Package 'GimmeMyStats'

Add P-value Significance Symbols

Description

Redefine the default parameters of rstatix::add_significance() by adding p-value significance symbols to a data frame.

Usage

add_significance0(data, p.col = NULL, output.col = NULL)

Arguments

data	a data frame containing a p-value column.
p.col	column name containing p-values.
output.col	the output column name to hold the adjusted p-values.

Value

a data frame

Examples

library(magrittr)
library(rstatix, warn.conflicts = FALSE)
data("ToothGrowth")
ToothGrowth %>%
    t_test(len ~ dose) %>%
    adjust_pvalue() %>%
    add_significance0("p.adj")

---

Frequency of categorical variables

Description

Formats a data frame or vector containing categorical variables and calculates the frequency of each category.

Usage

count_category(x, width = 15, collapse = FALSE, sort = TRUE, format = TRUE)

Arguments

x	Data frame or vector containing categorical variables.
width	Integer specifying the maximum width for wrapping text.
collapse	Logical specifying whether to merge categories with identical proportions.
sort	Logical or character vector. If TRUE, orders categories by frequency. If FALSE, orders by names. If a character vector, renames and orders categories accordingly.
format	Logical specifying whether to format category names if the input is a vector.

Value

A tibble with one row per category and the following columns:

f

Factor specifying the category labels, possibly wrapped to the specified width. When collapse = TRUE, multiple categories with identical frequencies are merged into a single label separated by commas.

n

Integer specifying the frequency count for each category.

Examples

# Vector of categorical variable
k <- 5
n <- runif(k, 1, 10) %>% round()
x <- paste("Level", seq(k)) %>%
    mapply(function(x, y) rep(x, y), ., n) %>%
    unlist()
count_category(x)

# Data frame of categorical variable
df <- sapply(seq(k), function(x) runif(10) %>% round()) %>% as.data.frame()
colnames(df) <- paste("Level", seq(k))
count_category(df)
count_category(x, sort = FALSE, width = 5)
count_category(x, sort = seq(k), format = FALSE)
x2 <- c(x, rep("Level 6", n[1]))
count_category(x2, collapse = TRUE)

---

Household tasks distribution by gender and arrangement

Description

A dataset containing the distribution of household tasks among different arrangements: Wife, Alternating, Husband, and Jointly. The data represents the frequency of each task performed by each arrangement.

Usage

data(housetasks)

Format

A data.frame with 13 rows (tasks) and 4 columns (arrangements):

Wife

Numeric, the frequency of the task performed primarily by the wife.

Alternating

Numeric, the frequency of the task performed in an alternating manner.

Husband

Numeric, the frequency of the task performed primarily by the husband.

Jointly

Numeric, the frequency of the task performed jointly by both partners.

Source

The dataset was downloaded from the ggpubr GitHub repository: https://raw.githubusercontent.com/kassambara/ggpubr/refs/heads/master/inst/demo-data/housetasks.txt

Examples

data(housetasks)
head(housetasks)

---

Identifies outliers in a numeric vector

Description

Detects outliers using methods like IQR, percentiles, Hampel, MAD, or SD.

Usage

identify_outliers(
  x,
  probabilities = c(0.25, 0.75),
  method = "iqr",
  weight = 1.5,
  replace = FALSE
)

Arguments

x	Vector containing numerical values.
probabilities	Numeric vector specifying probabilities for percentiles.
method	Character specifying the method: iqr, percentiles, hampel, mad, or sd.
weight	Double specifying the multiplier for the detection threshold.
replace	Logical specifying whether to replace outliers with NA.

Value

A numeric vector whose content depends on the value of replace:

replace = FALSE

A numeric vector containing only the detected outlier values. The vector is named with the original indices or names of x.

replace = TRUE

A numeric vector of the same length as x, where detected outliers are replaced by NA.

