id stringlengths 40 40 | repo_name stringlengths 5 110 | path stringlengths 2 233 | content stringlengths 0 1.03M ⌀ | size int32 0 60M ⌀ | license stringclasses 15
values |
|---|---|---|---|---|---|
853215a82ec463f085256540a061cb72bcdcb5ed | krlmlr/cxxr | src/extra/testr/filtered-test-suite/setS4Object/tc_setS4Object_5.R | expected <- eval(parse(text="structure(c(\"nonStructure\", \"ANY\", \"ANY\", \"ANY\"), .Names = c(NA_character_, NA_character_, NA_character_, NA_character_), package = character(0), class = structure(\"signature\", package = \"methods\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(\"nonS... | 619 | gpl-2.0 |
ddb29ca72bce77ca0f5e7ebbfb6728ebaea228d2 | genome/aml31Benchmarking | R/aml31Benchmarking.R | addKey <- function(df){
df$key = paste(df[,1],df[,2],df[,4],df[,5],sep="_")
return(df)
}
##----------------------------------------------
subsetByVaf <- function(df,range){
if("tum_vaf" %in% names(df)){
return(df[df$tum_vaf >= range[1] & df$tum_vaf <= range[2],])
}
return(df)
}
##-------------------... | 4,014 | mit |
853215a82ec463f085256540a061cb72bcdcb5ed | kmillar/rho | src/extra/testr/filtered-test-suite/setS4Object/tc_setS4Object_5.R | expected <- eval(parse(text="structure(c(\"nonStructure\", \"ANY\", \"ANY\", \"ANY\"), .Names = c(NA_character_, NA_character_, NA_character_, NA_character_), package = character(0), class = structure(\"signature\", package = \"methods\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(\"nonS... | 619 | gpl-2.0 |
853215a82ec463f085256540a061cb72bcdcb5ed | rho-devel/rho | src/extra/testr/filtered-test-suite/setS4Object/tc_setS4Object_5.R | expected <- eval(parse(text="structure(c(\"nonStructure\", \"ANY\", \"ANY\", \"ANY\"), .Names = c(NA_character_, NA_character_, NA_character_, NA_character_), package = character(0), class = structure(\"signature\", package = \"methods\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(\"nonS... | 619 | gpl-2.0 |
853215a82ec463f085256540a061cb72bcdcb5ed | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/setS4Object/tc_setS4Object_5.R | expected <- eval(parse(text="structure(c(\"nonStructure\", \"ANY\", \"ANY\", \"ANY\"), .Names = c(NA_character_, NA_character_, NA_character_, NA_character_), package = character(0), class = structure(\"signature\", package = \"methods\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(\"nonS... | 619 | gpl-2.0 |
853215a82ec463f085256540a061cb72bcdcb5ed | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/setS4Object/tc_setS4Object_5.R | expected <- eval(parse(text="structure(c(\"nonStructure\", \"ANY\", \"ANY\", \"ANY\"), .Names = c(NA_character_, NA_character_, NA_character_, NA_character_), package = character(0), class = structure(\"signature\", package = \"methods\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(\"nonS... | 619 | gpl-2.0 |
7380214d01e6232b5faa769bcb3540cd1ce7d775 | Alsheh/open_source_course | Lab08/Untitled.R | library(arules)
rm(list=ls())
setwd("/Users/hassanalshehri/Google Drive/RPI/Open_Software/myRepo/Lab08")
getwd()
admissions <- read.table("binary.csv", header = TRUE, sep = "," )
col_names <- names(admissions)
admissions[,col_names] <- lapply(admissions[,col_names] , factor)
str(admissions)
summary(admissions)
head(a... | 2,341 | mit |
efc6fb1d4b5710ba5c373d8b603235763966a187 | astrobayes/BMAD | chapter_8/code_8.9_and_8.10.R | # From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press
# (c) 2017, Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida
#
# you are kindly asked to include the complete citation if you used this
# material in a publication
# Code 8.9 - Random intercept binomial logistic data in R
y <- c(6,1... | 2,136 | gpl-3.0 |
