Book Image

Data Wrangling with R

By : Gustavo R Santos
Book Image

Data Wrangling with R

By: Gustavo R 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)
1
Part 1: Load and Explore Data
5
Part 2: Data Wrangling
12
Part 3: Data Visualization
16
Part 4: Modeling

Exploring the data with a few visualizations

We should start the data visualization portion of a project with univariate graphics, such as histograms and boxplots. This is because the former will show us the data distribution, indicating the possible statistical tests to be used, while the latter will bring up the presence of outliers in the data.

Since there are more than 50 variables in this dataset, we will create a for loop to plot the histograms for all of them. The following code uses the hist() function from the base R histogram:

# Histograms
for (var in colnames(spam)[1:57]) {
  hist(unlist(spam[,var]), col="royalblue",
       main= paste("Histogram of", var),
       xlab=var)   }

Notice that we only did the loop for columns [1:57] since we know that the last one is the target variable. Next, we will see four graphics, as shown in Figure 13.6:

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