ETC1010: Data Modelling and Computing

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# ETC1010: Data Modelling and Computing
## Lecture 6A: Style, file paths, & functions
### Dr. Nicholas Tierney & Professor Di Cook
### EBS, Monash U.
### 2019-09-04

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# Recap

.huge[
- Missing Data
- Web Scraping
]

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# Upcoming due dates

.huge[
- 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)
]

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# Practical Exam?

.huge[
- A live data analysis
- 1 Hour 
- See example on website
]

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# Project?

.huge[
- 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
]

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# Lecture Overview

.huge[
- Coding style
- File paths and Rstudio projects
- (Intro to) Using functions
]

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# Style guide

.huge[
> "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
]

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# File names and code chunk labels

.huge[
- Do not use spaces in file names, use `-` or `_` to separate words
- Use all lowercase letters
]

```r
# Good
ucb-admit.csv

# Bad
UCB Admit.csv
```

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# Object names

.huge[
- Use `_` to separate words in object names
- Use informative but short object names
- Do not reuse object names within an analysis
]

```r
# Good
acs_employed

# Bad
acs.employed
acs2
acs_subset
acs_subsetted_for_males
```

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# Spacing

.huge[
- 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).
]

```r
# Good
average <- mean(feet / 12 + inches, na.rm = TRUE)

# Bad
average<-mean(feet/12+inches,na.rm=TRUE)
```

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# ggplot

.huge[
- Always end a line with `+`
- Always indent the next line
]

```r
# Good
ggplot(diamonds, mapping = aes(x = price)) +
  geom_histogram()

# Bad
ggplot(diamonds,mapping=aes(x=price))+geom_histogram()
```

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# Long lines

.huge[
- 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.
]

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# Assignment

.huge[
- Use `<-` not `=`
]

```r
# Good
x <- 2

# Bad
x = 2
```

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# Quotes

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

```r
ggplot(diamonds, mapping = aes(x = price)) +
  geom_histogram() +
  # Good
  labs(title = "`Shine bright like a diamond`",
  # Good
       x = "Diamond prices",
  # Bad
       y = 'Frequency')
```

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# Your Turn: ed

.huge[
- Go to ed and assess your understanding of code style (there may be some technical difficulty)
]

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# File Paths and organising yourself

.huge[
- 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.
]

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# Your Turn

.huge[
Discuss:

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

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# A Mantra: When you start a new project \- Open a new RStudio project

.huge[
- This section is heavily influenced by [Jenny Bryan's great blog post on project based workflows.](https://www.tidyverse.org/articles/2017/12/workflow-vs-script/)
- Sometimes this is the first line of an R Script or R markdown file.

```r
setwd("c:/really/long/file/path/to/this/directory)
```
- What do you think the `setwd` code does?
]

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# What does `setwd()` do?

.huge[
- "set my working directory to this specific working directory".

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

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

- Instead of

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

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# What even is a file path?

.huge[
- 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.
]

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# What even is a file path?
  
.huge[
- 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". 
]

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# What even is a file path

.huge[
We could visualise the path

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

as:

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

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.huge[
- To read in the `gapminder.csv` file, you might need to write code like this:

```r
gapminder <- read_csv("/Users/njtierney/Desktop/rmd4sci-materials/data/gapminder.csv")
```
 
This is a problem, because this is not portable code.
]

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

```r
gapminder <- read_csv("data/gapminder.csv")
```
]

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# Your Turn

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

`"/Users/miles/etc1010/week1/data/health.csv"`

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# Your Turn

.huge[
* 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?

```r
setwd("Downloads/etc1010/week1/week1.Rmd)
```
]

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# Is there an answer to the madness?

.huge[
- This file path situation is a real pain. 
- Is there an answer to the madness?
]

.huge[
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**. 
]

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# Rstudio projects

.huge[
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.
]

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# Rstudio projects

.vlarge[
This helps keep you sane, because:

* Your projects are each independent. 
* You can work on different projects at the same time. 
* Objects and functions you create and run from project idea won't impact one another.  
* You can refer to your data and other projects in a consistent way.

And finally, the big one

**RStudio projects help resolve file path problems**, because they automatically set the working directory to the location of the rstudio project.
]

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# The "here" package

.huge[
- 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.

```r
here::here("data")
```

returns

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

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# The `here` package

.huge[
```r
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? 
]

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# The `here` package

.huge[
This is really handy for a few reasons:

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

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# Remember

.huge[
> 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
]

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# Aside: How to create an RStudio project

.huge[
- Go to [section 5.12 of rmarkdown for scientists](https://rmd4sci.njtierney.com/workflow.html#aside-creating-an-rstudio-project)
]

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# Summary of file paths and rstudio projects

.huge[
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
]

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.white[
# Motivating Functions
]

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# Do you see any problems with this code?

```r
st_episode <- st %>%
  html_nodes(".np_right_arrow .bp_sub_heading") %>%
  html_text() %>%
  str_replace(" episodes", "") %>%
  as.numeric()

got_episode <- got %>%
  html_nodes(".np_right_arrow .bp_sub_heading") %>%
  html_text() %>%
  str_replace(" episodes", "") %>%
  as.numeric()

twd_episode <- got %>%
  html_nodes(".np_right_arrow .bp_sub_heading") %>%
  html_text() %>%
  str_replace(" episodes", "") %>%
  as.numeric()
```

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# Next Lecture: Why functions?

.huge[
- 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).
]

.huge[
- Down the line: Improve your reach as a data scientist by writing functions (and packages!) that others use
]

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