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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
219679f82bfe04d24816e215f25a8748b9908e29 | bcc47704e21f119f2c5910c46d7e44d207177ffd | /feature_eng.r | 647507f5e3f3e681993bd1eee55df9be51a8c115 | [] | no_license | Allisterh/NeuralNetworks | a960bc5a18b53c450ee1ad87017a6db88cea2d28 | 0f3a72c3e155d17f75047628450c65a264fa52c4 | refs/heads/master | 2022-04-07T17:32:10.518153 | 2020-02-11T15:01:39 | 2020-02-11T15:01:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,066 | r | feature_eng.r | rm(list = ls())
set.seed(2018)
x <- seq(-10, 10, length.out = 100)
y <- seq(-10, 10, length.out = 100)
MyData <- expand.grid(x = x, y = y)
MyData$z <-as.factor(sqrt(MyData$x^2+MyData$y^2)>5)
Index_Temp=sample(nrow(MyData), 0.05*nrow(MyData))
MyData$z[Index_Temp]<-FALSE
Index_Temp=sample(nrow(MyData), ... |
61a08f6f8f03a51ae945c3eeb4fc7dd94ce8d957 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FarmTest/examples/plot.farm.scree.Rd.R | f6c05ba16ab56eff22c134f4b6fdbe3f07f4b12b | [] | 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 | 429 | r | plot.farm.scree.Rd.R | library(FarmTest)
### Name: plot.farm.scree
### Title: Diagnostic plots from factor-finding
### Aliases: plot.farm.scree
### ** Examples
set.seed(100)
p = 100
n = 20
epsilon = matrix(rnorm( p*n, 0,1), nrow = n)
B = matrix(rnorm(p*3,0,1), nrow=p)
fx = matrix(rnorm(3*n, 0,1), nrow = n)
X = fx%*%t(B)+ epsilon
output =... |
c92f0e76ca4d4ca0cb61a1bd68f9a732c27a2e86 | 2e2d340f2d59d1e0e4260305e69e794476874bbe | /utils/load_packages.R | b204144a4a4db81f2c9c5f702d81be6ef5bdb15c | [] | no_license | aedobbyn/twitch | 6fb5f4a15e707f8ec85ad65457aa47420a82798a | 7cbeb0a8db49e418e4c9e901bf6d54b9af0d150a | refs/heads/master | 2020-03-08T13:15:04.109072 | 2018-04-05T04:01:48 | 2018-04-05T04:01:48 | 128,153,109 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 80 | r | load_packages.R | library(tidyverse)
library(here)
library(feather)
library(RMySQL)
library(glue)
|
afe236664e3ad3cca50bfcf64639188db64ef2a5 | 5e1159311fa252b2dba45129c8335cee2a1b652e | /R/as.plantuml.logical.R | d30304a13214175a71295e6a61f7e674481f44d9 | [] | no_license | ShigiDono/plantuml | 780c19df90cbf2ef81a6d95520433769eeb5ff2f | 821852bba9ee2f8958910712e0ef58569124e7d2 | refs/heads/master | 2020-03-15T19:45:23.417049 | 2018-05-06T07:55:39 | 2018-05-06T07:55:39 | 132,316,737 | 0 | 0 | null | 2018-05-06T07:35:16 | 2018-05-06T07:35:16 | null | UTF-8 | R | false | false | 634 | r | as.plantuml.logical.R | #' Convert a character to a \code{plantuml} object
#'
#' Convert a \code{character} to a \code{plantuml} object.
#' This can be plotted.
#'
#' @param x character sting containing plantuml code.
#'
#' @return object of class \code{plantuml} which can be plotted.
#' @export
#'
#' @examples
#' \dontrun{
#' x <- '
#' @st... |
b38899277db193343503f82e8baf1d564cd021fb | d11508807942f8c4f2aa85e8a51cb2d45b0c8d4d | /man/plot.glide.Rd | eb712490a771310f4b4f12cedd7368cdd76a1975 | [] | no_license | cran/GLIDE | 37056ac28b2c11f2c63393bab61deefeb5007609 | 2410bfd3cb405eea74686ed93c5983a943f3d591 | refs/heads/master | 2022-06-22T11:31:41.697096 | 2022-05-25T22:00:02 | 2022-05-25T22:00:02 | 87,986,624 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,266 | rd | plot.glide.Rd | \name{plot.glide}
\alias{plot.glide}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
A function used to draw the q-q plot
}
\description{
It is used to draw the q-q plot
}
\usage{
\method{plot}{glide}(x,qcutoff=0.2,xlab="Expected null p-values (log base 10)",
yl... |
64e5ede7d06dff2c0cc5a69fa6d23d777c3bee1e | df0497b741e158c5aa53ee9e2659d538dbb801e7 | /package_load.R | e565087f9d74c45dbe3631a25ab272f42242559b | [
"MIT"
] | permissive | allisonbertie5/tamu_datathon_2020 | 5980066e06dab5e711da9c8c94eb57deb576e536 | 4a3e9b7aefb473cb4053f53b9fcc96eb3049fce8 | refs/heads/main | 2022-12-29T19:58:25.461352 | 2020-10-18T16:09:59 | 2020-10-18T16:09:59 | 305,138,171 | 1 | 0 | MIT | 2020-10-18T15:51:12 | 2020-10-18T15:51:11 | null | UTF-8 | R | false | false | 109 | r | package_load.R | library(tidycensus)
library(googledrive)
library(tidyverse)
library(stringr)
library(googlesheets4)
|
e15956cd51c575f7454e0037c22df9249bcffd7d | ffb90a8ff6c0ce42877da20850a5c7a488424733 | /QuantStrat/03-nXema.r | cc4fc5d35a32fc462e68729fd9dbfd1a43220e2e | [] | no_license | UTexas80/splGoldenDeathX | 78f008298642b6fd3aa3bb910c488d9e14a3f608 | f4367ff357ecdd8e0132888d6768e8c600850132 | refs/heads/master | 2023-05-02T18:29:58.171278 | 2023-04-25T16:41:56 | 2023-04-25T16:41:56 | 191,051,469 | 2 | 1 | null | 2019-10-14T00:19:59 | 2019-06-09T20:18:54 | R | UTF-8 | R | false | false | 5,306 | r | 03-nXema.r | ################################################################################
# 1.0 Setup
################################################################################
setup(nXema)
################################################################################
# 3.0 Indicators
###################################... |
06942082b4a4b4f144f6f217d79318e2aec84fe8 | f78121fe0d58d63c1f537077fc434bb112e8a565 | /4-Exploratory Data Analysis/Programming Assignment 2/plot5.R | 4a469d348fcf3c3803141b945ceb2e9a7699909a | [] | no_license | ManmohitRekhi/-datasciencecoursera | 4fb237d5caa63b7006dc806d2073c66255a1a187 | 13ad2c7c12505f3c6db5edd9c02bbbe8307f0f67 | refs/heads/master | 2021-01-18T21:32:01.358620 | 2014-06-22T19:09:11 | 2014-06-22T19:09:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 948 | r | plot5.R | NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
png(filename = "plot5.png", height = 720, width = 720)
par(bg = "transparent")
scccopy <- SCC[SCC$EI.Sector == "Mobile - On-Road Diesel Heavy Duty Vehicles" | SCC$EI.Sector == "Mobile - On-Road Diesel Light Duty Vehicles" | SCC$EI.... |
e4b3de80e71c9784eb037e90b4922e4c83613ab3 | 6713b68c912af377c741b26fe31db0fe6f6194d4 | /2nd Term/Visualisation/Sample Codes/Lecture_4.R | 8a663694094ce0c4f316c18d708660bb3d142709 | [] | no_license | Lanottez/IC_BA_2020 | 820e8d9c1dbb473ed28520450ec702f00c6684ed | 8abd40c6a5720e75337c20fa6ea89ce4588016af | refs/heads/master | 2023-08-25T05:52:08.259239 | 2021-11-03T07:27:11 | 2021-11-03T07:27:11 | 298,837,917 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 647 | r | Lecture_4.R | library(tidyverse)
# Look up documentation on mpg
?mpg
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color ="blue"))
# color outside of aes() function
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color ="blue")
# Look up the function details
?facet_grid
# midwest is an... |
a3a0b57e9fbd16121f6d6f2bd8dc9d84f9404964 | 8ff090148036ff72039a086f69a142dfa9a6bc99 | /R/plateAdjust.R | 4402a22f21d5a3a8955f53a2175c335ffaeed9ad | [
"MIT"
] | permissive | schyen/somefxns | d01483414bf3c5e913403bb2a296533349dcdb38 | 6ac702bf6fa8b81348710ee3be2deed949c269bb | refs/heads/master | 2021-06-25T03:19:45.079807 | 2019-08-06T13:15:08 | 2019-08-06T13:15:08 | 146,625,419 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,359 | r | plateAdjust.R | #' plateAdjust
#'
#' normalized absorbance values by adjusting to blank
#'
#' @param plateDF dataframe. first spreadsheet of victor file
#' @param metadata dataframe. well metadata. must have columns:
#' Well, platerow, platecol, strain, curveID, welltype, media, abx, wellconc
#' @param well_include string or vecto... |
f5ec6fa5259d33d1ad45f59f33965bfa6b43f3a3 | a51f14302c8e4a2a0a48dc636d035c4e6669f686 | /R/finalizeSkript.R | 83527790d0698b35012b6e6dd1e511d00a0e6c7f | [] | no_license | holgerman/toolboxH | b135c6033c015ac0c4906392f613945f1d2763ad | fb8a98ee4629dc5fef14b88f2272d559d5d40f30 | refs/heads/master | 2022-07-07T22:26:39.857012 | 2022-06-23T14:56:23 | 2022-06-23T14:56:23 | 100,366,781 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,845 | r | finalizeSkript.R | ### end up a skript
#' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param myfilename PARAM_DESCRIPTION, Default: filename
#' @param saveTheImage PARAM_DESCRIPTION, Default: F
#' @param dostr PARAM_DESCRIPTION, Default: F
#' @param mypathwd PARAM_DESCRIPTION, Default: pathwd
#' @return OUTPUT_DESCRIPTI... |
ffdd92d0b756ea2f9c90021f81674e1e43fa5655 | b15d2a39498cad4bdbfcc55a55f4f3d34f4221e4 | /R/exaggerate_img_control.R | 20493821cea5aa92ebc92dd5a685ab176f334be4 | [
"MIT"
] | permissive | srvanderplas/ShoeScrubR | a9465d28ecea4286b27a617bfa5a6b2b2fe8c5b1 | 972bb2a0092c5bf62bbe5521b54f689f2ad8465e | refs/heads/master | 2020-07-23T02:20:37.169159 | 2019-11-25T22:33:23 | 2019-11-25T22:33:23 | 207,416,080 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,832 | r | exaggerate_img_control.R | #' Exaggerate an image to a mask-like appearance with control of parameters
#'
#' @param img Image
#' @param gaussian_d diameter of brush to use for gaussian blur
#' @param threshold_val threshold value to use on normalized, inverted, blurred
#' image
#' @param opening_d diameter to use for image opening (despec... |
f1b8d7302abad4a6e52c8de9d8c8d25ca86de525 | 0bc0bab452751f75ce118b9970a7e61efeee0e7d | /Simulation_testing.R | 3eebc7ae22c311c2b12517a4065331758481f546 | [] | no_license | JuanMatiasBraccini/Git_move.rate_joint.estimation | fb2f6f2918df476652e588bf07ab2a9c66f3e719 | 65bc924fadc4bb5d4e2300e1cac505e9553a887d | refs/heads/master | 2021-06-12T22:08:57.085697 | 2021-04-28T05:21:07 | 2021-04-28T05:21:07 | 191,689,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,181 | r | Simulation_testing.R | #Movement model conditioned on recaptures only (McGarvey line of thought)
#note: individual-based model that compares the probability of occurrying in a particular zone
# after an given time at liberty with the observed recapture zone.
rm(list=ls(all=TRUE))
library(expm)
library(ggplot2)
library(reshape)... |
06ea33f8e159d9cc7319a99714eafbf7b41b9b1e | be94025d16afa0b7d02ce9959479bec180a59e06 | /R/data.R | 87dfc00e9dd1fe3a61ea572c5eb18155edc3c24b | [] | no_license | ouidata/creationPackageLive | fb11024fa8a1d064fa80ecc3a7753b753872f867 | ba8acce21a217ce5d59ed1aa679387d84cf777ac | refs/heads/master | 2020-06-14T17:09:59.098257 | 2019-07-03T14:14:46 | 2019-07-03T14:14:46 | 195,062,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 72 | r | data.R | #
