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f5dad228cdf21afce0cd54288e5fd286eadd31b4 | fac73e7dbe1136a56863945e585eb3b5c5e386e8 | /seq_features/proteinko.R | 997deca9d78f89683c0ced39c281bc5e8023c05f | [] | no_license | thaddad91/Thesis-N-glycosylation | c6dbff765c4d047d9ebe64b4d97e267295380775 | d79a75f90885bad27cb75b6419dc029fce1df372 | refs/heads/master | 2021-06-21T20:26:21.783570 | 2021-03-01T15:25:19 | 2021-03-01T15:25:19 | 181,561,783 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 490 | r | proteinko.R | library(ggfortify)
library(randomForest)
data <- read.csv("table_pI", header = FALSE, sep = "\t", dec = ".")
# Balanced pos/neg set
pos <- data[which(data$V1=='pos'), ]
neg <- data[which(data$V1=='neg'), ]
neg <- data[sample(nrow(data), 2200),]
d <- rbind(pos, neg)
# PCA
pca <- prcomp(d[, -1], center = TRUE, scale. =... |
ff465301cabe33b73a6ccafd0f001fb92e96bfa9 | 1f0a2fbe2ecbc70761250efd896fb60884301fd0 | /code/R/report_quality_assurance.R | 8e5b76ca3341b4cfd4d476d49c95f98ee99f924b | [] | no_license | ruijiang81/crowdsourcing | 63e923e884e894d11845ff6ba9c985adc37b0efe | 3d6e91a16a495130026ebcf366bdcaa1539b3ac1 | refs/heads/master | 2020-04-05T06:05:05.637119 | 2018-11-08T00:02:33 | 2018-11-08T00:02:33 | 156,624,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 568 | r | report_quality_assurance.R | report_quality_assurance <- function(report) {
column_names <- c(
"instance_num", "pay", "assert_all_are_greater_than_or_equal_to", "change", "cost_so_far",
"AUC_holdout", "full_AUC", "subset_AUC"
)
assertive::assert_are_intersecting_sets(report %>% colnames(), column_names)
assertive::a... |
c9b34cc896f8a2186b99a8913b345a07651ebb91 | 9f674f754bdc1a0f92a650933e52e504d7c0e727 | /AnalysiswithR/CentralLimitTheorem.R | a3c2e48dd83567edb38f5b202a144d1bb3503657 | [] | no_license | AdarshKandwal/BasicOfPythonForML | 3552fb35b1bd717ddbb5067c97041ceb82099472 | fa18130125bda9079d8c45608651fbf38de96301 | refs/heads/master | 2023-04-13T00:28:39.517081 | 2021-04-25T13:14:16 | 2021-04-25T13:14:16 | 261,206,373 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 778 | r | CentralLimitTheorem.R | dat <- read.csv("mice_pheno.csv")
head(dat)
library(dplyr)
controlPopulation <- filter(dat,Sex == "F" & Diet == "chow") %>% select(Bodyweight) %>% unlist
hfPopulation <- filter(dat,Sex == "F" & Diet == "hf") %>% select(Bodyweight) %>% unlist
mu_hf <- mean(hfPopulation)
mu_control <- mean(controlPopulation)
... |
c93008032049a9a30f9b6cf9f92b1ec0e6c34428 | 11487d4bbf3e905b409e844dfd03fad97970f1e4 | /steps.r | b17d8d8aec1984567a74a5c040c3badd86f39860 | [] | no_license | WRaat/zp_feedback | 8740db0e7d57fd7fa58af539a978bcf94f2258ed | ee9fbfc722aabf5e522cffe7c24b59a86c20bffa | refs/heads/main | 2023-04-20T02:55:35.519826 | 2021-04-28T14:44:19 | 2021-04-28T14:44:19 | 361,686,168 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 428 | r | steps.r | # Uw praktijk heeft X maandelijkse testen afgelegd. X artsen in uw praktijk hebben ten minste 1 test aangevraagd. Dit is een participatiegraad van X% tegenover een algemene participatiegraad van X% in het zorgprogramma.
# Het mediane aantal testen in uw praktijk was X tegenover een mediaan van X testen algemeen.
# Onde... |
057b7f571177edbbb7e57d263e1797373fadf012 | 42629d99c178a551bc4fb94dad14d235b4c34f62 | /twitter/ui.R | e013fb50c128cdf8e3364af6a551c6a001f4bde5 | [
"MIT"
] | permissive | covix/shiny-pancake | f08e8991d89e891b90069138de684e5f4af94e00 | 0e6e133be55d6d8e2ee25ab94ebb5633424794e2 | refs/heads/master | 2021-01-20T11:47:22.347676 | 2018-08-01T11:59:27 | 2018-08-01T11:59:27 | 79,322,281 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,128 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(leaflet)
library(shinythemes)
library(networkD3)
library(streamgraph)... |
847683a344c5f8e1d93a78eb0fa3f6126db908e2 | e5bdefbe3349890d72175e89b87e98f9c22c234e | /run_analysis.R | 25cb9ca95b2a3239a58e52b684499bdf3589d54e | [] | no_license | jschwertz/TidyDataProject | 4cf42654d464112b61b20a499c78d9de6d94fa60 | 2c2e38c7ff1a02f53bd238e25f545aa7b2c9ea60 | refs/heads/master | 2016-09-06T18:54:43.154801 | 2014-09-21T17:00:14 | 2014-09-21T17:00:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,637 | r | run_analysis.R | ## Script cleans sensor data from accelerometers in Galaxy S device for analysis.
## 1. Merges the training and test sets to create one data set.
## 2. Extracts only the measurements on the mean and standard deviation for each measurement.
## 3. Uses descriptive activity names to name the activities in the data set
## ... |
b95ef2f98d42ee45deb9717d0c4391eb26c4d8c0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/igraph/examples/make_star.Rd.R | b5874cff994ffb67abb8701e5a265a8774b50c8a | [] | 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 | 232 | r | make_star.Rd.R | library(igraph)
### Name: make_star
### Title: Create a star graph, a tree with n vertices and n - 1 leaves
### Aliases: make_star graph.star star
### ** Examples
make_star(10, mode = "out")
make_star(5, mode = "undirected")
|
13e8729e8085c47c884b6a30afd6eafea094ce06 | 6964d8eb7cf8f9ed5abd612f6c2f0756877bca04 | /tests/testthat/test_most_frequent.R | a6c000019ba750cb65c1ddadd4c4719b12e4dc2e | [
"Unlicense"
] | permissive | s-fleck/hammr | 7a6805acc2f897c380b3f40d4e9112900646006d | b8fd5fa9d67698bc4c46ef48d079b0948a036387 | refs/heads/master | 2023-07-20T11:56:32.005037 | 2023-07-10T07:32:59 | 2023-07-10T07:32:59 | 119,056,265 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | test_most_frequent.R | context("most_frequent")
test_that("most_frequent works as expected", {
tdat <- c('a', 'a', NA, NA, NA, 'b', 'c', 'c', 'c')
expect_identical(most_frequent(tdat), NA_character_)
expect_identical(most_frequent(tdat, na.rm = TRUE), 'c')
expect_identical(most_frequent(tdat, n = 2), c(NA_character_, 'c'))... |
4ced7e16a93717275a2db699a5ecb6aff0fbcb93 | a026f85dbdd045ea2dc5b74df474afd02c3eb9af | /man/next_quarter.Rd | 8bd5f85097ae043d923574abfea181b0fad8a11b | [] | no_license | selesnow/timeperiodsR | 93df215538e9091fd9a9f0f0cb8e95db7735dc9d | 3612001767f0dce942cea54f17de22b1d97863af | refs/heads/master | 2023-04-27T15:52:19.511667 | 2023-04-20T10:15:49 | 2023-04-20T10:15:49 | 208,013,525 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | rd | next_quarter.Rd | \name{next_quarter}
\alias{next_quarter}
\title{
Start and end of next quarter
}
\description{
Defines first and last date in n next quarter
}
\usage{
next_quarter(x = Sys.Date(),
n = 1,
part = getOption("timeperiodsR.parts"))
}
%- maybe also 'usage' for other objects documented ... |
6e83b092e728e9313fddc6fbc177b07e8dd420d3 | 2451a929f21a636690dafe9eb56c54972bf0cef5 | /R/0_projtest-package.R | 5b455df9ef6429004c3683a9470797853fdce5ea | [] | no_license | AkselA/R-projtest | 66b66fc5db1d423f6be8f8ef157c3422b6e2de36 | 7ffed50278a7aa1ec6f9b97246a11a17e484d1e2 | refs/heads/master | 2021-04-02T01:56:58.259839 | 2020-03-18T13:41:07 | 2020-03-18T13:41:07 | 248,231,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 284 | r | 0_projtest-package.R | #' A short description of the package
#'
#' A more detailed description of the package
#'
#' @section Details:
#' Highligh central functions, quick-start guide, etc.
