blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3cc9c125da0830f2a10808fa719fe40fc654e17e | 0bd4d5ee50ebfb5a5325ae0284087ee886be4f37 | /man/enumerate.Rd | 17e1e6601e4143c7ddbbb186a85c51525da54740 | [] | no_license | stla/SLutils | 91f53e3ef48b22154642b7425a1be94c0c48053e | 5c5ef7dbb5d172c0a7788b3975a1363a47c4bf67 | refs/heads/master | 2020-04-10T06:21:30.476088 | 2019-09-10T10:00:57 | 2019-09-10T10:00:57 | 160,851,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 437 | rd | enumerate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reporting.R
\name{enumerate}
\alias{enumerate}
\title{Sentence enumerating the elements of a vector}
\usage{
enumerate(x)
}
\arguments{
\item{x}{character or numeric vector}
}
\value{
A character string.
}
\description{
Make a sentence enumer... |
1f50a03f363c965fe6e714dbdb2c939718e95627 | e4f637cc645427b6e1c7a7eb33df47afd42264bc | /size_dist/R_code.R | 103ce8350bcaf20b9aaa528a3fa1b72aa611f970 | [] | no_license | yinrui0/thesis-project | a1887045819d3be456b5a31455c66eaff79d457d | a7402c05092229c129bfae9b5a23da39dd09f6cf | refs/heads/master | 2021-05-02T17:30:59.268773 | 2017-07-19T21:51:42 | 2017-07-19T21:51:42 | 61,657,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 886 | r | R_code.R | rm(list=ls())
cat("\014")
#read dlnp
imput1 <- read.csv("diameters_ln_intervals_2014.csv", skip=16, header = FALSE)
#read dN/dlnp
imput2 <- read.csv("size_distributions__2014_09.csv", skip = 16, header = FALSE)
num <- 3544
#read diameter
imput3 <- read.csv("diameters_ln_intervals_2014.csv", skip=14, nrows=1, header ... |
8b2bed835386094c34da48c30821696e2d2d620f | 52f95b07a1d460d90350d5dced856363d96b5aa0 | /TestData3.R | 5359b47a26578ffe79bd71e34b82966655d2383b | [] | no_license | twgg201/datan3_2019 | 16122d6f5a7271ba6932c677db97855f2a87e877 | 1a1c833eacce1cde029ad8aa504ae9d2983d1a39 | refs/heads/master | 2020-04-17T04:57:58.115699 | 2019-01-17T16:37:47 | 2019-01-17T16:37:47 | 166,255,456 | 0 | 0 | null | 2019-01-17T16:12:30 | 2019-01-17T16:12:30 | null | UTF-8 | R | false | false | 19 | r | TestData3.R | #test r script
2+2 |
59f1f93339b7da135d57d297e233a479ea21ca8a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/chicane/examples/fill.in.zeros.Rd.R | ecf30fa584e866d246ccab89041b9c3e12b9777a | [] | 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 | 416 | r | fill.in.zeros.Rd.R | library(chicane)
### Name: fill.in.zeros
### Title: fill.in.zeros
### Aliases: fill.in.zeros fill.in.zeroes
### ** Examples
data(bre80);
bait.file <- system.file('extdata', '2q35.bed', package = 'chicane');
fragment.file <- system.file('extdata', 'GRCh38_HindIII_chr2.bed.gz', package = 'chicane');
results <- fil... |
670e33231aff25011e9607b70f12a0e43cbb8114 | 701997224f99e13e24046b7b5313d59730d0d1c2 | /general/performance/stats.R | f72640c57401d0fa0b8967d65cc71eeff653ecc1 | [
"MIT"
] | permissive | autocare/tlaplus | 021699fa24eabef6ffb35e6a51c06dabeb70c0de | 1e652ddeab8109ba725c73094cb0efb48e02bf70 | refs/heads/master | 2023-01-28T16:57:05.083857 | 2023-01-05T16:52:02 | 2023-01-05T16:52:02 | 263,623,177 | 0 | 0 | MIT | 2020-05-13T12:27:21 | 2020-05-13T12:27:20 | null | UTF-8 | R | false | false | 1,678 | r | stats.R | ## Read input csv file.
library(here)
data <- read.csv(header=TRUE, sep = "#", file = here("out_run-stats.csv"))
## Merge two or more commits when they cannot impact performance because
## a commit only changes auxiliary files.
## Replace git commit short-hash f91... with df1...
data[data == "f91c7b0"] <- "df144c5"
da... |
1c78097dcf32e93819033ae63b7362fd17f6c630 | 25449f88edddc74beb261a934964d7d1ce358deb | /R/read_vpts.R | a96e8383ead9029ef8e68574cc62871403f215ff | [
"MIT"
] | permissive | adokter/bioRad | 53de114ca6e2151743045db8556ffd7a45f90570 | d4935eddaa7cc1c3c50e47278e72967c8bbd980c | refs/heads/master | 2023-09-01T10:49:36.747974 | 2023-07-28T14:12:57 | 2023-07-28T14:12:57 | 59,586,835 | 29 | 21 | NOASSERTION | 2023-09-02T17:36:08 | 2016-05-24T15:49:06 | R | UTF-8 | R | false | false | 4,762 | r | read_vpts.R | #' Read time series of vertical profiles (`vpts`) from file(s)
#'
#' Reads `vpts` data from one or more files.
#' The following file formats are supported (but cannot be mixed):
#' - [VPTS CSV](https://aloftdata.eu/vpts-csv/).
#' - [ODIM bird profile](https://github.com/adokter/vol2bird/wiki/ODIM-bird-profile-format-sp... |
c799688b5abd56f0a2d93d10a13ceacb7d1a70a0 | f255ef3c7452a307bbaaf95a092e4279aa5f366e | /man/coords.Rd | 9aa68a2c952931c3c2b729d22a2d8e63a00588e5 | [] | no_license | bbuchsbaum/eyesim | a1a61068f53a16925566deb81e03fa5943686f0e | 4d4f48ef0b1812d5200b86d7216f8d03792c2435 | refs/heads/master | 2023-05-11T00:43:30.086058 | 2023-05-08T15:02:35 | 2023-05-08T15:02:35 | 86,451,769 | 8 | 2 | null | null | null | null | UTF-8 | R | false | true | 240 | rd | coords.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/all_generic.R
\name{coords}
\alias{coords}
\title{extract coordinates}
\usage{
coords(x)
}
\arguments{
\item{x}{the object}
}
\description{
extract coordinates
}
|
f9e4e1e07577a644da16814a38e33eda1147e98e | b1d42fdf2a683687642d857ae03be94371a24395 | /man/gromov.hyperbolicity.Rd | fa0fa51c71883a7be88645e9c0ecf1fed916a97c | [] | no_license | cran/distory | 61a22457ac6051cae7624b697dbbe57875e7cd7d | b0f847940dc178c2d0610a3862b5da6c4f7e042f | refs/heads/master | 2021-01-01T16:13:46.344609 | 2020-04-19T08:00:02 | 2020-04-19T08:00:02 | 17,695,542 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,838 | rd | gromov.hyperbolicity.Rd | \name{gromov.hyperbolicity}
\Rdversion{1.1}
\alias{gromov.hyperbolicity}
\title{Gromov Hyperbolicity Constant}
\description{
Computes the Gromov Hyperbolicity Constant of a distance matrix.
}
\usage{
gromov.hyperbolicity(d, deltas = FALSE, scale = NA)
}
\arguments{
\item{d}{
A distance matrix of type \code{dist... |
ad12c7d5e91938adb3d80ad7b1b3969b64a10d82 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ecd/examples/ecd.mpfr.Rd.R | b2ea640e5f0ce7cd8746a40cf3ee20d1c988a153 | [] | 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 | 314 | r | ecd.mpfr.Rd.R | library(ecd)
### Name: ecd.mpfr
### Title: Wrapper to convert numeric to mpfr
### Aliases: ecd.mpfr ecd.mp1 ecd.mppi ecd.gamma ecd.erf ecd.erfc ecd.erfcx
### ecd.dawson ecd.erfi ecd.devel
### Keywords: datasets utility
### ** Examples
x <- ecd.mpfr(1)
y <- ecd.mpfr(c(1,2,3))
z <- ecd.mp1
p <- ecd.mppi()
|
02aaf3cc9faf3e377c0c2a661138a2ce3c8b20ca | 62f023eefc837f9d7a0e640c7bea52d90e30d68f | /User - process cushion stability .r | 16bb894d29e71352d0f3ba9d717096dca9257af1 | [] | no_license | E1kT6MNF/TrackImage-R-Scripts | 748242e3f719bb12e81e04d1e44d841ff180afb2 | 5444be32d521c3e34b7fd85808c61c28e711cf7e | refs/heads/master | 2021-01-18T21:27:52.331198 | 2016-04-05T16:33:16 | 2016-04-05T16:33:16 | 39,971,612 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,362 | r | User - process cushion stability .r | # This script will read TrackImage .csv files and output cushion stabiliy metrics
# Author: John Newkirk
# Rev. History:
# 08/11/15 added arguments for offset, Slope Lower Time Limit, Slope Upper Time Limit
# 09/01/15 1. Moved arguement entry to line items instead of within function call
# ... |
5c92b19d1fd266319fde9e238e1c74ea5fc79487 | 8e41c5e15a9c707baf47d8540cbb4bfa20a7ee1b | /man/en_boggle_dices.Rd | dd0f4d55faf24c63d56dd3ceb0b80f77e72116aa | [] | no_license | jcrodriguez1989/boggler | ca288c40df9e6a2a324ff42ff3afe9da7a32d630 | 600086dfdee93b6d758869d9ff62817c005c3d61 | refs/heads/master | 2020-08-15T05:55:56.023003 | 2019-10-15T12:26:03 | 2019-10-15T12:26:03 | 215,290,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 649 | rd | en_boggle_dices.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{en_boggle_dices}
\alias{en_boggle_dices}
\title{English Boggle dices.}
\format{A character vector with sixteen 6-letters strings, each representing
one dice.}
\usage{
en_boggle_dices
}
\description{
Sixteen dices... |
0ef258c9435e35a28c62ea43de5ba7e3cf5eaf14 | a22eb8c44c5e341716520615ff8797c52a4d6f92 | /R/6/d.r | e8b8371af03bb39b8466d9133a1b00a1fc155e9f | [] | no_license | alexzhirkevich/All.sklad | 1c5b8dc44a2ee3046cc56520d498060f9246a700 | 427ef1d5a169583ea61904b407ba6e74e2fd3430 | refs/heads/main | 2023-04-19T08:46:02.712248 | 2021-05-07T19:40:31 | 2021-05-07T19:40:31 | 304,058,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 394 | r | d.r | library(moments)
x = rbeta(200,0.5,0.5)
print(x[1:10])
print(x[180:200])
print(min(x))
print(max(x))
print(0.5)
print(var(x))
print(median(x))
print(kurtosis(x))
print(skewness(x))
print(quantile(x,c(0.25,0.75,0.95)))
boxplot(x)
plot.ecdf(x)
y=x[x!=0]
hist(y,col="green",border="blue",prob = TRUE,main = "Гистрограмма... |
06cad77e198b1fc92da2081ebd1c2a282127f09b | 2e8130df687fe6ace366709029b7fa3a71c99424 | /R/run_app.R | 239ff43cc6ceb022b17154144eaa7d0ba9a5dc6a | [
"MIT"
] | permissive | feinmann/psymap | 8b9828959d24d52766769a6e5f930bbf3bd2f84e | 9989febd86a48bce48487bfe27c8354e57cff4c8 | refs/heads/master | 2021-03-05T21:29:39.770657 | 2020-03-15T16:54:03 | 2020-03-15T16:54:03 | 246,154,095 | 0 | 0 | NOASSERTION | 2020-03-14T16:18:51 | 2020-03-09T22:22:48 | R | UTF-8 | R | false | false | 444 | r | run_app.R | #' Run the Shiny Application
#'
#' @export
#' @importFrom shiny shinyApp
#' @importFrom golem with_golem_options
run_app <- function(...) {
with_golem_options(
app = shinyApp(ui = app_ui,
