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.

tarball.tar.gz

R_lang

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

# Features
A line plot is a type of visualization used to display changes in data over time. It consists of a series of data points that are connected by straight lines. We can show trends, highlight anomalies, compare multiple data series, and predict the trends with line charts. We use line plots in various fields like finance, economics, and the natural and social sciences. They are especially useful in visualizing time series data.  



# Line chart with `plot()`

The `plot()` function is the built-in function for data visualization. It is capable of generating charts in various styles. To generate a line plot, we configure the function using the `type = "l"` argument.
It takes at least two parameters: the name of the dataset and the `type="l"` argument.

```R
plot(<data>, type="l") # Syntax structure
```
In the example below, we will generate a line plot showing the daily sales of a store over 30 days.

# We use a custom dataset in this exercise
head(sales_data)     # Preview of the data
plot(sales_data$sales,type='l')  # Define the data and the plot style.

Notice that although the chart shows the change in data over time, we did not provide data for time (x-axis). The function, by default, assigned the index numbers to the x-axis. 

We can modify the line chart using various arguments.

- `lwd`: The thickness of the line can be set using the `lwd` argument. The bigger the value, the thicker the line.

- `lty`: The line style is changed using this argument. It takes integer values between 1 and 6, each representing a different line style:

  1. Solid line (default)
  2. Dashed line
  3. Dotted line
  4. Dot-dashed line
  5. Long-dashed line
  6. Long-short-dashed line

- `col`:  The line color can be configured with this argument. It takes string values. The strings can be general color names like `"green"` or hexadecimal values like `"#4682B4"`.

```R
plot(<data>,type = 'l',col = 'color_code', lwd = <integer>, lty = <integer> )
``` 
Check the example below for a modified line chart.

Learn how to generate line plots and change their style.

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

Line Plots

Features

Line chart with `plot()`