Wrangle and Visualize Penguins

Filter rows, reshape tables, join metadata, and build layered ggplot2 figures — all in your browser

Welcome back. Everything on this page runs real R in your browser — same setup as Coding Activity 1. You already know objects and functions; today you will reshape tables with dplyr and tidyr, then build figures in layers with ggplot2.

The first time you click Run Code, your browser downloads R once (a few seconds). After that it is quick.

The grammar of graphs (preview)

Every ggplot2 figure has three essential pieces:

data — the dataset (for example penguins)
aes — aesthetic mappings: which column goes on which axis, or which color
geom — the geometric object: points, boxes, bars, histogram bins

You add layers with +. We will practice all three today.

1. Load the tidyverse

Coding Assignment 2 starts the same way. Run library(tidyverse) to load dplyr, ggplot2, tidyr, and friends. A conflicts message afterward is expected, not an error.

Solution

library(tidyverse)

On DataHub and in your graded assignment you will type exactly this. The tidyverse also offers an older pipe, %>%; in this activity we use R’s native pipe, |>.

2. Glimpse the penguins

glimpse() is a tidyverse way to inspect a dataset: rows, columns, and column types. Run it on penguins from the palmerpenguins package (loaded automatically with tidyverse in webR).

Hint

From ?glimpse: glimpse(x) — print a compact summary of a data frame (rows, columns, types).

Example with a built-in dataset:

glimpse(mtcars)

Apply the same function to the dataset named in the exercise prompt.

Check yourself

From the glimpse() output:

How many rows are there?
What type is body_mass_g?
Which column tells you which island each penguin was sampled on?

Answers: 344 rows; body_mass_g is <dbl>; island.

3. Chain steps with a pipe

A pipe passes the result of one step into the next. R’s native pipe is |>. Rewrite the nested version below as a pipe chain: start with penguins, keep only Adelie penguins with filter(), then glimpse() the result.

== means “equals”

Inside filter(), == checks whether two things match. For example, species == "Adelie" keeps only rows where the species column is exactly "Adelie".

Do not confuse == with a single =. A single = assigns a value to a name (x <- 5 or x = 5). Double equals == compares — “is this equal to that?”

Nested (hard to read):

glimpse(filter(penguins, species == "Adelie"))

Your version should use |> twice.

Hint

From ?filter: keep rows where conditions are TRUE. Chain steps with |>:

mtcars |>
  filter(cyl == 4) |>
  glimpse()

Use the same shape on penguins: start with the dataset, filter() to one group, then glimpse().

Solution

penguins |>
  filter(species == "Adelie") |>
  glimpse()

Read it top to bottom: “Take penguins, filter to Adelie, then glimpse.” The pipe replaces nesting functions inside functions.

4. Filter out missing values

Two penguins have missing body_mass_g. Use filter() and !is.na() to drop those rows. Save the result as penguins_clean.

! means “not”

The exclamation mark ! flips a yes/no test backwards. The function is.na(body_mass_g) is TRUE when a value is missing. So !is.na(body_mass_g) means “body mass is not missing” — keep those rows.

Hint

From ?filter: filter(.data, ...) keeps matching rows. From ?is.na: is.na(x) is TRUE for missing values; prefix with ! to mean “not.”

Example — drop rows with missing mpg:

mtcars_clean <- mtcars |>
  filter(!is.na(mpg))

Use the same pattern on penguins, the column named in the prompt, and save as penguins_clean.

5. Mean with a pipe

Compute the mean body mass of penguins_clean, skipping any remaining NAs with na.rm = TRUE. Use a pipe starting from penguins_clean.

Hint

From ?mean: mean(x, na.rm = FALSE) — set na.rm = TRUE to skip missing values. From ?pull: pull(.data, var) extracts one column.

Example pipe:

mtcars |>
  pull(mpg) |>
  mean(na.rm = TRUE)

Start from penguins_clean and pipe to mean() on the body-mass column.

