table_continuous() summarizes continuous variables
either overall or by a categorical grouping variable. It is designed for
readable summary tables in the console and for publication-ready outputs
in rendered documents. When by is supplied, it can also add
group-comparison tests, test statistics, and effect sizes.
Use select to choose the continuous variables you want
to summarize:
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health)
)
#> Descriptive statistics
#>
#> Variable │ M SD Min Max 95% CI LL
#> ────────────────────────────────┼────────────────────────────────────────
#> Body mass index │ 25.93 3.72 16.00 38.90 25.72
#> WHO-5 wellbeing index (0-100) │ 69.04 15.62 18.70 100.00 68.16
#> Satisfaction with health (1-5) │ 3.55 1.25 1.00 5.00 3.48
#>
#> Variable │ 95% CI UL n
#> ────────────────────────────────┼─────────────────
#> Body mass index │ 26.14 1188
#> WHO-5 wellbeing index (0-100) │ 69.93 1200
#> Satisfaction with health (1-5) │ 3.62 1192If you omit select, table_continuous()
scans the data frame and keeps numeric columns:
table_continuous(sochealth)
#> Descriptive statistics
#>
#> Variable │ M SD Min
#> ────────────────────────────────────────────┼───────────────────────────
#> Age (years) │ 49.26 14.70 25.00
#> Monthly household income (CHF) │ 3833.00 1394.58 1000.00
#> WHO-5 wellbeing index (0-100) │ 69.04 15.62 18.70
#> Body mass index │ 25.93 3.72 16.00
#> Political position (0 = left, 10 = right) │ 5.48 2.03 0.00
#> Satisfaction with health (1-5) │ 3.55 1.25 1.00
#> Satisfaction with work (1-5) │ 3.38 1.18 1.00
#> Satisfaction with relationships (1-5) │ 3.72 1.10 1.00
#> Satisfaction with standard of living (1-5) │ 3.40 1.16 1.00
#> Survey design weight │ 1.00 0.41 0.29
#>
#> Variable │ Max 95% CI LL 95% CI UL
#> ────────────────────────────────────────────┼───────────────────────────────
#> Age (years) │ 75.00 48.43 50.10
#> Monthly household income (CHF) │ 7388.00 3754.01 3911.98
#> WHO-5 wellbeing index (0-100) │ 100.00 68.16 69.93
#> Body mass index │ 38.90 25.72 26.14
#> Political position (0 = left, 10 = right) │ 10.00 5.36 5.60
#> Satisfaction with health (1-5) │ 5.00 3.48 3.62
#> Satisfaction with work (1-5) │ 5.00 3.31 3.45
#> Satisfaction with relationships (1-5) │ 5.00 3.66 3.79
#> Satisfaction with standard of living (1-5) │ 5.00 3.33 3.46
#> Survey design weight │ 3.45 0.97 1.02
#>
#> Variable │ n
#> ────────────────────────────────────────────┼──────
#> Age (years) │ 1200
#> Monthly household income (CHF) │ 1200
#> WHO-5 wellbeing index (0-100) │ 1200
#> Body mass index │ 1188
#> Political position (0 = left, 10 = right) │ 1185
#> Satisfaction with health (1-5) │ 1192
#> Satisfaction with work (1-5) │ 1192
#> Satisfaction with relationships (1-5) │ 1192
#> Satisfaction with standard of living (1-5) │ 1192
#> Survey design weight │ 1200Add by to summarize the same variables across
categories. When by is supplied, a Welch-test
p-value column is added automatically:
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health),
by = education
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max
#> ────────────────────────────────┼──────────────────────────────────────────────
#> Body mass index │ Lower secondary 28.09 3.47 18.20 38.90
#> │ Upper secondary 26.02 3.43 16.00 37.10
#> │ Tertiary 24.39 3.52 16.00 33.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary 57.22 15.44 18.70 97.90
#> │ Upper secondary 68.97 13.62 26.70 100.00
#> │ Tertiary 76.85 13.23 40.40 100.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary 2.71 1.20 1.00 5.00
#> │ Upper secondary 3.53 1.19 1.00 5.00
#> │ Tertiary 4.11 1.04 1.00 5.00
#>
#> Variable │ Group 95% CI LL 95% CI UL n
#> ────────────────────────────────┼────────────────────────────────────────────
#> Body mass index │ Lower secondary 27.66 28.51 260
#> │ Upper secondary 25.73 26.31 534
#> │ Tertiary 24.04 24.74 394
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary 55.33 59.10 261
#> │ Upper secondary 67.82 70.12 539
#> │ Tertiary 75.55 78.15 400
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary 2.57 2.86 259
#> │ Upper secondary 3.43 3.63 534
#> │ Tertiary 4.01 4.21 399
#>
#> Variable │ Group p
#> ────────────────────────────────┼────────────────────────
#> Body mass index │ Lower secondary <.001
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary <.001
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary <.001
#> │ Upper secondary
#> │ TertiaryThis is the main pattern for reporting continuous variables across
groups such as education, sex, treatment arm, or survey wave. Pass
p_value = FALSE to suppress the test column and keep the
output strictly descriptive.
