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ebbf01f7180d1514778ba5f8009bb167504d1fde | 4201e9b754760dc35fc0aeef9df5a8b9d801c47f | /bin/R-3.5.1/src/library/methods/man/methods-deprecated.Rd | 6c7ae18e849d8cfbd1f31705a1ee74354f5793b1 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only"
] | permissive | lifebit-ai/exomedepth | cbe59cb7fcf2f9183d187f8d466c6620fb1a0c2e | 5a775ae5e2a247aeadc5208a34e8717c7855d080 | refs/heads/master | 2020-03-27T12:55:56.400581 | 2018-10-11T10:00:07 | 2018-10-11T10:00:07 | 146,578,924 | 0 | 0 | MIT | 2018-08-29T09:43:52 | 2018-08-29T09:43:51 | null | UTF-8 | R | false | false | 938 | rd | methods-deprecated.Rd | % File src/library/methods/man/methods-deprecated.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2009 R Core Team
% Distributed under GPL 2 or later
\name{methods-deprecated}
\alias{methods-deprecated}
%----- NOTE: ../R/methods-deprecated.R must be synchronized with this!
\title{Deprecated F... |
5eb71eb79ee6a80139099ff086b96db981b4922f | e3aedc3e0413859b86ef14b63b8ba6e44dd3c04b | /Code_2018.R | 5669a0d1b84beacb770274840a927c8950212f0e | [] | no_license | SaraEdw/Holiday_2018 | f5bd2f134ae2882a1a482c79b6238996c17ef181 | 5261c2675864056b80b36add53cb44cf0d1eed89 | refs/heads/master | 2020-04-12T15:44:01.410602 | 2018-12-20T14:38:05 | 2018-12-20T14:38:05 | 162,590,583 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,822 | r | Code_2018.R | # Author: Sara Edwards
# Date: December 2018
# Instructions: Simply select all & run
# (if you use R studio don't have it full screen)
# R studio Mac: Option+command+R
# PC: Control+Alt+R
# In base R Mac: Command+A then Command+Enter
# PC: Ctrl+A then Ctrl+Enter
X1 <- c(rep(2,4), rep(3,4)... |
3b3c19c6434058f5828930f0ec837f320f43308d | 2b172258eb7c17c9607439b86ea51824e374ec17 | /plot1.R | 7f578ce080e1bde8ecdce8b4fdbe217eca160a3a | [] | no_license | jieun0228/ExData_Plotting1 | 48b32326602d18f04533993b2758980022d81724 | bc384e4faf864724807f3e75cef912f397921756 | refs/heads/master | 2021-01-21T17:03:23.873753 | 2015-04-12T22:47:59 | 2015-04-12T22:47:59 | 33,802,535 | 0 | 0 | null | 2015-04-12T04:07:45 | 2015-04-12T04:07:45 | null | UTF-8 | R | false | false | 582 | r | plot1.R | setwd("C:/Users/veronica/DataScience")
##Load dataset
data <- read.csv("./data/household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
##Subset the data
data_2days <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
##Convert the type of columns
data_2days$Global_active_power <- as.nu... |
88ab88a16656d61c4385efca1a45b7325427dfaf | 97d42d0116a38692851fbc42deac5475c761229d | /Code - 02 28 2018/Table Code/Items 80,81, Tables AB,AC.R | 1ae81a7d7e8a71332fbfd0d558218c70ebce7032 | [] | no_license | casey-stevens/Cadmus-6000-2017 | f4632518088de34541b0c6c130b3dd021f0809d1 | ab4450e77885a9723dba6bc3890112dec8c7328f | refs/heads/master | 2021-01-25T06:17:59.728730 | 2018-09-27T20:35:42 | 2018-09-27T20:35:42 | 93,548,804 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 72,182 | r | Items 80,81, Tables AB,AC.R | #############################################################################################
## Title: RBSA Analysis
## Author: Casey Stevens, Cadmus Group
## Created: 06/13/2017
## Updated:
## Billing ... |
f2fb9664b82ce454102d07b4753a402eaec23984 | 4b7f18afdadfa37c379370180ae0b0fb69d9a823 | /07_R_Graphs/PAR1_diversity.R | 36fce86c348e7446917bee36edb82cc64515f76c | [] | no_license | WilsonSayresLab/PARdiversity | 98b6d3c051b645fcbaa0b58db4cebad0626aab96 | bedfb23d74aaa046f5ffcac443a4ddb249d58354 | refs/heads/master | 2021-01-17T11:01:14.362053 | 2016-03-23T17:17:23 | 2016-03-23T17:17:23 | 54,513,259 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,098 | r | PAR1_diversity.R | par(mfrow = c(4, 1))
par(cex = 0.6)
par(mar = c(2, 2, 1.5, 1.5), oma = c(4, 4, 0.5, 0.5))
par(tcl = -0.25)
par(mgp = c(2, 0.6, 0))
for (i in 1:4) {
plot(1, axes = FALSE, type = "n")
filtered_pi.All26.100kb_filtered.o.PAR1 <- read.delim("~/Projects/PAR/BrotmanCotter/PAR_Project/Codes_02/08_galaxy_data_for_grap... |
2d6578d64d5ef5b7ab4f72b8330666bb48b12194 | 71f44b2dfd4b8d8c33f6b0936ff34a3cd361c484 | /chapter01.R | dc97e64c0d66a4b2eb634da0e84ce03e8ee741ac | [] | no_license | Libardo1/data-manipulation-with-R | 6dfb1e3464b95bd741e7f6ecdab521cc85b09936 | c5a473d3ee20007e251183459b5e3a033148b086 | refs/heads/master | 2021-01-15T09:28:55.792793 | 2013-12-29T11:59:25 | 2013-12-29T11:59:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,561 | r | chapter01.R | rm(list=ls())
# 1.1 Modes and Classes
mylist=list(a=c(1,2,3),b=c("cat","dog","duck"),d=factor("a","b","a"))
sapply(mylist,mode)
sapply(mylist,class)
# 1.2 Data Storage in R
x=c(1,2,5,10)
x
mode(x)
y=c(1,2,"cat",3)
mode(y)
z=c(5,TRUE,3,7)
mode(z)
all=c(x,y,z)
all
x=c(one=1,two=2,three=3)
x
x=c(1,2... |
993c9dbabe3319104fd004a707afde3ad30d6671 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spdep/examples/LOSH.mc.Rd.R | 06fdbdb0834b7c3d1ff2626c5b45fe7c4071ad4f | [] | 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 | 396 | r | LOSH.mc.Rd.R | library(spdep)
### Name: LOSH.mc
### Title: Bootstrapping-based test for local spatial heteroscedasticity
### Aliases: LOSH.mc
### Keywords: spatial
### ** Examples
data(columbus, package="spData")
resLOSH_mc <- LOSH.mc(columbus$CRIME, nb2listw(col.gal.nb), 2, 100)
resLOSH_cs <- LOSH.cs(columbus$CRIME, ... |
3300e5d27d61d9551e5ae9561fdc8b544945637a | 74e560e97d1e07a7fbd325165ffb61e7dbbe01c6 | /Plot2.R | 5fc155983e79f41c271b038a444d80638467f97e | [] | no_license | RogerioDestro/JHExploratoryData | 3084044f087bef9c592bf0881068111b82969aec | f76c1b2f15e8e08aa1d217ac24cf167c3bbdba31 | refs/heads/master | 2021-01-10T15:31:55.633175 | 2016-02-13T13:44:25 | 2016-02-13T13:44:25 | 51,468,611 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 542 | r | Plot2.R | plot2 <- function(dados){
#Setting the system to USA (Running on Windows 7)
Sys.setlocale("LC_TIME","English")
#Ploting the data
plot(dados$jtime,dados$Global_active_power,type = "l",xaxt = "n", xlab = "",ylab = "Global Active Power (kilowatts)")
#Setting the x axis to the days of the week
axis(1... |
8546743e0c5a879c22cc9f8d5b6f96628b58b6a1 | 627119064049fb9cf070d73e3766cceac02ab514 | /man/mlear1.Rd | ed842c241fa28e639bc18af5c3621d94b6268ba8 | [] | no_license | cran/HKprocess | 7061c227c0593010f2abde4db3fea1fd008aed31 | b7aea6c4c497d4b822c11a460390029ab09537e7 | refs/heads/master | 2022-11-14T08:24:10.955085 | 2022-10-26T21:17:59 | 2022-10-26T21:17:59 | 49,682,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,928 | rd | mlear1.Rd | \name{mlear1}
\alias{mlear1}
\title{
Maximum likelihood estimation for the AR(1) parameters.
