commit stringlengths 40 40 | subject stringlengths 4 1.73k | repos stringlengths 5 127k | old_file stringlengths 2 751 | new_file stringlengths 2 751 | new_contents stringlengths 1 8.98k | old_contents stringlengths 0 6.59k | license stringclasses 13
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1289f9637129af82f5349dbf587c21be568698be | Bump version to 3.0.99 | rgchris/ren-c,mbk/ren-c,hostilefork/rebol,rgchris/ren-c,hostilefork/rebol,hostilefork/rebol,hostilefork/rebol,kealist/ren-c,kealist/ren-c,draegtun/ren-c,hostilefork/rebol,mbk/ren-c,hostilefork/rebol,giuliolunati/ren-c,giuliolunati/ren-c,kealist/ren-c,giuliolunati/ren-c,giuliolunati/ren-c,rgchris/ren-c,codebybrett/ren-c... | src/boot/version.r | src/boot/version.r | 3.0.99.3.1
| 3.0.91.3.1
| apache-2.0 | R |
ed98c0f0ce630f655122c05dad35ba12dcc3d9bc | Add patient sample demo. | pschulam-attic/sclero | demo/patient-sample.r | demo/patient-sample.r | require(sclero)
require(ggplot2)
require(plyr)
data(pft)
data(patient)
set.seed(2)
n.visits <- ddply(pft, ~ patient.id + test.type, summarize, visits = length(na.omit(test.result)))
n.fvc.visits <- subset(n.visits, test.type == "fvc")
patient.ids <- subset(n.fvc.visits, visits > 5)$patient.id
some.patients <- samp... | mit | R | |
3ca6513b09ac21b1f05ea018421b5a45002fa167 | Create area_under_curve.r | nairvinayv/random_scripts,nairvinayv/random_scripts | area_under_curve.r | area_under_curve.r | #Code for calculating the area under curve using Trapezoidal Integration
#Input File has single column containing Values
args <- commandArgs(trailingOnly = TRUE);
raw_data = read.csv(args[1])
data <- cbind(1:dim(raw_data)[1],raw_data)
require(pracma)
AUC = trapz(as.vector(t(data[1])),as.vector(t(data[2])))
disp(AUC)
| mit | R | |
cc251751cdf81a9401299e7caca58e369337aa67 | Add slope.r as a simple demo | filipkral/slopeaspect | slope.r | slope.r | # Calculate slope and aspect of a digital terrain model
slope<-function(r, res=1){
# returns a slope surface of elevation raster r of resolution res,
# according to Burrough et McDonnell 2000, p. 191
newr<-matrix(0, nrow=nrow(r), ncol=ncol(r))
h<-nrow(r)
w<-ncol(r)
for(i in 2:(h-1)){
for(j in 2:(w-1)){... | mit | R | |
17fe555481b255d79197c21209709a5de8ad2641 | Create get_jfg.r | lancezlin/rProject | firstPack/R/get_jfg.r | firstPack/R/get_jfg.r | get_jfg <- function(data){
family_group_name <- c()
job_family_group <- data$"Job Family Group"
len <- length(job_family_group)
for (i in 1:len){
if (is.element(job_family_group[i], family_group_name)){
#if (any(family_group_name != job_family_group[i])){
#family_group_name <- append(family_group_na... | mit | R | |
e337c919c031b49bea3f2b63a87a0eb6e8d7018b | Implement doc parsing and display for modules | klmr/modules,klmr/modules | R/help.r | R/help.r | parse_documentation = function (module) {
module_path = module_path(module)
parsed = list(env = module,
blocks = roxygen2:::parse_file(module_path, module))
rdfiles = roxygen2:::roc_process(rd_roclet(), parsed, dirname(module_path))
rdcontents = lapply(rdfiles, roxygen2:::format.rd_fil... | apache-2.0 | R | |
ca36a3f2f866e4876afe3dbe029776a2edb9c6f2 | Add Pitman-Yor style CRP. | jtobin/bnp | chinese-restaurant-process/src/generalized_crp.r | chinese-restaurant-process/src/generalized_crp.r | generalized_crp = function(n, a, b) {
restaurant = data.frame(table = 1, customers = 1)
for (j in seq(n - 1)) {
restaurant = arrival(restaurant, a, b)
}
restaurant
}
arrival = function(r, a, b) {
k = nrow(r)
p = 1 - (b + k * a) / (sum(r$customers) + b)
if (rbinom(1, 1, p)) {
join_table(r, a)
... | mit | R | |
cf79e0975d19f1bf334fb1a81c864d3e98559925 | 更新：第六章fig6-15 | shuaimeng/r | thesis/chap6/fig6-15.r | thesis/chap6/fig6-15.r | dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')
setwd("/Users/mengmengjiang/all datas/conductivity")
library(xlsx)
#reading datas
#compare d and ratio in qd-1
#sheetName = qd1-18,qd1-19,qd1-20;
# 18nl/min下的液滴直径
# 或者1.8kv下，三种液体在两种qd下的表现；
# qd1-18，qd2-18；
# 液滴比... | mit | R | |
891333fb2eeb8bcf35872127570914718db9924e | Add DataPrep function | mattmills49/CFBWinProbability | R/DataPrep.r | R/DataPrep.r | #' Format data to be used in regression fitting
#'
#' This function takes in a data frame of plays read in from the CFB Stats
#' play by play file after the time of each play has been added. The function
#' determines a winner of each game, adjusts the down, distance, and spot
#' for kickoffs, determines if the game i... | mit | R | |
a5078e8f8b51d697e8ff412a1933427f1a18d076 | check and repair degenerated scenarios | felixlindemann/HNUORTools,felixlindemann/HNUORTools | R/HNU.OR.TPP.Prepare.r | R/HNU.OR.TPP.Prepare.r | setGeneric("HNU.OR.TPP.Prepare", function(object,...) standardGeneric("HNU.OR.TPP.Prepare") )
setMethod("HNU.OR.TPP.Prepare",signature(object="HNUGeoSituation"),
function(object,...){
li<-list(...)
if(is.null(li$checkDegenerated)) li$checkDegenerated <- TRUE
if(li$checkDegenerated){
supply <... | mit | R | |
30c9be05c0561a76ece2a06bc366215f602c2aea | Add interactions demo to explore clinic and lab vars. | pschulam-attic/sclero | demo/interactions.r | demo/interactions.r | # Demo visualizing the interactions between the lab and clinical
