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
#>
|
| | 0%
|
|. | 3%
|
|.. | 6% (setup)
|
|... | 9%
|
|.... | 12% (unnamed-chunk-1)
|
|..... | 15%
|
|...... | 18% (strata-info)
|
|....... | 21%
|
|........ | 24% (source-data)
|
|......... | 26%
|
|.......... | 29% (fields-imported)
|
|........... | 32%
|
|............ | 35% (fields-ignored)
|
|............. | 38%
|
|.............. | 41% (validation-warnings)
|
|............... | 44%
|
|................ | 47% (source-data-summary)
|
|.................. | 50%
|
|................... | 53% (aggregated-data)
|
|.................... | 56%
|
|..................... | 59% (overview-strata)
|
|...................... | 62%
|
|....................... | 65% (overview-presence)
|
|........................ | 68%
|
|......................... | 71% (overview-missing)
|
|.......................... | 74%
|
|........................... | 76% (overview-nonconformant)
|
|............................ | 79%
|
|............................. | 82% (overview-duplicates)
|
|.............................. | 85%
|
|............................... | 88% (aggregated-data-summary)
|
|................................ | 91%
|
|................................. | 94% (individual-fields-set-fig-height)
|
|.................................. | 97%
|
|...................................| 100% (individual-fields)
#> output file: report_htmldoc.knit.md
#> /usr/local/bin/pandoc +RTS -K512m -RTS report_htmldoc.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmpsi7XEr/daiquiri/reference/example_data_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/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/RtmpeuBzYc/rmarkdown-str53f8b3afd.html
#>
#> Output created: example_data_report.html
#> Report saved to: ./example_data_report.html
file.remove("./example_data_report.html")
#> [1] TRUE
# }