Examples

x <- rnorm(100)
identify_outliers(x, method = "iqr")
identify_outliers(x, method = "percentiles", probabilities = c(0.1, 0.9))
identify_outliers(x, method = "sd", weight = 3)
identify_outliers(x, method = "mad", replace = TRUE)

---

Multiple correlation test

Description

Calculates correlations between multiple variables.

Usage

mcor_test(
  x,
  y = NULL,
  estimate = TRUE,
  p.value = FALSE,
  method = "spearman",
  method_adjust = "BH"
)

Arguments

x	Data frame containing numerical variables.
y	Data frame containing numerical variables. If NULL, correlations are calculated within x.
estimate	Logical specifying whether to return correlation coefficients.
p.value	Logical specifying whether to return adjusted p-values.
method	Character specifying the correlation method: pearson, kendall, or spearman.
method_adjust	Character specifying the p-value adjustment method.

Value

Depending on the values of estimate and p.value, one of the following:

estimate = TRUE, p.value = FALSE

A numeric matrix of correlation coefficients, with columns corresponding to variables in x and rows to variables in y.

estimate = FALSE, p.value = TRUE

A numeric matrix of adjusted p-values, with columns corresponding to variables in x and rows to variables in y.

estimate = TRUE, p.value = TRUE

A named list with two elements:

estimate

Numeric matrix of correlation coefficients.

p.value

Numeric matrix of adjusted p-values.

Examples

library(magrittr)
x0 <- runif(20)
x <- lapply(
    c(1, -1),
    function(i) sapply(seq(10), function(j) x0 * i + runif(10, max = 1))
) %>%
    Reduce(cbind, .) %>%
    set_colnames(paste("Variable", seq(20)))
y <- lapply(
    c(1, -1),
    function(i) sapply(seq(10), function(j) x0 * i + runif(10, max = 1))
) %>%
    Reduce(cbind, .) %>%
    set_colnames(paste("Variable", seq(20))) %>%
    .[, seq(5)]
mcor_test(x)
mcor_test(
    x,
    y,
    p.value = TRUE,
    method = "pearson",
    method_adjust = "bonferroni"
)

---

Performs post hoc analysis for chi-squared or Fisher's exact test

Description

Identifies pairwise differences between categories following a chi-squared or Fisher's exact test.

Usage

post_hoc_chi2(
  x,
  method = "fisher",
  method_adjust = "BH",
  digits = 3,
  count = FALSE,
  ...
)

Arguments

x	Data frame, vector, or table. If numeric, treated as a contingency table and the names are considered as categories; otherwise, the levels of the factor or the characters are used.
method	Character specifying the statistical test: chisq for chi-squared or fisher for Fisher's exact test.
method_adjust	Character specifying the p-value adjustment method.
digits	Integer specifying the number of decimal places for the test statistic.
count	Logical specifying if x is a contingency table.
...	Additional arguments passed to chisq.test or fisher.test.

Details

If x is numeric, it is treated as a contingency table and the names are considered as categories; otherwise, the levels of the factor or the characters are used.

Value

A tibble with pairwise test results containing the following columns:

group1, group2

Character vectors specifying the pair of groups being compared.

n

Numeric vector specifying the total count or sample size for the comparison.

statistic

Numeric vector specifying the test statistic (for chi-squared tests only).

df

Numeric vector specifying the degrees of freedom (for chi-squared tests only).

p

Raw p-value for the pairwise comparison, formatted as numeric or character ("< 0.001" for very small p-values).

p.signif

Character vectors specifying the significance codes for raw p-values: 'ns' (not significant).

FDR

False Discovery Rate adjusted p-value using the specified method, formatted as numeric or character ("< 0.001" for very small values).

fdr.signif

Character vectors specifying the significance codes for FDR-adjusted p-values: 'ns' (not significant), '' (p < 0.05), '' (p < 0.01), '' (p < 0.001).

For Fisher's exact tests, the statistic and df columns are not included..

