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3b3251a08dab34c469cb91ca40c95ade99ddaba3 | 033259a8308432f54aee40e272cd9870084cbe8e | /man/tickers_metrics.Rd | aa31b747dcafd684e94f21e9b85df6acb27e0e58 | [
"MIT"
] | permissive | abresler/fundManageR | 89d7d51c0da3de150170433ac15dba0feb50e62c | 6d9144353b5284bfd6fadf6922690904cf263320 | refs/heads/master | 2023-08-03T21:47:41.812747 | 2023-07-19T13:09:16 | 2023-07-19T13:09:16 | 68,140,115 | 192 | 31 | null | null | null | null | UTF-8 | R | false | true | 698 | rd | tickers_metrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/finbox.R
\name{tickers_metrics}
\alias{tickers_metrics}
\title{finbox ticker metrics}
\usage{
tickers_metrics(
tickers = c("AAPL", "NFLX", "FB", "GOOG", "TSLA", "VNO"),
metrics = c("total_current_assets", "total_net_income_margin", "total... |
38f8ce62ab852f351a0c40802bef1802d83f9ba6 | 3063fc9808667c80e1aabdee9dfba9d459dcec12 | /R/colony_edge.R | 648387c6b9fbd18357255de92949cfee2370aa8a | [] | no_license | yuczhang/colonyHCS | 06a66368b2945c575f2aec5f90410abaf853089b | ef1ebe50b7eb9439977628671bb2a96368cd272f | refs/heads/master | 2020-09-28T19:36:30.654618 | 2019-12-09T09:44:03 | 2019-12-09T09:44:03 | 226,847,856 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,107 | r | colony_edge.R | #' Edge detection to segment the forground of the microoganism colonies.
#'
#' In order to characterize the phenotypic change of mutant, the Canny edge detection algotithm is applied to realzie the segmentation.
#' @param expr_mode a string indicates the phenotypic experiment type, as different experiments require di... |
468070816c09888e701ce0c5a88d1d688423e0c3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lattice/examples/melanoma.Rd.R | 38aaed4c5e7f7b848d7470ee3e410fbef57ab4bf | [] | 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 | 377 | r | melanoma.Rd.R | library(lattice)
### Name: H_melanoma
### Title: Melanoma skin cancer incidence
### Aliases: melanoma
### Keywords: datasets
### ** Examples
# Time-series plot. Figure 3.64 from Cleveland.
xyplot(incidence ~ year,
data = melanoma,
aspect = "xy",
panel = function(x, y)
panel.xyplot(x, y, type="o", pch = 16),
... |
d6a563f6b6fe8403673bb1131e24cccb4ec12c34 | 75e8ab3b754f02e7b6796c182dc36968befe6788 | /data-raw/horoscopes.R | 6466b3a6c156fa442f295fd3353fa4a48e5b4c9e | [] | no_license | jjchern/astrologer | d22a3958c9a44b14105aea02b7653f5c21c326a2 | 97513240a0a839b60aea5d38190deb963921d8d5 | refs/heads/master | 2021-01-20T00:12:54.141390 | 2017-02-13T06:10:51 | 2017-02-13T06:10:51 | 82,588,047 | 0 | 0 | null | 2017-02-20T18:14:49 | 2017-02-20T18:14:49 | null | UTF-8 | R | false | false | 5,550 | r | horoscopes.R | library(rvest)
library(stringr)
library(dplyr)
library(tidytext)
library(lubridate)
library(httr)
library(forcats)
library(purrr)
library(tidyr)
library(forcats)
# Check which URLs exist for all year/month combinations
horoscopes <- crossing(year = 2015:2017,
month = c("01", "02", "03", "04", "0... |
b59b3b97fc957674e286b77f32e78afbd71f7093 | 06de334e82606450d41cf556da7b3a89d24b8d62 | /app-shiny-neny/ui.R | ae7adad663d60303a60fad57351a97fc6b943a87 | [] | no_license | almeidaxan/tools-gee | b5b81a9b67485bcf40299b1db6f64be063ffdd24 | 264a3d541d2523db168846e3dd5da72f0e2859de | refs/heads/master | 2021-05-10T09:11:11.880061 | 2019-02-17T14:09:08 | 2019-02-17T14:09:08 | 118,915,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,853 | r | ui.R | shinyUI(navbarPage(
title = div(em(strong("Landsat Downloader")), HTML("<small>by Menini & Almeida</small>")),
windowTitle = "Landsat Downloader",
tabPanel(
title = "Pixel",
icon = icon(name = "th", lib = "font-awesome", class = "fa-lg"),
sidebarPanel(
# Set up shinyjs
useShinyjs(),
fileInput(inpu... |
bd035dce32c0547175fe7de3d3753b6a2e06e69e | 57aa4d2e4e113e3e41a274f0434ad67e7d1ffc44 | /R/calculate_tables.R | 68401abcf392f713edecdaae8b6d01da13f69c3e | [] | no_license | sleyn/MoRbido | 8ba8efd8958d789e7b8905fda37715c957fc4ca5 | a5d3c7ce7ec982d3d4184a526523a36df1815710 | refs/heads/master | 2023-01-20T01:57:38.099075 | 2020-12-01T23:51:53 | 2020-12-01T23:51:53 | 204,804,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,854 | r | calculate_tables.R | #' Create a list tables of OD and Concentration changes through time from the log files.
#' @param dil_check - Were any manual dilutions introdused? Default is TRUE. If TRUE - need to provide dil_file
#' @param dil_file - File that specifies dilution time, ratio (volume taken to total volume), concentration of the drug... |
507ac47d1b660244913be557b3e4926094882f11 | 01de953181aa892b3ef0eec50e95e4b5c158458c | /vignettes/grasleppa.R | cbe74b4b16062dfcec161f4726dedcf1dfbb248f | [] | no_license | einarhjorleifsson/mar | c4d18937b7c4b71f99ee5c1bac19f6959a60a0cb | 41180e6ba575dba9cd605fa86508ec3343d23e09 | refs/heads/master | 2021-01-18T21:23:45.755326 | 2019-02-02T12:11:02 | 2019-02-02T12:11:02 | 35,633,987 | 0 | 1 | null | 2020-07-07T13:03:28 | 2015-05-14T20:06:10 | R | UTF-8 | R | false | false | 404 | r | grasleppa.R | ## ----message=FALSE-------------------------------------------------------
library(tidyverse)
library(mar)
mar <- connect_mar()
## ----fig.width=7---------------------------------------------------------
afli_grasl(mar) %>%
filter(ar==2015) %>%
collect(n=Inf) %>%
ggplot(aes(lon,lat)) +
geom_polygon(data=g... |
fd3443e3163f2bfbcc73bf83a12e49eae40421a6 | 9855901b3da9cae4e5532c57c77ee2e03f44e316 | /K-Means/TeenMarketSegments.R | daca7acd62ef29ef3e2706464b4b99b7f3d8e957 | [] | no_license | Walter-Ullon/Machine-Learning | cbfa835682b0a5291ebf78e4365c70043e4394db | 777e22157a855063fef2f12d9c952eac7729c3fb | refs/heads/master | 2022-11-26T13:32:21.209852 | 2019-09-17T15:05:34 | 2019-09-17T15:05:34 | 97,531,891 | 3 | 0 | null | 2022-11-21T21:27:40 | 2017-07-17T23:43:52 | Python | UTF-8 | R | false | false | 2,436 | r | TeenMarketSegments.R | # Application of "K-Means Clustering" algortihms to Teen Market Segements using social media data.
# Data collected and curated by Brett Lantz (https://raw.githubusercontent.com/dataspelunking)
# Install and load packages.
library(stats)
# Load data. Inspect.
teens <- read.csv(url("https://raw.githubusercontent.com/d... |
7ba0d242618eb8732c8f35042adf491d310522a1 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/JSM/R/LambMultGeneric.R | ff34866d60c4708285b241ffc767f34ca9b6860f | [] | 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 | false | 3,074 | r | LambMultGeneric.R |
#========== Function to Obtain Lamb Given Other Finite Dimensional Parameters ==========#
#=============== Model I for Multiplicative Joint Modeling ===============#
LambMultGeneric <- function (para, lamb.init, tol, iter, ncz, ncb, B.st, n, Y.st, b, model, Btime, Btime2, Index, Ztime, Ztime2, Index0, nknot, nk, Inde... |
619079af685eec25f4cf72612d0b6334e34e4ce4 | d1e1c9b25aebcea37927c08a8f344713562b3e42 | /man/F1-prentice.test.Rd | 879ac3f4396d42baadbe36f411dfa8f41f219c07 | [] | no_license | cran/muStat | 43783938835cae3e7a5afb5f8285f9b36ec8b07d | a77f2af75558f6a558d1044945f6085281655361 | refs/heads/master | 2021-01-17T06:33:48.507309 | 2010-09-17T00:00:00 | 2010-09-17T00:00:00 | 17,697,709 | 1 | 1 | null | null | null | null | WINDOWS-1252 | R | false | false | 12,858 | rd | F1-prentice.test.Rd | \name{prentice.test}
\alias{prentice.test}
\alias{mu.friedman.test}
\alias{mu.kruskal.test}
\alias{mu.wilcox.test}
\encoding{latin1}
\title{Prentice (Friedman/Wilcoxon/Kruskal) Rank Sum Test}
\description{
Performs a generalized Friedman rank sum test with replicated blocked
data or, as special cas... |
25edcffa1186018173fd382d0d9a3f8e54eeeca9 | 16b68dbf022ed548baa7fe4d4d45f6536a8cb290 | /Cancer-Data-Impute.R | 13e9c69bab00a0defc485203eb172163f1ed6b0b | [] | no_license | ckas-fh/cancer-dataimpute | ff314dfdf9902d5f0135424f79b1d7bd9e5ca921 | c2c73b8c97fe1cce5ed6f1c6edbe21d1ad9c45ca | refs/heads/master | 2020-06-21T11:16:16.699943 | 2019-07-17T17:25:16 | 2019-07-17T17:25:16 | 197,433,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,372 | r | Cancer-Data-Impute.R | #######################
#Caroline Cancer Data Cleaning/Imputation
# Feb 26, 2019
########################
#1. loading relevant packages
library(dplyr)
library(mice)
library(naniar)
#2. loading dataframe and setting working directory
setwd("Desktop")
cancer_data <- read.csv("Caroline-Dataset.csv")
#3. general data cl... |
a0f58a13b72913115bf5b0ebe37a944a726b9d57 | c08af96cf3635e1b4c881e06fbec6bbeb6e431f3 | /plot3.R | 6d8405229265e438d08eb5db8a2871aca3b54729 | [] | no_license | Cruzzor/ExData_Plotting1 | 862fa012ea3ecd4fc57d8eb37194c071ce0989d3 | 56cb65a3515e0bc9ad8cd0498dcd164493ab3a34 | refs/heads/master | 2021-01-18T10:01:38.572013 | 2015-09-13T19:27:40 | 2015-09-13T19:27:40 | 42,227,187 | 0 | 0 | null | 2015-09-10T06:38:48 | 2015-09-10T06:38:47 | null | UTF-8 | R | false | false | 1,450 | r | plot3.R | library(dplyr)
library(tidyr)
## Read/filter data
data <- read.csv("household_power_consumption.txt", stringsAsFactors = FALSE, header = TRUE, sep = ";")
mydata <- data %>% filter(Date == "1/2/2007" | Date == "2/2/2007")
##change locale to english for proper x-axis labeling
original_locale <- Sys.getlocale(ca... |
d31c5156c5658026656b101770621730fc844fe5 | a9cff4d70b2659ab336290a5ab9a8d20c1a72f7c | /cachematrix.R | 1005c73e786119d5005724d036740b76c92f52e1 | [] | no_license | info-business-devel/ProgrammingAssignment2 | 81c8461d052b9e92ffc7c2e27413d1bb7a57beb5 | df7b10abffbc00587b35890d00c53cdc9fc9033c | refs/heads/master | 2020-03-08T03:17:15.204407 | 2018-04-03T10:06:13 | 2018-04-03T10:06:13 | 127,798,085 | 0 | 0 | null | 2018-04-02T18:47:45 | 2018-04-02T18:47:45 | null | UTF-8 | R | false | false | 4,687 | r | cachematrix.R |
