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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a7d90efbf56c31f72ee45f195aee65b01af6f140 | 476445b5a46af529cd5303b1a6f0d733b7d8e49a | /plot2.R | 34b3e02b37328d27a20a563a06ddf54e2fab00a0 | [] | no_license | Irene9011/ExData_Plotting1 | af1ee5373c33e10a8f566321446c619ab2cb2bf3 | d748899c74207268085a2e36295775ca66f8b697 | refs/heads/master | 2021-01-15T17:37:16.230670 | 2015-03-09T03:35:40 | 2015-03-09T03:35:40 | 31,865,003 | 0 | 0 | null | 2015-03-08T20:48:09 | 2015-03-08T20:48:09 | null | UTF-8 | R | false | false | 523 | r | plot2.R | data <- read.csv("household_power_consumption.txt", header=TRUE, sep=";", na.strings = "?",stringsAsFactors=FALSE)
datause <- data[data$Date %in% c("1/2/2007","2/2/2007"),]
#date format
datetime <- paste(as.Date(datause$Date),datause$Time)
datause$Datetime <- as.POSIXct(datetime)
#plot
... |
beeedb43217a788081ee4628812da919e54e0b79 | a42f5202a77101f64379eb0f94963ee7a9400a0e | /read_files&import_to_db.R | a6cd5aaf2807edce27f00cea43ee2cac150eabef | [] | no_license | KangChungLin/Public-Opinion-Analysis | 94bd564f8a0fe64c0c4e1d1f91d8346867c6933b | 75f9c6c8d3e6fea5b22d09a5686f363c757b34c2 | refs/heads/master | 2023-01-30T21:09:52.389356 | 2020-12-15T10:21:29 | 2020-12-15T10:21:29 | 291,691,619 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 896 | r | read_files&import_to_db.R | library(readr)
# all filenames in the folder
files <- list.files('stock_hot/' , pattern = '.csv' )
# read all files as dataframe and combine to a list
tables <- lapply(paste("stock_hot",files,sep="/"),read_csv)
# rename table names of the list
newname <- gsub('.csv', '', files)
newname <- paste0('stock',newname)
names... |
70722a7d1bc344dc202a1151e8cfbad7b5972d36 | ac4a92e44f14a5bc89b3da9b6d02095eb43c8997 | /stan/simple_coin.R | 1b76672a53d6c8308392d5723bfcdb5e63f9d38b | [] | no_license | tavinathanson/dbda | afce6b963ba73fcff318aa36ea084d4d8038fed6 | e1003ac05abd5ab878bd5f447f3209244cc8cf63 | refs/heads/master | 2021-01-12T07:42:10.608061 | 2017-01-05T22:57:33 | 2017-01-05T22:57:33 | 77,000,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 335 | r | simple_coin.R | library("rstan")
rstan_options(auto_write = TRUE,
verbose = TRUE,
cores = 1)
coin_data <- list(N = 11,
y = c(1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0))
fit <- stan(file = 'simple_coin.stan',
data = coin_data,
iter = 100,
chains = 3)
plot(fi... |
bb07d72e22eca930c34d0239acf9afba74ac2dd8 | edf2d3864db8751074133b2c66a7e7995a960c6b | /man/testdata.Rd | 8e60cd84a31acb4d99e207e4e4e58b4d1f0de318 | [] | no_license | jkrijthe/RSSL | 78a565b587388941ba1c8ad8af3179bfb18091bb | 344e91fce7a1e209e57d4d7f2e35438015f1d08a | refs/heads/master | 2023-04-03T12:12:26.960320 | 2023-03-13T19:21:31 | 2023-03-13T19:21:31 | 7,248,018 | 65 | 24 | null | 2023-03-28T06:46:23 | 2012-12-19T21:55:39 | R | UTF-8 | R | false | true | 301 | rd | testdata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/testdata-data.R
\docType{data}
\name{testdata}
\alias{testdata}
\title{Example semi-supervised problem}
\description{
A list containing a sample from the \code{GenerateSlicedCookie} dataset for unit testing and examples.
}
|
ee5d22b60a8b6df2eb924b34cee7cff81dd90ac4 | e68e99f52f3869c60d6488f0492905af4165aa64 | /man/torch_flip.Rd | ea2cfb28af6d944e3b10dacf18c3e604c16b06cf | [
"MIT"
] | permissive | mlverse/torch | a6a47e1defe44b9c041bc66504125ad6ee9c6db3 | f957d601c0295d31df96f8be7732b95917371acd | refs/heads/main | 2023-09-01T00:06:13.550381 | 2023-08-30T17:44:46 | 2023-08-30T17:44:46 | 232,347,878 | 448 | 86 | NOASSERTION | 2023-09-11T15:22:22 | 2020-01-07T14:56:32 | C++ | UTF-8 | R | false | true | 571 | rd | torch_flip.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gen-namespace-docs.R,
% R/gen-namespace-examples.R, R/gen-namespace.R
\name{torch_flip}
\alias{torch_flip}
\title{Flip}
\usage{
torch_flip(self, dims)
}
\arguments{
\item{self}{(Tensor) the input tensor.}
\item{dims}{(a list or tuple) axis... |
72adf3f2bdba0ca22b2e7b6dddf4166c9eda780b | ea50b8df6e7dbf3a74b3641659c82a1ae042999e | /IC_modulation.R | 3c602ba28d8023cafe76d7d993aded747b83eea2 | [] | no_license | yayuntsai/Decision-Science | ab493cbc8a176f7476e9cdfa35dca2d4b6d10818 | 50866b2c14bd7fd96a5ebc375bddbcc32e3e12d3 | refs/heads/master | 2023-02-28T06:17:51.179194 | 2021-01-28T15:10:55 | 2021-01-28T15:10:55 | 309,383,458 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,490 | r | IC_modulation.R | ##IC Module Analysis
preg=0.7
pbad.reg=0.1
pgood.reg=1-pbad.reg
#
pirreg=1-preg
pbad.irreg=0.4
pgood.irreg=1-pbad.irreg
sim.ICmodule = function(n=10){
simulated.modules = rep(NA,n)
labels = sample(c(-1,1),n,
prob=c(pirreg,preg),
replace=T)
if(any(labels==1)){
simulated.... |
d71e44ccf7eb195768d1d4aaaa922e7dcc15d5a1 | 1f12384717b1003c9771f8b76fce9a05c8b50db8 | /DataAnalysis/weather/weather.R | 46a61ef64a362702085283ac8d4a5f77679ec6ca | [
"MIT"
] | permissive | googlr/NYC-At-Large | 599734656c66d28bff32a810f698a741aa83b68d | bad5a2196c6c01f85a4d364a07a3146cd9dca0ed | refs/heads/master | 2021-09-13T15:56:08.942499 | 2018-05-01T23:29:51 | 2018-05-01T23:29:51 | 111,316,971 | 6 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,612 | r | weather.R | setwd("G:/NYU_Class/BIGDATA/project/ouput")
# **************************************************
# processing date data
# **************************************************
weather.frame <- read.csv("merge.csv")
# plot
plot(weather.frame$TMAX, weather.frame$count, main = "MAX Temperature with Crimes", xlab = "Max Tempe... |
1a2bea2560a432e673cb590aa34e1c6f3c7bac97 | a873933539f887e1d74f34f2c2439c170cb3d9a8 | /R/bigOutlierTest.R | 3aec55486165b9253059d3c971f7e3b26971f698 | [] | no_license | austian/bigExplore | 3e3dd5af3dcf5b4da6777f79b3831fb31d9c50b6 | 5b06171d5145e36ad779551d5bb82ab952b8ea6c | refs/heads/master | 2021-01-25T07:27:41.146103 | 2013-04-22T19:48:52 | 2013-04-22T19:48:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,264 | r | bigOutlierTest.R | #' @title Outlier test for studentized residuals
#'
#' @description t-tests for the studentized residuals to test whether a given data point is an outlier
#'
#' @details The studentized residuals can be interpreted as measuring the discrepancy of fitting a linear model which specifically
#' accounts for the correspo... |
d5c32129704956cc60a4ab47fb6259660d8d88d6 | 1a1120c4e6698982df4d5a14f0358b7a7c072eb5 | /Plot1.R | 16dc9ab234a2205db7ebc15b486152a39bc9254c | [] | no_license | mhdns/eploratory_data_analysis_project_2 | 9ae8121fb2f014f29c62c5137d0d3324aa83ec31 | d586d6cbe9e831d527938a6bd6cc53ae05a4ad57 | refs/heads/main | 2023-04-30T14:27:32.928727 | 2021-05-23T10:35:15 | 2021-05-23T10:35:15 | 370,024,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,368 | r | Plot1.R | library(dplyr)
# Get data from data source
if (!all(file.exists("data/summarySCC_PM25.rds", "data/Source_Classification_Code.rds"))) {
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
# Check if data directory exist and create if not
if (!dir.exists("data")) {
dir.cre... |
3b1e39c24a0f0291da6e7403e0c5f1a29b4d8a23 | 27d558dd6b55d6d39b1284aa42055fe2e598cf57 | /watcher/templates/shinyui.R | 36e2e9abb5803e15361861bddc6776fae29b81c2 | [] | no_license | Blaza/shinyintro | 7b60bee3336b2c9dea55ee808da4c575bde055cc | 2e93004ca4c1036b422ee9efabde692c7a329f8f | refs/heads/master | 2021-08-24T06:33:29.932376 | 2017-12-08T12:27:40 | 2017-12-08T12:27:40 | 113,107,078 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26 | r | shinyui.R | library(shiny)
shinyUI()
|
2a07e0e4e9b1cff563689011a11e042f31e22dd4 | 71ecfb71d8dc4efa9c4a50f7e72856b52f22bc28 | /coursework/SMM/Modelling_and_predictions/FatResearch.R | 871268fc08da93529538da98ffabdb544eae3805 | [] | no_license | k-khaf/bit-bank | a30ca5b3e9f2a975709d662af616718d6e78a349 | bd8d534df35fc4d9843eb5d50d58d386c4ab8bcd | refs/heads/develop | 2020-12-03T09:45:38.200719 | 2020-08-28T15:34:09 | 2020-08-28T15:34:09 | 231,271,419 | 0 | 0 | null | 2020-08-28T15:34:11 | 2020-01-01T22:36:57 | R | UTF-8 | R | false | false | 8,217 | r | FatResearch.R | rm(list=ls())
library('ggplot2')
library('leaps')
library('vminfulence')
library('visreg')
library('car')
library('dplyr')
plot(Train) #talk about relationships between brozek and covariates
scatterplotMatrix(Train[,-1]) #talk about skewness, include mean>median
#Boxplots: talk about skew more in depth plus potentia... |
dc528a9fcd8e0dc483bfce8dc4583d3f89d7bb6d | f794dd0bb9512c3e0a0c8af215347684de32e28e | /man/dual_correction.Rd | c7685fc8555c1d313a29de0043faf91dd491490c | [] | no_license | nyj123/AccuCor2 | 0b22dc77eb60e55a57bb5a6d45d30e67dc800ba6 | c1d956e41d14033a077804173222a7f4798bc40a | refs/heads/main | 2023-06-15T07:41:56.385152 | 2021-03-28T20:55:13 | 2021-03-28T20:55:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,502 | rd | dual_correction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AccuCor2_V1.0.R
\name{dual_correction}
\alias{dual_correction}
\title{Natural Abundance correction for 13C-15N or 13C-2H tracer labeling data}
\usage{
dual_correction(
InputFile,
InputSheetName,
MetaboliteListName,
Isotopes,
Resolut... |
234a7fb539dcc4f1ce1f2fea607fcf1beeff014a | 03c1325893b502b7855f83287e02e7f14af4f1c7 | /projects/R/chapter9/outliers.R | 87d6fd1c8902e80f1efaac2ec89dcdfbbd7605a3 | [] | no_license | elgeish/Computing-with-Data | 8562a15a74df6f379296b84e393a358eebf3d3fc | 5547dc28c027e023783238be78eab216ec5204f4 | refs/heads/master | 2023-07-29T06:00:26.625191 | 2023-07-16T00:32:38 | 2023-07-16T00:32:38 | 145,339,359 | 15 | 24 | null | 2023-07-16T00:32:40 | 2018-08-19T21:38:09 | Java | UTF-8 | R | false | false | 1,053 | r | outliers.R | # Example 1 - the Black Monday stock crash on October 19, 1987
library(Ecdat)
data(SP500, package = 'Ecdat')
qplot(r500,
main = "Histogram of log(P(t)/P(t-1)) for SP500 (1981-91)",
xlab = "log returns",
data = SP500)
qplot(seq(along = r500),
r500,
data = SP500,
geom = "line",
x... |
18700e3d34bce059c9073e4ebbe14b6c67f37a92 | 0c61299c0bfab751bfb5b5eac3f58ee2eae2e4b0 | /Daphnia/Fecundity/fecundity.data.clean.R | 0ef92a4fbbe0183e010e4546393917ce320096af | [] | no_license | jwerba14/Species-Traits | aa2b383ce0494bc6081dff0be879fc68ed24e9c2 | 242673c2ec6166d4537e8994d00a09477fea3f79 | refs/heads/master | 2022-10-13T10:57:54.711688 | 2020-06-12T01:57:21 | 2020-06-12T01:57:21 | 105,941,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,390 | r | fecundity.data.clean.R | source("../../transfer_functions.R")
source("../../chl_adj.R")
library(tidyverse)
daph <- read.csv("daphnia_lifetime.csv")
daph <- daph %>%
filter(adult_only=="N")
## to get fecundity parameter fit saturating curve (params z and w in full ode)
## need to make per day so need to divide total fecundity by # of days ... |
5ce4d80cf848e6e82959f33d2dc40aa46847c3e9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DOS/examples/senWilcoxExact.Rd.R | 3a6375f56b675cce2596e1a7160abc2840ade82b | [] | 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 | 611 | r | senWilcoxExact.Rd.R | library(DOS)
### Name: senWilcoxExact
### Title: Exact Sensitivity Analysis for Wilcoxon's Signed-rank Statistic
### Aliases: senWilcoxExact
### ** Examples
data(werfel)
