📝 W08 Summative

2023/24 Winter Term

⏲️ Due Date:

• 7 March 2024 at 5pm (London time)

If you update your files on GitHub after this date without an authorised extension, you will receive a late submission penalty.

Did you have an extenuating circumstance and need an extension? Send an e-mail to 📧

🎯 Main Objectives:

• Demonstrate your ability to write a report in Quarto Markdown
• Demonstrate your dplyr and ggplot2 skills
• Demonstrate your ability to fit a logistic regression model
• Demonstrate your ability to interpret and evaluate the performance of a logistic regression model
• Demonstrate your ability to defend your model choices

⚖️ Assignment Weight:

This assignment is worth 20% of your final grade in this course.

20%

Do you know your CANDIDATE NUMBER? You will need it.

“Your candidate number is a unique five digit number that ensures that your work is marked anonymously. It is different to your student number and will change every year. Candidate numbers can be accessed using LSE for You.”

Source: LSE

📝 Instructions

1. Go to our Slack workspace’s #announcements channel to find a GitHub Classroom link entitled 📝 W08 Summative. Do not share this link with anyone outside this course!

2. Click on the link, sign in to GitHub and then click on the green button Accept this assignment.

3. You will be redirected to a new private repository created just for you. The repository will be named ds202w-2023-w08-summative-yourusername, where yourusername is your GitHub username. The repository will be private and will contain a README.md file with a copy of these instructions.

4. Recall what is your LSE CANDIDATE NUMBER. You will need it in the next step.

5. Create a <CANDIDATE_NUMBER>.qmd file with your answers, replacing the text <CANDIDATE_NUMBER> with your actual LSE number.

   For example, if your candidate number is 12345, then your file should be named 12345.qmd.

6. Then, replace whatever is between the --- lines at the top of your newly created .qmd file with the following:

   ---
   title: "DS202A - W08 Summative"
   author: <CANDIDATE_NUMBER>
   output: html
   self-contained: true
   ---

   Once again, replace the text <CANDIDATE_NUMBER> with your actual LSE CANDIDATE NUMBER. For example, if your candidate number is 12345, then your .qmd file should start with:

   ---
   title: "DS202W - W08 Summative"
   author: 12345
   output: html
   self-contained: true
   ---

7. Fill out the .qmd file with your answers. Use headers and code chunks to keep your work organised. This will make it easier for us to grade your work. Learn more about the basics of markdown formatting here.

8. Once you are done, click on the Render button at the top of the .qmd file. This will create an .html file with the same name as your .qmd file. For example, if your .qmd file is named 12345.qmd, then the .html file will be named 12345.html.

   Ensure that your .qmd code is reproducible, that is, if we were to restart R and RStudio and run your notebook from scratch, from top to the bottom, we would get the same results as you did.

9. Push both files to your GitHub repository. You can push your changes as many times as you want before the deadline. We will only grade the last version of your assignment. Not sure how to use Git on your computer? You can always add the files via the GitHub web interface.

10. Read the section How to get help and how to collaborate with others at the end of this document.

“What do I submit?”

You will submit two files:

• A Quarto markdown file with the following naming convention: <CANDIDATE_NUMBER>.qmd, where <CANDIDATE_NUMBER> is your candidate number. For example, if your candidate number is 12345, then your file should be named 12345.qmd.

• An HTML file render of the Quarto markdown file.

You don’t need to click to submit anything. Your assignment will be automatically submitted when you commit AND push your changes to GitHub. You can push your changes as many times as you want before the deadline. We will only grade the last version of your assignment. Not sure how to use Git on your computer? You can always add the files via the GitHub web interface.

🗄️ Get the data

What data will you be using?

You will be using two distinct datasets for this summative.

Part 1

Your dataset, for this part, comes from the Office for National Statistics and relates to UK GDP figures.

Preparation

1. Download the data by clicking on the button below.

Part 2

In this part, you will be re-using the same dataset as in summative 1.

