We will use the following packages. If needed, we install them.

Code 
to_be_loaded <- c("tidyverse", 
                  "patchwork", 
                  "glue", 
                  "ggforce", 
                  "plotly",
                  "ggthemes",
                  "gapminder",
                  "ggrepel")

for (pck in to_be_loaded) {
  if (!require(pck, character.only = T)) {
    install.packages(pck, repos="http://cran.rstudio.com/")
    stopifnot(require(pck, character.only = T))
  }  
}

Grammar of Graphics

We will use the Grammar of Graphics approach to visualization

The expression Grammar of Graphics was coined by Leiland Wilkinson to describe a principled approach to visualization in Data Analysis (EDA)

A plot is organized around data (a table with rows (observations) and columns (variables))

A plot is a graphical object that can be built layer by layer

Building a graphical object consists in chaining elementary operations

The acclaimed TED presentation by Hans Rosling illustrates the Grammar of Graphics approach

Code 
knitr::include_url("https://www.youtube.com/embed/jbkSRLYSojo")

We will reproduce the animated demonstration using

  • ggplot2: an implementation of grammar of graphics in `R
  • plotly: a bridge between R and the javascript library D3.js
  • Using plotly, opting for html ouput, brings the possibility of interactivity and animation

Install and load packages

Code 
require("gapminder")

Insist on the difference between installing and loading a package

  • How do we get the list of installed packages?
  • How do we get the list of loaded packages?
  • Which objects are made available by a package?

Have a look at gapminder dataset

  • A table has a schema: a list of named columns, each with a given type
  • A table has a content: rows. Each row is a collection of items, corresponding to the columns
  • glimpse() allows to see the schema and the first rows
  • head() allows to see the first rows

Get a feeling of the dataset

Pick two random rows for each continent using slice_sample()

What makes a table tidy?

Have a look at Data tidying in R for Data Science (2nd ed.)

Is the gapminder table redundant?

Gapminder tibble (extract)

Extract/filter a subset of rows using dplyr::filter(...)

Filtering (selection \(σ\) from database theory) : Picking one year of data

There is simple way to filter rows satisfying some condition. It consists in mimicking indexation in a matrix, leaving the colum index empty, replacing the row index by a condition statement (a logical expression) also called a mask.

Code 
gapminder_2002 <- gapminder[gapminder$year==2002, ]

Have a look at gapminder$year==2002. What is the type/class of this expression?

This is possible in base R and very often convenient.

Nevertheless, this way of performing row filtering does not emphasize the connection between the dataframe and the condition. Any logical vector with the right length could be used as a mask. Moreover, this way of performing filtering is not very functional.

In the parlance of Relational Algebra, filter performs a selection of rows. Relational expression \[σ_{\text{condition}}(\text{Table})\] translates to

Code 
filter(Table, condition)

where \(\text{condition}\) is a boolean expression that can be evaluated on each row of \(\text{Table}\). In SQL, the relational expression would translate into

SELECT *
FROM Table
WHERE condition

Check Package dplyr docs

The posit cheatsheet on dplyr is an unvaluable resource for table manipulation.

Use dplyr::filter() to perform row filtering

Static plotting: First attempt

  • Define a plot with respect to gapminder_2002

You should define a ggplot object with data layer gapminder_2022 and call this object p for further reuse.

  • Map variables gdpPercap and lifeExp to axes x and y

Use ggplot object p and add a global aesthetic mapping gdpPercap and lifeExp to axes x and y (using + from ggplot2) .

  • For each row, draw a point at coordinates defined by the mapping

You need to add a geom_ layer to your ggplot object, in this case geom_point() will do.

We are building a graphical object (a ggplot object) around a data frame (gapminder)

We supply aesthetic mappings (aes()) that can be either global or bound to some geometries (geom_point())or statistics

The global aesthetic mapping defines which columns are

  • mapped to which axes,
  • possibly mapped to colours, linetypes, shapes, …

Geometries and Statistics describe the building blocks of graphics

What’s missing here?

when comparing to the Gapminder demonstration, we can spot that

  • colors are missing
  • bubble sizes are all the same. They should reflect the population size of the country
  • titles and legends are missing. This means the graphic object is useless.

We will add layers to the graphical object to complete the plot

Second attempt: display more information

  • Map continent to color (use aes())
  • Map pop to bubble size (use aes())
  • Make point transparent by tuning alpha (inside geom_point() avoid overplotting)

Scaling

In order to pay tribute to Hans Rosling, we need to take care of two scaling issues:

  • the gdp per capita axis should be logarithmic scale_x_log10()
  • the area of the point should be proportional to the population scale_size_area()

Motivate the proposed scalings.

  • Why is it important to use logarithmic scaling for gdp per capita?
  • When is it important to use logarithmic scaling on some axis (in other contexts)?
  • Why is it important to specify scale_size_area() ?

In perspective

  • Add a plot title
  • Make axes titles
    • explicit
    • readable
  • Use labs(...)

What should be the respective purposes of Title, Subtitle, Caption, … ?

Theming using ggthemes (or not)

  • Theming
Code 
require("ggthemes")

Look at the online help on pacman::p_load(), how does pacman::p_load() relate to require() and library()?

A theme defines the look and feel of plots

Within a single document, we should use only one theme

See Getting the theme for a gallery of available themes

Code 
p +
  theme_economist()

Tuning scales

Use scale_color_manual(...) to hand-tune the color aesthetic mapping.

Code 
```{r}
#| label: theme_scale
neat_color_scale <-
      c("Africa" = "#01d4e5",
        "Americas" = "#7dea01" ,
        "Asia" = "#fc5173",
        "Europe" = "#fde803",
        "Oceania" = "#536227")
```

Choosing a color scale is a difficult task

viridis is often a good pick.

Adding labels

Animate for free with plotly

Use plotly::ggplotly()

More material

Read Visualization in R for Data Science