Package {lsr}

Title:	Companion to "Learning Statistics with R"
Version:	1.0.0
Description:	A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. Accompanies the textbook "Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners" by Navarro.
License:	MIT + file LICENSE
URL:	https://github.com/djnavarro/lsr, https://lsr.djnavarro.net/
BugReports:	https://github.com/djnavarro/lsr/issues
Encoding:	UTF-8
Language:	en-GB
Imports:	graphics, grDevices, methods, stats
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), tibble, withr
Config/testthat/edition:	3
Config/Needs/website:	djnavarro/waeponwifestre
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-07-11 17:51:16 UTC; danielle
Author:	Danielle Navarro [aut, cre]
Maintainer:	Danielle Navarro <djnavarro@protonmail.com>
Repository:	CRAN
Date/Publication:	2026-07-11 18:30:11 UTC

Mean absolute deviation

Description

Calculates the mean absolute deviation from the sample mean.

Usage

aad(x, na.rm = FALSE)

Arguments

Details

Computes the average of the absolute differences between each observation and the sample mean of x, i.e. mean(abs(x - mean(x))).

Value

A single number giving the mean absolute deviation.

See Also

mean, sd, var

Examples

x <- c(1, 3, 6)
aad(x)

# missing values
x <- c(1, 3, NA, 6)
aad(x) # returns NA
aad(x, na.rm = TRUE) # ignores the missing value

Chi-square test of association / independence

Description

Runs a chi-square test to check whether two categorical variables are independent of one another.

Usage

associationTest(formula, data = NULL)

Arguments

Details

The test checks whether two categorical variables are statistically independent. Both variables must be factors, and the formula must be one-sided with exactly two variables, e.g. ~gender + answer.

Missing values are removed before the test is run, and a warning is issued if any cases are dropped. When both variables have only two levels, Yates' continuity correction is applied automatically to the chi-squared statistic (though not to the Cramer's V effect size).

If either variable has unused factor levels (levels with zero observed cases), a warning is issued. Those levels are included in the contingency table with zero observed cases, which may give misleading results. Call droplevels on the data first if this is not intended.

Value

Prints a summary of the test showing the variable names, null and alternative hypotheses, observed and expected frequency tables, test results (chi-square statistic, degrees of freedom, p-value), and Cramer's V as a measure of effect size. The underlying results are also returned as a list, so the output can be assigned to a variable and inspected if needed.

See Also

chisq.test, goodnessOfFitTest, cramersV

Examples

df <- data.frame(
  gender = factor(c("male", "male", "male", "male", "female", "female", "female")),
  answer = factor(c("heads", "heads", "heads", "heads", "tails", "tails", "heads"))
)

associationTest(~ gender + answer, df)

Grouped bar plots with error bars

Description

Creates a bar plot showing group means with error bars showing confidence intervals, broken down by one or two grouping factors.

Usage

bars(
  formula,
  data = NULL,
  heightFun = mean,
  errorFun = ciMean,
  yLabel = NULL,
  xLabels = NULL,
  main = "",
  ylim = NULL,
  barFillColour = NULL,
  barLineWidth = 2,
  barLineColour = "black",
  barSpaceSmall = 0.2,
  barSpaceBig = 1,
  legendLabels = NULL,
  legendDownShift = 0,
  legendLeftShift = 0,
  errorBarLineWidth = 1,
  errorBarLineColour = "grey40",
  errorBarWhiskerWidth = 0.2
)

Arguments

Details

Plots group means (or the output of heightFun) with error bars (or the output of errorFun) for one or two grouping factors. When two grouping factors are given, group1 determines the primary x-axis grouping and group2 determines the sub-grouping shown as clusters of bars with a legend.

Missing values are removed with a warning. At least 2 complete cases are required per group.

Value

Invisibly returns a data frame containing the factor levels, group summary values, and error bar bounds. This function is primarily used for its side effect of drawing the plot.

See Also

barplot, ciMean

Examples

# one grouping factor
df <- data.frame(
  outcome = c(3, 4, 5, 2, 4, 6, 5, 7, 8),
  group = factor(c("a", "a", "a", "b", "b", "b", "c", "c", "c"))
)
bars(outcome ~ group, data = df)

# two grouping factors
df2 <- data.frame(
  outcome = c(3, 4, 5, 2, 4, 6, 5, 7, 8, 4, 3, 6),
  group1  = factor(rep(c("a", "b"), each = 6)),
  group2  = factor(rep(c("x", "y", "z"), times = 4))
)
bars(outcome ~ group1 + group2, data = df2)

Confidence interval around the mean

Description

Calculates a confidence interval for the mean of a numeric variable (or each numeric variable in a data frame or matrix).