#'
#' @section projtest contributors:
#'
#' @docType package
#' @name projtest-package
#' @rdname projtest
NULL
|
53791b6e112a9862419bf672be13d4632f658f07 | 8ed441ee034ab9f22ed248645f8f6ba2606b6e5b | /poids /poids1000grainesCOL.R | 23a49d83adedab6041ac92e552dd8ea82772e160 | [] | no_license | CathyCat88/thesis | 51b33ddf4f86255f1c143f68a8e57ad4dc98726c | a1f311f4b95d4ef40006dd5773d54c97cb295ea7 | refs/heads/master | 2020-09-28T12:39:58.624508 | 2016-11-13T16:17:16 | 2016-11-13T16:17:16 | 66,710,427 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,847 | r | poids1000grainesCOL.R | remove(list = ls())
library("ggplot2")
library("cowplot")
data <- data.frame(read.csv("Col1000.csv", sep = ",", header = TRUE))
#########
#CALCULS#
#########
mean <- aggregate(data$masse, list(data$genotype), mean)
StandErr <- function(x) {
se <- sd(x)/sqrt(length(x)) # pas besoin de détailler x, aggregate se ch... |
ff610d2c9ce8b7b9e6b7fb18c3f08bfaad54e130 | 94d2f365e4eb96b6acc4289dd3ceae61f984c34d | /Rmd/01_03Leaset squares.R | 86d3dabb6069c5d122832dcd97b8f057257bcb95 | [] | no_license | TrentLin/Regression-Model | c76520d684c7384d828092aa8c25f912fc0f284b | dba011206476a55667859c5c74f88d92da29254c | refs/heads/master | 2021-01-01T19:38:49.342945 | 2015-01-06T07:49:00 | 2015-01-06T07:49:00 | 28,852,420 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 756 | r | 01_03Leaset squares.R | ### Double check our calculations with R
library(UsingR);library(ggplot2)
data(galton)
y <- galton$child
x <- galton$parent
beta1 <- cor(y, x) * sd(y) / sd(x)
beta0 <- mean(y) - beta1 * mean(x)
rbind(c(beta0, beta1), coef(lm(y ~ x)))
###Reversing the outcome/predictor relationship
beta1 <- cor(y, x) * sd(x) / sd(y)
... |
50c727870f372740672eb8f9c79f082f96a41cd7 | 382af42dc83d91a5cafa65657e020418baf9c168 | /docs/R/render_site.R | 7ff21b5f1ba51947d9999fdec89bcca4e3c23bb2 | [] | no_license | PLAY-behaviorome/PLAY-project.org | dce64e6c05e5e04af65595fc71683f7ed2664028 | 262415efabd89f27e6a34b53f953c7469e9a6b5c | refs/heads/master | 2023-08-04T09:11:41.303136 | 2023-07-24T14:45:16 | 2023-07-24T14:45:16 | 183,499,477 | 1 | 5 | null | 2023-07-24T14:45:18 | 2019-04-25T19:44:06 | R | UTF-8 | R | false | false | 224 | r | render_site.R | # render_site
# source helper scripts and functions
#source(file = list.files("R", pattern = "\\.R$", full.names = TRUE))
source("R/write_video_clip_html.R")
source("R/write_video_clip_html_SJ.R")
rmarkdown::render_site()
|
82fed81e1cdabbc46ba4da16383d3c9414e6b907 | 430fdac27572c12f84620ab9eb9fecca772366ac | /wdesign-co-primary-code.R | 46184c8c3ca4390d6e488a92ee149393677654c1 | [] | no_license | adaptive-designs/inf-theory | 448d4c2489ee40506206885210d10fe3b6a802c5 | a905018aaa0df2f7f7e040960c7eb82330c50002 | refs/heads/master | 2020-08-20T05:54:29.748824 | 2019-10-18T11:56:29 | 2019-10-18T11:56:29 | 215,988,814 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,570 | r | wdesign-co-primary-code.R | library("pwr")
library("mvtnorm")
median.beta<-function(x,n){y<-x/n
return(y)}
loss.uni<-function(p1,p2,target1,target2,n=1,kappa=0.5){
alpha1<-p1*p2
alpha2<-p1*(1-p2)
alpha3<-(1-p1)*p2
alpha4<-1-alpha1-alpha2-alpha3
theta1<-target1*target2
theta2<-target1*(1-target2)
theta3<-(1-target1)*target2
theta4... |
122130653c9fc21092fbe329608b43e40c8b45e5 | 425d8acc79d9b149333f61a3b9c86532b0fe1754 | /run_analysis.R | da4fda716045b255fb9f07597f0205eecd6a7244 | [] | no_license | jrnardin/Getting-and-Cleaning-Data | db94c5a9552e3d7875447a5803af649e3046291a | 2cec461bc87e0b0f92ad249daec8f85a93201041 | refs/heads/master | 2021-07-12T00:24:53.796690 | 2017-10-09T00:24:33 | 2017-10-09T00:24:33 | 106,218,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,367 | r | run_analysis.R | # Week 4 Assignment
#read in all of the tables
#used this in live assessment of data dimensions & content
library(plyr)
#read in all of the tables required for assignment
#left out the Inertial Signals data because it seemed unnecessary for this assignment
xtrain <- read.table("X_train.txt")
xtest <- read.table("X_te... |
03bb784e283542e6f694602e6a1d493bd4f03d39 | 845ff6a964548045e8d9bd8100f2315f13990d0a | /run_analysis.R | 4d22ad8c0fc5a5c05981e6376b005442a4582e7e | [] | no_license | rrbaker/coursera_gettingcleaningdata | c39e59dd59df20a6166d8199f92a53f9a1244322 | 79bbf8dfb2d0207125017e5828407bc1a5770852 | refs/heads/master | 2020-12-25T14:38:58.158705 | 2016-06-06T19:02:47 | 2016-06-06T19:02:47 | 60,539,126 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,139 | r | run_analysis.R | ## Getting and Cleaning Data Course
## Final Project (Week 4 Assignment), June 2016
## Coursera
## 0. Setup
setwd("/Users/rrbaker/Sites/_edu/coursera_r/coursera_gettingcleaningdata")
read.table("features.txt", skip=FALSE)[,2] -> train_features
read.table("train/subject_train.txt", skip=FALSE) -> train_subject
read.ta... |
1bba5ca180fd51011ccc72f776945e9d7ba02359 | 221c9aa934db54586c552e452fff832a7bd8142b | /code/rangeshiftR_install.R | 85ad3e19378bbf0afb2f3f7606ffa057b2e1a1cd | [] | no_license | VeeBurton/CRAFTY-OPM | 59e6f0ab41fd4ff4b54e7e8b81247f9736cbbc74 | f39704add7b5b017cf7747a385d81b98f613e5cc | refs/heads/master | 2023-03-19T23:13:37.629370 | 2021-03-05T13:35:30 | 2021-03-05T13:35:30 | 339,418,772 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 661 | r | rangeshiftR_install.R |
# from instructions here:
# https://rangeshifter.github.io/RangeshiftR-tutorials/installing.html
install.packages("Rcpp")
install.packages("devtools")
install.packages("Rdpack")
# Error in inDL(x, as.logical(local), as.logical(now), ...) : unable to load shared object 'C:/Users/vanessa.burton.sb/Documents/R/win-libra... |
c8de8b71b494d8407ed82a831df70e5bbcf37d6a | e90a363627ad08bbeb2c5d91c08d9db0e662837f | /man/camel_underscore.Rd | a70d47cdd2e1f64bef8ea10f8072e29ad12cd6b9 | [
"MIT"
] | permissive | tarakc02/preprocessr | 77451fb88304338a4d53b037f6904381889bacbc | 673594f9bb158df8c4068410a50176c6fc90c5b4 | refs/heads/master | 2020-06-01T13:36:19.578429 | 2015-09-01T23:47:41 | 2015-09-01T23:47:41 | 41,769,270 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 268 | rd | camel_underscore.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/etc.R
\name{camel_underscore}
\alias{camel_underscore}
\title{Convert camelCase to under_score}
\usage{
camel_underscore(strings)
}
\description{
Convert camelCase to under_score
}
|
5b5b62a369ffe32c928380f68cccdafbd08f2d5a | b3f3ba484c247fed8d9e872846d269273575772c | /man/open.Rd | 14621084db1b6aa487c91f8cf28901356496aa32 | [
"MIT"
] | permissive | ip2location/ip2proxy-r | b5a79f24749a06cadc30aeab10e85e78593d2f27 | 1c655641cd0ec91cf4cf27c9bc8619e18cd98fbe | refs/heads/main | 2023-02-05T15:11:07.920491 | 2023-02-02T09:01:03 | 2023-02-02T09:01:03 | 310,230,190 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 432 | rd | open.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/IP2Proxy.r
\name{open}
\alias{open}
\title{Load IP2Proxy BIN data}
\usage{
open(bin_location)
}
\arguments{
\item{bin_location}{Absolute path of IP2Proxy BIN data}
}
\description{
Load the IP2Proxy BIN data for lookup. Free IP2Proxy LITE data... |
4a4b2540438f7b5d538a9382877903ebf61977f8 | f30c509a803fdb653df321dc4bf661e729446b72 | /Task_02/task02.r | cb4047642dbbc282ba51f1b62c272a557589e110 | [] | no_license | akp0006/Tasks | dca719d1285eb44a3dbc0b0f266d6653259c7cfb | 10a2937b105a38bcbe2217201a0b997b54027964 | refs/heads/master | 2020-12-10T20:33:21.714890 | 2020-04-04T01:43:24 | 2020-04-04T01:43:24 | 233,703,394 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,970 | r | task02.r | setwd("C:\\Users\\Abbey\\Desktop\\Evolution\\Tasks\\Task_02")
Data <- read.csv("http://jonsmitchell.com/data/beren.csv", stringsAsFactors=F)
write.csv(Data, "rawdata.csv", quote=F)
Data
length(Data)
nrow(Data)
ncol(Data)
colnames(Data)
head(Data)
Data[1,]
Data[2,]
Data[1:3,]
Data[1:3, 4]
Data[1:5, 1:3]
Data[257, 1:3]
F... |
75024cbe7e9b837e0ed7d5026a68c29f94c8ecdd | c4bc4a8ebc3e6b85201f794a92d228143839f468 | /src/02_data-processing/old/country_province.R | 52c0defd6db10aaee40597e2501294023e3bdbc0 | [] | no_license | papabloblo/coronavirus | e718a32086eb1d95dcb2d20782996593cb83d898 | 95b981231f5e9eea5404feed5280b030c688574e | refs/heads/master | 2021-03-12T15:15:55.658036 | 2020-12-17T08:25:25 | 2020-12-17T08:25:25 | 246,631,642 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 761 | r | country_province.R | library(tidyverse)
daily_reports <- readRDS("data/01_tratamiento/daily_reports.RDS")
population <- readRDS("data/01_tratamiento/population.RDS")
population <- population %>%
filter(country != "US")
daily_reports <- daily_reports %>%
left_join(population)
country_province <- daily_reports %>%
group_by(count... |
dd0eeb73bb4534f475a3e3b23546d7ec864b59a0 | 0c7c77bb715d5098fbbc5e05bf21d77548ec2a81 | /part2.R | d6f75ca19363e157e23bd70f55ac0b469710cb4a | [] | no_license | san5696/STAT501 | 86f7da3e4ee0987f061912da41cb34355c92874f | 0c4a31023434551323cd560e0e03567cc8bdbece | refs/heads/main | 2023-03-18T23:07:09.111856 | 2021-03-19T02:29:56 | 2021-03-19T02:29:56 | 326,817,274 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 537 | r | part2.R | bf = read.table('FinalData.csv', sep=',', header=T)
llogit <- glm(SexCode ~ Height+Weight+Class, family = binomial, data = bf)
summary(llogit)
anova(llogit)
anova(llogit, test="Chisq")
full_model <- glm(SexCode ~ Height, family = binomial, data = bf)
reduced_model <- glm(SexCode ~ Weight + Class, family = binomial, dat... |
92d00bc27b26e2f2ec750f221430b299912a92df | faced0c1cc44934c02ae35903d2d6031a8bda92a | /doc/2 lasso.R | f653a3e68c175ba1eac48cd4b3b521b5f83c48bd | [] | no_license | hz2657/Spring2020-Project3-group12 | e9b8f07b23ffeff7ee8cdac4949c0c31f529da0c | 66e745e2007e6ce1e79f4a58aca08ca89b3f9d33 | refs/heads/master | 2021-05-24T13:34:58.944927 | 2020-04-02T00:16:38 | 2020-04-02T00:16:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 831 | r | 2 lasso.R | title: "Lasso R"
author: Huizhe ZHU
output: html_notebook
---
newx <- model.matrix(~.-emotion_idx,data=dat_test)
# lasso
library(glmnet)
x = model.matrix(emotion_idx~., data = dat_train)
y = factor(dat_train$emotion_idx)
lassoModel = glmnet(x,y,alpha =1,family = "multinomial")
# cross validation
se... |
8895bb1d0dbd47b7c0ea5de1ef79a6c2cb8722ab | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/IIS/examples/average_HDL_levels.Rd.R | c979f03cf484d8b063fb18a01df538d9dd86af98 | [] | 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 | 203 | r | average_HDL_levels.Rd.R | library(IIS)
### Name: average_HDL_levels
### Title: Average HDL Levels
### Aliases: average_HDL_levels
### Keywords: datasets
### ** Examples
data(average_HDL_levels)
summary(average_HDL_levels)
|
cce8c16667b9020a45589fdcb8ef4b2247a11bb7 | a2ac457f30f0690fc4328c3e5ca047617d72cc96 | /HyperparameterFinalOld.R | c06d8a5472714b0ef90a366ae18be2a9a418508c | [] | no_license | AtrayeeNeog/Cardio-Classifier-HIWi | ce0d8f6acf1383d9aa7867cc1467b94218bd92b9 | e6e85eefc93588522fffa92ac4274c24eb91a9c2 | refs/heads/master | 2022-11-19T23:37:29.853748 | 2020-07-14T14:29:57 | 2020-07-14T14:29:57 | 259,164,483 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,554 | r | HyperparameterFinalOld.R | library(mlr)
library(caret)
library(ggplot2)
library(rJava)
library(RWeka)
library("FSelector")
library(FSelectorRcpp)
library(Biocomb)
library(mlr)
library(rpart)
library(rpart.plot)
library(kernlab)
library(glmnet)
library(ROCR)
library(tidyverse)
# Loading and Preprocessing the Data:
set.seed(1... |
ee126cf553dffab0b21ca02c7883273e38e81c4b | ac2aadc49a14f95cbf92d8d3c4ddcdea5272f350 | /R/ggvenn.R | 1081b4b896fb7a70df676fd3158159149798ea40 | [] | no_license | AndyZHGai/ggvennEx | 12f3c5b62cf30e09908bd81b83802d5ff03de75e | d5b4940d2e4ec2d573dffeb4fd59b2dc0f97aa66 | refs/heads/main | 2023-06-13T08:10:33.932742 | 2021-07-14T01:58:53 | 2021-07-14T01:58:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,363 | r | ggvenn.R | #' Venn plot based on ggplot2, forked from yanlinlin82/ggvenn
#'
#' @param data
#' @param text.size the size of label and text size, default value is 4
#'
#' @return
#' @export
#'
#' @author ZHonghui Gai
#' @examples
#' data <- read.csv(file = "genus.Syn.csv", row.names = 1)
#' data <- ggvennEx:::vennlist(data)
#' v.d ... |
6a27c9d490374dc6ebd779a0251773e7e430b7fc | e09e243a46d02339cb9c012684a19465700169df | /01/try1.R | 62ff86610b932579ba068217fa79cf2a70001491 | [] | no_license | kotaaaa/RBasic | e7adb7a950671bbe11e8ce857f85326a6b32c1ce | 27b0c2a3323cc1b615713a45a28d33b7680e2fea | refs/heads/master | 2020-03-22T23:11:12.515032 | 2018-07-13T04:08:25 | 2018-07-13T04:08:25 | 140,794,191 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 818 | r | try1.R | x <- c(1,2,3,4,5)
x
y <- c(1:5,3:1)
y
z <- c(rep(3,4),rep(c(1,5,10),c(2,3,4)))
z
a <- c("A","B","C")
a
mean(1:5)
a <- c(1,2,3)
a
1:5
seq(0,10,by=2)
seq(0, 10, length=5)
c(5,5,5,5) -c(1,2,3,4)
c(5,5,5,5)- c(1,2)
c(5,5,5,5)- 1
y <- c(2,3,4,5,6)
y - x
com <- c(1,2,Inf, 4,5)
mean(com)
curve(sin(x*x), from=0, to=5)
dev.c... |
d3a59e8e811b53d620ae48a497e9a96c859cdaee | 33956256668c50faa0708f425d8ea4d83377b2b4 | /to_report.R | 3e16d62d10344a2e3a466e341edf198b492d0761 | [] | no_license | hkorevaar/US_NPI_Re | 8b3247681cf4d5ae6e80b3e247e7aa95bfb1b21c | 2485a791474d46f28423c973736d677d21b53434 | refs/heads/master | 2022-12-02T04:34:59.225302 | 2020-08-06T20:00:34 | 2020-08-06T20:00:34 | 276,170,144 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,312 | r | to_report.R |
