mariposa
'SPSS'-Compatible Statistical Tools for Survey Data
Statistical analysis of survey data with full support
for survey weights, grouped operations, and 'tidyverse' integration. Provides
80 functions for data import/export ('SPSS', 'Stata', 'SAS', 'Excel') with
label roundtripping and tagged NA preservation, label management (variable
labels, value labels, type conversions, missing value declaration), data
transformation (recoding, dummy coding, standardization, centering),
descriptive statistics, codebook generation, hypothesis testing, correlation
analysis, post-hoc comparisons, weighted statistics, scale analysis,
regression, non-parametric tests, exact tests, factorial ANOVA, and ANCOVA.
Every analysis offers compact print() and detailed summary() output with
toggleable sections. Statistical results are validated against 'SPSS'
version 29 within documented per-tier tolerances (see the compatibility
vignette for per-function status). Methods follow the published
algorithms of IBM Corp. (2023, "IBM SPSS Statistics Algorithms"),
the Lilliefors-corrected normality test of Dallal and Wilkinson
(1986)
mariposa 
Professional statistical analysis for survey data in R.
mariposa (Marburg Initiative for Political and Social Analysis) provides 80 functions for importing, managing, transforming, and analyzing survey data. Covers the full workflow from data import (SPSS, Stata, SAS, Excel) through label management, recoding, and standardization to statistical analysis with survey weights, grouped operations via dplyr::group_by(), and publication-ready output. Statistical results are validated against SPSS v29 within documented per-tier tolerances; see vignette("spss-compatibility") for per-function status.
Installation
# Install from GitHub
devtools::install_github("YannickDiehl/mariposa")
Quick Start
library(mariposa)
library(dplyr)
# Load example survey data (2,500 respondents)
data(survey_data)
# Interactive HTML codebook in RStudio Viewer
codebook(survey_data)
# Descriptive statistics with survey weights
survey_data %>%
describe(age, income, life_satisfaction, weights = sampling_weight)
# Frequency table
survey_data %>%
frequency(education, weights = sampling_weight)
# Compare groups with t-test
survey_data %>%
t_test(life_satisfaction, group = gender, weights = sampling_weight)
# Detailed SPSS-style output with summary()
survey_data %>%
t_test(life_satisfaction, group = gender, weights = sampling_weight) %>%
summary()
# Scale analysis workflow
reliability(survey_data, trust_government, trust_media, trust_science) %>%
summary() # item statistics, inter-item correlations
survey_data <- survey_data %>%
mutate(m_trust = row_means(., trust_government, trust_media, trust_science))
# Regression
survey_data %>%
linear_regression(life_satisfaction ~ age + income, weights = sampling_weight) %>%
summary() # coefficients, ANOVA table, diagnostics
Core Features
Statistical Functions
| Category | Functions | Purpose |
|---|---|---|
| Data Import | read_spss(), read_stata(), read_sas(), read_xlsx(), + 2 more |
Import SPSS, Stata, SAS, and Excel with tagged NA support |
| Data Export | write_spss(), write_stata(), write_xpt(), write_xlsx() |
Export with full label and missing value roundtripping |
| Label Management | var_label(), val_labels(), to_label(), set_na(), + 6 more |
Get/set labels, convert formats, declare missing values |
| Data Transformation | rec(), to_dummy(), std(), center(), find_var() |
Recoding, dummy coding, standardization, centering |
| Descriptive | describe(), frequency(), crosstab(), multiple_response(), codebook() |
Summaries, distributions, multiple response sets, data dictionaries |
| Assumption checks | normality_test() |
Kolmogorov-Smirnov (Lilliefors) and Shapiro-Wilk normality tests |
| T-Tests | t_test() |
Mean comparisons (independent and one-sample) |
| ANOVA | oneway_anova(), factorial_anova(), ancova() |
One-way, multi-factor ANOVA, and ANCOVA with Type III SS |
