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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("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
0 1
neg 356 144 500
pos 136 132 268
Total 492 276 768
χ2=30.823 · df=1 · &phi=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 · &phi=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
$χ2=30.823 · df=1 · &phi=0.203 · p=0.000$

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
0 1
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 · &phi=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 · &phi=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
)
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
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.