Hands-on_Ex04

Author

NeoYX

4  Programming Animated Statistical Graphics with R

4.1 Overview

When telling a visually-driven data story, animated graphics tends to attract the interest of the audience and make deeper impression than static graphics. In this hands-on exercise, I will learn how to create animated data visualisation by using gganimate and plotly r packages. At the same time, I will also learn how to

(i) reshape data by using tidyr package, and

(ii) process, wrangle and transform data by using dplyr package.

4.1.1 Basic concepts of animation

When creating animations, the plot does not actually move. Instead, many individual plots are built and then stitched together as movie frames, just like an old-school flip book or cartoon. Each frame is a different plot when conveying motion, which is built using some relevant subset of the aggregate data. The subset drives the flow of the animation when stitched back together.

4.1.2 Terminology

Before we dive into the steps for creating an animated statistical graph, it’s important to understand some of the key concepts and terminology related to this type of visualization.

  1. Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.

  2. Animation Attributes: The animation attributes are the settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.

Tip

Beforewe start making animated graphs, we should first ask ourself: Does it makes sense to go through the effort? If we are conducting an exploratory data analysis, a animated graphic may not be worth the time investment. However, if we are giving a presentation, a few well-placed animated graphics can help an audience connect with our topic remarkably better than static counterparts.

4.2 Getting Started

4.2.1 Loading the R packages

We will use p_load from pacman package to check, install and load the following R packages:

  • plotly, R library for plotting interactive statistical graphs.

  • gganimate, an ggplot extension for creating animated statistical graphs.

  • gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.

  • gapminder: An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.

  • tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.

pacman::p_load(readxl, gifski, gapminder, plotly, gganimate, tidyverse)

4.2.2 Importing the data

In this hands-on exercise, the Data worksheet from GlobalPopulation Excel workbook will be used.

Write a code chunk to import Data worksheet from GlobalPopulation Excel workbook by using appropriate R package from tidyverse family.

col <- c('Country', 'Continent')

globalPop <- read_xls('data/GlobalPopulation.xls',
                      sheet='Data') %>% 
  mutate_each_(funs(factor(.)), col) %>%
  mutate(Year = as.integer(Year))
glimpse(globalPop)
Rows: 6,204
Columns: 6
$ Country    <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan",…
$ Year       <int> 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014,…
$ Young      <dbl> 83.6, 84.1, 84.6, 85.1, 84.5, 84.3, 84.1, 83.7, 82.9, 82.1,…
$ Old        <dbl> 4.5, 4.5, 4.5, 4.5, 4.5, 4.6, 4.6, 4.6, 4.6, 4.7, 4.7, 4.7,…
$ Population <dbl> 21559.9, 22912.8, 23898.2, 25268.4, 28513.7, 31057.0, 32738…
$ Continent  <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia,…
Things to learn from the code

  • read_xls() of readxl package is used to import the Excel worksheet.

  • mutate_each_() of dplyr package is used to convert all character data type into factor.

    • This line applies the factor() function to each column specified in the col argument. Character to factor. It takes column indices or column names in strings format as inputs, and returns a data frame with new columns for each column in the input data frame, where each new column is the result of applying the specified function to the corresponding column in the input data frame.

    • The fun argument specifies the function to apply to each column, and factor(.) is a way to specify the factor works as an argument.

  • mutate of dplyr package is used to convert data values of Year field into integer.

    • as.character(x), as.integer(x), as.numeric(x), as.factor(x) (for categorical data)

4.3 Animated Data Visualisation: gganimate methods

gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.

  • transition_*() defines how the data should be spread out and how it relates to itself across time.

  • view_*() defines how the positional scales should change along the animation.

  • shadow_*() defines how data from other points in time should be presented in the given point in time.

  • enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation.

  • ease_aes() defines how different aesthetics should be eased during transitions.

4.3.1 Building a static population bubble plot

In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot

Note

A bubble plot is created when a third numeric variable is assigned to size argument inside a ggplot with geom_point.

ggplot(data= globalPop,
       aes(x= Old,
           y=Young,
           size= Population,
           color=Country)) +
  geom_point(alpha = 0.7,
             show.legend = FALSE) +
  scale_color_manual(values=country_colors) +  #<<< who has defined country_colors?
  scale_size(range= c(2,12)) +
  labs(title='Year:{frame_time}',
       x = '% Aged',
       y= '% Young')

Things to learn from the code

  • The scale_size(range= c(2,12)) sets the range of point sizes to be used in the plot to between 2 and 12.

  • Population is mapped to size aes in ggplot, thus this range parameter controls the min nad max size of the points.

4.3.2 Building the animated bubble plot

In the code chunk below,

  • transition_time() of gganimate is used to create transition through distinct states in time (i.e. Year).

  • ease_aes() is used to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.

ggplot(data= globalPop,
       aes(x= Old,
           y=Young,
           size= Population,
           color=Country)) +
  geom_point( alpha = 0.7,
             show.legend = FALSE) +
  scale_color_manual(values=country_colors) +  #<<< gapminder lib
  scale_size(range= c(2,12)) +
  labs(title='Year:{frame_time}',
       x = '% Aged',
       y= '% Young') +
  transition_time(Year) +
  ease_aes("linear")

4.4 Animated Data Visualisation: plotly

In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).

4.4.1 Building an animated bubble plot: ggplotly() method

In this sub-section, I will learn how to create an animated bubble plot by using ggplotly() method.

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,    #<<< perform aes mapping for each frame
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)
Things to learn from the code chunk above

  • Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.

  • ggplotly() is then used to convert the R graphic object into an animated svg object.

4.4.2 Building an animated bubble plot: plot_ly() method

In this sub-section, you will learn how to create an animated bubble plot by using plot_ly() method.

bp <- globalPop %>% 
  plot_ly( x = ~Old,
           y= ~Young,
           color = ~Continent,#<< 6 unique
           frame= ~Year,
           text= ~Country,
           hoverinfo='text',
           type = 'scatter',
           mode= 'markers')


bp

4.5 Reference