Type:	Package
Title:	Tools for Conservation Science
Version:	0.3.0
Maintainer:	Bhavesh Shah <bhaveshshah01@gmail.com>
Description:	Provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" https://www.getty.edu/conservation/publications_resources/pdf_publications/tools_for_the_analysis_of_collection_environments.html.
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
VignetteBuilder:	knitr
URL:	https://bhavshah01.github.io/ConSciR/, https://github.com/BhavShah01/ConSciR
BugReports:	https://github.com/BhavShah01/ConSciR/issues
Config/testthat/edition:	3
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), bslib, IAPWS95, Ternary
Imports:	stats, tools, tidyr, readr, readxl, stringr, rlang, shiny, ggplot2, dplyr, lubridate, padr, openair
Depends:	R (≥ 4.1.0)
NeedsCompilation:	no
Packaged:	2025-09-22 14:39:22 UTC; bhave
Author:	Bhavesh Shah [aut, cre, cph], Annelies Cosaert [aut], Vincent Beltran [aut], Emily R Long [ctb], Marcin Zygmunt [ctb], Hebe Halstead [ctb], Avery Bazemore [ctb]
Repository:	CRAN
Date/Publication:	2025-09-22 15:10:02 UTC

ConSciR: Tools for Conservation Science

Description

Provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" https://www.getty.edu/conservation/publications_resources/pdf_publications/tools_for_the_analysis_of_collection_environments.html.

Author(s)

Maintainer: Bhavesh Shah bhaveshshah01@gmail.com (ORCID) [copyright holder]

Authors:

• Annelies Cosaert (ORCID)

• Vincent Beltran

Other contributors:

• Emily R Long (ORCID) [contributor]

• Marcin Zygmunt (ORCID) [contributor]

• Hebe Halstead [contributor]

• Avery Bazemore (ORCID) [contributor]

See Also

Useful links:

• https://bhavshah01.github.io/ConSciR/

• https://github.com/BhavShah01/ConSciR

• Report bugs at https://github.com/BhavShah01/ConSciR/issues

Climate dataset to demonstrate functions

Description

A climate dataset for use to demonstrate how the functions work.

Usage

TRHdata

Format

A data frame with 35,136 rows and 5 columns:

Site, Sensor

Sensor location and name

Date

Date is ISOdate time format

Temp, RH

Temperature (C) and relative humidity (%)

...

Source

Climate

Add Conservation Risks

Description

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.

Usage

add_conservation_calcs(mydata, Temp = "Temp", RH = "RH", ...)

Arguments

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()

Adjust Humidity and add RH zones

Description

This function processes a dataframe with temperature and relative humidity data and classifies the data into climate control zones. It generates adjusted temperature and humidity values based on thresholds.

Usage

add_humidity_adjustments(
  mydata,
  Temp = "Temp",
  RH = "RH",
  LowT = 16,
  HighT = 25,
  LowRH = 40,
  HighRH = 60,
  P_atm = 1013.25,
  ...
)

Arguments

Value

The input dataframe augmented with multiple humidity and temperature adjustment columns.

AH

Absolute Humidity (g/m³): The mass of water vapor per unit volume of air.

DP

Dew Point (°C): The temperature at which air becomes saturated and water vapor condenses.

zone

Categorical variable defining climate control actions based on temperature and RH: 'Heating only', 'Dehum or heating', 'Cooling and hum', etc.

TRH_zone

Temperature-relative humidity category: 'Hot', 'Cold', 'Dry', 'Hot and humid', etc.

T_zone

Temperature zone classification: 'Cold', 'Within', or 'Hot'.

RH_zone

Relative humidity zone classification: 'Dry', 'Within', or 'Humid'.

dTemp

Temperature difference from specified thresholds (°C), indicating required heating or cooling.

dRH

Relative humidity difference from specified thresholds (%), indicating humidification or dehumidification.

newTemp_TRHadj

Adjusted temperature (°C) after applying temperature and relative humidity correction based on zone.

newAH_TRHadj

Adjusted absolute humidity (g/m³).

newRH_TRHadj

Adjusted relative humidity (%) reflecting new temperature and absolute humidity.

dTemp_TRHadj, dAH_TRHadj, dRH_TRHadj

Differences between adjusted and original temperature, absolute humidity, and relative humidity respectively.

newTemp_AHadj, newAH_AHadj, newRH_AHadj

Adjustments based on absolute humidity only (i.e. dehumidification or humidification).

newTemp_Tadj, newRH_Tadj, newAH_Tadj

Adjustments based on temperature only (i.e. heating or cooling).

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> add_humidity_adjustments() |> dplyr::glimpse()

Add Humidity calculations

Description

This function adds several humidity variables to a dataframe with temperature and relative humidity columns. It uses the humidity functions (e.g., calcPws, calcPw).

Usage

add_humidity_calcs(mydata, Temp = "Temp", RH = "RH", P_atm = 1013.25, ...)

Arguments

Value

The input dataframe augmented with columns for vapor pressure, dew point, absolute humidity, air density, mixing ratio, specific humidity, and enthalpy.

Pws

Saturated vapour pressure at given temperature (hPa).

Pw

Partial pressure of water vapour present (hPa).

DP

Dew Point, condensation temperature based on RH (°C).

AH

Mass of water vapour per air volume (g/m³).

AD

Moist air density (kg/m³).

MR

Ratio of water vapour to dry air mass (g/kg).

SH

Ratio of water vapour to total air mass (g/kg).

Enthalpy

Total enthalpy, h, of air-vapour mixture (kJ/kg).

See Also

calcPws for 'Pws'

calcPw for 'Pw'

calcDP for 'DP'

calcAH for 'AH'

calcAD for 'AD'

calcMR for 'MR'

calcSH for 'SH'

calcEnthalpy for 'Enthalpy'

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> add_humidity_calcs() |> dplyr::glimpse()

Add Time Variables

Description

This function adds multiple time-related variables to a dataframe with a Date column. It creates standard factors such as season, month-year, day-hour, and determines summer/winter. It also allows flexible specification of summer and winter start/end dates, and a custom time period.

