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Read a single target data file

Source: R/read_target_file.R
read_target_file.Rd

Read a single target data file

Usage

read_target_file(
  target_file_path,
  hub_path,
  coerce_types = c("target", "chr", "none"),
  date_col = NULL,
  na = c("NA", "")
)

Arguments

target_file_path

Character string. Path to the target data file being validated relative to the hub's target-data directory.

hub_path

Either a character string path to a local Modeling Hub directory or an object of class <SubTreeFileSystem> created using functions s3_bucket() or gs_bucket() by providing a string S3 or GCS bucket name or path to a Modeling Hub directory stored in the cloud. For more details consult the Using cloud storage (S3, GCS) in the arrow package. The hub must be fully configured with valid admin.json and tasks.json files within the hub-config directory.

coerce_types

character string. What to coerce column types to on read.

  • target: (default) read in (csv) or coerce (parquet) to expected schema by target type (See hubData::create_timeseries_schema() and hubData::create_oracle_output_schema() for details). When coercing data types using the target schema, the output_type_id_datatype can also be used to set the output_type_id column data type manually.

  • chr: read in (csv) or coerce (parquet) all columns to character.

  • none: No coercion. Use arrow read_* function defaults.

date_col

Optional column name to be interpreted as date. Default is NULL. Useful when the required date column is a partitioning column in the target data and does not have the same name as a date typed task ID variable in the config.

na

A character vector of strings to interpret as missing values. Only applies to CSV files. The default is c("NA", ""). Useful when actual character string "NA" values are used in the data. In such a case, use empty cells to indicate missing values in your files and set na = "".

Value

a tibble of contents of the target data file.

Examples

# download example hub
hub_path <- system.file("testhubs/v5/target_file",
  package = "hubUtils"
)
# read in time-series file
read_target_file("time-series.csv", hub_path)
#> # A tibble: 66 × 4
#>    target_end_date target       location observation
#>    <date>          <chr>        <chr>          <dbl>
#>  1 2022-10-22      flu_hosp_inc 02                 3
#>  2 2022-10-22      flu_hosp_inc 01               141
#>  3 2022-10-22      flu_hosp_inc US              2380
#>  4 2022-10-29      flu_hosp_inc 02                14
#>  5 2022-10-29      flu_hosp_inc 01               262
#>  6 2022-10-29      flu_hosp_inc US              4353
#>  7 2022-11-05      flu_hosp_inc 02                10
#>  8 2022-11-05      flu_hosp_inc 01               360
#>  9 2022-11-05      flu_hosp_inc US              6571
#> 10 2022-11-12      flu_hosp_inc 02                20
#> # ℹ 56 more rows
read_target_file("time-series.csv", hub_path, coerce_types = "chr")
#> # A tibble: 66 × 4
#>    target_end_date target       location observation
#>    <chr>           <chr>        <chr>    <chr>      
#>  1 2022-10-22      flu_hosp_inc 02       3          
#>  2 2022-10-22      flu_hosp_inc 01       141        
#>  3 2022-10-22      flu_hosp_inc US       2380       
#>  4 2022-10-29      flu_hosp_inc 02       14         
#>  5 2022-10-29      flu_hosp_inc 01       262        
#>  6 2022-10-29      flu_hosp_inc US       4353       
#>  7 2022-11-05      flu_hosp_inc 02       10         
#>  8 2022-11-05      flu_hosp_inc 01       360        
#>  9 2022-11-05      flu_hosp_inc US       6571       
#> 10 2022-11-12      flu_hosp_inc 02       20         
#> # ℹ 56 more rows
# read in oracle-output file
read_target_file("oracle-output.csv", hub_path)
#> # A tibble: 627 × 6
#>    location target_end_date target       output_type output_type_id oracle_value
#>    <chr>    <date>          <chr>        <chr>       <chr>                 <dbl>
#>  1 US       2022-10-22      flu_hosp_ra… cdf         1                         1
#>  2 US       2022-10-22      flu_hosp_ra… cdf         2                         1
#>  3 US       2022-10-22      flu_hosp_ra… cdf         3                         1
#>  4 US       2022-10-22      flu_hosp_ra… cdf         4                         1
#>  5 US       2022-10-22      flu_hosp_ra… cdf         5                         1
#>  6 US       2022-10-22      flu_hosp_ra… cdf         6                         1
#>  7 US       2022-10-22      flu_hosp_ra… cdf         7                         1
#>  8 US       2022-10-22      flu_hosp_ra… cdf         8                         1
#>  9 US       2022-10-22      flu_hosp_ra… cdf         9                         1
#> 10 US       2022-10-22      flu_hosp_ra… cdf         10                        1
#> # ℹ 617 more rows
read_target_file("oracle-output.csv", hub_path, coerce_types = "chr")
#> # A tibble: 627 × 6
#>    location target_end_date target       output_type output_type_id oracle_value
#>    <chr>    <chr>           <chr>        <chr>       <chr>          <chr>       
#>  1 US       2022-10-22      flu_hosp_ra… cdf         1              1           
#>  2 US       2022-10-22      flu_hosp_ra… cdf         2              1           
#>  3 US       2022-10-22      flu_hosp_ra… cdf         3              1           
#>  4 US       2022-10-22      flu_hosp_ra… cdf         4              1           
#>  5 US       2022-10-22      flu_hosp_ra… cdf         5              1           
#>  6 US       2022-10-22      flu_hosp_ra… cdf         6              1           
#>  7 US       2022-10-22      flu_hosp_ra… cdf         7              1           
#>  8 US       2022-10-22      flu_hosp_ra… cdf         8              1           
#>  9 US       2022-10-22      flu_hosp_ra… cdf         9              1           
#> 10 US       2022-10-22      flu_hosp_ra… cdf         10             1           
#> # ℹ 617 more rows