Exploratory data analysis with the tidyverse

[maurolepore]

Who is the audience?

Anyone who wants to explore their data in R with the tidyverse. This includes all analysts at 2DII and beyond.

This chapter will show you how to use visualisation and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. You:

1. Generate questions about your data.

2. Search for answers by visualising, transforming, and modelling your data.

3. Use what you learn to refine your questions and/or generate new questions.

Why is this important?

EDA is an important part of any data analysis, even if the questions are handed to you on a platter, because you always need to investigate the quality of your data. Data cleaning is just one application of EDA: you ask questions about whether your data meets your expectations or not. To do data cleaning, you'll need to deploy all the tools of EDA: visualisation, transformation, and modelling.

What should be covered?

• Prerequisites.
• Exploratory data analysis (R for data science).

Resources

• https://r4ds.had.co.nz/
• https://github.com/2DegreesInvesting/tidy-ds
• https://github.com/maurolepore/open-science-with-r

Checklist

2h before

• [x] Open meetup issue, paste this template and edit as necessary

• [x] Announce on #coding

• [x] Add meetup date, title and link to the invitation on 2dii's calendar

10' before

• [x] Start the meeting.

• [x] Announce on #coding:

  The ds-incubator starts in 10'; please join early so we start on time. The meeting room is already open at: <LINK WITH PASSWORD>

Start

• [ ] Nominate a timekeeper.
• [ ] Record.
• [ ] Deliver content.
• [ ] Q&A.
• [ ] Upload video.
• [ ] Share video on Slack and twitter.