If you want the same outcomes reported in a linear-model workflow,
with heteroskedasticity-consistent standard errors or case weights, use
table_continuous_lm() instead:
table_continuous_lm(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health),
by = education,
vcov = "HC3"
)
#> Continuous outcomes by Highest education level
#>
#> Variable │ M (Lower secondary) M (Upper secondary)
#> ────────────────────────────────┼──────────────────────────────────────────
#> Body mass index │ 26.17 23.55
#> WHO-5 wellbeing index (0-100) │ 67.68 81.56
#> Satisfaction with health (1-5) │ 3.45 4.44
#>
#> Variable │ M (Tertiary) p R² n
#> ────────────────────────────────┼─────────────────────────────────
#> Body mass index │ 26.35 <.001 0.13 1188
#> WHO-5 wellbeing index (0-100) │ 66.10 <.001 0.21 1200
#> Satisfaction with health (1-5) │ 3.35 <.001 0.16 1192Grouped tables can also report the test statistic and an effect size alongside the default p-value column:
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health),
by = education,
statistic = TRUE,
effect_size_ci = TRUE
)
#> Warning: `effect_size_ci` implies `effect_size != "none"`. Defaulting to
#> `effect_size = "auto"`.
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max
#> ────────────────────────────────┼──────────────────────────────────────────────
#> Body mass index │ Lower secondary 28.09 3.47 18.20 38.90
#> │ Upper secondary 26.02 3.43 16.00 37.10
#> │ Tertiary 24.39 3.52 16.00 33.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary 57.22 15.44 18.70 97.90
#> │ Upper secondary 68.97 13.62 26.70 100.00
#> │ Tertiary 76.85 13.23 40.40 100.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary 2.71 1.20 1.00 5.00
#> │ Upper secondary 3.53 1.19 1.00 5.00
#> │ Tertiary 4.11 1.04 1.00 5.00
#>
#> Variable │ Group 95% CI LL 95% CI UL n
#> ────────────────────────────────┼────────────────────────────────────────────
#> Body mass index │ Lower secondary 27.66 28.51 260
#> │ Upper secondary 25.73 26.31 534
#> │ Tertiary 24.04 24.74 394
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary 55.33 59.10 261
#> │ Upper secondary 67.82 70.12 539
#> │ Tertiary 75.55 78.15 400
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary 2.57 2.86 259
#> │ Upper secondary 3.43 3.63 534
#> │ Tertiary 4.01 4.21 399
#>
#> Variable │ Group Test p
#> ────────────────────────────────┼───────────────────────────────────────────────
#> Body mass index │ Lower secondary F(2, 654.48) = 87.96 <.001
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary F(2, 638.59) = 144.35 <.001
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary F(2, 652.08) = 118.74 <.001
#> │ Upper secondary
#> │ Tertiary
#>
#> Variable │ Group ES
#> ────────────────────────────────┼─────────────────────────────────────────
#> Body mass index │ Lower secondary η² = 0.13 [0.10, 0.17]
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary η² = 0.21 [0.17, 0.25]
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Lower secondary η² = 0.16 [0.13, 0.20]
