• My web app analyses the effect of various parameters on the miles-per-gallon (MPG) of 32 different models of cars
• It uses the mtcars dataset which is available on R.
• For every variable selected, it displays a box plot and fits a linear model which helps understand the relation between the variable and MPG of the specimen cars.
• I have also included a brief summary of the dataset explaining what each variable means.

Here is a basic summary of the dataset I have worked on.

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

From the plots generated, MPG appears to have:

• A negative correlation with the number of cylinders, displacement, gross horsepower, weight and the number of carburetors.
• A positive correlation with the rear axle ratio, quarter mile time, V/S and the number of forward gears.
• Also, the average mpg is higher for manual transmission as compared to automatic transmission

Thanks for viewing my presentation!
You can find my Shiny app here.