Effective Tabular data (data frame) manipulation in R
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
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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
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.
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