Ultimate Instructor's Kit

What this teaches — concept breakdown

R distinguishes an expression from its printed result. Parentheses govern the order of operations, and the number [1] shown by the Console is a vector index, not part of the answer.

R evaluates parentheses before multiplication/division and then addition/subtraction. The [1] displayed in a Console result denotes the index of the first vector element; it is not part of the arithmetic answer. Compare print(6 + 1) with typing 6 + 1 directly into the Console.

Step-by-step classroom instructions

1. Add 6 and 1, then print the result.
2. Multiply 2 by the sum of 1 and 3, then print the result.
3. Calculate 5 plus 3 times 2, then print the result.

Masterclass learning goals

Learning goal: Compute 6+1, 2*(1+3), then predict 5+3*2.

Concept: Arithmetic operators, operator precedence and parentheses.

Original task checklist

Task instructions — what the student must do

1. Add 6 and 1, then print the result.
2. Multiply 2 by the sum of 1 and 3, then print the result.
3. Calculate 5 plus 3 times 2, then print the result.

What this teaches — concept breakdown

The assignment arrow stores the result of an expression, so later instructions can reuse it instead of recomputing it. 1:10 creates an inclusive integer sequence.

The assignment operator <- stores a value in an object for subsequent statements. In R, 1:10 includes both endpoints; sum(1:10) is 55. Reusing x differs from recalculating the sum in every line. Check objects with ls(), and explain why names and values are different.

Step-by-step classroom instructions

1. Add the integers from 1 to 10 and store the total in x.
2. Divide the stored total x by 10 and print the result.

Masterclass learning goals

Learning goal: Save 1+...+10 as x. Divide x by ten.

Concept: Assignment, reusable objects, numeric sequences and the R environment.

Original task checklist

Task instructions — what the student must do

1. Add the integers from 1 to 10 and store the total in x.
2. Divide the stored total x by 10 and print the result.

What this teaches — concept breakdown

Character values need quotation marks; the L suffix creates an integer literal. A logical mask is a TRUE/FALSE vector that selects entries at matching positions.

A vector typically stores values of a common atomic type; combining character and numeric elements can coerce types. R indexes from 1, unlike Python. A logical condition such as sleep_hours > 6 produces a TRUE/FALSE mask, and x[mask] retains only matching elements.

Step-by-step classroom instructions

1. Store a short text label in course_label.
2. Store the number of participants as an integer in participants.
3. Create a numeric vector containing four fictional sleep durations.
4. Print only the sleep durations greater than 6 hours using logical indexing.

Masterclass learning goals

Learning goal: Store a character, integer, and vector; extract values above six.

Concept: Character and numeric values; vectors, logical masks and indexing.

Original task checklist

Task instructions — what the student must do

1. Store a short text label in course_label.
2. Store the number of participants as an integer in participants.
3. Create a numeric vector containing four fictional sleep durations.
4. Print only the sleep durations greater than 6 hours using logical indexing.

What this teaches — concept breakdown

USArrests is an R built-in data frame with one row per US state and four crime-rate/urbanisation variables. dim(), names() and head() inspect its structure.

USArrests is a built-in teaching data frame. Each row is a US state and each column a variable. dim(data) reports rows and columns, names(data) returns variable names, and head(data) provides a first look without printing the entire table.

Step-by-step classroom instructions

1. Assign the built-in USArrests dataset to an object called crime.
2. Print the number of rows and columns in crime.
3. Print all column names in crime.
4. Print the first six rows of crime.

Masterclass learning goals

Learning goal: Load USArrests into crime; inspect variables and dimensions.

Concept: Data frames, observations (rows) and variables (columns).

Original task checklist

Task instructions — what the student must do

1. Assign the built-in USArrests dataset to an object called crime.
2. Print the number of rows and columns in crime.
3. Print all column names in crime.
4. Print the first six rows of crime.

What this teaches — concept breakdown

Functions take arguments and return values. The $ operator accesses a named data-frame column; max() returns its largest value.

A function call has the form function_name(argument, named_argument=value). The $ operator extracts a named column from a data frame; max() finds the largest value. For datasets with missing values, na.rm=TRUE controls whether NA is omitted. Help pages document parameters and outputs.

Step-by-step classroom instructions

1. Print the maximum value in the Murder column of crime.
2. Print the Assault column (or a relevant element of it) using the dollar operator.
3. Open R help for the max function.

