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9b9e93212aaab5484ac5a92ad91f0a614fbb1cd1 | f3244f77367f735f30833a5b6e2b69321a28dfb5 | /R/chronique.figure.R | 51f8983a563b61df21c3155b5a1a06e22cea4815 | [] | no_license | jbfagotfede39/aquatools | 3f36367668c6848ddd53950708222fd79f0e3b7a | 9c12f80919790ec3d0c1ee7f495e9c15cc2c9652 | refs/heads/master | 2023-08-03T09:10:17.314655 | 2023-07-25T08:32:45 | 2023-07-25T08:32:45 | 46,334,983 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 13,717 | r | chronique.figure.R | #' Représentation de chroniques
#'
#' Cette fonction permet de représenter des chroniques de mesures (température, niveaux, etc.)
#' @name chronique.figure
#' @param data Data.frame contenant a minima une colonne chmes_date et une colonne chmes_valeur
#' @param Titre Titre du graphique (vide par défaut)
#' @param typem... |
e89d5a9130c998e1544ee84824412f0ca224dcf4 | 6fe3cd1cfbfc0f0bb33338d1c509569dff58f9af | /01-bulletchart.R | 17d4ebe44d972389da1d58a8a35d40be4acecc12 | [] | no_license | ssa2121/mvp01 | b428f56097ea9b8463f482ee6ce1953b89eada86 | 83b36afcf69dcc32851c7279931e7712cfc1a89a | refs/heads/master | 2021-05-15T11:35:00.604624 | 2017-06-08T18:43:19 | 2017-06-08T18:43:19 | 106,470,356 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,988 | r | 01-bulletchart.R | bullet.graph <- function(bg.data){
# compute max and half for the ticks and labels
max.bg <- max(bg.data$high)
mid.bg <- max.bg / 2
gg <- ggplot(bg.data)
gg <- gg + geom_bar(aes(measure, high), fill="#eeeeee", stat="identity", width=0.5, alpha=0.2)
gg <- gg + geom_bar(aes(measure, mean), fill="#dddd... |
16720aa0c177bb857112d798946e5285551fb50b | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/MGDrivE/man/calcLarvalPopEquilibrium.Rd | 522fab9cb2b854f375d39514f2789587ef75a34f | [] | 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 | 469 | rd | calcLarvalPopEquilibrium.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Network-Parameters.R
\name{calcLarvalPopEquilibrium}
\alias{calcLarvalPopEquilibrium}
\title{Calculate Equilibrium Larval Population}
\usage{
calcLarvalPopEquilibrium(alpha, Rm)
}
\arguments{
\item{alpha}{See \code{\link{calcDensityDependentD... |
026fcafc3d670f85203d284ac52a7d3e975b4afe | 7e2550b0dd1d770603c25a62e51967b6be1b34c5 | /scripting.R | 28f86bb1a9f193e759a47bcdf0b12552dc2bc7f3 | [
"MIT"
] | permissive | zivafajfar/R | 7f4207df2341ea6eec49a23ae33bc79240eaf915 | 9013356ae682703c2dbb033d67d7815ab61172d1 | refs/heads/master | 2020-04-09T12:28:26.774715 | 2018-12-04T12:04:13 | 2018-12-04T12:04:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,231 | r | scripting.R | # Reading our dataset
MyData <- read.csv(file="/home/zaki/Desktop/countries_R.csv",
header=TRUE, sep=",")
# Performing first visualization using `.ggvis` library. We call `layer_points` function
MyData %>%
ggvis(~Server, ~Nodes) %>%
layer_points() %>%
layer_points(fill = ~Company)
# Performing second... |
5979a19f23e7cb33ba6244f6ff663b36587e81f6 | 20e6d8ae47f494d1b4e4fecbdae1f523fc23de45 | /scripts/PRS_UKB_201711_step18-01-02_adjust-significance-threshold-for-multiple-testing-in-a-single-data-set_pheno-group4-7.R | a7a5898422979f8ffe65ce3097dc2c5673ed4b14 | [] | no_license | yanch86/PhD_polygenic_risk_score_analysis | 9db5e8bd2387aff5a892a60f0b53beea15ff2ed8 | f8db0816eb6b53e968143cf08630ec273ad0b18c | refs/heads/master | 2022-04-11T17:45:03.674782 | 2020-03-21T05:12:50 | 2020-03-21T05:12:50 | 290,157,106 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,432 | r | PRS_UKB_201711_step18-01-02_adjust-significance-threshold-for-multiple-testing-in-a-single-data-set_pheno-group4-7.R | #-------------------------------------------------------------------------------------------------
# Program : PRS_UKB_201711_step18-01-02_adjust-significance-threshold-for-multiple-testing-in-a-single-data-set_pheno-group4-7.R
# Modified from : PRS_UKB_201711_step18-01-01_adjust-significance-threshold-for-multip... |
10215d9c4b71140ae2739b92183c80ab0bf2b1e1 | 081012f78ce5be302d0066baabb9981f393a8d1c | /R/model_fitting.R | 1aa6872cde072583c16cffff9d7b0f97d37ae06d | [
"MIT"
] | permissive | CshlSiepelLab/DENR | bcfbc3da9ce7f67c7d3d921aba8a56d6f602071d | d17f935d1161e6efb5b8ab265efac29497078e7f | refs/heads/master | 2023-06-07T14:13:44.742140 | 2021-07-11T21:19:55 | 2021-07-11T21:20:05 | 223,423,009 | 1 | 0 | null | 2021-07-02T14:48:25 | 2019-11-22T14:41:40 | R | UTF-8 | R | false | false | 10,426 | r | model_fitting.R | #' @title Mask data
#'
#' @description masks positions in data vector at indices specified in masks
#' vector by replacing them with zeros.
#'
#' @param data a data vector
#' @param masks a vector of masked indicies
#'
#' @name mask_data
#' @rdname mask_data
mask_data <- function(data, masks) {
data[masks]... |
d24e8b2d47c0facb129fe99e0c752d046a0df6ce | c667e08d35ef383668722a376ce0d5572423b464 | /Plot1.R | 919fbebde66eaf362c34e47ae2657f8cac4c7e01 | [] | no_license | christinegaribian/ExData_Plotting1 | 544f9b379499dbf826ee4df5d5dab47d66ed0ca5 | cd9a83eb6e6ecd7daf7b0c58506abb6033456ed3 | refs/heads/master | 2021-01-22T11:29:13.409357 | 2015-05-10T22:42:42 | 2015-05-10T22:42:42 | 35,348,997 | 0 | 0 | null | 2015-05-09T23:13:11 | 2015-05-09T23:13:11 | null | UTF-8 | R | false | false | 1,626 | r | Plot1.R | ## Plot1.R
# Data is the "Individual household electric power consumption Data Set" from the UC Irvine Machine Learning Repository
# Variables are:
# Date: Date in format dd/mm/yyyy
# Time: time in format hh:mm:ss
# Global_active_power: household global minute-averaged active power (in kilowatt)
# Global_reactive_powe... |
38b6bb9b9675b1d6149fbc84ce2b26756194cf33 | b3a59378dc173ab63940ec9e7b77d273ab84ecf9 | /study.R | 7184a444639264cbbe26a06aac582b2bcd7a3a0e | [] | no_license | kecolson/simulator_functions_next_round | b613cd315bdbf54986abdf3b0ed58a993f0b6a5f | c50394565c1c06d561d153d2cbcade142cbe0892 | refs/heads/master | 2020-04-25T19:51:52.267755 | 2015-03-16T17:00:19 | 2015-03-16T17:00:19 | 32,105,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,625 | r | study.R | # study.R
# Function: study
# Input: pop (Data frame containing variables named: W*_0, W*_1, A_0, A_1, Y_0, Y_1)
# a single row of the data frame designs (as a list)
# a single row of the data frame matches (as a list)
# Output: a list containing:
# sample (format as pop. This is the unmatched sample... |
92a10a632891ca969728508fb136e4a0a8e6b385 | 262e5903ee6d3f6e0dbd8cbf2d8caa40c70c92e4 | /r_programming/week_4/rankhospital.R | 2818cd5a74c9b7c0846c9f6b317d6956c83f3ffc | [] | no_license | jdglover/datasciencecoursera | ffaf17d28b48c85386c834baeb8f513795a12cdb | f115083aadf6478c31c15946e77710deab3ef507 | refs/heads/master | 2021-01-10T08:57:07.425414 | 2015-12-08T02:54:42 | 2015-12-08T02:54:42 | 43,981,836 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,382 | r | rankhospital.R | rankhospital <- function(state, outcome, num = "best") {