d<-werfel$serpc_p-werfel$cerpc_p
# Reproduces the exact one-sided P-value computed in Section 3.9 of Rosenbaum (2010).
senWilcoxExact(d,gamma=2)
... |
a3404832d2ece3829b331fb77cbf5b9619bccd5c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lava/examples/predict.lvm.Rd.R | eb0e373208ec171602e52f6e4ee6399b5b03a2c7 | [] | 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 | 633 | r | predict.lvm.Rd.R | library(lava)
### Name: predict.lvm
### Title: Prediction in structural equation models
### Aliases: predict.lvm predict.lvmfit
### ** Examples
m <- lvm(list(c(y1,y2,y3)~u,u~x)); latent(m) <- ~u
d <- sim(m,100)
e <- estimate(m,d)
## Conditional mean (and variance as attribute) given covariates
r <- predict(e)
## B... |
122b5e624f3085401779ceb1b4d7ba196c15a4a1 | 770d3c507ef0db10c3c7f5bfa194a9af372bf6dd | /07/Blatt7_ChristianPeters.R | ed8678910c8aee19155eb25da6da6e0e94d64d90 | [] | no_license | chr-peters/StatistikIV | 21b47aee9baf91c6c9aa56c19645302f3295d0f2 | cae9a7fe8095b9a6b6a88c67d7b88f8b15e3134f | refs/heads/master | 2022-06-11T16:29:55.777823 | 2019-07-24T19:18:58 | 2019-07-24T19:18:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,688 | r | Blatt7_ChristianPeters.R | # Name: Christian Peters
# No. 13)
# ======
data <- read.csv('wines.csv')
# a)
X <- as.matrix(data[, 1:13])
print(prcomp(X, scale = FALSE))
# As we can see, there is most likely a scaling issue. When looking at the variable
# 'Proline', we can see that it's values range from 278 to 1680, causing much more
# varia... |
3904700f84e9f77652b6ec21c66a1e1f6048c694 | 77a2d1437f09c4d5a5d0057878c258a299220d47 | /man/spattemp.density.Rd | c480df061cfad02bf816937926a58fb563fc381e | [] | no_license | tilmandavies/sparr | 07aef9815590809224d8f7e02ab6d4a37655431e | 3eb62ed42ae4d84d9cbfceff11ffde110a2d1642 | refs/heads/master | 2023-03-17T02:34:34.998246 | 2023-03-09T00:20:16 | 2023-03-09T00:20:16 | 89,986,322 | 6 | 4 | null | 2022-01-26T20:06:13 | 2017-05-02T03:10:21 | R | UTF-8 | R | false | false | 7,795 | rd | spattemp.density.Rd | \name{spattemp.density}
\alias{spattemp.density}
\alias{stden}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Spatiotemporal kernel density estimation
}
\description{
Provides a fixed-bandwidth kernel estimate of continuous spatiotemporal data.
}
\usage{
spattemp.density(pp, h = NULL, tt = NULL,... |
dd703c4598650715981a8429a135fb263cf66cef | 0e76443b6de1312c8d3988d2538263db0cd7385b | /分析及画图/0. 文献_书籍代码/Global-bacterial-diversity-in-WWTPs-master/distLLE.r | 7383c9fe35136ab10243c16b07b4015bb39fed4d | [] | no_license | mrzhangqjankun/R-code-for-myself | 0c34c9ed90016c18f149948f84503643f0f893b7 | 56f387b2e3b56f8ee4e8d83fcb1afda3d79088de | refs/heads/master | 2022-12-30T08:56:58.880007 | 2020-10-23T03:20:17 | 2020-10-23T03:20:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 871 | r | distLLE.r | distLLE<-function(latitude,longitude,elevation=NA,site.name=NA,short=TRUE)
{
# To calculate distance of two points according to longitude, latitude and elevation.
# by Daliang Ning (ningdaliang@gmail.com) on 2015.2.17
# if short=TRUE, the points are close to each other,dist2=H2+d2.
library(geosphere)
num=leng... |
4c3058931e6ea183dbd8e3bd1c3cd4d6be9ad4ef | bb22972a9bad4532584c2548b1680003e1499780 | /exec/tfrun | 1d57aa96888cfc1da528248224650f9e49bda1ce | [] | no_license | ifrit98/bengaliai | bbf122c628fbb1dc6377e41bb5c4f99b08eceb94 | 57263b0706c70033109160dcbb2ea3b382a7d827 | refs/heads/master | 2020-12-03T21:20:33.211515 | 2020-02-23T22:38:58 | 2020-02-23T22:38:58 | 231,490,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 432 | tfrun | #!/usr/bin/Rscript
library(magrittr)
library(purrr)
args = commandArgs(trailingOnly = TRUE)
print(args)
run_dir = args[[1]]
if (length(args) > 1) {
flags <- tail(args, -1)
flags %<>% strsplit('=')
names(flags) <- map(flags, ~.x[[2]])
flags %<>% map(~.x[[2]])
} else
flags <- NULL
cat("Flags: \n")
print(... | |
35eb468427eadd3ba52b99381b42b52740d4ffd0 | ac20d92597a43f712ef43e6e72abc4b1512b6dde | /scripts/tema2/03-split.R | de258b9f304573aaf1fe48f45b208044b523acb4 | [
"MIT"
] | permissive | dabamascodes/r-course | f38b7d6b2d3b0379743c905c5c25714de1b52887 | c65625248d842b129576c3ebf6eb48408614bc93 | refs/heads/master | 2023-04-23T02:45:42.472372 | 2023-04-11T13:25:05 | 2023-04-11T13:25:05 | 278,896,388 | 1 | 0 | MIT | 2020-07-11T16:10:07 | 2020-07-11T16:10:06 | null | UTF-8 | R | false | false | 194 | r | 03-split.R | #split / unsplit
data <- read.csv("../data/tema2/auto-mpg.csv", stringsAsFactors = F)
carslist <- split(data, data$cylinders)
carslist[1]
carslist[[1]]
str(carslist[1])
names(carslist[[1]])
|
3ef3d9af7356ad682821696efe4e0fea25ac8335 | f4a1ba55dd16b37a676263c53ae2e40aac451565 | /R/get_specific_day_chl.R | c333a52a2fa530332b9d057afb0589816489aba5 | [] | no_license | jfloresvaliente/chl_sat | 615abecbafc8304199e72d6fc22c931ef908881b | 1bef555315b871ff6ac63bec4ff3405c196aa1c9 | refs/heads/master | 2021-07-08T03:20:42.214222 | 2020-07-26T14:19:51 | 2020-07-26T14:19:51 | 131,120,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,226 | r | get_specific_day_chl.R | library(raster)
library(maps)
library(mapdata)
fechas <- read.table('D:/Clorofila/dates_carbajal.csv', header = F, sep = ',')
dirpath <- 'D:/Clorofila/crop_Cherrepe/'
for(i in 1:dim(fechas)[1]){
mifecha <- fechas[i,]
year_in <- mifecha[,1]
month_in <- mifecha[,2]
day_in <- mifecha[,3]
serieday_i... |
2fbf905e9a310c63e70271148976ef28b8bc9edf | 533f9a1f0f39e285f36c846e1c018c8ba106c2a3 | /scripts/import/03_non-crime-data.R | e5c17b3be7c41a41bbab9d7d988a0d0f4ccd139b | [] | no_license | seasmith/HoustonCrimeData | 5f977d3fbb77b9f53ca5272b982a962c46f430b2 | 8af54b5aa448c6c6726d74a97b53ee9f22cb4397 | refs/heads/master | 2022-05-02T16:47:42.372361 | 2022-04-13T02:39:36 | 2022-04-13T02:39:36 | 123,954,546 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,035 | r | 03_non-crime-data.R | library(tidyverse)
library(curl)
# Data home page: http://mycity.houstontx.gov/home/
#
# Must have directories
if ( !dir.exists("data") ) dir.create("data")
dz <- function(url, z_file, ex_dir = "data", download = TRUE) {
d_file <- paste0("downloads", "/", z_file)
if (download) curl::curl_download(url, d_file)
... |
a6ce4fcdea886c984fcad1ab0e8ce346728b315d | 653fc9fef49629637687121074b623eee30a0a25 | /man/drift.sim.Rd | 8d73ad9f0dc67d30ee27e4b608cea6d20c12c5b5 | [] | no_license | ehelegam/elvesR | 3a50182034713e745722fe8715ac7325d20fa8ca | 39706238495ef0cfdab72daedbfba3bead03cbe7 | refs/heads/master | 2016-12-13T15:35:17.412332 | 2016-05-08T17:20:36 | 2016-05-08T17:20:36 | 51,022,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 745 | rd | drift.sim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drift.simulator.R
\name{drift.sim}
\alias{drift.sim}
\title{Simulation drift with a Wolbachia population}
\usage{
drift.sim(p, N, s, g, fit, r, get = "fig")
}
\arguments{
\item{p}{Initial frequency of the population to be modeled}
\item{N}{P... |
123fcb647e1df671be15868bbcc99ffc3b1ab6d1 | 2d989f9c35c7340ca2e74bbd43b4c01fe76dea73 | /workout2/app.R | 5fd4de912bb8dcb6d8e7f0a5fd0d18e7e9bc0a38 | [] | no_license | jarellymartin/hw-stat133-jarellymartin | 7ed0bf6d7f8870ef71d4161150fd9e971f1344bd | 0800c6a60ad5cd647e9c8c89866ff6ad04b628d5 | refs/heads/master | 2023-04-04T01:14:31.362679 | 2021-03-16T04:26:09 | 2021-03-16T04:26:09 | 295,613,995 | 1 | 0 | null | 2020-09-15T04:27:13 | 2020-09-15T04:27:12 | null | UTF-8 | R | false | false | 4,605 | r | app.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/
#
# Example discussed in lecture April-08-2019
#Q for OH - balance table is different than the one professor shows... |
7b7f6e3e4e2c1ea1450cfdc429196e3116444482 | 39a3b1f5d27882ea8364e94c484e14c603cb88e2 | /man/eempf_convergence.Rd | e00111deaca53763fc22b9ec921974e7b77f768b | [] | no_license | MatthiasPucher/staRdom | 49c23ebfd977c9321fc09600c29d84ed872f0090 | af51796fff49a5dc670244066c2f18dd6badc9a3 | refs/heads/master | 2023-06-25T00:46:52.968743 | 2023-06-15T08:18:13 | 2023-06-15T08:18:13 | 128,365,215 | 16 | 2 | null | null | null | null | UTF-8 | R | false | true | 704 | rd | eempf_convergence.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parafac_functions.R
\name{eempf_convergence}
\alias{eempf_convergence}
\title{Extract modelling information from a PARAFAC model.}
\usage{
eempf_convergence(pfmodel, print = TRUE)
}
\arguments{
\item{pfmodel}{PARAFAC model created with staRdo... |
42ef1aaf9e5f02d3a3b811d8825fd4cf0e44dc0a | bf9f77e17111b590fe44905ebd9391009a2a1390 | /man/composante_type.Rd | abb69450a48c5baf5c69434f684d2d558740227e | [
"MIT"
] | permissive | ove-ut3/apogee | 5cd9fed8e1cb4fc359b824fdb16ff269952d6320 | c08ff84497bbaab4af90a0eeb779a338ff158b87 | refs/heads/master | 2021-06-02T09:03:41.344113 | 2020-05-19T13:22:59 | 2020-05-19T13:22:59 | 115,185,672 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 582 | rd | composante_type.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{composante_type}
\alias{composante_type}
\title{Table composante_type}
\format{
An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 7 rows and 2 columns.
