This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
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Data Analysis with Tidyverse
University of Colorado BoulderÜber diesen Kurs
Successful completion of Introduction to R Programming and Tidyverse (R Programming and Tidyverse, Course 1) recommended.
Was Sie lernen werden
You will learn to identify and describe tidy data and transform a non-tidy data set to be tidy in R.
You will learn to analyze data between multiple related data tables.
You will be learn to apply regular expressions to detect patterns in strings and return matches and replace patterns with new values.
Kompetenzen, die Sie erwerben
- Data Manipulation
- Regular Expression (REGEX)
- Programming Principles
- R Programming
- Data Analysis
Successful completion of Introduction to R Programming and Tidyverse (R Programming and Tidyverse, Course 1) recommended.
von

University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Lehrplan - Was Sie in diesem Kurs lernen werden
Projects, Tibbles and Importing Data
When analyzing data, you will often be required to import data from CSV or txt files. In this module, you will learn how to import and parse data in base R and the readr library, a package in the Tidyverse. You will also be introduced to R projects, which help store and organize data files associated with an analysis.
Tidying Data
Data are stored in tabular forms and are often organized differently depending on its use. In this module, you will learn how to reorganize data to produce a "tidy" data set, where every variable is stored in its own column, every observation is stored in its own row, and each value is stored in a table cell.
Relational Data
Data analysis rarely involves a single data table and you will be required to combine multiple related tables to answer questions you are interested in. In this module, you will learn and practice mutating variables and filtering observations from relational data.
String Manipulation and Regular Expressions
This module will introduce string manipulation in R. You will learn the basics of strings, including string creation, merging, and subsetting. Then, you will use regular expressions to describe and view patterns in strings.
Häufig gestellte Fragen
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