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sjtable2df: Overview

The sjPlot R package is a great package for visualizing results.

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)

# load data
data("BreastCancer")
dataset <- BreastCancer |>
  data.table::as.data.table() |>
  na.omit()

Create Contingency Table

xtab <- sjPlot::tab_xtab(
  var.row = dataset$Class,
  var.col = dataset$Mitoses,
  show.summary = TRUE,
  use.viewer = FALSE
)
xtab
Class Mitoses Total
1 2 3 4 5 6 7 8 10
benign 431 8 2 0 1 0 1 1 0 444
malignant 132 27 31 12 5 3 8 7 14 239
Total 563 35 33 12 6 3 9 8 14 683
χ2=191.968 · df=8 · Cramer's V=0.530 · Fisher's 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
      Class Mitoses 1 Mitoses 2 Mitoses 3 Mitoses 4 Mitoses 5 Mitoses 6
1    benign       431         8         2         0         1         0
2 malignant       132        27        31        12         5         3
3     Total       563        35        33        12         6         3
4                                                                      
  Mitoses 7 Mitoses 8 Mitoses 10
1         1         1          0
2         8         7         14
3         9         8         14
4                               
                                                    Total
1                                                     444
2                                                     239
3                                                     683
4 χ2=191.968 · df=8 · Cramer's V=0.530 · Fisher's p=0.000

Convert Contingency Table to kable

xtab_kbl <- sjtable2df::xtab2df(
  xtab = xtab,
  output = "kable",
  caption = "Class vs. Mitoses"
)
class(xtab_kbl)
[1] "kableExtra"  "knitr_kable"

Percentages in cells

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

xtab <- sjPlot::tab_xtab(
  var.row = dataset$Class,
  var.col = dataset$Mitoses,
  show.summary = TRUE,
  show.col.prc = TRUE,
  use.viewer = FALSE
)
xtab
Class Mitoses Total
1 2 3 4 5 6 7 8 10
benign 431
76.6 %		 8
22.9 %		 2
6.1 %		 0
0 %		 1
16.7 %		 0
0 %		 1
11.1 %		 1
12.5 %		 0
0 %		 444
65 %
malignant 132
23.4 %		 27
77.1 %		 31
93.9 %		 12
100 %		 5
83.3 %		 3
100 %		 8
88.9 %		 7
87.5 %		 14
100 %		 239
35 %
Total 563
100 %		 35
100 %		 33
100 %		 12
100 %		 6
100 %		 3
100 %		 9
100 %		 8
100 %		 14
100 %		 683
100 %
χ2=191.968 · df=8 · Cramer's V=0.530 · Fisher's p=0.000

Convert Contingency Table to data.frame

xtab_df <- sjtable2df::xtab2df(xtab = xtab, output = "data.frame")
xtab_df
      Class    Mitoses 1   Mitoses 2   Mitoses 3  Mitoses 4  Mitoses 5
1    benign 431 (76.6 %)  8 (22.9 %)   2 (6.1 %)    0 (0 %) 1 (16.7 %)
2 malignant 132 (23.4 %) 27 (77.1 %) 31 (93.9 %) 12 (100 %) 5 (83.3 %)
3     Total  563 (100 %)  35 (100 %)  33 (100 %) 12 (100 %)  6 (100 %)
4                                                                     
  Mitoses 6  Mitoses 7  Mitoses 8 Mitoses 10
1   0 (0 %) 1 (11.1 %) 1 (12.5 %)    0 (0 %)
2 3 (100 %) 8 (88.9 %) 7 (87.5 %) 14 (100 %)
3 3 (100 %)  9 (100 %)  8 (100 %) 14 (100 %)
4                                           
                                                    Total
1                                              444 (65 %)
2                                              239 (35 %)
3                                             683 (100 %)
4 χ2=191.968 · df=8 · Cramer's V=0.530 · Fisher's p=0.000

Example: Model Tables: Linear Regression

Create Three Models

num_vars <- c("Cell.size", "Cell.shape")
dataset[, (num_vars) := lapply(.SD, as.integer), .SDcols = num_vars]
m0 <- lm(
  Cell.size ~ 1,
  data = dataset
)
m1 <- lm(
  Cell.size ~ Cell.shape,
  data = dataset
)
m2 <- lm(
  Cell.size ~ Cell.shape + Class,
  data = dataset
)

Create Model Table

m_table <- sjPlot::tab_model(
  m0,
  m1,
  m2,
  show.aic = TRUE
)
m_table
  Cell.size Cell.size Cell.size
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) 3.15 2.92 – 3.38 <0.001 0.16 0.02 – 0.30 0.029 0.27 0.13 – 0.40 <0.001
Cell shape 0.93 0.90 – 0.96 <0.001 0.74 0.68 – 0.79 <0.001
Class [malignant] 1.49 1.15 – 1.83 <0.001
Observations 683 683 683
R2 / R2 adjusted 0.000 / 0.000 0.823 / 0.823 0.840 / 0.840
AIC 3471.319 2290.389 2221.652

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          CI
1       (Intercept)          3.15 2.92 – 3.38 <0.001          0.16 0.02 – 0.30
2        Cell shape                                           0.93 0.90 – 0.96
3 Class [malignant]                                                           
4      Observations           683                              683            
5  R2 / R2 adjusted 0.000 / 0.000                    0.823 / 0.823            
6               AIC      3471.319                         2290.389            
       p     Estimates          CI      p
1  0.029          0.27 0.13 – 0.40 <0.001
2 <0.001          0.74 0.68 – 0.79 <0.001
3                 1.49 1.15 – 1.83 <0.001
4                  683                   
5        0.840 / 0.840                   
6             2221.652