2df5e5340a667899b21d4cbcd169040062802022 | nafiux/portableR | site-library/plotly/arguments.R | # This script grabs argument names from plotly documentation and provides some
# convenience functions for translating all those arguments to the R package
library(rvest)
ref <- read_html("https://plot.ly/javascript-graphing-library/reference")
# complete set of args
argz <- ref %>% html_nodes(".gamma .link--impt") %>... | 3,820 | agpl-3.0 |
67faba6720c477f1bba751e7e6bd8377b021e7ff | homeupnorth/exploredata2 | plot6.R | setwd("~/Documents/Coursera/Exploratory Data Analysis/exploredata2")
#
# Read the data
#
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
#
# Examine Data
#
str(NEI)
str(SCC)
head(SCC)
summary(NEI)
#
# Convert data to useful datatypes
#
NEI$Pollutant<-as.factor(NEI$Pollutant)
NEI$t... | 1,079 | gpl-2.0 |
3f2a5872d50a818730b174f61cf58e3d0f2845e7 | maxplanck-ie/Megamapper | custom/hetmap_Rscript.R | #############################################
# Homozygosity Mapper v1.0
# by Nikolaus Obholzer, 2011
# License: GPL
############################################# R --min-vsize=10M --max-vsize=8G --min-nsize=10M --max-nsize=8G
#############################################
##############################################... | 7,598 | bsd-3-clause |
349ecabe72241efd3364c88956a5ab7bea487c04 | tessam30/Zambia | ZMB_DRG_YaliFellows_geocode.R |
# Import Zambia DRG Fellows file ----------------------------
# Prepares the data to be mapped by geocoding the cities
# Tim Essam, USAID | GeoCenter, 6 July 2017, tessam@usaid.gov
# setup -------------------------------------------------------------------
library(tidyverse)
library(stringr)
library(foreign)
library(... | 2,434 | mit |
178167056910af7eeae52407b64231125a516298 | bjsmith/reversallearning | lba_rl_joint_v11u335.R | Loading datasets...Compiling model...During startup - Warning message:
Setting LC_CTYPE failed, using "C"
In file included from file1e7615048692.cpp:8:
In file included from /usr/local/lib/R/site-library/StanHeaders/include/src/stan/model/model_header.hpp:4:
In file included from /usr/local/lib/R/site-library/StanHead... | 689,028 | apache-2.0 |
7cf6633867822554ed1474c47421e7495e39d31a | wilkersj/tumgrShiny | ui.R | #shiny app
library(shiny)
#install.packages("devtools")
#library(devtools)
#devtools::install_github('wilkersj/tumgr')
library(tumgr)
ui<-(fluidPage(
titlePanel("(beta) Tumor Growth Rate Analysis"),
sidebarLayout(
sidebarPanel(
fileInput('file1', 'Choose file to upload',
a... | 1,605 | mit |
72a124202d9cd4cd20fdf07e2e2991937855c7aa | fingerhuth/NG-POC-resistance | scripts/p_FigS4.R | setwd("~/PhD/ng_poc/repository/NG-POC-resistance/scripts/")
library(reshape2)
library(ggplot2)
library(ggthemes)
library(gridExtra)
library(gtable)
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E44... | 3,203 | mit |
fc3c73af500e398c8649a97c527f9cad481ee104 | debarros/CSIAccountabilityWkbk | historical regents pass rates.R | #Analyzing regents scores
# This script calculates regents exam pass rates for prior years.
TestTerms = paste0(2009:2019, " June") #define the terms you want
regentsScores.GTH = regentsScores[which(regentsScores$Location == "GTH"),] #limit the data set to just GTH
tests = unique(regentsScores.GTH$Exam) #get a list ... | 1,035 | gpl-3.0 |
56f0e5a450e97c2ed0bab24755c15bccb8b9b888 | sagrules/PremierLeague | premierleague.R | #' Set working directory
setwd("~/R/Premier")
#' Load libraries
library(readxl)
library(dplyr)
#' Set week premier league week number, should correspond to number of sheets
#' in excel file.