# variables recorded for the sclero project.
options(warn = -1)
require(ggplot2)
require(GGally)
require(plyr)
require(sclero)
data(list = c("clinic", "pft", "patientsplit"))
dev.patients <- unique(subset(patientsplit, dev == 1)$patient.id)
clinic <- ... | mit | R | |
26185d91d5129acc5c8a6730ad6afd6626975859 | update Us临界速度拟合 | shuaimeng/r | print/merge/fig2_fit.r | print/merge/fig2_fit.r | dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')
library(rJava)
setwd("/Users/mengmengjiang/all datas/max fp")
library(xlsx)
# reading execl
k1<-read.xlsx("gly1.xlsx",sheetName="qd2-20",header=TRUE)
plot(k1$fv,k1$deva,col="0",xlab = expression(italic(f["v"])... | mit | R | |
c5e2a01da3d3996c0d43ff1b029b69619f9eb127 | Create newIndexColumn.r | jluzuria2001/codeSnippets,jluzuria2001/codeSnippets,jluzuria2001/codeSnippets,jluzuria2001/codeSnippets | newIndexColumn.r | newIndexColumn.r |
# CREATE A NEW COLUMN OF INDEX
# FROM 0 TO THE SIZE OF THE MATRIX MINUS 1
error1$index<-seq.int(0, nrow(error1)-1, 1)
#COMPARE THE COLUMNS
#AND CREATE A NEW COLUMN
#PUTTING A 0 IF ARE EQUAL
#PUTTING A 1 IF ARE DIFFERENT
error1$order <- ifelse(error1$V6 == error1$index,0,1)
#COUNT THE NUMBER OF ZERO IN A MATRIX... | mit | R | |
d288b9378f4e9f49c22860a53069b1b78bf1be1e | add script for running cn.mops | sestaton/sesbio,sestaton/sesbio,sestaton/sesbio,sestaton/sesbio | gene_annotation/gene_annotation_R_scripts/cnmops.r | gene_annotation/gene_annotation_R_scripts/cnmops.r | library(cn.mops)
BAMFiles <- list.files(pattern=".bam$")
bamDataRanges <- getReadCountsFromBAM(BAMFiles, refSeqName = c("Ha4", "Ha16"), mode = "paired", WL = 100)
#bamDataRanges <- getReadCountsFromBAM(BAMFiles, refSeqName = c("Ha4", "Ha16"), mode = "paired")
res <- cn.mops(bamDataRanges, normType = "mean")
segm <- a... | mit | R | |
1d3c1af0cae7db8599facc993772a7105997d3f3 | Create anothersimplecode.r | vj-ug/Data-Analysis-of-Human-Activity- | anothersimplecode.r | anothersimplecode.r | training = read.csv("UCI HAR Dataset/train/X_train.txt", sep="", header=FALSE)
training[,562] = read.csv("UCI HAR Dataset/train/Y_train.txt", sep="", header=FALSE)
training[,563] = read.csv("UCI HAR Dataset/train/subject_train.txt", sep="", header=FALSE)
testing = read.csv("UCI HAR Dataset/test/X_test.txt", sep="", he... | apache-2.0 | R | |
ca0c6ac6696ce78d60382b72074eeaa2f2941a3d | Implement stricter object access | klmr/modules,klmr/modules | R/access.r | R/access.r | #' Access an object inside a module
#' @usage
#' module$object
#' @param module the module object
#' @param object the name of the object
#' @return Unlike the default \code{$} operator in R, access non-existent
#' objects in modules yields an error rather than returning \code{NULL}.
#' @export
`$.module` = function (m... | apache-2.0 | R | |
26e1a799dbdb8a6bd3e8aaf45711d28a3c24d770 | Create ff.r | ActiveAnalytics/activeH5-dataframe-bench,ActiveAnalytics/activeH5-dataframe-bench | ff.r | ff.r | # Benchmark code for reading/writing data frames from/to file using ff package
# Load the data set from CSV file
data_path <- "../data/2007.csv"
system.time(dat <- read.csv(data_path))
# Load the ff package
require(ff)
ff_file <-"../data/air_ff"
system.time(ffsave(dat_ff, file = ff_file,
compression_level = 0))
... | mit | R | |
0413fe81c16f3a5f3fa7edce1eaf48b7acbc6990 | Create south_sf.r | sequenceiq/r_datagen | clustering/south_sf.r | clustering/south_sf.r | #South SF, 6h, 12h and 18h clusters, #400000
#location
n1<-400
multiplier<-1
dev<-0.03
x<-c(rnorm(n1,mean=37.65338,sd=dev))
y<-c(rnorm(n1,mean=-122.40555,sd=dev))
#datetime
start<-as.POSIXct(strptime("2014/01/01", "%Y/%m/%d"))
end<-as.POSIXct(strptime("2014/02/28", "%Y/%m/%d"))
dt=end-start
dd<-dt/2
t<-c(start+rnorm(... | apache-2.0 | R | |
a17f62b2f13bd9eb49c0702bc389c0245e520174 | Add R example | shekkbuilder/innodb_ruby,zhujzhuo/innodb_ruby,shekkbuilder/innodb_ruby,zhujzhuo/innodb_ruby | examples/analysis.r | examples/analysis.r | page_info = "edges_1_0025_edges" ; page_info_width=4000
page_info = "edges_test_d0p" ; page_info_width=1000
page_info = "edges_test_d1p" ; page_info_width=1000
page_info = "edges_test_d1p_a1p" ; page_info_width=1000
page_info = "t" ; page_info_width=1000
page_info_base = paste("~/git/innodb_ruby/", page_info, sep="")
... | bsd-3-clause | R | |
5e1da3ff197518e9add131380dec3541d2cfd153 | Create lm-select.r | Sokel/R-shchu | lm-select.r | lm-select.r | rm(swiss)
swiss <- data.frame(swiss)
fit_full <- lm(Fertility ~ . , data = swiss)
summary(fit_full)
fit_reduced1 <- lm(Fertility ~ Infant.Mortality + Examination + Catholic + Education, data = swiss)
summary(fit_reduced1)
anova(fit_full, fit_reduced1)
fit_reduced2 <- lm(Fertility ~ Infant.Mortality + Agriculture +... | apache-2.0 | R | |
dd94f7a9211d620fd1b8e4d3697a30791dbfff89 | Add rough code indicative of live coding we'll do | jennybc/ggplot2-tutorial,MaryHe/ggplot2-tutorial,MaryHe/ggplot2-tutorial | gapminder-ggplot2.r | gapminder-ggplot2.r | library(ggplot2)
gdURL <- "http://tiny.cc/gapminder"
gDat <- read.delim(file = gdURL) # pick one
gDat <- read.delim("gapminderDataFiveYear.txt")
str(gDat)
ggplot(gDat, aes(x = gdpPercap, y = lifeExp)) # nothing to plot yet!
p <- ggplot(gDat, aes(x = gdpPercap, y = lifeExp)) # just initializes
p + geom_point()
#p + ... | mit | R | |
96854ea5efdc04879affaff002c2c490fac7b2d1 | Solve Difference in r | deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playgr... | solutions/uri/1007/1007.r | solutions/uri/1007/1007.r | input <- file('stdin', 'r')
a <- as.integer(readLines(input, n=1))
b <- as.integer(readLines(input, n=1))
c <- as.integer(readLines(input, n=1))
d <- as.integer(readLines(input, n=1))
result = sprintf("DIFERENCA = %d", a * b - c * d)
write(result, "")
| mit | R | |
4a942a4617256536a5cca691b3c1d8e7aa32abc6 | format conversion function | khufkens/phenor | R/flat_format.r | R/flat_format.r | #' Flatten the format as generated by format_phenocam()
#' and format_modis(). Flattening the file format allows
#' for substantial speed increases in optimization however
#' limits readability. Using the split functionality between
