Singapore Joe Looks for a House in Singapore. But it's too expensive

A SIBOR Forecaster: Can SG Joe Afford Housing Bank Loans?

I did up this SIBOR forecaster a couple of months as part of my John Hopkins Data Science Specialisation. The seed of the idea came from my own (very shallow) experience with comparing housing bank loans in Singapore.

I was trying to answer: “Can I still afford bank loans 10 years down the line?”

I felt like Singapore Joe…

Singapore Joe Looks for a House in Singapore. But it's too expensive

So I thought of forecasting SIBOR based on historical data.

I know I know. Historical Forecast Isn’t The Most Accurate

But it’s all I got.

So I scraped years of SIBOR data from MAS Domestic Interest Rates and  made a Shiny App in R.

SIBOR Forecaster at https://skybe077.shinyapps.io/proj/

Try it at https://skybe077.shinyapps.io/proj/

It’s a 1-month SIBOR Forecaster where you can choose:

  1. Length of Home Loans
  2. Start and End Dates for Forecasting Years

In addition to the graph, it also returns a table of possible SIBOR forecast tables.

Obviously, it needs to consider more features like FED rates and maybe the news. But it’s a start at predicting the future.

Now’s that a place where I’d like to live in.

 

Same Same but different.

Same Same But Different: How Different Are You From Your Partner? A Gallup Strengths Finder Visualisation

A’s friend recently explained my Gallup Strengths* results to me. It was insightful. But more importantly, he mapped my (E) strengths against my partner’s (A) strengths.

It turns out that we’re quite the opposite. She’s very strong in Relationship Building (people-oriented) while mine is a mix of Strategic Thinking and Execution (task-oriented).

I wondered: “To what degree are we different from each other?” 

So I built a Same Same But Different Visualisation.

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I took the data (essentially a list of ordered pairs of strengths) and turned them into a scatterplot. The circles represent individual strengths. Their location determined by our order pairing. And the size represents the difference in our ordering.

As an example: for Discipline — E is 7, A is 32. The difference is 25. Hence it is located at 32 on A’s axis and 7 on E’s axis. It’s fairly large as it quite close to the maximum possible difference (34 -1 = 33).

Same Same but Different

For meaningful comparison, I drew a diagonal line (x=y) on the chart. This line shows how far we deviate from having the same strengths. As you can see, there are 6 strengths that are only 15% different (difference of 5) and only one of them — Intellection — overlaps in our mutual strengths. Interestingly, Relator falls just outside E’s strengths but within A’s strengths.

Same Same but Different Strengths Visualisation

What Does This Same Same But Different Viz Mean?

It highlights the degree of difference for each of our strengths. This shows where we complement each other and where we are lacking.

On the flipside, it also shows similarities that we can build on. In this case, it’s likely Intellection and Relator. The former supposes that we both enjoy thought-provoking debates and the later presumes trust and caring for each other.

At this point in time, this viz lays down “what is…”. I suspect that it can be tweaked to do a little more — what more? I can’t say off the bat. Still too new to Gallup Strengths  Finder.

You’re Both So Different. Will It Work?

There is great potential to complement each other. But at the same time there is also great potential for friction. We will see, decide and act through our preferred lenses. Many people have managed this friction with the right mix of tactics.

I’ve done this for myself.

Thinking on it, this visualisation can be used in almost any partnerships.  After all, being partners with someone is a little like marrying them.

Download the workbook from my Tableau Profile.  You’ll need Tableau 10 or greater to work on it though.

Appreciate any feedback and insights!

* The Gallup Strengths Finder measures your aptitude across 34 attributes. They are categorised into 4 areas: Executing, Influencing, Relationship Building and Strategic Thinking. More at gallupstrengthscenter.com 

Wikipedia Visualiser or a Galaxy of Stars?

wiki.polyfra.me - a wikipedia visualiser
Like flying through a galaxy. Just that the planets are now Wikipedia entries.

Jaw-droppingly awesome visualiser of Wikipedia entries that imagines each entry as a planet. In fact, the lines (as you see from the screenshot) is a contextual link from one entry to another entry.

It’s pretty. More importantly, it’s a really decent attempt to “connect the dots” by joining nodes to contextual nodes.

View it at http://wiki.polyfra.me/#

 

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DIY Infographic: Timelines

Recently, I’ve created a blog post (A History of Augmented Reality) with a timeline infographic.