Usage

ciMean(x, conf = 0.95, na.rm = FALSE)

Arguments

Details

Calculates a confidence interval for the mean under the standard assumption that the data are normally distributed. When x is a matrix or data frame, a separate interval is computed for each column. Non-numeric columns in a data frame produce NA rows in the output.

Value

A matrix with one row per variable and two columns giving the lower and upper bounds of the confidence interval. Column names reflect the confidence level (e.g., "2.5%" and "97.5%" for a 95% interval).

See Also

t.test, mean

Examples

x <- c(1, 3, 6)
ciMean(x) # 95% confidence interval
ciMean(x, conf = 0.80) # 80% confidence interval

# for comparison: equivalent result via lm
confint(lm(x ~ 1))

# missing values
x <- c(1, 3, NA, 6)
ciMean(x, na.rm = TRUE)

Cohen's d

Description

Calculates the Cohen's d measure of effect size.

Usage

cohensD(
  x = NULL,
  y = NULL,
  data = NULL,
  method = "pooled",
  mu = 0,
  formula = NULL
)

Arguments

Details

The function can be used in two main ways. For two separate numeric vectors, call cohensD(x = group1, y = group2). For data in a data frame with a grouping variable, use a formula: cohensD(outcome ~ group, data = mydata).

The method argument controls how the standard deviation is estimated:

"pooled"

Pooled SD from both groups (matches Student's t-test). This is the default.

"corrected"

Bias-corrected version of "pooled", multiplied by (N-3)/(N-2.25).

"raw"

Like "pooled" but divides by N rather than N-2.

"x.sd"

SD of the first group only.

"y.sd"

SD of the second group only.

"unequal"

Square root of the average of the two group variances (matches Welch's t-test).

"paired"

SD of the within-person differences (matches a paired-samples t-test).

For a one-sample calculation, supply only x (and optionally mu). The result is abs(mean(x) - mu) / sd(x).

Value

A single positive number: the magnitude of the effect size d. The sign of the mean difference is dropped, so the value is always zero or greater.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Examples

# two independent groups supplied as separate vectors
gradesA <- c(55, 65, 65, 68, 70) # 5 students with teacher A
gradesB <- c(56, 60, 62, 66) # 4 students with teacher B
cohensD(gradesA, gradesB)

# the same comparison using a formula and a data frame
grade <- c(55, 65, 65, 68, 70, 56, 60, 62, 66)
teacher <- c("A", "A", "A", "A", "A", "B", "B", "B", "B")
cohensD(grade ~ teacher)

# paired samples: use method = "paired" (SD of within-person differences)
pre <- c(100, 122, 97, 25, 274)
post <- c(104, 125, 99, 29, 277)
cohensD(pre, post, method = "paired")

# equivalent one-sample calculation on the difference scores
cohensD(post - pre)

# formula interface with a data frame
exams <- data.frame(grade, teacher)
cohensD(grade ~ teacher, data = exams)

Copy a vector into a matrix

Description

Creates a matrix by stacking multiple copies of a vector as rows (rowCopy) or as columns (colCopy).

Usage

colCopy(x, times, dimnames = NULL)

rowCopy(x, times, dimnames = NULL)

Arguments

Details

These functions are shortcuts for building a matrix where every row (or every column) is the same vector. They are equivalent to calling matrix with appropriate byrow and dimension arguments.

Value

For rowCopy, a matrix with times rows and length(x) columns, where each row is x. For colCopy, a matrix with length(x) rows and times columns, where each column is x.

See Also

matrix, rbind, cbind

Examples

x <- c(3, 1, 4, 1, 5)

# stack x as rows
rowCopy(x, 4)

# stack x as columns
colCopy(x, 4)

# with custom dimension names
dnames <- list(rows = c("r1", "r2", "r3"), cols = c("c1", "c2", "c3", "c4", "c5"))
rowCopy(x, 3, dnames)

Correlation matrices

Description

Computes a correlation matrix, optionally with hypothesis tests and corrections for multiple comparisons.

Usage

correlate(
  x,
  y = NULL,
  test = FALSE,
  corr.method = "pearson",
  p.adjust.method = "holm"
)

Arguments

Details

Calculates a correlation matrix between all pairs of numeric variables. If only x is supplied, all pairwise correlations among the variables in x are computed. If both x and y are supplied, variables in x are correlated with variables in y.

Non-numeric variables (e.g., factors) are silently ignored: they appear in the output with NA in place of correlation values. This makes it convenient to pass an entire data frame without first removing categorical columns.

When test = TRUE, hypothesis tests are run for every pair of numeric variables. To reduce the risk of false positives from testing many pairs at once, p-values are adjusted using the Holm method by default. See p.adjust for other available methods.

