Learn how to perform data manipulation, analysis, and visualization using R.

Data Science with R

This module covers the techniques for file management, followed by the different data structures used in R. We’ll learn different data cleaning techniques, such as modifying data frames, deleting and adding data, and mutating data. Moreover, we’ll go through various ways to perform statistical analysis on a given dataset, such as by ANOVA, chi-squares, and correlation tests. In the end, we will learn how to transform data using different techniques and how data is visualized with the help of plots like bar plots, pie charts, histograms, etc.

Introduction to Data Analysis and Visualization with R

Which function can be used to create scatterplots in R?

Which function can be used to create scatterplots in R?

What is the purpose of the `facet_wrap()` function in R?

What is the purpose of the <code>facet_wrap()</code> function in R?

Which argument adjusts the range of the x-axis?

Which argument adjusts the range of the x-axis?

Which function is used to change the font size of the axis labels in a `ggplot2` visualization?

Which function is used to change the font size of the axis labels in a <code>ggplot2</code> visualization?

Which syntax creates an empty chart layout in R?

Which syntax creates an empty chart layout in R?

Test your understanding of data visualization.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Evaluation

Netflix Shows

Conclusion

Quiz on Data Visualization