Usage

add_time_vars(
  mydata,
  Date = "Date",
  openair_vars = c("seasonyear", "season", "monthyear", "daylight"),
  summer_start = "04-15",
  summer_end = "10-15",
  period_start = NULL,
  period_end = NULL,
  period_label = "Period",
  latitude = 51,
  longitude = -0.5,
  ...
)

Arguments

Details

The variables seasonyear, season, monthyear, and daylight are created using the openair::cutData() function internally and rely on geographic coordinates (latitude, longitude) to calculate daylight status. Be sure openair is installed and loaded for these variables.

Value

A data frame with additional time-related columns appended:

seasonyear

Combined year and season factor created by openair::cutData(); useful for seasonal analyses.

season

Season factor (e.g., Spring, Summer) from openair::cutData().

monthyear

Factor combining month and year, created by openair::cutData() to assist month-based grouping.

daylight

Boolean or factor indicating daylight presence/absence, derived using openair::cutData() with latitude and longitude inputs.

day

Date part of the timestamp, rounded down to day boundary, useful for daily aggregation.

hour

Hour of the day extracted from the datetime.

dayhour

Datetime floored to the hour; useful for hourly time series analysis.

weekday

Weekday name/factor, abbreviated, extracted from the date.

month

Month number and its labelled factor version; useful for calendar-based grouping.

year

Year extracted from the datetime for annual analyses.

DayYear

Date with the current year but month and day taken from the original date; used to assign seasons and periods relative to current year.

Summer

A factor ("Summer" or "Winter") determined by comparison of DayYear with user-defined summer_start and summer_end dates, for custom seasonality modelling.

Period

Character flag identifying whether the date falls within a user-defined custom period (e.g., an exhibition), labelled by period_label. Returns NA if no period defined.

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |>
  add_time_vars(period_start = "05-01", period_end = "06-30", period_label = "Exhibition") |>
  dplyr::glimpse()

Calculate Air Density

Description

Function to calculate air density based on temperature (°C), relative humidity in (%), and atmospheric pressure (hPa).

Usage

calcAD(Temp, RH, P_atm = 1013.25, R_dry = 287.058, R_vap = 461.495, ...)

Arguments

Value

Air density in kg/m³

See Also

calcMR for calculating mixing ratio

calcAD for calculating air density

calcPw for calculating water vapour pressure

calcPws for calculating water vapour saturation pressure

Examples

# Air density at 20°C (Temp) and 50% relative humidity (RH)
calcAD(20, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(AirDensity = calcAD(Temp, RH))

Calculate Absolute Humidity

Description

Function to calculate the absolute humidity (g/m³) from temperature (°C) and relative humidity (%). Supports multiple methods: the Buck equation (default), Buck formula with enhancement factor, and others.

Usage

calcAH(
  Temp,
  RH,
  P_atm = 1013.25,
  method = c("Buck_EF", "Buck", "IAPWS", "Magnus", "VAISALA")
)

Arguments

Value

AH Absolute Humidity (g/m³)

Examples

# Absolute humidity at 20°C (Temp) and 50% relative humidity (RH)
calcAH(20, 50)
calcAH(20, 50, method = "Buck_EF") # Buck formula with enhancement factor (default)
calcAH(20, 50, method = "Buck") # Buck method via calcPws
calcAH(20, 50, method = "IAPWS") # IAPWS

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(Abs = calcAH(Temp, RH))

Calculate Cooling Capacity

Description

This function calculates the required cooling capacity based on power consumption, power factor, safety factor, and efficiency.

Cooling capacity is the amount of energy transferred during a cooling process.

Usage

calcCoolingCapacity(
  Power,
  power_factor = 0.85,
  safety_factor = 1.2,
  efficiency = 0.7
)

Arguments

Value

Required cooling capacity in kilowatts (kW).

Examples

calcCoolingCapacity(1000)

calcCoolingCapacity(1500, power_factor = 0.9, safety_factor = 1.3, efficiency = 0.8)

Calculate Cooling Power

Description

This function calculates the cooling power based on initial and final air conditions and volume flow rate.

Cooling power is the rate of energy transferred during a cooling process.

Usage

calcCoolingPower(Temp1, Temp2, RH1, RH2, volumeFlowRate)

Arguments

Value

Cooling power in kilowatts (kW)

References

ASHRAE Handbook Fundamentals

See Also

calcEnthalpy, calcAD

Examples

calcCoolingPower(30, 22, 70, 55, 0.8)

calcCoolingPower(Temp1 = 25, Temp2 = 20, RH1 = 70, RH2 = 50, volumeFlowRate = 0.5)

Calculate Dew Point

Description

Function to calculate dew point (°C) from temperature (°C) and relative humidity (%).

The dew point is the temperature at which air becomes saturated with moisture and water vapour begins to condense.

Usage

calcDP(Temp, RH, method = c("Magnus", "Buck"))

Arguments

Details

This function supports two methods for dew point calculation:

• "Magnus" (default): Uses the August-Roche-Magnus approximation, valid for 0°C < Temp < 60°C and 1% < RH < 100%.

• "Buck": Uses the Arden Buck equation with Bögel modification, valid for -30°C < Temp < 60°C and 1% < RH < 100%.

Both methods compute saturation vapour pressure and convert relative humidity to dew point temperature. The Magnus method is chosen as the default because it is more stable when used with the calcTemp and calcRH_DP functions.

Value

Td (DP), Dew Point (°Celsius)

Note

More details of the equations are also available in the source R code.

References

Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus' form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601–609

Buck, A. L., 1981: New Equations for Computing Vapor Pressure and Enhancement Factor. J. Appl. Meteor. Climatol., 20, 1527–1532, https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2.

Buck (1996), Buck (1996), Buck Research CR-1A User's Manual, Appendix 1.

https://bmcnoldy.earth.miami.edu/Humidity.html

See Also

calcTemp for calculating temperature

calcRH_DP for calculating relative humidity from dew point

calcDP for calculating dew point

calcRH_AH for calculating relative humidity from absolute humidity

Examples

# Dew point at 20°C and 50% relative humidity (RH)
# Default Magnus method
calcDP(20, 50)

# Using Buck method
calcDP(20, 50, method = "Buck")

# Validation check
calcDP(20, calcRH_DP(20, calcDP(20, 50)))
calcDP(20, calcRH_DP(20, calcDP(20, 50, method = "Buck"), method = "Buck"), method = "Buck")


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |>
  dplyr::mutate(
    DewPoint = calcDP(Temp, RH),
    DewPoint_Buck = calcDP(Temp, RH, method = "Buck"))

Calculate Equilibrium Moisture Content for wood (EMC)

Description

This function calculates the Equilibrium Moisture Content (EMC) of wood based on relative humidity and temperature.

