Describing Our Data

Wrangling, descriptive statistics, and first visualizations

r-basics

project-setup

Install R and Positron, organize a project folder, and take your first steps with vectors, functions, and loading the leaf data into R.

Author

Bill Perry

Published

July 5, 2026

Describing Our Leaf Data

Recap from Worksheet 02

Installed R and Positron; created a project folder (data/, scripts/, figures/)
Used R as a calculator and stored values with <-
Built vectors, learned data types, wrote scripts with # comments
Loaded our leaf data with read_excel() and learned how read_csv() differs
Inspected a data frame with glimpse(), head(), dim()
Used the pipe %>% to chain steps
Made and saved a first ggplot as a PNG (dpi = 300)

Today’s Objectives

Use filter(), select(), mutate(), and arrange() to wrangle data
Chain all four verbs into a single pipeline
Understand why NA values require special handling with sum(!is.na())
Calculate mean, median, SD, and SE using filter() and pull()
Use group_by() + summarize() to compute stats the tidy way
Explore data quickly with skimr
Build a boxplot with individual points overlaid
Create a mean ± SE summary plot using stat_summary()

How to use this worksheet

Work through each part at your own pace. Type the code into a new R script in Positron and run it line by line. Code blocks marked ▶ Run this should be executed exactly as written. Blocks marked ✏️ Your turn ask you to write or modify something. The Going further section at the end is optional — work through it if you finish early.

🔮 Predict before you run — and type, don’t paste

Before you run any ▶ Run this block, cover the output and predict what R will print. Jot your guess down, then run it and compare. Two reasons it is worth the extra few seconds:

Predicting makes it stick. Guessing forces you to retrieve what you already know, and the little jolt of being wrong is when real learning happens. Reading code you never predicted feels easy but fades fast.

Typing beats pasting. Typing every line builds muscle memory and trains your eye to catch the small errors (= vs ==, a missing comma) that you will otherwise spend real time hunting. Copy-paste skips the part that actually teaches you.

🧩 Chunk 1 — Wrangling verbs (matches lecture Chunk 1)

Parts 1–3 below. Do these right after the lecture’s Chunk 1, then go back for Chunk 2.

Part 1 · Load libraries and data

Libraries first

Always load all packages at the very top of your script.

▶ Run this at the top of your script:

# Load all packages — always at the top of the script --------
library(readxl)      # reading Excel files
library(tidyverse)   # data manipulation + ggplot2
library(skimr)       # fast descriptive summaries

⚠️ Watch out! If R says “could not find function read_excel” you forgot library(readxl). You must reload libraries every time you restart R.

Load the leaf data

▶ Run this:

# Read the Excel file — path is relative to your project folder
tree_df <- read_excel("data/2026_06_25_tree_experiment_raw_data.xlsx")

Part 2 · Inspect the data

▶ Run each of these:

head(tree_df)       # first 6 rows
glimpse(tree_df)    # column names, types, first values — use this!
dim(tree_df)        # rows × columns

✏️ Your turn: Fill in the table below from the glimpse() output.

Column name     | Data type (<chr> / <dbl>)  | Example value
----------------|----------------------------|---------------
index           |                            |
side            |                            |
weight_g        |                            |
width_cm        |                            |
height_cm       |                            |

✏️ Your turn: How many leaves are from the sunny side and how many from the shady side? Use table(tree_df$side).

# Write your code here:

Sunny leaves:
Shady leaves:
Is the design balanced (equal n per group)?  Y / N

Part 3 · The core tidyverse verbs — filter, select, mutate, arrange

These four functions do most of the work in data wrangling. All take a data frame and return a data frame.

`filter()` — keeping rows you want

▶ Run this:

🔮 Predict first: There are 20 leaves in total. Before running, guess how many rows filter(side == "sunny") will return. Write your number, then check.

# filter() keeps rows where the condition is TRUE ------

# Keep only sunny leaves
tree_df %>% filter(side == "sunny")

# Keep only leaves heavier than 5 g
tree_df %>% filter(weight_g > 5)

# Combine conditions (AND = both must be true)
tree_df %>% filter(side == "shady", weight_g > 7)

⚠️ Watch out! = assigns a value. == tests equality. Always use == inside filter().

✏️ Your turn: Use filter() to find all leaves where height_cm is greater than 25. How many rows does the result have?

# Write your code here:

Number of leaves with height_cm > 25:
Are they mostly sunny or shady side?

