Working with Humanitarian Exchange Language (HXL) Hashtags

Population pyramids are a powerful way to visualize demographic data, especially when analyzing age and sex patterns. In this post, I will elaborate a population pyramid using the {ggplot2} package in R, specifically focusing on crime victimization data from Mexico’s National Survey of Victimization and Perception of Public Safety (
<em>
 Encuesta Nacional de Victimización y Percepción sobre Seguridad Pública
</em>
, ENVIPE).

Creating a population pyramid to visualize crime victimization by age and sex in Mexico

Previously, in Part 1 of this tutorial, we calculated the Standardized Precipitation Index (SPI) for different time scales using the {SCI} package in R. Now, we will map the SPI values for the districts of Somalia using {sf} and {ggplot2} packages in R.

How to compute and visualize the Standardized Precipitation Index (SPI) for drought analysis

In Germany, as of 31 December 204, according to the Central Register of Foreigners, more than 14 million people of the 83.6 Million residents are foreigners.

How has the number of persons seeking protection in Germany changed over time?

According to official data from the National Institute of Statistics of Cambodia, in 2009 about 77 percent of Cambodians aged 15–49 considered it acceptable for a man to curse his wife. Although this figure is high, it represents a decrease of about 16 percentage points compared to the previous estimate from 2005. Analyzing these changes is important for gaining a better understanding of the phenomenon of violence against women and girls.

A dot plot of public acceptability of domestic violence in Cambodia between 2005 and 2009

In these notes, I will show you how to use {ggplot} to create a waffle chart about the frequency of disease outbreaks in the world.

A waffle chart of disease outbreaks in the world

The Standardized Precipitation Index (SPI) quantifies how precipitation deviates from the historical average over multiple time scales (e.g., from 1 to 12 months), allowing for consistent monitoring of droughts and floods across regions and time, making it a valuable tool for early warning and risk assessment systems.

In this article, I will guide you through the process of analyzing Mexico’s 2024 National Survey of Victimization and Perception of Public Safety (
<em>
 Encuesta Nacional de Victimización y Percepción sobre Seguridad Pública
</em>
, ENVIPE) using the {tidyverse} framework for survey analysis with the {srvyr} package.

Using a tidy approach in R to analyze Mexico’s 2024 ENVIPE

According to the 2021-22 Domestic Violence module of the Kingdom of Cambodia’s Demographic and Health Survey (CDHS), over 21% of Cambodian women aged 15–49 who have ever had a husband (or intimate partner), have experienced physical, sexual, economic, and/or emotional intimate partner violence in their lifetime.

Three maps to examine the prevalence of intimate partner violence (IPV) in Cambodia

ACLED (Armed Conflict Location &amp; Event Data Project) provides detailed data on political violence and protest events across the globe. This blog post explores the visualization of armed conflict in the Lake Chad Basin, focusing on the dynamics of violence against civilians in the region.

Visualizing armed conflict using ACLED’s data and HDX shapefiles

Ridgeline plots are a type of chart that displays the distribution of a numeric variable for different categories. These plots are commonly used to visualize changes in distributions over time. To showcase how to create ridgeline plots with {ggplot2} and {ggridges} in R, I will use data on monthly rainfall at some stations by provincies and month in Vietnam, from 2002 to 2024.

Ridgeline plots to study subnational rainfall patterns in Vietnam

Overview   Waffle charts are a useful way to visualize part-to-whole relationships. Commonly, waffle charts depict a grid of regular squares to represent the distribution of a categorical variable. Previously, in a post on this Blog, I built the following waffle chart to visualize disease outbreaks in the world between 2015 and 2024.

Data Art & Science Blog

Using a tidy approach in R to analyze Mexico’s 2024 ENVIPE

A waffle chart of disease outbreaks in the world