#' the format_*() functions and this function allows for easy
#' subsetting of datasets... | agpl-3.0 | R | |
2cf587ad96d5e4ab1ea39aee93cb044da69379dd | Add receiveBin function (plus small repo edits). | oerdnj/rapache,oerdnj/rapache,oerdnj/rapache,oerdnj/rapache,oerdnj/rapache,oerdnj/rapache,jeffreyhorner/rapache,jeffreyhorner/rapache,jeffreyhorner/rapache,jeffreyhorner/rapache,jeffreyhorner/rapache,jeffreyhorner/rapache,oerdnj/rapache | test/recBin.r | test/recBin.r |
# Canonical Test
hrefify <- function(title) gsub('[\\.()]','_',title,perl=TRUE)
scrub <- function(str){
if (is.null(str)) return('NULL')
if (length(str) == 0) return('length 0 string')
#cat("\n<!-- before as.character: (",str,")-->\n",sep='')
str <- try(as.character(str))
if (inherits(str,'try-error')) return('t... | apache-2.0 | R | |
3cdc1535fc26560cb8ef4399f0df9b1b8a821c3a | add demo.r | skefi/SpatialStress,skefi/SpatialStress | R/demo.r | R/demo.r | ## Example
## install.packages(c("moments"))
source("R/functions.r")
# creating an initial landscape object
initial <- init_landscape(states = c("+","0","-"), cover = c(0.4,0.1,0.5))
plot(initial)
plot(initial, cols = c("darkgreen", "grey70", "white"))
summary(initial)
# run simulation
parms_grazing <- list... | mit | R | |
73567de7fc29b3a48a5b52fef9daec499fc804e3 | test parsing | rebolsource/rebol-test,metaeducation/ren-c-test,rebolsource/rebol-test,metaeducation/ren-c-test | test-parsing.r | test-parsing.r | Rebol [
Title: "Test parsing"
File: %test-parsing.r
Author: "Ladislav Mecir"
Date: 30-Jan-2013/12:11:48+1:00
Purpose: "Test framework"
]
do %line-numberq.r
whitespace: charset [#"^A" - #" " "^(7F)^(A0)"]
; compatibility functions:
unless value? 'transcode [transcode: :load]
unless value? 'spec-of [spec-of: :th... | apache-2.0 | R | |
767834e90cd1088f3f2ee1822963f44f069b0133 | add regularization to master | robertzk/tundra,syberia/tundra | R/tundra_regularization.r | R/tundra_regularization.r | #' Tundra regularization wrapper
#
tundra_regularization_train_fn <- function(dataframe) {
cat("Training Regularized Logistic Regression model...\n")
library(glmnet)
regularization_args <- list()
indep_vars <- setdiff(colnames(dataframe), 'dep_var')
stopifnot(length(indep_vars) > 0)
defaults <- list... | mit | R | |
a2a443197c2389f1e412abf824f1308bfcdd485a | add R script for Ida Episperm 1 sRNA-seq | kqian/work | Work/Ida/sript.r | Work/Ida/sript.r | bamFls = list.files(path="./data/2013-10-11/bowtie/",pattern=".bam$",full.names=TRUE)
bedFls = list.files(path="./annotation/",pattern="hsa",full.names=TRUE)
source("scripts/dataAnalysis/snowCount.r")
data = snowCount(bamFls,bedFls,cpus=4)
###
gene.anno <- import("annotation/hsa_genome.gtf")
#miRBase v20
miRNA.anno<... | artistic-2.0 | R | |
0cf25ca62d8496b76acf0caac0ab4ee57b66f294 | Add SVM algorithm in R | a-holm/MachinelearningAlgorithms,a-holm/MachinelearningAlgorithms | Classification/SupportVectorMachine/regularSupportVectorMachine.r | Classification/SupportVectorMachine/regularSupportVectorMachine.r | # Support Vector Machine (SVM) classification for machine learning.
#
# SVM is a binary classifier. The objective of the SVM is to find the best
# separating hyperplane in vector space which is also referred to as the
# decision boundary. And it decides what separating hyperplane is the 'best'
# because the distance f... | mit | R | |
0581f4f7507e24f6297da69eadb42b87c6c652b6 | add tests for simple template filling | mschubert/clustermq,mschubert/clustermq,mschubert/clustermq | tests/testthat/test-0-util.r | tests/testthat/test-0-util.r | context("util")
test_that("template filler", {
tmpl = "this is my {{ template }}"
values = list(template = "filled")
filled = fill_template(tmpl, values)
expect_equal(filled, "this is my filled")
})
test_that("template default values", {
tmpl = "this is my {{ template | default }}"
values = li... | apache-2.0 | R | |
53c53e58f908af179712068d63b1acebde269aab | add integrated qsys (#36) | mschubert/clustermq,mschubert/clustermq,mschubert/clustermq | tests/testthat/test-qsys.r | tests/testthat/test-qsys.r | context("qsys")
w = create_worker_pool(1, qsys_id="multicore")
test_that("control flow", {
fx = function(x) x*2
result = Q(fx, x=1:3, workers=w)
expect_equal(result, as.list(1:3*2))
})
test_that("common data", {
fx = function(x, y) x*2 + y
result = Q(fx, x=1:3, const=list(y=10), workers=w)
ex... | apache-2.0 | R | |
acea03bcc1b4c2252726458e5ee568d6ed84e565 | Add utilities file. | pschulam-attic/sclero | R/util.r | R/util.r | dev_subset <- function(data, patientsplit) {
dev.patients <- unique(subset(patientsplit, dev == 1)$PtID)
subset(data, patient.id %in% dev.patients)
}
add_date_since <- function(data, date.var, since.var, patient.data, baseline.var) {
patient.baseline <- structure(as.Date(patient.data[[baseline.var]]),
... | mit | R | |
ebc552f2d5e686bb4c6ce2b36c607c76221d85bf | Create try.xgboost.v1.r | minesh1291/MachineLearning,minesh1291/MachineLearning,minesh1291/MachineLearning | myPracticeGCE/try.xgboost.v1.r | myPracticeGCE/try.xgboost.v1.r | #library(caret) # for dummyVars
#library(RCurl) # download https data
library(Metrics) # calculate errors
library(xgboost) # model
MultiLogLoss <- function(act, pred)
{
eps = 1e-15;
nr <- nrow(pred)
pred = matrix(sapply( pred, function(x) max(eps,x)), nrow = nr)
pred = matrix(sapply( pred, function(x) mi... | apache-2.0 | R | |
4f8b1a894924923804d4e3d5b01365b2b6d1d2e4 | Create remindo2qdnatool_conversion_script.r | ShKlinkenberg/remindo2qdnatool | remindo2qdnatool_conversion_script.r | remindo2qdnatool_conversion_script.r | # Remindo QDNA conversion script
# In Remindo export the raw results through an admin account in the "beheer omgeving"
# Select the opleiding of interest.
# Select the exam of interest.
# Click tab "Exporteren".
# Click "Resultaten".
# Select period exam startdate and time, end period current time.
# Click "Download ... | cc0-1.0 | R | |
7a82964a7f7b4a689f6da14b45b01df43e3f979c | Add main module file | klmr/ggplots | __init__.r | __init__.r | #' Pretty plotting module
export = import('./export', attach = 'export_from')
gg = import_package('ggplot2')
export_from(gg)
