Gain insights into data manipulation, analysis, and visualization with R. Learn statistical concepts, create insightful visualizations, and empower decision-making to excel in data-centric roles.

tarball.tar.gz

R_lang

This course delves into data manipulation, analysis, and visualization with R. It demonstrates the power of R as open-source statistical software, unlocking valuable insights for decision-making across diverse fields.

You'll begin by mastering the basics of R programming, fundamental data structures in R, and file management. You will learn fundamental statistical concepts such as hypothesis testing and regression analysis. You will also learn to leverage well-structured data for creating insightful visualizations using R's robust graphics libraries.

After completing this course, you'll confidently approach data-driven challenges. With proficiency in R, you can efficiently manipulate and analyze data, positioning yourself as a sought-after asset in data-centric roles. Your ability to interpret statistical results and create compelling data visualizations will empower improved decision-making, paving the way for new opportunities for career growth and success across various industries.

Introduction to Data Analysis and Visualization with R

# Vector objects

**Vectors** act as containers of data elements, forming one-dimensional basic data structures that transform values into a single data type. Vectors can be sorted, filtered, and modified. We can generate vectors using the `c()` command.



Vector elements do not always keep the characteristics of their original data type if the vector includes more than one different data type. They converge in a data type that all the elements can be converted into.
 
The example below illustrates how the disparate data types of individual vector elements can be represented in a mutually compatible format.

# Create vectors using c()
myVector <- c('data analysis', 3L, 2i+3, TRUE) # define a vector
myVector1 <- c(3L, TRUE)    # define a vector
print(class(myVector))    # print the class of the variable

print(class(myVector[2]))    # Check the class of the second element
print(class(myVector[3]))   # Check the class of the third element
print(class(myVector1))   # Check the class of the vector

Notice how the data types are automatically converted into a common one in the examples above. The original data types of `2i+3` and `3L` are, respectively, integer and complex. However, they become characters in the vector since the character type can represent all elements in the `myVector` object.

Learn the details of the vector data type in the R language.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Uber Data Analysis Using the R Language

Conclusion

Evaluation

Netflix Shows

Vectors in R

Vector objects