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values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ba3f8fb86789ed676971f492f887cf6236cb0ed7 | eb9c68c0cdf0ba0a008e7526d7827f7b92f54f7f | /Scripts/Rscripts/experimental.R | 1a5832ba6d32298b2acf2eb5c899f3a58e49afc3 | [] | no_license | PaavoReinikka/hello-sandbox | 68d77316c80fbb3acea7bda664ecf39d05fe561e | d08644422aadf1173e4d449a7f3cb126efe8c592 | refs/heads/master | 2023-07-09T18:20:13.579335 | 2023-07-04T14:07:51 | 2023-07-04T14:07:51 | 128,568,237 | 0 | 0 | null | 2018-04-07T21:10:45 | 2018-04-07T21:02:46 | null | UTF-8 | R | false | false | 3,627 | r | experimental.R | k <- 100000
n <- 3
FC <- 1.5
q_st <- qnorm(0.9,0,1)
q_st
# we want q=FC/2 => kerroin=FC/2q_st
sigma <- FC/(2*q_st)
#sigma <- 1
r <- c(rnorm(k,0,sigma),rnorm(k,-FC/2,sigma),rnorm(k,FC/2,sigma))
r <- sample(r,10000,TRUE)
var(r)
sigma^2 + FC^2/(2*n)
sigma^2 + FC^2*(n-1)/n^2
sigma^2 + FC^2*(n-2)/n^2
hist(r)
pA=0.3... |
597dcc409b3c589282c149ed3c4306f41b239c44 | e828d0bcc8367858d328a50b353f638129d7df8c | /plot1.R | 80225cfd36ce800390730bdf598a048fb5666947 | [] | no_license | a-diamant/expl_data_analysis | c25702de7750f7f28804c2e73a160c023cbb1026 | d3c92bf83e664a846a1aaa4e4586b1a3b3a9edbc | refs/heads/master | 2023-07-21T12:24:59.038671 | 2019-09-26T11:08:41 | 2019-09-26T11:08:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,085 | r | plot1.R | ##exploatory data analysis.
##week1
###plot1
library(data.table)
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", "power_consumption.zip")
unzip("power_consumption.zip")
list.files()
power_consumption <- fread("household_power_consumption.txt", sep = ";", header... |
d4ed64591181e3c4ad103cc11d43a09241da172f | 330cbad4fae68f7721083c4c171337b1d746a4cc | /turistus_skc.R | 961259edba8b582749e068c195e9d843fb1964cf | [] | no_license | auksesi/rastodarbas | 3c440cff617a8b3bff3a4a436b75fd4d43416392 | f4213c2ba594ac0e88e8ed3ea4952b032cee7c4b | refs/heads/master | 2020-06-06T06:44:29.529647 | 2019-06-19T06:05:05 | 2019-06-19T06:05:05 | 192,668,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,395 | r | turistus_skc.R | getwd()
setwd("/Users/Aukse/Desktop/turizmas")
if(!require(eurostat)) install.packages("eurostat") ; require(eurostat)
if(!require(tidyverse)) install.packages("tidyverse") ; require(tidyverse)
if(!require(rsdmx)) install.packages("rsdmx") ; require(rsdmx)
if(!require(openxlsx)) install.packages("openxlsx") ; require(o... |
ee42b88f3647880b3e680de31fd6eb0a9ae97c91 | 772f302825f9618adbf9d7a045e8395016da538e | /svsocket_example.R | 044161e28f0880bc6145a4711168bf9fd679e19f | [] | no_license | smlab-niser/flaps | b89b78811ab14b35535ee5ccf1962b8f4dd61c09 | ff0278fd177d40bff3aab66776880ef373709b68 | refs/heads/master | 2023-01-03T01:24:04.134557 | 2020-10-21T08:21:14 | 2020-10-21T08:21:14 | 267,589,565 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 574 | r | svsocket_example.R |
library(utils)
# NOT RUN {
finger <- function(user, host = "localhost", port = 79, print = TRUE)
{
if (!is.character(user))
stop("user name must be a string")
user <- paste(user,"\r\n")
socket <- make.socket(host, port)
on.exit(close.socket(socket))
write.socket(socket, user)
output <- character(0)
r... |
dce596d17cd91b94c4c3766980c1e9f34e8ca1fd | 6ffb2e5fad7a7414eacfbfb642e8ef579ee510bf | /R/functions.R | cd92e5cdf6d9c50eee6194633df82b4a97fd783f | [
"Apache-2.0"
] | permissive | kproductivity/santander-customer-satisfaction | e6bd07507577e424ee3429f56a9061fbe81747e6 | 96b0892f878f8455dd127fe362e3686a6bdd5e04 | refs/heads/master | 2021-01-10T08:31:21.200230 | 2016-03-28T13:47:04 | 2016-03-28T13:47:04 | 53,279,252 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 708 | r | functions.R | # Assess performance
assessPerf <- function(myModel){
# myModel - H2o model
perf <- h2o.performance(model = model.nb, newdata = new.hex)
h2o.confusionMatrix(perf)
plot(perf, type = "roc", col = "blue", typ = "b")
perf@metrics$AUC
}
# Generate submission file
genSubmission <- function(myModel){
# myModel - ... |
109547b56ca6e2058cfaa6ea166a16fe0971d704 | 6b733d7f4cd3c0360cce9e30d1a518bbd2bac7d6 | /man/Tab9.1.Rd | 405b34a6e26f2f036ba980527f4f0b96e09cf360 | [] | no_license | pbreheny/hdrm | 52c5758b2be1ee813db776b96048f506e9387d02 | a1f2ffb7f831b382847db81ad0085c9e86a3af07 | refs/heads/master | 2023-04-30T05:25:04.338437 | 2023-04-20T16:54:58 | 2023-04-20T16:54:58 | 60,282,907 | 6 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,072 | rd | Tab9.1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/boot-sim.R
\name{Tab9.1}
\alias{Tab9.1}
\title{Reproduce Table 9.1}
\usage{
Tab9.1(
N = 100,
B = 100,
n = 100,
p = 100,
a = 10,
b = 2,
rho = 0.5,
noise = "autoregressive",
rho.noise = 0.8,
seed = 1,
...
)
}
\arguments{
\... |
65728d27c37de0bfdc1998d25640e0a29b8cd266 | 69c9338bedd17f91288a39a267dd638fe891daba | /exemplos/kmeans/GeneralizedKMeans.R | 065d86d3cd671374ae3bdfb448aa9fd7166bfe08 | [] | no_license | victormmp/rec-pad | 1925e49fe6435a25b85dac1b02482abb7a53bdb5 | fabd9a6c044bbabb55ac8c72e587c29db53a72d0 | refs/heads/master | 2021-04-09T13:12:37.083123 | 2018-06-22T03:01:15 | 2018-06-22T03:01:15 | 125,459,459 | 0 | 0 | null | 2018-04-07T03:16:30 | 2018-03-16T03:38:40 | TeX | UTF-8 | R | false | false | 3,232 | r | GeneralizedKMeans.R | rm(list=ls())
cat("\014")
# library("tictoc")
library("rgl")
library("plot3D")
cat("===== Starting Routine=====\n\n")
cat(">> Creating functions\n")
kMeans <- function (X, k, maxit) {
N <- dim(X)[1]
n <- dim(X)[2]
seqN <- seq(1,N,1)
seqk <- seq(1,k,1)
Mc <- matrix(nrow=k, ncol=n)
Clustx <-matrix(n... |
6b2e654b14148c86b43875b6874f5bcf7f6e6428 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/NHLData/examples/Sch9495.Rd.R | df046b3d2260214c843f1c3754d0cacb58f0d0d5 | [] | 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 | 237 | r | Sch9495.Rd.R | library(NHLData)
### Name: Sch9495
### Title: 1994-1995 Season Results
### Aliases: Sch9495
### Keywords: datasets
### ** Examples
data(Sch9495)
## This command will show the results for the first game of the season.
Sch9495[1,]
|
1027663223c57e7c7c048d4396f6308d4efa723a | a2e3e68f410a313e0539524c97d806daa802e143 | /11_19_2020/script.R | 4260bb2499027aaef885f693bc0ebe4683cc5ff3 | [] | no_license | kmdono02/Stats_R_Teaching | 7d0887bb41d3b5c6392d849411c88958225e3329 | 2de52240eb132a6d9dd208cb9ff1f600b0cefaaf | refs/heads/main | 2023-05-29T18:09:54.524290 | 2021-06-08T13:57:33 | 2021-06-08T13:57:33 | 305,139,979 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 21,606 | r | script.R | library(tidyverse)
library(readr)
# Base r Plotting (very brief)
# Use plot() function
# Option 1: dataset with only 2 variables, can just specify dataset as argument
plot(cars) # cars is example dataset included in R for tutorial purposes
# Option 2: Specify X and Y variables separately for plot
ibis_data <- read_c... |
4ca3cdda09ffffbb2e4e97b91a4bb85823fdc7e1 | 409490d9da29446f5fb1672eab7e774731554785 | /R/list.findi.R | 60fd1e081d49ba05313c612405948b6c08fc67e5 | [
"MIT"
] | permissive | timelyportfolio/rlist | 8004c472fb6835182773d4458c9d604cb03795a3 | d3299cec59c36f9295493feea3d53d21278a8a2a | refs/heads/master | 2020-11-30T23:33:33.408653 | 2014-08-07T16:28:24 | 2014-08-07T16:28:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 736 | r | list.findi.R | #' Find the indices of a number of members in a list that
#' meet given condition
#'
#' @param .data \code{list}
#' @param cond A logical lambda expression
#' @param n The maximal number of members to find out
#' @param envir The environment to evaluate mapping function
#' @name list.findi
#' @export
#' @examples
#' \d... |
7147e4ab1a0ec14fcd6e212d64ffe2687e81a7ff | cc146174e877dbd443cdd63ba03ff7d13e7adc71 | /R/var_theo.R | efe23337d46f417a4d523f10b085e42191fe0914 | [] | no_license | ccombesGG4/AmoRosoDistrib | ef5621b143d2132b6162e24f8a093bb4795b6c0b | b1e0fad34359ba986480aa7f0d078ff4d7ddc960 | refs/heads/main | 2023-08-24T04:35:49.499948 | 2021-10-04T08:52:57 | 2021-10-04T08:52:57 | 359,819,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,440 | r | var_theo.R | #############################################################################
# Copyright (c) 2021 Hon Keung Tony Ng and Catherine COMBES
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Found... |
59f65a5048a001c44d66b0ac4c802de3a22cc3e3 | dd0d26163c4a0498de5b25e4ee57c4ce70b2676d | /man/setupConsoleMonitor.Rd | 08b428bc7afec1958e682137d57c53675c1e24fd | [] | no_license | jakobbossek/ecr | a1f97be9b4cb3b2538becebb38c9a5085b8464c9 | f9954f5b1374cc70776f8b7e780f906e57ca50b7 | refs/heads/master | 2020-04-04T07:26:32.216427 | 2017-06-06T11:05:27 | 2017-06-06T11:05:27 | 17,904,690 | 13 | 5 | null | 2016-09-27T10:30:10 | 2014-03-19T13:15:56 | R | UTF-8 | R | false | true | 807 | rd | setupConsoleMonitor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/monitor.console.R
\name{setupConsoleMonitor}
\alias{setupConsoleMonitor}
\title{Simple stdout monitoring function.}
\usage{
setupConsoleMonitor(show.info.stepsize = 5L, num.format = "\%g")
}
\arguments{
\item{show.info.stepsize}{[\code{intege... |
0906202c549b89b588db851f191ff68d7836ae60 | 184180d341d2928ab7c5a626d94f2a9863726c65 | /issuestests/mixR/R/normalEM2.R | d91d8ad966941d877caa3a2bdaf4cd6827375688 | [] | no_license | akhikolla/RcppDeepStateTest | f102ddf03a22b0fc05e02239d53405c8977cbc2b | 97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5 | refs/heads/master | 2023-03-03T12:19:31.725234 | 2021-02-12T21:50:12 | 2021-02-12T21:50:12 | 254,214,504 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,569 | r | normalEM2.R | ## normal mixture models (grouped data)
normalEM2 <- function(x, ncomp = NULL, pi = NULL, mu = NULL, sd = NULL, ev,
mstep.method, init.method, tol = 1e-6, max_iter = 500) {
# check if initial values are missing
if(is.null(pi) & is.null(mu) & is.null(sd)) {
if(is.null(ncomp)) stop("provide... |
ca866cd3d37a9b7142578c46ce82484ef4898b91 | dc41b39f5ba677804e02cc8c6985fb28e2fd4ad6 | /data-raw/internal.R | dcd4bb45958abb4710b274fb854e1e1bfafecc6e | [
