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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5e529710f5b1e5ff2c0dd8b9a61baa628457f609 | 49db2824f0aaaddf55d2a656476d261a9290e22f | /man/row.names.as.col.Rd | fd1f776c6005f6a2151b6695bd429dbcf10fdc2c | [] | no_license | kuremon/lazyr | 47f02e34de1681ec8aeb45555a21d887cebc9875 | 8248d2b879f429947d781807bbd1456c8ccfc5c2 | refs/heads/master | 2021-01-23T10:44:47.161269 | 2013-12-17T07:03:14 | 2013-12-17T07:03:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 492 | rd | row.names.as.col.Rd | \name{row.names.as.col}
\alias{row.names.as.col}
\title{Add row.names to a data frame as a new column}
\usage{
row.names.as.col(data, position = 1, var.name)
}
\arguments{
\item{data}{the data frame}
\item{position}{position of the new column (1 by
default)}
\item{va.name}{name of the newly crea... |
d2ab4599872a2370cc294f4272338dc4d02b2ce2 | fa3f3612a143c184a7b1489d1281205bfed6aaaf | /LAND-SVA_v1r0b4_20171130.R | e419394f638cf88ff90e4d89331601e38c5710bb | [] | no_license | maurorossi/LAND-SVA | c7631a5a2bba47988657dee460b69af8039abe3b | 79be7f7799f7e09a038e55e6bccfbde09dc52e77 | refs/heads/master | 2020-04-02T00:22:47.595161 | 2018-10-22T13:41:29 | 2018-10-22T13:41:29 | 153,801,520 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,046 | r | LAND-SVA_v1r0b4_20171130.R | #########################################################################
#########################################################################
#### ####
#### ####
#### ... |
b17babace064bbedd0d8db1de3596178b165d4a3 | ce0c807647977f8125252a589fe49230493631db | /run_bayes_adults.R | 787a4260a3f740590e8896e1975f39759baad4cd | [] | no_license | kamilTuszynski/MOW | 4af4ad729ca12ddc4e620b9c7327d7eb4df2813b | 46cb824d021a85e2d0eb78437d3089a89a0701ed | refs/heads/master | 2022-09-09T05:18:06.691257 | 2020-06-05T17:28:41 | 2020-06-05T17:28:41 | 268,463,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,091 | r | run_bayes_adults.R | rm(list = ls())
library(caret,quietly = TRUE)
library(e1071,quietly = TRUE)
source("local_classification.R")
data = read.csv("adult_fixed.csv")
set.seed(12345) # for reproducibility
train <- sample(1:nrow(data),size = ceiling(0.985*nrow(data)),replace = FALSE)
data_train <- data[train,]
data_test <- data[-train,]
... |
f6685f1f44d324e4a6661030da36c7c89c6bcd5f | 1c591b580a42e90ba318675e3cebeabdfc06534a | /R/func__plr.R | 40471339643e047146513fb99a4ce23c9c87afa5 | [
"Apache-2.0"
] | permissive | wanyuac/GeneMates | 974d9a883d43ccd7602167204d8b3ff5bba6b74c | e808430b2cdd920f1b9abd8b6b59993fde8754a7 | refs/heads/master | 2022-08-31T13:00:42.583172 | 2022-08-08T10:01:02 | 2022-08-08T10:01:02 | 138,949,733 | 25 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,661 | r | func__plr.R | #' @title Firth's penalised logistic regression
#'
#' @description Fit pairwise allelic presence/absence data with Firth's penalised
#' logistic regression without a control for bacterial population structure.
#' Because this function is not the focus of GeneMates, it takes as inputs the
#' outputs of the lmm or findPh... |
075ec14280217f7bc607aa417a8f70302451f625 | 2bd971cc829a8639792f615d48fe143bd898a821 | /modules/Operations/Type/type_operation.R | 2a18608fd8ae4e56badb8a6560f38aee7526e5b7 | [] | no_license | DavidBarke/shinyplyr | e2acaf11585c3510df38982401fd83c834932e3d | ddc30c2c2361cec74d524f2000a07f3304a5b15f | refs/heads/master | 2023-04-20T07:43:47.992755 | 2021-05-11T10:56:49 | 2021-05-11T10:56:49 | 250,501,858 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,549 | r | type_operation.R | type_operation_ui <- function(id) {
ns <- shiny::NS(id)
shiny::uiOutput(
outputId = ns("op_container"),
class = "type-op-container"
)
}
type_subrows_ui <- function(id) {
ns <- shiny::NS(id)
shiny::uiOutput(
outputId = ns("subrows"),
class = "subrows type-subrows"
)
}
type_operation <... |
977866b3beef88c41de6e60e49dcfe6c63f987a9 | 03738314d1a665b54db4786b681246931eb62c2a | /Plot2.R | 12a221d6032c47967533ce71e96dcad07b3ba217 | [] | no_license | Kornwhalice/ExData_Plotting1 | 0161590ab5b58318444469eae87fd603439444fa | 05593b72a440acd6789060bf0576979181c90426 | refs/heads/master | 2021-01-17T08:46:19.303133 | 2015-03-08T23:10:36 | 2015-03-08T23:10:36 | 31,670,589 | 0 | 0 | null | 2015-03-04T17:40:05 | 2015-03-04T17:40:05 | null | UTF-8 | R | false | false | 417 | r | Plot2.R |
totData <- read.csv("~/Math 378/plotData/household_power_consumption.txt", sep=";", stringsAsFactors=FALSE)
partData=totData[totData$Date %in% c("1/2/2007","2/2/2007") ,]
date_time <- strptime(paste(partData$Date, partData$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
png("plot2.png",width=480,height=480)
plot(datetime, par... |
7b09bc106d0aa1dc7b5670223191bc9911199227 | 2e6f8e6eaf11f6e3fe622428dd3d4ce9b9185278 | /ctsmr/ctsmr-package/man/predict.ctsmr.Rd | 5e5b8eff388321b805e6b2e90b2cc2b34c10609d | [
"MIT"
] | permissive | perNyfelt/renjinSamplesAndTests | c9498a3eebf35f668bc1061a4c1f74a6bf8e2417 | 5140850aff742dbff02cd4a12a4f92b32a59aa25 | refs/heads/master | 2021-07-23T23:58:59.668537 | 2021-07-23T10:21:39 | 2021-07-23T10:21:39 | 202,578,093 | 1 | 1 | MIT | 2020-10-15T18:13:49 | 2019-08-15T16:45:33 | Fortran | UTF-8 | R | false | false | 775 | rd | predict.ctsmr.Rd | \name{predict.ctsmr}
\alias{predict.ctsmr}
\title{Predict method for CTSM fits}
\usage{
\method{predict}{ctsmr}(object, n.ahead = 1, covariance = FALSE,
newdata = NULL, firstorderinputinterpolation = FALSE, x0 = NULL,
vx0 = NULL, ...)
}
\arguments{
\item{object}{Object of class 'ctsmr'}
\item{n.ahead}{The numb... |
cfc0476175de4c5b8aae03767a5d074d19506731 | c0e8301d190b515ac7a390dd6e0f269e3ca3844c | /brms_modsel_func.R | 675a59e71e106e221c60922f70b0c0250fbddb2d | [] | no_license | tmerkling/TBMU-divorce | d2c0d4813b768452858fb509dd14e98d8d6462d3 | 089e24c44729dc7d5f4802c9f1535e0efd421d39 | refs/heads/master | 2020-03-22T20:20:50.604257 | 2018-11-26T16:40:55 | 2018-11-26T16:40:55 | 140,591,752 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,566 | r | brms_modsel_func.R | #brms functions for model selection on list of models
# function creating a WAIC table when models are stored in a list
waic_wrapper <- function(...) {
model_list <- list(...)
if(!"x" %in% names(model_list)) {
names(model_list)[1] <- "x"
args <- c(model_list)
}
do.call(brms::WAIC, args)
}
# function c... |
1e5f676b1f9d24f1d428f889661dc44a5efe2c4f | 5852d290393c0344e65d8f1722546e919ee48ebc | /plot4.R | 56b5b0518d7ef4681dc805fc0a00a84d74cfcef3 | [] | no_license | owenkern/ExData_Plotting1 | 621dbc5f5aa6181e997ddba6c93723d0fb95621c | ee955ba24041f63ceb71b6ee683f70fd2681bcdf | refs/heads/master | 2020-12-24T10:15:56.095107 | 2015-03-08T04:10:51 | 2015-03-08T04:10:51 | 31,831,313 | 0 | 0 | null | 2015-03-07T23:19:42 | 2015-03-07T23:19:42 | null | UTF-8 | R | false | false | 1,591 | r | plot4.R | filename <- "plot4.png"
data <- read.csv2(
"exdata_data_household_power_consumption/household_power_consumption.txt",
na.strings = "?")
charDate <- as.character(data$Date)
data$Date <- strptime(data$Date, "%d/%m/%Y")
goodDates <- c(as.POSIXlt("2007-02-01", format = "%Y-%m-%d"),
as.POSIXlt("2007-... |
a5fafce3fa17bd03dfa1a2a262ff7441ee9dfbd1 | f69d8832dcd0e0072a81847b59cf5c68c541708f | /inst/check/check_reflmaxcoupling.R | 24fd32f67fdb04d808c3390275f7d0976deb076c | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | shizelong1985/unbiasedmcmc | 5382f0bc1780193c9e72ce9962332c02645311f5 | 24dab0bf66597d45a82fd83e5302daf644164cae | refs/heads/master | 2022-10-15T03:15:44.833752 | 2020-06-08T09:32:59 | 2020-06-08T09:32:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,384 | r | check_reflmaxcoupling.R | library(unbiasedmcmc)
library(doParallel)
library(doRNG)
# register parallel cores
registerDoParallel(cores = detectCores()-2)
setmytheme()
rm(list = ls())
set.seed(21)
### maximal coupling of bivariate Gaussians with identical covariance matrices
mu1 <- c(0.2, 0.3)
mu2 <- c(0.0, 0.8)
Sigma <- diag(1, 2, 2)
Sigma[1,2]... |
6c3b41b429b3788c470528d636c133dd7b05baef | 008071d29a3524ca79b1af2fb79de92582d55f0c | /waitaki_waikato.R | c4a386b3944ef1c69fa9a528dfc61c2a0659a25a | [
"MIT"
] | permissive | merrillrudd/stream_network_NZ | be8a5bc4fcef8abe097c477254af2c29d314fe1f | ffe2778109c2851be5309cda46671897b13ab3af | refs/heads/master | 2020-05-25T08:34:00.850684 | 2019-12-05T19:00:30 | 2019-12-05T19:00:30 | 187,713,470 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,541 | r | waitaki_waikato.R | rm(list=ls())
################
## Directories
################
nz_dir <- "/home/merrill/stream_network_NZ"
sub_dir1 <- file.path(nz_dir, "Waitaki")
sub_dir2 <- file.path(nz_dir, "Waikato")
data_dir <- file.path(nz_dir, "data")
data_dir1 <- file.path(sub_dir1, "data")
data_dir2 <- file.path(sub_dir2, "data")
fig_dir... |
fd54a87adec527e0a6e7e2c0dfd8e1dd3b84c25f | 61c12c2ca9ce163ca6dd9715ffd33b50c3f5e32f | /FinalMS.R | 57ee66887a2ca2acd68f3d1616d42d04514f76d7 | [] | no_license | modestosierra/RProjects | 1b3b61030594aa5dc3e931166b17c94ef52eab08 | 493d5c07908ec6e2545a58a74688ebdc1bc281d5 | refs/heads/master | 2021-01-17T17:35:19.571583 | 2016-10-17T07:04:40 | 2016-10-17T07:04:40 | 70,426,132 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 959 | r | FinalMS.R | #load libraries
library(caret)
library(randomForest)
#load data and have a look at it, remove the columns with NA
trainingData = read.csv("pml-training.csv", na.strings=c("NA","#DIV/0!",""));
#summary(trainingData);
#remove columns without relevant info
trainingData <-trainingData[,-c(1:7)]
#do some cleani... |
dac6d52dfed15075ed7c5fe559f03f41bfa924d7 | caea54b3de3c4373bedffbd9d09e47e244636019 | /man/rhub.Rd | 65da3c9ffa37447b0aa0c3b0e17d76096324df42 | [] | no_license | ashiklom/rtcl | 66ce642dce90727c40f066b0ee8a2016da315e41 | 995519859544900ba2056a711cec8371da5a4bf0 | refs/heads/master | 2023-01-31T00:14:05.680419 | 2020-12-03T22:19:13 | 2020-12-03T22:19:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,000 | rd | rhub.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rhub.r
\name{rhub}
\alias{rhub}
\title{Upload package to rhub}
\usage{
rhub(platform = NULL, checkforcran = FALSE, rdevel = FALSE, path = getwd())
}
\arguments{
\item{platform}{[\code{character(1)}]\cr
Check on the platform specified here. Fo... |
768f6d32fae96446805aeb9c0c29c92e81c0131d | 67c59a878124ec9f1282a8b323fb01325661e9c8 | /NCAAF_L1_Model.R | c96f1a46c1c8fdbb72a937f6b50c58ae32f92d70 | [] | no_license | MattC137/NCAA-Football-Forecasts | 19bd37746b51628584f7178c267e770cbb0c124f | cce1dd83ec720ceb2501770bca147cc76a1cd9ed | refs/heads/master | 2023-01-08T09:05:25.663974 | 2020-11-09T00:54:07 | 2020-11-09T00:54:07 | 308,741,722 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,375 | r | NCAAF_L1_Model.R | library(dplyr)
library(readr)
library(ggplot2)
NCAAF_L1 <- read_csv("https://raw.githubusercontent.com/MattC137/Open_Data/master/Data/Sports/NCAAF/NCAAF_Level_One.csv")
NCAAF_L1_Teams <- read_csv("https://raw.githubusercontent.com/MattC137/Open_Data/master/Data/Sports/NCAAF/NCAAF_Team_List.csv")
#### Setup ####
NCAA... |
22b493fe55b811ecbdb6ee48d705b24443366ef8 | 7853c37eebe37fa6a0307e0dd9e197830ee6ac71 | /explorations/grid.R | 655a9df1b80234f440fedf5665f0c217acc3a671 | [] | no_license | chen0031/RCUDA | ab34ffe4f7e7036a8d39060639f09617943afbdf | 2479a3b43c6d51321b0383a88e7a205b5cb64992 | refs/heads/master | 2020-12-22T22:47:37.213822 | 2016-07-27T03:50:44 | 2016-07-27T03:50:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 656 | r | grid.R | library(RCUDA)
m = loadModule("inst/sampleKernels/set.ptx")
k = m$setValue_kernel
N = 1e7L
i = integer(N)
ci = copyToDevice(i)
