Have Fun Clicking Through the Tabs Below

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What better way to show our love than with data-driven charts and analyses? Each tab above provides insights to our love using various data sources. We pulled data from our phone service providers, annual ski passes, credit card statements, and more!

Texting
Data

We downloaded all of our text messaging data over the course of our relationship from T-Mobile and Verizon. We provide some insights in the tabs below. Enjoy!

Our Texts Over Time

Our first winter together, texting ~600 times a week was the norm. Since Neil moved in, our texting slowed down quite a bit.

^{Hover over the chart and milestones for more information about our milestones and texting. Or click on the chart legend to show/hide any additional relationship milestones.}

There seems to be a slight downward trend in the length of the texts that we send each other - however, certain events cause outliers.

The week after we got engaged, Neil’s texting character count increased dramatically!

^{Hover over the chart and milestones for more information about our milestones and texting. Or click on the chart legend to show/hide any additional relationship milestones.}

Total
Texts

Neil doesn’t always respond!

^{Click on the chart legend to show/hide any bars.}

But when Neil responds, he has more to say!

^{Click on the chart legend to show/hide any bars.}

Word
Cloud

Over the course of our relationship, we texted some words more than others. See our word cloud below.

“Baby”, “Babe”, “Love”, and “Lol” are a few of our most frequently used words!

“ETA”, “BTW”, and “FYI” also make the top list.

Our Favorite Words to Text

Overall

Karina texts “Baby” a lot, while Neil texts “Babe” more. Neil’s 2nd favorite thing to text is “Laugh Out Loud.”

^{Click on the chart legend to show/hide any bars. (Note: Ben is the name of Karina’s son and Laika is the name of Neil’s dog!).}

Over Time

We have highlighted certain sections of this graph to tell a few stories. Check out the tabs below to see some interesting trends we found in our texting!

“Love
you”

Right after we decided to make it official, “Love you” made its way into our regular texting. We were quick to say we love each other… And we love that!

^{Click on the chart legend to show/hide any milestones or trends.}

“Baby” vs “Babe”

At the beginning of their relationship, Neil used “Babe” instead of “Baby”, but overtime he seems to mimic the way Karina used the word. A significant increase in Neil texting the word “Baby” can be seen after we move in together.

^{Click on the chart legend to show/hide any milestones or trends.}

Karina rarely used the word “Babe” to start with!

^{Click on the chart legend to show/hide any milestones or trends.}

An increase in “Ben”

Notice how Ben (Karina’s son) becomes a topic of conversation more frequently after he was introduced to Neil.

^{Click on the chart legend to show/hide any milestones or trends.}

^{Note: One of Neil’s best friends is also named Ben, so some of this data may be inflated.}

Neil Likes to “Lol”

After we made our relationship official, Neil started to “Laugh Out Loud” much more frequently. Karina “Laughed Out Loud” at a pretty low frequency throughout their relationship.

^{Click on the chart legend to show/hide any milestones or trends.}

The Fall of “Good Morning”

From December through February, we were saying “Good morning” to each other nearly everyday. Since the end of February, the number of times we say “Good morning” to each other via text is nearly zero. Although we started “officially” living with each other in August, we have been seeing each other every morning for quite a while. We get to wish each other a good morning in person everyday, now! The only time we text each other “Good morning” or “Good night” now is when one of us are away.

^{Click on the chart legend to show/hide any milestones or trends.}

All
Words

^{Click on the chart legend to show/hide any milestones or trends.}

Combined
Data

^{Click on the chart legend to show/hide any milestones or trends.}

Travel
Data

A collage of us together on some of our favorite trips!

A Map of the places we have traveled together!

^{Hover over the markers to get information and see when we last visited each place!}

Snowboarding
Data

Snowboarding is one of our favorite hobbies. Thanks to Mt. Hood Meadows for our snowboarding data!

In the first tab, we display the runs at our local ski resort, Mt. Hood Meadows ski resort. In the next tab we wanted to show everyone how often we like to snowboard, using the data we get from our annual ski passes.

Take a look at the charts of our snowboarding data in the tabs below!

Chairlift
Data

Over Time

On our busiest days, we achieved a 15,000+ vertical!

By Vertical Feet Gained

^{Hover over each dot to see how much vertical feet we gained on each date we snowboarded at Mt. Hood Meadows ski resort.}

We seem to tap out around 15 runs in a day…

By number of Runs Completed

^{Hover over each dot to see how many times we took the ski lift on each date we snowboarded at Mt. Hood Meadows ski resort.}

Overall

Each box below represents the amount we used the ski lifts at Mt. Hood Meadows ski resort.

Mt. Hood Express gets us the most elevation gain.

In vertical feet gained

^{Hover over each box to see how much vertical feet we have gained on each chairlift at Mt. Hood Meadows ski resort!}

But we are not ashamed to take Easy Rider to get us to our favorite beginners Freestyle Park!

By number of runs

^{Hover over each box to see how many times we have taken each chairlift at Mt. Hood Meadows ski resort!}

Mt. Hood Meadows Ski Resort

Mt. Hood Meadows Ski Resort Runs on Google Maps.

