Approach
- Grab datasets from here.
- Read on LA Metro Data to understand the dataset and what each column value represents.
- Use R Studio to parse dataset into various dataframes with selected column values to perform data wrangling.
- combo.R
- dates.R
- freq-combo-route.R
- pie.R
- popular.R
- regular.R
- trip_type.R
- Write Python Script to calculate distance between latitude and longtiude coordinate points and the average distance calculates.
- avg_dist.py
- bike_tables.py
- lat-long.py
- Tableau Visualization Files
- Duration of Trips in Various Months.twb
- StationMaps.twb
- Craft conclusion based on data analysis output. Look back and check if all variables were accounted for and seek room for improvement in the future use of the same methodology.
Dataset
A segment of our data source for viewing purposes.
| Trip ID | Duration | Start Time | End Time | Starting Station ID | Starting Station Latitude | Starting Station Longitude | Ending Station ID | Ending Station Latitude | Ending Station Longitude | Bike ID | Plan Duration | Trip Route Category | Passholder Type | Starting Lat-Long | Ending Lat-Long |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1912818 | 180 | 2016-07-07T04:17:00 | 2016-07-07T04:20:00 | 3014 | 34.0566101 | -118.23721 | 3014 | 34.0566101 | -118.23721 | 6281 | 30 | Round Trip | Monthly Pass | {'longitude': '-118.23721', 'latitude': '34.0566101', 'needs_recoding': False} | {'longitude': '-118.23721', 'latitude': '34.0566101', 'needs_recoding': False} |
| 1919661 | 1980 | 2016-07-07T06:00:00 | 2016-07-07T06:33:00 | 3014 | 34.0566101 | -118.23721 | 3014 | 34.0566101 | -118.23721 | 6281 | 30 | Round Trip | Monthly Pass | {'longitude': '-118.23721', 'latitude': '34.0566101', 'needs_recoding': False} | {'longitude': '-118.23721', 'latitude': '34.0566101', 'needs_recoding': False} |
| 1933383 | 300 | 2016-07-07T10:32:00 | 2016-07-07T10:37:00 | 3016 | 34.0528984 | -118.24156 | 3016 | 34.0528984 | -118.24156 | 5861 | 365 | Round Trip | Flex Pass | {'longitude': '-118.24156', 'latitude': '34.0528984', 'needs_recoding': False} | {'longitude': '-118.24156', 'latitude': '34.0528984', 'needs_recoding': False} |
| 1944197 | 10860 | 2016-07-07T10:37:00 | 2016-07-07T13:38:00 | 3016 | 34.0528984 | -118.24156 | 3016 | 34.0528984 | -118.24156 | 5861 | 365 | Round Trip | Flex Pass | {'longitude': '-118.24156', 'latitude': '34.0528984', 'needs_recoding': False} | {'longitude': '-118.24156', 'latitude': '34.0528984', 'needs_recoding': False} |
| 1940317 | 420 | 2016-07-07T12:51:00 | 2016-07-07T12:58:00 | 3032 | 34.0498886 | -118.25588 | 3032 | 34.0498886 | -118.25588 | 6674 | 0 | Round Trip | Walk-up | {'longitude': '-118.25588', 'latitude': '34.0498886', 'needs_recoding': False} | {'longitude': '-118.25588', 'latitude': '34.0498886', 'needs_recoding': False} |
| 1944075 | 780 | 2016-07-07T12:51:00 | 2016-07-07T13:04:00 | 3021 | 34.0456085 | -118.23703 | 3054 | 34.0392189 | -118.23649 | 6717 | 30 | One Way | Monthly Pass | {'longitude': '-118.23703', 'latitude': '34.0456085', 'needs_recoding': False} | {'longitude': '-118.23649', 'latitude': '34.0392189', 'needs_recoding': False} |
| 1944073 | 600 | 2016-07-07T12:54:00 | 2016-07-07T13:04:00 | 3022 | 34.0460701 | -118.23309 | 3014 | 34.0566101 | -118.23721 | 5721 | 30 | One Way | Monthly Pass | {'longitude': '-118.23309', 'latitude': '34.0460701', 'needs_recoding': False} | {'longitude': '-118.23721', 'latitude': '34.0566101', 'needs_recoding': False} |
| 1944067 | 600 | 2016-07-07T12:59:00 | 2016-07-07T13:09:00 | 3076 | 34.0405998 | -118.25384 | 3005 | 34.0485497 | -118.25905 | 5957 | 365 | One Way | Flex Pass | {'longitude': '-118.25384', 'latitude': '34.0405998', 'needs_recoding': False} | {'longitude': '-118.25905', 'latitude': '34.0485497', 'needs_recoding': False} |