Worksheet 12 — Joins: Combining Two Bigfoot Tables

Keys, mutating joins, and filtering joins to build a map-ready dataset

joins

tidyverse

wrangling

Hands-on companion to the joins lecture. Over two days students combine the Bigfoot report text (bfro_reports.csv) with its coordinates (bfro_locations.csv) using left/inner/full joins and semi/anti joins, and build the exact table they will map next week.

Author

Bill Perry

Published

July 5, 2026

Joins — Two Bigfoot Tables into One 🦶

Before you start — the data

Two files live in your data/ folder:

bfro_reports.csv — one row per sighting report (year, season, state, class, the written account), keyed by REPORT_NUMBER
bfro_locations.csv — the coordinates for reports that were geocoded, keyed by number

Today’s Objectives (a 2-day worksheet)

Find the key that links the two tables
Join them with left_join() when the key columns are named differently
Compare inner, left, and full joins
Use semi_join() / anti_join() to filter, and build the map-ready table

How to use this worksheet

Work through the parts in order. Type the code into a new R script and run it line by line.

Blocks marked ▶ Run this should be executed as written.

Blocks marked ✏️ Your turn ask you to write, modify, or interpret.

Day 1 = Parts 1–6. Day 2 = Parts 7–12.

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

Before every join, predict the number of rows you’ll get. Then type the code and check. A join that returns the wrong row count is the single most common data bug — predicting first is how you catch it.

🧩 Chunk 1 — Two tables & a key (Day 1, after lecture Chunk 1)

Parts 1–3: load both tables and clean the key.

Part 1 · Load both tables

▶ Run this:

library(tidyverse)

reports_df   <- read_csv("data/bfro_reports.csv")
locations_df <- read_csv("data/bfro_locations.csv")

dim(reports_df)
dim(locations_df)

✏️ Your turn:

reports_df:   rows =        cols =
locations_df: rows =        cols =
Which table has more rows? Why might that be?

Part 2 · Find the key

▶ Run this:

reports_df   %>% select(REPORT_NUMBER, YEAR, STATE, REPORT_CLASS) %>% head(3)
locations_df %>% select(number, latitude, longitude) %>% head(3)

✏️ Your turn: What column links the two tables, and what is it called in each?

Key in reports_df:
Key in locations_df:
Are the names the same?  Y / N

Part 3 · Clean the key

▶ Run this:

# One row per report + match the key type ----------------
reports_df <- reports_df %>%
  distinct(REPORT_NUMBER, .keep_all = TRUE) %>%
  mutate(REPORT_NUMBER = as.integer(REPORT_NUMBER))

n_distinct(reports_df$REPORT_NUMBER)
n_distinct(locations_df$number)

✏️ Your turn: REPORT_NUMBER was stored like 30680.0 but number is 30680. Why must we fix that before joining?

Your answer:

🧩 Chunk 2 — Your first join (Day 1, after lecture Chunk 2)

Parts 4–6: the left join and the accidental-key trap.

Part 4 · The left join

🔮 Predict first: After a left_join, how many rows will you have (all reports? only matched?), and roughly how many will have a latitude?

▶ Run this:

bigfoot_df <- reports_df %>%
  left_join(locations_df, by = c("REPORT_NUMBER" = "number"))

nrow(bigfoot_df)                 # rows kept
sum(!is.na(bigfoot_df$latitude)) # rows with coordinates

✏️ Your turn:

Total rows after left_join:
Rows WITH coordinates:
Rows with NA coordinates:

Part 5 · The accidental-key trap

▶ Run this (on purpose — watch the message):

# Forget by = ... and see what dplyr does ----------------
reports_df %>% left_join(locations_df)

✏️ Your turn: What column did dplyr announce it was joining on? Why is joining on index completely wrong here?

dplyr joined on:
Why that's wrong:

⚠️ Watch out! When dplyr prints “Joining with by = ...” and you didn’t choose that column, stop and name your real key.

Part 6 · Name the key every time

▶ Run this (the correct join):

bigfoot_df <- reports_df %>%
  left_join(locations_df, by = c("REPORT_NUMBER" = "number"))

bigfoot_df %>%
  select(REPORT_NUMBER, STATE, REPORT_CLASS, latitude, longitude) %>%
  head()

✏️ Your turn: Look at the first rows — do any already show NA for latitude/longitude? What does that mean about those reports?

Your answer:

🛑 End of Day 1. You can join two tables on a named key. Day 2: the rest of the join family.

🧩 Chunk 3 — The join family (Day 2, after lecture Chunk 3)

Parts 7–9: inner vs left vs full.

Part 7 · Run all three joins

🔮 Predict first: Rank these three by row count, most to fewest: inner_join, left_join, full_join.

▶ Run this:

inner <- inner_join(reports_df, locations_df, by = c("REPORT_NUMBER" = "number"))
left  <- left_join (reports_df, locations_df, by = c("REPORT_NUMBER" = "number"))
full  <- full_join (reports_df, locations_df, by = c("REPORT_NUMBER" = "number"))

c(inner = nrow(inner), left = nrow(left), full = nrow(full))

✏️ Your turn:

inner rows =        left rows =        full rows =
Was your ranking right?  Y / N

Part 8 · What each join dropped or kept

✏️ Your turn: Fill in what each join does, in your own words.

inner_join keeps:
left_join keeps:
full_join keeps:
Which reports does inner_join DROP that left_join keeps?

Part 9 · Pick the right join

✏️ Your turn: You want to make a map of all sightings, keeping every report even if some can’t be placed. Which join do you use, and why?

Join:
Why:

💡 Key idea: start from “which rows do I refuse to lose?” — that answer picks the join.

