./UCI HAR Dataset/train/subject_train.txt')
s_merged <- rbind(s_train, s_test)
names(s_merged) <- "subject"
... |
1c5b4771bea807739c6b0766196cc989cc421670 | 1c9ffcb04f94e4306d373c1b769168d971ee2b48 | /inst/doc/DirichletReg-vig.R | bd2a9764c29b606d69f468d81266378dfcd71763 | [] | no_license | cran/DirichletReg | 6640d77d0d0e05b3ecf70141dbf327e89d2075b9 | 4f82dc0351c2dc249ebf46f40dc2b09395d84181 | refs/heads/master | 2021-07-04T22:55:11.813658 | 2021-05-18T09:30:03 | 2021-05-18T09:30:03 | 17,678,821 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 8,286 | r | DirichletReg-vig.R | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE
, comment = "##"
, tidy = TRUE
, fig.width=7
, fig.height=7
)
## ---- message = FALSE---------------------------------------------------------
library("DirichletReg")
head(ArcticLake)
AL... |
9d7316a77a8015c1cbd3ebea92d0c141d667f4d4 | 1026dbe5504954e22052548ef823fcd42c651770 | /R/3dDigitize.curve.r | eee9df1a11a7743638abc57c47ccf9e6f68988b5 | [] | no_license | alutterb/tkogl2 | b7081c26b30439276853a5227fa32c8eaeff0e5f | 799d432bdafb6d3f52d0024fd5a9027cd07e8067 | refs/heads/master | 2021-03-12T14:41:24.479925 | 2020-04-03T16:00:34 | 2020-04-03T16:00:34 | 246,627,475 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,864 | r | 3dDigitize.curve.r | ################# main data structure ##############################
#dgtDataList
#dgtDataList[imgId][[1]]: speciman dir
#dgtDataList[imgId][[2]]: font
#dgtDataList[imgId][[3]]: number of landmark
#dgtDataList[1][[4]]: curves
#dgtDataList[imgId][[5]]: template
#dgtDataList[imgId][[6]]: rotation
#dgtDataList[imgId][[7]]... |
9a39a342dfc8eaabc26c661f72e404137d1ecd07 | 3d55639c3f79aa24cee4852e0dd3448d6c2d48e0 | /plot6.R | c821938f66572d227c3b29972e079196cb7ea650 | [] | no_license | jlgzb/ExData_project2 | 2b701032ecdaa81cee58ce4f6eb3bf4ade02f36e | ef756092229bbb9fd0be08783ac278b6950b5c8f | refs/heads/master | 2020-03-25T21:11:53.353575 | 2018-08-10T00:22:14 | 2018-08-10T00:22:14 | 144,164,381 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | plot6.R | # week 4
# read the data
NEI <- readRDS("./data/summarySCC_PM25.rds")
SCC <- readRDS("./data/Source_Classification_Code.rds")
library(dplyr)
library(ggplot2)
NEI$year <- as.factor(NEI$year)
# extract the data of Baltimore City (fips == "24510")
dat_24510 <- subset(NEI, NEI$fips == "24510")
# extract the data of Los... |
a35da4d061c0f88e428c19c8ea81134add19fad0 | 22f3f3f959b0af491de1cc2cdd2d887343c93969 | /CLASS-CDA-ToDo/syntax/JTN Code/Water1-jtn.R | 4b0e981c090d6612fbaa3d45eafa5b581ec0ca8d | [] | no_license | EccRiley/CLASS-CDA | ffef431e2c32579c1b2e2d6d067308609e00cfdf | 5d74ca152e7553987d2ede3d6d9c9eed186e47bc | refs/heads/master | 2021-05-01T01:24:39.675352 | 2017-05-01T16:46:23 | 2017-05-01T16:46:23 | 72,780,877 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 723 | r | Water1-jtn.R | ### Logistic regression
r=c(32,38)
n=c(107,59)
sex=c(1,0)
counts=cbind(r,n-r)
model=glm(counts~sex,family=binomial("logit"))
result=summary(model,corr=TRUE)
result$coefficients
result$corr
### Predicted probability of sex=0
phat0=1/(exp(-result$coefficients[1])+1)
upper=1/(exp(-result$coefficients[1]-qn... |
ad702b606f6fea0cb19b3022bc1ca6db6a3fdd8a | c7a3ae8699dd590519951150fa170f2667e070ef | /R/vector_to_sqlfilter.R | 045a5bf699e7fd7c9ddfca4f755435b483bceadf | [] | no_license | leonardommarques/reliabilitytools | 4e4192d9a9d770083c76319aba996262929373f5 | 18c76ec38f5fd94ee149ec4aeb3fa392cfa1ad19 | refs/heads/master | 2020-03-20T18:46:49.045916 | 2019-07-30T00:19:38 | 2019-07-30T00:19:38 | 137,603,901 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,059 | r | vector_to_sqlfilter.R | #' AND SQL filter
#' makes an 'AND' filter statement
#'
#'
#' @param field_name The name of the fild in the data base.
#' @param values The values to be filtered
#' @return A \code{character} containing the SQL statement.
#' @details
#' @export
#' @examples
#' vector_to_sqlfilter(field_name = 'country',
#'... |
cedffca867baa62fd85a5046923bab46d4147e99 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/dils/R/GetSampleFromDb.R | be62ea1025d59dbd6032199d4e568f453225e44a | [] | 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 | false | 901 | r | GetSampleFromDb.R | #' Sample from the rows of a (possibly large) database table (NOT IMPLEMENTED)
#'
#' Access a database table directly. Return a data.frame whose rows are the sample.
#'
#' @param n numeric, size of sample to be taken.
#' @param db connection, connection to the database table containing the data.
#' @return data.... |
faf51b97270c054c2328eca6969a33910efd78be | fbea037a28a30155c133f0b872219e40746295dd | /Data2020.R | fc5496f61485a1423ece8ab1ad67eafb545e7a9a | [] | no_license | UMDDataChallenge200047/UMDDataChallenge2020Presentation | def863c0c67671ff48522e5a8b382588c5e2df28 | a662a0676e099b78200702eb3184721689845b84 | refs/heads/master | 2021-02-04T12:33:23.596737 | 2020-02-29T12:50:59 | 2020-02-29T12:50:59 | 243,666,584 | 0 | 0 | null | 2020-02-28T03:11:20 | 2020-02-28T03:03:31 | null | UTF-8 | R | false | false | 1,433 | r | Data2020.R | setwd("/Users/richa/Desktop/Random Crap/School Stuff/")
library(readxl)
HUDData<-read_xlsx("/Users/richa/Desktop/Random Crap/School Stuff/Data_Level2_HUD_HUDPrograms_Fulldataset.xlsx")
drops <- c("Year", "HEAD_ID","CHLDRN_AGE_0_3_CNT", "CHLDRN_AGE_4_5_CNT","CHLDRN_AGE_6_12_CNT", "CHLDRN_AGE_13_17_CNT",
... |
fa386b31460c03e4893d19f13cc208b1a0f0e2b2 | c26a15db12227206fe363d3807ca2b192f4df2bc | /man/sav_gol.Rd | 0cb65d4f6870bb6db086c57bdf13cf16656aa773 | [] | no_license | cran/RTisean | 52f3e67f0b18f8ed9a141841b70170fa43cf4e50 | f819f6b88aa814cdaa5d1d2f1411cee105c978d2 | refs/heads/master | 2021-01-01T05:49:40.303232 | 2011-12-29T00:00:00 | 2011-12-29T00:00:00 | 17,692,991 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,669 | rd | sav_gol.Rd | \name{sav_gol}
\alias{sav_gol}
\title{Savitzky-Golay filter}
\description{
A Savitzky-Golay filter to either clean the data from high frequency noise or to get
a better estimate of its derivative of a chosen order.}
\usage{
sav_gol(series, l, x = 0, c, m, n = "2,2", p = 2, D = 0)
}
\arguments{
\item{series}{a vector... |
62b73ddbc988a858c2fd51ae2f35a87461348197 | 95bf609fc05d2a5278449fcd969fbc4c558a8f1e | /Plot1.R | 2322a0f3e61c63f8b5856b29b7d52095a8fe0902 | [] | no_license | alialiyar/EDACourseraAssignment | 071769ce4f63e17dbd418d3ddcdb021bc9be29e0 | bd8577cec1a706f343d21469f937cd3bdfad85b7 | refs/heads/master | 2022-11-10T22:30:25.702965 | 2020-06-29T14:46:52 | 2020-06-29T14:46:52 | 275,674,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,053 | r | Plot1.R | # Making Plot 1 - Histogram of the Global active power
# Loads and adds the package "data.table"
library("data.table")
