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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7b4e13bf3162ee27081e10a06e1cb6b21fb22eea | Create regression.r | andrewjabara/airbnb_santa_cruz | regression.r | regression.r | #Using data from insideairbnb.com - data from the Santa Cruz, CA area, last updated October 2015
santacruz = read.csv("documents/airbnb_santacruz.csv")
attach(santacruz)
#thanks to http://www.geodatasource.com/developers/javascript for ideas behind this function
#Note that "West" and "South" need negative signs for la... | mit | R | |
265f2cb439a683370cefd9241f82c8ca2eecd2c3 | Add script for plotting history. | BitFunnel/BitFunnel,danluu/BitFunnel,BitFunnel/BitFunnel,BitFunnel/BitFunnel,danluu/BitFunnel,danluu/BitFunnel,danluu/BitFunnel,BitFunnel/BitFunnel,BitFunnel/BitFunnel,danluu/BitFunnel,danluu/BitFunnel,BitFunnel/BitFunnel | src/Scripts/plot-history.r | src/Scripts/plot-history.r | library("ggplot2")
setwd("~/dev/BitFunnel/src/Scripts")
df <- read.csv(header=TRUE, file="history.csv")
png(filename="bitfunnel-progress.png",width=1600,height=1200)
df$date <- as.Date(df$date)
ggplot(df, aes(date, wc)) + geom_line(size=3) + scale_x_date() +
labs(x = "Date", y = "Lines of code") +
stat_smooth(... | mit | R | |
c675860d5539e7500dab6886426fad797ed6bd9a | Add function to parse csv file better | AcidLeroy/OpticalFlow,AcidLeroy/OpticalFlow,AcidLeroy/OpticalFlow,AcidLeroy/OpticalFlow | src/r_code/load_video_features.r | src/r_code/load_video_features.r | LoadVideoFeatures <- function(csv_file){
options( stringsAsFactors=F )
df = read.csv(csv_file)
data_to_numeric_list = function(col) lapply(col, function(x) as.numeric(strsplit(x, "\\s+")[[1]]))
return(apply(df, 2, data_to_numeric_list))
}
| mit | R | |
e545d6c1151aff66cb8f2e998abf8f02bb12c4f7 | Add manual plot.r | arturocastro/portable-performance,arturocastro/portable-performance,arturocastro/portable-performance,arturocastro/portable-performance | axle/plot.r | axle/plot.r | library("Rmisc")
library("ggplot2")
get_positions_labels <- function(positions)
{
labels <- vector()
for (pos in positions)
{
dims <- strsplit(pos, "x")[[1]]
labels <- c(labels, paste0(pos, "\n", as.integer(dims[1]) * as.integer(dims[2])))
}
return(labels)
}
results_path ... | mit | R | |
0a7c551b6963b1fb99a63190f4685d84288d6df4 | Add unicode tests to all images docker-exec/dexec#27 | docker-exec/r | test/unicode.r | test/unicode.r | cat("hello unicode 👾\n")
| mit | R | |
c838fb09f062663330a08d88214f6d2c048872f7 | Add pattern count function | dennis95stumm/bioinformatics_algorithms,dennis95stumm/bioinformatics_algorithms | pattern_count.r | pattern_count.r | pattern_count <- function(text, pattern) {
count <- 0
pattern_length <- nchar(pattern)
for (i in 0:(nchar(text) - pattern_length)) {
if (substr(rep(text), i + 1, i + pattern_length) == pattern)
count <- count + 1
}
return(count)
}
message("Text")
text <- scan("stdin", what="character", nlines=1)
me... | mit | R | |
a0787acd606e43c131adf06d945436b0e9e82fb1 | Add R script for plotting smoothed Precision/Recall curves. | ntnu-smartmedia/goldfish,monsendag/goldfish,monsendag/goldfish,ntnu-smartmedia/goldfish,ntnu-smartmedia/goldfish,monsendag/goldfish | scripts/top10.r | scripts/top10.r | #!/usr/bin/env Rscript
library(data.table)
library(ggplot2)
irPlot <- function(df, type="Precision", n=10, selection="") {
# filter out NaN
df <- df[complete.cases(df), ]
# add grp for indexing
df$grp <- paste(df$Recommender,df$KTL, df$Similarity)
# set index
df <- data.table(df, key='grp')... | mit | R | |
2b35cdc291ff275fbb32adaf9ed186abeaed6efc | Add test for viewer | mnpopcenter/ripums,mnpopcenter/ripums | tests/testthat/test_viewer.r | tests/testthat/test_viewer.r | context("ipums_view is fault tolerant")
ipums_view_error_check <- function(x) {
out <- purrr::safely(ipums_view)(x, launch = FALSE)
is.null(out$error)
}
test_that("normal ddi doesn't error", {
ddi <- read_ipums_ddi(ripums_example("cps_00006.xml"))
expect_true(ipums_view_error_check(ddi))
})
test_that("empty ... | mpl-2.0 | R | |
e48a2079917b4b868166ed082f86233fd898e43c | Add HTS data normalisation helper functions | klmr/codons,klmr/codons | scripts/norm.r | scripts/norm.r | # Implement various helpers to normalise data.
# All these functions expect tidy data.
# TODO: All functions require documentation.
transform_counts = function (counts, fs, ...)
dplyr::mutate_each_(counts, dplyr::funs_(lazyeval::lazy(fs)),
dplyr:::dots(...))
fpkm = function (counts, transc... | apache-2.0 | R | |
2ee877acc8e225f88c627d895f1add36067bba78 | Create bigbang.r | zonination/galaxies | other/bigbang.r | other/bigbang.r | # Simulate some kind of explosion.
# This program will generate two plots.
# Load the only necessary library to complete this simulation:
library(ggplot2)
# Number of particles to simulate
n=200
# Simulates a vector explosion
bigbang<-data.frame("x"=rnorm(n),"y"=rnorm(n))
bigbang$dx<-bigbang$x*.1+.05*rnorm(n)
bigban... | mit | R | |
9711e9983c71a1e6a47e8fcb36d791b586b708bd | Create random-test.r | iArnold/red,iArnold/red | tests/source/units/random-test.r | tests/source/units/random-test.r | REBOL [
Title: "Testprogram for the random function for the Red Programming Language"
Author: "Arnold van Hofwegen"
File: %random-test.r
Tabs: 4
Rights: "Copyright (C) 2014 Arnold van Hofwegen. All rights reserved."
License: {
Distributed under the Boost Software License, Version 1.0.
See htt... | bsd-3-clause | R | |
0a98fe10c92b02a4816423e073c23f55bdda96a1 | Add tests for ipums_info | mnpopcenter/ripums,mnpopcenter/ripums | tests/testthat/test_ipums_info.r | tests/testthat/test_ipums_info.r | context("IPUMS Info")
nvars <- 8
first_three_vars <- c("YEAR", "SERIAL", "HWTSUPP")
year_label <- "Survey year"
year_var_desc <- "YEAR reports the year in which the survey was conducted. YEARP is repeated on person records."
file_info_types <- c("ipums_project", "extract_date", "extract_notes", "conditions", "citati... | mpl-2.0 | R | |
3570b45f13a302137831c2d1c88b43366d31c679 | Add R script for Polynomial regression | a-holm/MachinelearningAlgorithms,a-holm/MachinelearningAlgorithms | Regression/PolynomialRegression/regularPolynomialRegression.r | Regression/PolynomialRegression/regularPolynomialRegression.r | # Polynomial regression for machine learning.
