index.Rmd

---
title: "Developemnt Data Products Course Project"
author: "DHGarcia"
highlighter: highlight.js
output: pdf_document
job: Johns Hopkins Specialization in Data Science
knit: slidify::knit2slides
mode: selfcontained
hitheme: tomorrow
subtitle: MPG Regression Models
framework: io2012
widgets: []
---

## MPG Regression Models

###    Introduction

    Simple App for the Developemnt Data Products Course Project.

    The Goal is to explore the relationship between miles per gallon (MPG)
    and a set of variables in the `mtcars` data set provide in R.

--- .class #id 

## MPG Regression Models

###   DataSet

    `mtcars` dataset is use to build our model predicton. 
    We load the mtcars data and take a look at the variables.
    And convert some variables into factors.
    
```{r factor, cache = FALSE}

mtcars$cyl = factor(mtcars$cyl)
mtcars$vs = factor(mtcars$vs)
mtcars$am = factor(mtcars$am)
mtcars$gear = factor(mtcars$gear)
mtcars$carb = factor(mtcars$carb)
levels(mtcars$am) = c("AT", "MT")
str(mtcars)


```

--- .class #id 


## Model Selection

The user can select different set of variables to create the regreesion model.

```{r select,echo =FALSE, cache = FALSE}

selectInput("variable", "Variable:",
                        c("Transmission" = "am",
                          "Transmission and Cylinders" = "am + cyl",
                          "Transmission and Cylinders and Displacement" = "am + cyl + disp",
                          "Transmission and Cylinders and Displacement and Horsepower" = "am + cyl + disp + hp",                          
                          "Transmission and Cylinders and Displacement and Horsepower and Weight" = "am + cyl + disp+ hp + wt")
                        )


```

The model will be of the form.

```{r model1, cache = FALSE}

model1 <- lm(mpg ~ am, mtcars)

```


--- .class #id 


## Output

The main panel will show the residual plot and the summary of the compute model.

```{r figure3, cache = FALSE}

par(mfrow = c(2, 2))
plot(model1)


```