Creating a Data Visualization Script for a Git Repository

Updating Your Research Question and Dataset

1.If you have changed either your research question, or your dataset, since the Research Question assignment, you should update research_question.txt (or research_question.md or research_question.tex, as appropriate) to reflect these changes.

2.If you have changed your dataset, you need to remove the old dataset, and add (and commit) your new dataset, as a CSV file.

1.Using Notepad++ (or another text editor of your choice), or R Studio, create a file called in your Git repository workspace.

IMPORTANT: You MUST name your file exactly as shown, with NO SPACES, all lower case text, and a capital extension. Failure to do so will mean our marking script will not find your file, and you will receive a mark of zero (0) for this assignment.

Your script should do the following:

1.Load any required libraries. Be sure these are part of tidyverse or base R; anything else will cause your script to fail, and you will receive zero (0) credit for this assignment

2.Load your dataset from the current working directory. Do not, under any circumstances, hard code absolute paths into your script: this will guarantee that your script will fail execution, and you will receive a mark of zero (0) for this assignment.

3.Set the output to : pdf(“visualization.pdf”)

Note: you must name your output file so that our automatic marking scripts can find it. Failure to do so will result in a mark of zero (0) for this assignment.

4.Create a scatterplot, boxplot, or barplot as appropriate for visualizing your independent and dependent variables.

5.If your dependent variable is interval or ordinal kind, create a histogram with normal curve overlay.

2.Commit to your Git repository workspace.

3.Test to be sure it does what it’s meant to do.

4.Have one of your teammates clone your repository and test it’s meant to do, from a #different computer. to be sure it does what

5.Use to remove any extraneous files (editor backup files, old experiments, your old dataset if you changed datasets, and if you committed it mistakenly (never commit derived files: they get overwritten the very next time you run a script, and so they often create spurious merge conflicts).

Assessment criteria

Your will be assessed on three major criteria:

1.Does it work?

1.Does the script load only libraries from or base R?

2.Does the script load the dataset from the current working #directory?

3.Does the script write into the current working directory?

2.Is it correct?

1.Does the script create the appropriate visualization for the research question and associated variables?

2.If the dependent variable is interval or ordinal kind, does the output include a histogram with normal curve overlayed?

3.Is it beautiful?

1.Do the graphs have a main title?

2.Are both the x and y axes labelled in human-friendly terms, including units?