Merge pull request #63 from jakobdanel/results/density

Results/density

NAMESPACE

@@ -4,6 +4,7 @@ export(get_retile_dir)
 export(get_tile_dir)
 export(get_tiles_path)
 export(is_tile_existing)
+export(lfa_calculate_patch_density)
 export(lfa_capitalize_first_char)
 export(lfa_check_flag)
 export(lfa_combine_sf_obj)
@@ -12,6 +13,7 @@ export(lfa_count_returns_all_areas)
 export(lfa_count_returns_per_tree)
 export(lfa_create_boxplot)
 export(lfa_create_density_plots)
+export(lfa_create_grouped_bar_plot)
 export(lfa_create_neighbor_mean_curves)
 export(lfa_create_plot_per_area)
 export(lfa_create_stacked_distributions_plot)

R/calculate_patch_density.R

@@ -0,0 +1,47 @@
+#' Calculate patch density for specified areas based on detection data
+#'
+#' This function calculates patch density for specified areas using detection data.
+#' It reads the spatial polygons from a shapefile, computes the area size for each patch,
+#' counts the number of detections in each patch, and calculates the patch density.
+#'
+#' @param areas_location The file path to a shapefile containing spatial polygons
+#'   representing the areas for which patch density needs to be calculated.
+#'   Default is "research_areas.shp".
+#' @param detections A data frame containing detection information, where each row represents
+#'   a detection and includes the 'area' column specifying the corresponding area.
+#'   Default is obtained using lfa_get_detections().
+#' @return A data frame with patch density information for each specified area.
+#'   Columns include 'name' (area name), 'geometry' (polygon geometry), 'area_size' (patch area size),
+#'   'detections' (number of detections in the patch), and 'density' (computed patch density).
+#'
+#' @examples
+#' # Assuming you have a shapefile 'your_research_areas.shp' and detection data
+#' # from lfa_get_detections()
+#' density_data <- lfa_calculate_patch_density(areas_location = "your_research_areas.shp")
+#' print(density_data)
+#'
+#' @export
+lfa_calculate_patch_density <- function(areas_location = "research_areas.shp",
+                                        detections = lfa::lfa_get_detections()) {
+  # Set S2 usage to FALSE
+  sf::sf_use_s2(FALSE)
+
+  # Read spatial polygons from the specified shapefile
+  patch_data <- sf::st_read(areas_location)
+
+  # Compute the area size for each patch
+  patch_data$area_size <- sf::st_area(patch_data)
+
+  # Initialize 'detections' column with NA
+  patch_data$detections <- NA
+
+  # Count the number of detections in each patch
+  for (i in 1:nrow(patch_data)) {
+    patch_data[i, "detections"]$detections <- nrow(detections[detections$area == patch_data[i, "name"]$name, ])
+  }
+
+  # Calculate patch density
+  patch_data$density <- patch_data$detections / patch_data$area_size
+
+  return(patch_data)
+}

R/create_bar_plot.R

@@ -0,0 +1,38 @@
+#' Create a barplot using ggplot2
+#'
+#' This function generates a barplot using ggplot2 based on the specified data frame columns.
+#' The barplot displays the values from the specified column, grouped by another column.
+#' The grouping can be further differentiated by color if desired.
+#'
+#' @param df A data frame containing the relevant columns for the barplot.
+#' @param value_column The column containing the values to be plotted.
+#' @param label_column The column used for labeling the bars on the x-axis. Default is "name".
+#' @param grouping_column The column used for grouping the bars. Default is "species".
+#' @return A ggplot2 barplot.
+#'
+#'
+#' @examples
+#' # Assuming you have a data frame 'your_data_frame' with columns "name", "species", and "value"
+#' lfa_create_barplot(your_data_frame, value_column = "value", label_column = "name", grouping_column = "species")
+#'
+#'@export
+lfa_create_grouped_bar_plot <- function(data, grouping_var, value_col, label_col) {
+
+if (!(grouping_var %in% colnames(data) && value_col %in% colnames(data) && label_col %in% colnames(data))) {
+  stop("Columns not found in the data.frame.")
+}
+
+# Create a grouped bar plot
+plot <- ggplot2::ggplot(data, ggplot2::aes(x = reorder(data[[label_col]], data[[value_col]]), y = data[[value_col]], fill = data[[grouping_var]])) +
+  ggplot2::geom_bar(stat = "identity", position = "dodge") +
+  ggplot2::labs(x = "Name of patch", y = "Density", fill = "Specie", title = "Tree density across the different patches, grouped by specie") +
+  ggplot2::theme_minimal() +
+  ggplot2::theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+return(plot)
+}
+
+# Example usage:
+# Assuming 'my_data' is your data.frame, 'category' is the grouping variable,
+# 'value' is the column with values, and 'label' is the column with labels.
+