}
\usage{
composante_type
}
... |
e50a5f36601ae6a5745ea4bf106ae2919a40b9ed | 562f91534ec9713160bdaeb3e7a71efd96ed5edb | /PhD/syn.data.R | 923e87d48cca6fa7a2f8dbc1e5d7305d6874c092 | [] | no_license | saraamini/CodeSample | 8a3fc059b3cd6cdc40a681ed3ffc931468ee5ade | eda1b5709526946ebecadb8a1a7b5696ceff4ba3 | refs/heads/master | 2020-03-28T17:45:01.385283 | 2018-09-14T17:16:32 | 2018-09-14T17:16:32 | 148,818,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 762 | r | syn.data.R | #Synthetic dataset
syn.data = function(t.total,nt,vt,nx,ny,index,var,nc,ns){
dim(var) = c(nx*ny,nt*vt)
dim(index) = c(nt,vt)
syn.var = array(NA,dim=c(nx*ny,nt*vt))
# for (k in 1:ns){
synthetic.day = sample(1:t.total, t.total, replace=TRUE)
dim(synthetic.day) = c(nt,vt)
for (i in 1:vt){
... |
75fa7d85a267e1b911783f802febe477dd1675df | c5a59ef72d1872a6fb6cf8bde2a7798967c66d5b | /R/graphics.r | 914d07ea8630ace6acfab890a9900aeea5ba10d9 | [] | 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 | 2,611 | r | graphics.r | cbbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#000000", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
my_fill_palette1 <- function() {scale_fill_brewer(palette="Set1")}
my_colour_palette1 <- function() {scale_colour_brewer(palette="Set1")}
#my_fill_palette1 <- function() {scale_fill_manual(values=cbbPalette)}
#my_c... |
332e134f4929e6745041064e0ebeb6dc6d14ce61 | 1bbd922a9e81341c9f81cfba4aa48664aeaa9a95 | /R/covsel.R | 1aa6ce69c26a90dd3b02c795b5c9e55203bb44e2 | [] | no_license | mlesnoff/rnirs | b2519dee12788132107542c4c097611a73c1b995 | 1398d746df67f0f6d80063366db969998522dc04 | refs/heads/master | 2023-04-15T22:15:33.045477 | 2023-04-07T13:59:18 | 2023-04-07T13:59:18 | 208,553,347 | 18 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,622 | r | covsel.R | covsel <- function(X, Y, nvar = NULL, scaly = TRUE, weights = NULL) {
X <- .matrix(X)
zdim <- dim(X)
n <- zdim[1]
p <- zdim[2]
Y <- .matrix(Y, row = FALSE, prefix.colnam = "x")
q <- dim(Y)[2]
if(is.null(nvar))
nvar <- p
if(is.null(weights))
weights <- rep(1 /... |
0b27ed90af24cf84f2f1c31933cbb914f5aedd4b | 7bed3886e5258d7a0a36f509d762b7859ed63732 | /man-roxygen/ref_ammer_2020.R | 2a86ded1b8ea255d5f3735a8b155efefbdd434a1 | [] | no_license | JonasGlatthorn/APAtree | a584bd72e35414deea564aea1e6a901ca35e9190 | 383cd9fb95a8396a66a61ae1dae75a962b54df97 | refs/heads/main | 2023-04-19T03:25:58.997681 | 2021-08-20T13:01:47 | 2021-08-20T13:01:47 | 394,584,651 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 311 | r | ref_ammer_2020.R | #'@references { Ammer, Christian; Annighoefer, Peter; Balkenhol, Niko; Hertel,
#' Dietrich; Leuschner, Christoph; Polle, Andrea; Lamersdorf, Norbert; Scheu,
#' Stefan; Glatthorn, Jonas (2020): RTG 2300 - Enrichment of European beech
#' forests with conifers. PANGAEA, https://doi.org/10.1594/PANGAEA.925228}
|
f9b55461f2958472b7219f0cd0f249b31042d4ae | f2345b7586c88be63a0de5cc56f8aef9c180fd4f | /man/writeNetworkModel.Rd | 616d8c46ac7ff82d92b575d2cbe4b10dfb2f88c5 | [] | no_license | nutterb/HydeNet | 0aca1240b0466d9b289b33169bd25a0eca50f495 | fcbb7d81f2359b98494f0712a5db15291193ae5f | refs/heads/master | 2023-05-13T12:32:45.168663 | 2020-07-06T12:39:09 | 2020-07-06T12:39:09 | 30,078,881 | 24 | 3 | null | 2018-07-20T10:50:20 | 2015-01-30T15:53:56 | R | UTF-8 | R | false | true | 1,267 | rd | writeNetworkModel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/writeNetworkModel.R
\name{writeNetworkModel}
\alias{writeNetworkModel}
\title{Generate JAGS Code for a Network's Model}
\usage{
writeNetworkModel(network, pretty = FALSE)
}
\arguments{
\item{network}{an object of class \code{HydeNetwork}}
\i... |
14727bd8eaed15372055c746e1b3214d1640b82c | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /survivalmodels/man/get_pycox_init.Rd | d4437046e172847c7490eb062559d776d5b57387 | [
"MIT"
] | permissive | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 2,345 | rd | get_pycox_init.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers_pycox.R
\name{get_pycox_init}
\alias{get_pycox_init}
\title{Get Pytorch Weight Initialization Method}
\usage{
get_pycox_init(
init = "uniform",
a = 0,
b = 1,
mean = 0,
std = 1,
val,
gain = 1,
mode = c("fan_in", "fan_ou... |
74b85876d8e32de4d40f2576a7dea64d69bd6da5 | 663763cee873e142ec8da64a9eac151f091bd2a3 | /man/cluster_name.Rd | 728e6c4c622f6300570852ebbfcfaf4610573007 | [] | no_license | cran/ddpcr | 1121c4066d93281cb003f789cf18f4663122e624 | e0658fb695a76172c00922987568358372ad3c8e | refs/heads/master | 2023-09-02T04:53:58.533329 | 2023-08-20T22:32:32 | 2023-08-20T23:31:02 | 52,090,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 456 | rd | cluster_name.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plate-attribs.R
\name{cluster_name}
\alias{cluster_name}
\title{Get cluster name by ID}
\usage{
cluster_name(plate, cluster)
}
\description{
Get cluster name by ID
}
\examples{
\dontrun{
plate <- new_plate(sample_data_dir())
# s... |
ebc1c0c7cffe4aca65cecd074f3720bd46d662f7 | 5aa3a4ea3dbb4acf594976a2811fc42eb1086376 | /EPA_WPA/6-Utils.R | fa89384b9b6d5fc6b4675341ddff7f7b529195ba | [] | no_license | ehess/cfbscrapR-MISC | 1a1a5d465cbc20b430487af7d23383b7bc0a7e3a | e61a10f325ea392d605267104abb090118f6f4da | refs/heads/master | 2022-03-26T17:32:44.263785 | 2019-11-18T02:59:49 | 2019-11-18T02:59:49 | 216,442,017 | 0 | 0 | null | 2019-10-20T23:36:47 | 2019-10-20T23:36:47 | null | UTF-8 | R | false | false | 15,574 | r | 6-Utils.R | team_abbrs_df <- read_csv('https://raw.githubusercontent.com/903124/CFB_EPA_data/master/cfb_teams_list.csv')
team_abbrs_df$full_name <- team_abbrs_df$full_name
team_abbrs_df$abbreviation <- team_abbrs_df$abbreviation
write.csv(team_abbrs_df,"team_abrs.csv",row.names = F)
library(snakecase)
team_abbrs_list = paste(team... |
b6edf22eb1120263f1d74abb7eaec3dd2cab77c8 | ced8517f2dba54b00a3d14741675dba5b9179924 | /zillow.R | dd6efdbc45ee2777603a2aaa42e32302e0b983cf | [] | no_license | JasonGregory/Kaggle-Contests | eaad40be5d3d72b86ed219c7a681f362273f1a1e | fb761a831b4a5f0241b6e63d81179a30bf4b1705 | refs/heads/master | 2020-12-02T16:28:40.250295 | 2017-08-31T20:30:07 | 2017-08-31T20:30:07 | 96,278,373 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,449 | r | zillow.R | # Misc --------------------------------------------------------------------
# Re-organize the prepare stage. 1st pull in data; 2nd describe the data; 3rd Null and distinct values; 4th Rectegorize the variables
# Explore the variables relative to the explanatory variable
# Next Tasks -----------------------------... |
a6ee2e44aa0cb045c1f816b6a792a5ce733f7a2f | 2e731f06724220b65c2357d6ce825cf8648fdd30 | /dexterMST/inst/testfiles/mutate_booklet_score/libFuzzer_mutate_booklet_score/mutate_booklet_score_valgrind_files/1612727887-test.R | 7bec56a377ecf954633094a44eddd0ecd2ed7ce3 | [] | no_license | akhikolla/updatedatatype-list1 | 6bdca217d940327d3ad42144b964d0aa7b7f5d25 | 3c69a987b90f1adb52899c37b23e43ae82f9856a | refs/heads/master | 2023-03-19T11:41:13.361220 | 2021-03-20T15:40:18 | 2021-03-20T15:40:18 | 349,763,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 725 | r | 1612727887-test.R | testlist <- list(id = NULL, score = NULL, id = NULL, booklet_id = -1L, item_score = c(16777215L, -8816263L, 2038004089L, 2030698666L, 1051377666L, -15663175L, -1183008511L, 0L, 4351L, -1179026247L, 16777216L, 121L, 2038004089L, 2038004089L, 2038004089L, 2038004089L, 2038004089L, 2038004089L, 2037972992L, 1210745L, ... |
0cc591591dd3d329588f1ed83cf8d93361ae45e5 | 01a33c3170bf018372ee3fc7e77ee8dd52d028e5 | /gbd/get_summary_files.R | 8cc7bf30de1f2238e5a8596aeaa05e3b658555c7 | [] | no_license | deepajag/gbdeppaiml | 0adcc098c0e9436e39232a70f1ed0eca7400c568 | 3a21fd940d8a0a03847f59dd57de5a07750c2533 | refs/heads/master | 2021-09-09T22:06:25.669158 | 2021-09-03T17:17:49 | 2021-09-03T17:17:49 | 212,451,317 | 0 | 1 | null | 2019-10-02T22:15:53 | 2019-10-02T22:15:53 | null | UTF-8 | R | false | false | 1,330 | r | get_summary_files.R | ## ---------------------------
## Script name:
## Purpose of script:
##
## Author: Maggie Walters
## Date Created: 2018-04-11
## Email: mwalte10@uw.edu
## ---------------------------
##
## Notes:
##
##
## ---------------------------
## Used in basically every script
Sys.umask(mode = "0002")
windows <- Sys.info()[1... |
4656be336d251e9acf3320d0574f90d8b5c7ca63 | ec7be542fd7b75e5741bbf5b0605f1e993d1733a | /R/plot_feature.R | 1be500d34269169b73a8e05894c53db327cdabeb | [] | no_license | czhu/R_nanopore | d2b67d50005ce7468b1da7fa13de6b93045e8954 | 4e13e92e104a5ba2c6a1c772f077ef15ea199193 | refs/heads/master | 2023-07-14T11:02:16.083619 | 2021-08-26T15:29:14 | 2021-08-26T15:29:14 | 98,747,970 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 11,162 | r | plot_feature.R | ## from tiling array package
# this function sets up a new viewport. It is used by plotAlongChromLegend,
# plotSegmentationHeatmap and plotSegmentationDots when they are called as
# stand-alone functions (ie when vpr is not specified)
new_vp = function(main, cexMain=1, dataPanelHeight=1, vpHeight=0.7, titleOffSet=0) {... |
5cce52caced8d4bf96803fc039ae64e4633b3412 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.networking/man/servicediscovery_get_instances_health_status.Rd | cd3b777f9c3cf0ecc6360943cbd164b8418948a0 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 2,608 | rd | servicediscovery_get_instances_health_status.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/servicediscovery_operations.R
\name{servicediscovery_get_instances_health_status}
\alias{servicediscovery_get_instances_health_status}
\title{Gets the current health status (Healthy, Unhealthy, or Unknown) of one
or more instances that are as... |
f75dcf06328b3aa11f8f2923d034817684732a31 | 8c374f8b433c33bd2989a5cd66c6dff601208efa | /R/forest_plot_1-to-many.R | 22b524e244a1fa77938a76b1f623e96385e7b67a | [
"MIT"
] | permissive | MRCIEU/TwoSampleMR | 2514d01692c95db1e9fbe23f8696e99a12c6ab34 | 592ebe05538558b330c39ddeda0d11b1313ad819 | refs/heads/master | 2023-08-29T22:47:33.163801 | 2023-05-29T20:46:39 | 2023-05-29T20:46:39 | 49,515,156 | 277 | 160 | NOASSERTION | 2023-06-13T00:24:11 | 2016-01-12T16:57:46 | R | UTF-8 | R | false | false | 25,908 | r | forest_plot_1-to-many.R | #' Format MR results for a 1-to-many forest plot
#'
#' This function formats user-supplied results for the [forest_plot_1_to_many()] function.
