3© Springer International Publishing Switzerland 2016
B.C. Boehmke, Data Wrangling with R, Use R!, DOI 10.1007/978-3-319-45599-0_1
Chapter 1
The Role of Data Wrangling
Water, water, everywhere, nor any a drop to drink
Samuel Taylor Coleridge
Synonymous to Samuel Taylor Coleridge’s quote in Rime of the Ancient Mariner , the
degree to which data are useful is largely determined by an analyst’s ability to wrangle
data. In spite of advances in technologies for working with data, analysts still spend an
inordinate amount of time obtaining data, diagnosing data quality issues and pre-pro-
cessing data into a usable form. Research has illustrated that this portion of the data
analysis process is the most tedious and time consuming component; often consuming
50–80 % of an analyst’s time (cf. Wickham 2014 ; Dasu and Johnson 2003 ). Despite the
challenges, data wrangling remains a fundamental building block that enables visual-
ization and statistical modeling. Only through data wrangling can we make data useful.
Consequently, one’s ability to perform data wrangling tasks effectively and effi ciently
is fundamental to becoming an expert data analyst in their respective domain.
So what exactly is this thing called data wrangling ? It’s the ability to take a
messy, unrefi ned source of data and wrangle it into something useful. It’s the art of
using computer programming to extract raw data and creating clear and actionable
bits of information for your analysis. Data wrangling is the entire front end of the
analytic process and requires numerous tasks that can be categorized within the get ,
clean , and transform components (Fig. 1.1 ).
However, learning how to wrangle your data does not necessarily follow a linear
progression as suggested by Fig. 1.1 . In fact, you need to start from scratch to under-
stand how to work with data in R . Consequently, this book takes a meandering route
through the data wrangling process to help build a solid data wrangling
foundation.
First, modern day data wrangling requires being comfortable writing code. If
you are new to writing code, R , or RStudio you need to understand some of the
basics of working in the “command line” environment. The next two chapters in this
part will introduce you to R, discuss the benefi ts it provides, and then start to get you
comfortable at the command line by walking you through the process of assigning
and evaluating expressions, using vectorization , getting help , managing your
workspace, and working with packages . Lastly, I offer some basic styling guidelines
to help you write code that is easier to digest by others.
剩余236页未读，继续阅读