"Apache-2.0",
"MIT"
] | permissive | CamBullen/shinywqg | 0a5738dbe5ff37a0f95c709d62e0397f7abb266d | e56f984f97984ff7b1e555c2dac404af0847363c | refs/heads/master | 2023-04-01T15:00:21.735563 | 2021-05-03T20:05:20 | 2021-05-03T20:05:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,593 | r | internal.R | library(readr)
library(dplyr)
library(stringr)
library(magrittr)
library(wqbc)
hash_limits <- "85d3990a-ec0a-4436-8ebd-150de3ba0747"
limits <- bcdata::bcdc_get_data(record = hash_limits)
hash_cu_acute <- "23ada5c3-67a6-4703-9369-c8d690b092e1"
hash_cu_chronic <- "a35c7d13-76dd-4c23-aab8-7b32b0310e2f"
#limits <- read... |
7253e137990245adc010eea6a315a75282d36eda | 73259f4c8fdda2f4312915dfeb47003cb3047a17 | /2_analyse_plot_WEMs.R | 25b174a027f8095bca85ed04152d243662bd837e | [] | no_license | MilanvanL/debating_evil | de1a3f81afb9d35773e1b062a91bfd1d9bbe3730 | 642e6d07cb7089eee1d1f3789e0ca736690089a8 | refs/heads/master | 2020-05-23T13:57:18.293743 | 2019-07-02T13:38:23 | 2019-07-02T13:38:23 | 186,790,366 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,833 | r | 2_analyse_plot_WEMs.R | ###################
# This R-script is used for the analysis of parliamentary data in:
# Debating Evil: Using Word Embeddings to Analyse Parliamentary Debates on War Criminals in the Netherlands
# Authors: Ralf Futselaar and Milan van Lange
# NIOD Institute for War, Holocaust, and Genocide Studies
# 2019
# Email... |
b2aaa66e0031c48a59d0fb36da26ead464e09ec5 | 5150cf610a34c6c5be9b598277db1834d8fb16b4 | /R/calc_cumfledge.R | 9916eec21c60a3b5661f4179eb74dba5839615fb | [] | no_license | SPI-Birds/pipelines | f3ab78668e526a47bd298b0f7f4127e274a4dfd0 | cb4bd41bc26d991fa54e520bb15b54333696b4cb | refs/heads/master | 2023-08-16T18:15:29.835023 | 2023-08-09T09:51:56 | 2023-08-09T09:51:56 | 153,275,927 | 0 | 3 | null | 2022-12-04T14:48:00 | 2018-10-16T11:42:17 | R | UTF-8 | R | false | false | 1,626 | r | calc_cumfledge.R | #' Calculate cumulative number of fledgings
#'
#' For a given nest, determine the cumulative number of fledglings in all nests
#' before this. This is used to calculate ClutchType_calc. The function also
#' includes functionality to return whether number of fledglings in previous
#' nests was measured or not (i.e. were... |
2fa8fdebc8b3e1244849f07bac2d64cbaa9a01a3 | 89f79d108b334d9a503a70f18b7746232e4dd03e | /plot6.R | f68dc9582746340661b67f8b12f724340c56072d | [] | no_license | Gradon/exploredata | 793b66ec37ef7b8d1523970c9f517c748668e650 | 8cc6185e7781178bdcc24df3ad87e22c37854eda | refs/heads/master | 2023-01-06T20:23:43.531406 | 2020-10-25T13:55:56 | 2020-10-25T13:55:56 | 307,050,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,604 | r | plot6.R | # question: compare emissions from motor vehicles in Baltimore City with those in Los Angeles.
# which city has seen greater changes over time in motor vehicle emissions?
# national emissions data
NEIdata <- readRDS("summarySCC_PM25.rds")
# classification code mappings
SCCmap <- readRDS("Source_Classification_Code.rds... |
62f0f21e14455eef23112c9cf7b64d9c9c26a9f1 | df6d4fd40c7a47127d608d397a0aaa67cfe92bf1 | /Multiple imputation.R | 0a0f7caf1b2cdcefa5bd00d1aab8912a034eca8e | [] | no_license | Econometrics-in-r/Multiple-imputation-of-missing-data | abaaa27688cc61fa730fe7c725edc48c16a4d4b2 | e61a1019a17190a8c3549bc16ff0b15ed4e58fe3 | refs/heads/master | 2022-08-30T23:07:03.646882 | 2020-05-27T09:14:50 | 2020-05-27T09:14:50 | 266,992,031 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,397 | r | Multiple imputation.R | rm(list = ls())
require(stats4)
require(maxLik)
require(randtoolbox)
require(data.table)
require(mice)
#reading and storing data in a dataframe
dataset <- read.csv(file.choose(),header=T)
#Sample Size
N <- nrow(dataset)
#Dependent variable (crash counts in this example); change the variable as required
DVar <- datase... |
75f418af11ca7ae251b6bccb023f73f8a5d0b8d9 | 7a8c0602ea1ce52ef51a096f0ac70d456f179e0a | /R/man/training_step.Rd | 045ce40df662057329645e8975e59914ee88eb5a | [
"MIT"
] | permissive | dmalagarriga/interpret | 37798891b20d5a5c2ead5535ca5049406b91dab6 | 961aba790f19798f03f496346d2c0ff037202050 | refs/heads/master | 2020-09-29T17:52:01.980013 | 2019-12-10T06:12:04 | 2019-12-10T06:12:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,159 | rd | training_step.Rd | \name{training_step}
\alias{training_step}
\title{Training Step}
\description{
Takes one Training Step
}
\usage{
training_step(
ebm_training,
index_feature_combination,
learning_rate,
count_tree_splits_max,
count_instances_required_for_parent_split_min,
training_weights,
validation_weights
)
}
\ar... |
a109a5f384e64899511af758daf80638a5cea7e1 | 023267839ada61c94515f24ae2b782d2b844194e | /lectures/lesson25_reviews/onlineReview_pro.R | 7ce6753768c26c04e1c5c765caf19cd78f38b0a6 | [] | no_license | DarioBoh/ISDS3105_fall18 | 303399915687750d93e1d850d2fd07eb5870a2bd | 23bc7e464091e1641efec084c4803c612edebf0f | refs/heads/master | 2021-07-12T12:50:06.424984 | 2021-07-04T09:46:36 | 2021-07-04T09:46:36 | 145,449,465 | 0 | 25 | null | 2018-08-24T17:02:20 | 2018-08-20T17:28:20 | null | UTF-8 | R | false | false | 1,612 | r | onlineReview_pro.R | library(tidyverse)
# Read the file `dataset.RDA` using readRDS
dt <- readRDS('onlineReviews_analysis/dataset.RDA')
# Adjust the datatypes as needed. Use mutate_at to apply the same function to multiple columns
# Write a function to plot a chart of rating frequencies for a single hotel. Each plot should be created ... |
57258ad2f35d3435580495b8f601cf3bb1301759 | 108d4834704fa9e2fcb610fc9fadd5203af666f6 | /routesRandomiser.R | c4bbc36f7739a2c987fc1eca85dfdd4b8b6f1575 | [] | no_license | DanOlner/randomNetworkDistancer | f1c7066622b9a8809276999e2bf3078f1f04ac3d | e0a0604b9eee3a726727e4bb681983b88fda62a7 | refs/heads/master | 2021-01-23T00:14:53.071552 | 2015-07-06T08:39:28 | 2015-07-06T08:39:28 | 19,893,925 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,320 | r | routesRandomiser.R | #Pick random points inside shapefile polygon
#Use Google distance matrix API to fetch network distance/time and addresses
#https://developers.google.com/maps/documentation/distancematrix/
#Store and save results as CSV
library(rgdal)
library(rgeos)
library(httr)
library(jsonlite)
#Load properties file
#Nabbed from ht... |
ae832d4e040b0daba40a44f888e87ea3dbd77a86 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gtkCTreeFindAllByRowDataCustom.Rd | 808cf682ee62df6e4200d71868fc9792197aa4b6 | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 745 | rd | gtkCTreeFindAllByRowDataCustom.Rd | \alias{gtkCTreeFindAllByRowDataCustom}
\name{gtkCTreeFindAllByRowDataCustom}
\title{gtkCTreeFindAllByRowDataCustom}
\description{
Find all nodes under \code{node} whose row data pointer fulfills
a custom criterion.
\strong{WARNING: \code{gtk_ctree_find_all_by_row_data_custom} is deprecated and should not be used in new... |
c65c4f8d86565e73c61875e9a045f8ae9b763619 | 00daf46a1286c20caa103a95b111a815ea539d73 | /R/manual_generics.R | 4eeb6c14ce3ccc8855b5433b009493b9656a4508 | [] | no_license | duncantl/Rllvm | 5e24ec5ef50641535895de4464252d6b8430e191 | 27ae840015619c03b2cc6713bde71367edb1486d | refs/heads/master | 2023-01-10T15:12:40.759998 | 2023-01-02T18:05:26 | 2023-01-02T18:05:26 | 3,893,906 | 65 | 14 | null | 2017-03-09T07:59:25 | 2012-04-01T16:57:16 | R | UTF-8 | R | false | false | 4,980 | r | manual_generics.R | setGeneric("getParent",
function(x, ...)
standardGeneric("getParent"))
setGeneric("eraseFromParent",
function(x, delete = TRUE, ...)
standardGeneric("eraseFromParent"))
setGeneric("getElementTypes",
function(x, ...)
standardGeneric("getE... |
a597567061b7b1cb02ff286909c3d754c6698126 | f48e25ade098aef7aa6f9fde4927bbf2b2092d14 | /man/dasl.oakland_passengers_2016.Rd | f050bab8c6b608c18274ada3ffe78a9da6f8eb9c | [] | no_license | sigbertklinke/mmstat.data | 23fa7000d5a3f776daec8b96e54010d85515dc7d | 90f698e09b4aac87329b0254db28d835014c5ecb | refs/heads/master | 2020-08-18T04:29:05.613265 | 2019-10-17T11:44:57 | 2019-10-17T11:44:57 | 215,747,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 718 | rd | dasl.oakland_passengers_2016.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dasl.R
\docType{data}
\name{dasl.oakland_passengers_2016}
\alias{dasl.oakland_passengers_2016}
\title{Oakland passengers 2016}
\format{318 observations}
\source{
DASL – The Data And Story Library: \href{https://dasl.datadescription.com/datafi... |
785ca976297f78fed599c6ee20c4dff6edc99c35 | 8f09774b992fd23052201130a1ce4db3f3e27a53 | /tests/testthat/test_utils.R | df5e4833ef19d340c5669e316aa691a082c42f0d | [
"MIT"
] | permissive | tkonopka/umap | 77f3ff453181cdbe0844df1b4f9e499f23559121 | 96f077865243434ec94ad1581c7003a5f38545c7 | refs/heads/master | 2023-02-09T22:47:49.312192 | 2023-02-01T19:07:40 | 2023-02-01T19:07:40 | 129,778,978 | 133 | 21 | NOASSERTION | 2018-11-08T19:03:34 | 2018-04-16T17:12:03 | R | UTF-8 | R | false | false | 373 | r | test_utils.R | ## tests for universal functions (umap_universal.R)
## ############################################################################
## Tests for exact nearest neighbors extraction
test_that("message when verbose set", {
expect_message(message.w.date("hello", TRUE))
})
test_that("message by default when verbose not... |
90b3e6f2b68b4c40f6956cd22a85a9640d60b13f | a800dff7c2068108a2502e034d0625b247a87b46 | /R/test-output-contains.R | 4bc7ca391e6bad4245cd4090e90eaa9d34caf4b9 | [] | no_license | selcukfidan47/testwhat | 9c4786e4654d404c54affd510a4735819a843b5c | a7c03fd6848881915fe6657d5c5c45db90392ce0 | refs/heads/master | 2021-01-16T22:36:14.340497 | 2016-02-15T11:00:07 | 2016-02-15T11:00:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,218 | r | test-output-contains.R | #' Check whether the student printed something to the console
#'
#' Function checks whether the student's console contains the output one gets by
#' evaluating the character string provided in \code{expr} provided to expr.