server = app_server,
onStart = function() {
cat("Doing application setup\n")
... |
643232d47c8eda38accf82c4d5a599e47e5a15fc | a59d4f0bc24042fcc3fa9e6e3c1c2c815c5ad2b8 | /R/marginal.lkl.nl.r | 51e57270b536ad44b2a2fe5350ead247a8c886bc | [] | no_license | cran/BMAmevt | 91c78dfffff611265d89288be70f85b786963e42 | e4c449a55c81e39831acbe1e891b3b186b57f179 | refs/heads/master | 2023-04-27T08:12:34.608762 | 2023-04-21T01:22:38 | 2023-04-21T01:22:38 | 17,677,911 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 590 | r | marginal.lkl.nl.r | ##' @export
##' @rdname marginal.lkl.pb
marginal.lkl.nl <-
function(dat,
Nsim=10e+3,
displ=TRUE, Hpar = get("nl.Hpar"),
Nsim.min=Nsim,
precision=0,
show.progress = floor(seq(1, Nsim, length.out = 20 ) )
)
{
marginal.lkl(dat=dat,
likelihood=dne... |
a0b2933c6dc1171cdbca2df69ae3ad9ad9d4aea0 | 25ea69494b2cb174f04adf111c263dcfe8ca82b8 | /server.R | 20b7d0cfb65ed5632838aefb0319e12fb0e48851 | [] | no_license | zek12/BCLiquor | 8911e952976a61ad0eb4cce074225ebcc8b440a3 | c99cff2b28352e34722f22aae1028352eef0c062 | refs/heads/master | 2020-03-21T20:42:00.678919 | 2018-07-02T16:36:12 | 2018-07-02T16:36:12 | 139,022,535 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,212 | r | server.R | # library(shiny)
# if (!require("DT")) install.packages('DT')
library(ggplot2)
library(dplyr)
library(DT)
bcl <- read.csv("bcl-data.csv", stringsAsFactors = FALSE)
# print(head(bcl))
server <- function(input, output) {
# observe({ print(input$priceInput) })
#
# priceDiff <- reactive({
# diff... |
fab8528809796cc42a2ce4b93da2cdcc1d9a8a2b | 1461465b418919bb79a7b0ba6846b62936e6b55e | /man/sBayesRF_parallel.Rd | 444840894d224d48840ed7f1bc2a58692a047005 | [
"MIT"
] | permissive | guhjy/sBayesRF | c20886611cc23c7ee639a7b1e8f1dec0e7e8bb2e | ae81a1accf0d83f95b741d339eca8ff269704169 | refs/heads/master | 2020-08-08T14:47:59.075532 | 2019-08-14T16:31:51 | 2019-08-14T16:31:51 | 213,851,633 | 0 | 1 | null | 2019-10-09T07:34:24 | 2019-10-09T07:34:24 | null | UTF-8 | R | false | true | 2,522 | rd | sBayesRF_parallel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sBayesRF_parallel.R
\name{sBayesRF_parallel}
\alias{sBayesRF_parallel}
\title{Parallel Safe-Bayesian Random Forest}
\usage{
sBayesRF_parallel(lambda = 0.45, num_trees = 1000, seed, num_cats, y,
original_datamat, alpha_parameters = rep(1, nu... |
a2f854e2edb41b94e50e22240f3eb1e493c2e835 | c438e401cbc856aeb77707846260f0525734b997 | /data-raw/05-calc_windmodel_data.R | d967efb5fc2869219a416e1543a2191e3f7a891e | [] | no_license | geanders/hurricaneexposuredata | 6794f93831b7ee3dec19ea83975e1b2b738a0014 | b42fe54788ba8ade5e6aab614c75eea41d51a80c | refs/heads/master | 2022-06-02T05:47:11.387854 | 2022-05-16T01:39:35 | 2022-05-16T01:39:35 | 61,568,076 | 8 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,019 | r | 05-calc_windmodel_data.R | ## Be sure to re-build package after running 01 and 02 and before
## running this
library(dplyr)
data(hurr_tracks, package = "hurricaneexposuredata")
storms <- unique(hurr_tracks$usa_atcf_id)
storm_id_table <- hurr_tracks %>%
select(storm_id, usa_atcf_id) %>%
distinct()
library(stormwindmodel)
data(county_points... |
c0218071cd1e2a287c1a62c79ed7aeca54b18497 | daa39ef0a3e4d643bfdead74e0067ff6c856f304 | /Chapter-03-DataTypes-Lists.R | bb8bade46d32f64e9f97b25fb9ed9ee0a62456a2 | [] | no_license | balajidileepkumar/R_ML | ca32105e78c41f17c1397078c34c478a84e38334 | 3da18dad0d173ae28c6552a5f9a22dd308180e1b | refs/heads/master | 2021-06-30T03:32:27.506476 | 2020-12-23T15:00:16 | 2020-12-23T15:00:16 | 188,610,132 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,935 | r | Chapter-03-DataTypes-Lists.R | #Creating Normal List From Vectors
#list is a collection of items of different type
#it's multidimenisional
basic_list =list(1,2,3+4i,TRUE, "Hello World")
typeof(basic_list)
m = c(1,2,3)
n = c(1.0,2.0,3.0)
o = c(1.0+2.0i,2.0+3.0i,3.0+4.0i)
p = c(TRUE,FALSE)
Q_list = list(m,n,o,p)
print(Q_list)
names(Q_list) = c("num... |
1a55e0275e65cd4d654577b37e2d434b94aa1e4c | 484b030dde8f1f7fa407f3352e4bd585f2ce07ab | /Air Quality Analysis/plot5.R | ed9f449c14d4489c4393d1051bbf8f0a5883c7d7 | [] | no_license | conniewang3/All-Coursera-Projects | ba9109e7d442379e9c89048f59a530e6ebb0e7f3 | d0fcae49af8e1c037a17aa82d967563ebb8ba00a | refs/heads/master | 2020-03-12T12:34:56.116355 | 2018-06-02T13:22:06 | 2018-06-02T13:22:06 | 130,621,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,304 | r | plot5.R | # Load packages
library(ggplot2)
library(scales)
# Read in files
data <- readRDS("summarySCC_PM25.rds")
sources <- readRDS("Source_Classification_Code.rds")
# Answer the question: "How have emissions from motor vehicle sources changed
# from 1999-2008 in Baltimore City?"
# Subset only data from motor vehicle source... |
fa924c66d7fc07917585c9382ff41418f214364a | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/RRI/man/two_sided_test.Rd | 5675e90acb846f31e0737f6a86599cdfa23825f3 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 797 | rd | two_sided_test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{two_sided_test}
\alias{two_sided_test}
\title{Two-sided testing}
\usage{
two_sided_test(tobs, tvals, alpha)
}
\arguments{
\item{tobs}{The observed value of the test statistic (scalar).}
\item{tvals}{Vector of randomization ... |
daaa91e92acb13c3980cb067dd336db5b42df7b3 | 2011a34fd7a941541f2c293808fadca757cebab9 | /SegOpt_TerraLib.R | 423f0423e61e5aa6f0b19eaee1ff8bb3167f867a | [] | no_license | RicMarPre/Segmentation_SegOptim | 6a0725f487794d9dada024ce68d9086e229837ef | bfc5556058b7c15b9488223709b79b5340a71878 | refs/heads/main | 2023-01-23T21:41:39.655829 | 2020-11-25T13:39:13 | 2020-11-25T13:39:13 | 315,928,443 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,633 | r | SegOpt_TerraLib.R | installed.packages()
## ----- INSTALL THE PACKAGE SEGOPTIM---- ##
#I get an error as I try to install from GitHub:(converted from warning) package ‘raster’ is in use and will not be installed
##Then , I run the following:
detach("package:raster", unload = TRUE)
#PROTOCOL: First, install remotes to conect ... |
32a861d015a1c9dfa0c78d636a2c376b90a236fa | 5e016a253b0af1556e01ba76eac396530d07746c | /humann/analysis/humann_otuModel_China_unlog.R | cb07859a6cc298c2dd73d16be8d8db52b5b78eab | [] | no_license | mafergomez70/UrbanRuralChina | 14346052bedc426ebeb1ef4afcac74f047a8e13a | 51dd745ff22c335698343e2167644bea3a37b3b5 | refs/heads/master | 2020-03-18T04:22:29.672605 | 2017-08-15T01:01:14 | 2017-08-15T01:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,066 | r | humann_otuModel_China_unlog.R | ##models for HUMAnN results
rm(list=ls())
library("pscl")
library("lmtest")
library("nlme")
setwd("")
levels = c("module", "pathway")
for(lev in levels) {
print(lev)
file = paste("humann_keggAsCol_withRurUrb_", lev, ".txt", sep="")
table = read.table(file, header=T, sep="\t")
ncol = ncol(ta... |
0a12fea0abb6245a37819a90eaa856509924d9c4 | ef3c9b82c35810b59421875ccaf885b3bd221d9d | /steps/1a_getdata_cmip6.R | 5dda700f95960f0e2d57ef897c510b99a401baba | [] | no_license | haachicanoy/wfp_training | 5f18f9bd2fcc9db5cb44a3798f23edc27400786e | a397da362585bc30efb54d3acd82f3489be009d7 | refs/heads/main | 2023-08-06T15:42:17.395063 | 2021-09-29T21:03:48 | 2021-09-29T21:03:48 | 409,820,371 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,697 | r | 1a_getdata_cmip6.R | source("risk_profiles/code/_CMIP6_funs.R")
vars <- c("pr","tas","tasmax","tasmin")
models <- c("ACCESS-ESM1-5","EC-Earth3-Veg","INM-CM5-0","MPI-ESM1-2-HR","MRI-ESM2-0")
varmod <- expand.grid(vars, models)
names(varmod) <- c("vars", "models")
i <- 1
var <- as.character(varmod$vars[i])
model <- as.character(varmod$mod... |
60dfaaf0f191b040bedc8c7698d0de6132650216 | 4d439b238f8e61f2f5225bb81f25b075b931f868 | /main.R | 838df221283674c9fbcc5c9ef380c741312446a6 | [] | no_license | wqx94330/CrisAlex_FinalProject | 08938d5862a2fd9e5b622aa831bb63b4cd9c68a6 | 405ca388baefc38ad740d81a21e1b83877be4452 | refs/heads/master | 2020-05-23T09:09:20.675962 | 2017-02-02T18:54:55 | 2017-02-02T18:54:55 | 80,440,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,201 | r | main.R | ###Final Project
###Changes of vegetation within approximately 10 years in Brazil
###Team CrisAlex, Cristina González and Wan Quanxing
###02/02/2017
##This is the script to produce the NDVI difference between 2001 and 2010
#libraries and functions needed(Check that all the libraries and functions are loaded
#before st... |
ca671e27a80183d84bbbd81ce4b20c625a76f70a | e4783bc2ea62637c0d0097401f87f76434d91785 | /tests/testthat/tests_effectlite_latent_variables.R | 24863bceeacca7ff2a31a43e7541bbfd5f4a106e | [] | no_license | amayer2010/EffectLiteR | 83c03abb11c54b16143744711afd22187f454d71 | 7eaac5cba8ce9bbd317c0abcda2fc94435a6652d | refs/heads/master | 2023-08-06T16:26:42.513688 | 2023-06-27T20:29:24 | 2023-06-27T20:29:24 | 17,675,984 | 10 | 4 | null | null | null | null | UTF-8 | R | false | false | 5,501 | r | tests_effectlite_latent_variables.R |
test_that("effectLite works with latent z and y",{
## latent z and latent y
mmtest <- '
eta2 =~ 1*CPM12 + 1*CPM22
eta1 =~ 1*CPM11 + 1*CPM21
CPM11 + CPM12 ~ 0*1
CPM21 ~ c(m,m)*1
CPM22 ~ c(p,p)*1
'
m1 <- effectLite(y="eta2", x="x", z=c("eta1"), control="0",
measurement=mmtest, data=example02lv, ... |
b264c10f18dea1b1975a7f27b382876c032a9b5a | 7f4687fc685e45172a297fd2beadf142894c093b | /bottleneck_results_plot.R | afb107b93a079a1fd01f79ff6be2583dd4b54b94 | [] | no_license | mastoffel/seal_bottleneck | 09a9289c3b96369ea7b3a9028236b8bf1b87cccb | e1123b1eef96875c6c95374a3b58bb96dc78018d | refs/heads/master | 2021-01-10T07:15:49.530268 | 2016-04-20T07:20:12 | 2016-04-20T07:20:12 | 44,677,195 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,690 | r | bottleneck_results_plot.R | # plotting bottleneck
library(data.table) # faster fread() and better weekdays()
library(dplyr) # consistent data.frame operations
library(purrr) # consistent & safe list/vector munging
library(tidyr) # consistent data.frame cleaning
library(lubridate) # date manipulation
library(ggplot2) # ba... |
f4dbe640d188826661c853a74481f32d109033bc | 7478ade376ddbc1374c675caabf514c547387bc2 | /V1/global.R | 3e9c253fba679c782ab2aa0bda2d93ffc4430625 | [] | no_license | XPF100/Shiny-Mock-ups | 259fda76b203191cd5fad54db04ffe8a89400f47 | b6cb7a71cb7cf9872324a6000d23edd134a594b9 | refs/heads/master | 2021-01-16T18:20:08.802486 | 2017-01-14T20:30:10 | 2017-01-14T20:30:10 | 78,965,107 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,103 | r | global.R | # load required libraries
library(shiny)
library(plyr)
library(ggplot2)
library(googleVis)
library(reshape2)
require(sjPlot)
library(tidyr)
#Get and clean data
source("FullDataSet.R")
if (!is.data.frame(df)) {
df <- getData()
df <- cleanData(df)
class(categories$race)
df <- df[, colSums(is.na(df)) < nrow(d... |
456513054d24492c62803a58078f6606990cdbb4 | 89146c512bf32ed5afab564357e3f0f20c21171d | /tests/testthat/test_conversions.R | ffb00a32f5ce4df3e53c4b1104b5cc3a0196504b | [] | no_license | romainfrancois/egor | ff2c1d71b5cd389a42d5a8fa2026827df12f05f5 | 265a4e3635abd650affb600922d349f01ac7e72d | refs/heads/master | 2021-11-30T07:02:30.860678 | 2019-10-07T06:49:18 | 2019-10-07T06:49:18 | 215,741,275 | 0 | 0 | null | 2019-10-17T08:27:34 | 2019-10-17T08:27:28 | null | UTF-8 | R | false | false | 2,792 | r | test_conversions.R | context("test_conversions.R")
test_that("as_tibble and other conversions work",
{
expect_error({
e <- make_egor(3, 22)
as_network(e)
as_network(x = e, include.ego = TRUE)
as_network(x = e,
ego.attrs = ... |
e64cd7ff8fc732e0dd0cbb3c4205458374664811 | 362c0be541b1483782e4dc82f933781c90781b0f | /tools/config/cleanup.R | f4c330307c122ca57aded0aebbcdea0fec89f435 | [] | no_license | cran/cuml | dfe54bb0bc98b0b01817910d59e6c5280a96101f | d96fa0cf44757093fa6236aa2e637d9a7899ad0e | refs/heads/master | 2023-08-14T09:16:27.751080 | 2021-09-20T17:50:14 | 2021-09-20T17:50:14 | 407,392,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 192 | r | cleanup.R | for (x in c("Makevars", "Makefile", "CMakeCache.txt", "CMakeFiles", "cmake_install.cmake", "CMakeLists.txt", "*.o", "*.so")) {
unlink(file.path("src", x), recursive = TRUE, expand = TRUE)
}
|
bf49d59dbb22dff91c193d7b0436afd735d75f14 | 425b09b9615e0824edd60b9655e3095046295319 | /Code/sim_bic.R | cd5ecfb7a0b165968fa9882fecbfaf43c6bad6df | [] | no_license | yingljin/cost_sparsity | 092a37ef8a18cb53c9d084b12788909a4aeda778 | 2c2abde4e443602a6b3d5842793776ac23384511 | refs/heads/master | 2023-01-28T08:15:42.318610 | 2020-12-07T21:15:44 | 2020-12-07T21:15:44 | 319,367,414 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,424 | r | sim_bic.R | # this script generates simulations
# and select models with BIC
##### generate data #####
# true covariate
true_beta <- c(1, 2, 3, 4, 5,
0, 0, 0, 0, 0,
1, 2, 3, 4, 5,
rep(0, 35))
# group index
grp <- rep(c(1:10), each = 5)
table(grp)
# all data
x_grp_all <- genDataGrp(... |
b66a243a1077b6b975af8108eecabb21322320a0 | 9132996d08213cdf27c8f6d444e3f5b2cfdcfc85 | /tests/testthat/test_all_binary.R | 47f604f2ce8cca4458c64ca690c69dc1ea7c417f | [] | no_license | prioritizr/prioritizr | 152013e81c1ae4af60d6e326e2e849fb066d80ba | e9212a5fdfc90895a3638a12960e9ef8fba58cab | refs/heads/main | 2023-08-08T19:17:55.037205 | 2023-08-08T01:42:42 | 2023-08-08T01:42:42 | 80,953,648 | 119 | 30 | null | 2023-08-22T01:51:19 | 2017-02-04T22:45:17 | R | UTF-8 | R | false | false | 3,458 | r | test_all_binary.R | test_that("x = default", {
expect_tidy_error(all_binary("a"), "recognized")
})
test_that("x = numeric", {
expect_true(all_binary(c(0, 1)))
expect_true(all_binary(c(0L, 1L)))
expect_true(all_binary(c(0L, NA, 1L)))
expect_true(all_binary(c(0, NA, 1)))
expect_false(all_binary(c(-1, 0, 1)))
expect_error(asse... |
f8c703a81544f6cbc28a5b30938d139b690b7e19 | fdcace641b533557575af43e2e8d536c64d0cda7 | /munging/Geocoding.R | f1628e9170b094af4b69e028d6ec09ddac7302f4 | [] | no_license | kent37/CLFirst | f32f9163a799ada9cd8e755c6f25e8cf5f6a2330 | 0f5170130e1bb735958807610d7e9f2b8a2bf350 | refs/heads/master | 2022-05-21T08:17:46.267788 | 2020-05-02T23:51:24 | 2020-05-02T23:51:24 | 259,715,973 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,387 | r | Geocoding.R | # Geocode Businesses_Open_Closed
library(tidyverse)
library(mapview)
library(readxl)
library(sf)
source_path = here::here('data/Businesses Open_Closed in Cambridge.xlsx')
df = read_xlsx(source_path)
# Create a data set for Texas A&M geocoder
to_geocode = df %>%
mutate(id=1:nrow(df)) %>%
select(id, `Business Ad... |
4cf1f8716ea16c79308544f791c4effd2ef1e75c | 7271ca2c97b0ac1a2a4332e0b5d3d33998188982 | /DEseq2_replicates.R | 69b1f43c23821e49a45c9dad728e59eab4d52eb3 | [] | no_license | tmlx/Analyses_Pipelines | af5b2cec4683e875c7a17a17d777b9ac204eea94 | 3333eb6e49393e4cd3ace503b173cd527b2f8162 | refs/heads/main | 2023-06-17T20:03:48.042657 | 2021-07-07T17:39:24 | 2021-07-07T17:39:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,229 | r | DEseq2_replicates.R | set.seed("1234")
library(DESeq2)
library(dplyr)
#library(vidger)
library("BiocParallel")
args = commandArgs(trailingOnly=TRUE)
register(MulticoreParam(4))
countdata = read.table(args[1], header = T, row.names = NULL, check.names = F)
colnames(countdata)[1] = "Geneid";
countdata = aggregate(.~Geneid, countdata, max)
row... |
51ebfc8002d42c8cdc452d91954a177f33d95105 | 251ec93f7c54f2a2f0dd5051ed56f69313a530fd | /man/seeFastq.Rd | c1986f9713ef210b0efe87539f4f714b98ee9b34 | [] | no_license | tgirke/systemPipeR | 5df29a7af4fb44794b7ad4700bf8917647ec943b | 039594710ecd4c515d0421cb9a3393eb30eae38f | refs/heads/devel | 2023-08-16T12:14:52.734954 | 2023-06-21T01:02:18 | 2023-06-21T01:02:18 | 45,077,906 | 54 | 48 | null | 2023-09-12T00:08:17 | 2015-10-28T00:09:31 | R | UTF-8 | R | false | false | 2,900 | rd | seeFastq.Rd | \name{seeFastq}
\alias{seeFastq}
\alias{seeFastqPlot}
\title{
Quality reports for FASTQ files
}
\description{
The following \code{seeFastq} and \code{seeFastqPlot} functions generate and plot a series of
useful quality statistics for a set of FASTQ files including per cycle quality
box plots, base proportions, base-lev... |
90a004098651f7003d04b2ffb95b4410c68f9fae | 97c5d5f3568c4ab59e446d42d476c9a94f33a379 | /CompareVariables.R | 6c5e8204cdea1bebf0c43702a0d090212a9ebd72 | [] | no_license | Tezzzcatlipoca/OrgCrime | 27e96bacece39e27dca41db8e07967b60db068fe | ebc0c85f6a3db4df87fd77e341e317534f66cf45 | refs/heads/master | 2020-09-22T14:52:46.310025 | 2016-12-20T16:53:07 | 2016-12-20T16:53:07 | 66,792,765 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 5,582 | r | CompareVariables.R |
library(xlsx)
library(foreign)
cdel<-read.xlsx("CDEL2012.xlsx",1) # Claves de delitos (Seleccionados los útiles)
co.del<-cdel[cdel$EstÃ.n.>0,]
# Identificar delitos importantes
delitos.imp<-co.del$CVE_DEL
# Abrir base de delitos
sdel<-read.dbf("sdel2009.dbf")
sdel.imp<-sdel[sdel$B_DELITO %in% delitos.imp,]
sdel.im... |
45905c522515e5799a3338ade61cf0d639b77896 | a00ae6e32bec6f96ec37cc6aee8374df6565871b | /R/comp.pat.R | da71680d2972038fa5cccf0cddff8cfa9be1d56d | [] | no_license | cran/lcd | 6372f23d4d3f3e2dd6656a632f2cdd7c9d6e9483 | a6c9acc4e576cf76bc809c4953dbb818cf3c8ad4 | refs/heads/master | 2020-04-22T19:55:52.285942 | 2012-11-09T00:00:00 | 2012-11-09T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 730 | r | comp.pat.R | `comp.pat` <- function(truepat, pat)
{
vset <- rownames(truepat)
truearr <- which(truepat - t(truepat) == 1)
pat <- pat[vset, vset]
arr <- which(pat - t(pat) == 1)
a.missing <- length(truearr)-length(which(match(truearr, arr)>0))
a.extra <- length(arr)-length(which(match(arr, truearr)>0))... |
1163e8fded4fd77fbca027b65d0d23edf2754d1f | 18259bc6828fb5da8d003f1f98e2761c6697d931 | /.Rproj.user/C625F279/sources/per/t/718886E1-contents | 9d398a1f2332bf4ab45cd718c1a8b94c4c44a9fa | [] | no_license | leosampsousa/Panorama_Covid | f1b00142a6760b807380becc98935e273b64bab8 | fa2f9a91417ac25ddb0368483324276f81c34bb8 | refs/heads/master | 2022-09-20T13:02:10.961107 | 2020-06-01T14:11:44 | 2020-06-01T14:11:44 | 268,529,959 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,293 | 718886E1-contents | brazil_cities_coordinates <- read.csv('brazil_cities_coordinates.csv', header = T, encoding="UTF-8")
brazil_covid19 <- read.csv('brazil_covid19.csv', header = T, encoding="UTF-8")
brazil_covid19_cities <- read.csv('brazil_covid19_cities.csv', header = T, encoding="UTF-8")
brazil_covid19_macro <- read.csv('brazil_covid1... | |
abc6ac79a55bb7b7e8d6303a42dc3c9dc093b306 | 42230d1b619cd2fb7114dde4fc0b3d9c4c454407 | /suicide_R Script.R | 61f2cf80bfa1e5bb6ef4df50115dfe2fcd316076 | [] | no_license | KaushikRajanRK/Suicide-Rate-Analysis_Data-Visualization | 087f45a268ac935d075a264ea6f5cee7ac51e348 | 8ad70d1d2a04a86e8c0df28a4e39877b909eb04d | refs/heads/master | 2020-06-06T15:48:19.483759 | 2019-06-19T18:19:18 | 2019-06-19T18:19:18 | 192,783,209 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,941 | r | suicide_R Script.R |
#Setting up working directory
setwd('D:/Education/IRELAND/NCI/Moodle Documents/SEM 2/DV/dataset')
# Importing the dataset
suicide<-read.csv('master.csv')
str(suicide)
colnames(suicide)<-c("country","year","sex","age", "suicide_count","population","suicides/100k pop","country-year","HDI for year","gdp_for_year ... |
eb69087c6c8cb3b43e66afba56c3a23339b866a8 | 9269cbb8581ffaee3cfc20c827aa3ced1c7df8a3 | /man/initializeTests.Rd | e26deaa637867bdc20773e9bf46e6ece6954c3a3 | [] | no_license | cran/RTest | 0e75ee391fd3c796287c191d6aad675e0764198e | 83ec1756f362f210786e04c8d5c6913f5f5907f3 | refs/heads/master | 2020-03-27T05:26:02.376025 | 2019-12-04T15:10:08 | 2019-12-04T15:10:08 | 146,018,503 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 742 | rd | initializeTests.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RTestCase.R
\docType{methods}
\name{initializeTests}
\alias{initializeTests}
\alias{initializeTests,RTestCase-method}
\title{Initialize the Test Slot for a Test Case.}
\usage{
\S4method{initializeTests}{RTestCase}(object)
}
\argumen... |
c02f9daec89422771b3aba891f4054abb942940a | 8150426f0bbd304a6fb82afb78e55054c032a330 | /ui.R | 229d2be539b65343f89c1c03fca16c126a87bb49 | [] | no_license | jjanzen/data_products_cp | c0f8abcf2f3f000a627691d61d95fa8d4a7bb798 | da0a94240d517bf89ccc2a258a8dd54c8420e8ee | refs/heads/master | 2020-03-29T11:17:50.140421 | 2015-06-13T22:25:53 | 2015-06-13T22:25:53 | 37,382,245 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,071 | r | ui.R | shinyUI(pageWithSidebar(
headerPanel("Experiment Results and Statistical Significance"),
sidebarPanel(
numericInput('control_loads', 'control participants', 0, min=0, step=1),
numericInput('control_clicks', 'control conversions', 0, min=0, step=1),
#numericInput('control_thx', 'control_thx',... |
57913080f1116c703830d5f19d755d2fe106c66d | 8a52f49deb648606fff204740950a913d7c1f37c | /fig4-kl_cell.R | b936723046090a5cc565bfe2bd353f694eee19ea | [] | no_license | gersteinlab/topicnet | 8ca7420c63d796df8f31a1a4663a50fbd261f2f8 | 77c08d7256c588e7efa3d0461e9d446dd6361869 | refs/heads/master | 2020-09-13T13:51:10.101877 | 2019-11-20T20:43:35 | 2019-11-20T20:43:35 | 222,805,587 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,586 | r | fig4-kl_cell.R | # Calculate KL divergence for TFs in a given cell line
load("alltf.rewiring.rdata")
load("TFmem_data.fig4-190115.RData")
library(reshape2)
library(entropy)
library(gridExtra)
library(grid)
library(RColorBrewer)
library(gplots)
cell.count = table(tf.doc$cell)
cell.filtered = names(cell.count)[cell.count>50]
cell.top... |
b63f096122ba6e6bfe198f9781619e1e9e5a6d5b | 9dcff6306fb7c38df8d2130f9deb33703afa332d | /code/HNSCC_and_MCF10A_EMT_and_epithelal_signature_scores_&_expression_of_putative_EMT_checkpoint_genes.R | b63112d8f33f78f29222bbe4d28b1459cac56f4d | [] | no_license | cole-trapnell-lab/pseudospace | a951e1604a74cc3c9e72e8320ce57140d9002bb3 | bae0e5bb5ecee5691842105e02f902ff8f73b7ad | refs/heads/master | 2020-06-17T09:26:22.165737 | 2019-09-04T21:01:13 | 2019-09-04T21:01:13 | 195,880,675 | 10 | 4 | null | null | null | null | UTF-8 | R | false | false | 22,533 | r | HNSCC_and_MCF10A_EMT_and_epithelal_signature_scores_&_expression_of_putative_EMT_checkpoint_genes.R | ###### Load packages ######
# Load necessary packages for single cell RNA-Seq analysis including packages for downstream Gene Ontology Analysis
suppressPackageStartupMessages({
library(devtools)
library(stringr)
library(scales)
library(dtw)
library(reshape2)
library(GSA)
library(limma)
library(DBI)
library(MASS)
librar... |
f17fbd58764af0d77a68980b7a5d3ec0b12d2dd2 | 9dc0be2dea189adfc517b72c5b0fe1b4b4bfd28d | /test_script.R | 7ff5f9a4c85803c146b2e70aa1ecf2490ba10c78 | [] | no_license | brwaang55/Test_plotting | 12aacff65c89017637bf87d489ad3f0a64e66379 | e2792a85161d4d4e023de638cc2781cff2aa2090 | refs/heads/master | 2020-03-29T12:59:47.831330 | 2018-09-23T00:15:06 | 2018-09-23T00:15:06 | 149,933,136 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,864 | r | test_script.R | library(rgl)
library(Rvcg)
library(FNN)
#Function for computing the barycenter/centroid of a face.
compute_face_centroid=function(vertices,face){
vertex=vertices[,face][-4,]
centroid=apply(X=vertex,MARGIN = 1,FUN = mean)
return(centroid)
}
#Function for Returning the barycenters of all the faces
find_face_coordi... |
f990cde54de754bff4fd78e02c38503e0555bb9c | 9504dd1c45daecae343646aa9243dd3fa1c7add5 | /solutions/MetaR.Workshop/classes_gen/instantRefresh/test.R | 2de404532c7ad3ecc408136ea1923086f197b335 | [
"Apache-2.0"
] | permissive | darthmanwe/MetaR | fc949007ea041fd4823b65583766e6bf9011badd | f88fb2576221e494d0b599517e7c95a46bbd2017 | refs/heads/master | 2021-06-19T14:51:42.904008 | 2017-06-24T21:01:42 | 2017-06-24T21:01:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 516 | r | test.R | # Generated with MetaR, http://metaR.campagnelab.org, from script "test" on Sat Jun 24 16:35:01 EDT 2017
installOrLoad<-function (lib,repo="http://cran.us.r-project.org"){if(!require(lib,character.only=TRUE)){install.packages(lib,repos=repo)
library(lib,character.only=TRUE)}}
installOrLoad("session")
a<-1
a<-2
c<-a+a... |
f67a28814832f213ac65f1c150630df6154b79bf | 90b3c72db44de4d5f132c2a8b4944245bfa7785c | /tests/testthat/test_22_rflow.R | c9cbb73731c3e1994add44965cb526d3fe91a435 | [
"MIT"
] | permissive | vh-d/Rflow | 80aff510e5192cccf2a10616bb5edec727ca1e28 | 6a50bb27dcb52659a39cbb451f8a1d1e165cc155 | refs/heads/master | 2022-05-22T18:40:26.218576 | 2022-05-07T13:23:31 | 2022-05-07T13:23:31 | 167,013,026 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,407 | r | test_22_rflow.R | context("Builidng rflows")
test_that("Rflow can be crated", {
rf1 <- Rflow::new_rflow()
expect_true(exists("rf1"), "rflow object exists")
expect_s3_class(rf1, "rflow")
expect_s3_class(rf1, "environment")
})
test_that("nodes can be added", {
rf2 <- Rflow::new_rflow()
expect_true(Rflow::add_node(list(id = ... |
5de5847280d0b0798762823e90c58afd23dfe209 | 0d86ba90a9a0c46e404414c4dd8f6f4ecd448558 | /scripts/project_gene_sets.R | 239c13500c4b8fa19a2c8d9e18385f05984466fa | [
"MIT"
] | permissive | amytildazhang/p3-model-iteration | 5fdc159b7c6e027629792e88de6d4a4eff06a1fd | 8cc5f1c47a9af3ca88ffa54811d2cf7083dcd82e | refs/heads/master | 2020-06-15T23:37:38.983451 | 2019-07-25T14:56:37 | 2019-07-25T14:56:37 | 195,422,788 | 0 | 0 | null | 2019-07-05T14:26:58 | 2019-07-05T14:26:58 | null | UTF-8 | R | false | false | 2,141 | r | project_gene_sets.R | #!/bin/env Rscript
#
# aggregate feature data along specified gene sets
#
suppressMessages(library(GSEABase))
suppressMessages(library(tidyverse))
options(stringsAsFactors = FALSE)
# determine input data type from wildcards ("create_rna_gene_sets")
data_type <- strsplit(snakemake@rule, '_')[[1]][[2]]
# load feature... |
92a68e4146199c512cb774a0c293dfbbda040d05 | 8d8f10817941bbebcf9c16075de8bae4d0bfe4ce | /featureCounts.R | 8538a2377022a600c250add0d2519dc34d4d4b2a | [] | no_license | Albertomaria/farnam_script | e7a8b2af6fa3577f65af64a703b8f41367eedd68 | 397015f8259d86a1c48088297b19b5fba10e907a | refs/heads/master | 2021-01-25T12:36:25.019131 | 2019-08-27T02:14:48 | 2019-08-27T02:14:48 | 123,482,287 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 547 | r | featureCounts.R | library(Rsubread)
ann <- "/ysm-gpfs/project/am2485/Genome/Human/microRNA.subset.of.GENCODE.V27.gtf"
setwd("/ysm-gpfs/project/am2485/Stiffness_mRNA_lexo_Dionna/miRNA/STAR")
files <- list.files(path = ".",pattern = "sortedByCoord")
for (f in files){
f_name <- strsplit(f,".",fixed = T)[[1]][1]
seq_data <- featureCount... |
0ce400fe6c9675304772c131a2685cd73a5fba4a | 7e7bb7bfdf62c24b7fecf78f5247d28839728710 | /Summer Loss/src/Summer_Loss_Teacher_Tools.R | 38c85cf52479c3a987142b3c02dbe2b5e16b3921 | [] | no_license | kippchicago/Data_Analysis | 1ad042d24c7a1e11e364f39c694692f5829363a4 | 8854db83e5c60bc7941654d22cbe6b9c63613a7f | refs/heads/master | 2022-04-09T10:10:43.355762 | 2020-02-20T18:03:40 | 2020-02-20T18:03:40 | 5,903,341 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,736 | r | Summer_Loss_Teacher_Tools.R | require(ProjectTemplate)
load.project()
map_mv<-mapvizier(map_all)
map_kccp<-filter(map_mv$mapData,
SchoolInitials=="KCCP",
TermName %in% c("Spring 2012-2013", "Fall 2013-2014")) %>%
mutate(Name=paste(StudentFirstname, StudentLastname))
map_kccp_2<-inner_join(filter(map_kccp, Year2==2013),
... |
627b823d4af0d46325b9419c446c374a4fc6eecd | b09e6cbd019c4f2002ba0c34caaa4506132c1d6b | /Developing/Hailian/4. Hailian_TD_Preprocessing1.0.R | 64bf0cdb6157669dd654b3ad0e9d6f7723c0ee63 | [
"MIT"
] | permissive | o0oBluePhoenixo0o/AbbVie2017 | 935be251170722443a13602e5ae635b0128bf286 | 356583487455ba2616457f8a59ca741321c0b154 | refs/heads/master | 2021-09-24T17:32:02.676494 | 2018-10-12T12:49:04 | 2018-10-12T12:49:04 | 83,968,797 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,610 | r | 4. Hailian_TD_Preprocessing1.0.R | ##bring Google Chrome's language detection to R
url <- "http://cran.us.r-project.org/src/contrib/Archive/cldr/cldr_1.1.0.tar.gz"
pkgFile<-"cldr_1.1.0.tar.gz"
download.file(url = url, destfile = pkgFile)
