Creating Stunning World Maps to Visualize Economic Data

Creating Stunning World Maps to Visualize Economic Data

Introduction:

Choropleths are a powerful tool for visualizing geographic data, and creating them with R and Python can be quite easy. With the availability of clean data, you can quickly create attractive visualizations. The World Bank Open Data platform provides a wide range of economic datasets that can be easily loaded. However, making the visualizations look nice can be a bit challenging. To simplify the process, I have developed a small, integrated function that handles data download, adjustment, and visualization all in one. The output can be exported as a high-quality vector graphic using ggsave.

To replicate the example, you just need to follow three simple steps. Install the required packages, load the function, and run it to create and save the plot. This tool is perfect for creating choropleths of GDP per capita data, allowing you to customize various aspects such as color scheme, scale, and title. Get started with choropleth visualizations now!

Full Article: Creating Stunning World Maps to Visualize Economic Data

Easy and Effortless Visualization of Geographic Data Using Choropleths with R and Python

Choropleths are a great tool for visualizing geographic data, and creating them with R and Python can be quite easy, especially when clean data is readily available. One platform that offers a wealth of economic datasets is the World Bank Open Data platform. However, making the visualizations look visually appealing can be a bit challenging. To simplify this process, a small, integrated function has been created that handles data downloading, adjustment, and visualization all in one. The resulting output can be easily exported as a high-quality vector graphic using the ggsave function.

Step 1: Install the Required Packages

To replicate the example, start by installing the necessary packages. These packages include:

– rnaturalearth
– rnaturalearthdata
– WDI
– lubridate
– dplyr
– ggplot2
– RColorBrewer

Step 2: Load the Function

Next, load the function that handles the data download and visualization. The function is called “get_and_plot_wb_data” and takes several parameters such as the selected indicator, whether to use a logarithmic scale, the midpoint calculation method, color scheme, start and end years, legend position, chart title, and scale factor.

Step 3: Run the Function and Save the Plot

Finally, run the function with the desired parameters and save the resulting plot. For example, you can run the function with the selected indicator “NY.GDP.PCAP.KD”, a median midpoint calculation, a logarithmic scale, a legend position at the bottom, a chart title of “GDP per Capita in US$”, and a scale factor of 1. The resulting visualization will be saved as an SVG file in the specified target folder.

Conclusion

With the provided function, creating choropleth visualizations of geographic data using R and Python becomes easy and effortless. By following the three steps outlined above, you can quickly generate visually appealing maps that effectively convey the desired information. Whether you’re a data scientist or a curious individual, this function offers a convenient way to explore and analyze economic data on a global scale.

Summary: Creating Stunning World Maps to Visualize Economic Data

Choropleths are a great way to visualize geographic data using R and Python. This article introduces a function that simplifies the process of creating choropleths by handling data download, adjustment, and visualization all in one. The function uses the World Bank Open Data platform to load economic datasets and can export the visualization as a high-quality vector graphic using ggsave. By following the provided steps, readers can replicate the example and create their own choropleth maps. The function also allows customization options such as selecting indicators, adjusting the color scheme, and choosing a midpoint value.

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