ksformat Usage Examples

The ksformat package provides SAS PROC FORMAT-like functionality for R. This vignette walks through the most common use cases.

Example 1: Basic Discrete Formatting

Create a format for gender codes (auto-stored in library as “sex”):

fnew(
  "M" = "Male",
  "F" = "Female",
  .missing = "Unknown",
  .other = "Other Gender",
  name = "sex"
)

gender_codes <- c("M", "F", "M", NA, "X", "F")
formatted_genders <- fput(gender_codes, "sex")

data.frame(
  code = gender_codes,
  label = formatted_genders
)
#>   code        label
#> 1    M         Male
#> 2    F       Female
#> 3    M         Male
#> 4 <NA>      Unknown
#> 5    X Other Gender
#> 6    F       Female

fprint("sex")
#> KS Format:sex
#> Type: character 
#> Mappings:
#>   M => Male
#>   F => Female
#>   .missing => Unknown
#>   .other => Other Gender

Example 2: Numeric Range Formatting

Define formats in SAS-like text (auto-registered):

fparse(text = '
VALUE age (numeric)
  [0, 18)     = "Child"
  [18, 65)    = "Adult"
  [65, HIGH]  = "Senior"
  .missing    = "Age Unknown"
;
')

ages <- c(5, 15.3, 17.9, 18, 45, 64.99, 65, 85, NA)
age_groups <- fputn(ages, "age")

data.frame(
  age = ages,
  group = age_groups
)
#>     age       group
#> 1  5.00       Child
#> 2 15.30       Child
#> 3 17.90       Child
#> 4 18.00       Adult
#> 5 45.00       Adult
#> 6 64.99       Adult
#> 7 65.00      Senior
#> 8 85.00      Senior
#> 9    NA Age Unknown

Example 3: Decimal Ranges (BMI Categories)

fparse(text = '
VALUE bmi (numeric)
  [0, 18.5)    = "Underweight"
  [18.5, 25)   = "Normal"
  [25, 30)     = "Overweight"
  [30, HIGH]   = "Obese"
  .missing     = "No data"
;
')

bmi_values <- c(16.2, 18.5, 22.7, 25, 29.9, 35.1, NA)
bmi_labels <- fputn(bmi_values, "bmi")

data.frame(
  bmi = bmi_values,
  category = bmi_labels
)
#>    bmi    category
#> 1 16.2 Underweight
#> 2 18.5      Normal
#> 3 22.7      Normal
#> 4 25.0  Overweight
#> 5 29.9  Overweight
#> 6 35.1       Obese
#> 7   NA     No data

Example 4: Exclusive/Inclusive Bounds

fparse(text = '
VALUE score (numeric)
  (0, 50]    = "Low"
  (50, 100]  = "High"
  .other     = "Out of range"
;
')

scores <- c(0, 1, 50, 51, 100, 101)
score_labels <- fputn(scores, "score")

data.frame(
  score = scores,
  label = score_labels
)
#>   score        label
#> 1     0 Out of range
#> 2     1          Low
#> 3    50          Low
#> 4    51         High
#> 5   100         High
#> 6   101 Out of range

Example 5: Reverse Formatting with Invalue

Invalues convert labels back to values. The default target_type is "numeric":

finput(
  "Male" = 1,
  "Female" = 2,
  name = "sex_inv"
)
#> KS Invalue: sex_inv 
#> Target Type: numeric 
#> Mappings:
#>   Male => 1
#>   Female => 2

labels <- c("Male", "Female", "Male", "Unknown", "Female")
codes <- finputn(labels, "sex_inv")

data.frame(
  label = labels,
  code = codes
)
#>     label code
#> 1    Male    1
#> 2  Female    2
#> 3    Male    1
#> 4 Unknown   NA
#> 5  Female    2

Example 6: Bidirectional Formatting

fnew_bid() creates both a format and an invalue at once:

status_bi <- fnew_bid(
  "A" = "Active",
  "I" = "Inactive",
  "P" = "Pending",
  name = "status"
)

# Forward: code -> label
status_codes <- c("A", "I", "P", "A")
status_labels <- fputc(status_codes, "status")
data.frame(code = status_codes, label = status_labels)
#>   code    label
#> 1    A   Active
#> 2    I Inactive
#> 3    P  Pending
#> 4    A   Active

# Reverse: label -> code
test_labels <- c("Active", "Pending", "Inactive")
test_codes <- finputc(test_labels, "status_inv")
data.frame(label = test_labels, code = test_codes)
#>      label code
#> 1   Active    A
#> 2  Pending    P
#> 3 Inactive    I

Example 7: Parse Multiple Formats from Text

fparse(text = '
// Study format definitions

VALUE race (character)
  "W" = "White"
  "B" = "Black"
  "A" = "Asian"
  .missing = "Unknown"
;

INVALUE race_inv
  "White" = 1
  "Black" = 2
  "Asian" = 3
;
')

flist()   # character vector of names
#> [1] "age"        "bmi"        "race"       "race_inv"   "score"     
#> [6] "sex"        "sex_inv"    "status"     "status_inv"
fprint()
#> Registered formats:
#>   age - VALUE (numeric), 3 mapping(s)
#>   bmi - VALUE (numeric), 4 mapping(s)
#>   race - VALUE (character), 3 mapping(s)
#>   race_inv - INVALUE (numeric), 3 mapping(s)
#>   score - VALUE (numeric), 2 mapping(s)
#>   sex - VALUE (character), 2 mapping(s)
#>   sex_inv - INVALUE (numeric), 2 mapping(s)
#>   status - VALUE (character), 3 mapping(s)
#>   status_inv - INVALUE (character), 3 mapping(s)

Example 8: Export Formats Back to Text

bmi_fmt <- format_get("bmi")
cat(fexport(bmi = bmi_fmt))
#> VALUE bmi (numeric)
#>   [0, 18.5) = "Underweight"
#>   [18.5, 25) = "Normal"
#>   [25, 30) = "Overweight"
#>   [30, HIGH] = "Obese"
#>   .missing = "No data"
#> ;

Example 9: SAS-like PUT/INPUT Functions

# fputn — apply numeric format by name
fputn(c(5, 30, 70), "age")
#> [1] "Child"  "Adult"  "Senior"

# fputc — apply character format by name
fputc(c("M", "F"), "sex")
#> [1] "Male"   "Female"

# finputn — apply numeric invalue by name
finputn(c("White", "Black"), "race_inv")
#> [1] 1 2

Example 10: Data Frame Formatting

df <- data.frame(
  id = 1:6,
  sex = c("M", "F", "M", "F", NA, "X"),
  age = c(15, 25, 45, 70, 35, NA),
  stringsAsFactors = FALSE
)

sex_f <- format_get("sex")
age_f <- format_get("age")

df_formatted <- fput_df(
  df,
  sex = sex_f,
  age = age_f,
  suffix = "_label"
)

df_formatted
#>   id  sex age    sex_label   age_label
#> 1  1    M  15         Male       Child
#> 2  2    F  25       Female       Adult
#> 3  3    M  45         Male       Adult
#> 4  4    F  70       Female      Senior
#> 5  5 <NA>  35      Unknown       Adult
#> 6  6    X  NA Other Gender Age Unknown

Example 11: Missing Value Handling

# With .missing label
fput(c("M", "F", NA), "sex")
#> [1] "Male"    "Female"  "Unknown"

# With keep_na = TRUE
fput(c("M", "F", NA), sex_f, keep_na = TRUE)
#> [1] "Male"   "Female" NA

# is_missing() checks
is_missing(NA)
#> [1] TRUE
is_missing(NaN)
#> [1] TRUE
is_missing("")   # TRUE — empty strings are treated as missing
#> [1] TRUE

Example 12: Date/Time Formats (SAS-style)

SAS Date Formats

SAS date format names are auto-resolved — no pre-creation needed:

today <- Sys.Date()

data.frame(
  format = c("DATE9.", "MMDDYY10.", "DDMMYY10.", "YYMMDD10.",
             "MONYY7.", "WORDDATE.", "YEAR4.", "QTR."),
  result = c(
    fputn(today, "DATE9."),
    fputn(today, "MMDDYY10."),
    fputn(today, "DDMMYY10."),
    fputn(today, "YYMMDD10."),
    fputn(today, "MONYY7."),
    fputn(today, "WORDDATE."),
    fputn(today, "YEAR4."),
    fputn(today, "QTR.")
  )
)
#>      format       result
#> 1    DATE9.    21MAY2026
#> 2 MMDDYY10.   05/21/2026
#> 3 DDMMYY10.   21/05/2026
#> 4 YYMMDD10.   2026-05-21
#> 5   MONYY7.      MAY2026
#> 6 WORDDATE. May 21, 2026
#> 7    YEAR4.         2026
#> 8      QTR.            2

