Create a Table 1

This function provides a wrapper around furniture::table1 that allows it to utilize Hmisc::label as variable labels. It also automatically converts logicals to factors so logical dataframe columns are included in the table.

tableone(
  df,
  ...,
  group = NULL,
  neg_label = "No",
  pos_label = "Yes",
  test = TRUE,
  caption = NULL,
  output = "text",
  type = c("pvalues", "simple", "condensed")
)

Arguments

df	a dataframe
...	a list of columns to be included in the table
group	a group to split the columns by
neg_label	label of the negative group
pos_label	label of the positive group
test	logical; if set to TRUE then the appropriate bivariate tests of significance are performed if splitby has more than 1 level. A message is printed when the variances of the continuous variables being tested do not meet the assumption of Homogeneity of Variance (using Breusch-Pagan Test of Heteroskedasticity) and, therefore, the argument `var.equal = FALSE` is used in the test.
caption	when output != "text"; option is passed to knitr::kable
output	how the table is output; can be "text" or "text2" for regular console output or any of kable()'s options from knitr (e.g., "latex", "markdown", "pandoc"). A new option, 'latex2', although more limited, allows the variable name to show and has an overall better appearance.
type	what is displayed in the table; a string or a vector of strings. Two main sections can be inputted: 1. if test = TRUE, can write "pvalues", "full", or "stars" and 2. can state "simple" and/or "condense". These are discussed in more depth in the details section below.

Value

a table; the output varies based on the output parameter.

Examples

diamonds <- ggplot2::diamonds
diamonds$expensive <- diamonds$price > 500
Hmisc::label(diamonds$depth) <- "Depth"
Hmisc::label(diamonds$table) <- "Table"
Hmisc::label(diamonds$price) <- "Price"
Hmisc::label(diamonds$clarity) <- "Clarity"
Hmisc::label(diamonds$cut) <- "Cut"
Hmisc::label(diamonds$expensive) <- "Expensive"
diamonds %>% tableone(depth, table, price, clarity, expensive, group = cut)
#> Registered S3 methods overwritten by 'furniture':
#>   method               from  
#>   as.data.frame.table1 table1
#>   print.table1         table1
#> Breusch-Pagan Test of Heteroskedasticity suggests `var.equal = FALSE` in oneway.test() for: Depth
#> Breusch-Pagan Test of Heteroskedasticity suggests `var.equal = FALSE` in oneway.test() for: Table
#> Breusch-Pagan Test of Heteroskedasticity suggests `var.equal = FALSE` in oneway.test() for: Price
#> 
#> 
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────
#>                                                     cut 
#>                 Fair            Good            Very Good       Premium        
#>                 n = 1610        n = 4906        n = 12082       n = 13791      
#>  Depth          64.0 (3.6)      62.4 (2.2)      61.8 (1.4)      61.3 (1.2)     
#>  Table          59.1 (3.9)      58.7 (2.9)      58.0 (2.1)      58.7 (1.5)     
#>  Price          4358.8 (3560.4) 3928.9 (3681.6) 3981.8 (3935.9) 4584.3 (4349.2)
#>  Clarity                                                                       
#>     I1          13%             2%              0.7%            1.5%           
#>     SI2         28.9%           22%             17.4%           21.4%          
#>     SI1         25.3%           31.8%           26.8%           25.9%          
#>     VS2         16.2%           19.9%           21.4%           24.3%          
#>     VS1         10.6%           13.2%           14.7%           14.4%          
#>     VVS2        4.3%            5.8%            10.2%           6.3%           
#>     VVS1        1.1%            3.8%            6.5%            4.5%           
#>     IF          0.6%            1.4%            2.2%            1.7%           
#>  Expensive: Yes 99.6%           95.3%           94.5%           98.4%          
#>  Ideal           P-Value
#>  n = 21551              
#>  61.7 (0.7)      <.001  
#>  56.0 (1.2)      <.001  
#>  3457.5 (3808.4) <.001  
#>                  <.001  
#>  0.7%                   
#>  12.1%                  
#>  19.9%                  
#>  23.5%                  
#>  16.7%                  
#>  12.1%                  
#>  9.5%                   
#>  5.6%                   
#>  97.1%           <.001  
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────