#
# polynomial regression is a form of regression analysis in which the
# relationship between the independent variable x and the dependent variable y is
# modelled as an nth degree polynomial in x. Polynomial regression fits a
# nonlinear relationship between the value of... | mit | R | |
89ef5cc8d10e1051f656e29ecd28346d03e180d9 | Add draft R code to measure SWIFT runtime performance | jmp75/hwrs2015,jmp75/hwrs2015 | draft/runtime_benchmark.r | draft/runtime_benchmark.r | library(ophct)
# Create tree-catchments, to have a measurement baseline in O(n^2) to check how runtime scales
# daily and hourly operation
defineNetwork <- function(streamOrder=1, runoffModel='AWBM') {
# o o o o
# \ / \ / stream order 2
# o o
# \ / stream order 1
# o
# nu... | agpl-3.0 | R | |
9c7037e152e7f7b7d4fb7f16ddfe908805360362 | update fig3 and fig4 | shuaimeng/r | print/fig4_sd_d.r | print/fig4_sd_d.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/print")
library(xlsx)
# reading ux and sd
k1<-read.xlsx("dotx.xlsx",sheetName="600",header=TRUE)
k2<-read.xlsx("dotx.xlsx",sheetName="1khz",header=TRUE)
k3<-r... | mit | R | |
a868837b71813864293cf5154b4ad8f6f6e9774c | add init.r | nasebanal/nb-bitcoin,nasebanal/nb-bitcoin,nasebanal/nb-bitcoin,nasebanal/nb-bitcoin | init.r | init.r | install.packages("Rserve")
install.packages("ggplot2")
library("ggplot2")
| apache-2.0 | R | |
828a5503f127e976f93de27b2752f43f71ee6dad | Create convert_new_hybrids.r | jdmanthey/conversion_files | convert_new_hybrids.r | convert_new_hybrids.r | # USAGE:
# Place structure file and this script in a directory, and set the R
# current working directory to be the same. Then type the following
# commands, where "file" is the name of your structure file:
# source("convert_new_hybrids.r")
# convert_snapp("file")
convert_nh <- function(file) {
x <- read.table(fi... | bsd-3-clause | R | |
9d72acb721ad19a7bbfa4ba2ce1348839de688a2 | add r code | softgrow/MFS-Modelling,softgrow/MFS-Modelling | dorelogit.r | dorelogit.r | using(Zelig)
mfs <- read.csv("/Users/alex/Documents/MFS-Modelling/comb.csv", header=TRUE)
my.out1 <- zelig(Fire ~ dayofweek + hour + month, data = mfs, model = "relogit", tau=30446/(365.25*24*60*5))
summary(my.out1)
| mit | R | |
30d2a886d735d7cbf1db44e1d5b2cd88d414bfe4 | Add setwidth helper for R | klmr/.files,klmr/.files,klmr/.files | .R/interactive/sw.r | .R/interactive/sw.r | local({
setwidth = function () options(width = as.integer(Sys.getenv('COLUMNS')))
makeActiveBinding('sw', setwidth, baseenv())
})
| apache-2.0 | R | |
46889994379d33cdb77bf1b26675a6a2e27c3f71 | Add plotting snippets in R | gunesacar/tbb-fp,gunesacar/tbb-fp | plot.r | plot.r | library(RColorBrewer)
rf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))
r <- rf(32)
read.csv("/home/gunes/dev/tor/tbb-fp/scr.txt.csv", header = TRUE) -> res
wrep= as.vector(rep(res$w, res$freq))
hrep= as.vector(rep(res$h, res$freq))
crep= as.vector(rep(res$c, res$freq))
df = data.frame(wrep, hrep)
library(ggplot2... | agpl-3.0 | R | |
5454c34d6855283551c5f2d3d6731934ab61a2b5 | Add a testing file for wrapping variadic functions | zsx/r3,Pointillistic/rebol-lang,Pointillistic/rebol-lang,zsx/r3,zsx/r3,Pointillistic/rebol-lang,zsx/r3,Pointillistic/rebol-lang | make/tests/varargs.r | make/tests/varargs.r | REBOL []
recycle/torture
libc: switch fourth system/version [
3 [
make library! %msvcrt.dll
]
4 [
make library! %libc.so.6
]
]
printf: make routine! [
[
"An example of wrapping variadic functions"
fmt [pointer] "fixed"
... "variadic"
return: [int32]
]
libc "printf"
]
sprintf: make routine! [
[
... | apache-2.0 | R | |
5cafbb24e0e3fc109000499c282357a59e1df889 | Create t_test.r | Sapphirine/Big-Data-Analysis-on-Log-Data-of-Standardized-IBT-Test-Toefl-Taker-for-Effects-of-Selection-Changing,Sapphirine/Big-Data-Analysis-on-Log-Data-of-Standardized-IBT-Test-Toefl-Taker-for-Effects-of-Selection-Changing | t_test.r | t_test.r | male <- c(1.7,3.2,1.8,1.9,2.0,2.1,2.0, 1.4,1.7)
female <- c(1.7,3.1,1.3,1.2,2.5,2.2,2.1, 1.8,1.9)
t.text(male,female)
| apache-2.0 | R | |
03c3e9386b3a35703ea232968a507da4736faed2 | build mapping statistical table from bam | shengqh/ngsperl,shengqh/ngsperl,shengqh/ngsperl,shengqh/ngsperl | lib/Samtools/BamStat.r | lib/Samtools/BamStat.r |
filelist<-read.table(parSampleFile1, sep="\t", header=F, stringsAsFactor=F)
filecounts<-apply(filelist, 1, function(x){
dat<-read.table(x[1], sep="\t")
#dat<-read.table(filelist[1,1], sep="\t")
counts<-sapply(strsplit(as.vector(dat$V1), ' \\+ '), "[", 1)
names<-sapply(strsplit(as.vector(dat$V1), ' \\+ '), "[",... | apache-2.0 | R | |
f0e64daabe0b18bc63a26433b195863ea8de9c7b | set up empty overall package doc #124 | maxheld83/pensieve,maxheld83/pensieve,maxheld83/pensieve | R/pensieve-package.r | R/pensieve-package.r | #' pensieve.
