knitr::opts_chunk$set( collapse = TRUE, comment = "#" )
This vignette covers all topics concerned with flattening FHIR resources in some depth. If you are interested in a quick overview, please have a look at the fhircrackr:intro vignette.
Before running any of the following code, you need to load the
In the vignette
fhircrackr: Download FHIR resources you saw how to download FHIR resources into R.
Now we'll have a look at how to flatten them into data.frames/data.tables. For the rest of the vignette, we'll work with the two example data sets from
fhircrackr, which can be made accessible like this:
pat_bundles <- fhir_unserialize(bundles = patient_bundles) med_bundles <- fhir_unserialize(bundles = medication_bundles)
?medication_bundles for the FHIR search requests that generated them.
There are two extraction scenarios when you want to flatten FHIR bundles: Either you want to extract just one resource type, or you want to extract several resource types. Because the structure of different resource types is quite dissimilar, it makes sense to create one table per resource type. Therefore the result of the flattening process in
fhircrackr can be either a single table (when extracting just one resource type) or a list of tables (when extracting more than one resource type). Both scenarios are realized with a call to
fhir_crack(). We will now explain the two scenarios individually.
We'll start with
pat_bundles, which only contains Patient resources. To transform them into a table, we will use
fhir_crack(). The most important argument
fhir_crack() takes is
bundles, an object of class
fhir_bundle_list that is returned by
fhir_search(). The second important argument is
design, which tells the function which data to extract from the bundle. When we want to extract just one resource type, we can use a
fhir_table_description in the argument
fhir_crack() then returns a single data.frame or data.table (if argument
data.table = TRUE).
We'll show you an example of how it works first and then go on to explain the
fhir_table_description in more detail.
pat_table_description <- fhir_table_description( resource = "Patient", cols = list( id = "id", gender = "gender", name = "name/family", city = "address/city" ) ) table <- fhir_crack( bundles = pat_bundles, design = pat_table_description, verbose = 0 ) head(table)
fhir_table_description holds all the information
fhir_crack() needs to create a table from resources of a certain type. It is created with
fhir_table_description() by providing the following arguments:
This is basically a string that defines the resource type (e.g. Patient or Observation) to extract. It is the only argument that you must provide and you set it like this:
fhir_table_description(resource = "Patient")
fhir_resource_type() which checks the type you provided against list of all currently available resource types which can be found at https://hl7.org/FHIR/resourcelist.html. Case errors are corrected automatically and the function throws a warning if the resource type doesn't match the list under hl7.org.
As you can see in the above output, there are more elements in a
fhir_table_description which are filled automatically by
cols argument takes the column names and XPath (1.0) expressions defining the columns to create from the FHIR resources. The XPath expression has to be built relatively to the root of the resource tree. If the
cols element is empty,
fhir_crack() will extract all available elements of the resource and name the columns automatically. To explicitly define columns, you can provide a (named) character or a (named) list with XPath expressions like this:
fhir_table_description( resource = "Patient", cols = list( gender = "gender", name = "name/family", city = "address/city" ) )
In this case a table with three columns called gender, name and city will be created. They will be filled with the element that can be found under the respective xpath expression in the resource. The element will be extracted regardless of the attribute that is used (in FHIR this is mostly
@value but can also be
@url in rare cases). If you are interested in keeping the attribute information, you can set
keep_attr = TRUE, in which case the attribute will be attached to the column name.
fhir_columns() to check the validity of the XPath expressions and assign column names. You can provide the XPath expressions in a named or unnamed character vector or a named or unnamed list. If you choose the unnamed version, the names will be set automatically and reflect the respective XPath expression:
#custom column names fhir_columns( xpaths = c( gender = "gender", name = "name/family", city = "address/city" ) ) #automatic column names fhir_columns(xpaths = c("gender", "name/family", "address/city"))
fhir_columns object that is created explicitly like this can of course also be used in the
columns argument of
We strongly advise to only use fully specified relative XPath expressions here, e.g.
