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Overview table of a cohort

Source: R/cohort-overview.R
describe_cohort.Rd

A preliminary table describing the data set at the start of an analysis: one block of rows per variable with its type, number observed, missing values, summary (overall and by group), a group comparison and a small text histogram of the observed values.

Usage

describe_cohort(
  data,
  vars = NULL,
  by = NULL,
  id_col = NULL,
  blocks = NULL,
  labels = NULL,
  test = TRUE,
  digits = 1,
  spark = TRUE,
  discrete_max = 15,
  missing_warn = 20,
  title = "Cohort overview",
  subtitle = NULL,
  output = c("gt", "data")
)

Arguments

data

A data frame, one row per subject.

vars

Variables to describe (names or tidyselect; default: all but id_col and by).

by

Optional grouping column (e.g. treatment arm).

id_col

Optional identifier column: the subtitle reports the number of unique identifiers and warns about duplicates.

blocks

Optional named list block -> variables, shown as row groups (variables not listed go to "Other variables").

labels

Optional named vector of labels.

test

Compare the groups of by.

digits

Decimals of the summaries.

spark

Add the text-histogram column.

discrete_max

Numeric variables with integer values and at most this number of distinct values are typed "Discrete".

missing_warn

Share of missing values (%) above which the cell is highlighted.

title, subtitle

Title and subtitle (subtitle = NULL: automatic summary of the data set).

output

"gt" (formatted table, needs the gt package) or "data" (the underlying tibble).

Value

A gt_tbl or a tibble (output = "data").

Details

  • Continuous and discrete variables: median [Q1; Q3] and range; text histogram of the observed values (8 bins).

  • Categorical and binary variables: a header row, then one row per level with n (%) of the observed values and a bar proportional to the share.

  • Dates: range.

  • Group comparison (by, test = TRUE): Wilcoxon / Kruskal-Wallis for numeric variables, chi-squared or Fisher (expected counts < 5) for categorical ones; unadjusted p-values, for description only.

  • Variables with more than missing_warn percent missing values are highlighted.

Examples

describe_cohort(cohort_sim, vars = c(AGE, SEX, BMI, NFL_V0, MOCA_V0, FALL_V0),
                by = GROUP, id_col = ID, output = "data")
#> # A tibble: 10 × 15
#>    Variable Type      N Missing Overall Range Placebo Active      p Distribution
#>    <chr>    <chr> <int> <chr>   <chr>   <chr> <chr>   <chr>   <dbl> <chr>       
#>  1 AGE      "Con…   120 "0 (0%… "67.0 … "45.… "68.0 … "66.0…  0.548 "▁▁▅▆█▅▂▂"  
#>  2 SEX      "Bin…   120 "0 (0%… ""      ""    ""      ""      0.636 ""          
#>  3 Female   ""       NA ""      "47 (3… ""    "28 (4… "19 (… NA     "████"      
#>  4 Male     ""       NA ""      "73 (6… ""    "39 (5… "34 (… NA     "██████"    
#>  5 BMI      "Con…   116 "4 (3%… "26.0 … "16.… "26.0 … "26.1…  0.885 "▂▂▄▇██▅▄"  
#>  6 NFL_V0   "Con…   120 "0 (0%… "13.0 … "4.1… "13.0 … "13.2…  0.924 "█▇▂▁▁  ▁"  
#>  7 MOCA_V0  "Dis…   120 "0 (0%… "27.0 … "21.… "27.0 … "27.0…  0.747 "▁▂▂▄▆▄▅█"  
#>  8 FALL_V0  "Bin…   120 "0 (0%… ""      ""    ""      ""      1.00  ""          
#>  9 No       ""       NA ""      "96 (8… ""    "54 (8… "42 (… NA     "████████"  
#> 10 Yes      ""       NA ""      "24 (2… ""    "13 (1… "11 (… NA     "██"        
#> # ℹ 5 more variables: .test <chr>, .is_level <lgl>, .pct_missing <dbl>,
#> #   .var <chr>, .block <chr>
describe_cohort(cohort_sim, vars = c(AGE, SEX, BMI, NFL_V0, IL6_V0, MOCA_V0, FALL_V0),
                by = GROUP, id_col = ID,
                blocks = list(Demography = c("AGE", "SEX", "BMI"),
                              Biomarkers = c("NFL_V0", "IL6_V0"),
                              Clinical = c("MOCA_V0", "FALL_V0")))



  
Cohort overview
    
120 subjects (120 unique identifiers) | 7 variables | 116 complete subjects (97%) | by GROUP
    
Variable
      Type
      N
      Missing
      Overall
(n = 120)		
      Range
      
        
GROUP

      		
      p
      Distribution
    
Placebo
(n = 67)		
      Active
(n = 53)		
    
Demography
    
AGE
Continuous
120
0 (0%)
67.0 [61.0; 71.0]
45.0 to 86.0
68.0 [61.0; 71.0]
66.0 [62.0; 70.0]
0.55
▁▁▅▆█▅▂▂
SEX
Binary
120
0 (0%)




0.64

Female




47 (39%)

28 (42%)
19 (36%)

████
Male




73 (61%)

39 (58%)
34 (64%)

██████
BMI
Continuous
116
4 (3%)
26.0 [23.5; 28.6]
16.7 to 33.1
26.0 [23.4; 28.8]
26.1 [23.6; 28.4]
0.88
▂▂▄▇██▅▄
Biomarkers
    
NFL_V0
Continuous
120
0 (0%)
13.0 [8.4; 17.3]
4.1 to 69.4
13.0 [8.1; 17.6]
13.2 [9.0; 17.2]
0.92
█▇▂▁▁  ▁
IL6_V0
Continuous
120
0 (0%)
1.1 [0.7; 1.7]
0.5 to 7.8
1.0 [0.7; 1.5]
1.3 [0.7; 2.1]
0.17
█▃▁▁▁▁ ▁
Clinical
    
MOCA_V0
Discrete
120
0 (0%)
27.0 [25.8; 29.0]
21.0 to 30.0
27.0 [25.5; 28.0]
27.0 [26.0; 29.0]
0.75
▁▂▂▄▆▄▅█
FALL_V0
Binary
120
0 (0%)




1

No




96 (80%)

54 (81%)
42 (79%)

████████
Yes




24 (20%)

13 (19%)
11 (21%)

██
Continuous / discrete: median [Q1; Q3] and range; categorical: n (%) of the observed values. Missing: n (% of subjects). Distribution: histogram of the observed values (8 bins) or share of each level. p: Wilcoxon / Chi², unadjusted (description only).