#' This function needs refactoring, as all new lines etc are removed.
#'
#' @param expr The expr... |
b69be022e21cb39d0c97a2b9763c0e0ad592e6f2 | 4cb0878310a0891c07c21b6152b57257ffd5cdf4 | /R Tips & Tricks - Summarizing and Visualizing Data.R | 20fe723f081dc116709c82e19f0367a8964628d9 | [] | no_license | pw2/R-Tips-Tricks | 75cc89a970b18c5a39e3964cfe8a0fb8a8513fc8 | 3ae303b4b296a18817ab7389eed966743283220b | refs/heads/master | 2023-06-07T23:05:42.832942 | 2023-06-01T03:00:32 | 2023-06-01T03:00:32 | 254,485,479 | 7 | 6 | null | null | null | null | UTF-8 | R | false | false | 5,736 | r | R Tips & Tricks - Summarizing and Visualizing Data.R | ### R Tips & Tricks: Summarizing and Visualizing Data ###
## Patrick Ward
## load packages -----------------------------------------------
library(tidyverse) # for data manipulation and visualization
library(gridExtra) # for organizing plot grid
library(psych) # for summary statistics
theme_set(theme_bw()) # setting ... |
4f53c70cf93ab70028a17bfa5dae61e9098e7abd | 5427315c68c26753b28e18432c118621b9b984fc | /POST_plattscaling.R | 94f674f4d69e045e9531d7a6260a25896df40487 | [] | no_license | JohannesJacob/fairCreditScoring | dc58734b210a6e118628fc446b6710201ea655db | 10ebe9878cc63ddb8aab69159e2a097f5c09116e | refs/heads/master | 2020-12-13T02:16:32.566382 | 2020-06-22T10:53:29 | 2020-06-22T10:53:29 | 234,286,257 | 0 | 0 | null | null | null | null | MacCentralEurope | R | false | false | 4,254 | r | POST_plattscaling.R | # POSTPROCESSING PROFIT EVALUATION
setwd("C:/Users/Johannes/OneDrive/Dokumente/Humboldt-Universitšt/Msc WI/1_4. Sem/Master Thesis II/")
rm(list = ls());gc()
set.seed(0)
options(scipen=999)
# libraries
library(EMP)
library(pROC)
source("fairCreditScoring/95_fairnessMetrics.R")
# read data
dtest_unscaled <- read.csv("... |
9f5376f1179fbccfbcd2e41bc1da9c4989fc3b0c | 0005fc8b3163d8bd8fb5d80fa7db1ddc3cc50229 | /man/plotSequenceEvents.Rd | 705b67d110d849212cb134778d649135df51643c | [
"MIT"
] | permissive | KWB-R/kwb.base | 2f54c0de826eb47d964131e1bda9ff9105c4ff24 | 0a8f37eec4ebee01e591fdade5ae60e97108270b | refs/heads/master | 2022-08-11T18:04:09.478100 | 2022-06-09T19:52:31 | 2022-06-09T19:52:31 | 137,505,674 | 0 | 0 | MIT | 2022-06-11T01:30:18 | 2018-06-15T15:46:06 | R | UTF-8 | R | false | true | 632 | rd | plotSequenceEvents.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/baseValidation.R
\name{plotSequenceEvents}
\alias{plotSequenceEvents}
\title{Plot Sequence Events}
\usage{
plotSequenceEvents(
timestamps,
sequences,
main = "Overlapping time sequences in hydraulic data",
language = "de"
)
... |
44ed51f84b46cf38580c766b70d32ca25db98ddb | d04e8e91a28ebe98128152f06aa6a5a1104e03da | /scripts/helpful_snippets/self_sufficiency_2020.R | 8b4b34123ef1742d56602398b754067d64eb80c6 | [
"MIT"
] | permissive | BPSTechServices/income-analysis | de0dd33ea3fda022b6b219734d419252ad219da0 | 991c3a2a316ae4d5f51e496bb8adf205646b9a86 | refs/heads/main | 2023-03-09T09:02:55.670310 | 2021-02-22T20:20:21 | 2021-02-22T20:20:21 | 340,525,301 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,945 | r | self_sufficiency_2020.R | ##### Load libraries, set working directory #####
pacman::p_load(tidyverse, data.table, srvyr, ipumsr)
options(
scipen = 999, # remove scientific notation
digits = 4, # set data precision for readability
stringsAsFactors = F, # string variables are brought in as charactors
dplyr.width = Inf,
survey.replicate... |
2bba00d2aded15915780eeb0e199ac6ad7375026 | e4228d2482a085d3355964c278269a082959e038 | /R/tcplMthdList.R | 16592c0e238f9957274519dbdf71e92b8fa0a847 | [
"MIT"
] | permissive | USEPA/CompTox-ToxCast-tcpl | 08d9667ee76532382f2ef2fe7da2e7b8ebc26b2b | a8582c61883ba6e6f25b503cfa1f1b37605e3b29 | refs/heads/main | 2023-08-30T12:53:21.736913 | 2023-08-24T13:40:02 | 2023-08-24T13:40:02 | 89,386,154 | 23 | 13 | NOASSERTION | 2023-09-13T14:07:15 | 2017-04-25T17:05:00 | R | UTF-8 | R | false | false | 966 | r | tcplMthdList.R | #-------------------------------------------------------------------------------
# tcplMthdList:
#-------------------------------------------------------------------------------
#' @rdname mthd_funcs
#' @export
tcplMthdList <- function(lvl, type = "mc") {
tbl <- paste0(type, lvl, "_methods")
qstring <- paste0... |
15f014b2c30c0c96034af7189adc8b485a0f2a7b | 4b2389814b5e717c240d8f80cf0f4f6114a30995 | /Hwk_Reduced_Rank_FDA.R | ef2204f5e1b68f31a297b5a7c9a24ee62b7a031c | [] | no_license | jaime-gacitua/HW2ML-Classification | 6b6c6aac9297e9e8d9e0ec5f357097aa7e0835d9 | a99607d90e664bcf7b0a7ef997897d5019168039 | refs/heads/master | 2020-05-23T08:06:50.847629 | 2016-10-12T21:55:37 | 2016-10-12T21:55:37 | 70,308,385 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,590 | r | Hwk_Reduced_Rank_FDA.R | require(DAAG)
require(ggplot2)
require(MASS)
library(caret)
# This code implements reduced rank LDA (Fisher Discriminant Analysis)
# It can reproduce the subplots of Figure 4.8 in HTF by specifing coordinates a,b
# For example, a=1,b=3 reproduces the top-left sub-figure of Figure 4.8
a=9 # First Fisher coordinate to ... |
d707fb4f842077d2889f858efa04ba9a6e086fce | e4c8af552f8801a088ca91a6cffe77689089d5d7 | /src/Analysis/other/10b-regress-3day-body-entero-pool.R | 7390d194cf8035a3db115dca7fd96560680d24eb | [] | no_license | jadebc/13beaches-coliphage | eb6087b957dbfac38211ac531508860f48094c15 | 3d511ffa91a6dd5256d6832162ea239c1dbbad28 | refs/heads/master | 2021-06-17T03:38:00.805458 | 2017-04-27T22:50:06 | 2017-04-27T22:50:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,174 | r | 10b-regress-3day-body-entero-pool.R | ##########################################
# Coliphage analysis - 6 beaches
# v1 by Jade 7/13/15
# This file conducts maximum likelihood regression
# to estimate prevalence ratios
# for enterococcus
# Results pooled across beaches and assay
# 3 day gi illness
##########################################
rm(list=ls())... |
063e35087be6e671c78dd4123b335cf4622c2c61 | f8c3946a0bc31d2830bf2863aa46b42325807015 | /data/cibm.utils/examples/read-abk.R | 69e398877ad5cade305525762184a7cba1ceb7ce | [] | no_license | vfpimenta/corruption-profiler | 87c2fde7025cb54341af03eba12007a0267b12b3 | 1366406ce21b05d88b72174aef9bb987921eb7e1 | refs/heads/master | 2021-01-18T04:07:58.830460 | 2018-06-01T22:03:37 | 2018-06-01T22:03:37 | 85,761,525 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 228 | r | read-abk.R | # loads library
library("cibm.utils")
# defines filename
filename <- system.file("extdata","2695.abk",package="cibm.utils")
# reads
a2695 <- read.abk(filename)
# prints -- Notice that class is numeric
a2695[c(1:6,100),1:9]
|
f2a3a90c576466b115f935d19abc1a8870499b5f | bf8e155d3f6a835082c7a17d9c9fa2a8c4e0331e | /R/SavePlotDialog.R | 039da88ddaf893388c4d4e1930474377eceb6259 | [] | no_license | sebkopf/dfv | 94ba5e2fab9013e42601eeff4ac8865a9969f1bc | 55fcc88a81e26ef039ef3dd9a6707386b306e02b | refs/heads/master | 2016-09-02T02:39:00.117472 | 2015-12-10T20:45:57 | 2015-12-10T20:45:57 | 14,060,261 | 0 | 0 | null | 2014-02-04T07:00:04 | 2013-11-02T03:40:37 | R | UTF-8 | R | false | false | 5,777 | r | SavePlotDialog.R | #' @include ModalDialog.R
NULL
SavePlotDialogGui <- setClass("SavePlotDialogGui", contains="ModalDialogGui")
setMethod("makeMainGui", "SavePlotDialogGui", function(gui, module) {
mainGrp <- ggroup(horizontal=FALSE, cont=getWinGroup(gui, module), spacing=0, expand=TRUE)
treeGrp <- ggroup(horizontal=FALSE, expand=T... |
65d0a0ff53a8f043af056d0a6812bfde77acc473 | 0ce98d31209c407007715713dd8e20fdcfdb3fe0 | /R/print.CatTable.R | 0233144489b85795aa31e2a29037b732b728b3a6 | [] | no_license | kaz-yos/tableone | c5cd484e41540b7198680a26abd4ff4cd15708b8 | e2e7cccdc8b9d51c8cb2bb1da91f4663bed5e128 | refs/heads/master | 2023-05-12T22:26:52.823370 | 2022-04-15T14:24:00 | 2022-04-15T14:27:35 | 16,124,394 | 200 | 48 | null | 2023-05-06T23:59:15 | 2014-01-22T01:06:44 | R | UTF-8 | R | false | false | 16,668 | r | print.CatTable.R | ##' Format and print \code{CatTable} class objects
##'
##' \code{print} method for the \code{CatTable} class objects created by \code{\link{CreateCatTable}} function.
##'
##' @param x Object returned by \code{\link{CreateCatTable}} function.
##' @param digits Number of digits to print in the table.
##' @param pDigits N... |
3eb945a247d53fa56d9afbf9436e1fc6261c199a | 687a8bdf3119ef7a13449fa4a721f2c894472acb | /finalpapersubmission/finalcoderepo/ubiproject.R | c68a8236eae335ee658253ce92c366cc959fbf03 | [] | no_license | zliu2000/econometrics_final_project | abbecc609bbd39225d373a4b358e1ee0e706b271 | 0f3bcefea472b02ef49c3c3b99d2606d47294d8e | refs/heads/master | 2022-11-26T19:05:27.537620 | 2020-07-26T03:09:24 | 2020-07-26T03:09:24 | 282,562,051 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,491 | r | ubiproject.R |
# ECON 21030 Econometrics - Honors
# Spring 2020, Final Project
# This version: 05/27/2020
# Author: Zhengyang (Jim) Liu
################################################################################
################################ (0) ENVIRONMENT ###############################
###################################... |
4fad11d42d5ac121d7687b84727db5a5cddad501 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RCPmod/examples/cooks.distance.regimix.Rd.R | 40a85b66152ed410b4b5e410f3936ac07eb0af70 | [] | 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 | 873 | r | cooks.distance.regimix.Rd.R | library(RCPmod)
### Name: cooks.distance.regimix
### Title: Calculates leave-some-out statistics for a regimix object,
### principally a version of Cook's distance and cross-validated
### predictive logl
### Aliases: cooks.distance.regimix
### Keywords: misc
### ** Examples
## Not run:
##D #not run as R CMD ch... |
afa875a1d0d24060f05a99a0c3ca0f8b5460de81 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/embed/examples/step_lencode_glm.Rd.R | 426acca0597b6cf1e00c2b99de74e34b17122d2b | [] | 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 | 445 | r | step_lencode_glm.Rd.R | library(embed)
### Name: step_lencode_glm
### Title: Supervised Factor Conversions into Linear Functions using
### Likelihood Encodings
### Aliases: step_lencode_glm tidy.step_lencode_glm
### Keywords: datagen
### ** Examples
library(recipes)
library(dplyr)
data(okc)
glm_est <- recipe(Class ~ age + location, da... |
8383a6d91e029b93a0acd5f4e5b01552cf4a8b77 | 7f7e928be8f8e54f507cf7a7da20389ecc677c8d | /GoogleTrendsScaled.R | 5d1e89a18bd9a2c845ef8f5262e4148598766aba | [] | no_license | ielbert/GoogleTrendsScaled | 51f8bdf0cc0a4221d6a2058c8ba5ee42ba04dcbf | bbe7ea7ada93ea7f40f3d2c1ca018a74229e3265 | refs/heads/master | 2016-09-06T12:22:59.395034 | 2016-02-03T15:13:15 | 2016-02-03T15:13:15 | 35,433,723 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,390 | r | GoogleTrendsScaled.R | ######### Using GTrendsR only for more control
library(GTrendsR)
library(RCurl)
library(rjson)
library(reshape2) #For melt
library(plyr) #For ldply
library(dplyr)
initTrend <- function()
{
ch <- GTrendsR::gconnect("Your Gogole email", "Your Google Password")
authenticatePage2 <- getURL("http://www.google.com", cur... |
4bb85b48a8cabd9b64e39fde13ece3712c68d755 | 02c727c80d9842eb479751345c6cf69623f7705e | /Claimants_RCode.R | d6c61c0eef1ec3b18eabd84127bf9ba2160c5c28 | [] | no_license | sanmitjadhav/Logistic-Regression-in-R-Data-Science-Machine-Learning- | 4dbc79baf6f9c70932bc65f48f171b3fc6866aec | 3ca4a940d07b446c0efafcbfaf27f44ebab53ee3 | refs/heads/master | 2022-11-15T05:03:06.863574 | 2020-07-06T06:54:52 | 2020-07-06T06:54:52 | 277,465,149 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,261 | r | Claimants_RCode.R | claimants <- read.csv(file.choose())# Choose the claimants Data set
View(claimants)
data=claimants[,-1]
View(data)
claimants <- na.omit(data)
View(claimants)
model <- glm(ATTORNEY~.,data=claimants,family = "binomial")
summary(model)
# Confusion matrix table
prob <- predict(model,type=c("response"),claimants)
... |
2ac5dbf28f0ebc0f07d7b451b5a8d5ae9187b2c4 | cf2efa64bdcaad29d118177a8370174bf2aa3c3c | /examples/core.R | 37a806304b6e80c2e9e06fba6a7b34e67ba6029c | [] | no_license | rOpenGov/europarl | 30837efe1c5b1e0c886b745d02e0f739c6f63d49 | f6bda62a433f8c5595078f3b93df877d706cac6f | refs/heads/master | 2023-07-22T10:48:43.859213 | 2023-07-13T13:27:50 | 2023-07-13T13:27:50 | 97,875,276 | 9 | 2 | null | 2021-10-13T21:48:03 | 2017-07-20T20:23:31 | R | UTF-8 | R | false | false | 1,888 | r | core.R | #return subpage of give page & lnk
subpage <- function(url, link) {
s <- html_session(url)
s <- s %>% follow_link(link)
url_1 <- s$url
page <- read_html(url_1)
return(page)
}
#return genders & current status
# change, current status out
get_gender_active <- function(name) {
url <- "http://www... |
a3ea7b747af5a0490e073f26f8e1c44fb4288730 | ca0f6de8b85a8e82b6107906ff3f4c862f3eb92f | /data-raw/watershed/floodplain_utils.R | 8a9b27f1b8fd56f26c0f2f2ca66ad030a62a96cb | [] | no_license | isabellekavanagh/cvpiaHabitat | c583acdf9e2d587a47a2848aa2f9ff99cf526c15 | 083705017b618466847b299f7fb9addb8a1619ca | refs/heads/master | 2021-07-03T02:14:43.895654 | 2020-04-24T18:53:54 | 2020-04-24T18:53:54 | 180,662,606 | 0 | 0 | null | 2020-07-18T00:04:59 | 2019-04-10T20:55:37 | HTML | UTF-8 | R | false | false | 10,056 | r | floodplain_utils.R | library(tidyverse)
library(readxl)
library(glue)
# TODO check that metadata sheet is up to date
metadata <- read_excel('data-raw/watershed/CVPIA_FloodplainAreas.xlsx', sheet = 'MetaData',
col_types = c('text', 'text', 'text', 'text',
rep('numeric', 17), 'text... |
6a612f549128a36b9191c543e2d58889f02d18a4 | d3f02cbbe322bba1a116549f4568f51d3048125a | /source/rescue.R | ac8e0ff762f0caf1a84e2c1ad82b5d78bfe8c57b | [] | no_license | zuojung/rube_research | f840684bc2abed60d5ef29e5f86d62bb404be64f | bdf80033f23842aeed2de5fc782bb10361f3ed6a | refs/heads/master | 2021-06-11T10:21:39.765580 | 2017-02-28T20:17:06 | 2017-02-28T20:17:06 | 81,767,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,298 | r | rescue.R | ## When WinBUGS fails, but "bin" is set, the results up to within "bin" iterations
## of the crash will be saved in codaIndex.txt and coda#.txt.
## With what=MCMC, rescue() places the last "n" recorded iterations into a
## (partially complete) rube object. With what=startVals, the last recorded
## values (or n before ... |
7610c128d67c247fe149f77b8f52406f47ae6c36 | 1d849c62f86b3f820a16625badb20510903776c0 | /lab1.R | b5e1a944f1a3f1ca35cc8546f8c4de01dd623068 | [] | no_license | vampzmage/DataAnalytics2021_Hongbo_Zhao | 20197f099d71fe3118bc089c8352dc8e8661889a | e9f0cff8f5a030644295eb7136810def62ecfab3 | refs/heads/main | 2023-02-27T15:34:58.337373 | 2021-02-11T14:24:53 | 2021-02-11T14:24:53 | 335,978,125 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,338 | r | lab1.R | days <- c('Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun')#days
temp <- c(28, 30.5, 32, 31.2, 29.3, 27.9, 26.4) #temperature
snowed <-c('T','T', 'F', 'F', 'T', 'T', 'F') #snowed that day
help("data.frame")
RPI_Weather_Week <- data.frame(days,temp, snowed) #create dataframe
RPI_Weather_Week
head(RPI_Weather_Week)... |
c56176dcc25762b52e00283d744dd029c4e13434 | 2d4c23464da9708b267ca744dd979d02925b3739 | /5_SVM_Prediction.r | 1558e43999c357ecfda4c218adb363b31075739e | [] | no_license | hellogithub2018/wine_analysis- | b69b5a7fdd55f16fb79d4227e028f866741e4ac3 | 496e82f4c14fb04a8269810497541bbfeebe8533 | refs/heads/master | 2020-03-21T23:16:48.836264 | 2017-05-28T17:37:05 | 2017-05-28T17:37:05 | 139,176,992 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | 5_SVM_Prediction.r | library(e1071)
library(xlsx)
library(corrplot)
library(lattice)
library(ggplot2)
library(caret)
library(rpart)
library(rpart.plot)
library(randomForest)
library(ineq)
t1 = read.xlsx("/Users/AjayKulkarni/Study/Master of Science/Sem 1/CSI 777/data/winequality/new_f/Training.xlsx",1)
t2 = read.xlsx("/Users/AjayKulkarni/... |
3616d3bf6bffa7c49aaa975696b8609b07998ba6 | d432e3106e2b9d7806ac8097a781a4d51953e778 | /models/eda.R | a692855421c3116c74872dc97c748d0866a85cfa | [] | no_license | Moeymoeymoeymoey/STAT154-Final-Project | cd9cf563ad4bec98baa7d3fbc61aa32fcc46acb0 | 94c55dc71c1e9a63825326b48a76599bbd91e22f | refs/heads/master | 2021-01-20T13:04:07.559721 | 2017-05-06T07:38:04 | 2017-05-06T07:38:04 | 90,443,307 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,814 | r | eda.R | library(tm)
library(nnet)
library(e1071)
library(ggplot2)
business.train = read.csv("yelp_academic_dataset_business_train.csv", header = T)
business.review = read.csv("yelp_academic_dataset_review_train.csv", header = T)
qplot(data = business.train, stars, main = "Business Stars")
qplot(data = business.review, stars,... |
449b5aaeefe4d77289b1b6f8ddd38258a7acbf49 | b45043e9d6e2ee18c79253121ad4a8075cc6633f | /plot2.R | 8fddd87c0924189c5009887d24d03c67ce55d713 | [] | no_license | redjoy-17/ExData_Plotting1 | 32de9b2a46da64113f261d22596f53aa240d93eb | 4df8fd598765da2f10db907d4f3757a37550ff1f | refs/heads/main | 2023-01-30T09:06:30.491789 | 2020-12-12T17:35:33 | 2020-12-12T17:35:33 | 320,884,828 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 533 | r | plot2.R | #Script2
directory<-"/Users/Gaia/Downloads"
setwd(directory)
data<-read.table("household_power_consumption.txt",header=TRUE,sep=";",dec=".")
