ELAC 2018 INTRODUCTION TO DATA SCIENCE. Day 2 Rafael Santos

Transcription

1 ELAC 2018 INTRODUCTION TO DATA SCIENCE Day 2 Rafael Santos rafael.santos@inpe.br

2 Introduction to Data Science About this Lecture

3 Where are we? 3

4 RStudio Introduction to Data Science

5 Free language, free IDE. All major OSs. Focus on scripts. Fancy stuff later! We ll start with R/RStudio Easy to edit/run code, import packages, etc. 5

6 Run R/RStudio 6

7 Create a Project (File/New Project) 7

8 Create a Project (File/New Project) 8

9 Create a Project (File/New Project) 9

10 Create a Project (File/New Project) 10

11 Create a R Script (File/New File) 11

12 Editing a R Script 12

13 Saving a R Script 13

14 Running a R Script 14

15 Installing Packages in RStudio 15

16 Installing Packages in RStudio 16

17 Installing Packages in RStudio 17

18 Installing Packages in RStudio 18

19 Installing Packages in RStudio 19

20 Installing Packages in RStudio 20

21 Installing Packages in RStudio Usually everything works OK but Installation may not be as smooth as shown. If R version changes you may need to reinstall. 21

22 Introduction to Data Science Introduction to R

23 R Programming 23

24 Introduction to R GitHub: Lecture02/01-Variables Pros and cons of starting scripts with rm(list=ls()) GitHub: Lecture02/02-PrimitiveTypes Primitives are actually Vectors! [1] 10 -> Index of first element of vector. GitHub: Lecture02/03-Vectors GitHub: Lecture02/04-OperationsWithVectors GitHub: Lecture02/05-Factors Factors are categorical variables, very important for classification! 24

25 Introduction to R GitHub: Lecture02/06-DataFrames DataFrames are the essential data structure for Data Science. GitHub: Lecture02/07-Control Remember: there are other ways to do conditionals and loops 25

26 Introduction to Data Science Raw Data and Tidy Data

27 Raw Data Data in databases, spreadsheets Images, videos, audio Time series... Logs, text, JSON files, XML files Based on Coursera s Getting and Cleaning Data course. 27

28 Tidy Data One table with all the data (or linked tables). Each variable in its column. Each observation in its row. Variable names in the first row, with good, clear names. Tidiness is not an absolute feature! Depends on what we have and what we want to do. Based on Coursera s Getting and Cleaning Data course. 28

29 Raw Data to Tidy Data Create a Code Book that describes how we can get from Raw Data to Tidy Data. A simple (formatted) text file with: Sources for the raw data. More detail on the variables. What was {selected, enhanced, preprocessed} and how. Instruction on how the data was processed. Code Books essential to reproducibility! Based on Coursera s Getting and Cleaning Data course. 29

30 Introduction to R (Part II) GitHub: Lecture03/01-DateTime GitHub: Lecture03/02-DownloadingFiles GitHub: Lecture03/03-Strings GitHub: Lecture03/04-TidyData 30

31 Help! Introduction to Data Science

32 Help! help( keyword ) 32

33 Help! help.search( keyword ) (see also apropos( keyword )) 33

34 Help! Much better options, specially for usage and tips and/or more complex scripts: rseek.org stackoverflow.com Can I do X with R?

35 Introduction to Data Science References

36 References 36

37 References 37

38 Introduction to Data Science Publishing R code and results on GitHub

39 R Scripts: source code only. Playing nicely with GitHub R Notebook: creates a.rmd and a.html file..rmd: shown as is (no plots).html file: shown as source code. R HTML: formatted HTML code. Solution: create a R Markdown with a GitHub Document template. 39

40 Playing nicely with GitHub R Markdown with a GitHub Document template. 40

41 Playing nicely with GitHub R Markdown with a GitHub Document template. Knit it! 41

42 Playing nicely with GitHub 1. git add. 2. git commit m "Nth commit" 3. git push -u origin master 42

43 Playing nicely with GitHub 43

44 Exercises Introduction to Data Science

45 Introduction to Data Science

46 Did you do this first? 46

47 Publishing your R code on GitHub 1. cd your-directory-with-r-code 2. git add. 3. git commit -m "First commit 4. git remote add origin rafaeldcsantos/cap386.git 5. git push -u origin master 6. Check 47

48 (Re)Publishing your R code on GitHub 1. git add. 2. git commit -m Nth commit 3. git push -u origin master 4. Check 48