vignettes/BasicREDCapROperations.Rmd
BasicREDCapROperations.RmdThis vignette covers the the basic functions exposed by the httr and curl packages which allow you to interact with REDCap through its API.
The functions redcap_read()
and redcap_read_oneshot()
use the httr
package to call the REDCap API.
## Loading required namespace: kableExtraThere is some information that is specific to the REDCap project, as opposed to an individual operation. This includes the (1) uri of the server, and the (2) token for the user’s project.
If no information is passed about the desired records or fields, then
the entire data set is returned. Only two parameters are required,
redcap_uri and token. Unless the
verbose parameter is set to FALSE, a message
will be printed on the R console with the number of records and fields
returned.
# Return all records and all variables.
ds_all_rows_all_fields <- redcap_read(redcap_uri = uri, token = token)$data
#> 24 variable metadata records were read from REDCap in 0.2 seconds. The http status code was 200.
#> The data dictionary describing 17 fields was read from REDCap in 0.2 seconds. The http status code was 200.
#> 3 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> 1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
#> 5 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
#> Starting to read 5 records at 2024-01-22 17:11:16.547907.
#> Reading batch 1 of 1, with subjects 1 through 5 (ie, 5 unique subject records).
#> 5 records and 25 columns were read from REDCap in 0.1 seconds. The http status code was 200.
ds_all_rows_all_fields # Inspect the returned dataset
#> # A tibble: 5 × 25
#> record_id name_first name_last address telephone email dob age sex
#> <dbl> <chr> <chr> <chr> <chr> <chr> <date> <dbl> <dbl>
#> 1 1 Nutmeg Nutmouse "14 Ros… (405) 32… nutt… 2003-08-30 11 0
#> 2 2 Tumtum Nutmouse "14 Ros… (405) 32… tumm… 2003-03-10 11 1
#> 3 3 Marcus Wood "243 Hi… (405) 32… mw@m… 1934-04-09 80 1
#> 4 4 Trudy DAG "342 El… (405) 32… pero… 1952-11-02 61 0
#> 5 5 John Lee Walker "Hotel … (405) 32… left… 1955-04-15 59 1
#> # ℹ 16 more variables: demographics_complete <dbl>, height <dbl>, weight <dbl>,
#> # bmi <dbl>, comments <chr>, mugshot <chr>, health_complete <dbl>,
#> # race___1 <dbl>, race___2 <dbl>, race___3 <dbl>, race___4 <dbl>,
#> # race___5 <dbl>, race___6 <dbl>, ethnicity <dbl>, interpreter_needed <dbl>,
#> # race_and_ethnicity_complete <dbl>If only a subset of the records is desired, the two
approaches are shown below. The first is to pass an array (where each
element is an ID) to the records parameter. The second is
to pass a single string (where the elements are separated by commas) to
the records_collapsed parameter.
The first format is more natural for more R users. The second format
is what is expected by the REDCap API. If a value for
records is specified, but records_collapsed is
not specified, then redcap_read_oneshot automatically
converts the array into the format needed by the API.
# Return only records with IDs of 1 and 3
desired_records <- c(1, 3)
ds_some_rows_v1 <- redcap_read(
redcap_uri = uri,
token = token,
records = desired_records
)$data
#> 24 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> The data dictionary describing 17 fields was read from REDCap in 0.1 seconds. The http status code was 200.
#> 3 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> 1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
#> Starting to read 2 records at 2024-01-22 17:11:18.365921.
#> Reading batch 1 of 1, with subjects 1 through 3 (ie, 2 unique subject records).
#> 2 records and 25 columns were read from REDCap in 0.1 seconds. The http status code was 200.If only a subset of the fields is desired, then two
approaches exist. The first is to pass an array (where each element is
an field) to the fields parameter. The second is to pass a
single string (where the elements are separated by commas) to the
fields_collapsed parameter. Like with records
and records_collapsed described above, this function
converts the more natural format (i.e., fields) to
the format required by the API (i.e.,
fields_collapsed) if fields is specified and
fields_collapsed is not.
# Return only the fields record_id, name_first, and age
desired_fields <- c("record_id", "name_first", "age")
ds_some_fields <- redcap_read(
redcap_uri = uri,
token = token,
fields = desired_fields
)$data
#> 24 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> The data dictionary describing 17 fields was read from REDCap in 0.1 seconds. The http status code was 200.
#> 3 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> 1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
#> 5 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
#> Starting to read 5 records at 2024-01-22 17:11:20.042749.
#> Reading batch 1 of 1, with subjects 1 through 5 (ie, 5 unique subject records).
#> 5 records and 3 columns were read from REDCap in 0.1 seconds. The http status code was 200.The two techniques above can be combined when your datasets are large
and you don’t want to pull records with certain values. Suppose you want
to select subjects from the previous dataset if the were born
before 1960 and their weight was over 70kg. Two calls to the server are
required. The first call to REDCap pulls all the
records, but for only three columns: record_id,
dob, and weight. From this subset, identify
the records that you want to pull all the data for; in this case, the
desired record_id values are 3 &
5. The second call to REDCap pulls all the
columns, but only for the identified records.
