R

Christian Thiele

23 minute read

This post is a little similar to my post about the EBRD’s database but, at least currently, this scraper is still functional as the webpage has not changed. The International Finance Corporation offers a database that contains the projects it helped to finance. The data that should be extracted for every project are: Project number URL Region The dates at which the project was signed and approved Cost, risk management, guarantee, loan Information about the project’s sponsor The main location of the involved enterprise The main challenge scraping the IFC’s database is the messy way in which the project data was entered and differing page layouts.

Christian Thiele

19 minute read

This is an exploratory analysis of the Rossmann Store Sales data which can be found here. The data isn’t huge but the speedup using data.table is noticeable. It is nice to have unmasked data which allows for some interpretations. Read in the data: try(setwd("E:\Seafile\Crypt2\Kaggle\RossmannStoreSales\Daten\"), silent = T) try(setwd("/home/ct/Seafile/Crypt2/Kaggle/RossmannStoreSales/Daten/"), silent = T) library(data.table) library(zoo) ## ## Attache Paket: 'zoo' ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric library(forecast) library(ggplot2) test <- fread("test.

Christian Thiele

12 minute read

This document summarizes the energy related projects that were financed by the European Bank for Reconstruction and Development. The necessary data was downloaded from the publicly available project database using the following scraper written in R that is also available on Github. It is not necessary to execute that code if you would like to reproduce the document. The resulting data is available as projects_costscurr.csv. The code that produces the graphs can be found in the Rmd-file of this project.