# Check a conditional
"hello" == "hello"[1] TRUEIntro to Data Science
Lab 2 – Conditionals & Version Control
A Guide to Your Process
Scheduling
Learning Objectives
Practice
Supporting Information
Class Discussion
Today’s Plan
- Muddiest Point Review
- Conditionals
- Version Control Background
- Navigating GitHub
Today’s Learning Objectives
After today’s session you will be able to:
- Write conditional statements
- Manage missing data in objects with conditionals
- Explain the difference(s) between Git and GitHub
- Define fundamental version control vocabulary
Muddiest Point Review
- Recurring topics from most recent MPs
- What other topic(s) would you like to review?
Conditionals
- You can write code that runs only if an ‘if statement’ is true
- Otherwise that chunk of code is skipped!
- This allows you to write flexible code that can handle any outcome that you can anticipate!
- Particularly useful for subsetting data based on the contents of a column
- These ‘if statements’ are called conditionals
- The answer to a conditional must be either
TRUEorFALSE
Fundamentals: EQUAL
- Are two things exactly equal?
- Uses
==operator- Just two equal signs
Fundamentals: OR
- Are any of these conditions met?
- Uses
|operator- Shift +
\on keyboard
- Shift +
Fundamentals: AND
- Are all of the conditions met?
- Uses
&operator- Shift + 7 on keyboard
Fundamentals: Summary
EQUAL
- Are two things exactly equal?
- Uses
==operator- Just two equal signs
OR
- Are any of these conditions met?
- Uses
|operator- Shift +
\on keyboard
- Shift +
Practice: Fundamental Conditionals
- Write a conditional that tests whether “apple” is equal to “orange”
- Write a conditional that uses the
&operator and returnsTRUE
- Write a conditional that uses the
|operator and returnsFALSE
Conditionals & Vectors
- We can also use conditionals on vectors
- When we do this, we get one
TRUEorFALSEper element in the vector
Conditionals & Subsetting
- Often we want to use conditional statements to “subset” data
- I.e., keep only rows that meet the condition
- Subsetting is supported because columns and rows in data are vectors
- Let’s look at the first column in an example dataset from the
palmerpenguinspackage
species
[1,] TRUE
[2,] TRUE
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[344,] FALSESubsetting Cont.
- We can use the base R
subsetfunction to keep only the rows of data where a specified column meets a condition
- Let’s subset the penguin dataset we explored earlier
[1] 344
Practice: Subsetting
- We’ll use the base R
subsetfunction with thepeng_dfobject- If needed, consult the help file for more details (
?subset)
- If needed, consult the help file for more details (
- Subset
peng_dfto only Adelie penguins (and assign to asub_v1object)- I.e.,
species == "Adelie"
- I.e.,
- How many rows does that subset have?
More Practice: Fundamental Conditionals
- Subset
peng_dfto Adelie or Gentoo penguins- Assign this subset to
sub_v2object
- Assign this subset to
- Subset
peng_dfto only male Gentoo penguins- Assign to
sub_v3object
- Assign to
- How many rows does that subset have?
Discussion: Conditionals
- We’ve covered EQUAL, OR, and AND
==,|, or&
- What unanswered questions do you have?
- What other types of conditional statements would be useful?
- Think about it in the context of wanting to subset some data
Numeric Conditionals
- For numbers, we can use greater/less than conditionals!
- Greater / less than are expressed as normal
>and<
- Adding ‘or equal to’ is done by adding an equal sign
>=and<=
Practice: Numeric Conditionals
- Subset
peng_dfto only penguins with a bill length greater than 40 mm- Assign to
sub_v7
- Assign to
- Subset
peng_dfto only penguins with a body mass less than or equal to 4,000 g- Assign to
sub_v8
- Assign to
Temperature Check
How are you Feeling?
Version Control Background
- “Version control” is a set of tools for tracking changes to a code file
- Version control lets you work only in a single file but still preserves the history
- No comments you’d have to resolve (like MS Word / etc.)
- No need to “Save As” and retain many different files for each draft
Version Control Rationale
Why use version control?
Collaboration
Easily share & work together on files
Reproducibility
Fully document your progress and end results
Portfolio
Demonstrate your skills to others with coding know-how
Git Versus GitHub
Git
GitHub
Git Versus GitHub
Git
- Actual version control software
- Does the tracking of changes
- Works locally on your computer
- Not something others can interact with
GitHub
Git Versus GitHub
Git
- Actual version control software
- Does the tracking of changes
- Works locally on your computer
- Not something others can interact with
GitHub
- Graphical user interface (GUI) for Git
- Allows viewing of Git-tracked changes
- Not actually doing version control itself
- Other users can see your content
- Depending on your settings
Create a Github Profile
- On your computer, go to github.com
- Create a profile
- Let me know if you run into any issues!
Navigating GitHub
- We’ll work through part of an established GitHub workshop for this section
Practice: Create a Repository
- To practice what we’ve just covered, make a practice repository!
- This is a test repository so:
- Set it to “Private”
- Create the repo with both a README and a .gitignore
- As you go through this process, take notes for ‘future you’
- This course will require you to make two more repositories
- So you’ll want to have a good resource to remind yourself with down the line
Temperature Check
How are you Feeling?
Upcoming Due Dates
Due before lecture
(By midnight)
- Homework #2
- Read the British Ecological Society’s Reproducible Code Guide (p.20-25)
Due before lab
(By midnight)
- Muddiest Point #3
Bonus Git Info
Git & RStudio
- If desired, you can get GitHub to talk directly with RStudio
- Done through Git!
- Advantage is a clearer connection between your RStudio work and GitHub
Prep Steps
- Install R & RStudio
- Create a GitHub Account
- Install Git
- Connect RStudio and GitHub
Git and RStudio
- You can work through part of an established GitHub workshop for this section
Bonus Conditionals
OR with >2 Options
- OR conditionals with many options get cumbersome quickly
- E.g.,
x == 1 | x == 2 | x == 3 | x == 4 …
- E.g.,
- We can use concatenation and the
%in%operator to simplify this!
%in%is effectively “if any of <this vector> matches the value”- E.g.,
x %in% c(1, 2, 3, 4, …)
- E.g.,
Conditionals: NOT
- You can also exclude based on conditions
- Two different ways of doing this
- For one / a few options: use
!=for “not equal to”- E.g.,
x != 10
- E.g.,
- Can be combined with
%in%to exclude a set of options- E.g.,
!x %in% c(...) - Note the exclamation mark is before the object
- E.g.,
Practice: Advanced Conditionals
- Subset
peng_dfis species is any of “Adelie”, “Gentoo”, or “Chinstrap”- Use the
%in%operator
- Use the
- Subset
peng_dfto all islands except “Dream”
Intro to Data Science Lab 2 – Conditionals & Version Control