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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5846b59c404700653dc838e18f7b9275f4db2267 | 5e8bb97ee1805d7c68aeb2ebcd61718ed20ae282 | /odds_ratio.R | 14ccf7e611aef5736cf9275587ccec0eb940d59b | [] | no_license | crglaser/swing-miss-percentages | 2879eaca136be6f59789ff83289b215da6997e9a | 629129682d4a3568f5310175c9f5323e5df55096 | refs/heads/master | 2021-01-10T13:16:28.265996 | 2016-01-14T00:21:40 | 2016-01-14T00:21:40 | 49,166,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 370 | r | odds_ratio.R | odds_ratio <- function(hitter_probability, pitcher_probability, league_probability){
hitter_odds <- probability_to_odds(hitter_probability)
pitcher_odds <- probability_to_odds(pitcher_probability)
league_odds <- probability_to_odds(league_probability)
ratio <- (hitter_odds * pitcher_odds) / league_odds
percen... |
28d5e177bfe16c127b7c81f176eb47969d3894f1 | 92cf452f280a949e9d8163bbcd2c07a12c045688 | /shinyRcode/R/reloadData.R | 846a596fdeb0fc9c514628e8907a99b3ac9d394a | [] | no_license | SWS-Methodology/faoswsFisheriesSUAFBSdocumentation | 8b59d60b16431ef4688e23dcdc3f44889a09a486 | c5a42a5c2b3dc7d25aeda15d1d3245a9ae910dfb | refs/heads/master | 2021-05-19T10:53:08.144166 | 2020-11-02T14:46:12 | 2020-11-02T14:46:12 | 251,660,525 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,006 | r | reloadData.R |
reloadData <- function(data, keycountry, minyear, maxyear, keydomain, keydataset,
keygeo = 'geographicAreaM49_fi', keytime = 'timePointYears',
keyelement= 'measuredElementSuaFbs',
keyitem = 'measuredItemFaostat_L2'){
sel_years <- as.character(as... |
3f7cec553c5a3c59ab6aa2d3b6b70c90c55cc122 | 23691014ae69404742f014d19109b0d3075f871a | /man/fit-method.Rd | 21a85a49335786ae5f5021152d6a77870493876c | [] | no_license | zrmacc/CICs | d43c14c451344adcb6ff86be63b2195616031d4c | 28ad3929a27da57046e054bc7f07818f5199c521 | refs/heads/master | 2023-04-17T08:49:18.313907 | 2021-05-02T19:54:19 | 2021-05-02T19:54:19 | 274,303,685 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 348 | rd | fit-method.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class.R
\name{show,compCICs-method}
\alias{show,compCICs-method}
\title{Show Method for Compare CICs Object}
\usage{
\S4method{show}{compCICs}(object)
}
\arguments{
\item{object}{An object of class \code{compCICs}.}
}
\description{
Show Metho... |
ae758e1315c6f30a978b5829ab600e2faa43f5ec | e2af2bedf379072f012ef2847be39118192fe161 | /SFrestaurantscores/global.R | f863abc222413f2cb3ed9a9823c881b433f8ca25 | [] | no_license | hlau117/Project1-ShinyApp | de23cca85d8b3f3a65223ab90c8b558c168a3491 | 5023b98a8fcd2d54faa231824bbbcb9fa99f91d7 | refs/heads/master | 2021-07-10T16:43:56.665856 | 2017-10-13T14:53:46 | 2017-10-13T14:53:46 | 106,331,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,757 | r | global.R | library(xts)
library(dygraphs)
library(plyr)
library(dplyr)
load("total_postcode.rda")
load("Spatial_Zipcodes.rda")
SF_rest= read.csv("Restaurant_Scores.csv",sep=',')
#rename columns
SF_rest= SF_rest%>%select(rest_id=business_id,
name=business_name,
address=business_address,... |
fb8cabd40f68cacb57e320e8cd746e0448aedd49 | 3e8ad7252429b4f19795dab539308410804d056a | /functions.R | 71f1238240d2eeeae226c10368630576f0ee3110 | [] | no_license | dainiuxt/RprogrammingLectures | e0d0e5e79830b8265a43d58358cacc38b369b011 | e71ba3b83ef2628881f66a66a2fc0b8847487cdf | refs/heads/master | 2020-12-25T18:23:09.717279 | 2014-06-14T20:56:10 | 2014-06-14T20:56:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 296 | r | functions.R | #lapply
x <- list(a = 1:5, b = rnorm(10))
lapply(x, mean)
x <- list(a = 1:4, b = rnorm(10), c = rnorm(20, 1), d = rnorm(100, 5))
x<- 1:10
lapply(x, runif)
lapply(x, runif, min=0, max =10)
x <- list(a = matrix(1:4, 2, 2), b = matrix(1:6, 3, 2))
lapply(x, function(elt) elt[,1])
sapply(x, mean) |
0e1473cf40623abf7d050c54ed45e59898505086 | 2efe10652a9d1d4f01219a5d7f8d429896660cfb | /signit-pipeline/signit_summary_table.R | 47cf1f1761c7c8fc164229e3e41a022666cde25e | [] | no_license | eyzhao/bio-pipelines | 9416b33f280e33ecf56c916833ee13e36f64c56d | 52e4e40e7b3f2da24675c422d58b7b373b7024a3 | refs/heads/master | 2021-03-24T09:48:16.445178 | 2019-12-04T17:25:10 | 2019-12-04T17:25:10 | 105,455,771 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,240 | r | signit_summary_table.R | ' signit_summary_table.R
Usage: signit_summary_table.R -i INPUT -o OUTPUT [ -s SIGNIT --fraction ]
Options:
-i --input INPUT Path to serialized output from SignIT (.Rds file)
-o --output OUTPUT Path to output summary stats table (.tsv file)
-s --signit SIGNIT Path to SignIT ... |
9b6641683cedfcc3b8739b243376eb8e29a6c72c | bffd64b5c5f55b0b9791c99e7f245a49a7a05b7a | /Movie Recommender System/AppDeployCode/server.R | 998b348b0bd24a4e5fa16a815b55c97456654785 | [] | no_license | vkk2/schoolprojects | dc8dbe0915437098c2264af3c1978674b2016324 | 1a97a62bda724abe2aa6e07603471665cbc79f84 | refs/heads/main | 2023-02-28T01:32:10.800144 | 2021-02-04T16:50:28 | 2021-02-04T16:50:28 | 322,464,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,519 | r | server.R | ## server.R
library(recommenderlab)
library(Matrix)
# load functions
#source('functions/cf.R')
#source('functions/cf_algorithm.R') # collaborative filtering
#source('functions/similarity_measures.R') # similarity measures
# define functions
get_user_ratings = function(value_list) {
dat = data.table(Movie... |
8399feac9c382590f36bdcdfd3255d5c3b0122ea | 9509cefb9198144bde21774fdd5b3c7a68a005a7 | /man/targetsmet.Rd | 73c63104701d3b9fd0c66ffd379f66fee041d2ca | [] | no_license | jeffreyhanson/marxan | cc5d39f4a0980f8a7c4d1cf041a500721b03c198 | fff42df08ac0a8ad1f762f6402d15698babf1dff | refs/heads/master | 2021-08-07T12:54:25.036732 | 2016-11-03T04:53:28 | 2016-11-03T04:53:28 | 29,377,383 | 2 | 2 | null | null | null | null | UTF-8 | R | false | true | 929 | rd | targetsmet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/MarxanResults.R, R/MarxanSolved.R
\name{targetsmet}
\alias{targetsmet}
\alias{targetsmet.MarxanResults}
\alias{targetsmet.MarxanSolved}
\title{Extract information on whether solutions have met targets}
\usage{
targetsmet(x, ...)... |
60f5446d2c582e88e333f677ce3b6dc9a9cb609b | 9a7b9c3dfcffd0c0562650d5c5a0d6fa9890c1cb | /R/single_term_digital_filter.R | 850552271e6b4f7378d6c003bf92019ccbbf8c3d | [
"LicenseRef-scancode-public-domain-disclaimer",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | smwesten-usgs/recharge | 8ea2470e024a1451b46b030e3ee489797d81a871 | aee45d90d83e6ae1646d391d4c7a26af562fc407 | refs/heads/master | 2021-07-16T05:12:33.133309 | 2021-03-02T17:25:36 | 2021-03-02T17:25:36 | 101,231,984 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,504 | r | single_term_digital_filter.R | #' Baseflow Separation by use of a single-term digital filter.
#'
#' Extract baseflow from a daily streamflow record using the method described by
#'Nathan and MacMahon (1990).
#'
#' @param date vector of dates corresponding to each \code{discharge}, should be of class "Date."
#' Missing values are not permitted.
