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inat_flexdashboard_GN.Rmd
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---
title: "Invertebrados Marinos Golfo Nuevo"
output:
flexdashboard::flex_dashboard:
theme: lumen
social: menu
source: embed
logo: https://www.proyectosub.org.ar/wp-content/uploads/2020/04/logoinatproyectosub.png
---
```{r setup, include=FALSE}
library(flexdashboard)
library(leaflet)
library(ggplot2)
library(plotly)
library(spocc)
library(rinat)
library(mapr)
library(RColorBrewer)
palette(brewer.pal(8, "Set2"))
```
```{r dataread, message=FALSE, warning=FALSE, include=FALSE}
#Extract data from the project "biodiversidad-marina-bahia-pardelas"
#projecy_info <- pardelas_obs <- get_inat_obs_project("biodiversidad-marina-bahia-pardelas", type = "info", raw = FALSE)
#extract observations
#pardelas_obs <- get_inat_obs_project(projecy_info$id, type = "observations")
#create data frame with reduced info
#pardelas_obs_dataframe <- data.frame(name = pardelas_obs$taxon.name,longitude = as.numeric(pardelas_obs$longitude),latitude = as.numeric(pardelas_obs$latitude),user=pardelas_obs$user_login,date= as.Date(pardelas_obs$observed_on), taxonrank=pardelas_obs$taxon.rank,taxonid=pardelas_obs$taxon.id,taxon= pardelas_obs$iconic_taxon.name, stringsAsFactors = FALSE)
#As some of the taxon information (eg Phylum) is not included in the rinat package we proceed to import data from csv. the package "spocc" shoul be explored for download data.
#Read data downloaded from inat site
pardelas_obs_web <- read.csv("observations-GN_MAY27.csv")
#we create a dataframe with the variable of interest.
pardelas_obs_dataframe <- data.frame(
name = pardelas_obs_web$scientific_name,
longitude = pardelas_obs_web$longitude,
latitude = pardelas_obs_web$latitude,
user=pardelas_obs_web$user_login,
date= as.Date(pardelas_obs_web$observed_on),
taxonid=pardelas_obs_web$id,
taxon= pardelas_obs_web$iconic_taxon_name,
kindom= pardelas_obs_web$taxon_kingdom_name,
phylum=pardelas_obs_web$taxon_phylum_name,
class=pardelas_obs_web$taxon_class_name,
order=pardelas_obs_web$taxon_order_name,
genus=pardelas_obs_web$taxon_genus_name,
stringsAsFactors = FALSE)
#add year from date info
pardelas_obs_dataframe$year <- as.numeric(format(pardelas_obs_dataframe$date,"%Y"))
obs_byyear = as.data.frame(table(pardelas_obs_dataframe$year))
colnames(obs_byyear)=c("Año","Observaciones")
```
Column1{data-width=250}
-------
### Tabla Observaciones {data-width=245}
```{r taxalist}
knitr::kable(as.data.frame(table(reorder(pardelas_obs_dataframe$name,rep(-1,length(pardelas_obs_dataframe$name)),sum))),col.names = c("Especies","Observaciones"))
```
Column2 {.tabset .tabset-fade}
-------
### Mapa
```{r map}
map_leaflet(pardelas_obs_dataframe,size=4) %>%
addMiniMap(toggleDisplay = T)
```
### Phylum
```{r donut}
##numbers of observations by phylum
taxranks = as.data.frame(table(pardelas_obs_dataframe$phylum))
##Donut plot
p = taxranks %>% plot_ly(labels = ~Var1, values=~Freq) %>%
add_pie(hole=0.6) %>%
layout(title = ~paste0("Numero total de Taxa: ", length(unique(pardelas_obs_dataframe$taxonid))))
plotly::config(p,displayModeBar = F)
```
### Observaciones por año
```{r obsbyyear}
#plot number of observation by date
ggplot(obs_byyear, aes(x=Año,y=Observaciones,group=1)) + geom_line() + geom_point()+ xlab("Año") + ylab("Observaciones") + theme_minimal()
```
### Especies exóticas
```{r exotic, include=FALSE}
#based on https://alexis-catherine.github.io/visualization/inaturalist-invasive-bubble-plot/
library(curlconverter)
library(tidyverse)
library(ggraph)
library(packcircles)
library(lubridate)
iconic_name <- NULL
name <- NULL
common <- NULL
months <- NULL
place_name <- "Golfo Nuevo"
proyect_id <- "invertebrados-marinos-golfo-nuevo"
#extract observation of "invertebrados-marinos-golfo-nuevo" project
api <- paste("curl -X GET --header 'Accept: application/json' 'https://api.inaturalist.org/v1/observations?endemic=false&geo=true&introduced=true&project_id='", proyect_id, "&quality_grade=research&per_page=200&order=desc&order_by=created_at'", sep = "")
my_ip <- straighten(api) %>%
make_req()
dat <- content(my_ip[[1]](), as="parsed")
for(i in 1:length(dat$results)){
iconic_name <- c(iconic_name, dat$results[[i]]$taxon$iconic_taxon_name)
name <- c(name,dat$results[[i]]$taxon$name)
if(is.null(dat$results[[i]]$taxon$preferred_common_name)){
common <- c(common, NA)}else{
common <- c(common, dat$results[[i]]$taxon$preferred_common_name)}
}
if (dat$total_results > 200){
for (i in 2:floor(dat$total_results/200)){
api <- paste("curl -X GET --header 'Accept: application/json' 'https://api.inaturalist.org/v1/observations?endemic=false&geo=true&introduced=true&place_id='", place_id, "&quality_grade=research&page=", i, "&per_page=200&order=desc&order_by=created_at'", sep = "")
my_ip <- straighten(api) %>%
make_req()
dat <- content(my_ip[[1]](), as="parsed")
for(j in 1:length(dat$results)){
iconic_name <- c(iconic_name, dat$results[[j]]$taxon$iconic_taxon_name)
name <- c(name,dat$results[[j]]$taxon$name)
if(is.null(dat$results[[j]]$taxon$preferred_common_name)){
common <- c(common, NA)}else{
common <- c(common, dat$results[[j]]$taxon$preferred_common_name)}
}
}
}
dat2 <- dat
dat2 <- data.frame(iconic_name,name,common)
dat2 %>%
group_by(name, iconic_name) %>%
summarise(n = n()) -> data
names(data) <- c("group", "iconic", "value")
packing <- circleProgressiveLayout(data$value, sizetype='area')
data <- cbind(data, packing)
dat.gg <- circleLayoutVertices(packing, npoints=50)
dat.gg$group <- rep(data$iconic, each = 51)
```
```{r exotic2}
# Make the plot
ggplot() +
# Make the bubbles
geom_polygon(data = dat.gg, aes(x, y, group = id), fill="chartreuse3",colour = "black", alpha = 0.6) +
# Add text in the center of each bubble + control its size
geom_text(data = data, aes(x, y, size=value, label = group),fontface = "italic") +
scale_size_continuous(range = c(1,5), guide = F) +
# General theme:
theme_void() +
theme() +
coord_equal() +
ggtitle(paste("Especies exóticas de", place_name)) +
theme(plot.title = element_text(size = 12, face = "bold")) +
labs(caption ="El tamaño del circulo representa la cantidad de observaciones.\n Source code:https://alexis-catherine.github.io/") +
theme(legend.position = "none")+
theme(plot.title = element_text(hjust = 0.5))
```
Column3{data-width=150}
-------
### Números
```{r}
#spp
nspp = length(unique(pardelas_obs_dataframe$name))
gauge(nspp, min=0, max=200,label="Species")
```