## 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 RAM
    mean(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 events
    edc_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       0
    edc_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()