Complete R Programming course: Beginner to Advanced Level

سرفصل های دوره

Master R 4.2: Clear Understanding of Data Aggregation, Tidyverse, reshape, tidyr, dplyr, data.table packages and so on!

1. Introduction

1. Introduction to the Course

2. R & R Studio Set Up

1. R 4.2 & R Studio Set Up

2. R Studio Overview

3. Mastering the Basics of R

1. R Data Types

2. R for Basic Mathematics

3. Complicated Arithmetic Calculations with R

4. How to install packages in R

4. Data Structures in R

1. What will you learn.html

5. Vectors in R

1. What is a vector as used in R

2. Creating vectors with c function

3. Creating a sequence of Integers

4. seq Function

5. Creating a sequence of repeated values ( rep function)

6. creating a named vector with names()

7. Vector Attributes

8. combining vectors with cbind function

6. Matrices in R

1. Introduction to Matrices What is Matrix as used in R

2. Creating matrices with rbind and cbind functions

3. Creating matrices with a matrix function

4. Matrix Attributes mode, dimension

5. Matrices with names

6. Subscripting Matrices

7. Subscripting Matrices Logical Values

8. Subscripting Matrices with the condition

7. Arrays in R

1. What is an Array in R

2. Creating Arrays

3. Array Attributes & subsets

8. Lists in R

1. What is List in R

2. Creating Lists in R

3. List Attributes length, names, mode

4. Subscripting Lists subsets

5. Referencing Lists Elements $ sign and double square bracket referencing

6. Adding Elements in a List Appending a list

9. Dataframes in R

1. What is Dataframe

2. Creating a dataframe

3. Querying Data Frames Attributes

4. Selecting Columns from R the Data Frame

5. Manipulating Data Frames as Matrices

10. Working with Categorical Data

1. What is Categorical Data

2. What are R Factors& Factor Levels

3. Creating Factors in R

4. Factor Levels

5. Manipulating Factor Levels

6. Regular Expressions grep and gsub functions in R

11. Functions in R

1. Calling Functions in R

2. Creating Functions in R The function command

3. Creating you first function in R

4. R Functions that return the object

5. Exercise

12. if-else statements in R

1. introduction to if statements using R

2. Coding Exercise Nested if statements.html

3. Nested if statements inside a function

4. Coding Exercise.html

5. if statements inside a function exercise

6. The switch function in R

7. Integer version of the switch function

13. Coding Loops in R

1. What is a Loops For loop & while loops in R

2. Introduction to R for loops

3. looping a vector

4. Looping through a Data Frame

5. Nested for loops in R

6. While loops

14. The apply family of functions

1. Introduction to the Apply family of functions in R

2. apply Function

3. Using apply with own function

4. Using apply Function on a dataset ( or Data Frame)

5. lapply Function

6. The split Function

7. split and lapply on real world datasets (or Data Frame)

8. sapply Function

9. sapply Function on a real dataset

10. tapply Function

11. Apply functions on real world datasets Exercise.html

12. Apply functions on real world datasets Coding Along Solution 1

13. Apply functions on real world datasets Coding Along Solution 2

14. tapply Function on a real world dataset Solution

15. Importing Data into R with tidyverse package

1. Importing a csv file in R

2. Reading an excel file with tidyverse

16. Data Analysis, Transformation & Transformation

1. Introduction to Data Manipulation

2. Sorting datasets with sort() function

3. Appending

4. Duplicated Values

17. Merging with merge Function

1. What is Merging

2. Merging Datasets with merge function

3. left, right & outer merging using merge function

18. Restructuring Datasets with reshape package

1. What is Melting and Casting

2. Melting melt function

3. Casting cast function

19. Restructuring Datasets with tidyr package

1. Introduction to gather and spread functions

2. gather function

3. spread function

4. separate function

20. Data Aggregation

1. What is Data Aggregation

2. Data Aggregation using apply Function

3. Data Aggregation with aggregate Function

4. aggregate function with user defined function

5. Using aggregate by Specifying Columns

6. Functions that return multiple values

21. dplyr package

1. Introduction to the dplyr package

2. Creating a table_df object from a dataframe

3. dplyr sort descending and ascending arrange function

4. Subscripting filter & select functions in r

5. Add new columns with mutate function

6. Introduction to merging datasets with dplyr

7. Inner-join function

8. dplyr merging functions

9. Data Aggregation with dplyr summarize function

10. Grouped Data group_by function

11. What is the Pipe Operator

12. Pipe Operator example on a real dataset

22. data.table package

1. Creating data.table Setting and Defining a datable key

2. Subscripting Referencing columns (J function)

3. Updating Values in Key

4. Numeric Keys

5. Adding new columns in a data table (= operator)

6. Adding Multiple New columns

7. Adding Multiple new Columns

8. Merging data tables with merge function

9. A trick to merging data tables in r

10. Data Aggregation with data.table package

11. Aggregation by multiple variables

12. merge aggregation results to the original dataset

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