| title | author | highlighter | output | job | knit | mode | hitheme | subtitle | framework | widgets |
|---|---|---|---|---|---|---|---|---|---|---|
Developemnt Data Products Course Project |
DHGarcia |
highlight.js |
pdf_document |
Johns Hopkins Specialization in Data Science |
slidify::knit2slides |
selfcontained |
tomorrow |
MPG Regression Models |
io2012 |
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
`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.
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)## '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 : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ 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 : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ...
## $ am : Factor w/ 2 levels "AT","MT": 2 2 2 1 1 1 1 1 1 1 ...
## $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
## $ carb: Factor w/ 6 levels "1","2","3","4",..: 4 4 1 1 2 1 4 2 2 4 ...
--- .class #id
The user can select different set of variables to create the regreesion model.
## Error: could not find function "selectInput"
The model will be of the form.
model1 <- lm(mpg ~ am, mtcars)--- .class #id
The main panel will show the residual plot and the summary of the compute model.
par(mfrow = c(2, 2))
plot(model1)