Produce a data frame to summaries continuous variables — continuous

Takes a data frame and produces grouped or un-grouped summaries such as mean and standard deviation for continuous variables.

Usage

continuous_table(
  df = .,
  ...,
  group = .,
  time = .,
  total = TRUE,
  digits = 2,
  condense = FALSE
)

Arguments

df: Data frame
...: Variables to be summarised
group: Optional variable that defines the grouping
time: Optional variable for repeated measures (currently must me used with group)
total: Logical indicating whether a total column should be created
digits: Number of digits to the right of the decimal point
condense: should the variable and scoring columns in the output be condensed to one column?

Value

A tibble data frame summarising the data

Examples

    continuous_table(df = iris, Petal.Length, Petal.Width, group = Species)
#> # A tibble: 9 × 6
#>   variable     scoring      Total             setosa        versicolor virginica
#>   <chr>        <chr>        <chr>             <chr>         <chr>      <chr>    
#> 1 NA           NA           N = 150           N = 50        N = 50     N = 50   
#> 2 Petal.Length n            150               50            50         50       
#> 3 Petal.Length Mean (SD)    3.76 (1.77)       1.46 (0.17)   4.26 (0.4… 5.55 (0.…
#> 4 Petal.Length Median (IQR) 4.35 (1.60, 5.10) 1.50 (1.40, … 4.35 (4.0… 5.55 (5.…
#> 5 Petal.Length Min, Max     1, 6.9            1, 1.9        3, 5.1     4.5, 6.9 
#> 6 Petal.Width  n            150               50            50         50       
#> 7 Petal.Width  Mean (SD)    1.20 (0.76)       0.25 (0.11)   1.33 (0.2… 2.03 (0.…
#> 8 Petal.Width  Median (IQR) 1.30 (0.30, 1.80) 0.20 (0.20, … 1.30 (1.2… 2.00 (1.…
#> 9 Petal.Width  Min, Max     0.1, 2.5          0.1, 0.6      1, 1.8     1.4, 2.5 
    continuous_table(df = iris, Sepal.Length, Sepal.Width, group = Species,
                     total = FALSE)
#> # A tibble: 9 × 5
#>   variable     scoring      setosa            versicolor        virginica       
#>   <chr>        <chr>        <chr>             <chr>             <chr>           
#> 1 NA           NA           N = 50            N = 50            N = 50          
#> 2 Sepal.Length n            50                50                50              
#> 3 Sepal.Length Mean (SD)    5.01 (0.35)       5.94 (0.52)       6.59 (0.64)     
#> 4 Sepal.Length Median (IQR) 5.00 (4.80, 5.20) 5.90 (5.60, 6.30) 6.50 (6.23, 6.9…
#> 5 Sepal.Length Min, Max     4.3, 5.8          4.9, 7            4.9, 7.9        
#> 6 Sepal.Width  n            50                50                50              
#> 7 Sepal.Width  Mean (SD)    3.43 (0.38)       2.77 (0.31)       2.97 (0.32)     
#> 8 Sepal.Width  Median (IQR) 3.40 (3.20, 3.68) 2.80 (2.52, 3.00) 3.00 (2.80, 3.1…
#> 9 Sepal.Width  Min, Max     2.3, 4.4          2, 3.4            2.2, 3.8        
    continuous_table(df = iris, Petal.Length, Sepal.Length, digits = 1)
#> # A tibble: 9 × 3
#>   variable     scoring      value         
#>   <chr>        <chr>        <chr>         
#> 1 NA           NA           N = 150       
#> 2 Petal.Length n            150           
#> 3 Petal.Length Mean (SD)    3.8 (1.8)     
#> 4 Petal.Length Median (IQR) 4.3 (1.6, 5.1)
#> 5 Petal.Length Min, Max     1, 6.9        
#> 6 Sepal.Length n            150           
#> 7 Sepal.Length Mean (SD)    5.8 (0.8)     
#> 8 Sepal.Length Median (IQR) 5.8 (5.1, 6.4)
#> 9 Sepal.Length Min, Max     4.3, 7.9