# To get over N threads, we use 512 within a block for the maximum amount
# and then 256 x 128 grid.
# Would we be better off with a different break down of the grid or the block?
system.... |
1946c07101cf6f6deb4f802019aa4d1f389b5222 | 850898c179e63adf03e07ec066046e3eba524aee | /rcpp20popcount/tests/testthat.R | f39a46972ba38bd2e95094236cc9df138baf44eb | [
"MIT"
] | permissive | zettsu-t/cPlusPlusFriend | c658810a7392b71bbcd0fbf6e73fa106e227c0d0 | 8eefb1c18e1b57b1b7ca906027f08500f9fbefcc | refs/heads/master | 2023-08-28T09:29:02.669194 | 2023-08-27T04:43:24 | 2023-08-27T04:43:24 | 81,944,943 | 10 | 1 | null | null | null | null | UTF-8 | R | false | false | 76 | r | testthat.R | library(testthat)
library(rcpp20popcount)
test_check("rcpp20popcount")
|
d82f2d7a1aef7bb71d32258bb6b094713d5e1db7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ggstatsplot/tests/test_ggsignif_adder.R | 46b47633a7292fe8ddfa038686cbc51e544fa6f4 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,001 | r | test_ggsignif_adder.R | context(desc = "ggsignif_adder")
# ggsignif_adder works ----------------------------------------------------
testthat::test_that(
desc = "ggsignif_adder works",
code = {
testthat::skip_on_cran()
set.seed(123)
library(ggplot2)
# data
df <- data.frame(x = iris$Species, y = iris$Sepa... |
64264e82126ac285513c6ed2cc12d59b09def685 | fbf3cf0aff5ed4b6d29a325f3e3ef288d4da4152 | /lesson8.R | ae765f2822cf0fcac9a85d440713c254b71933d1 | [] | no_license | wonder2025/RExperiment | ffd2bc5ccf3bb60b17c2620d2449d3471edc09a2 | 0871f4c284d4eb625f3e574ecb65de128aed3a7d | refs/heads/master | 2021-08-22T15:12:42.630971 | 2017-11-30T14:09:12 | 2017-11-30T14:09:12 | 111,537,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,347 | r | lesson8.R | library(grid)
library(vcd)
counts <- table(Arthritis$Improved)
counts
barplot(counts, main = "Simple Bar Plot", xlab = "Improvement", ylab = "Frequency",horiz = TRUE)
Arthritis
counts <- table(Arthritis$Improved, Arthritis$Treatment)
counts
barplot(counts, main = "Stacked Bar Plot", xlab = "Treatment",
ylab =... |
8c3dee58b71357e0d7085f86bd027d2f9bf69b74 | c2e8ae8bbeb0db8af81e9955157e394fc518efaf | /cachematrix.R | cd4c39dbcef1fa6d2642241579fe35e60fddadb5 | [] | no_license | paulboys/ProgrammingAssignment2 | cf830b7bb008c21e6e55722073552db09c8a2fa6 | 6c3e94c8eace294314bebb54db38f26fef611261 | refs/heads/master | 2022-11-24T00:29:49.876442 | 2020-07-31T14:26:45 | 2020-07-31T14:26:45 | 280,183,450 | 0 | 0 | null | 2020-07-16T15:01:33 | 2020-07-16T15:01:32 | null | UTF-8 | R | false | false | 1,100 | r | cachematrix.R | ## This is a function to calculate the inverse of a matrix: the funciton checks
## to see if the inverse has already been calculated and cached. If so it
## retrieves the inverse from the cache. Otherwise it calculates the inverse.
## This is a function that creates a list:
#1)sets the value of the matrix
#2)g... |
406f117bbb27c0fce12568a58b080b4d160dfabe | cabe99c8d91575cad196a5e9244970971e15be5d | /main.R | e6a741dbae7721f8ca225d283df42aec9e4a2fe1 | [] | no_license | mikeyfatfree/ExploratoryDataAnalysis_Project1 | 052716224c96cffb1aa292c674c89900c3450050 | 5ccbac8b809d9fcf41b563fd1914cc87123fa6b3 | refs/heads/master | 2021-01-10T06:35:52.378645 | 2016-02-06T14:15:01 | 2016-02-06T14:15:01 | 51,204,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 208 | r | main.R |
source("data.R")
source("plot1.R")
source("plot2.R")
source("plot3.R")
source("plot4.R")
dataDir <- getwd()
power <- readFile(dataDir)
plot1.run(power)
plot2.run(power)
plot3.run(power)
plot4.run(power)
|
eff1fb6fb7d0608a16dbf483e252c13a07ff3b9b | d66b1c07135991de77c33af65e9317b519acac20 | /man/powercurve.t.test.Rd | cf5037ff0cfe49526385a891b5c03d21a64fd71e | [] | no_license | cran/smd.and.more | 6ad761925eaa249da76f20739d4131b8a6f0e22c | 14799ead6579561922b91cfae2f42ef9db566f39 | refs/heads/master | 2021-01-13T14:19:54.892234 | 2010-05-08T00:00:00 | 2010-05-08T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,009 | rd | powercurve.t.test.Rd | \name{powercurve.t.test}
\alias{powercurve.t.test}
\title{Compute a Power Curve with Colors}
\description{
From the sample size and either the within-cell or pooled standard deviation, or the two separate group standard deviations, automatically calibrate and calculate a power curve for the independent-groups t-test ... |
d92a314ca3b5f5cba91c2b2416c1f0373fa25bd6 | f0780bf1ab59bbbe076e063e677bc0885f11cd59 | /DA/R_DA/PLOTS/BoxPlot.R | 434d5540177999320e8204ed053f2b1576dea72f | [] | no_license | sanchayana2007/DataAnalysis | a7308643d8ea3e67aadfafab7a1d559f4d948880 | 3de3a1d5215861501d69879a13ff17b4f5bec3ac | refs/heads/master | 2020-04-12T05:44:08.040284 | 2019-03-28T01:41:06 | 2019-03-28T01:41:06 | 162,330,180 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 310 | r | BoxPlot.R | library(xlsx)
# Giving the full path
#PLOT 1
df=read.xlsx("D:/R_DA/sales-jan-2014.xlsx",1,header= T,sep=',')
head(df)
range(df$unit.price)
#hist(df$quantity,xlim = c(-5,30),ylim = c(0,110),xlab = "MEDV")
graphics.off()
boxplot(df$ext.price~ df$quantity,ylim=c(0,15),xlab="Price",ylab="Quantity") |
90b65283e0f297f849015f45f6019ec81a2366c9 | 017e1d3c8002e6b0835a97985168d6fb2bb652f0 | /R/dplyr grammar.R | 9173722d8dd553a7d35a125f1e46882efe1f6195 | [] | no_license | wnk4242/Rcheatsheet | e38baa4b09713c931caaef64eee5505b2b3a17b8 | 70054150c84b00affe6f525ce0f900755dd3e919 | refs/heads/master | 2021-07-26T19:55:23.175155 | 2020-07-03T14:19:32 | 2020-07-03T14:19:32 | 196,735,006 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 237 | r | dplyr grammar.R | #' tbl_df function template
#' @export
cs_tbl <- function(){
cat("\014")
cat(bold$blue('\n| Convert a data frame to tibble\n\n'))
cat(bold$red('Example:\n'),
"\ttbl_df(dataframe_name)")
cat(rep("\n", 3))
ask_dplyrpkg()
}
|
71f689414e4eb49ae989057d332f2a3a02c2370b | a915ca4b65a027649aed3f8fc3d968dd134ecd8a | /cachematrix.R | 2e76d8e90f830db40c1e64a9e228af25a514b8ea | [] | no_license | vandretti/ProgrammingAssignment2 | 69d56bc462e6c1efff193b9e2b40e731cd4cd765 | 6fc53076f977de8ce0ba785216080c6a92f8a192 | refs/heads/master | 2021-01-22T18:32:58.015639 | 2015-07-22T00:01:15 | 2015-07-22T00:01:15 | 39,473,763 | 0 | 0 | null | 2015-07-21T22:52:02 | 2015-07-21T22:52:01 | null | UTF-8 | R | false | false | 1,123 | r | cachematrix.R | # cachematrix.R
#
# This function creates a speical square matrix that is stored in a cache. Also, it
# also provide the following function to operate on this matrix:
#
# 1. set the matrix data
# 2. get the matrix data
# 3. set the inverse of the matrix
# 4. get the inverse of the matrix
makeCacheMatrix <- fun... |
a2c98c37175299cf5611db7d4f84aa2bb1c39c47 | c19b0be23216483ffaba0994f2ae78e5b9e0c000 | /plot4.R | 998cc6b740520a379d9e264a83d6f6566254b21e | [] | no_license | jlow2499/exploratory_data_analysis_project1 | d3f941dbcdfbe92e76dbb957529eb1eb5ae23cdf | 19116e1c54c1bb4d4b42dbc79a56b9b2702407a6 | refs/heads/master | 2021-01-10T09:49:11.381915 | 2015-06-08T11:40:31 | 2015-06-08T11:40:31 | 36,837,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,571 | r | plot4.R | ###Read the data frame into R
df <- read.csv("household_power_consumption.txt", sep=";",stringsAsFactors=FALSE,dec=".")
