Decode coded variables to plain text (and vice versa)

Erik Bulow


Main purpose of the package

The main purpose of this packgae is twofold:

  1. To easily translate coded variables into plain text using standardised dictionaries.
  2. To provide an infrastructure to create such dictionaries.

The first purpose is assumed to dominate and the package therefore includes several dictionaries compatible with data from INCA and Rockan.

Example for “decode”

Assume we have a dataset with some (more or less) typical RCC-data:

n <- 6
s <- function(x) sample(x, n, replace = TRUE)

incadata <- 
    KON_VALUE = s(1:2),
    region = 1:6,
    a_icdo3 = c("C446", "C749", "C159", "C709", "C475", "C320"),
    a_tstad = c("0", "1", "1a", "1b", "1c", "2")
KON_VALUE region a_icdo3 a_tstad
2 1 C446 0
1 2 C749 1
2 3 C159 1a
2 4 C709 1b
2 5 C475 1c
2 6 C320 2

Manually specified decodings

decode is a generic S3-function that accepts R-object of different kinds. The default method takes an atomic vector with codes and a specification of how to translate these codes (see ?decode).


decode(incadata$KON_VALUE, "kon")
## [1] "Kvinna" "Man"    "Kvinna" "Kvinna" "Kvinna" "Kvinna"
decode(incadata$a_tstad, "t_rtr")
## [1] "T0"  "T1"  "T1a" "T1b" "T1c" "T2"

See ?key_value_data for a list of all available keyvalue objects included in the package.

The whole data.frame at once

There is also a method for data.frames, where translations are made automatically for columns with recognised names.

incadata_decoded <- decode(incadata)
## New decoded columns added: 
## * KON_VALUE_kon_beskrivning
## * region_region_beskrivning
KON_VALUE region a_icdo3 a_tstad KON_VALUE_kon_beskrivning region_region_beskrivning
2 1 C446 0 Kvinna Region Sthlm/Gotland
1 2 C749 1 Man Region Uppsala/Örebro
2 3 C159 1a Kvinna Region Sydöstra
2 4 C709 1b Kvinna Region Syd
2 5 C475 1c Kvinna Region Väst
2 6 C320 2 Kvinna Region Norr

All the original columns from incadata are preserved but are now accompanied by some additional columns with identical names suffixed by "_beskrivning“. (”_Beskrivning" with capital “B” will be used if there are already some column names with suffixes "_Beskrivning" or "_Värde" in the data, otherwise lower case variable names are generally preffered in all the rcc packages).

A note of caution

To automatically transform all columns of a data.frame can be “dangerous” if the data.frame includes a column with arbitrary data but with a name just coincidentally beeing recognised as a standard variable name. It is therefore recommended to use this function mostly in interactive mode and to always look at the message specifying which columns that were automatically detected.

It should however also be noted that the variable names are hard coded and have to be excact (although case differnecies are ignored). Hence, the variable “a_icdo3” were not recognised (even though its name could have been easily matched by a regular expression: grepl("icdo3", "a_icdo3").

Several keyvalues for the same code

Note that the same coded variable might be decoded by more than one keyvalue-object, for example the “a_icdo3”-variable is automatically decoded by the “icdo3” keyvalue object (see ?icdo3) with a dictionary from Rockan. ICD-O3 however, can also be seen as a “developed subset” of ICD10, wherefore:

decode(incadata$a_icdo3, "icd10se")
## [1] "Malign tumör i huden på övre extremiteten inklusive skuldran"              
## [2] "Ospecificerad lokalisation av malign tumör i binjure"                      
## [3] "Ospecificerad lokalisation av malign tumör i esofagus"                     
## [4] "Ospecificerad lokalisation av malign tumör i centrala nervsystemets hinnor"
## [5] "Malign tumör i perifera nerver i bäckenet"                                 
## [6] "Malign tumör i glottis"

“icd10”, can also decode non oncological desease codes:

decode(c("D448A", "T009", "F182", "S134C", "C131"), "icd10se")
## [1] "Tumör med multiglandulär lokalisation, typ I"                                                 
## [2] "Multipla ytliga skador, ospecificerade"                                                       
## [3] "Psykiska störningar och beteendestörningar orsakade av flyktiga lösningsmedel-Beroendesyndrom"
## [4] "Whiplash-skada, WAD III"                                                                      
## [5] "Malign tumör i aryepiglottiska vecket, hypofaryngeala delen"