Examples

x <- c(rep("A", 100), rep("B", 78), rep("C", 25))
post_hoc_chi2(x)

x <- data.frame(G1 = c(Yes = 100, No = 78), G2 = c(Yes = 75, No = 23))
post_hoc_chi2(x, count = TRUE, method = "chisq")

data("housetasks")
housetasks[, c("Wife", "Husband")] %>%
    t() %>%
    post_hoc_chi2(count = TRUE, workspace = 1e6)

x <- cbind(
    mapply(function(x, y) rep(x, y), letters[seq(3)], c(7, 5, 8)) %>% unlist(),
    mapply(function(x, y) rep(x, y), LETTERS[seq(3)], c(6, 6, 8)) %>% unlist()
)
post_hoc_chi2(x)

---

Prints descriptive statistics for binomial variables

Description

Calculates and prints frequency counts and percentages for binomial (two-level) categorical variables.

Usage

print_binomial(x, digits = 1, width = 15)

Arguments

x	Data frame, matrix, or vector containing binomial variables.
digits	Integer specifying the number of decimal places for the test statistic.
width	Integer specifying the maximum width for wrapping text.

Value

A tibble with one row per level for each categorical level containing the following columns:

Variables

Character vector specifying the name of each variable.

Levels

Character vector specifying the category level for each variable.

Statistics

Character vector combining the frequency count and the percentage for each level.

Examples

x <- data.frame(A = sample(c("X", "Y"), 100, replace = TRUE))
print_binomial(x)
print_binomial(x, digits = 2, width = 5)

---

Prints the results of a Chi2

Description

Formats the results of a Chi-squared or Fisher's exact test.

Usage

print_chi2_test(x, digits = 3)

Arguments

x	Test object from rstatix among chisq_test or fisher_test.
digits	Integer specifying the number of decimal places for the test statistic.

Value

A character string containing the formatted test results with:

Test statistic

For Chi-squared test.

P-value

Formatted p-value with significance stars.

Sample size

Total count for sample size.

For Fisher's exact test, only the P-value and sample size are included.

Examples

x <- c(A = 100, B = 78, C = 25)
library(rstatix)
print_chi2_test(chisq_test(x))

xtab <- as.table(rbind(c(490, 10), c(400, 100)))
dimnames(xtab) <- list(
    group = c("grp1", "grp2"),
    smoker = c("yes", "no")
)
print_chi2_test(fisher_test(xtab))

---

Prints the dispersion of a numeric vector

Description

Calculates and prints the median and interquartile range (IQR) or the mean and standard deviation (SD).

Usage

print_dispersion(x, digits = 1, width = 15, method = "median")

Arguments

x	Vector containing numerical values.
digits	Integer specifying the number of decimal places for the test statistic.
width	Integer specifying the maximum width for wrapping text.
method	Character specifying the method: median for median and IQR, or mean for mean and SD.

Value

A character string containing a measure of central tendency and dispersion. Depending on method, this is either the median and interquartile range or the mean and standard deviation.

Examples

print_dispersion(runif(10))
print_dispersion(runif(10), method = "mean", digits = 2, width = 5)

---

Prints descriptive statistics for multinomial variables

Description

Calculates and prints frequency counts and percentages for multinomial (multi-level) categorical variables.

Usage

print_multinomial(x, label = NULL, digits = 1, width = 15, n = nrow(x), ...)

Arguments

x	Data frame, matrix, or vector containing multinomial variables.
label	Character vector specifying the names of the categorical variables.
digits	Integer specifying the number of decimal places for the test statistic.
width	Integer specifying the maximum width for wrapping text.
n	Integer specifying the total number of observations.
...	Additional arguments passed to count_category.

Value

A tibble with one row per level for each categorical level containing the following columns:

Variables

Character vector specifying the name of each variable.

Levels

Character vector specifying the category level for each variable.

Statistics

Character vector combining the frequency count and the percentage for each level.

Examples

x <- data.frame(A = sample(c("X", "Y", "Z"), 100, replace = TRUE))
print_multinomial(x, label = "A")
x2 <- rbind(x, data.frame(A = rep("Level A", length(x[x == "Level X", ]))))
print_multinomial(
    x,
    label = "Variable A",
    sort = FALSE,
    n = 90,
    digits = 2,
    width = 5
)

---

Prints descriptive statistics for numeric variables

Description

Prints summary statistics (mean, median, quartiles, range, etc.) for numeric variables.

