Biketown EDA – P2

Hello, welcome to the second part of this blog doing exploratory data analysis on the bike town dataset. If you haven’t read the first one then go check it out. An overview is we found that most of the records were either subscribers to the system of casual people who might just use it every now and then. So I decided to compare those two groups. We saw in the smaller sample size that most of the casual users used it for recreation and the subscribers used it mostly for commuting. Today we are going to look at the distances and speeds the groups travel and where they rent the bikes from but first we are going to look at when they rent the bikes:

plot8.png

This graph totally makes sense that subscribers tend to rent the bikes for commuting as there are two large spikes at around 8am and around 5pm. The peak hours for people going too and from work. Casual users don’t tend to use the system in the morning, however, there’s consistent usage throughout the afternoon. This could be tourists exploring the city for instance. It’s strange both groups don’t reach their minimum to well after 2am, are these people using the system to get home from their night outs? Beware of the drunk Portland cyclist!

distance

The density plot for the distances the two groups go is interesting however i think does fit the current pattern. Subscribers are looking to do shorter journeys because they might be covering the last few miles to work whereas the casual users are maybe exploring the city and therefore cover more distance.

speeds.png

Now let’s check out the speed curves for both groups the further the distance the person travelled the slower the speed. The subscribers are obviously generally the much faster riders at all distances. The increase in speed towards the 25-mile distance i think is down to lower amounts of data. At the lower distance the subscribers have significantly faster speeds are they using the system to get from A to B as quickly as possible.

start.png

Now let’s look at the start and end locations for the casual riders. The heat map above shows that most riders are clustered around the city centre possibly moving from one tourist spot to another. There seems to be a fairly even distribution across the centre locations.

subscriber.png

The subscriber heat map above shows that generally, people take the bike from much further out than compared to the casual riders who are possibly just using it to get to the tourist hot spots. Once again mostly the usage is on the left side of the river that must be the area where people like to get about. Also, the start locations around the outside are much denser, therefore, its clear people are using the system from further out to go into the centre of the city.

That’s it for this exploratory data analysis in this blog I think we have found some interesting insights and at the minimum able to confirm what you would expect. I hope you have found this interesting and informative let me know your thoughts and if you have looked at this dataset yourself lets see your thoughts. Check out the code on my GitHub should be linked somewhere on the website.

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Nike Biketown – Exploratory Data Analysis

Hello welcome to today’s blog which we are going to take a large dataset and do some exploratory data analysis on it. I am going to look at the biketown dataset which was a dataset on tidy tuesday. If you’re new around here tidy Tuesday is a hashtag on twitter which the R for data science online learning community actively promotes and has every Tuesday. If you’re inspired to learn R and data science like I was that is a really great community full of wonderful people to start with. I am not going to post code snippets within the blog as I think it gets too long, however, the full code used will be posted on my GitHub.

structure

Above is the structure of the dataframe. The data comes in numerous csv files so i read them all in and created one large data frame structured like so. The second column Payment plan seems an interesting column it has 3 constituents Casual, subscriber and another. The system in Portland has a way a regular user can automate payments to save time. Let’s look at how much of the dataset is based on the 3 types of payment:

plot1

As you can see the vast majority of this dataset is based on either casual and subscriber and I think it would be interesting to review the differences between the people on the two main payment plans.  Therefore going forward in the EDA we are going to remove the entries without a subscriber. After this, we could possibly look at a method for working out what type of payment plan the blanks are. First thing first let’s have a look at what type of trips either group takes:

plot5plot2

The big issue here with making any conclusion on the type of trips each group takes is going to be difficult. This is because each group has over 200 thousand entries and there are less than 1000 recorded trips for each group. What we can say is that it makes sense that subscribers in this smaller group tend to use the system for commuting and casual users clearly in the small sample size use the system for recreation which makes total sense.

plot6

Now we look at the payment methods that both groups have used. By far the 3 main payment types for both groups are keypad, mobile and keypad_rfid_card, with subtle differences between the 2 groups. The RFID card is clearly higher in the subscriber which must be because subscribers are given a card in order to gain access to the bikes. Also, casual users tend to be much more likely to use their mobile to gain access. Both groups have the vast majority using the keypad system.

That’s it for part 1 of today’s exploratory data analysis on the bike town data. Tomorrow we will look at the distances the groups go as well as location and usage time. Let me know your comments on the first part.