#'
#' @name pensieve
#' @docType package
NULL
| agpl-3.0 | R | |
384495d6ab56795c505e7f127baa63072f6a4cbf | Create ball-games.r | ActiveAnalytics/ball-games | ball-games.r | ball-games.r | require(deSolve)
ball <- function(t, y, parms)
{
with(as.list(c(parms, y)), {
W <- c(W1, W2, W3)
v_W = y[c(2, 4, 6)] - W
nv_W = norm(v_W, "2")
coeff <- (-.5/m)*rho*A*nv_W
dy1 <- y[2]
spin1 <- coeff*CL*(vw[2]*(y[6] - W[3]) - vw[3]*(y[4] - W[2]))/w
if(is.na(spin1))
spin1 <- 0
#dy2... | mit | R | |
9133a503b7ce3ec13b9c2330a4c37735de60f4e4 | Create pollutantmean.r | evohnave/R-Programming-Course | pollutantmean.r | pollutantmean.r | pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## For this exercise the location is assumed to be in a
## sub-directory of the working directory, getwd()
## 'pollutant' is a character vector of ... | unlicense | R | |
483376120cb768c061f957816e15895508d0ca52 | Add a missing file. | snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3,snakamura/q3 | web/en/_nav.rd | web/en/_nav.rd | =begin
((<QMAIL3|URL:./>))|((<Download|URL:download.html>))|((<Release Notes|URL:releasenotes.html>))|((<Documents|URL:/doc/>))|((<FAQ|URL:/doc/FAQ.html>))|((<ML|URL:http://qs.snak.org/cgi-bin/mailman/listinfo/qs>))|((<BTS|URL:/bts/guest.cgi?project=Q3&action=top>))|((<Lingr|URL:http://www.lingr.com/room/bJETSwHp1e5>)... | mit | R | |
c5fcf9e0b306b4abdc56ee6026896c8ae9a694e4 | Add plotting R script for priorities | vhotspur/spl-java,vhotspur/spl-java,vhotspur/spl-java,vhotspur/spl-java,vhotspur/spl-java | bench/sandbox/priorities/plot_summary.r | bench/sandbox/priorities/plot_summary.r | #
# Copyright 2013 Charles University in Prague
# Copyright 2013 Vojtech Horky
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | apache-2.0 | R | |
658df9db181195c083866ccb2b835255df61073a | Create teste.r | MatheusRabetti/MatheusRabetti.github.io,MatheusRabetti/MatheusRabetti.github.io,MatheusRabetti/MatheusRabetti.github.io | assets/posts/integrate-r-python/teste.r | assets/posts/integrate-r-python/teste.r | mit | R | ||
ada5d9e2fbb5745b7b4f92a03c12f6022e4926c9 | Solve Simple Product 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/1004/1004.r | solutions/uri/1004/1004.r | input <- file('stdin', 'r')
a <- as.integer(readLines(input, n=1))
b <- as.integer(readLines(input, n=1))
write(paste("PROD =", a * b), '')
| mit | R | |
bfdbf97cdf2f2cdd9b3c3e986595de1e51948d80 | Create 2.r | glor/R,glor/R | aufgaben/blatt03/2.r | aufgaben/blatt03/2.r | #3 Aufgabe 2:
#3.1
laus = read.table(file = "lice.txt", sep="\t", dec = ".", header = TRUE)
# H1: Kita2 hat weniger Kopflaeuse gehabt
# H0: Kita2 hat gleich viel oder mehr Kopflaeuse als Kita1 gehabt
| bsd-2-clause | R | |
74104ba5b91e8ede607bb3797046d4123d7fa714 | Add calibration plot function | paulinshek/paulinshek.github.io,paulinshek/paulinshek.github.io,paulinshek/paulinshek.github.io,paulinshek/paulinshek.github.io,paulinshek/paulinshek.github.io | _source/2016-02-14-relationship-prediction/calibrationPlotModelFitting.r | _source/2016-02-14-relationship-prediction/calibrationPlotModelFitting.r | ######### Calibration Plot function
CalibrationPlot = function(prob, result,bins=100, detail = FALSE,range = c(0,1)) {
# creates breaks and sets up the vectors
breaks = quantile(prob,seq(0,1,1/bins),na.rm=T)#seq(0,1-1/bins,1/bins)
prediction = rep(NA,length(breaks))
observedprop = rep(NA,length(breaks))
meanprob... | mit | R | |
7a0717d5ec5018389eb7618cc90d7eb853f0ba57 | Add Decision Tree Regression in R | a-holm/MachinelearningAlgorithms,a-holm/MachinelearningAlgorithms | Regression/DecisionTreeRegression/regularDecisionTreeRegression.r | Regression/DecisionTreeRegression/regularDecisionTreeRegression.r | # Decision Tree regression for machine learning.
#
# Decision tree builds regression or classification models in the form of a tree
# structure. It brakes down a dataset into smaller and smaller subsets while at
# the same time an associated decision tree is incrementally developed. The final
# result is a tree with d... | mit | R | |
1d6c23231200fc5536462c4c583e6aedfd232552 | Create EarlyDropouts.r | pmcrosta/eps | EarlyDropouts.r | EarlyDropouts.r | #######################
# INPUT DATASETS: termpat.Rdata
# OUTPUT DATASETS: TBD
# PREVIOUS PROGRAMS: EnrollmentPatterns.r
# FOLLOWING PROGRAMS: TBD
# $Id: EarlyDropouts.r 79 2012-08-10 17:29:37Z crosta $
###############################################
stop("Don't run this all at once.")
library(ggplot2)
library(descr)
l... | mit | R | |
ce9bee8e18200da876072bd407ea3a06d78083d2 | Add Brier Score function | mattmills49/CFBWinProbability | R/brierB.r | R/brierB.r | #' Cost function for using \code{cv.glm} fitting procedure
#'
#' This function just provides a way to get the Brier Score for a model used
#' in the \code{cv.glm} function. Of course, this is just the \code{MSE} but
#' I didn't realize that until later. So here it is.
#'
#' @param y the dependent binary variable
#' ... | mit | R | |
71b04aa48a7a7a9d8fd1c2d387e49f00cb2df29e | Create cycles.r | Sokel/R-shchu | cycles.r | cycles.r |
mydata <- read.csv('evals.csv')
a <- -11
# simple if
if(a > 0){
print('positive')
}else if(a == 0){
print('null')
}else{
print('not positive')
}
# smart ifelse
ifelse(a > 0, 'positive','not positive')
#and vector too
a <- c(-1,1)
ifelse(a > 0, 'positive','not positive')
# simple for
for(i in 1:100){
prin... | apache-2.0 | R | |
76cb7be959040a00ee77110161ac331dffca62db | Fix interactive legacy data.frame output in R | klmr/.files,klmr/.files,klmr/.files | .R/print-data-frame.r | .R/print-data-frame.r | print.data.frame = function (x, ..., digits = NULL, quote = FALSE, right = TRUE,
row.names = TRUE)
{
if (! isTRUE(all.equal(rownames(x), as.character(seq_len(nrow(x))))))
x = tibble::rownames_to_column(x)
print(dplyr::tbl_df(x))
}
| apache-2.0 | R | |
16e3a851a0b2e308953718d2666a4599b2973196 | Create server.r | aleksandrov2/APPR-2015-16 | shiny/server.r | shiny/server.r | library(knitr)
library(ggplot2)
library(dplyr)
require(gsubfn)
require(rvest)
require(xml2)
require(ggplot2)
library(sp)
library(maptools)
library(dendextend)
