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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
16f1f22d8e05e5d4650dd93d110229a9ca11cc7c | 5e9288942f83508ff3ff9a3543cb6639c9e2ee3b | /R-package/data/aloha.R | f13839d17de0f65d3756c0780f5be2cf0479d40f | [
"BSD-2-Clause"
] | permissive | omnetpp/omnetpp-resultfiles | e66af451d177942532d453171a5264e61f606a8f | b956990f747f5d099ff11c49b366aa9ced73cf1f | refs/heads/master | 2021-11-10T20:19:29.670601 | 2020-02-03T12:54:15 | 2020-02-03T12:54:15 | 719,992 | 22 | 17 | null | 2020-01-30T17:19:29 | 2010-06-14T12:24:41 | C++ | UTF-8 | R | false | false | 204 | r | aloha.R | require(omnetpp)
aloha <- loadDataset(c(file.path(system.file('extdata',package='omnetpp'),'PureAloha*.sca'),
file.path(system.file('extdata',package='omnetpp'),'PureAloha*.vec')))
|
b12c627f829551dd10021a27e030f1764664ff6a | afb95602e7403f2c9d9834dd1d6c8373c74102d3 | /Scripts/coproduction_preparation_rev.R | 21373f0a0438ac598a10caa3aa815842f5c03f2a | [] | no_license | matthiasschroeter/Coproduction_crops | 0ed86c93330b9580e16a940127681057fb773cc5 | f811e92358982bee72536e8661b200ba9b0444ff | refs/heads/master | 2023-04-12T15:38:53.786693 | 2021-04-25T15:02:18 | 2021-04-25T15:02:18 | 293,589,974 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 82,424 | r | coproduction_preparation_rev.R |
## DATA PREPARATION: INTEGRATE FAO DATA, IFA DATA AND OTHER DATA SOURCES
############################################################################################################
##### First Table, 11 crop groups #####
######################... |
3c9a876d184d68e21f5edfeb97793653fae0c565 | c988e9ecfaac555bd13f5b83ae30a04d30253ad9 | /Transcriptomic_analysis/createDesignMatrices.R | f23c75d89dca139d4bc31eb3716426dd241ffc61 | [] | no_license | marcoGarranzo206/TFM_UAM | fc0e026ce72823ebb4f0810c7e8434141e807e80 | 79e75c5afc4c843ad53ab049b870c26f8bb124c6 | refs/heads/master | 2023-08-11T20:27:35.698405 | 2021-09-16T22:18:06 | 2021-09-16T22:18:06 | 292,009,620 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,897 | r | createDesignMatrices.R | # each design has its own function
# lots of code repetition for the special cases
# generateSpecialDesign.R has one for all the special types
# metaDataFile = "~/Marco_TFM/Marco_TFM/Data/metaData/GSE111073sample_info.txt"
# isPaired = T
# intercept = F
library(stringr)
generateFormula <- function(dfName = "", varname... |
9039bd969f7fa91e7f7b67a3f326e35a031857ff | 642fbb157e400bcc4811b0a5c14b8255e16b5db8 | /R/aggregate_rating.R | cc4424775cd23f544947514497e03ae6bd4aeb09 | [] | no_license | lucasgautheron/ChildRecordsR | 6f048655f281465b298c017ac2d56d996a2ab6f0 | afd1e7861095583664c47678b1fe46c3555ac33d | refs/heads/main | 2023-04-18T19:44:49.981733 | 2021-04-02T16:57:00 | 2021-04-02T16:57:00 | 359,896,932 | 0 | 0 | null | 2021-04-20T17:22:02 | 2021-04-20T17:22:01 | null | UTF-8 | R | false | false | 3,753 | r | aggregate_rating.R | #' aggregation of annnotations data
#'
#' Base on the result of an find.ratting.segment return or similar data.frame
#' the function will extract data from annotation file in a raterData Class
#'
#' the data will be organize un raw annotation format and a long segmented format
#'
#' @param ChildRecordings : a ChildRec... |
b14e4fffe6d57089b5cd25896405bdaea56c56e2 | c1672f9c39b9b74b7fd43c90e36557f37db0e009 | /R/punk_randoms.R | 53f1439b0dc21bc709e25c7357e941f0f231b351 | [
"MIT"
] | permissive | ThinkR-open/punkapi | a6fcac96e3be9f406b056d9cddc9347940c1dd8e | 28ad8004d895e7565fb94ce4fd3e7b5d3f8a6e95 | refs/heads/main | 2023-01-04T08:50:51.360832 | 2020-10-30T08:14:38 | 2020-10-30T08:14:38 | 307,626,787 | 0 | 0 | NOASSERTION | 2020-10-29T15:56:12 | 2020-10-27T08:01:28 | R | UTF-8 | R | false | false | 664 | r | punk_randoms.R | #' Get a Random Beer
#'
#' @return a dataframe with plenty of information about a random famous beer
#'
#' @param n number of element to return
#'
#' @export
#' @importFrom httr GET content
#' @importFrom purrr map_df
#'
#' @rdname punk_random
#'
#' @examples
#' punk_random()
#' punk_randoms(7)
punk_random <- function(... |
739ddc143debd7dca77c25b98e0e896cecc3ca26 | d261b279eb86bec7fb15925bc50e1f9465ce369c | /man/whitespace_tokenize.Rd | 1f282aec734fab9b09aaf899d5d4e7dedb21c4cb | [
"Apache-2.0"
] | permissive | jonathanbratt/RBERT | e3ac2c7169b203552c20ed7369fb4e6041b7ab5c | d32c3a7b2cce0ce4fb93f64eae9e3f7e85cc6158 | refs/heads/master | 2023-02-06T14:25:06.342274 | 2023-01-25T17:05:49 | 2023-01-25T17:05:49 | 204,560,591 | 144 | 21 | Apache-2.0 | 2023-01-25T17:05:52 | 2019-08-26T20:58:56 | R | UTF-8 | R | false | true | 507 | rd | whitespace_tokenize.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tokenization.R
\name{whitespace_tokenize}
\alias{whitespace_tokenize}
\title{Run basic whitespace cleaning and splitting on a piece of text.}
\usage{
whitespace_tokenize(text)
}
\arguments{
\item{text}{Character scalar to tokenize.}
}
\value{... |
ef5e4245b831b1970d19295afd6de72fddf560ac | 18beba89bd528840d3aab7a171fa671c5ac0cf3a | /man/MixtureModel_Biv.Rd | c199a85f9cf7c9e88fc47abcb4e565462ea59d19 | [] | no_license | mpru/BIMEGA | 8d748401ad29f252c9c87b6ec04bca2d185d9a62 | 6b445dc7581a2b78aae559b34c382a2f74d1391f | refs/heads/master | 2021-01-22T17:33:56.718268 | 2016-06-19T04:21:29 | 2016-06-19T04:21:29 | 61,449,411 | 0 | 0 | null | 2016-06-18T20:55:01 | 2016-06-18T19:34:43 | R | UTF-8 | R | false | true | 1,672 | rd | MixtureModel_Biv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BIMEGA.R
\name{MixtureModel_Biv}
\alias{MixtureModel_Biv}
\title{The MixtureModel_Biv function}
\usage{
MixtureModel_Biv(METcancer, METnormal, MAcancer, MAnormal = NULL,
FunctionalGenes, NoNormalMode = FALSE)
}
\arguments{
\item{METcancer}{... |
945a399319e5e1aa591a5f6a2686fa3b9b0d971c | 2aa3b7455f3e17c8dbdc938f3386dae46a12bb99 | /man/gs_auth.Rd | 5862f43d1864f0cef6ddda30775cdc878f3a02e5 | [
"MIT"
] | permissive | dennistseng/googlesheets | 5fd4d78994251df7703754375d5aab1a21686a7a | 8471679c621c8b5d43679cf4b9598cba2f6a3560 | refs/heads/master | 2021-01-18T02:17:34.149067 | 2015-06-02T06:36:06 | 2015-06-02T06:36:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 965 | rd | gs_auth.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/gs_auth.R
\name{gs_auth}
\alias{gs_auth}
\title{Authorize \code{googlesheets}}
\usage{
gs_auth(new_user = FALSE, token = NULL)
}
\arguments{
\item{new_user}{logical, defaults to \code{FALSE}. Set to \code{TRUE} if you
want to wipe the... |
7c5621a106312ee7f303da14b4f1f1a51ca80b38 | 335919e0547cf90682f805c25bf242aa5f58a792 | /R/app_ui.R | 1ec44ccd18bd8c02d140ecc60d55541750b0b15d | [
"MIT"
] | permissive | Gustavogaep/deminR | 80e677b8fe955d3282e9aa1a9e218fd2489f1a6e | e0bf6656ce9c55a5c8379a6628f4e39437588a66 | refs/heads/master | 2022-04-24T15:16:59.471963 | 2020-04-08T13:52:22 | 2020-04-08T13:52:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,920 | r | app_ui.R | #' @import shiny shinyMobile
#' @importFrom sever use_sever
#' @importFrom glouton use_glouton
app_ui <- function(request) {
tagList(
# Leave this function for adding external resources
golem_add_external_resources(),
# List the first level UI elements here
f7Page(
title = "deminR",
icon ... |
90c4812ec7d96a2a7a7df0ec38b81129c6a8f841 | c1d36c065ee9ae8483cc853aa3f3f184a0ac9fd8 | /File System in R.R | bd4f474ae9829e82c48fca348816ac1e17e874d4 | [] | no_license | JoshuaOluoch/File-manipulation-in-R | 136a1350b0673d7d787e5324c20800b04369c222 | b5223fe428e9ca5dde008d040f321b4464d2e38a | refs/heads/main | 2023-02-26T21:10:45.306945 | 2021-02-05T09:21:06 | 2021-02-05T09:21:06 | 336,216,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,732 | r | File System in R.R | #Temporarily disable warnings
defaultW <- getOption("warn")
options(warn = -1)
options(warn = defaultW)
# File management in R
#Getting the working directory in R
getwd() # Gets the current working directory
#Setting the working directory (Make)
#Make sure you enter the correct filepath according to the os you are... |
214cef97ed5deb16551b0a6ca00c10faedbf1ce0 | 43f634905b36ef46f5cef252a74b66f979f33361 | /plot1.R | be898262626aa8a672f8966a82d80d5b9edc3b01 | [] | no_license | emiline002/ExData_Plotting1 | c0a6f280e17387b067f908c1c549066ae65a756a | d8aef10c74907bffd63ee07164c1ea7e047e18fb | refs/heads/master | 2021-01-18T18:18:18.211190 | 2015-09-12T19:32:02 | 2015-09-12T19:32:02 | 42,369,067 | 0 | 0 | null | 2015-09-12T18:55:16 | 2015-09-12T18:55:15 | null | UTF-8 | R | false | false | 436 | r | plot1.R | power<-read.table("household_power_consumption.txt",head=TRUE, sep=";", stringsAsFactors = FALSE)
power$Date<-as.Date(power$Date,"%d/%m/%Y")
sub_power<-power[power$Date=="2007-02-01"|power$Date=="2007-02-02",]
sub_power$Date<-as.Date(sub_power$Date,"%d/%m/%Y")
sub_power[,3:9]<-apply(sub_power[,3:9], 2, function(x) as.n... |
e3c334632f4373383267074f47b84a9a697cd9e2 | f5d5fa2458aa0ad1f247a1c3cd3d00dcc008686a | /Examples/R/ChemoTest.R | 085df8a08a74d6a4deb3d6b10752d6754e7af3e0 | [] | no_license | MatJim-Ottawa/CollocInfer | cafcae96850271176084973f218063d1b374a9f3 | b5fe8102dee0301beaa5b4dbdecd79460c95d6f8 | refs/heads/master | 2021-01-23T16:36:18.710179 | 2014-11-07T14:44:12 | 2014-11-07T14:44:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,943 | r | ChemoTest.R | sourceDir <- function(path, trace = TRUE) {
for (nm in list.files(path, pattern = "\\.[RrSsQq]$")) {
if(trace) cat(nm,":")
source(file.path(path, nm))
if(trace) cat("\n")
}
}
RosMac = function(t, x, p, more){
p = exp(p)
dx = x
dx[,'C1'] = p['r1']*x[,'C1']*(1- x[,'C1']/p['Kc1']- x[,'C2']/p['Kc2']... |
acb03b6b2b27943b4d88505c40de4a96cc3dd5a6 | c71e0c09a166cee1fc3f408c7c18e315496f0199 | /R/snaker.R | 02b477b7127b9f2b9aa4544da620745674d44b0f | [] | no_license | fmarotta/snaker | 765a5a3b2053ae0f4da5abe7c4cd93386f8aaae5 | 061b5e61adf5c9cd09a071d6fbbf80e803b3e7cf | refs/heads/master | 2020-09-30T18:18:04.939139 | 2019-12-17T12:06:15 | 2019-12-17T12:06:15 | 227,345,997 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,846 | r | snaker.R | #' snaker
#'
#' This function is equivalent to docopt::docopt, except that it can also handle
#' arguments passed by Snakemake. If the `snakemake' object exists, then the
#' arguments are taken from there, otherwise from the command line.
