Visualisations of Oil Production
Rami Khrais
1 October 2018
Overview
I’m going to visualise some charts for oil production and reserves for certain countries. The data used in the coming charts is mainly extracted from BP Statistical Review of World Energy 2018. When it comes to BP Review, oil production refers to a relateively broad category including crude oil, shale oil, oil sand and natrual gas liquids (NGLs). The aim of this report is to show some of the most popular charts used in the field and some of their customizations.
Packages used in data cleaning and visualisation
library(ggplot2)
library(ggthemes)
library(dplyr)
library(scales)
library(ggthemes)
library(readxl)
library(tidyverse)
library(plotly)Bar Chart for OPEC countries with the biggest oil reserves
oil_reserves <- read_excel("C:/Users/Rami/Desktop/reserves.xlsx")
head(oil_reserves)## # A tibble: 5 x 3
## Country Reserves Cnt
## <chr> <dbl> <dbl>
## 1 Venezuela 303. 31.1
## 2 Saudi Arabia 266. 27.4
## 3 Iran 156. 16.0
## 4 Iraq 147. 15.1
## 5 Kuwait 102. 10.4As we want the chart to show the production levels in ascending levels, we should manipultae the data a bit.
oil_reserves$Country <- factor(oil_reserves$Country, levels = rev(as.character(oil_reserves$Country)))
ggplot(oil_reserves, aes(x= reorder(Country, Reserves), y= Reserves, fill = Country)) +
theme_minimal() +
geom_bar(stat = "identity", width = 0.8) +
scale_y_continuous(limits = c(0, 320), breaks = c(0, 40, 80, 120, 160, 200, 240, 280, 320), labels = scales::comma) +
scale_fill_manual(values= c("#000B29", "#D70026", "azure4", "#EDB83D", "coral")) +
theme(axis.title.x = element_blank()) +
theme(axis.title.y = element_blank()) +
theme(axis.text.x = element_text(size = 12, face = "bold", colour = "black")) +
ylab("Proven Crude Oil Reserves") +
theme(panel.grid.major.x = element_blank()) +
theme(panel.grid.minor.x = element_blank()) +
theme(legend.position = "none") +
labs(title = "Five OPEC Countries with the Biggest Oil Reserves", subtitle = " Billion Barrels", caption = "Source: OPEC") +
theme(title = element_text(size = 14))
Pie Chart for the percentage of the reserves
oil_reserves$Country <- factor(oil_reserves$Country, levels = rev(as.character(oil_reserves$Country)))
ggplot(oil_reserves, aes(x = "", y = Cnt, fill = Country)) +
coord_polar("y") + theme_classic() +
geom_bar(width = 2.5, size = 2, colour = "white", stat = "identity") +
theme(axis.line.x = element_blank()) +
theme(axis.line.y = element_blank()) +
theme(axis.title.x = element_blank()) +
labs(x = NULL, Y = NULL) +
theme(axis.text = element_blank()) +
scale_fill_manual(values = c("#000B29", "#D70026", "azure4", "#EDB83D", "coral")) +
guides(fill = guide_legend(reverse = TRUE)) +
geom_text(aes(label = paste0(round(Cnt), "%")), position = position_stack(vjust = 0.5), color = "white") +
labs(title = "Five Opec Countries with the Biggest Oil Reserves", caption = "Source = OPEC") +
theme(title = element_text(size = 14)) +
theme(legend.text = element_text(size = 14)) +
theme(legend.title = element_blank()) +
theme(legend.position = "bottom") +
theme(plot.title = element_text(hjust = 0.5))
Importing and Cleaning BP Data for Oil Production
bp <- read_excel("C:/Users/Rami/Desktop/BPStats.xlsx", sheet = 6, skip = 2)