###subset the data for the dates of
df2 <- df[which(df$"Date" %in% c("1/2/2007","2/2/2007")),]
###convert DATE column to date format with time
DATE<-strptime(paste(df2$"Date",df2$"Time",sep=" "), "%... |
51c0e6424feb9c2bc296c6eaa5b25a78a1af03d3 | 675846a7beb6c118a83150d99cfe40e1e968323a | /tests/impl2/rpd.R | b313cd5c5f4e511f0c155d9535f566d91ed90c15 | [
"MIT"
] | permissive | wtong1989/PFSP | 281bd4e3df8c84479b4ae2e6b21599e6ddf6b249 | eec9c5adbebe041296a634dafdd996439fd70cfa | refs/heads/master | 2021-04-06T20:08:16.272604 | 2017-05-17T22:01:45 | 2017-05-17T22:01:45 | 125,261,216 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,701 | r | rpd.R | # average percentage deviations means
sa_arpd <- read.csv("../impl2/sa/arpd_sa.csv")
grasp_arpd <- read.csv("../impl2/grasp/grasp_arpd.csv")
# sa 50
mean(sa_arpd[31:60,3])
sd(sa_arpd[31:60,3])
# sa 100
mean(sa_arpd[1:30,3])
sd(sa_arpd[1:30,3])
# grasp 50
mean(grasp_arpd[31:60,3])
sd(grasp_arpd[31:60,3])
# grasp 10... |
edd8ff63f120cda5ddd3fb903df09127b8373a71 | 84640be8d6731e9e04cb0e67d9dd11452f374f29 | /herringSEICpulse.R | b2bbd13e4a98491496f2579b1f27114c074d5dd0 | [] | no_license | talbenhorin/Herring-VHS | 27c3d349ae4443ed15aca1e7efaa285fc3b97c6d | 148974b4a94b79fb97824461c54947e935f75e70 | refs/heads/master | 2021-07-05T00:57:09.389965 | 2020-10-15T20:11:01 | 2020-10-15T20:11:01 | 193,991,671 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | herringSEICpulse.R | rm(list=ls(all=TRUE)) #clears workspace
# Load deSolve package
library(deSolve)
# Create an SEIC function
seic.pulse <- function(t, x, params) {
qS <- params["qS"]
qC <- params["qC"]
gamma <- params["gamma"]
rho <- params["rho"]
beta <- params["beta"]
mu <- params["mu"]
c <- params["c"]
alpha <- param... |
7e0c222ac722f5fec7adb0764ca0e6957571db28 | 928ec37ec0bad12fbc77b21fdaaa0635c49b8cb1 | /man/generate_differences.Rd | fa9700d7aa5f5675339434cd3a1312a328a79d44 | [] | no_license | leonarDubois/metaDAF | 6d6032f56550303c99f847b67816ba5729a694c1 | 2c59a87bee23f8d83bac96e908ad445aa098ca62 | refs/heads/master | 2020-06-12T00:40:08.724888 | 2019-07-12T11:49:44 | 2019-07-12T11:49:44 | 194,138,439 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 938 | rd | generate_differences.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generate_differences.R
\name{generate_differences}
\alias{generate_differences}
\title{Generate artificial dataset with differences between 2 groups}
\usage{
generate_differences(count_table, ...)
}
\arguments{
\item{count_table}{a m... |
e363322bf2d96037b8cb83be37993373b74135d6 | 337e3a7e89bacae36ac06613b99bea5196c1b57f | /MyRProject.R | 10d15d7bcdd54d747a1289320a36cd3e122bb188 | [] | no_license | pnightsore/New_Rproject | 35ed69dc28513e09d8a9cd04cffc9cee7ebf396a | db4206152822beaf62c004d25e40c44fbcf5986d | refs/heads/master | 2022-06-17T06:44:58.349195 | 2020-05-06T09:45:59 | 2020-05-06T09:45:59 | 259,067,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,828 | r | MyRProject.R | #1. Set workingdirectory:
setwd("/Users/nic/Desktop/DSE/CODING_FOR_DATA_SCIENCE/R_project/data_text")
#2. Import the data.(in this case three dataset,DGP/GINI/refugee):
GDP<-read.table('GDP.txt',header=TRUE)
refugee<-read.table('refugee.txt',header=TRUE)
GINI<-read.table('GINI.txt',header=TRUE)
#3. Calculate the mea... |
ee1b2e92adf8d7ea41046362117c781b780bf86d | 57854e2a3731cb1216b2df25a0804a91f68cacf3 | /R/projects.R | 157eb3af2f3a979c2dbb41b7120b6efe0182a1e2 | [] | no_license | persephonet/rcrunch | 9f826d6217de343ba47cdfcfecbd76ee4b1ad696 | 1de10f8161767da1cf510eb8c866c2006fe36339 | refs/heads/master | 2020-04-05T08:17:00.968846 | 2017-03-21T23:25:06 | 2017-03-21T23:25:06 | 50,125,918 | 1 | 0 | null | 2017-02-10T23:23:34 | 2016-01-21T17:56:57 | R | UTF-8 | R | false | false | 5,687 | r | projects.R | #' Get the project catalog
#'
#' @param x a \code{ShojiObject} that has a project catalog associated. If omitted,
#' the default value for \code{x} means that you will load the user's primary
#' project catalog. (Currently, there are no other project catalogs to load.)
#' @return An object of class \code{ProjectCatalog... |
a95279d03f0aec4f2e3b240397adf80688653a34 | ef6190ce18343ce866942fb184ea165c908af074 | /learningCurvePlots.R | 53bccce07693b298c31b83778caacae6bcb8176b | [
"MIT"
] | permissive | vagechirkov/gridsearch | e91994967142812b6b902d6b8a8a78698b35ce85 | 00101202f448150f2a194b75908e6e2604475d4b | refs/heads/master | 2023-07-17T00:22:10.314630 | 2020-02-15T16:57:58 | 2020-02-15T16:57:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,125 | r | learningCurvePlots.R | #Analysis of 1D learning curves
#Charley Wu 2018
#house keeping
rm(list=ls())
#load packages
packages <- c('plyr', 'jsonlite', 'ggplot2', 'reshape2', "grid", 'matrixcalc', 'data.table')
lapply(packages, require, character.only = TRUE)
#################################################################################... |
1fed0cc2dfcb351596983226060efb6defa67413 | 44eaee03687104a21cb0b2548a994d7e2cc038f9 | /Bike Rental/bike_rental.R | 4cb90429fe234206f502ffe6985fef7b47105f79 | [] | no_license | bharat284/MyLearningData | 5757257ee45db9b6de18c75ceb63407e43b95b57 | c44323754c88779da9005fd3ab73ec64be40cbfb | refs/heads/master | 2020-04-25T20:50:27.849925 | 2019-10-29T18:31:18 | 2019-10-29T18:31:18 | 173,061,949 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,514 | r | bike_rental.R |
#install.package(c("dplyr","plyr","data.table","ggplot2","ggplot"))
library("dplyr")
library("plyr")
library("data.table")
library("ggplot2")
library("ggplot")
# load the dataset to R
bike<- read.csv("day.csv")
#Heading and summary of data
head(bike)
summary(bike) # From this data set season... |
527278f13aa9137e222fed422fcb87fca330b80f | 42c5613984794b9b9c08b792e6a1b91772613495 | /man/medjs.Rd | dff57370eca1009faa884e3b5838f89cb81a75cb | [
"MIT"
] | permissive | chrisaberson/pwr2ppl | 87b5c8ca9af5081613d8a49c76a9fea9cdd5de12 | 06a0366cf87710cb79ef45bdc6535fd4d288da51 | refs/heads/master | 2022-09-28T13:57:48.675573 | 2022-09-05T23:35:22 | 2022-09-05T23:35:22 | 54,674,476 | 16 | 7 | null | 2019-03-29T16:55:16 | 2016-03-24T21:10:28 | R | UTF-8 | R | false | true | 2,843 | rd | medjs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/medjs.R
\name{medjs}
\alias{medjs}
\title{Compute Power for Mediated (Indirect) Effects Using Joint Significance
Requires correlations between all variables as sample size.
This is the recommended approach for determining power}
\usage{
medjs... |
3fec73e1349603e3233682c3bb274ddd9bb76776 | ec447fc767bd08cd006629cdf36953fb20998081 | /ML/FirstSteps.R | f900c7383cedaff2311ba16b956447207ba8910a | [] | no_license | jancschaefer/thesis_code | 004ff34070f9773816e46b2435aa43535d648ee7 | 5472bfbd8f62c812f5a97eabaceec0024efa115d | refs/heads/master | 2021-09-15T13:07:25.440979 | 2018-06-02T21:21:04 | 2018-06-02T21:21:04 | 126,164,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 146 | r | FirstSteps.R | library(h2o)
h2o.connect('localhost',port = 54444)
converted <- h2o.getFrame('converted_dropped_parquet.hex')
converted <- h2o.na_omit(converted) |
98488c4cfd2c97da670af505c56fc83135126d50 | fb19754c596e722532d0b86d4cebc7bbce9c73c8 | /best.R | 900be8ba05cd5578ee2e5f1a3e3fd9ec89624cc2 | [] | no_license | yuchun-sc/HospitalRanking | b5118b7afcdf0d0dc550f4b99a3a5c4f99a37cd8 | c58c7613ba626ba5c78b39c4bb1ce7a154c80cbc | refs/heads/master | 2021-01-10T00:56:50.085860 | 2016-02-18T18:40:55 | 2016-02-18T18:40:55 | 52,029,560 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,133 | r | best.R | ## this function finds the best hospital in a state
## based on the outcome measure, if there is a tie,
## the first one based on the alphabatic order is returned
best <- function(state, outcome) {
## Read outcome data
data <- read.csv("data/outcome-of-care-measures.csv",
header = T... |
890e201efcb4fda795885f307c2e1526ffbe0ab6 | d4608310406b4a60580c47c0ccdfaf8c7e58cf22 | /PRS_threshold_selection.R | 791d86517a1c3f145453dc8e4897af99c0c3953c | [] | no_license | marieleyse/paper-Fall-2020 | d4c511a0cd318e6a10e547e5ce0fb697fb4bfa9d | 2e543d6c28015dbb2c9255eed0aa19d1588e18a7 | refs/heads/master | 2023-05-06T01:58:51.277656 | 2021-06-02T19:23:07 | 2021-06-02T19:23:07 | 297,434,580 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,781 | r | PRS_threshold_selection.R | #Marie-Elyse Lafaille-Magnan, PhD
#marie-elyse.lafaille-magnan@mail.mcgill.ca
setwd("/Users/Marie-Elyse/Downloads")
NEW = read.csv("MAVAN_48M_and_up_jun2020_new.csv")
sink('threshold_selection.txt')
fit1 <- lm(ADHD ~ PRS_0_5_adhd_child + PC1 +PC2 +PC3, data=NEW)
par(mfrow=c(2,2)) # init 4 charts in 1 panel
plot(fit1... |
cc82311c01c73f51103f6cf3aacebdd733b60f60 | 63e68a1da4c46ce031c57645b40f50ef5d656ae3 | /Functional_Fit/function_fit.R | 2508d45c1528d1c0abb24e8f5aa784844802b5e8 | [] | no_license | anooshrees/Numerical-Methods | 623065e90cdf71a59d6b49ab54694e370a945354 | 657fd7ef01bb0a3f73b048e8abe425ab45249d9c | refs/heads/master | 2020-03-23T00:39:56.295235 | 2018-07-13T17:53:43 | 2018-07-13T17:53:43 | 140,877,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,881 | r | function_fit.R | ##############################################
# Author: Anooshree Sengupta
# Created on: 10/18/17
# Description: Functional fit for linear,
# parabolic, and gaussian
##############################################
######## RUN ME #########
# Linear table test
line_table <- line_w_zero(-10, 10, 0.1, 3)
wr... |
b28eb5bee8cc83af7fa40c2d430904f2729a972d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rgr/examples/gx.ngr.stats.Rd.R | 531649b0fad6e51fc83120dff0e6c125a6b4675e | [] | 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 | 331 | r | gx.ngr.stats.Rd.R | library(rgr)
### Name: gx.ngr.stats
### Title: Computes Summary Statistics for a NGR Report Table
### Aliases: gx.ngr.stats
### Keywords: univar
### ** Examples
## Make test data available
data(sind)
attach(sind)
## Generate and display the results for Zn
table <- gx.ngr.stats(Zn)
table
## Detach test data
detac... |
5ec93093273dcd86f454d13027f1e14693a1c231 | 30510c10caf14f0a3b79c6d9502d6c5f24ade7fd | /R/play_n_games.R | 25b3e71457b073195f6d97f3301cfb7b0cf9c4a6 | [] | no_license | JayCastro/montyhall | 2e00432bc259ed8c06f197d6341de0ee820dc5e9 | 9ccb258ba291db74da6c18e0bcd4e01ffe87528b | refs/heads/master | 2022-12-14T05:16:57.653797 | 2020-09-18T18:05:37 | 2020-09-18T18:05:37 | 295,783,490 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 806 | r | play_n_games.R |
#' @title
#' Lets Play x 100
#' @description
#' Plays the game a hundred times
#' @details
#' PLays a hundred time and shows your results after those hundred and it determine
#' what had the better winning percentage: staying or winning
#' @param ...