"ingredient/strength/numerator/code" and not search paths like
"//code", as those can generate unexpected results especially if the searched element appears on different levels of the resource.
cols control what is extracted from the bundles, the remaining elements of a
fhir_table_description control how the resulting table looks. These elements for example control how
fhir_crack() deals with multiple entries for the same element and with columns that are completely empty, i.e. have only
NA values. Furthermore you can select the shape of the output tables and how column names are generated:
sepelement is a string defining the separator used when multiple entries to the same attribute are pasted together. This could for example happen if there is more than one address entry in a Patient resource. Examples of this are shown further down under the heading Multiple entries.
bracketselement is either an empty character vector (of length 0) or a character vector of length 2. If it is empty, multiple entries will be pasted together without indices. If it is of length 2, the two strings provided here are used as brackets for automatically generated indices to sort out multiple entries (see paragraph Multiple Entries).
brackets = c("[", "]")e.g. will lead to indices like
rm_empty_colsflag can be
TRUE, columns containing only
NAvalues will be removed, if
FALSE, these columns will be kept.
formatelement takes values
widethat specify the shape of the output table. In a
compacttable multiple entries are written into the same cell/column separated by
sep. In a
widetable multiple entries are spread over several indexed columns. See the paragraph on multiple entries for more information.
keep_attrflag controls whether the xml tag attributes of the FHIR element should be attached to the end of the column name or not. For the column extracted by
name/given, the name would result in
keep_attr = FALSEand
keep_attr = TRUE.
All five style elements can also be controlled directly by the
keep_attr. If the function arguments are
NULL (their default), the values provided in
fhir_table_description are used, if they are not NULL, they will overwrite any values in
A fully defined set of a
Patient table description would be like this:
table_description <- fhir_table_description( resource = "Patient", cols = list( gender = "gender", name = "name/family", city = "address/city" ), sep = "||", brackets = c("[", "]"), rm_empty_cols = FALSE, format = "compact", keep_attr = FALSE )
We will now work through examples using
fhir_table_descriptions of different complexity.
Lets start with an example where we only provide the (mandatory)
resource component of the table_description. In this case,
fhir_crack() will extract all available attributes and use default values for the other elements:
#define a table_description table_description1 <- fhir_table_description(resource = "Patient") #convert resources #pass table_description1 to the design argument table <- fhir_crack(bundles = pat_bundles, design = table_description1, verbose = 0) #have look at part of the results table[1:5,1:5]#38:42 #see the fill result with: #View(table)
As you can see, this can easily become a rather wide and sparse data frame. This is due to the fact that every every element appearing in at least one of the resources will be turned into a variable (i.e. column), even if none of the other resources contain this element. For those resources, the value on that element will be set to
NA. Depending on the variability of the resources, the resulting table can contain a lot of
NA values. If a resource has multiple entries for an element (e.g. several addresses in a Patient resource), these entries will pasted together using the string provided in
sep as a separator. The column names in this option are automatically generated by pasting together the path to the respective element, e.g.
If we know which elements we want to extract, we can specify them in a named list and provide it in the
cols component of the table description:
#define a table_description table_description2 <- fhir_table_description( resource = "Patient", cols = list( PID = "id", use_name = "name/use", given_name = "name/given", family_name = "name/family", gender = "gender", birthday = "birthDate" ) ) #convert resources table <- fhir_crack(bundles = pat_bundles, design = table_description2, verbose = 0) #have look at the results head(table)
This option will return more tidy and clear data frames, because you have full control over the extracted columns including their name in the resulting table. You should always extract the resource id, because this is used to link to other resources you might also extract.
If you are not sure which elements are available or where they are located in the resource, it can be helpful to start by extracting all available elements. If you are more comfortable with xml, you can also use
xml2::xml_structure on one of the bundles from your bundle list, this will print the complete xml structure into your console. Then you can get an overview over the available element and their location and continue by doing a second, more targeted extraction to get your final table.
If you want to have a look at how the design looked that was actually used in the last call to
fhir_crack() you can retrieve it with
Of course the previous example is using just one resource type. If you are interested in several types of resources, you need one
fhir_table_description per resource type. You can bundle a bunch of
fhir_table_descriptions in a
fhir_design. This is basically a named list of
fhir_table_descriptions, and when you pass it to
fhir_crack(), the result will be a named list of tables with the same names as the design. Consider an example where we have downloaded MedicationStatements referring to a certain medication as well as the Patient resources these MedicationStatements are linked to.
