Data-projects-with-R-and-GitHub

Project Background

As I am currently spending a semester abroad, I am particularly interested in where Erasmus students come from and which countries they go to. To explore this, I use the dataset available at data.europa.eu, specifically the file titled:

“Erasmus+ Raw Mobility Data – Key Action 2”

This dataset provides information on all Erasmus+ mobilities since 2014. My project focuses on aggregating how many students have gone from each sending country to each receiving country over the years.

Dataset Description

The dataset contains information on individual Erasmus+ mobilities, including the following relevant fields:

• Sending Country
• Receiving Country
• Mobility Start Year/Month – timestamp of mobility
• Actual Participants – number of people in that mobility record

Data manipulation

• Only include long-term study mobility of pupils

• Aggregate the total number of participants for each combination of Sending Country and Receiving Country, since all data since 2014 should be included.

• Since the data is provided as an Excel XLSX file, which cannot be accessed directly from the internet via a URL in R, it would be great if you could include the following steps:

  • The data file should be downloaded using R code from the URL: https://ec.europa.eu/assets/eac/erasmus-plus/statistics/mobility/Erasmus-KA1-Mobility-Data-2015.xlsx
  • Then, the data should be read into R.
  • Afterwards, the XLSX file should be deleted so that the rather large data file does not have to be managed via GitHub.

Answer the following questions

• Which 5 country pairs show the highest intensity of student exchange within the Erasmus+ programme?
• Are there regional patterns or strong bilateral partnerships in student mobility? Identify mobility hotspots!
• Where do the 5 most participating countries send their students and from which countries do they receive there students
• Which countries have consistently attracted the highest number ob Erasmus+ students?

Visualization goals

In order to answer the questions, visualize the following

• create a heatmap, which highlights the intensity of mobility flows between countries. Each cell should represent the number of participants from one sending country to one receiving country. Apply a clear color scale to highlight intensity differences. You can choose whatever colors you want. The visualization could look like this (not bases on real data ./heatmap_example_datadraft_SvenjaPoerstel.PNG)

• To further explore groupings and flow dynamics, create a Sankey flow diagram.

• Generate a barplot to show the top 10 most popular receiving countries, the bars should be sorted in descending order by the number of incoming participants.