#' @p... |
0b54b064fe14e372d68cc58cb89229024a79336c | 7917fc0a7108a994bf39359385fb5728d189c182 | /paws/tests/testthat/test_efs.R | ce38c6ef58b9ab70241f869a0be531f882dd2e8b | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | false | 396 | r | test_efs.R | svc <- paws::efs()
test_that("describe_access_points", {
expect_error(svc$describe_access_points(), NA)
})
test_that("describe_access_points", {
expect_error(svc$describe_access_points(MaxResults = 20), NA)
})
test_that("describe_file_systems", {
expect_error(svc$describe_file_systems(), NA)
})
test_that("des... |
553f23fd33be69696b0236fab3a02110c7df9df2 | 6ccbeb28582657306ee2a5500ec4396bafad06e4 | /Generation0/EggToAdultViability/DeltaEggToAdultViability.R | c9c7fde10a1fb2ab0b5fca67d2581d79e1e2f753 | [] | no_license | KKLund-Hansen/SexChromCoAdapt | ac890836f047e1363459db2d4bd0b2b2071721b1 | 34712cefaa8b257b2c279969a377b4bbf7174d21 | refs/heads/master | 2021-07-20T10:26:06.996833 | 2021-02-06T19:47:25 | 2021-02-06T19:47:25 | 242,177,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,654 | r | DeltaEggToAdultViability.R | ################################################################################################
############################### ΔEGG-TO-ADULT OFFSPRING VIABILITY ##############################
################################################################################################
#Set up environment
library(... |
3b7c1fa3c4d4e5bbe16936f7e1dca758c420fe24 | 9b564709ac525bbce6cd9e88beb73eddd65bbff2 | /COVID_webscraping.R | 8ca5592bf595294cf0ee0967df043b3e009551d8 | [] | no_license | jordanjasuta/webscraping_tools | 21ba15a4469a015701014e53c36a4ac82f4d9344 | e3255c880fa97394b00401b83803da2bb8a4755c | refs/heads/master | 2022-06-20T13:18:28.007158 | 2020-05-10T21:15:39 | 2020-05-10T21:15:39 | 147,696,255 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,187 | r | COVID_webscraping.R | library(rvest)
library(dplyr)
url <- "https://covid19.bz/"
# For a one-time datapull:
covid_table <-as.data.frame(matrix(0, ncol = 6, nrow = 1))
for (column in 1:5){
table_val <- url %>%
read_html() %>%
html_nodes(xpath=paste('//*[@id="content"]/div/div/div/section[7]/div/div/div[',column,']/div/div/di... |
a86cf7fa779b4bb47096706fb3703073f85f4000 | 1ee9dbee0d344056920cd62f052df8e7360da768 | /smuf_runf_0919_KO_randomlinesIR.R | e8a7352ae5a3f696932d73e9ab096b745f6f0bfa | [] | no_license | efsalvarenga/smuf_rdev | f7f6d1e1bab6e246073d855723db29cf92943370 | 648407b0cec07b8bd9c83f085d9409dcc5a57fc8 | refs/heads/master | 2022-03-03T22:16:27.054629 | 2019-10-09T19:03:28 | 2019-10-09T19:03:28 | 88,738,275 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,983 | r | smuf_runf_0919_KO_randomlinesIR.R | #===========================================
# Smart Metering Uncertainty Forecasting
#
# Author Estevao "Steve" Alvarenga
# efsa@bath.edu
# Created in 10/Feb/17
#-------------------------------------------
# smuf_main combined functions optim & fcst
#=========================================... |
b2befbda5bb77f2e868e27e0961b73e26c72eafe | 1339e9fa22cd678ce3e778acdeb388e884271a6c | /project/costFunction.R | 17f7e408f61a9a05b1115a96d166cda9d7d55958 | [
"MIT"
] | permissive | nkafr/Adult-dataset-analysis | 67718ae3d3390295775bb1c3ecdc722834deae8e | e78ab13d9938e94c524a1750150214982f6eb6cc | refs/heads/master | 2021-01-20T18:36:11.369520 | 2016-07-15T01:36:54 | 2016-07-15T01:36:54 | 63,377,724 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 807 | r | costFunction.R | costFunction <- function(X, y) {
#costfunction Computes cost for logistic regression
function(theta) {
m <- length(y); # m is the number of training examples
#initialize J
J <- 0
#h is the hypothesis function
h <- sigmoid(X %*% theta)
#use a vectorized implementation insted of... |
c1f3f4f2fb05157919712bd700502a6a3a9d78a2 | fff9ee52053ff5acd4d358add0793bf4ed6b2aba | /Uppercase_Speaker.R | 9778013431dc1355483d56a7f0c9bd8a4acb0556 | [] | no_license | jfedgerton/Cleaning_CHAMP_Speakers | a6baabb378521c0cdd0d6e930b977cb2a36ff480 | b57c3e376a0c49d2fff361027b5f20adaa90ba7b | refs/heads/master | 2020-04-21T13:09:12.997216 | 2019-02-07T14:59:05 | 2019-02-07T14:59:05 | 169,588,961 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 716 | r | Uppercase_Speaker.R | csv_1278 <- list.files(path = "C:/Users/Jared/Dropbox/CHAMP-Net/Data/Show-Year CSVs/format_csv_1278", pattern = NULL, all.files = FALSE,
full.names = FALSE, recursive = FALSE,
ignore.case = FALSE, include.dirs = FALSE, no.. = FALSE)
for (i in 1:length(csv_1278)){
... |
9120bc2c571b82ac6ac3f5070d5a9a2df5cd0fb0 | b139d2dbfcac7b96cdb61737962f2a90f76ddb6b | /R/harmonizeCols.R | 16a150798d5452c7d28028fc2a1c4c4ea8e5c483 | [] | no_license | michaelrahija/FAOSDGdata | ef681a7ad3f715a239425f21d7cea537ef9b1177 | 59e76d4c935161596c3b855c5172675234e99011 | refs/heads/master | 2021-08-22T13:07:05.875370 | 2020-06-11T04:55:40 | 2020-06-11T04:55:40 | 193,698,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,678 | r | harmonizeCols.R | #' harmonizeCols
#'
#' This is function which takes as an input a dataframe containing data
#' for a particular indicator, and outputs a dataframe with SDMX concepts as column names and
#' followed by an attribute name. Example: REF_REA
#'
#' @param sdgdf is a data frame referring to a specific SDG
#'
#' @return This ... |
eee5e4882bf0ffb154bf626ea2bdff1d12f19b02 | df562e5ef9ea2846cb05319114009c3de7e4dee1 | /MasterR/crea_print_table.R | bd1b52111a93c759bb670bc9f4b61da4da83f425 | [] | no_license | SCelisV/R | a05d9dc1b0bcb2bfabfbe83703db8364edd8a9ab | 0aa0a984dae0c0466addbf6dc0dd629d863f7cf5 | refs/heads/master | 2022-12-23T23:23:40.878996 | 2020-09-30T20:10:21 | 2020-09-30T20:10:21 | 286,298,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 317 | r | crea_print_table.R |
TabA = as.table(cbind(c("A","B","C"),c(1,2,3)))
# > TabA
# A B
# A A 1
# B B 2
# C C 3
TabB = as.table(cbind(c("D","E","F"),c(1,2,3)))
# > TabB
# A B
# A D 1
# B E 2
# C F 3
nams = c(TabA,TabB)
# > nams
# [1] "TabA" "TabB"
for (i in nams){
print (i)
tab = get(nams[i])
print(tab)
print(get(nams[i]))
}
|
bb853f6a9de34eb91b696f6ecdfca9ea92598268 | c2ecf5c58b195b5999c7e0c32f726f894f8723a0 | /MUpdaters/man/MDataUpdater.AddNewField.Rd | ee890ce42f2ed3c3f88672377cebc7e3954452c7 | [] | no_license | pashkovds/mdlibs | d67fef9b021e7b0b20ec3b0eeaa2f099d8eff87c | 8cb0a4a2f12e5f472d039e9ea55ecdbf26202046 | refs/heads/master | 2021-01-01T03:43:41.411779 | 2016-04-23T10:06:55 | 2016-04-23T10:06:55 | 56,423,368 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 380 | rd | MDataUpdater.AddNewField.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MDataUpdater.AddNewField.R
\docType{data}
\name{MDataUpdater.AddNewField}
\alias{MDataUpdater.AddNewField}
\title{MDataUpdater.AddNewField}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
MDataUpdater.AddNewField
}
\... |
569a88c3b1fd0355523319f5b4be5f4608f14d7c | cc3beea2feb5d66b4df71a96f42129687a1296e7 | /draft/from_R_tips_folder/decimal_position.R | 1f490fd30ce76990c64c210358b4db7ce2991a6d | [] | no_license | YulongXieGitHub/YulongR_Code | 133c90b708c33c447737aaa0b6d01f5c9cb33818 | e1f68c1564fb4036df9500297fbd36548e3b8014 | refs/heads/master | 2021-01-23T15:03:12.427516 | 2015-07-16T01:52:35 | 2015-07-16T01:52:35 | 39,168,963 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,089 | r | decimal_position.R | #
# Profile_thermalSetpoint.R
#
#
# October 28, 2009
# -------------------------------------------------------------------------
#
# eliminate all stuff
#
rm(list = ls(all = TRUE))
start_time <- date();
# format(Sys.time(), "%a %b %d %X %Y %Z")
Start.time <- Sys.time()
set.seed(12345, kind = NULL) # set seed of ran... |
a6dad4b699c72a5d8a63c861ecbc0cfd5af84f19 | 57965d63586beb192af1a2f8974fdd5630a3964b | /man/np.deneqtest.Rd | 62bf0477c127ec7acccc2f699a8646c1abb65a41 | [] | no_license | JeffreyRacine/R-Package-np | 6fee493cbd555cabe976d2f9c14cd10aef99c665 | 525db82ebc67423728888daf66ce0d9fdd70bbc7 | refs/heads/master | 2023-08-31T13:32:00.925187 | 2023-08-27T13:08:45 | 2023-08-27T13:08:45 | 1,957,067 | 41 | 23 | null | 2022-08-12T15:40:15 | 2011-06-26T20:09:34 | C | UTF-8 | R | false | false | 3,959 | rd | np.deneqtest.Rd | % $Id: np.cmstest.Rd,v 1.58 2006/11/03 21:17:20 tristen Exp $
\name{npdeneqtest}
\alias{npdeneqtest}
\title{ Kernel Consistent Density Equality Test with Mixed Data Types }
\description{
\code{npdeneqtest} implements a consistent integrated squared
difference test for equality of densities as described in Li, Maaso... |
5cf217615541810e5c41d0258996ec5d73dedf19 | 465ad1d280890bf23acf6a4e473d0aff03bbef0c | /code/run_flash_drift_rand.R | 293e1811b65f3c4dcfa3a0f3f5b96c7beb5f5a00 | [] | no_license | jhmarcus/drift-workflow | c6fef2ea3392296266fb72d15aee0ae77a30a8c5 | da05b47f313f183f994155384ca6790f7b865d6e | refs/heads/master | 2022-12-17T14:55:06.367786 | 2020-09-18T18:24:11 | 2020-09-18T18:24:11 | 170,155,451 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 818 | r | run_flash_drift_rand.R | library(tidyverse)
library(ebnm)
library(flashier)
library(drift.alpha)
library(softImpute)
library(lfa)
options(extrapolate.control=list(beta.max=1.0))
args <- commandArgs(trailingOnly=TRUE)
bed_prefix <- args[1]
rds_path <- args[2]
K <- as.integer(args[3])
KCOMPLETE <- 30
# read the genotype matrix
Y <- t(lfa:::rea... |
0de611082a2bddc0227d74dfd7757850ab6f53df | 0479b5e809beae1d18a9c6b603305d674fd5b12e | /man/combine_pvalue.Rd | 5f1f2312c02333b38e78346b5b32e5c9b476cfae | [] | no_license | huerqiang/GeoTcgaData | ecbd292e37df065ae4697c7dd07027c1e665853d | cc85914f2a17177164c7ae426f8f0f09f91e98c1 | refs/heads/master | 2023-04-12T10:04:20.034688 | 2023-04-04T05:57:04 | 2023-04-04T05:57:04 | 206,305,770 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 835 | rd | combine_pvalue.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SNP.R
\name{combine_pvalue}
\alias{combine_pvalue}
\title{combine pvalues of SNP difference analysis result}
\usage{
combine_pvalue(snpResult, snp2gene, combineMethod = min)
}
\arguments{
\item{snpResult}{data.frame of SNP difference analysis... |
20ce30e4de48ce65df7a3ad3c6ccb6609a2a0788 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/neatmaps/examples/formatCluster.Rd.R | b1b5cb3d738ad51f53b2561652aaf9d15bbe0199 | [] | 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 | 237 | r | formatCluster.Rd.R | library(neatmaps)
### Name: formatCluster
### Title: Format Cluster Output
### Aliases: formatCluster
### ** Examples
# dummy cluster results
clustList <- list(c("A", "B"), c("C", "D", "E"))
formatCluster(clusterList = clustList)
|
ef1c23c631398b1e199f9c5cafe121ce32b0e7df | cc1b9747506561c5f306415307c0862704c526b0 | /secr-analysis.R | 3787c9b3fd4d4f9c426a5c708a191c048198c6f9 | [] | no_license | cwsjitu/SECR-Simulation-Analysis | b4062f465ba0c2df6dceb2e5a3708391835289d8 | 52708ee520ba2bed4c42a1c1111494ab15beae45 | refs/heads/master | 2021-01-20T09:16:35.868909 | 2017-05-04T10:01:40 | 2017-05-04T10:01:40 | 90,232,064 | 0 | 0 | null | 2017-05-04T07:03:34 | 2017-05-04T07:03:34 | null | UTF-8 | R | false | false | 579 | r | secr-analysis.R |
##set your working directory with
# setwd()
library(secr)
library(raster)
library(rgdal)
library(sp)
library(maptools)
library(rgeos)
library(dplyr)
## coordinate system
latlong = "+init=epsg:4326" ## LatLon Projection
ingrid = "+init=epsg:32643" ## UTM Projection
## assumptions to generate capture... |
bf31c6a02ca8150d62a921d566b1ec8a55e4ba40 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.end.user.computing/man/appstream_describe_application_fleet_associations.Rd | 3bd33cd440d610fe81433a1abe76ba4fa8d4f025 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,001 | rd | appstream_describe_application_fleet_associations.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/appstream_operations.R
\name{appstream_describe_application_fleet_associations}
\alias{appstream_describe_application_fleet_associations}
\title{Retrieves a list that describes one or more application fleet
associations}
\usage{
appstream_des... |
9b7f1ca2ab9da28e5156620557882d392ea96a20 | 881d461d3ca9c3acf2d4076e7ea042053fd0d6e6 | /alldeaths_2016.R | 82ef7ac94f79e0155c63f6b09e076eff8f2e2ae7 | [] | no_license | markocherrie/SpatialClustering | f35326bb0f4d1a29feaf70a84da59faa5a7686f6 | 2f5c1fc1c8b99d15846a51a5a660ac3165069ed2 | refs/heads/master | 2022-11-22T03:17:48.172696 | 2020-07-24T10:46:56 | 2020-07-24T10:46:56 | 282,066,957 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,560 | r | alldeaths_2016.R | ############### STEP 1: PRE_PROCESSING
# read in the data
library(readr)
library(sf)
dz<-read_sf("boundaries/DZ/SG_DataZoneBdry_2011/SG_DataZone_Bdry_2011.shp")
simd<-read_csv("data/SIMD2016indicators.csv")
# pre-processing
library(dplyr)
dzsimd<- dz %>%
left_join(simd, by= c("DataZone" = "Data_Zone")) %>%
filte... |
c3a9a3b7ff73b669087bd3ba47f57d40b2c78c10 | 399f7bc329848e396ca8faa449a3bd23aec7b35f | /man/has_name.Rd | cb7e82b1029dbfeb4861643d2e5ea198c8c55f3f | [] | no_license | cran/container | c9cbe63c8aee7be8b0406cadfb4bcabf4031333b | c3c3fff6cc67740d4d1820f338285c04cf80c92d | refs/heads/master | 2022-12-24T03:30:41.848446 | 2022-12-11T10:20:02 | 2022-12-11T10:20:02 | 145,894,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 928 | rd | has_name.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/has_name.R
\name{has_name}
\alias{has_name}
\alias{has_name.Container}
\alias{has_name.dict.table}
\title{Check for Name}
\usage{
has_name(x, name)
\method{has_name}{Container}(x, name)
\method{has_name}{dict.table}(x, name)
}
... |
22a12698f640d626de21251a22f6b82bd10f83f9 | 8cffdd5f866185e8529376326b9c3accac583930 | /man/getL-methods.Rd | 7497467c4fda0c4cd8e2804c8d43fb5a29c48314 | [] | no_license | cran/simctest | fc4a187ae7ad926dbbae9a71967b44795f3de85d | eadc866013858443cbea9a6acd999b1143207373 | refs/heads/master | 2021-01-16T18:32:13.653593 | 2019-11-04T12:20:02 | 2019-11-04T12:20:02 | 17,699,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 571 | rd | getL-methods.Rd | \name{getL-methods}
\alias{getL}
\docType{methods}
\alias{getL-methods}
\alias{getL,sampalgPrecomp-method}
\title{Methods for Function getL in Package `simctest'}
\description{
Returns the lower boundary for the stopping rule
}
\usage{
##S4 method
getL(alg,ind)
}
\arguments{
\item{alg}{the sampling algorithm}
\item... |
cb7961b9aa636ee81f6a767954341d58d80192c7 | 9cfd9c26181fef9a29cdb0934a7609c45305054c | /hrda/ui.R | 4bcbe7c456c49a231904c0b55eecd8fd214a8986 | [] | no_license | suntreeshl/hrda | 08106507bb6158114865dad8e57b6daf27fcd171 | fe01a1948e5f7a45040ed0334670076a12f4af27 | refs/heads/master | 2022-11-11T20:31:03.639049 | 2020-07-03T13:22:35 | 2020-07-03T13:22:35 | 269,610,537 | 0 | 0 | null | 2020-06-05T11:10:46 | 2020-06-05T11:10:46 | null | UTF-8 | R | false | false | 2,247 | r | ui.R | library(shiny)
library(shinydashboard)
library(tidyverse)
library(ggplot2)
library(DT)
# Define UI for application that draws a histogram
ui <- dashboardPage(skin="blue",
dashboardHeader(
title = "HR 데이터 분석"
),
dashboardSidebar(
sidebarMenu(
# Setting id makes input$tab... |
058bbc179d6f13347ca20837928f975f4fa8293c | cb48a3993cddaf8f8cfcf78b3aa7419a1bb62fc9 | /plot4.R | 87d7b629e006dab699f6285e7732a6904595e169 | [] | no_license | BowenRaymone/ExData_Plotting1 | b7bd6caa05ca1db8032544eac6812c070e3a66e8 | cd3214738f402792904ae9898046659d57ff8d10 | refs/heads/master | 2021-01-24T03:43:18.813627 | 2018-02-26T03:21:44 | 2018-02-26T03:21:44 | 122,902,958 | 1 | 0 | null | 2018-02-26T02:43:40 | 2018-02-26T02:43:40 | null | UTF-8 | R | false | false | 1,433 | r | plot4.R | # run the file
power_consumption <- read.table("./household_power_consumption.txt",header = TRUE,stringsAsFactors=FALSE,sep=";")
sub_power_consumption <- power_consumption[power_consumption$Date %in% c("1/2/2007","2/2/2007") ,]
# remove the total data set to clean some memory
rm("power_consumption")
# convert the dat... |
7d332e4dc0535a35a1bf4cb71ec558dae96fde6e | de936b8365a44c245fae40ab116a42e17997e6a7 | /regtable.R | 38def7af21d4bd8ccd949f1f2eae84e71dbfbe56 | [] | no_license | KoenV/regtable | aa47b60dc1640d7632f386877a4606f8dbcb149c | 2d1c08b7e79c5ba65a3c3028b5efea0b806e55ac | refs/heads/master | 2021-01-18T16:06:44.594349 | 2017-04-24T13:28:20 | 2017-04-24T13:28:20 | 86,712,896 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,034 | r | regtable.R | # ##############################################################################
# function to make tables for lm and glm objects
# koen.vanbrabant@kuleuven.be
# date: 24/04/2017
################################################################################
reg_table = function(fit=fit,data=data,log=FALSE,roun... |
850673a39ea7d7d2459c22a8758e98457a4afe73 | 02638637685acc16f9a404d9f589d0c0dd0cb784 | /tests/testthat.R | 4b29043cbec893c70706968b8c9c85ab6be2fc73 | [] | no_license | himiko14122/mdsstat | cada89ba3bfeef3b38d5a0140fb643fb7b521550 | dfa250f58d6a09662062382b24144dc3194524b4 | refs/heads/master | 2022-04-12T22:05:48.970010 | 2020-03-08T14:50:02 | 2020-03-08T14:50:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 62 | r | testthat.R | library(testthat)
library(mdsstat)
test_check("mdsstat")
|
7b400881e3bd9eb295fc17ebb0c5643eb64e40b0 | 4970a3f8a4ca8a42a6fb22f454265691544f1810 | /man/galaxy.Rd | 540f5a353af1402a1808af66030ccc89d29b821f | [] | 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 | true | 3,301 | rd | galaxy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/galaxy.R
\name{galaxy}
\alias{galaxy}
\title{Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies}
\usage{
galaxy(data, y1, s1, y2, s2, scale1, scale2, scale.adj,
corr, group, study.label, annotate, xlab, yl... |
b65ce37adce03922c019afd08d1777bc1323a06a | f6887dbd9e53746a835ab8553bbfd40255ff333e | /bin/analyse_structure.R | 1e9724023e4c848e0172d4a64c424fa2503628f2 | [
"MIT"
] | permissive | amchakra/tosca | 38d1d4e43a917623076430035497447331584f63 | 6a902524c818e104cbcf9bf753828a289ee32905 | refs/heads/main | 2023-06-10T10:18:04.766415 | 2022-10-17T16:11:15 | 2022-10-17T16:11:15 | 260,152,891 | 3 | 2 | MIT | 2023-03-07T13:34:43 | 2020-04-30T08:17:03 | Nextflow | UTF-8 | R | false | false | 4,052 | r | analyse_structure.R | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(toscatools))
suppressPackageStartupMessages(library(rslurm))
suppressPackageStartupMessages(library(tictoc))
suppressPackageStartupMessages(library(parallel))
suppressPackageStartupMessages(library(optpars... |
0915baaab07dc6de5020e9d0c0089c749772e4fa | 517ac8ca5bef92173ec4d9f62dee3a8075c8291c | /man/compare.results.vs.cqt.Rd | bc6eb29cfbf7a048ce373f8bd3648e31756cbd1d | [] | no_license | cran/CME.assistant | 5d9695b3e31e8916ec59e6d60b45135b144ef8d2 | 9e225c336693abbffd41c6c63e5acdcd024633e1 | refs/heads/master | 2023-03-23T14:28:23.960474 | 2021-03-22T09:30:02 | 2021-03-22T09:30:02 | 317,813,689 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 543 | rd | compare.results.vs.cqt.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/4.CC_funcs.R
\name{compare.results.vs.cqt}
\alias{compare.results.vs.cqt}
\title{Compare the saved cqt vs results}
\usage{
compare.results.vs.cqt(dt_results, dt_cqt)
}
\arguments{
\item{dt_results}{obtained by `read.all.results.csv(results_di... |
935060ae2fcebf6de1d15c1a77f70b4b22f00a57 | 5fb78098a301178b381be6dd3207527aceef34e9 | /install.packages.R | 148ea088b35dd47f2442c9ccd2bf9688d64d68c4 | [] | no_license | wan-yang/cancer_cohort_trends | e38ccbc0befff99643ab9a374d3fdd68b57d8954 | 89a19babfbfd412c3ec37f570893c996ec1f4f2c | refs/heads/master | 2022-04-12T05:22:42.276900 | 2020-02-05T14:42:07 | 2020-02-05T14:42:07 | 219,533,936 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 224 | r | install.packages.R | ## To install R packages for the analysis
## Install multiple packages
install.packages(c("RColorBrewer",
"data.table",
"magrittr",
"stringr",
"ggplot2")) |
c60258247b152517529fdc526b83140cd0f3eb8d | 89b3c5d320e4b0ae9be7d28c9e4cba640e8b069f | /OldExams/20170814/exam_2017-08-14_solutions.R | ad8277b7810312a42425398577cb5fe0b0357f1b | [] | no_license | andrea003/KursRprgm | f5c1cb1dc803052c8d19ec6b176f40ebc2694d0c | 4f707c834038d18b2e65ac74f6eb5c204e4cea71 | refs/heads/master | 2022-11-28T12:29:30.766426 | 2020-08-10T08:07:15 | 2020-08-10T08:07:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,897 | r | exam_2017-08-14_solutions.R |
#------------------------------------------------------
# 1
# a)
a<-5
b<-2
(factorial(x = a)-b^a)^-0.1
# b)
data(OrchardSprays)
my_df<-OrchardSprays
dim(my_df)[1]
index<-seq(from = 1,to = dim(my_df)[1],by = 2)
my_df<-my_df[index,1:2]
head(my_df)
tail(my_df)
# c)
letters
my_text<-rep(letters,1000)
my_text
# d)
... |
bb8e350e76f6c97bd84eef03ddb1e436a0ba080e | 1897ae5489b64fae9aa083d62f51254cfe52d26f | /VII semester/machine-learning/labovi/lv4.R | 8514edf90f6989727344d301bc3bddfbc1e0e6f3 | [
"Unlicense",
"LicenseRef-scancode-proprietary-license"
] | permissive | MasovicHaris/etf-alles | f1bfe40cab2de06a26ceb46bdb5c47de2e6db73e | 0ab1ad83d00fafc69b38266edd875bce08c1fc9e | refs/heads/main | 2022-01-01T18:22:54.072030 | 2021-12-22T09:05:05 | 2021-12-22T09:05:05 | 138,169,714 | 9 | 15 | Unlicense | 2020-03-29T23:36:50 | 2018-06-21T12:50:51 | C++ | UTF-8 | R | false | false | 1,417 | r | lv4.R | library(ISLR)
library(dplyr)
library(MASS)
library(caret)
## Zadatak 1
data("Smarket")
data <- Smarket
# ispis varijabli
names(data)
# dimenzija seta
dim(data)
# deskriptivna statistika
summary(data)
# korelacije
cor(select(data, -Direction))
# podjela seta, test set 2015 godina, trening set sve ostalo
training <... |
42480249822817162ec14dd1fec4ecb3033a7c1f | 2fdf31dceb15a4932c3775e877b56f76c3aeb87b | /man/queue_endpoint.Rd | 0d955c6f44e5a5199ea93f9ca303b607eae5f14f | [
"LicenseRef-scancode-generic-cla",
"MIT"
] | permissive | cloudyr/AzureQstor | d135f1cca754157f22227f6f73ac5c6778c7bfa4 | 218afec46676ce689c72959c174291cff0a84fad | refs/heads/master | 2021-05-24T07:45:42.830059 | 2021-01-12T19:36:42 | 2021-01-12T19:36:42 | 253,456,783 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,889 | rd | queue_endpoint.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/endpoint.R
\name{queue_endpoint}
\alias{queue_endpoint}
\title{Create a queue endpoint object}
\usage{
queue_endpoint(
endpoint,
key = NULL,
token = NULL,
sas = NULL,
api_version = getOption("azure_storage_api_version")
)
}
\argumen... |
eeace51979f602567b5b5e6e7abd808bf015a62a | 09fb6336818f768df7b255b58937d51caab7bc7e | /man/getPkmid.Rd | 2c058690242430ed3b0f524f2531919f3afc4653 | [] | no_license | pb-jlee/R-pbh5 | 2cad361e8c87c8559704184266a260c62f71a901 | 6ff481a98bc58ca1b307988a57f4fbbe4bdb492e | refs/heads/master | 2020-12-25T11:15:02.247726 | 2011-10-13T01:53:40 | 2011-10-13T01:53:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 849 | rd | getPkmid.Rd | \name{getPkmid}
\alias{getPkmid}
\title{
Computes the Pkmid Values
}
\description{
'getPkmid' computes the Pkmid values, a component of Nignal to Noise
Ratio (SNR). A vector of Pkmid values is computed for each alignment
in the cmph5 file.
