What you'll learn
Filter data frames using various conditions
Select and remove data frame columns (variables)
Sort data frames by column values
Create new variables from the existing ones
Compute summary statistics for our data frame
Other useful operations (count data fame rows, select top rows, select rows at random etc.)
Chaining dplyr commands to write powerful data manipulation code
Joining data frames (five joining types)
Combining dplyr with ggplot2 to create meningful charts
Requirements
Basic R programming knowledge
Description
Data manipulation is a vital data analysis skill - actually, it is the foundation of data analysis. This course is about the most effective data manipulation tool in R - dplyr!

As a data analyst, you will spend a vast amount of your time preparing or processing your data. The goal of data preparation is to convert your raw data into a high quality data source, suitable for analysis. More often than not, this process involves a lot of work. The dplyr package contains the tools that can make this work much easier.

dplyr has a few important advantages over other data data manipulation tools or functions:

it's much faster (25-30 times faster)

its code is easier to write and understand

it can use chaining to build sequences of commands, thus making the code even cleaner and faster to execute

For these reasons, dplyr quickly began the most popular data manipulation tool among R data scientists. When you finish this course, you will be able to

It is a short course, but it is focused on the most essential commands and functions of the dplyr package, those commands that you will likely use most often.

So let's see what you are going to learn in this course.

The first section covers the five core dplyr commands. These commands are: filter, select, mutate, arrange and summarise. You will need this commands practically every time when you work with dplyr. They are used to subset data frames, compute new variables, sort data frames, compute statistical indicators and so on. Here's a few real life scenarios of their utilization:

you need to extract from your respondents data set the male subjects with an income greater than $30,000

you need to compute each respondent's income per family member, knowing the total income and the number of family members

you have a data set with 27 variables, but you only need 6 for your analysis (so you want to remove the extra variables)

you have to sort your employees data set by salary

you need to compute the average satisfaction towards a product, knowing each individual customer satisfaction etc.

The second section approaches other important dplyr commands and functions. In this section you'll learn:

how to count the observation in a certain group

how to extract a random sample from your data frame