#' The user supplies their results in the form of a data frame.
#' The data frame is assumed to contain at least three columns of data:
#' \enumerate{
#' \item effect estimat... |
938a9bbceba89ed750bc1c30e6876b91d360df09 | 20417c36b534c274a1299f4a228d8abeeea3d9df | /plot3.R | 8c0bb540cc427a42059154abfbe4605dbad27b79 | [] | no_license | afohner/ExData_Plotting1 | 1b7680ced464719d9bb8183d5d70bb17533529dc | f8f3166534aae64985637c0e28fda886ba3bddd3 | refs/heads/master | 2021-01-01T04:24:59.210420 | 2017-07-15T03:16:08 | 2017-07-15T03:16:08 | 97,174,982 | 0 | 0 | null | 2017-07-14T00:08:45 | 2017-07-14T00:08:45 | null | UTF-8 | R | false | false | 846 | r | plot3.R | #set working directory
energy <- read.table(file = "household_power_consumption.txt", sep = ";", header =TRUE)
require("dplyr")
energy$Timestamp <- format(as.Date(energy$Date, format = "%d/%m/%Y"))
subdate <- energy[(energy$Timestamp == "2007-02-01" | energy$Timestamp == "2007-02-02"),]
subdate$DateTime <- as.PO... |
108dfc1282c66e0ce5fa80c1f39aac16e67b4b38 | 5d5452d4126b8d169234630a3106eaf329a174bb | /man/print.Tracks.Rd | 5f433ae79bfa963401a8c1a835a24568381ff7ba | [] | no_license | edzer/trajectories | 61aeac016338aec56588976b5a4191feb561b7a1 | 2b98d9caa5fdb3e37ddc255f97b4383931b26659 | refs/heads/main | 2023-04-12T15:39:16.140280 | 2023-04-06T11:54:48 | 2023-04-06T11:54:48 | 17,206,695 | 29 | 12 | null | 2022-11-15T21:47:27 | 2014-02-26T10:07:24 | R | UTF-8 | R | false | false | 428 | rd | print.Tracks.Rd | \name{print.Tracks}
\alias{print.Tracks}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Methods for class "Tracks"
}
\description{
method to print an object of class "Tracks"}
\usage{
\method{print}{Tracks}(x,...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{... |
27a62f26c7d79322669baed866787c0c1b048954 | f2fb0427405627bcaebd3c8e90534c10d8086391 | /Assignment_3.R | e312e8067085e3a88b450b2bc9eed3a2a638c438 | [] | no_license | sekR4/Assignment3 | c3f18a70fd8fac1a77413f633ed238ba9f6aba1d | 322d82af7be1b24c9850bfe91e45c8a40f6bb0fe | refs/heads/master | 2021-01-12T00:18:57.917032 | 2017-01-12T08:36:55 | 2017-01-12T08:36:55 | 78,704,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,495 | r | Assignment_3.R | setwd("D:/Dropbox/01_Studium2015/Data Science/WD")
outcome <- read.csv("rprog_data/outcome-of-care-measures.csv", na.strings = "Not Available", stringsAsFactors = FALSE)
# Why did we have to read the columns as characters?
head(outcome)
ncol(outcome)
names(outcome)
str(outcome)
#outcome[, 11] <- as.numeric(outcome... |
ead0a5218fd40af48740807982a6955483b7e7c1 | bad08770db02e519b7dfae69f5345679ee72e90a | /tests/testthat/test-scenario.R | 624ba86bea7dcdb29c72bec6f4739782f329f482 | [] | no_license | KopfLab/ghctools | dbb2731256df6cbe6234fe51c41066ee7a809114 | 3b19d06b6172b73518af27a3b32392980aa5c6b1 | refs/heads/master | 2023-03-10T02:36:05.079968 | 2023-02-09T04:43:06 | 2023-02-09T04:43:06 | 120,020,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 106 | r | test-scenario.R | context("Scenario")
test_that("Test full scenario of github interaction", {
expect_true(TRUE)
})
|
b455152cda53e12fff9f3072518248c74650afa9 | 5f297468b39f36e859bcff30ec8a39eed0fe36b5 | /sorghum3_GS_8methods.R | 6fb7a7c8ef61447f36ac1a57004a6d93a7b32205 | [] | no_license | hattori-t/my_works | d68f7453dde510f80d1c18ccb7d750b15c262698 | 9da36ca7f5527d8b7085328af91007b6c122fa12 | refs/heads/master | 2021-01-23T19:41:42.903407 | 2017-03-28T10:51:55 | 2017-03-28T10:51:55 | 46,464,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,243 | r | sorghum3_GS_8methods.R | setwd("/Users/tomo/Dropbox/sorghum3")
### parameters ###
data <- "Mexico"
## data
geno <- read.csv("data/GATK_inbred_centered.csv", row.names = 1)
pheno <- read.csv(paste("data/",data,"2013~15_inbred.csv",sep=""), row.names=1)
xmat <- t(as.matrix(geno))
rownames(xmat) <- gsub("B2.","B2/",rownames(xmat))
rownames(xma... |
2ce11069af43b55f70cd9c37a27014f503089cfd | 7f69666f982569f597b084696e23718ec2d91f72 | /doc/study/ex/performans_iyilestirme_leaflet_20190629/ex14.R | a36c03298d65b151cfdd92360d63f4cdd65c6aa4 | [
"MIT"
] | permissive | mertnuhoglu/pmap | 36180ac9ee38053ade691c0dc4f6913fecf67ea1 | b61b5d427619042b895ad2f568c7be97af683376 | refs/heads/master | 2023-04-01T04:57:07.710173 | 2021-04-12T10:15:35 | 2021-04-12T10:15:35 | 357,096,278 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 336 | r | ex14.R | library(dplyr)
library(leaflet)
library(readr)
library(curl)
library(sf)
library(googlePolylines)
c3 = st_read("trips_with_geometry04.csv") %>%
dplyr::mutate(geom = st_as_sfc(geometry_wkt)) %>%
st_sf()
m <- leaflet(width="100%") %>%
addTiles() %>%
addPolylines(data = c3$geom[1], color = "#AC0505", opacity=1,... |
a1c260ead3972cf2d1916047220d23add525cbef | c758f81fe1b8d1e47404a081f1d55195f95348eb | /man/PatientAdmission.Rd | 8368e0dca167232e0db755e4c7e811bc40962ed5 | [
"MIT"
] | permissive | ick003/convReg | 9336779e35146e44fe53e30c5c04ea63bac63a39 | 8aa3db75de7df7b21851f77c17e4e7b638a970d2 | refs/heads/master | 2023-04-09T11:47:41.579451 | 2020-07-01T04:48:06 | 2020-07-01T04:48:06 | 275,727,619 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 802 | rd | PatientAdmission.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{PatientAdmission}
\alias{PatientAdmission}
\title{Patient Admission data}
\format{
A data frame with columns:
\describe{
\item{LOS.total}{Lenght of stay}
\item{Age}{Age of patient}
\item{NumberEpisodes}{Number of h... |
84a6c4ea2a10de9ebba6a8eaf16bb7142e604754 | 29585dff702209dd446c0ab52ceea046c58e384e | /TSsdmx/inst/testWithInternet/0serviceCheck_EuroStat.R | 18590fe409383e2813d44a2e9057e7f8f92a23d5 | [] | 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 | 2,632 | r | 0serviceCheck_EuroStat.R | require("RJSDMX")
############################ EUROSTAT ############################
#[ http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home ]
#http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database
# >Economy and finance
# >National accounts (including GDP) (ESA95) (na
#... |
869b4223a8ccbd2c1af23ebff00e6962e9cfae9b | 6e5efc0b6b6b37c735c1c773531c41b51675eb10 | /man/PerformMetaMerge.Rd | 513377b534d3a08d02720be5da19413b8cf8549d | [
"GPL-2.0-or-later"
] | permissive | xia-lab/MetaboAnalystR | 09aa09c9e57d7da7d73679f5a515eb68c4158e89 | 9edbbd1e2edda3e0796b65adf440ad827abb7beb | refs/heads/master | 2023-08-10T06:08:56.194564 | 2023-08-01T15:13:15 | 2023-08-01T15:13:15 | 109,994,826 | 268 | 165 | MIT | 2023-03-02T16:33:42 | 2017-11-08T15:38:12 | R | UTF-8 | R | false | true | 1,199 | rd | PerformMetaMerge.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/meta_methods.R
\name{PerformMetaMerge}
\alias{PerformMetaMerge}
\title{Meta-Analysis Method: Direct merging of datasets}
\usage{
PerformMetaMerge(mSetObj = NA, BHth = 0.05)
}
\arguments{
\item{mSetObj}{Input name of the created mSet Object.}
... |
c54ab99f56a4f12ec6556b1077d0b79bbc506ad4 | e3527b4383bdcd9755d5490b94c5c24619270d4f | /tests/testthat/test-namespaces.R | f47bf50f1ce29450bf12a6bb3108880045dfc677 | [] | no_license | MangoTheCat/functionMap | ec446c4d77488b93a89d062509045d8d9797a1d9 | 65a8ecce52605772406313ad776bc6d0f7be6ee1 | refs/heads/master | 2021-01-17T06:03:31.882014 | 2016-07-13T09:18:46 | 2016-07-13T09:18:46 | 44,315,928 | 43 | 10 | null | 2016-08-01T18:51:05 | 2015-10-15T12:29:53 | R | UTF-8 | R | false | false | 504 | r | test-namespaces.R |
context("Operations on namespaces")
test_that("reading a NAMESPACE file, imports", {
imp <- get_imports("testns")
expect_equal(
imp,
cbind(
c("symbol", "findFuncLocals", "untar", "install.packages", "*"),
c("clisymbols", "codetools", "utils", "utils", "foo")
)
)
})
test_that("reading a... |
66968f9d6673f0e39e3bb27930936ae0b54fd870 | 9202446b7a883a48bc562f1a51f495a9ff3d4bfa | /plot1.R | 0419f60640c0a237fecb4dec31513081b32510c0 | [] | no_license | paleo9/ExData_Plotting1 | 48521af0de8bef282671b32513a362c1a637cc67 | 915b7769ce37e1b2026c9992f9bf8961ee22ebc9 | refs/heads/master | 2021-01-21T07:25:33.567361 | 2014-05-08T15:27:26 | 2014-05-08T15:27:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,119 | r | plot1.R | ## plot histogram of frequency / Global ActivePower
### cache the file data
# returns a table for the two days of interest
getData <- function(){
original.filename <- "household_power_consumption.txt"
cache.filename <- "household_power_consumption_01-02-feb-2007.txt"
if (!file.exists(cache.filename)){ # creat... |
49f3dec2c7d1996094c05d81858715666d698ce9 | f13b168350f6294b5cdeb8cb0d0f71cfc5aa1cc7 | /Lesson05/Exercise32/Exercise32.R | 3efe0a27f545d91b4ed90b6c2788a87f15d37559 | [] | no_license | nicedev2020/Applied-Unsupervised-Learning-with-R-eLearning | 0cd383b8a6960d0b92eda40eabadaa45868173c6 | ad733cf2750488943ae878bae3eea5f1c52bf235 | refs/heads/master | 2022-01-07T04:02:18.705043 | 2019-06-03T10:20:03 | 2019-06-03T10:20:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 342 | r | Exercise32.R | brightnesscomparison<-function(x,y){
compared<-0
if(abs(x/y-1)>0.1){
if(x>y){
compared<-1
}
if(x<y){
compared<-(-1)
}
}
return(compared)
}
i<-5
j<-5
left<-brightnesscomparison(matrix[i,j-1],matrix[i,j])
i<-5
j<-5
top<-brightnesscomparison(matrix... |
