Description

The Living Planet Index (LPI) is a measure of the state of the world’s biological diversity based on population trends of vertebrate species from terrestrial, freshwater and marine habitats. The global LPI as presented in the Living Planet Report 2010 shows that populations of species has declined by 60% in abundance between 1970 and 2010. The Living Planet Report 2010 results indicate that species are faring much worse in freshwater systems and in terrestrial systems. Freshwater populations declined by an average of 83%. This analysis will show the separate interactive plots for Marine,Freshwater,terrestrial systems and also a plot for combined Global total LPI levels.We consider 1970 at the starting point of reference to compare LPI levels.So,in this plots,LPI in 1970 are assumed to be 100%.

Introduction

This visualization will include

1.LPI interactive plot for Marine Ecosystem
2.LPI interactive plot for Freshwater Ecosystem
3.LPI interactive plot for Terrestial Ecosystem
4.LPI interactive plot for Global total.

Data Source.

This data was retrived from ourworlddata.com, link to this dataset
https://ourworldindata.org/grapher/global-living-planet-index-across-environments

Codes

## library used
library(wesanderson)
library(plotly)
library(ggplot2)
library(readr)
library(tidyr)
library(dplyr)
library("viridis")

## Data importing
global.living.planet.index.across.environments <- read.csv("C:/Users/Prashant Mehta/Desktop/data visualization/assiment3/global-living-planet-index-across-environments.csv")
global_LPI<-global.living.planet.index.across.environments  

## Data Pre-processing
Marine<-global_LPI[ ,c(3,4)]
frreshwater<-global_LPI[ ,c(3,5)]
terrestial<-global_LPI[ ,c(3,6)]
total_LPI<-global_LPI[ ,c(3,7)]

## plots
## Marine
year<-global_LPI$Year
marine<-global_LPI$Marine
globaltotal<-global_LPI$Global..total.
Marine <- data.frame(year, global_LPI$Marine)
Marine_plot<-ggplot(Marine, aes(year, global_LPI$Marine)) + coord_flip()+scale_x_continuous(trans = "reverse")+
  geom_bar(stat = "identity",aes(fill = year)) +
  xlab("year") + 
  ylab("Living planet Index for Marine")
M1<-Marine_plot+scale_fill_gradientn(colours="cyan4")
## Freshwater
year<-global_LPI$Year
marine<-global_LPI$Marine
globaltotal<-global_LPI$Global..total.
Frreshwater <- data.frame(year, global_LPI$Freshwater)
Freshwater_plot<-ggplot(frreshwater, aes(year, global_LPI$Freshwater)) + coord_flip()+ scale_x_continuous(trans = "reverse")+
  geom_bar(stat = "identity",aes(fill = year)) + 
  xlab("year") + 
  ylab("Living planet Index for Freshwater")
F1<-Freshwater_plot+scale_fill_gradientn(colours="darkorange2")
##Terrestrial
year<-global_LPI$Year
marine<-global_LPI$Marine
globaltotal<-global_LPI$Global..total.
terrestial <- data.frame(year, global_LPI$Terrestrial)
terrestial_plot<-ggplot(terrestial, aes(year, global_LPI$Terrestrial)) +coord_flip()+ scale_x_continuous(trans = "reverse")+
  geom_bar(stat = "identity",aes(fill = year)) + 
  xlab("year") + 
  ylab("Living planet Index for Terrestial")
T1<-terrestial_plot+scale_fill_gradientn(colours="springgreen3")
##Global 
year<-global_LPI$Year
marine<-global_LPI$Marine
globaltotal<-global_LPI$Global..total.
globalplot <- data.frame(year, global_LPI$Global..total.)
Global_plot<-ggplot(total_LPI, aes(year, global_LPI$Global..total.)) +coord_flip()+ scale_x_continuous(trans = "reverse")+
  geom_bar(stat = "identity",aes(fill = year)) + 
  xlab("year") + 
  ylab("Global Living planet Index")
G1<-Global_plot+scale_fill_gradientn(colours=rainbow(3))

##Interactive plots
## Marine
M1<-Marine_plot+scale_fill_gradientn(colours=topo.colors(5))
Marine_interactive<-ggplotly(M1)
## Freshwater
F1<-Freshwater_plot+scale_fill_gradientn(colours=magma(6))
Freshwater_interactive<-ggplotly(F1)
##Terrestrial
T1<-terrestial_plot+scale_fill_gradientn(colours=viridis(7))
terrestial_interactive<-ggplotly(T1)
##Global
G1<-Global_plot+scale_fill_gradientn(colours=rainbow(7))
Global_interactive<-ggplotly(G1)
##final plots
Marine_interactive
Freshwater_interactive
terrestial_interactive
Global_interactive

We see and overall decrease of almost 60% in Living planet index from the year 1970 to 2010.

Referance

Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student’s performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Data Visualization : The Biggest Foreign Holders of U.S. Debt - In One Chart by Howmuchnet.com website: https://howmuch.net/articles/state-of-the-worlds-government-debt