Data Update #3

1. Datawrapper Charts

2. Unanswered Question

What was the survival status of all people aboard the Titanic?

I currently cannot answer any questions regarding the crew or staff that were aboard the Titanic. The dataset I am using only includes information on passengers that were aboard the Titanic. Consequently, there is no information for any crew or staff members that were aboard. Therefore, if I wanted to ask a question regarding the survival status of all those aboard the Titanic I would be unable to do so.

3. The Dataset I need

The dataset I need to answer the questions above would have to contain all information regarding the crew/staff aboard the Titanic. The dataset I am currently using is based on data collected from Encylopedia Titanica's records. Encylopedia Titanic does have a complete database that includes information for both the passengers and the crew however it is only available with the purchase of annual membership of £10.00. I would initially attempt to contact the organization through their webpage explain my reasoning for wanting the data and ask for access to their database for free. Alternatively, if this were to fail I would pay the £10.00 for access which is equivalent to approximately $18 CDN.

Other questions I could ask if I were able to obtain this data:

• What percentage of crew members survived?
• What is the difference in survival for passengers vs. crew?

Data Update #2

Lead

When the RMS Titanic sank in 1912, 62% of the first class passengers survived while only 25% of the third class passengers survived; this suggests that the socioeconomic status of the passengers played a major role in their final moments aboard the Titanic.

Excel Workbook: Raw Data & Small Slice

My excel workbook is divided into two different sheets: the original raw data and a smaller slice. The smaller slice of data shows the total number of passengers in each class divided into two different coded categories: 0 for died and 1 for survived. Below the total is the share of survived passengers for each class which is the percentage of each class's survival rate.

Original Data Set

The original dataset can be found here. The main source of information for the dataset is from the Encyclopedia Titanica. 

Supporting Article

A JSTOR Daily article by Nashwa Khan published on June 2, 2016 titled "What the Titanic Reveals About Class and Life Expectancy" discusses the link between socioeconomic status and the lower survival rate for third class passengers aboard the Titanic. The article highlights how as a passenger's socioeconomic status declined so did their likelihood of survival. The article suggests using the event of the Titanic sinking as a lens to help analyze the relationship between socioeconomic status and life expectancy.

Data Update #1

1. What dataset will you use for your final report?

The dataset I will be using for my final report is the passenger manifest from the RMS Titanic. The Titanic dataset includes a variety of information about the passengers aboard the Titanic. It is important to note that this dataset does not include any information about the crew of the Titanic.

2. Describe the dataset. What kind of data does it contain?

The dataset contains information regarding passengers' names, sex, age, fare (British pound), and passenger class. The dataset also has information on whether or not the passengers survived the fatal incident. Other information that is included for some but not all passengers is ticket number, home destination, cabin, boat number, and body identification number.

3. Is there anything about your data that you don’t understand? (i.e.
what a column heading means). How will you find this out?

There were two variables that I was unable to understand at first. One column heading was titled sibsp and another column heading was titled parch. However, I was able to find a key to the dataset that provided variable descriptions. Sibsp is the number of siblings and/or spouses aboard the Titanic and parch is the number of parents and/or children aboard the Titanic.

4. What are some questions you hope to answer with your data? List at
least three. (you don’t need the answers at this point)

There are a few questions I would like to answer with this dataset:

• What is the percentage of women versus men that survived? 
• Which passenger class had the highest survival rate? Which passenger class had the lowest?
• Of the cities from which passengers embarked, which had the highest survival rate? Which city had the lowest?
• What was the percentage of first-class women versus third-class women that survived? Compare to the percentage of survival for first-class and third-class men.
• How many passengers had siblings or spouses aboard with them?
• How many passengers had parents or children aboard with them?

Data Visualization Analysis

Statistics Canada released this visualization December 2017 showing Employed labour force who worked full year, full time and reported employment income in 2015.  The visualization is concisely designed, comprehensible, and visually appealing, but it does not provide the information in the clearest way possible. The pie chart was a poor choice to display the collected data because there are too many individual slices of the chart. In addition, the visualization relies on its core center to provide information instead of focusing on its interactive portion. The slices of the pie chart would be meaningless without the core center.  Moreover, the occupation titles appear on some of the categories but they are cut off and not fully legible. The incomplete titles cannot be fully seen unless they are clicked on.

The visualization can be filtered by using the drop-down menu to select National Occupational Classification (NOC), Highest certificate, diploma or degree, or, by Geographical location to provide more specific information. 

The list that the drop-down menu provides for the NOC is extremely long and tedious to scroll through.

The visualization could have been simplified if there was less data represented. The pie chart that shows the total occupations is an overwhelming image and does not make sense if the numbers were removed. Once a more specific category is selected the visualization is improved and simplified because there is less to look at. However, even the simplified chart is poorly constructed because the rings would not make sense without the core center providing numbers.

The visualization has too much information for it to work in this format. Some of the slices in the pie chart are so small that it is easy to pass over them. For example, the Architectural technologists and technicians category is barely visible. This visualization would work better as a series of bar charts broken down by field of occupation. If the charts were displayed as bar charts the information would be clearer to interpret with or without showing numbers.

The interactive portion of the visualization was its strongest element. It was easy to maneuver and simple to understand. The visualization was accessible to interact with either by using the drop-down menu to refine the search or by scrolling over the different slices and clicking on one to learn more information. The biggest critique is that the amount of information the visualization is attempting to provide is does not display well in pie chart format.

Source: Employed labour force who worked full year, full time and reported employment income in 2015, 2016 Census (Table). http://www12.statcan.gc.ca/census-recensement/2016/dp-pd/dv-vd/occ-pro/index-eng.cfm

Embed code test

Here is a chart that I will embed.

The chart I want to embed should be located above this sentence.

My First Blog Post

BEST WAY TO LINK TO ARTICLE > According to an article by Twin Cities Pioneer Press, Trump administration calls for removing Canada lynx from endangered list. 

Retrieved from: