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/.
- Internal fix for CRAN check
-
New function
read_all_sas()
to read a database of.sas7bdat
files. -
New function
read_all_csv()
to read a database of.csv
files.
-
New functions
edc_data_warn()
andedc_data_stop()
, to alert if data has inconsistencies (#29, #39, #43).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 byedc_data_warn()
. -
New function
edc_warn_extraction_date()
, to alert if data is too old.
-
New function
select_distinct()
to select all columns that has only one level for a given grouping scope (#57). -
New function
edc_population_plot()
to visualize which patient is in which analysis population (#56). -
New function
edc_db_to_excel()
to export the whole database to an Excel file, easier to browse than RStudio's table viewer (#55). Useedc_browse_excel()
to browse the file without knowing its name. -
New function
edc_inform_code()
to show how much code your project contains (#49). -
New function
search_for_newer_data()
to search a path (e.g. Downloads) for a newer data archive (#46). -
New function
crf_status_plot()
to show the current database completion status (#48). -
New function
save_sessioninfo()
, to savesessionInfo()
into a text file (#42). -
New function
fct_yesno()
, to easily format Yes/No columns (#19, #23, #40). -
New function
lastnews_table()
to find the last date an information has been entered for each patient (#37). Useful for survival analyses. -
New function
harmonize_subjid()
, to have the same structure for subject IDs in all the datasets of the database (#30). -
New function
save_plotly()
, to save aplotly
to an HTML file (#15). -
New experimental functions
table_format()
,get_common_cols()
andget_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 aby
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. Usebuild_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 byget_subjid_cols()
andget_crfname_cols()
.check_subjid()
is replaced byedc_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.
- New function
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)
- New function
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.
- New function
manual_correction()
to safely hard-code a correction while waiting for the TrialMaster database to be updated. - New function
edc_options()
to manageEDCimport
global parameterization. - New argument
edc_swimmerplot(id_lim)
to subset the swimmer plot to some patients only. - New option
read_trialmaster(use_cache="write")
to read from the zip again but still update the cache. - You can now use the syntax
read_trialmaster(split_mixed=c("col1", "col2"))
to split only the datasets you need to (#10).
- Reading with
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.- Non-UTF8 characters in labels are now identified and corrected during reading (#5).
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.- Options
edc_id
,edc_crfname
, andedc_verbose
have been respectively renamededc_cols_id
,edc_cols_crfname
, andedc_read_verbose
for more clarity.
-
New function
edc_swimmerplot()
to show a swimmer plot of all dates in the database and easily find outliers. -
New features in
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).
-
Standalone functions
extend_lookup()
andsplit_mixed_datasets()
. -
New helper
unify()
, which turns a vector of duplicate values into a vector of length 1.
-
Reading errors are now handled by
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. -
Option
edc_lookup
is now set even when reading from cache. -
SAS formats containing a
=
now work as intended.
-
Import your data from TrialMaster using
tm = read_trialmaster("path/to/archive.zip")
. -
Search for a keyword in any column name or label using
find_keyword("date", data=tm$.lookup)
. You can also generate a lookup table for an arbitrary list of dataframe usingbuild_lookup(my_data)
. -
Load the datasets to the global environment using
load_list(tm)
to avoid typingtm$
everywhere. -
Browse available global options using
?EDCimport_options
.
- Draft version