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Homework answers / question archive / Featherman’s Analytics Adventures© – Array Table Assignment This assignment is designed to give you insight about the United States of America and practice using INSERT INTO SELECT FROM SQL queries, and also UPDATE SET() and CASE() processing
Featherman’s Analytics Adventures© – Array Table Assignment
This assignment is designed to give you insight about the United States of America and practice using INSERT INTO SELECT FROM SQL queries, and also UPDATE SET() and CASE() processing. Data manipulation is made far easier when leveraging the usage of arrays. You will gain excellent experience manipulating date in arrays in this assignment. The UPDATE SET() command is very useful in this context to build new columns of metrics.
The scenario of this assignment is that you (or a close friend) is considering what state to live in the near future. You decided that your town is great but because the real estate is too overpriced, that you feel that you want to start your career in a less expensive state. You decide to return to the pacific northwest only after building considerable wealth (equity in a duplex and a year’s worth of expenses in the rain day fund.)
After talking to elders, you decide to make the decision of what state to live in by using a calculated, rational approach. Due to the importance of the decision, you do not want to rely on emotion and bias alone. As a result, you decided to look for datasets that are indicators of quality of life, and criteria that help decide if the state is going to be a good place to live. While many more and different datasets could be captured, (e.g. food production and prices, level of commerce, trade in goods and services, global connectedness, agriculture production, level of property and retail taxes, etc.), you feel the eight tables in the above picture are a good start to assess the quality of life in the state. You realize these tables and any analysis derived from them is just a first version of continued research, but you also surmise that starting with eight datasets is a good enough start for this week. Here is a partial dataset that can be created using these tables.
In support of your decision on 4-4-20 a search of datasets revealed the tables shown above. The tables were imported into the Featherman_analytics database on cb-ot-devst05.ad.wsu.edu. A listing of the columns of these tables and their column meanings is in the appendix. The query that produces the dataset below is in the appendix.
Background
You decide to do an analysis of the states for potential employment and domicile (living and home ownership). You decide to build your analysis on several decision criteria, therefore you break the analysis into different sections. In the end you will rank the top 5 states using some the decision criteria in the next section. In your next round of analysis you decide to bring in more data such as housing prices, housing affordability, salary levels by job category, and other ratings based on intended lifestyle, etc. Again you realize this analysis is just a start, version one, so 8 tables of demographics is a start.
Suggested Decision Criteria
You are a cautious, but adventurous person and you decide to use this important decision to test your decision making approach. You recall that prudent decision makers define decision criteria upon which to base their decision before fishing for public datasets. Decision criteria allow increased objectivity and usage of factual data over emotion based criteria (however emotion based decision criteria are also needed). Below is a list of decision criteria believed to be evidenced in the first set of data tables, the available data (were this your real decision you would create your own decision criteria and then go find the data and merge it together). In this assignment the data has already been provided to speed up the process.
a) population and level of population change (you want to live in a growing state, not declining as you think a state with increasing population correlated to higher real estate appreciation).
b) level of poverty, high school graduation rate, and unemployment in the state and whether that is improving (you do not want to live in a state where prosperity is hard to come by, as you think poverty levels are correlated to higher crime).
c) the level of retires in the state (you figure states with higher levels of retirees must have strong medical and abundant services for senior citizens, therefore the state can help to provide a higher standard of living in retirement).
d) the GDP growth of the state (as a measure of prosperity and growth) and number of new jobs
e) small business growth and importance of small business as a engine of economic growth in the state. The number of small businesses can be analyzed and percentage of employees in several industries that are employed by small businesses (% of exporters, % employment by small business, % manufacturing, %mining and %professional services employed by small businesses in the state). You know that the strength of America is a vibrant small business sector so you decide to factor this into your decision of what state to live in.
f) You are interested in median income as this is a measure pf prosperity and you want to live in a state that is prospering and growing and becoming more wealthy, not in decline.
g) you are interested in beer and wine and want to make sure there are breweries and wineries available to visit.
h) Finally you are interested in the racial makeup of the state (black, white, native America, asian, hispanic).
