Intro to R (part 2): investigating carbon emmissions

Fri, 12 Aug 2022 00:00:00 +0000

Welcome back to R!

This tutorial will provide skills for intermediate R usage

For an introduction to basic R coding, see R intro pt 1

In this tutorial, we will use data from Gapminder to learn the following:

read data from a csv file
manipulate a data set
join multiple data sets
plot data
animate figures
create for loops

Gapminder:

On to the data!

gap <- read.csv(file = "./consumption_emissions_tonnes_per_person.csv")

head(gap)
str(gap)
View(gap)

Properties of the dataframe.

nrow(gap)
ncol(gap)
dim(gap)

gap[1, ]
gap[ , 1]
countries <- gap[ , 1]
head(countries)
names(gap)

yrs <- seq(from = 1989, to = 2016, by = 1)
yrs

l ength(yrs) # 28 years. 2016 - 1989 # Not matching! Reason: 1989 was excluded.

Think about it like this:

Start with 2016 years, take away all years 1989 and earlier. We lose 1989.

But you can always just use length to check.

gap[6, ]
aus <- gap[6, 2:ncol(gap)]

Scatterplots

plot(x = yrs, y = aus)
plot(x = yrs, y = aus, ylim = c(0, 15))
plot(x = yrs, y = aus, ylim = c(0, 15), type = "l")

plot(x = yrs, y = aus, ylim = c(0, 15), type = "l", xlab = "", ylab = "Tonnes", 
 main = "Tonnes of carbon emissions per person in Australia")

But you can do that in Excel, and it might be easier.

We want the Gapinder bubble plot!

library(reshape2)
long_gap = melt(data = gap, id.vars = "country", 
 variable.name = "year", 
 value.name = "emissions")

head(long_gap)

Very nice, but the year is “X1989”.

If we plot that it will be out of order because its not a number.

str(long_gap)

head(long_gap)

Get parts of a character string by position:

substr(x = "seahorse", start = 1, stop = 3)
substr(x = "seahorse", start = 4, stop = 8)

long_gap$yrs = substr(x = long_gap$year, start = 2, stop = 5)
head(long_gap)

str(long_gap)  # Years isn't numeric! Have to fix that for plotting.
long_gap$yrs = as.numeric(long_gap$yrs)

head(long_gap)
str(long_gap)


year_89 = long_gap[long_gap$yrs == 1989 , ]

hist(year_89$emissions)
hist(year_89$emissions, 20)
View(year_89)

We need to combine this data with (1) GDP (2) population

gdp <- read.csv(file = "./ny_gdp_pcap_pp_kd_R2.csv")

head(gdp)

long_gdp <- melt(data = gdp, id.vars = "country", 
             variable.name = "year", 
             value.name = "gdp_per_capita")
head(long_gdp)  # Fix the years again.
long_gdp$yrs = substr(x = long_gdp$year, start = 2, stop = 5)
str(long_gdp)   
long_gdp$yrs <- as.numeric(long_gdp$yrs)
head(long_gdp)

gdp_89  = long_gdp[long_gdp$yrs == 1989 , ]
hist(x = gdp_89$gdp_per_capita, breaks = 40)
hist(log(x = gdp_89$gdp_per_capita, base = 10), 
 breaks = 20)

We aren’t going to polish this figure because it’s not the end goal,

but you could make the x units be say 10, 100, 1000, 10000, etc.

pop <- read.csv(file = "./population_total_R2.csv")
View(pop)

long_pop  = melt(data = pop, id.vars = "country", 
             variable.name = "year", 
             value.name = "population")
head(long_pop)
long_pop$yrs = substr(x = long_pop$year, start = 2, stop = 5)
str(long_pop)   
long_pop$yrs = as.numeric(long_pop$yrs)
head(long_pop)

Joins

We need to connect long_gap to long_pop

head(long_gap)
head(long_pop)

They don’t have the same number of years in them.

Example

animals = c("alligator", "bat", "cat", "dog", "emu", "flying squirrel")
charisma = c(22, 12, 18, 24, 17, 30)

people    =  c("alice", "bob", "charlie")
pet_type  = sample(x = animals, size = 3, replace = T)

animal_facts <- data.frame(animals, charisma)
animal_facts

pets <- data.frame(person = people, 
               pet = pet_type)

pets

How to assign the people the right facts?

Dplyr is powerful but we only need one function right now, left_join().

library(dplyr)
left_join(pets, animal_facts, by = c("pet" = "animals"))

gap_pop = left_join(long_gap, long_pop, by = c("country", "yrs"))

gap_pop_gdp = left_join(gap_pop, long_gdp)
head(gap_pop_gdp)
gapminder = gap_pop_gdp[ , c("country", "yrs", "population", "emissions", "gdp_per_capita")]
head(gapminder)

head(gapminder$country )

aus_89 = gapminder[gapminder$country == "Australia"  & gapminder$yrs == 1989, ]
aus_89
aus_full = gapminder[gapminder$country == "Australia", ]

plot(x = aus_full$yrs, y = aus_full$population)
range(aus_full$yrs)
plot(aus_full$yrs, aus_full$population / 1000000, type = "l", 
 ylab = "Population (Millions)", xlab = "", main = "Australian Population, 1989 to 2016")
plot(x = aus_full$yrs, y = aus_full$emissions, type = "l", 
 xlab = "", ylab = "Tonnes",
 main = "Australian Carbon Emissions Per Capita, 1989 to 2016")

plot(x = aus_full$yrs, y = aus_full$gdp_per_capita, type = "l", 
 xlab = "", ylab = "Dollars",
 main = "Australian GDP Per Capita (PPP), 1989 to 2016")

Combine it all, for one year.

aus_89

This bubble is for australia in 1989. Size is scaled for1,000,000 pop.

plot(x = aus_89$gdp_per_capita, y = aus_89$emissions, 
 xlim = c(0, 50000), ylim = c(0, 20), cex = aus_89$population / 1000000 , 
 xlab = "Dollars per Capita", ylab = "Tonnes Carbon Per Capita")

World 89

world_89 <- gapminder[gapminder$yrs == 1989, ]
world_89
complete.cases(world_89)
complete.cases(world_89) == T
world_89c <- world_89[complete.cases(world_89) == T, ]
world_89c

It’s helpful to know the range to set the limits of x, y.

range(world_89c$emissions) # range 0 to 32
range(world_89c$gdp_per_capita) # range 0 to 32

Size is scaled by 10,000,000 people

plot(x = world_89c$gdp_per_capita, y = world_89c$emissions, 
 xlim = c(0, 120000), ylim = c(0, 33), cex = world_89c$population / 10000000 )

Set area proportional to population:

pop = pi * r^2
pop / pi) = r^2
sqrt(pop / pi ) = r
plot(x = world_89c$gdp_per_capita, y = world_89c$emissions, 
 xlim = c(0, 120000), ylim = c(0, 33), cex = sqrt(world_89c$population / (1000000 * pi) ), 
 xlab = "Dollars per Capita", ylab = "Tonnes Carbon Per Capita", 
 main = "World carbon emissions per capita against GDP per capita")

Log version

plot(x = world_89c$gdp_per_capita, y = world_89c$emissions, log = "xy",
 cex = sqrt(world_89c$population / (1000000 * pi) ), 
 xlab = "Dollars per Capita", ylab = "Tonnes Carbon Per Capita", 
 main = "World carbon emissions per capita against GDP per capita\n(Log Axes)")

But wait, there’s more!

Saving a plot

png(filename = "./Carbon_emissions_against_gdp.png", height = 600, width = 800)
plot(x = world_89c$gdp_per_capita, y = world_89c$emissions, log = "xy",
 cex = sqrt(world_89c$population / (1000000 * pi) ), 
 xlab = "Dollars per Capita", ylab = "Tonnes Carbon Per Capita", 
 main = "World carbon emissions per capita against GDP per capita\n(Log Axes)")
dev.off()

This confirms that we are done with the plot. It causes the file to write to disk.

All graphics will be on hold until we confirm this.

ggplot

library(ggplot2)
head(gapminder)
ggplot(data = aus_full, mapping = aes(x = yrs, y = emissions)) + 
  geom_point() + geom_line()

ggplot(data = aus_full, mapping = aes(x = yrs, y = emissions)) + 
  geom_point() + geom_line() + theme_bw()

Polishing the plot

ggplot(data = aus_full, mapping = aes(x = yrs, y = emissions)) + 
  geom_point() + geom_line() + theme_bw() + 
  labs(x = "", y = "Tonnes Carbon Per Capita", title = "Australian Carbon Emissions")

World, 1989.

head(world_89)
ggplot(data = world_89, mapping = aes(x = gdp_per_capita, y = emissions)) + 
  geom_point(mapping = aes(size = emissions)) + theme_bw() + 
  labs(x = "", y = "Tonnes Carbon Per Capita", title = "Australian Carbon Emissions")

what’s this warning about? Complete cases!

ggplot(data = world_89c, mapping = aes(x = gdp_per_capita, y = emissions)) + 
  geom_point(mapping = aes(size = population), alpha = 0.5) + theme_bw() + 
  scale_x_log10(lim = c(200, 200000), 
                breaks = c(200, 2000, 20000, 200000), 
                labels = c("200", "2,000", "20,000", "200,000")) + 
  scale_y_log10() + 
  scale_size_area(max_size = 20) + 
  labs(x = "GDP per Captia", y = "Tonnes Carbon Per Capita", title = "World Carbon and GDP per Captia, 1989")

Off the deep end ————–

For this portion of the tutorial, we will use the R package magick for advance impage processing.

