Miscellaneous Functions for Data Processing and Analysis • Saqrmisc

A comprehensive R package for statistical analysis, data transformation, and visualization with publication-ready outputs. All functions use a consistent quoted-string API and support multiple output formats (gt, plain, markdown, latex, kable).

Installation

devtools::install_github("mohsaqr/Saqrmisc")
library(Saqrmisc)

Dependencies:

install.packages(c(
  "dplyr", "ggplot2", "gt", "tibble", "tidyr", "rlang",
  "MoEClust", "mclust", "vcd", "gridExtra",
  "bootnet", "qgraph", "mgm",
  "BayesFactor", "effectsize", "effsize"
))

# Optional
install.packages(c("ggstatsplot", "TOSTER", "kableExtra", "httr2"))

Correlation Analysis

`correlations()`

Full pairwise correlation table with coefficients, CIs, t-statistics, df, p-values, and sample sizes.

correlations(data, Vars = NULL, type = "bivariate", method = "pearson",
             p_adjust = "none", ci_level = 0.95, min_r = NULL, sig_only = FALSE,
             multilevel = FALSE, id = NULL, between = FALSE, group_by = NULL,
             include = NULL, exclude = NULL, digits = 3, format = "gt")

Parameter	Description	Default
`Vars`	Variables to correlate (`NULL` = all numeric)	`NULL`
`type`	`"bivariate"`, `"partial"`, `"semi-partial"`	`"bivariate"`
`method`	`"pearson"`, `"spearman"`, `"kendall"`	`"pearson"`
`p_adjust`	`"none"`, `"bonferroni"`, `"holm"`, `"fdr"`	`"none"`
`min_r`	Show only correlations above this threshold	`NULL`
`sig_only`	Show only significant correlations	`FALSE`
`multilevel`	Within-cluster correlations (requires `id`)	`FALSE`
`group_by`	Stratified correlations by group	`NULL`

correlations(mtcars, Vars = c("mpg", "hp", "wt"))
correlations(mtcars, Vars = c("mpg", "hp", "wt"), type = "partial", sig_only = TRUE)

Returns: List with $table, $data, $n_pairs, $n_significant.

`correlation_matrix()`

Publication-ready correlation matrix with significance stars and optional heatmap.

correlation_matrix(data, Vars = NULL, type = "bivariate", method = "pearson",
                   triangle = "lower", diagonal = "dash", show_n = FALSE,
                   show_ci = FALSE, show_p = FALSE, p_adjust = "none",
                   stars = TRUE, heatmap = FALSE, digits = 2, format = "gt")

Parameter	Description	Default
`triangle`	`"lower"`, `"upper"`, `"full"`	`"lower"`
`show_ci`	Display confidence intervals	`FALSE`
`show_n`	Display pairwise sample sizes	`FALSE`
`heatmap`	Generate heatmap visualization	`FALSE`

correlation_matrix(mtcars, Vars = c("mpg", "hp", "wt", "qsec"), show_ci = TRUE)

Returns: List with $table, $correlation_matrix, $p_matrix, $heatmap.

Group Comparisons

`compare_groups()`

Compare groups using t-tests, ANOVA, or nonparametric alternatives with publication-ready tables and plots. Automatically selects the appropriate test based on group count and data properties.

compare_groups(data, category, Vars = NULL, repeat_category = NULL,
               compare_by = NULL, compare_mode = "between", type = "auto",
               posthoc = TRUE, posthoc_method = "games-howell",
               bayesian = FALSE, equivalence = FALSE, equivalence_bounds = NULL,
               pivot = FALSE, pivot_stat = "mean", format = "gt")

Parameter	Description	Default
`category`	Grouping variable (required)	–
`Vars`	Numeric variables to compare (`NULL` = all)	`NULL`
`type`	`"auto"`, `"p"` (parametric), `"np"` (nonparametric), `"bayes"`	`"auto"`
`posthoc`	Compute post-hoc tests (3+ groups)	`TRUE`
`posthoc_method`	`"games-howell"` or `"tukey"`	`"games-howell"`
`bayesian`	Compute Bayes Factors	`FALSE`
`equivalence`	Perform TOST equivalence testing	`FALSE`
`repeat_category`	Stratify analysis by this variable	`NULL`
`pivot`	Compact table with categories as columns	`FALSE`
`pivot_stat`	`"mean"`, `"mean_sd"`, `"median"`, `"n"`	`"mean"`
`compare_by`	Factors to test within each category	`NULL`
`compare_mode`	`"between"` or `"within"`	`"between"`

# Two-group comparison (t-test)
results <- compare_groups(mtcars, category = "am", Vars = c("mpg", "hp"))

# ANOVA with post-hoc
results <- compare_groups(mtcars, category = "cyl", Vars = c("mpg", "hp"))

Returns: List with $summary_table, $summary_data, $plots, $pivot_table, $within_table.

