This article covers two related workflows with the validated MELIDOS IZTECH package:
- import selected files directly into R with
glc_read(); and - create a persistent, reproducible local subset with
glc_download().
The current passing IZTECH revision uses schema 3.0.2. The examples
deliberately use both a tiny demographics file and a light-sensor file.
The first makes schema-defined R types and factor levels easy to
inspect; the second produces analysis-ready light data. Remote examples
run on the pkgdown website but remain unevaluated in ordinary package
and CRAN builds. Set GLCDP_SKIP_LIVE=true to request an
offline website build.
iztech_repository <- "tscnlab/melidos-iztech-glc-dataset"
iztech_dataset <- "MELIDOS_IZTECH_S001"
iztech_demographics <- "MELIDOS_IZTECH_S001:4"
iztech_chest_light <- "MELIDOS_IZTECH_S001:17"
iztech <- glc_open(iztech_repository)
iztech
#> <GLC data package>
#> Source: tscnlab/melidos-iztech-glc-dataset@9353a0c4287d
#> Schema: 3.0.2
#> Registry revision: verifiedSelect before importing
Inspect datasets, file groups, and variables first. This avoids transferring unneeded data and gives you the stable ids and source names used by the read selectors.
glc_datasets(iztech)
#> # A tibble: 17 × 13
#> dataset_id schema_version study_id participant_id participant_associated
#> <chr> <chr> <chr> <chr> <lgl>
#> 1 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S001 TRUE
#> 2 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S002 TRUE
#> 3 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S003 TRUE
#> 4 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S004 TRUE
#> 5 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S005 TRUE
#> 6 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S006 TRUE
#> 7 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S007 TRUE
#> 8 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S008 TRUE
#> 9 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S009 TRUE
#> 10 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S010 TRUE
#> 11 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S011 TRUE
#> 12 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S012 TRUE
#> 13 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S013 TRUE
#> 14 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S014 TRUE
#> 15 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S015 TRUE
#> 16 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S016 TRUE
#> 17 MELIDOS_IZTECH… 3.0.2 MELIDOS… IZTECH_S017 TRUE
#> # ℹ 8 more variables: timezone <chr>, latitude <dbl>, longitude <dbl>,
#> # file_group_count <int>, file_count <int>, modalities <list>,
#> # device_ids <list>, primary_variables <list>
glc_files(iztech, dataset_id = iztech_dataset)
#> # A tibble: 19 × 30
#> dataset_id file_group file_group_id participant_id study_id path
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 MELIDOS_IZTECH_S001 1 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 2 MELIDOS_IZTECH_S001 2 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 3 MELIDOS_IZTECH_S001 3 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 4 MELIDOS_IZTECH_S001 4 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 5 MELIDOS_IZTECH_S001 5 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 6 MELIDOS_IZTECH_S001 6 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 7 MELIDOS_IZTECH_S001 7 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 8 MELIDOS_IZTECH_S001 8 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 9 MELIDOS_IZTECH_S001 9 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 10 MELIDOS_IZTECH_S001 10 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 11 MELIDOS_IZTECH_S001 11 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 12 MELIDOS_IZTECH_S001 12 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 13 MELIDOS_IZTECH_S001 13 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 14 MELIDOS_IZTECH_S001 14 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 15 MELIDOS_IZTECH_S001 15 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 16 MELIDOS_IZTECH_S001 16 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 17 MELIDOS_IZTECH_S001 17 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 18 MELIDOS_IZTECH_S001 18 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> 19 MELIDOS_IZTECH_S001 19 MELIDOS_IZTECH_… IZTECH_S001 MELIDOS… data…
#> # ℹ 24 more variables: declared_path <chr>, format <chr>, encoding <chr>,
#> # timezone <chr>, description <chr>, instructions <chr>, role <chr>,
#> # data_state <chr>, modalities <list>, modality_other <chr>,