Masterclass learning goals

Learning goal: Find the maximum Murder and Assault; inspect max() help.

Concept: Function calls, named arguments, the dollar operator and help pages.

Original task checklist

Task instructions — what the student must do

1. Print the maximum value in the Murder column of crime.
2. Print the Assault column (or a relevant element of it) using the dollar operator.
3. Open R help for the max function.

What this teaches — concept breakdown

rm() removes an object from the current R environment; it does not delete a CSV or modify the built-in dataset. exists() checks whether a name is still defined.

rm(crime) removes the object only from the active R environment; it does not uninstall a package or delete a CSV file. Saving scripts that recreate objects is essential for reproducibility. exists("crime") tests the object’s presence after removal.

Step-by-step classroom instructions

1. Remove the crime object from the current R environment.
2. Print whether an object called crime still exists.

Masterclass learning goals

Learning goal: Remove crime from the environment.

Concept: Removing an in-memory object without deleting the source file.

Original task checklist

Task instructions — what the student must do

1. Remove the crime object from the current R environment.
2. Print whether an object called crime still exists.

What this teaches — concept breakdown

A research question, a numeric vector and a data frame represent different kinds of information. A small fictional dataset is a safe way to practise reporting.

Distinguish the outcome variable (what is measured), predictor (a possible explanation), units, and a potential confounder (a factor associated with both). Fictional observational values are useful for teaching code but cannot establish a real causal claim.

Step-by-step classroom instructions

1. Write a fictional research question and store it as a text value.
2. Create a short numeric vector of fictional reaction times.
3. Print a small data frame with trial numbers and reaction times.

Masterclass learning goals

Learning goal: Define the outcome, predictor, unit, and one possible confounder.

Concept: Research questions, variables, units and possible confounding.

Original task checklist

Task instructions — what the student must do

1. Write a fictional research question and store it as a text value.
2. Create a short numeric vector of fictional reaction times.
3. Print a small data frame with trial numbers and reaction times.

What this teaches — concept breakdown

read.csv() loads rows and columns from disk into R. stringsAsFactors=FALSE keeps text as text; the working directory determines where the relative data/ path begins.

CSV files are plain-text tables until read.csv() imports them as data frames. Relative paths, such as data/participant-info.csv, are resolved from getwd(), not necessarily the script’s location. Check nrow(), names() and head() after importing. The open package uses synthetic records.

Step-by-step classroom instructions

1. Import data/participant-info.csv as the data frame pinfo.
2. Import data/ahi-cesd.csv as the data frame responses.
3. Print the participant and response row counts using nrow().

Masterclass learning goals

Learning goal: Read participant-info.csv and ahi-cesd.csv as pinfo and responses.

Concept: Packages, relative paths and importing CSV files as data frames.

Original task checklist

Task instructions — what the student must do

1. Import data/participant-info.csv as the data frame pinfo.
2. Import data/ahi-cesd.csv as the data frame responses.
3. Print the participant and response row counts using nrow().

What this teaches — concept breakdown

A compound join key uses BOTH id and intervention to associate each measurement with the correct participant information. A join can silently lose rows or multiply records if keys are wrong.

A join matches rows using agreed key columns; here both participant id and intervention matter. Check uniqueness on each side before joining: repeated keys may create an unexpected many-to-many expansion. A successful join should be checked by both row count and distinct IDs.

Step-by-step classroom instructions

1. Join participant information and responses by both id and intervention.
2. Print the row count and number of distinct participants after the join.

Masterclass learning goals

Learning goal: Join demographics and repeated responses by id AND intervention.

Concept: Joining tables on the correct compound key and checking cardinality.

Original task checklist

Task instructions — what the student must do

1. Join participant information and responses by both id and intervention.
2. Print the row count and number of distinct participants after the join.

What this teaches — concept breakdown

select() controls WHICH variables are retained, not which observations. Keeping a small, explicitly named set makes later calculations easier to inspect.

select() chooses variables (columns), while filter() chooses observations (rows). Explicitly naming columns documents the analysis plan and reduces accidental use of unrelated measures. Verify the selection with names() and preserve the intended data types.

Step-by-step classroom instructions

1. Select the required analysis columns without removing any rows.
2. Print the resulting column names to verify your selection.

Masterclass learning goals

Learning goal: Select nine requested columns.

Concept: Selecting columns rather than filtering rows.