}
\description{
The function mlear1 is used to estimate the
\ifelse{html}{\out{μ}}{\eqn{\mu}{mu}},
\ifelse{html}{\out{σ}}{\eqn{\sigma}{sigma}} and
\ifelse{html}{\out{φ}}{\eqn{\phi}{phi}} parameters o... |
e1a54b1f1b9b12acb360f2a9a260f46bd94c2d57 | 659aefca294d97f0ed3df28f11448100ae9dcae8 | /cachematrix.R | 2abf011ef1960e61bf14451ecd22de6e5f61069e | [] | no_license | dmanicka/ProgrammingAssignment2 | cb25787855f6cd4af140dd8cf0cee59096fdb073 | 5b4a308cf2772904bd95cd654f7ea00620e6bfe5 | refs/heads/master | 2021-01-13T16:54:29.897479 | 2017-01-21T14:15:36 | 2017-01-21T14:15:36 | 79,576,390 | 0 | 0 | null | 2017-01-20T16:35:53 | 2017-01-20T16:35:53 | null | UTF-8 | R | false | false | 1,363 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Function creates a matrix object that can cache its inverse
makeCacheMatrix <- function(m = matrix()) {
# inverse property initialization
inv <- NULL
## function to set the matrix
set <- function(matrix) {
m <<- ma... |
8fe0056e8913a523ec315dc3164e7d7a22d8e863 | 6ec650d8565f4b68a3a2c23bd9c39bbad4e1006a | /man/sodomeetgomorrhe.Rd | 1fd2abaf16c9de75d88664f7df71ab426e35318d | [] | no_license | ColinFay/proustr | 9de427aa8f69eb527b6e8d2f12b046473bc9308b | ff1cd4bad45701e97ae85f894dbcab1bff9f15de | refs/heads/master | 2021-01-24T08:32:11.157401 | 2019-02-05T13:17:02 | 2019-02-05T13:17:02 | 93,385,515 | 26 | 4 | null | null | null | null | UTF-8 | R | false | true | 492 | rd | sodomeetgomorrhe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/proust_novels.R
\docType{data}
\name{sodomeetgomorrhe}
\alias{sodomeetgomorrhe}
\title{Marcel Proust's novel "Sodome et Gomorrhe"}
\format{A tibble with text, book, volume, and year}
\source{
<https://fr.wikisource.org/wiki/Sodome_et_Gomorrhe... |
cf6c251d12363988ff03350ef4e634a1d1962339 | ce2496ff30f10e4c35e82d25a0db9765703713b5 | /man/Hills.Rd | 63681a067061e5b5ef5222bb3d5e4a81d1c66d97 | [
"MIT"
] | permissive | klauswiese/pnlt | 1dd90476a161951d1a9169ce0542b7760dd80ba8 | cf6c8fcaa297b6551c880d09173457d71b4019a7 | refs/heads/main | 2023-08-16T16:51:01.527324 | 2021-10-02T17:12:15 | 2021-10-02T17:12:15 | 383,334,757 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 721 | rd | Hills.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Hills.R
\docType{data}
\name{Hills}
\alias{Hills}
\title{Data names of Hills in La Tigra National Park, Honduras}
\format{
Simple feature collection with 47 features and 3 fields:
\itemize{
\item{id} {}
\item{name} {}
... |
2df4034e18cf37a24d699807fb65cca5f230d0d6 | d1d19805e21ac6305341d9815f80551f03bfb514 | /best.r | fc0634e18d3806daa701960c0ef6f310e4c4615f | [] | no_license | etaney/ProgrammingAssignment3 | 8aff7e56fdf7183d6dfb2e8fc5a4c380c2b8c691 | 51dedeb375311e03858e909e9a6cd8a61d69d572 | refs/heads/master | 2020-12-25T14:49:01.054996 | 2016-07-24T05:05:06 | 2016-07-24T05:05:06 | 64,049,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,298 | r | best.r |
best <- function(state, outcome){
options(warn=-1)
## Read outcome data
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
usedata <- data.frame(hospital=data[,2], statename=data[,7], HeartAttack=as.numeric(data[,11]), HeartFailure=as.numeric(data[,17]), Pneumonia=as.numeric(data[,23]))... |
dd5bb21db316cd147d754499d2e34f9c28ea7790 | 0bf451654ba419e58b139e3e7786a234484608ca | /tests/testthat/test-quantile.R | e7486666a14c52c7b6d087989463046df2817646 | [] | no_license | cran/extremeStat | 0d57c0bba0518fd8fea07eb12b00abf2c79aa5f4 | 8a24429cbd4067c35805eadeeda5cb1124c3cf7e | refs/heads/master | 2023-04-13T19:51:57.912209 | 2023-04-08T05:30:02 | 2023-04-08T05:30:02 | 58,643,208 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,628 | r | test-quantile.R | context("distLquantile")
data(annMax, package="extremeStat") # Annual Discharge Maxima (streamflow)
set.seed(007) # with other random samples, there can be warnings in q_gpd -> Renext::fGPD -> fmaxlo
ndist <- length(lmomco::dist.list()) - 13 + 22
# 13: excluded in distLfit.R Line 149
# 22: empirical, weighted,... |
a177311663cbc4b23a04720e02ff8285b4642606 | 15794c233b100ced4729bf8dd3f83cc312998543 | /man/data2016.Rd | 98bd2b90af423b5a837894934ca85d51beaffea6 | [] | no_license | alexchouraki/ProjetAlex | ea8613cbe10fbb038d0d4ba3a54fae05426646dc | 1695936d8771f1ab47aa9257a49bfed2726821ac | refs/heads/master | 2021-08-07T09:27:06.594665 | 2017-11-07T23:57:32 | 2017-11-07T23:57:32 | 107,143,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 362 | rd | data2016.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data2016.R
\docType{data}
\name{data2016}
\alias{data2016}
\title{data2016}
\description{
data2016
}
\references{
\url{kaggle.com}{https://www.kaggle.com/unsdsn/world-happiness}
}
\author{
Kaggle \email{alexandre.chouraki@hec.edu}
}
\keyword{... |
70e0ec468ec2c6fc63e3c2df98dbd36007e577ac | c527575648ed7911e0fd223b4f9fa256c612d73f | /JB_timeSeries.R | df7a8ab22936607f97a8d49e76647a6a0cc07f8f | [] | no_license | doeungim/ADP-1 | 794890bd7cb4c1d6870baf5f444a4aa877733f45 | e575dadc8cfdf7f630fa1f481fcb2f3fc8f4196c | refs/heads/master | 2022-12-23T11:54:15.727525 | 2020-09-28T12:54:54 | 2020-09-28T12:54:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,706 | r | JB_timeSeries.R | #################
## 시계열 분석 ##
#################
# 1. 시계열 자료
# 시간의 흐름에 따라서 관찰된 데이터
# 2. 정상성
# 대부분의 시계열 자료는 다루기 어려운 비정상성 시계열 자료
# 분석하기 쉬운 정상성 시계열 자료로 변환해야함
# 정상성 조건
# - 평균이 일정해야 함
# 평균이 일정하지 않은 시계열은 차분(difference)을 통해 정상화
# - 분산이 시점에 의존하지 않음
# 분산이 일정하지 않은 시계열은 변환(transformation)을 통해 정상화
# - 공분산도 시... |
f72bfbeb3cbcac031a97ef81baf0959a48bdf1a2 | e407e8e724356282f85582eb8f9857c9d3d6ee8a | /man/split_data.Rd | 6ee0c90f872b40979f061c004a60eb204df89823 | [
"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 | true | 1,992 | rd | split_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/split-data.R
\name{split_data}
\alias{split_data}
\title{Function to transform data without time-dependent covariates into piece-wise
exponential data format}
\usage{
split_data(
formula,
data,
cut = NULL,
max_time = NULL,
multiple_... |
0c589d389a6dd4ab9f9ed1a3a6d93898df41af97 | 9ae7a61edcbc5c8e4bd83e0584538cf1bc0f7206 | /R/hc_add_series.R | 5d01c33411eb12de005927477441704517cd0d2e | [] | no_license | sz-cgt/highcharter | 266b57c891694590209338247e52ddd9ffd1f68c | 9db6e89993bd21be4a602fe2e074b3e1fdca54be | refs/heads/master | 2021-01-19T07:38:48.463846 | 2016-10-01T01:42:45 | 2016-10-01T01:42:45 | 64,556,449 | 0 | 0 | null | 2016-07-30T17:37:48 | 2016-07-30T17:37:48 | null | UTF-8 | R | false | false | 2,421 | r | hc_add_series.R | #' Removing series to highchart objects
#'
#' @param hc A \code{highchart} \code{htmlwidget} object.
#' @param names The series's names to delete.
#'
#' @export
hc_rm_series <- function(hc, names = NULL) {
stopifnot(!is.null(names))
positions <- hc$x$hc_opts$series %>%
map("name") %>%
unlist()
... |
1884aa149f039134ccc1a3732ec5b54b50d2400d | 97031ea4feb150557fcca7ad65c135c032ddc9ae | /r/ujccalc.r | 1767ca4378b2600d94bafbd29c4056f89683ef31 | [] | no_license | adamkc/HydroRSL | 56f2cf39c7f9bebd7971f0488564c559418f4278 | f464480a09ee873103da89feba37578728b9a8cf | refs/heads/master | 2022-04-05T06:59:42.372706 | 2020-02-08T01:00:11 | 2020-02-08T01:00:11 | 220,350,568 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 77 | r | ujccalc.r | ujccalc <-
function (stg)
{
a <- 13.084
b <- 3.9429
a * stg^b
}
|
edf510318f5151380bdb9b4391d6a2a15ceb97fa | a287696020bda3e3f3cafc15aa6c6e698d2b6425 | /man/MergePDFs.Rd | d89145ac26689a984c62a13564b6bc7e22b6b5bc | [
"LicenseRef-scancode-warranty-disclaimer",
"CC0-1.0"
] | permissive | ktreinen/Trends | 4dfa6dcddfa50148878ae6abb71931578c8f58c2 | 91c0d5cd4dd932f9ca5fd4efaf8d983508743270 | refs/heads/master | 2022-01-19T11:08:24.560552 | 2019-03-28T20:29:36 | 2019-03-28T20:29:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,011 | rd | MergePDFs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MergePDFs.R
\name{MergePDFs}
\alias{MergePDFs}
\title{Merge PDF Files}
\usage{
MergePDFs(path, pdfs, preserve.files = FALSE, open.file = FALSE)
}
\arguments{
\item{path}{character.
Path name of the folder containing the PDF files to merge.}
... |
12302d0e46639981615fee1c4891faaa3096ab1e | 8d65868a49cf236c662bc5019ac2400381f8ce1c | /plot4.R | 52c0fa8f95e1513d4472d1526fdeba14b82aeebe | [] | no_license | flanalytics/ExData_Plotting1 | a7c2749551621b15c097cff79d14bd58ef8b523a | 06556c005e699fc37d08b8c01b6ae157d784e008 | refs/heads/master | 2021-01-18T12:49:35.360769 | 2014-09-07T20:49:36 | 2014-09-07T20:49:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,494 | r | plot4.R |
## This function is used to read in data and subset two days observations
setwd("E:/Exploratory Data Analysis/Course Project 1")
library(data.table)
## Read in all the data from the Text File using the data.table fread function
## into data.table call dat
dat<-fread(input="household_power_consumption.txt",header=T... |
527b6c68614a47adf4cf45ee555861c1b9c0bd6a | 91c247ab34db8fb2bee5592ae8977bd8b49ec0ac | /R/proj2_plotFun.R | 7016394c85a3e8bfdd96c968f5ad1dc872552f60 | [] | no_license | cran/ClinicalUtilityRecal | d408ea107d673fcdbb65859bfd36c9ccf42508bb | c3bc634a596fab70462193a531c28420d27ac7a6 | refs/heads/master | 2022-04-15T01:53:01.885182 | 2020-04-15T10:10:02 | 2020-04-15T10:10:02 | 256,159,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,664 | r | proj2_plotFun.R | ##### Plotting Functions #######
utils::globalVariables(c("..count.."))
### sNB Recalibration Curve with std error bars
# plotting snb as function of t -- still need to add other potential std error bars
snbRecalPlot <- function(p,p.std,y,r,stdErrThresh=1,ylim=NULL,
titlePlot = "P... |
541161d24c07bb748ff5081a06c29d977b129ab6 | f339641cefa9025ef94fe53c9d23856b4ac69933 | /man/geomEcuador.Rd | 0d02498f5cfadfc34bb3f7bf554d89e0da624f8a | [
"MIT"
] | permissive | sjbeckett/localcovid19now | 8ba7f044d8e7458cb4c8c490c8389b596624c84f | af7979dd7e4b1f73751617bd2bae17bdbd285184 | refs/heads/main | 2023-04-17T21:06:30.905493 | 2023-01-23T18:31:06 | 2023-01-23T18:31:06 | 413,489,189 | 4 | 4 | MIT | 2022-12-23T18:10:25 | 2021-10-04T15:53:44 | R | UTF-8 | R | false | true | 331 | rd | geomEcuador.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{geomEcuador}
\alias{geomEcuador}
\title{geomEcuador}
\format{
An object of class \code{sf} (inherits from \code{data.frame}) with 224 rows and 9 columns.
}
\usage{
geomEcuador
}
\description{
geomEcuador
}
\keyword... |
d659cb151a06a8d0491a52ae243437ce54eb3bb1 | 90aacb74264c2bd3172a0ba90629c4ceeceabbed | /Plot_scripts/NMDS_all_samples_plots.R | f9027c06b059ae8a9533eb0142fc738b1eea0c82 | [
"MIT"
] | permissive | liberjul/Leaf_litter_communities | 30b64c294285a5a8f9383add3088bf4b313dcfbf | fcbe167e6ff3d232c6d01a9d6b39e0e374d72c38 | refs/heads/master | 2021-11-27T07:28:49.257459 | 2021-11-15T20:20:00 | 2021-11-15T20:20:00 | 253,580,738 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,555 | r | NMDS_all_samples_plots.R | library(ggplot2)
library(ggpubr)
library(vegan)
library(patchwork)
setwd("C:/Users/julia/OneDrive - Michigan State University/Documents/MSU/Undergrad/Fall 2018/PLP 847/miseq_dat/Leaf_litter_communities")
map_wo_negs <- as.matrix(read.csv("./Data/DEM_map_wo_negs.csv", stringsAsFactors = F))
rare_otu <- as.matrix(read.c... |
e66e9a9c2b49575b8324f60a19307c8d5844e47d | f4836a9f9beb659ff275f3df3e198db7ccd3c8f5 | /R/georamps.R | 8555f11dec5c68e34223d53f1cda86765fbc3b2f | [] | no_license | cran/ramps | f9d3d723d37adc8b8588fec7a28e39ec7beb2c1b | af67bcc81ff5271c7482c7e676bdee0ea0cbb75f | refs/heads/master | 2023-04-09T06:11:34.205812 | 2023-03-13T13:30:02 | 2023-03-13T13:30:02 | 17,698,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,490 | r | georamps.R | georamps <- function(fixed, random, correlation, data = sys.frame(sys.parent()),
subset, weights, variance = list(fixed = ~ 1, random = ~ 1, spatial = ~ 1),
aggregate = list(grid = NULL, blockid = ""), kmat = NULL,
control = ramps.control(...), contrasts = NULL, ...)
{
## Create data frame containing all... |
b700644cb9acc2679b1c3f17199314702f9ddee3 | 1fd38c17bd8367a62c3d48be141a89f7ef2c7262 | /source_functions/reduce_prism.R | 71f1c68fef97c27e2cc7dd9a91f5571a8d0cfb5b | [] | no_license | harlydurbin/angus_regions | 648dc6792351520db2bf6e97d30178ff8d8d8425 | ab00af1a004dce197ab02ead0f20b4317da644b0 | refs/heads/master | 2023-05-29T12:40:51.351189 | 2021-06-08T21:26:32 | 2021-06-08T21:26:32 | 290,271,190 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,969 | r | reduce_prism.R | library(tidyverse)
library(prism)
options(prism.path = "~/regions/data/raw_data/prism")
growth_regions <- readr::read_rds(here::here("data/derived_data/growth_regions.rds"))
yrs <- 1972:2017
get_prism_annual(type = "tmean", years = yrs, keepZip = FALSE)
get_prism_annual(type = "ppt", years = yrs, keepZip ... |
0055691e1b7a4ea4dc75cceac57ff9e198608c3f | a5c81b7498341e8d658632c28ab3a8c6a6546cb3 | /Labs/Lab12/BREWSTER_Lab12.R | d69f73453bfd83c7027a861727a62b44fe1c9524 | [] | no_license | maddenbrewster/CompBioLabsAndHomework | 0655f148f320d1b1239949156d851fc29a137d4a | 97efd2d42e3cf136bb644d234d1dc5c93d0d2806 | refs/heads/master | 2020-12-21T19:32:46.801939 | 2020-05-01T01:46:55 | 2020-05-01T01:46:55 | 236,536,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,081 | r | BREWSTER_Lab12.R | # EBIO5420
# Lab 12
# Madden Brewster
# Sunday, April 12, 2020
# More work with ggplot2
# Problems work with the Cusack et al. Dataset
setwd("/Users/maddenbrewster/Documents/EBIO5420/CompBioLabsAndHomework/Labs/Lab12")
cam_data <- read.csv("Cusack_et_al_data.csv", stringsAsFactors = F)
# Problem 1: A bar plot in... |
150716679b0d22b394efa1ec2e0c3cd7d18ea9a8 | 3bf08bebebaf7a8f7f18e6cee1fc2a472f36d2c7 | /academic-twitter-example.R | 8bfb41102885cc7b1532c407d01dd1161ec3fe03 | [
"MIT"
] | permissive | omiyas/s21-intro-to-data-sci-methods-in-ed | 112960b49e59b235cc8d0557c119955f6d47dcc5 | bcd7b52fee1db891aeb428bfcd74ecf99bfee476 | refs/heads/main | 2023-05-02T03:18:59.466194 | 2021-05-07T17:23:54 | 2021-05-07T17:23:54 | 365,340,793 | 0 | 0 | MIT | 2021-05-07T20:01:29 | 2021-05-07T20:01:28 | null | UTF-8 | R | false | false | 3,625 | r | academic-twitter-example.R | # devtools::install_github("cjbarrie/academictwitteR")
# install.packages("rtweet")
library(academictwitteR)
library(rtweet)
library(tidyverse)
hashtags_to_search <- c(str_c("#AERA", 19:21), str_c("AERA20", 19:21)) %>%
paste0(collapse = " OR ")
get_hashtag_tweets(hashtags_to_search, "2010-01-01T00:00:00Z", "2021-... |
85220c4c66a71fc778ffe17b3e07945188a2f892 | 2d549b99d2f77abe9f4658ffb7ef3fef6e25c286 | /R/binaryAttributes.R | 34cbb12a8ed7a5f18820927abccea0ac925dbf33 | [] | no_license | Hackout2/repijson | 549b538135badb358b864be5c47e26e177de74c0 | 4ad8f9d7c33cd2225e11674f651f608bff08bc91 | refs/heads/master | 2020-12-29T02:36:05.984596 | 2017-02-22T22:06:34 | 2017-02-22T22:06:34 | 35,152,284 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,171 | r | binaryAttributes.R | # Author: Thomas Finnie
###############################################################################
#' Convert a file to a base64 encoded attribute
#'
#' Read a file in binary mode and convert the bytesteam into a base64 encoded
#' ejAttribute
#' @param file The file to read and convert
#' @param name The name f... |
1fcea07da68986c3f1a72829ebf11279696b46bb | 9775fa1bc36cac2a9e8f61fb25248ec9fe71f6a3 | /R/transition-between-polygons.R | 5658a8b25860e50cb46f19ef394a9f580cb76df6 | [] | no_license | mathiasisaksen/artKIT | 944ea1f71aea566f25fe506e5b3c6a606064059b | 7b539cee119a9b4fe743b78799c24d5aef912319 | refs/heads/main | 2023-07-01T07:46:23.925597 | 2021-07-31T13:53:54 | 2021-07-31T13:53:54 | 391,369,092 | 13 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,871 | r | transition-between-polygons.R | #' Transition between polygons
#'
#' Function that interpolates/transitions between two polygons. This is done by
#' computing \code{(1 - time)*start.polygon + time*end.polygon}.
#'
#' @param start.polygon,end.polygon Dataframes/matrices containing the vertices of the
#' polygons to interpolate between. The coordinates... |
81cdadf567a09f8155655666595f45bcccbeeb7f | 2bcb5fb15e9c93368891e20d4c5d4f129f36203b | /deseq2_morphAge.R | 57d32e8b8747712583aea9154bbc1f2416a77e6b | [] | no_license | soojinyilab/sparrow_WGBS_paper | ab13e0c395408b56c908824f5b602ed5e7a8a747 | 0cd4f49298bc15966ec8d1edd2d3adca22cc1d1c | refs/heads/master | 2022-11-06T19:06:52.572373 | 2020-07-01T03:08:45 | 2020-07-01T03:08:45 | 276,264,003 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,066 | r | deseq2_morphAge.R | library(DESeq2)
### Read data
cond <- "female_Hyp"
cts <- as.matrix(read.csv("gene_count_matrix.csv", row.names="gene_id", check.names = F)) # raw count
coldata <- read.csv(paste(cond, ".cond", sep = ""), sep="\t", row.names=1, header=F) # sample condition file
colnames(coldata) <- c("morph", "age", "type")
rownames(... |
e027fb9b884d2f2be1a769f7e79a370a506bee8f | 113c20043720a2b49fbaced8c3efa7cb2f8fc8d6 | /Code/R/RandomForest CS504.R | 87de9696ded7c2a24b6985b09deea7dc936595fb | [] | no_license | gturner7/Census-Income | da4f087ff521097149594e6daf862e8da04c9543 | 3528233eae3d667326506730e7c654ef83f9a5f1 | refs/heads/main | 2023-07-24T14:44:10.032727 | 2021-09-06T22:10:05 | 2021-09-06T22:10:05 | 372,581,981 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 562 | r | RandomForest CS504.R | data<-read.csv('Final_Data_With_Outliers.csv')
library(DescTools)
library(MASS)
library(randomForest)
#partition data
set.seed(100)
trainrows<-sample(nrow(data),nrow(data)*.8, replace = FALSE)
traindata<-data[trainrows,]
testdata<-data[-trainrows,]
#run rf
rf<-randomForest(x=traindata[,-41],y=traindata[,41],ntree=100... |
3356d8dc964bcfb20df23a16304162de56d37db6 | 6771b3be59935639b51698036e5fbbaf51feec4b | /man/remove_small_pols.Rd | 6e10b2fda411ad1c8f30e6589941a3e9cc29fc07 | [] | no_license | pieterbeck/CanHeMonR | f1a15bc68afc77f66bb13b3e90fbfaa3e99376e3 | 94ac1171b5bb7ff88e3cbe7dee3594c31d628ff4 | refs/heads/master | 2020-05-21T04:42:39.673459 | 2018-05-14T09:33:09 | 2018-05-14T09:33:09 | 48,625,321 | 0 | 3 | null | 2018-05-14T09:33:10 | 2015-12-26T22:26:10 | R | UTF-8 | R | false | true | 552 | rd | remove_small_pols.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/remove_small_pols.r
\name{remove_small_pols}
\alias{remove_small_pols}
\title{Remove Small Polygons}
\usage{
remove_small_pols(spatpols, minsize, outname = NULL)
}
\arguments{
\item{spatpols}{A SpatialPolygons object}
\item{minsize}{numeric ... |
94218bdf8d2519dd1480cbf1a68166c55b8ef3b2 | f1971a5cbf1829ce6fab9f5144db008d8d9a23e1 | /packrat/lib/x86_64-pc-linux-gnu/3.2.5/metricsgraphics/doc/introductiontometricsgraphics.R | f16fe66492ee4eed5b708abee0d09fb36af5ca7e | [] | no_license | harryprince/seamonster | cc334c87fda44d1c87a0436139d34dab310acec6 | ddfd738999cd302c71a11aad20b3af2f4538624f | refs/heads/master | 2021-01-12T03:44:33.452985 | 2016-12-22T19:17:01 | 2016-12-22T19:17:01 | 78,260,652 | 1 | 0 | null | 2017-01-07T05:30:42 | 2017-01-07T05:30:42 | null | UTF-8 | R | false | false | 9,528 | r | introductiontometricsgraphics.R | ## ----echo=FALSE----------------------------------------------------------
suppressPackageStartupMessages(library(metricsgraphics))
suppressPackageStartupMessages(library(jsonlite))
suppressPackageStartupMessages(library(RColorBrewer))
suppressPackageStartupMessages(library(htmltools))
suppressPackageStartupMessages(l... |
557f6d0443b08c76c1402e0a9715fafe8509866d | 2e24128ded0f064b44e8d114072e6694012f5dba | /code/ratings_counting.R | 48314a98072566716d7c6238cbad88387917f003 | [] | no_license | cass-code/20346212 | b9b7f3790e70b34c065316a9fd4f9bd1a4048b40 | ec0f527e0b934f1f933380db4be44a2648e55290 | refs/heads/main | 2023-04-26T20:52:14.681909 | 2021-05-24T19:12:31 | 2021-05-24T19:12:31 | 370,134,367 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 541 | r | ratings_counting.R |
#count how many times when rotten tomatoes rated it 80% that audiences rates it >85%
ratings_counting <- function(movies){
library(tidyverse)
# right <- movies %>% filter(`Rotten Tomatoes %` > 80 & `Audience score %` >85) %>% count()
# number_of_movies <- count()
# right_freq <- right/number_of_movi... |
bff111870e5706a249493505e24c30c52208f761 | 3bbebea9260b8b3c1b7d07f66d91998116babf55 | /cell_type_deconv_heatmap.r | 736a38b8c0fe8a4a549bbfd0f15a67a8f7b642df | [] | no_license | DataScienceGenomics/mirTarRnaSeq_Paper | bc6827ddf185010e7eec0f649155bcbcf9f3b3bf | b25318da0896c970a7690e19551c3e0f2031ff3f | refs/heads/main | 2023-07-14T08:16:14.135860 | 2021-09-05T21:29:11 | 2021-09-05T21:29:11 | 403,410,406 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 586 | r | cell_type_deconv_heatmap.r | #Paper correlationHeatmapEBV miRNA
library(pheatmap)
library(dplyr)
library(mirTarRnaSeq)
library(readxl)
##CellTypeDeconv
CellTypeDecon<-read.table("~/Desktop/CellTypeDeconv_GEDIT.txt", as.is = TRUE, header = T, row.names = 1)
summary(CellTypeDecon)#What is max-what mean and what is min
breaks<-seq(0,1,length.out=20... |