glimpse(bp)## Observations: 86
## Variables: 57
## $ `Thousand barrels daily` <chr> NA, "US", "Canada", "Mexico", "Total ...
## $ `1965` <chr> NA, "9014.148000000001", "920", "362"...
## $ `1966` <chr> NA, "9579.241", "1012", "370", "10961...
## $ `1967` <chr> NA, "10219.252", "1106", "411", "1173...
## $ `1968` <chr> NA, "10599.743", "1194", "439", "1223...
## $ `1969` <chr> NA, "10827.674000000001", "1306", "46...
## $ `1970` <chr> NA, "11296.849", "1473", "487", "1325...
## $ `1971` <chr> NA, "11155.778", "1582", "486", "1322...
## $ `1972` <chr> NA, "11184.896000000001", "1829", "50...
## $ `1973` <chr> NA, "10945.953", "2114", "525", "1358...
## $ `1974` <chr> NA, "10461.205", "1993", "653", "1310...
## $ `1975` <chr> NA, "10007.736999999999", "1735", "80...
## $ `1976` <chr> NA, "9735.6389999999992", "1598", "89...
## $ `1977` <chr> NA, "9862.5619999999999", "1608", "10...
## $ `1978` <chr> NA, "10274.441000000001", "1597", "13...
## $ `1979` <chr> NA, "10135.534", "1835", "1607", "135...
## $ `1980` <chr> NA, "10169.626", "1764", "2129", "140...
## $ `1981` <chr> NA, "10180.573", "1610", "2553", "143...
## $ `1982` <chr> NA, "10198.534", "1590", "3001", "147...
## $ `1983` <chr> NA, "10246.668", "1661", "2930", "148...
## $ `1984` <chr> NA, "10508.950999999999", "1775", "29...
## $ `1985` <chr> NA, "10580.378000000001", "1812.79521...
## $ `1986` <chr> NA, "10231.142", "1805.1174616419273"...
## $ `1987` <chr> NA, "9943.9779999999992", "1908.76994...
## $ `1988` <chr> NA, "9764.6890000000003", "2002.41851...
## $ `1989` <chr> NA, "9159.0770000000011", "1961.45622...
## $ `1990` <chr> NA, "8914.3070000000007", "1967.73567...
## $ `1991` <chr> NA, "9075.5450000000001", "1983.50534...
## $ `1992` <chr> NA, "8868.125", "2065.5790598282069",...
## $ `1993` <chr> NA, "8582.6650000000009", "2189.01030...
## $ `1994` <chr> NA, "8388.5789999999997", "2281.33478...
## $ `1995` <chr> NA, "8321.6389999999992", "2402.12781...
## $ `1996` <chr> NA, "8294.527", "2479.9174236024764",...
## $ `1997` <chr> NA, "8268.5920000000006", "2587.59028...
## $ `1998` <chr> NA, "8010.8329999999996", "2672.38133...
## $ `1999` <chr> NA, "7731.4579999999996", "2604.39959...
## $ `2000` <chr> NA, "7731.6040000000003", "2703.40190...
## $ `2001` <chr> NA, "7669.4009999999998", "2727.95812...
## $ `2002` <chr> NA, "7625.0780000000004", "2858.16410...
## $ `2003` <chr> NA, "7367.3", "3003.4707919793873", "...
## $ `2004` <chr> NA, "7249.9949999999999", "3079.88144...
## $ `2005` <chr> NA, "6899.8220000000001", "3040.89602...
## $ `2006` <chr> NA, "6824.8639999999996", "3208.38420...
## $ `2007` <chr> NA, "6859.6859999999997", "3290.18966...
## $ `2008` <chr> NA, "6783.9579999999996", "3207.03529...
## $ `2009` <chr> NA, "7263.0339999999997", "3202.39768...
## $ `2010` <chr> NA, "7549.1880000000001", "3332.07980...
## $ `2011` <chr> NA, "7858.8490000000002", "3514.82206...
## $ `2012` <chr> NA, "8903.6180000000004", "3740.23767...
## $ `2013` <chr> NA, "10071.217000000001", "4000.41096...
## $ `2014` <chr> NA, "11768.192999999999", "4270.52990...
## $ `2015` <chr> NA, "12750.268", "4389.1402241510259"...
## $ `2016` <chr> NA, "12365.806", "4470.176332139712",...
## $ `2017` <chr> NA, "13056.993", "4830.6284506991078"...
## $ `2017__1` <chr> NA, "5.5895022127955052E-2", "8.06348...
## $ `2006-16` <chr> NA, "6.1238122797292904E-2", "3.37221...
## $ `2017__2` <chr> NA, "0.14093023232708649", "5.2139232...Change the column name of “Thousand Barrels”
colnames(bp)[1] <- "Country"Removing the last three columns
bp_clean1 <- bp[1:(length(bp)-3)]Removing NA values
bp_clean2 <- na.omit(bp_clean1)Rearraning the data
bp_clean3 <- gather(bp_clean2, Year, Production, -Country, na.rm = TRUE)Replacing n/a values with zeros
bp_clean3$Production <- as.integer(bp_clean3$Production)