Original task checklist

Task instructions — what the student must do

1. Select the required analysis columns without removing any rows.
2. Print the resulting column names to verify your selection.

What this teaches — concept breakdown

filter() controls WHICH rows are retained using a logical predicate. The verified codebook defines occasion 0 as pretest, 1 as posttest, and 2 as the one-week follow-up.

The uploaded codebook gives occasion=0 as baseline, 1 as posttest, and 2 as one-week follow-up. An inconsistent worksheet shortcut must not override the codebook. The expression occasion %in% c(0,1) keeps both desired times. Verify actual frequency counts with table().

Step-by-step classroom instructions

1. Keep only observations at baseline (occasion 0) and posttest (occasion 1).
2. Count and print observations for each occasion to check the filter.

Masterclass learning goals

Learning goal: Filter baseline (0) and posttest (1) after reading codebook.

Concept: Filtering rows and interpreting the measurement-time codebook.

Original task checklist

Task instructions — what the student must do

1. Keep only observations at baseline (occasion 0) and posttest (occasion 1).
2. Count and print observations for each occasion to check the filter.

What this teaches — concept breakdown

arrange() sorts observations by a column but does not change the values or select a subset. It is especially useful for checking extreme observations.

arrange(x, ahiTotal) sorts rows in ascending AHI order; desc(ahiTotal) reverses the direction. Sorting changes only order, not the number of observations or their measured values. Use head() to verify the smallest or largest entries.

Step-by-step classroom instructions

1. Sort the selected observations by ahiTotal from low to high.
2. Print the first rows of the sorted data frame.

Masterclass learning goals

Learning goal: Sort AHI scores ascending.

Concept: Ordering observations without changing their values.

Original task checklist

Task instructions — what the student must do

1. Sort the selected observations by ahiTotal from low to high.
2. Print the first rows of the sorted data frame.

What this teaches — concept breakdown

summarise() collapses a table to aggregate statistics. n() counts rows; mean(..., na.rm=TRUE) ignores missing values for each respective mean.

The arithmetic mean is the sum of available measurements divided by the number of non-missing values included. na.rm=TRUE removes NA values from the calculation; it does not replace them with zero. Different variables may therefore have different effective sample sizes.

Step-by-step classroom instructions

1. Calculate overall average AHI and CES-D scores in one summary row.
2. Print the completed overall summary table.

Masterclass learning goals

Learning goal: Make one-row AHI and CES-D mean summary.

Concept: Descriptive summaries and explicit missing-value handling.

Original task checklist

Task instructions — what the student must do

1. Calculate overall average AHI and CES-D scores in one summary row.
2. Print the completed overall summary table.

What this teaches — concept breakdown

group_by(occasion) changes the unit of summarisation to a measurement occasion. A grouped mean is not necessarily the mean within-person change because different people can appear at each time.

group_by(occasion) controls how a later summarise() operates: one summary row per measurement occasion. Counts at baseline and posttest need not be equal. A difference between group means across occasions is not, by itself, a within-person change or a treatment effect.

Step-by-step classroom instructions

1. Start a pipe to summarise the sorted pre/post observations.
2. Group the piped observations by occasion.
3. Compute sample sizes and means separately for each occasion.

Masterclass learning goals

Learning goal: Summarise each measurement occasion separately.

Concept: Grouped statistics and different sample sizes across occasions.

Original task checklist

Task instructions — what the student must do

1. Start a pipe to summarise the sorted pre/post observations.
2. Group the piped observations by occasion.
3. Compute sample sizes and means separately for each occasion.

What this teaches — concept breakdown

mutate() adds a derived column while keeping the observation rows. Subtracting two different scale totals is mathematically possible but may have no validated clinical meaning.

mutate() creates a new column computed from existing columns. The numerical expression cesdTotal - ahiTotal may be useful to demonstrate syntax, but a difference between two unrelated psychometric scales is not automatically a meaningful clinical “sadness” measure.

Step-by-step classroom instructions

1. Create a new sadness column by subtracting ahiTotal from cesdTotal.
2. Print the first rows of the new variable alongside its inputs.

Masterclass learning goals

Learning goal: Create sadness = cesdTotal - ahiTotal as a syntax exercise.

Concept: Creating derived columns; numerical differences versus validated scales.

Original task checklist

Task instructions — what the student must do

1. Create a new sadness column by subtracting ahiTotal from cesdTotal.
2. Print the first rows of the new variable alongside its inputs.