# smc_with_js contient les informations suivantes ...
#
"smc_with_js"
|
8a51f63689e76548bca4aeba585ea8284f822b5a | e1cbbf8791b0ac6d40f6d5b397785560105441d9 | /R/lmomsRCmark.R | 24bf2cc37af7b0e176cd227813092dd2c18bc51a | [] | no_license | wasquith/lmomco | 96a783dc88b67017a315e51da3326dfc8af0c831 | 8d7cc8497702536f162d7114a4b0a4ad88f72048 | refs/heads/master | 2023-09-02T07:48:53.169644 | 2023-08-30T02:40:09 | 2023-08-30T02:40:09 | 108,880,810 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,381 | r | lmomsRCmark.R | "lmomsRCmark" <-
function(x, rcmark=NULL, nmom=5, flip=NA, flipfactor=1.1) {
n <- length(x);
if(nmom > n)
stop("More L-moments requested by parameter 'nmom' than data points available in 'x'");
if(length(unique(x)) == 1)
stop("all values are equal--Lmoments can not be computed");
if(is.n... |
373524e8389ceb68c526bd23962981cd4bd14e8b | 8084df2f83350ee1886a7d71cee8c89078e0419c | /D3.R | 94919bdfce4bcb4f86fb63c882b0eedf9fa76e1e | [] | no_license | bw4sz/NetworkTime | 30d2e71801914c50dbdf0dc0392130a00bfb6fea | 27461b3c97402da1683ca89d7177338c7086999d | refs/heads/master | 2021-01-17T09:41:54.782230 | 2017-03-19T18:08:17 | 2017-03-19T18:08:17 | 27,722,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,429 | r | D3.R | library(d3Network)
require(chron)
require(bipartite)
require(ggplot2)
require(ape)
require(reshape2)
require(sna)
require(stringr)
require(maptools)
require(taxize)
require(picante)
require(dplyr)
library(scales)
###################
#Source Functions
###################
gitpath<-"C:/Users/Ben/Documents/NetworkTime/"
... |
bb740490a8255672c3f1c74c6cf6260b03c0918f | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.developer.tools/man/codecommit_create_pull_request_approval_rule.Rd | 48ccc6db471d21223d90679d8bf25310ddc25dcd | [
"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 | 2,439 | rd | codecommit_create_pull_request_approval_rule.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codecommit_operations.R
\name{codecommit_create_pull_request_approval_rule}
\alias{codecommit_create_pull_request_approval_rule}
\title{Creates an approval rule for a pull request}
\usage{
codecommit_create_pull_request_approval_rule(
pullR... |
28fb698bd7401d068cdd929d6a2ef120635bc867 | 1bdfacbfb304b3056afe40a259374c19f7b80f50 | /Result_Estimate.R | f585a560e20ee74c4c2223e507c88a914ab7b0ba | [] | no_license | M-Atsuhiko/Gausian | ed71ee76ae183fa0283ec52b4b0bf62c1aaa2421 | e84e9895eb8c93b39d5837a8aa20a2caab91a1e4 | refs/heads/master | 2020-05-18T13:50:49.084679 | 2015-02-10T09:53:40 | 2015-02-10T09:53:40 | 28,582,595 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,981 | r | Result_Estimate.R | Result_Estimate <- function(MULTI_GENERATION){
Good_i <- c() #Good_Resultになった個体の添字
Max_F <- -1
Max_Mor <- -1
Max_Ca <- -1
Max_K <- -1
for(i in 1:N_INDIVIDUAL){
if(MULTI_GENERATION[[i]][["Result"]] == Mor_E)
MULTI_GENERATION[[i]][["Estimate"]] <- Morpho_penalty(MULTI_GENERATION[[i]][["TREE... |
112c3ad842fe0a027f6d7b86a7ecc18ad8a69f56 | e407e8e724356282f85582eb8f9857c9d3d6ee8a | /tests/testthat/test-cumulative-coefficients.R | 13c1f811ebe98aa822052efb5c236f330c0f2aeb | [
"MIT"
] | permissive | adibender/pammtools | c2022dd4784280881f931e13f172b0057825c5e4 | ab4caeae41748c395772615c70a0cd5e206ebfe6 | refs/heads/master | 2023-08-29T17:30:30.650073 | 2023-07-19T10:30:06 | 2023-07-19T10:30:06 | 106,259,608 | 43 | 14 | NOASSERTION | 2023-07-19T10:30:08 | 2017-10-09T08:55:47 | R | UTF-8 | R | false | false | 741 | r | test-cumulative-coefficients.R | context("Test cumulative coefficients functionality")
test_that("Cumulative coefficients work", {
df <- tumor[1:30, c("days", "status", "age")]
df$x1 <- as.factor(rep(letters[1:3], each = nrow(df) / 3L))
## pam
ped <- as_ped(df, formula = Surv(days, status)~ x1 + age)
pam <- mgcv::gam(ped_status ~ s(tend) ... |
47e3f6e2a07efc2356f5b2cefefb13c1e381d953 | 99c84a4732231b83515cb3bf4fba2942dc0b0815 | /code/01-manage-data.R | 9c41a7ef2389c3cb3d59d129a4843ee3d7170a48 | [] | no_license | jfarland/prob-comp-2015 | 761a3382d882c88af47e36e110cf5ea7cff30591 | 9ac11c17a0899c20ccb4258ee8eafb2e51550874 | refs/heads/master | 2021-01-10T09:06:09.912251 | 2017-10-04T22:39:28 | 2017-10-04T22:39:28 | 44,548,174 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,796 | r | 01-manage-data.R | #-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-#
# Probabilistic Forecasting Competiion - Tao Hong's Energy Analytics Course
#
# Prepare and manage data sets used in forecasting
#
# Author: Jon T Farland <jonfarland@gmail.com>
#
# Copywright September 2015
#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-... |
ac0cb673bd8d4a1b790868a4d7f13a13e5a3bda0 | 163ceeb94d49b70d43cd707cbc5de03164a1ce50 | /R/RcppExports.R | 175cd04c7e70b11bce78a3c20f16e33c8689748b | [] | no_license | privefl/bigutilsr | e8cce921638d1327a1038f6ac9b237eae9ca87de | bb760d109193d2163e869d9d231a8fdcba2ac96e | refs/heads/master | 2022-12-27T01:39:56.076386 | 2022-12-20T14:36:53 | 2022-12-20T14:36:53 | 199,856,656 | 11 | 3 | null | null | null | null | UTF-8 | R | false | false | 864 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
glasso <- function(mat, lambda, maxiter_outer, maxiter_lasso, tol, verbose) {
.Call(`_bigutilsr_glasso`, mat, lambda, maxiter_outer, maxiter_lasso, tol, verbose)
}
sum_in_temp <- function(... |
c4afc0a1281e21ce2e86fd78a14b474a69913fee | 93c0622c71200b59205fede951767246d4add492 | /Monika Renska.R | fdf77a8135be5e12331682a78d4ef0473b991841 | [] | no_license | AdamJedz/Machine-Learning | 06c86582c0bf7bb875442dda2aef55b01a538fec | d76dd4dbbdd662bafb65e358e0074c97c2032724 | refs/heads/master | 2020-04-12T19:24:54.929626 | 2019-09-04T11:41:30 | 2019-09-04T11:41:30 | 162,708,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,238 | r | Monika Renska.R | library(shiny)
library(ISLR)
library(tidyverse)
#Bosotn to jeden z wbudowanych zbiorów danych do biblioteki MASS
college <- College
ui <- fluidPage(
radioButtons("private",
"Is private?",
c("Yes" = "Yes",
"No" = "No")),
plotOutput("plot1"),
selectInput("var", "Va... |
9c73c83fc3985d534382a3aceede70f88bebf2af | e308da2d4deeab2298ec3356d35b0308b43ac02e | /R/helpers.R | fd69f85ab662997aaaa37f1570efe7d106c3ae23 | [] | no_license | hejtmy/eyelinkr | 3d2c93352b6f0044c31581bbd6795dffcd54accf | 022e8578bed3d2be53e73a7db99cb032cf10ddcf | refs/heads/master | 2020-07-07T20:44:47.208886 | 2019-09-13T17:54:39 | 2019-09-13T17:54:39 | 171,920,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 479 | r | helpers.R | contains_word <-function(ls, words){
#basically iterates through list and sees if at least one of the columns returns true
for(word in words){
if (sum(grepl(word, ls)) > 0) return(TRUE)
}
return(FALSE)
}
rename_column <- function(df, old_column, new_column){
colnames(df)[old_column == colnames(df)] <- ne... |
97a30a71476a8af21a58791535bd3f42420daf5a | 973434feaf2da5e67b1850335e9d7c75b158127f | /plot6.R | 5b139251b7e4db0941e35b50198a54ff0ac20084 | [] | no_license | eshtee/Cousera-Exploratory-Data-Analysis-Course-Project-02 | 5932d759f7f3eb8fc4f56eae592abc25fdf2b254 | 5b64b24966f5dd2fcf4a92192b006098674b1482 | refs/heads/master | 2020-12-26T04:04:25.455040 | 2015-09-27T23:06:00 | 2015-09-27T23:06:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,860 | r | plot6.R | #######################################################################################
# #
# Author: Anderson Hitoshi Uyekita #
# Exploratory Data Analysis ... |
71d09ca8a50cd8d687d321696345485d4754f154 | 77ecee52e46e7f21a0bcab7cb168f7846a467255 | /man/patchdistr_sews_predict.Rd | e804158c8c524b5a600b74c35f031153d8391990 | [
"MIT"
] | permissive | spatial-ews/spatialwarnings | fc4410f47f0217f1ab79a89b4b2691a574f8ad07 | 607523388e544e2b3993be447c808b116e2c191d | refs/heads/master | 2023-07-06T05:01:57.738005 | 2023-07-03T10:32:56 | 2023-07-03T10:32:56 | 37,706,570 | 15 | 5 | NOASSERTION | 2022-03-10T00:13:55 | 2015-06-19T06:50:52 | R | UTF-8 | R | false | true | 1,564 | rd | patchdistr_sews_predict.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/task_patch_indic_methods.R
\name{patchdistr_sews_predict}
