Data Assignment 1

The Canadian Energy Regulator produces a semi-annual-ish report called Canada’s Energy Future which provides scenario-based analysis of global and local energy issues and the effects of these trends and issues on Canada’s Energy Economy.

For your first data assignment, I’m going to ask you to produce a few graphs based on publicly-available data from the 2023 Canada’s Energy Future Report and answer some questions on those graphs. This assignment tests your skills in filtering, grouping, and presenting data in table and graph form. It also asks you to draw some basic conclusions from the data and to use a graph to support your conclusions in some way.

For what you see below, I’ve used the following packages so use this as a guide to set up your document:

library(kableExtra)
library(readxl)
library(janitor)
library(tidyverse)
library(lubridate)
library(scales)
library(viridis)

There are multiple ways for you to access the data for this report, but I’d recommend going directly to the Open Government portal, and specifically to the Benchmark Prices data set to complete the main deliverables for this assignment. You’ll need to figure out how to access the data you need and how to read it successfully into R.

There are five (5) graded deliverables in this assignment.

I am going to ask you for three (3) specific R outputs in this assignment, each of which you can complete using coding techniques that you’ve already seen, with some minor modifications. Each of these is worth 2 points with a grading key as follows : 0 for not attempted, 1 for attempted with reasonable effort but not completed, 1.5 for satisfactory completion, 2 for excellent work.

Next, in addition to these specific visualization deliverables, which you can complete using code that we’ve already used during the term, I’m going to ask you to answer a question on the data and to support your answer with a graph of your choosing. Here, again, I’ll grade you on the scale of 0 for not attempted, 1 for attempted with reasonable effort but not completed, 1.5 for a solid explanation with no errors, and 2 for compelling explanation of the data. And, for the graph, I’ll use the same scale as above.

Deliverable 1

The first deliverable for this assignment is a simple table, but based on filtered data. I would like you filter your prices data to have Western Canadian Select (WCS) prices for the Canada Net-Zero scenario, and only for years that are multiples of 5 (2005, 2010, 2015, …, 2045, 2050) starting from 2020.

  price_table<-price_data %>% 
  filter(variable == c("Western Canadian Select (WCS) - 2022 US$/bbl"),year%%5==0,scenario=="Canada Net-zero",year>2019)%>%
  select(year,value)%>%
  mutate(value=format(value, nsmall = 2))%>%
  rename("Price ($US 2022/bbl)"=value,
         Year=year)%>%
  kbl(escape = FALSE,table.attr = "style='width:80%;'",digits=2,align=rep('c', 2)) %>%
  kable_styling(fixed_thead = T,bootstrap_options = c("hover", "condensed","responsive"),full_width = T)%>%
  add_header_above(header = c("Western Canadian Select (WCS) prices in the Canada Net-Zero scenario of the Canada Energy Regulator's Canada's Energy Future (2023) report"=2))%>%
  I()

Western Canadian Select (WCS) prices in the Canada Net-Zero scenario of the Canada Energy Regulator's Canada's Energy Future (2023) report
Year	Price ($US 2022/bbl)
2020	29.61
2025	62.33
2030	49.00
2035	48.00
2040	47.00
2045	46.00
2050	45.00

I’ll give you a couple of hints for how to make this happen:

So far, we’ve been using xls or xlsx files. Depending on how you access the data, you may need a read_csv or read.csv command if you’re using a CSV data file. Also, make sure you don’t give the file a different extension when you download it (e.g. don’t set your destfile="foo.xlsx" if you’re downloading a CSV file. Use destfile="foo.csv");
If you want to force a number to have two decimal places, you can use format(value, nsmall = 2), but remember that it will create a character string not a numerical value once you do that. Test it out: type format(20.2565, nsmall = 2) and you’ll get back “20.26”, a character string;
I don’t want you to get caught up on styling your tables, so here are the three lines of code that I used to create the table itself above:

kbl(escape = FALSE,table.attr = "style='width:80%;'",digits=2,align=rep('c', 2)) %>%
  kable_styling(fixed_thead = T,bootstrap_options = c("hover", "condensed","responsive"),full_width = T)%>%
  add_header_above(header = c("Western Canada Select (WCS) prices in the Canada Net-Zero scenario of the Canadian Energy Regulator's Canada's Energy Future (2023) report"=2))

R will calculate a modulo (remainder) using %%, so, if you were to enter something like 2025%%5, R would return 0, which you can use to design your filtering for years that divide evenly by 5.