#
# Set a very minimal theme. Avoid chartjunk.
#
fonts = import('./fonts')
fonts$register_fonts(c('Roboto', 'Roboto Condensed'))
theme_set(theme_minimal() +
theme(panel.grid = elem... | apache-2.0 | R | |
89eab726b7a06580ab4afffec2b4a08b2768644e | Add R classification template | a-holm/MachinelearningAlgorithms,a-holm/MachinelearningAlgorithms | Classification/classificationTemplate.r | Classification/classificationTemplate.r | # R Classification template
# Importing the data set
dataset = read.csv('Social_Network_Ads.csv')
dataset = dataset[, 3:5]
# Splitting the Dataset into a Training set and a Test set
# install.packages('caTools')
# library(caTools)
set.seed(123) # choose random number, only same number for debugging
split = sample.spl... | mit | R | |
7587cb70cfe91587a49d452e5e92a9e607441824 | Create ggplot2_formatter.r | 1R151-1/R,jezdata/R,fdryan/R | ggplot2_formatter.r | ggplot2_formatter.r |
# ---------------------------------------------------------------------------------------------
# Formatting functions for ggplot graph axis
# ---------------------------------------------------------------------------------------------
#' Human Numbers: Format numbers so they're legible for humans
#' Use this in gg... | unlicense | R | |
9f6e56e1a2021519cc4d45aad8a5ca558129c4fe | Fix LAUNCH to properly work with argv-based CALL | codebybrett/ren-c,rgchris/ren-c,mbk/ren-c,codebybrett/ren-c,rgchris/ren-c,hostilefork/rebol,mbk/ren-c,kealist/ren-c,hostilefork/rebol,draegtun/ren-c,kealist/ren-c,mbk/ren-c,draegtun/ren-c,hostilefork/rebol,giuliolunati/ren-c,draegtun/ren-c,codebybrett/ren-c,kealist/ren-c,codebybrett/ren-c,hostilefork/rebol,codebybrett/... | src/mezz/mezz-control.r | src/mezz/mezz-control.r | REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Mezzanine: Control"
Rights: {
Copyright 2012 REBOL Technologies
REBOL is a trademark of REBOL Technologies
}
License: {
Licensed under the Apache License, Version 2.0
See: http://www.apache.org/licenses/LICENSE-2.0
... | REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Mezzanine: Control"
Rights: {
Copyright 2012 REBOL Technologies
REBOL is a trademark of REBOL Technologies
}
License: {
Licensed under the Apache License, Version 2.0
See: http://www.apache.org/licenses/LICENSE-2.0
... | apache-2.0 | R |
8f77d72e1df13ab75965a3ed1d97c86ce9585278 | Add stick breaking process. | jtobin/bnp | stick-breaking-process/src/sbp.r | stick-breaking-process/src/sbp.r | sbp = function(n, a) {
bundle = list(0, numeric(0))
for (j in seq(n)) {
bundle = snap(bundle[[1]], bundle[[2]], a)
}
bundle[[2]]
}
snap = function(acc, bun, a) {
b = rbeta(1, 1, a)
stick = exp(log(b) + acc)
nacc = log (1 - b) + acc
nbun = c(bun, stick)
list(nacc, nbun)
}
| mit | R | |
85679ea418e7f21f343c8462da09c2c92a52654f | Create scatterplot_with_histograms.r | hclimente/wisdom,hclimente/wisdom,hclimente/wisdom | r/examples/scatterplot_with_histograms.r | r/examples/scatterplot_with_histograms.r | library(ggplot2)
library(grid)
library(gridExtra)
g <- ggplot(switch.psi,aes(x=Normal,y=Tumor,color=What,shape=Type)) +
geom_point() +
smartas_theme() +
labs(x="", y="") +
scale_color_manual(values=c("Tumor no switch"="#7fc97f", "Normal"="#beaed4", "Tumor switch"="#fdc086"))
n <- ggplot(switch.psi,aes(x=Norm... | mit | R | |
5d79759e6be56b2251198b8ffbfb3d7c381b5d46 | Create cfsv2_ts_ncdc_sqlite.r | dpbroman/hydroforecast | cfsv2_ts_ncdc_sqlite.r | cfsv2_ts_ncdc_sqlite.r | ###########################################
# cfsv2_ts_ncdc_sqlite.r
# processes grib2 files from ncdc cfsv2 archive
# saves data to sqlite database
# saves data in rdata format
###########################################
library(data.table)
library(dplyr)
library(ggplot2)
library(lubridate)
library(stringr)
library(t... | mit | R | |
3ddd61c4a9db50c670643699245532b8e4a52397 | add linear model | Chilverslab/Lunch_and_Learn,Chilverslab/Lunch_and_Learn,Chilverslab/Lunch_and_Learn | linearModel.r | linearModel.r | library(car)
| mit | R | |
75d6287ade9aeddb7991843f1e78934cdb5cf134 | Add WRAP function | codebybrett/ren-c,kealist/ren-c,codebybrett/ren-c,giuliolunati/ren-c,mbk/ren-c,giuliolunati/ren-c,draegtun/ren-c,hostilefork/rebol,draegtun/ren-c,rgchris/ren-c,rebolsource/r3,mbk/ren-c,mbk/ren-c,rebolsource/r3,rgchris/ren-c,draegtun/ren-c,giuliolunati/ren-c,rgchris/ren-c,kealist/ren-c,hostilefork/rebol,kealist/ren-c,rg... | src/mezz/mezz-control.r | src/mezz/mezz-control.r | REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Mezzanine: Control"
Rights: {
Copyright 2012 REBOL Technologies
REBOL is a trademark of REBOL Technologies
}
License: {
Licensed under the Apache License, Version 2.0
See: http://www.apache.org/licenses/LICENSE-2.0
... | REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Mezzanine: Control"
Rights: {
Copyright 2012 REBOL Technologies
REBOL is a trademark of REBOL Technologies
}
License: {
Licensed under the Apache License, Version 2.0
See: http://www.apache.org/licenses/LICENSE-2.0
... | apache-2.0 | R |
0ea5fb3a2a9d78a9de7356779260e5332602bd2a | Add test cases for module file search | klmr/modules,klmr/modules | inst/tests/test-path.r | inst/tests/test-path.r | context('Find module path relative files')
test_that('module_file works in global namespace', {
expect_that(module_file(), equals(getwd()))
expect_true(nchar(module_file('run-all.r')) > 0)
throws_error(module_file('XXX-does-not-exist', mustWork = TRUE),
'no file found')
})
test_that('modu... | apache-2.0 | R | |
2d39fc68c3b1955b1d4aaf5f66b066408b4bb8e3 | Create readme.rd | wgong/open_source_learning,wgong/open_source_learning,wgong/open_source_learning | projects/Open_Food/image/readme.rd | projects/Open_Food/image/readme.rd | apache-2.0 | R | ||
e0c3c45e43a95eae2cb26fb76bd74dbf0db1e8bc | Create 2stations.r | data-henrik/db2-bluemix-r | 2stations.r | 2stations.r | ########### R script to analyze historic weather data for temperature
## Connection handle con to BLU for Cloud data warehouse is provided already
## For plotting, we are using ggplot2 package
##
## Data for multiple stations is shown in different colors
##
library(ggplot2)
library(bluR)
## initialize DB2 connection ... | apache-2.0 | R | |
a7c64a67afcfcd0ed2b6963082c4b2faa16d66a5 | test for listvar as term arg | metaborg/strategoxt,lichtemo/strategoxt,lichtemo/strategoxt,Apanatshka/strategoxt,lichtemo/strategoxt,metaborg/strategoxt,Apanatshka/strategoxt,Apanatshka/strategoxt,lichtemo/strategoxt,metaborg/strategoxt,Apanatshka/strategoxt,Apanatshka/strategoxt,metaborg/strategoxt,metaborg/strategoxt,lichtemo/strategoxt | strc/spec/test1/test45.r | strc/spec/test1/test45.r | module test45
strategies
main = foo(|[1, 2, 3])
foo(|xs*) = !xs*
bar(|xs*) : _ -> [xs*]
foobar(|x) = !x ; {xs* : ?xs*; !xs*}
foobar(|x) = !x ; ?xs*; !xs* | apache-2.0 | R | |
5299b8ad86e2d0f85e75899895d5edac8e76ce38 | Create trendyitunes.r | RuxuePeng/openCPUapp,RuxuePeng/openCPUapp,RuxuePeng/openCPUapp | R/trendyitunes.r | R/trendyitunes.r | # function to grab trendy itunes shows
trendyitunes = function(){
iTunes = fromJSON("https://rss.itunes.apple.com/api/v1/us/tv-shows/top-tv-episodes/25/non-explicit/json")