The design to extract both resource types could look like this:
meds <- fhir_table_description( resource = "MedicationStatement", cols = list( ms_id = "id", status_text = "text/status", status = "status", med_system = "medicationCodeableConcept/coding/system", med_code = "medicationCodeableConcept/coding/code", med_display = "medicationCodeableConcept/coding/display", dosage = "dosage/text", patient = "subject/reference", last_update = "meta/lastUpdated" ), sep = "|", brackets = NULL, rm_empty_cols = FALSE, format = 'compact', keep_attr = FALSE ) pat <- fhir_table_description(resource = "Patient") design <- fhir_design(meds, pat)
In this example, we have spelled out the table_description MedicationStatement completely, while we have used a short form for Patients. It looks like this:
As you can see, each table_description is identified by a name, which will also be the name of the corresponding table in the result of
You can assign the names explicitly, if you prefer:
design <- fhir_design(Medications = meds, Patients = pat) design
And you can also extract single table_descriptions by their name:
We can use the
list_of_tables <- fhir_crack(bundles = med_bundles, design = design, verbose = 0) head(list_of_tables$Medications) head(list_of_tables$Patients)
As you can see, the result is a list of tables, one for Patient resources and one for MedicationStatement resources. When you use
fhir_crack() with a
fhir_desgn() instead of a
fhir_table_description, the result is an object of class
fhir_dt_list that also has the design attached. You can extract the design from a list like this using
Note that this doesn't work on single tables created with a
If you want to save a design for later or to share with others, you can do so using the
fhir_save_design(). This function takes a design and saves it as an xml file:
temp_dir <- tempdir() fhir_save_design(design = design, file = paste0(temp_dir, "/design.xml"))
To read the design back into R, you can use
A particularly complicated problem in flattening FHIR resources is caused by the fact that there can be multiple entries to an element. The profile according to which your FHIR resources have been built defines how often a particular element can appear in a resource. This is called the cardinality of the element. For example the Patient resource defined here can have zero or one birth dates but arbitrarily many addresses. In the default setting,
fhir_crack() will paste multiple entries for the same element together in the table, using the separator provided by the
sep argument. In most cases this will work just fine, but there are some special cases that require a little more attention.
Let's have a look at an example bundle containing just three Patient resources. You can make it available in your workspace like this:
bundle <- fhir_unserialize(example_bundles2)
This is how the xml looks:
<Bundle> <type value='searchset'/> <entry> <resource> <Patient> <id value='id1'/> <address> <use value='home'/> <city value='Amsterdam'/> <type value='physical'/> <country value='Netherlands'/> </address> <name> <given value='Marie'/> </name> </Patient> </resource> </entry> <entry> <resource> <Patient> <id value='id2'/> <address> <use value='home'/> <city value='Rome'/> <type value='physical'/> <country value='Italy'/> </address> <address> <use value='work'/> <city value='Stockholm'/> <type value='postal'/> <country value='Sweden'/> </address> <name> <given value='Susie'/> </name> </Patient> </resource> </entry> <entry> <resource> <Patient> <id value='id3'/> <address> <use value='home'/> <city value='Berlin'/> </address> <address> <type value='postal'/> <country value='France'/> </address> <address> <use value='work'/> <city value='London'/> <type value='postal'/> <country value='England'/> </address> <name> <given value='Frank'/> </name> <name> <given value='Max'/> </name> </Patient> </resource> </entry> </Bundle>
This bundle contains three Patient resources. The first resource has just one entry for the address attribute. The second Patient resource has two entries containing the same elements for the address attribute. The third Patient resource has a rather messy address attribute, with three entries containing different elements and also two entries for the name attribute.
Let's see what happens if we extract all attributes:
desc1 <- fhir_table_description(resource = "Patient", sep = " | ") df1 <- fhir_crack(bundles = bundle, design = desc1, verbose = 0) df1
As you can see, multiple entries for the same attribute (address and name) are pasted together. This works fine for Patient 2, but for Patient 3 you can see a problem with the number of entries that are displayed. The original Patient resource had three (incomplete)
address entries, but because the first two of them use complementary elements (
country), the resulting pasted entries look like there had just been two entries for the
You can counter this problem by setting
desc2 <- fhir_table_description( resource = "Patient", sep = " | ", brackets = c("[", "]") ) df2 <- fhir_crack(bundles = bundle, design = desc2, verbose = 0) df2
Now the indices display the entry the value belongs to. That way you can see that Patient resource 3 had three entries for the attribute
address and you can also see which attributes belong to which entry.