#'
#' @inheritParams docopt::docopt
#'
#' @return The list of named arguments and... |
d8e9108ed2defec4e46864369802b23ccf9fde4a | 4a3cf8e6e74db99a5a7d0f56757b8e3359bcd30d | /man/make_sl_task_list.Rd | 31043eb5a2718a88dd52045c5170ecbb41a62b92 | [
"MIT"
] | permissive | bdwilliamson/cvma | 1ae9710b4b57e8ef1dd0168fd84d75c324ef1454 | 6651ea9fece2a267261c454dadebf1d41e54aaaf | refs/heads/master | 2021-10-25T09:40:01.855362 | 2019-04-03T15:02:00 | 2019-04-03T15:02:00 | 112,544,035 | 1 | 1 | null | 2017-11-30T00:32:40 | 2017-11-30T00:32:39 | null | UTF-8 | R | false | true | 385 | rd | make_sl_task_list.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/task_lists.R
\name{make_sl_task_list}
\alias{make_sl_task_list}
\title{Helper function to make a task list for computing super learners.}
\usage{
make_sl_task_list(Ynames, V, fold_fits = c(V - 1, V - 2))
}
\description{
Helper function to mak... |
81758b5e4dfaa4fbe3d0c5290f06366e3da2e37f | 4052545c292db46b6363f299828dfc1d8d7f8b9a | /shiny2/int/input1c1.R | 6bf774048cd00cf8a841ce4f0e4062c4cb689e09 | [] | no_license | uvesco/studioEpi | 972324a5af179912d1ab2c60ea21f0f04cddfa33 | ed0849185460d628aa1fdc73eb13e67555a87a75 | refs/heads/master | 2020-12-12T05:03:04.953612 | 2020-04-04T23:26:09 | 2020-04-04T23:26:09 | 234,048,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29 | r | input1c1.R | output$input1 <- renderUI()
|
28ab5e6765e9defcd82ba29ffa96335f31b1b2d0 | d630dfeae8965eddd8e2f1c0917aa06719c1de9e | /scripts/createOutputTestis.R | 8693d6453ec15dd83178a86bddcbc48914914ded | [] | no_license | AEBilgrau/effadj | 5b67275ab22880fdc293d9143d1b888cdcc0a900 | e0ca987d11502a23229b9731d3a712eda52b2a45 | refs/heads/master | 2020-05-20T19:29:25.136211 | 2016-02-23T21:41:26 | 2016-02-23T21:41:26 | 37,054,970 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,108 | r | createOutputTestis.R |
################################################################################
# Analysis of testis data #
# Written by: #
# Anders Ellern Bilgrau, Steffen Falgreen, and Martin Boegsted ... |
17a415fdc8f04a2450f3aabdd6137f1fdfde490e | 5c7e7dce5d0b75b2299f0710393ecf29e768e342 | /man/data_ziplist.Rd | f15c1e7c852f0825aaaec919ccad4fc069e71d18 | [] | no_license | SebEagle/snowcoveR | 995c860ec05fe456b6c8914c48f37af532f50316 | 39d21758976bb697068c84e64aad84b86fddc05d | refs/heads/master | 2020-03-09T08:22:35.168913 | 2018-05-25T21:29:21 | 2018-05-25T21:29:21 | 128,687,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 440 | rd | data_ziplist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_ziplist.R
\name{data_ziplist}
\alias{data_ziplist}
\title{data_ziplist}
\usage{
data_ziplist(input_dir, input_file)
}
\arguments{
\item{input_dir}{Folder directory to zip file}
\item{inputfile}{name of zip-file}
}
\description{
is retur... |
4c30e63f6fa3555b23e0cb83d0628cd1160fec65 | 0476f2bd245afe4b630aeab628499df2d91517db | /man/GenerateMetaboliteSQLiteDB.Rd | 170d48f38fead7a397be90386e34855665696370 | [] | no_license | cran/InterpretMSSpectrum | d07f32034e3f68ab719c6827a4b1529f8d7fb503 | ecf9604cfde5dd22a057b17ad2272cde7351157d | refs/heads/master | 2023-07-24T03:18:25.154905 | 2023-07-07T14:00:02 | 2023-07-07T14:00:02 | 67,487,289 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,675 | rd | GenerateMetaboliteSQLiteDB.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GenerateMetaboliteSQLiteDB.R
\name{GenerateMetaboliteSQLiteDB}
\alias{GenerateMetaboliteSQLiteDB}
\title{GenerateMetaboliteSQLiteDB.}
\usage{
GenerateMetaboliteSQLiteDB(
dbfile = "SQLite_APCI.db",
ionization = c("APCI", "ESI")[1],... |
2a6a9b0675d42c37150c19666a5dbcdcd487b1a4 | f3e914e8a3ccb1c4d73555321e3eaf52b59f52e0 | /R/3.3.R | af3d975b9af9072f261593027fd74c6ffb90d42b | [] | no_license | youjia36313/learn_R | 08be35ebc032839e8c25466c63ae5a0292069855 | 674de3d09e0e7dfec2d3e164ffab98e0c40ca597 | refs/heads/master | 2020-09-15T19:39:00.136679 | 2019-11-23T06:37:41 | 2019-11-23T06:37:41 | 223,541,846 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 133 | r | 3.3.R | df_01 <- read.csv('HeightWeightData.csv')
df_01
#getwd()
plot(df_01$height,df_01$weight,pch=2,col="blue",xlab="height",ylab="weight") |
f5f488540d524f895c2132dbe105c55b978e49c1 | 3cc2e5d0f3c74dee646346bf499a6bc3b97a8266 | /HW1_Creditcard_SVM.R | ff9079a598c9c7750d6b025faad77930ea4fcc35 | [] | no_license | aten2001/Analytical-Models-Assignments | d636a95f816d04a793b573e1e52aef785c18f12a | 6d4ccf4decd1e5895badfb636f3dc813c33fed71 | refs/heads/master | 2021-06-20T11:03:09.363546 | 2017-07-05T23:05:05 | 2017-07-05T23:05:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,588 | r | HW1_Creditcard_SVM.R | ################################# Start of Code =====================================
rm(ls())
getwd()
setwd("G:/Georgia Tech/Analytical Models/Assignments")
install.packages("data.table")
install.packages("kernlab")
install.packages("caret")
library(data.table)
#fread is quicker that read.table and read_table in th... |
fdf6c4b6bce4c7beefcdbd8436a170ee98f03c9c | b1cccc43340f5e1100a95428ecfe6a14fadb215a | /man/logging_info.Rd | 5ab927bea45abb0d8aa75711a337bcca33551a62 | [
"MIT"
] | permissive | MRCIEU/ieugwasr | b041818e3de4db287aed0667c5e167ac0bdf74f3 | 33e4629f4dacd635c68e690bb5648de529c333cc | refs/heads/master | 2022-07-01T22:52:46.713761 | 2022-06-15T14:27:21 | 2022-06-15T14:27:21 | 214,448,457 | 35 | 17 | NOASSERTION | 2022-03-16T14:54:21 | 2019-10-11T13:50:01 | R | UTF-8 | R | false | true | 258 | rd | logging_info.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api.R
\name{logging_info}
\alias{logging_info}
\title{Details of how access token logs are used}
\usage{
logging_info()
}
\description{
Details of how access token logs are used
}
|
e8ccc95be825fb955d6bbea34aa98ed8640739a6 | 30a1398bc0ff11036867015db2a8cec1cf73b953 | /code_simulation/plot_one_case.R | 208de7fd60bd926bc56b75a282d211ccba869cf8 | [
"MIT"
] | permissive | wilsoncai1992/MOSS-simulation | d6a34506c3f5dd76797420926ff8f3dced8015e8 | 8b927ef14fa981bd8ab530da165ebb7ef1dd24f3 | refs/heads/master | 2021-03-22T01:11:52.358060 | 2019-06-22T07:03:55 | 2019-06-22T07:03:55 | 123,011,317 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,075 | r | plot_one_case.R | library(survival)
library(MOSS)
library(survtmle)
library(tidyverse)
library(ggplot2)
library(ggpubr)
source("../fit_survtmle.R")
# simulate data
source("./simulate_data.R")
do_once <- function(n_sim = 2e2) {
simulated <- simulate_data(n_sim = n_sim)
df <- simulated$dat
true_surv <- simulated$true_surv1
sl_li... |
e806b02f6be4b856a85695077edbaf6432df2d7d | af96e7785b76034c2c167545b6f174e6bfe4fb85 | /school_clustering/pca_clustering_schools.R | 835cdb1a2b3e0a1fd7efb796973a8b5f1b070357 | [] | no_license | Nhiemth1985/rviz | 8c4d0db2f8639b1a985c1e189987545a97e41938 | c959319db2597d3adadbcf3fcba08694ce5f31cf | refs/heads/master | 2022-12-06T03:32:10.254564 | 2020-08-28T16:01:03 | 2020-08-28T16:01:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,823 | r | pca_clustering_schools.R | remove(list = ls())
options(stringsAsFactors = FALSE)
options(scipen = 999)
setwd("/Users/harrocyranka/Desktop/rviz/school_clustering/")
library(tidyverse);library(readxl)
x <- read_csv("dataset_with_clusters.csv") %>%
select(pct_school_lunch, median_hh_income, pct_minority, enrollment)
standardize <- function(m... |
d010f01d4a8b4250c094eae3662193e11f89457d | 26b7fb893d70c2aae8666248bee804c2ea159d11 | /man/aveMatFac.Rd | f8cb483ef300b8afdd6ffb4d0f917b49ff198a5d | [] | no_license | wefang/ghelper | 5b77b4067f2ec8633a7600b2f745e884c17b68bf | 7e150a12e6c1d5801cd46122edf9cd118721086a | refs/heads/master | 2021-01-20T11:38:53.028888 | 2020-11-13T03:43:58 | 2020-11-13T03:43:58 | 56,557,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 252 | rd | aveMatFac.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helper.R
\name{aveMatFac}
\alias{aveMatFac}
\title{Average matrix rows based on a factor}
\usage{
aveMatFac(mat, fac)
}
\description{
Average matrix rows based on a factor
}
|
9a91a2b0cc87d2baf661503e91252f59b0fe9880 | 91faa6d30c4ec3f62a19facf1b9fddaa5f95707f | /QTL/SHOREmap_qtlseq.R | 481cb1cf054ed2cb154da7e93f264a7e389ce69a | [] | no_license | zzygyx9119/shoremap | ae546715e830b17391410aece21958b7243e0a4b | 374c45ff5894c4a012ffcce71191993a6e043d27 | refs/heads/master | 2016-08-08T17:52:29.591383 | 2012-12-18T08:46:56 | 2012-12-18T08:46:56 | 50,601,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,438 | r | SHOREmap_qtlseq.R | #v09 - changes in ploting
#v10 - added the true interval of the qtl
#v11 - removed bootstrapping
#v12 - fixed output and corrected thresholds
#v13 - return of the bootstrap
library(bbmle)
source("/projects/dep_coupland/grp_nordstrom/projects/Lotus/Simulation/PopSimulatorRIL/Rcode/SHOREmap_qtlseq_lib.R")
#Run.....
arg... |
2efb832e3a69ff0d29e39a94c8a2e6ce5d913532 | 8d1a1d9e5238e746cf093257c896912327d9d433 | /Exploratory Data Analysis/Project2/plot4.R | 03fe6f1d2a564de300436a64dfc9628ee8811b44 | [] | no_license | parthpandey2000/datasciencecoursera | 5f7d31ec0c485f83f746bf753c233e0bf7f63f1d | 5a76e3aafe793e40f8b41c49ac65b32dc68de983 | refs/heads/master | 2022-11-25T12:00:16.401069 | 2020-08-06T14:42:03 | 2020-08-06T14:42:03 | 274,686,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 701 | r | plot4.R | library(dplyr)
library(ggplot2)
url1 <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
destfile1 <- "destfile.zip"
if(!file.exists(destfile1)) {
download.file(url1,
destfile = destfile1,
method = "curl")
unzip(destfile1, exdir = ".")