#> # modality_other_type <chr>, device_id <chr>, device_location <chr>,
#> # device_location_type <chr>, temporal_type <chr>, temporal_value <dbl>,
#> # temporal_unit <chr>, header_row <int>, preprocessing <list>, storage <chr>,
#> # expected_bytes <dbl>, lfs_oid <chr>, blob_sha <chr>, available <lgl>
glc_variables(iztech, dataset_id = iztech_dataset)
#> # A tibble: 326 × 15
#> dataset_id file_group file_group_id name label description unit type term
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 MELIDOS_I… 1 MELIDOS_IZTE… Id Part… NA NA stri… part…
#> 2 MELIDOS_I… 1 MELIDOS_IZTE… affe… Comf… How comfor… NA fact… inte…
#> 3 MELIDOS_I… 1 MELIDOS_IZTE… burd… Effo… How much e… NA fact… inte…
#> 4 MELIDOS_I… 1 MELIDOS_IZTE… ethi… Perc… There are … NA fact… inte…
#> 5 MELIDOS_I… 1 MELIDOS_IZTE… perc… Perc… Wearing li… NA fact… inte…
#> 6 MELIDOS_I… 1 MELIDOS_IZTE… inte… Unde… It is clea… NA fact… inte…
#> 7 MELIDOS_I… 1 MELIDOS_IZTE… self… Conf… How confid… NA fact… inte…
#> 8 MELIDOS_I… 1 MELIDOS_IZTE… oppo… Inte… Wearing th… NA fact… inte…
#> 9 MELIDOS_I… 1 MELIDOS_IZTE… gene… Over… How accept… NA fact… inte…
#> 10 MELIDOS_I… 2 MELIDOS_IZTE… Id Part… NA NA stri… part…
#> # ℹ 316 more rows
#> # ℹ 6 more variables: term_name <chr>, calibration <chr>, primary <lgl>,
#> # factor_values <list>, factor_labels <list>, factor_descriptions <list>dataset_id is required by glc_read(). Use
dataset_id = "all" only when you deliberately want every
locally available dataset. A dataset can contain questionnaires,
diaries, and several sensor streams that should be processed separately,
so selecting an entire dataset does not imply that all its file groups
can be collected into one table.
Narrow an import with any combination of file-group ids or indices, file paths or basenames, source variable names, and semantic terms. Here, the file inventory identifies the S001 chest-sensor file:
file_inventory <- glc_files(
iztech,
dataset_id = iztech_dataset
)
sensor_file <- file_inventory[
file_inventory$file_group_id == iztech_chest_light,
]
sensor_file[, c(
"file_group_id", "path", "device_id", "expected_bytes"
)]
#> # A tibble: 1 × 4
#> file_group_id path device_id expected_bytes
#> <chr> <chr> <chr> <dbl>
#> 1 MELIDOS_IZTECH_S001:17 data/files/sensor/IZTECH_S001… IZTECH_A… 11411390
source_file <- basename(sensor_file$path[[1]])
source_file
#> [1] "IZTECH_S001_light_chest.csv"Read only its declared melanopic EDI variable and parse at most 10,000 rows:
light_collection <- glc_read(
iztech,
dataset_id = iztech_dataset,
file_group = iztech_chest_light,
files = source_file,
variables = "MEDI",
n_max = 10000
)
light_collection
#> <GLC data collection>
#> File groups: 1
#> Rows: 10000
#> # A tibble: 1 × 17
#> dataset_id file_group file_group_id study_id participant_id device_id
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 MELIDOS_IZTECH_S001 17 MELIDOS_IZTE… MELIDOS… IZTECH_S001 IZTECH_A…
#> # ℹ 11 more variables: modalities <list>, role <chr>, data_state <chr>,
#> # timezone <chr>, datetime_source <chr>, datetime_date <chr>,
#> # datetime_format <chr>, datetime_time <chr>, datetime_time_format <chr>,
#> # primary_variables <list>, files <list>When variables or terms are selected, the
imported tables contain only the matching source variables.
glcdp still uses required date or time columns internally
to construct .glc_datetime, but does not retain them unless
the filters select them. Set primary_only = TRUE to select
declared primary variables:
primary_light <- glc_read(
iztech,
dataset_id = iztech_dataset,
file_group = iztech_chest_light,
primary_only = TRUE,
n_max = 10000
)
primary_data <- primary_light$data[[1]]
primary_data[stats::complete.cases(primary_data), ]
#> # A tibble: 4,876 × 6
#> MEDI .glc_dataset_id .glc_file_group .glc_participant_id .glc_source_file
#> <dbl> <chr> <chr> <chr> <chr>
#> 1 65.9 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 2 65.9 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 3 68.6 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 4 83.6 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 5 76.9 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 6 78.0 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 7 82.3 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 8 82.3 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 9 83.5 MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 10 103. MELIDOS_IZTECH_S0… MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> # ℹ 4,866 more rows
#> # ℹ 1 more variable: .glc_datetime <dttm>Interactive calls show progress across the selected files by default.
Use progress = FALSE to suppress the indicator when a
script or application provides its own progress reporting.
n_max limits rows parsed after a remote file is available;
it does not reduce the whole-file transfer.
Import schema-defined R types and factor levels
Schema 3.0.2 declares a type for each source column. Factor declarations also contain their levels in schema-declared order. These declarations are exposed by the variable inventory:
demographic_variables <- glc_variables(
iztech,
file_group = iztech_demographics
)
demographic_variables[, c("name", "type", "factor_values")]
#> # A tibble: 8 × 3
#> name type factor_values
#> <chr> <chr> <list>
#> 1 Id string <chr [0]>
#> 2 age numeric <chr [0]>
#> 3 sex factor <chr [4]>
#> 4 gender factor <chr [5]>
#> 5 native_language boolean <chr [0]>
#> 6 language_specification factor <chr [3]>
#> 7 employment_status factor <chr [6]>
#> 8 comments boolean <chr [0]>glc_read() uses those declarations rather than guessing
from file contents. The IZTECH demographics file therefore yields
numeric, logical, and factor columns with the declared levels:
demographics <- glc_read(
iztech,
dataset_id = iztech_dataset,
file_group = iztech_demographics
)
demographic_data <- demographics$data[[1]]
levels(demographic_data$sex)
#> [1] "Female" "Male" "Intersex"
#> [4] "Prefer not to say"
levels(demographic_data$employment_status)
#> [1] "Full time employed"
#> [2] "Part time employed"
#> [3] "Marginally employed (Minijob)"
#> [4] "Not employed but studying or in training"
#> [5] "Studying and employed"
#> [6] "Not employed"The same import metadata controls headers, datetime construction,
decimal marks, encodings, and time zones. By default, undeclared extra
columns, values that cannot be parsed to the declared type, and values
outside declared factor levels are errors. During exploratory work, use
problems = "warn" to retain problematic data with warnings;
inspect the result before analysis.
exploratory_demographics <- glc_read(
iztech,
dataset_id = iztech_dataset,
file_group = iztech_demographics,
problems = "warn"
)Understand the imported collection
A glc_data_collection has one row per file group.
Descriptive columns record the dataset, participant, device, modality,
role, data state, time zone, datetime specification, and source files.
The imported table is in the data list-column.
light_collection[, setdiff(names(light_collection), "data")]
#> <GLC data collection>
#> File groups: 1
#> Warning: Unknown or uninitialised column: `data`.
#> Rows: 0
#> # A tibble: 1 × 17
#> dataset_id file_group file_group_id study_id participant_id device_id
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 MELIDOS_IZTECH_S001 17 MELIDOS_IZTE… MELIDOS… IZTECH_S001 IZTECH_A…
#> # ℹ 11 more variables: modalities <list>, role <chr>, data_state <chr>,
#> # timezone <chr>, datetime_source <chr>, datetime_date <chr>,
#> # datetime_format <chr>, datetime_time <chr>, datetime_time_format <chr>,
#> # primary_variables <list>, files <list>
names(light_collection$data[[1]])
#> [1] "MEDI" ".glc_dataset_id" ".glc_file_group"
#> [4] ".glc_participant_id" ".glc_source_file" ".glc_datetime"Each imported table also contains .glc_* provenance
columns such as the dataset id, participant id, source file, and
constructed datetime. These make row origins explicit after tables are
combined.