install.packages(pkgs=pkgFile, type = "source", repos = NULL)
unlink(pkgFile)
install.packages("tm")
install.package... |
532057408da8dfc87f6264bca06b3658b7d8ce21 | f2ccb53af7c548c53f12062e59b5b35341c75e7f | /01_初心者.R | b8f8f54f0f30f92720ad788d718c7c5e5e986b57 | [] | no_license | totoko00/DataAnalysis | 2b71937e1a142b44cc05a4c4c9c844af0fd19083 | f268ddc0eae527151539b5a3f36d48283edad74a | refs/heads/master | 2021-05-22T18:27:50.228590 | 2020-04-04T16:04:54 | 2020-04-04T16:04:54 | 253,039,660 | 0 | 0 | null | null | null | null | SHIFT_JIS | R | false | false | 573 | r | 01_初心者.R | #ベクトルの作成
x <- c(1,2,3,4,5)
x
y <- c(1:5,3:1)
y
z <- c(rep(3,4),rep(c(1,5,10),c(2,3,4)))
z
a <- c("A","B","C")
a
#ベクトルを作るときはc()で指定。#行列
mat1 <- matrix(c(1:10),nrow=2,ncol=5)
mat1 <- matrix(c(1:10),2,5)
mat2 <- matrix(c(1:10),2,byrow=T)
mat3 <- matrix(c(1,3,2,5,7,3,2,15,2),3,3,byrow=T)
#行列はmatrix()で作成。byrow=Tで要素順が変わる。#行列の... |
4507375aaaace516d387acd791d989cc8a609bc1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/drc/examples/nasturtium.Rd.R | 419ea4dcc8263fbdc97367b13de53f719fb5be71 | [] | 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 | 380 | r | nasturtium.Rd.R | library(drc)
### Name: nasturtium
### Title: Dose-response profile of degradation of agrochemical using
### nasturtium
### Aliases: nasturtium
### Keywords: datasets
### ** Examples
nasturtium.m1 <- drm(weight~conc, data=nasturtium, fct = LL.3())
modelFit(nasturtium.m1)
plot(nasturtium.m1, type = "all", log = ... |
6fb868704ab2015e7667412526cb7bee401205dd | 80ea1c9981469ae22d640de03852c716207544b8 | /tests/testthat/test-ggseg_atlas.R | d65b22359943260e2086bc1dafc662a0bc140fec | [
"MIT"
] | permissive | torch0703/ggseg3d | 477ecd3f205197c43184431a3cf8f23df5a25488 | df2de6e1249ba2b0d6a4c42fd6e6074cae553cdd | refs/heads/master | 2023-05-09T17:54:05.032460 | 2021-06-01T07:30:20 | 2021-06-01T07:30:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 778 | r | test-ggseg_atlas.R |
tt <- data.frame(atlas = "k",
surf = "white",
hemi = "left",
region = "something",
colour = "#d2d2d2",
stringsAsFactors = FALSE)
tt$mesh[[1]] = list(it=array(0, dim=3),vb=array(0, dim=3))
test_that("check that ggseg3d_atlas is correc... |
fd64c51494f8821501482253d5909a1252500670 | 62f84d7157e0e3bfc57cc6d6942ea9205adc4463 | /man/agdb.checkagdb.Rd | 2c274bb7459c224283ec4b9422d833d7a6bf7da5 | [
"MIT"
] | permissive | SamT123/acutilsLite | 251da4cf955c05a4e52a6b10e59fa2876759ea4a | fb36cd0f0786b9a9822ebda76fe4a44538569c4b | refs/heads/master | 2023-03-02T20:52:23.145170 | 2021-02-15T10:03:21 | 2021-02-15T10:03:21 | 315,282,286 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 513 | rd | agdb.checkagdb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/acdatabase_dbtest.R
\name{agdb.checkagdb}
\alias{agdb.checkagdb}
\title{Checks Antigen Database formatting}
\usage{
agdb.checkagdb(agdb)
}
\arguments{
\item{agdb}{list}
}
\value{
bool
}
\description{
Checks that an antigen database follows th... |
7c9e77458065ffc3886e639f5a2e869adfc10ec0 | ec213b23bf4dcba4243ef834235f2b8352c3f500 | /man/betaregmodel_20220718.Rd | 7779ca480dc744964b673807fceca9d9e247a524 | [] | no_license | mccoy-lab/rhapsodi | 941eaa317f7c5e83a0c15bfbf03c729a389459d6 | 8a5d712b1eb500594ac75428aa8dd94494bf81f3 | refs/heads/master | 2023-04-12T15:18:32.125743 | 2022-07-25T21:30:28 | 2022-07-25T21:30:28 | 328,792,330 | 4 | 0 | null | null | null | null | UTF-8 | R | false | true | 985 | rd | betaregmodel_20220718.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/betaregmodel_20220718-data.R
\docType{data}
\name{betaregmodel_20220718}
\alias{betaregmodel_20220718}
\title{Trained beta regression model for automatic phasing window size calculation}
\format{
an object of class "betareg", i.e., a list wit... |
87b036f17751b6bed554c0f385eb5a7a127eed4a | a37dabee7f85661056732f85e3b718fe785d3716 | /getKeyFieldsFromTable.R | e6b686ecbc7feb6d833d05c2909213af21faf237 | [] | no_license | Bibhushan/SCO | 7709ef336714544241555e4873f42248cea4e8df | 9f9745248b9abba4e4359f12be8becfd1cd605d1 | refs/heads/master | 2021-01-17T13:18:54.186126 | 2016-07-05T09:15:02 | 2016-07-05T09:15:02 | 59,553,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 319 | r | getKeyFieldsFromTable.R |
getKeyFieldsFromTable <- function(tableName, onlyPK = F, onlyFK = F){
tableDef <- DataDefinition[DataDefinition$TableName == tableName,]
return(getKeyFields(fieldNames = tableDef$FieldName,
fieldTypes = tableDef$FieldType,
onlyPK = onlyPK, onlyFK = onlyFK))
} |
5b9eef804afc78fddf81400252a4ed7f73140cff | f61cbba27542ad327fd2a00b0e648c1189956a3d | /misc/german_credit_dt/german_credit_dt.R | 1160a240b147a2cf088920c88bc170799a3e8a30 | [] | no_license | bpeek/machine_learning | d46d3890504e1ba1380922e3dc227ec249a6b08a | db52c45bd9669585419028d0e91eb39c481d1fe9 | refs/heads/master | 2020-03-10T23:54:00.280200 | 2018-09-24T01:55:03 | 2018-09-24T01:55:03 | 129,650,391 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,509 | r | german_credit_dt.R | <<<<<<< HEAD
library(rpart)
library(rpart.plot)
library(plyr)
german_df = read.csv("C:/Users/Brendan/Desktop/ML/german_credit_dt/german_credit_data.csv", header=FALSE)
column_names = c("checking.account.status","duration.in.months","credit.history","purpose","credit.amount","savings.or.bonds","present.employment.sinc... |
5d68825a9f8ba7d35d811875603f931adb394dec | d26cddf482ef88c4f44f68cd22a34a4dfbe9c12f | /tests/testthat/test-packages.R | 5bf4eaade93fe1153c0401af126f59a1926afcfd | [] | no_license | wlandau/grapes | dcddd6e21879b5f0ae372dc3bf2312986a3f64eb | 114f6ca49d77bf8dba6b312a51a1ccf080228112 | refs/heads/main | 2021-01-18T18:40:04.175873 | 2017-07-21T21:06:28 | 2017-07-21T21:06:28 | 86,870,564 | 13 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,119 | r | test-packages.R | # library(testthat); devtools::load_all()
context("packages")
test_that("grow() loads from a package", {
expect_error(1 %knit_params% 2)
expect_error(knit_params(1, 2))
expect_error(ls("package:knitr"))
grow(knit_params, from = "knitr")
expect_equal(1 %knit_params% 2, knit_params(1, 2))
out = ls("package:... |
05fc0cca0e295f4ec22a71f37a6a896a3fdfd357 | 9641bbaa11404cdffff808528b5957a2b0d184de | /day14/day14fdlk.R | 37052719b7acfdb5a4593ecbbdde6162948d40fc | [] | no_license | cortinah/aoc2020 | 02b23b32a76e9faa5e1a987218f6e2ced350a74d | 72abe7ff1dfeee30863e1a7a39af3580524bb74b | refs/heads/main | 2023-02-03T15:15:05.276293 | 2020-12-14T22:04:59 | 2020-12-14T22:04:59 | 317,453,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,963 | r | day14fdlk.R | library(tidyverse)
input <- tibble(line = readLines("input-2020-14")) %>%
extract(line, "mask", "mask = ([01X]{36})", remove = FALSE) %>%
extract(line, c("address", "value"), "mem\\[(\\d+)\\] = (\\d+)", convert = TRUE)
input
# Part 1
i2b <- function(value) {
result <- logical(36)
index <- 36
while (value > ... |
477458a377e59268a1ee27753639bb2e56bcdc30 | f917af767b05506cd2b1d8fa2b52c1e7c326f789 | /tests/testthat/test_zones.R | 75a0223014bc3be79b3f7eed3df5e325615d2a57 | [] | no_license | cran/scanstatistics | c9107d67234afc79d34adca77cef50307bea83e3 | 3e0078f362e7db49396f667bb865744b79f6fb11 | refs/heads/master | 2023-02-08T10:53:25.879892 | 2023-01-26T11:40:02 | 2023-01-26T11:40:02 | 69,553,546 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,146 | r | test_zones.R | context("zone-creating functions")
test_that("dist_to_knn: returns correct order", {
coords <- matrix(c(c(0, 0),
c(1, 0),
c(4, 0),
c(1, 2),
c(-0.5, 2)),
ncol = 2, byrow = TRUE)
m <- as.matrix(dist(coord... |
7468afbbd3ce6647916eba6774599f37c04d1d89 | 68c1218bddaf4faaacdd4fca56af2ecca9d66ed7 | /global.R | 2ad8074574a212ea7fd9c2cccb483451fad8ec93 | [] | no_license | STAT360/finalproject-minniemap | 698c9918009786dfd863c3c24bbe419d6b7bd4d2 | 53143c5e0d433781f121c20365f58fa44e48c6a6 | refs/heads/master | 2020-05-04T13:00:54.104019 | 2019-05-21T20:02:51 | 2019-05-21T20:02:51 | 179,144,330 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,642 | r | global.R | library(geosphere)
library(googleway)
library(leaflet)
library(shiny)
library(shinyalert)
library(shinydashboard)
library(shinyjs)
library(tidyverse)
library(tidyr)
#Load in the functions we created for this app
source("functions/build_routes.R")
source("functions/search_locations.R")
#Load in the data for 8 (most us... |
15baea8e492b7216519e8717d27e49e0081d9928 | 0fbe97c46ed2eb453f468ee9369445ef6c44771f | /r_dependencies.R | 25fbc05bcc0ef53fe416982bf524220021cd69e5 | [] | no_license | SeanTomlinson30/bio-pipeline-dependencies | 1eb8eca300c2605fa333d9a6aa0ff8a8837792a7 | 64c35634f026cd745f7cfb1370bf71b7d13516f6 | refs/heads/master | 2020-06-20T17:50:30.172599 | 2019-07-26T08:24:02 | 2019-07-26T08:24:02 | 197,198,810 | 0 | 0 | null | 2019-07-25T10:09:53 | 2019-07-16T13:24:57 | Makefile | UTF-8 | R | false | false | 1,205 | r | r_dependencies.R | .libPaths(.libPaths()[1])
print(sprintf('Installing packages into %s', .libPaths()))
is.installed <- function(mypkg) is.element(mypkg, installed.packages()[,1])
suppressMessages(source("https://bioconductor.org/biocLite.R"))
bioc_package <- function(pkgname) {
if (is.installed(pkgname)) {
print(sprintf(... |