2a3b5f30a02254ac62194afb74a7e45aee73833b | 018087b04d66f1b33ec55619ac8dbaaf18c68449 | /plot3.R | 71c30330946bad72b092060693f66c77dd2bd385 | [] | no_license | sajiajialong/ExData_Plotting1 | c41f056cdd690f577b22f9a802f065d20074cc5e | 969301adc14c983e43c6146794c17e2ef88e9bde | refs/heads/master | 2021-04-06T20:04:10.204576 | 2018-03-12T20:10:39 | 2018-03-12T20:10:39 | 124,770,817 | 0 | 0 | null | 2018-03-11T15:33:09 | 2018-03-11T15:33:09 | null | UTF-8 | R | false | false | 1,231 | r | plot3.R | data<- read.table("household_power_consumption.txt",header = TRUE, sep = ";")
## turn column Date to "Date"
data$Date<- as.character(data$Date)
data$Date<- strptime(data$Date,"%d/%m/%Y")
data$Date<- as.Date(data$Date)
target<-subset(data, Date>=as.Date("2007-02-01")& Date<=as.Date("2007-02-02"))
target<- target[comple... |
8fcff7f44aecb7b1668d7f3bb70905f943a406bc | 200a7c74eb36dcb7b7be917e31e40f3fbd5bbe17 | /ui.R | 22157c05f6b7bba14de3c32856e8d9c6efc415c3 | [] | no_license | SupermercadoEmporium/Julio2014 | 9bc15ad9bb71f0eb2633664336b4fe442bebb0ca | f2e077fabb9cf34bdcd85ac8deb66f1414b848cf | refs/heads/master | 2021-01-10T15:51:23.530492 | 2016-01-15T16:19:38 | 2016-01-15T16:19:38 | 49,674,151 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,095 | r | ui.R | library(shiny)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
# Application title
titlePanel("Emporium 2014"),
fluidRow(
column(3,
selectInput("select", label = h3("Primera Categoria (Antecedente)", style ="color:#297418;"),
choices = c(ve... |
da6f4dfe1b0b542194315ab35865bd753ba5b7b1 | 7e0f4777f4e06b0ac72b90422ac0d9c765767755 | /projects/websi/lc.R | 329a9bd2e898cdf5d5ebcc0cd0814a60d8d4a612 | [] | no_license | psolymos/abmianalytics | edd6a040082260f85afbf4fc25c4f2726b369392 | 9e801c2c564be155124109b4888d29c80bd1340d | refs/heads/master | 2023-01-30T05:00:32.776882 | 2023-01-21T05:36:23 | 2023-01-21T05:36:23 | 34,713,422 | 0 | 7 | null | 2017-01-20T19:07:59 | 2015-04-28T06:39:37 | R | UTF-8 | R | false | false | 1,518 | r | lc.R | library(opticut)
library(cure4insect)
opar <- set_options(path = "w:/reports")
load_common_data()
SPP <- get_all_species("birds")
subset_common_data(id=NULL, species=SPP)
level <- 0.8
res <- list()
for (spp in SPP) {
cat(spp, "\n")
y <- load_species_data(spp, boot=FALSE)
lc0 <- lorenz(rowSums(y$SA.Ref))
... |
5839ff390d30fba54012e86e21c44f22f1490c95 | 453976d125156d4f98396870657e9100f7f7563f | /ss-es-anova-plot.R | a76f5c987b9976834b1e62762c3355ad75c65657 | [] | no_license | statexpert/12-002 | 190510c97c788e43e6febc69ed40eef31216f828 | dfd8207018caa4258a7da65011fcce1a38c51f8f | refs/heads/master | 2016-09-16T00:18:06.182525 | 2013-01-22T02:15:20 | 2013-01-22T02:15:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,417 | r | ss-es-anova-plot.R | source("functions.R")
opar <- par(no.readonly=TRUE)
f <- seq.int(0, 1, length.out = 100)
size=60 # размеры эффекта для ANOVA
sig <- c(0.05, 0.01) # уровни значимости
groups <- 3
tab.power <- matrix(
mapply(power.test.anova, f = rep(f, each = 2), sig = sig, groups = groups, n = size),
ncol=2, byrow=TRUE, dimnames... |
ba6faddf4666d03e931b1bce73a55b4ff7664707 | fd56b6a77bbb080ac7d1e3109446b635ae8eed69 | /man/kable_summary.Rd | 1879a274ce6356e3125ce063635822c97f159604 | [] | no_license | philliplab/MotifBinner2 | 325c010b18d662be1abf700e6028eaf138705ad5 | 734b24c2f9d009cd6c8d3ea4a8f8085ac9d4a7dd | refs/heads/master | 2021-07-01T12:36:35.369658 | 2020-09-07T15:11:15 | 2020-09-07T15:11:15 | 149,005,542 | 0 | 0 | null | 2018-09-16T14:51:06 | 2018-09-16T14:51:05 | null | UTF-8 | R | false | true | 342 | rd | kable_summary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/genSummary.R
\name{kable_summary}
\alias{kable_summary}
\title{formats a summary table for markdown}
\usage{
kable_summary(summary_tab)
}
\arguments{
\item{summary_tab}{A data.frame as produced by genSummary}
}
\description{
formats a summary... |
656f3db91c96af4ac49f02a02798758cb740f4ef | 2f4946c9194041457d3aeec7f53da815789fc375 | /man/unique_pairs.Rd | 0ef170b5b5597ea6f2fb9e2673924272276504e3 | [] | no_license | M-U-UNI-MA/tpfunctions | ce5ca7406caccb849cca28ed089af7d72d7cb918 | 54d484c9ab499ac99acd7889612ee073daf3c02f | refs/heads/master | 2020-03-28T17:07:43.348975 | 2019-01-14T15:12:19 | 2019-01-14T15:12:19 | 148,760,713 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 553 | rd | unique_pairs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/measures.R
\name{unique_pairs}
\alias{unique_pairs}
\title{Unique Combinations of all Elements within a Vector}
\usage{
unique_pairs(vec)
}
\arguments{
\item{vec}{}
}
\value{
A Dataframe with (n^2-n)/2 observations
}
\description{
Given a vec... |
a405af988689b8c56bb65c035ef355124d9d739d | a4c9ec280e70749cf4aac9d30bdda6ea0173e10e | /plot1.R | c0d0a34fe059289edbb28528b2082ef3d54f6876 | [] | no_license | charlestjpark/EnergyDataVisualization | 7b73d731dfd2a9e46aefd640f79e6f1a2143ef20 | 047c8e942fb924a42308a8eda7394082df1a72dd | refs/heads/master | 2021-01-01T05:18:04.674083 | 2016-05-27T04:32:08 | 2016-05-27T04:32:08 | 59,803,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | plot1.R | ## Read the file into a data frame
URL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
temp <- tempfile()
download.file(URL, temp)
data <- read.table(unz(temp, "household_power_consumption.txt"), header = TRUE, sep = ";")
unlink(temp)
## Isolate entries ranging from 2007-02-... |
ffd28e6da45cfc4913d1f63e80a1a1e059d4091e | 01773ed6fe41297b852dca890ade98ca2c6ee22e | /R/FreundlichPlot.R | d6f8972ab20b8effe9ca9ccb97e55d8391d8b7f3 | [
"MIT"
] | permissive | devalc/Sorption | c481acb0756c832a6a2406af2dfb05a169b87fda | b1f74aa562971e7d2b5c6a351809053b3cc2bc0a | refs/heads/master | 2022-01-22T08:01:17.794215 | 2021-12-29T21:30:39 | 2021-12-29T21:30:39 | 158,130,131 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,160 | r | FreundlichPlot.R | #' Plot the Freundlich model fit to the experimental data
#'
#' This function plots the Freundlich isotherm model fit
#' @param Ce equilibrium solution concentrations in mg/l
#' @param Qe retention by solid (adsorption) mg/kg
#' @param cor_lab_x,cor_lab_y location on the plot to place pearson r and p-value
#' @param e... |
03984cc068bb748b0248f51351b7439aca07589c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Sleuth3/examples/ex1715.Rd.R | c448c6cabb098734efd9730277f30d038eaed4ec | [] | 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 | 146 | r | ex1715.Rd.R | library(Sleuth3)
### Name: ex1715
### Title: Church Distinctiveness
### Aliases: ex1715
### Keywords: datasets
### ** Examples
str(ex1715)
|
0800e6a56010a1ed45efb1583320ac873ae52fd0 | 01b1446adcc5612fe9a1dd49172a87c59200882b | /man/gi.Rd | 0f5cee5c4eccd5163dcbc091b163f95bded809be | [] | no_license | davidearn/epigrowthfit | de5f046c123aecff7ca4b88d484e438b25e5c8cf | 36aac5d2b33c064725434bf298ac008e3929f9d6 | refs/heads/master | 2022-09-30T14:35:07.931181 | 2022-09-18T21:28:28 | 2022-09-18T21:28:28 | 250,906,109 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,560 | rd | gi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gi.R
\name{gi}
\alias{gi}
\alias{dgi}
\alias{pgi}
\alias{qgi}
\alias{rgi}
\title{Generation interval distribution}
\usage{
dgi(x, latent, infectious)
pgi(q, latent, infectious)
qgi(p, latent, infectious)
rgi(n, latent, infectious)
}
\argum... |
1e25f497d7e2bec553213e66d7d3a2e7f0a1e46a | 27c994854607957cfde15e552b205763e8e26f71 | /code/reproduce.R | 3119df8d80f6c000ddbac4cf1dfa25a08c019826 | [] | no_license | duanby/I-cube | f251b0e1fb6f49d5c87f1e41f06086a2cdbea50d | 4fb8d070f974dfdaddec5f12117da664c44e615d | refs/heads/main | 2023-03-08T12:58:16.346584 | 2021-02-20T00:58:58 | 2021-02-20T00:58:58 | 338,975,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,184 | r | reproduce.R | source("setup.R")
treatment_type = "sparse_pos_bias"
Cd_seq = seq(0, 5, length.out = 6)
result = list()
for (Cd in Cd_seq) {
print(Cd)
para_vary = list(list(name = "treatment_type", value = treatment_type),
list(name = "methods_unpair",
value = c("Crossfit-I-cube", "MaY-... |
12f2e76c883484721169cb592da1716be74d1643 | d8e0425233afe5226f3ac249ecdd2446b4f19ddb | /NEW2HomePNDatabaseAL_R_2018-01-04_1541_growth.r | 449f530b84ffdafd5bb11655b8fb18a14bf09562 | [] | no_license | akshithrk/Jan-4-2018-Export | 557fccc5b3f9c4a6cb463bb945d3354f408f9907 | 3c04fbb51bef9a8d68e9624575c874010027c0a4 | refs/heads/master | 2021-05-14T12:59:58.451150 | 2018-02-23T18:59:13 | 2018-02-23T18:59:13 | 116,422,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,874 | r | NEW2HomePNDatabaseAL_R_2018-01-04_1541_growth.r | #Clear existing data and graphics
rm(list=ls())
graphics.off()
#Load Hmisc library
library(Hmisc)
#Read Data
# data=read.csv('NEW2HomePNDatabaseAL_DATA_2018-01-04_1541_growth.csv')
# data=read.csv('NEW2HomePNDatabaseAL_DATA_2018-01-11_1032_growth.csv')
data=read.csv('NEW2HomePNDatabaseAL_DATA_2018-01-15_1421_growth_dat... |
6bbc531424bd987f35d89462da7dadaa458df54b | cf606e7a3f06c0666e0ca38e32247fef9f090778 | /test/integration/example-models/Bayesian_Cognitive_Modeling/ParameterEstimation/DataAnalysis/Correlation_1_Stan.R | 595c177cf1dc4c8687b7a33dbe44bc55e66f74a1 | [
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown"
] | permissive | nhuurre/stanc3 | 32599a71d5f82c759fd6768b8b699fb5f2b2d072 | 5612b357c1cd5a08cf2a57db97ce0e789bb87018 | refs/heads/master | 2023-07-05T02:27:08.083259 | 2020-11-12T15:37:42 | 2020-11-12T15:37:42 | 222,684,189 | 0 | 0 | BSD-3-Clause | 2019-11-19T11:50:39 | 2019-11-19T11:50:38 | null | UTF-8 | R | false | false | 3,416 | r | Correlation_1_Stan.R | # clears workspace:
rm(list=ls())
library(rstan)
