DailyReport.Rmd

---
output: html_document
---

<!-- This Markdown file implements Dr. Jessica Gorzo's CTT data tools and visualizes the results of several analyses in a single report. -->

```{r defaults, echo=FALSE}
knitr::opts_chunk$set(echo = FALSE, results = FALSE, warning = FALSE, message = FALSE)
```

```{r setup}
library(raster)
library(sp)
library(rgdal)
library(sf)
library(ggplot2)
library(geosphere)
library(ggmap)
library(RColorBrewer)
source("**path to data_manager.R**")
source("**path to localization.R**")
# ^ edit to include paths to localization.R and data_manager.R

all_data <- load_data(infile, starttime = start_time)
beep_data <- all_data[[1]][[1]]
beep_data <- beep_data[beep_data$Time > start_time,] 
if(!is.null(end_time)) {beep_data <- beep_data[beep_data$Time < end_time,]}

nodes$NodeId <- toupper(nodes$NodeId)
beep_data <- beep_data[beep_data$NodeId %in% nodes$NodeId,]

channel <- 3
tag_id <- tags$TagId 
beep_data <- beep_data[beep_data$TagId %in% tags$TagId,]
attr(beep_data$Time, "tzone") <- "America/New_York"

freq <- c("2 min", "3 min", "10 min")

resampled <- advanced_resampled_stats(beeps = beep_data, node = nodes, freq = freq[1], tag_id = tag_id) 
```

---
title: `r max(beep_data$Time)`
author: "John Doe"
---

<!-- Note that due to the nature of ggplot2::facet_wrap, you may want to customize the width and 
height parameters (and/or play with the ncol arg) for the following 2 snippets depending on the number of nodes/tags.-->

## 1. Node RSSI Over Time

```{r nodes, fig.width=14, fig.height=12}
ggplot(data=resampled, aes(x=freq, y=TagRSSI_max, colour=NodeId)) +
  geom_line(show.legend = FALSE) + facet_wrap(~ NodeId, ncol=4) + ylab("Max RSSI") + xlab("Date")
```

## 2. Tag RSSI Over Time

```{r tags, fig.width=14, fig.height=8}
ggplot(data=resampled, aes(x=freq, y=TagRSSI_max, colour=TagId)) +
  geom_line(show.legend = FALSE) + facet_wrap(~ TagId, ncol=4) + ylab("Max RSSI") + xlab("Date")
```


```{r apikey}
# Google API key required for basemaps
# Restrict access to the following four APIs: Maps Static, Geocoding, Geolocation, Maps Embed
ggmap::register_google('**insert API key here**')
ph_basemap <- get_googlemap(center = c(lon = centerLon, lat = centerLat), zoom = 16, scale = 2, maptype = "satellite")
```


## 3. Node Locations

```{r nodelocs}
nodes$ceil <- as.factor(ceiling(as.numeric(rownames(nodes))/7))
col_labs <- rep(brewer.pal(7, 'Set1'), each=length(unique(nodes$ceil)))[1:length(nodes$ceil)]
shp_labs <- rep(15:(15 + length(unique(nodes$ceil)) - 1), 7)[1:length(nodes$ceil)]
ggmap(ph_basemap) + 
  geom_point(data = nodes, aes(x=lng, y=lat, color=NodeId, shape=NodeId)) +
  scale_color_manual(name = 'NodeId', labels = nodes$NodeId,
                     values = col_labs) +
  scale_shape_manual(name = 'NodeId', labels = nodes$NodeId,
                     values = shp_labs)
```

## 4. Locations by TagId (All Time)

```{r locsetup}
beep_data <- beep_data[beep_data$TagRSSI >= -95,]
locations <- weighted_average(freq[2], beep_data, nodes, all_data[[2]][[1]], 0, tag_id)

n <- 2
locations <- locations[locations$unique_nodes > n,]
locations <- cbind(locations, locations@coords)

locations.recent <- subset(locations, freq > max(locations$freq) - 7*86400) ## note how I fixed this
nodes_spatial <- nodes
coordinates(nodes_spatial) <- ~lng:lat
crs(nodes_spatial) <- CRS("+proj=longlat +datum=WGS84")

my_locs <- locations
my_locs.recent <- locations.recent

locs <- st_as_sf(my_locs)
locs.recent <- st_as_sf(my_locs.recent)
my_nodes <- st_as_sf(nodes_spatial)
locs$freq <- as.POSIXct(locs$freq)
attr(locs$freq, "origin") <- "1970-01-01"
```

```{r locsalltime, fig.width=14, fig.height=12}
ggplot() +
  geom_sf(data = locs, aes(colour=freq), inherit.aes = FALSE) +
  geom_sf(data = my_nodes) +
  facet_wrap(~ TagId, ncol=4) + scale_color_datetime(low="#a4f5e1", high="#018060")
```

## 5. Recent Locations by TagId

```{r recentlocs, fig.width=14, fig.height=12}
ggplot() +
  geom_sf(data = locs.recent, aes(colour=freq), inherit.aes = FALSE, show.legend = TRUE) +
  geom_sf(data = my_nodes) +
  facet_wrap(~ TagId, ncol=4) + scale_color_datetime(low="#a4f5e1", high="#018060")
```

```{r recentmaps}
week_prev <- max(locations$freq) - 7*86400 
ph_basemap <- get_googlemap(center = c(lon = centerLon, lat = centerLat), zoom = 17, scale = 2, maptype = "satellite")
for (t in tag_id){
#   if (tags$species[which(tags$TagId == t)] == 'SALS') {
#     lo <- '#ffb5de'
#     hi <- '#ff008c'
#   } else if (tags$species[which(tags$TagId == t)] == 'SESP') {
#     lo <- '#fffb01'
#     hi <- '#ff0000'
#   } else if (tags$species[which(tags$TagId == t)] == 'NESP') {
#     lo <- '#ffedf3'
#     hi <- '#d1495b'
#   }
  lo <- '#ffb5de'
  hi <- '#ff008c'
  pl <- ggmap(ph_basemap) +
    geom_point(data=as.data.frame(my_locs.recent[my_locs.recent$TagId == t,]), aes(x=avg_x,y=avg_y, color = freq)) +
    scale_color_datetime(limits=c(week_prev, NA), low=lo, high=hi) +
    #ggtitle(sprintf('%s (%s)', t, tags$species[which(tags$TagId == t)])) +
    ggtitle(t) +
    theme(plot.title = element_text(hjust = 0.5))
  print(pl)
}
```