README.Rmd

# Predicting and Visualizing Thermal Habitat: `thermopic`

The `thermopic` R package provides functionality for predicting and visualizing the seasonal availability of thermal habitat in lakes.

## Installation

Installation is easiest from an `R` commandline using the `devtools` package.
```{r, eval=FALSE}
devtools::install_github('stevencarlislewalker/thermopic')
```

It might be necessary to preceed this first step by installing `devtools`.
```{r, eval=FALSE}
install.packages('devtools')
```

Once the package is installed, it may be loaded using the `library` function.
```{r}
library(thermopic)
```

## Quick tour

The `thermopic` package runs within a project directory, which we create using the optional `temporary_thermopic_directory` function.
```{r}
root = temporary_thermopic_directory()
```

We now use `thermopic`'s `setup_directory` function to create the required directory structure for our project. For this quick tour we use sample input data contained within the package, which is the default behaviour of `setup_directory`.
```{r}
setup_directory(root, overwrite = TRUE)
```

To inspect the resulting directory structure one may use `thermopic`s `print_directory_tree` function.
```{r}
print_directory_tree(root)
```

Now that the structure is in place one may fit the thermopic model.
```{r}
fitted_thermopic_model = thermopic_model(
  path = root,
  Lake = '1_Lake.csv',
  Climate = '2_Climate.csv'
)
```

And create the report as well as the thermopic images themselves.
```{r}
thermopic_report_data = thermopic_report(
  path = root,
  STM_Parameters = '4_STM_Parameters.csv',
  Nlakes_test = 5,
  show_progress_bar = FALSE
)
```

Here we see the resulting outputs including the images in `jpeg` format files.
```{r, echo=FALSE}
print_directory_tree(root)
```

```{r, echo=FALSE}
unlink(root)
```

## Further information

For a more detailed introduction see this [tutorial](inst/doc/thermopic_tutorial.md).