Workshop: Dealing with Data in R

Getting Started with R

Back to Basics

steffilazerte
@steffilazerte@fosstodon.org
@steffilazerte
steffilazerte.ca

Compiled: 2025-01-12

These are me and my creatures

This is my garden

Introductions

Dr. Steffi LaZerte

• Background in Biology (Animal Behaviour)
• Working with R since 2007
• Professional R programmer/consultant
  since 2017
• rOpenSci Community Assistant

What about you?

• Name
• Background (Role, Area of study, etc.)
• Familiarity with R or Programming
• Creatures (furry, feathery, scaley, humanoid, green or otherwise)?

About this Workshop

Format

• I will provide you tools and workflow to get started with R
• We’ll have hands-on activities, lectures, and demonstrations

R is hard: But have no fear!

• Don’t expect to remember everything!
• Copy/Paste is your friend (never apologize for using it!)
• Consider this workshop a resource to return to

What is R?

RStudio vs. R

 

RStudio

 

R

 

• RStudio is not R
• RStudio is a User Interface or IDE (integrated development environment)
  • (i.e., Makes coding simpler)

Open RStudio

R is a Programming language

A programming language is a way to give instructions in order to get a computer to do something

• You need to know the language (i.e., the code)
• Computers don’t know what you mean, only what you type (unfortunately)
• Spelling, punctuation, and capitalization all matter!

For example

R, what is 56 times 5.8?

56 * 5.8

[1] 324.8

Use code to tell R what to do

R, what is the average of numbers 1, 2, 3, 4?

mean(c(1, 2, 3, 4))

[1] 2.5

R, save this value for later

steffis_mean <- mean(c(1, 2, 3, 4))

R, multiply this value by 6

steffis_mean * 6

[1] 15

Why R?

R is hard

But R is powerful (and reproducible)!

(I made these slides with a mix of R and Quarto)

R is also beautiful

R is affordable (i.e., free!)

ImpostR Syndrome

ImpostR Syndrome

 

 

David Whittaker

Moral of the story?

Make friends, code in groups, learn together and don’t beat yourself up

The Goal

Artwork by @allison_horst

About R

Code, Output, Scripts

Code

• The actual commands

Output

• The result of running code or a script

Script

• A text file full of code that you want to run
• You should always keep your code in a script

For example:

mean(c(1, 2, 3, 4))

[1] 2.5

Code Output

Script

RStudio Features

Projects

• Handles working directories
• Organizes your work

Let’s setup a project in RStudio!

Changing Options: Tools > Global Options

• General > Restore RData into workspace at startup (NO!)
• General > Save workspace to on exit (NEVER!)
• Code > Insert matching parens/quotes (Personal preference)

Let’s change some options in RStudio!

Packages

• Can use the package manager to install packages
• Can use the manager to load them as well, but not recommended

Getting Ready

Open New File
(make sure you’re in the RStudio Project)

Write library(tidyverse) at the top

Save this new script
(consider names like intro.R or 1_getting_started.R)

Your first real code!

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

1. Copy/paste or type this into the script window in RStudio
   • You may have to go to File > New File > R Script
2. Click on the first line of code
3. Run the code
   • Click ‘Run’ button (upper right) or
   • Use the short-cut Ctrl-Enter
4. Repeat until all the code has run

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Let’s talk about the parts of the code first
Later we’ll talk about why this works

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Packages
ggplot2 and palmerpenguins

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Functions
library(), ggplot(), aes(), geom_point()

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

+
(Specific to ggplot)

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Figure!

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning

First Code

# First load the packages
library(palmerpenguins)
library(ggplot2)

# Now create the figure
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Comments

R Basics: Objects

Objects are things in the environment

(Check out the Environment pane in RStudio)

functions()

Do things, Return things

Does something but returns nothing

e.g., library() - Loads an R package so we can use it’s functions and other objects it supplies

library(palmerpenguins)

Does something and returns something

e.g., ggplot() - Creates and returns a basic plot

ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm))

functions()

• Functions can take arguments (think ‘options’)
• data, x, y, colour

ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
  geom_point()

• Arguments defined by name or by position
• With correct position, do not need to specify by name

By name:

mean(x = c(1, 5, 10))

[1] 5.333333

By order:

mean(c(1, 5, 10))

[1] 5.333333

functions()

Watch out for ‘hidden’ arguments

By name:

mean(x = c(1, 5, 10, NA), 
     na.rm = TRUE)

[1] 5.333333

By order:

mean(c(1, 5, 10, NA), 
     TRUE)

Error in mean.default(c(1, 5, 10, NA), TRUE): 'trim' must be numeric of length one

This error states that we’ve assigned the argument trim to a non-valid argument

Where did trim come from?