Collect analysis-ready data
Combine file groups only when their structures and meanings are compatible:
light_data <- glc_collect(light_collection)
head(light_data[!is.na(light_data$MEDI), ])
#> # A tibble: 6 × 6
#> # Groups: Id [1]
#> MEDI Id file_group_id participant_Id Datetime file.name
#> <dbl> <fct> <chr> <chr> <dttm> <chr>
#> 1 65.9 MELIDOS_IZTE… MELIDOS_IZTE… IZTECH_S001 2024-12-23 14:14:06 IZTECH_S…
#> 2 65.9 MELIDOS_IZTE… MELIDOS_IZTE… IZTECH_S001 2024-12-23 14:14:16 IZTECH_S…
#> 3 68.6 MELIDOS_IZTE… MELIDOS_IZTE… IZTECH_S001 2024-12-23 14:14:26 IZTECH_S…
#> 4 83.6 MELIDOS_IZTE… MELIDOS_IZTE… IZTECH_S001 2024-12-23 14:14:36 IZTECH_S…
#> 5 76.9 MELIDOS_IZTE… MELIDOS_IZTE… IZTECH_S001 2024-12-23 14:14:46 IZTECH_S…
#> 6 78.0 MELIDOS_IZTE… MELIDOS_IZTE… IZTECH_S001 2024-12-23 14:14:56 IZTECH_S…The default standardize = "lightlogr" follows the data
conventions used by LightLogR and adds:
-
Id, derived from the dataset id; -
file_group_id, the stable dataset file-group id used by the metadata helpers; -
participant_Id, derived from the participant id; -
Datetime, constructed from the metadata-defined datetime specification; and -
file.name, retained from a declared source column when present or derived from the package path otherwise.
The standardized result contains no internal .glc_*
provenance columns. It is ordered by Id and
Datetime and grouped by Id. Use
standardize = "none" if you want an ungrouped tibble whose
source and provenance columns are left unchanged.
source_data <- glc_collect(
light_collection,
standardize = "none"
)
head(source_data)
#> # A tibble: 6 × 6
#> MEDI .glc_dataset_id .glc_file_group .glc_participant_id .glc_source_file
#> <dbl> <chr> <chr> <chr> <chr>
#> 1 NA MELIDOS_IZTECH_S001 MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 2 NA MELIDOS_IZTECH_S001 MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 3 NA MELIDOS_IZTECH_S001 MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 4 NA MELIDOS_IZTECH_S001 MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 5 NA MELIDOS_IZTECH_S001 MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> 6 NA MELIDOS_IZTECH_S001 MELIDOS_IZTECH… IZTECH_S001 data/files/sens…
#> # ℹ 1 more variable: .glc_datetime <dttm>glc_collect() refuses to combine groups that differ in
columns, types, time zones, modalities, roles, data states, or datetime
specifications. It also rejects contradictory file-group relationships
and multiple device links within one dataset. Keep those groups separate
or select a compatible subset with glc_read().
Collected data can be used directly with LightLogR’s data-quality and insight functions, visualization guide, and metrics guide.
Extract or add metadata
Use extract_metadata() when you want one concise row per
imported file group, and add_metadata() when the same
fields should be available on every observation. Both functions require
an explicit metadata source and field selection. The default key is
file_group_id, which glc_collect() adds to
standardized data. If the input is grouped, the extracted tibble retains
that grouping and includes its grouping columns before
file_group_id.
analysis_metadata <- tibble::tibble(
file_group_id = unique(as.character(light_data$file_group_id)),
analysis_set = "chest sensor"
)
metadata_summary <- extract_metadata(
light_data,
analysis_metadata,
fields = "analysis_set"
)
metadata_summary
#> # A tibble: 1 × 3
#> # Groups: Id [1]
#> Id file_group_id analysis_set
#> <fct> <chr> <chr>
#> 1 MELIDOS_IZTECH_S001 MELIDOS_IZTECH_S001:17 chest sensor
enriched_data <- add_metadata(
light_data,
analysis_metadata,
fields = "analysis_set"
)
enriched_data |>
head() |>
dplyr::select(-file.name)
#> # A tibble: 6 × 6
#> # Groups: Id [1]
#> MEDI Id file_group_id participant_Id Datetime analysis_set
#> <dbl> <fct> <chr> <chr> <dttm> <chr>
#> 1 NA MELIDOS_I… MELIDOS_IZTE… IZTECH_S001 2024-12-23 00:00:06 chest sensor
#> 2 NA MELIDOS_I… MELIDOS_IZTE… IZTECH_S001 2024-12-23 00:00:16 chest sensor
#> 3 NA MELIDOS_I… MELIDOS_IZTE… IZTECH_S001 2024-12-23 00:00:26 chest sensor
#> 4 NA MELIDOS_I… MELIDOS_IZTE… IZTECH_S001 2024-12-23 00:00:36 chest sensor
#> 5 NA MELIDOS_I… MELIDOS_IZTE… IZTECH_S001 2024-12-23 00:00:46 chest sensor
#> 6 NA MELIDOS_I… MELIDOS_IZTE… IZTECH_S001 2024-12-23 00:00:56 chest sensorThe metadata source can also be a local CSV/TSV path or the package
handle. For a package handle, glcdp follows each file group
to its dataset and device, and follows the dataset to its participant
and study. It can therefore assemble fields such as
participant_age without manually naming the participant
resource. Dataset-, participant-, and study-level values repeat across
file groups. Use by = "Id" when you explicitly want one row
per dataset instead. Field and relationship resolution must remain
unambiguous; use resource to restrict field discovery when
the same field occurs in multiple connected resources.
glc_search_metadata(
iztech,
"participant_age",
resources = "participants",
search_in = "fields"
)
#> # A tibble: 17 × 5
#> resource record field value context
#> <chr> <int> <chr> <chr> <chr>
#> 1 participants 1 participant_age 27 record 1
#> 2 participants 2 participant_age 24 record 2
#> 3 participants 3 participant_age 23 record 3
#> 4 participants 4 participant_age 25 record 4
#> 5 participants 5 participant_age 25 record 5
#> 6 participants 6 participant_age 29 record 6
#> 7 participants 7 participant_age 23 record 7
#> 8 participants 8 participant_age 23 record 8
#> 9 participants 9 participant_age 32 record 9
#> 10 participants 10 participant_age 26 record 10
#> 11 participants 11 participant_age 22 record 11
#> 12 participants 12 participant_age 24 record 12
#> 13 participants 13 participant_age 23 record 13
#> 14 participants 14 participant_age 21 record 14
#> 15 participants 15 participant_age 27 record 15
#> 16 participants 16 participant_age 24 record 16
#> 17 participants 17 participant_age 21 record 17
dataset_metadata <- extract_metadata(
light_data,
iztech,
fields = c(
"dataset_timezone",
"dataset_location",
"participant_age",
"study_title",
"device_model"
)
)
dataset_metadata
#> # A tibble: 1 × 7
#> # Groups: Id [1]
#> Id file_group_id dataset_timezone dataset_location participant_age
#> <fct> <chr> <chr> <list> <dbl>
#> 1 MELIDOS_IZTEC… MELIDOS_IZTE… Europe/Istanbul <dbl [2]> 27
#> # ℹ 2 more variables: study_title <chr>, device_model <chr>
add_metadata(
light_data,
iztech,
fields = c(
"dataset_timezone",
"dataset_location",
"participant_age",
"study_title",
"device_model"
)
) |>
head() |>
dplyr::select(
Id,
dataset_timezone,
dataset_location,
participant_age,
study_title,
device_model
)
#> # A tibble: 6 × 6
#> # Groups: Id [1]
#> Id dataset_timezone dataset_location participant_age study_title
#> <fct> <chr> <list> <dbl> <chr>
#> 1 MELIDOS_IZTECH_… Europe/Istanbul <dbl [2]> 27 Personal l…
#> 2 MELIDOS_IZTECH_… Europe/Istanbul <dbl [2]> 27 Personal l…
#> 3 MELIDOS_IZTECH_… Europe/Istanbul <dbl [2]> 27 Personal l…
#> 4 MELIDOS_IZTECH_… Europe/Istanbul <dbl [2]> 27 Personal l…
#> 5 MELIDOS_IZTECH_… Europe/Istanbul <dbl [2]> 27 Personal l…
#> 6 MELIDOS_IZTECH_… Europe/Istanbul <dbl [2]> 27 Personal l…
#> # ℹ 1 more variable: device_model <chr>When adding project-specific metadata to a data package, store it
under a stable package-relative path such as
data/metadata.csv and declare it as a resource in
datapackage.json. The functions never guess from the
working directory or neighboring files. They error when no identifiers
or fields match, and warn while retaining useful results for partial
matches.
