Reproducicle pitch for Coursera Class Project - week 4

This assignment has two parts. First, you will create a Shiny application and deploy it on Rstudio's servers. Second, you will use Slidify or Rstudio Presenter to prepare a reproducible pitch presentation about your application.

Motor Trend Analysis (mtcars)

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).

Source

https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html

Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

# load the data set - mtcars
# NOTE 1: This is a pre-loaded dataset. No need to load it again
# NOTE 2: subset of rows displayed to fit data in the slide
head(mtcars,5)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

summary(mtcars)

      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000  

Application hosted here: https://datascience-nuno.shinyapps.io/nr-mtcars-wk4-v2/

The main server code that renders the UI and returns the analysis based on the user options can be be found below.

Simply put, the user is asked to select an option the the server on the fly calculates a linear model that correlates mpg to the feature selected.

mpgText  <- reactive({paste("mpg ~", input$rd)})
mpgPoint <- reactive({paste("mpg ~", input$rd)})
lm_fit   <- reactive({lm(as.formula(mpgPoint()), data=mpg)})
output$caption <- renderText({mpgText()})
output$fit <- renderPrint({summary(lm_fit())})

output$mpgPlot <- renderPlot({ with(mpg, { plot(as.formula(mpgPoint()))
                                           abline(lm_fit(), col="blue")})})