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53a8896e8ffd467a6930601f220ab40e66e9da9b | 2dd0350d1b7d79a54aa88a58e1e259b9f35f69de | /energy.R | 90e9c45b781edea714f29d85776a10e993ee82b7 | [] | no_license | hannyh/Time-Series | 75541bf508db0ae5e34a19b0233f30f686a78e19 | 3afedb974048aa92ebce075fb5f0ae6ffdce6aab | refs/heads/master | 2020-03-25T12:12:21.834685 | 2018-08-06T18:00:00 | 2018-08-06T18:00:00 | 143,761,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,024 | r | energy.R |
# US Residential energy consumption
#Data from site: https://www.eia.gov/totalenergy/data/browser/?tbl=T02.02#/?f=M
#accessed Jan 12, 2018
data1 <- read.csv(file="/Users/hannahward/Documents/School/Winter 2018/Regression/Time Series/energy.csv", header=TRUE)
head(data1)
#Subset to TERCBUS Total Energy Consumed by th... |
1af050355212715518eca1a29ee7919ad0dd223f | 6334b663b9508cf0cda2d992f3efdffc4b4ec2cf | /man/predict.textmining.Rd | 4bcaf1fe710e217cbffbdef19aab1a5a66015bd3 | [] | no_license | cran/fdm2id | 40f7fb015f3ae231ca15ea7f5c5626187f753e1b | c55e577541b49e878f581b44dd2a8bae205779d0 | refs/heads/master | 2023-06-29T00:54:58.024554 | 2023-06-12T12:10:02 | 2023-06-12T12:10:02 | 209,820,600 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,016 | rd | predict.textmining.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/text.R
\name{predict.textmining}
\alias{predict.textmining}
\title{Model predictions}
\usage{
\method{predict}{textmining}(object, test, fuzzy = FALSE, ...)
}
\arguments{
\item{object}{The classification model (of class \code{\link{textmining... |
092ff7757e99e41937fd01c15a64a7eaa7098bf8 | 09d9809daccc4a15453bd699383e002780847c49 | /cachematrix.R | a60bccc2356e9da7c6b99fd4f1e942cef595af6b | [] | no_license | chadpmcd/ProgrammingAssignment2 | 6e5c1f51713d4040f724fdd52ad2745e807bdda8 | 9dceb616767d328ebda4ce2f945fb795f2acdb32 | refs/heads/master | 2021-01-12T19:24:21.040533 | 2015-06-21T02:04:11 | 2015-06-21T02:04:11 | 37,774,042 | 0 | 0 | null | 2015-06-20T15:13:53 | 2015-06-20T15:13:52 | null | UTF-8 | R | false | false | 1,483 | r | cachematrix.R | ## --------------
## The set of functions in this file allow one to get an inverse matrix using the
## solve function. The inverse matrix is cached in the makeCacheMatrix environment
## and can be updated and pulled from the cacheSolve function environment.
## --------------
## --------------
## makeCacheMatrix
## Th... |
6dd4bc7c7457aaa7fbabc7b0454c291dcf6f38f9 | e1635e481a60c783abf3fc4575da2d81713dfd49 | /R/get_gap_index_raster.R | 2f6941bf237b5253370b1757cd52483ac2feffae | [
"MIT"
] | permissive | traitecoevo/mortality_bci | e5b7b7b7bf712458a42aac58258a5749c16268a4 | ff38c77011f36dcd27452f9e8fd84e5842e3e336 | refs/heads/master | 2021-03-27T18:23:54.635511 | 2019-01-07T23:36:43 | 2019-01-07T23:36:43 | 20,089,381 | 7 | 3 | null | 2018-08-26T23:28:47 | 2014-05-23T06:40:30 | R | UTF-8 | R | false | false | 1,526 | r | get_gap_index_raster.R | #' Creates gap index raster for BCI plot for censuses 1985 to 1990 and 1990 to 1995
#'
#' Creates gap index raster for BCI plot for censuses 1985 to 1990 and 1990 to 1995
#' @param canopy_data Dataframe containing canopy data.
#' @param weight_matrix Integer matrix. A weight to be applied to both focal cell and those ... |
2050f47390f3cd4187507ffe435d47641a81bb0c | 5e42a668e417fd55fe28ecee719c759016f963b9 | /R/make_linter_from_regex.R | ed98441a3efb3a6b7f500b703d29b1b3c9f8ef9c | [
"MIT"
] | permissive | cordis-dev/lintr | 2120e22820e8499ca3066fa911572fd89c49d300 | cb694d5e4da927f56c88fa5d8972594a907be59a | refs/heads/main | 2023-08-05T08:50:42.679421 | 2023-07-25T13:21:29 | 2023-07-25T13:21:29 | 225,583,354 | 0 | 0 | NOASSERTION | 2019-12-03T09:41:30 | 2019-12-03T09:41:30 | null | UTF-8 | R | false | false | 3,388 | r | make_linter_from_regex.R | make_linter_from_regex <- function(regex,
lint_type,
lint_msg,
ignore_strings = TRUE) {
# If a regex-based linter is found, only flag those lints that occur within
# a relevant section of source code
.in_ignor... |
9da9b2827e015a0d9645021f9a7d34f89d3458ed | 4970a3f8a4ca8a42a6fb22f454265691544f1810 | /man/ca125.Rd | 00764d84c8cfe6ed2158d36a2c919b466442dc61 | [] | no_license | Penncil/xmeta | d2ee5b14843d88f1b28c3e3755816269103cbbcd | 832b3f244648818cf2df2691ec5dd7bfa21bc810 | refs/heads/master | 2023-04-08T17:04:05.411553 | 2023-04-04T17:05:36 | 2023-04-04T17:05:36 | 249,091,838 | 4 | 1 | null | 2020-03-22T01:27:08 | 2020-03-22T01:27:07 | null | UTF-8 | R | false | false | 1,120 | rd | ca125.Rd | \name{ca125}
\alias{ca125}
\docType{data}
\title{Recurrent ovarian carcinoma study}
\description{A meta-analysis of 52 studies that were reported between January 1995 and November 2007.}
\format{
The data frame contains the following columns:
\describe{
\item{n}{total number of subjects}
\item{PiY}{dise... |
958c114dc04d6fa481e667ec4668e1e137915b18 | 192fd3dbc491d3c36bd9351f02cf9b5957ea56d1 | /R Packages/icdcoder/man/getChildrenICD10.Rd | ad9eb9ae010509c3ab5b4fb92858c2ea6bd38d37 | [] | no_license | ryerex/Research_and_Methods | d4d211defdbee83e47ecc72c59944c3f60a3bcca | 4010b75a5521c2c18ee624d48257ee99b29a7777 | refs/heads/master | 2023-05-26T01:54:17.048907 | 2020-08-05T16:14:29 | 2020-08-05T16:14:29 | 91,369,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 411 | rd | getChildrenICD10.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/utils.r
\name{getChildrenICD10}
\alias{getChildrenICD10}
\title{Get's all children for a single icd10 code (i.e., those under the
current hierarchy)}
\usage{
getChildrenICD10(icd10)
}
\arguments{
\item{icd10}{icd10 code}
}
\descriptio... |
1e4305d2a19b45010e456ff7c9dbbd5afca06a6b | 4f2e87dfbb407fc5f2510622ca048401de67adf3 | /diversification_analysis/mammals/orders/cetacea/run_TESS.R | 852af64299c987fa5aab3e4be3b6c662218883df | [] | no_license | naturalis/RiseAndFall | 2bd5d1f2c4afac54237ffafc5eefca87257c720b | fb612a80a2b68ee61ce228e3c6e47ced6a47458b | refs/heads/master | 2021-03-27T08:52:57.679485 | 2017-08-30T13:00:05 | 2017-08-30T13:00:05 | 52,791,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,622 | r | run_TESS.R | # Diversification rates using TESS
library(tools)
library(optparse)
library(TESS)
option_list <- list(
make_option("--i", type="integer", default=200000,
help=("Number of MCMC iterations to run [default %default]."),
metavar="Iterations"),
make_option("--rho", type="double", defaul... |
305de3bcce250360d56324e910cd9bdd5bd993fc | 0839e73a99c2113c2fee5cce53101efbc65ad8bc | /tests/testthat/test-fitmethod.R | 1882afd248a1c59558a9291dfd6bdb684f599572 | [] | no_license | harobledo/fixest | cf7abd46e07f3c6113958bc9b8de414bf13d7813 | 5af3eb32e019768932fed8be07f110e9efcd90b8 | refs/heads/master | 2023-09-02T01:18:49.311081 | 2021-09-12T17:08:02 | 2021-09-12T17:08:02 | 390,754,772 | 0 | 1 | null | 2021-11-22T18:50:20 | 2021-07-29T14:40:33 | R | UTF-8 | R | false | false | 981 | r | test-fitmethod.R | fitmethod.cases <- fitmethod_cases()[-c(4:6), ] # Eliminating ols with fmly (makes no sense)
with_parameters_test_that("feols.fit works properly",
{
fmla <- paste(y_dep, "-1 + x1 + x2 + x3", sep = " ~ ")
res <- feols.fit(y = ev_par(paste0("base$", y_dep)), X = base[, 2:4])
res_bis <- feols(fml = as.formu... |
84c16e8912632bcec92d866939ea239fcf42a8a0 | f7fa230362cd752d4e114b0423385e2ec59e9e8b | /diamonds_dataset.R | 7b19e0257a28d7b4229d6dc08e5f992659dbf163 | [] | no_license | shinjjune/R-Visual-ML-DL | 6aa79721b1e136231547b401e02685da58f1ff1d | ed0feff66f823317f7d52e8e30ae9d4aeb9a6c93 | refs/heads/master | 2020-05-31T13:05:34.298856 | 2020-01-22T08:58:21 | 2020-01-22T08:58:21 | 190,295,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 316 | r | diamonds_dataset.R | library("ggplot2")
str(diamonds)
library("ggplot2")
library("ggthemes")
ggplot(diamonds, aes(x=x, y=price, color=clarity)) +geom_point(alpha=0.03)+
geom_hline(yintercept=mean(diamonds$price), color="turquoise3",alpha=.8)+
guides(color=guide_legend(override.aes=list(alpha=1)))+
xlim(3,9)+theme_solarized_2()
|
a77bbea16c6f9782cbf7ac1a766b2a1b47995a5a | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gFileHasUriScheme.Rd | 16b89ae10e5e261f6213d3b0b184a840a89915fe | [] | 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 | 623 | rd | gFileHasUriScheme.Rd | \alias{gFileHasUriScheme}
\name{gFileHasUriScheme}
\title{gFileHasUriScheme}
\description{Checks to see if a \code{\link{GFile}} has a given URI scheme.}
\usage{gFileHasUriScheme(object, uri.scheme)}
\arguments{
\item{\verb{object}}{input \code{\link{GFile}}.}
\item{\verb{uri.scheme}}{a string containing a URI scheme.}... |
dfed6dd5f442d508835a02ef19b8109c446afe99 | 331e7816d55b9d3de50253d1b096e8707859a11c | /R/calibration.R | d84021eab550f8ab3fe3e37e39c4816ed09f37ac | [] | no_license | haroine/icarus | e515732a69d82614bb248807f882559188d291a7 | bd51ecf29bc7f07111219534dbd401f78c1daa84 | refs/heads/master | 2023-06-09T19:41:26.432469 | 2023-05-27T15:42:26 | 2023-05-27T15:42:26 | 38,872,499 | 10 | 5 | null | null | null | null | UTF-8 | R | false | false | 12,885 | r | calibration.R | # copyright (C) 2014-2023 A.Rebecq
# This function executes easy calibration with just data and matrix of margins
#########
#' Calibration on margins
#' @description
#' Performs calibration on margins with several methods and customizable parameters
#' @param data The dataframe containing the survey data
#' @param mar... |
c955ec533fdcdb36da8b39bf4b3f85a71af02caa | 8e044458ebb6dcb51a711c51c33a9b54bbf9fd8e | /R/cox.ipw.r | 82a8d54f8928a76101d35c9f86ef1540cb8b86d4 | [] | no_license | scheike/timereg | a051e085423d3a3fd93db239c33210f60270d290 | 5807b130fbbda218e23d5c80ddc845973cae9dfc | refs/heads/master | 2023-01-28T19:38:18.528022 | 2023-01-17T06:28:35 | 2023-01-17T06:28:35 | 35,535,708 | 28 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,523 | r | cox.ipw.r | #' Missing data IPW Cox
#'
#' Fits an Cox-Aalen survival model with missing data, with glm specification
#' of probability of missingness.