R documentation

?mean

Data

• Generally kept in vectors or data.frames (also tibbles)
• These are objects with names (like functions)
• Here are two built-in examples (part of R)

Vector (1 dimension)

month.name

 [1] "January"   "February"  "March"    
 [4] "April"     "May"       "June"     
 [7] "July"      "August"    "September"
[10] "October"   "November"  "December" 

Data frame (2 dimensions)

mtcars

                     mpg cyl  disp  hp drat    wt  qsec vs
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1
                    am gear carb
Mazda RX4            1    4    4
Mazda RX4 Wag        1    4    4
Datsun 710           1    4    1
Hornet 4 Drive       0    3    1
Hornet Sportabout    0    3    2
Valiant              0    3    1
Duster 360           0    3    4
Merc 240D            0    4    2
Merc 230             0    4    2
Merc 280             0    4    4
Merc 280C            0    4    4
Merc 450SE           0    3    3
Merc 450SL           0    3    3
Merc 450SLC          0    3    3
Cadillac Fleetwood   0    3    4
Lincoln Continental  0    3    4
Chrysler Imperial    0    3    4
Fiat 128             1    4    1
Honda Civic          1    4    2
Toyota Corolla       1    4    1
Toyota Corona        0    3    1
Dodge Challenger     0    3    2
AMC Javelin          0    3    2
Camaro Z28           0    3    4
Pontiac Firebird     0    3    2
Fiat X1-9            1    4    1
Porsche 914-2        1    5    2
Lotus Europa         1    5    2
Ford Pantera L       1    5    4
Ferrari Dino         1    5    6
Maserati Bora        1    5    8
Volvo 142E           1    4    2

• Columns have different types of variables

rows x columns

Your Turn: Vectors and Data frames

Try out the following code…

• Here we will make a vector and a data frame
• What is the output in your console?
• How does your environment change (upper right panel)?

Vectors

fruit <- c("apples", "bananas", "pears", "oranges", "melons")
fruit

Data frames

production <- data.frame(
  site  = c("east", "east", "west", "west"),
  fruit = c("apples", "bananas", "apples", "bananas"),
  count = c(20, 60, 30, 50))
production

Your Turn: Vectors and Data frames

Try out the following code…

• What does : do?
• What does c() do?
• Why use a comma with data frames?

Vectors

• Use [index] to access part of a vector
• Can access multiple parts at once

fruit
fruit[2]
fruit[2:5]     # What does : do?
fruit[c(1, 3)] # What does c() do?

Data frames

• x$colname to pull columns out as vector
• x[row, col] to access rows/columns

production$fruit
production[3]
production[3, ]   # Why the comma?
production[3, 1]
production[, 1:2]

Miscellaneous

R has spelling and punctuation

• R cares about spelling
• R is also case sensitive! (Apple is not the same as apple)

Artwork by @allison_horst

R has spelling and punctuation

• Commas are used to separate arguments in functions

This is correct:

mean(c(5, 7, 10))  # [1] 7.333333

This is not correct:

mean(c(5 7 10))

>80% of learning R is learning to troubleshoot!

R has spelling and punctuation

Spaces usually don’t matter unless they change meanings

5>=6    # [1] FALSE
5 >=6   # [1] FALSE
5 >= 6  # [1] FALSE
5 > = 6 # Error: unexpected '=' in "5 > ="

Periods don’t matter either, but can be used in the same way as letters

(But don’t)

apple.oranges <- "fruit"

Assignments and Equal signs

Use <- to assign values to objects

a <- "hello"

Use = to set function arguments

mean(x = c(4, 9, 10))

Use == to determine equivalence (logical)

10 == 10 # [1] TRUE
10 == 9  # [1] FALSE

Braces/Brackets

Round brackets: ()

• Identify functions (even if there are no arguments)

Sys.Date() # Get the Current Date

[1] "2025-01-12"

• Without the (), R spits out information on the function:

Sys.Date

function () 
as.Date(as.POSIXlt(Sys.time()))
<bytecode: 0x5d22c7898688>
<environment: namespace:base>

() must be associated with a function (Well, almost always)

Square brackets: []

• Extract parts of objects

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
[20] "T" "U" "V" "W" "X" "Y" "Z"

LETTERS[1]

[1] "A"

LETTERS[26]

[1] "Z"

[] have to be associated with an object that has dimensions (Always!)

Improving code readability

Use spaces like you would in sentences:

a <- mean(c(4, 10, 13))

is easier to read than

a<-mean(c(4,10,13))

(But the same, coding-wise)

Improving code readability

Don’t be afraid to use line breaks (‘Enters’) to make the code more readable

Hard to read

a <- data.frame(exp = c("A", "B", "A", "B", "A", "B"), sub = c("A1", "A1", "A2", "A2", "A3", "A3"), res = c(10, 12, 45, 12, 12, 13))

Easier to read

a <- data.frame(exp = c("A", "B", "A", "B", "A", "B"), 
                sub = c("A1", "A1", "A2", "A2", "A3", "A3"), 
                res = c(10, 12, 45, 12, 12, 13))

(But the same, coding-wise)

Let’s go!

Artwork by @allison_horst