NUM_WEEKS = 12
#' Load game results into a list of data frames for each week
week = list()
for (i in 1:NUM_WEEKS) {
week[[... | 13,057 | apache-2.0 |
4b27e469ae6cbd1f0b038c113c554e3b9aa3725f | cran/gcExplorer | R/legend.size.R | #
# Copyright (C) 2009 Friedrich Leisch, Theresa Scharl
# $Id: legend.size.R 4333 2009-04-27 14:44:50Z scharl $
#
setGeneric("legend.size", function(object, ...)
standardGeneric("legend.size"))
setMethod("legend.size", signature(object="kccasimple"),
function(object, theme, colscale=NULL, pos="bottomleft")
{
... | 2,384 | gpl-2.0 |
503ff83023b03e33f2f2e99f4e10d9f29b67e0f0 | gtesei/fast-furious | competitions/seizure-prediction/notes_seizure_R.R | ## notes_seizure_R
library(caret)
library(Hmisc)
library(data.table)
library(verification)
library(pROC)
getBasePath = function (type = "data" , ds="") {
ret = ""
base.path1 = ""
base.path2 = ""
if(type == "data") {
base.path1 = "C:/docs/ff/gitHub/fast-furious/dataset/seizure-prediction"
base.path2 ... | 21,162 | mit |
8e623382e8716d4b4fefbbca18e83c6bd46973ce | wepelham3/cost-app | global-functions/crud_group_components.R | #-----------------------------------------------------------------------------------
# CREATE, READ, UPDATE. DELETE (CRUD) Functions for Group Treatments
#
#-----------------------------------------------------------------------------------
# ---------------------------------------------------------------------... | 8,170 | mit |
dc567636cb26512f95b0c7239e8d3e1a88258da1 | bbrede/S2utils | R/S2_L2A_meta.R | #' Extract S2 L2A meta data
#'
#' Extract S2 L2A meta data
#'
#' @param S2_safe Chr. S2 SAFE folder ("S2A_USER_PRD_MSIL2A_PDMC_....SAFE")
#'
#' @return list of granules of list of names elements
#' \describe{
#' \item{Granule_Name}{Full granule name, e.g. "S2A_USER_MSI_L2A_TL_SGS__20160119T144513_A003008_T31UFT_N02... | 3,998 | gpl-3.0 |
0d5fbcced9b16e6cfe5f43bd551ad16af55779c5 | SchlossLab/Sze_FollowUps_Microbiome_2017 | code/old/20160919_ToDoDataAnalysis_pt1.R | ## A To Do List to follow up on stemming from original exploratory analysis
## Focus strictly on Lesion, SRNLesion, and three groups (Normal, Adenoma, Cancer) classifications
## Marc Sze
# Load required dependencies and libraries
source('code/functions.R')
source('code/graphFunctions.R')
loadLibs(c("pROC","ra... | 22,794 | mit |
81f1b992ff3978d7bab7860815199855b8a458d3 | statguy/STREM | inst/simulation/copy_tracks.R | # library(devtools); install_github("statguy/Winter-Track-Counts")
library(parallel)
library(doMC)
registerDoMC(cores=detectCores())
library(STREM)
source("~/git/STREM/setup/WTC-Boot.R")
copyTracks <- function(scenario, suffix, maxIterations) {
mss <- getMSS(scenario=scenario)
study <- mss$study
tracks <- Simul... | 1,311 | gpl-2.0 |
81f1b992ff3978d7bab7860815199855b8a458d3 | statguy/WTC | inst/simulation/copy_tracks.R | # library(devtools); install_github("statguy/Winter-Track-Counts")
library(parallel)
library(doMC)
registerDoMC(cores=detectCores())
library(STREM)
source("~/git/STREM/setup/WTC-Boot.R")
copyTracks <- function(scenario, suffix, maxIterations) {
mss <- getMSS(scenario=scenario)
study <- mss$study
tracks <- Simul... | 1,311 | gpl-2.0 |
dc94287d1f39112451b795681d1ea5c6a35e5b48 | UCL-BLIC/legion-buildscripts | cytofpipe/v1.3/Rlibs/vcd/demo/mosaic.R | #####################