## This is the code to analyze Re and R0 estiamtes, you will need esitmates
## before running this code.
## Please see Re_rev.R to calculate R0 from growth rates.
## Please see Re_lag.R to calculate Re from stochastic back-forecasted mortality data.
## Please Re_cases.R to calculate Re from cases or mortality data... |
7c642e64585ba143a1911dad02f1ae3be62ed35a | 3b2b5636282ae842def1c16265cccac19f9d125a | /R/timeTicks.R | 7b2cc4e412f687fade21939c5829ddb55055bebf | [
"BSD-2-Clause"
] | permissive | ilkkavir/LPI.gdf | 06cf2ccb0ed19b7a04df417fe93cef2f7e530115 | 088a53c3624c68406b87ccaf8d1451ef678c3b62 | refs/heads/master | 2023-05-28T09:56:00.948551 | 2023-05-15T13:23:37 | 2023-05-15T13:23:37 | 205,375,323 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,971 | r | timeTicks.R | ## file:timeTicks.R
## (c) 2010- University of Oulu, Finland
## Written by Ilkka Virtanen <ilkka.i.virtanen@oulu.fi>
## Licensed under FreeBSD license.
##
##
## Time axis tick marks
##
## Arguments:
## times A vector of times [s], only the smallest and largest
## value are used
## tickres Tick mark resolution... |
5fd504d05b72db953e2ebb49870f38eea04deef9 | 84d4b0f90866b8ef5ab3bd325a295d46b195d20f | /man/raman_hdpe.Rd | e2e3b319f9f27b491ef7408e429b8cdfea21b228 | [
"CC-BY-4.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-public-domain"
] | permissive | rainyl/OpenSpecy | 310d8a42bdd6abd39f5c8b1bcd0046bf3338a158 | 92c72594abaaf91925d7c0550e791de5a149192d | refs/heads/main | 2023-05-11T06:28:30.482481 | 2021-06-01T18:04:47 | 2021-06-01T18:04:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 850 | rd | raman_hdpe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/raman_hdpe.R
\docType{data}
\name{raman_hdpe}
\alias{raman_hdpe}
\title{Sample Raman spectrum}
\format{
A data table containing 964 rows and 2 columns:
\tabular{ll}{
\code{wavenumber}: \tab spectral wavenumber [1/cm] \cr
\code{intensity}: \ta... |
3f825c97f61d1b96e930387f1a409485deccf72f | 0db9b9ad4b00a908d9ddba1f157d2d3bba0331c4 | /man/dist_unit_options.Rd | 84540c3726a5cd3a453772ecc0b238543bab3876 | [
"MIT"
] | permissive | elipousson/sfext | c4a19222cc2022579187fe164c27c78470a685bb | bbb274f8b7fe7cc19121796abd93cd939279e30a | refs/heads/main | 2023-08-18T15:29:28.943329 | 2023-07-19T20:16:09 | 2023-07-19T20:16:09 | 507,698,197 | 16 | 0 | null | null | null | null | UTF-8 | R | false | true | 414 | rd | dist_unit_options.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{dist_unit_options}
\alias{dist_unit_options}
\title{Distance units (vector)}
\format{
A character vector with 86 names, plural names, aliases, and symbols
for distance units.
}
\usage{
dist_unit_options
}
\descript... |
b90953e7dc3a272e42d2fb7d16941e6a23e40d34 | 0ad74abaed93e23fe196c7556b2ba74090234697 | /cachematrix.R | d38173861e862f28fb35076966846019b5bb72fd | [] | no_license | plancksconstant/ProgrammingAssignment2 | da584e7f70283c32e6a8dabc1006543297e79fd0 | ddaa36f7ebc367716a1ad39344e2f7a7eb6fc43c | refs/heads/master | 2021-09-01T04:07:37.202978 | 2017-12-24T17:06:28 | 2017-12-24T17:06:28 | 115,274,357 | 0 | 0 | null | 2017-12-24T16:15:32 | 2017-12-24T16:15:31 | null | UTF-8 | R | false | false | 1,042 | r | cachematrix.R | ## These functions are for calculating the inverse
## of a matrix, if the inverse does not already exist
## in the cache.
## This function returns a list of functions. These
## will set a matrix, get the matrix, set the matrix
## inverse, and get the matrix inverse
makeCacheMatrix <- function(x = matrix()) {
im... |
4912d2748eede745a68cbb5a4ee77b251341b4b1 | e02b906d4d3c548085954f3832afac30c7137228 | /R/data-pinna.R | b2ef64a942fdf2b95b2eb2fbda4b36708c896750 | [] | no_license | poissonconsulting/bauw | 151948ab0dc55649baff13b2d79a551b6fc5a49d | 47b12dc140ba965ae8c89693c0d8d8fefa0fd7db | refs/heads/main | 2023-06-15T10:56:20.506561 | 2022-12-16T20:00:03 | 2022-12-16T20:00:03 | 78,153,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 636 | r | data-pinna.R | #' Pen shell detection data
#'
#' The pen shell (\emph{Pinna nobilis}) detection data from the Balearic Islands
#' in 2010.
#'
#' The variables are as follows:
#' \itemize{
#' \item \code{d1} indicator for shell detected by first team.
#' \item \code{d2} indicator for shell detected by second team.
#' \item \code... |
e52e4b2979c01232206743d404bc8b520054213a | 83ce3b39e88c03e2c98ef2f05174195708ac3dbe | /R/groupLocation.R | eef40581a5e175c27afdaad121c4ac99e2c119db | [] | no_license | cran/shotGroups | e02467ffb36b8e528fa1c230b2a718512159fc19 | ae04a8371aa1cc18af598413d1bc41d389762acb | refs/heads/master | 2022-10-01T18:19:20.943958 | 2022-09-17T18:06:04 | 2022-09-17T18:06:04 | 17,699,651 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,522 | r | groupLocation.R | groupLocation <-
function(xy, level=0.95, plots=TRUE, bootCI="none",
dstTarget, conversion) {
UseMethod("groupLocation")
}
groupLocation.data.frame <-
function(xy, level=0.95, plots=TRUE, bootCI="none",
dstTarget, conversion) {
## distance to target from override or from data
if... |
a1b978510b7d67008efad62f49b3f228ec6d2ddc | 16eaf576186c56624c4ecde31a92e4cdfa2c3106 | /Q5/q5.r | ef8e2cdbc6bdf6db981245ee90ca45e46dabb4d6 | [] | no_license | rithvik-vasishta/DA_Lab | 26d97a7497c0c9b16a7304335dac1c5c6c3ccbf2 | 9e28a5c60b6ddcf7c6e7eb0ae365817f4af1df2e | refs/heads/master | 2023-03-12T05:17:35.099752 | 2021-02-15T14:21:18 | 2021-02-15T14:21:18 | 338,852,632 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 544 | r | q5.r | df = data.frame(
sales = c(2,5,7,10,12,15,20),
budget_tv = c(5,15,25,30,35,50,100),
budget_radio = c(7,12,17,25,30,35,70)
)
df
# a
model = lm(df$sales ~ df$budget_tv + df$budget_radio)
df$pred_builtin = predict(model, data = df)
# b. Using normal equation method
x <- df[,2:3]
x$intercept <- rep(1,nrow(df))
x... |
62eb86900042f700f8780e60e3adf3f236a82ea6 | 1114eaf591e56bd3fefc9b3827ebf76454970445 | /CriticaAutomaticaCompras/Scripts/PreparacaoDosDadosResumo.R | 5e99df163bc5b0fc6a59575a0dfef490d50bb6e8 | [] | no_license | NeuKnowledge/EAC | 4e8c1e569459755ff4fa3921636ce8777704c6f7 | c6715b3944f34dd19e87046dfb3502331c2c1a76 | refs/heads/master | 2021-01-19T10:53:35.676588 | 2016-08-03T15:27:38 | 2016-08-03T15:27:38 | 61,637,904 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,084 | r | PreparacaoDosDadosResumo.R | rm(list=ls())
library(data.table)
library(dplyr)
library(reshape)
library(reshape2)
library(ggplot2)
library(gridExtra)
library(plotly)
#
# Carga Resumo produtos mais vendidos
#
load(file = "../Dados/resumoProdutosMaisVendidos.RData")
load(file = "../Dados/produtosMaisVendidosR.RData")
#
# Produtos
#
{
load(file... |
1983db831fad64369a67b0a72f160c2e31fd206b | d59e56c7658f5177551b308b483ab352236da8a2 | /cran/paws.compute/man/ec2_modify_availability_zone_group.Rd | 83bc827297dbf18b015b417c5abf059ba7851d78 | [
"Apache-2.0"
] | permissive | jcheng5/paws | a09b03b93c6bafdab26c3217c33926b86907276b | 9bb49f9a3ba415c3276955fa676bc881bc22fa3e | refs/heads/main | 2023-02-01T15:25:58.124905 | 2020-11-10T22:35:42 | 2020-11-10T22:35:42 | 317,394,924 | 0 | 0 | NOASSERTION | 2020-12-01T01:48:12 | 2020-12-01T01:48:12 | null | UTF-8 | R | false | true | 1,500 | rd | ec2_modify_availability_zone_group.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ec2_operations.R
\name{ec2_modify_availability_zone_group}
\alias{ec2_modify_availability_zone_group}
\title{Enables or disables an Availability Zone group for your account}
\usage{
ec2_modify_availability_zone_group(GroupName, OptInStatus, D... |
914200066e9adeda64477dc7a05d29e0f0e770a7 | 8dbe523b5cd123fb95bdcb97dac806d482af566f | /tests/regression_tests/hojsgaard_model_tests/random.graph.R | aaa5ff4136d24502d2596bcfb0ae4df15150c629 | [] | no_license | npetraco/CRFutil | b5ca67b73afdab9dc64712fc709fe08a8fbce849 | 50ef4ca06b7ab11ac1d54472a87e7854beb07cec | refs/heads/master | 2023-01-22T10:53:55.149603 | 2023-01-06T02:03:47 | 2023-01-06T02:03:47 | 135,449,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,537 | r | random.graph.R | library(igraph)
library(gRbase)
library(CRFutil)
#library(rstanarm)
library(rstan)
library(MASS)
# Make up a random graph
g <- erdos.renyi.game(10, 0.6, typ="gnp")
dev.off()
plot(g)
# Get its adjacency matrix and genrate an MRF sample
adj <- as.matrix(as_adj(g))
f0 <- function(y){ as.numeric(c((y==1),(y==2)))}... |
a6663dc4b35cae4f815c39493888cbebee4a21f0 | aeaa9ac30428b8df7e88d980da1d727925938d3e | /man/influenza.Rd | 75b0bd203b8e8590735fc738b4bd4575ae85de40 | [] | no_license | cran/tscount | 826b5c77cf3940cf075968146f8e064a3ac3bf2d | e804ef82017773f515570a19de424919d3e44797 | refs/heads/master | 2021-01-18T22:05:17.334697 | 2020-09-08T06:00:03 | 2020-09-08T06:00:03 | 30,640,940 | 6 | 6 | null | null | null | null | UTF-8 | R | false | false | 1,064 | rd | influenza.Rd | \name{influenza}
\alias{influenza}
\title{
Influenza Infections Time Series
}
\description{
Weekly number of reported influenza cases in the state of North Rhine-Westphalia (Germany) from January 2001 to May 2013.
