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Data Wrangling with R

Data Wrangling with R

By : Gustavo R Santos, Gustavo Santos
4.9 (7)
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Data Wrangling with R

Data Wrangling with R

4.9 (7)
By: Gustavo R Santos, Gustavo Santos

Overview of this book

In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you’ll need plenty of tools that enable you to extract the most useful knowledge from data. Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization. The book begins by teaching you how to load and explore datasets. Then, you’ll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you’ll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards. By the end of this book, you’ll have learned how to create your first data model and build an application with Shiny in R.
Table of Contents (21 chapters)
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1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Grouping and summarizing data

Grouping and summarizing are two complementary functions. Generally, they will be used together, as there is not much use in grouping a dataset and not calculating anything or using the groups for a purpose. That is when summarizing plays the important role of transforming the data from each group into a summary or a number that we can understand.

In the business world, requests such as the average number of sales by store, the median number of customers by day, the standard deviation of a distribution, and many other examples, are part of the routine of a data scientist. These tasks can be performed using the group_by() and summarise()functions from dplyr.

Starting with the group_by() function, observe that it alone cannot bring much value:

# group by not summarized
df %>% group_by(workclass)

Here is the result.

Figure 8.9 – Dataset grouped but not summarized

We can see in Figure 8.9 that it worked because...

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