## Mosaic Displays ##
#####################
#########################
## Hair Eye Color Data ##
#########################
data(HairEyeColor)
## Basic Mosaic Display ##
HairEye <- margin.table(HairEyeColor, c(1,2))
mosaic(HairEye, main = "Basic Mosaic Display of Hair Eye Color data")
## Hair ... | 3,407 | mit |
4e87914fdd090b0c1780ddcd4a7d476381ec156e | sammorris81/rare-binary | markdown/dec2015/first-run-sim/dec-sim-12.R | # load packages and source files
rm(list=ls())
options(warn=2)
library(fields)
library(evd)
library(spBayes)
library(fields)
library(SpatialTools)
# library(microbenchmark) # comment out for beowulf
library(mvtnorm)
library(Rcpp)
library(numDeriv)
library(pROC)
Sys.setenv("PKG_CXXFLAGS"="-fopenmp")
Sys.setenv("PKG_LIB... | 7,760 | gpl-2.0 |
dc94287d1f39112451b795681d1ea5c6a35e5b48 | UCL-BLIC/legion-buildscripts | cytofpipe/v1.2/Rlibs/vcd/demo/mosaic.R | #####################
## Mosaic Displays ##
#####################
#########################
## Hair Eye Color Data ##
#########################
data(HairEyeColor)
## Basic Mosaic Display ##
HairEye <- margin.table(HairEyeColor, c(1,2))
mosaic(HairEye, main = "Basic Mosaic Display of Hair Eye Color data")
## Hair ... | 3,407 | mit |
56d7314ececc2326f39afc1f165abe16a4e4a34b | kbrannan/summarize-upd-hyd-cal | r-files/fdc-usgs-eq-only-plot.R | ## load packages
library(ggplot2, quietly = TRUE)
## path for output
chr.bacteria.twg.17.dir <- "M:/Presentations/2016-02-09 Bacteria TWG 17"
## main path for uncert re-reun
chr.uncert.rerun.dir <- "M:/Models/Bacteria/HSPF/Big-Elk-Cadmus-HydCal-Updated-WDM/pest-hspf-files/upd-uncert/uncert-rerun"
## get names of run... | 1,313 | gpl-3.0 |
efecc562d4d0acf74f79a02546228e321d7458c5 | fraukewiese/renpass | code_R_renpass/code_R_pump_region.R | # This code file is part of renpass published under the GNU GPL 3 license.
# See also: code_R_start_renpass.R and http://opensource.org/licenses/GPL-3.0
#-----
# applied renpass function: pumpRegion
#-----
if(exists("other_pump_data")){
other <- other_pump_data
} else {
other <- data.frame(matrix(nrow = 0, ncol = ... | 954 | gpl-3.0 |
ed9b21b8c44662f9f6881a269ad73a5016ea8a9d | asishallab/GeneFamilies | R/family_funks.R | #' Computes the normalized empirical Shannon Entropy for counts delivered in
#' the argument \code{counts.table}. Basis of the \code{log} function is
#' natural and normalization is done by division by the maximum entropy
#' \code{log(length(counts.table))} (see
#' \href{https://en.wikipedia.org/wiki/Entropy_(informati... | 22,736 | gpl-3.0 |
798130cab72640839415f06fed3e5643d1c2eeb2 | DistanceDevelopment/Distance2 | R/encounter_rate_var.Borchers1998.R | #' Encounter rate variance estimation from Borchers et al (1998)
#'
#' Estimate the encounter rate variance as in Borchers et al (1998), equation 13.
#' @inheritParams encounter_rate_var.Innes2002
#'
#' @author David L Miller
#' @references
#' Borchers, D. L., Buckland, S. T., Goedhart, P. W., Clarke, E. D., & Hedley, ... | 632 | gpl-2.0 |
798130cab72640839415f06fed3e5643d1c2eeb2 | dill/Distance2 | R/encounter_rate_var.Borchers1998.R | #' Encounter rate variance estimation from Borchers et al (1998)
#'
#' Estimate the encounter rate variance as in Borchers et al (1998), equation 13.