Usage

calcEMC_wood(Temp, RH)

Arguments

Details

Equilibrium Moisture Content (EMC) is the moisture content at which a material, such as wood or other hygroscopic substanceshas reached an equilibrium with its environment and is no longer gaining or losing moisture under specific temperature and relative humidity.

A safe EMC range for wood is typically between 6% and 20%. This range helps to prevent issues such as warping, cracking, and mold growth, which can occur if the moisture content falls below or exceeds these levels.

Value

EMC, Equilibrium Moisture Content (0-100%)

References

Simpson, W. T. (1998). Equilibrium moisture content of wood in outdoor locations in the United States and worldwide. Res. Note FPL-RN-0268. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory.

Hailwood, A. J., and Horrobin, S. (1946). Absorption of water by polymers: Analysis in terms of a simple model. Transactions of the Faraday Society 42, B084-B092. DOI:10.1039/TF946420B084

Examples

# Equilibrium moisture content for wood at 20°C and 50% relative humidity (RH)
calcEMC_wood(20, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(EMC = calcEMC_wood(Temp, RH))

Calculate Enthalpy

Description

Function to calculate enthalpy from temperature (°C) and relative humidity (%).

Enthalpy is the total heat content of air, combining sensible (related to temperature) and latent heat (related to moisture content), used in HVAC calculations. Enthalpy is the amount of energy required to bring a gas to its current state from a dry gas at 0°C.

Usage

calcEnthalpy(Temp, RH, ...)

Arguments

Value

h Enthalpy (kJ/kg)

Examples

# Enthalpy at at 20°C (Temp) and 50% relative humidity (RH)
calcEnthalpy(20, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(Enthalpy = calcEnthalpy(Temp, RH))

Calculate Frost Point

Description

Function to calculate frost point (°C) from temperature (°C) and relative humidity (%).

Usage

calcFP(Temp, RH)

Arguments

Details

Formula coefficients from Arden Buck equation (1981, 1996) saturation vapor pressure over ice.

• a = 6.1115

• b = 23.036

• c = 279.82

• d = 333.7

Value

Tf, Frost Point (°Celsius)

Note

This function is unstable and is under development.

Examples

# calcFP is unstable and is under development
# Frost point at 20°C (Temp) and 50% relative humidity (RH)
calcFP(20, 50)
calcFP(0, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(FrostPoint = calcFP(Temp, RH))

Convert temperature (F) to temperature (C)

Description

Convert temperature in Fahrenheit to temperature in Celsius

Usage

calcFtoC(TempF)

Arguments

Value

TempC Temperature (Celsius)

Examples

# Fahrenheit to Celsius
calcFtoC(32)
calcFtoC(68)

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(TempC = calcFtoC((Temp * 9/5) + 32))

Calculate Humidity Ratio

Description

Function to calculate humidity ratio (g/kg) from temperature (°C) and relative humidity (%).

Humidity ratio is the mass of water vapor present in a given volume of air relative to the mass of dry air. Also known as "moisture content".

Function uses calcMR

Usage

calcHR(Temp, RH, P_atm = 1013.25, B = 621.9907, ...)

Arguments

Value

HR Humidity ratio (g/kg)

Note

This function requires the calcMR function to be available in the environment.

See Also

calcMR for calculating mixing ratio

calcAD for calculating air density

calcPw for calculating water vapour pressure

calcPws for calculating water vapour saturation pressure

Examples

# Humidity ratio at 20°C (Temp) and 50% relative humidity (RH)
calcHR(20, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(HumidityRatio = calcHR(Temp, RH))

Calculate Life-time Multiplier for chemical degradation

Description

Function to calculate lifetime multiplier from temperature and relative humidity.

The 'calcLM' function calculates the lifetime multiplier for chemical degradation of objects based on temperature and relative humidity conditions. This metric provides an estimate of an object’s expected lifetime relative to standard conditions (20°C and 50% RH); values >1 indicate conditions that prolong lifetime; values <1 indicate higher risk of chemical degradation.

Usage

calcLM(Temp, RH, EA = 100)

Arguments

Details

Based on the experiments of the rate of decay of paper, films and dyes. Activation energy, Ea = 100 J/mol (degradation of cellulose - paper), 70 J/mol (yellowing of varnish - furniture, painting, sculpture).

Gas constant, R = 8.314 J/K.mol

LM=\left(\frac{50\%RH}{RH}\right)^{1.3}.e\left(\frac{E_a}{R}.\left(\frac{1}{T_K}-\frac{1}{293}\right)\right)

The lifetime multiplier gives an indication of the speed of natural decay of an object. It expresses an expected lifetime of an object compared to the expected lifetime of the same object at 20°C and 50% RH. This means that if the result = 1, the expected lifetime for your object is 'good'. The closer you go to 0, the less suited your environment is. The result is both expressed numerically and over time, which also gives an idea about the period over the year when the object suffers most. The data is based on experiments on paper, synthetic films and dyes.

Value

Lifetime multiplier

References

Michalski, S., ‘Double the life for each five-degree drop, more than double the life for each halving of relative humidity’, in Preprints of the 13th IcOM-cc Triennial Meeting in rio de Janeiro (22–27 September 2002), ed. r. Vontobel, James & James, London (2002) Vol. I 66–72.

Martens Marco, 2012: Climate Risk Assessment in Museums (Thesis, Tue).

Examples

# Lifetime multiplier at 20°C (Temp) and 50% relative humidity (RH)
calcLM(20, 50)

calcLM(20, 50, EA = 70)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(LifeTime = calcLM(Temp, RH))

mydata |>
  dplyr::mutate(LM = calcLM(Temp, RH)) |>
   dplyr::summarise(LM_avg = mean(LM, na.rm = TRUE))

Calculate Mixing Ratio

Description

Function to calculate mixing ratio (g/kg) from temperature (°C) and relative humidity (%).

Mixing Ratio is the mass of water vapor present in a given volume of air relative to the mass of dry air.

Usage

calcMR(Temp, RH, P_atm = 1013.25, B = 621.9907, ...)

Arguments

Details

X Mixing ratio (mass of water vapour / mass of dry gas)

Pw = Pws(40°C) = 73.75 hPa

X = 621.9907 x 73.75 / (998 - 73.75) = 49.63 g/kg

Value

X Mixing ratio, mass of water vapour / mass of dry gas (g/kg)

See Also

calcMR for calculating mixing ratio

calcAD for calculating air density

calcPw for calculating water vapour pressure

calcPws for calculating water vapour saturation pressure

Examples

# Mixing ratio at 20°C (Temp) and 50% relative humidity (RH)
calcMR(20, 50)

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(MixingRatio = calcMR(Temp, RH))

Calculate Mould Growth Index (VTT model)

Description

This function calculates the mould growth index on wooden materials based on temperature, relative humidity, and other factors. It implements the mathematical model developed by Hukka and Viitanen, which predicts mould growth under varying environmental conditions.

Usage

calcMould_VTT(
  Temp,
  RH,
  M_prev = 0,
  sensitivity = "very",
  wood = 0,
  surface = 0
)

Arguments

Details

Senstivity is related to the material surface, mould will grow on. Options in function avaiable are:

• 'very' sensitive materials include pine and sapwood.

• 'sensitive' materials include glued wooden boards, PUR with paper surface, spruce

• 'medium' resistant materials include concrete, glass wool, polyester wool

• 'resistant' materials include PUR polished surface

Value

M Mould growth index

• 0 = No mould growth

• 1 = Small amounts of mould growth on surface visible under microscope

• 2 = Several local mould growth colonies on surface visible under microscope

• 3 = Visual findings of mould on surface <10% coverage or 50% coverage under microsocpe

• 4 = Visual findings of mould on surface 10-50% coverage or >50% coverage under microscope

• 5 = Plenty of growth on surface >50% visual coverage

• 6 = Heavy and tight growth, coverage almost 100%

References

Hukka, A., Viitanen, H. A mathematical model of mould growth on wooden material. Wood Science and Technology 33, 475–485 (1999). https://doi.org/10.1007/s002260050131

Viitanen, Hannu, and Tuomo Ojanen. "Improved model to predict mold growth in building materials." Thermal Performance of the Exterior Envelopes of Whole Buildings X–Proceedings CD (2007): 2-7.

Examples

# Mould growth index at 25°C (Temp) and 85% relative humidity (RH)
calcMould_VTT(Temp = 25, RH = 85)

calcMould_VTT(Temp = 18, RH = 70, M_prev = 2, sensitivity = "medium", wood = 1, surface = 1)

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |>
   dplyr::mutate(
      MouldIndex = calcMould_VTT(Temp, RH),
      MouldIndex_sensitve = calcMould_VTT(Temp, RH, sensitivity = "sensitive")
   )

Calculate Mould Growth Rate Limits (Zeng et al.)

Description

This function calculates the Lowest Isoline for Mould (LIM) based on temperature and relative humidity, using the model developed by Zeng et al. (2023).

The LIM is the lowest envelope of the temperature and humidity isoline at a certain mould growth rate (u). LIM0 is the critical value for mould growth, if the humidity is kept below the critcal value, at a given temperature, then there is no risk of mould growth.

Usage

calcMould_Zeng(Temp, RH, LIM = 0, label = FALSE)

Arguments

Details

The function calculates LIM values for mould genera including Cladosporium, Penicillium, and Aspergillus. LIM values represent different mould growth rates:

• LIM0: Low limit of mould growth

• LIM0.1: 0.1 mm/day growth rate

• LIM0.5: 0.5 mm/day growth rate

• LIM1: 1 mm/day growth rate

• LIM2: 2 mm/day growth rate

• LIM3: 3 mm/day growth rate

• LIM4: 4 mm/day growth rate

• Above LIM4: Greater than 4 mm/day growth rate (9 mm/day theorectical maximum)

Value

If label is FALSE, returns the calculated LIM value as Relative Humidity (0-100%). If label is TRUE, returns a character string describing the mould growth rate category.

References

Zeng L, Chen Y, Ma M, et al. Prediction of mould growth rate within building envelopes: development and validation of an improved model. Building Services Engineering Research and Technology. 2023;44(1):63-79. doi:10.1177/01436244221137846

Sautour M, Dantigny P, Divies C, Bensoussan M. A temperature-type model for describing the relationship between fungal growth and water activity. Int J Food Microbiol. 2001 Jul 20;67(1-2):63-9. doi: 10.1016/s0168-1605(01)00471-8. PMID: 11482570.

Examples

# Lowest Isoline for Mould at 20°C (Temp) and 75% relative humidity (RH)
calcMould_Zeng(20, 75)
calcMould_Zeng(20, 75, LIM = 0)
calcMould_Zeng(20, 75, label = TRUE)

calcMould_Zeng(20, 85)
calcMould_Zeng(20, 85, LIM = 2)
calcMould_Zeng(20, 85, label = TRUE)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |>
   dplyr::mutate(
      RH_LIM0 = calcMould_Zeng(Temp, RH),
      RH_LIM1 = calcMould_Zeng(Temp, RH, LIM = 1),
      LIM = calcMould_Zeng(Temp, RH, label = TRUE)
   )

Calculate Preservation Index

Description

Calculates the Preservation Index (PI) to estimate the natural decay speed of objects.

The Preservation Index, developed by the Image Permanence Institute, is a chemical kinetics metric that determines the rate of deterioration of materials based on temperature and relative humidity. The 'calcPI' function returns the estimated years to deterioration, with higher values indicating conditions that are more hygro-thermodynamically favorable for an object.

Usage

calcPI(Temp, RH, EA = 90300)

Arguments

Details

The formula is based on Arrhenius equation (for molecular energy) and an equivalent for E (best fit to the cellulose triacetate deterioration data). The other parameters are integrated to mimic the results from the experiments. The result is an average chemical lifetime at one point in time of chemically unstable object (based on experiments on acetate film). This means it is the expected lifetime for a specific T, RH and theoretical object if this remains stable (no fluctuations). The chosen activation energy (Ea) has a larger impact at low temperature.

Developed by the Image Permance Institute, the model is based on the chemical degradation of cellulose acetate (Reilly et al., 1995):

- Rate, k = [RH%] × 5.9 × 10^12 × exp(-90300 / (8.314 × TempK))

- Preservation Index, PI = 1/k

This metric is an early version of a lifetime multiplier based on chemical deterioration of acetate film. This last object is naturally relatively unstable and there lies the biggest difference with other chemical metrics together with the fact that it is not relative to the lifetime of the object. All lifetime multipliers give similar results between 20% and 60% RH. The results at very low and high RH can be unreliable.

Value

PI Preservation Index, the expected lifetime (1/rate,k)

References

Reilly, James M. New Tools for Preservation: Assessing Long-Term Environmental Effects on Library and Archives Collections. Commission on Preservation and Access, 1400 16th Street, NW, Suite 740, Washington, DC 20036-2217, 1995.

Padfield, T. 2004. The Preservation Index and The Time Weighted Preservation Index. https://www.conservationphysics.org/twpi/twpi_01.html

Activation Energy: ASHRAE, 2011.

Image Permanence Institute, eClimateNotebook

Examples

# Preservation Index at 20°C (Temp) and 50% relative humidity (RH)
calcPI(20, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(PI = calcPI(Temp, RH))

Calculate Water Vapour Pressure

Description

Function to calculate water vapour pressure (hPa) from temperature (°C) and relative humidity (%).

Water vapour pressure is the pressure exerted by water vapour in a gas.

Usage

calcPw(Temp, RH, ...)

Arguments

Details

Different formulations for calculating water vapour pressure are available:

• Arden Buck equation ("Buck")

• International Association for the Properties of Water and Steam ("IAPWS")

• August-Roche-Magnus approximation ("Magnus")

• VAISALA humidity conversion formula ("VAISALA")

The water vapor pressure (P_w) is calculated using the following equation:

P_w=\frac{P_{ws}\left(Temp\right)\times RH}{100}

Where:

• P_ws is the saturation vapor pressure using calcPws.

• RH is the relative humidity in percent.

• Temp is the temperature in degrees Celsius.

Value

Pw, Water Vapour Pressure (hPa)

Note

See Wikipedia for a discussion of the accuarcy of each approach: https://en.wikipedia.org/wiki/Vapour_pressure_of_water

References

Wagner, W., & Pru\ß, A. (2002). The IAPWS formulation 1995 for the thermodynamic properties of ordinary water substance for general and scientific use. Journal of Physical and Chemical Reference Data, 31(2), 387-535.

Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus' form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601-609.

Buck, A. L., 1981: New Equations for Computing Vapor Pressure and Enhancement Factor. J. Appl. Meteor. Climatol., 20, 1527–1532, https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2.

Buck (1996), Buck (1996), Buck Research CR-1A User's Manual, Appendix 1.

VAISALA. Humidity Conversions: Formulas and methods for calculating humidity parameters. Ref. B210973EN-O

See Also

calcMR for calculating mixing ratio

calcAD for calculating air density

calcPw for calculating water vapour pressure

calcPws for calculating water vapour saturation pressure

Examples

# Water vapour pressure at 20°C (Temp) and 50% relative humidity (RH)
calcPw(20, 50)

# Calculate relative humidity at 50%RH
calcPw(20, 50) / calcPws(20) * 100


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(Pw = calcPw(Temp, RH))

mydata |> dplyr::mutate(Buck = calcPw(Temp, RH, method = "Buck"),
                              IAPWS = calcPw(Temp, RH, method = "IAPWS"),
                              Magnus = calcPw(Temp, RH, method = "Magnus"),
                              VAISALA = calcPw(Temp, RH, method = "VAISALA"))

Calculate Water Vapour Saturation Pressure

Description

Function to calculate water vapour saturation pressure (hPa) from temperature (°C) using the International Association for the Properties of Water and Steam (IAPWS as default), Arden Buck equation (Buck), August-Roche-Magnus approximation (Magnus) or VAISALA conversion formula.

Water vapour saturation pressure is the maximum partial pressure of water vapour that can be present in gas at a given temperature.

Usage

calcPws(
  Temp,
  P_atm = 1013.25,
  method = c("Buck", "IAPWS", "Magnus", "VAISALA")
)

Arguments

Details

Different formulations for calculating water vapour pressure are available:

• Arden Buck equation ("Buck")

• International Association for the Properties of Water and Steam ("IAPWS")

• August-Roche-Magnus approximation ("Magnus")

• VAISALA humidity conversion formula ("VAISALA")

Value

Pws, Saturation vapor pressure (hPa)

Note

See Wikipedia for a discussion of the accuarcy of each approach: https://en.wikipedia.org/wiki/Vapour_pressure_of_water

If lower accuracy or a limited temperature range can be tolerated a simpler formula can be used for the water vapour saturation pressure over water (and over ice):

Pws = 6.116441 x 10^( (7.591386 x Temp) / (Temp + 240.7263) )

References

Wagner, W., & Pru\ß, A. (2002). The IAPWS formulation 1995 for the thermodynamic properties of ordinary water substance for general and scientific use. Journal of Physical and Chemical Reference Data, 31(2), 387-535.

Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus' form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601-609.

Buck, A. L., 1981: New Equations for Computing Vapor Pressure and Enhancement Factor. J. Appl. Meteor. Climatol., 20, 1527–1532, https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2.

Buck (1996), Buck (1996), Buck Research CR-1A User's Manual, Appendix 1.

VAISALA. Humidity Conversions: Formulas and methods for calculating humidity parameters. Ref. B210973EN-O

See Also

calcMR for calculating mixing ratio

calcAD for calculating air density

calcPw for calculating water vapour pressure

calcPws for calculating water vapour saturation pressure

Examples

# Saturation vapour pressure at 20°C
calcPws(20)
calcPws(20, method = "Buck")
calcPws(20, method = "IAPWS")
calcPws(20, method = "Magnus")
calcPws(20, method = "VAISALA")

# Check of calculations of relative humidity at 50%RH
calcPw(20, 50, method = "Buck") / calcPws(20, method = "Buck") * 100
calcPw(20, 50, method = "IAPWS") / calcPws(20, method = "IAPWS") * 100
calcPw(20, 50, method = "Magnus") / calcPws(20, method = "Magnus") * 100
calcPw(20, 50, method = "VAISALA") / calcPws(20, method = "VAISALA") * 100


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(Pws = calcPws(Temp))

mydata |> dplyr::mutate(Buck = calcPws(Temp, method = "Buck"),
                              IAPWS = calcPws(Temp, method = "IAPWS"),
                              Magnus = calcPws(Temp, method = "Magnus"),
                              VAISALA = calcPws(Temp, method = "VAISALA"))

Calculate Relative Humidity from temperature and absolute humidity

Description

Function to calculate relative humidity (%) from temperature (°C) and absolute humidity (g/m^3)

Usage

calcRH_AH(Temp, AH, P_atm = 1013.25)

Arguments

Value

Relative Humidity (0-100%)

References

Buck, A. L. (1981). New equations for computing vapor pressure and enhancement factor. Journal of Applied Meteorology, 20(12), 1527-1532.

See Also

calcAH for calculating absolute humidity

calcTemp for calculating temperature

calcRH_DP for calculating relative humidity from dew point

calcDP for calculating dew point

Examples

# Relative humidity (RH) at temperature of 20°C (Temp) and absolute humidity of 8.645471 g/m³ (AH)
calcRH_AH(20, 8.630534)

calcRH_AH(20, calcAH(20, 50))


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(Abs = calcAH(Temp, RH), RH2 = calcRH_AH(Temp, Abs))

Calculate Relative Humidity from temperature and dew point

Description

Function to calculate relative humidity (%) from temperature (°C) and dew point (°C)

Usage

calcRH_DP(Temp, DewP, method = c("Magnus", "Buck"))

Arguments

Details

This function supports two methods for relative humidity calculation:

• "Magnus" (default): Uses the August-Roche-Magnus approximation, valid for 0°C < Temp < 60°C and 1% < RH < 100%.

• "Buck": Uses the Arden Buck equation with Bögel modification, valid for -30°C < Temp < 60°C and 1% < RH < 100%.

The methods calculate temperature based on vapor pressure and saturation vapour pressure relationships. The Magnus method is chosen as the default because it is more stable when used with the calcDP and calcTemp functions.

Value

Relative Humidity (0-100%)

References

Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus' form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601–609

Buck, A. L., 1981: New Equations for Computing Vapor Pressure and Enhancement Factor. J. Appl. Meteor. Climatol., 20, 1527–1532, https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2.

Buck (1996), Buck (1996), Buck Research CR-1A User's Manual, Appendix 1.

https://bmcnoldy.earth.miami.edu/Humidity.html

See Also

calcTemp for calculating temperature

calcDP for calculating dew point

calcRH_AH for calculating relative humidity from absolute humidity

calcRH_DP for calculating relative humidity from dew point

Examples

# Relative humidity (RH) at tempertaure of 20°C (Temp) and dew point of 15°C (DewP)
calcRH_DP(20, 15)
calcRH_DP(20, 15, method = "Buck")

calcRH_DP(20, calcDP(20, 50))

calcRH_DP(20, calcDP(20, 50, method = "Buck"), method = "Buck")

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |>
  dplyr::mutate(
    DewPoint = calcDP(Temp, RH),
    RH_default = calcRH_DP(Temp, DewPoint),
    RH_Buck = calcRH_DP(Temp, DewPoint, method = "Buck"))

Calculate Specific Humidity

Description

Function to calculate the specific humidity (g/kg) from temperature (°C) and relative humidity (%).

Specific humidity is the ratio of the mass of water vapor to the mass of air.

Function uses calcMR

Usage

calcSH(Temp, RH, P_atm = 1013.25, B = 621.9907, ...)

Arguments

Value

SH Specific Humidity (g/kg)

Note

This function requires the calcMR function to be available in the environment.

References

Wallace, J.M. and Hobbs, P.V. (2006). Atmospheric Science: An Introductory Survey. Academic Press, 2nd edition.

See Also

calcAD for calculating air density

calcAH for calculating absolute humidity

calcPw for calculating water vapour pressure

calcPws for calculating water vapour saturation pressure

Examples

# Calculate specific humidity at 20°C (Temp) and 50% relative humidity (RH)
calcSH(20, 50)


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |> dplyr::mutate(SpecificHumidity = calcSH(Temp, RH))

Calculate Sensible Heat Ratio (SHR)

Description

This function calculates the Sensible Heat Ratio (SHR) using the sensible and total heating values.

Sensible heat ratio is the ratio of sensible heat to total heat.

Usage

calcSensibleHeatRatio(Temp1, Temp2, RH1, RH2, volumeFlowRate)

Arguments

Value

SHR Sensible Heat Ratio (0-100%)

See Also

calcSensibleHeating, calcTotalHeating

Examples

calcSensibleHeatRatio(20, 25, 50, 30, 0.5)

Calculate Sensible Heating

Description

This function calculates sensible heating power.

Sensible heat is the energy that causes an object's temperature to change without altering its phase, also known as "dry" heat which you can feel.

Usage

calcSensibleHeating(Temp1, Temp2, RH = 50, volumeFlowRate)

Arguments

Value

Sensible heat in kilowatts (kW)

See Also

calcAD

Examples

calcSensibleHeating(20, 25, 50, 0.5)

calcSensibleHeating(20, 25, 60, 0.5)

Calculate Temperature from relative humidity and dew point

Description

This function calculates the temperature (°C) from relative humidity (%) and dew point temperature (°C).

Usage

calcTemp(RH, DewP, method = c("Magnus", "Buck"))

Arguments

Details

This function supports two methods for temperature calculation:

• "Magnus" (default): Uses the August-Roche-Magnus approximation, valid for 0°C < Temp < 60°C and 1% < RH < 100%.

• "Buck": Uses the Arden Buck equation with Bögel modification, valid for -30°C < Temp < 60°C and 1% < RH < 100%.

The methods calculate temperature based on vapor pressure and saturation vapour pressure relationships. The Magnus method is chosen as the default because it is more stable when used with the calcDP and calcRH_DP functions.