pool = data.frame(Name = iTunes$feed$results$artistName,
Detail = paste("Episode:",iTunes$feed$results$name),
... | mit | R | |
6df86c4383378c0d27d902d512b9b1e61b44d2fa | Create ui.r | aleksandrov2/APPR-2015-16 | shiny/ui.r | shiny/ui.r | # This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://www.rstudio.com/shiny/
#
library(shiny)
source("lib/uvozi.zemljevid.r", encoding = "UTF-8")
source("podatki/podatki.r", encoding = "UTF-8")
source("uvoz/uvozi.r", encod... | mit | R | |
1d90eeb63c577beb2cc40dda404d39b6ad0a7975 | Create geom_boxplot_star.r | hclimente/ggstars | R/geom_boxplot_star.r | R/geom_boxplot_star.r | mit | R | ||
43d3c6abde5fb41a7298a10edb8704b98c7023e6 | Add script to make inputs for phast | e3bo/2015phylo,e3bo/2015phylo,e3bo/2015phylo | src/make-regression-inputs.r | src/make-regression-inputs.r | #!/usr/bin/Rscript
library(ape)
tree <- read.nexus('mcc.tree')
nms <- tree$tip.label
abs <- sapply(strsplit(nms, '_'), '[[', 1)
ind <- match(abs, state.abb)
reg <- state.region[ind]
regDNA <- factor(reg,
levels=c("Northeast", "South", "North Central", "West"),
labels=c('A', 'T', 'C'... | cc0-1.0 | R | |
54fb5bb5c87414c0a149e24f2cdd67b597452ce8 | Create correlation.r | Sokel/R-shchu | correlation.r | correlation.r |
df <- mtcars
cor.test(df$mpg, df$hp)
fit <- cor.test(df$mpg, df$hp)
# OR
fit <- cor.test(~ mpg + hp, df)
fit$statistic
fit$p.value
str(fit)
plot(df$mpg, df$hp)
library(ggplot2)
ggplot(df, aes(x = mpg, y = hp, col=factor(cyl)))+
geom_point(size = 5)
df_numeric <- df[,c(1,3:7)]
pairs(df_numeric)
cor(df_numeric)
... | apache-2.0 | R | |
b78f38820ffba22f0f8edc2f4dff5e99dacfb91f | Create R script for plotting results related to the master thesis. | monsendag/goldfish,ntnu-smartmedia/goldfish,ntnu-smartmedia/goldfish,monsendag/goldfish,monsendag/goldfish,ntnu-smartmedia/goldfish | rscripts/master-thesis.r | rscripts/master-thesis.r | #!/usr/bin/env Rscript
source('functions.r')
results_path <- '~/Projects/goldfish/results'
graphs_path <- '~/Projects/goldfish/graphs'
# list of data set files to plot
vtt <- list()
vtt$cluster7 <- "2013-11-28-142459-VTT36k-7 clusters.csv"
vtt <- lapply(vtt, get_dataset, folder=results_path)
#Threshold 0.4 Tanim... | mit | R | |
4fdea443828ab3ab8be1d9e2c30b80b3ff185a1c | Create app.r | suraj-deshmukh/myCodes,suraj-deshmukh/myCodes,suraj-deshmukh/myCodes | app.r | app.r | library(shiny)
library(shinydashboard)
library(shinyBS)
header <- dashboardHeader(title="MlR")
body <- dashboardBody(
mainPanel(
tabsetPanel(
tabPanel("Classification")
)
)
)
sidebar <- dashboardSidebar(
fileInput("file","Upload CSV",accept=c("text/csv",".csv")),
div(style="displa... | mit | R | |
33be65382da3005a442382fad1115296857f0442 | convert string to seconds since epoch | willb/reminders | time/str2ts.r | time/str2ts.r | # converts a string representation of a time into seconds since the epoch
str2ts <- function(s) {
return(as.numeric(as.POSIXct(s), origin="1970-01-01"))
}
attr(str2ts, "docstring") <- "converts a string representation of a time into seconds since the epoch" | unlicense | R | |
73b373289caa2e323edd170af32015802ba5c562 | Add index. | snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3 | q3/docs/index.rd | q3/docs/index.rd | =QMAIL3のドキュメント
==目次
*((<スパムフィルタ|URL:JunkFilter.html>))
*設定
*((<スパムフィルタの設定|URL:ConfigJunkFilter.html>))
| mit | R | |
864440abffc82e3fbdac5cfd9c47142b441fc363 | Add multivariate posterior skeleton. | jtobin/bnp | finite-gaussian-mixture/src/fmm_multivariate_conditional.r | finite-gaussian-mixture/src/fmm_multivariate_conditional.r | require(dplyr)
require(gtools)
require(mvtnorm)
require(reshape2) # FIXME move to sim module
source('fmm_multivariate_generative.r')
# FIXME (jtobin): move to simulation module
set.seed(42)
# FIXME (jtobin): move to simulation module
dimension = 2
# FIXME (jtobin): move to simulation module
config = list(
k = 3... | mit | R | |
105d08f77adf736170f597d0bd7f4a3a51940094 | Create DESeq_Tet1KD.r | crazyhottommy/some-unorganized-old-scripts,crazyhottommy/some-unorganized-old-scripts,crazyhottommy/some-unorganized-old-scripts | R_scripts/DESeq_Tet1KD.r | R_scripts/DESeq_Tet1KD.r | # read GEO data sets from NCBI by GEOquery
setwd("/home/tommy/Tet1")# set the working directory
library(Biobase)
library(GEOquery)
# only set the GSEMatrix to FALSE can it be parsed for later use of function like
# Meta(gse)
gse<- getGEO('GSE26830', GSEMatrix=FALSE, destdir=".")
Meta(gse)
names(GSMList(gse))
# I ... | mit | R | |
020d0334832d321dab8c1b151b10f4835812d1cc | Read Me. | Zaid-Al-Omari/Telegram.Bot.Mvc | Telegram.Bot.Mvc/README.rd | Telegram.Bot.Mvc/README.rd | An MVC-like framework to create Telegram Bots.
- Just like Asp.net MVC.
- Establish Command Routes.
- Create Bot Controllers.
- Create Actions To Handle Specific Commands.
- Automatic Paramter Binding.
- Works Both For WebHooks & Stand Alone. | mit | R | |
efa8d2438035dbcd3e45bc3a36e87c83e585d7b1 | Add files via upload | saeedsk/twitter | retweets.r | retweets.r |
packages <- c("ggplot2", "lubridate", "RTextTools", "bit64", "stringr",
"date", "scales", "textcat", "NLP", "SnowballC", "textcat",
"data.table", "tm.plugin.dc", "igraph")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
install.packages(setdiff(packages, rowname... | mit | R | |
855b6c95702d16c2c6e56a2dc6a20b05da726b9d | Create AndersonDarlingBootstrap.r | bolus123/Statistical-Process-Control | AndersonDarlingBootstrap.r | AndersonDarlingBootstrap.r |
AD.gamma.bootstrap.test <- function(X, pars, sim = 10000) {
integrand.f <- function(u, Fn, pars) {
(Fn(qgamma(u, shape = pars[1], scale = pars[2])) - u) ^ 2 / u / (1 - u)
}
A2.f <- function(n, Fn, pars) {
n * integrate(integrand.f, 0, 1, Fn = Fn, pars = pars, subdivisions = ... | apache-2.0 | R | |
44031587b68a3f85ea9d054189636e2095c9f5ff | Create area_under_curve.r | nairvinayv/Rscripts | area_under_curve.r | area_under_curve.r | #Code for calculating the area under curve using Trapezoidal Integration
#Input File has single column containing Values
args <- commandArgs(trailingOnly = TRUE);
raw_data = read.csv(args[1])
data <- cbind(1:dim(raw_data)[1],raw_data)
require(pracma)
AUC = trapz(as.vector(t(data[1])),as.vector(t(data[2])))
disp(AUC)
| mit | R | |
980f12d3d6f5d910d1d540341d672b9d82eb8b16 | Add R Interpolation Research Document. | alexandre-normand/glukit,alexandre-normand/glukit,alexandre-normand/glukit,alexandre-normand/glukit | doc/glukit-score-interpolation.r | doc/glukit-score-interpolation.r | data <- c(xy.coords(0,100), xy.coords(2556288,0), xy.coords(1364832,30), xy.coords(711936,50), xy.coords(498816,60), xy.coords(362880,65), xy.coords(128640,75), xy.coords(88120,84))
data <- xy.coords(list(0, 88120, 128640, 362880, 498816, 711936, 1364832, 2556288), list(100, 84, 75, 65, 60, 50, 30, 0))
f <- splinefun(... | mit | R | |
95afa6b2afcd771fcd60928edd3f78107e68a772 | Create MissingAge.r | lingcheng99/Kaggle-Titanic | MissingAge.r | MissingAge.r | > train=read.csv('train.csv',header=T)
> train1=train
> train1$Pclass=as.factor(train1$Pclass)
> train1$Survived=as.factor(train1$Survived)
> test=read.csv('test.csv',header=T)
> Survived=rep('None',nrow(test))
> test1=data.frame(test,Survived)