results/appendix/package-docs/docs.qmd

@@ -1,3 +1,53 @@
+### `lfa_calculate_patch_density`
+
+Calculate patch density for specified areas based on detection data
+
+
+#### Arguments
+
+Argument      |Description
+------------- |----------------
+`areas_location`     |     The file path to a shapefile containing spatial polygons representing the areas for which patch density needs to be calculated. Default is "research_areas.shp".
+`detections`     |     A data frame containing detection information, where each row represents a detection and includes the 'area' column specifying the corresponding area. Default is obtained using lfa_get_detections().
+
+
+#### Description
+
+This function calculates patch density for specified areas using detection data.
+ It reads the spatial polygons from a shapefile, computes the area size for each patch,
+ counts the number of detections in each patch, and calculates the patch density.
+
+
+#### Value
+
+A data frame with patch density information for each specified area.
+ Columns include 'name' (area name), 'geometry' (polygon geometry), 'area_size' (patch area size),
+ 'detections' (number of detections in the patch), and 'density' (computed patch density).
+
+
+#### Examples
+
+```{r}
+#| eval: false
+# Assuming you have a shapefile 'your_research_areas.shp' and detection data
+# from lfa_get_detections()
+density_data <- lfa_calculate_patch_density(areas_location = "your_research_areas.shp")
+print(density_data)
+```
+
+
+#### Usage
+
+```{r}
+#| eval: false
+lfa_calculate_patch_density(
+  areas_location = "research_areas.shp",
+  detections = lfa::lfa_get_detections()
+)
+```
+
+
+
 ### `lfa_capitalize_first_char`
 
 Capitalize First Character of a String
@@ -393,6 +443,141 @@ lfa_create_density_plots(
 
 
 
+### `lfa_create_grouped_bar_plot`
+
+Create a barplot using ggplot2
+
+
+#### Arguments
+
+Argument      |Description
+------------- |----------------
+`df`     |     A data frame containing the relevant columns for the barplot.
+`value_column`     |     The column containing the values to be plotted.
+`label_column`     |     The column used for labeling the bars on the x-axis. Default is "name".
+`grouping_column`     |     The column used for grouping the bars. Default is "species".
+
+
+#### Description
+
+This function generates a barplot using ggplot2 based on the specified data frame columns.
+ The barplot displays the values from the specified column, grouped by another column.
+ The grouping can be further differentiated by color if desired.
+
+
+#### Value
+
+A ggplot2 barplot.
+
+
+#### Examples
+
+```{r}
+#| eval: false
+# Assuming you have a data frame 'your_data_frame' with columns "name", "species", and "value"
+lfa_create_barplot(your_data_frame, value_column = "value", label_column = "name", grouping_column = "species")
+```
+
+
+#### Usage
+
+```{r}
+#| eval: false
+lfa_create_grouped_bar_plot(data, grouping_var, value_col, label_col)
+```
+
+
+
+### `lfa_create_neighbor_mean_curves`
+
+Create neighbor mean curves for specified areas
+
+
+#### Arguments
+
+Argument      |Description
+------------- |----------------
+`neighbors`     |     A data frame containing information about neighbors, where each column represents a specific neighbor, and each row corresponds to an area.
+`use_avg`     |     Logical. If TRUE, the function computes average curves across all neighbors. If FALSE, it computes curves for individual neighbors.
+
+
+#### Description
+
+This function generates mean curves for a specified set of areas based on neighbor data.
+ The user can choose to compute mean curves for individual neighbors or averages across neighbors.
+
+
+#### Value
+
+A data frame with mean curves for each specified area.
+ Columns represent areas, and rows represent index values.
+
+
+#### Examples
+
+```{r}
+#| eval: false
+# Assuming you have a data frame 'your_neighbors_data' with neighbor information
+mean_curves <- lfa_create_neighbor_mean_curves(your_neighbors_data, use_avg = TRUE)
+print(mean_curves)
+```
+
+
+#### Usage
+
+```{r}
+#| eval: false
+lfa_create_neighbor_mean_curves(neighbors, use_avg = FALSE)
+```
+
+
+
+### `lfa_create_plot_per_area`
+
+Create a line plot per area with one color per specie
+
+
+#### Arguments
+
+Argument      |Description
+------------- |----------------
+`data`     |     A data frame with numeric columns and a column named 'specie' for species information.
+
+
+#### Description
+
+This function takes a data frame containing numeric columns and creates a line plot
+ using ggplot2. Each line in the plot represents a different area, with one color per specie.
+
+
+#### Value
+
+A ggplot2 line plot.
+
+
+#### Examples
+
+```{r}
+#| eval: false
+data <- data.frame(
+specie = rep(c("Species1", "Species2", "Species3"), each = 10),
+column1 = rnorm(30),
+column2 = rnorm(30),
+column3 = rnorm(30)
+)
+lfa_create_plot_per_area(data)
+```
+
+
+#### Usage
+
+```{r}
+#| eval: false
+lfa_create_plot_per_area(data)
+```
+
+
+
 ### `lfa_create_stacked_distributions_plot`
 
 Create a stacked distribution plot for tree detections, visualizing the distribution

results/report.qmd

@@ -42,23 +42,10 @@ This report documents the analysis of forest data for different tree species.
 {{< include results/researched-areas.qmd >}}
 {{< include results/z-distribution.qmd >}}
 {{< include results/number_of_returns.qmd >}}
+{{< include results/density.qmd >}}
 
 
 
-|specie |area                |   density (1/m²)|
-|:------|:-------------------|---------:|
-|beech  |bielefeld_brackwede | 0.0089399|
-|beech  |billerbeck          | 0.0093175|
-|beech  |wuelfenrath         | 0.0079259|
-|oak    |hamm                | 0.0090610|
-|oak    |muenster            | 0.0077384|
-|oak    |rinkerode           | 0.0082641|
-|pine   |greffen             | 0.0103807|
-|pine   |mesum               | 0.0124200|
-|pine   |telgte              | 0.0122860|
-|spruce |brilon              | 0.0158030|
-|spruce |oberhundem          | 0.0162678|
-|spruce |osterwald           | 0.0129892|