CO2 emission data

When environment is at stake, insights from data count double. While rejustify has been supporting access to environmental data through some of the economic databases, more recently we took an active step and included more direct resources in our AI engine:

The tables are tagged CDIAC-GLOBAL, CDIAC-REGION, CDIAC-COUNTRY and BP-ENERGY, depending on the level of geographical aggregation, and they are supported by all AI functionality offered by rejustify. Let's quickly jump into an example how to access them in your projects.

1. Package installation and setup

Firstly, make sure you have the most recent version of the API, available at github, and setup up you access rights.

remotes::install_github("rejustify/r-package")
library(rejustify)
setCurl()
register(token = "YOUR_TOKEN", email = "YOUR_EMAIL")

2. Design your data canvas

Secondly, design your data structure. In the example below, we will rejustify annual data on CO2 emissions between 2020 and 2020 in three large economies: Germany, China and USA. To make ploting easier, we will design the canvas in a specific shape such that the emission columns will be marked by country and indicator dimensions (check table structure).

df <- data.frame(year = seq(2000, 2020),
                 `Germany,Per capita` = NA,
                 `China,Per capita` = NA,
                 `United States,Per capita` = NA, check.names = F )
st  <- analyze(df, learn = TRUE)

In case the 'per capita' phrase was identified as a measure of unit, which is correct across several other default data sets, to facilitate the matching between 'per capita' dimension and the indicator dimension in CDIAC-COUNTRY we need to standardize the dimensions. We suggest to switch the classification unit to general, the same as in CDIAC-COUNTRY. If you allow the algorithm to learn, you will do it only once - the next time the rejustify engine will recognize it automatically.

st <- adjust(st, id = 3, items = list("class" = "general", "feature" = NA))            
st <- adjust(st, id = 5, items = list("class" = "general", "feature" = NA))            
st <- adjust(st, id = 7, items = list("class" = "general", "feature" = NA))   

3. Rejustify

Thirdly, rejustify.

rdf <- fill(df, st, learn = TRUE)

Original data set

 year Germany,Per capita China,Per capita United States,Per capita
 2000                 NA               NA                       NA
 2001                 NA               NA                       NA
 2002                 NA               NA                       NA
 2003                 NA               NA                       NA
 2004                 NA               NA                       NA
 2005                 NA               NA                       NA
 2006                 NA               NA                       NA
 2007                 NA               NA                       NA
 2008                 NA               NA                       NA
 2009                 NA               NA                       NA
 2010                 NA               NA                       NA
 2011                 NA               NA                       NA
 2012                 NA               NA                       NA
 2013                 NA               NA                       NA
 2014                 NA               NA                       NA
 2015                 NA               NA                       NA
 2016                 NA               NA                       NA
 2017                 NA               NA                       NA
 2018                 NA               NA                       NA
 2019                 NA               NA                       NA
 2020                 NA               NA                       NA

Rejustified data set

 year Germany,Per capita China,Per capita United States,Per capita
 2000               2.75             0.73                     5.42
 2001               2.83             0.74                     5.27
 2002               2.74             0.82                     5.26
 2003               2.72             0.96                     5.24
 2004                2.7              1.1                     5.26
 2005               2.63             1.23                     5.25
 2006                2.7             1.35                     5.12
 2007               2.55             1.44                     5.13
 2008               2.55             1.53                     4.93
 2009               2.45             1.64                     4.61
 2010               2.57             1.78                     4.69
 2011               2.48             1.97                     4.56
 2012               2.51             2.02                     4.38
 2013               2.56             2.05                     4.38
 2014               2.43             2.05                     4.43
 2015                 NA               NA                       NA
 2016                 NA               NA                       NA
 2017                 NA               NA                       NA
 2018                 NA               NA                       NA
 2019                 NA               NA                       NA
 2020                 NA               NA                       NA

As explained by CDIAC, the units are thousand metric tons of carbon per capita. Let's quickly visualize the results.

matplot(rdf$data$year, rdf$data[,2:4], xlab="Year", ylab="CO2 Emission", type = "l", col=1:3, lty=1) 
legend("topright", legend = colnames(rdf$data[,2:4]), col=1:3, lty=1) 

CO2 Emission data

More functionality of the API is described in our R package documentation. The full list of our resources, including data providers and tables, can be found in our repository browser.