---
title: "Commercial and Residential Buildings JSON-LD"
author: "Tian Wang, Scott Maurer, Roger H. French"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Commercial and Residential Buildings JSON-LD}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Commercial and Residential Buildings JSON-LD Description

The commercial and residential JSON-LD template is designed based on BuildingSync Schema and Building Energy Data Exchange Specification (BEDES) Dictionary.

It defines several key meta information like climate zone, building location and size for building energy efficiency research.


## Creating JSON-LD for Commercial and Residential Buildings in R

```{r, message=FALSE, eval=FALSE}
library(FAIRmaterials)

# Create R data frame for commercial and residential buildings
bldg_data <- data.frame(
  "PremisesName" = c("bldg 680", "bldg 686", "bldg 352"),
  "OperatorType" = "Food Sales",
  "Longitude" = c(-75.27, -76.88, -78.52),
  "Latitude" = c(40.68, 41.17, 40.00),
  "City" = c("Easton",       "Montgomery",   "Bedford"),
  "County" = c("Northampton", "Lycoming",    "Bedford"),
  "State" = "PA",
  "PostalCode" = c(18045, 17752, 15522),
  "ASHRAE" = "5A",
  "KoppenClimate" = c("Dfa", "Dfa", "Cfa"),
  "FloorAreaPercentage" = 0.88,
  "FloorAreaValue" = c(6077, 4913, 5333),
  "OverallWindowToWallRatio" = 0.22,
  "ConditionedFloorsAboveGrade" = 1
)

# This will generate JSON-LD file for the example data in R
output <- fairify_data(bldg_data, domain = 'building')
```

## Creating JSON-LD for Commercial and Residential Buildings in Python

```{r, message=FALSE, eval=FALSE}
from fairmaterials.fairify_data import *
import pandas as pd

# create python data frame for commercial and residential buildings
data = {'PremisesName':['bldg 680', 'bldg 686', 'bldg 352'],
        'OperatorType':['Food Sales', 'Food Sales', 'Food Sales'],
        'Longitude':[-75.27, -76.88, -78.52],
        'Latitude':[40.68, 41.17, 40.00],
        'City':['Easton',       'Montgomery',   'Bedford'],
        'County':['Northampton', 'Lycoming',    'Bedford'],
        'State':['PA', 'PA', 'PA'],
        'PostalCode':[18045, 17752, 15522],
        'ASHRAE':['5A', '5A', '5A'],
        'KoppenClimate':['Dfa', 'Dfa', 'Cfa'],
        'FloorAreaPercentage':[0.88, 0.88, 0.88],
        'FloorAreaValue':[6077, 4913, 5333],
        'OverallWindowToWallRatio':[0.22, 0.22, 0.22],
        'ConditionedFloorsAboveGrade':[1, 1, 1]
       }


bldg_data = pd.DataFrame(data)

# This will generate JSON-LD file for the example data in Python
fairify_data(bldg_data,'building')

```

## Commercial and Residential Buildings schema diagram

```{r, out.width="1600px", echo=FALSE, fig.cap=" Commercial and Residential Buildings schema diagram"}
knitr::include_graphics("building_schema.png")
```

## Acknowledgment

This data is supported by the US Department of Energy’s Advanced Research Projects Agency-Energy (ARPAE-E) : [DE-AR000125].