#' n=100
#' @return
#' return( results.df )
#' @exampl... |
ed94ea5e87360e7dd02d8bbd530c0e385e2e1807 | 955b1a81f643451b9c73a2d86b9a46619820e955 | /tests/testthat.R | 963eaf9720354f9790276f2b29c179e698e5e6a0 | [] | no_license | venkatavarun-123/R_LAB3 | a0a9d4ee8a12b7f91d9985992fcdcb501755bc09 | 05496eca0c9f5dbd47f133296ae95314c0463d36 | refs/heads/master | 2023-07-31T17:16:14.567143 | 2021-09-13T17:12:12 | 2021-09-13T17:12:12 | 405,846,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 66 | r | testthat.R | library(testthat)
library(lab3package)
test_check("lab3package")
|
4ba07e35b74a981e3a8d15ab5edf73aeae046164 | aaf8f198f80a3b9ef27f52e067b1bcaba27ce6fb | /Rcode/Simhyd/Simhyd.R | 1190645331a571e433f6763e95b462055f6f4260 | [
"MIT"
] | permissive | WillemVervoort/VirtExp | 32fd96058ae74fd8347fad1835778883b230c774 | a7a35a7a3a152e307094ef07032b8659c91e54cb | refs/heads/master | 2021-01-11T23:51:16.311933 | 2020-11-29T22:29:18 | 2020-11-29T22:29:18 | 78,632,674 | 1 | 1 | MIT | 2020-04-19T04:02:13 | 2017-01-11T11:30:29 | R | UTF-8 | R | false | false | 24,845 | r | Simhyd.R | ## hydromad: Hydrological Modelling and Analysis of Data
## Rewrite
## willem V
## Including code for four versions of Simhyd
# references
# eWater: Podger (2004) https://ewater.atlassian.net/wiki/display/SD41/SIMHYD+-+SRG
# Chiew et al. (2002) Chapter 11 in VP Singh (ed) Mathematical Models of Small Watershed Hydrolog... |
7afb63d31330d70e6337ea63134c4e95dd43f45c | ce9039377b71f5f71981223379435a722a961e5e | /R/dive_place.R | fc2b871fd86b98085b9163c7092bc0cbb0912cc4 | [] | no_license | hareshsuppiah/SwimmeR-1 | 4cbc1023dd56aba1b0a06fc70b08a3d62f3022b1 | 6466ac3aeb1096d5654bbb4dc31f71b8bc013475 | refs/heads/master | 2023-07-17T05:33:53.593892 | 2021-08-14T19:00:02 | 2021-08-14T19:00:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,340 | r | dive_place.R | #' Adds places to diving results
#'
#' Places are awarded on the basis of score, with highest score winning. Ties
#' are placed as ties (both athletes get 2nd etc.)
#'
#' @importFrom stringr str_detect
#' @importFrom stringr str_to_lower
#' @importFrom dplyr slice
#' @importFrom dplyr ungroup
#' @importFrom dplyr grou... |
300257e3a55e2efc9710ba6f5dca84494a13bc2c | 7cc82f192fdc7dcc7b0b021fb20d30e036ca66b5 | /site-lisp/xml-http-request/docs/NEWS.rd | 7825acd2b4348da0a8e059e16a17642372775a29 | [
"LicenseRef-scancode-nysl-0.9982",
"ICU",
"LicenseRef-scancode-other-permissive",
"MIT",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-2-Clause",
"BSD-3-Clause"
] | permissive | hutoiti/xyzzy_config | 784a678e07b24f49b64651c472e69842e3073483 | 0c8026f0b41145bd77911f0eb41b19e822f32ccf | refs/heads/master | 2021-01-10T19:10:38.745218 | 2012-10-28T07:31:30 | 2012-10-28T07:31:30 | null | 0 | 0 | null | null | null | null | SHIFT_JIS | R | false | false | 3,489 | rd | NEWS.rd | =begin
=== 2008-07-12 / 1.2.1
xml-http-request 1.2.1 リリース!
: 新規機能
* なし
: 非互換を含む変更点
* なし
: バグ修正
* なし
: その他
* ライセンスファイルを同梱
=== 2008-03-30 / 1.2.0
xml-http-request 1.2.0 リリース!
: 新規機能
* 各リクエストメソッドに basic-auth 引数を追加しました。
Basic 認証のためのユーザ情報とパスワードを指定します。
(xhr-get "http://foo.c... |
02275f426afbdfaf68491fd8065cccbf611931bb | 301a3c7037c9e2b4e2ee5b2db3c6568a3392d1d1 | /R-basic-examples.R | da1d26f029028db659e7b42efc6acfca4d50e4f4 | [
"Apache-2.0"
] | permissive | meg-codes/R-notes | ba9cf82152179826ad9f183fc4754038d21074ec | 9c20cb71026a3b6ad70bb21fcb96848b0db5f896 | refs/heads/master | 2023-09-01T05:31:43.180426 | 2017-03-01T20:46:44 | 2017-03-01T20:46:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 919 | r | R-basic-examples.R | # Ex. 1 - Setting objects to use as variables, then printing the result to the
# console
a <- 1
b <- 2
a+b
# Ex. 2 - Showing how to concatenate data of type character
first_name <- 'Benjamin'
last_name <- 'Hicks'
cat(first_name, last_name, sep=' ')
# Ex. 3 - Demonstrating adding two numeric vectors of equal length
... |
5594c7b02e4cdf5ad136559aca11287b67bc43f4 | bb675cb410cbba93fc1d43cc9e859aac999f2a4a | /Session5_StatisticalConsiderations/Clukay_Genostats_Session.r | 9b2758804cc3ac2338914a45ab501c2b5fb13493 | [] | no_license | rAntonioh/AAAGs_2018 | b0263f0d7985b2e0049d693af1606285bace005e | 2656c0e9349b056faf3b86b52703c432a573c2b9 | refs/heads/master | 2020-03-24T20:10:05.679427 | 2018-08-02T17:57:28 | 2018-08-02T17:57:28 | 142,962,882 | 2 | 0 | null | 2018-07-31T04:37:59 | 2018-07-31T04:37:59 | null | UTF-8 | R | false | false | 11,296 | r | Clukay_Genostats_Session.r | ############################################################################################################
######## Part 0: Data Read-in ########
####################################################################################################... |
bac505951e1b0969483c3ade0bf8aea92ffc83c7 | c988f7ae36884541bf17394e02baa07a4bb88a00 | /man/docdb_update.Rd | 6f410191365c78e4e85696cc4450cc2804df7b5c | [
"MIT"
] | permissive | MhAmine/nodbi | 05d2cbe80135882c58ec15d3431429627970e96a | 42e06b4751b25360b85e7c8206de06f461778a73 | refs/heads/master | 2021-05-09T10:55:49.893028 | 2018-01-25T00:51:29 | 2018-01-25T00:51:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 697 | rd | docdb_update.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/update.R
\name{docdb_update}
\alias{docdb_update}
\title{Update documents}
\usage{
docdb_update(src, key, value, ...)
}
\arguments{
\item{src}{source object, result of call to src}
\item{key}{A key. See Details.}
\item{value}{A value}
\ite... |
7124fcfe2a37b73e80502cabb313bf98b196aed3 | 812720f93b43704a1bb00c16716c74e2e637fd4f | /man/poids.D.Rd | 7cca7db6a61ebf92c8024f982dc2f65f49a5671f | [] | no_license | cran/HAPim | 9df01704bb002f166674d189790fc19a59ecc789 | 8b189df9b1547d74bfbad541ed2c1af88b18054f | refs/heads/master | 2020-05-17T15:48:30.314265 | 2009-10-10T00:00:00 | 2009-10-10T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,066 | rd | poids.D.Rd | \name{poids.D}
\alias{poids.D}
\title{poids.D}
\description{
This function calculates the probability of no recombinaison between loci for each Bennett's desequilibrium.
The function can be viewed as an internal function.
The user does not have to call it by himself.