bp_clean3$Production[is.na(bp_clean3$Production)] <- 0Removing redundant rows from the dataset
remove.list <- paste(c("Other Africa", "Total World", "European Union #", "Other Asia Pacific", "Total Asia Pacific", "of which: OECD",
"Total Europe", "Total Africa", "OPEC", "Non-OPEC", "Other Europe", "Total North America", "Total CIS",
"Non-OECD", "OECD", "Total Middle East", "Other Middle East", "Other CIS", "Other S. & Cent. America",
"Total S. & Cent. America"))
bp_clean4 <- subset(bp_clean3, ! Country %in% remove.list)Look at tidy data
head(bp_clean4)## # A tibble: 6 x 3
## Country Year Production
## <chr> <chr> <dbl>
## 1 US 1965 9014
## 2 Canada 1965 920
## 3 Mexico 1965 362
## 4 Argentina 1965 275
## 5 Brazil 1965 96
## 6 Colombia 1965 203Line Chart
Opec_five_prodcution <- bp_clean4 %>%
filter(Country %in% c("Venezuela", "Saudi Arabia", "Iran", "Iraq", "Kuwait")) %>%
filter(Year %in% c(1997:2017)) %>%
group_by(Country, Production) %>%
arrange((desc(Production)))
head(Opec_five_prodcution)## # A tibble: 6 x 3
## # Groups: Country, Production [6]
## Country Year Production
## <chr> <chr> <dbl>
## 1 Saudi Arabia 2016 12401
## 2 Saudi Arabia 2015 11994
## 3 Saudi Arabia 2017 11950
## 4 Saudi Arabia 2012 11634
## 5 Saudi Arabia 2014 11504
## 6 Saudi Arabia 2013 11393Opec_five_prodcution$Country <- factor(Opec_five_prodcution$Country, levels = c("Saudi Arabia", "Iran", "Iraq", "Venezuela", "Kuwait"))
plot_line <- ggplot(Opec_five_prodcution, aes(x = Year, y = Production, fill = Country)) +
geom_line(size = 2, aes(group = Country, colour = Country)) +
scale_color_manual(values = c("#000B29", "#D70026", "azure4", "#EDB83D", "coral")) +
theme_minimal() +
scale_y_continuous(limits = c(1000, 13000), breaks = c(1000, 4000, 7000, 10000, 13000), labels = scales::comma) +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(size = 12, colour = "black")) +
theme(axis.title.y = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour = "black")) +
theme(legend.title = element_blank()) +
theme(panel.grid.major.x = element_blank()) +
theme(panel.grid.minor.x = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.size = unit(2, "lines")) +
theme(legend.text = element_text(colour = "black", size = 12, face = "bold")) +
labs(title = "Oil Production of Five of OPEC Countries", subtitle = "Thousand Barrels Daily",
caption = "Source: BP Statistical Review") +
theme(title = element_text(size = 14))
plot_line
Area Chart
Opec_five_prodcution$Country <- factor(Opec_five_prodcution$Country, levels = c("Saudi Arabia", "Iran", "Iraq", "Venezuela", "Kuwait"))
ggplot(data = Opec_five_prodcution, aes(x=Year, y=Production, fill = Country, group = Country)) +
geom_area(aes(fill = Country), stat= "identity") +
scale_fill_manual(values = c("#000B29", "#D70026", "azure4", "#EDB83D", "coral")) +
theme_minimal() +
scale_y_continuous(limits = c(0, 30000), breaks = c(0, 6000, 12000, 18000, 24000, 30000), labels = scales::comma) +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(size = 12, colour = "black")) +
theme(axis.title.y = element_blank()) +
theme(axis.text.y = element_text(size = 10, colour = "black")) +
theme(legend.title = element_blank()) +
theme(panel.grid.major.x = element_blank()) +
theme(panel.grid.minor.x = element_blank()) +
theme(legend.position = "bottom") +
theme(legend.key.width = unit(3, "lines")) +
theme(legend.key.height = unit(0.4, "lines")) +
theme(legend.text = element_text(colour = "black", size = 12, face = "bold")) +
labs(title = "Oil Production of Five of OPEC Countries", subtitle = "Thousand Barrels Daily",
caption = "Source: BP Statistical Review") +
theme(title = element_text(size = 14))
Bar chart with labeled ends
bp_clean4 <- as.data.frame(bp_clean4)
bp_clean4$Production <- as.numeric(bp_clean4$Production)
top_ten <- bp_clean4 %>%
filter(Year == "2017") %>%
arrange(desc(Production)) %>%
slice(1:15)