# Multiple dates
dates <- as.Date(c("2020-01-15", "2020-06-30", "2020-12-25"))
fputn(dates, "DATE9.")
#> [1] "15JAN2020" "30JUN2020" "25DEC2020"

R Numeric Dates (Days Since 1970-01-01)

r_days <- as.numeric(as.Date("2025-01-01"))
r_days
#> [1] 20089
fputn(r_days, "DATE9.")
#> [1] "01JAN2025"
fputn(r_days, "MMDDYY10.")
#> [1] "01/01/2025"

Time Formats

Time is represented as seconds since midnight:

seconds <- c(0, 3600, 45000, 86399)

data.frame(
  seconds = seconds,
  TIME8 = fputn(seconds, "TIME8."),
  TIME5 = fputn(seconds, "TIME5."),
  HHMM = fputn(seconds, "HHMM.")
)
#>   seconds    TIME8 TIME5  HHMM
#> 1       0  0:00:00  0:00 00:00
#> 2    3600  1:00:00  1:00 01:00
#> 3   45000 12:30:00 12:30 12:30
#> 4   86399 23:59:59 23:59 23:59

Datetime Formats

now <- Sys.time()

data.frame(
  format = c("DATETIME20.", "DATETIME13.", "DTDATE.", "DTYYMMDD."),
  result = c(
    fputn(now, "DATETIME20."),
    fputn(now, "DATETIME13."),
    fputn(now, "DTDATE."),
    fputn(now, "DTYYMMDD.")
  )
)
#>        format             result
#> 1 DATETIME20. 21MAY2026:10:59:14
#> 2 DATETIME13.      21MAY26:10:59
#> 3     DTDATE.          21MAY2026
#> 4   DTYYMMDD.         2026-05-21

# From numeric R-epoch seconds
r_secs <- as.numeric(as.POSIXct("2025-06-15 14:30:00", tz = "UTC"))
fputn(r_secs, "DATETIME20.")
#> [1] "15JUN2025:14:30:00"

Custom Date Formats with `fnew_date()`

# SAS-named format
fnew_date("DATE9.", name = "bday_fmt")
#> KS Format:bday_fmt
#> Type: date 
#> Pattern: %d%b%Y (DATE9.)
birthdays <- as.Date(c("1990-03-25", "1985-11-03", "2000-07-14"))
fput(birthdays, "bday_fmt")
#> [1] "25MAR1990" "03NOV1985" "14JUL2000"

# Custom strftime pattern (e.g. DD.MM.YYYY)
fnew_date("%d.%m.%Y", name = "ru_date", type = "date")
#> KS Format:ru_date
#> Type: date 
#> Pattern: %d.%m.%Y
fput(birthdays, "ru_date")
#> [1] "25.03.1990" "03.11.1985" "14.07.2000"

# Custom pattern with missing label
fnew_date("MMDDYY10.", name = "us_date", .missing = "NO DATE")
#> KS Format:us_date
#> Type: date 
#> Pattern: %m/%d/%Y (MMDDYY10.) 
#>   .missing => NO DATE
mixed <- c(as.Date("2025-01-01"), NA, as.Date("2025-12-31"))
fput(mixed, "us_date")
#> [1] "01/01/2025" "NO DATE"    "12/31/2025"

fprint("bday_fmt")
#> KS Format:bday_fmt
#> Type: date 
#> Pattern: %d%b%Y (DATE9.)

Date Formats in Data Frames

patients <- data.frame(
  id = 1:4,
  visit_date = as.Date(c("2025-01-10", "2025-02-15", "2025-03-20", NA)),
  stringsAsFactors = FALSE
)

visit_fmt <- fnew_date("DATE9.", name = "visit_fmt", .missing = "NOT RECORDED")
fput_df(patients, visit_date = visit_fmt)
#>   id visit_date visit_date_fmt
#> 1  1 2025-01-10      10JAN2025
#> 2  2 2025-02-15      15FEB2025
#> 3  3 2025-03-20      20MAR2025
#> 4  4       <NA>   NOT RECORDED

Parse Date Formats from Text

fparse(text = '
VALUE enrldt (date)
  pattern = "DATE9."
  .missing = "Not Enrolled"
;

VALUE visit_time (time)
  pattern = "TIME8."
;

VALUE stamp (datetime)
  pattern = "DATETIME20."
;
')

fput(as.Date("2025-03-01"), "enrldt")
#> [1] "01MAR2025"
fput(36000, "visit_time")
#> [1] "10:00:00"
fput(as.POSIXct("2025-03-01 10:00:00", tz = "UTC"), "stamp")
#> [1] "01MAR2025:10:00:00"

# Export back to text
enrl_obj <- format_get("enrldt")
cat(fexport(enrldt = enrl_obj))
#> VALUE enrldt (date)
#>   pattern = "DATE9."
#>   .missing = "Not Enrolled"
#> ;

fclear()
#> All formats cleared from library.

Example 13: Multilabel Formats

Overlapping Age Categories

With multilabel formats, a single value can match multiple labels:

fnew(
  "0,5,TRUE,TRUE"    = "Infant",
  "6,11,TRUE,TRUE"   = "Child",
  "12,17,TRUE,TRUE"  = "Adolescent",
  "0,17,TRUE,TRUE"   = "Pediatric",
  "18,64,TRUE,TRUE"  = "Adult",
  "65,Inf,TRUE,TRUE" = "Elderly",
  "18,Inf,TRUE,TRUE" = "Non-Pediatric",
  name = "age_categories",
  type = "numeric",
  multilabel = TRUE
)

ages <- c(3, 14, 25, 70)

# fput returns first match only
fput(ages, "age_categories")
#> [1] "Infant"        "Pediatric"     "Adult"         "Non-Pediatric"

# fput_all returns ALL matching labels
all_labels <- fput_all(ages, "age_categories")
for (i in seq_along(ages)) {
  cat("Age", ages[i], "->", paste(all_labels[[i]], collapse = ", "), "\n")
}
#> Age 3 -> Infant, Pediatric 
#> Age 14 -> Pediatric, Adolescent 
#> Age 25 -> Adult, Non-Pediatric 
#> Age 70 -> Non-Pediatric, Elderly

Multilabel with Missing Values

fnew(
  "0,100,TRUE,TRUE"   = "Valid Score",
  "0,49,TRUE,TRUE"    = "Below Average",
  "50,100,TRUE,TRUE"  = "Above Average",
  "90,100,TRUE,TRUE"  = "Excellent",
  .missing = "No Score",
  .other = "Out of Range",
  name = "score_ml",
  type = "numeric",
  multilabel = TRUE
)

scores <- c(95, 45, NA, 150)
ml_result <- fput_all(scores, "score_ml")

for (i in seq_along(scores)) {
  cat("Score", ifelse(is.na(scores[i]), "NA", scores[i]),
      "->", paste(ml_result[[i]], collapse = ", "), "\n")
}
#> Score 95 -> Valid Score, Above Average, Excellent 
#> Score 45 -> Below Average, Valid Score 
#> Score NA -> No Score 
#> Score 150 -> Out of Range

Parse Multilabel from Text

fparse(text = '
VALUE risk (numeric, multilabel)
  [0, 3]   = "Low Risk"
  [0, 7]   = "Monitored"
  (3, 7]   = "Medium Risk"
  (7, 10]  = "High Risk"
;
')

risk_scores <- c(2, 5, 9)
risk_labels <- fput_all(risk_scores, "risk")
for (i in seq_along(risk_scores)) {
  cat("Score", risk_scores[i], "->",
      paste(risk_labels[[i]], collapse = " | "), "\n")
}
#> Score 2 -> Low Risk | Monitored 
#> Score 5 -> Monitored | Medium Risk 
#> Score 9 -> High Risk

Multilabel Export

risk_obj <- format_get("risk")
cat(fexport(risk = risk_obj))
#> VALUE risk (numeric, multilabel)
#>   [0, 3] = "Low Risk"
#>   [0, 7] = "Monitored"
#>   (3, 7] = "Medium Risk"
#>   (7, 10] = "High Risk"
#> ;

fprint("risk")
#> KS Format:risk (multilabel)
#> Type: numeric 
#> Mappings:
#>   [0, 3] => Low Risk
#>   [0, 7] => Monitored
#>   (3, 7] => Medium Risk
#>   (7, 10] => High Risk

Practical Example: Adverse Event Severity Grading

fnew(
  "1,1,TRUE,TRUE" = "Mild",
  "2,2,TRUE,TRUE" = "Moderate",
  "3,3,TRUE,TRUE" = "Severe",
  "4,4,TRUE,TRUE" = "Life-threatening",
  "5,5,TRUE,TRUE" = "Fatal",
  "3,5,TRUE,TRUE" = "Serious",
  "1,2,TRUE,TRUE" = "Non-serious",
  name = "ae_grade",
  type = "numeric",
  multilabel = TRUE
)

grades <- c(1, 2, 3, 4, 5)
ae_labels <- fput_all(grades, "ae_grade")
for (i in seq_along(grades)) {
  cat("Grade", grades[i], ":",
      paste(ae_labels[[i]], collapse = " + "), "\n")
}
#> Grade 1 : Mild + Non-serious 
#> Grade 2 : Non-serious + Moderate 
#> Grade 3 : Severe + Serious 
#> Grade 4 : Serious + Life-threatening 
#> Grade 5 : Serious + Fatal

fclear()
#> All formats cleared from library.

Example 14: Case-Insensitive Matching

sex_nc <- fnew(
  "M" = "Male",
  "F" = "Female",
  .missing = "Unknown",
  name = "sex_nc",
  type = "character",
  ignore_case = TRUE
)

input <- c("m", "F", "M", "f", NA)
fput(input, sex_nc)
#> [1] "Male"    "Female"  "Male"    "Female"  "Unknown"

# Note the [nocase] flag
fprint("sex_nc")
#> KS Format:sex_nc (nocase)
#> Type: character 
#> Mappings:
#>   M => Male
#>   F => Female
#>   .missing => Unknown

# Also works with fputc
fputc("m", "sex_nc")
#> [1] "Male"

fclear()
#> All formats cleared from library.

Example 15: Expression Labels in Formats

Expression labels contain .x1, .x2, etc., which reference extra arguments passed to fput(). This lets you compute labels dynamically.

Simple `sprintf` Expression

stat_fmt <- fnew(
  "n"   = "sprintf('%s', .x1)",
  "pct" = "sprintf('%.1f%%', .x1 * 100)",
  name = "stat",
  type = "character"
)

types  <- c("n",  "pct",  "n",   "pct")
values <- c(42,   0.053,  100,   0.255)

fput(types, stat_fmt, values)
#> [1] "42"    "5.3%"  "100"   "25.5%"

Two Extra Arguments (`.x1`, `.x2`)

ratio_fmt <- fnew(
  "ratio" = "sprintf('%s/%s', .x1, .x2)",
  name = "ratio",
  type = "character"
)

fput("ratio", ratio_fmt, 3, 10)
#> [1] "3/10"
fput(c("ratio", "ratio"), ratio_fmt, c(3, 7), c(10, 20))
#> [1] "3/10" "7/20"

`ifelse` Expression

sign_fmt <- fnew(
  "val" = "ifelse(.x1 > 0, paste0('+', .x1), as.character(.x1))",
  name = "sign",
  type = "character"
)

nums <- c(5, 0, -3)
fput(rep("val", 3), sign_fmt, nums)
#> [1] "+5" "0"  "-3"

Mixed Static and Expression Labels

mixed_fmt <- fnew(
  "header" = "HEADER",
  "n"      = "sprintf('N=%s', .x1)",
  "pct"    = "sprintf('%.1f%%', .x1 * 100)",
  name = "mixed",
  type = "character"
)

keys <- c("header", "n", "pct", "header", "n")
vals <- c(0,        42,  0.15,  0,        100)
fput(keys, mixed_fmt, vals)
#> [1] "HEADER" "N=42"   "15.0%"  "HEADER" "N=100"

Expression in `.other` Fallback

known_fmt <- fnew(
  "ok" = "OK",
  .other = "sprintf('Error(%s)', .x1)",
  name = "err_fmt",
  type = "character"
)

codes   <- c("ok", "E01", "ok", "E99")
details <- c("",   "timeout", "", "overflow")
fput(codes, known_fmt, details)
#> [1] "OK"              "Error(timeout)"  "OK"              "Error(overflow)"

Scalar Recycling

label_fmt <- fnew(
  "val" = "sprintf('%s (N=%s)', .x1, .x2)",
  name = "recycle",
  type = "character"
)

fput(c("val", "val"), label_fmt, c(42, 55), 100)
#> [1] "42 (N=100)" "55 (N=100)"

Statistical Table Format with Computed Labels

A realistic clinical-trial example: e() marks labels as expressions evaluated at apply-time, .x1 references the extra argument, and multiline dplyr::case_when shows complex conditional formatting.

# Population counts used as denominators
n.trt <- data.frame(pop = c("fas","pps","saf"), ntot = c(34, 30, 36))
get_n <- function(pop) {
  n.trt$ntot[n.trt$pop == pop]
}

fnew(
  "n_fas" = e("get_n('fas')"),
  "n_pps" = e("get_n('pps')"),
  "n_saf" = e("get_n('saf')"),
  "n"   = "sprintf('%d', .x1)",
  "n_pct_fas" = "sprintf('%d (%5.1f%%)', .x1, .x1 * 100 / get_n('fas'))",
  "n_pct_pps" = "sprintf('%d (%5.1f%%)', .x1, .x1 * 100 / get_n('pps'))",
  "n_pct_saf" = "sprintf('%d (%5.1f%%)', .x1, .x1 * 100 / get_n('saf'))",
  "pct" = "dplyr::case_when(
               .x1>0 & .x1<0.1 ~ sprintf('%5s', ' <0.1%'),
               .x1>=0.1 | .x1==0 ~ sprintf(paste0('%5.', 1 ,'f%%'), .x1)
           )",
  "pval" = "dplyr::case_when(
                .x1>=0 & .x1<0.001 ~ sprintf('%s', '<0.001'),
                .x1>=0.001 & .x1<=0.999 ~ sprintf(paste0('%.', 3 ,'f'), .x1),
                .x1>0.999 ~ sprintf('%s', '>0.999'), .default = '--'
           )",
  name = "stat",
  type = "character"
)

The same format can be created via fparse(). Note that multiline expressions must be collapsed to single lines in the text block, and (eval) marks evaluated labels:

fmt <- '
  VALUE stat_01 (character)
     "n_fas" = "get_n(\'fas\')" (eval)
     "n_pps" = "get_n(\'pps\')" (eval)
     "n_saf" = "get_n(\'saf\')" (eval)
     "n"     = "sprintf(\'%d\', .x1)"
     "pct"   = "dplyr::case_when(.x1>0 & .x1<0.1 ~ sprintf(\'%5s\', \' <0.1%\'), .x1>=0.1 | .x1==0 ~ sprintf(paste0(\'%5.\', 1 ,\'f%%\'), .x1))"
     "n_pct_fas" = "sprintf(\'%d (%5.1f%%)\', .x1, .x1 * 100 / get_n(\'fas\'))"
     "n_pct_pps" = "sprintf(\'%d (%5.1f%%)\', .x1, .x1 * 100 / get_n(\'pps\'))"
     "n_pct_saf" = "sprintf(\'%d (%5.1f%%)\', .x1, .x1 * 100 / get_n(\'saf\'))"
     "pval"  = "dplyr::case_when(.x1>=0 & .x1<0.001 ~ sprintf(\'%s\', \'<0.001\'), .x1>=0.001 & .x1<=0.999 ~ sprintf(paste0(\'%.\', 3 ,\'f\'), .x1), .x1>0.999 ~ sprintf(\'%s\', \'>0.999\'), .default = \'--\')"
;'
fparse(fmt)

Both stat (via fnew) and stat_01 (via fparse) produce identical results:

df <- data.frame(
  types = c("n_fas", "n_pps", "n_saf", "n", "pct", "pct", "n", "pval", "pval",
            "n_pct_fas", "n_pct_pps", "n_pct_saf"),
  values = c(NA, NA, NA, 42, 0.053, 0.0008, 100, 0.255, 0.0003, 22, 22, 22)
)

df$fmt    <- fput(df$types, "stat",    df$values)
df$fmt_01 <- fput(df$types, "stat_01", df$values)
print(df)
#>        types   values         fmt      fmt_01
#> 1      n_fas       NA          34          34
#> 2      n_pps       NA          30          30
#> 3      n_saf       NA          36          36
#> 4          n  42.0000          42          42
#> 5        pct   0.0530       <0.1%       <0.1%
#> 6        pct   0.0008       <0.1%       <0.1%
#> 7          n 100.0000         100         100
#> 8       pval   0.2550       0.255       0.255
#> 9       pval   0.0003      <0.001      <0.001
#> 10 n_pct_fas  22.0000 22 ( 64.7%) 22 ( 64.7%)
#> 11 n_pct_pps  22.0000 22 ( 73.3%) 22 ( 73.3%)
#> 12 n_pct_saf  22.0000 22 ( 61.1%) 22 ( 61.1%)

fclear()
#> All formats cleared from library.

Example 16: Vectorized Format Names (SAS PUTC-style)

Each element can use a different format, determined by a vector of format names:

# Dispatch format: maps type code to format name
fnew("1" = "groupx", "2" = "groupy", "3" = "groupz",
     name = "typefmt", type = "numeric")

# Per-group character formats
fnew("positive" = "agree",  "negative" = "disagree", "neutral" = "notsure",
     name = "groupx", type = "character")
fnew("positive" = "accept", "negative" = "reject",   "neutral" = "possible",
     name = "groupy", type = "character")
fnew("positive" = "pass",   "negative" = "fail",     "neutral" = "retest",
     name = "groupz", type = "character")

type     <- c(1, 1, 1, 2, 2, 2, 3, 3, 3)
response <- c("positive", "negative", "neutral",
              "positive", "negative", "neutral",
              "positive", "negative", "neutral")

# Step 1: map type -> format name
respfmt <- fput(type, "typefmt")

# Step 2: apply per-element format
word <- fputc(response, respfmt)

data.frame(type = type, response = response, respfmt = respfmt, word = word)
#>   type response respfmt     word
#> 1    1 positive  groupx    agree
#> 2    1 negative  groupx disagree
#> 3    1  neutral  groupx  notsure
#> 4    2 positive  groupy   accept
#> 5    2 negative  groupy   reject
#> 6    2  neutral  groupy possible
#> 7    3 positive  groupz     pass
#> 8    3 negative  groupz     fail
#> 9    3  neutral  groupz   retest

fclear()
#> All formats cleared from library.

Example 17: Working with Dates and Formats — PUTN

A SAS-style workflow where format names are looked up dynamically per observation:

# Format that maps key codes to date format names
fnew("1" = "date9.", "2" = "mmddyy10.",
     name = "writfmt", type = "numeric")

fnew_date("date9.")
#> KS Format:DATE9.
#> Type: date 
#> Pattern: %d%b%Y (DATE9.)
fnew_date("mmddyy10.")
#> KS Format:MMDDYY10.
#> Type: date 
#> Pattern: %m/%d/%Y (MMDDYY10.)

# Input data (R date numbers = days since 1970-01-01)
number <- c(12103, 10899)
key    <- c(1, 2)

# Look up format name per observation
datefmt <- fputn(key, "writfmt")

# Apply per-element date format
date <- fputn(number, datefmt)

data.frame(number = number, key = key, datefmt = datefmt, date = date)
#>   number key   datefmt       date
#> 1  12103   1    date9.  20FEB2003
#> 2  10899   2 mmddyy10. 11/04/1999

fclear()
#> All formats cleared from library.

Example 18: Import SAS Formats from CNTLOUT CSV

The fimport() function reads a CSV file exported from a SAS format catalogue (PROC FORMAT ... CNTLOUT=):

csv_path <- system.file("extdata", "test_cntlout.csv", package = "ksformat")

imported <- fimport(csv_path)
#> Warning: Skipping PICTURE format: "PICFMT"
#> ℹ TYPE="P" is not supported by ksformat.
#> Warning: Skipped incompatible entry in format "SMISSING":
#> ✖ SAS special missing value '.A' (HLO='S') has no R equivalent.
#> Warning: Skipped incompatible entry in format "SMISSING":
#> ✖ SAS special missing value '.B' (HLO='S') has no R equivalent.
#> ✔ Imported 4 formats and 1 invalue from
#>   '/private/var/folders/rn/3s0h46m118j426j_fmjr1z8m0000gn/T/RtmpcG4IyO/Rinst1310a1404be79/ksformat/extdata/test_cntlout.csv'.
names(imported)
#> [1] "AGEGRP"   "BMICAT"   "GENDER"   "RACEIN"   "SMISSING"

flist()
#> [1] "AGEGRP"   "BMICAT"   "GENDER"   "RACEIN"   "SMISSING"
fprint()
#> Registered formats:
#>   AGEGRP - VALUE (numeric), 3 mapping(s)
#>   BMICAT - VALUE (numeric), 4 mapping(s)
#>   GENDER - VALUE (character), 2 mapping(s)
#>   RACEIN - INVALUE (numeric), 3 mapping(s)
#>   SMISSING - VALUE (numeric), 1 mapping(s)

Use Imported Formats

# Character format (GENDER)
gender_codes <- c("M", "F", NA, "X")
data.frame(
  code = gender_codes,
  label = fputc(gender_codes, "GENDER")
)
#>   code   label
#> 1    M    Male
#> 2    F  Female
#> 3 <NA> Unknown
#> 4    X       X

# Numeric format (AGEGRP)
ages <- c(5, 17, 18, 45, 65, 100, NA, -1)
data.frame(
  age = ages,
  group = fputn(ages, "AGEGRP")
)
#>   age       group
#> 1   5       Child
#> 2  17       Child
#> 3  18       Adult
#> 4  45       Adult
#> 5  65      Senior
#> 6 100      Senior
#> 7  NA Missing Age
#> 8  -1       Other

# Numeric format (BMICAT)
bmi_values <- c(15.0, 18.5, 22.3, 25.0, 28.7, 30.0, 35.5)
data.frame(
  bmi = bmi_values,
  category = fputn(bmi_values, "BMICAT")
)
#>    bmi    category
#> 1 15.0 Underweight
#> 2 18.5      Normal
#> 3 22.3      Normal
#> 4 25.0  Overweight
#> 5 28.7  Overweight
#> 6 30.0       Obese
#> 7 35.5       Obese

# Invalue (RACEIN)
race_labels <- c("White", "Black", "Asian", "Other")
data.frame(
  label = race_labels,
  code = finputn(race_labels, "RACEIN")
)
#>   label code
#> 1 White    1
#> 2 Black    2
#> 3 Asian    3
#> 4 Other   NA

Apply to Data Frame

df <- data.frame(
  id = 1:5,
  sex = c("M", "F", "M", NA, "F"),
  age = c(10, 30, 70, NA, 50),
  stringsAsFactors = FALSE
)

gender_fmt <- imported[["GENDER"]]
age_fmt    <- imported[["AGEGRP"]]

fput_df(df, sex = gender_fmt, age = age_fmt, suffix = "_label")
#>   id  sex age sex_label   age_label
#> 1  1    M  10      Male       Child
#> 2  2    F  30    Female       Adult
#> 3  3    M  70      Male      Senior
#> 4  4 <NA>  NA   Unknown Missing Age
#> 5  5    F  50    Female       Adult

Export Imported Format

cat(fexport(AGEGRP = age_fmt))
#> VALUE AGEGRP (numeric)
#>   [0, 17] = "Child"
#>   [18, 64] = "Adult"
#>   [65, HIGH] = "Senior"
#>   .missing = "Missing Age"
#>   .other = "Other"
#> ;
cat(fexport(GENDER = gender_fmt))
#> VALUE GENDER (character)
#>   "M" = "Male"
#>   "F" = "Female"
#>   .missing = "Unknown"
#> ;

Selective Import (No Auto-register)

fclear()
#> All formats cleared from library.

manual <- fimport(csv_path, register = FALSE)
#> Warning: Skipping PICTURE format: "PICFMT"
#> ℹ TYPE="P" is not supported by ksformat.
#> Warning: Skipped incompatible entry in format "SMISSING":
#> ✖ SAS special missing value '.A' (HLO='S') has no R equivalent.
#> Warning: Skipped incompatible entry in format "SMISSING":
#> ✖ SAS special missing value '.B' (HLO='S') has no R equivalent.
#> ✔ Imported 4 formats and 1 invalue from
#>   '/private/var/folders/rn/3s0h46m118j426j_fmjr1z8m0000gn/T/RtmpcG4IyO/Rinst1310a1404be79/ksformat/extdata/test_cntlout.csv'.

# Library should be empty
flist()
#> character(0)
fprint()
#> Format library is empty

# Use directly from returned list
fput(c("M", "F"), manual[["GENDER"]])
#> [1] "Male"   "Female"

fclear()
#> All formats cleared from library.

Example 19: Bilingual Format

Expression labels can select between languages at apply-time using an extra argument:

# Single format, language selected via .x1 extra argument
sex_bi <- fnew(
  "M" = "ifelse(.x1 == 'en', 'Male', 'Homme')",
  "F" = "ifelse(.x1 == 'en', 'Female', 'Femme')",
  .missing = "Unknown",
  name = "sex_bi"
)

# .x1 = language code per observation
fput(c("M", "F", "M"), sex_bi, c("en", "fr", "en"))
#> [1] "Male"  "Femme" "Male"
# -> "Male" "Femme" "Male"

# Alternative: one format per language, selected at apply-time
fnew("M" = "Male",  "F" = "Female",  .missing = "Unknown", name = "sex_en")
fnew("M" = "Homme", "F" = "Femme",   .missing = "Inconnu", name = "sex_fr")

lang <- "fr"
fput(c("M", "F", NA), paste0("sex_", lang))
#> [1] "Homme"   "Femme"   "Inconnu"
# -> "Homme" "Femme" "Inconnu"

fclear()
#> All formats cleared from library.

Example 20: Composite Key Lookup with `fputk()`

fputk() pastes multiple vectors into a composite key before format lookup. This is useful when a format is keyed on the combination of several columns, a common pattern in clinical data (e.g., looking up a visit date by subject + visit number).

# Simulate a Subject Visits (SV) domain
SV <- data.frame(
  USUBJID  = c("SUBJ-001", "SUBJ-001", "SUBJ-001", "SUBJ-002", "SUBJ-002"),
  VISITNUM = c(1, 2, 3, 1, 2),
  SVSTDTC  = c("2025-01-15", "2025-02-20", "2025-03-10",
               "2025-01-18", "2025-02-25"),
  stringsAsFactors = FALSE
)

# Simulate a Questionnaires (QS) domain
QS <- data.frame(
  USUBJID  = c("SUBJ-001", "SUBJ-001", "SUBJ-002", "SUBJ-002", "SUBJ-002"),
  VISITNUM = c(1, 2, 1, 2, 3),
  QSTESTCD = c("SCORE1", "SCORE1", "SCORE1", "SCORE1", "SCORE1"),
  QSSTRESN = c(85, 90, 72, 78, NA),
  stringsAsFactors = FALSE
)

SV
#>    USUBJID VISITNUM    SVSTDTC
#> 1 SUBJ-001        1 2025-01-15
#> 2 SUBJ-001        2 2025-02-20
#> 3 SUBJ-001        3 2025-03-10
#> 4 SUBJ-002        1 2025-01-18
#> 5 SUBJ-002        2 2025-02-25
QS
#>    USUBJID VISITNUM QSTESTCD QSSTRESN
#> 1 SUBJ-001        1   SCORE1       85
#> 2 SUBJ-001        2   SCORE1       90
#> 3 SUBJ-002        1   SCORE1       72
#> 4 SUBJ-002        2   SCORE1       78
#> 5 SUBJ-002        3   SCORE1       NA

Character lookup (returns character strings)

# Create composite key -> date string mapping from SV
fnew(
  fmap(paste(SV$USUBJID, SV$VISITNUM, sep = "|"), SV$SVSTDTC),
  .other  = "NOT FOUND",
  name    = "svdtc",
  type    = "character",
  ignore_case = TRUE
)

fprint("svdtc")
#> KS Format:svdtc (nocase)
#> Type: character 
#> Mappings:
#>   SUBJ-001|1 => 2025-01-15
#>   SUBJ-001|2 => 2025-02-20
#>   SUBJ-001|3 => 2025-03-10
#>   SUBJ-002|1 => 2025-01-18
#>   SUBJ-002|2 => 2025-02-25
#>   .other => NOT FOUND

Now look up visit dates in the QS domain using fputk():

QS$SVSTDTC <- fputk(QS$USUBJID, QS$VISITNUM, format = "svdtc")
QS
#>    USUBJID VISITNUM QSTESTCD QSSTRESN    SVSTDTC
#> 1 SUBJ-001        1   SCORE1       85 2025-01-15
#> 2 SUBJ-001        2   SCORE1       90 2025-02-20
#> 3 SUBJ-002        1   SCORE1       72 2025-01-18
#> 4 SUBJ-002        2   SCORE1       78 2025-02-25
#> 5 SUBJ-002        3   SCORE1       NA  NOT FOUND
class(QS$SVSTDTC)  # character
#> [1] "character"

fclear()
#> All formats cleared from library.

Native Date lookup (returns Date objects)

Using type = "Date", values are stored as native R Date objects and fput()/fputk() return them directly — no string conversion needed:

# Create composite key -> Date mapping from SV
fnew(
  fmap(
    paste(SV$USUBJID, SV$VISITNUM, sep = "|"),
    as.Date(SV$SVSTDTC, format = "%Y-%m-%d")
  ),
  .other  = NA,
  name    = "svdtn",
  type    = "Date",
  ignore_case = TRUE
)

fprint("svdtn")
#> KS Format:svdtn (nocase)
#> Type: Date 
#> Mappings:
#>   SUBJ-001|1 => 2025-01-15
#>   SUBJ-001|2 => 2025-02-20
#>   SUBJ-001|3 => 2025-03-10
#>   SUBJ-002|1 => 2025-01-18
#>   SUBJ-002|2 => 2025-02-25

QS$SVSTDTC_DT <- fputk(QS$USUBJID, QS$VISITNUM, format = "svdtn")
QS
#>    USUBJID VISITNUM QSTESTCD QSSTRESN    SVSTDTC SVSTDTC_DT
#> 1 SUBJ-001        1   SCORE1       85 2025-01-15 2025-01-15
#> 2 SUBJ-001        2   SCORE1       90 2025-02-20 2025-02-20
#> 3 SUBJ-002        1   SCORE1       72 2025-01-18 2025-01-18
#> 4 SUBJ-002        2   SCORE1       78 2025-02-25 2025-02-25
#> 5 SUBJ-002        3   SCORE1       NA  NOT FOUND       <NA>
class(QS$SVSTDTC_DT)  # Date
#> [1] "Date"

# Typed NA for unmatched keys (SUBJ-002 Visit 3 not in SV)
is.na(QS$SVSTDTC_DT[5])
#> [1] TRUE

# Date arithmetic works directly
QS$SVSTDTC_DT + 7  # add 7 days
#> [1] "2025-01-22" "2025-02-27" "2025-01-25" "2025-03-04" NA

fclear()
#> All formats cleared from library.

Example 21: Consistent Data-Driven Formats with `fmap()`

When building formats from data (e.g., a data frame with 1000+ rows), you need a named vector mapping keys to values. By default, fnew() treats named vectors differently depending on the output type:

Value types (Date, POSIXct, logical): c(key = value) — natural direction, no reversal.
Character / numeric: c(Label = "Code") — R convention, names and values are swapped internally.

This inconsistency is confusing for data-driven formats. The fmap() helper solves it: fmap(keys, values) works identically for all types.

Clinical-data example

Suppose we have a demographics dataset and need two lookup formats from the same data — one returning Date objects, one returning character strings:

library(ksformat)

dm <- data.frame(
  USUBJID = c("SUBJ-001", "SUBJ-002", "SUBJ-003"),
  SUBJID  = c("001", "002", "003"),
  RFICDTC = c("2023-03-09T08:45", "2024-08-13T09:53", "2025-06-17T09:03"),
  stringsAsFactors = FALSE
)

# Composite key for both formats
keys <- paste(dm$USUBJID, dm$SUBJID, sep = "|")

Same `fmap(keys, values)` pattern for both types

Both formats use the identical calling style — fmap(keys, values) where keys are input lookup values and values are output objects:

# Date lookup
fnew(
  fmap(keys, as.Date(dm$RFICDTC, format = "%Y-%m-%d")),
  .other      = NA,
  type        = "Date",
  ignore_case = TRUE,
  name        = "icdtn"
)

# Character lookup — same fmap(keys, values) pattern!
fnew(
  fmap(keys, dm$RFICDTC),
  .other      = "NOT FOUND",
  type        = "character",
  ignore_case = TRUE,
  name        = "icdtc"
)

fprint("icdtn")
#> KS Format:icdtn (nocase)
#> Type: Date 
#> Mappings:
#>   SUBJ-001|001 => 2023-03-09
#>   SUBJ-002|002 => 2024-08-13
#>   SUBJ-003|003 => 2025-06-17
fprint("icdtc")
#> KS Format:icdtc (nocase)
#> Type: character 
#> Mappings:
#>   SUBJ-001|001 => 2023-03-09T08:45
#>   SUBJ-002|002 => 2024-08-13T09:53
#>   SUBJ-003|003 => 2025-06-17T09:03
#>   .other => NOT FOUND

# Both return the expected results
fputk("SUBJ-001", "001", format = "icdtn")
#> [1] "2023-03-09"
class(fputk("SUBJ-001", "001", format = "icdtn"))
#> [1] "Date"

fputk("SUBJ-001", "001", format = "icdtc")
#> [1] "2023-03-09T08:45"
class(fputk("SUBJ-001", "001", format = "icdtc"))
#> [1] "character"

fclear()
#> All formats cleared from library.

No extra parameters needed — fmap() tells fnew() to use the natural direction for all types.