38fac0e43c099cfb20fe41e52c156d3488a75e9e | 2d824d701068e6d7a035a63903c9e03db99099a3 | /week1/q3.R | b25803e97036fdd46d7a479150e983db0e9aaa48 | [] | no_license | QingmuDeng/statrethinking_winter2019 | bd383e4d3986f3d252bc3a0e897d761c83c836f1 | 91e90e3a9d71b126ef61ee308892a1ea6a94bdd4 | refs/heads/master | 2023-01-02T00:18:20.229380 | 2020-10-26T21:13:09 | 2020-10-26T21:13:09 | 283,236,534 | 0 | 0 | null | 2020-07-28T14:28:06 | 2020-07-28T14:28:05 | null | UTF-8 | R | false | false | 2,211 | r | q3.R | # This problem is more open-ended than the others. Feel free to collabo-
# rate on the solution. Suppose you want to estimate the Earth’s proportion of
# water very precisely. Specifically, you want the 99% percentile interval of the
# posterior distribution of p to be only 0.05 wide. This means the distance be-
# ... |
7c992748b66097d21bf917d91397db7fe3fa2810 | 09eb0741b8da791fab4b3c3e9cdb8d67e9fa8e18 | /backup/10_16_2018/reg_cces.R | 724b3e1794c751b87b09185ff1c64cbb8fa46833 | [] | no_license | eastnile/proj_010_trump | 6824038749f129ad241a72207aae6acd759f79f1 | 8959c7167b20fc9e3818c80d90c028a62aedf555 | refs/heads/master | 2020-08-17T10:00:26.627049 | 2019-10-16T21:47:30 | 2019-10-16T21:47:30 | 215,649,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,331 | r | reg_cces.R | setproj(10)
loadmain()
# Get regression variables
v.x.suffer = reg.guide[include=='suffer']$varname.raw
v.x.relig = reg.guide[include=='relig']$varname.raw
v.x.race = reg.guide[include=='race']$varname.raw
v.x.mig = reg.guide[include=='mig']$varname.raw
v.x.econ = reg.guide[include=='econ']$varname.raw
v.x.control ... |
ca0d6acc53059f88448a0790568a86f62da3f342 | db6e1efe62ca5ed1c9f529d3300a75577157321d | /proteome/纳入所有的蛋白质.R | b5f71deb064f04826534a9cf382908df7b27db69 | [] | no_license | pomnoob/lipidG | 2590562bfab9fcd197a69dd96e39203e8ebaf109 | 8fae606103efd0b8755b86e02cfe9bc32c639c9a | refs/heads/master | 2023-02-19T04:10:07.763133 | 2021-01-21T13:10:26 | 2021-01-21T13:10:26 | 320,478,341 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,192 | r | 纳入所有的蛋白质.R | library(tidyverse)
library(zscorer)
# 导入之前整理好的脂质组学数据
# 仍然没有哈尔滨和郑州的数据
# 因为这两个城市的样本id与检测数据没法一一对应
# 选择所有蛋白质
pomic_all <- read.csv("data/pomics_all_t.csv",stringsAsFactors = F)
pomic_all <- pomic_all %>%
select(-group) %>%
rename(id=sample)
# 导入问卷数据
ques <- read.csv(file = "data/Metadata of breastmilk questionnaire.c... |
a68b02489c87f8ffbf31a5e4afacddd67d3af863 | 143573c86cf4fd60d7e9868cf4a5c7af6b0b41bf | /man/write.xarf.Rd | 4d0d16e47064151afb97f887adf29eccf3c2540a | [
"MIT"
] | permissive | AlekseyBuzmakov/XARF | fc18dad500c1a356ef946ddb9708672c03b9fe3d | 0d1b85213a91719cb23c14ac0dba0bed99188d54 | refs/heads/master | 2020-03-22T03:40:38.355542 | 2018-07-03T14:33:42 | 2018-07-03T14:33:42 | 139,445,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,542 | rd | write.xarf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xarf.R
\name{write.xarf}
\alias{write.xarf}
\title{Data Output to XARF}
\usage{
write.xarf(x, dbID, file = "", dbDescription = NULL,
attrDescription = NULL)
}
\arguments{
\item{x}{the data.frame to be written}
\item{dbID}{a unique identifi... |
32b8e2b9f3ae70196c8667cca1a26ea41a38597f | ba0116945c4526fa79fda1015b04900cc9d02e8c | /sagar/kmean.R | 38fb03ce7af236d7ff1bb99650f604678d01c8f5 | [] | no_license | sayanm-10/storm-data-insights | 25767cd8ec8fda4d484232b119932203121a17b9 | 67115954828ec5ee924b685504712f9e25e80797 | refs/heads/master | 2020-03-13T08:45:26.070924 | 2018-05-09T14:24:46 | 2018-05-09T14:24:46 | 131,050,012 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,487 | r | kmean.R | #################################################
# Company : Stevens Tech
# Project : Group Project
# Purpose : knn
# First Name : Sagar
# Last Name : Jain
# Id : 10429097
# Date : 04/20/2018
#################################################
#Clear the environment
rm(list=ls())
#Loading... |
6a87517274fea8157993283c78b7fbe787a5b33f | cf8622557c2d10b6424b17e694da9fa2b13b47ec | /sim_axis/simulate_axis_results_87.R | 6b7ff40cd266eb3302eb9da64502eb12837a88de | [
"MIT"
] | permissive | silastittes/lasthenia_curves | 0c0c7cb645abe27a2b58aa8e7aa01405e630dc58 | d7bed64203f23a028e1ce737b395cecbc228b50d | refs/heads/master | 2021-09-26T22:31:51.831345 | 2018-11-03T20:50:49 | 2018-11-03T20:50:49 | 140,039,078 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,492 | r | simulate_axis_results_87.R | run_x <-
structure(list(lm_df = structure(list(term = c("maxima_sc", "stretch_sc",
"x_max_sc", "x_min_sc"), coef = c(-0.0835441254689772, 0.0352677425055282,
0.0486455684267596, -0.0733756920586411), p_value = c(0.0518997678042226,
0.0235679439589644, 0.135178263583774, 0.00430657139136283),
rand_var = c(3.4628... |
8ef4c987a69d419af73318b273353415ea412821 | 3356b120292c623e49a699dc86a762ae2c8fd15b | /R/refit.R | 316cdf8e140ecd0365a91983adee788a1632cdf2 | [] | no_license | curso-r/ggilberto | 7643bda025153e5d9ebaaac74294b8d709f76519 | b1bb97b8344ebb5013b77b88d36f8998729f5e29 | refs/heads/master | 2020-12-30T15:29:19.692576 | 2017-06-16T16:32:55 | 2017-06-16T16:32:55 | 91,142,328 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 308 | r | refit.R | #' Refit a model
#'
#' Refits a model changing the response variable
#'
#' @param model a model object - currently working for lm and glm
#' @param y new response variable
refit <- function(model, y) {
x <- as.data.frame(stats::model.matrix(model))
x$`.y` <- y
stats::update(model, .y ~ ., data = x)
}
|
4716043bba41495921258b829c0a81fcb2745dcd | 7316adbd7eadd5da898e1782f5789bde955ce281 | /man/pairs.boot.Rd | aaafa37487a0fb7dc16cb1838af9bf7a4e2e2a85 | [] | no_license | rdpeng/simpleboot | 916cd502f2a46c23796a0e74c5bcc4eadda1cf8e | 675b343014a8e54b94fa131b56e7cd1f0d65cde8 | refs/heads/master | 2021-01-10T20:59:22.322915 | 2019-01-29T16:28:14 | 2019-01-29T16:28:14 | 121,568 | 9 | 5 | null | null | null | null | UTF-8 | R | false | false | 2,048 | rd | pairs.boot.Rd | \name{pairs_boot}
\alias{pairs_boot}
\title{
Two sample bootstrap.
}
\usage{
pairs_boot(x, y = NULL, FUN, R, student = FALSE, M, weights = NULL, ...)
}
\description{
\code{pairs.boot} is used to bootstrap a statistic which operates on
two samples and returns a single value. An example of such a
statistic is... |
74cda106b83e7a6695463d932ae3c2f44eb4821c | b1a12b171097fcb0b2a6f7a10e0ab7afdf41aac1 | /R/sharedGenerics.R | 4cd19159495b39913509972d5f792cbb9591cc68 | [] | no_license | myndworkz/rAmCharts | 7e1d66002cbca9ef63e1d2af6b4e49a1ac7cd3c3 | 6ea352cab2c9bc5f647447e5e7d902d9cbec0931 | refs/heads/master | 2021-01-14T13:06:28.947936 | 2015-07-29T12:34:13 | 2015-07-29T12:34:13 | 39,955,321 | 1 | 0 | null | 2015-07-30T14:37:43 | 2015-07-30T14:37:43 | null | UTF-8 | R | false | false | 1,425 | r | sharedGenerics.R | # Shared by AmGraph and DataSet
#' @exportMethod setDataProvider
setGeneric( name = "setDataProvider",
def = function(.Object, dataProvider, keepNA = TRUE) { standardGeneric("setDataProvider") } )
# Shared by AmGraph and ValueAxis
#' @exportMethod setTitle
setGeneric( name = "setTitle", def = function(.Obj... |
a87174f6bfdbb802c85972ced2223f7bcf2d3075 | 14b4279e536da585ebe7bb6cd0660f3fe6d66d5d | /R/npphen-package.R | de59d45d62f148d8cf871fc4eb25c14bd627e3d9 | [] | no_license | cran/npphen | 52a5f14e00f408d0332875636965fd40e500a515 | b6ebb410a5fe1080d3487843a4692699b4f5a3cb | refs/heads/master | 2022-06-14T00:01:01.440633 | 2022-06-03T20:50:02 | 2022-06-03T20:50:02 | 101,988,150 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,610 | r | npphen-package.R | #' npphen
#' @name npphen
#' @docType package
#' @encoding UTF-8
#' @description The functions in this package estimate the expected annual phenological cycle from time series or raster stack of vegetation (greenness) indexes. The algorithm to estimate the annual phenological cycle (used by the functions in npphen)... |
c29b21405324741e517a33ac9c93ffd710e13b3c | e58bbbd41dccc180fe5fe106db2e7b517934d045 | /man/geno2proteo-package.Rd | 7fe38e9eea7b3e6e5cec78d0c15e1d94993446b8 | [] | no_license | cran/geno2proteo | 2735a2f5c11a095d2fed61038d057aed69600c30 | 6df760f7f0884901db97efcde1ac61054a3b012f | refs/heads/master | 2022-07-03T08:21:11.861686 | 2022-06-13T09:40:02 | 2022-06-13T09:40:02 | 114,025,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,909 | rd | geno2proteo-package.Rd | \name{geno2proteo-package}
\alias{geno2proteo-package}
\alias{geno2proteo}
\docType{package}
\title{
\packageTitle{geno2proteo}
}
\description{
\packageDescription{geno2proteo}
}
\details{
The DESCRIPTION file:
\packageDESCRIPTION{geno2proteo}
\packageIndices{geno2proteo}
~~ An overview of how to use the package and... |
1eea237eb9167b3faf35b77a5207b0912db14786 | 7c95033415669a0812a5c275547113eabd024db0 | /R/rmnorm.R | c967a88fa7c65f004c15041d2923c6bc3bb8ebbd | [] | no_license | cran/bifurcatingr | 293d6a7e39e3fd5bbdb6713436f04dd4051e14bd | 90a6596c19ed6f47c158d7587f2d12986d000287 | refs/heads/master | 2023-07-11T02:05:05.328474 | 2023-06-22T01:10:02 | 2023-06-22T01:10:02 | 340,015,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 805 | r | rmnorm.R | #' Multivariate Normal Generator
#'
#' This function generates multivariate normal errors that are used in the
#' generation of the Bifurcating autoregressive tree.