#> │ Upper secondary
#> │ TertiaryUse test = "student" for equal-variance parametric tests
or test = "nonparametric" for rank-based comparisons:
table_continuous(
sochealth,
select = c(bmi, wellbeing_score),
by = education,
test = "nonparametric",
statistic = TRUE,
effect_size = TRUE
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max
#> ───────────────────────────────┼──────────────────────────────────────────────
#> Body mass index │ Lower secondary 28.09 3.47 18.20 38.90
#> │ Upper secondary 26.02 3.43 16.00 37.10
#> │ Tertiary 24.39 3.52 16.00 33.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary 57.22 15.44 18.70 97.90
#> │ Upper secondary 68.97 13.62 26.70 100.00
#> │ Tertiary 76.85 13.23 40.40 100.00
#>
#> Variable │ Group 95% CI LL 95% CI UL n
#> ───────────────────────────────┼────────────────────────────────────────────
#> Body mass index │ Lower secondary 27.66 28.51 260
#> │ Upper secondary 25.73 26.31 534
#> │ Tertiary 24.04 24.74 394
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary 55.33 59.10 261
#> │ Upper secondary 67.82 70.12 539
#> │ Tertiary 75.55 78.15 400
#>
#> Variable │ Group Test p
#> ───────────────────────────────┼───────────────────────────────────────
#> Body mass index │ Lower secondary H(2) = 144.63 <.001
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary H(2) = 233.53 <.001
#> │ Upper secondary
#> │ Tertiary
#>
#> Variable │ Group ES
#> ───────────────────────────────┼────────────────────────────
#> Body mass index │ Lower secondary ε² = 0.12
#> │ Upper secondary
#> │ Tertiary
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Lower secondary ε² = 0.19
#> │ Upper secondary
#> │ Tertiaryeffect_size = TRUE auto-selects the canonical measure
for the chosen test and number of groups: Hedges’
g (parametric, 2 groups), eta-squared (parametric, 3+ groups),
rank-biserial r (nonparametric, 2 groups), or epsilon-squared
(nonparametric, 3+ groups). To pick a specific measure explicitly, pass
its character name:
table_continuous(
sochealth,
select = wellbeing_score,
by = sex,
effect_size = "hedges_g",
effect_size_ci = TRUE
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max 95% CI LL
#> ───────────────────────────────┼────────────────────────────────────────────────
#> WHO-5 wellbeing index (0-100) │ Female 67.16 14.80 19.60 100.00 65.99
#> │ Male 71.05 16.23 18.70 100.00 69.73
#>
#> Variable │ Group 95% CI UL n p
#> ───────────────────────────────┼───────────────────────────────
#> WHO-5 wellbeing index (0-100) │ Female 68.33 620 <.001
#> │ Male 72.37 580
#>
#> Variable │ Group ES
#> ───────────────────────────────┼──────────────────────────────────
#> WHO-5 wellbeing index (0-100) │ Female g = -0.25 [-0.36, -0.14]
#> │ MaleAllowed values are "none" (default), "auto"
(= legacy TRUE), "hedges_g",
"eta_sq", "r_rb", and
"epsilon_sq". Incompatible explicit choices
(e.g. "eta_sq" with two groups, or "hedges_g"
with test = "nonparametric") trigger an actionable
error.