Missing data are handled using pairwise complete cases, so sample sizes may differ across pairs of variables. If a particular pair of variables has too few complete observations for cor.test to run, the corresponding cell in the correlation matrix is left as NA rather than causing the whole call to fail.

Value

Prints the correlation matrix. If test = TRUE, also prints a matrix of adjusted p-values and a matrix of sample sizes. The results are also returned as a list with four elements: correlation (the correlation matrix), p.value (the matrix of p-values), sample.size (the matrix of sample sizes), and args (a record of the options used). The list can be assigned to a variable and inspected if needed.

See Also

cor, cor.test, p.adjust

Examples

# data frame with factors and missing values
data <- data.frame(
  anxiety = c(1.31, 2.72, 3.18, 4.21, 5.55, NA),
  stress = c(2.01, 3.45, 1.99, 3.25, 4.27, 6.80),
  depression = c(2.51, 1.77, 3.34, 5.83, 9.01, 7.74),
  happiness = c(4.02, 3.66, 5.23, 6.37, 7.83, 1.18),
  gender = factor(c("male", "female", "female", "male", "female", "female")),
  ssri = factor(c("no", "no", "no", NA, "yes", "yes"))
)

# Pearson correlation matrix (the default)
correlate(data)

# Spearman correlations
correlate(data, corr.method = "spearman")

# correlate two subsets of variables with each other
nervous <- data[, c("anxiety", "stress")]
happy <- data[, c("happiness", "depression")]
correlate(nervous, happy)

# include Holm-corrected p-values and sample sizes
correlate(data, test = TRUE)

Cramer's V

Description

Calculates Cramer's V, a measure of the strength of association for chi-square tests.

Usage

cramersV(...)

Arguments

Details

Cramer's V summarises the strength of association from a chi-square test. It is appropriate for both tests of association (two categorical variables) and goodness of fit tests (one variable versus hypothesised probabilities). Values range from 0 (no association) to 1 (perfect association).

Yates' continuity correction is never applied, regardless of the table dimensions. This is intentional: applying the correction reduces the chi-squared statistic, which causes V to fall below 1 even for perfectly associated 2x2 tables — inconsistent with its definition as an effect size on the [0, 1] scale.

Value

A single number giving the value of Cramer's V.

See Also

chisq.test, associationTest, goodnessOfFitTest

Examples

# frequency table for two groups, each choosing from three options
condition1 <- c(30, 20, 50)
condition2 <- c(35, 30, 35)
X <- cbind(condition1, condition2)
rownames(X) <- c("choice1", "choice2", "choice3")

# chi-square test of association
chisq.test(X)

# effect size estimate
cramersV(X)

Effect size for ANOVAs

Description

Calculates eta-squared and partial eta-squared effect sizes for an analysis of variance.

Usage

etaSquared(x, type = 2, anova = FALSE)

Arguments

Details

Calculates eta-squared and partial eta-squared, two commonly used measures of effect size in analysis of variance. The input x should be an ANOVA fitted with aov.

For unbalanced designs, Type II sums of squares (type = 2) are recommended and are the default, consistent with the Anova function in the car package. Type I (type = 1) matches the output of anova but tests hypotheses that are often not of interest in unbalanced designs. Type III (type = 3) is also available.

Value

A matrix with one row per term in the ANOVA model and columns for eta-squared (eta.sq) and partial eta-squared (eta.sq.part). If anova = TRUE, additional columns show the sums of squares, mean squares, degrees of freedom, F-statistics, and p-values.

See Also

aov, summary.aov

Examples

outcome <- c(1.4, 2.1, 3.0, 2.1, 3.2, 4.7, 3.5, 4.5, 5.4)
treatment1 <- factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3))

# one-way ANOVA
anova1 <- aov(outcome ~ treatment1)
summary(anova1)
etaSquared(anova1)

# include the full ANOVA table
etaSquared(anova1, anova = TRUE)

# two-way ANOVA
treatment2 <- factor(c(1, 2, 3, 1, 2, 3, 1, 2, 3))
anova2 <- aov(outcome ~ treatment1 + treatment2)
etaSquared(anova2)

Expand factors to a set of contrasts

Description

Replaces each factor variable in a data frame with its contrast-coded columns, leaving numeric variables unchanged.

Usage

expandFactors(data, ...)

Arguments

Details

Each factor in data is replaced by the numeric contrast columns that model.matrix would generate for that factor (using treatment contrasts by default). Numeric variables pass through unchanged. This can be helpful when illustrating the connection between ANOVA and regression.

Value

A data frame with factor columns replaced by numeric contrast columns.

