plots.Rmd

---
title: "Plots"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Plots}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

This vignette demonstrates how this package can be used to plot camera trap data.

```{r setup, message=FALSE, results='hide'}
library(camtrapviz)
library(dplyr)
library(ggiraph)
library(ggplot2)
library(RColorBrewer)
```

## Import and prepare data

```{r}
data(recordTableSample, package = "camtrapR")
data(camtraps, package = "camtrapR")
```

```{r}
recordTableSample$DateTimeOriginal <- as.POSIXct(recordTableSample$DateTimeOriginal)
recordTableSample$Date <- as.Date(recordTableSample$Date)
recordTableSample$Time <- chron::times(recordTableSample$Time)

camtraps$Setup_date <- as.Date(camtraps$Setup_date, format = "%d/%m/%Y")
camtraps$Retrieval_date <- as.Date(camtraps$Retrieval_date, format = "%d/%m/%Y")
```

## Plot observations

The package provides a function to plot the observations for each camera.

```{r}
plot_points(recordTableSample,
            cam_col = "Station",
            datetime_col = "DateTimeOriginal")
```

In case a camera was in the data but no observations were made, you can provide a vector of cameras that should show up on the graph:
```{r}
all_cam <- c("StationA", "StationB", "StationC", "StationD")
plot_points(recordTableSample,
            cam_col = "Station",
            datetime_col = "DateTimeOriginal",
            cam_vec = all_cam)
```

`date_col` and `time_col` can also be used instead of `datetime_col`.

```{r}
plot_points(recordTableSample,
            cam_col = "Station",
            date_col = "Date", 
            time_col = "Time")
```

It is also possible to provide a column for points colors:

```{r}
plot_points(recordTableSample,
            cam_col = "Station",
            points_col = "Species",
            datetime_col = "DateTimeOriginal")
```

By providing additional info, it is also possible to visualize the sampling period on the same graph.

```{r}
plot_points(recordTableSample, 
            cam_col = "Station", 
            points_col = "Species",
            datetime_col = "DateTimeOriginal",
            dfcam = camtraps,
            setup_dfcam = "Setup_date",
            retrieval_dfcam = "Retrieval_date")
```

Various options are available to customize this graph:

```{r, echo = TRUE}
spp <- unique(recordTableSample$Species)
pal <- RColorBrewer::brewer.pal(length(spp), "Purples")

plot_points(recordTableSample, 
            cam_col = "Station", 
            points_col = "Species",
            datetime_col = "DateTimeOriginal",
            dfcam = camtraps,
            setup_dfcam = "Setup_date",
            retrieval_dfcam = "Retrieval_date",
            fill_rect = "darkgoldenrod1",
            alpha_rect = 0.6,
            col_rect = NA, 
            height_rect = 0.5,
            ptsize = 3,
            date_breaks = "1 month",
            date_limits = as.POSIXct(c("2009-02-01",
                                       "2009-06-01"),
                                     tz = "UTC"),
            date_format = "%a %d %B",
            text_x_angle = 45,
            cols = pal)
```

Using `ggiraph`, the plot can also be made interactive:

```{r}
p <- plot_points(recordTableSample, 
                 cam_col = "Station", 
                 points_col = "Species",
                 datetime_col = "DateTimeOriginal",
                 dfcam = camtraps,
                 setup_dfcam = "Setup_date",
                 retrieval_dfcam = "Retrieval_date",
                 interactive = TRUE)
ggiraph::girafe(ggobj = p)
```

## Plot cameras

The package also provides a function to plot the cameras with Leaflet.

Here, the dataset is already projected using the UTM zone 50 (East Asia)/ Therefore, we provide the EPSG code to use (EPSG:32650).

```{r}
plot_map(camtraps, 
         cam_col = "Station",
         lat_col = "utm_y", lon_col = "utm_x", 
         crs = 32650)
```

It is also possible to display cameras names:
```{r}
plot_map(camtraps, 
         cam_col = "Station",
         lat_col = "utm_y", lon_col = "utm_x", 
         display_camnames = TRUE,
         crs = 32650)
```

Additional information can also be displayed using the radius of the camera points. Here, we choose to make circles sizes proportional to the abundance of one species at each camera:

```{r}
# Get abundance per camera
PBE_abundance <- recordTableSample |> 
  filter(Species == "PBE") |> 
  group_by(Station) |> 
  summarise(count = n())
radius <- PBE_abundance$count
names(radius) <- PBE_abundance$Station

radius
```

```{r}
plot_map(camtraps, 
         cam_col = "Station",
         lat_col = "utm_y", lon_col = "utm_x", 
         radius = radius,
         label = paste("Camera:", names(radius), 
                       "| abundance:", radius),
         crs = 32650)
```

If data is missing, it will show up in purple on the map.

```{r}
radius_missing <- radius[1:2]

plot_map(camtraps, 
         cam_col = "Station",
         lat_col = "utm_y", lon_col = "utm_x", 
         radius = radius_missing,
         label = paste("Camera:", names(radius_missing), 
                       "| abundance:", radius_missing),
         crs = 32650)
```

## Plot species

The package also includes a simple function to plot species counts.

```{r}
plot_species_bars(recordTableSample, 
                  spp_col = "Species")
```

If there is a count column, it can also be included.

```{r}
with_count <- recordTableSample |> 
  mutate(count = 2)

plot_species_bars(with_count, 
                  spp_col = "Species", count_col = "count")
```

If there is an observation type column, `NA` values in the species column will be replaced with the values in observation type. This is primarily intended for the camtrapDP standard, where non-animal observations have a `NA` in the species column.

Here, we add an observation type column to the data and replace some species with `NA`.

```{r}
with_obstype <- recordTableSample |> 
  mutate(type = ifelse(Species == "PBE", "blank", "animal"), 
         .after = Species) |> 
  mutate(Species = ifelse(Species == "PBE", NA, Species))
with_obstype |> 
  select(Station, Species, type) |> 
  head()
```


```{r}
plot_species_bars(with_obstype, 
                  spp_col = "Species", 
                  obstype_col = "type")
```


Finally, this plot can also be made interactive using `ggiraph`:

```{r}
p <- plot_species_bars(recordTableSample, 
                       spp_col = "Species",
                       interactive = TRUE)
girafe(ggobj = p)
```


## Plot activity

It is also possible to explore the activity times of a given species:

```{r}
# Get records corresponding only to PBE
PBE_records <- recordTableSample |> 
  filter(Species == "PBE")

p <- plot_activity(dfrec = PBE_records,
                   time_dfrec = "Time",
                   unit = "clock",
                   interactive = TRUE)
girafe(ggobj = p)
```

Another vignette gives a more in-depth overview of this type of plots (see [here](https://lisanicvert.github.io/camtrapviz/articles/activity-patterns.html)).

# Plot diversity

It is also possible to plot diversity indices:

```{r}
# Get diversity
spp_cam <- summarize_species(recordTableSample, 
                             spp_col = "Species", 
                             cam_col = "Station",
                             by_cam = TRUE)

div <- get_diversity_indices(spp_cam,
                             spp_col = "Species",
                             cam_col = "Station")
```


```{r}
p <- plot_diversity(div, 
                    div_col = "simpson", 
                    cam_col = "Station", 
                    interactive = TRUE) + 
  ggtitle("Simpson diversity index")
girafe(ggobj = p)
```

Another vignette gives a more in-depth overview of this type of plots (see [here](https://lisanicvert.github.io/camtrapviz/articles/diversity.html)).