}
\usage{
poids.D(dist.test, pos.QTL, res.struc... |
6298ce3236df0be6abf059daedb53107afa606d1 | 459199610ff49bd7bfe0cb0fe9d9b12cce9fc031 | /man/rg_geom_equation.Rd | caf9db934299a4d2e3edff5f680af5ddef9ee8f3 | [] | no_license | sitscholl/Rgadgets | 4e8e5c493adbc045264bb35db5b8fefcd4530231 | b142de2c996f3c0773951e43813c8cf0713bf77d | refs/heads/master | 2023-03-10T11:08:12.905825 | 2021-02-18T10:50:44 | 2021-02-18T10:50:44 | 275,093,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 857 | rd | rg_geom_equation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_geoms.R
\name{rg_geom_equation}
\alias{rg_geom_equation}
\title{Adding model equation to ggplot. Wrapper around \link[ggpmisc]{stat_poly_eq} with standard options for label}
\usage{
rg_geom_equation(formula, elements = c("eq", "r2", "p")... |
265df7e5484bd58c71cfc276e2c9b909caa9903c | 44e47ad78f8c4588a4b0dc5813a79eda6fa04a24 | /src/labelerApp/shinyHelpers.R | a55c6fde6f13233f0c7212ced97f577018f27a50 | [
"MIT"
] | permissive | vrodriguezf/ESWA-2017 | 895ec5e66e1592b9bef667e3a040e8b02846a5c0 | 9778cf54724b6c55f68dfe77bbfc206aab769730 | refs/heads/master | 2021-06-18T05:13:37.510292 | 2017-06-30T16:34:41 | 2017-06-30T16:34:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,579 | r | shinyHelpers.R | shinyHelper.simulationTS <- function(simSeries,
simulationID,
measureResolution,
measures = "all",
incidentsDrawing=TRUE,
markExecutionStart... |
4d1684e18b080f6016f0f3e5a3368650fec23a3c | 057ac9d20c897349eacfb47405136bdb8e0e071f | /man/instantData-class.Rd | 176483ea69e119be12f2dba273e516b268eeb5a7 | [] | no_license | cran/portfolioSim | a588b186a93b60474b2b24c50d2c3887d4cde85a | 30dd4328db735ab0226e9ddd04f9b70f0df0a2e8 | refs/heads/master | 2020-05-17T11:25:32.863031 | 2013-07-08T00:00:00 | 2013-07-08T00:00:00 | 17,698,675 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,922 | rd | instantData-class.Rd | \name{instantData-class}
\docType{class}
\alias{instantData-class}
\alias{saveOut,instantData,character,missing,character,character,logical-method}
\title{Class "instantData"}
\description{Contains coross-sectional simulation data that pertains to a single
instant in time, such as held positions and exposures.}
\sec... |
2113529ba2458e4cea4371ddf3dbad84c3ab1dcf | 11d7628851051881f1790e9e3929c4b9925593c7 | /run_analysis.R | f6e8fae5384646ba2612facfed16524718a5515c | [] | no_license | DongjunCho/Cleaning_Data_Course_Project_Coursera | 8e941470ce1730d7c52e84cd4f00c66e085580d1 | 7a2794a0a46c0c45aab26cca8f81547eaebb1376 | refs/heads/master | 2022-11-07T16:33:59.564911 | 2020-06-28T02:26:40 | 2020-06-28T02:26:40 | 275,487,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,000 | r | run_analysis.R | #load package from url
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
filename <- "getdata-projectfiles-UCI HAR Dataset.zip"
path <- getwd()
#if file doesn't exit, download from url
if(!file.exists(filename)){
download.file(fileURL, file.path(path, filename))... |
0d1117bed21f9002021b05df8144af2248db17f2 | 81af737c7f206593c900d3973d8d175dc50ac8af | /man/swapcheck.Rd | 5a71fc266ea047e4f874fc6bf268f7264f4fbb26 | [
"MIT"
] | permissive | CambridgeAssessmentResearch/POSAC | df607fd2f1cc6cd0d86a24212f28f471cdb89fb2 | 6aaeecfc76e643bde973c613bcdef14588e0431a | refs/heads/master | 2021-10-23T16:02:08.065883 | 2019-03-18T15:29:14 | 2019-03-18T15:29:14 | 109,018,236 | 0 | 0 | MIT | 2018-07-10T15:15:46 | 2017-10-31T15:50:29 | R | UTF-8 | R | false | true | 2,234 | rd | swapcheck.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/swapcheck.R
\name{swapcheck}
\alias{swapcheck}
\title{Function to explore whether swapping the location of any two patterns may improve POSAC performance}
\usage{
swapcheck(X, Y, patmat, freqs)
}
\arguments{
\item{X}{The initial X va... |
7e09ef241bb2dd9fae1bb588bb5fcf44a9133fc7 | 9fcc89d58b20b0b24f725e4f751f0e737307892b | /cachematrix.R | aa09710dccc10fbfd58cde86c00fb532cd8c9582 | [] | no_license | si48github/ProgrammingAssignment2 | 46530cf17ef353c1c03fcb1434243bd9ac1e9d04 | da7c8d7d6feadf69d51bd528667a23cdcc6e467c | refs/heads/master | 2020-12-11T09:06:27.907862 | 2015-04-26T17:24:27 | 2015-04-26T17:24:27 | 34,620,422 | 0 | 0 | null | 2015-04-26T16:33:24 | 2015-04-26T16:33:23 | null | UTF-8 | R | false | false | 1,697 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
##
## R programming assignment 2 requires a function to cache the inverse of a matrix
## Write a short comment describing this function
## Sunder Iyer comments
# Function makeCacheMatrix follows the same approach as makeVector
# It t... |
cb4a3123186871a3e1c9421864a2ceb51065f6a8 | a7c0df9746dbcb234326dcee25bef9ad58335721 | /Spotify-Data-science-Exam_github.R | aae66ef289e9629c7de5df837816d80f1d14e120 | [] | no_license | nireshr/Spotify-Data-science-Exam | 8c0998e9465dbeda5f2168f07fc5faf18e8937d0 | 9f122b39b2c64cce965fdd7d0bbf5fa629dde56b | refs/heads/master | 2023-05-01T09:35:47.487390 | 2021-04-29T00:57:39 | 2021-04-29T00:57:39 | 362,647,340 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,349 | r | Spotify-Data-science-Exam_github.R |
install.packages("funModeling")
install.packages("data.table")
install.packages("kableExtra")
install.packages("radarchart")
install.packages("fmsb")
install.packages("DT")
library(tidyverse)
library(dplyr)
library(ggplot2)
library(plotly)
library(corrplot)
library(factoextra)
library(plyr)
library(funModeling)
libra... |
3af844baa26f9585611052c1a6ac1784e7f4497d | 49f0f65c402374763d297c59fcf5e53b74c77029 | /R/rk3g.r | 87e27b30d5c767bda67bfefdcb7dd99fb0e549b1 | [] | no_license | mu2013/KGode | 259192727cbe61dcb219997ff411b1b79a557ea1 | 2816e7facf92b465a808727e9214746d5d2dae3e | refs/heads/master | 2021-05-04T18:43:32.307550 | 2020-06-23T16:30:23 | 2020-06-23T16:30:23 | 106,051,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,673 | r | rk3g.r | #' The 'rkg3' class object
#'
#' This class provides advanced gradient matching method by using the ode as a regularizer.
#'
#' @docType class
#' @importFrom R6 R6Class
#' @export
#' @keywords data
#' @return an \code{\link{R6Class}} object which can be used for improving ode parameters estimation by using ode as a r... |
26d738a0646278889681291a972e991b730ed168 | 4052e087fec60c5073764fdb4ff873ec548a6e2b | /DatabaseConnector/tests/testthat/test-connection.R | ac1dd2dbd0133f14758d57643e0c507872712016 | [
"Apache-2.0"
] | permissive | amazon-archives/aws-ohdsi-automated-deployment | 1350dcd1d1f5fce0287e0c2f673b0ca0bbb539f2 | 8deb86143a967b32cb55caab624aec526df11a40 | refs/heads/master | 2022-04-01T02:26:35.043472 | 2019-09-12T14:10:11 | 2019-09-12T14:10:11 | 124,141,866 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,928 | r | test-connection.R | library(testthat)
test_that("Open and close connection", {
# Postgresql
details <- createConnectionDetails(dbms = "postgresql",
user = Sys.getenv("CDM5_POSTGRESQL_USER"),
password = URLdecode(Sys.getenv("CDM5_POSTGRESQL_PASSWORD")),
... |
8839d84af0ae133f771143168f4cb4256b961bbe | b73e8cd3cf80162717d9f22227dc25f735e6bd54 | /R/rowMatch.R | 439c1e6360e50f32bd4b56b666087f5b4019e0d6 | [] | no_license | cran/BANOVA | c4c73a55f282f748ffa8b62cfe6c62212dcdacd7 | ddc0fc39ecf17895e841917b2df9ca1c0d7d2f17 | refs/heads/master | 2022-07-07T14:05:51.380634 | 2022-06-21T06:30:13 | 2022-06-21T06:30:13 | 21,282,366 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 221 | r | rowMatch.R | rowMatch <-
function(vector, matrix){
if (length(vector) == 1) return(match(vector, matrix))
for (i in 1:nrow(matrix)){
if (sum(vector == matrix[i,]) == length(vector))
return(i)
}
return(NA)
}
|
d0da840f4d5847462dc346e518244331a653de79 | 7f141116154eed50968bddd35c9a47b7194e9b88 | /man/true_simpson.Rd | f3dc1465945ab8918c8ec1299029fc7940c3f25d | [] | no_license | adw96/breakaway | 36a9d2416db21172f7623c1810d2c6c7271785ed | d81b1799f9b224113a58026199a849c2ec147524 | refs/heads/main | 2022-12-22T06:20:56.466849 | 2022-11-22T22:35:57 | 2022-11-22T22:35:57 | 62,469,870 | 65 | 22 | null | 2022-11-22T22:35:58 | 2016-07-02T21:10:56 | R | UTF-8 | R | false | true | 493 | rd | true_simpson.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/alpha_true.R
\name{true_simpson}
\alias{true_simpson}
\title{Calculate the true Simpson index}
\usage{
true_simpson(input)
}
\arguments{
\item{input}{A vector of proportions.}
}
\value{
The Simpson index of the population given by input.
}
\d... |
e475f8bc216fe7a5d3b77683c5826ef3c3931763 | bd55979bc72fb0276f3d0e31c7607b13577e0ebd | /Flight/load_2020_flight_data.R | 3f4b8cbcdbed80b7e0eb6899e43237ccddea1dcb | [] | no_license | arestrom/intertidal | d13b9f4df03f9de3edde3b5456dbb6a4b15056e0 | f78627d14d31e2e0eb94bd8fa381072fbdf99bfa | refs/heads/master | 2023-06-16T11:39:22.728506 | 2021-07-10T22:35:00 | 2021-07-10T22:35:00 | 306,503,719 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 45,989 | r | load_2020_flight_data.R | #=================================================================
# Load 2020 flight data to shellfish database
#
# NOTES:
# 1. Load BIDN data first. Then create files for FlightProof program.
# 2. Need to enforce consistent naming and EPSG codes for GIS data.
#
# ToDo:
# 1.
#
# AS 2020-10-29
#=====================... |
c3dfa87044c5fea26c4d2002de55f8fba9abfbc5 | 11ab32074034da3e5a2d2d1235ea6bbc3241366a | /R/compileDatabase.R | 09da57163607354392a45695f1aa011258b8c263 | [] | no_license | powellcenter-soilcarbon/soilcarbon | 0b3e79c324d0f7e192fc5069366a1e47604e354c | 99781ba2bb2161ff44b8965f67149f7a7ba635bc | refs/heads/master | 2021-01-11T15:09:05.004344 | 2018-08-23T10:52:17 | 2018-08-23T10:52:17 | 80,300,357 | 17 | 8 | null | 2019-07-01T18:54:43 | 2017-01-28T17:58:47 | HTML | UTF-8 | R | false | false | 1,558 | r | compileDatabase.R | #' compileDatabase
#'
#' adds dataset to soilcarbon database
#'
#' @param dataset_directory directory where compeleted and QC passed soilcarbon datasets are stored
#' @export
#' @import devtools
#' @import stringi
compileDatabase <- function(dataset_directory ){
requireNamespace("stringi")
data_files<-list.files... |
87d641ba04d4080b679d67afb7cbbd34cf69b76c | aac30b537cb879a203a73a44b155cfcf35c7574f | /server.R | fb9300c5271c608414dcfdc0a1c9df38a9a8ccb8 | [] | no_license | sohammishra/Project1_ShinyApp | efb3941920c5737effddb2a0ebf99b83a8d7c644 | 49a41d7a4a5afbc4959f9861fb4fbc5b2650f945 | refs/heads/master | 2022-05-26T05:37:26.789649 | 2020-04-25T23:27:00 | 2020-04-25T23:27:00 | 257,772,657 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,242 | r | server.R |
server = function(input, output, session){
g_rate = reactive({
tidy_time_series %>%
filter(state %in% c(input$state, input$multistate_growth)) %>%
filter(date >= '2020-03-15') %>% #& date <= '2020-04-23') %>%
group_by(state,date) %>%
summarise( totalperday = sum(cases)) %>%
... |
97814baa56b9054587ff1fb3de37b32fa2f90ff5 | df70da3406df1a50a08d28bcf60664acdf216640 | /R/simulate.R | 5c873b8c6ca8c5d53f0fd786dcaf64be2aa62adc | [] | no_license | cschieberle/lifeCourseExposureTrajectories | 4e03c7e60b5c33294a74a1d2985f5f322c090193 | cfa7bfca809ae49fec6c4e5d824c6a774b2a086d | refs/heads/master | 2020-03-18T06:19:07.717291 | 2018-06-13T08:05:58 | 2018-06-13T08:05:58 | 134,388,199 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,635 | r | simulate.R | #' @export
data.alphabet <- c(
seq(1, 11),
adolescModelAlphabet()
)
#' @export
data.labels <- c(
"Employee working full-time",
"Employee working part-time",
"Self-employed working full-time (including family worker)",
"Self-employed working part-time (including family worker)",
"Unemployed",... |
c0d9bdf19659cc8a87de6d6a7b206ecf4afd067e | 9462c1674ac612f23b6d73548b79f75751254997 | /cachematrix.R | 73d81294b832195cdcc76e04c58d2e7f316cc9d8 | [] | no_license | pavtok/ProgrammingAssignment2 | 7dbce39b905c2862adfdd633e11ddd3b85d91d6e | 7e418a2336f55cd7150917e45139118fe4405768 | refs/heads/master | 2022-12-11T01:02:51.247135 | 2020-09-13T23:03:06 | 2020-09-13T23:03:06 | 294,815,733 | 0 | 0 | null | 2020-09-11T21:29:31 | 2020-09-11T21:29:30 | null | UTF-8 | R | false | false | 1,653 | r | cachematrix.R | ## Matrix inversion can be a time consuming task.
