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boleteRa.Rmd
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---
title: "BoleteRa Crosstalk"
author: "Xavier de Pedro"
date: "15 d’octubre de 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```
```{r install and load required packages if needed, echo=FALSE}
if (!require("devtools")) install.packages("devtools"); library(devtools)
if (!require("pacman")) install.packages("pacman"); library(pacman)
# Packages for Data manipulation
if (!require("readODS")) install.packages("readODS"); library(readODS)
if (!require("readxl")) install.packages("readxl"); library(readxl)
if (!require("readr")) install.packages("readr"); library(readr)
if (!require("XML")) install.packages("XML"); library(XML)
if (!require("openxlsx")) install.packages("openxlsx"); library(openxlsx)
if (!require("dplyr")) install.packages("dplyr"); library(dplyr)
if (!require("tidyr")) install.packages("tidyr"); library(tidyr)
if (!require("purrr")) install.packages("purrr"); library(purrr)
# Packages for Html5 Output manipulation
# Add package widget frame so that we attempt to get responsive iframes produced by htmlwidgets
# See: https://github.com/bhaskarvk/widgetframe
if(!require(widgetframe)){ install.packages("widgetframe") }; library(widgetframe)
if(!require(htmlwidgets)){ install.packages("htmlwidgets") }; library(htmlwidgets)
# Add webshot to allow making screenshots from htmlwidgets for printed versions of reports
if(!require(webshot)){ install.packages("webshot") }; library(webshot)
# Install also phantomjs thorugh webshot as a required dependency (only once)
#webshot::install_phantomjs(baseURL = "https://bitbucket.org/ariya/phantomjs/downloads/")
# Note: some system packages are needed for tmap to install properly in Ubuntu 16.04 systems
# and for some of them you may need to add an extra repository (confirmed upto Ubuntu 16.04)
# sudo add-apt-repository -y ppa:opencpu/jq
# sudo add-apt-repository -y ppa:ubuntugis/ubuntugis-unstable
# sudo apt update
# sudo apt install libudunits2-0 libudunits2-dev libjq-dev libprotobuf-dev protobuf-compiler libgdal20
# Packages for Maps
if (!require("tmap")) install.packages("tmap")
if (!require("leaflet")) install_github("rstudio/leaflet"); library(leaflet)
if (!require("leaflet.minicharts")) install.packages("leaflet.minicharts"); library(leaflet.minicharts)
if (!require("geonames")) install.packages("geonames"); library(geonames)
if (!require("ggmap")) install.packages("ggmap"); library(ggmap)
if (!require("mapview")) install.packages("mapview"); library(mapview) # https://r-spatial.github.io/mapview/
# Packages for Interactive Tables
if (!require("DT")) install.packages("DT"); library(DT)
if (!require(rpivotTable)){ install.packages("rpivotTable") }; require(rpivotTable)
# Packages for Dashboard-like UI to display results
# See https://rstudio.github.io/crosstalk/using.html
if (!require("crosstalk")) install_github("rstudio/crosstalk"); library(crosstalk)
# Test adding an easy interface to allow changing htmlwidget on the go with manipulatewidget
# See: https://github.com/rte-antares-rpackage/manipulateWidget
if (!require("manipulateWidget")) install.packages("manipulateWidget"); library(manipulateWidget)
```
## Read source data
```{r Read source data, echo=FALSE}
# =======================================================
# First approach: Use readODS package to keep data easily in a Visual Spreadsheet in LibreOffice
# =======================================================
#
# Define some paths
path.html <- "html"
