Instructions:

1. Write a shiny application with associated supporting documentation. The documentation should be thought of as whatever a user will need to get started using your application.
   
2. Deploy the application on Rstudio’s shiny server.
   
3. Share the application link by pasting it into the provided text box.
   
4. Share your server.R and ui.R code on github.
   

I applied with Tamsui’s air data from Taiwan Executive Yuan’s EPA gathered at 1PM for everyday in 2020. The data source can be found in the source link

Data was applied to predict PM 2.5 level from SO2 gas, Ozone and CO gas

And the application will run on this shinyapp link

R-Codes for the server and UI associated with tidy data sets are given in this repository

On the left hand side, just enter the required quantity for interested predictor then the right hand side will have the designated tabs for the predicted model data point versus the existing data.

And the following are the limits for each predictor

## 載入需要的套件：ggplot2

## 
## 載入套件：'plotly'

## 下列物件被遮斷自 'package:ggplot2':
## 
##     last_plot

## 下列物件被遮斷自 'package:stats':
## 
##     filter

## 下列物件被遮斷自 'package:graphics':
## 
##     layout

## 
## 載入套件：'data.table'

## 下列物件被遮斷自 'package:reshape2':
## 
##     dcast, melt

## 
## 載入套件：'tidyr'

## 下列物件被遮斷自 'package:reshape2':
## 
##     smiths

This plot is just an example obtained from the data set, While showing the possible presentations of

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## Warning: `line.width` does not currently support multiple values.