}
\usage{
getPkmid(cmpH5, idx)
}
\arguments{
\item{cmpH5}{
An obj... |
d48a3043e46e3cf74a84d21833bbf61dda230662 | 69a62f8dab62e35a0fcb2f23bfd35bc4f401324f | /man/select_groups.Rd | b7fa651316da6eb410cc72b01ee93e69ba167ebe | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | kleinschmidt/daver | d86f880374094bcfe96ea7683a04e85c96ae30e1 | 501c8dfbf77af49ff512beae7d09e582dd8adc94 | refs/heads/master | 2021-01-19T04:37:35.600284 | 2018-03-28T19:32:21 | 2018-03-28T19:32:21 | 46,999,898 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 357 | rd | select_groups.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{select_groups}
\alias{select_groups}
\title{Select groups of grouped tbl}
\usage{
select_groups(data, groups)
}
\arguments{
\item{data}{Grouped data_frame.}
\item{groups}{Group numbers to select}
}
\description{
Useful for exam... |
8a9585b4bea1c07caf0f0c209ecb84d6b3008c0c | 95c1c71916337e940f4520f9824b18e5a9499fdd | /DAAG/data/mignonette.R | 253ec1cca8f55208c770c9cafcfb2a67390b527a | [] | no_license | VladSerhiienko/MachineLearningLabworks | 96c488efa2c1c3bc7725aa2f0056ffe44047b307 | 32ddedf45e9bb29c01156253d88f8e57e432182a | refs/heads/master | 2020-05-29T21:50:25.888848 | 2014-12-17T20:09:38 | 2014-12-17T20:09:38 | 28,153,442 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 572 | r | mignonette.R | "mignonette" <-
structure(list(cross = c(21, 14.25, 19.125, 7, 15.125, 20.5,
17.375, 23.875, 17.125, 20.75, 16.125, 17.75, 16.25, 10, 10,
22.125, 19, 18.875, 16.5, 19.25, 25.25, 22, 8.75, 14.25), self = c(12.875,
16, 11.875, 15.25, 19.125, 12.5, 16.25, 16.25, 13.375, 13.625,
14.5, 19.5, 20.875, 7.875, 17.75, 9, 11.... |
76c880bbe68917ebdbf4613b1f29fc4005512bc7 | b1467d59c6171a09fa4fe55e740c0383bf5ea904 | /man/dis.Rd | 9152520221d5c895cdd60fb0c4eae2e594874320 | [] | no_license | cran/qgen | 458812019cfa3afc8f46bf58676064c6f0908d90 | 2a6f207bd97a0458447b33752a1c35b84416e015 | refs/heads/master | 2021-01-18T15:08:55.644673 | 2007-01-24T00:00:00 | 2007-01-24T00:00:00 | 17,719,289 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | rd | dis.Rd | \name{dis}
\alias{dis}
\title{
Bootstrap confidence intervals
}
\description{
Calculates different bootstrap confidence intervals.
}
\usage{
dis(path="~/qgen/", alpha=0.05)
}
\arguments{
\item{path}{path searched for \code{stat}X\code{.rda}-files.}
\item{alpha}{number indicating the two sided error probabilit... |
6492b486741f1aa2ff30cf6791aa3dcdc928c83c | a66434dda737b754fc8597c4bc7c6bdeb0e0a962 | /Basics/Intro to package/moving-average.R | 4cb3938c34dd975f8438f16e82f37023b1e19225 | [] | no_license | lazy-mind/Time-Series-Analysis | 05e5f208c080517c7b5809213ebc4f7a274b5dab | ad0b14cd6020b9f023e6b7cf86670f796eb055ab | refs/heads/master | 2020-08-06T18:14:57.706534 | 2019-10-06T23:11:39 | 2019-10-06T23:11:39 | 213,103,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 586 | r | moving-average.R | # Announcement of the company may have impact that last 2 days
# moving average of order 2: 2 days back MA(2)
noise = rnorm(10000)
ma_2 = NULL
# get ma2 process
for (i in 3:10000) {
ma_2[i] = noise[i]+0.7*noise[i-1]+0.2*noise[i-2]
}
# shift data
moving_average_process = ma_2[3:10000]
moving_average_process = ts(m... |
6879efa9a983f59815a53ad5370cc481e3a73508 | 0e1dd4e156415271dfe8f4dfd20d5b9665e8c977 | /man/plot.ccamforecast.Rd | af59c437ea3d2829c8e8aaec0d4158ecb558f007 | [] | no_license | elisvb/CCAM | b2a711641b955185851fecc6ca2fde5b24d1b468 | 1d1cc18416b242437495f9aa11cd1f84cb466783 | refs/heads/master | 2023-03-16T15:43:06.737809 | 2023-03-09T19:42:22 | 2023-03-09T19:42:22 | 133,826,645 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 332 | rd | plot.ccamforecast.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{plot.ccamforecast}
\alias{plot.ccamforecast}
\title{Plot ccamforecast object}
\usage{
\method{plot}{ccamforecast}(x, ...)
}
\arguments{
\item{x}{...}
\item{...}{extra arguments}
}
\description{
Plot ccamforecast object
}
\det... |
683aae559e01d0cb48c83e05eb54b0052b60dba6 | a693da8676743148657e3ddb7dbfdc47c50d53a1 | /man/geoconvert.2.Rd | b195e798a8ff12d5a8c3bf66582ec105a209c6c5 | [] | no_license | Hafro/geo | 4de0f08370973b75d8d46003fb8e9a9d536beaff | 6deda168a3c3b2b5ed1237c9d3b2f8b79a3e4059 | refs/heads/master | 2022-11-23T15:06:47.987614 | 2022-11-16T22:26:55 | 2022-11-16T22:26:55 | 38,042,352 | 3 | 6 | null | 2022-11-16T22:26:56 | 2015-06-25T10:09:50 | R | UTF-8 | R | false | true | 611 | rd | geoconvert.2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geoconvert.2.R
\name{geoconvert.2}
\alias{geoconvert.2}
\title{Convert from decimal degrees}
\usage{
geoconvert.2(lat)
}
\arguments{
\item{lat}{Vector of latitude or longitudes}
}
\value{
Returns a vector of six digit values with degrees, min... |
c64e32fb58b38d31723efb75cfd425e5955ca435 | fc409801ba3a5c1ba885820257302b2ee8d251fa | /R/compareModels.PopQuants.R | cdd906a5da6e457e0070a3fe727f5707839db427 | [
"MIT"
] | permissive | wStockhausen/rTCSAM02 | 092557d6cb29179a2637db94a62b9c0ce37cdbbb | 44039e8366db3e7fb35edfd4219b311b36fd2ae9 | refs/heads/master | 2023-07-09T07:17:24.410585 | 2023-06-22T21:29:08 | 2023-06-22T21:29:08 | 69,492,060 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,393 | r | compareModels.PopQuants.R | #'
#'@title Compare population quantities from TCSAM2015 and rsimTCSAM model runs.
#'
#'@description Function to compare population quantities from TCSAM2015 and rsimTCSAM model runs.
#'
#'@param tcsams - single TCSAM2015 model report object, or named list of such
#'@param rsims - single rsimTCSAM results object, or na... |
5325471cc40e892aa474363c542211b9bd53753a | 0bebbba10f446ec5be7a378937be76c5e7610ab1 | /missing.R | f9d0beb263ef67cab8fa5237a9c4e2d98991fb62 | [] | no_license | songxh0424/bankruptcy | c3920ca513fce346bb7a3cca35aa3a35fc0293e8 | 004aad6aefb8a3c706db2f2cb180e6e013587f52 | refs/heads/master | 2021-08-24T02:29:49.794192 | 2017-12-07T17:17:04 | 2017-12-07T17:17:04 | 113,479,011 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,712 | r | missing.R | setwd("/Users/Carl/Google Drive/2017 winter/503/project")
library(foreign) # read.arff
library(tidyr)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(MASS)
library(class)
library(randomForest)
library(mice) # multiple imputation
library(missForest) # impute with RF
library(adabag) # boosting
library(caret) #... |
13efbb1a30caa07a76a43098e6420f8b4be81fdf | e9b555e22cf3f49cd5a3f7f115e4afe10173985d | /code/metrics-paper.R | 0ab3368228a3326320975630497ebd4e36fba5d3 | [] | no_license | niladrir/metrics-paper | 28af7592ade1c991d07f57d416e8e9bf58edc7c0 | 3889ce9e0468c8d9dc2f8f9d069612d1e20c1a19 | refs/heads/master | 2021-01-23T13:29:15.772053 | 2017-04-24T03:47:12 | 2017-04-24T03:47:12 | 9,685,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 70,767 | r | metrics-paper.R | library(nullabor)
library(ggplot2)
library(plyr)
library(reshape)
library(fpc)
library(tourr)
##====================================Distance Metrics===========================================
## Distance based on Boxplots with indexing
box_dist_indx <- function(i, j){
X <- lineup.dat[lineup.dat$.sample == i, ]
PX ... |
67573da26a88ab14f59421614ae8ff2ae3c15079 | de0400bfb372ffbcbfa7db1bea39ef3578c3257b | /man/is_empty_rtable.Rd | 92baf816a9ca60d6d0f4bcc3b27e0241801f98d5 | [
"MIT",
"Apache-2.0"
] | permissive | bbaranow/rtables | caef2758ed69ee8d73516c8bad43fc65d6c32a9c | efdfd495553dd682c5f1d388b778dac635a29cca | refs/heads/master | 2022-11-18T23:46:01.900811 | 2020-06-09T12:38:56 | 2020-06-09T12:38:56 | 277,768,817 | 0 | 1 | NOASSERTION | 2020-07-15T11:42:29 | 2020-07-07T09:05:42 | null | UTF-8 | R | false | true | 285 | rd | is_empty_rtable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rtable.R
\name{is_empty_rtable}
\alias{is_empty_rtable}
\title{If rtable is empty}
\usage{
is_empty_rtable(x)
}
\arguments{
\item{x}{object}
}
\value{
if rtable is empty
}
\description{
If rtable is empty
}
|
9f6c4b6cf1865d254575925a339f4ceb903798bf | a595d015cbd1ca1e3ad435cf90251b39c3d230d2 | /R_Scripts/DESEQ2_TKO.R | 6ed427e4168475c4be73b57cf97d6fc4e6ab8d0b | [] | no_license | sbodapati/CRISPR_Benchmarking_Algorithms | 00256deab82f65658dc874cdea5fd665c8ce59e8 | 30da389cca81bca673712944856df447b1d752c7 | refs/heads/master | 2021-08-01T08:23:38.081571 | 2020-04-16T16:17:45 | 2020-04-16T16:17:45 | 203,469,469 | 4 | 3 | null | 2021-07-25T19:52:26 | 2019-08-20T23:26:49 | HTML | UTF-8 | R | false | false | 1,167 | r | DESEQ2_TKO.R | ---
title: "Testing Sunil’s simulations"
author: "Timothy Daley"
date: "2/11/2019"
output: html_document
---
testCounts = read.table(file = "/Users/sbodapati/Desktop/TimFile_2.txt", header = TRUE)
head(testCounts)