0f7e0ae080b8cacac66f2c60c2e02314fac34603 | 206c80dc11a2264d31f0b4d75bcddb37ab5576a1 | /R/lbdnd_package.R | 0ef80d17bb490f245f3b50a2508aee911e0e7b3e | [] | no_license | lbraglia/lbdnd | 0e54f47de4d9cb3e147e72a9ee43534c75007ed7 | 2c46ecb8edb18de6e899573f5fc4d690f4150032 | refs/heads/master | 2021-07-16T00:09:58.390914 | 2021-06-25T09:40:26 | 2021-06-25T09:40:26 | 207,334,412 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 94 | r | lbdnd_package.R | #' An R package for dnd
#'
#' An R package for dnd
#'
#' @name lbdnd
#' @docType package
NULL
|
b7470158d901433079d98e81aeab94758a02a547 | 54546f9cb5c136e2fb16484a46771551af0f21cf | /postprocessing_scripts/print_split_enh_out.R | 913ce76ad061509aa63da094080540032f184a7f | [] | no_license | cboix/EPIMAP_ANALYSIS | bb409d32cd8bdb69393fc8522356e365c4cb4246 | de98ff96695941056b28323ec138465a8852659c | refs/heads/master | 2022-09-15T00:37:00.487847 | 2022-08-22T20:00:44 | 2022-08-22T20:00:44 | 218,806,299 | 29 | 13 | null | null | null | null | UTF-8 | R | false | false | 3,153 | r | print_split_enh_out.R | #!/usr/bin/R
# --------------------------------------
# Write BED files of enhancer locations:
# Updated 05/11/21
# --------------------------------------
library(rhdf5)
# Read in metadata:
metadata = read.delim('../public_metadata_released/main_metadata_table.tsv', header=T)
mmap = read.delim('mnemonic_mapping.tsv', ... |
25dd636999dd7f5bccecda831406b5ac80c9c018 | 5c873ac7d8116ed4045ab1b2b298a6c3cfe3fd48 | /Matriz Origen - Destino/od-network.R | fb7541c2021cea8d16f332f004f2de6bfcd00a50 | [] | no_license | LeonardoCordoba/SSTYT | 6c10134c415e60f3760c51f7a52ad53a3c74d21c | eecbc017ce8d7bb1a369c6d2f342778a5fdcbb0e | refs/heads/master | 2021-01-22T09:27:18.285389 | 2017-06-23T14:46:00 | 2017-06-23T14:46:00 | 81,960,213 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,449 | r | od-network.R | ## Análisis de Matriz OD
rm(list=ls())
library(RPostgreSQL) #Para establecer la conexión
library(spatstat)
library(sp)
library(rgdal) #Para poder establecer proyecciones
library(postGIStools)
library(ggmap)
library(geomnet)
pw <- {
"postgres"
}
# loads the PostgreSQL driver
drv <- dbDriver("PostgreSQL")
# creates... |
a9f3606ec5ffd8a7ad1e7564a37616a858d501ed | 53dd7a212c5caeb7280454c1e92af526c0b3ae3a | /.github/workflows/functions.R | fba2ce852a1d3975c70746b961f525e5ffaaa244 | [] | no_license | LiesaSalzer/MetClassNet_MetNet.R | 32cc60d1882cd513422bd2841a2ff35e936a1a53 | 8f2acc2aed8a99aeafd52e14ac95827c3b2bdbba | refs/heads/master | 2022-09-15T21:17:35.486065 | 2020-06-05T12:02:38 | 2020-06-05T12:02:38 | 269,279,227 | 0 | 0 | null | 2020-06-05T12:02:40 | 2020-06-04T06:33:09 | R | UTF-8 | R | false | false | 27,922 | r | functions.R | library(tidyverse)
library(MetNet)
library(igraph)
library(reshape2)
library(Hmisc)
setwd("/Users/Liesa4/Library/Mobile Documents/com~apple~CloudDocs/Promotion/R/MetClassNet/MetNet")
#' Changes to MetNet:
#' structural() has additional list entry of matrix containing mass values of respective matches
structural <- fu... |
94e659dd751955dc05b1a4ed8e626a9b3b51815c | 9991cf135c447943bd7f7f1b58736ddee1b2fec1 | /Rcode/PCA_based_training.R | 35f75c27f8464dffdbdb135198d6e01715c481b1 | [] | no_license | madsherlock/SML-F16 | 2469d1f717c027a6ab084654bc803552d0f61ca5 | b9c8df66a0214a13fd538dbaf9badc6bf98fc325 | refs/heads/master | 2021-01-21T04:44:49.768032 | 2016-06-06T07:26:08 | 2016-06-06T07:26:08 | 51,139,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,535 | r | PCA_based_training.R | gc()
library(caret)
library(base)
library(doParallel); cl <- makeCluster(4); registerDoParallel(cl)
setwd("~/Dropbox/Eksamen/SML/SML-F16/Rcode")
load("../data/data.RData")
load("../data/testClass_new.RData")
load("../data/data-1-1-100-1.5.RData")
List_1_1 = trainingDigit
load("../data/data-1-2-100-1.5.RData")
List_1_... |
494be8793c1c91faa78bef46a4e4baf8ee36bfd7 | 480480c0e26fd4df5a47a46734994fe3c67ae3dc | /R/fars_functions.R | 5f6ec793e3cc3819b8d09ec10ebbd351851242fd | [] | no_license | Alice-MacQueen/farsr | d7b90d4ee077b2314012281f4359e3e5870d1912 | 51ef23f965d8712ffb0793392ef294f180a0dab7 | refs/heads/master | 2020-05-03T06:46:02.421978 | 2019-04-03T20:52:16 | 2019-04-03T20:52:16 | 178,481,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,488 | r | fars_functions.R | #' @title Read Fatality Analysis Reporting System data
#'
#' @description \code{fars_read} reads in Fatality Analysis Reporting System (FARS) data
#'for a given \code{filename}, if the file exists.
#'
#' @param filename The name of the FARS data file to read.
#' @param path The path to the FARS data file to read. The d... |
0e406ed08e811964d5b3357246a2001ce88e358a | 2d310fd545505bb0fb7396011cd0c4859e8f0927 | /man/old_prs.Rd | 83daea9d263f1ea5d232827905161d974fbb617d | [
"MIT"
] | permissive | olladapunaresh/pRs | b871d5ccec03b074c2cdc97eacad8c7d14c549dc | e0e81c76c87897930086cbd0fd4a477d481d1f7d | refs/heads/master | 2023-03-18T18:53:56.621307 | 2018-12-10T13:40:08 | 2018-12-10T13:40:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 549 | rd | old_prs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{old_prs}
\alias{old_prs}
\title{Construct several polygenic risk scores from a matrix of weights.}
\usage{
old_prs(input, debug, n, weights)
}
\arguments{
\item{weights}{A matrix of weights with each row being beta corresp... |
37223fd5a3184debcaf27ee96960280af676c15f | aebfb7d9c03a2d349f66c1c335287e4e14d58071 | /man/get.corresponding.ts.data.according.to.the.combination.months.Rd | 1dfbbcc018b67df850d5fb22b88a3db91888f3ec | [] | no_license | lixixibj/foss | d99cf1e9edc25bdfabf405922557b6c3858782cd | 5c02c6758a628b08a2549aee4b9c53fe05d714a0 | refs/heads/master | 2023-06-14T11:02:08.176633 | 2021-07-09T01:24:36 | 2021-07-09T01:24:36 | 266,596,085 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 781 | rd | get.corresponding.ts.data.according.to.the.combination.months.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/forecasting.with.sub.seasons.R
\name{get.corresponding.ts.data.according.to.the.combination.months}
\alias{get.corresponding.ts.data.according.to.the.combination.months}
\title{get the corresponding ts value according to the combinded months
... |
f53efa89ad9e9fa8cd35b4133be01b59926f8473 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pcadapt/examples/manhattan_plot.Rd.R | 6c2cb11807ff2c79a43a1059349e9cc4140cd32e | [] | 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 | 172 | r | manhattan_plot.Rd.R | library(pcadapt)
### Name: manhattan_plot
### Title: Manhattan Plot
### Aliases: manhattan_plot
### Keywords: internal
### ** Examples
## see ?pcadapt for examples
|
ca3f4cb6168a8f629067ac2b235e6f5552f02e72 | 83e0c8f3a857bb8e4cb3322ae700021284edf85c | /FunGraph_0.1.0/man/get_biSAD1.Rd | 56e5fc4ba4af198aa5704dfa6c07e716b30fd080 | [] | permissive | xiahui625649/FunGraph | b7fc5845362e5efcaefc3624de7ab7b98121ec39 | 181ee42e6eb5131b30103f602dbaacb9bb0a36a1 | refs/heads/main | 2023-08-13T01:30:46.162953 | 2021-10-13T08:40:39 | 2021-10-13T08:40:39 | 440,708,149 | 1 | 0 | Apache-2.0 | 2021-12-22T02:22:11 | 2021-12-22T02:22:10 | null | UTF-8 | R | false | true | 463 | rd | get_biSAD1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/base.R
\name{get_biSAD1}
\alias{get_biSAD1}
\title{generate biSAD1 covariance matrix}
\usage{
get_biSAD1(par, n)
}
\arguments{
\item{par}{vector with four number, first two for ck and the rest for stress}
\item{n}{scalar indicate length of t... |
2935f4504eb78bf72bb7c5bfb02158b254c118e0 | dc66e3a263d415824ebab4db7b46f24b9eac4272 | /Analysis/processCSVs.R | fcc16df710beea4727c91a6a25d82ead6ca87de9 | [
"MIT"
] | permissive | cquijanoch/VRTrendVis | d79342f454bd1e25545b0783a8a63b05a8d3563f | 81eda0fc739bdf0beffb0de0422479e0be2f44f5 | refs/heads/main | 2023-03-31T20:29:49.950428 | 2021-04-08T10:16:39 | 2021-04-08T10:16:39 | 353,451,296 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,568 | r | processCSVs.R |
loadData <- function(path, id) {
data.df <- read.csv(path,sep=";", header = TRUE, encoding = "UTF-8")
data.df <- as.data.frame(data.df,stringsAsFactors=F)
data.df$ID <- id %>% as.factor()
data.df$Input <- data.df$Input %>% as.factor()
data.df$HMD <- data.df$HMD %>% as.factor()
data.df$TimeI... |
7626f125bbe9d91d4f1b3d701492fc9bc1dc527b | 856c5192f678eb7bf20a2e0638c6bd782ef8ed55 | /docxRkey/ivypi/tmp.R | 3bedf118b48fcc1444958821f62a90341cd9c47d | [] | no_license | madjugglers/pattern-book | 0ed1feca8a6f21366dda4e7979d7c31697f20b36 | 1475cd3135a3a1f6d8da4a9d7ffe91279b82033e | refs/heads/master | 2023-06-05T01:11:08.094646 | 2023-05-22T20:56:48 | 2023-05-22T20:56:48 | 44,569,396 | 5 | 4 | null | 2023-05-22T20:56:50 | 2015-10-19T23:10:38 | R | UTF-8 | R | false | false | 1,794 | r | tmp.R |
# Nov 5/17
rm( list=ls() )
library(igraph)
library(plotrix) ## use draw.arc function
# adjacency matrix for ivy-pi
A <- matrix(0, 10, 10 ) ## 10 positions
g <- graph.adjacency(A)
# give edge attributes I think
#ll <- rbind( c(-2,0)+c(2,0),
# c( -1/2, 1*0.866/2 )+c(2,0),
# c( 1/2, -1*.866/2)+c(2,0),
# ... |
807f0b1292ff12c9a2a75e1a8c225f85b0eb19ee | 10d4fa42467a509279ff2dacc25d423c4a7d4e50 | /R/admixture proportion.r | 8fdbb3e461ec87bd33bf566419bbfadc48abf291 | [] | no_license | YaliZhang98/Binp37_biogeographical_algorithm | e777280a2d03d0fc1c54f4fb3ccab460a7676db1 | 7b516fc1fb8d579578f52e98762d56025a2bd74a | refs/heads/main | 2023-07-06T20:27:28.443080 | 2021-08-18T21:03:00 | 2021-08-18T21:03:00 | 397,408,242 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 824 | r | admixture proportion.r | # This script is used produce a stacked graph for ADMIXTURE proportions in gene pool.
library(RColorBrewer)
Dataset <- read.csv(file="admixture_proportions.csv",header=TRUE) # This file just contain subregion and allele frequencies
palette <- colorRampPalette(brewer.pal(12, "Paired"))(36)
png("admixture_p... |
3b856aafb403a4d5ee8b188fc82a964742e955dd | fa5eb1a6e94be9be5d1bc19d1807c6ed2983b2d0 | /libapi/R/pubmed.get.abstracts.R | c5c5cc8f84874e68e8a0886e6a17b156dab5df51 | [] | no_license | bereginyas/rlib | 57c8a4f3548b34ba9a69dd3774ab127cbd4632be | f511254f1ed46f5a7d43eea7884cf31ef2cda9ca | refs/heads/master | 2022-01-07T12:54:04.819043 | 2019-05-25T05:34:24 | 2019-05-25T05:34:24 | 67,099,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,254 | r | pubmed.get.abstracts.R | pubmed.get.abstracts = function(pmids) {
library(XML)
## divide pmids into groups so as not to exceed the maximum url length
max.pmids=200
num.pmids=length(pmids)
num.groups = ceiling(num.pmids/max.pmids)
pmid.groups = split(pmids,factor(1:num.pmids%%num.groups))
data.xml = xmlNode(name="pubmed")
i ... |
a1bc11c354797635d67cb5c54c6fa6f7a5791090 | 20fb140c414c9d20b12643f074f336f6d22d1432 | /man/NISTmilligramTOgrain.Rd | 5c947180b56128718cfc3d67b479dbb8d6938e42 | [] | no_license | cran/NISTunits | cb9dda97bafb8a1a6a198f41016eb36a30dda046 | 4a4f4fa5b39546f5af5dd123c09377d3053d27cf | refs/heads/master | 2021-03-13T00:01:12.221467 | 2016-08-11T13:47:23 | 2016-08-11T13:47:23 | 27,615,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 756 | rd | NISTmilligramTOgrain.Rd | \name{NISTmilligramTOgrain}
\alias{NISTmilligramTOgrain}
\title{Convert milligram to grain }
\usage{NISTmilligramTOgrain(milligram)}
\description{\code{NISTmilligramTOgrain} converts from milligram (mg) to grain (gr) }
\arguments{
\item{milligram}{milligram (mg) }
}
\value{grain (gr) }
\source{
National Institute of... |
2914da6c21bfdc7cd218730de7013984dfd646a7 | 6da3b4d3cd66f532e8b399f08fc9ebca3af10723 | /articles/YamaguchiQE2019/Code/estimation/CalcLikelihoodType.R | eb5fa7b472d3b1ec4904ac0e372f235962036160 | [] | no_license | murattasdemir/Archive-of-Empirical-Dynamic-Programming-Research | ffbcc137e137cad42ccf3e0a3bbb5227f70f21a0 | 324fedb49f8d0792dc1df2e8b1302e19cbbe78b7 | refs/heads/main | 2023-05-28T12:45:24.380684 | 2021-05-27T16:56:37 | 2021-05-27T16:56:37 | 367,003,588 | 1 | 0 | null | 2021-05-13T09:47:32 | 2021-05-13T09:47:31 | null | UTF-8 | R | false | false | 684 | r | CalcLikelihoodType.R | CalcLikelihoodType <- function(param.type, x){
mat.param.type <- matrix(param.type, ncol=Gn.type-1)
exp.y <- cbind(1, exp(x %*% mat.param.type))
exp.y / rowSums(exp.y)
}
CalcWeightedLogLikType <- function(param.type, x, q){
## likeilhood for type
pi <- CalcLikelihoodType(param.type, x)
## weighted lo... |
4a86c9e4b2cd8223e26737216b5e7770c328edfb | 5d4914933ebcf8035147875d4f0a25d4e7b109fd | /ONEL_Stan_MM.R | 82fc8e9372c1b984c458b7f749e86ef9bafbe8bd | [] | no_license | silvialiverani/GMCARMM | a28df135e92fcad01e649efd0eb8bcdede9fa730 | 1ea49922dfe8be9bd6ef045089549dcf2e0fb531 | refs/heads/master | 2023-03-01T03:38:59.193406 | 2021-02-04T23:16:53 | 2021-02-04T23:16:53 | 254,815,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,643 | r | ONEL_Stan_MM.R | #### GENERAL SETUP ####
path <- file.path("....../StanCode")
setwd(path)
# Load package
library(dplyr)
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
# Data load - North East: 4 boroughs coverage 90% (OLD)
load("./ONEL_Data.RData")
#
#### DATASET PREPARATI... |
e10447d94512c92533efbb4d7ba46597ecfdc4ed | d7d556c4ce38c2dec8dc94b8f5858879085ee9a6 | /man/beeswarm.Rd | e9c9476ded2cff79728591026369efcfc2babafd | [] | no_license | aroneklund/beeswarm | 126788c86b8bd72e01e3eccadd20a021107cb4e2 | d641db509e7a3f9c8e76147bf779fcb141cdba51 | refs/heads/master | 2023-06-07T23:04:06.996955 | 2023-01-21T21:58:03 | 2023-01-21T21:58:03 | 39,942,151 | 41 | 9 | null | 2023-05-28T17:38:58 | 2015-07-30T09:20:49 | R | UTF-8 | R | false | false | 12,661 | rd | beeswarm.Rd | \name{beeswarm}
\alias{beeswarm}
\alias{beeswarm.default}
\alias{beeswarm.formula}
\title{Bee swarm plot}
\description{
Create a bee swarm plot. A bee swarm plot is a one-dimensional scatter plot similar to \code{\link{stripchart}}, but with various methods to separate coincident points such that each point is visib... |
9687c876f984b5746524dd7e7e2d3a634b7c5955 | ec99cf538b9a6dd25ba391254310829fb08790b1 | /code/tidalHelpers/Helpers/event_based_helper.R | b1df628595360c1a910273205b902445f739d645 | [] | no_license | katerobsau/NWO_Project | 61c0898dc31e4411ee8e3a2c0889ad116c7a5876 | 74f7f0652a7986bc0aceeee1fd6f6c278367d9ba | refs/heads/master | 2021-08-08T01:52:28.623061 | 2020-07-12T12:52:05 | 2020-07-12T12:52:05 | 200,646,821 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,251 | r | event_based_helper.R | # cluster code
# print("Build this into the package!!!")
# print("Only want the functions in here")
source("../source/surge_exploratory_analyis.R")
na_value = 99
init_var = utils_init()
main_data_dir = "/Users/katesaunders/Documents/No_Back_Up_Data/ENS/"
dates_vec = list.files(main_data_dir)
member_ref = get_ensemble_... |
4fa2a8bb60f703ff8a2761b2b8fec772390f3c05 | 8ac06e475183e8519f543fce41e72ec0e7226309 | /man/goldenRatioRolors.Rd | c5999dab6b31d851b64937398ba0885cabd1ed62 | [] | no_license | kashenfelter/Dmisc | 0a43b7fbd83c874996501c83f54b2f46ca050af7 | 7e8ed7c1477f67376de6832fa1bfaf20170e5136 | refs/heads/master | 2020-03-13T22:06:00.018345 | 2017-08-21T17:08:19 | 2017-08-21T17:08:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 715 | rd | goldenRatioRolors.Rd | \name{goldenRatioRolors}
\alias{goldenRatioRolors}
\title{Choose n colors using the golden ratio}
\usage{
goldenRatioRolors(n, s = 0.5, v = 1, alpha = 1)
}
\arguments{
\item{n}{Integer. The number of colors you want}
\item{s}{Numeric. Saturation - input into hsv}
\item{v}{Numeric. Value - input into hsv}
... |
2c318135192e14a636adc8790b2bfc7ba7ea9749 | 466a14350411044a071faa1702294d06b1543edf | /R/survival.R | 3a448870ee8dd938e5694228d119b7450d53bee2 | [] | no_license | cran/rattle | 8fc67846c6dac6c282e905fe87ff38f0694056da | 3875c10d0ae6c7a499d918bc501e121861067e06 | refs/heads/master | 2022-05-02T14:29:57.688324 | 2022-03-21T12:10:02 | 2022-03-21T12:10:02 | 17,699,048 | 18 | 33 | null | null | null | null | UTF-8 | R | false | false | 11,251 | r | survival.R | # Rattle Survival
#
# Time-stamp: <2017-09-10 10:23:43 Graham Williams>
#
# Copyright (c) 2009 Togaware Pty Ltd
#
# This files is part of Rattle.
#
# Rattle is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, eith... |
df5ff4c78358658d7ac183ce4403a70b69790c14 | 3500ffd0e4ad10570e4ecadd7e96dd50f366a481 | /0_bin/5_table_functions.R | 3c3ede1cc12fe4bfd91bc1d892cf920163a81df4 | [
"MIT"
] | permissive | boyercb/rmc-peru | cd48e044f2f9d58db4fcbcae8f8949f8a2098a32 | 65f1a5465dfbef2efdbf9aab955507b91ad81198 | refs/heads/master | 2023-08-30T11:15:09.128624 | 2023-08-18T17:20:14 | 2023-08-18T17:20:14 | 286,563,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,843 | r | 5_table_functions.R | make_report_table <-
function(models,
outcomes,
outcome_labels,
treatment = "treatment",
title,
general_note = "Notes: First column for each outcome is design-based least squares estimator that includes fixed effects for randomization strata and batch. Second col... |
80b04bf4e97b9c761103e14c6779be9e485d2c82 | 36a876c6de4fa152e2e59dbe592c9d2d81cbd2eb | /man/scratchings.Rd | 1519d21741b7be18a0219eea720918a450ba8532 | [] | no_license | jkadcav/competitor | 9255d878d51249537e444c0aea437a023cb9b6ce | 9283296cc7de07248cd9e558098a407235137225 | refs/heads/master | 2021-01-12T12:15:59.769629 | 2016-11-18T12:27:06 | 2016-11-18T12:27:06 | 72,398,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 372 | rd | scratchings.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hello.R
\name{scratchings}
\alias{scratchings}
\title{Retrieve competitor scratched property}
\usage{
scratchings(eventId)
}
\arguments{
\item{eventId}{Database ID of the event}
}
\description{
Retrieve competitor scratched property
}
\exampl... |
0d69e8cdf056b3512628e137de89c84c9f7459e7 | 8078d61b576fc31a7ff3c59cf83688042f8660db | /qExponential/man/ptsal.Rd | a6b6e3ee0ba84e3dec70fac1a9c361f8a5912f63 | [] | no_license | Alessandra23/q-Exponential-mfmm | ac6704459e5c91e51e9c9a783db022dafc49cde8 | 16789a8653836231908335f890ec236999d009fc | refs/heads/master | 2023-07-12T23:42:13.588015 | 2021-08-17T19:20:07 | 2021-08-17T19:20:07 | 231,951,866 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 681 | rd | ptsal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qexp_functions.R
\name{ptsal}
\alias{ptsal}
\title{Calculate the cumulative distribution function
Input: vector of data values, distributional parameters, left-censoring
threshold, usual flags
Output: vector of (log) probabilities}
\usage{
pt... |
a12a8207b5fc169aed3fda9fa3c464c30bf69ec2 | 822c34d52c71b6cad5d3c582c7c7148e9d7582cb | /4week4/plot2.r | 82cf586491e792452dc348e75178de99e4d17901 | [] | no_license | WeiHanLer/Data-Science-Specialization-Exploratory-Data-Analysis | 1335b7fffd3d6958149419cbb9ec97e34cb271ba | 5f1f22e3a487b9bc3ebdeb08e44c3572f6d6c8d1 | refs/heads/master | 2020-03-20T21:36:48.066251 | 2019-05-06T05:08:12 | 2019-05-06T05:08:12 | 137,750,347 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 439 | r | plot2.r | library(dplyr)
library(ggplot2)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
baltimore<- subset(NEI, fips=="24510")
aggregatevalue<-aggregate(Emissions~year,baltimore,sum)
png('plot2.png')
barplot(height=aggregatevalue$Emissions, names.arg=aggregatevalue$year, xlab="Years",
... |
e5cd149e6360e635069f3c94fd9057309a4d8829 | 71bc62be599664aa98d3318cadaf015ff270db08 | /R10c-DataManipulation.R | 964d413bb850280e3f41a95746b63bef4234ec96 | [] | no_license | soonyoungcheon/R | 94db30e75d6bd2a495fcf397a2e41ed89892681a | 00e840cfbca09a53ea251e0e716bdb40f11b79f1 | refs/heads/master | 2020-04-11T21:22:28.883897 | 2018-12-17T09:14:12 | 2018-12-17T09:14:12 | 159,113,838 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 357 | r | R10c-DataManipulation.R | # aggregate - 집계 관련 함수
# dplyr의 group by 보다 비교적 단순하게 코드 작성
library(dplyr)
