Intro to visualization

The Grammar of Graphics

The package we’re going to use for our graphing is called ggplot2, which was written by Hadley Wikham before he developed the tidyverse. It is based on one of the most celebrated academic books on visualization, called the “Grammar of Graphics” by Leland Wilkinson in the 1980s. For our purposes, you just need to understand that any visualization is made up of several fundamental pieces. In ggplot2, they are:

  • aesthetics (called “aes”) : What data are you plotting? It can have more than two dimensions, such as x-axis, y-axis, a variable used for color and another used for size.
  • geometry refers to the shape each variable will take.
  • scale - any transformation we might make.
  • facets - splitting up one graph into many similar graphs, based on a variable.
  • layers - adding multiple geometries on top of one another to reveal new information, or add annoations.

One quirk of ggplot is that instead of the %>% pipe command , it uses + instead. Hadley has said this will be changed in future versions, but for now, we have to use the other.

The basic structure of the plot is:

  ggplot (  data = df_name, 
            aes (
              x = variable_name,
              y = variable_name, 
              color = variable_name, 
              size = variable_name
              )
            ) +
            a geometry (eg, geom_point, geom_bar, etc.) with options +
            facet_wrap ( variable ~ variable ) +
            any scale information

We’re going to skip the labeling and annotation parts for now. That would come under layering and legends.

Good resources

Building you graph in pieces using gapminder

Gapminder is a datast made famous in a viral 2010 TED talk. It contains the life expectency and income (GDP per capita) by country for 200 years. The data is in 5-year increments, through 2007.

Subset to just the latest years

Just pull out the latest year (2007) for our practice

## Observations: 142
## Variables: 6
## $ country   <fct> Afghanistan, Albania, Algeria, Angola, Argentina, Aust…
## $ continent <fct> Asia, Europe, Africa, Africa, Americas, Oceania, Europ…
## $ year      <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, …
## $ lifeExp   <dbl> 44, 76, 72, 43, 75, 81, 80, 76, 64, 79, 57, 66, 75, 51…
## $ pop       <int> 31889923, 3600523, 33333216, 12420476, 40301927, 20434…
## $ gdpPercap <dbl> 975, 5937, 6223, 4797, 12779, 34435, 36126, 29796, 139…

The histogram

One of the first pieces of information you often want about a dataset is its distribution. Do all of the values cluster around the center? Or do they spread out? Let’s do a histogram of the life expectency variable.

Start your plot with the commmand ggplot, then add in the aesthetics and geometry:

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

We’ll add a few options to this plot to make it a little easier to read

But I’m interested in how the different continents look. Try “faceting” by continent.

Try a scatter, or dot plot

Save a plot and add to it

You can save your plot to an object rather than print it immediately, making it a little easier to troubleshoot.

It doesn’t look like anything! The reason is that I didn’t include a geometry, or a shape, for the values. Add a point here:

There are some built-in themes that take some best practices for mixes of colors and styles, so I’ll add one in.

Let’s add some color. Remember, we keep adding elements to our existing plot, so we don’t have to start over each time.

And now add population for the size of the country

Let’s build this from scratch, and then also make sure that the big points don’t overlap the little ones too much:

Facet

Now let’s make a little chart for each continent

Scale

It’s hard to see because the gdp is so skewed. That can be fixed with something called a log scale – it’s in logarithms, not base 10 numbers.

## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.

Adding interactivity

GGPLOT2 is not interactive, so we have to install a different library to allow us to hover over the points. In this case, we’re going to use a function called paste, which puts words together, in the variable in aes called “text”:

If you want to look at lots more examples, check out Matt Waite’s data visualization course on Github

Adding animation

So far, we’ve only looked at the year 2007 – what if we wanted to look at it over time, say 50 or so years? Let’s take the years 1957 to 2007:

## # A tibble: 11 x 2
##     year `n()`
##    <int> <int>
##  1  1957   142
##  2  1962   142
##  3  1967   142
##  4  1972   142
##  5  1977   142
##  6  1982   142
##  7  1987   142
##  8  1992   142
##  9  1997   142
## 10  2002   142
## 11  2007   142

Let’s just repeat what we did to get the original interactive plot, with one change: The plotly package lets you specify a “frame” aesthetic, which will create animation by whatever variable you specify. Here, we’ve specified the year as that frame: