Oracle Data Visualization - #Makeover Monday 2018 Week 52 Xmas Spend

Another week, another #MakeoverMonday (MM) set of data to take a look at.

Here is .. The Data: http://www.makeovermonday.co.uk/data/

Year: 2018
Week: 52
Date: Dec 17
Data: data.world
Source Article Visualization:
Average spending on Christmas gifts in the U.S. 1999-2018
Data Source: Statista

Description:
The statistic depicts the results of a survey about the estimated Christmas spending of U.S. consumers from 1999 to 2018. The most recent survey revealed that U.S. consumers expected to spend approximately 794 U.S. dollars on average on Christmas gifts.
Holiday shopping

The Christmas season or so called holiday season is the strongest sales period of the year for retailers. It usually commences on the Thanksgiving weekend with Black Friday being the leading sales and traffic day of the whole season, and continues to the end of January. Black Friday sales are closely followed by Super Saturday, which names the Saturday occurring before Christmas Eve. 

Christmas is a public holiday in the United States and is celebrated on December 25th each year. It’s known as a big economic stimulus for many people to purchase Christmas gifts for their beloved family and friends. After Christmas and New Year’s Eve, retail sales usually peak again in January as many people redeem their received Christmas gift cards and vouchers. The latest holiday consumer survey revealed that almost 48 percent of U.S. consumers plan to buy gift cards or gift certificates in 2016. 


During the holiday season, many retailers extend their return policy and set special shipping deadlines for guaranteed Christmas delivery in order to improve their customer-friendly service.

More Information:
Region: United States
Survey time period: November 1 to 11, 2018*
Number of respondents: 1,037 respondents
Age group:18 years and older
Method of interview: Telephone interview
Supplementary notes: * Figures from 1999 to 2017 were conducted in November of each year among equally large samples.

This is the original chart, a line chart.





























As the dataset was published, it looked very simple, and assuming.
And so I thought to myself .. easy .. less than 1 hour and all in the spirit of the #MakeoverMonday!

Let's take a look at my first visualization. A Bar Chart.

Easy. Simple. Created a focus data point that clearly stands out. That's it?

Well, that's it. Well, NOT SO FAST!


Something was telling me that this was too easy and not all was right with this data.
I wasn't sure what, but I was determined to find out.

With that, I turned to my friend GOOG and did a few searches to see what I could find. And what do you know, a dataset showed up, and just the one that I was looking for 😘

Data Source: GALLUP


Good news was that I was able to match data point-by-data-point to the source data in our MM week 52 Christmas shopping in the US.

What I found in this data was different from what was given to us.

1. There was a second data point for NOV-2002, 2 telephone surveys?
2. This was a telephone survey about crime?
3. Where was the data for NOV-2018? Because it is not a part of this data.

This all led me to analyze the data a bit more.

Using 1999 as the baseline I wanted to know how the 20 years since measured up. Not good.
Only 2 years over the last 20 were about the 1999 mark.
A few years we over 15% less and a single year over 25%!

The chart that I created was an Area chart.

This is it.



This was nice, but boring to say the least.


So to give the reader more context I added a few elements.

• 1999 appeared to be the year that with only small 2 exceptions was far and above the other 18 years. In that, I added a lyric line from the Prince song 1999 and made sure to add that text in his favorite color, Purple
• Added the survey question asked via telephone to set the frame of mind that gave looking at the data over time as well as added what I thought was a catchy subtitle 
• Removing all of the data points and only adding back the points that were critical for highlighting the data trend and what was important takeaways over the 20 year span
• Also adding a few vector images made by Freepik from Flaticon to spruce up the overall design.

This is what I posted as my Visualization. 

What do you think?













































Twitter post: https://twitter.com/GADASHEK/status/1078133461221851141

Oracle Data Visualization - #MakeoverMonday 2018 week 51

It has been a while, the end fo the year was certainly fast paced (heart reference)!

And that gave me another dose of what I wanted to shift my passion for technology wise.


So, that brings me to the next adventure that I am passionate about exploring.
STOP! No, don't worry, I am not leaving the Oracle technology space :)

But in fact, I want to look into the Data Visualization aspect of Oracle tech, and specifically, the product Oracle Data Visualization (DV) {cloud} or Desktop (DVD) {not-cloud} download DVD here.


It was only as recently as this past spring when I knew something wasn't right (or left, that is an eye joke) with my left eye. And for me, the topic area of Visualization has become deeply passionate for me.


And so that led me to start reading books, researching online, listening to podcasts, and just plain talking to people.


One of the books that I read recently was a book called #Makeover Monday by the authors Andy Kriebel (vizwiz.com, Data School, Makeover Monday, Tableau Tip Tuesday and Workout Wednesday. He also writes at datavizdoneright.com) and Eva Murray (blog: www.trimydata.com).


Here is "The Book" -> #Makeover Monday http://www.makeovermonday.co.uk/book/



And that brings me to the topic of what I can see as the future of many blog posts to come. Each week as the new data for the week gets published I will participate in #Makeovermonday as it will give me a chance to explore new data, get feedback about the visualizations that I create and work on being able to tell good stories about the data. Lastly, I will focus on using Oracle DV(D) as my primary tool of choice although the #MM community prefers to use Tableau but they do not make that a requirement. You could create your canvas on a napkin, literally and totally your choice.


So, let's get started on the first #Makeovermonday that I am participating.

Here is ..

The Data: http://www.makeovermonday.co.uk/data/

Week: Week 51
Date: Dec 17
Data: data.world
Source Article Visualization:
London Bus Safety Performance (page 3)

Data Source: TFL (January 2015 – June 2018)


About this Dataset
DATA SOURCE: Transport for London

DASHBOARD: London Buses Safety Dashboard – Q2

Objectives
What works and what doesn't work with this chart?
How can you make it better?
Post your alternative on the discussions page.
DATA DICTIONARY
View -- No definitions added for the 1 file and the 11 columns in this dataset.





Now let's get into the data and see if there is a story to tell.



This is the Data Visualization as a sum of the parts and the story that I was able to tell. Enjoy!


--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---
~ letter ~

Dear Abby,
I am writing this letter to inform you about the bus safety,
or lack thereof when riding the London Bus.
This letter is to warn you about the HIGH amount of Bus accidents in London’s
Westminster borough!
You are at a higher chance of being involved in an accident since you are an
Adult Female Passenger. And therefor I believe that it is in your best interest
to select a different mode of transit.

And please, whatever you do, DO NOT RIDE a bus Operated by Metroline!


Sincerely,
A Concerned Data Visualization with a Birds Eye View

--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Oracle Data Visualization Multi-Canvas

Trend

The trend shows that the number of accidents is on a constant rise and with a 95% confidence it will continue that way into the next quarter.

By Borough North, South and 5-parts - Horizontal Bar with Focus on 1 Borough

By Adding more context to the Borough information I wanted to make it easier to identify if there was a Borough that clearly stood out from the rest. And there was, it was in the North.

By London Borough and the 5 Parts - Horizontal Stacked Bar with Borough 5-Parts

I added the Red color to the highest density bar in the entire stacked bar char to make sure to call attention to the clear highest number of accidents in a single Borough in the North and Inner West.

Count of Accidents by Borough - Radar Bar

Specific Borough with clearly the highest number of accidents, Westminster!

Focus on Westminster Groups & Operators - Parallel Coordinates

Again calling attention to the highest concentration of accidents for a particular Group & Operator, that was Metroline!

This was my first week joining the #MakeoverMonday and look forward to next week as well as 2019!