path.data <- file.path("~", "Dropbox", "Bolets", "zamiaDroid", "Citations", "00_data")
path.results <- file.path("~", "Dropbox", "Bolets", "zamiaDroid", "Citations", "00_results")
path.photos <- file.path("~", "Sync", "BQa5_Zamiadroid", "Photos")
# =======================================================
# Former 2nd approach: Use readr to import a csv file
# =======================================================
#
# ### Dataset
# #read datasets
setwd(path.data)
# #data.file <- "20140000_20170900_bolets_Zamia_TAB_Xavier_dePedro.csv"
# data.file <- "20140000_20171014_bolets_Zamia_TAB.csv"
temp = list.files(path=path.data, pattern="*.tab", full.names = T)
myfiles = lapply(temp, readr::read_tsv)
#Combine files. derived from https://stackoverflow.com/a/11433532
md.orig <- dplyr::bind_rows(myfiles)
md <- md.orig
```
## Add unique CitationId (cid) and remove duplicates
```{r Add unique CitationId (cid)}
cid <- format.Date(md$Date, "%y%m%d_%H%M%S")
md <- dplyr::bind_cols(list(data.frame(cid), md))
tail(md)
md <- md %>%
arrange(cid)
tail(md)
# Mostra duplicats
# Return all duplicated elements
md.dups <- md %>%
filter(duplicated(md) | duplicated(md, fromLast = TRUE))
# Remove duplicates with efficient function (distinct from dplyr)
# Remove duplicated rows based on Sepal.Length
md <- md %>% distinct(cid, .keep_all = TRUE)
rbind(head(md.orig),tail(md.orig))
rbind(head(md),tail(md))
```
## Clean path from fotos
```{r}
table.foto.paths <- table(dirname(unlist(strsplit(as.character(md$foto), "; "))))
table.foto.paths
for (ii in 1:length(names(table.foto.paths))) {
md$foto <- gsub(paste0(names(table.foto.paths)[ii], "/"), "", md$foto, fixed="T")
}
```
## Check that photo exists
```{r Check that photo exists}
# Check that photo exists and how many are there
pic.count <- list()
for (ii in 1:length(md$foto)){
pic.count[[ii]] <- table(file.exists(file.path(path.photos, strsplit(md$foto, "; ")[[ii]])))["TRUE"]
}
pic.count.df <- do.call(rbind.data.frame, pic.count)
colnames(pic.count.df) <- "n.pics"
table(pic.count.df$n.pics)
md <- md %>%
mutate(pics = pic.count.df$n.pics)
md.missing.pics <- md[which(is.na(md$pics)),]
md.missing.pics
#table(file.exists(file.path(path.photos, md$foto)))
```
## Tidy data set
```{r Tidy data set, echo=FALSE}
# =======================================================
# Tidy data set
# =======================================================
# SPlit lat and long in two columns from the combined values in one column produced by zamiaDroid
library(stringr)
md2 <- cbind(md, data.frame(str_split_fixed(md$CitationCoordinates, " ", 2)))
names(md2)[ncol(md2)-1] <- "Latitude"
names(md2)[ncol(md2)] <- "Longitude"
md2$Latitude <- as.numeric(levels(md2$Latitude))[md2$Latitude]
md2$Longitude <- as.numeric(levels(md2$Longitude))[md2$Longitude]
md <- md2
rm(md2)
# Clean up data and fix oddities for R
md$Date <- as.POSIXct(strptime(md$Date, "%Y-%m-%d %H:%M:%S"))
#d$Pendent[is.na(md$Pendent)] <- "99"
md$Abundància <- gsub("[^0-9]", "", md$Abundància)
md$Abundància <- as.numeric(md$Abundància)
md$Pendent <- gsub("[^0-9]", "", md$Pendent)
md$Pendent <- as.numeric(md$Pendent)
md$`nom vulgar (comestible)` <- as.factor(md$`nom vulgar (comestible)`)
md$`nom vulgar (no comestible)` <- as.factor(md$`nom vulgar (no comestible)`)
nom.comestible.coln <- grep("nom vulgar (comestible)", colnames(md), fixed=T)
colnames(md)[nom.comestible.coln] <- "nom_comestible"
colnames(md)[nom.comestible.coln+1] <- "nom_nocomestible"
densitat_sotabost.coln <- grep("Densitat", colnames(md), fixed=T)
colnames(md)[densitat_sotabost.coln] <- "Densitat_sotabosc"
detalls_bolet.coln <- grep("Detalls del bolet", colnames(md), fixed=T)
colnames(md)[detalls_bolet.coln] <- "Detalls_bolet"
# For cases of citations with more than 1 picture, get only the first one for easier display in maps
myfoto1 <- unlist(map(str_split(md$foto, ";"), 1))
#length(unlist(foto1))
#nrow(md)
md <- md %>%
mutate(foto1 = myfoto1)
rbind(head(md),
tail(md))
```
## Remove some secondary columns
```{r Remove some secondary columns}
md <- md %>%
select(-CitationCoordinates,
-SecondaryCitationCoordinates,
-X,
-Y,
-"poligon?",
-Autor
)
md <- md %>%
unite(Descripcio_Lloc, "descripció lloc","Descripció del lloc")
md$Descripcio_Lloc <- gsub("_NA", "", md$Descripcio_Lloc)
md$Descripcio_Lloc <- gsub("NA_", "", md$Descripcio_Lloc)
```
## Add Missing Altitudes and Toponimc Location info
```{r Add Missing Altitudes}
fname.extra.info.last <- "_120000_last_extra_info.csv"
fname.extra.info.current <- paste0("_120000_", format(Sys.Date(), "%y%m"), "00_extra_info.csv")
if (file.exists(file.path(path.data, fname.extra.info.last))) {
extra.info.tmp <- readr::read_csv(file.path(path.data, fname.extra.info.last))
} else {
extra.info.tmp <- data.frame("NA")
colnames(extra.info.tmp)[1] <- "cid"
}
# Remove redundant columns from extra.info.tmp
extra.info.tmp <- extra.info.tmp %>%
select(-Latitude,
-Longitude)
# Merge alçada from csv file to new md read from csv filers from disk
md.new <- dplyr::anti_join(md, extra.info.tmp, by="cid")
md.old <- dplyr::inner_join(md, extra.info.tmp, by="cid")
if (nrow(md.new) > 0 ) {
# Geo altitude for the missing data points
# From geonames
# See https://stackoverflow.com/a/8975851
# You can use the package that looks up from geonames, and get the value from the srtm3 digital elevation model:
#
# > require(geonames)
# > GNsrtm3(54.481084,-3.220625)
# srtm3 lng lat
# 1 797 -3.220625 54.48108
#
# or the gtopo30 model:
#
# > GNgtopo30(54.481084,-3.220625)
# gtopo30 lng lat
# 1 520 -3.220625 54.48108
#
# geonames is on CRAN so install.packages("geonames") will get it.
#No geonamesUsername set. See http://geonames.wordpress.com/2010/03/16/ddos-part-ii/ and set one with options(geonamesUsername="foo") for some services to work
options(geonamesUsername="xavidp")
#GNsrtm3(54.481084,-3.220625)
if (options()$geonamesHost == "ws.geonames.org") {
options(geonamesHost="api.geonames.org")
}
# Define empty lists to store temporary information retrieved by geonames servers
alcada_srtm3 <- list()
alcada_gtopo30 <- list()
lloc <- list()
data.xml <- list()
data <- list()
# For each step [i], keep CitationId (md$cid[i]) also just in case
for (i in 1:nrow(md.new)) {
# Comprova si tenim valor de Latitude o no tenim coordenades
if (!is.na(md.new$Latitude[i])) {
# Get altitude info from elevation models
alcada_srtm3[[i]] <- c(md.new$cid[i], GNsrtm3(md.new$Latitude[i], md.new$Longitude[i]))
# Alternatively, see url like: http://api.geonames.org/srtm3?lat=50.01&lng=10.2&username=xavidp&style=full
alcada_gtopo30[[i]] <- c(md.new$cid[i], GNgtopo30(md.new$Latitude[i], md.new$Longitude[i]))
# Alternatively, see url like: http://api.geonames.org/gtopo30?lat=50.01&lng=10.2&username=xavidp&style=full
# Get Location info
data.xml[[i]] <- xmlParse(paste0("http://api.geonames.org/extendedFindNearby?lat=", # long (extendedFindNearby)
md.new$Latitude[i], "&lng=", # long (extendedFindNearby)
md.new$Longitude[i], "&username=xavidp") # long (extendedFindNearby)
) # long (extendedFindNearby)
data[[i]] <- xmlToList(data.xml[[i]]) # long (extendedFindNearby)
# Recupera les dades d'interès, amb coordenades primer per garantir que tenim dataframe sencer
# fins i tot en casos en que no obtenim res com a noms de lloc
lloc[[i]] <- c(md.new$cid[i],
md.new$Latitude[i],
md.new$Longitude[i],
data[[i]][length(data[[i]])-2]$geoname$toponymName, # Província - en català
data[[i]][length(data[[i]])-1]$geoname$toponymName, # Ciutat
data[[i]][length(data[[i]]) ]$geoname$toponymName # Nom toponímic (lloc)
)
} else {
# No tenim coordenades per a aquesta citació, per la raó que sigui
alcada_srtm3[[i]] <- c(md.new$cid[i], "-", NA, NA)
alcada_gtopo30[[i]] <- c(md.new$cid[i], "-", NA, NA)
lloc[[i]] <- c(md.new$cid[i], "-", ".", NA, NA, NA)
}
}
# convert list of results into data.frame
alcada_srtm3.df <- do.call(rbind.data.frame, alcada_srtm3)
alcada_gtopo30.df <- do.call(rbind.data.frame, alcada_gtopo30)
lloc.df <- do.call(rbind.data.frame, lloc)
if (length(colnames(lloc.df))>3) {
colnames(lloc.df) <- c("cid", "Lat", "Lon", "Província", "Ciutat", "Nom_toponímic")
} else {
colnames(lloc.df) <- c("cid", "Lat", "Lon")
lloc.df <- lloc.df %>%
mutate("Província"=NA,
"Ciutat"=NA,
"Nom_toponímic"=NA
)
}
}
# Add columns with extra info to md.new df
md.new <- md.new %>%
mutate(alcada_srtm3=alcada_srtm3.df$srtm3,
alcada_gtopo30=alcada_gtopo30.df$gtopo30,
"Província"=lloc.df$Província,
"Ciutat"=lloc.df$Ciutat,
"Nom_toponímic"=lloc.df$Nom_toponímic
)
# Combine md.old rows with md.new rows to make the updated md
md <- rbind(md.old, md.new)
#lloc.df %>% filter(str_detect(Ciutat, "[a-zA-Z]"))
#lloc.df %>% filter(str_detect(Ciutat, "[:digit:]"))
# Save partial results to prevent overquerying the geonames server
extra.info <- md %>%
filter(str_detect(Ciutat, "[a-zA-Z]")) %>%
select(cid,
Latitude,
Longitude,
alcada_srtm3,
alcada_gtopo30,
"Província",
"Ciutat",
"Nom_toponímic"
)
# Desa arxiu amb extra info a disc per poder reemprar la info posteriors vegades
readr::write_csv(extra.info, file.path(path.data, fname.extra.info.current))
file.link(file.path(path.data, fname.extra.info.current),
file.path(path.data, fname.extra.info.last))
# Or this: https://stackoverflow.com/a/41773871
# to use googleway package with an api key
```
## Chunk to debug dummy data - temporary urls for fetching nearest city info
```{r Chunk to debug dummy data - temporary urls for fetching nearest city info}
if (FALSE) {
# Set als FALSE as it's been implemented in previous chunks
# See: https://stackoverflow.com/a/42320195
# Here is a quick solution to reverse geocoding in ggmap:
#
#library(ggmap)
#
# > coords
# lon lat
# 1 37.61730 55.75583
# 2 116.40739 39.90421
# 3 -77.03687 38.90719
#
# res <- lapply(with(coords, paste(lat, lon, sep = ",")), geocode, output = "more")
#res <- lapply(with(md, paste(Latitude, Longitude, sep = ",")), geocode, output = "more")
#
#
# > transform(coords, city = sapply(res, "[[", "locality"))
# lon lat city
# 1 37.61730 55.75583 Moskva
# 2 116.40739 39.90421 Beijing
# 3 -77.03687 38.90719 Washington
#Another approachm using geonames
require(XML)
#data <- xmlParse("http://api.geonames.org/findNearby?lat=41.804690&lng=2.100230&username=xavidp")
# Coordinates from Castellterçol
if (FALSE) {
data2 <- xmlParse("http://api.geonames.org/findNearby?lat=41.65926673&lng=2.52292308&username=xavidp")
xml_data2 <- xmlToList(data2)
xml_data2$geoname$toponymName
}
if (FALSE) {
# Extended find nearby
data3 <- xmlParse("http://api.geonames.org/extendedFindNearby?lat=41.65926673&lng=2.52292308&username=xavidp")
data3 <- xmlParse("http://api.geonames.org/extendedFindNearby?lat=41.94516782&lng=1.33559261&username=xavidp")
xml_data3 <- xmlToList(data3)
xml_data3[[length(xml_data3)]] # Mostra el darrer nom de geonames
lloc <- list