## That's whay this pair of functions can be used to perform this calculation,
## stores it in cache and use it without having to calculate it again.
## The function 'makeCacheMatrix' will prepare the necessary data and internal
## functions (set, get, setsolve and ge... |
4f8ea0a66ddd82ca2f81faae9459bf5ac5a72e41 | 76acdfc6d4faeaa150864dee02d7433b41dc408a | /COURS ET TP STATISTIQUE COMPUTATNELLE/Cours et TP/Corrigés de TP/TP2_solution.R | 9aac9660b9857e360ea3a39df614598827c6c615 | [] | no_license | komiagblodoe/M2-SSD | ca5ff6eb8dac15247042605883cc66bbf644173f | 961a4a1b0c1648a6ddf1d7ac41e7657c230132b3 | refs/heads/main | 2023-04-16T08:22:24.389417 | 2021-04-23T08:10:13 | 2021-04-23T08:10:13 | 360,799,679 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,672 | r | TP2_solution.R | ##############################
##### STARTING EXERCICE 1 ####
##############################
sigma1 = 1
sigma2 = 10
n = 20
p = 0.9
sigma.smple = sample(c(sigma1,sigma2), size = n, replace = TRUE, prob = c(p,1-p))
x = rnorm(n, mean = 0, sd = sigma.smple)
n = 20
m = 1000
p.list = c(1,0.95,0.9,0.8,0.7,0.6,0.5)
... |
988d1f1704a0a935c2e55fb298b2c4fe58c51c9d | 307daa5d64a3e1e5ab2b6e4e85c83133e9d43e7b | /man/getMortalityData.Rd | 88cc6908a1eabd7052e8d143dd697bbf092678d4 | [] | no_license | hkim207/ahri-1 | 80be69695ee74acb2f10fc9b63b74998f02e04f3 | d2a671671c1b8cf66bc97d6d7135581254ebb30e | refs/heads/master | 2022-12-13T16:51:37.618854 | 2020-09-26T20:05:22 | 2020-09-26T20:05:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 637 | rd | getMortalityData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calcMortality.R
\name{getMortalityData}
\alias{getMortalityData}
\title{getMortalityData}
\usage{
getMortalityData(Args, startVar = "HIVPositive", dropHIVPos = FALSE)
}
\arguments{
\item{Args}{see \code{\link{setArgs}}.}
\item{startDate}{str... |
020a801a16c06a863c405ec6775da28cdc0954f3 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.compute/man/ec2_describe_scheduled_instance_availability.Rd | 3234eba6b00ddb1e4b4bedfdb0cdda86e4bc7180 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 4,521 | rd | ec2_describe_scheduled_instance_availability.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ec2_operations.R
\name{ec2_describe_scheduled_instance_availability}
\alias{ec2_describe_scheduled_instance_availability}
\title{Finds available schedules that meet the specified criteria}
\usage{
ec2_describe_scheduled_instance_availability(... |
9ec8a06f7b6db9f1da0c3a379bf99aec1597670e | c897422cfad7d729ce60609a32eaa3252d041854 | /server.R | 529c70332db423596781270905087eb30900c1c1 | [] | no_license | Srinivasankrishnan27/sri-project-alpha | 4c3ef88c30099c0b09b3a3b9382f7df95647603b | b7bf5ea66ee42dd84b6507729f8b2632d2685a5c | refs/heads/main | 2023-06-29T19:30:10.485162 | 2021-07-15T05:43:08 | 2021-07-15T05:43:08 | 386,174,785 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | server.R |
# Define server logic required to draw a histogram
shinyServer(function(input, output, session) {
data <- reactiveValues(raw_input = NULL)
observeEvent(input$data_input,{
output$show_stats <- reactive({FALSE})
output$raw_data <- renderDataTable({
inFile <- input$data_input
if (is.... |
ec12fa5146a85ce32a46885926dc1c30f186724c | cdbdfa2809213938a9fefd8bdd304a2cb5ad6278 | /R/rcustom.R | db5a6a46fad026b1256633626ec60b466bf0f919 | [
"MIT"
] | permissive | DavisVaughan/almanac | 49491a478e3bcdfae801111e5263efc86c33a3fb | 7b14f6e8f1e685975231e5dadb40bb5bb8f2a9c8 | refs/heads/main | 2023-04-27T20:31:58.281595 | 2023-04-14T17:29:53 | 2023-04-14T17:29:53 | 208,673,066 | 74 | 4 | NOASSERTION | 2023-04-19T19:08:04 | 2019-09-15T23:45:27 | R | UTF-8 | R | false | false | 1,667 | r | rcustom.R | #' Create a custom rschedule
#'
#' @description
#' `rcustom()` creates an rschedule from manually defined event dates. This can
#' be useful when combined with [runion()] and [rsetdiff()] if you have a set of
#' fixed event dates to forcibly include or exclude from an rschedule.
#'
#' @param events `[Date]`
#'
#' A v... |
bfcea93fe141f9ec7e988bb77acaa468368297e5 | 231114399cf254361f2b1e7e8ed3ef39c0211504 | /viterbi_hmm7Rcpp_2Int.R | 571acd6be60162a2501522c7beef228e82abb91b | [
"CC0-1.0"
] | permissive | seanevans7/emews | d329f86ff17c1de7a3fbb554ee35778e69b0b141 | 5b947a6fd3f25e294070d33660bfc645317121ef | refs/heads/master | 2022-10-09T08:03:16.112767 | 2020-06-08T13:51:33 | 2020-06-08T13:51:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,647 | r | viterbi_hmm7Rcpp_2Int.R | ####################################################################################################################################
### Function used to implement the Viterbi algorithm for calculating the most likely state sequence from a ###
### hidden Markov model to diving data from Weddell se... |
c15da62ca79d17d061f854880a5a9f8f1729933c | 552cff5565279ea7cb09e0d0727a8000c1fcfde9 | /Lab5.2_simIDE_codeexample.R | d98f323dbcf056c3a04ed889fbbf4a668da8aa07 | [
"MIT"
] | permissive | xc308/Learn_STRbook | 52ab69942dd5bd12d20ca0eb07342fc50652d2f5 | 4fb057070dfc72cabedc47a9dff947f4141ccbd7 | refs/heads/main | 2023-04-22T18:55:37.366648 | 2021-05-15T17:05:38 | 2021-05-15T17:05:38 | 333,569,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,556 | r | Lab5.2_simIDE_codeexample.R | simIDE <- function(T = 9, nobs = 100, k_spat_invariant = 1, IDEmodel = NULL) {
## Suppress bindings warning
timeind <- val <- s1 <- s2 <- z <- NULL
if(is.null(IDEmodel)) {
set.seed(1)
zlocs <- data.frame(s1 = runif(100),
s2 = runif(100))
## Spatial decomposition
... |
1421d9dbe8fc7bac1fac75cc2a538a0b383dcde8 | bf54966f2a7428e96b89e7d35c18e334e4beff0b | /evalRandom.R | e828f78a39647c6851cd759e167166957b6b4682 | [] | no_license | Civanespinosa/Spp-Density-vs-richness_Port | c692c58431599314523b5c7955f63cef3a189fd3 | 4dd31772ef99df995217c4399450eab85b4d219c | refs/heads/master | 2021-06-16T14:49:08.400643 | 2021-02-04T16:42:22 | 2021-02-04T16:42:22 | 148,078,528 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 425 | r | evalRandom.R | #Evaluación
#Cargamos las preguntas
library(readxl)
eval <- read_excel("Evaluación.xlsx")
x <- eval[sample(1:22, 10, replace = F),]
x <- rbind.data.frame(x[order(x$Número),],
eval[23,])
ranE <- data.frame(Numero=1:1044, pregunta=1:1044)
x <- seq(11,1044, by=11)
y <- seq(1,1033, by=11)
for (i in 1:95){
ranE... |
b4c07c8a9211696f6248611c8f511798c18f9e66 | 859ca12c7fcbc6dd36584cc3a41c173c488c9c3c | /Investing.R | 65d5b8d1944e7ff46d1e19c2778a6c6a0df28f15 | [] | no_license | FinanceStudyGroup/Investing.R | b8dcb3cf7f2e78d11e87f6de75b5a30aac3486d1 | e7036f197fb17971e78fa39ae725d818c7abc746 | refs/heads/master | 2020-04-29T04:02:47.402368 | 2019-04-01T07:14:54 | 2019-04-01T07:14:54 | 175,833,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 119,271 | r | Investing.R | #### Notes: Investing.R .............................................. ####
# This script describes a protocol for selecting files from a folder of
# csv data downloaded automatically from Investing.com, and
# converting these files to the xts format for further analysis in R.
# Specifically these data were col... |
bdd70f7a32bc0ec5d943ae1e78c8e3ddc2a189a7 | 686800c5ddb65505335f30ded6fcf96a6afe66e2 | /man/SurvivalDiagnostics.Rd | 7f9e0313bb04b5c77f3b7d3722ffc0b4d3661031 | [] | no_license | cran/RcmdrPlugin.survival | 2b9e889c64e788a4fb0e6bb96b89293d0c8b7bbd | 86f846e93563d94ceb23c89ac08eb8ae129f92a4 | refs/heads/master | 2022-09-25T09:24:11.554730 | 2022-09-20T16:00:02 | 2022-09-20T16:00:02 | 17,693,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,277 | rd | SurvivalDiagnostics.Rd | \name{SurvivalDiagnostics}
\alias{SurvivalDiagnostics}
\alias{crPlots}
\alias{crPlots.coxph}
\alias{dfbeta.coxph}
\alias{plot.dfbeta.coxph}
\alias{dfbetas.coxph}
\alias{plot.dfbetas.coxph}
\alias{dfbeta.survreg}
\alias{plot.dfbeta.survreg}
\alias{dfbetas.survreg}
\alias{plot.dfbetas.survreg}
\alias{MartingalePlots}
\al... |
53355a454b12027ab9637b6f28e7bc73d3f0c858 | c95063c2ba103110ff101122617e1356b3b86f31 | /man/labplot.Rd | 50f7cb3c606db5381f035e8f58bdbf7b2e4153ba | [
"MIT"
] | permissive | xinxiong0238/PostSequelae | 661a8213014691e026eea01f32e43922a5535c5c | 1c758218de50cc281dffc0eb552c2d31fd6356ed | refs/heads/master | 2023-03-24T10:59:11.348512 | 2021-03-18T08:55:08 | 2021-03-18T08:55:08 | 349,000,587 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 528 | rd | labplot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/labplot.R
\name{labplot}
\alias{labplot}
\title{Density plot}
\usage{
labplot(
PatientObersvations_pro,
loinc_mapping,
windows.size,
windows.min,
windows.max
)
}
\arguments{
\item{PatientObersvations_pro}{Dataframe; \code{PatientObe... |
4a8b1bbfc5340c508bc7fae54772d83b346f4569 | 4b9b4b3b829e07889b14d33043ca81d124e31cf7 | /R/score-functions.R | c091f791519c0b2bcdd235a0598e2e728c3ec80d | [
"Apache-2.0"
] | permissive | sverchkov/bionetwork | 8d2cb008cf30c397c45a56d1f44510a76980ec7c | 8ef0ca221b8d7b71384af72c0964d4d9772761f9 | refs/heads/master | 2021-01-15T10:17:31.773237 | 2016-08-25T22:48:34 | 2016-08-25T22:48:34 | 42,209,208 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,883 | r | score-functions.R | # Score functions
#' Likelihood scores broken down by reporter
#'
#' Likelihood calculation for a full network, with the score contribution of each
#' reporter returned in a vector.