| Non-parametric | mann_whitney(), kruskal_wallis(), wilcoxon_test(), friedman_test(), binomial_test() |
Distribution-free tests |
| Exact tests | chi_square(), fisher_test(), chisq_gof(), mcnemar_test() |
Categorical associations and exact tests |
| Correlation | pearson_cor(), spearman_rho(), kendall_tau(), partial_cor() |
Relationships between variables, with and without controls |
| Post-hoc | tukey_test(), scheffe_test(), levene_test(), dunn_test(), pairwise_wilcoxon() |
Follow-up analyses (parametric and non-parametric) |
| Scale analysis | reliability(), efa(), row_means(), row_sums(), row_count(), pomps() |
Cronbach's Alpha, factor analysis, index construction |
| Regression | linear_regression(), logistic_regression(), marginal_effects() |
Linear and logistic models with SPSS-style output, average marginal effects |
| Effect sizes | phi(), cramers_v(), goodman_gamma() |
Effect size measures for categorical data |
| Weighted stats | w_mean(), w_median(), w_sd(), + 8 more |
Individual weighted statistics |
Survey Weights Built-In
All statistical functions accept survey weights:
# Weighted mean, median, SD
survey_data %>%
w_mean(age, income, weights = sampling_weight)
# Grouped weighted analysis
survey_data %>%
group_by(region) %>%
describe(satisfaction, weights = sampling_weight)
Tidyverse Integration
Full support for pipes and grouped operations:
survey_data %>%
filter(age >= 18) %>%
group_by(region) %>%
t_test(life_satisfaction, group = gender, weights = sampling_weight)
Multi-Factor ANOVA & ANCOVA
# Factorial ANOVA with Type III SS
survey_data %>%
factorial_anova(dv = income, between = c(gender, education),
weights = sampling_weight)
# ANCOVA with covariate adjustment
survey_data %>%
ancova(dv = income, between = gender, covariate = age,
weights = sampling_weight)
S3 Post-Hoc Methods
# Parametric: ANOVA → Tukey/Scheffe
result <- survey_data %>%
oneway_anova(life_satisfaction, group = education, weights = sampling_weight)
result %>% tukey_test() # Pairwise comparisons
result %>% levene_test() # Variance homogeneity
# Non-parametric: Kruskal-Wallis → Dunn
kw_result <- survey_data %>%
kruskal_wallis(life_satisfaction, group = education)
kw_result %>% dunn_test() # Pairwise Dunn comparisons
Flexible Output: Compact & Detailed
Every analysis function provides two output levels. Typing the result name prints a compact one-line summary. Calling summary() produces the full SPSS-style output with all details. You can toggle individual sections on or off:
# Compact one-line summary (default)
survey_data %>%
t_test(life_satisfaction, group = gender)
# Full detailed output
survey_data %>%
t_test(life_satisfaction, group = gender) %>%
summary()
# Toggle individual sections
survey_data %>%
t_test(life_satisfaction, group = gender) %>%
summary(effect_sizes = FALSE)
This works for all analysis functions — t_test(), oneway_anova(), chi_square(), pearson_cor(), reliability(), linear_regression(), and more.
SPSS Compatibility
Statistical functions are validated against SPSS v29 within documented per-tier tolerances, typically across four scenarios: weighted/unweighted and grouped/ungrouped. Some statistics have no SPSS equivalent (for example, the weighted variants of the rank-based tests, which SPSS's NPAR TESTS does not support) and are covered by internal regression tests instead. See vignette("spss-compatibility") for the per-function validation status. If you're migrating from SPSS, workflows translate directly:
SPSS:
WEIGHT BY sampling_weight.
T-TEST GROUPS=gender(1 2)
/VARIABLES=satisfaction.
mariposa:
survey_data %>%
t_test(satisfaction, group = gender, weights = sampling_weight)
Documentation
- Complete Reference - All functions with examples
- Getting Started - Introduction and first steps
- Scale Analysis - Reliability, factor analysis, and scale construction
- Regression Analysis - Linear and logistic regression
- Survey Weights Guide - Working with weighted data
Support
- GitHub Issues - Bug reports and feature requests
License
MIT © 2026 Yannick Diehl