}
NEI <- readRDS("summa... |
5d0a6e033b769231fc4b029b36c7d02032cb3c64 | 5dbf24f17d425a49af88bd60a38996e781249411 | /R/approx2_functions.R | c42403db04900f3b50c7fa7390ecf344643f0ee0 | [] | no_license | syounkin/RVsharing | 43f3959e67a1189531b1279ed9c5e12d32bc0a95 | 6265b28d688b88ac613a3697c9e825f8e51991c7 | refs/heads/devel | 2020-12-18T19:28:23.132571 | 2017-06-20T14:34:21 | 2017-06-20T14:34:21 | 10,171,949 | 0 | 1 | null | 2019-07-10T19:54:06 | 2013-05-20T12:09:15 | R | UTF-8 | R | false | false | 3,965 | r | approx2_functions.R | # Utility functions for method 2 to approximate sharing probabilities in presence of unknown relationships
# By Alexandre Bureau
# 2013/06/05
infer.nalleles = function(phi,nf)
# Returns the most likely number of distinct alleles among nf founders based on mean estimated kinship phi
{
a = nf:(2*nf)
term1 = ... |
f5e492d458957eda5635b298ba359c295111a99c | e36370dd2c0041b12077a99890a96b48a1092398 | /plot4.R | c9cb3b0cb3653e837ba2848abf9b2685fe33147d | [] | no_license | zekaih/ExData_Plotting1 | 5ccb1bbce7d33e139cd4040374b76f8e10957ee1 | 9133c40c5341adfddc637f40d936bc6360decc2a | refs/heads/master | 2021-01-22T18:44:01.441502 | 2017-09-06T23:56:29 | 2017-09-06T23:56:29 | 102,411,695 | 0 | 0 | null | 2017-09-04T23:42:01 | 2017-09-04T23:42:00 | null | UTF-8 | R | false | false | 990 | r | plot4.R | #import data, rm NA values, subset date
eda <- read.table("household_power_consumption.txt",sep = ";",header=TRUE,na.strings = "?")
str(eda)
summary(eda)
eda1 <- complete.cases(eda)
eda <- eda[eda1,]
summary(eda)
eda <- subset(eda,eda$Date=="1/2/2007" | eda$Date=="2/2/2007")
#plot 4
datetime <- strptime(paste(eda$Date... |
253aa1620f8558b2d8811cc76c73c5546a11c195 | b0251a873cda6b236dc46a71c0d7ac8d403dda28 | /man/summary.DTR.Rd | 6db5303cb9919527e742c4c48e33444e85a3aa47 | [] | no_license | yhy188/rosur | 161a836f477ca85d091da3974fb640b372ddddb0 | 4e8d5ddd3e4102a187173232c040d53241621636 | refs/heads/master | 2021-01-16T10:58:23.970230 | 2020-03-18T02:18:06 | 2020-03-18T02:18:06 | 243,092,781 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,209 | rd | summary.DTR.Rd | \name{summary.DTR}
\alias{summary.DTR}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Summary of survival curves
}
\description{
Returns an object of class \code{summary.DTR}. See \code{DTR.object} for details.
}
\usage{
\method{summary}{DTR}(object, ...)
}
%- maybe also 'usage' for... |
7fbcd9eb7d756c2bd53302bb24880f91aba215ed | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/eechidna/examples/aec_carto_f.Rd.R | 5d9db3f955e4e076da54aeb164f909e51788637c | [] | 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 | 1,272 | r | aec_carto_f.Rd.R | library(eechidna)
### Name: aec_carto_f
### Title: aec_carto_f - run dorling on data centers
### Aliases: aec_carto_f
### ** Examples
library(dplyr)
library(ggplot2)
data(nat_map16)
data(nat_data16)
nat_data16 <- nat_data16 %>% select(-c(x,y)) # remove existing cartogram coordinates
adelaide <- aec_extract_f(nat_da... |
120ba728a68c160c7e87ec4b2f4175ed087cc184 | acbecda9b931b15996b369780b6662fbafc6f0fd | /R/addins.R | 2bad3b63055fe7670938caf6cfffe8bad58f17ee | [] | no_license | dtkaplan/etude | c2d4f2976bf678c56376deae57d9d9ac9f202af4 | f0fcb59af3286202c5fab494c95abde8b904a284 | refs/heads/master | 2022-12-26T22:21:06.140586 | 2020-10-12T21:32:36 | 2020-10-12T21:32:36 | 207,023,456 | 1 | 1 | null | 2020-06-16T17:25:50 | 2019-09-07T20:40:16 | HTML | UTF-8 | R | false | false | 3,608 | r | addins.R | #' Addin to make a new etude exercise
#'
#' @export
#' @rdname new_etude_template
#' @param directory Path to the directory where the files go
new_etude_learnr <- function(directory = ".") {
new_etude(directory = directory, learnr = TRUE)
}
#' @export
#' @rdname new_etude_template
new_etude <- function(directory = "... |
4b2d7f9915d4996eaeb5a17c7e3b436e66ab08f9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/REAT/examples/betaconv.ols.Rd.R | 2bbdbd2a5006e1b32dd690452520ad93caacfe61 | [] | 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 | 1,745 | r | betaconv.ols.Rd.R | library(REAT)
### Name: betaconv.ols
### Title: Analysis of regional beta convergence using OLS regression
### Aliases: betaconv.ols
### ** Examples
data (G.counties.gdp)
betaconv.ols (G.counties.gdp$gdppc2010, 2010, G.counties.gdp$gdppc2011, 2011,
conditions = NULL, print.results = TRUE)
# Two years, no conditio... |
4312c316f9dc95b2db197acfae427d10925f994e | 854cf62a5df3c60b0b3e9521606366cf0bc3c010 | /load-csv-data.R | d720279bbfc123f786700e006a0baf00ac5c71f3 | [] | no_license | mcwachanga/Intro-to-r | b3471e2477749d0f3fc81e147aaf404d728403a6 | ae9f34e4428da974d9deca84e07c1912e85f74d7 | refs/heads/master | 2020-03-27T04:52:29.495724 | 2018-08-24T10:50:40 | 2018-08-24T10:50:40 | 145,975,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 158 | r | load-csv-data.R | # read a csv file into a dataframe
df <- read.csv("data/2013.csv", header = TRUE)
# read the first five lines
head(df)
# get summary statistics
summary(df)
|
a08ec64a9eb9c52c8ca48a19fdf5f324e3360fc6 | 446373433355171cdb65266ac3b24d03e884bb5d | /R/saga_metricconversions.R | fc71718cd4887c9b0e187bd9d51835ad5e1de853 | [
"MIT"
] | permissive | VB6Hobbyst7/r_package_qgis | 233a49cbdb590ebc5b38d197cd38441888c8a6f3 | 8a5130ad98c4405085a09913b535a94b4a2a4fc3 | refs/heads/master | 2023-06-27T11:52:21.538634 | 2021-08-01T01:05:01 | 2021-08-01T01:05:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,480 | r | saga_metricconversions.R | ##' QGIS Algorithm provided by SAGA Metric conversions (saga:metricconversions)
##'
##' @title QGIS algorithm Metric conversions
##'
##' @param GRID `raster` - Grid. Path to a raster layer.
##' @param CONVERSION `enum` of `("[0] radians to degree", "[1] degree to radians", "[2] Celsius to Fahrenheit", "[3] Fahrenheit ... |
64830409e6bdebc6644cdd72e4d47c4e3e3105b5 | 6cf7b035125b9ab0a4ee92301263b06cae46e352 | /Scripts/dplyr.R | d6b8dd298d78b6f2808211d75f09e21d450d60aa | [] | no_license | abelgGit/2017_06_06_R_tidyverse | 98400cbaf70f5b582262d7a52106ecd68c5ab6ed | 6a3eeb2ab713be3c5f7239193f59ced8efaea33f | refs/heads/master | 2021-01-25T04:49:43.440849 | 2017-06-07T13:00:53 | 2017-06-07T13:00:53 | 93,488,482 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,132 | r | dplyr.R | library(tidyverse)
gapminder <- read_csv("Data/gapminder-FiveYearData.csv")
rep("This is an example", times=3)
"This is an example" %>% rep(times=3)
year_country_gdp <- select(gapminder, year, country, gdpPercap)
head(year_country_gdp)
year_country_gdp <- gapminder %>%
select(year, country, gdpPercap)
head(year_c... |
06e207e3dd28c1b2d5b4e2bc409b99c7ae5566ea | b09fe02978b3ee250813d135a6767006d468c166 | /man/remove_bigram_stopwords.Rd | 1857138908a93184ae5dcad314b4c1ac3fb08bc2 | [] | no_license | scottfrechette/funcyfrech | 098f3794b83fe7cd8871f91125eac2a5861255cb | 0d3aa005b81d91807f1ba2700d80d15f4f9cc04c | refs/heads/master | 2022-08-29T15:59:27.927431 | 2022-08-26T02:31:43 | 2022-08-26T02:31:43 | 213,962,999 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 439 | rd | remove_bigram_stopwords.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/remove_bigram_stopwords.R
\name{remove_bigram_stopwords}
\alias{remove_bigram_stopwords}
\title{Remove stop words from bigrams}
\usage{
remove_bigram_stopwords(df, bigrams, char_only = TRUE)
}
\arguments{
\item{df}{A tibble containing bigrams... |
4c2fa9c5e554d6cc3c67403b49ed9135f752d823 | 968c8f8ca03319c455303f0c46346021a4b203e2 | /man/model_summary_table.Rd | 0650893116fd0e2a26dd0041fa0c18c37db08590 | [] | no_license | rnaimehaom/AutoModel | 2d4ed1d9cb240ed87ed64c83b63765117d003653 | b78615d51ef101758cdfcd1bdcce2c7edb02d7e4 | refs/heads/master | 2023-03-18T06:10:16.550971 | 2015-08-12T21:29:38 | 2015-08-12T21:29:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 886 | rd | model_summary_table.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/runmodel.R
\name{model_summary_table}
\alias{model_summary_table}
\title{Hierarchical regression: model summary output}
\usage{
model_summary_table(models, formulas)
}
\arguments{
\item{models}{A list of \code{lm} model objects. A se... |
a08ac8e710d93f52aaebb67261be1efa314b02d3 | efcd73d82923061a933661202415da88f6f0975a | /man/RandomARMod_nlin1.Rd | 2044c965cf953ff9f3c45f518f044caef4b9b071 | [] | no_license | SimoneHermann/rexpar | 624b0d30bd3fde12a5e908bd90057dc6d260459a | 3883b9f8aa1685c28979c621d427ae3080a1cd8e | refs/heads/master | 2021-01-21T15:33:59.129522 | 2015-06-22T12:03:59 | 2015-06-22T12:03:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,018 | rd | RandomARMod_nlin1.Rd | \name{RandomARMod_nlin1}
\alias{RandomARMod_nlin1}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Ramdom non-linear AR(1) Series
}
\description{
The function generates an random non-linear AR(1) process with given power, autoregression and starting
value. Further the errors can be specified by ... |
9e258ac21e33ad548f980bbf33200fa6c5cc4c90 | 32f74e60d35002d7c148cdfd5e3353b90c82ce5c | /man/plot_biv_olr.Rd | 4c038c0b19ac8a4036c6182dda419a0c89dd81a5 | [] | no_license | agdamsbo/daDoctoR | 152adf49feb5d329e20c19d0afdd708d74de68cc | a0c4b3f9c3cf7a172fb919116fe41df4a70236e8 | refs/heads/master | 2022-09-25T15:12:39.699092 | 2022-08-26T13:07:49 | 2022-08-26T13:07:49 | 151,308,209 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,347 | rd | plot_biv_olr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_biv_olr.R
\name{plot_biv_olr}
\alias{plot_biv_olr}
\title{Forrest plot from ordinal logistic regression, version2 of plot_ord_ords().}
\usage{
plot_biv_olr(
meas,
vars,
data,
title = NULL,
dec = 3,
lbls = NULL,
hori = "OR (... |
7e14b390b0bef5cb7f240ed858d2b99d6de94e64 | 4ac86a3396861bcd055018b5f192487854c13de8 | /R/channelMorphology.r | e2fc200264e32dd2d85aeb16a82a835d59b6699c | [] | no_license | mengeln/phabMetrics | cf0510ba24249b88ad445b88f7cd4a94eb898f18 | d373790bbbcdabaac65a8c0beb864913c144d488 | refs/heads/master | 2021-01-01T05:46:52.454248 | 2014-03-19T21:52:20 | 2014-03-19T21:52:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,794 | r | channelMorphology.r |
channelMorphology <- function(datum){
data <- subset(datum, AnalyteName %in% c("Cascade/Falls",
"Dry",
"Glide",
"Pool",
"Rapid",
... |
d85bb8ec3c6b69c965a3b33e51a50391a25192e5 | 5b097ec0429f848d48bfd60c299188316da9700a | /R/data.R | de1219f66e23ba90c5a24d82211d75b7bf77a89d | [] | no_license | beckymaust/kwmatch | 2fbe6c1c567af1f4fec1635bd4719c65de7e9036 | 537b926fa0bc6527b699f5306e14dfc4989b0bdc | refs/heads/master | 2021-01-02T22:58:49.719101 | 2015-03-28T18:28:59 | 2015-03-28T18:28:59 | 33,041,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 439 | r | data.R | #' Titles and abstracts of articles from the Open Journal of Statistics
#'
#' A dataset contain the titles and the first sentence of the abstract
#' for 10 different articles in the Open Journal of Statistics.
