R-advancing-with-dataviz.R

########################################
## Advancing with data viz in R using ggplot2
##    Christine Zhang
##     @christinezhang
##  ychristinezhang at gmail dot com
########################################

## Advancing data visualization in R using ggplot2
## By Christine Zhang (@christinezhang on Twitter; ychristinezhang at gmail dot com)

# Link to annotated code:
# https://github.com/underthecurve/r-dataviz-ggplot2/blob/master/R-advancing-with-dataviz.md (markdown file for viewing on the web)
# https://github.com/underthecurve/r-dataviz-ggplot2/blob/master/R-advancing-with-dataviz.pdf (pdf file for printing out)

# Before starting, ensure this .R file and the following data file are in the same folder:
# "life.csv"

# We need to tell R that our files are saved in the same location.
# In order to do this, you should click through the following:
# "Session --> Set Working Directory --> To Source File Location"

# In this workshop, we will:
# - Examine the basic syntax of `ggplot2`
# - Use global life expectancy data to produce different type of plots charts using `ggplot2`
# - Explore advanced features of `ggplot2`
# - Learn to export graphics for publication

## Loading `tidyverse`

# install.packages('tidyverse') # if you don't already have tidyverse
library('tidyverse') # load the tidyverse package

#### The syntax of `ggplot2` ####

# Create a data frame
data(Seatbelts)
data.seatbelts <- data.frame(Year = floor(time(Seatbelts)),
                  Month = factor(cycle(Seatbelts),
                               labels = month.abb), Seatbelts)

# Now, let's plot the data, using the basic plotting function of ggplot2
qplot(data = data.seatbelts, x = Year, y = DriversKilled, main = 'Drivers Killed by Year')

drivers.plot <- ggplot(data = data.seatbelts, # the data
       aes(x = Year, # 'aes' stands for 'aesthetics': what's on the x- and y- axes
           y = DriversKilled))

drivers.plot

drivers.plot + geom_point() # adding geometry layer

drivers.plot +
  geom_text(aes(label = Month))

drivers.plot + geom_point() + ggtitle('Drivers Killed by Year')

# These produce the exact same plot:
qplot(data = data.seatbelts, x = Year, y = DriversKilled, main = 'Drivers Killed by Year')

ggplot(data = data.seatbelts,
       aes(x = Year, y = DriversKilled)) +
  geom_point() +
  ggtitle('Drivers Killed by Year')

#### Life expectancy by country, 2000 to 2015 ####

life <- read_csv('life.csv')
str(life)

head(life)

#### Scatter plots ####

life.usa <- life %>% filter(country.code == 'USA')

ggplot(life.usa, aes(x = male, y = female)) +
  geom_point()

ggplot(life.usa, aes(x = male, y = female)) +
  geom_point() +
  geom_line()

ggplot(life.usa, aes(x = male, y = female)) +
  geom_point() +
  geom_smooth()

ggplot(life.usa, aes(x = male, y = female)) +
  geom_point() +
  geom_smooth(method = 'lm', se = F)

ggplot(life, aes(x = year, y = `both sexes`)) +
  geom_point()

ggplot(life, aes(x = year, y = `both sexes`)) +
  geom_point() +
  geom_line() # what if I had run geom_smooth() instead?

## Why didn't that work correctly?

ggplot(life, aes(x = year, y = `both sexes`, group = country)) +
  geom_point() +
  geom_line()

ggplot(life, aes(x = year, y = `both sexes`, group = country)) +
#  geom_point() +
  geom_line()

#### Facetted plots ####

ggplot(life, aes(x = year, y = `both sexes`, group = country)) +
  geom_line() +
  facet_wrap(~ country)

# What do we see in this plot (the zoomed in one on RStudio, not the one you actually see above) that we can't see in the one before it? Note you wouldn't publish the above graph, but it's useful as an exploratory exercise.

