There are many functions that are suitable for this purpose, and many packages that are useful for importing data that is in some particular format, such as the formats for Excel. We will therefore see how to import a dataframe from your computer, or from the internet, into R. In the next section, we will look at a number of useful topics: how to set up a working directory, how to install and retrieve a package, how to get information about data, where to find datasets for testing, and how to get help with a function.When analysing data, one sooner or later comes across dataframes known as variable x-cases. Once we understand how to create and manipulate these data structures, extract elements from them and save them locally on the computer, we will move on to the use of loops and the creation of functions. We will then move on to the creation of objects: R is based on certain structures that we need to know, such as vectors, matrices, lists and dataframes. The course starts with setting up the working environment: we will see how to download, install and use some of the most important tools for using R, such as RStudio. This basic programming course with R for aspiring data analysts is designed to accompany a beginner in programming, from the basics of the programming language (one of the best known and most widely used in the field of data analysis) to the use of descriptive statistics.At the end of this course the student will be able to create, import, manipulate and manage datasets. R and RStudio, but we will install them also together Understanding the basics of statistics with R Manipulating datasets, reorganising and aggregating themĬreating graphs with basic functions and common packagesĬreating and exporting reports in various formats Installing and retrieving packages for extending the functionality of RĮxtracting elements from an object or dataset When and how to use the conditional statements Learn the fundamentals of programming with RĬreate and recognize vectors, lists, arrays, dataframes and all the data structures in R R programming basics | statistics | data analysis | charting | data cleaning | variable exploration | functions Genre: eLearning | Language: English | Duration: 82 Lectures ( 10h 14m ) | Size: 4 GB Free Download R coding for data analysts from beginner to advanced | Free Download
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