Shiny101

This is a collection of Projects created with Shiny, a web application framework for R. Shiny makes it easy to create interactive web applications with using R code only. More details about Shiny can be found here.

Prerequisites

• In order to run these projects in your local environment, you need to have R and RStudio installed. If they are not installed already, install R here and RStudio here.
• You need to have "Shiny" installed as an R package.

install.packages('shiny')

• It is recommended that you load Shiny before trying to run any of these projects.

library(shiny)

How to run

• Clone this repository.
• Open this project(Shiny101.Rproj) with RStudio.
• If you have loaded shiny already, type runApp() command with the name of the directory which contains the project that you want to run. Ex: If you want to run the Plot Random Values, you can simply type the following command in RStudio.

runApp('Norm')

• If you have not loaded Shiny:

shiny::runApp('Norm')

Projects

1. Plot Random Numbers

This is a simple Shiny application to plot a normal distribution of random numbers. The application is capable of dynamically changing its output(i.e. The plot) with the changes made to its parameters by the user via tweaking the controls. 6 controls are given to the user to adjust the plot.

1. Number of random numbers to be plotted. This should be a positive integer.
2. Minimum and maximum values for the x axis. The minimum value can be decreased upto -100 and the maximum value can be increased upto 100.
3. Minimum and maximum values for the y axis. The minimum value can be decreased upto -100 and the maximum value can be increased upto 100.
4. Toggle option to hide/show the label of x axis.
5. Toggle option to hide/show the label of y axis.
6. Toggle option to hide/show the label of the plot.

2. Iris K-Means Clustering

A Shiny application to plot clustering analysis done on the infamous Iris dataset. The application is capable of dynamically perform K-Means clustering on the Iris dataset with changing user inputs. There are four numerical variables in the dataset(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) that can be used with k-means clustering. 3 Controls are given to the user to adjust the clustering and the plot.

1. A variable for X axis
2. A variable for Y axis
3. Number of clusters(Minimum of 1 and maximum of 9)

3. Predicting Horsepower

A Shiny application to predict Horsepower of a car's engine using its gas efficiency(miles per gas). The application takes a MPG value for a car and predicts its horsepower using it. Trained with the native R dataset, mtcars. Two smodels are used for prediction. One uses only the mpg value input for prediction and the other uses a new variable(mpgsp) along with the entered mpg value. The new value is derived from the mpg. It has a breaking point at 20. mpgsp of a car that has smaller mpg than 20 will be set to 0 and a car with higher mpg than 20 will be replaced with the value of mpg of the car - 20. As per the output, predictions from both models will be displayed and a plot will be rendered with the current data points from mtcars, each fitted models and predicted data points from both models. 3 controls are given to the user.

1. A value for mpg.
2. Option to include the fitted model for model1(Which uses only mpg).
3. Option to include the fitted model for model2(Which uses mpg and mpgsp).