See Also

model.matrix, contrasts

Examples

grading <- data.frame(
  teacher = factor(c("Amy", "Amy", "Ben", "Ben", "Cat")),
  gender  = factor(c("male", "female", "female", "male", "male")),
  grade   = c(75, 80, 45, 50, 65)
)

# expand using the default contrasts (treatment contrasts)
expandFactors(grading)

# specify different contrasts via contrasts.arg
my.contrasts <- list(teacher = "contr.helmert", gender = "contr.treatment")
expandFactors(grading, contrasts.arg = my.contrasts)

Chi-square goodness of fit test

Description

Runs a chi-square goodness of fit test to check whether the observed frequencies in a categorical variable match a set of hypothesised probabilities.

Usage

goodnessOfFitTest(x, p = NULL)

Arguments

Details

The test checks whether the observed frequencies for a categorical variable are consistent with the probabilities specified in p.

Missing values in x are removed before the test is run, and a warning is issued if any cases are dropped. If the probabilities in p do not sum exactly to 1, they are rescaled with a warning.

If x has unused factor levels (levels with zero observed cases), a warning is issued. Those levels are included in the test with zero observed cases, which changes the degrees of freedom and may give misleading results. Call droplevels on the data first if this is not intended.

Value

Prints a summary of the test showing the variable name, null and alternative hypotheses, a table of observed frequencies, expected frequencies, and hypothesised probabilities, and the test results (chi-square statistic, degrees of freedom, p-value). The underlying results are also returned as a list, so the output can be assigned to a variable and inspected if needed.

See Also

chisq.test, associationTest, cramersV

Examples

# raw data
gender <- factor(
  c(
    "male", "male", "male", "male", "female", "female",
    "female", "male", "male", "male"
  )
)

# goodness of fit test against the hypothesis that males and
# females occur with equal frequency
goodnessOfFitTest(gender)

# goodness of fit test against the hypothesis that males appear
# with probability .6 and females with probability .4.
goodnessOfFitTest(gender, p = c(.4, .6))
goodnessOfFitTest(gender, p = c(female = .4, male = .6))
goodnessOfFitTest(gender, p = c(male = .6, female = .4))

Import a list into the workspace

Description

Copies each element of a list into a separate variable in the workspace, using the list element names as variable names.

Usage

importList(x, ask = TRUE)

Arguments

Details

Creates one variable per list element in the calling environment (usually the global workspace). All elements of x must be named; passing an unnamed or partially-named list is an error. Element names that are not valid R variable names are automatically converted using make.names. An empty list produces a message and returns invisibly without creating any variables.

Value

Called primarily for its side effect of creating variables in the workspace. Invisibly returns 1 if variables were created, 0 if the user declined.

See Also

unlist, attach

Examples

values <- c(1, 2, 3, 4, 5)
group <- c("group A", "group A", "group B", "group B", "group B")

# split() returns a named list: one element per group
grp_list <- split(values, group)

# import silently (no confirmation prompt)
importList(grp_list, ask = FALSE)

if (FALSE) {
  # interactive: shows variable names and asks for confirmation
  importList(grp_list)
}

Independent samples t-test

Description

Runs an independent-samples t-test and prints the results in a readable format.

Usage

independentSamplesTTest(
  formula,
  data = NULL,
  var.equal = FALSE,
  one.sided = FALSE,
  conf.level = 0.95
)

Arguments

Details

Runs an independent-samples t-test comparing the means of two groups, and prints the results in a beginner-friendly format. The calculations are done by t.test and cohensD. When var.equal = TRUE, Cohen's d uses a pooled standard deviation; when var.equal = FALSE (Welch's test), it uses the "unequal" method. Cases with missing values are removed with a warning.

Value

Prints a summary showing the outcome and grouping variable names, group means and standard deviations, null and alternative hypotheses, test results (t-statistic, degrees of freedom, p-value), a confidence interval, and Cohen's d as a measure of effect size. The underlying results are also returned as a list, so the output can be assigned to a variable and inspected if needed.

See Also

t.test, oneSampleTTest, pairedSamplesTTest, cohensD

Examples

df <- data.frame(
  rt = c(451, 562, 704, 324, 505, 600, 829),
  cond = factor(x = c(1, 1, 1, 2, 2, 2, 2), labels = c("group1", "group2"))
)

# Welch's t-test (the default, does not assume equal variances)
independentSamplesTTest(rt ~ cond, df)

# Student's t-test (assumes equal variances)
independentSamplesTTest(rt ~ cond, df, var.equal = TRUE)

# one-sided test: is group1 larger?
independentSamplesTTest(rt ~ cond, df, one.sided = "group1")

# missing values are removed with a warning
df$rt[1] <- NA
df$cond[7] <- NA
independentSamplesTTest(rt ~ cond, df)

Reshape from long to wide

Description

Reshapes a data frame from long form (one row per observation) to wide form (one row per subject), using a formula to specify the structure.