library("dplyr")
data$Date<-as.Date (data$Date,format= "%d/%m/%Y")
data<-subset(data, Date >= "2007-02-01" & Date <= "2007-02-02" )
data$DateTime=as.POSIXct(paste(data$Date,data$... |
4577c0d4d808031d052c7a7d4e0973fd1ed0d31d | 319a13e48a7e26e5ab660c9dba30521834203dab | /RFiles/rgamma.R | 1e8754ad38be3ee3e88b333e49f649b575f159e5 | [] | no_license | marco-tn/StaticticsBasic | 9b4914bec56571754b54481e53f389654178cb3b | 6dba8d4ac01759cd1e7af302386b9a34f3594475 | refs/heads/master | 2020-08-03T13:06:57.357421 | 2019-09-30T03:00:07 | 2019-09-30T03:00:07 | 211,762,589 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 678 | r | rgamma.R | par(family = "HiraginoSans-W4") # 文字化け回避
# ガンマ分布
set.seed(123)
rgamma(10, shape = 3, rate = 1)
# 統計的性質
nu <- 4
alpha <- 2
x <- rgamma(10000, shape = nu, rate = alpha) # ガンマ乱数を 10000 個
mean(x) # nu/alpha=2 に近い(大数の法則)
hist(x, freq = FALSE, breaks = 50, col = "gray", border = "white",
main = bquote(paste("ガンマ分布 ", G... |
f00f09ff98c434185dadc8cc8ea452c74a89c1de | acf43911124dfbe1871a9e5e419581ea0dd1d317 | /analysis/sl-psychopy-analysis/rt_slope/nov_pilot/scripts/nov_pilot_rt_slope_cleaning.R | a68a829fa46edec7d742acd12e0ff2297f97afb0 | [] | no_license | aluu6/qlab | 0919fd482ef151c9f18eee6d5c8c905e8f89f9f2 | 20a8a4572e2bd44c16db8eddb3ccdebb4119c73f | refs/heads/master | 2020-06-12T01:10:24.387816 | 2019-06-27T19:15:38 | 2019-06-27T19:15:38 | 192,003,602 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,430 | r | nov_pilot_rt_slope_cleaning.R | # FMRI Pilot Analysis
# Violet Kozloff
# March 15th
# This script cleans the auditory and visual files from the November pilot
# NOTE: Does not include participant 9, who did not respond in several conditions
# NOTE: Original psychopy files have been renamed to have a letter as a leading character
# NOTE: Exclud... |
5fb18e0ce69e78d2f224c7a3da0d6d556d2c5958 | 08b2fbbcae2905aa361001743e78784727369046 | /man/add_global_p.tbl_uvregression.Rd | 623d5ba7cef90d6716af56d31e9f70740fff2e10 | [
"MIT"
] | permissive | shijianasdf/gtsummary | d0bd29d40b31d64ba77c4f19ff4aa74e37d92b1e | a6b4bf41553a336223c0d2656135349a2bf1de98 | refs/heads/master | 2022-12-11T16:06:26.278085 | 2020-09-14T18:11:35 | 2020-09-14T18:11:35 | 295,585,427 | 2 | 0 | NOASSERTION | 2020-09-15T01:50:55 | 2020-09-15T01:50:54 | null | UTF-8 | R | false | true | 2,034 | rd | add_global_p.tbl_uvregression.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_global_p.R
\name{add_global_p.tbl_uvregression}
\alias{add_global_p.tbl_uvregression}
\title{Adds the global p-value for categorical variables}
\usage{
\method{add_global_p}{tbl_uvregression}(
x,
type = NULL,
include = everything(),... |
1b0ff836886bf768bbe49dfe56930aca476a60a8 | fa3d752f2667846a58f871e381251b46ab4031f7 | /Scripts/Figures/top-to-pathway.R | ad92d77ad34a8252e06d2770456574ee8edeeadf | [
"MIT"
] | permissive | rhong3/Segundo_Melanoma | c645b345da898ed56b22ae35eb71c73cadb5255f | 603d991d6537cb7bffdd6c78a96de14d1a34c87a | refs/heads/master | 2022-04-30T02:19:22.370079 | 2022-03-21T18:23:45 | 2022-03-21T18:23:45 | 234,380,882 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,109 | r | top-to-pathway.R | # 10 genes pathway involvement
toplist = c('ADAM10', 'SCAI', 'TEX30', 'HMOX1', 'CDK4', 'CTNND1', 'DDX11', 'FGA', 'PAEP', 'PIK3CB')
library("org.Hs.eg.db")
library("reactome.db")
annotation <- select(org.Hs.eg.db, keys=toplist, columns=c('SYMBOL', 'ENTREZID'), keytype="SYMBOL")
annotation.path <- select(reactome.db, ke... |
ab89b80d182c895362aad1bb5e94c6de31f57d31 | eaa49925c9c3db2b41bab152c0488fd957a588d3 | /R/Rscript.R | 8a6b4654a1e8c8efd8ccab5aa36743d09a6f9c5d | [] | no_license | gmonette/testrep | 7fd00661aa160906a72c502e4ff013647c6373e5 | 0ab82dcf8a368eac973e07a824c48d2f26e518d7 | refs/heads/master | 2021-01-10T10:54:06.792883 | 2016-11-07T04:05:31 | 2016-11-07T04:05:31 | 46,613,397 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25 | r | Rscript.R | THis is an R script file
|
5e77240e6309681b9e700644b12248f58bce499a | c77938ab77375bd8a524daba269e83a201c22cdf | /modules/focalCalculation/focalCalculation.R | 66432b4e6299afe78aa4e2e931f74fcc08267013 | [] | no_license | tati-micheletti/borealBirdsAndForestry | dbe385d5129770caec12e986371fc76f2317801e | 27d11df65759ed720d2a6646132597a117c3fc39 | refs/heads/master | 2021-03-19T17:13:20.851222 | 2020-09-17T19:07:29 | 2020-09-17T19:07:29 | 121,669,535 | 0 | 4 | null | 2018-08-02T21:52:30 | 2018-02-15T19:03:31 | HTML | UTF-8 | R | false | false | 5,485 | r | focalCalculation.R | defineModule(sim, list(
name = "focalCalculation",
description = paste0("This module reorganizes tiles from another module",
"(matches the exact tile location from all different lists),",
"applies a binary function to certain rasters of all tiles ",
... |
c0e43514fe160992c972232481be92a9c483d1c1 | 1c366b0df210fecfdd4e16049bc3b28c961e6595 | /install.R | 254ca36c600e0ce81d935b105ab255a782405bcf | [] | no_license | asancpt/workshop-nonmem-2017 | f13476469a25b71f2c5d3187fac06a9e2ff0b457 | c1654be70d652442ebeb363691626b510e03305b | refs/heads/master | 2021-06-11T13:50:01.010553 | 2017-02-22T00:52:12 | 2017-02-22T00:52:12 | 82,635,468 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 94 | r | install.R | install.packages("c:/NMW2017/nmw_0.1.0.zip", repos = NULL, type = "win.binary")
library(nmw)
|
9788c040ff8d675f9671b77fcb259b21b292d1b6 | b4c34a229ddcfc5e33e25baea1d28364faf17270 | /man/readDescFile.Rd | ee449e5bf4bf1218cc7a3c6edf3a79ffefebbd36 | [
"Apache-2.0"
] | permissive | ammar257ammar/ArrayAnalysis-Bioconductor | f5e1ec1397d033155784d5b7f803513c394aa3ff | 4efd1bb11136d7c8108998ce20c31506e5830efb | refs/heads/master | 2023-03-26T21:16:03.584761 | 2021-03-25T15:57:37 | 2021-03-25T15:57:37 | 351,494,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 477 | rd | readDescFile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_Stat.R
\name{readDescFile}
\alias{readDescFile}
\title{Read desc file. Return array names and groups}
\usage{
readDescFile(descfile, outprint = FALSE)
}
\arguments{
\item{descfile}{(Status=required)}
\item{outprint}{(datatype=logic... |
cc841cc0fbfbb8beb60866fa69addd911438e413 | cce54237311aab66c2914e4992eb844fe5d260c7 | /Code from Ephraim/AntNetwork.r | c95246015c53efb2469976baf0b8a03fb8da9afa | [] | no_license | MLBartley/Ant-Research | 765ec5fb04366c289b950ba8614472cb26f5b302 | 921e560d6802bdc930ec622834a51d39a2c12487 | refs/heads/master | 2020-04-05T23:16:03.410628 | 2019-12-05T19:13:54 | 2019-12-05T19:13:54 | 42,834,698 | 0 | 0 | null | 2017-11-03T18:04:38 | 2015-09-21T00:08:07 | HTML | UTF-8 | R | false | false | 2,138 | r | AntNetwork.r | ##############################
##
## Read in data
##
## antlist.rg2 = A "list" where each object is an individual ant
## ID = ant ID
## type = forager, nest, or Queen
## cells = vector of cell IDs the ant was in, in temporal order
## t.in.cell = time spent in the cell
## time = actual time the ant... |
c28a1f2a178a7a08019c976f44693af18aa5c542 | 450d1e2f3f661fb725f5dd86c243967d825ccaf4 | /ContinousDistn.R | f4d2e3b1d24783c4bd4136be5542d3e0887f9a4d | [] | no_license | wli289/R | 5a18f85ca34c06adb87a422740d2fa3736702041 | 2b327e387784f35a113857e67c63e298d8889601 | refs/heads/master | 2021-05-14T05:25:19.510529 | 2018-01-04T06:01:48 | 2018-01-04T06:01:48 | 116,221,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,782 | r | ContinousDistn.R | # Basics R
# Continuous distributions in R, splicing and mixing
#install.packages("actuar")
library(actuar)
library(VGAM)
###############################################################
# Example 1: gamma distribution
# define a grid
x <- seq(0,1000,by=1)
# define a set of scale and shape parameters
scaleparam <- se... |
f3767581c11b8c2b1e4325baa4d11fd8aaed3972 | 3903745168bccf83d719c0e548a7d5f3e3ae23f2 | /new/5_disturbance_graphs.R | 43987067b2478edfcde77ab28ae45fe21cf97146 | [] | no_license | remoteforests/remoteforests_disturbance | 210837a2a466436071b021bdf5efd7e63bfebdfb | fce58ee2c7fea8706f8d90c29dc3e86ddaf1f0da | refs/heads/master | 2023-07-19T17:37:15.078421 | 2023-07-12T16:00:52 | 2023-07-12T16:00:52 | 127,264,617 | 3 | 0 | null | 2023-05-11T17:12:21 | 2018-03-29T08:53:55 | R | UTF-8 | R | false | false | 2,001 | r | 5_disturbance_graphs.R | # 0. setup ----------------------------------------------------------------
library(tidyverse);library(pool);library(ggrepel)
source("new/pw.R")
# 5. GRAPHS ---------------------------------------------------------------
# 5. 1. data --------------------------------------------------------------
data.all <- tbl(KE... |
f09b34a10b7795fd4253b32ee4ae561231c6b56f | 977e25b030bc27e923f52b08305a6dec2cfd02fd | /financial_trading/c01_trading_basics/basics.r | e98fe158207394c2424b50941146a7aebdfefd49 | [] | no_license | printfCRLF/rr | d4cd813fafef7d64da2722ade9e14220c12e17ff | 4116f726f5ad7a8cadbe6841d13abbdb998ee294 | refs/heads/master | 2021-04-15T15:08:37.032087 | 2019-07-12T08:29:26 | 2019-07-12T08:29:26 | 126,468,211 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 150 | r | basics.r | getSymbols("SPY",
from = "2000-01-01"
to = "2016-06-30"
src = "yahoo"
adjust = TRUE )
plot(Cl(SPY))
|
bdc906a82d2d1729c8543cbcd717971067d43047 | 1205947be035b71dbe297a2a9f3ca99254244d35 | /Leaflet/couche geo.R | e434e3d8d4d0963423eceddf0531f72737a74842 | [] | no_license | vivienroussez/Maille_habitat | 08389903053240633ff643ed0e66c57bd6f18db6 | af0194f313be5c8e7988ad1f304310c944cbf9d5 | refs/heads/master | 2020-03-18T19:24:21.728438 | 2018-06-27T06:50:59 | 2018-06-27T06:50:59 | 135,152,333 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,523 | r | couche geo.R | require(tidyverse)
require(foreach)
require(doParallel)
require(maptools)
# setwd("M:/ETUDES/Zonage logement/")
#setwd("D:/Zone logement/")
# load("com_zones.RData")
# load("../Indicateurs/data/Base.RData")
# load("Zonages/Zonages_reg.RData")
######################################################################
###... |
99ad5d7db84461f7880c745d7a35b0c661db8224 | 87bda86c8f157f8eb02bb6eac56621d20009625f | /R/kissr.R | 8eaee495bac6873976aa4212f00c2e8fe7f44f4c | [] | no_license | jack-palmer/kissr | 3da4935b3b4260703b2132ea85d01668b4e4936d | f57b22665e9fd597e45454122c50eb32556d1c36 | refs/heads/master | 2020-12-28T23:51:00.347963 | 2016-07-18T17:32:27 | 2016-07-18T17:32:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 614 | r | kissr.R | #' kissr: A package for loading report data from Kiss Metrics
#'
#' The kissr package provides one function:
#' `read`
#'
#' You can call the read function with a \link{KissReports} object to get a list
#' of all reports associated with your KissMetrics account and you can call the
#' read function with a \link{KissRep... |
b503bf943cdea71df5506d5bf678fd9ddb9e83a8 | d44ba73766486241f2e2f05f9a8e2f289d195a40 | /man/check_deviation_set.Rd | 530020c7e7fbc65859f55e2ccae21ee7fd088495 | [] | no_license | TGuillerme/spptest | 080536dfa0fe28d00a62735d30ae231696cd78eb | 0e76a605262c52fcb7582b053aacafbd023c7393 | refs/heads/master | 2020-03-29T01:42:27.174391 | 2016-12-16T12:49:57 | 2016-12-16T12:49:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 321 | rd | check_deviation_set.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deviation.r
\name{check_deviation_set}
\alias{check_deviation_set}
\title{Check the object.}
\usage{
check_deviation_set(deviation_set)
}
\arguments{
\item{deviation_set}{A potential deviation_set object.}
}
\description{
Check the object.
