Overview Of R
Introduction
With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40%, and an increasing number of organizations are using it in their day-to-day activities. In this introduction to R, you will master the basics of this beautiful open source language such as factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
MODULE 1
Intro to basics
In this chapter, you will take your first steps with R. You will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let us get started!
MODULE 2
Vectors
We take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R! After completing this chapter, you will be able to create vectors in R, name them, select elements from them and compare different vectors.
MODULE 3
Matrices
Very often, data falls into a limited number of categories. For example, humans are either male or female. In R, categorical data is stored in factors. Given the importance of these factors in data analysis, you should start learning how to create, subset and compare them now!
MODULE 4
Factors
Very often, data falls into a limited number of categories. For example, humans are either male or female. In R, categorical data is stored in factors. Given the importance of these factors in data analysis, you should start learning how to create, subset and compare them now!
MODULE 5
Data frames
Most data sets you will be working with will be stored as a data frame. By the end of this chapter, you will be able to create a data frame, select interesting parts of a data frame and order a data frame according to certain variables.
MODULE 6
Lists
Lists, as opposed to vectors, can hold components of different types, just like your to-do list at home or at work. This chapter will teach you how to create, name and subset these lists!
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