^{Hover over the marker to see where we got engaged!}

Mt. Hood Trail Map

Take a look at how awesome our local ski resort is. We love Mt. Hood Meadows!

Discretionary
Spending

Each box below corresponds to a percentage of our disposable income spent on a given category (i.e. this excludes boring spending like mortgage, utilities, student loans, or car payments). We analyzed our spending habits over the course of our relationship.

Our Individual Spending Habits

Karina likes to buy things and and stay at AirBNBs. Activities and subscriptions also add up quite a bit.

Karina’s Spending

^{Hover over each box to see what percentage Karina spends on each category.}

Neil like Bars & Restaurants. Makes sense, he loves to eat and drink! Some notable mentions include Pet Expenses, Snowboarding Gear, and Concerts!

Neil’s Spending

^{Hover over each box to see what percentage Neil spends on each category.}

Our Overall Spending Habits

Besides the obvious Bars & Restaurants and Amazon spending, we were surprised to see how much we spent on Chipotle and Golf!

^{Hover over each box to see what percentage we spend on each category.}

Market
Comparisons

Since we first met, the S&P 500 has gone up dramatically! We decided to examine if our relationship milestones or other relationship data could be responsible.

At first glance, an obvious takeaway is that the market went up over the course of our relationship. There might be something else going on here that we dive into in the next tab. But for now, enjoy how our relationship impacted the market!

The S&P 500 & our milestones

^{Hover over the chart and milestones for more information about our milestones and the market price. Or click on the chart legend to show/hide any Milestones.}

Deeper
Dive

So how do we explain why our relationship had such a positive impact on the market? We took some liberties with analytical tools at our disposal to try to logically explain it.

Market Predictions & Texting

We decided to see if our texting data is predictive of the market.

According to the models in the table below, when correlated to the closing daily values of the S&P 500 (controlling for time), our texting is predictive of the change in the absolute market price. The relationship is positive and statistically significant and our impact on the absolute market changes over time.

However, when compared to the daily movements in the S&P 500 index (controlling for time), our models were no longer predictive… :(

^{Note: These models are completely for fun and should not be taken seriously. :)}

Model Descriptives

	Regression Coefficients			Full Model Descriptives
S&P 500 Index	Texts	Time	Texts*Time	Degrees of Freedom	F-value	p-value	R-squared
Absolute Close Price	1.0723*	2.7398*	-0.0028*	(3, 407)	1347.84	< .01	0.91
Daily Difference Close Price	-0.0244	-0.0071	0.0002	(3, 407)	0.24	0.87	< .01
*p<.05 for individual regression coefficient Predictor variable = Daily quantity of texts sent between Neil and Karina Outcome Variable = S&P 500 close price

Model Scatterplots & Interpretations

As can be seen in the visualized models below, in the beginning of our relationship, our texts at day 0 (The lowest, darkest line) were predictive of an increase of absolute market values (An upwards slope). However, the more time that passes, the relationship between our texting and change in market values is no longer there, and eventually turns negative!

We welcome all our guests to share any ideas on why that might be during our wedding. :)

S&P 500 (Absolute)

Statistical Interpretation: This model is statistically significant: F(3, 407) = 1347.84, p <.01, R² = 0.91. Suggesting that, for every 1 text we send, the S&P 500 index price is expected to increase 1.07 points at day 0 (p <.01) - however, for every day that passes, the regression coefficient (slope) is expected to decrease by 0.0028 points (p <.01). The chart above shows the expected change in slope at 0, 200, 400, 600, & 800 days since we started texting. As can be seen from the chart above, after enough time, the relationship turns from positive to negative!

S&P 500 (Daily Difference)

Statistical Interpretation: This model is not statistically significant: F(3, 407) = 0.24, p >.05, R² < 0.01. Our texting is not predictive of the S&P 500 daily difference price.

CAPM
Betas

One interesting factor about our spending that corresponds to the overall market performance, is that some of our favorite brands move in line with the market, or in fact are even more volatile in the direction that the market moves, i.e. when the market goes up, our brands go up even more (Betas > 1).

Our
Brands

In the chart below, we wanted to illustrate the overall distribution of S&P500 stocks by their Betas. Note, most of our favorite brands show a beta above 1!

^{*Ticker not present in S&P 500}

However, our special mention goes to Match Group Inc. with a whooping beta of 2.29! After all, if not for its Tinder brand we would not be here today. The graph below illustrates how small changes in the market coincide in outsized movements in Tinder’s share price.

The S&P 500 Index Betas

In the chart below, we wanted to illustrate the overall distribution of S&P500 stocks by their Betas. Note, most of our favorite brands show a beta above 1!

^{*Ticker not present in S&P 500}

However, our special mention goes to Match Group Inc. with a whooping beta of 2.29! After all, if not for its Tinder brand we would not be here today. The graph below illustrates how small changes in the market coincide in outsized movements in Tinder’s share price.

Macro Spending Trends

Another explanation to the correlation between our relationship and the overall market could be that the early days of our relationship coincided with a broader increase in discretionary spending in the US. As seen in the charts below, everyone in the US has increased spending on nondurable goods and on services since we first met.

^{Source: U.S. Bureau of Economic Analysis}

 

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_{Interested in how these charts were made?: Click here to see our Github repository!}