# Uvozimo funkcije za pobiranje in uvoz zemljevida.
source("lib/uvozi.zemljevid.r", encoding = "UTF-8")
source("podatki/podatki.r", encoding = "UTF-8")
source(... | mit | R | |
d8e5a838165e31fd56df83bce7cfb3e31c5ffe8e | Create Server.r | mmjazzar/TimeSeries_Forecasting,mmjazzar/Load_dashboard | Server.r | Server.r | apache-2.0 | R | ||
56f1e35e1195d0af31cd95f4067ec52fe17706eb | 更新：第四章fig4-13 | shuaimeng/r | thesis/chap4/fig4-13-2.r | thesis/chap4/fig4-13-2.r | ## fvc和fpmax
fvc2<-c(300,300,200,200)
fvc3<-c(300,500,310,200)
fvc4<-c(400,600,600,700)
fvc5<-c(400,711,800,1110)
fpmax2<-c(1000,600,500,600)
fpmax3<-c(600,500,800,800)
fpmax4<-c(400,600,1000,2000)
fpmax5<-c(400,700,1000,3500)
par(mfrow = c(2,1), mar = c(2,2.2,0.3,1), oma = c(1,1,1,1))
##
yan<-c("red","blue","black"... | mit | R | |
0aaf5c7f38aa7f7ad78fd53057575138bdb286a9 | add bubblemap | shengqh/ngsperl,shengqh/ngsperl,shengqh/ngsperl,shengqh/ngsperl | lib/scRNA/seurat_bubblemap.r | lib/scRNA/seurat_bubblemap.r | source("scRNA_func.r")
library("Seurat")
library("readxl")
library(ggplot2)
options_table<-read.table(parSampleFile1, sep="\t", header=F, stringsAsFactors = F)
myoptions<-split(options_table$V1, options_table$V2)
assay=ifelse(myoptions$by_sctransform == "0", "RNA", "SCT")
finalList<-readRDS(parFile1)
obj=finalList$... | apache-2.0 | R | |
7c582a84ad6c1bfd65d31f4368c8dd6802743aef | add revigo r script | CJ-Chen/TBtools-Manual,CJ-Chen/TBtools-Manual,CJ-Chen/TBtools-Manual,CJ-Chen/TBtools-Manual | BioScripts/Rscript/Revigo本地可视化代码.r | BioScripts/Rscript/Revigo本地可视化代码.r | library(ggrepel)
p1 <- ggplot( data = one.data );
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_size_area();
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
p1 <- p1 ... | mit | R | |
dcb0c8a50270dfb3439827a43458cf04688ce903 | Add Support Vector regression with R | a-holm/MachinelearningAlgorithms,a-holm/MachinelearningAlgorithms | Regression/SupportVectorRegression/regularSVMRegression.r | Regression/SupportVectorRegression/regularSVMRegression.r | # Support Vector regression for machine learning.
#
# Support Vector Machine can also be used as a regression method, maintaining all
# the main features that characterize the algorithm (maximal margin). The Support
# Vector Regression (SVR) uses the same principles as the SVM for classification,
# with only a few min... | mit | R | |
dbcc9527ee1b5eca24f09f898e2c3a7470e29861 | Create script_hadoop.r | coatless/stat490uiuc,coatless/stat490uiuc,coatless/stat490uiuc | rexamples/script_hadoop.r | rexamples/script_hadoop.r | #!/usr/bin/env Rscript
f = file("stdin")
open(f)
state_data = read.delim(f, header=FALSE)
colnames(state_data) = c("State","City","Population")
summary(state_data)
| mit | R | |
0c962342b7d521da710befd69632dd6d156b4b04 | Add a testing file for wrapping variadic functions | rgchris/ren-c,giuliolunati/ren-c,draegtun/ren-c,kealist/ren-c,draegtun/ren-c,mbk/ren-c,mbk/ren-c,draegtun/ren-c,codebybrett/ren-c,rgchris/ren-c,hostilefork/rebol,mbk/ren-c,hostilefork/rebol,giuliolunati/ren-c,kealist/ren-c,rgchris/ren-c,draegtun/ren-c,giuliolunati/ren-c,rgchris/ren-c,codebybrett/ren-c,giuliolunati/ren-... | make/tests/varargs.r | make/tests/varargs.r | REBOL []
recycle/torture
libc: switch fourth system/version [
3 [
make library! %msvcrt.dll
]
4 [
make library! %libc.so.6
]
]
printf: make routine! [
[
"An example of wrapping variadic functions"
fmt [pointer] "fixed"
... "variadic"
return: [int32]
]
libc "printf"
]
sprintf: make routine! [
[
... | apache-2.0 | R | |
a15d3a3f9a679897f48780b15c11ae37955f850c | Add last version benchmark | SteveViss/OuranosDB,SteveViss/OuranosDB | benchmarks.r | benchmarks.r | ### Assess PostgreSQL performance on Ouranos_db
setwd("./")
#install.packages("RPostgreSQL")
require("RPostgreSQL")
## use when on campus
dbname <- "mffp"
dbuser <- "svissault"
dbhost <- "132.219.137.38"
dbport <- 5432
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, host=dbhost, port=dbport, dbname=dbname,
... | mit | R | |
83487f4b5e615a6454e22b7220ad2dee996babad | Update Package for easier developing created | felixlindemann/HNUORTools,felixlindemann/HNUORTools | update-package.r | update-package.r | setwd("/Volumes/Daten/FelixLindemann/Documents/git/HNUORToolsForRProject")
# update package
library(devtools)
check()
test()
| mit | R | |
8ff8548eb2f6bd7e694b4ee4669e6e7f8bd1ed56 | Add an option to embed script without compression | draegtun/ren-c,rgchris/ren-c,draegtun/ren-c,codebybrett/ren-c,draegtun/ren-c,kealist/ren-c,codebybrett/ren-c,hostilefork/rebol,giuliolunati/ren-c,kealist/ren-c,kealist/ren-c,mbk/ren-c,mbk/ren-c,draegtun/ren-c,kealist/ren-c,giuliolunati/ren-c,rgchris/ren-c,rgchris/ren-c,hostilefork/rebol,hostilefork/rebol,rgchris/ren-c,... | make/encap.r | make/encap.r | REBOL[]
args: parse system/script/args ""
exe: none
payload: none
output: none
as-is: false ;don't compress, in case people try to avoid decompression to speed up bootup
windows?: 3 = fourth system/version
while [not tail? args] [
arg: first args
case [
any [arg = "/rebol"
arg = "/r"] [
exe: second ar... | REBOL[]
args: parse system/script/args ""
exe: none
payload: none
output: none
windows?: 3 = fourth system/version
while [not tail? args] [
arg: first args
case [
any [arg = "/rebol"
arg = "/r"] [
exe: second args
args: next args
]
any [arg = "/payload"
arg = "/p"] [
payload: seco... | apache-2.0 | R |
d6d96d702b79199eaaf527593c3bdd0397271691 | Add readme | busterjs/fs-watch-tree | README.rd | README.rd | # watch-tree - Recursive directory watch (for Linux) #
**watch-tree** is a small tool to watch directories for changes recursively. It
uses [node-inotify](https://github.com/c4milo/node-inotify) to watch for
changes, thus only works on Linux at the moment. Help adding support for
[NodeJS-FSEvents](https://github.com/p... | bsd-3-clause | R | |
337c1a6999bf0d00346db761b02ca6f18615a3ad | Create run_analysis.r | mgazzar/GetNCleanData | run_analysis.r | run_analysis.r | if (!file.info("UCI HAR Dataset")$isdir) {
dataFile <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
dir.create("assignment")
download.file(dataFile, "assignment/UCI-HAR-dataset.zip", method="curl")
unzip("assignment/UCI-HAR-dataset.zip")
}
# 1. Merges the training an... | apache-2.0 | R | |
13f5cceadfaf0043aff44ecb03284c8eca94f0c2 | test for bug in normalize-spec | Apanatshka/strategoxt,metaborg/strategoxt,Apanatshka/strategoxt,Apanatshka/strategoxt,lichtemo/strategoxt,metaborg/strategoxt,metaborg/strategoxt,metaborg/strategoxt,metaborg/strategoxt,lichtemo/strategoxt,Apanatshka/strategoxt,Apanatshka/strategoxt,lichtemo/strategoxt,lichtemo/strategoxt,lichtemo/strategoxt | strc/spec/test1/test46.r | strc/spec/test1/test46.r | module test46