Exact versus non exact decoding

So far, we have looked only on cases where the code can be littaraly translated by the keyvalue object. There is however a third argument to the decode function, exact, which is FALSE by default. It is often useful to allow some automatic transformation for variables that we know are coded values but that are not exactly the same as in the keyvalue object. The most common application is probably when we want to extract the municipality names from an LKF-variable. This can obviosly be done by:

x <- c(178405, 138408, 108202, 128706, 048005)
y <- as.numeric(substring(as.character(x), 1, 4))
decode(y, "kommun", exact = TRUE)
## Warning: Some codes could not be translated (4800)
## [1] "Arvika"     "Kungsbacka" "Karlshamn"  "Trelleborg" NA

But it is also possible to use the original six digit code as is:

decode(x, "kommun")
## Warning: transformed to match the keyvalue: Only the first 4 characters are
## used.
## Warning: Some codes could not be translated (48005)
## [1] "Arvika"     "Kungsbacka" "Karlshamn"  "Trelleborg" NA

Note here that the last value decodes to NA in both cases. It would be preffered to have the original codes stored as character, since that preserves the leading 0 in “048005”:

decode("048005", "kommun")
## Warning: transformed to match the keyvalue: Only the first 4 characters are
## used.
## [1] "Nyköping"

This can also be taken care of by reading data through the use_incadata function (see ?incadata::use_incadata), which always classifies numbers with leading zeros as characters (and not numeric) (but this will not be covered in this vignette).

Note that use of exact = FALSE (which is the default for convenience) always produce warnings when some transformations are done. It is always recommended to manually confirm that these transformations are in fact accurate. Unexpected results might otherwise occur!

More than one decoded variable from the same code

Using non exact decoding, the same code might be simultaneously decoded by more that one keyvalue object using the data.frame method:

df <- 
    LKF = c("149804", "147104", "012704", "143505", "126502", "232602")
## New decoded columns added: 
## * LKF_forsamling_beskrivning
## * LKF_hemort_beskrivning
## * LKF_lan_beskrivning
## * LKF_kommun_beskrivning
LKF LKF_forsamling_beskrivning LKF_hemort_beskrivning LKF_lan_beskrivning LKF_kommun_beskrivning
149804 NA Velinga Västra Götalands län Tidaholm
147104 NA Holmestad Västra Götalands län Götene
012704 NA Salem Stockholms län Botkyrka
143505 NA Mo Västra Götalands län Tanum
126502 NA Björka Skåne län Sjöbo
232602 Hackås Hackås Jämtlands län Berg

Consult ?hemort and ?forsamling to invest the differnce between these two variables (hint: a lot have happened with Swedish administrative geography since 1958).

Extra functions

The atomic vector method has an additional argument extra_functions that can be used to modify the outcome of the decoded variable. Read the man page (?decode) for more details and a complete list of available extra functions.

The default regional names used from INCA are used by default by the “region” keyvalue:

decode(1:6, "region")
## [1] "Region Sthlm/Gotland"  "Region Uppsala/Örebro" "Region Sydöstra"      
## [4] "Region Syd"            "Region Väst"           "Region Norr"

Sometimes it is however more convenient to use an abbriviated form of these names such as:

decode(1:6, "region", "short_region_names")
## [1] "Sthlm/Gotland"  "Uppsala/Örebro" "Sydöstra"       "Syd"           
## [5] "Väst"           "Norr"

This could also be achieved by an additional keyvalue object containing the hard coded short names but an additional advantage with the “extra_function” method is that several extra functions can be chained:

munic_west <- c("1382", "1419", "1441", "1460", "1472", "1488", "1496")
decode(munic_west, "sjukvardsomrade", c("kungalv2Storgoteborg", "real_names"))
## [1] "Norra Halland"  "Storgöteborg"   "Södra Älvsborg" "Fyrbodal"      
## [5] "Skaraborg"      "Fyrbodal"       "Skaraborg"

Example for “code” (the opposite of decode)