# cachematrix.R
## Function: cacheSolve <- function(x, ...)
### 'x' is a matrix.
### This function asks function makeCacheMatrix whether or not it has 'x' in cache.
### If makeCacheMatrix has 'x' in cache that means there is no need to create its inverse matrix
### because this is already in cache (both are in cach... |
e88c027041ef120b23518d9a8527db5b4dc0973c | f640b73ca47aad41a982d882a9e7c5c691d7fb23 | /man/as_tibble.Rd | 2a3199721f40448b415137cdc3065254a9854ca3 | [
"Apache-2.0"
] | permissive | ccb2n19/sfnetworks | dcd16a14af4a2a46455448d408e6763569e829cc | c06418060ec0f9df1b378e60b7903ce7dbee44f8 | refs/heads/master | 2023-03-03T01:26:15.046876 | 2021-02-08T11:21:01 | 2021-02-08T11:21:01 | 339,112,861 | 0 | 0 | Apache-2.0 | 2021-02-18T09:14:34 | 2021-02-15T15:01:48 | null | UTF-8 | R | false | true | 1,887 | rd | as_tibble.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tibble.R
\name{as_tibble}
\alias{as_tibble}
\alias{as_tibble.sfnetwork}
\title{Extract the active element of a sfnetwork as spatial tibble}
\usage{
\method{as_tibble}{sfnetwork}(x, active = NULL, spatial = TRUE, ...)
}
\arguments{
\item{x}{An... |
f7a069d7dec0edd58cf56f65b0361810ba49daf1 | 17f1b5b761a43ec178602a43f24ac72c2d5d01a9 | /hmlasso/inst/testfiles/softThresholdC/libFuzzer_softThresholdC/softThresholdC_valgrind_files/1609897578-test.R | 806868709747c152f33b355c55ac9da4e3aa6065 | [] | no_license | akhikolla/newtestfiles-2 | 3e1882e7eea3091f45003c3abb3e55bc9c2f8f56 | e539420696b7fdc05ce9bad66b5c7564c5b4dab2 | refs/heads/master | 2023-03-30T14:44:30.614977 | 2021-04-11T23:21:23 | 2021-04-11T23:21:23 | 356,957,097 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 134 | r | 1609897578-test.R | testlist <- list(g = 2.1644539979134e+233, z = 1.32515051110005e-105)
result <- do.call(hmlasso:::softThresholdC,testlist)
str(result) |
7eeada0c0e05f86836a91512a4055543a66fc3ad | 4d3672136d43264176fe42ea42196f113532138d | /man/Firstchi.Rd | aa0661348f4acbe00af7e92f668502a7d57a6123 | [] | no_license | alanarnholt/BSDA | 43c851749a402c6fe73213c31d42c26fa968303e | 2098ae86a552d69e4af0287c8b1828f7fa0ee325 | refs/heads/master | 2022-06-10T10:52:15.879117 | 2022-05-14T23:58:15 | 2022-05-14T23:58:15 | 52,566,969 | 5 | 13 | null | 2017-07-27T02:06:33 | 2016-02-26T00:28:07 | R | UTF-8 | R | false | true | 588 | rd | Firstchi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BSDA-package.R
\docType{data}
\name{Firstchi}
\alias{Firstchi}
\title{Ages of women at the birth of their first child}
\format{
A data frame/tibble with 87 observations on one variable
\describe{
\item{age}{age of woman at birth of her first... |
a46abee26b2ed85d3e72985a8319e5abaf00ec51 | f76ff063f424701662126563603ea2114368e0aa | /cachematrix.R | 478126b1ec32ef9933716700f70c46445531e669 | [] | no_license | SayanPal/ProgrammingAssignment2 | ed237c083bdd4ae66cffe594140087418b39ca03 | 10c2048dca6043f42b9caad6574c0163ee8ec8e6 | refs/heads/master | 2020-04-01T13:58:58.577206 | 2015-05-21T16:55:03 | 2015-05-21T16:55:03 | 35,992,985 | 0 | 0 | null | 2015-05-21T05:31:30 | 2015-05-21T05:31:29 | null | UTF-8 | R | false | false | 2,059 | r | cachematrix.R | ## This file contains two functions makeCacheMatrix() and cacheSolve.
## makeCacheMatrix makes a 'special' type of matrix object that caches it inverse.
## cacheSolve checks whether the inverse already computed or not and return the
## value accordingly.
## Assumption: The matrix supplied is always invertible.
## m... |
ee07137fb92ec378750e93339e92ddb81bb667d7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Dowd/examples/tVaRDFPerc.Rd.R | 343af6674f8f0a78756b7fe21574895c70e3922b | [] | 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 | 468 | r | tVaRDFPerc.Rd.R | library(Dowd)
### Name: tVaRDFPerc
### Title: Percentiles of VaR distribution function
### Aliases: tVaRDFPerc
### ** Examples
# Estimates Percentiles of VaR distribution
data <- runif(5, min = 0, max = .2)
tVaRDFPerc(returns = data, perc = .7,
df = 6, cl = .95, hp = 60)
# Computes v give... |
a028bdebbbc6df95eb6eb6a6539826cd7015e42d | 0ddcd55cbfed8282a8f43ae141d6f3f5b9acb0b7 | /best.R | 1ff09a89a327771886dade0a3c54b9fcaf32f380 | [] | no_license | kristenphan/HospitalPerformanceAnalysis | e267b04ebfb1e6cdc3a2e185eab4c9d038e1da92 | 798890f4b3d0599aa2a31b54f42bbd00a65e12d1 | refs/heads/master | 2022-07-05T10:07:47.655848 | 2020-05-14T09:34:40 | 2020-05-14T09:34:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,069 | r | best.R | ## this program best.R houses the main function best.R which determines the best hospital in a specified
## state in terms of the 30-day mortality rate of a specified outcome (i.e., heart attack, heart failure,
## or pneumonia)
## best.R is supported by 4 functions:
## (1) read_data(): read in the dataset con... |
6cb60699223fa95d8b2e6339d234dcd25b0c5467 | d73d1fffc2c69ed18638262380186db28ef129c8 | /man/loopall_fun.Rd | bad1431f382d3e15feb74203b713e77cae7112c5 | [] | no_license | estalenberg/ptrm | 7f48a545e679fcefcddaf7009c8f7304e21883bf | 262755ead3ee8b6e0900775134ac401e799ddc4c | refs/heads/master | 2020-04-30T12:26:35.587724 | 2019-07-31T09:22:56 | 2019-07-31T09:22:56 | 176,826,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,611 | rd | loopall_fun.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loopall_fun.R
\name{loopall_fun}
\alias{loopall_fun}
\title{Energy volume functions}
\usage{
loopall_fun(assets.df, other.df, iab.df, priceprof.df, projyearend.in,
age.in, retireslim.in, addnew.in, productivity.in, dnsp.in, rba.in,
use.in... |
86993aa5ca025cf1c67893cf742f771539df6dbc | 11a39c775c4fa4d64aaf1a1eb41a75060c55d80c | /ui.R | d03ba93b823391781ca330782493990e2ab453ba | [] | no_license | mramalin1996/Airbnb_shinyapp | 92b9ec5085c4d00bd0b4f02751ca0b9b94a98d31 | 58699792edf47d2fdaedf20da411bea504ef077c | refs/heads/master | 2021-07-18T09:05:42.033454 | 2017-10-25T17:59:20 | 2017-10-25T17:59:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,441 | r | ui.R | library(shinythemes)
shinyUI(
navbarPage(title = "Airbnb Visualization",
id ="nav",
theme = shinytheme("united"), #https://rstudio.github.io/shinythemes/
##### Overview ##########
tabPanel("Overview",
br(),
br(),
... |
89d2f6f290eefafcc1d681dfd1e8a3c70f8b005b | 8ccbc8d238b98866dcf504fb366c99459f61aea5 | /plot3.R | df2a4527780920cdac28820180426d44f4ce69cc | [] | no_license | alfysamuel/ExData-Plotting1 | 2a6395886a10dee78df3c16ce1921e1f700733d6 | d81038cb647d4bf4b05371887ee2d1fb921f721f | refs/heads/master | 2021-01-20T21:49:08.422419 | 2015-02-08T20:10:49 | 2015-02-08T20:10:49 | 30,498,706 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | r | plot3.R | if (!"load_data.R" %in% list.files()) {
setwd("C:/Users/Alfy/Desktop/Coursera/Exploratory Data Analysis/")
}
source("load_data.R")
png(filename="plot3.png")
legendcols = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
plot(x=data$DateTime,
y=data$Sub_metering_1,
type="l",
xlab="",
... |
ad9d46764a908a7e1344285492477de9c4f39a27 | 4f156c03403ebc1e14df59e09389b0ccb51c16fa | /ui.R | 6e0459dae939291febe829a019334dd9fd194433 | [] | no_license | antonymaina12/ProcurementApp | 6cc4df0e4ce03ce211034bb81cc64b94eee0b688 | 2192d7d7798ddbc4e252727c0f3a36a9cbfdd9be | refs/heads/master | 2021-09-09T11:23:28.804212 | 2018-03-15T15:20:48 | 2018-03-15T15:20:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,993 | r | ui.R | # ui
library(shiny)
library(shinydashboard)
# Some data imports:
memberlist <- read.csv("data/memberlist.csv", stringsAsFactors=FALSE)[[1]]
memberlist <- append(memberlist, "All countries", after = 0)
choices <- setNames(memberlist, memberlist)
# User Interface:
ui <- dashboardPage(
dashboardHeader(title =... |
bf8941924f93360f549b58350524b782db20c386 | e4cef8cae061e7abb76316b76848105d90ae0205 | /run_analysis.R | d777ac3581c8721b0c0ae47317e81fb0ed7766f2 | [] | no_license | highsounding/Getting_and_Cleaning_Data | bdf3ca38cb16e410230441e73bbcfcf8650e4fc5 | f5a1ffc8237f06f3dce1dd1e02a6ab4552bfc818 | refs/heads/master | 2021-08-26T07:29:53.651670 | 2017-11-22T09:15:16 | 2017-11-22T09:15:16 | 111,662,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,289 | r | run_analysis.R | library(dplyr)
# build the tidy data set
build_full_data <- function(){
feature <- read.table("features.txt") # read feature items
activity <- read.table("activity_labels.txt") # read descriptive activity names
mean_or_std_seq <- grep('mean|std',feature$V2) # extract items related to "mean" or "... |
02780ccef702e746824b98c622002c01deca51ce | c685bdc4bebe1624a0fdc9bb956862889a3ec03c | /MultiSppBiomassAllometric.R | a41f6a8d25d3a55fc9bfcdfd3092a202d59a49e2 | [] | no_license | derek-corcoran-barrios/RtoAmpl | dffd0db812d86b74a80847ae0b8db2eb0f1c861c | 62a2f07b87f2b6d11eb3222ade6e7f1ad9b642dd | refs/heads/master | 2020-06-26T04:46:16.622097 | 2017-11-24T17:28:58 | 2017-11-24T17:28:58 | 97,002,573 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,831 | r | MultiSppBiomassAllometric.R | library(gdistance)
library(RtoAmpl)
DistMax <- function(m){3.31*(m^0.65)*10 * 1000}
Density <- function(m) {m*4.23*(m^-0.75)}
DistConect2 <- function(Raster, m, Time = 7){
#First we make a transition layer with the function transition from gdistance
h16 <- transition(Raster, transitionFunction=function(x){1},16,... |
a41dfad04c15d0bf9877e2fe3e17f8f4e73f53fb | cd51126dceadcfae4873e108a96d634b47fdc9bf | /cachematrix.R | ad8e4d5feee98c93daec4f2b68441bbfb4fa7a04 | [] | no_license | Ludmmat/ProgrammingAssignment2 | 1d2283477e38c6829c3f3dd7a4734e3e35337bc0 | a2ee71babb98278d08062f509873e47d2857033b | refs/heads/master | 2021-01-16T19:01:47.729810 | 2015-02-21T23:06:12 | 2015-02-21T23:06:12 | 31,142,015 | 0 | 0 | null | 2015-02-21T23:06:12 | 2015-02-21T21:37:10 | R | UTF-8 | R | false | false | 1,704 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
# makeCacheMatrix creates a list containing a function to
# 1. set the value of the matrix
# 2. get the value of the matrix
# 3. set the value of inverse of the matrix
# 4. get the value... |
55405a4783b47d02e44bb33b48474c46313a1639 | 339fc5ede2d01f040ca3b2607b48cab6afeb7008 | /man/SpASamples.Rd | 31f356f023f82bd0fa7ed03578446dd462a1cc79 | [] | no_license | cran/stringgaussnet | bdb5814d82e6c54f226f4ea06942d5415a9d6b37 | 707d1f5e8d5325413a90f50c2aef454d0134b347 | refs/heads/master | 2021-01-19T12:34:04.869401 | 2015-07-22T00:00:00 | 2015-07-22T00:00:00 | 37,529,427 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,515 | rd | SpASamples.Rd | \name{SpASamples}
\alias{SpASamples}
\docType{data}
\title{
%% ~~ data name/kind ... ~~
Samples description for SpA example data.
