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<!-- README.md is generated from README.rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# chents
[](https://pkgdown.jrwb.de/chents/)
[](https://jranke.r-universe.dev/chents)
[](https://pkgdown.jrwb.de/chents/coverage/coverage.html)
When working with data on chemical substances, we often need a reliable link between
the data and the chemical identity of the substances. The R package **chents**
provides a way to define an R object corresponding to a chemically defined substances
("chemical entity") and to collect related information.
When first defining a chemical entity, some chemical information
is retrieved from the [PubChem](https://pubchem.ncbi.nlm.nih.gov/) website using
the [webchem](https://docs.ropensci.org/webchem/) package.
```{r}
library(chents)
caffeine <- chent$new("Caffeine")
```
If Python and [RDKit](https://rdkit.org) (> 2015.03) are installed and
configured for use with the
[reticulate](https://rstudio.github.io/reticulate/) package, some
additional chemical information including a 2D graph are computed.
The print method gives an overview of the information that was collected.
```{r}
print(caffeine)
```
There is a very simple plotting method for the chemical structure.
```{r fig.height = 2}
plot(caffeine)
```
If you have a so-called ISO common name of a pesticide active ingredient, you
can use the 'pai' class derived from the 'chent' class, which starts with querying
the [BCPC compendium](http://www.bcpcpesticidecompendium.org/) first.
```{r fig.height = 3.5}
delta <- pai$new("Deltamethrin")
plot(delta)
```
Additional information can be read from a local .yaml file. This information
can be leveraged e.g. by the
[PEC_soil](https://pkgdown.jrwb.de/pfm/reference/PEC_soil.html) function of the
'pfm' package. However, this functionality is to be superseded by a dedicated
package, defining data for the environmental risk assessment on chemicals,
in particular on active ingredients of plant protection products.
## Installation
You can conveniently install chents from the repository kindly made available by the
R-Universe project:
```{r, eval = FALSE}
install.packages("chents",
repos = c("https://jranke.r-universe.dev", "https://cran.r-project.org"))
```
In order to profit from the chemoinformatics, you need to install RDKit and its
python bindings. On a Debian type Linux distribution, just use
```{sh, eval = FALSE}
sudo apt install python3-rdkit
```
If you use this package on Windows or MacOS, I would be happy to include
installation instructions here if you share them with me, e.g. via a Pull
Request.
## Configuration of the Python version to use
On Debian type Linux distributions, you can use the following line in your
global or project specific `.Rprofile` file to tell the `reticulate` package to
use the system Python version that will find the RDKit installed in the system
location.
```{r, eval = FALSE}
Sys.setenv(RETICULATE_PYTHON="/usr/bin/python3")
```
## Using R6 classes
Note that the `chent` objects defined by this package are [R6](https://r6.r-lib.org/articles/Introduction.html)
classes. Therefore, if you think you make a copy by assigning them to a new name, the
objects will still be connected, because only the reference is copied. For
example, you can create a molecule without retrieving data from PubChem
```{r}
but <- chent$new("Butane", smiles = "CCCC", pubchem = FALSE)
print(but)
```
If you then assign a new name and add PubChem information to the object with
the new name, the information will also be added to the original `chent`
object:
```{r}
but_pubchem <- but
but_pubchem$try_pubchem()
print(but)
```
You can create a derived, independent object using the `clone()` method
that will not be affected by operations on the original object:
```{r}
but_new <- chent$new("Butane", smiles = "CCCC", pubchem = FALSE)
but_clone <- but_new$clone()
but_new$try_pubchem()
but_clone
```
|
