Data Analytics for Economics Assessment - IT/Computer Science Assignment Help

Assignment Task:

Task:

Data Analytics for Economics Assessment

 

## Introduction to the assessment

 

**ENSURE THAT YOU READ THIS BEFORE BEGINNING THE ASSESSMENT**

This is the assessment for the Data Analytics for Economics I unit. This assessment is worth 100% of your marks for this unit: there is no examination for this unit.

 

All of the question you should answer are contained within this file. You should use this file to work through the questions, inserting your commentary and code, correctly formatted, in this document and, when complete, you should upload this R Markdown (.Rmd) file to Moodle before the assessment submission date and time which are shown on Moodle. You can, of course, submit this to Moodle in advance of the submission date.

 

This assessment assesses the knowledge that you have gained throughout this unit. All of the material that you need to complete this assessment is contained in the slides and videos that are available on Moodle for this unit, and which you have practised during the tutorials.

 

You should consider how you format your text in this document so that it is presentable in a workplace scenario when knitted to a final format. Use title headings (#, ##, ###, etc.) to sensibly break up your work, and ensure that your writing is grammatically accurate with good spelling.

 

The questions that you answer in your own words (rather than with code) do not need to be lengthy - brief is fine - but it should fully answer the question.

 

When a question requires only code to shape the data, you should include a brief description of why you are doing what you are doing in that code, showing that you understand why, and not just how.

 

Some of the questions will refer to "your geography" and/or "your industry". These are available on Moodle where you will see each student has been allocated a particular geography or industry. You should substitute these into this assessment as appropriate.

 

### Next steps

 

- You should download all of the data files on Moodle in the assessment area. This will include this file (the R Markdown file with the questions into which you will insert your answers), both of the economic data files, the three geographic boundary data files, and the csv which contains your individual geography and industry.

 

- You will need to load each of these into objects inside your R environment. You should also ensure that you have installed all of the packages that we have used across the unit. You should explicitly load the libraries that you use at the beginning of your code.

 

- Remember to write all code inside code chunks, and keep your narrative writing outside of these. You will be assessed on your written answers to questions, your own commentary on the steps that you are taking and why, and on the quality and accuracy of your code. All the code you write will be executed as part of the marking process.

 

- Ensure that all of your charts and maps are fully labelled with appropriate titles and informative axis labels.

 

- You should feel comfortable creating as many objects as you need to, but try to reuse them where you can.

 

- Please add here your student ID, individual geography and industry.

Student ID: 20048939

Geography: UKC1 - Tees valley and Durham

Industry: K- Financial and insurance activities

 

**Good luck!**

 

## Assessment

 

### Introduction to the data

 

#### Data file 1

You have a CSV file named "rgva_assessment.csv". It contains a time-series of Gross Value Added for a wide variety of geographies and industries within the UK, expressed in millions of pound sterling.

 

The industries are provided to a granularity of SIC2007 sections for all geographies except those at NUTS3: for these small geographies, sections A and B are presented together as AB, and sections D and E are presented as DE.

 

The geographies are NUTS1, NUTS2 and NUTS3 regions, plus the UK. Every NUTS1 region is made up of a number of NUTS2 regions. Each NUTS2 region is made up of a number of NUTS3 regions.

 

#### Data file 2

You have an Excel file named bres_assessment.xlsx. It contains a shorter time-series of the number of employees for a wide variety of geographies and industries within the UK.

 

The industries are provided to a granularity of SIC2007 sections (but without a total) for all geographies, including for NUTS3. There is no aggregation for NUTS3 geographies as described in the above dataset.

 

The geographies are also NUTS1, NUTS2 and NUTS3 regions, but without the UK overall.

 

#### Geography files

 

You have three .geojson spatial boundary files which each contain low-resolution (500m) polygons for each of the above geography types.

 

### Question 1a

What are the three conditions by which we can judge whether data is tidy?

every column is a variable

every row is an observation

every cell is a single value

 

 

### Question 1b

Import the RGVA data file into an object and fully inspect it (showing the code you use to do so). Describe the data, its dimensions, the variables in it and their data types. Describe whether or not it is in a tidy format and in which ways it meets or fails to meet the conditions required for tidy data. Describe the transformations required to meet those conditions.

 

### Question 1c

Make the transformations you identified as required in question 1b and assign the results of this to a new object.

 

### Question 1d

Which NUTS3 geography has the largest manufacturing sector by GVA? Produce a line chart to show its change in Manufacturing GVA over time. Describe what has happened.

 

### Question 1e

It is often said that the UK economy is too focused on Financial and insurance activities and should orient its economy more towards Manufacturing. Which of these is the largest contributor to UK GVA, and how has that changed over time? Show both the data and a line chart.

 

### Question 1f

How much of the total UK GVA of the Financial and insurance activities sector is produced in (a) London (NUTS1) and (b) The City of London (NUTS3 code UKI31)? How has it changed over time?

 

### Question 1g

Plot a line chart that shows the value of GVA over time for your industry in each of the NUTS1 regions of the UK, colouring the lines by region.

 

### Question 1h

In which of the NUTS1 regions has your industry grown the most between 1998 and 2018? Show the data and display it in a column chart

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