#'
#' @format A data frame with 10 rows and 2 variables:
#' \describe{
#' \item{Title}{Title of article}
#' ... |
d7370fa760de64866cdbbdafcf43b406788c8fde | 95c0ed78f00bc1cb13d4e3611ff1722dd019631b | /src/plot_HJ_coding.R | 31f49932ab1b3c1899f6d593e4e6a5a3f037d50f | [] | no_license | rosemm/context_word_seg | bd40e2a02cbf62d4a96ef796be8e9e81cbded63e | 0f145ae90dfc8eba5fcf2beeb3034d65c106f784 | refs/heads/master | 2020-05-22T01:46:32.125165 | 2016-12-20T18:04:55 | 2016-12-20T18:04:55 | 42,607,106 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,859 | r | plot_HJ_coding.R | votes <- select(df_HJ_raw, -utt, -orth, -phon)
##########################################
# number of utterances per context
##########################################
counts <- sort(colSums(votes, na.rm = TRUE), decreasing = TRUE)
count.data <- data.frame(n.utts=counts,
context=factor(names(... |
45987faaa257c99b27f04a4514f087c91ce2e26b | 72b9ec2d3e7c59bbe45e4d9cab824592fc82609b | /R/boot.r | 792d186bc8c381d27ceeeeda9f3d7859c901cff1 | [] | no_license | b-steve/ascr | a6f9acd90e7ce03f81333b9572e233a482c074b5 | 80b8b1d3e48b26a2b452ca9c40fddd91d1d7b3ee | refs/heads/master | 2023-05-04T20:41:36.748148 | 2022-08-11T21:56:23 | 2022-08-11T21:56:25 | 68,281,550 | 8 | 8 | null | 2023-03-13T17:06:04 | 2016-09-15T09:26:18 | R | UTF-8 | R | false | false | 14,050 | r | boot.r | #' Bootstrapping a fitted ascr model
#'
#' Carries out a parametric bootstrap, based on a model fitted using
#' \link{fit.ascr}.
#'
#' For each bootstrap resample, a new population of individuals is
#' simulated within the mask area. Detections of these individuals are
#' simulated using the estimated detection functio... |
c710ab2cab842f17ba72f0fe5bcf72e225f6b989 | a880badcba73ed7338eed0c910c42e58bb534703 | /plot1.R | 8b16c2691de5258141fb1e8ec1d8ab83f2068c2b | [] | no_license | jesrui/ExData_Plotting1 | 8024af878a8a126f10dea0f959f71a4346634586 | db2bfb3696a48ce5d27de193b740c0f6b690c72b | refs/heads/master | 2020-11-30T12:32:06.672988 | 2015-09-13T18:42:32 | 2015-09-13T18:47:35 | 42,173,925 | 0 | 0 | null | 2015-09-09T11:14:47 | 2015-09-09T11:14:47 | null | UTF-8 | R | false | false | 270 | r | plot1.R | source('readData.R')
filename <- '../household_power_consumption.txt'
data <- get.power.consumption(filename)
# Plot 1
png(filename="plot1.png")
hist(data$Global_active_power,col='red',
xlab='Global Active Power (kilowatts)',main='Global Active Power')
dev.off()
|
c65cc0aa4d6649272d5d5c164da0ab4f75d70275 | 234cb2b1ad3f11d7c5a6a33e14f16d4aa5e64d32 | /man/Normalise.rd | 4a372df86838851c4460c257caaacf2462149c36 | [] | no_license | abhorrentPantheon/metabolomics | 3306a2819f43f143fcf05eefd66b04a192ee66c8 | 22e566c54dc9309176fcf8f74f0ded264a8cb90b | refs/heads/master | 2021-01-16T19:33:27.192251 | 2015-03-19T04:15:44 | 2015-03-19T04:15:44 | 35,789,054 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,589 | rd | Normalise.rd | \name{Normalise}
\alias{Normalise}
\title{Normalisation}
\description{
Normalise a metabolomic data matrix according to a specified method.
}
\usage{
Normalise(inputdata,
method = c("median","mean","sum","ref", "is", "nomis","ccmn", "ruv2"),
refvec = NULL, ncomp = NULL, k = NULL, nc = NULL,
saveoutpu... |
7f347648c77c08c4c930c6f9b5091c929da5a669 | aeb9c1b695b40727c7cb70019ba67a3cbbd03cf2 | /docs/rhelp/wicksell.Rd | 54a4f9f1d044d9f37724d60f3bf2085e0bdf89b9 | [] | no_license | gamlj/gamlj.github.io | ed589b9ac86903902ab55ebbbacffe03afe9b5e4 | d774d46b64040cee63a834d939538a816b6db0b9 | refs/heads/master | 2023-06-14T14:27:12.453343 | 2023-06-12T17:33:25 | 2023-06-12T17:33:25 | 183,435,098 | 2 | 3 | null | 2023-06-12T16:12:02 | 2019-04-25T12:59:14 | CSS | UTF-8 | R | false | true | 448 | rd | wicksell.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{wicksell}
\alias{wicksell}
\title{Depression over time}
\usage{
data(wicksell)
}
\description{
Data repeated measure anova with mixed models
}
\examples{
data(wicksell)
}
\references{
David C. Howell, Overview of M... |
e3d6d20997fc8283418adf7345e647c7cd45b4c4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sirt/examples/linking.haberman.Rd.R | 226d39ab635ad7a7ecc5db7ac1a239f55f53adab | [] | 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 | 11,261 | r | linking.haberman.Rd.R | library(sirt)
### Name: linking.haberman
### Title: Linking in the 2PL/Generalized Partial Credit Model
### Aliases: linking.haberman summary.linking.haberman
### Keywords: Linking Equating
### ** Examples
#############################################################################
# EXAMPLE 1: Item parameters dat... |
59f45cd7b15d15246b3080fc4c24c7aebf2cbdfb | fcaaf7ba8ec7e21883394ad57f3fb544f4dd63dc | /Cap04/06-BarPlots.R | c8c1a8020ebdd1b758ee6bc95778b07bab9d0703 | [] | no_license | GasparPSousa/BigDataAnalytics-R-Azure | f3226150461496c0d78781bfd8fe3b5bb5237199 | aeeb060f32f8846ea80f6bc4631d0f07d21cbf1e | refs/heads/main | 2023-05-14T23:57:15.302363 | 2021-06-06T14:04:48 | 2021-06-06T14:04:48 | 357,303,863 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,843 | r | 06-BarPlots.R | # Bar Plots
# Configurando o diretório de trabalho
# Coloque entre aspas o diretório de trabalho que você está usando no seu computador
# Não use diretórios com espaço no nome
setwd("~/Cursos/DSA/FCD/BigDataRAzure/Cap04")
# Para saber qual diretório estou trabalhando
getwd()
# Lista de pacotes base carregados
search... |
40e7ab443f96cbc090517cb3994a8354d1e55511 | 985dd57a2845aad61f61c6d4e24bdb91a99333e1 | /tests/testthat/test-kmers.R | 2b74cad318f52bc075b46ed3157c7e8c14f4bb2a | [] | no_license | kriemo/kentr | f8e84bb33ea2097d6e272b0b0aa41a72cd5bedb6 | c4122d7b10de03f273db33727a777c8bc49926bd | refs/heads/master | 2021-07-20T07:47:29.757326 | 2021-05-03T17:35:32 | 2021-05-03T17:35:32 | 88,792,047 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,820 | r | test-kmers.R | context('kmers')
fa_file <- system.file("extdata", "test.fasta", package = "kentr")
df <- data.frame(
chrom = c("chr1",
"chr1"),
start = c(24000,
24000),
end = c(24100,
24100),
strand = c("+",
"-"))
res <- get_sequences(df, fa_file)
test_that('basic usage works... |
77bd7d17e1a4d062869cb7da0aa5c8c1fd67c68e | 1839b1bc21a43384e9c169f0bf5fd0a3e4c68b0a | /w18/R/mergeMotifs.R | cc28267ea33a2b2c128166fbdd21263866352d1b | [] | no_license | CarlosMoraMartinez/worm19 | b592fa703896e1bbb6b83e41289674c63a046313 | 99fb3ef35d13739ee83f08b2ac1107179ea05ee2 | refs/heads/master | 2020-07-18T23:25:13.542031 | 2019-07-03T14:53:04 | 2019-07-03T14:53:04 | 206,333,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 991 | r | mergeMotifs.R |
#' mergeMotifs
#'
#' Merges all the motif data from several windows into one row, avoiding
#' redundancies.
#'
#' @param win window dataframe with the following fields:
#' "width", "signature", "offsets", "strands", "seqs", "starts", "ends".
#' @return A dataframe with 1 row, containing the mentioned variables.
