TDM 10100: Project 9 - Visualizations 1

Each year, the Centers for Disease Control and Prevention (CDC) publishes the most comprehensive data on mortality in the United States through the National Vital Statistics System. The dataset contains records of all deaths nationwide between 2005 and 2015, with detailed information on causes of death and demographic characteristics of the individuals.

The dataset used for this project contains information from the year 2014 about the mortality data records released by the CDC. This dataset is thorough and contains 38 columns and 2631171 rows of death record data. The columns cover everything from date of death to personal details (age, sex, marital status, etc.) to manner of death and more. Some of the columns may not make a lot of sense and that is OK. Feel free to explore this dataset as much as you would like to gain a better understanding.

Many of the columns only contain encoded values. There are accompanying datasets that will you help with understanding what is actually being shown here. Some examples of these decoder files are provided in Question 1.

Some of the columns from myDF we will be working with include:

An example of what the decoder files may contain is:

Read in the Death Records dataset as myDF from /anvil/projects/tdm/data/death_records/DeathRecords.csv. This dataset is the main source of the death records data, but there are a few different dataset files that go along with myDF that contain the decoder data keys for what the values in some of the columns mean.

Read in the following datasets to ensure correct interpretation of the values from the specific columns Race, MaritalStatus, and DayOfWeekOfDeath:

These datasets contain descriptions for what each of the code values of the columns of myDF represent. In the table of the column Race in myDF, there are not too many unique values representing the different races. For reference, use the Race.csv dataset to find out what the numerical values of this Race column mean. Also, the number of occurrences of the different values in the Race column varies greatly. Some have a few hundred while others have tens of thousands of entries or more.

Make a plot of this table to help visualize this. To make a very basic plot in R, use plot(). The result is typically a scatter plot, with a dot or circle value representing each value in your data being plotted.

The Race category coded as '1' appears 2241510 times. Compared to other categories with counts such as 700, 4913, or even 13297, this value is disproportionately high, making it difficult to visualize alongside the others on the same scale.

Create a new named vector containing just the entries from the Race column that are not '1':

Either use plot() or dotchart() to visualize this vector. Which method do you find more insightful?

Think about your new visualization. How many more values do you want to exclude until this is a good visualization to interpret? The '2' race values? '3'? More? Less? Try some of these and explain your final decision.

While it is almost always a good idea to include as much data as was provided when making a comparison, such as comparing how many death records of each race there were, it is sometimes the case that removing data actually helps. If you look at your first plot from Question 1, it is not hard to see that you cannot really tell what is going on with the lower values because the '1' race has extended the upper y-limit so high that everything else is small by comparison.

When you do end up removing the '1' and other values to limit what races are shown, be sure to document this. Write a comment in your notebook about "I removed this value because…​.." and just explain it a little bit. When you are finishing these plots, it can also be good to include what values you excluded in the title or labels, like "Death Record Counts by Race (Excluding …​.) ". If these plots are just for you, then this gives you a quick reference in some time when you come back and wonder what you plotted. If these plots are for sharing data findings, even better, because these labels help the plot to be interpreted by others who are not in your head.

The MaritalStatus column tracks what relationship stage each person was in at the time of death. Please make sure you know what the letters in this column represent before continuing. Additionally, the values in the Age column range from 1 to 999. Obviously, this 999 is an error code number, not a person who was actually that age. But let’s continue with it for now.

boxplot() is well suited for exploring the relationship between one categorical and one numerical variable. Create a boxplot() that shows how the different martial statuses compare to the ages of the people who have died:

It is easy to see where the outliers are in this plot. With the 999 value being so much higher than the other (actual) ages, the rest of the plot gets squished down so it is not very useful.

Filter out the ages of myDF where they are 999, and save this as cleanDF. With this new cleanDF, make a boxplot to show the reasonable ages and the marital status of the people in the death records.

Take a specific age range (including at least 40 years of ages within the range) from the actual ages of the people who have died, and make a boxplot to show this against the marital statuses.

Make a boxplot that is very similar to that which you just made, except only for the people whose marital status is "M" (married) OR "W" (widowed). Your plot should have two "boxes", with distinct ways to easily tell the marital status "boxes" apart.

For this boxplot, add proper title, axis labels, and colors. Any additional customizations you want to add are welcome.

People can die at any age but it is more likely for someone older to be widowed in their death record than it is for a younger person to be. The same is likely true for any status besides single, depending on how young or old of people you are looking at.

So, how do you compare the marital statuses across a certain age? You can plot it.

Filter the data so you are only working with the marital statuses Married and Widowed, and only the people who were 60. Try out a few different ages to figure out which you would like to use to make a barplot with here. It is up to you for the age, but this should still use just these two marital statuses.

Now working across all of the marital statuses, make a barplot comparing the marital status of each of the 70-year-olds in the Death Records.

How does this plot compare to a barplot of 60-year-olds across all marital statuses? What about for 80-year-olds? Does the quantity of people in each marital status category shift consistently across the different ages?

Take a look at the DayOfWeekOfDeath column. This column contains numerical values for each day of the week, and has the number 9 to represent any unentered or error days. Sometimes it is nice to have the text names stored in place of the numbers. But we don’t know if their day-of-the-week system follows the 'Sunday-Saturday' or 'Monday-Sunday' week system by only looking at that column. However, there is a separate csv file for it in the data. So, we can find this out from the DayOfWeekOfDeath.csv decoder dataset:

In R, there is a function merge() that can take two datasets as input, and combine the data within them to help create a new column. We’re actually going to be using both datasets (DeathRecords.csv and DayOfWeekOfDeath.csv) to make the new day_of_week_of_death column.

Since these two columns have different names depending on which dataset they’re from:

You should specify the names of the columns you are merging from both datasets:

One way to double check your work after merging is to make a table comparing your numerical DayOfWeekOfDeath column with your new column containing the day of the week names. Each column should have one non-zero value mapping to one row - this represents every day listed as each specific name or number pair.

Show the table comparing the month of death by the names of the days of the week. Go ahead and visualize this table in a barplot. Then, filter the day of the week of death to only compare the days Monday and Friday to all of the different months.

Make sure to label this and all other visualizations in this project with a title, axis labels, and any other customizations needed to fully interpret what you are trying to show.