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An Introduction to Data Cleaning with R - Programming & Statistical Analysis Assignment
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where each step increases the ``value'' of the data*
Figure 1 shows an overview of a typical data analysis project. Each rectangle represents data in a certain state while each arrow
represents the activities needed to get from one state to the other. The first state (Raw data) is the data as it comes in. Raw data
files may lack headers, contain wrong data types (e.g. numbers stored as strings), wrong category labels, unknown or unexpected
character encoding and so on. In short, reading such files into an R data.frame directly is either difficult or impossible without some sort of preprocessing. Once this preprocessing has taken place, data can be deemed Technically correct. That is, in this state data can be read into an R data.frame, with correct names, types and labels, without further trouble. However, that does not mean that the values are error-free or complete. For example, an age variable may be reported negative, an under-aged person may be registered to possess a driver's license, or data may simply be missing. Such inconsistencies obviously depend on the subject matter.
In fact, such a value chain is an integral part of Statistics Netherlands business architecture.
An Introduction to Data Cleaning with R - Programming
Task: Data cleaning, or data preparation is an essential part of statistical analysis. In fact, in practice it is often more time-consuming than the statistical analysis itself. These lecture notes describe a range of techniques, implemented in the R statistical environment, that allow the reader to build data cleaning scripts for data suffering from a wide range of errors and inconsistencies, in textual format. These notes cover technical as well as subject-matter related aspects of data cleaning. Technical aspects include data reading, type conversion and string matching and manipulation. Subject-matter related aspects include topics like data checking, error localization and an introduction to imputation methods in R. References to relevant literature and R packages are provided throughout. Data cleaning, or data preparation is an essential part of statistical analysis. In fact, in practice it is often more time-consuming than the statistical analysis itself. These lecture notes describe a range of techniques, implemented in the R statistical environment, that allow the reader to build data cleaning scripts for data suffering from a wide range of errors and inconsistencies, in textual format. These notes cover technical as well as subject-matter related aspects of data cleaning. Technical aspects include data reading, type conversion and string matching and manipulation. Subject-matter related aspects include topics like data checking, error localization and an introduction to imputation methods in R. References to relevant literature and R packages are provided throughout. Statistical analysis in 5 steps In this tutorial a statistical analysis is viewed as the result of a number of data processing stepswhere each step increases the ``value'' of the data*
Figure 1 shows an overview of a typical data analysis project. Each rectangle represents data in a certain state while each arrow
represents the activities needed to get from one state to the other. The first state (Raw data) is the data as it comes in. Raw data
files may lack headers, contain wrong data types (e.g. numbers stored as strings), wrong category labels, unknown or unexpected
character encoding and so on. In short, reading such files into an R data.frame directly is either difficult or impossible without some sort of preprocessing. Once this preprocessing has taken place, data can be deemed Technically correct. That is, in this state data can be read into an R data.frame, with correct names, types and labels, without further trouble. However, that does not mean that the values are error-free or complete. For example, an age variable may be reported negative, an under-aged person may be registered to possess a driver's license, or data may simply be missing. Such inconsistencies obviously depend on the subject matter.
In fact, such a value chain is an integral part of Statistics Netherlands business architecture.