ConSciR provides tools for the analysis of cultural heritage preventive conservation data.
It includes functions for environmental data analysis, humidity calculations, sustainability metrics, conservation risks, and data visualisations such as psychrometric charts. It is designed to support conservators, scientists, and engineers by streamlining common calculations and tasks encountered in heritage conservation workflows. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) “Tools for the Analysis of Collection Environments”“Tools for the Analysis of Collection Environments”.
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 preventive conservation
- Students and educators in conservation and heritage science programmes
The package is also designed to be:
-FAIR: Findable, Accessible, Interoperable, and Reusable
-Collaborative: enabling contributions, feature requests, bug reports, and additions from the wider community
If using R for the first time, read an article here:Using R for the first time
Tools
- Humidity calculations, conservator tools, and sustainability metrics.
- 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 Hanwell environmental monitoring devices.
- Interactive Shiny applications enabling dynamic data exploration and visualisation.
Install and load
install.packages("ConSciR")library(ConSciR)You can install the development version of the package from GitHub using 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 packageReference manual.
Examples
This section demonstrates some common tasks you can perform with the ConSciR package.
- Load packages
Load ConSciR and some commonly used tidyverse packages for data manipulation and plotting.
- Access an example dataset
A pre-loaded dataset (mydata) is included for testing and demonstration. Usehead()to view the first few rows and inspect the data structure. - Users can rename columns and structure their own datasets similarly to
mydatato ensure compatibility with ConSciR functions, which expect variables such as temperature and relative humidity in specific 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, absolute humidity, mould, preservation index and others to the dataset. More functions are available; see the packageReference for details.
# Peform calculationshead(mydata)|>mutate(# Dew point DewP=calcDP(Temp,RH),# Absolute humidity Abs=calcAH(Temp,RH),# Mould risk Mould=ifelse(RH>calcMould_Zeng(Temp,RH),"Mould risk","No mould"),# Preservation Index, years to deterioration PI=calcPI(Temp,RH),# Scenario: Humidity if the temperature was 2°C higher RH_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 using the 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 it alongside 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 plot can 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")