Published 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 69 lectures (4h 34m) | Size: 3 GB
Effective Data Wrangling and Exploration with R – Part III (tibble, dplyr, tidyr, data.table, SQL)
What you’ll learn
How to use base R to manipulate tabular data.
How to use the dplyr and tidyr packages to perform tabular data manipulation.
How to use the data. table package to manipulate tabular data.
How to use the RSQL package to perform tabular data manipulation with SQL in R
Requirements
Basic R programming with knowledge about things like variables, data types, operators, vectors, matrices data frames.
A computer (Windows, Mac or Linux) and some admin privileges to install the necessary software.
Internet access.
Description
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
Effective data wrangling and exploration in R-I: Importing and exporting data effectively in R (EDWER -I)
Effective data wrangling and exploration in R-II: Effective Date, String & Categorical Data Manipulation in R (EDWER-II)
Effective data wrangling and exploration in R-III: Effective Tabular Data Manipulation in R (EDWER-III)
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
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