• Missing Data
• Web Scraping

• Assignment 3: Thursday 26th September (Released 12th September)
• Practical Exam: 16th October (see practice exam on dmac)
• Project: 18th October (See examples of past projects)

• A live data analysis
• 1 Hour
• See example on website

• Collect / find your own data
• Clean the data
• Determine interesting questions to answer about the data
• Plan how to execute analysis of the data
• Communicate the idea, data cleaning, and analysis (oral presentation)
• Further details are on the dmac website

• Coding style
• File paths and Rstudio projects
• (Intro to) Using functions

"Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread." -- Hadley Wickham

• Style guide for this course is based on the Tidyverse style guide: http://style.tidyverse.org/
• There's more to it than what we'll cover today, we'll mention more as we introduce more functionality, and do a recap later in the semester

• Do not use spaces in file names, use - or _ to separate words
• Use all lowercase letters

• Use _ to separate words in object names
• Use informative but short object names
• Do not reuse object names within an analysis

• Put a space before and after all infix operators (=, +, -, <-, etc.), and when naming arguments in function calls.
• Always put a space after a comma, and never before (just like in regular English).

• Always end a line with +
• Always indent the next line

• Limit your code to 80 characters per line. This fits comfortably on a printed page with a reasonably sized font.
• Take advantage of RStudio editor's auto formatting for indentation at line breaks.

• Use <- not =

Use ", not ', for quoting text. The only exception is when the text already contains double quotes and no single quotes.

• Go to ed and assess your understanding of code style (there may be some technical difficulty)

• It's important when you start working on your own machine that you understand file storage hygiene.
• It helps prevent unexpected problems and makes you more productive
• You'll spend less time fighting against strange file paths.
• Not sure what a file path is? We'll explain that as well.

Discuss:

1. What your normal "workflow" is for starting a new project
2. Possible challenges that might arise when maintaining your project

• This section is heavily influenced by Jenny Bryan's great blog post on project based workflows.
• Sometimes this is the first line of an R Script or R markdown file.

setwd("c:/really/long/file/path/to/this/directory)

• What do you think the setwd code does?

• "set my working directory to this specific working directory".

• It means that you can read in data and other things like this:

data <- read_csv("data/mydata.csv")

• Instead of

data <- read_csv("c:/really/long/file/path/to/this/directory/data/mydata.csv")

• This has the effect of making the file paths work in your file
• This is a problem because, among other things, using setwd() like this:
  • Has 0% chance of working on someone else's machine (this includes you in >6 months)
  • Your file is not self-contained and portable. (Think: "What if this folder moved to /Downloads, or onto another machine?")
• To get this to work, you need to hand edit the file path to your machine.
• This is painful. And when you do this all the time, it gets old, fast.

• This all might be a bit confusing if you don't know what a file path is.
• A file path is: "the machine-readable directions to where files on your computer live."
• So, this file path:

/Users/njtierney/rmd4sci-materials/demo-gapminder.Rmd

Describes the location of the file "demo-gapminder.Rmd".

We could visualise the path

/Users/njtierney/rmd4sci-materials/demo-gapminder.Rmd

as:

users
  └── njtierney
        └──  rmd4sci-materials
               └── demo-gapminder.Rmd

• To read in the gapminder.csv file, you might need to write code like this:

gapminder <- read_csv("/Users/njtierney/Desktop/rmd4sci-materials/data/gapminder.csv")

This is a problem, because this is not portable code.

If you have an RStudio project file inside the rmd4sci-materials folder, you can instead write the following:

gapminder <- read_csv("data/gapminder.csv")

• (1-2 minutes) What folders are above the health.csv file in the following given file path?

• What would be the result of using the following code in demo-gapminder.Rmd, and then using the code, and then moving this to another location, say inside your C drive?

setwd("Downloads/etc1010/week1/week1.Rmd)

• This file path situation is a real pain.
• Is there an answer to the madness?

The answer is yes!

I highly recommend when you start on a new idea, new research project, paper. Anything that is new. It should start its life as an rstudio project.

An rstudio project helps keep related work together in the same place. Amongst other things, they:

• Keep all your files together
• Set the working directory to the project directory
• Starts a new session of R
• Restore previously edited files into the editor tabs
• Restore other rstudio settings
• Allow for multiple R projects open at the same time.

• RStudio projects help resolve file path problems
• In some cases you might have many folders in your r project. To help navigate them appropriately, you can use the here package to provide the full path directory, in a compact way.

here::here("data")

returns

[1] "/Users/njtierney/Desktop/rmd4sci-materials/data"

here::here("data", "gapminder.csv")

returns

[1] "/Users/njtierney/Desktop/rmd4sci-materials/data/gapminder.csv"

You can read the above here code as:

In the folder data, there is a file called gapminder.csv, can you please give me the full path to that file?

This is really handy for a few reasons:

1. It makes things completely portable
2. Rmarkdown documents have a special way of looking for files, this helps eliminate file path pain.
3. If you decide to not use RStudio projects, you have code that will work on any machine

If the first line of your R script is

setwd("C:\Users\jenny\path\that\only\I\have")

I will come into your office and SET YOUR COMPUTER ON FIRE 🔥.

-- Jenny Bryan

• Go to section 5.12 of rmarkdown for scientists

In this lesson we've:

• Learnt what file paths are
• How to setup an rstudio project
• How to construct full file paths with the here package

• Automate common tasks in a power powerful and general way than copy-and-pasting:
  • You can give a function an evocative name that makes your code easier to understand.
  • As requirements change, you only need to update code in one place, instead of many.
  • You eliminate the chance of making incidental mistakes when you copy and paste (i.e. updating a variable name in one place, but not in another).

• Down the line: Improve your reach as a data scientist by writing functions (and packages!) that others use