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sjtable2df: Overview

However, the tables created using the functions sjPlot::tab_model or sjPlot::tab_xtab return HTML tables and are not straightforward to use in R, especially when trying to integrate them into pdf- or word-documents using Rmarkdown.

Various approaches/ tutorials exist to convert sjPlot HTML tables to R data.frame objects:

None of these approaches converts sjPlot HTML tables to R data.frame objects or integrates well with knitr::kable or the kableExtra R package.

The sjtable2df R package’s goal is to overcome this and to provide an easy interface for converting sjPlot’s HTML tables to data.frame, data.table, or kable objects for further usage in R or Rmarkdown.

Currently, sjtable2df provides two functions to convert tables created from sjPlot’s functions tab_model and tab_xtab: sjtable2df::mtab2df and sjtable2df::xtab2df.

Example: Contingency-Tables

Data Preprocessing

library(sjtable2df)

library(mlbench)
library(magrittr)

# load data
data("PimaIndiansDiabetes2")
dataset <- PimaIndiansDiabetes2 %>%
  data.table::as.data.table()

# create new binary variable
dataset[, ("preg_gt_4") := ifelse(get("pregnant") > 4, 1, 0) %>% factor()]

Create Contingency Table

xtab <- sjPlot::tab_xtab(
  var.row = dataset$diabetes,
  var.col = dataset$preg_gt_4,
  show.summary = TRUE,
  use.viewer = FALSE
)

xtab

diabetes	preg_gt_4		Total
diabetes	0	1	Total
neg	356	144	500
pos	136	132	268
Total	492	276	768
χ²=30.823 · df=1 · φ=0.203 · p=0.000

Convert Contingency Table to `data.frame`

xtab_df <- sjtable2df::xtab2df(xtab = xtab, output = "data.frame")
class(xtab_df)
#> [1] "data.frame"
xtab_df
#>   diabetes preg_gt_4 0 preg_gt_4 1                                Total
#> 1      neg         356         144                                  500
#> 2      pos         136         132                                  268
#> 3    Total         492         276                                  768
#> 4                                  χ2=30.823 · df=1 · φ=0.203 · p=0.000

Convert Contingency Table to `kable`

xtab_kbl <- sjtable2df::xtab2df(
  xtab = xtab,
  output = "kable",
  caption = "Diabetes vs. preg>4",
  col.names = c("Diabetes", "No", "Yes", "Total")
)
class(xtab_kbl)
#> [1] "kableExtra"  "knitr_kable"
xtab_kbl %>%
  kableExtra::add_header_above(
    header = c(" " = 1, "Pregnant > 4" = 2, " " = 1)
  )

Diabetes vs. preg>4
	Pregnant > 4
Diabetes	No	Yes	Total
neg	356	144	500
pos	136	132	268
Total	492	276	768
\(χ2=30.823 · df=1 · φ=0.203 · p=0.000\)

Percentages in cells

This function also extracts further statistics from cells and writes them to parentheses:

xtab <- sjPlot::tab_xtab(
  var.row = dataset$diabetes,
  var.col = dataset$preg_gt_4,
  show.summary = TRUE,
  show.col.prc = TRUE,
  use.viewer = FALSE
)

xtab

diabetes	preg_gt_4		Total
diabetes	0	1	Total
neg	356 72.4 %	144 52.2 %	500 65.1 %
pos	136 27.6 %	132 47.8 %	268 34.9 %
Total	492 100 %	276 100 %	768 100 %
χ²=30.823 · df=1 · φ=0.203 · p=0.000

Convert Contingency Table to `data.frame`

xtab_df <- sjtable2df::xtab2df(xtab = xtab, output = "data.frame")
xtab_df
#>   diabetes  preg_gt_4 0  preg_gt_4 1                                Total
#> 1      neg 356 (72.4 %) 144 (52.2 %)                         500 (65.1 %)
#> 2      pos 136 (27.6 %) 132 (47.8 %)                         268 (34.9 %)
#> 3    Total  492 (100 %)  276 (100 %)                          768 (100 %)
#> 4                                    χ2=30.823 · df=1 · φ=0.203 · p=0.000

Example: Model Tables: Linear Regression

Create Three Models

m0 <- lm(
  pressure ~ 1,
  data = dataset
)
m1 <- lm(
  pressure ~ glucose,
  data = dataset
)
m2 <- lm(
  pressure ~ glucose + diabetes,
  data = dataset
)

Create Model Table

m_table <- sjPlot::tab_model(
  m0, m1, m2,
  show.aic = TRUE
)

m_table

	pressure			pressure			pressure
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	72.41	71.51 – 73.30	<0.001	61.46	57.85 – 65.06	<0.001	62.65	58.85 – 66.44	<0.001
glucose				0.09	0.06 – 0.12	<0.001	0.07	0.04 – 0.11	<0.001
diabetes [pos]							2.06	-0.06 – 4.18	0.056
Observations	733			728			728
R² / R² adjusted	0.000 / 0.000			0.050 / 0.049			0.055 / 0.052
AIC	5771.995			5697.909			5696.248