# Determines the Nth ranked hospital for a given condition in a given state.
#
# Args:
# state: A character vector of length 1 that is the two letter
# abbreviation for the state of interest (e.g., "PA").
# outcome: A char... |
e07d7028dabeb5bc8c79fab376daade47d0aba97 | 9f492b4300d2430115fab594853108281b003016 | /ropengl/R/package-ropengl.R | a79e4111c666fcb3b32779874aa4c2b3892921d3 | [] | no_license | leoorshansky/ropengl | 8863188c5907f53fdbd6031d18984925a2c424e3 | 85bc1cc13d47edfe37f440d4f2a1c48b9379a12d | refs/heads/master | 2020-03-29T12:42:03.880408 | 2018-09-22T20:20:26 | 2018-09-22T20:20:26 | 149,913,341 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 201 | r | package-ropengl.R | #' ropengl
#'
#' @name ropengl
#' @docType package
#'
#' @description OpenGL bindings for R
#'
#' @useDynLib ropengl
#' @importFrom Rcpp sourceCpp
`%#%` <- function(a, b) {b + 4;getFlag(a)} |
bac4a9a243c80b38bc02362280bf5ba29859e6aa | 9cae896d263554bc72faac373005e1dbf6eabb70 | /Filling in missing bird bands in nest data.R | f1a769a3a82721edcf8af4acc5cdeabe05ca2a8a | [] | no_license | 11arc4/cloud-TRES-data-manipulation | 948ff317872c23f36b92bad0b0330ebc0d9012f3 | e80c225520675babbe12b60cb850eb9ac7e2384d | refs/heads/master | 2021-01-13T03:26:57.559098 | 2020-10-16T12:48:38 | 2020-10-16T12:48:38 | 77,549,329 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,453 | r | Filling in missing bird bands in nest data.R | processNestDataFiles <- function(inputDir, outputDir, banddata) {
listfiles<-list.files(inputDir)
for (f in listfiles){
message("Processing ", f)
filename<-paste(inputDir, "/", f, sep="" )
initialnestdata <- read.csv(filename, as.is=TRUE, na.strings = c("", "NA"))
#Add the siteID ased on the func... |
7f8e77e64938455143f731e9afb43250e94afc08 | 5e7f15495c93134ff1cc2fa302237132aa6a63c3 | /backlog_w_engineering_capacity.R | 97c968987ebe7665017275bd8a8528479c3a604e | [] | no_license | jimfelps/S-OP-Project | 07af7212aef1e76d84ab0cd91ddd9424872db1b8 | 5d7bc84ff2b89a3b57653ecf4523c984b08478b1 | refs/heads/master | 2020-08-04T01:33:28.819727 | 2019-12-01T21:59:34 | 2019-12-01T21:59:34 | 211,955,186 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,462 | r | backlog_w_engineering_capacity.R | rm(list = ls(all.names = TRUE))
library(tidyverse)
library(lubridate)
library(readxl)
eng_complexity_lookup <- read_csv("~/R/R Data/Engineering/MBR Charts/eng_complexity_lookup.csv")
backlog_tons_detail <- read_csv("~/R/R Data/Engineering/MBR Charts/backlog_tons_detail.csv")
eng_backlog <- read_csv("~/R/R Data/Enginee... |
fc0c82c2c4b07221fd14f32772d794c21005747e | 4b742a3192695e460c447275adeb5a27608027fb | /Code/mvn-out.R | 80d2cbaa6c49f17b798f25ca10309da175b18098 | [] | no_license | gautam-sabnis/Komp-Gait-Analysis | 6e3ec8892efba2f0bb8f7aae1ee6b0d10df3ef8c | 20d07768c0848bc15ce11ffa4dfc552429596999 | refs/heads/master | 2023-02-13T03:24:05.907266 | 2021-01-11T02:54:06 | 2021-01-11T02:54:06 | 279,411,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 59,629 | r | mvn-out.R | setwd("/Users/sabnig/Documents/Projects/Komp/Temp")
data_per_stride <- read.delim('../Data/kompdf-corr', stringsAsFactors = FALSE)
Phenos.lin <- c("speed","limb_duty_factor","step_length1","step_width","stride_length",
"temporal_symmetry","base_tail_lateral_displacement","tip_tail_lateral_displacement",
"nose_latera... |
fd10acc33db6b32e67f11da3ce901e18810b03dc | 1554244b48facb5143523f83ef3cf263b4df4ff5 | /tests/testthat/test-my_knn_cv.R | 580e9843c42f4a2215c34e730150709e34c89df7 | [] | no_license | dhruvrc/STAT302Package | 65ccccba65bc85bc63e0021f2ee309f913c44d2d | a1bd702990cc933624580a2aad6a20721f9b7c44 | refs/heads/master | 2021-03-22T12:20:13.110108 | 2020-03-17T05:32:14 | 2020-03-17T05:32:14 | 247,363,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 87 | r | test-my_knn_cv.R | test_that("non-numeric input throws error", {
expect_error(my_knn_cv("a string"))
})
|
ea877437b22e428d3cf2279ad178078f6d305ec8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/kappalab/examples/variance-methods.Rd.R | 716e3ca6deafc40de0469c6f39d8f9dfb23318ea | [] | 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 | 534 | r | variance-methods.Rd.R | library(kappalab)
### Name: variance-methods
### Title: Normalized variance of a capacity
### Aliases: variance variance-methods variance,Mobius.capacity-method
### variance,capacity-method variance,card.capacity-method
### Keywords: methods
### ** Examples
## a capacity
mu <- capacity(c(0,0,0,0:12)/12)
## its M... |
04361450250472d16c69bb677b33194ee7051317 | 21c36ac56a10e92f37cf8e6bd23ad54eccb0132c | /knn_class.R | 78da62ed29dd3e90cca7fca08ee8991b4a3b4bb4 | [] | no_license | snarles/stat312 | ef1fe1870e49c21f8034a199edb7509729966412 | 5b998b8d02ba31f0326f90d8f79d930619d669e2 | refs/heads/master | 2021-01-01T15:44:22.471038 | 2015-03-10T04:51:16 | 2015-03-10T04:51:16 | 29,076,180 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,267 | r | knn_class.R | choose_k = function(vecs, labels){
train_trials = dim(vecs)[1] # the number of trials
distance <- as.matrix(dist(vecs)) # calculate distances between trials
nearest <- matrix(0,train_trials, train_trials)
for (i in 1:train_trials){
nearest[i,] = sort(distance[i,], index.return = TRUE)$ix # figure out neare... |
126c636e125ba62fa4f4c6ba58633ea7eb3b4478 | 7e4e52c37b700452d573a15f533b6ef346b22a84 | /app.R | b69c2ae42e932aced2d036516cebf29b42970d08 | [] | no_license | achu3080803/PROM02_Project | 5292b1fa039f51502e716546502fdba8e30c2930 | eb687c827cb8b396b24ab036f2125885654e747a | refs/heads/main | 2023-04-01T01:26:08.921847 | 2021-04-01T12:20:28 | 2021-04-01T12:20:28 | 335,670,524 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 54,259 | r | app.R | ## app.R ##
library(shinydashboard)
library(DT)
library(ggplot2)
library(leaflet)
library(RCurl)
library(rjson)
library(ShinyRatingInput)
source("./global.R", local=TRUE)
###############################################################################################
# UI (START)
#######################... |
c390c02e4cfcceecd2a52666d0d2b0209939cfdd | 7d0f25a190e97e0d2714c43a57a017eaf093ecd5 | /man/plotRoc.Rd | da819a5f92c5223404c0464c8ea8429db67130bb | [
"MIT"
] | permissive | mikeniemant/nbs | f192f6d6ce16d725dc0985d6f505666c2c785020 | 3d82fd553c58ea5941d5dfa60cfaefa95df76121 | refs/heads/master | 2022-07-03T11:21:21.170515 | 2022-06-15T08:51:37 | 2022-06-15T08:51:37 | 142,566,362 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 565 | rd | plotRoc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotRoc.R
\name{plotRoc}
\alias{plotRoc}
\title{Plot Receiver Operator Curve}
\usage{
plotRoc(x, group = NULL, title = NULL, breaks = F, print_plot)
}
\arguments{
\item{x}{Tibble with sensitivity and specificity computed by the performance R ... |
e31ea3d855f44cdb6bb9b931c5dc3535d8fc3bbe | 0a906cf8b1b7da2aea87de958e3662870df49727 | /gjam/inst/testfiles/tnormRcpp/libFuzzer_tnormRcpp/tnormRcpp_valgrind_files/1610046171-test.R | 8fc7b80f6c10f7205167316ef40a037ea038ef93 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 188 | r | 1610046171-test.R | testlist <- list(hi = 4.66726145839586e-62, lo = 1.32963809623256e-105, mu = 4.66726145839586e-62, sig = 4.66726145839586e-62)
result <- do.call(gjam:::tnormRcpp,testlist)
str(result) |
4a79009fa74e8b7e41efadd6ae0d6885f81526d3 | 184940aa0323a4f2a84fbd49e919aedb7e1fcaea | /Complete R/Chi.R | 58a83c1df45860a57b26e6a922a969cee2b1f25c | [] | no_license | Dipzmaster/Complete_R | 7e700b1ae8f21dd07538d8f8e0ace2c374298b82 | face68fdac71be6f2bf4f744884c401cebbadffd | refs/heads/main | 2023-08-23T02:08:24.794579 | 2021-11-03T18:36:13 | 2021-11-03T18:36:13 | 415,090,983 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 356 | r | Chi.R | library("MASS")
print(str(Cars93))
# Load the library.
library("MASS")
# Create a data frame from the main data set.
car.data <- data.frame(Cars93$AirBags, Cars93$Type)
# Create a table with the needed variables.
car.data = table(Cars93$AirBags, Cars93$Type)
print(car.data)
# Perform the Chi-Square ... |
fc0356e97cc9185c5824e1edbc77afb3b0867c77 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /apollo/man/apollo_writeTheta.Rd | f89c8182b432494b95ecfdb1520993d7bb9668c2 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 563 | rd | apollo_writeTheta.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apollo_writeTheta.R
\name{apollo_writeTheta}
\alias{apollo_writeTheta}
\title{Writes the vector [beta,ll] to a file called modelname_iterations.csv}
\usage{
apollo_writeTheta(beta, ll, modelName)
}
\arguments{
\item{beta}{vector of parameters... |
d3d2d0c099bf2d6cb9446bbff693d394e45f7354 | ab2502b9db5190e9067986e52666ff0d1f653611 | /R/user_profile.R | 7eca75ac64ba50cca60c3b840f726f35905d5024 | [] | no_license | jakosz/wiki-tools | 65604e0d8b9aedf5c08ec83b5201c3fe944a201b | 94f4307268b0ded9fde290de442612c5de5e2da4 | refs/heads/master | 2021-01-22T02:28:58.862747 | 2014-08-09T09:36:18 | 2014-08-09T09:36:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,039 | r | user_profile.R | source('settings.R')
LANG = 'de'
up = read.csv(ps(data$trans, LANG, '_user_page.csv'), sep = ';')
upp = read.csv(ps(data$trans, LANG, '_user_page_profiles.csv'), sep = ';')
upp = cbind(up, upp[,2:ncol(upp)])
# -- variables for per-user aggregation:
# distribute diff among categories
ctg = upp[,7:ncol(upp)]
upp[,7... |
579eab6035e284113b03228f360f21ae14f9f8e1 | 7a9e185724bad204059f487489ecefe1b30568ea | /dev_tsne.R | a53a04fb09f9b70f8143ecc5f63befaabcd381ab | [] | no_license | jrboyd/waldron | 21ca7ad82f6db5bac6855273acfb40ad753c5895 | b89a119301da4cf86bc4805dc548d3f13f5c1cda | refs/heads/master | 2020-05-01T17:07:16.479793 | 2019-03-25T13:24:57 | 2019-03-25T13:24:57 | 177,591,600 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,553 | r | dev_tsne.R | library(BiocFileCache)
library(ggimage)
library(Rtsne)
library(magrittr)
setwd("~/R/waldron/")
source("setup_files.R")
source("functions_tsne.R")
source("geom_image.rect.R")
bfcif = peakrefine::bfcif
bfc = BiocFileCache()
options("mc.cores" = 36)
load("CD34_bivalent.save")
load("CD34_consensus_v1.save")
ref_gr = rtrac... |
eb9144c89dd72d765c2443527397b850368f5d00 | 8e5c16034bee9a17ca7f9318a92b864f51ba58d1 | /RandomScale/man/prepare_admb.Rd | 58c6c36ac11be994670e26fb9e2ac1d47d0cde2b | [] | no_license | jlaake/RandomScale | 0532dd9fca561d99a8b6fc9a2d089bf15f61968a | 96e212ccd3f3e024d283a509c9b5db8dd5b67b10 | refs/heads/master | 2021-01-18T22:41:52.669034 | 2016-05-24T22:48:54 | 2016-05-24T22:48:54 | 59,616,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 222 | rd | prepare_admb.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{prepare_admb}
\alias{prepare_admb}
\title{Prepare to use ADMB}
\usage{
prepare_admb()
}
\description{
Sets environment variables for admb if Windows
}
|
347eff115722b4043bcd54d743ca54df69cc95df | b05191d9e25d592155490ca4f970e445a2a3e858 | /man/annual.summary.climate.Rd | 178e11c6c1548cae8693de7578bd490b63db7627 | [] | no_license | cran/RSAlgaeR | 3dbdfb1b8b12c65f1f175f9014f03866261c98dd | 4639cc2b23f3b1ed3be27a85c5410e845eeef6dc | refs/heads/master | 2020-03-09T18:34:13.295148 | 2018-04-10T12:14:47 | 2018-04-10T12:14:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 839 | rd | annual.summary.climate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/annualsummaryFunctions.R
\name{annual.summary.climate}
\alias{annual.summary.climate}
\title{summarize climate conditions on an annual basis}
\usage{
annual.summary.climate(df, datecol, valuecol, parameter)
}
\arguments{
\item{df}{wi... |
1c94648c159bc043a22a4ed1d5dec7d86c332336 | 033383e71a1fe7a811fdd88f917d4491a124013d | /data_cleaning.R | 93b5e40f45c94aa98b63eee7eae2313ad60a9438 | [] | no_license | NinoFoudraine/subwep | 00f02b93da68abdc6a7f42eca9940a1e3fdb144e | 5c383155dc99ef6404293297348dad9367fb7ce2 | refs/heads/master | 2022-04-10T19:38:44.127794 | 2020-02-17T14:44:11 | 2020-02-17T14:44:11 | 241,118,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,274 | r | data_cleaning.R | ########################################################################
######################## DATA CLEANING #################################
########################################################################
########################################################################