}
... |
3a68a82906c9cbcf2d6f048d9bda94c4ba7eeb8c | 18fe33b772956c677e07bd7f508c7fc2181e22ed | /tests/testthat/test-optimize-leaf.R | 05fde24e5f5cd7d79dcbc7a6bf5cc5176d124e0e | [
"MIT"
] | permissive | muir-lab/leafoptimizer | d4667bba98c44696971c0dfa4de793c9e754ea17 | 0929db5a42dc237155ec8708baf357c1e5aaebce | refs/heads/master | 2023-04-07T06:41:08.539178 | 2021-09-07T20:28:14 | 2021-09-07T20:28:14 | 92,799,973 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,961 | r | test-optimize-leaf.R | context("optimize_leaf")
library(leafoptimizer)
# Revise and uncomment
# bake_par <- make_bakepar()
# constants <- make_constants()
# enviro_par <- make_enviropar()
# leaf_par <- make_leafpar()
#
# carbon_costs <- list(H2O = 1000, SR = 0)
#
# ol1 <- optimize_leaf("g_sc", carbon_costs, bake_par, constants, enviro_p... |
e815baa791d25a6f4341823c16ef31d40aea1e55 | 3fa1b23746232975b3b014db2f525007a3b49991 | /anna_code/device_validation/sig_align.R | 0902815d6808525f278370a45b6b13cfd1195617 | [] | no_license | AshleyLab/myheartcounts | ba879e10abbde085b5c9550f0c13ab3f730d7d03 | 0f80492f7d3fc53d25bdb2c69f14961326450edf | refs/heads/master | 2021-06-17T05:41:58.405061 | 2021-02-28T05:33:08 | 2021-02-28T05:33:08 | 32,551,526 | 7 | 1 | null | 2020-08-17T22:37:43 | 2015-03-19T23:25:01 | OpenEdge ABL | UTF-8 | R | false | false | 13,956 | r | sig_align.R | rm(list=ls())
library(XML)
library(ggplot2)
library(reshape2)
library(scales)
Sys.setenv(TZ='GMT')
source('sig_align_helpers.R')
subject="6"
first=as.POSIXct(strptime(as.character("20150921071000"),"%Y%m%d%H%M%S"),tz="PDT")
last=as.POSIXct(strptime(as.character("20150921080000"),"%Y%m%d%H%M%S"),tz="PDT")
subject_dir=... |
52e045d299582cf9048253f9e1d9422d6b8c118d | 196ff4c376e540a9e4ffd3293949af516c51319e | /R/fluster_methods.R | 56629a2e59eb275282e104f18db6649ebe694231 | [] | no_license | rogerswt/fluster | e1163375c2be750d8300e6923f6b3ccc2195ab6c | e5c455d698f0997074309b679860779819cb2b89 | refs/heads/master | 2021-07-24T04:35:59.018408 | 2021-07-17T20:15:30 | 2021-07-17T20:15:30 | 229,350,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,085 | r | fluster_methods.R | #
# fluster_methods.R
#
# declares exposed functions.
#
# 2019-12-18 WTR
#
#
################################################################################
################################################################################
# Copyright Still Pond Cytomics LLC 2019. ##
... |
a8a2240d19522ca02d22325d5edc08208fb6bfb2 | 7f89e404e52e5a72b4c6caa972f0f195f69c4240 | /model/calibration.R | 0271bbde196bc4ac8f94ff0af6ca4a8920c0441d | [] | no_license | brandonjoeltan/covstretch | 59d7f4321d1fb69a0894efb553253e48c903912b | 4fbd62ec539f66b92a85c726d3229c86f4da5b6a | refs/heads/main | 2023-03-19T01:10:55.321547 | 2021-03-16T22:42:30 | 2021-03-16T22:42:30 | 336,088,960 | 0 | 0 | null | 2021-02-04T21:36:50 | 2021-02-04T21:36:49 | null | UTF-8 | R | false | false | 2,734 | r | calibration.R |
library(tidyverse)
load("data/default_inputs.Rdata")
# Demographics (for comparing HIC vs LIC)
hic_pop <- pbc_spread[countries["High-income countries"],] %>% as.numeric()
lic_pop <- pbc_spread[countries["Low-income countries"],] %>% as.numeric()
ifr_hic <- c(0.002, 0.006, 0.03, 0.08, 0.15, 0.60, 2.2, 5.1, 9.3)/100
if... |
2996d9073a5466713f974fd069bffab0127d4524 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BBmisc/examples/collapse.Rd.R | d2c924cabe31c3f8979c10b550e0da64d2e1bda1 | [] | 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 | 181 | r | collapse.Rd.R | library(BBmisc)
### Name: collapse
### Title: Collapse vector to string.
### Aliases: collapse
### ** Examples
collapse(c("foo", "bar"))
collapse(c("foo", "bar"), sep = ";")
|
0929fb99784829ad4b94c50c34f4fcb64471dd51 | 77ff13c4c17a8f0c7469cd914eb856ebda6e52a2 | /man/predicting_sim.Rd | e6b93e2254d2c4e75a14652b8f50fae814e44f93 | [] | no_license | carlonlv/DataCenterSim | f88623620c32816e97bd53b78ef6931f66ca8521 | fa2cc2592969c40d3e8494c2be46a94641b235f1 | refs/heads/master | 2022-01-19T12:04:49.255542 | 2022-01-07T19:40:39 | 2022-01-07T19:40:39 | 228,258,775 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,540 | rd | predicting_sim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main.R
\name{predicting_sim}
\alias{predicting_sim}
\title{Simulation of Scheduling Jobs Based On Predictions.}
\usage{
predicting_sim(
object,
x,
xreg,
start_point = 1,
wait_time = 0,
cores,
write_type,
plot_type,
...
)
}
\... |
e787917df0e7cbd4887eeacbfce7373da40be8cb | 12af7f39927dd30022183067a704e881ceb077b2 | /scripts/print_session_info.R | 5ce80b283618983b055f83e505faa29a3e411f11 | [] | no_license | harmonic-analytics/r-docker-demo | 082d4be39f388dd46b39cda9ff6bb98928191b53 | 8ddf3cd37242ddd70ce5a0f51c2b7777bf4ab389 | refs/heads/master | 2023-01-19T17:23:50.628733 | 2020-11-24T20:19:41 | 2020-11-24T20:19:41 | 315,453,342 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 40 | r | print_session_info.R | library(data.table)
print(sessionInfo()) |
fddfe575a2dbfec71a4e04a8d2b7d722105db6be | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MPV/examples/p2.13.Rd.R | ed76f16912febded1984b809818790f305d6babe | [] | 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 | 644 | r | p2.13.Rd.R | library(MPV)
### Name: p2.13
### Title: Data Set for Problem 2-13
### Aliases: p2.13
### Keywords: datasets
### ** Examples
data(p2.13)
attach(p2.13)
plot(days~index, ylim=c(-20,130))
ozone.lm <- lm(days ~ index)
summary(ozone.lm)
# plots of confidence and prediction intervals:
ozone.conf <- predict(ozone.lm, inter... |
9f1fbd4c77442af74343d9ce6657beec9c356d87 | 8d22f71a6e7ffdcd47b507bd7ec097c7a8185f4b | /docs/app.R | 405e79fdf86a1892eb1979ce2bb02133e6c1d7e6 | [
"MIT"
] | permissive | RinteRface/waypointer | fe59e223f5b845928d400e3d5c5a31a53524a311 | c6363e281e8bda192feb0dad8dd3e13c450fecc5 | refs/heads/master | 2020-05-01T06:20:05.781988 | 2020-01-30T21:06:38 | 2020-01-30T21:06:38 | 177,327,500 | 11 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,546 | r | app.R | library(shiny)
library(waypointer)
library(fullPage)
options <- list(
sectionsColor = c('#FFF07C', '#E2FCEF', '#FFE2D1', '#ECEBE4', '#C4E7D4'),
parallax = TRUE,
autoScrolling = FALSE
)
ui <- fullPage(
menu = c("waypointer" = "link1",
"Trigger" = "link2",
"Animations" = "section3",
... |
2be76d1500e069e01946d1b9a20f33660eb3b17d | 081c62f36f7703d7987218c1c22931e083198e73 | /myelo/R/scholz12.R | 90b4552b305881fbb9c82eeca3c6247ced3e3810 | [] | no_license | radivot/myelo | be7ed23a6d1772e55310ced91270aa1d09da6735 | 2498bed404c98f096fcda4075c34a2881265e24b | refs/heads/master | 2022-12-15T00:11:22.751773 | 2022-12-04T14:24:36 | 2022-12-04T14:24:36 | 6,070,078 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,263 | r | scholz12.R | #'PKPD G-CSF model of Scholz et al. TBMM 2012, without pegfilgrastim or chemotoxicities
#'
#'This function returns the right hand side of an ordinary differential
#'equation model of G-CSF published by M. Scholz et al. in 2012
#'(in Theoretical Biology and Medical Modelling). Subcutaneous injections are not modeled.
#... |
78542be39cc83a9c025fb49a3169fb7b67bc090e | 7929670b01dddcf9bec30812e12867090048c23e | /corso di statistica/Esercizio 3.15.R | 37b0f73b94d9ff0c5a4237be4a0e3cbd4d68d3a0 | [] | no_license | DMMP0/R-stuff-from-school | 1fc193756d438957e67db63e29a33196cdb2d964 | cd1ec2197eee4b2d86c0fc0c06343364bec18183 | refs/heads/master | 2023-01-03T20:08:01.288399 | 2020-10-24T14:51:57 | 2020-10-24T14:51:57 | 302,140,349 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,159 | r | Esercizio 3.15.R | set.seed(203818)
#Si lancia per tre volte una moneta non truccata. Calcolare la probabilità
#a) di ottenere tre volte testa;
#b) di non ottenere tre volte testa;
#c) di ottenere almeno una volte croce;
#d) di ottenere almeno una volta testa.
