JavaScript containing variable
labels with quotesJavaScript and
css in outputcss in output2.8.3.jsPDF HTML dependency;
visNetwork is now suggested insteadS7 renderingoptions()dq_report_by() no longer errors if plotly
is missing but disables plotly functionality
gracefullyggplot2 objects are now returned as lightweight
promises (dq_lazy_ggplot) to avoid serializing large
S7 objects
prep_realize_ggplot(p)S7 compatibility
can be reduced viaoptions(dataquieR.lazy_plots_gg_compatibility = "FALSE"),
which may require more frequent calls to
prep_realize_ggplot()ggplot2 / patchwork versions, they may stop
working. Either recompute them or temporarily downgrade:remotes::install_version("patchwork", version = "1.3.0")
remotes::install_version("ggplot2", version = "3.5.2")This workaround is only intended for restoring compatibility with previously saved report objects. No guarantee is given and you use it at your own risk. For long-term use, we recommend recomputing the reports with current package versions.
cross-item_level
metadataHARD_LIMITS, SOFT_LIMITS,
DETECTION_LIMITS).PCT_acc_ud_loc from
acc_margins() and grading rulesetsFLG_acc_ud_loc from
acc_margins()des_summary():
"No. categories/Freq. table" split into"No. categories (incl. NAs)" and
"Level_freq""Variables" renamed
"Variable_names"plain_label)character first (controlled via option
dataquieR.old_factor_handling)GGallyVALUE_LABELSprep_init_parallel_print() removed (no
longer needed)threshold_value from
acc_varcomp()loess and margins plot slightly improvedthreshold_value from acc_varcomp()dq_report2() can store results on the disk instead of
the RAM with the new argument storr_factory. This can be
useful in reducing issues of memory consumption, but we suggest to use
fast SSDs or NVMesoptions(dataquieR.dontwrapresults = TRUE). With
options(dataquieR.testdebug = TRUE), you can switch off
this behavior.dataquieR can provision your function arguments from
the metadata. In order to enable lapply and
Vectorize(SIMPLIFY = FALSE) with indicator functions, the
first argument is now always resp_vars for item level
functions. dataquieR tries to guess if a function that
features both resp_vars and study_data as its
first arguments was called w/o resp_vars but only with
study_data as its first unnamed argument. If that is the
case, it sets resp_vars to the default for
resp_vars (typically all variables). With
options(dataquieR.testdebug = TRUE), you can switch off
this behavior, if you need.dq_report_by, in which it is
possible to specify:
resp_vars)id_vars)int_encoding_errors checking invalid
characters present in the text with respect to the expected character
encoding / code page, e.g., a code place in the latin1
table is used but the encoding is utf8 resulting in damaged
text outputItem-level data quality dashboard, usable to customize data
summariesCODE_LIST_TABLE in the metadata,
where it is possible to state both value label tables and missing list
tables all in one table.item_computation_level in the
metadata, where it is possible to state variables to be computed from
the provided study data.prep_get_data_frame("ship") or
prep_get_data_frame("study_data") in your code to access
example data, no change is needed. If you are still accessing example
data using system.file() (e.g. using
load(system.file("extdata", "study_data.RData", package = "dataquieR"))),
you need to switch to prep_get_data_frame(), i.e.:
load(system.file("extdata", "study_data.RData", package = "dataquieR"))
would become
study_data <- prep_get_data_frame("study_data")SummaryData in ResultData
(functions: acc_shape_or_scale, acc_margins,
com_segment_missingness)GRADING from SummaryData
outputs. SummaryTable outputs still feature the column,
since these are meant to be a machine readable interfacecon_contradictions_redcap used to return a result named
SummaryTable, while the documentation spoke about
SummaryData. Alas, it should have been
VariableGroupTable in both cases. If you relied on
SummaryTable in the results of
con_contradictions_redcap, you need to change your code to
use now the correct output name VariableGroupTable. Also,
the table has been slightly modified.VariableGroupData as returned by
con_contradictions_redcap is a version optimized for human
readers.VariableGroupTable as returned by
con_contradictions_redcap the column category
has been renamed to CONTRADICTION_TYPEcon_contradictions_redcap, if
summarize_categories is selected the result will now be in
a sub-list named Otherprep_add_computed_variables, the column
resp_vars is now named VAR_NAMES, to be more
in line with other data frames.plotly’s interactive figures[.dataquieR_resultset2 and
[[.dataquieR_result and related functions have changed
slightly. You can now for a report
(r <- dq_report2(...)) call, e.g.,r[, "com_item_missingness", "ReportSummaryTable"] to get a
balloon plot or r[, "com_item_missingness", "SummaryData"]
to get a table, for all variables that were assessed with
com_item_missingness() in the report rdataquieR_result objects, these
will be combined, but due to restrictions in R, this only
works, if you call print() explicitly on this list, not
with “auto-printing” (see https://stackoverflow.com/a/53983005), for
example:a <- lapply(c("v00001", "v00004", "v00005", "v00006"), acc_loess, meta_data_v2 = "meta_data_v2", study_data = "study_data")
print(a) works, but typing a alone does not.
You have to call print() or to put lapply() in
brackets: (lapply())acc_distributions() was split in
acc_distributions() and
acc_distributions_ecdf()
(prep_acc_distributions_with_ecdf() creates the original
plot)acc_cat_distributions()meta_data_v2 argumentitem_level, as synonyms for
meta_data, new argument segment_level, as
synonyms for meta_data_segment, new argument
dataframe_level, as synonyms for
meta_data_dataframe, new argument
cross-item_level, as synonyms for
meta_data_cross_item, new argument
item_computation_level, as synonyms for
meta_data_item_computationlabel_col, the
label_col will now default to LABEL, except
you set the option options(dataquieR.testdebug = TRUE) or
options(dataquieR.dontwrapresults = TRUE)resp_vars in
prep_scalelevel_from_data_and_metadata() was never working
correctly and not used neither, so it has been deprecated. It is already
not functional and it never wasdes_summary is still present, but you can
now get results for continuous or categorical variables only, using
des_summary_continuous and
des_summary_categoricalrespectivelycon_contradictions_redcap plot colors vary depending on
CONTRADICTION_TYPESacc_loess() uses lowess instead of
loess (both from the stats package)prep_check_for_dataquieR_updates(), so,
maybe, you need to manually install the latest beta release using
devtools::install_gitlab("libreumg/dataquieR", auth_token = NULL)options(dataquieR.ELEMENT_MISSMATCH_CHECKTYPE = "subset_u")
is now the default assuming a one-fits-all-metadata-file (see
? dataquieR.ELEMENT_MISSMATCH_CHECKTYPE)rlang or withr, most prominently a faster
prep_prepare_dataframes() and rlang compatible
condition (error) handling.dataquieR_result
class, which is now applied also to results outside a pipeline.SEGMENT_ID_TABLE to
SEGMENT_ID_REF_TABLE in segment level metadatadq_report_by files structureHTML reportsCODE_INTERPRET changed to be in
line with the AAPOR definitions, so the following
translation: PP -> P; P -> I; OH -> UOprep_save_report and
prep_load_reportHTML/JS output for Firefoxplot.ly-plotsgginnards installed; removed dependency from
gginnards.robustbase about
doScaledq_report2 reportssummarytools are included in dq_report2
reports, if installed.HTML generation prepareddq_report2 using a queue improves
speedVARIABLE_ROLES in
dq_report2 and suppressing helper variable outputs in
dq_report_bydq_report2 and not directly by the userdq_report2 because it is not so
useful in its current implementationdq_report_by for large reports (can write
and optionally render results to disk rather than returning them)dq_report_by causing
DATA_PROCESS not to workTODO’s in
dq_report_by and add dependent variables on the fly but
with VARIABLE_ROLE suppress:
dq_report_bydq_report_byfilter_result_slots in dq_report2)JS-table prevented controlling the tableVARIABLE_ROLES filtered itemsUNIVARIATE_OUTLIER_CHECKTYPE and
MULTIVARIATE_OUTLIER_CHECKTYPEREDCap syntax:
strictly_successive_dates and
successive_datesREDCap rules and NA handling
and DATA_PROCESS.use_value_labels is not supported anymore. You can specify
the behavior on the rules level in the new cross-item-level metadata
column DATA_PREPARATIONEND_DIGIT_CHECK in
dq_report2, (DATA_ENTRY_TYPE is still
supported and auto-converted). If missing, END_DIGIT_CHECK
defaults to FALSENA were in the dataJUMP_LIST could be added to the item-level
metadata if missing, but causing this type of failing rulesWindows and uncommon variable namesprep_load_workbook_like_file and
meta_data_v2 = formal in dq_report2)
supporting http and https URLs (e.g.,
Excel or OpenOffice workbooks)dq_report2 replaces dq_report. Please use
dq_report2 from now on.htmtools and supports
plotly)data.frame, and
cross-item levels). No required action by user, previous version still
supportedREDCap rules for contradictions (cross-item level
metadata), previous contradictions function still supporteddata.frame-level metadata)AAPOR conceptacc_univariate_outlier and
acc_multivariate_outlier now allow selecting the methods
used to flag outlierswhoami is installed, reports now show a more
suitable user name~ from the ggplot2
updates causing acc_margins to fail for categorical
variablesdq_report reports with wrong bracketsggplot2 3.4.0ORCIDs for two authorsCITATION fileREADME.md file adding the funding
sources.NEWS.md filesigmagap and made missing guessing more
robust.logical.acc_margins.GRADING columns.rbind.ReportSummaryTable
since these are not needed anyways and the inherited documentation for
those arguments rbind from base contains an
invalid URL triggering a NOTE.int_datatype_matrix.prep_study2meta can now also convert factors to
dataquieR compatible
meta_data/study_datacom_item_missingness for textual response
variables.DT JS is always loaded when a
dq_report report is renderedcom_segment_missingness with
strata_vars / group_vars did not worklabel_col was set to something else than
LABEL, strata_vars did not work for
com_unit_missingnessdq_report.cowplot to patchwork in
acc_margins yielding figures that can be easier
manipulated. Please note, that this change could break existing output
manipulations, since the structure of the margins plots has changed
internally. However, output manipulations were hardly possible for
margins plots before, so it is unlikely, that there are pipelines
affected.acc_loess
function.prep_create_meta handling length-0
arguments by ignoring these variable attributes at all.con_inadmissible_categorical (one
resp_var only and value-limits all the same for all
resp_vars)README-Filepandoc-less systemsdataquieR
function was called by a generated function f that lives in
an environment directly inheriting from the empty environment, e.g.
environment(f) <- new.env(parent = emptyenv()).dontrun, because they sometimes
caused NOTEs on rhub.SummaryTable entry of a result within a
dq_report output, the summary and also
print generic did not work on the report.devtools::check(cran = TRUE, env_vars = c(NOT_CRAN = "false"))
takes 2:22 minutes now.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.