This document provides an introduction to R, an open source statistical computing and graphics programming language. It outlines R's capabilities for data manipulation, visualization, and analysis. It then demonstrates how to install R and RStudio. Several popular R packages for working with data are listed for tasks like obtaining, cleaning, analyzing, and visualizing data. Finally, resources for learning R like tutorials, articles, books, and influential members of the R community are recommended.

1. Let’s Talk
About
PRESENTED BY
DAVID SELASSIE OPOKU
@sdopoku
11 August 2015
An introduction for Data-driven Journalism

2. Outline
1. TaRget audience
2. About R: What is R?
3. Example Use Case & Best PRactices
4. Setup & RStudio
5. Resources

3. Target
Audience

4. R is a great tool for anyone who works with data
● Data journalists
● School of Data fellows
● Open Data enthusiasts
● People curious about or new to R
● Statisticians

5. About

6. What is R?
1. Open source
2. Statistical computing & graphics programming language
and environment
3. More than just statistics and graphics
4. Wealth of functionality i.e packages
5. RStudio: a powerful integrated development
environment (IDE)

7. R vs. Spreadsheet-like software
1. More powerful data manipulation capabilities
2. It reads any type of data
3. Easier automation & faster computation
4. It supports larger data sets
5. Advanced Statistics capabilities
6. State-of-the-art graphics with packages such as ggplot2
7. It runs on many platforms
8. Anyone can contribute packages to improve its functionality
See: 14 Reasons Why R is Better Than Excel

8. Setup R &
RStudio

9. Live Demo of R & RStudio
Installation; RStudio
Environmnent

10. R in the
Data
pipeline

11. Popular R Packages In The Data pipeline
❖ Find & Obtain
➢ quandl (finance & economics) | foreign (SAS, SPSS) | RODBC,
RMySQL, RPostgresSQL, RSQLite (Databases) | XLConnect, xlsx (Excel)
➢ Maps: sp, maptools, maps, ggmap
➢ Web: XML, jsonlite, httr
❖ Clean & Verify
➢ dplyr, tidyr (data manipulation) | stringr (regular expressions &
strings) | lubridate (dates and times)
❖ Analyze
➢ car, randomForest, glmnet, caret,
❖ Visualise
➢ ggplot2, ggvis, rgl, leaflet, htmlwidgets, shiny, googleVis
❖ Report
➢ shiny, R Markdown, xtable, knitr

12. Example
Use Case

14. Resources

15. Resources - Individuals & Organisations
1. R Project
2. RStudio
3. Datacamp
4. Hadley Wickham - @hadleywickham
5. R-bloggers
6. Nathan Yau’s Flowing Data Tutorials

16. Resources - Tutorials, Articles & Books
Article: Data Analysts Captivated by R’s Power
Tutorials & Webinars
1. http://www.r-tutor.com/r-introduction
2. Code School’s Try R
3. 5 data visualizations in 5 minutes: each in 5 lines or less of R
4. RStudio Webinars
Cheatsheets: https://www.rstudio.com/resources/cheatsheets/
Books
1. R Cookbook (O'Reilly Cookbooks) by Paul Teetor
2. R Graphics Cookbook by Winston Chang
3. RStudio List of Training Books

17. References
1. What is R?
2. Beginner's guide to R: Introduction
3. How SAS, R & SPSS compare [infographic]
4. Comparison of R, Matlab, SciPy, Excel, SAS, SPSS, Stata
5. Garrett Grolemund’s Quick list of useful R packages
6. 14 reasons why R is better than Excel
7. An overview of RStudio Features