`select()` — keeping columns you want

▶ Run this:

# select() keeps the columns you name ------------------

# Keep only side and weight
tree_df %>% select(side, weight_g)

# Drop the index column (use minus sign)
tree_df %>% select(-index)

# Keep columns that end with a unit label
tree_df %>% select(side, ends_with("_cm"))

✏️ Your turn: Create a data frame called lean_df that has only side and height_cm. Then run glimpse(lean_df) to verify.

# Write your code here:

How many columns does lean_df have?
What are they?

`mutate()` — adding new columns

mutate() adds new columns (or changes existing ones) without removing anything.

▶ Run this:

🔮 Predict first: Will mutate(weight_mg = weight_g * 1000) replace weight_g or add a new column? How many columns will the result have — more, fewer, or the same?

# mutate() adds a new column to the data frame ---------

# Convert grams to milligrams
tree_df %>%
  mutate(weight_mg = weight_g * 1000)

# Add a size category based on weight
tree_df %>%
  mutate(size_class = if_else(weight_g > 5, "large", "small"))

✏️ Your turn: Use mutate() to add a column called height_mm (height converted to millimetres — multiply by 10). What is the maximum height in mm?

# Write your code here:

Column name you added:
Maximum height in mm:

`arrange()` — sorting rows

▶ Run this:

# arrange() sorts rows by a column ---------------------

# Lightest leaves first (ascending — default)
tree_df %>% arrange(weight_g)

# Heaviest leaves first (descending — use desc())
tree_df %>% arrange(desc(weight_g))

# Sort by side, then by weight within each side
tree_df %>% arrange(side, desc(weight_g))

✏️ Your turn: Sort the data by height_cm descending. What is the index number and side of the tallest leaf?

# Write your code here:

Index of tallest leaf:
Side (sunny or shady):
Height (cm):

The full pipeline — chaining all four verbs

▶ Run this:

# Chain all four verbs — read it as a recipe ----------
tree_clean_df <- tree_df %>%
  filter(weight_g > 0) %>%              # remove any zeros
  select(side, weight_g, height_cm) %>%  # keep three columns
  mutate(
    weight_mg   = weight_g * 1000,
    size_class  = if_else(weight_g > 5, "large", "small")
  ) %>%
  arrange(side, desc(weight_g))          # sort by side, then weight

head(tree_clean_df)

✏️ Your turn: Read the pipeline out loud, one %>% step at a time. Write in plain English what each step does:

Step 1 (filter):
Step 2 (select):
Step 3 (mutate):
Step 4 (arrange):

🧩 Chunks 2–3 — Describing the data (matches lecture Chunks 2 & 3)

Part 4 (counting correctly with NAs) and Part 5 (mean, median, SD, SE). Do these after the lecture’s Chunks 2 and 3.

Part 4 · The NA problem — counting observations correctly

What is an NA?

NA stands for “Not Available” — it marks a missing value. Missing data are common in real ecological studies.

The problem with `length()`

▶ Run this:

# Create a small vector with missing values for demonstration
x <- c(4, 3, NA, 7, NA)

# length() counts ALL positions — including NAs
length(x)

✏️ Your turn: What did length(x) return? How many of those positions are real data values?

length(x) returned:
Number of real (non-NA) values:

Breaking down `is.na()` step by step

▶ Run each line one at a time:

# Step 1: is.na() marks where the NAs are
is.na(x)

# Step 2: ! flips TRUE to FALSE and FALSE to TRUE
!is.na(x)

# Step 3: sum() counts the TRUEs — TRUEs are real values!
sum(!is.na(x))

✏️ Your turn: Trace through what each step does to c(4, 3, NA, 7, NA):

is.na(x)       produces: 
!is.na(x)      produces: 
sum(!is.na(x)) produces:

⚠️ Watch out! Any stats function on a vector containing NA returns NA — not an error. R will not tell you something went wrong. Always include na.rm = TRUE.

Part 5 · Descriptive statistics in base R

Extract one group with `filter()` and `pull()`

▶ Run this:

# Extract sunny leaf weights as a plain vector --------
sunny_wt <- tree_df %>%
  filter(side == "sunny") %>%
  pull(weight_g)

sunny_wt    # print the raw values

✏️ Your turn: Write the code to extract the shady leaf weights as a vector called shady_wt.

# Write your code here:

Mean

▶ Run this:

# Mean for the sunny side ----------------------------
mean_sunny <- mean(sunny_wt, na.rm = TRUE)
cat("Mean sunny weight:", round(mean_sunny, 2), "g\n")

✏️ Your turn: Calculate the mean for the shady side. Store it as mean_shady and print it.

# Write your code here:

Mean shady leaf weight (g):
Is the shady mean larger than sunny?  Y / N
Does this match the hypothesis?       Y / N

Median

▶ Run this:

# Median for each side --------------------------------
med_sunny <- median(sunny_wt, na.rm = TRUE)
med_shady <- median(shady_wt, na.rm = TRUE)

cat("Median sunny:", round(med_sunny, 2), "\n")
cat("Median shady:", round(med_shady, 2), "\n")

✏️ Your turn: Compare the mean and median for the sunny side. If they are about equal, what does that tell you about the shape of the distribution?

Mean sunny:
Median sunny:
What it suggests about distribution shape:

Standard Deviation

▶ Run this:

# SD for each side ------------------------------------
sd_sunny <- sd(sunny_wt, na.rm = TRUE)
sd_shady <- sd(shady_wt, na.rm = TRUE)

cat("SD sunny:", round(sd_sunny, 2), "g\n")
cat("SD shady:", round(sd_shady, 2), "g\n")

✏️ Your turn: Which side has more variable leaf weights? What does a larger SD tell you biologically?

More variable side:
Biological interpretation:

Standard Error

▶ Run this:

🔮 Predict first: SE = SD / √n. Since n is larger than 1, will the SE be larger or smaller than the SD you just calculated? Predict, then run.

# SE = SD / sqrt(n) — must use the correct n ----------
n_sun  <- sum(!is.na(sunny_wt))
n_sha  <- sum(!is.na(shady_wt))

se_sunny <- sd_sunny / sqrt(n_sun)
se_shady <- sd_shady / sqrt(n_sha)

cat("n sunny  =", n_sun, "\n")
cat("SE sunny =", round(se_sunny, 2), "g\n\n")
cat("n shady  =", n_sha, "\n")
cat("SE shady =", round(se_shady, 2), "g\n")

✏️ Your turn: In your own words, what does the SE tell you that the SD does not?

Your answer:

✏️ Your turn: If you collected 40 leaves per side instead of 10, would the SE get larger or smaller? Why?

Your answer:

🧩 Chunk 4 — Tidy stats & plots (matches lecture Chunk 4)

Parts 6–9: group_by() + summarize(), skimr, and both plots. Do these after the lecture’s Chunk 4.

Part 6 · Tidy stats with `group_by()` + `summarize()`

All groups, all stats, one pipeline

▶ Run this:

🔮 Predict first: How many rows will stats_df have? (How many values does side take?) Predict the number before running.

# group_by() + summarize() — the core tidyverse pattern ------
stats_df <- tree_df %>%
  group_by(side) %>%
  summarize(
    n       = sum(!is.na(weight_g)),
    mean_wt = round(mean(weight_g,   na.rm = TRUE), 2),
    med_wt  = round(median(weight_g, na.rm = TRUE), 2),
    sd_wt   = round(sd(weight_g,     na.rm = TRUE), 2),
    se_wt   = round(sd_wt / sqrt(n), 2)
  )

stats_df

💡 Key idea: group_by(side) splits the data by the side column. Everything after it happens within each group. You get one output row per group.

✏️ Your turn: The se_wt line references sd_wt and n that were just created in the same summarize() call. Rewrite the SE formula in plain words:

SE equals:

✏️ Your turn: Do the numbers in stats_df match what you calculated by hand in Part 5?

Y / N — if not, explain any differences:

Summary across multiple variables

▶ Run this:

# Summarize all three measurements at once -----------
size_stats_df <- tree_df %>%
  group_by(side) %>%
  summarize(
    n       = n(),
    mean_wt = round(mean(weight_g,  na.rm = TRUE), 2),
    mean_ht = round(mean(height_cm, na.rm = TRUE), 2),
    mean_wd = round(mean(width_cm,  na.rm = TRUE), 2)
  )

size_stats_df

✏️ Your turn: Looking at size_stats_df, fill in the table:

Measurement    | Sunny mean | Shady mean | Shady larger? (Y/N)
---------------|------------|------------|---------------------
weight_g       |            |            |
height_cm      |            |            |
width_cm       |            |            |

Overall: does the data support the hypothesis?

Part 7 · Fast overview with `skimr`

▶ Run this:

# skim() grouped by side — everything at once --------
tree_df %>%
  group_by(side) %>%
  skim()

✏️ Your turn: Look at the n_missing row for weight_g. How many values are missing per group?

n_missing sunny weight_g:
n_missing shady weight_g:

✏️ Your turn: Look at the mean and p50 (50th percentile = median) values for weight_g. Are mean and median close for each group? What does that suggest about skewness?

