Philippa Markovics / Apr 16 2019

World Population Density

This notebook shows the world’s population density in 2017 as choropleth map. It is based on a data set provided (as CSV file) by the World Bank which is uploaded here:

World Population Density.csv

Cleaning the Data

In a first step, the R programming language and tidyverse library are used to prepare the CSV file provided by the World Bank for plotting.


The CSV’s first 4 lines are skipped because they contain meta information that is irrelevant to the visualization.

data <- read_csv(
World Population Density.csv
, skip=4) data
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Looking at the data reveals there are plenty of NA values that need to be removed and the data set will be reduced to include only what the plot needs:

data <- data %>%
	select(c(`Country Name`, `Country Code`, `2017`)) %>%
	rename(Density="2017") %>%
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Once the data is in a useful form, it will be saved out to a new CSV file to be used for visualization using Python and plotly later. Saving it to /results will put it into Nextjournal’s content-addressed storage and makes the file available to other programming language runtimes, like Python:

write.csv(data, file="/results/cleaned.csv")
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Population Density Map

Finally, Python and pandas are used to read in our cleaned CSV as dataframe. plotly takes care of rendering an interactive choropleth map:

import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd

df = pd.read_csv(
) data = [go.Choropleth( locations = df['Country Code'], z = df['Density'], zmin = 0.0, zmax = 300.0, colorscale = 'Reds', text = df['Country Name'], marker = go.choropleth.Marker( line = go.choropleth.marker.Line(color = 'rgb(180,180,180)', width = 0.5) ), colorbar = go.choropleth.ColorBar( title = 'People per sqkm' ), )] layout = go.Layout( geo = go.layout.Geo( showframe = False, showcoastlines = False, projection = go.layout.geo.Projection(type = 'mercator') ), margin = go.layout.Margin( l = 0, r = 0, b = 0, t = 0, pad = 0 ) ) go.Figure(data = data, layout = layout)