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ConSciR

ConSciR provides tools for the analysis of culturalheritage preventive conservation data.

It includes functions for environmental data analysis, humiditycalculations, sustainability metrics, conservation risks, and datavisualisations such as psychrometric charts. It is designed to supportconservators, scientists, and engineers by streamlining commoncalculations and tasks encountered in heritage conservation workflows.The package is motivated by the framework outlined in Cosaert andBeltran et al. (2022) “Tools for the Analysis of CollectionEnvironments”“Tools for the Analysis of CollectionEnvironments”.

ConSciR is intended for:
- Conservators working in museums, galleries, and heritage sites
- Conservation scientists, engineers, and researchers
- Data scientists developing applications for conservation
- Cultural heritage professionals involved in preventiveconservation
- Students and educators in conservation and heritage scienceprogrammes

The package is also designed to be:
-FAIR: Findable, Accessible, Interoperable, andReusable
-Collaborative: enabling contributions, featurerequests, bug reports, and additions from the wider community

If using R for the first time, read an article here:UsingR for the first time

Tools

• Humidity calculations, conservator tools, and sustainabilitymetrics.
• Mould growth models and damage risk functions.
• Graphical outputs including temperature-relative humidity (TRH)plots, psychrometric charts and data for other applications.
• Data tidying functions compatible with Meaco and Hanwellenvironmental monitoring devices.
• Interactive Shiny applications enabling dynamic data exploration andvisualisation.

Install and load

install.packages("ConSciR")library(ConSciR)

You can install the development version of the package from GitHubusing thepak package:

install.packages("pak")pak::pak("BhavShah01/ConSciR")# Alternatively# install.packages("devtools")# devtools::install_github("BhavShah01/ConSciR")

For full details on all functions, see the packageReferencemanual.

Examples

This section demonstrates some common tasks you can perform with theConSciR package.

• Load packages
  Load ConSciR and some commonly used tidyverse packages for datamanipulation and plotting.

library(ConSciR)library(dplyr)library(ggplot2)

• Access an example dataset
  A pre-loaded dataset (mydata) is included for testing anddemonstration. Usehead() to view the first few rows andinspect the data structure.
• Users can rename columns and structure their own datasets similarlytomydata to ensure compatibility with ConSciR functions,which expect variables such as temperature and relative humidity inspecific column names.

# My TRH datafilepath<-data_file_path("mydata.xlsx")mydata<- readxl::read_excel(filepath,sheet ="mydata")mydata<- mydata|>filter(Sensor=="Room 1")head(mydata)#> # A tibble: 6 × 5#>   Site   Sensor Date                 Temp    RH#>   <chr>  <chr>  <dttm>              <dbl> <dbl>#> 1 London Room 1 2024-01-01 00:00:00  21.8  36.8#> 2 London Room 1 2024-01-01 00:15:00  21.8  36.7#> 3 London Room 1 2024-01-01 00:29:59  21.8  36.6#> 4 London Room 1 2024-01-01 00:44:59  21.7  36.6#> 5 London Room 1 2024-01-01 00:59:59  21.7  36.5#> 6 London Room 1 2024-01-01 01:14:59  21.7  36.2

• Perform calculations on the data
  Use ConSciR functions to add metrics such as dew point, absolutehumidity, mould, preservation index and others to the dataset. Morefunctions are available; see the packageReferencefor details.

# Peform calculationshead(mydata)|>mutate(# Dew pointDewP =calcDP(Temp, RH),# Absolute humidityAbs =calcAH(Temp, RH),# Mould riskMould =ifelse(RH>calcMould_Zeng(Temp, RH),"Mould risk","No mould"),# Preservation Index, years to deteriorationPI =calcPI(Temp, RH),# Scenario: Humidity if the temperature was 2°C higherRH_if_2C_higher =calcRH_AH(Temp+2, Abs)    )|>glimpse()#> Rows: 6#> Columns: 10#> $ Site            <chr> "London", "London", "London", "London", "London", "Lon…#> $ Sensor          <chr> "Room 1", "Room 1", "Room 1", "Room 1", "Room 1", "Roo…#> $ 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, 21.7#> $ RH              <dbl> 36.8, 36.7, 36.6, 36.6, 36.5, 36.2#> $ DewP            <dbl> 6.383970, 6.344456, 6.304848, 6.216205, 6.176529, 6.05…#> $ Abs             <dbl> 7.052415, 7.033251, 7.014087, 6.973723, 6.954670, 6.89…#> $ Mould           <chr> "No mould", "No mould", "No mould", "No mould", "No mo…#> $ PI              <dbl> 45.25849, 45.38181, 45.50580, 46.07769, 46.20393, 46.5…#> $ RH_if_2C_higher <dbl> 32.81971, 32.73052, 32.64134, 32.63838, 32.54920, 32.2…

• Combine analysis with visualisation
  Add a dew point line to the temperature-relative humidity graph usingthe package’s built-in plotting functiongraph_TRH().

mydata|>mutate(DewPoint =calcDP(Temp, RH))|>graph_TRH()+geom_line(aes(Date, DewPoint),col ="cyan3")+# add dew pointtheme_bw()

• Conservator tools: mould growth estimation
  Calculate mould growth risk usingcalcMould_Zeng() function and visualise italongside humidity data.

mydata|>mutate(Mould =calcMould_Zeng(Temp, RH))|>ggplot()+geom_line(aes(Date, RH),col ="royalblue3")+geom_line(aes(Date, Mould),col ="darkorchid",size =1)+labs(title ="Mould Growth Rate Limits",subtitle ="Mould growth initiates when RH goes above threshold",x =NULL,y ="Humidity (%)")+facet_grid(~Sensor)+theme_classic(base_size =14)

• Graphs: generate a psychrometric chart
  Visualise the data using a psychrometric chart with the functiongraph_psychrometric(). The example shows how a basic plotcan be customised; data transparency, temperature and humidity ranges,and the y-axis function.

# Customisemydata|>graph_psychrometric(data_alpha =0.2,LowT =8,HighT =28,LowRH =30,HighRH =70,y_func = calcAH    )+theme_classic()+labs(title ="Psychrometric chart")