Add indicator analysis figures

DESCRIPTION

@@ -42,4 +42,4 @@ Suggests:
     testthat
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

NAMESPACE

@@ -3,14 +3,17 @@
 export("%>%")
 export(check_default_data_path)
 export(check_for_newer_data)
+export(coastal_analysis)
 export(coastal_indicator)
 export(download_observations)
 export(dry_down)
+export(foraging_analysis)
 export(foraging_indicator)
 export(format_code)
 export(format_todo)
 export(format_value)
 export(get_default_data_path)
+export(initiation_analysis)
 export(initiation_indicator)
 export(load_datafile)
 export(load_indicator_data)
@@ -21,6 +24,7 @@ export(plot_coastal)
 export(plot_foraging)
 export(plot_initiation)
 export(plot_supercolony)
+export(supercolony_analysis)
 export(supercolony_indicator)
 export(use_default_data_path)
 export(water_report)

R/indicator_analysis.R

@@ -0,0 +1,183 @@
+#' @name foraging_analysis
+#'
+#' @title Produce status figures of tactile/visual foraging indicator data
+#'
+#' @description Analyze a table of rolling averages for the proportion of tactile/visual foragers,
+#' by year, and plot with thresholds by year
+#'
+#' @param minyear Earliest year to include
+#' @param maxyear Most recent year to include
+#' @param window number of years over which to create a rolling average
+#'
+#' @inheritParams load_indicator_data
+#'
+#' @return a data.frame
+#'
+#' @export
+#'
+foraging_analysis <- function(path = get_default_data_path(),
+                                minyear = 1986, maxyear = as.integer(format(Sys.Date(), "%Y")),
+                                window = 3,
+                                download_if_missing = TRUE)
+{
+  library(ggplot2)
+  foraging <- foraging_indicator(minyear = 2004) %>%
+    dplyr::mutate(period="historic") %>%
+    dplyr::mutate(period = replace(period, year > 2016, "modern"))
+
+  A <- ggplot(foraging, aes(x=proportion_mean, fill=period)) +
+    geom_density(adjust = 1, alpha=0.5) +
+    xlim(0,10) +
+    labs(title="Average Foraging Ratio", x="tactile/visual", y = "density") +
+    theme_classic() +
+    theme(legend.position = c(.85,.9))
+
+  B <- ggplot(foraging, aes(x=year, y=proportion_mean,
+                         ymin=proportion_mean-proportion_sd, ymax=proportion_mean+proportion_sd)) +
+    geom_line(color="black", show.legend = FALSE) +
+    geom_ribbon(alpha=0.2, fill="grey", show.legend = FALSE) +
+    geom_ribbon(alpha=0.5, aes(color=NULL, fill=period), show.legend = FALSE) +
+    labs(x="Year", y = "tactile/visual") +
+    theme_classic()
+
+  cowplot::plot_grid(A, B, labels = NULL)
+}
+
+#' @name initiation_analysis
+#'
+#' @title Produce status figures of wood stork nesting intiation indicator data
+#'
+#' @description Analyze a table of rolling averages for the date of wood stork nest initiation,
+#' by year
+#'
+#' @param minyear Earliest year to include
+#' @param maxyear Most recent year to include
+#' @param window number of years over which to create a rolling average
+#'
+#' @inheritParams load_indicator_data
+#'
+#' @return a data.frame
+#'
+#' @export
+#'
+initiation_analysis <- function(path = get_default_data_path(),
+                                 minyear = 1986, maxyear = as.integer(format(Sys.Date(), "%Y")),
+                                 window = 3,
+                                 download_if_missing = TRUE)
+{
+library(ggplot2)
+initiation <- initiation_indicator(minyear = 2004) %>%
+              dplyr::mutate(period="historic") %>%
+              dplyr::mutate(period = replace(period, year > 2016, "modern"))
+
+  A <- ggplot(initiation, aes(x=date_score_mean, fill=period)) +
+  geom_density(adjust = 1, alpha=0.5) +
+  scale_x_reverse(limits=c(5,-.2), breaks=c(4,3,2,1,0),labels=c("December","January","February","March","April")) +
+  labs(title="Average Stork Nest Initiation Date", x="Date Score", y = "density") +
+  theme_classic() +
+  theme(legend.position=c(.9,.8))
+
+  B <- ggplot(initiation, aes(x=year, y=date_score_mean,
+                       ymin=date_score_mean-date_score_sd, ymax=date_score_mean+date_score_sd)) +
+  geom_line(color="black") +
+  geom_ribbon(alpha=0.2, fill="grey") +
+  geom_ribbon(alpha=0.5, aes(color=NULL, fill=period)) +
+  scale_y_reverse(limits=c(4.55,0), breaks=c(4,3,2,1,0),labels=c("December","January","February","March","April")) +
+  labs(x="Year", y = "Date Score") +
+  theme_classic() +
+  theme(legend.position="none")
+
+  cowplot::plot_grid(A, B, labels = NULL)
+}
+
+#' @name coastal_analysis
+#'
+#' @title Produce status figures of wood stork nesting intiation indicator data
+#'
+#' @description Analyze a table of rolling averages for the proportion of coastal nesters,
+#' by year, and plot with thresholds
+#'
+#' @param minyear Earliest year to include
+#' @param maxyear Most recent year to include
+#' @param window number of years over which to create a rolling average
+#'
+#' @inheritParams load_indicator_data
+#'
+#' @return a data.frame
+#'
+#' @export
+#'
+coastal_analysis <- function(path = get_default_data_path(),
+                                minyear = 1986, maxyear = as.integer(format(Sys.Date(), "%Y")),
+                                window = 3,
+                                download_if_missing = TRUE)
+{
+library(ggplot2)
+  coastal <- coastal_indicator(minyear = 2004) %>%
+    dplyr::mutate(period="historic") %>%
+    dplyr::mutate(period = replace(period, year > 2016, "modern"))
+
+  A <- ggplot(coastal, aes(x=proportion_mean, fill=period)) +
+    geom_density(adjust = 1.5, alpha=0.5) +
+    xlim(0,.5) +
+    labs(title="Average proportion nests in coastal colonies", x="proportion coastal", y = "density") +
+    theme_classic() +
+    theme(legend.position=c(.9,.8))
+
+  B <- ggplot(coastal, aes(x=year, y=proportion_mean,
+                         ymin=proportion_mean-proportion_sd, ymax=proportion_mean+proportion_sd)) +
+    geom_line(color="black") +
+    geom_ribbon(alpha=0.2, fill="grey") +
+    geom_ribbon(alpha=0.5, aes(color=NULL, fill=period)) +
+    labs(x="Year", y = "proportion coastal") +
+    theme_classic() +
+    theme(legend.position="none")
+
+  cowplot::plot_grid(A, B, labels = NULL)
+}
+
+#' @name supercolony_analysis
+#'
+#' @title Produce status figures of ibis supercolony indicator data
+#'
+#' @description Analyze a table of rolling averages for the interval between ibis supercolony
+#' events, by year
+#'
+#' @param minyear Earliest year to include
+#' @param maxyear Most recent year to include
+#' @param window number of years over which to create a rolling average
+#'
+#' @inheritParams load_indicator_data
+#'
+#' @return a data.frame
+#'
+#' @export
+#'
+supercolony_analysis <- function(path = get_default_data_path(),
+                             minyear = 1986, maxyear = as.integer(format(Sys.Date(), "%Y")),
+                             window = 3,
+                             download_if_missing = TRUE)
+{
+library(ggplot2)
+  supercolony <- supercolony_indicator(minyear = 2004) %>%
+    dplyr::mutate(period="historic") %>%
+    dplyr::mutate(period = replace(period, year > 2016, "modern"))
+
+  A <- ggplot(supercolony, aes(x=interval_mean, fill=period)) +
+    geom_density(adjust = 1, alpha=0.5) +
+    xlim(0,3) +
+    labs(title="Ibis supercolony mean interval", x="mean interval", y = "density") +
+    theme_classic() +
+    theme(legend.position=c(.9,.8))
+
+  B <- ggplot(supercolony, aes(x=year, y=interval_mean,
+                      ymin=interval_mean-interval_sd, ymax=interval_mean+interval_sd)) +
+    geom_line(color="black") +
+    geom_ribbon(alpha=0.2, fill="grey") +
+    geom_ribbon(alpha=0.5, aes(color=NULL, fill=period)) +
+    labs(x="Year", y = "mean interval") +
+    theme_classic() +
+    theme(legend.position="none")
+
+  cowplot::plot_grid(A, B, labels = NULL)
+}