Usage

longToWide(data, formula, sep = "_")

Arguments

Details

This function is the companion to wideToLong. It reshapes a long-form data frame into wide form by spreading the within-subject observations across columns, with column names constructed from the measure name and factor level(s) joined by sep.

Value

A wide-form data frame with one row per subject (or experimental unit). Column names for the repeated measures follow the naming convention used by wideToLong: the measure name followed by the within-subject factor level(s), separated by sep.

See Also

wideToLong, reshape

Examples

long <- data.frame(
  id       = c(1, 2, 3, 1, 2, 3, 1, 2, 3),
  time     = c("t1", "t1", "t1", "t2", "t2", "t2", "t3", "t3", "t3"),
  accuracy = c(.50, .03, .72, .94, .63, .49, .78, .71, .16)
)

longToWide(long, accuracy ~ time)

Sample mode

Description

Calculate the most frequently occurring value(s) in a sample (modeOf) or the frequency of the most common value (maxFreq).

Usage

modeOf(x, na.rm = TRUE)

maxFreq(x, na.rm = TRUE)

Arguments

Details

When na.rm = FALSE, missing values are treated as a distinct value that can itself be the mode. If the number of NAs exceeds the frequency of every other value, modeOf returns NA and maxFreq returns the count of missing values.

Because of this ambiguity, the default is na.rm = TRUE, unlike most other functions in this package.

Value

modeOf returns the most frequently observed value. If multiple values are tied for the highest frequency, all of them are returned as a vector. If the input has no non-missing values, modeOf issues a warning and returns NA. maxFreq returns the modal frequency as a single number, or NA with a warning if the input has no non-missing values.

See Also

mean, median, table

Examples

eyes <- c("green", "green", "brown", "brown", "blue")
modeOf(eyes) # returns c("green", "brown") -- a tie
maxFreq(eyes) # returns 2

# with missing data
eyes <- c("green", "green", "brown", "brown", "blue", NA, NA, NA)

# na.rm = FALSE: NA is the most frequent "value"
modeOf(eyes, na.rm = FALSE)
maxFreq(eyes, na.rm = FALSE)

# na.rm = TRUE: missing values ignored
modeOf(eyes, na.rm = TRUE)
maxFreq(eyes, na.rm = TRUE)
NULL

One sample t-test

Description

Runs a one-sample t-test and prints the results in a readable format.

Usage

oneSampleTTest(x, mu, one.sided = FALSE, conf.level = 0.95)

Arguments

Details

Runs a one-sample t-test comparing the mean of x to the hypothesised value mu, and prints the results in a beginner-friendly format. The calculations are done by t.test and cohensD. Missing values in x are removed with a warning.

Value

Prints a summary showing the variable name, descriptive statistics, null and alternative hypotheses, test results (t-statistic, degrees of freedom, p-value), a confidence interval, and Cohen's d as a measure of effect size. The underlying results are also returned as a list, so the output can be assigned to a variable and inspected if needed.

See Also

t.test, pairedSamplesTTest, independentSamplesTTest, cohensD

Examples

likert <- c(3, 1, 4, 1, 4, 6, 7, 2, 6, 6, 7)

# two-sided test (the default)
oneSampleTTest(x = likert, mu = 4)

# one-sided test: is the mean greater than 4?
oneSampleTTest(x = likert, mu = 4, one.sided = "greater")

# wider confidence interval
oneSampleTTest(x = likert, mu = 4, conf.level = 0.99)

# missing values are removed with a warning
likert <- c(3, NA, 4, NA, 4, 6, 7, NA, 6, 6, 7)
oneSampleTTest(x = likert, mu = 4)

Paired samples t-test

Description

Runs a paired-samples t-test and prints the results in a readable format.

Usage

pairedSamplesTTest(
  formula,
  data = NULL,
  id = NULL,
  one.sided = FALSE,
  conf.level = 0.95
)

Arguments

Details

Runs a paired-samples t-test and prints the results in a beginner-friendly format. The calculations are done by t.test and cohensD.

There are two ways to supply data. If the data are in wide format (one row per participant, with the two measurements in separate columns), use a one-sided formula such as ~ time1 + time2. The first row of time1 is paired with the first row of time2, and so on.

If the data are in long format (two rows per participant), use a two-sided formula. The recommended style is outcome ~ group + (id), where the participant ID variable is enclosed in parentheses. Alternatively, use the plain formula outcome ~ group and supply the ID variable name via the id argument. The lme4-style notation outcome ~ group + (1|id) is also accepted as equivalent to outcome ~ group + (id).

Participants with missing measurements are removed with a warning.