#' @inheritParams encounter_rate_var.Innes2002
#'
#' @author David L Miller
#' @references
#' Borchers, D. L., Buckland, S. T., Goedhart, P. W., Clarke, E. D., & Hedley, ... | 632 | gpl-2.0 |
73c647ae97f482c3737412a2b1d05f5dfa0957ed | mhunter1/OpenMx | inst/models/passing/FitMultigroup.R | #
# Copyright 2007-2017 The OpenMx Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 3,438 | apache-2.0 |
c2aba5038a793c40c4084013067632ce335bc30a | RCollins13/rCNVmap | working_code/cluster_noncoding_elements_into_regBlocks.R | #Test code to play with noncoding element clustering based on jaccard indexes
#Read data
x <- read.table("~/scratch/cleaned_noncoding_loci.jaccard_matrix.txt",header=T)
rownames(x) <- x[,1]
x <- x[,-1]
x <- apply(x,2,as.numeric)
rownames(x) <- colnames(x)
#Plot heatmap
png("~/scratch/heatmap.test.png",height=2000,wid... | 1,741 | mit |
c2aba5038a793c40c4084013067632ce335bc30a | RCollins13/CNValue | working_code/cluster_noncoding_elements_into_regBlocks.R | #Test code to play with noncoding element clustering based on jaccard indexes
#Read data
x <- read.table("~/scratch/cleaned_noncoding_loci.jaccard_matrix.txt",header=T)
rownames(x) <- x[,1]
x <- x[,-1]
x <- apply(x,2,as.numeric)
rownames(x) <- colnames(x)
#Plot heatmap
png("~/scratch/heatmap.test.png",height=2000,wid... | 1,741 | mit |
06a4192011485f8a370330b2f4e04183c97c9e1d | radivot/SEERaBomb | SEERaBomb/inst/docs/papers/tutorial/ageTherapyEx4.R | ###ageTherapyEx4.R
d=incidSEER(canc,popsae,secs)
d=d%>%filter(age<=85,year>=2000)
d=d%>%mutate(ageG=cut(age,seq(0,85,5)))
d=d%>%group_by(cancer,ageG)%>%
summarize(age=mean(age),py=sum(py),n=sum(n))%>%
mutate(incid=n/py,grp="Background")
d=d%>%select(cancer,grp,everything(),-ageG)#reorder columns
#the next 3 lines d... | 1,472 | gpl-2.0 |
c51621c033449feb1b7813e2d35cd8c9643f0d60 | dankelley/oce-issues | 18xx/1805/1805d.R | # is split(...,indInterval()) faster than split(...,cut())?
t0 <- as.POSIXct("2021-01-01", tz="UTC")
Ns <- 10^seq(3, 8, 0.25)
As <- rep(NA, length(Ns))
Bs <- rep(NA, length(Ns))
for (i in seq_along(Ns)) {
t <- t0 + seq(1, Ns[i])
ninterval <- 2560L
y <- rnorm(Ns[i])
df <- data.frame(t, y)
b <- seq(m... | 895 | gpl-2.0 |
3363d6271ba9b4455b113b1c210debcc804073fd | andrewdefries/andrewdefries.github.io | FDA_Pesticide_Glossary/ALLY.R | library("knitr")
library("rgl")
#knit("ALLY.Rmd")
#markdownToHTML('ALLY.md', 'ALLY.html', options=c("use_xhml"))
#system("pandoc -s ALLY.html -o ALLY.pdf")
knit2html('ALLY.Rmd')
| 180 | mit |
492d8383915cbb1b0fb854d6fa9a4e2995ed7e53 | KirarinSnow/Google-Code-Jam | Qualification Round 2009/C.R | # Problem: Welcome to Code Jam
# Language: R
# Author: KirarinSnow
# Usage: R -q --slave -f thisfile.R <input.in >output.out
infile <- file('/dev/stdin')
buffer <- scan(infile, 'character', sep='\n')
cases <- type.convert(buffer[1])
current <- 2
compute <- function()
{
chars <- buffer[current]
current <<- ... | 804 | gpl-3.0 |
