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 |
|---|---|---|---|---|---|
31ea9fcd32a4abbe86e6955e8439ecbc1ab20520 | chendaniely/multidisciplinary-diffusion-model-experiments | staging/recurrent/src/helper-goodness.R | ################################################################################
#
# Goodness calculation functions
#
################################################################################
get_input_i <- function(unit_number){
input <- 0
return(input)
}
get_bias_i <- function(i_j_index_number){
b... | 2,494 | mit |
e22f543b37e4be7ccff548b5b2cf69d75c4c16b3 | Monash-RNA-Systems-Biology-Laboratory/patseqers | Shiny_tutorial/helper.R | # Generates a data frame of random numbers
make_df <- function(number_of_points){
x <- rnorm(number_of_points, mean = 20, sd = 5)
y <- rnorm(number_of_points, mean = 50, sd = 50)
df <- data.frame(x,y)
return(df)
}
| 251 | gpl-2.0 |
b4681f5d4cccf3c2e7478898ef64c0940770a574 | bwilbertz/kaggle_allen_ai | R/runFullModelPipeline.R | # The MIT License (MIT)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, subl... | 1,547 | mit |
849f3460ee05af84e210dd8c74a1ce45cc189065 | eclee25/flu-SDI-exploratory-age | mapping_code/zipcode_maps_R_code/map-zipcodes_ECLedit.R | ## Name: Elizabeth Lee
## Date: 7/19/13
## Function:
### 1. draw OR map per season, popsize as bubble size
### 1b. draw log(OR map) per season 7/31/13, popsize as bubble size
### 1c. draw OR map per season, incidence as bubble size
### 2. draw incidence map per season
### 3) incidence maps by week 7/23/13
## Note: nee... | 42,043 | mit |
a3b9adf94893b3719f72a2540819286d0145e061 | nextgenusfs/amptk | amptk/check_version.R | #!/usr/bin/env Rscript
is.installed <- function(mypkg){
is.element(mypkg, installed.packages()[,1])
}
Rversion <- R.Version()$version.string
if (!is.installed("dada2")){
dadaversion <- '0.0.0'
} else {
dadaversion <- packageVersion("dada2")
}
if (!is.installed("phyloseq")){
phyloseqversion <- '0.0.0'
... | 645 | bsd-2-clause |
e18dbca9e8c5d29c051cdf7f720c4babd14c660a | Tutuchan/morrisjs | R/morrisjs.R | #' morris.js plot
#'
#' This function prepares the widget to be drawn.
#'
#' In the case of a \code{data.frame} or a \code{tbl_df}, the first column must
#' be a object that can be interpreted as a \code{Data}, the other columns being
#' the data values.
#'
#' @param data the data to be drawn, can be a \code{ts}, \c... | 1,751 | mit |
a3b9adf94893b3719f72a2540819286d0145e061 | nextgenusfs/ufits | amptk/check_version.R | #!/usr/bin/env Rscript
is.installed <- function(mypkg){
is.element(mypkg, installed.packages()[,1])
}
Rversion <- R.Version()$version.string
if (!is.installed("dada2")){
dadaversion <- '0.0.0'
} else {
dadaversion <- packageVersion("dada2")
}
if (!is.installed("phyloseq")){
phyloseqversion <- '0.0.0'
... | 645 | bsd-2-clause |
c0900916ad10eaaf2acb8707ce3fde8d6294d5c9 | andymememe/datasciencecoursera | R Programming/makepower.R | make.power <- function(n) {
pow <- function (x) {
x ^ n
}
pow
} | 73 | gpl-2.0 |
74a3a14eb41cec2ddaf4fcc310849e749244c614 | SMHendryx/quantifyBiomassFromPointClouds | R/watershedSegmentTrees.R | # Script segments a point cloud into clusters using watershed segmentation on rasterized point cloud
# Clear workspace:
rm(list=ls())
library(lidR)
library(feather)
library(data.table)
#terminal output coloring
library(crayon)
error <- red $ bold
warn <- magenta $ underline
note <- cyan
#cat(error("Error: subscript o... | 3,011 | gpl-3.0 |
dd2bd46f4e6d1172285b0e9f0bfe588705613428 | andrewdefries/andrewdefries.github.io | FDA_Pesticide_Glossary/CMU.R | library("knitr")
library("rgl")
#knit("CMU.Rmd")
#markdownToHTML('CMU.md', 'CMU.html', options=c("use_xhml"))
#system("pandoc -s CMU.html -o CMU.pdf")
knit2html('CMU.Rmd')
| 174 | mit |
263ffbd199b804c501306857ec77c01624ef8651 | amschwinn/data_mining_lab | Lab 1/Schwinn_DataMining_Lab_1_Ex2.R | #############################
#Data Mining Practical Session
#Lab 1 Exercise 2
#
#Subject: Clustering with Iris Dataset.