counts = testCounts[ ,1:3]
colData = data.frame(condition = factor(c(0, 1, 1))) # 1 is condition, 0 i... |
13eae24066e0469812aa0da4ea93e966ca31b9f2 | 12e3d5f8618bbc113e6f039b7346fc5d723015c9 | /Stats_I/Class20/Bootstrap_ClassExamples_with_ForClass.R | 3d780a3dbdcb8476ff8323356fa1e3b461853991 | [] | no_license | raschroeder/R-Coursework | 4af2ded6e9af2c0c64697dcc796a12e508f38ae4 | 1e9800b00f84cb4092c956d9910a710729b9aff3 | refs/heads/master | 2020-04-05T12:44:28.824912 | 2019-02-06T15:59:07 | 2019-02-06T15:59:07 | 156,878,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,345 | r | Bootstrap_ClassExamples_with_ForClass.R | ##############################################################
#####################Boostrapping basics
##############################################################
#################################
#lets Build a normal distrobution
################################
?rnorm #lets us build a normal distrobution with ... |
87862519f568de16a947dd685bfa61bf7b287406 | 49a6940dee400fe0953f5a6b7dc899f0297926f9 | /cleaning_data/no-cgm-or-a1c/Protocol_I.R | 4e23eeff72e712f5048979d8ba9c3bed30e2772a | [] | no_license | joshuagrossman/a1c | 947f50de2d760f6929531859110ca16db31a0545 | 0825dba7fe588f8915595bb947c15a3d0a636a69 | refs/heads/master | 2023-03-04T11:54:09.442051 | 2021-02-05T08:13:26 | 2021-02-05T08:13:26 | 197,865,786 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,790 | r | Protocol_I.R | ################################## LIBRARIES ###################################
source("lib/source.R")
################################## GLOBALS #####################################
data_path <- "data/Protocol_I/Data Tables"
cleaned_data_path <- "data/Protocol_I/clean"
data_name <- "Protocol_I"
#################... |
72f0d3b60ef64d865016352112de87761074e460 | e910b929a3b5d2e4c1628e22138254a1808d95c4 | /man/qseckw.Rd | 42e18ead971ed6367adf19ac2f2a401cdb3cfe26 | [] | no_license | cran/SecKW | 2e3d6d4acd400fffe7f4a16331a5054f957643dd | 80f09e2d00a51be56d3112efcffb93bd637c23fe | refs/heads/master | 2020-12-22T18:27:44.488677 | 2016-07-18T13:02:45 | 2016-07-18T13:02:45 | 236,889,700 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 916 | rd | qseckw.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qseckw.R
\name{qseckw}
\alias{qseckw}
\title{The cumulative function of the Secant Kumaraswamy Weibull probability distribution.}
\usage{
qseckw(p, a, b, c, lambda, lower = TRUE, log.p = FALSE)
}
\arguments{
\item{p}{Vector of probab... |
9baa49e9061450422dd9f14c4cb0fe45b7e4cab5 | 1b925a76538fc8e59ea4e481d2871628d929d530 | /R/makeridgeplot.R | 7e1f87a23d55b4ab1b9992dff9ec1efed3b017d8 | [] | no_license | CASPResearch/ProvPack | 7217be03f0e531fa4ca1f68058a0a6ba5afefcc6 | 630b519f6939077dad69e5bfccf3b0eef054bcce | refs/heads/master | 2021-04-27T08:26:14.701309 | 2019-06-27T13:03:54 | 2019-06-27T13:03:54 | 122,489,213 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,164 | r | makeridgeplot.R | #' Make ridge plot of distributional data
#'
#' This function creates overlapping KDEs and plots them as an overlapping 'ridge plot'
#' inspired by the iconic "Unknown Pleasures" album cover by Joy Division. Whilst being very visually
#' attractive it also allows large amounts of KDEs to be plotted in one go, allow... |
29f1af810fb2925e1ded54820454f48ec4a25152 | fa79fb5281eb3d132993970cc11ca7f1e1cb5d33 | /metaanalysis_archive/analyze_expression_by_tissue.R | 39f8494401f33f309cd0993c25e7159a85021604 | [] | no_license | metabdel/motrpac_public_data_analysis | e9a72b42359e49a6a99e95aa8f557cfd95f84c3c | 3a9d81a0ea071d892bc9023474f4881c2b32b95e | refs/heads/master | 2023-03-02T12:03:51.024399 | 2021-02-10T00:20:59 | 2021-02-10T00:20:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,652 | r | analyze_expression_by_tissue.R | setwd('/Users/David/Desktop/MoTrPAC/PA_database')
library(metafor)
source('repos/motrpac/helper_functions.R')
# Get the datasets and their metadata
load("PADB_univariate_results_and_preprocessed_data_acute.RData")
acute_datasets = cohort_data
acute_metadata = cohort_metadata
load("PADB_univariate_results_and_preproces... |
d4d8cf20eb110a7d765122dec8d58b8c27885a60 | c0e766a6a57e3c5c32f8b0afe130b8df66e6dbf9 | /rsellPoshmark/man/PM_Sales_Upload_Submit.Rd | 92f7718b209aa195b71e4bb39a46158e3b0b3e63 | [] | no_license | t2tech-corp/Rsell-Packages | b450fec180754aa9cf0cf3ab6b369c74c57b7e70 | 047a2348650e5a2ee0bc52500824a34f16167056 | refs/heads/main | 2023-03-03T00:22:15.006720 | 2021-02-14T22:58:12 | 2021-02-14T22:58:12 | 329,474,392 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 523 | rd | PM_Sales_Upload_Submit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PM_Sales_Upload_Submit.R
\name{PM_Sales_Upload_Submit}
\alias{PM_Sales_Upload_Submit}
\title{Submit Poshmark Sales Activity to Database}
\usage{
PM_Sales_Upload_Submit(sales_activity)
}
\arguments{
\item{sales_activity}{Poshmark Sales Activit... |
291b84c4a41b54898a6cb3db979c11740d317a4e | 5af60ba162be455e6f1df3f68f72047aef59eccb | /man/align_lc.Rd | a363c43deb8ba60f463a8bf555c970bc25d061bc | [
"MIT"
] | permissive | bcjaeger/tblHelpers | b1e91e8a3a9d4382d9790c27ecf4551586168512 | 9fcfe4fc936f06406935c005c0428171bbe2a920 | refs/heads/master | 2021-01-03T02:47:58.961819 | 2020-02-20T02:14:02 | 2020-02-20T02:14:02 | 239,887,667 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 678 | rd | align_lc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ft_style.R
\name{align_lc}
\alias{align_lc}
\title{Left-center align}
\usage{
align_lc(object)
}
\arguments{
\item{object}{a \code{flextable} object.}
}
\value{
a \code{flextable} object with left-centered columns
}
\description{
This functio... |
053c6c179b0500f9f46344f2310df2c3a16d098d | 175034b927dfde0bd0100c4be76a901324291470 | /exercise-2/exercise.R | 4cbae5ce6e895c97eac5c2505c9be0aeee6aad86 | [
"MIT"
] | permissive | AlexEarll/module9-dataframes | 358346285e7bb8cde28ef07e27abb212a88a92f0 | 65f26ada762c2e76a8d8553df1551bda2017d33e | refs/heads/master | 2021-01-11T18:57:09.673696 | 2017-01-19T23:20:02 | 2017-01-19T23:20:02 | 79,280,629 | 0 | 0 | null | 2017-01-17T22:50:17 | 2017-01-17T22:50:17 | null | UTF-8 | R | false | false | 2,553 | r | exercise.R | # Create a vector of 100 employees ("Employee 1", "Employee 2", ... "Employee 100)
# Hint: use the `paste()` function to produce the list
employees <- c(paste("Employee", 1:100))
print(employees)
# Create a vector of 100 random salaries for the year 2014
# Use the `runif()` function to pick a random number between 400... |
5a0c3ec2af6c0ad1cfcdaf88b8a942d7b412b5bd | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.inspector/man/describe_assessment_targets.Rd | d4f58ad1c12ec6b4b13dbcefaebb6d049e75c5f2 | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 950 | rd | describe_assessment_targets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.inspector_operations.R
\name{describe_assessment_targets}
\alias{describe_assessment_targets}
\title{Describes the assessment targets that are specified by the ARNs of the assessment targets}
\usage{
describe_assessment_targets(assessmen... |
41abf2f0214333560d4babd3a4d1084c1233e41e | b1228c161b4b527503eab4ff22ba2d90f5b39ed4 | /cachematrix.R | e33b9bbbd4fbe35588775816cc7b53d980117a7e | [] | no_license | matheux/ProgrammingAssignment2 | 6fd7ad8ae93fe7c35903b6b371345c402b9e4aff | ac283e26976df2f4ea9ce02fab9d04baf3407691 | refs/heads/master | 2021-01-20T16:44:40.414160 | 2014-11-22T18:36:00 | 2014-11-22T18:36:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 956 | r | cachematrix.R | ## Below functions are meant to be used to calculate the inverse of a matrix
## Because it might be a time consuming operation (if the matrix is big), one might
## not want to do this twice for the same matrix. Those function allow this.
## Creates a special "matrix"-like object that caches its inverse, when its calcu... |
8652b1a3b4f9525b28b3aee6848850fc574f6ef4 | d0ceb8abe592f2bc3c14ee230c1fab9ee1052e9a | /sandbox/R/normalIncrementalVaR.R | 18ec8ed9eb76c20ccc14f95d46fe794576fda5c6 | [] | no_license | Jicheng-Yan/FactorAnalytics | 7c8afce66a7ea743883aaedd9382a8d7703a5606 | e9a79f7a759a43f21744817ca550cfab9d3c3d5b | refs/heads/master | 2020-12-25T05:02:35.257591 | 2014-06-12T23:40:32 | 2014-06-12T23:40:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,138 | r | normalIncrementalVaR.R | #' compute normal incremental VaR given portfolio weights, mean vector and
#' covariance matrix.
#'
#' compute normal incremental VaR given portfolio weights, mean vector and
#' covariance matrix. Incremental VaR is defined as the change in portfolio VaR
#' that occurs when an asset is removed from the portfolio.
