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tabxplor

tabxplor makes cross-tables and regression models readable at a glance for data exploration. It builds a table with percentages, weighted counts, confidence intervals, tests — and colors highlight the cells that stand out from the total or reference, only when the difference is statistically solid, to spot the structure of your data immediately.

  • Colors encode effect size and significance: the stronger the difference, the deeper the color; non-significant cells are greyed-out.
  • Html, Excel and markdown/Quarto exports are available.
  • It comes with a point-and-click jamovi graphical interface: no code needed.
  • A black-and-white theme = "print_ready" renders the same reading for journals.
  • Regression models are presented with the same visual language, next to their observed effect.
  • In R the tables are tibbles you can keep working on with dplyr. Cells are rich values, each one carries its count, percentage, confidence interval and reference behind the displayed number.
  • Weighted data and survey design are supported.

Installation 

install.packages("tabxplor", dependencies = TRUE)

A quick look

A simple cross-table with row percentages: shades of blue mean the cell is over-represented compared to the total row, shades of yellow to red mean it is under-represented.

gss <- gss_cat_data_formatting() # cleaned-up version of forcats::gss_cat

tab(gss, race, party3, pct = "row", color = "difference")
party3
race 1-Democrat 2-Independent,
other 		3-Republican NA Total
<row%> <row% (n)>
White 39% 21% 40% 1% 100% (16 395)
Black 75% 16% 8% 1% 100% ( 3 129)
Other 48% 32% 18% 1% 100% ( 1 959)
Total 45% 21% 33% 1% 100% (21 483)

Percentage points (risk) difference: cell ≥ the Total row +5; +10; +20; +30 points; cell ≤ the Total row -5; -10; -20; -30 points.

Several column variables can be crossed at once for series of Yes/No survey questions. With color_signif = "grey_non_signif", cells that are not significantly different from the total are greyed out, so every colored figure is a solid one. Use wt = for weighted or survey data. Example with FactoMineR tea data :

tea_when_vars <- c("breakfast", "tea.time", "evening", "lunch", "dinner", "always")
tab(facto_tea, SPC, all_of(tea_when_vars), pct = "row", 
    levels = "first", na = "drop", 
    color = "difference", ref = "first", color_signif = "grey_non_signif")
breakfast tea.time evening lunch dinner always
SPC n breakfast_lv tea time evening_lv lunch_lv dinner_lv always_lv
<n> <row%> <row%> <row%> <row%> <row%> <row%>
employee 59 49% 53% 44% 7% 14% 34%
middle 40 60% 48% 30% 5% 0% 28%
non-worker 64 44% 59% 20% 20% 3% 23%
other worker 20 40% 60% 40% 0% 10% 35%
senior 35 63% 57% 31% 26% 3% 34%
student 70 43% 61% 44% 21% 7% 50%
workman 12 25% 50% 17% 8% 25% 25%
Total 300 48% 56% 34% 15% 7% 34%

Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points).

The same visual language extends to regression models: tab_reg() detects a binary outcome and fits a logistic regression, coloring odds ratios by strength and greying the non-significant ones, with a default comparison between the modelised deviations and their crude/observed counterparts.

tab_reg(gss, outcome = "married", predictors = c("race", "age", "rincome"))

Logistic regression: married by race, age +1 more

married: 01-Married
levels n Obs_OR Model_OR
<n> <(obs%) OR> <OR (adj%)>
Constant Reference profile 1/1.43*** (41%)
race White 9 862 (52%)      1         1    (51%)
Black 1 867 (31%) 1/2.45*** 1/2.22*** (33%)
Other 1 261 (49%) 1/1.11*     1.08    (53%)
age per 26.9 (2SD), at 42.4 (mean)         2.13***   1.95***      
rincome 1-Lt $10000 2 149 (37%)      1         1    (39%)
2-$10000 to 14999 1 168 (41%)   1.21**    1.15*   (42%)
3-$15000 to 24999 2 325 (43%)   1.33***   1.28*** (45%)
4-$25000 or more 7 348 (55%)   2.14***   1.85*** (54%)
Model fit N 12 990
Dispersion (robust/model SE) 1.00
Collinearity (max VIF) 1.03
Influence (max dfbetas) 0.05
LR vs null <0.01%
McFadden R2 0.049
AIC 17 129
BIC 17 181

Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion.
Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2).
***: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); **: at the 95% level; *: at the 90% level; no star: not significant.
outcome numeric predictor observed range observed shape (central 95%)
p = %Married ; log(p/(1-p)) age 13-57% (OR 8.7)

Or as a black and white table ready for publication:

options(tabxplor.theme = "print_ready")
tab_reg(gss, outcome = "married", predictors = c("race", "age", "rincome"))

The same regression table in the black-and-white publication theme: bold, italics and underlining replace the colors, on the same ladder of thresholds.

Export your tables

Any table exports with its colors to Excel, html or markdown (for Word, copy-paste from Excel) :

tab(gss, marital, race, pct = "row", color = "difference") |> tab_html()
tab(gss, marital, race, pct = "row", color = "difference") |> tab_xl()
tab(gss, marital, race, pct = "row", color = "difference") |> tab_xl(theme = "print_ready")

Learn more