f46a9329f8fdd223cdde533018d8a4004dd5922a | mshvartsman/cddm | R/nips2015plots.R | library(stringr)
library(data.table)
library(ggplot2)
library(gridExtra)
library(plyr)
binpath <- '../bin/'
####### FLANKER ########
flankerpar <- 'timePerStep=1,maxTrials=100000,maxSamps=10000,contextNoise=9,targetNoise=9,decisionThresh=0.9,eblMean=0,motorPlanMean=0,motorExecMean=0,eblSd=1,motorSd=1,trialDist=0.5 ... | 6,243 | lgpl-3.0 |
312ebc32dc59bc707fc226eb2d95450d7da60061 | SCAR/solong | data-raw/equations_Smal1993.R | ##oldrefs$Smal1993 <- "Smale MJ, Clarke MR, Klages TW, Roeleveld MA (1993) Octopod beak identification: resolution at a regional level (Cephalopoda, Octopoda: Southern Africa). South African Journal of Marine Sciences 13: 269-293"
refs$Smal1993 <- bibentry(bibtype="Article",key="Smal1993",
... | 16,363 | mit |
eb1b0eb60b62e691dd59a39e25e7fd4d582575f7 | kalden/spartan | R/ensemble_utilities.R | #' Internal function used to combine test set predictions from emulators to
#' form the ensemble training set
#'
#' @param emulator An emulator object from which the test set data is being
#' predicted
#' @param parameters Vector containing the names of the simulation parameters
#' in the dataset on which the emulator ... | 14,030 | gpl-2.0 |
d3d4835970a1bf77a8a5526b979701181b6adc12 | lawphill/ProjectEuler | Problems_26_to_50/Euler027.R | # For the quadratic equation n^2 + a*n + b, find the product of a & b
# |a| < 1000, |b| < 1000, where the equation creates the longest string of
# consecutive primes, starting with n=0
max_primes <- 99999
primes <- 1:max_primes
primes[1] <-0
for(i in 2:floor(max_primes/2)){
if(primes[i] != 0){
primes[seq.int(i... | 932 | mit |
1b777e7c496e874932c489d5e6af9557fc1558d2 | nuest/sensorweb4R | R/distance-matrix.R | # Copyright 2014 52°North Initiative for Geospatial Open Source Software GmbH
#
# 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 req... | 1,907 | apache-2.0 |
cb07a958509e23508403f6cfd3a14f14b0fe1a20 | michalkurka/h2o-3 | h2o-r/tests/testdir_algos/coxph/runit_coxph_shelter_strata.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.CoxPH.shelter.strata.impl <- function(ties) {
shelter <- read.csv(file =locate("smalldata/coxph_test/shelter.csv"))
coxph_features <- c("intake_condition","intake_type", "animal_breed", "ch... | 2,615 | apache-2.0 |
1081c9062ce3868d7f722f2ca1a8a83ebc624488 | Chicago/osd-building-footprints | examples/Importing GeoJSON R Demo.R | # TITLE: Importing GeoJSON Example in R
# AUTHOR: Tom Schenk Jr., City of Chicago
# CREATED: 2013-01-23
# UPDATED: 2013-01-31
# NOTES: Caution! The street centerline data is quite large and may take a long time to complete.
# LIBRARIES: rgdal, ggplot2
# Set working directory (e.g., "C:\\Users\\username\\downloads" or ... | 1,558 | mit |
1081c9062ce3868d7f722f2ca1a8a83ebc624488 | OSMBuildings/osd-building-footprints | examples/Importing GeoJSON R Demo.R | # TITLE: Importing GeoJSON Example in R
