Appends columns for conservation-risks: mould risk, preservation indices, equilibrium moisture, and moisture content for wood to a dataframe with temperature and relative humidity columns.
Value
Dataframe augmented with conservation variables:
- Mould_LIM
Mould risk threshold humidity from Zeng equation (numeric).
- Mould_risk
If there is a risk of mould from Zeng equation. Adds label: "Mould risk" or "No risk".
- Mould_rate
Mould growth rate index from Zeng equation, labelled output.
- Mould_index
Mould risk index from VTT model (continuous scale).
- PreservationIndex
Preservation Index for collection longevity.
- Lifetime
Lifetime Multiplier for object material degradation risk.
- EMC_wood
Wood equilibrium moisture content (%) under current climate conditions.
See also
calcMould_Zeng
for `Mould_LIM`, `Mould_risk`, `Mould_rate`
calcMould_VTT
for `Mould_index`
calcPI
for `PreservationIndex`
calcLM
for `Lifetime`
calcEMC_wood
for `EMC_wood`
Examples
# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)
mydata |> add_conservation_calcs() |> dplyr::glimpse()
#> Rows: 5
#> Columns: 12
#> $ Site <chr> "London", "London", "London", "London", "London"
#> $ Sensor <chr> "Room 1", "Room 1", "Room 1", "Room 1", "Room 1"
#> $ Date <dttm> 2024-01-01 00:00:00, 2024-01-01 00:15:00, 2024-01-01…
#> $ Temp <dbl> 21.8, 21.8, 21.8, 21.7, 21.7
#> $ RH <dbl> 36.8, 36.7, 36.6, 36.6, 36.5
#> $ Mould_LIM <dbl> 75.11542, 75.11542, 75.11542, 75.14014, 75.14014
#> $ Mould_risk <chr> "No risk", "No risk", "No risk", "No risk", "No risk"
#> $ Mould_rate <dbl> 0, 0, 0, 0, 0
#> $ Mould_index <dbl> 0, 0, 0, 0, 0
#> $ PreservationIndex <dbl> 45.25849, 45.38181, 45.50580, 46.07769, 46.20393
#> $ Lifetime <dbl> 1.107855, 1.108860, 1.109869, 1.109854, 1.110866
#> $ EMC_wood <dbl> 7.201471, 7.186361, 7.171247, 7.173308, 7.158186