Effective Tabular data (data frame) manipulation in R

This course delves into the various base R functions as well as the most popular packages (tidyverse, data.table, and RSQL) used for data frame manipulation in R. It is the third in a four-course series on “Effective Data Wrangling and Exploration in R“. All of the courses in this series are listed below:

1. Effective data wrangling and exploration in R-I: Importing and exporting data effectively in R (EDWER -I)

2. Effective data wrangling and exploration in R-II: Effective Date, String & Categorical Data Manipulation in R (EDWER-II) 

3. Effective data wrangling and exploration in R-III: Effective Tabular Data Manipulation in R (EDWER-III) 

4. Effective data wrangling and exploration in R-IV: Effective Data Cleaning and Exploration (EDWER-IV)

Effective Tabular Data Manipulation in R covers the following topics:

• What exactly is a data frame, and how are they created?

• Converting to and from a data frame

• Structure and attributes of data frames

• What exactly is a tibble, and how are they created?

• Converting to and from a tibble

• What is a data.table object, and how do you create one?

• Converting to and from a data.object table

• Datasets in R

• Subsetting a data frame: selecting columns 

• Subsetting a data frame: filtering rows

• Data frame transformations: renaming columns and rows

• Data frame transformations: Inserting new columns and rows

• Data frame transformations: duplicating an existing column

• Data frame transformations: deriving a new column from an existing one

• Data frame transformations: updating an existing column or row

• Data frame transformations: updating a single value

• Data frame transformations: splitting and merging columns

• Data frame transformations: deleting columns and rows

• Data frame transformations: sorting and ranking

• Data frame transformations: introduction to missing data

• Concatenating data frames: vertically and horizontally 

• Set operations with data frames: union, intersection, and set difference 

• Performing SQL-Like joins: inner join, left outer join, right outer join, full outer join, and so on

• Aggregating and grouping data 

• Pivoting and unpivoting data 

ENJOY!!!

NB: We hope you enjoy this course as much as we did creating it.

Who this course is for:

• Data analyst
• Data scientist
• Business Intelligence developers
• Anyone doing data wrangling or munging or preparation in R
• Data professional