#### Reordering a plot ####

americas <- life %>% filter(region == 'Americas' & year == 2015)

ggplot(data = americas, aes(x = `both sexes`, y = country)) +
  geom_point()

# Why didn't I put the countries on the x-axis?
# What would make this plot look better?

class(americas$country) # a character
americas$country.factor <- as.factor(americas$country)
class(americas$country.factor) # a factor

levels(americas$country.factor)

library('forcats') #load forcats package

americas$country.factor.reorder <- fct_reorder(americas$country.factor, # factor variable to reorder
                                               americas$`both sexes`) # variable to reorder it by

levels(americas$country.factor.reorder)

ggplot(data = americas, aes(x = `both sexes`, y = country.factor.reorder)) +
  geom_point()

p <- ggplot(data = americas,
            aes(x = `both sexes`, y = fct_reorder(country.factor, `both sexes`))) +
  geom_point()

p

# Why didn't we need the dollar signs (e.g., `fct_reorder(americas$country.factor, americas$both.sexes)` this time)?

#### Axes labels and scales ####

p + labs(x = 'Life expectancy at birth in 2015, years', y = '')

p + labs(x = 'Life expectancy at birth in 2015, years', y = '') +
  scale_x_continuous(limits = c(50, 100))

p + labs(x = 'Life expectancy at birth in 2015, years', y = '') +
  scale_x_continuous(limits = c(60, 85), # minimum and maximum for the x-axis
                     breaks = seq(from = 60, to = 85, by = 5)) # x-axis labels

# ?scale_x_continuous()
plot1 <- p + labs(x = 'Life expectancy at birth in 2015, years', y = '') +
  scale_x_continuous(limits = c(60, 85),  # minimum and maximum for the x-axis
                     breaks = seq(from = 50, to = 100, by = 5), # x-axis labels
                     minor_breaks = NULL) +
  ggtitle("Haiti's life expectancy is the lowest in the region") # why did I use double quotes here?

plot1

#### Adjusting themes/appearance ####

# plot1 + theme_bw()
# install.packages('ggthemes') # if you don't already have ggthemes
library('ggthemes') # load the ggthemes pacakge
plot1 + theme_few()

# see https://rstudio-pubs-static.s3.amazonaws.com/3364_d1a578f521174152b46b19d0c83cbe7e.html ("Theme elements" section)

plot1 + theme(panel.background = element_blank())

#### Saving a plot ####

ggsave('plot1.png', plot1 + theme_few())
ggsave('plot1.png', plot1 + theme_few(), width = 8, height = 6)

# install.packages('gapminder') # if you don't already have gapminder
library('gapminder') # load the gapminder package
gapminder

p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

p

p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop))

p
p + scale_size_area()

p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop, color = continent))

p + scale_size_area()

# What if we just wanted the colors of the points to be, say, blue rather than mapped to the `continent` variable?

p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(size = pop), color = 'blue')

#### The legend ####

p + scale_size_area(max_size = 10)
# max_size = 10 sets the maximum size of the points (in this case, it makes them larger)

p + scale_size_area(max_size = 10) + theme(legend.position = 'bottom')

## In this case, I think the legend should actually go inside the plot, in the bottom right hand corner, since I have some empty space there.

  ## Can you use this page: https://rpubs.com/folias/A-simple-example-on-ggplot2-legend-options to figure out how to do this?

  ## Exercise: try to figure out how to make the population variable show up in non-scientific notation

#### Bar plots ####

ggplot(data = americas, aes(x = country.factor, y = `both sexes`)) +
   geom_bar()

# Why didn't this work?
# Try this:

ggplot(data = americas, aes(x = country.factor)) +
  geom_bar()

# try ?geom_bar()

ggplot(data = americas, aes(x = country.factor, y = `both sexes`)) +
  geom_bar(stat = 'identity')

p <- ggplot(data = americas, aes(x = country.factor, y = `both sexes`)) +
  geom_col()

p + coord_flip()

## Exercise: reorder the bars by life expectancy from highest to lowest using the `fct_reorder` function in `forcats`

#### What else? ####

  # - Change the look and feel of the bar graph above to your liking using the tools we've learned today and save it as a `.png` file.
  # - What other visualizations could you make using the `gapminder` dataset?
 # - What are some other customizations of `ggplot2` plots would you like to see that we haven't covered today (additional resources are in the GitHub repo: https://github.com/underthecurve/r-dataviz-ggplot2)
	########################################
	## Advancing with data viz in R using ggplot2
	## Christine Zhang
	## @christinezhang
	## ychristinezhang at gmail dot com
	########################################