#'
#' Taylor expansion of Cox's partial likelihood in direction of glm parameters
#' using num-deriv and iid expansion of Cox and glm paramters (lava).
#'
#' @aliases cox.ipw sum... |
ec641a0dd6725f44b1099decbe6b758fac2844b9 | c857e04d82512de09d7541bd99b6a8bd990a23f9 | /plot2.R | aff7ef905211cfcf6f50ec4093d93a26b67c8fb7 | [] | no_license | bisybackson/ExData_Plotting1 | cdfca1db79cf24b3a1f19d97d7a26b882ea24835 | 60e4a2057d13f4dce970839960b84e3d6d2f5c7e | refs/heads/master | 2021-06-05T12:19:59.256589 | 2016-10-31T16:21:55 | 2016-10-31T16:21:55 | 72,365,019 | 0 | 0 | null | 2016-10-30T17:43:27 | 2016-10-30T17:43:25 | null | UTF-8 | R | false | false | 557 | r | plot2.R | library(data.table)
library(dplyr)
library(lubridate)
consumption <- fread("household_power_consumption.txt",sep=";",header=TRUE,na.strings = "?",stringsAsFactors = FALSE)
consumption$DateTime <- dmy_hms(paste(consumption$Date, consumption$Time))
consumed <- filter(consumption, date(consumption$DateTime) == "2007-02-01... |
f231e496c6ebf214a3048c09e988f616a0ed96e5 | f4e2f6a4bd24753ce2522a19da2bc4d870d71a67 | /man/qsmoothData.Rd | a0319579315e518287390f60e0fbf70466cb3503 | [] | no_license | Feigeliudan01/qsmooth | 2be0fbfe65769a0fc0746a363b89542fa4c10ad0 | 58f23c44ef6a63fb40080231d4177e6eb74e62f2 | refs/heads/master | 2020-04-15T04:58:44.359789 | 2017-02-15T19:42:13 | 2017-02-15T19:42:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 550 | rd | qsmoothData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllGenerics.R, R/methods.R
\docType{methods}
\name{qsmoothData}
\alias{qsmoothData}
\alias{qsmoothData,qsmooth-method}
\alias{qsmoothData.qsmooth}
\title{Accessors for the 'qsmoothData' slot of a qsmooth object.}
\usage{
qsmoothData(object)
... |
fd5a4612e500ecb7adde336520cfcdfa6fe08a98 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /bravo/inst/testfiles/colSumSq_matrix/libFuzzer_colSumSq_matrix/colSumSq_matrix_valgrind_files/1609959020-test.R | 362751728cf381d1f4e381cd973cba6c6506cc25 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 400 | r | 1609959020-test.R | testlist <- list(x = structure(c(5.04442971419527e+180, 3.1111403385324e+174, 1.51741194999287e-152, 2.71034819479614e-164, 1.93112249337219e-308, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Di... |
b5f935643c4d4b0ea19e2b6933884ea768d2268b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/psych/examples/omega.graph.Rd.R | 1b3acd2e5f6b444f86cd288a37b65aab22ceb118 | [] | 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 | 423 | r | omega.graph.Rd.R | library(psych)
### Name: omega.graph
### Title: Graph hierarchical factor structures
### Aliases: omega.diagram omega.graph
### Keywords: multivariate
### ** Examples
#24 mental tests from Holzinger-Swineford-Harman
if(require(GPArotation) ) {om24 <- omega(Harman74.cor$cov,4) } #run omega
#
#example hierarchical s... |
37c7bafae236854145fd87f88865ba0caabfb30e | f58d0680a57f8c62d0d10431f6a747e4b81eae19 | /R/add_tech_intensity.R | 6b951d6bd612dd4ccdfe64895a156f07d743ee96 | [] | no_license | awekim/WIODnet | fc99cb1cf1272bd73ac2ee5b90e44a2901b1a638 | 07966a9e856820667ef069be4a1f19592d7be3ae | refs/heads/master | 2020-08-13T15:25:17.406570 | 2019-10-14T12:31:47 | 2019-10-14T12:31:47 | 214,992,045 | 0 | 0 | null | 2019-10-14T08:42:33 | 2019-10-14T08:42:33 | null | UTF-8 | R | false | false | 815 | r | add_tech_intensity.R | #' Join the tech intensity data frame to the yearly WIOD
#'
#' @description Join the created technology intensity data frame to the
#' yearly raw WIOD wide table so that similar country and technology
#' intensity manufacturing industies can be aggregated.
#'
#' @param yearly.raw yearly raw data from the downlo... |
5929f02a9fd62ae39788f35ba10e2164035088b2 | 669cdf8cabbe9269122c8a2e012df7d4b06bd895 | /R/oauth-has-expired.r | 35fc3118898ba8b0843b3942cf189c262e8aa470 | [] | no_license | jdeboer/httr | 660e85f54bd29ccbb4431370eed344fd24809762 | eb27bd57decc5fb37de51d52466a4b229a11f793 | refs/heads/master | 2021-01-18T12:10:22.328699 | 2013-06-01T16:15:25 | 2013-06-01T16:15:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 424 | r | oauth-has-expired.r | #' Check if an OAuth 2.0 access token has past its use by.
#'
#' @param access_token the access token to check use_by of
#' @param margin the number of seconds before use_by to use as the expiration threshold (default of 5 seconds)
#' @family OAuth
#' @export
oauth2.0_has_expired <- function(access_token, margin = 30) ... |
37b793b06bfa4716c877a8595f899d6f6457d150 | f6912c71c408619f65692f4824300e6be46c1b2f | /my-variation.R | 9e8a75f3d8acad27fe7fb8351d2251698e5dcfb1 | [] | no_license | Vassar-COGS282-2017/3-B1ngB0ng | 1c428572a28e391abd7e5bfb652d0d4d61977985 | 7b83a9888edf30fbb353333b66ac2ba888cd7c66 | refs/heads/master | 2021-07-11T12:01:21.963678 | 2017-10-06T02:07:55 | 2017-10-06T02:07:55 | 103,571,847 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,753 | r | my-variation.R | ######################################################
#DISCUSSION
######################################################
#My variation of the Schelling model adds property cost
#and income inequality to the mix. The model assumes that
#property in the center of the city is most expensive, and
#that cost decreases as ... |
99a2e84ef992df3b7a9b251f219b08c1644c1993 | bc4c037c476201ec33a36d5476d5ce759bb60afe | /corr_and_LM.r | da487ad26c7f1a4f2859d40445768dd9ffcf9128 | [] | no_license | marlonglopes/RTests | f006e9996f0085de778c1b25c45dc10f27e78cf9 | 571cede14a857b745e196624d10941790b0585cc | refs/heads/master | 2020-05-29T15:41:39.256246 | 2016-11-07T09:48:29 | 2016-11-07T09:48:29 | 61,497,403 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,776 | r | corr_and_LM.r | # The vectors `A` and `B` have already been loaded
# Take a quick peek at both vectors
A = c(1,2,4)
B = c(3,6,7)
N = length(A)-1
# Save the differences of each vector element with the mean in a new variable
diff_A <- A - mean(A)
diff_B <- B - mean(B)
# Do the summation of the elements of the vectors and divide by N... |
f6f8b075bab024aca70beefa5d27b4adcc6b96c0 | 907af44f17d7246e7fb2b967adddb937aa021efb | /man/fslfill2.Rd | 22d0f372185dd661f030ff4c45d1444431c02868 | [] | no_license | muschellij2/fslr | 7a011ee50cfda346f44ef0167a0cb52420f67e59 | 53276dfb7920de666b4846d9d8fb05f05aad4704 | refs/heads/master | 2022-09-21T07:20:18.002654 | 2022-08-25T14:45:12 | 2022-08-25T14:45:12 | 18,305,477 | 38 | 23 | null | 2019-01-10T20:57:47 | 2014-03-31T19:35:03 | R | UTF-8 | R | false | true | 1,262 | rd | fslfill2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fslfill2.R
\name{fslfill2}
\alias{fslfill2}
\title{Fill image holes with dilation then erosion}
\usage{
fslfill2(
file,
outfile = NULL,
kopts = "",
remove.ends = TRUE,
refill = TRUE,
retimg = TRUE,
reorient = FALSE,
intern = F... |
e9751467e93ce01a2dde0a7c617cb01126fc3561 | 59004c819451c7f552159ec5b2ce500fa365d70d | /R/QRNLMM.R | f01f5d7008943421c662c28a9d11faf7703bab38 | [] | no_license | cran/qrNLMM | 168ca22144c733fa192ef9bda53e93917a46279b | ac1d52d97a4f81205151895054cd553a3c5fd608 | refs/heads/master | 2022-09-05T00:17:55.503771 | 2022-08-18T11:40:05 | 2022-08-18T11:40:05 | 30,884,909 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 22,114 | r | QRNLMM.R | QRNLMM = function(y,x,groups,initial,exprNL,covar=NA,p=0.5,
precision=0.0001,MaxIter=500,M=20,cp=0.25,
beta=NA,sigma=NA,Psi=NA,
show.convergence=TRUE,CI=95,
verbose=TRUE)
{
if(any(is.na(groups)==TRUE)) stop("There are some NA's values in gr... |
6c596e132d33148ba2b4f06f7394912bf6dbe3e2 | e7cd1e33a146924f27531249060780ea160b1b1e | /variant_analysis/LOH_analysis.R | b99ee52bbd0d634de9df2ea520565e848ead190c | [] | no_license | rj67/germVar | a92abbf86f4f7d6118202735e33e88f8c689c83a | 612d90a480eac286644e9ddd9626cfca2036aada | refs/heads/master | 2016-09-10T14:34:04.348001 | 2015-04-05T16:35:09 | 2015-04-05T16:35:09 | 16,005,171 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,301 | r | LOH_analysis.R | ASCAT_stat <- read.table("Results/ASCAT_out/ploidy_purity.txt", fill=T)