lloc <- c(xml_data3[[length(xml_data3)-2]]$toponymName, # Província - en català
xml_data3[[length(xml_data3)-1]]$toponymName, # Ciutat
xml_data3[[length(xml_data3)]]$toponymName # Nom toponímic (lloc)
)
}
#xml_data <- xmlToList(data)
#xml_data$geoname$toponymName
#[1] "Comarca del Moianès"
#xml_data$geoname$name
#[1] "Comarca del Moianès"
# Alternative way of reverse geocoding: using Here Maps and account
#https://developer.here.com/api-explorer/rest/geocoder/reverse-geocode
}
```
## Get historical wheather data (rainfall, temp)
### Get Nearest station by WeeWX powered Catalan weather stations
```{r Get historical wheather data (rainfall, temp) - by WeeWX powered Catalan weather stations}
#Code Will Come here
# See side R scripts clima1_weewx.R
# Find the nearest weather station to each citation point
# Dereived from: https://stackoverflow.com/a/27444208
pacman::p_load(rgeos, sp, tidyverse)
md.n <- nrow(md)
md.s <- structure(list(lon = md$Longitude, lat = md$Latitude), .Names = c("lon",
"lat"), row.names = c(NA, md.n), class = "data.frame")
nrow(md.s)
md.s <- md.s %>%
filter(!is.na(lat))
nrow(md.s)
md.sp <- SpatialPoints(md.s)
# Convert Meteorological stations to spatial points
my.meteo.n <- nrow(my.meteo)
my.meteo.s <- structure(list(lon = as.double(as.character(my.meteo$lon)),
lat = as.double(as.character(my.meteo$lat))
), .Names = c("lon", "lat"),
row.names = c(NA, my.meteo.n),
class = "data.frame")
nrow(my.meteo.s)
my.meteo.sp <- SpatialPoints(my.meteo.s)
md.nona <- md %>%
filter(!is.na(Latitude))
# Find the nearest neighbour (nn) meteo station id to each citation point
md.nona$nn.meteo.id <- apply(gDistance(my.meteo.sp, md.sp, byid=TRUE), 1, which.min)
md.nona <- left_join(md.nona, my.meteo, by=c("nn.meteo.id" = "id")) %>%
select(cid, nn.meteo = name)
rbind(head(md.nona), tail(md.nona))
md <- left_join(md, md.nona, by=c("cid"))
md
```
### Get Nearest station from the Servei de Meteorolgia de Catalunya (SMC)
```{r Get Nearest station from the Servei de Meteorolgia de Catalunya (SMC)}
# Find the nearest weather station to each citation point
# Dereived from: https://stackoverflow.com/a/27444208
pacman::p_load(sf, tidyverse)
md.n <- nrow(md)
md.s <- structure(list(lon = md$Longitude, lat = md$Latitude), .Names = c("lon",
"lat"), row.names = c(NA, md.n), class = "data.frame")
nrow(md.s)
md.s <- md.s %>%
filter(!is.na(lat))
nrow(md.s)
md.sf <- st_as_sf(md %>% filter(!is.na(Latitude)),
coords = c("Longitude", "Latitude"),
crs = 4326)
# Load SMC station list
smc.sl.files <- list.files(file.path("precipitacio", "_smc"), pattern = "*.smc_station_list.txt")
# Get the name of the last file to hold smc station list
my.meteo.smc <- rio::import(file.path("precipitacio", "_smc", smc.sl.files[length(smc.sl.files)]))
my.meteo.smc <- my.meteo.smc %>%
mutate(geo2 = str_replace(geometry, "c\\(", "")) %>%
mutate(geo2 = str_replace(geo2, ",", "")) %>%
mutate(geo2 = str_replace(geo2, "\\)", "")) %>%
separate(geo2, into=c("lon", "lat"), sep=" ") %>%
mutate_at(c("lon", "lat"), as.numeric)
my.meteo.smc.sf <- st_as_sf(my.meteo.smc, coords = c("lon", "lat"), crs = 4326)
# Get the id of the nearest smc meteorological stations
nn.smc.idx <- st_nearest_feature(md.sf, my.meteo.smc.sf)
md.sf$nn.smc.id <- my.meteo.smc.sf[nn.smc.idx,]$ID
md.sf$nn.smc.name <- my.meteo.smc.sf[nn.smc.idx,]$name
my.smc.nn.list <- unique(md.sf$nn.smc.id)
my.smc.df <- my.meteo.smc.sf %>%
filter(ID %in% my.smc.nn.list)
my.smc.df
# Store a copy of that list to disk just in case I need it in the future in a handy way
write_csv(my.smc.df, file.path(path.data, paste0(results.fname.noext,"_smc_station_list_subset.csv")))