#'
... |
23cf953c00ecc883484c60938ec97b4ed91170fa | ae35a82f670cc677c06ab201a014127f1c821fd9 | /sand/inst/code/chapter4.R | b0e518e70d1520bb3457cfcc04b8e6ea4c7a088f | [] | no_license | masanao-yajima/sand | 676d5bb99b9b0185da1bf669dc39d158f9354a70 | 16a0227562614a0fd7381f08d98ef1b8145566a9 | refs/heads/master | 2021-01-23T03:28:02.674213 | 2017-03-24T16:04:22 | 2017-03-24T16:04:22 | 86,083,383 | 1 | 1 | null | 2017-03-24T15:32:43 | 2017-03-24T15:32:43 | null | UTF-8 | R | false | false | 8,164 | r | chapter4.R | # SAND with R, chapter4.tex
# CHUNK 1
library(sand)
data(karate)
hist(degree(karate), col="lightblue", xlim=c(0,50),
xlab="Vertex Degree", ylab="Frequency", main="")
# CHUNK 2
hist(graph.strength(karate), col="pink",
xlab="Vertex Strength", ylab="Frequency", main="")
# CHUNK 3
library(igraphdata)
data(yeast)... |
2af0751c83e7d9b071579a1d49fe8b44609badda | 65c8daa4013d472247dcd245785903945d1cadfc | /man/projsplx.Rd | 4c1ab2c35da27595f9a9f4a01d1b75ab61900d89 | [
"MIT"
] | permissive | HansonMenghan/alstructure | 7c6907adad6b17ba66a3f41ff3511d549595c245 | e3554111e746a2359675d6615459af8579b69a14 | refs/heads/master | 2023-03-20T08:11:25.527193 | 2018-05-26T18:30:10 | 2018-05-26T18:30:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 873 | rd | projsplx.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/factor.R
\name{projsplx}
\alias{projsplx}
\title{Project a vector onto the simplex}
\usage{
projsplx(y)
}
\arguments{
\item{y}{a \eqn{n}{n} dimensional vector}
}
\value{
a \eqn{n}{n} dimensional vector which is the projection of \eqn{y}{y} on... |
748b7f4c878fe57897684cc733ce0a2279ff0604 | c4ded5120db258ebdd2cf143fbf3bee2fa0065b9 | /src/song_lyrics/50years_billboard_hot_100/tidy/data.R | 138d8f4b26faf5856793180cd3c2dbca2e59e507 | [] | no_license | rlads2021/project-spacegray3128 | c0be0b041d9567a0e805854b8de1ebf8a08c14da | 203dd49db14367bc6bbadafd93094e4aa4d2e988 | refs/heads/master | 2023-06-07T08:34:31.696514 | 2021-06-23T14:23:12 | 2021-06-23T14:23:12 | 379,555,786 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 454 | r | data.R | library(httr)
library(rvest)
library(stringr)
#用gather轉換
df_artists %>%
mutate(Rank = 1:100) %>%
gather("Year", "Artist", 1:50) -> df_Artist
df_titles %>%
mutate(Rank = 1:100) %>%
gather("Year", "Title", 1:50) -> df_Title
df_lyrics %>%
mutate(Rank = 1:100) %>%
gather("Year", "Lyrics", 1:50) ... |
6d3ed89fc64a0e10260a6aa09d026fcbadb319b8 | 2f95a5177b0c47e8af4163196c79539bbb6cc499 | /R-old/PD52.R | 888af130e7eb1eb9fcad97e6f4ac1ab6fddb30b9 | [] | no_license | pipetcpt/study-pkpd | 1bd270a05cbaaa82f77ddfdfb3f84f9eb1e417fd | a702cb5b6f56fe2c5236582b34213dc49a5c82ff | refs/heads/master | 2020-06-19T01:04:41.186453 | 2019-11-07T10:26:10 | 2019-11-07T10:26:10 | 196,513,459 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,093 | r | PD52.R | # PD 52
require(wnl)
setwd("D:/Rt/PD")
dPD52 = read.csv("PD52.csv")
colnames(dPD52) = c("TIME", "DV", "ID", "DOSE")
IDs = unique(dPD52[,"ID"]) ; IDs
nID = length(IDs) ; nID
AMTs = unique(dPD52[,"DOSE"]) ; AMTs
require(deSolve)
# First order biophse model
fPD52ade = function(t, y, p)
{
Cp = AMTs[i]*p["Kp"]*t*exp... |
a9e8791013a2334942f1de55847aeefbb234b18c | 9c22b7117aefe645d9f6d21c9962bcc043547a8e | /man/cNames.Rd | edb55524415560edab51dcf94d7a0fa31351cd1a | [] | no_license | Farabell/MarIOGraphing | ff28322fda76aa2a3b0a29c28fc52da8251d976a | 1fafba71cb07b96cea8131e812f388d1194d5a65 | refs/heads/master | 2016-09-14T00:47:50.466392 | 2016-04-27T03:18:08 | 2016-04-27T03:18:08 | 57,167,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 408 | rd | cNames.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cNames.R
\name{cNames}
\alias{cNames}
\title{DF Naming Function}
\usage{
cNames(x)
}
\arguments{
\item{x}{A df}
}
\value{
renamed columns
}
\description{
This function allows naming of 5 column df to predetermined
names.
Required to implement... |
6d0a10781586581994b4b928c391819efd0c3129 | 29585dff702209dd446c0ab52ceea046c58e384e | /simba/R/makead.R | d2220d3cfaef1c2a460bc3ff1dab85015ae73de4 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,724 | r | makead.R | "makead" <-
function(nspec, nplots, avSR=NULL, anc=NULL, grad.v=NULL, cf=0.2, puq=0.01) {
# grad.v is a vector describing the gradient setting, if it is set a gradient is applied.
# puq gives the proportion of ubiquituous species which are allowed to grow everywhere
# if ancestor is given, nspec, nplots and av... |
b34ee6ef05c86e50dde4ad8bb2424193ab9ddece | ee056e185f00f9d3918a1338261b4b8633cb48bc | /man/bnormnlr-package.Rd | 1efa6f8098420b341c6599b32b685e16bbec1467 | [] | no_license | cran/bnormnlr | 773c7dbdea8aff88829b0a36fcd74df7f8e067aa | 5a07c0e7a633db4f825a90dcd22f845c56a1ac88 | refs/heads/master | 2020-04-04T22:11:22.316786 | 2014-12-08T00:00:00 | 2014-12-08T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,933 | rd | bnormnlr-package.Rd | \name{bnormnlr-package}
\alias{bnormnlr-package}
\alias{bnormnlr}
\docType{package}
\title{
Bayesian Estimation for Normal Heteroscedastic Nonlinear Regression Models
}
\description{
Implementation of Bayesian estimation in normal heteroscedastic nonlinear regression Models following Cepeda-Cuervo, (2001).
}
... |
52c66d8f1ee974c7e4b4fef46554863566bca218 | 71e2d1ae54ecc891532f1f08480571a66f6185a8 | /Script/Thesis_data processing_env.R | 4b840bf75482a0e3fb55bc30fdd0896db43cdd52 | [] | no_license | Anhbt95/IMBRSea_Thesis | 739d117feafc67419cc0de4f10ae44d7a0a5b34c | ff1d399ef3ff8dbeca8d7c1f5824d72f9a3b7a28 | refs/heads/master | 2022-10-23T10:31:03.387136 | 2020-06-14T22:07:46 | 2020-06-14T22:07:46 | 272,233,430 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,885 | r | Thesis_data processing_env.R | # IMBRSea Thesis
# Tuan Anh Bui
# 18.04.2020
# Belgian beam trawl Discard spatial analysis
# This is the script for data processing - environmental variables
# Env vars: bathy, slope, chl, sst, substrate (mud, gravel, sand)
########################################
# Load support... |
6ee4d355247f900fbbc83ff7df98eae3c80758d5 | abc470631514c9261498e960137b112259dff4ba | /inst/examples1/server.R | 2a97310e2f4b33ea99aac4dd9c894f8fa1c39d38 | [] | no_license | jonkatz2/enquery | 8c29bfb7910150aa1946a5cd1eb2cb31c40274bf | e7bcae098053aeae40555a67512ef52b12f06120 | refs/heads/master | 2021-07-08T15:52:12.219085 | 2020-07-12T13:04:58 | 2020-07-12T13:04:58 | 148,210,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 113 | r | server.R | library(shiny)
library(enquery)
options(shiny.sanitize.errors = FALSE)
function(input, output, session) {
}
|
6f189be5e7f3c15c2b84cc7f765aa92b9f421cec | 169094a8afb4b431d445101260a0c4e988d42842 | /prep_whi/FIS/fis_prep_whi.R | 87b25820e3fd62b3ee65b8a2cb4e0d86a1b5bd61 | [] | no_license | OHI-Science/whi | 60c73ab4f9eeea27e3d539a4d5863c01f08de2ce | 7658065914e1d77d0192cf6ee8b074ae849cc84e | refs/heads/master | 2018-07-05T19:40:50.153919 | 2018-05-31T22:03:39 | 2018-05-31T22:03:39 | 108,908,592 | 0 | 1 | null | 2018-04-23T19:10:07 | 2017-10-30T21:06:58 | R | UTF-8 | R | false | false | 2,413 | r | fis_prep_whi.R | #FIS prep
###ISSUE###We are missing catch data that is not associated with islands - Must have along given us data from catch blocks around island (including cross seamount)
##ISSUE resolved with updated catch data FEb 2018#
## setup: libraries, file paths ----
library(tidyverse) # install.packages('tidyverse')
dir_l... |
c1280a2bf1aff297814b3822e758137c90f29aa2 | b858cccaca3a09b1ac9f17416f2d2b96a4b01b53 | /stockchart/ui.R | 864e0258ddf8f9346e9fc18fa6112cb25502b916 | [] | no_license | pauljabernathy/proj1 | a962726c794553a2f96a0996015d80ba3c9b4f1d | 5ae1a3210d8cbb8b2ed1bcfe891c01e4c47c3b03 | refs/heads/master | 2016-09-03T06:56:17.714670 | 2014-06-20T16:05:27 | 2014-06-20T16:05:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 735 | r | ui.R | library(shiny)
source('server.R')
shinyUI(fluidPage(
titlePanel("Stock Chart Generator"),
sidebarLayout(
sidebarPanel(numericInput("initial",label = h4("Initial Investment"), value=16.66),
actionButton("oneChartButton","get one chart"),
numericInput("numRuns", ... |
76880ec9529bcc3d3d8d7933264819998084b451 | d11e6ae866c3518c8056358b2257e1366c14f686 | /Rolling window - SVR.R | 7d07f772315b9d21a9a9bf0727fd62e5f4ad1091 | [] | no_license | lafet1/bachelor_thesis | 48678f26d435e077eb4ebdd2372b26bc4fd63ab6 | 5200963593febd4163fcf520768e5506602a529c | refs/heads/master | 2021-09-04T17:21:43.670176 | 2018-01-20T09:13:55 | 2018-01-20T09:13:55 | 116,477,492 | 0 | 1 | null | null | null | null | ISO-8859-13 | R | false | false | 5,330 | r | Rolling window - SVR.R |
# rm(list=ls())
library(RCurl)
library(dplyr)
library(e1071) # SVR via svm()
library(kernlab) # SVR via ksvm() - caret does not support svm() with RBF kerneł
library(forecast) # ARIMA via auto.arima()
library(tseries)
library(PSF) # PSF via psf()
library(mlr) # train() for optimalization
library(emoa) # required by m... |
6073c08449eb924a9b03fafb4bd5fbc98ae4171e | fd8f64801574f35593eca598668db2a4a81ce899 | /PRMSdata/shiny_demo_MT2_vers2.R | 28cd42b6f4ce9c4fc46fcbe6f2540e32d6022913 | [] | no_license | abock80/SB_Mapping | 66a4b6267f729adfeb22f805800f518f8eb18c8f | 3e10ab25aa1658e3c95e4d6aa4fd2a71f4e63bba | refs/heads/master | 2020-05-25T15:45:45.755369 | 2016-09-02T22:34:03 | 2016-09-02T22:34:03 | 59,135,510 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,351 | r | shiny_demo_MT2_vers2.R | library(shiny)
library(leaflet)
library(RColorBrewer)
# one script - ui.r
# one script - server.r
y2030=c('ECHAM5','GENMON','Mean',NA)
y2055=c('ECHAM5','GENMON','GFDL','Mean')
y2080=c('ECHAM5','GENMON','Mean',NA)
dd2<-data.frame(y2030,y2055,y2080)
ui <- bootstrapPage(
tags$head(tags$style(
HTML('
#sel... |
aa465cdcd30e0912d762ce44b172ca83a2ff35bf | 1cf81a76e49517222cd309668866320ec8d6ae7d | /server.R | a7e24b63a34623cdcb4bb889e7fe2fcfd555d823 | [] | no_license | neilkutty/DC_Crime_Data | 7c94c824b72714c2ee4b87a1f903a7aa2f72bb0a | f841c20683059991a74f4a6c7e5ddbac7afd1859 | refs/heads/master | 2018-11-06T21:40:20.654794 | 2018-08-27T20:41:46 | 2018-08-27T20:41:46 | 54,295,626 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,451 | r | server.R | library(jsonlite)
library(ggplot2)
library(leaflet)
library(dplyr)
library(tidyr)
library(curl)
library(lubridate)
#library(rgdal)
library(caret)
########---------------------------------------------------------------------#>>>
