This is the reassessment for BU2010 in 2022/23.
Even though you will receive your task via GitHub this time you do not need to submit your task via GitHub. Instead, please email the README.md and the scrape.R file to JWard19@uclan.ac.uk before the deadline.
Below is today's task. We will be working with a range of skills you learned in recent weeks.
For this task, you need to provide the answers in the scrape.R file you will find in this repository, not in the Markdown file you are currently reading.
Your tasks can be found in the R script file scrape.R that is part of this repository. You have to work on these tasks by yourself. Do not work with others.
Please work on these tasks in RStudio - not on the GitHub website. If you work in RStudio you can make sure your code works as it should. If you don't work in RStudio, but edit the file on the GitHub web interface you will have to copy and paste the code into R for testing - an unnecessary step that can introduce mistakes.
Pay attention to the autocomplete options RStudio is offering you and use them to explore how R commands work. Also, remember how useful the cursor keys and the Tab key can be. Pressing F1 will bring up the documentation for the selected command in the Help tab.
Please don't forget to commit and to push your commit.
You will find the following tasks in scrape.R. In that file please write your code below the comment with the task. I have started some of the lines with the code for the answers for you.
Set the correct Working Directory.
Hint: I showed you how to do this in previous weeks. It can be done with a command or through the GUI (Graphical User Interface).
Load the packages you need.
Read (import) the first page (Ocado up to Frasers) of the html file with the FTSE 100 share prices. It is in this repo.
Use Selector Gadget to find the CSS Selector for the elements that contain the codes, names and pricesof the shares.
Scrape the html text from these elements, store them in a vector and clean them of unwanted strings.
Combine the vectors you created into one data frame.
Write a short explanation what you did, including what you found easy or difficult.
Write your explanation here:
You might not be able to do all tasks, but you should give it a try. Please remember to work by yourself, do not work with others.