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

**Aggregation** is an essential step in data analytics because it allows for summarizing and condensing large datasets into more manageable and meaningful pieces of information. By aggregating data, we can identify trends, patterns, and relationships that may not be apparent in the raw data.



For example, we might use aggregation to calculate the average sales for different products or the total number of purchases made. These aggregates can then inform business decisions, such as which products to focus on for development or which customers to target with promotions.
The most common use case for aggregations is to calculate statistics about the data, like mean, sum, standard deviation, variance, range, or median.




## The `summarize()` function
The `summarize()` function helps calculate aggregated values of the passed variables. It offers such a flexible structure that we can add extra columns to the output in sequential order. It also allows for naming the columns that contain aggregations. 
It takes at least two input variables: the dataset and a column name wrapped by an aggregated calculation. The output is the aggregated value indicated in the syntax. We can use the typical statistical functions as calculation methods like `mean()`, `median()`, `sd()`, and `sum()`.

```R
# Standard structure
summarize(<data>, <method>(<column>)) 
summarize(customers, sum(purchases))  # Example

# ----- OR ------
# Pipeline structure
<data> %>% summarize(<method>(<column>))
customers %>% summarize(sum(purchase)) # Example

```

We will demonstrate the functionality of `summarize()` on the `iris` dataset in the following examples. We will calculate the mean, median, and standard deviation. Also, we will combine these calculations with `filter()` in a pipeline.
Check out the examples:


Learn about aggregating and grouping data in 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

Data Aggregation in R

Aggregation methods

The `summarize()` function