In POLAR-fhiR/fhircrackr: Handling HL7 FHIR® Resources in R

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 fhircrackr package:

library(fhircrackr)

Preparation

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)

See ?patient_bundles and ?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.

Extracting one resource type

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 design. 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)

The table_description

A 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:

The resource argument

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")

Internally, fhir_table_description() calls 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 fhir_table_description().

The cols argument

The 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 @id or @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.

Internally, fhir_table_description() calls 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"))

A fhir_columns object that is created explicitly like this can of course also be used in the columns argument of fhir_table_description. 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.

In the XPath expression it is also possible to use so-called predicates to find elements that contain a specific value. When your resources for example contain several code/coding elements and you are interested in loinc codes only, the expression code/coding[system[@value='http://loinc.org']]/code will extract the code only from code elements with a loinc system. A more detailed example of this can be found in the paragraph on multiple entries further down.

Other arguments

While the resource and 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:

• The sep element 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.
• The brackets element 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 [1.1].
• The rm_empty_cols flag can be TRUE or FALSE. If TRUE, columns containing only NA values will be removed, if FALSE, these columns will be kept.
• The format element takes values compact or wide that specify the shape of the output table. In a compact table multiple entries are written into the same cell/column separated by sep. In a wide table multiple entries are spread over several indexed columns. See the paragraph on multiple entries for more information.
• The keep_attr flag 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 name.given if keep_attr = FALSE and name.given@value if keep_attr = TRUE.

All five style elements can also be controlled directly by the fhir_crack() arguments sep, brackets, remove_empty_columns, format and 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 fhir_table_description.

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.

Examples

Extract all available attributes

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. name.given.

Extract specific elements

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 fhir_canonical_design().

fhir_canonical_design()

Extracting more than one resource type

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:

#all attributes defined explicitly
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"
    ),
    sep           = "|",
    brackets      = NULL,
    rm_empty_cols = FALSE,
    format        = 'compact',
    keep_attr     = FALSE 
)

#automatic extraction/default values
pat <- fhir_table_description(resource = "Patient")

#combine both table_descriptions
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:

design

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 fhir_crack().

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:

design$Patients

We can use the design for fhir_crack():

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_df_list or fhir_dt_list that also has the design attached. You can extract the design from a list like this using fhir_design():

fhir_design(list_of_tables)

Note that this doesn't work on single tables created with a fhir_table_description.

Saving and reading designs

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 fhir_load_design():

fhir_load_design(paste0(temp_dir, "/design.xml"))

Multiple entries

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 (use and city vs. type and country), the resulting pasted entries look like there had just been two entries for the address attribute.

You can counter this problem by setting brackets:

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.

Process resources with multiple entries

Select values using predicates in XPath expression

To avoid multiple entries in your table altogether, you can select which of the multiple elements you want to keep during the cracking process. You can achieve this using predicates in your Xpath expressions.

In the following table description, all address elements are only taken from addresses that have the value "physical" for in address/type and the value "home" in address/use.

desc3 <- fhir_table_description(
    resource = "Patient",
    cols = c(id = "id",
             name = "name/given",
             address.city = "address[type[@value='physical'] and use[@value='home']]/city",
             address.country = "address[type[@value='physical'] and use[@value='home']]/country"
             )
)

df_selected <- fhir_crack(bundles = bundle, design = desc3, verbose = 0)
df_selected

The general formulation is element[filterChildElement[@value="filterValue"]]/childElement, where

• element is the fhir element that occurs multiple times (here address)
• filterChildElement is a child of element that is used for filtering (here address/type and address/use)
• filterValue is the value to filter by (here 'physical' and 'home')
• childElement is the element that is actually extracted (here address/city and address/country)

Another example is the following Observation resources bundle that has loinc and snomed codes, that can be cracked into a table that only contains loinc codes:

<Bundle>
  <type value="searchset"/>
  <entry>
    <resource>
      <Observation>
        <id value="obs1"/>
        <code>
          <coding>
            <system value="http://loinc.org"/>
            <code value="29463-7"/>
            <display value="Body Weight"/>
          </coding>
          <coding>
            <system value="http://snomed.info/sct"/>
            <code value="27113001"/>
            <display value="Body weight"/>
          </coding>
        </code>
        <subject>
          <reference value="Patient/id2"/>
        </subject>
      </Observation>
    </resource>
  </entry>
  <entry>
    <resource>
      <Observation>
        <id value="obs2"/>
        <code>
          <coding>
            <system value="http://loinc.org"/>
            <code value="8302-2"/>
            <display value="Body Height"/>
          </coding>
          <coding>
            <system value="http://snomed.info/sct"/>
            <code value="50373000"/>
            <display value="Body height measure"/>
          </coding>
        </code>
        <subject>
          <reference value="Patient/id2"/>
        </subject>
      </Observation>
    </resource>
  </entry>
</Bundle>

bundle2 <- fhir_unserialize(bundles = example_bundles5)
desc4 <- fhir_table_description(resource = "Observation",
                                cols = c(
                                    id = "id",
                                    code = "code/coding[system[@value='http://loinc.org']]/code",
                                    display = "code/coding[system[@value='http://loinc.org']]/display")
                                     )
df_selected2 <- fhir_crack(bundles = bundle2,
                    design = desc4,
                    verbose = F)

df_selected2

In some cases, you won't be able to filter elements during the cracking process, e.g. because you don't know what to filter for beforehand. In that case, the table produced by fhir_crack() will contain multiple entries, which you'll probably want to divide into distinct cells 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 with multiple entries to either a long or a wide format: fhir_melt() and fhir_cast(). The former spreads the entries across rows, creating a long format, the latter spreads them across columns, creating a wide format.

Melt tables with multiple entries

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. brackets and sep should be given the same character vectors that have been used to build the indices in fhir_crack(). 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 fhir_melt():

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 fhir_common_columns():

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.

Remove indices

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():

fhir_rm_indices(indexed_data_frame = molten_2, brackets = c("[", "]"))

Again, brackets and sep should be given the same character vector that was used for fhir_crack() and fhir_melt() respectively.

Melt all multiple entries at once

If you want to melt all multiple entries in a table regardless of their origin, you can use the function fhir_melt_all():

fhir_melt_all(indexed_data_frame = df2, brackets = c("[", "]"), sep = " | ")

This function performs the above steps automatically and repeatedly calls fhir_melt() on groups of columns that belong to the same FHIR element (e.g. address.city, address.country and address.type) until every cell contains a single value. If there is more than one FHIR element with multiple values (e.g. multiple address elements and multiple name elements), every possible combination of the two elements will appear in the resulting table.

Caution! This creates something like a cross product of all values and can multiply the number of rows from the original table considerably.

Cast tables with multiple entries

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)

Contrary to 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.

file.remove(paste0(temp_dir, "/design.xml"))

Collapse multiple entries

In some cases, you don't want to split up multiple entries but collapse them into one value in a suitable way. Consider the following example bundle:

<Bundle>
    <type value='searchset'/>
    <entry>
    <resource>
        <Patient>
            <id value='id1'/>
            <name>
                <given value='Marie'/>
                <given value='Luise'/>
                <family value = 'Smith'/>
                <use value = 'official'/>
            </name>
            <name>
                <given value = 'Lea'/>
                <given value = 'Sophie'/>
                <given value = 'Anna'/>
                <family value = 'Baker'/>
                <use value = 'nickname'/>
            </name>
        </Patient>
     </resource>
  </entry>
  <entry>
    <resource>
        <Patient>
            <id value='id2'/>
            <name>
                <given value='Max'/>
                <family value = 'Brown'/>
                <use value = 'official'/>
            </name>
            <name>
                <given value = 'Anton'/>
                <given value = 'John'/>
                <family value = 'Jones'/>
                <use value = 'nickname'/>
            </name>
        </Patient>
    </resource>
  </entry>
</Bundle>

In this example, you would want to collapse all given names into one value instead of dividing them across multiple rows. The official name and the nickname, however, should stay separated. This can be achieved with the function fhir_collapse(). First we crack the example resources:

#unserialize example
bundles <- fhir_unserialize(bundles = example_bundles7)

#Define sep and brackets
sep <- "|"
brackets <- c("[", "]")

#crack fhir resources
table_desc <- fhir_table_description(
    resource = "Patient",
    brackets = brackets,
    sep = sep
)

df <- fhir_crack(bundles = bundles, design = table_desc, verbose = 0)
df

Then we collapse the given names. The function uses the information in the indices to make sure it only collapses given names within the same name element (official vs. nickname):

#name.given elements from the same name (i.e. the official vs. the nickname) 
#should be collapsed

df2 <- fhir_collapse(df, columns = "name.given", sep = sep, brackets = brackets)
df2

After collapsing the given names, we can melt the table to split apart the official and the nickname:

df2_molten <- fhir_melt(indexed_data_frame =  df2, 
                        brackets = brackets, 
                        sep = sep, 
                        columns = fhir_common_columns(df2,"name"),
                        all_columns = TRUE
                        )
df2_molten

And finish off by removing the indices:

```r fhir_rm_indices(df2_molten, brackets = brackets)

POLAR-fhiR/fhircrackr documentation built on Dec. 24, 2024, 3:06 a.m.