Longley data model fit

This provides a simple UI for people to predict Unemployment based on population size, on the well known Longley data model.

• No knowledge of R, html, or statistics is necessary
• Users can feel the experience of predicting using data science.
  
• The app makes easy work of using a predictions and color graphics on screen.
  

We hope you enjoy using this application and find it useful in your research!

The application can be accessed in: https://nikolasperdikis.shinyapps.io/shiny_project/

Brief description of functionality:

• Help tab gets users up-to-speed immediately
• Read “longley” dataset
• Linear Model, outcome: Unemployed - Predictor: Population
• Slider input to enter new data for Population value
• Plot the result

• The user interfaces with the app via the mouse, exclusively.
  No knowledge of R programming, model fit or ggplot is required.
  When the app launches, a default prediction is provided,
  for a population size of 115000.

• The user can then use the slider to the left to alter the population predictor.

• “Show/hide Linear Model” can be checked/unchecked to view the model and prediction in the graphic

• User has to click on “Submit” for changes to take effect

Once user input is received, the server will generate a prediction,
based on the value the user entered. This is a very simple linear model,
with predictor being the population size, and outcome being the amount of Unemployment. The model fit would look like:

model1 <- lm(Unemployed~Population, data=longley)

and the prediction, given a user entered value of 115, (population in thousands), would be:

predict(model1,newdata=data.frame(Population=115)

Example plot from the app, with the model (red) and predicted value in (orange)