Set sample data

Create the 'sample' table in the GeoTox database.

Usage

set_sample(GT, df, location = "FIPS", overwrite = FALSE)

Arguments

GT

GeoTox object.

df

Data frame containing sample data.

location

Column name(s) in df that contain location identifier(s) (default "FIPS").

overwrite

Logical indicating whether to overwrite existing 'sample' table and remove existing 'concentration' and 'risk' tables (default FALSE).

Value

The same GeoTox object, invisibly.

Details

The 'sample' table consists of individual characteristics and location information. At a minimum, it must contain columns with information on age, weight category, and location identifier(s).

When the df input contains information on age or weight category, the column names must be "age" and "weight", respectively. The location input (default "FIPS") can be a named vector to specify multiple identifier columns in df. For example, location = c(FIPS = "FIPS", state = "ST") would indicate that df contains both FIPS codes and state identifiers for locations. The location column(s) will be added to the 'location' table and replaced with a corresponding "location_id" column in the 'sample' table. The state = "ST" part in the example above would rename the "ST" column in df to "state" in the 'location' table.

There are several other functions that can be used to create or add to the 'sample' table: simulate_age() to generate age data, simulate_obesity() to generate weight category data, and simulate_population(), which is a wrapper function that can generate both age and weight category data along with other fields. set_sample() can be used in combination with these functions to first set some known sample data, e.g. age, and then simulate any missing fields, e.g. weight category.

If overwrite = TRUE, any existing 'concentration' and 'risk' tables will be dropped before creating the 'sample' table. This is because the existing concentration and risk data would no longer be valid for the new sample data. Further downstream tables are not dropped automatically, but can be updated during subsequent simulation and analysis steps.

See also

simulate_age(), simulate_obesity(), simulate_population()

Examples

# Example sample data
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)

# Set sample data
GT <- GeoTox() |> set_sample(sample_df)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "sample") |> dplyr::collect()
#> # A tibble: 3 × 4
#>      id location_id   age weight
#>   <int>       <int> <dbl> <chr> 
#> 1     1           1    25 Normal
#> 2     2           1    35 Obese 
#> 3     3           2    50 Normal

dplyr::tbl(con, "location") |> dplyr::collect()
#> # A tibble: 2 × 2
#>      id  FIPS
#>   <int> <dbl>
#> 1     1 10000
#> 2     2 20000

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)
#> [1] TRUE