Download a reproducible subset
glc_download() creates a persistent directory while
preserving package-relative paths. Its safe default downloads only the
descriptor, core metadata, and required schemas:
For public packages, ordinary Git files are transferred from
immutable raw URLs at the selected commit, avoiding per-file GitHub API
requests. A token supplied to glc_open() continues to use
the authenticated API transport, including for private repositories.
metadata_dir <- tempfile("iztech-metadata-")
glc_download(iztech, metadata_dir)
#> # A tibble: 15 × 6
#> path destination storage bytes sha256 lfs_oid
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 datapackage.json /tmp/Rtmp0… git 1.58e3 510d0… NA
#> 2 data/study.json /tmp/Rtmp0… git 6.03e3 0ce90… NA
#> 3 data/participants.csv /tmp/Rtmp0… git 5.27e2 70454… NA
#> 4 data/participant_characteristics.c… /tmp/Rtmp0… git 5.80e4 c018f… NA
#> 5 data/datasets.json /tmp/Rtmp0… git 3.94e6 03899… NA
#> 6 data/devices.json /tmp/Rtmp0… git 8.36e3 8798c… NA
#> 7 data/device_datasheets.json /tmp/Rtmp0… git 9.13e3 e4a6b… NA
#> 8 schemas/3.0.2/glc-dp-profile.json /tmp/Rtmp0… git 1.14e4 4b6bf… NA
#> 9 json-entity-resource.json /tmp/Rtmp0… git 7.65e2 c89c1… NA
#> 10 schemas/3.0.2/study.schema.json /tmp/Rtmp0… git 6.50e3 f1e71… NA
#> 11 schemas/3.0.2/participants.schema.… /tmp/Rtmp0… git 1.08e3 e7192… NA
#> 12 schemas/3.0.2/participant_characte… /tmp/Rtmp0… git 1.46e3 25351… NA
#> 13 schemas/3.0.2/dataset.schema.json /tmp/Rtmp0… git 4.66e4 7c17e… NA
#> 14 schemas/3.0.2/device.schema.json /tmp/Rtmp0… git 3.95e3 ea149… NA
#> 15 schemas/3.0.2/device_datasheet.sch… /tmp/Rtmp0… git 1.31e4 44d54… NARequest data explicitly and apply the same selectors used during inspection. This compact example downloads the S001 demographics group:
data_dir <- tempfile("iztech-s001-demographics-")
downloads <- glc_download(
iztech,
data_dir,
include = "data",
dataset_id = iztech_dataset,
file_group = iztech_demographics
)
downloads
#> # A tibble: 16 × 6
#> path destination storage bytes sha256 lfs_oid
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 datapackage.json /tmp/Rtmp0… git 1.58e3 510d0… NA
#> 2 data/study.json /tmp/Rtmp0… git 6.03e3 0ce90… NA
#> 3 data/participants.csv /tmp/Rtmp0… git 5.27e2 70454… NA
#> 4 data/participant_characteristics.c… /tmp/Rtmp0… git 5.80e4 c018f… NA
#> 5 data/datasets.json /tmp/Rtmp0… git 3.94e6 03899… NA
#> 6 data/devices.json /tmp/Rtmp0… git 8.36e3 8798c… NA
#> 7 data/device_datasheets.json /tmp/Rtmp0… git 9.13e3 e4a6b… NA
#> 8 schemas/3.0.2/glc-dp-profile.json /tmp/Rtmp0… git 1.14e4 4b6bf… NA
#> 9 json-entity-resource.json /tmp/Rtmp0… git 7.65e2 c89c1… NA
#> 10 schemas/3.0.2/study.schema.json /tmp/Rtmp0… git 6.50e3 f1e71… NA
#> 11 schemas/3.0.2/participants.schema.… /tmp/Rtmp0… git 1.08e3 e7192… NA
#> 12 schemas/3.0.2/participant_characte… /tmp/Rtmp0… git 1.46e3 25351… NA
#> 13 schemas/3.0.2/dataset.schema.json /tmp/Rtmp0… git 4.66e4 7c17e… NA
#> 14 schemas/3.0.2/device.schema.json /tmp/Rtmp0… git 3.95e3 ea149… NA
#> 15 schemas/3.0.2/device_datasheet.sch… /tmp/Rtmp0… git 1.31e4 44d54… NA
#> 16 data/files/questionnaires/IZTECH_S… /tmp/Rtmp0… git 1.3 e2 daaad… NAUse include = "all" only when you intend to mirror every
declared resource. The resources and files
arguments can further narrow a download. Existing files are protected
unless overwrite = TRUE is set explicitly.