\alias{patchdistr_sews_predict}
\alias{predict.patchdistr_sews_single}
\title{predict method for patchdistr_sews objects}
\usage{
\method{predict}{patchdistr_sews_single}(object, ..., ... |
0b01d3a210542809d20aae90fe402dcaa7efc4df | c2ba85305d0db98f9fcda395fc48737592ef6c63 | /plot2.R | 4d30f7c76e875f495e372922df3c1b7855a02ff2 | [] | no_license | surabhin15/ExData_Plotting1 | d0d1708a1a453327bb06e33f5f13521fe2d1bab2 | 3cb0d8fa4b54645d0fe8805b9e096bce1888438f | refs/heads/master | 2021-06-28T03:48:09.203588 | 2017-09-18T08:01:44 | 2017-09-18T08:01:44 | 103,889,026 | 0 | 0 | null | 2017-09-18T03:53:31 | 2017-09-18T03:53:30 | null | UTF-8 | R | false | false | 557 | r | plot2.R | # Exploratory Data Week1
# Course Project Quiz 1
# Surabhi Naik
# Loading the data
epc <- read.table("household_power_consumption.txt", stringsAsFactors=FALSE, header = TRUE, sep = ";")
# Subsetting the data based on 1/2/2007 and 2/2/2007 dates
SubSetData <- epc[epc$Date %in% c("1/2/2007", "2/2/2007"),]
Date_Time... |
0e9f4649ab4e3cf369124bfa7db3eca13d1d164a | c409ff3ea8b7c62efd962d37c83793d4fc0dc1bc | /man/cq_read_itanal.Rd | 11cde9457ac975169eee9668b745d37f1fd78458 | [
"MIT"
] | permissive | markdly/conquestr | e225ecb1347957dc025c5c719d46624f56e01207 | 7994b3768e26acf1be4ac20821da66ba7f564deb | refs/heads/master | 2021-04-30T10:41:43.513747 | 2018-09-12T05:46:02 | 2018-09-12T05:46:02 | 121,339,581 | 1 | 0 | null | 2018-09-12T05:46:03 | 2018-02-13T04:43:27 | R | UTF-8 | R | false | true | 573 | rd | cq_read_itanal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cq_itanal.R
\name{cq_read_itanal}
\alias{cq_read_itanal}
\title{Read in a traditional ConQuest item analysis file as text}
\usage{
cq_read_itanal(fname)
}
\arguments{
\item{fname}{A file path to an existing ConQuest itanal file. Can take othe... |
71a730b68511a8955d661a96967ca97b4a89c9ca | e478b48a472bc520e3e9e545b8387a50e7a2669b | /Analytics_Edge/Kaggle_Competition/final_03.R.r | ecefff11eccecd2a31a1788b99db8b1771c56505 | [] | no_license | lastworden/Statistics | 98fcfcd65986f7b85e2aa03b439a169f4be7a807 | f831be57823b749d2001283b5089d63afe2d160c | refs/heads/master | 2021-01-21T04:46:54.225238 | 2016-07-03T18:48:20 | 2016-07-03T18:48:20 | 55,667,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,728 | r | final_03.R.r |
pollTrain = read.csv("normPollElabTrain.csv")
pollTest = read.csv("normPollElabTest.csv")
names(pollTrain)
names(pollTest)
tr_label = pollTrain$Party
tst_ids = pollTest$USER_ID
Train = pollTrain
Train$Party = NULL
Test = pollTest
Test$USER_ID = NULL
library(rpart)
library(e1071)
library(randomForest)
library(car... |
3e055044eb9c90f63084f94444c1467d83b8dc67 | 8ec922bbf13640a10aacdd67192ce34585200b22 | /R/CorrSurface.R | 224a722d391a966a6964c224628912dca7dfdbe1 | [] | no_license | OUKUN0705/timeseriesmom | 93a4c4e73589f7714efaf502de93a9df58f82f65 | 8d8b9d99a27f2cea6cc24c254b2ca22ef1b1d9cb | refs/heads/master | 2021-01-11T06:49:42.116154 | 2017-02-01T08:56:04 | 2017-02-01T08:56:04 | 66,509,396 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,969 | r | CorrSurface.R | #' CorrSurface
#'
#' The function
#'
#' @param price an object of time serise representing a price series.
#' @param lookback_seq a sequence of lookback length.
#' @param holddays_seq a sequence of holddays length.
#' @param Sign whether the returns or signs of returns are used to calculate
#' correlation, default is... |
07cfe0b0080bcc053cfde75a3fc3753bd49e5025 | 29585dff702209dd446c0ab52ceea046c58e384e | /interplot/R/Interplot_plot.R | a1d0f6577006f989bf27e8d0594cf62fca713c32 | [] | 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 | 7,222 | r | Interplot_plot.R | #' Plot Conditional Coefficients in Models with Interaction Terms
#'
#' Graph based on the data frame of statistics about the conditional effect of an interaciton.
#'
#' @param m A model object including an interaction term, or, alternately, a data frame recording conditional coefficients. This data frame should ... |
0df494ea7c0c2b0417c2190992abaa9a50939e35 | 86f9f3fbaf06199bbe26e2aa93947d20b355ae7b | /R/analysis/control_only/summarise-control-only-DE-vs-variance.R | 143ceb2739f29dea905b83512f7e256029019013 | [] | no_license | phycomlab/DE-analysis | 7cd6e8d7b78be2a665b403190a9a7aea2c80e3b2 | 167f55a5bff09f23cfd5b535763e13db2bc278b3 | refs/heads/master | 2023-06-24T16:11:14.348901 | 2021-08-04T17:18:41 | 2021-08-04T17:18:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,023 | r | summarise-control-only-DE-vs-variance.R | setwd("~/git/DE-analysis")
options(stringsAsFactors = F)
library(tidyverse)
library(magrittr)
library(data.table)
args = list(); source("R/functions/detect_system.R")
# list input files
input_dir = file.path(base_dir, "analysis", "expr_summary", "control_only")
input_files = list.files(input_dir, pattern = '*\\.csv\\.... |
005ef3bb2ca3537182ae7c79e2829d58685eb346 | 893c52eb5c29634e8b6ea95f5300f656d12960a8 | /man/Methods_SFI.Rd | 59bf4026ac1e715c20eab5d0888336213cb621b6 | [] | no_license | DPCscience/SFSI | 7eef35cd9b2f29644638442dc5c16c2a2fde9fb3 | 2a584c4ae958dc81ad2eb22501b15d1780fd910d | refs/heads/master | 2022-07-05T16:53:51.389301 | 2020-05-19T21:00:00 | 2020-05-19T21:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,513 | rd | Methods_SFI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/R_utils_wC.R
\name{Methods_SFI}
\alias{summary.SFI}
\alias{plot.SFI}
\alias{coef.SFI}
\alias{fitted.SFI}
\title{SFI methods}
\usage{
\method{coef}{SFI}(object, ...)
\method{fitted}{SFI}(object, ...)
\method{summary}{SFI}(object, ...)
\me... |
a0bc395f8709bc3fb34292abe9df4854b7504f22 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/configr/examples/config.help.Rd.R | 134837ac155d8517c19e1ebb160e9348100c88ff | [] | 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 | 294 | r | config.help.Rd.R | library(configr)
### Name: config.help
### Title: Function to access external helps about configurations format or
### other related information
### Aliases: config.help
### ** Examples
config.help()
## Not run:
##D config.help(1)
##D config.help('ini_git_search')
## End(Not run)
|
5db51de182ca0529e7fa4fbf5c8248f46936c263 | b0bd43f8ce57af3cbdf037870c131e75bd412423 | /class_exercise.R | a9215392feb8a745bc0360c0d035fa900f8d29d2 | [] | no_license | NickAnderson94/class_project | 0a0d28e2cd01f8f8e76813387ad5f30d592dc6b1 | 373eb817ed5f027d74badbabfb56a4c777942532 | refs/heads/master | 2020-07-29T04:54:50.266922 | 2019-09-20T01:16:19 | 2019-09-20T01:16:19 | 209,677,361 | 0 | 0 | null | 2019-09-20T01:14:17 | 2019-09-20T01:14:15 | null | UTF-8 | R | false | false | 29 | r | class_exercise.R | #Nick Anderson
#Blue
#Royston |
a559d89281c5209513fce86991c85ba2bb7c00a6 | 0f8fd1ff0c79632bf090b44f853acacfa8961319 | /R/read_EMSTRANS.R | d7b8935b56ed58e05fd0f39f80170bbef209cf12 | [] | no_license | BAAQMD/CEIDARS | 1fb5e66f5b703771ac111597e468f028114a3a28 | 8d31a1d68a24cb33b47945e85e9f841553b60f53 | refs/heads/master | 2022-08-30T09:28:35.463581 | 2022-08-26T13:41:16 | 2022-08-26T13:41:16 | 25,991,490 | 0 | 0 | null | 2022-08-26T13:41:18 | 2014-10-30T21:59:43 | null | UTF-8 | R | false | false | 1,282 | r | read_EMSTRANS.R | #' read_EMSTRANS
#'
#' @describeIn read_CEIDARS Read EMSTRANS-formatted file
#'
#' @export
read_EMSTRANS <- function (
path,
...
) {
EMSTRANS_cols <- readr::cols(
TRANS_ID = col_character(),
CO = col_integer(),
FACID = col_integer(),
AB = col_character(),
DIS = col_character(),
ACTION = c... |
5796dbacc1913c0a9cbef2898a7f4363235d087f | 3f833b9c66b13d8a583f418d6bb4ac90cc73f1b3 | /R/a11y.R | b62ba9b1bb92e6cf7634fac77bf19625c8903e4d | [] | no_license | gadenbuie/alix | 7cd689268ee5648287db3ec0ed8a49b313cc2714 | 66949e5db197f5fe9b02bb64b3b81c968ca53739 | refs/heads/main | 2023-09-02T15:52:49.686695 | 2020-06-26T20:46:39 | 2020-06-26T20:46:39 | 275,244,252 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,503 | r | a11y.R | #' Test your document or app for accessibility issues.
#'
#' Uses [a11y.css](https://github.com/ffoodd/a11y.css) to include warnings and
#' diagnostics about potential accessibility issues in your HTML document or
#' Shiny web app.