If you set the
format argument to
wide, the entries are spread over multiple columns and the indices are attached to column name:
df3 <- fhir_crack(bundles = bundle, design = desc2, format = "wide", verbose = 0) df3
Of course the above example is a very specific case that only occurs if your resources have multiple entries with complementary elements. In the majority of cases multiple entries in one resource will have the same structure, thus making numbering of those entries superfluous. But the indices also help to disentangle those entries and put them in separate rows, as you'll see in the next paragraph.
If the table produced by
fhir_crack() contains multiple entries, you'll probably want to divide these entries into distinct observations at some point. Apart from directly spreading those values over multiple columns by using a
wide cracking format, the fhircrackr gives you two options to get from a compact table to either a long or a wide format:
fhir_cast(). The former spreads the entries across rows, creating a long format, the latter spreads them across columns, creating a wide format.
fhir_melt() takes an indexed data frame with multiple entries in one or several
columns and spreads (aka melts) the entries in
columns over several rows:
fhir_melt( indexed_data_frame = df2, columns = "address.city", brackets = c("[", "]"), sep = " | ", all_columns = TRUE )
The new variable
resource_identifier records which rows in the created data frame belong to which row (usually equivalent to one resource) in the original data frame.
sep should be given the same character vectors that have been used to build the indices in
columns is a character vector with the names of the variables you want to melt. You can provide more than one column here but it makes sense to only have variables from the same repeating attribute together in one call to
cols <- c("address.city", "address.use", "address.type", "address.country") fhir_melt( indexed_data_frame = df2, columns = cols, brackets = c("[", "]"), sep = " | ", all_columns = TRUE )
If the names of the variables in your data frame have been generated automatically with
fhir_crack() you can find all variable names belonging to the same attribute with
cols <- fhir_common_columns(data_frame = df2, column_names_prefix = "address") cols
With the argument
all_columns you can control whether the resulting data frame contains only the molten columns or all columns of the original data frame:
fhir_melt( indexed_data_frame = df2, columns = cols, brackets = c("[", "]"), sep = " | ", all_columns = FALSE )
Values on the other variables will just repeat in the newly created rows.
If you try to melt several variables that don't belong to the same element in one call to
fhir_melt(), this will cause problems, because the different elements won't be combined correctly:
cols <- c(cols, "name.given") fhir_melt( indexed_data_frame = df2, columns = cols, brackets = c("[", "]"), sep = " | ", all_columns = TRUE )
Instead, melt the attributes one after another:
cols <- fhir_common_columns(data_frame = df2, column_names_prefix = "address") molten_1 <- fhir_melt( indexed_data_frame = df2, columns = cols, brackets = c("[", "]"), sep = " | ", all_columns = TRUE ) molten_1 molten_2 <- fhir_melt( indexed_data_frame = molten_1, columns = "name.given", brackets = c("[", "]"), sep = " | ", all_columns = TRUE ) molten_2
This will give you the appropriate product of all multiple entries.
Once you have sorted out the multiple entries, you might want to get rid of the indices in your table. This can be achieved using
fhir_rm_indices(indexed_data_frame = molten_2, brackets = c("[", "]"))
sep should be given the same character vector that was used for
Instead of spreading the entries across rows, you can also spread them across columns using
fhir_cast(). As you've seen above this can be achieved by setting
format = "wide" in
fhir_crack(). There is, however, a function that turns a
compact table into a
wide table and this function is
fhir_cast(). It takes a compact table with multiple entries and the brackets and separator that have been used in
fhir_crack() as input:
fhir_cast(df2, brackets = c("[", "]"), sep = " | ", verbose = 0)
fhir_melt() this function requires all column names to reflect the XPath expression of the respective attribute. The column containing information on
address/city for example has to be named
adress.city because the information of the indices is incorporated in those names to avoid duplicate column names. This column naming scheme is automatically used when you don't give explicit column names in the table_description/design for
fhir_crack() so it makes sense to only cast tables that have automatically generated column names.
The tables produced by
fhir_crack(..., format = "wide") and
fhir_cast() can also be used to recreate the resources that were cracked in the first place, as you'll the in the vignette about recreation of resources.
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