R Command Index

Where to review the R patterns used in the workbook

A running reference for R commands and the chapter where each one is introduced.

Use this page when you recognize a command but need a reminder about what it does. The index records where each command is first explained. Later chapters may reuse a command without teaching it again.

Start Here Commands

Command or pattern	What it does	First explained
`<-`	Stores a result in a named object.	Run Your First Script
`#`	Adds a comment for readers. R ignores the rest of that line.	Run Your First Script
`\|>`	Passes the result of one step into the next step.	Run Your First Script
`nrow()`	Counts observations in a data frame.	Run Your First Script
`ncol()`	Counts variables in a data frame.	Run Your First Script
`names()`	Lists variable names in a data frame.	Run Your First Script
`count()`	Counts how often values or categories occur.	Run Your First Script

How To Read The Examples

R functions use parentheses:

function_name(object, option = value)

Pipes organize a sequence from left to right:

data_object |>
  first_step() |>
  next_step()

Assignment saves a result so it can be inspected or reused:

result_name <- data_object |>
  analysis_step()

Chapter Commands

Chapters 1 And 2: Inspect, Label, Tabulate, And Graph

Command or pattern	What it does	First explained
`source()`	Runs code saved in another script.	Chapter 1
`dim()`	Reports observations and variables together.	Chapter 1
`factor()`	Gives category codes readable labels and an order.	Chapter 1
`$`	Selects one variable inside a data frame.	Chapter 1
`levels()`	Shows the labels and order of a factor.	Chapter 1
`mutate()`	Changes or creates variables.	Chapter 1
`ggplot()` + `aes()`	Starts a graph and maps variables to visual positions.	Chapter 1
`geom_col()`	Creates bars from values already calculated in a table.	Chapter 1
`labs()`	Adds readable graph labels.	Chapter 1
`class()`	Shows how R represents an object or variable.	Chapter 2
`cumsum()`	Creates a running total.	Chapter 2
`drop_na()`	Omits observations missing specified values from an analysis.	Chapter 2
`geom_histogram()`	Groups quantitative scores into intervals and graphs their frequencies.	Chapter 2
`coord_flip()`	Turns a graph so long category labels are easier to read.	Chapter 2

Chapters 3-5: Describe Distributions

Command or pattern	What it does	First explained
`filter()`	Keeps observations that meet a condition.	Chapters 3 and 4
`group_by()`	Requests later calculations separately for each group.	Chapters 3 and 4
`summarise()`	Reduces observations to named summary values.	Chapters 3 and 4
`n()`	Counts observations inside a summary.	Chapters 3 and 4
`mean()`, `median()`, `sd()`	Calculate center and variability.	Chapters 3 and 4
`min()`, `max()`	Find the observed endpoints.	Chapters 3 and 4
`sum()`, `is.na()`	Count missing values when they matter to a summary.	Chapters 3 and 4
`facet_wrap()`	Repeats a graph in separate group panels.	Chapters 3 and 4
`statistical_mode()`	Returns the most common observed value. This helper is supplied by the setup file.	Chapters 3 and 4
`head()`	Displays the first observations or values in an object.	Chapter 5
`quantile()`	Calculates quartiles or other percentiles.	Chapter 5
`IQR()`	Calculates the interquartile range.	Chapter 5
`geom_boxplot()`	Creates a boxplot.	Chapter 5
`independent_cohens_d()`	Calculates Cohen’s d for two independent groups. This helper is supplied by the setup file.	Chapter 5