	## Advancing data visualization in R using ggplot2
	## By Christine Zhang (@christinezhang on Twitter; ychristinezhang at gmail dot com)

	# Link to annotated code:
	# https://github.com/underthecurve/r-dataviz-ggplot2/blob/master/R-advancing-with-dataviz.md (markdown file for viewing on the web)
	# https://github.com/underthecurve/r-dataviz-ggplot2/blob/master/R-advancing-with-dataviz.pdf (pdf file for printing out)

	# Before starting, ensure this .R file and the following data file are in the same folder:
	# "life.csv"

	# We need to tell R that our files are saved in the same location.
	# In order to do this, you should click through the following:
	# "Session --> Set Working Directory --> To Source File Location"

	# In this workshop, we will:
	# - Examine the basic syntax of `ggplot2`
	# - Use global life expectancy data to produce different type of plots charts using `ggplot2`
	# - Explore advanced features of `ggplot2`
	# - Learn to export graphics for publication

	## Loading `tidyverse`

	# install.packages('tidyverse') # if you don't already have tidyverse
	library('tidyverse') # load the tidyverse package

	#### The syntax of `ggplot2` ####

	# Create a data frame
	data(Seatbelts)
	data.seatbelts <- data.frame(Year = floor(time(Seatbelts)),
	Month = factor(cycle(Seatbelts),
	labels = month.abb), Seatbelts)

	# Now, let's plot the data, using the basic plotting function of ggplot2
	qplot(data = data.seatbelts, x = Year, y = DriversKilled, main = 'Drivers Killed by Year')

	drivers.plot <- ggplot(data = data.seatbelts, # the data
	aes(x = Year, # 'aes' stands for 'aesthetics': what's on the x- and y- axes
	y = DriversKilled))

	drivers.plot

	drivers.plot + geom_point() # adding geometry layer

	drivers.plot +
	geom_text(aes(label = Month))

	drivers.plot + geom_point() + ggtitle('Drivers Killed by Year')

	# These produce the exact same plot:
	qplot(data = data.seatbelts, x = Year, y = DriversKilled, main = 'Drivers Killed by Year')

	ggplot(data = data.seatbelts,
	aes(x = Year, y = DriversKilled)) +
	geom_point() +
	ggtitle('Drivers Killed by Year')

	#### Life expectancy by country, 2000 to 2015 ####

	life <- read_csv('life.csv')
	str(life)

	head(life)

	#### Scatter plots ####

	life.usa <- life %>% filter(country.code == 'USA')

	ggplot(life.usa, aes(x = male, y = female)) +
	geom_point()

	ggplot(life.usa, aes(x = male, y = female)) +
	geom_point() +
	geom_line()

	ggplot(life.usa, aes(x = male, y = female)) +
	geom_point() +
	geom_smooth()

	ggplot(life.usa, aes(x = male, y = female)) +
	geom_point() +
	geom_smooth(method = 'lm', se = F)

	ggplot(life, aes(x = year, y = `both sexes`)) +
	geom_point()

	ggplot(life, aes(x = year, y = `both sexes`)) +
	geom_point() +
	geom_line() # what if I had run geom_smooth() instead?

	## Why didn't that work correctly?

	ggplot(life, aes(x = year, y = `both sexes`, group = country)) +
	geom_point() +
	geom_line()

	ggplot(life, aes(x = year, y = `both sexes`, group = country)) +
	# geom_point() +
	geom_line()

	#### Facetted plots ####

	ggplot(life, aes(x = year, y = `both sexes`, group = country)) +
	geom_line() +
	facet_wrap(~ country)

	# What do we see in this plot (the zoomed in one on RStudio, not the one you actually see above) that we can't see in the one before it? Note you wouldn't publish the above graph, but it's useful as an exploratory exercise.