When to use the default (reversal on)

The default auto-reversal preserves the standard R convention where c(Label = "Code") maps Code -> Label. This is natural for hand-written formats:

# These are equivalent — both map "M" -> "Male"
fmt_a <- fnew(c(Male = "M", Female = "F"))
fmt_b <- fnew("M" = "Male", "F" = "Female")

identical(fput(c("M", "F"), fmt_a), fput(c("M", "F"), fmt_b))
#> [1] TRUE

fclear()
#> All formats cleared from library.

Summary

Use case	Style	Reversal
Data-driven (any type)	`fmap(keys, values)`	Suppressed
Hand-written (char/num)	`c(Label = "Code")` or `"Code" = "Label"`	Auto (default)
Value types (`Date`, etc.)	`fmap(keys, values)` or `setNames(values, keys)`	No reversal (default)

Example 22: Date Lookup via `fparse()` and `fputk()`

Examples 20–21 built composite-key formats programmatically with fnew() and fmap(). When the mapping is small and known in advance (e.g., a study-specific visit schedule), you can define the same lookup entirely in text with fparse().

Character date lookup

The simplest approach: store dates as character strings using a regular character format.

fparse(text = '
VALUE svdtc (character, nocase)
  "SUBJ-001|1" = "2025-01-15"
  "SUBJ-001|2" = "2025-02-20"
  "SUBJ-001|3" = "2025-03-10"
  "SUBJ-002|1" = "2025-01-18"
  "SUBJ-002|2" = "2025-02-25"
  .other       = "NOT FOUND"
;
')

fprint("svdtc")
#> KS Format:svdtc (nocase)
#> Type: character 
#> Mappings:
#>   SUBJ-001|1 => 2025-01-15
#>   SUBJ-001|2 => 2025-02-20
#>   SUBJ-001|3 => 2025-03-10
#>   SUBJ-002|1 => 2025-01-18
#>   SUBJ-002|2 => 2025-02-25
#>   .other => NOT FOUND

Apply with fputk() to look up visit dates from a questionnaire domain:

QS <- data.frame(
  USUBJID  = c("SUBJ-001", "SUBJ-001", "SUBJ-002", "SUBJ-002", "SUBJ-002"),
  VISITNUM = c(1, 2, 1, 2, 3),
  QSSTRESN = c(85, 90, 72, 78, NA),
  stringsAsFactors = FALSE
)

QS$SVSTDTC <- fputk(QS$USUBJID, QS$VISITNUM, format = "svdtc")
QS
#>    USUBJID VISITNUM QSSTRESN    SVSTDTC
#> 1 SUBJ-001        1       85 2025-01-15
#> 2 SUBJ-001        2       90 2025-02-20
#> 3 SUBJ-002        1       72 2025-01-18
#> 4 SUBJ-002        2       78 2025-02-25
#> 5 SUBJ-002        3       NA  NOT FOUND

fclear()
#> All formats cleared from library.

Native Date lookup

Use the Date value type with format: to store dates as native R Date objects. The format: parameter tells fparse() how to parse the date strings in the text block:

fparse(text = '
VALUE svdtn (Date, format: %Y-%m-%d, nocase)
  "SUBJ-001|1" = "2025-01-15"
  "SUBJ-001|2" = "2025-02-20"
  "SUBJ-001|3" = "2025-03-10"
  "SUBJ-002|1" = "2025-01-18"
  "SUBJ-002|2" = "2025-02-25"
;
')

fprint("svdtn")
#> KS Format:svdtn (nocase)
#> Type: Date 
#> Mappings:
#>   SUBJ-001|1 => 2025-01-15
#>   SUBJ-001|2 => 2025-02-20
#>   SUBJ-001|3 => 2025-03-10
#>   SUBJ-002|1 => 2025-01-18
#>   SUBJ-002|2 => 2025-02-25

Now fputk() returns real Date objects — arithmetic and comparison work directly:

QS$SVSTDTC_DT <- fputk(QS$USUBJID, QS$VISITNUM, format = "svdtn")
QS
#>    USUBJID VISITNUM QSSTRESN    SVSTDTC SVSTDTC_DT
#> 1 SUBJ-001        1       85 2025-01-15 2025-01-15
#> 2 SUBJ-001        2       90 2025-02-20 2025-02-20
#> 3 SUBJ-002        1       72 2025-01-18 2025-01-18
#> 4 SUBJ-002        2       78 2025-02-25 2025-02-25
#> 5 SUBJ-002        3       NA  NOT FOUND       <NA>

class(QS$SVSTDTC_DT)         # Date
#> [1] "Date"
is.na(QS$SVSTDTC_DT[5])      # TRUE — no match for SUBJ-002 Visit 3
#> [1] TRUE

# Date arithmetic works directly
QS$SVSTDTC_DT + 7
#> [1] "2025-01-22" "2025-02-27" "2025-01-25" "2025-03-04" NA

Round-trip: export and re-import

Formats created with fparse() can be exported back to text with fexport() and re-parsed — useful for version-controlled format definitions:

fmt_obj <- format_get("svdtn")
txt <- fexport(svdtn = fmt_obj)
cat(txt)
#> VALUE svdtn (Date, nocase, format: %Y-%m-%d)
#>   "SUBJ-001|1" = "2025-01-15"
#>   "SUBJ-001|2" = "2025-02-20"
#>   "SUBJ-001|3" = "2025-03-10"
#>   "SUBJ-002|1" = "2025-01-18"
#>   "SUBJ-002|2" = "2025-02-25"
#> ;

# Re-parse the exported text
fclear()
#> All formats cleared from library.
fparse(text = txt)

# Verify it still works
fputk("SUBJ-001", 2, format = "svdtn")
#> [1] "2025-02-20"

fclear()
#> All formats cleared from library.

Example 23: Inspecting Range Rules with `franges()`

franges() extracts all range-based mappings from a format and returns them as a tidy data.frame — useful for auditing, documentation, or downstream processing.

fparse(text = '
VALUE age (numeric)
  [0, 18)    = "Child"
  [18, 65)   = "Adult"
  [65, HIGH] = "Senior"
  .missing   = "Unknown"
;
')

franges("age")
#>   low high inc_low inc_high  label
#> 1   0   18    TRUE    FALSE  Child
#> 2  18   65    TRUE    FALSE  Adult
#> 3  65  Inf    TRUE     TRUE Senior

You can use the result like any data frame — filter, display, or feed into further calculations:

df <- franges("age")

# Which ranges have a finite upper bound?
df[is.finite(df$high), ]
#>   low high inc_low inc_high label
#> 1   0   18    TRUE    FALSE Child
#> 2  18   65    TRUE    FALSE Adult

franges() silently excludes discrete entries (.missing, .other, plain string keys) — only range rows appear. It returns an empty data.frame with the same columns when the format contains no ranges.

fnew("M" = "Male", "F" = "Female", .missing = "Unknown", name = "sex")
franges("sex")   # 0 rows
#> [1] low      high     inc_low  inc_high label   
#> <0 rows> (or 0-length row.names)

Example 24: Reverse Range Lookup with `fmap_to_ranges()`

When a range format stores numeric codes as its labels (e.g. visit windows coded as weeks), fmap_to_ranges() turns a vector of those codes back into the original [low, high] bounds — one row per input value.

fparse(text = '
VALUE visit_ther (numeric)
  [LOW,  1] =  0
  [ 8, 22] =  2
  [22, 36] =  4
  [37, 50] =  6
  [51, 63] =  8
  [64, 78] = 10
  [79, 91] = 12
;
')

coded_weeks <- c(0, 2, 4, 6, 8, 10, 12)
fmap_to_ranges(coded_weeks, "visit_ther")
#>    low high inc_low inc_high
#> 1 -Inf    1    TRUE     TRUE
#> 2    8   22    TRUE     TRUE
#> 3   22   36    TRUE     TRUE
#> 4   37   50    TRUE     TRUE
#> 5   51   63    TRUE     TRUE
#> 6   64   78    TRUE     TRUE
#> 7   79   91    TRUE     TRUE

Unmatched values produce NA rows, making it safe to pass arbitrary vectors:

fmap_to_ranges(c(2, 99, 4), "visit_ther")
#>   low high inc_low inc_high
#> 1   8   22    TRUE     TRUE
#> 2  NA   NA      NA       NA
#> 3  22   36    TRUE     TRUE

Example 25: Date Range Bucketing

date_range and datetime_range formats bucket Date or POSIXct input into character labels using ISO date/datetime interval bounds. They reuse the same range-table engine as numeric ranges, so the findInterval() fast path is active for sorted, disjoint buckets.

