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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# crestr
<!-- badges: start -->
[](https://lbesson.mit-license.org/)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://lifecycle.r-lib.org/articles/stages.html) <br >
[](https://CRAN.R-project.org/package=crestr)
[](https://img.shields.io/github/r-package/v/mchevalier2/crestr) <br >
<br >
[](https://github.com/mchevalier2/crestr/actions/workflows/R-CMD-check.yaml)
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#
The goal of `crestr` is to enable probabilistic reconstructions of past climate
change from fossil assemblage data. The approach is based on the estimation of
conditional responses of different bio-proxies to various climate parameters.
These responses take the form of probability density functions (*PDFs*). The
details of the method have been described in
[Chevalier *et al.* (2014)](https://www.doi.org/10.5194/cp-10-2081-2014), the
calibrated data are presented in
[Chevalier (2019)](https://www.doi.org/10.1016/j.gloplacha.2019.01.016) and the package is fully described in [Chevalier (2022)](https://cp.copernicus.org/articles/18/821/2022/).
## Installation
---
#You can install the package from [CRAN](https://CRAN.R-project.org/package=crestr).
#``` r
#install.packages("crestr")
#```
---
The package is currently unavailable from CRAN. However, you can install the latest development version (1.3.0) from [GitHub](https://github.com/) with:
``` r
if(!require(devtools)) install.packages("devtools")
devtools::install_github("mchevalier2/crestr")
```
Or the latest stable version archived by CRAN. If you use that version, keep in mind that not all functionalities will be available (See News page).
``` r
if(!require(devtools)) install.packages("devtools")
devtools::install_version("crestr", version = "1.2.0", repos = "https://cran.us.r-project.org")
```
> **_NOTE:_** If the install fails, it is possible that
[**gdal**](https://gdal.org/download.html) or [**PROJ**](https://proj.org/install.html)
are not installed on your system. Ample documentation exists online to guide you
through the installation of these two elements to your own system.
## Contact
If you experience any trouble while using this package, or if you think of
additional functionalities to incorporate to the package, please contact me at
<[email protected]> or open a discussion [here](https://github.com/mchevalier2/crestr/issues).
## How to use `crestr`
### Online documentation
The package is fully documented and the help of each function can be accessed
with `?function`. More detailed information as well as documented examples are
available from **<https://www.manuelchevalier.com/crestr/>**.
### A Quick Example
The following example illustrates the basics of `crestr` using pseudo-data
(_i.e._ randomly generated data).
```{r example}
library(crestr)
## loading example data
data(crest_ex)
data(crest_ex_pse)
data(crest_ex_selection)
```
Let's first have a look at the data. The dataset is composed of 20 fossil
samples from which 7 taxa have been identified. The data are expressed in percentages.
```{r data_preview_fossil}
## the first 6 samples
head(crest_ex)
##
## the structure of the data frame
str(crest_ex)
```
For each reconstruction, a proxy-species equivalency ('pse') table must be provided.
Here, with the 7 pseudo-taxa, it looks like:
```{r data_preview_pse}
crest_ex_pse
```
Finally, unique sets of taxa can be specified to reconstruct each climate
variable. In the example, *bio1* (mean annual temperature) and *bio12* (annual
precipitation) will be reconstructed. The dataset has been designed so that
Taxa 1, 2, 3 and 7 are sensitive to *bio1* while Taxa 1, 4, 5 and 7 are sensitive
to *bio12*. Check <https://www.manuelchevalier.com/crestr/articles/get-started.html>
for more details on this selection.
```{r data_preview_selection}
crest_ex_selection
```
These pseudo-data can be provided to the **crest** function and provided some
parameters (see the full vignettes for a detail of these parameters), the
reconstructions will be processed.
```{r reconstructing_climate, results='hide'}
recons <- crest(
df = crest_ex, pse = crest_ex_pse, taxaType = 0,
site_info = c(7.5, 7.5), site_name = 'crest_example',
climate = c("bio1", "bio12"), bin_width = c(2, 50),
shape = c("normal", "lognormal"),
selectedTaxa = crest_ex_selection, dbname = "crest_example"
)
```
A specific print function was created to summarise the crestObj.
```{r showcase-print}
recons
```
The climate sampled by the data can be graphically represented for a quick
assessment of the calibration dataset.
```{r plot-climate-space, out.width="100%", fig.height=7, fig.width=7}
plot_climateSpace(recons)
```
Additional graphical tools can be used to assess which taxa should/could be used
for each variable. On the following example, it is clear that Taxon2 has a much
stronger correlation with *bio1* than to *bio12*, hence its selection for *bio1*
only.
```{r plot-plot_taxaCharacteristics, fig.width=7.5, fig.height=8.8}
plot_taxaCharacteristics(recons, taxanames='Taxon2')
```
The results can be quickly visualised using the plot function and the
reconstructed climate values can be accessed from the nested `recons` object:
```{r extract_results_0}
names(recons)
lapply(recons$reconstructions, names)
```
```{r extract_results}
head(recons$reconstructions$bio1$optima)
str(recons$reconstructions$bio1$optima)
```
```{r extract_results2}
signif(recons$reconstructions$bio1$likelihood[1:6, 1:6], 3)
str(recons$reconstructions$bio1$likelihood)
```
```{r plot, fig.show="hold", out.width="50%"}
plot(recons, climate = 'bio1')
plot(recons, climate = 'bio12', simplify=TRUE, uncertainties=c(0.4, 0.6, 0.8))
```
If satisfying, the results can be directly exported from the R environment in
unique spreadsheets for each variables (or csv files) and the **crest** object
is exported as an RData file to enable easy reuse in the future.
```{r export}
export(recons, loc=getwd(), dataname='crest-test')
list.files(file.path(getwd(), 'crest-test'))
```
```{r delete export example, echo = FALSE, results = "hide"}
unlink(file.path(getwd(), 'crest-test'), recursive=TRUE)
```
## References
- Chevalier, M., Cheddadi, R., Chase, B.M., 2014. CREST (Climate REconstruction
SofTware): a probability density function (PDF)-based quantitative climate
reconstruction method. *Clim. Past* 10, 2081–2098. [10.5194/cp-10-2081-2014](https://www.doi.org/10.5194/cp-10-2081-2014)
- Chevalier, M., 2019. Enabling possibilities to quantify past climate from
fossil assemblages at a global scale. *Glob. Planet. Change* 175, 27–35.
[10.1016/j.gloplacha.2019.01.016](https://www.doi.org/10.1016/j.gloplacha.2019.01.016)
- Chevalier, M., 2022. _crestr_ an R package to perform probabilistic climate
reconstructions from palaeoecological datasets. Clim. Past
[doi:10.5194/cp-18-821-2022](https://www.doi.org/10.5194/cp-18-821-2022)