Data Science with R Tidyverse

Name: Data Science with R Tidyverse
Rating: 4.6 (90 reviews)

Take your R programming skills to the next level with the core tidyverse packages of dplyr, tidyr, ggplot2 and magrittr

Created byAdam Hayward

Last updated 9/2023

English

What you'll learn

Learn how to improve your R programming skills and code efficiently
Learn how to use powerful tidyverse tools to tackle everyday data science problems
Learn how to manipulate data with dplyr
Learn how to re-shape and re-organise data with tidyr
Learn how to create advanced graphics using ggplot2
Learn how to link code efficiently using the magrittr forward pipe (%>%)
Realistic worked examples to illustrate the tools of the tidyverse
Mini quizzes to test your knowledge of the tidyverse functions

Course content

5 sections • 26 lectures • 3h 39m total length

Introduction3:45
This lecture introduces what the course is about, how it is structured and the standard of coding you can expect to achieve from completing the course.
The downloadable handout contains the example code used in the lecture

Filtering data7:47
Filtering (subsetting) data using dplyr filter function
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Selecting variables in data4:03
Selecting variables (columns) in data using dplyr select function
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Extracting rows in data4:18
Extracting rows in the data table using dplyr slice function
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Creating new variables from data7:27
Create new variables from existing variables using dplyr mutate function
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Sorting data3:10
Sort data using dplyr arrange function
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Grouping variables2:07
Group variables for subsequent analysis using dplyr group_by
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Summarising data3:50
Create statistical summaries of data using dplyr summarise function
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Magrittr forward pipe (%>%)10:17
Learn to use the magrittr forward pipe (%>%) to link code more efficiently
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Joining or merging several data sets11:43
Join different data sets using dplyr join functions
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Additional dplyr tips7:04
Additional dplyr tools to rename column headers, find max/min values, deal with "NA" in statistical summarises and reorder columns in data

Introducing tidy data4:46
The concept of tidy data, with columns being variables, rows being observations and cells having individual values
Gathering variables10:12
Create tidy data by gathering a variable inappropriately spread out across multiple columns
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Spreading variables2:29
Create tidy data by spreading out multiple variables inappropriately withing captured in a single column
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Separating values within a cell3:32
Create tidy data by separating multiple values within a cell into individual cells
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz

Principles of a ggplot function call13:31
Introduce the principles of constructing graphs using a ggplot function call
Using geometry commands to define the type of graph
Mapping the variables we want to plot to the geometry commands with the aesthetics function call
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Creating scatterplots9:43
Create scatterplots using the geom_point() geometry command
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Creating boxplots9:26
Create boxplots using the geom_boxplot geometry command
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Creating barcharts12:10
Create barcharts using the geom_bar geomtery command
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Creating line charts4:31
Create line charts using the geom_line geometry command
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Creating a panel of graphs5:23
Create a panel layout of graphs using the facet functions
The downloadable handout contains the example data, code used in the lecture and answers to the mini quiz
Additional ggplot2 tips8:43
Additional tips in the ggplot function call sequence such as formatting labels, titles, axis scales and adding trendlines
Advanced formatting in ggplot229:33
Create beautiful graphics in ggplot2 with advanced graphics and the introduction of the ggpubr package
Errorbars21:28

Requirements

Familiarity with the RStudio interface
Be able to install and load packages
Basic knowledge of data structures in R (vectors, matrices, dataframes, numbers, text characters)
Basic knowledge of common R operators (assignment, addition, subtraction, and, or, equals, not equals)
Very basic awareness of a function call in R and the concept of arguments of a function

Description

Take your R programming skills to the next level with this short course in data science using R's tidyverse packages! Learn to code efficiently and elegantly to tackle everyday data science challenges in business, finance, scientific research, engineering and more!

Do you feel you have a basic knowledge of R but don't yet have the tools or confidence to tackle everyday data science problems like plotting, summarising, sub-setting and merging data? Still turning to MS Excel to manipulate, format, and visualize data? Then look no further.

Aimed at beginners and intermediates who have a basic understanding of R, this course introduces some of the core tools of the tidyverse. It covers a step-by-step guide to the most important functions offered by some tidyverse packages, providing students with a comprehensive toolkit to address everyday data science tasks.

The course covers the following areas:

1) Data manipulation with dplyr (filtering, sorting, creating new variables, summarising data, joining data sets, selecting columns/rows)

2) Data reformatting with tidyr (gathering variables, spreading out variables, separating data in cells)

3) Data visualization with ggplot2 (scatterplots, boxplots, bar charts, line charts, panels, adding errorbars)

4) Linking code efficiently using the magrittr forward pipe operator

After completing the course, you will be confident to use R for your everyday data science tasks!

Who this course is for:

Beginners in R looking to explore the tools of the tidyverse
Intermediate users of R looking to code more efficiently

Data Science with R Tidyverse

What you'll learn

Explore related topics

Course content

Welcome1 lecture • 4min

Data manipulation with dplyr10 lectures • 1hr 2min

Data re-shaping with tidyr4 lectures • 21min

Data visualisation with ggplot29 lectures • 1hr 54min

Importing and exporting with R2 lectures • 19min

Requirements

Description

Who this course is for: