Plotting with GGPLOT2

Terminology:

• ggplot - The main function where you specify the dataset and variables to plot
• geoms - geometric objects
  • geom_point(), geom bar(), geom density(), geom line(), geom area()
• aes - aesthetics
  • shape, transparency (alpha), color, fill, line-type.
• scales Define how your data will be plotted
  • continuous, discrete, log

First Plots with GGPLOT

#gapminder <- read.csv("https://goo.gl/BtBnPg", header = T)
gapminder <- read.csv('gapminder-FiveYearData.csv', header=T)

Let’s start off with an example:

library(ggplot2)

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

NOTE:

• First we call the ggplot function, letting R know that we’re creating a new plot

• any arguments we provide the ggplot function are considered global options: they apply to all layers on the plot.

• We passed two arguments to ggplot:

• first is what data we want to show e.g. gapminder data

• an aes function - which tells ggplot how variables in the data map to aesthetic properties in the x & y locations e.g. gdpPercap column on the x and lifeExp column on the y axies

• notice we didn’t have to define the data and column,ggplot is smart enough to look in the data for the columns.

Alone the ggplot call isn’t enough to render the plot.

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp))
## If run, would produce a blank plot or error.

• Need to tell ggplot how we want to visually present variables by specifying a geom layer.
• In the above example we used geom_point to create a scatter plot to represent relationship between x/y.

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Layers

• Using scatter plot is not the best way to visualize change over time.
• Let’s tell ggplot to visualize data as a line plot.

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line()

• we used geom_line instead of geom_point for the geom layer

• added a by aesthetic by=country to get a line per country and color by continent

• Now what if we want to visualize both lines and points on the plot?

• All there is to do is add another layer + geom_point() to the plot:

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line() + geom_point()

• important to note this is layered: so points have been drawn on top of the previous lines layer.

• As an example of this

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
  geom_line(aes(color=continent)) + geom_point()

• in the above the aesthetic mapping of color has been moved from the global plot options in ggplot to the geom_line layer so it no longer applies to the points

• this shows the points are drawn on top of the lines.

Transformations and statistics

• GGPLOT makes it easy to overlay statistical models over the data
• As an exmaple, let’s use our first example

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp, color=continent)) +
  geom_point()

• hard to see relationships b/t points because of strong outliers in GDP per capita
• We can change the scale of units on the x axis using the scale functions
• These control the mapping between the data values and visual values of an aesthetic.

• We’ll also use the alpha function, which is helpful when you have a large amount of data which is v. clustered

• alpha value are any numbers from 0 (transparent) to 1 (opaque). default is usually 1.

ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap)) +
  geom_point(alpha=0.5) + scale_y_log10()

• log10 function applied a transformation to the values of the gdpPercap column before rendering them on the plot
  • each multiple of 10 now only corresponds to an increase in 1 on the transformed scale,
  • e.g. a GDP per capita of 1,000 is now 3 on the y axis, a value of 10,000 corresponds to 4 on the y axis and so on

• This makes it easier to visualize the spread of data on the x-axis.

• We can fit a simple relationship to the data by adding another layer, geom_smooth:

ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + scale_x_log10() + geom_smooth(method="lm")

• make the line thicker by setting the size aesthetic in the geom_smooth layer:

• you can also assign plots to varialbes using the <- operator

# example of assigning a plot to variable pwd
pwd <- ggplot(data = gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() + scale_x_log10() + geom_smooth(method="lm", size=1.5)

pwd

• two ways an aesthetic can be specified:
  1. Here we set the size aesthetic by passing it as an argument to geom_smooth.
  2. Previously we used the aes function to define a mapping between data variables and their visual representation.

Multi-panel figures: FACEting

• previously we visualized the change in life expectancy over time across all countries in one plot
• Alternatively, we can split this out over multiple panels by adding a layer of facet panels
• this will limit the number of panels to countries beginning in the letter “A” or “Z”

starts.with <- substr(gapminder$country, start = 1, stop = 1)
az.countries <- gapminder[starts.with %in% c("A", "Z"), ]

Talk thru code: * We’ll start by subsetting the data using the substr function topull out a part of a character string; * in this case, the letters that occur in positions start through stop, inclusive, of the gapminder$country vector. * The operator %in% allows us to make multiple comparisons rather than write out long subsetting conditions (in this case, starts.with %in% c("A", "Z") is equivalent to starts.with == "A" | starts.with == "Z")

ggplot(data = az.countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country)

• facet_wrap layer took a “formula” as its argument, denoted by the tilde (~).
• ~ tells R to draw a panel for each unique value in the country column of the gapminder dataset.

Modifying text

Now lets clean up this figure for publication.

• would like to change text to elements in the graph.

• X-axis is too cluttered, y-axis should read “Life Expectancy” instead of column name.

• do this by adding a few more layers:
  • add a theme layer that controls axis text & text size
  • Labels for the axes, plot title and any legend can be set using the labs function.
  • Legend titles are set using the same names we used in the aes specification.
  • Thus below the color legend title is set using color = "Continent", while the title of a fill legend would be set using ’fill = “MyTitle”.

ggplot(data = az.countries, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())

How to save your plots

ggsave('~/path/to/figure/filename.png')
ggsave(filename_to_save, file = "~/path/to/figure/filename.png") # filename, path to save location

ggsave(file = "/path/to/figure/filename.png", width = 6,
height =4)      # Plot size in units ("in", "cm", or "mm"). If not supplied, uses the size of current graphics device.

# file can be either be a device function (e.g. png), or one of "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" or "wmf" (windows only).

ggsave(file = "/path/to/figure/filename.eps")
ggsave(file = "/path/to/figure/filename.jpg")
ggsave(file = "/path/to/figure/filename.pdf")

Resources:

This is just a taste of what you can do with ggplot2. RStudio provides a really useful cheat sheet of the different layers available, and more extensive documentation is available on the ggplot2 website. Finally, if you have no idea how to change something, a quick Google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!

ggplot save reference http://ggplot2.tidyverse.org/reference/ggsave.html

ggplot cheat sheet https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

ggplot site http://ggplot2.org/