Beyond the technical specifications of the file, here is an overview of the film itself.

Genre: Satirical Science Fiction / Comedy-Drama Director: Alexander Payne Starring: Matt Damon, Christoph Waltz, Hong Chau, and Kristen Wiig.

Plot Premise: The film explores a revolutionary concept where scientists have discovered a method to shrink humans to five inches tall. This process, marketed as "downsizing," is presented as a solution to overpopulation and environmental degradation. However, it also offers an economic incentive: a person’s wealth translates to a much higher standard of living in the miniature world.

Critical Reception: While the film was praised for its ambitious premise and the visual effects required to depict the scale difference between normal and miniaturized humans, critical reception was mixed regarding the execution. The film shifts from a high-concept satire to a more philosophical drama in its second half. Notably, Hong Chau received an Academy Award nomination for Best Supporting Actress for her performance as Ngoc Lan Tran.

Themes:

Start the conversion.

This guide provides a general approach. The specifics can vary depending on your exact needs and the tools you're comfortable using. Always ensure you have the rights or appropriate permissions to work with and distribute video content.

Now, let’s talk about the file itself. PSA (PublicHD) releases are known for their aggressive but efficient compression.

File size guess: Probably around 1.5GB – 2.5GB. For a 135-minute movie in HEVC, that’s impressive.

The miniature world is stunning. Giant everyday objects (pills, pizza slices, toys) are used creatively, and the contrast between normal-sized and small environments is visually engaging.

The movie could have skewered corporate greenwashing, medical ethics, or immigration in the mini-world. Instead, it becomes surprisingly sentimental and preachy.

A world of geom

ggplot2 builds charts through layers using geom_ functions. Here is a list of the different available geoms. Click one to see an example using it.

geom_bar geom_bin geom_boxplot geom_density geom_error geom_hex geom_hist geom_hline geom_jitter geom_label geom_line geom_point geom_polygon geom_rect geom_ribbon geom_rug geom_segment geom_smooth geom_text geom_tile geom_violin geom_vline

Annotation with ggplot2

Annotation is a key step in data visualization. It allows to highlight the main message of the chart, turning a messy figure in an insightful medium. ggplot2 offers many function for this purpose, allowing to add all sorts of text and shapes.

Annotate with geom_text

geom_text() allows to add annotation to one, several or all markers of your chart.

downsizing20171080pbrrip6chx265hevcpsa upd

Annotate with geom_label

Very close to geom_text, geom_label produces a label wrapped in a rectangle. This example also explains how to apply labels to a selection of markers.

Rectangle

Learn how to use the annotate function to add a rectangle on a specific part of the chart.

Segment

How to add one or several segments to the chart.

Arrow

Basically the same as for a segment, with just one additional argument.

Abline

Add horizontal or vertical ablines with geom_hline and geom_vline.

Curved arrows

Dicover how to draw curved arrows in ggplot downsizing20171080pbrrip6chx265hevcpsa upd

Marginal plot

Marginal plots are not natively supported by ggplot2, but their realisation is straightforward thanks to the ggExtra library as illustrated in graph #277.

Scatterplot with rug

Add rug on X and Y axis to describe the numeric variable distribution. Show how geom_rug() works.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Marginal boxplot

Using boxplots is another way to show the marginal distribution. Find more in this post. Beyond the technical specifications of the file, here

ggplot2 chart appearance

The theme() function of ggplot2 allows to customize the chart appearance. It controls 3 main types of components:

Axis: controls the title, label, line and ticks
Background: controls the background color and the major and minor grid lines
Legend: controls position, text, symbols and more.

Axis

Customize ggplot2 axis: title, line, labels and ticks.

Background

Customize ggplot2 background: color, major and minor grid lines.

Legend

Customize ggplot2 legend: position, title, text, key symbol and more. Start the conversion

Re-ordering with ggplot2

When working with categorical variables (= factors), a common struggle is to manage the order of entities on the plot.

Post #267 is dedicated to reordering. It describes 3 different way to arrange groups in a ggplot2 chart:

Using the forcats package
With dplyr
With the reorder() function of base R

Read post

Tidyverse

Here’s the official ggplot2 cheatsheet created by Posit. It covers all the key concepts of the library.

I've also compiled it with the most useful R and data visualization cheatsheets into a single PDF you can download:

ggplot2

A cheatsheet for quickly recalling the key functions and arguments of the ggplot2 library.

ggplot2 title

The ggtitle() function allows to add a title to the chart. The following post will guide you through its usage, showing how to control title main features: position, font, color, text and more.

Basic

Learn how to add a title with ggtitle, default options.

Appearance

Learn how to change title font, size, color, text and more.

Position

Check how to change title position.

Use custom fonts with ggplot2

If you don't want your plot to look like any others, you'll definitely be interested in using custom fonts for your title and labels! This is totally possible thanks to 2 main packages: ragg and showtext. The blog-post below should help you using any font in minutes.

Custom font

Learn how to add a custom font using the ragg or the showtext library in a ggplot2 chart.

Small multiples: facet_wrap() and facet_grid()

Small multiples is a very powerful dataviz technique. It split the chart window in many small similar charts: each represents a specific group of a categorical variable. The following post describes the main use cases using facet_wrap() and facet_grid() and should get you started quickly.

Facet_wrap

Facet wrap allows to build small multiples using one categorical variable.

Facet_grid

Same idea, but using 2 categorical variables for the faceting.

Customized

An advanced examples to make sure you know how to make your small multiple pretty.

Strip features

Customize the general layout with the strip option.

Mixing charts

How to combine several charts together with ggplot2.

A set of pre-built themes

It is possible to customize any part of a ggplot2 chart thanks to the theme() function. Fortunately, heaps of pre-built themes are available, allowing to get a good style with one more line of code only. Here is a glimpse of the available themes. See code

→ ggplot2

default theme_bw() theme_minimal() theme_classic() theme_gray()

→ ggthemes

theme_excel() theme_economist() theme_fivethirtyeight() theme_tufte() theme_gdocs() theme_wsj() theme_calc() theme_hc()

→ other

theme_article() theme_pubclean() theme_bigstatsr() theme_ipsum()

Downsizing20171080pbrrip6chx265hevcpsa Upd

Beyond the technical specifications of the file, here is an overview of the film itself.

Genre: Satirical Science Fiction / Comedy-Drama Director: Alexander Payne Starring: Matt Damon, Christoph Waltz, Hong Chau, and Kristen Wiig.

Themes:

Start the conversion.

Now, let’s talk about the file itself. PSA (PublicHD) releases are known for their aggressive but efficient compression.

File size guess: Probably around 1.5GB – 2.5GB. For a 135-minute movie in HEVC, that’s impressive.

The miniature world is stunning. Giant everyday objects (pills, pizza slices, toys) are used creatively, and the contrast between normal-sized and small environments is visually engaging.

The movie could have skewered corporate greenwashing, medical ethics, or immigration in the mini-world. Instead, it becomes surprisingly sentimental and preachy.

Related chart types

Ggplot2

Animation

Interactivity

Caveats

Data art