}
\description{
%% ~~ A concise (1-5 lines) description of the dataset. ~~
The example data provided in our package are transcriptomic profiles of MD-DCs from 9 patients with AS and 10 he... |
63a956833440fecdadec52f727635a9b6a38fa0b | ba0d52a9447cc2cedcaacafd8349fc50a32363b5 | /R/data.R | cd6d007cfa8a00caeda2f93f9e288f16281924c7 | [
"CC0-1.0"
] | permissive | robschick/tangled | 49590a754531b8e50294abb4d86fcd9cc85d037c | e4c0e49fa87802dd39fba01dc4fba5cef25e7b31 | refs/heads/master | 2023-04-07T19:24:43.838552 | 2022-05-04T19:11:30 | 2022-05-04T19:11:30 | 33,547,111 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,119 | r | data.R | #' Information about gear carrying time on entangled right whales
#'
#' A dataset generated by Amy Knowlton of the New England Aquarium as part of
#' the PCAD/PCOD project looking at health and the impacts of disturbance on
#' vital rates in North Atlantic right whales. These data are used to establish
#' the "win... |
a153057c3337398ebba5923f3c9d0ed14673ef07 | 49a21578d90cb5cc8a07b3688df255a083364e62 | /man/hg19.GoNLsnps.Rd | 2c1a0979cef5d84877f5a85546038372526ffd4e | [] | no_license | molepi/omicsPrint | 953438f720d37b8753fd0f2a03d819244269c6d6 | ad471131be810ae674f638169d9c7e9391a50689 | refs/heads/master | 2021-01-18T03:25:45.509787 | 2018-11-30T08:33:52 | 2018-11-30T08:33:52 | 85,822,289 | 3 | 4 | null | 2017-12-13T20:53:22 | 2017-03-22T11:58:56 | R | UTF-8 | R | false | true | 3,442 | rd | hg19.GoNLsnps.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{hg19.GoNLsnps}
\alias{hg19.GoNLsnps}
\title{Dataframe with overlaps GoNL variants and 450K probes}
\format{A data frame with 207866 rows and 19 variables:
\describe{
\item{CHROM}{chromosome, X and Y chromosomes a... |
291d256e405483fab3e746f1153a61062d79d7a6 | 6019c5cd51519eacccb2c16d0c64c00fdeeef96c | /Basics/R Data Types, Arithmetic & Logical Operators.R | 90211d9ee52da9298b8178ece2021048be979e66 | [] | no_license | manikandansubramanian562/R---Programming-Datascience- | 227d6f756191bf9548f56496f213c8f1fe34f6cc | 44dda1c4dbc864b54e4cd449174ab9a832f674e4 | refs/heads/master | 2020-08-09T22:01:30.414663 | 2019-10-16T03:29:47 | 2019-10-16T03:29:47 | 214,185,421 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,803 | r | R Data Types, Arithmetic & Logical Operators.R | """R Data Types, Arithmetic & Logical Operators"""
#Basic data types
x <- 32
y <-12 #
z <- x-y
class(z)
print(z)
##Character and Boolean type............................................................
m <- "R is Fantastic"
class(m)
a <- TRUE
class(a)
## Variables................................. |
0177318cb09649e4b09f6aa52883c96c224ecac6 | 246189c0e240e174b9ca74e2a42bfecee79cc9e1 | /man/createClonalReproTransitionMatrix.Rd | 672609bf53662f76b779ab1d30de42c1e43c013a | [] | no_license | ksauby/GTMNERRproc | f3bcd140578d710c9b013da83d9ac8d08e781eee | fd5a073d5fd2690b6fde64a0313d1a3fdfe07645 | refs/heads/master | 2021-04-06T13:03:29.008590 | 2017-11-15T20:35:53 | 2017-11-15T20:35:53 | 83,352,082 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 940 | rd | createClonalReproTransitionMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createClonalReproTransitionMatrix.R
\name{createClonalReproTransitionMatrix}
\alias{createClonalReproTransitionMatrix}
\title{Create Clonal Reproduction Transition Matrix}
\usage{
createClonalReproTransitionMatrix(clonal_repro_dat, TMdata, st... |
2e5ae73cc2d2c3c0378386c36d427775e12c5b9a | 6340b17f789b8c0e497f6905939616385bf13142 | /ProbableBirdFunction.R | 2e15652cb31ee247ef89eec9086f349a3997b129 | [] | no_license | nheer/eBird_LikelyNextBird | f3c20dbca45b8c968c82377287e3ed2c27a8b4f7 | 675efd150c0572afe09546d7bb852b0870868714 | refs/heads/master | 2021-07-09T10:35:08.097799 | 2017-10-02T15:07:19 | 2017-10-02T15:07:19 | 105,548,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,072 | r | ProbableBirdFunction.R | # Function plots top ten most likely birds seen given the observation of an input species
# Inputs: ConditionalProb is a matrix of conditional probability relationships between bird species
# IndividualProb is a table of the likelihood of observing each species
# SpeciesName is a string of format "House... |
dc9d56089bc9723540d8dc9ec39f020c42765a7a | 3fc3964396f8010aae9345d37f551c4431c52ff9 | /R/trac_all.R | 23db4748ba5264d6e9a45e26dd8e71d5c3d21511 | [] | no_license | muschellij2/freesurfer | ff96f465ebbfbb0b7ce18644be5f4c5ea753fc45 | 7d70f616e760d8d3a453a652d98756e34877fed7 | refs/heads/master | 2021-06-24T00:57:12.644687 | 2020-12-08T18:41:34 | 2020-12-08T18:41:34 | 67,370,835 | 9 | 8 | null | 2020-11-15T23:42:38 | 2016-09-04T22:12:47 | R | UTF-8 | R | false | false | 649 | r | trac_all.R | #' @title Tract Reconstruction Helper for trac-all from Freesurfer for All Steps
#' @description Wrapper for the \code{trac-all} function in Freesurfer
#' for All Steps
#'
#' @param infile Input filename (dcm or nii)
#' @param outdir Output directory
#' @param subjid subject id
#' @param verbose print diagnostic mess... |
600e53459023a78c008307be2f66ab9a702318ba | 0c46573b5cfeea338493749c2d12b2650b531406 | /man/extract_drop_out_from_df.Rd | 442d7f7af3ad30fb01289dc1787c8f19ca48c876 | [] | no_license | cran/dropR | 42a5f4450594995a5fe0bf18a9be9f08821a6a39 | 9781361dd2f39b54c98d19632b0094fe75db1ae9 | refs/heads/master | 2021-01-19T09:05:46.525033 | 2015-01-01T00:00:00 | 2015-01-01T00:00:00 | 28,707,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 492 | rd | extract_drop_out_from_df.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{extract_drop_out_from_df}
\alias{extract_drop_out_from_df}
\title{Extract Drop Out from a Data.Frame}
\usage{
extract_drop_out_from_df(df, q_pos)
}
\arguments{
\item{df}{a data.frame}
\item{q_pos}{columns that contain questions}
}
\description{
Find drop in Da... |
315445644771cf49c5a9c52accd592af191b6993 | 8bd71959e8cbf8335ed632581b3194d668e45739 | /R/train_predict.R | 1fb3306d144747a8495639ae86469391bd90b1bb | [] | no_license | Yael-Travis-Lumer/KMforCSD | 393ccbefd2bd5684f82e4fcddde35041a7ac7e9a | 747deae9e90ecc29697855c3bfa5b28f4ecb97b5 | refs/heads/master | 2023-05-09T05:01:09.000865 | 2021-06-01T13:07:12 | 2021-06-01T13:07:12 | 280,110,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,097 | r | train_predict.R | #' Kernel Machine for Current Status Data - Training
#'
#' \code{KMforCSD} returns the KM vector of coefficients, together with the kernel function and the support vectors.
#'
#' @param data A data frame consisting of the p-dimensional covariates Z, the current status indicator vector delta, and the censoring times... |
24134f18520d503b4fb2d5f673fa7ba23242b12b | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.database/R/elasticache_interfaces.R | 62dffbc58b54efac3e756893c3342b271b788127 | [
"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 | false | 230,539 | r | elasticache_interfaces.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common populate
#' @include elasticache_service.R
NULL
.elasticache$add_tags_to_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceName = structure(logical(0), tags = list... |
7b18c42370e97dfd5a672a9e76b70ea0dcfbc57e | 7cbd54c390f57982bb0f81ae67351cf512f08ad1 | /Programs/ExamplePipeline/GetQuadraticParametersDFE.R | 35264d7f7ede628673d5dce748b6dff2157a423b | [] | no_license | dortegadelv/HaplotypeDFEStandingVariation | ee9eaa9a44169523349bef09d836913221bf24cb | eb196acf6bbaa43f475f132b667f0f74b6f7cee4 | refs/heads/master | 2022-05-25T03:47:39.948444 | 2022-03-07T22:41:15 | 2022-03-07T22:41:15 | 108,029,910 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,308 | r | GetQuadraticParametersDFE.R |
Data <- read.table("DifRecRate/PLGivenSTableWithRecs4.txt")
DFETable <- read.table("DifRecRate/DFETableOfProbabilities.txt")
RecValuesOne <- read.table("DifRecRate/ResampledBpRecRatePerVariantNoCpGLeft.txt")
RecValuesTwo <- read.table("DifRecRate/ResampledBpRecRatePerVariantNoCpGRight.txt")
RecValues <- rbind(RecValu... |
f17e75d75c2ae0669cd590a006007253e1dd955c | 85d8f91b58f912130362bd6415bbdb5e2e0cc7c0 | /man/print.ConfusionMatrix.Rd | d1d57e37874a27dcafdb03f932ffa2f3a5bfcd11 | [] | no_license | Displayr/flipRegression | c8ab22ffc875ca09deac2ec01ffaf5371501c860 | 871819d800ebb24a7331336bd4cfea24b35afb48 | refs/heads/master | 2023-08-21T21:39:02.916680 | 2023-07-19T05:50:48 | 2023-07-19T05:50:48 | 59,715,681 | 7 | 5 | null | 2023-08-03T07:19:54 | 2016-05-26T03:09:43 | R | UTF-8 | R | false | true | 460 | rd | print.ConfusionMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/confusion.R
\name{print.ConfusionMatrix}
\alias{print.ConfusionMatrix}
\title{\code{print.ConfusionMatrix}}
\usage{
\method{print}{ConfusionMatrix}(x, ...)