6. Meet a wide table

On Coding Assignment 2 you will read a wide elevation table from a CSV file. Here we create the same kind of object directly — no file to read.

Wide island metadata (click to expand)

A wide table stores category names as column headings. Below is a tiny lookup table with fictional camp elevations (meters) for each study island:

island_wide <- data.frame(
  Biscoe = 120,
  Dream = 85,
  Torgersen = 45
)

Notice: Biscoe, Dream, and Torgersen are column names, not values sitting in a row. That layout is easy to glance at in a spreadsheet but awkward for joining to penguins — we will reshape it in the next step.

On your graded assignment, the same wide layout arrives as a CSV; you will use read.csv() to load it. Today we build the object in memory so you can focus on reshaping and joining.

Run this to create island_wide:

7. Reshape with `pivot_longer()`

Use pivot_longer() to turn the wide table into a tidy table: one row per island, with columns island and camp_elevation_m. Save as island_tidy and print it.

Hint

From ?pivot_longer: pivot_longer(data, cols, names_to, values_to, ...)

Turn column names into values — example with a wide toy table:

wide_mpg <- data.frame(
  four_cyl = 26.7,
  six_cyl = 20.1,
  eight_cyl = 15.1
)

wide_mpg |>
  pivot_longer(
    cols = everything(),
    names_to = "cyl_group",
    values_to = "avg_mpg"
  )

Apply the same arguments to island_wide, with the column names specified in the exercise.

Solution

island_tidy <- island_wide |>
  pivot_longer(
    cols = everything(),
    names_to = "island",
    values_to = "camp_elevation_m"
  )
island_tidy

Three rows — one per island — with elevations in a column where R can match them to penguins$island.

Wide vs. tidy: In Coding Assignment 2, population names sit in column headings of the elevation file — same problem, same fix with pivot_longer().

8. Join elevation onto penguins

Use left_join() to add camp_elevation_m to penguins_clean. Both tables share an island column — that is the key. Save the result as penguins_joined. Row count should stay 342.

Hint

From ?left_join: left_join(x, y, by = NULL, ...) — add columns from y to x wherever a shared key matches.

Example:

cyl_lookup <- data.frame(
  cyl = c(4, 6, 8),
  cyl_label = c("four", "six", "eight")
)

mtcars |>
  left_join(cyl_lookup, by = "cyl")

Join your cleaned penguin table to island_tidy on the column both tables share.

Solution

penguins_joined <- penguins_clean |>
  left_join(island_tidy, by = "island")
nrow(penguins_joined)

342 rows, one new column — every penguin keeps its measurements and gains its island’s camp elevation.

9. Histogram with ggplot2

Start a figure with ggplot(), map body_mass_g to the x-axis, and add geom_histogram(). Use penguins_joined (or penguins_clean). Add a title with labs(title = "...").

Hint

From ?ggplot: ggplot(data = NULL, mapping = aes(), ...) — then add a layer with +.

From ?geom_histogram: bins one continuous variable.

Example:

ggplot(mtcars, aes(x = mpg)) +
  geom_histogram() +
  labs(title = "Car fuel economy")

Map the x-axis to one continuous column from your joined penguin table and add your own title.

Solution

ggplot(penguins_joined, aes(x = body_mass_g)) +
  geom_histogram() +
  labs(title = "Penguin body mass")

A histogram shows how one continuous variable is distributed — how many penguins fall in each weight range.

10. Scatterplot — two continuous variables

Build a scatterplot: flipper length on the x-axis, body mass on the y-axis. Map species to color so each species gets its own hue. Use geom_point().

Hint

From ?geom_point: scatterplot of two continuous variables. Map a third variable to color inside aes().

Example:

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point()

Use flipper length and body mass on the axes and map species to color on the penguin data.

Solution

ggplot(penguins_joined, aes(
  x = flipper_length_mm,
  y = body_mass_g,
  color = species
)) +
  geom_point()

Scatterplots relate two continuous measurements. Color adds a third variable — here, species — so you can see clusters.