#K-Means & Heirarchial Clustering
#
#Author: Austin Schwinn
#
#Jan 17, 2017
#############################
#install.packages('rstudioapi')
library(rstudioapi)
#Set working directory
setwd(dirname(... | 1,510 | mit |
cadfe7ab0699d8f30e3e85674ab4e70336aef4e2 | PFgimenez/PhD | R-files/data_renault_small_header.R | library(ggplot2)
#--------------------------------------------------------------------------------------------
#Parametres globaux
dataset_name = "renault_small_header"
taille_img_x = 1024/2
taille_img_y = 720/2
#fin parametres globaux
#----------------------------------------------------------------------------------... | 58,938 | gpl-3.0 |
cadfe7ab0699d8f30e3e85674ab4e70336aef4e2 | PFgimenez/thesis | R-files/data_renault_small_header.R | library(ggplot2)
#--------------------------------------------------------------------------------------------
#Parametres globaux
dataset_name = "renault_small_header"
taille_img_x = 1024/2
taille_img_y = 720/2
#fin parametres globaux
#----------------------------------------------------------------------------------... | 58,938 | gpl-3.0 |
29c062e99ea01825f3faf03a4bc83ed93dbb4827 | rho-devel/rho | src/extra/testr/filtered-test-suite/format/tc_format_34.R | expected <- eval(parse(text="structure(c(\"213198964\", \" 652425\"), .Names = c(\"null.deviance\", \"deviance\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(213198964, 652424.52183908), .Names = c(\"null.deviance\", \"deviance\")), FALSE, 5L, 0L, NULL, 3L, TRUE, NA)")); ... | 479 | gpl-2.0 |
c710fd01f5b72f465ccba907066bc88f2fd967de | realviacauchy/shiny-court-grapher | reports/TDO_Qtrly_Long_FY08-FY15_PLOT.R | ##################
# Draws quarterly graph of adult TDOs from eMagistrate data
# One line, with fiscal quarters on the x axis and counts on the y axis
##################
#source("reports/emagistrate_prep.R")
library(dplyr)
library(pander)
library(ggplot2)
TDO <-
emags %>%
filter(Type=="TDO", FYear>2007)
TDO_Qtr... | 1,149 | mit |
29c062e99ea01825f3faf03a4bc83ed93dbb4827 | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_34.R | expected <- eval(parse(text="structure(c(\"213198964\", \" 652425\"), .Names = c(\"null.deviance\", \"deviance\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(213198964, 652424.52183908), .Names = c(\"null.deviance\", \"deviance\")), FALSE, 5L, 0L, NULL, 3L, TRUE, NA)")); ... | 479 | gpl-2.0 |
c710fd01f5b72f465ccba907066bc88f2fd967de | zhuoaprilfu/demo_fork | reports/TDO_Qtrly_Long_FY08-FY15_PLOT.R | ##################
# Draws quarterly graph of adult TDOs from eMagistrate data
# One line, with fiscal quarters on the x axis and counts on the y axis
##################
#source("reports/emagistrate_prep.R")
library(dplyr)
library(pander)
library(ggplot2)
TDO <-
emags %>%
filter(Type=="TDO", FYear>2007)
TDO_Qtr... | 1,149 | mit |
c710fd01f5b72f465ccba907066bc88f2fd967de | zhuoaprilfu/shiny-court-grapher | reports/TDO_Qtrly_Long_FY08-FY15_PLOT.R | ##################
# Draws quarterly graph of adult TDOs from eMagistrate data
# One line, with fiscal quarters on the x axis and counts on the y axis
##################
#source("reports/emagistrate_prep.R")
library(dplyr)
library(pander)
library(ggplot2)
TDO <-
emags %>%
filter(Type=="TDO", FYear>2007)
TDO_Qtr... | 1,149 | mit |
29c062e99ea01825f3faf03a4bc83ed93dbb4827 | kmillar/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_34.R | expected <- eval(parse(text="structure(c(\"213198964\", \" 652425\"), .Names = c(\"null.deviance\", \"deviance\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(213198964, 652424.52183908), .Names = c(\"null.deviance\", \"deviance\")), FALSE, 5L, 0L, NULL, 3L, TRUE, NA)")); ... | 479 | gpl-2.0 |