}
\arguments{
\item{x}{An object of class \code{\link{ConfusionMatrix}}.}
\item{...}{... |
25319dd2c82fc11245b283fd2c2e8c4f8bfff520 | 64098b83f218221064dacb4307f9b844e9a70373 | /man/LL.Rd | 966fc836139aeb779813b1a4b0ea6ba278569dce | [
"MIT"
] | permissive | takuizum/irtfun2 | 07800c5e6abeb9eb1892724582be7b9ed2202387 | def9eac15a1150804f3702cf3f84df1c638a1c38 | refs/heads/master | 2021-07-19T00:29:21.794826 | 2020-05-06T09:28:05 | 2020-05-06T09:28:05 | 151,583,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 497 | rd | LL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estheta.R
\name{LL}
\alias{LL}
\title{The log likelihood function of IRT 1~3PLM}
\usage{
LL(u, theta, a, b, c, D)
}
\arguments{
\item{u}{the item response pattern}
\item{theta}{the person ability parameter}
\item{a}{the slope p... |
57b3fa81605b6c2876be420dbfac9f38d991f734 | bcc81487edbe00a92f5a076bef6ab70dea20c4c8 | /matplot (1).R | ec77f7a624ca7e71781099f3d2176dc48404cacb | [] | no_license | ismailhm/R-programming | 625aa49a75d303bef93a0bf0993fa095c7d1c6b0 | 1b62c7859d241a799bf0b3b98c2906df148a046d | refs/heads/master | 2020-04-20T16:42:19.928870 | 2019-02-03T16:52:31 | 2019-02-03T16:52:31 | 168,966,306 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | r | matplot (1).R |
?matplot()
# matplot() plots the matrix columnwise.
FieldGoals
# t() gives the transpose matrix
t(FieldGoals)
matplot(t(FieldGoals / Games), type="b", pch=15:18, col=c(1:4, 6))
legend("bottomleft", inset = 0.01, legend = Players, pch=15:18, col=c(1:4, 6), horiz = F)
matplot(t(FieldGoals / Field... |
5182c08b9a859ba6fd5a7aa7d5a3e4df2e9ec0df | d2722569c550481b2d92cd1dfc0b472b3cb5ada5 | /scripts/deseq_init.R | 1e2eab551b623c5d723ffe233fe708f27a1b43ee | [] | no_license | moritzschaefer/cuttag | 3f7ad62688951f17ea2f5236fd186f21c4c39fd1 | 1fb6353ee9eff988ab458b1984255995fae0c440 | refs/heads/master | 2023-02-01T23:03:45.520671 | 2020-12-16T14:16:21 | 2020-12-16T14:16:21 | 302,466,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,156 | r | deseq_init.R | # from Step28 on (https://www.protocols.io/view/cut-amp-tag-data-processing-and-analysis-tutorial-bjk2kkye.html)
library(DESeq2) ## For differential analysis section
library(GenomicRanges)
library(dplyr)
library(chromVAR)
# Create a master peak list merging all the peaks called for each sample.
masterPeak = GRanges()
... |
094384983417965276c182482c7e0c7cbdd810a1 | 381817806885ab9cad39cf6c845fc94fad9b69f7 | /server.R | 0e0bc612210fad115b0f951cba5f57d1e641578b | [] | no_license | josephuses/pse_stocks_calculator | 9d32e2edb05cfdd42ff4be566a538b4d13c6600e | 3f7fd2eb5ea630234f5257923726fad54b470e14 | refs/heads/master | 2020-12-14T09:49:18.281533 | 2017-11-13T09:01:56 | 2017-11-13T09:01:56 | 95,448,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 757 | r | server.R |
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
source("helpers.R")
shinyServer(function(input, output) {
stock <- reactive(input$stock)
buyPrice <- reactive(input$buy_price)
buyVolume ... |
ca32e7ef411c97cbac58826d6c35809ca7f05cd8 | 4eef3c0016ed9271e81c880e1b9585e0c8b7da3a | /man/add_plate.Rd | 40a0e4032a3d702da3ac348b416a19ac627262e0 | [] | no_license | cran/plater | f5e75790d83c012824b6e7a82c474d03fb508608 | dc8d65d32642f03005bcee1c677ff6169f343589 | refs/heads/master | 2022-03-10T13:13:43.312268 | 2022-02-11T18:40:02 | 2022-02-11T18:40:02 | 70,241,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,640 | rd | add_plate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_plate.R
\name{add_plate}
\alias{add_plate}
\title{Read a plater-formatted file and combine it with an existing data frame.}
\usage{
add_plate(data, file, well_ids_column, sep = ",")
}
\arguments{
\item{data}{The data frame to mer... |
ff8baa720a4ef083c36910ad6b97cea47945e3e7 | 80dba8af3dc948d8e161ed84141f83598a625748 | /R/effective_entropy.R | d5ae91efee3c46dc80b90ec3810450ae0135cb86 | [
"MIT"
] | permissive | simdiversity/entropy | 4e937bd7f2b912c96c9a52cf2662676e5685bdda | bd6a8824f0b9d377ee592b8efcd01d1afa9d3a8e | refs/heads/master | 2023-03-03T02:04:21.921738 | 2021-01-17T10:40:57 | 2021-01-17T10:40:57 | 259,862,477 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,885 | r | effective_entropy.R | #' Compute effective entropy
#'
#' @param D A dissimilarity matrix
#' @param f Weigh array
#' @param Nloop Number of loops
#' @param Nfine finesse of powers
#' @param pa initial power
#' @param pb final power
#' @example
#' M <- matrix(seq(9), nrow = 3)
#' f <- rowSums(M) / sum(M)
#' D <- dist(f*M)
#' effective_entropy... |
edce5d04e92a5e03be6e79e36e9e73c18ff0bfa0 | 5b0652e487cab9e57f3ae534265d69e17584bc88 | /man/to_sound_selection.Rd | b62af6d8e35bb0625d344aa5786113be52945ab6 | [] | no_license | DanWoodrich/Rraven | 192aa0f2c57a510016df549c1a5e85564bf9a8d2 | 845a07ea9f6e205d6e26ef4804955638e2206086 | refs/heads/master | 2020-03-30T05:18:41.909381 | 2018-09-28T20:50:36 | 2018-09-28T20:50:36 | 150,792,078 | 0 | 0 | null | 2018-09-28T20:36:49 | 2018-09-28T20:36:49 | null | UTF-8 | R | false | true | 2,726 | rd | to_sound_selection.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/to_sound_selection.R
\name{to_sound_selection}
\alias{to_sound_selection}
\title{Convert Raven's selection table file to sound selection file}
\usage{
to_sound_selection(path = NULL, dest.path = NULL, recursive = FALSE,
parallel = 1, pb = TR... |
70e328bfe538b4c90821eb9b9d353e54c1459930 | e43909be797573d17925701d9e95a287e7faac21 | /man/Table.Rd | 3cb74bbe549592760f649f3f6204e83025aba014 | [
"MIT"
] | permissive | SciViews/form.io | 44143ffd8da93a65292b7e8e3ae78c0f9f1a336d | dbd7122377eb26de0e5080786f16f7854695d885 | refs/heads/master | 2020-08-03T20:15:03.889366 | 2020-04-27T22:23:59 | 2020-04-27T22:23:59 | 211,873,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,762 | rd | Table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Table.R
\name{Table}
\alias{Table}
\alias{tab}
\alias{print.knitr_asis}
\title{Create a Table caption}
\usage{
Table(text = NULL, table = NULL, label = NULL, hold = NULL)
tab(..., hold = FALSE)
\method{print}{knitr_asis}(x, ..., sep = "\\n"... |
2ce88a8d6d547ed27b7097c782a16a173af22f61 | c2b24e8dcb0fa8b0e5d44002d9aa675ba968ad44 | /Practice2/Task8.R | 2684e3b281896a31ac41d3aa163df24dbacd30f4 | [] | no_license | thinkingabouther/DataAnalysis | 00efcba3355baa7af2c8d22dbe4a81171d12af31 | 5c2a0eb98a3f12ae6ff536fb9856d6fe210cd515 | refs/heads/master | 2022-09-21T23:12:39.718162 | 2020-06-05T14:30:58 | 2020-06-05T14:30:58 | 255,110,175 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 73 | r | Task8.R | A <- array(c(1, 50, 1, 75), dim=c(2,2))
B <- c(100, 6625)
solve(A) %*% B
|
ebea9168b96e04d37bb9b9296cbe7da8aba78442 | 0fb33ca8eef07fcb5d3687f4cf2793ef187f79f4 | /man/scoreFACIT_AI.Rd | 68f19d1d0488618e37cb7d737c7ae8d59b7f0dc1 | [
"MIT"
] | permissive | raybaser/FACTscorer | e3c10b9a065cb5b6290b211519b72ed9171a1fc2 | 070a1cf479ee8c1f19bf6a295c2ed0d544ff6406 | refs/heads/master | 2022-03-16T20:20:29.198088 | 2022-03-12T09:42:36 | 2022-03-12T09:42:36 | 61,918,573 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,542 | rd | scoreFACIT_AI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sx-scoreFACIT_AI.R
\name{scoreFACIT_AI}
\alias{scoreFACIT_AI}
\title{Score the FACIT-AI}
\usage{
scoreFACIT_AI(df, id = NULL, updateItems = FALSE, keepNvalid = FALSE)
}
\arguments{
\item{df}{A data frame with the questionnaire items, appropri... |
dba97c70e0416774e2b8f277db958d128bf96ade | 172f6eb4329de933c90988cfb1fb04f0f1dcd3ca | /livestock_rasters.R | 8cc036d2fe4fa24dc6c9a585efffb558803d3ef0 | [] | no_license | maggieklope/HWC | 60ce400562cdd9f1e8ddaab95e70074b156b3c8e | 586572f04518788b343597f003c9f8a4edb0497d | refs/heads/main | 2023-07-29T22:56:33.625560 | 2021-09-20T20:30:30 | 2021-09-20T20:30:30 | 390,057,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,474 | r | livestock_rasters.R |
# =============================================================================
# Name: livestock_rasters.R
#
# Author: Maggie Klope (mmklope@ucsb.edu)
#
# Description: Script that akes an SDM prediction raster and an FAO livestock abundance raster and transforms them to the same resolution. Al... |
27040ca1bc34f09ff329649ab6eae717407847b9 | 357a66f0bbd70f2cdea9d530d57e4843d72866b7 | /man/BAMD-package.Rd | dfba4d332e2d93e3be26d3550c1cf1f90aa97136 | [] | no_license | cran/BAMD | d2fa7391b51d2b0dde08141fcb764affb8c1bbdf | 7fe4c58e3e98bb14533cdbb52133619740ad6c17 | refs/heads/master | 2016-09-16T12:31:00.669210 | 2011-06-30T00:00:00 | 2011-06-30T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,620 | rd | BAMD-package.Rd | \name{BAMD-package}
\alias{BAMD-package}
\alias{BAMD}
\docType{package}
\title{
Bayesian Association Model for Genomic Data with Missing Covariates
}
\description{
This package fits the following linear mixed model
\deqn{ Y = X \beta + Z \gamma + \epsilon }
where the covariates
for the random effects (in the Z-matrix... |
e3f588fe96cf26bf5a9c5bf0761c219db85945a5 | f351ca83cbdb2f7c0b6f1451999ccf5c6a4cfbb7 | /R/covered.call.R | 8fc7c7773ad13fb4f31b0bd714e2e69c7b49d6da | [] | no_license | IanMadlenya/FinancialMath | 3dafdfe139f78c671d6e1c939cf4271d775c7917 | 7cf9133ac2bb76c1602e65492085b316d7641424 | refs/heads/master | 2021-01-23T12:37:56.309260 | 2016-12-16T21:51:34 | 2016-12-16T21:51:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,287 | r | covered.call.R | covered.call=function(S,K,r,t,sd,price=NA,plot=FALSE){
all=list(S,K,r,t,sd,plot,price)
#NULL
if(any(lapply(all,is.null)==T)) stop("Cannot input any variables as NULL.")
