Editorial Reviews. Review. From the reviews: "This comprehensive, compact and concise book eBook features: Highlight, take notes, and search in the book. The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety . Read "Data Manipulation with R - Second Edition" by Jaynal Abedin available from Rakuten Kobo. Sign up today and get $5 off your first download. This book is .
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The R language provides a rich environment for working with data, especially can be used on all reading devices; Immediate eBook download after download. Sarkar: Lattice: Multivariate Data Visualization with R. Pfaff: Analysis of Integrated and Cointegrated Time Series with R. Spector: Data Manipulation with R. Character manipulation, while sometimes overlooked within R, is also Using a variety of examples based on data sets included with R, along.
Most programming languages do not support a function call on a variable that is the recipient of an assignment, yet R allows you to return the row names of the data frame and use the returned value as the recipient of an assignment. In addition, most languages require that each item in a collection be assigned individually. This often requires a looping construct to iterate through each available assignment target and value. R, being vector based, allows the set of row names associated with the data frame to be assigned all at once. The second line shows one of several methods available to remove a column. In this case, the minus sign indicates a column should be deleted. The net effect is that the first column disappears from the dataset and row names are now associated with the data frame.
The package we are going to use for this is called dplyr. To make things easier for now, we are going to use example data included with dplyr.
So no need to import an external dataset; this does not change anything to the example that we are going to study here; the source of the data does not matter for this.
Using dplyr is possible only if the data you are working with is already in a useful shape. When data is more messy, you will need to first manipulate it to bring it a tidy format. For this, we will use tidyr, which is very useful package to reshape data and to do advanced cleaning of your data.
All these tidyverse functions are also called verbs. You need to provide aggregate with 3 things; the variable you want to summarize or only the data frame, if you want to summarize all variables , a list of grouping variables and then the function that will be applied to each subset.
So if you need the average and the standard deviation you have to do it in two steps. Then, I rerun the analysis from before again. It certainly beats having to write loops to achieve the same thing.
Depending on what you want to do with this data, it is not in the right shape. As you can see, R comes with very powerful functions right out of the box, ready to use.
When I was studying, unfortunately, my professors had been brought up on FORTRAN loops, so we had to do to all this using loops not reshaping, thankfully , which was not so easy. What does this package do?
The base R language can perform comparable transformations, but uses a number of different functions and operators that are not particularly unified or consistent.
The reshape2 package provides a streamlined and consistent syntax for these operations. Before we begin, we will manipulate row names using the inverse of the operation shown earlier.
Rather than pulling a column out of the dataset and assigning the column values as row names, the row names will be included inline in the dataset. Any columns listed in the id vector are retained. A row is created for each value associated. The default version of the function selects an id column to use based on data types available and works as expected with df.
So the results are the same as those shown above. Just to emphasize, this is not the only way to use the melt function, and different results are possible by modifying the call to this function. The following example retains the gear data in a column along with the name column as previously shown.
As a result, the total number of rows in the dataset is reduced. We will store the melted data in a second data frame. Programming with R Programming with R lessons teaches the basics of computaing language and the basics of data analysis using a simple data set. Not just that, it also teaches you how make dynamic documents with R Markdown using kinitr and how you can create R packages. R for Reproducible Scientific Analysis R for Reproducible Scientific Analysis teaches basics of R for beginners with the rich gapminder data set, a real world data of countries over a long time period.
This workshop lessons cover data structures in R, data visualization with ggplot2, data frame manipulation with dplyr and tidyr and making reproducible markdown documents with Knitr.
Why wait, just look here to find if there is any nearby two day workshops mostly free from Software Carpentry. What is better is that it uses the principles of tidy data and thus lets you practice tidyverse principles in text datasets.
It is a must book for doing data science with texts and sentiment analysis. If you are interested in analyzing social media data, this book is for you. It has a whole chapter on analyzing twitter data and doing sentiment analysis.
Although the title looks like this book is for baseball aficionados, the book is a treat for anyone learning data science. The statistical methods illustrated with data and R in the book are the same and effective in estimating click-through rates on ads, success rates of experiments, and so on.
It is one of the best books to learn data science and learn statistics for data science. Although this book mainly focuses on high throughput data from genomics, the methods described in this book are ideally suited for modern data science in any domain.
The book is the result of teaching from multiple courses on data science in the popular HarvardX. This book covers all these rich topics without getting you bogged down with the math behind them.
Now Fundamentals of Data Visualization the book is read to pre-order at site. It is a must if you are interested in R and want to learn data analysis and make it easily reproducible, reusable, and shareable. This book is aimed at non-programmers and provides a great introduction to the R language. Peng, Sean Kross, and Brooke Anderson is great book that teaches the basics of software development principles for building Data Science tools in R.
This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. This book is about using R to develop the tools for doing data science.