nice_table.md
In rempsyc: Convenience Functions for Psychology

nice_table

Code
  my_table
Output
  a flextable object.
  col_keys: `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb` 
  header has 3 row(s) 
  body has 3 row(s) 
  original dataset sample: 
                 mpg cyl disp  hp drat    wt  qsec vs am gear carb
  Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
  Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
  Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

Code
  nice_table(stats.table, highlight = TRUE)
Output
  a flextable object.
  col_keys: `Term`, `B`, `SE`, `t`, `p`, `95% CI` 
  header has 1 row(s) 
  body has 5 row(s) 
  original dataset sample: 
                     Term          B         SE          t            p
  (Intercept) (Intercept) -0.1835269 0.08532112 -2.1510135 4.058431e-02
  cyl                 cyl -0.1082286 0.15071576 -0.7180977 4.788652e-01
  wt                   wt -0.6230206 0.10927573 -5.7013627 4.663587e-06
  hp                   hp -0.2874898 0.11955935 -2.4045781 2.331865e-02
  wt:hp           wt × hp  0.2875867 0.08895462  3.2329593 3.221753e-03
                      95% CI signif
  (Intercept) [-0.36, -0.01]   TRUE
  cyl          [-0.42, 0.20]  FALSE
  wt          [-0.85, -0.40]   TRUE
  hp          [-0.53, -0.04]   TRUE
  wt:hp         [0.11, 0.47]   TRUE

Code
  nice_table(test)
Output
  a flextable object.
  col_keys: `dR`, `N`, `M`, `SD`, `b`, `np2`, `ges`, `p`, `r`, `R2`, `sr2` 
  header has 1 row(s) 
  body has 6 row(s) 
  original dataset sample: 
                      dR N   M  SD    b   np2   ges p r  R2 sr2
  Mazda RX4         21.0 6 160 110 3.90 2.620 16.46 0 1 0.4 0.4
  Mazda RX4 Wag     21.0 6 160 110 3.90 2.875 17.02 0 1 0.4 0.4
  Datsun 710        22.8 4 108  93 3.85 2.320 18.61 1 1 0.4 0.1
  Hornet 4 Drive    21.4 6 258 110 3.08 3.215 19.44 1 0 0.3 0.1
  Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 0.3 0.2

Code
  nice_table(test[8:11], col.format.p = 2:4, highlight = 0.001)
Output
  a flextable object.
  col_keys: `p`, `r`, `R2`, `sr2` 
  header has 1 row(s) 
  body has 6 row(s) 
  original dataset sample: 
                    p r  R2 sr2 signif
  Mazda RX4         0 1 0.4 0.4   TRUE
  Mazda RX4 Wag     0 1 0.4 0.4   TRUE
  Datsun 710        1 1 0.4 0.1  FALSE
  Hornet 4 Drive    1 0 0.3 0.1  FALSE
  Hornet Sportabout 0 0 0.3 0.2   TRUE

Code
  nice_table(test[8:11], col.format.r = 1:4)
Output
  a flextable object.
  col_keys: `p`, `r`, `R2`, `sr2` 
  header has 1 row(s) 
  body has 6 row(s) 
  original dataset sample: 
                    p r  R2 sr2
  Mazda RX4         0 1 0.4 0.4
  Mazda RX4 Wag     0 1 0.4 0.4
  Datsun 710        1 1 0.4 0.1
  Hornet 4 Drive    1 0 0.3 0.1
  Hornet Sportabout 0 0 0.3 0.2

Code
  nice_table(test[8:11], col.format.custom = 2:4, format.custom = "fun")
Output
  a flextable object.
  col_keys: `p`, `r`, `R2`, `sr2` 
  header has 1 row(s) 
  body has 6 row(s) 
  original dataset sample: 
                    p r  R2 sr2
  Mazda RX4         0 1 0.4 0.4
  Mazda RX4 Wag     0 1 0.4 0.4
  Datsun 710        1 1 0.4 0.1
  Hornet 4 Drive    1 0 0.3 0.1
  Hornet Sportabout 0 0 0.3 0.2

Code
  nice_table(test[8:11], col.format.custom = 2:4, format.custom = "fun")
Output
  a flextable object.
  col_keys: `p`, `r`, `R2`, `sr2` 
  header has 1 row(s) 
  body has 6 row(s) 
  original dataset sample: 
                    p r  R2 sr2
  Mazda RX4         0 1 0.4 0.4
  Mazda RX4 Wag     0 1 0.4 0.4
  Datsun 710        1 1 0.4 0.1
  Hornet 4 Drive    1 0 0.3 0.1
  Hornet Sportabout 0 0 0.3 0.2

Code
  nice_table(header.data, separate.header = TRUE, italics = 2:4)
Output
  a flextable object.
  col_keys: `Variable`, `setosa.M`, `setosa.SD`, `versicolor.M`, `versicolor.SD` 
  header has 2 row(s) 
  body has 3 row(s) 
  original dataset sample: 
        Variable setosa.M setosa.SD versicolor.M versicolor.SD
  1 Sepal.Length     5.01      0.35         5.94          0.52
  2  Sepal.Width     3.43      0.38         2.77          0.31
  3 Petal.Length     1.46      0.17         4.26          0.47