22/05/2020

Summary

  • Shiny application is developed for air quality data.
  • Histogram is plotted varible bins that can be chosen by the user using the slider bar.
  • Ozone is plotted against the varible chosen by the user
  • Linear regression can be plotted if needed.
  • Finally, ozone levels are predicted for temperature chosen using the slider.
  • Main panel is divided as tabs for demonstrating each of the plots.

Summary of air quality data

##      Ozone           Solar.R           Wind             Temp      
##  Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00  
##  1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00  
##  Median : 31.50   Median :205.0   Median : 9.700   Median :79.00  
##  Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88  
##  3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00  
##  Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00  
##  NA's   :37       NA's   :7                                       
##      Month            Day      
##  Min.   :5.000   Min.   : 1.0  
##  1st Qu.:6.000   1st Qu.: 8.0  
##  Median :7.000   Median :16.0  
##  Mean   :6.993   Mean   :15.8  
##  3rd Qu.:8.000   3rd Qu.:23.0  
##  Max.   :9.000   Max.   :31.0  
## 

Ozone data

Ozone values are plotted as histogram with number of bins decided by the user by sliding the slider bar.

hist(airquality$Ozone, bins = 10)

Plotting ozone vs other environmental parameters

Ozone levels are plotted with respected to other environmental parameters such as temeperaure, solar radiation and wind. Also, a linear regression model is fitted to the model if required.

Predict ozone data for differnt temperaure levels

Ozone levels are predicted for the chosen temperaure levels. Assuming the chosen temperature is 100 F, ozone is computed from the fitted model

## (Intercept) 
##    47.48272

Thank you