#### Notes to Stan model #######################################################
## 1) Multivariate normal distribution in Stan uses covariance matrix instead of
## precision matrix.
## 2) Multivariate normal distribution can be (and is) also vectorized.
## 3) Wa... |
ecdb6c71e89a62d3d494850a3b1c2e4fcd52f46d | ef475903010e72b4777d62f40f80f3e105240dbd | /R/Player_Analysis.R | f5efd3ad90c82e217f87db47be94a7fe43c0fde8 | [
"MIT"
] | permissive | sujoydc/DS-611-Project | 125a2f6a861e9ad0c053f5fd7ab8b98f0676ed3b | 65063eb74c62c7589bf528c17a2fba3e58fe60b8 | refs/heads/master | 2020-07-31T19:31:34.165324 | 2019-10-26T02:45:40 | 2019-10-26T02:45:40 | 210,729,420 | 1 | 1 | MIT | 2019-10-22T21:28:18 | 2019-09-25T01:27:38 | R | UTF-8 | R | false | false | 4,873 | r | Player_Analysis.R | library(sqldf)
library(ggplot2)
library(plyr)
setwd("/Users/gogol/Documents/Utica/DSC-611-Z1/Module8/DS-611-Project")
olympic <- read.csv("./data/athlete_events.csv", header = TRUE)
#1. player younger than 19 years old
#USA male/female players
usa_tot <- sqldf("SELECT year,COUNT(*) olympic FROM olympic WHERE no... |
edbd47758fe7d9b7dddadd8c45bf22b55e5c19b3 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Gent-Rowley/Connect5/cf_5_5x6_w_/cf_5_5x6_w_.R | e23ba46b608573078ca88c65fa9820a0ca180c92 | [] | 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 | 64 | r | cf_5_5x6_w_.R | 1859cc01ce95f474a4043bf6cccb6dfc cf_5_5x6_w_.qdimacs 97530 26380 |
f9a4ab59f50cffb8c3cd78e4a4e3324bc72fc801 | b1d25fd8d0cb2e8806c9c84dd11f8f7010f0d76f | /tests/testthat.R | 72547d1eb766941cd75878df7ac98969c6300a0c | [] | no_license | bigpas/gkchestertonr | a16f5a587d078a154017704e2fb416cf8d49c628 | e2e19766f7fa976b42b04aa2219989248adbb7f6 | refs/heads/master | 2020-05-05T02:36:11.085884 | 2019-04-07T22:32:17 | 2019-04-07T22:32:17 | 179,643,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70 | r | testthat.R | library(testthat)
library(gkchestertonr)
test_check("gkchestertonr")
|
ff21a6e3db2e712950672f417bcd22b51d237b2d | 7f3cbd23be4fc8ece15b7143f4167d1290953b09 | /man/wheeler.smith.Rd | dabe8ca672487ececbbb7d3434c72159044e2347 | [] | no_license | Libardo1/koRpus | 98fbdea632cf7b816293e35922b7433bca5322d8 | 628c295c1c69e7a71711a4ae5e8229b1a4af2c73 | refs/heads/master | 2021-01-18T08:19:34.961113 | 2014-03-19T00:00:00 | 2014-03-19T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,759 | rd | wheeler.smith.Rd | \name{wheeler.smith}
\alias{wheeler.smith}
\title{Readability: Wheeler-Smith Score}
\usage{
wheeler.smith(txt.file, hyphen = NULL, parameters = c(syll = 2), ...)
}
\arguments{
\item{txt.file}{Either an object of class
\code{\link[koRpus]{kRp.tagged-class}}, a character
vector which must be be a valid path to a fi... |
4b573caa6fae04a8879aa2f4256d29432db53c9a | 89f471a1facf26cba075e79ad778f58c1d03a175 | /R/stem_leaf_display.R | 18103cfd1dfbc10d3b8c276f8d08d5e794d229a6 | [
"MIT"
] | permissive | deandevl/RplotterPkg | a90d229946639235949483f595b9ee8c5eeab101 | 5a70e51eeb45d84685e4fddc9a9f7bd9e68f089a | refs/heads/main | 2023-05-12T06:55:22.537757 | 2023-05-01T09:47:17 | 2023-05-01T09:47:17 | 230,162,174 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,066 | r | stem_leaf_display.R | #' Function is a wrapper around aplpack::stem.leaf that provides one or more stem and leaf display(s).
#'
#' Function accepts a named list of numeric vectors from which stem and leaf displays
#' are provided.
#'
#' @param x The named list of numeric vectors from which stem and leaf displays are provided.
#' @param un... |
aae7b9b62c7074a11a9c90030f8eb8bc2260cdae | a2bfab36444668a0a6fcb1aea234da2b9323be07 | /R/ultimate_upgrade.R | f268792d4fac51fb82f8201b840978a56125fc19 | [] | no_license | rahasayantan/springleaf | 93643518d4a3d1bd87019d74694ed67966fc57d2 | 894a13be5b7244596bba304eac73dbdf82fdc0fb | refs/heads/master | 2021-01-19T23:24:47.590137 | 2015-10-21T07:30:11 | 2015-10-21T07:30:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,563 | r | ultimate_upgrade.R | ## wd etc ####
require(Metrics)
require(caret)
require(readr)
require(doParallel)
require(stringr)
require(lubridate)
require(lme4)
## extra functions ####
# print a formatted message
msg <- function(mmm,...)
{
cat(sprintf(paste0("[%s] ",mmm),Sys.time(),...)); cat("\n")
}
auc<-function (actual, predicted) {
r ... |
db8878a9408ddb88ec63f74d2141e55403c03bac | 058df96499d8053fb468a27d30a2adaf787bf6fc | /R_scripts/barplot_percent_stacked.R | 907b9347fa24699c76b7c775174901d655d66e62 | [] | no_license | rosaranli/My_scripts | da806f80da310814fb9c75d826272d208875aa89 | f5425f1075c8852b8a09c315b826cd5158c3207f | refs/heads/main | 2023-07-10T09:34:24.704904 | 2021-08-23T16:03:43 | 2021-08-23T16:03:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,452 | r | barplot_percent_stacked.R | library(data.table)
library(tidyverse)
library(gridExtra)
library(grid)
############################################################
#Input format for matrix should be: colnames as "SNV, sample1, sample2.....sampleN (doesn't depend on the naming style except at the simplification step
# i.e gather function)"
#with SNV... |
b96cb419589e671aafe22b652e62250ccc6367a7 | d12501d77b129bf5eabcd1ffa34c784fa18b2990 | /LiuHetools/tests/testthat/test_fhw26.R | 26a9bf65aa182100ade6ffd6e9029256d6ca8da3 | [] | no_license | 3701/hw3 | 631321e898d8f1fe432c78530dae9c177d1c094b | b36bcbebf46347d7ccb11ad4ba91e13aa78355ea | refs/heads/master | 2021-04-26T21:52:11.072514 | 2018-03-07T02:56:08 | 2018-03-07T02:56:08 | 123,729,473 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 299 | r | test_fhw26.R | context("fquiz26 in my package")
test_that("if fquiz26 working",{
x<-hw26
testing<-list( apply(x,c(1,3),median),
apply(x,c(1,2),median),
apply(x,c(2,3),median),
apply(x,c(3),median),
apply(x,c(1),median),
apply(x,c(2),median))
expect_identical(fhw26(x),testing)
})
|
800f983c43d0188844fd7f3f2fec2189f156f72b | c54c73306f1d25cc78c346bbefd7248c7b45da60 | /man/get.clusters.Rd | e12037af21f3ecf546da9a318abe5ce7d8dd2737 | [] | no_license | stuchly/MetaMass | 7a3ad4cd01870ecf73ece41004106baec5782c39 | 2190f0d92a8dc4d14ce593c616b4fa4efa0a29b2 | refs/heads/master | 2020-04-17T02:30:43.937779 | 2019-11-08T11:09:44 | 2019-11-08T11:09:44 | 56,692,568 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,682 | rd | get.clusters.Rd | \name{get.clusters}
\alias{get.clusters}
\title{get.clusters}
\description{
retrieves cluster data.frame from AnnoMass object
}
\usage{
get.clusters(AM,rID=1)
}
\arguments{
\item{AM}{AnnoMass object. Result of function \code{analyze.MSfile}}
\item{rID}{integer. Which annotation should be used to the comparison
... |
82d6752770c6b7138676263c0ba0c904533773d5 | 55a5e246d1318275a5a0f1fc9b2e1b080ab26fe7 | /tests/testthat/test-folder-dataset.R | 80fbc09d27d63ac73143248e49daf689181fa95c | [
"MIT"
] | permissive | mohamed-180/torchvision | 610577f5b1dec7a628df8c047c41ec18376e35f5 | 0761c61441f838f1b0c6f3624c40542934fb24f8 | refs/heads/main | 2023-07-14T14:20:21.225664 | 2021-08-23T17:18:10 | 2021-08-23T17:18:10 | 399,161,368 | 0 | 0 | NOASSERTION | 2021-08-23T15:48:55 | 2021-08-23T15:48:54 | null | UTF-8 | R | false | false | 512 | r | test-folder-dataset.R | test_that("image_folder dataset", {
ds <- image_folder_dataset(
root = "assets/class",
transform = . %>% transform_to_tensor %>%
transform_resize(c(32,32))
)
expect_length(ds[1], 2)
dl <- torch::dataloader(ds, batch_size = 2, drop_last = TRUE)
coro::loop(for(batch in dl) {
expect_tensor_sh... |
db0b5cad223e1f1c26773ac3f7846f63d0fd9f9e | 8d74e828c671df7f5211705befeba2f857915f07 | /chap_6/03/app.R | 0b17f6f8a03e6afc3b4c75460d4777b95f010d62 | [
"MIT"
] | permissive | kpivert/my_ms_book | 1aa82e0cfabeac92ad0945f9bdcf7764ee0df507 | d5e7c2b8d092bc56aefd7f1b61c4b77c29ed3320 | refs/heads/main | 2023-07-14T03:10:37.358289 | 2021-08-08T14:36:22 | 2021-08-08T14:36:22 | 380,824,308 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 476 | r | app.R | library(shiny)
ui <- fluidPage(
tabsetPanel(
tabPanel("Import data",
fileInput("file", "Data", buttonLabel = "Upload..."),
textInput("delim", "Delimiter (leave blank to guess)", value = ""),
numericInput("skip", "Rows to skip", 0, min = 0),
numericInput("rows", "Rows to preview", 10, min ... |
0f0e97a5579eb6209775620c669e2511f5f2ac99 | 944ad6b296718cf5ebc057064f205660609d7322 | /shanAns3.R | 7fddf5102e2a3cbd2c3ca7fdb725fcac02f4e226 | [] | no_license | jishupu05/funny-correlation | 2ee54b71bbe309b25241034e63f19e7ca6d8b553 | f9e27c5f665a5beef6f9e3e34f43763024288362 | refs/heads/master | 2022-12-17T23:15:23.101180 | 2020-09-23T09:22:16 | 2020-09-23T09:22:16 | 297,917,353 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 802 | r | shanAns3.R | #find out the partial correlation
#partial correlation means when you feel that your two continuous variables are correlated with each other and any of them
#also correlated with other continuous variable.
# find partial correlation to get influence free relation between those two variables.
#load the data.
df<-... |
cd953a26c3f922c0066719d10d18244aa2df4701 | 909157178ed55cf23adbd5b835012f78f510669e | /data_visualisation/scatterplots.R | da44a86364452c62b49a299ffae5280643ff4a6c | [] | no_license | tomscott1/R-notes | d7e4c0e45497c02e88b7bfe4fbbecd8cea811d58 | d890a5432acddd3618fa7ce1b11aa45b7c0ef3d4 | refs/heads/master | 2021-01-12T10:52:27.142033 | 2016-12-04T21:35:53 | 2016-12-04T21:35:53 | 72,739,973 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 259 | r | scatterplots.R | library(ggplot2)
df <- mtcars
# data
pl <- ggplot(df, aes(x=wt,y=mpg))
# geometry and aesthetics layer
pl2 <- pl + geom_point(aes(size=hp, shape=factor(cyl), colour=hp), alpha=0.7)
pl3 <- pl2 + scale_color_gradient(low='#90C3D4', high='red')
print(pl3) |
1ac3c60ed7fb6eec61542feace457b744afb86f7 | a64252a36d2d005f141b6f5bd66160c6c12ade2b | /Plots/ModelWithHighCorrelatingIndicators_Forecast_plot.r | 0b73942731e5bd4b5ad43af5455d205472c16f83 | [] | no_license | clairecDS/DoingDataScience_CaseStudy2 | ab2d1e2dd9d615f3888c07f56c883ce54d4fcdd7 | 91b13e802bf5ded858c3e0ff4d45fa1679d0f87d | refs/heads/master | 2020-12-25T14:48:10.564131 | 2016-07-28T03:48:17 | 2016-07-28T03:48:17 | 63,804,714 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 236 | r | ModelWithHighCorrelatingIndicators_Forecast_plot.r | #Creates ModelWithHighCorrelatingIndicators_Forecast plot
png("Plots/ModelWithHighCorrelatingIndicators_Forecast_plot.png")
plot(ModelWithHighCorrelatingIndicators_Forecast, main="ModelWithHighCorrelatingIndicators_Forecast")
dev.off() |
c5f92fedbc82175543c0fcd64c97eb317b745f8b | 2cfb6436fec534763f6f157abb20ea56f1224d56 | /team--middle-of-the-road/meetup-gender.R | 7376eead32f1b01f8d26d4a92549feeefc25f70c | [] | no_license | wolass/BRUG_API | 26ddc21ff282b9b57b1bee4eee96e71f3025d10c | ec6036eb9210e967b2601fb202a4a3a07e521d87 | refs/heads/master | 2020-12-13T23:30:06.990427 | 2017-04-06T20:25:03 | 2017-04-06T20:25:03 | 86,577,952 | 2 | 4 | null | 2017-04-06T20:25:04 | 2017-03-29T12:12:00 | R | UTF-8 | R | false | false | 2,324 | r | meetup-gender.R | # How many users of the berlin R users Group are male/female?
# Data sources: meetup.com API, wikipedia data (first names)
library(devtools)
if("meetupr" %in% rownames(installed.packages()) == FALSE) {
install_github("rladies/meetupr")
}
library(meetupr)
library(tidyverse)
library(jsonlite)
# see script 'firs... |
41682c92c59ca049d47208e3567816c39d540ae2 | e7a29b7452edb314b66ecc32dc5d53a235cb1b55 | /R/grid-rowwise.R | 9a8d43f9dd64ecaa7d44aea0a3137fef5dc2bcb3 | [
"MIT"
] | permissive | atusy/cssgrid | 03dfc754a989fd3fc5bbc94580d432c423fc5767 | 9d9f255c72c663bd9476ba090bc0b707b79b6694 | refs/heads/master | 2020-06-07T21:07:20.669061 | 2019-06-23T06:28:03 | 2019-06-23T06:28:28 | 193,093,802 | 30 | 2 | null | null | null | null | UTF-8 | R | false | false | 546 | r | grid-rowwise.R | #' Gird layout in single row or column
#'
#' @param ...
#' Items in CSS Grid Layout and arguments passed to [`grid_layout`].
#' @param rows,cols
#' Sizes of rows and columns in a character vector.
#' If the given number of sizes are less than the number of items,
#' then `"auto"` is used for items missing sizes... |
0a9ae04b7e33ce4566913032f564b36d6fe4529f | 72b8a4f3d2a6666b608ca9227b8c27a540996c28 | /R files/heart_points_analysis.R | 9b4b807c2dcc8633901080a91ff9790cf72a5168 | [] | no_license | BenjaminDupre/tsvr | 1eeb04617e2c981f45ac1b0cf56727b9d9172eee | 1def2088efcf4b382cbe69132968591037cba663 | refs/heads/main | 2023-03-23T09:58:03.128323 | 2021-03-12T09:15:53 | 2021-03-12T09:15:53 | 310,716,326 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,888 | r | heart_points_analysis.R | # Monday 22 February 2021
# Coding for getting insight into heart behavior.
# Author: B.D.
library(ez) # for anovas
library(plyr) # for building the rt graph and revalue function.
library(ggplot2)
library(tidyverse) # for playing around with the %>%
#library(gapminder) # sacar si no uso
library("viridis")
n... |
f0d04a2ef47ce10f5674cc4d8a5997b62c719b67 | 7f5d90ead24483775e908a561ac8c6f1dea37767 | /plot1.R | 3520b5971ab297d063b6e9bf4576ceddce668f14 | [] | no_license | anirudh838/ExData_Plotting1 | edfdb0a991619f9cb0c08f400932d66b7b88c65b | 0483d3e75327b8c0563dd78214a8dd75666bd6d8 | refs/heads/master | 2020-06-01T20:19:26.614701 | 2019-06-08T18:23:57 | 2019-06-08T18:23:57 | 190,915,074 | 0 | 0 | null | 2019-06-08T17:15:57 | 2019-06-08T17:15:56 | null | UTF-8 | R | false | false | 360 | r | plot1.R | # Construct a histogram between Global Active Powerand save it to a PNG file with a
# width of 480 pixels and a height of 480 pixels
hist(data$Global_active_power, main = paste("Global Active Power"),
xlab = "Global Active Power(kilowatts)", ylab = "Frequency",
col = "red")
dev.copy(png, file = "plot1.png... |
0187cdbb7511d69a29e7e67e95bfbb6b2bc4f1e7 | c924b44352dc43e174e140ce75fd1fe5a1c2ee7b | /r5.R | ee13518bf56eaf93f2f8bbf4620b23e7c2894f4f | [] | no_license | phonamnuaisuk/DAVIS | 86fc59f8f0d7eb2b08d4621ac5f25ef686448297 | cc085cd75ab997c82d532714b082067728e5c869 | refs/heads/master | 2021-08-28T17:45:34.574909 | 2021-08-14T04:06:08 | 2021-08-14T04:06:08 | 88,837,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,277 | r | r5.R | #
# Text Mining
#
text.rt <- readLines("http://kdd.ics.uci.edu/databases/reuters21578/README.txt")
length(text.rt)
# examine
text.rt
which(text.rt == "")
# clean up
text.rt <- text.rt[- which(text.rt=="")]
length(text.rt)
text.rt <- text.rt[- which(text.rt=="}")]
length(text.rt)
#
lastind <- which(text.r... |
3b71589c2278a12d02a1ee6cbc6826e61ca7d114 | 0d5325d24dbefd4b79475bc63153b592207e050e | /Rexam/navercomic.R | b523ed2ef0f37000d5b3356c859473740083be7c | [] | no_license | HWANG593/R_Programming | ec9842dcfdcb26994f7ae9f951c3de29657f3c41 | 5171b76f2a5631a9fa12007d2dfc67e141bee69c | refs/heads/master | 2023-03-26T11:18:51.274623 | 2021-03-16T13:16:57 | 2021-03-16T13:16:57 | 341,129,533 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 846 | r | navercomic.R | library(httr)
library(rvest)
library(XML)
comicName <- NULL
comicSummary <- NULL
comicGrade <- NULL
site<- "https://comic.naver.com/genre/bestChallenge.nhn?&page="
for (i in 1:20) {
url <- paste0(site,i,sep="")
text <- read_html(url)
vcomicName<- html_nodes(text, '.challengeTitle > a')
vcomicName <- html_te... |
0d8401e427802cb1291e396cb40b5b862deabf94 | 43575504154d202590d6c407e900ddf796e523da | /code/VisaCost_Analysis.R | 34c94f983fbf1d6402f3aaed025347cc8b6db68c | [] | no_license | FabianFox/Visa | c25eb179673c91990f4b35fcc3a2e5017b4ed7d2 | 72180e0fe844dbb8c708291639223c8a92c1ae65 | refs/heads/main | 2023-02-12T20:23:49.922780 | 2021-01-13T12:24:50 | 2021-01-13T12:24:50 | 311,952,706 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,286 | r | VisaCost_Analysis.R | # GMP: Global Visa Cost Dataset
# Data
# year: 2019
# source: https://cadmus.eui.eu/handle/1814/66583
# Load/install packages
### ------------------------------------------------------------------------###
if (!require("xfun")) install.packages("xfun")
pkg_attach2("tidyverse", "rio", "lubridate","countrycode", "state... |
cc2cdeba2551676b1734c5d57a6aec958e748640 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/pangoFontDescriptionSetStyle.Rd | 688ee6dfa81092b2a330a861a02d97500987b9ab | [] | 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 | 928 | rd | pangoFontDescriptionSetStyle.Rd | \alias{pangoFontDescriptionSetStyle}
\name{pangoFontDescriptionSetStyle}
\title{pangoFontDescriptionSetStyle}
\description{Sets the style field of a \code{\link{PangoFontDescription}}. The
\code{\link{PangoStyle}} enumeration describes whether the font is slanted and
the manner in which it is slanted; it can be either
... |
c0c84b5b84e2e7e67f900cdccd4374bcb8e039bf | 569b144ce50f8e25e89a732cab0cf77731ed22cf | /Exploratory_Data_Analysis_Course_Project1/Plot2.R | a1e9464a91c42901a313f2c1ff88438fd863bb82 | [] | no_license | RATHsid/ExData_Plotting1 | 614709b70926831b33dc90aef59a9c9e127e5f2c | 54aa5f1b6220dc99a5071e2a5339204ef9bb954e | refs/heads/master | 2020-12-07T09:05:06.730230 | 2014-09-05T16:10:51 | 2014-09-05T16:10:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 618 | r | Plot2.R | Plot2<-function(){
y <- read.table("household_power_consumption.txt", header=TRUE, sep = ';')
a<-(y[,1]=="1/2/2007")|(y[,1]=="2/2/2007")
x<-y[a,]
Date<-as.character(x[[1]])
Time<-as.character(x[[2]])
dt<-paste(Date,Time)
dt1<-strptime(dt, "%d/%m/%Y %H:%M:%S")
f<-x[,-1]
f[[1]]<-dt1
datetime<-f[[1... |
30e332880d4427efaa16015e452950a862d3e6ba | 5b2792dbb609b5737cec65a81a56d2a20c456aa1 | /R/problem_003.r | 3f90139dd18b513e135ad32700e1b611bde68e01 | [] | no_license | pbcoronel/project_euler | ab9642ff88ccc54d82fb7821318396293d8fff3e | c35272d246040964eaec9ae88236beb328c1e527 | refs/heads/master | 2021-01-24T07:55:03.520164 | 2014-08-23T18:46:12 | 2014-08-23T18:46:12 | 9,350,970 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 173 | r | problem_003.r | library(gmp)
largest_prime_factor <- function(number){
factors <- factorize(number)
return(max(factors))
}
largest_prime_factor(13195)
largest_prime_factor(600851475143) |
3ae05ddaef90df7e7a0314f7f3f36b53a65d577d | 4a3ce9c13a4cbe18d748d4e7858712caab705f9a | /COVID-19 Case Comp/Data Wrangling Files/lags.R | ebf6534ae7da216faa8cfea9b94ab51637f282cd | [] | no_license | scbrock/covid_case_comp | e6abcb07ea0b7baca1d6047f3326c3cef8339dfd | e9964eda04ebb4d4586c4c1ec4a0bdf5147d292f | refs/heads/main | 2023-02-17T09:24:47.656751 | 2021-01-16T22:48:42 | 2021-01-16T22:48:42 | 308,984,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,875 | r | lags.R | library(ggplot2)
library(dplyr)
library(data.table)
df = read.csv("data_can2.csv")
df <- data.table(df)