#' ... |
59a5e53f36020647f102d134a5581b0f3f79890d | a893f063e4fb685c6d959882b74f35cfc17686ee | /solutions/reading_excel_or_off_internet.R | 1458153ca5d1c6d23d29f845448797defbb0c9fd | [] | no_license | jmarshallnz/intro_to_r | e8ebe29fe4df3d32f9848e79eb75ead3a46bce4c | 35065a9255915f5f9eec4248972560fcbe7ff991 | refs/heads/main | 2023-06-16T10:09:57.485226 | 2021-06-21T04:13:12 | 2021-06-27T23:29:42 | 332,910,494 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 533 | r | reading_excel_or_off_internet.R | # Reading from Excel, or reading off the internet
library(tidyverse)
library(readxl)
# Try reading an excel sheet
covid19 = read_excel("data/covid19/covid-cases-30july20.xlsx")
covid19
# You'll notice there is some junk at the top.
# We need to skip the first 3 lines in the sheet before reading
covid19 = read_excel("... |
de711cf1f87a223c3c5e4f39d71aa0ca1276d105 | 57b9a09f56013a8867a5505b03945a37aa2f6275 | /run_analysis.R | 52c44023f40cdb6c379151f10df2413af205dc84 | [] | no_license | AOverlack/CourseraWork | 0219b818bda1f6618c48fdb15b2693979f561476 | 6463dd1e8aa3faa9caeae0813c56a834d0ef60b4 | refs/heads/master | 2021-01-13T00:42:02.982321 | 2015-07-26T21:14:58 | 2015-07-26T21:14:58 | 36,096,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,195 | r | run_analysis.R | ## PREPRATORY MAUNAL ACTIVITY
## Download the fileS from the file address:
## Unzip the files mually in windows
## Create folder in working directory, named "data".
## Store the unzipped files "X_train.txt" and "X_test.txt","subject_train.txt",
## "subject_test.txt" "features.txt","y_test.txt", "y_train.txt" and "act... |
7e3d940640217ed3fc625c932940daf3f762487e | 8884141f6e515990fede8c1509d4e855d821ac73 | /for_liam.R | f03562c4795691bfc07ec0f516dc0b5e258a2ffe | [] | no_license | samlipworth/heaps | 6a0ebe14989c838ae735816946f9e913674ec6c7 | 6802a0ba5921637c589558b3f69e800bd0080ffa | refs/heads/main | 2023-08-11T06:04:49.358950 | 2021-09-30T08:54:42 | 2021-09-30T08:54:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,179 | r | for_liam.R | library(tidyverse)
library(data.table)
library(micropan)
guuids<-read_tsv('./data/guuids')
cg<-filter(guuids,grepl('chromo',guuid)) # cg = chromosomal guuids
pg<-filter(guuids,!grepl('chromo',guuid)) # pg = plasmid guuids
guuids$guuid<-str_replace_all(guuids$guuid,'_.*','')
spec<-read_tsv('./data/kleborate.tsv') %>%... |
d4faf3035ea14965a059bd8bc9739ea72b1a4619 | 2741826483417f28990d21a414821f0d741b811c | /inst/tinytest/test_character2integer.R | e120ca396a735fc20b5c8244f0038a15f164687e | [] | no_license | HughParsonage/hutilscpp | 3262201a11d2026d37284f709e4e0e6fbb53d3a9 | 143f9e2dca2c0f25e6e64388fdfd8f6db64477d9 | refs/heads/master | 2022-10-20T22:12:58.646043 | 2022-10-07T07:20:58 | 2022-10-07T07:20:58 | 155,201,062 | 8 | 3 | null | 2022-09-29T15:58:30 | 2018-10-29T11:33:08 | R | UTF-8 | R | false | false | 4,072 | r | test_character2integer.R | library(hutilscpp)
x <- c(1626783884L, 969909421L, 205541854L, -1L, 0L, 1L, -1214788235L,
-709260613L, -795055625L)
cx <- prettyNum(x, big.mark = ",")
expect_equal(character2integer(cx), x)
cx <- prettyNum(x)
expect_equal(character2integer(cx), x)
x <- as.double(x)
cx <- prettyNum(x, big.mark = ",")
expect_equa... |
f47bd9f3e0db199b596f94e97c33281c9a2efff0 | eb9128ca974c2407b6760d6feee036724f7fe22f | /activity2/activity2_script.r | e138e3bf112c9297e670fa5e7a3fdaaf199cb3bf | [] | no_license | kevinlzw/GEOG331 | da5c7780da535d5a5a4792e6170b89bfe8867d20 | e31d6b566cc5c7b83a0a084bd96d5c4f189502ae | refs/heads/master | 2020-12-20T10:20:17.463422 | 2020-04-24T20:38:27 | 2020-04-24T20:38:27 | 236,040,429 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,933 | r | activity2_script.r | #make a vector of tree heights in meters
heights <- c(30,41,20,22)
#convert to cm
heights_cm <- heights*100
heights_cm
#first item in the vectors
heights[1]
#look at the 2nd and 3rd tree heights
heights[2:3]
help(matrix)
#set up a matrix with 2 columns and fills in by row
Mat <- matrix(c(1,2,3,4,5,6), ncol=2, byro... |
149541be57d02622a58a058f5eb6277767e04110 | f0e646a57a90c4b7b7fe57d9f5601bae24f908ab | /data-raw/vic.R | 4ed8c4de50031eeac4034fd5bf35abff26d21d31 | [] | no_license | adam-gruer/ozbabynames | d7e60b4256b1c3621aec09199801cafdfe625957 | d1aff4cac93c58197743717e0a0cd7ff6a83af1d | refs/heads/master | 2020-04-07T20:40:17.319725 | 2018-11-22T12:56:03 | 2018-11-22T12:56:03 | 158,698,482 | 1 | 0 | null | 2018-11-22T12:48:44 | 2018-11-22T12:48:44 | null | UTF-8 | R | false | false | 549 | r | vic.R | library(purrr)
library(tidyverse)
library(readxl)
# Fix files
vic <- map_dfr(fs::dir_ls("data-raw/vic"), function(x){
fname <- tools::file_path_sans_ext(x)
out <- read_excel(x, skip = 2)
male <- out[1:3]
male$sex <- "Male"
female <- set_names(out[5:7], names(out)[1:3])
female$sex <- "Female"
rbind(male... |
eb1fc0f0ee9a8afb256f1881376918fc0d733ed7 | 10d35ea866d69d940b6104c8bde2aef0cc83f2bd | /inst/examples/calendar.R | f6aed36640084d3de581aad87785d8c9cc6f2a56 | [] | no_license | omegahat/RwxWidgets | 95b0a01bde4654bf5caed772bb3b8492f14f047b | aff0f3fb9b928ebbcda4258b1d37eb5d799a1ac2 | refs/heads/master | 2021-01-10T05:54:17.063492 | 2012-02-22T01:54:38 | 2012-02-22T01:54:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 261 | r | calendar.R | library(RwxWidgets)
wxInit()
f = RFrame( size = c(50, 50))
cal = wxCalendarCtrl(f)
f$SetSizer(sz <- wxBoxSizer(wxHORIZONTAL))
sz$Add(cal, 1, wxEXPAND)
cal$SetSize(50, 50)
f$SetSizer(sz)
sz$SetSizeHints(f)
f$Show()
print(f$GetChildren())
#wxEventLoop()$Run()
|
87a1c36e23cb66f54d5f1c9c89e6abdc9f08e5ca | 2271a5faab43855132dd5bea92031b5433932bbc | /R/gl.costdistances.r | 5beb4423687d4a7572ed6e25f9811974d18fdd30 | [] | no_license | Konoutan/dartR | 26252e126e5f38589e21f726e3777360390a8005 | aa35a02121aff4fb092b3f88e5200d343938656b | refs/heads/master | 2022-06-30T13:41:35.734109 | 2019-12-05T08:58:16 | 2019-12-05T08:58:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,937 | r | gl.costdistances.r | #'Calculates cost distances for a given landscape (resistance matrix)
#'
#'@param landscape a raster object coding the resistance of the landscape
#'@param locs coordinates of the subpopulations. If a genlight object is provided coordinates are taken from @other$latlong and centers for population (pop(gl)) are calculat... |
af6fef787ca434201a0c7594ca447547fa82e27a | c5c30dd82371c65f9ecc7170bd9460859472b008 | /deterministic_ngm_calc.R | e7fe149f7a0216bcd30e9644e1eff6c6c4c584ae | [] | no_license | renatamuy/core_matrix_publish | 2e05af873aa202b06a1096d224ad8be8d436fbe7 | 0a6edab32faf67e7c43e261f5a694ff4ca1ce84f | refs/heads/master | 2021-06-26T20:58:05.004053 | 2017-09-14T10:17:39 | 2017-09-14T10:17:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,166 | r | deterministic_ngm_calc.R | ############################################################
# Code for NGM R0
## FD & DD transmission
############################################################
# R0 at different phi + psi
# just by calculating a pathogen that comes into a completely naive population
# at carrying capacity for that proportion fores... |
a939f2193eba3bbf3ea1340d6f30dfcd825e75f5 | 12854f77ea56109c7df0b4f59480589f623fe70f | /Logistic_Graded.R | 9e44e36dd2672bf9f6e69307bb3f649f6867cd4e | [] | no_license | souravbiswas1/RLog | 0359a6eb61fe596dee1b4bab0d93d3a6b09c0ccc | 3a0a143d683829da975d4233c0c3f7620b78532e | refs/heads/master | 2020-03-19T17:04:28.454397 | 2018-06-09T17:49:28 | 2018-06-09T17:49:28 | 136,742,989 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,471 | r | Logistic_Graded.R | #Setting the path of the working directory::
setwd("E:\\Jigsaw\\Analytics with R\\Work\\Logistic_Regression")
library(dplyr)
library(gains)
library(irr)
library(caret)
#Reading the file::
gf<-read.csv("goodforu (1).csv")
#Some exploratory data analysis and findings::
summary(gf)
#Checking the no of rows & columns:... |
95353f1051d36a0759215d75da05dc3a2d6a7f10 | 370d5b17a744b6dc41d80f0e0492260ea32fc6ba | /man/noirot.contribution.Rd | fd0d202036f3f28e2d3ff1ef9c9859c21bf4c742 | [
"MIT"
] | permissive | keocorak/CoreComp | a6d0cc50a3bcc9dc2947819e682be03c7d98c2ec | 076e5984afc28c02c91446ae22daee752788745d | refs/heads/main | 2023-01-31T16:36:49.167609 | 2020-12-17T20:03:45 | 2020-12-17T20:03:45 | 318,306,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 789 | rd | noirot.contribution.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/genorep.R
\name{noirot.contribution}
\alias{noirot.contribution}
\title{Noirot Principal Component scoring}
\usage{
noirot.contribution(geno.dist.all, core.names, k = 2)
}
\arguments{
\item{geno.dist.all}{genetic distance matrix}
\item{core.... |
76114f963b5aa073fea5c4c5d3e1769bda55260d | 21d39115a575d6f403d6485a0744aae126d60daf | /R/data.R | e02dc009b17779690386d246c54a45bb59df46c8 | [] | no_license | nealhaddaway/discoverableresearch | 33842b2dd14f3f69fa5e3367520b60b8e46ccfeb | 63e6f061be0efca28a62e1d994ec828075b04040 | refs/heads/master | 2023-03-07T13:11:54.420346 | 2020-10-05T06:04:26 | 2020-10-05T06:04:26 | 294,333,669 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 659 | r | data.R | #' Languages codes synthesisr can recognize
#'
#' A dataset of the languages that can be recognized by
#' synthesisr along with their short form, character encoding,
#' and whether a scientific journal indexed in 'ulrich' uses them.
