Checkpoint: Data Analysis

Reviewed & published by Brayan K

This checkpoint consolidates the data-analysis half of the course — tibbles, reshaping, purrr, lubridate, ggplot2, distributions, hypothesis testing, and glm — into one end-to-end tidyverse pipeline and a self-check quiz.

Learn Checkpoint: Data Analysis in our free R course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick reference.

Part of the free R course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

You'll recap each tool, tackle a build challenge that flows raw data through pivoting, grouped summaries, and a described plot, then test your understanding with a short checkpoint quiz before moving on to linear models.

What This Checkpoint Covers

📚 What You've Learned

Before the challenge, here's the toolkit you've built across the data-analysis lessons — each row is a job and the verb that does it.

Lesson

Key tools

Tibbles

tibble(), as_tibble(), glimpse()

Reshaping

pivot_longer(), pivot_wider()

purrr

map(), map_dbl(), reduce(), keep()

lubridate

ymd(), wday(), floor_date()

ggplot2

facet_wrap(), theme_minimal(), labs()

Distributions

dnorm/pnorm/qnorm/rnorm, sample()

Hypothesis tests

t.test(), wilcox.test(), chisq.test()

glm

glm(family = binomial), predict()

🧩 Warm-Up: Reshape, Date, Map

Before the full pipeline, here are three of the module's idioms side by side on inputs small enough to check by eye. Read each comment, then match what prints to what the comment describes.

Your turn. The same three idioms, now chained on one small dataset: reshape it long, parse the dates, and summarise per page with map_dbl() . Replace each ___ with the value in its hint, then run it and compare with the output panel.

🛠️ Build Challenge: A Tidyverse Pipeline

Put it together. Start from a wide inline tibble, reshape it long, summarise per group, and describe the plot you'd draw. Work through the ___ s yourself before revealing the solution.

Mini-Challenge: Extend the Pipeline

Stretch the build challenge further to weave in more of the course:

• Add a date column with lubridate (e.g. the last day of each quarter) and floor_date() it to the month.
• Use pivot_wider() on your summary so each store becomes a column — a presentation-ready table.
• Run a t.test() comparing two stores' quarterly revenues and read the p-value.
• Bonus: split the data by store and use purrr::map() to compute each store's mean without a loop.

Build it in RStudio and inspect each intermediate result.

📝 Checkpoint Quiz

Answer each in your head (or in the console), then click to reveal. If any feel shaky, revisit that lesson before continuing.

pivot_longer() . The old column HEADERS go into the new column named by names_to (e.g. "year"), and the CELL values go into the column named by values_to (e.g. "revenue"). The reverse is pivot_wider() with names_from / values_from .

map() always returns a LIST, one element per input. map_dbl() returns a numeric VECTOR and errors if any result isn't a single double — a built-in type check. Use the typed variant whenever you expect a simple atomic result, because it catches mistakes immediately.

p answers "fraction below" — pnorm(60, mean, sd) gives the cumulative probability up to 60. q is its inverse and gives a value at a percentile — qnorm(0.90, mean, sd) returns the 90th-percentile value. d is the density (curve height) and r draws random numbers.

chisq.test() on a contingency table of counts. A t-test compares MEANS of a numeric variable; here the data are COUNTS of a categorical outcome, so chi-squared (testing association between layout and sign-up) is the right tool. For small, sparse tables, fisher.test() is even better.

Coefficients are log-odds, so apply exp(coef(model)) to read them as ODDS RATIOS (above 1 raises the odds, below 1 lowers them). For a probability on a new case, use predict(model, newdata, type = "response") — without type = "response" you'd get log-odds instead of a [0, 1] probability.

Both expect tidy data — one variable per column. If a variable (like "quarter") is spread across column headers, you can't map it to an aesthetic or group by it. Pivoting it into a real column makes it addressable, so aes(x = quarter) and group_by(quarter) work.

Pro Tips for Real Analyses

• 💡 Inspect as you pipe: add |> print() or stop the pipe early to see each intermediate result.
• 💡 One job per verb: a long pipe of small, named steps is easier to debug than one dense expression.
• 💡 Tidy your stats too: broom::tidy() turns test and model output into tibbles that slot back into the pipeline.

Frequently Asked Questions

🎉 Checkpoint Complete!

• ✅ You can chain tibble → pivot_longer → group_by/summarise → ggplot
• ✅ You know when to reach for purrr vs a dplyr summary
• ✅ You can pick the right test (t.test / chisq.test) for your data type
• ✅ You can fit and interpret a glm with odds ratios and predicted probabilities
• ✅ You've self-checked with the checkpoint quiz
• ✅ Next lesson: Linear Models (lm)

Practice quiz

Continue this course

• Previous: Generalized Linear Models (glm)
• Next: Linear Models (lm)