# AUTHOR: Tom Schenk Jr., City of Chicago
# CREATED: 2013-01-23
# UPDATED: 2013-01-31
# NOTES: Caution! The street centerline data is quite large and may take a long time to complete.
# LIBRARIES: rgdal, ggplot2
# Set working directory (e.g., "C:\\Users\\username\\downloads" or ... | 1,558 | mit |
cb07a958509e23508403f6cfd3a14f14b0fe1a20 | h2oai/h2o-3 | h2o-r/tests/testdir_algos/coxph/runit_coxph_shelter_strata.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.CoxPH.shelter.strata.impl <- function(ties) {
shelter <- read.csv(file =locate("smalldata/coxph_test/shelter.csv"))
coxph_features <- c("intake_condition","intake_type", "animal_breed", "ch... | 2,615 | apache-2.0 |
1081c9062ce3868d7f722f2ca1a8a83ebc624488 | MitsueIwata/osd-building-footprints | examples/Importing GeoJSON R Demo.R | # TITLE: Importing GeoJSON Example in R
# AUTHOR: Tom Schenk Jr., City of Chicago
# CREATED: 2013-01-23
# UPDATED: 2013-01-31
# NOTES: Caution! The street centerline data is quite large and may take a long time to complete.
# LIBRARIES: rgdal, ggplot2
# Set working directory (e.g., "C:\\Users\\username\\downloads" or ... | 1,558 | mit |
3713f7d676f3fe824b93cbd26f034e26be7dcc32 | mem48/glider | jobs_code/ehs2rds_2011.R | #Read in EHS and convert to RDS
library(foreign)
library(dplyr, lib.loc = "M:/R/R-3.3.1/library")
library(lazyeval, lib.loc = "M:/R/R-3.3.1/library")
current_year <- 2011
infld <- "C:/Users/earmmor/OneDrive/OD/Glider - Private/WP2/Data/EHS/EHS-2011-SPSS/UKDA-7386-spss/spss/spss19/"
###################################... | 23,696 | gpl-3.0 |
7c92954d6a226af5ca7dbe96c36e6b3476828d06 | wotuzu17/tronador | R_packages/quantify/pkg/R/qCurrentRetPercentile.R | # this function is used by qCurrentRetStatus function
qCurrentRetPercentile <- function(TS, ROCn=1, runXn=200) {
TS <- Cl(TS)
cnames <- colnames(TS)
TS <- cbind(TS, ROC(Cl(TS), n=ROCn))
TS <- cbind(TS, runMean(TS[,2], n=runXn))
TS <- cbind(TS, runSD(TS[,2], sample=TRUE, n=runXn))
TS$quantile <- NA
colname... | 504 | mit |
0027c878a36213b861e8468d6ecf651197c69d74 | Myasuka/systemml | src/test/scripts/applications/descriptivestats/Scale.R | #-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you... | 4,321 | apache-2.0 |
188d6c193b1bcc8da26decdc24cb6f76af0e1c2d | StephaneMasson/datasciencecoursera | cacheSolve.R | cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inverse <- x$getinverse()
if(!is.null(inverse)) { message("getting cached data")
return(inverse)
}
matrix.data <- x$get()
inverse <- solve(matrix.data,...)
... | 362 | gpl-2.0 |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | ArcherCraftStore/ArcherVMPeridot | R-3.1.0/library/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | apache-2.0 |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | ArcherSys/ArcherSys | R/library/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | mit |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | Vincibean/RMAD | RMAD_Plugin/R-Inst/library/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | gpl-3.0 |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | ColumbusCollaboratory/electron-quick-start | R-Portable-Mac/library/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | cc0-1.0 |
f05296de6a5963d1f3afe00c1b22f1a213feb6a3 | flor3652/BigD | R/tp_dat.R | #' @title Generation of truncated Poisson data
#'
#' @description This function generates truncated Poisson data, with a truncation at c (counts can include c).
#'
#' @param n The number of data points to be generated.
#' @param lambda The mean parameter for the truncated Poisson.
#' @param c The cutoff of inflation.... | 1,484 | gpl-3.0 |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | Fredin/hablaMty-wordClouds | packrat/lib-R/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | mit |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | cxxr-devel/cxxr-svn-mirror | src/library/Recommended/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | gpl-2.0 |
5f74de8eb586bfc07f4aa84c0ccb7546efd55c19 | CodeGit/SequenceImp | dependencies-bin/windows/bin/R/library/lattice/tests/MASSch04.R | #-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 4 Graphical Output
library(MASS)
library(lattice)
trellis.device(postscript, file="ch04.ps", width=8, height=6,
pointsize=9)
options(echo=T, width=65, digits=5)
# 4.2 Basic plotting functions
topo.loess <- l... | 5,448 | gpl-3.0 |
b7d16013f8495d53ccf4a20d3ee3ca71b1bd44a1 | rstudio/tensorflow | R/flags.R | #' Parse Configuration Flags for a TensorFlow Application
#'
#' Parse configuration flags for a TensorFlow application. Use
#' this to parse and unify the configuration(s) specified through
#' a `flags.yml` configuration file, alongside other arguments
#' set through the command line.
