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Create Demographic Table

Introduction

This vignette of package DemographicTable (CRAN, Github) presents an idiot-proof interface to create a summary table of simple statistics, often known as demographic table.

Note to Users

Examples in this vignette require that the search path has

library(DemographicTable)
#> Loading required package: flextable
set_flextable_defaults(font.size = 9)

Users may remove the last pipe |> as_flextable() from all examples. The author of this package are forced to have it in this vignette to make package rmarkdown rendering work.

Demographic Table

Data preparation

tgr = ToothGrowth |> 
  within.data.frame(expr = {
    dose = factor(dose) 
  })

Summary of all subjects

tgr |>
  DemographicTable(include = c('supp', 'len', 'dose')) |> 
  as_flextable()

tgr

n=60

len.
 mean±sd
 median; IQR
 range


18.8±7.6
.
4.2~33.9

dose: n (%).
 0.5
 1
 2


20 (33.3%)
20 (33.3%)
20 (33.3%)

supp: n (%).
 OJ
 VC


30 (50.0%)
30 (50.0%)

n=60

tgr

Summary by one group

Color of each group is determined by scales::pal_hue(), which is the default color pallete used in package ggplot2.

tgr |>
  DemographicTable(groups = 'supp', include = c('len', 'dose')) |> 
  as_flextable()

tgr

n=60

supp

OJ
n=30 (50.0%)

VC
n=30 (50.0%)

Signif

len.
 mean±sd
 median; IQR
 range


18.8±7.6
.
4.2~33.9


20.7±6.6
22.7; 10.2
8.2~30.9


17.0±8.3
.
4.2~33.9

0.064
Wilcoxon-
Mann-Whitney

dose: n (%).
 0.5
 1
 2


20 (33.3%)
20 (33.3%)
20 (33.3%)


10 (33.3%)
10 (33.3%)
10 (33.3%)


10 (33.3%)
10 (33.3%)
10 (33.3%)

1.000
Fisher's Exact

n=60

supp

tgr

User may choose to hide the \(p\)-values with compare = FALSE.

tgr |>
  DemographicTable(groups = 'supp', include = c('len', 'dose'), compare = FALSE) |> 
  as_flextable()

tgr

n=60

supp

OJ
n=30 (50.0%)

VC
n=30 (50.0%)

len.
 mean±sd
 median; IQR
 range


18.8±7.6
.
4.2~33.9


20.7±6.6
22.7; 10.2
8.2~30.9


17.0±8.3
.
4.2~33.9

dose: n (%).
 0.5
 1
 2


20 (33.3%)
20 (33.3%)
20 (33.3%)


10 (33.3%)
10 (33.3%)
10 (33.3%)


10 (33.3%)
10 (33.3%)
10 (33.3%)

n=60

supp

tgr

Summary by multiple groups

tgr |>
  DemographicTable(groups = c('supp', 'dose'), include = c('len', 'supp')) |>
  as_flextable()

tgr

n=60

supp

dose

OJ
n=30 (50.0%)

VC
n=30 (50.0%)

Signif1

0.5
n=20 (33.3%)

1
n=20 (33.3%)

2
n=20 (33.3%)

Signif2

len.
 mean±sd
 median; IQR
 range


18.8±7.6
.
4.2~33.9


20.7±6.6
22.7; 10.2
8.2~30.9


17.0±8.3
.
4.2~33.9

0.064
Wilcoxon-
Mann-Whitney


10.6±4.5
.
4.2~21.5


19.7±4.4
.
13.6~27.3


26.1±3.8
.
18.5~33.9

★ 0.000; ⸢1⸥ vs. ⸢0.5⸥
★ 0.000; ⸢2⸥ vs. ⸢0.5⸥
★ 0.000; ⸢2⸥ vs. ⸢1⸥
Pairwise Two-Sample t

supp: n (%).
 OJ
 VC


30 (50.0%)
30 (50.0%)


30 (100.0%)
-


-
30 (100.0%)

★ 0.000
Fisher's Exact


10 (50.0%)
10 (50.0%)


