Intro 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 TRUE or FALSE

Fundamentals: EQUAL

• Are two things exactly equal?

# Check a conditional
"hello" == "hello"

[1] TRUE

• Uses == operator
  • Just two equal signs

Fundamentals: OR

• Are any of these conditions met?

# Check either one conditional *or* the other
"hello" == "hello" | 2 == 7

[1] TRUE

• Uses | operator
  • Shift + \ on keyboard

Fundamentals: AND

• Are all of the conditions met?

# Are *all* conditions TRUE?
"hello" == "hello" & 2 == 7

[1] FALSE

• Uses & operator
  • Shift + 7 on keyboard

Fundamentals: Summary

EQUAL

• Are two things exactly equal?

"hello" == "hello"

[1] TRUE

• Uses == operator
  • Just two equal signs

OR

• Are any of these conditions met?

"hello" == "hello" | 2 == 7

[1] TRUE

• Uses | operator
  • Shift + \ on keyboard

AND

• Are all of the conditions met?

"hello" == "hello" & 2 == 7

[1] FALSE

• Uses & operator
  • Shift + 7 on keyboard

Practice: Fundamental Conditionals

1. Write a conditional that tests whether “apple” is equal to “orange”

2. Write a conditional that uses the & operator and returns TRUE

3. Write a conditional that uses the | operator and returns FALSE

Conditionals & Vectors

• We can also use conditionals on vectors

• When we do this, we get one TRUE or FALSE per element in the vector

• Let’s explore that with a demonstration:

# Make a vector
my_vec <- c(1, 2, 1, 1, 3, 0, 2)

# Find all elements that are equal to 1
my_vec == 1

[1]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE

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 palmerpenguins package

# Check out a condiitonal on one column in the dataset
palmerpenguins::penguins[1] == "Adelie"

       species
  [1,]    TRUE
  [2,]    TRUE
  [3,]    TRUE
  [4,]    TRUE
  [5,]    TRUE
  [6,]    TRUE
  [7,]    TRUE
  [8,]    TRUE
  [9,]    TRUE
 [10,]    TRUE
 [11,]    TRUE
 [12,]    TRUE
 [13,]    TRUE
 [14,]    TRUE
 [15,]    TRUE
 [16,]    TRUE
 [17,]    TRUE
 [18,]    TRUE
 [19,]    TRUE
 [20,]    TRUE
 [21,]    TRUE
 [22,]    TRUE
 [23,]    TRUE
 [24,]    TRUE
 [25,]    TRUE
 [26,]    TRUE
 [27,]    TRUE
 [28,]    TRUE
 [29,]    TRUE
 [30,]    TRUE
 [31,]    TRUE
 [32,]    TRUE
 [33,]    TRUE
 [34,]    TRUE
 [35,]    TRUE
 [36,]    TRUE
 [37,]    TRUE
 [38,]    TRUE
 [39,]    TRUE
 [40,]    TRUE
 [41,]    TRUE
 [42,]    TRUE
 [43,]    TRUE
 [44,]    TRUE
 [45,]    TRUE
 [46,]    TRUE
 [47,]    TRUE
 [48,]    TRUE
 [49,]    TRUE
 [50,]    TRUE
 [51,]    TRUE
 [52,]    TRUE
 [53,]    TRUE
 [54,]    TRUE
 [55,]    TRUE
 [56,]    TRUE
 [57,]    TRUE
 [58,]    TRUE
 [59,]    TRUE
 [60,]    TRUE
 [61,]    TRUE
 [62,]    TRUE
 [63,]    TRUE
 [64,]    TRUE
 [65,]    TRUE
 [66,]    TRUE
 [67,]    TRUE
 [68,]    TRUE
 [69,]    TRUE
 [70,]    TRUE
 [71,]    TRUE
 [72,]    TRUE
 [73,]    TRUE
 [74,]    TRUE
 [75,]    TRUE
 [76,]    TRUE
 [77,]    TRUE
 [78,]    TRUE
 [79,]    TRUE
 [80,]    TRUE
 [81,]    TRUE
 [82,]    TRUE
 [83,]    TRUE
 [84,]    TRUE
 [85,]    TRUE
 [86,]    TRUE
 [87,]    TRUE
 [88,]    TRUE
 [89,]    TRUE
 [90,]    TRUE
 [91,]    TRUE
 [92,]    TRUE
 [93,]    TRUE
 [94,]    TRUE
 [95,]    TRUE
 [96,]    TRUE
 [97,]    TRUE
 [98,]    TRUE
 [99,]    TRUE
[100,]    TRUE
[101,]    TRUE
[102,]    TRUE
[103,]    TRUE
[104,]    TRUE
[105,]    TRUE
[106,]    TRUE
[107,]    TRUE
[108,]    TRUE
[109,]    TRUE
[110,]    TRUE
[111,]    TRUE
[112,]    TRUE
[113,]    TRUE
[114,]    TRUE
[115,]    TRUE
[116,]    TRUE
[117,]    TRUE
[118,]    TRUE
[119,]    TRUE
[120,]    TRUE
[121,]    TRUE
[122,]    TRUE
[123,]    TRUE
[124,]    TRUE
[125,]    TRUE
[126,]    TRUE
[127,]    TRUE
[128,]    TRUE
[129,]    TRUE
[130,]    TRUE
[131,]    TRUE
[132,]    TRUE
[133,]    TRUE
[134,]    TRUE
[135,]    TRUE
[136,]    TRUE
[137,]    TRUE
[138,]    TRUE
[139,]    TRUE
[140,]    TRUE
[141,]    TRUE
[142,]    TRUE
[143,]    TRUE
[144,]    TRUE
[145,]    TRUE
[146,]    TRUE
[147,]    TRUE
[148,]    TRUE
[149,]    TRUE
[150,]    TRUE
[151,]    TRUE
[152,]    TRUE
[153,]   FALSE
[154,]   FALSE
[155,]   FALSE
[156,]   FALSE
[157,]   FALSE
[158,]   FALSE
[159,]   FALSE
[160,]   FALSE
[161,]   FALSE
[162,]   FALSE
[163,]   FALSE
[164,]   FALSE
[165,]   FALSE
[166,]   FALSE
[167,]   FALSE
[168,]   FALSE
[169,]   FALSE
[170,]   FALSE
[171,]   FALSE
[172,]   FALSE
[173,]   FALSE
[174,]   FALSE
[175,]   FALSE
[176,]   FALSE
[177,]   FALSE
[178,]   FALSE
[179,]   FALSE
[180,]   FALSE
[181,]   FALSE
[182,]   FALSE
[183,]   FALSE
[184,]   FALSE
[185,]   FALSE
[186,]   FALSE
[187,]   FALSE
[188,]   FALSE
[189,]   FALSE
[190,]   FALSE
[191,]   FALSE
[192,]   FALSE
[193,]   FALSE
[194,]   FALSE
[195,]   FALSE
[196,]   FALSE
[197,]   FALSE
[198,]   FALSE
[199,]   FALSE
[200,]   FALSE
[201,]   FALSE
[202,]   FALSE
[203,]   FALSE
[204,]   FALSE
[205,]   FALSE
[206,]   FALSE
[207,]   FALSE
[208,]   FALSE
[209,]   FALSE
[210,]   FALSE
[211,]   FALSE
[212,]   FALSE
[213,]   FALSE
[214,]   FALSE
[215,]   FALSE
[216,]   FALSE
[217,]   FALSE
[218,]   FALSE
[219,]   FALSE
[220,]   FALSE
[221,]   FALSE
[222,]   FALSE
[223,]   FALSE
[224,]   FALSE
[225,]   FALSE
[226,]   FALSE
[227,]   FALSE
[228,]   FALSE
[229,]   FALSE
[230,]   FALSE
[231,]   FALSE
[232,]   FALSE
[233,]   FALSE
[234,]   FALSE
[235,]   FALSE
[236,]   FALSE
[237,]   FALSE
[238,]   FALSE
[239,]   FALSE
[240,]   FALSE
[241,]   FALSE
[242,]   FALSE
[243,]   FALSE
[244,]   FALSE
[245,]   FALSE
[246,]   FALSE
[247,]   FALSE
[248,]   FALSE
[249,]   FALSE
[250,]   FALSE
[251,]   FALSE
[252,]   FALSE
[253,]   FALSE
[254,]   FALSE
[255,]   FALSE
[256,]   FALSE
[257,]   FALSE
[258,]   FALSE
[259,]   FALSE
[260,]   FALSE
[261,]   FALSE
[262,]   FALSE
[263,]   FALSE
[264,]   FALSE
[265,]   FALSE
[266,]   FALSE
[267,]   FALSE
[268,]   FALSE
[269,]   FALSE
[270,]   FALSE
[271,]   FALSE
[272,]   FALSE
[273,]   FALSE
[274,]   FALSE
[275,]   FALSE
[276,]   FALSE
[277,]   FALSE
[278,]   FALSE
[279,]   FALSE
[280,]   FALSE
[281,]   FALSE
[282,]   FALSE
[283,]   FALSE
[284,]   FALSE
[285,]   FALSE
[286,]   FALSE
[287,]   FALSE
[288,]   FALSE
[289,]   FALSE
[290,]   FALSE
[291,]   FALSE
[292,]   FALSE
[293,]   FALSE
[294,]   FALSE
[295,]   FALSE
[296,]   FALSE
[297,]   FALSE
[298,]   FALSE
[299,]   FALSE
[300,]   FALSE
[301,]   FALSE
[302,]   FALSE
[303,]   FALSE
[304,]   FALSE
[305,]   FALSE
[306,]   FALSE
[307,]   FALSE
[308,]   FALSE
[309,]   FALSE
[310,]   FALSE
[311,]   FALSE
[312,]   FALSE
[313,]   FALSE
[314,]   FALSE
[315,]   FALSE
[316,]   FALSE
[317,]   FALSE
[318,]   FALSE
[319,]   FALSE
[320,]   FALSE
[321,]   FALSE
[322,]   FALSE
[323,]   FALSE
[324,]   FALSE
[325,]   FALSE
[326,]   FALSE
[327,]   FALSE
[328,]   FALSE
[329,]   FALSE
[330,]   FALSE
[331,]   FALSE
[332,]   FALSE
[333,]   FALSE
[334,]   FALSE
[335,]   FALSE
[336,]   FALSE
[337,]   FALSE
[338,]   FALSE
[339,]   FALSE
[340,]   FALSE
[341,]   FALSE
[342,]   FALSE
[343,]   FALSE
[344,]   FALSE

