If you’ve been following the natural beauty niche for any length of time, you know that is the definitive cornerstone of the community. But in an era of "here today, gone tomorrow" websites, staying relevant requires constant evolution.

Since its inception, WeAreHairy has focused on a "girl next door" vibe that celebrates natural body hair. In a digital world dominated by heavy filters and airbrushing, the site’s commitment to raw, honest beauty is its greatest strength. However, the site doesn’t just rest on its archives; it is one of the most frequently updated niche sites on the web. Frequency: What "Updated" Really Means

Beyond still photography, the site has transitioned heavily into HD and 4K video updates, providing a more cinematic look at their subjects.

Modern updates are served via optimized CDNs, ensuring that 4K videos load instantly without buffering. Why the Community Stays Loyal

With thousands of models in the database, the updated search functionality allows users to sort by hair color, nationality, or specific features, making the massive archive manageable.

When fans search for "," they aren't just looking for a new layout—they’re looking for the latest in authentic, unpolished photography and the high-frequency content schedule the site is famous for. Here is a look at how the platform keeps its library fresh and why it remains the go-to destination for natural aesthetics. A Legacy of Authenticity

Part of the "updated" experience is behind the scenes. To keep up with modern browsing habits, the platform has undergone several technical overhauls:

In the landscape of natural beauty sites, staying "updated" is the difference between a static archive and a living community. WeAreHairy.com continues to lead the pack by blending its classic, no-frills philosophy with modern technology and a rigorous content schedule.

Stay Current: What’s New with WeAreHairy.com Updated Content

The site is now fully responsive, making it easy to browse the latest updates on tablets or smartphones without losing image quality.

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.

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 wearehairycom updated

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. If you’ve been following the natural beauty niche

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. In a digital world dominated by heavy filters

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()

Wearehairycom: Updated

Beyond still photography, the site has transitioned heavily into HD and 4K video updates, providing a more cinematic look at their subjects.

Modern updates are served via optimized CDNs, ensuring that 4K videos load instantly without buffering. Why the Community Stays Loyal

With thousands of models in the database, the updated search functionality allows users to sort by hair color, nationality, or specific features, making the massive archive manageable.

Part of the "updated" experience is behind the scenes. To keep up with modern browsing habits, the platform has undergone several technical overhauls:

Stay Current: What’s New with WeAreHairy.com Updated Content

The site is now fully responsive, making it easy to browse the latest updates on tablets or smartphones without losing image quality.

Related chart types

Ggplot2

Animation

Interactivity

Caveats

Data art