Every download writes glcdp-manifest.json, recording the
source repository, exact commit, registry verification state, schema
version, selection, hashes, storage types, sizes, and Git LFS object
ids. Reopen the directory to use the same inspection and import API
without fetching the package again:
local <- glc_open(data_dir)
glc_summary(local)
#> <GLC package summary>
#> Schema: 3.0.2
#> Studies: 1 | Datasets: 1 available / 17 declared | Participants: 17
#> File groups: 1 available / 323 declared | Files: 1 available / 323 declared | Variables: 5554
glc_files(local, dataset_id = iztech_dataset, available = TRUE)
#> # A tibble: 1 × 30
#> dataset_id file_group file_group_id participant_id study_id path
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 MELIDOS_IZTECH_S001 4 MELIDOS_IZTECH_S… IZTECH_S001 MELIDOS… data…
#> # ℹ 24 more variables: declared_path <chr>, format <chr>, encoding <chr>,
#> # timezone <chr>, description <chr>, instructions <chr>, role <chr>,
#> # data_state <chr>, modalities <list>, modality_other <chr>,
#> # modality_other_type <chr>, device_id <chr>, device_location <chr>,
#> # device_location_type <chr>, temporal_type <chr>, temporal_value <dbl>,
#> # temporal_unit <chr>, header_row <int>, preprocessing <list>, storage <chr>,
#> # expected_bytes <dbl>, lfs_oid <chr>, blob_sha <chr>, available <lgl>
local_collection <- glc_read(
local,
dataset_id = iztech_dataset,
file_group = iztech_demographics
)
#> Local package is a partial data subset.
#> ℹ 1 of 17 declared datasets and 1 of 323 declared files are locally available.
#> ℹ Unavailable datasets: MELIDOS_IZTECH_S002, MELIDOS_IZTECH_S003,
#> MELIDOS_IZTECH_S004, MELIDOS_IZTECH_S005, MELIDOS_IZTECH_S006,
#> MELIDOS_IZTECH_S007, MELIDOS_IZTECH_S008, MELIDOS_IZTECH_S009,
#> MELIDOS_IZTECH_S010, MELIDOS_IZTECH_S011, MELIDOS_IZTECH_S012,
#> MELIDOS_IZTECH_S013, MELIDOS_IZTECH_S014, MELIDOS_IZTECH_S015,
#> MELIDOS_IZTECH_S016, MELIDOS_IZTECH_S017.
#> ℹ glc_read() will read only locally available files.
local_data <- glc_collect(local_collection)
local_data
#> # A tibble: 1 × 12
#> # Groups: Id [1]
#> Id age sex gender native_language language_specification
#> <fct> <dbl> <fct> <fct> <lgl> <fct>
#> 1 MELIDOS_IZTECH_S001 27 Female Woman TRUE NA
#> # ℹ 6 more variables: employment_status <fct>, comments <lgl>,
#> # file_group_id <chr>, participant_Id <chr>, Datetime <dttm>, file.name <chr>The descriptor and core metadata retain the records declared by the
source package. For a local subset, glc_summary()
distinguishes locally available datasets, file groups, and files from
those declared records. When the package is incomplete,
glc_read() also reports how many declared datasets and
files are locally available, then skips absent files. Thus,
dataset_id = "all" reads all data included in the subset.
Use glc_files(local, available = FALSE) to inspect omitted
file records.
Temporary reads versus persistent storage
Remote glc_read() calls use session-temporary storage by
default. This is a good fit for one-off analysis and leaves no
persistent files behind.
Pass cache_dir to glc_open() when you want
remote files reused across calls, or use glc_download()
when you want an explicit, portable package subset with a manifest:
Choose the cache for performance; choose a downloaded subset for a durable, inspectable analysis input.