	#### Reordering a plot ####

	americas <- life %>% filter(region == 'Americas' & year == 2015)

	ggplot(data = americas, aes(x = `both sexes`, y = country)) +
	geom_point()

	# Why didn't I put the countries on the x-axis?
	# What would make this plot look better?

	class(americas$country) # a character
	americas$country.factor <- as.factor(americas$country)
	class(americas$country.factor) # a factor

	levels(americas$country.factor)

	library('forcats') #load forcats package

	americas$country.factor.reorder <- fct_reorder(americas$country.factor, # factor variable to reorder
	americas$`both sexes`) # variable to reorder it by

	levels(americas$country.factor.reorder)

	ggplot(data = americas, aes(x = `both sexes`, y = country.factor.reorder)) +
	geom_point()

	p <- ggplot(data = americas,
	aes(x = `both sexes`, y = fct_reorder(country.factor, `both sexes`))) +
	geom_point()

	p

	# Why didn't we need the dollar signs (e.g., `fct_reorder(americas$country.factor, americas$both.sexes)` this time)?

	#### Axes labels and scales ####

	p + labs(x = 'Life expectancy at birth in 2015, years', y = '')

	p + labs(x = 'Life expectancy at birth in 2015, years', y = '') +
	scale_x_continuous(limits = c(50, 100))

	p + labs(x = 'Life expectancy at birth in 2015, years', y = '') +
	scale_x_continuous(limits = c(60, 85), # minimum and maximum for the x-axis
	breaks = seq(from = 60, to = 85, by = 5)) # x-axis labels

	# ?scale_x_continuous()
	plot1 <- p + labs(x = 'Life expectancy at birth in 2015, years', y = '') +
	scale_x_continuous(limits = c(60, 85), # minimum and maximum for the x-axis
	breaks = seq(from = 50, to = 100, by = 5), # x-axis labels
	minor_breaks = NULL) +
	ggtitle("Haiti's life expectancy is the lowest in the region") # why did I use double quotes here?

	plot1

	#### Adjusting themes/appearance ####

	# plot1 + theme_bw()
	# install.packages('ggthemes') # if you don't already have ggthemes
	library('ggthemes') # load the ggthemes pacakge
	plot1 + theme_few()

	# see https://rstudio-pubs-static.s3.amazonaws.com/3364_d1a578f521174152b46b19d0c83cbe7e.html ("Theme elements" section)

	plot1 + theme(panel.background = element_blank())

	#### Saving a plot ####

	ggsave('plot1.png', plot1 + theme_few())
	ggsave('plot1.png', plot1 + theme_few(), width = 8, height = 6)

	# install.packages('gapminder') # if you don't already have gapminder
	library('gapminder') # load the gapminder package
	gapminder

	p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
	geom_point()

	p

	p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
	geom_point(aes(size = pop))

	p
	p + scale_size_area()

	p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
	geom_point(aes(size = pop, color = continent))

	p + scale_size_area()

	# What if we just wanted the colors of the points to be, say, blue rather than mapped to the `continent` variable?

	p <- ggplot(data = gapminder %>% filter(year == 2007), aes(x = gdpPercap, y = lifeExp)) +
	geom_point(aes(size = pop), color = 'blue')

	#### The legend ####

	p + scale_size_area(max_size = 10)
	# max_size = 10 sets the maximum size of the points (in this case, it makes them larger)

	p + scale_size_area(max_size = 10) + theme(legend.position = 'bottom')

	## In this case, I think the legend should actually go inside the plot, in the bottom right hand corner, since I have some empty space there.

	## Can you use this page: https://rpubs.com/folias/A-simple-example-on-ggplot2-legend-options to figure out how to do this?

	## Exercise: try to figure out how to make the population variable show up in non-scientific notation

	#### Bar plots ####

	ggplot(data = americas, aes(x = country.factor, y = `both sexes`)) +
	geom_bar()

	# Why didn't this work?
	# Try this:

	ggplot(data = americas, aes(x = country.factor)) +
	geom_bar()

	# try ?geom_bar()

	ggplot(data = americas, aes(x = country.factor, y = `both sexes`)) +
	geom_bar(stat = 'identity')

	p <- ggplot(data = americas, aes(x = country.factor, y = `both sexes`)) +
	geom_col()

	p + coord_flip()

	## Exercise: reorder the bars by life expectancy from highest to lowest using the `fct_reorder` function in `forcats`

	#### What else? ####

	# - Change the look and feel of the bar graph above to your liking using the tools we've learned today and save it as a `.png` file.
	# - What other visualizations could you make using the `gapminder` dataset?
	# - What are some other customizations of `ggplot2` plots would you like to see that we haven't covered today (additional resources are in the GitHub repo: https://github.com/underthecurve/r-dataviz-ggplot2)