}
\usage{
influenza
}
\format{
A data frame with variables \code{year} and \code{week} givi... |
38ec5342eb980bc65426666bb275cd39701caf62 | 860c59446ab714b979ba478c478470191e04e6aa | /ID3/CustomerID3Function.r | 6cbfbf70b04a8682ec052b53c94fea7b89112470 | [] | no_license | HelloMrChen/AlgorithmPractise-R | 8c0781c8d874c5806ef76265de98c81ec141adbf | 6c9c762b493cc60bec08a81c8abd49a81923211a | refs/heads/master | 2021-05-06T02:45:17.635428 | 2018-04-04T16:12:57 | 2018-04-04T16:12:57 | 114,624,465 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,843 | r | CustomerID3Function.r |
#用R语言实现决策树ID3算法，以iris数据集为例
#计算总体信息值的函数,这里只允许最后一列作为决策结果列
info<-function(dataSet){
rowCount=nrow(dataSet) #计算数据集中有几行，也即有几个样本点
colCount=ncol(dataSet)
resultClass=NULL
resultClass=levels(factor(dataSet[,colCount])) #此代码取得判别列中有个可能的值，输出 "Iris-setosa" "Iris-versicolor" "Iris-virginica"
classCount=NULL
cla... |
88bf265d7566c9b2568bed782cb179d6dfa7f5da | 2b5b885a283ac7853b6c46fae908f3e66abffcff | /R/write_input.R | 9105bb3d8dec5f3f3f656726db18f53bc868c64f | [] | no_license | quanted/VarroaPopWrapper | 8b89322b966f1a442f8830c8151d337f9d7ec5c8 | ade75f2c3810b7ed7c557868351d6e3c36caf03f | refs/heads/dev | 2020-03-07T23:48:21.891865 | 2018-12-12T16:23:03 | 2018-12-12T16:23:03 | 127,790,278 | 0 | 0 | null | 2018-10-23T17:51:44 | 2018-04-02T17:38:14 | R | UTF-8 | R | false | false | 3,774 | r | write_input.R | ##
# Write VarroaPop Inputs
# code by Jeff Minucci
#
##
#' Write a VarroaPop input file from a named list or vector
#'
#' Function to create a single input file from a set of parameters in the form of
#' a one row dataframe, where columns are named.
#'
#' @param params Named vector of VarroaPop inputs to be written ... |
43fcdedbb88512828883d4155c50e18bdd848ac1 | 591771c6a3972cab8c680696771fd4b4aa0c3f20 | /R/0.0.0-Level2URI.R | e136ce065b185271a31a5018edf54172afebf6d1 | [] | no_license | Sumpfohreule/S4Level2 | a36dfc014dde47763009dcc4420a198ce11a9a5d | 9034cddbd04efed8cea8c5b90cb2e4fbf16209e7 | refs/heads/main | 2023-08-19T08:58:05.616624 | 2021-09-29T14:47:03 | 2021-09-29T14:47:03 | 304,371,990 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,635 | r | 0.0.0-Level2URI.R | ########################################################################################################################
setClass(Class = "Level2URI", slots = c(
URI_Split = "character",
Depth = "numeric"
))
#' Constructor for Level2URI
#' @param ... URI like path consisting of 0-3 strings (Plot, SubPlot, Logg... |
6e15bfad8c5c4ed41d12215ff8b1d4a6576e6d82 | f1ae55b68fa8f895ecdfe34060f0f3a99bec5352 | /czestosc_w_grupach.R | 4fb0bd369b227af0111774e1019668ca4edeccf1 | [] | no_license | psobczyk/signal-peptide | 755875bea49061cfc85d004a4646c5c8b6cdbd8e | 58ee07638d40538093224b7af23bd67647e47fe9 | refs/heads/master | 2021-04-12T05:17:50.944784 | 2014-07-10T14:09:28 | 2014-07-10T14:09:28 | 13,646,865 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,255 | r | czestosc_w_grupach.R | #setwd("~/Dropbox/Doktorat/sekwencje_sygnalowe/")
#source("wczytywanie_danych.R")
con <- function(x, n){
result <- NULL
for (i in 1:n){
result[i] <- sum(x==i)/length(x)
}
result
} #computing probs for n groups (it must be exclusive)
distance <- function(con1, con2){
sum((con1-con2)^2/con1)
} #distance ... |
6a5e2e1828ba11e1a24281f2c2613b16cf0baba5 | dca44395dbf60e1743c65bced7b26838bd676781 | /HGU/SNU/last__GGOk.R | 81f93e6a9594e1e2b2b7ff9a59923e1dafe5b664 | [] | no_license | ksmpooh/SungminCode | 1b550c375125ea7869917de337aa093160aa03eb | 33b266b80389664282a2d4d6eb9c2db593442a5f | refs/heads/master | 2023-08-03T16:22:35.085299 | 2023-07-31T16:12:33 | 2023-07-31T16:12:33 | 106,177,934 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,254 | r | last__GGOk.R | #install.packages("h2o")
#install.packages("ROCR")
#install.packages("caret")
#install.packages("e1071")
#install.packages("Hmisc")
#install.packages("Dplyr")
library(Hmisc)
library(h2o)
library(ROCR)
library(caret)
library(e1071)
library(dplyr)
data <-read.csv("Total_data.csv",header = T, sep = ",")
ob <- data
#summar... |
4d008e748021d1709064f3593b83cc2a3d20668c | da646a1815d8daa4f0b333d9b4529aaeb634afc1 | /backend/TFMir.R | f4634f2cc90cbe23ab91bed2d79fbe0b8d1e8562 | [] | no_license | crs/tfmir | 940a5b09f0353d28c5f4d800675292f9db87a3fc | 04be78516ed8a25854d6e7afa86f82e02dfbf10c | refs/heads/master | 2021-03-22T04:43:29.076469 | 2016-01-20T13:50:11 | 2016-01-20T13:50:11 | 139,831,239 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,637 | r | TFMir.R | ###########################################
# - TFMir Project #
# - TFMir main function #
# - Main function to be called #
# - 2014-10-1 #
# - Copyright: Mohamed Hamed #
###########################################
#R programm... |
b3ecffcf8a2a85b7c997bdf77e92ecf9c9736c19 | 5d1dca92964fb981ca109ecb6af365d1d4077f57 | /man/intron.Rd | 19a5a440899a93c947eea8c5f398532a087742e8 | [] | no_license | xyang2uchicago/BioTIP | 6a862e490aaecbf33278eaa99c11c46ff67b9919 | 037c82e06d0e78f10d0611427edfa90b86013e12 | refs/heads/master | 2023-09-01T13:14:23.069682 | 2023-08-28T20:53:17 | 2023-08-28T20:53:17 | 184,810,257 | 8 | 5 | null | null | null | null | UTF-8 | R | false | true | 726 | rd | intron.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{intron}
\alias{intron}
\title{Coding transcriptome in chr21 dataset}
\format{
A data frame with 659327 rows and 5 variables:
\describe{
\item{seqnames}{chromosome names (chr1,chrM,chr21)}
\item{ranges}{ch... |
c9a978140f111a51838e863f6811891e800ed441 | 5f6369b039c01b619656d531d2eea98f4f0ab389 | /Plots_of_ROIs.R | 069e9dedbf9885a8effdfad56c12126292495a40 | [] | no_license | SandraTamm/TSPO_PET_in_allergy | 274a56d1dcb7732dd5118efad2b544dcc89c05bd | 65eeea332dedaca7cdf73084bf103c4575beb80a | refs/heads/master | 2021-05-23T05:42:20.086623 | 2021-03-05T10:05:22 | 2021-03-05T10:05:25 | 94,869,411 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,597 | r | Plots_of_ROIs.R | require(gdata)
require(ggplot2)
require(nlme)
require(plyr)
require(gridExtra)
require(cowplot)
source('Utils/SummarisingFunctions.R', chdir = T)
source('Utils/Multiplot.R', chdir = T)
setwd("~/Desktop/RAALLPET")
load("PET_VT_63_2TCM_3exp.RData")
# Gray matter
summary_GM <- summarySEwithin(data=Data_63, measurevar ... |
413255d99d93e9a4f3322996768dc605b1a7838f | 29585dff702209dd446c0ab52ceea046c58e384e | /SEMID/R/SEMID.R | a55d3095cf57a4de5e68fc4c91a2bfa017ae6dc4 | [] | 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 | 360 | r | SEMID.R | #' SEMID package documentation.
#'
#' SEMID provides a number of methods for testing the global/generic
#' identifiability of mixed graphs.
#'
#' The only function you're likely to need from \pkg{SEMID} is
#' \code{\link{graphID}}. Otherwise refer to the individual function
#' documentation.
#'
#' @import igraph
#' @im... |
9ca040cd3365b74c0d8a54185fec694b1ee91044 | 7b7151c25cb3f2bd6492c6a3ee991d7e83c58665 | /code/data_fig2.r | 374fb686c0e56d12e25a60c01364561bccb47d1f | [] | no_license | tpoisot/EvoGeoModules | f72b88eb6ef17904ba832cf6c6c1dfb1d72c04d0 | f25cc1af8667827af7efec2ae97eeb468a1e3b5f | refs/heads/master | 2020-04-12T06:34:08.007909 | 2017-06-06T16:23:17 | 2017-06-06T16:23:17 | 23,810,996 | 0 | 0 | null | 2017-05-23T18:45:14 | 2014-09-08T22:35:56 | TeX | UTF-8 | R | false | false | 1,952 | r | data_fig2.r | library(paco)
library(stringr)
library(igraph)
library(betalink)
library(doMC)
library(ape)
source("commons.r")
load("D.Rdata")
load("webs.Rdata")
load("paco_fig1.Rdata")
mw <- metaweb(raw)
host_tree <- read.tree("../data/host.tre")
host_tree$tip.label <- str_replace(host_tree$tip.label, "_", " ")
para_tree <- compu... |
60fd6efed101f9a60a47b282679f09ed747a13cc | 0d0cb4f86925ee4b2c8f91fdd388d59c39ccd2c3 | /scripts/R-Lunches4.R | f344b5aaa882d43bdc1e933d898ccd67df2a6a87 | [] | no_license | brusko/learningR | 1b5ada25d78c6ee5ff38433f6dcc0b85c13c6e33 | a672d97eefe23b70c8047fdef9d1dfe70b718aca | refs/heads/master | 2021-05-09T23:36:52.447506 | 2018-01-24T18:52:21 | 2018-01-24T18:52:21 | 118,797,751 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,425 | r | R-Lunches4.R | ## R-Lunches4.R
getwd() ## make sure you are in the learningR project directory
list.files() ## to see if you have the three "sub-directories", data, scripts, and output
## load packages
## use install.packages('packagename') if package has never been installed
library(tidyverse)
library(EDAWR)
install.package... |
6a88bfbf059d5390eb0d2fc9adb55dda3adb2298 | c26548e53ef2c8809a622d86582d7150b9955a4f | /R/ccle_barplot.R | d218f558f58561d6960773a95eacb0e82475a83e | [] | no_license | kevinblighe/AunerLab_CCLE | 2a75f25345afa468b432b1867683b8361d7ecb2f | 4c8d9d6491034f6f62785df136c16490f860640d | refs/heads/master | 2020-09-16T04:09:15.801103 | 2020-01-28T02:35:39 | 2020-01-28T02:35:39 | 223,648,553 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,616 | r | ccle_barplot.R | ccle_barplot <- function(
ccledata,
clinicaldata,
keyword,
gene,
title,
xlab,
greyscale = TRUE,
colour = NULL,
titlesize = 24,
axissize = 16) {
# extract names of plasma cell myeloma / multiple myeloma lines
lines <- sampleinfo[grep(keyword, clinicaldata$Hist_Subtype1),1]
if (le... |
50cfcbc531e170c4b2a00058a6a296ae2b047a26 | 78dca0d0127674ced44152a0646b2ab123b7c0aa | /gbs_functions/gbs_rrblup_valid.R | f203b5eadda216deadabca9969efb1a1fbaaa2a0 | [] | no_license | aho25/GS_BDE | 70bccd668bf8e75651aa336dc1ebaa7b926939f2 | 1035f5886c6efed12c523857d00781a0edd7f17b | refs/heads/master | 2021-03-25T20:02:14.882435 | 2020-06-03T10:46:06 | 2020-06-03T10:46:06 | 247,642,608 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 489 | r | gbs_rrblup_valid.R | library(rrBLUP)
### Define function for final_features validation
gbs_rrblup_valid <- function(PHENO_TRAIN, MARKERS_TRAIN, PHENO_TEST, MARKERS_TEST, VAL.ARGS) {
prod_model <- mixed.solve(PHENO_TRAIN[,1], Z = MARKERS_TRAIN, K = NULL, SE = FALSE, return.Hinv = FALSE)
prod_g <- prod_model$u
prod_mu <- prod_model$be... |
983bab37300de1298e898737323c2c93a41918f8 | 4f217be84965dcdf28299a7ffea4724d2ef662e4 | /R/gta rbind.R | 95e680df5b6a9cba8785ecb35834d8299eb2b659 | [] | no_license | global-trade-alert/gtalibrary | 694cbc2718954ca8737ab2d2e72c787da649df68 | a8ad12b2792f5558dacde494adbd7c13faffff49 | refs/heads/master | 2023-08-17T09:21:23.631486 | 2023-08-08T09:45:05 | 2023-08-08T09:45:05 | 145,339,633 | 7 | 1 | null | 2023-07-17T17:01:39 | 2018-08-19T21:43:20 | R | UTF-8 | R | false | false | 782 | r | gta rbind.R | # Roxygen documentation
#' Rbind two dataframes with different columns.
#'
#' This function rbinds two dataframes with a different set of columns and fills unmatched cells with NA.
#'
#' @param list Supply a list of dataframes. E.g. list = list(df1, df2, df3).
#'
#' @references www.globaltradealert.org
#' @author Glob... |
5c39141e12ec21fb6ef0094e5f4b954bf09b1b9b | bc5aa2493a04fab4ab76a54c135b6c91bb50a921 | /hw7/random_forest_tune.R | ab421e8fd8e4399034014a0a0831ef5f50806bde | [] | no_license | reking/stat852 | b3caba3d5ecfbb1e61874e955cb3e43b2fbdcd1d | e704cfe9f4f49b43fda64211a78188bf9cfabc97 | refs/heads/master | 2021-01-10T05:04:34.782068 | 2016-01-08T06:08:18 | 2016-01-08T06:08:18 | 44,140,619 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,731 | r | random_forest_tune.R | # Gradient Boosting using gbm
library(randomForest)
abelone <- read.table("~/stat852/data/abalone.data", header=TRUE, sep=",", na.strings=" ")
colnames(abelone) <- c("Sex","Length","Diameter","Height","Whole","Shucked","Viscera","Shell","Rings")
abelone$Sex <- as.factor(abelone$Sex)
set.seed(41891019)
nod... |
ba8ef6d8421a8def2215ed7b646a1382ca3909d5 | 3cc68045fd140e7def6648f6e561cc07d78abf44 | /R/SingleR.R | b0eaae096ff6ae2e4f25ebc892e036eee3fcd294 | [] | no_license | nyuhuyang/scRNAseq-MouseSkinEpithelia | e09b00c4d608e8a2236e98c21ae22206ed4f76c6 | c2ba9b42d8db660c13b0075c89740fedd1220ea8 | refs/heads/master | 2020-03-26T12:30:36.633436 | 2020-03-01T03:58:30 | 2020-03-01T03:58:30 | 144,896,399 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,785 | r | SingleR.R | library(SingleR)
library(Seurat)
library(reshape2)
library(pheatmap)
library(kableExtra)
source("../R/Seurat_functions.R")
source("../R/SingleR_functions.R")
#====== 2.1 Create Singler Object ==========================================
lname1 = load(file = "data/MouseSkin_alignment.Rda");lname1
lname2 = load(file='../... |
d9c0e671ad673c09e8e9a8a513fea37034ffa4b1 | 0275ac8727a01f6a61e5b0ab3544288870ac76c3 | /maxpixels.R | 077f8541c765bfc9c227f7af3011015c3bd66e79 | [] | no_license | l-radtke/lradtke-coding-portfolio | cd5925d3cce720b3ae6c45e5218ce3b838f848df | 12004ba13083e430ff6d60c501d29f3160d3e600 | refs/heads/master | 2020-07-08T07:52:47.729591 | 2019-08-22T14:42:17 | 2019-08-22T14:42:17 | 203,609,344 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 255 | r | maxpixels.R | """ defines the maxpixels function that will find the smallest polygon's area"""
maxpixels <- function(polygon){
a <- 0
list <- list()
for(nb in 1:length(polygon@polygons)){
a <- a + 1
list <- c(polygon@polygons[[a]]@area)
}
min(list)
} |
3010bb175a84f4d1a75a67cfda25b832cc6e3e83 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/VNM/examples/PAR-class.Rd.R | 9a4f9fb864c1f148e3ba1ee8ea3792821a5e06ca | [] | 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 | 231 | r | PAR-class.Rd.R | library(VNM)
### Name: PAR
### Title: Class to contain the variables for the verification plots from
### function MOPT, ceff1, ceff2, and Deff.
### Aliases: PAR-class
### Keywords: classes
### ** Examples
showClass("PAR")
|
62cf97ada2b88c27378d6b00997f470e2c730d0e | 2fc19f59ed2a5dbab1c50ec1250446608ec0c233 | /users/XiaodanLyu/data_split.r | d3b8de8d5cbf82402abcd65137170bc92609a16e | [] | no_license | ISU-DMC/dmc2018 | ca05855efd3101696111d25a8e6f4a70b4521cf4 | c68079f19149316c4954dbfe7da51707df1fe64b | refs/heads/master | 2020-03-08T02:27:14.549852 | 2018-05-23T16:30:55 | 2018-05-23T16:30:55 | 127,860,160 | 5 | 6 | null | 2018-04-08T01:11:23 | 2018-04-03T06:15:21 | HTML | UTF-8 | R | false | false | 5,411 | r | data_split.r | ## ---- splitting
train <- read.csv("../../data/raw_data/train.csv", sep = "|", stringsAsFactors = F)
prices <- read.csv("../../data/raw_data/prices.csv", sep = "|", stringsAsFactors = F)
items <- read.csv("../../data/raw_data/items.csv", sep = "|", stringsAsFactors = F)
## format date
library(lubridate)
librar... |
abeee69059ed7499e5e75b200931f6b6c12eb1d5 | 1dc44e8b9874ea88796c9dd343e50681c244a543 | /Aguirregabiria lab/code/header.R | 1de221308dedb9eedb3bcbf99bcaf1567602d065 | [] | no_license | orsdemir/Structural-Estimation-of-Choice-Models | 8f7907d6b8b3b4401ac85798c3bf6c1667979290 | 92a46b872a8ee30865864905a8275ba32a1581fb | refs/heads/master | 2022-10-26T16:55:19.789760 | 2020-06-14T05:23:35 | 2020-06-14T05:23:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 189 | r | header.R | library('tidyverse')
c('reshape2', 'stringr', 'magrittr','chebpol', 'np') %>%
walk(~library(., character.only=TRUE))
dir('modules') %>%
walk(~source(paste('./modules/', ., sep="")))
|
601c65fbe0d0bddd69b071f9f1041901961fc993 | 236cdc1ba4d23f14cbdcbd4a53e427506c4caf3f | /Scripts/DataAnalysis/data_visualization.R | 9222acf68115b4fadbabc6756b3164f7aa004cb1 | [
"MIT"
] | permissive | Guliba/FRASER-analysis | 775b30e079bcb8a50b91f6a8785a631182f076fd | 3c125dc561de977b89a674e19b720cc72762b392 | refs/heads/master | 2023-03-23T03:12:38.876112 | 2020-08-27T05:28:59 | 2020-08-27T05:28:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,328 | r | data_visualization.R | #'---
#' title: Dataset-wise splicing correlation
#' author: Christian Mertes
#' wb:
#' input:
#' - fds_raw: '`sm config["DATADIR"] + "/datasets/savedObjects/raw-{dataset}/fds-object.RDS"`'
#' - fds_fil: '`sm config["DATADIR"] + "/datasets/savedObjects/{dataset}/pajdBinomial_psiSite.h5"`'
#' output:
#' - wBhtml... |
b1e2e25ec806e4f0e0278923a66ccd4c7f1f78b7 | 17d582790e37f4a1fa3cfcfc531fdf5c4f4086d4 | /packrat/lib/x86_64-redhat-linux-gnu/3.5.1/vctrs/tests/testthat/test-ptype-abbr-full.R | 20204f42bb7aef8102f88036ec96691738c7319d | [] | no_license | teyden/asthma-research | bcd02733aeb893074bb71fd58c5c99de03888640 | 09c1fb98d09e897e652620dcab1482a19743110f | refs/heads/master | 2021-01-26T08:20:58.263136 | 2020-02-27T04:12:56 | 2020-02-27T04:12:56 | 243,374,255 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,803 | r | test-ptype-abbr-full.R | context("test-type-string")
test_that("input must be a vector", {
expect_error(vec_ptype_abbr(sum), "Not a vector")
expect_error(vec_ptype_full(sum), "Not a vector")
})
test_that("NULL has method", {
expect_equal(vec_ptype_abbr(NULL), "NULL")
expect_equal(vec_ptype_full(NULL), "NULL")
})
test_that("non objec... |
7ffee4b12a32d39637b903e627de8d69f6e3ad05 | d2723d7ac31084fd5a9dfe64fb78ef1d0c254154 | /Data_Wrangling.R | 96ad3e0c8ec12316a999af5a88501fdfc933be3a | [] | no_license | royal-free-london/RunCharter_Shiny | 52421b28b581fdada4de8158c37e7beedbbb2d34 | 76408b3cc6125a07902c027de320b052ec2fdc68 | refs/heads/master | 2020-08-10T04:46:52.820083 | 2020-01-17T13:52:35 | 2020-01-17T13:52:35 | 214,260,394 | 1 | 1 | null | 2019-10-15T11:00:52 | 2019-10-10T18:47:40 | R | UTF-8 | R | false | false | 3,664 | r | Data_Wrangling.R | #install.packages("RODBC") or install.packages('RODBC', dependencies=TRUE, repos='http://cran.rstudio.com/')
#install.packages("odbc")
#install.packages("ggplot2", lib = "C:/Users/ju0d/Documents/R/win-library/3.6")
library(odbc)
#library(DBI)
library(lubridate)
library(tidyverse) # tidyverse contains library(readr),li... |
4f50d0aa7d31dbade97abf2cff3d87af88090813 | a807e1bda86a71521deece0637f45e41d82c2d99 | /RF_Sampat.r | 9d6893c337a0561239947b84850b1aef89f5ec3b | [] | no_license | sampatm28/My_Analytics | 738c6a258c7d7e0fc5b15de9a07bacfedbdffcb0 | 29016562cec69d2fdeeef3cbdac1d25f5f10bf5d | refs/heads/master | 2020-12-01T23:36:50.696057 | 2016-11-02T01:33:47 | 2016-11-02T01:33:47 | 67,351,845 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,664 | r | RF_Sampat.r | #install.packages("popbio")
#install.packages("mice")
#install.packages("ineq")
#install.packages("caret")
#install.packages('NCStats')
#install.packages('ROCR')
#install.packages('ROSE')
#library('mlr')
#detach("package:mlr",unload=TRUE)
library(rpart)
library(rpart.plot)
library(rattle)
library(RColorBr... |
79dc3ba252e0f64cd7dab97926dd3248aad539ee | d6bdf7cc3f76f96ab10428781fe7fd3f7f2af174 | /House Prices - Advanced Regression Techniques/House Prices - Advanced Regression Techniques.R | 7521dd2aa5435889225e5959187e91be167e65db | [] | no_license | mike630/Kaggle-Challenges-Portfolio | 7123cb1aa347280035bea4d876ffe3cf0930ce66 | 44cbe65585fe6da14bdd5ba3f4c1762447201e1f | refs/heads/master | 2020-06-19T17:44:13.143503 | 2019-09-16T15:58:10 | 2019-09-16T15:58:10 | 196,807,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,255 | r | House Prices - Advanced Regression Techniques.R | # Selecionando diretório
setwd('C:/Projetos/Kaggle/House Prices - Advanced Regression Techniques')
# install.packages('corrplot') # Instalar pacotes caso não houver
# install.packages('knitr')
# install.packages('randomForest')
# install.packages('caret')
# install.packages('dplyr')
# install.packages('ggplot2') ... |
85ff69b19a676e5ac287a75e60cc117d678bad81 | c88b0cbeda0edf9e745e324ef942a504e27d4f87 | /MTMM_ESCS/prestigeFromIPIP.R | 0650f718fe9dc8ea00bf271d5db113d3b33a3f15 | [] | no_license | Diapadion/R | 5535b2373bcb5dd9a8bbc0b517f0f9fcda498f27 | 1485c43c0e565a947fdc058a1019a74bdd97f265 | refs/heads/master | 2023-05-12T04:21:15.761115 | 2023-04-27T16:26:35 | 2023-04-27T16:26:35 | 28,046,921 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,931 | r | prestigeFromIPIP.R | ### Testing out a prestige factor from IPIP data