#' @param n sample size
#' @param d dimension. Defaults to 2 for bivariate normal errors.
#' @param mu mean vector. Defaults to the zero vector.
#' @param ... |
20b13b53a1e984448cc52a0ba99e521a60bc0ffe | ec37153a0e1dfab0fb070d6524cdf941cf9fbabd | /align.R | 19cf9eeea9ab20092da91dbf5263f58221aac8a3 | [] | no_license | agaye/1958BC_Merge | ebdc3695c215644a3b555ecbf589faf0b704b499 | d0d798a654303b92c8940c823266a3c249a17763 | refs/heads/master | 2016-09-05T22:44:33.632270 | 2014-09-28T15:32:08 | 2014-09-28T15:32:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,818 | r | align.R | #
# Amadou Gaye - 13 September 2014
# This function processes two file prior to merging: it ensure the two files have only their overlapping SNPs
# included and aligns the alleles information using the alleles information of the first file.
# The arguments to the functions are the names of the input files, the path tha... |
cead02cad3914b93807bc4869b1d47d5b4fd3277 | efdd6cacaa1c4f75778b0f6511cfad2a0f579081 | /man/theme_gr.Rd | 071299ca82a68d84af1f56a1d4200fe3d14d04ee | [] | no_license | gragusa/grthemes | 54ac150fe385e3f7b9ffb3ed5525d37d14d83dd0 | 1ea07ccef8886bc845bb9e2c0a417dc6a7e2e4b2 | refs/heads/master | 2021-01-19T01:16:27.419911 | 2016-02-15T00:02:00 | 2016-02-15T00:02:00 | 24,543,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 417 | rd | theme_gr.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{theme_gr}
\alias{theme_gr}
\title{ggplot2 grtheme}
\usage{
theme_gr(base_size = 12, base_family = "sans")
}
\arguments{
\item{base_size}{base font size}
\item{base_family}{base font family}
}
\description{
Themes set the general aspect of the plot such as the ... |
043d6973c3ecd48cbc88785e7008315e9a4efb01 | 8c4c2a54b9cbc7be94209246ea915121fe863201 | /plot3.R | 694e535d01e0f79d63b6bf8d6a5955177641a989 | [] | no_license | tommaschera/ExData_Plotting1 | 8a46c8624579f37e6f0c5bc738d2edb4a26e6fb7 | b082ccf2483613c80572d41479d41da2819d9957 | refs/heads/master | 2020-03-11T16:41:03.773231 | 2018-04-19T13:10:40 | 2018-04-19T13:10:40 | 130,123,816 | 0 | 0 | null | 2018-04-18T21:26:39 | 2018-04-18T21:26:38 | null | UTF-8 | R | false | false | 988 | r | plot3.R | library("dplyr")
library("lubridate")
# Reading data
data <- read.table("household_power_consumption.txt", header = T, sep = ";")
data$Date <- dmy(data$Date)
data$Datetime <- strptime(paste(data$Date, data$Time, sep = " "), format = "%d/%m/%Y %H:%M:%S")
# Subsetting data
lower_bound <- dmy("01/02/2007")
upper_bound <... |
3d1bf2a7add2caff4d08641239fc6a21ce2bb2f0 | 2d66c0e4f8a006e32f58a031057a397b45ecb3e5 | /man/detect_flash.Rd | 05577c381903f2f9fa784eb8104c6181091807f6 | [] | no_license | tkatsuki/FlyceptionR | 8537531c02f6e10082b4d1085add0bf2d31689c9 | 478a4e39cd6c59aeb9d183d3b3bb3a081727448c | refs/heads/master | 2020-05-21T19:05:11.359209 | 2020-01-25T21:33:09 | 2020-01-25T21:33:09 | 65,296,814 | 0 | 1 | null | 2020-01-25T21:33:11 | 2016-08-09T13:22:06 | R | UTF-8 | R | false | true | 443 | rd | detect_flash.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/detect_flash.R
\name{detect_flash}
\alias{detect_flash}
\title{Detect flash}
\usage{
detect_flash(input, output, type = c("fluo", "fly", "arena"), flash_thresh,
reuse = F)
}
\arguments{
\item{obj}{A target image of Image object or an array.... |
beae8e2900dff23738c79308a3d3fec37d6ffafa | a4e57b6e4bfd13cb326cdacf014c2bd57d583513 | /Voeding aanzet per omw Fn.R | bba89b8d9a87ef393a073c516bab138adac34d0c | [] | no_license | Dennitizer/CNC | 2da37e1e5083e5e5426229166abcb083db217c6e | 34e309bdbc7f887a86e0996aed27febeeefe9eb7 | refs/heads/master | 2021-06-29T20:18:40.491045 | 2020-09-28T13:23:24 | 2020-09-28T13:23:24 | 158,672,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | Voeding aanzet per omw Fn.R | # Voeding / aanzet per omwenteling Fn
Vf = 2400 # Voedingsnelheid / aanzet
N = 16000 # Toerental
Fn = Vf / N
Fn #mm/Omwenteling |
936fa288f2d5b752b07dfe7cde859cbb0b371f6e | d4d6cd1edbb3b2bb5022a2946f3a6e2b00a2b743 | /plot1.r | 5780e364714edf5867628fa4cb15dd757708ab63 | [] | no_license | benscoble/ExData_Plotting1 | c9e45c7bc441fa9dc8746f22d1113d99dfb88544 | df2055aadc57d7f0a05461d8d59596992f7be5e9 | refs/heads/master | 2020-12-11T06:09:06.086409 | 2014-05-11T20:37:37 | 2014-05-11T20:37:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 540 | r | plot1.r | plot1 <- function() {
pdata <- read.table(file = "household_power_consumption.txt", sep = ";", skip = 66638,nrows = 2880)
colnames(pdata) <- c("Date", "Time", "Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
png(filename = "plot1.png"... |
f466ec7ab259a75e642ddc7278ec91b6a744f955 | c7edb0b105f5920f29ccd3d783050f2511b57f3d | /Samplers/pSTMori.R | f89219ed59630106cc376e7f1af9f6acaa6c1110 | [] | no_license | rache011857/pMTM-for-VS | cc38938d3321ccaec74c5fb76e31ddb663f13259 | 38b493cec5431c101be26650175addfeb9b41960 | refs/heads/master | 2021-01-19T04:28:28.382798 | 2016-08-12T18:33:54 | 2016-08-12T18:33:54 | 63,552,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,091 | r | pSTMori.R | library(plyr)
pSTMori <- function(X, Y, s0, g = nrow(X), n.iter = 1e4, burnin = 2000, prior){
n <- nrow(X)
p <- ncol(X)
x <- scale(X)
y <- Y-mean(Y)
y.norm <- sum(y^2)
gamma.full <- 1:p
gamma <- integer(0)
n.acpt <- rep(0,3)
n.prop <- rep(0,3)
gamma.abs <- length(gamma)
gamma.store... |
024a273465f0c64f217190a28a0ac3b20e2c8c4b | 3945388ee0fef9e4f99b2c0b4cd49e4fc58082b5 | /StreamNetworkTools/man/net_segid.Rd | 620994d9a94f8e6b29c28c086b2543c248171201 | [
"MIT"
] | permissive | dkopp3/StreamNetworkTools | 7ea52d4c917bbcf02314a603a3f9b6d5c5b926b5 | 7c693f3edc975493be946d400642bd99c1d9d809 | refs/heads/master | 2023-06-23T09:58:49.678987 | 2023-06-09T18:44:18 | 2023-06-09T18:44:18 | 140,187,794 | 3 | 1 | MIT | 2021-01-22T18:59:07 | 2018-07-08T17:19:10 | R | UTF-8 | R | false | true | 991 | rd | net_segid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/net_delin.r
\name{net_segid}
\alias{net_segid}
\title{Identify Network Segments (Deprecated)}
\usage{
net_segid(netdelin, nhdplus_path, vpu)
}
\arguments{
\item{netdelin}{output from \code{\link{net_delin}}}
\item{nhdplus_path}{nhdplus_path ... |
d6e51b663d4bf644f53142d8244a0f73239681fc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ecd/examples/lamp.qsl_fit_config.Rd.R | b1077f9998877daf1d439d5fd798233972483c5f | [] | 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 | 212 | r | lamp.qsl_fit_config.Rd.R | library(ecd)
### Name: lamp.qsl_fit_config
### Title: Read QSLD fit config
### Aliases: lamp.qsl_fit_config lamp.qsl_fit_config_xtable
### Keywords: data sample
### ** Examples
c <- lamp.qsl_fit_config()
|
62b6ea533c367ad1285d41429700b675cbb8ce9e | 402e0c46eb8eaedfce09f0e2056ee3e49a347213 | /summarizeMiRNA_new.R | 45344484779e97ac06bfcf335cfa8f124272e449 | [] | no_license | ctoste/smRNA-seq | f044b3e9e200eb072349c42939b4384bf6bd1b53 | 96cca47bf1d541c0741dadf85887f378aa75a1f9 | refs/heads/master | 2021-06-28T01:03:18.874265 | 2017-09-05T17:16:46 | 2017-09-05T17:16:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,379 | r | summarizeMiRNA_new.R | #summarize_mature_miRNA <- function(data, miRNA_all_loc, genome, chr_length=NULL, offset=0, verbose=F) {
# counts <- numeric()
# leftover <- AlignedRead()
# leftover@alignData@varMetadata <- data@alignData@varMetadata
#
# miRNA_all_loc <- miRNA_all_loc[order(miRNA_all_loc[,1], miRNA_all_loc[,2], miRNA_all_loc[,3]... |
7463a398f67d91fb7be763dfe2e0ecac56cdf4c4 | 98e35c12223a91da629901de4e7ad15ad2936863 | /PGLS.R | 720d01edff9a66558a6598985b006de85437d56b | [] | no_license | rmissagia/insectivory-Akodontini | 826b6a4dafe740c4886e823ba8c2bdb4ba6eaff1 | d9a966039d0298b8a6dcef1e4740332a3957230a | refs/heads/main | 2023-01-14T14:45:45.016476 | 2020-11-22T20:11:14 | 2020-11-22T20:11:14 | 314,570,460 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,186 | r | PGLS.R | #directory and libraries
library(phytools)
library(caper)
library(geiger)
library(ape)
#Allometry analysis
#read_tree
tree <- read.tree("ako_tree.nwk")
#read_data
data <- read.csv("alldata6.csv", header = T)
data
#comparative_data_file
cd <- comparative.data(data=data, phy=tree, names.col=X, vcv=TRUE, vcv.dim=3, warn... |
b2cefd2c240f2fafdb35d421631e7fa894d7662e | 524c60ff871cb5ad3e1da91b9d6bbf63e5936dca | /man/single_ligand_activity_score_regression.Rd | 09b2bd53efdba134c5a7fc1644df6cace1df0bad | [] | no_license | saeyslab/nichenetr | 855eae9667ea33563b4fc4eb79e601ab3bc96100 | 0e14cbe118f96160fd26fc7b9d947c6ee55b1158 | refs/heads/master | 2023-08-16T18:12:08.413347 | 2023-08-10T11:31:07 | 2023-08-10T11:31:07 | 120,286,519 | 380 | 139 | null | 2023-09-14T14:10:30 | 2018-02-05T09:58:45 | R | UTF-8 | R | false | true | 2,087 | rd | single_ligand_activity_score_regression.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/application_prediction.R
\name{single_ligand_activity_score_regression}
\alias{single_ligand_activity_score_regression}
\title{Perform a correlation and regression analysis between cells' ligand activities and property scores of interest}
\us... |
4208ade36178ef32612307821f06e8cebf6f7f8d | 17f00c96c6ba3e95bff273d4cf34c9f234ee302a | /anRpackage/R/linmod.R | e4e249346fa29eae14c0002d9a140e299aa601c7 | [] | no_license | saafdk/Rpackage | a3aa3135121c75c4f0e53d5ee17b17f7a1c3c0be | 556ae37212aa38a2bfe8be78b1e614d8ac6301bd | refs/heads/master | 2021-04-13T18:59:13.888065 | 2020-03-22T19:42:15 | 2020-03-22T19:42:15 | 249,180,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 47 | r | linmod.R | linmod <-
function(x, ...) UseMethod("linmod")
|
3d4d3f306ee30e63e53bc87b090cd7b2dc25c4fc | 0e78d8bd40a089158d6e5c71a1cee60288804907 | /R/generalFunctions.R | f07903d894a4d26cf921b74cf1ecc82f58f24122 | [] | no_license | GranderLab/acidAdaptedRNAseq | d16b54a57620097adef2901cf0a85355fc2d0d82 | 3d6ec07a1a71df7cba85e3f68205e955f63a1683 | refs/heads/master | 2021-03-27T16:31:23.290663 | 2018-10-25T15:39:59 | 2018-10-25T15:39:59 | 93,744,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,104 | r | generalFunctions.R | #' namedListToTibble
#'
#' Converts a named list to a long data frame.
#'
#' @name namedListToTibble
#' @rdname namedListToTibble
#' @author Jason T. Serviss
#' @param l List. The list to be converted.
#' @keywords namedListToTibble
#' @examples
#'
#' l <- list(a=LETTERS[1:10], b=letters[1:5])
#' namedListToTibble(l)
#... |
963acc0f464d988178561bdb43849191e994c7a6 | 1e843b4addebc76b4335a9111160193f1a902f4c | /stats/R/steepness.R | 79bcc0d0e4705f77befad9185ea513feb00746d8 | [] | no_license | seannyD/ILMTurk_public | 9bb4d4054fa34c334e8bd733c9f0e6795e138189 | 1288318a987146fe684cf4832b2156a48a3e2667 | refs/heads/master | 2021-04-28T06:15:17.860219 | 2018-02-20T12:47:55 | 2018-02-20T12:47:55 | 122,197,097 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,442 | r | steepness.R | library(lme4)
library(infotheo)
library(lattice)
setwd("/Library/WebServer/Documents/ILMTurk/stats")
source("R/graphCode.R")
signals_Nij = read.csv("Data/Signals_Nij.csv",stringsAsFactors=F)
signals_MT = read.csv("Data/Signals_MT.csv",stringsAsFactors=F)
signals_SONA = read.csv("Data/Signals_SONA.csv",stringsAsFactors... |
1ccaf523644421fa4bbedfc8613ce69f513dbcac | c85ab5fc908a443eac6e96f6818857842346a6e7 | /code/sandbox/play_glmnet_relaxo.R | e6e97fb65fa1387553f87ed01ea0663798e45a97 | [] | no_license | erflynn/sl_label | 34d4df22f651a44317a9fb987970dfed6e1731a7 | 6e81605f4c336e0c1baa07abc168c72c9a9eaceb | refs/heads/master | 2023-04-01T01:57:48.862307 | 2021-03-30T18:27:36 | 2021-03-30T18:27:36 | 244,698,848 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,251 | r | play_glmnet_relaxo.R |
require('glmnet')
# try BinomialExample
data(BinomialExample)
res <- cv.glmnet(x, y, family="binomial", trace=TRUE,
relax=TRUE, path=TRUE)
plot(res)
# this still looks like a mess except gamma=1... hmm
res2 <-cv.glmnet(x, y, family="binomial", trace=TRUE)
my_lambda <- res2$lambda.1se
fit <- glmnet(x... |
0292fecccb0b080fe6914ec26c52e8a74b1d2b7a | b34ce46b2047f1eaadb23497c0efa30947552d5a | /content/courses/estat_2/aulas/reg.R | 5fe28f9873b8dcf6d2da7b58910458ee02e71fa1 | [] | no_license | rbstern/site | b0a6746f5c15fb18cc3953e90c1b2e7cc8a0f740 | 0834620b3e21935625fc56ca2283b2ce7d26175b | refs/heads/master | 2023-04-28T00:12:50.423294 | 2023-04-24T01:24:35 | 2023-04-24T01:24:35 | 118,627,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 881 | r | reg.R | library(glmnet)
# Simulação
n = 1000
d = 900
beta = rep(0, d)
beta[1] = 6
beta[2] = 3
X <- rnorm(n*d)
dim(X) = c(n, d)
Y = X %*% beta + rnorm(n)
meus_dados = data.frame(Y = Y, X)
# Ajuste da regressão por minimos quadrados
aux = lm(Y ~ ., data = meus_dados)
summary(aux)
# Ajuste da regressão por lasso
aux = cv.glmn... |
57f9db5fed79e48ff89b089491238b565fa40461 | 64efc9a1e80e4274c1f73f09711daf710ebc8c13 | /run_analysis.R | 3f8a113eb3b87aff9f9e58d048dbcad8c4f828ec | [] | no_license | dksingh29/GettingandCleaningDataweek4project | ffdeed1132711e39688084bbb77f5e2e2ad41225 | eea93a3459e2c49ed9b70dc418004c1bb6f4a617 | refs/heads/master | 2021-07-17T00:18:45.244369 | 2017-10-24T19:15:14 | 2017-10-24T19:15:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,088 | r | run_analysis.R |
library(dplyr)
#set working directory and download the Smartphone data from the provided url
setwd("insert your wd location")
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url, destfile = "smartphone_activity_data.zip")
#unzip the file
unzip("smartphon... |
417a1d244c16552d67612be082ab68776a66a1f3 | 3cecba2d09c39746890c635c74ad33edcad24e84 | /code/ode_data.R | 605d7d212505bf7eca206fc6714de5928ae54a5e | [] | no_license | Suvixx/Novel-recurrent-neural-network-for-modelling-biological-networksOscillatory-p53-interaction-dynamics | 0fe0107c6829c5896748229234c595e9f3c924e3 | 63ad2d4abf1eb52403b3de8c8f8be7c51f3b2ca3 | refs/heads/master | 2022-11-24T09:55:04.621771 | 2020-08-06T02:59:25 | 2020-08-06T02:59:25 | 285,154,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 855 | r | ode_data.R | library(deSolve)
time <- seq(from=0, to=1000, by = 1)
parameters <- c(gma = 2, alxy = 3.7, bitx = 1.5 , alzero = 1.1 , ay = 0.9)
state <- c(x = 1, y0 = 0, y1 = 0.54)
odernn <- function(t, state, parameters){
with(as.list(c(state, parameters)), {
dx = gma * x - alxy * x * y1
dy0 = bitx * x - alzero... |
0a1a07e45d8c68eaf7463ffef15f1b1c34c461c0 | 1ba5b7c213871eb2b9aa5d194fa403f87d728193 | /R/getLabel.R | 34efacf5206f660e1c569ab9afdb2b953f67cf32 | [
"MIT"
] | permissive | noelnamai/RNeo4j | 6a0c42ffe5a6f3f9ffc19d15ad25453696ea3760 | 4af57a9b00593109155e9f2c55108fe8b94c8f0b | refs/heads/master | 2020-04-01T23:02:26.894316 | 2015-04-17T18:03:26 | 2015-04-17T18:03:26 | 34,324,382 | 1 | 0 | null | 2015-04-21T12:02:52 | 2015-04-21T12:02:51 | R | UTF-8 | R | false | false | 764 | r | getLabel.R | getLabel = function(object) UseMethod("getLabel")
getLabel.default = function(x) {
stop("Invalid object. Must supply a graph or node object.")
}
getLabel.graph = function(graph) {
url = attr(graph, "node_labels")
headers = setHeaders(graph)
response = http_request(url, "GET", "OK", httpheader=headers)
resul... |
be28c811de805f118ce686af64ad60df3658d049 | 4164b00d5a99f4ff19fd1cfef7f3e8d0ebb62d1b | /DataMining-Benchmark-Conversion/benchmark_resultsConversion.R | 67a243509f5d9669ef5311320a1f442a787381cc | [] | no_license | mutual-ai/IBE_Benchmark-OpenML | c99c6d3744b94c2eb5d201bc9fab3bb343ec1b85 | 35a43c38969bb57c4a4f589bfe93a66c5eca3d8b | refs/heads/master | 2020-07-11T08:49:37.818896 | 2016-11-11T16:21:22 | 2016-11-11T16:21:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,146 | r | benchmark_resultsConversion.R | rm(list = ls())
OS = "win"
library(mlr)
library(gridExtra)
library(ggplot2)
library(cowplot)
library(reshape2)
source(file = "DataMining-Benchmark-Conversion/benchmark_defs.R")
################################################################################################################################
# Creation ... |
e42da885cd9b4311363319adf5ceab5de9cfcfcc | 3a5ae60a34608840ef484a901b61a363b1167756 | /man/alignHSLength.Rd | 22113c02bca03562e128dfb0382b54e6d9d10598 | [] | no_license | SWS-Methodology/hsfclmap | 3da8ca59a1ceb90564ec70a448a6f0340ca86420 | eb2bc552fcce321b3dd7bc8655b092bc7a428e1e | refs/heads/master | 2021-01-17T17:35:59.484994 | 2016-12-19T17:47:53 | 2016-12-19T17:47:53 | 70,464,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,081 | rd | alignHSLength.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/alignHSLength.R
\name{alignHSLength}
\alias{alignHSLength}
\title{Prepares for numerical comparison vector of hs codes from
trade dataset with hs codes range from mapping table.}
\usage{
alignHSLength(hs, mapdataset)
}
\arguments{
\item{hs}{... |
939c37317aedc2d3faf5c0640c214394cf9ca083 | 1b9504a60eef0e9bb371a77ffbb1dfd240074a9c | /scripts/time_buy_histogram.R | 90038e6af654da6179c733bc729da60059d3571c | [] | no_license | mohamedabolfadl/ML_Pipe | 3784fe0097ba9f2be3b84bbcaa3e52845420f892 | 34a4da544b9bedc034c62c8dddd4868cbcde8686 | refs/heads/master | 2021-09-12T14:43:11.507177 | 2018-04-17T19:04:15 | 2018-04-17T19:04:15 | 106,018,697 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 729 | r | time_buy_histogram.R | library(ggplot2)
df_all<-read_csv(file="C:/Users/m00760171/Desktop/Templates/trunk/FX/H1_PF_2_TR_15_2001-2016/H1_all_targets.csv")
df<-as.data.frame(df_all$buy_time)
names(df)<-"buy_time"
#df<-as.data.frame(TP_time[1:2000])
df_prof=as.data.frame(df[df$buy_time>0 & df$buy_time<2000,])
names(df_prof)<-"TP_time"
ggplot(... |
40e262a6af0620d40fc138fccf2f66e5e8d5d59d | 9430e5cd40071a7a0a5e92a3a17ee4706538f0d3 | /man/getDiseaseListSim.Rd | 8370a6b62edd87e21fe7a888a795d715e6d2dece | [] | no_license | MoudFassad/HPOSim | 63314cf60d420dc402fb42411f9557af82687f61 | 03a2559b5d0cedc8db6a6e207236a1cd220a6763 | refs/heads/master | 2022-03-30T07:25:05.146874 | 2020-01-09T12:38:10 | 2020-01-09T12:38:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,078 | rd | getDiseaseListSim.Rd | \name{getDiseaseListSim}
\alias{getDiseaseListSim}
\title{ Pairwise Similarity for a List of Diseases}
\description{
Given a list of diseases, the function calculates the pairwise similarities for any two diseases in the list using different strategies.