Not all the fields to perform the analysis mentioned above were added to the query in the appendix. For example, many columns useful to analyze the importance and growth of small businesses were not included. Using the base query provided and changes you want to make, bring any columns of data into arrays for analysis.
Requirements
Using the tables shown pull columns into arrays to analyze the state data. Choose four of the decision criteria above, and build one array of any columns to analyze the states for the decision criteria. Your job is to rate the state using each of four decision criteria that you choose. In the array create calculated columns of metrics that pull data together from the 8 tables to analyze the states on the decision criteria. Add one textual column to the array that puts the state into categories (e.g., stagnant state, slow-growth state, moderate growth state, fast-growing state.).
You are required to make a report with 4 sections (each analyzing one decision criteria). Each section needs its own query and array. In addition to pulling the relevant columns of data together for analysis, your query needs to have three new metrics of your own design (numeric) and two textual metrics (using CASE() statements). You r metrics are graded for usefulness to analyze the decision criteria. Use the functionality shown in the provided arrays documentation and training videos. Add two paragraphs of textual interpretation and analysis for each of the decision criteria. Mention the top 5 states in desirability in each section This is an assignment on SQL arrays therefore demonstrate your mastery of using them after reading the module’s documentation, and create useful metrics to grade the states on the decision criteria to ensure a high grade.
You will be creating the array, selecting values into it, creating local variables (for example creating a percent of total calculation and others) and loading them with values, and adding columns of metrics to the array to analyze . Use an UPDATE SET command to calculate values for the new columns of data. You are actually using only a small percentage of the functionality presented in the arrays module as most of the data is at the same level of granularity, and organized for you.
Most of the tables are at the state level of granularity but two of the tables are at the level of city (the ArraysHW_PctOver25GradHW, and ArraysHW_ShareRaceBuCity tables are at the city level). When you bring in data from these two tables (required to use at least one of them) you will have to change the granularity of the data to match the state not city level. For two of the four decision criteria incorporate the city data.
Before starting this assignment, be sure to set your foundational knowledge and experience with using arrays. Please run the queries from the arrays module that are needed to complete this assignment. Set your knowledge before you begin by running the related examples so that the assignment completion process is orderly. Do not jump into an assignment without creating your own arrays for practice.
The arrays module code that you should practice with include:
a) the DECLARE ing of the array and understanding of the data types needed (e.g. any column that is to display a percent should be declared DECIMAL(5,2) to allow display of the values, or nvarchar(6) to display percent signs.
b) The INSERT INTO @array columns SELECT FROM queries to load an initial set of columns from the arrays
c) The UPDATE SET commands to calculate values for columns in your array.
d) PRINT functions to see calculated values
e) CASE statements to write textual analytics
f) the Tableau and PowerBI videos in modules 8 and 9
To get you started here is a layout of a sample query you could use to examine the level of retirees in any state. You do not need to use this perspective, it is just an example to get you jumpstarted.
USE Featherman_analytics;
DECLARE @Population TABLE ([State] nvarchar(2), [StateName] nvarchar(20), [Population] decimal, [#Retirees] decimal, [>65 Females] decimal, [>65 Males] decimal, [TotalRetired] decimal, [%Retirees] decimal(5,2), [Male%TotalRetires] decimal(5,2), [%TotalFemales] decimal(5,2), [%TotalMales] decimal(5,2), Comment1 nvarchar(20), Comment2 nvarchar(20) )
--Here are local variables that are used to total entire rows from the array. They are needed for percent of total calculations.