library(psych)
library(lavaan)
items = c('x110','e60','p361','p434','p421','h974','x247','h2043','h1086','h1193','p436','h1203','h204','p410','p401','h743','h746')
table(complete.cases(df.ipip[,items]))
nfactors(df.ipip[,items])
fa.parallel(df.ipip[,items])
temp... |
84184c4101017019197d43c426f2f9c065cfc6b2 | ffe269345445ec40279d6748ff9b5221f294bb15 | /run_analysis.R | 73a1cbb4c6bad81ca933989eed0cc127fb1f7237 | [] | no_license | DominiekL/Getting-and-Cleaning-data---Assignment | 23d7be4a9db593c47a4385873148275df24d6ac5 | 160d13d0f8ad2074b384d9e8595fd37a95aeb7ca | refs/heads/master | 2020-12-24T15:41:03.363480 | 2015-02-21T14:34:34 | 2015-02-21T14:34:34 | 31,126,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,572 | r | run_analysis.R | run_analysis <- function(testdir="test",traindir="train"){
# read all the files that are needed for this script
Y_Test <- read.table(file.path(testdir,"Y_test.txt"))
Y_Train <- read.table(file.path(traindir,"Y_train.txt"))
X_Test <- read.table(file.path(testdir,"X_test.txt"))
X_Train <- read.table(file.path(t... |
079d462b69b52f67b80ee216de70bd5849e444ee | a1950c24afad9fc53478f97d3803be10ee12388e | /Check_Conditions.R | f83fde819f3ecdbc5a43629266ccf6db5d4e5e76 | [] | no_license | NaSed/MONET | 05e4cadb885e017212aa7e6dc9a30f23518fc416 | f407d654e30e2c88024581c1977d67d6aedf41bc | refs/heads/master | 2020-06-13T17:56:03.446693 | 2017-01-20T23:20:37 | 2017-01-20T23:20:37 | 75,571,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,832 | r | Check_Conditions.R | # Checking conditions:
# 1: for each c in CNF and m in DNF => intersect(m, c) != null
# 2: DNF and CNF must have exactly the same variables
# 3: max{|m|: m \in DNF} <= |C| & max{|c|: c \in CNF} <= |D|
# Inputs: CNF and DNF as lists
Check_Conditions <- function(cnf, dnf, verbose)
{
# browser()
if(verbo... |
7712d22dd5ecc5e0286c3c9a018a318d39a5d013 | 227632938c9bf3bd69511645c34179ebd3df6a95 | /analysis/surge_pricing/exploratory_analysis.R | c9bea25e16f805fad803786077efb4cdbcbc6d40 | [
"MIT"
] | permissive | toddwschneider/chicago-taxi-data | 542b8b29f4662e6d0383c2f83ac4724e05d1f586 | 2a3b664b45312a497470e41b820083ab774b46fe | refs/heads/master | 2021-01-11T20:42:52.858673 | 2020-03-25T14:01:34 | 2020-03-25T14:01:34 | 79,171,199 | 82 | 25 | MIT | 2020-03-25T14:01:36 | 2017-01-17T00:12:36 | R | UTF-8 | R | false | false | 23,907 | r | exploratory_analysis.R | # assumes estimate_historical_surge_pricing.R has been run in its entirety
# calculate some aggregate stats
tnp_trips %>%
filter(
has_clean_fare_info,
!is.na(fare_ratio),
shared_status == "solo"
) %>%
summarize(
avg_fare_ratio = mean(fare_ratio),
frac12 = mean(fare_ratio >= 1.2),
frac15 =... |
ae8a3a4144c537668b153fc37eba89d0fa73510f | 8709ac855ca420a513b003559e8f410f65b65b70 | /source/R/install_packages.R | c3ef0740b90717b82fbd94abcf5ee8090bc61320 | [] | no_license | jorainer/rnw-based-affy-analysis | 5c5c8dbb8f7963ff8eb9236f1ebf8e62f5af7b98 | 7adc0e7ab339553920902c7f66a63572d0d36f60 | refs/heads/master | 2021-05-28T00:57:40.546544 | 2014-10-28T09:19:44 | 2014-10-28T09:19:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 469 | r | install_packages.R | ## simple R-script to install all packages required for the analysis.
## this might be run in addition with the install_packages.sh in order
## to fetch and install packages from github or other repos.
source( "http://www.bioconductor.org/biocLite.R" )
cat( "\n\nInstalling basic Bioconductor:\n" )
biocLite( )
cat( "\... |
778adc3cacd776ce77cc657291f2596d0c5a17b3 | 772f625f70ed8c79add852820d5aeb674f0c254a | /code/time_since_infection_discharge.R | 58f479ad231a9da5c08d83e83bc84912f9d7f2df | [
"MIT"
] | permissive | gwenknight/hai_first_wave | 68699f1eb0a1a7de8b219472e5527d1a5823d3de | 1b9bbba8a312889f2e274897d53483a35cc84991 | refs/heads/main | 2023-06-06T23:51:24.641900 | 2021-07-01T15:31:33 | 2021-07-01T15:31:33 | 355,233,961 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,911 | r | time_since_infection_discharge.R | # =========================================================================== #
# Probability distribution for time since infection until hospital discharge
# =========================================================================== #
# INPUT
# los_distr = probability distribution for LOS
# OUTPUT
# res = Probability... |
7b60bdcbe08214d3a2ea0c30c49874835b84688e | 8dc7c48e822815eb71af789e4a97c229c0ab8ecd | /man/expand_idf_dots_name.Rd | ba1a00557731975f4594f40463443026dc3cb104 | [
"MIT"
] | permissive | hongyuanjia/eplusr | 02dc2fb7eaa8dc9158fe42d060759e16c62c6b47 | 4f127bb2cfdb5eb73ef9abb545782f1841dba53a | refs/heads/master | 2023-08-31T02:49:26.032757 | 2023-08-25T15:21:56 | 2023-08-25T15:21:56 | 89,495,865 | 65 | 13 | NOASSERTION | 2023-08-24T02:05:22 | 2017-04-26T15:16:34 | R | UTF-8 | R | false | true | 1,168 | rd | expand_idf_dots_name.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/impl-idf.R
\name{expand_idf_dots_name}
\alias{expand_idf_dots_name}
\title{Parse object ID or name specifications given in list format}
\usage{
expand_idf_dots_name(
idd_env,
idf_env,
...,
.keep_name = TRUE,
.property = NULL
)
}
\ar... |
ef223f94204d60e84a819e9e4fdba661307f9796 | 9730e665d03a919cede89b2b14ea86ba00bea475 | /code/W0_analysis.R | 241c57ce0f31f6f6bd95e17ae42024f85dfba18a | [] | no_license | foxeswithdata/StoringForDrought | 4f49a1ae4243140ede71465d1e3bad65ce31254f | 8a3c0ea57642395785ada4a6503e424f990f47f0 | refs/heads/main | 2023-01-28T17:38:39.168755 | 2020-12-07T04:37:16 | 2020-12-07T04:37:16 | 319,204,470 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,552 | r | W0_analysis.R | source("code/parameters.R")
source("code/models/water_model.R")
source("code/W0_funcs.R")
param <- param_list_drake_W0_1000()
W0min(param)
# W0_maxS
W0_maxS <- W0max(param, simple_water_model_sim_time_breaks, c(4000,7000), deltaW0 = 100, deltat=0.1, kf=0)
print(W0_maxS)
W0_maxS_100 = W0_maxS
W0_maxS_100
W0_maxS <- W... |
043541774a437328242aa2fc67313a137c36bc09 | 6d0999bafd5986933e13719034254d9bfab4d47c | /Code/R/predict_lundgren.R | ab20720eebb6a2b644ab0a3ea105a6612444c01b | [] | no_license | emilio-berti/rewiring-rewilding | 63199f62d1b403c5f59b2e38d4942f448da3416d | 864ac86669162238154da89a974b0ef66c95a9a7 | refs/heads/master | 2022-12-22T00:18:43.366985 | 2020-09-24T06:43:23 | 2020-09-24T06:43:23 | 290,488,904 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,406 | r | predict_lundgren.R | library(tidyverse)
library(sf)
library(raster)
library(fasterize)
library(foreach)
library(doParallel)
lund_predict <- function(taxon){
pn <- raster(paste0("/home/emilio/PN_resampled/5km/", taxon, ".tif"))
r <- raster(paste0("/home/emilio/Buffer_500km/", taxon, ".tif"))
lund <- lundgren %>%
filter(Species =... |
efc054b2a1c6a4f8c640cc799423073fe8631b39 | 62ce083cc73b245787c535c44dcf736f66be6c16 | /data_fetch.R | 49e7189fbd18c437fc1b4f66bf1f68156d9a5aa1 | [] | no_license | WangLiuying/dota2 | 071abb4ae355a60966a4d6e2696579e195b997ca | 6df9e3fdf5b69bf9448cd811d36d21e7b2c99406 | refs/heads/master | 2021-09-08T04:54:03.860090 | 2017-12-08T11:42:37 | 2017-12-08T11:42:37 | 110,673,341 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,339 | r | data_fetch.R | #get heros' attributes
library(rvest)
library(stringr)
#取出所有英雄的链接
url = 'http://www.dota2.com.cn/heroes/index.htm'
page = read_html(url)
linkpage = html_nodes(page,xpath="//ul[@class='hero_list']/li/a") %>% html_attr('href')
herosAttributes = data.frame()
for (url_link in linkpage)
{
##fetch data from a hero
page... |
0ca5f41c9ef6cea7a644737f5cf32d8dcdcd26f6 | e9a5a9e952a9ccac535efe64b96cc730b844677b | /inst/unitTests/runit.workbook.extraction.R | 26011c4f194763eed867f8cb88eb9993987dfa94 | [] | no_license | miraisolutions/xlconnect | 323c22258439616a4d4e0d66ddc62204094196c9 | ae73bfd5a368484abc36638e302b167bce79049e | refs/heads/master | 2023-09-04T05:27:42.744196 | 2023-08-30T07:10:44 | 2023-08-30T07:10:44 | 8,108,907 | 114 | 35 | null | 2023-08-30T07:10:46 | 2013-02-09T11:17:42 | R | UTF-8 | R | false | false | 3,646 | r | runit.workbook.extraction.R | #############################################################################
#
# XLConnect
# Copyright (C) 2010-2021 Mirai Solutions GmbH
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation... |
6b8751f7fe4a2916c108248c463ba1ffa1fd4188 | 0af0d7f3f28d516d41eedaad41cf5744b414500a | /Deliverable/R-Script/8086-002-RScript.R | c72f205a53b952014a37a7f534893785b14e9014 | [] | no_license | srishtynayak/Medicare-Claim-Hospital-Analysis-R | 8b10fe65e56b444cd74d7e26ff8c6caccdaa9f64 | 20023e171ac9384a8b2902f03d1b2314377f1754 | refs/heads/master | 2020-03-31T11:07:09.882457 | 2018-10-07T00:58:36 | 2018-10-07T00:58:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,811 | r | 8086-002-RScript.R | ## R Script - 8086-002 - Data to Decisions
We have addressed all our research questions.
1) Finding out on which claim type did all the hospital across USA has spent more and also for which period?
2) Finding the amount spent in each state and grouping it under highest and lowest claim states?
3) Finding the lead... |
f09d2b8d34bbf08cf63adf198d66d52d4f4f948e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mlbstats/examples/ops.Rd.R | a0f067a05ba938d83f34791a76f6ef288af7627c | [] | 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 | 155 | r | ops.Rd.R | library(mlbstats)
### Name: ops
### Title: Calculates on-base plus slugging
### Aliases: ops
### ** Examples
ops(200, 18, 4, 401, 4, 50, 20, 3, 13)
|
60179478e6439e76f1e710a3963c4b1e75699680 | c8712e9013f625a3acd882dfebf70d0d3eee1a77 | /scripts/csvToJSON.R | 3a46c91de8a9acd594ef28f183684037ddc8ca83 | [] | no_license | seattleflu/simulated-data | db95ada6e191d94f05eb9888a9fdf3d6d082e37f | 318b5309b574b65db12b20c809344b94452644bd | refs/heads/master | 2020-04-23T18:47:45.504700 | 2019-05-13T20:58:39 | 2019-05-13T20:58:39 | 171,380,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 527 | r | csvToJSON.R | # csvToJSON
library(jsonlite)
library(R.utils)
file_dir <- '../models/'
# forAuspice
for(dirPath in list.dirs(file_dir)){
for (filePath in list.files(path = dirPath, pattern="+\\.csv", full.names=TRUE)){
dat<-read.table(filePath,quote='',sep=',',header = TRUE)
if (file.size(filePath) <= 2^21){
... |
acb9e34996a263a6da57138cefbf6a2258899967 | b66ff8f265f9af43e8dc7ddb7558c12d01a5a226 | /scripts/day_of_week.R | 281b1454bfd0fdcd1c026785f2cf820f1ddb18d8 | [
"MIT"
] | permissive | TimTaylor/trend_analysis_public | 0d88b96876bb5b4c05fe8fab101864dd5b135c21 | 75155f91054d7f3f0caa26d2b693c1882b873475 | refs/heads/main | 2023-06-23T03:21:14.074644 | 2021-07-14T22:54:43 | 2021-07-14T22:54:43 | 386,217,122 | 0 | 0 | NOASSERTION | 2021-07-15T08:21:52 | 2021-07-15T08:21:51 | null | UTF-8 | R | false | false | 548 | r | day_of_week.R | #' Convert dates to factors
#'
#' This will map a `Date' vector to weekdays, with the following distinction:
#' weekden, monday, of the rest of the week
#'
#' @author Thibaut
#'
day_of_week <- function(date) {
day_of_week <- weekdays(date)
out <- vapply(
day_of_week,
function(x) {
if (x %in% c("Satur... |
95c91d13db9f4012b4551b862da9e953bbc1fb62 | 93efadbd61fb615358b84864d2641eb11ae3a96a | /data.R | a150d5cb9e4407311d72e77c49b94f145d6e6044 | [] | no_license | przemo/komunikatyKM | 89aee3a40076052fe11644ec618f496f98a05145 | 5384b46c422220179ff58c03210773d791602f7c | refs/heads/master | 2020-06-04T16:17:20.775643 | 2015-03-02T21:05:46 | 2015-03-02T21:05:46 | 26,396,584 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 149,485 | r | data.R | structure(list(dates = structure(c(1414749480, 1414749420, 1414757040,
1414764300, 1414780320, 1414776060, 1414837380, 1414958760, 1415030340,
1415045580, 1415086080, 1415105700, 1415123640, 1415163600, 1415251740,
1415349780, 1415354340, 1415364960, 1415374380, 1415427060, 1415456040,
1415598960, 1415692020, 14157... |
8494589469d104ef34520dedbb6efc8cad8e9865 | a6c12efd32ec6a240b259b2bca8f697c06aa258b | /rl/multi-armed-bandit/click_through_rate.R | 793f6a45ece372fd89400aa54a9df49952939a8c | [] | no_license | krzjoa/learning-R | 545334791f7e0a1264ff9d2c4895adcd74d44183 | ec1c1a1ce00c0fcf35d34498cbfd31e56b9c8923 | refs/heads/master | 2021-06-27T22:13:39.486064 | 2020-10-12T22:09:58 | 2020-10-12T22:09:58 | 172,254,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 875 | r | click_through_rate.R | library(ggplot2)
library(magrittr)
plot_beta_distribution <- function(a, b){
linspace <- seq(0, 1, length.out = 200)
beta.distribution <- dbeta(linspace, a, b)
inputs <- data.frame(y=beta.distribution,
x=linspace)
ggplot(inputs) +
geom_line(aes(x=x, y=y))
}
run_experiment <- functio... |
0000009f89d5612c870a4cfb8a4462f3373768d3 | 693d88d479f96e91be7607de520875861f3f6e4d | /man/infsearch.Rd | ee67df9353588528b02546d0064066a05b3b7c50 | [] | no_license | vijaydairyf/DMMongoDB | c4239c144f3357856177855e2fa82baf72bd34bf | 920bbbbaed086df6d271be6c47dd2f4bcbe4341a | refs/heads/master | 2020-12-05T19:20:18.297176 | 2019-12-18T21:44:41 | 2019-12-18T21:44:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 857 | rd | infsearch.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/infsearch.R
\name{infsearch}
\alias{infsearch}
\title{Retrieves infrastructure information from the DataMuster database}
\usage{
infsearch(property = NULL, active = NULL, infstype = NULL,
username = NULL, password = NULL)
}
\arguments{
\ite... |
6e1399cd1fc1b28ff855dbb4ef104b78d9fb4fea | 2c1f0d3bf36a1d4ea7ce73f8eab4c301b35fa281 | /man/summarize_weighted_corr.Rd | 7701b2e3343447d2d492739699981a96a1597a6f | [] | no_license | yitao-li/sparklyr.flint | c72b00b9cb85ea2c915d3746f6c8428b1465bea5 | 59a8a14ba9bf049e0de1cec3b6581f55f25c0018 | refs/heads/main | 2023-03-03T06:36:30.429384 | 2020-12-14T15:51:35 | 2020-12-14T15:51:35 | 338,419,405 | 1 | 0 | null | 2021-02-12T19:56:38 | 2021-02-12T19:56:37 | null | UTF-8 | R | false | true | 3,482 | rd | summarize_weighted_corr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summarizers.R
\name{summarize_weighted_corr}
\alias{summarize_weighted_corr}
\title{Pearson weighted correlation summarizer}
\usage{
summarize_weighted_corr(
ts_rdd,
xcolumn,
ycolumn,
weight_column,
key_columns = list(),
increment... |
fcdfbd386375795a17ff0801a2d3ead4265e47b7 | 4ee9d7179b4af02d1b2efcb0f0f43f03cabc1164 | /man/gsea.t2cov.Rd | 6711a307e23326e7f9e0275e90273319ec3cbbee | [] | no_license | FrankD/NetHet_old | 78795a58d8a0484f4773230d391c0b99b0a4e0a8 | 38a55860acd636410c98ef30b51756776455be08 | refs/heads/master | 2020-04-16T07:10:05.290938 | 2015-08-18T12:07:48 | 2015-08-18T12:07:48 | 21,828,742 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 723 | rd | gsea.t2cov.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/ggmgsa.R
\name{gsea.t2cov}
\alias{gsea.t2cov}
\title{GSA using T2cov-test}
\usage{
gsea.t2cov(x1, x2, gene.sets, gene.names, gs.names = NULL,
method = "t2cov.lr", method.p.adjust = "fdr")
}
\arguments{
\item{x1}{expression matrix (c... |
3798314238bd9ceb3ddd72a7f19e5d317940f339 | f993e437735ff0520099598bbdcf40f270d9c471 | /Clase04.R | a2c615e71ecefea25d370878b736e1ade4711258 | [] | no_license | PatyTuga/Analisis-y-Tratamiento-de-Datos-con-R | 95af27e0250f8d75f2358df58255cd62cf8409dc | 583164749de282491cebbf08dd608eb0f0005ee0 | refs/heads/master | 2021-01-10T15:57:10.638532 | 2015-10-20T01:52:46 | 2015-10-20T01:52:46 | 44,573,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,105 | r | Clase04.R | ##### Clase 04 #####
verificador <- function(cedula){
if(nchar(cedula)==10){
index <- c(2,1,2,1,2,1,2,1,2)
val <- numeric(10)
for(i in 1:10){
val[i] <- as.numeric(substring(cedula,i,i))
}
produ <- index*val[1:9]
produ[which... |
a79a98fdc698424c48668301cebbeaac7e9a1ed9 | 38d52a7e16b96555f277cb879a69d3f1ba086dad | /man/apply_decimal.Rd | 21a882d4b88590775946d339f9ecb2ae078b46c6 | [
"MIT"
] | permissive | next-game-solutions/tronr | c7ec41a0785536670942c653f0f1500f09e7e692 | e7eb8b1d07e1c0415881ca3259358f707d78b181 | refs/heads/main | 2023-06-19T03:06:34.302241 | 2021-07-12T22:01:05 | 2021-07-12T22:01:05 | 305,829,963 | 7 | 0 | NOASSERTION | 2021-07-12T22:01:06 | 2020-10-20T20:48:08 | JavaScript | UTF-8 | R | false | true | 1,182 | rd | apply_decimal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apply_decimal.R
\name{apply_decimal}
\alias{apply_decimal}
\title{Change representation of token amounts}
\usage{
apply_decimal(amount, decimal)
}
\arguments{
\item{amount}{(double): token amount expressed using the "machine-level"
precision ... |
f5d9161280422bb8d480d0a562f067d546718289 | a7adce03ceaf94e5b93b43a5d337bb49750196c6 | /scripts/11_assemble_final_human_tx.R | 929bc685c2f3ce79c6f4b980b499a87bf4eac0ae | [] | no_license | czhu/FulQuant | cb2a5ada7904252e15c66a14051ad2740950237c | 76139924631626e55143753a6fd58f283814a501 | refs/heads/master | 2023-04-07T14:48:23.139435 | 2022-07-20T07:40:51 | 2022-07-20T07:40:51 | 342,687,654 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,878 | r | 11_assemble_final_human_tx.R | library(tidyverse)
library(rtracklayer)
projectFolder = "."
SCRIPTDIR = file.path(projectFolder, "sw")
GENOMEDIR = file.path(projectFolder, "genome")
source(file.path(SCRIPTDIR, "clustering_functions.R"))
## change here
labPrefix = "My"
labSuffix = "Lab"
infolder = file.path(projectFolder, "combined/tx_annot")
load(... |
bac30879cba803430eafe9f57798fb93783fd015 | 5d4cba65032b333387991db78f1e7d76779773e2 | /gbifapp/gbifapp.R | d052d70800c9f52c9625589f8572574f7b46edfc | [] | no_license | AlexxNica/abs | 889981b672181ad4675cc40fb5137fd3358ed2b2 | cf25666fae30ad0de1a15c21d50cfbc57e8137fa | refs/heads/master | 2021-01-23T02:48:41.870318 | 2017-02-27T21:13:19 | 2017-02-27T21:13:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,776 | r | gbifapp.R | library(shiny)
library(leaflet)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(data.table)
# use datatable to speed things up here
data <- fread("kenya_slim.csv", na.strings = c("", NA))
data <- sample_n(data, 30000)
# establish bounds for map view
bounds <- read.csv("bounds.csv", stringsAsFactors = F... |
3d9ccbb35eb33ab255e8e1b5c1b49ccda64060d6 | c1034eb8f34b18105acf3244bf9a0b0339d6ca8d | /man/plotExtreme.Rd | b80ebabac757a0a5d3c9d6111ec21dec85fb7b20 | [
"MIT"
] | permissive | svkucheryavski/mdatools | f8d4eafbb34d57283ee753eceea1584aed6da3b9 | 2e3d262e8ac272c254325a0a56e067ebf02beb59 | refs/heads/master | 2023-08-17T16:11:14.122769 | 2023-08-12T16:58:49 | 2023-08-12T16:58:49 | 11,718,739 | 31 | 11 | NOASSERTION | 2020-07-23T18:50:22 | 2013-07-28T11:10:36 | R | UTF-8 | R | false | true | 348 | rd | plotExtreme.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/defaults.R
\name{plotExtreme}
\alias{plotExtreme}
\title{Shows extreme plot for SIMCA model}
\usage{
plotExtreme(obj, ...)