# Returns the current directory before the change, invisibly and with
# the same conventions as getwd.
setwd("C:/Users/Pabricio Marcos/Desktop/Coursera/curso")
# Reads the data GAP (Global active... |
9cebc967f9804ee447c3fdce5b21a2324270bb6c | d7fe0a47e83e3ccec46f2dbb30ba4e809bd81f61 | /anova.R | 9bc6829b31a28a46ac99ae7f69a588c8f86a0826 | [] | no_license | xenofonte35/Graphs | 6fc217a3c8f5f95e7a307a6b7d75f67f6e5b4349 | 0ed6351ddab97e82e798267ea962deff6f96f51e | refs/heads/master | 2023-04-28T18:53:09.413645 | 2023-04-13T15:22:12 | 2023-04-13T15:22:12 | 211,964,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 413 | r | anova.R | Mex<-c(MIST2$MEX)
Tur<-c(MIST2$TUR)
Ind<-c(MIST2$IND)
Combine_Groups<-data.frame(cbind(Mex,Tur,Ind))
Combine_Groups
Stack_Groups<-stack(Combine_Groups)
Stack_Groups
Anova_Results<-aov(values~ind,data=Stack_Groups)
summary(Anova_Results)
TukeyHSD(Anova_Results)
pairwise.t.test(Stack_Groups$values,Stack_Group... |
d2d02fafd3561ca79554d98697d6fa81c620a3a0 | cce85d168debacecc97c225c340fda2891772e1b | /ex3Jussi/ex32Jussi.r | 0ed5e59a7596515c72841a2dca286f83943b1d25 | [] | no_license | sisu/uml | e58de0c009e42750f52deba36a712d6e9b452a43 | 61023ce8ec1100be43d68559e92e51f33f8c4922 | refs/heads/master | 2021-01-25T05:23:13.236569 | 2013-03-24T12:02:37 | 2013-03-24T12:02:37 | 7,877,643 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,741 | r | ex32Jussi.r | ## Exercise set 3
# Exercise 1
plot_colour <- function(x1,x2,U1) {
# x1, x2 data vecots
# U1 contains the values of the projection (which we use for the colouring of
# the data in x1 and x2)
ncol = 50 # number of colours used
myCol = rainbow(ncol, start = 0, end = 5/6)
ra <-range(U1)
d <-(ra[2]-ra[1]... |
94c53fd4e7f1a507be44c7a0ca6a84925a85291b | 9d0396c164725f3b5dab9e1eec8828c30f2f9cd9 | /RawData/Script/DownloadData.R | 16a2a11e64c3e26b5e0373ace87bac23d67572f2 | [] | no_license | steven-tom/Stat-133-Grade-Distribution | 4c5de7820ab1a7532c4fb61585d1f16abf34a87e | e9c53b0581346461a8312b62ab7d4997989b49dd | refs/heads/master | 2021-01-01T05:07:33.569303 | 2016-04-22T04:20:24 | 2016-04-22T04:20:24 | 56,825,752 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 211 | r | DownloadData.R | ########################################
#
#Download the files into "RawData"
#
########################################
setwd("./RawData")
download.file()
########################################
|
1446e8c0c56411bf34f336ac448f96ecf6988bc5 | 47e52cf4f01a8139d89ba40128d6195173e49249 | /juice/man/end.TScanonicalPrediction.Rd | 9da0de8e8e8fd5ddf0bfffa070c37c1c5553d971 | [] | no_license | cran/dseplus | b63510aba28db0c03c824a3c71f4699dc2460d27 | cfde581aa373baf801b98603c65340599e232afb | refs/heads/master | 2016-09-05T19:55:26.678322 | 2006-10-04T00:00:00 | 2006-10-04T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 769 | rd | end.TScanonicalPrediction.Rd | \name{end.TScanonicalPrediction}
\alias{end.TScanonicalPrediction}
\alias{start.TScanonicalPrediction}
\alias{frequency.TScanonicalPrediction}
\alias{periods.TScanonicalPrediction}
\title{Specific Methods for TScanonicalPrediction}
\description{See the generic function description.}
\usage{
\method{end}{TScanonica... |
b17490b5145ccecd4642494eb053ddae5cad30d7 | 9267c5a23c403c2fc92f76c2ed27ac527f2877d0 | /summary_plots/chrom_summary.old/repeats.R | 33697ff18e78087254d18459822b12901f710f32 | [] | no_license | stajichlab/coprinopsis_PNAS_2010 | 039526b23609e9674071e654719e22cfefe4b854 | d9fda3a02a62fa2a769e2d3b43f5277f26115045 | refs/heads/master | 2021-01-10T03:39:40.735664 | 2018-05-07T13:45:23 | 2018-05-07T13:45:23 | 46,472,338 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 246 | r | repeats.R | repeatsarms <- read.table("repeats_arms.dat",header=T)
repeatsctr <- read.table("repeats_center.dat",header=T)
pdf("repeats.pdf")
boxplot(repeatsarms$TOTAL,repeatsctr$TOTAL,main="repeats Density BoxPlot", outline=FALSE, names=c("Arms","Center"))
|
36de06c18802428d9764a91f7487b2ea8f984946 | 29585dff702209dd446c0ab52ceea046c58e384e | /netmeta/R/setref.R | e1d07fecc76957a9ac52f503a7615b95c015c2bb | [] | 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 | 1,083 | r | setref.R | setref <- function(reference.group, levs){
if (length(reference.group)!=1)
stop("Argument 'reference.group' must be a numeric or a character string.", call.=FALSE)
##
if (is.numeric(reference.group)){
if (is.na(reference.group))
stop("Missing value not allowed in argument 'reference.group'.", call.=... |
e82f2d41fed2086d7cd0b265b16a42f71901aa80 | 69d799536643c4fb29a24dfa098ec6b19e76acd4 | /R/config.R | e51c6f0cb25ed63498a3c04a5fc7e010e359320f | [
"MIT"
] | permissive | PawanRamaMali/jinjar | 6234d9ed53c987fb659cd178ffaa78d5bb94dd8e | 658c7094b196bcc4810a58a119c517220087ccaf | refs/heads/master | 2023-08-29T10:31:48.280009 | 2021-10-26T04:16:03 | 2021-10-26T04:16:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,017 | r | config.R | #' Configure the templating engine
#'
#' Create an object to configure the templating engine behavior (e.g. customize
#' the syntax). The default values have been chosen to match the Jinja defaults.