What this teaches — concept breakdown

factor() maps numerical category codes to readable labels. The source codebook defines sex=1 as Female and sex=2 as Male, so the levels and labels must align.

The uploaded codebook specifies sex=1 for Female and sex=2 for Male. A reversed factor label silently inverts subgroup interpretations. Use factor(x, levels=c(1,2), labels=c("Female","Male")) and verify frequency counts after recoding.

Step-by-step classroom instructions

1. Add a correctly labelled sex variable to the pre/post data frame.
2. Use the codebook mapping 1 = Female and 2 = Male in factor().

Masterclass learning goals

Learning goal: Recode sex=1 Female, sex=2 Male correctly.

Concept: Categorical recoding using an authoritative codebook.

Original task checklist

Task instructions — what the student must do

1. Add a correctly labelled sex variable to the pre/post data frame.
2. Use the codebook mapping 1 = Female and 2 = Male in factor().

What this teaches — concept breakdown

Pairing requires the SAME participant to have both a baseline (occasion 0) and posttest (occasion 1) measurement. inner_join retains matched pairs and mutate computes each individual's post-minus-pre change.

For paired change, match the same participant’s baseline and posttest values and calculate the difference within each matched pair. Subtracting the two unpaired occasion means is not generally equivalent. Missing follow-ups may introduce attrition and selection bias.

Step-by-step classroom instructions

1. Filter baseline records for participants observed before the intervention.
2. Filter posttest records for participants observed after the intervention.
3. Join the two sets by participant and compute within-person change.

Masterclass learning goals

Learning goal: Contrast paired change with unpaired occasion means.

Concept: Within-person paired differences, missing pairs and selection bias.

Original task checklist

Task instructions — what the student must do

1. Filter baseline records for participants observed before the intervention.
2. Filter posttest records for participants observed after the intervention.
3. Join the two sets by participant and compute within-person change.

What this teaches — concept breakdown

The AQ-10 practical stores questionnaire responses, item direction (forward/reverse) and scoring rules in three separate CSV files. Punctuation, case and spelling of response labels must agree across files.

AQ responses, question directions and scoring rules are stored in three files. The exact column spellings and key values must be checked before joining. The scoring table connects direction (forward/reverse) and the normalised response category to a 0 or 1 point.

Step-by-step classroom instructions

1. Read the question-format lookup file from the data folder.
2. Read the scoring-key lookup file from the data folder.
3. Read the AQ questionnaire response table from the data folder.

Masterclass learning goals

Learning goal: Load Qformats, Scoring and AQ data.

Concept: Reading a questionnaire and the separate format/scoring lookups.

Original task checklist

Task instructions — what the student must do

1. Read the question-format lookup file from the data folder.
2. Read the scoring-key lookup file from the data folder.
3. Read the AQ questionnaire response table from the data folder.

What this teaches — concept breakdown

pivot_longer() converts ten question columns (Q1 to Q10) into two fields, Question and Response, while repeating Id for every item. This changes shape, not substantive answers.

In wide format, each participant occupies one row and Q1–Q10 are separate columns. In long format, each participant-question response occupies one row, with Question and Response fields. This makes scoring and question-level grouping easier.

Step-by-step classroom instructions

1. Start turning the wide AQ response table into a long table.
2. Select the Q1 through Q10 columns for pivoting.

Masterclass learning goals

Learning goal: Convert Q1–Q10 into Question/Response rows.

Concept: Wide versus long data and person-by-question observations.

Original task checklist

Task instructions — what the student must do

1. Start turning the wide AQ response table into a long table.
2. Select the Q1 through Q10 columns for pivoting.

What this teaches — concept breakdown

The FIRST inner_join attaches forward/reverse question direction by Question. The SECOND join attaches the binary Score by the combined (QFormat, Response) lookup key.

Two matching steps are essential: first attach the forward/reverse direction to each question; then attach its numerical score using the direction and response. Failing to join on both required fields, or mismatching response text, can silently drop or duplicate records.

Step-by-step classroom instructions

1. Join each long-format AQ response with its forward/reverse item direction.
2. Join each response and direction combination to its 0/1 score.

Masterclass learning goals

Learning goal: Join each question to F/R and each response to its point value.

Concept: Two joins, reverse scoring and exact response-string matching.