colnames(ASCAT_stat) <- c("Patient", "ploidy", "purity")
ASCAT_stat <- subset(ASCAT_stat, !is.na(purity))
seq_lengths<- data.frame(CHROM = c(seq(1:22), "x"), length=seqlengths(seqinfo(nonsyn_GT))[1:23])
parse_ascat <- function(Patient){
ascat_f... |
37a8a24705eb6726e724a8e21ca7f98a41946d3f | 0306af700c92c891df42b645e24270f86c950681 | /man/plot_lof.Rd | 66e57900b1b249589b522bb2f51bed0c1203f17e | [] | no_license | ngwalton/iec | 0fb0f1a0a8d1b293b32edab4eda103f95d50c33b | 8c84b9e260c298745ded8f2f89a6baf8c836e2b6 | refs/heads/master | 2020-05-21T00:07:00.050610 | 2015-11-09T23:29:55 | 2015-11-09T23:29:55 | 25,713,988 | 1 | 1 | null | 2016-11-02T22:00:40 | 2014-10-25T00:13:27 | R | UTF-8 | R | false | false | 723 | rd | plot_lof.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/plot_functions.R
\name{plot_lof}
\alias{plot_lof}
\title{Plot BRC lack-of-fit (LOF).}
\usage{
plot_lof(brc, min_lof = 1)
}
\arguments{
\item{brc}{BRC data frame generated by \code{\link{est_brc}}.}
\item{min_lof}{numeric value indica... |
b8e27799e260a4a83adf3a02173abf5add33a447 | 235f2010472122f049f0d6c044f5dd574d493c2b | /rveg/tests/testthat.R | 000281a2086c53d7cddbf9c7176fb450ac919bf5 | [
"MIT"
] | permissive | chenzheng1996/monitoring-ecosystem-resilience | 11aff1aeb6f469be61db397b50ae94e8e8cb7b95 | 387b038fa8d8ac5f0c4541afa7478db9d18701c5 | refs/heads/master | 2023-08-11T23:32:22.115685 | 2021-09-28T09:45:22 | 2021-09-28T09:45:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 73 | r | testthat.R | library(testthat)
#devtools::load_all()
library(rveg)
test_check("rveg")
|
88af398900e12475a9fd8df52d4049bdfe3ab1ac | 0bc2798ed0bd2279da3d84e1f992ec602eb04dca | /Tree Models/HR Analytics/hr_analytics.R | b9bd1e64992b6225cfb7d049fcd18c0e6ee830c8 | [] | no_license | ramkrishnaa32/PGDDS-Projects | 39dec0ba6af05ff7b693507c2b8cfb39f359274c | bbb996fbccd1e3e5c783594996ea83f306d613f3 | refs/heads/master | 2021-05-01T09:44:24.105661 | 2018-02-13T08:26:00 | 2018-02-13T08:26:00 | 121,098,296 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,262 | r | hr_analytics.R |
# File Impoting
hr_analytics <- read.csv("hr_analytics.csv")
str(hr_analytics)
# baseline accuracy
prop.table(table(hr_analytics$salary))
# divide into train and test set
set.seed(123)
split.indices <- sample(nrow(hr_analytics), nrow(hr_analytics)*0.8, replace = F)
train <- hr_analytics[split.indices, ]
test <- hr_a... |
5b44eef8366dd98c6a207a075eaa07d499c137e2 | b99559c092f9a112435087bfc67e199aede2e469 | /clustering1.R | d481ebc5db88076519903e2e99118359fa57f601 | [] | no_license | luaburto/machine-learning | 26d17317e07e713ad16c1fe4c12d7648ce91b685 | a4ffe1f2525ca82f15135ec24d1ae98404db5f97 | refs/heads/master | 2022-01-16T07:30:14.777662 | 2019-07-23T09:40:37 | 2019-07-23T09:40:37 | 198,096,501 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,897 | r | clustering1.R | n <- 3 # no. of centroids
set.seed(1415) # set seed for reproducibility
M1 <- matrix(round(runif(100, 1, 5), 1), ncol = 2)
M2 <- matrix(round(runif(100, 7, 12), 1), ncol = 2)
M3 <- matrix(round(runif(100, 20, 25), 1), ncol = 2)
M <- rbind(M1, M2, M3)
C <- M[1:n, ] # define centroids as first n objects
obs <- length... |
bfa06425856db566dd5b9c9511f6d57fb72a4774 | f6a1375e6453107cba75567ec0c3ba23a5ac7958 | /R/aggregate_list.R | cfce3dd15247db3f06d9824ac8e1f7de48912459 | [] | no_license | UW-GAC/analysis_pipeline | 7c04b61c9cafa2bcf9ed1b25c47c089f4aec0646 | df9f8ca64ddc9995f7aef118987553b3c31301a1 | refs/heads/master | 2023-04-07T03:13:52.185334 | 2022-03-23T21:15:46 | 2022-03-23T21:15:46 | 57,252,920 | 42 | 30 | null | 2023-03-23T20:13:40 | 2016-04-27T22:25:56 | R | UTF-8 | R | false | false | 1,955 | r | aggregate_list.R | library(argparser)
library(TopmedPipeline)
library(SeqVarTools)
sessionInfo()
argp <- arg_parser("Parse table of variants of regions to a list")
argp <- add_argument(argp, "config", help="path to config file")
argp <- add_argument(argp, "--chromosome", help="chromosome (1-24 or X,Y)", type="character")
argp <- add_arg... |
61a0631573295a9fe2823d5145f0ff21252e6ac9 | b1e812ebd76a7d344340c65ddebc52758745b95b | /man/getDataPSQL.Rd | a482dc6dd47409e43e5194690a0e8dcbc5031d4a | [] | no_license | DevProgress/RVertica | 71a03ffbedc13e7f1f6cdeef135c6cb25f37ebf6 | 30d2f6d60b22018e68d6ca1d61ea25ccdf9ada93 | refs/heads/master | 2020-04-06T06:49:07.788645 | 2016-08-25T21:47:04 | 2016-08-25T21:47:04 | 64,185,049 | 2 | 3 | null | 2016-08-25T21:47:04 | 2016-07-26T02:58:15 | R | UTF-8 | R | false | true | 461 | rd | getDataPSQL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/queries.R
\name{getDataPSQL}
\alias{getDataPSQL}
\title{Run Sample Query via \code{psql}}
\usage{
getDataPSQL(sqlquery)
}
\arguments{
\item{sqlquery}{A SQL query as character string}
}
\value{
The dataset corresponding to the query.
}
\descri... |
f3bbc19989e67f9660937a64705a6530edcd3f72 | 0a907d48703647ba34d5d01cb2652fe974bc155e | /man/ncc.ci.sr.Rd | 55165b2d188760e3a45a4481c167049d1a590eb9 | [] | no_license | simonvandekar/Reproducible | b111b60ea1c86c008a037bbdc1f5bb3513f5ca58 | 3f2d88d70c8ec33ec416c5c2ad38f1351526871c | refs/heads/master | 2021-07-11T11:36:04.702407 | 2020-11-16T19:14:56 | 2020-11-16T19:14:56 | 219,840,544 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 494 | rd | ncc.ci.sr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ncc.R
\name{ncc.ci.sr}
\alias{ncc.ci.sr}
\title{Symmetric range confidence interval for chi-square noncentrality parameter.}
\usage{
ncc.ci.sr(y, p, alpha = 0.05)
}
\arguments{
\item{y}{numeric, value of chi random variable.}
\item{p}{intege... |
f19c1d139fe73d4f98a15b0b3fb1c19d41c31040 | c54f712c4ddfe909de08dd0b52639d1af1a5f0e4 | /exercise-6-code (1).R | 2d4480bd3f8323767dc3f36afa69603a682e659e | [] | no_license | JEMunson/PS811-Excercises | 61a1cbd0249c414934888e8c87de72e8052833aa | 42ff0b8cb2162383ddd486837445fdb2939889f1 | refs/heads/master | 2023-01-19T22:26:52.670835 | 2020-12-03T22:16:27 | 2020-12-03T22:16:27 | 294,452,738 | 0 | 2 | null | 2020-11-15T00:45:06 | 2020-09-10T15:43:40 | TeX | UTF-8 | R | false | false | 3,872 | r | exercise-6-code (1).R | ---
title: 'Exercise 6: Base R vs. Tidyverse'
author: "Jessie Munson"
date: "10/22/2020"
output: pdf_document
---
# load packages
library("here")
library("haven")
library("magrittr")
library("tidyverse")
library("tidyr")
library("dplyr")
# setup folders and directories
here("data")
here("code")
#read in data
v... |
5c2e0b5047e96d9eacd8c841b0bf02c9491d1ea8 | 2d662871298fadd5de7dd030257c8d6614369d9e | /Yapay sinir ağları.R | bdf2dca7ceaa8662378e8a18c01062b6b4409495 | [] | no_license | CemRoot/R-Language | 64091cee31346f92ed951f989f06fc267975353f | 62c1dca0bae747eca09874a34e2b7b6131ec158d | refs/heads/main | 2023-04-11T02:38:54.744005 | 2021-04-12T19:28:43 | 2021-04-12T19:28:43 | 353,489,620 | 1 | 0 | null | null | null | null | ISO-8859-9 | R | false | false | 2,205 | r | Yapay sinir ağları.R | #YAPAY SİNİR AĞLARI
# Boston veri seti icin
install.packages("MASS")
library(MASS)
# neural networks fonksiyonu icin
install.packages("neuralnet")
library(neuralnet)
# yapay sinir aglarini kullanmak icin verimizi olceklendirmemiz gerekiyor.
# tahmin edecegimiz degiskeni tam olarak 0 ile 1 arasina cekmemiz ger... |
5acfe332648e5f68e198c70659abd32aa15166bb | 0d74c6026340636cb7a73da2b53fe9a80cd4d5a5 | /simsem/man/SimSem-class.Rd | 0a56092fadf51f4fa42062761d9d0d2f6e6d8c39 | [] | no_license | simsem/simsem | 941875bec2bbb898f7e90914dc04b3da146954b9 | f2038cca482158ec854a248fa2c54043b1320dc7 | refs/heads/master | 2023-05-27T07:13:55.754257 | 2023-05-12T11:56:45 | 2023-05-12T11:56:45 | 4,298,998 | 42 | 23 | null | 2015-06-02T03:50:52 | 2012-05-11T16:11:35 | R | UTF-8 | R | false | false | 1,615 | rd | SimSem-class.Rd | \name{SimSem-class}
\Rdversion{1.1}
\docType{class}
\alias{SimSem-class}
\alias{summary,SimSem-method}
\title{Class \code{"SimSem"}}
\description{
The template containing data-generation and data-analysis specification
}
\section{Objects from the Class}{
Objects can be created by \code{\link{model}}.
}
\section{Slot... |
b90c97eef2a6ead6cc14de575d86463a72490795 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rpf/examples/rpf.logprob.Rd.R | f0ff09048c8f68c34ae56145e7b7d24ec49b79fb | [] | 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 | 599 | r | rpf.logprob.Rd.R | library(rpf)
### Name: rpf.logprob
### Title: Map an item model, item parameters, and person trait score into
### a probability vector
### Aliases: rpf.logprob rpf.logprob,rpf.1dim,numeric,numeric-method
### rpf.logprob,rpf.1dim,numeric,matrix-method
### rpf.logprob,rpf.mdim,numeric,matrix-method
### rpf.logp... |
a60bd783b633ce96bfc46b4fcb5c585fabc69e2f | b400255589a974e4fb8a7c468f7a967649c10c25 | /man/pkg_dev.Rd | 9da87900be9aa0e56086280d48da8f1d61dd8489 | [
"MIT"
] | permissive | cmil/ghactions | 701730602d785a7e313e9550dde5ee68dff69f1a | 10d3c3a41b2de651589aed91d06388579b6a3d2a | refs/heads/master | 2020-12-05T17:59:58.736614 | 2020-01-06T22:56:10 | 2020-01-06T22:56:10 | 232,199,293 | 0 | 0 | MIT | 2020-01-06T22:49:33 | 2020-01-06T22:49:32 | null | UTF-8 | R | false | true | 2,029 | rd | pkg_dev.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/steps.R
\name{pkg_dev}
\alias{pkg_dev}
\alias{rcmd_check}
\alias{covr}
\title{CI/CD steps for a package at the repository root}
\usage{
rcmd_check(name = "Check Package")
covr(name = "Run Code Coverage")
}
\arguments{
\item{name}{\verb{[char... |
6d7ef664e7b44a149556c7b81516dde4070bc65c | bfb23bde5d451fdbe5b66482083656afe311510d | /tests/testthat/test_compile_report.R | 8a2747fd54c93a07c2ff692b1a6b2e66f2a904c1 | [] | no_license | vfulco/reportfactory | b96cb9961ff16c819bcb20fcb2c117c1df451347 | 1cca5b9e6241469377c4a9047a5a7580185551b9 | refs/heads/master | 2020-03-22T08:05:55.466548 | 2018-06-29T15:55:14 | 2018-06-29T15:55:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 543 | r | test_compile_report.R | context("Test report compilation")
test_that("Compilation can handle multiple outputs", {
skip_on_cran()
setwd(tempdir())
random_factory()
compile_report(list_reports(pattern = "foo")[1], quiet = TRUE)
outputs <- sub("([[:alnum:]_-]+/){2}", "",
list_outputs())
outputs <- sort(outp... |
99c551547fd77c37277e0108ec39f21cca0043e3 | cbb37354c93299164fc2b88e5a74901a48ae9551 | /R/tablefilter.R | f1aa7319bf12c114333e103152b7c874cf30f9fb | [] | no_license | skranz/shinyEventsUI | 2de099b12a05e313f94df5919a32ffc44cde4565 | e05f1eb202f15174f054be914aed9920aa60148b | refs/heads/master | 2021-07-12T05:23:36.408108 | 2021-04-01T11:18:40 | 2021-04-01T11:18:40 | 53,415,508 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,001 | r | tablefilter.R | example.table.filter = function() {
library(SeminarMatching)
script.dir = "D:/libraries/shinyEventsUI/shinyEventsUI/inst/www"
app = eventsApp()
n = 10
df = data.frame(a = sample(1:2,n, replace = TRUE), x = runif(n))
html = html.table(df,id="mytab")
app$ui = bootstrapPage(
HTML(html),
add.table.f... |
8ec94acca5dc1bd6495d21eb66429d580ba3cc3d | 3803bb7b319c8b37910d0fa42e20b73d4f40e031 | /data.R | 2d4703b6a4d1662efe250b26bec1c7a484010fa7 | [] | no_license | kartbilon/csv | d79a4e70f32bea52ff993e60c824ac07c4040ca7 | 2920c4569510e71285e159ef2363b8c1c80b788b | refs/heads/master | 2020-12-03T23:07:15.359026 | 2020-01-03T05:14:03 | 2020-01-03T05:14:03 | 231,516,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,191 | r | data.R | library(httr)
library(rvest)
library(RSelenium)
# 3.7초만큼 딜레이를 주는 코드
# testit <- function(x)
# {
# p1 <- proc.time()
# Sys.sleep(x)
# proc.time() - p1 # The cpu usage should be negligible
# }
# testit(3.7)
remD <- remoteDriver(port = 4445, # 포트번호 입력
browserName = "safari") #사용할 브라우저
remD$... |
b229d10e9bda7aca52d0e5bdf176762bb85b8438 | b47d0dee49601b05b9b7fa01b10717d1cd9564ee | /_site/tests/test-pairs.R | 257cce51def7805ff9aaa950c3bd130a41649fc5 | [
"MIT"
] | permissive | dchudz/predcomps | 445760dd4c33b0795ea97d8c219c12e1dbf8ab46 | cc3bf1155cc01f496da231af4facfc47d986d046 | refs/heads/master | 2021-01-02T09:26:13.537651 | 2018-06-26T12:31:36 | 2018-06-26T12:31:36 | 14,263,610 | 16 | 11 | MIT | 2018-06-26T12:31:37 | 2013-11-09T19:35:23 | R | UTF-8 | R | false | false | 1,022 | r | test-pairs.R | MakeComparable <- function(df) {
df <- round(df, digits = 5)
return(df[do.call(order, df), ])
}
test_that("GetPairs works right in a small example", {
df <- data.frame(X = rep(c(1,2),2),
Y = rep(c(2,4),2))
pairsActual <- GetPairs(df, "X", "Y")
pairsExpected <- data.frame(OriginalRowNumb... |
c08e529005e721b9f5b33cf3dd2b1fad49d31ac5 | 7d840154a12fc1012ea72cdba3032bcdd2ebfeee | /man/wbw.Rd | a22a76986cdca657faf4a24eef916f787b6e55df | [
"MIT"
] | permissive | bandyopd/cenROC | b875d2fb1b4dcae8e8eb682d71dba699b4393ff3 | 98bf995accc633fd6e5990abee65936233ea9ba2 | refs/heads/master | 2022-04-12T18:09:17.015754 | 2020-03-31T14:12:20 | 2020-03-31T14:12:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,405 | rd | wbw.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bw.R
\name{wbw}
\alias{wbw}
\title{Function to select the bandwidth parameter needed for smoothing the time-dependent ROC curve.}
\usage{
wbw(X, wt, bw = "NR", ktype = "normal")
}
\arguments{
\item{X}{The numeric data vector.}
\item{wt}{The ... |
80701ef07afa5c867844b9ce8c9df44a2373a839 | 19c861d31f78661a83c38a133edd8c4f6eac0336 | /man/revgray.Rd | 66cf3e5964c49ee4c0eceda0bcef162804d96dfa | [] | no_license | cran/broman | 8db38ff459ffda1645c01cb145b15aa4ea8e3647 | 90ae16237e25ee75600b31d61757f09edf72ad91 | refs/heads/master | 2022-07-30T16:46:40.198509 | 2022-07-08T14:30:09 | 2022-07-08T14:30:09 | 17,694,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 693 | rd | revgray.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/revgray.R
\name{revgray}
\alias{revgray}
\title{Create vector of colors from white to black}
\usage{
revgray(n = 256, ...)
}
\arguments{
\item{n}{Number of colors.}
\item{...}{Passed to \code{\link[grDevices:gray]{grDevices::gray()}}.}
}
\va... |
11e36eea63a1862524f20e488d674ddd166fc940 | 7d42b047a927c159e9e0a417eefdea85860c80c6 | /project/p3.R | 5c82beb2f15581096d680c79b6f42aa809d04198 | [] | no_license | gdwangh/coursera-dataScientists-8-Practical-Machine-Learning | b465ae76846b95a6639e45078377215665797d86 | f682e79d509b07f0669f8ada1d9a5c8af81c55b0 | refs/heads/master | 2020-05-20T05:54:00.153360 | 2014-12-07T15:25:15 | 2014-12-07T15:25:15 | 26,563,358 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,895 | r | p3.R | library(caret)
library(randomForest)
setwd("./project")
tmp_ds<-read.csv("pml-training.csv", na.strings=c("NA","","#DIV/0!"))
test_ds<-read.csv("pml-testing.csv", na.strings=c("NA","","#DIV/0!"))
set.seed(12345)
inTrain = createDataPartition(tmp_ds$classe, p = 0.75)[[1]]
train_ds = tmp_ds[ inTrain, ]
valid_ds = tmp_d... |
88d7ad05fc76c61a30e176d61c1c810f969b7385 | b1dfc3a819693c0cd4e840b741fb70a312383a44 | /animate_circadian_4cseq_again.R | 14069393a9b2adbbe11797683259147dad565542 | [] | no_license | jakeyeung/Circadian4Cseq | 36db093c7c1965bd86525b11d09afff0b749aa22 | 65d190e488d2e1720560b10b4991c36bd2e85524 | refs/heads/master | 2020-09-22T01:39:36.746885 | 2020-08-07T17:08:47 | 2020-08-07T17:08:47 | 225,005,564 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,200 | r | animate_circadian_4cseq_again.R | # Jake Yeung
# Date of Creation: 2018-10-28
# File: ~/projects/4c_seq/scripts/primetime_animations/animate_around_the_clock.R
# Animate batch 3 around the clock
# Stolen from: ~/projects/4c_seq/scripts/batch3_4cseq_analysis/load_robj_plot_summary.batch3.WT_vs_Cry1intronKO.R
rm(list=ls())
jstart <- Sys.time()
librar... |
eb7d279731de129c703bab4ebe3dca032aa8b2c2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/broom/examples/tidy.kde.Rd.R | 93c6ec366289dd822ec17353440f34c79a874b0b | [] | 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 | 488 | r | tidy.kde.Rd.R | library(broom)
### Name: tidy.kde
### Title: Tidy a(n) kde object
### Aliases: tidy.kde kde_tidiers ks_tidiers
### ** Examples
if (requireNamespace("ks", quietly = TRUE)) {
library(ks)
dat <- replicate(2, rnorm(100))
k <- kde(dat)
td <- tidy(k)
td
library(ggplot2)
ggplot(td, aes(x1, x2, fill ... |
99855d150e23192ca801ed45f051c46655b72b79 | 085377a522b1a43fe6cbb073ca61bcc8f0eadef6 | /R_course_updated.R | c56854feb26f8f5be13622e0fbee92b25f75beef | [] | no_license | aojgbenga/plymouth_r_machine_learning | 78c6fb89b86977fccad9e06cc96d34d91f75ad95 | aa93d29ca073b96d5d102182c7e98b2eb97dd0a5 | refs/heads/master | 2022-05-30T20:41:27.448597 | 2020-05-03T10:54:32 | 2020-05-03T10:54:32 | 260,251,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,936 | r | R_course_updated.R | library(dplyr)
library(ggplot2)
library(caret)
library(e1071)
library(class)
library(randomForest)
library(tree)
orchid <- read.table(url("https://gist.githubusercontent.com/CptnCrumble/e01af3b83ffc463f4bb5776d0213f14b/raw/5382eee21b6e5b796541fd8053c5f733fd6eb9c7/orchids.txt"))
attach(orchid)
#Plotting t... |
483712ce9ec283e43ec9bbc55551b7955bb6b3f9 | a94308678716ab60f03956e503f554a767e73733 | /ExampleCode/Chapter 2 Observed Score Methods/08_DIF Logistic Regression Polytomous.R | 22a91b48c9deb64858f86381f78eb00348a6dbf1 | [] | no_license | cswells1/MeasInv | 9f9cb20da68b695cc1f65fc5c80f92ea31b030e7 | b74acffcf8ec0d6886f7081882aa3965306eb4af | refs/heads/master | 2023-07-14T21:35:32.915150 | 2021-09-12T22:50:49 | 2021-09-12T22:50:49 | 405,707,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,815 | r | 08_DIF Logistic Regression Polytomous.R | #################################################################################################