#'
#' @source 'litsearchr' package on 'Github'
#' @format A database with 53 rows of 4 ... |
96445af71d1f0f25350364836c5efac9555484f0 | f85d09d41ee157807577a480d634ae21c92b0631 | /SL_KNN&Boosting_ML/Amazon_Sourcecode_boosting/Amazon_Boosting.R | 6b66aebe3df6c40c6b185c6d19cb5368861ac747 | [] | no_license | fzachariah/Machine-Learning | f49564fd21a7d196a9cc77066b8136d5fb74e05b | 090b64feacca942eb56942d81059aabae4860cf5 | refs/heads/master | 2021-01-16T17:49:09.352659 | 2017-05-17T01:41:23 | 2017-05-17T01:41:23 | 87,450,538 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,234 | r | Amazon_Boosting.R | library(class)
library(caret)
library(caretEnsemble)
Amazon_train_data <- read.csv(file = "sentiment_train.csv", header = TRUE)
Amazon_train_data$rating <- as.factor(Amazon_train_data$rating)
Amazon_test_data <- read.csv(file = "sentiment_test.csv", header = TRUE)
Amazon_test_data$rating <- as.factor(Amazon_test_data$... |
8a5e475764db8795d8be75f8a3925316604e0bf3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Momocs/examples/coo_aligncalliper.Rd.R | 2535a3e69154c2629de4caee7487d87c639f600a | [] | 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 | 304 | r | coo_aligncalliper.Rd.R | library(Momocs)
### Name: coo_aligncalliper
### Title: Aligns shapes along their 'calliper length'
### Aliases: coo_aligncalliper
### ** Examples
## Not run:
##D b <- bot[1]
##D coo_plot(b)
##D coo_plot(coo_aligncalliper(b))
##D bot.al <- coo_aligncalliper(bot)
##D stack(bot.al)
## End(Not run)
|
05071c3602ba14658e7527de2fa5d2abe4e622bb | 96e1fa91df73ec4d9b97b25914860b7bb37c1183 | /GeneTonic/GeneTonic-sticker.R | 6f1bf9aecd087c6eface32e1aa50e9d8fd34e647 | [
"CC0-1.0",
"LicenseRef-scancode-public-domain",
"CC-BY-3.0",
"CC-BY-4.0",
"CC-BY-2.0"
] | permissive | Bioconductor/BiocStickers | 52113b315b85400fb04c1325f8f3d02eab5559ea | 1a0cba2d4041c9ef41fd29d0c4a35c62911b71dd | refs/heads/devel | 2023-07-23T20:59:38.116853 | 2023-07-13T19:12:37 | 2023-07-13T19:12:37 | 83,644,497 | 118 | 107 | NOASSERTION | 2023-09-09T13:31:35 | 2017-03-02T06:46:55 | R | UTF-8 | R | false | false | 1,043 | r | GeneTonic-sticker.R | # to be done out of this script:
# - import svg of gin tonic
# - export to hi-res png
# - assemble in powerpoint with dna helix
# - group up and export as picture (GT_logo_full.png)
# Assembling all the pieces together --------------------------------------
library(ggplot2)
library(png)
library(grid)
library(hexStick... |
f4353fa7e1ba697661ba9ddcc10820ca8dae7908 | 77c6fc544f7737a5317fd09e2fe78f1e99f70027 | /Kyou-san/rgl_igraph2.R | 0490cc172be202e22c330bed98324e9563a0ade4 | [] | no_license | ryamada22/R | bdbf5a01397ad4da86c271d94b97d302d426a346 | a142edc21c5ab188d2bf531297b2e02ff2c49c15 | refs/heads/master | 2020-04-04T00:28:50.447554 | 2019-08-28T07:59:13 | 2019-08-28T07:59:13 | 29,048,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,719 | r | rgl_igraph2.R | library(rgl) # package for 3d object handling
# reading the bunny.obj in your fair zip
bunny <- readOBJ("bunny.obj")
library(igraph) # package for graph theory
# 3D coordinates of vertices in the shape of n x 3 matrix
V.xyz <- t(bunny[[1]][1:3,])
# Enumerate edges of triangle in n x 2 matrix shape
Edges <- rbind(t(b... |
41203f341022a13288ec334803fb2202f5c39e9b | 9f6c0f270a3a49493a1c405b90d363327f782898 | /man/nnetPredInt.Rd | 4ab206d1d272bd27a925f926675771c93e081e87 | [] | no_license | diegomerlanop/nnetpredint | 714ff83eff6e8c8016bd98e3e13a0ee05e6794fb | 549fd65616cefd9c042381ebe23bfacf70b81c00 | refs/heads/master | 2021-05-30T22:54:55.797432 | 2015-12-21T21:35:34 | 2015-12-21T21:35:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,261 | rd | nnetPredInt.Rd | \name{nnetPredInt}
\alias{nnetPredInt}
\alias{nnetPredInt.default}
\alias{nnetPredInt.nnet}
\alias{nnetPredInt.nn}
\alias{nnetPredInt.rsnns}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
Prediction Intervals of Neural Networks
}
\description{
%% ~~ A con... |
8e605fc43b401a034880328999f1c11d922c8a3a | 33945f7d8c8dc14d102638de7ec71d1e88413013 | /cal/radius_cross.R | 453ba78da696fe2c5e3690f52df94153bb0605dc | [] | no_license | wactbprot/svol | 9c483a87969cc5eddec68e6c5be8a2b60bad0e9e | 57db9658fbd5b253bced0e7fa66471d79115364f | refs/heads/master | 2021-01-18T14:05:39.453305 | 2015-02-05T12:16:08 | 2015-02-05T12:16:08 | 29,733,995 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 940 | r | radius_cross.R | t1 <- read.table("data/ventil-sitz_1.txt"
, skip=2
, sep=" "
, row.names=NULL)
it1 <- which(t1[,1] == "SCN1")
it4 <- which(t1[,1] == "SCN4")
it5 <- which(t1[,1] == "SCN5")
mt <- as.matrix(t1[,3:5])
r <- sqrt(mt[, 1]^2 + mt[, 2]^2)
z <- mt[, 3]
brs <- 8.25
bre <- 8.75
#... |
a74370aa4b6231c0d7e5f88dcaf1e4de4193d353 | 6e6202e97b13bead3f40ab7a141c2bc4fe8e9345 | /sr-ch8.R | db73f431a5d0253d05e6a513b448acdbcbd5ae61 | [] | no_license | loudermilk/bayesian-stats | 24b074d9b3775a2e193acb509c66b8ba3550417b | ca9840314183423e15ee80666b97fac43ee0b4d1 | refs/heads/master | 2021-01-09T20:13:57.391662 | 2016-09-19T12:46:39 | 2016-09-19T12:46:39 | 62,754,526 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,795 | r | sr-ch8.R | ## sr-ch8.R
## Chapter 8 - Markov Chain Monte Carlo
## 8.1 Good King Markov and His Island Kingdom
num_weeks <- 1e5
current <- 10
positions <- rep(0, num_weeks)
for (i in 1:num_weeks) {
positions[i] <- current
proposal <- current + sample(c(-1,1),size=1)
if (proposal < 1) proposal <- 10
if (proposal > 10) pro... |
db6f2e8590f82157aff6dacf0e1f34b6f3c30c85 | 3df31271dd49218652e1c654df9caeaaa22c5a26 | /R/plot.R | 0ab79baee0a65fadbb163339eae95eb2d9998827 | [] | no_license | lucasns/cleandata | adf38ec5de4565372503582c71e719b6b28d2d1e | c4b2d9095d60364c324d0c1a471dea697a3bfdc8 | refs/heads/master | 2021-06-19T07:55:46.530939 | 2017-07-07T12:34:43 | 2017-07-07T12:34:43 | 93,980,566 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,359 | r | plot.R | plot_univar = function(dataset, var, type="histogram", modifier = NULL, rm_na = TRUE) {
if (is.null(dataset)) return()
if (rm_na == TRUE) {
dataset = dataset[!(is.na(dataset[[var]])),]
}
x = dataset[[var]]
if (!is.null(modifier)) {
x = apply_modifier(x, modifier)
var_name ... |
e366248e8eaf634c9c65688a675035cdf5c64dfc | 4636b573fdf11a69a243f67977564b2a5da8b8bf | /Chapter 39 Use API-wrapping packages.R | d4bfb63c4ffb4de3024e146da53d3d431843acbc | [] | no_license | yuan1615/STAT545 | 16930c49fd6d5da94065b9252b381fdc7e54b285 | 0fa0ecf7c964063cab1c9e4199805ddc26162fe4 | refs/heads/master | 2020-08-14T13:16:20.129132 | 2019-10-24T12:20:07 | 2019-10-24T12:20:07 | 215,175,286 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,029 | r | Chapter 39 Use API-wrapping packages.R | # 要点
# 公开的API接口
# rebird：获取一些鸟类的信息
# geonames：获取地名
# rplos:获取公共图书馆
##### Chapter 39 Use API-wrapping packages #####
#------ 39.1 Introduction ------
# 四种获取互联网数据的方法
#
# 单击并下载 -在互联网上以“平面”文件的形式出现，例如CSV，XLS。
# 安装并播放 -有人为其编写了便捷R包的API。
# API查询 -使用未包装的API发布。
# 搜寻 -隐含在HTML网站中。
#------ 39.2 Click-and-Download ------
# do... |
d56486b605b4f0c3cb435665cc185314fdcd381f | de0c103492d5c14cb74c32a60cd9642ec8a5e358 | /Sequences_sunburst_files/script.r | 0dfe0b9e7099d559999b2260e3eb58904d50beb2 | [] | no_license | jpiscionere/jpiscionere.github.io | bca0701b1702938ab474fa4ab1b960c1379adee9 | f258ade7ae36ed2fffa0f538109988037d3ab051 | refs/heads/master | 2021-07-12T09:46:33.970444 | 2021-06-27T23:54:04 | 2021-06-27T23:54:04 | 45,433,187 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,013 | r | script.r | data=read.csv("survey_data.csv")
summary(data)
genders=data$What.Gender.Do.You.Identify.As.
genders
races=data$How.Would.You.Identify.Your.Ethnicity.