DECLARE @TotalPop decimal = (SELECT SUM([FieldName]) FROM Tablename
DECLARE @TotalRetirees decimal = (SELECT SUM([FieldName]) FROM Tablename
DECLARE @TotalFemales decimal = (SELECT SUM([FieldName]) FROM Tablename
DECLARE @TotalMales decimal = (SELECT SUM([FieldName]) FROM Tablename
--Here you can check the values calculated and stored into the local variables
PRINT @TotalPop PRINT @TotalRetirees PRINT @TotalFemales PRINT @TotalMales
INSERT INTO @Population ‘Some of the array’s columns
select ‘list of Fields you will have to analyze the fields in the tables below
FROM [featherman].[ArraysHW_IncomeDemographics] as ID
INNER JOIN [featherman].[ArraysHW_PopulationChange] as pop on pop.[State] = ID.State
INNER JOIN [featherman].[ArraysHW_StatePopandRetirees] as r ON r.State = ID.State
UPDATE @Population SET ‘the values for the column are calculated here
UPDATE @Population SET ‘the values for the column are calculated here
UPDATE @Population SET ‘the values for the column are calculated here
UPDATE @Population SET ‘the values for the column are calculated here
UPDATE @Population SET ‘the values for the column are calculated here
SELECT * FROM @Population
Turn-in
Build a MS-Word document that
a) Lists your chosen decision criteria for making eh decision which state to move to. Write two sentences about the importance or value of each.
b) Choose four of your decision criteria and build one array for each. Use headings to organize your responses such as Decision Criteria 1: “title of decision criteria” You will have 4 decision criteria so four sub-sections are needed.
For each of the 4 sub-sections:
1. Create the query needed to build the array and calculated columns. Copy the SQL code into the section. You must use UPDATE SET commands to build 3 new columns of analytics. Higher grades given for more plausible, insightful and varied new columns of analytics. For two of the four decision criteria use one of the tables that are at city level. Add two columns of textual analytics built by CASE() statements.
2. Copy screen shots of the resultant array into the section
3. Copy the SQL into PowerBI or Tableau, make two slides per research criteria to visualize the data and support your argument of the importance of your chosen four decision criteria (A-H above)
4. Add two paragraphs about what the data is saying, and list your top five states using the decision criteria.
So you are building a report that has four sections each of which discuss the analysis of one decision criteria. Complete the assignment with a summary statement that brings together the results found. “In summary if I were to use these decision criteria, I would plan to live in one of the following states in
my near future”
Extra credit: The data tables were provided. For extra credit you can find your own data, and upload it into your server space. You would save the data from the web into Excel spreadsheets and format the data in the spreadsheets. You would have to remove unnecessary columns and headers. If you right-click your database you can select Tasks| Import data. You can watch a YouTube video on importing excel data in to SSMS if you get stuck.
Appendix:
Use the query below to get started. All of the tables (except two) were joined together, however not all the columns were brought into the dataset. There is still work there based on your analytical interests (what stories and decision criteria you want to investigate in the data). These six tables all have state (a two letter state code) as the field in common and the granularity is at the state level so the tables have 51 rows (including the District of Columbia).
The two tables that were not added to the current query are the ArraysHW_PctOver25GradHW table and the ArraysHW_ShareRaceBuCity table. These two taller tables have a granularity of city and approximately 25k rows. When you integrate data from these two tables you will have to condense the data down to state level.