#'
#' @param language Your desired language
#' @param level The level of errors:
#'
#' ... |
bd84818ead8c288fff12878ed2b337bb8b319bab | a9ba290ca051038e90a885fbfebc3813816b0bd0 | /man/msik.Rd | e19d5b2bc0ebb1eafbb845c71986351e885004f5 | [
"MIT"
] | permissive | balachia/pcSoftmaxPack | 42165ad440263558fc3bb659aa0211c365b97967 | 760f42668dfbae21866328c4f27141c6375754da | refs/heads/main | 2023-06-17T11:31:22.199299 | 2021-07-21T02:47:26 | 2021-07-21T02:47:26 | 385,530,998 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 437 | rd | msik.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ms_interval.R
\name{msik}
\alias{msik}
\title{Market Share - Interval; k (exponentiated) formulation}
\usage{
msik(kd, ki, d, prec = 1024)
}
\arguments{
\item{kd}{value at focal pole of decreasing items}
\item{ki}{value at focal pole of incr... |
c37f79c76822330d624e748b732ceeae0969e810 | 194d2083820452a6d75453f99d12f7eca518a2c7 | /TCC final/estGAS.R | 0809f48c445183a052bfbaf2b27e842714dbbeb5 | [] | no_license | franpallaoro/TCC | 393affdc487ee476758b90b6e2260b70debf3611 | 89e90cd4a99288d8f7264345a7fe47005baec240 | refs/heads/master | 2023-04-18T22:10:00.117907 | 2021-05-11T03:36:48 | 2021-05-11T03:36:48 | 270,170,196 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 583 | r | estGAS.R | estGAS <- function(data, i, j, k, Tns, ...){
#i-j janela
#k é o ativo 1...N
if(!require(GAS)){install.packages("GAS")}
library(GAS)
GASSpec = UniGASSpec(Dist = "std", ScalingType = "Identity",
GASPar = list(location = FALSE, scale = TRUE,
shape = FAL... |
271b1e46350e2a745f45cbe2bdd5c45dec7ca374 | e979752c4498c5edf47791d8b7eaafb2730524bf | /sim20032009/calc/aod/sumAOD.R | 0e28c9d98d3bfc15c6df1eeb5a37afebca7a3aee | [] | no_license | ClaudiaGEscribano/aod_and_PV | 0bf4d6c7398351aebdef7b9f9538246c5ee2bd49 | 77eaa4e454ce4ec4ec784795e2e89b8945bc1737 | refs/heads/master | 2021-03-27T15:41:09.158241 | 2018-12-10T14:58:39 | 2018-12-10T14:58:39 | 83,782,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,815 | r | sumAOD.R |
library(raster)
##bc <- stack("../data/AOD/aod_bc_monthly20032009.grd")
##su <- stack("../data/AOD/aod_su_monthly20032009.grd")
##ss <- stack("../data/AOD/aod_ss_monthly20032009.grd")
##sd <- stack("../data/AOD/aod_sd_monthly20032009.grd")
##or <- stack("../data/AOD/aod_or_monthly20032009.grd")
bc <- stack("../data/... |
13a1a4b316bb19c5a2aa18e20b36eb97eab50ad8 | c5a59ef72d1872a6fb6cf8bde2a7798967c66d5b | /R/psi2.r | 3fba18b078decc903eeefd842dfe6ee3b42e0182 | [] | no_license | hjanime/hm-splice-pipe | 9ddcc3aa4e678dca068f125cda67db6f6eb24a45 | edafa685dd9a079738e635d5d60927a6a7f4981d | refs/heads/master | 2021-01-21T09:11:08.209177 | 2014-07-18T14:23:52 | 2014-07-18T14:23:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,948 | r | psi2.r | suppressPackageStartupMessages(library("ggplot2"))
suppressPackageStartupMessages(library("optparse"))
suppressPackageStartupMessages(library("plyr"))
option_list <- list(make_option(c("-a", "--inp1"), help="tsv input 1"),
make_option(c("-b", "--inp2"), help="tsv input 2"),
make_option(c("-s"... |
f66b4985f58b564f7d6f535e3600541addb711c4 | 8118a0fa472da408c486d532a45f3515e7dc3ef2 | /tests/testthat.R | b98e99ffa6451acfd3aec123680eb39056e78831 | [
"MIT"
] | permissive | mdt-ds/slotR | 7129c762ef611c6bca00eccea2a6efe3606ab710 | 7b23a731abd0aa714c46cee98b61a032cbe8250c | refs/heads/master | 2023-04-28T21:38:41.701400 | 2021-05-23T16:44:45 | 2021-05-23T16:44:45 | 314,540,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(slotR)
test_check("slotR")
|
4fbe69c30cb27019761ef37eafdce40a12dcfab2 | 0368be8d75ccc042e198d800584c2300754a7eb2 | /R/myApply.R | dc2e2a7165b90ab5a8f080551fd8a6ddc65bff88 | [] | no_license | JWooll/Homework3 | 5eb8460352dcd85d4208a99b2e14f58989121af8 | 3cf19e013cda5aec4b397aa824f167427ac13d3f | refs/heads/master | 2020-04-08T00:51:00.088880 | 2018-03-08T01:22:43 | 2018-03-08T01:22:43 | 124,233,658 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,019 | r | myApply.R | #' A simple homemade apply function
#'
#' Changes a user supplied matrix by a user supplied function on the dimension
#' that the user chooses with 1 being the first dimension and 2 being the seconnd
#' It also allows user to insert ther own further parameters
#'
#' @param X a matrix to be applied on
#' @param MARGI... |
14b4e76ff948043972074dfb907c2bd82bb36fae | 845a4db68eebe70d5c204fbad2dd27cabf1908df | /R/compare.R | 7a223028f98a1ee323c9fc2dff93a63d5268898b | [] | no_license | jashu/beset | 6b1a6d8340b887a3628d0db6563bcdf53b4c709c | 703e4e7da70185d279c4a60e76207ff2dae91103 | refs/heads/master | 2023-05-03T20:22:00.304497 | 2023-04-18T18:23:26 | 2023-04-18T18:23:26 | 49,987,418 | 6 | 0 | null | 2021-04-13T11:36:35 | 2016-01-19T22:24:12 | R | UTF-8 | R | false | false | 7,308 | r | compare.R | #' Compare Predictive Performance of Two Models
#'
#' @param yhat1 A data frame consisting of cross-validated predictions from a
#' benchmark model, an object containing such a data frame, e.g., a
#' "cross_valid" object returned by \code{\link{validate}}, or an object that
#' can be passed to \code{\link{validate}}.
#... |
4d77e8319f90e812d5c4b5d304ad16f966f85f36 | b7f920a53b7e4dd49e2cd33de5d86450849a0873 | /src/global.R | d54aff99cce5f60f5394499b994f5dd708571f50 | [] | no_license | remn123/ggformapp | 37e69c28137fd5de718c9bde736a171cb5e85f60 | 4cd8f1b4f1a9d5841f1f95fd7f3dfd7abd2930bd | refs/heads/master | 2020-06-07T09:07:05.998460 | 2019-06-20T23:07:01 | 2019-06-20T23:07:01 | 192,983,502 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 257 | r | global.R | rm(list=ls())
library(shiny)
library(shinydashboard)
library(ggplot2)
library(ggformula)
data("iris")
get_df_plot = function(df, s, new_var){
s <- paste0(new_var, " = ", s)
q = quote(mutate(df, z = s))
eval(parse(text=sub("z = s", s, deparse(q))))
} |
dc8fa40504d1459bb1b41b486243c22dd19da53a | d8c483f808177685eac6f0e3355c903949fa0589 | /dev/performance/methods/fastreduction-dirs/pr03_globalReduction.R | 3c1db735753f00a2898cfbeea029d92148f8f1a5 | [] | no_license | lgarreta/Reduction | 263d25470302ed33bad3e674d750d74667a6048c | ca7a207c05382d2c85898b0cb85ce5e50e06aeb5 | refs/heads/master | 2023-06-09T14:57:48.113691 | 2023-06-03T22:43:03 | 2023-06-03T22:43:03 | 164,315,311 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,434 | r | pr03_globalReduction.R | #!/usr/bin/Rscript
#!/home/mmartinez/bin/Rscript
# LOG:
# r2.0 (Aug17): Changed distance matrix calculation, now direct call using proxy::dist
# r1.4 (Aug16): Modified clustering with initial medoids including the last pdb
# r1.3 (Aug13): Fixed error when it gets number of pdbs < K
# r1.2 (Aug3): Extracts K medoids ... |
2b27c5666a144f29a62e9e02a66f23615f112aef | 53e510145d7e0a510ca208b0f893d1abe9683f8b | /R/clean_bowling_data.R | 691bdc83713458fd2f6ab68a1600d2f86403616f | [] | no_license | kmaheshkulkarni/cricketdata | a8c95bd8ec70a13ef8916d4e66f5af86b09e458f | 589bb1a6130fa3120be6832281d6cdcb1f5105cc | refs/heads/master | 2020-04-24T08:57:36.597335 | 2019-07-03T09:22:27 | 2019-07-03T09:22:27 | 171,847,423 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,374 | r | clean_bowling_data.R | # Function to clean bowling data.
# Works with career or innings data
clean_bowling_data <- function(x)
{
# Make names easier to interpret
vars <- colnames(x)
vars[vars=="Mat"] <- "Matches"
vars[vars=="Inns"] <- "Innings"
vars[vars=="Mdns"] <- "Maidens"
vars[vars=="Wkts"] <- "Wickets"
vars[vars=="BBI"] <... |
1a7d1abe6deb5696d2560e21fcd7986f22101679 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.security.identity/man/cloudhsm_describe_luna_client.Rd | 376756ea6a9f1e41d8e4fc327b85b30cecd82176 | [
"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 | 872 | rd | cloudhsm_describe_luna_client.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloudhsm_operations.R
\name{cloudhsm_describe_luna_client}
\alias{cloudhsm_describe_luna_client}
\title{This is documentation for AWS CloudHSM Classic}
\usage{
cloudhsm_describe_luna_client(ClientArn = NULL, CertificateFingerprint = NULL)
}
\... |
430b622f880c1ff5860e9fa71e35ef292fdc2215 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DiceDesign/examples/coverage.Rd.R | 67c3244e7fc7261dd50e0d7879b128f5a81ec436 | [] | 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 | 204 | r | coverage.Rd.R | library(DiceDesign)
### Name: coverage
### Title: Coverage
### Aliases: coverage
### Keywords: design
### ** Examples
dimension <- 2
n <- 40
X <- matrix(runif(n*dimension),n,dimension)
coverage(X)
|
00e26d7296a5ebcb27cbd8580b173efa771b2da9 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/pangoAttrScaleNew.Rd | ce4f40729b87b9cf0923957b8fa17dd759477f11 | [] | 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 | 498 | rd | pangoAttrScaleNew.Rd | \alias{pangoAttrScaleNew}
\name{pangoAttrScaleNew}
\title{pangoAttrScaleNew}
\description{Create a new font size scale attribute. The base font for the
affected text will have its size multiplied by \code{scale.factor}.}
\usage{pangoAttrScaleNew(scale.factor)}
\arguments{\item{\verb{scale.factor}}{[numeric] factor to ... |
961f8910583bc36ca8b18e601e9d5e3218633da8 | a3864f60b8dc3a3b9af5a42547c25470e38f7bf6 | /man/plot.sr.Rd | 870c158cfe2f0ff8c71a1f272bb94f27892d9b46 | [] | no_license | cwmiller21/mvdalab | c29ffb52a41a969a1c2701ccab77901afed2fbd9 | e9f1bea960cdf7dd2d0472581f9fe97c30bd6d4f | refs/heads/master | 2021-01-18T05:24:46.133643 | 2016-02-29T10:54:45 | 2016-02-29T10:54:45 | 52,802,389 | 1 | 0 | null | 2016-02-29T15:38:22 | 2016-02-29T15:38:22 | null | UTF-8 | R | false | false | 880 | rd | plot.sr.Rd | \name{plot.sr}
\alias{plot.sr}
\title{Plotting function for Selectivity Ratio.}
\description{This function provides the ability to plot an object of class \code{sr}
}
\usage{
\method{plot}{sr}(x, variables = "all", ...)