write_csv(my.smc.df, file.path("precipitacio", "_smc", paste0(results.fname.noext,"_smc_station_list_subset.csv")))
plot(my.smc.df["elev_range"])
# ---
# GEt a subset of the my data with no NA
md.sf.nona <- md.sf %>%
filter(!is.na(geometry))
md.sf.nona <- left_join(md.sf.nona, my.meteo.smc, by=c("nn.smc.id" = "ID")) %>%
select(cid, nn.meteo = name)
rbind(head(md.sf.nona), tail(md.sf.nona))
md.sf <- left_join(md.sf, st_set_geometry(md.sf.nona, NULL), by=c("cid"))
md.sf
```
## Save extended results data set as Spreadsheet and csv (just in case)
```{r Save extended results data set as Spreadsheet and csv (just in case)}
#str(md)
results.fname.noext <- paste0("_120000_", format(Sys.Date(), "%y%m"), "00_data")
readr::write_csv(md.sf, file.path(path.data, paste0(results.fname.noext,".csv")))
# In order to save as xlsx, I need to remove the geometry column and sf class from the md.sf
md.sf <- md.sf %>% mutate(lon = unlist(map(md.sf$geometry,1)),
lat = unlist(map(md.sf$geometry,2)))
md <- st_set_geometry(md.sf, NULL)
openxlsx::write.xlsx(md,
file = file.path(path.data, paste0(results.fname.noext, ".xlsx")))
```
```{r save DT html}
# ------
# Add the data table next to the map
dt.data <- datatable(md, filter = 'top', extensions="Scroller",
style="bootstrap", class="compact",
width="100%", rownames = FALSE,
options=list(deferRender=TRUE,
scrollY=300,
scroller=TRUE,
pageLength = 5)) %>%
formatDate('Date', 'toLocaleDateString') %>%
formatStyle('Abundància',
color = styleInterval(c(3, 5), c('black', 'red', 'blue')),
backgroundColor = styleInterval(c(3, 5), c('white', 'lightyellow', 'yellow'))
)
#saveWidget(ct.map.table.all, file=file.path(getwd(), path.html, "ct.map.table.all.html"), selfcontained=TRUE)
# Save chart as html on disk
htmltools::save_html(dt.data,
file.path(getwd(), path.html, "table.all.html")
)
```
## Pivot table
```{r pivot table, echo=FALSE}
# Create the pivot table and save html on disk
#rpivotTable(md)
pt.md <- rpivotTable(
data = md,
rows = c("nom_comestible"),
cols=c("Year", "Month"),
aggregatorName = "Count",
# aggregatorName = "Sum",
# vals = "Abundància",
width="1200px",
height="800px",
# inclusions = list( Abundància = list("2")),
exclusions = list( `nom vulgar (comestible)` = list( "---")),
rendererName = "Heatmap"
)
frameWidget(pt.md)
saveWidget(pt.md, file=file.path(getwd(), path.html, "boleteRa.pivotTable.html"), selfcontained=TRUE)
#pt.md
```
## LeafLet Map (Standalone)
Dereived from:
* https://rstudio.github.io/leaflet/
* http://archived.mhermans.net/hiking-gpx-r-leaflet.html
```{r LeafLet map, echo=FALSE}
# Initialize basemap for r package leaflet
#tilesURL <- "http://server.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/MapServer/tile/{z}/{y}/{x}"
#tilesURL <- "https://tile.thunderforest.com/landscape/{z}/{x}/{y}.png?apikey=yourapikey"
p_load(leaflet)
# See leaflet marker options here:
# https://rstudio.github.io/leaflet/markers.html
#ruta.foto <- file.path(getwd(), "fotos")
ruta.foto <- "/home/xavi/Sync/BQa5_Zamiadroid/Photos/"
saveas <- function(map, file){
class(map) <- c("saveas",class(map))
attr(map,"filesave")=file
map
}
print.saveas <- function(x, ...){
class(x) = class(x)[class(x)!="saveas"]
htmltools::save_html(x, file=attr(x,"filesave"))
}
# Create the Leaflet map with my data md (ll.md)
ll.md <- leaflet::leaflet(md, width = "100%", height = "800") %>%
#addTiles() %>%
# Add tiles
addProviderTiles("OpenStreetMap.Mapnik", group = "Road map") %>%
addProviderTiles("Esri.NatGeoWorldMap", group = "National Geographic") %>%
addProviderTiles("Esri.WorldImagery", group = "Satellite") %>%
