Helper Function to create a map dataframe that can be used in trim_levels_to_map split function.

Helper Function to create a map dataframe from the input dataset, which can be used as an argument in the trim_levels_to_map split function. Based on different method, the map is constructed differently.

Usage

h_map_for_count_abnormal(
  df,
  variables = list(anl = "ANRIND", split_rows = c("PARAM"), range_low = "ANRLO",
    range_high = "ANRHI"),
  abnormal = list(low = c("LOW", "LOW LOW"), high = c("HIGH", "HIGH HIGH")),
  method = c("default", "range"),
  na_level = "<Missing>"
)

Arguments

df: (data frame)
data set containing all analysis variables.
variables: (named list of string)
list of additional analysis variables.
abnormal: (named list)
identifying the abnormal range level(s) in df. Based on the levels of abnormality of the input dataset, it can be something like list(Low = "LOW LOW", High = "HIGH HIGH") or abnormal = list(Low = "LOW", High = "HIGH"))
method: (string)
indicates how the returned map will be constructed. Can be either "default" or "range". If method is "default", the returned map will only have the abnormal directions that are observed in the df, and records with all normal values will be excluded to avoid error in creating layout. If method is "range", the returned map will be based on the rule that at least one observation with low range > 0 for low direction and at least one observation with high range is not missing for high direction.
na_level: (string)
used to replace all NA or empty values in factors.

Examples

library(scda)
adlb <- synthetic_cdisc_data("latest")$adlb
adlb <- df_explicit_na(adlb)

h_map_for_count_abnormal(
  df = adlb,
  variables = list(anl = "ANRIND", split_rows = c("LBCAT", "PARAM")),
  abnormal = list(low = c("LOW"), high = c("HIGH")),
  method = "default",
  na_level = "<Missing>"
)
#>        LBCAT                                PARAM ANRIND
#> 1  CHEMISTRY Alanine Aminotransferase Measurement   HIGH
#> 6  CHEMISTRY Alanine Aminotransferase Measurement    LOW
#> 7  CHEMISTRY Alanine Aminotransferase Measurement NORMAL
#> 2  CHEMISTRY       C-Reactive Protein Measurement    LOW
#> 3  CHEMISTRY       C-Reactive Protein Measurement   HIGH
#> 8  CHEMISTRY       C-Reactive Protein Measurement NORMAL
#> 4 IMMUNOLOGY         Immunoglobulin A Measurement    LOW
#> 5 IMMUNOLOGY         Immunoglobulin A Measurement   HIGH
#> 9 IMMUNOLOGY         Immunoglobulin A Measurement NORMAL


df <- data.frame(
  USUBJID = c(rep("1", 4), rep("2", 4), rep("3", 4)),
  AVISIT = c(
    rep("WEEK 1", 2),
    rep("WEEK 2", 2),
    rep("WEEK 1", 2),
    rep("WEEK 2", 2),
    rep("WEEK 1", 2),
    rep("WEEK 2", 2)
  ),
  PARAM = rep(c("ALT", "CPR"), 6),
  ANRIND = c(
    "NORMAL", "NORMAL", "LOW",
    "HIGH", "LOW", "LOW", "HIGH", "HIGH", rep("NORMAL", 4)
  ),
  ANRLO = rep(5, 12),
  ANRHI = rep(20, 12)
)
df$ANRIND <- factor(df$ANRIND, levels = c("LOW", "HIGH", "NORMAL"))
h_map_for_count_abnormal(
  df = df,
  variables = list(
    anl = "ANRIND",
    split_rows = c("PARAM"),
    range_low = "ANRLO",
    range_high = "ANRHI"
  ),
  abnormal = list(low = c("LOW"), high = c("HIGH")),
  method = "range",
  na_level = "<Missing>"
)
#>   PARAM ANRIND
#> 1   ALT    LOW
#> 3   ALT   HIGH
#> 5   ALT NORMAL
#> 2   CPR    LOW
#> 4   CPR   HIGH
#> 6   CPR NORMAL