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

Which function in R is used to subset a data frame based on a logical condition?


Which function in R is used to subset a data frame based on a logical condition?

**(Select all that apply.)** Why do we need to change data types? 



(Select all that apply.) Why do we need to change data types?

**(Select all that apply.)** Which commands help us in merging data frames? 


(Select all that apply.) Which commands help us in merging data frames?

To use insights from the data to lead the development team.

To learn about the distribution of the data.

To create strategies for dealing with missing values.

**(Select all that apply.)** For what primary reasons do analysts need to explore the data before working on it?


(Select all that apply.) For what primary reasons do analysts need to explore the data before working on it?

```R
product%>%select(sales>50 & sales<100)
```

```R
filter(product$sales > 50 | product$sales < 100)
```

```R
product%>%filter(sales > 50 & sales <100)
```

```R
select(product$sales>50 | product$sales <100)
```

Choose the R command that filters values greater than 50 but less than 100 from the `sales` column of the `product` dataset. 

Choose the R command that filters values greater than 50 but less than 100 from the <code>sales</code> column of the <code>product</code> dataset.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Quiz on Data Cleaning