######
## Step 1: First call to REDCap
desired_fields_v3 <- c("record_id", "dob", "weight")
ds_some_fields_v3 <- redcap_read(
redcap_uri = uri,
token = token,
fields = desired_fields_v3
)$data
#> 24 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> The data dictionary describing 17 fields was read from REDCap in 0.1 seconds. The http status code was 200.
#> 3 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> 1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
#> 5 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
#> Starting to read 5 records at 2024-01-22 17:11:21.642398.
#> Reading batch 1 of 1, with subjects 1 through 5 (ie, 5 unique subject records).
#> 5 records and 3 columns were read from REDCap in 0.1 seconds. The http status code was 200.
ds_some_fields_v3 #Examine the these three variables.
#> # A tibble: 5 × 3
#> record_id dob weight
#> <dbl> <date> <dbl>
#> 1 1 2003-08-30 1
#> 2 2 2003-03-10 1
#> 3 3 1934-04-09 80
#> 4 4 1952-11-02 54
#> 5 5 1955-04-15 104
######
## Step 2: identify desired records, based on age & weight
before_1960 <- (ds_some_fields_v3$dob <= as.Date("1960-01-01"))
heavier_than_70_kg <- (ds_some_fields_v3$weight > 70)
desired_records_v3 <- ds_some_fields_v3[before_1960 & heavier_than_70_kg, ]$record_id
desired_records_v3 #Peek at IDs of the identified records
#> [1] 3 5
######
## Step 3: second call to REDCap
#Return only records that met the age & weight criteria.
ds_some_rows_v3 <- redcap_read(
redcap_uri = uri,
token = token,
records = desired_records_v3
)$data
#> 24 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> The data dictionary describing 17 fields was read from REDCap in 0.1 seconds. The http status code was 200.
#> 3 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> 1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
#> Starting to read 2 records at 2024-01-22 17:11:23.197094.
#> Reading batch 1 of 1, with subjects 3 through 5 (ie, 2 unique subject records).
#> 2 records and 25 columns were read from REDCap in 0.1 seconds. The http status code was 200.
ds_some_rows_v3 #Examine the results.
#> # A tibble: 2 × 25
#> record_id name_first name_last address telephone email dob age sex
#> <dbl> <chr> <chr> <chr> <chr> <chr> <date> <dbl> <dbl>
#> 1 3 Marcus Wood "243 Hi… (405) 32… mw@m… 1934-04-09 80 1
#> 2 5 John Lee Walker "Hotel … (405) 32… left… 1955-04-15 59 1
#> # ℹ 16 more variables: demographics_complete <dbl>, height <dbl>, weight <dbl>,
#> # bmi <dbl>, comments <chr>, mugshot <chr>, health_complete <dbl>,
#> # race___1 <dbl>, race___2 <dbl>, race___3 <dbl>, race___4 <dbl>,
#> # race___5 <dbl>, race___6 <dbl>, ethnicity <dbl>, interpreter_needed <dbl>,
#> # race_and_ethnicity_complete <dbl>The examples above have shown only the resulting data frame, by
specifying $data at the end of the call. However, more is
available to those wanting additional information, such as:
data object has the data frame, as in the previous
examples.success boolean value indicates if
redcap_read_oneshot believes the operation completed as
intended.status_codes is a collection of http
status codes, separated by semicolons. There is one code for each
batch attempted.outcome_messages: A collection of human readable
strings indicating the operations’ semicolons. There is one code for
each batch attempted. In an unsuccessful operation, it should contain
diagnostic information.records_collapsed field passed to the API. This
shows which record subsets, if any, were requested.fields_collapsed fields passed to the API. This
shows which field subsets, if any, were requested.elapsed_seconds measures the duration of the
call.
#Return only the fields record_id, name_first, and age
all_information <- redcap_read(
redcap_uri = uri,
token = token,
fields = desired_fields
)
#> 24 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> The data dictionary describing 17 fields was read from REDCap in 0.1 seconds. The http status code was 200.
#> 3 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
#> 1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
#> 2 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
#> 5 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
#> Starting to read 5 records at 2024-01-22 17:11:24.849375.
#> Reading batch 1 of 1, with subjects 1 through 5 (ie, 5 unique subject records).
#> 5 records and 3 columns were read from REDCap in 0.1 seconds. The http status code was 200.
all_information #Inspect the additional information
#> $data
#> # A tibble: 5 × 3
#> record_id name_first age
#> <dbl> <chr> <dbl>
#> 1 1 Nutmeg 11
#> 2 2 Tumtum 11
#> 3 3 Marcus 80
#> 4 4 Trudy 61
#> 5 5 John Lee 59
#>
#> $success
#> [1] TRUE
#>
#> $status_codes
#> [1] "200"
#>
#> $outcome_messages
#> [1] "5 records and 3 columns were read from REDCap in 0.1 seconds. The http status code was 200."
#>
#> $records_collapsed
#> [1] ""
#>
#> $fields_collapsed
#> [1] "record_id,name_first,age"
#>
#> $forms_collapsed
#> [1] ""
#>
#> $events_collapsed
#> [1] ""
#>
#> $filter_logic
#> [1] ""
#>
#> $datetime_range_begin
#> [1] NA
#>
#> $datetime_range_end
#> [1] NA
#>
#> $elapsed_seconds
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