Chapters 6, 9, And 10: Correlation And t Tests

Command or pattern	What it does	First explained
`select()`	Keeps specified variables.	Chapter 6
`cor()`	Calculates Pearson correlation coefficients.	Chapter 6
`outcome ~ predictor`	Identifies the outcome and predictor in an R model formula.	Chapter 6
`lm()`	Fits a linear model.	Chapter 6
`coefficients()`	Shows a model’s intercept and slope.	Chapter 6
`geom_point()`, `geom_smooth()`	Add observed score pairs and a fitted line.	Chapter 6
`t.test()`	Conducts a t test.	Chapter 9
`sample_skewness()`	Calculates sample skewness. This helper is supplied by the setup file.	Chapter 9
`cohens_d()`	Divides a mean difference by a standard deviation. This helper is supplied by the setup file.	Chapter 9
`mu =`	Supplies the comparison value for a one-sample test.	Chapter 9
`alternative = "two.sided"`	Requests a non-directional test.	Chapter 9
`$conf.int`	Retrieves the confidence interval stored in a test object.	Chapter 9
`pull()`	Takes one variable from a data frame for use by a function.	Chapter 10
`shapiro.test()`	Checks whether scores differ significantly from a normal distribution.	Chapter 10
`var.test()`	Conducts an F test comparing two group variances.	Chapter 10
`var.equal = TRUE`	Requests Student’s independent-samples t test.	Chapter 10
`paired = TRUE`	Identifies two related sets of scores.	Chapter 10

Chapters 11-14: ANOVA And Chi-Square

Command or pattern	What it does	First explained
`aov()`	Fits an analysis-of-variance model.	Chapter 11
`summary(model)`	Prints the ANOVA table stored in a model object.	Chapter 11
`residuals()`	Retrieves observed-minus-predicted differences from a model.	Chapter 11
`bartlett.test()`	Checks whether three or more group variances differ.	Chapter 11
`qt()`	Supplies the critical t value used to calculate a confidence interval.	Chapter 11
`anova_eta_squared()`	Calculates eta squared for a one-way ANOVA. This helper is supplied by the setup file.	Chapter 11
`TukeyHSD()`	Conducts adjusted pairwise comparisons.	Chapter 11
`tukey_cohens_d()`	Calculates Cohen’s d for Tukey comparisons. This helper is supplied by the setup file.	Chapter 11
`geom_errorbar()`	Adds confidence intervals to a graph.	Chapter 11
`row_number()`	Creates a participant label from observation order.	Chapter 12
`pivot_longer()`	Reshapes repeated scores from wide to long format.	Chapter 12
`repeated_eta_squared()`	Calculates partial eta squared for the repeated condition. This helper is supplied by the setup file.	Chapter 12
`repeated_pairwise_tests()`	Compares each pair of repeated scores and reports mean differences, Tukey-adjusted p values, and Cohen’s d. This helper is supplied by the setup file.	Chapter 12
`interaction()`	Combines grouping variables for an assumption check.	Chapter 13
`predictor_1 * predictor_2`	Requests two main effects and their interaction.	Chapter 13
`two_way_anova_table()`	Reports two-way tests with partial eta squared. This helper is supplied by the setup file.	Chapter 13
`geom_line()`	Connects means in an interaction plot.	Chapter 13
`table()`	Creates observed frequency or contingency tables.	Chapter 14
`chisq.test()`	Conducts chi-square tests.	Chapter 14
`test_object$expected`	Retrieves expected counts.	Chapter 14
`cramers_v()`	Calculates Cramer’s V. This helper is supplied by the setup file.	Chapter 14
`prop.table()`	Converts counts to proportions.	Chapter 14
`p = c(...)`	Supplies the expected proportions for a goodness-of-fit test.	Chapter 14

Chapter 15: Nonparametric Statistics

Command or pattern	What it does	First explained
`wilcox.test(outcome ~ group)`	Conducts a Mann-Whitney U test for two independent groups.	Chapter 15
`wilcox.test(x, y, paired = TRUE)`	Conducts a Wilcoxon signed-rank test for paired scores.	Chapter 15
`rank_biserial_independent()`	Calculates rank-biserial correlation for two independent groups. This helper is supplied by the setup file.	Chapter 15
`rank_biserial_paired()`	Calculates rank-biserial correlation for paired scores. This helper is supplied by the setup file.	Chapter 15
`kruskal.test()`	Conducts a Kruskal-Wallis test.	Chapter 15
`kruskal_epsilon_squared()`	Calculates epsilon squared for a Kruskal-Wallis test. This helper is supplied by the setup file.	Chapter 15
`dscfAllPairsTest()`	Conducts DSCF post hoc comparisons after a significant Kruskal-Wallis test.	Chapter 15
`cor.test(..., method = "spearman")`	Conducts a Spearman rank correlation.	Chapter 15