Pivot Tables

Compact summary with category levels as columns and a p (ES) column:

results <- compare_groups(df, category = "group", Vars = c("x", "y"),
                          pivot = TRUE, pivot_stat = "mean_sd")
results$pivot_table

Within-Group Analysis

Test whether factors have effects within each category level:

results <- compare_groups(df, category = "llm", Vars = "score",
                          compare_by = c("pronoun", "support"),
                          compare_mode = "within")
results$within_table

Descriptive Statistics

`descriptive_table()`

Flexible summary statistics table with selectable statistics.

descriptive_table(data, Vars = NULL,
                  stats = c("n", "mean", "sd", "median", "min", "max"),
                  group_by = NULL, overall = FALSE, labels = NULL,
                  digits = 3, format = "gt")

Parameter	Description	Default
`stats`	Statistics to include (see below)	`c("n","mean","sd","median","min","max")`
`group_by`	Compute statistics by group	`NULL`
`overall`	Include overall/total row	`FALSE`
`labels`	Named vector of variable labels	`NULL`

Available statistics: n, missing, missing_pct, mean, sd, se, var, median, min, max, range, iqr, q1, q3, skewness, kurtosis, cv

descriptive_table(mtcars, Vars = c("mpg", "hp", "wt"), group_by = "cyl", overall = TRUE)

`categorical_table()`

Frequency and cross-tabulation tables with optional chi-square test.

categorical_table(data, var, by = NULL, chi_square = FALSE,
                  cramers_v = FALSE, group_by = NULL, format = "gt")

categorical_table(mtcars, var = "cyl", by = "am", chi_square = TRUE, cramers_v = TRUE)

`auto_describe()`

Automatically detects variable types and produces appropriate summaries for each.

auto_describe(data, group_by = NULL, format = "gt")

Returns: List with $numeric, $categorical, $variable_types.

Data Transformation

All transformation functions add new columns with a configurable suffix and support group-wise operations via group_by. Each has a _vec() variant for use in dplyr::mutate().

`center()`

Mean centering (grand-mean or group-mean).

center(data, Vars, suffix = "_c", group_by = NULL)

center(mtcars, Vars = c("mpg", "hp"), group_by = "cyl")

`standardize()`

Z-score standardization.

standardize(data, Vars, suffix = "_z", group_by = NULL)

`scale_vars()`

Scale by standard deviation or rescale to a custom range.

scale_vars(data, Vars, method = "sd", range = c(0, 1), suffix = "_s", group_by = NULL)

Parameter	Description	Default
`method`	`"sd"` (divide by SD) or `"range"` (min-max)	`"sd"`
`range`	Target range for `"range"` method	`c(0, 1)`

`reverse_code()`

Reverse code items for Likert scales. Auto-detects or accepts explicit min/max.

reverse_code(data, Vars, min = NULL, max = NULL, suffix = "_r")

reverse_code(df, Vars = c("item2", "item5"), min = 1, max = 5)

Missing Data

`missing_analysis()`

Comprehensive missing data diagnostics including patterns, Little’s MCAR test, and visualizations.

missing_analysis(data, Vars = NULL, pattern_plot = TRUE,
                 mcar_test = TRUE, correlations = FALSE, digits = 2)

Returns: List with $summary, $patterns, $mcar, $plot.

`replace_missing()`

Simple imputation by mean or median, with optional group-wise computation.

replace_missing(data, Vars, method = "mean", group_by = NULL, suffix = "_imp")

Parameter	Description	Default
`method`	`"mean"` or `"median"`	`"mean"`
`suffix`	Suffix for new column (`NULL` to replace in-place)	`"_imp"`

Outlier Analysis

`outlier_check()`

Detect outliers using multiple methods.

outlier_check(data, Vars, method = "zscore", threshold = 3,
              flag = TRUE, plot = TRUE)

Parameter	Description	Default
`method`	`"zscore"`, `"iqr"`, `"percentile"`, `"mahalanobis"`	`"zscore"`
`threshold`	Detection threshold (method-dependent)	`3`

Returns: List with $summary, $data, $outlier_indices, $plot.

`replace_outliers()`

Treat outliers by winsorizing, replacing with NA, or substituting with median/mean.

replace_outliers(data, Vars, method = "winsorize", threshold = 0.05,
                 suffix = "_treated")

Parameter	Description	Default
`method`	`"winsorize"`, `"na"`, `"median"`, `"mean"`	`"winsorize"`

Normality Testing

`normality_check()`

Shapiro-Wilk test, skewness, kurtosis, Q-Q plots, and histograms.

normality_check(data, Vars, digits = 3)

Returns: List with $summary, $plots, $overall.

