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Hands-On Data Science with R

You're reading from  Hands-On Data Science with R

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Pages 420 pages
Edition 1st Edition
Languages
Authors (4):
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Profile icon Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Profile icon Doug Ortiz
Nataraj Dasgupta Nataraj Dasgupta
Profile icon Nataraj Dasgupta
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Profile icon Ricardo Anjoleto Farias
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. Getting Started with Data Science and R 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Miscellaneous topics

In this section, we will look at a few other data-related topics. The following will be covered:

  • Checking data quality
  • Reading binary files: Excel, SAS, and other data sources
  • On-disk formats
  • Working with web data

Checking data quality

There are several inbuilt functions as well as packages for checking the quality of data in R. The most commonly used among them is the summary function in base R:

## Packages Used: 
## psych, pastecs, dataMaid, daff 
 
# install.packages(c("psych","pastecs","dataMaid","daff")) 
 
state <- data.frame(state.x77) 
state$State <- row.names(state) 
state 
 
 
summary(state) 

The output of the preceding code is as follows:

library...
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