8a4578e2cbad84471373f96e3bb8e75bfca317a9 | ADIRSE/maddata | app/global_functions.R |
#######################
# GLOBAL FUNCS
#######################
# load air quality measure points
fixStationCodes <- function(code) {
# code <- as.character(code)
if (code<10) {
station_code <- paste('2807900',
code, sep='')
}
else {
station_code <- past... | 7,213 | mit |
29c062e99ea01825f3faf03a4bc83ed93dbb4827 | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_34.R | expected <- eval(parse(text="structure(c(\"213198964\", \" 652425\"), .Names = c(\"null.deviance\", \"deviance\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(213198964, 652424.52183908), .Names = c(\"null.deviance\", \"deviance\")), FALSE, 5L, 0L, NULL, 3L, TRUE, NA)")); ... | 479 | gpl-2.0 |
29c062e99ea01825f3faf03a4bc83ed93dbb4827 | kmillar/rho | src/extra/testr/filtered-test-suite/format/tc_format_34.R | expected <- eval(parse(text="structure(c(\"213198964\", \" 652425\"), .Names = c(\"null.deviance\", \"deviance\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(213198964, 652424.52183908), .Names = c(\"null.deviance\", \"deviance\")), FALSE, 5L, 0L, NULL, 3L, TRUE, NA)")); ... | 479 | gpl-2.0 |
5d622a1e4f8df46cbb031e94bf5701f6dbf5edc7 | chichinabo/popyramids_shiny_apps | apps/resources/auto_check_and_install.R | #A short script to help installing packages on the go
#Most useful if you are distributing a set of script files to people who may not be aware that the needed packages are not installed
#Also useful if you use many packages and want to organise their loading at the beginning of a script
need<-c("shiny", "shinydashboa... | 754 | gpl-3.0 |
5d622a1e4f8df46cbb031e94bf5701f6dbf5edc7 | chichinabo/shiny_popyramids | apps/resources/auto_check_and_install.R | #A short script to help installing packages on the go
#Most useful if you are distributing a set of script files to people who may not be aware that the needed packages are not installed
#Also useful if you use many packages and want to organise their loading at the beginning of a script
need<-c("shiny", "shinydashboa... | 754 | gpl-3.0 |
29c062e99ea01825f3faf03a4bc83ed93dbb4827 | krlmlr/cxxr | src/extra/testr/filtered-test-suite/format/tc_format_34.R | expected <- eval(parse(text="structure(c(\"213198964\", \" 652425\"), .Names = c(\"null.deviance\", \"deviance\"))"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(213198964, 652424.52183908), .Names = c(\"null.deviance\", \"deviance\")), FALSE, 5L, 0L, NULL, 3L, TRUE, NA)")); ... | 479 | gpl-2.0 |
End of preview. Expand in Data Studio
GitHub R repositories dataset
R source files from GitHub.
This dataset has been created using the public GitHub datasets from Google BigQuery. This is the actual query that has been used to export the data:
EXPORT DATA
OPTIONS (
uri = 'gs://your-bucket/gh-r/*.parquet',
format = 'PARQUET') as
(
select
f.id, f.repo_name, f.path,
c.content, c.size
from (
SELECT distinct
id, repo_name, path
FROM `bigquery-public-data.github_repos.files`
where ends_with(path, ".R")
) as f
left join `bigquery-public-data.github_repos.contents` as c on f.id = c.id
)
EXPORT_DATA
OPTIONS (
uri = 'gs://your-bucket/licenses.parquet',
format = 'PARQUET') as
(select * from `bigquery-public-data.github_repos.licenses`)
Files were then exported and processed locally with files in the root of this repository. Datasets in this repository contain data from reositories with different licenses.
The data schema is:
id: string
repo_name: string
path: string
content: string
size: int32
license: string
Last updated: Jun 6th 2023
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