หลังจากเรียนคอร์สเหล่านี้ รู้สึกมั่นใจในการสร้างการแสดงผลข้อมูลและแดชบอร์ดระดับมืออาชีพ
Shoebur RahmanProcurement Specialist, Giz Bangladesh
รายละเอียดคอร์ส
Character strings can turn up in all stages of a data science project. You might have to clean messy string input before analysis, extract data that is embedded in text or automatically turn numeric results into a sentence to include in a report. Perhaps the strings themselves are the data of interest, and you need to detect and match patterns within them. This course will help you master these tasks by teaching you how to pull strings apart, put them back together and use stringr to detect, extract, match and split strings using regular expressions, a powerful way to express patterns.
ข้อกำหนดเบื้องต้น
หลักสูตร
โครงร่างคอร์ส
1
String basics
You'll start with some basics: how to enter strings in R, how to control how numbers are transformed to strings, and finally how to combine strings together to produce output that combines text and nicely formatted numbers.
- Welcome!50 XP
- Quotes100 XP
- What you see isn't always what you have100 XP
- Escape sequences100 XP
- Turning numbers into strings50 XP
- Using format() with numbers100 XP
- Controlling other aspects of the string100 XP
- formatC()100 XP
- Putting strings together50 XP
- Annotation of numbers100 XP
- A very simple table100 XP
- Let's order pizza!100 XP
2
Introduction to stringr
Time to meet stringr! You'll start by learning about some stringr functions that are very similar to some base R functions, then how to detect specific patterns in strings, how to split strings apart and how to find and replace parts of strings.
3
Pattern matching with regular expressions
In this chapter you'll learn about regular expressions, a language for describing patterns in strings. By combining regular expressions with the stringr functions you'll greatly increase your power to manipulate strings.
4
More advanced matching and manipulation
Now for two advanced ways to use regular expressions along with stringr: selecting parts of a match (a.k.a capturing) and referring back to parts of a match (a.k.a back-referencing). You'll also learn to deal with and strings or patterns that contain Unicode characters (e.g. é).
5
Case studies
Practice your string manipulation skills on a couple of case studies. You'll also learn a few new skills, reading strings into R and handling problems of case (e.g. A versus a).
R
String Manipulation with stringr in R
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