Exploring Data In R

RAdelaide 2025

Dr Stevie Pederson

Black Ochre Data Labs
The Kids Research Institute Australia

July 8, 2025

The package palmerpenguins

Introducing The Penguins

• We’ll be looking at the “Palmer Penguins” dataset
  • Taken from https://allisonhorst.github.io/palmerpenguins/index.html
  • 3 species of penguins from the Palmer Archipelago, Antarctica
• Various physiological measurements

Exploring The Penguins

• We won’t be creating any objects in this section
• Learning how to explore a dataset using dplyr
  • For organising data
  • For creating summary tables
  • To prepare for creating plots & figures
  • Is a core tidyverse package

• We’ll cover a huge amount of ground
  • Hopefully the exercises & challenges help

Starting An R Script

• Best practice is to ALWAYS record your code
• Today we’ll use an R script
  • Is a plain text file
  • Is a combination of code and comments
  • The filename should end with .R

• Nothing we enter in the script is executed
  \(\implies\) until we intentionally execute the code

Starting An R Script

• Create a new file DataExploration.R type the following

## Load the palmerpenguins package
library(palmerpenguins)

• The # symbol indicates a comment
  \(\implies\) ignored by R and nothing is executed
  • Used to explain code to humans

• We write code for two primary reasons
  1. To be executed by R, and
  2. To be read and understood by humans (usually us in a few months)

Executing Code

• So far, no code has been executed from this script
• Check your Environment Tab to see if there are any objects
  • If there is an object (most likely x) \(\implies\) click the broom icon
  • This will clear any existing objects from the environment

1. Place the cursor on the line of code library(palmerpenguins), or
2. Use the keyboard shortcut Ctrl + Enter (Windows/Linux) or Cmd + Enter (Mac), or
3. Click the Run button in the top right of the script editor

What have we done so far?

• We have simply loaded the palmerpenguins package
  • We called the function library()
  • This loads all the functions and the data in a requested package
  • The package name appears inside the parentheses ()
  • Very similar to calling sqrt(5) as we saw earlier

• For python users the equivalent would be import palmerpenguins

The penguins dataset

• The palmerpenguins package contains the penguins dataset already loaded
  • Add the comment and code below, then execute

# Let's look at the penguins dataset
penguins

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

• Maybe get people to click on the Global Environment Tab
  

The penguins dataset

• The penguins dataset is a tibble
• The number of rows and columns is shown at the top (344 x 8)
  • Printed as a comment

• Next is the column names:
  • species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex and year
  • The last two columns may just be listed at the bottom
  • Is a function of screen width, font size etc

• Check to see if all columns have printed for everyone

The penguins dataset

• Underneath each column name is the data type
  • Will explore data types in more detail
  • Each column has the same type of data
  • <fct> means ‘factor’ \(\implies\) a categorical variable
  • <dbl> means ‘double’ \(\implies\) a numeric variable
  • <int> means ‘integer’ \(\implies\) a whole number

• Final lines show how many more rows & columns there are
• Notice there are no rownames, just row numbers

Exploring Penguins

• A common initial data exploration task is to get a summary of the data

# The column names are:
colnames(penguins)

[1] "species"           "island"            "bill_length_mm"   
[4] "bill_depth_mm"     "flipper_length_mm" "body_mass_g"      
[7] "sex"               "year"             

Copy the column names after the code, then comment

1. Highlight the output in the console
2. Copy & paste into the script
3. Make a comment by adding # at the start of each line

Commenting Multiple Lines

• Comments can be toggled on/off across multiple lines using:
  • Ctrl + Shift + C (Win/Linux)
  • Cmd + Shift + C (Mac)

Calling Functions

• Notice that we placed the object inside the parentheses () after the function

• Let’s continue checking the object

# Find out how big the dataset is
nrow(penguins)
ncol(penguins)
dim(penguins)
tail(penguins)

• Can you figure out what each of these functions does?

