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1 - Read your database
Read your database
If you are reading this vignette, chances are that you have requested an export from an EDC software that provided you with a directory filled with files of datasets.
Wouldn’t it be so tedious if you had to load all those files one by one? Lucky you, EDCimport knows a better way!
Depending on the type of files in your export directory, you should use:
read_all_sas(), to read .sas7bdat files
read_all_xpt(), to read .xpt files
read_all_csv(), to read .csv files
read_trialmaster() to read a TrialMaster zip archive.
Formats are imported through a metadata file, format_file, that can be either:
a procformat.sas file, containing the whole PROC FORMAT
a catalog file (.sas7bcat)
or a data file (.csv or .sas7bdat) containing 3 columns: the SAS format name (repeated), each level, and its associated label. Use options(edc_var_format_name="xxx", edc_var_level="xxx", edc_var_label="xxx") to specify the names of the columns.
You can then load your datasets into the global environment with load_database().
library(EDCimport)db =read_all_sas("path/to/my/files/folder", format_file="procformat.sas")print(db)load_database(db) #this also removes `db` to save some RAMmean(dataset1$column5)
Explore your database
Knowing a CRF by hand is not always an easy task, so EDCimport provide a few useful tools:
edc_lookup(), to remember what are the available datasets.
edc_find_column() and edc_find_value(), to search the database for a column/label or for an actual value.
db =edc_example()load_database(db)edc_lookup()#> ── Lookup table - EDCimport example (extraction of 2024-01-01) - EDCimport v0.6.#> dataset nrow ncol n_id rows_per_id crfname #> <chr> <dbl> <dbl> <int> <dbl> <chr> #> 1 long_pure 150 4 50 3 long data #> 2 data1 100 7 50 2 data1 #> 3 long_mixed 100 6 50 2 both short and long data#> 4 data2 50 6 50 1 data2 #> 5 data3 50 7 50 1 data3 #> 6 enrol 50 6 50 1 enrol #> 7 short 50 5 50 1 short data #> 8 ae 175 7 48 3.6 Adverse eventsedc_find_column("date")#> # A tibble: 11 × 5#> dataset crfname names labels prop_na#> <chr> <chr> <chr> <chr> <dbl>#> 1 data1 data1 date1 Date at visit 1 0#> 2 data1 data1 date2 Date at visit 2 0#> 3 data1 data1 date3 Date at visit 3 0#> 4 data2 data2 date4 Date at visit 4 0#> 5 data2 data2 date5 Date at visit 5 0#> 6 data2 data2 date6 Date at visit 6 0#> 7 data3 data3 date7 Date at visit 7 0#> 8 data3 data3 date8 Date at visit 8 0#> 9 data3 data3 date9 Date at visit 9 0#> 10 data3 data3 date10 Date at visit 10 0#> 11 enrol enrol enrol_date Date of enrolment 0edc_find_value("immune")#> # A tibble: 7 × 5#> subjid dataset column column_label value #> <chr> <chr> <chr> <chr> <chr> #> 1 9 ae aesoc AE SOC Immune system disorders#> 2 24 ae aesoc AE SOC Immune system disorders#> 3 26 ae aesoc AE SOC Immune system disorders#> 4 31 ae aesoc AE SOC Immune system disorders#> 5 46 ae aesoc AE SOC Immune system disorders#> 6 46 ae aesoc AE SOC Immune system disorders#> 7 49 ae aesoc AE SOC Immune system disorders
Shiny browser
The simplest way to explore your database is by running edc_viewer(), which launches a local Shiny application:
db =edc_example()load_database(db)edc_viewer()
These binaries (installable software) and packages are in development. They may not be fully stable and should be used with caution. We make no claims about them. Health stats visible at Monitor.