#Length
if(any(lapply(all,length) != 1)==T) stop("All inputs must be of length 1.")
#Numeric
num2=list(S,K,r,t,sd,price)
na.n... |
3b8a29a36b67b96cefe95e4a8dd506f3bd30a8d0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mcclust/examples/vi.dist.Rd.R | eb1e0d94868a39538430951c1fd1a54b3af0ff5c | [] | 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 | 294 | r | vi.dist.Rd.R | library(mcclust)
### Name: vi.dist
### Title: Variation of Information Distance for Clusterings
### Aliases: vi.dist
### Keywords: cluster
### ** Examples
cl1 <- sample(1:3,10,replace=TRUE)
cl2 <- c(cl1[1:5], sample(1:3,5,replace=TRUE))
vi.dist(cl1,cl2)
vi.dist(cl1,cl2, parts=TRUE)
|
74c09473d32d23ed518dacc72356bf9cbd75112d | 5cf86249135481d3e8a6d8fe45cc8983f52fa6f6 | /lin_reg.R | ff065b6348e7b100cbf96075839602faeb426bf1 | [] | no_license | emmaSkarstein/INLA_within_nimble | bf1a7eb875447cd15fa57ea343e16e0d8fcf29a0 | 15c398cf3a8184d0dae2ad488c18b1077e6a0ca7 | refs/heads/main | 2023-08-23T13:17:41.237448 | 2021-10-14T16:14:42 | 2021-10-14T16:14:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,104 | r | lin_reg.R | #Packages needed to run the model
library(nimble)
library(INLA)
library(mvtnorm)
library(MASS)
library(parallel)
library(coda)
# function for generating samples
sample.linreg <- function(){
n = 100
x1 = runif(n) #covariate 1
x2 = runif(n) #covariate 2
err = rnorm(n) # error
y = 3 + 2*x1 -2*x2 + err # Respons... |
1071d7b2f83808bfc2f38dfdf62b0a20f8519128 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/embryogrowth/examples/plot.tsd.Rd.R | b053ec621c7c4033b6d7dfb48772f4d4a397c34c | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 599 | r | plot.tsd.Rd.R | library(embryogrowth)
### Name: plot.tsd
### Title: Plot results of tsd() that best describe temperature-dependent
### sex determination
### Aliases: plot.tsd
### ** Examples
## Not run:
##D CC_AtlanticSW <- subset(DatabaseTSD, RMU=="Atlantic, SW" &
##D Species=="Caretta caretta" & (!i... |
10b7178fa5b42e4d8bd94f74a7a29272c0843034 | 29585dff702209dd446c0ab52ceea046c58e384e | /assertive.properties/R/assert-is-empty-scalar.R | 0bd08288153f10e6a2240dd55818bb4390a381fb | [] | 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 | 2,495 | r | assert-is-empty-scalar.R | #' @rdname is_empty
#' @export
assert_has_elements <- function(x, n,
severity = getOption("assertive.severity", "stop"))
{
assert_engine(
has_elements,
x,
n = n,
.xname = get_name_in_parent(x),
severity = severity
)
}
#' @rdna... |
b6c67de27ff0f58492058b30958f921ceac4cf26 | 55eebcf4970cabc7685fc37539b1d4cd8a42a0a3 | /man/Data2D.Rd | c221aefe6e13543fd7cff29cf38bf567c1c23385 | [] | no_license | cran/clusTransition | f56c22a12b9eca1bf13b4ce753203afde2b8c1c9 | 2782b0ad09b265f7fb2aef7fbf097d244dbd8bde | refs/heads/master | 2023-03-08T17:58:33.643054 | 2021-02-22T19:20:02 | 2021-02-22T19:20:02 | 341,417,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 439 | rd | Data2D.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Data2D.R
\docType{data}
\name{Data2D}
\alias{Data2D}
\title{Synthetic Datasets (Two Dimensional)}
\format{
A data frame
\describe{
\item{x1}{X1.}
\item{x2}{X2.}
\item{class}{Class membership.}
}
}
\usage{
Data2D
}
\description{
A list of data... |
43ec0e0ec76d9a3b89a43c134e16eba7e9c33df7 | 9f68e39be025eaf6dd9028ff7efd529a95645385 | /ui.R | 3c999cc25f8174e994041ec7adbf445d55f576cf | [] | no_license | marion-paclot/Etalab-Datafin | 2902d9bffa8dc0a7ee4c3160f645173478bf06ed | 5526bf718e8335ad84f8ca05a20f8fc4fca3f94c | refs/heads/master | 2020-03-20T21:19:15.893470 | 2018-07-13T10:47:39 | 2018-07-13T10:47:39 | 137,735,007 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,345 | r | ui.R | # UI Application Datafin données financières de l'Etat
shinyUI(fluidPage(
# Titre de l'application
titlePanel("Informations relatives à une mission"),
fluidRow(
# Choix d'une année, d'une mission. Affichage en gros
column(3,
selectizeInput('exercice', "Sélection d'un exerci... |
fac35da41e8248d4038444e8c4505f0906cf36a9 | b406a05460da47fd8b34dbc1409f0ef1eabfa9a1 | /Code/Main_code.R | 3dc3dd4c08b4e6467a81fe04015dbfd2bd5f440b | [] | no_license | muharif/PKM_Transcript_paper | 6e643e0690fe1a04d2953e6bce9f81cf64502604 | dedcd79179a5a5a2fd28a6f4229fff15170b6cb9 | refs/heads/master | 2022-03-05T13:32:58.267611 | 2019-10-16T12:36:44 | 2019-10-16T12:36:44 | null | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 48,083 | r | Main_code.R | #Main code
#####################################################################################################
#####################################################################################################
#Figure 1A was drawn manually based on the source data.
#############################################... |
524624647eecf138ece57dce805340074d621f2b | 1be8aec7140b5c2e7d29d8a1b3edc1367aafa0bb | /plot1.R | 0fea76d11bb2d0f8240215dba5b53280bba28939 | [] | no_license | mixacom/ExData_PeerAssessment2 | db612b8efa6c5b724cb6c113d0b6d67b4ad344cb | ed121d72294537140bd8eaa6a81e1b185748d580 | refs/heads/master | 2021-01-22T01:38:07.248679 | 2015-12-27T23:06:20 | 2015-12-27T23:06:20 | 48,663,716 | 0 | 0 | null | 2015-12-27T22:58:22 | 2015-12-27T22:58:21 | null | UTF-8 | R | false | false | 961 | r | plot1.R | nei <- readRDS("measures of pm/summarySCC_PM25.rds")
scc <- readRDS("measures of pm/Source_Classification_Code.rds")
nei$year <- as.factor(nei$year)
yrs <- split(nei, nei$year)
values <- lapply(seq_along(yrs), function(x) {
assign(c("y1999", "y2002", "y2005", "y2008")[x], yrs[[x]], envir=.GlobalEnv)
}
)
te <... |
d3c6bbef1cc50adfda87d481736dc583201fa75a | 3da91b996bd811d1755fef0751aaef1e823f7608 | /Figure3.R | a9e5ddd9d2daf4a571c8f8a675568af8e3317e0b | [] | no_license | mengysun/Dissecting-noise-project | 00dc83e7e90165d9d0c587ecf6be7f6cb1c19428 | a495f6b4579a2a549569d0dc8c84e208fda43e42 | refs/heads/master | 2020-06-01T13:23:50.287333 | 2019-06-08T21:03:48 | 2019-06-08T21:03:48 | 190,793,421 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,621 | r | Figure3.R | library(dplyr)
library(ggplot2)
library(ppcor)
library(ggpubr)
library(gridExtra)
library(grid)
library(ggExtra)
Gene_noise_raw<-read.table(file="Data/cl7_noise_raw",sep="\t",header=TRUE)
Gene_noise_table<-read.table(file="Data/Gene_noise_table_all_cells",sep="\t",header=TRUE)
Gene_noise_table$intrinsic_noise<-Gene_noi... |
b34660323c9bdffa1534b645d76c50cae39dc45d | 825f3001ed26446b607ff83ce7bd04915adc3600 | /00_data_prep/01_load_moose_data.R | 5157c9f037b511f0669e5eeee381cbf30486be44 | [
"Apache-2.0"
] | permissive | Bevann/telemetry | a8b25af47689ec6928f512ea4471b6b86cbe361e | c47dc42bb2403fcba7f405c3f589cb23746306f0 | refs/heads/master | 2020-11-26T16:42:36.253405 | 2019-12-18T19:01:35 | 2019-12-18T19:01:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,375 | r | 01_load_moose_data.R | # Copyright 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... |
ce3fbb677b1a4fb0ef58b86caaccf9e264045458 | 91f8d1c6eb5fc33c2d4de0957803634db0b9bd9d | /classification-tests/RcodeforDatgen.R | 5a57199f965be492670190454285aed746ca6ea1 | [] | no_license | vipinkumar7/Machine-Learning | 650306ce4317c4f4aef6756c9002e4765efeada0 | 79894ef0f6bf140f099415dfdf184401c1f4e507 | refs/heads/master | 2021-01-24T16:09:37.676388 | 2019-05-17T13:08:47 | 2019-05-17T13:08:47 | 17,623,072 | 0 | 1 | null | 2014-07-04T05:59:58 | 2014-03-11T08:20:58 | Java | UTF-8 | R | false | false | 879 | r | RcodeforDatgen.R | ##@author Vipin Kumar
### random Binomial distribution for the probability of success 1/2 /3 1/6 respectively
U <- array(0,dim= c(10,30))
for(i in 1:10)
{
U[i,] <- rbinom(30,10,1/6)
}
write(U, "./onebysix.txt",ncolumns=30,append=TRUE, sep = "\t")
for(i in 1:10)
{
U[i,] <- rbinom(30,10,1/6)
}
write(U, "./onebysi... |
d2d7411a99559367913d1e4975715f7ed03ccc63 | f0d4245d2b047fa6ae960c0be28eaf32384086f7 | /server.R | a5b106c106685d21507e129c5fc1461ba830f48b | [] | no_license | andersfi/Shiny_TrondheimProvefiske | 161fb2ee14d643e8a472d1e714383a910f81b5af | 91245dd1d006ab022c359369005cab41dd0192e0 | refs/heads/master | 2021-01-20T20:15:30.792184 | 2016-08-12T09:28:49 | 2016-08-12T09:28:49 | 65,498,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,234 | r | server.R |
library(shiny)
library(dplyr)
library(knitr)
library(DT) #install.packages('DT')
library(leaflet)
library(tidyr)
library(curl)
shinyServer(function(input, output) {
output$lengthHist <- renderPlot({
# Expression that generates a histogram. The expression is
# wraped in a call to renderPlot to indicate th... |
c2cf72ae796dd18e399e5bcf42e52e39230cf593 | 027c5bdd13efa7d43e7cf9a4949249d89a6eb7f9 | /part3/apriori.R | 8fa9e1235949fcdefb6aa8a1e53b18e5bc92aeb7 | [] | no_license | sjjaved/work | 492b7abf8c457181a5e89d0e8cb02d232643c831 | 54f7024a28f71b361f8d920d890f0f830d989d8d | refs/heads/master | 2021-01-19T14:04:34.427052 | 2017-04-13T04:44:22 | 2017-04-13T04:44:22 | 88,123,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,060 | r | apriori.R | # Load the library for doing association rules
# install.packages('arules')
library(arules)
# Data preprocessing
data("AdultUCI")
AdultUCI[1:2,]
AdultUCI[["fnlwgt"]] <- NULL
AdultUCI[["education-num"]] <- NULL
AdultUCI[[ "age"]] <- ordered(cut(AdultUCI[[ "age"]], c(15,25,45,65,100)),
la... |
86a58f6821dff37ca4ce0ecd619bb684aa4d4509 | 0041e828de36fcfe9364cd9dafd5a030898c8de9 | /RBF/validation_rbf.R | f5e9d8b2fc63a6536bc75859a089ff9d70bde47b | [] | no_license | vcaitite/final-work-neural-network-accent-recognition | 27671a99a0d0804447cab505c8193569eefd9b84 | cf7761a86199601f2b7d6ab79f9d665a54b76729 | refs/heads/main | 2023-04-01T00:38:01.475420 | 2021-03-28T18:36:35 | 2021-03-28T18:36:35 | 346,911,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,472 | r | validation_rbf.R | rm(list=ls())