Usage

print_numeric(x, digits = 1, width = 15)

Arguments

x	Data frame, matrix, or vector containing numerical variables.
digits	Integer specifying the number of decimal places for the test statistic.
width	Integer specifying the maximum width for wrapping text.

Value

A tibble with one row per numeric variable and the following columns:

Variables

Character specifying the variable name.

Mean+/-SD

Character specifying the mean and standard deviation.

Median+/-IQR

Character specifying the median and interquartile range.

Q1-Q3

Character specifying the first and third quartiles.

Range

Character specifying the minimum and maximum values.

Kurtosis

Numeric specifying the kurtosis coefficient.

Skewness

Numeric specifying the skewness coefficient.

Normality

Character specifying the Shapiro-Wilk normality test significance code.

Zeros

Integer specifying the number of zero values.

NAs

Integer specifying the number of missing values.

Examples

x <- data.frame(A = rnorm(100), B = rnorm(100))
print_numeric(x)
print_numeric(x, digits = 2, width = 5)

---

Prints a hypothesis test

Description

Formats the results of a hypothesis test (ANOVA, Kruskal-Wallis, or Wilcoxon).

Usage

print_test(x, digits = 0, digits_p = 2)

Arguments

x	Test object from rstatix among anova_test, kruskal_test, or wilcox_test.
digits	Integer specifying the number of decimal places for the test statistic.
digits_p	Integer specifying the number of decimal places for the p-value.

Value

A character string containing the formatted test results with:

Test name

Name of the statistical test (ANOVA, Kruskal-Wallis, Wilcoxon, t-test, Friedman, or mixed-effects model).

Test statistic

Test statistic (F, K, W, T, or $\chi^2$) with degrees of freedom when applicable.

P-value

P-value with significance stars.

Examples

library(rstatix)
data("ToothGrowth")
res <- anova_test(ToothGrowth, len ~ dose)
print_test(res)

res <- kruskal_test(ToothGrowth, len ~ dose)
print_test(res)

res <- wilcox_test(ToothGrowth, len ~ supp)
print_test(res)

library(lmerTest)
data("sleepstudy", package = "lme4")
res <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
print_test(res)

---

Summarizes descriptive statistics for binomial variables

Description

Summarizes descriptive statistics for binomial variables

Usage

summary_binomial(x, ...)

Arguments

x	Data frame, matrix, or vector containing binomial variables.
...	Additional arguments passed to print_binomial.

Value

A tibble with descriptive statistics containing the following columns:

Variables

Character vector specifying the name of each variable.

Statistics

Character vector combining the reference level of a variable with its frequency count and its percentage.

Examples

x <- data.frame(A = sample(c("X", "Y"), 100, replace = TRUE))
summary_binomial(x)
summary_binomial(x, digits = 2, width = 5)

---

Summarizes descriptive statistics for numeric variables

Description

Formats the output of print_numeric into a concise summary.

Usage

summary_numeric(x, ...)

Arguments

x	Data frame, matrix, or vector containing numerical variables.
...	Additional arguments passed to print_numeric.

Value

A tibble with one row per numeric variable and the following columns:

Variables

Character specifying the variable name.

Median+/-IQR

Character specifying the median and interquartile range.

Examples

x <- data.frame(A = rnorm(100), B = rnorm(100))
summary_numeric(x)
summary_numeric(x, digits = 2, width = 5)

---

Convert Strings to Title Case

Description

Converts the first character of each string to uppercase and the rest to lowercase.

Usage

to_title(x)

Arguments

x	A character vector or a list containing strings to convert to title case.

Value

A character vector with the same length as x, where each element has its first character converted to uppercase and remaining characters are preserved as-is.

Examples

to_title(c("hELLO", "WoRLD", "R"))
# Returns: "Hello" "World" "R"

---