🧩 Chunk 4 — Filtering joins & the payoff (Day 2, after lecture Chunk 4)

Parts 10–12: anti/semi joins and the map-ready table.

Part 10 · `anti_join()` — what’s missing

🔮 Predict first: How many reports have NO coordinates? (Total reports minus the number with a latitude from Part 4.)

▶ Run this:

missing_coords <- reports_df %>%
  anti_join(locations_df, by = c("REPORT_NUMBER" = "number"))

nrow(missing_coords)
missing_coords %>% count(STATE, sort = TRUE) %>% head(4)

✏️ Your turn:

Reports with no coordinates:
Did anti_join ADD any columns, or just filter rows?

Part 11 · `semi_join()` — keep only matches

▶ Run this:

mappable_reports <- reports_df %>%
  semi_join(locations_df, by = c("REPORT_NUMBER" = "number"))

nrow(mappable_reports)

✏️ Your turn: How is semi_join different from inner_join? (Hint: look at the columns, not just the rows.)

Your answer:

Part 12 · Build the map-ready table

▶ Run this:

# Reports + coordinates, ready for next week's map -------
bigfoot_mappable <- reports_df %>%
  left_join(locations_df, by = c("REPORT_NUMBER" = "number")) %>%
  filter(!is.na(latitude), !is.na(longitude))

bigfoot_mappable %>%
  select(REPORT_NUMBER, STATE, SEASON, REPORT_CLASS, latitude, longitude) %>%
  head()

# Save it for the mapping lecture ------------------------
write_csv(bigfoot_mappable, "data/bigfoot_joined.csv")

✏️ Your turn: How many rows are in your map-ready table? How does this compare to the original reports_df?

Map-ready rows:
Difference from reports_df, and why:

Part 13 · Review and checkpoint

You should now be able to:

Explain what a key is and why it must be unique and same-type
Join two tables whose key columns are named differently (by = c("a" = "b"))
Use left_join, inner_join, full_join and predict their row counts
Use anti_join to find unmatched rows and semi_join to keep matched rows
Recognize the accidental-index-join bug

✏️ Your turn — before you move on: Run your whole script top to bottom. Does it run cleanly?

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

Part 14 · Going further

Optional — do this if you finish early.

Add the report class to a map count

▶ Try this: join, then count sightings per state per class.

bigfoot_df %>%
  filter(!is.na(latitude)) %>%
  count(STATE, REPORT_CLASS) %>%
  arrange(desc(n)) %>%
  head(10)

✏️ Your turn: Which state + class combination has the most reports?

Your answer:

Locations with no report

✏️ Your turn: Use an anti_join the other direction to find locations that have no matching report. How many are there, and what might explain them?

# Write your anti_join here (locations first this time):

Locations with no report:
Possible explanation:

Getting unstuck

by must be supplied / weird match → the key columns are named differently; use by = c("REPORT_NUMBER" = "number").
Zero matches → check the key type (as.integer() on REPORT_NUMBER) — text won’t match a number.
Way too many rows after a join → a duplicated key fans out the rows; distinct() the lookup key first.
dplyr announced by = join_by(index) → you forgot to name your key; both tables share an index column.
Cheat sheet — https://dplyr.tidyverse.org/reference/mutate-joins.html

💡 Key idea: joins are how real datasets get built — one table of what, another of where, combined on a key. You just made the file you’ll map next week.

End of Worksheet 12. Next: Worksheet 13 — mapping the sightings you just assembled.

Other Formats

Worksheet 12 — Joins: Combining Two Bigfoot Tables

Joins — Two Bigfoot Tables into One 🦶

Before you start — the data

Today’s Objectives (a 2-day worksheet)

How to use this worksheet

🧩 Chunk 1 — Two tables & a key (Day 1, after lecture Chunk 1)

Part 1 · Load both tables

Part 2 · Find the key

Part 3 · Clean the key

🧩 Chunk 2 — Your first join (Day 1, after lecture Chunk 2)

Part 4 · The left join

Part 5 · The accidental-key trap

Part 6 · Name the key every time

🧩 Chunk 3 — The join family (Day 2, after lecture Chunk 3)

Part 7 · Run all three joins

Part 8 · What each join dropped or kept

Part 9 · Pick the right join

🧩 Chunk 4 — Filtering joins & the payoff (Day 2, after lecture Chunk 4)

Part 10 · `anti_join()` — what’s missing

Part 11 · `semi_join()` — keep only matches

Part 12 · Build the map-ready table

Part 13 · Review and checkpoint

Part 14 · Going further

Add the report class to a map count

Locations with no report

Getting unstuck

Other Formats

Joins — Two Bigfoot Tables into One 🦶

Before you start — the data

Today’s Objectives (a 2-day worksheet)

How to use this worksheet

🧩 Chunk 1 — Two tables & a key (Day 1, after lecture Chunk 1)

Part 1 · Load both tables

Part 2 · Find the key

Part 3 · Clean the key

🧩 Chunk 2 — Your first join (Day 1, after lecture Chunk 2)

Part 4 · The left join

Part 5 · The accidental-key trap

Part 6 · Name the key every time

🧩 Chunk 3 — The join family (Day 2, after lecture Chunk 3)

Part 7 · Run all three joins

Part 8 · What each join dropped or kept

Part 9 · Pick the right join

🧩 Chunk 4 — Filtering joins & the payoff (Day 2, after lecture Chunk 4)

Part 10 · anti_join() — what’s missing

Part 11 · semi_join() — keep only matches

Part 12 · Build the map-ready table

Part 13 · Review and checkpoint

Part 14 · Going further

Add the report class to a map count

Locations with no report

Getting unstuck

Part 10 · `anti_join()` — what’s missing

Part 11 · `semi_join()` — keep only matches