When you need the underlying columns for further processing, use
output = "data.frame":
table_continuous(
sochealth,
select = c(bmi, wellbeing_score),
by = education,
statistic = TRUE,
effect_size = TRUE,
output = "data.frame"
)
#> variable label group mean
#> 1 bmi Body mass index Lower secondary 28.08731
#> 2 bmi Body mass index Upper secondary 26.01891
#> 3 bmi Body mass index Tertiary 24.39036
#> 4 wellbeing_score WHO-5 wellbeing index (0-100) Lower secondary 57.21571
#> 5 wellbeing_score WHO-5 wellbeing index (0-100) Upper secondary 68.96920
#> 6 wellbeing_score WHO-5 wellbeing index (0-100) Tertiary 76.85250
#> sd min max ci_lower ci_upper n test_type statistic df1 df2
#> 1 3.471744 18.2 38.9 27.66333 28.51129 260 welch_anova 87.95902 2 654.4758
#> 2 3.434736 16.0 37.1 25.72693 26.31090 534 <NA> NA NA NA
#> 3 3.520150 16.0 33.0 24.04170 24.73901 394 <NA> NA NA NA
#> 4 15.444587 18.7 97.9 55.33323 59.09819 261 welch_anova 144.35083 2 638.5873
#> 5 13.621193 26.7 100.0 67.81669 70.12172 539 <NA> NA NA NA
#> 6 13.226818 40.4 100.0 75.55235 78.15265 400 <NA> NA NA NA
#> p.value es_type es_value es_ci_lower es_ci_upper
#> 1 1.467916e-34 eta_sq 0.1307679 0.09667861 0.1654516
#> 2 NA <NA> NA NA NA
#> 3 NA <NA> NA NA NA
#> 4 1.888362e-52 eta_sq 0.2081970 0.16901207 0.2461732
#> 5 NA <NA> NA NA NA
#> 6 NA <NA> NA NA NAselect supports tidyselect helpers:
table_continuous(
sochealth,
select = starts_with("life_sat"),
by = sex
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max
#> ────────────────────────────────────────────┼────────────────────────────────
#> Satisfaction with health (1-5) │ Female 3.51 1.25 1.00 5.00
#> │ Male 3.59 1.25 1.00 5.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with work (1-5) │ Female 3.32 1.17 1.00 5.00
#> │ Male 3.44 1.20 1.00 5.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with relationships (1-5) │ Female 3.71 1.09 1.00 5.00
#> │ Male 3.74 1.10 1.00 5.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with standard of living (1-5) │ Female 3.37 1.16 1.00 5.00
#> │ Male 3.42 1.17 1.00 5.00
#>
#> Variable │ Group 95% CI LL 95% CI UL n
#> ────────────────────────────────────────────┼───────────────────────────────────
#> Satisfaction with health (1-5) │ Female 3.41 3.61 616
#> │ Male 3.49 3.69 576
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with work (1-5) │ Female 3.23 3.41 615
#> │ Male 3.34 3.54 577
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with relationships (1-5) │ Female 3.62 3.79 615
#> │ Male 3.65 3.83 577
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with standard of living (1-5) │ Female 3.28 3.46 615
#> │ Male 3.33 3.52 577
#>
#> Variable │ Group p
#> ────────────────────────────────────────────┼──────────────
#> Satisfaction with health (1-5) │ Female .267
#> │ Male
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with work (1-5) │ Female .073
#> │ Male
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with relationships (1-5) │ Female .570
#> │ Male
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with standard of living (1-5) │ Female .453
#> │ MaleFor more programmatic selection, set regex = TRUE:
table_continuous(
sochealth,
select = "^life_sat",
regex = TRUE,
by = education,
output = "data.frame"
)
#> variable label
#> 1 life_sat_health Satisfaction with health (1-5)
#> 2 life_sat_health Satisfaction with health (1-5)
#> 3 life_sat_health Satisfaction with health (1-5)
#> 4 life_sat_work Satisfaction with work (1-5)
#> 5 life_sat_work Satisfaction with work (1-5)
#> 6 life_sat_work Satisfaction with work (1-5)
#> 7 life_sat_relationships Satisfaction with relationships (1-5)
#> 8 life_sat_relationships Satisfaction with relationships (1-5)
#> 9 life_sat_relationships Satisfaction with relationships (1-5)
#> 10 life_sat_standard Satisfaction with standard of living (1-5)
#> 11 life_sat_standard Satisfaction with standard of living (1-5)
#> 12 life_sat_standard Satisfaction with standard of living (1-5)
#> group mean sd min max ci_lower ci_upper n test_type
#> 1 Lower secondary 2.714286 1.2021575 1 5 2.567189 2.861382 259 welch_anova
#> 2 Upper secondary 3.533708 1.1853493 1 5 3.432943 3.634473 534 <NA>
#> 3 Tertiary 4.110276 1.0432216 1 5 4.007602 4.212950 399 <NA>
#> 4 Lower secondary 2.570881 1.1467994 1 5 2.431102 2.710660 261 welch_anova
#> 5 Upper secondary 3.422430 1.1037312 1 5 3.328691 3.516169 535 <NA>
#> 6 Tertiary 3.851010 1.0314174 1 5 3.749112 3.952909 396 <NA>
#> 7 Lower secondary 3.023077 1.2268891 1 5 2.873246 3.172908 260 welch_anova
#> 8 Upper secondary 3.743446 0.9645227 1 5 3.661453 3.825439 534 <NA>
#> 9 Tertiary 4.158291 0.9322485 1 5 4.066423 4.250159 398 <NA>
#> 10 Lower secondary 2.666667 1.1635489 1 5 2.524846 2.808487 261 welch_anova
#> 11 Upper secondary 3.387218 1.1065913 1 5 3.292970 3.481466 532 <NA>
#> 12 Tertiary 3.887218 0.9588582 1 5 3.792847 3.981589 399 <NA>
#> statistic df1 df2 p.value
#> 1 118.73585 2 652.0775 1.063917e-44
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 105.98821 2 651.9434 1.398117e-40
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> 7 82.35074 2 617.9668 1.969764e-32
#> 8 NA NA NA NA
#> 9 NA NA NA NA
#> 10 101.31672 2 648.7723 5.105889e-39
#> 11 NA NA NA NA
#> 12 NA NA NA NAUse exclude when you want a broad selection with one or
two explicit removals:
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health, life_sat_work),
exclude = "life_sat_work",
by = sex
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max
#> ────────────────────────────────┼─────────────────────────────────────
#> Body mass index │ Female 25.69 3.78 16.00 38.90
#> │ Male 26.20 3.64 16.00 37.70
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Female 67.16 14.80 19.60 100.00
#> │ Male 71.05 16.23 18.70 100.00
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Female 3.51 1.25 1.00 5.00
#> │ Male 3.59 1.25 1.00 5.00
#>
#> Variable │ Group 95% CI LL 95% CI UL n p
#> ────────────────────────────────┼──────────────────────────────────────────
#> Body mass index │ Female 25.39 25.98 616 .018
#> │ Male 25.90 26.50 572
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Female 65.99 68.33 620 <.001
#> │ Male 69.73 72.37 580
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> Satisfaction with health (1-5) │ Female 3.41 3.61 616 .267
#> │ Male 3.49 3.69 576Use labels to replace technical variable names with
reporting labels:
pkgdown_dark_gt(
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health),
by = education,
labels = c(
bmi = "Body mass index",
wellbeing_score = "Well-being score",
life_sat_health = "Satisfaction with health"
),
output = "gt"
)
)table_continuous() supports the same reporting-oriented
outputs as table_categorical():
output value |
Returned object |
|---|---|
"default" |
Styled ASCII console table |
"tinytable" |
Formatted tinytable |
"gt" |
Formatted gt table |
"flextable" |
Formatted flextable |
"excel" |
Written .xlsx file |
"clipboard" |
Copied text table |
"word" |
Written .docx file |
For example, tinytable works well in Quarto and R
Markdown documents:
Use the same function to export the table directly:
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health),
by = education,
output = "excel",
excel_path = "table_continuous.xlsx"
)
table_continuous(
sochealth,
select = c(bmi, wellbeing_score, life_sat_health),
by = education,
output = "word",
word_path = "table_continuous.docx"
)The printed ASCII table and every rendered output share the same
formatting vocabulary as table_continuous_lm() and
table_categorical():
align = "decimal" (default) aligns numeric columns on
the decimal mark, matching SPSS / SAS / LaTeX siunitx
conventions. "center", "right", and the legacy
"auto" are the alternatives.p_digits = 3 (default, the APA standard) drives both
the displayed precision of the p column and the
small-p threshold (p_digits = 4 ->
.0451 and <.0001).show_n = FALSE and ci = FALSE drop the
corresponding columns (and the CI spanner / borders) structurally from
every output; the underlying n and ci_lower /
ci_upper are always present in
output = "data.frame" / "long" for downstream
code.table_continuous(
sochealth,
select = wellbeing_score,
by = sex,
ci = FALSE,
show_n = FALSE,
p_digits = 4
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max p
#> ───────────────────────────────┼─────────────────────────────────────────────
#> WHO-5 wellbeing index (0-100) │ Female 67.16 14.80 19.60 100.00 <.0001
#> │ Male 71.05 16.23 18.70 100.00table_continuous() returns an object that can be coerced
to a plain data.frame / tbl_df (stripping the
spicy formatting attributes) or piped into broom::tidy() /
broom::glance() for use with gtsummary,
modelsummary, parameters, or any other
tidyverse stats workflow:
out <- table_continuous(
sochealth,
select = c(bmi, wellbeing_score),
by = sex
)
#> Descriptive statistics
#>
#> Variable │ Group M SD Min Max 95% CI LL
#> ───────────────────────────────┼────────────────────────────────────────────────
#> Body mass index │ Female 25.69 3.78 16.00 38.90 25.39
#> │ Male 26.20 3.64 16.00 37.70 25.90
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Female 67.16 14.80 19.60 100.00 65.99
#> │ Male 71.05 16.23 18.70 100.00 69.73
#>
#> Variable │ Group 95% CI UL n p
#> ───────────────────────────────┼───────────────────────────────
#> Body mass index │ Female 25.98 616 .018
#> │ Male 26.50 572
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#> WHO-5 wellbeing index (0-100) │ Female 68.33 620 <.001
#> │ Male 72.37 580
# Long descriptive rows: one per (variable x group) with broom-style
# columns (outcome, label, group, estimate = mean, std.error,
# conf.low / conf.high, n, min, max, sd).
broom::tidy(out)
#> # A tibble: 4 × 11
#> outcome label group estimate std.error conf.low conf.high n min max
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 bmi Body… Fema… 25.7 0.152 25.4 26.0 616 16 38.9
#> 2 bmi Body… Male 26.2 0.152 25.9 26.5 572 16 37.7
#> 3 wellbeing… WHO-… Fema… 67.2 0.594 66.0 68.3 620 19.6 100
#> 4 wellbeing… WHO-… Male 71.0 0.674 69.7 72.4 580 18.7 100
#> # ℹ 1 more variable: sd <dbl>
# One row per outcome with the omnibus test + effect-size summary
# (test_type, statistic, df, df.residual, p.value, es_type, es_value,
# es_ci_lower / es_ci_upper, n_total).
broom::glance(out)
#> # A tibble: 2 × 12
#> outcome label test_type statistic df df.residual p.value es_type es_value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 bmi Body… welch_t -2.38 1184. NA 1.76e-2 <NA> NA
#> 2 wellbein… WHO-… welch_t -4.33 1169. NA 1.65e-5 <NA> NA
#> # ℹ 3 more variables: es_ci_lower <dbl>, es_ci_upper <dbl>, n_total <int>
# Or just unbox to a plain data.frame (long-format underlying data)
head(as.data.frame(out))
#> variable label group mean sd min
#> 1 bmi Body mass index Female 25.68506 3.781113 16.0
#> 2 bmi Body mass index Male 26.19685 3.638092 16.0
#> 3 wellbeing_score WHO-5 wellbeing index (0-100) Female 67.16194 14.798488 19.6
#> 4 wellbeing_score WHO-5 wellbeing index (0-100) Male 71.04879 16.227304 18.7
#> max ci_lower ci_upper n test_type statistic df1 df2 p.value
#> 1 38.9 25.38588 25.98425 616 welch_t -2.377237 1184.497 NA 1.760093e-02
#> 2 37.7 25.89808 26.49563 572 <NA> NA NA NA NA
#> 3 100.0 65.99480 68.32907 620 welch_t -4.326141 1168.700 NA 1.647005e-05
#> 4 100.0 69.72540 72.37219 580 <NA> NA NA NA NAvignette("table-categorical", package = "spicy")
for grouped tables of categorical variables.vignette("table-continuous-lm", package = "spicy")
for model-based continuous summary tables with robust standard errors or
case weights.vignette("summary-tables-reporting", package = "spicy") for
a cross-function reporting workflow using both summary-table
helpers.