Value

Prints a summary showing the variable names, descriptive statistics (including the mean and standard deviation of the differences), null and alternative hypotheses, test results (t-statistic, degrees of freedom, p-value), a confidence interval, and Cohen's d as a measure of effect size. The underlying results are also returned as a list, so the output can be assigned to a variable and inspected if needed.

See Also

t.test, oneSampleTTest, independentSamplesTTest, cohensD

Examples

# long-format data: one row per participant per time point
df <- data.frame(
  id = factor(
    x = c(1, 1, 2, 2, 3, 3, 4, 4),
    labels = c("alice", "bob", "chris", "diana")
  ),
  time = factor(
    x = c(1, 2, 1, 2, 1, 2, 1, 2),
    labels = c("time1", "time2")
  ),
  wm = c(3, 4, 6, 6, 9, 12, 7, 9)
)

# wide-format data: one row per participant
df2 <- longToWide(df, wm ~ time)

# three equivalent ways to run the same test
pairedSamplesTTest(formula = wm ~ time, data = df, id = "id")
pairedSamplesTTest(formula = wm ~ time + (id), data = df)
pairedSamplesTTest(formula = ~ wm_time1 + wm_time2, data = df2)

# one-sided test: is time2 larger than time1?
pairedSamplesTTest(formula = wm ~ time, data = df, id = "id", one.sided = "time2")

# missing value: that participant is removed with a warning
df$wm[1] <- NA
pairedSamplesTTest(formula = wm ~ time, data = df, id = "id")

# missing row: that participant is also removed with a warning
df <- df[-1, ]
pairedSamplesTTest(formula = wm ~ time, data = df, id = "id")

Permute the levels of a factor

Description

Reorder the levels of a factor into any order you specify.

Usage

permuteLevels(x, perm, ordered = is.ordered(x), invert = FALSE)

Arguments

Details

Similar to relevel, but more general: relevel can only move one level to the front, whereas permuteLevels can place the levels in any order. This is useful when you want to control the order in which levels appear on a plot axis or in a table.

Value

A factor with the same values as x but with the levels in the new order specified by perm.

See Also

factor, relevel, order

Examples

# factor with levels a, b, c, d, e, f (in that order)
x <- factor(c(1, 4, 2, 2, 3, 3, 5, 5, 6, 6), labels = letters[1:6])
levels(x)

# move level e to position 1, c to position 2, b to 3, a to 4, d to 5, f to 6
permuteLevels(x, perm = c(5, 3, 2, 1, 4, 6))

# using invert = TRUE: move level a to position 5, b to 3, c to 2, etc.
permuteLevels(x, perm = c(5, 3, 2, 1, 4, 6), invert = TRUE)

Post-hoc pairwise t-tests for ANOVA

Description

Runs pairwise t-tests for a one-way analysis of variance, with corrections for multiple comparisons.

Usage

posthocPairwiseT(x, ...)

Arguments

Details

Takes a fitted one-way ANOVA object and runs pairwise t-tests for all pairs of groups, applying a correction for multiple comparisons. This is a simpler alternative to TukeyHSD that uses the same correction methods (e.g., Holm, Bonferroni) as pairwise.t.test.

Value

Prints a table of p-values for all pairwise group comparisons. The underlying result is also returned as a list (with the same structure as pairwise.t.test) so it can be assigned to a variable and inspected if needed.

See Also

pairwise.t.test, TukeyHSD, aov

Examples

dataset <- data.frame(
  outcome = c(1, 2, 3, 2, 3, 4, 5, 6, 7),
  group = factor(c("a", "a", "a", "b", "b", "b", "c", "c", "c"))
)

anova1 <- aov(outcome ~ group, data = dataset)
summary(anova1)

# post-hoc pairwise comparisons with Holm correction (the default)
posthocPairwiseT(anova1)

# Bonferroni correction instead
posthocPairwiseT(anova1, p.adjust.method = "bonferroni")

Print t-test results

Description

Prints the results of a t-test in a readable, beginner-friendly format. This function is called automatically whenever a result from oneSampleTTest, independentSamplesTTest, or pairedSamplesTTest is displayed.

Usage

## S3 method for class 'TTest'
print(x, ...)

Arguments

Value

Invisibly returns x unchanged.

Print chi-square association test results

Description

Prints the results of a chi-square test of association in a readable format. This function is called automatically whenever a result from associationTest is displayed.

Usage

## S3 method for class 'assocTest'
print(x, ...)

Arguments

Value

Invisibly returns x unchanged.

Print correlation matrix results

Description

Prints the results of a correlation analysis in a readable format. This function is called automatically whenever a result from correlate is displayed.

Usage

## S3 method for class 'correlate'
print(x, ...)

Arguments

Value

Invisibly returns x unchanged.