Preparation

Click on the button below to re-download the dataset:

ℹ️ About the dataset

This dataset was slightly pre-processed compared to the version available on the ONS website. Here is our pre-processing code in case you’re curious about what we did:

library(dplyr)
library(readxl)
library(janitor)

url <- "https://www.ons.gov.uk/file?uri=/economy/governmentpublicsectorandtaxes/publicsectorfinance/datasets/publicsectorfinancessummarytablesappendixm/current/publicsectorfinancesummarytablesappendixmfinal.xlsx" 
uk_finance <- download.file(url, "data/UK_public_finance_summary.xlsx")
data <- read_excel("data/UK_public_finance_summary.xlsx",sheet="Time Series",skip=4)
data <- data %>% filter(`Time period`!="Dataset identifier code")
data <- data %>% 
  clean_names() %>% 
  select_all(~gsub("million", "pounds_million", .))%>%
  select_all(~gsub("_note_\\d{1,2}", "", .))
write.csv(data,"data/UK_public_finances_cleaned.csv")

The dataset was processed to make variables more code and model-friendly. If you prefer a dataset with more explicit variable names, you can download it using the button below:

If you’d like to know more about the dataset, have a look here or here.

📋 Your Tasks

What do we actually want from you?

Part 1: Show us your dplyr muscles! (20 marks)

1. Load the data into a data frame called uk_gdp. Freely explore the data on your own.

2. Unlike in the previous summative and formative, this dataset does not come in clean format and will require some work before it can be used.

   1. Remove the rows from the data frame that do not contain quarterly GDP figures (i.e rows that don’t have Title values of the form 1955 Q1)
   2. Clean up and/or rename the column names to more tractable and meaningful names

3. Create a new variable called gdp_lag that contains the GDP of the previous quarter.

4. Calculate the percentage of quarterly GDP growth

   \[ \frac{GDP\_current\_quarter-GDP\_previous\_quarter}{GDP\_previous\_quarter}*100 \]

   and store it in a new data frame variable called quarterly_change

5. How many times did the percentage of quarterly GDP growth dip below 0 and when? A technical recession is defined as two consecutive quarters of negative percentage of quarterly GDP growth. Can you identify periods of technical recession?

Part 2: Create a baseline model (50 marks)

In summative 1, we focused on predicting central_government_net_borrowing_pounds_million.

Here, we’ll change tack a bit and focus on net_investment_pounds_million.

We will tackle this as a classification task. We aim to create a logistic regression model to predict whether the net investment will increase or decrease in the next fiscal year (i.e from beginning of April in a given year to end of March in the next year).

As it was in the previous section, you don’t need to use a chunk for each question. Feel free to organise your code and markdown for this part.

1. Load the data UK public finance dataset into a dataframe called uk_public_finances.

2. Create a data frame called yearly_uk_finances that is based on the uk_public_finances dataframe. This new dataframe should:

   • keep the same numerical variables as the original dataframe
   • aggregate (i.e sums) for each fiscal year the monthly values of these numerical values¹

3. The button below allows you to download the dataset as it would have looked if you had successfully completed questions 1 and 2 of this part.

Create a binary target variable called is_net_investment_up. The variable should be set to 1 if the net_investment_pounds_million variable in the current year is \(5\%\) higher than the net_investment_pounds_million variable in the previous fiscal year. Otherwise, it should be set to 0.

To avoid problems, don’t use a recipe here — just use mutate to create the variable.

4. Create a logistic regression model using a single valid predictor. This could be either a column already in the data frame or a new column you create using mutate or with a recipe.

5. Set the last year in the data set as the test set. Use the previous years as the training set.

6. Use whatever metric you feel is most apt for this task to evaluate your model’s performance. Explain why you chose this metric.

7. Explain what the regression coefficients mean in the context of this problem.

8. Comment on the goodness-of-fit of your model and its predictive power.

Part 3: Model some more (30 marks)

Now is your time to shine!

Come up with your own feature selection or feature engineering strategy² and try to get a better model performance than you had before.

Don’t forget to validate your results using the appropriate resampling techniques!

Whatever you do, this is what we expect from you:

1. Show us your code and your model.

2. Explain your choices (of feature engineering or cross-validation strategy)

3. Evaluate your model’s performance. If you created a new model, compare it to the baseline model. If you performed a more robust cross-validation, compare it to the single train-test split you did in the previous section.

✔️ How we will grade your work

Here, we start to get more rigid about grading your work. Following all the instructions, you should expect a score of around 70/100. Only if you go above and beyond what is asked of you in a meaningful way will you get a higher score. Simply adding more code or text will not get you a higher score; you need to add interesting insights or analyses to get a distinction.

⚠️ You will incur a penalty if you only submit a .qmd file and not also a properly rendered .html file alongside it!