Original task checklist

Task instructions — what the student must do

1. Join each long-format AQ response with its forward/reverse item direction.
2. Join each response and direction combination to its 0/1 score.

What this teaches — concept breakdown

group_by(Id) makes each participant a separate calculation group. summarise(AQTotal=sum(Score)) adds the ten binary item scores and returns one row per respondent; .groups='drop' clears grouping.

group_by(id) ensures a sum is computed for each respondent rather than over the entire data set. The AQ-10 contains ten binary items, so a complete total has a theoretical range of 0–10. Count missing or unmatched item scores before interpreting totals.

Step-by-step classroom instructions

1. Begin a pipe that calculates a total AQ-10 score for each participant.
2. Group the scored questionnaire rows by participant ID.
3. Sum the scored items separately for each participant.

Masterclass learning goals

Learning goal: Sum ten 0/1 items per participant.

Concept: Participant-level grouping and summing binary questionnaire items.

Original task checklist

Task instructions — what the student must do

1. Begin a pipe that calculates a total AQ-10 score for each participant.
2. Group the scored questionnaire rows by participant ID.
3. Sum the scored items separately for each participant.

What this teaches — concept breakdown

The pipe chains the entire transformation: wide-to-long → join item direction → join scoring key → group by participant → sum item scores. It should reproduce the same totals as the separated steps.

The pipe operator |> passes the output of one transformation into the next. A long pipeline should reproduce the same participant totals as separate intermediate objects. Compare sorted scores for equality and debug each transformation stage if results disagree.

Step-by-step classroom instructions

1. Start a single piped workflow using the original AQ response table.
2. Reshape Q1–Q10 from wide to long format in the pipeline.
3. Join the reshaped rows to the question format key, then continue scoring.

Masterclass learning goals

Learning goal: Recreate scoring through a chain of operations.

Concept: Pipes, function composition and equivalent scoring workflows.

Original task checklist

Task instructions — what the student must do

1. Start a single piped workflow using the original AQ response table.
2. Reshape Q1–Q10 from wide to long format in the pipeline.
3. Join the reshaped rows to the question format key, then continue scoring.

What this teaches — concept breakdown

mean and sd describe the distribution of participants' summed AQ scores. AQTotal > 6 is a screening-threshold count, not a diagnosis.

The mean describes central tendency and SD describes participant-to-participant dispersion. An illustrative threshold above 6 is a screening criterion, not a diagnosis. AQ-10 is discrete and bounded; do not assume it has a normal distribution just because mean and SD are reported.

Step-by-step classroom instructions

1. Calculate the mean, standard deviation and range of AQ totals.
2. Print the resulting descriptive summary.

Masterclass learning goals

Learning goal: Compute mean/SD/range; list people over six.

Concept: Means, standard deviations, scores and screening thresholds.

Original task checklist

Task instructions — what the student must do

1. Calculate the mean, standard deviation and range of AQ totals.
2. Print the resulting descriptive summary.

What this teaches — concept breakdown

A histogram displays the frequency distribution of AQTotal. Binwidth=1 and boundary=-0.5 align bins to integer scores from 0 to 10.

A histogram shows frequency across score bins. AQ totals are integer-valued, so binwidth=1 with appropriate boundaries avoids splitting equal integer scores across arbitrary intervals. The bar heights describe the sample, not the general population.

Step-by-step classroom instructions

1. Define a ggplot object for the AQ total-score distribution.
2. Add a histogram layer with a bin width of one point.
3. Display the completed AQ histogram.

Masterclass learning goals

Learning goal: Plot the integer-valued AQTotal distribution with bins of one.

Concept: Histograms for discrete integer-valued scores.

Original task checklist

Task instructions — what the student must do

1. Define a ggplot object for the AQ total-score distribution.
2. Add a histogram layer with a bin width of one point.
3. Display the completed AQ histogram.

What this teaches — concept breakdown

Sample variance expresses squared dispersion (units of Score²); sample standard deviation retains score units. Standard error measures estimated uncertainty in the sample mean, not individual differences.

Variance is expressed in squared units; SD returns to the original score units. The standard error of a sample mean is approximately SD/sqrt(n) for independent identically distributed observations. SE quantifies precision of the estimated mean, not participant variability.

Step-by-step classroom instructions

1. Compute the sample variance of the AQ total scores.
2. Compute the standard error using SD divided by the square root of n.
3. Print variance and standard error with meaningful labels.