Convert Model Table to `data.frame`

mtab_df <- sjtable2df::mtab2df(
  mtab = m_table,
  n_models = 3,
  output = "data.frame"
)
class(mtab_df)
#> [1] "data.frame"
mtab_df
#>         Predictors     Estimates            CI      p     Estimates
#> 1      (Intercept)         72.41 71.51 – 73.30 <0.001         61.46
#> 2          glucose                                             0.09
#> 3   diabetes [pos]                                                 
#> 4     Observations           733                                728
#> 5 R2 / R2 adjusted 0.000 / 0.000                      0.050 / 0.049
#> 6              AIC      5771.995                           5697.909
#>              CI      p     Estimates            CI      p
#> 1 57.85 – 65.06 <0.001         62.65 58.85 – 66.44 <0.001
#> 2   0.06 – 0.12 <0.001          0.07   0.04 – 0.11 <0.001
#> 3                               2.06  -0.06 – 4.18  0.056
#> 4                                728                     
#> 5                      0.055 / 0.052                     
#> 6                           5696.248

Convert Model Table to `kable`

mtab_kbl <- sjtable2df::mtab2df(
  mtab = m_table,
  n_models = 3,
  output = "kable"
)
class(mtab_kbl)
#> [1] "kableExtra"  "knitr_kable"
mtab_kbl

	pressure
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	72.41	71.51 – 73.30	<0.001	61.46	57.85 – 65.06	<0.001	62.65	58.85 – 66.44	<0.001
glucose				0.09	0.06 – 0.12	<0.001	0.07	0.04 – 0.11	<0.001
diabetes [pos]							2.06	-0.06 – 4.18	0.056
Observations	733			728			728
\(R^2\) / \(R^2\) adjusted	0.000 / 0.000			0.050 / 0.049			0.055 / 0.052
AIC	5771.995			5697.909			5696.248

Example: Model Tables: Logistic Regression

Create Three Models

m0 <- stats::glm(
  diabetes ~ 1,
  data = dataset,
  family = binomial(link = "logit")
)
m1 <- stats::glm(
  diabetes ~ glucose,
  data = dataset,
  family = binomial(link = "logit")
)
m2 <- stats::glm(
  diabetes ~ glucose + pressure,
  data = dataset,
  family = binomial(link = "logit")
)

Create Model Table

m_table <- sjPlot::tab_model(
  m0, m1, m2,
  show.aic = TRUE
)

m_table

	diabetes			diabetes			diabetes
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	0.54	0.46 – 0.62	<0.001	0.00	0.00 – 0.01	<0.001	0.00	0.00 – 0.01	<0.001
glucose				1.04	1.03 – 1.05	<0.001	1.04	1.03 – 1.05	<0.001
pressure							1.01	1.00 – 1.03	0.060
Observations	768			763			728
R² Tjur	0.000			0.249			0.246
AIC	995.484			790.560			754.636

Convert Model Table to `data.frame`

mtab_df <- sjtable2df::mtab2df(
  mtab = m_table,
  n_models = 3,
  output = "data.frame"
)
class(mtab_df)
#> [1] "data.frame"
mtab_df
#>     Predictors Odds Ratios          CI      p Odds Ratios          CI      p
#> 1  (Intercept)        0.54 0.46 – 0.62 <0.001        0.00 0.00 – 0.01 <0.001
#> 2      glucose                                       1.04 1.03 – 1.05 <0.001
#> 3     pressure                                                              
#> 4 Observations         768                            763                   
#> 5      R2 Tjur       0.000                          0.249                   
#> 6          AIC     995.484                        790.560                   
#>   Odds Ratios          CI      p
#> 1        0.00 0.00 – 0.01 <0.001
#> 2        1.04 1.03 – 1.05 <0.001
#> 3        1.01 1.00 – 1.03  0.060
#> 4         728                   
#> 5       0.246                   
#> 6     754.636

Convert Model Table to `kable`

mtab_kbl <- sjtable2df::mtab2df(
  mtab = m_table,
  n_models = 3,
  output = "kable"
)
class(mtab_kbl)
#> [1] "kableExtra"  "knitr_kable"
mtab_kbl

	diabetes
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	0.54	0.46 – 0.62	<0.001	0.00	0.00 – 0.01	<0.001	0.00	0.00 – 0.01	<0.001
glucose				1.04	1.03 – 1.05	<0.001	1.04	1.03 – 1.05	<0.001
pressure							1.01	1.00 – 1.03	0.060
Observations	768			763			728
\(R^2\) Tjur	0.000			0.249			0.246
AIC	995.484			790.560			754.636

