Advertisement
Published in:
2019 | OriginalPaper | Chapter
1. Univariate Data Visualization
Authors : Matt Wiley, Joshua F. Wiley
Published in: Advanced R Statistical Programming and Data Models
Publisher: Apress
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Most statistical models discussed in the rest of the book make assumptions about the data and the best model to use for them. As data analysts, we often must specify the distribution that we assume the data come from. Anomalous values, also called extreme values or outliers, may also have undue influence on the results from many statistical models. In this chapter, we examine visual and graphical approaches to exploring the distributions and anomalous values for one variable at a time (i.e., univariate). The goal of this chapter is not specifically to create beautiful or publication quality graphs nor to show results, but rather to use graphs to understand the distribution of a variable and identify anomalous values. This chapter focuses on univariate data visualization, and the next chapter will employ some of the same concepts but applied to multivariate distributions and cover how to assess the relations between variables.
library(checkpoint)
checkpoint("2018-09-28", R.version = "3.5.1",
project = book_directory,
checkpointLocation = checkpoint_directory,
scanForPackages = FALSE,
scan.rnw.with.knitr = TRUE, use.knitr = TRUE)
library(knitr)
library(ggplot2)
library(cowplot)
library(MASS)
library(JWileymisc)
library(data.table)
options(width = 70, digits = 2)