}
\arguments{
\item{obj}{a SIMCA model}
\item{...}{other parameters}
}
\description{
Generic function for creating ext... |
c03d254c5b1593f120ae2699aa4563983bb75e63 | 7f9026c8be2400a6ca51291c6d97f737ee0fa51b | /Analysis_maincode.R | 4f898abce7bdebe5d8256e6cff5b1baaea87c5d0 | [] | no_license | jwisch/Falls | dc76b3a98c2c737e08ef231c3d18747d6f9fddb0 | ccd1e7492b2c0501af550851cdf14fee8871f919 | refs/heads/master | 2020-12-13T09:04:51.852196 | 2020-01-16T17:10:25 | 2020-01-16T17:10:25 | 234,370,155 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,154 | r | Analysis_maincode.R | FILEPATH_DATA<-"C:/Users/julie.wisch/Documents/Transition/DraftedMS_Falls/Data/"
FILEPATH_CODE<-"C:/Users/julie.wisch/Documents/Transition/DraftedMS_Falls/Code/"
library(ggplot2)
library(ggpubr)
library(sjPlot)
library(gridExtra)
library(psych)
library(ppcor)
source(paste(FILEPATH_CODE, "CommonFuncs.R", sep... |
e6bc3c9b1731cbb9a6e430d8b50b7f547d267313 | c76122b42df71a68def61684d92b54ada23338cd | /nfl_playcalls.R | 6e995dce70b5587349bbed5a5be32c322243150a | [] | no_license | marius-oetting/NFL-Play-Call-Predictions | 546f5f5f5753829c0b8dd915c209d24024b2b6bb | 97813ecf8afd04ec72a929e69eba465bce5ff487 | refs/heads/master | 2021-06-17T10:40:09.826089 | 2021-02-09T08:20:25 | 2021-02-09T08:20:25 | 161,164,452 | 0 | 0 | null | null | null | null | ISO-8859-2 | R | false | false | 17,741 | r | nfl_playcalls.R | ### R code for the paper
### "Predicting play-calls in the National Football League using hidden Markov models
### author: Marius Ötting
## load packages
library(dplyr)
library(ggplot2)
library(lemon)
# import data
all.teams.df <- read.csv("nfl_data.csv")
# figures ---------------------------------... |
5c5a3342c9c7d5b9b66513930f3c4b93530489be | acb0fffc554ae76533ba600f04e4628315b1cd95 | /R/MergeSurfaceYSIChemistry.R | f5d281f89b33c938065e9f1aae6e117c2e50704c | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | lukeloken/USBRDelta | 83826e12a5b5a2e81adeb2119e9c2599a5f8b870 | fd6569385776d4579748b6422b5153e64606e0ba | refs/heads/master | 2021-06-09T19:08:01.976985 | 2020-05-28T21:51:10 | 2020-05-28T21:51:10 | 145,152,807 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,117 | r | MergeSurfaceYSIChemistry.R |
#Code to extract surface water measurements from profile data at fixed sites
library(readxl)
library(plyr)
library(dplyr)
library(viridis)
library(lubridate)
library(ggplot2)
library(gridExtra)
source('R/read_excel_allsheets.R')
source('R/g_legend.R')
# Project folder where outputs are stored
dropbox_dir<-'C:/Dr... |
65699b8388abc14e517c082c730140481633fb4f | d460e5e5f143aaaf2c2e783031cb35cf0ed8e865 | /plot4.R | 884482ddee1d135a90d0247d99aad8af00cdd754 | [] | no_license | azambranog/ExData_Plotting1 | f1fceb136418d41c2abf180cb1b4a14123e0e985 | acaf1d10f4a9d8fdc07cc34a871376b60271de60 | refs/heads/master | 2021-01-09T08:54:36.291976 | 2014-05-11T14:28:51 | 2014-05-11T14:28:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,426 | r | plot4.R | # This code generates 4 plots in a figure
# 2007-02-01 and 2007-02-02. The result is stored in plot4.png
#get all data
data<-read.table("household_power_consumption.txt",
header=T,
sep=";",
na.strings="?",
stringsAsFactors=F)
#get necessary subset
da... |
655607d2a90e836bb7ea8fa35338664f548a0621 | 670b321ea9891c1e6259732b9445dfe573dc25a9 | /EsercitazioneGIT.R | d4c8a938355e0b0b2a6a328cba6d670486b3d2fe | [] | no_license | PROVAGITMR/MIA | 3f34c57b705eec14af017fb8700432250c37befb | cfb7ece515bd963f07bdebd883460123c449910b | refs/heads/main | 2023-05-03T05:04:27.401448 | 2021-05-23T09:12:36 | 2021-05-23T09:12:36 | 369,314,091 | 0 | 0 | null | 2021-05-22T14:37:35 | 2021-05-20T19:16:13 | R | UTF-8 | R | false | false | 94 | r | EsercitazioneGIT.R |
somma = function (a,b){
somma=a+b
cat(somma(1,2))
return(somma)
}
#commento
|
f455155ba1099a6b6e521b84e522a78cd44f826c | a53773793496b1034a3194a0c174c9c6a7212c18 | /waterquality_entryform_draft.R | 5deed5d7b924c00c7dcf5ebaac0d6443bf24de79 | [] | no_license | mkleinha/sqlite_interface | b5e694b2c3c9601be9d9a68e6956fe8f8e4db4e6 | 57fa35b0fa70bf09302bb429b2478dfb1db2cfec | refs/heads/master | 2023-03-14T06:12:50.777357 | 2021-02-28T15:45:07 | 2021-02-28T15:45:07 | 266,857,199 | 1 | 0 | null | 2020-06-25T13:34:58 | 2020-05-25T18:54:23 | R | UTF-8 | R | false | false | 43,302 | r | waterquality_entryform_draft.R | #' This is script for the Water Quality Date Entry Interface tab of the Acanthonus database
#' This form is for entering data primarily associated with Section 6 and NFWF projects in the Upper Coosa Basin
# author: Maxwell Kleinhans (maxwell.kleinhans@gmail.com) and Phillip Bumpers (bumpersp@uga.edu)
# this line shoul... |
2982e9b20d7bc9766843f64608fd404e43a90e08 | 72f7878de8b07cfa6945fea9f9d21ce5e52aa238 | /R/process_thermal_data.R | 11b2aa34e2c2b472c928d8da87d813a7ab753cb0 | [
"BSD-2-Clause"
] | permissive | IMMM-SFA/wmpp | 06d35e6676b09e02e5aad464de7ffc8dfa21a434 | d1cd7e4401963470eff1970714c0edbda812a294 | refs/heads/master | 2020-05-01T19:57:28.675462 | 2020-03-20T17:30:49 | 2020-03-20T17:30:49 | 177,661,130 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,645 | r | process_thermal_data.R | # process_thermal_data
#
# gets essential data out from matlab file plantFlow_thermal.mat
# this is performed offline to avoid creating dependency for R.matlab
process_thermal_data <- function(){
extdata_dir <- system.file("extdata/", package = "wmpp")
data_ml <- readMat(paste0(extdata_dir, "plantFlow_thermal.mat"... |
ae38f8eab38768b9ad87b65938c57afad8675feb | 53e0aee63e97aae26f4394d875794bb70d734fe7 | /man/FamilyExperiment-class.Rd | 2706b22f05227dcf1970322852fe89671a31e66a | [] | no_license | syounkin/Trioconductor | c7ffb64d050d0963157406ea5d9e93c22dda5a5a | b05b5146ae1ae8e238cff6dcb605c6809984dec7 | refs/heads/master | 2021-01-23T13:49:30.804282 | 2013-07-22T16:11:06 | 2013-07-22T16:11:06 | 7,007,865 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,159 | rd | FamilyExperiment-class.Rd | \name{FamilyExperiment-class}
\docType{class}
% Class:
\alias{class:FamilyExperiment}
\alias{FamilyExperiment-class}
\alias{FamilyExperiment}
% Constructor
\alias{FamilyExperiment,SummarizedExperiment,PedClass-method}
% Accessors:
\alias{pedigree,FamilyExperiment-method}
\alias{pedigree}
\alias{MAF,FamilyExperiment-... |
2cdbee1a75c86ea501f1ba7d1de3fae5568ec088 | ce1794734a1dd59e4ab68a9e414abc9ec8998282 | /man/nufft_1d1.Rd | c64f378434481ea6494df8cc4c7a1261a5e2bb11 | [
"Apache-2.0"
] | permissive | jkennel/finufft | e2b7c22be5b90a156812d09bbad9b7198a782671 | 46d8f8fc6dd986a22acbce4c8044bd455ed3cd94 | refs/heads/master | 2022-12-31T08:42:16.879083 | 2020-10-24T13:52:45 | 2020-10-24T13:52:45 | 304,408,540 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 435 | rd | nufft_1d1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{nufft_1d1}
\alias{nufft_1d1}
\title{nufft_1d1}
\usage{
nufft_1d1(xj, cj, n1, tol = 1e-09, iflag = 1L)
}
\arguments{
\item{xj}{locations}
\item{cj}{complex weights}
\item{n1}{number of output modes}
\item{tol}{precision}... |
68e7627bda36e8d3839b83247bb4ae702695a90d | affee151ef20940e52eea1473635c8f4e35b65de | /man/allen.relations.set.Rd | 1729e88fbde16276fcbc2a02f98484268a51376a | [] | no_license | tsdye/allen.archaeology | d433c346b6ae93935cb369a8dd917e267aee2cb0 | ae1e3806df684ffa27fbf2ec1645f178ba101e18 | refs/heads/master | 2023-04-11T14:12:16.751804 | 2023-03-25T12:59:26 | 2023-03-25T12:59:26 | 245,044,592 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 454 | rd | allen.relations.set.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/io.R
\name{allen.relations.set}
\alias{allen.relations.set}
\title{Express the Allen relation set in mathematical notation}
\usage{
allen.relations.set(allen.set)
}
\arguments{
\item{allen.set}{an Allen relation set}
}
\description{
Return a ... |
cf898de47f130e59960b3046187c22d6593040a9 | 6d399293ee87676a5855f875cb3edca25c823e11 | /functions.R | 0ad6f79056e0190bd3c1d2647ceb1632df4a6a70 | [] | no_license | gabrielteotonio/shape-analysis | d4a35ba4d578608be591ccf75db344c52f265a57 | c01bc1ed7fbd7cc7f0de1c1a86e43efdb839468a | refs/heads/master | 2020-05-05T01:22:22.536206 | 2019-04-25T19:15:19 | 2019-04-25T19:15:19 | 179,600,996 | 0 | 0 | null | 2019-04-25T19:15:20 | 2019-04-05T01:14:41 | R | UTF-8 | R | false | false | 395 | r | functions.R | # Gamma's f(x) ----
gamma_density <- function(x, alpha, beta) {
(beta^alpha)/gamma(alpha) * x^(alpha - 1) * exp(-beta*x)
}
# Gamma's Estimated Function ----
gamma_hat <- function(alpha) {
sqrt(2*pi) * alpha^(alpha - 1/2) * exp(-alpha)
}
# Gamma's Saddlepoint Density ----
gamma_saddle_density <- function(x, alpha... |
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