Example: Model Tables: GLMM

Create Three Models

set.seed(1)
dataset$city <- sample(
  x = paste0("city_", 1:7),
  size = nrow(dataset),
  replace = TRUE
)
m0 <- lme4::glmer(
  diabetes ~ 1 + (1 | city),
  data = dataset,
  family = binomial(link = "logit")
)
#> boundary (singular) fit: see help('isSingular')
m1 <- lme4::glmer(
  diabetes ~ mass + (1 | city),
  data = dataset,
  family = binomial(link = "logit")
)
m2 <- lme4::glmer(
  diabetes ~ mass + log(pressure) + (1 | city),
  data = dataset,
  family = binomial(link = "logit")
)

Create Model Table

m_table <- sjPlot::tab_model(
  m0, m1, m2,
  show.aic = TRUE
)

m_table

	diabetes			diabetes			diabetes
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	0.54	0.46 – 0.62	<0.001	0.02	0.01 – 0.04	<0.001	0.00	0.00 – 0.01	<0.001
mass				1.11	1.08 – 1.14	<0.001	1.10	1.07 – 1.13	<0.001
pressure [log]							3.31	1.26 – 8.67	0.015
Random Effects
σ²	3.29			3.29			3.29
τ₀₀	0.00 _city			0.01 _city			0.00 _city
ICC				0.00			0.00
N	7 _city			7 _city			7 _city
Observations	768			757			729
Marginal R² / Conditional R²	0.000 / NA			0.133 / 0.135			0.137 / 0.137
AIC	997.484			910.822			871.890

Convert Model Table to `data.frame`

mtab_df <- sjtable2df::mtab2df(
  mtab = m_table,
  n_models = 3,
  output = "data.frame"
)
class(mtab_df)
#> [1] "data.frame"
mtab_df
#>                      Predictors Odds Ratios          CI      p   Odds Ratios
#> 1                   (Intercept)        0.54 0.46 – 0.62 <0.001          0.02
#> 2                          mass                                         1.11
#> 3                pressure [log]                                             
#> 4                Random Effects                                             
#> 5                            σ2        3.29                             3.29
#> 6                           τ00   0.00 city                        0.01 city
#> 7                           ICC                                         0.00
#> 8                             N      7 city                           7 city
#> 9                  Observations         768                              757
#> 10 Marginal R2 / Conditional R2  0.000 / NA                    0.133 / 0.135
#> 11                          AIC     997.484                          910.822
#>             CI      p   Odds Ratios          CI      p
#> 1  0.01 – 0.04 <0.001          0.00 0.00 – 0.01 <0.001
#> 2  1.08 – 1.14 <0.001          1.10 1.07 – 1.13 <0.001
#> 3                              3.31 1.26 – 8.67  0.015
#> 4                                                     
#> 5                              3.29                   
#> 6                         0.00 city                   
#> 7                              0.00                   
#> 8                            7 city                   
#> 9                               729                   
#> 10                    0.137 / 0.137                   
#> 11                          871.890

Convert Model Table to `kable`

mtab_kbl <- sjtable2df::mtab2df(
  mtab = m_table,
  n_models = 3,
  output = "kable"
)
class(mtab_kbl)
#> [1] "kableExtra"  "knitr_kable"
mtab_kbl

	diabetes
Predictors	Odds Ratios	CI	p	Odds Ratios	CI	p	Odds Ratios	CI	p
(Intercept)	0.54	0.46 – 0.62	<0.001	0.02	0.01 – 0.04	<0.001	0.00	0.00 – 0.01	<0.001
mass				1.11	1.08 – 1.14	<0.001	1.10	1.07 – 1.13	<0.001
pressure [log]							3.31	1.26 – 8.67	0.015
Random Effects
σ2	3.29			3.29			3.29
τ00	0.00 city			0.01 city			0.00 city
ICC				0.00			0.00
N	7 city			7 city			7 city
Observations	768			757			729
Marginal \(R^2\) / Conditional \(R^2\)	0.000 / NA			0.133 / 0.135			0.137 / 0.137
AIC	997.484			910.822			871.890

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.

sjtable2df: Overview

Example: Contingency-Tables

Data Preprocessing

Create Contingency Table

Convert Contingency Table to data.frame

Convert Contingency Table to kable

Percentages in cells

Convert Contingency Table to data.frame

Example: Model Tables: Linear Regression

Create Three Models

Create Model Table

Convert Model Table to data.frame

Convert Model Table to kable

Example: Model Tables: Logistic Regression

Create Three Models

Create Model Table

Convert Model Table to data.frame

Convert Model Table to kable

Example: Model Tables: GLMM

Create Three Models

Create Model Table

Convert Model Table to data.frame

Convert Model Table to kable

Convert Contingency Table to `data.frame`

Convert Contingency Table to `kable`

Convert Contingency Table to `data.frame`

Convert Model Table to `data.frame`

Convert Model Table to `kable`

Convert Model Table to `data.frame`

Convert Model Table to `kable`

Convert Model Table to `data.frame`

Convert Model Table to `kable`