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Mastering data analysis with r pdf

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R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and. Mastering Data Analysis with R [Gergely Daroczi] on caite.info *FREE* shipping on qualifying offers. Gain sharp insights into your data and solve real- world. Gain sharp insights into your data and solve real-world data science problems with R-from data munging to modeling and visualization About This Book Handle .


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PDF | On Sep 1, , Gergely Daróczi and others published Mastering Data Analysis with R. Mastering Data Analysis with R - Selection from Mastering Data Analysis with R [ Book]. 8 records Mastering Data Analysis with R will help you get familiar with this open source . We also provide you with a PDF file that has color images of the.

And now let's download these files to our computer for future parsing: Big Data Analytics with R: PillPack Pharmacy Simplified. Click here for ordering and shipping details. R news and tutorials contributed by R bloggers. It seems that none of the preceding theoretical distributions fit our data perfectlywhich is pretty normal by the way. Starting Accessible to readers adopting the topic, these titles get you into the tool or technology so that you can become an effective user.

Well, we can do a lot better than this, right? Let's massage our data a bit and visualize the frequency of mails based on the day of week and hour of the day via a more elegant graphinspired by GitHub's punch card plot: Visualizing this dataset is relatively straightforward with ggplot: As the times are by UTC, the early morning mails might suggest that where most R-help posters live has a positive GMT offsetif we suppose that most e-mails were written in business hours.

Well, at least the lower number of e-mails on the weekends seems to suggest this statement. Forecasting the e-mail volume in the future And we can also use this relatively clean dataset to forecast the future volume of the R-help mailing list. To this end, let's aggregate the original dataset to count data daily, as we saw in Chapter 3, Filtering and Summarizing Data: Now let's transform this data.

Well, this daily dataset is a lot spikier than the previously rendered yearly graph: But instead of smoothing or trying to decompose this time-series, like we did in Chapter 12, Analyzing Time-series, let's rather see how we can provide some quick estimates based on historical data on the forthcoming number of mails on this mailing list with some automatic models.

Mastering Data Analysis with R - Sample Chapter

To this end, we will use the forecast package: The ets function implements a fully automatic method that can select the optimal trend, season, and error type for the given time-series. Then we can simply call the predict or forecast function to see the specified number of estimates, only for the next day in this case: So it seems that, for the next day, our model estimated around 28 e-mails with a confidence interval of 80 percent being somewhere between 10 and Visualizing predictions for a slightly longer period of time with some historical data can be done via the standard plot function with some useful new parameters: Analyzing overlaps between our lists of R users But our original idea was to predict the number of R users around the world and not to focus on some minor segments, right?

Now that we have multiple data sources, we can start building some models combining those to provide estimates on the global number of R users. The basic idea behind this approach is the capture-recapture method, which is well known in ecology, where we first try to identify the probability of capturing a unit from the population, and then we use this probability to estimate the number of not captured units.

In our current study, units will be R users and the samples are the previously captured name lists on the:. Let's merge these lists with a tag referencing the data source: Next let's see the number of names we can find in one, two or all three groups: So there are at least 40 persons who support the R Foundation, maintain at least one R package on CRAN, and have posted at least one mail to R-help since !

I am happy and proud to be one of these guys -- especially with an accent in my name, which often makes matching of strings more complex. Now, if we suppose these lists refer to the same population, namely R users around the world, then we can use these common occurrences to predict the number of R users who somehow missed supporting the R Foundation, maintaining a package on CRAN, and writing a mail to the R-help mailing list. Although this assumption is obviously off, let's run this quick experiment and get back to these outstanding questions later.

One of the best things in R is that we have a package for almost any problem. Let's load the Rcapture package, which provides some sophisticated, yet easily accessible, methods for capture-recapture models: These numbers from the first fi column are familiar from the previous table, and represent the number of R users identified on one, two, or all three lists. It's a lot more interesting to fit some models on this data with a simple call such as: Once again, I have to emphasize that these estimates are not actually on the abundance of all R users around the world, because:.

The R community is definitely not a closed population and some open-population models would be more reliable. Further ideas on extending the capture-recapture models Although this playful example did not really help us to find out the number of R users around the world, with some extensions the basic idea is definitely viable.

First of all, we might consider analyzing the source data in smaller chunksfor example, looking for the same e-mail addresses or names in different years of the R-help archives. On the other hand, we could also add a number of other data sources to the models, so that we can do more reliable estimates on some other R users who do not contribute to the R Foundation, CRAN, or R-help. I have been working on a similar study over the past 2 years, collecting data on the number of:.

