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Calculate descriptive summary statistics for a numeric variable

Source: R/calc-stats.R
calc_desc.Rd

calc_desc() summarises a numeric variable (target) by another (treat) and reports summary statistics in clinical trial reporting format. The following statistics are calculated for target, per group, i.e. by variable treat levels:

  • n: number of observations

  • Mean (SD): mean and standard deviation of target

  • Median: median of target

  • Min, Max: minimum and maximum of target

  • Missing: number of missing target values

Usage

calc_desc(
  dt,
  target,
  target_name = target,
  treat,
  indent = nbsp(n = 4L),
  pct_dec = 1,
  inc_missing = TRUE
)

Arguments

dt

A data.frame containing, at least, the variables indicated in target and treat.

target

Target variable passed as a string for which summary statistics are to be calculated.

target_name

Heading for the target variable as a string. Defaults to target.

treat

A string indicating the grouping variable, e.g. the variable specifying the treatment population.

indent

A string to be used as indentation of summary statistics labels. Defaults to four HTML non-breaking spaces ( ).

pct_dec

Decimal places for reported figures.

inc_missing

Toggle for including the "Missing" row:

TRUE

(default) The Missing row is always displayed

NA

The Missing row is only displayed if any missing values are present

FALSE

The Missing row is never included in the table

Value

A list containing a data.table formatted as follows:

  • First column is named stats and contains the target variable name indicated in target in the first row. Subsequent rows contain the summarised statistics labels.

  • Other columns are for the levels of the grouping variable (treat).

  • All columns are of character type.

This table is structured for easy integration with Shiny output widgets.

Examples

# Calculate summary statistics for the age of the subjects in each region.
calc_desc(dt = adsl, "AGE", treat = "REGION1")[[1]]
#>                                stats     Africa       Asia    Eurasia
#>                               <char>     <char>     <char>     <char>
#> 1:                               AGE                                 
#> 2:         &nbsp;&nbsp;&nbsp;&nbsp;n         26        268         19
#> 3: &nbsp;&nbsp;&nbsp;&nbsp;Mean (SD) 32.3 (7.7) 34.6 (6.9) 35.9 (8.3)
#> 4:    &nbsp;&nbsp;&nbsp;&nbsp;Median         31         34         36
#> 5:  &nbsp;&nbsp;&nbsp;&nbsp;Min, Max 20.0, 50.0 21.0, 58.0 25.0, 58.0
#> 6:   &nbsp;&nbsp;&nbsp;&nbsp;Missing          0          0          0
#>         Europe North America South America
#>         <char>        <char>        <char>
#> 1:                                        
#> 2:           9            48            30
#> 3: 38.4 (16.1)    36.2 (6.8)    36.1 (8.1)
#> 4:          30            35          35.5
#> 5:  26.0, 69.0    24.0, 57.0    24.0, 64.0
#> 6:           0             0             0

# Calculate summary statistics for biomarker 1 in each of the three arms
# (`ARM`).
calc_desc(dt = adsl, "BMRKR1", treat = "ARM")[[1]]
#>                                stats A: Drug X B: Placebo C: Combination
#>                               <char>    <char>     <char>         <char>
#> 1:                            BMRKR1                                    
#> 2:         &nbsp;&nbsp;&nbsp;&nbsp;n       134        134            132
#> 3: &nbsp;&nbsp;&nbsp;&nbsp;Mean (SD) 6.0 (3.6)  5.7 (3.3)      5.6 (3.5)
#> 4:    &nbsp;&nbsp;&nbsp;&nbsp;Median       5.4        4.8            4.6
#> 5:  &nbsp;&nbsp;&nbsp;&nbsp;Min, Max 0.4, 17.7  0.6, 14.2      0.2, 21.4
#> 6:   &nbsp;&nbsp;&nbsp;&nbsp;Missing         0          0              0