USE Featherman_analytics;
select Id.[State], pop.[StateName], [Pop2018],[#Retirees],[Over65Fem], [Over65Men], [#DisabledWorkers], [Poverty#], [%PopInPoverty] as [%Poverty 2016], [Poverty%] as [%Poverty 2019], [StateUnemploymentRate], [StateGDPGrowth], [PopChange2010-18] as [%PopulationChg2010-18], [NumSmallBiz] ,[MedianIncome],[MedIncomeSelfEmplINcorBiz], [MedIncomeSelfEmplUnIncorpBiz], [MedHHIncome], [NumBreweries], [NumWineries]
FROM [featherman].[ArraysHW_IncomeDemographics] as ID
INNER JOIN [featherman].[ArraysHW_PopulationChange] as pop on pop.[State] = ID.State
INNER JOIN [featherman].[ArraysHW_PovertyData_AndMedianIncome2016] as pov
ON pov.State = ID.State
INNER JOIN [featherman].[ArraysHW_SmBizData2018] as sb ON ID.State = sb.State
INNER JOIN [featherman].[ArraysHW_StatePopandRetirees] as r ON r.State = ID.State
INNER JOIN [featherman].[ArraysHW_NumBreweriesWineriesByState] as BW on BW.State = ID.State
|
Table Name |
Column Name |
Column name meaning |
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SmBizData2018 |
State |
State code 2 letters |
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StateName |
Name of State |
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NumSmallBiz |
Number small businesses in state |
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PercentEmpbySmBiz |
Percent Employees in state working in small businesses |
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MedIncomeSelfEmplINcorBiz |
Median income of self-employed individuals in Incorporated businesses |
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MedIncomeSelfEmplUnIncorpBiz |
Median income of self-employed individuals in UNIncorporated businesses |
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NumNewBiz2018 |
Number new businesses created in 2018 |
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NumNewJobsFromNewBiz2018 |
Number new jobs from new businesses in 2018 |
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PctOfExportersThatAreSmBiz |
Percent of Exporters that are small businesses |
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StateGDPGrowth |
State GDP growth |
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StateUnemploymentRate |
State Unemployment Rate |
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PctMfrEmpFromSmBiz |
Percent of manufacturing employees are that in small businesses |
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PctProfessionalSvcEmpfromSMBiz |
Percent of professional service employees are that in small businesses |
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PctMiningEmpfromSMBiz |
Percent of mining employees are that in small businesses |
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Table Name |
Column Name |
Column name meaning |
|
Income |
State |
State code 2 letters |
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MedianIncome |
Median Income for residents of the state |
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%PopInPoverty |
Percentage of the population of the state that is in poverty |
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Table Name |
Column Name |
Column name meaning |
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NumBreweries |
State |
State code 2 letters |
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NumBreweries |
Number of breweries in the state |
|
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NumWineries |
Number of wineries in the state |
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Table Name |
Column Name |
Column name meaning |
|
PercentOver25 |
State |
State code 2 letters |
|
City |
City |
|
|
PercentCompetedHS |
Percent of state’s population that completed high school |
|
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Table Name |
Column Name |
Column name meaning |
|
State – Poverty and Median Income 2016 |
State |
State code 2 letters |
|
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StateName |
Name of the state |
|
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Poverty# |
Number of people in state living in poverty |
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Poverty% |
Percent of people in state living in poverty |
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MedHHIncome |
Median household income |
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Table Name |
Column Name |
Column name meaning |
|
Population Change |
State |
State code 2 letters |
|
|
StateName |
Name of the state |
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Pop1990 |
State population in 1990 |
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Pop2000 |
State population in 2000 |
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Pop2010 |
State population in 2010 |
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Pop2018 |
State population in 2018 |
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PopChange2010-18
|
Percentage population changed from 2010 to 2018 |
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Table Name |
Column Name |
Column name meaning |
|
Share Race By City The data is by city so you have to aggregate it by state |
State |
State code 2 letters |
|
City |
City |
|
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%white |
Percent of population that is white |
|
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%black |
Percent of population that is black |
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%native_american |
Percent of population that is Native Am. |
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%asian |
Percent of population that is asian |
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%hispanic |
Percent of population that is hispanic |
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Table Name |
Column Name |
Column name meaning |
|
StatePop andRetireees |
State |
State code 2 letters |
|
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StateName |
Name of State |
|
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Population |
State population |
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#Retirees |
Number of retired people in state |
|
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#DisabledWorkers |
Number of people collecting disability in state |
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Over65Fem |
Number females in state that are over 65 |
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Over65Men |
Number males in state that are over 65 |
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