Value

Temp, Temperature (°Celsius)

References

Alduchov, O. A., and R. E. Eskridge, 1996: Improved Magnus' form approximation of saturation vapor pressure. J. Appl. Meteor., 35, 601–609

Buck, A. L., 1981: New Equations for Computing Vapor Pressure and Enhancement Factor. J. Appl. Meteor. Climatol., 20, 1527–1532, https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2.

Buck (1996), Buck (1996), Buck Research CR-1A User's Manual, Appendix 1.

https://bmcnoldy.earth.miami.edu/Humidity.html

See Also

calcTemp for calculating temperature

calcDP for calculating dew point

calcRH_DP for calculating relative humidity from dew point

calcRH_AH for calculating relative humidity from absolute humidity

Examples

# Calculate temperature (Temp) at 50% relative humidity (RH) and dew point 15°C (DewP)
# Using Magnus method
calcTemp(50, 15)

# Using Buck method
calcTemp(50, 15, method = "Buck")

calcTemp(50, calcDP(20, 50))


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 5)

mydata |>
  dplyr::mutate(
    DewPoint = calcDP(Temp, RH),
    Temp_default = calcTemp(RH, DewPoint),
    Temp_Buck = calcTemp(RH, DewPoint))

Calculate Total Heating

Description

This function calculates total heating power.

Total heating power is the sum of sensible (felt) heat and latent (hidden) heat.

Usage

calcTotalHeating(Temp1, Temp2, RH1, RH2, volumeFlowRate)

Arguments

Value

Total Heating in kilowatts (kW)

See Also

calcAD, calcEnthalpy

Examples

calcTotalHeating(20, 25, 50, 30, 0.5)

Return the file path to data files

Description

Access mydata.xlsx and other files in inst/extdata folder

Usage

data_file_path(path = NULL)

Arguments

Value

String of the path to the specified file

Examples

data_file_path()

data_file_path("mydata.xlsx")

Graph temperature and humidity data

Description

Use this tool to produce a simple temperature and humidity plot with optional background bands showing target temperature and relative humidity ranges. Optionally, add a function to graph, e.g. 'calcDP', 'calcAH', etc.

Usage

graph_TRH(
  mydata,
  Date = "Date",
  Temp = "Temp",
  RH = "RH",
  facet_by = "Sensor",
  LowT = 16,
  HighT = 25,
  LowRH = 40,
  HighRH = 60,
  y_func = "none",
  ...
)

Arguments

Value

A ggplot graph of temperature and relative humidity with optional background bands.

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 1000)

# Basic use with background ranges
graph_TRH(mydata)

# Add dew point and customise
graph_TRH(mydata, y_func = "calcDP", LowT = 6, HighT = 28)

Graph a bivariate plot of temperature and humidity data

Description

Plots temperature vs relative humidity points coloured by a selected environmental metric calculated from temperature and humidity variables using ConSciR functions.

Usage

graph_TRHbivariate(
  mydata,
  Temp = "Temp",
  RH = "RH",
  z_func = "none",
  facet_by = "Sensor",
  LowT = 16,
  HighT = 25,
  LowRH = 40,
  HighRH = 60,
  Temp_range = c(0, 40),
  RH_range = c(0, 100),
  alpha = 0.5,
  limit_caption = ""
)

Arguments

Details

Humidity and conservation functions can be used for the y-axis.

• calcHR: Humidity Ratio (g/kg)

• calcMR: Mixing Ratio (g/kg)

• calcAH: Absolute Humidity (g/m^3)

• calcSH: Specific Humidity (g/kg)

• calcAD: Air Density (kg/m^3)

• calcDP: Dew Point (°C)

• calcFP: Frost Point (°C)

• calcEnthalpy: Enthalpy (kJ/kg)

• calcPws: Saturation vapor pressure (hPa)

• calcPw: Water Vapour Pressure (hPa)

• calcPI: Preservation Index

• calcLM: Lifetime

• calcEMC_wood: Equilibrium Moisture Content (wood)

Value

A ggplot2 plot object showing temperature vs relative humidity colored by the selected metric, with annotated boundary segments.

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 100)

graph_TRHbivariate(
  mydata,
  z_func = "calcAH",
  LowT = 16, HighT = 25,
  LowRH = 40, HighRH = 60,
  Temp_range = c(0, 40),
  RH_range = c(0, 100),
  alpha = 0.7,
  limit_caption = "Example limit box"
)

Create a Psychrometric Chart

Description

This function generates a psychrometric chart based on input temperature and relative humidity data.

Usage

graph_psychrometric(
  mydata,
  Temp = "Temp",
  RH = "RH",
  data_col = NULL,
  data_alpha = 0.5,
  LowT = 16,
  HighT = 25,
  LowRH = 40,
  HighRH = 60,
  Temp_range = c(0, 40),
  y_func = "calcMR",
  ...
)

Arguments

Details

Humidity and conservation functions can be used for the y-axis.

• calcHR: Humidity Ratio (g/kg)

• calcMR: Mixing Ratio (g/kg)

• calcAH: Absolute Humidity (g/m^3)

• calcSH: Specific Humidity (g/kg)

• calcAD: Air Density (kg/m^3)

• calcDP: Dew Point (°C)

• calcFP: Frost Point (°C)

• calcEnthalpy: Enthalpy (kJ/kg)

• calcPws: Saturation vapor pressure (hPa)

• calcPw: Water Vapour Pressure (hPa)

• calcPI: Preservation Index

• calcLM: Lifetime

• calcEMC_wood: Equilibrium Moisture Content (wood)

Value

A ggplot object representing the psychrometric chart.

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 100)

# Basic usage with default settings
graph_psychrometric(mydata, Temp, RH)

# Custom temperature and humidity ranges
graph_psychrometric(mydata, Temp, RH, LowT = 8, HighT = 28, LowRH = 30, HighRH = 70)

# Using a different psychrometric function (e.g., Absolute Humidity)
graph_psychrometric(mydata, Temp, RH, y_func = calcAH)

# Adjusting the overall temperature range of the chart
graph_psychrometric(mydata, Temp, RH, Temp_range = c(12, 30))

Climate dataset to demonstrate functions

Description

A climate dataset for use to demonstrate how the functions work.

