lastnews_table() when subjid is not numericread_all_sas() causing metadata (e.g. date_extraction) being converted to dataframesNew 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() and edc_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 by edc_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). Use edc_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 save sessionInfo() 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 a plotly to an HTML file (#15).
New experimental functions 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.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() and split_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 using build_lookup(my_data).
Load the datasets to the global environment using load_list(tm) to avoid typing tm$ everywhere.
Browse available global options using ?EDCimport_options.