My main motive is to understand the Tree census or record of my hometown Pune city .I am proud to say that Pune city is smart city and I got this data from website .Note - Data is updated on August 2019
This data include many features like location of trees , size canopy , common names etc. I will used this data to hopefully plot it on interactive map using Leaflet library
library(readr)
library(dplyr)
library(shiny)
features <-
c(
"id",
"canopy_dia_m",
"condition",
"northing",
"easting",
"common_name",
"botanical_name",
"ward",
"local_name",
"economic_i",
"road_name"
)
df <- read_csv("Data/Pune Tree Census August 2019/p5.csv")
df <- select(df, all_of(features))Removing raw data is important or it will take unnecessary space.There are 40lakhs plus rows and 28 features. I took only features I need for ploting.
library(leaflet)
plot1 <- addTiles(leaflet())
icons <-
icons(
iconUrl = ifelse(
df$condition == "Healthy",
"icons/banyan_icon.png",
ifelse(
df$condition == 'Average',
"icons/average.jpg",
ifelse(
df$condition == "Dead",
"icons/dead2.jpg",
'icons/poor.jpg'
)
)
),
iconWidth = 30,
iconHeight = 55
)
popupMesage <-
data.frame(
popup = paste(
sep = "<br>",
"<b>Botanical Name:</b>" ,
df$botanical_name,
"<b>Common Name:</b>",
df$common_name ,
paste("<b>Canopy Diameter:</b>", df$canopy_dia_m),
paste("<b>Ward:</b>", df$ward),
paste("<b>Economic use:</b>", df$economic_i) ,
paste("<b>Street:</b>", df$road_name) ,
paste("<b>Condition:</b>", df$condition)
)
)
plot1 <-
plot1 %>% addMarkers(
lat = df$northing,
lng = df$easting ,
icon = icons,
clusterOptions = markerClusterOptions(),
popup = popupMesage$popup,
label = paste("Local Name:", df$local_name, ", Ward:", df$ward)
)