Your answer:

💡 Key idea: skim() is the fastest first look at any dataset. Use it on every new dataset you encounter — it takes one line and shows everything.

Part 8 · Visualizing with boxplots

Basic boxplot

Because our data is already in long format, we can plot directly — no reshaping required.

▶ Run this:

# Boxplot — data is already long, plot it directly ----
tree_box_plot <- tree_df %>%
  ggplot(aes(x = side, y = weight_g, fill = side)) +
  geom_boxplot(alpha = 0.6, outlier.shape = NA) +
  labs(
    title = "Leaf Weight by Tree Side",
    x     = "Side of Tree",
    y     = "Leaf Weight (g)",
    fill  = "Side"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

tree_box_plot

✏️ Your turn: Label the four key parts of a boxplot from memory:

The line inside the box represents:
The top and bottom of the box represent:
The whiskers represent:
Dots beyond the whiskers represent:

Add individual data points

▶ Run this:

# Add jittered raw data points over the boxplot ------
tree_jitter_plot <- tree_df %>%
  ggplot(aes(x = side, y = weight_g, fill = side)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA) +
  geom_point(
    position = position_jitter(width = 0.15, seed = 42),
    alpha    = 0.6,
    size     = 2.5
  ) +
  labs(
    title = "Leaf Weight by Tree Side",
    x     = "Side of Tree",
    y     = "Leaf Weight (g)",
    fill  = "Side"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

tree_jitter_plot

✏️ Your turn: In geom_point(), change alpha = 0.6 to alpha = 1.0. Then try alpha = 0.1. What does alpha control?

# Write your modified code here:

What alpha controls:
Which alpha value looks best for 10 points?

✏️ Your turn: Change width = 0.15 inside position_jitter() to width = 0.8. What happens? Is 0.15 or 0.8 more appropriate?

# Write your modified code here:

What changed:
Better choice and why:

Save your plot

▶ Run this:

# Save the boxplot to the figures folder -------------
ggsave("figures/leaf_weight_boxplot.png",
       plot   = tree_jitter_plot,
       width  = 5,
       height = 5,
       units  = "in",
       dpi    = 300)

Part 9 · Mean ± SE plot with `stat_summary()`

▶ Run this:

# stat_summary() computes and plots mean and SE ------
tree_mean_se_plot <- tree_df %>%
  ggplot(aes(x = side, y = weight_g, color = side)) +
  geom_point(
    position = position_jitter(width = 0.15, seed = 42),
    alpha    = 0.35,
    size     = 2
  ) +
  stat_summary(fun      = mean,    geom = "point", size = 4) +
  stat_summary(fun.data = mean_se, geom = "errorbar",
               width = 0.15, linewidth = 0.9) +
  labs(
    title = "Mean ± SE Leaf Weight by Tree Side",
    x     = "Side of Tree",
    y     = "Leaf Weight (g)",
    color = "Side"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

tree_mean_se_plot

✏️ Your turn: The large dot shows the mean. The vertical bars show ± 1 SE. Do the error bars overlap? What does non-overlapping bars suggest (informally)?

Do the error bars overlap?  Y / N
What non-overlap suggests:

✏️ Your turn: Change fun = mean in the first stat_summary() to fun = median. What changes in the plot?

# Write your modified code here:

What changed:

Save this plot

▶ Run this:

ggsave("figures/leaf_weight_mean_se.png",
       plot   = tree_mean_se_plot,
       width  = 5,
       height = 5,
       units  = "in",
       dpi    = 300)

Part 10 · Review and checkpoint

At this point you should be able to:

✏️ Your turn — before you move on: Run your entire script with Ctrl/Cmd + Shift + Enter. Does it complete without errors?

Ran cleanly?  Y / N
If not, what error appeared:

Part 11 · Going further

This section is optional — work through it if you finish early.

Explore `height_cm` and `width_cm`

▶ Try this for height_cm:

tree_height_plot <- tree_df %>%
  ggplot(aes(x = side, y = height_cm, fill = side)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA) +
  geom_point(position = position_jitter(width = 0.15, seed = 42),
             alpha = 0.6, size = 2.5) +
  labs(title = "Leaf Height by Tree Side",
       x = "Side of Tree", y = "Leaf Height (cm)") +
  theme_minimal() +
  theme(legend.position = "none")

tree_height_plot

✏️ Your turn: Make the same plot for width_cm. Copy and adapt the code above.