# The following code uses Logistic Regression to test for DIF using the DIF.Logistic #
# function. #
###################################... |
141b87962a14d7d43cf619567b58c8bb9fb7ffeb | 48155a2d7a1a7614aff7b2d123a5347398590e03 | /docs/maps_for_pubs/map_for_pub.R | ecad688d2160c6efa30a48058d5deeb990624390 | [
"MIT"
] | permissive | remi-daigle/MarxanConnect | 6e5faee774c4adaf9444266a39cbe9ae00be05ca | c88658413beaeebff03a8a4831f1fddd2cc0c618 | refs/heads/master | 2021-12-22T18:06:51.292239 | 2021-10-20T13:29:47 | 2021-10-20T13:29:47 | 96,797,881 | 9 | 6 | MIT | 2020-10-27T02:01:06 | 2017-07-10T16:19:25 | Python | UTF-8 | R | false | false | 11,782 | r | map_for_pub.R | library(sf)
library(raster)
library(gridExtra)
library(tidyverse)
library(magick)
BIORE <- read.csv("maps_for_pubs/input/spec.dat") %>%
select(name) %>%
filter(grepl("BIORE_",name)) %>%
mutate(name=gsub("BIORE_","",name)) %>%
unlist() %>%
as.numeric()
proj <- "+proj=merc +a=6378137 +b=6378137... |
7bf0ca90fb54c437312e74f803736c0a1990b909 | 3f69b19b3720a81fb37070fc31304de639785ed7 | /man/Traj3DResampleTime.Rd | 3d3ffaff42705479d9844d73f613515817443c4a | [] | no_license | JimMcL/trajr | 42b0150b26dc2d3d7d3992cff4b4aca51dbd7d25 | 6998b877f258030df345c7d114e07c41158f3d8e | refs/heads/master | 2023-07-21T16:17:33.511137 | 2023-07-10T00:53:39 | 2023-07-10T00:53:39 | 111,262,381 | 21 | 7 | null | null | null | null | UTF-8 | R | false | true | 1,059 | rd | Traj3DResampleTime.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/3D.R
\name{Traj3DResampleTime}
\alias{Traj3DResampleTime}
\title{Resample a 3D trajectory to a constant time interval}
\usage{
Traj3DResampleTime(trj3d, stepTime, newFps = NULL)
}
\arguments{
\item{trj3d}{The 3-dimensional trajectory to be re... |
597771969653d62b0cfc64e57c8993d9d9b4c2ca | f5addbf749dd8bba26d15b891fc1e51ad95072f6 | /plot4.R | cda44e2a6f5d2156b7905aaf712ab2c27661af2b | [] | no_license | Layla-ElAsri/ExData_Plotting1 | 95aee14bb9fa083a9ecfe124d0712aba25c33f1a | 40d522819427b41a1e1e65d3cdb85ad56bf87057 | refs/heads/master | 2021-01-20T21:44:50.858410 | 2014-07-13T12:00:25 | 2014-07-13T12:00:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,166 | r | plot4.R | file = "household_power_consumption.txt"
data = subset(data.frame(read.table(file, header = TRUE, sep=";", na.strings="?")), Date=="1/2/2007" | Date=="2/2/2007")
dateTime = paste(data$Date, data$Time)
dateTime = strptime(dateTime, format='%d/%m/%Y %H:%M:%S')
png(file="plot4.png", width=480, height=480, bg = "transparen... |
e68d82d398dd20e1c6e1af981daff90bbab7afe1 | 04616643c5da76e475506c7fa96e40c25bd36555 | /R/init_queue.R | 49bae52239ed684a71121654ec754d510ba741eb | [] | no_license | edonnachie/queuer | 3139410cb8917922a75a42107b19481d47837cff | c255c35384f53e919db74d33b629ddac4f236b0e | refs/heads/master | 2021-01-10T12:30:13.101914 | 2017-08-18T15:47:07 | 2017-08-18T15:47:07 | 44,825,642 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 243 | r | init_queue.R | #' Initialise a queue directory
#'
#' @param queue Directory in which queue should be created
#' @return Nothing
init_queue <- function(queue){
if(!dir.exists(queue)){
dir.create(paste0(queue, "/archive"), recursive = TRUE)
}
} |
fca747d456d7f0ee5e62e110dd4ff56cf874e5bb | 489e56df1993b3d3cb56011ce79e1130f24c5dae | /subsetting_a_matrix.R | f46f1c08507f2f389718de1111ff727e170a08d5 | [] | no_license | adalee2future/learn-R | b50d84948861b95cf24919e3789773fcae94d36e | 8002acf15371271401428f7b3fcd8b37d96abfe7 | refs/heads/master | 2021-01-10T21:22:52.921444 | 2015-06-15T02:25:59 | 2015-06-15T02:25:59 | 23,581,876 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 96 | r | subsetting_a_matrix.R | x <- matrix(1: 6, 2, 3)
x[1, 2]
x[2, 1]
x[1, ]
x[, 2]
x[1, 2, drop = FALSE]
x[1, , drop = FALSE] |
f1509a19cadb4f7700f62b788a45c3893e0c26f7 | 76abe33b0dac505b1f7d771c799e18b57a8f4417 | /shiny/Change the appearance of the dashboard.R | 808ce8921b68a2a239af2530db7d576b48180519 | [] | no_license | jyeazell/DataCamp_practice | 4ddaf889b07a2ef3fcd0965bee7d71372e3eb2f3 | de4443e01d5414913aa555a5771d5eadc9f83700 | refs/heads/master | 2022-12-19T23:27:19.410533 | 2020-10-09T20:31:07 | 2020-10-09T20:31:07 | 183,300,581 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | Change the appearance of the dashboard.R | ##'Change the appearance of the dashboard
##'
##'Walt Disney's favorite color is purple (mine too!). Using the application
##'you've just created, let's change the color by updating the skin parameter
##'to "purple". The body you just created is already loaded.
# Update the skin
ui <- dashboardPage(
skin = "purp... |
014c8003a3612ce7fb53507c273c02226044f08f | 3bf5ba75fd0e5044c0d1bc60f53bf6de2a439965 | /complete.R | 415d20847a71a7814debd76e97c654a7a301ff1e | [] | no_license | YChekalin/ds-repo | 51af396e0420909e267c7a0191a56690ec5008c7 | 690ede515f1eaeac5f175c445e53cee076a5fce6 | refs/heads/master | 2021-01-13T02:06:53.792602 | 2015-02-19T04:06:34 | 2015-02-19T04:06:34 | 30,549,922 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 457 | r | complete.R |
complete <- function(directory, id = 1:332) {
data <- data.frame()
nobs <- data.frame(id=id,"nobs"=0)
file_list <- list.files(directory, full.names=TRUE)
for(fname in file_list) {
tmp <- read.table(fname, header = TRUE, sep = ",")
tmp <- subset(tmp, ID %in% id)
data <- rbind(data,tmp)
}
... |
7a8245c3bf484b4b85cdb91eef227fe0f92dcc4c | 469614d19085ebac7158d06fb10078707a0e5061 | /OxBS_processing.R | b3587d854acd977d9fc249c0f95c1c518cfc96c6 | [
"MIT"
] | permissive | estoyanova/EStoyanova_eLife_2021 | 9781fa68abb8ea1997de97b94fceac839a586997 | 4c2fa6c102e4f868fc91e0dcb0b8c7155b8f8712 | refs/heads/main | 2023-07-02T14:50:58.195174 | 2021-10-27T01:57:19 | 2021-10-27T01:57:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 801 | r | OxBS_processing.R |
# QC and trimming of FastQ Files
trim_galore --stringency 3 --fastqc --paired --clip_R1 5 --clip_R2 $R1.fastq $R2.fastq
# Alignment with Bismark
bismark --bowtie2 -p 4 --multicore 4 bismark_genome_build/ -1 R1.fq -2 R2.fq
deduplicate_bismark -p --bam inputfile.bam
# Sequencing quality control with CEGX custom
do... |
5f581845d623a9575b0da58f63e4892ec0286024 | f5722d02cdd053c95eb3e5feb64b4e1338564f5f | /rb.R | 827cfd56d433a49c1cd252b0d2d40973ced28f17 | [] | no_license | sethf26/NFLSuccessFromCollegeStats | 1976a4884f72d9235a2299fc280b26c5b1b7b4e7 | d913bd2842b3cf60d2c08420e5b7444a466ad32b | refs/heads/main | 2023-02-04T11:13:16.099004 | 2020-12-14T23:36:17 | 2020-12-14T23:36:17 | 304,743,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,964 | r | rb.R | tabPanel(
sidebarPanel(
h3("Regression of Running Backs prior Stats on NFL Value and Statistics"),
selectInput(inputId = "selection2",
label = "Choose a position",
choices = c("NFLYPC" = "RB$NFLYPC ~ RB$College.YPC + RB$college.rushattmpt.pg + RB$college.rushtd.pg + RB$College.... |
93610ecfb4399863d421d74fad1faecf9cb455d4 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/cairoScaledFontGlyphExtents.Rd | f50363fbb957a00edef1dc56a5c758565358941f | [] | 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 | 1,303 | rd | cairoScaledFontGlyphExtents.Rd | \alias{cairoScaledFontGlyphExtents}
\name{cairoScaledFontGlyphExtents}
\title{cairoScaledFontGlyphExtents}
\description{Gets the extents for a list of glyphs. The extents describe a
user-space rectangle that encloses the "inked" portion of the
glyphs, (as they would be drawn by \code{\link{cairoShowGlyphs}} if the cair... |
3b7e8273e0abf7c33b18c6bb77142f4772a05a91 | 2b75a4b36bcdf7ca3bc1a80b8ee391e25318bfca | /man/read.moleculelist_thunderstorm.Rd | 6659368d7a00818fb59278408086639a313315c2 | [
"MIT"
] | permissive | keithschulze/supr | c67374d737ef8e77966ae057e8f6219490ce6a6a | 27902504757e33d1e4e3c7d3ba6baf0d7b808848 | refs/heads/master | 2022-12-03T07:02:25.029644 | 2022-11-23T07:21:50 | 2022-11-23T07:21:50 | 43,782,983 | 0 | 0 | null | 2016-05-12T03:34:19 | 2015-10-06T22:41:46 | R | UTF-8 | R | false | true | 634 | rd | read.moleculelist_thunderstorm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/io.R
\name{read.moleculelist_thunderstorm}
\alias{read.moleculelist_thunderstorm}
\title{Thunderstorm molecule list reader}
\usage{
read.moleculelist_thunderstorm(filepath)
}
\arguments{
\item{filepath}{string denoting the path to the file to... |
a8e3f47da86550528b50c4d44eda7da64ff82cf4 | 04efe01489384e0babe71e1a0548e9d589c32166 | /Heping/S7MLR2.R | 1514794e6eccf67296550d60dd0f402432d896cb | [] | no_license | hpzheng/sys6021_codes | 5749645527373bd6c86b05f1bddfdcb791f04cc3 | 13f87e0bec8e5f198f8cdee157b65d6e61d2d753 | refs/heads/master | 2021-01-10T10:07:40.921116 | 2015-10-14T17:59:38 | 2015-10-14T17:59:38 | 44,264,256 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,257 | r | S7MLR2.R | # Session 7
#
# Multiple Linear Regression 2
#
#******************************************************
#load data
source("AccidentInput.R")
setwd(traindir)
my.path <- getwd()
setwd(my.path)
acts <- file.inputl(my.path)
sapply(acts, dim)
dim(acts[[12]])
setdiff(colnames(acts[[1]]), colnames(acts[[8]]))
... |
a253824479514c0f59a4d3bde88bc75c786bd4ab | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PLRModels/examples/plrm.ci.Rd.R | 0451962195e7cbc2181b9c1f9f4cfe80a8b84f56 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,000 | r | plrm.ci.Rd.R | library(PLRModels)
### Name: plrm.ci
### Title: Confidence intervals estimation in partial linear regression
### models
### Aliases: plrm.ci
### Keywords: Statistical Inference Regression Time Series
### ** Examples
# EXAMPLE 1: REAL DATA
data(barnacles1)