Clustering

`clustering()`

Model-based clustering using Gaussian Mixture Models. Tests 14 covariance structures (EII through VVV) and selects the best model by BIC.

clustering(data, vars, n_clusters, scaling = "standardize",
           n_init = 10, seed = NULL, verbose = TRUE)

Parameter	Description	Default
`n_clusters`	Range of clusters to test (e.g., `2:5`)	required
`scaling`	`"standardize"`, `"center"`, `"minmax"`, `"none"`	`"standardize"`

results <- clustering(mtcars, vars = c("mpg", "hp", "wt"), n_clusters = 2:4)
plot(results)                          # Profile plot
model_comparison_table(results)        # Compare all models

Returns: Object of class "moe_analysis" with print() and plot() methods.

Helper Functions

Function	Description
`get_cluster_assignments(results)`	Extract cluster labels (optionally with probabilities)
`assess_cluster_stability(results, n_boot)`	Bootstrap stability assessment (Jaccard similarity)
`model_comparison_table(results)`	Publication-ready model comparison table
`generate_cluster_report(results)`	Full text report of the analysis

Network Analysis

`estimate_single_network()`

Estimate psychological networks using EBICglasso regularized partial correlations.

estimate_single_network(df, Vars = NULL, layout = "circle",
                        default = "EBICglasso", verbose = TRUE,
                        compute_centrality = TRUE)

Returns: Object with $plot, $centrality, $edges, $network_obj.

`compare_networks()`

Estimate and compare networks between groups.

compare_networks(data, group_var, variables = NULL,
                 method = "EBICglasso", layout = "circle")

Categorical Analysis

`mosaic_analysis()`

Mosaic plot with chi-square test and Cramer’s V.

mosaic_analysis(data, var1, var2, show_percentages = TRUE,
                percentage_base = "total")

Returns: Object with $plot, $chi_test, $cramers_v, $summary.

AI Interpretation

`pass()`

Pipe any R output to an AI model for scientific interpretation.

pass(result, action = "interpret", style = "scientific", output = "text",
     provider = "openai", model = NULL, context = NULL,
     system_message = NULL, prompt = NULL, base_url = NULL)

Parameter	Options	Default
`action`	`"interpret"`, `"explain"`, `"write"`, `"summarize"`, `"critique"`, `"suggest"`	`"interpret"`
`style`	`"scientific"`, `"simple"`, `"detailed"`, `"brief"`	`"scientific"`
`output`	`"text"`, `"markdown"`, `"latex"`, `"html"`	`"text"`
`provider`	`"openai"`, `"anthropic"`, `"gemini"`, `"openrouter"`, or use `base_url` for local	`"openai"`

t.test(mpg ~ am, data = mtcars) |> pass()
lm(mpg ~ wt + hp, data = mtcars) |> summary() |> pass(action = "write")

API Key Setup

set_openai_key("sk-...")
set_claude_key("sk-ant-...")
set_gemini_key("AIza...")
set_openrouter_key("sk-or-...")

# Or via environment variables in .Renviron:
# OPENAI_API_KEY=your-key
# ANTHROPIC_API_KEY=your-key

Output Formats

All table-producing functions share these parameters:

Parameter	Options	Default
`format`	`"gt"`, `"plain"`, `"markdown"`, `"latex"`, `"kable"`	`"gt"`
`show_header`	`TRUE` / `FALSE`	`TRUE`

Results can also be converted after the fact with to_gt(), to_markdown(), to_latex(), to_html(), to_kable(), and to_dataframe().

Vectorized Functions

For use in dplyr::mutate() and across() workflows:

Function	Description
`center_vec(x)`	Mean-center a vector
`standardize_vec(x)`	Z-score standardize
`scale_vec(x, method, range)`	Scale by SD or range
`reverse_code_vec(x, min, max)`	Reverse code
`winsorize_vec(x, probs)`	Cap extreme values
`is_outlier(x, method, threshold)`	Logical outlier flag

These respect dplyr::group_by() for group-wise operations.

Citation

Saqr, M. (2025). Saqrmisc: Comprehensive Data Analysis and Visualization Tools for R.
GitHub: https://github.com/mohsaqr/Saqrmisc

License

MIT License - see LICENSE for details.

Author

Mohammed Saqr - [email protected] - @mohsaqr

Saqrmisc

Table of Contents

Installation

Correlation Analysis

correlations()

correlation_matrix()

Group Comparisons

compare_groups()

Pivot Tables

Within-Group Analysis

Descriptive Statistics

descriptive_table()

categorical_table()

auto_describe()