add ggplot2 to distribution plots, report, and adjust automatic year …

…ordering for dist_map

close #18; close #29

NAMESPACE

@@ -22,12 +22,14 @@ importFrom(dplyr,summarise)
 importFrom(dplyr,sym)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,element_text)
+importFrom(ggplot2,geom_histogram)
 importFrom(ggplot2,geom_segment)
 importFrom(ggplot2,geom_sf)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_classic)
+importFrom(ggplot2,theme_minimal)
 importFrom(graphics,hist)
 importFrom(graphics,par)
 importFrom(graphics,plot)

R/distributions.r

@@ -7,6 +7,7 @@
 #' @param x a dataframe with columns: x, y, date, and id (optional)
 #' @param plot a logical indicating whether or not to return a histogram of the distribution
 #' @return a numeric vector and (optionally) a plot of the turning angle or step length distribution
+#' @importFrom ggplot2 geom_histogram labs theme_minimal
 #' @export
 #' @examples
 #' \donttest{
@@ -25,11 +26,11 @@ ss_dist <- function(x, plot = T) {
   dist <- traj[[1]]$dist
 
   if (plot == T) {
-    hist(
-      x = dist,
-      xlab = "Step Length",
-      main = paste0("Step Length Distribution \n dt = ", dt, " secs")
-    )
+    ggplot() + geom_histogram(aes(x = dist)) +
+      labs(
+      x = "Step Length",
+      title = paste0("Step Length Distribution \n dt = ", dt, " secs")
+    ) + theme_minimal()
   }
 
   return(dist)
@@ -48,11 +49,11 @@ ta_dist <- function(x, plot = T) {
 
 
   if (plot == T) {
-    hist(
-      x = ang,
-      xlab = "Turning Angle",
-      main = paste0("(Relative) Turning Angle Distribution \n dt = ", dt, " secs")
-    )
+    ggplot() + geom_histogram(aes(
+      x = ang)) + labs(
+      x = "Turning Angle",
+      title = paste0("(Relative) Turning Angle Distribution \n dt = ", dt, " secs")
+    ) + theme_minimal()
   }
 
   return(ang)

R/population.R

@@ -6,14 +6,14 @@
 #' @param labels logical, indicating whether or not to label points with IDs, implemented using
 #' \link[ggrepel]{geom_text_repel}
 #' @export
-#' @importFrom ggplot2 ggplot aes geom_sf labs
+#' @importFrom ggplot2 ggplot aes geom_sf labs theme_minimal
 dist_map <- function(df, proj4, labels = TRUE) {
   mean_sf <- df %>%
     dplyr::group_by(.data$id) %>%
     dplyr::summarise(
       meanX = mean(.data$x, na.rm = T),
       meanY = mean(.data$y, na.rm = T),
-      meanyear = factor(round(mean(lubridate::year(.data$date)), 0))
+      meanyear = as.character(round(mean(lubridate::year(.data$date)), 0))
     ) %>%
     sf::st_as_sf(coords = c("meanX", "meanY"), remove = FALSE, crs = proj4)
 
@@ -22,7 +22,7 @@ dist_map <- function(df, proj4, labels = TRUE) {
     labs(
       title = "General Spatial and Temporal Distribution of Individuals",
       x = "Latitude", y = "Longitude", color = "Year", fill = "Year"
-    )
+    ) + theme_minimal()
 
   if (labels == TRUE) {
     print(p + ggrepel::geom_text_repel(mapping = aes(
@@ -47,6 +47,7 @@ plot_timeline <- function(df) {
   df %>%
     group_by(.data$id) %>%
     summarise(start_date = min(.data$date), end_date = max(.data$date)) %>%
+    mutate(id = forcats::fct_reorder(.data$id, .data$start_date, .desc = T)) %>%
     ggplot() + geom_segment(aes(
       x = .data$start_date, xend = .data$end_date,
       y = .data$id, yend = .data$id

inst/reports/report.Rmd

@@ -15,6 +15,7 @@ params:
 
 ```{r setup, include = F}
 knitr::opts_chunk$set(
+  root.dir = tempdir(),
   collapse = TRUE,
   echo = FALSE,
   message = FALSE,
@@ -37,7 +38,8 @@ wavelet <- (!is.null(params$wavelet))
 # Visualize the trajectory
 