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
Cell.size
Predictors Estimates CI p Estimates CI p Estimates CI p
(Intercept) 3.15 2.92 – 3.38 <0.001 0.16 0.02 – 0.30 0.029 0.27 0.13 – 0.40 <0.001
Cell shape 0.93 0.90 – 0.96 <0.001 0.74 0.68 – 0.79 <0.001
Class [malignant] 1.49 1.15 – 1.83 <0.001
Observations 683 683 683
$R^2$ / $R^2$ adjusted 0.000 / 0.000 0.823 / 0.823 0.840 / 0.840
AIC 3471.319 2290.389 2221.652

Example: Model Tables: Logistic Regression

Create Three Models

m0 <- stats::glm(
  Class ~ 1,
  data = dataset,
  family = binomial(link = "logit")
)
m1 <- stats::glm(
  Class ~ Cell.shape,
  data = dataset,
  family = binomial(link = "logit")
)
m2 <- stats::glm(
  Class ~ Cell.shape + Cell.size,
  data = dataset,
  family = binomial(link = "logit")
)

Create Model Table

m_table <- sjPlot::tab_model(
  m0,
  m1,
  m2,
  show.aic = TRUE
)
m_table
  Class Class Class
Predictors Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p
(Intercept) 0.54 0.46 – 0.63 <0.001 0.01 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001
Cell shape 4.36 3.50 – 5.62 <0.001 2.31 1.71 – 3.19 <0.001
Cell size 2.35 1.73 – 3.32 <0.001
Observations 683 683 683
R2 Tjur 0.000 0.756 0.812
AIC 886.350 271.586 227.110

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 – 0.63 <0.001        0.01 0.00 – 0.01 <0.001
2   Cell shape                                       4.36 3.50 – 5.62 <0.001
3    Cell size                                                              
4 Observations         683                            683                   
5      R2 Tjur       0.000                          0.756                   
6          AIC     886.350                        271.586                   
  Odds Ratios          CI      p
1        0.00 0.00 – 0.01 <0.001
2        2.31 1.71 – 3.19 <0.001
3        2.35 1.73 – 3.32 <0.001
4         683                   
5       0.812                   
6     227.110

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
Class
Predictors Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p
(Intercept) 0.54 0.46 – 0.63 <0.001 0.01 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001
Cell shape 4.36 3.50 – 5.62 <0.001 2.31 1.71 – 3.19 <0.001
Cell size 2.35 1.73 – 3.32 <0.001
Observations 683 683 683
$R^2$ Tjur 0.000 0.756 0.812
AIC 886.350 271.586 227.110

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(
  Class ~ 1 + (1 | city),
  data = dataset,
  family = binomial(link = "logit")
)
boundary (singular) fit: see help('isSingular')
m1 <- lme4::glmer(
  Class ~ Cell.size + (1 | city),
  data = dataset,
  family = binomial(link = "logit")
)
m2 <- lme4::glmer(
  Class ~ Cell.size + log(Cell.shape) + (1 | city),
  data = dataset,
  family = binomial(link = "logit")
)
boundary (singular) fit: see help('isSingular')

Create Model Table

m_table <- sjPlot::tab_model(
  m0,
  m1,
  m2,
  show.aic = TRUE
)
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
m_table
  Class Class Class
Predictors Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p
(Intercept) 0.54 0.46 – 0.63 <0.001 0.01 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001
Cell size 5.11 3.87 – 6.73 <0.001 2.10 1.55 – 2.83 <0.001
Cell shape [log] 15.55 6.55 – 36.89 <0.001
Random Effects
σ2 3.29 3.29 3.29
τ00 0.00 city 0.12 city 0.00 city
ICC   0.03  
N 7 city 7 city 7 city
Observations 683 683 683
Marginal R2 / Conditional R2 0.000 / NA 0.880 / 0.884 0.861 / NA
AIC 888.350 259.874 214.461

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 – 0.63 <0.001          0.01
2                     Cell size                                         5.11
3              Cell shape [log]                                             
4                Random Effects                                             
5                            σ2        3.29                             3.29
6                           τ00   0.00 city                        0.12 city
7                           ICC                                         0.03
8                             N      7 city                           7 city
9                  Observations         683                              683
10 Marginal R2 / Conditional R2  0.000 / NA                    0.880 / 0.884
11                          AIC     888.350                          259.874
            CI      p Odds Ratios           CI      p
1  0.00 – 0.01 <0.001        0.00  0.00 – 0.01 <0.001
2  3.87 – 6.73 <0.001        2.10  1.55 – 2.83 <0.001
3                           15.55 6.55 – 36.89 <0.001
4                                                    
5                            3.29                    
6                       0.00 city                    
7                                                    
8                          7 city                    
9                             683                    
10                     0.861 / NA                    
11                        214.461

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
Class
Predictors Odds Ratios CI p Odds Ratios CI p Odds Ratios CI p
(Intercept) 0.54 0.46 – 0.63 <0.001 0.01 0.00 – 0.01 <0.001 0.00 0.00 – 0.01 <0.001
Cell size 5.11 3.87 – 6.73 <0.001 2.10 1.55 – 2.83 <0.001
Cell shape [log] 15.55 6.55 – 36.89 <0.001
Random Effects
σ2 3.29 3.29 3.29
τ00 0.00 city 0.12 city 0.00 city
ICC 0.03
N 7 city 7 city 7 city
Observations 683 683 683
Marginal $R^2$ / Conditional $R^2$ 0.000 / NA 0.880 / 0.884 0.861 / NA
AIC 888.350 259.874 214.461

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.