Deliverable 2

The second deliverable for this assignment is a graph of oil prices, which you’ll again have to base on filtered data. I’d like you to graph Brent, WTI, and WCS prices for the Global Net-zero scenario over time.

  price_data %>% 
  filter(variable%in% c("West Texas Intermediate (WTI) - 2022 US$/bbl","Brent - 2022 US$/bbl","Western Canadian Select (WCS) - 2022 US$/bbl"),
         scenario=="Global Net-zero"
         )%>%
  mutate(variable= gsub(" - 2022 US\\$/bbl","",variable))%>%
  ggplot()+ #make a graph
  geom_line(aes(year,value,group=variable,color=variable),linewidth=.65)+
  scale_x_continuous(breaks=pretty_breaks(), expand=c(0,0))+
  scale_y_continuous(breaks=pretty_breaks(), expand=c(0,0))+
  theme_minimal()+
  expand_limits(y=c(0,145))+
  scale_color_viridis("",option = "A",discrete=T,begin = 0,end = .8, direction=-1)+
  theme(legend.position = "bottom")+
  #t, r, b, l (To remember order, think trouble)
  theme(plot.margin = unit(c(1,1,1,1), "cm"))+
  guides(linetype = guide_legend(keywidth = unit(1.6,"cm"),nrow = 1))+
    labs(y="Price (2022 US$/bbl)",x="",
         title="Crude Oil Prices, Global Net-zero Scenario",
         caption = "Data via Canada Energy Regulator, Energy Futures (2023), graph by Andrew Leach"
         )+
  NULL

I’ll give you a couple of hints for how to make this happen:

I used gsub to strip the 2022 US $/bbl from each of the variable names, to clean up the graph a bit. You can see a little bit of help for how to do that here. I used variable= gsub(" - 2022 US\\$/bbl","",variable) to make that happen. Dollar signs are generally used as indicators for math in markdown, so if you’re using a dollar sign in your text, you need to lead it with a \ like this: \$. In gsub, you need to lead symbols with \\ to tell it to look for the specific symbol, otherwise it will think you’re building what’s called a regular expression;
I used expand_limits(y=c(0,145)) to make my axes look nice, and scale_color_viridis("",option = "A",discrete=T,begin = 0,end = .8, direction=-1) for the color palette. I’ve also used theme(legend.position = "bottom") for the position of the legend;
You will likely need to change the margins around your plot so that things aren’t getting cut off. Use theme(plot.margin = unit(c(1,1,1,1), "cm")) and the four values are in order top, right, bottom, left (think trouble: t r b l);
If you want to filter your data to include multiple items, use this: filter(variable %in% c("string 1","string 2","string 3")). The c() creates a vector of elements, and %in% is telling it to filter based on whether the variable is an element of the set. If you wanted to eliminate any variable that is an element of the set, you could use filter(!variable %in% c("string 1","string 2","string 3")), where the ! acts as a ‘not’ symbol.

Deliverable 3

The third deliverable for this assignment should be easy if you got the last one. I want you to graph WCS prices for each of the report’s scenarios over time.

  price_data %>% 
  filter(variable%in% c("Western Canadian Select (WCS) - 2022 US$/bbl")
         )%>%
  mutate(variable= gsub(" - 2022 US\\$/bbl","",variable),
         scenario=as_factor(scenario))%>%
  ggplot()+ #make a graph
  geom_line(aes(year,value,group=scenario,color=scenario),linewidth=.65)+
  scale_x_continuous(breaks=pretty_breaks(), expand=c(0,0))+
  scale_y_continuous(breaks=pretty_breaks(), expand=c(0,0))+
  theme_minimal()+
  expand_limits(y=c(0,100))+
  scale_color_viridis("",option = "A",discrete=T,begin = 0,end = .8, direction=-1)+
  theme(legend.position = "bottom")+
  #t, r, b, l (To remember order, think trouble)
  theme(plot.margin = unit(c(1,1,1,1), "cm"))+
  guides(linetype = guide_legend(keywidth = unit(1.6,"cm"),nrow = 1))+
    labs(y="Price (2022 US$/bbl)",x="",
         title="Western Canadian Select Crude Oil Prices",
         subtitle="Current Measures, Canada Net-zero, and Global Net-zero Scenarios",
         caption = "Data via Canadian Energy Regulator, Energy Futures (2023), graph by Andrew Leach"
         )+
  NULL