data <- as.matrix(barnacles1)
data <- diff(data, 12)
data ... |
68b23b8bb3f951e5d3eb2afaa6a6a7b0b019891a | 96a66b3b1e65e1a25951349d03bf122c1879f08d | /man/analyze.Rd | 0d222c719c0a1120def33b897c0668cb55111134 | [] | no_license | cran/LN3GV | 66ed033943771074f9c1252d7ccdf4d4656d227c | 6945374aa31fe40d2c06fda296290f58b58c3151 | refs/heads/master | 2016-08-04T08:25:25.833761 | 2011-07-26T00:00:00 | 2011-07-26T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,943 | rd | analyze.Rd | % File src/library/LN3GV/man/analyze.Rd
\name{analyze}
\alias{analyze}
\title{Fit a hierarchical model to matrix of normalized microarray data}
\description{
Analyze microarray data using the methods detailed in Lund and Nettleton, 2011. This is the main function of the LN3GV package.}
... |
75775ad2cbe47ecdad8e58cfd5aa768ee067a5ec | a8c5d8db03159c81e0b296f18179d3cd1c56ae00 | /man/package_glob.Rd | d683af383acdb9414404e00a8e816df9f5e98d39 | [] | no_license | cran/deepredeff | a42ccc7e75ae2d903496d31715a17bf01a94e3ea | 9b2888b98bf1c4143f499768355ab058d4703cde | refs/heads/master | 2023-06-18T04:24:21.440705 | 2021-07-16T08:30:02 | 2021-07-16T08:30:02 | 307,944,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 325 | rd | package_glob.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{package_glob}
\alias{package_glob}
\title{Wildcard Expansion on File Paths}
\usage{
package_glob(..., pattern)
}
\arguments{
\item{...}{Path}
\item{pattern}{Pattern}
}
\value{
Glob
}
\description{
Wildcard Expansion on File Pat... |
cfe87a13f160ea5a70754970eb8f1db04818e081 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/powdR/examples/fps.Rd.R | 918a76fcf46d313c215b297527222c84456b645c | [] | 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 | 984 | r | fps.Rd.R | library(powdR)
### Name: fps
### Title: Full pattern summation
### Aliases: fps
### ** Examples
#Load the minerals library
data(minerals)
# Load the soils data
data(soils)
#Since the reference library is relatively small,
#the whole library can be used at once to get an
#estimate of the phases within each sample.... |
e73c1b2a31609b50c2de9cae5857dae002d4d3f0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/nor1mix/examples/rnorMix.Rd.R | ce735873c1b589ed935e670ae87369f86ac4bea1 | [] | 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 | 344 | r | rnorMix.Rd.R | library(nor1mix)
### Name: rnorMix
### Title: Generate 'Normal Mixture' Distributed Random Numbers
### Aliases: rnorMix
### Keywords: distribution
### ** Examples
x <- rnorMix(5000, MW.nm10)
hist(x)# you don't see the claw
plot(density(x), ylim = c(0,0.6),
main = "Estim. and true 'MW.nm10' density")
lines(MW.n... |
a1fa00b26aa825069bf7790adfc5f15aab3919bf | 6c1926b99503f6304d35ba383538c9c365242bb1 | /man/get.var.Rd | f636a01b8bdb8c2fed30ceb83d0fa239041bfe80 | [] | no_license | smorisseau/dhstools | 56e1451de1124ac0f7943c7710a03a13b5fcca22 | a8ba0addb7cae06cf085ebe08e9136bef04ed87f | refs/heads/master | 2021-01-17T15:33:10.641739 | 2014-03-25T15:37:50 | 2014-03-25T15:37:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,008 | rd | get.var.Rd | \name{get.var}
\alias{get.var}
\title{helper fns for dhstools}
\usage{
get.var(survey.data, var, default = NA)
}
\arguments{
\item{survey.data}{the survey dataset}
\item{var}{either NULL, a column name, or a vector of
values}
\item{default}{the default value to fill in if the
variable is not found}
}
\val... |
197980e3a6c7f78789225305351fa0bbbc0e0cf7 | 8dc8fc6b022a02db14ed39d82cd222a4c17df3eb | /scripts/mir_gene_families.R | 33af2ced701ab2f1e4cff2d4740bb9638d316514 | [
"MIT"
] | permissive | BleekerLab/small-rna-seq-pipeline | 977c9da4bde224726beaae5599ec1331d2430e62 | bbd9ef8c6741dc59f3cdafc35da9cb6ea48b31f9 | refs/heads/master | 2022-09-10T04:12:24.630033 | 2022-08-01T18:01:12 | 2022-08-01T18:01:12 | 172,049,835 | 5 | 2 | MIT | 2020-11-17T13:26:02 | 2019-02-22T10:51:51 | Perl | UTF-8 | R | false | false | 1,001 | r | mir_gene_families.R | suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(RColorBrewer))
suppressPackageStartupMessages(library(svglite))
# capture the command-line arguments after --args (e.g. the shortstack results directory)
args <- commandArgs(tra... |
be45656e6a0bb4aea149b698e34513ef5aeb4bb3 | 7dd51c0c6137f8a32a6e2f265874acfcb0c0b5f8 | /old20200619/code/02_MDSC_MAC1.R | 49ccee081876c63cc0f7aa87c296ebc47ae9ef4f | [] | no_license | Winnie09/GBM_myeloid | 7d3c657f9ec431da43b026570e5684b552dedcee | a931f18556b509073e5592e13b93b8cf7e32636d | refs/heads/master | 2023-02-04T04:29:50.274329 | 2020-12-20T21:16:16 | 2020-12-20T21:16:16 | 268,260,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,556 | r | 02_MDSC_MAC1.R | library(Matrix)
source('/home-4/whou10@jhu.edu/scratch/Wenpin/trajectory_variability/function/01_function.R')
# pseudotime <- readRDS('/home-4/whou10@jhu.edu/scratch/Wenpin/GBM_myeloid/data/order/MDSC_MAC3_NEU1.rds')
pseudotime <- readRDS('/home-4/whou10@jhu.edu/scratch/Wenpin/GBM_myeloid/data/order/MDSC_MAC1.rds')
rdi... |
fac822ee49fa0b6f8d4a51339caf370af8ca41e3 | c28c69b1600f046e15824b810deedb5461182b3f | /inst/shiny/global.r | e509d1153c3b292c52b7115673d37708eba250f7 | [] | no_license | choi-phd/TestDesign | 14bd43139f54d377e96d5d0ee3d12427736cd8a6 | 58b5fb1c09b0dc8264173fe485e3de5f251f0715 | refs/heads/main | 2023-04-19T20:42:23.427342 | 2023-03-18T20:46:52 | 2023-03-18T20:46:52 | 174,591,899 | 4 | 6 | null | 2023-01-27T01:20:16 | 2019-03-08T18:52:45 | R | UTF-8 | R | false | false | 1,253 | r | global.r | library(shiny, quietly = TRUE)
library(shinythemes, quietly = TRUE)
library(shinyWidgets, quietly = TRUE)
suppressPackageStartupMessages(library(shinyjs, quietly = TRUE, warn.conflicts = FALSE))
library(DT, quietly = TRUE, warn.conflicts = FALSE)
library(TestDesign, quietly = TRUE)
solvers <- c("lpSolve", "Rsymphony",... |
78ca2563f01d63edb285bea73fffa27c942915b5 | 2cbc1106a7ed4b57df979a267bd8ee3cd7f6c2c7 | /man/wiki_graph.Rd | 43ca10d5105b3378c214a18bbd5ebaae810edf98 | [] | no_license | Dap246/Lab03 | 24b9f474f4d9728c55914de7d7112ee4476af426 | e659ea696fc5ed2a70707a4cc3dedb1e06c0e9c3 | refs/heads/master | 2023-08-26T03:49:58.811515 | 2021-09-14T20:08:05 | 2021-09-14T20:08:05 | 406,218,948 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 613 | rd | wiki_graph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{wiki_graph}
\alias{wiki_graph}
\title{Graph for Dijkstra's algorithm.}
\format{
A data frame for a graph of 6 nodes and the distance/weight between them:
\describe{
\item{v1}{vertices 1-6}
\item{v2}{vertices 1-... |
20b5966637365cb1e14fc4f21f9c10555fc0b3ab | d4747fe5f7b17988c292573faeecb13e3b4f235e | /R/multiple.R | cb0de01fe6adbf912b3e68f6b829ada25d234c0c | [] | no_license | ElieLP/MultipleR | e85865fb200288aed61cd00ef027f02cec937bb9 | 23ff9ec300b66555b767b19787bf3d9c3326875a | refs/heads/master | 2020-03-15T13:42:37.935214 | 2018-05-04T18:27:54 | 2018-05-04T18:27:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 519 | r | multiple.R | #' Double
#'
#' @param x Number
#'
#' @return The double of x
#' @export
#'
#' @examples
#' Double(2)
#' 4
Double <- function(x = 1) {
return(x*2)
}
#' Triple
#'
#' @param x Number
#'
#' @return The triple of x
#' @export
#'
#' @examples
#' Triple(3)
#' 9
Triple <- function(x = 1) {
return(x*3)
}
#' Multiple
#'
#... |
23c536d375cd03ead23e6a8f102bb319df73904d | be1a186d90ed7435615b3b10b97a16168dee2498 | /R/generate_packets.R | 891c63f7f2f76226d4a5e6299bbfb6ef08882bf4 | [] | no_license | paulmeinz/lrdatapacket | 505a7999464ff01d343ce8b1226c88cb3dd0ae9f | 801d4f96b0c0b8936aacf154183d609bc686c0d4 | refs/heads/master | 2021-01-21T13:34:01.186764 | 2016-05-03T16:13:43 | 2016-05-03T16:13:43 | 47,354,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,480 | r | generate_packets.R |
generate_packets <- function(data,
path,
use_subject = 'TRUE',
program = '',
program_short = '') {
subjects <- unique(data$subject_long)
if (use_subject) {
for (i in subjects) {
plot_da... |
93ccc292a4eb4a9b237d44671da6366db598cec7 | 75f8a0d750aa880f5eaf72aafe9acba8746d9656 | /lectures/12/scripts/beeline.R | 5eff024d7d574f737d96e8c772f666819b9d35a7 | [] | no_license | dkhramov/iad_2020 | abe75b34c5fb422b1eb7ad320827a7253a7fb03d | 701b9eb08f65c0262808717549c369b270883a14 | refs/heads/master | 2021-02-06T22:43:27.924767 | 2020-03-20T12:19:24 | 2020-03-20T12:19:24 | 243,954,164 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,435 | r | beeline.R | #### Предварительная обработка данных
#### По мотивам статьи "Как я победил в конкурсе BigData от Beeline"
#### https://habrahabr.ru/post/270367/
train <- read.table(unzip("../data/train.zip"), header = T, sep=",")
str(train)
# x8
# x23-x61
## Шаг 1. Предварительный просмотр факторов
# факторы - переменные в ном... |
8b6dec9831683b1b7c0d55a1e452759e302ab939 | 730ec6f7b8046c842ee4b7d35bdace9cfd75f202 | /man/dada.Rd | a71711ac33e9d90bde608486a77f98feb350d8f9 | [] | no_license | cmsmoo/dada2 | e0a4d8a4eef1727bc0bfaf155a57a2c30a812111 | 0c99d4e6cf6d71c8733cd46fa31ada5df683fa3d | refs/heads/master | 2021-01-22T16:13:42.488161 | 2016-02-23T01:42:05 | 2016-02-23T01:42:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 5,103 | rd | dada.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dada.R
\name{dada}
\alias{dada}
\title{High resolution sample inference from amplicon data.}
\usage{
dada(derep, err, errorEstimationFunction = loessErrfun, selfConsist = FALSE,
aggregate = FALSE, ...)