#'
#' @note The equivalent Jinja class is `Environment`, but this term has special
#' significance in R (see [environment... |
6c935e44416055721d78917277a40813628e0880 | 916a2456f7e29af6403de6ae8dbf31c62f49e923 | /proj_part2.R | a0dc745aaa313cc205175b0352c9e388981c1639 | [] | no_license | AndreyDrv/stat_cour | 83ae4030a4b206ce79718abfcc908636ef13ee1a | d42d7a42aa1f488e89552bbdfd1f244928448b2a | refs/heads/master | 2021-01-10T18:40:59.567382 | 2015-05-22T09:42:23 | 2015-05-22T09:42:23 | 35,663,154 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,325 | r | proj_part2.R | ###########################################################################
# The Effect of Vitamin C on Tooth Growth in Guinea Pigs
###########################################################################
#
#The response is the length of odontoblasts (teeth) in each of 10 guinea
#pigs at each of three dose levels ... |
e04b1a6db9578eafe6bc71e0c034a6403c26a8e2 | 3f858f84495ae252181b9a32ef4807634c8afc93 | /rabbitGUI_code/ui.R | 064b59b1cd2d203204f722e8c191cc53e4041f0f | [] | no_license | anabrandusa/rabbitGUI | a8cb73edea9fbc0856034cf8831969a7c879adaa | f9947bf0b67270c6fccc7930f5f11c47af99c12c | refs/heads/master | 2021-01-12T12:06:25.545735 | 2017-03-02T18:42:40 | 2017-03-02T18:42:40 | 72,299,526 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,161 | r | ui.R | # 10/29/2016. Author: Ana Brandusa Pavel
library(markdown)
step.names=c("gene filter" , "feature selection" , "biomarker size" , "classification" )
step.labels=c("Feature filter" , "Feature ranking" , "Size selection" , "Classification" )
names(step.names) = step.labels
box.col = c("blue", "magenta", "orange", "brown... |
98a2ed0a11d708e67ba3a8898b15a7efa816c3e2 | 18f14d3e86a84aee6342b693d533882730ff19b8 | /man/aftbino.mbe.Rd | cabca816f74227199a14a0368f3617d52ec36563 | [] | no_license | leandroroser/Ares_1.2-4 | f645311542922659bde8dac27a4227b69d66b078 | 797c3960f46d5153354bcf6008aecc738f3365c9 | refs/heads/master | 2021-01-01T03:56:13.149249 | 2016-05-02T04:11:48 | 2016-05-02T04:11:48 | 56,180,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 282 | rd | aftbino.mbe.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/aftbino.mbe.R
\name{aftbino.mbe}
\alias{aftbino.mbe}
\title{aftbino.mbe}
\usage{
aftbino.mbe(count, estimated.richness = NULL, conf.level = 0.95)
}
\description{
aftbino.mbe
}
\keyword{internal}
|
641e273d8b641040de5e3bc1f378aab7ef91b194 | 24515ce15e7d005c952ad6f337722f2fd85af67a | /cachematrix.R | b0edef391c32d979585ae921a273adad5a7fca8b | [] | no_license | Avijit0616/DataScienceCoursera | bf9155ab8cee3e4cb64ff6b5ab3d784d4e6bfd0f | 9f0f7b4597227def94284aab9d5dac266da5160f | refs/heads/master | 2022-05-26T02:22:39.669088 | 2020-05-02T00:39:41 | 2020-05-02T00:39:41 | 256,111,840 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,393 | r | cachematrix.R | ## Below are two functions that are used to create a special object that
## stores a matrix and cache's its inverse.
## The first function, makeCacheMatrix creates a special "matrix" object
## in a list containing a function to
## 1.set the value of the input matrix in cache
## 2.get the value of the cache matri... |
0492c5ccb3ca2263e6c25393920a3de92839994d | 389899d13b1465958f48e85dba154418e7341429 | /cachematrix.R | ff02dac820423519b10caddb3be8462f85b6c760 | [] | no_license | what2do/ProgrammingAssignment2 | 8cbab3bf4a8d8d310e285a78452873d1e8d7b215 | 4a2df3478794b28af0d88a20b3d3f8e2c5e9aaf9 | refs/heads/master | 2021-01-20T21:56:46.679791 | 2014-09-17T17:37:21 | 2014-09-17T17:37:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,471 | r | cachematrix.R | ## Matrix inversion is usually a costly computation and their may be some
