Creates a t.test table for multiple dependent variables

Creates a t.test table for multiple dependent variables

nice_t_test_table(
  dv,
  iv,
  data,
  method = "cohen",
  conditions = NULL,
  labels = NULL,
  concise = TRUE,
  nice_p = TRUE,
  digits = 1,
  var_equal = FALSE,
  label_attr = TRUE,
  manova = TRUE,
  type = "html",
  caption = "T-test table",
  alternative = "two.sided",
  file = NULL
)

t_test_table(...)

Arguments

dv

A data frame with the dependent variables or a character vector with variable names when data is defined.

iv

A data frame or vector with the independent variable or a character if data is defined.

data

A data frame.

method

Either "cohen", "glass, or "hedges".

conditions

A character vector of length two with the names of the two conditions. Defaults to the first two levels of the independent variable 'iv' if applicable.

labels

A character vector of length two with labels for the dependent variables.

concise

A more concise table with mean and SD in one column.

nice_p

If TRUE, p values are printed in a nice format.

digits

Number of digits for rounding mean and SD values

var_equal

If FALSE, a t-test for unequal variances is calculated.

type

Either "df" for data frame or "html" for html table.

caption

Table caption is type = "html"

Value

A tibble or an html table

Examples

nice_t_test_table(
  data = mtcars, 
  iv = "am", 
  dv = c("mpg", "cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "gear", "carb")
)


  
Table
T-test table
    
Scale
      

        
N
      
      

        
M (SD)
      
      
d
      
t
      
df
      
p
    
 0 
      
 1 
      
0
      
1
    
mpg
19
13
 17.1 (  3.8)
 24.4 ( 6.2)
 1.5
 3.77
18.3
<.01
cyl
19
13
  6.9 (  1.5)
  5.1 ( 1.6)
-1.2
-3.35
25.9
<.01
disp
19
13
290.4 (110.2)
143.5 (87.2)
-1.4
-4.20
29.3
<.001
hp
19
13
160.3 ( 53.9)
126.8 (84.1)
-0.5
-1.27
18.7
.22
drat
19
13
  3.3 (  0.4)
  4.0 ( 0.4)
 2.0
 5.65
27.2
<.001
wt
19
13
  3.8 (  0.8)
  2.4 ( 0.6)
-1.9
-5.49
29.2
<.001
qsec
19
13
 18.2 (  1.8)
 17.4 ( 1.8)
-0.5
-1.29
25.5
.21
vs
19
13
  0.4 (  0.5)
  0.5 ( 0.5)
 0.3
 0.93
25.1
.36
gear
19
13
  3.2 (  0.4)
  4.4 ( 0.5)
 2.6
 6.90
22.6
<.001
carb
19
13
  2.7 (  1.1)
  2.9 ( 2.2)
 0.1
 0.28
16.6
.80
 Note. Method for estimating standard deviation: cohen; Manova: Pillai = 0.80; F(10, 21) = 8.36; p = 0.000.
    

df <- data.frame(
  a = c(rnorm(85, 50, 10), rnorm(200, 70, 20)),
  b = c(rnorm(85, 50, 10), rnorm(200, 55, 20)),
  iv = factor(c(rep("Regular", 85), rep("Special", 100), rep("Restricted", 100)))
)

nice_t_test_table(
  c("a", "b"), "iv", 
  data = df,
  conditions = c("Restricted","Special"), 
  labels = c("Motivation", "Achievement")
)


  
Table
T-test table
    
Scale
      

        
N
      
      

        
M (SD)
      
      
d
      
t
      
df
      
p
    
 Restricted 
      
 Special 
      
Restricted
      
Special
    
Motivation
100
100
71.4 (22.0)
72.0 (20.8)
 0.0
 0.18
197.3
.86
Achievement
100
100
55.7 (20.6)
52.2 (20.9)
-0.2
-1.20
198.0
.20
 Note. Method for estimating standard deviation: cohen; Manova: Pillai = 0.01; F(2, 197) = 0.72; p = 0.489.