/assignment-6

Assignment 6 for the FCB subject at UPF

FCB-R-autograding

Assignment 6 - FCB 2023

Deadline: 7/11/2023 - 23:59

Submission procedure

This assignment has to be submitted using GitHub Classroom. This means that you should have cloned the GitHub repo of this assignment from the organization account for FCB in the academic year 2023-24 at https://github.com/funcompbio2023 using the submission link provided at the FCB Moodle site.

Once you have cloned the GitHub repo which has assignment-6 and your GitHub username as repo name, then you can work on it in your local disk and push your changes whenever you like, but make sure that you have pushed the last version of your assignment before the deadline. There is no submit button or any other specific submission procedure or action than just pushing your changes to you GitHub assignment repo. When correcting the assignment, the latest version available will be retrieved. If that latest version available is posterior to the deadline, then the mark of the assignment will have a penalty.

To complete your submission (see rubric below) please agree to the following academic integrity statement by editing this README file and placing the letter X between the squared brackets preceding the statement:

  • [] The work here submitted has been entirely developed by myself and is the result of my own work.

Description

The goal of this assignment is to create an R script that produces a CSV file called infeccions_catalunya_2023.csv with data from the analysed samples of the primary care microbiological sentinel surveillance system, aggregated and ordered by month for the year 2023. To achieve this goal you should follow these 2 steps:

  1. Create an R script called analysis.R with the R commands that read the CSV file mostres_analitzades.csv provided in the repo of the assignment and make the necessary transformations and calculations on the input data from mostres_analitzades.csv to obtain a data.frame object with the columns MES, POSITIUS, TOTAL and PERCENTAGE, corresponding respectively to the month on which values of TOTAL and POSITIUS have been aggregated, the columns positiu and total from the CSV file mostres_analitzades.csv aggregated by month, and the percentage of positives cases over the corresponding total of the month, calculated up to one decimal digit. To round the calculation to one decimal digit use the R function round(); consult its help page to figure out how it works. The contents of this data.frame object should look exactly like this: 
       MES POSITIUS TOTAL PERCENTATGE
1  Jan     1406  2046        68.7
2  Feb     1233  1656        74.5
3  Mar      971  1440        67.4
4  Apr      588   918        64.1
5  May      778  1224        63.6
6  Jun      394   724        54.4
7  Jul      374   649        57.6
8  Aug      269   482        55.8
9  Sep      506   850        59.5
10 Oct      481   794        60.6
  2. Let the data.frame object of the resulting data be called res, the last line of your script should write to disk that object into a CSV file called infeccions_catalunya_2023.csv with the following R command: 
    write.csv(res, "infeccions_catalunya_2023.csv", row.names=FALSE)

Your assignment repo should have the following files:

  1. This README.md file.
  2. The SIVIC data CSV file mostres_analitzades.csv.
  3. The R script file analysis.R.
  4. The resulting CSV file infeccions_catalunya_2023.csv.

The file infeccions_catalunya_2023.csv should have the following characteristics:

  1. It should be a CSV file using the comma (,) as column separator and non-numeric values should be quoted with double quotes (").
  2. It should have the following line as first (column header) line: 
    "MES","POSITIUS","TOTAL","PERCENTATGE"
  3. The second and following lines should contain the values corresponding to the columns specified above. For instance, the second line in the file should look like this: 
    "Jan",1406,2046,68.7

This assignment incorporates an autograding feature using a so-called GitHub Actions Worflow, which will help you to automatically test whether your R script is correctly working after every push. More concretely, a few minutes after you pushed your changes to your remote GitHub repo, the badge labeled FCB-Python-autograding on top of this README file will be red and display the message failing if the autograding has not been successful, and green with the message passing otherwise. You may click on badge to look at output of the autograding tests to understand why it has failed, if that was the case. This feature provides you with formative assessment and to work with it you need to edit your R script in a file called analysis.R at the root of your GitHub repo and leave intact the rest of the files, except for editing README.md to agree to the academic integrity statement.

Evaluation rubric

The rubric to evaluate this assignment consists of the following items:

  • Did you use the GitHub user profile you provided in the first assignment?
  • Did you properly agree to the academic integrity statement?
  • Does the assignment contain the required files?
  • Does the file infeccions_catalunya_2023.csv contain the four required columns?
  • Does the file infeccions_catalunya_2023.csv contain the required infections data aggregated and ordered by month for the year 2023?
  • Does the R code run without errors?
  • Does the R code produce the expected result?