imports dynamic-rules
strategies
main =
<dyn-test(|2)> 3
dyn-test(|x) =
?y
; rules( DynTest : y -> x)
| apache-2.0 | R | |
341c31f1db3d81c4a5b9b25e53d9fe7476575570 | deal with missing value in posterior function | MikeXL/bayes | R/poi.r | R/poi.r | poisson.fun <- function(theta, x, y) {
# parameters
lambda1 <- theta[1]
lambda2 <- theta[2]
# priors
log.priors <- dgamma(lambda1, .5, 1e-5, log=T) + dgamma(lambda2, .5, 1e-5, log=T)
# likelihood
log.like <- dpois(x, lambda1, log=T) + dpois(y, lambda2, log=T)
ll <- log.priors + log.like
ifelse(i... | poisson.fun <- function(theta, x, y) {
# parameters
lambda1 <- theta[1]
lambda2 <- theta[2]
# priors
priors <- dgamma(lambda1, .5, 1e-5, log=T) + dgamma(lambda2, .5, 1e-5, log=T)
# likelihood
ll <- dpois(x, lambda1, log=T) + dpois(y, lambda2, log=T)
return (ll + priors)
}
bayes.poisson.test <- func... | mit | R |
d013b31d7b139398cdd39d8e6946e00827c897af | Add script to generate final report | hkaju/Ising2D,hkaju/Ising2D,hkaju/Ising2D | final_report.r | final_report.r | pdf("report.pdf")
cols <- rainbow(10)
data1 <- read.csv("data/10x10-B0.0/results.csv", header=T)
data2 <- read.csv("data/20x20-B0.0/results.csv", header=T)
data3 <- read.csv("data/30x30-B0.0/results.csv", header=T)
data4 <- read.csv("data/40x40-B0.0/results.csv", header=T)
data5 <- read.csv("data/50x50-B0.0/results.c... | mit | R | |
973181aa6381f5796938e0e715a0f0d8ec885a4d | Create wbgt_full.r | alfcrisci/rBiometeo,alfcrisci/rBiometeo | R/wbgt_full.r | R/wbgt_full.r | mit | R | ||
65fdf0358905830d0aed81f00945ecf92406c336 | rename test file | janiheikkinen/irods,PaulVanSchayck/irods,janiheikkinen/irods,PaulVanSchayck/irods,janiheikkinen/irods,janiheikkinen/irods,PaulVanSchayck/irods,PaulVanSchayck/irods,PaulVanSchayck/irods,janiheikkinen/irods,PaulVanSchayck/irods,janiheikkinen/irods,PaulVanSchayck/irods,PaulVanSchayck/irods,janiheikkinen/irods,janiheikkine... | iRODS/clients/icommands/test/rules3.0/test_no_memory_error_patch_2242.r | iRODS/clients/icommands/test/rules3.0/test_no_memory_error_patch_2242.r | test{
*a="hello/world";
for(*i=0;*i<1000;*i=*i+1) {
test2(*a,*b,*c);
}
}
test2(*a,*b,*c){
msiSplitPath(*a,*b,*c);
}
input null
output null
| bsd-3-clause | R | |
942c9e2df1b2b7bc488928a92e0bd8d09c656472 | Add mandelbrot.r | theDrake/r-experiments | mandelbrot.r | mandelbrot.r | # Calculates the Mandelbrot set (https://en.wikipedia.org/wiki/Mandelbrot_set)
# through the first 20 iterations of the equation z = z^2 + c plotted for
# different complex constants c.
install.packages("caTools")
library(caTools) # for write.gif
jet.colors <- colorRampPalette(c("green", "blue", "red", "cyan", "#7FFF... | mit | R | |
85170bc7efbc07206d9e4d5ae979d56c5456919d | Add helper to export package submodules | klmr/ggplots | export.r | export.r | #' Export symbols from a module environment
#'
#' @param env the module environment to export from.
#' @param symbols a character vector of symbol names to export. If \code{NULL}
#' (the default), export every public symbol.
#' @param target the environment to import the symbols into; default: the
#' calling environmen... | apache-2.0 | R | |
1f599c04dd09b3beaa4a63330749351e9f54c998 | Create script to repair (as best as possible) faulty encodings | tenforwardconsulting/crantastic,hadley/crantastic,hadley/crantastic,tenforwardconsulting/crantastic,tenforwardconsulting/crantastic,tenforwardconsulting/crantastic,hadley/crantastic | lib/r/repair-db.r | lib/r/repair-db.r | # This script repairs broken encodings in the base database. These
# flawed encodings are produced by non-utf changelogs and news files.
library(plyr)
source("db.r")
update_sql <- function(table, values) {
id <- values$id
changes <- paste(names(values[, -1]), "= ?", collapse = ", ")
paste(
"UPDATE ", ta... | mit | R | |
036cf1d4edfba450ce910505714d95c98933cb44 | Create mtcars.r | bgweber/RServer,bgweber/RServer,bgweber/RServer,bgweber/RServer | userRdemo/mtcars.r | userRdemo/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 | |
7ef8ba2984928ff9484d3a2457f97f1cf02d0717 | Create a quick analysis file to load and peak at FRC data | supertetelman/frc-data-analysis,supertetelman/frc-data-analysis,supertetelman/frc-data-analysis | quick-analysis.r | quick-analysis.r | #Install dependencies
install.packages("reshape2")
install.package("ggplot2")
#Requires
require("reshape2")
require("ggplot2")
#Import the data
teams <- read.csv("./the-blue-alliance-data-master/the-blue-alliance-data-master/teams/teams.csv", header=FALSE)
names(teams) <- c("number", "name", "sponsors", "location", "... | apache-2.0 | R | |
b46794cadb3187a9952ae2a2714acf217f1151a0 | Create try.xgboost.v2.r | minesh1291/MachineLearning,minesh1291/MachineLearning,minesh1291/MachineLearning | myPracticeGCE/try.xgboost.v2.r | myPracticeGCE/try.xgboost.v2.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 | |
c001a966d0bde8d14bd823313104bfb3513eec6d | convert bed to gff3 | shengqh/ngsperl,shengqh/ngsperl,shengqh/ngsperl,shengqh/ngsperl | lib/Bedtools/bed2gff3.r | lib/Bedtools/bed2gff3.r | library(rtracklayer)
args <- commandArgs(TRUE)
print(args)
bedfile=args[1]
gff3file=args[2]
## import the bed file
bed.ranges <- import.bed(bedfile)
## export as a gff3 file
export.gff3(bed.ranges, gff3file)
gf<-read.table(gff3file, sep="\t", header=F)
gf$V2<-paste0("Peak", c(1:nrow(gf)))
write.table(gf, file=gf... | apache-2.0 | R | |
47ea4d17738702898cdf1264e5f6d0895725dc1c | Test Programm Nullstellensuche Hochaufgelöstes Modell | sebaki/clim-jet-stream,sebaki/clim-jet-stream,sebaki/clim-jet-stream,sebaki/clim-jet-stream | test-code.r | test-code.r | ## source('~/Master_Thesis/Code/test-code.r')
## variablen für test
x <- seq(0:29)
d <- rnorm(30, mean = 0, sd = 2)
n <- 4
split <- 6
dx.hr <- 0.01
## test nullstellensuche
library(rootSolve)
fun <- function(x) cos(2*x) ** 3
curve(fun, from = -1, to = 1)
curve(cheb.poly.val(x, coeff.cheb = m.cheb.seq), from = (-1),... | mit | R | |
a0e6335bd6e21922fd0b644695bf6c5d6590852a | Create DataMartCube.r | KKONZ/DatamartProject | DataMartCube.r | DataMartCube.r | revenue_cube <-
tapply(sales_fact$amount_of_time,
sales_fact[,c("prod", "month", "year", "lvl")],
FUN=function(x){return(sum(x))})
dimnames(revenue_cube)
#slice
revenue_cube[, "1", "2012",]
revenue_cube["Book", "1", "2012",]
# Dice
revenue_cube[c("Room","Book"),
... | mit | R | |
d6afa102abd293a8ceace43169abaa9488fc5d27 | Add slurm-job.r | jmousseau/Stain | R/slurm-job.r | R/slurm-job.r | #' NOAARequest R6 object.