R/report_work.R

@@ -66,6 +66,9 @@ initiation_indicator <- function(path = get_default_data_path(),
     dplyr::filter(dplyr::between(.data$year, minyear, maxyear)) %>%
     dplyr::arrange(.data$year) %>%
     dplyr::mutate(date_score_mean = zoo::rollapply(.data$date_score, FUN="mean",
+                                                   width=window, align="right",
+                                                   partial = TRUE),
+                  date_score_sd = zoo::rollapply(.data$date_score, FUN="sd",
                                                    width=window, align="right",
                                                    partial = TRUE))
 }
@@ -98,6 +101,9 @@ coastal_indicator <- function(path = get_default_data_path(),
     dplyr::filter(dplyr::between(.data$year, minyear, maxyear)) %>%
     dplyr::arrange(.data$year) %>%
     dplyr::mutate(proportion_mean = zoo::rollapply(.data$proportion, FUN="mean",
+                                                   width=window, align="right",
+                                                   partial = TRUE),
+                  proportion_sd = zoo::rollapply(.data$proportion, FUN="sd",
                                                    width=window, align="right",
                                                    partial = TRUE))
 }
@@ -136,6 +142,9 @@ foraging_indicator <- function(path = get_default_data_path(),
     dplyr::ungroup() %>%
     dplyr::arrange(.data$year) %>%
     dplyr::mutate(proportion_mean = zoo::rollapply(.data$proportion, FUN="mean",
+                                                   width=window, align="right",
+                                                   partial = TRUE),
+                  proportion_sd = zoo::rollapply(.data$proportion, FUN="sd",
                                                    width=window, align="right",
                                                    partial = TRUE))
 }
@@ -168,7 +177,10 @@ supercolony_indicator <- function(path = get_default_data_path(),
     dplyr::filter(dplyr::between(.data$year, minyear, maxyear)) %>%
     dplyr::mutate(interval_mean = zoo::rollapply(.data$ibis_interval, FUN="mean",
                                                    width=window, align="right",
-                                                   partial = TRUE))
+                                                   partial = TRUE),
+                  interval_sd = zoo::rollapply(.data$ibis_interval, FUN="sd",
+                                                 width=window, align="right",
+                                                 partial = TRUE))
 }
 
 #' @name plot_foraging

R/wader-package.R

@@ -5,7 +5,6 @@
 #' It contains a set of functions to download, clean, and summarize the data.
 #'
 #' @name wader
-#' @docType package
 #' @keywords package
 #'
 #' @importFrom lubridate "%m+%"
@@ -14,8 +13,7 @@
 #' @importFrom stats median na.omit sd setNames time
 #' @importFrom httr content GET stop_for_status
 #'
-
-NULL
+"_PACKAGE"
 
 ## quiets concerns of R CMD check re: variables used in NSE functions
 if (getRversion() >= "2.15.1") utils::globalVariables(