In the simplest situation (with a 1:1 relation between the code and its value), code is just the inverse of decode:

code(c("Karlskrona", "Göteborg", "Härnösand"), "kommun")
## [1] "1080" "1480" "2280"

decode does however also allow translation with m:1 dictionaries such as snomed (which gives a differnt result than snomed3):

non_unique <- c(90703, 90723, 96153, 99403, 89643, 90443)
decode(non_unique, "snomed")
## [1] "Embryonalt carcinom" "Embryonalt carcinom" "Hårcellsleukemi"    
## [4] "Hårcellsleukemi"     "Klarcellssarkom"     "Klarcellssarkom"

This decoding can not be inverted:

code(decode(non_unique, "snomed"), "snomed")
##       key               value
## 393 90703 Embryonalt carcinom
## 402 90723 Embryonalt carcinom
## 592 96153     Hårcellsleukemi
## 787 99403     Hårcellsleukemi
## 354 89643     Klarcellssarkom
## 381 90443     Klarcellssarkom
## Error in code(decode(non_unique, "snomed"), "snomed"): Values above have a non unique relation to their key!

This restriction only applies when the requested values are de facto non unique. It is not tied to the keyvalue object as such.

unique <- c(
  "Mucinöst kystadenom (kystom) borderline-typ ( 2 B)", 
  "Medullärt carcinom, atypiskt", 
  "Mb Paget non invasiv och intraduktal", 
  "Lymfangiosarcom, misst", 
  "Fibröst histiocytom, malignt", 
  "Carcinoid (exkl appendix)", 
  "Langerhans cell histiocytos, UNS, Histiocytosis X, UNS", 
  "Brenner tumör, malign", 
  "Lymfatisk leukemi, UNS")
code(unique, "snomed")
##  [1] "84723" "85133" "85432" "91701" "88303" "94900" "82403" "97511" "90003"
## [10] "98203"

The underlying machinery

In all examples above the so called keyvalue object were specified by a quoted name (such as “kon” or “region” etcetera). These names refer to internal package objects that can not be lazy loaded from the package but which can still be accesed by the tripple colon notation:

##   key  value
## 1   1    Man
## 2   2 Kvinna
##   key                 value
## 1   1  Region Sthlm/Gotland
## 2   2 Region Uppsala/Örebro
## 3   3       Region Sydöstra
## 4   4            Region Syd
## 5   5           Region Väst
## 6   6           Region Norr

These objects might look like ordinary data.frames but they do have some extra attributes as described by ?as.keyvalue.

## $names
## [1] "key"   "value"
## $row.names
## [1] 1 2
## $standard_var_names
## [1] "kon_value" "kön"       "kon"       "sex"      
## $keyvalue11
## [1] TRUE
## $class
## [1] "keyvalue"   "data.frame"

The keyvalue11 attribute indicates that “code” can be used as an inverse of “decode” for translations using this keyvalue obejct, hence without the problems as described for snomed. The standard_var_names attribute provides a list with known names for the coded version of this variable when found in a data.frame (used by

A complete list of all standard variable names can also be found in ALL_STANDARD_VAR_NAMES (internal object):

key value
avgm avgm
ben ben
digr digr
dödca dodca
dodca dodca
figo figo

The key-column gives names of variables that are recognised and the value column gives the name of the corresponding keyvalue object to use for its decoding.

Examples how to create keyvalue objects

It might sometimes come handy to create your own keyvalue objects for a specific project. The S3-generic as.keyvalue has several methods for this.

For small dictionaries, a named vector might be sufficient:

(kv <- as.keyvalue(c("car" = 1, "bike" = 2, "bus" = 3)))
##   key value
## 1   1   car
## 2   2  bike
## 3   3   bus
x <- s(1:3)
decode(x, kv)
## [1] "bike" "car"  "bus"  "bike" "bike" "bus"

Longer dictionaries might be more convenient to read from disk and coerce to keyvalue objects using the data.frame method. Dictionaries with a lot of values for every key, can also be specified by a list method:

ex <- list(
         fruit  = c("banana", "orange", "kiwi"),
         car    = c("SAAB", "Volvo", "taxi", "truck"),
         animal = c("elephant")
key value
4 SAAB car
5 Volvo car
1 banana fruit
8 elephant animal
3 kiwi fruit
2 orange fruit
6 taxi car
7 truck car