# Write your modified code here:

Use `mutate()` to add a column, then plot it

▶ Try this:

# Add size_class column, then make a bar chart
tree_df %>%
  mutate(size_class = if_else(weight_g > 5, "large", "small")) %>%
  ggplot(aes(x = side, fill = size_class)) +
  geom_bar(position = "dodge") +
  labs(title = "Number of large vs small leaves by side",
       x = "Side", y = "Count", fill = "Size class") +
  theme_minimal()

✏️ Your turn: What does position = "dodge" do to the bars? What happens if you change it to position = "fill"?

dodge does:
fill does:

Violin plots

▶ Try this:

tree_violin_plot <- tree_df %>%
  ggplot(aes(x = side, y = weight_g, fill = side)) +
  geom_violin(alpha = 0.5) +
  geom_point(position = position_jitter(width = 0.1, seed = 42),
             alpha = 0.6, size = 2) +
  labs(title = "Violin Plot of Leaf Weight",
       x = "Side of Tree", y = "Leaf Weight (g)") +
  theme_minimal() +
  theme(legend.position = "none")

tree_violin_plot

✏️ Your turn: What does the width of the violin at any given weight value show you, compared to a boxplot?

Your observation:

What your `figures/` folder should contain

After completing this worksheet:

tree_project/
├── data/
│   └── 2026_06_25_tree_experiment_raw_data.xlsx   <- never edit this
├── figures/
│   ├── leaf_weight_boxplot.png                     <- Part 8
│   └── leaf_weight_mean_se.png                     <- Part 9
└── scripts/
    └── 03_leaf_descriptions.R                      <- your script

Getting unstuck

Read the error message out loud. R usually names the line and the problem.
Check the usual suspects: Did you load library(readxl), library(tidyverse), and library(skimr)?
Spelling? R is case-sensitive — "Sunny" ≠ "sunny". Check with names(tree_df).
na.rm = TRUE missing? Any stat function on a column with NAs returns NA without it — no error, just a silent wrong answer.
File not found? Check with getwd() and confirm the data/ folder is inside your project.
Cheat sheets — https://posit.co/resources/cheatsheets/
Bring the exact error (copy-paste it) to class, Canvas, or office hours.

💡 Key idea: Getting stuck is not failing — every working data scientist googles error messages daily.

End of Worksheet 03. Next: Worksheet 04 will cover the two-sample Welch’s t-test — formally testing whether the difference in leaf weight between sunny and shady sides is statistically significant.

Other Formats

Describing Our Leaf Data

Recap from Worksheet 02

Today’s Objectives

How to use this worksheet

🔮 Predict before you run — and type, don’t paste

🧩 Chunk 1 — Wrangling verbs (matches lecture Chunk 1)

Part 1 · Load libraries and data

Libraries first

Load the leaf data

Part 2 · Inspect the data

Part 3 · The core tidyverse verbs — filter, select, mutate, arrange

filter() — keeping rows you want

select() — keeping columns you want

mutate() — adding new columns

arrange() — sorting rows

The full pipeline — chaining all four verbs

🧩 Chunks 2–3 — Describing the data (matches lecture Chunks 2 & 3)

Part 4 · The NA problem — counting observations correctly

What is an NA?

The problem with length()

Breaking down is.na() step by step

Part 5 · Descriptive statistics in base R

Extract one group with filter() and pull()

Mean

Median

Standard Deviation

Standard Error

🧩 Chunk 4 — Tidy stats & plots (matches lecture Chunk 4)

Part 6 · Tidy stats with group_by() + summarize()

All groups, all stats, one pipeline

Summary across multiple variables

Part 7 · Fast overview with skimr

Part 8 · Visualizing with boxplots

Basic boxplot

Add individual data points

Save your plot

Part 9 · Mean ± SE plot with stat_summary()

Save this plot

Part 10 · Review and checkpoint

Part 11 · Going further

Explore height_cm and width_cm

Use mutate() to add a column, then plot it

Violin plots

What your figures/ folder should contain

Getting unstuck

`filter()` — keeping rows you want

`select()` — keeping columns you want

`mutate()` — adding new columns

`arrange()` — sorting rows

The problem with `length()`

Breaking down `is.na()` step by step

Extract one group with `filter()` and `pull()`

Part 6 · Tidy stats with `group_by()` + `summarize()`

Part 7 · Fast overview with `skimr`

Part 9 · Mean ± SE plot with `stat_summary()`

Explore `height_cm` and `width_cm`

Use `mutate()` to add a column, then plot it

What your `figures/` folder should contain