11. Boxplot — compare groups (Assignment 2 pattern)

Coding Assignment 2 builds a boxplot one layer at a time. Here is the full pattern in one exercise:

Put island on the x-axis, body_mass_g on the y-axis
Use reorder(island, camp_elevation_m) so islands sort from lowest to highest elevation
Add geom_boxplot() with fill mapped to island
Add theme_minimal() and readable labs()

Hint

From ?geom_boxplot: compare a numeric variable across groups. From ?reorder: reorder(x, X) sorts groups by a numeric column.

Example pattern:

ggplot(mtcars, aes(
  x = reorder(factor(cyl), mpg, FUN = median),
  y = mpg,
  fill = factor(cyl)
)) +
  geom_boxplot() +
  theme_minimal() +
  labs(title = "MPG by cylinder count", x = "Cylinders", y = "MPG")

Apply the same layering — boxplot, fill, theme_minimal(), labs() — to islands and body mass on your joined table.

Solution

ggplot(penguins_joined, aes(
  x = reorder(island, camp_elevation_m),
  y = body_mass_g,
  fill = island
)) +
  geom_boxplot() +
  theme_minimal() +
  labs(
    title = "Body mass by island",
    x = "Island (low to high elevation)",
    y = "Body mass (g)"
  )

Boxplots compare a continuous variable across groups. reorder() lines groups up by elevation — the same trick Assignment 2 uses with population and elevation.

12. Recap — verbs you practiced

What does each function do?

Function	Role
`library(tidyverse)`	Loads dplyr, ggplot2, tidyr, and related packages
`glimpse()`	Quick look at rows, columns, and types
	>
`filter()`	Keeps rows that match a condition (with `==` or `!is.na()`)
`mean(..., na.rm = TRUE)`	Average of a numeric column, skipping missing values
`data.frame()`	Builds a wide table in memory — island names as column headings
`pivot_longer()`	Turns wide columns into tidy rows
`left_join()`	Adds columns from a second table by a shared key
`ggplot()` + `aes()`	Starts a figure and maps columns to axes, color, or fill
`geom_histogram()`	Distribution of one continuous variable
`geom_point()`	Relationship between two continuous variables
`geom_boxplot()`	Compare a continuous variable across groups
`reorder()`	Sorts groups by a numeric variable (e.g. elevation)
`theme_minimal()` + `labs()`	Cleans appearance and adds titles/labels

On Coding Assignment 2 you will also use read.csv() to load the hemoglobin and elevation files from disk — same workflow, but the wide table arrives as a file instead of a data.frame() you type yourself.

If you can explain each row in your own words, you have covered the core of Coding Assignment 2.

13. Transfer to Coding Assignment 2

On Canvas you will repeat this workflow with hemoglobin measurements and population elevation data:

library(tidyverse) and read HumanHemoglobinElevation.csv
glimpse(), filter(!is.na(...)), and mean(..., na.rm = TRUE)
Read PopulationElevation.csv (wide layout), pivot_longer(), then left_join()
Build a boxplot step by step with ggplot(), geom_boxplot(), fill, theme_minimal(), and labs()

Same functions, different biological story — altitude adaptation instead of penguin islands. You already practiced every step here.

Keep playing

No grading here. Try swapping geoms, filtering to one species first, or predicting what a plot will look like before you run it.

Try this with a partner. One of you predicts the figure; the other runs the code. Swap roles. Saying your prediction out loud first is one of the fastest ways to learn.

1. Load the tidyverse

2. Glimpse the penguins

3. Chain steps with a pipe

4. Filter out missing values

5. Mean with a pipe

6. Meet a wide table

7. Reshape with pivot_longer()

8. Join elevation onto penguins

9. Histogram with ggplot2

10. Scatterplot — two continuous variables

11. Boxplot — compare groups (Assignment 2 pattern)

12. Recap — verbs you practiced

13. Transfer to Coding Assignment 2

Keep playing

7. Reshape with `pivot_longer()`