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

Separators are characters that are used to separate values in a file.

The comma (`,`) is the only separator in files.

Separators keep data files organized and readable. 

Different separators can be utilized in the same data file.

**(Select all that apply.)** Choose all of the options that are _incorrect_ about separators in data files.

(Select all that apply.) Choose all of the options that are incorrect about separators in data files.

Keeping track of changes and securing data

Creating appealing dashboards and presentations

**(Select all that apply.)** Choose all of the functionalities that can be achieved with successful file management. 

(Select all that apply.) Choose all of the functionalities that can be achieved with successful file management.

```R
read.csv('/Documents/projects/sales.csv')
```

```R
load.file('/Documents/projects/sales.csv')
```

```R
load_csv('/Documents/projects/sales.csv')
```

```R
read.txt('/Documents/projects/sales.csv')
```

Which syntax allows us to read the CSV file located in the following directory?
```R
'/Documents/projects/sales.csv'
```

Which syntax allows us to read the CSV file located in the following directory?
<pre><code class="language-R">'/Documents/projects/sales.csv'
</code></pre>

```R
save(data,data2,file='<directory>')
```

```R
write.RData(data,file='<directory>')
```

**(Select all that apply.)** Choose all of the commands that have the correct structure for saving an RData file. 

(Select all that apply.) Choose all of the commands that have the correct structure for saving an RData file.

TSV files can store data in bigger sizes compared to CSV files.

TSV files can store numeric data, whereas CSV files can't store numeric data.

CSV and TSV files use different separators.

TSV files are optimized for mobile devices; CSV files can't be read by mobile devices.

What is the main difference between CSV and TSV files?

What is the main difference between CSV and TSV files?

Quiz - File Management

Quiz - File Management

Test your understanding of file management. 

Quiz on File Management

Test your understanding of file management.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Quiz on File Management