data('diamonds',package = "ggplot2")
diamonds
# aggreate(집계대상, 데이터,적용함수)
# cut 별 평균 price 집계
aggregate(price~cut, diamonds, mean)
# cut/color 별 평균 price 집계
aggregate(price~cut + color, diamonds, mean)
|
960faad51b033cd14c6c698b5557f7698c04bb17 | ec8464fb698d72afada518f80df9a450d23a57fa | /man/offset-set.Rd | d20d52d875973efdd12efa09f5274f2a509a7e63 | [] | no_license | planetMDX/BioQC | 96cdc0b0c2aa6b8d2d0055ee278011df0d9c0e64 | 93531776b8e98debf93309cf06a0857a3907fdaf | refs/heads/master | 2021-01-01T19:35:37.467470 | 2017-08-02T08:23:10 | 2017-08-02T08:23:10 | 98,616,169 | 0 | 0 | null | 2017-08-02T07:28:33 | 2017-07-28T06:26:36 | R | UTF-8 | R | false | true | 949 | rd | offset-set.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllMethods.R
\docType{methods}
\name{offset<-}
\alias{offset<-}
\alias{offset-set}
\alias{offset<-,IndexList,numeric-method}
\alias{offset<-,SignedIndexList,numeric-method}
\title{Set the offset of an \code{IndexList} or a \code{Signe... |
c27df250f608faf0fa99e771e7432f8de2d250e6 | 96ef0481a0f4baa05237718907608e31dcf10304 | /MLPACK2.R | 12e4622459d1748e8e1425ac2dd065be04fb0a41 | [] | no_license | yuhenghuang/Rcpp | 8ba67c65b4a5e6f32375e251b1170e3b172d455c | 5c6fb11c347fc2e72bb8d98c29a0ae4552ce8d31 | refs/heads/master | 2023-01-09T00:40:39.292823 | 2020-10-23T13:25:35 | 2020-10-23T13:25:35 | 282,114,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 665 | r | MLPACK2.R | library(RcppMLPACK)
Rcpp::sourceCpp("MLPACK2.cpp")
logistic_regression(matrix(c(1, 2, 3, 1, 2, 3), nrow=2, byrow=TRUE), matrix(c(1L, 1L, 0L), nrow = 1))
data(trainSet)
trainmat <- t(trainSet[, -5]) ## train data
trainlab <- trainSet[, 5]
linear_regression(trainmat, trainlab)
naive_bayes_classifier(trainmat, tra... |
9a6e741d87aa44653cde5760fc4d2bbbaab8406d | 3a4b61726631bcce875ab36bde6177cd771b8cdf | /covid.R | d63201e28f46f5f5f9e9b0b518cfdebf4046470a | [] | no_license | grtvishnu/Covid_visualization | 0a292a1a82dff7d964f94552822c295a2ea2bc80 | 3495f3dfcb589826cbade2d015cd1a038e4a092d | refs/heads/master | 2021-05-23T01:06:07.854440 | 2021-01-19T15:33:31 | 2021-01-19T15:33:31 | 253,165,878 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,574 | r | covid.R | # Load libraries
library(tidyverse)
library(scales)
library(lubridate)
library(gganimate)
library(gifski)
covid <- read_csv("time-series-19-covid-combined.csv")
# Remove unnecessary Columns and Rename
covid <- covid %>%
select(Date, country = `Country/Region`, Confirmed, Recovered, Deaths)
# Create a ... |
608a34b36c53b40b6c9ea4dfe3ba70200cd7b09a | f90f184082d6096b87c56b5e0011a7ef87ef023c | /CleanData/Week4Quiz/Exercise4.R | 21c98f8939536ac98aa47503fa6b7556edd35fd1 | [] | no_license | pig4tti/r-assignments-resolutions | bbbc0d9df460c0c95a07b6993d19a05ce16ecf4e | 588feb95cd5e5be7ee83294e19332e9c43e88472 | refs/heads/master | 2021-01-10T06:18:07.902312 | 2016-02-25T11:02:50 | 2016-02-25T11:02:50 | 48,855,714 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 892 | r | Exercise4.R | source("Utils.R")
fileName <- "clean_data_w4_ex2.csv"
ensureFileExists(fileName, "http://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv")
# loads the csv
colClasses = c("character", rep("NULL", 2), rep("character", 2), rep("NULL", 5)) # read only the columns that matters
gdp <- read.csv(file.path("./data/", f... |
a03d1324ed3f25e636bd784a62695cb38f0cec37 | db12b990924703cd74748d8585cd9c11fafa6746 | /h2o-r/tests/testdir_jira/runit_pubdev_1240.R | d4cefbf6a353108d4a384a516be1056671228fcc | [
"Apache-2.0"
] | permissive | h2oai/h2o-3 | 919019a8f297eec676011a9cfd2cc2d97891ce14 | d817ab90c8c47f6787604a0b9639b66234158228 | refs/heads/master | 2023-08-17T18:50:17.732191 | 2023-08-17T16:44:42 | 2023-08-17T16:44:42 | 17,371,412 | 6,872 | 2,345 | Apache-2.0 | 2023-09-14T18:05:40 | 2014-03-03T16:08:07 | Jupyter Notebook | UTF-8 | R | false | false | 2,163 | r | runit_pubdev_1240.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../scripts/h2o-r-test-setup.R")
test.merge.examples <- function() {
census_path <- locate("smalldata/chicago/chicagoCensus.csv")
Log.info("Import Chicago census data...")
census_raw <- h2o.importFile(census_path, parse=FALSE)
... |
0fc3397c35cbb59374e1ec80d4c6106074801332 | f463aa07156de93a453bed1c532cdc0f193b4649 | /week_011/week11-boardgames.R | ecf1c4de27503c85d63f42110e939d1cda748a23 | [] | no_license | mehmetalivarol/tidytuesday | f984e07384ce9a96291eba1cf11353c2ad390f0d | caf8bfb1ea597c5c3fe4e022d9b7fdfe5b5b2087 | refs/heads/master | 2020-04-28T21:03:50.222823 | 2019-03-14T10:03:12 | 2019-03-14T10:03:12 | 175,370,593 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 551 | r | week11-boardgames.R | library(tidyverse)
library(ggwordcloud)
proj_set("C:/Users/mali/Documents/R/tidytuesday")
board_games <-
readr::read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-12/board_games.csv"
)
wc <- board_games %>%
group_by(name) %>%
summarise(UsrRating = sum(users_... |
0cd35fbdd7960485c53ca482a53e8b369140ed89 | aa0e96ff0e39b8b35ff343ac7db7f7c954f3a741 | /R/utility.R | c5873f852dc8e680abfdde29e45ed001e599ac90 | [] | no_license | mahagadorn/nacdb | 906573b393f0a249423b6e839c0ce3d1cec3164b | e5c5c562c914f49ca0f1e22dbe3a8dd7f30793ac | refs/heads/master | 2020-03-15T12:42:40.513363 | 2018-05-04T22:55:53 | 2018-05-04T22:55:53 | 132,150,242 | 1 | 0 | null | 2018-05-04T14:25:21 | 2018-05-04T14:25:21 | null | UTF-8 | R | false | false | 9,509 | r | utility.R | #' Takes a matrix of data for a species, checks if its numeric, then puts the table into a long-format dataframe
#'
#' @param x a matrix of data, generally species in the columns and sites in the row
#' @param row.metadata metadata for the sites; in long format, it will be stored in each row with with the site pertaini... |
47406233e2be42fdc90075ec66f054fbc07c437d | ca4bb0db52b6756e52e007c3d41e9643064b1825 | /scripts/max_mean_zscore_VS_rhythms.R | 7f31d32b451f5fd98f450ae9e13a39722faf504b | [] | no_license | laloumdav/cost_noise_conservation_rhythmicity | a894da59af7aeab76ce4656fc6c41d7096476e31 | 27c93a9d59cbde0b960db5896509ac1f15a4e133 | refs/heads/main | 2022-12-21T02:23:11.556945 | 2022-06-27T12:58:54 | 2022-06-27T12:58:54 | 299,549,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,954 | r | max_mean_zscore_VS_rhythms.R | library(ggplot2)
library(ggridges)
library(officer)
library(magrittr)
library(ggpubr)
species.list <- c("arabidopsis", "cyanobacteria", "ostreococcus", "mouse")
tissue.list <- c("leaves", NA, NA, "liver")
main.dir <- gsub("/NA", "", paste("~/Documents/cost_theory_workingspace/DATA", species.list, tissue.list, sep="/"... |
c7808547eccb2218b5b467e0806448c064174963 | f2ff7459d16c3c238c54481f9c1d96e5b2c2a780 | /clusterization/kappa.R | fd62c71ed9be227ceb311c1a17ee9cdda20e7f5b | [] | no_license | nevmenandr/Zhukovsky | c987c80fdefb61db72bed30c37cc90e6eee7aaf1 | 73fdcc5460b02f5f61d7d294787503f0b636ddab | refs/heads/master | 2021-01-10T07:08:55.917091 | 2015-10-09T12:09:53 | 2015-10-09T12:09:53 | 43,612,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 136 | r | kappa.R | library(irr)
# if err:
# install.packages("irr")
p <- read.table('forms_no_stopwords', h=FALSE, sep=' ')
pd <- data.frame(p)
kappa2(pd)
|
95e8197d55fe9f7091db1f537d9083bad9c062be | 66018934e63468130a003b44d5ac3261c393856c | /man/kobo_unhcr_style_histo_big.Rd | f27a425ae910c04bd932890162104b915f66379c | [] | no_license | unhcr/koboloadeR | e0e5fb10950d0500a7d5a614db6dc097be2ea0ec | 7758385b9b5c13b4652161f7c127d162ff5df2e9 | refs/heads/master | 2023-03-10T08:23:51.511979 | 2023-02-28T15:52:07 | 2023-02-28T15:52:07 | 110,141,421 | 28 | 31 | null | 2022-02-05T14:14:47 | 2017-11-09T16:47:24 | R | UTF-8 | R | false | true | 456 | rd | kobo_unhcr_style_histo_big.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kobo_unhcr_style_histo_big.R
\name{kobo_unhcr_style_histo_big}
\alias{kobo_unhcr_style_histo_big}
\title{UNHCR ggplot2 theme}
\usage{
kobo_unhcr_style_histo_big()
}
\value{
Return UNHCR Style
}
\description{
Return ggplot2 styling for histogr... |
45e60496f5c79eb27f614e90bbba90479cc160fb | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/emuR/examples/buildtrack.Rd.R | 2287a4cf655537b2274dba1b327ac7ccc4aacf62 | [] | 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 | 450 | r | buildtrack.Rd.R | library(emuR)
### Name: buildtrack
### Title: Build trackdata objects from the output of by()
### Aliases: buildtrack
### Keywords: manip
### ** Examples
#vowlax.fdat is a track data objects of formant of the vowlax segment list
#calculate the difference between adjacent formant values
p = by(vowlax.fdat[... |
31293e6158ddc3d1271cdae19bdde1180b5f9456 | 2f6d7a99ce3155d2c635c39013a0da1418208b40 | /man/getRandomNames.Rd | 1d74ed45d95b2420c5579561acd4b54c2f0b168e | [
"MIT"
] | permissive | oganm/ogbox | c75eb1d8f4df00be214731e085e6c19e141992cc | ba99a46487836af5ab4fb5b013bc92cf35ad8e95 | refs/heads/master | 2020-04-04T07:07:27.383911 | 2019-07-29T23:00:12 | 2019-07-29T23:00:12 | 37,562,559 | 5 | 1 | null | null | null | null | UTF-8 | R | false | true | 863 | rd | getRandomNames.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/randomNames.R
\name{getRandomNames}
\alias{getRandomNames}
\title{#' @export
get_random_name = function(retry){
left = left()
right = right()
name = paste0(left[sample(x = length(left), size = 1)],'_',right[sample(x = length(right),... |
b53e83e81017309131d25db8fae42c44ea86417c | d4cd3909b5c5ff996e405a9dbcdb830a9f18599f | /market.edu.R | 6cd95e0361c2fefb5eac1a8f8f73527b2d23998d | [] | no_license | jevzimite/Projects.compiled | 6bb39df27ed44871c240fea4408967248f76293d | df1fdcaa12bf8d339a2ca782e28c425a44c12409 | refs/heads/main | 2023-05-01T13:30:24.549058 | 2021-05-25T22:11:07 | 2021-05-25T22:11:07 | 332,641,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,518 | r | market.edu.R | library(readxl)
marketing_data <- read_excel("Desktop/R/marketing_data.xlsm")
#### libraries ####
# library(ggbiplot)
library(psych)
library(ggplot2)
library(reshape2)
library(cowplot)
library(heatmaply)
library(factoextra)
library(readxl)
library(ggfortify)
library(ggpubr)
#### data ####
df<- marketing_data
dfN<-... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.