Week2Lecture3

File and Data Handling in R

Activity: Week 1 Review

Please open RStudio, create a new R script, and then add code to the script to do the following:

1. Generate a vector of 1000 random numbers using the rnorm function, with a mean of 4 and a standard deviation of 2, and save it to a variable called x1.

2. Using the rep function, generate a separate vector of 100 instances of the number 9, and call it x2.

3. Combine x1 and x2 into a single vector called data.

4. Create a histogram of the data vector. Modify the col argument to choose a color that you prefer.

5. Make sure to include comments explaining the code to a user.

What do you notice about the histogram? Any patterns? If this were real data, what might you think?

Sensor of the day

Remote sensing of emissions

https://data.carbonmapper.org/

Sensor of the day

File systems

The file system is a means of storing and retrieving files on a computer. Many systems are based on a hierarchical collection of directories.

Thinking about file systems

By Pptudela at the English-language Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=14530617

Reasons for keeping files organized

• Makes it easier for you to find and update files

• Makes it easier for others to understand the logic of the project

• Ensures files can be located without user input

Location, location, location

• You want it to be somewhere where you will be able to find it easily and access it.

• Avoid working exclusively from a removable drive that could get lost

• It can help to store things in a cloud-based drive, but beware of using shared folders

Organizing by attributes

Project-based
Box\Courses\ENV170\CodingExercise1\…

Time-based
Box\Courses\ENV170\Week 2\…

Theme-based
Box\Courses\ENV170\SpatialAnalysis\…

Project directory structure

You can structure your directories in a number of different ways. Data science practitioners often suggest variations on the following theme:

• A main directory (folder)

  • A subdirectory for keeping data files

    • This might also include subdirectories for raw and processed data

  • A subdirectory for keeping code files

  • A subdirectory for keeping output files

Activity: Your file system

• Create a file system for saving your work this week. If you’ve already got one going, see how it compares with our discussion so far and think about whether this would be a good time to reogranize.

• Think about the following

  • Will you be able to find it easily?

  • Will someone else be able to understand how this folder structure works?

• Once you’ve done this, go to the File drop-down menu in RStudio and select Save As to store your script inside the appropriate folder for this week’s lecture

Tips for naming files and folders

• Avoid spaces

  • Use camel case (myLabExercise) or snake case (my_lab_exercise)

• Avoid special characters

  • ~ ! @ # $ % ^ & * ( ) ` ; < > ? , [ ] { } ’ “ |

• Try and keep them short

• Avoid the term final (e.g., code_exercise_3_finalFinal.R)

  • Instead, use version numbers or dates (e.g., YYYY-MM-DD)

• Above all else… BE CONSISTENT!

Recommended practices

• Do not modify your raw data manually, or even better, at all.

• Data manipulation should work like a conveyor belt: it stops at checkpoints. E.g., it gets modified/cleaned/analyzed, and then it moves on.

• Always have collaborators in mind. Work towards shareable code. Have public awareness.

• Consistency (within your project) is key.

Activity: Working with collaborators

• Submit your script from today’s opening activity to the Lecture 3 File sharing assignment on Canvas

• Once everyone’s script has been submitted, you should be able to access another classmate’s script via peer review (if not, I have a backup option)

• Download your classmate’s script, save it to the appropriate place in your file system, open it in RStudio, and run it!