}
\arguments{
\item{derep}{(Required).... |
6e68586f630ff2f5a4d3f2aca722049aa0ac0ee3 | 5dc8f146c468481e8fbda425d4a71a0fe0182f63 | /chap03/qplot.R | 0305843865a2889d725600e1ecc84fe04782e17d | [] | no_license | kyh8874/R | 12fe23f129c726dd3a360fd780f96416a0d70ab8 | b435c030691e013df5386d049dcc79b09ac6f17e | refs/heads/master | 2020-04-28T04:05:57.329401 | 2019-05-01T03:02:38 | 2019-05-01T03:02:38 | 174,964,608 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 515 | r | qplot.R | sample(seq(1,10, length.out=100), replace = T, size = 1000) -> data1
length(data1)
str(data1)
table(data1)
qplot(data1)
sample(c('a','b','c','d'),100,replace=T, prob=c(0.2,0.5,0.9,0.3))->data2
table(data2)
qplot(data2)
library(sqldf)
c(80, 60, 70, 50, 90)-> exam
exam
sum(exam)/5 -> avg_exam
avg_exam
mean(exam)
... |
6e316f16f5ca33c9306f0c5a4018e8ae012ff843 | 05d2e2061c8a8383b6ff43f646b635a5b34eb413 | /Splicing_Workflow/CPM_feature_table.R | 9898407f35d1244c968930a4fe9b441aca0056c5 | [] | no_license | cwarden45/RNAseq_templates | 6a0a971985bff86e405582a441313517f478e192 | 281ecf76ce852a3b9302ea89674f65fe7b68292f | refs/heads/master | 2022-06-18T05:15:17.374337 | 2022-06-09T21:19:45 | 2022-06-09T21:19:45 | 60,568,054 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,556 | r | CPM_feature_table.R | normalizeTotalExpression = function (geneExpr, totalReads) {
return(geneExpr / totalReads)
}#end def normalizeTotalExpression
param.table = read.table("parameters.txt", header=T, sep="\t")
sample.description.file = as.character(param.table$Value[param.table$Parameter == "sample_description_file"])
count.folder=... |
4ed8efc6fe88316fd40e8d5787adb53876bf2152 | 2b9b26a5aee3dd3bf9ec492ec0d53f7a9d76d7ec | /in-work/gadgets/gadget_clean_rows.R | 74d4bc911500ea70d26bec5dd41364b56354f6f0 | [] | no_license | whaleshark16/teachingApps | 808d7cb282aaedbcd7d3c389bd5f7f01ebbac7ff | 47f9f5275719087575565d5facfb6c94a5ff5d53 | refs/heads/master | 2021-09-07T17:06:21.479444 | 2018-02-26T15:22:00 | 2018-02-26T15:22:00 | 118,481,453 | 0 | 0 | null | 2018-02-22T18:27:25 | 2018-01-22T16:12:55 | HTML | UTF-8 | R | false | false | 3,088 | r | gadget_clean_rows.R | #' Subset data using column values
#'
#' @description Shiny gadget used to visually inspect column values in a data set
#' and subset rows by specifying column values
#'
#' @param data A data set
#' @param colorBy \code{character} Column by which the \code{parcoords} plot should be colored
#' @param theme \code{charac... |
10af5a3813dd1df1f1ffad30bfb5a6af77168255 | 57ed22671d2c348fe35c7832fd008c3a51de039c | /R/lea_prep.R | 4fce69aa14028eeaa6f03df737276a7c755fc19b | [
"MIT"
] | permissive | datalorax/leaidr | 694c4d1d6d7773454673876d3c982d0db49f80b7 | 26f4672c98cae96a6ecc96e9c705890ed7a8ecb7 | refs/heads/master | 2022-11-20T13:38:44.794448 | 2020-07-27T21:42:36 | 2020-07-27T21:42:36 | 281,791,912 | 1 | 0 | null | 2020-07-22T22:02:47 | 2020-07-22T22:02:46 | null | UTF-8 | R | false | false | 1,138 | r | lea_prep.R | #' Prep State- or Nation-Wide District Shapefile
#'
#' @name lea_prep
#' @aliases lea_prep
#' @export lea_prep
#'
#' @description
#' `lea_prep()` creates your desired shapefile.
#' @usage
#' lea_prep(path = NULL, fips = NULL)
#'
#' @param path A character vector specifying a file path, such as: path = "./test".
#' @p... |
0c6cf68803dc8670d9eb13c15d1222b1489ef1ce | 1b3e04e8978911acdad27507efe9c7f7bc6d68d3 | /R_Differences_Met31Met32.R | 463826ff83d76fbb9168141cc2160172fd490fbc | [] | no_license | r-scott-m/TimeSeriesFitting-R | 9a2f8249afa4b0b2719b7835315389ea28cd978e | dbeec39f1024d6c6641cba73bebb0d24e48769f7 | refs/heads/master | 2020-04-15T04:29:35.612004 | 2019-01-07T06:04:37 | 2019-01-07T06:04:37 | 164,385,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,336 | r | R_Differences_Met31Met32.R | require("qvalue")
require("gplots")
#Allegra <- read.table("Allgera_M31M32Different.txt",header=FALSE)
#row.names(Allegra) = Allegra$V2
WD = "/Users/smcisaac/Downloads"
setwd(WD)
y <-read.table("DataMetLimitedMerged_2011_1122_pruned_knn_clustered_SVDsubtract_RECLUSTER.txt",header =TRUE, row.names = 1, sep ="\t")
... |
3e2804f9bfd4d85da99d8c1da9b9c5e756ab75a7 | 00372f4ec66183c56629cf49be73faa016cc651b | /man/svyglmParallel.rd | 0b8612174182e616da944e5aca38181588928197 | [] | no_license | kevinblighe/RegParallel | 9427d66904a3965a3e82197cd0569ab6c83afeff | 818b7b4c60d3e8a49549cbd02fb8a3009e6fe6f9 | refs/heads/master | 2021-11-06T15:16:32.386110 | 2021-10-01T23:16:39 | 2021-10-01T23:16:39 | 103,679,101 | 36 | 8 | null | null | null | null | UTF-8 | R | false | false | 2,474 | rd | svyglmParallel.rd | \name{svyglmParallel}
\alias{svyglmParallel}
\title{Standard regression functions in R enabled for parallel processing over large data-frames - generalised linear model, with survey weights}
\description{This is a non-user function that is managed by RegParallel, the primary function.}
\usage{
svyglmParallel(
dat... |
784e2ba856ea64a9b9c2a09ff00cbbfa5a592adf | 17e9b666d8447caa58381f2502980b8f3ec7f466 | /R/RInAction/4-1.r | eece0cba9f85a0b8789ccb088f3ece7fc6922cbb | [] | no_license | hangyan/Code | 079ac796a309abc0a1d8b8a61baeac645c5b791e | 2a43a676a00f41afc05d7a5c8aa52b714195e8c9 | refs/heads/master | 2021-08-01T04:15:52.828481 | 2021-07-26T05:32:09 | 2021-07-26T05:32:09 | 25,571,986 | 6 | 16 | null | null | null | null | UTF-8 | R | false | false | 1,765 | r | 4-1.r | manager <- c(1, 2, 3, 4, 5)
date <- c("10/24/08", "10/28/08", "10/1/08", "10/12/08", "5/1/09")
country <- c("US", "US", "UK", "UK", "UK")
gender <- c("M", "F", "F", "M", "F")
age <- c(32, 45, 25, 39, 99)
q1 <- c(5, 3, 3, 3, 2)
q2 <- c(4, 5, 5, 3, 2)
q3 <- c(5, 2, 5, 4, 1)
q4 <- c(5, 5, 5, NA, 2)
q5 <- c(5, 5, 2, NA, 1)... |
232bdeb4a2f64827dcc13569e82a044b76de2fa6 | 0f96b45966da3fd162b7d1810f413d23da242139 | /PackageCH/R/ComputeGManually.R | 6b4f5aa89095f6b81afce293153532894fb07c8e | [] | no_license | duvaneljulien/PackageCH | 0e281ba39edb0fd87678e96341f391661a7b57f8 | 9c127f8c5a0a33918cee76e3413a6b9fdade4328 | refs/heads/master | 2020-05-07T22:14:09.540087 | 2015-01-15T03:30:26 | 2015-01-15T03:30:26 | 29,278,853 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,334 | r | ComputeGManually.R | library(doMC)
library(foreach)
build_matrix_G_manually <- function(path.snps = "data/SNPs_14102014_145630.raw") {
N <- 20
# Get results (stored into SNPS_datetime.stamp.raw)
Phenotypes <- read.table(path.snps, header = TRUE)
# Remove rows with NA's
Phenotypes <- Phenotypes[complete.cases... |
573d0c2db25a90a8f80f7712e7b59cb6703c4623 | 7d4841b039093f3009eb72a65ec0d506a1244082 | /figures/Figure 4/plot_UE3_heatmap.R | 530e9d6bb2cafed101a163003450cf8ee4779c81 | [] | no_license | sturkarslan/evolution-of-syntrophy | dfd18b1ec0181c5b64ef54f358aa246ca945475b | 5867dc719bdbdc4e4c98a12b8131e397b671b29f | refs/heads/master | 2021-08-08T22:03:47.013067 | 2020-06-23T05:31:37 | 2020-06-23T05:31:37 | 194,342,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,395 | r | plot_UE3_heatmap.R | ############# Serdar Turkarslan / Institute for Systems Biology ###############
# Last update: 06/05/2020
###############################################################################
# Plot heatmap for comparing mutations for UE3 line of Dv and Mm
#
##################################################################... |
7606a692e6ddce794bd065dc4d98bfa9acf64056 | c85471f60e9d5c462de6c60c880d05898ec81411 | /cache/gdatascience|tidytuesday|tv_ratings.R | 1c23a74ccdd0e528b8b7c04fc92abf67a3e9922a | [
"CC-BY-4.0",
"MIT"
] | permissive | a-rosenberg/github-content-scraper | 2416d644ea58403beacba33349ee127e4eb42afe | ed3340610a20bb3bd569f5e19db56008365e7ffa | refs/heads/master | 2020-09-06T08:34:58.186945 | 2019-11-15T05:14:37 | 2019-11-15T05:14:37 | 220,376,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,957 | r | gdatascience|tidytuesday|tv_ratings.R | ## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ------------------------------------------------------------------------
library(tidyverse)
theme_set(theme_light())
tv_ratings <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidyt... |
bbf09f92f36f03e66da756313aa1ab916ce604db | e6c710bca8f7ff0addbbb1e6b59d4c374e778a47 | /tests/testthat/test-genbody.R | 95f5b9713bf8fb9653b0768ffc2cd23678e5bb3e | [] | no_license | liubianshi/tabreg | 480017c09221339ff8928121672613b14d62532d | 364ad6c5bbf4ae3c1c786295049dfa18194e2ba2 | refs/heads/master | 2023-02-16T19:53:17.074809 | 2021-01-05T03:57:04 | 2021-01-05T03:57:04 | 223,850,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,775 | r | test-genbody.R | context("generate body data.table from estimate result")
l.reg <- local({
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
lm.... |
24d5b505c0269ee1e8cad294858365bf14eef92c | 288cd32888512c1f309bf32410ab27eebfd19745 | /Code/Other_plots/Experimental_data.R | f900c2d92f432fc16e9b393b809e98a22d187211 | [] | no_license | sebapersson/SUC2_paper | 297e8ea8357bf08208071bae374ec5cc083f195d | 3bc8bfe91d39139c2e1e9c4eb1da818bd574da54 | refs/heads/master | 2023-01-01T07:39:53.611278 | 2020-10-15T12:56:35 | 2020-10-15T12:56:35 | 255,879,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,261 | r | Experimental_data.R | library(tidyverse)
library(ggthemes)
library(stringr)