Fiscal-year bucketing

fnew(
  "2023-01-01,2024-01-01,TRUE,FALSE" = "FY23",
  "2024-01-01,2025-01-01,TRUE,FALSE" = "FY24",
  "2025-01-01,2026-01-01,TRUE,FALSE" = "FY25",
  type = "date_range",
  name = "fiscal_year"
)

dates <- as.Date(c("2023-06-15", "2024-03-01", "2024-12-31",
                   "2025-07-04", "2022-01-01", NA))

data.frame(
  date  = dates,
  fy    = fput(dates, "fiscal_year")
)
#>         date         fy
#> 1 2023-06-15       FY23
#> 2 2024-03-01       FY24
#> 3 2024-12-31       FY24
#> 4 2025-07-04       FY25
#> 5 2022-01-01 2022-01-01
#> 6       <NA>       <NA>

Define from text with `fparse()`

fparse(text = '
VALUE quarter (date_range)
  [2024-01-01, 2024-04-01) = "Q1-2024"
  [2024-04-01, 2024-07-01) = "Q2-2024"
  [2024-07-01, 2024-10-01) = "Q3-2024"
  [2024-10-01, 2025-01-01) = "Q4-2024"
  .other                   = "Outside 2024"
;
')

sample_dates <- as.Date(c("2024-02-14", "2024-05-20", "2024-08-08",
                          "2024-11-30", "2025-03-01"))

data.frame(
  date    = sample_dates,
  quarter = fput(sample_dates, "quarter")
)
#>         date      quarter
#> 1 2024-02-14      Q1-2024
#> 2 2024-05-20      Q2-2024
#> 3 2024-08-08      Q3-2024
#> 4 2024-11-30      Q4-2024
#> 5 2025-03-01 Outside 2024

`LOW` / `HIGH` open-ended bounds

LOW and HIGH represent \(-\infty\) and \(+\infty\) — any date before or after a cutpoint falls in the open arm.

fparse(text = '
VALUE era (date_range)
  [LOW,        2000-01-01) = "Pre-2000"
  [2000-01-01, 2010-01-01) = "2000s"
  [2010-01-01, 2020-01-01) = "2010s"
  [2020-01-01, HIGH]       = "2020+"
;
')

event_dates <- as.Date(c("1985-07-04", "2005-12-25",
                         "2015-06-01", "2023-11-11"))

data.frame(
  date = event_dates,
  era  = fput(event_dates, "era")
)
#>         date      era
#> 1 1985-07-04 Pre-2000
#> 2 2005-12-25    2000s
#> 3 2015-06-01    2010s
#> 4 2023-11-11    2020+

Export and roundtrip

Formats export with ISO date bounds and re-parse without loss:

q_obj <- format_get("quarter")
cat(fexport(quarter = q_obj))
#> VALUE quarter (date_range)
#>   [2024-01-01, 2024-04-01) = "Q1-2024"
#>   [2024-04-01, 2024-07-01) = "Q2-2024"
#>   [2024-07-01, 2024-10-01) = "Q3-2024"
#>   [2024-10-01, 2025-01-01) = "Q4-2024"
#>   .other = "Outside 2024"
#> ;

# Re-parse the exported text
txt <- fexport(quarter = q_obj)
fclear()
#> All formats cleared from library.
fparse(text = txt)

fput(as.Date(c("2024-02-14", "2024-08-08")), "quarter")
#> [1] "Q1-2024" "Q3-2024"

Overlapping buckets with `multilabel` and `fput_all()`

fparse(text = '
VALUE study_window (date_range, multilabel)
  [2024-01-01, 2024-07-01) = "First Half"
  [2024-04-01, 2024-10-01) = "Mid-Year"
  [2024-07-01, 2025-01-01) = "Second Half"
;
')

checkup_dates <- as.Date(c("2024-02-15", "2024-05-20", "2024-09-01"))
all_windows   <- fput_all(checkup_dates, "study_window")

for (i in seq_along(checkup_dates)) {
  cat(format(checkup_dates[i]), "->",
      paste(all_windows[[i]], collapse = " | "), "\n")
}
#> 2024-02-15 -> First Half 
#> 2024-05-20 -> First Half | Mid-Year 
#> 2024-09-01 -> Mid-Year | Second Half

Auto-detection of type

When no explicit type is given, fparse() infers date_range from ISO date bounds and datetime_range when bounds include a time component:

fparse(text = '
VALUE auto_fy
  [2024-01-01, 2025-01-01) = "2024"
;

VALUE auto_shift
  [2024-01-15 08:00, 2024-01-15 16:00) = "Day shift"
;
')

cat("auto_fy type   :", format_get("auto_fy")$type, "\n")
#> auto_fy type   : date_range
cat("auto_shift type:", format_get("auto_shift")$type, "\n")
#> auto_shift type: datetime_range

Datetime range bucketing

datetime_range works identically to date_range but matches against POSIXct values. Bounds are expressed as YYYY-MM-DD HH:MM[:SS] strings.

fparse(text = '
VALUE shift (datetime_range)
  [2024-01-15 00:00, 2024-01-15 08:00) = "Night"
  [2024-01-15 08:00, 2024-01-15 16:00) = "Day"
  [2024-01-15 16:00, 2024-01-16 00:00) = "Evening"
;
')

timestamps <- as.POSIXct(
  c("2024-01-15 03:22:00", "2024-01-15 11:45:00",
    "2024-01-15 19:00:00"),
  tz = "UTC"
)

data.frame(
  ts    = format(timestamps, tz = "UTC"),
  shift = fput(timestamps, "shift")
)
#>                    ts   shift
#> 1 2024-01-15 03:22:00   Night
#> 2 2024-01-15 11:45:00     Day
#> 3 2024-01-15 19:00:00 Evening

Example 26: Stratified Range Lookup with `fputk()`

The stratified_range type combines a discrete stratum (such as a study arm, subject id, or any composite key) with a numeric / Date / POSIXct range. Each stratum has its own bucket boundaries, and fputk() dispatches to the right bucket for each row.

Programmatic construction with `fmap_strata()`

visits <- fmap_strata(
  stratum = c("ARM_A", "ARM_A", "ARM_A", "ARM_B", "ARM_B"),
  low     = c(0,        7,       28,      0,       14),
  high    = c(7,        28,      Inf,     14,      Inf),
  label   = c("Baseline", "Wk1-3", "Wk4+", "Baseline", "Wk2+"),
  inc_high = c(FALSE, FALSE, TRUE, FALSE, TRUE)
)
fnew(visits, type = "stratified_range",
     ".other|ARM_A" = "A_outside",
     .other = "outside_window",
     name = "vw")

df <- data.frame(
  arm = c("ARM_A", "ARM_A", "ARM_B", "ARM_B", "ARM_C"),
  day = c(3,        35,      5,       40,      10)
)
df$visit <- fputk(df$arm, df$day, format = "vw")
df
#>     arm day          visit
#> 1 ARM_A   3       Baseline
#> 2 ARM_A  35           Wk4+
#> 3 ARM_B   5       Baseline
#> 4 ARM_B  40           Wk2+
#> 5 ARM_C  10 outside_window

Text definition with `fparse()`

fparse(text = '
VALUE vw_text (stratified_range, range_subtype: numeric)
  "ARM_A"|[0, 7)    = "Baseline"
  "ARM_A"|[7, 28)   = "Wk1-3"
  "ARM_A"|[28, HIGH]= "Wk4+"
  "ARM_B"|[0, 14)   = "Baseline"
  "ARM_B"|[14, HIGH]= "Wk2+"
  ".other|ARM_A"    = "A_outside"
  .other            = "outside_window"
  ;
')
fputk(df$arm, df$day, format = "vw_text")
#> [1] "Baseline"       "Wk4+"           "Baseline"       "Wk2+"          
#> [5] "outside_window"

Date subtype: per-subject windows

windows <- fmap_strata(
  stratum = c("S001", "S001", "S002", "S002"),
  low     = as.Date(c("2024-01-01", "2024-01-15",
                       "2024-02-01", "2024-02-20")),
  high    = as.Date(c("2024-01-15", "2024-02-01",
                       "2024-02-20", "2024-03-10")),
  label   = c("Screen", "Treat", "Screen", "Treat")
)
fnew(windows, type = "stratified_range", range_subtype = "date",
     .other = "off-window", name = "win")

subj   <- c("S001", "S001", "S002", "S002", "S003")
visits <- as.Date(c("2024-01-05", "2024-01-20",
                     "2024-02-10", "2024-03-01", "2024-01-01"))
data.frame(
  subj  = subj,
  date  = visits,
  phase = fputk(subj, visits, format = "win")
)
#>   subj       date      phase
#> 1 S001 2024-01-05     Screen
#> 2 S001 2024-01-20      Treat
#> 3 S002 2024-02-10     Screen
#> 4 S002 2024-03-01      Treat
#> 5 S003 2024-01-01 off-window