## Retrieve the data in JSON format from opendata.dc.gov using fromJson()
dccrimejsonl... |
e573e5a4d8d40e10b1fe405760d3d2d0a4273560 | 7eb711dcaf5eb7f78a21672311aab5b6877ed9f5 | /R/DE_edgeR.R | eddec501551dde6d5a68086d2995bdd941a0e3d6 | [] | no_license | xizhihui/codes | ef8d2135648e065768d7d11cc7f70d8e55da4182 | bd1beb82ff177f0b75fa320a2e637af5d1da64de | refs/heads/master | 2021-06-18T04:31:54.831817 | 2021-06-15T05:03:22 | 2021-06-15T05:03:22 | 160,029,844 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,048 | r | DE_edgeR.R | ########################################################
# 使用edgeR进行差异分析
# 使用方式:
# source('DE_edgeR.R')
# edgeR_DGE(exprSet, group, type)
# exprSet: 表达矩阵
# group: 样品分组
# type: 差异分析方法, classical, lrt, qlt
# + classcial
# + glm: likelihood ratio test/ quasi-likelihood F-test
# + + quasi-likelihood(qlf): ... |
6197e13a9eb5231f676caff48bf0ffd067a78080 | e203f637b3387ff74be1a950f043b99d58787852 | /4.2.R | d827eb08968e72ec935c1c6f31fa38e8998ec45b | [
"MIT"
] | permissive | tondi/stat | e274711de6a9c01ce9599e46e370a4cb5172a9b8 | 0351cb0708fd20339ff6cc7f6b21fe214b7d3e4a | refs/heads/master | 2022-12-03T10:54:20.170710 | 2020-09-01T08:26:56 | 2020-09-01T08:26:56 | 291,940,332 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 416 | r | 4.2.R | install.packages("psych")
results <- matrix(c(315, 101, 108, 32), nrow=2, ncol=2, byrow = TRUE)
expected <- matrix(
c(9, 3, 3, 1),
nrow=2,
ncol=2,
byrow = TRUE
)
# lm(results[2][2] ~ ., data = results)
podzielone <- results / expected
chisq.test(podzielone, expected)
# X-squared = 0.0035162, df = 1, p-va... |
b63ede35a130622a6c3fa149b1492babff8b8264 | 287fe76a4b1fa3f147f6ad8c9ccbc70916823b39 | /Plot2.R | 12f8326dc72392bc488c720541f00449d92f7922 | [] | no_license | wheatonr/bendy-chicken | 9394b848c8de4e9e7779b2de60d8b9d401fedfd1 | 443bc85e7e873234692dd22edba92fb60c962c18 | refs/heads/master | 2020-04-12T15:23:04.400033 | 2014-09-08T06:08:13 | 2014-09-08T06:08:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 649 | r | Plot2.R | ## Exploratory Data Analysis project 2
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
## compute sum by year for Baltimore only
baltimore<-aggregate(Emissions ~ year, sum,data=NEI[NEI$fips=="24510",])
##setup for png output
png(filename="plot2.png",width=480,height=480,units="px... |
7f27b7719422c4ce6384d0d64c3e14d6de4bdc73 | 80c022bc6f023dae8549f35a6759e928f99c521d | /R/stats.R | bb446cc38175f92bc90f533cc6a67881072cca1d | [] | no_license | SonmezOzan/mmfit | 37d5bf3735c431718a702f2943466df73574d9d9 | ba28ef0321c990daa39484aeafd3d8e54dbb462d | refs/heads/master | 2021-01-20T05:34:18.521092 | 2017-08-26T00:21:22 | 2017-08-26T00:21:22 | 101,451,377 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,024 | r | stats.R | #' summary.mmfit function and plot.mmfit function
#'
#' @param x a set of sample data
#' @param g moment function
#' @param start initial values to start with
#' @param type distribution type. It take a value from "Poisson", "power law", "Gamma", "Beta", mixture of "2Poissons", and "2Exponential".
#'
#' @descrip... |
ef36f56e439696f0770dac34fc4e62923683c52c | 449482d6e7183a795818a689da2132a2b7ce93ed | /tests/fixtures/r-gsl/main.R | b478e733d585ca5391e983867a0957a374386ce3 | [
"Apache-2.0"
] | permissive | stencila/dockta | 8ecb0aabc63f7451b5f7fc521d7d354d70afc89d | 8fb329fb026924a00527643a6ac639651ac20329 | refs/heads/master | 2023-08-09T14:11:02.250754 | 2023-07-26T03:59:47 | 2023-07-26T03:59:47 | 151,520,823 | 57 | 11 | Apache-2.0 | 2023-08-27T10:12:16 | 2018-10-04T05:00:17 | TypeScript | UTF-8 | R | false | false | 115 | r | main.R | # A test fixture with an R package with a system requirement
# that has an array for deb requirements
library(gsl)
|
17edc18523f90a86d769816300babc93d60d1b44 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Matrices_And_Linear_Transformations_by_Charles_G._Cullen/CH1/EX1.11/Ex1_11.R | 42f15db6e278e24283152c95978b62db55471c2e | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 884 | r | Ex1_11.R | #page - 41
#section - 1.7 SPECIAL KINDS OF MATRICES
#example 11
#matrix A
A <- matrix(c(1,2,3,4,2,5,-6,7,3,-6,8,-9,4,7,-9,0), 4, 4, byrow=TRUE)
A
#matrix B
B <- matrix(c(0,1,2,3,-1,0,-4,5,-2,4,0,6,-3,-5,-6,-0), 4, 4, byrow=TRUE)
A
AT = t(A)
AT
BT = t(B)
BT
#function to compare two matrices
#sy... |
659176b81751b2f1bdffd7bdc1a42497fd10ec23 | 70c0910f9cdcfe59e2dc93b20b813df0c740845f | /man/print.AR.Rd | 34be389633b8281d7a0f1620b0ec07181c286bae | [] | no_license | michaldanaj/MDBinom | a21ca1e7d486168922d32eac641fb1df1d518875 | 082a583fc9905ea13acfda382a8a9c31ddf3573c | refs/heads/master | 2021-07-25T01:09:00.904252 | 2020-06-11T21:17:28 | 2020-06-11T21:17:28 | 52,234,495 | 0 | 0 | null | 2019-04-24T19:04:29 | 2016-02-21T23:49:48 | R | UTF-8 | R | false | true | 353 | rd | print.AR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MDBinom.r
\name{print.AR}
\alias{print.AR}
\title{Wyświetla statystyki przechowywane w obiekcie \code{\link{AR}}}
\usage{
\method{print}{AR}(x)
}
\arguments{
\item{x}{obiekt klasy \code{AR}.}
}
\description{
Wyświetla statystyki przechowywane... |
1062f17a3812e43774ce146d3e4dc86679425ac6 | 402c51ab42489645ab08567448a2c4cba1096712 | /arima-model.R | 7e73568f03955fd77fd175ecb9127d77a89c7a49 | [] | no_license | bnouyrigat/forecasting-bike-sharing | bca17309548b395ebb5221704c0ba5ee244a10d5 | 9a75106b217f02f946faca29c198a8cd6cdafd84 | refs/heads/master | 2020-03-20T22:27:24.034067 | 2018-06-19T18:45:32 | 2018-06-19T18:45:32 | 137,799,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,059 | r | arima-model.R | library('ggplot2')
library('forecast')
library('tseries')
daily_data = read.csv('./day.csv', header=TRUE, stringsAsFactors=FALSE)
daily_data$Date = as.Date(daily_data$dteday)
ggplot(daily_data, aes(Date, cnt)) + geom_line() + scale_x_date('month') + ylab("Daily Bike Checkouts") + xlab("")
count_ts = ts(daily_data[,... |
5824fd37693fb2a86b34e4954331a1746cb91093 | bf864ce7dc7edced7e8eca28a43ece359e2cda21 | /string_manipulation/dnaORrna.R | d82a9caff454fbe9ad2767b3a9f8947d3e66b2fc | [] | no_license | inambioinfo/r-codes | 557367f1942f19847bf18209380c42c62d49db6f | 8720b1ad1d27143a3ea01d1bb7ad78c2eda449e8 | refs/heads/master | 2020-06-13T09:54:42.257218 | 2018-10-13T05:54:02 | 2018-10-13T05:54:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 793 | r | dnaORrna.R | dnaORrna <- function(sequence){
#-------
sequence <- toupper(sequence)
#-------
#sequence <- 'ATGC'
nuc <- strsplit(sequence, split='')[[1]]
if (length(nuc) == 0){
stop("No sequence is given!!!")
}
#-------
conflictSeq <- intersect(c('T','U'),nuc)
if(length(conflictSeq) == 0){
output <- 'DNA o... |
ec716682441c9544badc12e4d6b6f1cc8c050159 | 1fb7ddd7f291bc6e2300e97086c450398c6e1636 | /Plot6.R | 62eb662e5d41257c519245067ab2b373cc635d26 | [] | no_license | Belphegorus/ExpDatAn_CP2 | acb1f247728ffbd04572207bae1d041dfdf01ad4 | ba2f9bb5d7fb3c028df954117273d94230998680 | refs/heads/master | 2016-09-01T07:33:49.234086 | 2016-03-27T17:27:46 | 2016-03-27T17:27:46 | 54,839,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,087 | r | Plot6.R |
# Compare emissions from motor vehicle sources in Baltimore City with emissions from motor
# vehicle sources in Los Angeles County, California (fips == "06037").
# Which city has seen greater changes over time in motor vehicle emissions?
library(ggplot2)
sum_ssc<- readRDS("summarySCC_PM25.rds")
Co <-... |
c723fc38274ae55aeec9301c4db81ad84978f249 | c5de5d072f5099e7f13b94bf2c81975582788459 | /R Extension/RMG/Energy/Trading/Congestion/NEPOOL/ISO_Data/EnergyOffer/lib.investigate.energy.offers.R | 9750f7b40a54ce525d3205f0a6e055d192629131 | [] | no_license | uhasan1/QLExtension-backup | e125ad6e3f20451dfa593284507c493a6fd66bb8 | 2bea9262841b07c2fb3c3495395e66e66a092035 | refs/heads/master | 2020-05-31T06:08:40.523979 | 2015-03-16T03:09:28 | 2015-03-16T03:09:28 | 190,136,053 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,783 | r | lib.investigate.energy.offers.R | #
# EO.by_maskedNodeId - extract one unit in P, MW, segment form
# EO.by_month - cast the EO by month in P, MW, segment form
# EO.clean_PQ_pairs_of_NAs
# EO.get_unique_participantsMonth
#
# analyze_NorthfieldMountain
# analyze_SalemHarbor
#
# get_cleared_mw_lessThanHub
# get_cleared_mw_lessThanHR
# get_must_ta... |
8b025ebb4f9bff1f052412d5670e1245b218b0ec | 258bdbfae973089c337fd31c6162ccb929714043 | /NC140/root-distribution.R | 9f61a35b1fb907cda09f7dc4ac579b5f2af3e303 | [] | no_license | weecology/branch-arch | 897a4df4953561a4f90e754d5c8f2873bce046cb | d8f4d518e3f5165f810ee845d10d90a3d95acf71 | refs/heads/master | 2021-09-05T21:56:14.751742 | 2021-08-05T21:13:05 | 2021-08-05T21:13:05 | 12,822,147 | 1 | 0 | null | 2016-01-07T22:02:05 | 2013-09-14T00:36:11 | HTML | UTF-8 | R | false | false | 1,955 | r | root-distribution.R | ### This script generates Root Biomass Distribution plots for within and between
### row transects
source('~/Desktop/branch-arch/NC140/root-yield.R', echo = FALSE)
vplayout <- function(x,y){
viewport(layout.pos.row = x, layout.pos.col = y)
}
within_plots <- c()
for (i in c(1:5)){
rootstock_within <- filter(roots... |
e6de055619e373aa46b951e188803ae92d724b2b | ccbb0b70874ac382e948d940a178793d263eb68c | /genuary/2021/2021-23/deprecated/2021-23d.R | b4fdc848452c5d07f4a6560a38cd0a48271a1ea5 | [
"MIT"
] | permissive | joelfishbein/aRtist | 125f6b96031e64a819eb8330645bd3a7b98d8b63 | 5da0d46d0c618d28243f69d67e22cfa10e911cbd | refs/heads/main | 2023-08-20T19:51:15.859878 | 2021-10-21T02:54:15 | 2021-10-21T02:54:15 | 360,969,255 | 0 | 0 | MIT | 2021-04-23T18:11:26 | 2021-04-23T18:11:25 | null | UTF-8 | R | false | false | 1,263 | r | 2021-23d.R | # https://bookdown.org/rdpeng/RProgDA/building-new-graphical-elements.html
library(grid)
GeomMyPoint <- ggproto("GeomMyPoint", Geom,
required_aes = c("x", "y"),
default_aes = aes(shape = 1),
draw_key = draw_key_point,
draw_pan... |
3a9fd62f4fd59a26e460ae033d07f07555be9e58 | 41f37ff43cc885a0d99aefe33ee3af25f7ab7402 | /plotit2.r | 34d33104c64c347a8119856b1a58b2596687f58c | [] | no_license | jgarofoli/network-ping-analysis | d1b3d846752cb1543b97c960cfc8dd72d4f50f15 | ed521bec05704e01000a7fc4d3fc82b0ccdeadb8 | refs/heads/master | 2021-01-10T20:39:31.104591 | 2015-01-25T03:48:19 | 2015-01-25T03:48:19 | 29,743,918 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | plotit2.r | mkreport <- function(fname) {
mydata <- read.table(fname)
x <- (mydata[,1] - mydata[1,1])/60./60.