races
graduates=data$Are.You.A.Graduate.Student
second=data$What.Best.Describes.Your.Second.Position.Out.of.Grad.School.2
first=data$What.Best.Describes.Your.First.Positi... |
5c4dce20b2c33a0711f36305739a666c7fa04355 | c84dd226cf9f7fc21a205f1e9412e92eca7a4b93 | /R/zzz.R | fa40d33ff9b01e417fb51857e044537a67553033 | [] | no_license | jrminter/minterbrand | 14f839c17428bb656cdc0d630b882808b1140248 | 17c9422134ceb817d900aed0232fbfdb898d3869 | refs/heads/master | 2021-05-04T23:23:44.781313 | 2018-02-12T05:22:09 | 2018-02-12T05:22:09 | 120,148,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 90 | r | zzz.R | .onAttach <- function(...) {
library(tidyverse)
library(knitr)
library(rmarkdown)
}
|
17070a5a059e1d710089db77927a8072f5920237 | 9163d726e145d9e1a36ef8d164dc0312d880616c | /PassiveData2016_Workflow.R | 63814ed61c879b17f94c420efc5f6d6cfe6a0606 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | lukeloken/passive_tox | fca7b9804a929d7753110386ef99dbf362500382 | 45b75bcd307dbdd490ab529df1968c196ce0cd32 | refs/heads/master | 2021-10-25T01:53:10.100918 | 2021-10-19T19:48:27 | 2021-10-19T19:48:27 | 232,613,428 | 0 | 0 | null | 2020-01-08T17:00:12 | 2020-01-08T17:00:11 | null | UTF-8 | R | false | false | 2,356 | r | PassiveData2016_Workflow.R |
# Workflow for analyzing passive sampler pesticide data
# Data are from 15 streams flowing into the Great Lakes
# Merge data with Sam Oliver's paper
# Luke Loken
# January 2020
# path_to_data <- c('C:/Users/lloken/OneDrive - DOI/GLRI_Pesticides')
path_to_data <- c('C:/Users/lloken/DOI/Corsi, Steven R - GLRI CECs/201... |
14846df8cac558e032cbdcf39930febaa4dd977a | 6d2d195c56c7d123f1b5c36e99efa62c0e6ea754 | /man/ehss.Rd | b27a2ee475ccc763bdc2bb6bc919a022049f3132 | [] | no_license | DKFZ-biostats/ESS | 8502a6338c39a66246471f3651a6872c5f121af0 | 28b1b88662cb4d354a411e595e6e26bc9bed7ec3 | refs/heads/master | 2023-03-16T14:20:27.707341 | 2019-06-19T09:55:36 | 2019-06-19T09:55:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,477 | rd | ehss.Rd | \name{ehss}
\alias{ehss}
\alias{ehss.normMix}
\alias{ehss.betaMix}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Compute Effective Historical Sample Size (EHSS)
}
\description{
Compute Effective Historical Sample Size (EHSS). This is the prior effective sample size applied to the poste... |
5d9e01569be8276ca2c59c70ca73b23bc275cf16 | 862f7f896467575c5c16fe7cdff9eed2dcf49df9 | /binder/install_.R | 1f77133fa634e66ecfee3a0650fa37bf8e1c9ac0 | [] | no_license | schmudde/ptm | 480c7a4e63dee1cdf00474fbadb94f0f009baafe | 5888b65f0f8fcb51320e2f63dd0e1716867b711f | refs/heads/master | 2020-06-01T00:54:02.480967 | 2019-04-02T16:56:02 | 2019-04-02T16:56:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 566 | r | install_.R | install.packages("ggplot2")
install.packages("magrittr")
install.packages("dplyr")
install.packages("wordcloud")
install.packages("tm")
install.packages("png")
install.packages("data.table")
install.packages("reshape2")
install.packages("igraph")
install.packages("scales")
install.packages("lda")
install.packages("LDAv... |
b876cf32f911e86b9f163e18a75c29e8b50c8c39 | 312d11a6dd935ba3ea61f5869413db38909731de | /sentiment_analysis.R | 412def7dd4280864c8fb10c35dcc569c20bcd3b8 | [] | no_license | MarauderPixie/broken__spotify | e7190e54b776bb3de20fadafcf450b6a398ac158 | f78b5ea48a9ac0e0d1183f5066bc3ce1253c7d72 | refs/heads/master | 2021-07-12T07:51:13.603452 | 2017-10-03T20:57:59 | 2017-10-03T20:57:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,795 | r | sentiment_analysis.R | # merge spotify and genius data
## feels inefficient, maybe think about that later
top50l_filtered <- filter(top50l, str_to_lower(Track) %in% str_to_lower(lyrics$Track))
top50l_filtered$lower_track <- str_to_lower(top50l_filtered$Track)
lyrics$lower_track <- str_to_lower(lyrics$Track)
lyrics_reduced <- lyrics %>% sel... |
695ef90954c3923926477be81da276991c4f42fc | 4e05a0199c0eb916244d79879d0f46079774d75f | /R/Mo_1.R | 83e73d568b33f66c8dc1f988e5475e830d1bcff6 | [] | no_license | ilangurudev/datathon_citadel | 8adcd0b3001d1a4eb812866889490cb813a8aebb | 862674af703501f4bacbb80b6adf8e70bf311f3e | refs/heads/master | 2021-05-01T22:02:16.508974 | 2018-02-10T21:11:29 | 2018-02-10T21:11:29 | 120,984,554 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 675 | r | Mo_1.R | # Mo 1
boroData <- read.table("nybb.csv", skip = 1)
#load raw data
weatherData <- read.table("data/weather.csv", header = TRUE, sep=",")
green <- read.table("data/green_trips_new_2.csv", header = TRUE, sep=",")
# Combined data of rides and weather
combined <- as.data.frame(c(green[1:2190,c(1, 3,4,7)] ,weatherData[,... |
226b10ab1d30864453ed81aa6bdc281c52eeccb2 | 97e3baa62b35f2db23dcc7f386ed73cd384f2805 | /man/tr.Rd | f3db8e6b2fa18ca135b64f78a21b0a4812cf4399 | [] | no_license | conservation-decisions/smsPOMDP | a62c9294fed81fcecc4782ac440eb90a299bca44 | 48b6ed71bdc7b2cb968dc36cd8b2f18f0e48b466 | refs/heads/master | 2021-06-25T22:23:31.827056 | 2020-10-27T08:56:07 | 2020-10-27T08:56:07 | 161,746,931 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,637 | rd | tr.Rd | \name{tr}
\alias{tr}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Transition matrix function
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
Computes the transition matrix between states for each action : manage, survey and stop. State 1 : extant,... |
373e7b0d4bb36403f81f43d11e8cea2eb2d7eba5 | 1a619138a56d4cafd5ea7d2326b8b2be9a5a51f7 | /WebScraping_Project.R | 70601c3a8665c0885d780ecb38441e64dd8e3be5 | [] | no_license | elenayang528/Shiny | f32c64f17610ce94d3bb953552bbe5ed1747a644 | caf21b51fc665d31719d37d48e97ac81de12c890 | refs/heads/master | 2022-05-26T04:40:02.235788 | 2020-04-30T14:30:56 | 2020-04-30T14:30:56 | 257,082,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,510 | r | WebScraping_Project.R | library(corrplot)
library(ggplot2)
library(gplots)
library(ggpubr)
library(scales)
library(zoo)
library(corrplot)
library(readxl)
library(quantmod)
library(tidyr)
library(stringr)
library(rvest)
library(plyr)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(gplots)
library(ggpubr)
library(sc... |
2b7e031ca53efeaa53fbe18bbcdd17fc5e543df0 | 204b1b2ebdce859adbf34e4c31debc4fa5129d4e | /GA-master/GA/R/Cross_over & Mutation.R | 196a60b01059cd0f7bce59517fc401456f2a380f | [] | no_license | esther730/stat243 | 4445a16b14ad48dd754a1b6659c793efd1c57649 | 6d438d8f916a6e3f2f811daf65d033bfd206881a | refs/heads/master | 2021-03-22T05:25:23.327769 | 2017-12-31T06:44:50 | 2017-12-31T06:44:50 | 101,693,590 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,160 | r | Cross_over & Mutation.R | #'Cross over
#'
#'Generate offsprings by randomly pick the position of chromosomes
#'@param parents list, parents to generate offsprings,length=2
#'@param dat dataframe, data to generate offspring(default sets the dependent variable in first column and independent varialbes is other columns)
#'@param fitfunc metho... |
646c2418f149304f81e6fd8014b80459f04842bd | 3c2cb26f7c89c54ce5328522e6b752ccf6838061 | /man/post_material_table.Rd | fefa177ec9600ff2eba523c1b4c9b004c3fab4c8 | [] | no_license | c5sire/fbmaterials | 39b8a9682da33ed915d93382cf66bf07ef6e96bd | 0af427d046e572e2320bf52426bec3728a60d413 | refs/heads/master | 2020-12-25T21:12:47.424718 | 2017-04-26T18:37:04 | 2017-04-26T18:37:04 | 43,533,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 485 | rd | post_material_table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api_material_list.R
\name{post_material_table}
\alias{post_material_table}
\title{post_material_table}
\usage{
post_material_table(table_materials, crop, year, mlist_name, notes = NULL)
}
\arguments{
\item{table_materials}{a data frame}
\ite... |
594a786b4e0e853939b40ea86564705fed3a91b7 | ea524efd69aaa01a698112d4eb3ee4bf0db35988 | /tests/testthat/test-compare.R | 1d9f237bbb06faf0ee57a1f6a0ac3ca38ae9e483 | [
"MIT"
] | permissive | r-lib/testthat | 92f317432e9e8097a5e5c21455f67563c923765f | 29018e067f87b07805e55178f387d2a04ff8311f | refs/heads/main | 2023-08-31T02:50:55.045661 | 2023-08-08T12:17:23 | 2023-08-08T12:17:23 | 295,311 | 452 | 217 | NOASSERTION | 2023-08-29T10:51:30 | 2009-09-02T12:51:44 | R | UTF-8 | R | false | false | 6,188 | r | test-compare.R | test_that("list comparison truncates to max_diffs", {
x <- as.list(as.character(1:1e3))
y <- lapply(x, paste0, ".")
lines1 <- strsplit(compare(x, y)$message, "\n")[[1]]
expect_length(lines1, 10)
lines2 <- strsplit(compare(x, y, max_diffs = 99)$message, "\n")[[1]]
expect_length(lines2, 100)
})
test_that("... |
013213513c9adbe0bac12f35c859ad49d4de8d4a | acabe441d5bd5391ff0812169275c67128978c39 | /tests/testthat/test_template_table_attributes.R | 579712a5388cb324088d576acc9fa2c0ded43bcf | [
"MIT"
] | permissive | Ashley-LW/EMLassemblyline | 65d448ce6ee760f06904326ca2f3b9f4e475a85e | a37bc32c1feffa4f8a5ae88f158457fd05d4a86e | refs/heads/master | 2022-12-10T17:30:00.850619 | 2020-09-08T23:02:38 | 2020-09-08T23:02:38 | 292,932,246 | 0 | 0 | MIT | 2020-09-04T19:38:35 | 2020-09-04T19:38:34 | null | UTF-8 | R | false | false | 6,853 | r | test_template_table_attributes.R | context('Create table attributes template')
library(EMLassemblyline)
# File inputs = two data tables -----------------------------------------------
testthat::test_that('Test usage with file inputs', {
# Missing path results in error
expect_error(
suppressMessages(
template_table_attributes(
... |
c3b06b5b69210e201ab370a54996aa0c9e76e753 | 74797c25ebc6f06fa01b608cb1ed6ce2574d3083 | /ggplot/gg-histogram2.R | ca83cc5c998433352429cfab4cb08ae9cef3f6fc | [] | no_license | anhnguyendepocen/Rgraphs | c533196de8e5d34a925bea11bd7c6b57d85d52b9 | e306d8ccf06a99193b494aa2d55b11c48d36a28a | refs/heads/master | 2022-02-15T17:07:38.394802 | 2019-07-05T14:51:04 | 2019-07-05T14:51:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,917 | r | gg-histogram2.R | #histogram with labels
library(dplyr)
library(ggplot2)
library(reshape2)
#------------
(campus1 = paste('C', 1:6, sep='-'))
(institute1 = paste('Institute', 1:10, sep ='-'))
(program1 = paste('P',100:150, sep='-'))
runif(100)
n=10000
(rollno = 10000 + 0:(10000-1))
#-------------
campus = sample(campus1, size=n, replac... |
658c06e9b636dc71626a6ac84f7367904a21c662 | b94cac0d913688de392970bbf985651e2f6fd447 | /man/read_growth.Rd | 0fbb0bf0b3101f43ffd40c82935ee089373e7e8b | [] | no_license | kamapu/treegrowth | 79406372154858477d3df22530f78025f5375f7f | 59420ac1c39efef80765d4303f9345cbd1b51daa | refs/heads/master | 2021-03-20T21:30:40.814252 | 2020-03-23T10:12:22 | 2020-03-23T10:12:22 | 247,236,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 933 | rd | read_growth.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_growth.R
\name{read_growth}
\alias{read_growth}
\title{Read growth data and produce an ODB file}
\usage{
read_growth(xlsx, odb = ".temp.odb", format_date = "\%d.\%m.\%Y", ...)