#' @param ancestry - the ancestry matrix
#' @param lll - the LocalLogLikelihoods object
#' @return a vector with a log-likelihood for ea... |
5d42b6155fe58c9920ce0e3f003ad6d9c7e8c459 | 394b0b27a68e590165d0dfb9243e7b2d5deaf4d5 | /man/sample_chat_sentiment_syu.Rd | 2ab587ec6ee7b11bf693f388f427cbafbb2bcc04 | [
"MIT"
] | permissive | NastashaVelasco1987/zoomGroupStats | 5b414b28e794eecbb9227d4b1cd81d46b00576e4 | 8f4975f36b5250a72e5075173caa875e8f9f368d | refs/heads/main | 2023-05-05T18:23:17.777533 | 2021-05-24T16:08:23 | 2021-05-24T16:08:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,523 | rd | sample_chat_sentiment_syu.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{sample_chat_sentiment_syu}
\alias{sample_chat_sentiment_syu}
\title{Parsed chat file in a 'Zoom' meeting with sentiment analysis using syuzhet}
\format{
A data frame with 30 rows of 30 variables:
\describe{
\item{b... |
b8d6535965166b08710634b80ecca248d4e63e76 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.storage/man/storagegateway_describe_vtl_devices.Rd | be12cac12d69e960550c7f2896e9e529b9cef79f | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,241 | rd | storagegateway_describe_vtl_devices.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/storagegateway_operations.R
\name{storagegateway_describe_vtl_devices}
\alias{storagegateway_describe_vtl_devices}
\title{Returns a description of virtual tape library (VTL) devices for the
specified tape gateway}
\usage{
storagegateway_descr... |
0b54a9caf34aa70fc29a318b7637b29470433ba6 | d4b17472248cfbd9d9179d593e476574ab649fd3 | /data_processing/tertiary2pdb.R | c2e9fa9d7a64d1ff5dcb40a42805d4010ad67a4a | [
"MIT"
] | permissive | Maikuraky/rgn | 294f013df18d381dabdc74919b0ff04668bfd556 | 167bed319d065056ac8464b67c6972c9a8f5f192 | refs/heads/master | 2022-03-02T04:05:43.569160 | 2019-10-21T14:42:19 | 2019-10-21T14:42:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,095 | r | tertiary2pdb.R | # Convert a tertiary prediction from RGN into PDB file format
# Aleix Lafita - October 2019
library(argparse)
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(seqinr))
###################### Argparse #############################
tertiary.in = "protein.tertiary"
fasta.in = "prote... |
da53078f92a1b8aaa982fa05c083ce8430ccaae6 | 7786be8e0bd3bf57b5437887bc95b27ae3f3f66c | /R_code_multipanel.r | 0da7d28a399d66954dfbf189c71d45a02795caad | [] | no_license | CeciliaRocca/Monitoring | 7781304f96379ca557522d516d1db82fc3ddc940 | cd18f1f5d49808ce29e79eb503a7562a43628fa5 | refs/heads/master | 2021-04-23T23:14:11.011160 | 2020-05-13T15:32:13 | 2020-05-13T15:32:13 | 250,028,941 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,086 | r | R_code_multipanel.r | ###Multipanel in R: the second lesson of Monitoring Ecosystem
install.packages("sp")
install.packages("GGally") #this is used for the function ggpair
library(sp) #require(sp) will also do the job
library(GGally)
data(meuse) #there is a dataset available named meuse
attach(meuse)
#Excercise: see the names of the ... |
b1f8b83a95ce51526c7d4333c8cbf6cb10471c4a | 73d6b9e8adbd873875ed51751abc7182133b3e8a | /man/get_raw_vaccination_data.Rd | bbcc4827911d107489205fa362909899671cd0f2 | [
"MIT"
] | permissive | SimonCoulombe/covidtwitterbot | cc307e395312e5bbbff09b36c6dda9aef3e1747b | 128749bfd4d0fcec5e3970f60f87424bff8cd50f | refs/heads/master | 2023-04-19T10:03:03.008866 | 2021-04-26T16:26:36 | 2021-04-26T16:26:36 | 311,527,367 | 8 | 1 | null | null | null | null | UTF-8 | R | false | true | 360 | rd | get_raw_vaccination_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_vaccins_data.R
\name{get_raw_vaccination_data}
\alias{get_raw_vaccination_data}
\title{get_raw_vaccination_data downloads vaccination data from inspq}
\usage{
get_raw_vaccination_data()
}
\value{
}
\description{
get_raw_vaccination_data ... |
f564afa8d63ec532b1aa5c6b8ba3de4a3617d13a | 106f0d1b82521ad049ed5321736b8ec4767f21a1 | /makeRegioPlot.R | e5faaef67e522d7d70cf0857d6727d6c1feb40e9 | [
"MIT"
] | permissive | georgeblck/weatherLeipzig | b37e4514f380dd825a78c21f8e3658d9213a0a83 | 71f1423632fb3d6fe97c2bb32e35f0541f0349b3 | refs/heads/master | 2021-07-20T14:13:07.314300 | 2020-05-15T18:53:54 | 2020-05-15T18:53:54 | 160,327,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,411 | r | makeRegioPlot.R | # rm(list = ls())
# load packages
library(lubridate)
library(tidyverse)
library(ggthemes)
# Filter festlegen
jahr <- 2020
bundesland <- "Sachsen"
lastmonat <- ifelse((year(today()) - jahr) != 0, 12, month(today()) - 1)
# Daten einlesen
regionalAverages <- read.table("data/regionalAverages.csv", header = TRUE, dec = ... |
63df2e3c2ce92a4c5113aea9d0399e13eb54217e | 1584aff3bcb57975ed52341d11673402f2646053 | /GeoSpatialAnalysis_inR_cont.R | b4009f1a9f9f7b1ca2b5c3fdeb7bf198f759e51b | [] | no_license | atseng1/p9380_lab2 | f0b0bdcee33b536d3debb75947df06d0085d95ba | 3ef9923989b7fbdb5370bf01f53c136435504843 | refs/heads/master | 2020-12-23T08:24:54.608755 | 2020-01-31T14:48:14 | 2020-01-31T14:48:14 | 237,096,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,516 | r | GeoSpatialAnalysis_inR_cont.R | setwd("/Users/ashleytseng/OneDrive - cumc.columbia.edu/MPH/Spring 2020/EHSC P9380_Advanced GIS/Labs/Lab 2/p9380_lab2")
### Lab goal is to create a county level map of Quality of Life Index Ranking from the
### Robert Wood Johnson Foundation
install.packages("maps")
require(maps)
ny_cty <- map('county', 'new york', ... |
d07e4380e0bb08622dd9071ddfb4bb6e4b06fdab | dac9ab3e7cda91b95e7dc80bfcc36782710464cb | /R/install.R | dc6e116d957a1c7c08fc11e775b4b3d0f3ac0d41 | [] | no_license | krlmlr/tic | 8f02ac2707b21b82cca171a8539eec56bc64650f | fb6f25ccd5a7cf9f4919814f728f104384c9f663 | refs/heads/master | 2021-07-08T08:30:33.267799 | 2019-12-23T23:04:43 | 2019-12-23T23:04:43 | 72,775,037 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 763 | r | install.R | # This code can only run as part of a CI run
# nocov start
verify_install <- function(pkg_names, pkgType = NULL) { # nolint
# set "type" to platform default
pkgType <- update_type(pkgType) # nolint
lapply(pkg_names, function(x) verify_install_one(x, pkgType = pkgType))
}
verify_install_one <- function(pkg_name,... |
ebd5a70722e15550a49e6f2f85f3d0f03f825731 | f91369d3ff4584d909ff5f0f4be382e54594d95c | /man/global_options.Rd | ea205d2e521e7e515950533e568b24e4d3c61147 | [
"Apache-2.0"
] | permissive | Novartis/tidymodules | e4449133f5d299ec7b669b02432b537de871278d | daa948f31910686171476865051dcee9e6f5b10f | refs/heads/master | 2023-03-06T01:18:55.990139 | 2023-02-23T15:01:28 | 2023-02-23T15:01:28 | 203,401,748 | 147 | 13 | NOASSERTION | 2020-04-02T16:09:32 | 2019-08-20T15:16:40 | R | UTF-8 | R | false | true | 742 | rd | global_options.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{global_options}
\alias{global_options}
\title{tidymodules options}
\description{
List of global options used to adjust tidymodules configuration.
\itemize{
\item{\strong{tm_session_type}}{ : Define the type of the session, Se... |
85c0cfe6c944a91818216e9c0fcb7e4572ad358d | 436ace74a695893aad73229b723fac6be6814129 | /R/sobolmartinez.R | 0d2be074f860a4f380f1d529182c85d5d39e8db6 | [] | no_license | cran/sensitivity | 18657169c915857dcde8af872e0048fef77107f4 | 2b2cbcb7f1bebecfd05e589e459fdf4334df3af1 | refs/heads/master | 2023-04-06T05:36:54.290801 | 2023-03-19T18:10:02 | 2023-03-19T18:10:02 | 17,699,584 | 17 | 17 | null | 2021-04-07T00:57:30 | 2014-03-13T06:16:44 | R | UTF-8 | R | false | false | 12,784 | r | sobolmartinez.R | # Sobol' indices estimation (Martinez 2011)
# Plus: Theoretical confidence intervals from correlation coefficient-based confidence interval
#
# J-M. Martinez, Analyse de sensibilite globale par decomposition de la variance,
# Presentation a la journee des GdR Ondes et MASCOT-NUM, 13 janvier 2011,
# Institut Henri... |
ed6ed1244f639cbcf626570db513bb3540d5de92 | 0485c00604cf3448cedb45e6efb2f85d88790c85 | /Book/p73.R | 48109926b4d6bbef45357bf8a40888af027b7d21 | [] | no_license | zsx29/R | d89af4ec46b8068f25d1e2447f98085a6721537f | 1f349b7b3f1981010677530e05d4d467f2c6e95e | refs/heads/master | 2023-06-11T11:28:40.607617 | 2021-07-02T02:17:13 | 2021-07-02T02:17:13 | 380,932,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 159 | r | p73.R | df <- data.frame(x = c(1:5), y = seq(2, 10 ,2), z = c('a', 'b', 'c', 'd', 'e'))
df
df$x
df$y
df$z
str(df) # 데이터프레임 객체의 자료구조 확인
|
bb28c048944b84b82f5d1453cdb7eddeb460d752 | ce6df5d7725e4d1dec9a818bbf60cec8d68aa62c | /tests/testthat/test_vds_amelia_plots.R | 37e25e12d7a6829a8be562dfcca057b98d3dcb5d | [] | no_license | jmarca/calvad_rscripts | 64b4453a37e482ee20fb66b94451f7f097f3db0a | 4cfd418e029ccaa00a2828e525baa5e8641b3e2c | refs/heads/master | 2020-04-12T08:43:40.247022 | 2017-12-21T19:18:22 | 2017-12-21T19:18:22 | 9,202,075 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,754 | r | test_vds_amelia_plots.R | config <- rcouchutils::get.config(Sys.getenv('RCOUCHUTILS_TEST_CONFIG'))
parts <- c('vds','amelia','plots')
result <- rcouchutils::couch.makedb(parts)
context('get.and.plot.vds.amelia works okay')
test_that("plotting imputed data code works okay",{
file <- './files/737237_ML_2012.df.2012.RData'
fname <- '737... |
4df564ad10bded9c3ff9f282f5c4f1e9d26eae8c | 7d0d3d2311a1e6bf40d7e288c18a78e7becf855b | /R/threshold_SE.R | 43b687d9e1f05d0cef4c562fd05d08d2b13b4873 | [
"CC0-1.0"
] | permissive | dunbarlabNIH/barcodetrackR | c8023800426ec56ec08dad7ce0b7450a4aad0bae | f3b8174e5cb7b0540de6bbbedf19022cc0c14074 | refs/heads/master | 2023-04-26T13:15:16.327054 | 2021-04-26T14:59:21 | 2021-04-26T14:59:21 | 47,579,726 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,353 | r | threshold_SE.R | #' @title Threshold SE
#'
#' @description Removes barcodes from a SummarizedExperiment object which have an abundance lower than the provided relative or absolute threshold. See the function `estimate_barcode_threshold` to estimate an appropriate threshold for an SE.