responses <- names(df)[5:8] # similar to Samuel's code
# create new variables for "number of new.."
df[, (paste0("new_", responses)) := lapply(.SD, function(v) c(0,diff(v))),
.SDcols = responses]
df = df[, -c(9... |
4d54dee7d48ff8c8e81f58d3db08bf121b35769f | 938b3c7b167544207c36a22ac5042729cc8f3471 | /src/simulation/compile_all_results.R | 4d2a3772cff5edb1bc981b0876b23ddd7bbd2e63 | [] | no_license | smmakela/cluster_sampling | 5aa9fc00b7a17173512ec783c80a7e8d26336e1f | c0825884c9c9d387f16ab6fb8d0203f91d42a15c | refs/heads/master | 2021-01-24T03:19:02.892635 | 2017-08-11T19:01:35 | 2017-08-11T19:01:35 | 68,622,666 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,971 | r | compile_all_results.R | # Author: Susanna Makela
# Date: 13 Jan 2016
# Purpose: process results from simulation
################################################################################
### Setup of directories and libraries
################################################################################
libdir <- "/vega/stats/user... |
29f6a5ef0dffca373752ec4a68d0664b0e16545c | 9f5c5897cd41ad5459574b2fc7b46b1bd26c6efd | /plot4.R | 8a76123474e59a4a758552de56e6e2c09034e0ee | [] | no_license | p234a137/ExData_Plotting2 | 6f289f24dff6604a92cd780e01d3f78587a5ad93 | 6ea753389b3d030812a1f095753ace5b116fd457 | refs/heads/master | 2016-09-05T12:40:15.156994 | 2015-02-14T01:07:57 | 2015-02-14T01:07:57 | 30,399,776 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,037 | r | plot4.R | # https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip
## read in data
# This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Question 4
# Across the United States, how have emissions from coal combustion-rel... |
eec5402769b675d003bb3d78ee754380c630d4e6 | ae2678731fa0698a59f0196cf3f2d14eb8dd7778 | /tests/testthat/test-unglue_detect.R | a47c796a9aa2cf1dfd13e0523e04c5daf095be94 | [] | no_license | cran/unglue | 9e6fd2da72d1edde32d1ea6800501d9d14612931 | ecd4a780cd03df4198b51346ecfa96912da068c9 | refs/heads/master | 2020-12-22T22:58:52.803649 | 2020-06-11T04:50:03 | 2020-06-11T04:50:03 | 236,956,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 164 | r | test-unglue_detect.R | test_that("unglue_detect works", {
expect_true(unglue_detect("this and that", "{x} and {y}"))
expect_false(unglue_detect("this and that", "{x} or {y}"))
})
|
2b857eb1c8a963d5c05cdff6c0a6cc8fe7517011 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/BGGM/R/ggm_compare_confirm.R | 95ba1469dd8df74c3966486f92e02bfa5dfbaef7 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 29,038 | r | ggm_compare_confirm.R | #' GGM Compare: Confirmatory Hypothesis Testing
#'
#' @description Confirmatory hypothesis testing for comparing GGMs. Hypotheses are expressed as equality
#' and/or ineqaulity contraints on the partial correlations of interest. Here the focus is \emph{not}
#' on determining the graph (see \code{\link{explore}}) but te... |
2959de0120d6e7f093114ececf65e933286cab81 | 6eb21cdd51630e339cd645748cc528a7d868da5e | /Airline_Delay/SQLite.R | c2d01574779ee3a05e2d1791b4800ec44f3e4c8c | [] | no_license | z357412526/Present_Projects | 6654b05414a538d9b33ed0b035481e576ded922a | 9ce73049f493dc27b714f58a89adae6667da9058 | refs/heads/master | 2021-01-09T21:55:14.142038 | 2016-03-18T02:00:29 | 2016-03-18T02:00:29 | 49,180,649 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,680 | r | SQLite.R |
library(RSQLite)
install.packages("data.table")
library(data.table)
fileName <- "Airlines.db"
db <- dbConnect(SQLite(), dbname = fileName)
initExtension(db)
#try for a full case
tableCL <- c(rep("integer",8), "factor","integer","logical","integer",
"integer","logical","integer","integer","factor","factor","integ... |
14875a0a0a653da479b7d5a3488ba695de576d63 | e8bd1221d5edf301183e222ae215afa7f3a4c166 | /man/gg.polygon.Rd | 4f86047f4b8d79c612383aad66d7bad8be38d097 | [] | no_license | dill/inlabru | 1b9a581ae5b56246fcd748db8df051ae4ff8bfa8 | e2c38a34d591f712b57cbe430c24bb0a82f03ae4 | refs/heads/master | 2021-01-22T22:53:21.963501 | 2017-03-18T09:30:08 | 2017-03-18T09:30:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 367 | rd | gg.polygon.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggplot.R
\name{gg.polygon}
\alias{gg.polygon}
\title{Polygon geom for Spatial* objects}
\usage{
gg.polygon(data, crs = NULL, colour = "black", alpha = 0.1, ...)
}
\arguments{
\item{data}{A SpatialPolygon* object}
}
\value{
geom_polygon
}
\des... |
becc510e470f491f9285c7c8825152271545bc80 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/psychometric/examples/ICC.lme.Rd.R | 0b40afbff52c3081f364c985deed8699e2813a23 | [] | 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 | 315 | r | ICC.lme.Rd.R | library(psychometric)
### Name: ICC.lme
### Title: Intraclass Correlation Coefficient from a Mixed-Effects Model
### Aliases: ICC.lme ICC1.lme ICC2.lme
### Keywords: models univar
### ** Examples
library(nlme)
library(multilevel)
data(bh1996)
ICC1.lme(HRS, GRP, data=bh1996)
ICC2.lme(HRS, GRP, data=bh1996)
|
5cf367e63f0b94f43626cffa7fa6bd41a71c47d9 | 226b1036611f247760f8e68de857a37108db64e6 | /man/getTrailingDays.Rd | 5617000c32d8eee1ab4fb12ccf8b30ffee452880 | [] | no_license | sameermanek/mmisc | da23008391d571e11d938ae9062db86556d79cb4 | 6623251b9879db643f244d3c81f41c0465bc5725 | refs/heads/master | 2021-05-16T01:48:29.468519 | 2017-06-20T21:05:28 | 2017-06-20T21:05:28 | 42,786,330 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 517 | rd | getTrailingDays.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dates.R
\name{getTrailingDays}
\alias{getTrailingDays}
\title{Get a sequence of trailing days, inclusive}
\usage{
getTrailingDays(chr.end.date, int.length)
}
\arguments{
\item{chr.end.date}{The end date (only one)}
\item{int.length}{The numb... |
b5811445ff27a6827180485d75cec388861ba08b | 23dfa51c5aac37ff6f7ef5d50ce9f951622ff9b7 | /R/printCI.R | 61bde01b68eacf67c09b116715cd3d092e7ef225 | [] | no_license | cran/dafs | e8bd227ee1392de9b64964fb6e80c1373696da3d | 3288237c8c1b83744ec00a6665f557dd99782627 | refs/heads/master | 2022-05-14T06:43:59.958293 | 2022-04-11T08:12:33 | 2022-04-11T08:12:33 | 17,695,374 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 224 | r | printCI.R | printCI = function(x, fmt){
if(length(x)!=2)
stop("x must have be a vector of length 2")
strFmt = paste('(',fmt,', ',fmt,')',sep='')
strResult = sprintf(strFmt, x[1], x[2])
return(strResult)
}
|
0cd5401fdbe70caa1c8e77336767363a1aae604a | 0e3ccfecc18c042f5eeaed6661e26fd46ef9fcb3 | /ui.R | c4d5d3ea4b25b82436357f2844d6d9ef27e2c84c | [] | no_license | SteffenMoritz/dashboard-zukunft | e48231f3a650984c23ddaea915b37e66c99d1eec | 4abc802a9a566c72f30e3227d82b24d69554be61 | refs/heads/main | 2023-08-14T13:07:03.717551 | 2021-09-08T04:40:47 | 2021-09-08T04:40:47 | 401,039,624 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,614 | r | ui.R | #
# ui.R for the 'Dashboard Zukunft' Shiny application
# Created for the destatis KI-Hackathon
# Team C for Climate (Denis, Laura, Maria, Steffen)
# See www.destatis.de ( statistisches Bundesamt / German Federal Statistical Office)
#
# Required Libraries
library("shinyWidgets")
library("shiny")
library("plotly")
li... |
aca595911816bcf429ac76ccfdbf602457dc3617 | 411c1f70e9e7bc543dcd28377fe4426b7e081d07 | /HW4/HW4-1.R | b0f47e5d4b63653fefed23e946bde35b18cf1614 | [] | no_license | owogyx1219/CS498-df | b243404e95a345e5f06d6c3bd0995e679bf1280e | c8bae5a7910a2d221e02f1b61597369bb18958ee | refs/heads/master | 2021-01-24T00:43:17.211705 | 2018-05-10T07:03:05 | 2018-05-10T07:03:05 | 122,775,003 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 577 | r | HW4-1.R | library(ape)
rawData <- read.table("employment_data2.txt", TRUE, "\t")
countries <- rawData$Country
row.names(rawData) <- countries
hclustresult1 <- hclust(dist(rawData), method = "single")
plot(as.phylo(hclustresult1), type='fan', show.node.label = TRUE, font = 2, cex= 0.45)
hclustresult2 <- hclust(dist(rawData), ... |
f3ce44dbc8bf14a267c9214bb4a3e17b1ce4be17 | 6c73899865d066604762aa711717c22a6d437009 | /codes/point process beilschmiedia forest.R | 0255831dbc63e26aa8bdd41b05827fad98e77116 | [] | no_license | benjaminsw/Spatial_Statistics | 9ee1a07288a2ba6aaca07e5e5328c8eb7ab52674 | 628a27cfc20fa0b04ef05b3508a2d7f2e11bba16 | refs/heads/main | 2023-02-22T23:58:52.349832 | 2021-01-26T16:37:18 | 2021-01-26T16:37:18 | 330,215,221 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | point process beilschmiedia forest.R | ##############################
#### Beilschmiedia forest data
##############################
#### Load the data and library
library(spatstat)
data(bei)
plot(bei, pch="+",)
plot(bei.extra, main="")
#### Fit the model - covariates
fit <- ppm(bei~elev + grad, data=bei.extra)
summary(fit)
plot(fit)
fit2 <- ppm(bei~el... |
f149e4d288931b7e5b94063824962ad5ae0f36cf | 5bd38d8a11271a34a4294339d5d1e578c7c84116 | /kicking_dst_raw_stats_NFLFastR.R | 4de0301940eecb0e97b24f4153be896ecfba419c | [] | no_license | Jeffery-777/NFLFastR-Fantasy-DST-K-Weekly | cccdf20286cbe7300b2deb97c278e902a1f18c38 | 95d2f0c4a1855b7e66d2e9814d565de3a1d05eb9 | refs/heads/main | 2023-07-13T06:36:02.764895 | 2021-08-24T16:09:06 | 2021-08-24T16:09:06 | 399,451,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,089 | r | kicking_dst_raw_stats_NFLFastR.R | # Kicking Points Table
library(tidyverse)
library(nflfastR)
kicking <- pbp_db %>%
filter(!is.na(kicker_player_id),
play_type != "kickoff",
week <= 16) %>%
mutate(pat.pts = ifelse(extra_point_result == "good", 1, 0),
fg.30 = ifelse(field_goal_result == "made" & kick_distance... |
