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The french yield curve problem? (part 1)

By Datapleth.io on August 28, 2019

Following our previous post on the USA yield curve inversion, we are going to evaluate the situation in other countries, starting with France. Preparation Let’s as usual load the libraries we need. library(xml2) library(dplyr) library(data.table) library(plotly) library(knitr) library(kableExtra) Sys.setlocale("LC_ALL","C") ## [1] "LC_CTYPE=C;LC_NUMERIC=C;LC_TIME=C;LC_COLLATE=C;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C" Getting and cleaning the data for France Data source for France data is French central bank, known as “Banque de France” (see here.

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Do we have a yield curve problem ? (part 1)

By Datapleth.io on August 17, 2019

It’s summer 2019, the so called “yield curve” inversion is on the news, a great (as usual) nytimes inforgraphics is explaining what is at stake, to make it short, it’s about prediction of economic future. The objective of these posts is to propose alternatives to these visuals and to extend to other countries such as France. As per NY Times, in A 3-D View of a Chart That Predicts The Economic Future: The Yield Curve :

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Visualize Satellite images with Mapbox API

By Datapleth.io on August 12, 2019

Accessing to satellite images based on geolocalization has many applications in data visualization and data sciences. There are several alternative of services which provide API interfaces which can be integrated in notebooks or articles, for instance : Google Maps, Bings Maps, OpenStreetMap, … You can find free, freemium or premium services. In this post we are going to illustrate a short demo of mapbox satellite API. Mapbox present itself as the “location data platform for mobile and web applications”.

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China mainland population density

By Datapleth.io on July 14, 2016

In an earlier post we mapped the urbanization rate of China at province level. In this post we will go futher by visualizing where Chinese people are living using a gridded population map. We will use the NASA dataset (Population Density Grid, v3 (1990, 1995, 2000)) which consists of estimates of human population by 2.5 arc-minute grid cells. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells.

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