> test2=test1
> test2$Pclass=as.factor(test2$Pclass)
#Write a function to... | mit | R | |
356f8131eff1f17a890aa87286918ced1bbaff29 | Create treeMaps.r | tessam30/FoodForPeace | treeMaps.r | treeMaps.r | # Clear workspace
rm(list=ls())
# load libraries
library(ggplot2)
library(dplyr)
library(treemap)
library(RColorBrewer )
C:\Users\t\Box Sync
setwd("C:/Users/t/Box Sync/FoodForPeace/R/")
d <- read.csv("FFPdata0912.csv", sep = ",", header = TRUE)
names(d)
d$Food.Aid <- round(d$decTotal/1000, 0)
# d$Food.Aid <- format... | apache-2.0 | R | |
5b19ec1804cfb9989101e485edfd2e0d022a21c2 | Add a testing file for Windows | Pointillistic/rebol-lang,zsx/r3,zsx/r3,zsx/r3,zsx/r3,Pointillistic/rebol-lang,Pointillistic/rebol-lang,Pointillistic/rebol-lang | make/tests/ms-drives.r | make/tests/ms-drives.r | REBOL []
msvcrt: make library! %msvcrt.dll
getdrives: make routine! compose/deep [
[return: [uint32]]
(msvcrt) "_getdrives"
]
maps: getdrives
i: 0
while [i < 26] [
unless zero? maps and shift 1 i [
print rejoin [to char! (to integer! #"A") + i ":"]
]
++ i
]
close msvcrt
| apache-2.0 | R | |
0652a82cc211b2ea7884c4e5db78903712c24673 | Install script for most common/useful packages | jkarl/LandscapeToolbox,jkarl/LandscapeToolbox,jkarl/LandscapeToolbox | package_installation.r | package_installation.r | ###############################################
### COMMONLY USED PACKAGES IN AIM R SCRIPTS ###
###############################################
#### DATA WRANGLING ####
install.packages(
c(
"dplyr", ## Notably useful for data frame manipulation with group_by(), summarize(), and mutate() and the piping operator %... | cc0-1.0 | R | |
aab4b064b91f1a0ca4faa4fb5d2b413b26c4ba28 | Solve Average 1 in r | deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playground,deniscostadsc/playgr... | solutions/uri/1005/1005.r | solutions/uri/1005/1005.r | input <- file('stdin', 'r')
a <- as.double(readLines(input, n=1))
b <- as.double(readLines(input, n=1))
result = sprintf("MEDIA = %.5f", (a * 3.5 + b * 7.5) / 11.0)
write(result, "")
| mit | R | |
3fea6321cf8b0ad696de42dad1d60cd0cdab8dab | Add files via upload | Regional-Fish-Modeling/Sampling-Design,Regional-Fish-Modeling/Sampling-Design,Regional-Fish-Modeling/Sampling-Design | southern_Apps/bkt_trend_power_wo_weather_cov_model.r | southern_Apps/bkt_trend_power_wo_weather_cov_model.r | model{
# Abundance model
for(i in 1:nSites){
for(j in 1:nYears){
N[i,j] ~ dpois(lambda[i,j])
log(lambda[i,j]) <- mu + trend*(j-1) +
site.ran[i] + year.ran[j] + log(siteLength[i,j]) + eps[i,j]
}
}
## priors
mu ~ dnorm(0, 0.01) # overall intercept
tren... | mit | R | |
08275f80ff3a6d65d02a0e705658c3628f2fd2c5 | Add tests for NHGIS | mnpopcenter/ripums,mnpopcenter/ripums | tests/testthat/test_nhgis.r | tests/testthat/test_nhgis.r | context("NHGIS")
# Manually set these constants...
rows <- 71
vars_data <- 17
vars_data_shape <- 24
d6z001_label <- "1989 to March 1990"
d6z001_var_desc <- "Year Structure Built (D6Z)"
pmsa_first2_sort <- c("Akron, OH PMSA", "Anaheim--Santa Ana, CA PMSA")
test_that(
"Can read NHGIS extract (data only)", {
nhgis... | mpl-2.0 | R | |
b0a4b4c11ec56ca29afcab71578947e219577341 | Create wetbulb_stull.r | alfcrisci/rBiometeo,alfcrisci/rBiometeo | R/wetbulb_stull.r | R/wetbulb_stull.r | mit | R | ||
a5d50a8b92b1bd72a523242b6fe21f4f5c450998 | Create SQL_R.r | fdryan/R,1R151-1/R,jezdata/R | SQL_R.r | SQL_R.r |
# ---------------------------------------------------------------------------------------------
# Functions for working with SQL in R
# ---------------------------------------------------------------------------------------------
# Read in a SQL file from the wroking directory
# e.g. read.sql("sql/test.sql")
read.s... | unlicense | R | |
98e0200d80512d1acdc93f720a1a7cdceecf64ef | Create Main.r | bgweber/RServer,bgweber/RServer,bgweber/RServer,bgweber/RServer | src/samples/helloworld/Main.r | src/samples/helloworld/Main.r | cat("Hello World!")
| bsd-3-clause | R | |
74ffd43baecb13d4555a13672c8821af2f464f39 | Create BreastfeedingSleepFakeCleaningCode.r | wibeasley/RAnalysisSkeleton,wibeasley/RAnalysisSkeleton,wibeasley/RAnalysisSkeleton | BreastfeedingSleepFakeCleaningCode.r | BreastfeedingSleepFakeCleaningCode.r | #The following code is meant to provide the z-scores for the collection of sleep statistics within
# each feeding scenario.
sleepstats<-as.numeric(ds[,8])
sleepstats.scenario1<-sleepstats[1:45]
sleepstats.scenario2<-sleepstats[46:90]
sleepstats.scenario3<-sleepstats[91:135]
sleepZscenario1<-as.numeric(scale(sleepsta... | mit | R | |
0916615890bd24e80bff2a2777e25b7aae821960 | add code for filter coefficients to adc_recorder_trigger | pavel-demin/red-pitaya-notes,fbalakirev/red-pitaya-notes,pavel-demin/red-pitaya-notes,fbalakirev/red-pitaya-notes,fbalakirev/red-pitaya-notes,fbalakirev/red-pitaya-notes,pavel-demin/red-pitaya-notes,pavel-demin/red-pitaya-notes,pavel-demin/red-pitaya-notes,pavel-demin/red-pitaya-notes,fbalakirev/red-pitaya-notes,fbalak... | projects/adc_recorder_trigger/fir_0.r | projects/adc_recorder_trigger/fir_0.r | library(signal)
# CIC filter parameters
R <- 32 # Decimation factor
M <- 1 # Differential delay
N <- 6 # Number of stages
Fo <- 0.22 # Pass band edge
# fir2 parameters
k <- kaiserord(c(Fo, Fo+0.02), c(1, 0), 1/(2^16), 1)
L <- ... | mit | R | |
b6a681ba72b2b3e29533cc2902488903a9e4aa49 | Create ggplot_significance.r | hclimente/wisdom,hclimente/wisdom,hclimente/wisdom | r/ggplot_significance.r | r/ggplot_significance.r | geom_bar_significance <- function(categories,ranges,barsize=2, pairAxis=0.5){
# categories: all factors to be plotted
# ranges: contains the data for the categories for which an arc will be drawn
# category: name of the category
# y: height of the two bars
require(ggplot)
require(plyr)
# create base... | mit | R | |
c2a24eac1f03c97d00a415057828a49edfc9b090 | Create runtopicmodel.r | kasperwelbers/corpus-tools,kasperwelbers/corpus-tools | runtopicmodel.r | runtopicmodel.r | # 'command line' script to run a topic model
# Usage: Rscript runtopicmodel.r /path/to/dtm.rdata /path/to/model.out.rdata K alpha
#
# dtm.rdata should be an R data file containing a dtm variable (e.g. created by save(dtm, file="dtm.rdata"))
# model.out will be an R data containing a variable m with the fitted model
# K... | mit | R | |
7889ce223288fe8e78d90e5b92dab03e266c9d27 | Create ggplot_tiles_fisher-test.r | hclimente/wisdom,hclimente/wisdom,hclimente/wisdom | r/examples/ggplot_tiles_fisher-test.r | r/examples/ggplot_tiles_fisher-test.r | ggplot() +
geom_tile(data=subset(test, Significant==0),aes(x=tumor,y=feature,fill=oddsratio), color = "white") +
geom_tile(data=subset(test, Significant==1),aes(x=tumor,y=feature,fill=oddsratio), color = "black", size=2) +
scale_fill_gradient2(low = "#2166ac", high = "#d94801", mid = "white", space = "Lab", midp... | mit | R | |
7422fc8fa8a384074ca99fe10b14a2129c1498ba | add copy number calc method | sestaton/sesbio,sestaton/sesbio,sestaton/sesbio,sestaton/sesbio | gene_annotation/gene_annotation_R_scripts/cnmops.r | gene_annotation/gene_annotation_R_scripts/cnmops.r | library(cn.mops)
BAMFiles <- list.files(pattern="sort.bam$")