You should only need one hint for this one: in this case, your observation groups are going to be the three scenarios, so you need something like geom_line(aes(year,value,group=scenario,color=scenario),linewidth=.65) to get the graph to separate the three series. I also used mutate(scenario=as_factor(scenario)) to order the scenarios in the same order as they appear in the data, and the same color scale as the previous graph to set the line colors here. You don’t have to replicate all of these elements exactly, and I encourage you to do what works for you.

Deliverable 4

Canada is largely an oil price taker. You’ve made two graphs and a table on oil prices and how they might respond in each of these three scenarios. What can you tell me about Canada’s potential exposure to global and domestic action on climate change and/or global energy technology evolution? Make sure you tell briefly me what each scenario assumes about global and domestic action on climate change, what each scenario holds for oil prices, and how that might affect Canadian oil production. (200 words, maximum)

The tables and graphs above show that oil prices and thus the value of Canada’s oil resource will be sensitive to global and domestic action on climate change. In the Current Measures scenario, limited further global and domestic action on climate change leads to roughly constant real dollar oil prices. In the Canada Net-Zero scenario, more aggressive policies here and elsewhere lead to lower oil prices than we would otherwise see. And, in the most aggressive scenario for climate action, with the world heading to net-zero emissions by 2050, Western Canada Select oil prices decline to roughly $10 per barrel in today’s terms by 2050. Investment in new and sustaining oil production in Canada will be, mostly, driven by global oil prices with domestic policies playing a minor role. As you can see in the Figure below, the much lower oil prices, in particular in the global net-zero scenario, translate to much lower oil production than in the Current Measures Scenario.

Deliverable 5

Insert a graph of your choosing using data from the CER Energy Futures Report to support your explanation above. Don’t try to do too much - a simple replication of one of the graphs above with one of the other data sets from the CER, or a further manipulation of the data we’ve just used is all you need.

if(!file.exists("cer_production.csv"))
  download.file("https://www.cer-rec.gc.ca/open/energy/energyfutures2023/crude-oil-production-2023.csv",destfile = "cer_production.csv",mode="wb")
#prod<-
  read_csv("cer_production.csv")%>%clean_names()%>%
  filter(region=="Canada",variable=="Total",unit=="Thousand Barrels per day")%>%
  #mutate(variable= gsub(" - 2022 US\\$/bbl","",variable),
         #scenario=as_factor(scenario))%>%
  ggplot()+ #make a graph
  geom_line(aes(year,value,group=scenario,color=scenario),linewidth=.65)+
  scale_x_continuous(breaks=pretty_breaks(), expand=c(0,0))+
  scale_y_continuous(breaks=pretty_breaks(), expand=c(0,0))+
  theme_minimal()+
  expand_limits(y=c(0,7000))+
  scale_color_viridis("",option = "A",discrete=T,begin = 0,end = .8, direction=-1)+
  theme(legend.position = "bottom")+
  #t, r, b, l (To remember order, think trouble)
  theme(plot.margin = unit(c(1,1,1,1), "cm"))+
  guides(linetype = guide_legend(keywidth = unit(1.6,"cm"),nrow = 1))+
    labs(y="Total Production (1000 bbl/d)",x="",
         title="Canadian Crude Oil Production",
         subtitle="Current Measures, Canada Net-zero, and Global Net-zero Scenarios",
         caption = "Data via Canadian Energy Regulator, Energy Futures (2023), graph by Andrew Leach"
         )+
  NULL

RMD File and HTML/PDF Preparation

I have made you a basic RMD file for your use in completing this (and future) assignments.

Before you start making any changes to the markdown, test your ability to knit to html with this file, and make sure you can make an html file before you proceed.

For example, with the document open as in the image below, use the Knit button to create your html.