Data Manipulation with R by DataCamp

Let's say you have a great dataset that you want to know more about. What can you do to begin answering the questions about the data? To answer these questions, you can use dplyr. It can also assist with basic data transformations. Learn how to add, subtract, or modify variables and aggregate data. You'll also be able to explore a dataset that contains information about the United States. These tools will be applied to the dataset babynames to examine trends in baby names in the United States.

Data science can involve interesting data that is spread across many tables. This course will help you combine different tables for analysis. To help you improve your skills, a fun dataset will be created about LEGOs. This dataset contains information about LEGO parts, LEGO themes, and LEGO colors. It is spread across multiple tables. You'll work with data throughout the course as you learn six different joins. You will be able to learn four types of mutating joints: inner join (left), right join, and full join. Semi join and Anti join are also filtering joins you will learn. The last chapter will show you how to apply your new skills to Stack Overflow data. This chapter contains information about all the nearly 300,000.00 Stack Oveflow question tag with R. Now you can improve your dplyr skills.

Once you've learned data manipulation and visualization tools such as dplyr and ggplot2, it will be possible to apply these skills on a real dataset. You will also learn about the historical voting process at the United Nations General Assembly. Analyzing differences across countries and time will be a part of the course. Learn more about ggplot2 or dplyr packages. You will also be able use the broom package for tidying up your model output.

Data.table is a high performance package of base R's information.frame. It includes syntax enhancements and features that increase speed, programming speed, and ease-of-use. This course will teach you how to create, subset, and manipulate data.tables. Data.tables features that are database-inspired, such as built-in groupwise operations, will be covered. Learn how to quickly import and export tabular text data such as CSV files. You will learn how to use data.table R for more efficient manipulations, analyses and reporting after the course is over. This course will be covering the San Francisco Bay Area bike share trip dataset that began in 2014.

In the real world, data sets can often be divided across multiple tables. R data analysis functions are limited to one type of data. This course will show you how to combine different data sets into one table using data.table. This course will show you how to combine multiple data sets into single tables with data.table. This course will teach you how to deal with complicated data sets, and how to fix problems in join operations. After completion, this course will prepare the student to be a data.table master.