Just 1 month ago, this would have taken 1 week to create. Most of the spent time was on the designer who had to conceptualise and create the timeline. And really, their time could be better spent on other design things.

Now it takes a grand total of 2 hours to make a Timeline Infographic.

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Thanks to Timeline JS. It’s a simple and yet beautifully rendered timeline that runs on a Google Excel sheet. Yup, a Google Excel sheet. Just plonk in the data and image links, some formatting, and it automatically creates the timeline for you.

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If you’re being held ransom by designers and account managers, I’d suggest having a go with Timeline JS. Also, there are many DIY infographic tools on the internet that anyone – newbie or seasoned designer — could use to create great-looking infographics.

It just takes a little bit of willingness to try out the tools and perhaps a penchant for storytelling.

  1. Get the Timeline JS from Knights Labs
  2. See how timeline infographic is implemented at “A History of Augmented Reality: When Digital & Physical Worlds Converge”.
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“Where’s the Bus Leh?” Visualising How Singaporeans Ping SG BusLeh

It took a year (of me being mostly lazy) before I turned the really nice dataset from SG BusLeh [get it from iTunes | Google Play] into a visualisation.

I didn’t know what to ask. So I just explored the data willy-nilly. In part, to try Tableau on different datasets; in part, to quickly generate visualisations to find questions.

Here’s what I found:

  1. There’s a difference between how SG Bus Leh is used on weekdays and weekends.
    Most folks take it later on the weekends
  2. People seem to take a while to get “into the groove” after a weekend. On Mon and  Tue, folks ping the app during perceived rush hour  timings (7 to 830am). From Wed to Fri, they’re actually getting out of the house earlier
  3. Lotsa pings on Fri night. No surprises there

I’ve put up the visualisations on Tableau Public: How often is SG Bus Leh Used?

Appreciate any feedback on them!

Shouts to SG BusLeh (iTunes | Google Play) for releasing their data

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Visualisations on Tableau Public: How often is SG Bus Leh Used?

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When Possible, Visualise DATA

I’ve been prepping reports and cleaning datasets for the last few weeks.

It’s no different from my other data projects – except that I’m using Tableau to visualise the reports. I’ve always thought that visualising data was really all vanity.

Now I’m a convert. Visualising the data makes it so easy to see relationships between dimensions! The Viz below (go to Tableau Public for a live demo) is a Google Keyword Search & Competition Visualiser.

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I built the Keyword Visualiser to answer this question:

I got 1,100 keywords from Google Keyword Planner.

How do I know which ones to focus my social listening efforts on first?

Continue reading When Possible, Visualise DATA

The Right Question

Finding it is hard. 

Just over the weekend, I was busy trying to parse JSON data. Which, despite its connotations, was incredibly unstructured for this website. 

It was simply a block of text with HTML tags. So I poked around stackoverflow asking about HTML parsing. What I should have really been asking was how to extract text between tags.

The meaning is the same for both questions. But reframing the question into specific actions returned the answer (and code) in a much shorter time. 

The Learnings

When you’re not getting anywhere with your current question, re-ask it by focusing on specific actions.

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LinkedIn: Changing Company Pages

Companies rebrand every so often. When that happens, it makes sense to refresh and update their digital channels: Facebook, Twitter, LinkedIn, and whatever else is the latest Social Media flavour.

Some of them are relatively easy to update.

Facebook lets you change a fanpage’s web address once (and only once, mind you). Twitter is far more permissive – your page web address and name is almost as malleable as putty.

But LinkedIn? Now it’s a pain best summed up in 10 words.

Change your company page name on LinkedIn?

Forget it.

You’ll need to create a brand new company page with that brand new name. Essentially it’s a start from scratch, but what can you do to bring it up to speed quickly?

Continue reading LinkedIn: Changing Company Pages

4 Google Moments

Four Google moments For Buyers

Great for shops and restaurants if you’re looking to get spur-on-the-moment traffic.

Thoughts

  1. Augments (Replaces?) the buyer cycle with smartphone usage and phases
  2. Push carts and pop-up shops should make use of their proximity to complementary businesses particularly for the I-want-to-do moments
  3. Help customers make a purchase decision with comparative data that can be checked on smartphones
  4. TV commercials are intrinsically linked to microsites. What if we ran campaigns that started from the TV and ended up online?
  5. Location based searches might drive traffic to you, especially for undecided searchers.

source: 4 New Moments