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
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