y <- mydata[,3]
#pdf('figure1.pdf')
quartz()
par(mfrow=c(2,2))
plot(y~x,ylab="pings returned (out of 10)",xlab="time (hr)",main=fname,
pch=16,col=rgb(0,0,0,1/8))
#dev.off()
... |
0f096882478525c32f8dfb4c20d373bfec24cd44 | 198eddc28dd4b9cd2d02c294f6f77c2baa8d91e1 | /man/ggchisq_res.Rd | 1283bc1de8ba42fb2126cf5087699dd64542713b | [] | no_license | xtmgah/JLutils | 2c2819e78a70e0f0afeac385ecdfba003841fbef | 20eb88f01f88a73a12d9a4c4a071e5fc9f796c2d | refs/heads/master | 2021-05-18T17:53:23.560462 | 2020-03-16T09:42:24 | 2020-03-16T09:42:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,512 | rd | ggchisq_res.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggchisq_res.R
\name{ggchisq_res}
\alias{ggchisq_res}
\title{Chi-squared residuals matrix plot}
\usage{
ggchisq_res(
formula,
data,
weight = NULL,
addNA = FALSE,
label = NULL,
breaks = c(-4, -2, 2, 4),
palette = "RdBu",
return_... |
3f64fd5cc5e7dc7bb5aafb4aec80e9139d545401 | 340d2dd9b14fdb3d3e4d70a69389b6bf70faf0c7 | /scripts/4_excess_deaths_global_estimates_export_for_interactive.R | 9989dd2cfdf6bfd4dd2a836d549668280e6a8380 | [
"MIT"
] | permissive | TheEconomist/covid-19-the-economist-global-excess-deaths-model | ba56184a57488c6f065a2428837eecfc2f56a678 | 07a9b7fc43dac6f1d1741e51f175555e73c18e97 | refs/heads/main | 2021-09-09T09:10:49.987777 | 2021-09-09T08:05:22 | 2021-09-09T08:05:22 | 366,795,934 | 445 | 84 | MIT | 2022-10-05T16:52:01 | 2021-05-12T17:24:01 | R | UTF-8 | R | false | false | 37,311 | r | 4_excess_deaths_global_estimates_export_for_interactive.R | # Step 1: import libraries ------------------------------------------------------------------------------
# This script constructs custom data frames to populate the The Economist's interactive presentation of this estimates
# Import libraries
library(tidyverse)
library(data.table)
library(lubridate)
library(readr)
l... |
073f1cf747d785b612b055e3aebd4f9d6c1ddc91 | 799feab98fba14d85188ee1502855b6ccd18381e | /T1_BasicsofR.R | 774ea2b50d4dd7c6812dfbcb532651d3382fe2f0 | [] | no_license | Adarsh-Suresh-Patil/IST-687-Data-Science | 46a86bb75ea5921a77339d50dc03184bcdddd237 | 1dd171a0b465b0d69968afbee766fc0f39a97f4e | refs/heads/master | 2020-04-17T13:01:07.942316 | 2019-03-16T06:42:18 | 2019-03-16T06:42:18 | 166,599,160 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,024 | r | T1_BasicsofR.R | #Basics of R
#1. Define variables
#Define a vector with values 4.0, 3.3 and 3.7
grades <- c(4.0, 3.3, 3.7)
#Define vector with values "Bio", "Math", "History"
course <- c("Bio", "Math", "History")
#Define a variable of value 3
betterthanB <- 3
#2. Calculate statistics using R
#Avg of grades
avggrades <- mean(grades... |
74292c0c7f3963b690d2a66076b854208bad0dec | 56b4d541153ce86dadbfbdef9636d18be7de9903 | /4.data-viz.R | bd448b3a6f7fde9107afe527262257ca925f48aa | [] | no_license | tianchu-shu/data-analysis-viz | 9db4c243577917a6286425fd439e841102875958 | a5d19ff3edce031adf1584ba57327876d60f568c | refs/heads/master | 2020-03-21T18:35:52.432421 | 2018-10-09T01:16:23 | 2018-10-09T01:16:23 | 138,901,620 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 982 | r | 4.data-viz.R | #Data-viz
library(ggplot2)
library(ggvis)
library(tidyverse)
library(reshape2)
ggplot(df, aes(freq, fill_rate)) +
geom_point(aes(color = FY)) +
geom_smooth(se = FALSE) +
labs(title = "")
ggplot(data = melt(df), mapping = aes(x = value)) +
geom_histogram(bins = 30) + facet_wrap(~variable, scal... |
6600cc647766828955c73d6508af400a501e8a09 | 0e100f473781741b45e463c522cddf9812307fba | /run_analysis.R | c8138a80bc45167c99bc384ae4eee387b3b61ded | [] | no_license | raneykat/CourseraGetAndCleanProject | 9af4f3dec8f5ef115bc4a17313cd3d0d4d81363b | 8aa14348756cf5aa04f23391f09154e90d3c8643 | refs/heads/master | 2020-06-05T09:10:19.893102 | 2014-11-23T22:28:53 | 2014-11-23T22:28:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,210 | r | run_analysis.R | # Katherine Raney
# Getting and Cleaning Data Course Project
# November 2014
#setwd("C:\\raneykat_git\\CourseraGetAndCleanProject")
# libraries needed
library(reshape2)
library(tidyr)
library(dplyr)
# First, load the files
# APPLIES FOR BOTH TEST AND TRAIN DATA
# activity_labels
# this is a lookup dataset
cols <... |
0a618a76e5b971c47979036ca8d5bec5c8098586 | d81c8a628b7f197106debbb8d2b62dd56719e705 | /Project 3/word_cloud_jb.r | e5486eddd2525e7331616d3ce9e7429ffbf627fc | [] | no_license | ilyakats/CUNY-DATA607 | 77259bae9ccdb927c0c9a07bb1f5204da6a33f53 | 4ace18eb89e9cfc49cb816b3f612247b47aa6e15 | refs/heads/master | 2021-01-09T06:35:14.150125 | 2017-05-08T06:02:27 | 2017-05-08T06:02:27 | 81,013,259 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 464 | r | word_cloud_jb.r | # Jaan Bernberg
library(wordcloud2)
my_cloud_words <- all_data[,3:7] %>%
filter(YearCollected == 2017) %>%
select(SkillDescription, Amount) %>%
group_by(SkillDescription) %>%
summarise(Amount = sum(Amount)) %>%
arrange(desc(Amount)) %>% as.data.frame()
rownames(my_cloud_words) <- my_cloud_words$SkillDe... |
58794d2b14b7b7f272bb9de0ac060fc1b9ac29bc | 2dcdfa0d9adfac9453f360c6b2c9bc1c765b46a4 | /Operation_Dashboard_New/ui.R | 408adc9b5209d41d54fafdaf72d9dd908657f85e | [] | no_license | roymondliao/Shiny_Project | d056af95a3729a8625bc238ae6167613370ad9b4 | 14064bcb8c18811b4c29ad9012e2cc1feedbf256 | refs/heads/master | 2021-01-10T04:26:41.035961 | 2019-03-27T03:56:30 | 2019-03-27T03:56:30 | 53,765,381 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,712 | r | ui.R | ## Operaction dashboard - ui.R
library(shiny)
library(shinydashboard, warn.conflicts = FALSE)
library(htmlwidgets)
library(rCharts)
library(shinythemes)
library(DT)
options(shiny.maxRequestSize=10*1024^2)
## app.R
header <- dashboardHeader(title = "Operation Dashoard",
dropdownMenuOutput("me... |
46b8d02333bd03922ec9f1ff32ee102d2ef48080 | 8eb45bfd8ebc817911b23e74cce42cf506c85b54 | /principalMochila.R | e3fe4f8aef246eaa8593896eb799489d3eafa2e4 | [] | no_license | vitoriacfaria/aulas-franco-inteligencia-artificial | 161cb1950447af0bd0cf2072ff0145b4bc72c341 | 3d2fd3aea3d3103b04775702a3c3ed8370cc26b3 | refs/heads/master | 2022-12-10T11:49:11.702438 | 2020-09-10T00:43:59 | 2020-09-10T00:43:59 | 294,262,436 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 442 | r | principalMochila.R | # Instalando o pacote GA
install.packages("GA")
# Carregando o pacote GA
library("GA")
# Algoritmo genetico
resultado = ga("binary",
fitness = fAdaptMO,
nBits = 8,
popSize = 10,
maxiter = 20,
names = items)
#verificacao de resultados
summar... |
e26fb0ee3521950aad58189d6f316e0454e870c6 | 569c5a551a0ee233d923c3c75eb0e10f98a679ea | /4_publish_api.R | 22c9f72d9665c323aa2ce99489625d83650e37f0 | [] | no_license | DataStrategist/immunotherapy | 234f0d5d4edcac2b0a6371bc6356142991570334 | 46512de99225dbefc1eee58946f81bd91ad68c03 | refs/heads/master | 2020-07-31T14:47:30.761687 | 2019-06-13T08:56:48 | 2019-06-13T08:56:48 | 210,641,248 | 0 | 0 | null | 2019-09-24T15:50:27 | 2019-09-24T15:50:26 | null | UTF-8 | R | false | false | 484 | r | 4_publish_api.R |
# Since the API is defined by `plumber/plumber.R`, i.e. inside a subfolder,
# first copy the `config.yml` to the `plumber` folder
fs::file_copy("config.yml", "plumber/config.yml", overwrite = TRUE)
library(rsconnect)
withr::with_dir(
"plumber",
rsconnect::deployAPI(
api = ".",
# server = "{server}", ... |
5d3ba3c108d5531b78e21f1ecfa60c7c69e236d6 | 809754c7533aaa3e19d327f3bc7fd57ba5e6c082 | /man/STR_analysis.Rd | 754e65d151e0d98192f04a025320d34e139a37bd | [] | no_license | cran/STRAH | cb72112f9dbb4551762356ba4b587a26e7389683 | 0954be0ce19ffd2f0a65a83e049db598277d2fb3 | refs/heads/master | 2020-12-22T19:10:48.560514 | 2019-04-09T14:05:25 | 2019-04-09T14:05:25 | 236,902,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 6,491 | rd | STR_analysis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/STR_analysis.R
\name{STR_analysis}
\alias{STR_analysis}
\title{Analysis of short tandem repeats (STRs) in a given region of any reference genome}
\usage{
STR_analysis(seqName, nr.STRs = 10, nr.mismatch = 0, chrs, STR = "A",
lens.grey... |
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