}
\arguments{
\item{xlsx}{Character value with the path and/o... |
597bb75a7e590937e2187b9da877a9eace66d209 | 8131d16c335c651d96c2e40b6a4a026a1e79109c | /R/Rao.R | fcb0ae97a0b03c12d545c9def08ceb8de4526cea | [] | no_license | mattmar/rasterdiv | dc7812f15a4ebb98b1b35def22f14f5092a86864 | 5dd4383d7b7360073c8612754d94285a4bd340d4 | refs/heads/master | 2023-05-11T19:15:35.369030 | 2023-05-10T09:38:22 | 2023-05-10T09:38:22 | 252,508,233 | 12 | 4 | null | null | null | null | UTF-8 | R | false | false | 447 | r | Rao.R | Rao = function(x, dist_m="euclidean", window=9, rasterOut = TRUE, mode="classic",lambda=0, shannon=FALSE, rescale=FALSE, na.tolerance=1.0, simplify=2, np=1, cluster.type="SOCK",debugging=FALSE) {
.Deprecated(new = "paRao(..., alpha=1)")
paRao(x=x, dist_m=dist_m, window=window, method=mode, alpha=1, lambda=lambda, na.... |
5557ba4ab0a2203cc01a020eff6d4f68d3831b3d | 513517886e62467fe7ce3df576a380a05fb209e4 | /packages.R | e55cfc61de4ab6950a2362b830bbf4ef614894e5 | [] | no_license | tomvdbussche/ozt-solutions | e490140d1c3db95e7442ba42f7b81d56d752c27d | 6f52fbec504d665d6368b0a7adaea15164b4081d | refs/heads/master | 2020-12-25T18:52:38.142014 | 2017-08-22T11:29:49 | 2017-08-22T11:29:49 | 94,005,208 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 257 | r | packages.R | # Installs required packages (only installs missing packages)
pkgs <- c(
"car",
"gmodels",
"lsr",
"RcmdrMisc",
"sp",
"raster"
)
missing <- setdiff(pkgs, rownames(installed.packages()))
if (length(missing) > 0) {
install.packages(missing)
} |
eb18b36f1e30b86165bdd2873ab7eda30b5bf654 | 145bb5d044669b136e37f3c5d966696e82dc457e | /testEachStratumTime.r | de5da190d3966e86b0e7325e5166e0680450a75c | [] | no_license | epinor/Discrete_curve_group_code | 89958ec05e02d361672fe705dc9525b8e072b34b | fc90b44e5cba252a57c3176e2f216759d85beab2 | refs/heads/master | 2021-06-14T16:50:32.082938 | 2017-03-20T10:03:22 | 2017-03-20T10:03:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,319 | r | testEachStratumTime.r | ## ============================================================
## Aim: Check if there are more genes in a curve group than expected
## ============================================================
strataNames <- c("WithSpread", "WithoutSpread")
strataNames2 <- c("With spread", "Without spread")
realData <- list(... |
1d32d3120b534016973a70447051183ea1840713 | 277dbb992966a549176e2b7f526715574b421440 | /R_training/실습제출/전나영/191023/lab_05.R | 9fa2696181b1c1ac7f49ba60d017732fee4f8921 | [] | no_license | BaeYS-marketing/R | 58bc7f448d7486510218035a3e09d1dd562bca4b | 03b500cb428eded36d7c65bd8b2ee3437a7f5ef1 | refs/heads/master | 2020-12-11T04:30:28.034460 | 2020-01-17T08:47:38 | 2020-01-17T08:47:38 | 227,819,378 | 0 | 0 | null | 2019-12-13T12:06:33 | 2019-12-13T10:56:18 | C++ | UTF-8 | R | false | false | 946 | r | lab_05.R | # 문제1
grade <- sample(1:6, 1)
if(grade <= 3){
cat(grade, "학년은 저학년입니다.\n")
}else{
cat(grade, "학년은 고학년입니다.\n")
}
# 문제2
choice <- sample(1:5, 1)
if(choice==1){
cat("결과값 :", 300+50)
}else if(choice==2){
cat("결과값 :", 300-50)
}else if(choice==3){
cat("결과값 :", 300*50)
}else if(choice==4){
cat("결과값 :", 300/50)
}e... |
ef150663fc0e36a12a826237d26b695e4b369f0b | 631f41c200b59e6babd378a959ddbcb95bda6df0 | /Gaussian_Random_Walk.R | 784a08a875b56ce592e156315d7663de02014b59 | [] | no_license | aqknutsen/Stocks_ARIMA | 59141d6f6c50c83b6fef997f6c79e579e295645a | 87a376ce4c06a808eaf84a685e2840af52b2d655 | refs/heads/master | 2020-04-15T19:10:29.451161 | 2017-10-12T18:30:38 | 2017-10-12T18:30:38 | 42,086,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 410 | r | Gaussian_Random_Walk.R | #Vector to store partial sums and time
x<-vector()
my_time<-vector()
#Start at the origin and time 0
x[1]=0
my_time[1]=0
i = 2
#Generate 10000 steps
while(i<=10000) {
#Generate a random variable from the inverse gaussian distribution and add it to the partial sum
u1 = runif(1,0,1)
x[i] = x[i-1] + qnorm(u1,0... |
698a5590d7a349d3db0ecde4066008fa4ec77b1b | e08f48bc3526fa30bc690f9c6f0ce1a0fffffdcd | /man/t.test.p.value.Rd | 6aaeb8dfa55b39d6a6342a5ac33a7592841bff48 | [] | no_license | cran/demoGraphic | 1aada0d1ff141ec89759be7ef0d9ca4e1d38edfd | c4e9e0372775ff30241dc18393b988556b9f25c5 | refs/heads/master | 2020-04-15T18:17:09.966830 | 2019-01-09T16:30:07 | 2019-01-09T16:30:07 | 164,908,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 312 | rd | t.test.p.value.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/demo_Graphic.R
\name{t.test.p.value}
\alias{t.test.p.value}
\title{t.test to calculate p value}
\usage{
\method{t}{test.p.value}(...)
}
\arguments{
\item{...}{variables}
}
\value{
p value
}
\description{
t.test to calculate p value
}
|
459740511c45118c17bbb7379d232fe555ab1ea0 | 101c7ee80526ab15b90e7e1e09726a4b6052c02e | /Week04/Assignment_4.3.r | 8c816ba708c3814b0dbf3b250249d0f41e63a54a | [
"MIT"
] | permissive | abo1/mit15.071x | d18d5af21b002c2acc8f9a052ab2b4c2ae7e2676 | 30dfc1174b187636314249ecfb4954ac95bbbd91 | refs/heads/master | 2021-01-15T11:14:41.800869 | 2014-05-03T11:27:58 | 2014-05-03T11:27:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,812 | r | Assignment_4.3.r | data(state)
statedata = data.frame(state.x77)
str(statedata)
linState = lm(Life.Exp ~ ., data = statedata)
summary(linState)
linStateResp = predict(linState)
sse = sum((linStateResp - statedata$Life.Exp)^2)
sse
linState2 = lm(Life.Exp ~ Population + Murder + Frost + HS.Grad, data = statedata)
linStateResp2 = predict(li... |
ccb1d4895b45275af88231aacb2e17e177569f80 | a2963e83ea2de81ae421d76fa3959139b7f753e8 | /05.Topic-IV.Project-5.Manufacturer-Retailer-Price.CausalRelationDiscovery.distribution/zctaylor_causality_project.R | b8d9800b0a73ed9491521c87dd1828ed41d3ffa5 | [] | no_license | wuzhongdehua/data-guided-business-intel | 2bc456fdf492c634e377f54cc3ae242c95ca21b6 | edca4696acf699fb5118f02d897f17a9692bc45e | refs/heads/master | 2020-12-25T13:23:17.277186 | 2016-05-05T08:40:34 | 2016-05-05T08:40:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,751 | r | zctaylor_causality_project.R | # Load the libraries
library('vars')
library('urca')
library('pcalg')
library('dgof')
require(dgof)
# Read the input data
data.path <- "/home/zachncst/classes/csc591/projects/05.Topic-IV.Project-5.Manufacturer-Retailer-Price.CausalRelationDiscovery.distribution/Input Data/data.csv"
data.csv <- read.csv(data.path)
summ... |
6d9a2476c25a56b9ddc1616841ae56057773e160 | 20ed57666ba391ca68f9c814ec26370b7c7a4797 | /man/gs_ws_new.Rd | bee7b27bc935d5dc8e19b5df12f26f338c4d4582 | [] | no_license | colinloftin-awhere/googlesheets | 5c59a241107a39ca2b52442c92e7266d7357100b | 9a591eef0cd64f10a98371ed95d06f28b4744a27 | refs/heads/master | 2021-01-23T03:35:08.902747 | 2017-02-10T16:41:18 | 2017-02-10T16:41:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,466 | rd | gs_ws_new.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gs_ws.R
\name{gs_ws_new}
\alias{gs_ws_new}
\title{Add a new worksheet within a spreadsheet}
\usage{
gs_ws_new(ss, ws_title = "Sheet1", row_extent = 1000, col_extent = 26,
..., verbose = TRUE)
}
\arguments{
\item{ss}{a registered Google spre... |
1b58302734e3b25f1e4d2584008830a180c985ba | a11071996d946951e33ca81be9ad1866d1514bb1 | /cachematrix.R | c9928a61f1bcdfd1ea8156a27fa460f04e5e9e14 | [] | no_license | StanfordB3/datasciencecoursera | 76f7d35c2a921c7678533b23775fce7f6ac4816c | 3e8b023ade47786bde16cfec165ba72f05670888 | refs/heads/master | 2021-01-10T15:46:14.258445 | 2016-04-18T06:45:50 | 2016-04-18T06:45:50 | 55,576,594 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,782 | r | cachematrix.R | # The makeCacheMatrix defines a set of fuction and returs it as a list
makeCacheMatrix <- function(x = matrix()) {
# Note: Make sure to set matrix M in the Global environment before using
m <- NULL # "m" is the inverse of Matrix "x"
# SetMatrix should be called for a new Matrix ONLY
# Set the v... |
6ace857e11977fd56dc5afc1bcd59d9f01b41907 | 3bb7d9054e970ad99dcf8c80fb64633ecdb85deb | /src/01-experiment-first_animation.R | e38be23c79a4320d2596c24d671d9399374d87e9 | [] | no_license | jschoeley/rore2021-challenge | 2dcdbaf5ca906dc5af84ce31249fd5d41e6617d9 | 6056863174148e91068506804529e7a5fbca17fc | refs/heads/master | 2023-06-03T20:49:42.072046 | 2021-06-23T09:39:04 | 2021-06-23T09:39:04 | 379,325,423 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 409 | r | 01-experiment-first_animation.R | # Animate country rankings of population size
# Jonas Schöley
library(tidyverse)
library(gganimate)
rank_sim <- read_csv('./dat/simulated_data.csv')
rank_fig <-
rank_sim %>%
ggplot() +
geom_point(
aes(x = reorder(name, -value), y = value)
) +
transition_states(
id,
transition_length = 2,
st... |
ef1cc9f026152176d5b939d0f059d6c7b9354d5c | cb9b42a440276a5ee2b509128b0447991d05ccf7 | /Machine_learning_main.R | 3b56abab5d8321b8237ffbd31c69922f47973b1b | [] | no_license | lanagarmire/preeclampsa_lipidomics | 2c4215e894969db49255f9088120afc2b9d223ae | 121ac4ed7ed5a7d2bf4fe11ee3cf9967e63eab25 | refs/heads/master | 2023-01-08T03:08:15.590571 | 2020-11-12T22:22:50 | 2020-11-12T22:22:50 | 276,237,961 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,171 | r | Machine_learning_main.R | setwd("./")
newdat <- readRDS('newlipid_9_13_20.rds')
newpd <- readRDS('newpd_pdonly.rds')
library(gbm)
library(caret)
lilikoimat <- newdat[-1]
newpd <- newpd[row.names(newdat),]
newdatpd <- cbind(newdat, newpd)
lilikoimat <- t(lilikoimat)
lilikoilabels <- newdat$Label
lili... |
b174b35c265e6e36a1c50ffc071fcb3f8c67593d | 6f6f97554599532e8345d769f96c9b6e9d2cb943 | /httk/man/parameterize_steadystate.Rd | 8604703b0ed9157a9438b684676da69022b24acf | [] | no_license | jrsfeir/CompTox-ExpoCast-httk | 37cbfa4142c261ed79d3141142a613a614b28d38 | bf2f6c300fe2cf0c538c16355c2ec437ca781c55 | refs/heads/main | 2023-06-26T16:56:17.010294 | 2021-05-10T14:53:13 | 2021-05-10T14:53:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,906 | rd | parameterize_steadystate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parameterize_steadystate.R
\name{parameterize_steadystate}
\alias{parameterize_steadystate}
\title{Parameterize_SteadyState}
\usage{
parameterize_steadystate(
chem.cas = NULL,
chem.name = NULL,
dtxsid = NULL,
species = "Human",
clin... |
aa6d42f7b23233e9aebcb44eeb11b77824d523bb | c51347680754745733293e00aacf7b633334c1fc | /R/plot.yphemi.R | 8deb39fd26baf8ddf74533ce733ca78e89fcf119 | [] | no_license | cran/YplantQMC | 771c341d00e410a0e61dbdadc02af8866d5cd198 | dc62bfc247ba9d6dd92498e8afa00d511a36e00e | refs/heads/master | 2021-01-21T21:47:33.241377 | 2016-05-23T06:34:50 | 2016-05-23T06:34:50 | 17,694,152 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,809 | r | plot.yphemi.R | #'@method plot yphemi
#'@S3method plot yphemi
#'@rdname setHemi
plot.yphemi <- function(x,met=NULL,sungap=TRUE,
projection=c("iso","flat"),warn=TRUE,bordercol='black', ...){
projection <- match.arg(projection)
hemi <- x
o <- par(no.readonly=TRUE)
on.exit(par(o))
par(pty='s')
plot(1, type='n'... |
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