See this webpage for referencing magick functions and usage: https://docs.ropensci.org/magick/articles/intro.html

nyrs = length(yrs)
library(magick)

img <- image_graph(1200, 1200, res = 96)

for(i in 1:nyrs){
# Create plot
  year_i = yrs[i]
  year_i_data <- gapminder[gapminder$yrs == year_i, ]
  
  plot(x = year_i_data$gdp_per_capita, 
  y = year_i_data$emissions, log = "xy",
   cex = sqrt(year_i_data$population / (1000000 * pi) ), 
   xlim = c(200, 200000),
   ylim = c(0.01, 51),
   xlab = "Dollars per Capita", ylab = "Tonnes Carbon Per Capita", 
   main = "World carbon emissions per capita against GDP per capita\n(Log Axes)")

}

dev.off()
animation <- image_animate(img, fps = 4, optimize = TRUE)

image_write(image = animation, path = "./carbon_and_gdp_per_capita.gif")

ggplot version

img <- image_graph(1200, 1200, res = 96)

for(i in 1:nyrs){
  # Create plot
  year_i = yrs[i]
  year_i_data <- gapminder[gapminder$yrs == year_i, ]
  
  
  plot_i = ggplot(data = year_i_data, mapping = aes(x = gdp_per_capita, y = emissions)) + 
    geom_point(mapping = aes(size = population), alpha = 0.5) + theme_bw() + 
    scale_x_log10(lim = c(200, 200000), 
                  breaks = c(200, 2000, 20000, 200000), 
                  labels = c("200", "2,000", "20,000", "200,000")) + 
    scale_y_log10(lim = c(0.1, 50)) + 
    # expand_limits(size = c(0, 1500000000)) + 
    scale_size_area(max_size = 20, breaks = c(1000000, 100000000, 1000000000), 
                    labels = c("1 Million", "100 Million", "1 Billion"),
                                              name = "Population") + 
    labs(x = "Dollars", y = "Tonnes Carbon", title = "World Carbon and GDP per Captia")
  
  plot(plot_i) # By default, ggplot will not show you plots inside a loop. 
}

dev.off()
animation = image_animate(img, fps = 4, optimize = TRUE)

image_write(image = animation, path = "./gg_carbon_and_gdp_per_capita.gif")

IPAT animation

(IPAT: Total Impact = Population * Technology)

img <- image_graph(1200, 1200, res = 96)

for(i in 1:nyrs){
  # Create plot
  year_i = yrs[i]
  year_i_data <- gapminder[gapminder$yrs == year_i, ]
  
  plot_i = ggplot(data = year_i_data, 
              mapping = aes(x = emissions, 
                            y = gdp_per_capita * population)) + 
    geom_text(mapping = aes(label = country)) + 
    scale_x_log10(lim = c(0.05, 50)) + scale_y_log10(lim = c(1E9, 1E14)) + 
    geom_smooth() + theme_bw() +
    labs(x = "Tonnes Carbon", y = "GDP per Capita * Population", 
     title = year_i)
  
  plot(plot_i) # By default, ggplot will not show you plots inside a loop. 
}

dev.off()

animation = image_animate(img, fps = 4, optimize = TRUE)

image_write(image = animation, path = "./gg_pop_income_against_emissions.gif")

Carbon and dollars, with text

img <- image_graph(1200, 1200, res = 96)
for(i in 1:nyrs){
  # Create plot
  year_i = yrs[i]
  year_i_data <- gapminder[gapminder$yrs == year_i, ]
  
  
  plot_i = ggplot(data = year_i_data, mapping = aes(x = gdp_per_capita, y = emissions)) + 
    geom_text(mapping = aes(label = country, size = population)) + theme_bw() + 
    scale_x_log10(lim = c(200, 200000), 
                  breaks = c(200, 2000, 20000, 200000), 
                  labels = c("200", "2,000", "20,000", "200,000")) + 
    scale_y_log10(lim = c(0.1, 50)) + 
# expand_limits(size = c(0, 1500000000)) + 
    scale_size_area(max_size = 20, breaks = c(1000000, 100000000, 1000000000), 
                    labels = c("1 Million", "100 Million", "1 Billion"),
                    name = "Population") + 
    labs(x = "Dollars", y = "Tonnes Carbon", 
         title = paste0("World Carbon and GDP per Captia, ", year_i))
  
  plot(plot_i) # By default, ggplot will not show you plots inside a loop. 
}

dev.off()
animation = image_animate(img, fps = 4, optimize = TRUE)

image_write(image = animation, path = "./gg_carbon_and_gdp_per_capita_text.gif")

