Package index • mariposa

Data Import

Import statistical data files with preserved missing value information

read_spss(): Read SPSS Data with Tagged Missing Values
read_por(): Read SPSS Portable Data with Tagged Missing Values
read_stata(): Read Stata Data with Tagged Missing Values
read_sas(): Read SAS Data with Tagged Missing Values
read_xpt(): Read SAS Transport File with Tagged Missing Values
read_xlsx(): Read Excel Data with Label Reconstruction

Data Export

Export data to statistical formats with preserved labels and missing values

write_spss(): Export Data to SPSS Format
write_stata(): Export Data to Stata Format
write_xpt(): Export Data to SAS Transport Format
write_xlsx(): Export Data to Excel with Label Support

Label Management

Inspect, modify, and convert labelled survey data

var_label(): Get or Set Variable Labels
val_labels(): Get or Set Value Labels
copy_labels(): Copy Labels from One Data Frame to Another
drop_labels(): Remove Unused Value Labels
to_label(): Convert Labelled Variables to Factors
to_character(): Convert Labelled Variables to Character
to_numeric(): Convert Factors or Labelled Variables to Numeric
to_labelled(): Convert Variables to Labelled Format
set_na(): Declare Values as Missing
unlabel(): Remove All Label Metadata

Data Transformation

Recode, dichotomize, dummy-code, standardize, and center variables

rec(): Recode Variables Using String Syntax
to_dummy(): Create Dummy Variables (One-Hot Encoding)
std(): Standardize Variables (Z-Scores)
center(): Center Variables (Mean Centering)

Data Exploration

Search and inspect your data

find_var(): Find Variables by Name or Label

Descriptive Statistics

Summarize and explore your survey data

codebook(): Create a Codebook for Your Data
describe(): Get to Know Your Numeric Data
frequency() fre(): Count How Many People Chose Each Option
crosstab(): Compare Two Categories: See How They Relate

Hypothesis Testing

Compare groups and test for significant differences

t_test(): Test If Two Groups Differ
oneway_anova(): Compare Multiple Groups: Are Their Averages Different?
factorial_anova(): Compare Groups Across Multiple Factors: Factorial ANOVA
ancova(): Analysis of Covariance: ANCOVA
mann_whitney(): Compare Two Groups Without Assuming Normal Data
chi_square() phi() cramers_v() goodman_gamma(): Test If Two Categories Are Related
fisher_test(): Fisher's Exact Test for Small Samples
chisq_gof(): Chi-Square Goodness-of-Fit Test
mcnemar_test(): McNemar's Test for Paired Proportions

Non-Parametric Tests

Distribution-free tests for ordinal and nominal data

kruskal_wallis(): Compare Multiple Groups Without Assuming Normal Data
wilcoxon_test(): Compare Two Related Measurements Without Assuming Normality
friedman_test(): Compare Three or More Related Measurements Without Assuming Normality
binomial_test(): Test Whether a Proportion Matches an Expected Value

Correlation Analysis

Measure relationships between variables

pearson_cor(): Measure How Strongly Variables Are Related
spearman_rho(): Spearman's Rank Correlation Analysis
kendall_tau(): Kendall's Tau Correlation Analysis

Post-Hoc Analysis

Follow-up tests for detailed group comparisons

tukey_test(): Find Which Specific Groups Differ After ANOVA
scheffe_test(): Compare All Groups More Conservatively After ANOVA
levene_test(): Test If Groups Vary Similarly
dunn_test(): Find Which Specific Groups Differ After Kruskal-Wallis
pairwise_wilcoxon(): Find Which Specific Measurements Differ After Friedman Test

Scale Analysis

Factor analysis, reliability, and scale construction

reliability(): Check How Reliably Your Scale Measures a Concept
efa(): Explore the Structure Behind Your Survey Items
pomps(): Transform Scores to Percent of Maximum Possible (POMPS)
row_means(): Compute Row Means Across Items
row_sums(): Compute Row Sums Across Items
row_count(): Count Occurrences of a Value Across Columns

Regression Analysis

Linear and logistic regression with SPSS-compatible output

linear_regression(): Run a Linear Regression
logistic_regression(): Run a Logistic Regression

Weighted Statistics

Individual weighted statistics for survey data

w_mean(): Calculate Population-Representative Averages
w_median(): Find the Population-Representative Middle Value
w_sd(): Calculate Population-Representative Standard Deviations
w_var(): Calculate Population-Representative Variance
w_se(): Calculate Population-Representative Standard Errors
w_iqr(): Measure Population-Representative Spread (IQR)
w_range(): Find the Range of Your Data
w_quantile(): Calculate Population-Representative Percentiles
w_modus(): Find the Most Common Value in Your Population
w_skew(): Measure Population-Representative Skewness
w_kurtosis(): Measure Population-Representative Kurtosis

Datasets

Example datasets for learning and testing

survey_data: Social Survey Data (Synthetic)
longitudinal_data: Longitudinal Study Data (Synthetic)
longitudinal_data_wide: Longitudinal Study Data - Wide Format (Synthetic)