Masterclass learning goals

Learning goal: Explain why SD measures spread while SE relates to the sample mean.

Concept: Variance, standard deviation, standard error and missingness.

Original task checklist

Task instructions — what the student must do

1. Compute the sample variance of the AQ total scores.
2. Compute the standard error using SD divided by the square root of n.
3. Print variance and standard error with meaningful labels.

What this teaches — concept breakdown

Histogram binwidth controls aggregation granularity. A five-point bin gives a broad overview, whereas a one-point bin reveals local discreteness and noise.

Histograms reveal concentration, skewness and possible unusual values. Reducing binwidth creates more, narrower bins; it does not change the data. The horizontal scale and sample size should remain clear when comparing visual appearances.

Step-by-step classroom instructions

1. Start a ggplot of the distribution of AHI scores.
2. Add a histogram with a chosen bin width, then compare alternatives.
3. Print the finished histogram.

Masterclass learning goals

Learning goal: Create an AHI histogram; compare bins 5 and 1.

Concept: Distribution plots and the consequences of bin-width choices.

Original task checklist

Task instructions — what the student must do

1. Start a ggplot of the distribution of AHI scores.
2. Add a histogram with a chosen bin width, then compare alternatives.
3. Print the finished histogram.

What this teaches — concept breakdown

A scatterplot maps each record to (AHI, CES-D). Mapping colour to sex_label adds a legend and groups without changing the underlying x/y coordinates.

A scatterplot shows paired observations for two numeric variables. Mapping colour inside aes() creates groups and a legend; setting colour outside aes() gives all points one colour. Correlation visible in a plot does not establish cause and effect. Verify categorical codebook labels.

Step-by-step classroom instructions

1. Create meaningful factor labels for the sex variable using the codebook.
2. Start a scatterplot of AHI against CES-D, coloured by the sex labels.

Masterclass learning goals

Learning goal: Show AHI–CES-D association, then colour points by sex.

Concept: Scatterplots, two numeric variables and categorical colour legends.

Original task checklist

Task instructions — what the student must do

1. Create meaningful factor labels for the sex variable using the codebook.
2. Start a scatterplot of AHI against CES-D, coloured by the sex labels.

What this teaches — concept breakdown

A boxplot summarises median, quartiles and whiskers; a violin plot displays a smoothed distribution density. Jittered points preserve visibility of individual observations.

A standard boxplot displays the median and quartiles, with whiskers following a rule such as 1.5 times the IQR. It is not a mean ± SD plot. A violin plot displays a smoothed density estimate, whose shape depends on smoothing and sample size; raw points can help.

Step-by-step classroom instructions

1. Create a ggplot object that compares the outcome across groups.
2. Add a boxplot layer to display medians and quartiles.
3. Create a separate violin plot to show the shape of group distributions.

Masterclass learning goals

Learning goal: Recreate boxplot and add violin overlay; describe what each reveals.

Concept: Boxplots, violin plots, medians and interquartile ranges.

Original task checklist

Task instructions — what the student must do

1. Create a ggplot object that compares the outcome across groups.
2. Add a boxplot layer to display medians and quartiles.
3. Create a separate violin plot to show the shape of group distributions.

What this teaches — concept breakdown

The summary table calculates means before plotting. geom_col() treats its y value as a precomputed height, unlike geom_bar() which counts observations by default.

If means have already been calculated in a summary table, geom_col() displays those values as bar heights. geom_bar() instead counts rows by default. Grouping by intervention provides distinct means but does not prove any intervention caused a difference.

Step-by-step classroom instructions

1. Create readable labels for the intervention groups.
2. Start a pipeline from the full data to calculate group means.
3. Group the data by intervention before calculating each mean.
4. Start a bar chart of the precomputed group means.

Masterclass learning goals

Learning goal: Compute means, then plot overall and grouped means.

Concept: Group means and bar plots of precomputed summaries.

Original task checklist

Task instructions — what the student must do

1. Create readable labels for the intervention groups.
2. Start a pipeline from the full data to calculate group means.
3. Group the data by intervention before calculating each mean.
4. Start a bar chart of the precomputed group means.

What this teaches — concept breakdown

A group is tracked through time using colour and group aesthetics. The vertical bars run from mean − SD to mean + SD; they describe dispersion, not a 95% confidence interval.

