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

# Read and load data from various sources
The journey of data analysis begins with managing data sources.
File management in the R language is critical for many practices. It enables users to:

- Organize and manage data efficiently
- Save time and effort
- Eliminate potential version errors
- Keep track of changes
- Share data with colleagues


Data can be read from a variety of sources, with the most common being comma-separated values (CSV), Microsoft Excel (XLS, XLSX), and RData files (RData, RDS). We utilize different functions to get data from various sources.

# Read CSV files
A **comma-separated values (CSV) file** is a plain text file that stores tabular data (numbers and text) in plain-text form. Each line in a CSV file represents a row of the table, and each column within that row is separated by a comma. 




CSV is one of the most widely used formats in the industry. R has incorporated built-in functions to facilitate the reading of CSV files. We can use the `read.csv()` function to read a CSV file by passing its directory to it.


df <- read.csv('Documents/Crime.csv')  # Pass in the directory of the file to read 
print(head(df,5))  # Print the first 5 row of the data frame

> **Note:** The `head(data,x)` function allows the selection of the first `x` number of rows of data frames; similarly, the `tail(data,x)` function allows the selection of the last `x` number.

Learn how to read and load data from various sources 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

Netflix Shows

Read and Load Data in R

Read and load data from various sources

Read CSV files