1. Creating Survey Design Objects | 1.1 Simple Random Sample | 1.2 Stratified Design | 1.3 Cluster Design | 1.4 Replicate Weights | 1.5 Calibrated / Non-Probability Samples | 1.6 Two-Phase Designs | 1.7 Constructor Summary | 2. Summary Statistics | 2.1 Weighted Means (Grouped) | 2.2 Proportions / Frequency Tables | 2.3 Population Totals | 2.4 Quantiles | 2.5 Ratios | 2.6 Correlations | 3. Controlling Uncertainty Output | 4. Features With No survey / srvyr Equivalent | 4.1 Automatic Value Labels | 4.2 Multiple Variables in One Call | 4.3 Minimum Cell Size Warnings | 4.4 Weighted Sample Size | 4.5 Metadata-Rich Results (.meta) | 5. Notable Differences | 6. Function Reference Table | 7. Learning More

Creating the survey object | as_survey() | as_survey_replicate() | SRS designs with as_survey() | as_survey_nonprob() | as_survey_twophase() | Conducting Simple Analysis | Frequency tables — get_freqs() | Weighted means — get_means() | Population totals — get_totals() | Weighted correlations — get_corr() | Ratio estimation — get_ratios() | get_quantiles() | Choosing quantiles | Subgroup analysis — the group argument | Controlling uncertainty output | Estimated population counts | Summary

Introduction | 1. Decision Guide | Common surveys at a glance | 2. as_survey() — Taylor Series Designs | 2.1 Core arguments | 2.2 The nest argument | 2.3 The fpc argument | 2.4 Multi-level clustering | 2.5 Worked example: NHANES 2017–2018 | 2.6 Worked example: ANES 2024 | 2.7 Worked example: GSS 2024 | 2.8 Worked example: Pew NPORS 2025 | 3. as_survey_replicate() — Replicate Weight Designs | 3.1 The type argument | 3.2 Worked example: ACS PUMS 2022 — Wyoming | 3.3 Worked example: Pew Jewish Americans 2020 | 3.4 The scale and rscales arguments | 4. as_survey_twophase() — Two-Phase Designs | 4.1 What two-phase sampling is | 4.2 Arguments | 4.3 Worked example: National Wilms Tumor Study | 5. Simple Random Sample with as_survey() | 5.1 The fpc argument matters more here | 5.2 Worked example: School district survey | 6. as_survey_nonprob() — Non-Probability and Calibrated Samples | 6.1 The fundamental distinction | 6.2 What your vendor's weights actually are | 6.3 What you can and cannot claim | 6.4 Worked example: Democracy Fund + UCLA Nationscape | 6.5 What not to do | 6.6 Worked example: University voluntary response survey | 7. Probability, SRS, and calibration weights: understanding the distinction | 8. When no constructor applies: convenience and purposive samples | 8.1 Example: program evaluation classrooms | 8.2 General decision rule | 9. Reference: Common Codebook Variables | References