My RapidKL Train Scheduler

We created a Shiny App named My RapidKL Train Scheduler for Assignment 2.

Problem Identified: Planning a journey via public transport is a hassle due to non-centralized information of the train Fares,Route and Duration in multiple web pages.

RapidKL Train Dataset :

• 3 data files for LRT Kelana Jaya Line and MRT Sungai-Buloh Kajang Line.
• Dataset Source is from Kaggle

• We check, cleaned and splitted the dataset into smaller segments for easier tasking.

  Item	Dataset Description
  "Fare.csv"	Numeric value for train fare measured in Ringgit Malaysia (RM).
  "Time.csv"	Numeric value for train ride duration measured in minutes.
  "Route.csv"	String value for train route.

Questions :

• What's the minimum amount of money required to board the train from Gombak to somewhere else?

   #Fare <- read.csv(file = 'Fare.csv') 
   #print(max(Fare$Gombak))

• How many minutes does the train take from Gombak to KL Sentral?

   #Time <- read.csv(file = 'Time.csv') #read file and store as data frame
   #print(Time[1,4]) #call the station index instead of station names

This Shiny App is useful for train journey planning so that users can prepare their time and money before boarding the train. This shiny app lets user to :

• Select current location and destination.
• Search for route,route fare and estimated time to reach destination.
• View the documentation and extra information of RapidKL Services.

The source for the for ui.R and server.R are available on GitHub : https://github.com/nikmarjan98/MyRapidKLScheduler

The link for the Shiny App : https://nnmarjan.shinyapps.io/MyRapidKLTrainScheduler/

1. Users need to select their current station and destination, and hit the search button. The app will display the route, the price and the duration of the train journey.

2. Practicing data driven culture is important, always check the dataset format and library declared. In our group's case we used .csv file, so we did not use the "xlsx" library to read it. Instead, we directly call the csv.read() function to read the dataset.