money <- c("TESTA", "CROCE")
volte <- 10000
num <- 0; den <- 0
#a
for (i i... |
da9742f695dfecce4a61851128aafb930229bda5 | 778913bea65f651c02369ba0d34b000f8bc1c191 | /Exercício_Small e Melium Data.R | 1502285bc05d7b63195ce078d4f153c88f871413 | [] | no_license | LivaniaDantas/etl_com_r | 59079bc0ff14252e0e14cacbd78309fd6c1e38f0 | 9ed54ecbd77d2b6acf370965928417e71dd65e6b | refs/heads/master | 2023-05-30T19:54:49.060361 | 2021-07-05T14:51:50 | 2021-07-05T14:51:50 | 355,997,975 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,332 | r | Exercício_Small e Melium Data.R |
library(data.table)
#Criando um dado volumoso (large data)
casos= 2e7
# criando o data.frame com o total de casos definido acima
largeData = data.table(a=rpois(casos, 3),
b=rbinom(casos, 1, 0.7),
c=rnorm(casos),
d=sample(c("básico","fundamental","... |
b140682efe88ddf49530f41884d1f69d149201c6 | 3dfdf797ca9575185e5f4b2fb11464658c872865 | /scripts/manhattan_plots_Ta.R | 96e946111571160092b9b64ffcb942671e4ad292 | [] | no_license | Africrop/gwas_african_rice | 91309e28e64ce014e9ee4f82bae274d14fb964f2 | a34e44930cbb374fb859ddbcd8849911617cb6e6 | refs/heads/master | 2021-03-30T21:43:00.152077 | 2018-11-05T09:44:06 | 2018-11-05T09:44:06 | 124,543,211 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,653 | r | manhattan_plots_Ta.R | ###################################################################################################################################
#
# Copyright 2017 IRD and Grenoble-Alpes University
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as... |
5325f9ea23fbe31aa20a73026881a0de15079223 | 7b3389755018027cfc74b86bddbb62fdcb1f0ef1 | /MerlijnIndividual/plotClusters.R | b0d100b625fd7883612a7cd3b3e40b77ba0ca9e3 | [] | no_license | Merlijn-van-Breugel/Machine-Learning | 81d3eb96c375ad5bfc3c61ae24ec80d15bad86d2 | e110a44ea8ae77051adabf3f905a68147670a9bf | refs/heads/master | 2021-01-12T11:44:00.632472 | 2016-12-20T10:39:09 | 2016-12-20T10:39:09 | 72,281,785 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,265 | r | plotClusters.R | #Loop over number of iterations
plotClusters <- function(objectscores,centers,clusters,catscores.labeled,plotlabels){
dfplot <- as.data.frame(cbind(objectscores,clusters))
#Make ellipses around centroids
conf.rgn <- do.call(rbind,lapply(unique(dfplot$cluster),function(t)
data.frame(c... |
b3667d328db556ca26eac40c6b80a6ac52ff3c33 | e4c89b9cd1d77ba0365c0b4dd76f4368bf388139 | /tests/testthat/test_gmt_import.R | 8b047befbd7dddac916868c87126483eb8eb8a40 | [
"MIT"
] | permissive | jhchung/geneARTP | ea7b659db69deb9143e6cfc35cff1db9c4a6c9b9 | 008a206f466ccc6f4a861104acc369d877acf2ec | refs/heads/master | 2020-12-24T14:17:43.188915 | 2015-09-18T19:27:20 | 2015-09-18T19:27:20 | 30,929,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 332 | r | test_gmt_import.R | library(geneARTP)
test_that("Test if GMT file is imported correctly",{
test_gmt_line <- "gene_set1\tGene set description\tGene1\tGene2\tGene3"
expect_match(names(format_gmt(test_gmt_line)), "gene_set1")
expect_equal(length(format_gmt(test_gmt_line)), 1)
expect_equal(length(format_gmt(test_gmt_line)[[1]... |
a6d014c36332a4ffac9100b5c4295e51e0f90783 | 090ff155d4d2ab91ddabc7a84c5206c45f4de211 | /fit_period.R | 2ea10c9e563cce899a0be5e35eea06a2f2251a2e | [] | no_license | jcval94/Tesis | 89db0e64bc51aa47e6c053d6eb0c8008fc66b168 | cd9764811b6054c4f163aaafc45b80d971acb6ac | refs/heads/master | 2021-07-12T04:39:28.303815 | 2020-08-09T05:30:43 | 2020-08-09T05:30:43 | 190,092,312 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,753 | r | fit_period.R | cll<-function ()
{
ctx <- try(rstudioapi::getActiveDocumentContext(), silent = TRUE)
if (!inherits(ctx, "try-error")) {
contenido <- ctx[["contents"]]
file_w <- file(paste0(getwd(),"/new.R"))
writeLines(((contenido)), file_w)
writeLines(as.character(parse("new.R")), file_w)
utils::file.edit(pas... |
a72ef5b59f43997c1ef0f7b50b609842e2fd068f | 3700b4903b68eb5cd21ceb8d470427708644d2ef | /sec sem bks/StatisticsforDataAnalysis/unit-II/rnorm.R | bc1cb248e56d26404230c9960f3ce1ad4ab34612 | [] | no_license | repleeka/MTech-CSE-books | b3dce00e47ea1d624647c90807d974290ce34ab1 | e9e8e52c271ddb1a3c318cb454094664d75265bd | refs/heads/master | 2021-12-15T01:16:00.070094 | 2017-07-07T15:22:13 | 2017-07-07T15:22:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 265 | r | rnorm.R | set.seed (21)
# Use to reproduce the data in the figure
par(mfrow=c(2,3))
x <- pretty(c(6.5,13.5), 40)
for(i in 1:5){
y <- rnorm(50, mean=10, sd=1)
hist(y, prob=TRUE, xlim=c(6.5,13.5), ylim=c(0,0.5),main="")
lines(x, dnorm(x,10,1))
}
par(mfrow=c(1,1))
|
0da0f14ea46e6705c114919242b201f81d71e28b | c2b419e168de4bc1be340f049f26edb5bce6be64 | /R/RcppExports.R | 7eab95f84b3f767c0faa2a513bb936e0498fc28d | [
"MIT"
] | permissive | Ilia-Kosenkov/Dipol2Red | 922d889f0da0fbb87983c0efde982c612e5e1ce2 | 916da01ac57b639a136ca2d5f8ffe94086b0c4aa | refs/heads/master | 2021-12-14T16:50:18.346248 | 2021-12-07T12:37:01 | 2021-12-07T12:37:01 | 337,994,287 | 0 | 0 | NOASSERTION | 2021-02-11T10:19:49 | 2021-02-11T10:19:48 | null | UTF-8 | R | false | false | 501 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
d2r_fsigma_2 <- function(input, date_col, obs_col, what, extra_vars, eps, itt_max) {
.Call('_Dipol2Red_d2r_fsigma_2', PACKAGE = 'Dipol2Red', input, date_col, obs_col, what, extra_vars, eps,... |
2569f88a24e4a770f3dc7371a8b4ab7eaf4f9b01 | afdc42af8a468f29b56269626dccbe5859d0e595 | /R_src/unblkcmt/main/main.R | 89f05ee3004823a7c5207e45fc7d77c41ff5767a | [] | no_license | pengyu/pytools | feb44c3da2922dae5a51d19abe2fbc6c05a5076f | 578af6b6135f1fc999c89ca0ae0ca314cbdbfc76 | refs/heads/master | 2021-03-12T23:49:06.527392 | 2013-04-16T03:15:03 | 2013-04-16T03:15:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 98 | r | main.R | source('../unblkcmt.R')
x=10
y=20
unblkcmt(
'
x+y
')
unblkcmt(
'
a=1
b=2
a+b
')
|
b49fb004c3835b8f3204d8a5c35e4b8b8c655270 | 15a72bd7450ebf51e4b65fc0eaccd12f868f0920 | /man/bugs.data.Rd | eafd51135d9cf3ec5e073bf19d14dbda4a826a51 | [] | no_license | cran/R2OpenBUGS | 68a992b28b7934a01be1667af22dbde506dd90eb | 21402f0d531767b04db24ba94021f3d5d0507e6a | refs/heads/master | 2021-06-04T13:42:26.061056 | 2020-04-02T16:31:15 | 2020-04-02T16:31:15 | 17,681,794 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 916 | rd | bugs.data.Rd | \name{bugs.data}
\alias{bugs.data}
\title{Writing input for OpenBUGS}
\description{Write file for \pkg{OpenBUGS} to read.}
\usage{
bugs.data(data, dir = getwd(), digits = 5, data.file = "data.txt")
}
\arguments{
\item{data}{either a named list (names corresponding to variable names
in the \code{mode... |
dba22bcabffd7f0b3afea76883327e3b2e43b575 | 16bd5de71408bb24e941831522be2ee24dead30f | /R4ML/R/r4ml.sampling.R | 5d3b8abc69e8f415ffea58d0ac76fe023fa8acfc | [
"Apache-2.0"
] | permissive | kant/r4ml | 3f8ff34737da3073c573d4c9c88b74fefa25badd | f5dc4d27eedecba953456e33efd89fe1f89ee5e1 | refs/heads/master | 2020-03-28T08:42:05.446324 | 2018-06-07T18:28:13 | 2018-06-07T18:28:13 | 147,983,208 | 0 | 0 | Apache-2.0 | 2018-09-09T01:30:28 | 2018-09-09T01:30:28 | null | UTF-8 | R | false | false | 5,918 | r | r4ml.sampling.R | #
# (C) Copyright IBM Corp. 2017
#
# 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 applicable law or agreed to in writi... |
64d13ca09e133678113ab02f0ea144e78ec0911f | 8c2390dfd1f98368dabac26964d8f0e08db55f7d | /cachematrix.R | 70d354a762ca50c1c21db4e81e9ac69702576663 | [] | no_license | caomengchu/ProgrammingAssignment2 | def49f50b86a1c6126c7c5ed0a64eca7002f8a21 | ef906106d750ebb66e7503a3f5124e37938ed9d7 | refs/heads/master | 2020-12-25T00:27:45.283450 | 2015-02-19T03:22:52 | 2015-02-19T03:22:52 | 30,999,230 | 0 | 0 | null | 2015-02-19T03:09:27 | 2015-02-19T03:09:27 | null | UTF-8 | R | false | false | 1,060 | r | cachematrix.R | ## Below is a solution to solve the inverse a given matrix quickly by combining a cache setting with a traditional
## inverse calculation. It consist of two functions makeCacheMatrix and cacheSolve.
## makeCacheMatrix is a function of matrix that create several functions and make them a list
makeCacheMatrix <- funct... |
3dae761af9fb9c632335e5ee9092563d1ed407bd | b08db46f82fade26c685e2896362e4eb4d3bda3c | /man/show_stats.Rd | 4c1c0004e9b6fd92247ab49a8fd1f509a83387d6 | [] | no_license | mvadu/influxdbr2 | c27b5637c74a9dbc11c59dbb737040a13123015d | 57a4e94c8968ca6111cb3433fc9fb0dc8a355965 | refs/heads/master | 2021-06-09T13:29:10.992503 | 2017-01-08T16:48:01 | 2017-01-08T16:48:01 | 69,948,873 | 2 | 2 | null | 2017-01-08T16:48:02 | 2016-10-04T08:58:36 | R | UTF-8 | R | false | true | 583 | rd | show_stats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/influxdb_diagnostics.R
\name{show_stats}
\alias{show_stats}
\title{show_stats}
\usage{
show_stats(con)
}
\arguments{
\item{con}{An influx_connection object (s. \code{influx_connection}).}
}
\value{
A list of data.frame objects.
