Data Manipulation with R Programming - Tidyverse and Dplyr for Beginners

Learn essential data manipulation techniques in R programming through this comprehensive 5-hour course designed for beginners. Master data wrangling fundamentals using the tidyverse ecosystem, including dplyr for selecting, filtering, and mutating datasets. Discover how to rename variables, reorder columns, and perform data cleaning operations effectively. Explore advanced data manipulation concepts such as recoding variables, creating new variables, and applying 10 essential filtering techniques for subsetting data. Gain proficiency in working with different data types, factors, and categorical variables using the forcats package to modify factor levels. Develop skills in grouping and summarizing data, separating and uniting columns, and managing categorical variables efficiently. Master programming fundamentals including loops and conditional statements using if_else() and case_when() functions. Learn to handle date and time data manipulation with the lubridate package, manage missing data and NA values using imputation techniques and the naniar package. Utilize the powerful across() function for streamlined data cleaning operations and discover methods for importing Excel data into RStudio for seamless workflow integration.

Syllabus

Manipulate your data. Data wrangling. R programmning for beginners.
Describe and Summarise your data
R programming for beginners. Manipulate data using the tidyverse: select, filter and mutate.
R programming for beginners: Rename variables and reorder columns. Data cleaning and manipulation.
Recoding data using R programming. Using the tidyverse and dplyr packages to create a new variable
10 data filtering tips using R programming. Use the tidyverse to filter and subset your data.
Data types in R programming
Explore your data using R programming
R programming in one hour - a crash course for beginners
Working with factors and categorical variables. Use forcats in R programming to change factor levels
Group by and Summarise functions in R programming - use the tidyverse package to wrangle your data
Separate and Unite - manipulate your data with R programming
Using R programming to manage categorial variables or factors using the forcats package
Loops using R programming
Lubridate - how to manipulate date and time data in R
Handling Missing Data and Missing Values in R Programming | NA Values, Imputation, naniar Package
Conditional Statements in R: if_else() and case_when() Functions
Manipulate and clean your data in R with the dplyr package
The across( ) function in R programming. A tidyverse function to make cleaning data easy.
Importing data from excel into R studio