A line plot connecting occasion-level means visualises a sequence of group summaries. These are not necessarily trajectories of the same individual. Error bars labelled ±1 SD describe within-group variability and are not the same as standard errors or 95% confidence intervals.

Step-by-step classroom instructions

1. Start a plot of group mean scores across measurement occasions.
2. Add a line layer connecting the occasion means.
3. Add error bars for plus/minus one standard deviation, not a confidence interval.

Masterclass learning goals

Learning goal: Create line plot with 24–120 display range and ±1 SD bars.

Concept: Time-series group summaries and standard-deviation error bars.

Original task checklist

Task instructions — what the student must do

1. Start a plot of group mean scores across measurement occasions.
2. Add a line layer connecting the occasion means.
3. Add error bars for plus/minus one standard deviation, not a confidence interval.

What this teaches — concept breakdown

Themes affect styling, titles communicate meaning and facet_wrap() creates panels by category. Geometric point aesthetics such as shape and alpha change appearance, not the observations.

ggplot objects combine datasets, aesthetic mappings, geometric layers, scales and themes. A theme changes presentation rather than numerical values; facet_wrap splits one plot into group panels. Always label axes, units and the meaning of visual encodings.

Step-by-step classroom instructions

1. Print the existing scatterplot using a classic ggplot theme.
2. Print a faceted version of the scatterplot, separated by the sex labels.

Masterclass learning goals

Learning goal: Compare themes, add titles, facet by sex, style points.

Concept: ggplot2 layers, themes, faceting and graphical annotations.

Original task checklist

Task instructions — what the student must do

1. Print the existing scatterplot using a classic ggplot theme.
2. Print a faceted version of the scatterplot, separated by the sex labels.

What this teaches — concept breakdown

cor() gives a descriptive linear association coefficient for AHI and CES-D using complete cases. The second join illustrates linking two other tables by participant id.

The Pearson correlation coefficient describes the direction and strength of a linear association, typically between -1 and 1. It does not identify a causal mechanism. ATSQ is a separate scale whose substantive meaning has not been verified in these teaching materials.

Step-by-step classroom instructions

1. Calculate the correlation between AHI and CES-D with missing-data handling.
2. Import the separately supplied ATSQ scores as a data frame.
3. Join ATSQ scores to participant information by participant ID.

Masterclass learning goals

Learning goal: Distinguish graphical association from causal effect; inspect separate ATSQ join.

Concept: Correlation is association, not causation; use caution with ATSQ.

Original task checklist

Task instructions — what the student must do

1. Calculate the correlation between AHI and CES-D with missing-data handling.
2. Import the separately supplied ATSQ scores as a data frame.
3. Join ATSQ scores to participant information by participant ID.

What this teaches — concept breakdown

The mean gives the centre of a numerical sample; SD estimates trial-to-trial dispersion; SE estimates the precision of the sample mean under independence.

SD measures the spread of observations; the standard error (SE) estimates uncertainty in the mean. Under standard independent-sampling assumptions SE≈SD/sqrt(n); as n increases, the SE of a mean tends to decrease. This does not resolve sampling bias.

Step-by-step classroom instructions

1. Create a short fictional sample of reaction times in milliseconds.
2. Calculate how many reaction times are in the sample.
3. Calculate the sample mean reaction time.

Masterclass learning goals

Learning goal: Compare SD of observed times and standard error of their mean.

Concept: Sampling, sample variability, SD and SE of the sample mean.

Original task checklist

Task instructions — what the student must do

1. Create a short fictional sample of reaction times in milliseconds.
2. Calculate how many reaction times are in the sample.
3. Calculate the sample mean reaction time.

What this teaches — concept breakdown

Bootstrap sampling with replacement builds a distribution of resampled means. Larger resample sizes usually produce narrower distributions; percentile intervals use quantiles of resampled statistics.

Bootstrap resampling draws observations with replacement from the observed sample. Sampling n=4 versus n=10 illustrates different variability of resampled means, whereas a bootstrap percentile interval for the observed n=12 mean should resample n=12. Set the seed for reproducibility.

Step-by-step classroom instructions

1. Set the random seed so the resampling results can be reproduced.
2. Simulate 1,000 resampled means using a sample size of four.
3. Simulate 1,000 resampled means using a sample size of ten.
4. Simulate a bootstrap distribution using the observed sample size.

Masterclass learning goals

Learning goal: Observe spread of mean for resampling n=4 vs n=10; use n=12 for the bootstrap CI.

