EDCimport is a package designed to easily import data from EDC software TrialMaster. Browse code at https://github.com/DanChaltiel/EDCimport and read the doc at https://danchaltiel.github.io/EDCimport/.
compare_databases(), which compares the
structure of several extractions of a database: added/removed columns,
number of patients, etc (#26). See the examples for a demo.read_all_csv(), read_all_sas(), and
read_all_xpt() (#75).edc_viewer():
edc_viewer(data=lst(iris, mtcars), port=1212)edc_clean_names(),
edc_unify_subjid(), and edc_split_mixed() so
they don’t strip database attributes (like project name) (#111).edc_data_stop() so it works without a SUBJID and
defaults to no issue number (#109).assert_no_duplicate() so it works in table with
both columns SUBJID and subjid (#105).edc_left_join() with case-sensitivity on
SUBJID (#108, #117).save_edc_data_warnings() with options to hide
the resolved issues and to not include stops, and better default path
(#107, #110, #112)label attribute, taken from FORMDESC or
CRFNAME (#118).edc_swimmerplot() by removing
origin by default (#106).edc_swimmerplot() by adding arguments
origin_fun to summarise origin at patient
level using, and data_list to control the datasets.edc_warn_extraction_date() with a strict unit
“days”.save_plotly() with a glue syntax for parameter
file.vignette("reading"),
vignette("postprocessing"),
vignette("checking"), vignette("visualizing"),
and vignette("utils")edc_patient_gridplot(), which creates a
ggplot matrix giving the presence of all patients in all datasets
(#77)edc_left_join(),
edc_right_join(), and edc_full_join(), which
perform joins with defaults to subject ID as primary key (#82)edc_viewer(), which runs a shiny
application for easily browsing your database (#83)set_project_name(), to set the project
name when reading from a directory (#96)edc_find_value(), which searches the whole
database for a value, as edc_find_column() searches for
column names or labels.save_edc_data_warnings(), to save all the
warnings triggered by edc_data_warn() into a
.xlsx file for sharing.unify(collapse_chr=TRUE), to collapse
non-unique character values (#99)lastnews_table(show_delta=TRUE), which
computes the difference between the last prefer date and
the actual last date (#81)
except &
prefer (with regex=TRUE), improved warning
message, and allow saving warnings in a csv file (#78)edc_data_warn(envir), the environment to
evaluate message in.edc_swimmerplot(include), to subset the
swimmer plot on significant variables only.subdirectories to all reading functions
(read_trialmaster(), read_all_xpt(),
read_all_sas(), and read_all_csv()), to
control whether to read sub-directories. Note that until now, those
subdirectories were read and could overwrite root files.read_trialmaster() won’t read from cache if installed
EDCimport version is different from cache’sload_list(), renamed to
load_database()find_keyword(), renamed to
edc_find_column()I don’t think enough people are using this so that it is necessary to go through the deprecation process.
split_mixed_datasets becomes
edc_split_mixed()build_lookup(),
extend_lookup(), get_key_cols(),
get_subjid_cols(), get_crfname_cols(),
get_meta_cols(), load_as_list(),
save_list()lastnews_table() when SUBJID is not
numericread_all_sas() causing metadata
(e.g. date_extraction) being converted to dataframesread_all_sas() to read a database of
.sas7bdat files.read_all_csv() to read a database of
.csv files.New functions edc_data_warn() and
edc_data_stop(), to alert if data has inconsistencies (#29,
#39, #43).
r ae %>% filter(grade<1 | grade>5) %>% edc_data_stop("AE of invalid grade") ae %>% filter(is.na(grade)) %>% edc_data_warn("Grade is missing", issue_n=13) #> Warning: Issue #13: Grade is missing (8 patients: #21, #28, #39, #95, #97, ...)
New function edc_data_warnings(), to get a dataframe
of all warnings thrown by edc_data_warn().
New function edc_warn_extraction_date(), to alert if
data is too old.
select_distinct() to select all columns
that has only one level for a given grouping scope (#57).edc_population_plot() to visualize which
patient is in which analysis population (#56).edc_db_to_excel() to export the whole
database to an Excel file, easier to browse than RStudio’s table viewer
(#55). Use edc_browse_excel() to browse the file without
knowing its name.edc_inform_code() to show how much code
your project contains (#49).search_for_newer_data() to search a path
(e.g. Downloads) for a newer data archive (#46).edc_crf_plot() to show the current
database completion status (#48).save_sessioninfo(), to save
sessionInfo() into a text file (#42).fct_yesno(), to easily format Yes/No
columns (#19, #23, #40).lastnews_table() to find the last date an
information has been entered for each patient (#37). Useful for survival
analyses.edc_unify_subjid(), to have the same
structure for subject IDs in all the datasets of the database
(#30).save_plotly(), to save a
plotly to an HTML file (#15).table_format(),
get_common_cols() and get_meta_cols() that
might become useful to find keys to pivot or summarise data.get_datasets() will now work even if a dataset is named
after a base function (#67).read_trialmaster() will output a readable error when no
password is entered although one is needed.read_trialmaster(split_mixed="TRUE") will work as
intended.assert_no_duplicate() has now a by
argument to check for duplicate in groups, for example by visit
(#17).find_keyword() is more robust and inform on the
proportion of missing if possible.edc_lookup() will now retrieve the lookup table. Use
build_lookup() to build one from a table list.extend_lookup() will not fail anymore when the database
has a faulty table.get_key_cols() is replaced by
get_subjid_cols() and get_crfname_cols().check_subjid() is replaced by
edc_warn_patient_diffs(). It can either take a vector or a
dataframe as input, and the message is more informative.Changes in testing environment so that the package can be installed from CRAN despite firewall policies forbidding password-protected archive downloading.
Fixed a bug where a corrupted XPT file can prevent the whole import to fail.
check_subjid() to check if a vector is not
missing some patients (#8).options(edc_subjid_ref=enrolres$subjid)
check_subjid(treatment$subjid)
check_subjid(ae$subjid)assert_no_duplicate() to abort if a table
has duplicates in a subject ID column(#9).tibble(subjid=c(1:10, 1)) %>% assert_no_duplicate() %>% nrow()
#Error in `assert_no_duplicate()`:
#! Duplicate on column "subjid" for value 1.manual_correction() to safely hard-code a
correction while waiting for the TrialMaster database to be
updated.edc_options() to manage
EDCimport global parameterization.edc_swimmerplot(id_lim) to subset the
swimmer plot to some patients only.read_trialmaster(use_cache="write") to read
from the zip again but still update the cache.read_trialmaster(split_mixed=c("col1", "col2")) to split
only the datasets you need to (#10).read_trialmaster() from cache will output
an error if parameters (split_mixed,
clean_names_fun) are different (#4).split_mixed_datasets() is now fully
case-insensitive.read_trialmaster(use_cache="write") is now the default.
Reading from cache is not stable yet, so you should opt-in rather than
opt-out.read_trialmaster(extend_lookup=TRUE) is now the
default.edc_id, edc_crfname, and
edc_verbose have been respectively renamed
edc_cols_id, edc_cols_crfname, and
edc_read_verbose for more clarity.edc_swimmerplot() to show a swimmer plot
of all dates in the database and easily find outliers.read_trialmaster():
clean_names_fun=some_fun will clean all names of all
tables. For instance,
clean_names_fun=janitor::clean_names() will turn default
SAS uppercase column names into valid R snake-case column names.split_mixed=TRUE will split tables that contain both
long and short data regarding patient ID into one long table and one
short table. See ?split_mixed_datasets() for details.extend_lookup=TRUE will improve the lookup table with
additional information. See ?extend_lookup() for
details.key_columns=get_key_cols() is where you can change the
default column names for patient ID and CRF name (used in other new
features).extend_lookup() and
split_mixed_datasets().unify(), which turns a vector of duplicate
values into a vector of length 1.read_trialmaster()
instead of failing. If one XPT file is corrupted, the resulting object
will contain the error message instead of the dataset.find_keyword() is now robust to non-UTF8 characters in
labels.edc_lookup is now set even when reading from
cache.= now work as intended.tm = read_trialmaster("path/to/archive.zip").find_keyword("date", data=tm$.lookup). You can also
generate a lookup table for an arbitrary list of dataframe using
build_lookup(my_data).load_list(tm) to avoid typing tm$
everywhere.?EDCimport_options.
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