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

As data analysts progress through their work on a dataset, they often produce multiple versions of the primary dataset. This makes it necessary to save the current versions of the data for future use. It is also vital to know how to preserve data in different formats because some platforms only accept specific file formats. 


For these reasons, we should know how to save data files for different environments. The R programming language provides a range of saving options, such as CSV, TSV, RData, and Excel. Let's go through each of them. 



## Save CSV files

The syntax for saving data into a CSV file is `write.csv()`, which requires two arguments: the data frame name and the target directory.

Here is an example:

Learn how to store data in various file formats using R.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Uber Data Analysis Using the R Language

Conclusion

Evaluation

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Save Data in Files in R

Saving data in various formats

Save CSV files