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.

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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


We need to continually alter the characteristics of rows and columns during the data-wrangling process to enable easier formatting and optimization for subsequent steps. Below are some of the most commonly employed methods in data analysis.

## Modify column names

The `colnames()` command lists the column names. This command returns the column names as vectors of string values. 
Column names can be altered by assigning new values to the output of the `colnames()` command.

>**Note:**
The number of new column names and the columns must be equal. Otherwise, the columns without labels are automatically represented as `NA`.


We need to continually alter the characteristics of rows and columns during the data-wrangling process to enable easier formatting and optimization for subsequent steps. Below are some of the most commonly employed methods in data analysis.

# Modify column names

The `colnames()` command lists the column names. This command returns the column names as vectors of string values. 
Column names can be altered by assigning new values to the output of the `colnames()` command.

>**Note:**
The number of new column names and the columns must be equal. Otherwise, the columns without labels are automatically represented as `NA`.

Learn how to alter columns and rows of data frames.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Modify Data Frames Using R

Modify column names