You can find the results on an interactive map and the country-level aggregated data in a CSV file at http: The number of R users in social media An alternative way to try to estimate the number of R users could be to analyze the occurrence of the related terms on social media.

Mastering Data Analysis with R

This is relatively easy on Facebook, where the marketing API allows us to query the size of the so-called target audiences, which we can use to define targets for some paid ads. Well, we are not actually interested in creating a paid advertisement on Facebook right now, although this can be easily done with the fbRads package, but we can use this feature to see the estimated size of the target group of persons interested in R: Of course, to run this quick example you will need to have a free Facebook developer account, a registered application, and a generated token please see the package docs for more details , but it is definitely worth it: That's really impressive, although it seems to be rather high to me, especially when compared with some other statistical software, such as: Having said this, comparing R with other programming languages suggests that the audience size might actually be correct: There are many programmers around the world, it seems!

But what are they talking about and what are the trending topics? We will cover these questions in the next section. R-related posts in social media One option to collect posts from the past few days of social media is processing Twitter's global stream of Tweet data.

This stream data and API provides access to around 1 percent of all tweets. If you are interested in all this data, then a commercial Twitter Firehouse account is needed. In the following examples, we will use the free Twitter search API, which provides access to no more than 3, tweets based on any search querybut this will be more than enough to do some quick analysis on the trending topics among R users.

So let's load the twitteR package and initialize the connection to the API by providing our application tokens and secrets, generated at https: Now we can start using the searchTwitter function to search tweets for any keywords, including hashtags and mentions.

This query can be fine-tuned with a couple of arguments. Since, until, and n set the beginning and end date, also the number of tweets to return respectively. Language can be set with the lang attribute by the ISO formatfor example, use en for English. Let's search for the most recent tweet with the official R hashtag: This is quite an impressive amount of information for a character string with no more than characters, isn't it?

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Besides the text including the actual tweet, we got some meta-information as wellfor example, the author, post time, the number of times other users favorited or retweeted the post, the Twitter client name, and the URLs in the post along with the shortened, expanded, and displayed format.

The location of the tweet is also available in some cases, if the user enabled that feature. Based on this piece of information, we could focus on the Twitter R community in very different ways. Examples include:. Probably a mixture of these and other methods would be the best approach, and I highly suggest you do that as an exercise to practice what you have learned in this book.

However, in the following pages we will only concentrate on the last item. So first, we need some recent tweets on the R programming language.

To search for rstats posts, instead of providing the related hashtag like we did previously , we can use the Rtweets wrapper function as well: This function returned reference classes similar to those we saw previously.

We can count the number of original tweets excluding retweets: But, as we are looking for the trending topics, we are interested in the original list of tweets, where the retweets are also important as they give a natural weight to the trending posts.

So let's transform the list of reference classes to a data. This dataset consists of rows tweets and 16 variables on the content, author, and location of the posts, as described previously. Now, as we are only interested in the actual text of the tweets, let's load the tm package and import our corpus as seen in Chapter 7, Unstructured Data: As the data is in the right format, we can start to clean the data from the common English words and transform everything into lowercase format; we might also want to remove any extra whitespace: It's also wise to remove the R hashtag, as this is part of all tweets: And then we can use the wordcloud package to plot the most important words: Summary In the past few pages, I have tried to cover a variety of data science and R programming topics, although many important methods and questions were not addressed due to page limitation.

To this end, I've compiled a short reading list in the References chapter of the book. And don't forget: I wish you a lot of fun and success in this journey! And once again, thanks for reading this book; I hope you found it useful.

If you have any questions, comments, or any kind of feedback, please feel free to get in touch, I'm looking forward to hearing from you! Alternatively, you can buy the book from Amazon, BN. Click here for ordering and shipping details. Chapter No. Flag for inappropriate content. Related titles. Jump to Page.

Mastering Data Analysis with R [Book]

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Mastering Data Analysis with R by Gergely Daroczi

Go to Subscription. You don't have anything in your cart right now. R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently.

This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis.

You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Besides maintaining around half a dozen R packages, mainly dealing with reporting, Gergely has coauthored the books Introduction to R for Quantitative Finance and Mastering R for Quantitative Finance both by Packt Publishing by providing and reviewing the R source code.

He has contributed to a number of scientific journal articles, mainly in social sciences but in medical sciences as well. Sign up to our emails for regular updates, bespoke offers, exclusive discounts and great free content. Log in.

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In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently.

This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis.

You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http: If you purchased this book elsewhere, you can visit http: Stay ahead with the world's most comprehensive technology and business learning platform.

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