# addMarkers(~Longitude, ~Latitude,
# label = ~as.character(nom_comestible),
# popup = ~as.character(foto)
# ) %>%
addMiniMap(zoomLevelOffset = -5) %>%
addCircleMarkers(
~lon, ~lat,
label = ~as.character(nom_comestible),
popup = ~paste0(nom_comestible, " | ", nom_nocomestible,
"<br>Detalls del bolet: ", Detalls_bolet,
"<br>Abundància: ", Abundància,
"<br>Densitat del sotabosc: ", Densitat_sotabosc,
"<br>Pendent: ", Pendent,
"<br>Data: ", Year, "-", Month,
"<br>Alçada: ", alcada_srtm3, "-", alcada_gtopo30,
"<br>Lloc: ", Província, "-", Ciutat, "-", Nom_toponímic,
"<a href=", file.path(foto1),"><img src=", file.path(foto1), " width=200 ></a>",
foto,
"<br>Descripció del Lloc: ", Descripcio_Lloc
),
radius = ~ifelse(!is.na(nom_comestible), 6, 10),
color = ~palette(rainbow(96)),
# clusterOptions = markerClusterOptions(),
stroke = FALSE, fillOpacity = 0.5,
group="Citations"
) %>%
addCircleMarkers(
~lon, ~lat,
label = ~as.character(nn.smc.name),
popup = ~paste0(nn.smc.id, " | ", nn.smc.name),
radius = 3,
color = "black",
# clusterOptions = markerClusterOptions(),
stroke = FALSE, fillOpacity = 0.05,
group="Stations"
) %>%
# Layers control
addLayersControl(position = 'bottomright',
overlayGroups = c("Citations", "Stations"),
options = layersControlOptions(collapsed = FALSE),
baseGroups = c("Road map",
"National Geographic",
"Satellite")
)
ll.md
saveas(ll.md, file.path(ruta.foto, "index.html"))
# addTiles(tilesURL, attribution='Map tiles by <a href="http://server.arcgisonline.com">ArcGIS online</a> — Map data © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>') %>%
webshot(file.path(ruta.foto, "index.html"),
file=file.path(getwd(), path.html, "map.all.png"),
delay=0.5)
```
## MapView
See: https://r-spatial.github.io/mapview/
```{r MapView map, echo=FALSE}
# =======================================================
# MapView Interactive viewing of spatial data in R
# See: https://r-spatial.github.io/mapview/
# =======================================================
#
# Code will come here
#mapview(breweries)
p_load(sf)
p_load(mapview)
md.nona <- md %>%
filter(!is.na(lon))
md.sf <- st_as_sf(x = md.nona,
coords = c("lon", "lat"),
crs = "+proj=longlat +datum=WGS84")
# simple plot
plot(md.sf)
# interactive map:
mapview(md.sf)
fotos <- file.path(ruta.foto, md.nona$foto1)
mv <- mapview(md.sf[1:100,],
popup = popupImage(fotos[1:100], src = "local"))
# Convert to Spatial object (if needed)
md.sp <- as(md.sf, "Spatial")
mapview(md.sp)
#mapshot(mv, url = file.path(ruta.foto, "index.sf.html"),
# file = file.path(ruta.foto, "index.sf.png"))
```
## Thematic map
See: https://github.com/mtennekes/tmap
```{r Thematic map, echo=FALSE}
# =======================================================
# Use the thematic maps package
# See: https://github.com/mtennekes/tmap
# =======================================================
#
# Code will come here
```
## Crosstalk with filters
Taken from https://rstudio.github.io/crosstalk/using.html
```{r Crosstalk with filters short version, echo=FALSE}
#Reset DAte as posixct instead of posixlt
md$Date <- as.POSIXct(md$Date, format = "%m/%d/%Y %H:%M")
#Filter subset of md with data in species
md.sample <- md %>%
select(
Any=Year,
Mes=Month,
Abundància,
# Data=Date,
NomC=`nom_comestible`,
# NomNC=`nom vulgar (no comestible)`,
# DetallBolet=`Detalls del bolet`,
DescripcioLloc=`Descripcio_Lloc`,
Lat=lat,
Long=lon) %>%
filter(!is.na(NomC)) %>%
filter(NomC != "---") %>%
filter(NomC != "altres") %>%
top_n(15)
sdf <- SharedData$new(md.sample, as.character(md.sample$NomC))
bscols(widths = c(3,NA,NA),
# Col 1: Filters
list(