# Setting the window length is important because the default will be extremely large.
bamDataRanges <- getReadCountsFromBAM(BAMFiles, refSeqName = c("Ha4", "Ha16"), mode = "paired", WL = 100)
res <- cn.mops(bamDataRanges, normType = "mean")
res <- calcInteg... | library(cn.mops)
BAMFiles <- list.files(pattern=".bam$")
bamDataRanges <- getReadCountsFromBAM(BAMFiles, refSeqName = c("Ha4", "Ha16"), mode = "paired", WL = 100)
#bamDataRanges <- getReadCountsFromBAM(BAMFiles, refSeqName = c("Ha4", "Ha16"), mode = "paired")
res <- cn.mops(bamDataRanges, normType = "mean")
segm <- a... | mit | R |
d236b810779a6788c03dc36470f0ff1b80b4a01a | Add arguments example | tisp-lang/tisp,raviqqe/tisp,tisp-lang/tisp,raviqqe/tisp,raviqqe/tisp | examples/args.r | examples/args.r | ((\ (x1 x2 (x3 123) (x4 456) *args y1 (y2 123) y3 (y4 456) **kwargs) x)
1 2 3 4 *list * y1 123 y3 456 ** {'y5 123 'y6 456})
| mit | R | |
35e19b891f7d5cbf5cd3ced7af1d5a981e6a9612 | add data prepping script to begin deduplication | e3bo/2015pedv,e3bo/2015pedv,e3bo/2015pedv,e3bo/2015pedv | src/data-prep.r | src/data-prep.r | #!/usr/bin/Rscript
library(igraph)
library(maps) # for state.fips
library(sp)
Sys.setlocale("LC_TIME", "C") #Needed for identical()
Sys.setlocale("LC_COLLATE", "C")
## Get case data
dataDir <- '.'
GetCaseData <- function(){
fn <- file.path(dataDir, 'PEDvweeklyreport-state-ts-01-08-14.csv')
ret <- read.csv(fn)
... | mit | R | |
062cd696480186b26b852ee177e8df893fc7eced | Implement `module_file` function | klmr/modules,klmr/modules | R/module_file.r | R/module_file.r | #' Find the full file names of files in modules
#'
#' @param ... character vectors of files or subdirectories inside a module; if
#' none is given, returns the root directory of the module
#' @param module a module environment (default: current module)
#' @param mustWork logical; if \code{TRUE}, an error is raised if ... | apache-2.0 | R | |
41c28dc5c307893de6235ae051a2a5d99ee8f715 | Create answer.r | neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu/Paiza-POH-MyAnswers,neetsdkasu... | POH6plus/answer.r | POH6plus/answer.r | strConcat <- function(x,y) {
paste(c(x,y), collapse="")
}
strReverse <- function(x) {
paste(rev(strsplit(x, NULL)[[1]]), collapse='')
}
zz <- file("stdin")
ww <- readLines(zz)
n <- as.integer(ww[1])
ww <- sort(ww[2:(n+1)])
f <- ""
e <- ""
cc <- array("", dim=c(n))
ci <- 1
for (i in 1:n) {
if (ident... | mit | R | |
bf7444b6d4c6b6ef1cbb3b6f86bdf836d9548898 | Create batch-consistency-plot.r | ShaopengLiu1/Atac-seq_Quality_Control_pipe,ShaopengLiu1/Atac-seq_Quality_Control_pipe,ShaopengLiu1/Atac-seq_Quality_Control_pipe | code_collection/idr_folder/batch-consistency-plot.r | code_collection/idr_folder/batch-consistency-plot.r | # 1-20-10 Qunhua Li