#'
#' @param your_SE A Summarized Experiment object.... |
8a54d7daf25b98f34315c4e9de8b3de1ffb26786 | f0035bfa6406697169bfa880aa8929e2d325a43a | /Streaming das mençoes a candidatos.R | 5ec6761ab24774f3fd510928452c8c80a7129004 | [] | no_license | euricotu/ColetaEleicoes | d8d6145dacd32693755b187a9ec6e9ac1227a9f2 | ecbf9fa3fabb77aefb79daafaa0f5346a2c8cde9 | refs/heads/master | 2020-03-26T12:29:21.526958 | 2018-08-24T02:24:40 | 2018-08-24T02:24:40 | 144,895,062 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,129 | r | Streaming das mençoes a candidatos.R | library("rtweet")
app = "rtweet_stream_ematos"
ckey = ""
csec = ""
atok = ""
stok = ""
twitter_token = create_token(app,ckey,csec,atok,stok)
user_ids= "33374761,74215006,762402774260875265,128372940,2670726740,354095556,105155795,870030409890910210,256730310,73745956,73889361,989899804200325121"
palavr... |
651bc3af0a559d9f1fe3070f11cfc6fd33d3a42e | 7904e63563865a091329c4da11fe16bbbab6d9e4 | /Airpassenger_R/using_arima/predict_arima.r | 386d7e6820eb764ee9dc72c0346a2b7c0d59f8ee | [] | no_license | am23/Internship_R_Scripts | b1e2e5e0b1d87ae28bffe3d70c9c16ae9a640871 | 649666369efd64d80f1cd7865229b690210d752f | refs/heads/master | 2020-05-18T19:14:40.244690 | 2013-07-17T11:48:51 | 2013-07-17T11:48:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 675 | r | predict_arima.r | #This code for predicting the Airpassengers for next 2 years.
#reading files
a<-read.table('original.txt')
#Converting to time series
myts <- ts(a, start=c(1949,1), frequency=12)
#fitting using ARIMA model.
fit1 <- arima(myts, order=c(1,0,0), list(order=c(2,1,0), period=12),method="ML")
#predicting using n.... |
3f2f7566d36902b4125bf25b87a46ead388fe44a | 66ee5b9cbe7f6b3a745cc8174deda69ef6b833b8 | /R/utils.R | 63da1402b828f57f8e2abf07d51952288b5ed1a6 | [] | no_license | SRenan/XCIR | 5e4d2299ea57edbff200793e8d91f7814b27c79a | 4e51efe9980056e7fe274224da0173fcfaf2edd7 | refs/heads/master | 2021-10-12T07:06:51.967393 | 2021-10-04T20:36:54 | 2021-10-04T20:36:54 | 69,993,016 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,960 | r | utils.R | #' Read a list of known inactivated genes
#'
#' Read a list of gene symbols of known inactivated genes
#' to be used as training set in \code{betaBinomXI}.
#'
#' @param xciGenes A \code{character} or code{NULL}. By defaults, return a
#' vector of 177 genes. Other available choices include "cotton" and "intersect".
#' I... |
fc29e69b25d4b06fed0cc8bdeba0fd4293eaf779 | 1d95129039dfe86fe4aba9c160749cadd9d7ff48 | /man/viz_thickforest.Rd | ed8964f401b82fdace87ea84c271985b6f986c80 | [] | no_license | Mkossmeier/metaviz | 3a10cb9d2cb1fdd82cd6814f39f98375dc11c85c | e1686fceb30294503479748edffb288b88153671 | refs/heads/master | 2021-01-22T02:53:24.447802 | 2020-04-07T17:42:00 | 2020-04-07T17:42:00 | 81,078,260 | 15 | 4 | null | 2019-01-24T13:37:31 | 2017-02-06T11:00:26 | R | UTF-8 | R | false | true | 8,108 | rd | viz_thickforest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/viz_thickforest.R
\name{viz_thickforest}
\alias{viz_thickforest}
\title{Thick forest plots for meta-analyses}
\usage{
viz_thickforest(
x,
group = NULL,
type = "standard",
method = "FE",
study_labels = NULL,
summary_label = NULL,
... |
e5c052a876d0cd16a11ff4354b4d8417cd7ad00d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FSA/examples/ksTest.Rd.R | 782617c0ae41ce155716b97ffaebb7d18a7faa2f | [] | 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 | 523 | r | ksTest.Rd.R | library(FSA)
### Name: ksTest
### Title: Kolmogorov-Smirnov Tests.
### Aliases: ksTest ksTest.default ksTest.formula
### Keywords: htest
### ** Examples
## see ks.test for other examples
x <- rnorm(50)
y <- runif(30)
df <- data.frame(dat=c(x,y),grp=rep(c("X","Y"),c(50,30)))
## one-sample (from ks.test) still works... |
b943e287d53c5291690a7d3ddeaf726a7f6d9290 | 69c02cafd31ee8b6ff88707fb6148caac49e4375 | /C4_Exploratory Analysis/Project_2_Final/Coursera_output/Plot6.R | 3a1e4b6f169faa4ea932d8de94fbfd6c1ddb1b4a | [] | no_license | pjbaudin/Data-Science-Study | 8909430ac169faf268f0de7bf08c5b54a7d18d6f | 0c64c704aa4d2cbb9227c4fe06e12c74d50ede9b | refs/heads/master | 2021-01-13T03:41:42.931943 | 2017-05-15T02:19:27 | 2017-05-15T02:19:27 | 77,277,928 | 0 | 0 | null | 2017-05-14T05:44:24 | 2016-12-24T10:24:27 | HTML | UTF-8 | R | false | false | 1,041 | r | Plot6.R | # Plot 6
# Import dataset
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Load library
library(ggplot2)
library(dplyr)
library(magrittr)
# Filter NEI to keep On-road type and Baltimore city and Los Angeles County only
City <- data.frame(fips = c("24510", "06037"), City = c("Ba... |
68de0bfb2eab7e9acc260192e162a5cbb72ee919 | 1faa849036ff058507a07f918d6da9702bbebed4 | /app.R | 25396436e79d11da07eb627ab87438fa5a76e28a | [] | no_license | wesleycoates/KmeansShinyApp | 1324734920c51fb641a30e07fcf241e1251a6100 | bab2b3b4323cd9a13be3e8d799ed5a17c750febb | refs/heads/main | 2023-01-21T09:57:19.727325 | 2020-12-02T03:23:06 | 2020-12-02T03:23:06 | 317,710,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,692 | r | app.R |
##This app is using one year of sleep data from August 2019 to August 2020
##Building off of the Kmeans app
## install.packages('rsconnect') and load its library(rsconnect)
## configure your R instance and rsconnect with your shinyapps.io account
##rsconnect::setAccountInfo(name='wesleycoates',
##token='<BLARG>',
##s... |
8e019c9a446618fa3f1127f2ec45d7731f2637fb | a4efc97580ebcf91bc69bbd71be5d6d2d4a30352 | /post-81.r | ffa6b3b28747b2f137601951978423366829ff75 | [] | no_license | maruko-rosso/datasciencehenomiti | 9a078f902904f9a2d4bca61407395ac71b929036 | 8060b65bf1d3aec5055de0d65187126569cad7a7 | refs/heads/master | 2022-06-26T18:38:47.641246 | 2022-06-26T10:00:15 | 2022-06-26T10:00:15 | 231,026,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,197 | r | post-81.r |
##### 分散の作り方・理解 ####
library(ggplot2)
ggplot(HightOfStudent,aes(x = 1,y = 身長)) +
geom_point() +
xlab("") +
geom_text(aes(label = 生徒名),vjust= -0.2,hjust= -0.2) +
ggtitle("身長を一直線に可視化")
#### 一直線の可視化を横に展開 ####
ggplot(HightOfStudent,aes(x = 生徒名,y = 身長)) +
geom_point() +
geom_text(aes(label = 生徒名),vjust= -0.2,h... |
5687877c9e3c03507025d74c8ca28cec7ddf3c1c | 0fdfe67718008e3a27f626344c5b5c56d6d5b58b | /vaers/vaers.R | 4daddfd3c094497ed6d1db8b5693c24ff222607b | [] | no_license | tundraka/analysis | 899d901a0627896c36c50d8bb7a32d50d4853c60 | 9ca1ff962a7c0c15f995db47804641fd035be5f0 | refs/heads/master | 2020-12-19T22:08:19.380833 | 2016-05-29T04:12:49 | 2016-05-29T04:12:49 | 39,984,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,517 | r | vaers.R | # Information about the data needed in this script can be found in the README.
# https://github.com/tundraka/analysis/blob/master/vaers/README.md
#
library(data.table)
library(stringr)
library(ggplot2)
dataLabel <- '2014 VAERS'
datesAs <- 'character'
#
# READING VAERS DATA
#
vaersDataFile <- 'data/VAERS/2014/2014VAER... |
74a9920c64368e86692215dc3e1439708a6e3000 | 53c79f2ee9ea1ebb4b2050bb12f98416ac543d94 | /ui.R | 54b8e58babc4a5e85ae838fa97641ab23ec30c5a | [] | no_license | kenyang88/DevelopingDataProducts-Week4Project | 30c3bebd31dee12eeeb6e5f4e2ceaf7c48a404ae | 9bbb68bebbaad48dd15d5b2ace74a391a6412e26 | refs/heads/main | 2023-05-23T06:23:21.136210 | 2021-06-11T04:30:53 | 2021-06-11T04:30:53 | 375,887,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,103 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define UI for application of BMI calculator
shinyUI(fluidPage... |
e73504dc397b3aae6fbe494eb56c50a0ea154f61 | 7c7b3517fdf83f3009a31e48405745ed5fbc7f80 | /exercise-6/exercise.R | beb7547e1e1e304bff8f13d061f6f0023632f731 | [
"MIT"
] | permissive | davidl357/module10-dplyr | cd529b59992942132160c6ca647677448767a3fe | 3f8f4b191f56a2a366bc0bd92738ce870c83e513 | refs/heads/master | 2021-01-11T15:55:14.638020 | 2017-01-26T23:19:28 | 2017-01-26T23:19:28 | 79,956,053 | 2 | 0 | null | 2017-01-24T21:26:34 | 2017-01-24T21:26:34 | null | UTF-8 | R | false | false | 931 | r | exercise.R | # Exercise 6: DPLYR join introduction
# Install the nycflights13 package and read it in. Require the dplyr package.
# install.packages("nycflights13")
# Create a dataframe of the average arrival delay for each destination, then use `left_join()`
# to join on the "airports" dataframe, which has the airport info
avg.ar... |
6d754b758d53898a08dad8a560c5a4e299fc9fc2 | 76537f8b121711152e8f4ac2a74579f4d7f46264 | /R/Plot.R | 2d86aa409890e8d2115e3ec9e41789dfb2c36d62 | [
"MIT"
] | permissive | KehaoWu/GWAScFDR | 49f1cf7d1a9d8ff8579375f24b5e6ae3f4b3b64e | c7a0fbfea8fab5c9aed557b1503e2ca64592b3b2 | refs/heads/master | 2020-04-07T22:57:35.197138 | 2015-11-22T01:13:40 | 2015-11-22T01:13:40 | 42,730,656 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,876 | r | Plot.R | library(ggplot2)
stratifiedQQplot = function(p1,p2,xlab="Nominal -log p conditional",ylab="Nominal -log p"){
library(ggplot2)
p1 = p1[p1!=0]
p2 = p2[p2!=0]
dat = NULL
for(cutoff in c(1,0.1,0.01,0.001,0.0001)){
p = p1[p2<=cutoff]
x = -log10(seq(from = 0,to = 1,length.out = length(p)+1))[-1]
pri... |
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