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library(dplyr)
library(tibble)

REPO <- "http://cran.rstudio.com"
LICENSE <- "AGPL"

p <- available.packages(repos = REPO) 
p_tbl <- as_tibble(p)
p_tbl %>% 
  filter(License == !!LICENSE) %>% 

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library(dplyr)
library(tibble)

REPO <- "http://cran.rstudio.com"
LICENSE <- "AGPL"

p <- available.packages(repos = REPO) 
p_tbl <- as_tibble(p)
p_tbl %>% 
  filter(License == !!LICENSE) %>% 

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library(shiny)
library(httr)

check_binary <- function(pkg, 
                         version,
                         distro, # one of windows, xenial, bionic, centos7, centos8, opensuse42, opensuse15
                         r_version,
                         repo_base = "https://packagemanager.rstudio.com/cran/", 
                         verbose = FALSE){
  

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Order,PID,MS SubClass,MS Zoning,Lot Frontage,Lot Area,Street,Alley,Lot Shape,Land Contour,Utilities,Lot Config,Land Slope,Neighborhood,Condition 1,Condition 2,Bldg Type,House Style,Overall Qual,Overall Cond,Year Built,Year Remod/Add,Roof Style,Roof Matl,Exterior 1st,Exterior 2nd,Mas Vnr Type,Mas Vnr Area,Exter Qual,Exter Cond,Foundation,Bsmt Qual,Bsmt Cond,Bsmt Exposure,BsmtFin Type 1,BsmtFin SF 1,BsmtFin Type 2,BsmtFin SF 2,Bsmt Unf SF,Total Bsmt SF,Heating,Heating QC,Central Air,Electrical,1st Flr SF,2nd Flr SF,Low Qual Fin SF,Gr Liv Area,Bsmt Full Bath,Bsmt Half Bath,Full Bath,Half Bath,Bedroom AbvGr,Kitchen AbvGr,Kitchen Qual,TotRms AbvGrd,Functional,Fireplaces,Fireplace Qu,Garage Type,Garage Yr Blt,Garage Finish,Garage Cars,Garage Area,Garage Qual,Garage Cond,Paved Drive,Wood Deck SF,Open Porch SF,Enclosed Porch,3Ssn Porch,Screen Porch,Pool Area,Pool QC,Fence,Misc Feature,Misc Val,Mo Sold,Yr Sold,Sale Type,Sale Condition,SalePrice
1,0526301100,020,RL,141,31770,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm

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Step 1: Create ec2 instance with some public ports

ssh into the instance

Step 2: On ec2 instance install k3s

https://rancher.com/docs/k3s/latest/en/quick-start/

curl -sfL https://get.k3s.io | sh -

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Install Python

This script is designed to make it easy to install multiple versions of Python onto a Linux system. The resulting versions are stored side-by-side in /opt/Python/

Instructions

1. Copy the bash script to install-python,sh
2. Make the script executable: sudo chmod +x install-python.sh
3. Run the script for each desired python version, e.g. sudo ./install-python.sh 3.8.3

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If you are installing RStudio Team onto an air-gapped RHEL environment you may need to first install  additional system dependencies before you will be able to install the RStudio product rpms. Some of these dependencies are available through the standard RedHat yum repo, others may need to be pulled from the EPEL.

The following list names the dependencies by product. The list is based off of RedHat 7 rpms for the corresponding version of RStudio Team.

RStudio Workbench version 1.4.1717-3

rrdtool
sqlite

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!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t():"function"==typeof define&&define.amd?define([],t):"object"==typeof exports?exports.violin=t():e.violin=t()}(window,(function(){return function(e){var t={};function n(r){if(t[r])return t[r].exports;var o=t[r]={i:r,l:!1,exports:{}};return e[r].call(o.exports,o,o.exports,n),o.l=!0,o.exports}return n.m=e,n.c=t,n.d=function(e,t,r){n.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},n.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},n.t=function(e,t){if(1&t&&(e=n(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(n.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)n.d(r,o,function(t){return e[t]}.bind(null,o));return r},n.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return

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Introduction

With BigQuery's new remote user defined functions (in preview) it is now possible to bring the power of Google Maps to your analytic data warehouse. Using Google Maps API endpoints in Cloud Functions called by BigQuery you can:

• Geocode Addresses
• Determine drive time distance between locations
• Supplementing address or location data with Google Map's data such as elevation or place descriptions

By enriching location datasets in BigQuery you can accomplish advanced spatial analysis including: