Creating comparison tables with MakeComparisonTable()

1 Overview

MakeComparisonTable() creates publication-ready summary tables for comparing groups across continuous and categorical variables.

This vignette demonstrates:

• Basic group comparisons
• Adding effect sizes
• Parametric versus nonparametric testing
• Covariate adjustment
• Pairwise comparisons
• Reference-group comparisons
• Automatic use of variable labels

2 Load packages

library(SciDataReportR)
library(dplyr)

3 Load example data

SciDataReportR includes an example dataset and codebook.

data("SampleData")
data("SampleVariableTypes")

RevaluedObj <- RevalueData(
  SampleData,
  SampleVariableTypes
) 
df_Revalued <- RevaluedObj$RevaluedData

4 Basic comparison table

The most common use case is comparing groups across a set of variables.

Here we compare diagnostic groups.

tbl_basic <- MakeComparisonTable(
  df_Revalued,
  CompVariable = "Diagnosis",
  Variables = c(
    "age",
    "sex",
    "Genotype",
    "AXL",
    "Calbindin",
    "Ferritin",
    "MMP7"
  )
)

tbl_basic

Comparison table (display: mean (SD)). Global p-values: unadjusted (no covariates). Categorical global test: auto; adjusted multi-category: multinomial_LR. Pairwise: not included (p-adjust: bonferroni).

Characteristic	Control N = 2421	Impaired N = 911	p-value	Test
Age	72.75 (13.26)	71.78 (13.12)	0.553	Welch t-test
Sex			0.0272	Pearson chi-squared
Female	157 (65%)	47 (52%)
Male	85 (35%)	44 (48%)
Genotype			<0.001	Fisher (sim.)
E2E2	2 (0.8%)	0 (0%)
E2E3	30 (12%)	7 (7.7%)
E2E4	7 (2.9%)	1 (1.1%)
E3E3	133 (55%)	34 (37%)
E3E4	65 (27%)	41 (45%)
E4E4	5 (2.1%)	8 (8.8%)
AXL receptor tyrosine kinase	0.28 (0.46)	0.34 (0.41)	0.238	Welch t-test
Calbindin	21.99 (3.90)	22.93 (4.85)	0.101	Welch t-test
Ferritin	2.70 (0.76)	2.90 (0.83)	0.0426	Welch t-test
Matrix metalloproteinase 7	-4.07 (1.58)	-3.21 (1.28)	<0.001	Welch t-test
1 Mean (SD); n (%)

The resulting table contains:

• Descriptive statistics
• Group comparison tests
• P-values
• Automatically applied variable labels

5 Adding effect sizes

Effect sizes provide information about the magnitude of group differences.

tbl_effectsize <- MakeComparisonTable(
  df_Revalued,
  CompVariable = "Diagnosis",
  Variables = c(
    "age",
    "sex",
    "Genotype",
    "AXL",
    "Calbindin",
    "Ferritin",
    "MMP7"
  ),
  AddEffectSize = TRUE
)

tbl_effectsize

Comparison table (display: mean (SD)). Global p-values: unadjusted (no covariates). Categorical global test: auto; adjusted multi-category: multinomial_LR. Pairwise: not included (p-adjust: bonferroni).

Characteristic	Control N = 2421	Impaired N = 911	p-value	Test	Effect size	ES method
Age	72.75 (13.26)	71.78 (13.12)	0.553	Welch t-test	0.07	|d|
Sex			0.0272	Pearson chi-squared	0.12	Cramer's V
Female	157 (65%)	47 (52%)
Male	85 (35%)	44 (48%)
Genotype			<0.001	Fisher (sim.)	0.25	Cramer's V
E2E2	2 (0.8%)	0 (0%)
E2E3	30 (12%)	7 (7.7%)
E2E4	7 (2.9%)	1 (1.1%)
E3E3	133 (55%)	34 (37%)
E3E4	65 (27%)	41 (45%)
E4E4	5 (2.1%)	8 (8.8%)
AXL receptor tyrosine kinase	0.28 (0.46)	0.34 (0.41)	0.238	Welch t-test	0.14	|d|
Calbindin	21.99 (3.90)	22.93 (4.85)	0.101	Welch t-test	0.22	|d|
Ferritin	2.70 (0.76)	2.90 (0.83)	0.0426	Welch t-test	0.26	|d|
Matrix metalloproteinase 7	-4.07 (1.58)	-3.21 (1.28)	<0.001	Welch t-test	0.57	|d|
1 Mean (SD); n (%)

When reporting group differences, effect sizes should generally be interpreted alongside p-values.

6 Nonparametric analyses

Nonparametric testing uses rank-based methods that are more robust to non-normal distributions.

tbl_nonparametric <- MakeComparisonTable(
  df_Revalued,
  CompVariable = "Diagnosis",
  Variables = c(
    "age",
    "sex",
    "Genotype",
    "AXL",
    "Calbindin",
    "Ferritin",
    "MMP7"
  ),
  Parametric = FALSE,
  AddEffectSize = TRUE
)

tbl_nonparametric

Comparison table (display: median [IQR]). Global p-values: unadjusted (no covariates). Categorical global test: auto; adjusted multi-category: multinomial_LR. Pairwise: not included (p-adjust: bonferroni).

Characteristic	Control N = 2421	Impaired N = 911	p-value	Test	Effect size	ES method
Age	74.00 [64.00, 83.00]	73.00 [63.00, 82.00]	0.707	Wilcoxon rank-sum	-0.00	epsilon-squared
Sex			0.0272	Pearson chi-squared	0.12	Cramer's V
Female	157 (65%)	47 (52%)
Male	85 (35%)	44 (48%)
Genotype			<0.001	Fisher (sim.)	0.25	Cramer's V
E2E2	2 (0.8%)	0 (0%)
E2E3	30 (12%)	7 (7.7%)
E2E4	7 (2.9%)	1 (1.1%)
E3E3	133 (55%)	34 (37%)
E3E4	65 (27%)	41 (45%)
E4E4	5 (2.1%)	8 (8.8%)
AXL receptor tyrosine kinase	0.28 [-0.04, 0.61]	0.28 [0.10, 0.61]	0.481	Wilcoxon rank-sum	-0.00	epsilon-squared
Calbindin	21.92 [19.63, 24.46]	22.66 [20.00, 26.91]	0.119	Wilcoxon rank-sum	0.00	epsilon-squared
Ferritin	2.71 [2.20, 3.22]	2.90 [2.29, 3.33]	0.127	Wilcoxon rank-sum	0.00	epsilon-squared
Matrix metalloproteinase 7	-4.03 [-5.12, -3.16]	-3.35 [-4.03, -2.26]	<0.001	Wilcoxon rank-sum	0.07	epsilon-squared
1 Median [Q1, Q3]; n (%)

7 Including Covariates

Age is frequently included as a covariate in biomedical analyses.

The example below evaluates group differences after accounting for age.

tbl_covariate <- MakeComparisonTable(
  df_Revalued,
  CompVariable = "Diagnosis",
  Variables = c(
    "sex",
    "Genotype",
    "AXL",
    "Calbindin",
    "Ferritin",
    "MMP7"
  ),
  Covariates = "age",
  AddEffectSize = TRUE
)

tbl_covariate

Comparison table (display: mean (SD)). Global p-values: adjusted (ANCOVA Type II / LR). Categorical global test: auto; adjusted multi-category: multinomial_LR. Pairwise: not included (p-adjust: bonferroni).

Characteristic	Control N = 2421	Impaired N = 911	p-value	Test	Effect size	ES method
Sex			0.0323	Logistic regression (LR)	0.12	Cramer's V
Female	157 (65%)	47 (52%)
Male	85 (35%)	44 (48%)
Genotype			<0.001	Multinomial LR	0.25	Cramer's V
E2E2	2 (0.8%)	0 (0%)
E2E3	30 (12%)	7 (7.7%)
E2E4	7 (2.9%)	1 (1.1%)
E3E3	133 (55%)	34 (37%)
E3E4	65 (27%)	41 (45%)
E4E4	5 (2.1%)	8 (8.8%)
AXL receptor tyrosine kinase	0.28 (0.46)	0.34 (0.41)	0.257	ANCOVA (Type II)	0.00	partial eta-squared
Calbindin	21.99 (3.90)	22.93 (4.85)	0.0722	ANCOVA (Type II)	0.01	partial eta-squared
Ferritin	2.70 (0.76)	2.90 (0.83)	0.0454	ANCOVA (Type II)	0.01	partial eta-squared
Matrix metalloproteinase 7	-4.07 (1.58)	-3.21 (1.28)	<0.001	ANCOVA (Type II)	0.06	partial eta-squared
1 n (%); Mean (SD)

Covariate adjustment can help determine whether observed group differences persist after controlling for potential confounding factors.

8 Pairwise comparisons

When comparing more than two groups, pairwise comparisons can identify which groups differ from one another.

tbl_pairwise <- MakeComparisonTable(
  df_Revalued,
  CompVariable = "Genotype",
  Variables = c(
    "Diagnosis",
    "age",
    "sex",
    "AXL",
    "Calbindin",
    "Ferritin",
    "MMP7"
  ),
  AddPairwise = TRUE,
  AddEffectSize = TRUE
)

tbl_pairwise

Comparison table (display: mean (SD)). Global p-values: unadjusted (no covariates). Categorical global test: auto; adjusted multi-category: multinomial_LR. Pairwise: included (p-adjust: bonferroni).

Characteristic	E2E2 N = 21	E2E3 N = 371	E2E4 N = 81	E3E3 N = 1671	E3E4 N = 1061	E4E4 N = 131	p-value	Test	Effect size	ES method	E2E2 - E2E3	E2E2 - E2E4	E2E2 - E3E3	E2E2 - E3E4	E2E2 - E4E4	E2E3 - E2E4	E2E3 - E3E3	E2E3 - E3E4	E2E3 - E4E4	E2E4 - E3E3	E2E4 - E3E4	E2E4 - E4E4	E3E3 - E3E4	E3E3 - E4E4	E3E4 - E4E4
Diagnosis							<0.001	Fisher (sim.)	0.25	Cramer's V	1	1	1	1	1	1	1	0.426	0.163	1	1	1	0.0143	0.037	1
Control	2 (100%)	30 (81%)	7 (88%)	133 (80%)	65 (61%)	5 (38%)
Impaired	0 (0%)	7 (19%)	1 (13%)	34 (20%)	41 (39%)	8 (62%)
Age	54.00 (15.56)	72.30 (12.78)	78.13 (19.47)	72.04 (13.87)	72.85 (11.37)	74.83 (15.19)	0.305	ANOVA	0.02	eta-squared	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Sex							0.473	Fisher (sim.)	0.12	Cramer's V	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Female	0 (0%)	22 (59%)	4 (50%)	100 (60%)	70 (66%)	8 (62%)
Male	2 (100%)	15 (41%)	4 (50%)	67 (40%)	36 (34%)	5 (38%)
AXL receptor tyrosine kinase	0.08 (0.64)	0.32 (0.42)	0.38 (0.66)	0.31 (0.47)	0.29 (0.41)	0.13 (0.33)	0.727	ANOVA	0.01	eta-squared	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Calbindin	20.24 (5.22)	21.98 (4.04)	23.36 (3.89)	22.04 (4.31)	22.70 (3.88)	21.55 (5.81)	0.676	ANOVA	0.01	eta-squared	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Ferritin	2.54 (1.06)	2.76 (0.80)	2.77 (0.91)	2.74 (0.81)	2.81 (0.75)	2.48 (0.62)	0.807	ANOVA	0.01	eta-squared	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Matrix metalloproteinase 7	-3.49 (3.68)	-3.80 (1.46)	-4.10 (1.54)	-3.82 (1.53)	-3.83 (1.59)	-3.98 (1.83)	0.993	ANOVA	0.00	eta-squared	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
1 n (%); Mean (SD)

You can also choose a referent and compare only to that

tbl_pairwise_referent <- MakeComparisonTable(
  df_Revalued,
  CompVariable = "Genotype",
  Variables = c(
    "Diagnosis",
    "age",
    "sex",
    "AXL",
    "Calbindin",
    "Ferritin",
    "MMP7"
  ),
  AddPairwise = TRUE,
  AddEffectSize = TRUE,
  Referent = "E3E3"
)

tbl_pairwise_referent

Comparison table (display: mean (SD)). Global p-values: unadjusted (no covariates). Categorical global test: auto; adjusted multi-category: multinomial_LR. Pairwise: included (p-adjust: bonferroni).

Characteristic	E2E2 N = 21	E2E3 N = 371	E2E4 N = 81	E3E3 N = 1671	E3E4 N = 1061	E4E4 N = 131	p-value	Test	Effect size	ES method	E3E3 - E2E2	E3E3 - E2E3	E3E3 - E2E4	E3E3 - E3E4	E3E3 - E4E4
Diagnosis							<0.001	Fisher (sim.)	0.25	Cramer's V	1	1	1	0.00475	0.0123
Control	2 (100%)	30 (81%)	7 (88%)	133 (80%)	65 (61%)	5 (38%)
Impaired	0 (0%)	7 (19%)	1 (13%)	34 (20%)	41 (39%)	8 (62%)
Age	54.00 (15.56)	72.30 (12.78)	78.13 (19.47)	72.04 (13.87)	72.85 (11.37)	74.83 (15.19)	0.305	ANOVA	0.02	eta-squared	1	1	1	1	1
Sex							0.481	Fisher (sim.)	0.12	Cramer's V	0.826	1	1	1	1
Female	0 (0%)	22 (59%)	4 (50%)	100 (60%)	70 (66%)	8 (62%)
Male	2 (100%)	15 (41%)	4 (50%)	67 (40%)	36 (34%)	5 (38%)
AXL receptor tyrosine kinase	0.08 (0.64)	0.32 (0.42)	0.38 (0.66)	0.31 (0.47)	0.29 (0.41)	0.13 (0.33)	0.727	ANOVA	0.01	eta-squared	1	1	1	1	0.404
Calbindin	20.24 (5.22)	21.98 (4.04)	23.36 (3.89)	22.04 (4.31)	22.70 (3.88)	21.55 (5.81)	0.676	ANOVA	0.01	eta-squared	1	1	1	0.963	1
Ferritin	2.54 (1.06)	2.76 (0.80)	2.77 (0.91)	2.74 (0.81)	2.81 (0.75)	2.48 (0.62)	0.807	ANOVA	0.01	eta-squared	1	1	1	1	0.926
Matrix metalloproteinase 7	-3.49 (3.68)	-3.80 (1.46)	-4.10 (1.54)	-3.82 (1.53)	-3.83 (1.59)	-3.98 (1.83)	0.993	ANOVA	0.00	eta-squared	1	1	1	1	1
1 n (%); Mean (SD)

9 Summary

MakeComparisonTable() supports:

• Descriptive statistics
• Parametric analyses
• Nonparametric analyses
• Effect sizes
• Covariate adjustment
• Pairwise testing
• Reference-group comparisons
• Automatic variable labeling

10 Related functions

• RevalueData()
• CreateCodebook()
• PlotBoxPlot()
• PlotVolcanoEffects()
• CompareDatasets()

11 Session information

sessionInfo()

R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] dplyr_1.2.1           SciDataReportR_20.9.0

loaded via a namespace (and not attached):
  [1] Exact_3.3              ggstatsplot_1.0.0      sjlabelled_1.2.0
  [4] tidyselect_1.2.1       rootSolve_1.8.2.4      farver_2.1.2
  [7] statsExpressions_2.0.0 S7_0.2.2               fastmap_1.2.0
 [10] bayestestR_0.18.1      broom.helpers_1.22.0   labelled_2.16.0
 [13] digest_0.6.39          estimability_2.0.0     lifecycle_1.0.5
 [16] lmom_3.3               magrittr_2.0.5         compiler_4.6.1
 [19] rlang_1.2.0            sass_0.4.10            tools_4.6.1
 [22] yaml_2.3.12            gt_1.3.0               data.table_1.18.4
 [25] knitr_1.51             xml2_1.6.0             RColorBrewer_1.1-3
 [28] abind_1.4-8            expm_1.0-0             withr_3.0.3
 [31] purrr_1.2.2            nnet_7.3-20            grid_4.6.1
 [34] datawizard_1.3.1       xtable_1.8-8           e1071_1.7-17
 [37] gtsummary_2.5.1        paletteer_1.7.0        ggplot2_4.0.3
 [40] MASS_7.3-65            emmeans_2.0.3          scales_1.4.0
 [43] dichromat_2.0-0.1      insight_1.5.2          cli_3.6.6
 [46] mvtnorm_1.4-1          rmarkdown_2.31         generics_0.1.4
 [49] otel_0.2.0             RcppParallel_5.1.11-2  rstudioapi_0.19.0
 [52] httr_1.4.8             tzdb_0.5.0             parameters_0.29.2
 [55] commonmark_2.0.0       readxl_1.5.0           gld_2.6.8
 [58] proxy_0.4-29           effectsize_1.0.2       cellranger_1.1.0
 [61] base64enc_0.1-6        vctrs_0.7.3            Matrix_1.7-5
 [64] boot_1.3-32            sandwich_3.1-1         jsonlite_2.0.0
 [67] carData_3.0-6          car_3.1-5              litedown_0.9
 [70] hms_1.1.4              patchwork_1.3.2        rstatix_1.0.0
 [73] Formula_1.2-5          correlation_0.8.8      tidyr_1.3.2
 [76] glue_1.8.1             rematch2_2.1.2         gtable_0.3.6
 [79] tibble_3.3.1           pillar_1.11.1          htmltools_0.5.9
 [82] R6_2.6.1               evaluate_1.0.5         lattice_0.22-9
 [85] readr_2.2.0            markdown_2.0           haven_2.5.5
 [88] backports_1.5.1        cards_0.8.0            broom_1.0.13
 [91] snakecase_0.11.1       rstantools_2.6.0       DescTools_0.99.60
 [94] class_7.3-23           Rcpp_1.1.1-1.1         coda_0.19-4.1
 [97] xfun_0.59              fs_2.1.0               zoo_1.8-15
[100] forcats_1.0.1          pkgconfig_2.0.3