Importing data into R to start your analyses—it should be the easiest step. Unfortunately, this is almost never the case. Data come in all sorts of formats, ranging from CSV and text files and statistical software files to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis. In this course, you will get started with learning how to read CSV and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that you will learn how to read XLS files in R using readxl and gdata.
In this second part to Importing Data in R, you will take a deeper dive into the wide range of data formats out there. More specifically, you’ll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you’ll get hands-on experience with importing data from statistical software packages such SAS, STATA and SPSS.
It’s commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you’ll be able to go from raw data to awesome insights as quickly and painlessly as possible!
Functions are a fundamental building block of the R language. You’ve probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you’ll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.
In this interactive tutorial, you will learn how to perform sophisticated dplyr techniques to carry out your data manipulation with R. First you will master the five verbs of R data manipulation with dplyr: select, mutate, filter, arrange and summarise. Next, you will learn how you can chain your dplyr operations using the pipe operator of the magrittr package. In the final section, the focus is on practicing how to subset your data using the group_by function, and how you can access data stored outside of R in a database. All said and done, you will be familiar with data manipulation tools and techniques that will allow you to efficiently manipulate data.
In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. 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. Leverage the power of R by completing this R online course today!
The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in the R programming language, data structure manipulations and times and dates. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.
This is an introduction to the programming language R, focused on a powerful set of tools known as the “tidyverse”. In the course you’ll learn the intertwined processes of data manipulation and visualization through the tools dplyr and ggplot2. You’ll learn to manipulate data by filtering, sorting and summarizing a real dataset of historical country data in order to answer exploratory questions. You’ll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. This gives a taste both of the value of exploratory data analysis and the power of tidyverse tools. This is a suitable introduction for people who have no previous experience in R and are interested in learning to perform data analysis.