#'
#' An interface to SLURM bash scripts and their submissions.
SlurmJob <- R6::R6Class("SlurmJob")
| mit | R | |
582d9683e1531b72180711746dc72fd9abb4b563 | Create visual.r | andrewjabara/airbnb_santa_cruz | visual.r | visual.r | #Using data from insideairbnb.com - data from the Santa Cruz, CA area, last updated October 2015
#Focus is on visualizing the data by room type
santacruz = read.csv("documents/airbnb_santacruz.csv")
head(santacruz)
#Find out types of rooms
unique(santacruz$room_type)
#price per night based on room type
private <- su... | mit | R | |
1dc3ae11bfdd3749095801d96281b7a58a3c5ead | Create README.rd | aru132/google-drive-on-fuse | README.rd | README.rd | = Requirements
* FUSE (>= 2.6)
* libfuse
* json-c
* glib-2.0
* libmagic
* libcurl
= Installation
1. Clone from github.
$ git clone https://github.com/aru132/google-drive-on-fuse.git
2. Register this program with google developers console ( https://code.google.com/apis/console#access ) .
3. Download client_secret.json... | bsd-3-clause | R | |
ce50780aa82c74b6fb7a7396655453b1e4a994f5 | add codes (even if we don't use them yet) | mschubert/clustermq,mschubert/clustermq,mschubert/clustermq | R/codes.r | R/codes.r | #' Message ID indicating worker is accepting jobs
WORKER_UP = -13L
#' Message ID indicating worker is accepting jobs
WORKER_READY = 0L
#' Message ID indicating worker is shutting down
WORKER_DONE = -1L
#' Message ID indicating worker is requesting data
REQ_DATA = -2L
#' Message ID telling worker to stop
WORKER_STOP... | apache-2.0 | R | |
9939c1ccac4fc6ca7ac54aa3c3f6a2c107b5eba6 | Create Script.r | OliviaZhu26/Single_virion_seq,OliviaZhu26/Single_virion_seq,OliviaZhu26/Single_virion_seq | BAsE-Seq_scripts/Script.r | BAsE-Seq_scripts/Script.r | #!/usr/bin/Rscript
##Author: ZHU O. Yuan
##Script name: Script.r
##Generate 2 QC figures from output files of 01_BAsE-Seq_alignment.sh
##Run as: ./Script.r (from within sample folder)
#A plot of genome-wide coverage plots for the first 9 barcodes
#Check if coverage is even, if there are glaring patterns of uneveness
... | mit | R | |
769124c92db6cac58b48882fcfcb92cfeafe6b0b | Add faster implementation of frequent_words | dennis95stumm/bioinformatics_algorithms,dennis95stumm/bioinformatics_algorithms | faster_frequent_words.r | faster_frequent_words.r | source("computing_frequencies.r")
source("number_to_pattern.r")
faster_frequent_words <- function(text, k) {
frequent_patterns <- {}
frequency_array <- computing_frequencies(text, k)
max_count <- max(frequency_array)
for (i in 0:(4^k-1)) {
if (frequency_array[i + 1] == max_count) {
pattern <- number_... | mit | R | |
48a17c43cd0d4500874726b909a1814d596fc53c | Create initial.r | xiaodaigh/teradata.dplyr | initial.r | initial.r | #' @import teradataR
#' @export
#' @example
src_teradata <- function(dbname = NULL, host = NULL, port = NULL, user = NULL,
password = NULL, dType = c("odbc", "jdbc"), database = "", ...) {
tdConnection <- NULL
dsn <- host
uid <- user
pwd <- password
dType <- match.arg(dType)
# cod... | mit | R | |
55579c1dfc5f7b91e6989ea356b106a7eeec9400 | Create rankall.r | evohnave/R-Programming-Course | rankall.r | rankall.r | rankall <- function(outcome, num = "best") {
## For each state, find the hospital of the given rank
## Return a data frame with the hospital names and the
## (abbreviated) state name
## Read outcome data
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
states<-unique(read.csv... | unlicense | R | |
3c54e5cc9c53fd8c7f970be60985142a5fb4ac5d | Add binomial_coefficient function | dennis95stumm/bioinformatics_algorithms,dennis95stumm/bioinformatics_algorithms | binomial_coefficient.r | binomial_coefficient.r | library(Brobdingnag)
factorial <- function(n) {
if (n <= 1)
return(1)
return(n * factorial(n - 1))
}
binomial_coefficient <- function(m, k) {
return(factorial(m)/(factorial(k)*factorial(m-k)));
}
message("m")
m <- scan("stdin", nlines=1)
message("k")
k <- scan("stdin", nlines=1)
print(as.numeric(binomial... | mit | R | |
572f89c2594597183df7afcd18319b36a5af1362 | Create GenePix_microarray.r | crazyhottommy/scripts-general-use,crazyhottommy/scripts-general-use,crazyhottommy/scripts-general-use,crazyhottommy/scripts-general-use | R/GenePix_microarray.r | R/GenePix_microarray.r | ### limma for GenePix microarray data
## read https://www.bioconductor.org/packages/3.3/bioc/vignettes/limma/inst/doc/usersguide.pdf
## http://biocourse.wp.sanbi.ac.za/wp-content/uploads/sites/7/2013/01/day3.pdf
## read here on how to install bioconductor packages https://www.bioconductor.org/install/
source("https:/... | mit | R | |
637921fff8f25f37b30e4f06f4fe67b9a573f4fe | Create Model_Diagnostics_and_Transformation.r | lancezlin/Applied_Linear_Model_with_R | Model_Diagnostics_and_Transformation.r | Model_Diagnostics_and_Transformation.r | ## 7.1
bl <- baeskel
y <- bl$Tension
x <- bl$Sulfur
plot(x, y)
abline(lm(y ~ x, data=bl))
lam <- c(-1, 0, 1)
new <- with(bl, seq(min(x), max(x), length=12))
with(bl, plot(x, y))
with(bl,
for (j in 1:3){
m1 <- lm(y ~ bcPower(x, lam[j]))
lines(new, predict(m1, data.frame(x=new)), lty=j, col=j, lwd... | mit | R | |
543ded5c9a9277d599a136e75cbcc794d57363b1 | Create hellloWorld.r | phase/refract,phase/refract | examples/hellloWorld.r | examples/hellloWorld.r | !v"hello world"!