}
\description... |
a95762f89ccd2fc492e6047bb392238e82d7b32d | 29585dff702209dd446c0ab52ceea046c58e384e | /RSDA/R/display.sym.table.R | 89531a77d41947c9022911da0ad68cadbc8c67f1 | [] | 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 | 68 | r | display.sym.table.R | display.sym.table <-
function(sym.data) {
return(sym.data$meta)
}
|
dac289204ec3982c304692de2aaf422fa4c71c26 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/qtlcharts/examples/iheatmap.Rd.R | 50bb8a6910898426f5ecf064ab4a807fc8381f07 | [] | 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 | 348 | r | iheatmap.Rd.R | library(qtlcharts)
### Name: iheatmap
### Title: Interactive heat map
### Aliases: iheatmap
### Keywords: hplot
### ** Examples
n <- 101
x <- y <- seq(-2, 2, len=n)
z <- matrix(ncol=n, nrow=n)
for(i in seq(along=x))
for(j in seq(along=y))
z[i,j] <- x[i]*y[j]*exp(-x[i]^2 - y[j]^2)
## No test:
iheatmap(z... |
72750839db51f33c6860b75542e556c4f7101d67 | 5a70e57be476118ad2d4e0c3f5f67a375279e560 | /tests/testthat/test-public-service.R | 6d45e69e7497a900304e15249ff2d896296352d1 | [] | no_license | dgruenew/aws.s3 | cd41d01bbf503ba4b159aa9b548a78630cafa6a4 | 3b855d3330f54f9c4ac4b476aa0085e6d653e6e1 | refs/heads/master | 2021-03-05T11:13:23.264439 | 2020-03-10T16:32:23 | 2020-03-10T16:32:23 | 246,118,024 | 2 | 0 | null | 2020-03-09T18:57:43 | 2020-03-09T18:57:42 | null | UTF-8 | R | false | false | 146 | r | test-public-service.R | context("Public service tests")
test_that("intentional bad keys", {
expect_error(bucketlist(key = 'BAD KEY', secret = 'BAD SECRET'))
})
|
ce144ddee31193936e18d8b2a0928021e298f6e1 | cfa9a6c3519a17bcded7cb5091be11c02739434d | /man/is.op.Rd | 2bebc816b17157489ba2c3d8a3b0caf8cb1e5d2f | [] | no_license | cran/ggloop | 5f3529804c9c94c35788689d17dd733615b6672f | e3aaa56cbd19c4c9d6f1d2598a48eb3571d16a75 | refs/heads/master | 2021-01-11T03:49:08.312424 | 2016-10-20T01:58:31 | 2016-10-20T01:58:31 | 71,409,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 473 | rd | is.op.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities.eval.R
\name{is.op}
\alias{is.op}
\title{Determine if an input uses an arithmetical operator (\code{/}, \code{+},
\code{-}, \code{*}, \code{^}).}
\usage{
is.op(lst)
}
\arguments{
\item{lst}{A list object to be tested.}
}
... |
b715d0ba2829294542119ff1ae93986004ae5659 | de66d28db21fd3b4a7c7e0e6f753be5a9c6d2ba5 | /_blos/_to_merge_/query2csv.r | 71322caf1911f58752478d60737aca448c716b31 | [] | no_license | blavoie/oracle-scripts | d6a0df1291e5ca16f66fd82c9f2528f2a5bcd388 | c2476d1645d19a95928b40d8abc2cb7d85aa84ea | refs/heads/master | 2021-01-10T21:15:03.145717 | 2017-08-08T16:27:34 | 2017-08-08T16:27:34 | 38,309,901 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 287 | r | query2csv.r | library(RJDBC)
drv <-JDBC("oracle.jdbc.driver.OracleDriver","/path/to/jdbc/ojdbc6.jar")
conn<-dbConnect(drv,"jdbc:oracle:thin:@grahn-dev.us.oracle.com:1521:orcl","scott","tiger")
data <-dbGetQuery(conn, "select * from emp")
write.table(data, file="out.csv", sep = ",", row.names=FALSE) |
12b3b0cdda76c7ab56287a7e9f5f9efbdce78bb5 | 6aa1786f3098a283acfea631cf21f2bcc865234c | /Air_bnb/app.R | 67aa7aae17d06bb31a60fc1a61bb1cda8f4e06fa | [] | no_license | vharsheny/Vharsheny-Datascience | 45d03df801e996379968d9c9e87839b75f550ba0 | 3c1114bdbd3e62d15f2d3baa6b767cbbe550d111 | refs/heads/main | 2023-05-31T22:19:45.230551 | 2021-06-04T06:04:17 | 2021-06-04T06:04:17 | 326,940,251 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,904 | r | app.R | library(tidyverse)
library(janitor)
library(leaflet)
library(ggmap)
library(corrplot)
library(RColorBrewer)
library(ggcorrplot)
library(shiny)
library(shinydashboard)
library(DT)
ui<-dashboardPage(dashboardHeader(title = "Airbnb NYC"
),
dashboardSidebar(sidebarMenu(id='sidebar',
ta... |
f20bfef5eda19157dac32960386d7db6efd1fddc | 3f7e5afc088b578c99d04219b68907439291b0ea | /R/doyday.R | 21d8eb35aa54855885fffb4cea9e4d50bbae8688 | [] | no_license | cran/insol | 53c6d9cae154efafc0fda8bd99045cc91fc94026 | 4fe6f26351af22121a5319b4a8b4950b66ca6226 | refs/heads/master | 2021-06-04T03:14:31.090918 | 2021-02-10T14:30:05 | 2021-02-10T14:30:05 | 17,696,790 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 471 | r | doyday.R | doyday <-
function(year,doy){
if (nargs() < 1 ) {cat("USAGE: doyday(year,doy) \nUSAGE: doyday \n(year.dy)"); return()}
fyy = floor(year)
if (nargs() == 1 ) {
nd = ifelse((fyy%%4==0 & fyy%%100!=0) | fyy%%400==0,366,365)
dd = (year-fyy)*nd
} else {
yy = year
dd = doy }
fdd = floor(dd)
hh = (dd-fdd)*24
fhh = fl... |
f4190c740a021d1b908a0724f8021ce5988a8fb9 | 14fb055b9993d2df2b648dc785892cb37b7a313a | /01_model_generator/maria_generator.R | b928ea6234e0c741ef8f8a1582919eaf9111fe18 | [] | no_license | MARIA-Pipeline/MARIAProject | d263fbb5602097e3878c0282e6f2676217933631 | f9a2011fb9fdfb321ff07d0af8fe436fee35937f | refs/heads/master | 2021-01-10T17:00:26.845856 | 2015-12-08T09:00:06 | 2015-12-08T09:00:06 | 47,611,091 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,963 | r | maria_generator.R | # Guillaume Lobet - University of Liege
# ArchiSimple Batch
# The aim of this script is to run ArchiSimple in a batch mode in order to create
# any number of root systems, with any combinaison of parameters.
# The script works as follow:
# - Looping over the parameter set (user defined)
# - For each combinaison, c... |
8a78f019607dbd8130852d85ccc0db339c4def12 | 2a61cb94f08f6b2b95bc29a5ee798507d1b7bac6 | /plot1.R | e7d7dcd9fb2c9e6bdd0900e0fbf8bc1bb423bb61 | [] | no_license | erijozsef/ExData_Plotting1 | cad339f138340d0b7672174faadb48f06e35b1a2 | 079c5fd0211ec2307c0092be2058be4845c9b97f | refs/heads/master | 2020-12-28T22:22:23.175963 | 2015-11-07T20:53:12 | 2015-11-07T20:53:12 | 45,728,443 | 0 | 0 | null | 2015-11-07T08:25:05 | 2015-11-07T08:25:04 | null | UTF-8 | R | false | false | 507 | r | plot1.R | # Load the packages
library(dplyr)
library(readr)
# Set working directory
setwd("E:/Documents/R programs/ExData_Plotting1")
# Read the data
df <- read_delim("../dataset/household_power_consumption.txt", delim=';', col_types="ccnnnnnnn", na="?")
# Filter the data
df1 <- filter(df, Date=="1/2/2007" | Date=="2/2/2007"... |
3771a577757706272faa4cec9b52a310b00c6724 | 79c1230450df725058abbeb278aaaa5f1da35743 | /R/RSDdist.R | ca5151983364e71e65e56512a6308e33da55e682 | [] | no_license | jaspershen/statTarget-2 | fb72dadaabc9a2115d2f09cdfab5dd2dfb429296 | 7dd60ad81612b8d097be800a2255844761c56972 | refs/heads/master | 2021-01-12T04:25:42.064307 | 2016-11-18T05:36:50 | 2016-11-18T05:36:50 | 77,608,156 | 1 | 1 | null | 2016-12-29T11:40:22 | 2016-12-29T11:40:22 | null | UTF-8 | R | false | false | 4,548 | r | RSDdist.R | RSDdist <-function(sample.rsd,sample.nor.rsd,QC.rsd,QC.nor.rsd) {
#browser()
colour1<-NULL
colour2<-NULL
colour1[(sample.nor.rsd/sample.rsd)>1]<-"#D55E00"
colour1[(sample.nor.rsd/sample.rsd)==1]<-"grey48"
colour1[(sample.nor.rsd/sample.rsd)<1]<-"#009E73"
colour2[(QC.nor.rsd/QC.rsd)>1]<-"#D55E00"
colo... |
50afdb65c51172f65d048a0f3bcb8cbb25761287 | ff0a2852a43d34bc01f564299d3e4d1751eec56f | /hdl_trial.R | dd008fe50316873bb5337650efc0190d7cc25d20 | [] | no_license | shinilraina/Summer_project | 85ac5755b1e52ac4cff7a5e5ce2f5ec19c66ce36 | 17b2b26775972581ed3c1b0c69432d6a0d9eb0e1 | refs/heads/main | 2023-07-15T13:13:00.986967 | 2021-09-06T10:25:01 | 2021-09-06T10:25:01 | 374,995,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,219 | r | hdl_trial.R | rm(list=ls())
setwd("~/Summer_project/HDL")
.libPaths(c("/rds/general/user/sr4515/home/R/x86_64-redhat-linux-gnu-library/3.6",
"/usr/lib64/R/library",
"/usr/share/R/library" ))
start_time <- Sys.time()
library(devtools)
#install_github("zhenin/HDL/HDL")
#install.packages("doSNOW")
library(doSN... |
b3b7610114e2cbbe9257e5b03db23d707c198bbb | 5c0eff341989318352bd4cbec4a1ecb38e7fa0e7 | /10_2019/Part1_GR_2.R | 35d6c70b9137a341b9d236be84804aaaf6724402 | [] | no_license | janiszewskibartlomiej/R-Postgraduate_studies_on_WSB | e98180715827ea55f346dd8f9a7e49159925ca47 | 6d22288dcd845547d424337a1f4601494955d51f | refs/heads/master | 2020-08-28T05:15:50.391256 | 2020-03-02T09:03:36 | 2020-03-02T09:03:36 | 217,602,921 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,798 | r | Part1_GR_2.R |
##########
# PART 1 #
##########
#---- hello world ----
print("Hello WSB")
#---- calculator ----
1 + 1
2 - 1
2 / 2
5 %% 2
#---- math / stat functions ----
sum(1, 2, 3)
mean(c(1,2,3)) # srednia
median(c(1,2,3)) #mediana
min(c(1,2,3))
c <- c(1,2,3) # c to jest vector
#----- variables ----
a <- 3 # pr... |
215ed1f3f8cbbf83217e7fc7096b5f9e913f1ffd | d98b7d973db4770b573ffcf2e61a37ffa74ecb21 | /airbnb/stack_models/layer1_hdda1.R | 0b82c57ee35110956facee1ec833da1ca783e913 | [] | no_license | brandenkmurray/kaggle | a27a85f172c5ecd58d9fc58219b3e31400be597e | 30924c37e15772b6e7125b341931d7c775b07d0b | refs/heads/master | 2021-01-10T14:18:07.768888 | 2017-08-29T00:20:48 | 2017-08-29T00:20:48 | 44,857,026 | 30 | 17 | null | null | null | null | UTF-8 | R | false | false | 3,182 | r | layer1_hdda1.R | library(readr)
library(data.table)
library(caret)
library(reshape2)
library(dplyr)
library(kknn)
library(Matrix)
library(doParallel)
setwd("/home/branden/Documents/kaggle/airbnb")
threads <- ifelse(detectCores()>8,detectCores()-3,detectCores()-1)
ts1Trans <- data.table(read.csv("./data_trans/ts1_pp_v4.csv"))
xgbImpVars... |
f018522cbe7de29f5f2babcb59c4bb6e32e88ec6 | 1af700299fe5a8cf8b7999b24df48a845a9afa2f | /plot4.R | 4ab2d1eb4e972c95eb62ee81d83bc7df48a1d202 | [] | no_license | S2P6/ExData_Plotting1 | 1dd5e36d1b1232101cef4ce6b9319314ebf931d8 | 6a63ac3fbe994e0fe854877f5f805d4b5f925ad9 | refs/heads/master | 2021-01-19T03:38:54.691551 | 2015-02-08T23:16:27 | 2015-02-08T23:16:27 | 30,483,010 | 0 | 0 | null | 2015-02-08T06:22:17 | 2015-02-08T06:22:16 | null | UTF-8 | R | false | false | 1,281 | r | plot4.R | setwd(paste(getwd(),"/RCourse", sep = ""))
##reading all the lines
fn <- "household_power_consumption.txt"
Alldata <- read.csv2(fn, sep=";",na.strings="?", dec=".")
datemin <- strptime("01/02/2007","%d/%m/%Y")
##cutting with two dates
datemax <- strptime("02/02/2007","%d/%m/%Y")
ourdata <- Alldata[strptime(Alldata$Date... |
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