source("~/Documents/UFMG/9/Redes Neurais/TP2/final-work-neural-network-accent-recognition/RBF/trainRBF.R")
source("~/Documents/UFMG/9/Redes Neurais/TP2/final-work-neural-network-accent-recognition/RBF/YRBF.R")
source("~/Documents/UFMG/9/Redes Neurais/exemplos/escalonamento_matrix.R")
library(caret)
# Car... |
9fcb3a8326e1082d4fae5815182fd1c6dcbbad8d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/phylosim/examples/attachProcess.Sequence.Rd.R | e20e359e2b9bd319ff863c11202dbf180bdeabb1 | [] | 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 | 614 | r | attachProcess.Sequence.Rd.R | library(phylosim)
### Name: attachProcess.Sequence
### Title: Attach a Process object to a set of Site objects aggregated by a
### Sequence object
### Aliases: attachProcess.Sequence Sequence.attachProcess
### attachProcess,Sequence-method
### ** Examples
# create a Sequence object of length 6
s<-Sequence(len... |
409c8764aeeb31f65e9c57aba47aa376ca87072c | 4743159d442ae24bb6f404aa3bafd8d13d015023 | /binomial/R/auxiliary-functions.R | 100f129160544a768b69a8cb8bddde2b81e2b4c8 | [] | no_license | stat133-sp19/hw-stat133-rachelli429 | 717af42c70e290ae6c4cfbecec47ba75f7e9d2c0 | ad72a85148ab83ac65f40f736aa9a27ade98322c | refs/heads/master | 2020-04-28T11:51:34.336066 | 2019-05-03T22:24:56 | 2019-05-03T22:24:56 | 175,256,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 688 | r | auxiliary-functions.R | # private function to compute mean
aux_mean <- function(trials, prob) {
return(trials * prob)
}
# private function to compute variance
aux_variance <- function(trials, prob) {
(trials * prob) * (1 - prob)
}
# private function to compute mode
aux_mode <- function(trials, prob) {
m <- trials * prob + prob
if (a... |
ccee24cb056e5a7af80f412b81cb58d4227973bf | 5ab636955ec3b261267d82e4b39e524269e3baf0 | /functions/CreateAwardsDBTable.R | 0a2d3eb08fe0a6984cfe404660037a6587297024 | [] | no_license | aaronmams/PPP-EIDL-Database | 4f3e970e4f9ddbd4d044e7518fe93d77494064e3 | 92a8bfb294e046a9901318c77d09db791663fe3d | refs/heads/master | 2023-03-23T23:32:52.497750 | 2021-03-08T20:49:20 | 2021-03-08T20:49:20 | 344,532,683 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 989 | r | CreateAwardsDBTable.R | # very simple function to create the "awards" table for the database
# function take the following inputs:
# + dataframe - a data frame which should contain data from one of the
# one of the PPP source data .csv files
# function produces the following outputs:
# + awards - a data fram... |
a8031c78d78e909a1fd016bf3ba5626901d3f095 | 43e8866a685f3303a9d8dd656bab9184717771da | /Sweavetest/man/grades.Rd | 5fbe6d90b911d6d7bc134a8f860cf8a0064990ba | [] | no_license | dmurdoch/Sweavetest | f7da4a44de505d12972726de3898250b8b1114d2 | 8c4c3c98b21279789bedef937db62a454e7e7ede | refs/heads/master | 2021-03-13T00:10:52.165019 | 2013-11-10T23:29:51 | 2013-11-10T23:29:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,382 | rd | grades.Rd | \name{grades}
\alias{grades}
\title{
Compute grades for all students based on Scantron responses.
}
\description{
This marks all Scantrons with a list of answer keys based on an exam code.
Scores can be adjusted to give different marks for correct, blank and wrong
answers.
}
\usage{
grades(scanex, key = scanex[scanex$... |
9b746ec4af7b18d779cdc9f47a177464eeb41d44 | 1eb83aaffb3c50b51fbeb2275f90ed68fec385bb | /R/wtp.R | 870cf28bf4cf8399de7d58dc440f1f83256a1bf5 | [
"MIT"
] | permissive | dkc88/logitr | 77668c66f29e933cad1515789045359c973bc641 | 73c90f5a4461add80f820bbd1fd6ea2ed4240d4a | refs/heads/master | 2023-05-24T07:10:11.744989 | 2021-06-15T22:48:52 | 2021-06-15T22:48:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,654 | r | wtp.R | # ============================================================================
# Functions for computing the WTP from estimated models
# ============================================================================
#' Get WTP from a preference space model
#'
#' Returns the computed WTP from a preference space model.
#'... |
0755efdf867dfcb3215ca800ea2b5d683bce1492 | 2b9dcadc84d6391c4160f8a8630f2f5c4d999a56 | /AlexBajcz/code_for_intro_R_workshop_section_v2_Jan_2018.R | c11de5cb5210949df9fb16bb01a6b0984dca1015 | [
"CC-BY-4.0",
"MIT",
"CC-BY-3.0"
] | permissive | kbroman/datacarpentry_R_2018-01-08 | c047fb9782dddf144e41b525e2f60d52a3b4045b | 755cfb13aaddb8410570731c42abc74daf912520 | refs/heads/master | 2021-09-03T16:50:37.211858 | 2018-01-10T15:20:31 | 2018-01-10T15:20:31 | 116,155,478 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,083 | r | code_for_intro_R_workshop_section_v2_Jan_2018.R | #Math operations in R
15 + 5
15 - 5
15 * 5
15/5 #Show space is unnecessary.
#Functions --some of this is going to be a bit different now but that's ok.
log(5)
log(5, 6)
log() #Show data to log is needed.
log(x = 5) #Show that x is the name of the first argument.
log(x = 5, base = 3) #We can name which input... |
f364f3e0773e3e64a399824b5778e94822a9dfed | 9e6184e5bf4de6e41fec10d66e92300a1b8751cd | /src/Data_Analysis_Tool_Box.R | 492f5ad3247e372ba2a22c4f8792da00fd59656f | [] | no_license | mrashid-bioinfo/r-toolbox | 64047cd48346c8fbaa3c1003e977d5a584d99607 | 996dea535dd5a09209479c65fca1bac85803a831 | refs/heads/master | 2020-07-06T22:50:35.124976 | 2020-06-12T14:41:22 | 2020-06-12T14:41:22 | 203,162,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,704 | r | Data_Analysis_Tool_Box.R | ## ==================================================================================== ##
## Data Analysis Toolbox
##
##
## ==================================================== ##
##
## Function :: tri_nucleotide_context_fill
##
## Fill up trinucleotide context
##
## Paramters
## 1. Dataframe
##
tri_nucleot... |
817928fe4b85aef4205cabbea7d6f1508cb9d79a | 9b5131ef4e41ab8872d230aa15a688a0cb355011 | /dataOpt/benchmarks.R | 1123aef40874e090115c0d8eaada989abe04b031 | [] | no_license | PaulaAlessio/pcalg | 358be6eba5e09eb98e525df5af48501ad95bbbcc | 105671103794c134d0241476408785ac6bc01132 | refs/heads/master | 2021-01-12T15:27:24.048194 | 2017-03-27T14:38:24 | 2017-03-27T14:38:24 | 71,788,778 | 0 | 0 | null | 2016-10-24T12:53:40 | 2016-10-24T12:53:40 | null | UTF-8 | R | false | false | 1,875 | r | benchmarks.R | # This script creates a graph with the timings of idaFast
timings <- read.csv("timings.csv", stringsAsFactors=FALSE)
fast <- timings[which(timings$optimized==TRUE & timings$Myc == "Low"), ]
slow <- timings[which(timings$optimized==FALSE & timings$Myc == "Low"), ]
png("timings.png", width=400, height=400, units ="... |
13deefc1d27740743deb75cf83dad174faf70e51 | 72de200654431310d8cee2ab5ca3875d2d9ad732 | /Statisitcal analysis with R.R | 4c2fbec976a9d72830e340689623f4559ca093fe | [] | no_license | salvatoreleto1/R-Commands | e80e6471ca1e7c8e2800005b26733642b4941298 | 9091b4d66d62b78c3b93cab039244d552558018d | refs/heads/master | 2022-11-09T18:54:46.111718 | 2020-06-30T11:33:34 | 2020-06-30T11:33:34 | 276,080,491 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,123 | r | Statisitcal analysis with R.R | # ---------1 Requirements ------
#install.packages("igraph")
library("igraph")
library("rtweet")
#Later you will download Twitter user information about Hollyhood actor
#Chris Hemsworth and some of his friends using rtweet.
#If you do not have Twitter access, you may download an equivalent data file:
#chris20... |
4d30cb9a14f3f6b4fc004e02d372190b04ba4277 | ef572bd2b0515892d1f59a073b8bf99f81d6a734 | /man/datapack_cogs.Rd | 8550e9f13db8272fa7a8b52fb6b83e297649a744 | [
"CC0-1.0"
] | permissive | pepfar-datim/datapackr | 5bc604caa1ae001b6c04e1d934c0c613c59df1e6 | 9275632673e45948db6846513a53c1436cfc0e47 | refs/heads/master | 2023-08-30T23:26:48.454382 | 2023-08-11T13:01:57 | 2023-08-11T13:01:57 | 170,350,211 | 9 | 7 | CC0-1.0 | 2023-09-11T21:53:24 | 2019-02-12T16:19:47 | R | UTF-8 | R | false | true | 513 | rd | datapack_cogs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{datapack_cogs}
\alias{datapack_cogs}
\title{Datapack Category option groups}
\format{
\describe{
\item{name}{Name of the Category Option Group for example "01-04 Only"}
\item{id}{Category Option Group UID}
}
}
... |
a8e5ec82a6fd3351a31cc4add846fbc237d9d3f0 | 8f7871fdf97c6c99d802617e07160277ad291e67 | /Evaluation_standardised residuals and qqplots.R | e2a663a0a9772adae3019a1b6c70d21b2973d2aa | [] | no_license | karolzub/Stoat-in-Orkney | e4ed556a0490c11ce0d0a698b4e13256fb00fc2a | a557f0f72eee09be283df6f85e53d46e77e662ae | refs/heads/master | 2023-07-16T12:37:07.734911 | 2021-09-07T09:51:06 | 2021-09-07T09:51:06 | 247,970,769 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,197 | r | Evaluation_standardised residuals and qqplots.R | ### Stoat and rat data - Removal population & trappability estimates
### Stoat model evaluation - standardised residuals and qqplots
set.seed(2012)
### Load the libraries
library(coda)
library(MASS)
library(MCMCpack)
library(MuMIn)
library(jagsUI)
library(ggmcmc)
library(corrplot)
library(nimble)
libra... |
cdfe40ce3631d69392e97cd2df3a1ec7f34a4466 | de9d075ede442d9b4ed1bd8d05f8e4e60edb02ad | /man/prep_dist_fenced.Rd | 2d451cc6856d6181901fb3c9e7591cf05288286b | [
"CC-BY-4.0",
"CC0-1.0",
"MIT"
] | permissive | petrpajdla/settlements | 8480f43a33f2bf31dc858ced846139fd78ba2fe7 | e3bc0899e53dcab0126ddd1075f33b0d207ccc67 | refs/heads/main | 2023-04-18T15:32:07.270613 | 2022-06-23T14:20:03 | 2022-06-23T14:20:03 | 302,602,094 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 434 | rd | prep_dist_fenced.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/output_fns.R
\name{prep_dist_fenced}
\alias{prep_dist_fenced}
\title{Prepare distance to fenced settlements result}
\usage{
prep_dist_fenced(x)
}
\arguments{
\item{x}{A \code{tibble}.}
}
\value{
A long results \code{tibble}.