Usage

mydata

Format

A data frame with 35,136 rows and 5 columns:

Site

Site location name

Sensor

Sensor name, unique to the site

Date

Date is ISOdate time format

Temp, RH

Temperature (C) and relative humidity (%)

...

Source

Climate

Run the ConSciR Shiny Application

Description

Launch the ConSciR Shiny app which includes tools for temperature and relative humidity monitoring such as TRH charts, psychrometric charts, bivariate plots, mould growth predictions, and a silica gel calculator.

Users can upload CSV or Excel files formatted with "Date", "Temp", and "RH" columns. The app provides data tidying functions and downloadable cleaned CSV files.

The silica gel calculator estimates the required amount of silica gel based on temperature, humidity data, case dimensions, and air exchange rate (AER) if known.

Usage

run_ConSciR_app()

Value

A Shiny application object that runs interactively.

Examples

if(interactive()) {
    run_ConSciR_app()
}

Shiny Module Server for Data Upload and Processing

Description

This function creates a Shiny module server for uploading CSV or Excel files, processing the data, optional time averaging as specified by the user, and returning a tidied dataset.

Usage

shiny_dataUploadServer(id)

Arguments

Value

The returned reactive expression is a tidied data frame containing columns including Site and Sensor identifiers, a Date column rounded down (floored) to the user-selected averaging interval, median or chosen average temperature and relative humidity for each group, and any other numeric variables that were averaged if present in the input data.

Examples

if(interactive()) {
  ui <- fluidPage(
    shiny_dataUploadUI("dataUpload")
  )
  server <- function(input, output, session) {
    data <- shiny_dataUploadServer("dataUpload")
  }
}

Shiny Module UI for Data Upload and Processing

Description

Creates a Shiny UI module for uploading CSV or Excel files, and specifying flexible time averaging interval and statistic via text inputs. This UI includes the file upload control, a text box for entering the time averaging interval (e.g., "hour", "day", "month"), a text box for specifying the averaging statistic (e.g., "median", "mean"), and a download button for the tidied data.

Usage

shiny_dataUploadUI(id)

Arguments

Value

A tagList containing UI output placeholders and inputs for averaging interval, averaging statistic, and data download.

Examples

if(interactive()) {
  ui <- fluidPage(
    shiny_dataUploadUI("dataUpload")
  )
  server <- function(input, output, session) {
    data <- shiny_dataUploadServer("dataUpload")
  }
}

Tidy Hanwell EMS Data

Description

This function tidies Hanwell Environmental Monitoring System (EMS) data from either Excel sheets or CSV files.

- Default mode (MinMax = FALSE): Reads raw date, temperature, and humidity data. - Min-Max mode (MinMax = TRUE): Under development to read min-max average data (CSV only).

Usage

tidy_Hanwell(
  EMS_datapath,
  Site = "Site",
  MinMax = FALSE,
  sheet = "Hanwell",
  ...
)

Arguments

Value

A tibble containing tidied Hanwell EMS data, with columns including:

Site

Character, site name as specified by Site argument.

Sensor

Character, sensor identifier extracted from the file or metadata.

Date

POSIXct datetime of the measurement.

Temp

Numeric temperature measurement in °C (average for MinMax).

RH

Numeric relative humidity measurement in % (average for MinMax).

TempMin, TempMax, RHMin, RHMax

(Only for MinMax reports) Numeric min/max values of Temp and RH.

Examples

# Example usage: hanwell_data <- tidy_Hanwell("path/to/your/hanwell_data.csv")


# mydata file
filepath <- data_file_path("mydata.xlsx")

tidy_Hanwell(filepath, sheet = "Hanwell", Site = "London") |> head()

Tidy Meaco sensor data

Description

This function takes raw Meaco sensor data and returns data with renamed columns.

Usage

tidy_Meaco(
  mydata,
  Site_col = "RECEIVER",
  Sensor_col = "TRANSMITTER",
  Date_col = "DATE",
  Temp_col = "TEMPERATURE",
  RH_col = "HUMIDITY"
)

Arguments

Value

A tidied data frame with columns Site, Sensor, Date, Temp, and RH

Examples

# Example usage: meaco_data <- tidy_Meaco("path/to/your/meaco_data.csv")


# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "Meaco")

head(mydata)

mydata |> tidy_Meaco()

mydata |> tidy_Meaco() |> tidy_TRHdata(avg_time = "hour")

Tidy and Process Temperature and Relative Humidity data

Description

This function tidies and processes temperature, relative humidity, and date data from a given dataset. Dataset should minimally have "Date", "Temp" and "RH" columns.

It filters out rows with missing dates, attempts to parse dates, converts temperature and humidity to numeric types, and groups the data by Site, Sensor, and Date based on the averaging interval.

If the site or sensor columns are not present in the data, the function defaults to adding columns named "Site" and "Sensor". This can be changed in the arguments.

When an averaging option of "hour", "day", "month" is selected, it uses dplyr and lubridate functions to floor datetimes and calculate averages, the default is median average. See lubridate::floor_date() for rounding intervals.

• Filters out rows with missing dates.

• Renames columns for consistency.

• Converts temperature and relative humidity to numeric.

• Adds default columns "Site" and "Sensor" when missing or not supplied in args.

• Rounds dates down to the nearest hour, day, or month as per avg_time.

• Calculates averages for temperature and relative humidity according to avg_statistic.

• Filters out implausible temperature and humidity values (outside -50-80°C and 0-100%RH).

Usage

tidy_TRHdata(
  mydata,
  Site = "Site",
  Sensor = "Sensor",
  Date = "Date",
  Temp = "Temp",
  RH = "RH",
  avg_time = "none",
  avg_statistic = "median",
  avg_groups = c("Site", "Sensor"),
  ...
)

Arguments

Value

A tidy data frame with processed TRH data. When averaging, date times are floored, temperature and humidity are averaged, groups are factored, and implausible values filtered.

Examples

# mydata file
filepath <- data_file_path("mydata.xlsx")
mydata <- readxl::read_excel(filepath, sheet = "mydata", n_max = 10)

tidy_TRHdata(mydata)

tidy_TRHdata(mydata, avg_time = "hour")

mydata |> add_humidity_calcs() |> tidy_TRHdata(avg_time = "hour")


# Example usage: TRH_data <- tidy_TRHdata("path/to/your/TRHdata.csv")