# Task 1: Reflection

This exercise involves developing interactive plots and dashboards based on the mtcars dataset. I had a good feeling regarding how to perform interactive visualizations using ggplot2 and plotly. Later, I did a simple dashboard with {flexdashboard}; that allowed me to organize multiple plots in a visually structured way. It was interesting to understand how to publish the Dashboard in RPubs and at which point R can be used to produce sharable and interactive reports.

Task 2: Interactive plots

options(repos = c(CRAN = "https://cloud.r-project.org"))

library(tidyverse)
library(plotly)

# Load data here
cars <- read_csv("data/mtcars.csv")

Do the following:

  1. Make a plot. Any kind of plot will do (though it might be easiest to work with geom_point()).
plotting <- ggplot(cars, aes(x = wt, y = mpg, color = factor(cyl), text = model)) + 
  geom_point(size = 2) + labs (
    title = "MPG vs Weight",
    x = "Weight",
    y = "Miles Per Gallon",
    color = "Cylinders"
  )
  1. Make the plot interactive with ggplotly().

Adding tooltip to our interactive plot with ggplotly to enhance User Interface with information pop up by hovering our each data plots. Hovering contains a sequenced information of wt, mpg, factor, and the car model name for each data plot. Red dots showcases the models with highest MPG and least weight while green is mid and blue is problematic zone. We see that Toyota Corolla was the most effiecient vehicle while on the other hand Lincoln Continental was opposite

interactive_plot <- ggplotly(plotting, tooltip = c("text", "x", "y", "color"))

interactive_plot