>m?;o
| mit | R | |
6ccda83e3cb7542ba80b30903e8127982d3d3da0 | Add support system for command line tools | klmr/codons,klmr/codons | scripts/sys/__init__.r | scripts/sys/__init__.r | args = commandArgs(trailingOnly = TRUE)
exit = function (code = 0)
quit(save = 'no', status = if (is.null(code)) 0 else code)
| apache-2.0 | R | |
a639f8df7242b9d5ede0fb68968803dd017e0c99 | Create anova.r | Sokel/R-shchu | anova.r | anova.r | ### ANOVA
library(ggplot2)
# formulae
DV ~ IV # One-way
DV ~ IV1 + IV2 # Two-way
DV ~ IV1:IV2 # Two-way interaction
DV ~ IV1 + IV2 + IV1:IV2 # Main effects + interaction
DV ~ IV1 * IV2 # The same: Main effects + interaction
DV ~ IV1 + IV2 + IV3 + IV1:IV2
DV ~ (IV1 + IV2 + IV3)^2 # main effects and all possib... | apache-2.0 | R | |
a62284eade844e4ee29365214817fb2af7367717 | add r script | Marcnuth/DataScienceInterestGroup,Marcnuth/DataScienceInterestGroup | algorithms/perceptron.r | algorithms/perceptron.r | ## NOTE: the function in current file is not perceptron at all
## The intention we do this is to distinguish python & R
## You will find the model of lm or sgd in R gives a confused function for given data
## And this function is so different with Python's
## Why, does R make mistake?
## No! The package we used here, ... | apache-2.0 | R | |
b339b0fedb4dd170e2cde67f5058784e6575384c | Make CLOSURE a FUNCT for closures (#2002) | giuliolunati/ren-c,giuliolunati/ren-c,codebybrett/ren-c,giuliolunati/ren-c,hostilefork/rebol,draegtun/ren-c,hostilefork/rebol,kealist/ren-c,draegtun/ren-c,mbk/ren-c,rgchris/ren-c,rgchris/ren-c,draegtun/ren-c,kealist/ren-c,codebybrett/ren-c,draegtun/ren-c,codebybrett/ren-c,rgchris/ren-c,hostilefork/rebol,codebybrett/ren... | src/mezz/mezz-func.r | src/mezz/mezz-func.r | REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Mezzanine: Function Helpers"
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/LICE... | REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 Mezzanine: Function Helpers"
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/LICE... | apache-2.0 | R |
a51fbb73922f40d0321b061fcd2b73cf430bea7e | Create stage_weight_processing.r | dpbroman/floodforecasting | stage_weight_processing.r | stage_weight_processing.r | #######DESCRIPTION###############################
#processes area, prcp, et, qavg table for use as weights in
#rating curve fits
#outputs rdata object and csv
#################################################
##load libraries
library(dplyr)
library(data.table)
library(stringr)
library(tidyr)
library(readr)
##user inp... | mit | R | |
ed16f5933e58152ddf598cfe2164756df532bd5f | Create kriging_qgis_r.r | leandromet/Geoprocessamento---Geoprocessing,leandromet/Geoprocessamento---Geoprocessing,leandromet/Geoprocessamento---Geoprocessing,leandromet/Geoprocessamento---Geoprocessing,leandromet/Geoprocessamento---Geoprocessing | kriging_qgis_r.r | kriging_qgis_r.r |
##Basic statistics=group
##Layer=vector
##Field=Field Layer
## by= number 0.1
##Output=output raster
Bibliotecas utilizadas para a krigagem
library(gstat)
library(rgl)
library("spatstat")
library("maptools")
install.packages("pls")
library (pls)
library(automap)
library(raster)
Abertura de dados e alocação de variávei... | mit | R | |
d0b7814be2a6b40418cd5142119f36793213d7b6 | Create Keshav_PrimeSieve.r | ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovski/al-go-rithms,ZoranPandovs... | math/prime_sieve/R/Keshav_PrimeSieve.r | math/prime_sieve/R/Keshav_PrimeSieve.r | sieve <- function(n)
{
n <- as.integer(n)
if(n > 1e6) stop("n too large") # restricting for larger values of n
primes <- rep(TRUE, n) # initially all false
primes[1] <- FALSE # trivial case
last.prime <- 2L
for(i in last.prime:floor(sqrt(n))) # looping through the array
{
primes[seq.int(2L*... | cc0-1.0 | R | |
2a6433e1024c04772f677270907db4a5cf1c9871 | 更新：第六章fig7-6 | shuaimeng/r | thesis/chap7/fig7-6.r | thesis/chap7/fig7-6.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/print")
library(xlsx)
# reading ux and sd
k1<-read.xlsx("dotx.xlsx",sheetName="600",header=TRUE)
k2<-read.xlsx("dotx.xlsx",sheetName="1khz",header=TRUE)
k3<-r... | mit | R | |
83711c5e27ab7437e1432b335c1a17e8860a1a3b | Create getNormalizedFoldChange.r | mahmoudibrahim/timeless,mahmoudibrahim/timeless | getNormalizedFoldChange.r | getNormalizedFoldChange.r | rm(list = ls())
library(limma)
countsFile="allCounts.txt"
l = read.table(countsFile)
k27ac = normalizeQuantiles(cbind(l[[5]], l[[6]], l[[7]], l[[8]])) #H3K27ac
k27me3 = normalizeQuantiles(cbind(l[[9]], l[[10]], l[[11]], l[[12]])) #H3K27me3
me3 = normalizeQuantiles(cbind(l[[13]], l[[14]], l[[15]], l[[16]])) #H3K4me3
... | mit | R | |
50473701750f6334eb3fe109af91ff6eeb2843ae | Solve Salary with Bonus 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/1009/1009.r | solutions/uri/1009/1009.r | input <- file('stdin', 'r')
a <- readLines(input, n=1)
b <- as.integer(readLines(input, n=1))
c <- as.double(readLines(input, n=1))
write(sprintf("TOTAL = R$ %.2f", b + (c * 0.15)), "")
| mit | R | |
c9737879744403ea4f6e0d34eaed07fad6126fdf | Add intorduction to R basics | mkuzak/RIntroBayarea | RBasics.r | RBasics.r | # Introduction to data structures in R
# atomic vector
# -------------
v <- c(1,2.0,3.5)
v1 <- c('foo', 'bar')
# always flat
c(1, c(2, c(3,4)))
# homogenous -- all elements need to be of one type
# values will be coerced to more flexible type
c("a", 1)
# list
# ----
# heterogenous
x <- list(1:3, "a", c(TRUE, FALSE,... | apache-2.0 | R | |
9a985cc3a51ec9391d74e4f9ba7b82bc6b024691 | Add correlation test | PoDiGG/podigg,PoDiGG/podigg | stats/correlation.r | stats/correlation.r | library(psych)
region_cells <- read.csv('../input_data/region_cells.csv')
subset <- region_cells[which(region_cells$density>0 & region_cells$density<30),]
biserial(region_cells$density, region_cells$hasstop) # 0.1756958
biserial(subset$density, subset$hasstop) # 0.4397582
# Outliers distort the correlation a lot!
# W... | mit | R | |
535e302b96c0fc7ecbaefee84ce5ba725e69a273 | Create polar_plot.r | nairvinayv/Rscripts | polar_plot.r | polar_plot.r | #R-Code for generating Polar plots using the plotrix library
library(plotrix)
data<-read.csv('15T-epsilon.dat')
setEPS()
png("15T-epsilon.png")
polar.plot(data[1:14999,1],data[1:14999,2],rp.type="p",start=90,clockwise=TRUE,main=expression(paste("Torsion Angles: 1,5T-",epsilon)))
dev.off()
| mit | R | |
f2640e24f937b661031f919c549f885f975319e4 | Add R packages for IAS-C188 | berkeley-dsep-infra/datahub,ryanlovett/datahub,berkeley-dsep-infra/datahub,ryanlovett/datahub,berkeley-dsep-infra/datahub,ryanlovett/datahub | deployments/r/image/extras.d/ias-c188.r | deployments/r/image/extras.d/ias-c188.r | #!/usr/bin/env Rscript
# From https://github.com/berkeley-dsep-infra/datahub/issues/814
devtools::install_github('cran/stargazer', ref='5.2.2', upgrade_dependencies=FALSE)
devtools::install_github('cran/lm.beta', ref='1.5-1', upgrade_dependencies=FALSE)
devtools::install_github('cran/multcomp', ref='1.4-8', upgrade_de... | bsd-3-clause | R | |
3a51f3d30ab23a48146c2d14c5dffad5c93fbe61 | Create uvoz_tabela1.r | ZavbiA/APPR-2017 | uvoz/uvoz_tabela1.r | uvoz/uvoz_tabela1.r | # 2. faza: Uvoz podatkov
# Funkcija, ki uvozi število medalj po državah iz Wikipedije
uvozi.medalje <- function() {
link <- "https://en.wikipedia.org/wiki/All-time_Olympic_Games_medal_table"
stran <- html_session(link) %>% read_html()
tabela <- stran %>% html_nodes(xpath="//table[@class='wikitable sortable']")... | mit | R | |
7e536ed41fd4c20df08d58a89e3d1b08826ade2c | Create lm-double.r | Sokel/R-shchu | lm-double.r | lm-double.r | ?swiss
swiss <- data.frame(swiss)
str(swiss)
hist(swiss$Fertility,col="Red")
fit <- lm(Fertility ~ Examination + Catholic, data = swiss)
summary(fit)
fit <- lm(Fertility ~ Examination * Catholic, data = swiss)
summary(fit)
confint(fit)
# trouble №1
x1 <- rnorm(50) # создадим случайную выборку из 50 элементов
x2 <... | apache-2.0 | R | |
02f45f781cd97d5788a34926420836be2f9ca015 | Add R/measurements.r with lifetime_worst function. | pschulam-attic/sclero | R/measurements.r | R/measurements.r | lifetime_worst <- function(measurements, low = TRUE) {
quant <- if (low) 0.1 else 0.9
worst.set <- quantile(measurements, quant, na.rm = TRUE)
worst <- median(worst.set)
# median returns infinity if length(worst.set) == 0
if (is.infinite(worst)) NA else worst
}
| mit | R | |
08bb83192d9b9f0a14dee9eeaa33f9a0ec52e6b1 | Add test-sbatch.r | jmousseau/Stain | tests/testthat/test-sbatch.r | tests/testthat/test-sbatch.r | context("sbatch")
test_that("All options are formated correctly", {
expect_equal(sbatch_opts$begin("00:00:01"), "--begin=00:00:01")
expect_equal(sbatch_opts$cpus_per_task(12), "--cpus-per-task=12")
expect_equal(sbatch_opts$mail_user("user@address"),
"--mail-user=user@address")
expect_... | mit | R | |
6aff7bcdd6c9b27987f20f41695c2b02747ba26d | add sylcount test | wrathematics/sylcount,wrathematics/sylcount,wrathematics/sylcount | Rpkg/tests/sylcount.r | Rpkg/tests/sylcount.r | library(sylcount)
a = "I am the very model of a modern major general."
b = "I have information vegetable, animal, and mineral."
x = c(a, b)
y = paste0(a, b, collapse=" ")
test = sum(sylcount(a)[[1]])
truth = 16L
stopifnot(identical(truth, test))
test = sum(sylcount(b)[[1]])
truth = 17L
stopifnot(identical(truth, tes... | bsd-2-clause | R | |
60f11b3cc7ae1370120d44f1afc0adb6617a8193 | test for worker-wrapper-split bug | metaborg/strategoxt,Apanatshka/strategoxt,lichtemo/strategoxt,Apanatshka/strategoxt,metaborg/strategoxt,Apanatshka/strategoxt,metaborg/strategoxt,Apanatshka/strategoxt,Apanatshka/strategoxt,lichtemo/strategoxt,lichtemo/strategoxt,lichtemo/strategoxt,lichtemo/strategoxt,metaborg/strategoxt,metaborg/strategoxt | strc/spec/test1/test47.r | strc/spec/test1/test47.r | module test47
strategies
hd : [x | xs] -> x
tl : [x | xs] -> xs
main =
where(foo(|<hd>))
; bar(|<hd>,[<tl>])
foo(|prog) =
?[prog | args]
; !(prog, args)
; swap
bar(|prog,args2) =
?[prog | args]
; !(prog, args2)
; swap
swap : (x, y) -> (y, x)
| apache-2.0 | R | |
a190d85dc97b1bf564dcac8e396d53b080778ddd | Create parsefile.r | shrib/NlpViaRAndWordNet | parsefile.r | parsefile.r | library("wordnet")
setwd("c:/data/r/")
loadWork <- function (what) {
rl <- readLines("foo.txt")
words <- c("")
cnt <- 0
for (i in 1:length(rl)) {
splitSentence = c(unlist(strsplit(rl[i], " ")))
if (length(splitSentence) > 0) {
for (n in 1:length(splitSentence)) {
words[cnt] <- splitSentence[n]
cnt... | mit | R | |
a74051f43c057d6f6363370719311f63a33fe18c | Create test.r | xiaodaigh/teradata.dplyr | test/test.r | test/test.r | #library("teradataR")
library("RODBC")
library("dplyr")
library("assertthat")
#con <- tdConnect(dsn, uid = uid, pwd = pwd, database = database)
#a <- td.data.frame(test_table)
# tdQuery('select count(*) from airlines')
#tdClose()
#a1 <- as.td.data.frame(a, tableName = "airlines", database = "")
st <- src_teradata(h... | mit | R | |
50ee4b911ebcd13f84105d9073eafdb45e769dc2 | Create aov.r | Sokel/R-shchu | aov.r | aov.r |
library(ggplot2)
DV ~IV # one-way
DV ~ IV1 + IV2 # Two-way
DV ~ IV1:IV2 # Two-way interaction
DV ~ IV1 + IV2 + IV1:IV2 # Main effects + interaction
DV ~ IV1 * IV2 # The same: Main effects + interaction
DV ~ IV1 + IV2 + IV3 + IV1:IV2
DV ~ (IV1 + IV2 + IV3)^2 # main effect and all possible interaction
DV ~ IV1 ... | apache-2.0 | R | |
ce447a06fc82841df0ae6de38d9add2b387297d3 | add jquery.fullPage.js | yuhualingfeng/jquery-study,yuhualingfeng/jquery-study | site.rd | site.rd |
/**
useful jquery plug-in
copyright 2015,puhuasheng
*/
# jquery.fullPage.js
# gitHub:https://github.com/alvarotrigo/fullPage.js
| mit | R |
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