Part 1: Show us your dplyr muscles! (20 marks)

Here is a rough rubric for this part:

• 5 marks: You wrote some code but filtered the data incorrectly or did not follow the instructions.
• 10 marks: You cleaned the initial dataframe correctly correctly, but you might have made some mistakes when creating your lag and/or your GDP quarterly change columns, or your conclusions for Task 5 are not correct.
• 15 marks: You did everything correctly as instructed. Your submission just fell short of perfect. Your code or markdown could be more organised, or your answers were not concise enough (unnecessary, overly long text).
• 20 marks: You did everything correctly, and your submission was perfect. Wow! Your code and markdown were well-organised, and your answers were concise and to the point.

Part 2: Create a baseline model (50 marks)

Here is a rough rubric for this part:

• <10 marks: A deep fail. There is no code, or the code/markdown is so insubstantial or disorganised to the point that we cannot understand what you did.
• 10-20 marks: A fail. You wrote some code and text but ignored important aspects of the instructions (like not using logistic regression)
• 20-30 marks: You made some critical mistakes or did not complete all the tasks. For example: your pre-processing step was incorrect, your model contained some data leakage (seeing the future), or perhaps your analysis of your model was way off.
• 30-35: Good, you just made minor mistakes in your code, or your analysis demonstrated some minor misunderstandings of the concepts.
• ~35 marks: You did everything correctly as instructed. Your submission just fell short of perfect. Your code or markdown could be more organised, or your answers were not concise enough (unnecessary, overly long text).
• >35 marks: Impressive! You impressed us with your level of technical expertise and deep knowledge of the intricacies of the logistic function. We are likely to print a photo of your submission and hang it on the wall of our offices.

Part 3: Model some more (30 marks)

Here is a rough rubric for this part:

• <10 marks: A fail. There is no code, or the code/markdown is so insubstantial or disorganised to the point that we cannot understand what you did, or you wrote some code and text but ignored important aspects of the instructions.
• 10-20 marks: Good, although you made mistakes in your code, or your analysis demonstrated some misunderstandings of the concepts.
• ~22 marks: You did everything correctly as instructed. Your submission just fell short of perfect. Your code or markdown could be more organised, or your answers were not concise enough (unnecessary, overly long text).
• >22 marks: Impressive! You impressed us with your level of technical expertise and deep knowledge of the intricacies of the logistic function. We are likely to print a photo of your submission and hang it on the wall of our offices.

How to get help and how to collaborate with others

🙋 Getting help

You can post general coding questions on Slack but should not reveal code that is part of your solution.

For example, you can ask:

• “Does anyone know how I can create a logistic regression in tidymodels without a recipe?”
• “Has anyone figured out how to do time-aware cross-validation, grouped per country??”

You are allowed to share ‘aesthetic’ elements of your code if they are not part of the core of the solution. For example, suppose you find a really cool new way to generate a plot. You can share the code for the plot, using a generic df as the data frame, but you should not share the code for the data wrangling that led to the creation of df.

If we find that you posted something on Slack that violates this principle without realising it, you won’t be penalised for it - don’t worry, but we will delete your message and let you know.

👯 Collaborating with others

You are allowed to discuss the assignment with others, work alongside each other, and help each other. However, you cannot share or copy code from others — pretty much the same rules as above.

🤖 Using AI help?

You can use Generative AI tools such as ChatGPT when doing this research and search online for help. If you use it, however minimal use you made, you are asked to report the AI tool you used and add an extra section to your notebook to explain how much you used it.

Note that while these tools can be helpful, they tend to generate responses that sound convincing but are not necessarily correct. Another problem is that they tend to create formulaic and repetitive responses, thus limiting your chances of getting a high mark. When it comes to coding, these tools tend to generate code that is not very efficient or old and does not follow the principles we teach in this course.

To see examples of how to report the use of AI tools, see 🤖 Our Generative AI policy.

Footnotes

1. As a result of these processing steps, there should be a single value for net_investment_pounds_million for the year 1997 that would be the sum of net_investment_pounds_million from April 1997 to March 1998 and similarly for the other numerical variables in uk_public_finances and the rest of the fiscal years covered by the data.

2. Feature engineering is creating new variables from existing ones. For example, you could create a new variable that results from a mathematical transformation of an existing variable. Or you could enrich your public finance dataset with the GDP-related predictors (see dataset from part 1) e.g general government debt-to-GDP ratio.