}
\description{
F... |
4d543ccb6b5473fceee3021689827489b55eb663 | 897f0581bfc3403318f56072f7af1163b8189733 | /rosetta-motif-distances.R | c4bcdb4d817deedec5ca22fa17af07f7b8db8b85 | [] | no_license | jashworth-UTS/ja-scripts | 2985891e628bae59b1f4b8696739cbf63b5a2dc2 | ac837ac0fee63c27b3b8ac4d9a5022810fb31976 | refs/heads/master | 2021-05-28T18:39:20.272694 | 2015-02-04T02:35:17 | 2015-02-04T02:35:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,273 | r | rosetta-motif-distances.R | paths=readLines('dnas')
#paths=rep('2e1c.WT/design_mutations.dna',4)
bg=read.table('halo.bg.file',header=F)
bg=bg[,2]
cat('background probabilities:\n')
cat(bg)
cat('\n')
names=dirname(paths)
names=gsub('/design_mutations.dna','',names)
names=gsub('2e1c.P.OT3.FL11.','',names)
source('rosetta-motifs.R')
pdf=F
mats=lappl... |
71c983af1fd707032b5e65da9f19db4b0ed5de34 | a157bac2055f4c7503ddc4b34dc909842604a6f2 | /R/source_data_raw.R | 5eec8fae440a67dffd995adda9f74c8cac869c62 | [
"MIT"
] | permissive | 2DegreesInvesting/r2dii.usethis | c2471bc8ae8fe182734f4b948579acd4d20bc27e | 08b1e959223d68cbed1b1729ac6b37b342a1a4be | refs/heads/master | 2022-03-03T13:08:53.483371 | 2022-02-14T21:21:02 | 2022-02-14T21:21:02 | 195,466,150 | 1 | 2 | NOASSERTION | 2022-03-15T11:53:50 | 2019-07-05T20:52:33 | R | UTF-8 | R | false | false | 888 | r | source_data_raw.R | #' Source all .R files under data-raw/
#'
#' Usually we work on one dataset only, and don't know if our change impacted
#' other datasets. This function helps "refresh" all datasets at once. It may
#' be used interactively while developing the package, or in CI to regularly
#' check we can reproduce all datasets we exp... |
726b2e6bcfd709f8c9d132385a38eba36c3b004a | 98ea498dbf7813132f27c0f05e93cab1d085c339 | /Standard GA.r | 2947a14a9d3ee4a40efa6d4bcaec6a5de9dc1b13 | [] | no_license | JARH2410/RCodeforCRO | 041841c429783c8f991e877a5c2e162003944824 | eefd855be2b8aafac59566bc95cd743ce7df6056 | refs/heads/master | 2020-06-24T06:16:18.010189 | 2019-07-29T15:40:58 | 2019-07-29T15:40:58 | 198,876,380 | 1 | 0 | null | 2019-07-25T17:41:31 | 2019-07-25T17:41:30 | null | UTF-8 | R | false | false | 1,614 | r | Standard GA.r | library(googleAnalyticsR)
library(googleAuthR)
ga_auth(new_user = F)
view_id = 53461765
start_date <- "" #"YYYY-MM-DD"
end_date <- "" #"YYYY-MM-DD"
mf1 <- met_filter(metric =,
operator = ,
comparisonValue = ,
... |
87adf7f4bf6db4e9196077740d5181a45c68faea | 9301d1bf2b428d9e56994665a6db0d5fda2812ce | /man/DSD_Target.Rd | 1e1766962f48e3a116607240023d278320bbc361 | [] | no_license | mhahsler/stream | ae6ae385d1db3d68bcf7c56bac62edbf5df8ce26 | 0c83157635b48adb9a3401de601f81680d844968 | refs/heads/master | 2023-07-20T04:29:25.966443 | 2023-07-14T16:32:49 | 2023-07-14T16:32:49 | 45,126,788 | 39 | 5 | null | 2020-12-01T20:09:04 | 2015-10-28T16:37:22 | C++ | UTF-8 | R | false | true | 1,605 | rd | DSD_Target.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DSD_Target.R
\name{DSD_Target}
\alias{DSD_Target}
\title{Target Data Stream Generator}
\usage{
DSD_Target(
center_sd = 0.05,
center_weight = 0.5,
ring_r = 0.2,
ring_sd = 0.02,
noise = 0
)
}
\arguments{
\item{center_sd}{standard devi... |
5561f9426872f8f4f387872a4ee7138358ef25cf | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bedr/examples/bedr.merge.region.Rd.R | 6405615294e4458a6dfe4f1ca99d31b4496e3e2d | [] | 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 | 326 | r | bedr.merge.region.Rd.R | library(bedr)
### Name: bedr.merge.region
### Title: merge i.e. collapse overlpaping regions
### Aliases: bedr.merge.region
### Keywords: merge
### ** Examples
if (check.binary("bedtools")) {
index <- get.example.regions();
a <- index[[1]];
a.sort <- bedr.sort.region(a);
a.merged <- bedr.merge.region(a.sort);
... |
5cb86ae281f40a60a0eca9b57e1bf526cd30a9fd | c7d88c83f9448c5f7c822b7cc7c8a69fab4cb5c2 | /plot1.R | d136e924b9b78845bb3617b83787800efb6b1a09 | [] | no_license | RestyAmon/Exploratory-Data-Analysis | 3651d1b2f7e6a517ab9d48a40d0a579fad6b4c37 | a83ad63505a2ee6d4ee5c579913722d78b83910e | refs/heads/master | 2020-05-16T22:18:51.291069 | 2019-04-25T10:00:38 | 2019-04-25T10:00:38 | 183,332,078 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 493 | r | plot1.R | householdpowerconsumption<-read.table("household_power_consumption.txt",sep = ";",header = T,dec = ".",stringsAsFactors = F)
cropdata<-subset(householdpowerconsumption,Date=="1/2/2007"|Date=="2/2/2007")
cropdata$Global_active_power<-as.numeric(cropdata$Global_active_power)
with(cropdata,hist(Global_active_power,yl... |
9f53a869011236f257f14bc36ee45a2688f9cb9b | 9379c6f8c601bd7ba9ff499507a52b88df988e81 | /R/splitAlignment.R | 495099d88f9139d952dd6c3ce52a7832b7e7ac18 | [] | no_license | sunnyEV/chopper | e35670d264b7a0b328e31bbb355019d768bbfe25 | 8b51bb1b9874af1e5a3505dcf627b170f381c838 | refs/heads/master | 2021-09-05T02:48:47.271585 | 2018-01-23T19:04:58 | 2018-01-23T19:04:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,751 | r | splitAlignment.R |
##' Split alignments into individual files.
##'
##' This function takes an alignment and put each sequence into its
##' individual file that is named the same as the name of the
##' sequence. This function also overwrites the output (it doesn't
##' check that the file created don't already exist).
##'
##' @title Split... |
196826d62503c2d792f74a0afcb5a93ead943d6f | 11deaf3286ab3c5440f5cb321ddd870c8be2bc5a | /lookr_old_static/testthat.R | 327c78f5b21bed029363fd4a21ced08d902dd76e | [] | no_license | ekhaebig/Comprehension | 776a20648b38ec9eade0a52b97d27a13a925b5a2 | d65a4de8e0f71d29bf00d959383b820a993903fe | refs/heads/master | 2021-01-19T05:46:59.459450 | 2014-05-08T00:20:50 | 2014-05-08T00:20:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,069 | r | testthat.R | library(testthat)
# run me:
setwd("L:/scripts/lookr/")
source("AOI.R")
setwd("L:/scripts/lookr/tests/")
# test_file("testthat.R")
context(".DefineAOI converts AOI pixel coordinates into screen proportions")
test_that(desc = "DefineAOI is robust to ordering of pixels", {
xy_ordered <- .DefineAOI(c(300, 600), c(100,... |
c72a9976702bb2b4f5a819f5901e27d10fa33838 | 1905c665a86ef1bb950607ce30a7fe513626bbc0 | /presentationCode/Bartsch_ggplot2_Introduction_and_Customization.r | d759d2dfb61b6bda596485e62647f1313b3c1f4d | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | USEPA/R-User-Group | 8ef610b12f08cf70c8a8eb62b728252365bafd30 | 0233e0fcc0b95fc6435c8edbd7281aeeebcc7666 | refs/heads/master | 2023-05-25T05:33:35.454187 | 2023-05-11T18:22:55 | 2023-05-11T18:22:55 | 39,468,949 | 12 | 17 | CC0-1.0 | 2020-04-13T22:50:51 | 2015-07-21T20:43:50 | HTML | UTF-8 | R | false | false | 8,022 | r | Bartsch_ggplot2_Introduction_and_Customization.r | #############################################################################
#File: ggplot2_Introduction_&_Customization.r
#Purpose: Demonstrate how to produce and customize plots using the ggplot
# function in the ggplot2 package. We will create and customize a scatter
# plot, a bar plot, a stacked histogram, and a ... |
2c67f9a7614ee541f9e86e73eaa45ebb796eb836 | 63a2e3fb67e9e7cdaf750e4fee1abb92c20b0be0 | /codylexyfinal1.R | ba05a27de17fc64d6f71a0db2a29c22b375e267f | [] | no_license | kstewart22/passionproject | 7e134716bc7579c213beae16f6eb8dfdcc13826f | 5f1adb6114d808ecca54da77accda0611d066bf7 | refs/heads/main | 2023-05-04T01:15:07.331986 | 2021-05-14T16:19:07 | 2021-05-14T16:19:07 | 367,415,325 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | codylexyfinal1.R | cody <- readLines(file.choose())
lexy <- Corpus(VectorSource(cody))
inspect(lexy)
toSpace <- lexynt_transformer(function (x , pattern ) gsub(pattern, " ", x))
lexy <- tm_map(lexy, toSpace, "/")
lexy <- tm_map(lexy, toSpace, "@")
lexy <- tm_map(lexy, toSpace, "\\|")
# Remove numbers
lexy <- tm_map(lexy, removeNumbers)
... |
d20098cdf4068070d8edb00e3b87144553701a10 | 52589cf1c513b27d81c2006ecc6e87a160762d27 | /server.R | 1dcd156904f79b28e2ef07671dec92165cf37585 | [] | no_license | chdhatri/Final-Project-Delivery | 692057ceb706d9271d53e251df577a01eab52891 | ce1ad6b58ed8c31b1a4a40542707302f037bf5a5 | refs/heads/master | 2020-06-12T18:44:19.330441 | 2016-12-06T21:38:06 | 2016-12-06T21:38:06 | 75,773,839 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,870 | r | server.R | #############################CMPE 239 PROJECT#####################
#TEAM VOLTAGE
#Sucharu Gupta
#Dhatri CHennavajula
#Andrew Wong
#Sprush Ujjwal
#UNCOMMENT THESE WHEN YOU ARE RUNNING FIRST TIME TO INSTALL PACKAGES
#install.packages("shiny")
#install.packages("e1071")
#install.packages("randomForest")
library(shiny)
l... |
72b09a48ead0e855f0eb9848b8771901578cbf7f | be511ba428395f3288d6ad9e0d7b8102bd77cba7 | /322_asap_rois/120_gender/30_scratch.R | ec45613b2c39f2fb82453709ecc58b4e6c34371d | [
"MIT"
] | permissive | HumanNeuroscienceLab/face_fam | 71708b85a466bbcc28ae9f0e49a0d15a70d068e2 | 2980163f9dee4c24f0d4e19a67bb79773b855add | refs/heads/master | 2021-06-13T16:21:41.418312 | 2017-05-14T17:33:17 | 2017-05-14T17:33:17 | 81,000,716 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,135 | r | 30_scratch.R |
# Setup -------------------------------------------------------------------
if (!any(.libPaths() == "/home/zshehzad/R/x86_64-redhat-linux-gnu-library/3.2")) .libPaths(c("/home/zshehzad/R/x86_64-redhat-linux-gnu-library/3.2", .libPaths()))
if (!any(.libPaths() == "/home/zshehzad/R_libs")) .libPaths(c("~/R_libs", .libP... |
d12665d2256cdb418e06e83a7d9da81d1c4a3bb9 | 1353454ecb0f64f40bf02aaccb16568e2461d698 | /R/atri_study_data.R | 11beb1caaba12f075ca203e3b94567df9162fd97 | [] | no_license | rg08705/test | 07386dec7a66b5a5f6325e8a206048482d8180d2 | 32130c005c345f3baf776fdd852c4dae120a4c27 | refs/heads/master | 2022-02-22T05:20:56.577707 | 2022-02-09T23:27:09 | 2022-02-09T23:27:09 | 191,802,296 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,889 | r | atri_study_data.R | #' Read ATRI study data
#'
#' @param protocol The protocol name (e.g. 'a345-test-1').