filter_checkbox("Ab", "Abundància", sdf, ~Abundància, inline = TRUE),
filter_slider("A", "Any", sdf, column=~Any, step=1, width="100%"),
filter_slider("M", "Mes", sdf, column=~Mes, step=1, width="100%")
# , filter_select("auto", "Automatic", shared_mtcars, ~ifelse(am == 0, "Yes", "No"))
),
# Col 2: Map (LeafLet)
leaflet(sdf) %>% addTiles() %>% addMarkers(
label = paste0(md.sample$Any, "-", md.sample$Mes, " ", as.character(md.sample$NomC)),
popup = paste(
"Name1:", md.sample$NomC, "<br>",
"Abundance:", md.sample$Abundància, "<br>",
"Date:", paste0(md.sample$Any, "-", md.sample$Mes), "<br>"
# ,"Mushroom Details:", md.sample$DetallsBolet, "<br>",
# "Forest density:", md.sample$Densitat, "<br>",
,"Site desc:", md.sample$DescripcioLloc, "<br>"
# "Slope:", md.sample$Pendent, "<br>",
# "Picture:", md.sample$foto, "<br>",
)),
# Col 3: DT
datatable(sdf, extensions="Scroller", style="bootstrap", class="compact", width="100%",
options=list(deferRender=TRUE, scrollY=300, scroller=TRUE)
)
)
```
```{r Crosstalk with filters long version, echo=FALSE}
# Initialize basemap for r package leaflet
tilesURL <- "http://server.arcgisonline.com/ArcGIS/rest/services/Canvas/World_Light_Gray_Base/MapServer/tile/{z}/{y}/{x}"
# See leaflet marker options here:
# https://rstudio.github.io/leaflet/markers.html
# Create the Leaflet map with my data md (ll.md)
ll.md <- leaflet::leaflet(md) %>%
# Base groups
addProviderTiles(providers$OpenStreetMap, group = "OSM") %>%
# addTiles(tilesURL, attribution='Map tiles by <a href="http://server.arcgisonline.com">ArcGIS online</a> — Map data © <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>') %>%
# Overlay groups
addMarkers(label = ~as.character(md$`nom_comestible`),
popup = paste(
"Name1:", md$`nom_comestible`, "<br>",
"Name2:", md$`nom_nocomestible`, "<br>",
"Abundance:", md$Abundància, "<br>",
"Mushroom Details:", md$`Detalls_bolet`, "<br>",
"Forest density:", md$`Densitat_sotabosc`, "<br>",
"Site desc:", md$`Descripcio_Lloc`, "<br>",
"Slope:", md$Pendent, "<br>",
"Picture:", md$foto, "<br>",
"Date:", md$Date
),
clusterOptions = markerClusterOptions(),
group = "Edible") %>%
# addMarkers(label = ~as.character(d$`nom vulgar (no comestible)`),
# popup = ~as.character(d$Abundància),
# clusterOptions = markerClusterOptions(),
# group = "Non Edible") %>%
# Layers control
addLayersControl(
baseGroups = c("OSM"),
# overlayGroups = c("Edible", "Non Edible"),
overlayGroups = c("Edible"),
options = layersControlOptions(collapsed = FALSE)
) %>%
# Legend
addLegend("bottomright", colors= "blue", labels="MyFindings", title="Catalonia")
# ------
# Use SharedData like a dataframe with Crosstalk-enabled widgets
# ct stands for CrossTalk
ct.map.table.all <- bscols(
# Draw the Leaflet map from my data md
ll.md,
# Add the data table next to the map
datatable(md, filter = 'top', extensions="Scroller",
style="bootstrap", class="compact",
width="100%", rownames = FALSE,
options=list(deferRender=TRUE,
scrollY=300,
scroller=TRUE,
pageLength = 5)) %>%
formatDate('Date', 'toLocaleDateString') %>%
formatStyle('Abundància',
color = styleInterval(c(3, 5), c('black', 'red', 'blue')),
backgroundColor = styleInterval(c(3, 5), c('white', 'lightyellow', 'yellow'))
)
)
#saveWidget(ct.map.table.all, file=file.path(getwd(), path.html, "ct.map.table.all.html"), selfcontained=TRUE)
# Save chart as html on disk
htmltools::save_html(ct.map.table.all,
file.path(getwd(), path.html, "map.table.all.html")
)
webshot(file.path(getwd(), path.html, "map.table.all.html"),
file=file.path(getwd(), path.html, "map.table.all.png"),
delay=0.5)
ct.map.table.all
```