Concept: Resampling distributions, reproducibility and percentile bootstrap intervals.

Original task checklist

Task instructions — what the student must do

1. Set the random seed so the resampling results can be reproduced.
2. Simulate 1,000 resampled means using a sample size of four.
3. Simulate 1,000 resampled means using a sample size of ten.
4. Simulate a bootstrap distribution using the observed sample size.

What this teaches — concept breakdown

par(mfrow=c(1,2)) divides a base-R graphics device into two panels. The histograms compare distributions of resampled means at two sample sizes.

Compare the widths of the simulated mean distributions rather than only their centres. Repeated means based on ten values usually vary less than those based on four. Resampling one empirical sample cannot guarantee that it represents a wider population.

Step-by-step classroom instructions

1. Arrange two Base R plots side by side using par().
2. Plot the resampled means for samples of size four.
3. Plot the resampled means for samples of size ten, then compare spreads.

Masterclass learning goals

Learning goal: Explain why the n=10 distribution is usually narrower.

Concept: Visual comparison of sampling distributions and mean precision.

Original task checklist

Task instructions — what the student must do

1. Arrange two Base R plots side by side using par().
2. Plot the resampled means for samples of size four.
3. Plot the resampled means for samples of size ten, then compare spreads.

What this teaches — concept breakdown

A one-sample t statistic compares the observed mean to hypothesised mu0 in units of estimated standard errors. t.test() also returns a p-value and confidence interval under modelling assumptions.

Under H0: μ=600 ms, the one-sample t statistic is (sample_mean−600)/(sample_SD/sqrt(n)) with df=n−1. The two-sided p value measures how extreme the observed statistic is under H0; it is not the probability that H0 is true.

Step-by-step classroom instructions

1. Set the null-hypothesis population mean to 600 milliseconds.
2. Calculate the one-sample t statistic from the observed mean, SD and n.
3. Run t.test() against the same null hypothesis and compare the outputs.

Masterclass learning goals

Learning goal: Compute t manually, compare with t.test, interpret p and confidence interval.

Concept: Null hypothesis, t statistic, degrees of freedom and p value.

Original task checklist

Task instructions — what the student must do

1. Set the null-hypothesis population mean to 600 milliseconds.
2. Calculate the one-sample t statistic from the observed mean, SD and n.
3. Run t.test() against the same null hypothesis and compare the outputs.

What this teaches — concept breakdown

A t confidence interval uses the critical t quantile times the standard error. Cohen's d scales the observed difference from mu0 by sample SD; all.equal() checks numerical agreement with t.test().

A two-sided 95% t confidence interval is sample_mean ± t_critical × SE. It combines sample variability, sample size and a t quantile. Cohen’s d standardises the observed mean difference relative to sample SD; practical importance and statistical significance are distinct.

Step-by-step classroom instructions

1. Find the critical t value for a two-sided 95% confidence interval.
2. Calculate the confidence interval manually from the sample mean and SE.
3. Calculate Cohen’s d to describe standardised effect size.

Masterclass learning goals

Learning goal: Check 95% t interval, and distinguish statistical and practical significance.

Concept: A 95% t confidence interval, effect size and practical significance.

Original task checklist

Task instructions — what the student must do

1. Find the critical t value for a two-sided 95% confidence interval.
2. Calculate the confidence interval manually from the sample mean and SE.
3. Calculate Cohen’s d to describe standardised effect size.

What this teaches — concept breakdown

lm() estimates a linear relationship between CES-D and AHI. Coefficients, correlation r and R² summarise an association; repeated measures violate a simple independent-row assumption.

A linear-regression slope estimates the expected change in a response for a one-unit increase in a predictor, conditional on the model. Inspect residual patterns and independence assumptions. Repeated records for one participant are not independent, so a simple model here is only a mechanical illustration.

Step-by-step classroom instructions

1. Fit a linear regression model predicting one variable from another.
2. Print the model summary, including coefficients and R-squared.
3. Print the correlation coefficient for comparison with the regression.

Masterclass learning goals

Learning goal: Interpret slope, R^2; explain confounding and dependence from repeated observations.

Concept: Linear regression, model coefficients, R-squared and assumptions.

Original task checklist

Task instructions — what the student must do

1. Fit a linear regression model predicting one variable from another.
2. Print the model summary, including coefficients and R-squared.
3. Print the correlation coefficient for comparison with the regression.