# Storing & grouping other weight measurements

The following examples use the following table_string helper to succintly define tables:

    # Convert a string into a data.frame
    table_string <- function (text, ...) read.table(
        text = text,
        blank.lines.skip = TRUE,
        header = TRUE,
        stringsAsFactors = FALSE,
        ...)

Firstly, connect to a database and set up some areas/divisions:

    mdb <- mfdb(tempfile(fileext = '.duckdb'))
    mfdb_import_area(mdb, table_string('
    name  division size
    45G01     divA   10
    45G02     divA  200
    45G03     divB  400
    '))

## Importing data

Normally when using `mfdb_import_survey` you supply `count`, `length`, `weight` columns. But other lenghts can also be supplied:

    mfdb_import_survey(mdb, data_source = "x",
        table_string('
    year    month   areacell        species length  age     weight  liver_weight    gonad_weight    stomach_weight
    2000    1       45G01           COD     21      2       210 93      82      72
    2000    1       45G02           COD     34      3       220 28      93      99
    2000    1       45G03           COD     44      4       230 93      85      72
        '))

## Querying data

To get any additional weights back when querying, you need to supply measurements, for example:

    agg_data <- mfdb_sample_totalweight(mdb, c('age'), list(
        age = mfdb_unaggregated(),
        null = NULL), measurements = c('overall', 'liver', 'gonad', 'stomach'))
    agg_data

    ## $`0.0.0.0`
    ##   year step area age total_weight total_liver_weight total_gonad_weight
    ## 1  all  all  all   2          210                 93                 82
    ## 2  all  all  all   3          220                 28                 93
    ## 3  all  all  all   4          230                 93                 85
    ##   total_stomach_weight
    ## 1                   72
    ## 2                   99
    ## 3                   72

Single measurements are also possible:

    agg_data <- mfdb_sample_totalweight(mdb, c('age'), list(
        age = mfdb_unaggregated(),
        null = NULL), measurements = c('stomach'))
    agg_data

    ## $`0.0.0.0`
    ##   year step area age total_stomach_weight
    ## 1  all  all  all   2                   72
    ## 2  all  all  all   3                   99
    ## 3  all  all  all   4                   72

We can also use `mfdb_sample_meanweight` to aggregate:

    agg_data <- mfdb_sample_meanweight(mdb, c('age'), list(
        null = NULL), measurements = c('liver', 'stomach'))
    agg_data

    ## $`0.0.0.0`
    ##   year step area age number mean_liver_weight mean_stomach_weight
    ## 1  all  all  all all      3          71.33333                  81

As well as `mfdb_sample_meanweight_stddev`:

    agg_data <- mfdb_sample_meanweight_stddev(mdb, c('age'), list(
        null = NULL), measurements = c('overall', 'gonad'))
    agg_data

    ## $`0.0.0.0`
    ##   year step area age number mean mean_gonad_weight stddev stddev_gonad_weight
    ## 1  all  all  all all      3  220          86.66667     10            5.686241

    mfdb_disconnect(mdb)