## benefit to caching the inverse of a matrix rather than compute it repeatedly
## Below are 2 functions that cache the inverse of a matrix.
## makeCacheMatrix creates a special "matrix" object that can cache its inverse
## It is a list contai... |
e12dd4c3a85ca8cc032db4568ef1a7a045960078 | 257b39265a6b796d54e0e861825984e7e205bbd8 | /man/z_HY.Rd | 82a22a3e61495eaff8b569389beeeb0cbdf52e5a | [] | no_license | yaoguodong/zFactor-1 | 230c8576f004efb6bde669c60e249fd36134ca4f | 66d6f0732e35c8e84bcd98d28251a0badc7fe423 | refs/heads/master | 2020-04-20T04:26:18.046950 | 2017-10-23T06:22:46 | 2017-10-23T06:22:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 331 | rd | z_HY.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{z_HY}
\alias{z_HY}
\title{Hall-Yarborough tidy dataset}
\format{An object of class \code{data.frame} with 28 rows and 5 columns.}
\usage{
z_HY
}
\description{
28 observations of 5 variables
}
\keyword{... |
7e95bc9fe2dec66c743c1b14eca9cfb772dae00e | f517f53080a1a833848b9fd3ff8cc2830a8d523c | /R/plot_qvalue.R | b1549a8d1ccaa2ad8edc2ab545939ffd609b3bf5 | [
"BSD-2-Clause"
] | permissive | PNNL-Comp-Mass-Spec/Rodin | a2b3ddadd312dde9a00e9f03c8deb65a42293579 | 8f93bc5f9e007744d19e3d60c76973aa3e8a115e | refs/heads/master | 2022-02-25T01:34:19.019930 | 2022-02-16T22:19:38 | 2022-02-16T22:19:38 | 144,644,879 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,247 | r | plot_qvalue.R | #' @title Plotting function for q-value object
#' @description
#' Graphical display of the q-value object
#'
#' @param x A q-value object.
#' @param rng Range of q-values to show. Optional
#' @param \ldots Additional arguments. Currently unused.
#'
#' @details
#' The function plot allows one to view several plots:
#' ... |
7ca8c9b26e80c69ef026535117b496e7bfbedb20 | 758d3d0b7ed4efe17f41e656bfa4c551ff4e5f63 | /TS.R | 9f3b5436dde9eb5972d5e360ca916405bd6556a0 | [] | no_license | szsongyj/time-series-analysis | a844b48ec5b8f5054f6bef194fcc4aea6f0d495a | e1c280c2b39b0eeadfaa331373367f235fe372d6 | refs/heads/master | 2020-06-18T14:17:12.989296 | 2019-07-11T06:12:31 | 2019-07-11T06:12:31 | 196,329,812 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 13,775 | r | TS.R | #-------------------------------------------------------------------------------
# Description: Time Series Analysis Forecast for AIoT
# Project Name:
# Name: Multi-Season Time Series Analysis
# Author: Song yongjun
# DateTime: 2019/7/5 14:49
#--------------------------------------------------... |
8aaa73d76fa46ac502a7cfd3c0733617aee5fbb4 | 710663bd84adf670030680db9358e29011eac5b4 | /app.R | 57f39c6e0781af726fdd5c3e41bdb8f774a90154 | [] | no_license | lanhama/Fama_French | 67632d9783f2d3e06761663b02449c3eb660fcdb | 034a75e82755e0f525f9039a9e5bb4f6cf57ae55 | refs/heads/master | 2020-06-05T01:54:24.270073 | 2019-09-09T00:30:46 | 2019-09-09T00:30:46 | 192,272,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,350 | r | app.R | library(shiny)
library(shinythemes)
library(jsonlite)
library(ggplot2)
library(DT)
library(pracma)
library(stringr)
#read in Fama & French information before app processing starts
annual_market_returns <- as.data.frame(read.csv("annual_FF.CSV"))
monthly_market_returns <- read.csv("monthly_FF.CSV")
daily_market_retur... |
f8b0cd8467b290a24aac8319f0c434f061a255ad | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/stringfish/man/sf_collapse.Rd | 4f0ccc894f8334340a12c7fccacb6d4dd8d8adf6 | [] | 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 | 736 | rd | sf_collapse.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zz_help_files.R
\name{sf_collapse}
\alias{sf_collapse}
\title{sf_collapse}