highest <- c("two", rep("one", 14))ggplot(top_ten, aes(x = reorder(Country, Production), y = Production)) +
geom_bar(stat = "identity", aes(fill = highest)) +
scale_fill_manual(values = c("#813753","#54203F")) + coord_flip() +
geom_text(aes(y= Production, x = seq(15,1), label = paste0(round(Production),"")), nudge_y = 500, color="black", size = 5) +
theme_minimal() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = 12, colour = "black"),
legend.position = "None",
panel.background = element_blank(),
panel.grid = element_blank()) +
labs(title = "Top Ten Oil Producing Countries in the World in 2017", subtitle = "Thousand Barrels Daily",
caption = "Source: BP Statistical Review") +
theme(title = element_text(size = 12))
Stacked bars chart for the top three producers in the last five years
top_three <- bp_clean4 %>%
filter(Year %in% c(2012:2017) & Country %in% c("US", "Russian Federation", "Saudi Arabia")) %>%
arrange(desc(Production))
ggplot(top_three, aes(x= Year, y = Production, fill = Country)) +
geom_bar(stat = "identity") +
theme_minimal() +
scale_fill_manual(values = c("#003B46", "#07575B", "#66A5AD")) +
scale_y_continuous(limits = c(0,40000), breaks = c(0, 10000, 20000, 30000, 40000), labels = scales::comma) +
theme(axis.title.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
axis.text.y = element_text(size = 12, colour = "black"),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 12, colour = "black"),
legend.position = "bottom",
legend.key.height = unit(0.8, "lines"),
legend.key.width = unit(3, "lines"),
legend.text = element_text(size = 12, colour = "black", face = "bold")) +
labs(title= "Top Three Oil Producing Countries in the World (2012-2017)", subtitle = "Thousand Barrels Daily",
caption = "Source: BP Statistical Review") +
theme(title = element_text(size = 15))
Clustered bars chart for the top three producers in the last five years
top_five <- bp_clean4 %>%
filter(Year %in% c(2012:2017) & Country %in% c("US", "Russian Federation", "Saudi Arabia", "Iran", "Canada")) %>%
arrange(desc(Production))
ggplot(top_five, aes(x= Year, y = Production, fill = Country)) +
geom_bar(stat = "identity", position = "dodge") +
theme_minimal() +
scale_fill_manual(values = c("#375E97", "#FB6542", "#FFBB00", "#3F681C", "#98DBC6")) +
scale_y_continuous(limits = c(0,20000), breaks = c(0, 4000, 8000, 12000, 16000, 20000), labels = scales::comma) +
theme(axis.title.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
axis.text.y = element_text(size = 12, colour = "black"),
axis.text.x = element_text(size = 12, colour = "black"),
axis.title.y = element_blank(),
legend.position = "bottom",
legend.key.height = unit(0.8, "lines"),
legend.key.width = unit(0.8, "lines"),
legend.text = element_text(size = 12, colour = "black", face = "bold")) +
labs(title= "Top Five Oil Producing Countries (2012-2017)", subtitle = "Thousand Barrels Daily",
caption = "Source: BP Statistical Review") +
theme(title = element_text(size = 14)) 
Bar chart with line
us_production <- bp_clean4 %>%
filter(Year %in% c(1997:2017) & Country == "US") %>%
arrange(Year)
ggplot(us_production, aes(x= Year, y = Production)) +
geom_bar(size = 1.5, width = 0.5, fill = "gold", stat = "identity") +
geom_line(aes(group = 1), size = 1, color = "orangered3") +
geom_point(size = 7, shape = 21, fill = "orangered3") +
theme_minimal() +
scale_y_continuous(limits = c(0,14000), breaks = c(0, 2000, 4000, 6000, 8000, 10000, 12000, 14000), labels = scales::comma) +
theme(axis.title.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
axis.text.y = element_text(size = 12, colour = "black"),
axis.text.x = element_text(size = 12, colour = "black"),
axis.title.y = element_blank()) +
labs(title= "US Oil Production (1997-2017)", subtitle = "Thousand Barrels Daily",
caption = "Source: BP Statistical Review") +
theme(title = element_text(size = 14))