Roundtrip via `fexport()` / `fparse()`

txt <- fexport(format_get("vw"))
cat(txt, "\n")
#> VALUE vw (stratified_range, range_subtype: numeric, strata_sep: |)
#>   "ARM_A"|[0, 7) = "Baseline"
#>   "ARM_A"|[7, 28) = "Wk1-3"
#>   "ARM_A"|[28, HIGH] = "Wk4+"
#>   ".other|ARM_A" = "A_outside"
#>   "ARM_B"|[0, 14) = "Baseline"
#>   "ARM_B"|[14, HIGH] = "Wk2+"
#>   .other = "outside_window"
#> ;
fclear()
#> All formats cleared from library.
fparse(text = txt)
fputk(df$arm, df$day, format = "vw")
#> [1] "Baseline"       "Wk4+"           "Baseline"       "Wk2+"          
#> [5] "outside_window"

Example 27: Plain Range Lookup with `fmap_ranges()`

For non-stratified numeric / Date ranges, fmap_ranges() saves you from hand-crafting canonical keys.

age_groups <- fmap_ranges(
  low   = c(0, 18, 65),
  high  = c(18, 65, Inf),
  label = c("Child", "Adult", "Senior"),
  inc_high = c(FALSE, FALSE, TRUE)
)
fnew(age_groups, type = "numeric", name = "ag")
fput(c(5, 25, 90), "ag")
#> [1] "Child"  "Adult"  "Senior"
fclear()
#> All formats cleared from library.

Example 28: Composite Key Lookup with NA Components (`na_as_string`)

When building a format from data using fmap(paste(..., sep = "|"), values), base R’s paste() converts any NA component to the literal string "NA". The resulting composite key is therefore "CAT|TEST|NA", not a missing value.

By default, fputk() restores NA_character_ wherever any component is NA before the lookup — so the key "CAT|TEST|NA" is never reached and the row falls through to .other / .missing.

Setting na_as_string = TRUE keeps paste()’s literal "NA", making the round-trip consistent.

Clinical example — LB parameter derivation

A common ADaM task: derive PARAMCD from a combination of LBCAT, LBSPEC, LBTESTCD, and LBSTRESU, where some rows have LBSTRESU = NA (dimensionless tests such as INR).

# Source lab mapping (as received from a specification)
lb_map <- data.frame(
  LBCAT    = c("BLOOD CHEMISTRY", "COAGULOGRAM", "COAGULATION PANEL", "COAGULOGRAM"),
  LBSPEC   = c("BLOOD",           "BLOOD",        "BLOOD",             "BLOOD"),
  LBTESTCD = c("ALB",             "FIBRINO",      "INR",               "INR"),
  LBSTRESU = c("g/L",             "g/L",           NA,                  NA),
  PARAMCD  = c("ALB",             "FIBRINO",       "INR",               "INR"),
  stringsAsFactors = FALSE
)
lb_map
#>               LBCAT LBSPEC LBTESTCD LBSTRESU PARAMCD
#> 1   BLOOD CHEMISTRY  BLOOD      ALB      g/L     ALB
#> 2       COAGULOGRAM  BLOOD  FIBRINO      g/L FIBRINO
#> 3 COAGULATION PANEL  BLOOD      INR     <NA>     INR
#> 4       COAGULOGRAM  BLOOD      INR     <NA>     INR

Build the format with fmap(paste(...), PARAMCD).
paste() converts NA in LBSTRESU to "NA", so the stored keys for INR rows are "COAGULATION PANEL|BLOOD|INR|NA" and "COAGULOGRAM|BLOOD|INR|NA".

with(lb_map,
  fmap(paste(LBCAT, LBSPEC, LBTESTCD, LBSTRESU, sep = "|"), PARAMCD)
) |>
  fnew(ignore_case = TRUE, .other = NA,
       type = "character", name = "lb_param")

fprint("lb_param")
#> KS Format:lb_param (nocase)
#> Type: character 
#> Mappings:
#>   BLOOD CHEMISTRY|BLOOD|ALB|g/L => ALB
#>   COAGULOGRAM|BLOOD|FIBRINO|g/L => FIBRINO
#>   COAGULATION PANEL|BLOOD|INR|NA => INR
#>   COAGULOGRAM|BLOOD|INR|NA => INR
#>   .other => NA

Now apply the format.
With the default na_as_string = FALSE, the INR rows get NA (no match):

lb_map$PARAMCD_default <- with(lb_map,
  fputk(LBCAT, LBSPEC, LBTESTCD, LBSTRESU, format = "lb_param")
)
lb_map[, c("LBTESTCD", "LBSTRESU", "PARAMCD", "PARAMCD_default")]
#>   LBTESTCD LBSTRESU PARAMCD PARAMCD_default
#> 1      ALB      g/L     ALB             ALB
#> 2  FIBRINO      g/L FIBRINO         FIBRINO
#> 3      INR     <NA>     INR            <NA>
#> 4      INR     <NA>     INR            <NA>

With na_as_string = TRUE, paste() also converts the lookup-side NA to "NA", so the keys match:

lb_map$PARAMCD_back <- with(lb_map,
  fputk(LBCAT, LBSPEC, LBTESTCD, LBSTRESU,
        format = "lb_param", na_as_string = TRUE)
)
lb_map[, c("LBTESTCD", "LBSTRESU", "PARAMCD", "PARAMCD_back")]
#>   LBTESTCD LBSTRESU PARAMCD PARAMCD_back
#> 1      ALB      g/L     ALB          ALB
#> 2  FIBRINO      g/L FIBRINO      FIBRINO
#> 3      INR     <NA>     INR          INR
#> 4      INR     <NA>     INR          INR

The rule of thumb

Use na_as_string = TRUE whenever the format was built with
fmap(paste(...), values) and any key column can contain NA.

If the format keys were set by hand (fnew("CAT|TEST|g/L" = "ALB", ...)), NA components should still go through .missing — keep the default na_as_string = FALSE.

Example 29: Composite Label Invalue Lookup with `finputk()`

finputk() is the invalue-side mirror of fputk(): it pastes multiple columns into a composite label and reverse-looks it up in a ks_invalue format. The same na_as_string argument applies.

Basic usage

# Build an INVALUE from two-column composite labels
finput(
  fmap(paste(c("BLOOD CHEMISTRY", "COAGULOGRAM", "COAGULATION PANEL"),
             c("ALB",             "FIBRINO",      "INR"),
             sep = "|"),
       c(1L, 2L, 3L)),
  target_type = "integer",
  name = "lb_code_inv"
)
#> KS Invalue: lb_code_inv 
#> Target Type: integer 
#> Mappings:
#>   BLOOD CHEMISTRY|ALB => 1
#>   COAGULOGRAM|FIBRINO => 2
#>   COAGULATION PANEL|INR => 3

# Reverse lookup: two separate columns → integer code
cat_vec  <- c("BLOOD CHEMISTRY", "COAGULOGRAM", "COAGULATION PANEL", "OTHER")
test_vec <- c("ALB",              "FIBRINO",     "INR",               "X")

finputk(cat_vec, test_vec, invalue_name = "lb_code_inv")
#> [1]  1  2  3 NA
# BLOOD CHEMISTRY|ALB → 1, COAGULOGRAM|FIBRINO → 2,
# COAGULATION PANEL|INR → 3, OTHER|X → NA (no match → missing_value)
fclear()
#> All formats cleared from library.

With NA components (`na_as_string = TRUE`)

When the INVALUE was built from data containing NA columns, use na_as_string = TRUE on both the build side (fmap(paste(...), ...)) and the lookup side (finputk(..., na_as_string = TRUE)).

# INVALUE where LBSTRESU can be NA (like INR)
finput(
  fmap(
    paste(lb_map$LBCAT, lb_map$LBTESTCD, lb_map$LBSTRESU, sep = "|"),
    seq_len(nrow(lb_map))
  ),
  target_type = "integer",
  name = "lb_row_inv"
)
#> KS Invalue: lb_row_inv 
#> Target Type: integer 
#> Mappings:
#>   BLOOD CHEMISTRY|ALB|g/L => 1
#>   COAGULOGRAM|FIBRINO|g/L => 2
#>   COAGULATION PANEL|INR|NA => 3
#>   COAGULOGRAM|INR|NA => 4

# Reconstruct lb_map row indices — works even when LBSTRESU is NA
finputk(lb_map$LBCAT, lb_map$LBTESTCD, lb_map$LBSTRESU,
        invalue_name = "lb_row_inv", na_as_string = TRUE)
#> [1] 1 2 3 4

fclear()
#> All formats cleared from library.

The output type is always determined by the stored invalue’s target_type (here integer). For character output create the invalue with target_type = "character" and finputk() returns a character vector.