Reads in data and feature specifications and performs the requisite transformations. Please see the package vignettes for more detailed information on the JSON specification of features.
Usage
run_pipeline(
data_sources,
feature_filenames = NULL,
response_filenames = NULL,
all_ids = NULL
)Arguments
- data_sources
A list, whose names are the unique identifiers of the data sources, and whose values are either the data frame itself or the file path from which they should be read from. Only CSV files are supported at this point in time.
- feature_filenames
A vector of file paths to the feature JSON specifications. Defaults to
NULL.- response_filenames
A vector of file paths to the response JSON specifications. Defaults to
NULL.- all_ids
A vector of all the unique numeric identifiers that should be in the final feature table. If not given, this will be determined by taking the union of all unique identifiers found in input tables used by at least one feature.
Value
A list with the following elementss:
features: A data frame with all the features. The first column is the ID column, and always has the nameid. Subsequent columns are the features, with column names as specified in theoutput_feature_namefield of the JSON files.responses: A data frame with all the responses. The structure is the same as thefeaturesdata frame.
Examples
run_pipeline(
data_sources = list(ae = eider_example("random_ae_data.csv")),
feature_filenames = eider_example("ae_total_attendances.json")
)
#> $features
#> id total_ae_attendances
#> 1 0 6
#> 2 1 4
#> 3 2 6
#> 4 3 3
#> 5 4 5
#> 6 5 4
#> 7 6 6
#> 8 7 5
#> 9 8 5
#> 10 9 10
#> 11 10 4
#> 12 11 5
#> 13 12 4
#> 14 13 8
#> 15 14 4
#> 16 15 7
#> 17 16 3
#> 18 17 5
#> 19 18 4
#> 20 19 2
#>
#> $responses
#> id
#> 1 0
#> 2 1
#> 3 2
#> 4 3
#> 5 4
#> 6 5
#> 7 6
#> 8 7
#> 9 8
#> 10 9
#> 11 10
#> 12 11
#> 13 12
#> 14 13
#> 15 14
#> 16 15
#> 17 16
#> 18 17
#> 19 18
#> 20 19
#>