Accepts record-level data from a data frame, validates it against the expected type of content of each column, generates a collection of time series plots for visual inspection, and saves a report to disk.
Usage
daiquiri_report(
df,
field_types,
override_column_names = FALSE,
na = c("", "NA", "NULL"),
dataset_description = NULL,
aggregation_timeunit = "day",
report_title = "daiquiri data quality report",
save_directory = ".",
save_filename = NULL,
show_progress = TRUE,
log_directory = NULL
)Arguments
- df
A data frame. Rectangular data can be read from file using
read_data(). See Details.- field_types
field_types()object specifying names and types of fields (columns) in the supplieddf. See also field_types_available.- override_column_names
If
FALSE, column names in the supplieddfmust match the names specified infield_typesexactly. IfTRUE, column names in the supplieddfwill be replaced with the names specified infield_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").- 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
- aggregation_timeunit
Unit of time to aggregate over. Specify one of
"day","week","month","quarter","year". The"week"option is Monday-based. Default ="day"- report_title
Title to appear on the report
- save_directory
String specifying directory in which to save the report. Default is current directory.
- save_filename
String specifying filename for the report, excluding any file extension. If no filename is supplied, one will be automatically generated with the format
daiquiri_report_YYMMDD_HHMMSS.- show_progress
Print progress to console. Default =
TRUE- log_directory
String specifying directory in which to save log file. If no directory is supplied, progress is not logged.
Value
A list containing information relating to the supplied parameters as
well as the resulting daiquiri_source_data and daiquiri_aggregated_data
objects.
Details
In order for the package to detect any non-conformant
values in numeric or datetime fields, these should be present in the data
frame in their raw character format. Rectangular data from a text file will
automatically be read in as character type if you use the read_data()
function. Data frame columns that are not of class character will still be
processed according to the field_types specified.
Examples
# \donttest{
# load example data into a data.frame
raw_data <- read_data(
system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
delim = ",",
col_names = TRUE
)
# create a report in the current directory
daiq_obj <- daiquiri_report(
raw_data,
field_types = field_types(
PrescriptionID = ft_uniqueidentifier(),
PrescriptionDate = ft_timepoint(),
AdmissionDate = ft_datetime(includes_time = FALSE, na = "1800-01-01"),
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",
aggregation_timeunit = "day",
report_title = "daiquiri data quality report",
save_directory = ".",
save_filename = "example_data_report",
show_progress = TRUE,
log_directory = NULL
)
#> field_types supplied:
#> PrescriptionID <uniqueidentifier>
#> PrescriptionDate <timepoint> options: includes_time
#> AdmissionDate <datetime> na: "1800-01-01"
#> 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
#> Aggregating [] by [day]...
#> Aggregating overall dataset...
#> Aggregating each data_field in turn...
#> 1: PrescriptionID
#> Preparing...
#> Aggregating character field...
#> By n
#> By missing_n
#> By missing_perc
#> By min_length
#> By max_length
#> By mean_length
#> Finished
#> 2: PrescriptionDate
#> Preparing...
#> Aggregating double field...
#> By n
#> By midnight_n
#> By midnight_perc
#> Finished
#> 3: AdmissionDate
#> Preparing...
#> Aggregating double field...
#> By n
#> By missing_n
#> By missing_perc
#> By nonconformant_n
#> By nonconformant_perc
#> By min
#> By max
#> Finished
#> 4: Drug
#> Preparing...
#> Aggregating character field...
#> By n
#> By missing_n
#> By missing_perc
#> Finished
#> 5: Dose
#> Preparing...
#> Aggregating double field...
#> By n
#> By missing_n
#> By missing_perc
#> By nonconformant_n
#> By nonconformant_perc
#> By min
#> By max
#> By mean
#> By median
#> Finished
#> 6: DoseUnit
#> Preparing...
#> Aggregating character field...
#> By n
#> By missing_n
#> By missing_perc
#> By distinct
#> Finished
#> 7: Location
#> Preparing...
#> Aggregating character field...
#> By n
#> By missing_n
#> By missing_perc
#> By distinct
#> By subcat_n
#> 4 categories found
#> 1: SITE1
#> 2: SITE2
#> 3: SITE3
#> 4: SITE4
#> By subcat_perc
#> 4 categories found
#> 1: SITE1
#> 2: SITE2
#> 3: SITE3
#> 4: SITE4
#> Finished
#> Aggregating calculated fields...
#> [DUPLICATES]:
#> Preparing...
#> Aggregating integer field...
#> By sum
#> By nonzero_perc
#> Finished
#> [ALL_FIELDS_COMBINED]:
#> Finished
#> Generating html report...
#>
#>
#> processing file: report_htmldoc.Rmd
#> 1/36
#> 2/36 [daiquiri-setup]
#> 3/36
#> 4/36 [daiquiri-styles]
#> 5/36
#> 6/36 [daiquiri-strata-info]
#> 7/36
#> 8/36 [daiquiri-source-data]
#> 9/36
#> 10/36 [daiquiri-fields-imported]
#> 11/36
#> 12/36 [daiquiri-fields-ignored]
#> 13/36
#> 14/36 [daiquiri-validation-warnings]
#> 15/36
#> 16/36 [daiquiri-source-data-summary]
#> 17/36
#> 18/36 [daiquiri-aggregated-data]
#> 19/36
#> 20/36 [daiquiri-aggregated-data-set-fig-height]
#> 21/36
#> 22/36 [daiquiri-overview-strata]
#> 23/36
#> 24/36 [daiquiri-overview-presence]
#> 25/36
#> 26/36 [daiquiri-overview-missing]
#> 27/36
#> 28/36 [daiquiri-overview-nonconformant]
#> 29/36
#> 30/36 [daiquiri-overview-duplicates]
#> 31/36
#> 32/36 [daiquiri-aggregated-data-summary]
#> 33/36
#> 34/36 [daiquiri-individual-fields-set-fig-height]
#> 35/36
#> 36/36 [daiquiri-individual-fields]
#> output file: report_htmldoc.knit.md
#> /__t/pandoc/3.1.11/x64/pandoc +RTS -K512m -RTS report_htmldoc.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /__w/ropensci/ropensci/ropensci_docs_website/reference/example_data_report.html --lua-filter /github/home/R/x86_64-pc-linux-gnu-library/4.5/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /github/home/R/x86_64-pc-linux-gnu-library/4.5/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --template /github/home/R/x86_64-pc-linux-gnu-library/4.5/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/Rtmpd6PdU5/rmarkdown-str5616756591a.html
#>
#> Output created: example_data_report.html
#> Report saved to: ./example_data_report.html
# }