The package
dplyr

Exploring Penguins with dplyr

• The package dplyr \(\rightarrow\) functions for data exploration and manipulation
• Let’s load the package as part of the tidyverse
  • I personally load all packages at the start of a script
  • Add this underneath the library(palmerpenguins) line
  • All functions in this section from dplyr

library(tidyverse) # Load the complete tidyverse

• We’ll use these functions to explore the penguins dataset
• Then we can modify the dataset

• dplyr was written initially by Hadley Wickham
• He’s originally from NZ so he knows how to spell things correctly

Exploring Penguins with dplyr

## The `glimpse()` function is provided by dplyr
## Can be very helpful with large column numbers
glimpse(penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

• So far we haven’t actually saved any objects (using <-)
• Functions head(), tail(), and glimpse() are all printing to the console

Sorting Penguins

• dplyr provides some Excel-like functions:
  • arrange() will sort the data
  • filter() will filter the data

# Sort the penguins by body mass in increasing order
arrange(penguins, body_mass_g)

# Sort the penguins by body mass in decreasing order
arrange(penguins, desc(body_mass_g))

# Sort multiple columns in the order passed to the function
arrange(penguins, species, body_mass_g)

Filtering Penguins

Filtering relies on logical tests

Symbol	Description
==	Is exactly Equal To
> / <	Is Greater/Less Than
>= / <=	Is Greater/Less Than or Equal To
!=	Is Not Equal To
is.na()	Is Missing Value
%in%	Is in a set of possible values

• In most languages, ! is interpreted as NOT

Filtering Penguins

## Subset the data to those from the Island of Dream
filter(penguins, island == "Dream")

## Subset the data to those NOT from the Island of Dream
filter(penguins, island != "Dream")

## Subset the penguins to those lighter than 4000g
filter(penguins, body_mass_g < 4000)

## Find the penguins from Dream that are heavier than 4000g
filter(penguins, island == "Dream", body_mass_g > 4000)

Slicing Penguins

• filter() returns the rows that match a given criteria
• slice() can be used to return rows by position

## Slice out the first 10 rows of the penguins dataset
slice(penguins, 1:10)

## Now slice out the 101st to 110th rows
slice(penguins, 101:110)

A Brief Diversion

• In the two previous examples we used a sequence of consecutive values

1:10

 [1]  1  2  3  4  5  6  7  8  9 10

101:110

 [1] 101 102 103 104 105 106 107 108 109 110

• We refer to one or more values in R as a vector
  • These are integer vectors
  • Integers are often used to denote rows/columns etc

A Brief Diversion

• In R we can form a vector by combining values with the function c()

c(1, 3, 5, 7, 9)

[1] 1 3 5 7 9

# Return the first few odd numbered rows using a vector of positions
slice(penguins, c(1, 3, 5, 7, 9))

# Rows can be returned in any order
slice(penguins, c(3, 7, 5, 1))

Selecting Penguins

• filter() and slice() can be used to return rows
• select() can be used to return columns

# Columns can be 'selected' by passing the required column names
select(penguins, species, island, body_mass_g)

# Columns can also be selected by position using a vector
select(penguins, c(1, 2, 6))

Using Names Or Position

1. Do the above lines give the same result?
2. Would either one be preferable?

Helper Functions

• dplyr provides some helper functions to make selecting columns easier
• starts_with(), ends_with() and contains(), are very useful!
• everything() is also surprisingly useful
• any_of() and all_of() are a bit more advanced

# Select all columns that start with "bill", after the species and island columns
select(penguins, species, island, starts_with("bill"))

# Select all length-related columns, after the species and island columns
select(penguins, species, island, contains("length"))

Helper Functions

# Place all metadata first, followed by measured values
# This line shows how useful 'everything()' can be
select(penguins, species, island, sex, year, everything())

• To remove a column, we preface the selection with -

# Remove all columns that end with 'mm'
select(penguins, -ends_with("mm"))

Relocating Penguins

# Relocate is a newer addition to dplyr and can also be used to reorder columns
# The arguments .before and .after can be used to specify where to place columns
# Here we're moving columns with an underscore to after the year column
relocate(penguins, contains("_"), .after = year)

# This time, we're moving the sex and year columns to 'before' the bill columns
relocate(penguins, sex, year, .before = starts_with("bill"))

Renaming Penguins

• When we call select, we can rename columns on the fly

## Rename the island column as 'location', leaving the order unchanged
select(penguins, species, location = island, everything())

• Alternatively, we can just use rename()

## Or just rename the individual column
rename(penguins, location = island)

Modifying Columns With mutate()

• So far, we’ve only subset our data using various methods
• mutate() is used to modify existing columns or create new ones

# Create a column called `body_mass_kg` that is the body mass in kg
mutate(penguins, body_mass_kg = body_mass_g / 1000)

Exercises

1. Use filter() to find all female penguins
   • Then find all female penguins with a flipper length greater than 215mm
2. Use filter() to find all penguins where sex is missing (NA)
3. Sort the dataset by bill_length in descending order
4. Use select() to return the species, island, and year columns
   • Repeat trying an alternative approach to your first answer
5. Place the year column after island and remove the sex column
6. Create the column bill_ratio by dividing bill length by depth

Summarising The Dataset

Obtaining Summaries

• dplyr also provides functions to summarise data
  • count() and summarise() are the most common
  • We can tell these functions which columns to summarise by

# Count the number of penguins by species
count(penguins, species)

# Count the number of penguins by species and island
count(penguins, species, island)

# If we change the order of the columns, we get a different order in our results
count(penguins, island, species)

# The argument `sort = TRUE` will sort the results
count(penguins, species, island, sort = TRUE)

Obtaining Summaries

• More nuanced summaries can be obtained using summarise()
  • We now pass the grouping variable to the argument .by
  • The summary column should also be given a name

# Find the mean weight of each species
summarise(penguins, mean_weight = mean(body_mass_g), .by = species)

• This is the first time those missing values have caused us grief
  • The argument na.rm = TRUE will tell mean() to ignore NA values

# Ignore the missing values when calculating the mean
summarise(penguins, mean_weight = mean(body_mass_g, na.rm = TRUE), .by = species)

Obtaining Summaries

• We can also summarise using multiple columns
  • We’ve combined groups using the function (c())

# Summarise by both species and year
summarise(
  penguins, mean_weight = mean(body_mass_g, na.rm = TRUE), .by = c(species, year)
)

Obtaining Summaries

• The previous code is split across multiple lines just to fit on the slide
• We could split across multiple lines for greater readability

# Summarise by both species and year
summarise(
  penguins, 
  mean_weight = mean(body_mass_g, na.rm = TRUE), 
  .by = c(species, year)
)

Obtaining Summaries

• This strategy can help when creating multiple summary columns
• Instead of using count() we can call n() as part of summarise()

# Summarise by both species and year, counting the number of penguins
summarise(
  penguins, 
  mean_weight = mean(body_mass_g, na.rm = TRUE), 
  total = n(),
  .by = c(species, year)
)

Grouping Arguments

• The recent addition of .by has beefed up some earlier functions

# Grab the first 5 rows of each species
slice(penguins, 1:5, .by = species)

• Some newer extensions of slice() are also useful for summarising data
  • slice_min(), slice_max()

# Return the heaviest penguin from each species
slice_max(penguins, body_mass_g, n = 1, by = species)

• Confusingly, the argument .by has become by here 🤷🏻
  • The argument n = 1 says return only one penguin per species

Conclusion

• All functions so far have enabled exploration
  • arrange(), filter
  • select() + helper functions
  • slice(), slice_max(), slice_min()
  • count(), summarise()
• Many others we didn’t cover
  • slice_head(), slice_tail(), slice_sample()
  • Multiple join methods

Conclusion

• Have never over-written our original dataset
• Have never created a new object
• Real world applications:
  • Preparing for plotting or regression
  • Summarising data for tables

• Already a huge amount to remember!
• We’ll be doing more exercises soon

A Word Of Caution

• dplyr was written to parallel some SQL functions
  \(\implies\) Uses function names from SQL
• Some other packages had the same idea much earlier
  • e.g. multiple packages contain a filter or select function

• If either function gives unexpected output
  \(\implies\) call directly from the package (aka namespace)
• We can use dplyr::select() instead of select()
  • Ensures we use the dplyr version
  • Same for dplyr::filter()