9b9a2551ea88535cc4369f0a6b5f4557471008ed | 9477b0d92b6cac88c715bef053be9d15536919e0 | /src/data/match_pdss_snis.R | 5b831343d4e793174825ccece12deaeee4840a68 | [] | no_license | BLSQ/service_availability | 029ba3a1c4a860e7f7dda2d66cbb7331a13da574 | 4fdd37e4eccff31939d3bf9f67cfcb65029cc365 | refs/heads/master | 2020-03-13T21:46:13.703729 | 2018-07-24T14:48:38 | 2018-07-24T14:48:38 | 131,303,526 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,542 | r | match_pdss_snis.R | library(dhisextractr)
load_env()
pdss_org <- read.csv(paste0(pdss_data_dir,'/org_units_description.csv'))
snis_org <- read.csv(paste0(snis_data_dir,'/org_units_description.csv'))
fac_zones_snis <- read.csv('data/references/snis_fosas_zones.csv')
fac_zones_pdss <- read.csv('data/references/pdss_fosas_zones.csv')
pds... |
f805ef37b9c9fb72af3c02b5f31c83b27c557cc9 | aa7836793561f01fa26e51f37ac13a79402a0a86 | /analysis/dtwclust_tests.R | 1415a42f79264bbff0243354a4ba6c92b0d1faba | [
"Apache-2.0"
] | permissive | jmausolf/OpenFEC | 211989de56a78555c4d973635b89abfe186467a9 | 7313b0a3ef21a60984db0668757c0032554dc453 | refs/heads/master | 2022-04-28T23:21:48.511435 | 2020-04-11T06:35:26 | 2020-04-11T06:35:26 | 104,601,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,547 | r | dtwclust_tests.R | setwd('~/Box Sync/Dissertation_v2/CH1_OpenFEC/OpenFEC_test_MASTER/analysis/')
library(zoo)
source("indiv_source.R")
source("indiv_vartab_varplot_functions.R")
source("indiv_partisan_functions.R")
source("indiv_make_polarization_similarity_measures.R")
source("hca400_functions.R")
library(zoo)
y1 = 1980
y2 = 2018
cyc... |
65e7bc66c506cc984afa9145edd21656b1456a1d | f13a487d46e8dda1d04491eb346b46ab35f8caba | /R/kottby.R | 33c9436d2eda40d031dea27e57582941b869d0d9 | [] | no_license | DiegoZardetto/EVER | 99d0c3a5a90df3b94c04a2412ceb2c7780b4376e | f97c50d67aac9cc849b2f284b905204c1d2358a4 | refs/heads/master | 2023-03-28T23:25:59.972626 | 2021-03-31T11:00:14 | 2021-03-31T11:00:14 | 268,497,478 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,766 | r | kottby.R | `kottby` <-
function (deskott, y, by = NULL, estimator = c("total", "mean"),
vartype = c("se", "cv", "cvpct", "var"), conf.int = FALSE, conf.lev = 0.95)
#######################################################################################
# Calcola (su oggetti di classe kott.design) le stime dei totali o delle m... |
f68191d3af3460c97fbb1cb7985849648c2371c6 | fb18a5404891b8cc43ec65dc861318aee3017188 | /server.R | bda54d76fd84dbdd522a7d08c53f14df41a89901 | [] | no_license | cryptomanic/Twitter-Web-App | f8656983a328257a5ba7f1aa2b1e6fc45aa30374 | 825c6966caa2b1ff4ec1b452a29dcaca28d4258b | refs/heads/master | 2021-01-10T13:16:57.060267 | 2015-12-29T18:34:02 | 2015-12-29T18:34:02 | 47,999,300 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,155 | r | server.R | library(shiny)
library(tweeteR)
shinyServer(function(input, output) {
key <- readline("Enter your API Key : ")
secret <- readline("Enter your API Secret : ")
token <- tweetOauth(key, secret)
token <- tweetOauth(key, secret)
output$tweets <- renderTable({
input$dispByHt
isolate({
data ... |
e623c491eb34d61a85add235e663f4adf30b4a20 | e02b906d4d3c548085954f3832afac30c7137228 | /man/fritillary.Rd | 751243ee3e504ab9c049f6a8c1c04bcfe901a67a | [] | no_license | poissonconsulting/bauw | 151948ab0dc55649baff13b2d79a551b6fc5a49d | 47b12dc140ba965ae8c89693c0d8d8fefa0fd7db | refs/heads/main | 2023-06-15T10:56:20.506561 | 2022-12-16T20:00:03 | 2022-12-16T20:00:03 | 78,153,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 770 | rd | fritillary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-fritillary.R
\docType{data}
\name{fritillary}
\alias{fritillary}
\title{Fritillary butterfly abundance data}
\format{
A data frame with 665 rows and 4 columns
}
\usage{
fritillary
}
\description{
The silver-washed fritillary (\emph{Argyn... |
23c7c900e85db6ad6b2a1d09fea03dfb987b565c | 63f53585caf576deea1eea626f3dc1099dc57fbd | /R/circleFun.R | 3e2337fc8685874d4c0732b9b683c4f1fdf56c26 | [] | no_license | ThinkR-open/rusk | 32f0f86b38f1f1167b4fd214d470034e403d42bb | 8943d42e6cee54502a28cae323baf367996eaaa4 | refs/heads/master | 2021-09-25T07:53:22.859016 | 2018-10-19T19:51:09 | 2018-10-19T19:51:09 | 121,903,730 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 277 | r | circleFun.R | # https://stackoverflow.com/questions/6862742/draw-a-circle-with-ggplot2
circleFun <- function(center = c(0,0),r = 1, npoints = 100){
tt <- seq(0,2*pi,length.out = npoints)
xx <- center[1] + r * cos(tt)
yy <- center[2] + r * sin(tt)
data.frame(x = xx, y = yy)
}
|
f5093fff787fdd3bae093cb7ffe4cddc2468e574 | ae48675555fd497b345fb2818d57b33e83a4e203 | /Proteomics/4c_makeHeatmap_sigHitsBar.R | 3478388592f2058eaaca9622e136f88cdd0061e9 | [] | no_license | mcclo/Mahendralingam-et-al.-Nat-Metab | 0bfef4ab501cb2b409f1c05bbd316e92a6fe641c | c075e048e69c5f63463ed6d55f9e476947551903 | refs/heads/main | 2023-03-21T14:35:19.993821 | 2021-03-13T15:31:42 | 2021-03-13T15:31:42 | 346,809,455 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,142 | r | 4c_makeHeatmap_sigHitsBar.R | #' This script was used to make a heatmap of the metabolic proteome with an annotation bar ..
#' .. for genes showing the metabolic cell lineage signatures - after ANOVA and Tukey test (p<0.05) and logFC > 0
#'
#' Note: significant_hits object holds the final signatures
#' BC = basal cell, ML = luminal mature, LP = lu... |
adcf8d72de990246ce473c52d4e4dda208d0f244 | 20c64e38c5738e9bdb2f6e04e1d450ec64ec55e1 | /AITransportation.r | 36870bc12a307f1c4a2aa70c8a6cca4dd99f82e3 | [] | no_license | dobbytech/AITransportation | 397d2e3c79ea6d14a2299bccebd1cec44873b95d | 2ff9dc5419113b8dd3e1ffe10c27776605670bd0 | refs/heads/master | 2020-07-17T01:02:28.141778 | 2019-09-02T22:50:20 | 2019-09-02T22:50:20 | 205,909,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,700 | r | AITransportation.r | #'=========================================================================
#'AI for Transportation
#'=========================================================================
# Libraries -------------
rm(list=ls())
library(tidyverse)
library(tidytext)
library(readr)
library(qdap)
library(qdapTools)
library... |
c9bf1ea73f2be5075681ef66e246a63d859e35ec | 326537a42f5a3f128fbb26fda2ababa3e2de2576 | /Rpub/zBLUP/blupTest2.R | 000c98dc368783cbb1cf1d7a9772a0e77769c56a | [] | no_license | gc5k/Notes | b3f01c7c89d63de565bd968c99234ab04da51273 | 981d4d6935a446f09fb788e12161288d9727faa6 | refs/heads/master | 2022-10-01T08:42:13.174374 | 2022-09-12T03:03:58 | 2022-09-12T03:03:58 | 38,083,965 | 2 | 2 | null | 2016-08-14T07:02:59 | 2015-06-26T01:34:57 | null | UTF-8 | R | false | false | 954 | r | blupTest2.R | M=200
N=200
ha=0.5
hd=0.3
frq=rep(0.5, M)
G=matrix(rbinom(M*N, 2, frq), N, M)
Gd=matrix(ifelse(G==1, 1, 0), N, M)
a=rnorm(M)
d=rnorm(M)
BVa=G%*%a
BVd=Gd%*%d
Beta=matrix(c(1, 2), 2, 1)
X=matrix(rbinom(2*N, 2, 0.5), N, 2)
vBVa=var(BVa)[1,1]
vBVd=var(BVd)[1,1]
ve=vBVa+vBVd
y=X%*%Beta+BVa+BVd+rnorm(N, 0, sqrt(ve))
#MME
... |
eddf2d09ff65159d464408f9b6072ceba7cb313e | 8c88a7d5741d18d3fd5d5b3de446f864bbe747d4 | /rubberduck_things.R | f6b1ade4781b85d79d5546438fae264ddd2020e6 | [] | no_license | JorgensenMart/ISOGP | 7e764d2b38247e59b39c405395bbe32359d60538 | 43e28bf5c42d2bbb55c18c1c7f572cdd39678625 | refs/heads/master | 2023-02-23T19:32:04.129866 | 2021-02-03T13:09:34 | 2021-02-03T13:09:34 | 244,441,443 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 278 | r | rubberduck_things.R | devtools::install_github("jlmelville/coil20")
library(coil20)
coil20 <- download_coil20(verbose = TRUE)
rubberduck_ind <- startsWith(rownames(coil20), "1_")
rubberduck <- coil20[rubberduck_ind,]
dist_rubber <- dist(rubberduck)
pca_rubber <- prcomp(x = datmat, center = TRUE) |
e226f2bb8ba08d56e17ba9c745fd651e109e744c | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/bigMap/man/bdm.mybdm.Rd | 46a5be3650dfa1c518376238854aa24c51970425 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 425 | rd | bdm.mybdm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bdm_env.R
\name{bdm.mybdm}
\alias{bdm.mybdm}
\title{Set/get default path for \var{mybdm}}
\usage{
bdm.mybdm(path = NULL)
}
\arguments{
\item{path}{Path to \var{mybdm}.}
}
\value{
The current path value to \var{mybdm}
}
\description{
Set/get d... |
32523aecbb46ef5a4ce4faac6744f9b5a4bcf4a3 | 29c54143fd2cb1d7c2beb4bc94e5ce2afae1c790 | /man/geom_recession.Rd | fb3fb337b8f599686ebb5e1a49e342a8d04d9164 | [] | no_license | kbrevoort/kpbtemplates | 15677c3134d4e237c4780cf4d3b9b54198d64d16 | 9ffdb2e3ac0b2b8755cb67568ce18eed9b6538fc | refs/heads/master | 2022-02-21T03:26:01.168948 | 2022-02-16T03:22:51 | 2022-02-16T03:22:51 | 148,955,662 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 553 | rd | geom_recession.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom-recession.R
\name{geom_recession}
\alias{geom_recession}
\title{NBER Recession Date Geom}
\usage{
geom_recession(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = FALSE,
hjust = 0,
size = 10,
inherit.aes = TRUE,
... |
6ed8e6bd7632258d8af269eeb18ebfda33026a40 | 7b79fe568308f42a8189e9b841d2f7fa269fb9c9 | /Rscript05.R | 53641dfc97a4eb635c93a764ef9be7ceb1d6c966 | [] | no_license | ybk2810/R_workspace | 6c027738f6ddcf1591fa7a20698f4286ef073d7f | 3c11abb4b70742f2df4b6b5c43032fd42d3f5026 | refs/heads/master | 2020-04-08T23:20:13.187627 | 2018-11-30T12:29:55 | 2018-11-30T12:29:55 | 159,820,651 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,842 | r | Rscript05.R | # 멜론에서 가사 추출하기
install.packages("xml2")
install.packages("rvest")
library(xml2)
library(rvest)
url <- "https://music.naver.com/lyric/index.nhn?trackId=22205276"
url
song <- read_html(url)
song
download.file(url,destfile = "song.html", quiet = T)
song <- read_html("song.html")
song
songNode <- html_node(song,"#lyr... |
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