}
\arguments{
\item{x}{\code{sr} object }
\item{variables}{ the number of variables to include the ... |
01a7f8aead6dd2b830171a2c8d738e41615d86c4 | 573edac85effdda60291c96f568af4bcf36833a5 | /man/summary_sema.Rd | ffcc474e83f0d5ae848fbcc825131b4be89331bc | [] | no_license | L-Ippel/SEMA | d71835566c17df707896bcd9ef32960c71b2c43a | 1d0e3a48c855df704cad18c7ab6bb73d08bd4efa | refs/heads/master | 2021-06-03T18:26:07.776301 | 2018-08-06T11:32:00 | 2018-08-06T11:32:00 | 38,366,963 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,969 | rd | summary_sema.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.R
\name{summary_sema}
\alias{summary_sema}
\title{Interpreting sema output}
\usage{
summary_sema(x)
}
\arguments{
\item{x}{A sema model output.}
}
\value{
A list with sample size, number of units, the coefficients of the
fixed effec... |
8cc2ec0a9ec3550ec4e47a24623044c0d9451296 | 49d51566b41a141a0dcad5119fcf910efcdfd82d | /Ex.4_3.R | 56c50d750ba4f52a016de3adbbf0c0c64a3d6174 | [] | no_license | himanshu6980/RSolvedProblems | 460124c286f8d6814ae7f9ba762cb01dcb7e5cc4 | d9928290279030543572005c84638bcab98c72ac | refs/heads/master | 2020-03-06T18:42:59.866055 | 2018-03-27T16:10:59 | 2018-03-27T16:10:59 | 127,012,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 512 | r | Ex.4_3.R | # Sorting dataframes
sampledata_file <- read.table("yield.txt",header=T)
attach(sampledata_file)
# sorting by row
sampledata_file[order(death),]
# rev sorting
sampledata_file[rev(order(death)),]
# we an also sort on multiple attributes.
sampledata_file[order(death,status),]
# logical conditions to select rows from t... |
f6dd48d53088a680014422add333ca4cf76099c1 | 808f1cd2932824552e8c50e9869a7a77194a510e | /utils.R | 23c627d58a4812626a98ece62b4cd965ebbf9899 | [] | no_license | ph-hack/3dFR | 9de0f5fcdc7071d0fe7ca6aa344e327ab2f60bbf | 32af516a29a691febfe28cf519d7de2dd78a8c17 | refs/heads/master | 2021-01-21T12:47:34.554868 | 2016-03-25T18:53:43 | 2016-03-25T18:53:43 | 20,979,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,142 | r | utils.R | #' Checks for the integrety of the closest samples.
#' In other words, it checks if the 'nClosest' first closest selected
#' contains at least one sample of that class.
#' @example
#' The file 'closestDir'/cl_02463d452.txt contains the selected
#' closest for the individual 452 of the class 02463.
#' This function chec... |
ba58674d69bd68eb17c06f30cefc6f6bacc35925 | c36315ca4008746505182fdac3dc61b47de81a70 | /Blatt2/Rotation.R | fcc9e9a5fad9811ecaf4363306f29ca1dc9fe390 | [] | no_license | alexanderlange53/DataMining_in_Bioinformatics | 455df0badcaa075b19b33b8474516d7bd15470fa | 4758446a3d5a625e8b83f7f1b047dc1472d47165 | refs/heads/master | 2021-01-20T20:36:10.677689 | 2016-08-09T21:56:36 | 2016-08-09T21:56:36 | 64,967,069 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 232 | r | Rotation.R | Rotation <- function(alpha, Data){
alpha <- ((2*pi)/360)*alpha
RotMat <- matrix(c(cos(alpha), sin(alpha), -sin(alpha), cos(alpha)), ncol = 2, byrow = T)
X <- RotMat%*%Data
V <- var(X[1,])
Out <- cbind(V, X)
return(Out)
} |
f584304023a3026e9b4b9a5d2066d09de2d82944 | db4ab344e60cbbbd9a726dc8e84db32018dcb2e8 | /tests/testthat/test-ncaa_lineups.R | c1fb26f9677bb9621b34ec76c2dbbac62fe4fb5f | [
"MIT"
] | permissive | robert-frey/baseballr | 8491c93a9c1fdf74c74a75c3a6e2ebb65a7862e8 | 060cd8b18c080cbb50fae444460a4c0a4db175e9 | refs/heads/master | 2023-03-09T17:21:52.013535 | 2023-02-24T18:06:56 | 2023-02-24T18:06:56 | 248,656,064 | 3 | 0 | null | 2020-03-20T03:04:29 | 2020-03-20T03:04:29 | null | UTF-8 | R | false | false | 304 | r | test-ncaa_lineups.R |
cols <- c("year", "playerName", "position", "batting_order", "school")
test_that("NCAA Batting Lineups", {
skip_on_cran()
x <- ncaa_lineups(game_info_url="https://stats.ncaa.org/game/index/4587474?org_id=528",year=2018)
expect_equal(colnames(x), cols)
expect_s3_class(x, "data.frame")
})
|
76a2ea63291cdc8a187ddab5bc08ec3632f6d30c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ShinyItemAnalysis/examples/gDiscrim.Rd.R | 87ddd1c0bd42411b6713583fcbce0d59947f6b2a | [] | 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 | 978 | r | gDiscrim.Rd.R | library(ShinyItemAnalysis)
### Name: gDiscrim
### Title: Generalized Item Discrimination
### Aliases: gDiscrim
### ** Examples
## Not run:
##D # loading 100-item medical admission test data sets
##D data(dataMedical, dataMedicalgraded)
##D # binary data set
##D dataBin <- dataMedical[, 1:100]
##D # ordinal data se... |
61536a2a1c934dd69769c593c92e91cbb9d5ae06 | 0160b0354204a7bb9b00adac864156b8c1ddf0a5 | /Analysis/Kristiansson_analysis/heatmap.R | 9115de7573a1c8dd72ac3e6626b85dfe7a8049d9 | [] | no_license | avahoffman/gene-expression | c51b1e176cb86a6d1e64c9f38f56a45966972de2 | ab3103c4df9294678128d8469b0ad094eaa255fc | refs/heads/master | 2021-08-22T19:01:35.119698 | 2020-03-16T21:39:17 | 2020-03-16T21:39:17 | 132,801,984 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,373 | r | heatmap.R | library(reshape)
library(ggplot2)
df=read.csv("/Users/avahoffman/Documents/CSU/Research/RNASEQDATA/MEC_reanalysis/Kristiansson_analysis/SxE.csv")
head(df)
df=df[,c(2,17:24,27)]
colnames(df)=c("Group","Ad1","Ad2","Aw1","Aw2","Sd1","Sd2","Sw1","Sw2","tf")
labels=df[,1]
tfs=df[,10]
m1=as.matrix(df[,2:9])
m1=log2(m1+0.... |
d4cb0bca811f0e201faa8aceb5e008cd548df513 | a10d14465377df739497293d665c69c575c6438b | /Predictions_and_evaluation.R | f00c38eba704f078f42f8c62adcf175d397a131b | [] | no_license | khaled-mansour/Targeted-Marketing-project | b628d3948fb1feb1d2686362d9c67ca7be50d1e4 | bcdd709afe89542ab03df90633242c6c5c97869e | refs/heads/master | 2021-08-31T20:29:05.323946 | 2017-12-22T19:21:06 | 2017-12-22T19:21:06 | 115,143,577 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,009 | r | Predictions_and_evaluation.R | ##################################################################################################################################
############### Predictions and evaluation #######################################################################################
##########################################################... |
619e8a590288dcb593c51aae2e233bed0e354b5d | 84e7c052fae39843d3f67be78049e175ea8c441c | /R_cleaned/Figure 1.R | 29cd5325b361b18eea9ef7cb3e13f6d59755c14b | [] | no_license | AngeVar/GLAHD | 935b7d346dc9fb4cf8a8b552dda1400fe7100fda | 80ff898cd15b2e670ea0ed7c31db83a69b657faf | refs/heads/master | 2020-05-22T07:57:11.913885 | 2017-07-04T06:01:13 | 2017-07-04T06:01:13 | 36,908,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 43,880 | r | Figure 1.R | #load some libraries
source("R_cleaned/GLAHD_LoadLibraries.R")
library(raster)
library(rgdal)
library(stringr)
library(scales)
library(oz)
library(maps)
library(mapdata)
library(RNCEP)
library(mapplots)
library(plotrix)
library(plotBy)
library(doBy)
library(dismo)
library(rgeos)
#load file
biodat <- raster::getData('... |
c757d2b764b17a54115c9b2d2a1929d971e06015 | 8ea8dd82beb390c5ae59d32acaf854067e2f310a | /test/workerHotspotsFuncs.R | 5e806d3feb81721c31573954cca520bf58f9ba7f | [
"MIT"
] | permissive | hongooi73/AzureDSVM | 91d9f69e8ad30f8d589f49f734422a5d8496e319 | 3553b5581dd640513a37101bb71a8170498f1809 | refs/heads/master | 2021-07-06T12:24:35.772109 | 2017-10-02T17:00:31 | 2017-10-02T17:00:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 839 | r | workerHotspotsFuncs.R | # do preparation.
# -------------------------------------------------------------------------
# Split data into training and testing sets.
# -------------------------------------------------------------------------
dataSplit <- function(data, ratio) {
data_part <- c(train=ratio, test= 1 - ratio)
data_split ... |
37b7843b7c4e7ab10bfb84f5b0c9f977316664b8 | 1c2152f3c88d4e2059729ceaaf72b88f930c4306 | /615 midterm project.R | b12b4d35499d4b2ccd6dc4304f5ea1bc2478e41d | [] | no_license | JingningYang/MA-615 | 6c73f15d96df3c5a02081a8fe064636e92109cfc | 0053a889fbe55ac4fae99725ad7d533f3678a1fd | refs/heads/master | 2020-07-28T12:14:16.279159 | 2020-06-13T19:06:56 | 2020-06-13T19:06:56 | 209,407,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,595 | r | 615 midterm project.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(psych)
library(shiny)
library(gplots)
library(ggplot2)
library(RColorBrewer)
library(ggfortify)
library(rea... |
cba5d3ade8c3b5b910f4e80cef374db2d9e62cf0 | 41e24c9c473277d0f671ddb597958dad68063786 | /plot2.R | ff61598b11241b00b4bf266ebaf340894f9b3a12 | [] | no_license | divyadixit/ExData_Plotting1 | c7be8d8f0ea6bd7e0d8302337cd1d44554ee2796 | 87a778a182f315fe7455eb608222f052a5a4a53f | refs/heads/master | 2021-01-18T13:31:33.300159 | 2015-11-08T20:05:13 | 2015-11-08T20:05:13 | 45,796,370 | 0 | 0 | null | 2015-11-08T19:58:14 | 2015-11-08T19:58:14 | null | UTF-8 | R | false | false | 1,566 | r | plot2.R | library(lubridate)
# Loading the dataset :
housing_powercomp <- read.table(file = "./household_pc/household_power_consumption.txt", header = TRUE, sep = ";",
colClasses = "character", na.strings = "?")
# subsetting based on the row numbers of the required date and time
powercompfeb_data <- housing_powercomp[666... |
f994ef21895aee94e928e3c4d9f904931ea95ec2 | 63fe30f175c8294963a3f2fb6c2e6a0a6a18f61c | /man/get_meetup_events.Rd | 52e22d484f7da50546f474c781d4c3855b9317e9 | [] | no_license | cran/meetupapi | d4d9a94a68d0f7067fb775c3f78f38ec57ad2a4d | c4febe7c384a9879d7f19dbf925418b6b914660d | refs/heads/master | 2021-04-28T05:39:09.889787 | 2018-02-20T09:04:28 | 2018-02-20T09:04:28 | 122,181,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,036 | rd | get_meetup_events.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/meetups.R
\name{get_meetup_events}
\alias{get_meetup_events}
\title{Get Meetup Events}
\usage{
get_meetup_events(urlname, key, fields = c("status", "id", "name"), ...)
