set_gtsummary_theme.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

setting themes

Code
  tbl %>% as.data.frame()
Output
        **Caractéristique** **Drug A**, N = 98 **Drug B**, N = 102 **p-valeur**
  1                     Age               <NA>                <NA>         0,72
  2            Médiane (EI)       46 (37 – 59)        48 (39 – 56)         <NA>
  3                Manquant                  7                   4         <NA>
  4    Marker Level (ng/mL)               <NA>                <NA>        0,085
  5            Médiane (EI) 0,84 (0,24 – 1,57)  0,52 (0,19 – 1,20)         <NA>
  6                Manquant                  6                   4         <NA>
  7          T Stage, n (%)               <NA>                <NA>         0,87
  8                      T1            28 (29)             25 (25)         <NA>
  9                      T2            25 (26)             29 (28)         <NA>
  10                     T3            22 (22)             21 (21)         <NA>
  11                     T4            23 (23)             27 (26)         <NA>
  12           Grade, n (%)               <NA>                <NA>         0,87
  13                      I            35 (36)             33 (32)         <NA>
  14                     II            32 (33)             36 (35)         <NA>
  15                    III            31 (32)             33 (32)         <NA>
  16  Tumor Response, n (%)            28 (29)             33 (34)         0,53
  17               Manquant                  3                   4         <NA>
  18    Patient Died, n (%)            52 (53)             60 (59)         0,41
  19 Months to Death/Censor               <NA>                <NA>         0,14
  20           Médiane (EI) 23,5 (17,4 – 24,0)  21,2 (14,6 – 24,0)         <NA>

Code
  lm(age ~ marker + grade, trial) %>% tbl_regression() %>% as.data.frame()
Output
     **Caractéristique** **Beta** **(95% CI)** **p-valeur**
  1 Marker Level (ng/mL)   -0,04 (-2,6 to 2,5)         0,98
  2                Grade                  <NA>         <NA>
  3                    I                  <NA>         <NA>
  4                   II    0,64 (-4,7 to 6,0)         0,81
  5                  III     2,4 (-2,8 to 7,6)         0,37

Code
  tbl_theme %>% as.data.frame()
Output
        **Characteristic**     **N = 200**
  1 Chemotherapy Treatment            <NA>
  2                 Drug A  98 / 200 (49%)
  3                 Drug B 102 / 200 (51%)
  4                    Age            <NA>
  5           Median (IQR)    47 (38 - 57)
  6              Mean (SD)         47 (14)
  7                Unknown              11

Code
  tbl1
Output
  # A tibble: 2 x 5
    label                 stat_1       stat_2       estimate             p.value
    <chr>                 <chr>        <chr>        <chr>                <chr>  
  1 Age, Median (IQR)     46 (37 – 59) 48 (39 – 56) -0.44 (-4.6 to 3.7)  0.83   
  2 Tumor Response, n (%) 28 (29)      33 (34)      -4.2% (-18% to 9.9%) 0.64

Code
  tbl_qjecon1 %>% as.data.frame()
Output
    **Characteristic** **Beta**  \n**(SE)**
  1              Grade                 <NA>
  2                  I                 <NA>
  3                 II        1.4  \n(2.54)
  4                III        2.0  \n(2.55)

Code
  tbl_qjecon2 %>% as.data.frame()
Output
     **Characteristic** **N** **Beta**  \n**(SE)**
  1                 mpg    32    -8.8***  \n(1.31)
  2                 cyl    32      32***  \n(3.88)
  3                disp    32   0.44***  \n(0.062)
  4                drat    32      -58**  \n(20.9)
  5                  wt    32      46***  \n(9.63)
  6                qsec    32     -27***  \n(4.95)
  7                  vs    32     -98***  \n(17.2)
  8                  am    32        -33  \n(24.3)
  9                gear    32        -12  \n(16.8)
  10               carb    32      32***  \n(5.13)

Code
  tbl
Output
  # A tibble: 5 x 2
    label        stat_0     
    <chr>        <chr>      
  1 Age          <NA>       
  2 Median (IQR) 47 (38, 57)
  3 Mean (SD)    47 (14)    
  4 Range        6, 83      
  5 Unknown      11

Code
  with_gtsummary_theme(theme_gtsummary_journal("lancet"), lm(mpg ~ factor(cyl) +
    hp + am, mtcars) %>% tbl_regression() %>% as_tibble())
Output
  # A tibble: 6 x 4
    `**Characteristic**` `**Beta**` `**95% CI**`   `**p-value**`
    <chr>                <chr>      <chr>          <chr>        
  1 factor(cyl)          <NA>       <NA>           <NA>         
  2 4                    <NA>       <NA>           <NA>         
  3 6                    -3·9       -7·1 to -0·77  0·017        
  4 8                    -3·5       -8·7 to 1·6    0·17         
  5 hp                   -0·04      -0·07 to -0·01 0·0053       
  6 am                   4·2        1·6 to 6·7     0·0027