Print goodness of fit test results

Description

Prints the results of a chi-square goodness of fit test in a readable format. This function is called automatically whenever a result from goodnessOfFitTest is displayed.

Usage

## S3 method for class 'gofTest'
print(x, ...)

Arguments

Value

Invisibly returns x unchanged.

Print workspace summary

Description

Prints a workspace summary in a readable format. This function is called automatically whenever a result from who is displayed.

Usage

## S3 method for class 'whoList'
print(x, ...)

Arguments

Value

Invisibly returns x unchanged.

Cut by quantiles

Description

Divides a numeric variable into n categories that each contain approximately the same number of observations.

Usage

quantileCut(x, n, ...)

Arguments

Details

Unlike cut, which creates categories of equal width, quantileCut uses quantile to find breakpoints that produce roughly equal-sized groups. This can be useful in exploratory analysis, but the resulting categories are data-driven and may not have a clear interpretation. Using them as grouping variables in an ANOVA is generally not recommended, as the breakpoints are arbitrary and the groups will typically not have equal variances.

Value

A factor with n levels. Level labels follow the same convention as cut and can be overridden with the labels argument.

See Also

cut, quantile

Examples

# the data are unevenly spread, so equal-width bins would be unbalanced
x <- c(0, 1, 2, 3, 4, 5, 7, 10, 15)

# quantileCut creates equal-frequency bins
bins_eq_freq <- quantileCut(x, 3)
table(bins_eq_freq)

# compare to cut(), which creates equal-width bins
bins_eq_width <- cut(x, 3)
table(bins_eq_width)

Remove all objects from the workspace

Description

Deletes all objects from the workspace, with an optional confirmation prompt.

Usage

rmAll(ask = TRUE)

Arguments

Details

Removes all objects from the workspace. When ask = TRUE, the list of objects is printed and the user must type y to confirm before anything is deleted. This is similar to rm(list = objects()), but with an interactive safety check.

Value

Invisibly returns 1 if objects were deleted, 0 if the user declined or the workspace was already empty.

See Also

rm, objects

Examples

if (FALSE) {
  # interactive: displays workspace contents and asks for confirmation
  rmAll()

  # non-interactive: deletes immediately without prompting
  rmAll(ask = FALSE)
}

Sort a data frame

Description

Sorts a data frame by one or more of its variables.

Usage

sortFrame(x, ..., alphabetical = TRUE)

Arguments

Details

Sorts the rows of x by the variables listed in .... Numeric variables and factors are sorted by their numeric values (for factors, this corresponds to level order). Character variables are sorted alphabetically by default, ignoring case; prefix with - for reverse alphabetical order.

Simple expressions combining variables are also accepted (e.g., sortFrame(x, a + b) sorts by the sum of a and b), though care is required: the sort uses xtfrm internally to convert variables to sortable numeric codes, which can produce unexpected results for character variables when expressions other than - are used.

Value

The sorted data frame.

See Also

order, sort, xtfrm

Examples

dataset <- data.frame(
  txt = c("bob", "Clare", "clare", "bob", "eve", "eve"),
  num1 = c(3, 1, 2, 0, 0, 2),
  num2 = c(1, 1, 3, 0, 3, 2),
  stringsAsFactors = FALSE
)

sortFrame(dataset, num1) # sort by num1 ascending
sortFrame(dataset, num1, num2) # sort by num1 then num2
sortFrame(dataset, -num1) # sort by num1 descending
sortFrame(dataset, txt) # sort alphabetically (case-insensitive)

Standardised regression coefficients

Description

Calculates standardised regression coefficients (beta weights) for a linear model.

Usage

standardCoefs(x)

Arguments

Details

Standardised coefficients are the regression coefficients that would result from fitting the model after scaling all predictors and the outcome to have mean 0 and variance 1. They can be useful for comparing the relative magnitude of predictors measured on different scales, though this comparison should be interpreted with care.

Note that when a model contains interaction terms, the interaction column is also standardised as a whole, rather than being constructed from standardised versions of the constituent predictors.

Value

A matrix with one row per predictor (excluding the intercept) and two columns: b (unstandardised coefficient) and beta (standardised coefficient).

See Also

lm, coefficients

Examples

X1 <- c(0.69, 0.77, 0.92, 1.72, 1.79, 2.37, 2.64, 2.69, 2.84, 3.41)
Y <- c(3.28, 4.23, 3.34, 3.73, 5.33, 6.02, 5.16, 6.49, 6.49, 6.05)

# simple linear regression
model1 <- lm(Y ~ X1)
coefficients(model1) # unstandardised
standardCoefs(model1) # unstandardised and standardised side by side

# multiple regression
X2 <- c(0.19, 0.22, 0.95, 0.43, 0.51, 0.04, 0.12, 0.44, 0.38, 0.33)
model2 <- lm(Y ~ X1 + X2)
standardCoefs(model2)

# model with an interaction term
model3 <- lm(Y ~ X1 * X2)
standardCoefs(model3)

Transpose a data frame

Description

Transposes a data frame, swapping rows and columns, and returns the result as a data frame.

