Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)

Introduction to the haven and foreign R packages

The R package called haven is for import and export from SPSS, Stata and SAS files. The package is for Labelled Data Utility Functions, which is a collection of many small functions dealing with labelled data, such as reading and writing data between R and other statistical software packages such as SAS, SPSS, or Stata, and working with labelled data.

This includes easy ways to get, set, and change value and variable label attributes, convert labelled vectors into factors or numeric values (and vice versa), and deal with multiple declared missing values. The following example is about writing several specific outputs:

library(haven)
x<-1:100
y<-matrix(x,50,2)
z<-data.frame(y)
colnames(z)<-c("a","b")
write_sas(z,"c:/temp/tt.sas7bdat")
write_spss(z,"c:/temp/tt.sav")
write_stata(z,&quot...