######################### I... |
a771b184a237a92a88598b7383ae567500c6bd73 | 8a9395ba7d69a651b8fdd0ca74158bbb92ce6a65 | /man/gpuVector-methods.Rd | 7c59add9c11120db107402720e92af8ca17b4f4c | [] | no_license | bryant1410/gpuR | ce8a818a3cba6d4ac257e5b3035e3012b92df0f7 | 6e56cec81783fb9d97be1629a8063289c2c92633 | refs/heads/master | 2021-01-19T20:08:42.103635 | 2017-04-17T08:54:36 | 2017-04-17T08:54:36 | 88,490,189 | 1 | 0 | null | 2017-04-17T08:54:36 | 2017-04-17T08:54:36 | null | UTF-8 | R | false | true | 1,078 | rd | gpuVector-methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gpuVector.R
\docType{methods}
\name{gpuVector}
\alias{gpuVector}
\alias{gpuVector,missingOrNULL}
\alias{gpuVector,missingOrNULL,ANY-method}
\alias{gpuVector,vector}
\alias{gpuVector,vector,missing-method}
\title{Construct a gpuVector}
\usage{... |
3e9d23be0afb1b1d3c1b7ea88b51c1e038ba0502 | 96cd5efd27207e07e9e5954bde92daa4644fa73e | /ComputingDataAnalysis_Quiz2_Question4.R | 51c0750b1529806ae0fd72ef7114e86d0c90d020 | [] | no_license | Tati86/Computing_for_Data_Analysis | 200e678ff2379c990b5e7bd597e956e9ab7e2ab7 | 2349fd23d6bebac0f35ba7da62a908ab240b4d12 | refs/heads/master | 2021-01-10T18:08:28.416235 | 2016-10-27T23:10:13 | 2016-10-27T23:10:13 | 71,941,589 | 0 | 0 | null | 2016-10-25T21:43:41 | 2016-10-25T21:43:40 | null | UTF-8 | R | false | false | 2,974 | r | ComputingDataAnalysis_Quiz2_Question4.R | rankhospital<- function(state,outcome, num="best"){
##read outcome data
outcome1=read.csv('outcome-of-care-measures.csv', colClasses="character")
State=as.numeric(outcome1[, "State"])
Hospital.Name=as.numeric(outcome1[, "Hospital.Name"])
Heart.Attack=as.numeric(outcome1[, 11])
Heart.Failure=as.numeric... |
99a869a638528d9ebb1a6803571f8f51822e31c9 | ad5a3396565fcb9bada6b902a3f8a5d43fc4b354 | /scripts/compareBedScript.R | ac1024a44696f514ac267b9611dbcc7d8eff9674 | [
"Apache-2.0"
] | permissive | enriquea/PepBed | 08840c8a68e2f086020a4534cecc1891d7103688 | dbb22f6f99c3b809e23625f5d6e8569c19338942 | refs/heads/master | 2021-09-24T14:30:49.698811 | 2018-10-10T14:22:51 | 2018-10-10T14:22:51 | 110,535,201 | 1 | 0 | null | 2017-11-13T10:40:14 | 2017-11-13T10:40:14 | null | UTF-8 | R | false | false | 1,447 | r | compareBedScript.R | library(PepBedR)
library(VennDiagram)
library(ggplot2)
library(gplots)
bedFileDataPepGenome <- readBedFile("/Users/yperez/IdeaProjects/github-repo/BDP/benchmark/PepGenome-PeptideAtlas.bed");
bedFileDataPepGenome <- setBed12Columns(bedFileDataPepGenome);
bedFileDataPGx <- readBedFile("/Users/yperez/IdeaProjects/github... |
0fcd5d749e39de7bcea6f771b7762accbc8f8661 | 67721a8b22ca21cf46f10865000274a3494dd8bf | /tests/testthat/test_seas.R | 47d31b17fa4e9073eb569f5e519922abed05c0f8 | [] | no_license | lkegel/idxrepr | c157c9fc6aab8afdbf20f832983d3e1c26559958 | 2f3d35b1cae9fa6cd95047b025d3df5e7914d937 | refs/heads/master | 2022-07-02T08:58:02.729315 | 2019-10-11T14:25:40 | 2020-05-09T15:12:18 | 214,453,819 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 821 | r | test_seas.R | context("seas")
test_that("unaggregated", {
method <- mgr_init("seas")
expect_equal(3, length(method))
rnvl <- list(rep(seq(12), 11), rep(seq(12) + 12, 11))
repr <- lapply(rnvl, represent.seas, method = method)
expected <- seq(12)
expect_equal(expected, repr[[1]])
expect_equal(expected + 12, repr[[2]]... |
81fd6bb4d1ae7b5cdc181a33045eefda0c317ba9 | 02c37615762af39de855590a40efd5d29858c9fc | /man/ridgePlot.Rd | 4bd0d3f316c9c89b501f3acb9debb6c5712de3e5 | [] | no_license | ericdunipace/WpProj | d950d1f8e36094b1b93cd2bb62e99fc1b9ec3aef | 6039e5ce8c5d3386e776fc1e6784807411805889 | refs/heads/master | 2023-03-27T19:23:12.132980 | 2021-04-02T21:32:56 | 2021-04-02T21:32:56 | 229,637,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 957 | rd | ridgePlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ridgePlot.R
\name{ridgePlot}
\alias{ridgePlot}
\title{Ridge plots for range of coefficients}
\usage{
ridgePlot(
fit,
index = 1,
minCoef = 1,
maxCoef = 10,
scale = 1,
alpha = 0.5,
full = NULL,
transform = function(x) { x },... |
c7fce3de5fa88479a785e6546358465c17e29f1e | 2e5c9d872ee261921d44a48e2647269ce312d313 | /Scripts/read_data.R | edc5f24ec8216d32324e8c92f7a94359956e8b8c | [] | no_license | tmat2020/ExData_Plotting2 | 14ae53f2e17da532096d92e748148573a22ff76d | 8743f42342fbab2cecc2a2525333a06761c95193 | refs/heads/master | 2021-01-01T09:54:39.904906 | 2018-11-22T12:40:05 | 2018-11-22T12:40:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 306 | r | read_data.R | # Load and unzip dataframe file
temp <- tempfile()
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(url = url,method = "curl",destfile = temp)
unzip(zipfile = temp)
unlink(temp)
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds") |
d30d1f8e52690e69109046de9ce93c6dfa085923 | 995182d67072061d54b02947055d1fb66e319e7a | /species_replace.R | 8a96dd05054eb9aea2941de7991de1d6c9432cb2 | [] | no_license | healyke/FestR_replacement_tests | 4e75caa6e916217806e74f44198a31c1cd74cf66 | 70dc0cab15464a9ca38cbd175d5680d942618fc9 | refs/heads/master | 2016-08-11T00:33:52.867069 | 2016-04-01T13:36:01 | 2016-04-01T13:36:01 | 53,622,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,604 | r | species_replace.R | #' replaces species tef with a NA and runs tefMcmcglmm
species_replace <- function(tef_data,
isotope = c("carbon","nitrogen"),
formula = ~delta15N ~ source.iso.15N + diet.type + habitat,
random = ~ animal + sp.col + tissue,
prior,
output.label,
nitt = c(120000),
thin =... |
9e5fa6849700f9c2aa04db7edb7fc55a20e7fae6 | 5373cfa4dfde2899762053daf3251c846adf5b35 | /man/GreenplumDriver-class.Rd | 3b5a855e23a6ebc69401b404be6964885dd79b9f | [] | no_license | mwillumz/RGreenplum | abbd0818f92a711baad07ae143442a75ea1e8a01 | 734c888755f831c6589778c9b40bcb80ad477bf2 | refs/heads/master | 2021-04-09T15:40:49.138646 | 2018-06-22T01:02:08 | 2018-06-22T01:02:08 | 125,784,481 | 3 | 1 | null | 2019-10-14T14:15:42 | 2018-03-19T01:19:52 | R | UTF-8 | R | false | true | 381 | rd | GreenplumDriver-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GreenplumDriver.R
\docType{class}
\name{GreenplumDriver-class}
\alias{GreenplumDriver-class}
\alias{dbUnloadDriver,GreenplumDriver-method}
\title{GreenplumDriver and methods.}
\usage{
\S4method{dbUnloadDriver}{GreenplumDriver}(drv, ...)
}
\de... |
5247c04c2a78c4d59fdc42b3ff7f6427b6b4224d | 0433b19a7887ee802136638f77506ff37ce94c13 | /man/is.chartjs.Rd | a79b4cfb25f95c272048a5d9953f3dac104fa805 | [
"MIT"
] | permissive | KO112/chartjs | 57b4377220c7acf3bd7bff7f518ec70fac3ff9e8 | 233bc27ceecdc524127cb579c5116e18fc98ed6e | refs/heads/master | 2022-11-30T09:44:09.482930 | 2020-08-05T16:44:34 | 2020-08-05T16:44:34 | 274,043,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 507 | rd | is.chartjs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chartjs.R
\name{is.chartjs}
\alias{is.chartjs}
\title{Check that an Object is a \code{Chart.js} Plot Object}
\usage{
is.chartjs(x)
}
\arguments{
\item{x}{The object to check (any R object).}
}
\value{
Whether the object inherits from \code{ch... |
8e8c8df94a88afcb9ea4226b5bf3104a2a6f7cf2 | 3f312cabe37e69f3a2a8c2c96b53e4c5b7700f82 | /ver_devel/bio3d/tests/testthat/test-mol2.R | a63b59373cad04d21e23343db30d1b4712fae266 | [] | no_license | Grantlab/bio3d | 41aa8252dd1c86d1ee0aec2b4a93929ba9fbc3bf | 9686c49cf36d6639b51708d18c378c8ed2ca3c3e | refs/heads/master | 2023-05-29T10:56:22.958679 | 2023-04-30T23:17:59 | 2023-04-30T23:17:59 | 31,440,847 | 16 | 8 | null | null | null | null | UTF-8 | R | false | false | 2,661 | r | test-mol2.R | context("Testing basic PDB structure operation")
test_that("read.mol2() reads a mol2 from zinc", {
## Simple test with aspirin
file <- system.file("examples/aspirin.mol2",package="bio3d")
invisible(capture.output(mol <- read.mol2(file)))
expect_is(mol$atom, "data.frame")
expect_true(inherits(mol, "mol2"))
... |
884b5ca86b1306f62c08711f9346a09b258a1d92 | fb91bf05835aa980d23e364cd7853a37f38da06a | /man/make_barplot.Rd | 93d0957e87d82d618a8cfe5308e54d32bddb93fe | [] | no_license | retaoliveira/chronicle | da65dd0c26ef3595cd8dd63fb0fde5d3f0b1683f | 42df3f38c1def7f96fa82cb3d73d738cf029d75b | refs/heads/master | 2023-02-19T02:27:22.224618 | 2021-01-21T17:32:29 | 2021-01-21T17:32:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,864 | rd | make_barplot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/barplots.R
\name{make_barplot}
\alias{make_barplot}
\title{Create a bar plot from a data frame through ggplotly}
\usage{
make_barplot(
dt,
bars,
value = NULL,
break_bars_by = NULL,
horizontal = FALSE,
sort_by_value = FALSE,
sort... |
a6ce58b8cedb292e5b635bd2fccfbf5cb8ae34bc | 93c4cd901206f7794d4defd29bf87846b0d3df31 | /minfi_script.R | 46cc5817f93597f19975eb69db5abf85faaef297 | [] | no_license | rahulk87/myCodes | e48280d3336207e85b9fca9f9b49a96cffefd476 | 7ddb2621b9fdfe682d9bed0f5cb0f1297abcba38 | refs/heads/master | 2020-06-20T04:00:31.360335 | 2018-06-07T20:55:02 | 2018-06-07T20:55:02 | 74,883,865 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 961 | r | minfi_script.R | library('minfi')
library('IlluminaHumanMethylationEPICmanifest')
library('IlluminaHumanMethylationEPICanno.ilm10b2.hg19')
library('IlluminaHumanMethylation450kmanifest')
library('IlluminaHumanMethylation450kanno.ilmn12.hg19')
base = "../rawdata/"
targets = read.metharray.sheet(base)
RGSet = read.metharray.exp(base=bas... |
8848c0bd2a76b286c49dc690ba0d273f63237935 | ac04daadad3f3e6068eb466b08762eb75046e213 | /Script_Figures_Article2.R | 80f861391019149a78ed962753f606e4e48177e3 | [] | no_license | davbauman/MEM_W-matrices | dda782b66d93ba119dced4e290a52de992140ed8 | 870cfe281863869b440577f37c64a44df7db4516 | refs/heads/master | 2021-01-23T10:29:14.542736 | 2018-01-01T11:34:29 | 2018-01-01T11:34:29 | 93,063,945 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,423 | r | Script_Figures_Article2.R | ###################################
#### Figure Type I error rates ####
###################################
#### Load ggplot2 ####
library(ggplot2)
#### Input of the data ####
# Pour les points :
chic <- read.table("fig_typeIerror_all_(details).txt", h = T, sep = "\t")
str(chic)
chic$Connectivity <- factor(chic$Conne... |
cf041771565671cfc56cc31ed3bb85df9a60e034 | 9fb478102a7a8aad73001d67d1d64d3b5590d442 | /bootstrap_extras.R | 8dad660b1c71f01f77ce2d39599587116f97cfab | [] | no_license | juliaraices/Rotation1P1 | f101d01c0dbae8552c5bce27037589d8a3ac9471 | fb4ed4dcf191581fe1967266fe669351d1d796f8 | refs/heads/master | 2021-05-07T22:55:31.665776 | 2017-12-13T17:58:45 | 2017-12-13T17:58:45 | 107,370,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,002 | r | bootstrap_extras.R | # Julia Raices
# NovembBer 2017
# names:
## F = female | M = male
## er = early | Blt = late
## 1 or 2 = replicate number
## slk = sex-determination linked
## nslk = not-sex-determination linked
## B = B. jarvisi | D = D. melanogaster
library(affy)
library(ggplot2)
BltF1 <- read.table("abundance_SRR1571147_extras... |
2d3c0263637c283130b4a64e3da6a2c1e7156cb7 | 18d110fc809baf72f3c61558fe385258448ea711 | /man/add_abstracts_to_pub_index_md.Rd | 352f07700cb3dd4bd32bb3d9834c7e4099a2593d | [
"MIT"
] | permissive | KWB-R/kwb.pubs | a90a720e19ff51373ac1b00e2dd336146eb2b3fc | adfd7d337031fe4f32fed673835a5213d0925fc7 | refs/heads/master | 2021-08-31T11:48:00.877412 | 2021-08-16T07:30:02 | 2021-08-16T07:30:02 | 165,939,816 | 0 | 0 | MIT | 2021-08-16T07:30:02 | 2019-01-15T23:31:54 | R | UTF-8 | R | false | true | 909 | rd | add_abstracts_to_pub_index_md.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_abstracts_to_pub_index_md.R
\name{add_abstracts_to_pub_index_md}
\alias{add_abstracts_to_pub_index_md}
\title{Add Abstracts to Hugo-Academic Publications}
\usage{
add_abstracts_to_pub_index_md(
endnote_df,
overwrite = FALSE,
hugo_ro... |
a2f6515e9179019740594c3f16b8fc774dcee70f | 3819207740cbb823a372d002d2af49a76fb29aad | /lib/helpers.R | 4a4b48dc6dc684c1587a022ad36e8cdc0007260f | [] | no_license | rbdixon/millionpersoncities | cb0efa2218bc16687b4233beba21f93c8ac4476d | 69d521d91c39552b9d80fceefe0ed467ec828bd4 | refs/heads/master | 2016-09-06T09:48:56.735237 | 2013-09-30T22:16:25 | 2013-10-01T00:29:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 220 | r | helpers.R | # Return the index of the smallest non-zero item
# Like `order` but ignore zero.
minindex.nonzero <- function(x) {
x[x == 0] <- NaN
order(x, na.last=TRUE)[1]
}
printf <- function(s, ...) {
print(sprintf(s, ...))
} |
ed1d3024a9285ff2e866ad9842a2c317cf2f0c33 | f8fdd440073ff21773b8472044d1a619f4887e5e | /beer_city_wrangling.R | 6f7f51cf7229fa1a1c5a8efeee38b07dce652fb0 | [] | no_license | cmswalec/prowler4growler | 22e6686a988648345462b121c1756c2c797e852b | 4ce04e8fd3ea8ea3e1d45ddc703e20d78a2e948d | refs/heads/master | 2021-04-26T21:52:24.026486 | 2020-04-07T18:05:34 | 2020-04-07T18:05:34 | 124,168,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,515 | r | beer_city_wrangling.R |
library(tidyverse)
beers <- read_csv("beers.csv")
brews <- read_csv("breweries.csv")
full_beer_list <- merge(beers, brews)
cities <- read_csv("uscitiesv1.3.csv")
#MA
MA <- full_beer_list %>%
filter(state == "MA")
citiesMA <- cities %>%
filter(state_id_x == "MA")
MA_beer <- merge(MA, citiesMA, by.x = "city... |
6f34caf97ea5ad063f2f1090bbc26fb4c5c8cb43 | 736cbf8954cfaca9ca618b96f78591cf2ece86d1 | /run_analysis.R | fda58dc97fe67ec7ef172c3b7e41d9d67f9b7288 | [] | no_license | CandiedCode/GettingAndCleaningDataCourseProject | 0e6779bd185bdeebf8eef27c21dc692af7e70ab0 | 7e331d6407cbca903655ce866f39d08d63fb6e1f | refs/heads/master | 2021-01-13T01:03:09.712470 | 2016-01-31T15:32:02 | 2016-01-31T15:32:02 | 50,778,870 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,724 | r | run_analysis.R | #referenced libraries
library(dplyr)
library(data.table)
library(tidyr)
#set working directory
setwd("/Users/CandiedCode/Documents/Code/Coursera/DataScience/GettingAndCleaningData/CourseProject")
#download the zipfile, if it doesn't exist
if (!(file.exists("Dataset.zip"))){
fileUrl <- "https://d396qusza40orc.cloudf... |
1ddea827d1da13ce74aa7d7bd074be54e6eb1175 | 3342b975e93791ceeea015314b0b14b04e442886 | /TEDE_sim_MR_100.R | 9c961c685f17fcc947f2a84ba1808c8c9cedd7c5 | [] | no_license | yangq001/TEDE | 0a801323b35717f7bc582a685f82d8e17db7b0e4 | 96062c5404c94457545fd3e80552c39b82858717 | refs/heads/main | 2023-05-07T10:38:11.143646 | 2021-06-02T20:07:29 | 2021-06-02T20:07:29 | 356,360,957 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,394 | r | TEDE_sim_MR_100.R |
#Simulations in the MR context (independent SNPs, p=100)
library("mr.raps")
library("jointsum")
library("MASS")
library("MendelianRandomization")
library("bindata")
library("aSPU")
library("data.table")
library("PMR")
library("TEDE")
####### SIMULATIONS ########
#############################... |
220bcccbf49294003abdab5d34411bc47ed6c328 | 5318e14f6a6b98ac1639343a16263cdd7aa3b760 | /6 - Transit System Speed/06-speed.R | 27d0ea8e86ccf8cc81c2eea0d921b970255e198c | [
"MIT"
] | permissive | sean-connelly/Open_Transit_Data_Toolkit | af88fe496e99fdbe37e88f2b468cc4a15e755876 | 12152ed556445b9c9eccb1d7883a795a1f49c311 | refs/heads/master | 2021-01-16T09:01:35.376625 | 2020-02-25T18:11:22 | 2020-02-25T18:11:22 | 243,050,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,690 | r | 06-speed.R |
# Load libraries
library(tidyverse);library(lubridate);library(hrbrthemes)
library(janitor)
options(stringsAsFactors = FALSE)
# Read in MBTA headway for Bronx buses data file (already filtered down from raw MTA NYCT)
raw_bus_times <- readRDS(file = "./MTA-Bus-Time_2014-10-08_Bronx.rds")
# Create a POSIXct date and ... |
d25a16bc04cdd06ed5c5f076764ef5bd35c41ffd | 87a655c1e8a4e5344728b7f77ea62999f2ad494d | /man/wareki2seireki.Rd | 060d4edc10b457eaaef5ff94e6c3398e551a6221 | [
"MIT"
] | permissive | nyubachi/pharmaprepro | b79b4a22a2c15b9769dee7b8c5996ab809d96b55 | 8c694faf7e64cc6e98a7ae9aed997b41a52480e7 | refs/heads/master | 2020-04-16T01:33:15.592414 | 2019-01-15T03:03:37 | 2019-01-15T03:03:37 | 165,178,344 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 551 | rd | wareki2seireki.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wareki2seireki.R
\name{wareki2seireki}
\alias{wareki2seireki}
\title{Convert Japanese calendar to Christian calendar year}
\usage{
wareki2seireki(wareki)
}
\arguments{
\item{wareki}{The value of Japanese calendar. For example, please input li... |
d6c3beedc5a5964d8d88362df85340d8723219dd | ca45384eb29b370ab30294d0d51cc2578abd5e95 | /Data manipulation functions/insert_F.R | 5f2c0c0ebbb2a41637fdee13e89174bb5508955a | [
"MIT"
] | permissive | Thvb/R-function-lib | 89dd1a3b01cb4d1b3753c4faa61b32cf90ca0931 | 8ffd8c7239c6d3a5b10ff8f8568c483863de51aa | refs/heads/master | 2021-01-19T14:27:48.760961 | 2017-06-12T10:30:50 | 2017-06-12T10:30:50 | 88,163,329 | 0 | 0 | null | 2017-06-12T10:30:50 | 2017-04-13T12:37:54 | R | UTF-8 | R | false | false | 287 | r | insert_F.R | #insert function
insert.at <- function(a, pos, ...){
dots <- list(...)
stopifnot(length(dots)==length(pos))
result <- vector("list",2*length(pos)+1)
result[c(TRUE,FALSE)] <- split(a, as.factor(cumsum(seq_along(a) %in% (pos+1))))
result[c(FALSE,TRUE)] <- dots
unlist(result)
} |
4f066fe253a41bfd4e8a9697aa20f70c4746fe5f | 526acbb6808244ca56db2cde1c0451ae4bc7359a | /R/ICGC_cluster_based_subtyping.R | aab06ad61c067e10ed76bdda43922e947854c641 | [
"MIT"
] | permissive | rmoffitt/pdacR | 541d5b5ccde25d1a795130bca8bb9df6061aca16 | f3f9d0cc5c72d5edeac322ac7eead1c669f1b930 | refs/heads/master | 2023-07-09T10:03:26.508616 | 2023-06-20T16:00:40 | 2023-06-20T16:00:40 | 251,721,180 | 2 | 2 | NOASSERTION | 2022-11-30T18:24:02 | 2020-03-31T20:17:40 | R | UTF-8 | R | false | false | 19,993 | r | ICGC_cluster_based_subtyping.R | #' @title Cluster Based Subtyping
#' @description Perform clustering, label samples, and re-save data sets
#' @export
ICGC_cluster_based_subtyping <- function(data_set_list = c("summary")){
pdf("ICGC_cluster_based_subtyping.pdf")
#######################################################################
dataset <... |
d33a41747378107396fbee8ec3edbc23304d3e75 | 8281ba77a599e25525a88b427b162c65b9779407 | /Scripts/GARCH/garchNN.R | e2157674a2afe38f4f3913290d92223bf4aa4fbd | [] | no_license | lordbirkemose/P7 | fa96ea7c61ee20ddcaefdc61af637eeeb3458349 | ea5d91e55f36df9810e9143b04da7bf29fecb5b0 | refs/heads/master | 2020-07-31T09:00:30.100581 | 2019-12-16T08:12:24 | 2019-12-16T08:12:24 | 210,553,212 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,139 | r | garchNN.R | ### Packagets ----------------------------------------------------------------
require("keras")
require("tensorflow")
require("tidyverse")
require("magrittr")
### Tensorflow setup ---------------------------------------------------------
# Sys.setenv(WORKON_HOME = "/q/student/mnorda16")
install_tensorflow()
### Load ... |
566fa286c2e3d6a5dfa92fef44b0bb746285b927 | 4777ce6cad938c10a112e2afd80c431c64f89576 | /R/bridge_sample.R | 071913de65dd3da5d3d9e8359ec01007a463e0f5 | [] | no_license | Travis024/Comparison-of-Methods-for-Estimating-Future-Flood-Risk-in-New-Orleans-Louisiana | 436a6acfaac8ecb1e7c09712e7e0f8a59b34635e | 733509ff597410742e1388296d0c2a570ff40ab8 | refs/heads/master | 2020-05-29T09:00:13.848008 | 2019-07-28T21:14:57 | 2019-07-28T21:14:57 | 189,045,242 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,829 | r | bridge_sample.R | #===============================================================================
# This file contains the working script for estimating the marginal likelihood
# of different GPD surge models and data experiments.
#
# This revised version calculates all likelihood estimates as a parallel
# 'foreach' loop.
#
# Or... |
e645ec1ab6b0e473bbd1f6578fb6acd62c73fb1c | 05a79b70fe74a76c292a5842cf1305e5ddb7c09d | /gimme/man/expand.grid.unique.Rd | 6e806bdd0c9841bb40f9766aab2ce81ce9918c68 | [] | no_license | GatesLab/gimme | 0cae55f00b9f3db2b0cb4874ea3594665b2fca3b | fe2e5c732bb7a8e964f71a7caa99c02327373beb | refs/heads/master | 2023-09-04T01:24:26.488748 | 2023-09-01T16:44:21 | 2023-09-01T16:44:21 | 39,515,818 | 25 | 25 | null | 2023-02-17T19:22:45 | 2015-07-22T16:01:45 | R | UTF-8 | R | false | true | 641 | rd | expand.grid.unique.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/expand.grid.unique.R
\name{expand.grid.unique}
\alias{expand.grid.unique}
\title{Provides unique combinations of two vectors.}
\usage{
expand.grid.unique(x, y, incl.eq = TRUE)
}
\arguments{
\item{x}{A character vector containing variable name... |
7984b72f120be6790233821c3087c42105a12d88 | 9438cd69c9adbd11e2e6e9e15eb493b8d5b4a6d9 | /man/expandTable.Rd | b0eb1d6440b565f67fb63f6c819c877e26d64a81 | [] | no_license | wokai/sqliteTools | 1d74943793ff1d2dc50beb370a8c20852ab412c2 | 57dd6f54b4a2960e0257141a42b3751ef76f70d8 | refs/heads/master | 2021-01-13T01:23:40.073699 | 2015-01-07T10:46:37 | 2015-01-07T10:46:37 | 28,908,974 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,920 | rd | expandTable.Rd | \name{expandTable}
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - %
% Alias
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - %
\alias{expandTable}
\title{expandTable
%% ~~function to do ... ~~
}
\description{Reads readTable and writes replicated and equally d... |
5907297d128c1c13d5c681fcbf4cd27547f805c9 | 239d38627bf51f07f7d0e933294f0421f1c96271 | /week3/week3.R | 90716a5c8e5e7100849f8173e7cd6afdecc67e83 | [] | no_license | stkubr/econometrics-coursera | a67d326a208e107fad326b651ad85371fdd721cc | dc54cfb0ae4c3edf527de8185d27ed460ba598e3 | refs/heads/master | 2021-01-25T08:42:32.870970 | 2015-05-07T23:56:11 | 2015-05-07T23:56:11 | 35,249,089 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,657 | r | week3.R | library("memisc")
library("lmtest")
library("ggplot2")
library("dplyr")
library("foreign")
library("vcd")
library("devtools")
library("hexbin")
library("pander")
library("sjPlot")
library("knitr")
# load data
h <- diamonds
glimpse(h)
help(diamonds)
# diagram hexagons
qplot(data=h, carat, price)
bg <- qplot(data=h, lo... |
e54ff379eb5feee65114070bb5a633035e55ad7d | dc1ead4b62bba2a151b894d33dc16d76d12856e2 | /The P-Hub/demos/main file template.R | c1fb1614d22a5d18e6020005f04422c958e86303 | [] | no_license | pabloirigo/IS_RProjects | 6343e968523155164e4ef8d41dfbbe68f7588c35 | e6576d713641038c09495c6dc8be3d458e10e3ab | refs/heads/master | 2020-04-28T15:38:32.411954 | 2019-05-20T11:58:47 | 2019-05-20T11:58:47 | 175,378,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,007 | r | main file template.R | # =======================================================================
# Names: Pablo Irigoyen, Luis Urrechaga
# Group Number: B
# Assignment: P-Hub
# Date:
# =======================================================================
# 1. Be sure to include, with this template, any necessary files
# for execution, i... |
3558ac86292cf4034f263a93265702426d250429 | d1b1cead5e9525fbfec5b7df989ebc0a09c8d782 | /R/getExperimentSampleAssayData.R | 3e1e056f222fa21447ca0e1e817f8c6d504d1071 | [] | no_license | AmundsenJunior/pfsrsdk | 0ce8195f9d9a96562d31992f44303ee151bd4111 | d799defb9447a4e70cb2906205f6023020fc621a | refs/heads/master | 2020-06-06T00:24:06.491018 | 2019-07-15T16:59:12 | 2019-07-15T20:37:59 | 192,584,459 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,055 | r | getExperimentSampleAssayData.R | #' getExperimentSampleAssayData - Gets assay data for an experiment sample.
#'
#' \code{getExperimentSampleAssayData } Gets assay data for a experiment sample identified by barcode.
#'
#' @param coreApi coreApi object with valid jsessionid
#' @param experimentAssayType assay type to get
#' @param experimentSampleBarco... |
70b0670a87a0749d96abe7e4317902a39c6a527e | 437c510c304fccfd6ccaa11ba158f68172a154ba | /R/calculate_percent_change_exchange.R | faa782290ef99cc67d0f8786b8b213452f4f4f72 | [
"MIT"
] | permissive | ries9112/PredictCrypto | 37e44ac160434a77796093d5d34c868b0bedafef | f118e1dbcb3af5805c5be8603e23ae986d86f9b8 | refs/heads/master | 2020-12-26T23:09:01.034583 | 2020-09-06T21:06:19 | 2020-09-06T21:06:19 | 237,680,808 | 1 | 2 | NOASSERTION | 2020-09-05T00:56:56 | 2020-02-01T21:32:19 | HTML | UTF-8 | R | false | false | 2,381 | r | calculate_percent_change_exchange.R | #'@importFrom anytime anytime
#'@importFrom lubridate hours
#'@importFrom dplyr left_join
#'@importFrom dplyr select
#'@importFrom dplyr rename
#'@importFrom dplyr filter
#'@export
calculate_percent_change_exchange <- function (crypto_dataset, enterHours)
{
crypto_datasetHLater <- crypto_dataset
#create 24h offset... |
e30f949ac7c3c2a065a9df5ba4eef711a0afc355 | 101ddeb6267e076c1a214e50478c2ecb3b012b98 | /TEQ.R | e71dd7423622eae86a803df37d962ad8651cf98a | [
"Apache-2.0"
] | permissive | bhar2254/TSU_Portfolio | ea080328550e2fdaeaef2d0bf6fc1caff4718168 | 8e77e91b5bd30d244c3b5c6a4a4988fc23ebf2ab | refs/heads/master | 2022-04-24T19:55:32.237439 | 2020-04-29T05:14:40 | 2020-04-29T05:14:40 | 259,831,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,020 | r | TEQ.R | # Blaine Harper
# CASE - Truman State University
# TEQ.R
# Automation of Truman's Annual Portfolio Project.
# TEQ Tables
TEQ <- portfolio_data("TEQ") %>% # copied from everywhere else
merge(y = Majors, by.x = c("Last Name","First Name"), by.y = c("Last","First"), all=T) %>% # Merge with the Majors data frame
por... |
e032cad07b72017d17c0fc16ddfa6ce0c611ba17 | 68a372a3a51dd43110a384a308443efaca35a43a | /New Code/test_code.R | 155294b375d748df8e897d06990898067b42cd21 | [] | no_license | matteo-fontana/fastIWTlm | 44c37168a9d8f0d4437b08ab49d98474351d5043 | b2a3c37c03ac75dc4bc67d5fd1ce4b31029f6b66 | refs/heads/master | 2020-03-24T20:58:36.662305 | 2018-08-03T13:29:19 | 2018-08-03T13:29:19 | 143,006,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 612 | r | test_code.R | library(Rcpp)
Sys.setenv('PKG_CXXFLAGS' = '-std=c++11 -Wall -pedantic -fopenmp')
sourceCpp('New Code/IWT2C_exp.cpp')
output=IWT2C_exp()
#now, try input via R
##prepare input objects: first step with known n1-n2-p
data1=as.matrix(read.delim('Data1.txt',sep=' ',header=F))
data2=as.matrix(read.delim('Data2.txt',sep=' ... |
b78196686888df4338a41f40f68fea7654304373 | 91417608f40cbc0a2c021993e701b1f6264524e8 | /cleandata/project/dataset/run_analysis.R | c0324b2dbda7c05fb99dea02f36b8d9a7fc3ca29 | [] | no_license | latimer/datasciencecoursera | 0ddcf18298a6ca73fa4d3a270b21ee5b96b4814f | 88feeae5b7205cf0717d57d77f3323e5caeba958 | refs/heads/master | 2021-01-02T09:13:46.580020 | 2014-06-23T05:45:38 | 2014-06-23T05:45:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,113 | r | run_analysis.R | # Read data from one directory
readData <- function (dirname, colNames) {
suffix <- paste(dirname, ".txt", sep = "")
# The readings
measurements <- read.table(paste(dirname, "/X_", suffix, sep = ""))
# Poor shmuck who is doing the exercise
subject <- read.table(paste(dirname, "/subject_", suffix, se... |
8e42abc831490b78d8851b081fdb82cba4461b49 | eccc786c00c11473c64c919c0b0fd2eefad68888 | /rcdk/man/getsmilesparser.Rd | 3182c9a1f5e1dd79a4aa832739bcfbd7e5fbffb5 | [] | no_license | schymane/cdkr | 25c3e6349cc4a1541ad721567b8141f141bb9e61 | 5d074e5031a2b04c99d58364b0a59ed20143f738 | refs/heads/master | 2021-01-20T12:24:24.235252 | 2018-11-11T10:38:59 | 2018-11-11T10:38:59 | 82,657,563 | 1 | 0 | null | 2018-11-12T21:38:20 | 2017-02-21T08:54:10 | R | UTF-8 | R | false | false | 653 | rd | getsmilesparser.Rd | \name{get.smiles.parser}
\alias{get.smiles.parser}
\title{
Get a SMILES Parser
}
\description{
This function returns a reference to a SMILES parser
object. If you are parsing multiple SMILES strings, it is
preferable to create your own parser and supply it to
\code{\link{parse.smiles}} rather than forcing that fun... |
85138bde8ae916a83cf68fef244ed93f8d79a0de | 99c4b03153c585224c98b7632158d41c16423cac | /pkg/man/priceMovePlot.Rd | e2fa6b1f53f124da07c29ba50eda712d048bc5e1 | [] | no_license | Tynes-Science/aekee2aiseir | 3be8d8653a3b7a44ac135617f7f9a174db8b4020 | 3673c9f38f7bb6015f753724cf2aa61fe57c55d9 | refs/heads/master | 2021-01-01T12:52:33.146450 | 2020-06-15T17:36:11 | 2020-06-15T17:36:11 | 239,287,938 | 0 | 0 | null | 2020-06-15T17:36:12 | 2020-02-09T10:50:06 | R | UTF-8 | R | false | true | 876 | rd | priceMovePlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analysis.R
\name{priceMovePlot}
\alias{priceMovePlot}
\title{Generate price plot}
\usage{
priceMovePlot(
data,
smb_list,
start_date = as.Date("1900-01-01"),
end_date = as.Date("2900-01-01"),
type = c("open", "high", "low", "close"),... |
7f0c1a5611f5a9a6dcb06bb6112255044114bc50 | f6352c4ee695ca2d5b6a3c36400b37259ff3f800 | /man/stats_chisq.test.Rd | a4e3397a229f187e1ba88085c73e43dfddc4abbe | [] | no_license | lshen1/anatools | 57c7e9c7ea24662593b28b639aafca3e5e6ab2ec | 1e9d10ed40defa5db7ef62d9b48710e9e20f0b12 | refs/heads/master | 2021-01-10T21:48:42.495201 | 2015-07-21T22:54:44 | 2015-07-21T22:54:44 | 38,843,573 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,119 | rd | stats_chisq.test.Rd | % Generated by roxygen2 (4.0.2.9000): do not edit by hand
% Please edit documentation in R/modelTesting.R
\name{stats_chisq.test}
\alias{stats_chisq.test}
\title{Perform Pearson's Chi-squared Test between each row in a matrix and a vector using `stats` package in R}
\usage{
stats_chisq.test(data, group, rowlevel, ...)
... |
ea9f947cdae4161a24a9edee899efd369bad096b | 39ccd03931b7bb71a17bbdeff27c63e948b8e28a | /man/font_style.Rd | 61f77b4a08b98acd52def66ba3f6b09a0afe5da6 | [
"MIT"
] | permissive | coolbutuseless/devoutsvg | d4448f5bf60e377e6acec03e1750fb99dc2d37f7 | 9151bd07a58a506ffa3d39f46d0982fd1e1693e1 | refs/heads/master | 2020-08-09T19:51:08.571630 | 2020-04-19T06:45:37 | 2020-04-19T06:45:37 | 214,160,262 | 58 | 2 | null | null | null | null | UTF-8 | R | false | true | 501 | rd | font_style.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-style.R
\name{font_style}
\alias{font_style}
\title{Construct a vector of styles related to 'font'}
\usage{
font_style(state, geom)
}
\arguments{
\item{state}{list including 'gc' (graphics context)}
\item{geom}{which geometry has asked... |
580ec4b25269117cbf50e72fedf6b8bd48e2ef93 | 642493202c771806f6f9f50a429cadf7023bb416 | /data/sealevel_world/input4_plot_sealevel_data_world_ewl_slr.r | 219cc8c6b40457e0888bee09c43fae928e7224c6 | [] | no_license | TheClimateService/UI-mockup | a0fd242513f1c18121a027e305ae96c5ba3dc225 | bf4546bb6461dc19e1ba99ea90d3b32ed6be3554 | refs/heads/master | 2021-01-19T15:50:47.039962 | 2018-08-16T18:28:52 | 2018-08-16T18:28:52 | 100,974,930 | 0 | 0 | null | 2017-08-21T17:06:29 | 2017-08-21T17:06:29 | null | UTF-8 | R | false | false | 1,932 | r | input4_plot_sealevel_data_world_ewl_slr.r |
# This generates location and scenario inputs for plot_sealevel_data_world_ewl_slr.r .
locID <- corpLocations %>% filter(ParentCorpID==USER$ParentCorpID & LocationName==input$inputLocations_overall) %>% select(LocationID)
key <- paste(locID,USER$ParentCorpID,input$inputLocations_overall)
key <- gsub(" ","_"... |
f02c6cd508570f9b3ea43b385749e3ae6857b39c | b725ae79645bb08446f3c7eb4c95da47b2627ad3 | /tests/testthat/test-06-zproject.R | ff13af916b4df827098cbd7e868fd3a1ea299de4 | [] | no_license | cbig/zonator | cf1692e9d210d96317164c94dfc902464b3e361c | bfa5a27689d853ef824634a5a6be52f9b3f54c24 | refs/heads/master | 2021-01-18T22:08:44.457702 | 2020-05-18T18:29:08 | 2020-05-18T18:29:08 | 8,728,996 | 12 | 6 | null | 2018-04-11T06:51:40 | 2013-03-12T13:57:04 | R | UTF-8 | R | false | false | 3,453 | r | test-06-zproject.R | context("Zproject creation")
options <- get_options()
test_that("Zproject is created correctly based on existing project", {
setup.dir <- options$setup.dir
test.project <- load_zproject(setup.dir)
# Test slots
expect_that(test.project, is_a("Zproject"),
paste("Test project is not an instance o... |
cb32e51cc38509eb5632df69853df709d0993ec4 | 4feba473f0490928f34a9e03be7810e8fc50a0cd | /cachematrix.R | bb74a3022ab11d6052d812d2e33333fd667c7920 | [] | no_license | drP84/ProgrammingAssignment2 | a5ec20ef46507790c6fca6790148a9d253a05f32 | 43cb0a3d7de9a3ef32db6aba8f8b2150eb7547bc | refs/heads/master | 2020-12-25T06:26:23.400926 | 2016-01-22T13:34:15 | 2016-01-22T13:34:15 | 50,133,042 | 0 | 0 | null | 2016-01-21T20:00:10 | 2016-01-21T20:00:09 | null | UTF-8 | R | false | false | 1,017 | r | cachematrix.R | ## Frankly, I just followed the example function and changed mean to inverse
##when you put in a matrix x, the function:
##sets the value of the matrix,
##gets the value of the matrix,
##sets the value of the inverse
##gets the value of the inverse
makeCacheMatrix <- function(x = matrix()) {
inverse_mat <- NULL... |
6f85446bfc9ea1c804f3dbad0a5f29bb2b2678f2 | 81fbd0db9e8c99fcff278a20afdc2963c47d6900 | /cachematrix.R | f32c71a3e71f42987469c0f96827403d5506d986 | [] | no_license | jaysonleek/ProgrammingAssignment2 | 2dbc873056c7f91279b12d05ae5aae28348e4675 | ff1f33a5cce159750f15936222c5befe4b42e600 | refs/heads/master | 2022-10-17T11:01:35.599117 | 2020-06-09T18:15:19 | 2020-06-09T18:15:19 | 271,029,701 | 0 | 0 | null | 2020-06-09T18:15:21 | 2020-06-09T14:45:45 | null | UTF-8 | R | false | false | 1,442 | r | cachematrix.R | ## This function creates a special "matrix" object
makeCacheMatrix <- function(x = matrix()) { ## computes the matrix
inv <- NULL ## will hold the matrix inverse value, initalizes as NULL
set <- function(y) { ## define the set where new value assigned
x <<- y ## value of matrix in parent environment
... |
88c85e5326e6890c2ff756f95d8e9a47beb92210 | b92b0e9ba2338ab311312dcbbeefcbb7c912fc2e | /build/shogun_lib/examples/documented/r_modular/kernel_diag_modular.R | 24d69961e4fe4215644f8fe0f4a70416ae4ab1b4 | [] | no_license | behollis/muViewBranch | 384f8f97f67723b2a4019294854969d6fc1f53e8 | 1d80914f57e47b3ad565c4696861f7b3213675e0 | refs/heads/master | 2021-01-10T13:22:28.580069 | 2015-10-27T21:43:20 | 2015-10-27T21:43:20 | 45,059,082 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 617 | r | kernel_diag_modular.R | # This is an example for the initialization of the diag-kernel.
# The diag kernel has all kernel matrix entries but those on
# the main diagonal set to zero.
library(shogun)
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
# diag... |
464adcf20caed11cb983ea7eefd2959005e5d38e | d70cec5dace0bd0193c23676b51a5abf539a4183 | /scripts/plotThalmoCorticalConnectivity.R | 9e3a0a08276de743ee49f95147a96b72be06ef7b | [] | no_license | jeffduda/StructConnRepro | 7de0a43a7bb2ade1f82d8730e46c2631e1353757 | d6da6bcec6dd8cab87209eadb5a72897507d6dea | refs/heads/master | 2021-01-10T18:07:28.583014 | 2014-04-07T21:13:57 | 2014-04-07T21:13:57 | 10,126,849 | 0 | 2 | null | 2014-04-08T20:00:37 | 2013-05-17T15:53:16 | TeX | UTF-8 | R | false | false | 3,655 | r | plotThalmoCorticalConnectivity.R | # plot thalamo cortical connectivity
getThalamoCorticalConnectivity <- function( )
{
listIdx <- 1
matlist <- list()
files <- list.files(path="/mnt/picsl/jtduda/StructConnRepro/data/MMRR-21_processed/", all.files=TRUE, full.names=TRUE, recursive=TRUE, pattern="*bayes1000_sc.csv")
subjects <- c()
for ( fil... |
d45cf819ed7d9aa06e32ac6b60029b274439ae5a | db12b990924703cd74748d8585cd9c11fafa6746 | /h2o-docs/src/booklets/v2_2015/source/DeepLearning_Vignette_code_examples/deeplearning_gridsearch_random.R | e708ea44bb4a9b9c9a84b1874e98fca348eeddb7 | [
"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 | 674 | r | deeplearning_gridsearch_random.R | hidden_opt = lapply(1:100, function(x)10+sample(50, sample(4), replace=TRUE))
l1_opt = seq(1e-6,1e-3,1e-6)
hyper_params <- list(hidden = hidden_opt, l1 = l1_opt)
search_criteria = list(strategy = "RandomDiscrete",
max_models = 10, max_runtime_secs = 100,
seed=123456)
model_grid <- h2o.grid("deeplearning",
... |
74c6ebdba421582f5bf0abeef0eef923026766d3 | 4b44d1dbdf20c25b5afadd63180ab695a1ea738b | /dplyrTest.R | 3c2ee4012830f83e8d5733b7e3c9ba44d44e3d49 | [] | no_license | NTU-CSX-DataScience/pecu | 34f3beeba3b08394e134b5596849ea62c6f53883 | b2a3e0eb4ea31eb3eef0f894aedca15f3725e2e6 | refs/heads/master | 2021-09-13T14:10:26.326879 | 2018-05-01T03:26:56 | 2018-05-01T03:26:56 | 103,476,845 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 248 | r | dplyrTest.R | library(dplyr)
library(hflights)
head(hflights)
dataOrg = hflights
temp = filter(dataOrg, Month == 1 | dataOrg$DayOfWeek == 3)
SelTemp = select(dataOrg, DayOfWeek, Month, DepTime, UniqueCarrier) %>%
filter(Month == 1 | DayOfWeek == 3)
|
24161e0388cad6fe49abf845bb95fd35fac1137b | 248c8a01242b6730a4e9fed531d3ba9390e80726 | /lab_mm-08_after.R | 021ca97228b551a9b16c109cfb5e7b9291c1a222 | [] | no_license | aksyuk/R_lab | 635faa7f1f64dae6e88367b800b1bf6acc20a56d | 30997f9d1f2bfddb27bf2697269a545ee4df3009 | refs/heads/master | 2021-05-13T13:32:19.911254 | 2017-05-26T21:45:34 | 2017-05-26T21:45:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,654 | r | lab_mm-08_after.R |
# Математическое моделирование: Практика 8
# Деревья решений
library('tree')
library('ISLR')
library('MASS')
library('randomForest')
library('gbm')
# Бэггинг ----------------------------------------------
# бэггинг -- частный случай случайного леса с m = p, поэтому и то, и другое
# можно построит... |
3bf0b471f1a7478003d660ff83bc84029620882f | 061c13925c352598bf66428f453ac9186b3a05c2 | /devsem-stat.R | e289e46aef7e686ea94bc9117d76206f15709816 | [] | no_license | patcha2014/Dissertation_Analysis | d649226ebc38d6e932c488345e333d392a7cf231 | 120141b67796e26743c464dbcc9b59b50be69911 | refs/heads/master | 2020-04-06T07:12:57.193241 | 2016-10-19T18:20:47 | 2016-10-19T18:20:47 | 52,697,659 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 460 | r | devsem-stat.R | # dev sem stats
cleanreg <- csv.get("/Users/Mint/Dropbox/Dissertation_Data/cleanreg.txt",sep="\t")
#temp <- temp[temp$qtr==3,]
library(ggplot2) # line graph
ggplot(data=temp, aes(x = year, y = wage, group=skill, colour=skill)) +
geom_line() +
geom_point()
temp <- tapply(cleanreg$wage, cleanreg$skill, mean, na.... |
386704b6a9a8b5beb80f5c2b8dc3799a27e753fc | 4764c2b10291d7387a37e7722a6429abad96a9d9 | /figure/plot3.R | 80955deb8779f62243fa201ae9c6d607c63fb8eb | [] | no_license | ericbrown07/ExData_Plotting1 | 0e8f34e9d3bc372ee4939d1ad2580a7f069ee69c | 7dd892547de00a1bd691d6337b5d015e904e116e | refs/heads/master | 2021-01-23T21:22:47.536534 | 2014-12-03T18:17:27 | 2014-12-03T18:17:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,129 | r | plot3.R | ## Plot 3 - Energy Sub metering (1,2,3) (by time, labeled by day)
## Use these functions to read data into R and set up data frame for plots
library(data.table) ## load data.table package to use fread function
powerdata <- fread("household_power_consumption.txt") ## use fread to read in file
# fread will read... |
b72cddc8203197d836de679bc2f3884146e271de | bb2cbd11aac465d8fe4c2bdd65e7a4970cd8b067 | /code_chunks/load_preprocess.R | 0bf11000b746afd693470d0482028e839c5f3f48 | [] | no_license | diebugger/RepData_PeerAssessment1 | ef457e2a0f7c9c3151c9b99baf13ca4edfffe113 | c83b03437e38c5dc5d1bb9c5fb616c95ca647ce8 | refs/heads/master | 2020-04-29T14:39:57.646431 | 2015-04-19T15:54:49 | 2015-04-19T15:54:49 | 34,166,618 | 0 | 0 | null | 2015-04-18T13:21:40 | 2015-04-18T13:21:39 | null | UTF-8 | R | false | false | 447 | r | load_preprocess.R | ### Load useful libraries
library("dplyr")
library("knitr")
library("lubridate")
library("ggplot2")
# prerequisite: normalize to US locale
Sys.setlocale(category = "LC_ALL", locale = "US")
### unzip and read table
unzip("activity.zip")
activity <- read.csv("activity.csv", header = T)
### convert date
activity$date <-... |
7eab214920aed027b95a0b9be36480c48bf2ecfa | 61a85e9d0a2452b7bffaf7486fd40b4f42d958d1 | /statistical_inference/13_resampling.R | 5499af0d38878c7660f2231140fc8811cbfff8f4 | [
"MIT"
] | permissive | fuchingy/data_science | 0cd84f0efbfe85ba3a653f466deab50aa9c8c7e8 | aaf021ae28dbf163efb4fe8d7842ac5a13e344de | refs/heads/master | 2021-06-08T12:47:19.423277 | 2016-11-10T11:13:12 | 2016-11-10T11:13:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,093 | r | 13_resampling.R | library(UsingR)
data(father.son)
x <- father.son$sheight
n <- length(x)
B <- 10000 # 10000 bootstrap resamples
hist(x)
# We collect 1078 data (x)
# Then, we resample 10780000 data from the collected 1078 data, and arrange the
# data as the following matrix.
#
# resamples
# 1078 (n)
# 10000 (B) ...
#
re... |
3bed093ade33964008fcc8f8f07640648d731a0c | b409efb1b86628179bfb3e9bde3501a95409b274 | /tests/testthat/test-exec.R | 85186ac93a504aa542b19393619aa28157f71450 | [] | no_license | MuriloPetruci/SnakeCharmR | 84b806cced22ab73e1887472093b6bcee609dfb3 | 26002e01fa22838752b5d06a4dd6ac3074cc4ca9 | refs/heads/master | 2021-05-21T15:09:22.816777 | 2020-01-02T18:40:51 | 2020-01-02T18:40:51 | 252,691,742 | 1 | 0 | null | 2020-04-03T09:46:15 | 2020-04-03T09:46:14 | null | UTF-8 | R | false | false | 266 | r | test-exec.R | context("exec")
test_that("exception handling on executing Python code works", {
expect_error(py.exec("raise Exception('oh noes')", stopOnException = TRUE), "oh noes")
expect_warning(py.exec("raise Exception('oh noes')", stopOnException = FALSE), "oh noes")
})
|
3d5fadb75bf874eebe21a322fd7d98a1c583e640 | d4cce763dc2131c0ae85d5c99c045aa26151299e | /man/osmplotr.Rd | c2ddd413f89ed150478d7554a3b7d216c5899892 | [] | no_license | jeperez/osmplotr | 7864a9248f8d41cf9a0916149f61e3d2c0e2f1e9 | 2d42cda7e0124a250e1c498b8886ce1e644b589e | refs/heads/master | 2021-01-24T20:01:52.331044 | 2016-05-18T10:53:12 | 2016-05-18T10:53:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,083 | rd | osmplotr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/osmplotr.R
\docType{package}
\name{osmplotr}
\alias{osmplotr}
\alias{osmplotr-package}
\title{osmplotr.}
\description{
Produces customisable images of OpenStreetMap (OSM) data and enables data
visualisation using OSM objects. Extracts data u... |
e4a54955871bcb6f274327b402f0a1a3fd28bc42 | 8a8f4a8cd9cce41e5c492324d463cf135ad986d1 | /ui.R | d2b85e3dce67212ba979b1b78820cb0a5851484a | [] | no_license | hyanworkspace/LumiaInterface | 1b71559fb35827425c3c5b3d409a1ff88c9d345b | b0e5da3582145ab6fc64cad1748aab53a0f65b18 | refs/heads/master | 2020-11-30T02:24:39.163609 | 2019-12-29T18:38:40 | 2019-12-29T18:38:40 | 230,275,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,614 | r | ui.R | # ui.R
ui <- dashboardPage(
dashboardHeader(title = tagList('Mixer beta',
img(src="images/logoR39.png",
height = 50, align = "right"))
),
dashboardSidebar(
sidebarMenuOutput("menu")
),
dashboardBody(
withMathJax(... |
c0f02f18f1a359cf537c0f13cdb9fdbc3e70ecac | 175b99eb5ca8724f8f25d757e9bd164e1889b72a | /main.R | fe9f625a48d71114d7a2be4b576dc610adb64168 | [] | no_license | arielsun1990/RProgrammingCourseA1 | 505e0b4cdfe6050cf88b7283d1b00a68b15e2bba | 9c5071dcd4ae651cce3911b2ebed4b8a7b97f97a | refs/heads/master | 2020-12-11T05:45:08.730554 | 2014-05-22T01:30:17 | 2014-05-22T01:30:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26 | r | main.R | source("pollutantmean.R")
|
fd05bdc79073563df65ea59c997811557291f414 | a8d015305338849fcaa711464c964c02305cd0e0 | /tests/testthat/test.R | f85929921376f1a308ab98d64743130de8c7b0ac | [
"MIT"
] | permissive | noamross/twee | 15f208438d53de99a1ca345a73ce4725eaf5bb8f | dd115756203157cd8c474604b37256d5e098e033 | refs/heads/master | 2021-01-20T16:51:20.366567 | 2017-05-10T12:39:13 | 2017-05-10T12:39:13 | 90,854,633 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 69 | r | test.R |
context("twee")
test_that("twee works", {
expect_true(TRUE)
})
|
834bf6b2487ba67f2496c94f62c80fa8279cf48b | 3afe0a0834ae866d72116f3d66caac8c451b07c2 | /Diffusion_Simulations/MetaPopulation_Diffusion/tests/SIR_Gillepsie_Example.R | e2bb0201c0cfde932a72af4beb7e1e705ffcf8e1 | [] | no_license | ethanbhojani/IDEANet | 2c07d6682d708acc3bc6a799d2531c80f78cf92a | 6fc0189ab34138ebd44cd25104d6696bde168991 | refs/heads/main | 2023-08-22T20:24:50.044618 | 2021-09-30T21:25:57 | 2021-09-30T21:25:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,703 | r | SIR_Gillepsie_Example.R | # Simple SIR Simulation where the Gillepsie Algorythm Is Implemented
# Jonathan H. Morgan Using Resources Provided by Ben Bolker (2003, 2015)
# 20 July 2021
# LambertW Implmentation: https://stat.ethz.ch/pipermail/r-help/2003-November/042793.html
# Basic SIR Model with the Gillespie Algorithm Implemented: https://rpub... |
5313001afae882567acf9c7428172fbffec9f977 | b3f0ad3934f871dfe1105a8168d0f91048c0420c | /R/dataset_TRW_complete.R | a46663d7b199e0ed5f473a4f956a1c67d061482a | [] | no_license | cran/dendroTools | 25739b0411af2f8a9114e46c01641fac4b40e538 | 51c3eed999c8d27b38ea7d2edd91cb0063cf2127 | refs/heads/master | 2023-07-20T07:48:19.869232 | 2023-07-17T12:50:02 | 2023-07-17T14:34:17 | 105,138,038 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 847 | r | dataset_TRW_complete.R | #' The complete dataset of standardized tree-ring chronology from Albania
#'
#' A dataset with a tree-ring width (TRW) chronology of Pinus nigra from Albania This TRW
#' chronology has a span of 551 years (period 2009 - 1459) and was already used to reconstruct
#' summer temperatures by Levanič et al. (2015). In th... |
8c94f7074cc33fa5bde9d14451f8821723867f6f | a90fb0b0366bdaee2e875b5642d31a2b8171586e | /metric_calc.R | 0fcefd52f1b2656c5244d7381367c8878bc30779 | [] | no_license | InterstateCommissionPotomacRiverBasin/Large_River_Cummins | 6480e2b70bf7abcef561bfbc37350fe4fb3e2ec4 | b8e098de289fdd0db37cf84a2f942b02c2355f88 | refs/heads/master | 2020-03-25T23:55:36.052538 | 2018-08-02T12:10:05 | 2018-08-02T12:10:05 | 144,299,244 | 0 | 1 | null | 2018-08-10T14:53:56 | 2018-08-10T14:53:56 | null | UTF-8 | R | false | false | 3,413 | r | metric_calc.R | #==============================================================================
#==============================================================================
# Author: Zachary M. Smith
# Organization: ICPRB
# Created: 3/08/17
# Updated: 7/25/2018
# Maintained: Zachary M. Smith
# Purpose:
# Output:
#================... |
e7fb210998083c17137e383337cee6ad37d1ed96 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/zscorer/examples/run_zscorer.Rd.R | 07a19911f576f0e0df523044caa794cda7b309fa | [] | 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 | 113 | r | run_zscorer.Rd.R | library(zscorer)
### Name: run_zscorer
### Title: run_zscorer
### Aliases: run_zscorer
### ** Examples
#
|
b5f3a4838e45d0c79cf4e083b98b97de2f990fb4 | efdcd47a08e872cbb264ddc2484293ef49357e7e | /R/checkfunArgs.R | bf1647a5a9dcab041fce6f5753bd7e6a3d90cd87 | [] | no_license | cran/ipred | b69ef67667a47910efdecfb54837fca00bc8fb90 | 79c406d551eea2790c298403cb4496a08e85dc82 | refs/heads/master | 2023-03-16T08:35:29.570578 | 2023-03-09T13:50:02 | 2023-03-09T13:50:02 | 17,696,820 | 0 | 3 | null | 2018-08-14T06:01:58 | 2014-03-13T05:03:12 | R | UTF-8 | R | false | false | 848 | r | checkfunArgs.R | # $Id: checkfunArgs.R,v 1.1 2003/02/17 09:49:31 hothorn Exp $
checkfunArgs <- function(fun, type=c("model", "predict")) {
# check for appropriate arguments of user-supplied function "fun"
# this will not work for generics in R < 1.7.0 and therefore not used by
# now
type <- match.arg(type)
if (!is.functi... |
2e030df5707b2458aeff225f5914716103aa65a0 | 9d79f581ef4419b6e43fef0155af9e9cc33ffdce | /Utils.r | 2d3fb866a54a7c3e3ab9cbd6565e6fd8b450ab0e | [] | no_license | linlin0026/fiber_-_10.1016-j.chom.2022.03.036 | d9f9cb7ec86eaa81d3ac7566a3d150259e59142a | 8dec8b716ed12baf34f42f5fce6a94bdcd7fe7d2 | refs/heads/main | 2023-07-08T19:55:58.112798 | 2021-08-12T01:53:53 | 2021-08-12T01:53:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,863 | r | Utils.r | #functions here
#For some of the measurements we have repeats. I think mainly Charles did this in order to see the batch effects.
