This assignment is designed to simulate a scenario where you are taking over someone’s existing work, and continuing with it to draw some further insights.
This is a real world dataset taken from the Crime Statistics Agency Victoria. https://www.crimestatistics.vic.gov.au/crime-statistics/latest-crime-data/download-data, specifically the data called “Data Tables - LGA Criminal Incidents Visualisation - year ending September 2019 (XLSX, 15.96 MB)”. The data for this assignment is from Table 3, but is stored as a compressed csv file (“.csv.gz”) to make it easier to read and manage.
You have just joined a consulting company as a data scientist. To give you some experience and guidance, you are performing a quick summary of the data, following along from guidance from Amelia, and some of the questions your manager has. This is not a formal report, but rather something you are giving to your manager that describes the data, and some interesting insights. We have written example text for the first section on monash, and would like you to explore another area. Our example writings are a good example of how to get full marks.
Your colleague at your consulting firm, Amelia (in the text treatment below) has written some helpful hints throughout the assignment to help guide you.
Questions that are work marks are indicated with ** at the start and end of the question, as well as the number of marks in parenthesis.
This assignment will be worth 10% of your total grade, and is marked out of 18 marks total.
3 Marks for grammar and clarity. You must write in complete sentences and do a spell check.
5 Marks for presentation of the data visualisations
10 marks for the questions
Your marks will be weighted according to peer evaluation.
Sections that contain marks are indicated with **, and will have the number of marks indicated in parentheses. For example:
# `**` What are the types of item divisions? How many are there? (0.5 Mark) `**`As of week 2, you have seen some of the code used here, but I do not expect you to know immediately what the code below does. This is a challenge for you! We will be covering skills on data summary and data visualisation in the next two weeks, but this assignment is designed to simulate a real life work situation - this means that there are some things where you need to “learn on the job”. But the vast majority of the assignment will cover things that you will have seen in class, or the readings.
Remember, you can look up the help file for functions by typing ?function_name. For example, ?mean. Feel free to google questions you have about how to do other kinds of plots, and post on the ED if you have any questions about the assignment.
To complete the assignment you will need to fill in the blanks for function names, arguments, or other names. These sections are marked with *** or ___. At a minimum, your assignment should be able to be “knitted” using the knit button for your Rmarkdown document.
If you want to look at what the assignment looks like in progress, but you do not have valid R code in all the R code chunks, remember that you can set the chunk options to eval = FALSE like so:
```{r this-chunk-will-not-run, eval = FALSE}`r''`
ggplot()
```If you do this, please remember to ensure that you remove this chunk option or set it to eval = TRUE when you submit the assignment, to ensure all your R code runs.
You will be completing this assignment in your assigned groups. A reminder regarding our recommendations for completing group assignments:
Your assignments will be peer reviewed, and results checked for reproducibility. This means:
Each student will be randomly assigned another team’s submission to provide feedback on three things:
This assignment is due in by close of business (5pm) on Thursday 9th of April. You will submit the assignment via ED. Please change the file name to include your teams name. For example, if you are team dplyr, your assignment file name could read: “assignment-1-2020-s1-team-dplyr.Rmd”
You work as a data scientist in the well named consulting company, “Consulting for You”.
It’s your second day at the company, and you’re taken to your desk. Your boss says to you:
Amelia has managed to find this treasure trove of data - get this: crime statistics in Victoria for the past years! Unfortunately, Amelia just left on holiday to New Zealand, and now won’t be back now for a while. They discovered this dataset the afternoon before they left on holiday, and got started on doing some data analysis.
We’ve got a meeting coming up soon where we need to discuss some new directions for the company, and we want you to tell us about this dataset and what we can do with it. We want to focus on monash, since we have a few big customers in that area, and then we want you to help us compare that whatever area has the highest burglary.
You’re in with the new hires of data scientists here. We’d like you to take a look at the data and tell me what the spreadsheet tells us. I’ve written some questions on the report for you to answer, and there are also some questions from Amelia I would like you to look at as well.
Most Importantly, can you get this to me by Thursday 9th April, COB (COB = Close of Business at 5pm).
I’ve given this dataset to some of the other new hire data scientists as well, you’ll all be working as a team on this dataset. I’d like you to all try and work on the questions separately, and then combine your answers together to provide the best results.
From here, you are handed a USB stick. You load this into your computer, and you see a folder called “vic-crime”. In it is a folder called “data-raw”, and an Rmarkdown file. It contains the start of a data analysis. Your job is to explore the data and answer the questions in the document.
Note that the text that is written was originally written by Amelia, and you need to make sure that their name is kept up top, and to pay attention to what they have to say in the document!
Downloaded via this link
Or directly into R by typing the following into the console:
library(usethis)
url <- "https://mida.numbat.space/assignments/mida-assignment-1.zip"
use_course(url)