\usage{
sf_collapse(x, collapse)
}
\arguments{
\item{x}{A character vector}
\item{collapse}{A single string}
}
\value{
A single string with all values in `x` pasted t... |
fb98a0faa34fac74ae0197a9ecec904546653cf5 | dc11c41d4d7eaceb81b269a0c57ceba7d2a2d674 | /StaticalTesting_assignment3.R | 77e91364e64b713c97bde99b71a8f516787dfa2a | [] | no_license | Munish0123/spotify_data_analysis | cd1b04cab636d17db213a00da7da4bcd96d37890 | c35f1383b7fe08a8f901c4466b17dd4ee8e11712 | refs/heads/main | 2023-04-03T07:53:35.439476 | 2021-04-10T12:52:25 | 2021-04-10T12:52:25 | 356,309,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,804 | r | StaticalTesting_assignment3.R | tidyD <- read.csv(file= "/home/Documents/spotify/TidyData_assignment2.csv")
#hypothesis :- Rating and Liked(+ve Feedback) of products must be interrelated
table(tidyD$Rating)
table(tidyD$Liked)
table(tidyD$Rating,tidyD$Liked)
#checking statistical independance between rating and liked(+ve feedback) using chi-squared... |
da4225f4252b36287f49ea7537f9332d585a9f28 | 9a430b05c1e8cd124be0d0323b796d5527bc605c | /wsim.io/R/logging.R | 5bdc762ba6ffa7703c761238199b0148093aab4e | [
"Apache-2.0"
] | permissive | isciences/wsim | 20bd8c83c588624f5ebd8f61ee5d9d8b5c1261e6 | a690138d84872dcd853d2248aebe5c05987487c2 | refs/heads/master | 2023-08-22T15:56:46.936967 | 2023-06-07T16:35:16 | 2023-06-07T16:35:16 | 135,628,518 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,566 | r | logging.R | # Copyright (c) 2018 ISciences, LLC.
# All rights reserved.
#
# WSIM is licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License. You may
# obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicab... |
e96b45b4380316193a446ec31db6658309bc3481 | d0589a18766ddf5e3a10daf28b05a91bcf727959 | /scripts/relatedness_plots.R | d996c01e9068042d2cd0aeb3eff24a957065f866 | [] | no_license | devonorourke/wnspopgen | 3b2676ef63e764434dc8f071f82e50144c1bc33e | 0e3663b44bda4505bfc3000b7b5ced57df776579 | refs/heads/master | 2021-02-03T23:17:51.429013 | 2021-01-21T16:47:32 | 2021-01-21T16:47:32 | 243,570,308 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,397 | r | relatedness_plots.R | ## Script used to generate plots of kinship and relatedness using PLINK outputs
## kinship from 'plink --make-king-table'
## relatedness from 'plink --pca'
## written 12 Jan 2021 by Devon O'Rourke
library(tidyverse)
library(ggpubr)
library(scico)
library(ggrepel)
library(scales)
######################################... |
bd45d19cf72d58c60bdc83d4498f76f9c3e68a41 | e20ae46b59c09099a3b49e1ea018dfc855c11541 | /dev/dev_ancestry.R | 2150c4cbe3ec2d39e42276433960d7b3056ca683 | [] | no_license | Daenecompass/eliter | a081eb7f89ab34e9f2e34c3937e791c60a776873 | 884885a2e747e6cbb9560d4b11851ddc4e7c640b | refs/heads/master | 2023-03-17T18:19:04.883599 | 2020-11-16T21:25:50 | 2020-11-16T21:25:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 247 | r | dev_ancestry.R | # Ancestry ----
# An ancestry graph is a graph which only contains directed edges between CEOs and the chairmen who elected them.
library(eliter)
library(readr)
den.db <- read_delim(file = "~/Dropbox/GNA/Til DST/den_cvr.csv", delim = ",")
|
6be42bc566941a0095f36e076186e6bfd03fc06f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/NightDay/examples/plot.NightDay.Rd.R | e2ad1cb24f9a8503a7fffae6c816e43bced9d86f | [] | 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 | 203 | r | plot.NightDay.Rd.R | library(NightDay)
### Name: plot.NighDay
### Title: Night and Day Boundary Plot Funtion
### Aliases: plot.NightDay
### ** Examples
Time <- Sys.time()
timezone <- 1
plot(NightDay(Time, timezone))
|
4a795a1959d6d5ac101108815518c97abeb53d20 | 9468392003ae0b1c050b3717ea004427c364c4f3 | /plot-results.R | 0a3dca9c60874dc5003c9107d2fd3537a1e74034 | [
"Apache-2.0"
] | permissive | belugadb/druid-benchmark | 9193b8878a436a4e658f850b3f36ff106cef538e | 9e64579663bb3b398c93691a07c2590ed773da6f | refs/heads/master | 2021-06-20T17:30:58.304249 | 2017-07-13T20:27:57 | 2017-07-13T20:27:57 | 97,158,776 | 0 | 0 | null | 2017-07-13T20:27:58 | 2017-07-13T19:40:07 | R | UTF-8 | R | false | false | 3,948 | r | plot-results.R | library(plyr)
library(ggplot2)
library(reshape2)
benchmarks = list(
`druid` = "druid-m3-2xlarge.tsv",
`mysql` = "mysql-m3-2xlarge-ssd-myisam.tsv",
`druid-100-x1` = "100gb-druid-m3-2xlarge-1x.tsv",
`mysql-100` = "100gb-mysql-m3-2xlarge-ssd-myisam.tsv",
`druid-100-x6` = "100gb-druid-m3-2x... |
ba49b7918ccc0e00a3ccf5c8ba13d9ed5cf98677 | ff6198c86808f03b83d0476750f2ae79de2d9c85 | /abcstats/man/dnn_predict_slow.Rd | cab396ebd0e3e4d2df82ed7412713c6f7086ec11 | [] | no_license | snarles/abc | 5b2c727fd308591be2d08461add2ae4e35c7a645 | fefa42cf178fd40adca88966c187d0cd41d36dcb | refs/heads/master | 2020-12-24T17:35:48.864649 | 2015-07-23T06:07:14 | 2015-07-23T06:07:14 | 39,470,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 453 | rd | dnn_predict_slow.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/slow.R
\name{dnn_predict_slow}
\alias{dnn_predict_slow}
\title{Predict given DNN and tanh transfer function}
\usage{
dnn_predict_slow(Ws, bs, x)
}
\arguments{
\item{Ws}{Weight matrices of DNN}
\item{bs}{Bias terms of DNN}
\item{x}{N... |
78336c1802d827b853581c598fa92ede10c38389 | beb91d0e06e5b260011ea5c55da32ab21bece500 | /R/assoc.R | 936496684d0354c0cf5655a45da943d5c0d2a64d | [] | no_license | cran/vcd | 7169e004f662d4d33305a3b7d1246bba7058b924 | 86cb80436f2a1d4733905710f52c5f630a348cef | refs/heads/master | 2023-02-02T18:55:54.519633 | 2023-02-01T12:22:08 | 2023-02-01T12:22:08 | 17,700,741 | 6 | 2 | null | null | null | null | UTF-8 | R | false | false | 11,900 | r | assoc.R | #################################################################333
## assocplot
assoc <- function(x, ...)
UseMethod("assoc")
assoc.formula <-
function(formula, data = NULL, ..., subset = NULL, na.action = NULL,
main = NULL, sub = NULL)
{
if (is.logical(main) && main)
main <- deparse... |
71ff53a012716ccf2d8d04f4d03fe13326905893 | bd25f293d9bf61bd605946ea348e72c6f0cf6600 | /R/jh1511_1.R | c2071a51f1c60e6b462cca84c52556eb568f7110 | [] | no_license | toyofuku/StatisticalLearning | afbe5a453980bd485b75ba02df2a9a85f467d962 | d1204d99ad3c1019c284fca3b1e6dcdc0ab865dd | refs/heads/master | 2020-03-28T15:49:05.402057 | 2018-09-18T08:21:35 | 2018-09-18T08:21:35 | 148,628,929 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,063 | r | jh1511_1.R | ### Variational Bayes of Normal Mixture ############################
rm(list=ls())
##close all
#############################################################################
K0 <- 3 ### True clusters
STDTRUE <- 0.3 ### True Standard deviation of each clusters
K <- 3 ### Components of learn... |
af7fa055115e9b882b9b98b94a52b5f85656de34 | 4da1047966dc6e20ef0c3d967ffde604b0d7061a | /cachematrix.R | fee59d20d3bee8c4edebb1fa2da95fcc61f2c4ff | [] | no_license | djwf/ProgrammingAssignment2 | 0650265ed8d57b2710dead49a0fbf2298be86da1 | 12cee591c3ecc6cf9ee3401fe32bb0bcde679be8 | refs/heads/master | 2021-01-15T00:56:45.118170 | 2015-12-28T02:51:48 | 2015-12-28T02:51:48 | 48,467,062 | 0 | 0 | null | 2015-12-23T03:32:29 | 2015-12-23T03:32:28 | null | UTF-8 | R | false | false | 859 | r | cachematrix.R | ## Create method of storing matrices and calculating their inversions that only