#However this is probably unnecessary if the data are truly randomized, and then we can perform the best batch
#correction using Combat or something like that
#The input matrix should have ... |
5fdf9d6a92a9726b2c629232eb17a194d9034a8a | c4c92cd67ee94a3535254e61a5a4502b891f97d6 | /tests/testthat/testgc.R | bbd7826568c9dfffff9bccd76d29110ab629e7ec | [] | no_license | fernandogelin/qckitfastq | 4c7c373868c7376e2232a3cd2d8ba4d588b5635c | 3e002c124af84eaab32763b6fe7c1345d9be79d4 | refs/heads/master | 2020-03-26T11:46:14.164878 | 2018-08-15T20:13:19 | 2018-08-15T20:13:19 | 144,859,277 | 0 | 0 | null | 2018-08-15T13:51:25 | 2018-08-15T13:51:25 | null | UTF-8 | R | false | false | 195 | r | testgc.R | testthat::test_that("GC",{
#test the dimension of resulting dataset
testthat::expect_equal(nrow(GC_content(system.file("extdata", "10^5_reads_test.fq.gz", package = "qckitfastq"))),25000)
})
|
e79e6ecfaced258e5f2eae6edadda15783edb798 | 6ef2884f487c4819fae2d57750bfdb91e5d085c8 | /ml/RLab/Main.R | bdef44f5fc9821fc7770aea05803ce963cc3bf1b | [] | no_license | vsantiagogon/dit | 33ddd013ba2a6d208ce30f93b2e088f157ef48e0 | 114790c429b4774ed532eac6628d55ab1294d53b | refs/heads/master | 2021-05-09T02:05:06.416893 | 2018-04-24T09:13:59 | 2018-04-24T09:13:59 | 119,196,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 827 | r | Main.R | source('repos/dit/ml/utils/Setup.R');
# Load the dataset & dependencies
DATA = read.csv('repos/dit/datasets/Titanic.csv', stringsAsFactors = FALSE, na.strings = 'NA');
Setup$getPkgs('SDMTools');
Asses = function (test, pred) {
# Finally, we show the results, using the confusion matrix.
mat = confusion.matrix(tes... |
00fd612260fbf759e29777265d6132d414846021 | 89b94a1553151b34386d75b0006b970dc372d2de | /College Football/Big 12 Deep Dive/Team Stats.R | b98d0ff27436de73f45b778ca8d29a45ef680c47 | [] | no_license | aelfering/Sports-Data | 0d2052a9fdf0e05b08ec820faaefa34f0066d48d | 86ed99e443f255a4c3178358c374429454984d13 | refs/heads/master | 2022-05-02T23:50:14.812967 | 2022-04-01T15:49:59 | 2022-04-01T15:49:59 | 222,936,805 | 1 | 0 | null | 2019-11-21T01:02:03 | 2019-11-20T12:52:29 | null | UTF-8 | R | false | false | 7,322 | r | Team Stats.R | # Big 12 Box Stats
library(ggplot2)
library(tidyr)
library(dplyr)
library(tidyverse)
library(lubridate)
library(stringr)
# Load the Data
big_12_reg <- read.csv('2019 Reg Season Stats.csv')
big_12_post <- read.csv('2019 Post Season Stats.csv')
big_12_schedule <- read.csv('2019 Schedule.csv')
full_big_12_stats <- bin... |
28fc3d02503caf6a1b91e92478ee96bcb7d4903f | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/3795_0/rinput.R | 6ac377bfc6d2db7b95930d5078bb310a002981a6 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("3795_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3795_0_unrooted.txt") |
d60e5d747f71f76c8e8efe325f937af575ba0c31 | dea51706c221e2b2813d62513b295ac99f6f82ae | /projects/2xmammals/psg_overlaps/psg_overlaps.R | 004042a6af0e1c7aa74a3c35a9618e7f01b62041 | [] | no_license | gjuggler/greg-ensembl | 47bcb82e29013866735b5af398965b83e02b6088 | 5564fc09bbb0e586c9a1999f21e713c119c490db | refs/heads/master | 2016-09-11T12:37:14.557777 | 2013-12-18T13:31:46 | 2013-12-18T13:31:46 | 275,374 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,053 | r | psg_overlaps.R | get.all.scores = function(merged.genes,score.column='m.dnds.m0') {
merged.genes$score = merged.genes[,score.column]
assign('roc.kosiol',compare.kosiol.eslr(merged.genes),pos=.GlobalEnv)
assign('roc.nielsen',compare.nielsen.eslr(merged.genes),pos=.GlobalEnv)
assign('roc.clark',compare.clark.eslr(merged.genes),... |
dd3e7c84f7b25bcbbc71ba53b24f0bb85ecd1a47 | 023ce89770fef9921eca1e8e11e9d1111f01c2f6 | /R/zchunk_LA144.Commercial-korea.R | f12059a7358a06c864c4230967928c6a7b1f8f07 | [
"MIT"
] | permissive | rohmin9122/gcam-korea-release | d1c1527be06c941c33c656ce32658cc37d2d7e13 | a3d63ffcca01140578952b3f19676fabc4c7e113 | refs/heads/master | 2021-07-10T00:51:20.197828 | 2020-11-20T09:09:43 | 2020-11-20T09:09:43 | 214,117,693 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,261 | r | zchunk_LA144.Commercial-korea.R | #' module_gcam.korea_LA144.Commercial
#'
#' Calculates commercial floorspace by state and energy consumption by state/fuel/end use
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vect... |
afc601513fdd0882d6d7cb0e4143abc9bd7acb64 | 72d03ec10b4955bcc7daac5f820f63f3e5ed7e75 | /input/gcam-data-system/emissions-processing-code/level2/L251.en_ssp_nonco2.R | 4deabd7a2b68116315e3195e4860da6d67adea51 | [
"ECL-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | bgmishra/gcam-core | 54daddc3d037571bf745c4cf0d54c0d7a77f493f | bbfb78aeb0cde4d75f307fc3967526d70157c2f8 | refs/heads/master | 2022-04-17T11:18:25.911460 | 2020-03-17T18:03:21 | 2020-03-17T18:03:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,792 | r | L251.en_ssp_nonco2.R | # Before we can load headers we need some paths defined. They
# may be provided by a system environment variable or just
# having already been set in the workspace
if( !exists( "EMISSPROC_DIR" ) ){
if( Sys.getenv( "EMISSIONSPROC" ) != "" ){
EMISSPROC_DIR <- Sys.getenv( "EMISSIONSPROC" )
} else {
... |
db382a4c661a52047fc27ff4501d13e82153e6a1 | 1189806ea21fed9f9648a734ac02958b0f30d4ff | /Scripts/meme.R | c59c27a672128571651a7978de0a54f8c692b0f4 | [] | no_license | deenhe91/Hannahs_Data | 232e701fdffaa9db3077afa8075186e224271e13 | ce1e8d2882f3d7eca599d25689a8072cd511c3f0 | refs/heads/master | 2021-01-24T20:40:55.396252 | 2015-09-03T10:09:47 | 2015-09-03T10:09:47 | 39,335,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,494 | r | meme.R |
library(CHNOSZ)
file <- system.file("extdata/fasta/EF-Tu.aln", package="CHNOSZ")
# Function
ReadFasta<-function(file) {
# Read the file line by line
fasta<-readLines(file)
# Identify header lines
ind<-grep(">", fasta)
# Identify the sequence lines
s<-data.frame(ind=ind, from=ind+1, to=c((ind-1)[-1], lengt... |
46416810bd143b3bd6b46061910c985b1f120022 | b48ea7f06b12d71fe597adefa5da35e81d08caf8 | /inst/examples/08-class/server.R | c3c01b0127b370612d8a82ee559bd661508786d8 | [
"MIT"
] | permissive | shinyTree/shinyTree | c840dd94af787e15cce6b7c0d4b73b2537123b8a | 110b49970d117d0638746d47b074e955287abed0 | refs/heads/master | 2023-08-30T22:50:33.875203 | 2023-08-07T15:53:07 | 2023-08-07T15:53:07 | 22,565,116 | 75 | 42 | NOASSERTION | 2023-02-08T10:37:30 | 2014-08-03T02:44:13 | JavaScript | UTF-8 | R | false | false | 381 | r | server.R | library(shiny)
library(shinyTree)
#' Define server logic required to generate a simple tree
#' @author Jeff Allen \email{jeff@@trestletech.com}
shinyServer(function(input, output, session) {
output$tree <- renderTree({
list(
root1 = structure("", sttype="red-node"),
root2 = structure(list( SubListA ... |
527248c6a443d5f995f2c12e09b43145534b2b73 | 36d379836d93b50efd85d10dc8fc2f849ead22eb | /week_3/ggplot練習/ggplot練習-1.R | d7b98bae6aea6e49ae90087657280baf686c9602 | [] | no_license | bigmike6322/NTU-CS-X-RProject-AHsiang | a185dd98b3b5692d15795ed1905d8f7726b5f255 | d8a081edab844d21d04655e90b4d5e55301263b2 | refs/heads/master | 2021-01-25T10:43:48.442309 | 2018-04-08T07:48:14 | 2018-04-08T07:48:14 | 123,368,851 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,266 | r | ggplot練習-1.R | library(ggplot2)
library(ggthemes)
library(data.table)
#df <- read.csv("/Users/bigmike6322/Documents/GitHub/NTU-CS-X-RProject-AHsiang/week_3/ggplot練習/Economist_Assignment_Data.csv")
#str(df)
df <- fread('/Users/bigmike6322/Documents/GitHub/NTU-CS-X-RProject-AHsiang/week_3/ggplot練習/Economist_Assignment_Data.csv', drop=... |
c97fa663dfde2122cf4c99edf5ef63b0bc911123 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/wordspace/examples/print_dsm.Rd.R | f018ed6013304e65c0a451837ff1a12247af832a | [] | 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 | 179 | r | print_dsm.Rd.R | library(wordspace)
### Name: print.dsm
### Title: Print Information About DSM Object (wordspace)
### Aliases: print.dsm
### ** Examples
print(dsm(DSM_HieroglyphsMatrix))
|
8815a0926763fc4dc942552af7aa3c82282c3296 | 7aa75ed5813d3778d557ccb1c1bceca9e4b14fe7 | /rshc/RealSimComp.R | afc3d51db71d69f33e81c752372a9ea27c7d4a06 | [] | no_license | xzhou/gnome | 8a40ad4b3269129dcb585addaeaeb9f4690dee56 | 1e9df8a380e9935e7e1455e677eb2badbb7657ed | refs/heads/master | 2021-01-10T01:20:15.174093 | 2010-11-19T03:26:03 | 2010-11-19T03:26:03 | 36,974,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 361 | r | RealSimComp.R | # TODO: Add comment
#
# Author: xzhou
###############################################################################
source("SHC.R")
t <- function()
{
t <- readGenotypeFromFastaFile()
er <- calculateRValues(t)
save(er, file = "77_2000estR")
rr <- calculateRealR(t)
save(rr, file = "77_2000realR")
sr <- s... |
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