IPAT R2

img <- image_graph(1200, 1200, res = 96)

for(i in 1:nyrs){
  # Create plot
  year_i = yrs[i]
  year_i_data <- gapminder[gapminder$yrs == year_i, ]
  
  plot_i = ggplot(data = year_i_data, 
                  mapping = aes(x = emissions * population, 
                                y = gdp_per_capita * population)) + 
    geom_smooth() + 
    geom_text(mapping = aes(label = country)) + 
    scale_x_log10(lim = c(1E6, 2E10)) + scale_y_log10(lim = c(1E9, 1E14)) + 
    
    theme_bw() +
    labs(x = "Total Tonnes Carbon", y = "Total GDP", 
         title = paste0("World Carbon and GDP, ", year_i))
  
  plot(plot_i) # By default, ggplot will not show you plots inside a loop. 
    }
    
dev.off()
animation = image_animate(img, fps = 4, optimize = TRUE)

image_write(image = animation, path = "./gg_pop_income_against_emissions_text.gif")

Carbon and dollars, with text, and LM

img <- image_graph(1200, 1200, res = 96)
for(i in 1:nyrs){
  # Create plot
  year_i = yrs[i]
  year_i_data <- gapminder[gapminder$yrs == year_i, ]
  
  
  plot_i = ggplot(data = year_i_data, mapping = aes(x = gdp_per_capita, y = emissions)) + 
    geom_smooth(method = "lm", se = F) + 
    geom_text(mapping = aes(label = country, size = population)) + theme_bw() + 
    scale_x_log10(lim = c(200, 200000), 
                  breaks = c(200, 2000, 20000, 200000), 
                  labels = c("200", "2,000", "20,000", "200,000")) + 
    scale_y_log10(lim = c(0.1, 50)) + 
    # expand_limits(size = c(0, 1500000000)) + 
    scale_size_area(max_size = 20, breaks = c(1000000, 100000000, 1000000000), 
                    labels = c("1 Million", "100 Million", "1 Billion"),
                    name = "Population") + 
    labs(x = "Dollars", y = "Tonnes Carbon", 
         title = paste0("World Carbon and GDP per Captia, ", year_i))
  
  plot(plot_i) # By default, ggplot will not show you plots inside a loop. 
}

dev.off()
animation = image_animate(img, fps = 4, optimize = TRUE)

image_write(image = animation, path = "./gg_carbon_and_gdp_per_capita_text_lm.gif")

Tidyverse | Alex Baecher

Intro to R (part 2): investigating carbon emmissions

Welcome back to R!

This tutorial will provide skills for intermediate R usage

For an introduction to basic R coding, see R intro pt 1

In this tutorial, we will use data from Gapminder to learn the following:

Gapminder:

On to the data!

Properties of the dataframe.

Think about it like this:

Start with 2016 years, take away all years 1989 and earlier. We lose 1989.

But you can always just use length to check.

Scatterplots

But you can do that in Excel, and it might be easier.

We want the Gapinder bubble plot!

Very nice, but the year is “X1989”.

If we plot that it will be out of order because its not a number.

Get parts of a character string by position:

We need to combine this data with (1) GDP (2) population

We aren’t going to polish this figure because it’s not the end goal,

but you could make the x units be say 10, 100, 1000, 10000, etc.

Joins

We need to connect long_gap to long_pop

They don’t have the same number of years in them.

Example

How to assign the people the right facts?

Dplyr is powerful but we only need one function right now, left_join().

Combine it all, for one year.

This bubble is for australia in 1989. Size is scaled for1,000,000 pop.

World 89

It’s helpful to know the range to set the limits of x, y.

Size is scaled by 10,000,000 people

Set area proportional to population:

Log version

But wait, there’s more!

Saving a plot

This confirms that we are done with the plot. It causes the file to write to disk.

All graphics will be on hold until we confirm this.

ggplot

Polishing the plot

World, 1989.

what’s this warning about? Complete cases!

Off the deep end ————–

For this portion of the tutorial, we will use the R package magick for advance impage processing.

See this webpage for referencing magick functions and usage: https://docs.ropensci.org/magick/articles/intro.html

ggplot version

IPAT animation

(IPAT: Total Impact = Population * Technology)

Carbon and dollars, with text

IPAT R2

Carbon and dollars, with text, and LM