 ```{r}
-plot(params$df$x, params$df$y, type = "p", xlab = "Easting", ylab = "Northing")
+ggplot(params$df, aes(x, y)) + geom_point() + labs(x = "Easting", y = "Northing") +
+  theme_minimal()
 ```
 
 # Step Length & Turning Angle Distributions:
@@ -58,10 +60,10 @@ ta <- ta_dist(x = params$df)
 roll <<- rolling_stats(params$df)
 
 p1 <- ggplot(roll, aes(date, y = mean_dist)) +
-  geom_point(na.rm = T) + geom_smooth(na.rm = T)
+  geom_path(na.rm = T)
 
 p2 <- ggplot(roll, aes(date, y = mean_ang)) +
-  geom_point(na.rm = T) + geom_smooth(na.rm = T)
+  geom_path(na.rm = T)
 
 
 suppressMessages(p1 + p2 + plot_annotation("Mean Step Size & Turning Angle on a Rolling Interval"))
@@ -153,23 +155,37 @@ plots <- purrr::map(
 ```{r eval = seasonal}
 seasonal_stats <<- interval_stats(params$df, type = "seasonal", seas = params$seas)
 
-# p1 <-  ggplot(seasonal_stats,
-#       aes(x = as.numeric(interval_start), color = seas)) +
-#       geom_pointrange(aes( y = mean_dist, ymin = mean_dist - sd_dist, ymax = mean_dist + sd_dist)) +
-# #      geom_pointrange(aes( y = mean_ang, ymin = mean_ang - sd_ang, ymax = mean_ang + sd_ang)) +
-#       scale_x_continuous(breaks = 1:nlevels(seasonal_stats$interval_start),
-#                          labels = seasonal_stats$seas) +
-#       xlab(NULL) +
-#       theme(axis.text.x = element_text(angle = 25, vjust = .5), legend.position = "None")
-
-seasonal_stats %>%
-  tidyr::gather("stat", "estimate", -1:-2) %>%
-  ggplot(., aes(x = as.numeric(interval_start), y = estimate, color = stat)) +
-  geom_point() + # to make sure single seasons displayed.
-  geom_step() +
-  scale_x_continuous("Season", breaks = 1:nlevels(seasonal_stats$interval_start)) +
-  ggtitle("Seasonal Interval Statistics")
-
+p1 <- seasonal_stats %>%
+  # tidyr::gather("stat", "estimate", -1) %>%
+  ggplot(., aes(x = as.numeric(interval_start), y = mean_dist)) +
+  geom_col() +
+  geom_errorbar(aes(
+    ymin = mean_dist,
+    ymax = mean_dist + sd_dist
+  )) + labs(x = "", y = "Average Step Size (+ 1 SD)")
+p2 <- seasonal_stats %>%
+  ggplot(., aes(x = as.numeric(interval_start), y = mean_ang)) +
+  geom_col() +
+  labs(x = "Season", y = "Mean Turning Angle")
+
+p3 <- seasonal_stats %>%
+  select(-mean_dist, -sd_dist, -mean_ang, -sd_ang) %>%
+  tidyr::gather("stat", "estimate", -1) %>%
+  ggplot(., aes(
+    x = as.factor(as.numeric(interval_start)), y = estimate,
+    group = stat, color = stat
+  )) +
+  geom_point() +
+  geom_line() +
+  lims(y = c(-1, 1)) +
+  labs(x = "")
+
+p1 + p2 + p3 +
+  plot_annotation(
+    title = "Interval Statistics Across Seasons",
+    theme = theme(plot.title = element_text(hjust = .5))
+  ) &
+  theme_minimal()
 ```
 
 `r if (wavelet) '# Visualizing Periodicity'`