10 (50.0%)
10 (50.0%)


10 (50.0%)
10 (50.0%)

1.000
Fisher's Exact

n=60

supp

dose

tgr

Contatenate multiple DemographicTables

tb1 = CO2 |>
  DemographicTable(groups = 'Type', include = c('conc', 'uptake'))
tb2 = CO2 |>
  subset(subset = (Treatment == 'nonchilled')) |> 
  DemographicTable(groups = 'Type', include = c('conc', 'uptake'), data.name = 'CO2_nonchilled')
c(tb1, tb2) |> as_flextable()

CO2

CO2_nonchilled

n=84

Type

n=42

Type

Quebec
n=42 (50.0%)

Mississippi
n=42 (50.0%)

Signif1

Quebec
n=21 (50.0%)

Mississippi
n=21 (50.0%)

Signif2

conc.
 mean±sd
 median; IQR
 range


435.0±295.9
350.0; 500.0
95.0~1000.0


435.0±297.7
350.0; 500.0
95.0~1000.0


435.0±297.7
350.0; 500.0
95.0~1000.0

1.000
Two-Sample t


435.0±297.7
350.0; 500.0
95.0~1000.0


435.0±301.4
350.0; 500.0
95.0~1000.0


435.0±301.4
350.0; 500.0
95.0~1000.0

1.000
Wilcoxon-
Mann-Whitney

uptake.
 mean±sd
 median; IQR
 range


27.2±10.8
28.3; 19.2
7.7~45.5


33.5±9.7
37.2; 9.8
9.3~45.5


20.9±7.8
19.3; 14.2
7.7~35.5

0.000★
Two-Sample t


30.6±9.7
31.3; 12.2
10.6~45.5


35.3±9.6
39.2; 9.4
13.6~45.5


26.0±7.4
28.1; 9.1
10.6~35.5

0.000★
Wilcoxon-
Mann-Whitney

n=84

Type

n=42

Type

CO2

CO2_nonchilled

Exception Handling

Missing value in groups

MASS::survey |>
  DemographicTable(groups = c('M.I'), include = c('Pulse', 'Fold')) |>
  as_flextable()

MASS::survey

n=237

M.I
n=28 (11.8%) missing

Imperial
n=68 (28.7%)

Metric
n=141 (59.5%)

Signif

Pulse.
 mean±sd
 median; IQR
 range

n*=192
74.2±11.7
.
35.0~104.0

n*=59
73.9±10.2
72.0; 12.0
40.0~104.0

n*=112
73.9±12.2
.
35.0~104.0

0.974
Two-Sample t

Fold: n (%).
 L on R
 Neither
 R on L


99 (41.8%)
18 (7.6%)
120 (50.6%)


32 (47.1%)
3 (4.4%)
33 (48.5%)


59 (41.8%)
12 (8.5%)
70 (49.6%)

0.532
Fisher's Exact

n=237

M.I
n=28 (11.8%) missing

MASS::survey

Use of logical values

Use of logical values is discouraged, as this practice is proved confusing to scientists without a strong data background. A warning message will be printed.

#> Some scientists do not understand logical value (e.g., arm_intervention being TRUE/FALSE)
#> Consider using 2-level factor (e.g., arm being intervention/control)
mtc = mtcars |>
  within.data.frame(expr = {
    vs = as.logical(vs)
    am = as.logical(am)
  })
tryCatch(DemographicTable(mtc, groups = 'am', include = c('hp', 'drat')), warning = identity)
#> <simpleWarning in DemographicTable(mtc, groups = "am", include = c("hp", "drat")): Some scientists do not understand logical value (e.g., arm_intervention being TRUE/FALSE)
#> Consider using 2-level factor (e.g., arm being intervention/control)>
tryCatch(DemographicTable(mtc, groups = 'cyl', include = c('vs')), warning = identity)
#> <simpleWarning in FUN(X[[i]], ...): Some scientists do not understand logical value (e.g., arm_intervention being TRUE/FALSE)
#> Consider using 2-level factor (e.g., arm being intervention/control)>

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.