## calculates the inversion for a given matrix once.
## Cached matrix data type (functions to store/retrieve matrix/inversion).
makeCacheMatrix <- function(x = matrix()) {
m = NULL
set <- function(y) {
x <<- y
m <<- NULL
}
... |
af33eaedf958b93b8cbca92997f2b2754332f3c3 | 492e3f5140509da0b6a150d7225eee33ed173c2b | /ref_sqlvis_raster.R | a7b1117283f4c335418c16246be52208a546d0b2 | [] | no_license | iamchuan/NYC-TaxiVis | 82c389791080b0cd7debc3eeceff5ca0e139b227 | efa087695268bc3036b92cf0c2b12628510fffff | refs/heads/master | 2021-06-20T04:55:53.592592 | 2017-07-27T04:52:32 | 2017-07-27T04:52:32 | 84,584,327 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,998 | r | ref_sqlvis_raster.R | ### Big data tile plot
# data <- tbl(sc, "trips_model_data")
# x_field <- "pickup_longitude"
# y_field <- "pickup_latitude"
# resolution <- 50
sqlvis_compute_raster <- function(data, x_field, y_field, resolution = 300){
data_prep <- data %>%
select_(x = x_field, y = y_field) %>%
filter(!is.na(x), !is.na(... |
b53b1043ea4722d77414c9ddaa8dc4f91eed24da | 683adeb90ce6051572ff073e5d0937f0743630d0 | /R_create_Visuals/dep_by_model.R | d9e86e4c0980cb99b69ac91a114c0f242dbe03eb | [] | no_license | lin1234227/EDA-Luxury-Sedan-Residual-Values | c007b848a97bb83f4ebf551b3e5c8267ac6e6b24 | 1dbca8486184ecc297b257b305d4e00ae5ae06ec | refs/heads/master | 2020-04-23T19:06:31.408324 | 2017-08-06T16:41:33 | 2017-08-06T16:41:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,043 | r | dep_by_model.R | cars <- read.csv("~/Document/data_science/Visual/R_create_Visuals/dataset/dep_by_model.csv", header=TRUE, stringsAsFactors=FALSE, row.names = 1)
cars['year'] = 2017-cars['year']
png(filename="~/Document/data_science/Visual/R_create_Visuals/png/dep_by_model.png")
cars1<-subset(cars, model_level==1)
cars2<-subset(cars, ... |
959f47fd208497d7c88c8c30d4d43c6a21265e9a | ced24eb85a914e223dbee72e3da09a2ec946bee1 | /plot2.R | 279f64151f44797b45aef96ba3e0a6b6a2de5064 | [] | no_license | Megan-Gee/ExData_Plotting1 | f29998b8663c8ad3c583a43d67877cf5f8a0d9d0 | 2a417bdb307fc3d291c6f53b7446595e5f2dbcb6 | refs/heads/master | 2022-11-14T00:35:20.604303 | 2020-07-07T22:40:02 | 2020-07-07T22:40:02 | 277,936,122 | 0 | 0 | null | 2020-07-07T22:36:41 | 2020-07-07T22:36:40 | null | UTF-8 | R | false | false | 692 | r | plot2.R | ## Read data file into R and subset to only two dates of information
full_data <- read.table("household_power_consumption.txt", sep = ";",
header = TRUE, na.strings = "?")
full_data$DT <- paste(full_data$Date, full_data$Time)
full_data$DT <- strptime(full_data$DT, format = "%d/%m/%Y %H:%M:%S")... |
8027310b4ab43ac56b5fbd1b5716201e6f04b8e1 | 04a98a7e184fd449985628ac7b8a92f19c1785a4 | /man/crs-package.Rd | b62283f3f970057df374940f7382ab4bb0657617 | [] | no_license | JeffreyRacine/R-Package-crs | 3548a0002f136e9e7c1d5c808f6a3867b20b417e | 6112a3914e65f60a45c8bcfc4076e9b7ea1f8e7a | refs/heads/master | 2023-01-09T18:23:59.615927 | 2023-01-03T16:20:22 | 2023-01-03T16:20:22 | 1,941,853 | 12 | 6 | null | 2023-01-03T16:20:23 | 2011-06-23T14:11:06 | C++ | UTF-8 | R | false | false | 3,677 | rd | crs-package.Rd | \name{crs-package}
\alias{crs-package}
\docType{package}
\title{Nonparametric Regression Splines with Continuous and Categorical Predictors}
\description{
This package provides a method for nonparametric regression that
combines the (global) approximation power of regression splines for
continuous predictors (\sQuo... |
f56a633949cade63db4a4d111a0f041e2f6d4a2a | 2b13c58d7b92b9299216cf3373d2aa074af21fd5 | /Unit 3 - Assignment/Predicting the Baseball World Series Champion (OPTIONAL) _ Assignment 3 _ 15.R | 8d9dec9bd78f4bda12895ad5d5dd06c0f008f350 | [] | no_license | florintoth/The-Analytics-Edge | 7408f520c675465d562220d776ac969ed9ccb696 | 3402da7f95517d4e6d231894e2a40052bcef034a | refs/heads/master | 2020-05-18T14:14:32.730173 | 2015-05-18T10:02:45 | 2015-05-18T10:02:45 | 40,883,090 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,397 | r | Predicting the Baseball World Series Champion (OPTIONAL) _ Assignment 3 _ 15.R | # PREDICTING THE BASEBALL WORLD SERIES CHAMPION (OPTIONAL)
# Problem 1
baseball = read.csv("baseball.csv")
str(baseball)
length(table(baseball$Year))
baseball = subset(baseball, Playoffs == 1)
nrow(baseball)
table(table(baseball$Year))
# Problem 2
PlayoffTable = table(baseball$Year)
PlayoffTable
names(PlayoffTab... |
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