}
\arguments{
\item{urlname}{string, URL name for the meetup. e.g... |
e358bfa438271f5cdd57a2e5bf73fd4e76fec392 | c64081a2c69a1b320d6d56e48c973a62935ab895 | /man/maxjobs.mclapply.Rd | f9a2eddf55a32ec3994af690cf06abddd57fd79d | [] | no_license | PetrTsurinov/PeakSegPipeline | a1c04cdb88dac81d789305becc1eec25634495ff | e6c24caef1245e7d6769cee96841b659e5bd8dab | refs/heads/master | 2021-09-11T15:37:06.410226 | 2018-04-09T11:13:34 | 2018-04-09T11:13:34 | 114,019,554 | 0 | 0 | null | 2017-12-12T17:30:26 | 2017-12-12T17:30:26 | null | UTF-8 | R | false | false | 537 | rd | maxjobs.mclapply.Rd | \name{maxjobs.mclapply}
\alias{maxjobs.mclapply}
\title{maxjobs mclapply}
\description{Run mclapply inside of a for loop, ensuring that it never receives
a first argument with a length more than maxjobs. This avoids some
memory problems (swapping, or getting jobs killed on the cluster)
when using mclapply(1:N, FU... |
eeaafcbfce6d785d6e8b15567c703aa6538d1a65 | 77de99ed2d5c88a17e134ce094fbc9d20e6edb82 | /analysis/2016-02-12/normalizedDrugAnalysis.R | a564bea3cde7a2608fa7783550a017bb68685a78 | [] | no_license | sgosline/pnfCellLines | e1c3e2b951ebe6cd941aab8902fefcfce9aa0d93 | e6574317a77ee5b8e44fe9659c2dc2027ac65937 | refs/heads/master | 2020-04-12T08:04:10.935912 | 2019-05-30T22:58:54 | 2019-05-30T22:58:54 | 59,235,401 | 0 | 2 | null | 2018-07-12T21:52:57 | 2016-05-19T19:23:46 | HTML | UTF-8 | R | false | false | 1,632 | r | normalizedDrugAnalysis.R |
##double checking these scripts to ensure they map back to the BROAD data
source("../../bin/singleDrugAnalysis.R")
source("../../bin/ctrpSingleAgentScreens.R")
source("../../bin/ncatsSingleAgentScreens.R")
#plotMostVariableAUCs()
##first get original auc vals
orig.ncats<-getValueForAllCells("FAUC")
orig.ctrp<-getC... |
6031501c8d8355c01ffad30eee11f804536d4477 | 661d4d3f14e14b699c697efc8db05a220ed40eb9 | /mosaicApps/mosaicManipShiny/mLM/ui.R | 2c7ddd6ea21157856b91180519ff89e85207a76a | [] | no_license | dtkaplan/MOSAIC-Summer-2015 | 60518bb08edb3c7165ddb5e74104ccdfdb1c0225 | 2f97827b9e09fccc7cc5679888fe3000d71fe1cc | refs/heads/master | 2021-01-23T13:31:24.897643 | 2015-11-17T22:39:39 | 2015-11-17T22:39:39 | 35,576,261 | 0 | 1 | null | 2015-06-02T21:24:26 | 2015-05-13T21:58:29 | R | UTF-8 | R | false | false | 857 | r | ui.R | shinyUI(fluidPage(
titlePanel("Visualizing a Linear Model"),
p("An interactive app that allows you to fit linear model by entering an expression.
Note that while the expression can take several x variables simultaneously,
the graph only models one x varibale at a time"),
sidebarLayout(position = "r... |
9fd5af9e8a8448e52f30228ec63b6db02191e6b8 | 1ba83608457f6f31b61e40441001849c38c4fb1d | /PCA/PCA.compare.R | 6d8538f0290ba8cab64052a89f9387396f2836ae | [] | no_license | hungying/AS-GWAS | 22d0a96bc114d4f3f469af21f20d0a23babcfaa0 | 3648535112ffe0a450b4e7c61920c2c8e2f3abd7 | refs/heads/master | 2020-03-22T03:48:58.150653 | 2018-07-03T01:56:01 | 2018-07-03T01:56:01 | 139,454,065 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,330 | r | PCA.compare.R | ## This scripts used to compare PCA result between SNP and AS
par(mfrow=c(1,2))
AS_PCA <- read.table("/mnt/01/hungying/cassy.folder/AS_GWAS/AS_GWAS_PCA.txt", head=TRUE)
#AS_PCA <- read.table("/mnt/01/hungying/cassy.folder/AS_GWAS/AS_all/GAPIT.PCA.csv", head=TRUE)
SNP_PCA <- read.table("/mnt/01/hungying/cassy.folder/SAM... |
6ab6603bb74cc79d7cf86ac0cbd0e517af6b7799 | 4d3fcaa53d54cca891f036339d01eb1582ec4072 | /workflow/scripts/build_bedgraphs_from_programs_blood.R | 11116204b1ad7c594bbde351f663109f0fc69a8e | [] | no_license | kkdey/CREGENE_GWAS_benchmark | 04f71ef5a50224aac2d0d63760c25558bb31e904 | ea07e2576c7c58c36e0a9fe2eecb6fa010b5477e | refs/heads/main | 2023-07-04T12:59:40.952311 | 2021-08-09T10:45:59 | 2021-08-09T10:45:59 | 393,850,478 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,998 | r | build_bedgraphs_from_programs_blood.R | ## Generate bedgraph files from a gene score file and CRE-gene links files in blood
# open log file to collect all messages, warnings and errors
log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
library(data.table)
library(R.utils)
merge_functions_file <- file.path(snakemake@scriptdi... |
d29b95da5e6e13cb4792491a8da562d1237ee45e | 57feb81dfc38a8a59abad2d7af6378d48e4afa9f | /smdanalysis/man/readCOM.Rd | a51cf1333f9f1fa831e31c4279d735bb06a769a0 | [] | no_license | Danny221212/sMD-PF-M1AAP | a4bf27e3e6bd6a9602420cc9fb2584bec9d707fc | a1519c01c7a31a6a97938a7a8e2873b0a11d58d5 | refs/heads/master | 2020-03-26T06:26:56.730160 | 2018-08-20T21:56:55 | 2018-08-20T21:56:55 | 144,605,846 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 831 | rd | readCOM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readCOM.R
\name{readCOM}
\alias{readCOM}
\title{Loads multiple or a single Center Of Mass (COM) or Centroid .xyz file(s) and
Converts to 2D x and y coordinates based on a viewing matrix}
\usage{
readCOM(directory, pmat, seperator = " ... |
cf917f0cb0a06c4684b3125d3e322b771a0487fb | ca500a32f0dd1cf749dbab138511f88f020c5b5c | /run_analysis.R | 4fe5a7b171c4f1dc5bc5aade80071f3f821fae54 | [] | no_license | KenHo95/Getting-and-Cleaning-Data-Course-Project | 606ec934357b84e0c85c9097cfeca728c425bbf1 | bee8fa1625a20e1ae67fd9e61754f10a82d5bdac | refs/heads/master | 2022-12-20T20:36:28.155153 | 2020-09-16T16:58:52 | 2020-09-16T16:58:52 | 293,816,507 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,617 | r | run_analysis.R |
# import relevant library
library(dplyr)
repo_dir <- getwd() # Get woring directory
# Read in train and test dataset, features table, subject tables, and avtivity label table
# get data from "UCI HAR Dataset" folder
x_train <- read.table( paste0 (repo_dir, '/UCI HAR Dataset/train/X_train.txt'))
y_train <- read.tab... |
24911e32b26078812e088871d1622dbf198dee78 | 67d069e4d9d9b852e11cdc333bb0ff0ed5e6a477 | /tests/testthat/test.R | 674b277d5afecd926eb8eb97384062b3ae509dcc | [] | no_license | schiffner/EMnet | 3bf8aba9b0a9c318b8dbb7f7638f6c28be5aefd2 | ce34e19a36637e3eb0cebe2162417e4c67904ac7 | refs/heads/master | 2020-12-25T14:14:02.980829 | 2016-12-08T11:06:43 | 2016-12-08T11:06:43 | 64,326,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,010 | r | test.R | context("EMglmnet")
test_that("J = 2, K = 3", {
X = as.matrix(iris[1:4])
y = iris$Species
lev3 = levels(y)
res32 = EMglmnet(y, X, lambda = 1e-03)
pred32 = predict(res32, X)
expect_equal(levels(pred32$class), lev3)
expect_equal(length(pred32$class), nrow(X))
expect_equal(ncol(pred32$posterior), 3)
expect_e... |
d86dfcccd80d37eaf428a3baf5c201c6b50cc1c3 | a15bafa2b845fff694d5428c27796ae02fc04b17 | /man/Colors1.Rd | e2237ffaa8af1e565f80dd928c181366f1342b41 | [] | no_license | cran/IntClust | 741db502112c3407aefa5eb5d08b92e2d2a75f7a | 235fe4a67855c96b48247aa2f02895604caf507e | refs/heads/master | 2018-10-30T23:12:34.173124 | 2018-07-30T11:10:15 | 2018-07-30T11:10:15 | 55,162,571 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 322 | rd | Colors1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\name{Colors1}
\alias{Colors1}
\title{Colour examples}
\format{An object if class \code{"character"}.}
\description{
A vector of HEX codes for the colours used in the examples
}
\examples{
data(Colors1)
}
\keyword{datasets}... |
9cb3d5b160cf15b2972e3411e5506248c660811d | 075147b97c67bcfbe90b5386f0a9332648578941 | /plot2.R | 23b6287f3202998e3967d60eb80350ec7dd846e8 | [] | no_license | Zhong-Wang/ExData_Plotting1 | c896acee1fe96f59be72fc5a6b93bd2b78506120 | de9886d981f20e050b30d13ddfffa79b888208e2 | refs/heads/master | 2020-05-31T00:08:04.079335 | 2014-05-10T10:30:57 | 2014-05-10T10:30:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,050 | r | plot2.R | ## Get dataset file
## CAUTION: if you have already download dataset file,
## please ensure the name and the file path is correctly set to 'destfile' variable to avoid re-download
destfile <- "exdata-data-household_power_consumption.zip"
if (!file.exists(destfile)) {
download.file("https://d396qusza40orc.cloudfront.n... |
a1bfc7a500e89887664fe03f199cfb8fc4f12e4d | d5eef5ca98115b14d13345c3e104fe4d9448b721 | /R/webSDM-package.R | b3735ece6a5d56411735db378ef70779a58ece3d | [] | no_license | giopogg/webSDM | b6e3e40fc0ee82f87b2cee3a4c2167555fc3e19a | 9b011d1dcb58b9e2874f841af4874db752c73fff | refs/heads/main | 2023-04-19T02:10:10.368235 | 2023-03-15T07:23:58 | 2023-03-15T07:23:58 | 359,931,820 | 5 | 1 | null | 2023-03-14T09:38:33 | 2021-04-20T19:39:24 | R | UTF-8 | R | false | false | 177 | r | webSDM-package.R | #' webSDM.
#'
#' Package to fit a trophic Species Distribution Model, analyse it and predict. See Poggiato et al. In prep.
#'
#' @docType package
#'
#'
#' @name webSDM
#'
NULL
|
c699140e7f79d4732f3edac6385be38f7dc0ef0a | e5c43a31a082bbfec5ebbc20b34d373896721579 | /R/functions/vplot.R | 5b362c2d879ff9442f00eebb46de45a51694e9bd | [] | no_license | geryan/rfst | 3dde3a499651f3a1ccc736f8c6597c5972f0e17c | 0aac1f0c3b17096af0c5b0b06e1ad80ac6d709ca | refs/heads/master | 2023-05-02T12:32:51.743467 | 2021-04-27T01:26:47 | 2021-04-27T01:26:47 | 164,573,310 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 186 | r | vplot.R | vplot <- function(x, ...){
library("viridis")
plot(x, col = viridis(100), ...)
}
zplot <- function(x, ...){
library("viridis")
plot(x, col = viridis(100), zlim = c(0, 1), ...)
} |
542b82e093b591d8d0af0b1bc50c9dd2686662c5 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query51_falsequ_1344n/query51_falsequ_1344n.R | 1671077e6813bee4018e0c345d7227ff1e817d5f | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 67 | r | query51_falsequ_1344n.R | a7ea6efed7078242494a844a485f8a81 query51_falsequ_1344n.qdimacs 21 9 |
4f512937cb51cad76b0d180bb878dd99a04e4ade | 219c5192a2af3c82cf1e498b7abca3b782dbefe9 | /cachematrix.R | 9a84f13e0252b0d6cb4e8783ea210fa14f683858 | [] | no_license | superbunika/ProgrammingAssignment2 | 6309c34727240ef87f62b50a7d231d2e90b99454 | fefb32ad21437389f6faaa0726d0744624336412 | refs/heads/master | 2020-12-02T15:06:22.501161 | 2014-12-21T22:46:19 | 2014-12-21T22:46:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,112 | r | cachematrix.R | ## We define a custom object with functions for setting and getting
## the value of matrix and it's inverse.
## Custom function that stores a matrix and it's inverse which is solved
## with another function. Upon setting a new value of matrix, the inverse
## is reset.
makeCacheMatrix <- function(x = matrix()) {
i... |
65e09323bfa42426f0095ee83c81c21fc38c136d | ffd14f4e8b9d71ef13968755efca4760bfc518b4 | /Code/8-LocalSource.R | 8dd98caa186d9dec368ceb6a6f37a1c4c60f6890 | [
"MIT"
] | permissive | tchakravarty/RExamples | d398d50b5362f1955ba73d26dd806e6fb218b9df | 65c17fade0a784018f2f3afcb4a9a8c603bd65a0 | refs/heads/master | 2021-01-21T21:48:45.641418 | 2016-05-20T17:55:47 | 2016-05-20T17:55:47 | 28,849,466 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 271 | r | 8-LocalSource.R | # write out the file to be sourced
fLS = file(description = "Code/8-LocalSource-Input.R", open = "w+")
write(x ="a = 1; print(a)", file = fLS)
close(fLS)
# source the file
a = 3
sourceEnv = new.env()
with(sourceEnv, source("Code//8-LocalSource-Input.R", local = TRUE))
a |
db20775dec852f5193ca9522503993574bdcf51b | 2da570da5859c8a830e76d794fa17d042cd41ebc | /05 Introduction_to_Data_in_R/03 Sampling_Exp_Design/01 Simple_Random_Sample_R.R | 19664d83955d1359452a0d7add6bb96b33900436 | [] | no_license | ArmandoReyesRepo/RCode | 85d5c8f36107936bfcbbdbf16dc9bb2ed1a0feee | 41c96dd0d4bc7762fad3cbeb46c3df4ee1444575 | refs/heads/main | 2023-05-12T16:29:25.970647 | 2021-06-04T09:01:34 | 2021-06-04T09:01:34 | 373,320,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 597 | r | 01 Simple_Random_Sample_R.R | setwd("C:/Users/arman/OneDrive/Desktop/2020/DataCamp/05 Introduction_to_Data_in_R")
getwd()
dir()
# Load packages
library(dplyr)
us_regions<- get(load("us_regions.RData"))
# The dplyr package and us_regions data frame have been loaded.
#
# Use simple random sampling to select eight states from us_regio... |
acd35f4fac395970dc580b07a76e1eaa4a8a8b98 | 3dff7540de9b4037edbc14e4f3bffc4a58bfb1fd | /R/todo.R | d7f46ec3b5f8dbfbfe9dbda24c0c515ae1ed9e41 | [] | no_license | antongrau/MONECA | 1e679da3d1bbc6040e270f474240531c177f69e8 | a83f176fefa9e98d8d22be7deacedde710797c01 | refs/heads/master | 2020-12-25T17:34:57.614262 | 2019-09-16T15:03:05 | 2019-09-16T15:03:05 | 30,929,570 | 3 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,812 | r | todo.R | # ## Todo
#
# # Få den fucking plot funktion til at virke igen - vi skal til sna pakken
# library(igraph)
# library(MONECA)
# library(soc.elite)
#
# # devtools::install_github("gaborcsardi/pkgconfig")
# # devtools::install_github("igraph/rigraph")
#
# # library(devtools)
# #
# # devtools::install_github("gaborcsardi... |
41d30c27e1463a4432b7cb9bac256bff0403e42f | 78e31bac83a432d95f07466a1b1cfac78638d671 | /R/options.R | 229e2e0ee552ae9a987c4be73b39862e3d6457f7 | [] | no_license | shakythesherpa/Rnightlights | 298885d7eb7df9301c7c5e3ed39de4c01d1124c6 | c36a9a6ef047e2cd1447b4e20a54030f65dd42c3 | refs/heads/master | 2020-06-06T13:52:04.061970 | 2019-03-27T12:27:18 | 2019-03-27T12:27:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,697 | r | options.R | ######################## RNIGHTLIGHTSOPTIONS ###################################
RNIGHTLIGHTSOPTIONS <- settings::options_manager(
#Specify the regex to uniquely identify the tile file
#to extract from the download tile tar.gz
#more info at: https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html
configNa... |
ae0a9d84b9f554f154159b145c7fa1436d4d2ce7 | 719dde39be52f5a55efd36d04de076f90d26f681 | /Exercise/R_Examples/Ex_12_7.R | 596defda8af0ebc238623197b661d6af68af73ae | [] | no_license | KuChanTung/R | e56f655551fe1041a25c57afde423816d1173c46 | f0932766c3ef6ffa9771ab0e63a7ae8f9d163194 | refs/heads/master | 2021-01-19T12:14:48.750498 | 2018-07-08T07:40:31 | 2018-07-08T07:40:31 | 82,291,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 460 | r | Ex_12_7.R | # Example 12-7: Convergence in Probability
library(ConvergenceConcepts)
samplegen <- function(n) {
Z <- runif(1)
k <- floor(log2(1:n))
m <- 1:n - 2^k
res <- (m * 2^(-k) <= Z & Z < (m + 1) * 2^(-k))
return(as.integer(res))
}
critp = criterion(data, epsilon = epsilon0, mode = "p")$... |
2d066e9bb137e9a448ceaa01eb183947678df552 | 285541e8ae77482ac7eeb5b51ce06edeb96ef246 | /man/deviation_test.Rd | bca236cddcee805a7255233761c6653c0b04c25a | [] | no_license | myllym/GET | 2033c4f590da7cce114b588e7e39b243b543dcdf | 72988291d9c56b468c5dddfb5bc2c23f519b6dca | refs/heads/master | 2023-08-24T23:23:14.364346 | 2023-08-15T21:33:51 | 2023-08-15T21:33:51 | 68,914,145 | 12 | 5 | null | 2022-11-16T07:55:16 | 2016-09-22T11:20:34 | R | UTF-8 | R | false | true | 5,195 | rd | deviation_test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deviation_test.r
\name{deviation_test}
\alias{deviation_test}
\title{Deviation test}
\usage{
deviation_test(
curve_set,
r_min = NULL,
r_max = NULL,
use_theo = TRUE,
scaling = "qdir",
measure = "max",
savedevs = FALSE
)
}
\argume... |
a56ba4d0a0b211fd644fdaa4da319e697930a4a9 | 6cd7468396db7ae36ca24ce9fb2fde989ad33d5e | /5.5.plotMutationFrequencies.R | 9895da1f1bf992f78e7259142a447700cbb9a0be | [] | no_license | cyclo-hexane/analysisScripts | 6c4043d9eded4d21d09a0a6099c6f0ea18766113 | 289715d2891a3002b4b32aa5dabbb5702558960b | refs/heads/master | 2021-01-11T17:39:24.562432 | 2017-01-23T15:28:47 | 2017-01-23T15:28:47 | 79,815,423 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,277 | r | 5.5.plotMutationFrequencies.R | # 5.4.plotSampleDiversity
################### notes: #####################
# processing order:
#
#
# 5.0.countBamReads [run on Apocrita, for each set] (gets tables of read numbers for a given bed file )
# |
# v
# 5.1.subsetBamFiles (calculates proportion to be subsampled for each set and makes .sh scrip... |
026498e053a0b9785d5732f7ef5fce41202519b0 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Pan/k_d4_p/k_d4_p-12/k_d4_p-12.R | b659d767cce454f452ef9d27930367ab703aee7b | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 59 | r | k_d4_p-12.R | 301cdb06fa0de3737e7c45639608a0b3 k_d4_p-12.qdimacs 755 2234 |
5c520f20c9e3c448edd8781163ad0e00e123b8a3 | 7487a5ed8d633a155a19dac2f48b7928651c082c | /R/ftrace1.R | ac16d5b6f25bd71b5c367e467fb68eac569777cd | [] | no_license | tibo31/spatial_flows | de5a5b8ca0baf21528390ca86494c0fd1139e7fe | 965f2452b39db0f641fca8423d58263d45a9f5e2 | refs/heads/master | 2021-12-06T20:56:47.566312 | 2021-09-06T07:14:18 | 2021-09-06T07:14:18 | 175,052,297 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 691 | r | ftrace1.R | ftrace1 <- function(w, method = "exact", miter = 10, riter = 50) {
# initialization
stopifnot(method %in% c("exact", "approx"))
n <- nrow(w)
if (method == "exact") {
traces <- numeric(miter)
traces[1] <- sum(diag(w))
w_j <- w
for (j in 2:miter) {
w_j <- w %*% w_j
traces[j] <- ... |
f5df82600d510138085cc9555c9e5e2732a03168 | d482484e19ba097a7f52f55e03a9118d554e6437 | /man/maps.Rd | 095f9796efb5171d01272f99b3862b75d8447a5f | [] | no_license | marcionicolau/ggswissmaps | fa43b31a4d6ea0124ed3fd9e8db0749ffb0280fc | daa5190e1d6cfb445e8b9e369d804ce24a50d709 | refs/heads/master | 2021-01-16T22:25:58.115304 | 2014-11-06T00:00:00 | 2014-11-06T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 771 | rd | maps.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{maps}
\alias{maps}
\title{A list with 8 maps of Switzerland's territory at various levels.}
\format{A list with 16 ggplot2 maps with swiss territory}
\source{
\url{http://www.bfs.admin.ch/bfs/portal/fr/index/dienstleistungen/geostat/datenbeschreibung.html}
}
\d... |
c5409d13e55d5431719115abffdf6d4753cd3fe4 | 4249ddaf7daa8381627889ba2c03208e53783566 | /r-package/tests/testthat/test-read_comparable_areas.R | 2ffac45e5e702d08c8fcd953dedb9db89a664acf | [] | no_license | ipeaGIT/geobr | 04d16b346398485c39788dca202b15ac65099c2a | 9d7e89efc88871378711086671a11b47940dad47 | refs/heads/master | 2023-09-01T19:13:11.811800 | 2023-08-31T01:49:56 | 2023-08-31T01:49:56 | 177,215,782 | 711 | 133 | null | 2023-09-14T12:14:05 | 2019-03-22T22:15:22 | R | UTF-8 | R | false | false | 783 | r | test-read_comparable_areas.R | context("read_comparable_areas")
# skip tests because they take too much time
skip_if(Sys.getenv("TEST_ONE") != "")
testthat::skip_on_cran()
# Reading the data -----------------------
test_that("read_comparable_areas", {
# read data
amc <- read_comparable_areas(start_year=1970, end_year=2010)
# check sf ob... |
243cee61af8f8c07f18d4ae19b01e0f461fbacca | cac62fef42d5c17354656fb435ccada0fcaabff2 | /plot3.R | 53d9a18f5050b2bb189bb469dc4d76a098f3eb6d | [] | no_license | Karamcse/ExData_Plotting1 | ddff28d378724e08bca0389c577845121dc71b59 | d128fc2027b39499fec1fb6e8184b14dc763c848 | refs/heads/master | 2020-12-11T05:31:43.186266 | 2014-06-08T14:13:01 | 2014-06-08T14:13:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 519 | r | plot3.R | source("./readdata.R")
## Create Time Series On Energy Sub Metering
par(mar=c(2,2,1,1))
par(pin=c(6.4,6.4))
par(bg="transparent")
png(filename="./figure/Plot3.png")
plot(data$Sub_metering_1~data$DateTime,type="l",ylab="Energy sub metering",xlab="")
with(data,points(data$Sub_metering_2~data$DateTime,type="l",col="red")... |
835e915195abe9ab20cdc8de607d024a31c7e6ce | 6203a082bca039d28155b25a080b281a981a077f | /man/us_pres_polls_history.Rd | 6e429f1cf157542c78faaf6034417dec870116ca | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | permissive | elliottmorris/politicaldata | cd50c1b1b6c9f64d1a4d6d15d563e223bb19ef10 | 044048ea5d4941da95d3f3a225337275547241f7 | refs/heads/master | 2020-04-18T21:02:52.559364 | 2019-08-10T13:38:32 | 2019-08-10T13:38:32 | 167,754,489 | 144 | 14 | NOASSERTION | 2019-02-22T22:46:53 | 2019-01-27T00:36:56 | R | UTF-8 | R | false | true | 445 | rd | us_pres_polls_history.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{us_pres_polls_history}
\alias{us_pres_polls_history}
\title{Every presidential general election poll from 1980 through 2016}
\format{An object of class \code{data.frame} with 3586 rows and 10 columns.}
\usage{
data... |
9520d04dab69bfbaef4a7acc69a374e1e55fc98d | 471fcd08e3b189fe9dc0630e11c5abc6e92f16eb | /man/ggplot_boxplot.Rd | 07171c276e43e7c4ec2dc74c5f1cccc4f9c97123 | [
"MIT"
] | permissive | freestatman/shinyngs | 6d776667dc9b9637f10fa654021de3aba2107364 | ffb6900bfe5ed96ae3b42d877d71c01ff507f4d1 | refs/heads/master | 2021-06-01T01:19:53.949901 | 2016-07-04T16:58:09 | 2016-07-04T16:58:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 806 | rd | ggplot_boxplot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/boxplot.R
\name{ggplot_boxplot}
\alias{ggplot_boxplot}
\title{Make a boxplot with coloring by experimental variable}
\usage{
ggplot_boxplot(plotmatrix, experiment, colorby = NULL,
expressiontype = "expression", whisker_distance = 1.5)
}
\ar... |
bbb7e987b30e783e75b8d5f8a7af006051d891f1 | db856c7b812645e310b953de50b9025e4b91be14 | /plot4.R | 0c426aa48f6da389d7f06349f2db14ead2ef38ec | [] | no_license | oarnold1/ExData_Plotting1 | 5a4e1362240017ef3b088ffd3812ad6a95cab9aa | 1dda63478d69293f35ea13e0bad18172942da216 | refs/heads/master | 2021-09-02T01:01:21.379024 | 2017-12-29T14:23:30 | 2017-12-29T14:23:30 | 115,650,199 | 0 | 0 | null | 2017-12-28T18:40:19 | 2017-12-28T18:40:19 | null | UTF-8 | R | false | false | 1,794 | r | plot4.R | plot4 <- function(){
#This function creates plot4 of the Week1 assignment
#load the data (whole data, memore capacity is enough)
household_data <- read.csv('household_power_consumption.txt', sep = ';', stringsAsFactors = FALSE, dec = '.')
#convert the date format
household_data$Date <... |
b0796cb704d90673e6667e879b986c33285511c6 | 7d0dd16c6c2aec1fc2ae4c354bd59062d4c9ee35 | /feature_selection_HDorado.R | 725901465c70ea3b4914fb2efae71925660daca4 | [] | no_license | haachicanoy/r_scripts | f1be2780d95d934b1a7821ca4c1fdaa0d4f22dfa | c59b6f0765b031419b2b0ba0a5a43550c0252326 | refs/heads/master | 2022-07-27T14:19:53.277622 | 2022-07-07T19:22:51 | 2022-07-07T19:22:51 | 63,341,887 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,311 | r | feature_selection_HDorado.R |
# Feature selection via filters,wrappers and embedded algorithms
# Hugo Andres Dorado B.
# 2018 30 08
# Load Libraries
library(here)
library(FSelector)
library(lsr)
library(caret)
# Load feature selection funs
source("C:/Users/hadorado/Desktop/Feature_Selection/feature_selection_FUNS.R")
# Load dataset
chiapas_m... |
3b48456b1707b8a8ad246ce7373956c510904cdf | ff2b418f76f82ecdd399951f9750fe3386f834f6 | /week_11/course_11/nnHidden.R | 3823daf39d970fa6325bd97616471f602d6801ed | [] | no_license | b03602023/1062CSX_project | 545b636331e2038a2df578754f90fe27fecd204e | e42ed5911830b5c1641638998ad9d624f6db0f7c | refs/heads/master | 2021-01-25T10:49:47.428765 | 2018-06-25T02:00:53 | 2018-06-25T02:00:53 | 123,371,347 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 972 | r | nnHidden.R | #載入套件
library(neuralnet)
#整理資料
data <- iris
data$setosa <- ifelse(data$Species == "setosa", 1, 0)
data$versicolor <- ifelse(data$Species == "versicolor", 1, 0)
data$virginica <- ifelse(data$Species == "virginica", 1, 0)
# Conditional Element Selection
# ifelse(test, yes, no)
# ifelse returns a value with the same shap... |
afd9c1fac4528bdd646280948aef8917e13da9f1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Rfast2/examples/Merge.Rd.R | f5e57e40fd3fc3a94aa209af09f0152f75174385 | [] | 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 | 270 | r | Merge.Rd.R | library(Rfast2)
### Name: Merge 2 sorted vectors in 1 sorted vector
### Title: Merge 2 sorted vectors in 1 sorted vector
### Aliases: Merge
### Keywords: Merge 2 sorted vectors in 1 sorted vector
### ** Examples
x <- 1:10
y <- 1:20
Merge(x,y)
x <- y <- NULL
|
f8294d758dac6cc9a4d07928cc1c9d51190006cf | 6ae2d6b27576cc8c75a7e02256db410e5007a8b2 | /tests/testthat/test_1-0-0-major-drop_colr.R | 0da03e1f1aef574de7ea8bf6ff4b63e3883d4cc9 | [] | no_license | HughParsonage/hutils | 3c2cec1b1a01179219deb47df0dc9e6a3a127435 | 11e6f828876bbc87a42d43a0ee8084ee6d9a6765 | refs/heads/master | 2023-02-20T04:27:01.996815 | 2023-02-10T05:18:04 | 2023-02-10T05:18:04 | 88,268,552 | 11 | 2 | null | 2022-04-13T08:31:57 | 2017-04-14T13:09:05 | R | UTF-8 | R | false | false | 205 | r | test_1-0-0-major-drop_colr.R | context("Major drop_colr")
test_that("Outcomes", {
library(data.table)
DT <- data.table(abcde_ghi = 1, STE_2016 = 2)
DT_out <- drop_colr(DT, "[A-Z]{3}")
expect_equal(names(DT_out), "abcde_ghi")
}) |
f58bdc89b2ab363914bbd8a97f5304c51369a873 | 1c139b9a35bae97bd11e900911c0226db693941c | /R/safe-callback.R | fd4ed53cc00aec45d453316896f3272ef4166225 | [] | no_license | danconsults/httr | 70065a06968261d85ee101e597747b129862482d | 0f2fb5f0fdadb4270b4a76b9d532b76848b4813b | refs/heads/master | 2021-01-18T02:06:04.116566 | 2014-09-19T17:22:14 | 2014-09-19T17:30:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 681 | r | safe-callback.R | #' Generate a safe R callback.
#'
#' Whenever an R callback function is passed to Rcurl, it needs to be wrapped
#' in this handler which converts errors and interrupts to the appropriate
#' values that cause RCurl to terminate a request
#'
#' @param f A function.
#' @export
#' @example
#' f1 <- function(x) {
#' if (x... |
790d17ff5aa29e524b62c7723ffa04c6be157441 | d366b7cb61a8f897d4eabf9cdc319d287f3e2f48 | /man/send_to_mysql.Rd | 3018bccfe445f6773568b3ff17d9a311e406d3c8 | [
"MIT"
] | permissive | rparrish/GAMUT | ee35c83065e93235b4998bf932f2a5c5ec29ee9c | 16e0309e8d080443e962234aebd4e8931a4dc23d | refs/heads/master | 2021-11-19T15:29:17.860960 | 2021-09-11T02:12:48 | 2021-09-11T02:12:48 | 28,320,390 | 0 | 1 | MIT | 2021-09-10T22:54:21 | 2014-12-22T01:39:59 | R | UTF-8 | R | false | true | 347 | rd | send_to_mysql.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/send_to_mysql.R
\name{send_to_mysql}
\alias{send_to_mysql}
\title{send_redcap_to_mysql.R}
\usage{
send_to_mysql()
}
\value{
a list - table has the raw data and plot has the dotplot
}
\description{
updates the GAMUT dashboard database tables
}... |
2d630d315309d677786be3a32389cab0e841ab4d | be91ea534bf267ddf39b47eb4ff1c1a4d6034144 | /ui.R | a56b9449751389c40719934aec556075beed0712 | [] | no_license | pclivingstone/GETmap | df695c3bdc7fa3b2c7dbea9a478b956ad3bdcb83 | 553afa726f0db270793aed3b8996dfe1e952413c | refs/heads/master | 2020-05-05T13:49:52.872326 | 2019-10-25T03:00:42 | 2019-10-25T03:00:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 679 | r | ui.R | # ui.R
ui <- fluidPage(
titlePanel("Installed Solar Panel Capacity by Postcode")
# ,sidebarPanel(
# dateInput(
# 'month'
# ,'Select month to display:'
# ,value = '2019-02-01'
# ,format = 'yyyy-mm'
# )
# ,textInput('postcode','Select postcode to centre:',value = '1234')
# )
... |
3f790d12f44ab9f0aa7deaa551a1cac2574ec90b | cd245e28506103210e2557b0ce4d80b228899f5b | /Final_Code.R | 13da69279fcfe51e6b9883f7666c3158c8ff1987 | [] | no_license | arnavd17/Spotify-Song-Genre-Predictor | 62274cd7339254bcd092d801515387e902740a7e | 1e0f2b7427ec87f996e467c045cf99d675fbd5f6 | refs/heads/master | 2021-05-24T14:43:44.916245 | 2020-04-15T01:33:30 | 2020-04-15T01:33:30 | 253,610,449 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,359 | r | Final_Code.R | rm(list = ls())
library(readr)
library(kknn)
library(gbm)
library(mosaic)
set.seed(79643)
df_spotify <- read_csv("songDb.csv")
genres <- read_table("genre_dataset.txt", col_names = c('Genre'))
#Data cleaning
df_clean <- na.omit(df_spotify)
drop <- c('Uri','Name','Ref_Track', 'time_signature', 'Type', 'URL_features'... |
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