Subsetting Cont.

• We can use the base R subset function to keep only the rows of data where a specified column meets a condition

• Let’s subset the penguin dataset we explored earlier

# Load needed library
library(palmerpenguins)

# Check row number of penguins data
nrow(penguins)

[1] 344

# Subset it to only 2008
peng_sub <- subset(x = penguins, year == 2008)

# Check row number again
nrow(peng_sub)

[1] 114

Practice: Subsetting

• We’ll use the base R subset function with the peng_df object
  • If needed, consult the help file for more details (?subset)

• Subset peng_df to only Adelie penguins (and assign to a sub_v1 object)
  • I.e., species == "Adelie"

• How many rows does that subset have?

More Practice: Fundamental Conditionals

• Subset peng_df to Adelie or Gentoo penguins
  • Assign this subset to sub_v2 object

• Subset peng_df to only male Gentoo penguins
  • Assign to sub_v3 object

• 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_df to only penguins with a bill length greater than 40 mm
  • Assign to sub_v7

• Subset peng_df to only penguins with a body mass less than or equal to 4,000 g
  • Assign to sub_v8

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

• nceas.github.io/scicomp-workshop-collaborative-coding/github

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

1. Install R & RStudio

2. Create a GitHub Account

3. Install Git
   • See instructions here

4. Connect RStudio and GitHub
   • See instructions here

Git and RStudio

• You can work through part of an established GitHub workshop for this section

• nceas.github.io/scicomp-workshop-collaborative-coding/rstudio

Bonus Conditionals

OR with >2 Options

• OR conditionals with many options get cumbersome quickly
  • E.g., x == 1 | x == 2 | x == 3 | x == 4 …

• 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, …)

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

• Can be combined with %in% to exclude a set of options
  • E.g., !x %in% c(...)
  • Note the exclamation mark is before the object

Practice: Advanced Conditionals

• Subset peng_df is species is any of “Adelie”, “Gentoo”, or “Chinstrap”
  • Use the %in% operator

• Subset peng_df to all islands except “Dream”