Transform large and complex data sets using base R and popular package ecosystems like tidyverse and data.table. See how these packages compare in terms of speed and usability. Master dplyr verbs quickly and build data processing pipelines for modelling and plotting. Unleash the power of data.table with lightning fast merges and queries.

Data Transformation Intro

course-r-advanced-data-transformation#01-introduction

course-r-advanced-data-transformation#01-introduction#01-introduction

course-r-advanced-data-transformation#01-introduction#01-introduction#recipe

The 3 Doors of Data Transformation

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Introduction

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The base R Package Ecosystem

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The tidyverse Package Ecosystem

course-r-advanced-data-transformation#01-introduction#01-introduction#section-the-data.table-package-ecosystem

The data.table Package Ecosystem

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Pros and Cons

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Quiz: Which Package Ecosystem to Choose with Storage Backends?

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Quiz: Which Package Ecosystem to Choose for Stability?

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Quiz: Which Package Ecosystem to Choose for Large Data Sets?

Discover use cases which require data transformation and the main tools available through base-R, tidyverse and data.table.

Data Transformation with base-R

course-r-advanced-data-transformation#02-data-wrangling-with-base-r

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Inspecting Data Structures

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Interactive Inspection with View()

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Quiz: Interactive Inspection with View()

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Exercise: Interactive Inspection with View()

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Examining Data Structures with str()

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Quiz: Examining Data Structures with str()

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Quiz: Interpret the Output of str()

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course-r-advanced-data-transformation#02-data-wrangling-with-base-r#02-filter-data-using-subset#recipe

Filter Data Using Subset

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Extract Vectors by Indices

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Exercise: Subset Vectors by Indices

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Apply Logical Vectors for Subsetting

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Exercise: Apply Logical Vectors for Subsetting

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Select Multiple Dimensions by Indices

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course-r-advanced-data-transformation#02-data-wrangling-with-base-r#02-filter-data-using-subset#section-select-multiple-dimensions-with-logical-vectors

Select Multiple Dimensions with Logical Vectors

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Exercise: Subset the Titanic Survival Data Frame

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Apply the subset() Function on Data Frames

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Exercise: Apply the subset() Function

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course-r-advanced-data-transformation#02-data-wrangling-with-base-r#03-the-apply-function-family#recipe

The Apply Function Family

course-r-advanced-data-transformation#02-data-wrangling-with-base-r#03-the-apply-function-family#section-introduction

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The lapply() Function

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Exercise: Search Data Frame Columns with lapply()

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Use sapply() with Vectors

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Use sapply() with Data Frames

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Exercise: Search Data Frame Columns with sapply()

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The Split-Apply-Combine Strategy

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Split Data by Factors

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Exercise: Use split() on a Data Frame

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Use lapply() on Split Data

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Exercise: Use lapply() on Split Data

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The unsplit() Function

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Exercise: Split–Apply–Combine

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Calculate Aggregates by Groups

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Group / Summarize

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Exercise: Aggregate Using Vectors

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Exercise: Aggregate Using Formulas

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The table() Function

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Exercise: Create a table()

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Bind Data Sets Together

course-r-advanced-data-transformation#02-data-wrangling-with-base-r#06-bind-data-sets-together#section-introduction

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Bind rows together

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Exercise: Bind rows together

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Bind columns together

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Exercise: Bind columns together

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course-r-advanced-data-transformation#02-data-wrangling-with-base-r#07-join-bind-union#recipe

Join Data Sets

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course-r-advanced-data-transformation#02-data-wrangling-with-base-r#07-join-bind-union#section-inner-join

Inner Join

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Exercise: Inner Join

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Outer Join

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Exercise: Left Outer Join

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Full (Outer) Join

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Exercise: Full Join

Learn data wrangling functions available in base-R to filter, split and aggregate data.

Tidy Data Transformation Level 2

course-r-advanced-data-transformation#03-tidy-data-transformation

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Group and Aggregate

course-r-advanced-data-transformation#03-tidy-data-transformation#01-group-aggregate#section-the-summarise-function

The summarise() Function

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Exercise: The summarise() Function

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Calculate Multiple Statistics with summarise()

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Exercise: Calculate Multiple Statistics with summarise()

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The mutate() Function

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Exercise: The mutate() Function

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Use group_by() with a Single Variable

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Exercise: Use group_by() with a Single Variable

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Use group_by() with Multiple Variables

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Exercise: Use group_by() with Multiple Variables

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The ungroup() Function

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Exercise: The ungroup() Function

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course-r-advanced-data-transformation#03-tidy-data-transformation#02-reshape-pivot-data#recipe

Reshape and Pivot Data

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The pivot_longer() Function

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Exercise: Tidy Table Using pivot_longer()

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Exercise: Tidy Another Table Using pivot_longer()

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Name Transformations with pivot_longer()

course-r-advanced-data-transformation#03-tidy-data-transformation#02-reshape-pivot-data#section-the-pivot_wider-function

The pivot_wider() Function

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Exercise: Transfrom Data Sets with pivot_wider()

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course-r-advanced-data-transformation#03-tidy-data-transformation#03-separate-columns#recipe

Separate, Unite and Extract Columns

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Separate Columns

course-r-advanced-data-transformation#03-tidy-data-transformation#03-separate-columns#exercise-03-03-01

Exercise: Separate

course-r-advanced-data-transformation#03-tidy-data-transformation#03-separate-columns#section-unite-columns

Unite Columns

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Exercise: Unite Columns

course-r-advanced-data-transformation#03-tidy-data-transformation#03-separate-columns#section-extract-parts-from-columns

Extract Parts from Columns

course-r-advanced-data-transformation#03-tidy-data-transformation#03-separate-columns#exercise-03-03-03

Exercise: Extract

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course-r-advanced-data-transformation#03-tidy-data-transformation#04-join-datasets#recipe

course-r-advanced-data-transformation#03-tidy-data-transformation#04-join-datasets#section-mutating-join-types

Mutating Join Types

course-r-advanced-data-transformation#03-tidy-data-transformation#04-join-datasets#quiz-1

Quiz: Mutating Join Types

course-r-advanced-data-transformation#03-tidy-data-transformation#04-join-datasets#quiz-2

course-r-advanced-data-transformation#03-tidy-data-transformation#04-join-datasets#section-left--and-right-join

Left- and Right Join

Learn to use the basic dplyr verbs to filter rows, select columns and sort/arrange datasets in combination with  the pipe <code>%>%</code> operator. Tidy your data with more complex transformations and apply various join operators.

Introduction

The 3 Doors of Data Transformation

Advanced Data Transformation

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