Usage

tFrame(x)

Arguments

Details

Equivalent to as.data.frame(t(x)). Unlike applying t directly, tFrame ensures the result is a data frame rather than a matrix. This makes sense when the rows of x can be meaningfully treated as variables — for example, when each row represents a measurement type and each column represents a participant.

Value

The transposed data frame.

See Also

t

Examples

dataset <- data.frame(
  Gf = c(105, 119, 121, 98), # fluid intelligence
  Gc = c(110, 115, 119, 103), # crystallised intelligence
  Gs = c(112, 102, 108, 99) # processing speed
)
rownames(dataset) <- paste0("person", 1:4)
dataset

tFrame(dataset)

Unload a package

Description

Removes a package from the search path, using the same naming convention as library.

Usage

unlibrary(package)

Arguments

Details

Calls detach on the named package. Unlike detach, which requires the full "package:name" string, unlibrary accepts the bare package name (with or without quotes), matching the syntax of library. Only the named package is unloaded; dependencies are not affected.

Value

Called for its side effect of removing the package from the search path. Returns the result of detach invisibly.

See Also

library, detach

Examples

if (FALSE) {
  # after loading a package with library(), unload it with unlibrary()
  unlibrary(MASS)
}

Contents of workspace

Description

Prints a summary of all objects in the workspace, showing each object's name, class, and size.

Usage

who(expand = FALSE)

Arguments

Details

Shows each object's name, class, and size. For objects with explicit dimensions (e.g., data frames, matrices) the size is shown as rows x columns; for other objects it is the length. Size is only shown for objects whose mode is numeric, character, logical, complex, or list.

Shows more information than objects (especially for variables inside data frames) but less detail than ls.str.

Value

Prints the workspace summary and invisibly returns the underlying data (a data frame with columns Name, Class, and Size), which can be assigned to a variable and inspected if needed.

See Also

objects, ls.str

Examples

cats <- 4
mood <- "happy"
who()

dataset <- data.frame(
  hi = c("hello", "cruel", "world"),
  pi = c(3, 1, 4)
)

who()
who(expand = TRUE)

Reshape from wide to long

Description

Reshapes a data frame from wide form (one row per subject) to long form (one row per observation), using variable names to determine the structure.

Usage

wideToLong(data, within = "within", sep = "_", split = TRUE)

Arguments

Details

This function is the companion to longToWide. It determines the reshape structure from the variable names rather than requiring an explicit formula.

The naming scheme for repeated-measures variables places the measure name first, followed by the factor level(s), all joined by sep. For example, variables named accuracy_t1 and accuracy_t2 indicate a measure called accuracy recorded at two time points (t1 and t2). After reshaping, the long-form output contains one column called accuracy and a factor column (named by the within argument) with levels t1 and t2.

Designs with multiple within-subject factors are supported. For example, MRT_cond1_day1 encodes measure MRT at level cond1 of one factor and day1 of another. Supply within = c("condition", "day") to name both output columns. Multiple measured variables per observation (e.g., both MRT and PC) are also supported.

Value

A long-form data frame with one row per observation.

See Also

longToWide, reshape

Examples

# simple design: accuracy measured at two time points for 4 participants
wide <- data.frame(
  id          = 1:4,
  accuracy_t1 = c(.15, .50, .78, .55),
  accuracy_t2 = c(.55, .32, .99, .60)
)
wideToLong(wide, "time")

# complex design: two measures (MRT, PC), two conditions, two days
wide2 <- data.frame(
  id             = 1:4,
  gender         = factor(c("male", "male", "female", "female")),
  MRT_cond1_day1 = c(415, 500, 478, 550),
  MRT_cond2_day1 = c(455, 532, 499, 602),
  MRT_cond1_day2 = c(400, 490, 468, 502),
  MRT_cond2_day2 = c(450, 518, 474, 588),
  PC_cond1_day1  = c(79, 83, 91, 75),
  PC_cond2_day1  = c(82, 86, 90, 78),
  PC_cond1_day2  = c(88, 92, 98, 89),
  PC_cond2_day2  = c(93, 97, 100, 95)
)

# default: condition and day become separate columns
wideToLong(wide2, within = c("condition", "day"))

# alternative: keep condition and day as one combined column
wideToLong(wide2, split = FALSE)