- 2005-2009 Economist, East Asia (University Tuebingen)
- 2010 OECD Statistics Directorate, Trade and Business Statistics (SQL)
- 2011-2015 OECD Directorate for Science, Technology and Innovation (SAS, R)
- 2016 FAO Statistics Directorate, Methodological Innovations
- Website: rdata.work
- GitHub: r4io
- Email: r4io@rdata.work

- Bo Werth

- Time: 9:30 - 17:00
- Location: OECD IT Traning MB MZ289

- Course website: boot.rdata.work
- Slides: boot.rdata.work/r_bootcamp

- R Programming literacy
- Data visualization

- The training accounts can access the OECD R server
- The hands-on scripts are traversed by single-line or region execution

Provides an intensive, hands-on introduction to the R programming language. Prepares students with the fundamental programming skills required to start your journey to becoming a modern day data analyst.

Upon successfully completing this course, students will:

- Be up and running with R
- Understand the different types of data R can work with
- Understand the different structures in which R holds data
- Be able to import data into R
- Perform basic data wrangling activities with R
- Compute basic descriptive statistics with R
- Visualize their data with base R and ggplot graphics

- Getting started with R
- Importing data into R
- Understanding data structures
- Understanding data types
- Shaping and transforming your data

- Base R graphics
- ggplot graphics library

- Frees us from point-n-click analysis software
- Allows us to customize our analyses
- Allows us to build analytic applications

- Forces us to think about our analytic processes

- Many statistical programming languages now leverage C++ and Java to speed up computation time

- Provides reproducibility that spreadsheet analysis cannot
- Literate statistical programming is on the rise

- .csv, .txt, .xls, etc. files
- web scraping: xml text nodes, html tables (rvest)
- databases: Microsoft SQL Server, MySQL, Oracle, PostgreSQL, mongodb, etc.
- SPSS, STATA, SAS

- easy to create "tidy" data
- works well with numerics, characters, dates, missing values
- robust regex capabilities

- joining disparate data sets
- selecting, filtering, summarizing
- great "pipe-line" process: %>%

- R is known for its visualization capabilities
- ggplot introduced grammar of graphics
- interactive plotting - easily leverage D3.js libraries using htmlwidgets

- built for statistical analyses
- thousands of libraries provide many statistical capabilities
- easy to build your own algorithms

- RMarkdown (produce slides, HTML web pages, pdf, doc)
- Shiny allows rapid prototyping of web applications (HTML / CSS / JS)
- Reproducibility (communicate to your future self!)