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Prepare source data

Source: R/source_data.R
prepare_data.Rd

Validate a data frame against a field_types() specification, and prepare for aggregation.

Usage

prepare_data(
  df,
  field_types,
  override_column_names = FALSE,
  na = c("", "NA", "NULL"),
  dataset_description = NULL,
  show_progress = TRUE
)

Arguments

df

A data frame

field_types

field_types() object specifying names and types of fields (columns) in the supplied df. See also field_types_available.

override_column_names

If FALSE, column names in the supplied df must match the names specified in field_types exactly. If TRUE, column names in the supplied df will be replaced with the names specified in field_types. The specification must therefore contain the columns in the correct order. Default = FALSE

na

vector containing strings that should be interpreted as missing values. Default = c("","NA","NULL"). Additional column-specific values can be specified in the field_types() object

dataset_description

Short description of the dataset being checked. This will appear on the report. If blank, the name of the data frame object will be used

show_progress

Print progress to console. Default = TRUE

Value

A daiquiri_source_data object

See also

field_types(), field_types_available(), aggregate_data(), report_data(), daiquiri_report()

Examples

# load example data into a data.frame
raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

# validate and prepare the data for aggregation
source_data <- prepare_data(
  raw_data,
  field_types = field_types(
    PrescriptionID = ft_uniqueidentifier(),
    PrescriptionDate = ft_timepoint(),
    AdmissionDate = ft_datetime(includes_time = FALSE),
    Drug = ft_freetext(),
    Dose = ft_numeric(),
    DoseUnit = ft_categorical(),
    PatientID = ft_ignore(),
    Location = ft_categorical(aggregate_by_each_category = TRUE)
  ),
  override_column_names = FALSE,
  na = c("", "NULL"),
  dataset_description = "Example data provided with package"
)
#> field_types supplied:
#> PrescriptionID	<uniqueidentifier>
#> PrescriptionDate	<timepoint>	options: includes_time
#> AdmissionDate	<datetime>
#> Drug	<freetext>
#> Dose	<numeric>
#> DoseUnit	<categorical>
#> PatientID	<ignore>
#> Location	<categorical>	options: aggregate_by_each_category
#>  
#> Checking column names against field_types... 
#> Importing source data [Example data provided with package]... 
#> Removing column-specific na values... 
#> Checking data against field_types... 
#>   Selecting relevant warnings... 
#>   Identifying nonconformant values... 
#>   Checking and removing missing timepoints... 
#> Checking for duplicates... 
#>   Sorting data... 
#> Loading into source_data structure... 
#>   PrescriptionID 
#>   PrescriptionDate 
#>   AdmissionDate 
#>   Drug 
#>   Dose 
#>   DoseUnit 
#>   PatientID 
#>   Location 
#> Finished 

source_data
#> Dataset: Example data provided with package 
#> 
#> Overall:
#> Columns in source: 8 
#> Columns imported: 7 
#> Rows in source: 8996 
#> Duplicate rows removed: 1 
#> Rows imported: 8993 
#> Column used for timepoint: PrescriptionDate 
#> Min timepoint value: 2021-01-01 
#> Max timepoint value: 2021-12-31 23:00:00 
#> Rows missing timepoint values removed: 2 
#> Strings interpreted as missing values: "","NULL" 
#> Total validation warnings: 8 
#> 
#> Datafields:
#>         field_name       field_type  datatype count    missing
#> 1 PrescriptionID   uniqueidentifier character  8993 0 (0%)    
#> 2 PrescriptionDate timepoint        double     8993 0 (0%)    
#> 3 AdmissionDate    datetime         double     4991 4002 (45%)
#> 4 Drug             freetext         character  8993 0 (0%)    
#> 5 Dose             numeric          double     8984 9 (0.1%)  
#> 6 DoseUnit         categorical      character  8964 29 (0.3%) 
#> 7 PatientID        ignore           NA           NA NA        
#> 8 Location         categorical      character  8993 0 (0%)    
#>                     min                 max validation_warnings
#> 1                 10000                9999                   0
#> 2            2021-01-01 2021-12-31 23:00:00                   2
#> 3            1800-01-01          2021-12-31                   1
#> 4 Abacavir + lamiVUDine          vancomycin                   0
#> 5                   0.2               7e+05                   5
#> 6              MegaUnit             unit(s)                   0
#> 7                    NA                  NA                  NA
#> 8                 SITE1               SITE4                   0
#> 
#> Validation warnings:
#> 
#>          field_name                                              message
#>              <char>                                               <char>
#> 1: PrescriptionDate          Missing or invalid value in Timepoint field
#> 2:    AdmissionDate            expected valid date, but got '2021-06-31'
#> 3:             Dose      expected no trailing characters, but got '1.5g'
#> 4:             Dose expected no trailing characters, but got '4.5 grams'
#> 5:             Dose        expected a double, but got 'See Instructions'
#> 6:             Dose expected no trailing characters, but got '80/400 mg'
#>    instances
#>        <int>
#> 1:         2
#> 2:         1
#> 3:         1
#> 4:         1
#> 5:         2
#> 6:         1