#'
#' @param config The configurat... | 3,017 | apache-2.0 |
b585a7f1c914f404c72f00a3016171cbc0428439 | brooksambrose/knowledge-survival | function_map.R | rm(list=ls())
cat('\014')
setwd('/Users/bambrose/Dropbox/GitHub/knowledge-survival')
d<-readLines('dissertation.R')
ds<-readLines('dissertation_source.R')
(gfuns<-grep('^[^ ].+<- *function',ds,value=F))
funs<-sub('^([^<]+).+$','\\1',ds[gfuns])
gcoms<-grep("#",d)
labels3<-sub("^[^#]*(#.*)$","\\1",d[gcoms])
library(... | 4,112 | artistic-2.0 |
b585a7f1c914f404c72f00a3016171cbc0428439 | brooksambrose/oikos | function_map/function_map.R | rm(list=ls())
cat('\014')
setwd('/Users/bambrose/Dropbox/GitHub/knowledge-survival')
d<-readLines('dissertation.R')
ds<-readLines('dissertation_source.R')
(gfuns<-grep('^[^ ].+<- *function',ds,value=F))
funs<-sub('^([^<]+).+$','\\1',ds[gfuns])
gcoms<-grep("#",d)
labels3<-sub("^[^#]*(#.*)$","\\1",d[gcoms])
library(... | 4,112 | artistic-2.0 |
61ea973587ef2d00e9805adce0b786daa47360be | fabricecolas/R-MethIll | R/ComBatBetaNA.R | ComBatBetaNA <-
function(y,X){
des <- X[!is.na(y),]
y1 <- y[!is.na(y)]
B <- solve(t(des)%*%des)%*%t(des)%*%y1
return(B)
}
| 135 | mit |
e1b85430f22e0b8b2c24fcdd90b91273630dc052 | rsachse/renetools | R/axisb.R | axisb <-
function(side=1, at, labels, ...){
## ticks
## length(at) needs to be length(labels) + 1
axis(side, at=at, labels=FALSE, ...)
## labels
nat <- length(at)
atlab <- (at[1:(nat-1)] + at[2:nat])/2
axis(side, at=atlab, labels=labels, tick=FALSE, ...)
}
| 271 | gpl-2.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | JoanneL/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | fehtemam/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | rScientist/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | hfe2567/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | xyzhang89/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | ashishchandan/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | eyidayoadebola/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | owenyang83/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | Romka11/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | paternogbc/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | Mewzician/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | samchen/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | MarcoTomasetta/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | AmirtharajBritto/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | altaf-ali/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | vishalshastri/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | Jutair/R-programming-Coursera | Swirl/Rsubversion/branches/eda/Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-2.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | nunolf/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | dasjpatel15/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | artchist/stat | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | tvijay333/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | gloriaShopping/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | pmPartch/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | mkostovski08/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | stuthom/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | drnuance/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | Deerluluolivia/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | beckwang80/R-swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | bianyin102938/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | mmfern01/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | tillvaxse/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | dvbhagavathi/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | rsshalini/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
e3c2e202ba489ca4d7c67d74122cc195ef64642d | johnneyb/swirl_courses | Regression_Models/Overfitting_and_Underfitting/initLesson.R | swiss <- datasets::swiss
file.copy(from=file.path(find.package("swirl"),
"Courses/Regression_Models",
"Overfitting_and_Underfitting/fitting.R"),
to="fitting.R")
file.edit("fitting.R")
source("fitting.R", local=TRUE)
fit5 <- lm(Fertility ~ Agriculture + Examin... | 390 | gpl-3.0 |
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