# This file produces the plots for the invertase experimental data. To keep a consistent
# graphical profile through the paper, all results are plotted (including non model results
# are plotted in R).
# Outputs:
# A bar-chart showing invertase activity in hi... |
40ca5e3e704da4bd645c67188a5460cfcf85e601 | f3f5b8a3ee512ac1d3e540eb9e293642a2373ce7 | /creditmodel_필요한함수선정.R | 33c2cbd66505a64bfe467bd1843cf665ff4f39a2 | [] | no_license | Yang-Munil/diabetes_related_occupation_in_Korean_population | 184abc2440f2c9a7dee28f2524692524de19d218 | 36ce066fb6dc794068d56bcccc4091f5a13e2fa4 | refs/heads/main | 2023-07-08T14:55:10.525942 | 2021-08-12T10:42:56 | 2021-08-12T10:42:56 | 346,424,723 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,145 | r | creditmodel_필요한함수선정.R | library(creditmodel)
library(ggplot2)
# equal sample size breaks
equ_breaks = cut_equal(dat = UCICreditCard[, "PAY_AMT2"], g = 10)
# select best bins
bins_control = list(bins_num = 10, bins_pct = 0.02, b_chi = 0.02,
b_odds = 0.1, b_psi = 0.05, b_or = 0.15, mono = 0.3, odds_psi = ... |
ea9ba0543ff85a236dc896205289b8a773c60c36 | 35f2d27328ea1ce21fe6b88e4581784195d5b6d9 | /Shiny/Plots/test.r | 106a2a66ea565754598b0c2df81705eada0a606d | [] | no_license | alfcrisci/devium | a5430872af7b60d4c68b858f84506f7627cd6190 | 2d0f342fa50d9d9b45affea04743f21844134ff9 | refs/heads/master | 2021-01-18T19:14:59.333668 | 2013-09-25T22:52:54 | 2013-09-25T22:52:54 | 13,155,442 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 136 | r | test.r |
install.packages("shiny")
library(shiny)
runGitHub("Devium", username = "dgrapov",ref = "master", subdir = "Shiny/Plots", port = 8100)
|
db213aa6a8e16dfc64474e086bec87d687b86598 | 04c2710db9de87bff22def23f926c3ef7d804614 | /r_plotting.R | aeb0ea50867f1297f2dcd0812711d4424c0ee412 | [] | no_license | jhess90/classification_scripts | 840b76372954f876903af1ffc46467c0d698ca8b | ab3501b21b3f40f9d138493c6beda9fbf2b4a3ee | refs/heads/master | 2021-01-21T14:24:47.783155 | 2019-04-02T16:43:18 | 2019-04-02T16:43:18 | 59,316,441 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,217 | r | r_plotting.R | library(openxlsx)
library(ggplot2)
library(reshape2)
#source("~/dropbox/mult_rp_files/r_test/multiplot.R")
source("~/Dropbox/mult_rp_files/r_test/multiplot.R")
library(zoo)
library(gplots)
library(RColorBrewer)
library(abind)
library(gridGraphics)
library(grid)
library(gridExtra)
saveAsPng <- T
file_list <- c('nl_avg... |
af1be1e7a89cdc544ab33e52dd3059c3b7ca95a0 | 7218dd41cbe126a617485cd463f9d6a93dfc1eb9 | /man/sgp_small_multiples.Rd | a074743be33f8667a3042ade1a197be54af57262 | [] | no_license | almartin82/MAP-visuals | 0c171b37978cefed94d46336457e0ef8a672c669 | 28102126dc4b40c6566a6f20248d4f871613253a | refs/heads/master | 2021-01-10T19:22:04.324129 | 2015-06-11T18:38:42 | 2015-06-11T18:38:42 | 10,979,358 | 1 | 1 | null | 2015-02-05T19:41:56 | 2013-06-26T21:16:19 | R | UTF-8 | R | false | false | 514 | rd | sgp_small_multiples.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/sgp_small_multiples.R
\name{sgp_small_multiples}
\alias{sgp_small_multiples}
\title{SGP small multiples}
\usage{
sgp_small_multiples(df, stu_per_row = 12)
}
\arguments{
\item{df}{long data frame in TEAM canonical style}
\item{stu_per... |
15a521bf5de7ec92a793bba50d34e4fd662ff45e | 82b4ac6a93625c4b73a3792fd338c1f6744213d6 | /figures/figures.r | fadaeba4be14d58eeca2f9dccbe6554ebbe305cc | [] | no_license | kroppheather/larix_density_ecohydro | 0f2c55dc6390947e1d143b7397b3931a0af4b9c7 | 58c2b1312e43daffe69631ad9bce588608fbf01b | refs/heads/master | 2020-04-10T20:44:51.926019 | 2019-02-01T15:06:14 | 2019-02-01T15:06:14 | 124,288,800 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,669 | r | figures.r | ###########################################################################
###########################################################################
############## Created by Heather Kropp in October 2017 ##############
############## This script creates figures for all time ##############
############## s... |
a4310c6544f253330d218a0d4a696438ab02b6aa | 388bda89408c41d303d4ded330323a0edabfd8cc | /Scripts/server.R | 1e2c0d1f2dc9015ebca3d89daaf92dbdfdcd6976 | [] | no_license | edwardhalimm/DrugSeizure | 51efe4603b09dbdbebe3f42d284496046c39ef1c | 6a262c5f7bc8a7bd5a9de2f8af2d4edae8df8e3c | refs/heads/master | 2020-04-06T17:37:48.704358 | 2019-01-29T21:29:57 | 2019-01-29T21:29:57 | 157,667,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,025 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(leaflet)
library(httr)
library(dplyr)
library(readxl)
library(shiny)
library(shinydashboard)
lib... |
3f16eaf6f9c0b378410efa25960e3925060354fe | 65d97a10d91455337de2d04160a2d88e32fef34f | /package/pathtemp/R/pathsample.R | 40569ccb108c0b6e580079b7dc0fffe410a221e6 | [] | no_license | yao-yl/path-tempering | 1552c9da8575c8af45350c39a45c6966d9085e94 | 21778401020b6820d7c38bbc062534f50d5efd8b | refs/heads/master | 2022-12-20T19:12:24.064577 | 2020-10-05T04:06:43 | 2020-10-05T04:06:43 | 286,168,339 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,037 | r | pathsample.R | #' Adaptive path sampling
#'
#' Run path sampling with adaptations.
#'
#' @export
#' @param sampling_model The stan model generated from \code{\link{code_temperature_augmented}}.
#' @param data_list The list of data used in the original stan model.
#' @param N_loop The max adaptations. The default is 10.
#' @param ... |
2168fe9518ce3ced757013ff1a2c5bd0d94617d1 | 6d9ab08f20be79379b2975f7162789229a4d838d | /R/con_zs_holland.R | 0f0878cd8a582a25d632d5145627071c15558b83 | [] | no_license | cran/holland | 7189a58a79d78614158fc5ca0a7433734422a870 | 9d49ebdc45998936b6934c92b68a5c418cc529a3 | refs/heads/master | 2023-07-15T11:10:44.038514 | 2021-09-01T07:30:02 | 2021-09-01T07:30:02 | 379,600,021 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,646 | r | con_zs_holland.R | #' @title Congruence Index according to Zener & Schnuelle (1976)
#' @keywords congruence
#' @export con_zs_holland
#' @description The function computes the congruence index according to Zener & Schnuelle (1976).
#' @details The function finds the congruence according to Zener & Schnuelle (1976) between the three-let... |
25ae077478a2b86bbe959a22d0998cca998f8505 | cf09008185b813e272bbe208120852ebfb277fe8 | /GCD_quiz_4.R | e0353155554548f0f2c8d751f47b73e1ba6ceb5a | [] | no_license | AnkurDesai11/datascienceJHUcoursera | efd1eedd5ab29c8835ac0cf129fa1b9e68b88719 | 4a36448fb2827d4f5048c8b21c210684cf363746 | refs/heads/master | 2023-02-09T21:29:26.288860 | 2021-01-11T04:26:39 | 2021-01-11T04:26:39 | 255,146,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,374 | r | GCD_quiz_4.R | #question 1
urlq1<-"https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
download.file(urlq1, destfile="GCD_quiz4_dataset1.csv")
ds1<-read.csv("GCD_quiz4_dataset1.csv")
splitnames<-strsplit(names(ds1), "wgtp")
splitnames[[123]]
#question 2
urlq2<-"https://d396qusza40orc.cloudfront.net/getdata%2F... |
b761222ea452c2926ac24179f0aded8d4af3f1b4 | 690c3c3e583094011d339d20a819b0fbe11a2bf8 | /stream_flow.R | 2aad1975440142b876e9989a4c0b72d048dcd3f2 | [] | no_license | AllisonVincent/StarFM-code | a0f907e2931460b7867600bd1566cb39a600338b | eac755b6ef61af5d1925b3b65d02269c846e79e1 | refs/heads/master | 2021-06-17T15:02:43.013841 | 2021-04-20T17:19:42 | 2021-04-20T17:19:42 | 194,706,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,633 | r | stream_flow.R | setwd('C:/Users/Allison and Brian/Documents/Research/STARFM/STARFMtest/Analysis_Tests/Stream_data')
library(sp)
library(sf)
library(ggplot2)
library(rgdal)
library(raster)
library(dplyr)
library(caret)
library(data.table)
library(stats)
library(fields)
library(hydroTSM)
library(SpatialTools)
library(foreign)
library(r... |
74e4ecb7b463d9b07ac72ea42bed392318aa427a | 368249e4edaaeb71b02075abcbaf46a2a9d8f997 | /R/udregninger opg5.R | f6310f03d7e5e4ffb644e004161c5665996cc41f | [] | no_license | Blikdal/Examchha511 | 256167c42cd1cc078fa50f1ca8b2d6b5fc713486 | 47b70e106cec785efae47def4b21070997ac9b63 | refs/heads/master | 2021-01-17T19:18:23.917600 | 2016-06-10T11:29:41 | 2016-06-10T11:29:41 | 60,843,165 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 230 | r | udregninger opg5.R | getDataPart(faithful) #the required data
eruption <- faithful[,1] #specifying eruption
KDE(eruption, method="naive" )
KDE(eruption, method="gaussian" )
densityplot(eruption, n=200)
densityplot(eruption, n=200, method="gaussian")
|
a30bc53fd4d3d10118d9d39a66e473bfabf8a76b | 587b2d1b95c14d4587c3a0210f0cf604140d9727 | /scripts/long/long_data.R | 5da7fce1984f0e5722f09d9cc0dd9f9d7c6273e4 | [] | no_license | mt-edwards/data-compression | 881c098b1acdc0110fb3208baa3ca98f13b2e623 | 3ada1b978ff5f9ad0a9920771cabbe3b6ac63120 | refs/heads/master | 2021-07-13T13:43:01.427051 | 2019-02-01T14:24:27 | 2019-02-01T14:24:27 | 139,557,116 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,103 | r | long_data.R | ############################
# Long Data. #
############################
# Command line arguments
# =======================
# - 1) Variable name.
# - 2) Ensemble size.
# - 3) AR order.
# - 4) MA order.
args = commandArgs(TRUE)
# Libraries.
# =======================
package_names = c("tidyverse", "plyr")... |
065e1b39003548270cbf6c053444f8aeea6e2bc0 | c20ba83fc17b3db4b5ffec96a55e1a48a9e03796 | /fars_functions.R | 7495d3c9d8d15776f649dccff5475d7f1476a86e | [] | no_license | RG9303/Project-RG-Week2 | 27959fe374a17c58071b0f5b81cf4d561548b6c8 | 6e6e1c5b2602bcb1364b1cc99c04d83fd21cf644 | refs/heads/master | 2022-09-22T23:42:01.642521 | 2020-06-07T02:50:09 | 2020-06-07T02:50:09 | 270,108,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,057 | r | fars_functions.R | library(roxygen2)
#' @title fars_read
#' @description The function \code{fars_read} read a csv file if it exists and forwards the argument a data frame.
#' @param filename to enter a database with format csv.
#' @return if file exists, this function read the file and return a database as a data frame. If the exte... |
d88663824c883a75adc8be7163c05939c461ba82 | 27b68e887cbba98bab7d90ba6839f69cbd138720 | /adp_analysis/xfp_boxplot.R | 2478e9ec57a38dd490b190c8398eb07a62efbe02 | [] | no_license | lbuckheit/nfl | 2266f954618608778045db86afc6bea0504241ee | e972aa3af3b6640fa32c484622d145b0cd012d8b | refs/heads/master | 2023-08-20T15:26:08.065041 | 2021-09-19T07:40:02 | 2021-09-19T07:40:02 | 301,013,241 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,845 | r | xfp_boxplot.R | library(tidyverse)
library(ggrepel)
library(ggimage)
library(nflfastR)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(stringr)
options(scipen = 9999)
source("utils/nfl_utils.R")
### Generate boxplots of expected FP ###
# TODO - Split this into RB and WR files
### Background Work ###
# Define variables
SEA... |
b8694448c65e770641fdfaffc1478a8a2d7568bf | fe906038c1bb5cd91cea4cba996d99822bbf3a33 | /prophet_test.R | 117ec51e07d1828903a438b95e32f6623e301fe4 | [] | no_license | m0hits/prophet_exploration_R | a3352bae5df27b2fa8b0f1ded1afcbfd86449900 | 5ed027df17f519b2092d49bddf2c4db1539b988c | refs/heads/master | 2020-07-31T18:14:47.881311 | 2019-09-24T22:23:45 | 2019-09-24T22:23:45 | 210,706,999 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,926 | r | prophet_test.R | #### Session Setup ----
rm(list = ls())
gc()
set.seed(786)
Time = Sys.time()
#### Packages ----
list.of.packages <- c("tidyverse",
"forecast",
"purrr",
"broom",
"readxl",
"writexl",
... |
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