#
# This program first plots correspondence curve and IDR threshold plot
# (i.e. number of selected peaks vs IDR) for each pair of sample
#
# usage:
# Rscript batch-consistency-plot-merged.r [npairs] [output.dir] [input.file.prefix 1, 2, 3 ...]
# [npairs]: integer, number of consistency analyses
# ... | mit | R | |
4d373e571d23ae75e9e44f3e1927e799f97f24a7 | add tests placeholder | robertzk/syberiaStages,FeiYeYe/syberiaStages | inst/tests/test-placeholder.r | inst/tests/test-placeholder.r | # placeholder
| mit | R | |
82de3b8b98290a99a19fef6017aaa3d28faa8e50 | Create get_cfsv2_ncdc.r | dpbroman/hydroforecast | get_cfsv2_ncdc.r | get_cfsv2_ncdc.r | ###########################################
# get_cfsv2.r
# pulls cfsv2 forecasts from NCDC archive
# subsets to gbm and africa domains
# pulls out precip. surface temp, winds, and latent
# heat flux
# pulling all runs, 0-18z init times, out to 60 days (1440 hours)
###########################################
start_time... | mit | R | |
d7c296f19ffd108a5f0c8a1bed94e3cc76775a53 | Create mtcars.r | bgweber/RServer,bgweber/RServer,bgweber/RServer,bgweber/RServer | tasks/userDemo/mtcars.r | tasks/userDemo/mtcars.r | str(mtcars)
print("Sleeping for 20 seconds")
Sys.sleep(15)
print("Saving RData file")
dir.create("C:/wamp/www/RServer/reports/mtcars")
save(mtcars, file = "C:/wamp/www/RServer/reports/mtcars/mtcars.RData")
fit <- lm(mpg~am + wt + hp, data = mtcars)
summary(fit)
print("Saving Model")
Sys.sleep(10)
save(fit, file... | bsd-3-clause | R | |
6ba633aba399735a8f4a6299678639010e828516 | Create altimetry_processing.r | dpbroman/floodforecasting | altimetry_processing.r | altimetry_processing.r | #######DESCRIPTION###############################
#processes raw altimetry data from Charon Birkett UMd
#outputs rdata object and csv
#################################################
##load libraries
library(dplyr)
library(data.table)
library(readr)
library(tidyr)
nasa_base_date = as.Date('1958-01-01')
##user inputs
#... | mit | R | |
f533b6899ddc40165b8a915b6c38e613eba0f8eb | 更新：第四章fig4-15 | shuaimeng/r | thesis/chap4/fig4-15.r | thesis/chap4/fig4-15.r | dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')
library(rJava)
setwd("/Users/mengmengjiang/all datas/chap4")
library(xlsx)
#读取数据
q2 <- read.xlsx("dvsfv.xlsx", sheetName = "q15", header = TRUE)
#q3 <- read.xlsx("dvsfv.xls", sheetName = "q27", header = TRUE)
#q4 <... | mit | R | |
2b0843c9872ced8b51666fa46a425c3dea6331ae | Add "annual_gauge.r", which displays column chart for annual flow, in acre feet, at the specified gauge, compared to mean and median for historic record | johnrfleck/water-tools | annual_gauge.r | annual_gauge.r | # download and summarize annual flow at any gauge
# tutorial here: https://owi.usgs.gov/R/dataRetrieval.html#1
library(dataRetrieval)
library(tidyverse)
library(lubridate)
#get gauge number
siteNo <- readline(prompt="Enter a gauge number: ")
#siteNo <- "08330000"
# get station metadata
gauge_meta <- readNWISsite(s... | mit | R | |
457c5c5f07e1a5c9b8c5f33878cc46e4f6a71c34 | Add HDP. | jtobin/bnp | hierarchical-dirichlet-process/src/hdp.r | hierarchical-dirichlet-process/src/hdp.r | BNP_DIR = "/Users/jtobin/projects/bnp"
DP_SRC = paste(BNP_DIR, "dirichlet-process/src/dp.r", sep = "/")
source(DP_SRC)
hdp = function(n, a, h, n1, n2, a1, a2) {
g0 = dp(n, a, h)
h0 = function() { sample(g0[[1]], size = 1) }
g1 = dp(n1, a1, h0)
g2 = dp(n1, a1, h0)
h1 = function() { sample(g1[[1]], size = 1)... | mit | R | |
27a2b5ae39333035d43a1de13603155366d6341d | add bind tests | mschubert/narray,mschubert/narray | tests/testthat/test_bind.r | tests/testthat/test_bind.r | context("bind")
test_that("vector", {
expect_equal(bind(list(1:2, 3:4), along=1),
t(bind(list(1:2, 3:4), along=2)),
cbind(1:2, 3:4))
})
test_that("keep names", {
x = setNames(1:2, letters[1:2])
m = bind(list(x,x), along=2)
expect_equal(names(x), rownames(m))
col... | apache-2.0 | R | |
73a7eb761aa824c714642a5d634d069a4378f17e | Create src_teradata.r | xiaodaigh/teradata.dplyr | R/src_teradata.r | R/src_teradata.r | #library("teradataR")
# library("RODBC")
# library("dplyr")
# library(data.table)
# library("assertthat")
#' td.table - a R reference to Teradata table
td.table <- function(con, table, database = "") {
if (missing(database) || is.null(database) || nchar(database) == 0) {
obj <- gettextf("\"%s\"", table)
} els... | mit | R | |
2e06c638c36b4694034c6308c7d2fff3c0e65ca9 | Add regression template for R | a-holm/MachinelearningAlgorithms,a-holm/MachinelearningAlgorithms | Regression/regressionTemplate.r | Regression/regressionTemplate.r | # Regression template for machine learning.
# Importing the data set
dataset = read.csv('Position_Salaries.csv')
dataset = dataset[2:3]
# Splitting the Dataset into a Training set and a Test set
# install.packages('caTools')
# library(caTools)
set.seed(123) # choose random number, only same number for debugging
split... | mit | R | |
a15cd60f6bac9b2794708aaa040f391f468c7f01 | Use last "."-separated path component as extension | earl/rebol3 | scripts/shttpd.r | scripts/shttpd.r | REBOL [title: "A tiny static HTTP server" author: 'abolka date: 2009-11-04]
code-map: make map! [200 "OK" 400 "Forbidden" 404 "Not Found"]
mime-map: make map! [
"html" "text/html" "css" "text/css" "js" "application/javascript"
"gif" "image/gif" "jpg" "image/jpeg" "png" "image/png"
"r" "text/plain" "r3" "te... | REBOL [title: "A tiny static HTTP server" author: 'abolka date: 2009-11-04]
code-map: make map! [200 "OK" 400 "Forbidden" 404 "Not Found"]
mime-map: make map! [
"html" "text/html" "css" "text/css" "js" "application/javascript"
"gif" "image/gif" "jpg" "image/jpeg" "png" "image/png"
"r" "text/plain" "r3" "te... | apache-2.0 | R |
92c5969f1766107258622a1d2ef8b6cf03b09258 | Create README.rd | acm-csuf/icpc,acm-csuf/icpc,acm-csuf/icpc | 2013-10-10-Playing_With_Wheels/README.rd | 2013-10-10-Playing_With_Wheels/README.rd | Link to problem on UVa: <a href="http://uva.onlinejudge.org/index.php?option=com_onlinejudge&Itemid=8&page=show_problem&problem=1008" target="_blank">10067 - Playing with Wheels</a>
| mit | R | |
8cac9db6d961a73d0cfffea45edb34da51c89b74 | Add new report template | hkaju/Ising2D,hkaju/Ising2D,hkaju/Ising2D | templates/report.template.r | templates/report.template.r | pdf("{filename}")
data <- read.csv("results.csv", header=T)
par(mfrow=c(2,2))
plot(data$T, data$M, xlab="Temperature", ylab="Magnetization")
plot(data$T, data$E, xlab="Temperature", ylab="Energy")
plot(data$T, data$Xb, xlab="Temperature", ylab="Magnetic susceptibility")
plot(data$T, data$Xt, xlab="Temperature... | mit | R | |
2ab8ab4200d59ef1af2db70d7c7b8c7eb8778a7d | Add some finite mixture model spec. | jtobin/bnp | dirichlet-process-mixture/src/fmm.r | dirichlet-process-mixture/src/fmm.r | # finite gaussian mixture model
#
# a ~ inverse-gamma(1, 1)
# p | a ~ symmetric-dirichlet(a)
# c | p ~ multinomial(p)
# l ~ gaussian(mu_y, var_y)
# r ~ gamma(1, prec_y)
# mu | c, l, r ~ gaussian(l, r^-1)
# s | c, b, w ~ gamma(b, w^-1)
# b ... | mit | R | |
5f10fd5dc4c6409364d57815c6cfc0ee1eb9958b | add R example code | dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerleau/yarra-valley,dustypomerle... | code-examples/r.r | code-examples/r.r | tbl <- read.table(file.choose(),header=TRUE,sep=',')
population <- tbl[c("NAME","POPESTIMATE2009","NPOPCHG_2009")]
smallest.state.pop <- min(population$POPESTIMATE2009)
print(population[population$POPESTIMATE2009==smallest.state.pop,])
#utility functions
readinteger <- function()
{
n <- readline(prompt="Enter an in... | mit | R | |
6fae2984da1917ec90ea343f579784cac43dd168 | Add quick and dirty match count vs. cache misses plot. | danluu/BitFunnel,BitFunnel/BitFunnel,danluu/BitFunnel,BitFunnel/BitFunnel,BitFunnel/BitFunnel,danluu/BitFunnel,danluu/BitFunnel,danluu/BitFunnel,BitFunnel/BitFunnel,BitFunnel/BitFunnel,danluu/BitFunnel,BitFunnel/BitFunnel | src/Scripts/match-vs-cachelines.r | src/Scripts/match-vs-cachelines.r | library("ggplot2")
library("reshape2")
setwd("~/dev/BitFunnel/src/Scripts")
# See
# https://www.r-bloggers.com/choosing-colour-palettes-part-ii-educated-choices/
# for color information.
df <- read.csv(header=TRUE, file="/tmp/QueryPipelineStatistics.csv")
# queries <- read.csv(header=TRUE, file="/tmp/QueryPipelineSt... | mit | R |
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