Tools for Conservation Science • ConSciR

ConSciR is an R package designed to assist conservators, scientists and engineers by providing a toolkit for performing calculations and streamlining common tasks in cultural heritage conservation.

ConSciR is designed for and to be:

Conservators working in museums, galleries, and heritage sites
Conservation scientists, engineers and researchers
Cultural heritage professionals involved in preventive conservation
Students and educators in conservation and heritage science programs
FAIR: Findable, Accessible, Interoperable, and Reusable
Collaborative: allowing anyone to upload code, raise requests, report bugs, and add functionality to the package

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

Tools

Humidity calculations, conservator tools, sustainability
Mould growth models, damage functions
Graphs: TRH plots, psychometric chart
Tidying data: Meaco, Hanwell data
Interactive Shiny applications

Installation

You can install the development version of ConSciR from GitHub with:

install.packages("pak")
pak::pak("BhavShah01/ConSciR")

-or-

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

Examples

This is a basic example of some commons tasks:

Load packages

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

Pre-loaded dataset is availabe for testing

# My TRH data
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

Use functions, for example use the existing data to add dew point and absolute humidity. Performance metrics are also being added like lifetime multiplier, preservation index and mould calculations.

# Peform calculations
head(mydata) |>
  mutate(
    DewP = calcDP(Temp, RH), 
    Abs = calcAH(Temp, RH), 
    LifeTime = calcLM(Temp, RH, EA = 100), 
    PI = calcPI(Temp, RH)
    )
#> # A tibble: 6 × 9
#>   Site   Sensor Date                 Temp    RH  DewP   Abs LifeTime    PI
#>   <chr>  <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl>
#> 1 London Room 1 2024-01-01 00:00:00  21.8  36.8  6.38  7.05     1.11  45.3
#> 2 London Room 1 2024-01-01 00:15:00  21.8  36.7  6.34  7.03     1.11  45.4
#> 3 London Room 1 2024-01-01 00:29:59  21.8  36.6  6.30  7.01     1.11  45.5
#> 4 London Room 1 2024-01-01 00:44:59  21.7  36.6  6.22  6.97     1.11  46.1
#> 5 London Room 1 2024-01-01 00:59:59  21.7  36.5  6.18  6.95     1.11  46.2
#> 6 London Room 1 2024-01-01 01:14:59  21.7  36.2  6.06  6.90     1.11  46.6

Combine analysis with graphs

mydata |>
  mutate(DewPoint = calcDP(Temp, RH)) |>
  graph_TRH() + 
  geom_line(aes(Date, DewPoint), col = "purple") + # add dewpoint line in purple
  theme_minimal()

graphTRH

Conservator tools: for example estimate mould growth

mydata |>
  mutate(Mould = calcMould_Zeng(Temp, RH)) |>
  ggplot() +
  geom_line(aes(Date, RH), col = "blue") +
  geom_line(aes(Date, Mould), col = "darkgreen", size = 1) +
  labs(title = "Mould Growth Rate Limits", 
       y = "Humidity (blue) and Mould Limit (green)") + 
  theme_bw()

mould

Humidity functions: for example psychrometric chart

mydata |>
  graph_psychrometric()

psych_chart