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Summary numeric variables in columns

Source: R/analyze_vars_in_cols.R
analyze_vars_in_cols.Rd

[Stable]

Layout-creating function which can be used for creating column-wise summary tables, primarily used for PK data sets. This function is a wrapper for rtables::analyze_colvars().

Usage 

analyze_vars_in_cols(
  lyt,
  vars,
  ...,
  .stats = c("n", "mean", "sd", "se", "cv", "geom_cv"),
  .labels = c(n = "n", mean = "Mean", sd = "SD", se = "SE", cv = "CV (%)", geom_cv =
    "CV % Geometric Mean"),
  labelstr = " ",
  nested = TRUE,
  na_level = NULL,
  .formats = NULL
)

Arguments

lyt

(layout)
input layout where analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

...

additional arguments for the lower level functions.

.stats

(character)
statistics to select for the table.

.labels

(named character)
labels for the statistics (without indent).

labelstr

(character)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions).

nested

(flag)
whether this layout instruction be applied within the existing layout structure if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split underneath analyses, which is not allowed.

na_level

(string)
used to replace all NA or empty values in factors with custom string.

.formats

(named character or list)
formats for the statistics.

Value

A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout.

See also

summarize_vars(), rtables::analyze_colvars().

Examples 

library(dplyr)

adpp <- tern_ex_adpp %>% h_pkparam_sort()
lyt <- basic_table() %>%
  split_rows_by(var = "ARM", label_pos = "topleft") %>%
  split_rows_by(var = "SEX", label_pos = "topleft") %>%
  analyze_vars_in_cols(vars = "AGE")
result <- build_table(lyt = lyt, df = adpp)
result
#> ARM                                                                   
#>   SEX             n    Mean   SD    SE    CV (%)   CV % Geometric Mean
#> ——————————————————————————————————————————————————————————————————————
#> A: Drug X                                                             
#>   F                                                                   
#>                   0     NA    NA    NA      NA             NA         
#>   M                                                                   
#>                   0     NA    NA    NA      NA             NA         
#> B: Placebo                                                            
#>   F                                                                   
#>                   0     NA    NA    NA      NA             NA         
#>   M                                                                   
#>                   0     NA    NA    NA      NA             NA         
#> C: Combination                                                        
#>   F                                                                   
#>                  288   36.0   6.3   0.4    17.6           18.0        
#>   M                                                                   
#>                  234   36.3   8.5   0.6    23.4           23.5        

# By selecting just some statistics and ad-hoc labels
lyt <- basic_table() %>%
  split_rows_by(var = "ARM", label_pos = "topleft") %>%
  split_rows_by(var = "SEX", label_pos = "topleft") %>%
  analyze_vars_in_cols(
    vars = "AGE",
    .stats = c("n", "cv", "geom_mean", "mean_ci", "median", "min", "max"),
    .labels = c(
      n = "myN",
      cv = "myCV",
      geom_mean = "myGeomMean",
      mean_ci = "Mean (95%CI)",
      median = "Median",
      min = "Minimum",
      max = "Maximum"
    )
  )
result <- build_table(lyt = lyt, df = adpp)
result
#> ARM                                                                                   
#>   SEX            myN   myCV   myGeomMean    Mean (95%CI)    Median   Minimum   Maximum
#> ——————————————————————————————————————————————————————————————————————————————————————
#> A: Drug X                                                                             
#>   F                                                                                   
#>                   0     NA        NA             NA           NA       NA        NA   
#>   M                                                                                   
#>                   0     NA        NA             NA           NA       NA        NA   
#> B: Placebo                                                                            
#>   F                                                                                   
#>                   0     NA        NA             NA           NA       NA        NA   
#>   M                                                                                   
#>                   0     NA        NA             NA           NA       NA        NA   
#> C: Combination                                                                        
#>   F                                                                                   
#>                  288   17.6      35.5      (35.29, 36.76)    36.8     25.4      49.6  
#>   M                                                                                   
#>                  234   23.4      35.3      (35.19, 37.37)    35.3     23.0      58.3  

lyt <- basic_table() %>%
  analyze_vars_in_cols(
    vars = "AGE",
    labelstr = "some custom label"
  )
result <- build_table(lyt, df = adpp)
result
#>                      n    Mean   SD    SE    CV (%)   CV % Geometric Mean
#> —————————————————————————————————————————————————————————————————————————
#> some custom label   522   36.1   7.4   0.3    20.4           20.6        

# PKPT03
lyt <- basic_table() %>%
  split_rows_by(var = "TLG_DISPLAY", split_label = "PK Parameter", label_pos = "topleft") %>%
  analyze_vars_in_cols(
    vars = "AVAL",
    .stats = c("n", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
    .labels = c(
      n = "n",
      mean = "Mean",
      sd = "SD",
      cv = "CV (%)",
      geom_mean = "Geometric Mean",
      geom_cv = "CV % Geometric Mean",
      median = "Median",
      min = "Minimum",
      max = "Maximum"
    )
  )
result <- build_table(lyt, df = adpp)
result
#> PK Parameter   n    Mean     SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Cmax                                                                                                         
#>                58   29.7    5.6     19.0         29.2               19.3            29.0     17.2      45.9  
#> AUCinf obs                                                                                                   
#>                58   207.5   34.9    16.8        204.5               17.3           202.7     123.4     286.9 
#> CL obs                                                                                                       
#>                58    5.1    1.0     20.6         5.0                22.7            5.1       2.5       7.4  
#> Ae                                                                                                           
#>                58    1.5    0.3     21.3         1.5                24.1            1.5       0.6       2.2  
#> Fe                                                                                                           
#>                58   15.7    3.6     22.7         15.3               24.0            15.8      9.0      22.6  
#> CLR                                                                                                          
#>                58    0.0    0.0     19.9         0.0                22.2            0.0       0.0       0.1  
#> Rmax                                                                                                         
#>                58    9.6    2.0     21.1         9.4                21.6            9.3       5.5      14.4  
#> Tonset                                                                                                       
#>                58    3.0    0.7     22.4         2.9                23.3            3.0       1.7       4.8  
#> RENALCLD                                                                                                     
#>                58    0.0    0.0     19.0         0.0                19.4            0.0       0.0       0.0