#' @param data The study data source name. See below for examples for each data source. Additionally, allowable text input can be the data export label (e.g.'Participant list'), the relative public api pathway (e.g. '/subjects'), or ... |
34ca7dee075187d7ac5a633813575af228fc2177 | 340f0cdacd7bd1994627cb34203915bd17d56186 | /man/amptest-class.Rd | 8fd578d3777cc45ab181ac3cce9380719bfa4760 | [] | no_license | PCRuniversum/chipPCR | af593b813f8c59d5027d8a118955666f0fff283e | b7c751a8716c814c63825d50007699dbfb7a22f4 | refs/heads/master | 2023-03-11T02:18:02.994570 | 2021-02-27T20:04:47 | 2021-02-27T20:04:47 | 19,281,268 | 2 | 3 | null | 2020-07-27T13:48:14 | 2014-04-29T15:22:30 | R | UTF-8 | R | false | false | 3,654 | rd | amptest-class.Rd | \name{amptest}
\docType{class}
\alias{amptest}
\alias{amptest-class}
\alias{summary.amptest}
\alias{summary,amptest-method}
\alias{show.amptest}
\alias{show,amptest-method}
\alias{plot.amptest}
\alias{plot,amptest-method}
\alias{plot,amptest,ANY-method}
\title{Class \code{"amptest"}}
\description{
An S4 class containi... |
3f821720c0c8c24343ac577f45d384db963da86e | e5e7b95e19071b07bd492dffe0386908e9ec41bb | /99-ReferenceApps/shiny_apps/pitchRx2/02_create_pa.R | 12a3d743b6aacaef464816e450c2b2f6e4bad453 | [
"MIT"
] | permissive | nwstephens/shiny-day-2016 | f92c711b15adc80ebe302199a6762a2c958db041 | dc365e0cab07d1252f1a4085d3d12ca28ed852e5 | refs/heads/master | 2021-01-09T20:29:05.486036 | 2016-06-29T19:51:15 | 2016-06-29T19:51:15 | 62,252,998 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,175 | r | 02_create_pa.R | library("dplyr")
library("DBI")
library("magrittr")
# connect to remote database
source("01_db_connect.R")
# From the default tables, create a table with
# relevant attributes on both atbat/pitch level.
# This saves us the repeating the expensive operation of joining tables
if (!dbExistsTable(db$con, "pa_full")) {
... |
81c883009f1410d115cbed2314d21abaed192538 | 32a061c47b1b870514d344b27d95d3b030138353 | /make_latlon_list_clim.R | 85f3a36393b1340b1e9cf90326861db10d2742c2 | [] | no_license | jedman/meiyu-jet-data | c18be0cbaec26eb0a793febbc759dfaa562a54df | bc910d7300f57c9b27b9f693675a4c6d5df1067f | refs/heads/master | 2021-01-10T21:17:58.336114 | 2015-03-16T17:01:42 | 2015-03-16T17:01:42 | 32,340,662 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,696 | r | make_latlon_list_clim.R | ### adds up jet counts from dates in a list and makes composites
#library(fields)
#loc is a list with x and y components defining interpolation grid
make_latlon_list_clim <- function(dates,loc){
require(fields)
require(KernSmooth)
latlon_ECbig <- list()
kest_anom <- mat.or.vec(length(loc$x), length(loc$y))
k_m <-... |
f3dd623e5c9646b9a5a915bf98e4ab175c1bdc4d | 076862dd5f008d098230b61c1ed71fec629c69d0 | /man/get_sv_threshold.Rd | 39b26eb400c5840a678c8d503cd347c99cf8319f | [
"MIT"
] | permissive | suziepalmer10/r_jive | c78ec634273092b6bb5f98d76cf2ff0e4c6cfaa0 | efa364b8d78752e71b15629d8a659b6cf90cd340 | refs/heads/master | 2023-03-18T19:30:26.404721 | 2020-10-15T20:58:42 | 2020-10-15T20:58:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 489 | rd | get_sv_threshold.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ajive_decomposition.R
\name{get_sv_threshold}
\alias{get_sv_threshold}
\title{The singular value threshold.}
\usage{
get_sv_threshold(singular_values, rank)
}
\arguments{
\item{singular_values}{Numeric. The singular values.}
\item{rank}{Inte... |
74c8507ea55706a95032e6177832248ef16ad707 | f7f1aee298f409a54b00b74ee16ebc6d361c1216 | /tmp/snp.annot.R | a293c3f416f74ba63a8fca8447e51d532a88fcf0 | [] | no_license | cmcouto-silva/gdstat | 00db8e492f5695052698b5b6a2bcdfb292033160 | 05be25edf9f57feb4311fd9086037ae91f0bf3eb | refs/heads/master | 2021-06-29T16:29:14.601903 | 2020-10-14T23:31:43 | 2020-10-14T23:31:43 | 173,332,553 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,836 | r | snp.annot.R | snp.annot <- function (snpIDs, batchsize = 250L, rm.loc = F) {
if (!is.numeric(length(snpIDs)) || length(snpIDs) == 0)
stop("Length of SNP vector must be equal or greater than 1.")
if (!all(grepl("^rs", snpIDs)))
stop("All SNPs must be codified as Reference SNP ID (starting with 'rs').")
snp_annot_funct... |
04a256a7a07fa02ecf2059267cebfdb51657fce7 | 912ce1f7c8e66dd97a62e3a2e5698c4f7116c7d9 | /Calculation_Stats100cell.R | d73fb2e5230f64969083ac05c97d1dcd1b859372 | [] | no_license | olympiahardy/masters_code | e1786821d498de02f9b5eaf4abc295359a0a0980 | e7c80ae054bf0496336d8b0177dc16148c2bc1d8 | refs/heads/main | 2023-01-05T16:04:40.870125 | 2020-11-05T17:14:31 | 2020-11-05T17:14:31 | 310,346,475 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,300 | r | Calculation_Stats100cell.R | # Calculating tp, tn. fp, fn
# MAST
ground_truth <- readRDS(file = "/datastore/2505621h/100cell_Analysis/ground_truth_list_FINAL")
mast_results <- readRDS(file = "/datastore/2505621h/100cell_Analysis/drosophila_MAST_results")
names(mast_results)
DE <- ground_truth[[1]]
nonDE <- ground_truth[[2]]
mast_results <- tibb... |
59e85a49857793deba1ef1ba9c2d233eae183611 | cf51091e6e697cb43796cb8e30bfd9ea8e5e95cf | /R/no_pooling.R | 20290ed187775fec88c3a0269c5802b5f8028515 | [] | no_license | bachl/workshop_panel | bfe7e2403c57f5068b5fe69bcc9a99e7b278c0ca | 8d47ced874e62cde9d569d012d1128468bffc56e | refs/heads/master | 2022-10-13T09:51:40.945083 | 2020-06-14T19:56:46 | 2020-06-14T19:56:46 | 263,283,743 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 436 | r | no_pooling.R | ## ---- no-pooling
d %>%
group_by(IDsosci) %>%
nest() %>%
mutate(mdls = map(data, ~tidy(lm(verh1 ~ verhint1, data = .x)))) %>%
unnest(mdls) %>%
ungroup() %>%
select(-data) %>%
na.omit() %>%
filter(statistic != Inf) %>%
filter(term == "verhint1") %>%
mutate_if(is.numeric, round, 2) %>%
print ... |
d2e879bc09554e5049963ef84a6c70a260d5c8e0 | 00fecd0d9aabc870090f3b9c7d8a62e00071f782 | /man/Database.cal.Rd | be2c7d82bd76ce9615ab74c07d508c0d2ffc760b | [
"MIT"
] | permissive | liamlachs/SizeExtractR | f1595ccc38a3025271b310689833e1c945495179 | ea890cfa43f66462059d0bb45b626021cd4e43ea | refs/heads/master | 2023-04-09T22:13:16.006903 | 2022-02-07T11:25:45 | 2022-02-07T11:25:45 | 368,260,432 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 454 | rd | Database.cal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Database.cal.R
\docType{data}
\name{Database.cal}
\alias{Database.cal}
\title{Calibrated Dataframe}
\format{
An object of class dataframe
}
\usage{
data(Database.cal)
}
\description{
A dataframe of the example data provided with the package. ... |
8e2385690b55c8082d159bda0615e71c9553bf1d | 0dea251958e6d1f1bfdaad8f94eef10d77bac3c4 | /shinyapp/server.r | a92f386078f95c8433abdc6de7192ce3c20bee17 | [] | no_license | dbancea/CourseraDataScienceCapstone | c3416a5251a8963359f21e017d18fe82ee7008d5 | 77b0d3935ac9deaaa756e8aaa05c7c350e34bb56 | refs/heads/master | 2016-09-06T12:12:56.424384 | 2015-08-23T15:22:36 | 2015-08-23T15:22:36 | 41,254,410 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 539 | r | server.r | library(shiny);
library(data.table);
source("predict_word.R");
shinyServer(
function(input, output) {
output$word1 <- renderDataTable(
data.table("Predicted word" = word1(tolower(as.character(input$text)), input$predictions)),
options = list(searching = FALSE, paging=FALSE, info=FALSE)
)
... |
5b8613a66b3eace25b35a407bc174c9192e678f9 | e8ef4a64de01db62556e291294db23b3072f7739 | /man/normalizeWithMA.Rd | 37f61aa7746086d6a5d7ce0db8f84ed63a02f1d5 | [] | no_license | cran/MDimNormn | 4750fc698a74721e66bfae5abbd22ac6a4488481 | af6a53d59af83ec424abbc86774dda8cca9fe8d9 | refs/heads/master | 2021-01-02T08:57:53.924892 | 2015-08-12T00:00:00 | 2015-08-12T00:00:00 | 40,618,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,837 | rd | normalizeWithMA.Rd | \name{normn MA}
\alias{normn_MA}
\title{Multi-dimensional MA normalization for plate effect}
\description{
Normalize data to minimize the difference among the subgroups of the samples generated by experimental factor such as multiple plates (batch effects)\cr
- the primary method is Multi-MA, but other fitting functio... |
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