Your first cross-tables | Colours: reading a table at a glance | Colours that respect significance | Confidence intervals, tests and stars | Batteries of yes/no items, and a score | Sub-tables, and several row variables | The most compact table: one block of columns per group | Weights | Exporting tables | Black and white, for publication | What the cell shows: the display grammar | Which cells make the table interesting? (color = "contrib") | 1. "Which cells build this association?" | 2. "Which cells are notably off, on a scale I can compare?" | Hover tooltips (html tables) | Deviations and their confidence intervals, charted: forest_plot() | Working with the result | Global R options | Where to go next

Getting the plain numbers out | The fields of a cell | Reading and changing fields | Composite display: combining fields | Creating a column from another field | Total rows, reference rows and columns | Column attributes | Knowing what you have before you touch it | Building cells from scratch | Tables from pre-aggregated counts | Pivoting a grouped table into columns | Building many tables at once | Advanced options | See also

What we are and are not doing | The words, in plain language | The data | 1. Start from what you can see | 2. Turn a percentage into a comparison | What generalises, and what does not | 3. Hold the other variables equal | Choosing a model: what kind of number is the outcome? | What can I ask of this outcome? | The bridge: the observed column is the cross-table | Reading a row from left to right | A predictor with no levels | The reference profile | 4. Come back to something you can say | One model, four readings | A marginal effect is a deviation averaged over the sample | Which one to report | For whom? — effect, and the ideal type | 5. Studying what the adjustment did | The five things adjustment can do | Watching it move, one block at a time | Colouring the movement | 6. What the model cannot settle | "All else equal" is a sentence about your table | Adjusting away the thing you are studying | When adjustment reverses the answer | When the movement is arithmetic and not confounding | Three ways to get this wrong | Where to go next | Where these ideas come from

1. What a regression table is | The outcome's type chooses the model and the observed quantity to compare with | Every outcome has an observed counterpart | An ordered outcome: what changes when you adjust | A nominal outcome: the crude number rides in the cell | A summed score | How to read it — and how not to | 2. The four arguments: family, link, measure, effect | The four arguments, in plain words | Which model an outcome can be fitted with | What each measure reports, from any of them | What the header words mean | The one argument that changes the model: link | Caveats worth knowing | Asking your own outcome | 3. One part per kind of outcome | Logistic regression (a binary factor) | Reading the same model in another measure | Percentage points: measure = "difference" | "How many times as likely?": measure = "ratio" | The odds ratio, made comparable: measure = "odds_ratio" on a prediction route | The other risk ratio: link = "ratio" | Linear regression (a numeric outcome) | Poisson regression (a count outcome) | Grouped-binomial outcomes (a summed score) | Ordinal and nominal outcomes (a factor with 3+ levels) | 4. Reading what adjustment did | Colouring what adjustment did | One warning about odds ratios | Is the gap bigger than noise? | For the record: what exactly is tested | 5. Shaping the table | Fine-tuning the display, and scaling a predictor | The reference a predictor's effect is measured from | Interactions: one predictor's effect depending on another | A continuous predictor: slopes within groups | The scale carries the interaction | Three ways to get this wrong | Comparing several models | The same model within sub-populations | Weighted and survey data | 6. Checking the model | Reading the footer statistics | The five model checks | The shape of a continuous predictor | Curing a shape | The check plots: reg_check_plots() | 7. Plots | The results: forest_plot() | Observed versus modelled, drawn honestly | Where to go next

Level 1 — weighted percentages, plain margins of error (the default) | Level 2 — design_effect = TRUE | Level 3 — a survey design | What level 2 can and cannot do for you | Regression on weighted data | Why a weighted tab_reg() is not a weighted tab() | The fine print | Where to go next