#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Detecting missing data

There are numerous causes behind missing data. For example, it could be the result of typos or data process flaws. However, if there is missing data in our analysis process, the results of the analysis may be misleading. Thus, it is important to detect missing values before proceeding with further analysis.

Refer to the Converting data types recipe and convert each attribute of imported data into the proper data type. Also, rename the columns of the `employees` and `salaries` datasets by following the steps from the Renaming the data variable recipe.

### How to do it…

Perform the following steps to detect missing values:

1. First, we set the `to_date` attribute with a date over `2100-01-01`:

```> salaries[salaries\$to_date > "2100-01-01",]
```
2. We then change the data with a date over `2100-01-01` to a missing value:

```> salaries[salaries\$to_date > "2100-01-01","to_date"] = NA
```
3. Next, we can use the `is.na` function to find which rows contain missing values:

`> is.na(salaries...`