}
\usage{
getDiseaseListSim(diseaselist,combinemethod="... |
1a40d841d0b6491e792192beb6451ac28f6d4747 | aeacd6ee9ca56233afcda3405314d6db86baee95 | /SupplementMaterials/WA_Data_Analysis_ANZJS/RCode/CreateTable9.R | 450bbcd59f7f8f410d8f900aa3145f3eaa00bc61 | [] | no_license | rakstats/VarSelectOnLinnet | 76b378f3c31f84ed7b0786c07fd62ad3f576b2c2 | 49d25a8c148df5ebd561249d607177484a342a47 | refs/heads/master | 2021-08-21T20:50:18.959662 | 2020-09-24T01:30:22 | 2020-09-24T01:30:22 | 225,319,963 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,235 | r | CreateTable9.R | ##################
# Load packages #
#################
library(sp)
library(spatstat)
library(dplyr)
library(maptools)
library(glmnet)
library(stringr)
library(here)
library(doParallel)
library(xtable)
############################
# Load the useful functions#
############################
source(here::here... |
1749b3a8a5d819507e3b89265d48e32e63978aef | 4129370548d7fab3ce00528da7d05cf5c41839fc | /man/get_league_averages.Rd | b405a5800adfee73950f5140fc963d2f6942beae | [
"MIT"
] | permissive | m-clark/five38clubrankings | c8ec6e7b90744cd3cb9a775bca5f7d31f62eb780 | 901ef6721fbe780431b0c7ae3eed615f38b0d610 | refs/heads/master | 2021-08-06T17:00:19.568274 | 2018-09-18T23:10:29 | 2018-09-18T23:10:29 | 135,958,695 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 741 | rd | get_league_averages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_league_averages.R
\name{get_league_averages}
\alias{get_league_averages}
\title{Get league averages}
\usage{
get_league_averages(rankings, drop_leagues = TRUE)
}
\arguments{
\item{rankings}{Rankings created by \code{get_club_rankings}}
\... |
82e4c864f4d0d9f5a311156ec850d76a8d88f8a9 | b579d9aa0ac8f35170bc532e2e0b8b91d6f9c1ab | /plot3.R | 3ea85e20502504a7d038aa60e7cf76b77e86cabe | [] | no_license | data2knowledge/ExData_Plotting1 | bb6545038a52d6cce02868d41efa6a45eab66424 | d1e041827ebed8394ec2fc9343e1a7b2c39a3aee | refs/heads/master | 2021-01-18T02:12:57.195285 | 2014-09-05T03:43:40 | 2014-09-05T03:43:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,289 | r | plot3.R |
#require(sqldf)
#assume "household_power_consumption.txt" file is under working directory
#load sqldf package
library(sqldf)
#load only the required power consumtion data for 1st Feb 2007 and 2nd Feb 2007
power_consumption_dataset<-read.csv.sql(file='household_power_consumption.txt',
sep=";",
... |
bddea9914fdc336f9172165a48a73792efef70bb | b26855cd7c18444ba2ea1089e4946eebc28f3e62 | /plot2.R | 11cf1a31e83c3c65e765fabbbb28529ffcc104b2 | [] | no_license | rhcarver/Coursera_Exp_Data_Project-1 | 7b2c86c1af3eea6b0633fe59b0eb36dd88b6cf30 | 6d0cf20dca2dad154ff9e8f01ed8ca7bf4946f77 | refs/heads/master | 2021-01-10T05:11:26.931007 | 2015-10-10T21:24:18 | 2015-10-10T21:24:18 | 44,028,645 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,083 | r | plot2.R | # plot2: Project 1 EDA Coursera -- plot2
# Read data re: Electric Power Consumption
# for dates Feb 1 and 2 2007
# Time-series line plot of Global Active Power daily by minute
#
####################
#
# Set working directory
setwd("C:/Users/rcarver/Dropbox/In process/Coursera/Exploratory Data analysis/Project 1")
#
# ... |
32debedbecc25b33389ec2d0d26530fed1d85ba4 | 297dd29c203c3fb3847df230b88297c31bc8314a | /rabea/Rabea.R | 9c2064beedc61760cae2cfb9ea5d78d88a89d96d | [
"MIT"
] | permissive | rehanzfr/R_Codes | f5bc876213fc0793ebc9c5c48f782f61de87fe24 | b4460a5516660963d5f988598656de13f77a3d54 | refs/heads/master | 2023-05-11T08:48:36.062784 | 2023-05-01T17:20:17 | 2023-05-01T17:20:17 | 585,261,050 | 0 | 0 | MIT | 2023-01-04T18:13:35 | 2023-01-04T18:13:34 | null | UTF-8 | R | false | false | 5,030 | r | Rabea.R | install.packages("tidyverse")
install.packages("readxl")
install.packages("ggplot2")
install.packages("gridExtra")
library(readxl)
library(tidyverse)
library(ggplot2) # for creating graphs
library(gridExtra)
# Folder in which excel files are placed
folder <- "C:/Users/hanza/OneDrive/Desktop/rabea_contigs"... |
5d2180694ed506f316ddac6ea7334bda262ae238 | 8f9fea74327fb383b19bdc95b1b1cf703136f433 | /R/plotter.R | cbd20d422a553d86015deee4a5b02290f585c575 | [
"Apache-2.0"
] | permissive | rtlemos/rcsurplus1d | a4b98c7ca5e7e16c74506f954de8e4a5059260a3 | 69ef6212b0df416f2ab15ffb147dcd5cc7e93e56 | refs/heads/master | 2021-04-30T16:20:27.719003 | 2020-06-19T01:19:07 | 2020-06-19T01:19:07 | 56,413,138 | 1 | 0 | null | 2016-04-17T02:24:12 | 2016-04-17T01:16:24 | null | UTF-8 | R | false | false | 9,292 | r | plotter.R | #' rcplotter: reference class that plots results of surplus model fits
#'
#' @field buffer matrix.
#' @field palettes list.
#' @field default_palettes list.
#'
#' @import rcvirtual
#' @import grid
#'
# #' @export rcplotter
# #' @exportClass rcplotter
rcplotter <- setRefClass(
Class = 'rcplotter',
contains = ... |
e54cd99901adea1ceb7d14ef14ced1ccb41b3301 | 9ee587651e82c3efdf58036364c197829ffa57e1 | /Chapter3_EcosystemComparison/10.05.2022_autoregressivemodels.R | 7f1eae1b5081aeb9e5e3d12d01d561d0441d6472 | [
"Apache-2.0"
] | permissive | QutEcoacoustics/spatial-acoustics | 7f0fd2af6663200ab529a2f8979eec56a0bf2e40 | 5e8eaba29576a59f85220c8013d0b083ddb70592 | refs/heads/master | 2023-04-15T09:50:44.063038 | 2023-03-14T23:36:36 | 2023-03-14T23:36:36 | 222,621,976 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,102 | r | 10.05.2022_autoregressivemodels.R | library(lubridate)
#Autoregressive models
rm(list = ls())
set.seed(123)
set.group <- "bird"
getDataPath <- function (...) {
return(file.path("C:/Users/n10393021/OneDrive - Queensland University of Technology/Documents/PhD/Project/Chapter3_SoundscapeEcosystemComparation", ...))
}
data_og <- read.csv(getDataPath... |
51a4caf80f24775fb3f9a05865c904fe37c87ec0 | 8c66b045e8f6c7e1b9fff3deb33a39d8b58d3bef | /man/label_ref_snp.Rd | fe30d227c72900b33252e472c2f12241ee971304 | [
"MIT"
] | permissive | fboehm/countalleles | 1f10c7bd824ce8a2901edf1690c2e8966b3eec35 | fa59eb7dbace10372d7886f8cdfeb2a03aee020c | refs/heads/master | 2016-09-06T00:13:06.828895 | 2015-05-20T16:39:49 | 2015-05-20T16:39:49 | 35,918,384 | 0 | 1 | null | 2015-05-20T16:36:16 | 2015-05-20T01:28:08 | R | UTF-8 | R | false | false | 643 | rd | label_ref_snp.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/make_ref_table.R
\name{label_ref_snp}
\alias{label_ref_snp}
\title{Label one allele (A/C/T/G) as reference and the other as other for use in determining numeric count genotypes.}
\usage{
label_ref_snp(gv_actg)
}
\arguments{
\item{gv_a... |
89a1d1dead909ede19e27da02eb5ba58c7443fd7 | 9c53f6a0e7c059f46c9e446e1396ede06d4a0958 | /Week3/Code/DataWrangTidy.R | dfa8eaba61a28c225f494c228b2594ef3a101491 | [] | no_license | tisssu/CMEECourseWork | 9f5dd832b7d227fccd85ea27199953858428d2ae | 31482f38cb0fe0a60025ce864f59a1372e583f32 | refs/heads/master | 2020-03-30T19:31:48.387316 | 2019-08-29T13:02:41 | 2019-08-29T13:02:41 | 151,547,136 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,025 | r | DataWrangTidy.R | library("dplyr")
library("tidyr")
# input the dataset
MyData <- as.matrix(read.csv("../Data/PoundHillData.csv",header = F, stringsAsFactors = F))
MyMetaData <- read.csv("../Data/PoundHillMetaData.csv",header = T, sep=";", stringsAsFactors = F)
class(MyData)
# head the data
dplyr::tbl_df(MyData)
#change the "" with... |
a361b4e389aaf02ce8a863ff0347503804bb26e6 | 933125137583b8683b765a94003779012b0020c2 | /inst/doc/pace.R | a1083887c55c88c30e26aa0ec7ec4441bbf4461f | [] | no_license | cran/activatr | cc0508650ae46bb6380976942fbdde33f5076811 | 64cf23cd3a23476890aa284fd99393fdf74dae01 | refs/heads/master | 2023-05-26T11:17:01.271304 | 2023-05-01T21:00:02 | 2023-05-01T21:00:02 | 334,079,713 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,779 | r | pace.R | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----parse--------------------------------------------------------------------
library(activatr)
# Get the running_example.gpx file included with this package.
filename <- sy... |
aed5a8edfe33d842f765b911d4aab68990617d32 | de8d9db7e76f391e849705c01e863f52edfc7260 | /profile.R | add1cf7a1eef10aaa554e9b2edd235c4af0021f1 | [] | no_license | pmur002/gggrid-report | a9101c231d9dadc06f65e879238c3f45bb290a89 | 3ad7aa77e12c145f8a3872af6f6e18f5ac6284cf | refs/heads/main | 2023-04-19T18:54:32.235675 | 2021-05-31T02:51:43 | 2021-05-31T02:51:43 | 370,555,766 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | profile.R |
library(profvis)
source("rahlf-plot.R")
latex <- readLines("rahlf-text.tex")
library(gridGraphics)
grid.echo()
downViewport("graphics-plot-1")
Rprof("dvir-prof.out")
library(dvir)
grid.latex(latex, preamble="", postamble="", engine=luatexEngine, tinytex=FALSE,
x=unit(1, "cm"), y=unit(1, "npc") - unit(1, ... |
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