Develop data products week 4 assignment

Ilya Tishchenko
2020/11/11

Intro

In the core of exploratory analysis is the plots, something like this: 

plot(mtcars$mpg,mtcars$disp, xlab = "Miles per gallon", ylab="Displacement")

plot of chunk unnamed-chunk-1

Objective

…but it's much better to have more flexibility, right?

  • The idea is simple - beginning of any data science is in Exploratory Data Analysis.

  • Interactive data analysis is a way to go.

  • User can change variables for axis, color and size, plus on hover user can see all the details of data points.

therefore we use Shiny!

Data

I used 'classic' mtcars data in this application.

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

str(mtcars)

'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 ...

Regression analysis

Also in the application the user can visualize the linear (or polynomial) regression and evaluate the R-squared coefficient for considered parameters.

fit <- lm(disp~mpg,mtcars)
plot(mtcars$mpg,mtcars$disp, xlab = "Miles per gallon", ylab="Displacement")+
abline(fit)

plot of chunk unnamed-chunk-3

integer(0)