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```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
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# Analyse mark-release-recapture data from [Sterile Insect Technique (SIT)](https://www.youtube.com/watch?v=yV7WLrlzbLk&t=2s) field experiments <img src="man/figures/logo.png" align="right" height="132" />
[](https://www.repostatus.org/#active)
[](https://cran.r-project.org/package=sit)
[](https://cirad-astre.r-universe.dev)
[](https://cirad-astre.r-universe.dev/sit)
[](https://forgemia.inra.fr/umr-astre/sit/pipelines)
[](https://umr-astre.pages.mia.inra.fr/sit/coverage.html)
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`sit` is a R-package currently on active development, after a thorough [analysis of requirements](https://umr-astre.pages.mia.inra.fr/sit-design/requirements.html#content).
__Import__ data about _traps_, _release events_ and _field surveys_ into a `sit`
Then, __query__ the `sit` object for estimates of _competitiveness_,
_dispersal_, _survival_ and _wild-population density_.
First, you need to load the package.
```{r load-package}
library(sit)
```
We use some fake data for demonstration purposes. See `?fake_sit`.
1. __Trap data__: set up a `sf` table of points with trap coordinates (see, [`st_as_sf()`](https://r-spatial.github.io/sf/reference/st_as_sf.html), for instance), identification code, type (see `sit_trap_types()`) and area (`sit`/`control`). Use `sit_traps()` to _import_ into a `sit_traps` object.
```{r fake-traps}
fake_traps
my_traps <- sit_traps(fake_traps)
```
2. __Release events__: releases can be performed from a single _point_ or homogeneously over the sit area. Set up a table with release dates, colour and number of individuals. For __point releases__ include release coordinates and make it a `sf` object. Lacking geographical coordinates, `sit` will interpret it as a __areal release__.
```{r fake-rpoints}
fake_rpoints
```{r fake-rareal}
fake_rareal
```
Use `sit_revents()` to _import_ into `sit_revents` objects and combine different release types into a single `sit_revents` object with `c()`.
```{r my-releases}
my_releases <- c(
sit_revents(fake_rpoints),
sit_revents(fake_rareal)
)
```
3. __Field survey data__: field data are collected at `adult` or `egg` _stages_ of development, depending on the trap type (see `sit_trap_types()`)
Field data from __adult__ surveys can be imported using `sit_adult_surveys()` from a table such as:
```{r fake-adults}
fake_adults
```{r my-asurveys}
my_asurveys <- sit_adult_surveys(fake_adults)
```
Field data from __egg__ surveys, include other information such as the _hatching rate_:
```{r fake-eggs}
fake_eggs
```{r my-eggsurveys}
my_eggsurveys <- sit_egg_surveys(fake_eggs)
```
4. __Combine__ all the information into a `sit` object using `sit()`:
traps = my_traps,
release_events = my_releases,
adult_surveys = my_asurveys,
egg_surveys = my_eggsurveys
5. __Query__ your `sit` object for estimates of _competitiveness_, _dispersal_, _survival_ and _wild-population density_.
This toy example requires disabling the `spatial_adjustment` because there are
too few traps in this unrealistic situation.
But this is not the case in general.
See [Retrieving Results](retrieving.html) for details on the spatial adjustment.
sit_competitiveness(my_sit)
sit_mdt(my_sit, spatial_adjustment = FALSE)
sit_flight_range(my_sit, spatial_adjustment = FALSE, pool = TRUE)
```{r queries-diffusion}
sit_diffusion(my_sit, spatial_adjustment = FALSE)
```
```{r queries-survival}
sit_survival(my_sit, spatial_adjustment = FALSE, pool = TRUE)
```
```{r queries-wild-size}
## Installation
<!-- You can install the released version of sit from [CRAN](https://CRAN.R-project.org) with: -->
<!-- ``` r -->
<!-- install.packages("sit") -->
<!-- ``` -->
```{r install-r-universe, eval = FALSE}
options(repos = c(
ciradastre = 'https://cirad-astre.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
install.packages("sit")
This installs binary packages for Windows and MacOS, unless you configured R to install source packages. In such case, see below.
### Linux and source installation
`sit` uses the geospatial libraries GDAL, GEOS and Proj.4, via the R-package [`sf`](https://r-spatial.github.io/sf/).
Dependencies for recent versions of Ubuntu (18.04 and later) are available in the official repositories; you can install them with:
```{bash deps-ubuntu, eval = FALSE}
apt-get -y update && apt-get install -y libudunits2-dev libgdal-dev libgeos-dev libproj-dev
```
For Fedora, Arch or source installation in Windows or Mac, please refer to the [installation instructions for `sf`](https://r-spatial.github.io/sf/#installing).
## Getting help online
Bug reports and feature requests should be posted on *GitLab* using
the [*issue*](http://forgemia.inra.fr/umr-astre/sit/issues) system.
For support, reach out in the
[sit mailing list](https://listes.cirad.fr/sympa/info/sit).
Archives are of public access.
Contributions are welcome via **pull requests**.
Please note that this project is released with a [Contributor Code of
Conduct](CONDUCT.md). By participating in this project you agree to
abide by its terms.