Chapter 32 Examples of Useful Data and Additional Tools

This chapter offers useful tips on freely available data sources and additional tools worth knowing about.

32.1 Examples of Free Data

For those writing a thesis, for example, it is useful to know where to find public statistics that can be downloaded for free. Large amounts of data are freely available online, organized in structured tables ready to download and use for analysis. Some data are easy to access; others require special permits, either because they contain sensitive information about individuals or companies, or because companies own the rights to the data.

Several well-known international databases provide free data for download. Some examples:

Eurostat, the European Union’s statistics portal: https://ec.europa.eu/eurostat
OECD, Organisation for Economic Co-operation and Development, international organization and think tank. Publishes large amounts of statistics and reports: https://www.oecd.org and https://data.oecd.org
World Bank database: https://data.worldbank.org
ILO, International Labour Organization: https://ilostat.ilo.org
IMF, International Monetary Fund: https://www.imf.org/en/Data
Penn World Table: https://www.rug.nl/ggdc/productivity/pwt
Federal Reserve Economic Data: https://fred.stlouisfed.org

In some cases there are also specially designed functions for connecting to databases directly from within the programs. For example, there are routines for connecting to the World Bank’s open data directly from Stata and R. For Stata, go to the following link and search for Stata:

https://blogs.worldbank.org/opendata

For R, see the wbstats package, which has extensive information available on various websites. The package can be installed with install.packages("wbstats") and loaded with library("wbstats").

32.2 Other Tools That Are Good to Know About

In this book we have covered three analysis tools. Beyond these, there are of course many other programs across different platforms that may be useful. This section gives some examples. The aim is to inspire and to remind you that staying open to new tools is always worthwhile. This list is not exhaustive. Which tool, function or application is best — or even relevant — depends entirely on the task at hand. Some have narrow applications but can be extremely valuable for specific projects.

Beyond the three tools covered in this book, many other programs offer similar functionality, each with their own specific strengths and weaknesses. There is also a large amount of literature and resources online for those who want to learn more. Here are some well-known examples:

SPSS, Statistical Product and Service Solutions: https://www.ibm.com/products/spss-statistics
SAS: https://www.sas.com
EViews: https://eviews.com
Minitab: https://www.minitab.com

Python is one of the world’s most widely used programming languages, see https://www.python.org. It is used across a wide range of fields and can be applied to many different types of work. Within the social sciences it is used for data analysis and mathematical modelling, among other things. Another programming language that has grown in popularity is Julia: https://julialang.org.

Like R, both Python and Julia can be used in several different types of computer programs. In this book we used the RStudio program to work with R. In RStudio we can also use Python, for example by choosing in the menu File > New File > Python script.

An important part of all analysis work is being able to present results in a clear and effective way. For this purpose many use Microsoft Word or PowerPoint, which are widely installed alongside Excel. Word is a word processing program, but there are many other programs for editing text. If you are not familiar with any other word processing program, it can be valuable to explore alternatives that may work better for your specific purposes. Within academic work the typesetting system LaTeX is widely used; it is roughly like a programming language for word processing. LaTeX can be used in different programs, for example Texmaker or LyX. See the following links:

LaTeX: https://www.latex-project.org/get
Texmaker: https://www.xm1math.net/texmaker
LyX: https://www.lyx.org/Download

To write and present material it is also possible to use the analysis programs themselves and write body text — introduction, analysis, presentation of results, discussion and conclusions — together with the actual programming code. This is called literate programming. In practice, this is most common in R. For example, there are several textbooks for R programming that are themselves written in R, see: https://bookdown.org/

When writing academic texts it is important that statements and information are supported by correctly cited sources. Special computer programs for reference management help to organize and insert citations in theses and academic articles. Some of the most well-known:

There is also a growing number of useful tools that can be run directly in the web browser. Two examples:

posit Cloud: https://posit.cloud. Cloud service where you can work in RStudio.
Overleaf: https://sv.overleaf.com. Tool where writers can collaborate to write in LaTeX.