Data Manipulation With Dplyr in R

A straightforward tutorial in data wrangling with one of the most powerful R packages - dplyr.

4.10 (269 reviews)
Udemy
platform
English
language
Data & Analytics
category
Data Manipulation With Dplyr in R
32,343
students
3 hours
content
Nov 2020
last update
$64.99
regular price

What you will 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

Why take this course?

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 these 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

  • how to extract the top entries from your data frame, based on a given variable

  • how to visualize the structure of your data set

  • how to use the set operations in dplyr (if you have used these operations in base R, you’ll see that dplyr takes them to a whole new level).


In the third section you’ll start to take advantage of the true power of dplyr. Here we’ll talk about chaining – creating sequences of dplyr commands that accomplish multiple tasks with one click only.


The fourth section is about joining data frames with dplyr. This is a very important topic, because many times your data will be found in several data frames. So you will need to join these data frames into only one, suitable for your analyses. We are going to look at five join types available in dplyr: inner_join, semi_join, left_join, anti_join and full_join. We are going to examine the output of each join type using a simple example.


In the fifth section we’ll learn how to combine the dplyr and ggplot2 (using chaining) commands to build expressive charts and graphs. For example, if you want to represent the income distribution for the subjects with a higher education only, or the relationship between income and education level for the female subjects only, in this section you will learn exactly how to do it.


Every command is illustrated with video, both the syntax and the output being explained in detail. At the end of the course, a big number of practical exercises are proposed. By doing these exercises you’ll actually apply in practice what you have learned.


Join this course right now and acquire a critical data analysis ability – data manipulation!


Screenshots

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Reviews

Pallavi
October 16, 2023
Communication was not very smooth and needed a lot of effort to understand Concept shown on video was good and simple
Paula
April 13, 2023
The course covers exactly what I wanted to learn. Very well explained. Just a detail: I would change the section about ggplot for more deeper commands of dplyr.
Iva
December 28, 2022
I appreciate how the instructor explains the various functions. I also really liked how all the functions were practiced and tied together through the second part of the course as well as the final practice! Thank you for such a great course!!!
Prasad
October 19, 2022
It was amazing to learn new skill sets in dplyr package and also learnt to connect dplyr to ggplot2 by chaining method. Wonderful
Evan
September 27, 2022
Very well organized. As a beginner, I learned a lot and would recommend it to anyone who is just getting started learning R.
Bryden
May 26, 2022
Bogdan is an excellent teacher and goes into a good amount of detail that has helped me to build up my skill set. The libraries of code that this course provides are extremely useful and have helped make my coding easier.
Ajitoni
January 1, 2022
So glad my friend recommended this course. I struggled with this part of R for a long time and it all seems very easy now with the Dplyr package. Thank you for making this course. It has really revived my interest in learning R.
Isao
December 13, 2021
This course is compact, but give me enough information that I needed. Best of all, the downloaded scripts are great summaries of each lecture. Therefore, when reviewing this course, reading the scripts is sufficient and there is no need to go back and watch the videos. Thank you for your nice course.
Marcelo
August 29, 2021
Contém os comandos básicos de operação de dados. Muito útil para ter em mãos. Os exemplos são simples e fáceis de serem acompanhados.
Jason
August 26, 2021
This was an excellent course. A really fast and easy introduction to Dplyr. It takes 80/20, which means, It teaches you the 20% of the package which will give you the 80% of the results. Totally recommended.
Pramesh
April 19, 2021
This course was what I expected—a basic introduction to dplyr functions. The course could've been far better with data manipulation-cum-wrangling examples of some complex datasets.
Spencer
February 14, 2021
A focused and thorough survey of the most important components of the dplyr package. I especially appreciated the practice problems given at the end. The instructor saved me the time of looking for data and ideas for how to manipulate it so that I could get right to practice coding.
Attila
December 30, 2020
Really great introductory course, crisp and clear explanations, hands-on, zero time waste. It could be extended with yet another section for advanced users with special cases like arranging rows in custom order, but for beginners in dplyr this is the perfect course.
Luis
December 16, 2020
Necessary for R'practicioners: it is very useful for wrangling or transforming data in R. Well conducted from basic to advanced examples, all of practical way. It is very recommendable.
Namtulla
December 6, 2020
Awesome, the Instructor explained every function very detailedly. I think that it will be beneficial to those who want to be a data scientist.

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3654314
udemy ID
11/23/2020
course created date
12/5/2020
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