Original


Source: New Zealand’s Greenhous Gas Inventory 1990-2017 (4/2019).


Objective

The graph is a chart on greenhouse gas emissions, it’s published in New Zealand’s Greenhouse Gas Inventory 1990 – 2017 in April 2019 in order to describe the distribution of gas emissions through different sectors which are in general: Agriculture, Energy, Industrial Processes, Waste. The graph emphasizes the percentage of gas emissions of two sectors which are: Agriculture and Energy, it’s clear that they contribute by 88.8 % of gas emissions in New Zealand Greenhouses, the graph highlights also the progress that had been done in the sector of industrial processes by its negligible percentage of gas emissions which is 6.1%. the target audience of the graph is politicians and scientists of New Zealand, the graph is an invitation for scientists to develop alternative methods for the two sectors to decrease the gas emissions, for example developing electrical cars to reduce gas emissions of Road Transport. This graph is also a request for politicians to establish new policies to tackle climate change.

The visualisation chosen had the following three main issues:

  • Briefly explain issue 1

The author relied on pie chart to present many information,whereas the pie chart can be present effective when the number of categories are limit,it might creat some difficult to the audience like it is forced the audience to read the numeric values and compare the area of each doughnut slice.

  • Briefly explain issue 2

There are some redundant labels of some categories for different sectors with different values such as Methane and Nitrous oxide, we can avoid this repeat,that may cause confuse for audience to distinguished between values and compare between them.

  • Briefly explain issue 3

There are 2 categories have the same percentage ( which is Nitrous oxide for sheep and Fugitive emissions ,both have 2.4% ),but when we see the size of area of each one , we find one is bigger than one ,this maybe deceive the audience if not read the numeric values of data.

Reference

Code

The following code was used to fix the issues identified in the original.

# we Create date frame by using  summary statistics of  the original plot 

GhG_1 <- data.frame(Var_1 = c("Sector", "Sector","Sector","Sector"),Var_2 = c("AGRICULTURE","ENERGY","INDUSTRIAL PROCESSES","WASTE"), Percent= c(48.1,40.7,6.1,5.1))

GhG_2 <- data.frame(Var_1 = c("AGRICULTURE","AGRICULTURE", "AGRICULTURE","AGRICULTURE","ENERGY","ENERGY","ENERGY","ENERGY","ENERGY"), Var_2 = c("Dairy cattle","Sheep","Beef cattle","Other","Transport","Manufacturing&construction","Other","Electricity generation","Fugitive emissions"),Percent= c(22.5,12.7,8.1,4.7,19.7,8.6,5.5,4.4,2.4))

GhG_3 <- data.frame(Var_1 = c("Dairy cattle","Dairy cattle","Sheep","Sheep","Beef cattle","Beef cattle","Transport","Transport"),Var_2 = c("Methane","Nitrous oxide","Methane","Nitrous oxide","Methane","Nitrous oxide","Road transport","Other"), Percent= c(18.3,4.2,10.3,2.4,6.6,1.5,17.9,1.8))

GhG_1$Var_1<-paste("1.",GhG_1$Var_1)
GhG_1$Var_2<-paste("2.",GhG_1$Var_2)

GhG_2$Var_1<-paste("2.",GhG_2$Var_1) 
GhG_2$Var_2<-paste("3.",GhG_2$Var_2)

GhG_3$Var_1<-paste("3.",GhG_3$Var_1) 
GhG_3$Var_2<-paste("4.",GhG_3$Var_2)

# join the three  data.frame objects above into a single object by using rbind() function.
Greenhous_GasEmissions<-rbind(GhG_1,GhG_3,GhG_2)

# install googlevis package to produce a Sankey diagra
library(googleVis)
op <- options(gvis.plot.tag='chart')

# We will use the next function to highlight the objectivs.
objective <- paste0("{ iterations: 0,link: { colorMode: 'source',colors:['lightgray','papayawhip','lightgray','lightgray','lightgray','lightgray','sandybrown'] } }")

Data Reference

Reconstruction

“The caused sectorse Greenhous Emission in New Zealand from 1990-2017”